diff --git a/dev/.buildinfo b/dev/.buildinfo
index 0f77836294d56..961fb4b2dc54a 100644
--- a/dev/.buildinfo
+++ b/dev/.buildinfo
@@ -1,4 +1,4 @@
 # Sphinx build info version 1
 # This file records the configuration used when building these files. When it is not found, a full rebuild will be done.
-config: 396f019a82144c2f9ccdaf0ab9e98373
+config: da333e5632041208ee3343e22f9d4d07
 tags: 645f666f9bcd5a90fca523b33c5a78b7
diff --git a/dev/_downloads/006fc185672e58b056a5c134db26935c/plot_coin_segmentation.ipynb b/dev/_downloads/006fc185672e58b056a5c134db26935c/plot_coin_segmentation.ipynb
index 0592e23302ea2..36d7c71ab0df9 100644
--- a/dev/_downloads/006fc185672e58b056a5c134db26935c/plot_coin_segmentation.ipynb
+++ b/dev/_downloads/006fc185672e58b056a5c134db26935c/plot_coin_segmentation.ipynb
@@ -53,7 +53,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/00ae629d652473137a3905a5e08ea815/plot_iris_dtc.py b/dev/_downloads/00ae629d652473137a3905a5e08ea815/plot_iris_dtc.py
index 9d4298919d515..349f4a893511e 100644
--- a/dev/_downloads/00ae629d652473137a3905a5e08ea815/plot_iris_dtc.py
+++ b/dev/_downloads/00ae629d652473137a3905a5e08ea815/plot_iris_dtc.py
@@ -63,7 +63,7 @@
 
     # Plot the training points
     for i, color in zip(range(n_classes), plot_colors):
-        idx = np.where(y == i)
+        idx = np.asarray(y == i).nonzero()
         plt.scatter(
             X[idx, 0],
             X[idx, 1],
diff --git a/dev/_downloads/02192f99342d6d1323161978b6c80bfc/plot_ols_ridge.zip b/dev/_downloads/02192f99342d6d1323161978b6c80bfc/plot_ols_ridge.zip
index af118e817bee2..9c27cc79a151b 100644
Binary files a/dev/_downloads/02192f99342d6d1323161978b6c80bfc/plot_ols_ridge.zip and b/dev/_downloads/02192f99342d6d1323161978b6c80bfc/plot_ols_ridge.zip differ
diff --git a/dev/_downloads/02fe21a1f5d14bd1ebbe34aa905d9bf6/plot_multilabel.zip b/dev/_downloads/02fe21a1f5d14bd1ebbe34aa905d9bf6/plot_multilabel.zip
index ba5dc7d999866..32cd4754e4ba2 100644
Binary files a/dev/_downloads/02fe21a1f5d14bd1ebbe34aa905d9bf6/plot_multilabel.zip and b/dev/_downloads/02fe21a1f5d14bd1ebbe34aa905d9bf6/plot_multilabel.zip differ
diff --git a/dev/_downloads/0358b1921962fd7c6f5a94c5abc91476/plot_hashing_vs_dict_vectorizer.zip b/dev/_downloads/0358b1921962fd7c6f5a94c5abc91476/plot_hashing_vs_dict_vectorizer.zip
index dc2cd7b3dfe0a..af9b6cad306b2 100644
Binary files a/dev/_downloads/0358b1921962fd7c6f5a94c5abc91476/plot_hashing_vs_dict_vectorizer.zip and b/dev/_downloads/0358b1921962fd7c6f5a94c5abc91476/plot_hashing_vs_dict_vectorizer.zip differ
diff --git a/dev/_downloads/036b9372e2e7802453cbb994da7a6786/plot_linearsvc_support_vectors.py b/dev/_downloads/036b9372e2e7802453cbb994da7a6786/plot_linearsvc_support_vectors.py
index 021e1c6b55962..370f826d11a64 100644
--- a/dev/_downloads/036b9372e2e7802453cbb994da7a6786/plot_linearsvc_support_vectors.py
+++ b/dev/_downloads/036b9372e2e7802453cbb994da7a6786/plot_linearsvc_support_vectors.py
@@ -31,7 +31,7 @@
     # decision_function = np.dot(X, clf.coef_[0]) + clf.intercept_[0]
     # The support vectors are the samples that lie within the margin
     # boundaries, whose size is conventionally constrained to 1
-    support_vector_indices = np.where(np.abs(decision_function) <= 1 + 1e-15)[0]
+    support_vector_indices = (np.abs(decision_function) <= 1 + 1e-15).nonzero()[0]
     support_vectors = X[support_vector_indices]
 
     plt.subplot(1, 2, i + 1)
diff --git a/dev/_downloads/038d1885eb5f5ea53aca42da3031fe38/plot_document_clustering.zip b/dev/_downloads/038d1885eb5f5ea53aca42da3031fe38/plot_document_clustering.zip
index c0102c0569041..689cda248082f 100644
Binary files a/dev/_downloads/038d1885eb5f5ea53aca42da3031fe38/plot_document_clustering.zip and b/dev/_downloads/038d1885eb5f5ea53aca42da3031fe38/plot_document_clustering.zip differ
diff --git a/dev/_downloads/03c018a16384c69f3a89e473650d57ee/plot_svm_margin.zip b/dev/_downloads/03c018a16384c69f3a89e473650d57ee/plot_svm_margin.zip
index d79de56b9a942..f425e2c27f72a 100644
Binary files a/dev/_downloads/03c018a16384c69f3a89e473650d57ee/plot_svm_margin.zip and b/dev/_downloads/03c018a16384c69f3a89e473650d57ee/plot_svm_margin.zip differ
diff --git a/dev/_downloads/0486bf9e537e44cedd2a236d034bcd90/plot_pcr_vs_pls.ipynb b/dev/_downloads/0486bf9e537e44cedd2a236d034bcd90/plot_pcr_vs_pls.ipynb
index 678a9240cff12..3a3bb1e4020a5 100644
--- a/dev/_downloads/0486bf9e537e44cedd2a236d034bcd90/plot_pcr_vs_pls.ipynb
+++ b/dev/_downloads/0486bf9e537e44cedd2a236d034bcd90/plot_pcr_vs_pls.ipynb
@@ -125,7 +125,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/04b9a7769df5b331f4d94e1d065b4311/plot_voting_decision_regions.zip b/dev/_downloads/04b9a7769df5b331f4d94e1d065b4311/plot_voting_decision_regions.zip
index 6700a9c28a515..1eb8f66499362 100644
Binary files a/dev/_downloads/04b9a7769df5b331f4d94e1d065b4311/plot_voting_decision_regions.zip and b/dev/_downloads/04b9a7769df5b331f4d94e1d065b4311/plot_voting_decision_regions.zip differ
diff --git a/dev/_downloads/053b58bbfc8177072856c743b2c93424/plot_varimax_fa.zip b/dev/_downloads/053b58bbfc8177072856c743b2c93424/plot_varimax_fa.zip
index 4d2596094b2b0..5367248c2dcfd 100644
Binary files a/dev/_downloads/053b58bbfc8177072856c743b2c93424/plot_varimax_fa.zip and b/dev/_downloads/053b58bbfc8177072856c743b2c93424/plot_varimax_fa.zip differ
diff --git a/dev/_downloads/055e8313e28f2f3b5fd508054dfe5fe0/plot_roc_crossval.ipynb b/dev/_downloads/055e8313e28f2f3b5fd508054dfe5fe0/plot_roc_crossval.ipynb
index 7f27c788d7f59..0509f550ebe75 100644
--- a/dev/_downloads/055e8313e28f2f3b5fd508054dfe5fe0/plot_roc_crossval.ipynb
+++ b/dev/_downloads/055e8313e28f2f3b5fd508054dfe5fe0/plot_roc_crossval.ipynb
@@ -89,7 +89,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/05ca8a4e90b4cc2acd69f9e24b4a1f3a/plot_classifier_chain_yeast.ipynb b/dev/_downloads/05ca8a4e90b4cc2acd69f9e24b4a1f3a/plot_classifier_chain_yeast.ipynb
index 478d5c668fc16..ae87b350ece21 100644
--- a/dev/_downloads/05ca8a4e90b4cc2acd69f9e24b4a1f3a/plot_classifier_chain_yeast.ipynb
+++ b/dev/_downloads/05ca8a4e90b4cc2acd69f9e24b4a1f3a/plot_classifier_chain_yeast.ipynb
@@ -114,7 +114,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/061854726c268bcdae5cd1c330cf8c75/plot_sgd_penalties.ipynb b/dev/_downloads/061854726c268bcdae5cd1c330cf8c75/plot_sgd_penalties.ipynb
index d1eaae66e6d26..a89677ca20bb3 100644
--- a/dev/_downloads/061854726c268bcdae5cd1c330cf8c75/plot_sgd_penalties.ipynb
+++ b/dev/_downloads/061854726c268bcdae5cd1c330cf8c75/plot_sgd_penalties.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/067cd5d39b097d2c49dd98f563dac13a/plot_iterative_imputer_variants_comparison.ipynb b/dev/_downloads/067cd5d39b097d2c49dd98f563dac13a/plot_iterative_imputer_variants_comparison.ipynb
index 1a4b1e7c67a55..7cf41d89fd956 100644
--- a/dev/_downloads/067cd5d39b097d2c49dd98f563dac13a/plot_iterative_imputer_variants_comparison.ipynb
+++ b/dev/_downloads/067cd5d39b097d2c49dd98f563dac13a/plot_iterative_imputer_variants_comparison.ipynb
@@ -15,7 +15,7 @@
       },
       "outputs": [],
       "source": [
-        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\n\nfrom sklearn.datasets import fetch_california_housing\nfrom sklearn.ensemble import RandomForestRegressor\n\n# To use this experimental feature, we need to explicitly ask for it:\nfrom sklearn.experimental import enable_iterative_imputer  # noqa\nfrom sklearn.impute import IterativeImputer, SimpleImputer\nfrom sklearn.kernel_approximation import Nystroem\nfrom sklearn.linear_model import BayesianRidge, Ridge\nfrom sklearn.model_selection import cross_val_score\nfrom sklearn.neighbors import KNeighborsRegressor\nfrom sklearn.pipeline import make_pipeline\n\nN_SPLITS = 5\n\nrng = np.random.RandomState(0)\n\nX_full, y_full = fetch_california_housing(return_X_y=True)\n# ~2k samples is enough for the purpose of the example.\n# Remove the following two lines for a slower run with different error bars.\nX_full = X_full[::10]\ny_full = y_full[::10]\nn_samples, n_features = X_full.shape\n\n# Estimate the score on the entire dataset, with no missing values\nbr_estimator = BayesianRidge()\nscore_full_data = pd.DataFrame(\n    cross_val_score(\n        br_estimator, X_full, y_full, scoring=\"neg_mean_squared_error\", cv=N_SPLITS\n    ),\n    columns=[\"Full Data\"],\n)\n\n# Add a single missing value to each row\nX_missing = X_full.copy()\ny_missing = y_full\nmissing_samples = np.arange(n_samples)\nmissing_features = rng.choice(n_features, n_samples, replace=True)\nX_missing[missing_samples, missing_features] = np.nan\n\n# Estimate the score after imputation (mean and median strategies)\nscore_simple_imputer = pd.DataFrame()\nfor strategy in (\"mean\", \"median\"):\n    estimator = make_pipeline(\n        SimpleImputer(missing_values=np.nan, strategy=strategy), br_estimator\n    )\n    score_simple_imputer[strategy] = cross_val_score(\n        estimator, X_missing, y_missing, scoring=\"neg_mean_squared_error\", cv=N_SPLITS\n    )\n\n# Estimate the score after iterative imputation of the missing values\n# with different estimators\nestimators = [\n    BayesianRidge(),\n    RandomForestRegressor(\n        # We tuned the hyperparameters of the RandomForestRegressor to get a good\n        # enough predictive performance for a restricted execution time.\n        n_estimators=4,\n        max_depth=10,\n        bootstrap=True,\n        max_samples=0.5,\n        n_jobs=2,\n        random_state=0,\n    ),\n    make_pipeline(\n        Nystroem(kernel=\"polynomial\", degree=2, random_state=0), Ridge(alpha=1e3)\n    ),\n    KNeighborsRegressor(n_neighbors=15),\n]\nscore_iterative_imputer = pd.DataFrame()\n# iterative imputer is sensible to the tolerance and\n# dependent on the estimator used internally.\n# we tuned the tolerance to keep this example run with limited computational\n# resources while not changing the results too much compared to keeping the\n# stricter default value for the tolerance parameter.\ntolerances = (1e-3, 1e-1, 1e-1, 1e-2)\nfor impute_estimator, tol in zip(estimators, tolerances):\n    estimator = make_pipeline(\n        IterativeImputer(\n            random_state=0, estimator=impute_estimator, max_iter=25, tol=tol\n        ),\n        br_estimator,\n    )\n    score_iterative_imputer[impute_estimator.__class__.__name__] = cross_val_score(\n        estimator, X_missing, y_missing, scoring=\"neg_mean_squared_error\", cv=N_SPLITS\n    )\n\nscores = pd.concat(\n    [score_full_data, score_simple_imputer, score_iterative_imputer],\n    keys=[\"Original\", \"SimpleImputer\", \"IterativeImputer\"],\n    axis=1,\n)\n\n# plot california housing results\nfig, ax = plt.subplots(figsize=(13, 6))\nmeans = -scores.mean()\nerrors = scores.std()\nmeans.plot.barh(xerr=errors, ax=ax)\nax.set_title(\"California Housing Regression with Different Imputation Methods\")\nax.set_xlabel(\"MSE (smaller is better)\")\nax.set_yticks(np.arange(means.shape[0]))\nax.set_yticklabels([\" w/ \".join(label) for label in means.index.tolist()])\nplt.tight_layout(pad=1)\nplt.show()"
+        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\n\nfrom sklearn.datasets import fetch_california_housing\nfrom sklearn.ensemble import RandomForestRegressor\n\n# To use this experimental feature, we need to explicitly ask for it:\nfrom sklearn.experimental import enable_iterative_imputer  # noqa: F401\nfrom sklearn.impute import IterativeImputer, SimpleImputer\nfrom sklearn.kernel_approximation import Nystroem\nfrom sklearn.linear_model import BayesianRidge, Ridge\nfrom sklearn.model_selection import cross_val_score\nfrom sklearn.neighbors import KNeighborsRegressor\nfrom sklearn.pipeline import make_pipeline\n\nN_SPLITS = 5\n\nrng = np.random.RandomState(0)\n\nX_full, y_full = fetch_california_housing(return_X_y=True)\n# ~2k samples is enough for the purpose of the example.\n# Remove the following two lines for a slower run with different error bars.\nX_full = X_full[::10]\ny_full = y_full[::10]\nn_samples, n_features = X_full.shape\n\n# Estimate the score on the entire dataset, with no missing values\nbr_estimator = BayesianRidge()\nscore_full_data = pd.DataFrame(\n    cross_val_score(\n        br_estimator, X_full, y_full, scoring=\"neg_mean_squared_error\", cv=N_SPLITS\n    ),\n    columns=[\"Full Data\"],\n)\n\n# Add a single missing value to each row\nX_missing = X_full.copy()\ny_missing = y_full\nmissing_samples = np.arange(n_samples)\nmissing_features = rng.choice(n_features, n_samples, replace=True)\nX_missing[missing_samples, missing_features] = np.nan\n\n# Estimate the score after imputation (mean and median strategies)\nscore_simple_imputer = pd.DataFrame()\nfor strategy in (\"mean\", \"median\"):\n    estimator = make_pipeline(\n        SimpleImputer(missing_values=np.nan, strategy=strategy), br_estimator\n    )\n    score_simple_imputer[strategy] = cross_val_score(\n        estimator, X_missing, y_missing, scoring=\"neg_mean_squared_error\", cv=N_SPLITS\n    )\n\n# Estimate the score after iterative imputation of the missing values\n# with different estimators\nestimators = [\n    BayesianRidge(),\n    RandomForestRegressor(\n        # We tuned the hyperparameters of the RandomForestRegressor to get a good\n        # enough predictive performance for a restricted execution time.\n        n_estimators=4,\n        max_depth=10,\n        bootstrap=True,\n        max_samples=0.5,\n        n_jobs=2,\n        random_state=0,\n    ),\n    make_pipeline(\n        Nystroem(kernel=\"polynomial\", degree=2, random_state=0), Ridge(alpha=1e3)\n    ),\n    KNeighborsRegressor(n_neighbors=15),\n]\nscore_iterative_imputer = pd.DataFrame()\n# iterative imputer is sensible to the tolerance and\n# dependent on the estimator used internally.\n# we tuned the tolerance to keep this example run with limited computational\n# resources while not changing the results too much compared to keeping the\n# stricter default value for the tolerance parameter.\ntolerances = (1e-3, 1e-1, 1e-1, 1e-2)\nfor impute_estimator, tol in zip(estimators, tolerances):\n    estimator = make_pipeline(\n        IterativeImputer(\n            random_state=0, estimator=impute_estimator, max_iter=25, tol=tol\n        ),\n        br_estimator,\n    )\n    score_iterative_imputer[impute_estimator.__class__.__name__] = cross_val_score(\n        estimator, X_missing, y_missing, scoring=\"neg_mean_squared_error\", cv=N_SPLITS\n    )\n\nscores = pd.concat(\n    [score_full_data, score_simple_imputer, score_iterative_imputer],\n    keys=[\"Original\", \"SimpleImputer\", \"IterativeImputer\"],\n    axis=1,\n)\n\n# plot california housing results\nfig, ax = plt.subplots(figsize=(13, 6))\nmeans = -scores.mean()\nerrors = scores.std()\nmeans.plot.barh(xerr=errors, ax=ax)\nax.set_title(\"California Housing Regression with Different Imputation Methods\")\nax.set_xlabel(\"MSE (smaller is better)\")\nax.set_yticks(np.arange(means.shape[0]))\nax.set_yticklabels([\" w/ \".join(label) for label in means.index.tolist()])\nplt.tight_layout(pad=1)\nplt.show()"
       ]
     }
   ],
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/06c18f4675ecd124f98537d05e10abd0/plot_nca_dim_reduction.zip b/dev/_downloads/06c18f4675ecd124f98537d05e10abd0/plot_nca_dim_reduction.zip
index 6e6320f84f58c..4fc230f1d62f0 100644
Binary files a/dev/_downloads/06c18f4675ecd124f98537d05e10abd0/plot_nca_dim_reduction.zip and b/dev/_downloads/06c18f4675ecd124f98537d05e10abd0/plot_nca_dim_reduction.zip differ
diff --git a/dev/_downloads/06cfc926acb27652fb2aa5bfc583e7cb/plot_hashing_vs_dict_vectorizer.ipynb b/dev/_downloads/06cfc926acb27652fb2aa5bfc583e7cb/plot_hashing_vs_dict_vectorizer.ipynb
index 142442ac99d74..1c5324210a14e 100644
--- a/dev/_downloads/06cfc926acb27652fb2aa5bfc583e7cb/plot_hashing_vs_dict_vectorizer.ipynb
+++ b/dev/_downloads/06cfc926acb27652fb2aa5bfc583e7cb/plot_hashing_vs_dict_vectorizer.ipynb
@@ -323,7 +323,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/06e5cfec52777b1f220312b4ada9469d/plot_document_classification_20newsgroups.zip b/dev/_downloads/06e5cfec52777b1f220312b4ada9469d/plot_document_classification_20newsgroups.zip
index 12627734851b5..b7ac1209d50b8 100644
Binary files a/dev/_downloads/06e5cfec52777b1f220312b4ada9469d/plot_document_classification_20newsgroups.zip and b/dev/_downloads/06e5cfec52777b1f220312b4ada9469d/plot_document_classification_20newsgroups.zip differ
diff --git a/dev/_downloads/06ffeb4f0ded6447302acd5a712f8490/plot_nearest_centroid.ipynb b/dev/_downloads/06ffeb4f0ded6447302acd5a712f8490/plot_nearest_centroid.ipynb
index fd9a4e65e0692..56de89367da4f 100644
--- a/dev/_downloads/06ffeb4f0ded6447302acd5a712f8490/plot_nearest_centroid.ipynb
+++ b/dev/_downloads/06ffeb4f0ded6447302acd5a712f8490/plot_nearest_centroid.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/0785ea6d45bde062e5beedda88131215/plot_release_highlights_1_3_0.ipynb b/dev/_downloads/0785ea6d45bde062e5beedda88131215/plot_release_highlights_1_3_0.ipynb
index 7f6d9bb920324..cde1d7606f132 100644
--- a/dev/_downloads/0785ea6d45bde062e5beedda88131215/plot_release_highlights_1_3_0.ipynb
+++ b/dev/_downloads/0785ea6d45bde062e5beedda88131215/plot_release_highlights_1_3_0.ipynb
@@ -29,7 +29,7 @@
       },
       "outputs": [],
       "source": [
-        "import numpy as np\nfrom sklearn.cluster import HDBSCAN\nfrom sklearn.datasets import load_digits\nfrom sklearn.metrics import v_measure_score\n\nX, true_labels = load_digits(return_X_y=True)\nprint(f\"number of digits: {len(np.unique(true_labels))}\")\n\nhdbscan = HDBSCAN(min_cluster_size=15).fit(X)\nnon_noisy_labels = hdbscan.labels_[hdbscan.labels_ != -1]\nprint(f\"number of clusters found: {len(np.unique(non_noisy_labels))}\")\n\nprint(v_measure_score(true_labels[hdbscan.labels_ != -1], non_noisy_labels))"
+        "import numpy as np\n\nfrom sklearn.cluster import HDBSCAN\nfrom sklearn.datasets import load_digits\nfrom sklearn.metrics import v_measure_score\n\nX, true_labels = load_digits(return_X_y=True)\nprint(f\"number of digits: {len(np.unique(true_labels))}\")\n\nhdbscan = HDBSCAN(min_cluster_size=15).fit(X)\nnon_noisy_labels = hdbscan.labels_[hdbscan.labels_ != -1]\nprint(f\"number of clusters found: {len(np.unique(non_noisy_labels))}\")\n\nprint(v_measure_score(true_labels[hdbscan.labels_ != -1], non_noisy_labels))"
       ]
     },
     {
@@ -47,7 +47,7 @@
       },
       "outputs": [],
       "source": [
-        "import numpy as np\nfrom sklearn.preprocessing import TargetEncoder\n\nX = np.array([[\"cat\"] * 30 + [\"dog\"] * 20 + [\"snake\"] * 38], dtype=object).T\ny = [90.3] * 30 + [20.4] * 20 + [21.2] * 38\n\nenc = TargetEncoder(random_state=0)\nX_trans = enc.fit_transform(X, y)\n\nenc.encodings_"
+        "import numpy as np\n\nfrom sklearn.preprocessing import TargetEncoder\n\nX = np.array([[\"cat\"] * 30 + [\"dog\"] * 20 + [\"snake\"] * 38], dtype=object).T\ny = [90.3] * 30 + [20.4] * 20 + [21.2] * 38\n\nenc = TargetEncoder(random_state=0)\nX_trans = enc.fit_transform(X, y)\n\nenc.encodings_"
       ]
     },
     {
@@ -65,7 +65,7 @@
       },
       "outputs": [],
       "source": [
-        "import numpy as np\nfrom sklearn.tree import DecisionTreeClassifier\n\nX = np.array([0, 1, 6, np.nan]).reshape(-1, 1)\ny = [0, 0, 1, 1]\n\ntree = DecisionTreeClassifier(random_state=0).fit(X, y)\ntree.predict(X)"
+        "import numpy as np\n\nfrom sklearn.tree import DecisionTreeClassifier\n\nX = np.array([0, 1, 6, np.nan]).reshape(-1, 1)\ny = [0, 0, 1, 1]\n\ntree = DecisionTreeClassifier(random_state=0).fit(X, y)\ntree.predict(X)"
       ]
     },
     {
@@ -101,7 +101,7 @@
       },
       "outputs": [],
       "source": [
-        "import numpy as np\nfrom sklearn.model_selection import cross_val_score\nfrom sklearn.datasets import make_low_rank_matrix\nfrom sklearn.ensemble import HistGradientBoostingRegressor\n\nn_samples, n_features = 500, 10\nrng = np.random.RandomState(0)\nX = make_low_rank_matrix(n_samples, n_features, random_state=rng)\ncoef = rng.uniform(low=-10, high=20, size=n_features)\ny = rng.gamma(shape=2, scale=np.exp(X @ coef) / 2)\ngbdt = HistGradientBoostingRegressor(loss=\"gamma\")\ncross_val_score(gbdt, X, y).mean()"
+        "import numpy as np\n\nfrom sklearn.datasets import make_low_rank_matrix\nfrom sklearn.ensemble import HistGradientBoostingRegressor\nfrom sklearn.model_selection import cross_val_score\n\nn_samples, n_features = 500, 10\nrng = np.random.RandomState(0)\nX = make_low_rank_matrix(n_samples, n_features, random_state=rng)\ncoef = rng.uniform(low=-10, high=20, size=n_features)\ny = rng.gamma(shape=2, scale=np.exp(X @ coef) / 2)\ngbdt = HistGradientBoostingRegressor(loss=\"gamma\")\ncross_val_score(gbdt, X, y).mean()"
       ]
     },
     {
@@ -119,7 +119,7 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.preprocessing import OrdinalEncoder\nimport numpy as np\n\nX = np.array(\n    [[\"dog\"] * 5 + [\"cat\"] * 20 + [\"rabbit\"] * 10 + [\"snake\"] * 3], dtype=object\n).T\nenc = OrdinalEncoder(min_frequency=6).fit(X)\nenc.infrequent_categories_"
+        "import numpy as np\n\nfrom sklearn.preprocessing import OrdinalEncoder\n\nX = np.array(\n    [[\"dog\"] * 5 + [\"cat\"] * 20 + [\"rabbit\"] * 10 + [\"snake\"] * 3], dtype=object\n).T\nenc = OrdinalEncoder(min_frequency=6).fit(X)\nenc.infrequent_categories_"
       ]
     }
   ],
@@ -139,7 +139,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/07882f339cdda68a73e6335f34418af6/plot_theilsen.zip b/dev/_downloads/07882f339cdda68a73e6335f34418af6/plot_theilsen.zip
index f8d96946e6078..69fb6a70a6bdc 100644
Binary files a/dev/_downloads/07882f339cdda68a73e6335f34418af6/plot_theilsen.zip and b/dev/_downloads/07882f339cdda68a73e6335f34418af6/plot_theilsen.zip differ
diff --git a/dev/_downloads/07960f9087d379e9d0da6350d6ee3f41/plot_classification_probability.ipynb b/dev/_downloads/07960f9087d379e9d0da6350d6ee3f41/plot_classification_probability.ipynb
index 92ed174a58bd0..04d8e35a94a17 100644
--- a/dev/_downloads/07960f9087d379e9d0da6350d6ee3f41/plot_classification_probability.ipynb
+++ b/dev/_downloads/07960f9087d379e9d0da6350d6ee3f41/plot_classification_probability.ipynb
@@ -121,7 +121,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/07f60cdd0b8c83dd80140be675140b02/plot_face_compress.zip b/dev/_downloads/07f60cdd0b8c83dd80140be675140b02/plot_face_compress.zip
index 77f82cac9f6a7..7b88695bbfeb2 100644
Binary files a/dev/_downloads/07f60cdd0b8c83dd80140be675140b02/plot_face_compress.zip and b/dev/_downloads/07f60cdd0b8c83dd80140be675140b02/plot_face_compress.zip differ
diff --git a/dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip b/dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip
index b74958f76e2d4..fbb05aa5bc96e 100644
Binary files a/dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip and b/dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip differ
diff --git a/dev/_downloads/0837676cf643e44f0684e848d0967551/plot_compare_cross_decomposition.ipynb b/dev/_downloads/0837676cf643e44f0684e848d0967551/plot_compare_cross_decomposition.ipynb
index 121b4c5c10064..7eb051b228020 100644
--- a/dev/_downloads/0837676cf643e44f0684e848d0967551/plot_compare_cross_decomposition.ipynb
+++ b/dev/_downloads/0837676cf643e44f0684e848d0967551/plot_compare_cross_decomposition.ipynb
@@ -143,7 +143,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/083d8568c199bebbc1a847fc6c917e9e/plot_kernel_approximation.ipynb b/dev/_downloads/083d8568c199bebbc1a847fc6c917e9e/plot_kernel_approximation.ipynb
index f6fc72a337896..81439217c92ff 100644
--- a/dev/_downloads/083d8568c199bebbc1a847fc6c917e9e/plot_kernel_approximation.ipynb
+++ b/dev/_downloads/083d8568c199bebbc1a847fc6c917e9e/plot_kernel_approximation.ipynb
@@ -78,7 +78,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/0919b8971cbb94ffb3b06c80621a3283/plot_label_propagation_structure.zip b/dev/_downloads/0919b8971cbb94ffb3b06c80621a3283/plot_label_propagation_structure.zip
index c74b93c7b8124..a5d5e3dae04b3 100644
Binary files a/dev/_downloads/0919b8971cbb94ffb3b06c80621a3283/plot_label_propagation_structure.zip and b/dev/_downloads/0919b8971cbb94ffb3b06c80621a3283/plot_label_propagation_structure.zip differ
diff --git a/dev/_downloads/0a3f5445678ab43e77a0d7602ce7e53a/plot_nca_illustration.zip b/dev/_downloads/0a3f5445678ab43e77a0d7602ce7e53a/plot_nca_illustration.zip
index 927e6c2492d17..fa8450b3e402e 100644
Binary files a/dev/_downloads/0a3f5445678ab43e77a0d7602ce7e53a/plot_nca_illustration.zip and b/dev/_downloads/0a3f5445678ab43e77a0d7602ce7e53a/plot_nca_illustration.zip differ
diff --git a/dev/_downloads/0a90f2b8e2dadb7d37ca67b3f7adb656/plot_gradient_boosting_regularization.ipynb b/dev/_downloads/0a90f2b8e2dadb7d37ca67b3f7adb656/plot_gradient_boosting_regularization.ipynb
index 1b90aa802551f..1ab8600b968f4 100644
--- a/dev/_downloads/0a90f2b8e2dadb7d37ca67b3f7adb656/plot_gradient_boosting_regularization.ipynb
+++ b/dev/_downloads/0a90f2b8e2dadb7d37ca67b3f7adb656/plot_gradient_boosting_regularization.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/0aadb4e0dc9f402704c8a56152f01083/plot_lasso_dense_vs_sparse_data.ipynb b/dev/_downloads/0aadb4e0dc9f402704c8a56152f01083/plot_lasso_dense_vs_sparse_data.ipynb
index b8665e8bd4f5f..68a632e1f6238 100644
--- a/dev/_downloads/0aadb4e0dc9f402704c8a56152f01083/plot_lasso_dense_vs_sparse_data.ipynb
+++ b/dev/_downloads/0aadb4e0dc9f402704c8a56152f01083/plot_lasso_dense_vs_sparse_data.ipynb
@@ -71,7 +71,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/0af0092c704518874f82d38d725bb97f/plot_dict_face_patches.ipynb b/dev/_downloads/0af0092c704518874f82d38d725bb97f/plot_dict_face_patches.ipynb
index cda258c1b1499..d933dc2cf11fc 100644
--- a/dev/_downloads/0af0092c704518874f82d38d725bb97f/plot_dict_face_patches.ipynb
+++ b/dev/_downloads/0af0092c704518874f82d38d725bb97f/plot_dict_face_patches.ipynb
@@ -89,7 +89,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/0afbdae4d5c21dfbf1e7f98e95e973c5/plot_separating_hyperplane.zip b/dev/_downloads/0afbdae4d5c21dfbf1e7f98e95e973c5/plot_separating_hyperplane.zip
index 73ee57cfbf7a8..b6cf6bb91e274 100644
Binary files a/dev/_downloads/0afbdae4d5c21dfbf1e7f98e95e973c5/plot_separating_hyperplane.zip and b/dev/_downloads/0afbdae4d5c21dfbf1e7f98e95e973c5/plot_separating_hyperplane.zip differ
diff --git a/dev/_downloads/0b03eb096c3d32195335ba930a0b3662/plot_digits_agglomeration.zip b/dev/_downloads/0b03eb096c3d32195335ba930a0b3662/plot_digits_agglomeration.zip
index 003f50febb1af..17ed42e4ae589 100644
Binary files a/dev/_downloads/0b03eb096c3d32195335ba930a0b3662/plot_digits_agglomeration.zip and b/dev/_downloads/0b03eb096c3d32195335ba930a0b3662/plot_digits_agglomeration.zip differ
diff --git a/dev/_downloads/0b569e153dc27fb6eeb5063e271e8921/plot_rfe_digits.zip b/dev/_downloads/0b569e153dc27fb6eeb5063e271e8921/plot_rfe_digits.zip
index 8c443c71ea8a9..ff69557de5c40 100644
Binary files a/dev/_downloads/0b569e153dc27fb6eeb5063e271e8921/plot_rfe_digits.zip and b/dev/_downloads/0b569e153dc27fb6eeb5063e271e8921/plot_rfe_digits.zip differ
diff --git a/dev/_downloads/0b601219a14824c971bbf8bb797e8973/plot_logistic_path.ipynb b/dev/_downloads/0b601219a14824c971bbf8bb797e8973/plot_logistic_path.ipynb
index 80c690384cf63..520a24df71f6b 100644
--- a/dev/_downloads/0b601219a14824c971bbf8bb797e8973/plot_logistic_path.ipynb
+++ b/dev/_downloads/0b601219a14824c971bbf8bb797e8973/plot_logistic_path.ipynb
@@ -125,7 +125,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/0c15970ac17183d2bf864a9563081aeb/plot_calibration.ipynb b/dev/_downloads/0c15970ac17183d2bf864a9563081aeb/plot_calibration.ipynb
index b7e9d9c2eb660..a95223c49a051 100644
--- a/dev/_downloads/0c15970ac17183d2bf864a9563081aeb/plot_calibration.ipynb
+++ b/dev/_downloads/0c15970ac17183d2bf864a9563081aeb/plot_calibration.ipynb
@@ -89,7 +89,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/0c988b0c2bea0040fec13fe1055db95c/plot_pca_vs_fa_model_selection.ipynb b/dev/_downloads/0c988b0c2bea0040fec13fe1055db95c/plot_pca_vs_fa_model_selection.ipynb
index 8547a976c2278..9dc05afff8d60 100644
--- a/dev/_downloads/0c988b0c2bea0040fec13fe1055db95c/plot_pca_vs_fa_model_selection.ipynb
+++ b/dev/_downloads/0c988b0c2bea0040fec13fe1055db95c/plot_pca_vs_fa_model_selection.ipynb
@@ -71,7 +71,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/0dd5e0a7942f1446accf7dffd47aed28/plot_label_propagation_digits_active_learning.py b/dev/_downloads/0dd5e0a7942f1446accf7dffd47aed28/plot_label_propagation_digits_active_learning.py
index 1e03f528acdb8..eda6804fe3863 100644
--- a/dev/_downloads/0dd5e0a7942f1446accf7dffd47aed28/plot_label_propagation_digits_active_learning.py
+++ b/dev/_downloads/0dd5e0a7942f1446accf7dffd47aed28/plot_label_propagation_digits_active_learning.py
@@ -1,7 +1,7 @@
 """
-========================================
-Label Propagation digits active learning
-========================================
+=========================================
+Label Propagation digits: Active learning
+=========================================
 
 Demonstrates an active learning technique to learn handwritten digits
 using label propagation.
@@ -108,7 +108,7 @@
             sub.axis("off")
 
         # labeling 5 points, remote from labeled set
-        (delete_index,) = np.where(unlabeled_indices == image_index)
+        (delete_index,) = (unlabeled_indices == image_index).nonzero()
         delete_indices = np.concatenate((delete_indices, delete_index))
 
     unlabeled_indices = np.delete(unlabeled_indices, delete_indices)
diff --git a/dev/_downloads/1003e2b79d0fc902b8f0cbd0f449fd06/plot_birch_vs_minibatchkmeans.zip b/dev/_downloads/1003e2b79d0fc902b8f0cbd0f449fd06/plot_birch_vs_minibatchkmeans.zip
index e8c840d8eebcb..4b93480186cc7 100644
Binary files a/dev/_downloads/1003e2b79d0fc902b8f0cbd0f449fd06/plot_birch_vs_minibatchkmeans.zip and b/dev/_downloads/1003e2b79d0fc902b8f0cbd0f449fd06/plot_birch_vs_minibatchkmeans.zip differ
diff --git a/dev/_downloads/1054d40caffbd65c52b20dac784c7c5c/plot_elastic_net_precomputed_gram_matrix_with_weighted_samples.ipynb b/dev/_downloads/1054d40caffbd65c52b20dac784c7c5c/plot_elastic_net_precomputed_gram_matrix_with_weighted_samples.ipynb
index f4e18eee9ba49..c0b450cfe188a 100644
--- a/dev/_downloads/1054d40caffbd65c52b20dac784c7c5c/plot_elastic_net_precomputed_gram_matrix_with_weighted_samples.ipynb
+++ b/dev/_downloads/1054d40caffbd65c52b20dac784c7c5c/plot_elastic_net_precomputed_gram_matrix_with_weighted_samples.ipynb
@@ -89,7 +89,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/10754505339af88c10b8a48127535a4a/plot_quantile_regression.ipynb b/dev/_downloads/10754505339af88c10b8a48127535a4a/plot_quantile_regression.ipynb
index 005f23eb31b85..fd238b3582703 100644
--- a/dev/_downloads/10754505339af88c10b8a48127535a4a/plot_quantile_regression.ipynb
+++ b/dev/_downloads/10754505339af88c10b8a48127535a4a/plot_quantile_regression.ipynb
@@ -197,7 +197,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/10bb40e21b74618cdeed618ff1eae595/plot_det.ipynb b/dev/_downloads/10bb40e21b74618cdeed618ff1eae595/plot_det.ipynb
index 511aff4eb3b22..bf30f161d76de 100644
--- a/dev/_downloads/10bb40e21b74618cdeed618ff1eae595/plot_det.ipynb
+++ b/dev/_downloads/10bb40e21b74618cdeed618ff1eae595/plot_det.ipynb
@@ -40,7 +40,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Define the classifiers\n\nHere we define two different classifiers. The goal is to visually compare their\nstatistical performance across thresholds using the ROC and DET curves. There\nis no particular reason why these classifiers are chosen other classifiers\navailable in scikit-learn.\n\n"
+        "## Define the classifiers\n\nHere we define two different classifiers. The goal is to visually compare their\nstatistical performance across thresholds using the ROC and DET curves.\n\n"
       ]
     },
     {
@@ -51,14 +51,14 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.ensemble import RandomForestClassifier\nfrom sklearn.pipeline import make_pipeline\nfrom sklearn.svm import LinearSVC\n\nclassifiers = {\n    \"Linear SVM\": make_pipeline(StandardScaler(), LinearSVC(C=0.025)),\n    \"Random Forest\": RandomForestClassifier(\n        max_depth=5, n_estimators=10, max_features=1\n    ),\n}"
+        "from sklearn.dummy import DummyClassifier\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.pipeline import make_pipeline\nfrom sklearn.svm import LinearSVC\n\nclassifiers = {\n    \"Linear SVM\": make_pipeline(StandardScaler(), LinearSVC(C=0.025)),\n    \"Random Forest\": RandomForestClassifier(\n        max_depth=5, n_estimators=10, max_features=1, random_state=0\n    ),\n    \"Non-informative baseline\": DummyClassifier(),\n}"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Plot ROC and DET curves\n\nDET curves are commonly plotted in normal deviate scale. To achieve this the\nDET display transforms the error rates as returned by the\n:func:`~sklearn.metrics.det_curve` and the axis scale using\n`scipy.stats.norm`.\n\n"
+        "## Compare ROC and DET curves\n\nDET curves are commonly plotted in normal deviate scale. To achieve this the\nDET display transforms the error rates as returned by the\n:func:`~sklearn.metrics.det_curve` and the axis scale using\n`scipy.stats.norm`.\n\n"
       ]
     },
     {
@@ -69,14 +69,14 @@
       },
       "outputs": [],
       "source": [
-        "import matplotlib.pyplot as plt\n\nfrom sklearn.metrics import DetCurveDisplay, RocCurveDisplay\n\nfig, [ax_roc, ax_det] = plt.subplots(1, 2, figsize=(11, 5))\n\nfor name, clf in classifiers.items():\n    clf.fit(X_train, y_train)\n\n    RocCurveDisplay.from_estimator(clf, X_test, y_test, ax=ax_roc, name=name)\n    DetCurveDisplay.from_estimator(clf, X_test, y_test, ax=ax_det, name=name)\n\nax_roc.set_title(\"Receiver Operating Characteristic (ROC) curves\")\nax_det.set_title(\"Detection Error Tradeoff (DET) curves\")\n\nax_roc.grid(linestyle=\"--\")\nax_det.grid(linestyle=\"--\")\n\nplt.legend()\nplt.show()"
+        "import matplotlib.pyplot as plt\n\nfrom sklearn.dummy import DummyClassifier\nfrom sklearn.metrics import DetCurveDisplay, RocCurveDisplay\n\nfig, [ax_roc, ax_det] = plt.subplots(1, 2, figsize=(11, 5))\n\nax_roc.set_title(\"Receiver Operating Characteristic (ROC) curves\")\nax_det.set_title(\"Detection Error Tradeoff (DET) curves\")\n\nax_roc.grid(linestyle=\"--\")\nax_det.grid(linestyle=\"--\")\n\nfor name, clf in classifiers.items():\n    (color, linestyle) = (\n        (\"black\", \"--\") if name == \"Non-informative baseline\" else (None, None)\n    )\n    clf.fit(X_train, y_train)\n    RocCurveDisplay.from_estimator(\n        clf, X_test, y_test, ax=ax_roc, name=name, color=color, linestyle=linestyle\n    )\n    DetCurveDisplay.from_estimator(\n        clf, X_test, y_test, ax=ax_det, name=name, color=color, linestyle=linestyle\n    )\n\nplt.legend()\nplt.show()"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Notice that it is easier to visually assess the overall performance of\ndifferent classification algorithms using DET curves than using ROC curves. As\nROC curves are plot in a linear scale, different classifiers usually appear\nsimilar for a large part of the plot and differ the most in the top left\ncorner of the graph. On the other hand, because DET curves represent straight\nlines in normal deviate scale, they tend to be distinguishable as a whole and\nthe area of interest spans a large part of the plot.\n\nDET curves give direct feedback of the detection error tradeoff to aid in\noperating point analysis. The user can then decide the FNR they are willing to\naccept at the expense of the FPR (or vice-versa).\n\n"
+        "Notice that it is easier to visually assess the overall performance of\ndifferent classification algorithms using DET curves than using ROC curves. As\nROC curves are plot in a linear scale, different classifiers usually appear\nsimilar for a large part of the plot and differ the most in the top left\ncorner of the graph. On the other hand, because DET curves represent straight\nlines in normal deviate scale, they tend to be distinguishable as a whole and\nthe area of interest spans a large part of the plot.\n\nDET curves give direct feedback of the detection error tradeoff to aid in\noperating point analysis. The user can then decide the FNR they are willing to\naccept at the expense of the FPR (or vice-versa).\n\n## Non-informative classifier baseline for the ROC and DET curves\n\nThe diagonal black-dotted lines in the plots above correspond to a\n:class:`~sklearn.dummy.DummyClassifier` using the default \"prior\" strategy, to\nserve as baseline for comparison with other classifiers. This classifier makes\nconstant predictions, independent of the input features in `X`, making it a\nnon-informative classifier.\n\nTo further understand the non-informative baseline of the ROC and DET curves,\nwe recall the following mathematical definitions:\n\n$\\text{FPR} = \\frac{\\text{FP}}{\\text{FP} + \\text{TN}}$\n\n$\\text{FNR} = \\frac{\\text{FN}}{\\text{TP} + \\text{FN}}$\n\n$\\text{TPR} = \\frac{\\text{TP}}{\\text{TP} + \\text{FN}}$\n\nA classifier that always predict the positive class would have no true\nnegatives nor false negatives, giving $\\text{FPR} = \\text{TPR} = 1$ and\n$\\text{FNR} = 0$, i.e.:\n\n- a single point in the upper right corner of the ROC plane,\n- a single point in the lower right corner of the DET plane.\n\nSimilarly, a classifier that always predict the negative class would have no\ntrue positives nor false positives, thus $\\text{FPR} = \\text{TPR} = 0$\nand $\\text{FNR} = 1$, i.e.:\n\n- a single point in the lower left corner of the ROC plane,\n- a single point in the upper left corner of the DET plane.\n\n"
       ]
     }
   ],
@@ -96,7 +96,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/1160eee327e01cad702c4964e1c69f45/plot_custom_kernel.ipynb b/dev/_downloads/1160eee327e01cad702c4964e1c69f45/plot_custom_kernel.ipynb
index ce703f266eaa2..d6459a73a0166 100644
--- a/dev/_downloads/1160eee327e01cad702c4964e1c69f45/plot_custom_kernel.ipynb
+++ b/dev/_downloads/1160eee327e01cad702c4964e1c69f45/plot_custom_kernel.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/11651f4eebcf15ff7959e210e82dc45c/plot_linear_model_coefficient_interpretation.zip b/dev/_downloads/11651f4eebcf15ff7959e210e82dc45c/plot_linear_model_coefficient_interpretation.zip
index 544da4dabd377..9c692281d5746 100644
Binary files a/dev/_downloads/11651f4eebcf15ff7959e210e82dc45c/plot_linear_model_coefficient_interpretation.zip and b/dev/_downloads/11651f4eebcf15ff7959e210e82dc45c/plot_linear_model_coefficient_interpretation.zip differ
diff --git a/dev/_downloads/1180cd432166ec9764125420df10ec77/plot_poisson_regression_non_normal_loss.zip b/dev/_downloads/1180cd432166ec9764125420df10ec77/plot_poisson_regression_non_normal_loss.zip
index f7fa3fc6283d2..bcb1240d46614 100644
Binary files a/dev/_downloads/1180cd432166ec9764125420df10ec77/plot_poisson_regression_non_normal_loss.zip and b/dev/_downloads/1180cd432166ec9764125420df10ec77/plot_poisson_regression_non_normal_loss.zip differ
diff --git a/dev/_downloads/11cbd2b41092ecf5ba8083dfb7bae25a/plot_random_forest_regression_multioutput.ipynb b/dev/_downloads/11cbd2b41092ecf5ba8083dfb7bae25a/plot_random_forest_regression_multioutput.ipynb
index 0db66c054b24e..95439a715c096 100644
--- a/dev/_downloads/11cbd2b41092ecf5ba8083dfb7bae25a/plot_random_forest_regression_multioutput.ipynb
+++ b/dev/_downloads/11cbd2b41092ecf5ba8083dfb7bae25a/plot_random_forest_regression_multioutput.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/1273a3baa87138f2b817bfc78fe7ecb4/plot_feature_union.ipynb b/dev/_downloads/1273a3baa87138f2b817bfc78fe7ecb4/plot_feature_union.ipynb
index 7c5133c80e09f..f554e0a5c4d5a 100644
--- a/dev/_downloads/1273a3baa87138f2b817bfc78fe7ecb4/plot_feature_union.ipynb
+++ b/dev/_downloads/1273a3baa87138f2b817bfc78fe7ecb4/plot_feature_union.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/129e49615b0c1e7782384456dd7c3ec3/plot_rfe_with_cross_validation.zip b/dev/_downloads/129e49615b0c1e7782384456dd7c3ec3/plot_rfe_with_cross_validation.zip
index 2b2cc755a021f..4a509c1f26b46 100644
Binary files a/dev/_downloads/129e49615b0c1e7782384456dd7c3ec3/plot_rfe_with_cross_validation.zip and b/dev/_downloads/129e49615b0c1e7782384456dd7c3ec3/plot_rfe_with_cross_validation.zip differ
diff --git a/dev/_downloads/12a392e818ac5fa47dd91461855f3f77/plot_linearsvc_support_vectors.ipynb b/dev/_downloads/12a392e818ac5fa47dd91461855f3f77/plot_linearsvc_support_vectors.ipynb
index 8247b4d25deab..e8679fc8d86de 100644
--- a/dev/_downloads/12a392e818ac5fa47dd91461855f3f77/plot_linearsvc_support_vectors.ipynb
+++ b/dev/_downloads/12a392e818ac5fa47dd91461855f3f77/plot_linearsvc_support_vectors.ipynb
@@ -15,7 +15,7 @@
       },
       "outputs": [],
       "source": [
-        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nfrom sklearn.datasets import make_blobs\nfrom sklearn.inspection import DecisionBoundaryDisplay\nfrom sklearn.svm import LinearSVC\n\nX, y = make_blobs(n_samples=40, centers=2, random_state=0)\n\nplt.figure(figsize=(10, 5))\nfor i, C in enumerate([1, 100]):\n    # \"hinge\" is the standard SVM loss\n    clf = LinearSVC(C=C, loss=\"hinge\", random_state=42).fit(X, y)\n    # obtain the support vectors through the decision function\n    decision_function = clf.decision_function(X)\n    # we can also calculate the decision function manually\n    # decision_function = np.dot(X, clf.coef_[0]) + clf.intercept_[0]\n    # The support vectors are the samples that lie within the margin\n    # boundaries, whose size is conventionally constrained to 1\n    support_vector_indices = np.where(np.abs(decision_function) <= 1 + 1e-15)[0]\n    support_vectors = X[support_vector_indices]\n\n    plt.subplot(1, 2, i + 1)\n    plt.scatter(X[:, 0], X[:, 1], c=y, s=30, cmap=plt.cm.Paired)\n    ax = plt.gca()\n    DecisionBoundaryDisplay.from_estimator(\n        clf,\n        X,\n        ax=ax,\n        grid_resolution=50,\n        plot_method=\"contour\",\n        colors=\"k\",\n        levels=[-1, 0, 1],\n        alpha=0.5,\n        linestyles=[\"--\", \"-\", \"--\"],\n    )\n    plt.scatter(\n        support_vectors[:, 0],\n        support_vectors[:, 1],\n        s=100,\n        linewidth=1,\n        facecolors=\"none\",\n        edgecolors=\"k\",\n    )\n    plt.title(\"C=\" + str(C))\nplt.tight_layout()\nplt.show()"
+        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nfrom sklearn.datasets import make_blobs\nfrom sklearn.inspection import DecisionBoundaryDisplay\nfrom sklearn.svm import LinearSVC\n\nX, y = make_blobs(n_samples=40, centers=2, random_state=0)\n\nplt.figure(figsize=(10, 5))\nfor i, C in enumerate([1, 100]):\n    # \"hinge\" is the standard SVM loss\n    clf = LinearSVC(C=C, loss=\"hinge\", random_state=42).fit(X, y)\n    # obtain the support vectors through the decision function\n    decision_function = clf.decision_function(X)\n    # we can also calculate the decision function manually\n    # decision_function = np.dot(X, clf.coef_[0]) + clf.intercept_[0]\n    # The support vectors are the samples that lie within the margin\n    # boundaries, whose size is conventionally constrained to 1\n    support_vector_indices = (np.abs(decision_function) <= 1 + 1e-15).nonzero()[0]\n    support_vectors = X[support_vector_indices]\n\n    plt.subplot(1, 2, i + 1)\n    plt.scatter(X[:, 0], X[:, 1], c=y, s=30, cmap=plt.cm.Paired)\n    ax = plt.gca()\n    DecisionBoundaryDisplay.from_estimator(\n        clf,\n        X,\n        ax=ax,\n        grid_resolution=50,\n        plot_method=\"contour\",\n        colors=\"k\",\n        levels=[-1, 0, 1],\n        alpha=0.5,\n        linestyles=[\"--\", \"-\", \"--\"],\n    )\n    plt.scatter(\n        support_vectors[:, 0],\n        support_vectors[:, 1],\n        s=100,\n        linewidth=1,\n        facecolors=\"none\",\n        edgecolors=\"k\",\n    )\n    plt.title(\"C=\" + str(C))\nplt.tight_layout()\nplt.show()"
       ]
     }
   ],
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/12b6dbb270865986bd1c9bbf7ce24cb0/plot_voting_decision_regions.py b/dev/_downloads/12b6dbb270865986bd1c9bbf7ce24cb0/plot_voting_decision_regions.py
index d40d831fb911f..57f3f4b22b947 100644
--- a/dev/_downloads/12b6dbb270865986bd1c9bbf7ce24cb0/plot_voting_decision_regions.py
+++ b/dev/_downloads/12b6dbb270865986bd1c9bbf7ce24cb0/plot_voting_decision_regions.py
@@ -1,55 +1,111 @@
 """
-==================================================
-Plot the decision boundaries of a VotingClassifier
-==================================================
+===============================================================
+Visualizing the probabilistic predictions of a VotingClassifier
+===============================================================
 
 .. currentmodule:: sklearn
 
-Plot the decision boundaries of a :class:`~ensemble.VotingClassifier` for two
-features of the Iris dataset.
+Plot the predicted class probabilities in a toy dataset predicted by three
+different classifiers and averaged by the :class:`~ensemble.VotingClassifier`.
 
-Plot the class probabilities of the first sample in a toy dataset predicted by
-three different classifiers and averaged by the
-:class:`~ensemble.VotingClassifier`.
+First, three linear classifiers are initialized. Two are spline models with
+interaction terms, one using constant extrapolation and the other using periodic
+extrapolation. The third classifier is a :class:`~kernel_approximation.Nystroem`
+with the default "rbf" kernel.
 
-First, three exemplary classifiers are initialized
-(:class:`~tree.DecisionTreeClassifier`,
-:class:`~neighbors.KNeighborsClassifier`, and :class:`~svm.SVC`) and used to
-initialize a soft-voting :class:`~ensemble.VotingClassifier` with weights `[2,
-1, 2]`, which means that the predicted probabilities of the
-:class:`~tree.DecisionTreeClassifier` and :class:`~svm.SVC` each count 2 times
-as much as the weights of the :class:`~neighbors.KNeighborsClassifier`
-classifier when the averaged probability is calculated.
+In the first part of this example, these three classifiers are used to
+demonstrate soft-voting using :class:`~ensemble.VotingClassifier` with weighted
+average. We set `weights=[2, 1, 3]`, meaning the constant extrapolation spline
+model's predictions are weighted twice as much as the periodic spline model's,
+and the Nystroem model's predictions are weighted three times as much as the
+periodic spline.
+
+The second part demonstrates how soft predictions can be converted into hard
+predictions.
 
 """
 
 # Authors: The scikit-learn developers
 # SPDX-License-Identifier: BSD-3-Clause
 
-from itertools import product
+# %%
+# We first generate a noisy XOR dataset, which is a binary classification task.
 
 import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+from matplotlib.colors import ListedColormap
+
+n_samples = 500
+rng = np.random.default_rng(0)
+feature_names = ["Feature #0", "Feature #1"]
+common_scatter_plot_params = dict(
+    cmap=ListedColormap(["tab:red", "tab:blue"]),
+    edgecolor="white",
+    linewidth=1,
+)
+
+xor = pd.DataFrame(
+    np.random.RandomState(0).uniform(low=-1, high=1, size=(n_samples, 2)),
+    columns=feature_names,
+)
+noise = rng.normal(loc=0, scale=0.1, size=(n_samples, 2))
+target_xor = np.logical_xor(
+    xor["Feature #0"] + noise[:, 0] > 0, xor["Feature #1"] + noise[:, 1] > 0
+)
+
+X = xor[feature_names]
+y = target_xor.astype(np.int32)
+
+fig, ax = plt.subplots()
+ax.scatter(X["Feature #0"], X["Feature #1"], c=y, **common_scatter_plot_params)
+ax.set_title("The XOR dataset")
+plt.show()
+
+# %%
+# Due to the inherent non-linear separability of the XOR dataset, tree-based
+# models would often be preferred. However, appropriate feature engineering
+# combined with a linear model can yield effective results, with the added
+# benefit of producing better-calibrated probabilities for samples located in
+# the transition regions affected by noise.
+#
+# We define and fit the models on the whole dataset.
 
-from sklearn import datasets
 from sklearn.ensemble import VotingClassifier
-from sklearn.inspection import DecisionBoundaryDisplay
-from sklearn.neighbors import KNeighborsClassifier
-from sklearn.svm import SVC
-from sklearn.tree import DecisionTreeClassifier
-
-# Loading some example data
-iris = datasets.load_iris()
-X = iris.data[:, [0, 2]]
-y = iris.target
-
-# Training classifiers
-clf1 = DecisionTreeClassifier(max_depth=4)
-clf2 = KNeighborsClassifier(n_neighbors=7)
-clf3 = SVC(gamma=0.1, kernel="rbf", probability=True)
+from sklearn.kernel_approximation import Nystroem
+from sklearn.linear_model import LogisticRegression
+from sklearn.pipeline import make_pipeline
+from sklearn.preprocessing import PolynomialFeatures, SplineTransformer, StandardScaler
+
+clf1 = make_pipeline(
+    SplineTransformer(degree=2, n_knots=2),
+    PolynomialFeatures(interaction_only=True),
+    LogisticRegression(C=10),
+)
+clf2 = make_pipeline(
+    SplineTransformer(
+        degree=2,
+        n_knots=4,
+        extrapolation="periodic",
+        include_bias=True,
+    ),
+    PolynomialFeatures(interaction_only=True),
+    LogisticRegression(C=10),
+)
+clf3 = make_pipeline(
+    StandardScaler(),
+    Nystroem(gamma=2, random_state=0),
+    LogisticRegression(C=10),
+)
+weights = [2, 1, 3]
 eclf = VotingClassifier(
-    estimators=[("dt", clf1), ("knn", clf2), ("svc", clf3)],
+    estimators=[
+        ("constant splines model", clf1),
+        ("periodic splines model", clf2),
+        ("nystroem model", clf3),
+    ],
     voting="soft",
-    weights=[2, 1, 2],
+    weights=weights,
 )
 
 clf1.fit(X, y)
@@ -57,17 +113,106 @@
 clf3.fit(X, y)
 eclf.fit(X, y)
 
-# Plotting decision regions
-f, axarr = plt.subplots(2, 2, sharex="col", sharey="row", figsize=(10, 8))
-for idx, clf, tt in zip(
+# %%
+# Finally we use :class:`~inspection.DecisionBoundaryDisplay` to plot the
+# predicted probabilities. By using a diverging colormap (such as `"RdBu"`), we
+# can ensure that darker colors correspond to `predict_proba` close to either 0
+# or 1, and white corresponds to `predict_proba` of 0.5.
+
+from itertools import product
+
+from sklearn.inspection import DecisionBoundaryDisplay
+
+fig, axarr = plt.subplots(2, 2, sharex="col", sharey="row", figsize=(10, 8))
+for idx, clf, title in zip(
     product([0, 1], [0, 1]),
     [clf1, clf2, clf3, eclf],
-    ["Decision Tree (depth=4)", "KNN (k=7)", "Kernel SVM", "Soft Voting"],
+    [
+        "Splines with\nconstant extrapolation",
+        "Splines with\nperiodic extrapolation",
+        "RBF Nystroem",
+        "Soft Voting",
+    ],
 ):
-    DecisionBoundaryDisplay.from_estimator(
-        clf, X, alpha=0.4, ax=axarr[idx[0], idx[1]], response_method="predict"
+    disp = DecisionBoundaryDisplay.from_estimator(
+        clf,
+        X,
+        response_method="predict_proba",
+        plot_method="pcolormesh",
+        cmap="RdBu",
+        alpha=0.8,
+        ax=axarr[idx[0], idx[1]],
+    )
+    axarr[idx[0], idx[1]].scatter(
+        X["Feature #0"],
+        X["Feature #1"],
+        c=y,
+        **common_scatter_plot_params,
     )
-    axarr[idx[0], idx[1]].scatter(X[:, 0], X[:, 1], c=y, s=20, edgecolor="k")
-    axarr[idx[0], idx[1]].set_title(tt)
+    axarr[idx[0], idx[1]].set_title(title)
+    fig.colorbar(disp.surface_, ax=axarr[idx[0], idx[1]], label="Probability estimate")
 
 plt.show()
+
+# %%
+# As a sanity check, we can verify for a given sample that the probability
+# predicted by the :class:`~ensemble.VotingClassifier` is indeed the weighted
+# average of the individual classifiers' soft-predictions.
+#
+# In the case of binary classification such as in the present example, the
+# :term:`predict_proba` arrays contain the probability of belonging to class 0
+# (here in red) as the first entry, and the probability of belonging to class 1
+# (here in blue) as the second entry.
+
+test_sample = pd.DataFrame({"Feature #0": [-0.5], "Feature #1": [1.5]})
+predict_probas = [est.predict_proba(test_sample).ravel() for est in eclf.estimators_]
+for (est_name, _), est_probas in zip(eclf.estimators, predict_probas):
+    print(f"{est_name}'s predicted probabilities: {est_probas}")
+
+# %%
+print(
+    "Weighted average of soft-predictions: "
+    f"{np.dot(weights, predict_probas) / np.sum(weights)}"
+)
+
+# %%
+# We can see that manual calculation of predicted probabilities above is
+# equivalent to that produced by the `VotingClassifier`:
+
+print(
+    "Predicted probability of VotingClassifier: "
+    f"{eclf.predict_proba(test_sample).ravel()}"
+)
+
+# %%
+# To convert soft predictions into hard predictions when weights are provided,
+# the weighted average predicted probabilities are computed for each class.
+# Then, the final class label is then derived from the class label with the
+# highest average probability, which corresponds to the default threshold at
+# `predict_proba=0.5` in the case of binary classification.
+
+print(
+    "Class with the highest weighted average of soft-predictions: "
+    f"{np.argmax(np.dot(weights, predict_probas) / np.sum(weights))}"
+)
+
+# %%
+# This is equivalent to the output of `VotingClassifier`'s `predict` method:
+
+print(f"Predicted class of VotingClassifier: {eclf.predict(test_sample).ravel()}")
+
+# %%
+# Soft votes can be thresholded as for any other probabilistic classifier. This
+# allows you to set a threshold probability at which the positive class will be
+# predicted, instead of simply selecting the class with the highest predicted
+# probability.
+
+from sklearn.model_selection import FixedThresholdClassifier
+
+eclf_other_threshold = FixedThresholdClassifier(
+    eclf, threshold=0.7, response_method="predict_proba"
+).fit(X, y)
+print(
+    "Predicted class of thresholded VotingClassifier: "
+    f"{eclf_other_threshold.predict(test_sample)}"
+)
diff --git a/dev/_downloads/12fb3a51f4f60a9be2386b10ccc076cf/plot_gpr_noisy.zip b/dev/_downloads/12fb3a51f4f60a9be2386b10ccc076cf/plot_gpr_noisy.zip
index ca81d91fd57f8..2dc671d95d390 100644
Binary files a/dev/_downloads/12fb3a51f4f60a9be2386b10ccc076cf/plot_gpr_noisy.zip and b/dev/_downloads/12fb3a51f4f60a9be2386b10ccc076cf/plot_gpr_noisy.zip differ
diff --git a/dev/_downloads/133f2198d3ab792c75b39a63b0a99872/plot_cost_sensitive_learning.ipynb b/dev/_downloads/133f2198d3ab792c75b39a63b0a99872/plot_cost_sensitive_learning.ipynb
index 869ea6a90239a..6bdbcf53d1f1e 100644
--- a/dev/_downloads/133f2198d3ab792c75b39a63b0a99872/plot_cost_sensitive_learning.ipynb
+++ b/dev/_downloads/133f2198d3ab792c75b39a63b0a99872/plot_cost_sensitive_learning.ipynb
@@ -695,7 +695,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/138e7c706c17949c3098ff8074b03ce7/plot_release_highlights_1_2_0.py b/dev/_downloads/138e7c706c17949c3098ff8074b03ce7/plot_release_highlights_1_2_0.py
index e01372650b016..ee5316229dd9a 100644
--- a/dev/_downloads/138e7c706c17949c3098ff8074b03ce7/plot_release_highlights_1_2_0.py
+++ b/dev/_downloads/138e7c706c17949c3098ff8074b03ce7/plot_release_highlights_1_2_0.py
@@ -1,4 +1,4 @@
-# ruff: noqa
+# ruff: noqa: CPY001, E501
 """
 =======================================
 Release Highlights for scikit-learn 1.2
@@ -31,9 +31,10 @@
 # (some examples) <https://youtu.be/5bCg8VfX2x8>`__.
 
 import numpy as np
-from sklearn.datasets import load_iris
-from sklearn.preprocessing import StandardScaler, KBinsDiscretizer
+
 from sklearn.compose import ColumnTransformer
+from sklearn.datasets import load_iris
+from sklearn.preprocessing import KBinsDiscretizer, StandardScaler
 
 X, y = load_iris(as_frame=True, return_X_y=True)
 sepal_cols = ["sepal length (cm)", "sepal width (cm)"]
@@ -78,6 +79,7 @@
 # :class:`~metrics.PredictionErrorDisplay` provides a way to analyze regression
 # models in a qualitative manner.
 import matplotlib.pyplot as plt
+
 from sklearn.metrics import PredictionErrorDisplay
 
 fig, axs = plt.subplots(nrows=1, ncols=2, figsize=(12, 5))
@@ -109,8 +111,8 @@
 X = X.select_dtypes(["number", "category"]).drop(columns=["body"])
 
 # %%
-from sklearn.preprocessing import OrdinalEncoder
 from sklearn.pipeline import make_pipeline
+from sklearn.preprocessing import OrdinalEncoder
 
 categorical_features = ["pclass", "sex", "embarked"]
 model = make_pipeline(
diff --git a/dev/_downloads/1393861b58df827d4c681b80a5be2472/plot_kmeans_digits.zip b/dev/_downloads/1393861b58df827d4c681b80a5be2472/plot_kmeans_digits.zip
index 6d7528f5545e8..e2fb057fe8673 100644
Binary files a/dev/_downloads/1393861b58df827d4c681b80a5be2472/plot_kmeans_digits.zip and b/dev/_downloads/1393861b58df827d4c681b80a5be2472/plot_kmeans_digits.zip differ
diff --git a/dev/_downloads/13db5212719118ea59532c291af3a8f9/plot_bisect_kmeans.ipynb b/dev/_downloads/13db5212719118ea59532c291af3a8f9/plot_bisect_kmeans.ipynb
index a4da43b20db18..fa057883e818a 100644
--- a/dev/_downloads/13db5212719118ea59532c291af3a8f9/plot_bisect_kmeans.ipynb
+++ b/dev/_downloads/13db5212719118ea59532c291af3a8f9/plot_bisect_kmeans.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/151e78f803ff9f3154dd7dbbaf45e0b0/plot_release_highlights_1_6_0.zip b/dev/_downloads/151e78f803ff9f3154dd7dbbaf45e0b0/plot_release_highlights_1_6_0.zip
index 7b892e41f9869..4a045e76eb3ef 100644
Binary files a/dev/_downloads/151e78f803ff9f3154dd7dbbaf45e0b0/plot_release_highlights_1_6_0.zip and b/dev/_downloads/151e78f803ff9f3154dd7dbbaf45e0b0/plot_release_highlights_1_6_0.zip differ
diff --git a/dev/_downloads/1548afe7d3147cdbff3b5cba88ffc124/plot_sparse_coding.zip b/dev/_downloads/1548afe7d3147cdbff3b5cba88ffc124/plot_sparse_coding.zip
index 16410535d461d..02725cbebf6c6 100644
Binary files a/dev/_downloads/1548afe7d3147cdbff3b5cba88ffc124/plot_sparse_coding.zip and b/dev/_downloads/1548afe7d3147cdbff3b5cba88ffc124/plot_sparse_coding.zip differ
diff --git a/dev/_downloads/15dc6d7a809edf988a7328336a25faec/plot_column_transformer.ipynb b/dev/_downloads/15dc6d7a809edf988a7328336a25faec/plot_column_transformer.ipynb
index a87eb107a8983..a70973a044de2 100644
--- a/dev/_downloads/15dc6d7a809edf988a7328336a25faec/plot_column_transformer.ipynb
+++ b/dev/_downloads/15dc6d7a809edf988a7328336a25faec/plot_column_transformer.ipynb
@@ -143,7 +143,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/161b401efba4b01936c9871f71688386/plot_species_kde.zip b/dev/_downloads/161b401efba4b01936c9871f71688386/plot_species_kde.zip
index c2c92311d2a71..94966d51cf356 100644
Binary files a/dev/_downloads/161b401efba4b01936c9871f71688386/plot_species_kde.zip and b/dev/_downloads/161b401efba4b01936c9871f71688386/plot_species_kde.zip differ
diff --git a/dev/_downloads/16260993c16a6d249d6df4cb11cf8174/plot_theilsen.ipynb b/dev/_downloads/16260993c16a6d249d6df4cb11cf8174/plot_theilsen.ipynb
index d555e50c1ce25..6b9948ff6ffea 100644
--- a/dev/_downloads/16260993c16a6d249d6df4cb11cf8174/plot_theilsen.ipynb
+++ b/dev/_downloads/16260993c16a6d249d6df4cb11cf8174/plot_theilsen.ipynb
@@ -71,7 +71,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/1630d9144544d31ca40f70d5ce894b1c/plot_roc_curve_visualization_api.zip b/dev/_downloads/1630d9144544d31ca40f70d5ce894b1c/plot_roc_curve_visualization_api.zip
index 40dcdb3c27f00..af9fcb3365718 100644
Binary files a/dev/_downloads/1630d9144544d31ca40f70d5ce894b1c/plot_roc_curve_visualization_api.zip and b/dev/_downloads/1630d9144544d31ca40f70d5ce894b1c/plot_roc_curve_visualization_api.zip differ
diff --git a/dev/_downloads/175dc7cd79fedbe3d38829513430275a/plot_forest_importances.zip b/dev/_downloads/175dc7cd79fedbe3d38829513430275a/plot_forest_importances.zip
index f21cc18a61bd7..bb04f72dfb8b6 100644
Binary files a/dev/_downloads/175dc7cd79fedbe3d38829513430275a/plot_forest_importances.zip and b/dev/_downloads/175dc7cd79fedbe3d38829513430275a/plot_forest_importances.zip differ
diff --git a/dev/_downloads/179a84f8da8ce09af733c9a82135ca4d/plot_oneclass.ipynb b/dev/_downloads/179a84f8da8ce09af733c9a82135ca4d/plot_oneclass.ipynb
index 347dc701d7be1..5df4cd8b86920 100644
--- a/dev/_downloads/179a84f8da8ce09af733c9a82135ca4d/plot_oneclass.ipynb
+++ b/dev/_downloads/179a84f8da8ce09af733c9a82135ca4d/plot_oneclass.ipynb
@@ -57,7 +57,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/1916a2a88b97d84ae7b7fb44d3f5503b/plot_anomaly_comparison.ipynb b/dev/_downloads/1916a2a88b97d84ae7b7fb44d3f5503b/plot_anomaly_comparison.ipynb
index c257f2352a5bd..1385f8ca3b425 100644
--- a/dev/_downloads/1916a2a88b97d84ae7b7fb44d3f5503b/plot_anomaly_comparison.ipynb
+++ b/dev/_downloads/1916a2a88b97d84ae7b7fb44d3f5503b/plot_anomaly_comparison.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/1929d77b217d252c180ea45cab60f1c0/plot_kmeans_assumptions.zip b/dev/_downloads/1929d77b217d252c180ea45cab60f1c0/plot_kmeans_assumptions.zip
index a4be358e2934b..7a12fdce0add9 100644
Binary files a/dev/_downloads/1929d77b217d252c180ea45cab60f1c0/plot_kmeans_assumptions.zip and b/dev/_downloads/1929d77b217d252c180ea45cab60f1c0/plot_kmeans_assumptions.zip differ
diff --git a/dev/_downloads/1969c243dbe2c542ebd3887f2ecde211/plot_coin_ward_segmentation.zip b/dev/_downloads/1969c243dbe2c542ebd3887f2ecde211/plot_coin_ward_segmentation.zip
index a1672869efd60..3ab187c427a1b 100644
Binary files a/dev/_downloads/1969c243dbe2c542ebd3887f2ecde211/plot_coin_ward_segmentation.zip and b/dev/_downloads/1969c243dbe2c542ebd3887f2ecde211/plot_coin_ward_segmentation.zip differ
diff --git a/dev/_downloads/19e9c0cb24a132133cef3b311caaf199/plot_nca_illustration.ipynb b/dev/_downloads/19e9c0cb24a132133cef3b311caaf199/plot_nca_illustration.ipynb
index d2584fc0418f5..d2085838606e1 100644
--- a/dev/_downloads/19e9c0cb24a132133cef3b311caaf199/plot_nca_illustration.ipynb
+++ b/dev/_downloads/19e9c0cb24a132133cef3b311caaf199/plot_nca_illustration.ipynb
@@ -71,7 +71,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/1a2ab00bbfd4eb80e0afca13d83e2a14/plot_lda_qda.ipynb b/dev/_downloads/1a2ab00bbfd4eb80e0afca13d83e2a14/plot_lda_qda.ipynb
index dce74a932455b..f750392ff0a09 100644
--- a/dev/_downloads/1a2ab00bbfd4eb80e0afca13d83e2a14/plot_lda_qda.ipynb
+++ b/dev/_downloads/1a2ab00bbfd4eb80e0afca13d83e2a14/plot_lda_qda.ipynb
@@ -114,7 +114,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/1a643dc4b5b4cf8540a70446f140456f/plot_lasso_lasso_lars_elasticnet_path.ipynb b/dev/_downloads/1a643dc4b5b4cf8540a70446f140456f/plot_lasso_lasso_lars_elasticnet_path.ipynb
index 353624fc1d11f..7a3c1f8ce8689 100644
--- a/dev/_downloads/1a643dc4b5b4cf8540a70446f140456f/plot_lasso_lasso_lars_elasticnet_path.ipynb
+++ b/dev/_downloads/1a643dc4b5b4cf8540a70446f140456f/plot_lasso_lasso_lars_elasticnet_path.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/1acfe5256fc92dfaed2c1033304ed297/plot_gradient_boosting_regularization.zip b/dev/_downloads/1acfe5256fc92dfaed2c1033304ed297/plot_gradient_boosting_regularization.zip
index cdb1b93814876..f494521f177f4 100644
Binary files a/dev/_downloads/1acfe5256fc92dfaed2c1033304ed297/plot_gradient_boosting_regularization.zip and b/dev/_downloads/1acfe5256fc92dfaed2c1033304ed297/plot_gradient_boosting_regularization.zip differ
diff --git a/dev/_downloads/1b3f17ff0f112d5b77cbdb90f1c17046/plot_set_output.py b/dev/_downloads/1b3f17ff0f112d5b77cbdb90f1c17046/plot_set_output.py
index e74d94957c685..f3e5be13f5182 100644
--- a/dev/_downloads/1b3f17ff0f112d5b77cbdb90f1c17046/plot_set_output.py
+++ b/dev/_downloads/1b3f17ff0f112d5b77cbdb90f1c17046/plot_set_output.py
@@ -10,7 +10,7 @@
 the `set_output` method or globally by setting `set_config(transform_output="pandas")`.
 For details, see
 `SLEP018 <https://scikit-learn-enhancement-proposals.readthedocs.io/en/latest/slep018/proposal.html>`__.
-"""  # noqa
+"""  # noqa: CPY001
 
 # %%
 # First, we load the iris dataset as a DataFrame to demonstrate the `set_output` API.
diff --git a/dev/_downloads/1ba22806e611e4b95c469e74c4b23796/sklearn_is_fitted.ipynb b/dev/_downloads/1ba22806e611e4b95c469e74c4b23796/sklearn_is_fitted.ipynb
index e3079434652ce..1d6cf509f6fa1 100644
--- a/dev/_downloads/1ba22806e611e4b95c469e74c4b23796/sklearn_is_fitted.ipynb
+++ b/dev/_downloads/1ba22806e611e4b95c469e74c4b23796/sklearn_is_fitted.ipynb
@@ -42,7 +42,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/1c4a422dfa5bd721501d19a2b7e2499b/plot_species_kde.ipynb b/dev/_downloads/1c4a422dfa5bd721501d19a2b7e2499b/plot_species_kde.ipynb
index b5140f6346575..c1c221601cb53 100644
--- a/dev/_downloads/1c4a422dfa5bd721501d19a2b7e2499b/plot_species_kde.ipynb
+++ b/dev/_downloads/1c4a422dfa5bd721501d19a2b7e2499b/plot_species_kde.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/1c8e15dadd5530db0ffab60f34a29a64/plot_discretization_classification.zip b/dev/_downloads/1c8e15dadd5530db0ffab60f34a29a64/plot_discretization_classification.zip
index 42ef17a3d8f41..cbf3e7f0d217b 100644
Binary files a/dev/_downloads/1c8e15dadd5530db0ffab60f34a29a64/plot_discretization_classification.zip and b/dev/_downloads/1c8e15dadd5530db0ffab60f34a29a64/plot_discretization_classification.zip differ
diff --git a/dev/_downloads/1c9575e30c88815f6de0280064b2c594/plot_missing_values.zip b/dev/_downloads/1c9575e30c88815f6de0280064b2c594/plot_missing_values.zip
index 090e7315fcf93..70cf9dcf3975c 100644
Binary files a/dev/_downloads/1c9575e30c88815f6de0280064b2c594/plot_missing_values.zip and b/dev/_downloads/1c9575e30c88815f6de0280064b2c594/plot_missing_values.zip differ
diff --git a/dev/_downloads/1e0968da80ca868bbdf21c1d0547f68c/plot_lle_digits.ipynb b/dev/_downloads/1e0968da80ca868bbdf21c1d0547f68c/plot_lle_digits.ipynb
index 98aded27adbbd..a92924fa18205 100644
--- a/dev/_downloads/1e0968da80ca868bbdf21c1d0547f68c/plot_lle_digits.ipynb
+++ b/dev/_downloads/1e0968da80ca868bbdf21c1d0547f68c/plot_lle_digits.ipynb
@@ -87,7 +87,7 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.decomposition import TruncatedSVD\nfrom sklearn.discriminant_analysis import LinearDiscriminantAnalysis\nfrom sklearn.ensemble import RandomTreesEmbedding\nfrom sklearn.manifold import (\n    MDS,\n    TSNE,\n    Isomap,\n    LocallyLinearEmbedding,\n    SpectralEmbedding,\n)\nfrom sklearn.neighbors import NeighborhoodComponentsAnalysis\nfrom sklearn.pipeline import make_pipeline\nfrom sklearn.random_projection import SparseRandomProjection\n\nembeddings = {\n    \"Random projection embedding\": SparseRandomProjection(\n        n_components=2, random_state=42\n    ),\n    \"Truncated SVD embedding\": TruncatedSVD(n_components=2),\n    \"Linear Discriminant Analysis embedding\": LinearDiscriminantAnalysis(\n        n_components=2\n    ),\n    \"Isomap embedding\": Isomap(n_neighbors=n_neighbors, n_components=2),\n    \"Standard LLE embedding\": LocallyLinearEmbedding(\n        n_neighbors=n_neighbors, n_components=2, method=\"standard\"\n    ),\n    \"Modified LLE embedding\": LocallyLinearEmbedding(\n        n_neighbors=n_neighbors, n_components=2, method=\"modified\"\n    ),\n    \"Hessian LLE embedding\": LocallyLinearEmbedding(\n        n_neighbors=n_neighbors, n_components=2, method=\"hessian\"\n    ),\n    \"LTSA LLE embedding\": LocallyLinearEmbedding(\n        n_neighbors=n_neighbors, n_components=2, method=\"ltsa\"\n    ),\n    \"MDS embedding\": MDS(n_components=2, n_init=1, max_iter=120, n_jobs=2),\n    \"Random Trees embedding\": make_pipeline(\n        RandomTreesEmbedding(n_estimators=200, max_depth=5, random_state=0),\n        TruncatedSVD(n_components=2),\n    ),\n    \"Spectral embedding\": SpectralEmbedding(\n        n_components=2, random_state=0, eigen_solver=\"arpack\"\n    ),\n    \"t-SNE embedding\": TSNE(\n        n_components=2,\n        max_iter=500,\n        n_iter_without_progress=150,\n        n_jobs=2,\n        random_state=0,\n    ),\n    \"NCA embedding\": NeighborhoodComponentsAnalysis(\n        n_components=2, init=\"pca\", random_state=0\n    ),\n}"
+        "from sklearn.decomposition import TruncatedSVD\nfrom sklearn.discriminant_analysis import LinearDiscriminantAnalysis\nfrom sklearn.ensemble import RandomTreesEmbedding\nfrom sklearn.manifold import (\n    MDS,\n    TSNE,\n    Isomap,\n    LocallyLinearEmbedding,\n    SpectralEmbedding,\n)\nfrom sklearn.neighbors import NeighborhoodComponentsAnalysis\nfrom sklearn.pipeline import make_pipeline\nfrom sklearn.random_projection import SparseRandomProjection\n\nembeddings = {\n    \"Random projection embedding\": SparseRandomProjection(\n        n_components=2, random_state=42\n    ),\n    \"Truncated SVD embedding\": TruncatedSVD(n_components=2),\n    \"Linear Discriminant Analysis embedding\": LinearDiscriminantAnalysis(\n        n_components=2\n    ),\n    \"Isomap embedding\": Isomap(n_neighbors=n_neighbors, n_components=2),\n    \"Standard LLE embedding\": LocallyLinearEmbedding(\n        n_neighbors=n_neighbors, n_components=2, method=\"standard\"\n    ),\n    \"Modified LLE embedding\": LocallyLinearEmbedding(\n        n_neighbors=n_neighbors, n_components=2, method=\"modified\"\n    ),\n    \"Hessian LLE embedding\": LocallyLinearEmbedding(\n        n_neighbors=n_neighbors, n_components=2, method=\"hessian\"\n    ),\n    \"LTSA LLE embedding\": LocallyLinearEmbedding(\n        n_neighbors=n_neighbors, n_components=2, method=\"ltsa\"\n    ),\n    \"MDS embedding\": MDS(n_components=2, n_init=1, max_iter=120, eps=1e-6),\n    \"Random Trees embedding\": make_pipeline(\n        RandomTreesEmbedding(n_estimators=200, max_depth=5, random_state=0),\n        TruncatedSVD(n_components=2),\n    ),\n    \"Spectral embedding\": SpectralEmbedding(\n        n_components=2, random_state=0, eigen_solver=\"arpack\"\n    ),\n    \"t-SNE embedding\": TSNE(\n        n_components=2,\n        max_iter=500,\n        n_iter_without_progress=150,\n        n_jobs=2,\n        random_state=0,\n    ),\n    \"NCA embedding\": NeighborhoodComponentsAnalysis(\n        n_components=2, init=\"pca\", random_state=0\n    ),\n}"
       ]
     },
     {
@@ -143,7 +143,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/1e308ad8f0f51110ea07c8d3b552c5bf/plot_pcr_vs_pls.zip b/dev/_downloads/1e308ad8f0f51110ea07c8d3b552c5bf/plot_pcr_vs_pls.zip
index 7f4e2258ac2a3..cb1d2855d45c4 100644
Binary files a/dev/_downloads/1e308ad8f0f51110ea07c8d3b552c5bf/plot_pcr_vs_pls.zip and b/dev/_downloads/1e308ad8f0f51110ea07c8d3b552c5bf/plot_pcr_vs_pls.zip differ
diff --git a/dev/_downloads/1e455640a8ffb0f44808048624f12d3d/plot_incremental_pca.ipynb b/dev/_downloads/1e455640a8ffb0f44808048624f12d3d/plot_incremental_pca.ipynb
index b27d76905036a..6121bd005c18f 100644
--- a/dev/_downloads/1e455640a8ffb0f44808048624f12d3d/plot_incremental_pca.ipynb
+++ b/dev/_downloads/1e455640a8ffb0f44808048624f12d3d/plot_incremental_pca.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/1e69769246e8d916462fd0c67f19c605/plot_set_output.zip b/dev/_downloads/1e69769246e8d916462fd0c67f19c605/plot_set_output.zip
index 091c7d2eb6e42..4b12f126cb323 100644
Binary files a/dev/_downloads/1e69769246e8d916462fd0c67f19c605/plot_set_output.zip and b/dev/_downloads/1e69769246e8d916462fd0c67f19c605/plot_set_output.zip differ
diff --git a/dev/_downloads/1ecb1284f0b785cd0011e155bf44657c/plot_lof_novelty_detection.ipynb b/dev/_downloads/1ecb1284f0b785cd0011e155bf44657c/plot_lof_novelty_detection.ipynb
index e4d1629313726..3999fc65ce3d0 100644
--- a/dev/_downloads/1ecb1284f0b785cd0011e155bf44657c/plot_lof_novelty_detection.ipynb
+++ b/dev/_downloads/1ecb1284f0b785cd0011e155bf44657c/plot_lof_novelty_detection.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/1ed4d16a866c9fe4d86a05477e6d0664/plot_svm_scale_c.ipynb b/dev/_downloads/1ed4d16a866c9fe4d86a05477e6d0664/plot_svm_scale_c.ipynb
index 6f38593baed52..54a7d916cfb4b 100644
--- a/dev/_downloads/1ed4d16a866c9fe4d86a05477e6d0664/plot_svm_scale_c.ipynb
+++ b/dev/_downloads/1ed4d16a866c9fe4d86a05477e6d0664/plot_svm_scale_c.ipynb
@@ -136,7 +136,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
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diff --git a/dev/_downloads/1f948ff6f5face5a362672c4e36dd01e/plot_mini_batch_kmeans.ipynb b/dev/_downloads/1f948ff6f5face5a362672c4e36dd01e/plot_mini_batch_kmeans.ipynb
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diff --git a/dev/_downloads/215c560d29193ab9b0a495609bc74802/plot_monotonic_constraints.ipynb b/dev/_downloads/215c560d29193ab9b0a495609bc74802/plot_monotonic_constraints.ipynb
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diff --git a/dev/_downloads/21b82d82985712b5de6347f382c77c86/plot_partial_dependence.ipynb b/dev/_downloads/21b82d82985712b5de6347f382c77c86/plot_partial_dependence.ipynb
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diff --git a/dev/_downloads/21ba44171c2107e5285a530bdf3dd0f6/plot_digits_linkage.ipynb b/dev/_downloads/21ba44171c2107e5285a530bdf3dd0f6/plot_digits_linkage.ipynb
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diff --git a/dev/_downloads/23fb33f64b3c23edf25165a3a4f04237/plot_successive_halving_iterations.ipynb b/dev/_downloads/23fb33f64b3c23edf25165a3a4f04237/plot_successive_halving_iterations.ipynb
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       },
       "outputs": [],
       "source": [
-        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nfrom scipy.stats import randint\n\nfrom sklearn import datasets\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.experimental import enable_halving_search_cv  # noqa\nfrom sklearn.model_selection import HalvingRandomSearchCV"
+        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nfrom scipy.stats import randint\n\nfrom sklearn import datasets\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.experimental import enable_halving_search_cv  # noqa: F401\nfrom sklearn.model_selection import HalvingRandomSearchCV"
       ]
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     {
@@ -78,7 +78,7 @@
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diff --git a/dev/_downloads/26485aa00d25776ba2b4e87114e5ad56/plot_manifold_sphere.zip b/dev/_downloads/26485aa00d25776ba2b4e87114e5ad56/plot_manifold_sphere.zip
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diff --git a/dev/_downloads/28056df7ed8b04d495f832aaab1b8c3e/plot_semi_supervised_versus_svm_iris.ipynb b/dev/_downloads/28056df7ed8b04d495f832aaab1b8c3e/plot_semi_supervised_versus_svm_iris.ipynb
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+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/2840d928d4f93cd381486b35c2031752/plot_stock_market.ipynb b/dev/_downloads/2840d928d4f93cd381486b35c2031752/plot_stock_market.ipynb
index 174c23461eb1b..8a78ea5119758 100644
--- a/dev/_downloads/2840d928d4f93cd381486b35c2031752/plot_stock_market.ipynb
+++ b/dev/_downloads/2840d928d4f93cd381486b35c2031752/plot_stock_market.ipynb
@@ -105,7 +105,7 @@
       },
       "outputs": [],
       "source": [
-        "import matplotlib.pyplot as plt\nfrom matplotlib.collections import LineCollection\n\nplt.figure(1, facecolor=\"w\", figsize=(10, 8))\nplt.clf()\nax = plt.axes([0.0, 0.0, 1.0, 1.0])\nplt.axis(\"off\")\n\n# Plot the graph of partial correlations\npartial_correlations = edge_model.precision_.copy()\nd = 1 / np.sqrt(np.diag(partial_correlations))\npartial_correlations *= d\npartial_correlations *= d[:, np.newaxis]\nnon_zero = np.abs(np.triu(partial_correlations, k=1)) > 0.02\n\n# Plot the nodes using the coordinates of our embedding\nplt.scatter(\n    embedding[0], embedding[1], s=100 * d**2, c=labels, cmap=plt.cm.nipy_spectral\n)\n\n# Plot the edges\nstart_idx, end_idx = np.where(non_zero)\n# a sequence of (*line0*, *line1*, *line2*), where::\n#            linen = (x0, y0), (x1, y1), ... (xm, ym)\nsegments = [\n    [embedding[:, start], embedding[:, stop]] for start, stop in zip(start_idx, end_idx)\n]\nvalues = np.abs(partial_correlations[non_zero])\nlc = LineCollection(\n    segments, zorder=0, cmap=plt.cm.hot_r, norm=plt.Normalize(0, 0.7 * values.max())\n)\nlc.set_array(values)\nlc.set_linewidths(15 * values)\nax.add_collection(lc)\n\n# Add a label to each node. The challenge here is that we want to\n# position the labels to avoid overlap with other labels\nfor index, (name, label, (x, y)) in enumerate(zip(names, labels, embedding.T)):\n    dx = x - embedding[0]\n    dx[index] = 1\n    dy = y - embedding[1]\n    dy[index] = 1\n    this_dx = dx[np.argmin(np.abs(dy))]\n    this_dy = dy[np.argmin(np.abs(dx))]\n    if this_dx > 0:\n        horizontalalignment = \"left\"\n        x = x + 0.002\n    else:\n        horizontalalignment = \"right\"\n        x = x - 0.002\n    if this_dy > 0:\n        verticalalignment = \"bottom\"\n        y = y + 0.002\n    else:\n        verticalalignment = \"top\"\n        y = y - 0.002\n    plt.text(\n        x,\n        y,\n        name,\n        size=10,\n        horizontalalignment=horizontalalignment,\n        verticalalignment=verticalalignment,\n        bbox=dict(\n            facecolor=\"w\",\n            edgecolor=plt.cm.nipy_spectral(label / float(n_labels)),\n            alpha=0.6,\n        ),\n    )\n\nplt.xlim(\n    embedding[0].min() - 0.15 * np.ptp(embedding[0]),\n    embedding[0].max() + 0.10 * np.ptp(embedding[0]),\n)\nplt.ylim(\n    embedding[1].min() - 0.03 * np.ptp(embedding[1]),\n    embedding[1].max() + 0.03 * np.ptp(embedding[1]),\n)\n\nplt.show()"
+        "import matplotlib.pyplot as plt\nfrom matplotlib.collections import LineCollection\n\nplt.figure(1, facecolor=\"w\", figsize=(10, 8))\nplt.clf()\nax = plt.axes([0.0, 0.0, 1.0, 1.0])\nplt.axis(\"off\")\n\n# Plot the graph of partial correlations\npartial_correlations = edge_model.precision_.copy()\nd = 1 / np.sqrt(np.diag(partial_correlations))\npartial_correlations *= d\npartial_correlations *= d[:, np.newaxis]\nnon_zero = np.abs(np.triu(partial_correlations, k=1)) > 0.02\n\n# Plot the nodes using the coordinates of our embedding\nplt.scatter(\n    embedding[0], embedding[1], s=100 * d**2, c=labels, cmap=plt.cm.nipy_spectral\n)\n\n# Plot the edges\nstart_idx, end_idx = non_zero.nonzero()\n# a sequence of (*line0*, *line1*, *line2*), where::\n#            linen = (x0, y0), (x1, y1), ... (xm, ym)\nsegments = [\n    [embedding[:, start], embedding[:, stop]] for start, stop in zip(start_idx, end_idx)\n]\nvalues = np.abs(partial_correlations[non_zero])\nlc = LineCollection(\n    segments, zorder=0, cmap=plt.cm.hot_r, norm=plt.Normalize(0, 0.7 * values.max())\n)\nlc.set_array(values)\nlc.set_linewidths(15 * values)\nax.add_collection(lc)\n\n# Add a label to each node. The challenge here is that we want to\n# position the labels to avoid overlap with other labels\nfor index, (name, label, (x, y)) in enumerate(zip(names, labels, embedding.T)):\n    dx = x - embedding[0]\n    dx[index] = 1\n    dy = y - embedding[1]\n    dy[index] = 1\n    this_dx = dx[np.argmin(np.abs(dy))]\n    this_dy = dy[np.argmin(np.abs(dx))]\n    if this_dx > 0:\n        horizontalalignment = \"left\"\n        x = x + 0.002\n    else:\n        horizontalalignment = \"right\"\n        x = x - 0.002\n    if this_dy > 0:\n        verticalalignment = \"bottom\"\n        y = y + 0.002\n    else:\n        verticalalignment = \"top\"\n        y = y - 0.002\n    plt.text(\n        x,\n        y,\n        name,\n        size=10,\n        horizontalalignment=horizontalalignment,\n        verticalalignment=verticalalignment,\n        bbox=dict(\n            facecolor=\"w\",\n            edgecolor=plt.cm.nipy_spectral(label / float(n_labels)),\n            alpha=0.6,\n        ),\n    )\n\nplt.xlim(\n    embedding[0].min() - 0.15 * np.ptp(embedding[0]),\n    embedding[0].max() + 0.10 * np.ptp(embedding[0]),\n)\nplt.ylim(\n    embedding[1].min() - 0.03 * np.ptp(embedding[1]),\n    embedding[1].max() + 0.03 * np.ptp(embedding[1]),\n)\n\nplt.show()"
       ]
     }
   ],
@@ -125,7 +125,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/285b194a4740110cb23e241031123972/plot_johnson_lindenstrauss_bound.ipynb b/dev/_downloads/285b194a4740110cb23e241031123972/plot_johnson_lindenstrauss_bound.ipynb
index 7e2a9e8c5def1..9e52e14c3c0df 100644
--- a/dev/_downloads/285b194a4740110cb23e241031123972/plot_johnson_lindenstrauss_bound.ipynb
+++ b/dev/_downloads/285b194a4740110cb23e241031123972/plot_johnson_lindenstrauss_bound.ipynb
@@ -114,7 +114,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/28df2114703b91f224d70205e9b75a7d/plot_concentration_prior.ipynb b/dev/_downloads/28df2114703b91f224d70205e9b75a7d/plot_concentration_prior.ipynb
index 8e102474086fa..fdcc32d774844 100644
--- a/dev/_downloads/28df2114703b91f224d70205e9b75a7d/plot_concentration_prior.ipynb
+++ b/dev/_downloads/28df2114703b91f224d70205e9b75a7d/plot_concentration_prior.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/29998264311e172e4afe243096ca2c93/plot_cost_complexity_pruning.ipynb b/dev/_downloads/29998264311e172e4afe243096ca2c93/plot_cost_complexity_pruning.ipynb
index 0a5deba1cfb5c..c449c82d26a80 100644
--- a/dev/_downloads/29998264311e172e4afe243096ca2c93/plot_cost_complexity_pruning.ipynb
+++ b/dev/_downloads/29998264311e172e4afe243096ca2c93/plot_cost_complexity_pruning.ipynb
@@ -125,7 +125,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/2a14e362a70d246e83fa6a89ca069cee/plot_sparse_coding.py b/dev/_downloads/2a14e362a70d246e83fa6a89ca069cee/plot_sparse_coding.py
index 778f718c2ac87..a3456b553486c 100644
--- a/dev/_downloads/2a14e362a70d246e83fa6a89ca069cee/plot_sparse_coding.py
+++ b/dev/_downloads/2a14e362a70d246e83fa6a89ca069cee/plot_sparse_coding.py
@@ -106,7 +106,7 @@ def ricker_matrix(width, resolution, n_components):
         dictionary=D, transform_algorithm="threshold", transform_alpha=20
     )
     x = coder.transform(y.reshape(1, -1))
-    _, idx = np.where(x != 0)
+    _, idx = (x != 0).nonzero()
     x[0, idx], _, _, _ = np.linalg.lstsq(D[idx, :].T, y, rcond=None)
     x = np.ravel(np.dot(x, D))
     squared_error = np.sum((y - x) ** 2)
diff --git a/dev/_downloads/2a405f4296fe09a86824e347e7ccf7e5/plot_discretization.zip b/dev/_downloads/2a405f4296fe09a86824e347e7ccf7e5/plot_discretization.zip
index 1d0bdf98ef5e7..2836f2f1a1201 100644
Binary files a/dev/_downloads/2a405f4296fe09a86824e347e7ccf7e5/plot_discretization.zip and b/dev/_downloads/2a405f4296fe09a86824e347e7ccf7e5/plot_discretization.zip differ
diff --git a/dev/_downloads/2a5eb3b98b9fe593b9bfeb548a54175d/plot_sparse_logistic_regression_mnist.ipynb b/dev/_downloads/2a5eb3b98b9fe593b9bfeb548a54175d/plot_sparse_logistic_regression_mnist.ipynb
index 795fec62b5961..ad13ce0485d27 100644
--- a/dev/_downloads/2a5eb3b98b9fe593b9bfeb548a54175d/plot_sparse_logistic_regression_mnist.ipynb
+++ b/dev/_downloads/2a5eb3b98b9fe593b9bfeb548a54175d/plot_sparse_logistic_regression_mnist.ipynb
@@ -15,7 +15,7 @@
       },
       "outputs": [],
       "source": [
-        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\n\nimport time\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nfrom sklearn.datasets import fetch_openml\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.utils import check_random_state\n\n# Turn down for faster convergence\nt0 = time.time()\ntrain_samples = 5000\n\n# Load data from https://www.openml.org/d/554\nX, y = fetch_openml(\"mnist_784\", version=1, return_X_y=True, as_frame=False)\n\nrandom_state = check_random_state(0)\npermutation = random_state.permutation(X.shape[0])\nX = X[permutation]\ny = y[permutation]\nX = X.reshape((X.shape[0], -1))\n\nX_train, X_test, y_train, y_test = train_test_split(\n    X, y, train_size=train_samples, test_size=10000\n)\n\nscaler = StandardScaler()\nX_train = scaler.fit_transform(X_train)\nX_test = scaler.transform(X_test)\n\n# Turn up tolerance for faster convergence\nclf = LogisticRegression(C=50.0 / train_samples, penalty=\"l1\", solver=\"saga\", tol=0.1)\nclf.fit(X_train, y_train)\nsparsity = np.mean(clf.coef_ == 0) * 100\nscore = clf.score(X_test, y_test)\n# print('Best C % .4f' % clf.C_)\nprint(\"Sparsity with L1 penalty: %.2f%%\" % sparsity)\nprint(\"Test score with L1 penalty: %.4f\" % score)\n\ncoef = clf.coef_.copy()\nplt.figure(figsize=(10, 5))\nscale = np.abs(coef).max()\nfor i in range(10):\n    l1_plot = plt.subplot(2, 5, i + 1)\n    l1_plot.imshow(\n        coef[i].reshape(28, 28),\n        interpolation=\"nearest\",\n        cmap=plt.cm.RdBu,\n        vmin=-scale,\n        vmax=scale,\n    )\n    l1_plot.set_xticks(())\n    l1_plot.set_yticks(())\n    l1_plot.set_xlabel(\"Class %i\" % i)\nplt.suptitle(\"Classification vector for...\")\n\nrun_time = time.time() - t0\nprint(\"Example run in %.3f s\" % run_time)\nplt.show()"
+        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\n\nimport time\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nfrom sklearn.datasets import fetch_openml\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.utils import check_random_state\n\n# Turn down for faster convergence\nt0 = time.time()\ntrain_samples = 5000\n\n# Load data from https://www.openml.org/d/554\nX, y = fetch_openml(\"mnist_784\", version=1, return_X_y=True, as_frame=False)\n\nrandom_state = check_random_state(0)\npermutation = random_state.permutation(X.shape[0])\nX = X[permutation]\ny = y[permutation]\nX = X.reshape((X.shape[0], -1))\n\nX_train, X_test, y_train, y_test = train_test_split(\n    X, y, train_size=train_samples, test_size=10000\n)\n\nscaler = StandardScaler()\nX_train = scaler.fit_transform(X_train)\nX_test = scaler.transform(X_test)\n\n# Turn up tolerance for faster convergence\nclf = LogisticRegression(C=50.0 / train_samples, penalty=\"l1\", solver=\"saga\", tol=0.1)\nclf.fit(X_train, y_train)\nsparsity = np.mean(clf.coef_ == 0) * 100\nscore = clf.score(X_test, y_test)\n# print('Best C % .4f' % clf.C_)\nprint(\"Sparsity with L1 penalty: %.2f%%\" % sparsity)\nprint(\"Test score with L1 penalty: %.4f\" % score)\n\ncoef = clf.coef_.copy()\nplt.figure(figsize=(10, 5))\nscale = np.abs(coef).max()\nfor i in range(10):\n    l1_plot = plt.subplot(2, 5, i + 1)\n    l1_plot.imshow(\n        coef[i].reshape(28, 28),\n        interpolation=\"nearest\",\n        cmap=plt.cm.RdBu,\n        vmin=-scale,\n        vmax=scale,\n    )\n    l1_plot.set_xticks(())\n    l1_plot.set_yticks(())\n    l1_plot.set_xlabel(f\"Class {i}\")\nplt.suptitle(\"Classification vector for...\")\n\nrun_time = time.time() - t0\nprint(\"Example run in %.3f s\" % run_time)\nplt.show()"
       ]
     }
   ],
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/2b2bebba7f9fb4d03b9c12d63c8b44ad/plot_topics_extraction_with_nmf_lda.py b/dev/_downloads/2b2bebba7f9fb4d03b9c12d63c8b44ad/plot_topics_extraction_with_nmf_lda.py
index faeef5ae15a11..a6f774d01e2de 100644
--- a/dev/_downloads/2b2bebba7f9fb4d03b9c12d63c8b44ad/plot_topics_extraction_with_nmf_lda.py
+++ b/dev/_downloads/2b2bebba7f9fb4d03b9c12d63c8b44ad/plot_topics_extraction_with_nmf_lda.py
@@ -50,7 +50,7 @@ def plot_top_words(model, feature_names, n_top_words, title):
 
         ax = axes[topic_idx]
         ax.barh(top_features, weights, height=0.7)
-        ax.set_title(f"Topic {topic_idx +1}", fontdict={"fontsize": 30})
+        ax.set_title(f"Topic {topic_idx + 1}", fontdict={"fontsize": 30})
         ax.tick_params(axis="both", which="major", labelsize=20)
         for i in "top right left".split():
             ax.spines[i].set_visible(False)
diff --git a/dev/_downloads/2b824c234439fb855e512d2465db9d8e/plot_scaling_importance.zip b/dev/_downloads/2b824c234439fb855e512d2465db9d8e/plot_scaling_importance.zip
index c69f2e112cc29..0bec2c1ce45cd 100644
Binary files a/dev/_downloads/2b824c234439fb855e512d2465db9d8e/plot_scaling_importance.zip and b/dev/_downloads/2b824c234439fb855e512d2465db9d8e/plot_scaling_importance.zip differ
diff --git a/dev/_downloads/2c8efe31be0d68b7945dbfbff0788dd3/plot_prediction_latency.ipynb b/dev/_downloads/2c8efe31be0d68b7945dbfbff0788dd3/plot_prediction_latency.ipynb
index eead66cf2c131..e32b7c0367498 100644
--- a/dev/_downloads/2c8efe31be0d68b7945dbfbff0788dd3/plot_prediction_latency.ipynb
+++ b/dev/_downloads/2c8efe31be0d68b7945dbfbff0788dd3/plot_prediction_latency.ipynb
@@ -107,7 +107,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/2cb37d76ae6c02821275d6ef8b96620f/plot_ica_blind_source_separation.zip b/dev/_downloads/2cb37d76ae6c02821275d6ef8b96620f/plot_ica_blind_source_separation.zip
index 4e8db085b95f4..a316b684bd58a 100644
Binary files a/dev/_downloads/2cb37d76ae6c02821275d6ef8b96620f/plot_ica_blind_source_separation.zip and b/dev/_downloads/2cb37d76ae6c02821275d6ef8b96620f/plot_ica_blind_source_separation.zip differ
diff --git a/dev/_downloads/2dd2f734250e8481fde18f8cd2e3820c/plot_target_encoder.zip b/dev/_downloads/2dd2f734250e8481fde18f8cd2e3820c/plot_target_encoder.zip
index e216628212be8..fbd2aa04c4723 100644
Binary files a/dev/_downloads/2dd2f734250e8481fde18f8cd2e3820c/plot_target_encoder.zip and b/dev/_downloads/2dd2f734250e8481fde18f8cd2e3820c/plot_target_encoder.zip differ
diff --git a/dev/_downloads/2e18b618565acbb9b5918136a879a534/plot_logistic_path.zip b/dev/_downloads/2e18b618565acbb9b5918136a879a534/plot_logistic_path.zip
index 126d2cdfb7666..cba1c605e4dcc 100644
Binary files a/dev/_downloads/2e18b618565acbb9b5918136a879a534/plot_logistic_path.zip and b/dev/_downloads/2e18b618565acbb9b5918136a879a534/plot_logistic_path.zip differ
diff --git a/dev/_downloads/2e4791a177381a6102b21e44083615c8/plot_poisson_regression_non_normal_loss.ipynb b/dev/_downloads/2e4791a177381a6102b21e44083615c8/plot_poisson_regression_non_normal_loss.ipynb
index 937e388b34238..6cf39c59c764f 100644
--- a/dev/_downloads/2e4791a177381a6102b21e44083615c8/plot_poisson_regression_non_normal_loss.ipynb
+++ b/dev/_downloads/2e4791a177381a6102b21e44083615c8/plot_poisson_regression_non_normal_loss.ipynb
@@ -269,7 +269,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/2e6d841155147c9fbce4ea3837b924b9/plot_release_highlights_0_24_0.ipynb b/dev/_downloads/2e6d841155147c9fbce4ea3837b924b9/plot_release_highlights_0_24_0.ipynb
index f5848c488f2e3..98492c39ef57f 100644
--- a/dev/_downloads/2e6d841155147c9fbce4ea3837b924b9/plot_release_highlights_0_24_0.ipynb
+++ b/dev/_downloads/2e6d841155147c9fbce4ea3837b924b9/plot_release_highlights_0_24_0.ipynb
@@ -22,7 +22,7 @@
       },
       "outputs": [],
       "source": [
-        "import numpy as np\nfrom scipy.stats import randint\nfrom sklearn.experimental import enable_halving_search_cv  # noqa\nfrom sklearn.model_selection import HalvingRandomSearchCV\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.datasets import make_classification\n\nrng = np.random.RandomState(0)\n\nX, y = make_classification(n_samples=700, random_state=rng)\n\nclf = RandomForestClassifier(n_estimators=10, random_state=rng)\n\nparam_dist = {\n    \"max_depth\": [3, None],\n    \"max_features\": randint(1, 11),\n    \"min_samples_split\": randint(2, 11),\n    \"bootstrap\": [True, False],\n    \"criterion\": [\"gini\", \"entropy\"],\n}\n\nrsh = HalvingRandomSearchCV(\n    estimator=clf, param_distributions=param_dist, factor=2, random_state=rng\n)\nrsh.fit(X, y)\nrsh.best_params_"
+        "import numpy as np\nfrom scipy.stats import randint\n\nfrom sklearn.datasets import make_classification\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.experimental import enable_halving_search_cv  # noqa: F401\nfrom sklearn.model_selection import HalvingRandomSearchCV\n\nrng = np.random.RandomState(0)\n\nX, y = make_classification(n_samples=700, random_state=rng)\n\nclf = RandomForestClassifier(n_estimators=10, random_state=rng)\n\nparam_dist = {\n    \"max_depth\": [3, None],\n    \"max_features\": randint(1, 11),\n    \"min_samples_split\": randint(2, 11),\n    \"bootstrap\": [True, False],\n    \"criterion\": [\"gini\", \"entropy\"],\n}\n\nrsh = HalvingRandomSearchCV(\n    estimator=clf, param_distributions=param_dist, factor=2, random_state=rng\n)\nrsh.fit(X, y)\nrsh.best_params_"
       ]
     },
     {
@@ -54,7 +54,7 @@
       },
       "outputs": [],
       "source": [
-        "import numpy as np\nfrom sklearn import datasets\nfrom sklearn.semi_supervised import SelfTrainingClassifier\nfrom sklearn.svm import SVC\n\nrng = np.random.RandomState(42)\niris = datasets.load_iris()\nrandom_unlabeled_points = rng.rand(iris.target.shape[0]) < 0.3\niris.target[random_unlabeled_points] = -1\nsvc = SVC(probability=True, gamma=\"auto\")\nself_training_model = SelfTrainingClassifier(svc)\nself_training_model.fit(iris.data, iris.target)"
+        "import numpy as np\n\nfrom sklearn import datasets\nfrom sklearn.semi_supervised import SelfTrainingClassifier\nfrom sklearn.svm import SVC\n\nrng = np.random.RandomState(42)\niris = datasets.load_iris()\nrandom_unlabeled_points = rng.rand(iris.target.shape[0]) < 0.3\niris.target[random_unlabeled_points] = -1\nsvc = SVC(probability=True, gamma=\"auto\")\nself_training_model = SelfTrainingClassifier(svc)\nself_training_model.fit(iris.data, iris.target)"
       ]
     },
     {
@@ -72,7 +72,7 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.feature_selection import SequentialFeatureSelector\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.datasets import load_iris\n\nX, y = load_iris(return_X_y=True, as_frame=True)\nfeature_names = X.columns\nknn = KNeighborsClassifier(n_neighbors=3)\nsfs = SequentialFeatureSelector(knn, n_features_to_select=2)\nsfs.fit(X, y)\nprint(\n    \"Features selected by forward sequential selection: \"\n    f\"{feature_names[sfs.get_support()].tolist()}\"\n)"
+        "from sklearn.datasets import load_iris\nfrom sklearn.feature_selection import SequentialFeatureSelector\nfrom sklearn.neighbors import KNeighborsClassifier\n\nX, y = load_iris(return_X_y=True, as_frame=True)\nfeature_names = X.columns\nknn = KNeighborsClassifier(n_neighbors=3)\nsfs = SequentialFeatureSelector(knn, n_features_to_select=2)\nsfs.fit(X, y)\nprint(\n    \"Features selected by forward sequential selection: \"\n    f\"{feature_names[sfs.get_support()].tolist()}\"\n)"
       ]
     },
     {
@@ -90,7 +90,7 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.datasets import fetch_covtype\nfrom sklearn.pipeline import make_pipeline\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import MinMaxScaler\nfrom sklearn.kernel_approximation import PolynomialCountSketch\nfrom sklearn.linear_model import LogisticRegression\n\nX, y = fetch_covtype(return_X_y=True)\npipe = make_pipeline(\n    MinMaxScaler(),\n    PolynomialCountSketch(degree=2, n_components=300),\n    LogisticRegression(max_iter=1000),\n)\nX_train, X_test, y_train, y_test = train_test_split(\n    X, y, train_size=5000, test_size=10000, random_state=42\n)\npipe.fit(X_train, y_train).score(X_test, y_test)"
+        "from sklearn.datasets import fetch_covtype\nfrom sklearn.kernel_approximation import PolynomialCountSketch\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.pipeline import make_pipeline\nfrom sklearn.preprocessing import MinMaxScaler\n\nX, y = fetch_covtype(return_X_y=True)\npipe = make_pipeline(\n    MinMaxScaler(),\n    PolynomialCountSketch(degree=2, n_components=300),\n    LogisticRegression(max_iter=1000),\n)\nX_train, X_test, y_train, y_test = train_test_split(\n    X, y, train_size=5000, test_size=10000, random_state=42\n)\npipe.fit(X_train, y_train).score(X_test, y_test)"
       ]
     },
     {
@@ -126,7 +126,7 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.ensemble import RandomForestRegressor\nfrom sklearn.datasets import fetch_california_housing\n\n# from sklearn.inspection import plot_partial_dependence\nfrom sklearn.inspection import PartialDependenceDisplay\n\nX, y = fetch_california_housing(return_X_y=True, as_frame=True)\nfeatures = [\"MedInc\", \"AveOccup\", \"HouseAge\", \"AveRooms\"]\nest = RandomForestRegressor(n_estimators=10)\nest.fit(X, y)\n\n# plot_partial_dependence has been removed in version 1.2. From 1.2, use\n# PartialDependenceDisplay instead.\n# display = plot_partial_dependence(\ndisplay = PartialDependenceDisplay.from_estimator(\n    est,\n    X,\n    features,\n    kind=\"individual\",\n    subsample=50,\n    n_jobs=3,\n    grid_resolution=20,\n    random_state=0,\n)\ndisplay.figure_.suptitle(\n    \"Partial dependence of house value on non-location features\\n\"\n    \"for the California housing dataset, with BayesianRidge\"\n)\ndisplay.figure_.subplots_adjust(hspace=0.3)"
+        "from sklearn.datasets import fetch_california_housing\nfrom sklearn.ensemble import RandomForestRegressor\n\n# from sklearn.inspection import plot_partial_dependence\nfrom sklearn.inspection import PartialDependenceDisplay\n\nX, y = fetch_california_housing(return_X_y=True, as_frame=True)\nfeatures = [\"MedInc\", \"AveOccup\", \"HouseAge\", \"AveRooms\"]\nest = RandomForestRegressor(n_estimators=10)\nest.fit(X, y)\n\n# plot_partial_dependence has been removed in version 1.2. From 1.2, use\n# PartialDependenceDisplay instead.\n# display = plot_partial_dependence(\ndisplay = PartialDependenceDisplay.from_estimator(\n    est,\n    X,\n    features,\n    kind=\"individual\",\n    subsample=50,\n    n_jobs=3,\n    grid_resolution=20,\n    random_state=0,\n)\ndisplay.figure_.suptitle(\n    \"Partial dependence of house value on non-location features\\n\"\n    \"for the California housing dataset, with BayesianRidge\"\n)\ndisplay.figure_.subplots_adjust(hspace=0.3)"
       ]
     },
     {
@@ -144,7 +144,7 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.tree import DecisionTreeRegressor\nfrom sklearn.model_selection import train_test_split\nimport numpy as np\n\nn_samples, n_features = 1000, 20\nrng = np.random.RandomState(0)\nX = rng.randn(n_samples, n_features)\n# positive integer target correlated with X[:, 5] with many zeros:\ny = rng.poisson(lam=np.exp(X[:, 5]) / 2)\nX_train, X_test, y_train, y_test = train_test_split(X, y, random_state=rng)\nregressor = DecisionTreeRegressor(criterion=\"poisson\", random_state=0)\nregressor.fit(X_train, y_train)"
+        "import numpy as np\n\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.tree import DecisionTreeRegressor\n\nn_samples, n_features = 1000, 20\nrng = np.random.RandomState(0)\nX = rng.randn(n_samples, n_features)\n# positive integer target correlated with X[:, 5] with many zeros:\ny = rng.poisson(lam=np.exp(X[:, 5]) / 2)\nX_train, X_test, y_train, y_test = train_test_split(X, y, random_state=rng)\nregressor = DecisionTreeRegressor(criterion=\"poisson\", random_state=0)\nregressor.fit(X_train, y_train)"
       ]
     },
     {
@@ -171,7 +171,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/2eaa49c025f80c826512eda4a8add5c3/plot_caching_nearest_neighbors.ipynb b/dev/_downloads/2eaa49c025f80c826512eda4a8add5c3/plot_caching_nearest_neighbors.ipynb
index e471be8fc094e..3d86bdffca161 100644
--- a/dev/_downloads/2eaa49c025f80c826512eda4a8add5c3/plot_caching_nearest_neighbors.ipynb
+++ b/dev/_downloads/2eaa49c025f80c826512eda4a8add5c3/plot_caching_nearest_neighbors.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/2f1e361eb1d2ca997cf5da2e2651b7bb/plot_column_transformer.zip b/dev/_downloads/2f1e361eb1d2ca997cf5da2e2651b7bb/plot_column_transformer.zip
index 08ac4412d0083..a5bd2c1f51dd8 100644
Binary files a/dev/_downloads/2f1e361eb1d2ca997cf5da2e2651b7bb/plot_column_transformer.zip and b/dev/_downloads/2f1e361eb1d2ca997cf5da2e2651b7bb/plot_column_transformer.zip differ
diff --git a/dev/_downloads/2f3ef774a6d7e52e1e6b7ccbb75d25f0/plot_gradient_boosting_quantile.py b/dev/_downloads/2f3ef774a6d7e52e1e6b7ccbb75d25f0/plot_gradient_boosting_quantile.py
index 01ab647359c47..dbe3a99b045dd 100644
--- a/dev/_downloads/2f3ef774a6d7e52e1e6b7ccbb75d25f0/plot_gradient_boosting_quantile.py
+++ b/dev/_downloads/2f3ef774a6d7e52e1e6b7ccbb75d25f0/plot_gradient_boosting_quantile.py
@@ -241,11 +241,12 @@ def coverage_fraction(y, y_low, y_high):
 # cross-validation on the pinball loss with alpha=0.05:
 
 # %%
-from sklearn.experimental import enable_halving_search_cv  # noqa
-from sklearn.model_selection import HalvingRandomSearchCV
-from sklearn.metrics import make_scorer
 from pprint import pprint
 
+from sklearn.experimental import enable_halving_search_cv  # noqa: F401
+from sklearn.metrics import make_scorer
+from sklearn.model_selection import HalvingRandomSearchCV
+
 param_grid = dict(
     learning_rate=[0.05, 0.1, 0.2],
     max_depth=[2, 5, 10],
diff --git a/dev/_downloads/2f60e3212e8896cf10a907dfe4a6a631/plot_gradient_boosting_quantile.zip b/dev/_downloads/2f60e3212e8896cf10a907dfe4a6a631/plot_gradient_boosting_quantile.zip
index c3f4d3a04f748..b5c6bc1ea8e65 100644
Binary files a/dev/_downloads/2f60e3212e8896cf10a907dfe4a6a631/plot_gradient_boosting_quantile.zip and b/dev/_downloads/2f60e3212e8896cf10a907dfe4a6a631/plot_gradient_boosting_quantile.zip differ
diff --git a/dev/_downloads/2f7b3682e03ebd7066495c3252414a44/plot_lasso_dense_vs_sparse_data.zip b/dev/_downloads/2f7b3682e03ebd7066495c3252414a44/plot_lasso_dense_vs_sparse_data.zip
index 34d12bb5d04d4..a5c565dc326d3 100644
Binary files a/dev/_downloads/2f7b3682e03ebd7066495c3252414a44/plot_lasso_dense_vs_sparse_data.zip and b/dev/_downloads/2f7b3682e03ebd7066495c3252414a44/plot_lasso_dense_vs_sparse_data.zip differ
diff --git a/dev/_downloads/2f90cc6b2b011c5fa616a3a47c2ed8d1/plot_permutation_importance_multicollinear.zip b/dev/_downloads/2f90cc6b2b011c5fa616a3a47c2ed8d1/plot_permutation_importance_multicollinear.zip
index bdd793bd6a71c..a8537257822aa 100644
Binary files a/dev/_downloads/2f90cc6b2b011c5fa616a3a47c2ed8d1/plot_permutation_importance_multicollinear.zip and b/dev/_downloads/2f90cc6b2b011c5fa616a3a47c2ed8d1/plot_permutation_importance_multicollinear.zip differ
diff --git a/dev/_downloads/2fce81c38823a4c8e6cf7beebb010f00/plot_gradient_boosting_oob.zip b/dev/_downloads/2fce81c38823a4c8e6cf7beebb010f00/plot_gradient_boosting_oob.zip
index c0f1f9a75d8e6..3263d0290923e 100644
Binary files a/dev/_downloads/2fce81c38823a4c8e6cf7beebb010f00/plot_gradient_boosting_oob.zip and b/dev/_downloads/2fce81c38823a4c8e6cf7beebb010f00/plot_gradient_boosting_oob.zip differ
diff --git a/dev/_downloads/3018e0d07b27e25beba9764116709763/plot_ica_blind_source_separation.ipynb b/dev/_downloads/3018e0d07b27e25beba9764116709763/plot_ica_blind_source_separation.ipynb
index 3df409d956123..a61bc2e9d4405 100644
--- a/dev/_downloads/3018e0d07b27e25beba9764116709763/plot_ica_blind_source_separation.ipynb
+++ b/dev/_downloads/3018e0d07b27e25beba9764116709763/plot_ica_blind_source_separation.ipynb
@@ -89,7 +89,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/3019a99f3f124514f9a0650bcb33fa27/plot_spectral_coclustering.zip b/dev/_downloads/3019a99f3f124514f9a0650bcb33fa27/plot_spectral_coclustering.zip
index 0e21dddf3a75d..675cfc7219fe8 100644
Binary files a/dev/_downloads/3019a99f3f124514f9a0650bcb33fa27/plot_spectral_coclustering.zip and b/dev/_downloads/3019a99f3f124514f9a0650bcb33fa27/plot_spectral_coclustering.zip differ
diff --git a/dev/_downloads/305aa8bba883838e1ca0d690e78e9fbd/plot_sgd_separating_hyperplane.ipynb b/dev/_downloads/305aa8bba883838e1ca0d690e78e9fbd/plot_sgd_separating_hyperplane.ipynb
index 404d9bbc54f41..e5bd69a3dd641 100644
--- a/dev/_downloads/305aa8bba883838e1ca0d690e78e9fbd/plot_sgd_separating_hyperplane.ipynb
+++ b/dev/_downloads/305aa8bba883838e1ca0d690e78e9fbd/plot_sgd_separating_hyperplane.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/30cf609ec45ebd9e95d54912b37002fa/plot_lasso_lars_ic.zip b/dev/_downloads/30cf609ec45ebd9e95d54912b37002fa/plot_lasso_lars_ic.zip
index 5801d3332dd6a..820cbd25662e0 100644
Binary files a/dev/_downloads/30cf609ec45ebd9e95d54912b37002fa/plot_lasso_lars_ic.zip and b/dev/_downloads/30cf609ec45ebd9e95d54912b37002fa/plot_lasso_lars_ic.zip differ
diff --git a/dev/_downloads/31e6facb4e4fb576c8a7d28e24eaba3b/plot_lle_digits.zip b/dev/_downloads/31e6facb4e4fb576c8a7d28e24eaba3b/plot_lle_digits.zip
index 4abb258a4226e..ad583440991e2 100644
Binary files a/dev/_downloads/31e6facb4e4fb576c8a7d28e24eaba3b/plot_lle_digits.zip and b/dev/_downloads/31e6facb4e4fb576c8a7d28e24eaba3b/plot_lle_digits.zip differ
diff --git a/dev/_downloads/32b1787884d41cf7a0b36640001d381f/plot_permutation_tests_for_classification.zip b/dev/_downloads/32b1787884d41cf7a0b36640001d381f/plot_permutation_tests_for_classification.zip
index ab560923288f7..20f6a8ac7519a 100644
Binary files a/dev/_downloads/32b1787884d41cf7a0b36640001d381f/plot_permutation_tests_for_classification.zip and b/dev/_downloads/32b1787884d41cf7a0b36640001d381f/plot_permutation_tests_for_classification.zip differ
diff --git a/dev/_downloads/3316f301d7c7651c033565a5ae51c295/plot_release_highlights_1_3_0.py b/dev/_downloads/3316f301d7c7651c033565a5ae51c295/plot_release_highlights_1_3_0.py
index ebb109e524f1d..f7faad08c9b1e 100644
--- a/dev/_downloads/3316f301d7c7651c033565a5ae51c295/plot_release_highlights_1_3_0.py
+++ b/dev/_downloads/3316f301d7c7651c033565a5ae51c295/plot_release_highlights_1_3_0.py
@@ -1,4 +1,4 @@
-# ruff: noqa
+# ruff: noqa: CPY001
 """
 =======================================
 Release Highlights for scikit-learn 1.3
@@ -50,6 +50,7 @@
 # making it more robust to parameter selection than :class:`cluster.DBSCAN`.
 # More details in the :ref:`User Guide <hdbscan>`.
 import numpy as np
+
 from sklearn.cluster import HDBSCAN
 from sklearn.datasets import load_digits
 from sklearn.metrics import v_measure_score
@@ -71,6 +72,7 @@
 # estimate of the average target values for observations belonging to that category.
 # More details in the :ref:`User Guide <target_encoder>`.
 import numpy as np
+
 from sklearn.preprocessing import TargetEncoder
 
 X = np.array([["cat"] * 30 + ["dog"] * 20 + ["snake"] * 38], dtype=object).T
@@ -92,6 +94,7 @@
 # :ref:`sphx_glr_auto_examples_ensemble_plot_hgbt_regression.py` for a usecase
 # example of this feature in :class:`~ensemble.HistGradientBoostingRegressor`.
 import numpy as np
+
 from sklearn.tree import DecisionTreeClassifier
 
 X = np.array([0, 1, 6, np.nan]).reshape(-1, 1)
@@ -128,9 +131,10 @@
 # Gamma deviance loss function via `loss="gamma"`. This loss function is useful for
 # modeling strictly positive targets with a right-skewed distribution.
 import numpy as np
-from sklearn.model_selection import cross_val_score
+
 from sklearn.datasets import make_low_rank_matrix
 from sklearn.ensemble import HistGradientBoostingRegressor
+from sklearn.model_selection import cross_val_score
 
 n_samples, n_features = 500, 10
 rng = np.random.RandomState(0)
@@ -148,9 +152,10 @@
 # into a single output for each feature. The parameters to enable the gathering of
 # infrequent categories are `min_frequency` and `max_categories`.
 # See the :ref:`User Guide <encoder_infrequent_categories>` for more details.
-from sklearn.preprocessing import OrdinalEncoder
 import numpy as np
 
+from sklearn.preprocessing import OrdinalEncoder
+
 X = np.array(
     [["dog"] * 5 + ["cat"] * 20 + ["rabbit"] * 10 + ["snake"] * 3], dtype=object
 ).T
diff --git a/dev/_downloads/3344592b61c45b04f094dd038d2e2efd/plot_column_transformer_mixed_types.zip b/dev/_downloads/3344592b61c45b04f094dd038d2e2efd/plot_column_transformer_mixed_types.zip
index d783e09440827..411cbb981e794 100644
Binary files a/dev/_downloads/3344592b61c45b04f094dd038d2e2efd/plot_column_transformer_mixed_types.zip and b/dev/_downloads/3344592b61c45b04f094dd038d2e2efd/plot_column_transformer_mixed_types.zip differ
diff --git a/dev/_downloads/3438aba177365cb595921cf18806dfa7/plot_classifier_comparison.ipynb b/dev/_downloads/3438aba177365cb595921cf18806dfa7/plot_classifier_comparison.ipynb
index 0924d6641976d..d57beae484cf1 100644
--- a/dev/_downloads/3438aba177365cb595921cf18806dfa7/plot_classifier_comparison.ipynb
+++ b/dev/_downloads/3438aba177365cb595921cf18806dfa7/plot_classifier_comparison.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/35620548aa1dcefbc9e36e7330feafdc/plot_forest_hist_grad_boosting_comparison.zip b/dev/_downloads/35620548aa1dcefbc9e36e7330feafdc/plot_forest_hist_grad_boosting_comparison.zip
index 8fbd1fe341fc3..7afc8253cf943 100644
Binary files a/dev/_downloads/35620548aa1dcefbc9e36e7330feafdc/plot_forest_hist_grad_boosting_comparison.zip and b/dev/_downloads/35620548aa1dcefbc9e36e7330feafdc/plot_forest_hist_grad_boosting_comparison.zip differ
diff --git a/dev/_downloads/3577ee5e1121d4b5da38feb779aa44bb/plot_roc_curve_visualization_api.ipynb b/dev/_downloads/3577ee5e1121d4b5da38feb779aa44bb/plot_roc_curve_visualization_api.ipynb
index 7ff61ff63b378..dc11c2aa6b1fc 100644
--- a/dev/_downloads/3577ee5e1121d4b5da38feb779aa44bb/plot_roc_curve_visualization_api.ipynb
+++ b/dev/_downloads/3577ee5e1121d4b5da38feb779aa44bb/plot_roc_curve_visualization_api.ipynb
@@ -89,7 +89,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/358cfc6f7881b63fd4067f3b13ced9ab/plot_svm_tie_breaking.ipynb b/dev/_downloads/358cfc6f7881b63fd4067f3b13ced9ab/plot_svm_tie_breaking.ipynb
index 2a7f00ceb92bc..2e5fca5793fe7 100644
--- a/dev/_downloads/358cfc6f7881b63fd4067f3b13ced9ab/plot_svm_tie_breaking.ipynb
+++ b/dev/_downloads/358cfc6f7881b63fd4067f3b13ced9ab/plot_svm_tie_breaking.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/35ddd809a204588ba5e0d93a2ac44682/plot_feature_union.zip b/dev/_downloads/35ddd809a204588ba5e0d93a2ac44682/plot_feature_union.zip
index 15acdacfecd43..68d8374f1d3db 100644
Binary files a/dev/_downloads/35ddd809a204588ba5e0d93a2ac44682/plot_feature_union.zip and b/dev/_downloads/35ddd809a204588ba5e0d93a2ac44682/plot_feature_union.zip differ
diff --git a/dev/_downloads/35ec767dbdc0119a20d1fd2022298627/plot_gmm_pdf.ipynb b/dev/_downloads/35ec767dbdc0119a20d1fd2022298627/plot_gmm_pdf.ipynb
index 6c2dec4e6aa37..955c08a20c4f8 100644
--- a/dev/_downloads/35ec767dbdc0119a20d1fd2022298627/plot_gmm_pdf.ipynb
+++ b/dev/_downloads/35ec767dbdc0119a20d1fd2022298627/plot_gmm_pdf.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/3749b413371f6991d683ca525228e765/plot_digits_classification.zip b/dev/_downloads/3749b413371f6991d683ca525228e765/plot_digits_classification.zip
index 8939f701c3bd9..70fa2995578b9 100644
Binary files a/dev/_downloads/3749b413371f6991d683ca525228e765/plot_digits_classification.zip and b/dev/_downloads/3749b413371f6991d683ca525228e765/plot_digits_classification.zip differ
diff --git a/dev/_downloads/37d005a18100ae3c52b798da37735337/plot_random_forest_embedding.zip b/dev/_downloads/37d005a18100ae3c52b798da37735337/plot_random_forest_embedding.zip
index 3560fe0fa03be..408e1c7ac5afd 100644
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diff --git a/dev/_downloads/388f5c580f2084392fb899ca1bc53867/plot_label_propagation_digits.zip b/dev/_downloads/388f5c580f2084392fb899ca1bc53867/plot_label_propagation_digits.zip
index 7c0213578a49b..07536f2f9e267 100644
Binary files a/dev/_downloads/388f5c580f2084392fb899ca1bc53867/plot_label_propagation_digits.zip and b/dev/_downloads/388f5c580f2084392fb899ca1bc53867/plot_label_propagation_digits.zip differ
diff --git a/dev/_downloads/389fb4950ddfe12a741e6ac5b7d79193/plot_sgd_early_stopping.ipynb b/dev/_downloads/389fb4950ddfe12a741e6ac5b7d79193/plot_sgd_early_stopping.ipynb
index e6e6c39d7f67c..2349608649029 100644
--- a/dev/_downloads/389fb4950ddfe12a741e6ac5b7d79193/plot_sgd_early_stopping.ipynb
+++ b/dev/_downloads/389fb4950ddfe12a741e6ac5b7d79193/plot_sgd_early_stopping.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/38e826c9e3778d7de78b2fc671fd7903/plot_adaboost_regression.ipynb b/dev/_downloads/38e826c9e3778d7de78b2fc671fd7903/plot_adaboost_regression.ipynb
index 5a1822b9cf330..605b3edc85a04 100644
--- a/dev/_downloads/38e826c9e3778d7de78b2fc671fd7903/plot_adaboost_regression.ipynb
+++ b/dev/_downloads/38e826c9e3778d7de78b2fc671fd7903/plot_adaboost_regression.ipynb
@@ -78,7 +78,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/3992a64e742c874f5df2ba26ae9c049b/plot_grid_search_text_feature_extraction.ipynb b/dev/_downloads/3992a64e742c874f5df2ba26ae9c049b/plot_grid_search_text_feature_extraction.ipynb
index d9a71cfc9174f..72a7c93917487 100644
--- a/dev/_downloads/3992a64e742c874f5df2ba26ae9c049b/plot_grid_search_text_feature_extraction.ipynb
+++ b/dev/_downloads/3992a64e742c874f5df2ba26ae9c049b/plot_grid_search_text_feature_extraction.ipynb
@@ -201,7 +201,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/3a435b85c0a6eebd325c2b99faad732b/plot_swissroll.zip b/dev/_downloads/3a435b85c0a6eebd325c2b99faad732b/plot_swissroll.zip
index b3f3dcb28d6b3..67064311c3555 100644
Binary files a/dev/_downloads/3a435b85c0a6eebd325c2b99faad732b/plot_swissroll.zip and b/dev/_downloads/3a435b85c0a6eebd325c2b99faad732b/plot_swissroll.zip differ
diff --git a/dev/_downloads/3ad3d8de76dc79e282240ff9d4aec996/plot_causal_interpretation.zip b/dev/_downloads/3ad3d8de76dc79e282240ff9d4aec996/plot_causal_interpretation.zip
index 26b36170877f2..467ff4c6ff66f 100644
Binary files a/dev/_downloads/3ad3d8de76dc79e282240ff9d4aec996/plot_causal_interpretation.zip and b/dev/_downloads/3ad3d8de76dc79e282240ff9d4aec996/plot_causal_interpretation.zip differ
diff --git a/dev/_downloads/3b25b9158f1c0a1564eb0f4c9e531d46/plot_underfitting_overfitting.ipynb b/dev/_downloads/3b25b9158f1c0a1564eb0f4c9e531d46/plot_underfitting_overfitting.ipynb
index 59e27ecfbd697..61cf65011fc57 100644
--- a/dev/_downloads/3b25b9158f1c0a1564eb0f4c9e531d46/plot_underfitting_overfitting.ipynb
+++ b/dev/_downloads/3b25b9158f1c0a1564eb0f4c9e531d46/plot_underfitting_overfitting.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/3c9b7bcd0b16f172ac12ffad61f3b5f0/plot_stack_predictors.ipynb b/dev/_downloads/3c9b7bcd0b16f172ac12ffad61f3b5f0/plot_stack_predictors.ipynb
index ccdfdea99dc82..36d80e1ff994d 100644
--- a/dev/_downloads/3c9b7bcd0b16f172ac12ffad61f3b5f0/plot_stack_predictors.ipynb
+++ b/dev/_downloads/3c9b7bcd0b16f172ac12ffad61f3b5f0/plot_stack_predictors.ipynb
@@ -194,7 +194,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/3d2c40ab2cfee918c269fd63cd2bc466/plot_model_complexity_influence.zip b/dev/_downloads/3d2c40ab2cfee918c269fd63cd2bc466/plot_model_complexity_influence.zip
index d03df42974561..7095f2204ce29 100644
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diff --git a/dev/_downloads/3d40c54d0288de1437e5299f8039b95a/plot_release_highlights_0_24_0.zip b/dev/_downloads/3d40c54d0288de1437e5299f8039b95a/plot_release_highlights_0_24_0.zip
index c49c894cec1af..8600a13d56964 100644
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diff --git a/dev/_downloads/3d6718e20e6b7b05b7cdaa333a6d05a4/plot_bisect_kmeans.zip b/dev/_downloads/3d6718e20e6b7b05b7cdaa333a6d05a4/plot_bisect_kmeans.zip
index d824eb03b9cf3..2930fcc57417c 100644
Binary files a/dev/_downloads/3d6718e20e6b7b05b7cdaa333a6d05a4/plot_bisect_kmeans.zip and b/dev/_downloads/3d6718e20e6b7b05b7cdaa333a6d05a4/plot_bisect_kmeans.zip differ
diff --git a/dev/_downloads/3d67d60c4ffe2f928db30cd3174e5389/plot_ward_structured_vs_unstructured.zip b/dev/_downloads/3d67d60c4ffe2f928db30cd3174e5389/plot_ward_structured_vs_unstructured.zip
index 4c54f52490a29..fecf599885cb7 100644
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diff --git a/dev/_downloads/3d6c2c74b5e16738ba82279166b9bf46/plot_faces_decomposition.zip b/dev/_downloads/3d6c2c74b5e16738ba82279166b9bf46/plot_faces_decomposition.zip
index 01e8593ffac33..7f5cc08f29582 100644
Binary files a/dev/_downloads/3d6c2c74b5e16738ba82279166b9bf46/plot_faces_decomposition.zip and b/dev/_downloads/3d6c2c74b5e16738ba82279166b9bf46/plot_faces_decomposition.zip differ
diff --git a/dev/_downloads/3daf4e9ab9d86061e19a11d997a09779/plot_tomography_l1_reconstruction.ipynb b/dev/_downloads/3daf4e9ab9d86061e19a11d997a09779/plot_tomography_l1_reconstruction.ipynb
index 97adaae9ed657..65bbf8072ab87 100644
--- a/dev/_downloads/3daf4e9ab9d86061e19a11d997a09779/plot_tomography_l1_reconstruction.ipynb
+++ b/dev/_downloads/3daf4e9ab9d86061e19a11d997a09779/plot_tomography_l1_reconstruction.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/3e3ff82825c1590c99d9a44b3a517f55/plot_multiclass_overview.zip b/dev/_downloads/3e3ff82825c1590c99d9a44b3a517f55/plot_multiclass_overview.zip
index 33e32bc51a444..ba2a4470a90be 100644
Binary files a/dev/_downloads/3e3ff82825c1590c99d9a44b3a517f55/plot_multiclass_overview.zip and b/dev/_downloads/3e3ff82825c1590c99d9a44b3a517f55/plot_multiclass_overview.zip differ
diff --git a/dev/_downloads/3ed102fa8211c8d36f2331f0c5e1dcef/plot_model_complexity_influence.ipynb b/dev/_downloads/3ed102fa8211c8d36f2331f0c5e1dcef/plot_model_complexity_influence.ipynb
index ac0dd91feb7fb..02db34e3a787e 100644
--- a/dev/_downloads/3ed102fa8211c8d36f2331f0c5e1dcef/plot_model_complexity_influence.ipynb
+++ b/dev/_downloads/3ed102fa8211c8d36f2331f0c5e1dcef/plot_model_complexity_influence.ipynb
@@ -114,7 +114,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/3f7191b01d0103d1886c959ed7687c4d/plot_bicluster_newsgroups.ipynb b/dev/_downloads/3f7191b01d0103d1886c959ed7687c4d/plot_bicluster_newsgroups.ipynb
index a44f4b44e292e..ae9db1a5291c1 100644
--- a/dev/_downloads/3f7191b01d0103d1886c959ed7687c4d/plot_bicluster_newsgroups.ipynb
+++ b/dev/_downloads/3f7191b01d0103d1886c959ed7687c4d/plot_bicluster_newsgroups.ipynb
@@ -15,7 +15,7 @@
       },
       "outputs": [],
       "source": [
-        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\nfrom collections import Counter\nfrom time import time\n\nimport numpy as np\n\nfrom sklearn.cluster import MiniBatchKMeans, SpectralCoclustering\nfrom sklearn.datasets import fetch_20newsgroups\nfrom sklearn.feature_extraction.text import TfidfVectorizer\nfrom sklearn.metrics.cluster import v_measure_score\n\n\ndef number_normalizer(tokens):\n    \"\"\"Map all numeric tokens to a placeholder.\n\n    For many applications, tokens that begin with a number are not directly\n    useful, but the fact that such a token exists can be relevant.  By applying\n    this form of dimensionality reduction, some methods may perform better.\n    \"\"\"\n    return (\"#NUMBER\" if token[0].isdigit() else token for token in tokens)\n\n\nclass NumberNormalizingVectorizer(TfidfVectorizer):\n    def build_tokenizer(self):\n        tokenize = super().build_tokenizer()\n        return lambda doc: list(number_normalizer(tokenize(doc)))\n\n\n# exclude 'comp.os.ms-windows.misc'\ncategories = [\n    \"alt.atheism\",\n    \"comp.graphics\",\n    \"comp.sys.ibm.pc.hardware\",\n    \"comp.sys.mac.hardware\",\n    \"comp.windows.x\",\n    \"misc.forsale\",\n    \"rec.autos\",\n    \"rec.motorcycles\",\n    \"rec.sport.baseball\",\n    \"rec.sport.hockey\",\n    \"sci.crypt\",\n    \"sci.electronics\",\n    \"sci.med\",\n    \"sci.space\",\n    \"soc.religion.christian\",\n    \"talk.politics.guns\",\n    \"talk.politics.mideast\",\n    \"talk.politics.misc\",\n    \"talk.religion.misc\",\n]\nnewsgroups = fetch_20newsgroups(categories=categories)\ny_true = newsgroups.target\n\nvectorizer = NumberNormalizingVectorizer(stop_words=\"english\", min_df=5)\ncocluster = SpectralCoclustering(\n    n_clusters=len(categories), svd_method=\"arpack\", random_state=0\n)\nkmeans = MiniBatchKMeans(\n    n_clusters=len(categories), batch_size=20000, random_state=0, n_init=3\n)\n\nprint(\"Vectorizing...\")\nX = vectorizer.fit_transform(newsgroups.data)\n\nprint(\"Coclustering...\")\nstart_time = time()\ncocluster.fit(X)\ny_cocluster = cocluster.row_labels_\nprint(\n    f\"Done in {time() - start_time:.2f}s. V-measure: \\\n{v_measure_score(y_cocluster, y_true):.4f}\"\n)\n\n\nprint(\"MiniBatchKMeans...\")\nstart_time = time()\ny_kmeans = kmeans.fit_predict(X)\nprint(\n    f\"Done in {time() - start_time:.2f}s. V-measure: \\\n{v_measure_score(y_kmeans, y_true):.4f}\"\n)\n\n\nfeature_names = vectorizer.get_feature_names_out()\ndocument_names = list(newsgroups.target_names[i] for i in newsgroups.target)\n\n\ndef bicluster_ncut(i):\n    rows, cols = cocluster.get_indices(i)\n    if not (np.any(rows) and np.any(cols)):\n        import sys\n\n        return sys.float_info.max\n    row_complement = np.nonzero(np.logical_not(cocluster.rows_[i]))[0]\n    col_complement = np.nonzero(np.logical_not(cocluster.columns_[i]))[0]\n    # Note: the following is identical to X[rows[:, np.newaxis],\n    # cols].sum() but much faster in scipy <= 0.16\n    weight = X[rows][:, cols].sum()\n    cut = X[row_complement][:, cols].sum() + X[rows][:, col_complement].sum()\n    return cut / weight\n\n\nbicluster_ncuts = list(bicluster_ncut(i) for i in range(len(newsgroups.target_names)))\nbest_idx = np.argsort(bicluster_ncuts)[:5]\n\nprint()\nprint(\"Best biclusters:\")\nprint(\"----------------\")\nfor idx, cluster in enumerate(best_idx):\n    n_rows, n_cols = cocluster.get_shape(cluster)\n    cluster_docs, cluster_words = cocluster.get_indices(cluster)\n    if not len(cluster_docs) or not len(cluster_words):\n        continue\n\n    # categories\n    counter = Counter(document_names[doc] for doc in cluster_docs)\n\n    cat_string = \", \".join(\n        f\"{(c / n_rows * 100):.0f}% {name}\" for name, c in counter.most_common(3)\n    )\n\n    # words\n    out_of_cluster_docs = cocluster.row_labels_ != cluster\n    out_of_cluster_docs = np.where(out_of_cluster_docs)[0]\n    word_col = X[:, cluster_words]\n    word_scores = np.array(\n        word_col[cluster_docs, :].sum(axis=0)\n        - word_col[out_of_cluster_docs, :].sum(axis=0)\n    )\n    word_scores = word_scores.ravel()\n    important_words = list(\n        feature_names[cluster_words[i]] for i in word_scores.argsort()[:-11:-1]\n    )\n\n    print(f\"bicluster {idx} : {n_rows} documents, {n_cols} words\")\n    print(f\"categories   : {cat_string}\")\n    print(f\"words        : {', '.join(important_words)}\\n\")"
+        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\nfrom collections import Counter\nfrom time import time\n\nimport numpy as np\n\nfrom sklearn.cluster import MiniBatchKMeans, SpectralCoclustering\nfrom sklearn.datasets import fetch_20newsgroups\nfrom sklearn.feature_extraction.text import TfidfVectorizer\nfrom sklearn.metrics.cluster import v_measure_score\n\n\ndef number_normalizer(tokens):\n    \"\"\"Map all numeric tokens to a placeholder.\n\n    For many applications, tokens that begin with a number are not directly\n    useful, but the fact that such a token exists can be relevant.  By applying\n    this form of dimensionality reduction, some methods may perform better.\n    \"\"\"\n    return (\"#NUMBER\" if token[0].isdigit() else token for token in tokens)\n\n\nclass NumberNormalizingVectorizer(TfidfVectorizer):\n    def build_tokenizer(self):\n        tokenize = super().build_tokenizer()\n        return lambda doc: list(number_normalizer(tokenize(doc)))\n\n\n# exclude 'comp.os.ms-windows.misc'\ncategories = [\n    \"alt.atheism\",\n    \"comp.graphics\",\n    \"comp.sys.ibm.pc.hardware\",\n    \"comp.sys.mac.hardware\",\n    \"comp.windows.x\",\n    \"misc.forsale\",\n    \"rec.autos\",\n    \"rec.motorcycles\",\n    \"rec.sport.baseball\",\n    \"rec.sport.hockey\",\n    \"sci.crypt\",\n    \"sci.electronics\",\n    \"sci.med\",\n    \"sci.space\",\n    \"soc.religion.christian\",\n    \"talk.politics.guns\",\n    \"talk.politics.mideast\",\n    \"talk.politics.misc\",\n    \"talk.religion.misc\",\n]\nnewsgroups = fetch_20newsgroups(categories=categories)\ny_true = newsgroups.target\n\nvectorizer = NumberNormalizingVectorizer(stop_words=\"english\", min_df=5)\ncocluster = SpectralCoclustering(\n    n_clusters=len(categories), svd_method=\"arpack\", random_state=0\n)\nkmeans = MiniBatchKMeans(\n    n_clusters=len(categories), batch_size=20000, random_state=0, n_init=3\n)\n\nprint(\"Vectorizing...\")\nX = vectorizer.fit_transform(newsgroups.data)\n\nprint(\"Coclustering...\")\nstart_time = time()\ncocluster.fit(X)\ny_cocluster = cocluster.row_labels_\nprint(\n    f\"Done in {time() - start_time:.2f}s. V-measure: \\\n{v_measure_score(y_cocluster, y_true):.4f}\"\n)\n\n\nprint(\"MiniBatchKMeans...\")\nstart_time = time()\ny_kmeans = kmeans.fit_predict(X)\nprint(\n    f\"Done in {time() - start_time:.2f}s. V-measure: \\\n{v_measure_score(y_kmeans, y_true):.4f}\"\n)\n\n\nfeature_names = vectorizer.get_feature_names_out()\ndocument_names = list(newsgroups.target_names[i] for i in newsgroups.target)\n\n\ndef bicluster_ncut(i):\n    rows, cols = cocluster.get_indices(i)\n    if not (np.any(rows) and np.any(cols)):\n        import sys\n\n        return sys.float_info.max\n    row_complement = np.nonzero(np.logical_not(cocluster.rows_[i]))[0]\n    col_complement = np.nonzero(np.logical_not(cocluster.columns_[i]))[0]\n    # Note: the following is identical to X[rows[:, np.newaxis],\n    # cols].sum() but much faster in scipy <= 0.16\n    weight = X[rows][:, cols].sum()\n    cut = X[row_complement][:, cols].sum() + X[rows][:, col_complement].sum()\n    return cut / weight\n\n\nbicluster_ncuts = list(bicluster_ncut(i) for i in range(len(newsgroups.target_names)))\nbest_idx = np.argsort(bicluster_ncuts)[:5]\n\nprint()\nprint(\"Best biclusters:\")\nprint(\"----------------\")\nfor idx, cluster in enumerate(best_idx):\n    n_rows, n_cols = cocluster.get_shape(cluster)\n    cluster_docs, cluster_words = cocluster.get_indices(cluster)\n    if not len(cluster_docs) or not len(cluster_words):\n        continue\n\n    # categories\n    counter = Counter(document_names[doc] for doc in cluster_docs)\n\n    cat_string = \", \".join(\n        f\"{(c / n_rows * 100):.0f}% {name}\" for name, c in counter.most_common(3)\n    )\n\n    # words\n    out_of_cluster_docs = cocluster.row_labels_ != cluster\n    out_of_cluster_docs = out_of_cluster_docs.nonzero()[0]\n    word_col = X[:, cluster_words]\n    word_scores = np.array(\n        word_col[cluster_docs, :].sum(axis=0)\n        - word_col[out_of_cluster_docs, :].sum(axis=0)\n    )\n    word_scores = word_scores.ravel()\n    important_words = list(\n        feature_names[cluster_words[i]] for i in word_scores.argsort()[:-11:-1]\n    )\n\n    print(f\"bicluster {idx} : {n_rows} documents, {n_cols} words\")\n    print(f\"categories   : {cat_string}\")\n    print(f\"words        : {', '.join(important_words)}\\n\")"
       ]
     }
   ],
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/40f4aad91af595a370d7582e3a23bed7/plot_roc.ipynb b/dev/_downloads/40f4aad91af595a370d7582e3a23bed7/plot_roc.ipynb
index f4c6f4e61e040..42953aebee744 100644
--- a/dev/_downloads/40f4aad91af595a370d7582e3a23bed7/plot_roc.ipynb
+++ b/dev/_downloads/40f4aad91af595a370d7582e3a23bed7/plot_roc.ipynb
@@ -134,7 +134,7 @@
       },
       "outputs": [],
       "source": [
-        "print(f\"y_score:\\n{y_score[0:2,:]}\")\nprint()\nprint(f\"y_score.ravel():\\n{y_score[0:2,:].ravel()}\")"
+        "print(f\"y_score:\\n{y_score[0:2, :]}\")\nprint()\nprint(f\"y_score.ravel():\\n{y_score[0:2, :].ravel()}\")"
       ]
     },
     {
@@ -271,7 +271,7 @@
       },
       "outputs": [],
       "source": [
-        "pair_scores = []\nmean_tpr = dict()\n\nfor ix, (label_a, label_b) in enumerate(pair_list):\n    a_mask = y_test == label_a\n    b_mask = y_test == label_b\n    ab_mask = np.logical_or(a_mask, b_mask)\n\n    a_true = a_mask[ab_mask]\n    b_true = b_mask[ab_mask]\n\n    idx_a = np.flatnonzero(label_binarizer.classes_ == label_a)[0]\n    idx_b = np.flatnonzero(label_binarizer.classes_ == label_b)[0]\n\n    fpr_a, tpr_a, _ = roc_curve(a_true, y_score[ab_mask, idx_a])\n    fpr_b, tpr_b, _ = roc_curve(b_true, y_score[ab_mask, idx_b])\n\n    mean_tpr[ix] = np.zeros_like(fpr_grid)\n    mean_tpr[ix] += np.interp(fpr_grid, fpr_a, tpr_a)\n    mean_tpr[ix] += np.interp(fpr_grid, fpr_b, tpr_b)\n    mean_tpr[ix] /= 2\n    mean_score = auc(fpr_grid, mean_tpr[ix])\n    pair_scores.append(mean_score)\n\n    fig, ax = plt.subplots(figsize=(6, 6))\n    plt.plot(\n        fpr_grid,\n        mean_tpr[ix],\n        label=f\"Mean {label_a} vs {label_b} (AUC = {mean_score :.2f})\",\n        linestyle=\":\",\n        linewidth=4,\n    )\n    RocCurveDisplay.from_predictions(\n        a_true,\n        y_score[ab_mask, idx_a],\n        ax=ax,\n        name=f\"{label_a} as positive class\",\n    )\n    RocCurveDisplay.from_predictions(\n        b_true,\n        y_score[ab_mask, idx_b],\n        ax=ax,\n        name=f\"{label_b} as positive class\",\n        plot_chance_level=True,\n        despine=True,\n    )\n    ax.set(\n        xlabel=\"False Positive Rate\",\n        ylabel=\"True Positive Rate\",\n        title=f\"{target_names[idx_a]} vs {label_b} ROC curves\",\n    )\n\nprint(f\"Macro-averaged One-vs-One ROC AUC score:\\n{np.average(pair_scores):.2f}\")"
+        "pair_scores = []\nmean_tpr = dict()\n\nfor ix, (label_a, label_b) in enumerate(pair_list):\n    a_mask = y_test == label_a\n    b_mask = y_test == label_b\n    ab_mask = np.logical_or(a_mask, b_mask)\n\n    a_true = a_mask[ab_mask]\n    b_true = b_mask[ab_mask]\n\n    idx_a = np.flatnonzero(label_binarizer.classes_ == label_a)[0]\n    idx_b = np.flatnonzero(label_binarizer.classes_ == label_b)[0]\n\n    fpr_a, tpr_a, _ = roc_curve(a_true, y_score[ab_mask, idx_a])\n    fpr_b, tpr_b, _ = roc_curve(b_true, y_score[ab_mask, idx_b])\n\n    mean_tpr[ix] = np.zeros_like(fpr_grid)\n    mean_tpr[ix] += np.interp(fpr_grid, fpr_a, tpr_a)\n    mean_tpr[ix] += np.interp(fpr_grid, fpr_b, tpr_b)\n    mean_tpr[ix] /= 2\n    mean_score = auc(fpr_grid, mean_tpr[ix])\n    pair_scores.append(mean_score)\n\n    fig, ax = plt.subplots(figsize=(6, 6))\n    plt.plot(\n        fpr_grid,\n        mean_tpr[ix],\n        label=f\"Mean {label_a} vs {label_b} (AUC = {mean_score:.2f})\",\n        linestyle=\":\",\n        linewidth=4,\n    )\n    RocCurveDisplay.from_predictions(\n        a_true,\n        y_score[ab_mask, idx_a],\n        ax=ax,\n        name=f\"{label_a} as positive class\",\n    )\n    RocCurveDisplay.from_predictions(\n        b_true,\n        y_score[ab_mask, idx_b],\n        ax=ax,\n        name=f\"{label_b} as positive class\",\n        plot_chance_level=True,\n        despine=True,\n    )\n    ax.set(\n        xlabel=\"False Positive Rate\",\n        ylabel=\"True Positive Rate\",\n        title=f\"{target_names[idx_a]} vs {label_b} ROC curves\",\n    )\n\nprint(f\"Macro-averaged One-vs-One ROC AUC score:\\n{np.average(pair_scores):.2f}\")"
       ]
     },
     {
@@ -334,7 +334,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/41475a5e6fc6360930267b5cd092c937/plot_classification.zip b/dev/_downloads/41475a5e6fc6360930267b5cd092c937/plot_classification.zip
index f646324de8fee..a873b27146823 100644
Binary files a/dev/_downloads/41475a5e6fc6360930267b5cd092c937/plot_classification.zip and b/dev/_downloads/41475a5e6fc6360930267b5cd092c937/plot_classification.zip differ
diff --git a/dev/_downloads/4158158e379fe1c9e1700dd8a6125179/plot_pca_vs_fa_model_selection.zip b/dev/_downloads/4158158e379fe1c9e1700dd8a6125179/plot_pca_vs_fa_model_selection.zip
index 7075c26d584f8..706040555263c 100644
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diff --git a/dev/_downloads/419245a165589a136566a555741c80e8/plot_gpr_on_structured_data.zip b/dev/_downloads/419245a165589a136566a555741c80e8/plot_gpr_on_structured_data.zip
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diff --git a/dev/_downloads/4212c599ad39f31d06640bbe0ddf2807/plot_pipeline_display.zip b/dev/_downloads/4212c599ad39f31d06640bbe0ddf2807/plot_pipeline_display.zip
index 929052def09f1..ea0805bca3d5a 100644
Binary files a/dev/_downloads/4212c599ad39f31d06640bbe0ddf2807/plot_pipeline_display.zip and b/dev/_downloads/4212c599ad39f31d06640bbe0ddf2807/plot_pipeline_display.zip differ
diff --git a/dev/_downloads/43f17c8225b2c8e15e22a7edf7adb8b5/plot_mean_shift.ipynb b/dev/_downloads/43f17c8225b2c8e15e22a7edf7adb8b5/plot_mean_shift.ipynb
index a4604ec820658..2af7a6d8e84ae 100644
--- a/dev/_downloads/43f17c8225b2c8e15e22a7edf7adb8b5/plot_mean_shift.ipynb
+++ b/dev/_downloads/43f17c8225b2c8e15e22a7edf7adb8b5/plot_mean_shift.ipynb
@@ -89,7 +89,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/44775ca43ce935c840a877c3d2ebed6c/plot_ridge_coeffs.ipynb b/dev/_downloads/44775ca43ce935c840a877c3d2ebed6c/plot_ridge_coeffs.ipynb
index 6ac6a174ee977..0e057f0694b29 100644
--- a/dev/_downloads/44775ca43ce935c840a877c3d2ebed6c/plot_ridge_coeffs.ipynb
+++ b/dev/_downloads/44775ca43ce935c840a877c3d2ebed6c/plot_ridge_coeffs.ipynb
@@ -96,7 +96,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/44ae219cc6359efae1cf5a6dc3e4b2e8/plot_rbf_parameters.zip b/dev/_downloads/44ae219cc6359efae1cf5a6dc3e4b2e8/plot_rbf_parameters.zip
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diff --git a/dev/_downloads/45165f5550aea2cda6696d5586546067/plot_gpc_isoprobability.zip b/dev/_downloads/45165f5550aea2cda6696d5586546067/plot_gpc_isoprobability.zip
index de805b125ebc3..e609060273393 100644
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diff --git a/dev/_downloads/452ef11734eef7e89e9b6f7bb4bcb108/plot_isolation_forest.zip b/dev/_downloads/452ef11734eef7e89e9b6f7bb4bcb108/plot_isolation_forest.zip
index b57ddbcc626e6..7a1d97af3152e 100644
Binary files a/dev/_downloads/452ef11734eef7e89e9b6f7bb4bcb108/plot_isolation_forest.zip and b/dev/_downloads/452ef11734eef7e89e9b6f7bb4bcb108/plot_isolation_forest.zip differ
diff --git a/dev/_downloads/45916745bb89ca49be3a50aa80e65e3f/plot_nested_cross_validation_iris.ipynb b/dev/_downloads/45916745bb89ca49be3a50aa80e65e3f/plot_nested_cross_validation_iris.ipynb
index 1c880d8994fdd..9d7a596b96b2f 100644
--- a/dev/_downloads/45916745bb89ca49be3a50aa80e65e3f/plot_nested_cross_validation_iris.ipynb
+++ b/dev/_downloads/45916745bb89ca49be3a50aa80e65e3f/plot_nested_cross_validation_iris.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/461290890e86d787be8ca355c3c65e4a/plot_svm_regression.zip b/dev/_downloads/461290890e86d787be8ca355c3c65e4a/plot_svm_regression.zip
index 63a0367350d81..9d21e8c0f7090 100644
Binary files a/dev/_downloads/461290890e86d787be8ca355c3c65e4a/plot_svm_regression.zip and b/dev/_downloads/461290890e86d787be8ca355c3c65e4a/plot_svm_regression.zip differ
diff --git a/dev/_downloads/46b6a23d83637bf0f381ce9d8c528aa2/plot_pca_iris.ipynb b/dev/_downloads/46b6a23d83637bf0f381ce9d8c528aa2/plot_pca_iris.ipynb
index b7c2158cfa6aa..540cacfdbc415 100644
--- a/dev/_downloads/46b6a23d83637bf0f381ce9d8c528aa2/plot_pca_iris.ipynb
+++ b/dev/_downloads/46b6a23d83637bf0f381ce9d8c528aa2/plot_pca_iris.ipynb
@@ -96,7 +96,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/46c19b52b5a5ab5796725eb7e0688309/plot_gpr_on_structured_data.ipynb b/dev/_downloads/46c19b52b5a5ab5796725eb7e0688309/plot_gpr_on_structured_data.ipynb
index c783b46db2728..dba5cb05e77f5 100644
--- a/dev/_downloads/46c19b52b5a5ab5796725eb7e0688309/plot_gpr_on_structured_data.ipynb
+++ b/dev/_downloads/46c19b52b5a5ab5796725eb7e0688309/plot_gpr_on_structured_data.ipynb
@@ -100,7 +100,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/471829dadf19abf3dd2b87b08c9ffc92/plot_gmm_covariances.ipynb b/dev/_downloads/471829dadf19abf3dd2b87b08c9ffc92/plot_gmm_covariances.ipynb
index 0a398fb47e2f7..3922a27d40679 100644
--- a/dev/_downloads/471829dadf19abf3dd2b87b08c9ffc92/plot_gmm_covariances.ipynb
+++ b/dev/_downloads/471829dadf19abf3dd2b87b08c9ffc92/plot_gmm_covariances.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/4727c89312a4840b10accd3381f1b354/plot_permutation_importance.zip b/dev/_downloads/4727c89312a4840b10accd3381f1b354/plot_permutation_importance.zip
index ae240c265a3cd..5904d28b3075e 100644
Binary files a/dev/_downloads/4727c89312a4840b10accd3381f1b354/plot_permutation_importance.zip and b/dev/_downloads/4727c89312a4840b10accd3381f1b354/plot_permutation_importance.zip differ
diff --git a/dev/_downloads/47511aeb2706f3183261493a52ece97b/plot_transformed_target.zip b/dev/_downloads/47511aeb2706f3183261493a52ece97b/plot_transformed_target.zip
index a6bca0e6837c0..abb827629b9f5 100644
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diff --git a/dev/_downloads/477739095a6a24d74492bccf73397462/plot_johnson_lindenstrauss_bound.zip b/dev/_downloads/477739095a6a24d74492bccf73397462/plot_johnson_lindenstrauss_bound.zip
index 02cfbe0ebc613..5f21bb56071f6 100644
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diff --git a/dev/_downloads/4796ad47eaaf6dee0097a73a3c92ca19/plot_display_object_visualization.zip b/dev/_downloads/4796ad47eaaf6dee0097a73a3c92ca19/plot_display_object_visualization.zip
index 455805577b3c9..ce7b7fa55cbc9 100644
Binary files a/dev/_downloads/4796ad47eaaf6dee0097a73a3c92ca19/plot_display_object_visualization.zip and b/dev/_downloads/4796ad47eaaf6dee0097a73a3c92ca19/plot_display_object_visualization.zip differ
diff --git a/dev/_downloads/47f00be4ac7158f9188c680e1a05ef7b/plot_optics.zip b/dev/_downloads/47f00be4ac7158f9188c680e1a05ef7b/plot_optics.zip
index aa9f749b4e04f..67299121b6a28 100644
Binary files a/dev/_downloads/47f00be4ac7158f9188c680e1a05ef7b/plot_optics.zip and b/dev/_downloads/47f00be4ac7158f9188c680e1a05ef7b/plot_optics.zip differ
diff --git a/dev/_downloads/47f024d726d245e034c7690b4664721f/plot_classification.ipynb b/dev/_downloads/47f024d726d245e034c7690b4664721f/plot_classification.ipynb
index 824bfbd4cfe23..071a663ddf571 100644
--- a/dev/_downloads/47f024d726d245e034c7690b4664721f/plot_classification.ipynb
+++ b/dev/_downloads/47f024d726d245e034c7690b4664721f/plot_classification.ipynb
@@ -96,7 +96,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/493307eb257cfb3d4e056ee73a41842e/plot_sparse_cov.ipynb b/dev/_downloads/493307eb257cfb3d4e056ee73a41842e/plot_sparse_cov.ipynb
index 20300468f1af9..8e42dfa219a2c 100644
--- a/dev/_downloads/493307eb257cfb3d4e056ee73a41842e/plot_sparse_cov.ipynb
+++ b/dev/_downloads/493307eb257cfb3d4e056ee73a41842e/plot_sparse_cov.ipynb
@@ -100,7 +100,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/4941b506cc56c9cec00d40992e2a4207/plot_permutation_importance_multicollinear.ipynb b/dev/_downloads/4941b506cc56c9cec00d40992e2a4207/plot_permutation_importance_multicollinear.ipynb
index d51a96cc129bb..e1d343b449944 100644
--- a/dev/_downloads/4941b506cc56c9cec00d40992e2a4207/plot_permutation_importance_multicollinear.ipynb
+++ b/dev/_downloads/4941b506cc56c9cec00d40992e2a4207/plot_permutation_importance_multicollinear.ipynb
@@ -161,7 +161,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/4969738e4f46f5753249be090ab7b3f5/plot_gmm_init.ipynb b/dev/_downloads/4969738e4f46f5753249be090ab7b3f5/plot_gmm_init.ipynb
index 6d30a92230dfe..6d22f98fbddbd 100644
--- a/dev/_downloads/4969738e4f46f5753249be090ab7b3f5/plot_gmm_init.ipynb
+++ b/dev/_downloads/4969738e4f46f5753249be090ab7b3f5/plot_gmm_init.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/49cd91d05440a1c88b074430761aeb76/plot_cv_indices.ipynb b/dev/_downloads/49cd91d05440a1c88b074430761aeb76/plot_cv_indices.ipynb
index 4a705242c87fa..8f3bd3db8198d 100644
--- a/dev/_downloads/49cd91d05440a1c88b074430761aeb76/plot_cv_indices.ipynb
+++ b/dev/_downloads/49cd91d05440a1c88b074430761aeb76/plot_cv_indices.ipynb
@@ -125,7 +125,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/49fae0b4f6ab58738dcbf62236756548/plot_successive_halving_iterations.py b/dev/_downloads/49fae0b4f6ab58738dcbf62236756548/plot_successive_halving_iterations.py
index 31c1a0b9d5b34..986be49ac0bef 100644
--- a/dev/_downloads/49fae0b4f6ab58738dcbf62236756548/plot_successive_halving_iterations.py
+++ b/dev/_downloads/49fae0b4f6ab58738dcbf62236756548/plot_successive_halving_iterations.py
@@ -20,7 +20,7 @@
 
 from sklearn import datasets
 from sklearn.ensemble import RandomForestClassifier
-from sklearn.experimental import enable_halving_search_cv  # noqa
+from sklearn.experimental import enable_halving_search_cv  # noqa: F401
 from sklearn.model_selection import HalvingRandomSearchCV
 
 # %%
diff --git a/dev/_downloads/4a71403d3ce9170ab7db4afc50d661a7/plot_underfitting_overfitting.zip b/dev/_downloads/4a71403d3ce9170ab7db4afc50d661a7/plot_underfitting_overfitting.zip
index e207990c9cd0f..9a5728c07d9f7 100644
Binary files a/dev/_downloads/4a71403d3ce9170ab7db4afc50d661a7/plot_underfitting_overfitting.zip and b/dev/_downloads/4a71403d3ce9170ab7db4afc50d661a7/plot_underfitting_overfitting.zip differ
diff --git a/dev/_downloads/4bdf97c300a97c8d6b10374db210d15c/plot_cv_indices.zip b/dev/_downloads/4bdf97c300a97c8d6b10374db210d15c/plot_cv_indices.zip
index 0a7045a325e8d..f085f47a065d4 100644
Binary files a/dev/_downloads/4bdf97c300a97c8d6b10374db210d15c/plot_cv_indices.zip and b/dev/_downloads/4bdf97c300a97c8d6b10374db210d15c/plot_cv_indices.zip differ
diff --git a/dev/_downloads/4c1663175b07cf9608b07331aa180eb7/plot_logistic_multinomial.ipynb b/dev/_downloads/4c1663175b07cf9608b07331aa180eb7/plot_logistic_multinomial.ipynb
index 2e1d457da988e..2c27d777bdcfc 100644
--- a/dev/_downloads/4c1663175b07cf9608b07331aa180eb7/plot_logistic_multinomial.ipynb
+++ b/dev/_downloads/4c1663175b07cf9608b07331aa180eb7/plot_logistic_multinomial.ipynb
@@ -125,7 +125,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/4c773264381a88c3d3933952c6040058/plot_swissroll.ipynb b/dev/_downloads/4c773264381a88c3d3933952c6040058/plot_swissroll.ipynb
index 846fbfdf69043..966d5f7521a3a 100644
--- a/dev/_downloads/4c773264381a88c3d3933952c6040058/plot_swissroll.ipynb
+++ b/dev/_downloads/4c773264381a88c3d3933952c6040058/plot_swissroll.ipynb
@@ -139,7 +139,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/4cf0456267ced0f869a458ef4776d4c5/plot_release_highlights_1_1_0.py b/dev/_downloads/4cf0456267ced0f869a458ef4776d4c5/plot_release_highlights_1_1_0.py
index 2a529e9ccd269..fdb11f887f3db 100644
--- a/dev/_downloads/4cf0456267ced0f869a458ef4776d4c5/plot_release_highlights_1_1_0.py
+++ b/dev/_downloads/4cf0456267ced0f869a458ef4776d4c5/plot_release_highlights_1_1_0.py
@@ -1,4 +1,4 @@
-# ruff: noqa
+# ruff: noqa: CPY001
 """
 =======================================
 Release Highlights for scikit-learn 1.1
@@ -28,9 +28,10 @@
 # -----------------------------------------------------------------
 # :class:`~ensemble.HistGradientBoostingRegressor` can model quantiles with
 # `loss="quantile"` and the new parameter `quantile`.
-from sklearn.ensemble import HistGradientBoostingRegressor
-import numpy as np
 import matplotlib.pyplot as plt
+import numpy as np
+
+from sklearn.ensemble import HistGradientBoostingRegressor
 
 # Simple regression function for X * cos(X)
 rng = np.random.RandomState(42)
@@ -66,12 +67,12 @@
 # This enables :class:`~pipeline.Pipeline` to construct the output feature names for
 # more complex pipelines:
 from sklearn.compose import ColumnTransformer
-from sklearn.preprocessing import OneHotEncoder, StandardScaler
-from sklearn.pipeline import make_pipeline
-from sklearn.impute import SimpleImputer
-from sklearn.feature_selection import SelectKBest
 from sklearn.datasets import fetch_openml
+from sklearn.feature_selection import SelectKBest
+from sklearn.impute import SimpleImputer
 from sklearn.linear_model import LogisticRegression
+from sklearn.pipeline import make_pipeline
+from sklearn.preprocessing import OneHotEncoder, StandardScaler
 
 X, y = fetch_openml(
     "titanic", version=1, as_frame=True, return_X_y=True, parser="pandas"
@@ -115,9 +116,10 @@
 # the gathering of infrequent categories are `min_frequency` and
 # `max_categories`. See the :ref:`User Guide <encoder_infrequent_categories>`
 # for more details.
-from sklearn.preprocessing import OneHotEncoder
 import numpy as np
 
+from sklearn.preprocessing import OneHotEncoder
+
 X = np.array(
     [["dog"] * 5 + ["cat"] * 20 + ["rabbit"] * 10 + ["snake"] * 3], dtype=object
 ).T
@@ -184,6 +186,7 @@
 # learning when the data is not readily available from the start, or when the
 # data does not fit into memory.
 import numpy as np
+
 from sklearn.decomposition import MiniBatchNMF
 
 rng = np.random.RandomState(0)
@@ -202,7 +205,7 @@
 X_reconstructed = W @ H
 
 print(
-    f"relative reconstruction error: ",
+    "relative reconstruction error: ",
     f"{np.sum((X - X_reconstructed) ** 2) / np.sum(X**2):.5f}",
 )
 
@@ -215,10 +218,11 @@
 # previous clustering: a cluster is split into two new clusters repeatedly
 # until the target number of clusters is reached, giving a hierarchical
 # structure to the clustering.
-from sklearn.datasets import make_blobs
-from sklearn.cluster import KMeans, BisectingKMeans
 import matplotlib.pyplot as plt
 
+from sklearn.cluster import BisectingKMeans, KMeans
+from sklearn.datasets import make_blobs
+
 X, _ = make_blobs(n_samples=1000, centers=2, random_state=0)
 
 km = KMeans(n_clusters=5, random_state=0, n_init="auto").fit(X)
diff --git a/dev/_downloads/4cfbe7582ae87a68ed98582a86d0bb9c/plot_mlp_training_curves.zip b/dev/_downloads/4cfbe7582ae87a68ed98582a86d0bb9c/plot_mlp_training_curves.zip
index 8d266884b9eaf..8d0eb3965cda6 100644
Binary files a/dev/_downloads/4cfbe7582ae87a68ed98582a86d0bb9c/plot_mlp_training_curves.zip and b/dev/_downloads/4cfbe7582ae87a68ed98582a86d0bb9c/plot_mlp_training_curves.zip differ
diff --git a/dev/_downloads/4dcec41c6039a7291948a1846cd34240/plot_label_propagation_digits_active_learning.zip b/dev/_downloads/4dcec41c6039a7291948a1846cd34240/plot_label_propagation_digits_active_learning.zip
index d4f96e5a0ff1b..d571bab80d4d0 100644
Binary files a/dev/_downloads/4dcec41c6039a7291948a1846cd34240/plot_label_propagation_digits_active_learning.zip and b/dev/_downloads/4dcec41c6039a7291948a1846cd34240/plot_label_propagation_digits_active_learning.zip differ
diff --git a/dev/_downloads/4eaf86254084752c8a966ae3e3b83708/plot_gmm_selection.zip b/dev/_downloads/4eaf86254084752c8a966ae3e3b83708/plot_gmm_selection.zip
index 57d52671ce9c7..881814da98f71 100644
Binary files a/dev/_downloads/4eaf86254084752c8a966ae3e3b83708/plot_gmm_selection.zip and b/dev/_downloads/4eaf86254084752c8a966ae3e3b83708/plot_gmm_selection.zip differ
diff --git a/dev/_downloads/4ee88a807e060ca374ab95e0d8d819ed/plot_ica_vs_pca.ipynb b/dev/_downloads/4ee88a807e060ca374ab95e0d8d819ed/plot_ica_vs_pca.ipynb
index 333f45be77521..5fcce019a141b 100644
--- a/dev/_downloads/4ee88a807e060ca374ab95e0d8d819ed/plot_ica_vs_pca.ipynb
+++ b/dev/_downloads/4ee88a807e060ca374ab95e0d8d819ed/plot_ica_vs_pca.ipynb
@@ -71,7 +71,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/4f07b03421908788913e044918d8ed1e/plot_release_highlights_0_23_0.py b/dev/_downloads/4f07b03421908788913e044918d8ed1e/plot_release_highlights_0_23_0.py
index be9b5fc3b257e..00c36969ec18b 100644
--- a/dev/_downloads/4f07b03421908788913e044918d8ed1e/plot_release_highlights_0_23_0.py
+++ b/dev/_downloads/4f07b03421908788913e044918d8ed1e/plot_release_highlights_0_23_0.py
@@ -1,4 +1,4 @@
-# ruff: noqa
+# ruff: noqa: CPY001
 """
 ========================================
 Release Highlights for scikit-learn 0.23
@@ -35,9 +35,10 @@
 # 'poisson' loss as well.
 
 import numpy as np
-from sklearn.model_selection import train_test_split
-from sklearn.linear_model import PoissonRegressor
+
 from sklearn.ensemble import HistGradientBoostingRegressor
+from sklearn.linear_model import PoissonRegressor
+from sklearn.model_selection import train_test_split
 
 n_samples, n_features = 1000, 20
 rng = np.random.RandomState(0)
@@ -63,11 +64,11 @@
 # this feature.
 
 from sklearn import set_config
-from sklearn.pipeline import make_pipeline
-from sklearn.preprocessing import OneHotEncoder, StandardScaler
-from sklearn.impute import SimpleImputer
 from sklearn.compose import make_column_transformer
+from sklearn.impute import SimpleImputer
 from sklearn.linear_model import LogisticRegression
+from sklearn.pipeline import make_pipeline
+from sklearn.preprocessing import OneHotEncoder, StandardScaler
 
 set_config(display="diagram")
 
@@ -94,12 +95,13 @@
 # parallelism instead of relying on joblib, so the `n_jobs` parameter has no
 # effect anymore. For more details on how to control the number of threads,
 # please refer to our :ref:`parallelism` notes.
-import scipy
 import numpy as np
-from sklearn.model_selection import train_test_split
+import scipy
+
 from sklearn.cluster import KMeans
 from sklearn.datasets import make_blobs
 from sklearn.metrics import completeness_score
+from sklearn.model_selection import train_test_split
 
 rng = np.random.RandomState(0)
 X, y = make_blobs(random_state=rng)
@@ -126,11 +128,12 @@
 # example, see :ref:`sphx_glr_auto_examples_ensemble_plot_hgbt_regression.py`.
 import numpy as np
 from matplotlib import pyplot as plt
-from sklearn.model_selection import train_test_split
+
+from sklearn.ensemble import HistGradientBoostingRegressor
 
 # from sklearn.inspection import plot_partial_dependence
 from sklearn.inspection import PartialDependenceDisplay
-from sklearn.ensemble import HistGradientBoostingRegressor
+from sklearn.model_selection import train_test_split
 
 n_samples = 500
 rng = np.random.RandomState(0)
@@ -173,10 +176,11 @@
 # The two linear regressors :class:`~sklearn.linear_model.Lasso` and
 # :class:`~sklearn.linear_model.ElasticNet` now support sample weights.
 
-from sklearn.model_selection import train_test_split
+import numpy as np
+
 from sklearn.datasets import make_regression
 from sklearn.linear_model import Lasso
-import numpy as np
+from sklearn.model_selection import train_test_split
 
 n_samples, n_features = 1000, 20
 rng = np.random.RandomState(0)
diff --git a/dev/_downloads/4f099a716faccfd3dfbe8161514623b1/plot_mnist_filters.zip b/dev/_downloads/4f099a716faccfd3dfbe8161514623b1/plot_mnist_filters.zip
index 70d043f0d30d3..f450b4cacb677 100644
Binary files a/dev/_downloads/4f099a716faccfd3dfbe8161514623b1/plot_mnist_filters.zip and b/dev/_downloads/4f099a716faccfd3dfbe8161514623b1/plot_mnist_filters.zip differ
diff --git a/dev/_downloads/4f4cd09070f9e2e8fba523ca3ba243f1/plot_gmm.zip b/dev/_downloads/4f4cd09070f9e2e8fba523ca3ba243f1/plot_gmm.zip
index 1366bb6dfd3f0..45a1dea7aefce 100644
Binary files a/dev/_downloads/4f4cd09070f9e2e8fba523ca3ba243f1/plot_gmm.zip and b/dev/_downloads/4f4cd09070f9e2e8fba523ca3ba243f1/plot_gmm.zip differ
diff --git a/dev/_downloads/4fe7ce5d502ee21f3c344f775829354a/plot_bias_variance.py b/dev/_downloads/4fe7ce5d502ee21f3c344f775829354a/plot_bias_variance.py
index e1b37c03360f6..72134841c78ea 100644
--- a/dev/_downloads/4fe7ce5d502ee21f3c344f775829354a/plot_bias_variance.py
+++ b/dev/_downloads/4fe7ce5d502ee21f3c344f775829354a/plot_bias_variance.py
@@ -177,8 +177,8 @@ def generate(n_samples, noise, n_repeat=1):
 
     plt.subplot(2, n_estimators, n_estimators + n + 1)
     plt.plot(X_test, y_error, "r", label="$error(x)$")
-    plt.plot(X_test, y_bias, "b", label="$bias^2(x)$"),
-    plt.plot(X_test, y_var, "g", label="$variance(x)$"),
+    plt.plot(X_test, y_bias, "b", label="$bias^2(x)$")
+    plt.plot(X_test, y_var, "g", label="$variance(x)$")
     plt.plot(X_test, y_noise, "c", label="$noise(x)$")
 
     plt.xlim([-5, 5])
diff --git a/dev/_downloads/5034bd1c22037f56c1da2e8d84719607/plot_classifier_chain_yeast.zip b/dev/_downloads/5034bd1c22037f56c1da2e8d84719607/plot_classifier_chain_yeast.zip
index c35b6b45f39eb..22eafe6e6be41 100644
Binary files a/dev/_downloads/5034bd1c22037f56c1da2e8d84719607/plot_classifier_chain_yeast.zip and b/dev/_downloads/5034bd1c22037f56c1da2e8d84719607/plot_classifier_chain_yeast.zip differ
diff --git a/dev/_downloads/503dcbe9fdb65a8f83bd6e34b3adc769/plot_covariance_estimation.ipynb b/dev/_downloads/503dcbe9fdb65a8f83bd6e34b3adc769/plot_covariance_estimation.ipynb
index 26e3e9aeb2ffa..c99656443931c 100644
--- a/dev/_downloads/503dcbe9fdb65a8f83bd6e34b3adc769/plot_covariance_estimation.ipynb
+++ b/dev/_downloads/503dcbe9fdb65a8f83bd6e34b3adc769/plot_covariance_estimation.ipynb
@@ -114,7 +114,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/512757d5cc898dc59c01eb79cc80a273/plot_gradient_boosting_regression.zip b/dev/_downloads/512757d5cc898dc59c01eb79cc80a273/plot_gradient_boosting_regression.zip
index c61cfee0a8a98..797918c286544 100644
Binary files a/dev/_downloads/512757d5cc898dc59c01eb79cc80a273/plot_gradient_boosting_regression.zip and b/dev/_downloads/512757d5cc898dc59c01eb79cc80a273/plot_gradient_boosting_regression.zip differ
diff --git a/dev/_downloads/517bdba67b49ba04ad16cff789c15fba/plot_gpr_noisy_targets.ipynb b/dev/_downloads/517bdba67b49ba04ad16cff789c15fba/plot_gpr_noisy_targets.ipynb
index 0a5973a1e6023..5501ed8ec5d7c 100644
--- a/dev/_downloads/517bdba67b49ba04ad16cff789c15fba/plot_gpr_noisy_targets.ipynb
+++ b/dev/_downloads/517bdba67b49ba04ad16cff789c15fba/plot_gpr_noisy_targets.ipynb
@@ -179,7 +179,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/51833337bfc73d152b44902e5baa50ff/plot_lasso_lars_ic.ipynb b/dev/_downloads/51833337bfc73d152b44902e5baa50ff/plot_lasso_lars_ic.ipynb
index 6e52b25c08fc9..d5cc569cfae71 100644
--- a/dev/_downloads/51833337bfc73d152b44902e5baa50ff/plot_lasso_lars_ic.ipynb
+++ b/dev/_downloads/51833337bfc73d152b44902e5baa50ff/plot_lasso_lars_ic.ipynb
@@ -147,7 +147,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/51e6f272e94e3b63cfd48c4b41fbaa10/plot_feature_selection_pipeline.ipynb b/dev/_downloads/51e6f272e94e3b63cfd48c4b41fbaa10/plot_feature_selection_pipeline.ipynb
index 1753f2dd9bcc9..c138267763371 100644
--- a/dev/_downloads/51e6f272e94e3b63cfd48c4b41fbaa10/plot_feature_selection_pipeline.ipynb
+++ b/dev/_downloads/51e6f272e94e3b63cfd48c4b41fbaa10/plot_feature_selection_pipeline.ipynb
@@ -132,7 +132,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/530196dec3cf8d9c844ea06e4cbdd1be/plot_feature_transformation.zip b/dev/_downloads/530196dec3cf8d9c844ea06e4cbdd1be/plot_feature_transformation.zip
index f7f5cfe10fa6c..f4541210027bf 100644
Binary files a/dev/_downloads/530196dec3cf8d9c844ea06e4cbdd1be/plot_feature_transformation.zip and b/dev/_downloads/530196dec3cf8d9c844ea06e4cbdd1be/plot_feature_transformation.zip differ
diff --git a/dev/_downloads/5318b93274c12da04cc124d3efa940af/plot_hdbscan.zip b/dev/_downloads/5318b93274c12da04cc124d3efa940af/plot_hdbscan.zip
index 27b3109299c56..d141187e2ecca 100644
Binary files a/dev/_downloads/5318b93274c12da04cc124d3efa940af/plot_hdbscan.zip and b/dev/_downloads/5318b93274c12da04cc124d3efa940af/plot_hdbscan.zip differ
diff --git a/dev/_downloads/53490cdb42c3c07ba8cccd1c4ed4dca4/plot_release_highlights_1_4_0.ipynb b/dev/_downloads/53490cdb42c3c07ba8cccd1c4ed4dca4/plot_release_highlights_1_4_0.ipynb
index 30bc0c8753644..3ba9a09e6f8a6 100644
--- a/dev/_downloads/53490cdb42c3c07ba8cccd1c4ed4dca4/plot_release_highlights_1_4_0.ipynb
+++ b/dev/_downloads/53490cdb42c3c07ba8cccd1c4ed4dca4/plot_release_highlights_1_4_0.ipynb
@@ -40,7 +40,7 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.ensemble import HistGradientBoostingClassifier\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import roc_auc_score\n\nX_train, X_test, y_train, y_test = train_test_split(X_adult, y_adult, random_state=0)\nhist = HistGradientBoostingClassifier(categorical_features=\"from_dtype\")\n\nhist.fit(X_train, y_train)\ny_decision = hist.decision_function(X_test)\nprint(f\"ROC AUC score is {roc_auc_score(y_test, y_decision)}\")"
+        "from sklearn.ensemble import HistGradientBoostingClassifier\nfrom sklearn.metrics import roc_auc_score\nfrom sklearn.model_selection import train_test_split\n\nX_train, X_test, y_train, y_test = train_test_split(X_adult, y_adult, random_state=0)\nhist = HistGradientBoostingClassifier(categorical_features=\"from_dtype\")\n\nhist.fit(X_train, y_train)\ny_decision = hist.decision_function(X_test)\nprint(f\"ROC AUC score is {roc_auc_score(y_test, y_decision)}\")"
       ]
     },
     {
@@ -58,7 +58,7 @@
       },
       "outputs": [],
       "source": [
-        "import polars as pl\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.preprocessing import OneHotEncoder\nfrom sklearn.compose import ColumnTransformer\n\ndf = pl.DataFrame(\n    {\"height\": [120, 140, 150, 110, 100], \"pet\": [\"dog\", \"cat\", \"dog\", \"cat\", \"cat\"]}\n)\npreprocessor = ColumnTransformer(\n    [\n        (\"numerical\", StandardScaler(), [\"height\"]),\n        (\"categorical\", OneHotEncoder(sparse_output=False), [\"pet\"]),\n    ],\n    verbose_feature_names_out=False,\n)\npreprocessor.set_output(transform=\"polars\")\n\ndf_out = preprocessor.fit_transform(df)\ndf_out"
+        "import polars as pl\n\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.preprocessing import OneHotEncoder, StandardScaler\n\ndf = pl.DataFrame(\n    {\"height\": [120, 140, 150, 110, 100], \"pet\": [\"dog\", \"cat\", \"dog\", \"cat\", \"cat\"]}\n)\npreprocessor = ColumnTransformer(\n    [\n        (\"numerical\", StandardScaler(), [\"height\"]),\n        (\"categorical\", OneHotEncoder(sparse_output=False), [\"pet\"]),\n    ],\n    verbose_feature_names_out=False,\n)\npreprocessor.set_output(transform=\"polars\")\n\ndf_out = preprocessor.fit_transform(df)\ndf_out"
       ]
     },
     {
@@ -87,7 +87,7 @@
       },
       "outputs": [],
       "source": [
-        "import numpy as np\nfrom sklearn.ensemble import RandomForestClassifier\n\nX = np.array([0, 1, 6, np.nan]).reshape(-1, 1)\ny = [0, 0, 1, 1]\n\nforest = RandomForestClassifier(random_state=0).fit(X, y)\nforest.predict(X)"
+        "import numpy as np\n\nfrom sklearn.ensemble import RandomForestClassifier\n\nX = np.array([0, 1, 6, np.nan]).reshape(-1, 1)\ny = [0, 0, 1, 1]\n\nforest = RandomForestClassifier(random_state=0).fit(X, y)\nforest.predict(X)"
       ]
     },
     {
@@ -105,7 +105,7 @@
       },
       "outputs": [],
       "source": [
-        "import matplotlib.pyplot as plt\nfrom sklearn.inspection import PartialDependenceDisplay\nfrom sklearn.ensemble import RandomForestRegressor\n\nn_samples = 500\nrng = np.random.RandomState(0)\nX = rng.randn(n_samples, 2)\nnoise = rng.normal(loc=0.0, scale=0.01, size=n_samples)\ny = 5 * X[:, 0] + np.sin(10 * np.pi * X[:, 0]) - noise\n\nrf_no_cst = RandomForestRegressor().fit(X, y)\nrf_cst = RandomForestRegressor(monotonic_cst=[1, 0]).fit(X, y)\n\ndisp = PartialDependenceDisplay.from_estimator(\n    rf_no_cst,\n    X,\n    features=[0],\n    feature_names=[\"feature 0\"],\n    line_kw={\"linewidth\": 4, \"label\": \"unconstrained\", \"color\": \"tab:blue\"},\n)\nPartialDependenceDisplay.from_estimator(\n    rf_cst,\n    X,\n    features=[0],\n    line_kw={\"linewidth\": 4, \"label\": \"constrained\", \"color\": \"tab:orange\"},\n    ax=disp.axes_,\n)\ndisp.axes_[0, 0].plot(\n    X[:, 0], y, \"o\", alpha=0.5, zorder=-1, label=\"samples\", color=\"tab:green\"\n)\ndisp.axes_[0, 0].set_ylim(-3, 3)\ndisp.axes_[0, 0].set_xlim(-1, 1)\ndisp.axes_[0, 0].legend()\nplt.show()"
+        "import matplotlib.pyplot as plt\n\nfrom sklearn.ensemble import RandomForestRegressor\nfrom sklearn.inspection import PartialDependenceDisplay\n\nn_samples = 500\nrng = np.random.RandomState(0)\nX = rng.randn(n_samples, 2)\nnoise = rng.normal(loc=0.0, scale=0.01, size=n_samples)\ny = 5 * X[:, 0] + np.sin(10 * np.pi * X[:, 0]) - noise\n\nrf_no_cst = RandomForestRegressor().fit(X, y)\nrf_cst = RandomForestRegressor(monotonic_cst=[1, 0]).fit(X, y)\n\ndisp = PartialDependenceDisplay.from_estimator(\n    rf_no_cst,\n    X,\n    features=[0],\n    feature_names=[\"feature 0\"],\n    line_kw={\"linewidth\": 4, \"label\": \"unconstrained\", \"color\": \"tab:blue\"},\n)\nPartialDependenceDisplay.from_estimator(\n    rf_cst,\n    X,\n    features=[0],\n    line_kw={\"linewidth\": 4, \"label\": \"constrained\", \"color\": \"tab:orange\"},\n    ax=disp.axes_,\n)\ndisp.axes_[0, 0].plot(\n    X[:, 0], y, \"o\", alpha=0.5, zorder=-1, label=\"samples\", color=\"tab:green\"\n)\ndisp.axes_[0, 0].set_ylim(-3, 3)\ndisp.axes_[0, 0].set_xlim(-1, 1)\ndisp.axes_[0, 0].legend()\nplt.show()"
       ]
     },
     {
@@ -159,7 +159,7 @@
       },
       "outputs": [],
       "source": [
-        "import sklearn\nfrom sklearn.metrics import get_scorer\nfrom sklearn.datasets import make_regression\nfrom sklearn.linear_model import Lasso\nfrom sklearn.model_selection import GridSearchCV, cross_validate, GroupKFold\n\n# For now by default metadata routing is disabled, and need to be explicitly\n# enabled.\nsklearn.set_config(enable_metadata_routing=True)\n\nn_samples = 100\nX, y = make_regression(n_samples=n_samples, n_features=5, noise=0.5)\nrng = np.random.RandomState(7)\ngroups = rng.randint(0, 10, size=n_samples)\nsample_weights = rng.rand(n_samples)\nestimator = Lasso().set_fit_request(sample_weight=True)\nhyperparameter_grid = {\"alpha\": [0.1, 0.5, 1.0, 2.0]}\nscoring_inner_cv = get_scorer(\"neg_mean_squared_error\").set_score_request(\n    sample_weight=True\n)\ninner_cv = GroupKFold(n_splits=5)\n\ngrid_search = GridSearchCV(\n    estimator=estimator,\n    param_grid=hyperparameter_grid,\n    cv=inner_cv,\n    scoring=scoring_inner_cv,\n)\n\nouter_cv = GroupKFold(n_splits=5)\nscorers = {\n    \"mse\": get_scorer(\"neg_mean_squared_error\").set_score_request(sample_weight=True)\n}\nresults = cross_validate(\n    grid_search,\n    X,\n    y,\n    cv=outer_cv,\n    scoring=scorers,\n    return_estimator=True,\n    params={\"sample_weight\": sample_weights, \"groups\": groups},\n)\nprint(\"cv error on test sets:\", results[\"test_mse\"])\n\n# Setting the flag to the default `False` to avoid interference with other\n# scripts.\nsklearn.set_config(enable_metadata_routing=False)"
+        "import sklearn\nfrom sklearn.datasets import make_regression\nfrom sklearn.linear_model import Lasso\nfrom sklearn.metrics import get_scorer\nfrom sklearn.model_selection import GridSearchCV, GroupKFold, cross_validate\n\n# For now by default metadata routing is disabled, and need to be explicitly\n# enabled.\nsklearn.set_config(enable_metadata_routing=True)\n\nn_samples = 100\nX, y = make_regression(n_samples=n_samples, n_features=5, noise=0.5)\nrng = np.random.RandomState(7)\ngroups = rng.randint(0, 10, size=n_samples)\nsample_weights = rng.rand(n_samples)\nestimator = Lasso().set_fit_request(sample_weight=True)\nhyperparameter_grid = {\"alpha\": [0.1, 0.5, 1.0, 2.0]}\nscoring_inner_cv = get_scorer(\"neg_mean_squared_error\").set_score_request(\n    sample_weight=True\n)\ninner_cv = GroupKFold(n_splits=5)\n\ngrid_search = GridSearchCV(\n    estimator=estimator,\n    param_grid=hyperparameter_grid,\n    cv=inner_cv,\n    scoring=scoring_inner_cv,\n)\n\nouter_cv = GroupKFold(n_splits=5)\nscorers = {\n    \"mse\": get_scorer(\"neg_mean_squared_error\").set_score_request(sample_weight=True)\n}\nresults = cross_validate(\n    grid_search,\n    X,\n    y,\n    cv=outer_cv,\n    scoring=scorers,\n    return_estimator=True,\n    params={\"sample_weight\": sample_weights, \"groups\": groups},\n)\nprint(\"cv error on test sets:\", results[\"test_mse\"])\n\n# Setting the flag to the default `False` to avoid interference with other\n# scripts.\nsklearn.set_config(enable_metadata_routing=False)"
       ]
     },
     {
@@ -177,7 +177,7 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.decomposition import PCA\nimport scipy.sparse as sp\nfrom time import time\n\nX_sparse = sp.random(m=1000, n=1000, random_state=0)\nX_dense = X_sparse.toarray()\n\nt0 = time()\nPCA(n_components=10, svd_solver=\"arpack\").fit(X_sparse)\ntime_sparse = time() - t0\n\nt0 = time()\nPCA(n_components=10, svd_solver=\"arpack\").fit(X_dense)\ntime_dense = time() - t0\n\nprint(f\"Speedup: {time_dense / time_sparse:.1f}x\")"
+        "from time import time\n\nimport scipy.sparse as sp\n\nfrom sklearn.decomposition import PCA\n\nX_sparse = sp.random(m=1000, n=1000, random_state=0)\nX_dense = X_sparse.toarray()\n\nt0 = time()\nPCA(n_components=10, svd_solver=\"arpack\").fit(X_sparse)\ntime_sparse = time() - t0\n\nt0 = time()\nPCA(n_components=10, svd_solver=\"arpack\").fit(X_dense)\ntime_dense = time() - t0\n\nprint(f\"Speedup: {time_dense / time_sparse:.1f}x\")"
       ]
     }
   ],
@@ -197,7 +197,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/535778bfbc9b4881da3e662bc2ea8484/plot_multi_metric_evaluation.zip b/dev/_downloads/535778bfbc9b4881da3e662bc2ea8484/plot_multi_metric_evaluation.zip
index 75fbc0c5e05ff..2edc8287509bc 100644
Binary files a/dev/_downloads/535778bfbc9b4881da3e662bc2ea8484/plot_multi_metric_evaluation.zip and b/dev/_downloads/535778bfbc9b4881da3e662bc2ea8484/plot_multi_metric_evaluation.zip differ
diff --git a/dev/_downloads/53884c828bd5d2cdd1543e33376e33ea/plot_release_highlights_1_2_0.zip b/dev/_downloads/53884c828bd5d2cdd1543e33376e33ea/plot_release_highlights_1_2_0.zip
index fa071e81c7ad8..8dfb7f317c3b6 100644
Binary files a/dev/_downloads/53884c828bd5d2cdd1543e33376e33ea/plot_release_highlights_1_2_0.zip and b/dev/_downloads/53884c828bd5d2cdd1543e33376e33ea/plot_release_highlights_1_2_0.zip differ
diff --git a/dev/_downloads/53ba3c905c26674dcfea44efc1b5aa23/plot_gmm_sin.zip b/dev/_downloads/53ba3c905c26674dcfea44efc1b5aa23/plot_gmm_sin.zip
index 1c425636fb689..a8134395f547a 100644
Binary files a/dev/_downloads/53ba3c905c26674dcfea44efc1b5aa23/plot_gmm_sin.zip and b/dev/_downloads/53ba3c905c26674dcfea44efc1b5aa23/plot_gmm_sin.zip differ
diff --git a/dev/_downloads/53efb390bba6a1c7eadd27ed210a1830/plot_segmentation_toy.zip b/dev/_downloads/53efb390bba6a1c7eadd27ed210a1830/plot_segmentation_toy.zip
index 0c96463b8d296..e3e45452fe577 100644
Binary files a/dev/_downloads/53efb390bba6a1c7eadd27ed210a1830/plot_segmentation_toy.zip and b/dev/_downloads/53efb390bba6a1c7eadd27ed210a1830/plot_segmentation_toy.zip differ
diff --git a/dev/_downloads/5411a737a70dedec6dd709c217d0ce3f/plot_calibration_curve.zip b/dev/_downloads/5411a737a70dedec6dd709c217d0ce3f/plot_calibration_curve.zip
index 569ca7bb3c996..53fe29d32ce78 100644
Binary files a/dev/_downloads/5411a737a70dedec6dd709c217d0ce3f/plot_calibration_curve.zip and b/dev/_downloads/5411a737a70dedec6dd709c217d0ce3f/plot_calibration_curve.zip differ
diff --git a/dev/_downloads/54823a4305997fc1281f34ce676fb43e/plot_iterative_imputer_variants_comparison.py b/dev/_downloads/54823a4305997fc1281f34ce676fb43e/plot_iterative_imputer_variants_comparison.py
index f06875a5f7fcd..d2a68d351ce8a 100644
--- a/dev/_downloads/54823a4305997fc1281f34ce676fb43e/plot_iterative_imputer_variants_comparison.py
+++ b/dev/_downloads/54823a4305997fc1281f34ce676fb43e/plot_iterative_imputer_variants_comparison.py
@@ -55,7 +55,7 @@
 from sklearn.ensemble import RandomForestRegressor
 
 # To use this experimental feature, we need to explicitly ask for it:
-from sklearn.experimental import enable_iterative_imputer  # noqa
+from sklearn.experimental import enable_iterative_imputer  # noqa: F401
 from sklearn.impute import IterativeImputer, SimpleImputer
 from sklearn.kernel_approximation import Nystroem
 from sklearn.linear_model import BayesianRidge, Ridge
diff --git a/dev/_downloads/54c7ea3b3671861fbfb2161a6f0ab6d0/plot_nca_classification.ipynb b/dev/_downloads/54c7ea3b3671861fbfb2161a6f0ab6d0/plot_nca_classification.ipynb
index 48638ec7f0272..7d49268ada266 100644
--- a/dev/_downloads/54c7ea3b3671861fbfb2161a6f0ab6d0/plot_nca_classification.ipynb
+++ b/dev/_downloads/54c7ea3b3671861fbfb2161a6f0ab6d0/plot_nca_classification.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/553e920694cd9978815f721f868d3509/plot_out_of_core_classification.zip b/dev/_downloads/553e920694cd9978815f721f868d3509/plot_out_of_core_classification.zip
index f75fee0193517..8c99bc920a9f3 100644
Binary files a/dev/_downloads/553e920694cd9978815f721f868d3509/plot_out_of_core_classification.zip and b/dev/_downloads/553e920694cd9978815f721f868d3509/plot_out_of_core_classification.zip differ
diff --git a/dev/_downloads/55c6bb66860ffa17d0769be421b7683f/plot_tuned_decision_threshold.zip b/dev/_downloads/55c6bb66860ffa17d0769be421b7683f/plot_tuned_decision_threshold.zip
index 511355f48fe1e..7e111784465bf 100644
Binary files a/dev/_downloads/55c6bb66860ffa17d0769be421b7683f/plot_tuned_decision_threshold.zip and b/dev/_downloads/55c6bb66860ffa17d0769be421b7683f/plot_tuned_decision_threshold.zip differ
diff --git a/dev/_downloads/578d975e1bd2f15587da4ccbce0a7d14/plot_gpc_xor.ipynb b/dev/_downloads/578d975e1bd2f15587da4ccbce0a7d14/plot_gpc_xor.ipynb
index 1226fc9202050..468fa3b79ee7c 100644
--- a/dev/_downloads/578d975e1bd2f15587da4ccbce0a7d14/plot_gpc_xor.ipynb
+++ b/dev/_downloads/578d975e1bd2f15587da4ccbce0a7d14/plot_gpc_xor.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/579bd5b3e1f615788637846e3011bb6f/plot_affinity_propagation.zip b/dev/_downloads/579bd5b3e1f615788637846e3011bb6f/plot_affinity_propagation.zip
index 66cefd0919d4c..aeb5d4dc85c5e 100644
Binary files a/dev/_downloads/579bd5b3e1f615788637846e3011bb6f/plot_affinity_propagation.zip and b/dev/_downloads/579bd5b3e1f615788637846e3011bb6f/plot_affinity_propagation.zip differ
diff --git a/dev/_downloads/581dc0b49de1be345ed0b6010e5f873d/plot_nnls.ipynb b/dev/_downloads/581dc0b49de1be345ed0b6010e5f873d/plot_nnls.ipynb
index 6740c4410ab68..d995eeb919915 100644
--- a/dev/_downloads/581dc0b49de1be345ed0b6010e5f873d/plot_nnls.ipynb
+++ b/dev/_downloads/581dc0b49de1be345ed0b6010e5f873d/plot_nnls.ipynb
@@ -125,7 +125,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/583de4ea98c6544c52ea4c57e62b1813/plot_sparse_logistic_regression_20newsgroups.ipynb b/dev/_downloads/583de4ea98c6544c52ea4c57e62b1813/plot_sparse_logistic_regression_20newsgroups.ipynb
index 35a82da9587d8..1668916336409 100644
--- a/dev/_downloads/583de4ea98c6544c52ea4c57e62b1813/plot_sparse_logistic_regression_20newsgroups.ipynb
+++ b/dev/_downloads/583de4ea98c6544c52ea4c57e62b1813/plot_sparse_logistic_regression_20newsgroups.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/592b2521e44501266ca5339d1fb123cb/plot_rfe_with_cross_validation.py b/dev/_downloads/592b2521e44501266ca5339d1fb123cb/plot_rfe_with_cross_validation.py
index 4e3e45384e026..951b82bffa46d 100644
--- a/dev/_downloads/592b2521e44501266ca5339d1fb123cb/plot_rfe_with_cross_validation.py
+++ b/dev/_downloads/592b2521e44501266ca5339d1fb123cb/plot_rfe_with_cross_validation.py
@@ -22,9 +22,12 @@
 
 from sklearn.datasets import make_classification
 
+n_features = 15
+feat_names = [f"feature_{i}" for i in range(15)]
+
 X, y = make_classification(
     n_samples=500,
-    n_features=15,
+    n_features=n_features,
     n_informative=3,
     n_redundant=2,
     n_repeated=0,
@@ -71,7 +74,12 @@
 import matplotlib.pyplot as plt
 import pandas as pd
 
-cv_results = pd.DataFrame(rfecv.cv_results_)
+data = {
+    key: value
+    for key, value in rfecv.cv_results_.items()
+    if key in ["n_features", "mean_test_score", "std_test_score"]
+}
+cv_results = pd.DataFrame(data)
 plt.figure()
 plt.xlabel("Number of features selected")
 plt.ylabel("Mean test accuracy")
@@ -91,3 +99,17 @@
 # cross-validation technique. The test accuracy decreases above 5 selected
 # features, this is, keeping non-informative features leads to over-fitting and
 # is therefore detrimental for the statistical performance of the models.
+
+# %%
+import numpy as np
+
+for i in range(cv.n_splits):
+    mask = rfecv.cv_results_[f"split{i}_support"][
+        rfecv.n_features_
+    ]  # mask of features selected by the RFE
+    features_selected = np.ma.compressed(np.ma.masked_array(feat_names, mask=1 - mask))
+    print(f"Features selected in fold {i}: {features_selected}")
+# %%
+# In the five folds, the selected features are consistent. This is good news,
+# it means that the selection is stable across folds, and it confirms that
+# these features are the most informative ones.
diff --git a/dev/_downloads/59c5e476396afdc4788c35a49f6289b9/plot_multioutput_face_completion.ipynb b/dev/_downloads/59c5e476396afdc4788c35a49f6289b9/plot_multioutput_face_completion.ipynb
index a7d229bbd48be..f0f447ce8ee21 100644
--- a/dev/_downloads/59c5e476396afdc4788c35a49f6289b9/plot_multioutput_face_completion.ipynb
+++ b/dev/_downloads/59c5e476396afdc4788c35a49f6289b9/plot_multioutput_face_completion.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/5a847ed2e1c03e450c5f9dee339423ad/plot_sparse_logistic_regression_mnist.py b/dev/_downloads/5a847ed2e1c03e450c5f9dee339423ad/plot_sparse_logistic_regression_mnist.py
index 22e4e9cd48e60..e4a44e989b565 100644
--- a/dev/_downloads/5a847ed2e1c03e450c5f9dee339423ad/plot_sparse_logistic_regression_mnist.py
+++ b/dev/_downloads/5a847ed2e1c03e450c5f9dee339423ad/plot_sparse_logistic_regression_mnist.py
@@ -75,7 +75,7 @@
     )
     l1_plot.set_xticks(())
     l1_plot.set_yticks(())
-    l1_plot.set_xlabel("Class %i" % i)
+    l1_plot.set_xlabel(f"Class {i}")
 plt.suptitle("Classification vector for...")
 
 run_time = time.time() - t0
diff --git a/dev/_downloads/5afb7e185d86921e913f71e03a88ba38/plot_prediction_latency.zip b/dev/_downloads/5afb7e185d86921e913f71e03a88ba38/plot_prediction_latency.zip
index 1b44731679f8c..09b97a5c3219e 100644
Binary files a/dev/_downloads/5afb7e185d86921e913f71e03a88ba38/plot_prediction_latency.zip and b/dev/_downloads/5afb7e185d86921e913f71e03a88ba38/plot_prediction_latency.zip differ
diff --git a/dev/_downloads/5b58ff41853cd5fd1f2f388574fbf6db/plot_image_denoising.zip b/dev/_downloads/5b58ff41853cd5fd1f2f388574fbf6db/plot_image_denoising.zip
index 3a7439b0ceb8f..14f495a02ec40 100644
Binary files a/dev/_downloads/5b58ff41853cd5fd1f2f388574fbf6db/plot_image_denoising.zip and b/dev/_downloads/5b58ff41853cd5fd1f2f388574fbf6db/plot_image_denoising.zip differ
diff --git a/dev/_downloads/5bb69fbfba6a6762ece8fe57d5044636/plot_voting_regressor.ipynb b/dev/_downloads/5bb69fbfba6a6762ece8fe57d5044636/plot_voting_regressor.ipynb
index a4a709be87280..36d15f15f6cd7 100644
--- a/dev/_downloads/5bb69fbfba6a6762ece8fe57d5044636/plot_voting_regressor.ipynb
+++ b/dev/_downloads/5bb69fbfba6a6762ece8fe57d5044636/plot_voting_regressor.ipynb
@@ -89,7 +89,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/5c5a28f56fff2d59a099a9617c8f1b5a/plot_mini_batch_kmeans.zip b/dev/_downloads/5c5a28f56fff2d59a099a9617c8f1b5a/plot_mini_batch_kmeans.zip
index 41cef16047b43..98e2574458b92 100644
Binary files a/dev/_downloads/5c5a28f56fff2d59a099a9617c8f1b5a/plot_mini_batch_kmeans.zip and b/dev/_downloads/5c5a28f56fff2d59a099a9617c8f1b5a/plot_mini_batch_kmeans.zip differ
diff --git a/dev/_downloads/5e3b69cd18b43dd31a46a35ccba413e7/plot_forest_importances.ipynb b/dev/_downloads/5e3b69cd18b43dd31a46a35ccba413e7/plot_forest_importances.ipynb
index 0d66a6794519e..97eb846b2b97c 100644
--- a/dev/_downloads/5e3b69cd18b43dd31a46a35ccba413e7/plot_forest_importances.ipynb
+++ b/dev/_downloads/5e3b69cd18b43dd31a46a35ccba413e7/plot_forest_importances.ipynb
@@ -150,7 +150,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/5f322361bcf53bfae6ef8f25492b6bbb/plot_grid_search_digits.zip b/dev/_downloads/5f322361bcf53bfae6ef8f25492b6bbb/plot_grid_search_digits.zip
index a122201179175..2f380a56c14c1 100644
Binary files a/dev/_downloads/5f322361bcf53bfae6ef8f25492b6bbb/plot_grid_search_digits.zip and b/dev/_downloads/5f322361bcf53bfae6ef8f25492b6bbb/plot_grid_search_digits.zip differ
diff --git a/dev/_downloads/603c968ffea3a0291127213cb66729c8/plot_nearest_centroid.zip b/dev/_downloads/603c968ffea3a0291127213cb66729c8/plot_nearest_centroid.zip
index 8ebd1a9160144..6ef9422a5c82f 100644
Binary files a/dev/_downloads/603c968ffea3a0291127213cb66729c8/plot_nearest_centroid.zip and b/dev/_downloads/603c968ffea3a0291127213cb66729c8/plot_nearest_centroid.zip differ
diff --git a/dev/_downloads/604c0a9de0e1b80dae9e6754fdb27014/plot_manifold_sphere.ipynb b/dev/_downloads/604c0a9de0e1b80dae9e6754fdb27014/plot_manifold_sphere.ipynb
index 186882bf6ce22..76ceed74f0ef1 100644
--- a/dev/_downloads/604c0a9de0e1b80dae9e6754fdb27014/plot_manifold_sphere.ipynb
+++ b/dev/_downloads/604c0a9de0e1b80dae9e6754fdb27014/plot_manifold_sphere.ipynb
@@ -15,7 +15,7 @@
       },
       "outputs": [],
       "source": [
-        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\n\nfrom time import time\n\nimport matplotlib.pyplot as plt\n\n# Unused but required import for doing 3d projections with matplotlib < 3.2\nimport mpl_toolkits.mplot3d  # noqa: F401\nimport numpy as np\nfrom matplotlib.ticker import NullFormatter\n\nfrom sklearn import manifold\nfrom sklearn.utils import check_random_state\n\n# Variables for manifold learning.\nn_neighbors = 10\nn_samples = 1000\n\n# Create our sphere.\nrandom_state = check_random_state(0)\np = random_state.rand(n_samples) * (2 * np.pi - 0.55)\nt = random_state.rand(n_samples) * np.pi\n\n# Sever the poles from the sphere.\nindices = (t < (np.pi - (np.pi / 8))) & (t > ((np.pi / 8)))\ncolors = p[indices]\nx, y, z = (\n    np.sin(t[indices]) * np.cos(p[indices]),\n    np.sin(t[indices]) * np.sin(p[indices]),\n    np.cos(t[indices]),\n)\n\n# Plot our dataset.\nfig = plt.figure(figsize=(15, 8))\nplt.suptitle(\n    \"Manifold Learning with %i points, %i neighbors\" % (1000, n_neighbors), fontsize=14\n)\n\nax = fig.add_subplot(251, projection=\"3d\")\nax.scatter(x, y, z, c=p[indices], cmap=plt.cm.rainbow)\nax.view_init(40, -10)\n\nsphere_data = np.array([x, y, z]).T\n\n# Perform Locally Linear Embedding Manifold learning\nmethods = [\"standard\", \"ltsa\", \"hessian\", \"modified\"]\nlabels = [\"LLE\", \"LTSA\", \"Hessian LLE\", \"Modified LLE\"]\n\nfor i, method in enumerate(methods):\n    t0 = time()\n    trans_data = (\n        manifold.LocallyLinearEmbedding(\n            n_neighbors=n_neighbors, n_components=2, method=method, random_state=42\n        )\n        .fit_transform(sphere_data)\n        .T\n    )\n    t1 = time()\n    print(\"%s: %.2g sec\" % (methods[i], t1 - t0))\n\n    ax = fig.add_subplot(252 + i)\n    plt.scatter(trans_data[0], trans_data[1], c=colors, cmap=plt.cm.rainbow)\n    plt.title(\"%s (%.2g sec)\" % (labels[i], t1 - t0))\n    ax.xaxis.set_major_formatter(NullFormatter())\n    ax.yaxis.set_major_formatter(NullFormatter())\n    plt.axis(\"tight\")\n\n# Perform Isomap Manifold learning.\nt0 = time()\ntrans_data = (\n    manifold.Isomap(n_neighbors=n_neighbors, n_components=2)\n    .fit_transform(sphere_data)\n    .T\n)\nt1 = time()\nprint(\"%s: %.2g sec\" % (\"ISO\", t1 - t0))\n\nax = fig.add_subplot(257)\nplt.scatter(trans_data[0], trans_data[1], c=colors, cmap=plt.cm.rainbow)\nplt.title(\"%s (%.2g sec)\" % (\"Isomap\", t1 - t0))\nax.xaxis.set_major_formatter(NullFormatter())\nax.yaxis.set_major_formatter(NullFormatter())\nplt.axis(\"tight\")\n\n# Perform Multi-dimensional scaling.\nt0 = time()\nmds = manifold.MDS(2, max_iter=100, n_init=1, random_state=42)\ntrans_data = mds.fit_transform(sphere_data).T\nt1 = time()\nprint(\"MDS: %.2g sec\" % (t1 - t0))\n\nax = fig.add_subplot(258)\nplt.scatter(trans_data[0], trans_data[1], c=colors, cmap=plt.cm.rainbow)\nplt.title(\"MDS (%.2g sec)\" % (t1 - t0))\nax.xaxis.set_major_formatter(NullFormatter())\nax.yaxis.set_major_formatter(NullFormatter())\nplt.axis(\"tight\")\n\n# Perform Spectral Embedding.\nt0 = time()\nse = manifold.SpectralEmbedding(\n    n_components=2, n_neighbors=n_neighbors, random_state=42\n)\ntrans_data = se.fit_transform(sphere_data).T\nt1 = time()\nprint(\"Spectral Embedding: %.2g sec\" % (t1 - t0))\n\nax = fig.add_subplot(259)\nplt.scatter(trans_data[0], trans_data[1], c=colors, cmap=plt.cm.rainbow)\nplt.title(\"Spectral Embedding (%.2g sec)\" % (t1 - t0))\nax.xaxis.set_major_formatter(NullFormatter())\nax.yaxis.set_major_formatter(NullFormatter())\nplt.axis(\"tight\")\n\n# Perform t-distributed stochastic neighbor embedding.\nt0 = time()\ntsne = manifold.TSNE(n_components=2, random_state=0)\ntrans_data = tsne.fit_transform(sphere_data).T\nt1 = time()\nprint(\"t-SNE: %.2g sec\" % (t1 - t0))\n\nax = fig.add_subplot(2, 5, 10)\nplt.scatter(trans_data[0], trans_data[1], c=colors, cmap=plt.cm.rainbow)\nplt.title(\"t-SNE (%.2g sec)\" % (t1 - t0))\nax.xaxis.set_major_formatter(NullFormatter())\nax.yaxis.set_major_formatter(NullFormatter())\nplt.axis(\"tight\")\n\nplt.show()"
+        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\n\nfrom time import time\n\nimport matplotlib.pyplot as plt\n\n# Unused but required import for doing 3d projections with matplotlib < 3.2\nimport mpl_toolkits.mplot3d  # noqa: F401\nimport numpy as np\nfrom matplotlib.ticker import NullFormatter\n\nfrom sklearn import manifold\nfrom sklearn.utils import check_random_state\n\n# Variables for manifold learning.\nn_neighbors = 10\nn_samples = 1000\n\n# Create our sphere.\nrandom_state = check_random_state(0)\np = random_state.rand(n_samples) * (2 * np.pi - 0.55)\nt = random_state.rand(n_samples) * np.pi\n\n# Sever the poles from the sphere.\nindices = (t < (np.pi - (np.pi / 8))) & (t > (np.pi / 8))\ncolors = p[indices]\nx, y, z = (\n    np.sin(t[indices]) * np.cos(p[indices]),\n    np.sin(t[indices]) * np.sin(p[indices]),\n    np.cos(t[indices]),\n)\n\n# Plot our dataset.\nfig = plt.figure(figsize=(15, 8))\nplt.suptitle(\n    \"Manifold Learning with %i points, %i neighbors\" % (1000, n_neighbors), fontsize=14\n)\n\nax = fig.add_subplot(251, projection=\"3d\")\nax.scatter(x, y, z, c=p[indices], cmap=plt.cm.rainbow)\nax.view_init(40, -10)\n\nsphere_data = np.array([x, y, z]).T\n\n# Perform Locally Linear Embedding Manifold learning\nmethods = [\"standard\", \"ltsa\", \"hessian\", \"modified\"]\nlabels = [\"LLE\", \"LTSA\", \"Hessian LLE\", \"Modified LLE\"]\n\nfor i, method in enumerate(methods):\n    t0 = time()\n    trans_data = (\n        manifold.LocallyLinearEmbedding(\n            n_neighbors=n_neighbors, n_components=2, method=method, random_state=42\n        )\n        .fit_transform(sphere_data)\n        .T\n    )\n    t1 = time()\n    print(\"%s: %.2g sec\" % (methods[i], t1 - t0))\n\n    ax = fig.add_subplot(252 + i)\n    plt.scatter(trans_data[0], trans_data[1], c=colors, cmap=plt.cm.rainbow)\n    plt.title(\"%s (%.2g sec)\" % (labels[i], t1 - t0))\n    ax.xaxis.set_major_formatter(NullFormatter())\n    ax.yaxis.set_major_formatter(NullFormatter())\n    plt.axis(\"tight\")\n\n# Perform Isomap Manifold learning.\nt0 = time()\ntrans_data = (\n    manifold.Isomap(n_neighbors=n_neighbors, n_components=2)\n    .fit_transform(sphere_data)\n    .T\n)\nt1 = time()\nprint(\"%s: %.2g sec\" % (\"ISO\", t1 - t0))\n\nax = fig.add_subplot(257)\nplt.scatter(trans_data[0], trans_data[1], c=colors, cmap=plt.cm.rainbow)\nplt.title(\"%s (%.2g sec)\" % (\"Isomap\", t1 - t0))\nax.xaxis.set_major_formatter(NullFormatter())\nax.yaxis.set_major_formatter(NullFormatter())\nplt.axis(\"tight\")\n\n# Perform Multi-dimensional scaling.\nt0 = time()\nmds = manifold.MDS(2, max_iter=100, n_init=1, random_state=42)\ntrans_data = mds.fit_transform(sphere_data).T\nt1 = time()\nprint(\"MDS: %.2g sec\" % (t1 - t0))\n\nax = fig.add_subplot(258)\nplt.scatter(trans_data[0], trans_data[1], c=colors, cmap=plt.cm.rainbow)\nplt.title(\"MDS (%.2g sec)\" % (t1 - t0))\nax.xaxis.set_major_formatter(NullFormatter())\nax.yaxis.set_major_formatter(NullFormatter())\nplt.axis(\"tight\")\n\n# Perform Spectral Embedding.\nt0 = time()\nse = manifold.SpectralEmbedding(\n    n_components=2, n_neighbors=n_neighbors, random_state=42\n)\ntrans_data = se.fit_transform(sphere_data).T\nt1 = time()\nprint(\"Spectral Embedding: %.2g sec\" % (t1 - t0))\n\nax = fig.add_subplot(259)\nplt.scatter(trans_data[0], trans_data[1], c=colors, cmap=plt.cm.rainbow)\nplt.title(\"Spectral Embedding (%.2g sec)\" % (t1 - t0))\nax.xaxis.set_major_formatter(NullFormatter())\nax.yaxis.set_major_formatter(NullFormatter())\nplt.axis(\"tight\")\n\n# Perform t-distributed stochastic neighbor embedding.\nt0 = time()\ntsne = manifold.TSNE(n_components=2, random_state=0)\ntrans_data = tsne.fit_transform(sphere_data).T\nt1 = time()\nprint(\"t-SNE: %.2g sec\" % (t1 - t0))\n\nax = fig.add_subplot(2, 5, 10)\nplt.scatter(trans_data[0], trans_data[1], c=colors, cmap=plt.cm.rainbow)\nplt.title(\"t-SNE (%.2g sec)\" % (t1 - t0))\nax.xaxis.set_major_formatter(NullFormatter())\nax.yaxis.set_major_formatter(NullFormatter())\nplt.axis(\"tight\")\n\nplt.show()"
       ]
     }
   ],
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/607c99671400a5055ef516d1aabd00c1/plot_adaboost_multiclass.ipynb b/dev/_downloads/607c99671400a5055ef516d1aabd00c1/plot_adaboost_multiclass.ipynb
index 487c61287ad32..41d29de2ea3da 100644
--- a/dev/_downloads/607c99671400a5055ef516d1aabd00c1/plot_adaboost_multiclass.ipynb
+++ b/dev/_downloads/607c99671400a5055ef516d1aabd00c1/plot_adaboost_multiclass.ipynb
@@ -157,7 +157,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/60bf71ece62c4a5b5fe3e40007de265b/plot_gpc.ipynb b/dev/_downloads/60bf71ece62c4a5b5fe3e40007de265b/plot_gpc.ipynb
index cb977189c527a..dd7a92174b5db 100644
--- a/dev/_downloads/60bf71ece62c4a5b5fe3e40007de265b/plot_gpc.ipynb
+++ b/dev/_downloads/60bf71ece62c4a5b5fe3e40007de265b/plot_gpc.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/60fcc8b24d902a4f93feb6592e544e02/plot_calibration.zip b/dev/_downloads/60fcc8b24d902a4f93feb6592e544e02/plot_calibration.zip
index d5c3cb974df67..7a76ad30ecd2b 100644
Binary files a/dev/_downloads/60fcc8b24d902a4f93feb6592e544e02/plot_calibration.zip and b/dev/_downloads/60fcc8b24d902a4f93feb6592e544e02/plot_calibration.zip differ
diff --git a/dev/_downloads/6142de78d839661549389d7029cf98b0/plot_sgd_weighted_samples.ipynb b/dev/_downloads/6142de78d839661549389d7029cf98b0/plot_sgd_weighted_samples.ipynb
index e7da1fde4f6a5..07b5622752ed2 100644
--- a/dev/_downloads/6142de78d839661549389d7029cf98b0/plot_sgd_weighted_samples.ipynb
+++ b/dev/_downloads/6142de78d839661549389d7029cf98b0/plot_sgd_weighted_samples.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/622bed78541c980857399459cc7188ca/plot_outlier_detection_bench.zip b/dev/_downloads/622bed78541c980857399459cc7188ca/plot_outlier_detection_bench.zip
index 0690312f04384..df48a6d8517e2 100644
Binary files a/dev/_downloads/622bed78541c980857399459cc7188ca/plot_outlier_detection_bench.zip and b/dev/_downloads/622bed78541c980857399459cc7188ca/plot_outlier_detection_bench.zip differ
diff --git a/dev/_downloads/6383d955c013c730f9d211f15e261f38/plot_successive_halving_heatmap.py b/dev/_downloads/6383d955c013c730f9d211f15e261f38/plot_successive_halving_heatmap.py
index 4d9b676443e5e..c46068532e52e 100644
--- a/dev/_downloads/6383d955c013c730f9d211f15e261f38/plot_successive_halving_heatmap.py
+++ b/dev/_downloads/6383d955c013c730f9d211f15e261f38/plot_successive_halving_heatmap.py
@@ -18,7 +18,7 @@
 import pandas as pd
 
 from sklearn import datasets
-from sklearn.experimental import enable_halving_search_cv  # noqa
+from sklearn.experimental import enable_halving_search_cv  # noqa: F401
 from sklearn.model_selection import GridSearchCV, HalvingGridSearchCV
 from sklearn.svm import SVC
 
diff --git a/dev/_downloads/639968ec6cece5483e8b9df39f02a7c4/plot_voting_regressor.zip b/dev/_downloads/639968ec6cece5483e8b9df39f02a7c4/plot_voting_regressor.zip
index 07763aea94d45..1dd11b57f6fc0 100644
Binary files a/dev/_downloads/639968ec6cece5483e8b9df39f02a7c4/plot_voting_regressor.zip and b/dev/_downloads/639968ec6cece5483e8b9df39f02a7c4/plot_voting_regressor.zip differ
diff --git a/dev/_downloads/64040a7010774aea8b93dc426b3edcae/plot_feature_selection_pipeline.zip b/dev/_downloads/64040a7010774aea8b93dc426b3edcae/plot_feature_selection_pipeline.zip
index 898b2c5831166..53edb5ec1e9ea 100644
Binary files a/dev/_downloads/64040a7010774aea8b93dc426b3edcae/plot_feature_selection_pipeline.zip and b/dev/_downloads/64040a7010774aea8b93dc426b3edcae/plot_feature_selection_pipeline.zip differ
diff --git a/dev/_downloads/646b2c083be2d0f557b369a51cec966d/plot_mlp_training_curves.ipynb b/dev/_downloads/646b2c083be2d0f557b369a51cec966d/plot_mlp_training_curves.ipynb
index 419cde55dc881..bd9a7757b9a8a 100644
--- a/dev/_downloads/646b2c083be2d0f557b369a51cec966d/plot_mlp_training_curves.ipynb
+++ b/dev/_downloads/646b2c083be2d0f557b369a51cec966d/plot_mlp_training_curves.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/64788a28f138e0d4ba98dbbbb9116be5/plot_rbm_logistic_classification.ipynb b/dev/_downloads/64788a28f138e0d4ba98dbbbb9116be5/plot_rbm_logistic_classification.ipynb
index c164108e91aff..d82f9676c7324 100644
--- a/dev/_downloads/64788a28f138e0d4ba98dbbbb9116be5/plot_rbm_logistic_classification.ipynb
+++ b/dev/_downloads/64788a28f138e0d4ba98dbbbb9116be5/plot_rbm_logistic_classification.ipynb
@@ -143,7 +143,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/64a4ce2171987c74f955483a03ac45b5/plot_metadata_routing.ipynb b/dev/_downloads/64a4ce2171987c74f955483a03ac45b5/plot_metadata_routing.ipynb
index 9fa5ed0dde4af..45f2b4f7599b4 100644
--- a/dev/_downloads/64a4ce2171987c74f955483a03ac45b5/plot_metadata_routing.ipynb
+++ b/dev/_downloads/64a4ce2171987c74f955483a03ac45b5/plot_metadata_routing.ipynb
@@ -668,7 +668,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/6522aa1dd16bb328d88cb09cbc08eded/plot_mnist_filters.ipynb b/dev/_downloads/6522aa1dd16bb328d88cb09cbc08eded/plot_mnist_filters.ipynb
index c84416e7788c0..7581cc356aa57 100644
--- a/dev/_downloads/6522aa1dd16bb328d88cb09cbc08eded/plot_mnist_filters.ipynb
+++ b/dev/_downloads/6522aa1dd16bb328d88cb09cbc08eded/plot_mnist_filters.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/65b807da1fd0f3cbb60c1425fddba026/plot_multilabel.ipynb b/dev/_downloads/65b807da1fd0f3cbb60c1425fddba026/plot_multilabel.ipynb
index d4627b6266cc3..a091d89926663 100644
--- a/dev/_downloads/65b807da1fd0f3cbb60c1425fddba026/plot_multilabel.ipynb
+++ b/dev/_downloads/65b807da1fd0f3cbb60c1425fddba026/plot_multilabel.ipynb
@@ -15,7 +15,7 @@
       },
       "outputs": [],
       "source": [
-        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nfrom sklearn.cross_decomposition import CCA\nfrom sklearn.datasets import make_multilabel_classification\nfrom sklearn.decomposition import PCA\nfrom sklearn.multiclass import OneVsRestClassifier\nfrom sklearn.svm import SVC\n\n\ndef plot_hyperplane(clf, min_x, max_x, linestyle, label):\n    # get the separating hyperplane\n    w = clf.coef_[0]\n    a = -w[0] / w[1]\n    xx = np.linspace(min_x - 5, max_x + 5)  # make sure the line is long enough\n    yy = a * xx - (clf.intercept_[0]) / w[1]\n    plt.plot(xx, yy, linestyle, label=label)\n\n\ndef plot_subfigure(X, Y, subplot, title, transform):\n    if transform == \"pca\":\n        X = PCA(n_components=2).fit_transform(X)\n    elif transform == \"cca\":\n        X = CCA(n_components=2).fit(X, Y).transform(X)\n    else:\n        raise ValueError\n\n    min_x = np.min(X[:, 0])\n    max_x = np.max(X[:, 0])\n\n    min_y = np.min(X[:, 1])\n    max_y = np.max(X[:, 1])\n\n    classif = OneVsRestClassifier(SVC(kernel=\"linear\"))\n    classif.fit(X, Y)\n\n    plt.subplot(2, 2, subplot)\n    plt.title(title)\n\n    zero_class = np.where(Y[:, 0])\n    one_class = np.where(Y[:, 1])\n    plt.scatter(X[:, 0], X[:, 1], s=40, c=\"gray\", edgecolors=(0, 0, 0))\n    plt.scatter(\n        X[zero_class, 0],\n        X[zero_class, 1],\n        s=160,\n        edgecolors=\"b\",\n        facecolors=\"none\",\n        linewidths=2,\n        label=\"Class 1\",\n    )\n    plt.scatter(\n        X[one_class, 0],\n        X[one_class, 1],\n        s=80,\n        edgecolors=\"orange\",\n        facecolors=\"none\",\n        linewidths=2,\n        label=\"Class 2\",\n    )\n\n    plot_hyperplane(\n        classif.estimators_[0], min_x, max_x, \"k--\", \"Boundary\\nfor class 1\"\n    )\n    plot_hyperplane(\n        classif.estimators_[1], min_x, max_x, \"k-.\", \"Boundary\\nfor class 2\"\n    )\n    plt.xticks(())\n    plt.yticks(())\n\n    plt.xlim(min_x - 0.5 * max_x, max_x + 0.5 * max_x)\n    plt.ylim(min_y - 0.5 * max_y, max_y + 0.5 * max_y)\n    if subplot == 2:\n        plt.xlabel(\"First principal component\")\n        plt.ylabel(\"Second principal component\")\n        plt.legend(loc=\"upper left\")\n\n\nplt.figure(figsize=(8, 6))\n\nX, Y = make_multilabel_classification(\n    n_classes=2, n_labels=1, allow_unlabeled=True, random_state=1\n)\n\nplot_subfigure(X, Y, 1, \"With unlabeled samples + CCA\", \"cca\")\nplot_subfigure(X, Y, 2, \"With unlabeled samples + PCA\", \"pca\")\n\nX, Y = make_multilabel_classification(\n    n_classes=2, n_labels=1, allow_unlabeled=False, random_state=1\n)\n\nplot_subfigure(X, Y, 3, \"Without unlabeled samples + CCA\", \"cca\")\nplot_subfigure(X, Y, 4, \"Without unlabeled samples + PCA\", \"pca\")\n\nplt.subplots_adjust(0.04, 0.02, 0.97, 0.94, 0.09, 0.2)\nplt.show()"
+        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nfrom sklearn.cross_decomposition import CCA\nfrom sklearn.datasets import make_multilabel_classification\nfrom sklearn.decomposition import PCA\nfrom sklearn.multiclass import OneVsRestClassifier\nfrom sklearn.svm import SVC\n\n\ndef plot_hyperplane(clf, min_x, max_x, linestyle, label):\n    # get the separating hyperplane\n    w = clf.coef_[0]\n    a = -w[0] / w[1]\n    xx = np.linspace(min_x - 5, max_x + 5)  # make sure the line is long enough\n    yy = a * xx - (clf.intercept_[0]) / w[1]\n    plt.plot(xx, yy, linestyle, label=label)\n\n\ndef plot_subfigure(X, Y, subplot, title, transform):\n    if transform == \"pca\":\n        X = PCA(n_components=2).fit_transform(X)\n    elif transform == \"cca\":\n        X = CCA(n_components=2).fit(X, Y).transform(X)\n    else:\n        raise ValueError\n\n    min_x = np.min(X[:, 0])\n    max_x = np.max(X[:, 0])\n\n    min_y = np.min(X[:, 1])\n    max_y = np.max(X[:, 1])\n\n    classif = OneVsRestClassifier(SVC(kernel=\"linear\"))\n    classif.fit(X, Y)\n\n    plt.subplot(2, 2, subplot)\n    plt.title(title)\n\n    zero_class = (Y[:, 0]).nonzero()\n    one_class = (Y[:, 1]).nonzero()\n    plt.scatter(X[:, 0], X[:, 1], s=40, c=\"gray\", edgecolors=(0, 0, 0))\n    plt.scatter(\n        X[zero_class, 0],\n        X[zero_class, 1],\n        s=160,\n        edgecolors=\"b\",\n        facecolors=\"none\",\n        linewidths=2,\n        label=\"Class 1\",\n    )\n    plt.scatter(\n        X[one_class, 0],\n        X[one_class, 1],\n        s=80,\n        edgecolors=\"orange\",\n        facecolors=\"none\",\n        linewidths=2,\n        label=\"Class 2\",\n    )\n\n    plot_hyperplane(\n        classif.estimators_[0], min_x, max_x, \"k--\", \"Boundary\\nfor class 1\"\n    )\n    plot_hyperplane(\n        classif.estimators_[1], min_x, max_x, \"k-.\", \"Boundary\\nfor class 2\"\n    )\n    plt.xticks(())\n    plt.yticks(())\n\n    plt.xlim(min_x - 0.5 * max_x, max_x + 0.5 * max_x)\n    plt.ylim(min_y - 0.5 * max_y, max_y + 0.5 * max_y)\n    if subplot == 2:\n        plt.xlabel(\"First principal component\")\n        plt.ylabel(\"Second principal component\")\n        plt.legend(loc=\"upper left\")\n\n\nplt.figure(figsize=(8, 6))\n\nX, Y = make_multilabel_classification(\n    n_classes=2, n_labels=1, allow_unlabeled=True, random_state=1\n)\n\nplot_subfigure(X, Y, 1, \"With unlabeled samples + CCA\", \"cca\")\nplot_subfigure(X, Y, 2, \"With unlabeled samples + PCA\", \"pca\")\n\nX, Y = make_multilabel_classification(\n    n_classes=2, n_labels=1, allow_unlabeled=False, random_state=1\n)\n\nplot_subfigure(X, Y, 3, \"Without unlabeled samples + CCA\", \"cca\")\nplot_subfigure(X, Y, 4, \"Without unlabeled samples + PCA\", \"pca\")\n\nplt.subplots_adjust(0.04, 0.02, 0.97, 0.94, 0.09, 0.2)\nplt.show()"
       ]
     }
   ],
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/66bd3d78a17dbf797557992df72ff73e/plot_regression.zip b/dev/_downloads/66bd3d78a17dbf797557992df72ff73e/plot_regression.zip
index d26de22a75b4c..75d0100db4070 100644
Binary files a/dev/_downloads/66bd3d78a17dbf797557992df72ff73e/plot_regression.zip and b/dev/_downloads/66bd3d78a17dbf797557992df72ff73e/plot_regression.zip differ
diff --git a/dev/_downloads/6740e8b7b0e74561d2572efc253b6034/plot_sgd_iris.zip b/dev/_downloads/6740e8b7b0e74561d2572efc253b6034/plot_sgd_iris.zip
index aac2ef5966bd5..f3591cacf76e2 100644
Binary files a/dev/_downloads/6740e8b7b0e74561d2572efc253b6034/plot_sgd_iris.zip and b/dev/_downloads/6740e8b7b0e74561d2572efc253b6034/plot_sgd_iris.zip differ
diff --git a/dev/_downloads/67703ae8c65716668dd87c31a24a069b/plot_det.py b/dev/_downloads/67703ae8c65716668dd87c31a24a069b/plot_det.py
index bf72fc8ade61f..873d00d696d95 100644
--- a/dev/_downloads/67703ae8c65716668dd87c31a24a069b/plot_det.py
+++ b/dev/_downloads/67703ae8c65716668dd87c31a24a069b/plot_det.py
@@ -60,10 +60,9 @@
 # ----------------------
 #
 # Here we define two different classifiers. The goal is to visually compare their
-# statistical performance across thresholds using the ROC and DET curves. There
-# is no particular reason why these classifiers are chosen other classifiers
-# available in scikit-learn.
+# statistical performance across thresholds using the ROC and DET curves.
 
+from sklearn.dummy import DummyClassifier
 from sklearn.ensemble import RandomForestClassifier
 from sklearn.pipeline import make_pipeline
 from sklearn.svm import LinearSVC
@@ -71,13 +70,14 @@
 classifiers = {
     "Linear SVM": make_pipeline(StandardScaler(), LinearSVC(C=0.025)),
     "Random Forest": RandomForestClassifier(
-        max_depth=5, n_estimators=10, max_features=1
+        max_depth=5, n_estimators=10, max_features=1, random_state=0
     ),
+    "Non-informative baseline": DummyClassifier(),
 }
 
 # %%
-# Plot ROC and DET curves
-# -----------------------
+# Compare ROC and DET curves
+# --------------------------
 #
 # DET curves are commonly plotted in normal deviate scale. To achieve this the
 # DET display transforms the error rates as returned by the
@@ -86,22 +86,29 @@
 
 import matplotlib.pyplot as plt
 
+from sklearn.dummy import DummyClassifier
 from sklearn.metrics import DetCurveDisplay, RocCurveDisplay
 
 fig, [ax_roc, ax_det] = plt.subplots(1, 2, figsize=(11, 5))
 
-for name, clf in classifiers.items():
-    clf.fit(X_train, y_train)
-
-    RocCurveDisplay.from_estimator(clf, X_test, y_test, ax=ax_roc, name=name)
-    DetCurveDisplay.from_estimator(clf, X_test, y_test, ax=ax_det, name=name)
-
 ax_roc.set_title("Receiver Operating Characteristic (ROC) curves")
 ax_det.set_title("Detection Error Tradeoff (DET) curves")
 
 ax_roc.grid(linestyle="--")
 ax_det.grid(linestyle="--")
 
+for name, clf in classifiers.items():
+    (color, linestyle) = (
+        ("black", "--") if name == "Non-informative baseline" else (None, None)
+    )
+    clf.fit(X_train, y_train)
+    RocCurveDisplay.from_estimator(
+        clf, X_test, y_test, ax=ax_roc, name=name, color=color, linestyle=linestyle
+    )
+    DetCurveDisplay.from_estimator(
+        clf, X_test, y_test, ax=ax_det, name=name, color=color, linestyle=linestyle
+    )
+
 plt.legend()
 plt.show()
 
@@ -117,3 +124,35 @@
 # DET curves give direct feedback of the detection error tradeoff to aid in
 # operating point analysis. The user can then decide the FNR they are willing to
 # accept at the expense of the FPR (or vice-versa).
+#
+# Non-informative classifier baseline for the ROC and DET curves
+# --------------------------------------------------------------
+#
+# The diagonal black-dotted lines in the plots above correspond to a
+# :class:`~sklearn.dummy.DummyClassifier` using the default "prior" strategy, to
+# serve as baseline for comparison with other classifiers. This classifier makes
+# constant predictions, independent of the input features in `X`, making it a
+# non-informative classifier.
+#
+# To further understand the non-informative baseline of the ROC and DET curves,
+# we recall the following mathematical definitions:
+#
+# :math:`\text{FPR} = \frac{\text{FP}}{\text{FP} + \text{TN}}`
+#
+# :math:`\text{FNR} = \frac{\text{FN}}{\text{TP} + \text{FN}}`
+#
+# :math:`\text{TPR} = \frac{\text{TP}}{\text{TP} + \text{FN}}`
+#
+# A classifier that always predict the positive class would have no true
+# negatives nor false negatives, giving :math:`\text{FPR} = \text{TPR} = 1` and
+# :math:`\text{FNR} = 0`, i.e.:
+#
+# - a single point in the upper right corner of the ROC plane,
+# - a single point in the lower right corner of the DET plane.
+#
+# Similarly, a classifier that always predict the negative class would have no
+# true positives nor false positives, thus :math:`\text{FPR} = \text{TPR} = 0`
+# and :math:`\text{FNR} = 1`, i.e.:
+#
+# - a single point in the lower left corner of the ROC plane,
+# - a single point in the upper left corner of the DET plane.
diff --git a/dev/_downloads/67dd3ba8fc7589bc10a0d9191f17ab66/plot_display_object_visualization.ipynb b/dev/_downloads/67dd3ba8fc7589bc10a0d9191f17ab66/plot_display_object_visualization.ipynb
index c1af0f86e2907..00154a78a131d 100644
--- a/dev/_downloads/67dd3ba8fc7589bc10a0d9191f17ab66/plot_display_object_visualization.ipynb
+++ b/dev/_downloads/67dd3ba8fc7589bc10a0d9191f17ab66/plot_display_object_visualization.ipynb
@@ -125,7 +125,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/68fdea23e50d165632d4bd4e36453cd5/plot_release_highlights_1_1_0.ipynb b/dev/_downloads/68fdea23e50d165632d4bd4e36453cd5/plot_release_highlights_1_1_0.ipynb
index a7d21c14addc1..2351acd35d110 100644
--- a/dev/_downloads/68fdea23e50d165632d4bd4e36453cd5/plot_release_highlights_1_1_0.ipynb
+++ b/dev/_downloads/68fdea23e50d165632d4bd4e36453cd5/plot_release_highlights_1_1_0.ipynb
@@ -22,7 +22,7 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.ensemble import HistGradientBoostingRegressor\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n# Simple regression function for X * cos(X)\nrng = np.random.RandomState(42)\nX_1d = np.linspace(0, 10, num=2000)\nX = X_1d.reshape(-1, 1)\ny = X_1d * np.cos(X_1d) + rng.normal(scale=X_1d / 3)\n\nquantiles = [0.95, 0.5, 0.05]\nparameters = dict(loss=\"quantile\", max_bins=32, max_iter=50)\nhist_quantiles = {\n    f\"quantile={quantile:.2f}\": HistGradientBoostingRegressor(\n        **parameters, quantile=quantile\n    ).fit(X, y)\n    for quantile in quantiles\n}\n\nfig, ax = plt.subplots()\nax.plot(X_1d, y, \"o\", alpha=0.5, markersize=1)\nfor quantile, hist in hist_quantiles.items():\n    ax.plot(X_1d, hist.predict(X), label=quantile)\n_ = ax.legend(loc=\"lower left\")"
+        "import matplotlib.pyplot as plt\nimport numpy as np\n\nfrom sklearn.ensemble import HistGradientBoostingRegressor\n\n# Simple regression function for X * cos(X)\nrng = np.random.RandomState(42)\nX_1d = np.linspace(0, 10, num=2000)\nX = X_1d.reshape(-1, 1)\ny = X_1d * np.cos(X_1d) + rng.normal(scale=X_1d / 3)\n\nquantiles = [0.95, 0.5, 0.05]\nparameters = dict(loss=\"quantile\", max_bins=32, max_iter=50)\nhist_quantiles = {\n    f\"quantile={quantile:.2f}\": HistGradientBoostingRegressor(\n        **parameters, quantile=quantile\n    ).fit(X, y)\n    for quantile in quantiles\n}\n\nfig, ax = plt.subplots()\nax.plot(X_1d, y, \"o\", alpha=0.5, markersize=1)\nfor quantile, hist in hist_quantiles.items():\n    ax.plot(X_1d, hist.predict(X), label=quantile)\n_ = ax.legend(loc=\"lower left\")"
       ]
     },
     {
@@ -47,7 +47,7 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.compose import ColumnTransformer\nfrom sklearn.preprocessing import OneHotEncoder, StandardScaler\nfrom sklearn.pipeline import make_pipeline\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.feature_selection import SelectKBest\nfrom sklearn.datasets import fetch_openml\nfrom sklearn.linear_model import LogisticRegression\n\nX, y = fetch_openml(\n    \"titanic\", version=1, as_frame=True, return_X_y=True, parser=\"pandas\"\n)\nnumeric_features = [\"age\", \"fare\"]\nnumeric_transformer = make_pipeline(SimpleImputer(strategy=\"median\"), StandardScaler())\ncategorical_features = [\"embarked\", \"pclass\"]\n\npreprocessor = ColumnTransformer(\n    [\n        (\"num\", numeric_transformer, numeric_features),\n        (\n            \"cat\",\n            OneHotEncoder(handle_unknown=\"ignore\", sparse_output=False),\n            categorical_features,\n        ),\n    ],\n    verbose_feature_names_out=False,\n)\nlog_reg = make_pipeline(preprocessor, SelectKBest(k=7), LogisticRegression())\nlog_reg.fit(X, y)"
+        "from sklearn.compose import ColumnTransformer\nfrom sklearn.datasets import fetch_openml\nfrom sklearn.feature_selection import SelectKBest\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.pipeline import make_pipeline\nfrom sklearn.preprocessing import OneHotEncoder, StandardScaler\n\nX, y = fetch_openml(\n    \"titanic\", version=1, as_frame=True, return_X_y=True, parser=\"pandas\"\n)\nnumeric_features = [\"age\", \"fare\"]\nnumeric_transformer = make_pipeline(SimpleImputer(strategy=\"median\"), StandardScaler())\ncategorical_features = [\"embarked\", \"pclass\"]\n\npreprocessor = ColumnTransformer(\n    [\n        (\"num\", numeric_transformer, numeric_features),\n        (\n            \"cat\",\n            OneHotEncoder(handle_unknown=\"ignore\", sparse_output=False),\n            categorical_features,\n        ),\n    ],\n    verbose_feature_names_out=False,\n)\nlog_reg = make_pipeline(preprocessor, SelectKBest(k=7), LogisticRegression())\nlog_reg.fit(X, y)"
       ]
     },
     {
@@ -83,7 +83,7 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.preprocessing import OneHotEncoder\nimport numpy as np\n\nX = np.array(\n    [[\"dog\"] * 5 + [\"cat\"] * 20 + [\"rabbit\"] * 10 + [\"snake\"] * 3], dtype=object\n).T\nenc = OneHotEncoder(min_frequency=6, sparse_output=False).fit(X)\nenc.infrequent_categories_"
+        "import numpy as np\n\nfrom sklearn.preprocessing import OneHotEncoder\n\nX = np.array(\n    [[\"dog\"] * 5 + [\"cat\"] * 20 + [\"rabbit\"] * 10 + [\"snake\"] * 3], dtype=object\n).T\nenc = OneHotEncoder(min_frequency=6, sparse_output=False).fit(X)\nenc.infrequent_categories_"
       ]
     },
     {
@@ -126,7 +126,7 @@
       },
       "outputs": [],
       "source": [
-        "import numpy as np\nfrom sklearn.decomposition import MiniBatchNMF\n\nrng = np.random.RandomState(0)\nn_samples, n_features, n_components = 10, 10, 5\ntrue_W = rng.uniform(size=(n_samples, n_components))\ntrue_H = rng.uniform(size=(n_components, n_features))\nX = true_W @ true_H\n\nnmf = MiniBatchNMF(n_components=n_components, random_state=0)\n\nfor _ in range(10):\n    nmf.partial_fit(X)\n\nW = nmf.transform(X)\nH = nmf.components_\nX_reconstructed = W @ H\n\nprint(\n    f\"relative reconstruction error: \",\n    f\"{np.sum((X - X_reconstructed) ** 2) / np.sum(X**2):.5f}\",\n)"
+        "import numpy as np\n\nfrom sklearn.decomposition import MiniBatchNMF\n\nrng = np.random.RandomState(0)\nn_samples, n_features, n_components = 10, 10, 5\ntrue_W = rng.uniform(size=(n_samples, n_components))\ntrue_H = rng.uniform(size=(n_components, n_features))\nX = true_W @ true_H\n\nnmf = MiniBatchNMF(n_components=n_components, random_state=0)\n\nfor _ in range(10):\n    nmf.partial_fit(X)\n\nW = nmf.transform(X)\nH = nmf.components_\nX_reconstructed = W @ H\n\nprint(\n    \"relative reconstruction error: \",\n    f\"{np.sum((X - X_reconstructed) ** 2) / np.sum(X**2):.5f}\",\n)"
       ]
     },
     {
@@ -144,7 +144,7 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.datasets import make_blobs\nfrom sklearn.cluster import KMeans, BisectingKMeans\nimport matplotlib.pyplot as plt\n\nX, _ = make_blobs(n_samples=1000, centers=2, random_state=0)\n\nkm = KMeans(n_clusters=5, random_state=0, n_init=\"auto\").fit(X)\nbisect_km = BisectingKMeans(n_clusters=5, random_state=0).fit(X)\n\nfig, ax = plt.subplots(1, 2, figsize=(10, 5))\nax[0].scatter(X[:, 0], X[:, 1], s=10, c=km.labels_)\nax[0].scatter(km.cluster_centers_[:, 0], km.cluster_centers_[:, 1], s=20, c=\"r\")\nax[0].set_title(\"KMeans\")\n\nax[1].scatter(X[:, 0], X[:, 1], s=10, c=bisect_km.labels_)\nax[1].scatter(\n    bisect_km.cluster_centers_[:, 0], bisect_km.cluster_centers_[:, 1], s=20, c=\"r\"\n)\n_ = ax[1].set_title(\"BisectingKMeans\")"
+        "import matplotlib.pyplot as plt\n\nfrom sklearn.cluster import BisectingKMeans, KMeans\nfrom sklearn.datasets import make_blobs\n\nX, _ = make_blobs(n_samples=1000, centers=2, random_state=0)\n\nkm = KMeans(n_clusters=5, random_state=0, n_init=\"auto\").fit(X)\nbisect_km = BisectingKMeans(n_clusters=5, random_state=0).fit(X)\n\nfig, ax = plt.subplots(1, 2, figsize=(10, 5))\nax[0].scatter(X[:, 0], X[:, 1], s=10, c=km.labels_)\nax[0].scatter(km.cluster_centers_[:, 0], km.cluster_centers_[:, 1], s=20, c=\"r\")\nax[0].set_title(\"KMeans\")\n\nax[1].scatter(X[:, 0], X[:, 1], s=10, c=bisect_km.labels_)\nax[1].scatter(\n    bisect_km.cluster_centers_[:, 0], bisect_km.cluster_centers_[:, 1], s=20, c=\"r\"\n)\n_ = ax[1].set_title(\"BisectingKMeans\")"
       ]
     }
   ],
@@ -164,7 +164,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/69531acbb2cc9d87ef86f75c8eaba68d/plot_sgdocsvm_vs_ocsvm.ipynb b/dev/_downloads/69531acbb2cc9d87ef86f75c8eaba68d/plot_sgdocsvm_vs_ocsvm.ipynb
index ceefe1174cf75..9a9ddd78ef44b 100644
--- a/dev/_downloads/69531acbb2cc9d87ef86f75c8eaba68d/plot_sgdocsvm_vs_ocsvm.ipynb
+++ b/dev/_downloads/69531acbb2cc9d87ef86f75c8eaba68d/plot_sgdocsvm_vs_ocsvm.ipynb
@@ -68,7 +68,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/6953689dfdc5dd401dda89604bbdaefb/plot_time_series_lagged_features.ipynb b/dev/_downloads/6953689dfdc5dd401dda89604bbdaefb/plot_time_series_lagged_features.ipynb
index c42205e5ea5c9..777ea9319b48a 100644
--- a/dev/_downloads/6953689dfdc5dd401dda89604bbdaefb/plot_time_series_lagged_features.ipynb
+++ b/dev/_downloads/6953689dfdc5dd401dda89604bbdaefb/plot_time_series_lagged_features.ipynb
@@ -285,7 +285,7 @@
       },
       "outputs": [],
       "source": [
-        "quantile_list = [0.05, 0.5, 0.95]\n\nfor quantile in quantile_list:\n    model = HistGradientBoostingRegressor(loss=\"quantile\", quantile=quantile)\n    cv_results = cross_validate(\n        model,\n        X,\n        y,\n        cv=ts_cv,\n        scoring=scoring,\n        n_jobs=2,\n    )\n    time = cv_results[\"fit_time\"]\n    scores[\"fit_time\"].append(f\"{time.mean():.2f} \u00b1 {time.std():.2f} s\")\n\n    scores[\"loss\"].append(f\"quantile {int(quantile*100)}\")\n    for key, value in cv_results.items():\n        if key.startswith(\"test_\"):\n            metric = key.split(\"test_\")[1]\n            scores = consolidate_scores(cv_results, scores, metric)\n\nscores_df = pl.DataFrame(scores)\nscores_df"
+        "quantile_list = [0.05, 0.5, 0.95]\n\nfor quantile in quantile_list:\n    model = HistGradientBoostingRegressor(loss=\"quantile\", quantile=quantile)\n    cv_results = cross_validate(\n        model,\n        X,\n        y,\n        cv=ts_cv,\n        scoring=scoring,\n        n_jobs=2,\n    )\n    time = cv_results[\"fit_time\"]\n    scores[\"fit_time\"].append(f\"{time.mean():.2f} \u00b1 {time.std():.2f} s\")\n\n    scores[\"loss\"].append(f\"quantile {int(quantile * 100)}\")\n    for key, value in cv_results.items():\n        if key.startswith(\"test_\"):\n            metric = key.split(\"test_\")[1]\n            scores = consolidate_scores(cv_results, scores, metric)\n\nscores_df = pl.DataFrame(scores)\nscores_df"
       ]
     },
     {
@@ -384,7 +384,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/69878e8e2864920aa874c5a68cecf1d3/plot_species_distribution_modeling.py b/dev/_downloads/69878e8e2864920aa874c5a68cecf1d3/plot_species_distribution_modeling.py
index 5b0d30bc4c8bf..e2edda813c25d 100644
--- a/dev/_downloads/69878e8e2864920aa874c5a68cecf1d3/plot_species_distribution_modeling.py
+++ b/dev/_downloads/69878e8e2864920aa874c5a68cecf1d3/plot_species_distribution_modeling.py
@@ -109,7 +109,7 @@ def create_species_bunch(species_name, train, test, coverages, xgrid, ygrid):
 
 
 def plot_species_distribution(
-    species=("bradypus_variegatus_0", "microryzomys_minutus_0")
+    species=("bradypus_variegatus_0", "microryzomys_minutus_0"),
 ):
     """
     Plot the species distribution.
@@ -194,7 +194,7 @@ def plot_species_distribution(
         Z = np.ones((data.Ny, data.Nx), dtype=np.float64)
 
         # We'll predict only for the land points.
-        idx = np.where(land_reference > -9999)
+        idx = (land_reference > -9999).nonzero()
         coverages_land = data.coverages[:, idx[0], idx[1]].T
 
         pred = clf.decision_function((coverages_land - mean) / std)
diff --git a/dev/_downloads/69f1bc3bab6ea5d622c5dd4cbd78227f/plot_successive_halving_heatmap.ipynb b/dev/_downloads/69f1bc3bab6ea5d622c5dd4cbd78227f/plot_successive_halving_heatmap.ipynb
index d81a3705f4b94..846db97c54fef 100644
--- a/dev/_downloads/69f1bc3bab6ea5d622c5dd4cbd78227f/plot_successive_halving_heatmap.ipynb
+++ b/dev/_downloads/69f1bc3bab6ea5d622c5dd4cbd78227f/plot_successive_halving_heatmap.ipynb
@@ -15,7 +15,7 @@
       },
       "outputs": [],
       "source": [
-        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\n\nfrom time import time\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\n\nfrom sklearn import datasets\nfrom sklearn.experimental import enable_halving_search_cv  # noqa\nfrom sklearn.model_selection import GridSearchCV, HalvingGridSearchCV\nfrom sklearn.svm import SVC"
+        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\n\nfrom time import time\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\n\nfrom sklearn import datasets\nfrom sklearn.experimental import enable_halving_search_cv  # noqa: F401\nfrom sklearn.model_selection import GridSearchCV, HalvingGridSearchCV\nfrom sklearn.svm import SVC"
       ]
     },
     {
@@ -78,7 +78,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/6a464fa14bf1d3ca99fc9a199d81a8b3/plot_compare_cross_decomposition.zip b/dev/_downloads/6a464fa14bf1d3ca99fc9a199d81a8b3/plot_compare_cross_decomposition.zip
index a09aa164842e0..d12a6a1e73ba0 100644
Binary files a/dev/_downloads/6a464fa14bf1d3ca99fc9a199d81a8b3/plot_compare_cross_decomposition.zip and b/dev/_downloads/6a464fa14bf1d3ca99fc9a199d81a8b3/plot_compare_cross_decomposition.zip differ
diff --git a/dev/_downloads/6b00e458f3e282f1cc421f077b2fcad1/plot_spectral_biclustering.ipynb b/dev/_downloads/6b00e458f3e282f1cc421f077b2fcad1/plot_spectral_biclustering.ipynb
index 04b916ca968e8..0e549b0b0b197 100644
--- a/dev/_downloads/6b00e458f3e282f1cc421f077b2fcad1/plot_spectral_biclustering.ipynb
+++ b/dev/_downloads/6b00e458f3e282f1cc421f077b2fcad1/plot_spectral_biclustering.ipynb
@@ -157,7 +157,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/6bf322ce1724c13e6e0f8f719ebd253c/plot_kmeans_digits.ipynb b/dev/_downloads/6bf322ce1724c13e6e0f8f719ebd253c/plot_kmeans_digits.ipynb
index d4151ae164cc4..f784101fa27e3 100644
--- a/dev/_downloads/6bf322ce1724c13e6e0f8f719ebd253c/plot_kmeans_digits.ipynb
+++ b/dev/_downloads/6bf322ce1724c13e6e0f8f719ebd253c/plot_kmeans_digits.ipynb
@@ -107,7 +107,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/6c079edbd131768ca2f3068e9fdb94eb/plot_topics_extraction_with_nmf_lda.zip b/dev/_downloads/6c079edbd131768ca2f3068e9fdb94eb/plot_topics_extraction_with_nmf_lda.zip
index 64afd6c9fd8f4..9dd794054485c 100644
Binary files a/dev/_downloads/6c079edbd131768ca2f3068e9fdb94eb/plot_topics_extraction_with_nmf_lda.zip and b/dev/_downloads/6c079edbd131768ca2f3068e9fdb94eb/plot_topics_extraction_with_nmf_lda.zip differ
diff --git a/dev/_downloads/6c50dbd9c6dc52f3da913f8d8f82274d/plot_ensemble_oob.ipynb b/dev/_downloads/6c50dbd9c6dc52f3da913f8d8f82274d/plot_ensemble_oob.ipynb
index cd20dedfde1d3..134cf9d6c63de 100644
--- a/dev/_downloads/6c50dbd9c6dc52f3da913f8d8f82274d/plot_ensemble_oob.ipynb
+++ b/dev/_downloads/6c50dbd9c6dc52f3da913f8d8f82274d/plot_ensemble_oob.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/6caa16249d07b4f3e57d5f3bf102b137/plot_ridge_path.ipynb b/dev/_downloads/6caa16249d07b4f3e57d5f3bf102b137/plot_ridge_path.ipynb
index be1df6b33844b..7d1d26aa7ea33 100644
--- a/dev/_downloads/6caa16249d07b4f3e57d5f3bf102b137/plot_ridge_path.ipynb
+++ b/dev/_downloads/6caa16249d07b4f3e57d5f3bf102b137/plot_ridge_path.ipynb
@@ -71,7 +71,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/6cd2f23417e24a8a2a445c34f1b57930/plot_lasso_and_elasticnet.ipynb b/dev/_downloads/6cd2f23417e24a8a2a445c34f1b57930/plot_lasso_and_elasticnet.ipynb
index 9077596883940..e0cd8ed17bdf8 100644
--- a/dev/_downloads/6cd2f23417e24a8a2a445c34f1b57930/plot_lasso_and_elasticnet.ipynb
+++ b/dev/_downloads/6cd2f23417e24a8a2a445c34f1b57930/plot_lasso_and_elasticnet.ipynb
@@ -204,7 +204,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/6cf81265f73116706dcf276ed0aa52df/plot_gpc_iris.zip b/dev/_downloads/6cf81265f73116706dcf276ed0aa52df/plot_gpc_iris.zip
index d626d8ce164e7..d55a58dbd551c 100644
Binary files a/dev/_downloads/6cf81265f73116706dcf276ed0aa52df/plot_gpc_iris.zip and b/dev/_downloads/6cf81265f73116706dcf276ed0aa52df/plot_gpc_iris.zip differ
diff --git a/dev/_downloads/6d4f620ec6653356eb970c2a6ed62081/plot_calibration_curve.ipynb b/dev/_downloads/6d4f620ec6653356eb970c2a6ed62081/plot_calibration_curve.ipynb
index e93a932035d00..2f0a748ed1335 100644
--- a/dev/_downloads/6d4f620ec6653356eb970c2a6ed62081/plot_calibration_curve.ipynb
+++ b/dev/_downloads/6d4f620ec6653356eb970c2a6ed62081/plot_calibration_curve.ipynb
@@ -165,7 +165,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/6d7313d090e7392c48a23c84b6fdec0c/plot_lw_vs_oas.ipynb b/dev/_downloads/6d7313d090e7392c48a23c84b6fdec0c/plot_lw_vs_oas.ipynb
index f6a69f08f7e24..367e498f6166e 100644
--- a/dev/_downloads/6d7313d090e7392c48a23c84b6fdec0c/plot_lw_vs_oas.ipynb
+++ b/dev/_downloads/6d7313d090e7392c48a23c84b6fdec0c/plot_lw_vs_oas.ipynb
@@ -46,7 +46,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/6ebaebc92484478ceb119d24fe9df21c/plot_pipeline_display.ipynb b/dev/_downloads/6ebaebc92484478ceb119d24fe9df21c/plot_pipeline_display.ipynb
index 59ccaeca64b52..865e7a35d4147 100644
--- a/dev/_downloads/6ebaebc92484478ceb119d24fe9df21c/plot_pipeline_display.ipynb
+++ b/dev/_downloads/6ebaebc92484478ceb119d24fe9df21c/plot_pipeline_display.ipynb
@@ -179,7 +179,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/6ef4b0956dab0972b88de2f2a3bf20d7/plot_calibration_multiclass.zip b/dev/_downloads/6ef4b0956dab0972b88de2f2a3bf20d7/plot_calibration_multiclass.zip
index dfd130d0c660d..b3c9bf4925ce6 100644
Binary files a/dev/_downloads/6ef4b0956dab0972b88de2f2a3bf20d7/plot_calibration_multiclass.zip and b/dev/_downloads/6ef4b0956dab0972b88de2f2a3bf20d7/plot_calibration_multiclass.zip differ
diff --git a/dev/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip b/dev/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip
index a52e42b71dfbb..ce573918d3a83 100644
Binary files a/dev/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip and b/dev/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip differ
diff --git a/dev/_downloads/6f4a6a0d8063b616c4aa4db2865de57c/plot_svm_anova.ipynb b/dev/_downloads/6f4a6a0d8063b616c4aa4db2865de57c/plot_svm_anova.ipynb
index cf8e62644f713..d0df16c7502b4 100644
--- a/dev/_downloads/6f4a6a0d8063b616c4aa4db2865de57c/plot_svm_anova.ipynb
+++ b/dev/_downloads/6f4a6a0d8063b616c4aa4db2865de57c/plot_svm_anova.ipynb
@@ -89,7 +89,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/6f4d2fbb0386afc6ddc42f47a6c85a7c/plot_agglomerative_dendrogram.zip b/dev/_downloads/6f4d2fbb0386afc6ddc42f47a6c85a7c/plot_agglomerative_dendrogram.zip
index b3b277fd04a67..bb69d47323837 100644
Binary files a/dev/_downloads/6f4d2fbb0386afc6ddc42f47a6c85a7c/plot_agglomerative_dendrogram.zip and b/dev/_downloads/6f4d2fbb0386afc6ddc42f47a6c85a7c/plot_agglomerative_dendrogram.zip differ
diff --git a/dev/_downloads/7011de1f31ecdc52f138d7e582a6a455/plot_voting_probas.ipynb b/dev/_downloads/7011de1f31ecdc52f138d7e582a6a455/plot_voting_probas.ipynb
deleted file mode 100644
index 4b3c05bb4736e..0000000000000
--- a/dev/_downloads/7011de1f31ecdc52f138d7e582a6a455/plot_voting_probas.ipynb
+++ /dev/null
@@ -1,43 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "\n# Plot class probabilities calculated by the VotingClassifier\n\n.. currentmodule:: sklearn\n\nPlot the class probabilities of the first sample in a toy dataset predicted by\nthree different classifiers and averaged by the\n:class:`~ensemble.VotingClassifier`.\n\nFirst, three exemplary classifiers are initialized\n(:class:`~linear_model.LogisticRegression`, :class:`~naive_bayes.GaussianNB`,\nand :class:`~ensemble.RandomForestClassifier`) and used to initialize a\nsoft-voting :class:`~ensemble.VotingClassifier` with weights `[1, 1, 5]`, which\nmeans that the predicted probabilities of the\n:class:`~ensemble.RandomForestClassifier` count 5 times as much as the weights\nof the other classifiers when the averaged probability is calculated.\n\nTo visualize the probability weighting, we fit each classifier on the training\nset and plot the predicted class probabilities for the first sample in this\nexample dataset.\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nfrom sklearn.ensemble import RandomForestClassifier, VotingClassifier\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.naive_bayes import GaussianNB\n\nclf1 = LogisticRegression(max_iter=1000, random_state=123)\nclf2 = RandomForestClassifier(n_estimators=100, random_state=123)\nclf3 = GaussianNB()\nX = np.array([[-1.0, -1.0], [-1.2, -1.4], [-3.4, -2.2], [1.1, 1.2]])\ny = np.array([1, 1, 2, 2])\n\neclf = VotingClassifier(\n    estimators=[(\"lr\", clf1), (\"rf\", clf2), (\"gnb\", clf3)],\n    voting=\"soft\",\n    weights=[1, 1, 5],\n)\n\n# predict class probabilities for all classifiers\nprobas = [c.fit(X, y).predict_proba(X) for c in (clf1, clf2, clf3, eclf)]\n\n# get class probabilities for the first sample in the dataset\nclass1_1 = [pr[0, 0] for pr in probas]\nclass2_1 = [pr[0, 1] for pr in probas]\n\n\n# plotting\n\nN = 4  # number of groups\nind = np.arange(N)  # group positions\nwidth = 0.35  # bar width\n\nfig, ax = plt.subplots()\n\n# bars for classifier 1-3\np1 = ax.bar(ind, np.hstack(([class1_1[:-1], [0]])), width, color=\"green\", edgecolor=\"k\")\np2 = ax.bar(\n    ind + width,\n    np.hstack(([class2_1[:-1], [0]])),\n    width,\n    color=\"lightgreen\",\n    edgecolor=\"k\",\n)\n\n# bars for VotingClassifier\np3 = ax.bar(ind, [0, 0, 0, class1_1[-1]], width, color=\"blue\", edgecolor=\"k\")\np4 = ax.bar(\n    ind + width, [0, 0, 0, class2_1[-1]], width, color=\"steelblue\", edgecolor=\"k\"\n)\n\n# plot annotations\nplt.axvline(2.8, color=\"k\", linestyle=\"dashed\")\nax.set_xticks(ind + width)\nax.set_xticklabels(\n    [\n        \"LogisticRegression\\nweight 1\",\n        \"GaussianNB\\nweight 1\",\n        \"RandomForestClassifier\\nweight 5\",\n        \"VotingClassifier\\n(average probabilities)\",\n    ],\n    rotation=40,\n    ha=\"right\",\n)\nplt.ylim([0, 1])\nplt.title(\"Class probabilities for sample 1 by different classifiers\")\nplt.legend([p1[0], p2[0]], [\"class 1\", \"class 2\"], loc=\"upper left\")\nplt.tight_layout()\nplt.show()"
-      ]
-    }
-  ],
-  "metadata": {
-    "kernelspec": {
-      "display_name": "Python 3",
-      "language": "python",
-      "name": "python3"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.10.16"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 0
-}
\ No newline at end of file
diff --git a/dev/_downloads/7012baed63b9a27f121bae611b8285c2/plot_cyclical_feature_engineering.ipynb b/dev/_downloads/7012baed63b9a27f121bae611b8285c2/plot_cyclical_feature_engineering.ipynb
index 678c6f714c126..7e6657375b673 100644
--- a/dev/_downloads/7012baed63b9a27f121bae611b8285c2/plot_cyclical_feature_engineering.ipynb
+++ b/dev/_downloads/7012baed63b9a27f121bae611b8285c2/plot_cyclical_feature_engineering.ipynb
@@ -731,7 +731,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/70af03f765a8f15d7c1d63e836e68590/plot_stock_market.py b/dev/_downloads/70af03f765a8f15d7c1d63e836e68590/plot_stock_market.py
index 74f60ffa00c15..40f778c785723 100644
--- a/dev/_downloads/70af03f765a8f15d7c1d63e836e68590/plot_stock_market.py
+++ b/dev/_downloads/70af03f765a8f15d7c1d63e836e68590/plot_stock_market.py
@@ -213,7 +213,7 @@
 )
 
 # Plot the edges
-start_idx, end_idx = np.where(non_zero)
+start_idx, end_idx = non_zero.nonzero()
 # a sequence of (*line0*, *line1*, *line2*), where::
 #            linen = (x0, y0), (x1, y1), ... (xm, ym)
 segments = [
diff --git a/dev/_downloads/70d94cdb64ace13b2b2bcb80c1b9986a/plot_bayesian_ridge_curvefit.zip b/dev/_downloads/70d94cdb64ace13b2b2bcb80c1b9986a/plot_bayesian_ridge_curvefit.zip
index 1cc55bb79d184..bcc6b5b3740c1 100644
Binary files a/dev/_downloads/70d94cdb64ace13b2b2bcb80c1b9986a/plot_bayesian_ridge_curvefit.zip and b/dev/_downloads/70d94cdb64ace13b2b2bcb80c1b9986a/plot_bayesian_ridge_curvefit.zip differ
diff --git a/dev/_downloads/71a61bc97b8485c9c30708afd6dbe763/plot_partial_dependence.zip b/dev/_downloads/71a61bc97b8485c9c30708afd6dbe763/plot_partial_dependence.zip
index 460374a9f909f..4380f899a35f7 100644
Binary files a/dev/_downloads/71a61bc97b8485c9c30708afd6dbe763/plot_partial_dependence.zip and b/dev/_downloads/71a61bc97b8485c9c30708afd6dbe763/plot_partial_dependence.zip differ
diff --git a/dev/_downloads/72d877fe6a0a97bfa8950a086a14a445/plot_agglomerative_dendrogram.ipynb b/dev/_downloads/72d877fe6a0a97bfa8950a086a14a445/plot_agglomerative_dendrogram.ipynb
index 0e2a740e4b951..ed707ef8fcdd5 100644
--- a/dev/_downloads/72d877fe6a0a97bfa8950a086a14a445/plot_agglomerative_dendrogram.ipynb
+++ b/dev/_downloads/72d877fe6a0a97bfa8950a086a14a445/plot_agglomerative_dendrogram.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
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diff --git a/dev/_downloads/72de0e9b7e7a5e07ea3b341b61af5277/plot_adaboost_regression.zip b/dev/_downloads/72de0e9b7e7a5e07ea3b341b61af5277/plot_adaboost_regression.zip
index 3faf1231c227f..21f11c5f2889d 100644
Binary files a/dev/_downloads/72de0e9b7e7a5e07ea3b341b61af5277/plot_adaboost_regression.zip and b/dev/_downloads/72de0e9b7e7a5e07ea3b341b61af5277/plot_adaboost_regression.zip differ
diff --git a/dev/_downloads/73099ec6cbf7cd0f232cf582342fdfd2/plot_adjusted_for_chance_measures.zip b/dev/_downloads/73099ec6cbf7cd0f232cf582342fdfd2/plot_adjusted_for_chance_measures.zip
index 939f20d12b905..fad6ffb8d71e6 100644
Binary files a/dev/_downloads/73099ec6cbf7cd0f232cf582342fdfd2/plot_adjusted_for_chance_measures.zip and b/dev/_downloads/73099ec6cbf7cd0f232cf582342fdfd2/plot_adjusted_for_chance_measures.zip differ
diff --git a/dev/_downloads/733ff7845fe2f197ecd0c72afcf23651/plot_randomized_search.ipynb b/dev/_downloads/733ff7845fe2f197ecd0c72afcf23651/plot_randomized_search.ipynb
index 6ae507e637b72..350ed4a408c05 100644
--- a/dev/_downloads/733ff7845fe2f197ecd0c72afcf23651/plot_randomized_search.ipynb
+++ b/dev/_downloads/733ff7845fe2f197ecd0c72afcf23651/plot_randomized_search.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
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diff --git a/dev/_downloads/7394345a6f70a1637fe759e076256013/plot_self_training_varying_threshold.ipynb b/dev/_downloads/7394345a6f70a1637fe759e076256013/plot_self_training_varying_threshold.ipynb
index 9b0a0b38070d2..7426e9f7e0fa7 100644
--- a/dev/_downloads/7394345a6f70a1637fe759e076256013/plot_self_training_varying_threshold.ipynb
+++ b/dev/_downloads/7394345a6f70a1637fe759e076256013/plot_self_training_varying_threshold.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
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diff --git a/dev/_downloads/73ec8f08ae3ece02509539ee03cc3cdd/approximate_nearest_neighbors.ipynb b/dev/_downloads/73ec8f08ae3ece02509539ee03cc3cdd/approximate_nearest_neighbors.ipynb
index aca1d771daed7..dc161f56f929a 100644
--- a/dev/_downloads/73ec8f08ae3ece02509539ee03cc3cdd/approximate_nearest_neighbors.ipynb
+++ b/dev/_downloads/73ec8f08ae3ece02509539ee03cc3cdd/approximate_nearest_neighbors.ipynb
@@ -114,7 +114,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
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diff --git a/dev/_downloads/74a474baa9f199005f835804a7281678/plot_lasso_lasso_lars_elasticnet_path.zip b/dev/_downloads/74a474baa9f199005f835804a7281678/plot_lasso_lasso_lars_elasticnet_path.zip
index e0f17caa5a0c9..d0a3abebc7bfa 100644
Binary files a/dev/_downloads/74a474baa9f199005f835804a7281678/plot_lasso_lasso_lars_elasticnet_path.zip and b/dev/_downloads/74a474baa9f199005f835804a7281678/plot_lasso_lasso_lars_elasticnet_path.zip differ
diff --git a/dev/_downloads/74b690b6fca856df4027d12c67c15acf/plot_mds.zip b/dev/_downloads/74b690b6fca856df4027d12c67c15acf/plot_mds.zip
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diff --git a/dev/_downloads/751870ccff9f33e50613aab6084f86e5/plot_scalable_poly_kernels.zip b/dev/_downloads/751870ccff9f33e50613aab6084f86e5/plot_scalable_poly_kernels.zip
index b22d64e6edfd7..ecd2fef3a1497 100644
Binary files a/dev/_downloads/751870ccff9f33e50613aab6084f86e5/plot_scalable_poly_kernels.zip and b/dev/_downloads/751870ccff9f33e50613aab6084f86e5/plot_scalable_poly_kernels.zip differ
diff --git a/dev/_downloads/751db3d5e6b909ff00972495eaae53df/plot_document_clustering.ipynb b/dev/_downloads/751db3d5e6b909ff00972495eaae53df/plot_document_clustering.ipynb
index 1055efe8b34be..d4199a72f67ce 100644
--- a/dev/_downloads/751db3d5e6b909ff00972495eaae53df/plot_document_clustering.ipynb
+++ b/dev/_downloads/751db3d5e6b909ff00972495eaae53df/plot_document_clustering.ipynb
@@ -287,7 +287,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
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diff --git a/dev/_downloads/753b1eab15cb6e02bef128e5ec8dbbbe/plot_gmm_init.zip b/dev/_downloads/753b1eab15cb6e02bef128e5ec8dbbbe/plot_gmm_init.zip
index d420e8417e9ad..9b9b3fd238c0c 100644
Binary files a/dev/_downloads/753b1eab15cb6e02bef128e5ec8dbbbe/plot_gmm_init.zip and b/dev/_downloads/753b1eab15cb6e02bef128e5ec8dbbbe/plot_gmm_init.zip differ
diff --git a/dev/_downloads/757941223692da355c1f7de747af856d/plot_compare_calibration.ipynb b/dev/_downloads/757941223692da355c1f7de747af856d/plot_compare_calibration.ipynb
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+++ b/dev/_downloads/757941223692da355c1f7de747af856d/plot_compare_calibration.ipynb
@@ -89,7 +89,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
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diff --git a/dev/_downloads/75a08bb798ae7156529a808a0e08e7b4/plot_gpr_prior_posterior.ipynb b/dev/_downloads/75a08bb798ae7156529a808a0e08e7b4/plot_gpr_prior_posterior.ipynb
index bd0be76c31902..e82d30bfc73eb 100644
--- a/dev/_downloads/75a08bb798ae7156529a808a0e08e7b4/plot_gpr_prior_posterior.ipynb
+++ b/dev/_downloads/75a08bb798ae7156529a808a0e08e7b4/plot_gpr_prior_posterior.ipynb
@@ -216,7 +216,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
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diff --git a/dev/_downloads/764d061a261a2e06ad21ec9133361b2d/plot_precision_recall.ipynb b/dev/_downloads/764d061a261a2e06ad21ec9133361b2d/plot_precision_recall.ipynb
index 427dc98d3c32a..251b3ab0191a6 100644
--- a/dev/_downloads/764d061a261a2e06ad21ec9133361b2d/plot_precision_recall.ipynb
+++ b/dev/_downloads/764d061a261a2e06ad21ec9133361b2d/plot_precision_recall.ipynb
@@ -197,7 +197,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
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diff --git a/dev/_downloads/76835ae504ba9a29a0e409a26f38aa8d/plot_digits_denoising.zip b/dev/_downloads/76835ae504ba9a29a0e409a26f38aa8d/plot_digits_denoising.zip
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diff --git a/dev/_downloads/768c66e612686c51be7a0d956e60a0a8/plot_inductive_clustering.ipynb b/dev/_downloads/768c66e612686c51be7a0d956e60a0a8/plot_inductive_clustering.ipynb
index 0d5adefba4ad4..4aed30c4076ca 100644
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+++ b/dev/_downloads/768c66e612686c51be7a0d956e60a0a8/plot_inductive_clustering.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
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diff --git a/dev/_downloads/770d16dafa0c41ebe338b3627690c266/plot_sgd_early_stopping.zip b/dev/_downloads/770d16dafa0c41ebe338b3627690c266/plot_sgd_early_stopping.zip
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diff --git a/dev/_downloads/7716523ca12b85a020d7a525dff641cc/plot_varimax_fa.ipynb b/dev/_downloads/7716523ca12b85a020d7a525dff641cc/plot_varimax_fa.ipynb
index 87b9d254c1761..8b257d3d2d161 100644
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@@ -89,7 +89,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
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diff --git a/dev/_downloads/77b640d8771f7ecdeb2dbc948ebce90a/plot_kmeans_plusplus.ipynb b/dev/_downloads/77b640d8771f7ecdeb2dbc948ebce90a/plot_kmeans_plusplus.ipynb
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@@ -35,7 +35,7 @@
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       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
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+      "version": "3.10.17"
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diff --git a/dev/_downloads/7b16734166ab4280e940d7fb89dd6113/plot_discretization_strategies.zip b/dev/_downloads/7b16734166ab4280e940d7fb89dd6113/plot_discretization_strategies.zip
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diff --git a/dev/_downloads/7b414ce0c39e11cf961fd4fa23008246/plot_target_encoder.ipynb b/dev/_downloads/7b414ce0c39e11cf961fd4fa23008246/plot_target_encoder.ipynb
index bc58cbb0cb3af..3c2997039d057 100644
--- a/dev/_downloads/7b414ce0c39e11cf961fd4fa23008246/plot_target_encoder.ipynb
+++ b/dev/_downloads/7b414ce0c39e11cf961fd4fa23008246/plot_target_encoder.ipynb
@@ -186,7 +186,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
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diff --git a/dev/_downloads/7b4d196feed2f34aae28e18d96971f8d/plot_kmeans_stability_low_dim_dense.zip b/dev/_downloads/7b4d196feed2f34aae28e18d96971f8d/plot_kmeans_stability_low_dim_dense.zip
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diff --git a/dev/_downloads/7be24b5c175982744260a2ced8398a5a/plot_release_highlights_1_0_0.py b/dev/_downloads/7be24b5c175982744260a2ced8398a5a/plot_release_highlights_1_0_0.py
index 264cb1d5a557e..03213076b326e 100644
--- a/dev/_downloads/7be24b5c175982744260a2ced8398a5a/plot_release_highlights_1_0_0.py
+++ b/dev/_downloads/7be24b5c175982744260a2ced8398a5a/plot_release_highlights_1_0_0.py
@@ -1,4 +1,4 @@
-# ruff: noqa
+# ruff: noqa: CPY001
 """
 =======================================
 Release Highlights for scikit-learn 1.0
@@ -89,6 +89,7 @@
 # refer to the :ref:`User Guide <spline_transformer>`.
 
 import numpy as np
+
 from sklearn.preprocessing import SplineTransformer
 
 X = np.arange(5).reshape(5, 1)
@@ -147,9 +148,10 @@
 # is used to check that the column names of the dataframe passed in
 # non-:term:`fit`, such as :term:`predict`, are consistent with features in
 # :term:`fit`:
-from sklearn.preprocessing import StandardScaler
 import pandas as pd
 
+from sklearn.preprocessing import StandardScaler
+
 X = pd.DataFrame([[1, 2, 3], [4, 5, 6]], columns=["a", "b", "c"])
 scalar = StandardScaler().fit(X)
 scalar.feature_names_in_
@@ -162,9 +164,10 @@
 # will be added to all other transformers in future releases. Additionally,
 # :meth:`compose.ColumnTransformer.get_feature_names_out` is available to
 # combine feature names of its transformers:
+import pandas as pd
+
 from sklearn.compose import ColumnTransformer
 from sklearn.preprocessing import OneHotEncoder
-import pandas as pd
 
 X = pd.DataFrame({"pet": ["dog", "cat", "fish"], "age": [3, 7, 1]})
 preprocessor = ColumnTransformer(
diff --git a/dev/_downloads/7c06490f380b1e20e9558c6c5fde70ed/plot_adjusted_for_chance_measures.ipynb b/dev/_downloads/7c06490f380b1e20e9558c6c5fde70ed/plot_adjusted_for_chance_measures.ipynb
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@@ -150,7 +150,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
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@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
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diff --git a/dev/_downloads/7c67ecf521b229ecc7e544b6321fb996/plot_concentration_prior.zip b/dev/_downloads/7c67ecf521b229ecc7e544b6321fb996/plot_concentration_prior.zip
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diff --git a/dev/_downloads/7c8070aa163e648367101a22133c5711/plot_forest_hist_grad_boosting_comparison.ipynb b/dev/_downloads/7c8070aa163e648367101a22133c5711/plot_forest_hist_grad_boosting_comparison.ipynb
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+++ b/dev/_downloads/7c8070aa163e648367101a22133c5711/plot_forest_hist_grad_boosting_comparison.ipynb
@@ -132,7 +132,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
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diff --git a/dev/_downloads/7ce809adad0d67b96c1df3b7fcd74567/plot_mlp_alpha.ipynb b/dev/_downloads/7ce809adad0d67b96c1df3b7fcd74567/plot_mlp_alpha.ipynb
index 3f1a4d235b5e4..70e3a6d8d6134 100644
--- a/dev/_downloads/7ce809adad0d67b96c1df3b7fcd74567/plot_mlp_alpha.ipynb
+++ b/dev/_downloads/7ce809adad0d67b96c1df3b7fcd74567/plot_mlp_alpha.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
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diff --git a/dev/_downloads/7dd63f4a8fba0f2153a1e27ffc8abc36/plot_grid_search_text_feature_extraction.zip b/dev/_downloads/7dd63f4a8fba0f2153a1e27ffc8abc36/plot_grid_search_text_feature_extraction.zip
index 5678ee4ab822c..00b228c689183 100644
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diff --git a/dev/_downloads/7e9cf82b8b60275dd7851470d151af5f/plot_kmeans_stability_low_dim_dense.ipynb b/dev/_downloads/7e9cf82b8b60275dd7851470d151af5f/plot_kmeans_stability_low_dim_dense.ipynb
index 868e59ce8dcc7..7d048d3113030 100644
--- a/dev/_downloads/7e9cf82b8b60275dd7851470d151af5f/plot_kmeans_stability_low_dim_dense.ipynb
+++ b/dev/_downloads/7e9cf82b8b60275dd7851470d151af5f/plot_kmeans_stability_low_dim_dense.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/7ea565549e75a394bc13798f2a296900/plot_omp.zip b/dev/_downloads/7ea565549e75a394bc13798f2a296900/plot_omp.zip
index 38568f51f73bd..8275bd52bf9c9 100644
Binary files a/dev/_downloads/7ea565549e75a394bc13798f2a296900/plot_omp.zip and b/dev/_downloads/7ea565549e75a394bc13798f2a296900/plot_omp.zip differ
diff --git a/dev/_downloads/7ff1697c60d48929305821f39296dbb9/plot_document_classification_20newsgroups.ipynb b/dev/_downloads/7ff1697c60d48929305821f39296dbb9/plot_document_classification_20newsgroups.ipynb
index 826cb8f3095bf..16e3188306567 100644
--- a/dev/_downloads/7ff1697c60d48929305821f39296dbb9/plot_document_classification_20newsgroups.ipynb
+++ b/dev/_downloads/7ff1697c60d48929305821f39296dbb9/plot_document_classification_20newsgroups.ipynb
@@ -247,7 +247,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/8025056f1d24411e898c2d0086371880/plot_optics.ipynb b/dev/_downloads/8025056f1d24411e898c2d0086371880/plot_optics.ipynb
index 6c90fae955dae..1af1e95bea82a 100644
--- a/dev/_downloads/8025056f1d24411e898c2d0086371880/plot_optics.ipynb
+++ b/dev/_downloads/8025056f1d24411e898c2d0086371880/plot_optics.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/803ca1bcd8dd2c364836a6784144355b/plot_cluster_comparison.ipynb b/dev/_downloads/803ca1bcd8dd2c364836a6784144355b/plot_cluster_comparison.ipynb
index 524f43a62566e..3a5d472d4253b 100644
--- a/dev/_downloads/803ca1bcd8dd2c364836a6784144355b/plot_cluster_comparison.ipynb
+++ b/dev/_downloads/803ca1bcd8dd2c364836a6784144355b/plot_cluster_comparison.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/80b08d3f67c5579a3b581e92003ce6c3/plot_sgd_separating_hyperplane.zip b/dev/_downloads/80b08d3f67c5579a3b581e92003ce6c3/plot_sgd_separating_hyperplane.zip
index 06c31ab0a111a..977504cb36644 100644
Binary files a/dev/_downloads/80b08d3f67c5579a3b581e92003ce6c3/plot_sgd_separating_hyperplane.zip and b/dev/_downloads/80b08d3f67c5579a3b581e92003ce6c3/plot_sgd_separating_hyperplane.zip differ
diff --git a/dev/_downloads/80f1673b50d88ee3f77b8540882e9964/plot_lof_outlier_detection.zip b/dev/_downloads/80f1673b50d88ee3f77b8540882e9964/plot_lof_outlier_detection.zip
index c9900f02f5077..de2645fec5537 100644
Binary files a/dev/_downloads/80f1673b50d88ee3f77b8540882e9964/plot_lof_outlier_detection.zip and b/dev/_downloads/80f1673b50d88ee3f77b8540882e9964/plot_lof_outlier_detection.zip differ
diff --git a/dev/_downloads/80fef09514fd851560e999a5b7daa303/plot_roc.py b/dev/_downloads/80fef09514fd851560e999a5b7daa303/plot_roc.py
index 1fc2dedf2943e..a482ad5f4ab95 100644
--- a/dev/_downloads/80fef09514fd851560e999a5b7daa303/plot_roc.py
+++ b/dev/_downloads/80fef09514fd851560e999a5b7daa303/plot_roc.py
@@ -152,9 +152,9 @@
 #
 # We can briefly demo the effect of :func:`numpy.ravel`:
 
-print(f"y_score:\n{y_score[0:2,:]}")
+print(f"y_score:\n{y_score[0:2, :]}")
 print()
-print(f"y_score.ravel():\n{y_score[0:2,:].ravel()}")
+print(f"y_score.ravel():\n{y_score[0:2, :].ravel()}")
 
 # %%
 # In a multi-class classification setup with highly imbalanced classes,
@@ -359,7 +359,7 @@
     plt.plot(
         fpr_grid,
         mean_tpr[ix],
-        label=f"Mean {label_a} vs {label_b} (AUC = {mean_score :.2f})",
+        label=f"Mean {label_a} vs {label_b} (AUC = {mean_score:.2f})",
         linestyle=":",
         linewidth=4,
     )
diff --git a/dev/_downloads/82643d7dc9e517c462b1325c8c1c93a5/plot_tree_regression.zip b/dev/_downloads/82643d7dc9e517c462b1325c8c1c93a5/plot_tree_regression.zip
index 1ced824519a8b..a289389ae5a47 100644
Binary files a/dev/_downloads/82643d7dc9e517c462b1325c8c1c93a5/plot_tree_regression.zip and b/dev/_downloads/82643d7dc9e517c462b1325c8c1c93a5/plot_tree_regression.zip differ
diff --git a/dev/_downloads/82b113d6a139ff1d978ca9188dc8f15c/plot_gmm_sin.ipynb b/dev/_downloads/82b113d6a139ff1d978ca9188dc8f15c/plot_gmm_sin.ipynb
index 67e665980bb2f..6f45fb118ea10 100644
--- a/dev/_downloads/82b113d6a139ff1d978ca9188dc8f15c/plot_gmm_sin.ipynb
+++ b/dev/_downloads/82b113d6a139ff1d978ca9188dc8f15c/plot_gmm_sin.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/82c79cb352203b04e0caab27b7e3f5e7/plot_gpr_noisy_targets.zip b/dev/_downloads/82c79cb352203b04e0caab27b7e3f5e7/plot_gpr_noisy_targets.zip
index 1bcc8330d0c56..019d71ccd4f29 100644
Binary files a/dev/_downloads/82c79cb352203b04e0caab27b7e3f5e7/plot_gpr_noisy_targets.zip and b/dev/_downloads/82c79cb352203b04e0caab27b7e3f5e7/plot_gpr_noisy_targets.zip differ
diff --git a/dev/_downloads/83b230a9377aa30995aae41940c0d3a4/plot_learning_curve.zip b/dev/_downloads/83b230a9377aa30995aae41940c0d3a4/plot_learning_curve.zip
index fef94344b428a..44a948db1b2c9 100644
Binary files a/dev/_downloads/83b230a9377aa30995aae41940c0d3a4/plot_learning_curve.zip and b/dev/_downloads/83b230a9377aa30995aae41940c0d3a4/plot_learning_curve.zip differ
diff --git a/dev/_downloads/83d33d2afcbf708f386433bb1abb0785/plot_mahalanobis_distances.ipynb b/dev/_downloads/83d33d2afcbf708f386433bb1abb0785/plot_mahalanobis_distances.ipynb
index e24218b127e36..cb3e31ff956a5 100644
--- a/dev/_downloads/83d33d2afcbf708f386433bb1abb0785/plot_mahalanobis_distances.ipynb
+++ b/dev/_downloads/83d33d2afcbf708f386433bb1abb0785/plot_mahalanobis_distances.ipynb
@@ -107,7 +107,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/8452fc8dfe9850cfdaa1b758e5a2748b/plot_gradient_boosting_early_stopping.ipynb b/dev/_downloads/8452fc8dfe9850cfdaa1b758e5a2748b/plot_gradient_boosting_early_stopping.ipynb
index 491922dec9dd9..dd5b0a3fcdd54 100644
--- a/dev/_downloads/8452fc8dfe9850cfdaa1b758e5a2748b/plot_gradient_boosting_early_stopping.ipynb
+++ b/dev/_downloads/8452fc8dfe9850cfdaa1b758e5a2748b/plot_gradient_boosting_early_stopping.ipynb
@@ -121,7 +121,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/85657ba7194505e55a2b4b79bdf37ce4/plot_huber_vs_ridge.zip b/dev/_downloads/85657ba7194505e55a2b4b79bdf37ce4/plot_huber_vs_ridge.zip
index 41f1e7ff64539..1c9ffc89cd3eb 100644
Binary files a/dev/_downloads/85657ba7194505e55a2b4b79bdf37ce4/plot_huber_vs_ridge.zip and b/dev/_downloads/85657ba7194505e55a2b4b79bdf37ce4/plot_huber_vs_ridge.zip differ
diff --git a/dev/_downloads/85b2b4f3ecdf3d37834c318b4889b4c0/plot_svm_regression.ipynb b/dev/_downloads/85b2b4f3ecdf3d37834c318b4889b4c0/plot_svm_regression.ipynb
index daa961cc9b860..f70b7eb0526c8 100644
--- a/dev/_downloads/85b2b4f3ecdf3d37834c318b4889b4c0/plot_svm_regression.ipynb
+++ b/dev/_downloads/85b2b4f3ecdf3d37834c318b4889b4c0/plot_svm_regression.ipynb
@@ -89,7 +89,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/85e330311348be5bd40d46fbc15a91be/plot_svm_anova.zip b/dev/_downloads/85e330311348be5bd40d46fbc15a91be/plot_svm_anova.zip
index 5b0b71ceac588..1268b4f92554d 100644
Binary files a/dev/_downloads/85e330311348be5bd40d46fbc15a91be/plot_svm_anova.zip and b/dev/_downloads/85e330311348be5bd40d46fbc15a91be/plot_svm_anova.zip differ
diff --git a/dev/_downloads/862c762bdafef83db4c2c9e803ed6295/plot_release_highlights_1_6_0.ipynb b/dev/_downloads/862c762bdafef83db4c2c9e803ed6295/plot_release_highlights_1_6_0.ipynb
index 5ede5b9f0a967..33b681d7ce4e8 100644
--- a/dev/_downloads/862c762bdafef83db4c2c9e803ed6295/plot_release_highlights_1_6_0.ipynb
+++ b/dev/_downloads/862c762bdafef83db4c2c9e803ed6295/plot_release_highlights_1_6_0.ipynb
@@ -22,7 +22,7 @@
       },
       "outputs": [],
       "source": [
-        "import time\nfrom sklearn.datasets import make_classification\nfrom sklearn.frozen import FrozenEstimator\nfrom sklearn.linear_model import SGDClassifier\nfrom sklearn.model_selection import FixedThresholdClassifier\n\nX, y = make_classification(n_samples=1000, random_state=0)\n\nstart = time.time()\nclassifier = SGDClassifier().fit(X, y)\nprint(f\"Fitting the classifier took {(time.time() - start) * 1_000:.2f} milliseconds\")\n\nstart = time.time()\nthreshold_classifier = FixedThresholdClassifier(\n    estimator=FrozenEstimator(classifier), threshold=0.9\n).fit(X, y)\nprint(\n    f\"Fitting the threshold classifier took {(time.time() - start) * 1_000:.2f} \"\n    \"milliseconds\"\n)"
+        "import time\n\nfrom sklearn.datasets import make_classification\nfrom sklearn.frozen import FrozenEstimator\nfrom sklearn.linear_model import SGDClassifier\nfrom sklearn.model_selection import FixedThresholdClassifier\n\nX, y = make_classification(n_samples=1000, random_state=0)\n\nstart = time.time()\nclassifier = SGDClassifier().fit(X, y)\nprint(f\"Fitting the classifier took {(time.time() - start) * 1_000:.2f} milliseconds\")\n\nstart = time.time()\nthreshold_classifier = FixedThresholdClassifier(\n    estimator=FrozenEstimator(classifier), threshold=0.9\n).fit(X, y)\nprint(\n    f\"Fitting the threshold classifier took {(time.time() - start) * 1_000:.2f} \"\n    \"milliseconds\"\n)"
       ]
     },
     {
@@ -61,7 +61,7 @@
       },
       "outputs": [],
       "source": [
-        "import numpy as np\nfrom sklearn.ensemble import ExtraTreesClassifier\n\nX = np.array([0, 1, 6, np.nan]).reshape(-1, 1)\ny = [0, 0, 1, 1]\n\nforest = ExtraTreesClassifier(random_state=0).fit(X, y)\nforest.predict(X)"
+        "import numpy as np\n\nfrom sklearn.ensemble import ExtraTreesClassifier\n\nX = np.array([0, 1, 6, np.nan]).reshape(-1, 1)\ny = [0, 0, 1, 1]\n\nforest = ExtraTreesClassifier(random_state=0).fit(X, y)\nforest.predict(X)"
       ]
     },
     {
@@ -116,7 +116,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/86c2bd1cbaae0fc61dd3ddfc8ccfe64e/plot_iterative_imputer_variants_comparison.zip b/dev/_downloads/86c2bd1cbaae0fc61dd3ddfc8ccfe64e/plot_iterative_imputer_variants_comparison.zip
index a35f8100cf51e..84b162989c8f8 100644
Binary files a/dev/_downloads/86c2bd1cbaae0fc61dd3ddfc8ccfe64e/plot_iterative_imputer_variants_comparison.zip and b/dev/_downloads/86c2bd1cbaae0fc61dd3ddfc8ccfe64e/plot_iterative_imputer_variants_comparison.zip differ
diff --git a/dev/_downloads/86c888008757148890daaf43d664fa71/plot_tweedie_regression_insurance_claims.py b/dev/_downloads/86c888008757148890daaf43d664fa71/plot_tweedie_regression_insurance_claims.py
index 3acc2b5f1472f..ea2365a71d48a 100644
--- a/dev/_downloads/86c888008757148890daaf43d664fa71/plot_tweedie_regression_insurance_claims.py
+++ b/dev/_downloads/86c888008757148890daaf43d664fa71/plot_tweedie_regression_insurance_claims.py
@@ -606,8 +606,9 @@ def score_estimator(
             "predicted, frequency*severity model": np.sum(
                 exposure * glm_freq.predict(X) * glm_sev.predict(X)
             ),
-            "predicted, tweedie, power=%.2f"
-            % glm_pure_premium.power: np.sum(exposure * glm_pure_premium.predict(X)),
+            "predicted, tweedie, power=%.2f" % glm_pure_premium.power: np.sum(
+                exposure * glm_pure_premium.predict(X)
+            ),
         }
     )
 
diff --git a/dev/_downloads/87939fa0b9f88b9ec889e5fc62f83214/plot_t_sne_perplexity.zip b/dev/_downloads/87939fa0b9f88b9ec889e5fc62f83214/plot_t_sne_perplexity.zip
index 38ff956490231..ed0c232f62bbc 100644
Binary files a/dev/_downloads/87939fa0b9f88b9ec889e5fc62f83214/plot_t_sne_perplexity.zip and b/dev/_downloads/87939fa0b9f88b9ec889e5fc62f83214/plot_t_sne_perplexity.zip differ
diff --git a/dev/_downloads/883c6b4b0cc369a10ddb336c09a9e4dd/plot_likelihood_ratios.py b/dev/_downloads/883c6b4b0cc369a10ddb336c09a9e4dd/plot_likelihood_ratios.py
index 24a8f2ef1759e..e4c1a6662ffa3 100644
--- a/dev/_downloads/883c6b4b0cc369a10ddb336c09a9e4dd/plot_likelihood_ratios.py
+++ b/dev/_downloads/883c6b4b0cc369a10ddb336c09a9e4dd/plot_likelihood_ratios.py
@@ -40,7 +40,7 @@ class proportion than the target application.
 from sklearn.datasets import make_classification
 
 X, y = make_classification(n_samples=10_000, weights=[0.9, 0.1], random_state=0)
-print(f"Percentage of people carrying the disease: {100*y.mean():.2f}%")
+print(f"Percentage of people carrying the disease: {100 * y.mean():.2f}%")
 
 # %%
 # A machine learning model is built to diagnose if a person with some given
diff --git a/dev/_downloads/898b30acf62919d918478efbe526195f/plot_digits_pipe.ipynb b/dev/_downloads/898b30acf62919d918478efbe526195f/plot_digits_pipe.ipynb
index 70a730d8b03ee..81a27096f8e30 100644
--- a/dev/_downloads/898b30acf62919d918478efbe526195f/plot_digits_pipe.ipynb
+++ b/dev/_downloads/898b30acf62919d918478efbe526195f/plot_digits_pipe.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/899c34960ade693d4b26e3b301f99416/plot_cluster_comparison.zip b/dev/_downloads/899c34960ade693d4b26e3b301f99416/plot_cluster_comparison.zip
index a2ef4e8f46bcc..d0cdc3c3d4ae8 100644
Binary files a/dev/_downloads/899c34960ade693d4b26e3b301f99416/plot_cluster_comparison.zip and b/dev/_downloads/899c34960ade693d4b26e3b301f99416/plot_cluster_comparison.zip differ
diff --git a/dev/_downloads/899ea87b7908a2db16ce24485576e5db/plot_train_error_vs_test_error.zip b/dev/_downloads/899ea87b7908a2db16ce24485576e5db/plot_train_error_vs_test_error.zip
index 2b1d7e68ddc6d..5f6241ff3a440 100644
Binary files a/dev/_downloads/899ea87b7908a2db16ce24485576e5db/plot_train_error_vs_test_error.zip and b/dev/_downloads/899ea87b7908a2db16ce24485576e5db/plot_train_error_vs_test_error.zip differ
diff --git a/dev/_downloads/89b02c3e866d0979d0024f34b90877de/plot_rfe_digits.ipynb b/dev/_downloads/89b02c3e866d0979d0024f34b90877de/plot_rfe_digits.ipynb
index 3ec77eed7c537..2ba9b30daaab7 100644
--- a/dev/_downloads/89b02c3e866d0979d0024f34b90877de/plot_rfe_digits.ipynb
+++ b/dev/_downloads/89b02c3e866d0979d0024f34b90877de/plot_rfe_digits.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/8b139be9c3b091b5f6bbd2651331dadc/plot_ridge_path.zip b/dev/_downloads/8b139be9c3b091b5f6bbd2651331dadc/plot_ridge_path.zip
index f86b95fc0dd2c..bcc6b6191f70e 100644
Binary files a/dev/_downloads/8b139be9c3b091b5f6bbd2651331dadc/plot_ridge_path.zip and b/dev/_downloads/8b139be9c3b091b5f6bbd2651331dadc/plot_ridge_path.zip differ
diff --git a/dev/_downloads/8b88532e3291dedc081d198e7ea23254/plot_nca_classification.zip b/dev/_downloads/8b88532e3291dedc081d198e7ea23254/plot_nca_classification.zip
index e78a0838c17b7..0c7a7d94bf5f7 100644
Binary files a/dev/_downloads/8b88532e3291dedc081d198e7ea23254/plot_nca_classification.zip and b/dev/_downloads/8b88532e3291dedc081d198e7ea23254/plot_nca_classification.zip differ
diff --git a/dev/_downloads/8b98cea0e0ec1ca3cc503c13ddac0537/plot_t_sne_perplexity.ipynb b/dev/_downloads/8b98cea0e0ec1ca3cc503c13ddac0537/plot_t_sne_perplexity.ipynb
index 3f8e85c923875..2712008f43ec2 100644
--- a/dev/_downloads/8b98cea0e0ec1ca3cc503c13ddac0537/plot_t_sne_perplexity.ipynb
+++ b/dev/_downloads/8b98cea0e0ec1ca3cc503c13ddac0537/plot_t_sne_perplexity.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/8bafbf478bc8e9392d50f1e4c9ce3c4e/plot_iris_svc.zip b/dev/_downloads/8bafbf478bc8e9392d50f1e4c9ce3c4e/plot_iris_svc.zip
index 7fec0344ecd56..acabb4f5d7d2c 100644
Binary files a/dev/_downloads/8bafbf478bc8e9392d50f1e4c9ce3c4e/plot_iris_svc.zip and b/dev/_downloads/8bafbf478bc8e9392d50f1e4c9ce3c4e/plot_iris_svc.zip differ
diff --git a/dev/_downloads/8c6c0b05c72467c8a5dd82b81fce1ce3/plot_frozen_examples.zip b/dev/_downloads/8c6c0b05c72467c8a5dd82b81fce1ce3/plot_frozen_examples.zip
index 19e648a08d644..bdae5b5da6490 100644
Binary files a/dev/_downloads/8c6c0b05c72467c8a5dd82b81fce1ce3/plot_frozen_examples.zip and b/dev/_downloads/8c6c0b05c72467c8a5dd82b81fce1ce3/plot_frozen_examples.zip differ
diff --git a/dev/_downloads/8c73025f63b2513cd701f6a378d77281/plot_elastic_net_precomputed_gram_matrix_with_weighted_samples.zip b/dev/_downloads/8c73025f63b2513cd701f6a378d77281/plot_elastic_net_precomputed_gram_matrix_with_weighted_samples.zip
index 7bd08b22244f8..6922e92e18d5f 100644
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diff --git a/dev/_downloads/8c94a38055d85a44378c1684b4c671ac/plot_all_scaling.zip b/dev/_downloads/8c94a38055d85a44378c1684b4c671ac/plot_all_scaling.zip
index aaf4c264e71a9..aa9fd2c121eeb 100644
Binary files a/dev/_downloads/8c94a38055d85a44378c1684b4c671ac/plot_all_scaling.zip and b/dev/_downloads/8c94a38055d85a44378c1684b4c671ac/plot_all_scaling.zip differ
diff --git a/dev/_downloads/8d09950dfaf03c48cddf217f4acf8b65/plot_bias_variance.ipynb b/dev/_downloads/8d09950dfaf03c48cddf217f4acf8b65/plot_bias_variance.ipynb
index 22dca1d0ba7c7..252d3e41d442b 100644
--- a/dev/_downloads/8d09950dfaf03c48cddf217f4acf8b65/plot_bias_variance.ipynb
+++ b/dev/_downloads/8d09950dfaf03c48cddf217f4acf8b65/plot_bias_variance.ipynb
@@ -15,7 +15,7 @@
       },
       "outputs": [],
       "source": [
-        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nfrom sklearn.ensemble import BaggingRegressor\nfrom sklearn.tree import DecisionTreeRegressor\n\n# Settings\nn_repeat = 50  # Number of iterations for computing expectations\nn_train = 50  # Size of the training set\nn_test = 1000  # Size of the test set\nnoise = 0.1  # Standard deviation of the noise\nnp.random.seed(0)\n\n# Change this for exploring the bias-variance decomposition of other\n# estimators. This should work well for estimators with high variance (e.g.,\n# decision trees or KNN), but poorly for estimators with low variance (e.g.,\n# linear models).\nestimators = [\n    (\"Tree\", DecisionTreeRegressor()),\n    (\"Bagging(Tree)\", BaggingRegressor(DecisionTreeRegressor())),\n]\n\nn_estimators = len(estimators)\n\n\n# Generate data\ndef f(x):\n    x = x.ravel()\n\n    return np.exp(-(x**2)) + 1.5 * np.exp(-((x - 2) ** 2))\n\n\ndef generate(n_samples, noise, n_repeat=1):\n    X = np.random.rand(n_samples) * 10 - 5\n    X = np.sort(X)\n\n    if n_repeat == 1:\n        y = f(X) + np.random.normal(0.0, noise, n_samples)\n    else:\n        y = np.zeros((n_samples, n_repeat))\n\n        for i in range(n_repeat):\n            y[:, i] = f(X) + np.random.normal(0.0, noise, n_samples)\n\n    X = X.reshape((n_samples, 1))\n\n    return X, y\n\n\nX_train = []\ny_train = []\n\nfor i in range(n_repeat):\n    X, y = generate(n_samples=n_train, noise=noise)\n    X_train.append(X)\n    y_train.append(y)\n\nX_test, y_test = generate(n_samples=n_test, noise=noise, n_repeat=n_repeat)\n\nplt.figure(figsize=(10, 8))\n\n# Loop over estimators to compare\nfor n, (name, estimator) in enumerate(estimators):\n    # Compute predictions\n    y_predict = np.zeros((n_test, n_repeat))\n\n    for i in range(n_repeat):\n        estimator.fit(X_train[i], y_train[i])\n        y_predict[:, i] = estimator.predict(X_test)\n\n    # Bias^2 + Variance + Noise decomposition of the mean squared error\n    y_error = np.zeros(n_test)\n\n    for i in range(n_repeat):\n        for j in range(n_repeat):\n            y_error += (y_test[:, j] - y_predict[:, i]) ** 2\n\n    y_error /= n_repeat * n_repeat\n\n    y_noise = np.var(y_test, axis=1)\n    y_bias = (f(X_test) - np.mean(y_predict, axis=1)) ** 2\n    y_var = np.var(y_predict, axis=1)\n\n    print(\n        \"{0}: {1:.4f} (error) = {2:.4f} (bias^2) \"\n        \" + {3:.4f} (var) + {4:.4f} (noise)\".format(\n            name, np.mean(y_error), np.mean(y_bias), np.mean(y_var), np.mean(y_noise)\n        )\n    )\n\n    # Plot figures\n    plt.subplot(2, n_estimators, n + 1)\n    plt.plot(X_test, f(X_test), \"b\", label=\"$f(x)$\")\n    plt.plot(X_train[0], y_train[0], \".b\", label=\"LS ~ $y = f(x)+noise$\")\n\n    for i in range(n_repeat):\n        if i == 0:\n            plt.plot(X_test, y_predict[:, i], \"r\", label=r\"$\\^y(x)$\")\n        else:\n            plt.plot(X_test, y_predict[:, i], \"r\", alpha=0.05)\n\n    plt.plot(X_test, np.mean(y_predict, axis=1), \"c\", label=r\"$\\mathbb{E}_{LS} \\^y(x)$\")\n\n    plt.xlim([-5, 5])\n    plt.title(name)\n\n    if n == n_estimators - 1:\n        plt.legend(loc=(1.1, 0.5))\n\n    plt.subplot(2, n_estimators, n_estimators + n + 1)\n    plt.plot(X_test, y_error, \"r\", label=\"$error(x)$\")\n    plt.plot(X_test, y_bias, \"b\", label=\"$bias^2(x)$\"),\n    plt.plot(X_test, y_var, \"g\", label=\"$variance(x)$\"),\n    plt.plot(X_test, y_noise, \"c\", label=\"$noise(x)$\")\n\n    plt.xlim([-5, 5])\n    plt.ylim([0, 0.1])\n\n    if n == n_estimators - 1:\n        plt.legend(loc=(1.1, 0.5))\n\nplt.subplots_adjust(right=0.75)\nplt.show()"
+        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nfrom sklearn.ensemble import BaggingRegressor\nfrom sklearn.tree import DecisionTreeRegressor\n\n# Settings\nn_repeat = 50  # Number of iterations for computing expectations\nn_train = 50  # Size of the training set\nn_test = 1000  # Size of the test set\nnoise = 0.1  # Standard deviation of the noise\nnp.random.seed(0)\n\n# Change this for exploring the bias-variance decomposition of other\n# estimators. This should work well for estimators with high variance (e.g.,\n# decision trees or KNN), but poorly for estimators with low variance (e.g.,\n# linear models).\nestimators = [\n    (\"Tree\", DecisionTreeRegressor()),\n    (\"Bagging(Tree)\", BaggingRegressor(DecisionTreeRegressor())),\n]\n\nn_estimators = len(estimators)\n\n\n# Generate data\ndef f(x):\n    x = x.ravel()\n\n    return np.exp(-(x**2)) + 1.5 * np.exp(-((x - 2) ** 2))\n\n\ndef generate(n_samples, noise, n_repeat=1):\n    X = np.random.rand(n_samples) * 10 - 5\n    X = np.sort(X)\n\n    if n_repeat == 1:\n        y = f(X) + np.random.normal(0.0, noise, n_samples)\n    else:\n        y = np.zeros((n_samples, n_repeat))\n\n        for i in range(n_repeat):\n            y[:, i] = f(X) + np.random.normal(0.0, noise, n_samples)\n\n    X = X.reshape((n_samples, 1))\n\n    return X, y\n\n\nX_train = []\ny_train = []\n\nfor i in range(n_repeat):\n    X, y = generate(n_samples=n_train, noise=noise)\n    X_train.append(X)\n    y_train.append(y)\n\nX_test, y_test = generate(n_samples=n_test, noise=noise, n_repeat=n_repeat)\n\nplt.figure(figsize=(10, 8))\n\n# Loop over estimators to compare\nfor n, (name, estimator) in enumerate(estimators):\n    # Compute predictions\n    y_predict = np.zeros((n_test, n_repeat))\n\n    for i in range(n_repeat):\n        estimator.fit(X_train[i], y_train[i])\n        y_predict[:, i] = estimator.predict(X_test)\n\n    # Bias^2 + Variance + Noise decomposition of the mean squared error\n    y_error = np.zeros(n_test)\n\n    for i in range(n_repeat):\n        for j in range(n_repeat):\n            y_error += (y_test[:, j] - y_predict[:, i]) ** 2\n\n    y_error /= n_repeat * n_repeat\n\n    y_noise = np.var(y_test, axis=1)\n    y_bias = (f(X_test) - np.mean(y_predict, axis=1)) ** 2\n    y_var = np.var(y_predict, axis=1)\n\n    print(\n        \"{0}: {1:.4f} (error) = {2:.4f} (bias^2) \"\n        \" + {3:.4f} (var) + {4:.4f} (noise)\".format(\n            name, np.mean(y_error), np.mean(y_bias), np.mean(y_var), np.mean(y_noise)\n        )\n    )\n\n    # Plot figures\n    plt.subplot(2, n_estimators, n + 1)\n    plt.plot(X_test, f(X_test), \"b\", label=\"$f(x)$\")\n    plt.plot(X_train[0], y_train[0], \".b\", label=\"LS ~ $y = f(x)+noise$\")\n\n    for i in range(n_repeat):\n        if i == 0:\n            plt.plot(X_test, y_predict[:, i], \"r\", label=r\"$\\^y(x)$\")\n        else:\n            plt.plot(X_test, y_predict[:, i], \"r\", alpha=0.05)\n\n    plt.plot(X_test, np.mean(y_predict, axis=1), \"c\", label=r\"$\\mathbb{E}_{LS} \\^y(x)$\")\n\n    plt.xlim([-5, 5])\n    plt.title(name)\n\n    if n == n_estimators - 1:\n        plt.legend(loc=(1.1, 0.5))\n\n    plt.subplot(2, n_estimators, n_estimators + n + 1)\n    plt.plot(X_test, y_error, \"r\", label=\"$error(x)$\")\n    plt.plot(X_test, y_bias, \"b\", label=\"$bias^2(x)$\")\n    plt.plot(X_test, y_var, \"g\", label=\"$variance(x)$\")\n    plt.plot(X_test, y_noise, \"c\", label=\"$noise(x)$\")\n\n    plt.xlim([-5, 5])\n    plt.ylim([0, 0.1])\n\n    if n == n_estimators - 1:\n        plt.legend(loc=(1.1, 0.5))\n\nplt.subplots_adjust(right=0.75)\nplt.show()"
       ]
     }
   ],
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/8daa075623a97eefa5be2c4a6eb55992/plot_regression.ipynb b/dev/_downloads/8daa075623a97eefa5be2c4a6eb55992/plot_regression.ipynb
index 2e9e2b3c35cbf..5a281c0af5005 100644
--- a/dev/_downloads/8daa075623a97eefa5be2c4a6eb55992/plot_regression.ipynb
+++ b/dev/_downloads/8daa075623a97eefa5be2c4a6eb55992/plot_regression.ipynb
@@ -71,7 +71,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/8ed2480d8058dc8515097893b64d815b/plot_permutation_tests_for_classification.ipynb b/dev/_downloads/8ed2480d8058dc8515097893b64d815b/plot_permutation_tests_for_classification.ipynb
index 0778b36d4a72d..e372bb3fe2c05 100644
--- a/dev/_downloads/8ed2480d8058dc8515097893b64d815b/plot_permutation_tests_for_classification.ipynb
+++ b/dev/_downloads/8ed2480d8058dc8515097893b64d815b/plot_permutation_tests_for_classification.ipynb
@@ -132,7 +132,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/8f282611d5fe24935e33b3c53d30ab1b/plot_multi_task_lasso_support.zip b/dev/_downloads/8f282611d5fe24935e33b3c53d30ab1b/plot_multi_task_lasso_support.zip
index 272cd0d0270b1..1e183a3cee8ba 100644
Binary files a/dev/_downloads/8f282611d5fe24935e33b3c53d30ab1b/plot_multi_task_lasso_support.zip and b/dev/_downloads/8f282611d5fe24935e33b3c53d30ab1b/plot_multi_task_lasso_support.zip differ
diff --git a/dev/_downloads/901ad159d788e1122a5616f537985e74/plot_lasso_model_selection.ipynb b/dev/_downloads/901ad159d788e1122a5616f537985e74/plot_lasso_model_selection.ipynb
index c0844da11c9e4..f516486912718 100644
--- a/dev/_downloads/901ad159d788e1122a5616f537985e74/plot_lasso_model_selection.ipynb
+++ b/dev/_downloads/901ad159d788e1122a5616f537985e74/plot_lasso_model_selection.ipynb
@@ -226,7 +226,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/905bd7d135e7fe5fdec55e4f0aa77420/plot_voting_decision_regions.ipynb b/dev/_downloads/905bd7d135e7fe5fdec55e4f0aa77420/plot_voting_decision_regions.ipynb
index a1efc7c4e28bf..4de6921bd8bfd 100644
--- a/dev/_downloads/905bd7d135e7fe5fdec55e4f0aa77420/plot_voting_decision_regions.ipynb
+++ b/dev/_downloads/905bd7d135e7fe5fdec55e4f0aa77420/plot_voting_decision_regions.ipynb
@@ -4,7 +4,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# Plot the decision boundaries of a VotingClassifier\n\n.. currentmodule:: sklearn\n\nPlot the decision boundaries of a :class:`~ensemble.VotingClassifier` for two\nfeatures of the Iris dataset.\n\nPlot the class probabilities of the first sample in a toy dataset predicted by\nthree different classifiers and averaged by the\n:class:`~ensemble.VotingClassifier`.\n\nFirst, three exemplary classifiers are initialized\n(:class:`~tree.DecisionTreeClassifier`,\n:class:`~neighbors.KNeighborsClassifier`, and :class:`~svm.SVC`) and used to\ninitialize a soft-voting :class:`~ensemble.VotingClassifier` with weights `[2,\n1, 2]`, which means that the predicted probabilities of the\n:class:`~tree.DecisionTreeClassifier` and :class:`~svm.SVC` each count 2 times\nas much as the weights of the :class:`~neighbors.KNeighborsClassifier`\nclassifier when the averaged probability is calculated.\n"
+        "\n# Visualizing the probabilistic predictions of a VotingClassifier\n\n.. currentmodule:: sklearn\n\nPlot the predicted class probabilities in a toy dataset predicted by three\ndifferent classifiers and averaged by the :class:`~ensemble.VotingClassifier`.\n\nFirst, three linear classifiers are initialized. Two are spline models with\ninteraction terms, one using constant extrapolation and the other using periodic\nextrapolation. The third classifier is a :class:`~kernel_approximation.Nystroem`\nwith the default \"rbf\" kernel.\n\nIn the first part of this example, these three classifiers are used to\ndemonstrate soft-voting using :class:`~ensemble.VotingClassifier` with weighted\naverage. We set `weights=[2, 1, 3]`, meaning the constant extrapolation spline\nmodel's predictions are weighted twice as much as the periodic spline model's,\nand the Nystroem model's predictions are weighted three times as much as the\nperiodic spline.\n\nThe second part demonstrates how soft predictions can be converted into hard\npredictions.\n"
       ]
     },
     {
@@ -15,7 +15,162 @@
       },
       "outputs": [],
       "source": [
-        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\n\nfrom itertools import product\n\nimport matplotlib.pyplot as plt\n\nfrom sklearn import datasets\nfrom sklearn.ensemble import VotingClassifier\nfrom sklearn.inspection import DecisionBoundaryDisplay\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.svm import SVC\nfrom sklearn.tree import DecisionTreeClassifier\n\n# Loading some example data\niris = datasets.load_iris()\nX = iris.data[:, [0, 2]]\ny = iris.target\n\n# Training classifiers\nclf1 = DecisionTreeClassifier(max_depth=4)\nclf2 = KNeighborsClassifier(n_neighbors=7)\nclf3 = SVC(gamma=0.1, kernel=\"rbf\", probability=True)\neclf = VotingClassifier(\n    estimators=[(\"dt\", clf1), (\"knn\", clf2), (\"svc\", clf3)],\n    voting=\"soft\",\n    weights=[2, 1, 2],\n)\n\nclf1.fit(X, y)\nclf2.fit(X, y)\nclf3.fit(X, y)\neclf.fit(X, y)\n\n# Plotting decision regions\nf, axarr = plt.subplots(2, 2, sharex=\"col\", sharey=\"row\", figsize=(10, 8))\nfor idx, clf, tt in zip(\n    product([0, 1], [0, 1]),\n    [clf1, clf2, clf3, eclf],\n    [\"Decision Tree (depth=4)\", \"KNN (k=7)\", \"Kernel SVM\", \"Soft Voting\"],\n):\n    DecisionBoundaryDisplay.from_estimator(\n        clf, X, alpha=0.4, ax=axarr[idx[0], idx[1]], response_method=\"predict\"\n    )\n    axarr[idx[0], idx[1]].scatter(X[:, 0], X[:, 1], c=y, s=20, edgecolor=\"k\")\n    axarr[idx[0], idx[1]].set_title(tt)\n\nplt.show()"
+        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We first generate a noisy XOR dataset, which is a binary classification task.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nfrom matplotlib.colors import ListedColormap\n\nn_samples = 500\nrng = np.random.default_rng(0)\nfeature_names = [\"Feature #0\", \"Feature #1\"]\ncommon_scatter_plot_params = dict(\n    cmap=ListedColormap([\"tab:red\", \"tab:blue\"]),\n    edgecolor=\"white\",\n    linewidth=1,\n)\n\nxor = pd.DataFrame(\n    np.random.RandomState(0).uniform(low=-1, high=1, size=(n_samples, 2)),\n    columns=feature_names,\n)\nnoise = rng.normal(loc=0, scale=0.1, size=(n_samples, 2))\ntarget_xor = np.logical_xor(\n    xor[\"Feature #0\"] + noise[:, 0] > 0, xor[\"Feature #1\"] + noise[:, 1] > 0\n)\n\nX = xor[feature_names]\ny = target_xor.astype(np.int32)\n\nfig, ax = plt.subplots()\nax.scatter(X[\"Feature #0\"], X[\"Feature #1\"], c=y, **common_scatter_plot_params)\nax.set_title(\"The XOR dataset\")\nplt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Due to the inherent non-linear separability of the XOR dataset, tree-based\nmodels would often be preferred. However, appropriate feature engineering\ncombined with a linear model can yield effective results, with the added\nbenefit of producing better-calibrated probabilities for samples located in\nthe transition regions affected by noise.\n\nWe define and fit the models on the whole dataset.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn.ensemble import VotingClassifier\nfrom sklearn.kernel_approximation import Nystroem\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.pipeline import make_pipeline\nfrom sklearn.preprocessing import PolynomialFeatures, SplineTransformer, StandardScaler\n\nclf1 = make_pipeline(\n    SplineTransformer(degree=2, n_knots=2),\n    PolynomialFeatures(interaction_only=True),\n    LogisticRegression(C=10),\n)\nclf2 = make_pipeline(\n    SplineTransformer(\n        degree=2,\n        n_knots=4,\n        extrapolation=\"periodic\",\n        include_bias=True,\n    ),\n    PolynomialFeatures(interaction_only=True),\n    LogisticRegression(C=10),\n)\nclf3 = make_pipeline(\n    StandardScaler(),\n    Nystroem(gamma=2, random_state=0),\n    LogisticRegression(C=10),\n)\nweights = [2, 1, 3]\neclf = VotingClassifier(\n    estimators=[\n        (\"constant splines model\", clf1),\n        (\"periodic splines model\", clf2),\n        (\"nystroem model\", clf3),\n    ],\n    voting=\"soft\",\n    weights=weights,\n)\n\nclf1.fit(X, y)\nclf2.fit(X, y)\nclf3.fit(X, y)\neclf.fit(X, y)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Finally we use :class:`~inspection.DecisionBoundaryDisplay` to plot the\npredicted probabilities. By using a diverging colormap (such as `\"RdBu\"`), we\ncan ensure that darker colors correspond to `predict_proba` close to either 0\nor 1, and white corresponds to `predict_proba` of 0.5.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from itertools import product\n\nfrom sklearn.inspection import DecisionBoundaryDisplay\n\nfig, axarr = plt.subplots(2, 2, sharex=\"col\", sharey=\"row\", figsize=(10, 8))\nfor idx, clf, title in zip(\n    product([0, 1], [0, 1]),\n    [clf1, clf2, clf3, eclf],\n    [\n        \"Splines with\\nconstant extrapolation\",\n        \"Splines with\\nperiodic extrapolation\",\n        \"RBF Nystroem\",\n        \"Soft Voting\",\n    ],\n):\n    disp = DecisionBoundaryDisplay.from_estimator(\n        clf,\n        X,\n        response_method=\"predict_proba\",\n        plot_method=\"pcolormesh\",\n        cmap=\"RdBu\",\n        alpha=0.8,\n        ax=axarr[idx[0], idx[1]],\n    )\n    axarr[idx[0], idx[1]].scatter(\n        X[\"Feature #0\"],\n        X[\"Feature #1\"],\n        c=y,\n        **common_scatter_plot_params,\n    )\n    axarr[idx[0], idx[1]].set_title(title)\n    fig.colorbar(disp.surface_, ax=axarr[idx[0], idx[1]], label=\"Probability estimate\")\n\nplt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "As a sanity check, we can verify for a given sample that the probability\npredicted by the :class:`~ensemble.VotingClassifier` is indeed the weighted\naverage of the individual classifiers' soft-predictions.\n\nIn the case of binary classification such as in the present example, the\n:term:`predict_proba` arrays contain the probability of belonging to class 0\n(here in red) as the first entry, and the probability of belonging to class 1\n(here in blue) as the second entry.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "test_sample = pd.DataFrame({\"Feature #0\": [-0.5], \"Feature #1\": [1.5]})\npredict_probas = [est.predict_proba(test_sample).ravel() for est in eclf.estimators_]\nfor (est_name, _), est_probas in zip(eclf.estimators, predict_probas):\n    print(f\"{est_name}'s predicted probabilities: {est_probas}\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "print(\n    \"Weighted average of soft-predictions: \"\n    f\"{np.dot(weights, predict_probas) / np.sum(weights)}\"\n)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We can see that manual calculation of predicted probabilities above is\nequivalent to that produced by the `VotingClassifier`:\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "print(\n    \"Predicted probability of VotingClassifier: \"\n    f\"{eclf.predict_proba(test_sample).ravel()}\"\n)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "To convert soft predictions into hard predictions when weights are provided,\nthe weighted average predicted probabilities are computed for each class.\nThen, the final class label is then derived from the class label with the\nhighest average probability, which corresponds to the default threshold at\n`predict_proba=0.5` in the case of binary classification.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "print(\n    \"Class with the highest weighted average of soft-predictions: \"\n    f\"{np.argmax(np.dot(weights, predict_probas) / np.sum(weights))}\"\n)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "This is equivalent to the output of `VotingClassifier`'s `predict` method:\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "print(f\"Predicted class of VotingClassifier: {eclf.predict(test_sample).ravel()}\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Soft votes can be thresholded as for any other probabilistic classifier. This\nallows you to set a threshold probability at which the positive class will be\npredicted, instead of simply selecting the class with the highest predicted\nprobability.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn.model_selection import FixedThresholdClassifier\n\neclf_other_threshold = FixedThresholdClassifier(\n    eclf, threshold=0.7, response_method=\"predict_proba\"\n).fit(X, y)\nprint(\n    \"Predicted class of thresholded VotingClassifier: \"\n    f\"{eclf_other_threshold.predict(test_sample)}\"\n)"
       ]
     }
   ],
@@ -35,7 +190,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/90cf257940769245392cef28e5ab05b4/plot_lasso_and_elasticnet.zip b/dev/_downloads/90cf257940769245392cef28e5ab05b4/plot_lasso_and_elasticnet.zip
index 49f8f15d97242..774579b48ee7c 100644
Binary files a/dev/_downloads/90cf257940769245392cef28e5ab05b4/plot_lasso_and_elasticnet.zip and b/dev/_downloads/90cf257940769245392cef28e5ab05b4/plot_lasso_and_elasticnet.zip differ
diff --git a/dev/_downloads/917c94a6c4500bbf4185faeba5504398/plot_multiclass_overview.ipynb b/dev/_downloads/917c94a6c4500bbf4185faeba5504398/plot_multiclass_overview.ipynb
index 462b6df6b3fa8..065f2a6bd6a95 100644
--- a/dev/_downloads/917c94a6c4500bbf4185faeba5504398/plot_multiclass_overview.ipynb
+++ b/dev/_downloads/917c94a6c4500bbf4185faeba5504398/plot_multiclass_overview.ipynb
@@ -132,7 +132,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/91999ecc168932f9034d0cbc1cc248fa/plot_affinity_propagation.ipynb b/dev/_downloads/91999ecc168932f9034d0cbc1cc248fa/plot_affinity_propagation.ipynb
index f5f23adb30719..8d0774c4d4616 100644
--- a/dev/_downloads/91999ecc168932f9034d0cbc1cc248fa/plot_affinity_propagation.ipynb
+++ b/dev/_downloads/91999ecc168932f9034d0cbc1cc248fa/plot_affinity_propagation.ipynb
@@ -89,7 +89,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/91a0c94f9f7c19d59a0ad06e77512326/plot_gpr_co2.ipynb b/dev/_downloads/91a0c94f9f7c19d59a0ad06e77512326/plot_gpr_co2.ipynb
index 09ef49e027c0a..ffd99959744bb 100644
--- a/dev/_downloads/91a0c94f9f7c19d59a0ad06e77512326/plot_gpr_co2.ipynb
+++ b/dev/_downloads/91a0c94f9f7c19d59a0ad06e77512326/plot_gpr_co2.ipynb
@@ -298,7 +298,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/9235188b9f885ba3542bf0529565fdfe/plot_gpr_noisy.ipynb b/dev/_downloads/9235188b9f885ba3542bf0529565fdfe/plot_gpr_noisy.ipynb
index ba2a1aa410a21..725c8cd96ada3 100644
--- a/dev/_downloads/9235188b9f885ba3542bf0529565fdfe/plot_gpr_noisy.ipynb
+++ b/dev/_downloads/9235188b9f885ba3542bf0529565fdfe/plot_gpr_noisy.ipynb
@@ -234,7 +234,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/923fcad5e07de1ce7dc8dcbd7327c178/plot_release_highlights_0_23_0.ipynb b/dev/_downloads/923fcad5e07de1ce7dc8dcbd7327c178/plot_release_highlights_0_23_0.ipynb
index b02b7917a0bd0..acad9f2f57858 100644
--- a/dev/_downloads/923fcad5e07de1ce7dc8dcbd7327c178/plot_release_highlights_0_23_0.ipynb
+++ b/dev/_downloads/923fcad5e07de1ce7dc8dcbd7327c178/plot_release_highlights_0_23_0.ipynb
@@ -22,7 +22,7 @@
       },
       "outputs": [],
       "source": [
-        "import numpy as np\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.linear_model import PoissonRegressor\nfrom sklearn.ensemble import HistGradientBoostingRegressor\n\nn_samples, n_features = 1000, 20\nrng = np.random.RandomState(0)\nX = rng.randn(n_samples, n_features)\n# positive integer target correlated with X[:, 5] with many zeros:\ny = rng.poisson(lam=np.exp(X[:, 5]) / 2)\nX_train, X_test, y_train, y_test = train_test_split(X, y, random_state=rng)\nglm = PoissonRegressor()\ngbdt = HistGradientBoostingRegressor(loss=\"poisson\", learning_rate=0.01)\nglm.fit(X_train, y_train)\ngbdt.fit(X_train, y_train)\nprint(glm.score(X_test, y_test))\nprint(gbdt.score(X_test, y_test))"
+        "import numpy as np\n\nfrom sklearn.ensemble import HistGradientBoostingRegressor\nfrom sklearn.linear_model import PoissonRegressor\nfrom sklearn.model_selection import train_test_split\n\nn_samples, n_features = 1000, 20\nrng = np.random.RandomState(0)\nX = rng.randn(n_samples, n_features)\n# positive integer target correlated with X[:, 5] with many zeros:\ny = rng.poisson(lam=np.exp(X[:, 5]) / 2)\nX_train, X_test, y_train, y_test = train_test_split(X, y, random_state=rng)\nglm = PoissonRegressor()\ngbdt = HistGradientBoostingRegressor(loss=\"poisson\", learning_rate=0.01)\nglm.fit(X_train, y_train)\ngbdt.fit(X_train, y_train)\nprint(glm.score(X_test, y_test))\nprint(gbdt.score(X_test, y_test))"
       ]
     },
     {
@@ -40,7 +40,7 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn import set_config\nfrom sklearn.pipeline import make_pipeline\nfrom sklearn.preprocessing import OneHotEncoder, StandardScaler\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.compose import make_column_transformer\nfrom sklearn.linear_model import LogisticRegression\n\nset_config(display=\"diagram\")\n\nnum_proc = make_pipeline(SimpleImputer(strategy=\"median\"), StandardScaler())\n\ncat_proc = make_pipeline(\n    SimpleImputer(strategy=\"constant\", fill_value=\"missing\"),\n    OneHotEncoder(handle_unknown=\"ignore\"),\n)\n\npreprocessor = make_column_transformer(\n    (num_proc, (\"feat1\", \"feat3\")), (cat_proc, (\"feat0\", \"feat2\"))\n)\n\nclf = make_pipeline(preprocessor, LogisticRegression())\nclf"
+        "from sklearn import set_config\nfrom sklearn.compose import make_column_transformer\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.pipeline import make_pipeline\nfrom sklearn.preprocessing import OneHotEncoder, StandardScaler\n\nset_config(display=\"diagram\")\n\nnum_proc = make_pipeline(SimpleImputer(strategy=\"median\"), StandardScaler())\n\ncat_proc = make_pipeline(\n    SimpleImputer(strategy=\"constant\", fill_value=\"missing\"),\n    OneHotEncoder(handle_unknown=\"ignore\"),\n)\n\npreprocessor = make_column_transformer(\n    (num_proc, (\"feat1\", \"feat3\")), (cat_proc, (\"feat0\", \"feat2\"))\n)\n\nclf = make_pipeline(preprocessor, LogisticRegression())\nclf"
       ]
     },
     {
@@ -58,7 +58,7 @@
       },
       "outputs": [],
       "source": [
-        "import scipy\nimport numpy as np\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.cluster import KMeans\nfrom sklearn.datasets import make_blobs\nfrom sklearn.metrics import completeness_score\n\nrng = np.random.RandomState(0)\nX, y = make_blobs(random_state=rng)\nX = scipy.sparse.csr_matrix(X)\nX_train, X_test, _, y_test = train_test_split(X, y, random_state=rng)\nkmeans = KMeans(n_init=\"auto\").fit(X_train)\nprint(completeness_score(kmeans.predict(X_test), y_test))"
+        "import numpy as np\nimport scipy\n\nfrom sklearn.cluster import KMeans\nfrom sklearn.datasets import make_blobs\nfrom sklearn.metrics import completeness_score\nfrom sklearn.model_selection import train_test_split\n\nrng = np.random.RandomState(0)\nX, y = make_blobs(random_state=rng)\nX = scipy.sparse.csr_matrix(X)\nX_train, X_test, _, y_test = train_test_split(X, y, random_state=rng)\nkmeans = KMeans(n_init=\"auto\").fit(X_train)\nprint(completeness_score(kmeans.predict(X_test), y_test))"
       ]
     },
     {
@@ -76,7 +76,7 @@
       },
       "outputs": [],
       "source": [
-        "import numpy as np\nfrom matplotlib import pyplot as plt\nfrom sklearn.model_selection import train_test_split\n\n# from sklearn.inspection import plot_partial_dependence\nfrom sklearn.inspection import PartialDependenceDisplay\nfrom sklearn.ensemble import HistGradientBoostingRegressor\n\nn_samples = 500\nrng = np.random.RandomState(0)\nX = rng.randn(n_samples, 2)\nnoise = rng.normal(loc=0.0, scale=0.01, size=n_samples)\ny = 5 * X[:, 0] + np.sin(10 * np.pi * X[:, 0]) - noise\n\ngbdt_no_cst = HistGradientBoostingRegressor().fit(X, y)\ngbdt_cst = HistGradientBoostingRegressor(monotonic_cst=[1, 0]).fit(X, y)\n\n# plot_partial_dependence has been removed in version 1.2. From 1.2, use\n# PartialDependenceDisplay instead.\n# disp = plot_partial_dependence(\ndisp = PartialDependenceDisplay.from_estimator(\n    gbdt_no_cst,\n    X,\n    features=[0],\n    feature_names=[\"feature 0\"],\n    line_kw={\"linewidth\": 4, \"label\": \"unconstrained\", \"color\": \"tab:blue\"},\n)\n# plot_partial_dependence(\nPartialDependenceDisplay.from_estimator(\n    gbdt_cst,\n    X,\n    features=[0],\n    line_kw={\"linewidth\": 4, \"label\": \"constrained\", \"color\": \"tab:orange\"},\n    ax=disp.axes_,\n)\ndisp.axes_[0, 0].plot(\n    X[:, 0], y, \"o\", alpha=0.5, zorder=-1, label=\"samples\", color=\"tab:green\"\n)\ndisp.axes_[0, 0].set_ylim(-3, 3)\ndisp.axes_[0, 0].set_xlim(-1, 1)\nplt.legend()\nplt.show()"
+        "import numpy as np\nfrom matplotlib import pyplot as plt\n\nfrom sklearn.ensemble import HistGradientBoostingRegressor\n\n# from sklearn.inspection import plot_partial_dependence\nfrom sklearn.inspection import PartialDependenceDisplay\nfrom sklearn.model_selection import train_test_split\n\nn_samples = 500\nrng = np.random.RandomState(0)\nX = rng.randn(n_samples, 2)\nnoise = rng.normal(loc=0.0, scale=0.01, size=n_samples)\ny = 5 * X[:, 0] + np.sin(10 * np.pi * X[:, 0]) - noise\n\ngbdt_no_cst = HistGradientBoostingRegressor().fit(X, y)\ngbdt_cst = HistGradientBoostingRegressor(monotonic_cst=[1, 0]).fit(X, y)\n\n# plot_partial_dependence has been removed in version 1.2. From 1.2, use\n# PartialDependenceDisplay instead.\n# disp = plot_partial_dependence(\ndisp = PartialDependenceDisplay.from_estimator(\n    gbdt_no_cst,\n    X,\n    features=[0],\n    feature_names=[\"feature 0\"],\n    line_kw={\"linewidth\": 4, \"label\": \"unconstrained\", \"color\": \"tab:blue\"},\n)\n# plot_partial_dependence(\nPartialDependenceDisplay.from_estimator(\n    gbdt_cst,\n    X,\n    features=[0],\n    line_kw={\"linewidth\": 4, \"label\": \"constrained\", \"color\": \"tab:orange\"},\n    ax=disp.axes_,\n)\ndisp.axes_[0, 0].plot(\n    X[:, 0], y, \"o\", alpha=0.5, zorder=-1, label=\"samples\", color=\"tab:green\"\n)\ndisp.axes_[0, 0].set_ylim(-3, 3)\ndisp.axes_[0, 0].set_xlim(-1, 1)\nplt.legend()\nplt.show()"
       ]
     },
     {
@@ -94,7 +94,7 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.model_selection import train_test_split\nfrom sklearn.datasets import make_regression\nfrom sklearn.linear_model import Lasso\nimport numpy as np\n\nn_samples, n_features = 1000, 20\nrng = np.random.RandomState(0)\nX, y = make_regression(n_samples, n_features, random_state=rng)\nsample_weight = rng.rand(n_samples)\nX_train, X_test, y_train, y_test, sw_train, sw_test = train_test_split(\n    X, y, sample_weight, random_state=rng\n)\nreg = Lasso()\nreg.fit(X_train, y_train, sample_weight=sw_train)\nprint(reg.score(X_test, y_test, sw_test))"
+        "import numpy as np\n\nfrom sklearn.datasets import make_regression\nfrom sklearn.linear_model import Lasso\nfrom sklearn.model_selection import train_test_split\n\nn_samples, n_features = 1000, 20\nrng = np.random.RandomState(0)\nX, y = make_regression(n_samples, n_features, random_state=rng)\nsample_weight = rng.rand(n_samples)\nX_train, X_test, y_train, y_test, sw_train, sw_test = train_test_split(\n    X, y, sample_weight, random_state=rng\n)\nreg = Lasso()\nreg.fit(X_train, y_train, sample_weight=sw_train)\nprint(reg.score(X_test, y_test, sw_test))"
       ]
     }
   ],
@@ -114,7 +114,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/92ee804b84e076734477cbf10677d350/plot_forest_iris.zip b/dev/_downloads/92ee804b84e076734477cbf10677d350/plot_forest_iris.zip
index 353eeeccda046..bbcb69cd8cb9c 100644
Binary files a/dev/_downloads/92ee804b84e076734477cbf10677d350/plot_forest_iris.zip and b/dev/_downloads/92ee804b84e076734477cbf10677d350/plot_forest_iris.zip differ
diff --git a/dev/_downloads/93afb18fff95760fe854241141b4bdae/plot_cv_predict.ipynb b/dev/_downloads/93afb18fff95760fe854241141b4bdae/plot_cv_predict.ipynb
index 2d4256a88278b..866f49b888b68 100644
--- a/dev/_downloads/93afb18fff95760fe854241141b4bdae/plot_cv_predict.ipynb
+++ b/dev/_downloads/93afb18fff95760fe854241141b4bdae/plot_cv_predict.ipynb
@@ -96,7 +96,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/93cd12369459b2e432d0a2665e19ef8a/plot_voting_probas.py b/dev/_downloads/93cd12369459b2e432d0a2665e19ef8a/plot_voting_probas.py
deleted file mode 100644
index 848358ca1d208..0000000000000
--- a/dev/_downloads/93cd12369459b2e432d0a2665e19ef8a/plot_voting_probas.py
+++ /dev/null
@@ -1,97 +0,0 @@
-"""
-===========================================================
-Plot class probabilities calculated by the VotingClassifier
-===========================================================
-
-.. currentmodule:: sklearn
-
-Plot the class probabilities of the first sample in a toy dataset predicted by
-three different classifiers and averaged by the
-:class:`~ensemble.VotingClassifier`.
-
-First, three exemplary classifiers are initialized
-(:class:`~linear_model.LogisticRegression`, :class:`~naive_bayes.GaussianNB`,
-and :class:`~ensemble.RandomForestClassifier`) and used to initialize a
-soft-voting :class:`~ensemble.VotingClassifier` with weights `[1, 1, 5]`, which
-means that the predicted probabilities of the
-:class:`~ensemble.RandomForestClassifier` count 5 times as much as the weights
-of the other classifiers when the averaged probability is calculated.
-
-To visualize the probability weighting, we fit each classifier on the training
-set and plot the predicted class probabilities for the first sample in this
-example dataset.
-
-"""
-
-# Authors: The scikit-learn developers
-# SPDX-License-Identifier: BSD-3-Clause
-
-import matplotlib.pyplot as plt
-import numpy as np
-
-from sklearn.ensemble import RandomForestClassifier, VotingClassifier
-from sklearn.linear_model import LogisticRegression
-from sklearn.naive_bayes import GaussianNB
-
-clf1 = LogisticRegression(max_iter=1000, random_state=123)
-clf2 = RandomForestClassifier(n_estimators=100, random_state=123)
-clf3 = GaussianNB()
-X = np.array([[-1.0, -1.0], [-1.2, -1.4], [-3.4, -2.2], [1.1, 1.2]])
-y = np.array([1, 1, 2, 2])
-
-eclf = VotingClassifier(
-    estimators=[("lr", clf1), ("rf", clf2), ("gnb", clf3)],
-    voting="soft",
-    weights=[1, 1, 5],
-)
-
-# predict class probabilities for all classifiers
-probas = [c.fit(X, y).predict_proba(X) for c in (clf1, clf2, clf3, eclf)]
-
-# get class probabilities for the first sample in the dataset
-class1_1 = [pr[0, 0] for pr in probas]
-class2_1 = [pr[0, 1] for pr in probas]
-
-
-# plotting
-
-N = 4  # number of groups
-ind = np.arange(N)  # group positions
-width = 0.35  # bar width
-
-fig, ax = plt.subplots()
-
-# bars for classifier 1-3
-p1 = ax.bar(ind, np.hstack(([class1_1[:-1], [0]])), width, color="green", edgecolor="k")
-p2 = ax.bar(
-    ind + width,
-    np.hstack(([class2_1[:-1], [0]])),
-    width,
-    color="lightgreen",
-    edgecolor="k",
-)
-
-# bars for VotingClassifier
-p3 = ax.bar(ind, [0, 0, 0, class1_1[-1]], width, color="blue", edgecolor="k")
-p4 = ax.bar(
-    ind + width, [0, 0, 0, class2_1[-1]], width, color="steelblue", edgecolor="k"
-)
-
-# plot annotations
-plt.axvline(2.8, color="k", linestyle="dashed")
-ax.set_xticks(ind + width)
-ax.set_xticklabels(
-    [
-        "LogisticRegression\nweight 1",
-        "GaussianNB\nweight 1",
-        "RandomForestClassifier\nweight 5",
-        "VotingClassifier\n(average probabilities)",
-    ],
-    rotation=40,
-    ha="right",
-)
-plt.ylim([0, 1])
-plt.title("Class probabilities for sample 1 by different classifiers")
-plt.legend([p1[0], p2[0]], ["class 1", "class 2"], loc="upper left")
-plt.tight_layout()
-plt.show()
diff --git a/dev/_downloads/944f38d1c7814149bd2ee3473837dcc7/wikipedia_principal_eigenvector.zip b/dev/_downloads/944f38d1c7814149bd2ee3473837dcc7/wikipedia_principal_eigenvector.zip
index bfb2c9d8dbe32..776757be3f374 100644
Binary files a/dev/_downloads/944f38d1c7814149bd2ee3473837dcc7/wikipedia_principal_eigenvector.zip and b/dev/_downloads/944f38d1c7814149bd2ee3473837dcc7/wikipedia_principal_eigenvector.zip differ
diff --git a/dev/_downloads/948a4dfa149766b475b1cf2515f289d1/wikipedia_principal_eigenvector.ipynb b/dev/_downloads/948a4dfa149766b475b1cf2515f289d1/wikipedia_principal_eigenvector.ipynb
index 8d166b29388eb..2a3fea28775d7 100644
--- a/dev/_downloads/948a4dfa149766b475b1cf2515f289d1/wikipedia_principal_eigenvector.ipynb
+++ b/dev/_downloads/948a4dfa149766b475b1cf2515f289d1/wikipedia_principal_eigenvector.ipynb
@@ -125,7 +125,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/949ed208b2147ed2b3e348e81fef52be/plot_rfe_with_cross_validation.ipynb b/dev/_downloads/949ed208b2147ed2b3e348e81fef52be/plot_rfe_with_cross_validation.ipynb
index 361f2160bf1f3..4af5127db4861 100644
--- a/dev/_downloads/949ed208b2147ed2b3e348e81fef52be/plot_rfe_with_cross_validation.ipynb
+++ b/dev/_downloads/949ed208b2147ed2b3e348e81fef52be/plot_rfe_with_cross_validation.ipynb
@@ -33,7 +33,7 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.datasets import make_classification\n\nX, y = make_classification(\n    n_samples=500,\n    n_features=15,\n    n_informative=3,\n    n_redundant=2,\n    n_repeated=0,\n    n_classes=8,\n    n_clusters_per_class=1,\n    class_sep=0.8,\n    random_state=0,\n)"
+        "from sklearn.datasets import make_classification\n\nn_features = 15\nfeat_names = [f\"feature_{i}\" for i in range(15)]\n\nX, y = make_classification(\n    n_samples=500,\n    n_features=n_features,\n    n_informative=3,\n    n_redundant=2,\n    n_repeated=0,\n    n_classes=8,\n    n_clusters_per_class=1,\n    class_sep=0.8,\n    random_state=0,\n)"
       ]
     },
     {
@@ -69,7 +69,7 @@
       },
       "outputs": [],
       "source": [
-        "import matplotlib.pyplot as plt\nimport pandas as pd\n\ncv_results = pd.DataFrame(rfecv.cv_results_)\nplt.figure()\nplt.xlabel(\"Number of features selected\")\nplt.ylabel(\"Mean test accuracy\")\nplt.errorbar(\n    x=cv_results[\"n_features\"],\n    y=cv_results[\"mean_test_score\"],\n    yerr=cv_results[\"std_test_score\"],\n)\nplt.title(\"Recursive Feature Elimination \\nwith correlated features\")\nplt.show()"
+        "import matplotlib.pyplot as plt\nimport pandas as pd\n\ndata = {\n    key: value\n    for key, value in rfecv.cv_results_.items()\n    if key in [\"n_features\", \"mean_test_score\", \"std_test_score\"]\n}\ncv_results = pd.DataFrame(data)\nplt.figure()\nplt.xlabel(\"Number of features selected\")\nplt.ylabel(\"Mean test accuracy\")\nplt.errorbar(\n    x=cv_results[\"n_features\"],\n    y=cv_results[\"mean_test_score\"],\n    yerr=cv_results[\"std_test_score\"],\n)\nplt.title(\"Recursive Feature Elimination \\nwith correlated features\")\nplt.show()"
       ]
     },
     {
@@ -78,6 +78,24 @@
       "source": [
         "From the plot above one can further notice a plateau of equivalent scores\n(similar mean value and overlapping errorbars) for 3 to 5 selected features.\nThis is the result of introducing correlated features. Indeed, the optimal\nmodel selected by the RFE can lie within this range, depending on the\ncross-validation technique. The test accuracy decreases above 5 selected\nfeatures, this is, keeping non-informative features leads to over-fitting and\nis therefore detrimental for the statistical performance of the models.\n\n"
       ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n\nfor i in range(cv.n_splits):\n    mask = rfecv.cv_results_[f\"split{i}_support\"][\n        rfecv.n_features_\n    ]  # mask of features selected by the RFE\n    features_selected = np.ma.compressed(np.ma.masked_array(feat_names, mask=1 - mask))\n    print(f\"Features selected in fold {i}: {features_selected}\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "In the five folds, the selected features are consistent. This is good news,\nit means that the selection is stable across folds, and it confirms that\nthese features are the most informative ones.\n\n"
+      ]
     }
   ],
   "metadata": {
@@ -96,7 +114,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/950834c93bfbd20e8a6c673a9fe66ca9/plot_gradient_boosting_categorical.zip b/dev/_downloads/950834c93bfbd20e8a6c673a9fe66ca9/plot_gradient_boosting_categorical.zip
index e776fa1718df4..bf766563100ed 100644
Binary files a/dev/_downloads/950834c93bfbd20e8a6c673a9fe66ca9/plot_gradient_boosting_categorical.zip and b/dev/_downloads/950834c93bfbd20e8a6c673a9fe66ca9/plot_gradient_boosting_categorical.zip differ
diff --git a/dev/_downloads/95e2652922af032381167a5aa13f2b36/plot_f_test_vs_mi.ipynb b/dev/_downloads/95e2652922af032381167a5aa13f2b36/plot_f_test_vs_mi.ipynb
index 6c47418234a3b..4efac9c7b572d 100644
--- a/dev/_downloads/95e2652922af032381167a5aa13f2b36/plot_f_test_vs_mi.ipynb
+++ b/dev/_downloads/95e2652922af032381167a5aa13f2b36/plot_f_test_vs_mi.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/95f7eb8e9a7edff8210ddbf69e096837/plot_unveil_tree_structure.zip b/dev/_downloads/95f7eb8e9a7edff8210ddbf69e096837/plot_unveil_tree_structure.zip
index fffa691f3baaa..65b2ca4c2df79 100644
Binary files a/dev/_downloads/95f7eb8e9a7edff8210ddbf69e096837/plot_unveil_tree_structure.zip and b/dev/_downloads/95f7eb8e9a7edff8210ddbf69e096837/plot_unveil_tree_structure.zip differ
diff --git a/dev/_downloads/97af0ebf12a51edb939605ce096cae13/plot_polynomial_interpolation.ipynb b/dev/_downloads/97af0ebf12a51edb939605ce096cae13/plot_polynomial_interpolation.ipynb
index 6c8951ce7dee7..d5d633748023e 100644
--- a/dev/_downloads/97af0ebf12a51edb939605ce096cae13/plot_polynomial_interpolation.ipynb
+++ b/dev/_downloads/97af0ebf12a51edb939605ce096cae13/plot_polynomial_interpolation.ipynb
@@ -143,7 +143,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/97ec5b2cda9af9d409cf94ba3ae5e270/plot_sgd_iris.ipynb b/dev/_downloads/97ec5b2cda9af9d409cf94ba3ae5e270/plot_sgd_iris.ipynb
index 3dbc9e43c5e51..8ce3016dac5e2 100644
--- a/dev/_downloads/97ec5b2cda9af9d409cf94ba3ae5e270/plot_sgd_iris.ipynb
+++ b/dev/_downloads/97ec5b2cda9af9d409cf94ba3ae5e270/plot_sgd_iris.ipynb
@@ -15,7 +15,7 @@
       },
       "outputs": [],
       "source": [
-        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nfrom sklearn import datasets\nfrom sklearn.inspection import DecisionBoundaryDisplay\nfrom sklearn.linear_model import SGDClassifier\n\n# import some data to play with\niris = datasets.load_iris()\n\n# we only take the first two features. We could\n# avoid this ugly slicing by using a two-dim dataset\nX = iris.data[:, :2]\ny = iris.target\ncolors = \"bry\"\n\n# shuffle\nidx = np.arange(X.shape[0])\nnp.random.seed(13)\nnp.random.shuffle(idx)\nX = X[idx]\ny = y[idx]\n\n# standardize\nmean = X.mean(axis=0)\nstd = X.std(axis=0)\nX = (X - mean) / std\n\nclf = SGDClassifier(alpha=0.001, max_iter=100).fit(X, y)\nax = plt.gca()\nDecisionBoundaryDisplay.from_estimator(\n    clf,\n    X,\n    cmap=plt.cm.Paired,\n    ax=ax,\n    response_method=\"predict\",\n    xlabel=iris.feature_names[0],\n    ylabel=iris.feature_names[1],\n)\nplt.axis(\"tight\")\n\n# Plot also the training points\nfor i, color in zip(clf.classes_, colors):\n    idx = np.where(y == i)\n    plt.scatter(\n        X[idx, 0],\n        X[idx, 1],\n        c=color,\n        label=iris.target_names[i],\n        edgecolor=\"black\",\n        s=20,\n    )\nplt.title(\"Decision surface of multi-class SGD\")\nplt.axis(\"tight\")\n\n# Plot the three one-against-all classifiers\nxmin, xmax = plt.xlim()\nymin, ymax = plt.ylim()\ncoef = clf.coef_\nintercept = clf.intercept_\n\n\ndef plot_hyperplane(c, color):\n    def line(x0):\n        return (-(x0 * coef[c, 0]) - intercept[c]) / coef[c, 1]\n\n    plt.plot([xmin, xmax], [line(xmin), line(xmax)], ls=\"--\", color=color)\n\n\nfor i, color in zip(clf.classes_, colors):\n    plot_hyperplane(i, color)\nplt.legend()\nplt.show()"
+        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nfrom sklearn import datasets\nfrom sklearn.inspection import DecisionBoundaryDisplay\nfrom sklearn.linear_model import SGDClassifier\n\n# import some data to play with\niris = datasets.load_iris()\n\n# we only take the first two features. We could\n# avoid this ugly slicing by using a two-dim dataset\nX = iris.data[:, :2]\ny = iris.target\ncolors = \"bry\"\n\n# shuffle\nidx = np.arange(X.shape[0])\nnp.random.seed(13)\nnp.random.shuffle(idx)\nX = X[idx]\ny = y[idx]\n\n# standardize\nmean = X.mean(axis=0)\nstd = X.std(axis=0)\nX = (X - mean) / std\n\nclf = SGDClassifier(alpha=0.001, max_iter=100).fit(X, y)\nax = plt.gca()\nDecisionBoundaryDisplay.from_estimator(\n    clf,\n    X,\n    cmap=plt.cm.Paired,\n    ax=ax,\n    response_method=\"predict\",\n    xlabel=iris.feature_names[0],\n    ylabel=iris.feature_names[1],\n)\nplt.axis(\"tight\")\n\n# Plot also the training points\nfor i, color in zip(clf.classes_, colors):\n    idx = (y == i).nonzero()\n    plt.scatter(\n        X[idx, 0],\n        X[idx, 1],\n        c=color,\n        label=iris.target_names[i],\n        edgecolor=\"black\",\n        s=20,\n    )\nplt.title(\"Decision surface of multi-class SGD\")\nplt.axis(\"tight\")\n\n# Plot the three one-against-all classifiers\nxmin, xmax = plt.xlim()\nymin, ymax = plt.ylim()\ncoef = clf.coef_\nintercept = clf.intercept_\n\n\ndef plot_hyperplane(c, color):\n    def line(x0):\n        return (-(x0 * coef[c, 0]) - intercept[c]) / coef[c, 1]\n\n    plt.plot([xmin, xmax], [line(xmin), line(xmax)], ls=\"--\", color=color)\n\n\nfor i, color in zip(clf.classes_, colors):\n    plot_hyperplane(i, color)\nplt.legend()\nplt.show()"
       ]
     }
   ],
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/98345ee267d0372eda8faf906905730e/plot_missing_values.py b/dev/_downloads/98345ee267d0372eda8faf906905730e/plot_missing_values.py
index 9d61ffc4964ee..851bfd419453b 100644
--- a/dev/_downloads/98345ee267d0372eda8faf906905730e/plot_missing_values.py
+++ b/dev/_downloads/98345ee267d0372eda8faf906905730e/plot_missing_values.py
@@ -92,7 +92,7 @@ def add_missing_values(X_full, y_full):
 from sklearn.ensemble import RandomForestRegressor
 
 # To use the experimental IterativeImputer, we need to explicitly ask for it:
-from sklearn.experimental import enable_iterative_imputer  # noqa
+from sklearn.experimental import enable_iterative_imputer  # noqa: F401
 from sklearn.impute import IterativeImputer, KNNImputer, SimpleImputer
 from sklearn.model_selection import cross_val_score
 from sklearn.pipeline import make_pipeline
diff --git a/dev/_downloads/983937feb1a5f82dab78ed11a2af1217/plot_time_series_lagged_features.py b/dev/_downloads/983937feb1a5f82dab78ed11a2af1217/plot_time_series_lagged_features.py
index f2eb039e35fe0..7c5b75e12ccfd 100644
--- a/dev/_downloads/983937feb1a5f82dab78ed11a2af1217/plot_time_series_lagged_features.py
+++ b/dev/_downloads/983937feb1a5f82dab78ed11a2af1217/plot_time_series_lagged_features.py
@@ -265,7 +265,7 @@ def consolidate_scores(cv_results, scores, metric):
     time = cv_results["fit_time"]
     scores["fit_time"].append(f"{time.mean():.2f} ± {time.std():.2f} s")
 
-    scores["loss"].append(f"quantile {int(quantile*100)}")
+    scores["loss"].append(f"quantile {int(quantile * 100)}")
     for key, value in cv_results.items():
         if key.startswith("test_"):
             metric = key.split("test_")[1]
diff --git a/dev/_downloads/9846b34238b553e03157c49723da2b04/plot_manifold_sphere.py b/dev/_downloads/9846b34238b553e03157c49723da2b04/plot_manifold_sphere.py
index 7c666c4b7fb7b..d52d99be4d087 100644
--- a/dev/_downloads/9846b34238b553e03157c49723da2b04/plot_manifold_sphere.py
+++ b/dev/_downloads/9846b34238b553e03157c49723da2b04/plot_manifold_sphere.py
@@ -50,7 +50,7 @@
 t = random_state.rand(n_samples) * np.pi
 
 # Sever the poles from the sphere.
-indices = (t < (np.pi - (np.pi / 8))) & (t > ((np.pi / 8)))
+indices = (t < (np.pi - (np.pi / 8))) & (t > (np.pi / 8))
 colors = p[indices]
 x, y, z = (
     np.sin(t[indices]) * np.cos(p[indices]),
diff --git a/dev/_downloads/985f759e90739932d90206b8295630d6/plot_forest_iris.ipynb b/dev/_downloads/985f759e90739932d90206b8295630d6/plot_forest_iris.ipynb
index b4a24bac049aa..3329b2ab6ba38 100644
--- a/dev/_downloads/985f759e90739932d90206b8295630d6/plot_forest_iris.ipynb
+++ b/dev/_downloads/985f759e90739932d90206b8295630d6/plot_forest_iris.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/98a587e4b782bdfca89898083ebe7b05/plot_multioutput_face_completion.zip b/dev/_downloads/98a587e4b782bdfca89898083ebe7b05/plot_multioutput_face_completion.zip
index 7acdbf01534d9..b61b4d8a887e4 100644
Binary files a/dev/_downloads/98a587e4b782bdfca89898083ebe7b05/plot_multioutput_face_completion.zip and b/dev/_downloads/98a587e4b782bdfca89898083ebe7b05/plot_multioutput_face_completion.zip differ
diff --git a/dev/_downloads/98b05156f2b58b0d71d37e809c834124/plot_randomized_search.zip b/dev/_downloads/98b05156f2b58b0d71d37e809c834124/plot_randomized_search.zip
index ba811bd52ce4d..2f692837c8ca8 100644
Binary files a/dev/_downloads/98b05156f2b58b0d71d37e809c834124/plot_randomized_search.zip and b/dev/_downloads/98b05156f2b58b0d71d37e809c834124/plot_randomized_search.zip differ
diff --git a/dev/_downloads/98db344b333b9fbda7598300288d1b1e/plot_map_data_to_normal.zip b/dev/_downloads/98db344b333b9fbda7598300288d1b1e/plot_map_data_to_normal.zip
index 9405948e81ba6..b00dc32dcaf6c 100644
Binary files a/dev/_downloads/98db344b333b9fbda7598300288d1b1e/plot_map_data_to_normal.zip and b/dev/_downloads/98db344b333b9fbda7598300288d1b1e/plot_map_data_to_normal.zip differ
diff --git a/dev/_downloads/99402c7ce7a6ab7fafcb48caa3c9447b/plot_pca_iris.zip b/dev/_downloads/99402c7ce7a6ab7fafcb48caa3c9447b/plot_pca_iris.zip
index 6575a01469c8b..ad9527684a17d 100644
Binary files a/dev/_downloads/99402c7ce7a6ab7fafcb48caa3c9447b/plot_pca_iris.zip and b/dev/_downloads/99402c7ce7a6ab7fafcb48caa3c9447b/plot_pca_iris.zip differ
diff --git a/dev/_downloads/99ccb624369d00fa81b6030937f80b04/plot_ransac.ipynb b/dev/_downloads/99ccb624369d00fa81b6030937f80b04/plot_ransac.ipynb
index 2ae5ea2b547e1..78b621285fc8e 100644
--- a/dev/_downloads/99ccb624369d00fa81b6030937f80b04/plot_ransac.ipynb
+++ b/dev/_downloads/99ccb624369d00fa81b6030937f80b04/plot_ransac.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/9ad55bf68758018c9961815802c65e18/plot_confusion_matrix.ipynb b/dev/_downloads/9ad55bf68758018c9961815802c65e18/plot_confusion_matrix.ipynb
index e8bc3b9845c48..b7a654462e4c5 100644
--- a/dev/_downloads/9ad55bf68758018c9961815802c65e18/plot_confusion_matrix.ipynb
+++ b/dev/_downloads/9ad55bf68758018c9961815802c65e18/plot_confusion_matrix.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/9c824e9beef1b72c9f1ad3f39de0bf57/plot_label_propagation_structure.ipynb b/dev/_downloads/9c824e9beef1b72c9f1ad3f39de0bf57/plot_label_propagation_structure.ipynb
index ff3139819fdd1..26b5d73a64842 100644
--- a/dev/_downloads/9c824e9beef1b72c9f1ad3f39de0bf57/plot_label_propagation_structure.ipynb
+++ b/dev/_downloads/9c824e9beef1b72c9f1ad3f39de0bf57/plot_label_propagation_structure.ipynb
@@ -4,7 +4,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# Label Propagation learning a complex structure\n\nExample of LabelPropagation learning a complex internal structure\nto demonstrate \"manifold learning\". The outer circle should be\nlabeled \"red\" and the inner circle \"blue\". Because both label groups\nlie inside their own distinct shape, we can see that the labels\npropagate correctly around the circle.\n"
+        "\n# Label Propagation circles: Learning a complex structure\n\nExample of LabelPropagation learning a complex internal structure\nto demonstrate \"manifold learning\". The outer circle should be\nlabeled \"red\" and the inner circle \"blue\". Because both label groups\nlie inside their own distinct shape, we can see that the labels\npropagate correctly around the circle.\n"
       ]
     },
     {
@@ -87,7 +87,7 @@
       },
       "outputs": [],
       "source": [
-        "output_labels = label_spread.transduction_\noutput_label_array = np.asarray(output_labels)\nouter_numbers = np.where(output_label_array == outer)[0]\ninner_numbers = np.where(output_label_array == inner)[0]\n\nplt.figure(figsize=(4, 4))\nplt.scatter(\n    X[outer_numbers, 0],\n    X[outer_numbers, 1],\n    color=\"navy\",\n    marker=\"s\",\n    lw=0,\n    s=10,\n    label=\"outer learned\",\n)\nplt.scatter(\n    X[inner_numbers, 0],\n    X[inner_numbers, 1],\n    color=\"c\",\n    marker=\"s\",\n    lw=0,\n    s=10,\n    label=\"inner learned\",\n)\nplt.legend(scatterpoints=1, shadow=False, loc=\"center\")\nplt.title(\"Labels learned with Label Spreading (KNN)\")\nplt.show()"
+        "output_labels = label_spread.transduction_\noutput_label_array = np.asarray(output_labels)\nouter_numbers = (output_label_array == outer).nonzero()[0]\ninner_numbers = (output_label_array == inner).nonzero()[0]\n\nplt.figure(figsize=(4, 4))\nplt.scatter(\n    X[outer_numbers, 0],\n    X[outer_numbers, 1],\n    color=\"navy\",\n    marker=\"s\",\n    lw=0,\n    s=10,\n    label=\"outer learned\",\n)\nplt.scatter(\n    X[inner_numbers, 0],\n    X[inner_numbers, 1],\n    color=\"c\",\n    marker=\"s\",\n    lw=0,\n    s=10,\n    label=\"inner learned\",\n)\nplt.legend(scatterpoints=1, shadow=False, loc=\"center\")\nplt.title(\"Labels learned with Label Spreading (KNN)\")\nplt.show()"
       ]
     }
   ],
@@ -107,7 +107,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/9d2f119ab4a1b6f1454c43b796f2c6a6/plot_kernel_ridge_regression.ipynb b/dev/_downloads/9d2f119ab4a1b6f1454c43b796f2c6a6/plot_kernel_ridge_regression.ipynb
index 4ad0631ea3c04..312e9d160e8df 100644
--- a/dev/_downloads/9d2f119ab4a1b6f1454c43b796f2c6a6/plot_kernel_ridge_regression.ipynb
+++ b/dev/_downloads/9d2f119ab4a1b6f1454c43b796f2c6a6/plot_kernel_ridge_regression.ipynb
@@ -153,7 +153,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/9d4dd9a87478475252fc9d2e46a7c787/plot_feature_agglomeration_vs_univariate_selection.zip b/dev/_downloads/9d4dd9a87478475252fc9d2e46a7c787/plot_feature_agglomeration_vs_univariate_selection.zip
index 54395f60dc46f..b29cdf10b9450 100644
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diff --git a/dev/_downloads/9d886125f15994580789a53f5790be5f/plot_classification_probability.zip b/dev/_downloads/9d886125f15994580789a53f5790be5f/plot_classification_probability.zip
index 73d9e3eae5340..8898efc4833f9 100644
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diff --git a/dev/_downloads/9d97cc4ed755b7f2c7f9311bccc89a00/plot_lle_digits.py b/dev/_downloads/9d97cc4ed755b7f2c7f9311bccc89a00/plot_lle_digits.py
index 34b221ca0cd1d..d53816536158f 100644
--- a/dev/_downloads/9d97cc4ed755b7f2c7f9311bccc89a00/plot_lle_digits.py
+++ b/dev/_downloads/9d97cc4ed755b7f2c7f9311bccc89a00/plot_lle_digits.py
@@ -10,7 +10,6 @@
 # Authors: The scikit-learn developers
 # SPDX-License-Identifier: BSD-3-Clause
 
-
 # %%
 # Load digits dataset
 # -------------------
@@ -131,7 +130,7 @@ def plot_embedding(X, title):
     "LTSA LLE embedding": LocallyLinearEmbedding(
         n_neighbors=n_neighbors, n_components=2, method="ltsa"
     ),
-    "MDS embedding": MDS(n_components=2, n_init=1, max_iter=120, n_jobs=2),
+    "MDS embedding": MDS(n_components=2, n_init=1, max_iter=120, eps=1e-6),
     "Random Trees embedding": make_pipeline(
         RandomTreesEmbedding(n_estimators=200, max_depth=5, random_state=0),
         TruncatedSVD(n_components=2),
diff --git a/dev/_downloads/9db681d93e93ba2254d53b5f5a4403f1/plot_random_forest_regression_multioutput.zip b/dev/_downloads/9db681d93e93ba2254d53b5f5a4403f1/plot_random_forest_regression_multioutput.zip
index daef379fc02e6..71931c522aeb9 100644
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diff --git a/dev/_downloads/9e97ddcf188484151f4cb4c62e3be741/plot_compare_methods.zip b/dev/_downloads/9e97ddcf188484151f4cb4c62e3be741/plot_compare_methods.zip
index d8d55600a56d5..25555885130a3 100644
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diff --git a/dev/_downloads/a0e7bd40b2cd97ee01b5c2e0bf212505/plot_kmeans_silhouette_analysis.zip b/dev/_downloads/a0e7bd40b2cd97ee01b5c2e0bf212505/plot_kmeans_silhouette_analysis.zip
index 41ef696da9721..19bb432173929 100644
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diff --git a/dev/_downloads/a0eb10a653f425408929c3d52384e9b0/plot_partial_dependence_visualization_api.zip b/dev/_downloads/a0eb10a653f425408929c3d52384e9b0/plot_partial_dependence_visualization_api.zip
index 297a2a6078394..55fd93050475b 100644
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diff --git a/dev/_downloads/a12e68b62bdda2af020f3efce6bb3545/approximate_nearest_neighbors.zip b/dev/_downloads/a12e68b62bdda2af020f3efce6bb3545/approximate_nearest_neighbors.zip
index 677ef3330f0dc..1355d98199b49 100644
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diff --git a/dev/_downloads/a16535f422dac30dcba2599b105c4a84/plot_roc_crossval.zip b/dev/_downloads/a16535f422dac30dcba2599b105c4a84/plot_roc_crossval.zip
index 5d8d5f264d969..06f3a82d5d273 100644
Binary files a/dev/_downloads/a16535f422dac30dcba2599b105c4a84/plot_roc_crossval.zip and b/dev/_downloads/a16535f422dac30dcba2599b105c4a84/plot_roc_crossval.zip differ
diff --git a/dev/_downloads/a2f99bb3ce0cbeefa9d2e44fe9daf4cd/plot_gpc_iris.ipynb b/dev/_downloads/a2f99bb3ce0cbeefa9d2e44fe9daf4cd/plot_gpc_iris.ipynb
index 2503b468a0dee..b6f97048422ea 100644
--- a/dev/_downloads/a2f99bb3ce0cbeefa9d2e44fe9daf4cd/plot_gpc_iris.ipynb
+++ b/dev/_downloads/a2f99bb3ce0cbeefa9d2e44fe9daf4cd/plot_gpc_iris.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/a32d6321e82346eded216e3c68f044c6/plot_sparse_cov.zip b/dev/_downloads/a32d6321e82346eded216e3c68f044c6/plot_sparse_cov.zip
index 1e2fe24cc3780..af9be70bc0fc6 100644
Binary files a/dev/_downloads/a32d6321e82346eded216e3c68f044c6/plot_sparse_cov.zip and b/dev/_downloads/a32d6321e82346eded216e3c68f044c6/plot_sparse_cov.zip differ
diff --git a/dev/_downloads/a36aaa401f3fc0a911c024ab92f37ae4/plot_anomaly_comparison.zip b/dev/_downloads/a36aaa401f3fc0a911c024ab92f37ae4/plot_anomaly_comparison.zip
index 68bde002e2cf3..5b6da4b3cb4b5 100644
Binary files a/dev/_downloads/a36aaa401f3fc0a911c024ab92f37ae4/plot_anomaly_comparison.zip and b/dev/_downloads/a36aaa401f3fc0a911c024ab92f37ae4/plot_anomaly_comparison.zip differ
diff --git a/dev/_downloads/a373b9fdc21005d9a66ecf3df90eb49a/plot_birch_vs_minibatchkmeans.ipynb b/dev/_downloads/a373b9fdc21005d9a66ecf3df90eb49a/plot_birch_vs_minibatchkmeans.ipynb
index 288cb28db92d1..13353fe55d9fe 100644
--- a/dev/_downloads/a373b9fdc21005d9a66ecf3df90eb49a/plot_birch_vs_minibatchkmeans.ipynb
+++ b/dev/_downloads/a373b9fdc21005d9a66ecf3df90eb49a/plot_birch_vs_minibatchkmeans.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/a390b24658dd58aba65067ab405e1ee5/plot_digits_linkage.zip b/dev/_downloads/a390b24658dd58aba65067ab405e1ee5/plot_digits_linkage.zip
index dc8892fdfd10b..cf6df0e61aea4 100644
Binary files a/dev/_downloads/a390b24658dd58aba65067ab405e1ee5/plot_digits_linkage.zip and b/dev/_downloads/a390b24658dd58aba65067ab405e1ee5/plot_digits_linkage.zip differ
diff --git a/dev/_downloads/a396baaf1f191c4fd27c35ae5b37420c/plot_likelihood_ratios.ipynb b/dev/_downloads/a396baaf1f191c4fd27c35ae5b37420c/plot_likelihood_ratios.ipynb
index 4ef038d757afe..33075327ffdc4 100644
--- a/dev/_downloads/a396baaf1f191c4fd27c35ae5b37420c/plot_likelihood_ratios.ipynb
+++ b/dev/_downloads/a396baaf1f191c4fd27c35ae5b37420c/plot_likelihood_ratios.ipynb
@@ -33,7 +33,7 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.datasets import make_classification\n\nX, y = make_classification(n_samples=10_000, weights=[0.9, 0.1], random_state=0)\nprint(f\"Percentage of people carrying the disease: {100*y.mean():.2f}%\")"
+        "from sklearn.datasets import make_classification\n\nX, y = make_classification(n_samples=10_000, weights=[0.9, 0.1], random_state=0)\nprint(f\"Percentage of people carrying the disease: {100 * y.mean():.2f}%\")"
       ]
     },
     {
@@ -269,7 +269,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/a3fe1b9a65568eb1b8610be42fd84cbb/plot_ensemble_oob.zip b/dev/_downloads/a3fe1b9a65568eb1b8610be42fd84cbb/plot_ensemble_oob.zip
index ad97bc2c76d3a..245c126c52e72 100644
Binary files a/dev/_downloads/a3fe1b9a65568eb1b8610be42fd84cbb/plot_ensemble_oob.zip and b/dev/_downloads/a3fe1b9a65568eb1b8610be42fd84cbb/plot_ensemble_oob.zip differ
diff --git a/dev/_downloads/a440a8b10138c855100ed5820fdb36b6/plot_missing_values.ipynb b/dev/_downloads/a440a8b10138c855100ed5820fdb36b6/plot_missing_values.ipynb
index 7b195a0d397f1..14a8cb85a5ea6 100644
--- a/dev/_downloads/a440a8b10138c855100ed5820fdb36b6/plot_missing_values.ipynb
+++ b/dev/_downloads/a440a8b10138c855100ed5820fdb36b6/plot_missing_values.ipynb
@@ -51,7 +51,7 @@
       },
       "outputs": [],
       "source": [
-        "rng = np.random.RandomState(0)\n\nfrom sklearn.ensemble import RandomForestRegressor\n\n# To use the experimental IterativeImputer, we need to explicitly ask for it:\nfrom sklearn.experimental import enable_iterative_imputer  # noqa\nfrom sklearn.impute import IterativeImputer, KNNImputer, SimpleImputer\nfrom sklearn.model_selection import cross_val_score\nfrom sklearn.pipeline import make_pipeline\n\nN_SPLITS = 4\nregressor = RandomForestRegressor(random_state=0)"
+        "rng = np.random.RandomState(0)\n\nfrom sklearn.ensemble import RandomForestRegressor\n\n# To use the experimental IterativeImputer, we need to explicitly ask for it:\nfrom sklearn.experimental import enable_iterative_imputer  # noqa: F401\nfrom sklearn.impute import IterativeImputer, KNNImputer, SimpleImputer\nfrom sklearn.model_selection import cross_val_score\nfrom sklearn.pipeline import make_pipeline\n\nN_SPLITS = 4\nregressor = RandomForestRegressor(random_state=0)"
       ]
     },
     {
@@ -204,7 +204,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/a444bb0cccc6e91b6043fdced497ce82/plot_robust_fit.zip b/dev/_downloads/a444bb0cccc6e91b6043fdced497ce82/plot_robust_fit.zip
index b64b7066c0c35..7c55968f058ea 100644
Binary files a/dev/_downloads/a444bb0cccc6e91b6043fdced497ce82/plot_robust_fit.zip and b/dev/_downloads/a444bb0cccc6e91b6043fdced497ce82/plot_robust_fit.zip differ
diff --git a/dev/_downloads/a471e25495d24d0086e798aa9ca44a89/plot_release_highlights_1_6_0.py b/dev/_downloads/a471e25495d24d0086e798aa9ca44a89/plot_release_highlights_1_6_0.py
index 7e842659f018a..503af8c076fbb 100644
--- a/dev/_downloads/a471e25495d24d0086e798aa9ca44a89/plot_release_highlights_1_6_0.py
+++ b/dev/_downloads/a471e25495d24d0086e798aa9ca44a89/plot_release_highlights_1_6_0.py
@@ -1,4 +1,4 @@
-# ruff: noqa
+# ruff: noqa: CPY001, E501
 """
 =======================================
 Release Highlights for scikit-learn 1.6
@@ -33,6 +33,7 @@
 # or to pass a pre-fitted model to some of the meta-estimators. Here's a short example:
 
 import time
+
 from sklearn.datasets import make_classification
 from sklearn.frozen import FrozenEstimator
 from sklearn.linear_model import SGDClassifier
@@ -122,6 +123,7 @@
 # :class:`ensemble.ExtraTreesRegressor` now support missing values. More details in the
 # :ref:`User Guide <tree_missing_value_support>`.
 import numpy as np
+
 from sklearn.ensemble import ExtraTreesClassifier
 
 X = np.array([0, 1, 6, np.nan]).reshape(-1, 1)
diff --git a/dev/_downloads/a4ca19d099be76c1c7cbb53108c7b00b/plot_release_highlights_1_1_0.zip b/dev/_downloads/a4ca19d099be76c1c7cbb53108c7b00b/plot_release_highlights_1_1_0.zip
index b88cd55ef6e36..a80fe341c5af2 100644
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diff --git a/dev/_downloads/a687ddc099cdaf81b25dcd61b9cb7672/plot_inductive_clustering.zip b/dev/_downloads/a687ddc099cdaf81b25dcd61b9cb7672/plot_inductive_clustering.zip
index b1a7383b9af28..d741c22e58c96 100644
Binary files a/dev/_downloads/a687ddc099cdaf81b25dcd61b9cb7672/plot_inductive_clustering.zip and b/dev/_downloads/a687ddc099cdaf81b25dcd61b9cb7672/plot_inductive_clustering.zip differ
diff --git a/dev/_downloads/a72c1f35af8f7695645ce87422b177bf/plot_label_propagation_digits_active_learning.ipynb b/dev/_downloads/a72c1f35af8f7695645ce87422b177bf/plot_label_propagation_digits_active_learning.ipynb
index b91fe30eab1c5..fb395d0f428a5 100644
--- a/dev/_downloads/a72c1f35af8f7695645ce87422b177bf/plot_label_propagation_digits_active_learning.ipynb
+++ b/dev/_downloads/a72c1f35af8f7695645ce87422b177bf/plot_label_propagation_digits_active_learning.ipynb
@@ -4,7 +4,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# Label Propagation digits active learning\n\nDemonstrates an active learning technique to learn handwritten digits\nusing label propagation.\n\nWe start by training a label propagation model with only 10 labeled points,\nthen we select the top five most uncertain points to label. Next, we train\nwith 15 labeled points (original 10 + 5 new ones). We repeat this process\nfour times to have a model trained with 30 labeled examples. Note you can\nincrease this to label more than 30 by changing `max_iterations`. Labeling\nmore than 30 can be useful to get a sense for the speed of convergence of\nthis active learning technique.\n\nA plot will appear showing the top 5 most uncertain digits for each iteration\nof training. These may or may not contain mistakes, but we will train the next\nmodel with their true labels.\n"
+        "\n# Label Propagation digits: Active learning\n\nDemonstrates an active learning technique to learn handwritten digits\nusing label propagation.\n\nWe start by training a label propagation model with only 10 labeled points,\nthen we select the top five most uncertain points to label. Next, we train\nwith 15 labeled points (original 10 + 5 new ones). We repeat this process\nfour times to have a model trained with 30 labeled examples. Note you can\nincrease this to label more than 30 by changing `max_iterations`. Labeling\nmore than 30 can be useful to get a sense for the speed of convergence of\nthis active learning technique.\n\nA plot will appear showing the top 5 most uncertain digits for each iteration\nof training. These may or may not contain mistakes, but we will train the next\nmodel with their true labels.\n"
       ]
     },
     {
@@ -15,7 +15,7 @@
       },
       "outputs": [],
       "source": [
-        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom scipy import stats\n\nfrom sklearn import datasets\nfrom sklearn.metrics import classification_report, confusion_matrix\nfrom sklearn.semi_supervised import LabelSpreading\n\ndigits = datasets.load_digits()\nrng = np.random.RandomState(0)\nindices = np.arange(len(digits.data))\nrng.shuffle(indices)\n\nX = digits.data[indices[:330]]\ny = digits.target[indices[:330]]\nimages = digits.images[indices[:330]]\n\nn_total_samples = len(y)\nn_labeled_points = 40\nmax_iterations = 5\n\nunlabeled_indices = np.arange(n_total_samples)[n_labeled_points:]\nf = plt.figure()\n\nfor i in range(max_iterations):\n    if len(unlabeled_indices) == 0:\n        print(\"No unlabeled items left to label.\")\n        break\n    y_train = np.copy(y)\n    y_train[unlabeled_indices] = -1\n\n    lp_model = LabelSpreading(gamma=0.25, max_iter=20)\n    lp_model.fit(X, y_train)\n\n    predicted_labels = lp_model.transduction_[unlabeled_indices]\n    true_labels = y[unlabeled_indices]\n\n    cm = confusion_matrix(true_labels, predicted_labels, labels=lp_model.classes_)\n\n    print(\"Iteration %i %s\" % (i, 70 * \"_\"))\n    print(\n        \"Label Spreading model: %d labeled & %d unlabeled (%d total)\"\n        % (n_labeled_points, n_total_samples - n_labeled_points, n_total_samples)\n    )\n\n    print(classification_report(true_labels, predicted_labels))\n\n    print(\"Confusion matrix\")\n    print(cm)\n\n    # compute the entropies of transduced label distributions\n    pred_entropies = stats.distributions.entropy(lp_model.label_distributions_.T)\n\n    # select up to 5 digit examples that the classifier is most uncertain about\n    uncertainty_index = np.argsort(pred_entropies)[::-1]\n    uncertainty_index = uncertainty_index[\n        np.isin(uncertainty_index, unlabeled_indices)\n    ][:5]\n\n    # keep track of indices that we get labels for\n    delete_indices = np.array([], dtype=int)\n\n    # for more than 5 iterations, visualize the gain only on the first 5\n    if i < 5:\n        f.text(\n            0.05,\n            (1 - (i + 1) * 0.183),\n            \"model %d\\n\\nfit with\\n%d labels\" % ((i + 1), i * 5 + 10),\n            size=10,\n        )\n    for index, image_index in enumerate(uncertainty_index):\n        image = images[image_index]\n\n        # for more than 5 iterations, visualize the gain only on the first 5\n        if i < 5:\n            sub = f.add_subplot(5, 5, index + 1 + (5 * i))\n            sub.imshow(image, cmap=plt.cm.gray_r, interpolation=\"none\")\n            sub.set_title(\n                \"predict: %i\\ntrue: %i\"\n                % (lp_model.transduction_[image_index], y[image_index]),\n                size=10,\n            )\n            sub.axis(\"off\")\n\n        # labeling 5 points, remote from labeled set\n        (delete_index,) = np.where(unlabeled_indices == image_index)\n        delete_indices = np.concatenate((delete_indices, delete_index))\n\n    unlabeled_indices = np.delete(unlabeled_indices, delete_indices)\n    n_labeled_points += len(uncertainty_index)\n\nf.suptitle(\n    (\n        \"Active learning with Label Propagation.\\nRows show 5 most \"\n        \"uncertain labels to learn with the next model.\"\n    ),\n    y=1.15,\n)\nplt.subplots_adjust(left=0.2, bottom=0.03, right=0.9, top=0.9, wspace=0.2, hspace=0.85)\nplt.show()"
+        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom scipy import stats\n\nfrom sklearn import datasets\nfrom sklearn.metrics import classification_report, confusion_matrix\nfrom sklearn.semi_supervised import LabelSpreading\n\ndigits = datasets.load_digits()\nrng = np.random.RandomState(0)\nindices = np.arange(len(digits.data))\nrng.shuffle(indices)\n\nX = digits.data[indices[:330]]\ny = digits.target[indices[:330]]\nimages = digits.images[indices[:330]]\n\nn_total_samples = len(y)\nn_labeled_points = 40\nmax_iterations = 5\n\nunlabeled_indices = np.arange(n_total_samples)[n_labeled_points:]\nf = plt.figure()\n\nfor i in range(max_iterations):\n    if len(unlabeled_indices) == 0:\n        print(\"No unlabeled items left to label.\")\n        break\n    y_train = np.copy(y)\n    y_train[unlabeled_indices] = -1\n\n    lp_model = LabelSpreading(gamma=0.25, max_iter=20)\n    lp_model.fit(X, y_train)\n\n    predicted_labels = lp_model.transduction_[unlabeled_indices]\n    true_labels = y[unlabeled_indices]\n\n    cm = confusion_matrix(true_labels, predicted_labels, labels=lp_model.classes_)\n\n    print(\"Iteration %i %s\" % (i, 70 * \"_\"))\n    print(\n        \"Label Spreading model: %d labeled & %d unlabeled (%d total)\"\n        % (n_labeled_points, n_total_samples - n_labeled_points, n_total_samples)\n    )\n\n    print(classification_report(true_labels, predicted_labels))\n\n    print(\"Confusion matrix\")\n    print(cm)\n\n    # compute the entropies of transduced label distributions\n    pred_entropies = stats.distributions.entropy(lp_model.label_distributions_.T)\n\n    # select up to 5 digit examples that the classifier is most uncertain about\n    uncertainty_index = np.argsort(pred_entropies)[::-1]\n    uncertainty_index = uncertainty_index[\n        np.isin(uncertainty_index, unlabeled_indices)\n    ][:5]\n\n    # keep track of indices that we get labels for\n    delete_indices = np.array([], dtype=int)\n\n    # for more than 5 iterations, visualize the gain only on the first 5\n    if i < 5:\n        f.text(\n            0.05,\n            (1 - (i + 1) * 0.183),\n            \"model %d\\n\\nfit with\\n%d labels\" % ((i + 1), i * 5 + 10),\n            size=10,\n        )\n    for index, image_index in enumerate(uncertainty_index):\n        image = images[image_index]\n\n        # for more than 5 iterations, visualize the gain only on the first 5\n        if i < 5:\n            sub = f.add_subplot(5, 5, index + 1 + (5 * i))\n            sub.imshow(image, cmap=plt.cm.gray_r, interpolation=\"none\")\n            sub.set_title(\n                \"predict: %i\\ntrue: %i\"\n                % (lp_model.transduction_[image_index], y[image_index]),\n                size=10,\n            )\n            sub.axis(\"off\")\n\n        # labeling 5 points, remote from labeled set\n        (delete_index,) = (unlabeled_indices == image_index).nonzero()\n        delete_indices = np.concatenate((delete_indices, delete_index))\n\n    unlabeled_indices = np.delete(unlabeled_indices, delete_indices)\n    n_labeled_points += len(uncertainty_index)\n\nf.suptitle(\n    (\n        \"Active learning with Label Propagation.\\nRows show 5 most \"\n        \"uncertain labels to learn with the next model.\"\n    ),\n    y=1.15,\n)\nplt.subplots_adjust(left=0.2, bottom=0.03, right=0.9, top=0.9, wspace=0.2, hspace=0.85)\nplt.show()"
       ]
     }
   ],
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/a79c521bd835e8739e06d7dafbfc4eb4/plot_omp.ipynb b/dev/_downloads/a79c521bd835e8739e06d7dafbfc4eb4/plot_omp.ipynb
index 7f098b8e4be74..07369daac74b8 100644
--- a/dev/_downloads/a79c521bd835e8739e06d7dafbfc4eb4/plot_omp.ipynb
+++ b/dev/_downloads/a79c521bd835e8739e06d7dafbfc4eb4/plot_omp.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/a88ca03d46b8289eaed2fc08e505bee2/plot_grid_search_refit_callable.zip b/dev/_downloads/a88ca03d46b8289eaed2fc08e505bee2/plot_grid_search_refit_callable.zip
index bac431f29493f..32aaba9885cdd 100644
Binary files a/dev/_downloads/a88ca03d46b8289eaed2fc08e505bee2/plot_grid_search_refit_callable.zip and b/dev/_downloads/a88ca03d46b8289eaed2fc08e505bee2/plot_grid_search_refit_callable.zip differ
diff --git a/dev/_downloads/a8ae7970b854e7e2bea48bb7688bd7b6/plot_random_multilabel_dataset.ipynb b/dev/_downloads/a8ae7970b854e7e2bea48bb7688bd7b6/plot_random_multilabel_dataset.ipynb
index 81eece858b5a3..1d3c233cea4b5 100644
--- a/dev/_downloads/a8ae7970b854e7e2bea48bb7688bd7b6/plot_random_multilabel_dataset.ipynb
+++ b/dev/_downloads/a8ae7970b854e7e2bea48bb7688bd7b6/plot_random_multilabel_dataset.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/a91fcd567e6bc2540b34f44d599d3531/plot_lof_outlier_detection.ipynb b/dev/_downloads/a91fcd567e6bc2540b34f44d599d3531/plot_lof_outlier_detection.ipynb
index 6a8d95546a399..fdb3f34b9b20d 100644
--- a/dev/_downloads/a91fcd567e6bc2540b34f44d599d3531/plot_lof_outlier_detection.ipynb
+++ b/dev/_downloads/a91fcd567e6bc2540b34f44d599d3531/plot_lof_outlier_detection.ipynb
@@ -89,7 +89,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/a97bf662e52d471b04e1ab480c0ad7f2/plot_tweedie_regression_insurance_claims.ipynb b/dev/_downloads/a97bf662e52d471b04e1ab480c0ad7f2/plot_tweedie_regression_insurance_claims.ipynb
index 17da54007ac4c..21a0e69289783 100644
--- a/dev/_downloads/a97bf662e52d471b04e1ab480c0ad7f2/plot_tweedie_regression_insurance_claims.ipynb
+++ b/dev/_downloads/a97bf662e52d471b04e1ab480c0ad7f2/plot_tweedie_regression_insurance_claims.ipynb
@@ -224,7 +224,7 @@
       },
       "outputs": [],
       "source": [
-        "res = []\nfor subset_label, X, df in [\n    (\"train\", X_train, df_train),\n    (\"test\", X_test, df_test),\n]:\n    exposure = df[\"Exposure\"].values\n    res.append(\n        {\n            \"subset\": subset_label,\n            \"observed\": df[\"ClaimAmount\"].values.sum(),\n            \"predicted, frequency*severity model\": np.sum(\n                exposure * glm_freq.predict(X) * glm_sev.predict(X)\n            ),\n            \"predicted, tweedie, power=%.2f\"\n            % glm_pure_premium.power: np.sum(exposure * glm_pure_premium.predict(X)),\n        }\n    )\n\nprint(pd.DataFrame(res).set_index(\"subset\").T)"
+        "res = []\nfor subset_label, X, df in [\n    (\"train\", X_train, df_train),\n    (\"test\", X_test, df_test),\n]:\n    exposure = df[\"Exposure\"].values\n    res.append(\n        {\n            \"subset\": subset_label,\n            \"observed\": df[\"ClaimAmount\"].values.sum(),\n            \"predicted, frequency*severity model\": np.sum(\n                exposure * glm_freq.predict(X) * glm_sev.predict(X)\n            ),\n            \"predicted, tweedie, power=%.2f\" % glm_pure_premium.power: np.sum(\n                exposure * glm_pure_premium.predict(X)\n            ),\n        }\n    )\n\nprint(pd.DataFrame(res).set_index(\"subset\").T)"
       ]
     },
     {
@@ -262,7 +262,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/a9a5b8f39dd796eae478e4c9c39cd207/plot_mds.ipynb b/dev/_downloads/a9a5b8f39dd796eae478e4c9c39cd207/plot_mds.ipynb
index ddedfb5500ad8..5948f6da8b12d 100644
--- a/dev/_downloads/a9a5b8f39dd796eae478e4c9c39cd207/plot_mds.ipynb
+++ b/dev/_downloads/a9a5b8f39dd796eae478e4c9c39cd207/plot_mds.ipynb
@@ -4,7 +4,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# Multi-dimensional scaling\n\nAn illustration of the metric and non-metric MDS on generated noisy data.\n\nThe reconstructed points using the metric MDS and non metric MDS are slightly\nshifted to avoid overlapping.\n"
+        "\n# Multi-dimensional scaling\n\nAn illustration of the metric and non-metric MDS on generated noisy data.\n"
       ]
     },
     {
@@ -15,7 +15,115 @@
       },
       "outputs": [],
       "source": [
-        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\n\nimport numpy as np\nfrom matplotlib import pyplot as plt\nfrom matplotlib.collections import LineCollection\n\nfrom sklearn import manifold\nfrom sklearn.decomposition import PCA\nfrom sklearn.metrics import euclidean_distances\n\n# Generate the data\nEPSILON = np.finfo(np.float32).eps\nn_samples = 20\nrng = np.random.RandomState(seed=3)\nX_true = rng.randint(0, 20, 2 * n_samples).astype(float)\nX_true = X_true.reshape((n_samples, 2))\n\n# Center the data\nX_true -= X_true.mean()\n\n# Compute pairwise Euclidean distances\ndistances = euclidean_distances(X_true)\n\n# Add noise to the distances\nnoise = rng.rand(n_samples, n_samples)\nnoise = noise + noise.T\nnp.fill_diagonal(noise, 0)\ndistances += noise\n\nmds = manifold.MDS(\n    n_components=2,\n    max_iter=3000,\n    eps=1e-9,\n    random_state=42,\n    dissimilarity=\"precomputed\",\n    n_jobs=1,\n)\nX_mds = mds.fit(distances).embedding_\n\nnmds = manifold.MDS(\n    n_components=2,\n    metric=False,\n    max_iter=3000,\n    eps=1e-12,\n    dissimilarity=\"precomputed\",\n    random_state=42,\n    n_jobs=1,\n    n_init=1,\n)\nX_nmds = nmds.fit_transform(distances)\n\n# Rescale the data\nX_mds *= np.sqrt((X_true**2).sum()) / np.sqrt((X_mds**2).sum())\nX_nmds *= np.sqrt((X_true**2).sum()) / np.sqrt((X_nmds**2).sum())\n\n# Rotate the data\npca = PCA(n_components=2)\nX_true = pca.fit_transform(X_true)\nX_mds = pca.fit_transform(X_mds)\nX_nmds = pca.fit_transform(X_nmds)\n\n# Align the sign of PCs\nfor i in [0, 1]:\n    if np.corrcoef(X_mds[:, i], X_true[:, i])[0, 1] < 0:\n        X_mds[:, i] *= -1\n    if np.corrcoef(X_nmds[:, i], X_true[:, i])[0, 1] < 0:\n        X_nmds[:, i] *= -1\n\nfig = plt.figure(1)\nax = plt.axes([0.0, 0.0, 1.0, 1.0])\n\ns = 100\nplt.scatter(X_true[:, 0], X_true[:, 1], color=\"navy\", s=s, lw=0, label=\"True Position\")\nplt.scatter(X_mds[:, 0], X_mds[:, 1], color=\"turquoise\", s=s, lw=0, label=\"MDS\")\nplt.scatter(X_nmds[:, 0], X_nmds[:, 1], color=\"darkorange\", s=s, lw=0, label=\"NMDS\")\nplt.legend(scatterpoints=1, loc=\"best\", shadow=False)\n\n# Plot the edges\nstart_idx, end_idx = np.where(X_mds)\n# a sequence of (*line0*, *line1*, *line2*), where::\n#            linen = (x0, y0), (x1, y1), ... (xm, ym)\nsegments = [\n    [X_true[i, :], X_true[j, :]] for i in range(len(X_true)) for j in range(len(X_true))\n]\nedges = distances.max() / (distances + EPSILON) * 100\nnp.fill_diagonal(edges, 0)\nedges = np.abs(edges)\nlc = LineCollection(\n    segments, zorder=0, cmap=plt.cm.Blues, norm=plt.Normalize(0, edges.max())\n)\nlc.set_array(edges.flatten())\nlc.set_linewidths(np.full(len(segments), 0.5))\nax.add_collection(lc)\n\nplt.show()"
+        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Dataset preparation\n\nWe start by uniformly generating 20 points in a 2D space.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\nfrom matplotlib import pyplot as plt\nfrom matplotlib.collections import LineCollection\n\nfrom sklearn import manifold\nfrom sklearn.decomposition import PCA\nfrom sklearn.metrics import euclidean_distances\n\n# Generate the data\nEPSILON = np.finfo(np.float32).eps\nn_samples = 20\nrng = np.random.RandomState(seed=3)\nX_true = rng.randint(0, 20, 2 * n_samples).astype(float)\nX_true = X_true.reshape((n_samples, 2))\n\n# Center the data\nX_true -= X_true.mean()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Now we compute pairwise distances between all points and add\na small amount of noise to the distance matrix. We make sure\nto keep the noisy distance matrix symmetric.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# Compute pairwise Euclidean distances\ndistances = euclidean_distances(X_true)\n\n# Add noise to the distances\nnoise = rng.rand(n_samples, n_samples)\nnoise = noise + noise.T\nnp.fill_diagonal(noise, 0)\ndistances += noise"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Here we compute metric and non-metric MDS of the noisy distance matrix.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "mds = manifold.MDS(\n    n_components=2,\n    max_iter=3000,\n    eps=1e-9,\n    n_init=1,\n    random_state=42,\n    dissimilarity=\"precomputed\",\n    n_jobs=1,\n)\nX_mds = mds.fit(distances).embedding_\n\nnmds = manifold.MDS(\n    n_components=2,\n    metric=False,\n    max_iter=3000,\n    eps=1e-12,\n    dissimilarity=\"precomputed\",\n    random_state=42,\n    n_jobs=1,\n    n_init=1,\n)\nX_nmds = nmds.fit_transform(distances)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Rescaling the non-metric MDS solution to match the spread of the original data.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "X_nmds *= np.sqrt((X_true**2).sum()) / np.sqrt((X_nmds**2).sum())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "To make the visual comparisons easier, we rotate the original data and both MDS\nsolutions to their PCA axes. And flip horizontal and vertical MDS axes, if needed,\nto match the original data orientation.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# Rotate the data\npca = PCA(n_components=2)\nX_true = pca.fit_transform(X_true)\nX_mds = pca.fit_transform(X_mds)\nX_nmds = pca.fit_transform(X_nmds)\n\n# Align the sign of PCs\nfor i in [0, 1]:\n    if np.corrcoef(X_mds[:, i], X_true[:, i])[0, 1] < 0:\n        X_mds[:, i] *= -1\n    if np.corrcoef(X_nmds[:, i], X_true[:, i])[0, 1] < 0:\n        X_nmds[:, i] *= -1"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Finally, we plot the original data and both MDS reconstructions.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "fig = plt.figure(1)\nax = plt.axes([0.0, 0.0, 1.0, 1.0])\n\ns = 100\nplt.scatter(X_true[:, 0], X_true[:, 1], color=\"navy\", s=s, lw=0, label=\"True Position\")\nplt.scatter(X_mds[:, 0], X_mds[:, 1], color=\"turquoise\", s=s, lw=0, label=\"MDS\")\nplt.scatter(X_nmds[:, 0], X_nmds[:, 1], color=\"darkorange\", s=s, lw=0, label=\"NMDS\")\nplt.legend(scatterpoints=1, loc=\"best\", shadow=False)\n\n# Plot the edges\nstart_idx, end_idx = X_mds.nonzero()\n# a sequence of (*line0*, *line1*, *line2*), where::\n#            linen = (x0, y0), (x1, y1), ... (xm, ym)\nsegments = [\n    [X_true[i, :], X_true[j, :]] for i in range(len(X_true)) for j in range(len(X_true))\n]\nedges = distances.max() / (distances + EPSILON) * 100\nnp.fill_diagonal(edges, 0)\nedges = np.abs(edges)\nlc = LineCollection(\n    segments, zorder=0, cmap=plt.cm.Blues, norm=plt.Normalize(0, edges.max())\n)\nlc.set_array(edges.flatten())\nlc.set_linewidths(np.full(len(segments), 0.5))\nax.add_collection(lc)\n\nplt.show()"
       ]
     }
   ],
@@ -35,7 +143,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/a9b380a057ec6333fd7e96273331a9b8/plot_semi_supervised_newsgroups.zip b/dev/_downloads/a9b380a057ec6333fd7e96273331a9b8/plot_semi_supervised_newsgroups.zip
index e764bd11910fc..3dfe4853ca9b6 100644
Binary files a/dev/_downloads/a9b380a057ec6333fd7e96273331a9b8/plot_semi_supervised_newsgroups.zip and b/dev/_downloads/a9b380a057ec6333fd7e96273331a9b8/plot_semi_supervised_newsgroups.zip differ
diff --git a/dev/_downloads/aa8e07ce1b796a15ada1d9f0edce48b5/plot_discretization_classification.ipynb b/dev/_downloads/aa8e07ce1b796a15ada1d9f0edce48b5/plot_discretization_classification.ipynb
index b77ce35de93a9..e8782f76042d8 100644
--- a/dev/_downloads/aa8e07ce1b796a15ada1d9f0edce48b5/plot_discretization_classification.ipynb
+++ b/dev/_downloads/aa8e07ce1b796a15ada1d9f0edce48b5/plot_discretization_classification.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/aba5d73e169336038beb51608b748211/plot_linearsvc_support_vectors.zip b/dev/_downloads/aba5d73e169336038beb51608b748211/plot_linearsvc_support_vectors.zip
index 9da739787bc85..4e8b3679d3211 100644
Binary files a/dev/_downloads/aba5d73e169336038beb51608b748211/plot_linearsvc_support_vectors.zip and b/dev/_downloads/aba5d73e169336038beb51608b748211/plot_linearsvc_support_vectors.zip differ
diff --git a/dev/_downloads/abe774818c06ccab43a59d593e3b0249/plot_estimator_representation.ipynb b/dev/_downloads/abe774818c06ccab43a59d593e3b0249/plot_estimator_representation.ipynb
index 156396230a830..0d59d60a4ad0e 100644
--- a/dev/_downloads/abe774818c06ccab43a59d593e3b0249/plot_estimator_representation.ipynb
+++ b/dev/_downloads/abe774818c06ccab43a59d593e3b0249/plot_estimator_representation.ipynb
@@ -71,7 +71,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/acc912c1f80e1cb0e32675b5f7686075/plot_lda.ipynb b/dev/_downloads/acc912c1f80e1cb0e32675b5f7686075/plot_lda.ipynb
index 3a679b792dbcb..00edaead8d6b5 100644
--- a/dev/_downloads/acc912c1f80e1cb0e32675b5f7686075/plot_lda.ipynb
+++ b/dev/_downloads/acc912c1f80e1cb0e32675b5f7686075/plot_lda.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/ad1c1da0830674700828e658b74f6cf6/plot_tree_regression.ipynb b/dev/_downloads/ad1c1da0830674700828e658b74f6cf6/plot_tree_regression.ipynb
index 6c944004a3f94..a0cc4dd672feb 100644
--- a/dev/_downloads/ad1c1da0830674700828e658b74f6cf6/plot_tree_regression.ipynb
+++ b/dev/_downloads/ad1c1da0830674700828e658b74f6cf6/plot_tree_regression.ipynb
@@ -200,7 +200,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/adc9be3b7acc279025dad9ee4ce92038/plot_discretization_strategies.ipynb b/dev/_downloads/adc9be3b7acc279025dad9ee4ce92038/plot_discretization_strategies.ipynb
index 91b8a092bc856..ccb5fc70129e6 100644
--- a/dev/_downloads/adc9be3b7acc279025dad9ee4ce92038/plot_discretization_strategies.ipynb
+++ b/dev/_downloads/adc9be3b7acc279025dad9ee4ce92038/plot_discretization_strategies.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/ae2d0a2ad69c5df5b93e5ea5c87d56b2/plot_release_highlights_1_5_0.ipynb b/dev/_downloads/ae2d0a2ad69c5df5b93e5ea5c87d56b2/plot_release_highlights_1_5_0.ipynb
index a30bea8f043d6..c706368f94432 100644
--- a/dev/_downloads/ae2d0a2ad69c5df5b93e5ea5c87d56b2/plot_release_highlights_1_5_0.ipynb
+++ b/dev/_downloads/ae2d0a2ad69c5df5b93e5ea5c87d56b2/plot_release_highlights_1_5_0.ipynb
@@ -22,7 +22,7 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.datasets import make_classification\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.metrics import ConfusionMatrixDisplay\n\n\nX, y = make_classification(n_samples=10_000, weights=[0.9, 0.1], random_state=0)\nX_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)\n\nclassifier_05 = LogisticRegression(C=1e6, random_state=0).fit(X_train, y_train)\n_ = ConfusionMatrixDisplay.from_estimator(classifier_05, X_test, y_test)"
+        "from sklearn.datasets import make_classification\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.metrics import ConfusionMatrixDisplay\nfrom sklearn.model_selection import train_test_split\n\nX, y = make_classification(n_samples=10_000, weights=[0.9, 0.1], random_state=0)\nX_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)\n\nclassifier_05 = LogisticRegression(C=1e6, random_state=0).fit(X_train, y_train)\n_ = ConfusionMatrixDisplay.from_estimator(classifier_05, X_test, y_test)"
       ]
     },
     {
@@ -76,7 +76,7 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.model_selection import TunedThresholdClassifierCV\nfrom sklearn.metrics import make_scorer\n\ncustom_scorer = make_scorer(\n    custom_score, response_method=\"predict\", greater_is_better=True\n)\ntuned_classifier = TunedThresholdClassifierCV(\n    classifier_05, cv=5, scoring=custom_scorer\n).fit(X, y)\n\nprint(f\"Tuned decision threshold: {tuned_classifier.best_threshold_:.3f}\")\nprint(f\"Custom score: {custom_score(y_test, tuned_classifier.predict(X_test)):.2f}\")"
+        "from sklearn.metrics import make_scorer\nfrom sklearn.model_selection import TunedThresholdClassifierCV\n\ncustom_scorer = make_scorer(\n    custom_score, response_method=\"predict\", greater_is_better=True\n)\ntuned_classifier = TunedThresholdClassifierCV(\n    classifier_05, cv=5, scoring=custom_scorer\n).fit(X, y)\n\nprint(f\"Tuned decision threshold: {tuned_classifier.best_threshold_:.3f}\")\nprint(f\"Custom score: {custom_score(y_test, tuned_classifier.predict(X_test)):.2f}\")"
       ]
     },
     {
@@ -144,7 +144,7 @@
       },
       "outputs": [],
       "source": [
-        "import numpy as np\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\n\nX = np.array([[0, 1, 2], [3, 4, 5]])\ncolumn_transformer = ColumnTransformer(\n    [(\"std_scaler\", StandardScaler(), [0]), (\"one_hot\", OneHotEncoder(), [1, 2])]\n)\n\ncolumn_transformer.fit(X)\n\nprint(column_transformer[\"std_scaler\"])\nprint(column_transformer[\"one_hot\"])"
+        "import numpy as np\n\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.preprocessing import OneHotEncoder, StandardScaler\n\nX = np.array([[0, 1, 2], [3, 4, 5]])\ncolumn_transformer = ColumnTransformer(\n    [(\"std_scaler\", StandardScaler(), [0]), (\"one_hot\", OneHotEncoder(), [1, 2])]\n)\n\ncolumn_transformer.fit(X)\n\nprint(column_transformer[\"std_scaler\"])\nprint(column_transformer[\"one_hot\"])"
       ]
     },
     {
@@ -200,7 +200,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/aec731a57fcba7fde8f5e3d94ffc7c69/plot_logistic_l1_l2_sparsity.ipynb b/dev/_downloads/aec731a57fcba7fde8f5e3d94ffc7c69/plot_logistic_l1_l2_sparsity.ipynb
index f7c7f7f99cb73..75890fa75beb3 100644
--- a/dev/_downloads/aec731a57fcba7fde8f5e3d94ffc7c69/plot_logistic_l1_l2_sparsity.ipynb
+++ b/dev/_downloads/aec731a57fcba7fde8f5e3d94ffc7c69/plot_logistic_l1_l2_sparsity.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/af8345a01f32fc7a8b3c9693bb4aca30/plot_grid_search_refit_callable.ipynb b/dev/_downloads/af8345a01f32fc7a8b3c9693bb4aca30/plot_grid_search_refit_callable.ipynb
index a8f8d6d8d33dc..138d6551f3dfd 100644
--- a/dev/_downloads/af8345a01f32fc7a8b3c9693bb4aca30/plot_grid_search_refit_callable.ipynb
+++ b/dev/_downloads/af8345a01f32fc7a8b3c9693bb4aca30/plot_grid_search_refit_callable.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/aff4193b21ef6ea0af509058be609d18/plot_kernel_ridge_regression.zip b/dev/_downloads/aff4193b21ef6ea0af509058be609d18/plot_kernel_ridge_regression.zip
index abd4e0c169bee..8042349bf6bc7 100644
Binary files a/dev/_downloads/aff4193b21ef6ea0af509058be609d18/plot_kernel_ridge_regression.zip and b/dev/_downloads/aff4193b21ef6ea0af509058be609d18/plot_kernel_ridge_regression.zip differ
diff --git a/dev/_downloads/b05e6cdf6d51481f37bf29b0bb92995e/plot_kmeans_assumptions.ipynb b/dev/_downloads/b05e6cdf6d51481f37bf29b0bb92995e/plot_kmeans_assumptions.ipynb
index 617bd22a63d16..b53d9446b469b 100644
--- a/dev/_downloads/b05e6cdf6d51481f37bf29b0bb92995e/plot_kmeans_assumptions.ipynb
+++ b/dev/_downloads/b05e6cdf6d51481f37bf29b0bb92995e/plot_kmeans_assumptions.ipynb
@@ -150,7 +150,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/b12810e5af9bfe7f99d2564c84262560/plot_coin_segmentation.zip b/dev/_downloads/b12810e5af9bfe7f99d2564c84262560/plot_coin_segmentation.zip
index 48c79520ec4e3..dfc5ce086b60b 100644
Binary files a/dev/_downloads/b12810e5af9bfe7f99d2564c84262560/plot_coin_segmentation.zip and b/dev/_downloads/b12810e5af9bfe7f99d2564c84262560/plot_coin_segmentation.zip differ
diff --git a/dev/_downloads/b22a06b90f4e4dff100e395092c650be/plot_random_forest_embedding.ipynb b/dev/_downloads/b22a06b90f4e4dff100e395092c650be/plot_random_forest_embedding.ipynb
index 0fe55e3fc716e..862aeeada4131 100644
--- a/dev/_downloads/b22a06b90f4e4dff100e395092c650be/plot_random_forest_embedding.ipynb
+++ b/dev/_downloads/b22a06b90f4e4dff100e395092c650be/plot_random_forest_embedding.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/b26574ccf9c31e12ab2afd8d683f3279/plot_topics_extraction_with_nmf_lda.ipynb b/dev/_downloads/b26574ccf9c31e12ab2afd8d683f3279/plot_topics_extraction_with_nmf_lda.ipynb
index 8becce9589a29..4c35617abcf2e 100644
--- a/dev/_downloads/b26574ccf9c31e12ab2afd8d683f3279/plot_topics_extraction_with_nmf_lda.ipynb
+++ b/dev/_downloads/b26574ccf9c31e12ab2afd8d683f3279/plot_topics_extraction_with_nmf_lda.ipynb
@@ -15,7 +15,7 @@
       },
       "outputs": [],
       "source": [
-        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\n\nfrom time import time\n\nimport matplotlib.pyplot as plt\n\nfrom sklearn.datasets import fetch_20newsgroups\nfrom sklearn.decomposition import NMF, LatentDirichletAllocation, MiniBatchNMF\nfrom sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\n\nn_samples = 2000\nn_features = 1000\nn_components = 10\nn_top_words = 20\nbatch_size = 128\ninit = \"nndsvda\"\n\n\ndef plot_top_words(model, feature_names, n_top_words, title):\n    fig, axes = plt.subplots(2, 5, figsize=(30, 15), sharex=True)\n    axes = axes.flatten()\n    for topic_idx, topic in enumerate(model.components_):\n        top_features_ind = topic.argsort()[-n_top_words:]\n        top_features = feature_names[top_features_ind]\n        weights = topic[top_features_ind]\n\n        ax = axes[topic_idx]\n        ax.barh(top_features, weights, height=0.7)\n        ax.set_title(f\"Topic {topic_idx +1}\", fontdict={\"fontsize\": 30})\n        ax.tick_params(axis=\"both\", which=\"major\", labelsize=20)\n        for i in \"top right left\".split():\n            ax.spines[i].set_visible(False)\n        fig.suptitle(title, fontsize=40)\n\n    plt.subplots_adjust(top=0.90, bottom=0.05, wspace=0.90, hspace=0.3)\n    plt.show()\n\n\n# Load the 20 newsgroups dataset and vectorize it. We use a few heuristics\n# to filter out useless terms early on: the posts are stripped of headers,\n# footers and quoted replies, and common English words, words occurring in\n# only one document or in at least 95% of the documents are removed.\n\nprint(\"Loading dataset...\")\nt0 = time()\ndata, _ = fetch_20newsgroups(\n    shuffle=True,\n    random_state=1,\n    remove=(\"headers\", \"footers\", \"quotes\"),\n    return_X_y=True,\n)\ndata_samples = data[:n_samples]\nprint(\"done in %0.3fs.\" % (time() - t0))\n\n# Use tf-idf features for NMF.\nprint(\"Extracting tf-idf features for NMF...\")\ntfidf_vectorizer = TfidfVectorizer(\n    max_df=0.95, min_df=2, max_features=n_features, stop_words=\"english\"\n)\nt0 = time()\ntfidf = tfidf_vectorizer.fit_transform(data_samples)\nprint(\"done in %0.3fs.\" % (time() - t0))\n\n# Use tf (raw term count) features for LDA.\nprint(\"Extracting tf features for LDA...\")\ntf_vectorizer = CountVectorizer(\n    max_df=0.95, min_df=2, max_features=n_features, stop_words=\"english\"\n)\nt0 = time()\ntf = tf_vectorizer.fit_transform(data_samples)\nprint(\"done in %0.3fs.\" % (time() - t0))\nprint()\n\n# Fit the NMF model\nprint(\n    \"Fitting the NMF model (Frobenius norm) with tf-idf features, \"\n    \"n_samples=%d and n_features=%d...\" % (n_samples, n_features)\n)\nt0 = time()\nnmf = NMF(\n    n_components=n_components,\n    random_state=1,\n    init=init,\n    beta_loss=\"frobenius\",\n    alpha_W=0.00005,\n    alpha_H=0.00005,\n    l1_ratio=1,\n).fit(tfidf)\nprint(\"done in %0.3fs.\" % (time() - t0))\n\n\ntfidf_feature_names = tfidf_vectorizer.get_feature_names_out()\nplot_top_words(\n    nmf, tfidf_feature_names, n_top_words, \"Topics in NMF model (Frobenius norm)\"\n)\n\n# Fit the NMF model\nprint(\n    \"\\n\" * 2,\n    \"Fitting the NMF model (generalized Kullback-Leibler \"\n    \"divergence) with tf-idf features, n_samples=%d and n_features=%d...\"\n    % (n_samples, n_features),\n)\nt0 = time()\nnmf = NMF(\n    n_components=n_components,\n    random_state=1,\n    init=init,\n    beta_loss=\"kullback-leibler\",\n    solver=\"mu\",\n    max_iter=1000,\n    alpha_W=0.00005,\n    alpha_H=0.00005,\n    l1_ratio=0.5,\n).fit(tfidf)\nprint(\"done in %0.3fs.\" % (time() - t0))\n\ntfidf_feature_names = tfidf_vectorizer.get_feature_names_out()\nplot_top_words(\n    nmf,\n    tfidf_feature_names,\n    n_top_words,\n    \"Topics in NMF model (generalized Kullback-Leibler divergence)\",\n)\n\n# Fit the MiniBatchNMF model\nprint(\n    \"\\n\" * 2,\n    \"Fitting the MiniBatchNMF model (Frobenius norm) with tf-idf \"\n    \"features, n_samples=%d and n_features=%d, batch_size=%d...\"\n    % (n_samples, n_features, batch_size),\n)\nt0 = time()\nmbnmf = MiniBatchNMF(\n    n_components=n_components,\n    random_state=1,\n    batch_size=batch_size,\n    init=init,\n    beta_loss=\"frobenius\",\n    alpha_W=0.00005,\n    alpha_H=0.00005,\n    l1_ratio=0.5,\n).fit(tfidf)\nprint(\"done in %0.3fs.\" % (time() - t0))\n\n\ntfidf_feature_names = tfidf_vectorizer.get_feature_names_out()\nplot_top_words(\n    mbnmf,\n    tfidf_feature_names,\n    n_top_words,\n    \"Topics in MiniBatchNMF model (Frobenius norm)\",\n)\n\n# Fit the MiniBatchNMF model\nprint(\n    \"\\n\" * 2,\n    \"Fitting the MiniBatchNMF model (generalized Kullback-Leibler \"\n    \"divergence) with tf-idf features, n_samples=%d and n_features=%d, \"\n    \"batch_size=%d...\" % (n_samples, n_features, batch_size),\n)\nt0 = time()\nmbnmf = MiniBatchNMF(\n    n_components=n_components,\n    random_state=1,\n    batch_size=batch_size,\n    init=init,\n    beta_loss=\"kullback-leibler\",\n    alpha_W=0.00005,\n    alpha_H=0.00005,\n    l1_ratio=0.5,\n).fit(tfidf)\nprint(\"done in %0.3fs.\" % (time() - t0))\n\ntfidf_feature_names = tfidf_vectorizer.get_feature_names_out()\nplot_top_words(\n    mbnmf,\n    tfidf_feature_names,\n    n_top_words,\n    \"Topics in MiniBatchNMF model (generalized Kullback-Leibler divergence)\",\n)\n\nprint(\n    \"\\n\" * 2,\n    \"Fitting LDA models with tf features, n_samples=%d and n_features=%d...\"\n    % (n_samples, n_features),\n)\nlda = LatentDirichletAllocation(\n    n_components=n_components,\n    max_iter=5,\n    learning_method=\"online\",\n    learning_offset=50.0,\n    random_state=0,\n)\nt0 = time()\nlda.fit(tf)\nprint(\"done in %0.3fs.\" % (time() - t0))\n\ntf_feature_names = tf_vectorizer.get_feature_names_out()\nplot_top_words(lda, tf_feature_names, n_top_words, \"Topics in LDA model\")"
+        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\n\nfrom time import time\n\nimport matplotlib.pyplot as plt\n\nfrom sklearn.datasets import fetch_20newsgroups\nfrom sklearn.decomposition import NMF, LatentDirichletAllocation, MiniBatchNMF\nfrom sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\n\nn_samples = 2000\nn_features = 1000\nn_components = 10\nn_top_words = 20\nbatch_size = 128\ninit = \"nndsvda\"\n\n\ndef plot_top_words(model, feature_names, n_top_words, title):\n    fig, axes = plt.subplots(2, 5, figsize=(30, 15), sharex=True)\n    axes = axes.flatten()\n    for topic_idx, topic in enumerate(model.components_):\n        top_features_ind = topic.argsort()[-n_top_words:]\n        top_features = feature_names[top_features_ind]\n        weights = topic[top_features_ind]\n\n        ax = axes[topic_idx]\n        ax.barh(top_features, weights, height=0.7)\n        ax.set_title(f\"Topic {topic_idx + 1}\", fontdict={\"fontsize\": 30})\n        ax.tick_params(axis=\"both\", which=\"major\", labelsize=20)\n        for i in \"top right left\".split():\n            ax.spines[i].set_visible(False)\n        fig.suptitle(title, fontsize=40)\n\n    plt.subplots_adjust(top=0.90, bottom=0.05, wspace=0.90, hspace=0.3)\n    plt.show()\n\n\n# Load the 20 newsgroups dataset and vectorize it. We use a few heuristics\n# to filter out useless terms early on: the posts are stripped of headers,\n# footers and quoted replies, and common English words, words occurring in\n# only one document or in at least 95% of the documents are removed.\n\nprint(\"Loading dataset...\")\nt0 = time()\ndata, _ = fetch_20newsgroups(\n    shuffle=True,\n    random_state=1,\n    remove=(\"headers\", \"footers\", \"quotes\"),\n    return_X_y=True,\n)\ndata_samples = data[:n_samples]\nprint(\"done in %0.3fs.\" % (time() - t0))\n\n# Use tf-idf features for NMF.\nprint(\"Extracting tf-idf features for NMF...\")\ntfidf_vectorizer = TfidfVectorizer(\n    max_df=0.95, min_df=2, max_features=n_features, stop_words=\"english\"\n)\nt0 = time()\ntfidf = tfidf_vectorizer.fit_transform(data_samples)\nprint(\"done in %0.3fs.\" % (time() - t0))\n\n# Use tf (raw term count) features for LDA.\nprint(\"Extracting tf features for LDA...\")\ntf_vectorizer = CountVectorizer(\n    max_df=0.95, min_df=2, max_features=n_features, stop_words=\"english\"\n)\nt0 = time()\ntf = tf_vectorizer.fit_transform(data_samples)\nprint(\"done in %0.3fs.\" % (time() - t0))\nprint()\n\n# Fit the NMF model\nprint(\n    \"Fitting the NMF model (Frobenius norm) with tf-idf features, \"\n    \"n_samples=%d and n_features=%d...\" % (n_samples, n_features)\n)\nt0 = time()\nnmf = NMF(\n    n_components=n_components,\n    random_state=1,\n    init=init,\n    beta_loss=\"frobenius\",\n    alpha_W=0.00005,\n    alpha_H=0.00005,\n    l1_ratio=1,\n).fit(tfidf)\nprint(\"done in %0.3fs.\" % (time() - t0))\n\n\ntfidf_feature_names = tfidf_vectorizer.get_feature_names_out()\nplot_top_words(\n    nmf, tfidf_feature_names, n_top_words, \"Topics in NMF model (Frobenius norm)\"\n)\n\n# Fit the NMF model\nprint(\n    \"\\n\" * 2,\n    \"Fitting the NMF model (generalized Kullback-Leibler \"\n    \"divergence) with tf-idf features, n_samples=%d and n_features=%d...\"\n    % (n_samples, n_features),\n)\nt0 = time()\nnmf = NMF(\n    n_components=n_components,\n    random_state=1,\n    init=init,\n    beta_loss=\"kullback-leibler\",\n    solver=\"mu\",\n    max_iter=1000,\n    alpha_W=0.00005,\n    alpha_H=0.00005,\n    l1_ratio=0.5,\n).fit(tfidf)\nprint(\"done in %0.3fs.\" % (time() - t0))\n\ntfidf_feature_names = tfidf_vectorizer.get_feature_names_out()\nplot_top_words(\n    nmf,\n    tfidf_feature_names,\n    n_top_words,\n    \"Topics in NMF model (generalized Kullback-Leibler divergence)\",\n)\n\n# Fit the MiniBatchNMF model\nprint(\n    \"\\n\" * 2,\n    \"Fitting the MiniBatchNMF model (Frobenius norm) with tf-idf \"\n    \"features, n_samples=%d and n_features=%d, batch_size=%d...\"\n    % (n_samples, n_features, batch_size),\n)\nt0 = time()\nmbnmf = MiniBatchNMF(\n    n_components=n_components,\n    random_state=1,\n    batch_size=batch_size,\n    init=init,\n    beta_loss=\"frobenius\",\n    alpha_W=0.00005,\n    alpha_H=0.00005,\n    l1_ratio=0.5,\n).fit(tfidf)\nprint(\"done in %0.3fs.\" % (time() - t0))\n\n\ntfidf_feature_names = tfidf_vectorizer.get_feature_names_out()\nplot_top_words(\n    mbnmf,\n    tfidf_feature_names,\n    n_top_words,\n    \"Topics in MiniBatchNMF model (Frobenius norm)\",\n)\n\n# Fit the MiniBatchNMF model\nprint(\n    \"\\n\" * 2,\n    \"Fitting the MiniBatchNMF model (generalized Kullback-Leibler \"\n    \"divergence) with tf-idf features, n_samples=%d and n_features=%d, \"\n    \"batch_size=%d...\" % (n_samples, n_features, batch_size),\n)\nt0 = time()\nmbnmf = MiniBatchNMF(\n    n_components=n_components,\n    random_state=1,\n    batch_size=batch_size,\n    init=init,\n    beta_loss=\"kullback-leibler\",\n    alpha_W=0.00005,\n    alpha_H=0.00005,\n    l1_ratio=0.5,\n).fit(tfidf)\nprint(\"done in %0.3fs.\" % (time() - t0))\n\ntfidf_feature_names = tfidf_vectorizer.get_feature_names_out()\nplot_top_words(\n    mbnmf,\n    tfidf_feature_names,\n    n_top_words,\n    \"Topics in MiniBatchNMF model (generalized Kullback-Leibler divergence)\",\n)\n\nprint(\n    \"\\n\" * 2,\n    \"Fitting LDA models with tf features, n_samples=%d and n_features=%d...\"\n    % (n_samples, n_features),\n)\nlda = LatentDirichletAllocation(\n    n_components=n_components,\n    max_iter=5,\n    learning_method=\"online\",\n    learning_offset=50.0,\n    random_state=0,\n)\nt0 = time()\nlda.fit(tf)\nprint(\"done in %0.3fs.\" % (time() - t0))\n\ntf_feature_names = tf_vectorizer.get_feature_names_out()\nplot_top_words(lda, tf_feature_names, n_top_words, \"Topics in LDA model\")"
       ]
     }
   ],
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/b327cd0a0802e17daefbd5de7ade2705/plot_svm_scale_c.zip b/dev/_downloads/b327cd0a0802e17daefbd5de7ade2705/plot_svm_scale_c.zip
index 254fa19538a37..9c4e044326e1a 100644
Binary files a/dev/_downloads/b327cd0a0802e17daefbd5de7ade2705/plot_svm_scale_c.zip and b/dev/_downloads/b327cd0a0802e17daefbd5de7ade2705/plot_svm_scale_c.zip differ
diff --git a/dev/_downloads/b367e30cc681ed484e0148f4ce9eccb0/plot_calibration_multiclass.ipynb b/dev/_downloads/b367e30cc681ed484e0148f4ce9eccb0/plot_calibration_multiclass.ipynb
index 3e44631b41208..5e0e7a42e026e 100644
--- a/dev/_downloads/b367e30cc681ed484e0148f4ce9eccb0/plot_calibration_multiclass.ipynb
+++ b/dev/_downloads/b367e30cc681ed484e0148f4ce9eccb0/plot_calibration_multiclass.ipynb
@@ -157,7 +157,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/b49810e68af99a01e25ba2dfc951b687/plot_train_error_vs_test_error.ipynb b/dev/_downloads/b49810e68af99a01e25ba2dfc951b687/plot_train_error_vs_test_error.ipynb
index f51f72dde7c14..b77f9ebfcaa0b 100644
--- a/dev/_downloads/b49810e68af99a01e25ba2dfc951b687/plot_train_error_vs_test_error.ipynb
+++ b/dev/_downloads/b49810e68af99a01e25ba2dfc951b687/plot_train_error_vs_test_error.ipynb
@@ -132,7 +132,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/b55408ba1cd122d5939c6ec627dc6a10/plot_time_series_lagged_features.zip b/dev/_downloads/b55408ba1cd122d5939c6ec627dc6a10/plot_time_series_lagged_features.zip
index dc23d467b860b..8a133f8e50c75 100644
Binary files a/dev/_downloads/b55408ba1cd122d5939c6ec627dc6a10/plot_time_series_lagged_features.zip and b/dev/_downloads/b55408ba1cd122d5939c6ec627dc6a10/plot_time_series_lagged_features.zip differ
diff --git a/dev/_downloads/b5ac5dfd67b0aab146fcb9faaac8480c/plot_gradient_boosting_quantile.ipynb b/dev/_downloads/b5ac5dfd67b0aab146fcb9faaac8480c/plot_gradient_boosting_quantile.ipynb
index 3be354a19103b..75496e60cbc57 100644
--- a/dev/_downloads/b5ac5dfd67b0aab146fcb9faaac8480c/plot_gradient_boosting_quantile.ipynb
+++ b/dev/_downloads/b5ac5dfd67b0aab146fcb9faaac8480c/plot_gradient_boosting_quantile.ipynb
@@ -231,7 +231,7 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.experimental import enable_halving_search_cv  # noqa\nfrom sklearn.model_selection import HalvingRandomSearchCV\nfrom sklearn.metrics import make_scorer\nfrom pprint import pprint\n\nparam_grid = dict(\n    learning_rate=[0.05, 0.1, 0.2],\n    max_depth=[2, 5, 10],\n    min_samples_leaf=[1, 5, 10, 20],\n    min_samples_split=[5, 10, 20, 30, 50],\n)\nalpha = 0.05\nneg_mean_pinball_loss_05p_scorer = make_scorer(\n    mean_pinball_loss,\n    alpha=alpha,\n    greater_is_better=False,  # maximize the negative loss\n)\ngbr = GradientBoostingRegressor(loss=\"quantile\", alpha=alpha, random_state=0)\nsearch_05p = HalvingRandomSearchCV(\n    gbr,\n    param_grid,\n    resource=\"n_estimators\",\n    max_resources=250,\n    min_resources=50,\n    scoring=neg_mean_pinball_loss_05p_scorer,\n    n_jobs=2,\n    random_state=0,\n).fit(X_train, y_train)\npprint(search_05p.best_params_)"
+        "from pprint import pprint\n\nfrom sklearn.experimental import enable_halving_search_cv  # noqa: F401\nfrom sklearn.metrics import make_scorer\nfrom sklearn.model_selection import HalvingRandomSearchCV\n\nparam_grid = dict(\n    learning_rate=[0.05, 0.1, 0.2],\n    max_depth=[2, 5, 10],\n    min_samples_leaf=[1, 5, 10, 20],\n    min_samples_split=[5, 10, 20, 30, 50],\n)\nalpha = 0.05\nneg_mean_pinball_loss_05p_scorer = make_scorer(\n    mean_pinball_loss,\n    alpha=alpha,\n    greater_is_better=False,  # maximize the negative loss\n)\ngbr = GradientBoostingRegressor(loss=\"quantile\", alpha=alpha, random_state=0)\nsearch_05p = HalvingRandomSearchCV(\n    gbr,\n    param_grid,\n    resource=\"n_estimators\",\n    max_resources=250,\n    min_resources=50,\n    scoring=neg_mean_pinball_loss_05p_scorer,\n    n_jobs=2,\n    random_state=0,\n).fit(X_train, y_train)\npprint(search_05p.best_params_)"
       ]
     },
     {
@@ -323,7 +323,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/b5ad104e8ecfa33bea1e5b896063738a/plot_cv_predict.zip b/dev/_downloads/b5ad104e8ecfa33bea1e5b896063738a/plot_cv_predict.zip
index 9425eeccd6434..cbf595899aacb 100644
Binary files a/dev/_downloads/b5ad104e8ecfa33bea1e5b896063738a/plot_cv_predict.zip and b/dev/_downloads/b5ad104e8ecfa33bea1e5b896063738a/plot_cv_predict.zip differ
diff --git a/dev/_downloads/b5d1ec88ae06ced89813c50d00effe51/plot_species_distribution_modeling.ipynb b/dev/_downloads/b5d1ec88ae06ced89813c50d00effe51/plot_species_distribution_modeling.ipynb
index 84afeec19e578..2e9ffe60d7d7c 100644
--- a/dev/_downloads/b5d1ec88ae06ced89813c50d00effe51/plot_species_distribution_modeling.ipynb
+++ b/dev/_downloads/b5d1ec88ae06ced89813c50d00effe51/plot_species_distribution_modeling.ipynb
@@ -15,7 +15,7 @@
       },
       "outputs": [],
       "source": [
-        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\n\nfrom time import time\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nfrom sklearn import metrics, svm\nfrom sklearn.datasets import fetch_species_distributions\nfrom sklearn.utils import Bunch\n\n# if basemap is available, we'll use it.\n# otherwise, we'll improvise later...\ntry:\n    from mpl_toolkits.basemap import Basemap\n\n    basemap = True\nexcept ImportError:\n    basemap = False\n\n\ndef construct_grids(batch):\n    \"\"\"Construct the map grid from the batch object\n\n    Parameters\n    ----------\n    batch : Batch object\n        The object returned by :func:`fetch_species_distributions`\n\n    Returns\n    -------\n    (xgrid, ygrid) : 1-D arrays\n        The grid corresponding to the values in batch.coverages\n    \"\"\"\n    # x,y coordinates for corner cells\n    xmin = batch.x_left_lower_corner + batch.grid_size\n    xmax = xmin + (batch.Nx * batch.grid_size)\n    ymin = batch.y_left_lower_corner + batch.grid_size\n    ymax = ymin + (batch.Ny * batch.grid_size)\n\n    # x coordinates of the grid cells\n    xgrid = np.arange(xmin, xmax, batch.grid_size)\n    # y coordinates of the grid cells\n    ygrid = np.arange(ymin, ymax, batch.grid_size)\n\n    return (xgrid, ygrid)\n\n\ndef create_species_bunch(species_name, train, test, coverages, xgrid, ygrid):\n    \"\"\"Create a bunch with information about a particular organism\n\n    This will use the test/train record arrays to extract the\n    data specific to the given species name.\n    \"\"\"\n    bunch = Bunch(name=\" \".join(species_name.split(\"_\")[:2]))\n    species_name = species_name.encode(\"ascii\")\n    points = dict(test=test, train=train)\n\n    for label, pts in points.items():\n        # choose points associated with the desired species\n        pts = pts[pts[\"species\"] == species_name]\n        bunch[\"pts_%s\" % label] = pts\n\n        # determine coverage values for each of the training & testing points\n        ix = np.searchsorted(xgrid, pts[\"dd long\"])\n        iy = np.searchsorted(ygrid, pts[\"dd lat\"])\n        bunch[\"cov_%s\" % label] = coverages[:, -iy, ix].T\n\n    return bunch\n\n\ndef plot_species_distribution(\n    species=(\"bradypus_variegatus_0\", \"microryzomys_minutus_0\")\n):\n    \"\"\"\n    Plot the species distribution.\n    \"\"\"\n    if len(species) > 2:\n        print(\n            \"Note: when more than two species are provided,\"\n            \" only the first two will be used\"\n        )\n\n    t0 = time()\n\n    # Load the compressed data\n    data = fetch_species_distributions()\n\n    # Set up the data grid\n    xgrid, ygrid = construct_grids(data)\n\n    # The grid in x,y coordinates\n    X, Y = np.meshgrid(xgrid, ygrid[::-1])\n\n    # create a bunch for each species\n    BV_bunch = create_species_bunch(\n        species[0], data.train, data.test, data.coverages, xgrid, ygrid\n    )\n    MM_bunch = create_species_bunch(\n        species[1], data.train, data.test, data.coverages, xgrid, ygrid\n    )\n\n    # background points (grid coordinates) for evaluation\n    np.random.seed(13)\n    background_points = np.c_[\n        np.random.randint(low=0, high=data.Ny, size=10000),\n        np.random.randint(low=0, high=data.Nx, size=10000),\n    ].T\n\n    # We'll make use of the fact that coverages[6] has measurements at all\n    # land points.  This will help us decide between land and water.\n    land_reference = data.coverages[6]\n\n    # Fit, predict, and plot for each species.\n    for i, species in enumerate([BV_bunch, MM_bunch]):\n        print(\"_\" * 80)\n        print(\"Modeling distribution of species '%s'\" % species.name)\n\n        # Standardize features\n        mean = species.cov_train.mean(axis=0)\n        std = species.cov_train.std(axis=0)\n        train_cover_std = (species.cov_train - mean) / std\n\n        # Fit OneClassSVM\n        print(\" - fit OneClassSVM ... \", end=\"\")\n        clf = svm.OneClassSVM(nu=0.1, kernel=\"rbf\", gamma=0.5)\n        clf.fit(train_cover_std)\n        print(\"done.\")\n\n        # Plot map of South America\n        plt.subplot(1, 2, i + 1)\n        if basemap:\n            print(\" - plot coastlines using basemap\")\n            m = Basemap(\n                projection=\"cyl\",\n                llcrnrlat=Y.min(),\n                urcrnrlat=Y.max(),\n                llcrnrlon=X.min(),\n                urcrnrlon=X.max(),\n                resolution=\"c\",\n            )\n            m.drawcoastlines()\n            m.drawcountries()\n        else:\n            print(\" - plot coastlines from coverage\")\n            plt.contour(\n                X, Y, land_reference, levels=[-9998], colors=\"k\", linestyles=\"solid\"\n            )\n            plt.xticks([])\n            plt.yticks([])\n\n        print(\" - predict species distribution\")\n\n        # Predict species distribution using the training data\n        Z = np.ones((data.Ny, data.Nx), dtype=np.float64)\n\n        # We'll predict only for the land points.\n        idx = np.where(land_reference > -9999)\n        coverages_land = data.coverages[:, idx[0], idx[1]].T\n\n        pred = clf.decision_function((coverages_land - mean) / std)\n        Z *= pred.min()\n        Z[idx[0], idx[1]] = pred\n\n        levels = np.linspace(Z.min(), Z.max(), 25)\n        Z[land_reference == -9999] = -9999\n\n        # plot contours of the prediction\n        plt.contourf(X, Y, Z, levels=levels, cmap=plt.cm.Reds)\n        plt.colorbar(format=\"%.2f\")\n\n        # scatter training/testing points\n        plt.scatter(\n            species.pts_train[\"dd long\"],\n            species.pts_train[\"dd lat\"],\n            s=2**2,\n            c=\"black\",\n            marker=\"^\",\n            label=\"train\",\n        )\n        plt.scatter(\n            species.pts_test[\"dd long\"],\n            species.pts_test[\"dd lat\"],\n            s=2**2,\n            c=\"black\",\n            marker=\"x\",\n            label=\"test\",\n        )\n        plt.legend()\n        plt.title(species.name)\n        plt.axis(\"equal\")\n\n        # Compute AUC with regards to background points\n        pred_background = Z[background_points[0], background_points[1]]\n        pred_test = clf.decision_function((species.cov_test - mean) / std)\n        scores = np.r_[pred_test, pred_background]\n        y = np.r_[np.ones(pred_test.shape), np.zeros(pred_background.shape)]\n        fpr, tpr, thresholds = metrics.roc_curve(y, scores)\n        roc_auc = metrics.auc(fpr, tpr)\n        plt.text(-35, -70, \"AUC: %.3f\" % roc_auc, ha=\"right\")\n        print(\"\\n Area under the ROC curve : %f\" % roc_auc)\n\n    print(\"\\ntime elapsed: %.2fs\" % (time() - t0))\n\n\nplot_species_distribution()\nplt.show()"
+        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\n\nfrom time import time\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nfrom sklearn import metrics, svm\nfrom sklearn.datasets import fetch_species_distributions\nfrom sklearn.utils import Bunch\n\n# if basemap is available, we'll use it.\n# otherwise, we'll improvise later...\ntry:\n    from mpl_toolkits.basemap import Basemap\n\n    basemap = True\nexcept ImportError:\n    basemap = False\n\n\ndef construct_grids(batch):\n    \"\"\"Construct the map grid from the batch object\n\n    Parameters\n    ----------\n    batch : Batch object\n        The object returned by :func:`fetch_species_distributions`\n\n    Returns\n    -------\n    (xgrid, ygrid) : 1-D arrays\n        The grid corresponding to the values in batch.coverages\n    \"\"\"\n    # x,y coordinates for corner cells\n    xmin = batch.x_left_lower_corner + batch.grid_size\n    xmax = xmin + (batch.Nx * batch.grid_size)\n    ymin = batch.y_left_lower_corner + batch.grid_size\n    ymax = ymin + (batch.Ny * batch.grid_size)\n\n    # x coordinates of the grid cells\n    xgrid = np.arange(xmin, xmax, batch.grid_size)\n    # y coordinates of the grid cells\n    ygrid = np.arange(ymin, ymax, batch.grid_size)\n\n    return (xgrid, ygrid)\n\n\ndef create_species_bunch(species_name, train, test, coverages, xgrid, ygrid):\n    \"\"\"Create a bunch with information about a particular organism\n\n    This will use the test/train record arrays to extract the\n    data specific to the given species name.\n    \"\"\"\n    bunch = Bunch(name=\" \".join(species_name.split(\"_\")[:2]))\n    species_name = species_name.encode(\"ascii\")\n    points = dict(test=test, train=train)\n\n    for label, pts in points.items():\n        # choose points associated with the desired species\n        pts = pts[pts[\"species\"] == species_name]\n        bunch[\"pts_%s\" % label] = pts\n\n        # determine coverage values for each of the training & testing points\n        ix = np.searchsorted(xgrid, pts[\"dd long\"])\n        iy = np.searchsorted(ygrid, pts[\"dd lat\"])\n        bunch[\"cov_%s\" % label] = coverages[:, -iy, ix].T\n\n    return bunch\n\n\ndef plot_species_distribution(\n    species=(\"bradypus_variegatus_0\", \"microryzomys_minutus_0\"),\n):\n    \"\"\"\n    Plot the species distribution.\n    \"\"\"\n    if len(species) > 2:\n        print(\n            \"Note: when more than two species are provided,\"\n            \" only the first two will be used\"\n        )\n\n    t0 = time()\n\n    # Load the compressed data\n    data = fetch_species_distributions()\n\n    # Set up the data grid\n    xgrid, ygrid = construct_grids(data)\n\n    # The grid in x,y coordinates\n    X, Y = np.meshgrid(xgrid, ygrid[::-1])\n\n    # create a bunch for each species\n    BV_bunch = create_species_bunch(\n        species[0], data.train, data.test, data.coverages, xgrid, ygrid\n    )\n    MM_bunch = create_species_bunch(\n        species[1], data.train, data.test, data.coverages, xgrid, ygrid\n    )\n\n    # background points (grid coordinates) for evaluation\n    np.random.seed(13)\n    background_points = np.c_[\n        np.random.randint(low=0, high=data.Ny, size=10000),\n        np.random.randint(low=0, high=data.Nx, size=10000),\n    ].T\n\n    # We'll make use of the fact that coverages[6] has measurements at all\n    # land points.  This will help us decide between land and water.\n    land_reference = data.coverages[6]\n\n    # Fit, predict, and plot for each species.\n    for i, species in enumerate([BV_bunch, MM_bunch]):\n        print(\"_\" * 80)\n        print(\"Modeling distribution of species '%s'\" % species.name)\n\n        # Standardize features\n        mean = species.cov_train.mean(axis=0)\n        std = species.cov_train.std(axis=0)\n        train_cover_std = (species.cov_train - mean) / std\n\n        # Fit OneClassSVM\n        print(\" - fit OneClassSVM ... \", end=\"\")\n        clf = svm.OneClassSVM(nu=0.1, kernel=\"rbf\", gamma=0.5)\n        clf.fit(train_cover_std)\n        print(\"done.\")\n\n        # Plot map of South America\n        plt.subplot(1, 2, i + 1)\n        if basemap:\n            print(\" - plot coastlines using basemap\")\n            m = Basemap(\n                projection=\"cyl\",\n                llcrnrlat=Y.min(),\n                urcrnrlat=Y.max(),\n                llcrnrlon=X.min(),\n                urcrnrlon=X.max(),\n                resolution=\"c\",\n            )\n            m.drawcoastlines()\n            m.drawcountries()\n        else:\n            print(\" - plot coastlines from coverage\")\n            plt.contour(\n                X, Y, land_reference, levels=[-9998], colors=\"k\", linestyles=\"solid\"\n            )\n            plt.xticks([])\n            plt.yticks([])\n\n        print(\" - predict species distribution\")\n\n        # Predict species distribution using the training data\n        Z = np.ones((data.Ny, data.Nx), dtype=np.float64)\n\n        # We'll predict only for the land points.\n        idx = (land_reference > -9999).nonzero()\n        coverages_land = data.coverages[:, idx[0], idx[1]].T\n\n        pred = clf.decision_function((coverages_land - mean) / std)\n        Z *= pred.min()\n        Z[idx[0], idx[1]] = pred\n\n        levels = np.linspace(Z.min(), Z.max(), 25)\n        Z[land_reference == -9999] = -9999\n\n        # plot contours of the prediction\n        plt.contourf(X, Y, Z, levels=levels, cmap=plt.cm.Reds)\n        plt.colorbar(format=\"%.2f\")\n\n        # scatter training/testing points\n        plt.scatter(\n            species.pts_train[\"dd long\"],\n            species.pts_train[\"dd lat\"],\n            s=2**2,\n            c=\"black\",\n            marker=\"^\",\n            label=\"train\",\n        )\n        plt.scatter(\n            species.pts_test[\"dd long\"],\n            species.pts_test[\"dd lat\"],\n            s=2**2,\n            c=\"black\",\n            marker=\"x\",\n            label=\"test\",\n        )\n        plt.legend()\n        plt.title(species.name)\n        plt.axis(\"equal\")\n\n        # Compute AUC with regards to background points\n        pred_background = Z[background_points[0], background_points[1]]\n        pred_test = clf.decision_function((species.cov_test - mean) / std)\n        scores = np.r_[pred_test, pred_background]\n        y = np.r_[np.ones(pred_test.shape), np.zeros(pred_background.shape)]\n        fpr, tpr, thresholds = metrics.roc_curve(y, scores)\n        roc_auc = metrics.auc(fpr, tpr)\n        plt.text(-35, -70, \"AUC: %.3f\" % roc_auc, ha=\"right\")\n        print(\"\\n Area under the ROC curve : %f\" % roc_auc)\n\n    print(\"\\ntime elapsed: %.2fs\" % (time() - t0))\n\n\nplot_species_distribution()\nplt.show()"
       ]
     }
   ],
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/b5dd907aa4383b0fd8d5380f1d1a07ac/plot_target_encoder_cross_val.zip b/dev/_downloads/b5dd907aa4383b0fd8d5380f1d1a07ac/plot_target_encoder_cross_val.zip
index e749d24596e75..5db1c62b80484 100644
Binary files a/dev/_downloads/b5dd907aa4383b0fd8d5380f1d1a07ac/plot_target_encoder_cross_val.zip and b/dev/_downloads/b5dd907aa4383b0fd8d5380f1d1a07ac/plot_target_encoder_cross_val.zip differ
diff --git a/dev/_downloads/b64609eaa844207d7f926823a464efd3/plot_release_highlights_1_0_0.zip b/dev/_downloads/b64609eaa844207d7f926823a464efd3/plot_release_highlights_1_0_0.zip
index 9d492b1e0545f..2bd2327a88540 100644
Binary files a/dev/_downloads/b64609eaa844207d7f926823a464efd3/plot_release_highlights_1_0_0.zip and b/dev/_downloads/b64609eaa844207d7f926823a464efd3/plot_release_highlights_1_0_0.zip differ
diff --git a/dev/_downloads/b77b3494346415433e29621203cd753c/plot_cost_complexity_pruning.zip b/dev/_downloads/b77b3494346415433e29621203cd753c/plot_cost_complexity_pruning.zip
index 2c051431a39e5..bad3725636b9b 100644
Binary files a/dev/_downloads/b77b3494346415433e29621203cd753c/plot_cost_complexity_pruning.zip and b/dev/_downloads/b77b3494346415433e29621203cd753c/plot_cost_complexity_pruning.zip differ
diff --git a/dev/_downloads/b7e32fe54d613dce0d3c376377af061d/plot_outlier_detection_bench.py b/dev/_downloads/b7e32fe54d613dce0d3c376377af061d/plot_outlier_detection_bench.py
index cef58e20d75eb..600eceb1a06b3 100644
--- a/dev/_downloads/b7e32fe54d613dce0d3c376377af061d/plot_outlier_detection_bench.py
+++ b/dev/_downloads/b7e32fe54d613dce0d3c376377af061d/plot_outlier_detection_bench.py
@@ -88,12 +88,12 @@ def fit_predict(estimator, X):
     tic = perf_counter()
     if estimator[-1].__class__.__name__ == "LocalOutlierFactor":
         estimator.fit(X)
-        y_pred = estimator[-1].negative_outlier_factor_
+        y_score = estimator[-1].negative_outlier_factor_
     else:  # "IsolationForest"
-        y_pred = estimator.fit(X).decision_function(X)
+        y_score = estimator.fit(X).decision_function(X)
     toc = perf_counter()
     print(f"Duration for {model_name}: {toc - tic:.2f} s")
-    return y_pred
+    return y_score
 
 
 # %%
@@ -138,7 +138,7 @@ def fit_predict(estimator, X):
 
 # %%
 y_true = {}
-y_pred = {"LOF": {}, "IForest": {}}
+y_score = {"LOF": {}, "IForest": {}}
 model_names = ["LOF", "IForest"]
 cat_columns = ["protocol_type", "service", "flag"]
 
@@ -150,7 +150,7 @@ def fit_predict(estimator, X):
         lof_kw={"n_neighbors": int(n_samples * anomaly_frac)},
         iforest_kw={"random_state": 42},
     )
-    y_pred[model_name]["KDDCup99 - SA"] = fit_predict(model, X)
+    y_score[model_name]["KDDCup99 - SA"] = fit_predict(model, X)
 
 # %%
 # Forest covertypes dataset
@@ -185,7 +185,7 @@ def fit_predict(estimator, X):
         lof_kw={"n_neighbors": int(n_samples * anomaly_frac)},
         iforest_kw={"random_state": 42},
     )
-    y_pred[model_name]["forestcover"] = fit_predict(model, X)
+    y_score[model_name]["forestcover"] = fit_predict(model, X)
 
 # %%
 # Ames Housing dataset
@@ -242,7 +242,7 @@ def fit_predict(estimator, X):
         lof_kw={"n_neighbors": int(n_samples * anomaly_frac)},
         iforest_kw={"random_state": 42},
     )
-    y_pred[model_name]["ames_housing"] = fit_predict(model, X)
+    y_score[model_name]["ames_housing"] = fit_predict(model, X)
 
 # %%
 # Cardiotocography dataset
@@ -271,7 +271,7 @@ def fit_predict(estimator, X):
         lof_kw={"n_neighbors": int(n_samples * anomaly_frac)},
         iforest_kw={"random_state": 42},
     )
-    y_pred[model_name]["cardiotocography"] = fit_predict(model, X)
+    y_score[model_name]["cardiotocography"] = fit_predict(model, X)
 
 # %%
 # Plot and interpret results
@@ -299,7 +299,7 @@ def fit_predict(estimator, X):
     for model_idx, model_name in enumerate(model_names):
         display = RocCurveDisplay.from_predictions(
             y_true[dataset_name],
-            y_pred[model_name][dataset_name],
+            y_score[model_name][dataset_name],
             pos_label=pos_label,
             name=model_name,
             ax=ax,
@@ -346,10 +346,10 @@ def fit_predict(estimator, X):
 for model_idx, (linestyle, n_neighbors) in enumerate(zip(linestyles, n_neighbors_list)):
     model.set_params(localoutlierfactor__n_neighbors=n_neighbors)
     model.fit(X)
-    y_pred = model[-1].negative_outlier_factor_
+    y_score = model[-1].negative_outlier_factor_
     display = RocCurveDisplay.from_predictions(
         y,
-        y_pred,
+        y_score,
         pos_label=pos_label,
         name=f"n_neighbors = {n_neighbors}",
         ax=ax,
@@ -386,10 +386,10 @@ def fit_predict(estimator, X):
 ):
     model = make_pipeline(preprocessor, lof)
     model.fit(X)
-    y_pred = model[-1].negative_outlier_factor_
+    y_score = model[-1].negative_outlier_factor_
     display = RocCurveDisplay.from_predictions(
         y,
-        y_pred,
+        y_score,
         pos_label=pos_label,
         name=str(preprocessor).split("(")[0],
         ax=ax,
@@ -438,10 +438,10 @@ def fit_predict(estimator, X):
 ):
     model = make_pipeline(preprocessor, lof)
     model.fit(X)
-    y_pred = model[-1].negative_outlier_factor_
+    y_score = model[-1].negative_outlier_factor_
     display = RocCurveDisplay.from_predictions(
         y,
-        y_pred,
+        y_score,
         pos_label=pos_label,
         name=str(preprocessor).split("(")[0],
         ax=ax,
diff --git a/dev/_downloads/ba0cfc16d7953e1c2c6912b6beca1e91/plot_release_highlights_1_5_0.py b/dev/_downloads/ba0cfc16d7953e1c2c6912b6beca1e91/plot_release_highlights_1_5_0.py
index 7a4e9f61597fd..ef389a5db290b 100644
--- a/dev/_downloads/ba0cfc16d7953e1c2c6912b6beca1e91/plot_release_highlights_1_5_0.py
+++ b/dev/_downloads/ba0cfc16d7953e1c2c6912b6beca1e91/plot_release_highlights_1_5_0.py
@@ -1,4 +1,4 @@
-# ruff: noqa
+# ruff: noqa: CPY001
 """
 =======================================
 Release Highlights for scikit-learn 1.5
@@ -30,10 +30,9 @@
 # problem. :class:`~model_selection.FixedThresholdClassifier` allows wrapping any
 # binary classifier and setting a custom decision threshold.
 from sklearn.datasets import make_classification
-from sklearn.model_selection import train_test_split
 from sklearn.linear_model import LogisticRegression
 from sklearn.metrics import ConfusionMatrixDisplay
-
+from sklearn.model_selection import train_test_split
 
 X, y = make_classification(n_samples=10_000, weights=[0.9, 0.1], random_state=0)
 X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
@@ -90,8 +89,8 @@ def custom_score(y_observed, y_pred):
 # Tuning the threshold to optimize this custom metric gives a smaller threshold
 # that allows more samples to be classified as the positive class. As a result,
 # the average gain per prediction improves.
-from sklearn.model_selection import TunedThresholdClassifierCV
 from sklearn.metrics import make_scorer
+from sklearn.model_selection import TunedThresholdClassifierCV
 
 custom_scorer = make_scorer(
     custom_score, response_method="predict", greater_is_better=True
@@ -161,8 +160,9 @@ def custom_score(y_observed, y_pred):
 # The transformers of a :class:`~compose.ColumnTransformer` can now be directly
 # accessed using indexing by name.
 import numpy as np
+
 from sklearn.compose import ColumnTransformer
-from sklearn.preprocessing import StandardScaler, OneHotEncoder
+from sklearn.preprocessing import OneHotEncoder, StandardScaler
 
 X = np.array([[0, 1, 2], [3, 4, 5]])
 column_transformer = ColumnTransformer(
diff --git a/dev/_downloads/bad2c14c5cfbd2a6e2c7d563be123e11/plot_sgd_loss_functions.ipynb b/dev/_downloads/bad2c14c5cfbd2a6e2c7d563be123e11/plot_sgd_loss_functions.ipynb
index 83d21264b8448..13a005c4d9df7 100644
--- a/dev/_downloads/bad2c14c5cfbd2a6e2c7d563be123e11/plot_sgd_loss_functions.ipynb
+++ b/dev/_downloads/bad2c14c5cfbd2a6e2c7d563be123e11/plot_sgd_loss_functions.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/baf3c3e7a8255dce4078f2fb913ebe12/plot_gmm_pdf.zip b/dev/_downloads/baf3c3e7a8255dce4078f2fb913ebe12/plot_gmm_pdf.zip
index 09bf00bc77a4b..e97e8019d3015 100644
Binary files a/dev/_downloads/baf3c3e7a8255dce4078f2fb913ebe12/plot_gmm_pdf.zip and b/dev/_downloads/baf3c3e7a8255dce4078f2fb913ebe12/plot_gmm_pdf.zip differ
diff --git a/dev/_downloads/bb136fd1099bc957fc4cc8e783dd9b2e/plot_confusion_matrix.zip b/dev/_downloads/bb136fd1099bc957fc4cc8e783dd9b2e/plot_confusion_matrix.zip
index 3fdb13ea0deb8..654d515076e20 100644
Binary files a/dev/_downloads/bb136fd1099bc957fc4cc8e783dd9b2e/plot_confusion_matrix.zip and b/dev/_downloads/bb136fd1099bc957fc4cc8e783dd9b2e/plot_confusion_matrix.zip differ
diff --git a/dev/_downloads/bb1a82e9d9265cd75fea1470589796a9/plot_stack_predictors.zip b/dev/_downloads/bb1a82e9d9265cd75fea1470589796a9/plot_stack_predictors.zip
index 0635971056509..e5d2c751fc379 100644
Binary files a/dev/_downloads/bb1a82e9d9265cd75fea1470589796a9/plot_stack_predictors.zip and b/dev/_downloads/bb1a82e9d9265cd75fea1470589796a9/plot_stack_predictors.zip differ
diff --git a/dev/_downloads/bbe339f46fa25869444a824be9486f14/plot_covariance_estimation.zip b/dev/_downloads/bbe339f46fa25869444a824be9486f14/plot_covariance_estimation.zip
index 968427b246f67..703060f1f6a08 100644
Binary files a/dev/_downloads/bbe339f46fa25869444a824be9486f14/plot_covariance_estimation.zip and b/dev/_downloads/bbe339f46fa25869444a824be9486f14/plot_covariance_estimation.zip differ
diff --git a/dev/_downloads/bbfe808211963096bb78580f7fc28b10/plot_iris_dtc.zip b/dev/_downloads/bbfe808211963096bb78580f7fc28b10/plot_iris_dtc.zip
index 6cf21271ddacf..32e7793894d40 100644
Binary files a/dev/_downloads/bbfe808211963096bb78580f7fc28b10/plot_iris_dtc.zip and b/dev/_downloads/bbfe808211963096bb78580f7fc28b10/plot_iris_dtc.zip differ
diff --git a/dev/_downloads/bc4cacb86f284cd0b3913166a69c9fb2/plot_iris_dtc.ipynb b/dev/_downloads/bc4cacb86f284cd0b3913166a69c9fb2/plot_iris_dtc.ipynb
index 36419ccaa7410..17e87023e6105 100644
--- a/dev/_downloads/bc4cacb86f284cd0b3913166a69c9fb2/plot_iris_dtc.ipynb
+++ b/dev/_downloads/bc4cacb86f284cd0b3913166a69c9fb2/plot_iris_dtc.ipynb
@@ -51,7 +51,7 @@
       },
       "outputs": [],
       "source": [
-        "import matplotlib.pyplot as plt\nimport numpy as np\n\nfrom sklearn.datasets import load_iris\nfrom sklearn.inspection import DecisionBoundaryDisplay\nfrom sklearn.tree import DecisionTreeClassifier\n\n# Parameters\nn_classes = 3\nplot_colors = \"ryb\"\nplot_step = 0.02\n\n\nfor pairidx, pair in enumerate([[0, 1], [0, 2], [0, 3], [1, 2], [1, 3], [2, 3]]):\n    # We only take the two corresponding features\n    X = iris.data[:, pair]\n    y = iris.target\n\n    # Train\n    clf = DecisionTreeClassifier().fit(X, y)\n\n    # Plot the decision boundary\n    ax = plt.subplot(2, 3, pairidx + 1)\n    plt.tight_layout(h_pad=0.5, w_pad=0.5, pad=2.5)\n    DecisionBoundaryDisplay.from_estimator(\n        clf,\n        X,\n        cmap=plt.cm.RdYlBu,\n        response_method=\"predict\",\n        ax=ax,\n        xlabel=iris.feature_names[pair[0]],\n        ylabel=iris.feature_names[pair[1]],\n    )\n\n    # Plot the training points\n    for i, color in zip(range(n_classes), plot_colors):\n        idx = np.where(y == i)\n        plt.scatter(\n            X[idx, 0],\n            X[idx, 1],\n            c=color,\n            label=iris.target_names[i],\n            edgecolor=\"black\",\n            s=15,\n        )\n\nplt.suptitle(\"Decision surface of decision trees trained on pairs of features\")\nplt.legend(loc=\"lower right\", borderpad=0, handletextpad=0)\n_ = plt.axis(\"tight\")"
+        "import matplotlib.pyplot as plt\nimport numpy as np\n\nfrom sklearn.datasets import load_iris\nfrom sklearn.inspection import DecisionBoundaryDisplay\nfrom sklearn.tree import DecisionTreeClassifier\n\n# Parameters\nn_classes = 3\nplot_colors = \"ryb\"\nplot_step = 0.02\n\n\nfor pairidx, pair in enumerate([[0, 1], [0, 2], [0, 3], [1, 2], [1, 3], [2, 3]]):\n    # We only take the two corresponding features\n    X = iris.data[:, pair]\n    y = iris.target\n\n    # Train\n    clf = DecisionTreeClassifier().fit(X, y)\n\n    # Plot the decision boundary\n    ax = plt.subplot(2, 3, pairidx + 1)\n    plt.tight_layout(h_pad=0.5, w_pad=0.5, pad=2.5)\n    DecisionBoundaryDisplay.from_estimator(\n        clf,\n        X,\n        cmap=plt.cm.RdYlBu,\n        response_method=\"predict\",\n        ax=ax,\n        xlabel=iris.feature_names[pair[0]],\n        ylabel=iris.feature_names[pair[1]],\n    )\n\n    # Plot the training points\n    for i, color in zip(range(n_classes), plot_colors):\n        idx = np.asarray(y == i).nonzero()\n        plt.scatter(\n            X[idx, 0],\n            X[idx, 1],\n            c=color,\n            label=iris.target_names[i],\n            edgecolor=\"black\",\n            s=15,\n        )\n\nplt.suptitle(\"Decision surface of decision trees trained on pairs of features\")\nplt.legend(loc=\"lower right\", borderpad=0, handletextpad=0)\n_ = plt.axis(\"tight\")"
       ]
     },
     {
@@ -89,7 +89,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/bc5bb4c753c40c49f0db9713bda0d1fd/plot_estimator_representation.zip b/dev/_downloads/bc5bb4c753c40c49f0db9713bda0d1fd/plot_estimator_representation.zip
index 1c4f5f8333a5d..1d98dc526a4ac 100644
Binary files a/dev/_downloads/bc5bb4c753c40c49f0db9713bda0d1fd/plot_estimator_representation.zip and b/dev/_downloads/bc5bb4c753c40c49f0db9713bda0d1fd/plot_estimator_representation.zip differ
diff --git a/dev/_downloads/bc627aa67e9e5146b84d53d996f25245/plot_gradient_boosting_early_stopping.zip b/dev/_downloads/bc627aa67e9e5146b84d53d996f25245/plot_gradient_boosting_early_stopping.zip
index cc048fc62b415..82a236acbeb03 100644
Binary files a/dev/_downloads/bc627aa67e9e5146b84d53d996f25245/plot_gradient_boosting_early_stopping.zip and b/dev/_downloads/bc627aa67e9e5146b84d53d996f25245/plot_gradient_boosting_early_stopping.zip differ
diff --git a/dev/_downloads/bd48035d82fe0719ccbd66ac2192f65b/plot_feature_transformation.ipynb b/dev/_downloads/bd48035d82fe0719ccbd66ac2192f65b/plot_feature_transformation.ipynb
index a976610b8d03d..e5741c43149a4 100644
--- a/dev/_downloads/bd48035d82fe0719ccbd66ac2192f65b/plot_feature_transformation.ipynb
+++ b/dev/_downloads/bd48035d82fe0719ccbd66ac2192f65b/plot_feature_transformation.ipynb
@@ -183,7 +183,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/be7e9c5a81790b318c3a8028ced647ff/plot_linkage_comparison.ipynb b/dev/_downloads/be7e9c5a81790b318c3a8028ced647ff/plot_linkage_comparison.ipynb
index 3e66d12b335ef..7f6624d7c62cd 100644
--- a/dev/_downloads/be7e9c5a81790b318c3a8028ced647ff/plot_linkage_comparison.ipynb
+++ b/dev/_downloads/be7e9c5a81790b318c3a8028ced647ff/plot_linkage_comparison.ipynb
@@ -71,7 +71,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/bee6912c62835d8f46bc3fbcb465cb4b/plot_adaboost_multiclass.zip b/dev/_downloads/bee6912c62835d8f46bc3fbcb465cb4b/plot_adaboost_multiclass.zip
index 316663fc1e388..be49e97b07c1a 100644
Binary files a/dev/_downloads/bee6912c62835d8f46bc3fbcb465cb4b/plot_adaboost_multiclass.zip and b/dev/_downloads/bee6912c62835d8f46bc3fbcb465cb4b/plot_adaboost_multiclass.zip differ
diff --git a/dev/_downloads/c0510ef77613771032b63870b7baaa31/plot_robust_vs_empirical_covariance.ipynb b/dev/_downloads/c0510ef77613771032b63870b7baaa31/plot_robust_vs_empirical_covariance.ipynb
index 818200c2057da..9cb4461dcbb44 100644
--- a/dev/_downloads/c0510ef77613771032b63870b7baaa31/plot_robust_vs_empirical_covariance.ipynb
+++ b/dev/_downloads/c0510ef77613771032b63870b7baaa31/plot_robust_vs_empirical_covariance.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/c08598f3ffe66017f7cad294026ee0b9/plot_out_of_core_classification.ipynb b/dev/_downloads/c08598f3ffe66017f7cad294026ee0b9/plot_out_of_core_classification.ipynb
index 05acc6627965e..ed35349db8c79 100644
--- a/dev/_downloads/c08598f3ffe66017f7cad294026ee0b9/plot_out_of_core_classification.ipynb
+++ b/dev/_downloads/c08598f3ffe66017f7cad294026ee0b9/plot_out_of_core_classification.ipynb
@@ -33,7 +33,7 @@
       },
       "outputs": [],
       "source": [
-        "class ReutersParser(HTMLParser):\n    \"\"\"Utility class to parse a SGML file and yield documents one at a time.\"\"\"\n\n    def __init__(self, encoding=\"latin-1\"):\n        HTMLParser.__init__(self)\n        self._reset()\n        self.encoding = encoding\n\n    def handle_starttag(self, tag, attrs):\n        method = \"start_\" + tag\n        getattr(self, method, lambda x: None)(attrs)\n\n    def handle_endtag(self, tag):\n        method = \"end_\" + tag\n        getattr(self, method, lambda: None)()\n\n    def _reset(self):\n        self.in_title = 0\n        self.in_body = 0\n        self.in_topics = 0\n        self.in_topic_d = 0\n        self.title = \"\"\n        self.body = \"\"\n        self.topics = []\n        self.topic_d = \"\"\n\n    def parse(self, fd):\n        self.docs = []\n        for chunk in fd:\n            self.feed(chunk.decode(self.encoding))\n            for doc in self.docs:\n                yield doc\n            self.docs = []\n        self.close()\n\n    def handle_data(self, data):\n        if self.in_body:\n            self.body += data\n        elif self.in_title:\n            self.title += data\n        elif self.in_topic_d:\n            self.topic_d += data\n\n    def start_reuters(self, attributes):\n        pass\n\n    def end_reuters(self):\n        self.body = re.sub(r\"\\s+\", r\" \", self.body)\n        self.docs.append(\n            {\"title\": self.title, \"body\": self.body, \"topics\": self.topics}\n        )\n        self._reset()\n\n    def start_title(self, attributes):\n        self.in_title = 1\n\n    def end_title(self):\n        self.in_title = 0\n\n    def start_body(self, attributes):\n        self.in_body = 1\n\n    def end_body(self):\n        self.in_body = 0\n\n    def start_topics(self, attributes):\n        self.in_topics = 1\n\n    def end_topics(self):\n        self.in_topics = 0\n\n    def start_d(self, attributes):\n        self.in_topic_d = 1\n\n    def end_d(self):\n        self.in_topic_d = 0\n        self.topics.append(self.topic_d)\n        self.topic_d = \"\"\n\n\ndef stream_reuters_documents(data_path=None):\n    \"\"\"Iterate over documents of the Reuters dataset.\n\n    The Reuters archive will automatically be downloaded and uncompressed if\n    the `data_path` directory does not exist.\n\n    Documents are represented as dictionaries with 'body' (str),\n    'title' (str), 'topics' (list(str)) keys.\n\n    \"\"\"\n\n    DOWNLOAD_URL = (\n        \"http://archive.ics.uci.edu/ml/machine-learning-databases//"\n        \"reuters21578-mld/reuters21578.tar.gz\"\n    )\n    ARCHIVE_SHA256 = \"3bae43c9b14e387f76a61b6d82bf98a4fb5d3ef99ef7e7075ff2ccbcf59f9d30\"\n    ARCHIVE_FILENAME = \"reuters21578.tar.gz\"\n\n    if data_path is None:\n        data_path = Path(get_data_home()) / \"reuters\"\n    else:\n        data_path = Path(data_path)\n    if not data_path.exists():\n        \"\"\"Download the dataset.\"\"\"\n        print(\"downloading dataset (once and for all) into %s\" % data_path)\n        data_path.mkdir(parents=True, exist_ok=True)\n\n        def progress(blocknum, bs, size):\n            total_sz_mb = \"%.2f MB\" % (size / 1e6)\n            current_sz_mb = \"%.2f MB\" % ((blocknum * bs) / 1e6)\n            if _not_in_sphinx():\n                sys.stdout.write(\"\\rdownloaded %s / %s\" % (current_sz_mb, total_sz_mb))\n\n        archive_path = data_path / ARCHIVE_FILENAME\n\n        urlretrieve(DOWNLOAD_URL, filename=archive_path, reporthook=progress)\n        if _not_in_sphinx():\n            sys.stdout.write(\"\\r\")\n\n        # Check that the archive was not tampered:\n        assert sha256(archive_path.read_bytes()).hexdigest() == ARCHIVE_SHA256\n\n        print(\"untarring Reuters dataset...\")\n        with tarfile.open(archive_path, \"r:gz\") as fp:\n            fp.extractall(data_path, filter=\"data\")\n        print(\"done.\")\n\n    parser = ReutersParser()\n    for filename in data_path.glob(\"*.sgm\"):\n        for doc in parser.parse(open(filename, \"rb\")):\n            yield doc"
+        "class ReutersParser(HTMLParser):\n    \"\"\"Utility class to parse a SGML file and yield documents one at a time.\"\"\"\n\n    def __init__(self, encoding=\"latin-1\"):\n        HTMLParser.__init__(self)\n        self._reset()\n        self.encoding = encoding\n\n    def handle_starttag(self, tag, attrs):\n        method = \"start_\" + tag\n        getattr(self, method, lambda x: None)(attrs)\n\n    def handle_endtag(self, tag):\n        method = \"end_\" + tag\n        getattr(self, method, lambda: None)()\n\n    def _reset(self):\n        self.in_title = 0\n        self.in_body = 0\n        self.in_topics = 0\n        self.in_topic_d = 0\n        self.title = \"\"\n        self.body = \"\"\n        self.topics = []\n        self.topic_d = \"\"\n\n    def parse(self, fd):\n        self.docs = []\n        for chunk in fd:\n            self.feed(chunk.decode(self.encoding))\n            for doc in self.docs:\n                yield doc\n            self.docs = []\n        self.close()\n\n    def handle_data(self, data):\n        if self.in_body:\n            self.body += data\n        elif self.in_title:\n            self.title += data\n        elif self.in_topic_d:\n            self.topic_d += data\n\n    def start_reuters(self, attributes):\n        pass\n\n    def end_reuters(self):\n        self.body = re.sub(r\"\\s+\", r\" \", self.body)\n        self.docs.append(\n            {\"title\": self.title, \"body\": self.body, \"topics\": self.topics}\n        )\n        self._reset()\n\n    def start_title(self, attributes):\n        self.in_title = 1\n\n    def end_title(self):\n        self.in_title = 0\n\n    def start_body(self, attributes):\n        self.in_body = 1\n\n    def end_body(self):\n        self.in_body = 0\n\n    def start_topics(self, attributes):\n        self.in_topics = 1\n\n    def end_topics(self):\n        self.in_topics = 0\n\n    def start_d(self, attributes):\n        self.in_topic_d = 1\n\n    def end_d(self):\n        self.in_topic_d = 0\n        self.topics.append(self.topic_d)\n        self.topic_d = \"\"\n\n\ndef stream_reuters_documents(data_path=None):\n    \"\"\"Iterate over documents of the Reuters dataset.\n\n    The Reuters archive will automatically be downloaded and uncompressed if\n    the `data_path` directory does not exist.\n\n    Documents are represented as dictionaries with 'body' (str),\n    'title' (str), 'topics' (list(str)) keys.\n\n    \"\"\"\n\n    DOWNLOAD_URL = \"https://kdd.ics.uci.edu/databases/reuters21578/reuters21578.tar.gz/"\n    ARCHIVE_SHA256 = \"3bae43c9b14e387f76a61b6d82bf98a4fb5d3ef99ef7e7075ff2ccbcf59f9d30\"\n    ARCHIVE_FILENAME = \"reuters21578.tar.gz\"\n\n    if data_path is None:\n        data_path = Path(get_data_home()) / \"reuters\"\n    else:\n        data_path = Path(data_path)\n    if not data_path.exists():\n        \"\"\"Download the dataset.\"\"\"\n        print(\"downloading dataset (once and for all) into %s\" % data_path)\n        data_path.mkdir(parents=True, exist_ok=True)\n\n        def progress(blocknum, bs, size):\n            total_sz_mb = \"%.2f MB\" % (size / 1e6)\n            current_sz_mb = \"%.2f MB\" % ((blocknum * bs) / 1e6)\n            if _not_in_sphinx():\n                sys.stdout.write(\"\\rdownloaded %s / %s\" % (current_sz_mb, total_sz_mb))\n\n        archive_path = data_path / ARCHIVE_FILENAME\n\n        urlretrieve(DOWNLOAD_URL, filename=archive_path, reporthook=progress)\n        if _not_in_sphinx():\n            sys.stdout.write(\"\\r\")\n\n        # Check that the archive was not tampered:\n        assert sha256(archive_path.read_bytes()).hexdigest() == ARCHIVE_SHA256\n\n        print(\"untarring Reuters dataset...\")\n        with tarfile.open(archive_path, \"r:gz\") as fp:\n            fp.extractall(data_path, filter=\"data\")\n        print(\"done.\")\n\n    parser = ReutersParser()\n    for filename in data_path.glob(\"*.sgm\"):\n        for doc in parser.parse(open(filename, \"rb\")):\n            yield doc"
       ]
     },
     {
@@ -89,7 +89,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/c0a901203201090b01ac6d929a31ce08/plot_kernel_pca.ipynb b/dev/_downloads/c0a901203201090b01ac6d929a31ce08/plot_kernel_pca.ipynb
index 5e78bcc854846..ec9975e7f0091 100644
--- a/dev/_downloads/c0a901203201090b01ac6d929a31ce08/plot_kernel_pca.ipynb
+++ b/dev/_downloads/c0a901203201090b01ac6d929a31ce08/plot_kernel_pca.ipynb
@@ -136,7 +136,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/c1445b023cf96a45dff2bda6de47c0ef/plot_svm_tie_breaking.zip b/dev/_downloads/c1445b023cf96a45dff2bda6de47c0ef/plot_svm_tie_breaking.zip
index a8306a5b5918a..c8260643f1e35 100644
Binary files a/dev/_downloads/c1445b023cf96a45dff2bda6de47c0ef/plot_svm_tie_breaking.zip and b/dev/_downloads/c1445b023cf96a45dff2bda6de47c0ef/plot_svm_tie_breaking.zip differ
diff --git a/dev/_downloads/c158013efa5fd61eaa463a2f88014a07/plot_tuned_decision_threshold.ipynb b/dev/_downloads/c158013efa5fd61eaa463a2f88014a07/plot_tuned_decision_threshold.ipynb
index 1d9dfe4f3fc52..0ab9c6f6c1b4b 100644
--- a/dev/_downloads/c158013efa5fd61eaa463a2f88014a07/plot_tuned_decision_threshold.ipynb
+++ b/dev/_downloads/c158013efa5fd61eaa463a2f88014a07/plot_tuned_decision_threshold.ipynb
@@ -186,7 +186,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/c15cce0dbcd8722cb5638987eff985c0/plot_release_highlights_1_4_0.py b/dev/_downloads/c15cce0dbcd8722cb5638987eff985c0/plot_release_highlights_1_4_0.py
index af07e60f34b56..5ce256b065e48 100644
--- a/dev/_downloads/c15cce0dbcd8722cb5638987eff985c0/plot_release_highlights_1_4_0.py
+++ b/dev/_downloads/c15cce0dbcd8722cb5638987eff985c0/plot_release_highlights_1_4_0.py
@@ -1,4 +1,4 @@
-# ruff: noqa
+# ruff: noqa: CPY001
 """
 =======================================
 Release Highlights for scikit-learn 1.4
@@ -41,8 +41,8 @@
 # treats the columns with categorical dtypes as categorical features in the
 # algorithm:
 from sklearn.ensemble import HistGradientBoostingClassifier
-from sklearn.model_selection import train_test_split
 from sklearn.metrics import roc_auc_score
+from sklearn.model_selection import train_test_split
 
 X_train, X_test, y_train, y_test = train_test_split(X_adult, y_adult, random_state=0)
 hist = HistGradientBoostingClassifier(categorical_features="from_dtype")
@@ -56,9 +56,9 @@
 # -----------------------------
 # scikit-learn's transformers now support polars output with the `set_output` API.
 import polars as pl
-from sklearn.preprocessing import StandardScaler
-from sklearn.preprocessing import OneHotEncoder
+
 from sklearn.compose import ColumnTransformer
+from sklearn.preprocessing import OneHotEncoder, StandardScaler
 
 df = pl.DataFrame(
     {"height": [120, 140, 150, 110, 100], "pet": ["dog", "cat", "dog", "cat", "cat"]}
@@ -87,6 +87,7 @@
 # missing values going to the left and right nodes. More details in the
 # :ref:`User Guide <tree_missing_value_support>`.
 import numpy as np
+
 from sklearn.ensemble import RandomForestClassifier
 
 X = np.array([0, 1, 6, np.nan]).reshape(-1, 1)
@@ -103,8 +104,9 @@
 # trees, random forests, extra-trees, and exact gradient boosting. Here, we show this
 # feature for random forest on a regression problem.
 import matplotlib.pyplot as plt
-from sklearn.inspection import PartialDependenceDisplay
+
 from sklearn.ensemble import RandomForestRegressor
+from sklearn.inspection import PartialDependenceDisplay
 
 n_samples = 500
 rng = np.random.RandomState(0)
@@ -161,10 +163,10 @@
 # <metadata_routing_models>`. For instance, this is how you can do a nested
 # cross-validation with sample weights and :class:`~model_selection.GroupKFold`:
 import sklearn
-from sklearn.metrics import get_scorer
 from sklearn.datasets import make_regression
 from sklearn.linear_model import Lasso
-from sklearn.model_selection import GridSearchCV, cross_validate, GroupKFold
+from sklearn.metrics import get_scorer
+from sklearn.model_selection import GridSearchCV, GroupKFold, cross_validate
 
 # For now by default metadata routing is disabled, and need to be explicitly
 # enabled.
@@ -216,10 +218,12 @@
 # materializing large sparse matrices when performing the
 # eigenvalue decomposition of the data set covariance matrix.
 #
-from sklearn.decomposition import PCA
-import scipy.sparse as sp
 from time import time
 
+import scipy.sparse as sp
+
+from sklearn.decomposition import PCA
+
 X_sparse = sp.random(m=1000, n=1000, random_state=0)
 X_dense = X_sparse.toarray()
 
diff --git a/dev/_downloads/c19d26d7653b439c29998e17425b7dad/plot_custom_kernel.zip b/dev/_downloads/c19d26d7653b439c29998e17425b7dad/plot_custom_kernel.zip
index 2ca781b1f23f6..e5b69a2547543 100644
Binary files a/dev/_downloads/c19d26d7653b439c29998e17425b7dad/plot_custom_kernel.zip and b/dev/_downloads/c19d26d7653b439c29998e17425b7dad/plot_custom_kernel.zip differ
diff --git a/dev/_downloads/c23136a84c063f9259187cbd78ec30ed/plot_dbscan.zip b/dev/_downloads/c23136a84c063f9259187cbd78ec30ed/plot_dbscan.zip
index d9f32da6b6598..339a759669d07 100644
Binary files a/dev/_downloads/c23136a84c063f9259187cbd78ec30ed/plot_dbscan.zip and b/dev/_downloads/c23136a84c063f9259187cbd78ec30ed/plot_dbscan.zip differ
diff --git a/dev/_downloads/c2cfe11afa629e514b58e6b27dbf5ae7/plot_gpr_co2.zip b/dev/_downloads/c2cfe11afa629e514b58e6b27dbf5ae7/plot_gpr_co2.zip
index 6c7fcd04129a4..5b13fbb0a7b49 100644
Binary files a/dev/_downloads/c2cfe11afa629e514b58e6b27dbf5ae7/plot_gpr_co2.zip and b/dev/_downloads/c2cfe11afa629e514b58e6b27dbf5ae7/plot_gpr_co2.zip differ
diff --git a/dev/_downloads/c2fa5e42f146df31090b4867fccadd4a/plot_pca_vs_lda.zip b/dev/_downloads/c2fa5e42f146df31090b4867fccadd4a/plot_pca_vs_lda.zip
index 197cb214ddf2d..3774f6b77ae7a 100644
Binary files a/dev/_downloads/c2fa5e42f146df31090b4867fccadd4a/plot_pca_vs_lda.zip and b/dev/_downloads/c2fa5e42f146df31090b4867fccadd4a/plot_pca_vs_lda.zip differ
diff --git a/dev/_downloads/c38a99335d08742ba0737174364ad279/plot_ard.zip b/dev/_downloads/c38a99335d08742ba0737174364ad279/plot_ard.zip
index d6832edefc3c5..ff42b092f108b 100644
Binary files a/dev/_downloads/c38a99335d08742ba0737174364ad279/plot_ard.zip and b/dev/_downloads/c38a99335d08742ba0737174364ad279/plot_ard.zip differ
diff --git a/dev/_downloads/c3a0ceabf65f8c894895af414e685ee9/plot_adaboost_twoclass.ipynb b/dev/_downloads/c3a0ceabf65f8c894895af414e685ee9/plot_adaboost_twoclass.ipynb
index 4b8c62c535883..e7d7ebbc2e5f5 100644
--- a/dev/_downloads/c3a0ceabf65f8c894895af414e685ee9/plot_adaboost_twoclass.ipynb
+++ b/dev/_downloads/c3a0ceabf65f8c894895af414e685ee9/plot_adaboost_twoclass.ipynb
@@ -15,7 +15,7 @@
       },
       "outputs": [],
       "source": [
-        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nfrom sklearn.datasets import make_gaussian_quantiles\nfrom sklearn.ensemble import AdaBoostClassifier\nfrom sklearn.inspection import DecisionBoundaryDisplay\nfrom sklearn.tree import DecisionTreeClassifier\n\n# Construct dataset\nX1, y1 = make_gaussian_quantiles(\n    cov=2.0, n_samples=200, n_features=2, n_classes=2, random_state=1\n)\nX2, y2 = make_gaussian_quantiles(\n    mean=(3, 3), cov=1.5, n_samples=300, n_features=2, n_classes=2, random_state=1\n)\nX = np.concatenate((X1, X2))\ny = np.concatenate((y1, -y2 + 1))\n\n# Create and fit an AdaBoosted decision tree\nbdt = AdaBoostClassifier(DecisionTreeClassifier(max_depth=1), n_estimators=200)\nbdt.fit(X, y)\n\nplot_colors = \"br\"\nplot_step = 0.02\nclass_names = \"AB\"\n\nplt.figure(figsize=(10, 5))\n\n# Plot the decision boundaries\nax = plt.subplot(121)\ndisp = DecisionBoundaryDisplay.from_estimator(\n    bdt,\n    X,\n    cmap=plt.cm.Paired,\n    response_method=\"predict\",\n    ax=ax,\n    xlabel=\"x\",\n    ylabel=\"y\",\n)\nx_min, x_max = disp.xx0.min(), disp.xx0.max()\ny_min, y_max = disp.xx1.min(), disp.xx1.max()\nplt.axis(\"tight\")\n\n# Plot the training points\nfor i, n, c in zip(range(2), class_names, plot_colors):\n    idx = np.where(y == i)\n    plt.scatter(\n        X[idx, 0],\n        X[idx, 1],\n        c=c,\n        s=20,\n        edgecolor=\"k\",\n        label=\"Class %s\" % n,\n    )\nplt.xlim(x_min, x_max)\nplt.ylim(y_min, y_max)\nplt.legend(loc=\"upper right\")\n\nplt.title(\"Decision Boundary\")\n\n# Plot the two-class decision scores\ntwoclass_output = bdt.decision_function(X)\nplot_range = (twoclass_output.min(), twoclass_output.max())\nplt.subplot(122)\nfor i, n, c in zip(range(2), class_names, plot_colors):\n    plt.hist(\n        twoclass_output[y == i],\n        bins=10,\n        range=plot_range,\n        facecolor=c,\n        label=\"Class %s\" % n,\n        alpha=0.5,\n        edgecolor=\"k\",\n    )\nx1, x2, y1, y2 = plt.axis()\nplt.axis((x1, x2, y1, y2 * 1.2))\nplt.legend(loc=\"upper right\")\nplt.ylabel(\"Samples\")\nplt.xlabel(\"Score\")\nplt.title(\"Decision Scores\")\n\nplt.tight_layout()\nplt.subplots_adjust(wspace=0.35)\nplt.show()"
+        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nfrom sklearn.datasets import make_gaussian_quantiles\nfrom sklearn.ensemble import AdaBoostClassifier\nfrom sklearn.inspection import DecisionBoundaryDisplay\nfrom sklearn.tree import DecisionTreeClassifier\n\n# Construct dataset\nX1, y1 = make_gaussian_quantiles(\n    cov=2.0, n_samples=200, n_features=2, n_classes=2, random_state=1\n)\nX2, y2 = make_gaussian_quantiles(\n    mean=(3, 3), cov=1.5, n_samples=300, n_features=2, n_classes=2, random_state=1\n)\nX = np.concatenate((X1, X2))\ny = np.concatenate((y1, -y2 + 1))\n\n# Create and fit an AdaBoosted decision tree\nbdt = AdaBoostClassifier(DecisionTreeClassifier(max_depth=1), n_estimators=200)\nbdt.fit(X, y)\n\nplot_colors = \"br\"\nplot_step = 0.02\nclass_names = \"AB\"\n\nplt.figure(figsize=(10, 5))\n\n# Plot the decision boundaries\nax = plt.subplot(121)\ndisp = DecisionBoundaryDisplay.from_estimator(\n    bdt,\n    X,\n    cmap=plt.cm.Paired,\n    response_method=\"predict\",\n    ax=ax,\n    xlabel=\"x\",\n    ylabel=\"y\",\n)\nx_min, x_max = disp.xx0.min(), disp.xx0.max()\ny_min, y_max = disp.xx1.min(), disp.xx1.max()\nplt.axis(\"tight\")\n\n# Plot the training points\nfor i, n, c in zip(range(2), class_names, plot_colors):\n    idx = (y == i).nonzero()\n    plt.scatter(\n        X[idx, 0],\n        X[idx, 1],\n        c=c,\n        s=20,\n        edgecolor=\"k\",\n        label=\"Class %s\" % n,\n    )\nplt.xlim(x_min, x_max)\nplt.ylim(y_min, y_max)\nplt.legend(loc=\"upper right\")\n\nplt.title(\"Decision Boundary\")\n\n# Plot the two-class decision scores\ntwoclass_output = bdt.decision_function(X)\nplot_range = (twoclass_output.min(), twoclass_output.max())\nplt.subplot(122)\nfor i, n, c in zip(range(2), class_names, plot_colors):\n    plt.hist(\n        twoclass_output[y == i],\n        bins=10,\n        range=plot_range,\n        facecolor=c,\n        label=\"Class %s\" % n,\n        alpha=0.5,\n        edgecolor=\"k\",\n    )\nx1, x2, y1, y2 = plt.axis()\nplt.axis((x1, x2, y1, y2 * 1.2))\nplt.legend(loc=\"upper right\")\nplt.ylabel(\"Samples\")\nplt.xlabel(\"Score\")\nplt.title(\"Decision Scores\")\n\nplt.tight_layout()\nplt.subplots_adjust(wspace=0.35)\nplt.show()"
       ]
     }
   ],
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/c3bc3113489c0b7c9a698b430d691ddc/plot_compare_gpr_krr.ipynb b/dev/_downloads/c3bc3113489c0b7c9a698b430d691ddc/plot_compare_gpr_krr.ipynb
index e38e80490cd13..74db1e4c6fd68 100644
--- a/dev/_downloads/c3bc3113489c0b7c9a698b430d691ddc/plot_compare_gpr_krr.ipynb
+++ b/dev/_downloads/c3bc3113489c0b7c9a698b430d691ddc/plot_compare_gpr_krr.ipynb
@@ -320,7 +320,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/c3f95dc25241c64632f9c3378fd4e89b/plot_target_encoder_cross_val.ipynb b/dev/_downloads/c3f95dc25241c64632f9c3378fd4e89b/plot_target_encoder_cross_val.ipynb
index e0a4fc1f89d89..a4c2c2ed31a8e 100644
--- a/dev/_downloads/c3f95dc25241c64632f9c3378fd4e89b/plot_target_encoder_cross_val.ipynb
+++ b/dev/_downloads/c3f95dc25241c64632f9c3378fd4e89b/plot_target_encoder_cross_val.ipynb
@@ -222,7 +222,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/c4b61c2bcffecd3661bffe3c79ef6e0b/plot_discretization.ipynb b/dev/_downloads/c4b61c2bcffecd3661bffe3c79ef6e0b/plot_discretization.ipynb
index 28a13e6ce4016..1100beee2c39d 100644
--- a/dev/_downloads/c4b61c2bcffecd3661bffe3c79ef6e0b/plot_discretization.ipynb
+++ b/dev/_downloads/c4b61c2bcffecd3661bffe3c79ef6e0b/plot_discretization.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/c52226c1b0f858148e3768010bf9b091/sklearn_is_fitted.zip b/dev/_downloads/c52226c1b0f858148e3768010bf9b091/sklearn_is_fitted.zip
index 9b8fc6f4c1e29..f9a435f8ed2fb 100644
Binary files a/dev/_downloads/c52226c1b0f858148e3768010bf9b091/sklearn_is_fitted.zip and b/dev/_downloads/c52226c1b0f858148e3768010bf9b091/sklearn_is_fitted.zip differ
diff --git a/dev/_downloads/c536eb92f539255e80e2b3ef5200e7a1/plot_gradient_boosting_regression.ipynb b/dev/_downloads/c536eb92f539255e80e2b3ef5200e7a1/plot_gradient_boosting_regression.ipynb
index 03cc1f3731d90..4ba9a4a18ac7a 100644
--- a/dev/_downloads/c536eb92f539255e80e2b3ef5200e7a1/plot_gradient_boosting_regression.ipynb
+++ b/dev/_downloads/c536eb92f539255e80e2b3ef5200e7a1/plot_gradient_boosting_regression.ipynb
@@ -125,7 +125,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/c545073653cd4e480bed940f553b41fc/plot_gpc_xor.zip b/dev/_downloads/c545073653cd4e480bed940f553b41fc/plot_gpc_xor.zip
index cc5a6123c2f80..ad17ba8d01748 100644
Binary files a/dev/_downloads/c545073653cd4e480bed940f553b41fc/plot_gpc_xor.zip and b/dev/_downloads/c545073653cd4e480bed940f553b41fc/plot_gpc_xor.zip differ
diff --git a/dev/_downloads/c5d41d4d7d1dab3e49804c2e2c4222e8/plot_ard.ipynb b/dev/_downloads/c5d41d4d7d1dab3e49804c2e2c4222e8/plot_ard.ipynb
index 076717171f316..da9864d52b475 100644
--- a/dev/_downloads/c5d41d4d7d1dab3e49804c2e2c4222e8/plot_ard.ipynb
+++ b/dev/_downloads/c5d41d4d7d1dab3e49804c2e2c4222e8/plot_ard.ipynb
@@ -175,7 +175,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/c6a456b2390718e4dc79945608262e0b/plot_huber_vs_ridge.ipynb b/dev/_downloads/c6a456b2390718e4dc79945608262e0b/plot_huber_vs_ridge.ipynb
index 517c5e58d7672..f2bb1e124180a 100644
--- a/dev/_downloads/c6a456b2390718e4dc79945608262e0b/plot_huber_vs_ridge.ipynb
+++ b/dev/_downloads/c6a456b2390718e4dc79945608262e0b/plot_huber_vs_ridge.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/c6bab6fd031af67403ba45efde42d16c/plot_adaboost_twoclass.zip b/dev/_downloads/c6bab6fd031af67403ba45efde42d16c/plot_adaboost_twoclass.zip
index c14469ff57f13..6be743ef52bb4 100644
Binary files a/dev/_downloads/c6bab6fd031af67403ba45efde42d16c/plot_adaboost_twoclass.zip and b/dev/_downloads/c6bab6fd031af67403ba45efde42d16c/plot_adaboost_twoclass.zip differ
diff --git a/dev/_downloads/c6e2877780eeb2421a441896c8ec77b7/plot_label_propagation_structure.py b/dev/_downloads/c6e2877780eeb2421a441896c8ec77b7/plot_label_propagation_structure.py
index 8a1798c84edf4..323cfb2a110cf 100644
--- a/dev/_downloads/c6e2877780eeb2421a441896c8ec77b7/plot_label_propagation_structure.py
+++ b/dev/_downloads/c6e2877780eeb2421a441896c8ec77b7/plot_label_propagation_structure.py
@@ -1,7 +1,7 @@
 """
-==============================================
-Label Propagation learning a complex structure
-==============================================
+=======================================================
+Label Propagation circles: Learning a complex structure
+=======================================================
 
 Example of LabelPropagation learning a complex internal structure
 to demonstrate "manifold learning". The outer circle should be
@@ -78,8 +78,8 @@
 # when the label was unknown.
 output_labels = label_spread.transduction_
 output_label_array = np.asarray(output_labels)
-outer_numbers = np.where(output_label_array == outer)[0]
-inner_numbers = np.where(output_label_array == inner)[0]
+outer_numbers = (output_label_array == outer).nonzero()[0]
+inner_numbers = (output_label_array == inner).nonzero()[0]
 
 plt.figure(figsize=(4, 4))
 plt.scatter(
diff --git a/dev/_downloads/c75a28fb9c5813c4fe15345695d1bdcf/plot_cost_sensitive_learning.zip b/dev/_downloads/c75a28fb9c5813c4fe15345695d1bdcf/plot_cost_sensitive_learning.zip
index 9a49df20b03b1..c46578de27682 100644
Binary files a/dev/_downloads/c75a28fb9c5813c4fe15345695d1bdcf/plot_cost_sensitive_learning.zip and b/dev/_downloads/c75a28fb9c5813c4fe15345695d1bdcf/plot_cost_sensitive_learning.zip differ
diff --git a/dev/_downloads/c763870a5d2681d445fc65fcf63bba31/plot_sparse_coding.ipynb b/dev/_downloads/c763870a5d2681d445fc65fcf63bba31/plot_sparse_coding.ipynb
index 3d51e7bf9139e..5726e7444d806 100644
--- a/dev/_downloads/c763870a5d2681d445fc65fcf63bba31/plot_sparse_coding.ipynb
+++ b/dev/_downloads/c763870a5d2681d445fc65fcf63bba31/plot_sparse_coding.ipynb
@@ -15,7 +15,7 @@
       },
       "outputs": [],
       "source": [
-        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nfrom sklearn.decomposition import SparseCoder\n\n\ndef ricker_function(resolution, center, width):\n    \"\"\"Discrete sub-sampled Ricker (Mexican hat) wavelet\"\"\"\n    x = np.linspace(0, resolution - 1, resolution)\n    x = (\n        (2 / (np.sqrt(3 * width) * np.pi**0.25))\n        * (1 - (x - center) ** 2 / width**2)\n        * np.exp(-((x - center) ** 2) / (2 * width**2))\n    )\n    return x\n\n\ndef ricker_matrix(width, resolution, n_components):\n    \"\"\"Dictionary of Ricker (Mexican hat) wavelets\"\"\"\n    centers = np.linspace(0, resolution - 1, n_components)\n    D = np.empty((n_components, resolution))\n    for i, center in enumerate(centers):\n        D[i] = ricker_function(resolution, center, width)\n    D /= np.sqrt(np.sum(D**2, axis=1))[:, np.newaxis]\n    return D\n\n\nresolution = 1024\nsubsampling = 3  # subsampling factor\nwidth = 100\nn_components = resolution // subsampling\n\n# Compute a wavelet dictionary\nD_fixed = ricker_matrix(width=width, resolution=resolution, n_components=n_components)\nD_multi = np.r_[\n    tuple(\n        ricker_matrix(width=w, resolution=resolution, n_components=n_components // 5)\n        for w in (10, 50, 100, 500, 1000)\n    )\n]\n\n# Generate a signal\ny = np.linspace(0, resolution - 1, resolution)\nfirst_quarter = y < resolution / 4\ny[first_quarter] = 3.0\ny[np.logical_not(first_quarter)] = -1.0\n\n# List the different sparse coding methods in the following format:\n# (title, transform_algorithm, transform_alpha,\n#  transform_n_nozero_coefs, color)\nestimators = [\n    (\"OMP\", \"omp\", None, 15, \"navy\"),\n    (\"Lasso\", \"lasso_lars\", 2, None, \"turquoise\"),\n]\nlw = 2\n\nplt.figure(figsize=(13, 6))\nfor subplot, (D, title) in enumerate(\n    zip((D_fixed, D_multi), (\"fixed width\", \"multiple widths\"))\n):\n    plt.subplot(1, 2, subplot + 1)\n    plt.title(\"Sparse coding against %s dictionary\" % title)\n    plt.plot(y, lw=lw, linestyle=\"--\", label=\"Original signal\")\n    # Do a wavelet approximation\n    for title, algo, alpha, n_nonzero, color in estimators:\n        coder = SparseCoder(\n            dictionary=D,\n            transform_n_nonzero_coefs=n_nonzero,\n            transform_alpha=alpha,\n            transform_algorithm=algo,\n        )\n        x = coder.transform(y.reshape(1, -1))\n        density = len(np.flatnonzero(x))\n        x = np.ravel(np.dot(x, D))\n        squared_error = np.sum((y - x) ** 2)\n        plt.plot(\n            x,\n            color=color,\n            lw=lw,\n            label=\"%s: %s nonzero coefs,\\n%.2f error\" % (title, density, squared_error),\n        )\n\n    # Soft thresholding debiasing\n    coder = SparseCoder(\n        dictionary=D, transform_algorithm=\"threshold\", transform_alpha=20\n    )\n    x = coder.transform(y.reshape(1, -1))\n    _, idx = np.where(x != 0)\n    x[0, idx], _, _, _ = np.linalg.lstsq(D[idx, :].T, y, rcond=None)\n    x = np.ravel(np.dot(x, D))\n    squared_error = np.sum((y - x) ** 2)\n    plt.plot(\n        x,\n        color=\"darkorange\",\n        lw=lw,\n        label=\"Thresholding w/ debiasing:\\n%d nonzero coefs, %.2f error\"\n        % (len(idx), squared_error),\n    )\n    plt.axis(\"tight\")\n    plt.legend(shadow=False, loc=\"best\")\nplt.subplots_adjust(0.04, 0.07, 0.97, 0.90, 0.09, 0.2)\nplt.show()"
+        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nfrom sklearn.decomposition import SparseCoder\n\n\ndef ricker_function(resolution, center, width):\n    \"\"\"Discrete sub-sampled Ricker (Mexican hat) wavelet\"\"\"\n    x = np.linspace(0, resolution - 1, resolution)\n    x = (\n        (2 / (np.sqrt(3 * width) * np.pi**0.25))\n        * (1 - (x - center) ** 2 / width**2)\n        * np.exp(-((x - center) ** 2) / (2 * width**2))\n    )\n    return x\n\n\ndef ricker_matrix(width, resolution, n_components):\n    \"\"\"Dictionary of Ricker (Mexican hat) wavelets\"\"\"\n    centers = np.linspace(0, resolution - 1, n_components)\n    D = np.empty((n_components, resolution))\n    for i, center in enumerate(centers):\n        D[i] = ricker_function(resolution, center, width)\n    D /= np.sqrt(np.sum(D**2, axis=1))[:, np.newaxis]\n    return D\n\n\nresolution = 1024\nsubsampling = 3  # subsampling factor\nwidth = 100\nn_components = resolution // subsampling\n\n# Compute a wavelet dictionary\nD_fixed = ricker_matrix(width=width, resolution=resolution, n_components=n_components)\nD_multi = np.r_[\n    tuple(\n        ricker_matrix(width=w, resolution=resolution, n_components=n_components // 5)\n        for w in (10, 50, 100, 500, 1000)\n    )\n]\n\n# Generate a signal\ny = np.linspace(0, resolution - 1, resolution)\nfirst_quarter = y < resolution / 4\ny[first_quarter] = 3.0\ny[np.logical_not(first_quarter)] = -1.0\n\n# List the different sparse coding methods in the following format:\n# (title, transform_algorithm, transform_alpha,\n#  transform_n_nozero_coefs, color)\nestimators = [\n    (\"OMP\", \"omp\", None, 15, \"navy\"),\n    (\"Lasso\", \"lasso_lars\", 2, None, \"turquoise\"),\n]\nlw = 2\n\nplt.figure(figsize=(13, 6))\nfor subplot, (D, title) in enumerate(\n    zip((D_fixed, D_multi), (\"fixed width\", \"multiple widths\"))\n):\n    plt.subplot(1, 2, subplot + 1)\n    plt.title(\"Sparse coding against %s dictionary\" % title)\n    plt.plot(y, lw=lw, linestyle=\"--\", label=\"Original signal\")\n    # Do a wavelet approximation\n    for title, algo, alpha, n_nonzero, color in estimators:\n        coder = SparseCoder(\n            dictionary=D,\n            transform_n_nonzero_coefs=n_nonzero,\n            transform_alpha=alpha,\n            transform_algorithm=algo,\n        )\n        x = coder.transform(y.reshape(1, -1))\n        density = len(np.flatnonzero(x))\n        x = np.ravel(np.dot(x, D))\n        squared_error = np.sum((y - x) ** 2)\n        plt.plot(\n            x,\n            color=color,\n            lw=lw,\n            label=\"%s: %s nonzero coefs,\\n%.2f error\" % (title, density, squared_error),\n        )\n\n    # Soft thresholding debiasing\n    coder = SparseCoder(\n        dictionary=D, transform_algorithm=\"threshold\", transform_alpha=20\n    )\n    x = coder.transform(y.reshape(1, -1))\n    _, idx = (x != 0).nonzero()\n    x[0, idx], _, _, _ = np.linalg.lstsq(D[idx, :].T, y, rcond=None)\n    x = np.ravel(np.dot(x, D))\n    squared_error = np.sum((y - x) ** 2)\n    plt.plot(\n        x,\n        color=\"darkorange\",\n        lw=lw,\n        label=\"Thresholding w/ debiasing:\\n%d nonzero coefs, %.2f error\"\n        % (len(idx), squared_error),\n    )\n    plt.axis(\"tight\")\n    plt.legend(shadow=False, loc=\"best\")\nplt.subplots_adjust(0.04, 0.07, 0.97, 0.90, 0.09, 0.2)\nplt.show()"
       ]
     }
   ],
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/c82bd3e95b1fad595c0ed8d47e58d845/plot_digits_kde_sampling.ipynb b/dev/_downloads/c82bd3e95b1fad595c0ed8d47e58d845/plot_digits_kde_sampling.ipynb
index 4527d109a8519..13d851171d6f6 100644
--- a/dev/_downloads/c82bd3e95b1fad595c0ed8d47e58d845/plot_digits_kde_sampling.ipynb
+++ b/dev/_downloads/c82bd3e95b1fad595c0ed8d47e58d845/plot_digits_kde_sampling.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/c8c7c5458b84586cacd8498015126bc4/plot_coin_ward_segmentation.ipynb b/dev/_downloads/c8c7c5458b84586cacd8498015126bc4/plot_coin_ward_segmentation.ipynb
index 76e35207f311c..a5c13153ca5d0 100644
--- a/dev/_downloads/c8c7c5458b84586cacd8498015126bc4/plot_coin_ward_segmentation.ipynb
+++ b/dev/_downloads/c8c7c5458b84586cacd8498015126bc4/plot_coin_ward_segmentation.ipynb
@@ -125,7 +125,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/c8d6400be1e93975c77a6f188d6c9cd0/plot_sgdocsvm_vs_ocsvm.zip b/dev/_downloads/c8d6400be1e93975c77a6f188d6c9cd0/plot_sgdocsvm_vs_ocsvm.zip
index 11b33b934613c..d28ff2d62c563 100644
Binary files a/dev/_downloads/c8d6400be1e93975c77a6f188d6c9cd0/plot_sgdocsvm_vs_ocsvm.zip and b/dev/_downloads/c8d6400be1e93975c77a6f188d6c9cd0/plot_sgdocsvm_vs_ocsvm.zip differ
diff --git a/dev/_downloads/c8db473878b6afea8e75e36dc828f109/plot_compare_methods.ipynb b/dev/_downloads/c8db473878b6afea8e75e36dc828f109/plot_compare_methods.ipynb
index a1ce787605acc..7db6453ca1037 100644
--- a/dev/_downloads/c8db473878b6afea8e75e36dc828f109/plot_compare_methods.ipynb
+++ b/dev/_downloads/c8db473878b6afea8e75e36dc828f109/plot_compare_methods.ipynb
@@ -134,7 +134,7 @@
       },
       "outputs": [],
       "source": [
-        "md_scaling = manifold.MDS(\n    n_components=n_components,\n    max_iter=50,\n    n_init=4,\n    random_state=0,\n    normalized_stress=False,\n)\nS_scaling = md_scaling.fit_transform(S_points)\n\nplot_2d(S_scaling, S_color, \"Multidimensional scaling\")"
+        "md_scaling = manifold.MDS(\n    n_components=n_components,\n    max_iter=50,\n    n_init=1,\n    random_state=0,\n    normalized_stress=False,\n)\nS_scaling = md_scaling.fit_transform(S_points)\n\nplot_2d(S_scaling, S_color, \"Multidimensional scaling\")"
       ]
     },
     {
@@ -190,7 +190,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/c93b4e0b422f79bfcb544a032cebe455/plot_robust_fit.ipynb b/dev/_downloads/c93b4e0b422f79bfcb544a032cebe455/plot_robust_fit.ipynb
index 5455f79cc5b60..fc2b8ac6a7459 100644
--- a/dev/_downloads/c93b4e0b422f79bfcb544a032cebe455/plot_robust_fit.ipynb
+++ b/dev/_downloads/c93b4e0b422f79bfcb544a032cebe455/plot_robust_fit.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/c9688d36cfbf43a68f3613b58110ceaa/plot_scaling_importance.ipynb b/dev/_downloads/c9688d36cfbf43a68f3613b58110ceaa/plot_scaling_importance.ipynb
index 09fe748a7452e..d60bc4e175e94 100644
--- a/dev/_downloads/c9688d36cfbf43a68f3613b58110ceaa/plot_scaling_importance.ipynb
+++ b/dev/_downloads/c9688d36cfbf43a68f3613b58110ceaa/plot_scaling_importance.ipynb
@@ -150,7 +150,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/c9e0ce7ab0d4145aa1e41ca5fc31178e/plot_incremental_pca.zip b/dev/_downloads/c9e0ce7ab0d4145aa1e41ca5fc31178e/plot_incremental_pca.zip
index 3041b181ede2a..6355ffe160023 100644
Binary files a/dev/_downloads/c9e0ce7ab0d4145aa1e41ca5fc31178e/plot_incremental_pca.zip and b/dev/_downloads/c9e0ce7ab0d4145aa1e41ca5fc31178e/plot_incremental_pca.zip differ
diff --git a/dev/_downloads/ca0bfe2435d9b3fffe21c713e63d3a6f/plot_learning_curve.ipynb b/dev/_downloads/ca0bfe2435d9b3fffe21c713e63d3a6f/plot_learning_curve.ipynb
index bbcff38a10742..ef9556a6d8cc3 100644
--- a/dev/_downloads/ca0bfe2435d9b3fffe21c713e63d3a6f/plot_learning_curve.ipynb
+++ b/dev/_downloads/ca0bfe2435d9b3fffe21c713e63d3a6f/plot_learning_curve.ipynb
@@ -125,7 +125,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/cb3f1818f509a9fcaea18d325eeb2e71/plot_dict_face_patches.zip b/dev/_downloads/cb3f1818f509a9fcaea18d325eeb2e71/plot_dict_face_patches.zip
index 7b9a0bd6ffa7a..9fc8308c859ea 100644
Binary files a/dev/_downloads/cb3f1818f509a9fcaea18d325eeb2e71/plot_dict_face_patches.zip and b/dev/_downloads/cb3f1818f509a9fcaea18d325eeb2e71/plot_dict_face_patches.zip differ
diff --git a/dev/_downloads/cb5b0b55b4ddb01e9ad80e6e28417c64/plot_scalable_poly_kernels.ipynb b/dev/_downloads/cb5b0b55b4ddb01e9ad80e6e28417c64/plot_scalable_poly_kernels.ipynb
index 9014b4d8d2023..e8b34131a8fd7 100644
--- a/dev/_downloads/cb5b0b55b4ddb01e9ad80e6e28417c64/plot_scalable_poly_kernels.ipynb
+++ b/dev/_downloads/cb5b0b55b4ddb01e9ad80e6e28417c64/plot_scalable_poly_kernels.ipynb
@@ -168,7 +168,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/cb9a8a373677fb481fe43a11d8fa0e94/plot_hgbt_regression.ipynb b/dev/_downloads/cb9a8a373677fb481fe43a11d8fa0e94/plot_hgbt_regression.ipynb
index b3f383a6c8a1f..512339e71c16f 100644
--- a/dev/_downloads/cb9a8a373677fb481fe43a11d8fa0e94/plot_hgbt_regression.ipynb
+++ b/dev/_downloads/cb9a8a373677fb481fe43a11d8fa0e94/plot_hgbt_regression.ipynb
@@ -233,7 +233,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/cc6cb3aee22cfec68cced9928e78b9af/plot_kernel_pca.zip b/dev/_downloads/cc6cb3aee22cfec68cced9928e78b9af/plot_kernel_pca.zip
index aafbf7e45ead1..8a2c6d54dbfcf 100644
Binary files a/dev/_downloads/cc6cb3aee22cfec68cced9928e78b9af/plot_kernel_pca.zip and b/dev/_downloads/cc6cb3aee22cfec68cced9928e78b9af/plot_kernel_pca.zip differ
diff --git a/dev/_downloads/ccd24a43e08ccb154430edf7f927cfc7/plot_bicluster_newsgroups.zip b/dev/_downloads/ccd24a43e08ccb154430edf7f927cfc7/plot_bicluster_newsgroups.zip
index 014815e77c83f..8601f78adbf10 100644
Binary files a/dev/_downloads/ccd24a43e08ccb154430edf7f927cfc7/plot_bicluster_newsgroups.zip and b/dev/_downloads/ccd24a43e08ccb154430edf7f927cfc7/plot_bicluster_newsgroups.zip differ
diff --git a/dev/_downloads/cd5de29451c4f8624f47d18def81839c/plot_gradient_boosting_categorical.ipynb b/dev/_downloads/cd5de29451c4f8624f47d18def81839c/plot_gradient_boosting_categorical.ipynb
index acb3f7fafce36..6ed480c43aa25 100644
--- a/dev/_downloads/cd5de29451c4f8624f47d18def81839c/plot_gradient_boosting_categorical.ipynb
+++ b/dev/_downloads/cd5de29451c4f8624f47d18def81839c/plot_gradient_boosting_categorical.ipynb
@@ -175,7 +175,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/cda53b33015268619bc212d32b7000b9/plot_compare_methods.py b/dev/_downloads/cda53b33015268619bc212d32b7000b9/plot_compare_methods.py
index 30ce4e5d8d897..6203a4afc436d 100644
--- a/dev/_downloads/cda53b33015268619bc212d32b7000b9/plot_compare_methods.py
+++ b/dev/_downloads/cda53b33015268619bc212d32b7000b9/plot_compare_methods.py
@@ -166,7 +166,7 @@ def add_2d_scatter(ax, points, points_color, title=None):
 md_scaling = manifold.MDS(
     n_components=n_components,
     max_iter=50,
-    n_init=4,
+    n_init=1,
     random_state=0,
     normalized_stress=False,
 )
diff --git a/dev/_downloads/cde909454db5f1c683f557f051f7934e/plot_select_from_model_diabetes.zip b/dev/_downloads/cde909454db5f1c683f557f051f7934e/plot_select_from_model_diabetes.zip
index 4748db09d20d4..8a3072604ac44 100644
Binary files a/dev/_downloads/cde909454db5f1c683f557f051f7934e/plot_select_from_model_diabetes.zip and b/dev/_downloads/cde909454db5f1c683f557f051f7934e/plot_select_from_model_diabetes.zip differ
diff --git a/dev/_downloads/ce1d170a6b8e6097fd3c01ab24b276f5/plot_successive_halving_heatmap.zip b/dev/_downloads/ce1d170a6b8e6097fd3c01ab24b276f5/plot_successive_halving_heatmap.zip
index fa85050522dcf..a12e83e10139f 100644
Binary files a/dev/_downloads/ce1d170a6b8e6097fd3c01ab24b276f5/plot_successive_halving_heatmap.zip and b/dev/_downloads/ce1d170a6b8e6097fd3c01ab24b276f5/plot_successive_halving_heatmap.zip differ
diff --git a/dev/_downloads/ce35bcc69acbd491cf7ac77fa17889d5/plot_classifier_comparison.zip b/dev/_downloads/ce35bcc69acbd491cf7ac77fa17889d5/plot_classifier_comparison.zip
index f1dfc800ef664..28c6cd22d0dc9 100644
Binary files a/dev/_downloads/ce35bcc69acbd491cf7ac77fa17889d5/plot_classifier_comparison.zip and b/dev/_downloads/ce35bcc69acbd491cf7ac77fa17889d5/plot_classifier_comparison.zip differ
diff --git a/dev/_downloads/cea3fc06a4f342c635cadafa63b33319/plot_weighted_samples.ipynb b/dev/_downloads/cea3fc06a4f342c635cadafa63b33319/plot_weighted_samples.ipynb
index eb6ec97afa300..cc4aa6dac91b1 100644
--- a/dev/_downloads/cea3fc06a4f342c635cadafa63b33319/plot_weighted_samples.ipynb
+++ b/dev/_downloads/cea3fc06a4f342c635cadafa63b33319/plot_weighted_samples.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/cf0f90f46eb559facf7f63f124f61e04/plot_linear_model_coefficient_interpretation.ipynb b/dev/_downloads/cf0f90f46eb559facf7f63f124f61e04/plot_linear_model_coefficient_interpretation.ipynb
index 62164ce1dbfcb..e9b1c660b7471 100644
--- a/dev/_downloads/cf0f90f46eb559facf7f63f124f61e04/plot_linear_model_coefficient_interpretation.ipynb
+++ b/dev/_downloads/cf0f90f46eb559facf7f63f124f61e04/plot_linear_model_coefficient_interpretation.ipynb
@@ -713,7 +713,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/d15e2c63b604233bb04599c0a1a02c8f/plot_tomography_l1_reconstruction.zip b/dev/_downloads/d15e2c63b604233bb04599c0a1a02c8f/plot_tomography_l1_reconstruction.zip
index fc8d5e3cd3d47..6d40c425d13a0 100644
Binary files a/dev/_downloads/d15e2c63b604233bb04599c0a1a02c8f/plot_tomography_l1_reconstruction.zip and b/dev/_downloads/d15e2c63b604233bb04599c0a1a02c8f/plot_tomography_l1_reconstruction.zip differ
diff --git a/dev/_downloads/d20432c5cccdd8b208184031b83a8cf9/plot_release_highlights_0_24_0.py b/dev/_downloads/d20432c5cccdd8b208184031b83a8cf9/plot_release_highlights_0_24_0.py
index a7369317da3e0..d09250ba6ff64 100644
--- a/dev/_downloads/d20432c5cccdd8b208184031b83a8cf9/plot_release_highlights_0_24_0.py
+++ b/dev/_downloads/d20432c5cccdd8b208184031b83a8cf9/plot_release_highlights_0_24_0.py
@@ -1,4 +1,4 @@
-# ruff: noqa
+# ruff: noqa: CPY001, E501
 """
 ========================================
 Release Highlights for scikit-learn 0.24
@@ -51,10 +51,11 @@
 
 import numpy as np
 from scipy.stats import randint
-from sklearn.experimental import enable_halving_search_cv  # noqa
-from sklearn.model_selection import HalvingRandomSearchCV
-from sklearn.ensemble import RandomForestClassifier
+
 from sklearn.datasets import make_classification
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.experimental import enable_halving_search_cv  # noqa: F401
+from sklearn.model_selection import HalvingRandomSearchCV
 
 rng = np.random.RandomState(0)
 
@@ -118,6 +119,7 @@
 # Read more in the :ref:`User guide <self_training>`.
 
 import numpy as np
+
 from sklearn import datasets
 from sklearn.semi_supervised import SelfTrainingClassifier
 from sklearn.svm import SVC
@@ -140,9 +142,9 @@
 # (backward selection), based on a cross-validated score maximization.
 # See the :ref:`User Guide <sequential_feature_selection>`.
 
+from sklearn.datasets import load_iris
 from sklearn.feature_selection import SequentialFeatureSelector
 from sklearn.neighbors import KNeighborsClassifier
-from sklearn.datasets import load_iris
 
 X, y = load_iris(return_X_y=True, as_frame=True)
 feature_names = X.columns
@@ -163,11 +165,11 @@
 # :class:`~sklearn.preprocessing.PolynomialFeatures`.
 
 from sklearn.datasets import fetch_covtype
-from sklearn.pipeline import make_pipeline
-from sklearn.model_selection import train_test_split
-from sklearn.preprocessing import MinMaxScaler
 from sklearn.kernel_approximation import PolynomialCountSketch
 from sklearn.linear_model import LogisticRegression
+from sklearn.model_selection import train_test_split
+from sklearn.pipeline import make_pipeline
+from sklearn.preprocessing import MinMaxScaler
 
 X, y = fetch_covtype(return_X_y=True)
 pipe = make_pipeline(
@@ -194,8 +196,8 @@
 # prediction on a feature for each sample separately, with one line per sample.
 # See the :ref:`User Guide <individual_conditional>`
 
-from sklearn.ensemble import RandomForestRegressor
 from sklearn.datasets import fetch_california_housing
+from sklearn.ensemble import RandomForestRegressor
 
 # from sklearn.inspection import plot_partial_dependence
 from sklearn.inspection import PartialDependenceDisplay
@@ -232,10 +234,11 @@
 # splitting criterion. Setting `criterion="poisson"` might be a good choice
 # if your target is a count or a frequency.
 
-from sklearn.tree import DecisionTreeRegressor
-from sklearn.model_selection import train_test_split
 import numpy as np
 
+from sklearn.model_selection import train_test_split
+from sklearn.tree import DecisionTreeRegressor
+
 n_samples, n_features = 1000, 20
 rng = np.random.RandomState(0)
 X = rng.randn(n_samples, n_features)
diff --git a/dev/_downloads/d22cc4079af5678a112fa5aa77700680/plot_separating_hyperplane_unbalanced.ipynb b/dev/_downloads/d22cc4079af5678a112fa5aa77700680/plot_separating_hyperplane_unbalanced.ipynb
index b7817d74bd68d..73ee80d3ba9d8 100644
--- a/dev/_downloads/d22cc4079af5678a112fa5aa77700680/plot_separating_hyperplane_unbalanced.ipynb
+++ b/dev/_downloads/d22cc4079af5678a112fa5aa77700680/plot_separating_hyperplane_unbalanced.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/d24b370bfcf725dbb3feedb9cb874c73/plot_outlier_detection_wine.zip b/dev/_downloads/d24b370bfcf725dbb3feedb9cb874c73/plot_outlier_detection_wine.zip
index 0d95fa333f953..39b2eae011cc8 100644
Binary files a/dev/_downloads/d24b370bfcf725dbb3feedb9cb874c73/plot_outlier_detection_wine.zip and b/dev/_downloads/d24b370bfcf725dbb3feedb9cb874c73/plot_outlier_detection_wine.zip differ
diff --git a/dev/_downloads/d2f474d24fdee2c16d20414456da98c4/plot_agglomerative_clustering.ipynb b/dev/_downloads/d2f474d24fdee2c16d20414456da98c4/plot_agglomerative_clustering.ipynb
index 946c96f37556d..b6993efd3e2f4 100644
--- a/dev/_downloads/d2f474d24fdee2c16d20414456da98c4/plot_agglomerative_clustering.ipynb
+++ b/dev/_downloads/d2f474d24fdee2c16d20414456da98c4/plot_agglomerative_clustering.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/d3bae9b99abb4dfdba5f7c8588be8746/plot_separating_hyperplane_unbalanced.zip b/dev/_downloads/d3bae9b99abb4dfdba5f7c8588be8746/plot_separating_hyperplane_unbalanced.zip
index 42ed09d71d6b9..055fe6d7ebed2 100644
Binary files a/dev/_downloads/d3bae9b99abb4dfdba5f7c8588be8746/plot_separating_hyperplane_unbalanced.zip and b/dev/_downloads/d3bae9b99abb4dfdba5f7c8588be8746/plot_separating_hyperplane_unbalanced.zip differ
diff --git a/dev/_downloads/d45483409febd18772e1fe41233d258e/plot_gradient_boosting_oob.ipynb b/dev/_downloads/d45483409febd18772e1fe41233d258e/plot_gradient_boosting_oob.ipynb
index ab1657f8bbf64..d1bcf546bbfd4 100644
--- a/dev/_downloads/d45483409febd18772e1fe41233d258e/plot_gradient_boosting_oob.ipynb
+++ b/dev/_downloads/d45483409febd18772e1fe41233d258e/plot_gradient_boosting_oob.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/d46573f9713a1a8e4ec685dbe05d7696/plot_caching_nearest_neighbors.zip b/dev/_downloads/d46573f9713a1a8e4ec685dbe05d7696/plot_caching_nearest_neighbors.zip
index c5fda7e1c24f1..2db6e6f49e191 100644
Binary files a/dev/_downloads/d46573f9713a1a8e4ec685dbe05d7696/plot_caching_nearest_neighbors.zip and b/dev/_downloads/d46573f9713a1a8e4ec685dbe05d7696/plot_caching_nearest_neighbors.zip differ
diff --git a/dev/_downloads/d55388904f5399e98ed36e971c4da3cf/plot_rbf_parameters.ipynb b/dev/_downloads/d55388904f5399e98ed36e971c4da3cf/plot_rbf_parameters.ipynb
index feb5a4f86657e..bf2daa479df1a 100644
--- a/dev/_downloads/d55388904f5399e98ed36e971c4da3cf/plot_rbf_parameters.ipynb
+++ b/dev/_downloads/d55388904f5399e98ed36e971c4da3cf/plot_rbf_parameters.ipynb
@@ -179,7 +179,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/d58abbe7bc4eabf74b338ceadfa1c679/plot_svm_kernels.zip b/dev/_downloads/d58abbe7bc4eabf74b338ceadfa1c679/plot_svm_kernels.zip
index 1db9b360ace96..71e14fd9d6bac 100644
Binary files a/dev/_downloads/d58abbe7bc4eabf74b338ceadfa1c679/plot_svm_kernels.zip and b/dev/_downloads/d58abbe7bc4eabf74b338ceadfa1c679/plot_svm_kernels.zip differ
diff --git a/dev/_downloads/d59ecf1ac6d8f981abe3e795cad51cbd/plot_grid_search_stats.zip b/dev/_downloads/d59ecf1ac6d8f981abe3e795cad51cbd/plot_grid_search_stats.zip
index 0f010df7badf3..895b0103492de 100644
Binary files a/dev/_downloads/d59ecf1ac6d8f981abe3e795cad51cbd/plot_grid_search_stats.zip and b/dev/_downloads/d59ecf1ac6d8f981abe3e795cad51cbd/plot_grid_search_stats.zip differ
diff --git a/dev/_downloads/d5d294a9765ce8e38cb5160192969e05/plot_logistic.zip b/dev/_downloads/d5d294a9765ce8e38cb5160192969e05/plot_logistic.zip
index eb63884f2080e..458d2d917f677 100644
Binary files a/dev/_downloads/d5d294a9765ce8e38cb5160192969e05/plot_logistic.zip and b/dev/_downloads/d5d294a9765ce8e38cb5160192969e05/plot_logistic.zip differ
diff --git a/dev/_downloads/d5dfa4bf7bbcb5bde05d8b9e576a0de1/plot_logistic_l1_l2_sparsity.zip b/dev/_downloads/d5dfa4bf7bbcb5bde05d8b9e576a0de1/plot_logistic_l1_l2_sparsity.zip
index 6056d06f26645..aee220fcb20f5 100644
Binary files a/dev/_downloads/d5dfa4bf7bbcb5bde05d8b9e576a0de1/plot_logistic_l1_l2_sparsity.zip and b/dev/_downloads/d5dfa4bf7bbcb5bde05d8b9e576a0de1/plot_logistic_l1_l2_sparsity.zip differ
diff --git a/dev/_downloads/d64ed0728005aaeba058d3ccec909e73/plot_mds.py b/dev/_downloads/d64ed0728005aaeba058d3ccec909e73/plot_mds.py
index afea676b245a8..9d9828fc448f5 100644
--- a/dev/_downloads/d64ed0728005aaeba058d3ccec909e73/plot_mds.py
+++ b/dev/_downloads/d64ed0728005aaeba058d3ccec909e73/plot_mds.py
@@ -5,14 +5,17 @@
 
 An illustration of the metric and non-metric MDS on generated noisy data.
 
-The reconstructed points using the metric MDS and non metric MDS are slightly
-shifted to avoid overlapping.
-
 """
 
 # Authors: The scikit-learn developers
 # SPDX-License-Identifier: BSD-3-Clause
 
+# %%
+# Dataset preparation
+# -------------------
+#
+# We start by uniformly generating 20 points in a 2D space.
+
 import numpy as np
 from matplotlib import pyplot as plt
 from matplotlib.collections import LineCollection
@@ -31,6 +34,11 @@
 # Center the data
 X_true -= X_true.mean()
 
+# %%
+# Now we compute pairwise distances between all points and add
+# a small amount of noise to the distance matrix. We make sure
+# to keep the noisy distance matrix symmetric.
+
 # Compute pairwise Euclidean distances
 distances = euclidean_distances(X_true)
 
@@ -40,10 +48,14 @@
 np.fill_diagonal(noise, 0)
 distances += noise
 
+# %%
+# Here we compute metric and non-metric MDS of the noisy distance matrix.
+
 mds = manifold.MDS(
     n_components=2,
     max_iter=3000,
     eps=1e-9,
+    n_init=1,
     random_state=42,
     dissimilarity="precomputed",
     n_jobs=1,
@@ -62,10 +74,16 @@
 )
 X_nmds = nmds.fit_transform(distances)
 
-# Rescale the data
-X_mds *= np.sqrt((X_true**2).sum()) / np.sqrt((X_mds**2).sum())
+# %%
+# Rescaling the non-metric MDS solution to match the spread of the original data.
+
 X_nmds *= np.sqrt((X_true**2).sum()) / np.sqrt((X_nmds**2).sum())
 
+# %%
+# To make the visual comparisons easier, we rotate the original data and both MDS
+# solutions to their PCA axes. And flip horizontal and vertical MDS axes, if needed,
+# to match the original data orientation.
+
 # Rotate the data
 pca = PCA(n_components=2)
 X_true = pca.fit_transform(X_true)
@@ -79,6 +97,9 @@
     if np.corrcoef(X_nmds[:, i], X_true[:, i])[0, 1] < 0:
         X_nmds[:, i] *= -1
 
+# %%
+# Finally, we plot the original data and both MDS reconstructions.
+
 fig = plt.figure(1)
 ax = plt.axes([0.0, 0.0, 1.0, 1.0])
 
@@ -89,7 +110,7 @@
 plt.legend(scatterpoints=1, loc="best", shadow=False)
 
 # Plot the edges
-start_idx, end_idx = np.where(X_mds)
+start_idx, end_idx = X_mds.nonzero()
 # a sequence of (*line0*, *line1*, *line2*), where::
 #            linen = (x0, y0), (x1, y1), ... (xm, ym)
 segments = [
diff --git a/dev/_downloads/d731b5f8ec6b42fcb1e93a30584080e8/plot_release_highlights_0_22_0.zip b/dev/_downloads/d731b5f8ec6b42fcb1e93a30584080e8/plot_release_highlights_0_22_0.zip
index 5abab67692844..0e2d833473693 100644
Binary files a/dev/_downloads/d731b5f8ec6b42fcb1e93a30584080e8/plot_release_highlights_0_22_0.zip and b/dev/_downloads/d731b5f8ec6b42fcb1e93a30584080e8/plot_release_highlights_0_22_0.zip differ
diff --git a/dev/_downloads/d9b8062b664fa0515a7849d4a28e98ed/plot_multilabel.py b/dev/_downloads/d9b8062b664fa0515a7849d4a28e98ed/plot_multilabel.py
index 9d08ad3fa7907..4c88dbe1838f2 100644
--- a/dev/_downloads/d9b8062b664fa0515a7849d4a28e98ed/plot_multilabel.py
+++ b/dev/_downloads/d9b8062b664fa0515a7849d4a28e98ed/plot_multilabel.py
@@ -71,8 +71,8 @@ def plot_subfigure(X, Y, subplot, title, transform):
     plt.subplot(2, 2, subplot)
     plt.title(title)
 
-    zero_class = np.where(Y[:, 0])
-    one_class = np.where(Y[:, 1])
+    zero_class = (Y[:, 0]).nonzero()
+    one_class = (Y[:, 1]).nonzero()
     plt.scatter(X[:, 0], X[:, 1], s=40, c="gray", edgecolors=(0, 0, 0))
     plt.scatter(
         X[zero_class, 0],
diff --git a/dev/_downloads/da2a64f1701e08e61149af1143e91f0b/plot_sgd_weighted_samples.zip b/dev/_downloads/da2a64f1701e08e61149af1143e91f0b/plot_sgd_weighted_samples.zip
index 7e47c5af00d71..e37a0dc7857c2 100644
Binary files a/dev/_downloads/da2a64f1701e08e61149af1143e91f0b/plot_sgd_weighted_samples.zip and b/dev/_downloads/da2a64f1701e08e61149af1143e91f0b/plot_sgd_weighted_samples.zip differ
diff --git a/dev/_downloads/dbb11e3bf79f5227c33c4f2a04ec206a/plot_self_training_varying_threshold.zip b/dev/_downloads/dbb11e3bf79f5227c33c4f2a04ec206a/plot_self_training_varying_threshold.zip
index 6ecf9c53ac575..239e12e4b19bb 100644
Binary files a/dev/_downloads/dbb11e3bf79f5227c33c4f2a04ec206a/plot_self_training_varying_threshold.zip and b/dev/_downloads/dbb11e3bf79f5227c33c4f2a04ec206a/plot_self_training_varying_threshold.zip differ
diff --git a/dev/_downloads/dc6079cc1c70df637397032629974c2c/plot_gpc.zip b/dev/_downloads/dc6079cc1c70df637397032629974c2c/plot_gpc.zip
index 479aed7c6f700..9819941409f33 100644
Binary files a/dev/_downloads/dc6079cc1c70df637397032629974c2c/plot_gpc.zip and b/dev/_downloads/dc6079cc1c70df637397032629974c2c/plot_gpc.zip differ
diff --git a/dev/_downloads/dcbef7d65782fe41603cd93588b9b909/plot_robust_vs_empirical_covariance.zip b/dev/_downloads/dcbef7d65782fe41603cd93588b9b909/plot_robust_vs_empirical_covariance.zip
index 5f4ff45c45065..8bee9ea6e599c 100644
Binary files a/dev/_downloads/dcbef7d65782fe41603cd93588b9b909/plot_robust_vs_empirical_covariance.zip and b/dev/_downloads/dcbef7d65782fe41603cd93588b9b909/plot_robust_vs_empirical_covariance.zip differ
diff --git a/dev/_downloads/dd28338257df6d2a7e6b9ff5f2743272/plot_outlier_detection_wine.ipynb b/dev/_downloads/dd28338257df6d2a7e6b9ff5f2743272/plot_outlier_detection_wine.ipynb
index de4cfc4276ab0..7bef91a44d0bb 100644
--- a/dev/_downloads/dd28338257df6d2a7e6b9ff5f2743272/plot_outlier_detection_wine.ipynb
+++ b/dev/_downloads/dd28338257df6d2a7e6b9ff5f2743272/plot_outlier_detection_wine.ipynb
@@ -82,7 +82,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/ddc1f7dc483de25e01897025606d794f/plot_hdbscan.py b/dev/_downloads/ddc1f7dc483de25e01897025606d794f/plot_hdbscan.py
index 64d4936694bf3..eee221d578ca3 100644
--- a/dev/_downloads/ddc1f7dc483de25e01897025606d794f/plot_hdbscan.py
+++ b/dev/_downloads/ddc1f7dc483de25e01897025606d794f/plot_hdbscan.py
@@ -40,7 +40,7 @@ def plot(X, labels, probabilities=None, parameters=None, ground_truth=False, ax=
             # Black used for noise.
             col = [0, 0, 0, 1]
 
-        class_index = np.where(labels == k)[0]
+        class_index = (labels == k).nonzero()[0]
         for ci in class_index:
             ax.plot(
                 X[ci, 0],
diff --git a/dev/_downloads/de462b38777cec6da8a8387da7db8f3a/plot_mean_shift.zip b/dev/_downloads/de462b38777cec6da8a8387da7db8f3a/plot_mean_shift.zip
index a6d56e1311bda..375ac88ae4027 100644
Binary files a/dev/_downloads/de462b38777cec6da8a8387da7db8f3a/plot_mean_shift.zip and b/dev/_downloads/de462b38777cec6da8a8387da7db8f3a/plot_mean_shift.zip differ
diff --git a/dev/_downloads/de80e18b1c5bc8b447bbce5b9750c567/plot_sgd_iris.py b/dev/_downloads/de80e18b1c5bc8b447bbce5b9750c567/plot_sgd_iris.py
index 46dc2e7c31cd1..e8aaf3a2e13a2 100644
--- a/dev/_downloads/de80e18b1c5bc8b447bbce5b9750c567/plot_sgd_iris.py
+++ b/dev/_downloads/de80e18b1c5bc8b447bbce5b9750c567/plot_sgd_iris.py
@@ -55,7 +55,7 @@
 
 # Plot also the training points
 for i, color in zip(clf.classes_, colors):
-    idx = np.where(y == i)
+    idx = (y == i).nonzero()
     plt.scatter(
         X[idx, 0],
         X[idx, 1],
diff --git a/dev/_downloads/df17584e78fbab6ddca8edf79def6190/plot_gmm.ipynb b/dev/_downloads/df17584e78fbab6ddca8edf79def6190/plot_gmm.ipynb
index 41bbb0183152a..ae0a34db28e39 100644
--- a/dev/_downloads/df17584e78fbab6ddca8edf79def6190/plot_gmm.ipynb
+++ b/dev/_downloads/df17584e78fbab6ddca8edf79def6190/plot_gmm.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/df1b185d030fd32d53b4b2303d64816c/plot_release_highlights_1_2_0.ipynb b/dev/_downloads/df1b185d030fd32d53b4b2303d64816c/plot_release_highlights_1_2_0.ipynb
index 1674d7f6804d6..4e4565f4cc2ab 100644
--- a/dev/_downloads/df1b185d030fd32d53b4b2303d64816c/plot_release_highlights_1_2_0.ipynb
+++ b/dev/_downloads/df1b185d030fd32d53b4b2303d64816c/plot_release_highlights_1_2_0.ipynb
@@ -22,7 +22,7 @@
       },
       "outputs": [],
       "source": [
-        "import numpy as np\nfrom sklearn.datasets import load_iris\nfrom sklearn.preprocessing import StandardScaler, KBinsDiscretizer\nfrom sklearn.compose import ColumnTransformer\n\nX, y = load_iris(as_frame=True, return_X_y=True)\nsepal_cols = [\"sepal length (cm)\", \"sepal width (cm)\"]\npetal_cols = [\"petal length (cm)\", \"petal width (cm)\"]\n\npreprocessor = ColumnTransformer(\n    [\n        (\"scaler\", StandardScaler(), sepal_cols),\n        (\n            \"kbin\",\n            KBinsDiscretizer(encode=\"ordinal\", quantile_method=\"averaged_inverted_cdf\"),\n            petal_cols,\n        ),\n    ],\n    verbose_feature_names_out=False,\n).set_output(transform=\"pandas\")\n\nX_out = preprocessor.fit_transform(X)\nX_out.sample(n=5, random_state=0)"
+        "import numpy as np\n\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.datasets import load_iris\nfrom sklearn.preprocessing import KBinsDiscretizer, StandardScaler\n\nX, y = load_iris(as_frame=True, return_X_y=True)\nsepal_cols = [\"sepal length (cm)\", \"sepal width (cm)\"]\npetal_cols = [\"petal length (cm)\", \"petal width (cm)\"]\n\npreprocessor = ColumnTransformer(\n    [\n        (\"scaler\", StandardScaler(), sepal_cols),\n        (\n            \"kbin\",\n            KBinsDiscretizer(encode=\"ordinal\", quantile_method=\"averaged_inverted_cdf\"),\n            petal_cols,\n        ),\n    ],\n    verbose_feature_names_out=False,\n).set_output(transform=\"pandas\")\n\nX_out = preprocessor.fit_transform(X)\nX_out.sample(n=5, random_state=0)"
       ]
     },
     {
@@ -58,7 +58,7 @@
       },
       "outputs": [],
       "source": [
-        "import matplotlib.pyplot as plt\nfrom sklearn.metrics import PredictionErrorDisplay\n\nfig, axs = plt.subplots(nrows=1, ncols=2, figsize=(12, 5))\n_ = PredictionErrorDisplay.from_estimator(\n    hist_no_interact, X, y, kind=\"actual_vs_predicted\", ax=axs[0]\n)\n_ = PredictionErrorDisplay.from_estimator(\n    hist_no_interact, X, y, kind=\"residual_vs_predicted\", ax=axs[1]\n)"
+        "import matplotlib.pyplot as plt\n\nfrom sklearn.metrics import PredictionErrorDisplay\n\nfig, axs = plt.subplots(nrows=1, ncols=2, figsize=(12, 5))\n_ = PredictionErrorDisplay.from_estimator(\n    hist_no_interact, X, y, kind=\"actual_vs_predicted\", ax=axs[0]\n)\n_ = PredictionErrorDisplay.from_estimator(\n    hist_no_interact, X, y, kind=\"residual_vs_predicted\", ax=axs[1]\n)"
       ]
     },
     {
@@ -105,7 +105,7 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.preprocessing import OrdinalEncoder\nfrom sklearn.pipeline import make_pipeline\n\ncategorical_features = [\"pclass\", \"sex\", \"embarked\"]\nmodel = make_pipeline(\n    ColumnTransformer(\n        transformers=[(\"cat\", OrdinalEncoder(), categorical_features)],\n        remainder=\"passthrough\",\n    ),\n    HistGradientBoostingRegressor(random_state=0),\n).fit(X, y)"
+        "from sklearn.pipeline import make_pipeline\nfrom sklearn.preprocessing import OrdinalEncoder\n\ncategorical_features = [\"pclass\", \"sex\", \"embarked\"]\nmodel = make_pipeline(\n    ColumnTransformer(\n        transformers=[(\"cat\", OrdinalEncoder(), categorical_features)],\n        remainder=\"passthrough\",\n    ),\n    HistGradientBoostingRegressor(random_state=0),\n).fit(X, y)"
       ]
     },
     {
@@ -168,7 +168,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/df790541d4c6bdebcc75018a2459467a/plot_release_highlights_0_22_0.ipynb b/dev/_downloads/df790541d4c6bdebcc75018a2459467a/plot_release_highlights_0_22_0.ipynb
index b2c8b5b331e6a..99dfc5e2df0ec 100644
--- a/dev/_downloads/df790541d4c6bdebcc75018a2459467a/plot_release_highlights_0_22_0.ipynb
+++ b/dev/_downloads/df790541d4c6bdebcc75018a2459467a/plot_release_highlights_0_22_0.ipynb
@@ -215,7 +215,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/df96f413a13b9b7958a4a343650cd091/plot_stock_market.zip b/dev/_downloads/df96f413a13b9b7958a4a343650cd091/plot_stock_market.zip
index c3154785b13a5..0313bd852f58c 100644
Binary files a/dev/_downloads/df96f413a13b9b7958a4a343650cd091/plot_stock_market.zip and b/dev/_downloads/df96f413a13b9b7958a4a343650cd091/plot_stock_market.zip differ
diff --git a/dev/_downloads/dfc0bb239d89efaffb5847c5579be995/plot_metadata_routing.zip b/dev/_downloads/dfc0bb239d89efaffb5847c5579be995/plot_metadata_routing.zip
index e3f66e4ce305c..5bab850c86bf1 100644
Binary files a/dev/_downloads/dfc0bb239d89efaffb5847c5579be995/plot_metadata_routing.zip and b/dev/_downloads/dfc0bb239d89efaffb5847c5579be995/plot_metadata_routing.zip differ
diff --git a/dev/_downloads/e1ab2ee0fb9ec140eb5421f55efcbebf/plot_agglomerative_clustering.zip b/dev/_downloads/e1ab2ee0fb9ec140eb5421f55efcbebf/plot_agglomerative_clustering.zip
index 769a29ae72d86..f95157077fd2e 100644
Binary files a/dev/_downloads/e1ab2ee0fb9ec140eb5421f55efcbebf/plot_agglomerative_clustering.zip and b/dev/_downloads/e1ab2ee0fb9ec140eb5421f55efcbebf/plot_agglomerative_clustering.zip differ
diff --git a/dev/_downloads/e1d99a2f5c5c55550ce32108208f6477/plot_ward_structured_vs_unstructured.ipynb b/dev/_downloads/e1d99a2f5c5c55550ce32108208f6477/plot_ward_structured_vs_unstructured.ipynb
index 8252979054167..76fe2fc20155d 100644
--- a/dev/_downloads/e1d99a2f5c5c55550ce32108208f6477/plot_ward_structured_vs_unstructured.ipynb
+++ b/dev/_downloads/e1d99a2f5c5c55550ce32108208f6477/plot_ward_structured_vs_unstructured.ipynb
@@ -143,7 +143,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/e23929e86fa0a415fb85ef2834a3ff7d/plot_set_output.ipynb b/dev/_downloads/e23929e86fa0a415fb85ef2834a3ff7d/plot_set_output.ipynb
index e16a49b35f817..98acd5b57c71d 100644
--- a/dev/_downloads/e23929e86fa0a415fb85ef2834a3ff7d/plot_set_output.ipynb
+++ b/dev/_downloads/e23929e86fa0a415fb85ef2834a3ff7d/plot_set_output.ipynb
@@ -269,7 +269,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/e38f4849bd47832b7b365f2fa9d31dd6/plot_compare_reduction.ipynb b/dev/_downloads/e38f4849bd47832b7b365f2fa9d31dd6/plot_compare_reduction.ipynb
index 9c4396d4aa509..c14b12173d9c2 100644
--- a/dev/_downloads/e38f4849bd47832b7b365f2fa9d31dd6/plot_compare_reduction.ipynb
+++ b/dev/_downloads/e38f4849bd47832b7b365f2fa9d31dd6/plot_compare_reduction.ipynb
@@ -89,7 +89,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/e4310e2da2aadb3d26dad89e2830f503/plot_mlp_alpha.zip b/dev/_downloads/e4310e2da2aadb3d26dad89e2830f503/plot_mlp_alpha.zip
index 6c5b1cf557600..da3f7412035dc 100644
Binary files a/dev/_downloads/e4310e2da2aadb3d26dad89e2830f503/plot_mlp_alpha.zip and b/dev/_downloads/e4310e2da2aadb3d26dad89e2830f503/plot_mlp_alpha.zip differ
diff --git a/dev/_downloads/e44c57031af568237dc334b0d317a929/plot_nca_dim_reduction.ipynb b/dev/_downloads/e44c57031af568237dc334b0d317a929/plot_nca_dim_reduction.ipynb
index 3359bdf9cdb08..93693adefa243 100644
--- a/dev/_downloads/e44c57031af568237dc334b0d317a929/plot_nca_dim_reduction.ipynb
+++ b/dev/_downloads/e44c57031af568237dc334b0d317a929/plot_nca_dim_reduction.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/e4c20bdc523a6a0313bebc9cf95a9883/plot_lda_qda.zip b/dev/_downloads/e4c20bdc523a6a0313bebc9cf95a9883/plot_lda_qda.zip
index 6a9d112c024f1..99c248db87e93 100644
Binary files a/dev/_downloads/e4c20bdc523a6a0313bebc9cf95a9883/plot_lda_qda.zip and b/dev/_downloads/e4c20bdc523a6a0313bebc9cf95a9883/plot_lda_qda.zip differ
diff --git a/dev/_downloads/e4e5f5cf3f2329fa034f4cc5287020f8/plot_nested_cross_validation_iris.zip b/dev/_downloads/e4e5f5cf3f2329fa034f4cc5287020f8/plot_nested_cross_validation_iris.zip
index 73fc17d88659e..5ec0223c2e5fb 100644
Binary files a/dev/_downloads/e4e5f5cf3f2329fa034f4cc5287020f8/plot_nested_cross_validation_iris.zip and b/dev/_downloads/e4e5f5cf3f2329fa034f4cc5287020f8/plot_nested_cross_validation_iris.zip differ
diff --git a/dev/_downloads/e5984993ae4c01e36276dcfed2bf0838/plot_svm_margin.ipynb b/dev/_downloads/e5984993ae4c01e36276dcfed2bf0838/plot_svm_margin.ipynb
index f6392e06cd009..00e0790e0ab69 100644
--- a/dev/_downloads/e5984993ae4c01e36276dcfed2bf0838/plot_svm_margin.ipynb
+++ b/dev/_downloads/e5984993ae4c01e36276dcfed2bf0838/plot_svm_margin.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/e60e99adef360baabc49b925646a39d9/plot_all_scaling.ipynb b/dev/_downloads/e60e99adef360baabc49b925646a39d9/plot_all_scaling.ipynb
index 911b82f430cc2..88c2273ee1df5 100644
--- a/dev/_downloads/e60e99adef360baabc49b925646a39d9/plot_all_scaling.ipynb
+++ b/dev/_downloads/e60e99adef360baabc49b925646a39d9/plot_all_scaling.ipynb
@@ -215,7 +215,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/e68419b513284db108081422c73a5667/plot_bicluster_newsgroups.py b/dev/_downloads/e68419b513284db108081422c73a5667/plot_bicluster_newsgroups.py
index aed7037086168..054fb0ba399e1 100644
--- a/dev/_downloads/e68419b513284db108081422c73a5667/plot_bicluster_newsgroups.py
+++ b/dev/_downloads/e68419b513284db108081422c73a5667/plot_bicluster_newsgroups.py
@@ -147,7 +147,7 @@ def bicluster_ncut(i):
 
     # words
     out_of_cluster_docs = cocluster.row_labels_ != cluster
-    out_of_cluster_docs = np.where(out_of_cluster_docs)[0]
+    out_of_cluster_docs = out_of_cluster_docs.nonzero()[0]
     word_col = X[:, cluster_words]
     word_scores = np.array(
         word_col[cluster_docs, :].sum(axis=0)
diff --git a/dev/_downloads/e6a6423db5b5d652b6a6deafc8ca7ee0/plot_separating_hyperplane.ipynb b/dev/_downloads/e6a6423db5b5d652b6a6deafc8ca7ee0/plot_separating_hyperplane.ipynb
index 0676aea6e3467..d4e435fe76889 100644
--- a/dev/_downloads/e6a6423db5b5d652b6a6deafc8ca7ee0/plot_separating_hyperplane.ipynb
+++ b/dev/_downloads/e6a6423db5b5d652b6a6deafc8ca7ee0/plot_separating_hyperplane.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/e6b5bf0cfe4380adcb59097ff327369c/plot_semi_supervised_versus_svm_iris.zip b/dev/_downloads/e6b5bf0cfe4380adcb59097ff327369c/plot_semi_supervised_versus_svm_iris.zip
index 91735e96334de..6116c58d91523 100644
Binary files a/dev/_downloads/e6b5bf0cfe4380adcb59097ff327369c/plot_semi_supervised_versus_svm_iris.zip and b/dev/_downloads/e6b5bf0cfe4380adcb59097ff327369c/plot_semi_supervised_versus_svm_iris.zip differ
diff --git a/dev/_downloads/e7311e6599ab6ff3129117a6d8c302ec/plot_segmentation_toy.ipynb b/dev/_downloads/e7311e6599ab6ff3129117a6d8c302ec/plot_segmentation_toy.ipynb
index 36d393a97abdb..5ec55cf86837b 100644
--- a/dev/_downloads/e7311e6599ab6ff3129117a6d8c302ec/plot_segmentation_toy.ipynb
+++ b/dev/_downloads/e7311e6599ab6ff3129117a6d8c302ec/plot_segmentation_toy.ipynb
@@ -143,7 +143,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/e75dc2347a2a807d30226ce1a4a562ae/plot_polynomial_interpolation.zip b/dev/_downloads/e75dc2347a2a807d30226ce1a4a562ae/plot_polynomial_interpolation.zip
index ebd7a028af7cf..7c3ab59429583 100644
Binary files a/dev/_downloads/e75dc2347a2a807d30226ce1a4a562ae/plot_polynomial_interpolation.zip and b/dev/_downloads/e75dc2347a2a807d30226ce1a4a562ae/plot_polynomial_interpolation.zip differ
diff --git a/dev/_downloads/e7d5500e87a046a110ca0daebd702588/plot_agglomerative_clustering_metrics.ipynb b/dev/_downloads/e7d5500e87a046a110ca0daebd702588/plot_agglomerative_clustering_metrics.ipynb
index 96d79e4044eba..94bbae90ba260 100644
--- a/dev/_downloads/e7d5500e87a046a110ca0daebd702588/plot_agglomerative_clustering_metrics.ipynb
+++ b/dev/_downloads/e7d5500e87a046a110ca0daebd702588/plot_agglomerative_clustering_metrics.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/e9634f730904cc10c4d965d5e91bf2a1/plot_sparse_logistic_regression_20newsgroups.zip b/dev/_downloads/e9634f730904cc10c4d965d5e91bf2a1/plot_sparse_logistic_regression_20newsgroups.zip
index 805eda6ea3c6b..a7076c52cc665 100644
Binary files a/dev/_downloads/e9634f730904cc10c4d965d5e91bf2a1/plot_sparse_logistic_regression_20newsgroups.zip and b/dev/_downloads/e9634f730904cc10c4d965d5e91bf2a1/plot_sparse_logistic_regression_20newsgroups.zip differ
diff --git a/dev/_downloads/e9bea5fda5f2e724b4f11b783c9afee5/plot_mahalanobis_distances.zip b/dev/_downloads/e9bea5fda5f2e724b4f11b783c9afee5/plot_mahalanobis_distances.zip
index 31e3f0be25968..031e392117189 100644
Binary files a/dev/_downloads/e9bea5fda5f2e724b4f11b783c9afee5/plot_mahalanobis_distances.zip and b/dev/_downloads/e9bea5fda5f2e724b4f11b783c9afee5/plot_mahalanobis_distances.zip differ
diff --git a/dev/_downloads/ea57d7ab1588de8f5bd1afc68f20de2f/plot_transformed_target.ipynb b/dev/_downloads/ea57d7ab1588de8f5bd1afc68f20de2f/plot_transformed_target.ipynb
index 1e8420adb9c8b..bf0638fe1c964 100644
--- a/dev/_downloads/ea57d7ab1588de8f5bd1afc68f20de2f/plot_transformed_target.ipynb
+++ b/dev/_downloads/ea57d7ab1588de8f5bd1afc68f20de2f/plot_transformed_target.ipynb
@@ -165,7 +165,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/eacb6a63c887dafcff02b3cee64854ef/plot_outlier_detection_bench.ipynb b/dev/_downloads/eacb6a63c887dafcff02b3cee64854ef/plot_outlier_detection_bench.ipynb
index 918d15d17932d..ec67a01ea06e5 100644
--- a/dev/_downloads/eacb6a63c887dafcff02b3cee64854ef/plot_outlier_detection_bench.ipynb
+++ b/dev/_downloads/eacb6a63c887dafcff02b3cee64854ef/plot_outlier_detection_bench.ipynb
@@ -51,7 +51,7 @@
       },
       "outputs": [],
       "source": [
-        "from time import perf_counter\n\n\ndef fit_predict(estimator, X):\n    tic = perf_counter()\n    if estimator[-1].__class__.__name__ == \"LocalOutlierFactor\":\n        estimator.fit(X)\n        y_pred = estimator[-1].negative_outlier_factor_\n    else:  # \"IsolationForest\"\n        y_pred = estimator.fit(X).decision_function(X)\n    toc = perf_counter()\n    print(f\"Duration for {model_name}: {toc - tic:.2f} s\")\n    return y_pred"
+        "from time import perf_counter\n\n\ndef fit_predict(estimator, X):\n    tic = perf_counter()\n    if estimator[-1].__class__.__name__ == \"LocalOutlierFactor\":\n        estimator.fit(X)\n        y_score = estimator[-1].negative_outlier_factor_\n    else:  # \"IsolationForest\"\n        y_score = estimator.fit(X).decision_function(X)\n    toc = perf_counter()\n    print(f\"Duration for {model_name}: {toc - tic:.2f} s\")\n    return y_score"
       ]
     },
     {
@@ -87,7 +87,7 @@
       },
       "outputs": [],
       "source": [
-        "y_true = {}\ny_pred = {\"LOF\": {}, \"IForest\": {}}\nmodel_names = [\"LOF\", \"IForest\"]\ncat_columns = [\"protocol_type\", \"service\", \"flag\"]\n\ny_true[\"KDDCup99 - SA\"] = y\nfor model_name in model_names:\n    model = make_estimator(\n        name=model_name,\n        categorical_columns=cat_columns,\n        lof_kw={\"n_neighbors\": int(n_samples * anomaly_frac)},\n        iforest_kw={\"random_state\": 42},\n    )\n    y_pred[model_name][\"KDDCup99 - SA\"] = fit_predict(model, X)"
+        "y_true = {}\ny_score = {\"LOF\": {}, \"IForest\": {}}\nmodel_names = [\"LOF\", \"IForest\"]\ncat_columns = [\"protocol_type\", \"service\", \"flag\"]\n\ny_true[\"KDDCup99 - SA\"] = y\nfor model_name in model_names:\n    model = make_estimator(\n        name=model_name,\n        categorical_columns=cat_columns,\n        lof_kw={\"n_neighbors\": int(n_samples * anomaly_frac)},\n        iforest_kw={\"random_state\": 42},\n    )\n    y_score[model_name][\"KDDCup99 - SA\"] = fit_predict(model, X)"
       ]
     },
     {
@@ -116,7 +116,7 @@
       },
       "outputs": [],
       "source": [
-        "y_true[\"forestcover\"] = y\nfor model_name in model_names:\n    model = make_estimator(\n        name=model_name,\n        lof_kw={\"n_neighbors\": int(n_samples * anomaly_frac)},\n        iforest_kw={\"random_state\": 42},\n    )\n    y_pred[model_name][\"forestcover\"] = fit_predict(model, X)"
+        "y_true[\"forestcover\"] = y\nfor model_name in model_names:\n    model = make_estimator(\n        name=model_name,\n        lof_kw={\"n_neighbors\": int(n_samples * anomaly_frac)},\n        iforest_kw={\"random_state\": 42},\n    )\n    y_score[model_name][\"forestcover\"] = fit_predict(model, X)"
       ]
     },
     {
@@ -163,7 +163,7 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.compose import make_column_selector as selector\n\ncategorical_columns_selector = selector(dtype_include=\"category\")\ncat_columns = categorical_columns_selector(X)\n\ny_true[\"ames_housing\"] = y\nfor model_name in model_names:\n    model = make_estimator(\n        name=model_name,\n        categorical_columns=cat_columns,\n        lof_kw={\"n_neighbors\": int(n_samples * anomaly_frac)},\n        iforest_kw={\"random_state\": 42},\n    )\n    y_pred[model_name][\"ames_housing\"] = fit_predict(model, X)"
+        "from sklearn.compose import make_column_selector as selector\n\ncategorical_columns_selector = selector(dtype_include=\"category\")\ncat_columns = categorical_columns_selector(X)\n\ny_true[\"ames_housing\"] = y\nfor model_name in model_names:\n    model = make_estimator(\n        name=model_name,\n        categorical_columns=cat_columns,\n        lof_kw={\"n_neighbors\": int(n_samples * anomaly_frac)},\n        iforest_kw={\"random_state\": 42},\n    )\n    y_score[model_name][\"ames_housing\"] = fit_predict(model, X)"
       ]
     },
     {
@@ -192,7 +192,7 @@
       },
       "outputs": [],
       "source": [
-        "y_true[\"cardiotocography\"] = y\nfor model_name in model_names:\n    model = make_estimator(\n        name=model_name,\n        lof_kw={\"n_neighbors\": int(n_samples * anomaly_frac)},\n        iforest_kw={\"random_state\": 42},\n    )\n    y_pred[model_name][\"cardiotocography\"] = fit_predict(model, X)"
+        "y_true[\"cardiotocography\"] = y\nfor model_name in model_names:\n    model = make_estimator(\n        name=model_name,\n        lof_kw={\"n_neighbors\": int(n_samples * anomaly_frac)},\n        iforest_kw={\"random_state\": 42},\n    )\n    y_score[model_name][\"cardiotocography\"] = fit_predict(model, X)"
       ]
     },
     {
@@ -210,7 +210,7 @@
       },
       "outputs": [],
       "source": [
-        "import math\n\nfrom sklearn.metrics import RocCurveDisplay\n\ncols = 2\npos_label = 0  # mean 0 belongs to positive class\ndatasets_names = y_true.keys()\nrows = math.ceil(len(datasets_names) / cols)\n\nfig, axs = plt.subplots(nrows=rows, ncols=cols, squeeze=False, figsize=(10, rows * 4))\n\nfor ax, dataset_name in zip(axs.ravel(), datasets_names):\n    for model_idx, model_name in enumerate(model_names):\n        display = RocCurveDisplay.from_predictions(\n            y_true[dataset_name],\n            y_pred[model_name][dataset_name],\n            pos_label=pos_label,\n            name=model_name,\n            ax=ax,\n            plot_chance_level=(model_idx == len(model_names) - 1),\n            chance_level_kw={\"linestyle\": \":\"},\n        )\n    ax.set_title(dataset_name)\n_ = plt.tight_layout(pad=2.0)  # spacing between subplots"
+        "import math\n\nfrom sklearn.metrics import RocCurveDisplay\n\ncols = 2\npos_label = 0  # mean 0 belongs to positive class\ndatasets_names = y_true.keys()\nrows = math.ceil(len(datasets_names) / cols)\n\nfig, axs = plt.subplots(nrows=rows, ncols=cols, squeeze=False, figsize=(10, rows * 4))\n\nfor ax, dataset_name in zip(axs.ravel(), datasets_names):\n    for model_idx, model_name in enumerate(model_names):\n        display = RocCurveDisplay.from_predictions(\n            y_true[dataset_name],\n            y_score[model_name][dataset_name],\n            pos_label=pos_label,\n            name=model_name,\n            ax=ax,\n            plot_chance_level=(model_idx == len(model_names) - 1),\n            chance_level_kw={\"linestyle\": \":\"},\n        )\n    ax.set_title(dataset_name)\n_ = plt.tight_layout(pad=2.0)  # spacing between subplots"
       ]
     },
     {
@@ -228,7 +228,7 @@
       },
       "outputs": [],
       "source": [
-        "X = X_forestcover\ny = y_true[\"forestcover\"]\n\nn_samples = X.shape[0]\nn_neighbors_list = (n_samples * np.array([0.2, 0.02, 0.01, 0.001])).astype(np.int32)\nmodel = make_pipeline(RobustScaler(), LocalOutlierFactor())\n\nlinestyles = [\"solid\", \"dashed\", \"dashdot\", \":\", (5, (10, 3))]\n\nfig, ax = plt.subplots()\nfor model_idx, (linestyle, n_neighbors) in enumerate(zip(linestyles, n_neighbors_list)):\n    model.set_params(localoutlierfactor__n_neighbors=n_neighbors)\n    model.fit(X)\n    y_pred = model[-1].negative_outlier_factor_\n    display = RocCurveDisplay.from_predictions(\n        y,\n        y_pred,\n        pos_label=pos_label,\n        name=f\"n_neighbors = {n_neighbors}\",\n        ax=ax,\n        plot_chance_level=(model_idx == len(n_neighbors_list) - 1),\n        chance_level_kw={\"linestyle\": (0, (1, 10))},\n        linestyle=linestyle,\n        linewidth=2,\n    )\n_ = ax.set_title(\"RobustScaler with varying n_neighbors\\non forestcover dataset\")"
+        "X = X_forestcover\ny = y_true[\"forestcover\"]\n\nn_samples = X.shape[0]\nn_neighbors_list = (n_samples * np.array([0.2, 0.02, 0.01, 0.001])).astype(np.int32)\nmodel = make_pipeline(RobustScaler(), LocalOutlierFactor())\n\nlinestyles = [\"solid\", \"dashed\", \"dashdot\", \":\", (5, (10, 3))]\n\nfig, ax = plt.subplots()\nfor model_idx, (linestyle, n_neighbors) in enumerate(zip(linestyles, n_neighbors_list)):\n    model.set_params(localoutlierfactor__n_neighbors=n_neighbors)\n    model.fit(X)\n    y_score = model[-1].negative_outlier_factor_\n    display = RocCurveDisplay.from_predictions(\n        y,\n        y_score,\n        pos_label=pos_label,\n        name=f\"n_neighbors = {n_neighbors}\",\n        ax=ax,\n        plot_chance_level=(model_idx == len(n_neighbors_list) - 1),\n        chance_level_kw={\"linestyle\": (0, (1, 10))},\n        linestyle=linestyle,\n        linewidth=2,\n    )\n_ = ax.set_title(\"RobustScaler with varying n_neighbors\\non forestcover dataset\")"
       ]
     },
     {
@@ -246,7 +246,7 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.preprocessing import MinMaxScaler, SplineTransformer, StandardScaler\n\npreprocessor_list = [\n    None,\n    RobustScaler(),\n    StandardScaler(),\n    MinMaxScaler(),\n    SplineTransformer(),\n]\nexpected_anomaly_fraction = 0.02\nlof = LocalOutlierFactor(n_neighbors=int(n_samples * expected_anomaly_fraction))\n\nfig, ax = plt.subplots()\nfor model_idx, (linestyle, preprocessor) in enumerate(\n    zip(linestyles, preprocessor_list)\n):\n    model = make_pipeline(preprocessor, lof)\n    model.fit(X)\n    y_pred = model[-1].negative_outlier_factor_\n    display = RocCurveDisplay.from_predictions(\n        y,\n        y_pred,\n        pos_label=pos_label,\n        name=str(preprocessor).split(\"(\")[0],\n        ax=ax,\n        plot_chance_level=(model_idx == len(preprocessor_list) - 1),\n        chance_level_kw={\"linestyle\": (0, (1, 10))},\n        linestyle=linestyle,\n        linewidth=2,\n    )\n_ = ax.set_title(\"Fixed n_neighbors with varying preprocessing\\non forestcover dataset\")"
+        "from sklearn.preprocessing import MinMaxScaler, SplineTransformer, StandardScaler\n\npreprocessor_list = [\n    None,\n    RobustScaler(),\n    StandardScaler(),\n    MinMaxScaler(),\n    SplineTransformer(),\n]\nexpected_anomaly_fraction = 0.02\nlof = LocalOutlierFactor(n_neighbors=int(n_samples * expected_anomaly_fraction))\n\nfig, ax = plt.subplots()\nfor model_idx, (linestyle, preprocessor) in enumerate(\n    zip(linestyles, preprocessor_list)\n):\n    model = make_pipeline(preprocessor, lof)\n    model.fit(X)\n    y_score = model[-1].negative_outlier_factor_\n    display = RocCurveDisplay.from_predictions(\n        y,\n        y_score,\n        pos_label=pos_label,\n        name=str(preprocessor).split(\"(\")[0],\n        ax=ax,\n        plot_chance_level=(model_idx == len(preprocessor_list) - 1),\n        chance_level_kw={\"linestyle\": (0, (1, 10))},\n        linestyle=linestyle,\n        linewidth=2,\n    )\n_ = ax.set_title(\"Fixed n_neighbors with varying preprocessing\\non forestcover dataset\")"
       ]
     },
     {
@@ -264,7 +264,7 @@
       },
       "outputs": [],
       "source": [
-        "X = X_cardiotocography\ny = y_true[\"cardiotocography\"]\n\nn_samples, expected_anomaly_fraction = X.shape[0], 0.025\nlof = LocalOutlierFactor(n_neighbors=int(n_samples * expected_anomaly_fraction))\n\nfig, ax = plt.subplots()\nfor model_idx, (linestyle, preprocessor) in enumerate(\n    zip(linestyles, preprocessor_list)\n):\n    model = make_pipeline(preprocessor, lof)\n    model.fit(X)\n    y_pred = model[-1].negative_outlier_factor_\n    display = RocCurveDisplay.from_predictions(\n        y,\n        y_pred,\n        pos_label=pos_label,\n        name=str(preprocessor).split(\"(\")[0],\n        ax=ax,\n        plot_chance_level=(model_idx == len(preprocessor_list) - 1),\n        chance_level_kw={\"linestyle\": (0, (1, 10))},\n        linestyle=linestyle,\n        linewidth=2,\n    )\nax.set_title(\n    \"Fixed n_neighbors with varying preprocessing\\non cardiotocography dataset\"\n)\nplt.show()"
+        "X = X_cardiotocography\ny = y_true[\"cardiotocography\"]\n\nn_samples, expected_anomaly_fraction = X.shape[0], 0.025\nlof = LocalOutlierFactor(n_neighbors=int(n_samples * expected_anomaly_fraction))\n\nfig, ax = plt.subplots()\nfor model_idx, (linestyle, preprocessor) in enumerate(\n    zip(linestyles, preprocessor_list)\n):\n    model = make_pipeline(preprocessor, lof)\n    model.fit(X)\n    y_score = model[-1].negative_outlier_factor_\n    display = RocCurveDisplay.from_predictions(\n        y,\n        y_score,\n        pos_label=pos_label,\n        name=str(preprocessor).split(\"(\")[0],\n        ax=ax,\n        plot_chance_level=(model_idx == len(preprocessor_list) - 1),\n        chance_level_kw={\"linestyle\": (0, (1, 10))},\n        linestyle=linestyle,\n        linewidth=2,\n    )\nax.set_title(\n    \"Fixed n_neighbors with varying preprocessing\\non cardiotocography dataset\"\n)\nplt.show()"
       ]
     }
   ],
@@ -284,7 +284,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/eb2eb8ac4033d060dc6864d179f8a96e/plot_sgd_penalties.zip b/dev/_downloads/eb2eb8ac4033d060dc6864d179f8a96e/plot_sgd_penalties.zip
index 41db576309abd..1157180dde9d7 100644
Binary files a/dev/_downloads/eb2eb8ac4033d060dc6864d179f8a96e/plot_sgd_penalties.zip and b/dev/_downloads/eb2eb8ac4033d060dc6864d179f8a96e/plot_sgd_penalties.zip differ
diff --git a/dev/_downloads/eb87d6211b2c0a7c2dc460a9e28b1f6a/plot_digits_classification.ipynb b/dev/_downloads/eb87d6211b2c0a7c2dc460a9e28b1f6a/plot_digits_classification.ipynb
index e6e5c26652336..bffe5fdbd4c5b 100644
--- a/dev/_downloads/eb87d6211b2c0a7c2dc460a9e28b1f6a/plot_digits_classification.ipynb
+++ b/dev/_downloads/eb87d6211b2c0a7c2dc460a9e28b1f6a/plot_digits_classification.ipynb
@@ -143,7 +143,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/ebb639877592982f79ada0c577699e8d/plot_semi_supervised_newsgroups.ipynb b/dev/_downloads/ebb639877592982f79ada0c577699e8d/plot_semi_supervised_newsgroups.ipynb
index 99a9eb9fae66c..2f083c8135070 100644
--- a/dev/_downloads/ebb639877592982f79ada0c577699e8d/plot_semi_supervised_newsgroups.ipynb
+++ b/dev/_downloads/ebb639877592982f79ada0c577699e8d/plot_semi_supervised_newsgroups.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/ebb9718da412e32cda94d39670b58bd0/plot_rbm_logistic_classification.zip b/dev/_downloads/ebb9718da412e32cda94d39670b58bd0/plot_rbm_logistic_classification.zip
index 39827c05874cd..9710da416f5e9 100644
Binary files a/dev/_downloads/ebb9718da412e32cda94d39670b58bd0/plot_rbm_logistic_classification.zip and b/dev/_downloads/ebb9718da412e32cda94d39670b58bd0/plot_rbm_logistic_classification.zip differ
diff --git a/dev/_downloads/ec1f4d8025c9e2e1a227310276945765/plot_digits_agglomeration.ipynb b/dev/_downloads/ec1f4d8025c9e2e1a227310276945765/plot_digits_agglomeration.ipynb
index 818ad231c3b72..f860e0d9b7706 100644
--- a/dev/_downloads/ec1f4d8025c9e2e1a227310276945765/plot_digits_agglomeration.ipynb
+++ b/dev/_downloads/ec1f4d8025c9e2e1a227310276945765/plot_digits_agglomeration.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/ecddb0e5ddf1515ce4a8abec5287ef30/plot_isotonic_regression.zip b/dev/_downloads/ecddb0e5ddf1515ce4a8abec5287ef30/plot_isotonic_regression.zip
index 112e29d23eeeb..2fb4743d621b7 100644
Binary files a/dev/_downloads/ecddb0e5ddf1515ce4a8abec5287ef30/plot_isotonic_regression.zip and b/dev/_downloads/ecddb0e5ddf1515ce4a8abec5287ef30/plot_isotonic_regression.zip differ
diff --git a/dev/_downloads/ecdf1fb2540fbc3ff9b429164cb880c2/plot_precision_recall.zip b/dev/_downloads/ecdf1fb2540fbc3ff9b429164cb880c2/plot_precision_recall.zip
index 35de17d010419..d13be12bf9b82 100644
Binary files a/dev/_downloads/ecdf1fb2540fbc3ff9b429164cb880c2/plot_precision_recall.zip and b/dev/_downloads/ecdf1fb2540fbc3ff9b429164cb880c2/plot_precision_recall.zip differ
diff --git a/dev/_downloads/ed5e2dba642062278ab833dd7617cfe0/plot_gmm_selection.ipynb b/dev/_downloads/ed5e2dba642062278ab833dd7617cfe0/plot_gmm_selection.ipynb
index feb8791a52ec9..602e9335e231b 100644
--- a/dev/_downloads/ed5e2dba642062278ab833dd7617cfe0/plot_gmm_selection.ipynb
+++ b/dev/_downloads/ed5e2dba642062278ab833dd7617cfe0/plot_gmm_selection.ipynb
@@ -136,7 +136,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/ed9632132a75f297eac3f60b74cd47aa/plot_cyclical_feature_engineering.zip b/dev/_downloads/ed9632132a75f297eac3f60b74cd47aa/plot_cyclical_feature_engineering.zip
index a225bd739548a..f1da675d5810e 100644
Binary files a/dev/_downloads/ed9632132a75f297eac3f60b74cd47aa/plot_cyclical_feature_engineering.zip and b/dev/_downloads/ed9632132a75f297eac3f60b74cd47aa/plot_cyclical_feature_engineering.zip differ
diff --git a/dev/_downloads/ee702db469ee8b432e74db09a687ae1d/plot_sgd_loss_functions.zip b/dev/_downloads/ee702db469ee8b432e74db09a687ae1d/plot_sgd_loss_functions.zip
index 8599b141aad79..5953e078babf0 100644
Binary files a/dev/_downloads/ee702db469ee8b432e74db09a687ae1d/plot_sgd_loss_functions.zip and b/dev/_downloads/ee702db469ee8b432e74db09a687ae1d/plot_sgd_loss_functions.zip differ
diff --git a/dev/_downloads/ee8e74bb66ae2967f890e19f28090b37/plot_spectral_coclustering.ipynb b/dev/_downloads/ee8e74bb66ae2967f890e19f28090b37/plot_spectral_coclustering.ipynb
index fea47d0c4c150..441c7aa7dd04b 100644
--- a/dev/_downloads/ee8e74bb66ae2967f890e19f28090b37/plot_spectral_coclustering.ipynb
+++ b/dev/_downloads/ee8e74bb66ae2967f890e19f28090b37/plot_spectral_coclustering.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/eecca55484b603c335baba1e320dddaf/plot_ransac.zip b/dev/_downloads/eecca55484b603c335baba1e320dddaf/plot_ransac.zip
index e8464dbd699b1..ee5df3a5c9cf4 100644
Binary files a/dev/_downloads/eecca55484b603c335baba1e320dddaf/plot_ransac.zip and b/dev/_downloads/eecca55484b603c335baba1e320dddaf/plot_ransac.zip differ
diff --git a/dev/_downloads/eeecd96e36ce9fc8a3bb2061edde8dbc/plot_lof_novelty_detection.zip b/dev/_downloads/eeecd96e36ce9fc8a3bb2061edde8dbc/plot_lof_novelty_detection.zip
index 868e5ba6876bb..d44dead1200f8 100644
Binary files a/dev/_downloads/eeecd96e36ce9fc8a3bb2061edde8dbc/plot_lof_novelty_detection.zip and b/dev/_downloads/eeecd96e36ce9fc8a3bb2061edde8dbc/plot_lof_novelty_detection.zip differ
diff --git a/dev/_downloads/eeee1f686d08b12fa5d592cccddded23/plot_agglomerative_clustering_metrics.zip b/dev/_downloads/eeee1f686d08b12fa5d592cccddded23/plot_agglomerative_clustering_metrics.zip
index f2ac2d5cecbe2..2433612300fd3 100644
Binary files a/dev/_downloads/eeee1f686d08b12fa5d592cccddded23/plot_agglomerative_clustering_metrics.zip and b/dev/_downloads/eeee1f686d08b12fa5d592cccddded23/plot_agglomerative_clustering_metrics.zip differ
diff --git a/dev/_downloads/ef504a3cb245a55fde178198c8218b4a/plot_hgbt_regression.zip b/dev/_downloads/ef504a3cb245a55fde178198c8218b4a/plot_hgbt_regression.zip
index f360316188cc2..fad1f89aaa9ce 100644
Binary files a/dev/_downloads/ef504a3cb245a55fde178198c8218b4a/plot_hgbt_regression.zip and b/dev/_downloads/ef504a3cb245a55fde178198c8218b4a/plot_hgbt_regression.zip differ
diff --git a/dev/_downloads/eff5561dfcda3a1dd15da0cd7d817e0d/plot_logistic.ipynb b/dev/_downloads/eff5561dfcda3a1dd15da0cd7d817e0d/plot_logistic.ipynb
index 4bfbf3091e721..ab0e34788adb9 100644
--- a/dev/_downloads/eff5561dfcda3a1dd15da0cd7d817e0d/plot_logistic.ipynb
+++ b/dev/_downloads/eff5561dfcda3a1dd15da0cd7d817e0d/plot_logistic.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/f0440afe8f6b99019c4fd181a9f57c59/plot_adaboost_twoclass.py b/dev/_downloads/f0440afe8f6b99019c4fd181a9f57c59/plot_adaboost_twoclass.py
index c499a9f6dc44b..18a2a10841c1c 100644
--- a/dev/_downloads/f0440afe8f6b99019c4fd181a9f57c59/plot_adaboost_twoclass.py
+++ b/dev/_downloads/f0440afe8f6b99019c4fd181a9f57c59/plot_adaboost_twoclass.py
@@ -65,7 +65,7 @@
 
 # Plot the training points
 for i, n, c in zip(range(2), class_names, plot_colors):
-    idx = np.where(y == i)
+    idx = (y == i).nonzero()
     plt.scatter(
         X[idx, 0],
         X[idx, 1],
diff --git a/dev/_downloads/f0ca25cfb6bdf6b7e17e64061d600126/plot_tweedie_regression_insurance_claims.zip b/dev/_downloads/f0ca25cfb6bdf6b7e17e64061d600126/plot_tweedie_regression_insurance_claims.zip
index a422ce7af6fa0..633e9a6406a2d 100644
Binary files a/dev/_downloads/f0ca25cfb6bdf6b7e17e64061d600126/plot_tweedie_regression_insurance_claims.zip and b/dev/_downloads/f0ca25cfb6bdf6b7e17e64061d600126/plot_tweedie_regression_insurance_claims.zip differ
diff --git a/dev/_downloads/f1268276b88d7501262b137a5c139a16/plot_frozen_examples.ipynb b/dev/_downloads/f1268276b88d7501262b137a5c139a16/plot_frozen_examples.ipynb
index f6bdcafbccd7d..2b7417ddc6724 100644
--- a/dev/_downloads/f1268276b88d7501262b137a5c139a16/plot_frozen_examples.ipynb
+++ b/dev/_downloads/f1268276b88d7501262b137a5c139a16/plot_frozen_examples.ipynb
@@ -114,7 +114,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/f170f118a3121751597248ab2244e237/plot_release_highlights_1_4_0.zip b/dev/_downloads/f170f118a3121751597248ab2244e237/plot_release_highlights_1_4_0.zip
index 20cea38ea0ecb..bc1b86117002e 100644
Binary files a/dev/_downloads/f170f118a3121751597248ab2244e237/plot_release_highlights_1_4_0.zip and b/dev/_downloads/f170f118a3121751597248ab2244e237/plot_release_highlights_1_4_0.zip differ
diff --git a/dev/_downloads/f1a137ac46ba39a9ac507c515d184d90/plot_multi_task_lasso_support.ipynb b/dev/_downloads/f1a137ac46ba39a9ac507c515d184d90/plot_multi_task_lasso_support.ipynb
index 7939c7635b304..b583a3498c3f2 100644
--- a/dev/_downloads/f1a137ac46ba39a9ac507c515d184d90/plot_multi_task_lasso_support.ipynb
+++ b/dev/_downloads/f1a137ac46ba39a9ac507c515d184d90/plot_multi_task_lasso_support.ipynb
@@ -89,7 +89,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/f1e887db7b101f4c858db7db12e9c7e2/plot_select_from_model_diabetes.ipynb b/dev/_downloads/f1e887db7b101f4c858db7db12e9c7e2/plot_select_from_model_diabetes.ipynb
index 6966f81f1109c..3e145f2c4c3d6 100644
--- a/dev/_downloads/f1e887db7b101f4c858db7db12e9c7e2/plot_select_from_model_diabetes.ipynb
+++ b/dev/_downloads/f1e887db7b101f4c858db7db12e9c7e2/plot_select_from_model_diabetes.ipynb
@@ -150,7 +150,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/f1fbe67eb1c0d4cc6f257fe17aeecd87/plot_voting_probas.zip b/dev/_downloads/f1fbe67eb1c0d4cc6f257fe17aeecd87/plot_voting_probas.zip
deleted file mode 100644
index 696522072f4c0..0000000000000
Binary files a/dev/_downloads/f1fbe67eb1c0d4cc6f257fe17aeecd87/plot_voting_probas.zip and /dev/null differ
diff --git a/dev/_downloads/f2677dfc7ce435a13dbd8d5fc7298413/plot_release_highlights_1_0_0.ipynb b/dev/_downloads/f2677dfc7ce435a13dbd8d5fc7298413/plot_release_highlights_1_0_0.ipynb
index 14fdacb71e21e..86e93b7dfc6f6 100644
--- a/dev/_downloads/f2677dfc7ce435a13dbd8d5fc7298413/plot_release_highlights_1_0_0.ipynb
+++ b/dev/_downloads/f2677dfc7ce435a13dbd8d5fc7298413/plot_release_highlights_1_0_0.ipynb
@@ -29,7 +29,7 @@
       },
       "outputs": [],
       "source": [
-        "import numpy as np\nfrom sklearn.preprocessing import SplineTransformer\n\nX = np.arange(5).reshape(5, 1)\nspline = SplineTransformer(degree=2, n_knots=3)\nspline.fit_transform(X)"
+        "import numpy as np\n\nfrom sklearn.preprocessing import SplineTransformer\n\nX = np.arange(5).reshape(5, 1)\nspline = SplineTransformer(degree=2, n_knots=3)\nspline.fit_transform(X)"
       ]
     },
     {
@@ -54,7 +54,7 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.preprocessing import StandardScaler\nimport pandas as pd\n\nX = pd.DataFrame([[1, 2, 3], [4, 5, 6]], columns=[\"a\", \"b\", \"c\"])\nscalar = StandardScaler().fit(X)\nscalar.feature_names_in_"
+        "import pandas as pd\n\nfrom sklearn.preprocessing import StandardScaler\n\nX = pd.DataFrame([[1, 2, 3], [4, 5, 6]], columns=[\"a\", \"b\", \"c\"])\nscalar = StandardScaler().fit(X)\nscalar.feature_names_in_"
       ]
     },
     {
@@ -72,7 +72,7 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.compose import ColumnTransformer\nfrom sklearn.preprocessing import OneHotEncoder\nimport pandas as pd\n\nX = pd.DataFrame({\"pet\": [\"dog\", \"cat\", \"fish\"], \"age\": [3, 7, 1]})\npreprocessor = ColumnTransformer(\n    [\n        (\"numerical\", StandardScaler(), [\"age\"]),\n        (\"categorical\", OneHotEncoder(), [\"pet\"]),\n    ],\n    verbose_feature_names_out=False,\n).fit(X)\n\npreprocessor.get_feature_names_out()"
+        "import pandas as pd\n\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.preprocessing import OneHotEncoder\n\nX = pd.DataFrame({\"pet\": [\"dog\", \"cat\", \"fish\"], \"age\": [3, 7, 1]})\npreprocessor = ColumnTransformer(\n    [\n        (\"numerical\", StandardScaler(), [\"age\"]),\n        (\"categorical\", OneHotEncoder(), [\"pet\"]),\n    ],\n    verbose_feature_names_out=False,\n).fit(X)\n\npreprocessor.get_feature_names_out()"
       ]
     },
     {
@@ -138,7 +138,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/f2e78295c97b04635d9e749896f8e08b/plot_isotonic_regression.ipynb b/dev/_downloads/f2e78295c97b04635d9e749896f8e08b/plot_isotonic_regression.ipynb
index 3b2fdf0d95a20..abf49586e2a9f 100644
--- a/dev/_downloads/f2e78295c97b04635d9e749896f8e08b/plot_isotonic_regression.ipynb
+++ b/dev/_downloads/f2e78295c97b04635d9e749896f8e08b/plot_isotonic_regression.ipynb
@@ -78,7 +78,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/f2f93218efc289130afe6ccd4aae9a02/plot_lasso_model_selection.zip b/dev/_downloads/f2f93218efc289130afe6ccd4aae9a02/plot_lasso_model_selection.zip
index b11a6d18a26cc..0a8a031bc8bce 100644
Binary files a/dev/_downloads/f2f93218efc289130afe6ccd4aae9a02/plot_lasso_model_selection.zip and b/dev/_downloads/f2f93218efc289130afe6ccd4aae9a02/plot_lasso_model_selection.zip differ
diff --git a/dev/_downloads/f39c19ddd9f1c49a604c054eff707568/plot_isolation_forest.ipynb b/dev/_downloads/f39c19ddd9f1c49a604c054eff707568/plot_isolation_forest.ipynb
index 7b84c2a545148..1e806f6e177ce 100644
--- a/dev/_downloads/f39c19ddd9f1c49a604c054eff707568/plot_isolation_forest.ipynb
+++ b/dev/_downloads/f39c19ddd9f1c49a604c054eff707568/plot_isolation_forest.ipynb
@@ -125,7 +125,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/f499e804840a40d11222872e84726eef/plot_digits_denoising.ipynb b/dev/_downloads/f499e804840a40d11222872e84726eef/plot_digits_denoising.ipynb
index 780adc5faee88..aecf13b66b43b 100644
--- a/dev/_downloads/f499e804840a40d11222872e84726eef/plot_digits_denoising.ipynb
+++ b/dev/_downloads/f499e804840a40d11222872e84726eef/plot_digits_denoising.ipynb
@@ -161,7 +161,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/f4a89bf823d814fee03a693df158d83a/plot_grid_search_digits.ipynb b/dev/_downloads/f4a89bf823d814fee03a693df158d83a/plot_grid_search_digits.ipynb
index 128cf2383e066..a8ed7680476b1 100644
--- a/dev/_downloads/f4a89bf823d814fee03a693df158d83a/plot_grid_search_digits.ipynb
+++ b/dev/_downloads/f4a89bf823d814fee03a693df158d83a/plot_grid_search_digits.ipynb
@@ -186,7 +186,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/f4caac5f151abedf4fe41507b2ef12a6/plot_weighted_samples.zip b/dev/_downloads/f4caac5f151abedf4fe41507b2ef12a6/plot_weighted_samples.zip
index 7baf249760309..1b96d366e606f 100644
Binary files a/dev/_downloads/f4caac5f151abedf4fe41507b2ef12a6/plot_weighted_samples.zip and b/dev/_downloads/f4caac5f151abedf4fe41507b2ef12a6/plot_weighted_samples.zip differ
diff --git a/dev/_downloads/f52666c44d104a3e37802015751177fe/plot_face_compress.ipynb b/dev/_downloads/f52666c44d104a3e37802015751177fe/plot_face_compress.ipynb
index 6ffa77fd61f56..acd6a9cea0cc0 100644
--- a/dev/_downloads/f52666c44d104a3e37802015751177fe/plot_face_compress.ipynb
+++ b/dev/_downloads/f52666c44d104a3e37802015751177fe/plot_face_compress.ipynb
@@ -201,7 +201,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/f5437b9b38b73a00640f05b987811126/plot_kde_1d.zip b/dev/_downloads/f5437b9b38b73a00640f05b987811126/plot_kde_1d.zip
index ef5e57cc658b9..311f8511fb1ee 100644
Binary files a/dev/_downloads/f5437b9b38b73a00640f05b987811126/plot_kde_1d.zip and b/dev/_downloads/f5437b9b38b73a00640f05b987811126/plot_kde_1d.zip differ
diff --git a/dev/_downloads/f57e1ee55d4c7a51949d5c26b3af07bb/plot_multi_metric_evaluation.ipynb b/dev/_downloads/f57e1ee55d4c7a51949d5c26b3af07bb/plot_multi_metric_evaluation.ipynb
index bbb3f77f4f148..343a59f770005 100644
--- a/dev/_downloads/f57e1ee55d4c7a51949d5c26b3af07bb/plot_multi_metric_evaluation.ipynb
+++ b/dev/_downloads/f57e1ee55d4c7a51949d5c26b3af07bb/plot_multi_metric_evaluation.ipynb
@@ -71,7 +71,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/f57e626755a909b821ad476093ada898/plot_ridge_coeffs.zip b/dev/_downloads/f57e626755a909b821ad476093ada898/plot_ridge_coeffs.zip
index c27a54b37c57c..7d98c80c8780a 100644
Binary files a/dev/_downloads/f57e626755a909b821ad476093ada898/plot_ridge_coeffs.zip and b/dev/_downloads/f57e626755a909b821ad476093ada898/plot_ridge_coeffs.zip differ
diff --git a/dev/_downloads/f66c3a9f3631c05c52d31172371922ab/plot_spectral_biclustering.zip b/dev/_downloads/f66c3a9f3631c05c52d31172371922ab/plot_spectral_biclustering.zip
index 9e3dfe9112aa3..e94e4e37488bd 100644
Binary files a/dev/_downloads/f66c3a9f3631c05c52d31172371922ab/plot_spectral_biclustering.zip and b/dev/_downloads/f66c3a9f3631c05c52d31172371922ab/plot_spectral_biclustering.zip differ
diff --git a/dev/_downloads/f6d77c1d67a1e980437128d300e94c4e/plot_feature_selection.zip b/dev/_downloads/f6d77c1d67a1e980437128d300e94c4e/plot_feature_selection.zip
index 664b77fe2abde..9ff655a001c14 100644
Binary files a/dev/_downloads/f6d77c1d67a1e980437128d300e94c4e/plot_feature_selection.zip and b/dev/_downloads/f6d77c1d67a1e980437128d300e94c4e/plot_feature_selection.zip differ
diff --git a/dev/_downloads/f726f6c50f1cc13e1afb7561fa005d16/plot_image_denoising.ipynb b/dev/_downloads/f726f6c50f1cc13e1afb7561fa005d16/plot_image_denoising.ipynb
index 170e651c0a24e..67789bbfff774 100644
--- a/dev/_downloads/f726f6c50f1cc13e1afb7561fa005d16/plot_image_denoising.ipynb
+++ b/dev/_downloads/f726f6c50f1cc13e1afb7561fa005d16/plot_image_denoising.ipynb
@@ -125,7 +125,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/f7a387851c5762610f4e8197e52bbbca/plot_unveil_tree_structure.ipynb b/dev/_downloads/f7a387851c5762610f4e8197e52bbbca/plot_unveil_tree_structure.ipynb
index 61c1558c5854d..2a9bc93df60b1 100644
--- a/dev/_downloads/f7a387851c5762610f4e8197e52bbbca/plot_unveil_tree_structure.ipynb
+++ b/dev/_downloads/f7a387851c5762610f4e8197e52bbbca/plot_unveil_tree_structure.ipynb
@@ -132,7 +132,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/f7c999465d2f8d68e0c04bec778aa48e/plot_out_of_core_classification.py b/dev/_downloads/f7c999465d2f8d68e0c04bec778aa48e/plot_out_of_core_classification.py
index a698c8e1c66e2..ad0ff9638e41c 100644
--- a/dev/_downloads/f7c999465d2f8d68e0c04bec778aa48e/plot_out_of_core_classification.py
+++ b/dev/_downloads/f7c999465d2f8d68e0c04bec778aa48e/plot_out_of_core_classification.py
@@ -142,10 +142,7 @@ def stream_reuters_documents(data_path=None):
 
     """
 
-    DOWNLOAD_URL = (
-        "http://archive.ics.uci.edu/ml/machine-learning-databases/"
-        "reuters21578-mld/reuters21578.tar.gz"
-    )
+    DOWNLOAD_URL = "https://kdd.ics.uci.edu/databases/reuters21578/reuters21578.tar.gz"
     ARCHIVE_SHA256 = "3bae43c9b14e387f76a61b6d82bf98a4fb5d3ef99ef7e7075ff2ccbcf59f9d30"
     ARCHIVE_FILENAME = "reuters21578.tar.gz"
 
diff --git a/dev/_downloads/f7f600b44fff5cb66c5bb505ac77ef2a/plot_ica_vs_pca.zip b/dev/_downloads/f7f600b44fff5cb66c5bb505ac77ef2a/plot_ica_vs_pca.zip
index cb47b34b93112..01f2a8ab45ea9 100644
Binary files a/dev/_downloads/f7f600b44fff5cb66c5bb505ac77ef2a/plot_ica_vs_pca.zip and b/dev/_downloads/f7f600b44fff5cb66c5bb505ac77ef2a/plot_ica_vs_pca.zip differ
diff --git a/dev/_downloads/f84209ea397909becdc84b5de1a5b047/plot_iris_svc.ipynb b/dev/_downloads/f84209ea397909becdc84b5de1a5b047/plot_iris_svc.ipynb
index 60c4d81c41baa..a7fcbb439d373 100644
--- a/dev/_downloads/f84209ea397909becdc84b5de1a5b047/plot_iris_svc.ipynb
+++ b/dev/_downloads/f84209ea397909becdc84b5de1a5b047/plot_iris_svc.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/f8915489864d3c3a6162a9d08c2ddc8b/plot_sparse_logistic_regression_mnist.zip b/dev/_downloads/f8915489864d3c3a6162a9d08c2ddc8b/plot_sparse_logistic_regression_mnist.zip
index 81712c6b0565c..b75da0c75a46f 100644
Binary files a/dev/_downloads/f8915489864d3c3a6162a9d08c2ddc8b/plot_sparse_logistic_regression_mnist.zip and b/dev/_downloads/f8915489864d3c3a6162a9d08c2ddc8b/plot_sparse_logistic_regression_mnist.zip differ
diff --git a/dev/_downloads/f927b32733adb25c5c8208225278f50c/plot_gpc_isoprobability.ipynb b/dev/_downloads/f927b32733adb25c5c8208225278f50c/plot_gpc_isoprobability.ipynb
index fa5d36eed56ec..74687c24fb876 100644
--- a/dev/_downloads/f927b32733adb25c5c8208225278f50c/plot_gpc_isoprobability.ipynb
+++ b/dev/_downloads/f927b32733adb25c5c8208225278f50c/plot_gpc_isoprobability.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/f92fcbee358b8c7063475033e6e9cde0/plot_hdbscan.ipynb b/dev/_downloads/f92fcbee358b8c7063475033e6e9cde0/plot_hdbscan.ipynb
index d8d243a5be342..960ff4b0e413d 100644
--- a/dev/_downloads/f92fcbee358b8c7063475033e6e9cde0/plot_hdbscan.ipynb
+++ b/dev/_downloads/f92fcbee358b8c7063475033e6e9cde0/plot_hdbscan.ipynb
@@ -26,7 +26,7 @@
       },
       "outputs": [],
       "source": [
-        "import matplotlib.pyplot as plt\nimport numpy as np\n\nfrom sklearn.cluster import DBSCAN, HDBSCAN\nfrom sklearn.datasets import make_blobs\n\n\ndef plot(X, labels, probabilities=None, parameters=None, ground_truth=False, ax=None):\n    if ax is None:\n        _, ax = plt.subplots(figsize=(10, 4))\n    labels = labels if labels is not None else np.ones(X.shape[0])\n    probabilities = probabilities if probabilities is not None else np.ones(X.shape[0])\n    # Black removed and is used for noise instead.\n    unique_labels = set(labels)\n    colors = [plt.cm.Spectral(each) for each in np.linspace(0, 1, len(unique_labels))]\n    # The probability of a point belonging to its labeled cluster determines\n    # the size of its marker\n    proba_map = {idx: probabilities[idx] for idx in range(len(labels))}\n    for k, col in zip(unique_labels, colors):\n        if k == -1:\n            # Black used for noise.\n            col = [0, 0, 0, 1]\n\n        class_index = np.where(labels == k)[0]\n        for ci in class_index:\n            ax.plot(\n                X[ci, 0],\n                X[ci, 1],\n                \"x\" if k == -1 else \"o\",\n                markerfacecolor=tuple(col),\n                markeredgecolor=\"k\",\n                markersize=4 if k == -1 else 1 + 5 * proba_map[ci],\n            )\n    n_clusters_ = len(set(labels)) - (1 if -1 in labels else 0)\n    preamble = \"True\" if ground_truth else \"Estimated\"\n    title = f\"{preamble} number of clusters: {n_clusters_}\"\n    if parameters is not None:\n        parameters_str = \", \".join(f\"{k}={v}\" for k, v in parameters.items())\n        title += f\" | {parameters_str}\"\n    ax.set_title(title)\n    plt.tight_layout()"
+        "import matplotlib.pyplot as plt\nimport numpy as np\n\nfrom sklearn.cluster import DBSCAN, HDBSCAN\nfrom sklearn.datasets import make_blobs\n\n\ndef plot(X, labels, probabilities=None, parameters=None, ground_truth=False, ax=None):\n    if ax is None:\n        _, ax = plt.subplots(figsize=(10, 4))\n    labels = labels if labels is not None else np.ones(X.shape[0])\n    probabilities = probabilities if probabilities is not None else np.ones(X.shape[0])\n    # Black removed and is used for noise instead.\n    unique_labels = set(labels)\n    colors = [plt.cm.Spectral(each) for each in np.linspace(0, 1, len(unique_labels))]\n    # The probability of a point belonging to its labeled cluster determines\n    # the size of its marker\n    proba_map = {idx: probabilities[idx] for idx in range(len(labels))}\n    for k, col in zip(unique_labels, colors):\n        if k == -1:\n            # Black used for noise.\n            col = [0, 0, 0, 1]\n\n        class_index = (labels == k).nonzero()[0]\n        for ci in class_index:\n            ax.plot(\n                X[ci, 0],\n                X[ci, 1],\n                \"x\" if k == -1 else \"o\",\n                markerfacecolor=tuple(col),\n                markeredgecolor=\"k\",\n                markersize=4 if k == -1 else 1 + 5 * proba_map[ci],\n            )\n    n_clusters_ = len(set(labels)) - (1 if -1 in labels else 0)\n    preamble = \"True\" if ground_truth else \"Estimated\"\n    title = f\"{preamble} number of clusters: {n_clusters_}\"\n    if parameters is not None:\n        parameters_str = \", \".join(f\"{k}={v}\" for k, v in parameters.items())\n        title += f\" | {parameters_str}\"\n    ax.set_title(title)\n    plt.tight_layout()"
       ]
     },
     {
@@ -233,7 +233,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/f99bb35c32eb8028063a1428c3999b84/plot_permutation_importance.ipynb b/dev/_downloads/f99bb35c32eb8028063a1428c3999b84/plot_permutation_importance.ipynb
index a940d75a4668b..5984d817e1da3 100644
--- a/dev/_downloads/f99bb35c32eb8028063a1428c3999b84/plot_permutation_importance.ipynb
+++ b/dev/_downloads/f99bb35c32eb8028063a1428c3999b84/plot_permutation_importance.ipynb
@@ -237,7 +237,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/f9d87a4251e5f99e531dd4a41c01b46c/plot_lw_vs_oas.zip b/dev/_downloads/f9d87a4251e5f99e531dd4a41c01b46c/plot_lw_vs_oas.zip
index bd44b139d29fe..2399f77685497 100644
Binary files a/dev/_downloads/f9d87a4251e5f99e531dd4a41c01b46c/plot_lw_vs_oas.zip and b/dev/_downloads/f9d87a4251e5f99e531dd4a41c01b46c/plot_lw_vs_oas.zip differ
diff --git a/dev/_downloads/fac66cd1688e423f58fe5d640246cb2d/plot_face_recognition.zip b/dev/_downloads/fac66cd1688e423f58fe5d640246cb2d/plot_face_recognition.zip
index 3e43faf0233d0..837acd9c88eaa 100644
Binary files a/dev/_downloads/fac66cd1688e423f58fe5d640246cb2d/plot_face_recognition.zip and b/dev/_downloads/fac66cd1688e423f58fe5d640246cb2d/plot_face_recognition.zip differ
diff --git a/dev/_downloads/fbad5f36a76ec3e17c024c7b920e5552/plot_partial_dependence_visualization_api.ipynb b/dev/_downloads/fbad5f36a76ec3e17c024c7b920e5552/plot_partial_dependence_visualization_api.ipynb
index 05f2cd14fe731..35da6fce335c5 100644
--- a/dev/_downloads/fbad5f36a76ec3e17c024c7b920e5552/plot_partial_dependence_visualization_api.ipynb
+++ b/dev/_downloads/fbad5f36a76ec3e17c024c7b920e5552/plot_partial_dependence_visualization_api.ipynb
@@ -161,7 +161,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/fbde8dfb8bd7af110ec5f186216b4186/plot_compare_gpr_krr.zip b/dev/_downloads/fbde8dfb8bd7af110ec5f186216b4186/plot_compare_gpr_krr.zip
index a9b61e68fde59..279ef9f181622 100644
Binary files a/dev/_downloads/fbde8dfb8bd7af110ec5f186216b4186/plot_compare_gpr_krr.zip and b/dev/_downloads/fbde8dfb8bd7af110ec5f186216b4186/plot_compare_gpr_krr.zip differ
diff --git a/dev/_downloads/fc26dc0b9d906466315a49a860435409/plot_map_data_to_normal.ipynb b/dev/_downloads/fc26dc0b9d906466315a49a860435409/plot_map_data_to_normal.ipynb
index f942589c14537..6e4667b549e37 100644
--- a/dev/_downloads/fc26dc0b9d906466315a49a860435409/plot_map_data_to_normal.ipynb
+++ b/dev/_downloads/fc26dc0b9d906466315a49a860435409/plot_map_data_to_normal.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/_downloads/fc62e5184f789c3ec66281b9f35fe4cd/plot_kernel_approximation.zip b/dev/_downloads/fc62e5184f789c3ec66281b9f35fe4cd/plot_kernel_approximation.zip
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diff --git a/dev/_images/sphx_glr_plot_voting_probas_001.png b/dev/_images/sphx_glr_plot_voting_probas_001.png
deleted file mode 100644
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diff --git a/dev/_images/sphx_glr_plot_voting_probas_thumb.png b/dev/_images/sphx_glr_plot_voting_probas_thumb.png
deleted file mode 100644
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diff --git a/dev/_sources/auto_examples/applications/plot_cyclical_feature_engineering.rst.txt b/dev/_sources/auto_examples/applications/plot_cyclical_feature_engineering.rst.txt
index 5a7174c5afe9d..303820964bd50 100644
--- a/dev/_sources/auto_examples/applications/plot_cyclical_feature_engineering.rst.txt
+++ b/dev/_sources/auto_examples/applications/plot_cyclical_feature_engineering.rst.txt
@@ -2568,7 +2568,7 @@ instead of `RidgeCV`.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 12.148 seconds)
+   **Total running time of the script:** (0 minutes 14.675 seconds)
 
 
 .. _sphx_glr_download_auto_examples_applications_plot_cyclical_feature_engineering.py:
diff --git a/dev/_sources/auto_examples/applications/plot_digits_denoising.rst.txt b/dev/_sources/auto_examples/applications/plot_digits_denoising.rst.txt
index 4632767453050..94cc48e0bae76 100644
--- a/dev/_sources/auto_examples/applications/plot_digits_denoising.rst.txt
+++ b/dev/_sources/auto_examples/applications/plot_digits_denoising.rst.txt
@@ -312,7 +312,7 @@ will depend of the parameters `n_components`, `gamma`, and `alpha`.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 6.435 seconds)
+   **Total running time of the script:** (0 minutes 7.148 seconds)
 
 
 .. _sphx_glr_download_auto_examples_applications_plot_digits_denoising.py:
diff --git a/dev/_sources/auto_examples/applications/plot_face_recognition.rst.txt b/dev/_sources/auto_examples/applications/plot_face_recognition.rst.txt
index 14b1dab655bac..7ac430f6cb23f 100644
--- a/dev/_sources/auto_examples/applications/plot_face_recognition.rst.txt
+++ b/dev/_sources/auto_examples/applications/plot_face_recognition.rst.txt
@@ -171,9 +171,9 @@ dataset): unsupervised feature extraction / dimensionality reduction
  .. code-block:: none
 
     Extracting the top 150 eigenfaces from 966 faces
-    done in 0.073s
+    done in 0.093s
     Projecting the input data on the eigenfaces orthonormal basis
-    done in 0.005s
+    done in 0.006s
 
 
 
@@ -211,7 +211,7 @@ Train a SVM classification model
  .. code-block:: none
 
     Fitting the classifier to the training set
-    done in 4.765s
+    done in 5.943s
     Best estimator found by grid search:
     SVC(C=np.float64(76823.03433306457), class_weight='balanced',
         gamma=np.float64(0.0034189458230957995))
@@ -255,7 +255,7 @@ Quantitative evaluation of the model quality on the test set
  .. code-block:: none
 
     Predicting people's names on the test set
-    done in 0.042s
+    done in 0.049s
                        precision    recall  f1-score   support
 
          Ariel Sharon       0.75      0.69      0.72        13
@@ -372,7 +372,7 @@ tensorflow to implement such models.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 5.528 seconds)
+   **Total running time of the script:** (0 minutes 6.882 seconds)
 
 
 .. _sphx_glr_download_auto_examples_applications_plot_face_recognition.py:
diff --git a/dev/_sources/auto_examples/applications/plot_model_complexity_influence.rst.txt b/dev/_sources/auto_examples/applications/plot_model_complexity_influence.rst.txt
index 86ce065522ac7..f12d6debb0b7b 100644
--- a/dev/_sources/auto_examples/applications/plot_model_complexity_influence.rst.txt
+++ b/dev/_sources/auto_examples/applications/plot_model_complexity_influence.rst.txt
@@ -386,49 +386,49 @@ ensemble is not as detrimental.
 
     Benchmarking SGDClassifier(alpha=0.001, l1_ratio=0.25, loss='modified_huber',
                   n_iter_no_change=2, penalty='elasticnet', tol=0.1)
-    Complexity: 4948 | Hamming Loss (Misclassification Ratio): 0.2675 | Pred. Time: 0.057344s
+    Complexity: 4948 | Hamming Loss (Misclassification Ratio): 0.2675 | Pred. Time: 0.059860s
 
     Benchmarking SGDClassifier(alpha=0.001, l1_ratio=0.5, loss='modified_huber',
                   n_iter_no_change=2, penalty='elasticnet', tol=0.1)
-    Complexity: 1847 | Hamming Loss (Misclassification Ratio): 0.3264 | Pred. Time: 0.043617s
+    Complexity: 1847 | Hamming Loss (Misclassification Ratio): 0.3264 | Pred. Time: 0.045086s
 
     Benchmarking SGDClassifier(alpha=0.001, l1_ratio=0.75, loss='modified_huber',
                   n_iter_no_change=2, penalty='elasticnet', tol=0.1)
-    Complexity: 997 | Hamming Loss (Misclassification Ratio): 0.3383 | Pred. Time: 0.035609s
+    Complexity: 997 | Hamming Loss (Misclassification Ratio): 0.3383 | Pred. Time: 0.037784s
 
     Benchmarking SGDClassifier(alpha=0.001, l1_ratio=0.9, loss='modified_huber',
                   n_iter_no_change=2, penalty='elasticnet', tol=0.1)
-    Complexity: 802 | Hamming Loss (Misclassification Ratio): 0.3582 | Pred. Time: 0.032368s
+    Complexity: 802 | Hamming Loss (Misclassification Ratio): 0.3582 | Pred. Time: 0.034099s
 
     Benchmarking NuSVR(C=1000.0, gamma=3.0517578125e-05, nu=0.05)
-    Complexity: 18 | MSE: 5558.7313 | Pred. Time: 0.000218s
+    Complexity: 18 | MSE: 5558.7313 | Pred. Time: 0.000187s
 
     Benchmarking NuSVR(C=1000.0, gamma=3.0517578125e-05, nu=0.1)
-    Complexity: 36 | MSE: 5289.8022 | Pred. Time: 0.000286s
+    Complexity: 36 | MSE: 5289.8022 | Pred. Time: 0.000260s
 
     Benchmarking NuSVR(C=1000.0, gamma=3.0517578125e-05, nu=0.2)
-    Complexity: 72 | MSE: 5193.8353 | Pred. Time: 0.000440s
+    Complexity: 72 | MSE: 5193.8353 | Pred. Time: 0.000412s
 
     Benchmarking NuSVR(C=1000.0, gamma=3.0517578125e-05, nu=0.35)
-    Complexity: 124 | MSE: 5131.3279 | Pred. Time: 0.000640s
+    Complexity: 124 | MSE: 5131.3279 | Pred. Time: 0.000643s
 
     Benchmarking NuSVR(C=1000.0, gamma=3.0517578125e-05)
-    Complexity: 178 | MSE: 5149.0779 | Pred. Time: 0.000869s
+    Complexity: 178 | MSE: 5149.0779 | Pred. Time: 0.000876s
 
     Benchmarking GradientBoostingRegressor(learning_rate=0.05, max_depth=2, n_estimators=10)
-    Complexity: 10 | MSE: 4066.4812 | Pred. Time: 0.000263s
+    Complexity: 10 | MSE: 4066.4812 | Pred. Time: 0.000178s
 
     Benchmarking GradientBoostingRegressor(learning_rate=0.05, max_depth=2, n_estimators=25)
-    Complexity: 25 | MSE: 3551.1723 | Pred. Time: 0.000286s
+    Complexity: 25 | MSE: 3551.1723 | Pred. Time: 0.000207s
 
     Benchmarking GradientBoostingRegressor(learning_rate=0.05, max_depth=2, n_estimators=50)
-    Complexity: 50 | MSE: 3445.2171 | Pred. Time: 0.000322s
+    Complexity: 50 | MSE: 3445.2171 | Pred. Time: 0.000240s
 
     Benchmarking GradientBoostingRegressor(learning_rate=0.05, max_depth=2, n_estimators=75)
-    Complexity: 75 | MSE: 3433.0358 | Pred. Time: 0.000360s
+    Complexity: 75 | MSE: 3433.0358 | Pred. Time: 0.000279s
 
     Benchmarking GradientBoostingRegressor(learning_rate=0.05, max_depth=2)
-    Complexity: 100 | MSE: 3456.0602 | Pred. Time: 0.000391s
+    Complexity: 100 | MSE: 3456.0602 | Pred. Time: 0.000306s
 
 
 
@@ -451,7 +451,7 @@ under-fitting or over-fitting.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 4.715 seconds)
+   **Total running time of the script:** (0 minutes 5.231 seconds)
 
 
 .. _sphx_glr_download_auto_examples_applications_plot_model_complexity_influence.py:
diff --git a/dev/_sources/auto_examples/applications/plot_out_of_core_classification.rst.txt b/dev/_sources/auto_examples/applications/plot_out_of_core_classification.rst.txt
index 94eeb01262220..2818d6a5a1eb9 100644
--- a/dev/_sources/auto_examples/applications/plot_out_of_core_classification.rst.txt
+++ b/dev/_sources/auto_examples/applications/plot_out_of_core_classification.rst.txt
@@ -80,7 +80,7 @@ The dataset used in this example is Reuters-21578 as provided by the UCI ML
 repository. It will be automatically downloaded and uncompressed on first
 run.
 
-.. GENERATED FROM PYTHON SOURCE LINES 52-187
+.. GENERATED FROM PYTHON SOURCE LINES 52-184
 
 .. code-block:: Python
 
@@ -177,10 +177,7 @@ run.
 
         """
 
-        DOWNLOAD_URL = (
-            "http://archive.ics.uci.edu/ml/machine-learning-databases/"
-            "reuters21578-mld/reuters21578.tar.gz"
-        )
+        DOWNLOAD_URL = "https://kdd.ics.uci.edu/databases/reuters21578/reuters21578.tar.gz"
         ARCHIVE_SHA256 = "3bae43c9b14e387f76a61b6d82bf98a4fb5d3ef99ef7e7075ff2ccbcf59f9d30"
         ARCHIVE_FILENAME = "reuters21578.tar.gz"
 
@@ -226,7 +223,7 @@ run.
 
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 188-193
+.. GENERATED FROM PYTHON SOURCE LINES 185-190
 
 Main
 ----
@@ -234,7 +231,7 @@ Main
 Create the vectorizer and limit the number of features to a reasonable
 maximum
 
-.. GENERATED FROM PYTHON SOURCE LINES 193-329
+.. GENERATED FROM PYTHON SOURCE LINES 190-326
 
 .. code-block:: Python
 
@@ -383,53 +380,53 @@ maximum
  .. code-block:: none
 
     Test set is 878 documents (108 positive)
-                     SGD classifier :          962 train docs (   132 positive)    878 test docs (   108 positive) accuracy: 0.915 in 0.62s ( 1552 docs/s)
-              Perceptron classifier :          962 train docs (   132 positive)    878 test docs (   108 positive) accuracy: 0.855 in 0.62s ( 1545 docs/s)
-          NB Multinomial classifier :          962 train docs (   132 positive)    878 test docs (   108 positive) accuracy: 0.877 in 0.63s ( 1525 docs/s)
-      Passive-Aggressive classifier :          962 train docs (   132 positive)    878 test docs (   108 positive) accuracy: 0.933 in 0.63s ( 1517 docs/s)
+                     SGD classifier :          962 train docs (   132 positive)    878 test docs (   108 positive) accuracy: 0.915 in 0.75s ( 1282 docs/s)
+              Perceptron classifier :          962 train docs (   132 positive)    878 test docs (   108 positive) accuracy: 0.855 in 0.75s ( 1276 docs/s)
+          NB Multinomial classifier :          962 train docs (   132 positive)    878 test docs (   108 positive) accuracy: 0.877 in 0.77s ( 1254 docs/s)
+      Passive-Aggressive classifier :          962 train docs (   132 positive)    878 test docs (   108 positive) accuracy: 0.933 in 0.77s ( 1246 docs/s)
 
 
-                     SGD classifier :         3911 train docs (   517 positive)    878 test docs (   108 positive) accuracy: 0.938 in 1.74s ( 2245 docs/s)
-              Perceptron classifier :         3911 train docs (   517 positive)    878 test docs (   108 positive) accuracy: 0.936 in 1.74s ( 2241 docs/s)
-          NB Multinomial classifier :         3911 train docs (   517 positive)    878 test docs (   108 positive) accuracy: 0.885 in 1.75s ( 2232 docs/s)
-      Passive-Aggressive classifier :         3911 train docs (   517 positive)    878 test docs (   108 positive) accuracy: 0.941 in 1.75s ( 2229 docs/s)
+                     SGD classifier :         3911 train docs (   517 positive)    878 test docs (   108 positive) accuracy: 0.938 in 2.21s ( 1769 docs/s)
+              Perceptron classifier :         3911 train docs (   517 positive)    878 test docs (   108 positive) accuracy: 0.936 in 2.21s ( 1766 docs/s)
+          NB Multinomial classifier :         3911 train docs (   517 positive)    878 test docs (   108 positive) accuracy: 0.885 in 2.22s ( 1758 docs/s)
+      Passive-Aggressive classifier :         3911 train docs (   517 positive)    878 test docs (   108 positive) accuracy: 0.941 in 2.23s ( 1755 docs/s)
 
 
-                     SGD classifier :         6821 train docs (   891 positive)    878 test docs (   108 positive) accuracy: 0.952 in 2.84s ( 2399 docs/s)
-              Perceptron classifier :         6821 train docs (   891 positive)    878 test docs (   108 positive) accuracy: 0.952 in 2.85s ( 2397 docs/s)
-          NB Multinomial classifier :         6821 train docs (   891 positive)    878 test docs (   108 positive) accuracy: 0.900 in 2.85s ( 2391 docs/s)
-      Passive-Aggressive classifier :         6821 train docs (   891 positive)    878 test docs (   108 positive) accuracy: 0.953 in 2.86s ( 2389 docs/s)
+                     SGD classifier :         6821 train docs (   891 positive)    878 test docs (   108 positive) accuracy: 0.952 in 3.46s ( 1970 docs/s)
+              Perceptron classifier :         6821 train docs (   891 positive)    878 test docs (   108 positive) accuracy: 0.952 in 3.47s ( 1968 docs/s)
+          NB Multinomial classifier :         6821 train docs (   891 positive)    878 test docs (   108 positive) accuracy: 0.900 in 3.48s ( 1962 docs/s)
+      Passive-Aggressive classifier :         6821 train docs (   891 positive)    878 test docs (   108 positive) accuracy: 0.953 in 3.48s ( 1960 docs/s)
 
 
-                     SGD classifier :         9759 train docs (  1276 positive)    878 test docs (   108 positive) accuracy: 0.949 in 3.96s ( 2466 docs/s)
-              Perceptron classifier :         9759 train docs (  1276 positive)    878 test docs (   108 positive) accuracy: 0.953 in 3.96s ( 2465 docs/s)
-          NB Multinomial classifier :         9759 train docs (  1276 positive)    878 test docs (   108 positive) accuracy: 0.909 in 3.97s ( 2460 docs/s)
-      Passive-Aggressive classifier :         9759 train docs (  1276 positive)    878 test docs (   108 positive) accuracy: 0.958 in 3.97s ( 2459 docs/s)
+                     SGD classifier :         9759 train docs (  1276 positive)    878 test docs (   108 positive) accuracy: 0.949 in 4.72s ( 2068 docs/s)
+              Perceptron classifier :         9759 train docs (  1276 positive)    878 test docs (   108 positive) accuracy: 0.953 in 4.72s ( 2066 docs/s)
+          NB Multinomial classifier :         9759 train docs (  1276 positive)    878 test docs (   108 positive) accuracy: 0.909 in 4.73s ( 2062 docs/s)
+      Passive-Aggressive classifier :         9759 train docs (  1276 positive)    878 test docs (   108 positive) accuracy: 0.958 in 4.73s ( 2061 docs/s)
 
 
-                     SGD classifier :        11680 train docs (  1499 positive)    878 test docs (   108 positive) accuracy: 0.944 in 4.92s ( 2372 docs/s)
-              Perceptron classifier :        11680 train docs (  1499 positive)    878 test docs (   108 positive) accuracy: 0.956 in 4.93s ( 2371 docs/s)
-          NB Multinomial classifier :        11680 train docs (  1499 positive)    878 test docs (   108 positive) accuracy: 0.915 in 4.93s ( 2367 docs/s)
-      Passive-Aggressive classifier :        11680 train docs (  1499 positive)    878 test docs (   108 positive) accuracy: 0.950 in 4.94s ( 2366 docs/s)
+                     SGD classifier :        11680 train docs (  1499 positive)    878 test docs (   108 positive) accuracy: 0.944 in 5.88s ( 1986 docs/s)
+              Perceptron classifier :        11680 train docs (  1499 positive)    878 test docs (   108 positive) accuracy: 0.956 in 5.88s ( 1985 docs/s)
+          NB Multinomial classifier :        11680 train docs (  1499 positive)    878 test docs (   108 positive) accuracy: 0.915 in 5.89s ( 1983 docs/s)
+      Passive-Aggressive classifier :        11680 train docs (  1499 positive)    878 test docs (   108 positive) accuracy: 0.950 in 5.89s ( 1982 docs/s)
 
 
-                     SGD classifier :        14625 train docs (  1865 positive)    878 test docs (   108 positive) accuracy: 0.965 in 6.06s ( 2412 docs/s)
-              Perceptron classifier :        14625 train docs (  1865 positive)    878 test docs (   108 positive) accuracy: 0.903 in 6.06s ( 2411 docs/s)
-          NB Multinomial classifier :        14625 train docs (  1865 positive)    878 test docs (   108 positive) accuracy: 0.924 in 6.07s ( 2408 docs/s)
-      Passive-Aggressive classifier :        14625 train docs (  1865 positive)    878 test docs (   108 positive) accuracy: 0.957 in 6.07s ( 2407 docs/s)
+                     SGD classifier :        14625 train docs (  1865 positive)    878 test docs (   108 positive) accuracy: 0.965 in 7.05s ( 2075 docs/s)
+              Perceptron classifier :        14625 train docs (  1865 positive)    878 test docs (   108 positive) accuracy: 0.903 in 7.05s ( 2074 docs/s)
+          NB Multinomial classifier :        14625 train docs (  1865 positive)    878 test docs (   108 positive) accuracy: 0.924 in 7.06s ( 2072 docs/s)
+      Passive-Aggressive classifier :        14625 train docs (  1865 positive)    878 test docs (   108 positive) accuracy: 0.957 in 7.06s ( 2071 docs/s)
 
 
-                     SGD classifier :        17360 train docs (  2179 positive)    878 test docs (   108 positive) accuracy: 0.957 in 7.09s ( 2447 docs/s)
-              Perceptron classifier :        17360 train docs (  2179 positive)    878 test docs (   108 positive) accuracy: 0.933 in 7.10s ( 2446 docs/s)
-          NB Multinomial classifier :        17360 train docs (  2179 positive)    878 test docs (   108 positive) accuracy: 0.932 in 7.10s ( 2443 docs/s)
-      Passive-Aggressive classifier :        17360 train docs (  2179 positive)    878 test docs (   108 positive) accuracy: 0.952 in 7.11s ( 2442 docs/s)
+                     SGD classifier :        17360 train docs (  2179 positive)    878 test docs (   108 positive) accuracy: 0.957 in 8.14s ( 2132 docs/s)
+              Perceptron classifier :        17360 train docs (  2179 positive)    878 test docs (   108 positive) accuracy: 0.933 in 8.14s ( 2132 docs/s)
+          NB Multinomial classifier :        17360 train docs (  2179 positive)    878 test docs (   108 positive) accuracy: 0.932 in 8.15s ( 2129 docs/s)
+      Passive-Aggressive classifier :        17360 train docs (  2179 positive)    878 test docs (   108 positive) accuracy: 0.952 in 8.15s ( 2129 docs/s)
 
 
 
 
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 330-339
+.. GENERATED FROM PYTHON SOURCE LINES 327-336
 
 Plot results
 ------------
@@ -441,7 +438,7 @@ measured on the first 1000 samples, held out as a validation set.
 To limit the memory consumption, we queue examples up to a fixed amount
 before feeding them to the learner.
 
-.. GENERATED FROM PYTHON SOURCE LINES 339-436
+.. GENERATED FROM PYTHON SOURCE LINES 336-433
 
 .. code-block:: Python
 
@@ -583,7 +580,7 @@ before feeding them to the learner.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 8.066 seconds)
+   **Total running time of the script:** (0 minutes 9.139 seconds)
 
 
 .. _sphx_glr_download_auto_examples_applications_plot_out_of_core_classification.py:
diff --git a/dev/_sources/auto_examples/applications/plot_outlier_detection_wine.rst.txt b/dev/_sources/auto_examples/applications/plot_outlier_detection_wine.rst.txt
index 961e86df7c3e7..7e1c23b88b48c 100644
--- a/dev/_sources/auto_examples/applications/plot_outlier_detection_wine.rst.txt
+++ b/dev/_sources/auto_examples/applications/plot_outlier_detection_wine.rst.txt
@@ -214,7 +214,7 @@ the data scatter matrix and the risk of over-fitting the data.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.363 seconds)
+   **Total running time of the script:** (0 minutes 0.377 seconds)
 
 
 .. _sphx_glr_download_auto_examples_applications_plot_outlier_detection_wine.py:
diff --git a/dev/_sources/auto_examples/applications/plot_prediction_latency.rst.txt b/dev/_sources/auto_examples/applications/plot_prediction_latency.rst.txt
index 93f2ede3eaa4a..2e21f748c6a78 100644
--- a/dev/_sources/auto_examples/applications/plot_prediction_latency.rst.txt
+++ b/dev/_sources/auto_examples/applications/plot_prediction_latency.rst.txt
@@ -473,7 +473,7 @@ Benchmark throughput
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 17.477 seconds)
+   **Total running time of the script:** (0 minutes 18.169 seconds)
 
 
 .. _sphx_glr_download_auto_examples_applications_plot_prediction_latency.py:
diff --git a/dev/_sources/auto_examples/applications/plot_species_distribution_modeling.rst.txt b/dev/_sources/auto_examples/applications/plot_species_distribution_modeling.rst.txt
index d9d7c863438d0..29b225b0ae8c1 100644
--- a/dev/_sources/auto_examples/applications/plot_species_distribution_modeling.rst.txt
+++ b/dev/_sources/auto_examples/applications/plot_species_distribution_modeling.rst.txt
@@ -82,7 +82,7 @@ References
 
      Area under the ROC curve : 0.993919
 
-    time elapsed: 5.87s
+    time elapsed: 8.29s
 
 
 
@@ -167,7 +167,7 @@ References
 
 
     def plot_species_distribution(
-        species=("bradypus_variegatus_0", "microryzomys_minutus_0")
+        species=("bradypus_variegatus_0", "microryzomys_minutus_0"),
     ):
         """
         Plot the species distribution.
@@ -252,7 +252,7 @@ References
             Z = np.ones((data.Ny, data.Nx), dtype=np.float64)
 
             # We'll predict only for the land points.
-            idx = np.where(land_reference > -9999)
+            idx = (land_reference > -9999).nonzero()
             coverages_land = data.coverages[:, idx[0], idx[1]].T
 
             pred = clf.decision_function((coverages_land - mean) / std)
@@ -306,7 +306,7 @@ References
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 6.021 seconds)
+   **Total running time of the script:** (0 minutes 8.461 seconds)
 
 
 .. _sphx_glr_download_auto_examples_applications_plot_species_distribution_modeling.py:
diff --git a/dev/_sources/auto_examples/applications/plot_stock_market.rst.txt b/dev/_sources/auto_examples/applications/plot_stock_market.rst.txt
index c8cb1c1bf011b..570113a9f3664 100644
--- a/dev/_sources/auto_examples/applications/plot_stock_market.rst.txt
+++ b/dev/_sources/auto_examples/applications/plot_stock_market.rst.txt
@@ -247,7 +247,7 @@ its fluctuations.
 .. raw:: html
 
     <div class="output_subarea output_html rendered_html output_result">
-    <style>#sk-container-id-27 {
+    <style>#sk-container-id-28 {
       /* Definition of color scheme common for light and dark mode */
       --sklearn-color-text: #000;
       --sklearn-color-text-muted: #666;
@@ -278,15 +278,15 @@ its fluctuations.
       }
     }
 
-    #sk-container-id-27 {
+    #sk-container-id-28 {
       color: var(--sklearn-color-text);
     }
 
-    #sk-container-id-27 pre {
+    #sk-container-id-28 pre {
       padding: 0;
     }
 
-    #sk-container-id-27 input.sk-hidden--visually {
+    #sk-container-id-28 input.sk-hidden--visually {
       border: 0;
       clip: rect(1px 1px 1px 1px);
       clip: rect(1px, 1px, 1px, 1px);
@@ -298,7 +298,7 @@ its fluctuations.
       width: 1px;
     }
 
-    #sk-container-id-27 div.sk-dashed-wrapped {
+    #sk-container-id-28 div.sk-dashed-wrapped {
       border: 1px dashed var(--sklearn-color-line);
       margin: 0 0.4em 0.5em 0.4em;
       box-sizing: border-box;
@@ -306,7 +306,7 @@ its fluctuations.
       background-color: var(--sklearn-color-background);
     }
 
-    #sk-container-id-27 div.sk-container {
+    #sk-container-id-28 div.sk-container {
       /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
          but bootstrap.min.css set `[hidden] { display: none !important; }`
          so we also need the `!important` here to be able to override the
@@ -316,7 +316,7 @@ its fluctuations.
       position: relative;
     }
 
-    #sk-container-id-27 div.sk-text-repr-fallback {
+    #sk-container-id-28 div.sk-text-repr-fallback {
       display: none;
     }
 
@@ -332,14 +332,14 @@ its fluctuations.
 
     /* Parallel-specific style estimator block */
 
-    #sk-container-id-27 div.sk-parallel-item::after {
+    #sk-container-id-28 div.sk-parallel-item::after {
       content: "";
       width: 100%;
       border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
       flex-grow: 1;
     }
 
-    #sk-container-id-27 div.sk-parallel {
+    #sk-container-id-28 div.sk-parallel {
       display: flex;
       align-items: stretch;
       justify-content: center;
@@ -347,28 +347,28 @@ its fluctuations.
       position: relative;
     }
 
-    #sk-container-id-27 div.sk-parallel-item {
+    #sk-container-id-28 div.sk-parallel-item {
       display: flex;
       flex-direction: column;
     }
 
-    #sk-container-id-27 div.sk-parallel-item:first-child::after {
+    #sk-container-id-28 div.sk-parallel-item:first-child::after {
       align-self: flex-end;
       width: 50%;
     }
 
-    #sk-container-id-27 div.sk-parallel-item:last-child::after {
+    #sk-container-id-28 div.sk-parallel-item:last-child::after {
       align-self: flex-start;
       width: 50%;
     }
 
-    #sk-container-id-27 div.sk-parallel-item:only-child::after {
+    #sk-container-id-28 div.sk-parallel-item:only-child::after {
       width: 0;
     }
 
     /* Serial-specific style estimator block */
 
-    #sk-container-id-27 div.sk-serial {
+    #sk-container-id-28 div.sk-serial {
       display: flex;
       flex-direction: column;
       align-items: center;
@@ -386,14 +386,14 @@ its fluctuations.
 
     /* Pipeline and ColumnTransformer style (default) */
 
-    #sk-container-id-27 div.sk-toggleable {
+    #sk-container-id-28 div.sk-toggleable {
       /* Default theme specific background. It is overwritten whether we have a
       specific estimator or a Pipeline/ColumnTransformer */
       background-color: var(--sklearn-color-background);
     }
 
     /* Toggleable label */
-    #sk-container-id-27 label.sk-toggleable__label {
+    #sk-container-id-28 label.sk-toggleable__label {
       cursor: pointer;
       display: flex;
       width: 100%;
@@ -406,13 +406,13 @@ its fluctuations.
       gap: 0.5em;
     }
 
-    #sk-container-id-27 label.sk-toggleable__label .caption {
+    #sk-container-id-28 label.sk-toggleable__label .caption {
       font-size: 0.6rem;
       font-weight: lighter;
       color: var(--sklearn-color-text-muted);
     }
 
-    #sk-container-id-27 label.sk-toggleable__label-arrow:before {
+    #sk-container-id-28 label.sk-toggleable__label-arrow:before {
       /* Arrow on the left of the label */
       content: "▸";
       float: left;
@@ -420,13 +420,13 @@ its fluctuations.
       color: var(--sklearn-color-icon);
     }
 
-    #sk-container-id-27 label.sk-toggleable__label-arrow:hover:before {
+    #sk-container-id-28 label.sk-toggleable__label-arrow:hover:before {
       color: var(--sklearn-color-text);
     }
 
     /* Toggleable content - dropdown */
 
-    #sk-container-id-27 div.sk-toggleable__content {
+    #sk-container-id-28 div.sk-toggleable__content {
       max-height: 0;
       max-width: 0;
       overflow: hidden;
@@ -435,12 +435,12 @@ its fluctuations.
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-27 div.sk-toggleable__content.fitted {
+    #sk-container-id-28 div.sk-toggleable__content.fitted {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
-    #sk-container-id-27 div.sk-toggleable__content pre {
+    #sk-container-id-28 div.sk-toggleable__content pre {
       margin: 0.2em;
       border-radius: 0.25em;
       color: var(--sklearn-color-text);
@@ -448,79 +448,79 @@ its fluctuations.
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-27 div.sk-toggleable__content.fitted pre {
+    #sk-container-id-28 div.sk-toggleable__content.fitted pre {
       /* unfitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
-    #sk-container-id-27 input.sk-toggleable__control:checked~div.sk-toggleable__content {
+    #sk-container-id-28 input.sk-toggleable__control:checked~div.sk-toggleable__content {
       /* Expand drop-down */
       max-height: 200px;
       max-width: 100%;
       overflow: auto;
     }
 
-    #sk-container-id-27 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
+    #sk-container-id-28 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
       content: "▾";
     }
 
     /* Pipeline/ColumnTransformer-specific style */
 
-    #sk-container-id-27 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-28 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-27 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-28 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
     /* Estimator-specific style */
 
     /* Colorize estimator box */
-    #sk-container-id-27 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-28 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-27 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-28 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
-    #sk-container-id-27 div.sk-label label.sk-toggleable__label,
-    #sk-container-id-27 div.sk-label label {
+    #sk-container-id-28 div.sk-label label.sk-toggleable__label,
+    #sk-container-id-28 div.sk-label label {
       /* The background is the default theme color */
       color: var(--sklearn-color-text-on-default-background);
     }
 
     /* On hover, darken the color of the background */
-    #sk-container-id-27 div.sk-label:hover label.sk-toggleable__label {
+    #sk-container-id-28 div.sk-label:hover label.sk-toggleable__label {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
     /* Label box, darken color on hover, fitted */
-    #sk-container-id-27 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
+    #sk-container-id-28 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
     /* Estimator label */
 
-    #sk-container-id-27 div.sk-label label {
+    #sk-container-id-28 div.sk-label label {
       font-family: monospace;
       font-weight: bold;
       display: inline-block;
       line-height: 1.2em;
     }
 
-    #sk-container-id-27 div.sk-label-container {
+    #sk-container-id-28 div.sk-label-container {
       text-align: center;
     }
 
     /* Estimator-specific */
-    #sk-container-id-27 div.sk-estimator {
+    #sk-container-id-28 div.sk-estimator {
       font-family: monospace;
       border: 1px dotted var(--sklearn-color-border-box);
       border-radius: 0.25em;
@@ -530,18 +530,18 @@ its fluctuations.
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-27 div.sk-estimator.fitted {
+    #sk-container-id-28 div.sk-estimator.fitted {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
     /* on hover */
-    #sk-container-id-27 div.sk-estimator:hover {
+    #sk-container-id-28 div.sk-estimator:hover {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-27 div.sk-estimator.fitted:hover {
+    #sk-container-id-28 div.sk-estimator.fitted:hover {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-2);
     }
@@ -629,7 +629,7 @@ its fluctuations.
 
     /* "?"-specific style due to the `<a>` HTML tag */
 
-    #sk-container-id-27 a.estimator_doc_link {
+    #sk-container-id-28 a.estimator_doc_link {
       float: right;
       font-size: 1rem;
       line-height: 1em;
@@ -644,26 +644,26 @@ its fluctuations.
       border: var(--sklearn-color-unfitted-level-1) 1pt solid;
     }
 
-    #sk-container-id-27 a.estimator_doc_link.fitted {
+    #sk-container-id-28 a.estimator_doc_link.fitted {
       /* fitted */
       border: var(--sklearn-color-fitted-level-1) 1pt solid;
       color: var(--sklearn-color-fitted-level-1);
     }
 
     /* On hover */
-    #sk-container-id-27 a.estimator_doc_link:hover {
+    #sk-container-id-28 a.estimator_doc_link:hover {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-3);
       color: var(--sklearn-color-background);
       text-decoration: none;
     }
 
-    #sk-container-id-27 a.estimator_doc_link.fitted:hover {
+    #sk-container-id-28 a.estimator_doc_link.fitted:hover {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-3);
     }
-    </style><div id="sk-container-id-27" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>GraphicalLassoCV(alphas=array([ 0.03162278,  0.05994843,  0.11364637,  0.21544347,  0.40842387,
-            0.77426368,  1.46779927,  2.7825594 ,  5.27499706, 10.        ]))</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-107" type="checkbox" checked><label for="sk-estimator-id-107" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>GraphicalLassoCV</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.covariance.GraphicalLassoCV.html">?<span>Documentation for GraphicalLassoCV</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>GraphicalLassoCV(alphas=array([ 0.03162278,  0.05994843,  0.11364637,  0.21544347,  0.40842387,
+    </style><div id="sk-container-id-28" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>GraphicalLassoCV(alphas=array([ 0.03162278,  0.05994843,  0.11364637,  0.21544347,  0.40842387,
+            0.77426368,  1.46779927,  2.7825594 ,  5.27499706, 10.        ]))</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-117" type="checkbox" checked><label for="sk-estimator-id-117" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>GraphicalLassoCV</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.covariance.GraphicalLassoCV.html">?<span>Documentation for GraphicalLassoCV</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>GraphicalLassoCV(alphas=array([ 0.03162278,  0.05994843,  0.11364637,  0.21544347,  0.40842387,
             0.77426368,  1.46779927,  2.7825594 ,  5.27499706, 10.        ]))</pre></div> </div></div></div></div>
     </div>
     <br />
@@ -800,7 +800,7 @@ axis.
     )
 
     # Plot the edges
-    start_idx, end_idx = np.where(non_zero)
+    start_idx, end_idx = non_zero.nonzero()
     # a sequence of (*line0*, *line1*, *line2*), where::
     #            linen = (x0, y0), (x1, y1), ... (xm, ym)
     segments = [
@@ -874,7 +874,7 @@ axis.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 2.321 seconds)
+   **Total running time of the script:** (0 minutes 7.065 seconds)
 
 
 .. _sphx_glr_download_auto_examples_applications_plot_stock_market.py:
diff --git a/dev/_sources/auto_examples/applications/plot_time_series_lagged_features.rst.txt b/dev/_sources/auto_examples/applications/plot_time_series_lagged_features.rst.txt
index 6f5d0ce39a656..b04c714e2cb38 100644
--- a/dev/_sources/auto_examples/applications/plot_time_series_lagged_features.rst.txt
+++ b/dev/_sources/auto_examples/applications/plot_time_series_lagged_features.rst.txt
@@ -178,7 +178,7 @@ and `"winter"` present in the dataset to confirm they are balanced.
       white-space: pre-wrap;
     }
     </style>
-    <small>shape: (4, 2)</small><table border="1" class="dataframe"><thead><tr><th>season</th><th>count</th></tr><tr><td>cat</td><td>u32</td></tr></thead><tbody><tr><td>&quot;2&quot;</td><td>4409</td></tr><tr><td>&quot;1&quot;</td><td>4242</td></tr><tr><td>&quot;3&quot;</td><td>4232</td></tr><tr><td>&quot;0&quot;</td><td>4496</td></tr></tbody></table></div>
+    <small>shape: (4, 2)</small><table border="1" class="dataframe"><thead><tr><th>season</th><th>count</th></tr><tr><td>cat</td><td>u32</td></tr></thead><tbody><tr><td>&quot;3&quot;</td><td>4232</td></tr><tr><td>&quot;1&quot;</td><td>4242</td></tr><tr><td>&quot;2&quot;</td><td>4409</td></tr><tr><td>&quot;0&quot;</td><td>4496</td></tr></tbody></table></div>
     </div>
     <br />
     <br />
@@ -571,7 +571,7 @@ distribution without making strong assumptions on its shape.
         time = cv_results["fit_time"]
         scores["fit_time"].append(f"{time.mean():.2f} ± {time.std():.2f} s")
 
-        scores["loss"].append(f"quantile {int(quantile*100)}")
+        scores["loss"].append(f"quantile {int(quantile * 100)}")
         for key, value in cv_results.items():
             if key.startswith("test_"):
                 metric = key.split("test_")[1]
@@ -596,7 +596,7 @@ distribution without making strong assumptions on its shape.
       white-space: pre-wrap;
     }
     </style>
-    <small>shape: (6, 8)</small><table border="1" class="dataframe"><thead><tr><th>loss</th><th>fit_time</th><th>MAPE</th><th>RMSE</th><th>MAE</th><th>pinball_loss_05</th><th>pinball_loss_50</th><th>pinball_loss_95</th></tr><tr><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td></tr></thead><tbody><tr><td>&quot;squared_error&quot;</td><td>&quot;0.32 ± 0.01 s&quot;</td><td>&quot;0.36 ± 0.07&quot;</td><td>&quot;62.3 ± 3.5&quot;</td><td>&quot;39.1 ± 2.3&quot;</td><td>&quot;17.7 ± 1.3&quot;</td><td>&quot;19.5 ± 1.1&quot;</td><td>&quot;21.4 ± 2.4&quot;</td></tr><tr><td>&quot;poisson&quot;</td><td>&quot;0.34 ± 0.01 s&quot;</td><td>&quot;0.32 ± 0.07&quot;</td><td>&quot;64.2 ± 4.0&quot;</td><td>&quot;39.3 ± 2.8&quot;</td><td>&quot;16.7 ± 1.5&quot;</td><td>&quot;19.7 ± 1.4&quot;</td><td>&quot;22.6 ± 3.0&quot;</td></tr><tr><td>&quot;absolute_error&quot;</td><td>&quot;0.43 ± 0.01 s&quot;</td><td>&quot;0.32 ± 0.06&quot;</td><td>&quot;64.6 ± 3.8&quot;</td><td>&quot;39.9 ± 3.2&quot;</td><td>&quot;17.1 ± 1.1&quot;</td><td>&quot;19.9 ± 1.6&quot;</td><td>&quot;22.7 ± 3.1&quot;</td></tr><tr><td>&quot;quantile 5&quot;</td><td>&quot;0.56 ± 0.00 s&quot;</td><td>&quot;0.41 ± 0.01&quot;</td><td>&quot;145.6 ± 20.9&quot;</td><td>&quot;92.5 ± 16.2&quot;</td><td>&quot;5.9 ± 0.9&quot;</td><td>&quot;46.2 ± 8.1&quot;</td><td>&quot;86.6 ± 15.3&quot;</td></tr><tr><td>&quot;quantile 50&quot;</td><td>&quot;0.58 ± 0.02 s&quot;</td><td>&quot;0.32 ± 0.06&quot;</td><td>&quot;64.6 ± 3.8&quot;</td><td>&quot;39.9 ± 3.2&quot;</td><td>&quot;17.1 ± 1.1&quot;</td><td>&quot;19.9 ± 1.6&quot;</td><td>&quot;22.7 ± 3.1&quot;</td></tr><tr><td>&quot;quantile 95&quot;</td><td>&quot;0.56 ± 0.00 s&quot;</td><td>&quot;1.07 ± 0.27&quot;</td><td>&quot;99.6 ± 8.7&quot;</td><td>&quot;72.0 ± 6.1&quot;</td><td>&quot;62.9 ± 7.4&quot;</td><td>&quot;36.0 ± 3.1&quot;</td><td>&quot;9.1 ± 1.3&quot;</td></tr></tbody></table></div>
+    <small>shape: (6, 8)</small><table border="1" class="dataframe"><thead><tr><th>loss</th><th>fit_time</th><th>MAPE</th><th>RMSE</th><th>MAE</th><th>pinball_loss_05</th><th>pinball_loss_50</th><th>pinball_loss_95</th></tr><tr><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td></tr></thead><tbody><tr><td>&quot;squared_error&quot;</td><td>&quot;0.36 ± 0.02 s&quot;</td><td>&quot;0.36 ± 0.07&quot;</td><td>&quot;62.3 ± 3.5&quot;</td><td>&quot;39.1 ± 2.3&quot;</td><td>&quot;17.7 ± 1.3&quot;</td><td>&quot;19.5 ± 1.1&quot;</td><td>&quot;21.4 ± 2.4&quot;</td></tr><tr><td>&quot;poisson&quot;</td><td>&quot;0.38 ± 0.01 s&quot;</td><td>&quot;0.32 ± 0.07&quot;</td><td>&quot;64.2 ± 4.0&quot;</td><td>&quot;39.3 ± 2.8&quot;</td><td>&quot;16.7 ± 1.5&quot;</td><td>&quot;19.7 ± 1.4&quot;</td><td>&quot;22.6 ± 3.0&quot;</td></tr><tr><td>&quot;absolute_error&quot;</td><td>&quot;0.49 ± 0.02 s&quot;</td><td>&quot;0.32 ± 0.06&quot;</td><td>&quot;64.6 ± 3.8&quot;</td><td>&quot;39.9 ± 3.2&quot;</td><td>&quot;17.1 ± 1.1&quot;</td><td>&quot;19.9 ± 1.6&quot;</td><td>&quot;22.7 ± 3.1&quot;</td></tr><tr><td>&quot;quantile 5&quot;</td><td>&quot;0.61 ± 0.05 s&quot;</td><td>&quot;0.41 ± 0.01&quot;</td><td>&quot;145.6 ± 20.9&quot;</td><td>&quot;92.5 ± 16.2&quot;</td><td>&quot;5.9 ± 0.9&quot;</td><td>&quot;46.2 ± 8.1&quot;</td><td>&quot;86.6 ± 15.3&quot;</td></tr><tr><td>&quot;quantile 50&quot;</td><td>&quot;0.66 ± 0.03 s&quot;</td><td>&quot;0.32 ± 0.06&quot;</td><td>&quot;64.6 ± 3.8&quot;</td><td>&quot;39.9 ± 3.2&quot;</td><td>&quot;17.1 ± 1.1&quot;</td><td>&quot;19.9 ± 1.6&quot;</td><td>&quot;22.7 ± 3.1&quot;</td></tr><tr><td>&quot;quantile 95&quot;</td><td>&quot;0.68 ± 0.05 s&quot;</td><td>&quot;1.07 ± 0.27&quot;</td><td>&quot;99.6 ± 8.7&quot;</td><td>&quot;72.0 ± 6.1&quot;</td><td>&quot;62.9 ± 7.4&quot;</td><td>&quot;36.0 ± 3.1&quot;</td><td>&quot;9.1 ± 1.3&quot;</td></tr></tbody></table></div>
     </div>
     <br />
     <br />
@@ -842,7 +842,7 @@ series forecasting, that enables dynamic predictions of future values.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 9.360 seconds)
+   **Total running time of the script:** (0 minutes 10.327 seconds)
 
 
 .. _sphx_glr_download_auto_examples_applications_plot_time_series_lagged_features.py:
diff --git a/dev/_sources/auto_examples/applications/plot_tomography_l1_reconstruction.rst.txt b/dev/_sources/auto_examples/applications/plot_tomography_l1_reconstruction.rst.txt
index 87a9fb1b6ba2a..acdd1a59fef61 100644
--- a/dev/_sources/auto_examples/applications/plot_tomography_l1_reconstruction.rst.txt
+++ b/dev/_sources/auto_examples/applications/plot_tomography_l1_reconstruction.rst.txt
@@ -180,7 +180,7 @@ contributed to fewer projections than the central disk.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 9.552 seconds)
+   **Total running time of the script:** (0 minutes 9.836 seconds)
 
 
 .. _sphx_glr_download_auto_examples_applications_plot_tomography_l1_reconstruction.py:
diff --git a/dev/_sources/auto_examples/applications/plot_topics_extraction_with_nmf_lda.rst.txt b/dev/_sources/auto_examples/applications/plot_topics_extraction_with_nmf_lda.rst.txt
index ac71c1875e145..acd1ac84727de 100644
--- a/dev/_sources/auto_examples/applications/plot_topics_extraction_with_nmf_lda.rst.txt
+++ b/dev/_sources/auto_examples/applications/plot_topics_extraction_with_nmf_lda.rst.txt
@@ -86,30 +86,30 @@ proportional to (n_samples * iterations).
  .. code-block:: none
 
     Loading dataset...
-    done in 1.109s.
+    done in 1.284s.
     Extracting tf-idf features for NMF...
-    done in 0.240s.
+    done in 0.312s.
     Extracting tf features for LDA...
-    done in 0.235s.
+    done in 0.310s.
 
     Fitting the NMF model (Frobenius norm) with tf-idf features, n_samples=2000 and n_features=1000...
-    done in 0.064s.
+    done in 0.080s.
 
 
      Fitting the NMF model (generalized Kullback-Leibler divergence) with tf-idf features, n_samples=2000 and n_features=1000...
-    done in 1.295s.
+    done in 1.452s.
 
 
      Fitting the MiniBatchNMF model (Frobenius norm) with tf-idf features, n_samples=2000 and n_features=1000, batch_size=128...
-    done in 0.075s.
+    done in 0.084s.
 
 
      Fitting the MiniBatchNMF model (generalized Kullback-Leibler divergence) with tf-idf features, n_samples=2000 and n_features=1000, batch_size=128...
-    done in 0.217s.
+    done in 0.229s.
 
 
      Fitting LDA models with tf features, n_samples=2000 and n_features=1000...
-    done in 2.572s.
+    done in 2.577s.
 
 
 
@@ -150,7 +150,7 @@ proportional to (n_samples * iterations).
 
             ax = axes[topic_idx]
             ax.barh(top_features, weights, height=0.7)
-            ax.set_title(f"Topic {topic_idx +1}", fontdict={"fontsize": 30})
+            ax.set_title(f"Topic {topic_idx + 1}", fontdict={"fontsize": 30})
             ax.tick_params(axis="both", which="major", labelsize=20)
             for i in "top right left".split():
                 ax.spines[i].set_visible(False)
@@ -326,7 +326,7 @@ proportional to (n_samples * iterations).
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 11.217 seconds)
+   **Total running time of the script:** (0 minutes 12.584 seconds)
 
 
 .. _sphx_glr_download_auto_examples_applications_plot_topics_extraction_with_nmf_lda.py:
diff --git a/dev/_sources/auto_examples/bicluster/plot_bicluster_newsgroups.rst.txt b/dev/_sources/auto_examples/bicluster/plot_bicluster_newsgroups.rst.txt
index c04210b213002..bf39404309b0b 100644
--- a/dev/_sources/auto_examples/bicluster/plot_bicluster_newsgroups.rst.txt
+++ b/dev/_sources/auto_examples/bicluster/plot_bicluster_newsgroups.rst.txt
@@ -51,9 +51,9 @@ achieve a better V-measure than clusters found by MiniBatchKMeans.
 
     Vectorizing...
     Coclustering...
-    Done in 1.23s. V-measure: 0.4415
+    Done in 1.35s. V-measure: 0.4415
     MiniBatchKMeans...
-    Done in 2.19s. V-measure: 0.3015
+    Done in 2.71s. V-measure: 0.3015
 
     Best biclusters:
     ----------------
@@ -212,7 +212,7 @@ achieve a better V-measure than clusters found by MiniBatchKMeans.
 
         # words
         out_of_cluster_docs = cocluster.row_labels_ != cluster
-        out_of_cluster_docs = np.where(out_of_cluster_docs)[0]
+        out_of_cluster_docs = out_of_cluster_docs.nonzero()[0]
         word_col = X[:, cluster_words]
         word_scores = np.array(
             word_col[cluster_docs, :].sum(axis=0)
@@ -230,7 +230,7 @@ achieve a better V-measure than clusters found by MiniBatchKMeans.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 6.088 seconds)
+   **Total running time of the script:** (0 minutes 7.279 seconds)
 
 
 .. _sphx_glr_download_auto_examples_bicluster_plot_bicluster_newsgroups.py:
diff --git a/dev/_sources/auto_examples/bicluster/plot_spectral_biclustering.rst.txt b/dev/_sources/auto_examples/bicluster/plot_spectral_biclustering.rst.txt
index 017c63202666c..85e5f9bf31708 100644
--- a/dev/_sources/auto_examples/bicluster/plot_spectral_biclustering.rst.txt
+++ b/dev/_sources/auto_examples/bicluster/plot_spectral_biclustering.rst.txt
@@ -261,7 +261,7 @@ labels are represented by different shades of blue.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.501 seconds)
+   **Total running time of the script:** (0 minutes 0.641 seconds)
 
 
 .. _sphx_glr_download_auto_examples_bicluster_plot_spectral_biclustering.py:
diff --git a/dev/_sources/auto_examples/bicluster/plot_spectral_coclustering.rst.txt b/dev/_sources/auto_examples/bicluster/plot_spectral_coclustering.rst.txt
index 32d787cf80852..f5b458d1b25d2 100644
--- a/dev/_sources/auto_examples/bicluster/plot_spectral_coclustering.rst.txt
+++ b/dev/_sources/auto_examples/bicluster/plot_spectral_coclustering.rst.txt
@@ -120,7 +120,7 @@ the biclusters.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.346 seconds)
+   **Total running time of the script:** (0 minutes 0.376 seconds)
 
 
 .. _sphx_glr_download_auto_examples_bicluster_plot_spectral_coclustering.py:
diff --git a/dev/_sources/auto_examples/calibration/plot_calibration.rst.txt b/dev/_sources/auto_examples/calibration/plot_calibration.rst.txt
index 6d37cc746e3c3..b0121d93b641f 100644
--- a/dev/_sources/auto_examples/calibration/plot_calibration.rst.txt
+++ b/dev/_sources/auto_examples/calibration/plot_calibration.rst.txt
@@ -235,7 +235,7 @@ Plot data and the predicted probabilities
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.360 seconds)
+   **Total running time of the script:** (0 minutes 0.369 seconds)
 
 
 .. _sphx_glr_download_auto_examples_calibration_plot_calibration.py:
diff --git a/dev/_sources/auto_examples/calibration/plot_calibration_curve.rst.txt b/dev/_sources/auto_examples/calibration/plot_calibration_curve.rst.txt
index 8c0b6a28be7e3..506d7a78a6133 100644
--- a/dev/_sources/auto_examples/calibration/plot_calibration_curve.rst.txt
+++ b/dev/_sources/auto_examples/calibration/plot_calibration_curve.rst.txt
@@ -633,7 +633,7 @@ References
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 2.282 seconds)
+   **Total running time of the script:** (0 minutes 2.484 seconds)
 
 
 .. _sphx_glr_download_auto_examples_calibration_plot_calibration_curve.py:
diff --git a/dev/_sources/auto_examples/calibration/plot_calibration_multiclass.rst.txt b/dev/_sources/auto_examples/calibration/plot_calibration_multiclass.rst.txt
index 69766a7e48679..6ae936b02e74f 100644
--- a/dev/_sources/auto_examples/calibration/plot_calibration_multiclass.rst.txt
+++ b/dev/_sources/auto_examples/calibration/plot_calibration_multiclass.rst.txt
@@ -1283,7 +1283,7 @@ All in all, the One-vs-Rest multiclass-calibration strategy implemented in
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 1.356 seconds)
+   **Total running time of the script:** (0 minutes 1.393 seconds)
 
 
 .. _sphx_glr_download_auto_examples_calibration_plot_calibration_multiclass.py:
diff --git a/dev/_sources/auto_examples/calibration/plot_compare_calibration.rst.txt b/dev/_sources/auto_examples/calibration/plot_compare_calibration.rst.txt
index f586cea799918..0660183e696eb 100644
--- a/dev/_sources/auto_examples/calibration/plot_compare_calibration.rst.txt
+++ b/dev/_sources/auto_examples/calibration/plot_compare_calibration.rst.txt
@@ -352,7 +352,7 @@ References
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 2.968 seconds)
+   **Total running time of the script:** (0 minutes 3.287 seconds)
 
 
 .. _sphx_glr_download_auto_examples_calibration_plot_compare_calibration.py:
diff --git a/dev/_sources/auto_examples/classification/plot_classification_probability.rst.txt b/dev/_sources/auto_examples/classification/plot_classification_probability.rst.txt
index 0ba9316d05c31..cc7fdcf04f119 100644
--- a/dev/_sources/auto_examples/classification/plot_classification_probability.rst.txt
+++ b/dev/_sources/auto_examples/classification/plot_classification_probability.rst.txt
@@ -414,7 +414,7 @@ necessarily the case for other datasets.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 2.598 seconds)
+   **Total running time of the script:** (0 minutes 2.623 seconds)
 
 
 .. _sphx_glr_download_auto_examples_classification_plot_classification_probability.py:
diff --git a/dev/_sources/auto_examples/classification/plot_classifier_comparison.rst.txt b/dev/_sources/auto_examples/classification/plot_classifier_comparison.rst.txt
index 65d5f5937df96..d26e15fa6af1d 100644
--- a/dev/_sources/auto_examples/classification/plot_classifier_comparison.rst.txt
+++ b/dev/_sources/auto_examples/classification/plot_classifier_comparison.rst.txt
@@ -192,7 +192,7 @@ set.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 2.097 seconds)
+   **Total running time of the script:** (0 minutes 2.187 seconds)
 
 
 .. _sphx_glr_download_auto_examples_classification_plot_classifier_comparison.py:
diff --git a/dev/_sources/auto_examples/classification/plot_digits_classification.rst.txt b/dev/_sources/auto_examples/classification/plot_digits_classification.rst.txt
index cfca61fed8e67..05025b7aa621e 100644
--- a/dev/_sources/auto_examples/classification/plot_digits_classification.rst.txt
+++ b/dev/_sources/auto_examples/classification/plot_digits_classification.rst.txt
@@ -315,7 +315,7 @@ as follows:
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.417 seconds)
+   **Total running time of the script:** (0 minutes 0.439 seconds)
 
 
 .. _sphx_glr_download_auto_examples_classification_plot_digits_classification.py:
diff --git a/dev/_sources/auto_examples/classification/plot_lda.rst.txt b/dev/_sources/auto_examples/classification/plot_lda.rst.txt
index 7ef6abb25afa6..97a357cc6ae47 100644
--- a/dev/_sources/auto_examples/classification/plot_lda.rst.txt
+++ b/dev/_sources/auto_examples/classification/plot_lda.rst.txt
@@ -142,7 +142,7 @@ Shrinkage (OAS) estimators of covariance can improve classification.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 7.973 seconds)
+   **Total running time of the script:** (0 minutes 7.778 seconds)
 
 
 .. _sphx_glr_download_auto_examples_classification_plot_lda.py:
diff --git a/dev/_sources/auto_examples/classification/plot_lda_qda.rst.txt b/dev/_sources/auto_examples/classification/plot_lda_qda.rst.txt
index ce168967ebd02..2385ae75d06d1 100644
--- a/dev/_sources/auto_examples/classification/plot_lda_qda.rst.txt
+++ b/dev/_sources/auto_examples/classification/plot_lda_qda.rst.txt
@@ -318,7 +318,7 @@ single covariance matrix.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.404 seconds)
+   **Total running time of the script:** (0 minutes 0.407 seconds)
 
 
 .. _sphx_glr_download_auto_examples_classification_plot_lda_qda.py:
diff --git a/dev/_sources/auto_examples/cluster/plot_adjusted_for_chance_measures.rst.txt b/dev/_sources/auto_examples/cluster/plot_adjusted_for_chance_measures.rst.txt
index 6c18f9eef52e8..4a51c57f2cb05 100644
--- a/dev/_sources/auto_examples/cluster/plot_adjusted_for_chance_measures.rst.txt
+++ b/dev/_sources/auto_examples/cluster/plot_adjusted_for_chance_measures.rst.txt
@@ -322,6 +322,903 @@ clusters similar or equal to the number of samples.
    :class: sphx-glr-single-img
 
 
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    /home/circleci/project/sklearn/metrics/cluster/_supervised.py:50: UserWarning:
+
+    The number of unique classes is greater than 50% of the number of samples.
+
+    /home/circleci/project/sklearn/metrics/cluster/_supervised.py:50: UserWarning:
+
+    The number of unique classes is greater than 50% of the number of samples.
+
+    /home/circleci/project/sklearn/metrics/cluster/_supervised.py:49: UserWarning:
+
+    The number of unique classes is greater than 50% of the number of samples.
+
+    /home/circleci/project/sklearn/metrics/cluster/_supervised.py:49: UserWarning:
+
+    The number of unique classes is greater than 50% of the number of samples.
+
+    /home/circleci/project/sklearn/metrics/cluster/_supervised.py:50: UserWarning:
+
+    The number of unique classes is greater than 50% of the number of samples.
+
+    /home/circleci/project/sklearn/metrics/cluster/_supervised.py:49: UserWarning:
+
+    The number of unique classes is greater than 50% of the number of samples.
+
+    /home/circleci/project/sklearn/metrics/cluster/_supervised.py:50: UserWarning:
+
+    The number of unique classes is greater than 50% of the number of samples.
+
+    /home/circleci/project/sklearn/metrics/cluster/_supervised.py:49: UserWarning:
+
+    The number of unique classes is greater than 50% of the number of samples.
+
+    /home/circleci/project/sklearn/metrics/cluster/_supervised.py:50: UserWarning:
+
+    The number of unique classes is greater than 50% of the number of samples.
+
+    /home/circleci/project/sklearn/metrics/cluster/_supervised.py:49: UserWarning:
+
+    The number of unique classes is greater than 50% of the number of samples.
+
+    /home/circleci/project/sklearn/metrics/cluster/_supervised.py:50: UserWarning:
+
+    The number of unique classes is greater than 50% of the number of samples.
+
+    /home/circleci/project/sklearn/metrics/cluster/_supervised.py:49: UserWarning:
+
+    The number of unique classes is greater than 50% of the number of samples.
+
+    /home/circleci/project/sklearn/metrics/cluster/_supervised.py:50: UserWarning:
+
+    The number of unique classes is greater than 50% of the number of samples.
+
+    /home/circleci/project/sklearn/metrics/cluster/_supervised.py:49: UserWarning:
+
+    The number of unique classes is greater than 50% of the number of samples.
+
+    /home/circleci/project/sklearn/metrics/cluster/_supervised.py:50: UserWarning:
+
+    The number of unique classes is greater than 50% of the number of samples.
+
+    /home/circleci/project/sklearn/metrics/cluster/_supervised.py:49: UserWarning:
+
+    The number of unique classes is greater than 50% of the number of samples.
+
+    /home/circleci/project/sklearn/metrics/cluster/_supervised.py:50: UserWarning:
+
+    The number of unique classes is greater than 50% of the number of samples.
+
+    /home/circleci/project/sklearn/metrics/cluster/_supervised.py:49: UserWarning:
+
+    The number of unique classes is greater than 50% of the number of samples.
+
+    /home/circleci/project/sklearn/metrics/cluster/_supervised.py:50: UserWarning:
+
+    The number of unique classes is greater than 50% of the number of samples.
+
+    /home/circleci/project/sklearn/metrics/cluster/_supervised.py:49: UserWarning:
+
+    The number of unique classes is greater than 50% of the number of samples.
+
+    /home/circleci/project/sklearn/metrics/cluster/_supervised.py:50: UserWarning:
+
+    The number of unique classes is greater than 50% of the number of samples.
+
+    /home/circleci/project/sklearn/metrics/cluster/_supervised.py:49: UserWarning:
+
+    The number of unique classes is greater than 50% of the number of samples.
+
+    /home/circleci/project/sklearn/metrics/cluster/_supervised.py:50: UserWarning:
+
+    The number of unique classes is greater than 50% of the number of samples.
+
+    /home/circleci/project/sklearn/metrics/cluster/_supervised.py:49: UserWarning:
+
+    The number of unique classes is greater than 50% of the number of samples.
+
+    /home/circleci/project/sklearn/metrics/cluster/_supervised.py:50: UserWarning:
+
+    The number of unique classes is greater than 50% of the number of samples.
+
+    /home/circleci/project/sklearn/metrics/cluster/_supervised.py:49: UserWarning:
+
+    The number of unique classes is greater than 50% of the number of samples.
+
+    /home/circleci/project/sklearn/metrics/cluster/_supervised.py:50: UserWarning:
+
+    The number of unique classes is greater than 50% of the number of samples.
+
+    /home/circleci/project/sklearn/metrics/cluster/_supervised.py:49: UserWarning:
+
+    The number of unique classes is greater than 50% of the number of samples.
+
+    /home/circleci/project/sklearn/metrics/cluster/_supervised.py:50: UserWarning:
+
+    The number of unique classes is greater than 50% of the number of samples.
+
+    /home/circleci/project/sklearn/metrics/cluster/_supervised.py:49: UserWarning:
+
+    The number of unique classes is greater than 50% of the number of samples.
+
+    /home/circleci/project/sklearn/metrics/cluster/_supervised.py:50: UserWarning:
+
+    The number of unique classes is greater than 50% of the number of samples.
+
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+    The number of unique classes is greater than 50% of the number of samples.
+
+    /home/circleci/project/sklearn/metrics/cluster/_supervised.py:50: UserWarning:
+
+    The number of unique classes is greater than 50% of the number of samples.
+
+    /home/circleci/project/sklearn/metrics/cluster/_supervised.py:49: UserWarning:
+
+    The number of unique classes is greater than 50% of the number of samples.
+
+    /home/circleci/project/sklearn/metrics/cluster/_supervised.py:50: UserWarning:
+
+    The number of unique classes is greater than 50% of the number of samples.
+
+    /home/circleci/project/sklearn/metrics/cluster/_supervised.py:50: UserWarning:
+
+    The number of unique classes is greater than 50% of the number of samples.
+
+    /home/circleci/project/sklearn/metrics/cluster/_supervised.py:49: UserWarning:
+
+    The number of unique classes is greater than 50% of the number of samples.
+
+    /home/circleci/project/sklearn/metrics/cluster/_supervised.py:49: UserWarning:
+
+    The number of unique classes is greater than 50% of the number of samples.
+
+    /home/circleci/project/sklearn/metrics/cluster/_supervised.py:49: UserWarning:
+
+    The number of unique classes is greater than 50% of the number of samples.
+
+    /home/circleci/project/sklearn/metrics/cluster/_supervised.py:50: UserWarning:
+
+    The number of unique classes is greater than 50% of the number of samples.
+
+    /home/circleci/project/sklearn/metrics/cluster/_supervised.py:49: UserWarning:
+
+    The number of unique classes is greater than 50% of the number of samples.
+
+    /home/circleci/project/sklearn/metrics/cluster/_supervised.py:50: UserWarning:
+
+    The number of unique classes is greater than 50% of the number of samples.
+
+    /home/circleci/project/sklearn/metrics/cluster/_supervised.py:49: UserWarning:
+
+    The number of unique classes is greater than 50% of the number of samples.
+
+    /home/circleci/project/sklearn/metrics/cluster/_supervised.py:49: UserWarning:
+
+    The number of unique classes is greater than 50% of the number of samples.
+
+    /home/circleci/project/sklearn/metrics/cluster/_supervised.py:50: UserWarning:
+
+    The number of unique classes is greater than 50% of the number of samples.
+
+    /home/circleci/project/sklearn/metrics/cluster/_supervised.py:49: UserWarning:
+
+    The number of unique classes is greater than 50% of the number of samples.
+
+    /home/circleci/project/sklearn/metrics/cluster/_supervised.py:50: UserWarning:
+
+    The number of unique classes is greater than 50% of the number of samples.
+
+    /home/circleci/project/sklearn/metrics/cluster/_supervised.py:49: UserWarning:
+
+    The number of unique classes is greater than 50% of the number of samples.
+
+    /home/circleci/project/sklearn/metrics/cluster/_supervised.py:50: UserWarning:
+
+    The number of unique classes is greater than 50% of the number of samples.
+
+    /home/circleci/project/sklearn/metrics/cluster/_supervised.py:49: UserWarning:
+
+    The number of unique classes is greater than 50% of the number of samples.
+
+    /home/circleci/project/sklearn/metrics/cluster/_supervised.py:50: UserWarning:
+
+    The number of unique classes is greater than 50% of the number of samples.
+
+    /home/circleci/project/sklearn/metrics/cluster/_supervised.py:49: UserWarning:
+
+    The number of unique classes is greater than 50% of the number of samples.
+
+    /home/circleci/project/sklearn/metrics/cluster/_supervised.py:50: UserWarning:
+
+    The number of unique classes is greater than 50% of the number of samples.
+
+    /home/circleci/project/sklearn/metrics/cluster/_supervised.py:49: UserWarning:
+
+    The number of unique classes is greater than 50% of the number of samples.
+
+    /home/circleci/project/sklearn/metrics/cluster/_supervised.py:50: UserWarning:
+
+    The number of unique classes is greater than 50% of the number of samples.
+
+    /home/circleci/project/sklearn/metrics/cluster/_supervised.py:49: UserWarning:
+
+    The number of unique classes is greater than 50% of the number of samples.
+
+    /home/circleci/project/sklearn/metrics/cluster/_supervised.py:50: UserWarning:
+
+    The number of unique classes is greater than 50% of the number of samples.
+
+    /home/circleci/project/sklearn/metrics/cluster/_supervised.py:49: UserWarning:
+
+    The number of unique classes is greater than 50% of the number of samples.
+
+    /home/circleci/project/sklearn/metrics/cluster/_supervised.py:50: UserWarning:
+
+    The number of unique classes is greater than 50% of the number of samples.
+
+    /home/circleci/project/sklearn/metrics/cluster/_supervised.py:49: UserWarning:
+
+    The number of unique classes is greater than 50% of the number of samples.
+
+    /home/circleci/project/sklearn/metrics/cluster/_supervised.py:50: UserWarning:
+
+    The number of unique classes is greater than 50% of the number of samples.
+
+    /home/circleci/project/sklearn/metrics/cluster/_supervised.py:49: UserWarning:
+
+    The number of unique classes is greater than 50% of the number of samples.
+
+    /home/circleci/project/sklearn/metrics/cluster/_supervised.py:50: UserWarning:
+
+    The number of unique classes is greater than 50% of the number of samples.
+
+    /home/circleci/project/sklearn/metrics/cluster/_supervised.py:49: UserWarning:
+
+    The number of unique classes is greater than 50% of the number of samples.
+
+    /home/circleci/project/sklearn/metrics/cluster/_supervised.py:50: UserWarning:
+
+    The number of unique classes is greater than 50% of the number of samples.
+
+    /home/circleci/project/sklearn/metrics/cluster/_supervised.py:49: UserWarning:
+
+    The number of unique classes is greater than 50% of the number of samples.
+
+    /home/circleci/project/sklearn/metrics/cluster/_supervised.py:50: UserWarning:
+
+    The number of unique classes is greater than 50% of the number of samples.
+
+    /home/circleci/project/sklearn/metrics/cluster/_supervised.py:49: UserWarning:
+
+    The number of unique classes is greater than 50% of the number of samples.
+
+    /home/circleci/project/sklearn/metrics/cluster/_supervised.py:50: UserWarning:
+
+    The number of unique classes is greater than 50% of the number of samples.
+
+    /home/circleci/project/sklearn/metrics/cluster/_supervised.py:49: UserWarning:
+
+    The number of unique classes is greater than 50% of the number of samples.
+
+    /home/circleci/project/sklearn/metrics/cluster/_supervised.py:50: UserWarning:
+
+    The number of unique classes is greater than 50% of the number of samples.
+
+    /home/circleci/project/sklearn/metrics/cluster/_supervised.py:49: UserWarning:
+
+    The number of unique classes is greater than 50% of the number of samples.
+
+    /home/circleci/project/sklearn/metrics/cluster/_supervised.py:50: UserWarning:
+
+    The number of unique classes is greater than 50% of the number of samples.
+
+
 
 
 
@@ -350,7 +1247,7 @@ of clusters.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 1.019 seconds)
+   **Total running time of the script:** (0 minutes 1.060 seconds)
 
 
 .. _sphx_glr_download_auto_examples_cluster_plot_adjusted_for_chance_measures.py:
diff --git a/dev/_sources/auto_examples/cluster/plot_affinity_propagation.rst.txt b/dev/_sources/auto_examples/cluster/plot_affinity_propagation.rst.txt
index d8937a15463fa..643eb90308056 100644
--- a/dev/_sources/auto_examples/cluster/plot_affinity_propagation.rst.txt
+++ b/dev/_sources/auto_examples/cluster/plot_affinity_propagation.rst.txt
@@ -164,7 +164,7 @@ Plot result
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.290 seconds)
+   **Total running time of the script:** (0 minutes 0.298 seconds)
 
 
 .. _sphx_glr_download_auto_examples_cluster_plot_affinity_propagation.py:
diff --git a/dev/_sources/auto_examples/cluster/plot_agglomerative_clustering.rst.txt b/dev/_sources/auto_examples/cluster/plot_agglomerative_clustering.rst.txt
index d388382b656f7..ae13c86821a2c 100644
--- a/dev/_sources/auto_examples/cluster/plot_agglomerative_clustering.rst.txt
+++ b/dev/_sources/auto_examples/cluster/plot_agglomerative_clustering.rst.txt
@@ -58,21 +58,21 @@ which is well known to have this percolation instability.
     *
 
       .. image-sg:: /auto_examples/cluster/images/sphx_glr_plot_agglomerative_clustering_002.png
-         :alt: n_cluster=3, connectivity=False, linkage=average (time 0.04s), linkage=complete (time 0.04s), linkage=ward (time 0.04s), linkage=single (time 0.01s)
+         :alt: n_cluster=3, connectivity=False, linkage=average (time 0.04s), linkage=complete (time 0.03s), linkage=ward (time 0.04s), linkage=single (time 0.01s)
          :srcset: /auto_examples/cluster/images/sphx_glr_plot_agglomerative_clustering_002.png
          :class: sphx-glr-multi-img
 
     *
 
       .. image-sg:: /auto_examples/cluster/images/sphx_glr_plot_agglomerative_clustering_003.png
-         :alt: n_cluster=30, connectivity=True, linkage=average (time 0.11s), linkage=complete (time 0.10s), linkage=ward (time 0.14s), linkage=single (time 0.02s)
+         :alt: n_cluster=30, connectivity=True, linkage=average (time 0.10s), linkage=complete (time 0.10s), linkage=ward (time 0.13s), linkage=single (time 0.02s)
          :srcset: /auto_examples/cluster/images/sphx_glr_plot_agglomerative_clustering_003.png
          :class: sphx-glr-multi-img
 
     *
 
       .. image-sg:: /auto_examples/cluster/images/sphx_glr_plot_agglomerative_clustering_004.png
-         :alt: n_cluster=3, connectivity=True, linkage=average (time 0.10s), linkage=complete (time 0.11s), linkage=ward (time 0.15s), linkage=single (time 0.02s)
+         :alt: n_cluster=3, connectivity=True, linkage=average (time 0.10s), linkage=complete (time 0.10s), linkage=ward (time 0.13s), linkage=single (time 0.02s)
          :srcset: /auto_examples/cluster/images/sphx_glr_plot_agglomerative_clustering_004.png
          :class: sphx-glr-multi-img
 
@@ -145,7 +145,7 @@ which is well known to have this percolation instability.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 1.891 seconds)
+   **Total running time of the script:** (0 minutes 1.882 seconds)
 
 
 .. _sphx_glr_download_auto_examples_cluster_plot_agglomerative_clustering.py:
diff --git a/dev/_sources/auto_examples/cluster/plot_agglomerative_clustering_metrics.rst.txt b/dev/_sources/auto_examples/cluster/plot_agglomerative_clustering_metrics.rst.txt
index 2ecd4c6e04285..a9e2ed4782e58 100644
--- a/dev/_sources/auto_examples/cluster/plot_agglomerative_clustering_metrics.rst.txt
+++ b/dev/_sources/auto_examples/cluster/plot_agglomerative_clustering_metrics.rst.txt
@@ -228,7 +228,7 @@ thus the clustering puts them in the same cluster.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 1.087 seconds)
+   **Total running time of the script:** (0 minutes 1.210 seconds)
 
 
 .. _sphx_glr_download_auto_examples_cluster_plot_agglomerative_clustering_metrics.py:
diff --git a/dev/_sources/auto_examples/cluster/plot_agglomerative_dendrogram.rst.txt b/dev/_sources/auto_examples/cluster/plot_agglomerative_dendrogram.rst.txt
index 91ac56e4a9bc3..dcae3f88a375e 100644
--- a/dev/_sources/auto_examples/cluster/plot_agglomerative_dendrogram.rst.txt
+++ b/dev/_sources/auto_examples/cluster/plot_agglomerative_dendrogram.rst.txt
@@ -87,7 +87,7 @@ using AgglomerativeClustering and the dendrogram method available in scipy.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.088 seconds)
+   **Total running time of the script:** (0 minutes 0.099 seconds)
 
 
 .. _sphx_glr_download_auto_examples_cluster_plot_agglomerative_dendrogram.py:
diff --git a/dev/_sources/auto_examples/cluster/plot_birch_vs_minibatchkmeans.rst.txt b/dev/_sources/auto_examples/cluster/plot_birch_vs_minibatchkmeans.rst.txt
index b6429c7eff130..f5dfe6d08a649 100644
--- a/dev/_sources/auto_examples/cluster/plot_birch_vs_minibatchkmeans.rst.txt
+++ b/dev/_sources/auto_examples/cluster/plot_birch_vs_minibatchkmeans.rst.txt
@@ -54,7 +54,7 @@ step before the final (global) clustering step that further reduces these
 
     BIRCH without global clustering as the final step took 0.46 seconds
     n_clusters : 158
-    BIRCH with global clustering as the final step took 0.46 seconds
+    BIRCH with global clustering as the final step took 0.45 seconds
     n_clusters : 100
     Time taken to run MiniBatchKMeans 0.21 seconds
 
@@ -157,7 +157,7 @@ step before the final (global) clustering step that further reduces these
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 3.346 seconds)
+   **Total running time of the script:** (0 minutes 3.427 seconds)
 
 
 .. _sphx_glr_download_auto_examples_cluster_plot_birch_vs_minibatchkmeans.py:
diff --git a/dev/_sources/auto_examples/cluster/plot_bisect_kmeans.rst.txt b/dev/_sources/auto_examples/cluster/plot_bisect_kmeans.rst.txt
index 60e346a6d7555..cfbf2eb3cd64f 100644
--- a/dev/_sources/auto_examples/cluster/plot_bisect_kmeans.rst.txt
+++ b/dev/_sources/auto_examples/cluster/plot_bisect_kmeans.rst.txt
@@ -103,7 +103,7 @@ present for regular K-Means.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 1.045 seconds)
+   **Total running time of the script:** (0 minutes 1.114 seconds)
 
 
 .. _sphx_glr_download_auto_examples_cluster_plot_bisect_kmeans.py:
diff --git a/dev/_sources/auto_examples/cluster/plot_cluster_comparison.rst.txt b/dev/_sources/auto_examples/cluster/plot_cluster_comparison.rst.txt
index 0461ef520aa87..bb950745a7764 100644
--- a/dev/_sources/auto_examples/cluster/plot_cluster_comparison.rst.txt
+++ b/dev/_sources/auto_examples/cluster/plot_cluster_comparison.rst.txt
@@ -322,7 +322,7 @@ dimensional data.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 5.843 seconds)
+   **Total running time of the script:** (0 minutes 5.825 seconds)
 
 
 .. _sphx_glr_download_auto_examples_cluster_plot_cluster_comparison.py:
diff --git a/dev/_sources/auto_examples/cluster/plot_coin_segmentation.rst.txt b/dev/_sources/auto_examples/cluster/plot_coin_segmentation.rst.txt
index 1209506fe1d05..278c7ef316e09 100644
--- a/dev/_sources/auto_examples/cluster/plot_coin_segmentation.rst.txt
+++ b/dev/_sources/auto_examples/cluster/plot_coin_segmentation.rst.txt
@@ -147,21 +147,21 @@ Compute and visualize the resulting regions
     *
 
       .. image-sg:: /auto_examples/cluster/images/sphx_glr_plot_coin_segmentation_001.png
-         :alt: Spectral clustering: kmeans, 1.58s
+         :alt: Spectral clustering: kmeans, 1.99s
          :srcset: /auto_examples/cluster/images/sphx_glr_plot_coin_segmentation_001.png
          :class: sphx-glr-multi-img
 
     *
 
       .. image-sg:: /auto_examples/cluster/images/sphx_glr_plot_coin_segmentation_002.png
-         :alt: Spectral clustering: discretize, 1.46s
+         :alt: Spectral clustering: discretize, 1.89s
          :srcset: /auto_examples/cluster/images/sphx_glr_plot_coin_segmentation_002.png
          :class: sphx-glr-multi-img
 
     *
 
       .. image-sg:: /auto_examples/cluster/images/sphx_glr_plot_coin_segmentation_003.png
-         :alt: Spectral clustering: cluster_qr, 1.45s
+         :alt: Spectral clustering: cluster_qr, 1.81s
          :srcset: /auto_examples/cluster/images/sphx_glr_plot_coin_segmentation_003.png
          :class: sphx-glr-multi-img
 
@@ -170,9 +170,9 @@ Compute and visualize the resulting regions
 
  .. code-block:: none
 
-    Spectral clustering: kmeans, 1.58s
-    Spectral clustering: discretize, 1.46s
-    Spectral clustering: cluster_qr, 1.45s
+    Spectral clustering: kmeans, 1.99s
+    Spectral clustering: discretize, 1.89s
+    Spectral clustering: cluster_qr, 1.81s
 
 
 
@@ -180,7 +180,7 @@ Compute and visualize the resulting regions
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 4.852 seconds)
+   **Total running time of the script:** (0 minutes 6.140 seconds)
 
 
 .. _sphx_glr_download_auto_examples_cluster_plot_coin_segmentation.py:
diff --git a/dev/_sources/auto_examples/cluster/plot_coin_ward_segmentation.rst.txt b/dev/_sources/auto_examples/cluster/plot_coin_ward_segmentation.rst.txt
index 4378bcd6d0bf6..869c472c76429 100644
--- a/dev/_sources/auto_examples/cluster/plot_coin_ward_segmentation.rst.txt
+++ b/dev/_sources/auto_examples/cluster/plot_coin_ward_segmentation.rst.txt
@@ -152,7 +152,7 @@ Compute clustering
  .. code-block:: none
 
     Compute structured hierarchical clustering...
-    Elapsed time: 0.159s
+    Elapsed time: 0.157s
     Number of pixels: 4697
     Number of clusters: 27
 
@@ -201,7 +201,7 @@ is finding a large in the background.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.327 seconds)
+   **Total running time of the script:** (0 minutes 0.326 seconds)
 
 
 .. _sphx_glr_download_auto_examples_cluster_plot_coin_ward_segmentation.py:
diff --git a/dev/_sources/auto_examples/cluster/plot_dbscan.rst.txt b/dev/_sources/auto_examples/cluster/plot_dbscan.rst.txt
index a879bca0229da..5d4296b624999 100644
--- a/dev/_sources/auto_examples/cluster/plot_dbscan.rst.txt
+++ b/dev/_sources/auto_examples/cluster/plot_dbscan.rst.txt
@@ -255,7 +255,7 @@ black.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.164 seconds)
+   **Total running time of the script:** (0 minutes 0.166 seconds)
 
 
 .. _sphx_glr_download_auto_examples_cluster_plot_dbscan.py:
diff --git a/dev/_sources/auto_examples/cluster/plot_dict_face_patches.rst.txt b/dev/_sources/auto_examples/cluster/plot_dict_face_patches.rst.txt
index 19fcf99ef1a23..2d8e2a2e64889 100644
--- a/dev/_sources/auto_examples/cluster/plot_dict_face_patches.rst.txt
+++ b/dev/_sources/auto_examples/cluster/plot_dict_face_patches.rst.txt
@@ -157,7 +157,7 @@ Learn the dictionary of images
     Partial fit of 2200 out of 2400
     Partial fit of 2300 out of 2400
     Partial fit of 2400 out of 2400
-    done in 1.17s.
+    done in 1.37s.
 
 
 
@@ -193,7 +193,7 @@ Plot the results
 
 
 .. image-sg:: /auto_examples/cluster/images/sphx_glr_plot_dict_face_patches_001.png
-   :alt: Patches of faces Train time 1.2s on 3200 patches
+   :alt: Patches of faces Train time 1.4s on 3200 patches
    :srcset: /auto_examples/cluster/images/sphx_glr_plot_dict_face_patches_001.png
    :class: sphx-glr-single-img
 
@@ -204,7 +204,7 @@ Plot the results
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 2.139 seconds)
+   **Total running time of the script:** (0 minutes 2.558 seconds)
 
 
 .. _sphx_glr_download_auto_examples_cluster_plot_dict_face_patches.py:
diff --git a/dev/_sources/auto_examples/cluster/plot_digits_agglomeration.rst.txt b/dev/_sources/auto_examples/cluster/plot_digits_agglomeration.rst.txt
index 1b34cc6bc3655..b11cee2dcd7a1 100644
--- a/dev/_sources/auto_examples/cluster/plot_digits_agglomeration.rst.txt
+++ b/dev/_sources/auto_examples/cluster/plot_digits_agglomeration.rst.txt
@@ -93,7 +93,7 @@ feature agglomeration.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.119 seconds)
+   **Total running time of the script:** (0 minutes 0.129 seconds)
 
 
 .. _sphx_glr_download_auto_examples_cluster_plot_digits_agglomeration.py:
diff --git a/dev/_sources/auto_examples/cluster/plot_digits_linkage.rst.txt b/dev/_sources/auto_examples/cluster/plot_digits_linkage.rst.txt
index fa5a79fd3d088..f6c694ca87d83 100644
--- a/dev/_sources/auto_examples/cluster/plot_digits_linkage.rst.txt
+++ b/dev/_sources/auto_examples/cluster/plot_digits_linkage.rst.txt
@@ -86,10 +86,10 @@ random resampling of the dataset.
 
     Computing embedding
     Done.
-    ward :  0.05s
+    ward :  0.06s
     average :       0.05s
-    complete :      0.05s
-    single :        0.02s
+    complete :      0.06s
+    single :        0.03s
 
 
 
@@ -164,7 +164,7 @@ random resampling of the dataset.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 1.454 seconds)
+   **Total running time of the script:** (0 minutes 1.720 seconds)
 
 
 .. _sphx_glr_download_auto_examples_cluster_plot_digits_linkage.py:
diff --git a/dev/_sources/auto_examples/cluster/plot_face_compress.rst.txt b/dev/_sources/auto_examples/cluster/plot_face_compress.rst.txt
index 6df84f1f26f70..b1f22a107f43b 100644
--- a/dev/_sources/auto_examples/cluster/plot_face_compress.rst.txt
+++ b/dev/_sources/auto_examples/cluster/plot_face_compress.rst.txt
@@ -404,7 +404,7 @@ a 64-bit float representation.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 2.104 seconds)
+   **Total running time of the script:** (0 minutes 2.545 seconds)
 
 
 .. _sphx_glr_download_auto_examples_cluster_plot_face_compress.py:
diff --git a/dev/_sources/auto_examples/cluster/plot_feature_agglomeration_vs_univariate_selection.rst.txt b/dev/_sources/auto_examples/cluster/plot_feature_agglomeration_vs_univariate_selection.rst.txt
index f73f4909bd5a2..4920784a47d80 100644
--- a/dev/_sources/auto_examples/cluster/plot_feature_agglomeration_vs_univariate_selection.rst.txt
+++ b/dev/_sources/auto_examples/cluster/plot_feature_agglomeration_vs_univariate_selection.rst.txt
@@ -198,7 +198,7 @@ Ward agglomeration followed by BayesianRidge
            ...,
            [-0.849835, ..., -1.091621]], shape=(1600, 100)), connectivity=<COOrdinate sparse matrix of dtype 'int64'
             with 7840 stored elements and shape (1600, 1600)>, n_clusters=None, return_distance=False)
-    ________________________________________________________ward_tree - 0.0s, 0.0min
+    ________________________________________________________ward_tree - 0.1s, 0.0min
     ________________________________________________________________________________
     [Memory] Calling sklearn.cluster._agglomerative.ward_tree...
     ward_tree(array([[ 0.905206, ..., -0.675318],
@@ -314,7 +314,7 @@ Attempt to remove the temporary cachedir, but don't worry if it fails
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.474 seconds)
+   **Total running time of the script:** (0 minutes 0.540 seconds)
 
 
 .. _sphx_glr_download_auto_examples_cluster_plot_feature_agglomeration_vs_univariate_selection.py:
diff --git a/dev/_sources/auto_examples/cluster/plot_hdbscan.rst.txt b/dev/_sources/auto_examples/cluster/plot_hdbscan.rst.txt
index defec6a3201d2..15a61f07d8009 100644
--- a/dev/_sources/auto_examples/cluster/plot_hdbscan.rst.txt
+++ b/dev/_sources/auto_examples/cluster/plot_hdbscan.rst.txt
@@ -72,7 +72,7 @@ We first define a couple utility functions for convenience.
                 # Black used for noise.
                 col = [0, 0, 0, 1]
 
-            class_index = np.where(labels == k)[0]
+            class_index = (labels == k).nonzero()[0]
             for ci in class_index:
                 ax.plot(
                     X[ci, 0],
@@ -470,7 +470,7 @@ and the minimum spanning tree. All we need to do is specify the `cut_distance`
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 14.100 seconds)
+   **Total running time of the script:** (0 minutes 15.012 seconds)
 
 
 .. _sphx_glr_download_auto_examples_cluster_plot_hdbscan.py:
diff --git a/dev/_sources/auto_examples/cluster/plot_inductive_clustering.rst.txt b/dev/_sources/auto_examples/cluster/plot_inductive_clustering.rst.txt
index e5149ee63e18c..b18debfc09d7d 100644
--- a/dev/_sources/auto_examples/cluster/plot_inductive_clustering.rst.txt
+++ b/dev/_sources/auto_examples/cluster/plot_inductive_clustering.rst.txt
@@ -164,7 +164,7 @@ extends clustering by inducing a classifier from the cluster labels.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 1.968 seconds)
+   **Total running time of the script:** (0 minutes 2.139 seconds)
 
 
 .. _sphx_glr_download_auto_examples_cluster_plot_inductive_clustering.py:
diff --git a/dev/_sources/auto_examples/cluster/plot_kmeans_assumptions.rst.txt b/dev/_sources/auto_examples/cluster/plot_kmeans_assumptions.rst.txt
index fd2e4eceafe73..8941cb3832f5f 100644
--- a/dev/_sources/auto_examples/cluster/plot_kmeans_assumptions.rst.txt
+++ b/dev/_sources/auto_examples/cluster/plot_kmeans_assumptions.rst.txt
@@ -313,7 +313,7 @@ to restart it several times to avoid convergence to a local minimum.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 1.111 seconds)
+   **Total running time of the script:** (0 minutes 1.141 seconds)
 
 
 .. _sphx_glr_download_auto_examples_cluster_plot_kmeans_assumptions.py:
diff --git a/dev/_sources/auto_examples/cluster/plot_kmeans_digits.rst.txt b/dev/_sources/auto_examples/cluster/plot_kmeans_digits.rst.txt
index b993544b4010b..a489cbf512641 100644
--- a/dev/_sources/auto_examples/cluster/plot_kmeans_digits.rst.txt
+++ b/dev/_sources/auto_examples/cluster/plot_kmeans_digits.rst.txt
@@ -223,8 +223,8 @@ We will compare three approaches:
     __________________________________________________________________________________
     init            time    inertia homo    compl   v-meas  ARI     AMI     silhouette
     k-means++       0.034s  69545   0.598   0.645   0.621   0.469   0.617   0.158
-    random          0.036s  69735   0.681   0.723   0.701   0.574   0.698   0.173
-    PCA-based       0.012s  69513   0.600   0.647   0.622   0.468   0.618   0.162
+    random          0.037s  69735   0.681   0.723   0.701   0.574   0.698   0.173
+    PCA-based       0.013s  69513   0.600   0.647   0.622   0.468   0.618   0.162
     __________________________________________________________________________________
 
 
@@ -310,7 +310,7 @@ we can use :class:`~sklearn.decomposition.PCA` to project into a
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 2.665 seconds)
+   **Total running time of the script:** (0 minutes 1.200 seconds)
 
 
 .. _sphx_glr_download_auto_examples_cluster_plot_kmeans_digits.py:
diff --git a/dev/_sources/auto_examples/cluster/plot_kmeans_plusplus.rst.txt b/dev/_sources/auto_examples/cluster/plot_kmeans_plusplus.rst.txt
index 47f0cf1045cf6..937da0bf77e65 100644
--- a/dev/_sources/auto_examples/cluster/plot_kmeans_plusplus.rst.txt
+++ b/dev/_sources/auto_examples/cluster/plot_kmeans_plusplus.rst.txt
@@ -80,7 +80,7 @@ K-Means++ is used as the default initialization for :ref:`k_means`.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.057 seconds)
+   **Total running time of the script:** (0 minutes 0.059 seconds)
 
 
 .. _sphx_glr_download_auto_examples_cluster_plot_kmeans_plusplus.py:
diff --git a/dev/_sources/auto_examples/cluster/plot_kmeans_silhouette_analysis.rst.txt b/dev/_sources/auto_examples/cluster/plot_kmeans_silhouette_analysis.rst.txt
index ba247f8fecea3..d9ae0e5eccff9 100644
--- a/dev/_sources/auto_examples/cluster/plot_kmeans_silhouette_analysis.rst.txt
+++ b/dev/_sources/auto_examples/cluster/plot_kmeans_silhouette_analysis.rst.txt
@@ -244,7 +244,7 @@ verified from the labelled scatter plot on the right.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 1.063 seconds)
+   **Total running time of the script:** (0 minutes 1.229 seconds)
 
 
 .. _sphx_glr_download_auto_examples_cluster_plot_kmeans_silhouette_analysis.py:
diff --git a/dev/_sources/auto_examples/cluster/plot_kmeans_stability_low_dim_dense.rst.txt b/dev/_sources/auto_examples/cluster/plot_kmeans_stability_low_dim_dense.rst.txt
index 54fdc5de95dd5..464cd882d3bd8 100644
--- a/dev/_sources/auto_examples/cluster/plot_kmeans_stability_low_dim_dense.rst.txt
+++ b/dev/_sources/auto_examples/cluster/plot_kmeans_stability_low_dim_dense.rst.txt
@@ -192,7 +192,7 @@ clusters widely spaced.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 1.282 seconds)
+   **Total running time of the script:** (0 minutes 1.275 seconds)
 
 
 .. _sphx_glr_download_auto_examples_cluster_plot_kmeans_stability_low_dim_dense.py:
diff --git a/dev/_sources/auto_examples/cluster/plot_linkage_comparison.rst.txt b/dev/_sources/auto_examples/cluster/plot_linkage_comparison.rst.txt
index 7e4381e53e683..0ef203ea44e0e 100644
--- a/dev/_sources/auto_examples/cluster/plot_linkage_comparison.rst.txt
+++ b/dev/_sources/auto_examples/cluster/plot_linkage_comparison.rst.txt
@@ -239,7 +239,7 @@ Run the clustering and plot
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 1.837 seconds)
+   **Total running time of the script:** (0 minutes 1.869 seconds)
 
 
 .. _sphx_glr_download_auto_examples_cluster_plot_linkage_comparison.py:
diff --git a/dev/_sources/auto_examples/cluster/plot_mean_shift.rst.txt b/dev/_sources/auto_examples/cluster/plot_mean_shift.rst.txt
index 532c068a1fd9a..46c8c9529b7fc 100644
--- a/dev/_sources/auto_examples/cluster/plot_mean_shift.rst.txt
+++ b/dev/_sources/auto_examples/cluster/plot_mean_shift.rst.txt
@@ -149,7 +149,7 @@ Plot result
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.425 seconds)
+   **Total running time of the script:** (0 minutes 0.410 seconds)
 
 
 .. _sphx_glr_download_auto_examples_cluster_plot_mean_shift.py:
diff --git a/dev/_sources/auto_examples/cluster/plot_mini_batch_kmeans.rst.txt b/dev/_sources/auto_examples/cluster/plot_mini_batch_kmeans.rst.txt
index 361d9d6b5fb3b..663eb22718e2e 100644
--- a/dev/_sources/auto_examples/cluster/plot_mini_batch_kmeans.rst.txt
+++ b/dev/_sources/auto_examples/cluster/plot_mini_batch_kmeans.rst.txt
@@ -247,7 +247,7 @@ Plotting the results
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.170 seconds)
+   **Total running time of the script:** (0 minutes 0.167 seconds)
 
 
 .. _sphx_glr_download_auto_examples_cluster_plot_mini_batch_kmeans.py:
diff --git a/dev/_sources/auto_examples/cluster/plot_optics.rst.txt b/dev/_sources/auto_examples/cluster/plot_optics.rst.txt
index 155244ba65114..2f7c89a71c13a 100644
--- a/dev/_sources/auto_examples/cluster/plot_optics.rst.txt
+++ b/dev/_sources/auto_examples/cluster/plot_optics.rst.txt
@@ -143,7 +143,7 @@ thresholds in DBSCAN.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 1.570 seconds)
+   **Total running time of the script:** (0 minutes 1.546 seconds)
 
 
 .. _sphx_glr_download_auto_examples_cluster_plot_optics.py:
diff --git a/dev/_sources/auto_examples/cluster/plot_segmentation_toy.rst.txt b/dev/_sources/auto_examples/cluster/plot_segmentation_toy.rst.txt
index 638dd1637076f..4c1b70e023355 100644
--- a/dev/_sources/auto_examples/cluster/plot_segmentation_toy.rst.txt
+++ b/dev/_sources/auto_examples/cluster/plot_segmentation_toy.rst.txt
@@ -236,7 +236,7 @@ circles as the region sizes are easier to balance in this case.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.420 seconds)
+   **Total running time of the script:** (0 minutes 0.496 seconds)
 
 
 .. _sphx_glr_download_auto_examples_cluster_plot_segmentation_toy.py:
diff --git a/dev/_sources/auto_examples/cluster/plot_ward_structured_vs_unstructured.rst.txt b/dev/_sources/auto_examples/cluster/plot_ward_structured_vs_unstructured.rst.txt
index 2a5184b1877b4..439fedaae2429 100644
--- a/dev/_sources/auto_examples/cluster/plot_ward_structured_vs_unstructured.rst.txt
+++ b/dev/_sources/auto_examples/cluster/plot_ward_structured_vs_unstructured.rst.txt
@@ -262,7 +262,7 @@ Plotting the structured hierarchical clusters.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.391 seconds)
+   **Total running time of the script:** (0 minutes 0.397 seconds)
 
 
 .. _sphx_glr_download_auto_examples_cluster_plot_ward_structured_vs_unstructured.py:
diff --git a/dev/_sources/auto_examples/compose/plot_column_transformer.rst.txt b/dev/_sources/auto_examples/compose/plot_column_transformer.rst.txt
index 9bbda1c80cf30..a8c44c6c94ea5 100644
--- a/dev/_sources/auto_examples/compose/plot_column_transformer.rst.txt
+++ b/dev/_sources/auto_examples/compose/plot_column_transformer.rst.txt
@@ -338,7 +338,7 @@ topics for ``X_test``. Performance metrics of our pipeline are then printed.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 2.508 seconds)
+   **Total running time of the script:** (0 minutes 2.661 seconds)
 
 
 .. _sphx_glr_download_auto_examples_compose_plot_column_transformer.py:
diff --git a/dev/_sources/auto_examples/compose/plot_column_transformer_mixed_types.rst.txt b/dev/_sources/auto_examples/compose/plot_column_transformer_mixed_types.rst.txt
index 3707cc0da553f..5a1425774c7f3 100644
--- a/dev/_sources/auto_examples/compose/plot_column_transformer_mixed_types.rst.txt
+++ b/dev/_sources/auto_examples/compose/plot_column_transformer_mixed_types.rst.txt
@@ -206,7 +206,7 @@ representation of the estimator is displayed:
 .. raw:: html
 
     <div class="output_subarea output_html rendered_html output_result">
-    <style>#sk-container-id-66 {
+    <style>#sk-container-id-67 {
       /* Definition of color scheme common for light and dark mode */
       --sklearn-color-text: #000;
       --sklearn-color-text-muted: #666;
@@ -237,15 +237,15 @@ representation of the estimator is displayed:
       }
     }
 
-    #sk-container-id-66 {
+    #sk-container-id-67 {
       color: var(--sklearn-color-text);
     }
 
-    #sk-container-id-66 pre {
+    #sk-container-id-67 pre {
       padding: 0;
     }
 
-    #sk-container-id-66 input.sk-hidden--visually {
+    #sk-container-id-67 input.sk-hidden--visually {
       border: 0;
       clip: rect(1px 1px 1px 1px);
       clip: rect(1px, 1px, 1px, 1px);
@@ -257,7 +257,7 @@ representation of the estimator is displayed:
       width: 1px;
     }
 
-    #sk-container-id-66 div.sk-dashed-wrapped {
+    #sk-container-id-67 div.sk-dashed-wrapped {
       border: 1px dashed var(--sklearn-color-line);
       margin: 0 0.4em 0.5em 0.4em;
       box-sizing: border-box;
@@ -265,7 +265,7 @@ representation of the estimator is displayed:
       background-color: var(--sklearn-color-background);
     }
 
-    #sk-container-id-66 div.sk-container {
+    #sk-container-id-67 div.sk-container {
       /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
          but bootstrap.min.css set `[hidden] { display: none !important; }`
          so we also need the `!important` here to be able to override the
@@ -275,7 +275,7 @@ representation of the estimator is displayed:
       position: relative;
     }
 
-    #sk-container-id-66 div.sk-text-repr-fallback {
+    #sk-container-id-67 div.sk-text-repr-fallback {
       display: none;
     }
 
@@ -291,14 +291,14 @@ representation of the estimator is displayed:
 
     /* Parallel-specific style estimator block */
 
-    #sk-container-id-66 div.sk-parallel-item::after {
+    #sk-container-id-67 div.sk-parallel-item::after {
       content: "";
       width: 100%;
       border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
       flex-grow: 1;
     }
 
-    #sk-container-id-66 div.sk-parallel {
+    #sk-container-id-67 div.sk-parallel {
       display: flex;
       align-items: stretch;
       justify-content: center;
@@ -306,28 +306,28 @@ representation of the estimator is displayed:
       position: relative;
     }
 
-    #sk-container-id-66 div.sk-parallel-item {
+    #sk-container-id-67 div.sk-parallel-item {
       display: flex;
       flex-direction: column;
     }
 
-    #sk-container-id-66 div.sk-parallel-item:first-child::after {
+    #sk-container-id-67 div.sk-parallel-item:first-child::after {
       align-self: flex-end;
       width: 50%;
     }
 
-    #sk-container-id-66 div.sk-parallel-item:last-child::after {
+    #sk-container-id-67 div.sk-parallel-item:last-child::after {
       align-self: flex-start;
       width: 50%;
     }
 
-    #sk-container-id-66 div.sk-parallel-item:only-child::after {
+    #sk-container-id-67 div.sk-parallel-item:only-child::after {
       width: 0;
     }
 
     /* Serial-specific style estimator block */
 
-    #sk-container-id-66 div.sk-serial {
+    #sk-container-id-67 div.sk-serial {
       display: flex;
       flex-direction: column;
       align-items: center;
@@ -345,14 +345,14 @@ representation of the estimator is displayed:
 
     /* Pipeline and ColumnTransformer style (default) */
 
-    #sk-container-id-66 div.sk-toggleable {
+    #sk-container-id-67 div.sk-toggleable {
       /* Default theme specific background. It is overwritten whether we have a
       specific estimator or a Pipeline/ColumnTransformer */
       background-color: var(--sklearn-color-background);
     }
 
     /* Toggleable label */
-    #sk-container-id-66 label.sk-toggleable__label {
+    #sk-container-id-67 label.sk-toggleable__label {
       cursor: pointer;
       display: flex;
       width: 100%;
@@ -365,13 +365,13 @@ representation of the estimator is displayed:
       gap: 0.5em;
     }
 
-    #sk-container-id-66 label.sk-toggleable__label .caption {
+    #sk-container-id-67 label.sk-toggleable__label .caption {
       font-size: 0.6rem;
       font-weight: lighter;
       color: var(--sklearn-color-text-muted);
     }
 
-    #sk-container-id-66 label.sk-toggleable__label-arrow:before {
+    #sk-container-id-67 label.sk-toggleable__label-arrow:before {
       /* Arrow on the left of the label */
       content: "▸";
       float: left;
@@ -379,13 +379,13 @@ representation of the estimator is displayed:
       color: var(--sklearn-color-icon);
     }
 
-    #sk-container-id-66 label.sk-toggleable__label-arrow:hover:before {
+    #sk-container-id-67 label.sk-toggleable__label-arrow:hover:before {
       color: var(--sklearn-color-text);
     }
 
     /* Toggleable content - dropdown */
 
-    #sk-container-id-66 div.sk-toggleable__content {
+    #sk-container-id-67 div.sk-toggleable__content {
       max-height: 0;
       max-width: 0;
       overflow: hidden;
@@ -394,12 +394,12 @@ representation of the estimator is displayed:
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-66 div.sk-toggleable__content.fitted {
+    #sk-container-id-67 div.sk-toggleable__content.fitted {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
-    #sk-container-id-66 div.sk-toggleable__content pre {
+    #sk-container-id-67 div.sk-toggleable__content pre {
       margin: 0.2em;
       border-radius: 0.25em;
       color: var(--sklearn-color-text);
@@ -407,79 +407,79 @@ representation of the estimator is displayed:
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-66 div.sk-toggleable__content.fitted pre {
+    #sk-container-id-67 div.sk-toggleable__content.fitted pre {
       /* unfitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
-    #sk-container-id-66 input.sk-toggleable__control:checked~div.sk-toggleable__content {
+    #sk-container-id-67 input.sk-toggleable__control:checked~div.sk-toggleable__content {
       /* Expand drop-down */
       max-height: 200px;
       max-width: 100%;
       overflow: auto;
     }
 
-    #sk-container-id-66 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
+    #sk-container-id-67 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
       content: "▾";
     }
 
     /* Pipeline/ColumnTransformer-specific style */
 
-    #sk-container-id-66 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-67 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-66 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-67 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
     /* Estimator-specific style */
 
     /* Colorize estimator box */
-    #sk-container-id-66 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-67 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-66 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-67 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
-    #sk-container-id-66 div.sk-label label.sk-toggleable__label,
-    #sk-container-id-66 div.sk-label label {
+    #sk-container-id-67 div.sk-label label.sk-toggleable__label,
+    #sk-container-id-67 div.sk-label label {
       /* The background is the default theme color */
       color: var(--sklearn-color-text-on-default-background);
     }
 
     /* On hover, darken the color of the background */
-    #sk-container-id-66 div.sk-label:hover label.sk-toggleable__label {
+    #sk-container-id-67 div.sk-label:hover label.sk-toggleable__label {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
     /* Label box, darken color on hover, fitted */
-    #sk-container-id-66 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
+    #sk-container-id-67 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
     /* Estimator label */
 
-    #sk-container-id-66 div.sk-label label {
+    #sk-container-id-67 div.sk-label label {
       font-family: monospace;
       font-weight: bold;
       display: inline-block;
       line-height: 1.2em;
     }
 
-    #sk-container-id-66 div.sk-label-container {
+    #sk-container-id-67 div.sk-label-container {
       text-align: center;
     }
 
     /* Estimator-specific */
-    #sk-container-id-66 div.sk-estimator {
+    #sk-container-id-67 div.sk-estimator {
       font-family: monospace;
       border: 1px dotted var(--sklearn-color-border-box);
       border-radius: 0.25em;
@@ -489,18 +489,18 @@ representation of the estimator is displayed:
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-66 div.sk-estimator.fitted {
+    #sk-container-id-67 div.sk-estimator.fitted {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
     /* on hover */
-    #sk-container-id-66 div.sk-estimator:hover {
+    #sk-container-id-67 div.sk-estimator:hover {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-66 div.sk-estimator.fitted:hover {
+    #sk-container-id-67 div.sk-estimator.fitted:hover {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-2);
     }
@@ -588,7 +588,7 @@ representation of the estimator is displayed:
 
     /* "?"-specific style due to the `<a>` HTML tag */
 
-    #sk-container-id-66 a.estimator_doc_link {
+    #sk-container-id-67 a.estimator_doc_link {
       float: right;
       font-size: 1rem;
       line-height: 1em;
@@ -603,25 +603,25 @@ representation of the estimator is displayed:
       border: var(--sklearn-color-unfitted-level-1) 1pt solid;
     }
 
-    #sk-container-id-66 a.estimator_doc_link.fitted {
+    #sk-container-id-67 a.estimator_doc_link.fitted {
       /* fitted */
       border: var(--sklearn-color-fitted-level-1) 1pt solid;
       color: var(--sklearn-color-fitted-level-1);
     }
 
     /* On hover */
-    #sk-container-id-66 a.estimator_doc_link:hover {
+    #sk-container-id-67 a.estimator_doc_link:hover {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-3);
       color: var(--sklearn-color-background);
       text-decoration: none;
     }
 
-    #sk-container-id-66 a.estimator_doc_link.fitted:hover {
+    #sk-container-id-67 a.estimator_doc_link.fitted:hover {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-3);
     }
-    </style><div id="sk-container-id-66" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,
+    </style><div id="sk-container-id-67" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,
                      ColumnTransformer(transformers=[(&#x27;num&#x27;,
                                                       Pipeline(steps=[(&#x27;imputer&#x27;,
                                                                        SimpleImputer(strategy=&#x27;median&#x27;)),
@@ -633,10 +633,10 @@ representation of the estimator is displayed:
                                                                        OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)),
                                                                       (&#x27;selector&#x27;,
                                                                        SelectPercentile(percentile=50,
-                                                                                        score_func=&lt;function chi2 at 0x7fb58f5c1cf0&gt;))]),
+                                                                                        score_func=&lt;function chi2 at 0x7fd851052050&gt;))]),
                                                       [&#x27;embarked&#x27;, &#x27;sex&#x27;,
                                                        &#x27;pclass&#x27;])])),
-                    (&#x27;classifier&#x27;, LogisticRegression())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-257" type="checkbox" ><label for="sk-estimator-id-257" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>Pipeline</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.pipeline.Pipeline.html">?<span>Documentation for Pipeline</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,
+                    (&#x27;classifier&#x27;, LogisticRegression())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-271" type="checkbox" ><label for="sk-estimator-id-271" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>Pipeline</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.pipeline.Pipeline.html">?<span>Documentation for Pipeline</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,
                      ColumnTransformer(transformers=[(&#x27;num&#x27;,
                                                       Pipeline(steps=[(&#x27;imputer&#x27;,
                                                                        SimpleImputer(strategy=&#x27;median&#x27;)),
@@ -648,10 +648,10 @@ representation of the estimator is displayed:
                                                                        OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)),
                                                                       (&#x27;selector&#x27;,
                                                                        SelectPercentile(percentile=50,
-                                                                                        score_func=&lt;function chi2 at 0x7fb58f5c1cf0&gt;))]),
+                                                                                        score_func=&lt;function chi2 at 0x7fd851052050&gt;))]),
                                                       [&#x27;embarked&#x27;, &#x27;sex&#x27;,
                                                        &#x27;pclass&#x27;])])),
-                    (&#x27;classifier&#x27;, LogisticRegression())])</pre></div> </div></div><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-258" type="checkbox" ><label for="sk-estimator-id-258" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>preprocessor: ColumnTransformer</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.compose.ColumnTransformer.html">?<span>Documentation for preprocessor: ColumnTransformer</span></a></div></label><div class="sk-toggleable__content fitted"><pre>ColumnTransformer(transformers=[(&#x27;num&#x27;,
+                    (&#x27;classifier&#x27;, LogisticRegression())])</pre></div> </div></div><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-272" type="checkbox" ><label for="sk-estimator-id-272" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>preprocessor: ColumnTransformer</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.compose.ColumnTransformer.html">?<span>Documentation for preprocessor: ColumnTransformer</span></a></div></label><div class="sk-toggleable__content fitted"><pre>ColumnTransformer(transformers=[(&#x27;num&#x27;,
                                      Pipeline(steps=[(&#x27;imputer&#x27;,
                                                       SimpleImputer(strategy=&#x27;median&#x27;)),
                                                      (&#x27;scaler&#x27;, StandardScaler())]),
@@ -661,8 +661,8 @@ representation of the estimator is displayed:
                                                       OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)),
                                                      (&#x27;selector&#x27;,
                                                       SelectPercentile(percentile=50,
-                                                                       score_func=&lt;function chi2 at 0x7fb58f5c1cf0&gt;))]),
-                                     [&#x27;embarked&#x27;, &#x27;sex&#x27;, &#x27;pclass&#x27;])])</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-259" type="checkbox" ><label for="sk-estimator-id-259" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>num</div></div></label><div class="sk-toggleable__content fitted"><pre>[&#x27;age&#x27;, &#x27;fare&#x27;]</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-260" type="checkbox" ><label for="sk-estimator-id-260" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>SimpleImputer</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.impute.SimpleImputer.html">?<span>Documentation for SimpleImputer</span></a></div></label><div class="sk-toggleable__content fitted"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-261" type="checkbox" ><label for="sk-estimator-id-261" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>StandardScaler</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.StandardScaler.html">?<span>Documentation for StandardScaler</span></a></div></label><div class="sk-toggleable__content fitted"><pre>StandardScaler()</pre></div> </div></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-262" type="checkbox" ><label for="sk-estimator-id-262" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>cat</div></div></label><div class="sk-toggleable__content fitted"><pre>[&#x27;embarked&#x27;, &#x27;sex&#x27;, &#x27;pclass&#x27;]</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-263" type="checkbox" ><label for="sk-estimator-id-263" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>OneHotEncoder</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.OneHotEncoder.html">?<span>Documentation for OneHotEncoder</span></a></div></label><div class="sk-toggleable__content fitted"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-264" type="checkbox" ><label for="sk-estimator-id-264" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>SelectPercentile</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.feature_selection.SelectPercentile.html">?<span>Documentation for SelectPercentile</span></a></div></label><div class="sk-toggleable__content fitted"><pre>SelectPercentile(percentile=50, score_func=&lt;function chi2 at 0x7fb58f5c1cf0&gt;)</pre></div> </div></div></div></div></div></div></div></div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-265" type="checkbox" ><label for="sk-estimator-id-265" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>LogisticRegression</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.linear_model.LogisticRegression.html">?<span>Documentation for LogisticRegression</span></a></div></label><div class="sk-toggleable__content fitted"><pre>LogisticRegression()</pre></div> </div></div></div></div></div></div>
+                                                                       score_func=&lt;function chi2 at 0x7fd851052050&gt;))]),
+                                     [&#x27;embarked&#x27;, &#x27;sex&#x27;, &#x27;pclass&#x27;])])</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-273" type="checkbox" ><label for="sk-estimator-id-273" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>num</div></div></label><div class="sk-toggleable__content fitted"><pre>[&#x27;age&#x27;, &#x27;fare&#x27;]</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-274" type="checkbox" ><label for="sk-estimator-id-274" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>SimpleImputer</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.impute.SimpleImputer.html">?<span>Documentation for SimpleImputer</span></a></div></label><div class="sk-toggleable__content fitted"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-275" type="checkbox" ><label for="sk-estimator-id-275" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>StandardScaler</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.StandardScaler.html">?<span>Documentation for StandardScaler</span></a></div></label><div class="sk-toggleable__content fitted"><pre>StandardScaler()</pre></div> </div></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-276" type="checkbox" ><label for="sk-estimator-id-276" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>cat</div></div></label><div class="sk-toggleable__content fitted"><pre>[&#x27;embarked&#x27;, &#x27;sex&#x27;, &#x27;pclass&#x27;]</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-277" type="checkbox" ><label for="sk-estimator-id-277" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>OneHotEncoder</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.OneHotEncoder.html">?<span>Documentation for OneHotEncoder</span></a></div></label><div class="sk-toggleable__content fitted"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-278" type="checkbox" ><label for="sk-estimator-id-278" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>SelectPercentile</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.feature_selection.SelectPercentile.html">?<span>Documentation for SelectPercentile</span></a></div></label><div class="sk-toggleable__content fitted"><pre>SelectPercentile(percentile=50, score_func=&lt;function chi2 at 0x7fd851052050&gt;)</pre></div> </div></div></div></div></div></div></div></div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-279" type="checkbox" ><label for="sk-estimator-id-279" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>LogisticRegression</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.linear_model.LogisticRegression.html">?<span>Documentation for LogisticRegression</span></a></div></label><div class="sk-toggleable__content fitted"><pre>LogisticRegression()</pre></div> </div></div></div></div></div></div>
     </div>
     <br />
     <br />
@@ -780,7 +780,7 @@ can use this information to dispatch the categorical columns to the
 .. raw:: html
 
     <div class="output_subarea output_html rendered_html output_result">
-    <style>#sk-container-id-67 {
+    <style>#sk-container-id-68 {
       /* Definition of color scheme common for light and dark mode */
       --sklearn-color-text: #000;
       --sklearn-color-text-muted: #666;
@@ -811,15 +811,15 @@ can use this information to dispatch the categorical columns to the
       }
     }
 
-    #sk-container-id-67 {
+    #sk-container-id-68 {
       color: var(--sklearn-color-text);
     }
 
-    #sk-container-id-67 pre {
+    #sk-container-id-68 pre {
       padding: 0;
     }
 
-    #sk-container-id-67 input.sk-hidden--visually {
+    #sk-container-id-68 input.sk-hidden--visually {
       border: 0;
       clip: rect(1px 1px 1px 1px);
       clip: rect(1px, 1px, 1px, 1px);
@@ -831,7 +831,7 @@ can use this information to dispatch the categorical columns to the
       width: 1px;
     }
 
-    #sk-container-id-67 div.sk-dashed-wrapped {
+    #sk-container-id-68 div.sk-dashed-wrapped {
       border: 1px dashed var(--sklearn-color-line);
       margin: 0 0.4em 0.5em 0.4em;
       box-sizing: border-box;
@@ -839,7 +839,7 @@ can use this information to dispatch the categorical columns to the
       background-color: var(--sklearn-color-background);
     }
 
-    #sk-container-id-67 div.sk-container {
+    #sk-container-id-68 div.sk-container {
       /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
          but bootstrap.min.css set `[hidden] { display: none !important; }`
          so we also need the `!important` here to be able to override the
@@ -849,7 +849,7 @@ can use this information to dispatch the categorical columns to the
       position: relative;
     }
 
-    #sk-container-id-67 div.sk-text-repr-fallback {
+    #sk-container-id-68 div.sk-text-repr-fallback {
       display: none;
     }
 
@@ -865,14 +865,14 @@ can use this information to dispatch the categorical columns to the
 
     /* Parallel-specific style estimator block */
 
-    #sk-container-id-67 div.sk-parallel-item::after {
+    #sk-container-id-68 div.sk-parallel-item::after {
       content: "";
       width: 100%;
       border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
       flex-grow: 1;
     }
 
-    #sk-container-id-67 div.sk-parallel {
+    #sk-container-id-68 div.sk-parallel {
       display: flex;
       align-items: stretch;
       justify-content: center;
@@ -880,28 +880,28 @@ can use this information to dispatch the categorical columns to the
       position: relative;
     }
 
-    #sk-container-id-67 div.sk-parallel-item {
+    #sk-container-id-68 div.sk-parallel-item {
       display: flex;
       flex-direction: column;
     }
 
-    #sk-container-id-67 div.sk-parallel-item:first-child::after {
+    #sk-container-id-68 div.sk-parallel-item:first-child::after {
       align-self: flex-end;
       width: 50%;
     }
 
-    #sk-container-id-67 div.sk-parallel-item:last-child::after {
+    #sk-container-id-68 div.sk-parallel-item:last-child::after {
       align-self: flex-start;
       width: 50%;
     }
 
-    #sk-container-id-67 div.sk-parallel-item:only-child::after {
+    #sk-container-id-68 div.sk-parallel-item:only-child::after {
       width: 0;
     }
 
     /* Serial-specific style estimator block */
 
-    #sk-container-id-67 div.sk-serial {
+    #sk-container-id-68 div.sk-serial {
       display: flex;
       flex-direction: column;
       align-items: center;
@@ -919,14 +919,14 @@ can use this information to dispatch the categorical columns to the
 
     /* Pipeline and ColumnTransformer style (default) */
 
-    #sk-container-id-67 div.sk-toggleable {
+    #sk-container-id-68 div.sk-toggleable {
       /* Default theme specific background. It is overwritten whether we have a
       specific estimator or a Pipeline/ColumnTransformer */
       background-color: var(--sklearn-color-background);
     }
 
     /* Toggleable label */
-    #sk-container-id-67 label.sk-toggleable__label {
+    #sk-container-id-68 label.sk-toggleable__label {
       cursor: pointer;
       display: flex;
       width: 100%;
@@ -939,13 +939,13 @@ can use this information to dispatch the categorical columns to the
       gap: 0.5em;
     }
 
-    #sk-container-id-67 label.sk-toggleable__label .caption {
+    #sk-container-id-68 label.sk-toggleable__label .caption {
       font-size: 0.6rem;
       font-weight: lighter;
       color: var(--sklearn-color-text-muted);
     }
 
-    #sk-container-id-67 label.sk-toggleable__label-arrow:before {
+    #sk-container-id-68 label.sk-toggleable__label-arrow:before {
       /* Arrow on the left of the label */
       content: "▸";
       float: left;
@@ -953,13 +953,13 @@ can use this information to dispatch the categorical columns to the
       color: var(--sklearn-color-icon);
     }
 
-    #sk-container-id-67 label.sk-toggleable__label-arrow:hover:before {
+    #sk-container-id-68 label.sk-toggleable__label-arrow:hover:before {
       color: var(--sklearn-color-text);
     }
 
     /* Toggleable content - dropdown */
 
-    #sk-container-id-67 div.sk-toggleable__content {
+    #sk-container-id-68 div.sk-toggleable__content {
       max-height: 0;
       max-width: 0;
       overflow: hidden;
@@ -968,12 +968,12 @@ can use this information to dispatch the categorical columns to the
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-67 div.sk-toggleable__content.fitted {
+    #sk-container-id-68 div.sk-toggleable__content.fitted {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
-    #sk-container-id-67 div.sk-toggleable__content pre {
+    #sk-container-id-68 div.sk-toggleable__content pre {
       margin: 0.2em;
       border-radius: 0.25em;
       color: var(--sklearn-color-text);
@@ -981,79 +981,79 @@ can use this information to dispatch the categorical columns to the
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-67 div.sk-toggleable__content.fitted pre {
+    #sk-container-id-68 div.sk-toggleable__content.fitted pre {
       /* unfitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
-    #sk-container-id-67 input.sk-toggleable__control:checked~div.sk-toggleable__content {
+    #sk-container-id-68 input.sk-toggleable__control:checked~div.sk-toggleable__content {
       /* Expand drop-down */
       max-height: 200px;
       max-width: 100%;
       overflow: auto;
     }
 
-    #sk-container-id-67 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
+    #sk-container-id-68 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
       content: "▾";
     }
 
     /* Pipeline/ColumnTransformer-specific style */
 
-    #sk-container-id-67 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-68 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-67 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-68 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
     /* Estimator-specific style */
 
     /* Colorize estimator box */
-    #sk-container-id-67 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-68 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-67 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-68 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
-    #sk-container-id-67 div.sk-label label.sk-toggleable__label,
-    #sk-container-id-67 div.sk-label label {
+    #sk-container-id-68 div.sk-label label.sk-toggleable__label,
+    #sk-container-id-68 div.sk-label label {
       /* The background is the default theme color */
       color: var(--sklearn-color-text-on-default-background);
     }
 
     /* On hover, darken the color of the background */
-    #sk-container-id-67 div.sk-label:hover label.sk-toggleable__label {
+    #sk-container-id-68 div.sk-label:hover label.sk-toggleable__label {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
     /* Label box, darken color on hover, fitted */
-    #sk-container-id-67 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
+    #sk-container-id-68 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
     /* Estimator label */
 
-    #sk-container-id-67 div.sk-label label {
+    #sk-container-id-68 div.sk-label label {
       font-family: monospace;
       font-weight: bold;
       display: inline-block;
       line-height: 1.2em;
     }
 
-    #sk-container-id-67 div.sk-label-container {
+    #sk-container-id-68 div.sk-label-container {
       text-align: center;
     }
 
     /* Estimator-specific */
-    #sk-container-id-67 div.sk-estimator {
+    #sk-container-id-68 div.sk-estimator {
       font-family: monospace;
       border: 1px dotted var(--sklearn-color-border-box);
       border-radius: 0.25em;
@@ -1063,18 +1063,18 @@ can use this information to dispatch the categorical columns to the
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-67 div.sk-estimator.fitted {
+    #sk-container-id-68 div.sk-estimator.fitted {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
     /* on hover */
-    #sk-container-id-67 div.sk-estimator:hover {
+    #sk-container-id-68 div.sk-estimator:hover {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-67 div.sk-estimator.fitted:hover {
+    #sk-container-id-68 div.sk-estimator.fitted:hover {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-2);
     }
@@ -1162,7 +1162,7 @@ can use this information to dispatch the categorical columns to the
 
     /* "?"-specific style due to the `<a>` HTML tag */
 
-    #sk-container-id-67 a.estimator_doc_link {
+    #sk-container-id-68 a.estimator_doc_link {
       float: right;
       font-size: 1rem;
       line-height: 1em;
@@ -1177,64 +1177,64 @@ can use this information to dispatch the categorical columns to the
       border: var(--sklearn-color-unfitted-level-1) 1pt solid;
     }
 
-    #sk-container-id-67 a.estimator_doc_link.fitted {
+    #sk-container-id-68 a.estimator_doc_link.fitted {
       /* fitted */
       border: var(--sklearn-color-fitted-level-1) 1pt solid;
       color: var(--sklearn-color-fitted-level-1);
     }
 
     /* On hover */
-    #sk-container-id-67 a.estimator_doc_link:hover {
+    #sk-container-id-68 a.estimator_doc_link:hover {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-3);
       color: var(--sklearn-color-background);
       text-decoration: none;
     }
 
-    #sk-container-id-67 a.estimator_doc_link.fitted:hover {
+    #sk-container-id-68 a.estimator_doc_link.fitted:hover {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-3);
     }
-    </style><div id="sk-container-id-67" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,
+    </style><div id="sk-container-id-68" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,
                      ColumnTransformer(transformers=[(&#x27;num&#x27;,
                                                       Pipeline(steps=[(&#x27;imputer&#x27;,
                                                                        SimpleImputer(strategy=&#x27;median&#x27;)),
                                                                       (&#x27;scaler&#x27;,
                                                                        StandardScaler())]),
-                                                      &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb5845dccd0&gt;),
+                                                      &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd8413c07c0&gt;),
                                                      (&#x27;cat&#x27;,
                                                       Pipeline(steps=[(&#x27;encoder&#x27;,
                                                                        OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)),
                                                                       (&#x27;selector&#x27;,
                                                                        SelectPercentile(percentile=50,
-                                                                                        score_func=&lt;function chi2 at 0x7fb58f5c1cf0&gt;))]),
-                                                      &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb5845ddc30&gt;)])),
-                    (&#x27;classifier&#x27;, LogisticRegression())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-266" type="checkbox" ><label for="sk-estimator-id-266" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>Pipeline</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.pipeline.Pipeline.html">?<span>Documentation for Pipeline</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,
+                                                                                        score_func=&lt;function chi2 at 0x7fd851052050&gt;))]),
+                                                      &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd8413c3040&gt;)])),
+                    (&#x27;classifier&#x27;, LogisticRegression())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-280" type="checkbox" ><label for="sk-estimator-id-280" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>Pipeline</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.pipeline.Pipeline.html">?<span>Documentation for Pipeline</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,
                      ColumnTransformer(transformers=[(&#x27;num&#x27;,
                                                       Pipeline(steps=[(&#x27;imputer&#x27;,
                                                                        SimpleImputer(strategy=&#x27;median&#x27;)),
                                                                       (&#x27;scaler&#x27;,
                                                                        StandardScaler())]),
-                                                      &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb5845dccd0&gt;),
+                                                      &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd8413c07c0&gt;),
                                                      (&#x27;cat&#x27;,
                                                       Pipeline(steps=[(&#x27;encoder&#x27;,
                                                                        OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)),
                                                                       (&#x27;selector&#x27;,
                                                                        SelectPercentile(percentile=50,
-                                                                                        score_func=&lt;function chi2 at 0x7fb58f5c1cf0&gt;))]),
-                                                      &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb5845ddc30&gt;)])),
-                    (&#x27;classifier&#x27;, LogisticRegression())])</pre></div> </div></div><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-267" type="checkbox" ><label for="sk-estimator-id-267" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>preprocessor: ColumnTransformer</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.compose.ColumnTransformer.html">?<span>Documentation for preprocessor: ColumnTransformer</span></a></div></label><div class="sk-toggleable__content fitted"><pre>ColumnTransformer(transformers=[(&#x27;num&#x27;,
+                                                                                        score_func=&lt;function chi2 at 0x7fd851052050&gt;))]),
+                                                      &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd8413c3040&gt;)])),
+                    (&#x27;classifier&#x27;, LogisticRegression())])</pre></div> </div></div><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-281" type="checkbox" ><label for="sk-estimator-id-281" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>preprocessor: ColumnTransformer</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.compose.ColumnTransformer.html">?<span>Documentation for preprocessor: ColumnTransformer</span></a></div></label><div class="sk-toggleable__content fitted"><pre>ColumnTransformer(transformers=[(&#x27;num&#x27;,
                                      Pipeline(steps=[(&#x27;imputer&#x27;,
                                                       SimpleImputer(strategy=&#x27;median&#x27;)),
                                                      (&#x27;scaler&#x27;, StandardScaler())]),
-                                     &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb5845dccd0&gt;),
+                                     &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd8413c07c0&gt;),
                                     (&#x27;cat&#x27;,
                                      Pipeline(steps=[(&#x27;encoder&#x27;,
                                                       OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)),
                                                      (&#x27;selector&#x27;,
                                                       SelectPercentile(percentile=50,
-                                                                       score_func=&lt;function chi2 at 0x7fb58f5c1cf0&gt;))]),
-                                     &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb5845ddc30&gt;)])</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-268" type="checkbox" ><label for="sk-estimator-id-268" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>num</div></div></label><div class="sk-toggleable__content fitted"><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb5845dccd0&gt;</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-269" type="checkbox" ><label for="sk-estimator-id-269" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>SimpleImputer</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.impute.SimpleImputer.html">?<span>Documentation for SimpleImputer</span></a></div></label><div class="sk-toggleable__content fitted"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-270" type="checkbox" ><label for="sk-estimator-id-270" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>StandardScaler</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.StandardScaler.html">?<span>Documentation for StandardScaler</span></a></div></label><div class="sk-toggleable__content fitted"><pre>StandardScaler()</pre></div> </div></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-271" type="checkbox" ><label for="sk-estimator-id-271" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>cat</div></div></label><div class="sk-toggleable__content fitted"><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb5845ddc30&gt;</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-272" type="checkbox" ><label for="sk-estimator-id-272" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>OneHotEncoder</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.OneHotEncoder.html">?<span>Documentation for OneHotEncoder</span></a></div></label><div class="sk-toggleable__content fitted"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-273" type="checkbox" ><label for="sk-estimator-id-273" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>SelectPercentile</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.feature_selection.SelectPercentile.html">?<span>Documentation for SelectPercentile</span></a></div></label><div class="sk-toggleable__content fitted"><pre>SelectPercentile(percentile=50, score_func=&lt;function chi2 at 0x7fb58f5c1cf0&gt;)</pre></div> </div></div></div></div></div></div></div></div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-274" type="checkbox" ><label for="sk-estimator-id-274" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>LogisticRegression</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.linear_model.LogisticRegression.html">?<span>Documentation for LogisticRegression</span></a></div></label><div class="sk-toggleable__content fitted"><pre>LogisticRegression()</pre></div> </div></div></div></div></div></div>
+                                                                       score_func=&lt;function chi2 at 0x7fd851052050&gt;))]),
+                                     &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd8413c3040&gt;)])</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-282" type="checkbox" ><label for="sk-estimator-id-282" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>num</div></div></label><div class="sk-toggleable__content fitted"><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd8413c07c0&gt;</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-283" type="checkbox" ><label for="sk-estimator-id-283" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>SimpleImputer</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.impute.SimpleImputer.html">?<span>Documentation for SimpleImputer</span></a></div></label><div class="sk-toggleable__content fitted"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-284" type="checkbox" ><label for="sk-estimator-id-284" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>StandardScaler</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.StandardScaler.html">?<span>Documentation for StandardScaler</span></a></div></label><div class="sk-toggleable__content fitted"><pre>StandardScaler()</pre></div> </div></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-285" type="checkbox" ><label for="sk-estimator-id-285" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>cat</div></div></label><div class="sk-toggleable__content fitted"><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd8413c3040&gt;</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-286" type="checkbox" ><label for="sk-estimator-id-286" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>OneHotEncoder</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.OneHotEncoder.html">?<span>Documentation for OneHotEncoder</span></a></div></label><div class="sk-toggleable__content fitted"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-287" type="checkbox" ><label for="sk-estimator-id-287" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>SelectPercentile</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.feature_selection.SelectPercentile.html">?<span>Documentation for SelectPercentile</span></a></div></label><div class="sk-toggleable__content fitted"><pre>SelectPercentile(percentile=50, score_func=&lt;function chi2 at 0x7fd851052050&gt;)</pre></div> </div></div></div></div></div></div></div></div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-288" type="checkbox" ><label for="sk-estimator-id-288" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>LogisticRegression</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.linear_model.LogisticRegression.html">?<span>Documentation for LogisticRegression</span></a></div></label><div class="sk-toggleable__content fitted"><pre>LogisticRegression()</pre></div> </div></div></div></div></div></div>
     </div>
     <br />
     <br />
@@ -1322,7 +1322,7 @@ the parameter space will be evaluated.
 .. raw:: html
 
     <div class="output_subarea output_html rendered_html output_result">
-    <style>#sk-container-id-68 {
+    <style>#sk-container-id-69 {
       /* Definition of color scheme common for light and dark mode */
       --sklearn-color-text: #000;
       --sklearn-color-text-muted: #666;
@@ -1353,15 +1353,15 @@ the parameter space will be evaluated.
       }
     }
 
-    #sk-container-id-68 {
+    #sk-container-id-69 {
       color: var(--sklearn-color-text);
     }
 
-    #sk-container-id-68 pre {
+    #sk-container-id-69 pre {
       padding: 0;
     }
 
-    #sk-container-id-68 input.sk-hidden--visually {
+    #sk-container-id-69 input.sk-hidden--visually {
       border: 0;
       clip: rect(1px 1px 1px 1px);
       clip: rect(1px, 1px, 1px, 1px);
@@ -1373,7 +1373,7 @@ the parameter space will be evaluated.
       width: 1px;
     }
 
-    #sk-container-id-68 div.sk-dashed-wrapped {
+    #sk-container-id-69 div.sk-dashed-wrapped {
       border: 1px dashed var(--sklearn-color-line);
       margin: 0 0.4em 0.5em 0.4em;
       box-sizing: border-box;
@@ -1381,7 +1381,7 @@ the parameter space will be evaluated.
       background-color: var(--sklearn-color-background);
     }
 
-    #sk-container-id-68 div.sk-container {
+    #sk-container-id-69 div.sk-container {
       /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
          but bootstrap.min.css set `[hidden] { display: none !important; }`
          so we also need the `!important` here to be able to override the
@@ -1391,7 +1391,7 @@ the parameter space will be evaluated.
       position: relative;
     }
 
-    #sk-container-id-68 div.sk-text-repr-fallback {
+    #sk-container-id-69 div.sk-text-repr-fallback {
       display: none;
     }
 
@@ -1407,14 +1407,14 @@ the parameter space will be evaluated.
 
     /* Parallel-specific style estimator block */
 
-    #sk-container-id-68 div.sk-parallel-item::after {
+    #sk-container-id-69 div.sk-parallel-item::after {
       content: "";
       width: 100%;
       border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
       flex-grow: 1;
     }
 
-    #sk-container-id-68 div.sk-parallel {
+    #sk-container-id-69 div.sk-parallel {
       display: flex;
       align-items: stretch;
       justify-content: center;
@@ -1422,28 +1422,28 @@ the parameter space will be evaluated.
       position: relative;
     }
 
-    #sk-container-id-68 div.sk-parallel-item {
+    #sk-container-id-69 div.sk-parallel-item {
       display: flex;
       flex-direction: column;
     }
 
-    #sk-container-id-68 div.sk-parallel-item:first-child::after {
+    #sk-container-id-69 div.sk-parallel-item:first-child::after {
       align-self: flex-end;
       width: 50%;
     }
 
-    #sk-container-id-68 div.sk-parallel-item:last-child::after {
+    #sk-container-id-69 div.sk-parallel-item:last-child::after {
       align-self: flex-start;
       width: 50%;
     }
 
-    #sk-container-id-68 div.sk-parallel-item:only-child::after {
+    #sk-container-id-69 div.sk-parallel-item:only-child::after {
       width: 0;
     }
 
     /* Serial-specific style estimator block */
 
-    #sk-container-id-68 div.sk-serial {
+    #sk-container-id-69 div.sk-serial {
       display: flex;
       flex-direction: column;
       align-items: center;
@@ -1461,14 +1461,14 @@ the parameter space will be evaluated.
 
     /* Pipeline and ColumnTransformer style (default) */
 
-    #sk-container-id-68 div.sk-toggleable {
+    #sk-container-id-69 div.sk-toggleable {
       /* Default theme specific background. It is overwritten whether we have a
       specific estimator or a Pipeline/ColumnTransformer */
       background-color: var(--sklearn-color-background);
     }
 
     /* Toggleable label */
-    #sk-container-id-68 label.sk-toggleable__label {
+    #sk-container-id-69 label.sk-toggleable__label {
       cursor: pointer;
       display: flex;
       width: 100%;
@@ -1481,13 +1481,13 @@ the parameter space will be evaluated.
       gap: 0.5em;
     }
 
-    #sk-container-id-68 label.sk-toggleable__label .caption {
+    #sk-container-id-69 label.sk-toggleable__label .caption {
       font-size: 0.6rem;
       font-weight: lighter;
       color: var(--sklearn-color-text-muted);
     }
 
-    #sk-container-id-68 label.sk-toggleable__label-arrow:before {
+    #sk-container-id-69 label.sk-toggleable__label-arrow:before {
       /* Arrow on the left of the label */
       content: "▸";
       float: left;
@@ -1495,13 +1495,13 @@ the parameter space will be evaluated.
       color: var(--sklearn-color-icon);
     }
 
-    #sk-container-id-68 label.sk-toggleable__label-arrow:hover:before {
+    #sk-container-id-69 label.sk-toggleable__label-arrow:hover:before {
       color: var(--sklearn-color-text);
     }
 
     /* Toggleable content - dropdown */
 
-    #sk-container-id-68 div.sk-toggleable__content {
+    #sk-container-id-69 div.sk-toggleable__content {
       max-height: 0;
       max-width: 0;
       overflow: hidden;
@@ -1510,12 +1510,12 @@ the parameter space will be evaluated.
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-68 div.sk-toggleable__content.fitted {
+    #sk-container-id-69 div.sk-toggleable__content.fitted {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
-    #sk-container-id-68 div.sk-toggleable__content pre {
+    #sk-container-id-69 div.sk-toggleable__content pre {
       margin: 0.2em;
       border-radius: 0.25em;
       color: var(--sklearn-color-text);
@@ -1523,79 +1523,79 @@ the parameter space will be evaluated.
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-68 div.sk-toggleable__content.fitted pre {
+    #sk-container-id-69 div.sk-toggleable__content.fitted pre {
       /* unfitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
-    #sk-container-id-68 input.sk-toggleable__control:checked~div.sk-toggleable__content {
+    #sk-container-id-69 input.sk-toggleable__control:checked~div.sk-toggleable__content {
       /* Expand drop-down */
       max-height: 200px;
       max-width: 100%;
       overflow: auto;
     }
 
-    #sk-container-id-68 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
+    #sk-container-id-69 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
       content: "▾";
     }
 
     /* Pipeline/ColumnTransformer-specific style */
 
-    #sk-container-id-68 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-69 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-68 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-69 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
     /* Estimator-specific style */
 
     /* Colorize estimator box */
-    #sk-container-id-68 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-69 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-68 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-69 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
-    #sk-container-id-68 div.sk-label label.sk-toggleable__label,
-    #sk-container-id-68 div.sk-label label {
+    #sk-container-id-69 div.sk-label label.sk-toggleable__label,
+    #sk-container-id-69 div.sk-label label {
       /* The background is the default theme color */
       color: var(--sklearn-color-text-on-default-background);
     }
 
     /* On hover, darken the color of the background */
-    #sk-container-id-68 div.sk-label:hover label.sk-toggleable__label {
+    #sk-container-id-69 div.sk-label:hover label.sk-toggleable__label {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
     /* Label box, darken color on hover, fitted */
-    #sk-container-id-68 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
+    #sk-container-id-69 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
     /* Estimator label */
 
-    #sk-container-id-68 div.sk-label label {
+    #sk-container-id-69 div.sk-label label {
       font-family: monospace;
       font-weight: bold;
       display: inline-block;
       line-height: 1.2em;
     }
 
-    #sk-container-id-68 div.sk-label-container {
+    #sk-container-id-69 div.sk-label-container {
       text-align: center;
     }
 
     /* Estimator-specific */
-    #sk-container-id-68 div.sk-estimator {
+    #sk-container-id-69 div.sk-estimator {
       font-family: monospace;
       border: 1px dotted var(--sklearn-color-border-box);
       border-radius: 0.25em;
@@ -1605,18 +1605,18 @@ the parameter space will be evaluated.
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-68 div.sk-estimator.fitted {
+    #sk-container-id-69 div.sk-estimator.fitted {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
     /* on hover */
-    #sk-container-id-68 div.sk-estimator:hover {
+    #sk-container-id-69 div.sk-estimator:hover {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-68 div.sk-estimator.fitted:hover {
+    #sk-container-id-69 div.sk-estimator.fitted:hover {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-2);
     }
@@ -1704,7 +1704,7 @@ the parameter space will be evaluated.
 
     /* "?"-specific style due to the `<a>` HTML tag */
 
-    #sk-container-id-68 a.estimator_doc_link {
+    #sk-container-id-69 a.estimator_doc_link {
       float: right;
       font-size: 1rem;
       line-height: 1em;
@@ -1719,37 +1719,37 @@ the parameter space will be evaluated.
       border: var(--sklearn-color-unfitted-level-1) 1pt solid;
     }
 
-    #sk-container-id-68 a.estimator_doc_link.fitted {
+    #sk-container-id-69 a.estimator_doc_link.fitted {
       /* fitted */
       border: var(--sklearn-color-fitted-level-1) 1pt solid;
       color: var(--sklearn-color-fitted-level-1);
     }
 
     /* On hover */
-    #sk-container-id-68 a.estimator_doc_link:hover {
+    #sk-container-id-69 a.estimator_doc_link:hover {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-3);
       color: var(--sklearn-color-background);
       text-decoration: none;
     }
 
-    #sk-container-id-68 a.estimator_doc_link.fitted:hover {
+    #sk-container-id-69 a.estimator_doc_link.fitted:hover {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-3);
     }
-    </style><div id="sk-container-id-68" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>RandomizedSearchCV(estimator=Pipeline(steps=[(&#x27;preprocessor&#x27;,
+    </style><div id="sk-container-id-69" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>RandomizedSearchCV(estimator=Pipeline(steps=[(&#x27;preprocessor&#x27;,
                                                   ColumnTransformer(transformers=[(&#x27;num&#x27;,
                                                                                    Pipeline(steps=[(&#x27;imputer&#x27;,
                                                                                                     SimpleImputer(strategy=&#x27;median&#x27;)),
                                                                                                    (&#x27;scaler&#x27;,
                                                                                                     StandardScaler())]),
-                                                                                   &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb5845dccd0&gt;),
+                                                                                   &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd8413c07c0&gt;),
                                                                                   (&#x27;cat&#x27;,
                                                                                    Pipeline(steps=[(&#x27;encoder&#x27;,
                                                                                                     OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)),
                                                                                                    (&#x27;s...
-                                                                                                                     score_func=&lt;function chi2 at 0x7fb58f5c1cf0&gt;))]),
-                                                                                   &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb5845ddc30&gt;)])),
+                                                                                                                     score_func=&lt;function chi2 at 0x7fd851052050&gt;))]),
+                                                                                   &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd8413c3040&gt;)])),
                                                  (&#x27;classifier&#x27;,
                                                   LogisticRegression())]),
                        param_distributions={&#x27;classifier__C&#x27;: [0.1, 1.0, 10, 100],
@@ -1759,19 +1759,19 @@ the parameter space will be evaluated.
                                                                                         70],
                                             &#x27;preprocessor__num__imputer__strategy&#x27;: [&#x27;mean&#x27;,
                                                                                      &#x27;median&#x27;]},
-                       random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-275" type="checkbox" ><label for="sk-estimator-id-275" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>RandomizedSearchCV</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.model_selection.RandomizedSearchCV.html">?<span>Documentation for RandomizedSearchCV</span></a><span class="sk-estimator-doc-link ">i<span>Not fitted</span></span></div></label><div class="sk-toggleable__content "><pre>RandomizedSearchCV(estimator=Pipeline(steps=[(&#x27;preprocessor&#x27;,
+                       random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-289" type="checkbox" ><label for="sk-estimator-id-289" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>RandomizedSearchCV</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.model_selection.RandomizedSearchCV.html">?<span>Documentation for RandomizedSearchCV</span></a><span class="sk-estimator-doc-link ">i<span>Not fitted</span></span></div></label><div class="sk-toggleable__content "><pre>RandomizedSearchCV(estimator=Pipeline(steps=[(&#x27;preprocessor&#x27;,
                                                   ColumnTransformer(transformers=[(&#x27;num&#x27;,
                                                                                    Pipeline(steps=[(&#x27;imputer&#x27;,
                                                                                                     SimpleImputer(strategy=&#x27;median&#x27;)),
                                                                                                    (&#x27;scaler&#x27;,
                                                                                                     StandardScaler())]),
-                                                                                   &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb5845dccd0&gt;),
+                                                                                   &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd8413c07c0&gt;),
                                                                                   (&#x27;cat&#x27;,
                                                                                    Pipeline(steps=[(&#x27;encoder&#x27;,
                                                                                                     OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)),
                                                                                                    (&#x27;s...
-                                                                                                                     score_func=&lt;function chi2 at 0x7fb58f5c1cf0&gt;))]),
-                                                                                   &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb5845ddc30&gt;)])),
+                                                                                                                     score_func=&lt;function chi2 at 0x7fd851052050&gt;))]),
+                                                                                   &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd8413c3040&gt;)])),
                                                  (&#x27;classifier&#x27;,
                                                   LogisticRegression())]),
                        param_distributions={&#x27;classifier__C&#x27;: [0.1, 1.0, 10, 100],
@@ -1781,32 +1781,32 @@ the parameter space will be evaluated.
                                                                                         70],
                                             &#x27;preprocessor__num__imputer__strategy&#x27;: [&#x27;mean&#x27;,
                                                                                      &#x27;median&#x27;]},
-                       random_state=0)</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-276" type="checkbox" ><label for="sk-estimator-id-276" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>estimator: Pipeline</div></div></label><div class="sk-toggleable__content "><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,
+                       random_state=0)</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-290" type="checkbox" ><label for="sk-estimator-id-290" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>estimator: Pipeline</div></div></label><div class="sk-toggleable__content "><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,
                      ColumnTransformer(transformers=[(&#x27;num&#x27;,
                                                       Pipeline(steps=[(&#x27;imputer&#x27;,
                                                                        SimpleImputer(strategy=&#x27;median&#x27;)),
                                                                       (&#x27;scaler&#x27;,
                                                                        StandardScaler())]),
-                                                      &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb5845dccd0&gt;),
+                                                      &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd8413c07c0&gt;),
                                                      (&#x27;cat&#x27;,
                                                       Pipeline(steps=[(&#x27;encoder&#x27;,
                                                                        OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)),
                                                                       (&#x27;selector&#x27;,
                                                                        SelectPercentile(percentile=50,
-                                                                                        score_func=&lt;function chi2 at 0x7fb58f5c1cf0&gt;))]),
-                                                      &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb5845ddc30&gt;)])),
-                    (&#x27;classifier&#x27;, LogisticRegression())])</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-277" type="checkbox" ><label for="sk-estimator-id-277" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>preprocessor: ColumnTransformer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.compose.ColumnTransformer.html">?<span>Documentation for preprocessor: ColumnTransformer</span></a></div></label><div class="sk-toggleable__content "><pre>ColumnTransformer(transformers=[(&#x27;num&#x27;,
+                                                                                        score_func=&lt;function chi2 at 0x7fd851052050&gt;))]),
+                                                      &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd8413c3040&gt;)])),
+                    (&#x27;classifier&#x27;, LogisticRegression())])</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-291" type="checkbox" ><label for="sk-estimator-id-291" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>preprocessor: ColumnTransformer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.compose.ColumnTransformer.html">?<span>Documentation for preprocessor: ColumnTransformer</span></a></div></label><div class="sk-toggleable__content "><pre>ColumnTransformer(transformers=[(&#x27;num&#x27;,
                                      Pipeline(steps=[(&#x27;imputer&#x27;,
                                                       SimpleImputer(strategy=&#x27;median&#x27;)),
                                                      (&#x27;scaler&#x27;, StandardScaler())]),
-                                     &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb5845dccd0&gt;),
+                                     &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd8413c07c0&gt;),
                                     (&#x27;cat&#x27;,
                                      Pipeline(steps=[(&#x27;encoder&#x27;,
                                                       OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)),
                                                      (&#x27;selector&#x27;,
                                                       SelectPercentile(percentile=50,
-                                                                       score_func=&lt;function chi2 at 0x7fb58f5c1cf0&gt;))]),
-                                     &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb5845ddc30&gt;)])</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-278" type="checkbox" ><label for="sk-estimator-id-278" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>num</div></div></label><div class="sk-toggleable__content "><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb5845dccd0&gt;</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-279" type="checkbox" ><label for="sk-estimator-id-279" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>SimpleImputer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.impute.SimpleImputer.html">?<span>Documentation for SimpleImputer</span></a></div></label><div class="sk-toggleable__content "><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div> </div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-280" type="checkbox" ><label for="sk-estimator-id-280" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>StandardScaler</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.StandardScaler.html">?<span>Documentation for StandardScaler</span></a></div></label><div class="sk-toggleable__content "><pre>StandardScaler()</pre></div> </div></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-281" type="checkbox" ><label for="sk-estimator-id-281" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>cat</div></div></label><div class="sk-toggleable__content "><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb5845ddc30&gt;</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-282" type="checkbox" ><label for="sk-estimator-id-282" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>OneHotEncoder</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.OneHotEncoder.html">?<span>Documentation for OneHotEncoder</span></a></div></label><div class="sk-toggleable__content "><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</pre></div> </div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-283" type="checkbox" ><label for="sk-estimator-id-283" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>SelectPercentile</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.feature_selection.SelectPercentile.html">?<span>Documentation for SelectPercentile</span></a></div></label><div class="sk-toggleable__content "><pre>SelectPercentile(percentile=50, score_func=&lt;function chi2 at 0x7fb58f5c1cf0&gt;)</pre></div> </div></div></div></div></div></div></div></div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-284" type="checkbox" ><label for="sk-estimator-id-284" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>LogisticRegression</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.linear_model.LogisticRegression.html">?<span>Documentation for LogisticRegression</span></a></div></label><div class="sk-toggleable__content "><pre>LogisticRegression()</pre></div> </div></div></div></div></div></div></div></div></div></div></div>
+                                                                       score_func=&lt;function chi2 at 0x7fd851052050&gt;))]),
+                                     &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd8413c3040&gt;)])</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-292" type="checkbox" ><label for="sk-estimator-id-292" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>num</div></div></label><div class="sk-toggleable__content "><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd8413c07c0&gt;</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-293" type="checkbox" ><label for="sk-estimator-id-293" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>SimpleImputer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.impute.SimpleImputer.html">?<span>Documentation for SimpleImputer</span></a></div></label><div class="sk-toggleable__content "><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div> </div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-294" type="checkbox" ><label for="sk-estimator-id-294" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>StandardScaler</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.StandardScaler.html">?<span>Documentation for StandardScaler</span></a></div></label><div class="sk-toggleable__content "><pre>StandardScaler()</pre></div> </div></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-295" type="checkbox" ><label for="sk-estimator-id-295" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>cat</div></div></label><div class="sk-toggleable__content "><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd8413c3040&gt;</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-296" type="checkbox" ><label for="sk-estimator-id-296" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>OneHotEncoder</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.OneHotEncoder.html">?<span>Documentation for OneHotEncoder</span></a></div></label><div class="sk-toggleable__content "><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</pre></div> </div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-297" type="checkbox" ><label for="sk-estimator-id-297" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>SelectPercentile</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.feature_selection.SelectPercentile.html">?<span>Documentation for SelectPercentile</span></a></div></label><div class="sk-toggleable__content "><pre>SelectPercentile(percentile=50, score_func=&lt;function chi2 at 0x7fd851052050&gt;)</pre></div> </div></div></div></div></div></div></div></div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-298" type="checkbox" ><label for="sk-estimator-id-298" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>LogisticRegression</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.linear_model.LogisticRegression.html">?<span>Documentation for LogisticRegression</span></a></div></label><div class="sk-toggleable__content "><pre>LogisticRegression()</pre></div> </div></div></div></div></div></div></div></div></div></div></div>
     </div>
     <br />
     <br />
@@ -1997,7 +1997,7 @@ not used for hyperparameter tuning.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 1.210 seconds)
+   **Total running time of the script:** (0 minutes 1.460 seconds)
 
 
 .. _sphx_glr_download_auto_examples_compose_plot_column_transformer_mixed_types.py:
diff --git a/dev/_sources/auto_examples/compose/plot_compare_reduction.rst.txt b/dev/_sources/auto_examples/compose/plot_compare_reduction.rst.txt
index e5ccc55850a35..04b397822a876 100644
--- a/dev/_sources/auto_examples/compose/plot_compare_reduction.rst.txt
+++ b/dev/_sources/auto_examples/compose/plot_compare_reduction.rst.txt
@@ -111,7 +111,7 @@ Illustration of ``Pipeline`` and ``GridSearchCV``
 .. raw:: html
 
     <div class="output_subarea output_html rendered_html output_result">
-    <style>#sk-container-id-69 {
+    <style>#sk-container-id-70 {
       /* Definition of color scheme common for light and dark mode */
       --sklearn-color-text: #000;
       --sklearn-color-text-muted: #666;
@@ -142,15 +142,15 @@ Illustration of ``Pipeline`` and ``GridSearchCV``
       }
     }
 
-    #sk-container-id-69 {
+    #sk-container-id-70 {
       color: var(--sklearn-color-text);
     }
 
-    #sk-container-id-69 pre {
+    #sk-container-id-70 pre {
       padding: 0;
     }
 
-    #sk-container-id-69 input.sk-hidden--visually {
+    #sk-container-id-70 input.sk-hidden--visually {
       border: 0;
       clip: rect(1px 1px 1px 1px);
       clip: rect(1px, 1px, 1px, 1px);
@@ -162,7 +162,7 @@ Illustration of ``Pipeline`` and ``GridSearchCV``
       width: 1px;
     }
 
-    #sk-container-id-69 div.sk-dashed-wrapped {
+    #sk-container-id-70 div.sk-dashed-wrapped {
       border: 1px dashed var(--sklearn-color-line);
       margin: 0 0.4em 0.5em 0.4em;
       box-sizing: border-box;
@@ -170,7 +170,7 @@ Illustration of ``Pipeline`` and ``GridSearchCV``
       background-color: var(--sklearn-color-background);
     }
 
-    #sk-container-id-69 div.sk-container {
+    #sk-container-id-70 div.sk-container {
       /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
          but bootstrap.min.css set `[hidden] { display: none !important; }`
          so we also need the `!important` here to be able to override the
@@ -180,7 +180,7 @@ Illustration of ``Pipeline`` and ``GridSearchCV``
       position: relative;
     }
 
-    #sk-container-id-69 div.sk-text-repr-fallback {
+    #sk-container-id-70 div.sk-text-repr-fallback {
       display: none;
     }
 
@@ -196,14 +196,14 @@ Illustration of ``Pipeline`` and ``GridSearchCV``
 
     /* Parallel-specific style estimator block */
 
-    #sk-container-id-69 div.sk-parallel-item::after {
+    #sk-container-id-70 div.sk-parallel-item::after {
       content: "";
       width: 100%;
       border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
       flex-grow: 1;
     }
 
-    #sk-container-id-69 div.sk-parallel {
+    #sk-container-id-70 div.sk-parallel {
       display: flex;
       align-items: stretch;
       justify-content: center;
@@ -211,28 +211,28 @@ Illustration of ``Pipeline`` and ``GridSearchCV``
       position: relative;
     }
 
-    #sk-container-id-69 div.sk-parallel-item {
+    #sk-container-id-70 div.sk-parallel-item {
       display: flex;
       flex-direction: column;
     }
 
-    #sk-container-id-69 div.sk-parallel-item:first-child::after {
+    #sk-container-id-70 div.sk-parallel-item:first-child::after {
       align-self: flex-end;
       width: 50%;
     }
 
-    #sk-container-id-69 div.sk-parallel-item:last-child::after {
+    #sk-container-id-70 div.sk-parallel-item:last-child::after {
       align-self: flex-start;
       width: 50%;
     }
 
-    #sk-container-id-69 div.sk-parallel-item:only-child::after {
+    #sk-container-id-70 div.sk-parallel-item:only-child::after {
       width: 0;
     }
 
     /* Serial-specific style estimator block */
 
-    #sk-container-id-69 div.sk-serial {
+    #sk-container-id-70 div.sk-serial {
       display: flex;
       flex-direction: column;
       align-items: center;
@@ -250,14 +250,14 @@ Illustration of ``Pipeline`` and ``GridSearchCV``
 
     /* Pipeline and ColumnTransformer style (default) */
 
-    #sk-container-id-69 div.sk-toggleable {
+    #sk-container-id-70 div.sk-toggleable {
       /* Default theme specific background. It is overwritten whether we have a
       specific estimator or a Pipeline/ColumnTransformer */
       background-color: var(--sklearn-color-background);
     }
 
     /* Toggleable label */
-    #sk-container-id-69 label.sk-toggleable__label {
+    #sk-container-id-70 label.sk-toggleable__label {
       cursor: pointer;
       display: flex;
       width: 100%;
@@ -270,13 +270,13 @@ Illustration of ``Pipeline`` and ``GridSearchCV``
       gap: 0.5em;
     }
 
-    #sk-container-id-69 label.sk-toggleable__label .caption {
+    #sk-container-id-70 label.sk-toggleable__label .caption {
       font-size: 0.6rem;
       font-weight: lighter;
       color: var(--sklearn-color-text-muted);
     }
 
-    #sk-container-id-69 label.sk-toggleable__label-arrow:before {
+    #sk-container-id-70 label.sk-toggleable__label-arrow:before {
       /* Arrow on the left of the label */
       content: "▸";
       float: left;
@@ -284,13 +284,13 @@ Illustration of ``Pipeline`` and ``GridSearchCV``
       color: var(--sklearn-color-icon);
     }
 
-    #sk-container-id-69 label.sk-toggleable__label-arrow:hover:before {
+    #sk-container-id-70 label.sk-toggleable__label-arrow:hover:before {
       color: var(--sklearn-color-text);
     }
 
     /* Toggleable content - dropdown */
 
-    #sk-container-id-69 div.sk-toggleable__content {
+    #sk-container-id-70 div.sk-toggleable__content {
       max-height: 0;
       max-width: 0;
       overflow: hidden;
@@ -299,12 +299,12 @@ Illustration of ``Pipeline`` and ``GridSearchCV``
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-69 div.sk-toggleable__content.fitted {
+    #sk-container-id-70 div.sk-toggleable__content.fitted {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
-    #sk-container-id-69 div.sk-toggleable__content pre {
+    #sk-container-id-70 div.sk-toggleable__content pre {
       margin: 0.2em;
       border-radius: 0.25em;
       color: var(--sklearn-color-text);
@@ -312,79 +312,79 @@ Illustration of ``Pipeline`` and ``GridSearchCV``
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-69 div.sk-toggleable__content.fitted pre {
+    #sk-container-id-70 div.sk-toggleable__content.fitted pre {
       /* unfitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
-    #sk-container-id-69 input.sk-toggleable__control:checked~div.sk-toggleable__content {
+    #sk-container-id-70 input.sk-toggleable__control:checked~div.sk-toggleable__content {
       /* Expand drop-down */
       max-height: 200px;
       max-width: 100%;
       overflow: auto;
     }
 
-    #sk-container-id-69 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
+    #sk-container-id-70 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
       content: "▾";
     }
 
     /* Pipeline/ColumnTransformer-specific style */
 
-    #sk-container-id-69 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-70 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-69 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-70 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
     /* Estimator-specific style */
 
     /* Colorize estimator box */
-    #sk-container-id-69 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-70 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-69 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-70 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
-    #sk-container-id-69 div.sk-label label.sk-toggleable__label,
-    #sk-container-id-69 div.sk-label label {
+    #sk-container-id-70 div.sk-label label.sk-toggleable__label,
+    #sk-container-id-70 div.sk-label label {
       /* The background is the default theme color */
       color: var(--sklearn-color-text-on-default-background);
     }
 
     /* On hover, darken the color of the background */
-    #sk-container-id-69 div.sk-label:hover label.sk-toggleable__label {
+    #sk-container-id-70 div.sk-label:hover label.sk-toggleable__label {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
     /* Label box, darken color on hover, fitted */
-    #sk-container-id-69 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
+    #sk-container-id-70 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
     /* Estimator label */
 
-    #sk-container-id-69 div.sk-label label {
+    #sk-container-id-70 div.sk-label label {
       font-family: monospace;
       font-weight: bold;
       display: inline-block;
       line-height: 1.2em;
     }
 
-    #sk-container-id-69 div.sk-label-container {
+    #sk-container-id-70 div.sk-label-container {
       text-align: center;
     }
 
     /* Estimator-specific */
-    #sk-container-id-69 div.sk-estimator {
+    #sk-container-id-70 div.sk-estimator {
       font-family: monospace;
       border: 1px dotted var(--sklearn-color-border-box);
       border-radius: 0.25em;
@@ -394,18 +394,18 @@ Illustration of ``Pipeline`` and ``GridSearchCV``
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-69 div.sk-estimator.fitted {
+    #sk-container-id-70 div.sk-estimator.fitted {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
     /* on hover */
-    #sk-container-id-69 div.sk-estimator:hover {
+    #sk-container-id-70 div.sk-estimator:hover {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-69 div.sk-estimator.fitted:hover {
+    #sk-container-id-70 div.sk-estimator.fitted:hover {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-2);
     }
@@ -493,7 +493,7 @@ Illustration of ``Pipeline`` and ``GridSearchCV``
 
     /* "?"-specific style due to the `<a>` HTML tag */
 
-    #sk-container-id-69 a.estimator_doc_link {
+    #sk-container-id-70 a.estimator_doc_link {
       float: right;
       font-size: 1rem;
       line-height: 1em;
@@ -508,25 +508,25 @@ Illustration of ``Pipeline`` and ``GridSearchCV``
       border: var(--sklearn-color-unfitted-level-1) 1pt solid;
     }
 
-    #sk-container-id-69 a.estimator_doc_link.fitted {
+    #sk-container-id-70 a.estimator_doc_link.fitted {
       /* fitted */
       border: var(--sklearn-color-fitted-level-1) 1pt solid;
       color: var(--sklearn-color-fitted-level-1);
     }
 
     /* On hover */
-    #sk-container-id-69 a.estimator_doc_link:hover {
+    #sk-container-id-70 a.estimator_doc_link:hover {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-3);
       color: var(--sklearn-color-background);
       text-decoration: none;
     }
 
-    #sk-container-id-69 a.estimator_doc_link.fitted:hover {
+    #sk-container-id-70 a.estimator_doc_link.fitted:hover {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-3);
     }
-    </style><div id="sk-container-id-69" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>GridSearchCV(estimator=Pipeline(steps=[(&#x27;scaling&#x27;, MinMaxScaler()),
+    </style><div id="sk-container-id-70" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>GridSearchCV(estimator=Pipeline(steps=[(&#x27;scaling&#x27;, MinMaxScaler()),
                                            (&#x27;reduce_dim&#x27;, &#x27;passthrough&#x27;),
                                            (&#x27;classify&#x27;,
                                             LinearSVC(dual=False,
@@ -537,8 +537,8 @@ Illustration of ``Pipeline`` and ``GridSearchCV``
                                              NMF(max_iter=1000)],
                               &#x27;reduce_dim__n_components&#x27;: [2, 4, 8]},
                              {&#x27;classify__C&#x27;: [1, 10, 100, 1000],
-                              &#x27;reduce_dim&#x27;: [SelectKBest(score_func=&lt;function mutual_info_classif at 0x7fb58f5c0160&gt;)],
-                              &#x27;reduce_dim__k&#x27;: [2, 4, 8]}])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-285" type="checkbox" ><label for="sk-estimator-id-285" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>GridSearchCV</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.model_selection.GridSearchCV.html">?<span>Documentation for GridSearchCV</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>GridSearchCV(estimator=Pipeline(steps=[(&#x27;scaling&#x27;, MinMaxScaler()),
+                              &#x27;reduce_dim&#x27;: [SelectKBest(score_func=&lt;function mutual_info_classif at 0x7fd8510504c0&gt;)],
+                              &#x27;reduce_dim__k&#x27;: [2, 4, 8]}])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-299" type="checkbox" ><label for="sk-estimator-id-299" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>GridSearchCV</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.model_selection.GridSearchCV.html">?<span>Documentation for GridSearchCV</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>GridSearchCV(estimator=Pipeline(steps=[(&#x27;scaling&#x27;, MinMaxScaler()),
                                            (&#x27;reduce_dim&#x27;, &#x27;passthrough&#x27;),
                                            (&#x27;classify&#x27;,
                                             LinearSVC(dual=False,
@@ -549,10 +549,10 @@ Illustration of ``Pipeline`` and ``GridSearchCV``
                                              NMF(max_iter=1000)],
                               &#x27;reduce_dim__n_components&#x27;: [2, 4, 8]},
                              {&#x27;classify__C&#x27;: [1, 10, 100, 1000],
-                              &#x27;reduce_dim&#x27;: [SelectKBest(score_func=&lt;function mutual_info_classif at 0x7fb58f5c0160&gt;)],
-                              &#x27;reduce_dim__k&#x27;: [2, 4, 8]}])</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-286" type="checkbox" ><label for="sk-estimator-id-286" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>best_estimator_: Pipeline</div></div></label><div class="sk-toggleable__content fitted"><pre>Pipeline(steps=[(&#x27;scaling&#x27;, MinMaxScaler()),
+                              &#x27;reduce_dim&#x27;: [SelectKBest(score_func=&lt;function mutual_info_classif at 0x7fd8510504c0&gt;)],
+                              &#x27;reduce_dim__k&#x27;: [2, 4, 8]}])</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-300" type="checkbox" ><label for="sk-estimator-id-300" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>best_estimator_: Pipeline</div></div></label><div class="sk-toggleable__content fitted"><pre>Pipeline(steps=[(&#x27;scaling&#x27;, MinMaxScaler()),
                     (&#x27;reduce_dim&#x27;, PCA(iterated_power=7, n_components=8)),
-                    (&#x27;classify&#x27;, LinearSVC(C=1, dual=False, max_iter=10000))])</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-287" type="checkbox" ><label for="sk-estimator-id-287" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>MinMaxScaler</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.MinMaxScaler.html">?<span>Documentation for MinMaxScaler</span></a></div></label><div class="sk-toggleable__content fitted"><pre>MinMaxScaler()</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-288" type="checkbox" ><label for="sk-estimator-id-288" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>PCA</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.decomposition.PCA.html">?<span>Documentation for PCA</span></a></div></label><div class="sk-toggleable__content fitted"><pre>PCA(iterated_power=7, n_components=8)</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-289" type="checkbox" ><label for="sk-estimator-id-289" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>LinearSVC</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.svm.LinearSVC.html">?<span>Documentation for LinearSVC</span></a></div></label><div class="sk-toggleable__content fitted"><pre>LinearSVC(C=1, dual=False, max_iter=10000)</pre></div> </div></div></div></div></div></div></div></div></div></div></div>
+                    (&#x27;classify&#x27;, LinearSVC(C=1, dual=False, max_iter=10000))])</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-301" type="checkbox" ><label for="sk-estimator-id-301" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>MinMaxScaler</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.MinMaxScaler.html">?<span>Documentation for MinMaxScaler</span></a></div></label><div class="sk-toggleable__content fitted"><pre>MinMaxScaler()</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-302" type="checkbox" ><label for="sk-estimator-id-302" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>PCA</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.decomposition.PCA.html">?<span>Documentation for PCA</span></a></div></label><div class="sk-toggleable__content fitted"><pre>PCA(iterated_power=7, n_components=8)</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-303" type="checkbox" ><label for="sk-estimator-id-303" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>LinearSVC</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.svm.LinearSVC.html">?<span>Documentation for LinearSVC</span></a></div></label><div class="sk-toggleable__content fitted"><pre>LinearSVC(C=1, dual=False, max_iter=10000)</pre></div> </div></div></div></div></div></div></div></div></div></div></div>
     </div>
     <br />
     <br />
@@ -652,7 +652,7 @@ a transformer is costly.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 44.030 seconds)
+   **Total running time of the script:** (0 minutes 50.514 seconds)
 
 
 .. _sphx_glr_download_auto_examples_compose_plot_compare_reduction.py:
diff --git a/dev/_sources/auto_examples/compose/plot_digits_pipe.rst.txt b/dev/_sources/auto_examples/compose/plot_digits_pipe.rst.txt
index 3eaaca16e787a..08ef6a3eb054f 100644
--- a/dev/_sources/auto_examples/compose/plot_digits_pipe.rst.txt
+++ b/dev/_sources/auto_examples/compose/plot_digits_pipe.rst.txt
@@ -131,7 +131,7 @@ We use a GridSearchCV to set the dimensionality of the PCA
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 1.111 seconds)
+   **Total running time of the script:** (0 minutes 1.470 seconds)
 
 
 .. _sphx_glr_download_auto_examples_compose_plot_digits_pipe.py:
diff --git a/dev/_sources/auto_examples/compose/plot_feature_union.rst.txt b/dev/_sources/auto_examples/compose/plot_feature_union.rst.txt
index e4e769e734818..c1476bf9c713f 100644
--- a/dev/_sources/auto_examples/compose/plot_feature_union.rst.txt
+++ b/dev/_sources/auto_examples/compose/plot_feature_union.rst.txt
@@ -287,7 +287,7 @@ dataset and is only used to illustrate the usage of FeatureUnion.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.459 seconds)
+   **Total running time of the script:** (0 minutes 0.466 seconds)
 
 
 .. _sphx_glr_download_auto_examples_compose_plot_feature_union.py:
diff --git a/dev/_sources/auto_examples/compose/plot_transformed_target.rst.txt b/dev/_sources/auto_examples/compose/plot_transformed_target.rst.txt
index 785efd1fded83..f0f4b51f55fa2 100644
--- a/dev/_sources/auto_examples/compose/plot_transformed_target.rst.txt
+++ b/dev/_sources/auto_examples/compose/plot_transformed_target.rst.txt
@@ -381,7 +381,7 @@ better model fit.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 1.336 seconds)
+   **Total running time of the script:** (0 minutes 1.683 seconds)
 
 
 .. _sphx_glr_download_auto_examples_compose_plot_transformed_target.py:
diff --git a/dev/_sources/auto_examples/covariance/plot_covariance_estimation.rst.txt b/dev/_sources/auto_examples/covariance/plot_covariance_estimation.rst.txt
index 1e5532b3be51a..a882667eb3807 100644
--- a/dev/_sources/auto_examples/covariance/plot_covariance_estimation.rst.txt
+++ b/dev/_sources/auto_examples/covariance/plot_covariance_estimation.rst.txt
@@ -249,7 +249,7 @@ cross-validation, or with the LedoitWolf and OAS estimates.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.421 seconds)
+   **Total running time of the script:** (0 minutes 0.524 seconds)
 
 
 .. _sphx_glr_download_auto_examples_covariance_plot_covariance_estimation.py:
diff --git a/dev/_sources/auto_examples/covariance/plot_lw_vs_oas.rst.txt b/dev/_sources/auto_examples/covariance/plot_lw_vs_oas.rst.txt
index b9bb150d79873..174fe12d5f878 100644
--- a/dev/_sources/auto_examples/covariance/plot_lw_vs_oas.rst.txt
+++ b/dev/_sources/auto_examples/covariance/plot_lw_vs_oas.rst.txt
@@ -153,7 +153,7 @@ Chen et al., IEEE Trans. on Sign. Proc., Volume 58, Issue 10, October 2010.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 2.455 seconds)
+   **Total running time of the script:** (0 minutes 2.785 seconds)
 
 
 .. _sphx_glr_download_auto_examples_covariance_plot_lw_vs_oas.py:
diff --git a/dev/_sources/auto_examples/covariance/plot_mahalanobis_distances.rst.txt b/dev/_sources/auto_examples/covariance/plot_mahalanobis_distances.rst.txt
index 1ddc50a0e3ca4..59389176d6144 100644
--- a/dev/_sources/auto_examples/covariance/plot_mahalanobis_distances.rst.txt
+++ b/dev/_sources/auto_examples/covariance/plot_mahalanobis_distances.rst.txt
@@ -320,7 +320,7 @@ distribution of inlier samples for robust MCD based Mahalanobis distances.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.265 seconds)
+   **Total running time of the script:** (0 minutes 0.320 seconds)
 
 
 .. _sphx_glr_download_auto_examples_covariance_plot_mahalanobis_distances.py:
diff --git a/dev/_sources/auto_examples/covariance/plot_robust_vs_empirical_covariance.rst.txt b/dev/_sources/auto_examples/covariance/plot_robust_vs_empirical_covariance.rst.txt
index 1c06048827f96..c90109cb3b043 100644
--- a/dev/_sources/auto_examples/covariance/plot_robust_vs_empirical_covariance.rst.txt
+++ b/dev/_sources/auto_examples/covariance/plot_robust_vs_empirical_covariance.rst.txt
@@ -224,7 +224,7 @@ References
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 2.489 seconds)
+   **Total running time of the script:** (0 minutes 3.272 seconds)
 
 
 .. _sphx_glr_download_auto_examples_covariance_plot_robust_vs_empirical_covariance.py:
diff --git a/dev/_sources/auto_examples/covariance/plot_sparse_cov.rst.txt b/dev/_sources/auto_examples/covariance/plot_sparse_cov.rst.txt
index 3b1a02a98e28c..23f71e8aa8047 100644
--- a/dev/_sources/auto_examples/covariance/plot_sparse_cov.rst.txt
+++ b/dev/_sources/auto_examples/covariance/plot_sparse_cov.rst.txt
@@ -247,7 +247,7 @@ Plot the results
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.436 seconds)
+   **Total running time of the script:** (0 minutes 0.574 seconds)
 
 
 .. _sphx_glr_download_auto_examples_covariance_plot_sparse_cov.py:
diff --git a/dev/_sources/auto_examples/cross_decomposition/plot_compare_cross_decomposition.rst.txt b/dev/_sources/auto_examples/cross_decomposition/plot_compare_cross_decomposition.rst.txt
index d633d2b913475..3264665c1302a 100644
--- a/dev/_sources/auto_examples/cross_decomposition/plot_compare_cross_decomposition.rst.txt
+++ b/dev/_sources/auto_examples/cross_decomposition/plot_compare_cross_decomposition.rst.txt
@@ -336,7 +336,7 @@ CCA (PLS mode B with symmetric deflation)
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.190 seconds)
+   **Total running time of the script:** (0 minutes 0.249 seconds)
 
 
 .. _sphx_glr_download_auto_examples_cross_decomposition_plot_compare_cross_decomposition.py:
diff --git a/dev/_sources/auto_examples/cross_decomposition/plot_pcr_vs_pls.rst.txt b/dev/_sources/auto_examples/cross_decomposition/plot_pcr_vs_pls.rst.txt
index d32dcbd90770c..62aebd11c7c2f 100644
--- a/dev/_sources/auto_examples/cross_decomposition/plot_pcr_vs_pls.rst.txt
+++ b/dev/_sources/auto_examples/cross_decomposition/plot_pcr_vs_pls.rst.txt
@@ -292,7 +292,7 @@ component which has the most preditive power on the target.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.507 seconds)
+   **Total running time of the script:** (0 minutes 0.606 seconds)
 
 
 .. _sphx_glr_download_auto_examples_cross_decomposition_plot_pcr_vs_pls.py:
diff --git a/dev/_sources/auto_examples/datasets/plot_random_multilabel_dataset.rst.txt b/dev/_sources/auto_examples/datasets/plot_random_multilabel_dataset.rst.txt
index 954c3c3fe214a..2d9126c3d0e5a 100644
--- a/dev/_sources/auto_examples/datasets/plot_random_multilabel_dataset.rst.txt
+++ b/dev/_sources/auto_examples/datasets/plot_random_multilabel_dataset.rst.txt
@@ -158,7 +158,7 @@ feature distinguishes a particular class.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.128 seconds)
+   **Total running time of the script:** (0 minutes 0.183 seconds)
 
 
 .. _sphx_glr_download_auto_examples_datasets_plot_random_multilabel_dataset.py:
diff --git a/dev/_sources/auto_examples/decomposition/plot_faces_decomposition.rst.txt b/dev/_sources/auto_examples/decomposition/plot_faces_decomposition.rst.txt
index 9cf2c8f119f0b..f4749cf94e8dd 100644
--- a/dev/_sources/auto_examples/decomposition/plot_faces_decomposition.rst.txt
+++ b/dev/_sources/auto_examples/decomposition/plot_faces_decomposition.rst.txt
@@ -609,7 +609,7 @@ coefficients are positively constrained.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 7.399 seconds)
+   **Total running time of the script:** (0 minutes 8.319 seconds)
 
 
 .. _sphx_glr_download_auto_examples_decomposition_plot_faces_decomposition.py:
diff --git a/dev/_sources/auto_examples/decomposition/plot_ica_blind_source_separation.rst.txt b/dev/_sources/auto_examples/decomposition/plot_ica_blind_source_separation.rst.txt
index 30c600a532f44..8ebe50829bd90 100644
--- a/dev/_sources/auto_examples/decomposition/plot_ica_blind_source_separation.rst.txt
+++ b/dev/_sources/auto_examples/decomposition/plot_ica_blind_source_separation.rst.txt
@@ -159,7 +159,7 @@ Plot results
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.354 seconds)
+   **Total running time of the script:** (0 minutes 0.431 seconds)
 
 
 .. _sphx_glr_download_auto_examples_decomposition_plot_ica_blind_source_separation.py:
diff --git a/dev/_sources/auto_examples/decomposition/plot_ica_vs_pca.rst.txt b/dev/_sources/auto_examples/decomposition/plot_ica_vs_pca.rst.txt
index d0c932e4cb363..ff38d54de78ea 100644
--- a/dev/_sources/auto_examples/decomposition/plot_ica_vs_pca.rst.txt
+++ b/dev/_sources/auto_examples/decomposition/plot_ica_vs_pca.rst.txt
@@ -176,7 +176,7 @@ Plot results
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.365 seconds)
+   **Total running time of the script:** (0 minutes 0.413 seconds)
 
 
 .. _sphx_glr_download_auto_examples_decomposition_plot_ica_vs_pca.py:
diff --git a/dev/_sources/auto_examples/decomposition/plot_image_denoising.rst.txt b/dev/_sources/auto_examples/decomposition/plot_image_denoising.rst.txt
index 106b8ada44457..4814f5e77cf56 100644
--- a/dev/_sources/auto_examples/decomposition/plot_image_denoising.rst.txt
+++ b/dev/_sources/auto_examples/decomposition/plot_image_denoising.rst.txt
@@ -244,7 +244,7 @@ Learn the dictionary from reference patches
 
 
 .. image-sg:: /auto_examples/decomposition/images/sphx_glr_plot_image_denoising_002.png
-   :alt: Dictionary learned from face patches Train time 14.8s on 22692 patches
+   :alt: Dictionary learned from face patches Train time 14.9s on 22692 patches
    :srcset: /auto_examples/decomposition/images/sphx_glr_plot_image_denoising_002.png
    :class: sphx-glr-single-img
 
@@ -254,7 +254,7 @@ Learn the dictionary from reference patches
  .. code-block:: none
 
     Learning the dictionary...
-    2.0 iterations / 125 steps in 14.81.
+    2.0 iterations / 125 steps in 14.86.
 
 
 
@@ -330,7 +330,7 @@ Extract noisy patches and reconstruct them using the dictionary
     *
 
       .. image-sg:: /auto_examples/decomposition/images/sphx_glr_plot_image_denoising_005.png
-         :alt: Least-angle regression 4 atoms (time: 8.6s), Image, Difference (norm: 13.35)
+         :alt: Least-angle regression 4 atoms (time: 8.5s), Image, Difference (norm: 13.35)
          :srcset: /auto_examples/decomposition/images/sphx_glr_plot_image_denoising_005.png
          :class: sphx-glr-multi-img
 
@@ -350,13 +350,13 @@ Extract noisy patches and reconstruct them using the dictionary
     done in 0.00s.
     Orthogonal Matching Pursuit
     1 atom...
-    done in 0.54s.
+    done in 0.53s.
     Orthogonal Matching Pursuit
     2 atoms...
-    done in 1.12s.
+    done in 1.11s.
     Least-angle regression
     4 atoms...
-    done in 8.57s.
+    done in 8.52s.
     Thresholding
      alpha=0.1...
     done in 0.09s.
@@ -367,7 +367,7 @@ Extract noisy patches and reconstruct them using the dictionary
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 26.263 seconds)
+   **Total running time of the script:** (0 minutes 26.305 seconds)
 
 
 .. _sphx_glr_download_auto_examples_decomposition_plot_image_denoising.py:
diff --git a/dev/_sources/auto_examples/decomposition/plot_incremental_pca.rst.txt b/dev/_sources/auto_examples/decomposition/plot_incremental_pca.rst.txt
index f77cac1ab57fe..ebba7714fab47 100644
--- a/dev/_sources/auto_examples/decomposition/plot_incremental_pca.rst.txt
+++ b/dev/_sources/auto_examples/decomposition/plot_incremental_pca.rst.txt
@@ -109,7 +109,7 @@ incremental approaches.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.226 seconds)
+   **Total running time of the script:** (0 minutes 0.232 seconds)
 
 
 .. _sphx_glr_download_auto_examples_decomposition_plot_incremental_pca.py:
diff --git a/dev/_sources/auto_examples/decomposition/plot_kernel_pca.rst.txt b/dev/_sources/auto_examples/decomposition/plot_kernel_pca.rst.txt
index 1d8f5d423b9b9..2eae721cbdf8a 100644
--- a/dev/_sources/auto_examples/decomposition/plot_kernel_pca.rst.txt
+++ b/dev/_sources/auto_examples/decomposition/plot_kernel_pca.rst.txt
@@ -283,7 +283,7 @@ the mapping.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.541 seconds)
+   **Total running time of the script:** (0 minutes 0.626 seconds)
 
 
 .. _sphx_glr_download_auto_examples_decomposition_plot_kernel_pca.py:
diff --git a/dev/_sources/auto_examples/decomposition/plot_pca_iris.rst.txt b/dev/_sources/auto_examples/decomposition/plot_pca_iris.rst.txt
index 2873b4bc0f5ac..ce11e2ddd952a 100644
--- a/dev/_sources/auto_examples/decomposition/plot_pca_iris.rst.txt
+++ b/dev/_sources/auto_examples/decomposition/plot_pca_iris.rst.txt
@@ -193,7 +193,7 @@ transformation, we see that we can identify each species using only the first fe
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 2.047 seconds)
+   **Total running time of the script:** (0 minutes 2.112 seconds)
 
 
 .. _sphx_glr_download_auto_examples_decomposition_plot_pca_iris.py:
diff --git a/dev/_sources/auto_examples/decomposition/plot_pca_vs_fa_model_selection.rst.txt b/dev/_sources/auto_examples/decomposition/plot_pca_vs_fa_model_selection.rst.txt
index dc5b73b1c3236..da023b40b2fa2 100644
--- a/dev/_sources/auto_examples/decomposition/plot_pca_vs_fa_model_selection.rst.txt
+++ b/dev/_sources/auto_examples/decomposition/plot_pca_vs_fa_model_selection.rst.txt
@@ -227,7 +227,7 @@ Fit the models
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 2.932 seconds)
+   **Total running time of the script:** (0 minutes 3.376 seconds)
 
 
 .. _sphx_glr_download_auto_examples_decomposition_plot_pca_vs_fa_model_selection.py:
diff --git a/dev/_sources/auto_examples/decomposition/plot_pca_vs_lda.rst.txt b/dev/_sources/auto_examples/decomposition/plot_pca_vs_lda.rst.txt
index 624f1e98e7f7f..26271806c46d1 100644
--- a/dev/_sources/auto_examples/decomposition/plot_pca_vs_lda.rst.txt
+++ b/dev/_sources/auto_examples/decomposition/plot_pca_vs_lda.rst.txt
@@ -124,7 +124,7 @@ LDA, in contrast to PCA, is a supervised method, using known class labels.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.188 seconds)
+   **Total running time of the script:** (0 minutes 0.235 seconds)
 
 
 .. _sphx_glr_download_auto_examples_decomposition_plot_pca_vs_lda.py:
diff --git a/dev/_sources/auto_examples/decomposition/plot_sparse_coding.rst.txt b/dev/_sources/auto_examples/decomposition/plot_sparse_coding.rst.txt
index 07149979ac22a..21d3081c98b06 100644
--- a/dev/_sources/auto_examples/decomposition/plot_sparse_coding.rst.txt
+++ b/dev/_sources/auto_examples/decomposition/plot_sparse_coding.rst.txt
@@ -139,7 +139,7 @@ is performed in order to stay on the same order of magnitude.
             dictionary=D, transform_algorithm="threshold", transform_alpha=20
         )
         x = coder.transform(y.reshape(1, -1))
-        _, idx = np.where(x != 0)
+        _, idx = (x != 0).nonzero()
         x[0, idx], _, _, _ = np.linalg.lstsq(D[idx, :].T, y, rcond=None)
         x = np.ravel(np.dot(x, D))
         squared_error = np.sum((y - x) ** 2)
@@ -158,7 +158,7 @@ is performed in order to stay on the same order of magnitude.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.266 seconds)
+   **Total running time of the script:** (0 minutes 0.264 seconds)
 
 
 .. _sphx_glr_download_auto_examples_decomposition_plot_sparse_coding.py:
diff --git a/dev/_sources/auto_examples/decomposition/plot_varimax_fa.rst.txt b/dev/_sources/auto_examples/decomposition/plot_varimax_fa.rst.txt
index 6335976060da2..4e7ea6f5cd1e0 100644
--- a/dev/_sources/auto_examples/decomposition/plot_varimax_fa.rst.txt
+++ b/dev/_sources/auto_examples/decomposition/plot_varimax_fa.rst.txt
@@ -186,7 +186,7 @@ Run factor analysis with Varimax rotation
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.391 seconds)
+   **Total running time of the script:** (0 minutes 0.418 seconds)
 
 
 .. _sphx_glr_download_auto_examples_decomposition_plot_varimax_fa.py:
diff --git a/dev/_sources/auto_examples/ensemble/index.rst.txt b/dev/_sources/auto_examples/ensemble/index.rst.txt
index bd38ea078f91e..291870f91b35e 100644
--- a/dev/_sources/auto_examples/ensemble/index.rst.txt
+++ b/dev/_sources/auto_examples/ensemble/index.rst.txt
@@ -306,23 +306,6 @@ Examples concerning the :mod:`sklearn.ensemble` module.
     </div>
 
 
-.. raw:: html
-
-    <div class="sphx-glr-thumbcontainer" tooltip="Plot the class probabilities of the first sample in a toy dataset predicted by three different classifiers and averaged by the VotingClassifier.">
-
-.. only:: html
-
-  .. image:: /auto_examples/ensemble/images/thumb/sphx_glr_plot_voting_probas_thumb.png
-    :alt:
-
-  :ref:`sphx_glr_auto_examples_ensemble_plot_voting_probas.py`
-
-.. raw:: html
-
-      <div class="sphx-glr-thumbnail-title">Plot class probabilities calculated by the VotingClassifier</div>
-    </div>
-
-
 .. raw:: html
 
     <div class="sphx-glr-thumbcontainer" tooltip="A voting regressor is an ensemble meta-estimator that fits several base regressors, each on the whole dataset. Then it averages the individual predictions to form a final prediction. We will use three different regressors to predict the data: GradientBoostingRegressor, RandomForestRegressor, and LinearRegression). Then the above 3 regressors will be used for the VotingRegressor.">
@@ -340,23 +323,6 @@ Examples concerning the :mod:`sklearn.ensemble` module.
     </div>
 
 
-.. raw:: html
-
-    <div class="sphx-glr-thumbcontainer" tooltip="Plot the decision boundaries of a VotingClassifier for two features of the Iris dataset.">
-
-.. only:: html
-
-  .. image:: /auto_examples/ensemble/images/thumb/sphx_glr_plot_voting_decision_regions_thumb.png
-    :alt:
-
-  :ref:`sphx_glr_auto_examples_ensemble_plot_voting_decision_regions.py`
-
-.. raw:: html
-
-      <div class="sphx-glr-thumbnail-title">Plot the decision boundaries of a VotingClassifier</div>
-    </div>
-
-
 .. raw:: html
 
     <div class="sphx-glr-thumbcontainer" tooltip="Plot the decision surfaces of forests of randomized trees trained on pairs of features of the iris dataset.">
@@ -425,6 +391,23 @@ Examples concerning the :mod:`sklearn.ensemble` module.
     </div>
 
 
+.. raw:: html
+
+    <div class="sphx-glr-thumbcontainer" tooltip="Plot the predicted class probabilities in a toy dataset predicted by three different classifiers and averaged by the VotingClassifier.">
+
+.. only:: html
+
+  .. image:: /auto_examples/ensemble/images/thumb/sphx_glr_plot_voting_decision_regions_thumb.png
+    :alt:
+
+  :ref:`sphx_glr_auto_examples_ensemble_plot_voting_decision_regions.py`
+
+.. raw:: html
+
+      <div class="sphx-glr-thumbnail-title">Visualizing the probabilistic predictions of a VotingClassifier</div>
+    </div>
+
+
 .. thumbnail-parent-div-close
 
 .. raw:: html
@@ -452,11 +435,10 @@ Examples concerning the :mod:`sklearn.ensemble` module.
    /auto_examples/ensemble/plot_monotonic_constraints
    /auto_examples/ensemble/plot_adaboost_multiclass
    /auto_examples/ensemble/plot_ensemble_oob
-   /auto_examples/ensemble/plot_voting_probas
    /auto_examples/ensemble/plot_voting_regressor
-   /auto_examples/ensemble/plot_voting_decision_regions
    /auto_examples/ensemble/plot_forest_iris
    /auto_examples/ensemble/plot_gradient_boosting_quantile
    /auto_examples/ensemble/plot_bias_variance
    /auto_examples/ensemble/plot_adaboost_twoclass
+   /auto_examples/ensemble/plot_voting_decision_regions
 
diff --git a/dev/_sources/auto_examples/ensemble/plot_adaboost_multiclass.rst.txt b/dev/_sources/auto_examples/ensemble/plot_adaboost_multiclass.rst.txt
index ca195a5e74224..5d73b2ecf9440 100644
--- a/dev/_sources/auto_examples/ensemble/plot_adaboost_multiclass.rst.txt
+++ b/dev/_sources/auto_examples/ensemble/plot_adaboost_multiclass.rst.txt
@@ -380,7 +380,7 @@ weights are built to counter-balance the worse performing weak learners.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 4.289 seconds)
+   **Total running time of the script:** (0 minutes 4.521 seconds)
 
 
 .. _sphx_glr_download_auto_examples_ensemble_plot_adaboost_multiclass.py:
diff --git a/dev/_sources/auto_examples/ensemble/plot_adaboost_regression.rst.txt b/dev/_sources/auto_examples/ensemble/plot_adaboost_regression.rst.txt
index e1e487a52f06f..e0aac4a394cbb 100644
--- a/dev/_sources/auto_examples/ensemble/plot_adaboost_regression.rst.txt
+++ b/dev/_sources/auto_examples/ensemble/plot_adaboost_regression.rst.txt
@@ -141,7 +141,7 @@ single decision tree regressor and AdaBoost regressor, could fit the data.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.504 seconds)
+   **Total running time of the script:** (0 minutes 0.474 seconds)
 
 
 .. _sphx_glr_download_auto_examples_ensemble_plot_adaboost_regression.py:
diff --git a/dev/_sources/auto_examples/ensemble/plot_adaboost_twoclass.rst.txt b/dev/_sources/auto_examples/ensemble/plot_adaboost_twoclass.rst.txt
index c040e30b80112..d8bf7e4d17bf5 100644
--- a/dev/_sources/auto_examples/ensemble/plot_adaboost_twoclass.rst.txt
+++ b/dev/_sources/auto_examples/ensemble/plot_adaboost_twoclass.rst.txt
@@ -98,7 +98,7 @@ with a decision score above some value.
 
     # Plot the training points
     for i, n, c in zip(range(2), class_names, plot_colors):
-        idx = np.where(y == i)
+        idx = (y == i).nonzero()
         plt.scatter(
             X[idx, 0],
             X[idx, 1],
@@ -141,7 +141,7 @@ with a decision score above some value.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.710 seconds)
+   **Total running time of the script:** (0 minutes 0.769 seconds)
 
 
 .. _sphx_glr_download_auto_examples_ensemble_plot_adaboost_twoclass.py:
diff --git a/dev/_sources/auto_examples/ensemble/plot_bias_variance.rst.txt b/dev/_sources/auto_examples/ensemble/plot_bias_variance.rst.txt
index 81b8eb4dfdf70..60d538997b934 100644
--- a/dev/_sources/auto_examples/ensemble/plot_bias_variance.rst.txt
+++ b/dev/_sources/auto_examples/ensemble/plot_bias_variance.rst.txt
@@ -221,8 +221,8 @@ References
 
         plt.subplot(2, n_estimators, n_estimators + n + 1)
         plt.plot(X_test, y_error, "r", label="$error(x)$")
-        plt.plot(X_test, y_bias, "b", label="$bias^2(x)$"),
-        plt.plot(X_test, y_var, "g", label="$variance(x)$"),
+        plt.plot(X_test, y_bias, "b", label="$bias^2(x)$")
+        plt.plot(X_test, y_var, "g", label="$variance(x)$")
         plt.plot(X_test, y_noise, "c", label="$noise(x)$")
 
         plt.xlim([-5, 5])
@@ -237,7 +237,7 @@ References
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 1.220 seconds)
+   **Total running time of the script:** (0 minutes 1.339 seconds)
 
 
 .. _sphx_glr_download_auto_examples_ensemble_plot_bias_variance.py:
diff --git a/dev/_sources/auto_examples/ensemble/plot_ensemble_oob.rst.txt b/dev/_sources/auto_examples/ensemble/plot_ensemble_oob.rst.txt
index 81d078f5f26ba..f2ebe1da8e27d 100644
--- a/dev/_sources/auto_examples/ensemble/plot_ensemble_oob.rst.txt
+++ b/dev/_sources/auto_examples/ensemble/plot_ensemble_oob.rst.txt
@@ -137,7 +137,7 @@ error stabilizes.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 3.866 seconds)
+   **Total running time of the script:** (0 minutes 3.885 seconds)
 
 
 .. _sphx_glr_download_auto_examples_ensemble_plot_ensemble_oob.py:
diff --git a/dev/_sources/auto_examples/ensemble/plot_feature_transformation.rst.txt b/dev/_sources/auto_examples/ensemble/plot_feature_transformation.rst.txt
index 9b07905618d6d..35fa39ac4955f 100644
--- a/dev/_sources/auto_examples/ensemble/plot_feature_transformation.rst.txt
+++ b/dev/_sources/auto_examples/ensemble/plot_feature_transformation.rst.txt
@@ -1071,18 +1071,18 @@ Therefore, we wrapped the call to `apply` within a `FunctionTransformer`.
       background-color: var(--sklearn-color-fitted-level-3);
     }
     </style><div id="sk-container-id-21" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;functiontransformer&#x27;,
-                     FunctionTransformer(func=&lt;function rf_apply at 0x7fb5860d4700&gt;,
+                     FunctionTransformer(func=&lt;function rf_apply at 0x7fd843d2d7e0&gt;,
                                          kw_args={&#x27;model&#x27;: RandomForestClassifier(max_depth=3,
                                                                                   n_estimators=10,
                                                                                   random_state=10)})),
                     (&#x27;onehotencoder&#x27;, OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)),
                     (&#x27;logisticregression&#x27;, LogisticRegression(max_iter=1000))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-92" type="checkbox" ><label for="sk-estimator-id-92" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>Pipeline</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.pipeline.Pipeline.html">?<span>Documentation for Pipeline</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>Pipeline(steps=[(&#x27;functiontransformer&#x27;,
-                     FunctionTransformer(func=&lt;function rf_apply at 0x7fb5860d4700&gt;,
+                     FunctionTransformer(func=&lt;function rf_apply at 0x7fd843d2d7e0&gt;,
                                          kw_args={&#x27;model&#x27;: RandomForestClassifier(max_depth=3,
                                                                                   n_estimators=10,
                                                                                   random_state=10)})),
                     (&#x27;onehotencoder&#x27;, OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)),
-                    (&#x27;logisticregression&#x27;, LogisticRegression(max_iter=1000))])</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-93" type="checkbox" ><label for="sk-estimator-id-93" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>rf_apply</div><div class="caption">FunctionTransformer</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.FunctionTransformer.html">?<span>Documentation for FunctionTransformer</span></a></div></label><div class="sk-toggleable__content fitted"><pre>FunctionTransformer(func=&lt;function rf_apply at 0x7fb5860d4700&gt;,
+                    (&#x27;logisticregression&#x27;, LogisticRegression(max_iter=1000))])</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-93" type="checkbox" ><label for="sk-estimator-id-93" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>rf_apply</div><div class="caption">FunctionTransformer</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.FunctionTransformer.html">?<span>Documentation for FunctionTransformer</span></a></div></label><div class="sk-toggleable__content fitted"><pre>FunctionTransformer(func=&lt;function rf_apply at 0x7fd843d2d7e0&gt;,
                         kw_args={&#x27;model&#x27;: RandomForestClassifier(max_depth=3,
                                                                  n_estimators=10,
                                                                  random_state=10)})</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-94" type="checkbox" ><label for="sk-estimator-id-94" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>OneHotEncoder</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.OneHotEncoder.html">?<span>Documentation for OneHotEncoder</span></a></div></label><div class="sk-toggleable__content fitted"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-95" type="checkbox" ><label for="sk-estimator-id-95" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>LogisticRegression</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.linear_model.LogisticRegression.html">?<span>Documentation for LogisticRegression</span></a></div></label><div class="sk-toggleable__content fitted"><pre>LogisticRegression(max_iter=1000)</pre></div> </div></div></div></div></div></div>
@@ -1533,16 +1533,16 @@ Therefore, we wrapped the call to `apply` within a `FunctionTransformer`.
       background-color: var(--sklearn-color-fitted-level-3);
     }
     </style><div id="sk-container-id-22" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;functiontransformer&#x27;,
-                     FunctionTransformer(func=&lt;function gbdt_apply at 0x7fb57bf40820&gt;,
+                     FunctionTransformer(func=&lt;function gbdt_apply at 0x7fd8436de4d0&gt;,
                                          kw_args={&#x27;model&#x27;: GradientBoostingClassifier(n_estimators=10,
                                                                                       random_state=10)})),
                     (&#x27;onehotencoder&#x27;, OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)),
                     (&#x27;logisticregression&#x27;, LogisticRegression(max_iter=1000))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-96" type="checkbox" ><label for="sk-estimator-id-96" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>Pipeline</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.pipeline.Pipeline.html">?<span>Documentation for Pipeline</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>Pipeline(steps=[(&#x27;functiontransformer&#x27;,
-                     FunctionTransformer(func=&lt;function gbdt_apply at 0x7fb57bf40820&gt;,
+                     FunctionTransformer(func=&lt;function gbdt_apply at 0x7fd8436de4d0&gt;,
                                          kw_args={&#x27;model&#x27;: GradientBoostingClassifier(n_estimators=10,
                                                                                       random_state=10)})),
                     (&#x27;onehotencoder&#x27;, OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)),
-                    (&#x27;logisticregression&#x27;, LogisticRegression(max_iter=1000))])</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-97" type="checkbox" ><label for="sk-estimator-id-97" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>gbdt_apply</div><div class="caption">FunctionTransformer</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.FunctionTransformer.html">?<span>Documentation for FunctionTransformer</span></a></div></label><div class="sk-toggleable__content fitted"><pre>FunctionTransformer(func=&lt;function gbdt_apply at 0x7fb57bf40820&gt;,
+                    (&#x27;logisticregression&#x27;, LogisticRegression(max_iter=1000))])</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-97" type="checkbox" ><label for="sk-estimator-id-97" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>gbdt_apply</div><div class="caption">FunctionTransformer</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.FunctionTransformer.html">?<span>Documentation for FunctionTransformer</span></a></div></label><div class="sk-toggleable__content fitted"><pre>FunctionTransformer(func=&lt;function gbdt_apply at 0x7fd8436de4d0&gt;,
                         kw_args={&#x27;model&#x27;: GradientBoostingClassifier(n_estimators=10,
                                                                      random_state=10)})</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-98" type="checkbox" ><label for="sk-estimator-id-98" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>OneHotEncoder</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.OneHotEncoder.html">?<span>Documentation for OneHotEncoder</span></a></div></label><div class="sk-toggleable__content fitted"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-99" type="checkbox" ><label for="sk-estimator-id-99" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>LogisticRegression</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.linear_model.LogisticRegression.html">?<span>Documentation for LogisticRegression</span></a></div></label><div class="sk-toggleable__content fitted"><pre>LogisticRegression(max_iter=1000)</pre></div> </div></div></div></div></div></div>
     </div>
@@ -1617,7 +1617,7 @@ We can finally show the different ROC curves for all the models.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 2.698 seconds)
+   **Total running time of the script:** (0 minutes 3.462 seconds)
 
 
 .. _sphx_glr_download_auto_examples_ensemble_plot_feature_transformation.py:
diff --git a/dev/_sources/auto_examples/ensemble/plot_forest_hist_grad_boosting_comparison.rst.txt b/dev/_sources/auto_examples/ensemble/plot_forest_hist_grad_boosting_comparison.rst.txt
index 286830280ff80..40551c8ea84ed 100644
--- a/dev/_sources/auto_examples/ensemble/plot_forest_hist_grad_boosting_comparison.rst.txt
+++ b/dev/_sources/auto_examples/ensemble/plot_forest_hist_grad_boosting_comparison.rst.txt
@@ -307,7 +307,7 @@ folds of the cross-validation.
 
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@@ -343,7 +343,7 @@ accuracy trade-off of the RF models in this case.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 54.339 seconds)
+   **Total running time of the script:** (1 minutes 1.135 seconds)
 
 
 .. _sphx_glr_download_auto_examples_ensemble_plot_forest_hist_grad_boosting_comparison.py:
diff --git a/dev/_sources/auto_examples/ensemble/plot_forest_importances.rst.txt b/dev/_sources/auto_examples/ensemble/plot_forest_importances.rst.txt
index fce04691b75ca..4561b40e84cf2 100644
--- a/dev/_sources/auto_examples/ensemble/plot_forest_importances.rst.txt
+++ b/dev/_sources/auto_examples/ensemble/plot_forest_importances.rst.txt
@@ -560,7 +560,7 @@ deviation of accumulation of the impurity decrease within each tree.
 
  .. code-block:: none
 
-    Elapsed time to compute the importances: 0.009 seconds
+    Elapsed time to compute the importances: 0.007 seconds
 
 
 
@@ -628,7 +628,7 @@ and can be computed on a left-out test set.
 
  .. code-block:: none
 
-    Elapsed time to compute the importances: 0.503 seconds
+    Elapsed time to compute the importances: 0.630 seconds
 
 
 
@@ -673,7 +673,7 @@ permutation importance to fully omit a feature.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 1.013 seconds)
+   **Total running time of the script:** (0 minutes 1.273 seconds)
 
 
 .. _sphx_glr_download_auto_examples_ensemble_plot_forest_importances.py:
diff --git a/dev/_sources/auto_examples/ensemble/plot_forest_iris.rst.txt b/dev/_sources/auto_examples/ensemble/plot_forest_iris.rst.txt
index a4125c790c870..2039d137d9a9e 100644
--- a/dev/_sources/auto_examples/ensemble/plot_forest_iris.rst.txt
+++ b/dev/_sources/auto_examples/ensemble/plot_forest_iris.rst.txt
@@ -232,7 +232,7 @@ samples are built sequentially and so do not use multiple cores.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 6.220 seconds)
+   **Total running time of the script:** (0 minutes 7.610 seconds)
 
 
 .. _sphx_glr_download_auto_examples_ensemble_plot_forest_iris.py:
diff --git a/dev/_sources/auto_examples/ensemble/plot_gradient_boosting_categorical.rst.txt b/dev/_sources/auto_examples/ensemble/plot_gradient_boosting_categorical.rst.txt
index ffc3c64787b3b..c7734ad0bc391 100644
--- a/dev/_sources/auto_examples/ensemble/plot_gradient_boosting_categorical.rst.txt
+++ b/dev/_sources/auto_examples/ensemble/plot_gradient_boosting_categorical.rst.txt
@@ -424,7 +424,7 @@ to the baseline model that just dropped the categorical features altogether.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 3.707 seconds)
+   **Total running time of the script:** (0 minutes 3.925 seconds)
 
 
 .. _sphx_glr_download_auto_examples_ensemble_plot_gradient_boosting_categorical.py:
diff --git a/dev/_sources/auto_examples/ensemble/plot_gradient_boosting_early_stopping.rst.txt b/dev/_sources/auto_examples/ensemble/plot_gradient_boosting_early_stopping.rst.txt
index b3deb2d69cf62..7ca3b7393687f 100644
--- a/dev/_sources/auto_examples/ensemble/plot_gradient_boosting_early_stopping.rst.txt
+++ b/dev/_sources/auto_examples/ensemble/plot_gradient_boosting_early_stopping.rst.txt
@@ -273,7 +273,7 @@ practical benefits of early stopping:
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 3.347 seconds)
+   **Total running time of the script:** (0 minutes 3.577 seconds)
 
 
 .. _sphx_glr_download_auto_examples_ensemble_plot_gradient_boosting_early_stopping.py:
diff --git a/dev/_sources/auto_examples/ensemble/plot_gradient_boosting_oob.rst.txt b/dev/_sources/auto_examples/ensemble/plot_gradient_boosting_oob.rst.txt
index 93180d8302737..3bee1b242b4c1 100644
--- a/dev/_sources/auto_examples/ensemble/plot_gradient_boosting_oob.rst.txt
+++ b/dev/_sources/auto_examples/ensemble/plot_gradient_boosting_oob.rst.txt
@@ -189,7 +189,7 @@ but is computationally more demanding.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 10.376 seconds)
+   **Total running time of the script:** (0 minutes 10.543 seconds)
 
 
 .. _sphx_glr_download_auto_examples_ensemble_plot_gradient_boosting_oob.py:
diff --git a/dev/_sources/auto_examples/ensemble/plot_gradient_boosting_quantile.rst.txt b/dev/_sources/auto_examples/ensemble/plot_gradient_boosting_quantile.rst.txt
index ad04c7f1fed21..91e9b821dbed1 100644
--- a/dev/_sources/auto_examples/ensemble/plot_gradient_boosting_quantile.rst.txt
+++ b/dev/_sources/auto_examples/ensemble/plot_gradient_boosting_quantile.rst.txt
@@ -292,18 +292,18 @@ Measure the models with :func:`~sklearn.metrics.mean_squared_error` and
 
     <div class="output_subarea output_html rendered_html output_result">
     <style type="text/css">
-    #T_5e273_row0_col3, #T_5e273_row1_col0, #T_5e273_row2_col1, #T_5e273_row3_col2 {
+    #T_e9b5c_row0_col3, #T_e9b5c_row1_col0, #T_e9b5c_row2_col1, #T_e9b5c_row3_col2 {
       font-weight: bold;
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     </style>
-    <table id="T_5e273">
+    <table id="T_e9b5c">
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-          <th id="T_5e273_level0_col0" class="col_heading level0 col0" >pbl=0.05</th>
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-          <th id="T_5e273_level0_col2" class="col_heading level0 col2" >pbl=0.95</th>
-          <th id="T_5e273_level0_col3" class="col_heading level0 col3" >MSE</th>
+          <th id="T_e9b5c_level0_col0" class="col_heading level0 col0" >pbl=0.05</th>
+          <th id="T_e9b5c_level0_col1" class="col_heading level0 col1" >pbl=0.50</th>
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+          <td id="T_e9b5c_row0_col0" class="data row0 col0" >0.715413</td>
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-          <td id="T_5e273_row3_col3" class="data row3 col3" >28.667219</td>
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+          <td id="T_e9b5c_row3_col0" class="data row3 col0" >3.909909</td>
+          <td id="T_e9b5c_row3_col1" class="data row3 col1" >2.145957</td>
+          <td id="T_e9b5c_row3_col2" class="data row3 col2" >0.382005</td>
+          <td id="T_e9b5c_row3_col3" class="data row3 col3" >28.667219</td>
         </tr>
       </tbody>
     </table>
@@ -392,18 +392,18 @@ We then do the same on the test set.
 
     <div class="output_subarea output_html rendered_html output_result">
     <style type="text/css">
-    #T_447f7_row1_col0, #T_447f7_row2_col1, #T_447f7_row2_col3, #T_447f7_row3_col2 {
+    #T_8ab45_row1_col0, #T_8ab45_row2_col1, #T_8ab45_row2_col3, #T_8ab45_row3_col2 {
       font-weight: bold;
     }
     </style>
-    <table id="T_447f7">
+    <table id="T_8ab45">
       <thead>
         <tr>
           <th class="blank level0" >&nbsp;</th>
-          <th id="T_447f7_level0_col0" class="col_heading level0 col0" >pbl=0.05</th>
-          <th id="T_447f7_level0_col1" class="col_heading level0 col1" >pbl=0.50</th>
-          <th id="T_447f7_level0_col2" class="col_heading level0 col2" >pbl=0.95</th>
-          <th id="T_447f7_level0_col3" class="col_heading level0 col3" >MSE</th>
+          <th id="T_8ab45_level0_col0" class="col_heading level0 col0" >pbl=0.05</th>
+          <th id="T_8ab45_level0_col1" class="col_heading level0 col1" >pbl=0.50</th>
+          <th id="T_8ab45_level0_col2" class="col_heading level0 col2" >pbl=0.95</th>
+          <th id="T_8ab45_level0_col3" class="col_heading level0 col3" >MSE</th>
         </tr>
         <tr>
           <th class="index_name level0" >model</th>
@@ -415,32 +415,32 @@ We then do the same on the test set.
       </thead>
       <tbody>
         <tr>
-          <th id="T_447f7_level0_row0" class="row_heading level0 row0" >mse</th>
-          <td id="T_447f7_row0_col0" class="data row0 col0" >0.917281</td>
-          <td id="T_447f7_row0_col1" class="data row0 col1" >0.767498</td>
-          <td id="T_447f7_row0_col2" class="data row0 col2" >0.617715</td>
-          <td id="T_447f7_row0_col3" class="data row0 col3" >6.692901</td>
+          <th id="T_8ab45_level0_row0" class="row_heading level0 row0" >mse</th>
+          <td id="T_8ab45_row0_col0" class="data row0 col0" >0.917281</td>
+          <td id="T_8ab45_row0_col1" class="data row0 col1" >0.767498</td>
+          <td id="T_8ab45_row0_col2" class="data row0 col2" >0.617715</td>
+          <td id="T_8ab45_row0_col3" class="data row0 col3" >6.692901</td>
         </tr>
         <tr>
-          <th id="T_447f7_level0_row1" class="row_heading level0 row1" >q 0.05</th>
-          <td id="T_447f7_row1_col0" class="data row1 col0" >0.144204</td>
-          <td id="T_447f7_row1_col1" class="data row1 col1" >1.245961</td>
-          <td id="T_447f7_row1_col2" class="data row1 col2" >2.347717</td>
-          <td id="T_447f7_row1_col3" class="data row1 col3" >15.648026</td>
+          <th id="T_8ab45_level0_row1" class="row_heading level0 row1" >q 0.05</th>
+          <td id="T_8ab45_row1_col0" class="data row1 col0" >0.144204</td>
+          <td id="T_8ab45_row1_col1" class="data row1 col1" >1.245961</td>
+          <td id="T_8ab45_row1_col2" class="data row1 col2" >2.347717</td>
+          <td id="T_8ab45_row1_col3" class="data row1 col3" >15.648026</td>
         </tr>
         <tr>
-          <th id="T_447f7_level0_row2" class="row_heading level0 row2" >q 0.50</th>
-          <td id="T_447f7_row2_col0" class="data row2 col0" >0.412021</td>
-          <td id="T_447f7_row2_col1" class="data row2 col1" >0.607752</td>
-          <td id="T_447f7_row2_col2" class="data row2 col2" >0.803483</td>
-          <td id="T_447f7_row2_col3" class="data row2 col3" >5.874771</td>
+          <th id="T_8ab45_level0_row2" class="row_heading level0 row2" >q 0.50</th>
+          <td id="T_8ab45_row2_col0" class="data row2 col0" >0.412021</td>
+          <td id="T_8ab45_row2_col1" class="data row2 col1" >0.607752</td>
+          <td id="T_8ab45_row2_col2" class="data row2 col2" >0.803483</td>
+          <td id="T_8ab45_row2_col3" class="data row2 col3" >5.874771</td>
         </tr>
         <tr>
-          <th id="T_447f7_level0_row3" class="row_heading level0 row3" >q 0.95</th>
-          <td id="T_447f7_row3_col0" class="data row3 col0" >4.354394</td>
-          <td id="T_447f7_row3_col1" class="data row3 col1" >2.355445</td>
-          <td id="T_447f7_row3_col2" class="data row3 col2" >0.356497</td>
-          <td id="T_447f7_row3_col3" class="data row3 col3" >34.852774</td>
+          <th id="T_8ab45_level0_row3" class="row_heading level0 row3" >q 0.95</th>
+          <td id="T_8ab45_row3_col0" class="data row3 col0" >4.354394</td>
+          <td id="T_8ab45_row3_col1" class="data row3 col1" >2.355445</td>
+          <td id="T_8ab45_row3_col2" class="data row3 col2" >0.356497</td>
+          <td id="T_8ab45_row3_col3" class="data row3 col3" >34.852774</td>
         </tr>
       </tbody>
     </table>
@@ -548,15 +548,16 @@ To confirm this hypothesis, we tune the hyper-parameters of a new regressor
 of the 5th percentile by selecting the best model parameters by
 cross-validation on the pinball loss with alpha=0.05:
 
-.. GENERATED FROM PYTHON SOURCE LINES 244-274
+.. GENERATED FROM PYTHON SOURCE LINES 244-275
 
 .. code-block:: Python
 
-    from sklearn.experimental import enable_halving_search_cv  # noqa
-    from sklearn.model_selection import HalvingRandomSearchCV
-    from sklearn.metrics import make_scorer
     from pprint import pprint
 
+    from sklearn.experimental import enable_halving_search_cv  # noqa: F401
+    from sklearn.metrics import make_scorer
+    from sklearn.model_selection import HalvingRandomSearchCV
+
     param_grid = dict(
         learning_rate=[0.05, 0.1, 0.2],
         max_depth=[2, 5, 10],
@@ -599,7 +600,7 @@ cross-validation on the pinball loss with alpha=0.05:
 
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 275-283
+.. GENERATED FROM PYTHON SOURCE LINES 276-284
 
 We observe that the hyper-parameters that were hand-tuned for the median
 regressor are in the same range as the hyper-parameters suitable for the 5th
@@ -610,7 +611,7 @@ need to redefine the `scoring` metric used to select the best model, along
 with adjusting the alpha parameter of the inner gradient boosting estimator
 itself:
 
-.. GENERATED FROM PYTHON SOURCE LINES 283-298
+.. GENERATED FROM PYTHON SOURCE LINES 284-299
 
 .. code-block:: Python
 
@@ -646,7 +647,7 @@ itself:
 
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 299-307
+.. GENERATED FROM PYTHON SOURCE LINES 300-308
 
 The result shows that the hyper-parameters for the 95th percentile regressor
 identified by the search procedure are roughly in the same range as the hand-tuned
@@ -657,7 +658,7 @@ that is comprised by the predictions of those two tuned quantile regressors.
 Note that the prediction of the upper 95th percentile has a much coarser shape
 than the prediction of the lower 5th percentile because of the outliers:
 
-.. GENERATED FROM PYTHON SOURCE LINES 307-323
+.. GENERATED FROM PYTHON SOURCE LINES 308-324
 
 .. code-block:: Python
 
@@ -689,7 +690,7 @@ than the prediction of the lower 5th percentile because of the outliers:
 
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 324-329
+.. GENERATED FROM PYTHON SOURCE LINES 325-330
 
 The plot looks qualitatively better than for the untuned models, especially
 for the shape of the of lower quantile.
@@ -697,7 +698,7 @@ for the shape of the of lower quantile.
 We now quantitatively evaluate the joint-calibration of the pair of
 estimators:
 
-.. GENERATED FROM PYTHON SOURCE LINES 329-330
+.. GENERATED FROM PYTHON SOURCE LINES 330-331
 
 .. code-block:: Python
 
@@ -715,7 +716,7 @@ estimators:
 
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 331-332
+.. GENERATED FROM PYTHON SOURCE LINES 332-333
 
 .. code-block:: Python
 
@@ -733,7 +734,7 @@ estimators:
 
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 333-338
+.. GENERATED FROM PYTHON SOURCE LINES 334-339
 
 The calibration of the tuned pair is sadly not better on the test set: the
 width of the estimated confidence interval is still too narrow.
@@ -744,7 +745,7 @@ better assess the variability of those estimates.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 9.901 seconds)
+   **Total running time of the script:** (0 minutes 11.467 seconds)
 
 
 .. _sphx_glr_download_auto_examples_ensemble_plot_gradient_boosting_quantile.py:
diff --git a/dev/_sources/auto_examples/ensemble/plot_gradient_boosting_regression.rst.txt b/dev/_sources/auto_examples/ensemble/plot_gradient_boosting_regression.rst.txt
index 19c6ff588ccf8..eb04902ae4424 100644
--- a/dev/_sources/auto_examples/ensemble/plot_gradient_boosting_regression.rst.txt
+++ b/dev/_sources/auto_examples/ensemble/plot_gradient_boosting_regression.rst.txt
@@ -277,7 +277,7 @@ show that they overlap with 0.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 1.388 seconds)
+   **Total running time of the script:** (0 minutes 1.520 seconds)
 
 
 .. _sphx_glr_download_auto_examples_ensemble_plot_gradient_boosting_regression.py:
diff --git a/dev/_sources/auto_examples/ensemble/plot_gradient_boosting_regularization.rst.txt b/dev/_sources/auto_examples/ensemble/plot_gradient_boosting_regularization.rst.txt
index 350456ee7da61..39794af7a8894 100644
--- a/dev/_sources/auto_examples/ensemble/plot_gradient_boosting_regularization.rst.txt
+++ b/dev/_sources/auto_examples/ensemble/plot_gradient_boosting_regularization.rst.txt
@@ -125,7 +125,7 @@ analogous to the random splits in Random Forests
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 8.336 seconds)
+   **Total running time of the script:** (0 minutes 8.808 seconds)
 
 
 .. _sphx_glr_download_auto_examples_ensemble_plot_gradient_boosting_regularization.py:
diff --git a/dev/_sources/auto_examples/ensemble/plot_hgbt_regression.rst.txt b/dev/_sources/auto_examples/ensemble/plot_hgbt_regression.rst.txt
index 2bf84c941c327..80e70b6c564ec 100644
--- a/dev/_sources/auto_examples/ensemble/plot_hgbt_regression.rst.txt
+++ b/dev/_sources/auto_examples/ensemble/plot_hgbt_regression.rst.txt
@@ -653,7 +653,7 @@ reason why we do no use the `common_params` in this section as done before.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 22.240 seconds)
+   **Total running time of the script:** (0 minutes 25.355 seconds)
 
 
 .. _sphx_glr_download_auto_examples_ensemble_plot_hgbt_regression.py:
diff --git a/dev/_sources/auto_examples/ensemble/plot_isolation_forest.rst.txt b/dev/_sources/auto_examples/ensemble/plot_isolation_forest.rst.txt
index dff356c8bc95d..313c7b6374e99 100644
--- a/dev/_sources/auto_examples/ensemble/plot_isolation_forest.rst.txt
+++ b/dev/_sources/auto_examples/ensemble/plot_isolation_forest.rst.txt
@@ -657,7 +657,7 @@ values close to `1` and are more likely to be inliers.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.451 seconds)
+   **Total running time of the script:** (0 minutes 0.478 seconds)
 
 
 .. _sphx_glr_download_auto_examples_ensemble_plot_isolation_forest.py:
diff --git a/dev/_sources/auto_examples/ensemble/plot_monotonic_constraints.rst.txt b/dev/_sources/auto_examples/ensemble/plot_monotonic_constraints.rst.txt
index 73984135125a6..7f964add5a05b 100644
--- a/dev/_sources/auto_examples/ensemble/plot_monotonic_constraints.rst.txt
+++ b/dev/_sources/auto_examples/ensemble/plot_monotonic_constraints.rst.txt
@@ -1058,7 +1058,7 @@ monotonic constraints by passing a dictionary:
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.625 seconds)
+   **Total running time of the script:** (0 minutes 0.644 seconds)
 
 
 .. _sphx_glr_download_auto_examples_ensemble_plot_monotonic_constraints.py:
diff --git a/dev/_sources/auto_examples/ensemble/plot_random_forest_embedding.rst.txt b/dev/_sources/auto_examples/ensemble/plot_random_forest_embedding.rst.txt
index 6ee52a7872e02..7f0aff8ef3168 100644
--- a/dev/_sources/auto_examples/ensemble/plot_random_forest_embedding.rst.txt
+++ b/dev/_sources/auto_examples/ensemble/plot_random_forest_embedding.rst.txt
@@ -146,7 +146,7 @@ original data.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.326 seconds)
+   **Total running time of the script:** (0 minutes 0.358 seconds)
 
 
 .. _sphx_glr_download_auto_examples_ensemble_plot_random_forest_embedding.py:
diff --git a/dev/_sources/auto_examples/ensemble/plot_random_forest_regression_multioutput.rst.txt b/dev/_sources/auto_examples/ensemble/plot_random_forest_regression_multioutput.rst.txt
index 34ee49d9c946a..c0d9bd24d388a 100644
--- a/dev/_sources/auto_examples/ensemble/plot_random_forest_regression_multioutput.rst.txt
+++ b/dev/_sources/auto_examples/ensemble/plot_random_forest_regression_multioutput.rst.txt
@@ -131,7 +131,7 @@ x and y coordinate as output.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.563 seconds)
+   **Total running time of the script:** (0 minutes 0.561 seconds)
 
 
 .. _sphx_glr_download_auto_examples_ensemble_plot_random_forest_regression_multioutput.py:
diff --git a/dev/_sources/auto_examples/ensemble/plot_stack_predictors.rst.txt b/dev/_sources/auto_examples/ensemble/plot_stack_predictors.rst.txt
index fa81b91a179ed..c04623934bde4 100644
--- a/dev/_sources/auto_examples/ensemble/plot_stack_predictors.rst.txt
+++ b/dev/_sources/auto_examples/ensemble/plot_stack_predictors.rst.txt
@@ -634,19 +634,19 @@ We will first design the pipeline required for the tree-based models.
     }
     </style><div id="sk-container-id-13" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>ColumnTransformer(transformers=[(&#x27;simpleimputer&#x27;,
                                      SimpleImputer(add_indicator=True),
-                                     &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb58512ebf0&gt;),
+                                     &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd840886200&gt;),
                                     (&#x27;ordinalencoder&#x27;,
                                      OrdinalEncoder(encoded_missing_value=-2,
                                                     handle_unknown=&#x27;use_encoded_value&#x27;,
                                                     unknown_value=-1),
-                                     &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb58e4f58a0&gt;)])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-34" type="checkbox" ><label for="sk-estimator-id-34" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>ColumnTransformer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.compose.ColumnTransformer.html">?<span>Documentation for ColumnTransformer</span></a><span class="sk-estimator-doc-link ">i<span>Not fitted</span></span></div></label><div class="sk-toggleable__content "><pre>ColumnTransformer(transformers=[(&#x27;simpleimputer&#x27;,
+                                     &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd840886cb0&gt;)])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-34" type="checkbox" ><label for="sk-estimator-id-34" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>ColumnTransformer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.compose.ColumnTransformer.html">?<span>Documentation for ColumnTransformer</span></a><span class="sk-estimator-doc-link ">i<span>Not fitted</span></span></div></label><div class="sk-toggleable__content "><pre>ColumnTransformer(transformers=[(&#x27;simpleimputer&#x27;,
                                      SimpleImputer(add_indicator=True),
-                                     &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb58512ebf0&gt;),
+                                     &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd840886200&gt;),
                                     (&#x27;ordinalencoder&#x27;,
                                      OrdinalEncoder(encoded_missing_value=-2,
                                                     handle_unknown=&#x27;use_encoded_value&#x27;,
                                                     unknown_value=-1),
-                                     &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb58e4f58a0&gt;)])</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-35" type="checkbox" ><label for="sk-estimator-id-35" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>simpleimputer</div></div></label><div class="sk-toggleable__content "><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb58512ebf0&gt;</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-36" type="checkbox" ><label for="sk-estimator-id-36" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>SimpleImputer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.impute.SimpleImputer.html">?<span>Documentation for SimpleImputer</span></a></div></label><div class="sk-toggleable__content "><pre>SimpleImputer(add_indicator=True)</pre></div> </div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-37" type="checkbox" ><label for="sk-estimator-id-37" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>ordinalencoder</div></div></label><div class="sk-toggleable__content "><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb58e4f58a0&gt;</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-38" type="checkbox" ><label for="sk-estimator-id-38" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>OrdinalEncoder</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.OrdinalEncoder.html">?<span>Documentation for OrdinalEncoder</span></a></div></label><div class="sk-toggleable__content "><pre>OrdinalEncoder(encoded_missing_value=-2, handle_unknown=&#x27;use_encoded_value&#x27;,
+                                     &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd840886cb0&gt;)])</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-35" type="checkbox" ><label for="sk-estimator-id-35" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>simpleimputer</div></div></label><div class="sk-toggleable__content "><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd840886200&gt;</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-36" type="checkbox" ><label for="sk-estimator-id-36" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>SimpleImputer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.impute.SimpleImputer.html">?<span>Documentation for SimpleImputer</span></a></div></label><div class="sk-toggleable__content "><pre>SimpleImputer(add_indicator=True)</pre></div> </div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-37" type="checkbox" ><label for="sk-estimator-id-37" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>ordinalencoder</div></div></label><div class="sk-toggleable__content "><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd840886cb0&gt;</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-38" type="checkbox" ><label for="sk-estimator-id-38" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>OrdinalEncoder</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.OrdinalEncoder.html">?<span>Documentation for OrdinalEncoder</span></a></div></label><div class="sk-toggleable__content "><pre>OrdinalEncoder(encoded_missing_value=-2, handle_unknown=&#x27;use_encoded_value&#x27;,
                    unknown_value=-1)</pre></div> </div></div></div></div></div></div></div></div></div>
     </div>
     <br />
@@ -1102,18 +1102,18 @@ is a linear model.
                                                       StandardScaler()),
                                                      (&#x27;simpleimputer&#x27;,
                                                       SimpleImputer(add_indicator=True))]),
-                                     &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb58512ebf0&gt;),
+                                     &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd840886200&gt;),
                                     (&#x27;onehotencoder&#x27;,
                                      OneHotEncoder(handle_unknown=&#x27;ignore&#x27;),
-                                     &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb58e4f58a0&gt;)])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-39" type="checkbox" ><label for="sk-estimator-id-39" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>ColumnTransformer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.compose.ColumnTransformer.html">?<span>Documentation for ColumnTransformer</span></a><span class="sk-estimator-doc-link ">i<span>Not fitted</span></span></div></label><div class="sk-toggleable__content "><pre>ColumnTransformer(transformers=[(&#x27;pipeline&#x27;,
+                                     &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd840886cb0&gt;)])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-39" type="checkbox" ><label for="sk-estimator-id-39" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>ColumnTransformer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.compose.ColumnTransformer.html">?<span>Documentation for ColumnTransformer</span></a><span class="sk-estimator-doc-link ">i<span>Not fitted</span></span></div></label><div class="sk-toggleable__content "><pre>ColumnTransformer(transformers=[(&#x27;pipeline&#x27;,
                                      Pipeline(steps=[(&#x27;standardscaler&#x27;,
                                                       StandardScaler()),
                                                      (&#x27;simpleimputer&#x27;,
                                                       SimpleImputer(add_indicator=True))]),
-                                     &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb58512ebf0&gt;),
+                                     &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd840886200&gt;),
                                     (&#x27;onehotencoder&#x27;,
                                      OneHotEncoder(handle_unknown=&#x27;ignore&#x27;),
-                                     &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb58e4f58a0&gt;)])</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-40" type="checkbox" ><label for="sk-estimator-id-40" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>pipeline</div></div></label><div class="sk-toggleable__content "><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb58512ebf0&gt;</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-41" type="checkbox" ><label for="sk-estimator-id-41" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>StandardScaler</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.StandardScaler.html">?<span>Documentation for StandardScaler</span></a></div></label><div class="sk-toggleable__content "><pre>StandardScaler()</pre></div> </div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-42" type="checkbox" ><label for="sk-estimator-id-42" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>SimpleImputer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.impute.SimpleImputer.html">?<span>Documentation for SimpleImputer</span></a></div></label><div class="sk-toggleable__content "><pre>SimpleImputer(add_indicator=True)</pre></div> </div></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-43" type="checkbox" ><label for="sk-estimator-id-43" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>onehotencoder</div></div></label><div class="sk-toggleable__content "><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb58e4f58a0&gt;</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-44" type="checkbox" ><label for="sk-estimator-id-44" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>OneHotEncoder</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.OneHotEncoder.html">?<span>Documentation for OneHotEncoder</span></a></div></label><div class="sk-toggleable__content "><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</pre></div> </div></div></div></div></div></div></div></div></div>
+                                     &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd840886cb0&gt;)])</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-40" type="checkbox" ><label for="sk-estimator-id-40" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>pipeline</div></div></label><div class="sk-toggleable__content "><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd840886200&gt;</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-41" type="checkbox" ><label for="sk-estimator-id-41" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>StandardScaler</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.StandardScaler.html">?<span>Documentation for StandardScaler</span></a></div></label><div class="sk-toggleable__content "><pre>StandardScaler()</pre></div> </div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-42" type="checkbox" ><label for="sk-estimator-id-42" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>SimpleImputer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.impute.SimpleImputer.html">?<span>Documentation for SimpleImputer</span></a></div></label><div class="sk-toggleable__content "><pre>SimpleImputer(add_indicator=True)</pre></div> </div></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-43" type="checkbox" ><label for="sk-estimator-id-43" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>onehotencoder</div></div></label><div class="sk-toggleable__content "><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd840886cb0&gt;</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-44" type="checkbox" ><label for="sk-estimator-id-44" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>OneHotEncoder</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.OneHotEncoder.html">?<span>Documentation for OneHotEncoder</span></a></div></label><div class="sk-toggleable__content "><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</pre></div> </div></div></div></div></div></div></div></div></div>
     </div>
     <br />
     <br />
@@ -1578,29 +1578,29 @@ to combine their outputs together.
                                                                        StandardScaler()),
                                                                       (&#x27;simpleimputer&#x27;,
                                                                        SimpleImputer(add_indicator=True))]),
-                                                      &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb58512ebf0&gt;),
+                                                      &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd840886200&gt;),
                                                      (&#x27;onehotencoder&#x27;,
                                                       OneHotEncoder(handle_unknown=&#x27;ignore&#x27;),
-                                                      &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb58e4f58a0&gt;)])),
+                                                      &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd840886cb0&gt;)])),
                     (&#x27;lassocv&#x27;, LassoCV())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-45" type="checkbox" ><label for="sk-estimator-id-45" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>Pipeline</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.pipeline.Pipeline.html">?<span>Documentation for Pipeline</span></a><span class="sk-estimator-doc-link ">i<span>Not fitted</span></span></div></label><div class="sk-toggleable__content "><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,
                      ColumnTransformer(transformers=[(&#x27;pipeline&#x27;,
                                                       Pipeline(steps=[(&#x27;standardscaler&#x27;,
                                                                        StandardScaler()),
                                                                       (&#x27;simpleimputer&#x27;,
                                                                        SimpleImputer(add_indicator=True))]),
-                                                      &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb58512ebf0&gt;),
+                                                      &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd840886200&gt;),
                                                      (&#x27;onehotencoder&#x27;,
                                                       OneHotEncoder(handle_unknown=&#x27;ignore&#x27;),
-                                                      &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb58e4f58a0&gt;)])),
+                                                      &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd840886cb0&gt;)])),
                     (&#x27;lassocv&#x27;, LassoCV())])</pre></div> </div></div><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-46" type="checkbox" ><label for="sk-estimator-id-46" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>columntransformer: ColumnTransformer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.compose.ColumnTransformer.html">?<span>Documentation for columntransformer: ColumnTransformer</span></a></div></label><div class="sk-toggleable__content "><pre>ColumnTransformer(transformers=[(&#x27;pipeline&#x27;,
                                      Pipeline(steps=[(&#x27;standardscaler&#x27;,
                                                       StandardScaler()),
                                                      (&#x27;simpleimputer&#x27;,
                                                       SimpleImputer(add_indicator=True))]),
-                                     &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb58512ebf0&gt;),
+                                     &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd840886200&gt;),
                                     (&#x27;onehotencoder&#x27;,
                                      OneHotEncoder(handle_unknown=&#x27;ignore&#x27;),
-                                     &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb58e4f58a0&gt;)])</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-47" type="checkbox" ><label for="sk-estimator-id-47" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>pipeline</div></div></label><div class="sk-toggleable__content "><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb58512ebf0&gt;</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-48" type="checkbox" ><label for="sk-estimator-id-48" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>StandardScaler</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.StandardScaler.html">?<span>Documentation for StandardScaler</span></a></div></label><div class="sk-toggleable__content "><pre>StandardScaler()</pre></div> </div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-49" type="checkbox" ><label for="sk-estimator-id-49" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>SimpleImputer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.impute.SimpleImputer.html">?<span>Documentation for SimpleImputer</span></a></div></label><div class="sk-toggleable__content "><pre>SimpleImputer(add_indicator=True)</pre></div> </div></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-50" type="checkbox" ><label for="sk-estimator-id-50" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>onehotencoder</div></div></label><div class="sk-toggleable__content "><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb58e4f58a0&gt;</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-51" type="checkbox" ><label for="sk-estimator-id-51" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>OneHotEncoder</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.OneHotEncoder.html">?<span>Documentation for OneHotEncoder</span></a></div></label><div class="sk-toggleable__content "><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</pre></div> </div></div></div></div></div></div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-52" type="checkbox" ><label for="sk-estimator-id-52" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>LassoCV</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.linear_model.LassoCV.html">?<span>Documentation for LassoCV</span></a></div></label><div class="sk-toggleable__content "><pre>LassoCV()</pre></div> </div></div></div></div></div></div>
+                                     &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd840886cb0&gt;)])</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-47" type="checkbox" ><label for="sk-estimator-id-47" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>pipeline</div></div></label><div class="sk-toggleable__content "><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd840886200&gt;</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-48" type="checkbox" ><label for="sk-estimator-id-48" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>StandardScaler</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.StandardScaler.html">?<span>Documentation for StandardScaler</span></a></div></label><div class="sk-toggleable__content "><pre>StandardScaler()</pre></div> </div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-49" type="checkbox" ><label for="sk-estimator-id-49" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>SimpleImputer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.impute.SimpleImputer.html">?<span>Documentation for SimpleImputer</span></a></div></label><div class="sk-toggleable__content "><pre>SimpleImputer(add_indicator=True)</pre></div> </div></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-50" type="checkbox" ><label for="sk-estimator-id-50" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>onehotencoder</div></div></label><div class="sk-toggleable__content "><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd840886cb0&gt;</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-51" type="checkbox" ><label for="sk-estimator-id-51" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>OneHotEncoder</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.OneHotEncoder.html">?<span>Documentation for OneHotEncoder</span></a></div></label><div class="sk-toggleable__content "><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</pre></div> </div></div></div></div></div></div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-52" type="checkbox" ><label for="sk-estimator-id-52" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>LassoCV</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.linear_model.LassoCV.html">?<span>Documentation for LassoCV</span></a></div></label><div class="sk-toggleable__content "><pre>LassoCV()</pre></div> </div></div></div></div></div></div>
     </div>
     <br />
     <br />
@@ -2040,31 +2040,31 @@ to combine their outputs together.
     </style><div id="sk-container-id-16" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,
                      ColumnTransformer(transformers=[(&#x27;simpleimputer&#x27;,
                                                       SimpleImputer(add_indicator=True),
-                                                      &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb58512ebf0&gt;),
+                                                      &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd840886200&gt;),
                                                      (&#x27;ordinalencoder&#x27;,
                                                       OrdinalEncoder(encoded_missing_value=-2,
                                                                      handle_unknown=&#x27;use_encoded_value&#x27;,
                                                                      unknown_value=-1),
-                                                      &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb58e4f58a0&gt;)])),
+                                                      &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd840886cb0&gt;)])),
                     (&#x27;randomforestregressor&#x27;,
                      RandomForestRegressor(random_state=42))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-53" type="checkbox" ><label for="sk-estimator-id-53" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>Pipeline</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.pipeline.Pipeline.html">?<span>Documentation for Pipeline</span></a><span class="sk-estimator-doc-link ">i<span>Not fitted</span></span></div></label><div class="sk-toggleable__content "><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,
                      ColumnTransformer(transformers=[(&#x27;simpleimputer&#x27;,
                                                       SimpleImputer(add_indicator=True),
-                                                      &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb58512ebf0&gt;),
+                                                      &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd840886200&gt;),
                                                      (&#x27;ordinalencoder&#x27;,
                                                       OrdinalEncoder(encoded_missing_value=-2,
                                                                      handle_unknown=&#x27;use_encoded_value&#x27;,
                                                                      unknown_value=-1),
-                                                      &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb58e4f58a0&gt;)])),
+                                                      &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd840886cb0&gt;)])),
                     (&#x27;randomforestregressor&#x27;,
                      RandomForestRegressor(random_state=42))])</pre></div> </div></div><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-54" type="checkbox" ><label for="sk-estimator-id-54" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>columntransformer: ColumnTransformer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.compose.ColumnTransformer.html">?<span>Documentation for columntransformer: ColumnTransformer</span></a></div></label><div class="sk-toggleable__content "><pre>ColumnTransformer(transformers=[(&#x27;simpleimputer&#x27;,
                                      SimpleImputer(add_indicator=True),
-                                     &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb58512ebf0&gt;),
+                                     &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd840886200&gt;),
                                     (&#x27;ordinalencoder&#x27;,
                                      OrdinalEncoder(encoded_missing_value=-2,
                                                     handle_unknown=&#x27;use_encoded_value&#x27;,
                                                     unknown_value=-1),
-                                     &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb58e4f58a0&gt;)])</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-55" type="checkbox" ><label for="sk-estimator-id-55" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>simpleimputer</div></div></label><div class="sk-toggleable__content "><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb58512ebf0&gt;</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-56" type="checkbox" ><label for="sk-estimator-id-56" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>SimpleImputer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.impute.SimpleImputer.html">?<span>Documentation for SimpleImputer</span></a></div></label><div class="sk-toggleable__content "><pre>SimpleImputer(add_indicator=True)</pre></div> </div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-57" type="checkbox" ><label for="sk-estimator-id-57" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>ordinalencoder</div></div></label><div class="sk-toggleable__content "><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb58e4f58a0&gt;</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-58" type="checkbox" ><label for="sk-estimator-id-58" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>OrdinalEncoder</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.OrdinalEncoder.html">?<span>Documentation for OrdinalEncoder</span></a></div></label><div class="sk-toggleable__content "><pre>OrdinalEncoder(encoded_missing_value=-2, handle_unknown=&#x27;use_encoded_value&#x27;,
+                                     &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd840886cb0&gt;)])</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-55" type="checkbox" ><label for="sk-estimator-id-55" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>simpleimputer</div></div></label><div class="sk-toggleable__content "><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd840886200&gt;</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-56" type="checkbox" ><label for="sk-estimator-id-56" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>SimpleImputer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.impute.SimpleImputer.html">?<span>Documentation for SimpleImputer</span></a></div></label><div class="sk-toggleable__content "><pre>SimpleImputer(add_indicator=True)</pre></div> </div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-57" type="checkbox" ><label for="sk-estimator-id-57" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>ordinalencoder</div></div></label><div class="sk-toggleable__content "><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd840886cb0&gt;</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-58" type="checkbox" ><label for="sk-estimator-id-58" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>OrdinalEncoder</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.OrdinalEncoder.html">?<span>Documentation for OrdinalEncoder</span></a></div></label><div class="sk-toggleable__content "><pre>OrdinalEncoder(encoded_missing_value=-2, handle_unknown=&#x27;use_encoded_value&#x27;,
                    unknown_value=-1)</pre></div> </div></div></div></div></div></div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-59" type="checkbox" ><label for="sk-estimator-id-59" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>RandomForestRegressor</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.ensemble.RandomForestRegressor.html">?<span>Documentation for RandomForestRegressor</span></a></div></label><div class="sk-toggleable__content "><pre>RandomForestRegressor(random_state=42)</pre></div> </div></div></div></div></div></div>
     </div>
     <br />
@@ -2507,31 +2507,31 @@ to combine their outputs together.
     </style><div id="sk-container-id-17" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,
                      ColumnTransformer(transformers=[(&#x27;simpleimputer&#x27;,
                                                       SimpleImputer(add_indicator=True),
-                                                      &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb58512ebf0&gt;),
+                                                      &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd840886200&gt;),
                                                      (&#x27;ordinalencoder&#x27;,
                                                       OrdinalEncoder(encoded_missing_value=-2,
                                                                      handle_unknown=&#x27;use_encoded_value&#x27;,
                                                                      unknown_value=-1),
-                                                      &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb58e4f58a0&gt;)])),
+                                                      &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd840886cb0&gt;)])),
                     (&#x27;histgradientboostingregressor&#x27;,
                      HistGradientBoostingRegressor(random_state=0))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-60" type="checkbox" ><label for="sk-estimator-id-60" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>Pipeline</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.pipeline.Pipeline.html">?<span>Documentation for Pipeline</span></a><span class="sk-estimator-doc-link ">i<span>Not fitted</span></span></div></label><div class="sk-toggleable__content "><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,
                      ColumnTransformer(transformers=[(&#x27;simpleimputer&#x27;,
                                                       SimpleImputer(add_indicator=True),
-                                                      &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb58512ebf0&gt;),
+                                                      &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd840886200&gt;),
                                                      (&#x27;ordinalencoder&#x27;,
                                                       OrdinalEncoder(encoded_missing_value=-2,
                                                                      handle_unknown=&#x27;use_encoded_value&#x27;,
                                                                      unknown_value=-1),
-                                                      &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb58e4f58a0&gt;)])),
+                                                      &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd840886cb0&gt;)])),
                     (&#x27;histgradientboostingregressor&#x27;,
                      HistGradientBoostingRegressor(random_state=0))])</pre></div> </div></div><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-61" type="checkbox" ><label for="sk-estimator-id-61" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>columntransformer: ColumnTransformer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.compose.ColumnTransformer.html">?<span>Documentation for columntransformer: ColumnTransformer</span></a></div></label><div class="sk-toggleable__content "><pre>ColumnTransformer(transformers=[(&#x27;simpleimputer&#x27;,
                                      SimpleImputer(add_indicator=True),
-                                     &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb58512ebf0&gt;),
+                                     &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd840886200&gt;),
                                     (&#x27;ordinalencoder&#x27;,
                                      OrdinalEncoder(encoded_missing_value=-2,
                                                     handle_unknown=&#x27;use_encoded_value&#x27;,
                                                     unknown_value=-1),
-                                     &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb58e4f58a0&gt;)])</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-62" type="checkbox" ><label for="sk-estimator-id-62" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>simpleimputer</div></div></label><div class="sk-toggleable__content "><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb58512ebf0&gt;</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-63" type="checkbox" ><label for="sk-estimator-id-63" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>SimpleImputer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.impute.SimpleImputer.html">?<span>Documentation for SimpleImputer</span></a></div></label><div class="sk-toggleable__content "><pre>SimpleImputer(add_indicator=True)</pre></div> </div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-64" type="checkbox" ><label for="sk-estimator-id-64" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>ordinalencoder</div></div></label><div class="sk-toggleable__content "><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb58e4f58a0&gt;</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-65" type="checkbox" ><label for="sk-estimator-id-65" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>OrdinalEncoder</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.OrdinalEncoder.html">?<span>Documentation for OrdinalEncoder</span></a></div></label><div class="sk-toggleable__content "><pre>OrdinalEncoder(encoded_missing_value=-2, handle_unknown=&#x27;use_encoded_value&#x27;,
+                                     &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd840886cb0&gt;)])</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-62" type="checkbox" ><label for="sk-estimator-id-62" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>simpleimputer</div></div></label><div class="sk-toggleable__content "><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd840886200&gt;</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-63" type="checkbox" ><label for="sk-estimator-id-63" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>SimpleImputer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.impute.SimpleImputer.html">?<span>Documentation for SimpleImputer</span></a></div></label><div class="sk-toggleable__content "><pre>SimpleImputer(add_indicator=True)</pre></div> </div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-64" type="checkbox" ><label for="sk-estimator-id-64" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>ordinalencoder</div></div></label><div class="sk-toggleable__content "><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd840886cb0&gt;</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-65" type="checkbox" ><label for="sk-estimator-id-65" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>OrdinalEncoder</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.OrdinalEncoder.html">?<span>Documentation for OrdinalEncoder</span></a></div></label><div class="sk-toggleable__content "><pre>OrdinalEncoder(encoded_missing_value=-2, handle_unknown=&#x27;use_encoded_value&#x27;,
                    unknown_value=-1)</pre></div> </div></div></div></div></div></div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-66" type="checkbox" ><label for="sk-estimator-id-66" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>HistGradientBoostingRegressor</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.ensemble.HistGradientBoostingRegressor.html">?<span>Documentation for HistGradientBoostingRegressor</span></a></div></label><div class="sk-toggleable__content "><pre>HistGradientBoostingRegressor(random_state=0)</pre></div> </div></div></div></div></div></div>
     </div>
     <br />
@@ -2980,60 +2980,60 @@ to combine their outputs together.
                                    Pipeline(steps=[(&#x27;columntransformer&#x27;,
                                                     ColumnTransformer(transformers=[(&#x27;simpleimputer&#x27;,
                                                                                      SimpleImputer(add_indicator=True),
-                                                                                     &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb58512ebf0&gt;),
+                                                                                     &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd840886200&gt;),
                                                                                     (&#x27;ordinalencoder&#x27;,
                                                                                      OrdinalEncoder(encoded_missing_value=-2,
                                                                                                     handle_unknown=&#x27;use_encoded_value&#x27;,
                                                                                                     unknown_v...
-                                                                                     &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb58512ebf0&gt;),
+                                                                                     &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd840886200&gt;),
                                                                                     (&#x27;ordinalencoder&#x27;,
                                                                                      OrdinalEncoder(encoded_missing_value=-2,
                                                                                                     handle_unknown=&#x27;use_encoded_value&#x27;,
                                                                                                     unknown_value=-1),
-                                                                                     &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb58e4f58a0&gt;)])),
+                                                                                     &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd840886cb0&gt;)])),
                                                    (&#x27;histgradientboostingregressor&#x27;,
                                                     HistGradientBoostingRegressor(random_state=0))]))],
                       final_estimator=RidgeCV())</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-67" type="checkbox" ><label for="sk-estimator-id-67" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>StackingRegressor</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.ensemble.StackingRegressor.html">?<span>Documentation for StackingRegressor</span></a><span class="sk-estimator-doc-link ">i<span>Not fitted</span></span></div></label><div class="sk-toggleable__content "><pre>StackingRegressor(estimators=[(&#x27;Random Forest&#x27;,
                                    Pipeline(steps=[(&#x27;columntransformer&#x27;,
                                                     ColumnTransformer(transformers=[(&#x27;simpleimputer&#x27;,
                                                                                      SimpleImputer(add_indicator=True),
-                                                                                     &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb58512ebf0&gt;),
+                                                                                     &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd840886200&gt;),
                                                                                     (&#x27;ordinalencoder&#x27;,
                                                                                      OrdinalEncoder(encoded_missing_value=-2,
                                                                                                     handle_unknown=&#x27;use_encoded_value&#x27;,
                                                                                                     unknown_v...
-                                                                                     &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb58512ebf0&gt;),
+                                                                                     &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd840886200&gt;),
                                                                                     (&#x27;ordinalencoder&#x27;,
                                                                                      OrdinalEncoder(encoded_missing_value=-2,
                                                                                                     handle_unknown=&#x27;use_encoded_value&#x27;,
                                                                                                     unknown_value=-1),
-                                                                                     &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb58e4f58a0&gt;)])),
+                                                                                     &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd840886cb0&gt;)])),
                                                    (&#x27;histgradientboostingregressor&#x27;,
                                                     HistGradientBoostingRegressor(random_state=0))]))],
                       final_estimator=RidgeCV())</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><label>Random Forest</label></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-68" type="checkbox" ><label for="sk-estimator-id-68" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>columntransformer: ColumnTransformer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.compose.ColumnTransformer.html">?<span>Documentation for columntransformer: ColumnTransformer</span></a></div></label><div class="sk-toggleable__content "><pre>ColumnTransformer(transformers=[(&#x27;simpleimputer&#x27;,
                                      SimpleImputer(add_indicator=True),
-                                     &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb58512ebf0&gt;),
+                                     &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd840886200&gt;),
                                     (&#x27;ordinalencoder&#x27;,
                                      OrdinalEncoder(encoded_missing_value=-2,
                                                     handle_unknown=&#x27;use_encoded_value&#x27;,
                                                     unknown_value=-1),
-                                     &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb58e4f58a0&gt;)])</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-69" type="checkbox" ><label for="sk-estimator-id-69" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>simpleimputer</div></div></label><div class="sk-toggleable__content "><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb58512ebf0&gt;</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-70" type="checkbox" ><label for="sk-estimator-id-70" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>SimpleImputer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.impute.SimpleImputer.html">?<span>Documentation for SimpleImputer</span></a></div></label><div class="sk-toggleable__content "><pre>SimpleImputer(add_indicator=True)</pre></div> </div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-71" type="checkbox" ><label for="sk-estimator-id-71" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>ordinalencoder</div></div></label><div class="sk-toggleable__content "><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb58e4f58a0&gt;</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-72" type="checkbox" ><label for="sk-estimator-id-72" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>OrdinalEncoder</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.OrdinalEncoder.html">?<span>Documentation for OrdinalEncoder</span></a></div></label><div class="sk-toggleable__content "><pre>OrdinalEncoder(encoded_missing_value=-2, handle_unknown=&#x27;use_encoded_value&#x27;,
+                                     &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd840886cb0&gt;)])</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-69" type="checkbox" ><label for="sk-estimator-id-69" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>simpleimputer</div></div></label><div class="sk-toggleable__content "><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd840886200&gt;</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-70" type="checkbox" ><label for="sk-estimator-id-70" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>SimpleImputer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.impute.SimpleImputer.html">?<span>Documentation for SimpleImputer</span></a></div></label><div class="sk-toggleable__content "><pre>SimpleImputer(add_indicator=True)</pre></div> </div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-71" type="checkbox" ><label for="sk-estimator-id-71" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>ordinalencoder</div></div></label><div class="sk-toggleable__content "><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd840886cb0&gt;</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-72" type="checkbox" ><label for="sk-estimator-id-72" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>OrdinalEncoder</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.OrdinalEncoder.html">?<span>Documentation for OrdinalEncoder</span></a></div></label><div class="sk-toggleable__content "><pre>OrdinalEncoder(encoded_missing_value=-2, handle_unknown=&#x27;use_encoded_value&#x27;,
                    unknown_value=-1)</pre></div> </div></div></div></div></div></div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-73" type="checkbox" ><label for="sk-estimator-id-73" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>RandomForestRegressor</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.ensemble.RandomForestRegressor.html">?<span>Documentation for RandomForestRegressor</span></a></div></label><div class="sk-toggleable__content "><pre>RandomForestRegressor(random_state=42)</pre></div> </div></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><label>Lasso</label></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-74" type="checkbox" ><label for="sk-estimator-id-74" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>columntransformer: ColumnTransformer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.compose.ColumnTransformer.html">?<span>Documentation for columntransformer: ColumnTransformer</span></a></div></label><div class="sk-toggleable__content "><pre>ColumnTransformer(transformers=[(&#x27;pipeline&#x27;,
                                      Pipeline(steps=[(&#x27;standardscaler&#x27;,
                                                       StandardScaler()),
                                                      (&#x27;simpleimputer&#x27;,
                                                       SimpleImputer(add_indicator=True))]),
-                                     &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb58512ebf0&gt;),
+                                     &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd840886200&gt;),
                                     (&#x27;onehotencoder&#x27;,
                                      OneHotEncoder(handle_unknown=&#x27;ignore&#x27;),
-                                     &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb58e4f58a0&gt;)])</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-75" type="checkbox" ><label for="sk-estimator-id-75" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>pipeline</div></div></label><div class="sk-toggleable__content "><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb58512ebf0&gt;</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-76" type="checkbox" ><label for="sk-estimator-id-76" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>StandardScaler</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.StandardScaler.html">?<span>Documentation for StandardScaler</span></a></div></label><div class="sk-toggleable__content "><pre>StandardScaler()</pre></div> </div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-77" type="checkbox" ><label for="sk-estimator-id-77" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>SimpleImputer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.impute.SimpleImputer.html">?<span>Documentation for SimpleImputer</span></a></div></label><div class="sk-toggleable__content "><pre>SimpleImputer(add_indicator=True)</pre></div> </div></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-78" type="checkbox" ><label for="sk-estimator-id-78" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>onehotencoder</div></div></label><div class="sk-toggleable__content "><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb58e4f58a0&gt;</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-79" type="checkbox" ><label for="sk-estimator-id-79" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>OneHotEncoder</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.OneHotEncoder.html">?<span>Documentation for OneHotEncoder</span></a></div></label><div class="sk-toggleable__content "><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</pre></div> </div></div></div></div></div></div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-80" type="checkbox" ><label for="sk-estimator-id-80" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>LassoCV</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.linear_model.LassoCV.html">?<span>Documentation for LassoCV</span></a></div></label><div class="sk-toggleable__content "><pre>LassoCV()</pre></div> </div></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><label>Gradient Boosting</label></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-81" type="checkbox" ><label for="sk-estimator-id-81" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>columntransformer: ColumnTransformer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.compose.ColumnTransformer.html">?<span>Documentation for columntransformer: ColumnTransformer</span></a></div></label><div class="sk-toggleable__content "><pre>ColumnTransformer(transformers=[(&#x27;simpleimputer&#x27;,
+                                     &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd840886cb0&gt;)])</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-75" type="checkbox" ><label for="sk-estimator-id-75" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>pipeline</div></div></label><div class="sk-toggleable__content "><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd840886200&gt;</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-76" type="checkbox" ><label for="sk-estimator-id-76" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>StandardScaler</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.StandardScaler.html">?<span>Documentation for StandardScaler</span></a></div></label><div class="sk-toggleable__content "><pre>StandardScaler()</pre></div> </div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-77" type="checkbox" ><label for="sk-estimator-id-77" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>SimpleImputer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.impute.SimpleImputer.html">?<span>Documentation for SimpleImputer</span></a></div></label><div class="sk-toggleable__content "><pre>SimpleImputer(add_indicator=True)</pre></div> </div></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-78" type="checkbox" ><label for="sk-estimator-id-78" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>onehotencoder</div></div></label><div class="sk-toggleable__content "><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd840886cb0&gt;</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-79" type="checkbox" ><label for="sk-estimator-id-79" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>OneHotEncoder</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.OneHotEncoder.html">?<span>Documentation for OneHotEncoder</span></a></div></label><div class="sk-toggleable__content "><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</pre></div> </div></div></div></div></div></div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-80" type="checkbox" ><label for="sk-estimator-id-80" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>LassoCV</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.linear_model.LassoCV.html">?<span>Documentation for LassoCV</span></a></div></label><div class="sk-toggleable__content "><pre>LassoCV()</pre></div> </div></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><label>Gradient Boosting</label></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-81" type="checkbox" ><label for="sk-estimator-id-81" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>columntransformer: ColumnTransformer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.compose.ColumnTransformer.html">?<span>Documentation for columntransformer: ColumnTransformer</span></a></div></label><div class="sk-toggleable__content "><pre>ColumnTransformer(transformers=[(&#x27;simpleimputer&#x27;,
                                      SimpleImputer(add_indicator=True),
-                                     &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb58512ebf0&gt;),
+                                     &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd840886200&gt;),
                                     (&#x27;ordinalencoder&#x27;,
                                      OrdinalEncoder(encoded_missing_value=-2,
                                                     handle_unknown=&#x27;use_encoded_value&#x27;,
                                                     unknown_value=-1),
-                                     &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb58e4f58a0&gt;)])</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-82" type="checkbox" ><label for="sk-estimator-id-82" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>simpleimputer</div></div></label><div class="sk-toggleable__content "><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb58512ebf0&gt;</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-83" type="checkbox" ><label for="sk-estimator-id-83" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>SimpleImputer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.impute.SimpleImputer.html">?<span>Documentation for SimpleImputer</span></a></div></label><div class="sk-toggleable__content "><pre>SimpleImputer(add_indicator=True)</pre></div> </div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-84" type="checkbox" ><label for="sk-estimator-id-84" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>ordinalencoder</div></div></label><div class="sk-toggleable__content "><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb58e4f58a0&gt;</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-85" type="checkbox" ><label for="sk-estimator-id-85" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>OrdinalEncoder</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.OrdinalEncoder.html">?<span>Documentation for OrdinalEncoder</span></a></div></label><div class="sk-toggleable__content "><pre>OrdinalEncoder(encoded_missing_value=-2, handle_unknown=&#x27;use_encoded_value&#x27;,
+                                     &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd840886cb0&gt;)])</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-82" type="checkbox" ><label for="sk-estimator-id-82" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>simpleimputer</div></div></label><div class="sk-toggleable__content "><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd840886200&gt;</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-83" type="checkbox" ><label for="sk-estimator-id-83" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>SimpleImputer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.impute.SimpleImputer.html">?<span>Documentation for SimpleImputer</span></a></div></label><div class="sk-toggleable__content "><pre>SimpleImputer(add_indicator=True)</pre></div> </div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-84" type="checkbox" ><label for="sk-estimator-id-84" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>ordinalencoder</div></div></label><div class="sk-toggleable__content "><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd840886cb0&gt;</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-85" type="checkbox" ><label for="sk-estimator-id-85" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>OrdinalEncoder</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.OrdinalEncoder.html">?<span>Documentation for OrdinalEncoder</span></a></div></label><div class="sk-toggleable__content "><pre>OrdinalEncoder(encoded_missing_value=-2, handle_unknown=&#x27;use_encoded_value&#x27;,
                    unknown_value=-1)</pre></div> </div></div></div></div></div></div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-86" type="checkbox" ><label for="sk-estimator-id-86" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>HistGradientBoostingRegressor</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.ensemble.HistGradientBoostingRegressor.html">?<span>Documentation for HistGradientBoostingRegressor</span></a></div></label><div class="sk-toggleable__content "><pre>HistGradientBoostingRegressor(random_state=0)</pre></div> </div></div></div></div></div></div></div></div></div><div class="sk-item"><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><label>final_estimator</label></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-87" type="checkbox" ><label for="sk-estimator-id-87" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>RidgeCV</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.linear_model.RidgeCV.html">?<span>Documentation for RidgeCV</span></a></div></label><div class="sk-toggleable__content "><pre>RidgeCV()</pre></div> </div></div></div></div></div></div></div></div></div></div></div>
     </div>
     <br />
@@ -3107,7 +3107,7 @@ regressors.
 
 
 .. image-sg:: /auto_examples/ensemble/images/sphx_glr_plot_stack_predictors_001.png
-   :alt: Single predictors versus stacked predictors, Random Forest Evaluation in 1.12 seconds, Lasso Evaluation in 0.23 seconds, Gradient Boosting Evaluation in 0.47 seconds, Stacking Regressor Evaluation in 9.30 seconds
+   :alt: Single predictors versus stacked predictors, Random Forest Evaluation in 1.09 seconds, Lasso Evaluation in 0.23 seconds, Gradient Boosting Evaluation in 0.49 seconds, Stacking Regressor Evaluation in 9.43 seconds
    :srcset: /auto_examples/ensemble/images/sphx_glr_plot_stack_predictors_001.png
    :class: sphx-glr-single-img
 
@@ -3124,7 +3124,7 @@ computationally expensive.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 22.643 seconds)
+   **Total running time of the script:** (0 minutes 23.230 seconds)
 
 
 .. _sphx_glr_download_auto_examples_ensemble_plot_stack_predictors.py:
diff --git a/dev/_sources/auto_examples/ensemble/plot_voting_decision_regions.rst.txt b/dev/_sources/auto_examples/ensemble/plot_voting_decision_regions.rst.txt
index e6ebf22bd6b69..f63eec4512b4e 100644
--- a/dev/_sources/auto_examples/ensemble/plot_voting_decision_regions.rst.txt
+++ b/dev/_sources/auto_examples/ensemble/plot_voting_decision_regions.rst.txt
@@ -18,34 +18,90 @@
 .. _sphx_glr_auto_examples_ensemble_plot_voting_decision_regions.py:
 
 
-==================================================
-Plot the decision boundaries of a VotingClassifier
-==================================================
+===============================================================
+Visualizing the probabilistic predictions of a VotingClassifier
+===============================================================
 
 .. currentmodule:: sklearn
 
-Plot the decision boundaries of a :class:`~ensemble.VotingClassifier` for two
-features of the Iris dataset.
+Plot the predicted class probabilities in a toy dataset predicted by three
+different classifiers and averaged by the :class:`~ensemble.VotingClassifier`.
 
-Plot the class probabilities of the first sample in a toy dataset predicted by
-three different classifiers and averaged by the
-:class:`~ensemble.VotingClassifier`.
+First, three linear classifiers are initialized. Two are spline models with
+interaction terms, one using constant extrapolation and the other using periodic
+extrapolation. The third classifier is a :class:`~kernel_approximation.Nystroem`
+with the default "rbf" kernel.
 
-First, three exemplary classifiers are initialized
-(:class:`~tree.DecisionTreeClassifier`,
-:class:`~neighbors.KNeighborsClassifier`, and :class:`~svm.SVC`) and used to
-initialize a soft-voting :class:`~ensemble.VotingClassifier` with weights `[2,
-1, 2]`, which means that the predicted probabilities of the
-:class:`~tree.DecisionTreeClassifier` and :class:`~svm.SVC` each count 2 times
-as much as the weights of the :class:`~neighbors.KNeighborsClassifier`
-classifier when the averaged probability is calculated.
+In the first part of this example, these three classifiers are used to
+demonstrate soft-voting using :class:`~ensemble.VotingClassifier` with weighted
+average. We set `weights=[2, 1, 3]`, meaning the constant extrapolation spline
+model's predictions are weighted twice as much as the periodic spline model's,
+and the Nystroem model's predictions are weighted three times as much as the
+periodic spline.
+
+The second part demonstrates how soft predictions can be converted into hard
+predictions.
+
+.. GENERATED FROM PYTHON SOURCE LINES 27-31
+
+.. code-block:: Python
+
+
+    # Authors: The scikit-learn developers
+    # SPDX-License-Identifier: BSD-3-Clause
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 32-33
+
+We first generate a noisy XOR dataset, which is a binary classification task.
+
+.. GENERATED FROM PYTHON SOURCE LINES 33-65
+
+.. code-block:: Python
+
+
+    import matplotlib.pyplot as plt
+    import numpy as np
+    import pandas as pd
+    from matplotlib.colors import ListedColormap
+
+    n_samples = 500
+    rng = np.random.default_rng(0)
+    feature_names = ["Feature #0", "Feature #1"]
+    common_scatter_plot_params = dict(
+        cmap=ListedColormap(["tab:red", "tab:blue"]),
+        edgecolor="white",
+        linewidth=1,
+    )
+
+    xor = pd.DataFrame(
+        np.random.RandomState(0).uniform(low=-1, high=1, size=(n_samples, 2)),
+        columns=feature_names,
+    )
+    noise = rng.normal(loc=0, scale=0.1, size=(n_samples, 2))
+    target_xor = np.logical_xor(
+        xor["Feature #0"] + noise[:, 0] > 0, xor["Feature #1"] + noise[:, 1] > 0
+    )
+
+    X = xor[feature_names]
+    y = target_xor.astype(np.int32)
+
+    fig, ax = plt.subplots()
+    ax.scatter(X["Feature #0"], X["Feature #1"], c=y, **common_scatter_plot_params)
+    ax.set_title("The XOR dataset")
+    plt.show()
 
-.. GENERATED FROM PYTHON SOURCE LINES 25-74
 
 
 
 .. image-sg:: /auto_examples/ensemble/images/sphx_glr_plot_voting_decision_regions_001.png
-   :alt: Decision Tree (depth=4), KNN (k=7), Kernel SVM, Soft Voting
+   :alt: The XOR dataset
    :srcset: /auto_examples/ensemble/images/sphx_glr_plot_voting_decision_regions_001.png
    :class: sphx-glr-single-img
 
@@ -53,36 +109,56 @@ classifier when the averaged probability is calculated.
 
 
 
-.. code-block:: Python
+.. GENERATED FROM PYTHON SOURCE LINES 66-73
 
+Due to the inherent non-linear separability of the XOR dataset, tree-based
+models would often be preferred. However, appropriate feature engineering
+combined with a linear model can yield effective results, with the added
+benefit of producing better-calibrated probabilities for samples located in
+the transition regions affected by noise.
 
-    # Authors: The scikit-learn developers
-    # SPDX-License-Identifier: BSD-3-Clause
+We define and fit the models on the whole dataset.
 
-    from itertools import product
+.. GENERATED FROM PYTHON SOURCE LINES 73-116
+
+.. code-block:: Python
 
-    import matplotlib.pyplot as plt
 
-    from sklearn import datasets
     from sklearn.ensemble import VotingClassifier
-    from sklearn.inspection import DecisionBoundaryDisplay
-    from sklearn.neighbors import KNeighborsClassifier
-    from sklearn.svm import SVC
-    from sklearn.tree import DecisionTreeClassifier
-
-    # Loading some example data
-    iris = datasets.load_iris()
-    X = iris.data[:, [0, 2]]
-    y = iris.target
-
-    # Training classifiers
-    clf1 = DecisionTreeClassifier(max_depth=4)
-    clf2 = KNeighborsClassifier(n_neighbors=7)
-    clf3 = SVC(gamma=0.1, kernel="rbf", probability=True)
+    from sklearn.kernel_approximation import Nystroem
+    from sklearn.linear_model import LogisticRegression
+    from sklearn.pipeline import make_pipeline
+    from sklearn.preprocessing import PolynomialFeatures, SplineTransformer, StandardScaler
+
+    clf1 = make_pipeline(
+        SplineTransformer(degree=2, n_knots=2),
+        PolynomialFeatures(interaction_only=True),
+        LogisticRegression(C=10),
+    )
+    clf2 = make_pipeline(
+        SplineTransformer(
+            degree=2,
+            n_knots=4,
+            extrapolation="periodic",
+            include_bias=True,
+        ),
+        PolynomialFeatures(interaction_only=True),
+        LogisticRegression(C=10),
+    )
+    clf3 = make_pipeline(
+        StandardScaler(),
+        Nystroem(gamma=2, random_state=0),
+        LogisticRegression(C=10),
+    )
+    weights = [2, 1, 3]
     eclf = VotingClassifier(
-        estimators=[("dt", clf1), ("knn", clf2), ("svc", clf3)],
+        estimators=[
+            ("constant splines model", clf1),
+            ("periodic splines model", clf2),
+            ("nystroem model", clf3),
+        ],
         voting="soft",
-        weights=[2, 1, 2],
+        weights=weights,
     )
 
     clf1.fit(X, y)
@@ -90,25 +166,722 @@ classifier when the averaged probability is calculated.
     clf3.fit(X, y)
     eclf.fit(X, y)
 
-    # Plotting decision regions
-    f, axarr = plt.subplots(2, 2, sharex="col", sharey="row", figsize=(10, 8))
-    for idx, clf, tt in zip(
+
+
+
+
+
+.. raw:: html
+
+    <div class="output_subarea output_html rendered_html output_result">
+    <style>#sk-container-id-27 {
+      /* Definition of color scheme common for light and dark mode */
+      --sklearn-color-text: #000;
+      --sklearn-color-text-muted: #666;
+      --sklearn-color-line: gray;
+      /* Definition of color scheme for unfitted estimators */
+      --sklearn-color-unfitted-level-0: #fff5e6;
+      --sklearn-color-unfitted-level-1: #f6e4d2;
+      --sklearn-color-unfitted-level-2: #ffe0b3;
+      --sklearn-color-unfitted-level-3: chocolate;
+      /* Definition of color scheme for fitted estimators */
+      --sklearn-color-fitted-level-0: #f0f8ff;
+      --sklearn-color-fitted-level-1: #d4ebff;
+      --sklearn-color-fitted-level-2: #b3dbfd;
+      --sklearn-color-fitted-level-3: cornflowerblue;
+
+      /* Specific color for light theme */
+      --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));
+      --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));
+      --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));
+      --sklearn-color-icon: #696969;
+
+      @media (prefers-color-scheme: dark) {
+        /* Redefinition of color scheme for dark theme */
+        --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));
+        --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));
+        --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));
+        --sklearn-color-icon: #878787;
+      }
+    }
+
+    #sk-container-id-27 {
+      color: var(--sklearn-color-text);
+    }
+
+    #sk-container-id-27 pre {
+      padding: 0;
+    }
+
+    #sk-container-id-27 input.sk-hidden--visually {
+      border: 0;
+      clip: rect(1px 1px 1px 1px);
+      clip: rect(1px, 1px, 1px, 1px);
+      height: 1px;
+      margin: -1px;
+      overflow: hidden;
+      padding: 0;
+      position: absolute;
+      width: 1px;
+    }
+
+    #sk-container-id-27 div.sk-dashed-wrapped {
+      border: 1px dashed var(--sklearn-color-line);
+      margin: 0 0.4em 0.5em 0.4em;
+      box-sizing: border-box;
+      padding-bottom: 0.4em;
+      background-color: var(--sklearn-color-background);
+    }
+
+    #sk-container-id-27 div.sk-container {
+      /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
+         but bootstrap.min.css set `[hidden] { display: none !important; }`
+         so we also need the `!important` here to be able to override the
+         default hidden behavior on the sphinx rendered scikit-learn.org.
+         See: https://github.com/scikit-learn/scikit-learn/issues/21755 */
+      display: inline-block !important;
+      position: relative;
+    }
+
+    #sk-container-id-27 div.sk-text-repr-fallback {
+      display: none;
+    }
+
+    div.sk-parallel-item,
+    div.sk-serial,
+    div.sk-item {
+      /* draw centered vertical line to link estimators */
+      background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));
+      background-size: 2px 100%;
+      background-repeat: no-repeat;
+      background-position: center center;
+    }
+
+    /* Parallel-specific style estimator block */
+
+    #sk-container-id-27 div.sk-parallel-item::after {
+      content: "";
+      width: 100%;
+      border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
+      flex-grow: 1;
+    }
+
+    #sk-container-id-27 div.sk-parallel {
+      display: flex;
+      align-items: stretch;
+      justify-content: center;
+      background-color: var(--sklearn-color-background);
+      position: relative;
+    }
+
+    #sk-container-id-27 div.sk-parallel-item {
+      display: flex;
+      flex-direction: column;
+    }
+
+    #sk-container-id-27 div.sk-parallel-item:first-child::after {
+      align-self: flex-end;
+      width: 50%;
+    }
+
+    #sk-container-id-27 div.sk-parallel-item:last-child::after {
+      align-self: flex-start;
+      width: 50%;
+    }
+
+    #sk-container-id-27 div.sk-parallel-item:only-child::after {
+      width: 0;
+    }
+
+    /* Serial-specific style estimator block */
+
+    #sk-container-id-27 div.sk-serial {
+      display: flex;
+      flex-direction: column;
+      align-items: center;
+      background-color: var(--sklearn-color-background);
+      padding-right: 1em;
+      padding-left: 1em;
+    }
+
+
+    /* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is
+    clickable and can be expanded/collapsed.
+    - Pipeline and ColumnTransformer use this feature and define the default style
+    - Estimators will overwrite some part of the style using the `sk-estimator` class
+    */
+
+    /* Pipeline and ColumnTransformer style (default) */
+
+    #sk-container-id-27 div.sk-toggleable {
+      /* Default theme specific background. It is overwritten whether we have a
+      specific estimator or a Pipeline/ColumnTransformer */
+      background-color: var(--sklearn-color-background);
+    }
+
+    /* Toggleable label */
+    #sk-container-id-27 label.sk-toggleable__label {
+      cursor: pointer;
+      display: flex;
+      width: 100%;
+      margin-bottom: 0;
+      padding: 0.5em;
+      box-sizing: border-box;
+      text-align: center;
+      align-items: start;
+      justify-content: space-between;
+      gap: 0.5em;
+    }
+
+    #sk-container-id-27 label.sk-toggleable__label .caption {
+      font-size: 0.6rem;
+      font-weight: lighter;
+      color: var(--sklearn-color-text-muted);
+    }
+
+    #sk-container-id-27 label.sk-toggleable__label-arrow:before {
+      /* Arrow on the left of the label */
+      content: "▸";
+      float: left;
+      margin-right: 0.25em;
+      color: var(--sklearn-color-icon);
+    }
+
+    #sk-container-id-27 label.sk-toggleable__label-arrow:hover:before {
+      color: var(--sklearn-color-text);
+    }
+
+    /* Toggleable content - dropdown */
+
+    #sk-container-id-27 div.sk-toggleable__content {
+      max-height: 0;
+      max-width: 0;
+      overflow: hidden;
+      text-align: left;
+      /* unfitted */
+      background-color: var(--sklearn-color-unfitted-level-0);
+    }
+
+    #sk-container-id-27 div.sk-toggleable__content.fitted {
+      /* fitted */
+      background-color: var(--sklearn-color-fitted-level-0);
+    }
+
+    #sk-container-id-27 div.sk-toggleable__content pre {
+      margin: 0.2em;
+      border-radius: 0.25em;
+      color: var(--sklearn-color-text);
+      /* unfitted */
+      background-color: var(--sklearn-color-unfitted-level-0);
+    }
+
+    #sk-container-id-27 div.sk-toggleable__content.fitted pre {
+      /* unfitted */
+      background-color: var(--sklearn-color-fitted-level-0);
+    }
+
+    #sk-container-id-27 input.sk-toggleable__control:checked~div.sk-toggleable__content {
+      /* Expand drop-down */
+      max-height: 200px;
+      max-width: 100%;
+      overflow: auto;
+    }
+
+    #sk-container-id-27 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
+      content: "▾";
+    }
+
+    /* Pipeline/ColumnTransformer-specific style */
+
+    #sk-container-id-27 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
+      color: var(--sklearn-color-text);
+      background-color: var(--sklearn-color-unfitted-level-2);
+    }
+
+    #sk-container-id-27 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+      background-color: var(--sklearn-color-fitted-level-2);
+    }
+
+    /* Estimator-specific style */
+
+    /* Colorize estimator box */
+    #sk-container-id-27 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
+      /* unfitted */
+      background-color: var(--sklearn-color-unfitted-level-2);
+    }
+
+    #sk-container-id-27 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+      /* fitted */
+      background-color: var(--sklearn-color-fitted-level-2);
+    }
+
+    #sk-container-id-27 div.sk-label label.sk-toggleable__label,
+    #sk-container-id-27 div.sk-label label {
+      /* The background is the default theme color */
+      color: var(--sklearn-color-text-on-default-background);
+    }
+
+    /* On hover, darken the color of the background */
+    #sk-container-id-27 div.sk-label:hover label.sk-toggleable__label {
+      color: var(--sklearn-color-text);
+      background-color: var(--sklearn-color-unfitted-level-2);
+    }
+
+    /* Label box, darken color on hover, fitted */
+    #sk-container-id-27 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
+      color: var(--sklearn-color-text);
+      background-color: var(--sklearn-color-fitted-level-2);
+    }
+
+    /* Estimator label */
+
+    #sk-container-id-27 div.sk-label label {
+      font-family: monospace;
+      font-weight: bold;
+      display: inline-block;
+      line-height: 1.2em;
+    }
+
+    #sk-container-id-27 div.sk-label-container {
+      text-align: center;
+    }
+
+    /* Estimator-specific */
+    #sk-container-id-27 div.sk-estimator {
+      font-family: monospace;
+      border: 1px dotted var(--sklearn-color-border-box);
+      border-radius: 0.25em;
+      box-sizing: border-box;
+      margin-bottom: 0.5em;
+      /* unfitted */
+      background-color: var(--sklearn-color-unfitted-level-0);
+    }
+
+    #sk-container-id-27 div.sk-estimator.fitted {
+      /* fitted */
+      background-color: var(--sklearn-color-fitted-level-0);
+    }
+
+    /* on hover */
+    #sk-container-id-27 div.sk-estimator:hover {
+      /* unfitted */
+      background-color: var(--sklearn-color-unfitted-level-2);
+    }
+
+    #sk-container-id-27 div.sk-estimator.fitted:hover {
+      /* fitted */
+      background-color: var(--sklearn-color-fitted-level-2);
+    }
+
+    /* Specification for estimator info (e.g. "i" and "?") */
+
+    /* Common style for "i" and "?" */
+
+    .sk-estimator-doc-link,
+    a:link.sk-estimator-doc-link,
+    a:visited.sk-estimator-doc-link {
+      float: right;
+      font-size: smaller;
+      line-height: 1em;
+      font-family: monospace;
+      background-color: var(--sklearn-color-background);
+      border-radius: 1em;
+      height: 1em;
+      width: 1em;
+      text-decoration: none !important;
+      margin-left: 0.5em;
+      text-align: center;
+      /* unfitted */
+      border: var(--sklearn-color-unfitted-level-1) 1pt solid;
+      color: var(--sklearn-color-unfitted-level-1);
+    }
+
+    .sk-estimator-doc-link.fitted,
+    a:link.sk-estimator-doc-link.fitted,
+    a:visited.sk-estimator-doc-link.fitted {
+      /* fitted */
+      border: var(--sklearn-color-fitted-level-1) 1pt solid;
+      color: var(--sklearn-color-fitted-level-1);
+    }
+
+    /* On hover */
+    div.sk-estimator:hover .sk-estimator-doc-link:hover,
+    .sk-estimator-doc-link:hover,
+    div.sk-label-container:hover .sk-estimator-doc-link:hover,
+    .sk-estimator-doc-link:hover {
+      /* unfitted */
+      background-color: var(--sklearn-color-unfitted-level-3);
+      color: var(--sklearn-color-background);
+      text-decoration: none;
+    }
+
+    div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,
+    .sk-estimator-doc-link.fitted:hover,
+    div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,
+    .sk-estimator-doc-link.fitted:hover {
+      /* fitted */
+      background-color: var(--sklearn-color-fitted-level-3);
+      color: var(--sklearn-color-background);
+      text-decoration: none;
+    }
+
+    /* Span, style for the box shown on hovering the info icon */
+    .sk-estimator-doc-link span {
+      display: none;
+      z-index: 9999;
+      position: relative;
+      font-weight: normal;
+      right: .2ex;
+      padding: .5ex;
+      margin: .5ex;
+      width: min-content;
+      min-width: 20ex;
+      max-width: 50ex;
+      color: var(--sklearn-color-text);
+      box-shadow: 2pt 2pt 4pt #999;
+      /* unfitted */
+      background: var(--sklearn-color-unfitted-level-0);
+      border: .5pt solid var(--sklearn-color-unfitted-level-3);
+    }
+
+    .sk-estimator-doc-link.fitted span {
+      /* fitted */
+      background: var(--sklearn-color-fitted-level-0);
+      border: var(--sklearn-color-fitted-level-3);
+    }
+
+    .sk-estimator-doc-link:hover span {
+      display: block;
+    }
+
+    /* "?"-specific style due to the `<a>` HTML tag */
+
+    #sk-container-id-27 a.estimator_doc_link {
+      float: right;
+      font-size: 1rem;
+      line-height: 1em;
+      font-family: monospace;
+      background-color: var(--sklearn-color-background);
+      border-radius: 1rem;
+      height: 1rem;
+      width: 1rem;
+      text-decoration: none;
+      /* unfitted */
+      color: var(--sklearn-color-unfitted-level-1);
+      border: var(--sklearn-color-unfitted-level-1) 1pt solid;
+    }
+
+    #sk-container-id-27 a.estimator_doc_link.fitted {
+      /* fitted */
+      border: var(--sklearn-color-fitted-level-1) 1pt solid;
+      color: var(--sklearn-color-fitted-level-1);
+    }
+
+    /* On hover */
+    #sk-container-id-27 a.estimator_doc_link:hover {
+      /* unfitted */
+      background-color: var(--sklearn-color-unfitted-level-3);
+      color: var(--sklearn-color-background);
+      text-decoration: none;
+    }
+
+    #sk-container-id-27 a.estimator_doc_link.fitted:hover {
+      /* fitted */
+      background-color: var(--sklearn-color-fitted-level-3);
+    }
+    </style><div id="sk-container-id-27" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>VotingClassifier(estimators=[(&#x27;constant splines model&#x27;,
+                                  Pipeline(steps=[(&#x27;splinetransformer&#x27;,
+                                                   SplineTransformer(degree=2,
+                                                                     n_knots=2)),
+                                                  (&#x27;polynomialfeatures&#x27;,
+                                                   PolynomialFeatures(interaction_only=True)),
+                                                  (&#x27;logisticregression&#x27;,
+                                                   LogisticRegression(C=10))])),
+                                 (&#x27;periodic splines model&#x27;,
+                                  Pipeline(steps=[(&#x27;splinetransformer&#x27;,
+                                                   SplineTransformer(degree=2,
+                                                                     extrapolation=&#x27;periodic&#x27;,
+                                                                     n_knots=4)),
+                                                  (&#x27;polynomialfeatures&#x27;,
+                                                   PolynomialFeatures(interaction_only=True)),
+                                                  (&#x27;logisticregression&#x27;,
+                                                   LogisticRegression(C=10))])),
+                                 (&#x27;nystroem model&#x27;,
+                                  Pipeline(steps=[(&#x27;standardscaler&#x27;,
+                                                   StandardScaler()),
+                                                  (&#x27;nystroem&#x27;,
+                                                   Nystroem(gamma=2,
+                                                            random_state=0)),
+                                                  (&#x27;logisticregression&#x27;,
+                                                   LogisticRegression(C=10))]))],
+                     voting=&#x27;soft&#x27;, weights=[2, 1, 3])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-107" type="checkbox" ><label for="sk-estimator-id-107" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>VotingClassifier</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.ensemble.VotingClassifier.html">?<span>Documentation for VotingClassifier</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>VotingClassifier(estimators=[(&#x27;constant splines model&#x27;,
+                                  Pipeline(steps=[(&#x27;splinetransformer&#x27;,
+                                                   SplineTransformer(degree=2,
+                                                                     n_knots=2)),
+                                                  (&#x27;polynomialfeatures&#x27;,
+                                                   PolynomialFeatures(interaction_only=True)),
+                                                  (&#x27;logisticregression&#x27;,
+                                                   LogisticRegression(C=10))])),
+                                 (&#x27;periodic splines model&#x27;,
+                                  Pipeline(steps=[(&#x27;splinetransformer&#x27;,
+                                                   SplineTransformer(degree=2,
+                                                                     extrapolation=&#x27;periodic&#x27;,
+                                                                     n_knots=4)),
+                                                  (&#x27;polynomialfeatures&#x27;,
+                                                   PolynomialFeatures(interaction_only=True)),
+                                                  (&#x27;logisticregression&#x27;,
+                                                   LogisticRegression(C=10))])),
+                                 (&#x27;nystroem model&#x27;,
+                                  Pipeline(steps=[(&#x27;standardscaler&#x27;,
+                                                   StandardScaler()),
+                                                  (&#x27;nystroem&#x27;,
+                                                   Nystroem(gamma=2,
+                                                            random_state=0)),
+                                                  (&#x27;logisticregression&#x27;,
+                                                   LogisticRegression(C=10))]))],
+                     voting=&#x27;soft&#x27;, weights=[2, 1, 3])</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><label>constant splines model</label></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-108" type="checkbox" ><label for="sk-estimator-id-108" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>SplineTransformer</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.SplineTransformer.html">?<span>Documentation for SplineTransformer</span></a></div></label><div class="sk-toggleable__content fitted"><pre>SplineTransformer(degree=2, n_knots=2)</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-109" type="checkbox" ><label for="sk-estimator-id-109" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>PolynomialFeatures</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.PolynomialFeatures.html">?<span>Documentation for PolynomialFeatures</span></a></div></label><div class="sk-toggleable__content fitted"><pre>PolynomialFeatures(interaction_only=True)</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-110" type="checkbox" ><label for="sk-estimator-id-110" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>LogisticRegression</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.linear_model.LogisticRegression.html">?<span>Documentation for LogisticRegression</span></a></div></label><div class="sk-toggleable__content fitted"><pre>LogisticRegression(C=10)</pre></div> </div></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><label>periodic splines model</label></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-111" type="checkbox" ><label for="sk-estimator-id-111" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>SplineTransformer</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.SplineTransformer.html">?<span>Documentation for SplineTransformer</span></a></div></label><div class="sk-toggleable__content fitted"><pre>SplineTransformer(degree=2, extrapolation=&#x27;periodic&#x27;, n_knots=4)</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-112" type="checkbox" ><label for="sk-estimator-id-112" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>PolynomialFeatures</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.PolynomialFeatures.html">?<span>Documentation for PolynomialFeatures</span></a></div></label><div class="sk-toggleable__content fitted"><pre>PolynomialFeatures(interaction_only=True)</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-113" type="checkbox" ><label for="sk-estimator-id-113" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>LogisticRegression</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.linear_model.LogisticRegression.html">?<span>Documentation for LogisticRegression</span></a></div></label><div class="sk-toggleable__content fitted"><pre>LogisticRegression(C=10)</pre></div> </div></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><label>nystroem model</label></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-114" type="checkbox" ><label for="sk-estimator-id-114" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>StandardScaler</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.StandardScaler.html">?<span>Documentation for StandardScaler</span></a></div></label><div class="sk-toggleable__content fitted"><pre>StandardScaler()</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-115" type="checkbox" ><label for="sk-estimator-id-115" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>Nystroem</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.kernel_approximation.Nystroem.html">?<span>Documentation for Nystroem</span></a></div></label><div class="sk-toggleable__content fitted"><pre>Nystroem(gamma=2, random_state=0)</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-116" type="checkbox" ><label for="sk-estimator-id-116" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>LogisticRegression</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.linear_model.LogisticRegression.html">?<span>Documentation for LogisticRegression</span></a></div></label><div class="sk-toggleable__content fitted"><pre>LogisticRegression(C=10)</pre></div> </div></div></div></div></div></div></div></div></div></div></div>
+    </div>
+    <br />
+    <br />
+
+.. GENERATED FROM PYTHON SOURCE LINES 117-121
+
+Finally we use :class:`~inspection.DecisionBoundaryDisplay` to plot the
+predicted probabilities. By using a diverging colormap (such as `"RdBu"`), we
+can ensure that darker colors correspond to `predict_proba` close to either 0
+or 1, and white corresponds to `predict_proba` of 0.5.
+
+.. GENERATED FROM PYTHON SOURCE LINES 121-157
+
+.. code-block:: Python
+
+
+    from itertools import product
+
+    from sklearn.inspection import DecisionBoundaryDisplay
+
+    fig, axarr = plt.subplots(2, 2, sharex="col", sharey="row", figsize=(10, 8))
+    for idx, clf, title in zip(
         product([0, 1], [0, 1]),
         [clf1, clf2, clf3, eclf],
-        ["Decision Tree (depth=4)", "KNN (k=7)", "Kernel SVM", "Soft Voting"],
+        [
+            "Splines with\nconstant extrapolation",
+            "Splines with\nperiodic extrapolation",
+            "RBF Nystroem",
+            "Soft Voting",
+        ],
     ):
-        DecisionBoundaryDisplay.from_estimator(
-            clf, X, alpha=0.4, ax=axarr[idx[0], idx[1]], response_method="predict"
+        disp = DecisionBoundaryDisplay.from_estimator(
+            clf,
+            X,
+            response_method="predict_proba",
+            plot_method="pcolormesh",
+            cmap="RdBu",
+            alpha=0.8,
+            ax=axarr[idx[0], idx[1]],
+        )
+        axarr[idx[0], idx[1]].scatter(
+            X["Feature #0"],
+            X["Feature #1"],
+            c=y,
+            **common_scatter_plot_params,
         )
-        axarr[idx[0], idx[1]].scatter(X[:, 0], X[:, 1], c=y, s=20, edgecolor="k")
-        axarr[idx[0], idx[1]].set_title(tt)
+        axarr[idx[0], idx[1]].set_title(title)
+        fig.colorbar(disp.surface_, ax=axarr[idx[0], idx[1]], label="Probability estimate")
 
     plt.show()
 
 
+
+
+.. image-sg:: /auto_examples/ensemble/images/sphx_glr_plot_voting_decision_regions_002.png
+   :alt: Splines with constant extrapolation, Splines with periodic extrapolation, RBF Nystroem, Soft Voting
+   :srcset: /auto_examples/ensemble/images/sphx_glr_plot_voting_decision_regions_002.png
+   :class: sphx-glr-single-img
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 158-166
+
+As a sanity check, we can verify for a given sample that the probability
+predicted by the :class:`~ensemble.VotingClassifier` is indeed the weighted
+average of the individual classifiers' soft-predictions.
+
+In the case of binary classification such as in the present example, the
+:term:`predict_proba` arrays contain the probability of belonging to class 0
+(here in red) as the first entry, and the probability of belonging to class 1
+(here in blue) as the second entry.
+
+.. GENERATED FROM PYTHON SOURCE LINES 166-172
+
+.. code-block:: Python
+
+
+    test_sample = pd.DataFrame({"Feature #0": [-0.5], "Feature #1": [1.5]})
+    predict_probas = [est.predict_proba(test_sample).ravel() for est in eclf.estimators_]
+    for (est_name, _), est_probas in zip(eclf.estimators, predict_probas):
+        print(f"{est_name}'s predicted probabilities: {est_probas}")
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    constant splines model's predicted probabilities: [0.11272662 0.88727338]
+    periodic splines model's predicted probabilities: [0.99726573 0.00273427]
+    nystroem model's predicted probabilities: [0.3185838 0.6814162]
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 173-178
+
+.. code-block:: Python
+
+    print(
+        "Weighted average of soft-predictions: "
+        f"{np.dot(weights, predict_probas) / np.sum(weights)}"
+    )
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    Weighted average of soft-predictions: [0.3630784 0.6369216]
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 179-181
+
+We can see that manual calculation of predicted probabilities above is
+equivalent to that produced by the `VotingClassifier`:
+
+.. GENERATED FROM PYTHON SOURCE LINES 181-187
+
+.. code-block:: Python
+
+
+    print(
+        "Predicted probability of VotingClassifier: "
+        f"{eclf.predict_proba(test_sample).ravel()}"
+    )
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    Predicted probability of VotingClassifier: [0.3630784 0.6369216]
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 188-193
+
+To convert soft predictions into hard predictions when weights are provided,
+the weighted average predicted probabilities are computed for each class.
+Then, the final class label is then derived from the class label with the
+highest average probability, which corresponds to the default threshold at
+`predict_proba=0.5` in the case of binary classification.
+
+.. GENERATED FROM PYTHON SOURCE LINES 193-199
+
+.. code-block:: Python
+
+
+    print(
+        "Class with the highest weighted average of soft-predictions: "
+        f"{np.argmax(np.dot(weights, predict_probas) / np.sum(weights))}"
+    )
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    Class with the highest weighted average of soft-predictions: 1
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 200-201
+
+This is equivalent to the output of `VotingClassifier`'s `predict` method:
+
+.. GENERATED FROM PYTHON SOURCE LINES 201-204
+
+.. code-block:: Python
+
+
+    print(f"Predicted class of VotingClassifier: {eclf.predict(test_sample).ravel()}")
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    Predicted class of VotingClassifier: [1]
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 205-209
+
+Soft votes can be thresholded as for any other probabilistic classifier. This
+allows you to set a threshold probability at which the positive class will be
+predicted, instead of simply selecting the class with the highest predicted
+probability.
+
+.. GENERATED FROM PYTHON SOURCE LINES 209-219
+
+.. code-block:: Python
+
+
+    from sklearn.model_selection import FixedThresholdClassifier
+
+    eclf_other_threshold = FixedThresholdClassifier(
+        eclf, threshold=0.7, response_method="predict_proba"
+    ).fit(X, y)
+    print(
+        "Predicted class of thresholded VotingClassifier: "
+        f"{eclf_other_threshold.predict(test_sample)}"
+    )
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    Predicted class of thresholded VotingClassifier: [0]
+
+
+
+
+
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.665 seconds)
+   **Total running time of the script:** (0 minutes 0.702 seconds)
 
 
 .. _sphx_glr_download_auto_examples_ensemble_plot_voting_decision_regions.py:
diff --git a/dev/_sources/auto_examples/ensemble/plot_voting_probas.rst.txt b/dev/_sources/auto_examples/ensemble/plot_voting_probas.rst.txt
deleted file mode 100644
index 668ee5eeb9e38..0000000000000
--- a/dev/_sources/auto_examples/ensemble/plot_voting_probas.rst.txt
+++ /dev/null
@@ -1,178 +0,0 @@
-
-.. DO NOT EDIT.
-.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
-.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
-.. "auto_examples/ensemble/plot_voting_probas.py"
-.. LINE NUMBERS ARE GIVEN BELOW.
-
-.. only:: html
-
-    .. note::
-        :class: sphx-glr-download-link-note
-
-        :ref:`Go to the end <sphx_glr_download_auto_examples_ensemble_plot_voting_probas.py>`
-        to download the full example code. or to run this example in your browser via JupyterLite or Binder
-
-.. rst-class:: sphx-glr-example-title
-
-.. _sphx_glr_auto_examples_ensemble_plot_voting_probas.py:
-
-
-===========================================================
-Plot class probabilities calculated by the VotingClassifier
-===========================================================
-
-.. currentmodule:: sklearn
-
-Plot the class probabilities of the first sample in a toy dataset predicted by
-three different classifiers and averaged by the
-:class:`~ensemble.VotingClassifier`.
-
-First, three exemplary classifiers are initialized
-(:class:`~linear_model.LogisticRegression`, :class:`~naive_bayes.GaussianNB`,
-and :class:`~ensemble.RandomForestClassifier`) and used to initialize a
-soft-voting :class:`~ensemble.VotingClassifier` with weights `[1, 1, 5]`, which
-means that the predicted probabilities of the
-:class:`~ensemble.RandomForestClassifier` count 5 times as much as the weights
-of the other classifiers when the averaged probability is calculated.
-
-To visualize the probability weighting, we fit each classifier on the training
-set and plot the predicted class probabilities for the first sample in this
-example dataset.
-
-.. GENERATED FROM PYTHON SOURCE LINES 25-98
-
-
-
-.. image-sg:: /auto_examples/ensemble/images/sphx_glr_plot_voting_probas_001.png
-   :alt: Class probabilities for sample 1 by different classifiers
-   :srcset: /auto_examples/ensemble/images/sphx_glr_plot_voting_probas_001.png
-   :class: sphx-glr-single-img
-
-
-
-
-
-.. code-block:: Python
-
-
-    # Authors: The scikit-learn developers
-    # SPDX-License-Identifier: BSD-3-Clause
-
-    import matplotlib.pyplot as plt
-    import numpy as np
-
-    from sklearn.ensemble import RandomForestClassifier, VotingClassifier
-    from sklearn.linear_model import LogisticRegression
-    from sklearn.naive_bayes import GaussianNB
-
-    clf1 = LogisticRegression(max_iter=1000, random_state=123)
-    clf2 = RandomForestClassifier(n_estimators=100, random_state=123)
-    clf3 = GaussianNB()
-    X = np.array([[-1.0, -1.0], [-1.2, -1.4], [-3.4, -2.2], [1.1, 1.2]])
-    y = np.array([1, 1, 2, 2])
-
-    eclf = VotingClassifier(
-        estimators=[("lr", clf1), ("rf", clf2), ("gnb", clf3)],
-        voting="soft",
-        weights=[1, 1, 5],
-    )
-
-    # predict class probabilities for all classifiers
-    probas = [c.fit(X, y).predict_proba(X) for c in (clf1, clf2, clf3, eclf)]
-
-    # get class probabilities for the first sample in the dataset
-    class1_1 = [pr[0, 0] for pr in probas]
-    class2_1 = [pr[0, 1] for pr in probas]
-
-
-    # plotting
-
-    N = 4  # number of groups
-    ind = np.arange(N)  # group positions
-    width = 0.35  # bar width
-
-    fig, ax = plt.subplots()
-
-    # bars for classifier 1-3
-    p1 = ax.bar(ind, np.hstack(([class1_1[:-1], [0]])), width, color="green", edgecolor="k")
-    p2 = ax.bar(
-        ind + width,
-        np.hstack(([class2_1[:-1], [0]])),
-        width,
-        color="lightgreen",
-        edgecolor="k",
-    )
-
-    # bars for VotingClassifier
-    p3 = ax.bar(ind, [0, 0, 0, class1_1[-1]], width, color="blue", edgecolor="k")
-    p4 = ax.bar(
-        ind + width, [0, 0, 0, class2_1[-1]], width, color="steelblue", edgecolor="k"
-    )
-
-    # plot annotations
-    plt.axvline(2.8, color="k", linestyle="dashed")
-    ax.set_xticks(ind + width)
-    ax.set_xticklabels(
-        [
-            "LogisticRegression\nweight 1",
-            "GaussianNB\nweight 1",
-            "RandomForestClassifier\nweight 5",
-            "VotingClassifier\n(average probabilities)",
-        ],
-        rotation=40,
-        ha="right",
-    )
-    plt.ylim([0, 1])
-    plt.title("Class probabilities for sample 1 by different classifiers")
-    plt.legend([p1[0], p2[0]], ["class 1", "class 2"], loc="upper left")
-    plt.tight_layout()
-    plt.show()
-
-
-.. rst-class:: sphx-glr-timing
-
-   **Total running time of the script:** (0 minutes 0.366 seconds)
-
-
-.. _sphx_glr_download_auto_examples_ensemble_plot_voting_probas.py:
-
-.. only:: html
-
-  .. container:: sphx-glr-footer sphx-glr-footer-example
-
-    .. container:: binder-badge
-
-      .. image:: images/binder_badge_logo.svg
-        :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/ensemble/plot_voting_probas.ipynb
-        :alt: Launch binder
-        :width: 150 px
-
-    .. container:: lite-badge
-
-      .. image:: images/jupyterlite_badge_logo.svg
-        :target: ../../lite/lab/index.html?path=auto_examples/ensemble/plot_voting_probas.ipynb
-        :alt: Launch JupyterLite
-        :width: 150 px
-
-    .. container:: sphx-glr-download sphx-glr-download-jupyter
-
-      :download:`Download Jupyter notebook: plot_voting_probas.ipynb <plot_voting_probas.ipynb>`
-
-    .. container:: sphx-glr-download sphx-glr-download-python
-
-      :download:`Download Python source code: plot_voting_probas.py <plot_voting_probas.py>`
-
-    .. container:: sphx-glr-download sphx-glr-download-zip
-
-      :download:`Download zipped: plot_voting_probas.zip <plot_voting_probas.zip>`
-
-
-.. include:: plot_voting_probas.recommendations
-
-
-.. only:: html
-
- .. rst-class:: sphx-glr-signature
-
-    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_
diff --git a/dev/_sources/auto_examples/ensemble/plot_voting_regressor.rst.txt b/dev/_sources/auto_examples/ensemble/plot_voting_regressor.rst.txt
index e6f6a2ed99786..e7e42a8f70377 100644
--- a/dev/_sources/auto_examples/ensemble/plot_voting_regressor.rst.txt
+++ b/dev/_sources/auto_examples/ensemble/plot_voting_regressor.rst.txt
@@ -592,7 +592,7 @@ prediction made by :class:`~ensemble.VotingRegressor`.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 1.001 seconds)
+   **Total running time of the script:** (0 minutes 1.051 seconds)
 
 
 .. _sphx_glr_download_auto_examples_ensemble_plot_voting_regressor.py:
diff --git a/dev/_sources/auto_examples/feature_selection/plot_f_test_vs_mi.rst.txt b/dev/_sources/auto_examples/feature_selection/plot_f_test_vs_mi.rst.txt
index 087473a608554..62108379867ea 100644
--- a/dev/_sources/auto_examples/feature_selection/plot_f_test_vs_mi.rst.txt
+++ b/dev/_sources/auto_examples/feature_selection/plot_f_test_vs_mi.rst.txt
@@ -87,7 +87,7 @@ perception for this example. Both methods correctly mark x_3 as irrelevant.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.219 seconds)
+   **Total running time of the script:** (0 minutes 0.310 seconds)
 
 
 .. _sphx_glr_download_auto_examples_feature_selection_plot_f_test_vs_mi.py:
diff --git a/dev/_sources/auto_examples/feature_selection/plot_feature_selection.rst.txt b/dev/_sources/auto_examples/feature_selection/plot_feature_selection.rst.txt
index 6c5d7d7eb8241..6ca3ea03f18fb 100644
--- a/dev/_sources/auto_examples/feature_selection/plot_feature_selection.rst.txt
+++ b/dev/_sources/auto_examples/feature_selection/plot_feature_selection.rst.txt
@@ -260,7 +260,7 @@ and will thus improve classification.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.173 seconds)
+   **Total running time of the script:** (0 minutes 0.204 seconds)
 
 
 .. _sphx_glr_download_auto_examples_feature_selection_plot_feature_selection.py:
diff --git a/dev/_sources/auto_examples/feature_selection/plot_feature_selection_pipeline.rst.txt b/dev/_sources/auto_examples/feature_selection/plot_feature_selection_pipeline.rst.txt
index 668b9c359934d..cff8a8f8a4f15 100644
--- a/dev/_sources/auto_examples/feature_selection/plot_feature_selection_pipeline.rst.txt
+++ b/dev/_sources/auto_examples/feature_selection/plot_feature_selection_pipeline.rst.txt
@@ -109,7 +109,7 @@ classifier which will be trained.
 .. raw:: html
 
     <div class="output_subarea output_html rendered_html output_result">
-    <style>#sk-container-id-28 {
+    <style>#sk-container-id-29 {
       /* Definition of color scheme common for light and dark mode */
       --sklearn-color-text: #000;
       --sklearn-color-text-muted: #666;
@@ -140,15 +140,15 @@ classifier which will be trained.
       }
     }
 
-    #sk-container-id-28 {
+    #sk-container-id-29 {
       color: var(--sklearn-color-text);
     }
 
-    #sk-container-id-28 pre {
+    #sk-container-id-29 pre {
       padding: 0;
     }
 
-    #sk-container-id-28 input.sk-hidden--visually {
+    #sk-container-id-29 input.sk-hidden--visually {
       border: 0;
       clip: rect(1px 1px 1px 1px);
       clip: rect(1px, 1px, 1px, 1px);
@@ -160,7 +160,7 @@ classifier which will be trained.
       width: 1px;
     }
 
-    #sk-container-id-28 div.sk-dashed-wrapped {
+    #sk-container-id-29 div.sk-dashed-wrapped {
       border: 1px dashed var(--sklearn-color-line);
       margin: 0 0.4em 0.5em 0.4em;
       box-sizing: border-box;
@@ -168,7 +168,7 @@ classifier which will be trained.
       background-color: var(--sklearn-color-background);
     }
 
-    #sk-container-id-28 div.sk-container {
+    #sk-container-id-29 div.sk-container {
       /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
          but bootstrap.min.css set `[hidden] { display: none !important; }`
          so we also need the `!important` here to be able to override the
@@ -178,7 +178,7 @@ classifier which will be trained.
       position: relative;
     }
 
-    #sk-container-id-28 div.sk-text-repr-fallback {
+    #sk-container-id-29 div.sk-text-repr-fallback {
       display: none;
     }
 
@@ -194,14 +194,14 @@ classifier which will be trained.
 
     /* Parallel-specific style estimator block */
 
-    #sk-container-id-28 div.sk-parallel-item::after {
+    #sk-container-id-29 div.sk-parallel-item::after {
       content: "";
       width: 100%;
       border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
       flex-grow: 1;
     }
 
-    #sk-container-id-28 div.sk-parallel {
+    #sk-container-id-29 div.sk-parallel {
       display: flex;
       align-items: stretch;
       justify-content: center;
@@ -209,28 +209,28 @@ classifier which will be trained.
       position: relative;
     }
 
-    #sk-container-id-28 div.sk-parallel-item {
+    #sk-container-id-29 div.sk-parallel-item {
       display: flex;
       flex-direction: column;
     }
 
-    #sk-container-id-28 div.sk-parallel-item:first-child::after {
+    #sk-container-id-29 div.sk-parallel-item:first-child::after {
       align-self: flex-end;
       width: 50%;
     }
 
-    #sk-container-id-28 div.sk-parallel-item:last-child::after {
+    #sk-container-id-29 div.sk-parallel-item:last-child::after {
       align-self: flex-start;
       width: 50%;
     }
 
-    #sk-container-id-28 div.sk-parallel-item:only-child::after {
+    #sk-container-id-29 div.sk-parallel-item:only-child::after {
       width: 0;
     }
 
     /* Serial-specific style estimator block */
 
-    #sk-container-id-28 div.sk-serial {
+    #sk-container-id-29 div.sk-serial {
       display: flex;
       flex-direction: column;
       align-items: center;
@@ -248,14 +248,14 @@ classifier which will be trained.
 
     /* Pipeline and ColumnTransformer style (default) */
 
-    #sk-container-id-28 div.sk-toggleable {
+    #sk-container-id-29 div.sk-toggleable {
       /* Default theme specific background. It is overwritten whether we have a
       specific estimator or a Pipeline/ColumnTransformer */
       background-color: var(--sklearn-color-background);
     }
 
     /* Toggleable label */
-    #sk-container-id-28 label.sk-toggleable__label {
+    #sk-container-id-29 label.sk-toggleable__label {
       cursor: pointer;
       display: flex;
       width: 100%;
@@ -268,13 +268,13 @@ classifier which will be trained.
       gap: 0.5em;
     }
 
-    #sk-container-id-28 label.sk-toggleable__label .caption {
+    #sk-container-id-29 label.sk-toggleable__label .caption {
       font-size: 0.6rem;
       font-weight: lighter;
       color: var(--sklearn-color-text-muted);
     }
 
-    #sk-container-id-28 label.sk-toggleable__label-arrow:before {
+    #sk-container-id-29 label.sk-toggleable__label-arrow:before {
       /* Arrow on the left of the label */
       content: "▸";
       float: left;
@@ -282,13 +282,13 @@ classifier which will be trained.
       color: var(--sklearn-color-icon);
     }
 
-    #sk-container-id-28 label.sk-toggleable__label-arrow:hover:before {
+    #sk-container-id-29 label.sk-toggleable__label-arrow:hover:before {
       color: var(--sklearn-color-text);
     }
 
     /* Toggleable content - dropdown */
 
-    #sk-container-id-28 div.sk-toggleable__content {
+    #sk-container-id-29 div.sk-toggleable__content {
       max-height: 0;
       max-width: 0;
       overflow: hidden;
@@ -297,12 +297,12 @@ classifier which will be trained.
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-28 div.sk-toggleable__content.fitted {
+    #sk-container-id-29 div.sk-toggleable__content.fitted {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
-    #sk-container-id-28 div.sk-toggleable__content pre {
+    #sk-container-id-29 div.sk-toggleable__content pre {
       margin: 0.2em;
       border-radius: 0.25em;
       color: var(--sklearn-color-text);
@@ -310,79 +310,79 @@ classifier which will be trained.
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-28 div.sk-toggleable__content.fitted pre {
+    #sk-container-id-29 div.sk-toggleable__content.fitted pre {
       /* unfitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
-    #sk-container-id-28 input.sk-toggleable__control:checked~div.sk-toggleable__content {
+    #sk-container-id-29 input.sk-toggleable__control:checked~div.sk-toggleable__content {
       /* Expand drop-down */
       max-height: 200px;
       max-width: 100%;
       overflow: auto;
     }
 
-    #sk-container-id-28 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
+    #sk-container-id-29 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
       content: "▾";
     }
 
     /* Pipeline/ColumnTransformer-specific style */
 
-    #sk-container-id-28 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-29 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-28 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-29 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
     /* Estimator-specific style */
 
     /* Colorize estimator box */
-    #sk-container-id-28 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-29 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-28 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-29 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
-    #sk-container-id-28 div.sk-label label.sk-toggleable__label,
-    #sk-container-id-28 div.sk-label label {
+    #sk-container-id-29 div.sk-label label.sk-toggleable__label,
+    #sk-container-id-29 div.sk-label label {
       /* The background is the default theme color */
       color: var(--sklearn-color-text-on-default-background);
     }
 
     /* On hover, darken the color of the background */
-    #sk-container-id-28 div.sk-label:hover label.sk-toggleable__label {
+    #sk-container-id-29 div.sk-label:hover label.sk-toggleable__label {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
     /* Label box, darken color on hover, fitted */
-    #sk-container-id-28 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
+    #sk-container-id-29 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
     /* Estimator label */
 
-    #sk-container-id-28 div.sk-label label {
+    #sk-container-id-29 div.sk-label label {
       font-family: monospace;
       font-weight: bold;
       display: inline-block;
       line-height: 1.2em;
     }
 
-    #sk-container-id-28 div.sk-label-container {
+    #sk-container-id-29 div.sk-label-container {
       text-align: center;
     }
 
     /* Estimator-specific */
-    #sk-container-id-28 div.sk-estimator {
+    #sk-container-id-29 div.sk-estimator {
       font-family: monospace;
       border: 1px dotted var(--sklearn-color-border-box);
       border-radius: 0.25em;
@@ -392,18 +392,18 @@ classifier which will be trained.
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-28 div.sk-estimator.fitted {
+    #sk-container-id-29 div.sk-estimator.fitted {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
     /* on hover */
-    #sk-container-id-28 div.sk-estimator:hover {
+    #sk-container-id-29 div.sk-estimator:hover {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-28 div.sk-estimator.fitted:hover {
+    #sk-container-id-29 div.sk-estimator.fitted:hover {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-2);
     }
@@ -491,7 +491,7 @@ classifier which will be trained.
 
     /* "?"-specific style due to the `<a>` HTML tag */
 
-    #sk-container-id-28 a.estimator_doc_link {
+    #sk-container-id-29 a.estimator_doc_link {
       float: right;
       font-size: 1rem;
       line-height: 1em;
@@ -506,25 +506,25 @@ classifier which will be trained.
       border: var(--sklearn-color-unfitted-level-1) 1pt solid;
     }
 
-    #sk-container-id-28 a.estimator_doc_link.fitted {
+    #sk-container-id-29 a.estimator_doc_link.fitted {
       /* fitted */
       border: var(--sklearn-color-fitted-level-1) 1pt solid;
       color: var(--sklearn-color-fitted-level-1);
     }
 
     /* On hover */
-    #sk-container-id-28 a.estimator_doc_link:hover {
+    #sk-container-id-29 a.estimator_doc_link:hover {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-3);
       color: var(--sklearn-color-background);
       text-decoration: none;
     }
 
-    #sk-container-id-28 a.estimator_doc_link.fitted:hover {
+    #sk-container-id-29 a.estimator_doc_link.fitted:hover {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-3);
     }
-    </style><div id="sk-container-id-28" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;selectkbest&#x27;, SelectKBest(k=3)), (&#x27;linearsvc&#x27;, LinearSVC())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-108" type="checkbox" ><label for="sk-estimator-id-108" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>Pipeline</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.pipeline.Pipeline.html">?<span>Documentation for Pipeline</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>Pipeline(steps=[(&#x27;selectkbest&#x27;, SelectKBest(k=3)), (&#x27;linearsvc&#x27;, LinearSVC())])</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-109" type="checkbox" ><label for="sk-estimator-id-109" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>SelectKBest</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.feature_selection.SelectKBest.html">?<span>Documentation for SelectKBest</span></a></div></label><div class="sk-toggleable__content fitted"><pre>SelectKBest(k=3)</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-110" type="checkbox" ><label for="sk-estimator-id-110" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>LinearSVC</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.svm.LinearSVC.html">?<span>Documentation for LinearSVC</span></a></div></label><div class="sk-toggleable__content fitted"><pre>LinearSVC()</pre></div> </div></div></div></div></div></div>
+    </style><div id="sk-container-id-29" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;selectkbest&#x27;, SelectKBest(k=3)), (&#x27;linearsvc&#x27;, LinearSVC())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-118" type="checkbox" ><label for="sk-estimator-id-118" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>Pipeline</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.pipeline.Pipeline.html">?<span>Documentation for Pipeline</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>Pipeline(steps=[(&#x27;selectkbest&#x27;, SelectKBest(k=3)), (&#x27;linearsvc&#x27;, LinearSVC())])</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-119" type="checkbox" ><label for="sk-estimator-id-119" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>SelectKBest</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.feature_selection.SelectKBest.html">?<span>Documentation for SelectKBest</span></a></div></label><div class="sk-toggleable__content fitted"><pre>SelectKBest(k=3)</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-120" type="checkbox" ><label for="sk-estimator-id-120" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>LinearSVC</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.svm.LinearSVC.html">?<span>Documentation for LinearSVC</span></a></div></label><div class="sk-toggleable__content fitted"><pre>LinearSVC()</pre></div> </div></div></div></div></div></div>
     </div>
     <br />
     <br />
@@ -633,7 +633,7 @@ features by the first step.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.013 seconds)
+   **Total running time of the script:** (0 minutes 0.014 seconds)
 
 
 .. _sphx_glr_download_auto_examples_feature_selection_plot_feature_selection_pipeline.py:
diff --git a/dev/_sources/auto_examples/feature_selection/plot_rfe_digits.rst.txt b/dev/_sources/auto_examples/feature_selection/plot_rfe_digits.rst.txt
index 8f22d21c740a6..79e8f565d43a7 100644
--- a/dev/_sources/auto_examples/feature_selection/plot_rfe_digits.rst.txt
+++ b/dev/_sources/auto_examples/feature_selection/plot_rfe_digits.rst.txt
@@ -92,7 +92,7 @@ at the center of the image tend to be more predictive than those near the edges.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 2.689 seconds)
+   **Total running time of the script:** (0 minutes 3.180 seconds)
 
 
 .. _sphx_glr_download_auto_examples_feature_selection_plot_rfe_digits.py:
diff --git a/dev/_sources/auto_examples/feature_selection/plot_rfe_with_cross_validation.rst.txt b/dev/_sources/auto_examples/feature_selection/plot_rfe_with_cross_validation.rst.txt
index c78a0972c8456..ea8d058065834 100644
--- a/dev/_sources/auto_examples/feature_selection/plot_rfe_with_cross_validation.rst.txt
+++ b/dev/_sources/auto_examples/feature_selection/plot_rfe_with_cross_validation.rst.txt
@@ -50,16 +50,19 @@ of 2 additional redundant (i.e. correlated) features has the effect that the
 selected features vary depending on the cross-validation fold. The remaining
 features are non-informative as they are drawn at random.
 
-.. GENERATED FROM PYTHON SOURCE LINES 22-37
+.. GENERATED FROM PYTHON SOURCE LINES 22-40
 
 .. code-block:: Python
 
 
     from sklearn.datasets import make_classification
 
+    n_features = 15
+    feat_names = [f"feature_{i}" for i in range(15)]
+
     X, y = make_classification(
         n_samples=500,
-        n_features=15,
+        n_features=n_features,
         n_informative=3,
         n_redundant=2,
         n_repeated=0,
@@ -76,7 +79,7 @@ features are non-informative as they are drawn at random.
 
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 38-43
+.. GENERATED FROM PYTHON SOURCE LINES 41-46
 
 Model training and selection
 ----------------------------
@@ -84,7 +87,7 @@ Model training and selection
 We create the RFE object and compute the cross-validated scores. The scoring
 strategy "accuracy" optimizes the proportion of correctly classified samples.
 
-.. GENERATED FROM PYTHON SOURCE LINES 43-64
+.. GENERATED FROM PYTHON SOURCE LINES 46-67
 
 .. code-block:: Python
 
@@ -122,7 +125,7 @@ strategy "accuracy" optimizes the proportion of correctly classified samples.
 
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 65-70
+.. GENERATED FROM PYTHON SOURCE LINES 68-73
 
 In the present case, the model with 3 features (which corresponds to the true
 generative model) is found to be the most optimal.
@@ -130,7 +133,7 @@ generative model) is found to be the most optimal.
 Plot number of features VS. cross-validation scores
 ---------------------------------------------------
 
-.. GENERATED FROM PYTHON SOURCE LINES 70-86
+.. GENERATED FROM PYTHON SOURCE LINES 73-94
 
 .. code-block:: Python
 
@@ -138,7 +141,12 @@ Plot number of features VS. cross-validation scores
     import matplotlib.pyplot as plt
     import pandas as pd
 
-    cv_results = pd.DataFrame(rfecv.cv_results_)
+    data = {
+        key: value
+        for key, value in rfecv.cv_results_.items()
+        if key in ["n_features", "mean_test_score", "std_test_score"]
+    }
+    cv_results = pd.DataFrame(data)
     plt.figure()
     plt.xlabel("Number of features selected")
     plt.ylabel("Mean test accuracy")
@@ -162,7 +170,7 @@ Plot number of features VS. cross-validation scores
 
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 87-94
+.. GENERATED FROM PYTHON SOURCE LINES 95-102
 
 From the plot above one can further notice a plateau of equivalent scores
 (similar mean value and overlapping errorbars) for 3 to 5 selected features.
@@ -172,10 +180,45 @@ cross-validation technique. The test accuracy decreases above 5 selected
 features, this is, keeping non-informative features leads to over-fitting and
 is therefore detrimental for the statistical performance of the models.
 
+.. GENERATED FROM PYTHON SOURCE LINES 104-112
+
+.. code-block:: Python
+
+    import numpy as np
+
+    for i in range(cv.n_splits):
+        mask = rfecv.cv_results_[f"split{i}_support"][
+            rfecv.n_features_
+        ]  # mask of features selected by the RFE
+        features_selected = np.ma.compressed(np.ma.masked_array(feat_names, mask=1 - mask))
+        print(f"Features selected in fold {i}: {features_selected}")
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+    Features selected in fold 0: ['feature_3' 'feature_4' 'feature_8' 'feature_14']
+    Features selected in fold 1: ['feature_3' 'feature_4' 'feature_8' 'feature_14']
+    Features selected in fold 2: ['feature_3' 'feature_4' 'feature_8' 'feature_14']
+    Features selected in fold 3: ['feature_3' 'feature_4' 'feature_8' 'feature_14']
+    Features selected in fold 4: ['feature_3' 'feature_4' 'feature_8' 'feature_14']
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 113-116
+
+In the five folds, the selected features are consistent. This is good news,
+it means that the selection is stable across folds, and it confirms that
+these features are the most informative ones.
+
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.674 seconds)
+   **Total running time of the script:** (0 minutes 0.838 seconds)
 
 
 .. _sphx_glr_download_auto_examples_feature_selection_plot_rfe_with_cross_validation.py:
diff --git a/dev/_sources/auto_examples/feature_selection/plot_select_from_model_diabetes.rst.txt b/dev/_sources/auto_examples/feature_selection/plot_select_from_model_diabetes.rst.txt
index abf9a0d482d27..21aa1a11d62f4 100644
--- a/dev/_sources/auto_examples/feature_selection/plot_select_from_model_diabetes.rst.txt
+++ b/dev/_sources/auto_examples/feature_selection/plot_select_from_model_diabetes.rst.txt
@@ -197,7 +197,7 @@ above the coefficient of third most important feature.
  .. code-block:: none
 
     Features selected by SelectFromModel: ['s1' 's5']
-    Done in 0.002s
+    Done in 0.003s
 
 
 
@@ -258,9 +258,9 @@ both approaches here.
  .. code-block:: none
 
     Features selected by forward sequential selection: ['bmi' 's5']
-    Done in 0.197s
+    Done in 0.270s
     Features selected by backward sequential selection: ['bmi' 's5']
-    Done in 0.561s
+    Done in 0.709s
 
 
 
@@ -488,13 +488,13 @@ to perform the feature selection.
     tol: -0.01
     Features selected: ['worst perimeter']
     ROC AUC score: 0.975
-    Done in 17.527s
+    Done in 22.408s
 
     tol: -0.001
     Features selected: ['radius error' 'fractal dimension error' 'worst texture'
      'worst perimeter' 'worst concave points']
     ROC AUC score: 0.997
-    Done in 17.086s
+    Done in 19.988s
 
     tol: -0.0001
     Features selected: ['mean compactness' 'mean concavity' 'mean concave points' 'radius error'
@@ -502,7 +502,7 @@ to perform the feature selection.
      'fractal dimension error' 'worst texture' 'worst perimeter' 'worst area'
      'worst concave points' 'worst symmetry']
     ROC AUC score: 0.998
-    Done in 14.632s
+    Done in 17.049s
 
 
 
@@ -516,7 +516,7 @@ decreases as the values of `tol` come closer to zero.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 50.137 seconds)
+   **Total running time of the script:** (1 minutes 0.575 seconds)
 
 
 .. _sphx_glr_download_auto_examples_feature_selection_plot_select_from_model_diabetes.py:
diff --git a/dev/_sources/auto_examples/frozen/plot_frozen_examples.rst.txt b/dev/_sources/auto_examples/frozen/plot_frozen_examples.rst.txt
index 525dd64dba167..1d31432fac4dd 100644
--- a/dev/_sources/auto_examples/frozen/plot_frozen_examples.rst.txt
+++ b/dev/_sources/auto_examples/frozen/plot_frozen_examples.rst.txt
@@ -211,7 +211,7 @@ classifier using :class:`~sklearn.calibration.CalibratedClassifierCV`.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.017 seconds)
+   **Total running time of the script:** (0 minutes 0.018 seconds)
 
 
 .. _sphx_glr_download_auto_examples_frozen_plot_frozen_examples.py:
diff --git a/dev/_sources/auto_examples/gaussian_process/plot_compare_gpr_krr.rst.txt b/dev/_sources/auto_examples/gaussian_process/plot_compare_gpr_krr.rst.txt
index 8daf4294a60ff..44554f0eb5bc8 100644
--- a/dev/_sources/auto_examples/gaussian_process/plot_compare_gpr_krr.rst.txt
+++ b/dev/_sources/auto_examples/gaussian_process/plot_compare_gpr_krr.rst.txt
@@ -352,7 +352,7 @@ parameter and the kernel parameters.
 
  .. code-block:: none
 
-    Time for KernelRidge fitting: 3.685 seconds
+    Time for KernelRidge fitting: 3.839 seconds
 
 
 
@@ -489,7 +489,7 @@ estimate the noise in the dataset.
 
  .. code-block:: none
 
-    Time for GaussianProcessRegressor fitting: 0.028 seconds
+    Time for GaussianProcessRegressor fitting: 0.031 seconds
 
 
 
@@ -714,7 +714,7 @@ also increases.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 4.285 seconds)
+   **Total running time of the script:** (0 minutes 4.481 seconds)
 
 
 .. _sphx_glr_download_auto_examples_gaussian_process_plot_compare_gpr_krr.py:
diff --git a/dev/_sources/auto_examples/gaussian_process/plot_gpc.rst.txt b/dev/_sources/auto_examples/gaussian_process/plot_gpc.rst.txt
index 45333c1e86fa2..0adeca25cc349 100644
--- a/dev/_sources/auto_examples/gaussian_process/plot_gpc.rst.txt
+++ b/dev/_sources/auto_examples/gaussian_process/plot_gpc.rst.txt
@@ -187,7 +187,7 @@ hyperparameters used in the first figure by black dots.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 1.891 seconds)
+   **Total running time of the script:** (0 minutes 2.138 seconds)
 
 
 .. _sphx_glr_download_auto_examples_gaussian_process_plot_gpc.py:
diff --git a/dev/_sources/auto_examples/gaussian_process/plot_gpc_iris.rst.txt b/dev/_sources/auto_examples/gaussian_process/plot_gpc_iris.rst.txt
index eed694a1db9f8..06ec8593075de 100644
--- a/dev/_sources/auto_examples/gaussian_process/plot_gpc_iris.rst.txt
+++ b/dev/_sources/auto_examples/gaussian_process/plot_gpc_iris.rst.txt
@@ -101,7 +101,7 @@ assigning different length-scales to the two feature dimensions.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 2.348 seconds)
+   **Total running time of the script:** (0 minutes 5.657 seconds)
 
 
 .. _sphx_glr_download_auto_examples_gaussian_process_plot_gpc_iris.py:
diff --git a/dev/_sources/auto_examples/gaussian_process/plot_gpc_isoprobability.rst.txt b/dev/_sources/auto_examples/gaussian_process/plot_gpc_isoprobability.rst.txt
index 8065a1165f9e3..973898d77db4e 100644
--- a/dev/_sources/auto_examples/gaussian_process/plot_gpc_isoprobability.rst.txt
+++ b/dev/_sources/auto_examples/gaussian_process/plot_gpc_isoprobability.rst.txt
@@ -142,7 +142,7 @@ the predicted probabilities.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.126 seconds)
+   **Total running time of the script:** (0 minutes 0.152 seconds)
 
 
 .. _sphx_glr_download_auto_examples_gaussian_process_plot_gpc_isoprobability.py:
diff --git a/dev/_sources/auto_examples/gaussian_process/plot_gpc_xor.rst.txt b/dev/_sources/auto_examples/gaussian_process/plot_gpc_xor.rst.txt
index c76d6fdc4244d..7e34ac5bf8546 100644
--- a/dev/_sources/auto_examples/gaussian_process/plot_gpc_xor.rst.txt
+++ b/dev/_sources/auto_examples/gaussian_process/plot_gpc_xor.rst.txt
@@ -108,7 +108,7 @@ stationary kernels often obtain better results.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.390 seconds)
+   **Total running time of the script:** (0 minutes 0.459 seconds)
 
 
 .. _sphx_glr_download_auto_examples_gaussian_process_plot_gpc_xor.py:
diff --git a/dev/_sources/auto_examples/gaussian_process/plot_gpr_co2.rst.txt b/dev/_sources/auto_examples/gaussian_process/plot_gpr_co2.rst.txt
index e7c640ee07641..a1139010ffa56 100644
--- a/dev/_sources/auto_examples/gaussian_process/plot_gpr_co2.rst.txt
+++ b/dev/_sources/auto_examples/gaussian_process/plot_gpr_co2.rst.txt
@@ -477,7 +477,7 @@ meaning since they would not have been expressed in ppm.
 .. raw:: html
 
     <div class="output_subarea output_html rendered_html output_result">
-    <style>#sk-container-id-30 {
+    <style>#sk-container-id-31 {
       /* Definition of color scheme common for light and dark mode */
       --sklearn-color-text: #000;
       --sklearn-color-text-muted: #666;
@@ -508,15 +508,15 @@ meaning since they would not have been expressed in ppm.
       }
     }
 
-    #sk-container-id-30 {
+    #sk-container-id-31 {
       color: var(--sklearn-color-text);
     }
 
-    #sk-container-id-30 pre {
+    #sk-container-id-31 pre {
       padding: 0;
     }
 
-    #sk-container-id-30 input.sk-hidden--visually {
+    #sk-container-id-31 input.sk-hidden--visually {
       border: 0;
       clip: rect(1px 1px 1px 1px);
       clip: rect(1px, 1px, 1px, 1px);
@@ -528,7 +528,7 @@ meaning since they would not have been expressed in ppm.
       width: 1px;
     }
 
-    #sk-container-id-30 div.sk-dashed-wrapped {
+    #sk-container-id-31 div.sk-dashed-wrapped {
       border: 1px dashed var(--sklearn-color-line);
       margin: 0 0.4em 0.5em 0.4em;
       box-sizing: border-box;
@@ -536,7 +536,7 @@ meaning since they would not have been expressed in ppm.
       background-color: var(--sklearn-color-background);
     }
 
-    #sk-container-id-30 div.sk-container {
+    #sk-container-id-31 div.sk-container {
       /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
          but bootstrap.min.css set `[hidden] { display: none !important; }`
          so we also need the `!important` here to be able to override the
@@ -546,7 +546,7 @@ meaning since they would not have been expressed in ppm.
       position: relative;
     }
 
-    #sk-container-id-30 div.sk-text-repr-fallback {
+    #sk-container-id-31 div.sk-text-repr-fallback {
       display: none;
     }
 
@@ -562,14 +562,14 @@ meaning since they would not have been expressed in ppm.
 
     /* Parallel-specific style estimator block */
 
-    #sk-container-id-30 div.sk-parallel-item::after {
+    #sk-container-id-31 div.sk-parallel-item::after {
       content: "";
       width: 100%;
       border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
       flex-grow: 1;
     }
 
-    #sk-container-id-30 div.sk-parallel {
+    #sk-container-id-31 div.sk-parallel {
       display: flex;
       align-items: stretch;
       justify-content: center;
@@ -577,28 +577,28 @@ meaning since they would not have been expressed in ppm.
       position: relative;
     }
 
-    #sk-container-id-30 div.sk-parallel-item {
+    #sk-container-id-31 div.sk-parallel-item {
       display: flex;
       flex-direction: column;
     }
 
-    #sk-container-id-30 div.sk-parallel-item:first-child::after {
+    #sk-container-id-31 div.sk-parallel-item:first-child::after {
       align-self: flex-end;
       width: 50%;
     }
 
-    #sk-container-id-30 div.sk-parallel-item:last-child::after {
+    #sk-container-id-31 div.sk-parallel-item:last-child::after {
       align-self: flex-start;
       width: 50%;
     }
 
-    #sk-container-id-30 div.sk-parallel-item:only-child::after {
+    #sk-container-id-31 div.sk-parallel-item:only-child::after {
       width: 0;
     }
 
     /* Serial-specific style estimator block */
 
-    #sk-container-id-30 div.sk-serial {
+    #sk-container-id-31 div.sk-serial {
       display: flex;
       flex-direction: column;
       align-items: center;
@@ -616,14 +616,14 @@ meaning since they would not have been expressed in ppm.
 
     /* Pipeline and ColumnTransformer style (default) */
 
-    #sk-container-id-30 div.sk-toggleable {
+    #sk-container-id-31 div.sk-toggleable {
       /* Default theme specific background. It is overwritten whether we have a
       specific estimator or a Pipeline/ColumnTransformer */
       background-color: var(--sklearn-color-background);
     }
 
     /* Toggleable label */
-    #sk-container-id-30 label.sk-toggleable__label {
+    #sk-container-id-31 label.sk-toggleable__label {
       cursor: pointer;
       display: flex;
       width: 100%;
@@ -636,13 +636,13 @@ meaning since they would not have been expressed in ppm.
       gap: 0.5em;
     }
 
-    #sk-container-id-30 label.sk-toggleable__label .caption {
+    #sk-container-id-31 label.sk-toggleable__label .caption {
       font-size: 0.6rem;
       font-weight: lighter;
       color: var(--sklearn-color-text-muted);
     }
 
-    #sk-container-id-30 label.sk-toggleable__label-arrow:before {
+    #sk-container-id-31 label.sk-toggleable__label-arrow:before {
       /* Arrow on the left of the label */
       content: "▸";
       float: left;
@@ -650,13 +650,13 @@ meaning since they would not have been expressed in ppm.
       color: var(--sklearn-color-icon);
     }
 
-    #sk-container-id-30 label.sk-toggleable__label-arrow:hover:before {
+    #sk-container-id-31 label.sk-toggleable__label-arrow:hover:before {
       color: var(--sklearn-color-text);
     }
 
     /* Toggleable content - dropdown */
 
-    #sk-container-id-30 div.sk-toggleable__content {
+    #sk-container-id-31 div.sk-toggleable__content {
       max-height: 0;
       max-width: 0;
       overflow: hidden;
@@ -665,12 +665,12 @@ meaning since they would not have been expressed in ppm.
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-30 div.sk-toggleable__content.fitted {
+    #sk-container-id-31 div.sk-toggleable__content.fitted {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
-    #sk-container-id-30 div.sk-toggleable__content pre {
+    #sk-container-id-31 div.sk-toggleable__content pre {
       margin: 0.2em;
       border-radius: 0.25em;
       color: var(--sklearn-color-text);
@@ -678,79 +678,79 @@ meaning since they would not have been expressed in ppm.
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-30 div.sk-toggleable__content.fitted pre {
+    #sk-container-id-31 div.sk-toggleable__content.fitted pre {
       /* unfitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
-    #sk-container-id-30 input.sk-toggleable__control:checked~div.sk-toggleable__content {
+    #sk-container-id-31 input.sk-toggleable__control:checked~div.sk-toggleable__content {
       /* Expand drop-down */
       max-height: 200px;
       max-width: 100%;
       overflow: auto;
     }
 
-    #sk-container-id-30 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
+    #sk-container-id-31 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
       content: "▾";
     }
 
     /* Pipeline/ColumnTransformer-specific style */
 
-    #sk-container-id-30 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-31 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-30 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-31 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
     /* Estimator-specific style */
 
     /* Colorize estimator box */
-    #sk-container-id-30 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-31 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-30 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-31 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
-    #sk-container-id-30 div.sk-label label.sk-toggleable__label,
-    #sk-container-id-30 div.sk-label label {
+    #sk-container-id-31 div.sk-label label.sk-toggleable__label,
+    #sk-container-id-31 div.sk-label label {
       /* The background is the default theme color */
       color: var(--sklearn-color-text-on-default-background);
     }
 
     /* On hover, darken the color of the background */
-    #sk-container-id-30 div.sk-label:hover label.sk-toggleable__label {
+    #sk-container-id-31 div.sk-label:hover label.sk-toggleable__label {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
     /* Label box, darken color on hover, fitted */
-    #sk-container-id-30 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
+    #sk-container-id-31 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
     /* Estimator label */
 
-    #sk-container-id-30 div.sk-label label {
+    #sk-container-id-31 div.sk-label label {
       font-family: monospace;
       font-weight: bold;
       display: inline-block;
       line-height: 1.2em;
     }
 
-    #sk-container-id-30 div.sk-label-container {
+    #sk-container-id-31 div.sk-label-container {
       text-align: center;
     }
 
     /* Estimator-specific */
-    #sk-container-id-30 div.sk-estimator {
+    #sk-container-id-31 div.sk-estimator {
       font-family: monospace;
       border: 1px dotted var(--sklearn-color-border-box);
       border-radius: 0.25em;
@@ -760,18 +760,18 @@ meaning since they would not have been expressed in ppm.
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-30 div.sk-estimator.fitted {
+    #sk-container-id-31 div.sk-estimator.fitted {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
     /* on hover */
-    #sk-container-id-30 div.sk-estimator:hover {
+    #sk-container-id-31 div.sk-estimator:hover {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-30 div.sk-estimator.fitted:hover {
+    #sk-container-id-31 div.sk-estimator.fitted:hover {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-2);
     }
@@ -859,7 +859,7 @@ meaning since they would not have been expressed in ppm.
 
     /* "?"-specific style due to the `<a>` HTML tag */
 
-    #sk-container-id-30 a.estimator_doc_link {
+    #sk-container-id-31 a.estimator_doc_link {
       float: right;
       font-size: 1rem;
       line-height: 1em;
@@ -874,25 +874,25 @@ meaning since they would not have been expressed in ppm.
       border: var(--sklearn-color-unfitted-level-1) 1pt solid;
     }
 
-    #sk-container-id-30 a.estimator_doc_link.fitted {
+    #sk-container-id-31 a.estimator_doc_link.fitted {
       /* fitted */
       border: var(--sklearn-color-fitted-level-1) 1pt solid;
       color: var(--sklearn-color-fitted-level-1);
     }
 
     /* On hover */
-    #sk-container-id-30 a.estimator_doc_link:hover {
+    #sk-container-id-31 a.estimator_doc_link:hover {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-3);
       color: var(--sklearn-color-background);
       text-decoration: none;
     }
 
-    #sk-container-id-30 a.estimator_doc_link.fitted:hover {
+    #sk-container-id-31 a.estimator_doc_link.fitted:hover {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-3);
     }
-    </style><div id="sk-container-id-30" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>GaussianProcessRegressor(kernel=50**2 * RBF(length_scale=50) + 2**2 * RBF(length_scale=100) * ExpSineSquared(length_scale=1, periodicity=1) + 0.5**2 * RationalQuadratic(alpha=1, length_scale=1) + 0.1**2 * RBF(length_scale=0.1) + WhiteKernel(noise_level=0.01))</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-114" type="checkbox" checked><label for="sk-estimator-id-114" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>GaussianProcessRegressor</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.gaussian_process.GaussianProcessRegressor.html">?<span>Documentation for GaussianProcessRegressor</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>GaussianProcessRegressor(kernel=50**2 * RBF(length_scale=50) + 2**2 * RBF(length_scale=100) * ExpSineSquared(length_scale=1, periodicity=1) + 0.5**2 * RationalQuadratic(alpha=1, length_scale=1) + 0.1**2 * RBF(length_scale=0.1) + WhiteKernel(noise_level=0.01))</pre></div> </div></div></div></div>
+    </style><div id="sk-container-id-31" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>GaussianProcessRegressor(kernel=50**2 * RBF(length_scale=50) + 2**2 * RBF(length_scale=100) * ExpSineSquared(length_scale=1, periodicity=1) + 0.5**2 * RationalQuadratic(alpha=1, length_scale=1) + 0.1**2 * RBF(length_scale=0.1) + WhiteKernel(noise_level=0.01))</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-124" type="checkbox" checked><label for="sk-estimator-id-124" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>GaussianProcessRegressor</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.gaussian_process.GaussianProcessRegressor.html">?<span>Documentation for GaussianProcessRegressor</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>GaussianProcessRegressor(kernel=50**2 * RBF(length_scale=50) + 2**2 * RBF(length_scale=100) * ExpSineSquared(length_scale=1, periodicity=1) + 0.5**2 * RationalQuadratic(alpha=1, length_scale=1) + 0.1**2 * RBF(length_scale=0.1) + WhiteKernel(noise_level=0.01))</pre></div> </div></div></div></div>
     </div>
     <br />
     <br />
@@ -1003,7 +1003,7 @@ indicating that the data can be very well explained by the model.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 3.924 seconds)
+   **Total running time of the script:** (0 minutes 4.609 seconds)
 
 
 .. _sphx_glr_download_auto_examples_gaussian_process_plot_gpr_co2.py:
diff --git a/dev/_sources/auto_examples/gaussian_process/plot_gpr_noisy.rst.txt b/dev/_sources/auto_examples/gaussian_process/plot_gpr_noisy.rst.txt
index 6a08acab2ec92..db61aede02f41 100644
--- a/dev/_sources/auto_examples/gaussian_process/plot_gpr_noisy.rst.txt
+++ b/dev/_sources/auto_examples/gaussian_process/plot_gpr_noisy.rst.txt
@@ -448,7 +448,7 @@ of hyperparameters despite the bad initial values.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 5.434 seconds)
+   **Total running time of the script:** (0 minutes 5.948 seconds)
 
 
 .. _sphx_glr_download_auto_examples_gaussian_process_plot_gpr_noisy.py:
diff --git a/dev/_sources/auto_examples/gaussian_process/plot_gpr_noisy_targets.rst.txt b/dev/_sources/auto_examples/gaussian_process/plot_gpr_noisy_targets.rst.txt
index 655178d565882..9e5c5206d1451 100644
--- a/dev/_sources/auto_examples/gaussian_process/plot_gpr_noisy_targets.rst.txt
+++ b/dev/_sources/auto_examples/gaussian_process/plot_gpr_noisy_targets.rst.txt
@@ -305,7 +305,7 @@ variable.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.458 seconds)
+   **Total running time of the script:** (0 minutes 0.551 seconds)
 
 
 .. _sphx_glr_download_auto_examples_gaussian_process_plot_gpr_noisy_targets.py:
diff --git a/dev/_sources/auto_examples/gaussian_process/plot_gpr_on_structured_data.rst.txt b/dev/_sources/auto_examples/gaussian_process/plot_gpr_on_structured_data.rst.txt
index e63c2f85585f7..9bb8cd1cc4779 100644
--- a/dev/_sources/auto_examples/gaussian_process/plot_gpr_on_structured_data.rst.txt
+++ b/dev/_sources/auto_examples/gaussian_process/plot_gpr_on_structured_data.rst.txt
@@ -293,7 +293,7 @@ Classification
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.208 seconds)
+   **Total running time of the script:** (0 minutes 0.231 seconds)
 
 
 .. _sphx_glr_download_auto_examples_gaussian_process_plot_gpr_on_structured_data.py:
diff --git a/dev/_sources/auto_examples/gaussian_process/plot_gpr_prior_posterior.rst.txt b/dev/_sources/auto_examples/gaussian_process/plot_gpr_prior_posterior.rst.txt
index 1957e66ea2029..e04b906648b44 100644
--- a/dev/_sources/auto_examples/gaussian_process/plot_gpr_prior_posterior.rst.txt
+++ b/dev/_sources/auto_examples/gaussian_process/plot_gpr_prior_posterior.rst.txt
@@ -503,7 +503,7 @@ Matérn kernel
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 1.432 seconds)
+   **Total running time of the script:** (0 minutes 1.591 seconds)
 
 
 .. _sphx_glr_download_auto_examples_gaussian_process_plot_gpr_prior_posterior.py:
diff --git a/dev/_sources/auto_examples/impute/plot_iterative_imputer_variants_comparison.rst.txt b/dev/_sources/auto_examples/impute/plot_iterative_imputer_variants_comparison.rst.txt
index 8490ade47717a..59597c23a56d1 100644
--- a/dev/_sources/auto_examples/impute/plot_iterative_imputer_variants_comparison.rst.txt
+++ b/dev/_sources/auto_examples/impute/plot_iterative_imputer_variants_comparison.rst.txt
@@ -88,7 +88,7 @@ complex and costly missing values imputation strategies.
     from sklearn.ensemble import RandomForestRegressor
 
     # To use this experimental feature, we need to explicitly ask for it:
-    from sklearn.experimental import enable_iterative_imputer  # noqa
+    from sklearn.experimental import enable_iterative_imputer  # noqa: F401
     from sklearn.impute import IterativeImputer, SimpleImputer
     from sklearn.kernel_approximation import Nystroem
     from sklearn.linear_model import BayesianRidge, Ridge
@@ -191,7 +191,7 @@ complex and costly missing values imputation strategies.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 5.772 seconds)
+   **Total running time of the script:** (0 minutes 5.621 seconds)
 
 
 .. _sphx_glr_download_auto_examples_impute_plot_iterative_imputer_variants_comparison.py:
diff --git a/dev/_sources/auto_examples/impute/plot_missing_values.rst.txt b/dev/_sources/auto_examples/impute/plot_missing_values.rst.txt
index e610a3a3ea73a..f617028899456 100644
--- a/dev/_sources/auto_examples/impute/plot_missing_values.rst.txt
+++ b/dev/_sources/auto_examples/impute/plot_missing_values.rst.txt
@@ -140,7 +140,7 @@ imputed data. Let's look at each imputer separately:
     from sklearn.ensemble import RandomForestRegressor
 
     # To use the experimental IterativeImputer, we need to explicitly ask for it:
-    from sklearn.experimental import enable_iterative_imputer  # noqa
+    from sklearn.experimental import enable_iterative_imputer  # noqa: F401
     from sklearn.impute import IterativeImputer, KNNImputer, SimpleImputer
     from sklearn.model_selection import cross_val_score
     from sklearn.pipeline import make_pipeline
@@ -462,7 +462,7 @@ results (otherwise known as a 'long tail').
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 9.536 seconds)
+   **Total running time of the script:** (0 minutes 8.855 seconds)
 
 
 .. _sphx_glr_download_auto_examples_impute_plot_missing_values.py:
diff --git a/dev/_sources/auto_examples/index.rst.txt b/dev/_sources/auto_examples/index.rst.txt
index fac5819ab7ea9..f8d6e0f1619c8 100644
--- a/dev/_sources/auto_examples/index.rst.txt
+++ b/dev/_sources/auto_examples/index.rst.txt
@@ -1822,23 +1822,6 @@ Examples concerning the :mod:`sklearn.ensemble` module.
     </div>
 
 
-.. raw:: html
-
-    <div class="sphx-glr-thumbcontainer" tooltip="Plot the class probabilities of the first sample in a toy dataset predicted by three different classifiers and averaged by the VotingClassifier.">
-
-.. only:: html
-
-  .. image:: /auto_examples/ensemble/images/thumb/sphx_glr_plot_voting_probas_thumb.png
-    :alt:
-
-  :ref:`sphx_glr_auto_examples_ensemble_plot_voting_probas.py`
-
-.. raw:: html
-
-      <div class="sphx-glr-thumbnail-title">Plot class probabilities calculated by the VotingClassifier</div>
-    </div>
-
-
 .. raw:: html
 
     <div class="sphx-glr-thumbcontainer" tooltip="A voting regressor is an ensemble meta-estimator that fits several base regressors, each on the whole dataset. Then it averages the individual predictions to form a final prediction. We will use three different regressors to predict the data: GradientBoostingRegressor, RandomForestRegressor, and LinearRegression). Then the above 3 regressors will be used for the VotingRegressor.">
@@ -1856,23 +1839,6 @@ Examples concerning the :mod:`sklearn.ensemble` module.
     </div>
 
 
-.. raw:: html
-
-    <div class="sphx-glr-thumbcontainer" tooltip="Plot the decision boundaries of a VotingClassifier for two features of the Iris dataset.">
-
-.. only:: html
-
-  .. image:: /auto_examples/ensemble/images/thumb/sphx_glr_plot_voting_decision_regions_thumb.png
-    :alt:
-
-  :ref:`sphx_glr_auto_examples_ensemble_plot_voting_decision_regions.py`
-
-.. raw:: html
-
-      <div class="sphx-glr-thumbnail-title">Plot the decision boundaries of a VotingClassifier</div>
-    </div>
-
-
 .. raw:: html
 
     <div class="sphx-glr-thumbcontainer" tooltip="Plot the decision surfaces of forests of randomized trees trained on pairs of features of the iris dataset.">
@@ -1941,6 +1907,23 @@ Examples concerning the :mod:`sklearn.ensemble` module.
     </div>
 
 
+.. raw:: html
+
+    <div class="sphx-glr-thumbcontainer" tooltip="Plot the predicted class probabilities in a toy dataset predicted by three different classifiers and averaged by the VotingClassifier.">
+
+.. only:: html
+
+  .. image:: /auto_examples/ensemble/images/thumb/sphx_glr_plot_voting_decision_regions_thumb.png
+    :alt:
+
+  :ref:`sphx_glr_auto_examples_ensemble_plot_voting_decision_regions.py`
+
+.. raw:: html
+
+      <div class="sphx-glr-thumbnail-title">Visualizing the probabilistic predictions of a VotingClassifier</div>
+    </div>
+
+
 .. thumbnail-parent-div-close
 
 .. raw:: html
@@ -5027,52 +5010,52 @@ Examples concerning the :mod:`sklearn.semi_supervised` module.
 
 .. raw:: html
 
-    <div class="sphx-glr-thumbcontainer" tooltip="Demonstrates an active learning technique to learn handwritten digits using label propagation.">
+    <div class="sphx-glr-thumbcontainer" tooltip="Example of LabelPropagation learning a complex internal structure to demonstrate &quot;manifold learning&quot;. The outer circle should be labeled &quot;red&quot; and the inner circle &quot;blue&quot;. Because both label groups lie inside their own distinct shape, we can see that the labels propagate correctly around the circle.">
 
 .. only:: html
 
-  .. image:: /auto_examples/semi_supervised/images/thumb/sphx_glr_plot_label_propagation_digits_active_learning_thumb.png
+  .. image:: /auto_examples/semi_supervised/images/thumb/sphx_glr_plot_label_propagation_structure_thumb.png
     :alt:
 
-  :ref:`sphx_glr_auto_examples_semi_supervised_plot_label_propagation_digits_active_learning.py`
+  :ref:`sphx_glr_auto_examples_semi_supervised_plot_label_propagation_structure.py`
 
 .. raw:: html
 
-      <div class="sphx-glr-thumbnail-title">Label Propagation digits active learning</div>
+      <div class="sphx-glr-thumbnail-title">Label Propagation circles: Learning a complex structure</div>
     </div>
 
 
 .. raw:: html
 
-    <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates the power of semisupervised learning by training a Label Spreading model to classify handwritten digits with sets of very few labels.">
+    <div class="sphx-glr-thumbcontainer" tooltip="Demonstrates an active learning technique to learn handwritten digits using label propagation.">
 
 .. only:: html
 
-  .. image:: /auto_examples/semi_supervised/images/thumb/sphx_glr_plot_label_propagation_digits_thumb.png
+  .. image:: /auto_examples/semi_supervised/images/thumb/sphx_glr_plot_label_propagation_digits_active_learning_thumb.png
     :alt:
 
-  :ref:`sphx_glr_auto_examples_semi_supervised_plot_label_propagation_digits.py`
+  :ref:`sphx_glr_auto_examples_semi_supervised_plot_label_propagation_digits_active_learning.py`
 
 .. raw:: html
 
-      <div class="sphx-glr-thumbnail-title">Label Propagation digits: Demonstrating performance</div>
+      <div class="sphx-glr-thumbnail-title">Label Propagation digits: Active learning</div>
     </div>
 
 
 .. raw:: html
 
-    <div class="sphx-glr-thumbcontainer" tooltip="Example of LabelPropagation learning a complex internal structure to demonstrate &quot;manifold learning&quot;. The outer circle should be labeled &quot;red&quot; and the inner circle &quot;blue&quot;. Because both label groups lie inside their own distinct shape, we can see that the labels propagate correctly around the circle.">
+    <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates the power of semisupervised learning by training a Label Spreading model to classify handwritten digits with sets of very few labels.">
 
 .. only:: html
 
-  .. image:: /auto_examples/semi_supervised/images/thumb/sphx_glr_plot_label_propagation_structure_thumb.png
+  .. image:: /auto_examples/semi_supervised/images/thumb/sphx_glr_plot_label_propagation_digits_thumb.png
     :alt:
 
-  :ref:`sphx_glr_auto_examples_semi_supervised_plot_label_propagation_structure.py`
+  :ref:`sphx_glr_auto_examples_semi_supervised_plot_label_propagation_digits.py`
 
 .. raw:: html
 
-      <div class="sphx-glr-thumbnail-title">Label Propagation learning a complex structure</div>
+      <div class="sphx-glr-thumbnail-title">Label Propagation digits: Demonstrating performance</div>
     </div>
 
 
diff --git a/dev/_sources/auto_examples/inspection/plot_causal_interpretation.rst.txt b/dev/_sources/auto_examples/inspection/plot_causal_interpretation.rst.txt
index adc8000a39a21..ae9bdaaffcda3 100644
--- a/dev/_sources/auto_examples/inspection/plot_causal_interpretation.rst.txt
+++ b/dev/_sources/auto_examples/inspection/plot_causal_interpretation.rst.txt
@@ -340,7 +340,7 @@ estimations.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 2.025 seconds)
+   **Total running time of the script:** (0 minutes 2.112 seconds)
 
 
 .. _sphx_glr_download_auto_examples_inspection_plot_causal_interpretation.py:
diff --git a/dev/_sources/auto_examples/inspection/plot_linear_model_coefficient_interpretation.rst.txt b/dev/_sources/auto_examples/inspection/plot_linear_model_coefficient_interpretation.rst.txt
index c192e3bf51afc..84a15e376dfae 100644
--- a/dev/_sources/auto_examples/inspection/plot_linear_model_coefficient_interpretation.rst.txt
+++ b/dev/_sources/auto_examples/inspection/plot_linear_model_coefficient_interpretation.rst.txt
@@ -663,7 +663,7 @@ First, we fit the model.
 .. raw:: html
 
     <div class="output_subarea output_html rendered_html output_result">
-    <style>#sk-container-id-32 {
+    <style>#sk-container-id-33 {
       /* Definition of color scheme common for light and dark mode */
       --sklearn-color-text: #000;
       --sklearn-color-text-muted: #666;
@@ -694,15 +694,15 @@ First, we fit the model.
       }
     }
 
-    #sk-container-id-32 {
+    #sk-container-id-33 {
       color: var(--sklearn-color-text);
     }
 
-    #sk-container-id-32 pre {
+    #sk-container-id-33 pre {
       padding: 0;
     }
 
-    #sk-container-id-32 input.sk-hidden--visually {
+    #sk-container-id-33 input.sk-hidden--visually {
       border: 0;
       clip: rect(1px 1px 1px 1px);
       clip: rect(1px, 1px, 1px, 1px);
@@ -714,7 +714,7 @@ First, we fit the model.
       width: 1px;
     }
 
-    #sk-container-id-32 div.sk-dashed-wrapped {
+    #sk-container-id-33 div.sk-dashed-wrapped {
       border: 1px dashed var(--sklearn-color-line);
       margin: 0 0.4em 0.5em 0.4em;
       box-sizing: border-box;
@@ -722,7 +722,7 @@ First, we fit the model.
       background-color: var(--sklearn-color-background);
     }
 
-    #sk-container-id-32 div.sk-container {
+    #sk-container-id-33 div.sk-container {
       /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
          but bootstrap.min.css set `[hidden] { display: none !important; }`
          so we also need the `!important` here to be able to override the
@@ -732,7 +732,7 @@ First, we fit the model.
       position: relative;
     }
 
-    #sk-container-id-32 div.sk-text-repr-fallback {
+    #sk-container-id-33 div.sk-text-repr-fallback {
       display: none;
     }
 
@@ -748,14 +748,14 @@ First, we fit the model.
 
     /* Parallel-specific style estimator block */
 
-    #sk-container-id-32 div.sk-parallel-item::after {
+    #sk-container-id-33 div.sk-parallel-item::after {
       content: "";
       width: 100%;
       border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
       flex-grow: 1;
     }
 
-    #sk-container-id-32 div.sk-parallel {
+    #sk-container-id-33 div.sk-parallel {
       display: flex;
       align-items: stretch;
       justify-content: center;
@@ -763,28 +763,28 @@ First, we fit the model.
       position: relative;
     }
 
-    #sk-container-id-32 div.sk-parallel-item {
+    #sk-container-id-33 div.sk-parallel-item {
       display: flex;
       flex-direction: column;
     }
 
-    #sk-container-id-32 div.sk-parallel-item:first-child::after {
+    #sk-container-id-33 div.sk-parallel-item:first-child::after {
       align-self: flex-end;
       width: 50%;
     }
 
-    #sk-container-id-32 div.sk-parallel-item:last-child::after {
+    #sk-container-id-33 div.sk-parallel-item:last-child::after {
       align-self: flex-start;
       width: 50%;
     }
 
-    #sk-container-id-32 div.sk-parallel-item:only-child::after {
+    #sk-container-id-33 div.sk-parallel-item:only-child::after {
       width: 0;
     }
 
     /* Serial-specific style estimator block */
 
-    #sk-container-id-32 div.sk-serial {
+    #sk-container-id-33 div.sk-serial {
       display: flex;
       flex-direction: column;
       align-items: center;
@@ -802,14 +802,14 @@ First, we fit the model.
 
     /* Pipeline and ColumnTransformer style (default) */
 
-    #sk-container-id-32 div.sk-toggleable {
+    #sk-container-id-33 div.sk-toggleable {
       /* Default theme specific background. It is overwritten whether we have a
       specific estimator or a Pipeline/ColumnTransformer */
       background-color: var(--sklearn-color-background);
     }
 
     /* Toggleable label */
-    #sk-container-id-32 label.sk-toggleable__label {
+    #sk-container-id-33 label.sk-toggleable__label {
       cursor: pointer;
       display: flex;
       width: 100%;
@@ -822,13 +822,13 @@ First, we fit the model.
       gap: 0.5em;
     }
 
-    #sk-container-id-32 label.sk-toggleable__label .caption {
+    #sk-container-id-33 label.sk-toggleable__label .caption {
       font-size: 0.6rem;
       font-weight: lighter;
       color: var(--sklearn-color-text-muted);
     }
 
-    #sk-container-id-32 label.sk-toggleable__label-arrow:before {
+    #sk-container-id-33 label.sk-toggleable__label-arrow:before {
       /* Arrow on the left of the label */
       content: "▸";
       float: left;
@@ -836,13 +836,13 @@ First, we fit the model.
       color: var(--sklearn-color-icon);
     }
 
-    #sk-container-id-32 label.sk-toggleable__label-arrow:hover:before {
+    #sk-container-id-33 label.sk-toggleable__label-arrow:hover:before {
       color: var(--sklearn-color-text);
     }
 
     /* Toggleable content - dropdown */
 
-    #sk-container-id-32 div.sk-toggleable__content {
+    #sk-container-id-33 div.sk-toggleable__content {
       max-height: 0;
       max-width: 0;
       overflow: hidden;
@@ -851,12 +851,12 @@ First, we fit the model.
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-32 div.sk-toggleable__content.fitted {
+    #sk-container-id-33 div.sk-toggleable__content.fitted {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
-    #sk-container-id-32 div.sk-toggleable__content pre {
+    #sk-container-id-33 div.sk-toggleable__content pre {
       margin: 0.2em;
       border-radius: 0.25em;
       color: var(--sklearn-color-text);
@@ -864,79 +864,79 @@ First, we fit the model.
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-32 div.sk-toggleable__content.fitted pre {
+    #sk-container-id-33 div.sk-toggleable__content.fitted pre {
       /* unfitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
-    #sk-container-id-32 input.sk-toggleable__control:checked~div.sk-toggleable__content {
+    #sk-container-id-33 input.sk-toggleable__control:checked~div.sk-toggleable__content {
       /* Expand drop-down */
       max-height: 200px;
       max-width: 100%;
       overflow: auto;
     }
 
-    #sk-container-id-32 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
+    #sk-container-id-33 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
       content: "▾";
     }
 
     /* Pipeline/ColumnTransformer-specific style */
 
-    #sk-container-id-32 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-33 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-32 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-33 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
     /* Estimator-specific style */
 
     /* Colorize estimator box */
-    #sk-container-id-32 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-33 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-32 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-33 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
-    #sk-container-id-32 div.sk-label label.sk-toggleable__label,
-    #sk-container-id-32 div.sk-label label {
+    #sk-container-id-33 div.sk-label label.sk-toggleable__label,
+    #sk-container-id-33 div.sk-label label {
       /* The background is the default theme color */
       color: var(--sklearn-color-text-on-default-background);
     }
 
     /* On hover, darken the color of the background */
-    #sk-container-id-32 div.sk-label:hover label.sk-toggleable__label {
+    #sk-container-id-33 div.sk-label:hover label.sk-toggleable__label {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
     /* Label box, darken color on hover, fitted */
-    #sk-container-id-32 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
+    #sk-container-id-33 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
     /* Estimator label */
 
-    #sk-container-id-32 div.sk-label label {
+    #sk-container-id-33 div.sk-label label {
       font-family: monospace;
       font-weight: bold;
       display: inline-block;
       line-height: 1.2em;
     }
 
-    #sk-container-id-32 div.sk-label-container {
+    #sk-container-id-33 div.sk-label-container {
       text-align: center;
     }
 
     /* Estimator-specific */
-    #sk-container-id-32 div.sk-estimator {
+    #sk-container-id-33 div.sk-estimator {
       font-family: monospace;
       border: 1px dotted var(--sklearn-color-border-box);
       border-radius: 0.25em;
@@ -946,18 +946,18 @@ First, we fit the model.
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-32 div.sk-estimator.fitted {
+    #sk-container-id-33 div.sk-estimator.fitted {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
     /* on hover */
-    #sk-container-id-32 div.sk-estimator:hover {
+    #sk-container-id-33 div.sk-estimator:hover {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-32 div.sk-estimator.fitted:hover {
+    #sk-container-id-33 div.sk-estimator.fitted:hover {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-2);
     }
@@ -1045,7 +1045,7 @@ First, we fit the model.
 
     /* "?"-specific style due to the `<a>` HTML tag */
 
-    #sk-container-id-32 a.estimator_doc_link {
+    #sk-container-id-33 a.estimator_doc_link {
       float: right;
       font-size: 1rem;
       line-height: 1em;
@@ -1060,25 +1060,25 @@ First, we fit the model.
       border: var(--sklearn-color-unfitted-level-1) 1pt solid;
     }
 
-    #sk-container-id-32 a.estimator_doc_link.fitted {
+    #sk-container-id-33 a.estimator_doc_link.fitted {
       /* fitted */
       border: var(--sklearn-color-fitted-level-1) 1pt solid;
       color: var(--sklearn-color-fitted-level-1);
     }
 
     /* On hover */
-    #sk-container-id-32 a.estimator_doc_link:hover {
+    #sk-container-id-33 a.estimator_doc_link:hover {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-3);
       color: var(--sklearn-color-background);
       text-decoration: none;
     }
 
-    #sk-container-id-32 a.estimator_doc_link.fitted:hover {
+    #sk-container-id-33 a.estimator_doc_link.fitted:hover {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-3);
     }
-    </style><div id="sk-container-id-32" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,
+    </style><div id="sk-container-id-33" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,
                      ColumnTransformer(remainder=&#x27;passthrough&#x27;,
                                        transformers=[(&#x27;onehotencoder&#x27;,
                                                       OneHotEncoder(drop=&#x27;if_binary&#x27;),
@@ -1089,7 +1089,7 @@ First, we fit the model.
                     (&#x27;transformedtargetregressor&#x27;,
                      TransformedTargetRegressor(func=&lt;ufunc &#x27;log10&#x27;&gt;,
                                                 inverse_func=&lt;ufunc &#x27;exp10&#x27;&gt;,
-                                                regressor=Ridge(alpha=1e-10)))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-116" type="checkbox" ><label for="sk-estimator-id-116" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>Pipeline</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.pipeline.Pipeline.html">?<span>Documentation for Pipeline</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,
+                                                regressor=Ridge(alpha=1e-10)))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-126" type="checkbox" ><label for="sk-estimator-id-126" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>Pipeline</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.pipeline.Pipeline.html">?<span>Documentation for Pipeline</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,
                      ColumnTransformer(remainder=&#x27;passthrough&#x27;,
                                        transformers=[(&#x27;onehotencoder&#x27;,
                                                       OneHotEncoder(drop=&#x27;if_binary&#x27;),
@@ -1100,13 +1100,13 @@ First, we fit the model.
                     (&#x27;transformedtargetregressor&#x27;,
                      TransformedTargetRegressor(func=&lt;ufunc &#x27;log10&#x27;&gt;,
                                                 inverse_func=&lt;ufunc &#x27;exp10&#x27;&gt;,
-                                                regressor=Ridge(alpha=1e-10)))])</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-117" type="checkbox" ><label for="sk-estimator-id-117" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>ColumnTransformer</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.compose.ColumnTransformer.html">?<span>Documentation for ColumnTransformer</span></a></div></label><div class="sk-toggleable__content fitted"><pre>ColumnTransformer(remainder=&#x27;passthrough&#x27;,
+                                                regressor=Ridge(alpha=1e-10)))])</pre></div> </div></div><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-127" type="checkbox" ><label for="sk-estimator-id-127" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>columntransformer: ColumnTransformer</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.compose.ColumnTransformer.html">?<span>Documentation for columntransformer: ColumnTransformer</span></a></div></label><div class="sk-toggleable__content fitted"><pre>ColumnTransformer(remainder=&#x27;passthrough&#x27;,
                       transformers=[(&#x27;onehotencoder&#x27;,
                                      OneHotEncoder(drop=&#x27;if_binary&#x27;),
                                      [&#x27;RACE&#x27;, &#x27;OCCUPATION&#x27;, &#x27;SECTOR&#x27;, &#x27;MARR&#x27;,
                                       &#x27;UNION&#x27;, &#x27;SEX&#x27;, &#x27;SOUTH&#x27;])],
-                      verbose_feature_names_out=False)</pre></div> </div></div><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-118" type="checkbox" ><label for="sk-estimator-id-118" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>transformedtargetregressor: TransformedTargetRegressor</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.compose.TransformedTargetRegressor.html">?<span>Documentation for transformedtargetregressor: TransformedTargetRegressor</span></a></div></label><div class="sk-toggleable__content fitted"><pre>TransformedTargetRegressor(func=&lt;ufunc &#x27;log10&#x27;&gt;, inverse_func=&lt;ufunc &#x27;exp10&#x27;&gt;,
-                               regressor=Ridge(alpha=1e-10))</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-119" type="checkbox" ><label for="sk-estimator-id-119" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>regressor: Ridge</div></div></label><div class="sk-toggleable__content fitted"><pre>Ridge(alpha=1e-10)</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-120" type="checkbox" ><label for="sk-estimator-id-120" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>Ridge</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.linear_model.Ridge.html">?<span>Documentation for Ridge</span></a></div></label><div class="sk-toggleable__content fitted"><pre>Ridge(alpha=1e-10)</pre></div> </div></div></div></div></div></div></div></div></div></div></div>
+                      verbose_feature_names_out=False)</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-128" type="checkbox" ><label for="sk-estimator-id-128" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>onehotencoder</div></div></label><div class="sk-toggleable__content fitted"><pre>[&#x27;RACE&#x27;, &#x27;OCCUPATION&#x27;, &#x27;SECTOR&#x27;, &#x27;MARR&#x27;, &#x27;UNION&#x27;, &#x27;SEX&#x27;, &#x27;SOUTH&#x27;]</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-129" type="checkbox" ><label for="sk-estimator-id-129" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>OneHotEncoder</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.OneHotEncoder.html">?<span>Documentation for OneHotEncoder</span></a></div></label><div class="sk-toggleable__content fitted"><pre>OneHotEncoder(drop=&#x27;if_binary&#x27;)</pre></div> </div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-130" type="checkbox" ><label for="sk-estimator-id-130" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>remainder</div></div></label><div class="sk-toggleable__content fitted"><pre>[&#x27;EDUCATION&#x27;, &#x27;EXPERIENCE&#x27;, &#x27;AGE&#x27;]</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-131" type="checkbox" ><label for="sk-estimator-id-131" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>passthrough</div></div></label><div class="sk-toggleable__content fitted"><pre>passthrough</pre></div> </div></div></div></div></div></div></div><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-132" type="checkbox" ><label for="sk-estimator-id-132" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>transformedtargetregressor: TransformedTargetRegressor</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.compose.TransformedTargetRegressor.html">?<span>Documentation for transformedtargetregressor: TransformedTargetRegressor</span></a></div></label><div class="sk-toggleable__content fitted"><pre>TransformedTargetRegressor(func=&lt;ufunc &#x27;log10&#x27;&gt;, inverse_func=&lt;ufunc &#x27;exp10&#x27;&gt;,
+                               regressor=Ridge(alpha=1e-10))</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-133" type="checkbox" ><label for="sk-estimator-id-133" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>regressor: Ridge</div></div></label><div class="sk-toggleable__content fitted"><pre>Ridge(alpha=1e-10)</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-134" type="checkbox" ><label for="sk-estimator-id-134" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>Ridge</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.linear_model.Ridge.html">?<span>Documentation for Ridge</span></a></div></label><div class="sk-toggleable__content fitted"><pre>Ridge(alpha=1e-10)</pre></div> </div></div></div></div></div></div></div></div></div></div></div>
     </div>
     <br />
     <br />
@@ -1710,7 +1710,7 @@ The model will stay unchanged.
 .. raw:: html
 
     <div class="output_subarea output_html rendered_html output_result">
-    <style>#sk-container-id-33 {
+    <style>#sk-container-id-34 {
       /* Definition of color scheme common for light and dark mode */
       --sklearn-color-text: #000;
       --sklearn-color-text-muted: #666;
@@ -1741,15 +1741,15 @@ The model will stay unchanged.
       }
     }
 
-    #sk-container-id-33 {
+    #sk-container-id-34 {
       color: var(--sklearn-color-text);
     }
 
-    #sk-container-id-33 pre {
+    #sk-container-id-34 pre {
       padding: 0;
     }
 
-    #sk-container-id-33 input.sk-hidden--visually {
+    #sk-container-id-34 input.sk-hidden--visually {
       border: 0;
       clip: rect(1px 1px 1px 1px);
       clip: rect(1px, 1px, 1px, 1px);
@@ -1761,7 +1761,7 @@ The model will stay unchanged.
       width: 1px;
     }
 
-    #sk-container-id-33 div.sk-dashed-wrapped {
+    #sk-container-id-34 div.sk-dashed-wrapped {
       border: 1px dashed var(--sklearn-color-line);
       margin: 0 0.4em 0.5em 0.4em;
       box-sizing: border-box;
@@ -1769,7 +1769,7 @@ The model will stay unchanged.
       background-color: var(--sklearn-color-background);
     }
 
-    #sk-container-id-33 div.sk-container {
+    #sk-container-id-34 div.sk-container {
       /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
          but bootstrap.min.css set `[hidden] { display: none !important; }`
          so we also need the `!important` here to be able to override the
@@ -1779,7 +1779,7 @@ The model will stay unchanged.
       position: relative;
     }
 
-    #sk-container-id-33 div.sk-text-repr-fallback {
+    #sk-container-id-34 div.sk-text-repr-fallback {
       display: none;
     }
 
@@ -1795,14 +1795,14 @@ The model will stay unchanged.
 
     /* Parallel-specific style estimator block */
 
-    #sk-container-id-33 div.sk-parallel-item::after {
+    #sk-container-id-34 div.sk-parallel-item::after {
       content: "";
       width: 100%;
       border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
       flex-grow: 1;
     }
 
-    #sk-container-id-33 div.sk-parallel {
+    #sk-container-id-34 div.sk-parallel {
       display: flex;
       align-items: stretch;
       justify-content: center;
@@ -1810,28 +1810,28 @@ The model will stay unchanged.
       position: relative;
     }
 
-    #sk-container-id-33 div.sk-parallel-item {
+    #sk-container-id-34 div.sk-parallel-item {
       display: flex;
       flex-direction: column;
     }
 
-    #sk-container-id-33 div.sk-parallel-item:first-child::after {
+    #sk-container-id-34 div.sk-parallel-item:first-child::after {
       align-self: flex-end;
       width: 50%;
     }
 
-    #sk-container-id-33 div.sk-parallel-item:last-child::after {
+    #sk-container-id-34 div.sk-parallel-item:last-child::after {
       align-self: flex-start;
       width: 50%;
     }
 
-    #sk-container-id-33 div.sk-parallel-item:only-child::after {
+    #sk-container-id-34 div.sk-parallel-item:only-child::after {
       width: 0;
     }
 
     /* Serial-specific style estimator block */
 
-    #sk-container-id-33 div.sk-serial {
+    #sk-container-id-34 div.sk-serial {
       display: flex;
       flex-direction: column;
       align-items: center;
@@ -1849,14 +1849,14 @@ The model will stay unchanged.
 
     /* Pipeline and ColumnTransformer style (default) */
 
-    #sk-container-id-33 div.sk-toggleable {
+    #sk-container-id-34 div.sk-toggleable {
       /* Default theme specific background. It is overwritten whether we have a
       specific estimator or a Pipeline/ColumnTransformer */
       background-color: var(--sklearn-color-background);
     }
 
     /* Toggleable label */
-    #sk-container-id-33 label.sk-toggleable__label {
+    #sk-container-id-34 label.sk-toggleable__label {
       cursor: pointer;
       display: flex;
       width: 100%;
@@ -1869,13 +1869,13 @@ The model will stay unchanged.
       gap: 0.5em;
     }
 
-    #sk-container-id-33 label.sk-toggleable__label .caption {
+    #sk-container-id-34 label.sk-toggleable__label .caption {
       font-size: 0.6rem;
       font-weight: lighter;
       color: var(--sklearn-color-text-muted);
     }
 
-    #sk-container-id-33 label.sk-toggleable__label-arrow:before {
+    #sk-container-id-34 label.sk-toggleable__label-arrow:before {
       /* Arrow on the left of the label */
       content: "▸";
       float: left;
@@ -1883,13 +1883,13 @@ The model will stay unchanged.
       color: var(--sklearn-color-icon);
     }
 
-    #sk-container-id-33 label.sk-toggleable__label-arrow:hover:before {
+    #sk-container-id-34 label.sk-toggleable__label-arrow:hover:before {
       color: var(--sklearn-color-text);
     }
 
     /* Toggleable content - dropdown */
 
-    #sk-container-id-33 div.sk-toggleable__content {
+    #sk-container-id-34 div.sk-toggleable__content {
       max-height: 0;
       max-width: 0;
       overflow: hidden;
@@ -1898,12 +1898,12 @@ The model will stay unchanged.
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-33 div.sk-toggleable__content.fitted {
+    #sk-container-id-34 div.sk-toggleable__content.fitted {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
-    #sk-container-id-33 div.sk-toggleable__content pre {
+    #sk-container-id-34 div.sk-toggleable__content pre {
       margin: 0.2em;
       border-radius: 0.25em;
       color: var(--sklearn-color-text);
@@ -1911,79 +1911,79 @@ The model will stay unchanged.
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-33 div.sk-toggleable__content.fitted pre {
+    #sk-container-id-34 div.sk-toggleable__content.fitted pre {
       /* unfitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
-    #sk-container-id-33 input.sk-toggleable__control:checked~div.sk-toggleable__content {
+    #sk-container-id-34 input.sk-toggleable__control:checked~div.sk-toggleable__content {
       /* Expand drop-down */
       max-height: 200px;
       max-width: 100%;
       overflow: auto;
     }
 
-    #sk-container-id-33 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
+    #sk-container-id-34 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
       content: "▾";
     }
 
     /* Pipeline/ColumnTransformer-specific style */
 
-    #sk-container-id-33 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-34 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-33 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-34 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
     /* Estimator-specific style */
 
     /* Colorize estimator box */
-    #sk-container-id-33 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-34 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-33 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-34 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
-    #sk-container-id-33 div.sk-label label.sk-toggleable__label,
-    #sk-container-id-33 div.sk-label label {
+    #sk-container-id-34 div.sk-label label.sk-toggleable__label,
+    #sk-container-id-34 div.sk-label label {
       /* The background is the default theme color */
       color: var(--sklearn-color-text-on-default-background);
     }
 
     /* On hover, darken the color of the background */
-    #sk-container-id-33 div.sk-label:hover label.sk-toggleable__label {
+    #sk-container-id-34 div.sk-label:hover label.sk-toggleable__label {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
     /* Label box, darken color on hover, fitted */
-    #sk-container-id-33 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
+    #sk-container-id-34 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
     /* Estimator label */
 
-    #sk-container-id-33 div.sk-label label {
+    #sk-container-id-34 div.sk-label label {
       font-family: monospace;
       font-weight: bold;
       display: inline-block;
       line-height: 1.2em;
     }
 
-    #sk-container-id-33 div.sk-label-container {
+    #sk-container-id-34 div.sk-label-container {
       text-align: center;
     }
 
     /* Estimator-specific */
-    #sk-container-id-33 div.sk-estimator {
+    #sk-container-id-34 div.sk-estimator {
       font-family: monospace;
       border: 1px dotted var(--sklearn-color-border-box);
       border-radius: 0.25em;
@@ -1993,18 +1993,18 @@ The model will stay unchanged.
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-33 div.sk-estimator.fitted {
+    #sk-container-id-34 div.sk-estimator.fitted {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
     /* on hover */
-    #sk-container-id-33 div.sk-estimator:hover {
+    #sk-container-id-34 div.sk-estimator:hover {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-33 div.sk-estimator.fitted:hover {
+    #sk-container-id-34 div.sk-estimator.fitted:hover {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-2);
     }
@@ -2092,7 +2092,7 @@ The model will stay unchanged.
 
     /* "?"-specific style due to the `<a>` HTML tag */
 
-    #sk-container-id-33 a.estimator_doc_link {
+    #sk-container-id-34 a.estimator_doc_link {
       float: right;
       font-size: 1rem;
       line-height: 1em;
@@ -2107,25 +2107,25 @@ The model will stay unchanged.
       border: var(--sklearn-color-unfitted-level-1) 1pt solid;
     }
 
-    #sk-container-id-33 a.estimator_doc_link.fitted {
+    #sk-container-id-34 a.estimator_doc_link.fitted {
       /* fitted */
       border: var(--sklearn-color-fitted-level-1) 1pt solid;
       color: var(--sklearn-color-fitted-level-1);
     }
 
     /* On hover */
-    #sk-container-id-33 a.estimator_doc_link:hover {
+    #sk-container-id-34 a.estimator_doc_link:hover {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-3);
       color: var(--sklearn-color-background);
       text-decoration: none;
     }
 
-    #sk-container-id-33 a.estimator_doc_link.fitted:hover {
+    #sk-container-id-34 a.estimator_doc_link.fitted:hover {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-3);
     }
-    </style><div id="sk-container-id-33" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,
+    </style><div id="sk-container-id-34" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,
                      ColumnTransformer(transformers=[(&#x27;onehotencoder&#x27;,
                                                       OneHotEncoder(drop=&#x27;if_binary&#x27;),
                                                       [&#x27;RACE&#x27;, &#x27;OCCUPATION&#x27;,
@@ -2138,7 +2138,7 @@ The model will stay unchanged.
                     (&#x27;transformedtargetregressor&#x27;,
                      TransformedTargetRegressor(func=&lt;ufunc &#x27;log10&#x27;&gt;,
                                                 inverse_func=&lt;ufunc &#x27;exp10&#x27;&gt;,
-                                                regressor=Ridge(alpha=1e-10)))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-121" type="checkbox" ><label for="sk-estimator-id-121" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>Pipeline</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.pipeline.Pipeline.html">?<span>Documentation for Pipeline</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,
+                                                regressor=Ridge(alpha=1e-10)))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-135" type="checkbox" ><label for="sk-estimator-id-135" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>Pipeline</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.pipeline.Pipeline.html">?<span>Documentation for Pipeline</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,
                      ColumnTransformer(transformers=[(&#x27;onehotencoder&#x27;,
                                                       OneHotEncoder(drop=&#x27;if_binary&#x27;),
                                                       [&#x27;RACE&#x27;, &#x27;OCCUPATION&#x27;,
@@ -2151,13 +2151,13 @@ The model will stay unchanged.
                     (&#x27;transformedtargetregressor&#x27;,
                      TransformedTargetRegressor(func=&lt;ufunc &#x27;log10&#x27;&gt;,
                                                 inverse_func=&lt;ufunc &#x27;exp10&#x27;&gt;,
-                                                regressor=Ridge(alpha=1e-10)))])</pre></div> </div></div><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-122" type="checkbox" ><label for="sk-estimator-id-122" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>columntransformer: ColumnTransformer</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.compose.ColumnTransformer.html">?<span>Documentation for columntransformer: ColumnTransformer</span></a></div></label><div class="sk-toggleable__content fitted"><pre>ColumnTransformer(transformers=[(&#x27;onehotencoder&#x27;,
+                                                regressor=Ridge(alpha=1e-10)))])</pre></div> </div></div><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-136" type="checkbox" ><label for="sk-estimator-id-136" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>columntransformer: ColumnTransformer</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.compose.ColumnTransformer.html">?<span>Documentation for columntransformer: ColumnTransformer</span></a></div></label><div class="sk-toggleable__content fitted"><pre>ColumnTransformer(transformers=[(&#x27;onehotencoder&#x27;,
                                      OneHotEncoder(drop=&#x27;if_binary&#x27;),
                                      [&#x27;RACE&#x27;, &#x27;OCCUPATION&#x27;, &#x27;SECTOR&#x27;, &#x27;MARR&#x27;,
                                       &#x27;UNION&#x27;, &#x27;SEX&#x27;, &#x27;SOUTH&#x27;]),
                                     (&#x27;standardscaler&#x27;, StandardScaler(),
-                                     [&#x27;EDUCATION&#x27;, &#x27;EXPERIENCE&#x27;, &#x27;AGE&#x27;])])</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-123" type="checkbox" ><label for="sk-estimator-id-123" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>onehotencoder</div></div></label><div class="sk-toggleable__content fitted"><pre>[&#x27;RACE&#x27;, &#x27;OCCUPATION&#x27;, &#x27;SECTOR&#x27;, &#x27;MARR&#x27;, &#x27;UNION&#x27;, &#x27;SEX&#x27;, &#x27;SOUTH&#x27;]</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-124" type="checkbox" ><label for="sk-estimator-id-124" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>OneHotEncoder</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.OneHotEncoder.html">?<span>Documentation for OneHotEncoder</span></a></div></label><div class="sk-toggleable__content fitted"><pre>OneHotEncoder(drop=&#x27;if_binary&#x27;)</pre></div> </div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-125" type="checkbox" ><label for="sk-estimator-id-125" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>standardscaler</div></div></label><div class="sk-toggleable__content fitted"><pre>[&#x27;EDUCATION&#x27;, &#x27;EXPERIENCE&#x27;, &#x27;AGE&#x27;]</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-126" type="checkbox" ><label for="sk-estimator-id-126" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>StandardScaler</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.StandardScaler.html">?<span>Documentation for StandardScaler</span></a></div></label><div class="sk-toggleable__content fitted"><pre>StandardScaler()</pre></div> </div></div></div></div></div></div></div><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-127" type="checkbox" ><label for="sk-estimator-id-127" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>transformedtargetregressor: TransformedTargetRegressor</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.compose.TransformedTargetRegressor.html">?<span>Documentation for transformedtargetregressor: TransformedTargetRegressor</span></a></div></label><div class="sk-toggleable__content fitted"><pre>TransformedTargetRegressor(func=&lt;ufunc &#x27;log10&#x27;&gt;, inverse_func=&lt;ufunc &#x27;exp10&#x27;&gt;,
-                               regressor=Ridge(alpha=1e-10))</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-128" type="checkbox" ><label for="sk-estimator-id-128" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>regressor: Ridge</div></div></label><div class="sk-toggleable__content fitted"><pre>Ridge(alpha=1e-10)</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-129" type="checkbox" ><label for="sk-estimator-id-129" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>Ridge</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.linear_model.Ridge.html">?<span>Documentation for Ridge</span></a></div></label><div class="sk-toggleable__content fitted"><pre>Ridge(alpha=1e-10)</pre></div> </div></div></div></div></div></div></div></div></div></div></div>
+                                     [&#x27;EDUCATION&#x27;, &#x27;EXPERIENCE&#x27;, &#x27;AGE&#x27;])])</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-137" type="checkbox" ><label for="sk-estimator-id-137" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>onehotencoder</div></div></label><div class="sk-toggleable__content fitted"><pre>[&#x27;RACE&#x27;, &#x27;OCCUPATION&#x27;, &#x27;SECTOR&#x27;, &#x27;MARR&#x27;, &#x27;UNION&#x27;, &#x27;SEX&#x27;, &#x27;SOUTH&#x27;]</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-138" type="checkbox" ><label for="sk-estimator-id-138" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>OneHotEncoder</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.OneHotEncoder.html">?<span>Documentation for OneHotEncoder</span></a></div></label><div class="sk-toggleable__content fitted"><pre>OneHotEncoder(drop=&#x27;if_binary&#x27;)</pre></div> </div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-139" type="checkbox" ><label for="sk-estimator-id-139" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>standardscaler</div></div></label><div class="sk-toggleable__content fitted"><pre>[&#x27;EDUCATION&#x27;, &#x27;EXPERIENCE&#x27;, &#x27;AGE&#x27;]</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-140" type="checkbox" ><label for="sk-estimator-id-140" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>StandardScaler</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.StandardScaler.html">?<span>Documentation for StandardScaler</span></a></div></label><div class="sk-toggleable__content fitted"><pre>StandardScaler()</pre></div> </div></div></div></div></div></div></div><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-141" type="checkbox" ><label for="sk-estimator-id-141" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>transformedtargetregressor: TransformedTargetRegressor</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.compose.TransformedTargetRegressor.html">?<span>Documentation for transformedtargetregressor: TransformedTargetRegressor</span></a></div></label><div class="sk-toggleable__content fitted"><pre>TransformedTargetRegressor(func=&lt;ufunc &#x27;log10&#x27;&gt;, inverse_func=&lt;ufunc &#x27;exp10&#x27;&gt;,
+                               regressor=Ridge(alpha=1e-10))</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-142" type="checkbox" ><label for="sk-estimator-id-142" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>regressor: Ridge</div></div></label><div class="sk-toggleable__content fitted"><pre>Ridge(alpha=1e-10)</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-143" type="checkbox" ><label for="sk-estimator-id-143" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>Ridge</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.linear_model.Ridge.html">?<span>Documentation for Ridge</span></a></div></label><div class="sk-toggleable__content fitted"><pre>Ridge(alpha=1e-10)</pre></div> </div></div></div></div></div></div></div></div></div></div></div>
     </div>
     <br />
     <br />
@@ -2332,7 +2332,7 @@ is best suited for prediction.
 .. raw:: html
 
     <div class="output_subarea output_html rendered_html output_result">
-    <style>#sk-container-id-34 {
+    <style>#sk-container-id-35 {
       /* Definition of color scheme common for light and dark mode */
       --sklearn-color-text: #000;
       --sklearn-color-text-muted: #666;
@@ -2363,15 +2363,15 @@ is best suited for prediction.
       }
     }
 
-    #sk-container-id-34 {
+    #sk-container-id-35 {
       color: var(--sklearn-color-text);
     }
 
-    #sk-container-id-34 pre {
+    #sk-container-id-35 pre {
       padding: 0;
     }
 
-    #sk-container-id-34 input.sk-hidden--visually {
+    #sk-container-id-35 input.sk-hidden--visually {
       border: 0;
       clip: rect(1px 1px 1px 1px);
       clip: rect(1px, 1px, 1px, 1px);
@@ -2383,7 +2383,7 @@ is best suited for prediction.
       width: 1px;
     }
 
-    #sk-container-id-34 div.sk-dashed-wrapped {
+    #sk-container-id-35 div.sk-dashed-wrapped {
       border: 1px dashed var(--sklearn-color-line);
       margin: 0 0.4em 0.5em 0.4em;
       box-sizing: border-box;
@@ -2391,7 +2391,7 @@ is best suited for prediction.
       background-color: var(--sklearn-color-background);
     }
 
-    #sk-container-id-34 div.sk-container {
+    #sk-container-id-35 div.sk-container {
       /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
          but bootstrap.min.css set `[hidden] { display: none !important; }`
          so we also need the `!important` here to be able to override the
@@ -2401,7 +2401,7 @@ is best suited for prediction.
       position: relative;
     }
 
-    #sk-container-id-34 div.sk-text-repr-fallback {
+    #sk-container-id-35 div.sk-text-repr-fallback {
       display: none;
     }
 
@@ -2417,14 +2417,14 @@ is best suited for prediction.
 
     /* Parallel-specific style estimator block */
 
-    #sk-container-id-34 div.sk-parallel-item::after {
+    #sk-container-id-35 div.sk-parallel-item::after {
       content: "";
       width: 100%;
       border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
       flex-grow: 1;
     }
 
-    #sk-container-id-34 div.sk-parallel {
+    #sk-container-id-35 div.sk-parallel {
       display: flex;
       align-items: stretch;
       justify-content: center;
@@ -2432,28 +2432,28 @@ is best suited for prediction.
       position: relative;
     }
 
-    #sk-container-id-34 div.sk-parallel-item {
+    #sk-container-id-35 div.sk-parallel-item {
       display: flex;
       flex-direction: column;
     }
 
-    #sk-container-id-34 div.sk-parallel-item:first-child::after {
+    #sk-container-id-35 div.sk-parallel-item:first-child::after {
       align-self: flex-end;
       width: 50%;
     }
 
-    #sk-container-id-34 div.sk-parallel-item:last-child::after {
+    #sk-container-id-35 div.sk-parallel-item:last-child::after {
       align-self: flex-start;
       width: 50%;
     }
 
-    #sk-container-id-34 div.sk-parallel-item:only-child::after {
+    #sk-container-id-35 div.sk-parallel-item:only-child::after {
       width: 0;
     }
 
     /* Serial-specific style estimator block */
 
-    #sk-container-id-34 div.sk-serial {
+    #sk-container-id-35 div.sk-serial {
       display: flex;
       flex-direction: column;
       align-items: center;
@@ -2471,14 +2471,14 @@ is best suited for prediction.
 
     /* Pipeline and ColumnTransformer style (default) */
 
-    #sk-container-id-34 div.sk-toggleable {
+    #sk-container-id-35 div.sk-toggleable {
       /* Default theme specific background. It is overwritten whether we have a
       specific estimator or a Pipeline/ColumnTransformer */
       background-color: var(--sklearn-color-background);
     }
 
     /* Toggleable label */
-    #sk-container-id-34 label.sk-toggleable__label {
+    #sk-container-id-35 label.sk-toggleable__label {
       cursor: pointer;
       display: flex;
       width: 100%;
@@ -2491,13 +2491,13 @@ is best suited for prediction.
       gap: 0.5em;
     }
 
-    #sk-container-id-34 label.sk-toggleable__label .caption {
+    #sk-container-id-35 label.sk-toggleable__label .caption {
       font-size: 0.6rem;
       font-weight: lighter;
       color: var(--sklearn-color-text-muted);
     }
 
-    #sk-container-id-34 label.sk-toggleable__label-arrow:before {
+    #sk-container-id-35 label.sk-toggleable__label-arrow:before {
       /* Arrow on the left of the label */
       content: "▸";
       float: left;
@@ -2505,13 +2505,13 @@ is best suited for prediction.
       color: var(--sklearn-color-icon);
     }
 
-    #sk-container-id-34 label.sk-toggleable__label-arrow:hover:before {
+    #sk-container-id-35 label.sk-toggleable__label-arrow:hover:before {
       color: var(--sklearn-color-text);
     }
 
     /* Toggleable content - dropdown */
 
-    #sk-container-id-34 div.sk-toggleable__content {
+    #sk-container-id-35 div.sk-toggleable__content {
       max-height: 0;
       max-width: 0;
       overflow: hidden;
@@ -2520,12 +2520,12 @@ is best suited for prediction.
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-34 div.sk-toggleable__content.fitted {
+    #sk-container-id-35 div.sk-toggleable__content.fitted {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
-    #sk-container-id-34 div.sk-toggleable__content pre {
+    #sk-container-id-35 div.sk-toggleable__content pre {
       margin: 0.2em;
       border-radius: 0.25em;
       color: var(--sklearn-color-text);
@@ -2533,79 +2533,79 @@ is best suited for prediction.
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-34 div.sk-toggleable__content.fitted pre {
+    #sk-container-id-35 div.sk-toggleable__content.fitted pre {
       /* unfitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
-    #sk-container-id-34 input.sk-toggleable__control:checked~div.sk-toggleable__content {
+    #sk-container-id-35 input.sk-toggleable__control:checked~div.sk-toggleable__content {
       /* Expand drop-down */
       max-height: 200px;
       max-width: 100%;
       overflow: auto;
     }
 
-    #sk-container-id-34 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
+    #sk-container-id-35 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
       content: "▾";
     }
 
     /* Pipeline/ColumnTransformer-specific style */
 
-    #sk-container-id-34 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-35 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-34 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-35 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
     /* Estimator-specific style */
 
     /* Colorize estimator box */
-    #sk-container-id-34 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-35 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-34 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-35 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
-    #sk-container-id-34 div.sk-label label.sk-toggleable__label,
-    #sk-container-id-34 div.sk-label label {
+    #sk-container-id-35 div.sk-label label.sk-toggleable__label,
+    #sk-container-id-35 div.sk-label label {
       /* The background is the default theme color */
       color: var(--sklearn-color-text-on-default-background);
     }
 
     /* On hover, darken the color of the background */
-    #sk-container-id-34 div.sk-label:hover label.sk-toggleable__label {
+    #sk-container-id-35 div.sk-label:hover label.sk-toggleable__label {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
     /* Label box, darken color on hover, fitted */
-    #sk-container-id-34 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
+    #sk-container-id-35 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
     /* Estimator label */
 
-    #sk-container-id-34 div.sk-label label {
+    #sk-container-id-35 div.sk-label label {
       font-family: monospace;
       font-weight: bold;
       display: inline-block;
       line-height: 1.2em;
     }
 
-    #sk-container-id-34 div.sk-label-container {
+    #sk-container-id-35 div.sk-label-container {
       text-align: center;
     }
 
     /* Estimator-specific */
-    #sk-container-id-34 div.sk-estimator {
+    #sk-container-id-35 div.sk-estimator {
       font-family: monospace;
       border: 1px dotted var(--sklearn-color-border-box);
       border-radius: 0.25em;
@@ -2615,18 +2615,18 @@ is best suited for prediction.
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-34 div.sk-estimator.fitted {
+    #sk-container-id-35 div.sk-estimator.fitted {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
     /* on hover */
-    #sk-container-id-34 div.sk-estimator:hover {
+    #sk-container-id-35 div.sk-estimator:hover {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-34 div.sk-estimator.fitted:hover {
+    #sk-container-id-35 div.sk-estimator.fitted:hover {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-2);
     }
@@ -2714,7 +2714,7 @@ is best suited for prediction.
 
     /* "?"-specific style due to the `<a>` HTML tag */
 
-    #sk-container-id-34 a.estimator_doc_link {
+    #sk-container-id-35 a.estimator_doc_link {
       float: right;
       font-size: 1rem;
       line-height: 1em;
@@ -2729,25 +2729,25 @@ is best suited for prediction.
       border: var(--sklearn-color-unfitted-level-1) 1pt solid;
     }
 
-    #sk-container-id-34 a.estimator_doc_link.fitted {
+    #sk-container-id-35 a.estimator_doc_link.fitted {
       /* fitted */
       border: var(--sklearn-color-fitted-level-1) 1pt solid;
       color: var(--sklearn-color-fitted-level-1);
     }
 
     /* On hover */
-    #sk-container-id-34 a.estimator_doc_link:hover {
+    #sk-container-id-35 a.estimator_doc_link:hover {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-3);
       color: var(--sklearn-color-background);
       text-decoration: none;
     }
 
-    #sk-container-id-34 a.estimator_doc_link.fitted:hover {
+    #sk-container-id-35 a.estimator_doc_link.fitted:hover {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-3);
     }
-    </style><div id="sk-container-id-34" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,
+    </style><div id="sk-container-id-35" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,
                      ColumnTransformer(transformers=[(&#x27;onehotencoder&#x27;,
                                                       OneHotEncoder(drop=&#x27;if_binary&#x27;),
                                                       [&#x27;RACE&#x27;, &#x27;OCCUPATION&#x27;,
@@ -2762,7 +2762,7 @@ is best suited for prediction.
                                                 inverse_func=&lt;ufunc &#x27;exp10&#x27;&gt;,
                                                 regressor=RidgeCV(alphas=array([1.e-10, 1.e-09, 1.e-08, 1.e-07, 1.e-06, 1.e-05, 1.e-04, 1.e-03,
            1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04, 1.e+05,
-           1.e+06, 1.e+07, 1.e+08, 1.e+09, 1.e+10]))))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-130" type="checkbox" ><label for="sk-estimator-id-130" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>Pipeline</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.pipeline.Pipeline.html">?<span>Documentation for Pipeline</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,
+           1.e+06, 1.e+07, 1.e+08, 1.e+09, 1.e+10]))))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-144" type="checkbox" ><label for="sk-estimator-id-144" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>Pipeline</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.pipeline.Pipeline.html">?<span>Documentation for Pipeline</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,
                      ColumnTransformer(transformers=[(&#x27;onehotencoder&#x27;,
                                                       OneHotEncoder(drop=&#x27;if_binary&#x27;),
                                                       [&#x27;RACE&#x27;, &#x27;OCCUPATION&#x27;,
@@ -2777,17 +2777,17 @@ is best suited for prediction.
                                                 inverse_func=&lt;ufunc &#x27;exp10&#x27;&gt;,
                                                 regressor=RidgeCV(alphas=array([1.e-10, 1.e-09, 1.e-08, 1.e-07, 1.e-06, 1.e-05, 1.e-04, 1.e-03,
            1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04, 1.e+05,
-           1.e+06, 1.e+07, 1.e+08, 1.e+09, 1.e+10]))))])</pre></div> </div></div><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-131" type="checkbox" ><label for="sk-estimator-id-131" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>columntransformer: ColumnTransformer</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.compose.ColumnTransformer.html">?<span>Documentation for columntransformer: ColumnTransformer</span></a></div></label><div class="sk-toggleable__content fitted"><pre>ColumnTransformer(transformers=[(&#x27;onehotencoder&#x27;,
+           1.e+06, 1.e+07, 1.e+08, 1.e+09, 1.e+10]))))])</pre></div> </div></div><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-145" type="checkbox" ><label for="sk-estimator-id-145" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>columntransformer: ColumnTransformer</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.compose.ColumnTransformer.html">?<span>Documentation for columntransformer: ColumnTransformer</span></a></div></label><div class="sk-toggleable__content fitted"><pre>ColumnTransformer(transformers=[(&#x27;onehotencoder&#x27;,
                                      OneHotEncoder(drop=&#x27;if_binary&#x27;),
                                      [&#x27;RACE&#x27;, &#x27;OCCUPATION&#x27;, &#x27;SECTOR&#x27;, &#x27;MARR&#x27;,
                                       &#x27;UNION&#x27;, &#x27;SEX&#x27;, &#x27;SOUTH&#x27;]),
                                     (&#x27;standardscaler&#x27;, StandardScaler(),
-                                     [&#x27;EDUCATION&#x27;, &#x27;EXPERIENCE&#x27;, &#x27;AGE&#x27;])])</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-132" type="checkbox" ><label for="sk-estimator-id-132" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>onehotencoder</div></div></label><div class="sk-toggleable__content fitted"><pre>[&#x27;RACE&#x27;, &#x27;OCCUPATION&#x27;, &#x27;SECTOR&#x27;, &#x27;MARR&#x27;, &#x27;UNION&#x27;, &#x27;SEX&#x27;, &#x27;SOUTH&#x27;]</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-133" type="checkbox" ><label for="sk-estimator-id-133" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>OneHotEncoder</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.OneHotEncoder.html">?<span>Documentation for OneHotEncoder</span></a></div></label><div class="sk-toggleable__content fitted"><pre>OneHotEncoder(drop=&#x27;if_binary&#x27;)</pre></div> </div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-134" type="checkbox" ><label for="sk-estimator-id-134" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>standardscaler</div></div></label><div class="sk-toggleable__content fitted"><pre>[&#x27;EDUCATION&#x27;, &#x27;EXPERIENCE&#x27;, &#x27;AGE&#x27;]</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-135" type="checkbox" ><label for="sk-estimator-id-135" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>StandardScaler</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.StandardScaler.html">?<span>Documentation for StandardScaler</span></a></div></label><div class="sk-toggleable__content fitted"><pre>StandardScaler()</pre></div> </div></div></div></div></div></div></div><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-136" type="checkbox" ><label for="sk-estimator-id-136" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>transformedtargetregressor: TransformedTargetRegressor</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.compose.TransformedTargetRegressor.html">?<span>Documentation for transformedtargetregressor: TransformedTargetRegressor</span></a></div></label><div class="sk-toggleable__content fitted"><pre>TransformedTargetRegressor(func=&lt;ufunc &#x27;log10&#x27;&gt;, inverse_func=&lt;ufunc &#x27;exp10&#x27;&gt;,
+                                     [&#x27;EDUCATION&#x27;, &#x27;EXPERIENCE&#x27;, &#x27;AGE&#x27;])])</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-146" type="checkbox" ><label for="sk-estimator-id-146" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>onehotencoder</div></div></label><div class="sk-toggleable__content fitted"><pre>[&#x27;RACE&#x27;, &#x27;OCCUPATION&#x27;, &#x27;SECTOR&#x27;, &#x27;MARR&#x27;, &#x27;UNION&#x27;, &#x27;SEX&#x27;, &#x27;SOUTH&#x27;]</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-147" type="checkbox" ><label for="sk-estimator-id-147" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>OneHotEncoder</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.OneHotEncoder.html">?<span>Documentation for OneHotEncoder</span></a></div></label><div class="sk-toggleable__content fitted"><pre>OneHotEncoder(drop=&#x27;if_binary&#x27;)</pre></div> </div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-148" type="checkbox" ><label for="sk-estimator-id-148" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>standardscaler</div></div></label><div class="sk-toggleable__content fitted"><pre>[&#x27;EDUCATION&#x27;, &#x27;EXPERIENCE&#x27;, &#x27;AGE&#x27;]</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-149" type="checkbox" ><label for="sk-estimator-id-149" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>StandardScaler</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.StandardScaler.html">?<span>Documentation for StandardScaler</span></a></div></label><div class="sk-toggleable__content fitted"><pre>StandardScaler()</pre></div> </div></div></div></div></div></div></div><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-150" type="checkbox" ><label for="sk-estimator-id-150" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>transformedtargetregressor: TransformedTargetRegressor</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.compose.TransformedTargetRegressor.html">?<span>Documentation for transformedtargetregressor: TransformedTargetRegressor</span></a></div></label><div class="sk-toggleable__content fitted"><pre>TransformedTargetRegressor(func=&lt;ufunc &#x27;log10&#x27;&gt;, inverse_func=&lt;ufunc &#x27;exp10&#x27;&gt;,
                                regressor=RidgeCV(alphas=array([1.e-10, 1.e-09, 1.e-08, 1.e-07, 1.e-06, 1.e-05, 1.e-04, 1.e-03,
            1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04, 1.e+05,
-           1.e+06, 1.e+07, 1.e+08, 1.e+09, 1.e+10])))</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-137" type="checkbox" ><label for="sk-estimator-id-137" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>regressor: RidgeCV</div></div></label><div class="sk-toggleable__content fitted"><pre>RidgeCV(alphas=array([1.e-10, 1.e-09, 1.e-08, 1.e-07, 1.e-06, 1.e-05, 1.e-04, 1.e-03,
+           1.e+06, 1.e+07, 1.e+08, 1.e+09, 1.e+10])))</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-151" type="checkbox" ><label for="sk-estimator-id-151" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>regressor: RidgeCV</div></div></label><div class="sk-toggleable__content fitted"><pre>RidgeCV(alphas=array([1.e-10, 1.e-09, 1.e-08, 1.e-07, 1.e-06, 1.e-05, 1.e-04, 1.e-03,
            1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04, 1.e+05,
-           1.e+06, 1.e+07, 1.e+08, 1.e+09, 1.e+10]))</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-138" type="checkbox" ><label for="sk-estimator-id-138" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>RidgeCV</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.linear_model.RidgeCV.html">?<span>Documentation for RidgeCV</span></a></div></label><div class="sk-toggleable__content fitted"><pre>RidgeCV(alphas=array([1.e-10, 1.e-09, 1.e-08, 1.e-07, 1.e-06, 1.e-05, 1.e-04, 1.e-03,
+           1.e+06, 1.e+07, 1.e+08, 1.e+09, 1.e+10]))</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-152" type="checkbox" ><label for="sk-estimator-id-152" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>RidgeCV</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.linear_model.RidgeCV.html">?<span>Documentation for RidgeCV</span></a></div></label><div class="sk-toggleable__content fitted"><pre>RidgeCV(alphas=array([1.e-10, 1.e-09, 1.e-08, 1.e-07, 1.e-06, 1.e-05, 1.e-04, 1.e-03,
            1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04, 1.e+05,
            1.e+06, 1.e+07, 1.e+08, 1.e+09, 1.e+10]))</pre></div> </div></div></div></div></div></div></div></div></div></div></div>
     </div>
@@ -3203,7 +3203,7 @@ Lessons learned
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 12.806 seconds)
+   **Total running time of the script:** (0 minutes 12.624 seconds)
 
 
 .. _sphx_glr_download_auto_examples_inspection_plot_linear_model_coefficient_interpretation.py:
diff --git a/dev/_sources/auto_examples/inspection/plot_partial_dependence.rst.txt b/dev/_sources/auto_examples/inspection/plot_partial_dependence.rst.txt
index a1b4a7beee922..ac7010b04314f 100644
--- a/dev/_sources/auto_examples/inspection/plot_partial_dependence.rst.txt
+++ b/dev/_sources/auto_examples/inspection/plot_partial_dependence.rst.txt
@@ -372,7 +372,7 @@ numerical features and encode the categorical features with a
 .. raw:: html
 
     <div class="output_subarea output_html rendered_html output_result">
-    <style>#sk-container-id-35 {
+    <style>#sk-container-id-36 {
       /* Definition of color scheme common for light and dark mode */
       --sklearn-color-text: #000;
       --sklearn-color-text-muted: #666;
@@ -403,15 +403,15 @@ numerical features and encode the categorical features with a
       }
     }
 
-    #sk-container-id-35 {
+    #sk-container-id-36 {
       color: var(--sklearn-color-text);
     }
 
-    #sk-container-id-35 pre {
+    #sk-container-id-36 pre {
       padding: 0;
     }
 
-    #sk-container-id-35 input.sk-hidden--visually {
+    #sk-container-id-36 input.sk-hidden--visually {
       border: 0;
       clip: rect(1px 1px 1px 1px);
       clip: rect(1px, 1px, 1px, 1px);
@@ -423,7 +423,7 @@ numerical features and encode the categorical features with a
       width: 1px;
     }
 
-    #sk-container-id-35 div.sk-dashed-wrapped {
+    #sk-container-id-36 div.sk-dashed-wrapped {
       border: 1px dashed var(--sklearn-color-line);
       margin: 0 0.4em 0.5em 0.4em;
       box-sizing: border-box;
@@ -431,7 +431,7 @@ numerical features and encode the categorical features with a
       background-color: var(--sklearn-color-background);
     }
 
-    #sk-container-id-35 div.sk-container {
+    #sk-container-id-36 div.sk-container {
       /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
          but bootstrap.min.css set `[hidden] { display: none !important; }`
          so we also need the `!important` here to be able to override the
@@ -441,7 +441,7 @@ numerical features and encode the categorical features with a
       position: relative;
     }
 
-    #sk-container-id-35 div.sk-text-repr-fallback {
+    #sk-container-id-36 div.sk-text-repr-fallback {
       display: none;
     }
 
@@ -457,14 +457,14 @@ numerical features and encode the categorical features with a
 
     /* Parallel-specific style estimator block */
 
-    #sk-container-id-35 div.sk-parallel-item::after {
+    #sk-container-id-36 div.sk-parallel-item::after {
       content: "";
       width: 100%;
       border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
       flex-grow: 1;
     }
 
-    #sk-container-id-35 div.sk-parallel {
+    #sk-container-id-36 div.sk-parallel {
       display: flex;
       align-items: stretch;
       justify-content: center;
@@ -472,28 +472,28 @@ numerical features and encode the categorical features with a
       position: relative;
     }
 
-    #sk-container-id-35 div.sk-parallel-item {
+    #sk-container-id-36 div.sk-parallel-item {
       display: flex;
       flex-direction: column;
     }
 
-    #sk-container-id-35 div.sk-parallel-item:first-child::after {
+    #sk-container-id-36 div.sk-parallel-item:first-child::after {
       align-self: flex-end;
       width: 50%;
     }
 
-    #sk-container-id-35 div.sk-parallel-item:last-child::after {
+    #sk-container-id-36 div.sk-parallel-item:last-child::after {
       align-self: flex-start;
       width: 50%;
     }
 
-    #sk-container-id-35 div.sk-parallel-item:only-child::after {
+    #sk-container-id-36 div.sk-parallel-item:only-child::after {
       width: 0;
     }
 
     /* Serial-specific style estimator block */
 
-    #sk-container-id-35 div.sk-serial {
+    #sk-container-id-36 div.sk-serial {
       display: flex;
       flex-direction: column;
       align-items: center;
@@ -511,14 +511,14 @@ numerical features and encode the categorical features with a
 
     /* Pipeline and ColumnTransformer style (default) */
 
-    #sk-container-id-35 div.sk-toggleable {
+    #sk-container-id-36 div.sk-toggleable {
       /* Default theme specific background. It is overwritten whether we have a
       specific estimator or a Pipeline/ColumnTransformer */
       background-color: var(--sklearn-color-background);
     }
 
     /* Toggleable label */
-    #sk-container-id-35 label.sk-toggleable__label {
+    #sk-container-id-36 label.sk-toggleable__label {
       cursor: pointer;
       display: flex;
       width: 100%;
@@ -531,13 +531,13 @@ numerical features and encode the categorical features with a
       gap: 0.5em;
     }
 
-    #sk-container-id-35 label.sk-toggleable__label .caption {
+    #sk-container-id-36 label.sk-toggleable__label .caption {
       font-size: 0.6rem;
       font-weight: lighter;
       color: var(--sklearn-color-text-muted);
     }
 
-    #sk-container-id-35 label.sk-toggleable__label-arrow:before {
+    #sk-container-id-36 label.sk-toggleable__label-arrow:before {
       /* Arrow on the left of the label */
       content: "▸";
       float: left;
@@ -545,13 +545,13 @@ numerical features and encode the categorical features with a
       color: var(--sklearn-color-icon);
     }
 
-    #sk-container-id-35 label.sk-toggleable__label-arrow:hover:before {
+    #sk-container-id-36 label.sk-toggleable__label-arrow:hover:before {
       color: var(--sklearn-color-text);
     }
 
     /* Toggleable content - dropdown */
 
-    #sk-container-id-35 div.sk-toggleable__content {
+    #sk-container-id-36 div.sk-toggleable__content {
       max-height: 0;
       max-width: 0;
       overflow: hidden;
@@ -560,12 +560,12 @@ numerical features and encode the categorical features with a
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-35 div.sk-toggleable__content.fitted {
+    #sk-container-id-36 div.sk-toggleable__content.fitted {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
-    #sk-container-id-35 div.sk-toggleable__content pre {
+    #sk-container-id-36 div.sk-toggleable__content pre {
       margin: 0.2em;
       border-radius: 0.25em;
       color: var(--sklearn-color-text);
@@ -573,79 +573,79 @@ numerical features and encode the categorical features with a
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-35 div.sk-toggleable__content.fitted pre {
+    #sk-container-id-36 div.sk-toggleable__content.fitted pre {
       /* unfitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
-    #sk-container-id-35 input.sk-toggleable__control:checked~div.sk-toggleable__content {
+    #sk-container-id-36 input.sk-toggleable__control:checked~div.sk-toggleable__content {
       /* Expand drop-down */
       max-height: 200px;
       max-width: 100%;
       overflow: auto;
     }
 
-    #sk-container-id-35 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
+    #sk-container-id-36 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
       content: "▾";
     }
 
     /* Pipeline/ColumnTransformer-specific style */
 
-    #sk-container-id-35 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-36 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-35 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-36 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
     /* Estimator-specific style */
 
     /* Colorize estimator box */
-    #sk-container-id-35 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-36 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-35 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-36 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
-    #sk-container-id-35 div.sk-label label.sk-toggleable__label,
-    #sk-container-id-35 div.sk-label label {
+    #sk-container-id-36 div.sk-label label.sk-toggleable__label,
+    #sk-container-id-36 div.sk-label label {
       /* The background is the default theme color */
       color: var(--sklearn-color-text-on-default-background);
     }
 
     /* On hover, darken the color of the background */
-    #sk-container-id-35 div.sk-label:hover label.sk-toggleable__label {
+    #sk-container-id-36 div.sk-label:hover label.sk-toggleable__label {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
     /* Label box, darken color on hover, fitted */
-    #sk-container-id-35 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
+    #sk-container-id-36 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
     /* Estimator label */
 
-    #sk-container-id-35 div.sk-label label {
+    #sk-container-id-36 div.sk-label label {
       font-family: monospace;
       font-weight: bold;
       display: inline-block;
       line-height: 1.2em;
     }
 
-    #sk-container-id-35 div.sk-label-container {
+    #sk-container-id-36 div.sk-label-container {
       text-align: center;
     }
 
     /* Estimator-specific */
-    #sk-container-id-35 div.sk-estimator {
+    #sk-container-id-36 div.sk-estimator {
       font-family: monospace;
       border: 1px dotted var(--sklearn-color-border-box);
       border-radius: 0.25em;
@@ -655,18 +655,18 @@ numerical features and encode the categorical features with a
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-35 div.sk-estimator.fitted {
+    #sk-container-id-36 div.sk-estimator.fitted {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
     /* on hover */
-    #sk-container-id-35 div.sk-estimator:hover {
+    #sk-container-id-36 div.sk-estimator:hover {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-35 div.sk-estimator.fitted:hover {
+    #sk-container-id-36 div.sk-estimator.fitted:hover {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-2);
     }
@@ -754,7 +754,7 @@ numerical features and encode the categorical features with a
 
     /* "?"-specific style due to the `<a>` HTML tag */
 
-    #sk-container-id-35 a.estimator_doc_link {
+    #sk-container-id-36 a.estimator_doc_link {
       float: right;
       font-size: 1rem;
       line-height: 1em;
@@ -769,39 +769,39 @@ numerical features and encode the categorical features with a
       border: var(--sklearn-color-unfitted-level-1) 1pt solid;
     }
 
-    #sk-container-id-35 a.estimator_doc_link.fitted {
+    #sk-container-id-36 a.estimator_doc_link.fitted {
       /* fitted */
       border: var(--sklearn-color-fitted-level-1) 1pt solid;
       color: var(--sklearn-color-fitted-level-1);
     }
 
     /* On hover */
-    #sk-container-id-35 a.estimator_doc_link:hover {
+    #sk-container-id-36 a.estimator_doc_link:hover {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-3);
       color: var(--sklearn-color-background);
       text-decoration: none;
     }
 
-    #sk-container-id-35 a.estimator_doc_link.fitted:hover {
+    #sk-container-id-36 a.estimator_doc_link.fitted:hover {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-3);
     }
-    </style><div id="sk-container-id-35" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>ColumnTransformer(transformers=[(&#x27;num&#x27;, QuantileTransformer(n_quantiles=100),
+    </style><div id="sk-container-id-36" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>ColumnTransformer(transformers=[(&#x27;num&#x27;, QuantileTransformer(n_quantiles=100),
                                      [&#x27;temp&#x27;, &#x27;feel_temp&#x27;, &#x27;humidity&#x27;,
                                       &#x27;windspeed&#x27;]),
                                     (&#x27;cat&#x27;, OneHotEncoder(handle_unknown=&#x27;ignore&#x27;),
                                      Index([&#x27;season&#x27;, &#x27;month&#x27;, &#x27;hour&#x27;, &#x27;holiday&#x27;, &#x27;weekday&#x27;, &#x27;workingday&#x27;,
            &#x27;weather&#x27;],
-          dtype=&#x27;object&#x27;))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-139" type="checkbox" ><label for="sk-estimator-id-139" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>ColumnTransformer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.compose.ColumnTransformer.html">?<span>Documentation for ColumnTransformer</span></a><span class="sk-estimator-doc-link ">i<span>Not fitted</span></span></div></label><div class="sk-toggleable__content "><pre>ColumnTransformer(transformers=[(&#x27;num&#x27;, QuantileTransformer(n_quantiles=100),
+          dtype=&#x27;object&#x27;))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-153" type="checkbox" ><label for="sk-estimator-id-153" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>ColumnTransformer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.compose.ColumnTransformer.html">?<span>Documentation for ColumnTransformer</span></a><span class="sk-estimator-doc-link ">i<span>Not fitted</span></span></div></label><div class="sk-toggleable__content "><pre>ColumnTransformer(transformers=[(&#x27;num&#x27;, QuantileTransformer(n_quantiles=100),
                                      [&#x27;temp&#x27;, &#x27;feel_temp&#x27;, &#x27;humidity&#x27;,
                                       &#x27;windspeed&#x27;]),
                                     (&#x27;cat&#x27;, OneHotEncoder(handle_unknown=&#x27;ignore&#x27;),
                                      Index([&#x27;season&#x27;, &#x27;month&#x27;, &#x27;hour&#x27;, &#x27;holiday&#x27;, &#x27;weekday&#x27;, &#x27;workingday&#x27;,
            &#x27;weather&#x27;],
-          dtype=&#x27;object&#x27;))])</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-140" type="checkbox" ><label for="sk-estimator-id-140" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>num</div></div></label><div class="sk-toggleable__content "><pre>[&#x27;temp&#x27;, &#x27;feel_temp&#x27;, &#x27;humidity&#x27;, &#x27;windspeed&#x27;]</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-141" type="checkbox" ><label for="sk-estimator-id-141" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>QuantileTransformer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.QuantileTransformer.html">?<span>Documentation for QuantileTransformer</span></a></div></label><div class="sk-toggleable__content "><pre>QuantileTransformer(n_quantiles=100)</pre></div> </div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-142" type="checkbox" ><label for="sk-estimator-id-142" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>cat</div></div></label><div class="sk-toggleable__content "><pre>Index([&#x27;season&#x27;, &#x27;month&#x27;, &#x27;hour&#x27;, &#x27;holiday&#x27;, &#x27;weekday&#x27;, &#x27;workingday&#x27;,
+          dtype=&#x27;object&#x27;))])</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-154" type="checkbox" ><label for="sk-estimator-id-154" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>num</div></div></label><div class="sk-toggleable__content "><pre>[&#x27;temp&#x27;, &#x27;feel_temp&#x27;, &#x27;humidity&#x27;, &#x27;windspeed&#x27;]</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-155" type="checkbox" ><label for="sk-estimator-id-155" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>QuantileTransformer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.QuantileTransformer.html">?<span>Documentation for QuantileTransformer</span></a></div></label><div class="sk-toggleable__content "><pre>QuantileTransformer(n_quantiles=100)</pre></div> </div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-156" type="checkbox" ><label for="sk-estimator-id-156" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>cat</div></div></label><div class="sk-toggleable__content "><pre>Index([&#x27;season&#x27;, &#x27;month&#x27;, &#x27;hour&#x27;, &#x27;holiday&#x27;, &#x27;weekday&#x27;, &#x27;workingday&#x27;,
            &#x27;weather&#x27;],
-          dtype=&#x27;object&#x27;)</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-143" type="checkbox" ><label for="sk-estimator-id-143" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>OneHotEncoder</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.OneHotEncoder.html">?<span>Documentation for OneHotEncoder</span></a></div></label><div class="sk-toggleable__content "><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</pre></div> </div></div></div></div></div></div></div></div></div>
+          dtype=&#x27;object&#x27;)</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-157" type="checkbox" ><label for="sk-estimator-id-157" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>OneHotEncoder</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.OneHotEncoder.html">?<span>Documentation for OneHotEncoder</span></a></div></label><div class="sk-toggleable__content "><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</pre></div> </div></div></div></div></div></div></div></div></div>
     </div>
     <br />
     <br />
@@ -839,7 +839,7 @@ encode the categorical features using a
 .. raw:: html
 
     <div class="output_subarea output_html rendered_html output_result">
-    <style>#sk-container-id-36 {
+    <style>#sk-container-id-37 {
       /* Definition of color scheme common for light and dark mode */
       --sklearn-color-text: #000;
       --sklearn-color-text-muted: #666;
@@ -870,15 +870,15 @@ encode the categorical features using a
       }
     }
 
-    #sk-container-id-36 {
+    #sk-container-id-37 {
       color: var(--sklearn-color-text);
     }
 
-    #sk-container-id-36 pre {
+    #sk-container-id-37 pre {
       padding: 0;
     }
 
-    #sk-container-id-36 input.sk-hidden--visually {
+    #sk-container-id-37 input.sk-hidden--visually {
       border: 0;
       clip: rect(1px 1px 1px 1px);
       clip: rect(1px, 1px, 1px, 1px);
@@ -890,7 +890,7 @@ encode the categorical features using a
       width: 1px;
     }
 
-    #sk-container-id-36 div.sk-dashed-wrapped {
+    #sk-container-id-37 div.sk-dashed-wrapped {
       border: 1px dashed var(--sklearn-color-line);
       margin: 0 0.4em 0.5em 0.4em;
       box-sizing: border-box;
@@ -898,7 +898,7 @@ encode the categorical features using a
       background-color: var(--sklearn-color-background);
     }
 
-    #sk-container-id-36 div.sk-container {
+    #sk-container-id-37 div.sk-container {
       /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
          but bootstrap.min.css set `[hidden] { display: none !important; }`
          so we also need the `!important` here to be able to override the
@@ -908,7 +908,7 @@ encode the categorical features using a
       position: relative;
     }
 
-    #sk-container-id-36 div.sk-text-repr-fallback {
+    #sk-container-id-37 div.sk-text-repr-fallback {
       display: none;
     }
 
@@ -924,14 +924,14 @@ encode the categorical features using a
 
     /* Parallel-specific style estimator block */
 
-    #sk-container-id-36 div.sk-parallel-item::after {
+    #sk-container-id-37 div.sk-parallel-item::after {
       content: "";
       width: 100%;
       border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
       flex-grow: 1;
     }
 
-    #sk-container-id-36 div.sk-parallel {
+    #sk-container-id-37 div.sk-parallel {
       display: flex;
       align-items: stretch;
       justify-content: center;
@@ -939,28 +939,28 @@ encode the categorical features using a
       position: relative;
     }
 
-    #sk-container-id-36 div.sk-parallel-item {
+    #sk-container-id-37 div.sk-parallel-item {
       display: flex;
       flex-direction: column;
     }
 
-    #sk-container-id-36 div.sk-parallel-item:first-child::after {
+    #sk-container-id-37 div.sk-parallel-item:first-child::after {
       align-self: flex-end;
       width: 50%;
     }
 
-    #sk-container-id-36 div.sk-parallel-item:last-child::after {
+    #sk-container-id-37 div.sk-parallel-item:last-child::after {
       align-self: flex-start;
       width: 50%;
     }
 
-    #sk-container-id-36 div.sk-parallel-item:only-child::after {
+    #sk-container-id-37 div.sk-parallel-item:only-child::after {
       width: 0;
     }
 
     /* Serial-specific style estimator block */
 
-    #sk-container-id-36 div.sk-serial {
+    #sk-container-id-37 div.sk-serial {
       display: flex;
       flex-direction: column;
       align-items: center;
@@ -978,14 +978,14 @@ encode the categorical features using a
 
     /* Pipeline and ColumnTransformer style (default) */
 
-    #sk-container-id-36 div.sk-toggleable {
+    #sk-container-id-37 div.sk-toggleable {
       /* Default theme specific background. It is overwritten whether we have a
       specific estimator or a Pipeline/ColumnTransformer */
       background-color: var(--sklearn-color-background);
     }
 
     /* Toggleable label */
-    #sk-container-id-36 label.sk-toggleable__label {
+    #sk-container-id-37 label.sk-toggleable__label {
       cursor: pointer;
       display: flex;
       width: 100%;
@@ -998,13 +998,13 @@ encode the categorical features using a
       gap: 0.5em;
     }
 
-    #sk-container-id-36 label.sk-toggleable__label .caption {
+    #sk-container-id-37 label.sk-toggleable__label .caption {
       font-size: 0.6rem;
       font-weight: lighter;
       color: var(--sklearn-color-text-muted);
     }
 
-    #sk-container-id-36 label.sk-toggleable__label-arrow:before {
+    #sk-container-id-37 label.sk-toggleable__label-arrow:before {
       /* Arrow on the left of the label */
       content: "▸";
       float: left;
@@ -1012,13 +1012,13 @@ encode the categorical features using a
       color: var(--sklearn-color-icon);
     }
 
-    #sk-container-id-36 label.sk-toggleable__label-arrow:hover:before {
+    #sk-container-id-37 label.sk-toggleable__label-arrow:hover:before {
       color: var(--sklearn-color-text);
     }
 
     /* Toggleable content - dropdown */
 
-    #sk-container-id-36 div.sk-toggleable__content {
+    #sk-container-id-37 div.sk-toggleable__content {
       max-height: 0;
       max-width: 0;
       overflow: hidden;
@@ -1027,12 +1027,12 @@ encode the categorical features using a
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-36 div.sk-toggleable__content.fitted {
+    #sk-container-id-37 div.sk-toggleable__content.fitted {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
-    #sk-container-id-36 div.sk-toggleable__content pre {
+    #sk-container-id-37 div.sk-toggleable__content pre {
       margin: 0.2em;
       border-radius: 0.25em;
       color: var(--sklearn-color-text);
@@ -1040,79 +1040,79 @@ encode the categorical features using a
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-36 div.sk-toggleable__content.fitted pre {
+    #sk-container-id-37 div.sk-toggleable__content.fitted pre {
       /* unfitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
-    #sk-container-id-36 input.sk-toggleable__control:checked~div.sk-toggleable__content {
+    #sk-container-id-37 input.sk-toggleable__control:checked~div.sk-toggleable__content {
       /* Expand drop-down */
       max-height: 200px;
       max-width: 100%;
       overflow: auto;
     }
 
-    #sk-container-id-36 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
+    #sk-container-id-37 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
       content: "▾";
     }
 
     /* Pipeline/ColumnTransformer-specific style */
 
-    #sk-container-id-36 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-37 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-36 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-37 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
     /* Estimator-specific style */
 
     /* Colorize estimator box */
-    #sk-container-id-36 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-37 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-36 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-37 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
-    #sk-container-id-36 div.sk-label label.sk-toggleable__label,
-    #sk-container-id-36 div.sk-label label {
+    #sk-container-id-37 div.sk-label label.sk-toggleable__label,
+    #sk-container-id-37 div.sk-label label {
       /* The background is the default theme color */
       color: var(--sklearn-color-text-on-default-background);
     }
 
     /* On hover, darken the color of the background */
-    #sk-container-id-36 div.sk-label:hover label.sk-toggleable__label {
+    #sk-container-id-37 div.sk-label:hover label.sk-toggleable__label {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
     /* Label box, darken color on hover, fitted */
-    #sk-container-id-36 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
+    #sk-container-id-37 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
     /* Estimator label */
 
-    #sk-container-id-36 div.sk-label label {
+    #sk-container-id-37 div.sk-label label {
       font-family: monospace;
       font-weight: bold;
       display: inline-block;
       line-height: 1.2em;
     }
 
-    #sk-container-id-36 div.sk-label-container {
+    #sk-container-id-37 div.sk-label-container {
       text-align: center;
     }
 
     /* Estimator-specific */
-    #sk-container-id-36 div.sk-estimator {
+    #sk-container-id-37 div.sk-estimator {
       font-family: monospace;
       border: 1px dotted var(--sklearn-color-border-box);
       border-radius: 0.25em;
@@ -1122,18 +1122,18 @@ encode the categorical features using a
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-36 div.sk-estimator.fitted {
+    #sk-container-id-37 div.sk-estimator.fitted {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
     /* on hover */
-    #sk-container-id-36 div.sk-estimator:hover {
+    #sk-container-id-37 div.sk-estimator:hover {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-36 div.sk-estimator.fitted:hover {
+    #sk-container-id-37 div.sk-estimator.fitted:hover {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-2);
     }
@@ -1221,7 +1221,7 @@ encode the categorical features using a
 
     /* "?"-specific style due to the `<a>` HTML tag */
 
-    #sk-container-id-36 a.estimator_doc_link {
+    #sk-container-id-37 a.estimator_doc_link {
       float: right;
       font-size: 1rem;
       line-height: 1em;
@@ -1236,25 +1236,25 @@ encode the categorical features using a
       border: var(--sklearn-color-unfitted-level-1) 1pt solid;
     }
 
-    #sk-container-id-36 a.estimator_doc_link.fitted {
+    #sk-container-id-37 a.estimator_doc_link.fitted {
       /* fitted */
       border: var(--sklearn-color-fitted-level-1) 1pt solid;
       color: var(--sklearn-color-fitted-level-1);
     }
 
     /* On hover */
-    #sk-container-id-36 a.estimator_doc_link:hover {
+    #sk-container-id-37 a.estimator_doc_link:hover {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-3);
       color: var(--sklearn-color-background);
       text-decoration: none;
     }
 
-    #sk-container-id-36 a.estimator_doc_link.fitted:hover {
+    #sk-container-id-37 a.estimator_doc_link.fitted:hover {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-3);
     }
-    </style><div id="sk-container-id-36" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>ColumnTransformer(sparse_threshold=1,
+    </style><div id="sk-container-id-37" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>ColumnTransformer(sparse_threshold=1,
                       transformers=[(&#x27;cat&#x27;, OrdinalEncoder(),
                                      Index([&#x27;season&#x27;, &#x27;month&#x27;, &#x27;hour&#x27;, &#x27;holiday&#x27;, &#x27;weekday&#x27;, &#x27;workingday&#x27;,
            &#x27;weather&#x27;],
@@ -1262,7 +1262,7 @@ encode the categorical features using a
                                     (&#x27;num&#x27;, &#x27;passthrough&#x27;,
                                      [&#x27;temp&#x27;, &#x27;feel_temp&#x27;, &#x27;humidity&#x27;,
                                       &#x27;windspeed&#x27;])],
-                      verbose_feature_names_out=False)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-144" type="checkbox" ><label for="sk-estimator-id-144" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>ColumnTransformer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.compose.ColumnTransformer.html">?<span>Documentation for ColumnTransformer</span></a><span class="sk-estimator-doc-link ">i<span>Not fitted</span></span></div></label><div class="sk-toggleable__content "><pre>ColumnTransformer(sparse_threshold=1,
+                      verbose_feature_names_out=False)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-158" type="checkbox" ><label for="sk-estimator-id-158" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>ColumnTransformer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.compose.ColumnTransformer.html">?<span>Documentation for ColumnTransformer</span></a><span class="sk-estimator-doc-link ">i<span>Not fitted</span></span></div></label><div class="sk-toggleable__content "><pre>ColumnTransformer(sparse_threshold=1,
                       transformers=[(&#x27;cat&#x27;, OrdinalEncoder(),
                                      Index([&#x27;season&#x27;, &#x27;month&#x27;, &#x27;hour&#x27;, &#x27;holiday&#x27;, &#x27;weekday&#x27;, &#x27;workingday&#x27;,
            &#x27;weather&#x27;],
@@ -1270,9 +1270,9 @@ encode the categorical features using a
                                     (&#x27;num&#x27;, &#x27;passthrough&#x27;,
                                      [&#x27;temp&#x27;, &#x27;feel_temp&#x27;, &#x27;humidity&#x27;,
                                       &#x27;windspeed&#x27;])],
-                      verbose_feature_names_out=False)</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-145" type="checkbox" ><label for="sk-estimator-id-145" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>cat</div></div></label><div class="sk-toggleable__content "><pre>Index([&#x27;season&#x27;, &#x27;month&#x27;, &#x27;hour&#x27;, &#x27;holiday&#x27;, &#x27;weekday&#x27;, &#x27;workingday&#x27;,
+                      verbose_feature_names_out=False)</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-159" type="checkbox" ><label for="sk-estimator-id-159" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>cat</div></div></label><div class="sk-toggleable__content "><pre>Index([&#x27;season&#x27;, &#x27;month&#x27;, &#x27;hour&#x27;, &#x27;holiday&#x27;, &#x27;weekday&#x27;, &#x27;workingday&#x27;,
            &#x27;weather&#x27;],
-          dtype=&#x27;object&#x27;)</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-146" type="checkbox" ><label for="sk-estimator-id-146" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>OrdinalEncoder</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.OrdinalEncoder.html">?<span>Documentation for OrdinalEncoder</span></a></div></label><div class="sk-toggleable__content "><pre>OrdinalEncoder()</pre></div> </div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-147" type="checkbox" ><label for="sk-estimator-id-147" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>num</div></div></label><div class="sk-toggleable__content "><pre>[&#x27;temp&#x27;, &#x27;feel_temp&#x27;, &#x27;humidity&#x27;, &#x27;windspeed&#x27;]</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-148" type="checkbox" ><label for="sk-estimator-id-148" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>passthrough</div></div></label><div class="sk-toggleable__content "><pre>passthrough</pre></div> </div></div></div></div></div></div></div></div></div>
+          dtype=&#x27;object&#x27;)</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-160" type="checkbox" ><label for="sk-estimator-id-160" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>OrdinalEncoder</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.OrdinalEncoder.html">?<span>Documentation for OrdinalEncoder</span></a></div></label><div class="sk-toggleable__content "><pre>OrdinalEncoder()</pre></div> </div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-161" type="checkbox" ><label for="sk-estimator-id-161" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>num</div></div></label><div class="sk-toggleable__content "><pre>[&#x27;temp&#x27;, &#x27;feel_temp&#x27;, &#x27;humidity&#x27;, &#x27;windspeed&#x27;]</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-162" type="checkbox" ><label for="sk-estimator-id-162" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>passthrough</div></div></label><div class="sk-toggleable__content "><pre>passthrough</pre></div> </div></div></div></div></div></div></div></div></div>
     </div>
     <br />
     <br />
@@ -1327,7 +1327,7 @@ single-variable partial dependence plots.
  .. code-block:: none
 
     Training MLPRegressor...
-    done in 0.607s
+    done in 0.658s
     Test R2 score: 0.61
 
 
@@ -1411,7 +1411,7 @@ We will plot the averaged partial dependence.
  .. code-block:: none
 
     Computing partial dependence plots...
-    done in 0.649s
+    done in 0.543s
 
 
 
@@ -1454,7 +1454,7 @@ specific preprocessor we created for this model.
  .. code-block:: none
 
     Training HistGradientBoostingRegressor...
-    done in 0.132s
+    done in 0.131s
     Test R2 score: 0.62
 
 
@@ -1511,7 +1511,7 @@ features.
  .. code-block:: none
 
     Computing partial dependence plots...
-    done in 1.198s
+    done in 1.118s
 
 
 
@@ -1589,7 +1589,7 @@ selected ICEs for the temperature and humidity features.
  .. code-block:: none
 
     Computing partial dependence plots and individual conditional expectation...
-    done in 0.537s
+    done in 0.508s
 
 
 
@@ -1710,7 +1710,7 @@ heatmap.
  .. code-block:: none
 
     Computing partial dependence plots...
-    done in 8.215s
+    done in 8.146s
 
 
 
@@ -1771,7 +1771,7 @@ non-linear feature interactions.
  .. code-block:: none
 
     Computing partial dependence plots...
-    done in 7.819s
+    done in 7.991s
 
 
 
@@ -1839,7 +1839,7 @@ the season, the weather, and the target would be as follow:
  .. code-block:: none
 
     Computing partial dependence plots...
-    done in 0.382s
+    done in 0.374s
 
 
 
@@ -1962,7 +1962,7 @@ but with custom values
  .. code-block:: none
 
     Computing partial dependence plots with custom evaluation values...
-    done in 0.479s
+    done in 0.555s
 
 
 
@@ -1970,7 +1970,7 @@ but with custom values
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 25.670 seconds)
+   **Total running time of the script:** (0 minutes 25.682 seconds)
 
 
 .. _sphx_glr_download_auto_examples_inspection_plot_partial_dependence.py:
diff --git a/dev/_sources/auto_examples/inspection/plot_permutation_importance.rst.txt b/dev/_sources/auto_examples/inspection/plot_permutation_importance.rst.txt
index b43bc7dbff6d1..5c68cbf7a92cb 100644
--- a/dev/_sources/auto_examples/inspection/plot_permutation_importance.rst.txt
+++ b/dev/_sources/auto_examples/inspection/plot_permutation_importance.rst.txt
@@ -148,7 +148,7 @@ the following preprocessing steps:
 .. raw:: html
 
     <div class="output_subarea output_html rendered_html output_result">
-    <style>#sk-container-id-37 {
+    <style>#sk-container-id-38 {
       /* Definition of color scheme common for light and dark mode */
       --sklearn-color-text: #000;
       --sklearn-color-text-muted: #666;
@@ -179,15 +179,15 @@ the following preprocessing steps:
       }
     }
 
-    #sk-container-id-37 {
+    #sk-container-id-38 {
       color: var(--sklearn-color-text);
     }
 
-    #sk-container-id-37 pre {
+    #sk-container-id-38 pre {
       padding: 0;
     }
 
-    #sk-container-id-37 input.sk-hidden--visually {
+    #sk-container-id-38 input.sk-hidden--visually {
       border: 0;
       clip: rect(1px 1px 1px 1px);
       clip: rect(1px, 1px, 1px, 1px);
@@ -199,7 +199,7 @@ the following preprocessing steps:
       width: 1px;
     }
 
-    #sk-container-id-37 div.sk-dashed-wrapped {
+    #sk-container-id-38 div.sk-dashed-wrapped {
       border: 1px dashed var(--sklearn-color-line);
       margin: 0 0.4em 0.5em 0.4em;
       box-sizing: border-box;
@@ -207,7 +207,7 @@ the following preprocessing steps:
       background-color: var(--sklearn-color-background);
     }
 
-    #sk-container-id-37 div.sk-container {
+    #sk-container-id-38 div.sk-container {
       /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
          but bootstrap.min.css set `[hidden] { display: none !important; }`
          so we also need the `!important` here to be able to override the
@@ -217,7 +217,7 @@ the following preprocessing steps:
       position: relative;
     }
 
-    #sk-container-id-37 div.sk-text-repr-fallback {
+    #sk-container-id-38 div.sk-text-repr-fallback {
       display: none;
     }
 
@@ -233,14 +233,14 @@ the following preprocessing steps:
 
     /* Parallel-specific style estimator block */
 
-    #sk-container-id-37 div.sk-parallel-item::after {
+    #sk-container-id-38 div.sk-parallel-item::after {
       content: "";
       width: 100%;
       border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
       flex-grow: 1;
     }
 
-    #sk-container-id-37 div.sk-parallel {
+    #sk-container-id-38 div.sk-parallel {
       display: flex;
       align-items: stretch;
       justify-content: center;
@@ -248,28 +248,28 @@ the following preprocessing steps:
       position: relative;
     }
 
-    #sk-container-id-37 div.sk-parallel-item {
+    #sk-container-id-38 div.sk-parallel-item {
       display: flex;
       flex-direction: column;
     }
 
-    #sk-container-id-37 div.sk-parallel-item:first-child::after {
+    #sk-container-id-38 div.sk-parallel-item:first-child::after {
       align-self: flex-end;
       width: 50%;
     }
 
-    #sk-container-id-37 div.sk-parallel-item:last-child::after {
+    #sk-container-id-38 div.sk-parallel-item:last-child::after {
       align-self: flex-start;
       width: 50%;
     }
 
-    #sk-container-id-37 div.sk-parallel-item:only-child::after {
+    #sk-container-id-38 div.sk-parallel-item:only-child::after {
       width: 0;
     }
 
     /* Serial-specific style estimator block */
 
-    #sk-container-id-37 div.sk-serial {
+    #sk-container-id-38 div.sk-serial {
       display: flex;
       flex-direction: column;
       align-items: center;
@@ -287,14 +287,14 @@ the following preprocessing steps:
 
     /* Pipeline and ColumnTransformer style (default) */
 
-    #sk-container-id-37 div.sk-toggleable {
+    #sk-container-id-38 div.sk-toggleable {
       /* Default theme specific background. It is overwritten whether we have a
       specific estimator or a Pipeline/ColumnTransformer */
       background-color: var(--sklearn-color-background);
     }
 
     /* Toggleable label */
-    #sk-container-id-37 label.sk-toggleable__label {
+    #sk-container-id-38 label.sk-toggleable__label {
       cursor: pointer;
       display: flex;
       width: 100%;
@@ -307,13 +307,13 @@ the following preprocessing steps:
       gap: 0.5em;
     }
 
-    #sk-container-id-37 label.sk-toggleable__label .caption {
+    #sk-container-id-38 label.sk-toggleable__label .caption {
       font-size: 0.6rem;
       font-weight: lighter;
       color: var(--sklearn-color-text-muted);
     }
 
-    #sk-container-id-37 label.sk-toggleable__label-arrow:before {
+    #sk-container-id-38 label.sk-toggleable__label-arrow:before {
       /* Arrow on the left of the label */
       content: "▸";
       float: left;
@@ -321,13 +321,13 @@ the following preprocessing steps:
       color: var(--sklearn-color-icon);
     }
 
-    #sk-container-id-37 label.sk-toggleable__label-arrow:hover:before {
+    #sk-container-id-38 label.sk-toggleable__label-arrow:hover:before {
       color: var(--sklearn-color-text);
     }
 
     /* Toggleable content - dropdown */
 
-    #sk-container-id-37 div.sk-toggleable__content {
+    #sk-container-id-38 div.sk-toggleable__content {
       max-height: 0;
       max-width: 0;
       overflow: hidden;
@@ -336,12 +336,12 @@ the following preprocessing steps:
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-37 div.sk-toggleable__content.fitted {
+    #sk-container-id-38 div.sk-toggleable__content.fitted {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
-    #sk-container-id-37 div.sk-toggleable__content pre {
+    #sk-container-id-38 div.sk-toggleable__content pre {
       margin: 0.2em;
       border-radius: 0.25em;
       color: var(--sklearn-color-text);
@@ -349,79 +349,79 @@ the following preprocessing steps:
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-37 div.sk-toggleable__content.fitted pre {
+    #sk-container-id-38 div.sk-toggleable__content.fitted pre {
       /* unfitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
-    #sk-container-id-37 input.sk-toggleable__control:checked~div.sk-toggleable__content {
+    #sk-container-id-38 input.sk-toggleable__control:checked~div.sk-toggleable__content {
       /* Expand drop-down */
       max-height: 200px;
       max-width: 100%;
       overflow: auto;
     }
 
-    #sk-container-id-37 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
+    #sk-container-id-38 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
       content: "▾";
     }
 
     /* Pipeline/ColumnTransformer-specific style */
 
-    #sk-container-id-37 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-38 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-37 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-38 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
     /* Estimator-specific style */
 
     /* Colorize estimator box */
-    #sk-container-id-37 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-38 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-37 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-38 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
-    #sk-container-id-37 div.sk-label label.sk-toggleable__label,
-    #sk-container-id-37 div.sk-label label {
+    #sk-container-id-38 div.sk-label label.sk-toggleable__label,
+    #sk-container-id-38 div.sk-label label {
       /* The background is the default theme color */
       color: var(--sklearn-color-text-on-default-background);
     }
 
     /* On hover, darken the color of the background */
-    #sk-container-id-37 div.sk-label:hover label.sk-toggleable__label {
+    #sk-container-id-38 div.sk-label:hover label.sk-toggleable__label {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
     /* Label box, darken color on hover, fitted */
-    #sk-container-id-37 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
+    #sk-container-id-38 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
     /* Estimator label */
 
-    #sk-container-id-37 div.sk-label label {
+    #sk-container-id-38 div.sk-label label {
       font-family: monospace;
       font-weight: bold;
       display: inline-block;
       line-height: 1.2em;
     }
 
-    #sk-container-id-37 div.sk-label-container {
+    #sk-container-id-38 div.sk-label-container {
       text-align: center;
     }
 
     /* Estimator-specific */
-    #sk-container-id-37 div.sk-estimator {
+    #sk-container-id-38 div.sk-estimator {
       font-family: monospace;
       border: 1px dotted var(--sklearn-color-border-box);
       border-radius: 0.25em;
@@ -431,18 +431,18 @@ the following preprocessing steps:
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-37 div.sk-estimator.fitted {
+    #sk-container-id-38 div.sk-estimator.fitted {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
     /* on hover */
-    #sk-container-id-37 div.sk-estimator:hover {
+    #sk-container-id-38 div.sk-estimator:hover {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-37 div.sk-estimator.fitted:hover {
+    #sk-container-id-38 div.sk-estimator.fitted:hover {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-2);
     }
@@ -530,7 +530,7 @@ the following preprocessing steps:
 
     /* "?"-specific style due to the `<a>` HTML tag */
 
-    #sk-container-id-37 a.estimator_doc_link {
+    #sk-container-id-38 a.estimator_doc_link {
       float: right;
       font-size: 1rem;
       line-height: 1em;
@@ -545,25 +545,25 @@ the following preprocessing steps:
       border: var(--sklearn-color-unfitted-level-1) 1pt solid;
     }
 
-    #sk-container-id-37 a.estimator_doc_link.fitted {
+    #sk-container-id-38 a.estimator_doc_link.fitted {
       /* fitted */
       border: var(--sklearn-color-fitted-level-1) 1pt solid;
       color: var(--sklearn-color-fitted-level-1);
     }
 
     /* On hover */
-    #sk-container-id-37 a.estimator_doc_link:hover {
+    #sk-container-id-38 a.estimator_doc_link:hover {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-3);
       color: var(--sklearn-color-background);
       text-decoration: none;
     }
 
-    #sk-container-id-37 a.estimator_doc_link.fitted:hover {
+    #sk-container-id-38 a.estimator_doc_link.fitted:hover {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-3);
     }
-    </style><div id="sk-container-id-37" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;preprocess&#x27;,
+    </style><div id="sk-container-id-38" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;preprocess&#x27;,
                      ColumnTransformer(transformers=[(&#x27;cat&#x27;,
                                                       OrdinalEncoder(encoded_missing_value=-1,
                                                                      handle_unknown=&#x27;use_encoded_value&#x27;,
@@ -574,7 +574,7 @@ the following preprocessing steps:
                                                       [&#x27;age&#x27;, &#x27;sibsp&#x27;, &#x27;parch&#x27;,
                                                        &#x27;fare&#x27;, &#x27;random_num&#x27;])],
                                        verbose_feature_names_out=False)),
-                    (&#x27;classifier&#x27;, RandomForestClassifier(random_state=42))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-149" type="checkbox" ><label for="sk-estimator-id-149" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>Pipeline</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.pipeline.Pipeline.html">?<span>Documentation for Pipeline</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>Pipeline(steps=[(&#x27;preprocess&#x27;,
+                    (&#x27;classifier&#x27;, RandomForestClassifier(random_state=42))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-163" type="checkbox" ><label for="sk-estimator-id-163" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>Pipeline</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.pipeline.Pipeline.html">?<span>Documentation for Pipeline</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>Pipeline(steps=[(&#x27;preprocess&#x27;,
                      ColumnTransformer(transformers=[(&#x27;cat&#x27;,
                                                       OrdinalEncoder(encoded_missing_value=-1,
                                                                      handle_unknown=&#x27;use_encoded_value&#x27;,
@@ -585,7 +585,7 @@ the following preprocessing steps:
                                                       [&#x27;age&#x27;, &#x27;sibsp&#x27;, &#x27;parch&#x27;,
                                                        &#x27;fare&#x27;, &#x27;random_num&#x27;])],
                                        verbose_feature_names_out=False)),
-                    (&#x27;classifier&#x27;, RandomForestClassifier(random_state=42))])</pre></div> </div></div><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-150" type="checkbox" ><label for="sk-estimator-id-150" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>preprocess: ColumnTransformer</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.compose.ColumnTransformer.html">?<span>Documentation for preprocess: ColumnTransformer</span></a></div></label><div class="sk-toggleable__content fitted"><pre>ColumnTransformer(transformers=[(&#x27;cat&#x27;,
+                    (&#x27;classifier&#x27;, RandomForestClassifier(random_state=42))])</pre></div> </div></div><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-164" type="checkbox" ><label for="sk-estimator-id-164" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>preprocess: ColumnTransformer</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.compose.ColumnTransformer.html">?<span>Documentation for preprocess: ColumnTransformer</span></a></div></label><div class="sk-toggleable__content fitted"><pre>ColumnTransformer(transformers=[(&#x27;cat&#x27;,
                                      OrdinalEncoder(encoded_missing_value=-1,
                                                     handle_unknown=&#x27;use_encoded_value&#x27;,
                                                     unknown_value=-1),
@@ -593,8 +593,8 @@ the following preprocessing steps:
                                     (&#x27;num&#x27;, SimpleImputer(),
                                      [&#x27;age&#x27;, &#x27;sibsp&#x27;, &#x27;parch&#x27;, &#x27;fare&#x27;,
                                       &#x27;random_num&#x27;])],
-                      verbose_feature_names_out=False)</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-151" type="checkbox" ><label for="sk-estimator-id-151" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>cat</div></div></label><div class="sk-toggleable__content fitted"><pre>[&#x27;pclass&#x27;, &#x27;sex&#x27;, &#x27;embarked&#x27;, &#x27;random_cat&#x27;]</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-152" type="checkbox" ><label for="sk-estimator-id-152" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>OrdinalEncoder</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.OrdinalEncoder.html">?<span>Documentation for OrdinalEncoder</span></a></div></label><div class="sk-toggleable__content fitted"><pre>OrdinalEncoder(encoded_missing_value=-1, handle_unknown=&#x27;use_encoded_value&#x27;,
-                   unknown_value=-1)</pre></div> </div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-153" type="checkbox" ><label for="sk-estimator-id-153" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>num</div></div></label><div class="sk-toggleable__content fitted"><pre>[&#x27;age&#x27;, &#x27;sibsp&#x27;, &#x27;parch&#x27;, &#x27;fare&#x27;, &#x27;random_num&#x27;]</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-154" type="checkbox" ><label for="sk-estimator-id-154" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>SimpleImputer</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.impute.SimpleImputer.html">?<span>Documentation for SimpleImputer</span></a></div></label><div class="sk-toggleable__content fitted"><pre>SimpleImputer()</pre></div> </div></div></div></div></div></div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-155" type="checkbox" ><label for="sk-estimator-id-155" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>RandomForestClassifier</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.ensemble.RandomForestClassifier.html">?<span>Documentation for RandomForestClassifier</span></a></div></label><div class="sk-toggleable__content fitted"><pre>RandomForestClassifier(random_state=42)</pre></div> </div></div></div></div></div></div>
+                      verbose_feature_names_out=False)</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-165" type="checkbox" ><label for="sk-estimator-id-165" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>cat</div></div></label><div class="sk-toggleable__content fitted"><pre>[&#x27;pclass&#x27;, &#x27;sex&#x27;, &#x27;embarked&#x27;, &#x27;random_cat&#x27;]</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-166" type="checkbox" ><label for="sk-estimator-id-166" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>OrdinalEncoder</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.OrdinalEncoder.html">?<span>Documentation for OrdinalEncoder</span></a></div></label><div class="sk-toggleable__content fitted"><pre>OrdinalEncoder(encoded_missing_value=-1, handle_unknown=&#x27;use_encoded_value&#x27;,
+                   unknown_value=-1)</pre></div> </div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-167" type="checkbox" ><label for="sk-estimator-id-167" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>num</div></div></label><div class="sk-toggleable__content fitted"><pre>[&#x27;age&#x27;, &#x27;sibsp&#x27;, &#x27;parch&#x27;, &#x27;fare&#x27;, &#x27;random_num&#x27;]</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-168" type="checkbox" ><label for="sk-estimator-id-168" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>SimpleImputer</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.impute.SimpleImputer.html">?<span>Documentation for SimpleImputer</span></a></div></label><div class="sk-toggleable__content fitted"><pre>SimpleImputer()</pre></div> </div></div></div></div></div></div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-169" type="checkbox" ><label for="sk-estimator-id-169" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>RandomForestClassifier</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.ensemble.RandomForestClassifier.html">?<span>Documentation for RandomForestClassifier</span></a></div></label><div class="sk-toggleable__content fitted"><pre>RandomForestClassifier(random_state=42)</pre></div> </div></div></div></div></div></div>
     </div>
     <br />
     <br />
@@ -811,7 +811,7 @@ to overfit by setting `min_samples_leaf` at 20 data points.
 .. raw:: html
 
     <div class="output_subarea output_html rendered_html output_result">
-    <style>#sk-container-id-38 {
+    <style>#sk-container-id-39 {
       /* Definition of color scheme common for light and dark mode */
       --sklearn-color-text: #000;
       --sklearn-color-text-muted: #666;
@@ -842,15 +842,15 @@ to overfit by setting `min_samples_leaf` at 20 data points.
       }
     }
 
-    #sk-container-id-38 {
+    #sk-container-id-39 {
       color: var(--sklearn-color-text);
     }
 
-    #sk-container-id-38 pre {
+    #sk-container-id-39 pre {
       padding: 0;
     }
 
-    #sk-container-id-38 input.sk-hidden--visually {
+    #sk-container-id-39 input.sk-hidden--visually {
       border: 0;
       clip: rect(1px 1px 1px 1px);
       clip: rect(1px, 1px, 1px, 1px);
@@ -862,7 +862,7 @@ to overfit by setting `min_samples_leaf` at 20 data points.
       width: 1px;
     }
 
-    #sk-container-id-38 div.sk-dashed-wrapped {
+    #sk-container-id-39 div.sk-dashed-wrapped {
       border: 1px dashed var(--sklearn-color-line);
       margin: 0 0.4em 0.5em 0.4em;
       box-sizing: border-box;
@@ -870,7 +870,7 @@ to overfit by setting `min_samples_leaf` at 20 data points.
       background-color: var(--sklearn-color-background);
     }
 
-    #sk-container-id-38 div.sk-container {
+    #sk-container-id-39 div.sk-container {
       /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
          but bootstrap.min.css set `[hidden] { display: none !important; }`
          so we also need the `!important` here to be able to override the
@@ -880,7 +880,7 @@ to overfit by setting `min_samples_leaf` at 20 data points.
       position: relative;
     }
 
-    #sk-container-id-38 div.sk-text-repr-fallback {
+    #sk-container-id-39 div.sk-text-repr-fallback {
       display: none;
     }
 
@@ -896,14 +896,14 @@ to overfit by setting `min_samples_leaf` at 20 data points.
 
     /* Parallel-specific style estimator block */
 
-    #sk-container-id-38 div.sk-parallel-item::after {
+    #sk-container-id-39 div.sk-parallel-item::after {
       content: "";
       width: 100%;
       border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
       flex-grow: 1;
     }
 
-    #sk-container-id-38 div.sk-parallel {
+    #sk-container-id-39 div.sk-parallel {
       display: flex;
       align-items: stretch;
       justify-content: center;
@@ -911,28 +911,28 @@ to overfit by setting `min_samples_leaf` at 20 data points.
       position: relative;
     }
 
-    #sk-container-id-38 div.sk-parallel-item {
+    #sk-container-id-39 div.sk-parallel-item {
       display: flex;
       flex-direction: column;
     }
 
-    #sk-container-id-38 div.sk-parallel-item:first-child::after {
+    #sk-container-id-39 div.sk-parallel-item:first-child::after {
       align-self: flex-end;
       width: 50%;
     }
 
-    #sk-container-id-38 div.sk-parallel-item:last-child::after {
+    #sk-container-id-39 div.sk-parallel-item:last-child::after {
       align-self: flex-start;
       width: 50%;
     }
 
-    #sk-container-id-38 div.sk-parallel-item:only-child::after {
+    #sk-container-id-39 div.sk-parallel-item:only-child::after {
       width: 0;
     }
 
     /* Serial-specific style estimator block */
 
-    #sk-container-id-38 div.sk-serial {
+    #sk-container-id-39 div.sk-serial {
       display: flex;
       flex-direction: column;
       align-items: center;
@@ -950,14 +950,14 @@ to overfit by setting `min_samples_leaf` at 20 data points.
 
     /* Pipeline and ColumnTransformer style (default) */
 
-    #sk-container-id-38 div.sk-toggleable {
+    #sk-container-id-39 div.sk-toggleable {
       /* Default theme specific background. It is overwritten whether we have a
       specific estimator or a Pipeline/ColumnTransformer */
       background-color: var(--sklearn-color-background);
     }
 
     /* Toggleable label */
-    #sk-container-id-38 label.sk-toggleable__label {
+    #sk-container-id-39 label.sk-toggleable__label {
       cursor: pointer;
       display: flex;
       width: 100%;
@@ -970,13 +970,13 @@ to overfit by setting `min_samples_leaf` at 20 data points.
       gap: 0.5em;
     }
 
-    #sk-container-id-38 label.sk-toggleable__label .caption {
+    #sk-container-id-39 label.sk-toggleable__label .caption {
       font-size: 0.6rem;
       font-weight: lighter;
       color: var(--sklearn-color-text-muted);
     }
 
-    #sk-container-id-38 label.sk-toggleable__label-arrow:before {
+    #sk-container-id-39 label.sk-toggleable__label-arrow:before {
       /* Arrow on the left of the label */
       content: "▸";
       float: left;
@@ -984,13 +984,13 @@ to overfit by setting `min_samples_leaf` at 20 data points.
       color: var(--sklearn-color-icon);
     }
 
-    #sk-container-id-38 label.sk-toggleable__label-arrow:hover:before {
+    #sk-container-id-39 label.sk-toggleable__label-arrow:hover:before {
       color: var(--sklearn-color-text);
     }
 
     /* Toggleable content - dropdown */
 
-    #sk-container-id-38 div.sk-toggleable__content {
+    #sk-container-id-39 div.sk-toggleable__content {
       max-height: 0;
       max-width: 0;
       overflow: hidden;
@@ -999,12 +999,12 @@ to overfit by setting `min_samples_leaf` at 20 data points.
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-38 div.sk-toggleable__content.fitted {
+    #sk-container-id-39 div.sk-toggleable__content.fitted {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
-    #sk-container-id-38 div.sk-toggleable__content pre {
+    #sk-container-id-39 div.sk-toggleable__content pre {
       margin: 0.2em;
       border-radius: 0.25em;
       color: var(--sklearn-color-text);
@@ -1012,79 +1012,79 @@ to overfit by setting `min_samples_leaf` at 20 data points.
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-38 div.sk-toggleable__content.fitted pre {
+    #sk-container-id-39 div.sk-toggleable__content.fitted pre {
       /* unfitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
-    #sk-container-id-38 input.sk-toggleable__control:checked~div.sk-toggleable__content {
+    #sk-container-id-39 input.sk-toggleable__control:checked~div.sk-toggleable__content {
       /* Expand drop-down */
       max-height: 200px;
       max-width: 100%;
       overflow: auto;
     }
 
-    #sk-container-id-38 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
+    #sk-container-id-39 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
       content: "▾";
     }
 
     /* Pipeline/ColumnTransformer-specific style */
 
-    #sk-container-id-38 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-39 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-38 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-39 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
     /* Estimator-specific style */
 
     /* Colorize estimator box */
-    #sk-container-id-38 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-39 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-38 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-39 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
-    #sk-container-id-38 div.sk-label label.sk-toggleable__label,
-    #sk-container-id-38 div.sk-label label {
+    #sk-container-id-39 div.sk-label label.sk-toggleable__label,
+    #sk-container-id-39 div.sk-label label {
       /* The background is the default theme color */
       color: var(--sklearn-color-text-on-default-background);
     }
 
     /* On hover, darken the color of the background */
-    #sk-container-id-38 div.sk-label:hover label.sk-toggleable__label {
+    #sk-container-id-39 div.sk-label:hover label.sk-toggleable__label {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
     /* Label box, darken color on hover, fitted */
-    #sk-container-id-38 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
+    #sk-container-id-39 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
     /* Estimator label */
 
-    #sk-container-id-38 div.sk-label label {
+    #sk-container-id-39 div.sk-label label {
       font-family: monospace;
       font-weight: bold;
       display: inline-block;
       line-height: 1.2em;
     }
 
-    #sk-container-id-38 div.sk-label-container {
+    #sk-container-id-39 div.sk-label-container {
       text-align: center;
     }
 
     /* Estimator-specific */
-    #sk-container-id-38 div.sk-estimator {
+    #sk-container-id-39 div.sk-estimator {
       font-family: monospace;
       border: 1px dotted var(--sklearn-color-border-box);
       border-radius: 0.25em;
@@ -1094,18 +1094,18 @@ to overfit by setting `min_samples_leaf` at 20 data points.
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-38 div.sk-estimator.fitted {
+    #sk-container-id-39 div.sk-estimator.fitted {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
     /* on hover */
-    #sk-container-id-38 div.sk-estimator:hover {
+    #sk-container-id-39 div.sk-estimator:hover {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-38 div.sk-estimator.fitted:hover {
+    #sk-container-id-39 div.sk-estimator.fitted:hover {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-2);
     }
@@ -1193,7 +1193,7 @@ to overfit by setting `min_samples_leaf` at 20 data points.
 
     /* "?"-specific style due to the `<a>` HTML tag */
 
-    #sk-container-id-38 a.estimator_doc_link {
+    #sk-container-id-39 a.estimator_doc_link {
       float: right;
       font-size: 1rem;
       line-height: 1em;
@@ -1208,25 +1208,25 @@ to overfit by setting `min_samples_leaf` at 20 data points.
       border: var(--sklearn-color-unfitted-level-1) 1pt solid;
     }
 
-    #sk-container-id-38 a.estimator_doc_link.fitted {
+    #sk-container-id-39 a.estimator_doc_link.fitted {
       /* fitted */
       border: var(--sklearn-color-fitted-level-1) 1pt solid;
       color: var(--sklearn-color-fitted-level-1);
     }
 
     /* On hover */
-    #sk-container-id-38 a.estimator_doc_link:hover {
+    #sk-container-id-39 a.estimator_doc_link:hover {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-3);
       color: var(--sklearn-color-background);
       text-decoration: none;
     }
 
-    #sk-container-id-38 a.estimator_doc_link.fitted:hover {
+    #sk-container-id-39 a.estimator_doc_link.fitted:hover {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-3);
     }
-    </style><div id="sk-container-id-38" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;preprocess&#x27;,
+    </style><div id="sk-container-id-39" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;preprocess&#x27;,
                      ColumnTransformer(transformers=[(&#x27;cat&#x27;,
                                                       OrdinalEncoder(encoded_missing_value=-1,
                                                                      handle_unknown=&#x27;use_encoded_value&#x27;,
@@ -1238,7 +1238,7 @@ to overfit by setting `min_samples_leaf` at 20 data points.
                                                        &#x27;fare&#x27;, &#x27;random_num&#x27;])],
                                        verbose_feature_names_out=False)),
                     (&#x27;classifier&#x27;,
-                     RandomForestClassifier(min_samples_leaf=20, random_state=42))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-156" type="checkbox" ><label for="sk-estimator-id-156" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>Pipeline</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.pipeline.Pipeline.html">?<span>Documentation for Pipeline</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>Pipeline(steps=[(&#x27;preprocess&#x27;,
+                     RandomForestClassifier(min_samples_leaf=20, random_state=42))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-170" type="checkbox" ><label for="sk-estimator-id-170" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>Pipeline</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.pipeline.Pipeline.html">?<span>Documentation for Pipeline</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>Pipeline(steps=[(&#x27;preprocess&#x27;,
                      ColumnTransformer(transformers=[(&#x27;cat&#x27;,
                                                       OrdinalEncoder(encoded_missing_value=-1,
                                                                      handle_unknown=&#x27;use_encoded_value&#x27;,
@@ -1250,7 +1250,7 @@ to overfit by setting `min_samples_leaf` at 20 data points.
                                                        &#x27;fare&#x27;, &#x27;random_num&#x27;])],
                                        verbose_feature_names_out=False)),
                     (&#x27;classifier&#x27;,
-                     RandomForestClassifier(min_samples_leaf=20, random_state=42))])</pre></div> </div></div><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-157" type="checkbox" ><label for="sk-estimator-id-157" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>preprocess: ColumnTransformer</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.compose.ColumnTransformer.html">?<span>Documentation for preprocess: ColumnTransformer</span></a></div></label><div class="sk-toggleable__content fitted"><pre>ColumnTransformer(transformers=[(&#x27;cat&#x27;,
+                     RandomForestClassifier(min_samples_leaf=20, random_state=42))])</pre></div> </div></div><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-171" type="checkbox" ><label for="sk-estimator-id-171" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>preprocess: ColumnTransformer</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.compose.ColumnTransformer.html">?<span>Documentation for preprocess: ColumnTransformer</span></a></div></label><div class="sk-toggleable__content fitted"><pre>ColumnTransformer(transformers=[(&#x27;cat&#x27;,
                                      OrdinalEncoder(encoded_missing_value=-1,
                                                     handle_unknown=&#x27;use_encoded_value&#x27;,
                                                     unknown_value=-1),
@@ -1258,8 +1258,8 @@ to overfit by setting `min_samples_leaf` at 20 data points.
                                     (&#x27;num&#x27;, SimpleImputer(),
                                      [&#x27;age&#x27;, &#x27;sibsp&#x27;, &#x27;parch&#x27;, &#x27;fare&#x27;,
                                       &#x27;random_num&#x27;])],
-                      verbose_feature_names_out=False)</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-158" type="checkbox" ><label for="sk-estimator-id-158" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>cat</div></div></label><div class="sk-toggleable__content fitted"><pre>[&#x27;pclass&#x27;, &#x27;sex&#x27;, &#x27;embarked&#x27;, &#x27;random_cat&#x27;]</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-159" type="checkbox" ><label for="sk-estimator-id-159" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>OrdinalEncoder</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.OrdinalEncoder.html">?<span>Documentation for OrdinalEncoder</span></a></div></label><div class="sk-toggleable__content fitted"><pre>OrdinalEncoder(encoded_missing_value=-1, handle_unknown=&#x27;use_encoded_value&#x27;,
-                   unknown_value=-1)</pre></div> </div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-160" type="checkbox" ><label for="sk-estimator-id-160" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>num</div></div></label><div class="sk-toggleable__content fitted"><pre>[&#x27;age&#x27;, &#x27;sibsp&#x27;, &#x27;parch&#x27;, &#x27;fare&#x27;, &#x27;random_num&#x27;]</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-161" type="checkbox" ><label for="sk-estimator-id-161" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>SimpleImputer</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.impute.SimpleImputer.html">?<span>Documentation for SimpleImputer</span></a></div></label><div class="sk-toggleable__content fitted"><pre>SimpleImputer()</pre></div> </div></div></div></div></div></div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-162" type="checkbox" ><label for="sk-estimator-id-162" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>RandomForestClassifier</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.ensemble.RandomForestClassifier.html">?<span>Documentation for RandomForestClassifier</span></a></div></label><div class="sk-toggleable__content fitted"><pre>RandomForestClassifier(min_samples_leaf=20, random_state=42)</pre></div> </div></div></div></div></div></div>
+                      verbose_feature_names_out=False)</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-172" type="checkbox" ><label for="sk-estimator-id-172" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>cat</div></div></label><div class="sk-toggleable__content fitted"><pre>[&#x27;pclass&#x27;, &#x27;sex&#x27;, &#x27;embarked&#x27;, &#x27;random_cat&#x27;]</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-173" type="checkbox" ><label for="sk-estimator-id-173" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>OrdinalEncoder</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.OrdinalEncoder.html">?<span>Documentation for OrdinalEncoder</span></a></div></label><div class="sk-toggleable__content fitted"><pre>OrdinalEncoder(encoded_missing_value=-1, handle_unknown=&#x27;use_encoded_value&#x27;,
+                   unknown_value=-1)</pre></div> </div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-174" type="checkbox" ><label for="sk-estimator-id-174" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>num</div></div></label><div class="sk-toggleable__content fitted"><pre>[&#x27;age&#x27;, &#x27;sibsp&#x27;, &#x27;parch&#x27;, &#x27;fare&#x27;, &#x27;random_num&#x27;]</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-175" type="checkbox" ><label for="sk-estimator-id-175" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>SimpleImputer</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.impute.SimpleImputer.html">?<span>Documentation for SimpleImputer</span></a></div></label><div class="sk-toggleable__content fitted"><pre>SimpleImputer()</pre></div> </div></div></div></div></div></div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-176" type="checkbox" ><label for="sk-estimator-id-176" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>RandomForestClassifier</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.ensemble.RandomForestClassifier.html">?<span>Documentation for RandomForestClassifier</span></a></div></label><div class="sk-toggleable__content fitted"><pre>RandomForestClassifier(min_samples_leaf=20, random_state=42)</pre></div> </div></div></div></div></div></div>
     </div>
     <br />
     <br />
@@ -1375,7 +1375,7 @@ still valid.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 5.553 seconds)
+   **Total running time of the script:** (0 minutes 4.826 seconds)
 
 
 .. _sphx_glr_download_auto_examples_inspection_plot_permutation_importance.py:
diff --git a/dev/_sources/auto_examples/inspection/plot_permutation_importance_multicollinear.rst.txt b/dev/_sources/auto_examples/inspection/plot_permutation_importance_multicollinear.rst.txt
index 5574fa92ce314..8a4584cfd2326 100644
--- a/dev/_sources/auto_examples/inspection/plot_permutation_importance_multicollinear.rst.txt
+++ b/dev/_sources/auto_examples/inspection/plot_permutation_importance_multicollinear.rst.txt
@@ -345,7 +345,7 @@ features:
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 5.523 seconds)
+   **Total running time of the script:** (0 minutes 4.717 seconds)
 
 
 .. _sphx_glr_download_auto_examples_inspection_plot_permutation_importance_multicollinear.py:
diff --git a/dev/_sources/auto_examples/kernel_approximation/plot_scalable_poly_kernels.rst.txt b/dev/_sources/auto_examples/kernel_approximation/plot_scalable_poly_kernels.rst.txt
index 4a7f51a7ab6f1..260f8f91ae0cc 100644
--- a/dev/_sources/auto_examples/kernel_approximation/plot_scalable_poly_kernels.rst.txt
+++ b/dev/_sources/auto_examples/kernel_approximation/plot_scalable_poly_kernels.rst.txt
@@ -400,7 +400,7 @@ https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary.html
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 34.642 seconds)
+   **Total running time of the script:** (0 minutes 35.296 seconds)
 
 
 .. _sphx_glr_download_auto_examples_kernel_approximation_plot_scalable_poly_kernels.py:
diff --git a/dev/_sources/auto_examples/linear_model/plot_ard.rst.txt b/dev/_sources/auto_examples/linear_model/plot_ard.rst.txt
index 39bf9db031c4f..b0bcfedac00ce 100644
--- a/dev/_sources/auto_examples/linear_model/plot_ard.rst.txt
+++ b/dev/_sources/auto_examples/linear_model/plot_ard.rst.txt
@@ -351,7 +351,7 @@ models fail when extrapolating.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.614 seconds)
+   **Total running time of the script:** (0 minutes 0.670 seconds)
 
 
 .. _sphx_glr_download_auto_examples_linear_model_plot_ard.py:
diff --git a/dev/_sources/auto_examples/linear_model/plot_bayesian_ridge_curvefit.rst.txt b/dev/_sources/auto_examples/linear_model/plot_bayesian_ridge_curvefit.rst.txt
index b3338b249e225..1d5a192075d4c 100644
--- a/dev/_sources/auto_examples/linear_model/plot_bayesian_ridge_curvefit.rst.txt
+++ b/dev/_sources/auto_examples/linear_model/plot_bayesian_ridge_curvefit.rst.txt
@@ -167,7 +167,7 @@ Plot the true and predicted curves with log marginal likelihood (L)
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.265 seconds)
+   **Total running time of the script:** (0 minutes 0.296 seconds)
 
 
 .. _sphx_glr_download_auto_examples_linear_model_plot_bayesian_ridge_curvefit.py:
diff --git a/dev/_sources/auto_examples/linear_model/plot_elastic_net_precomputed_gram_matrix_with_weighted_samples.rst.txt b/dev/_sources/auto_examples/linear_model/plot_elastic_net_precomputed_gram_matrix_with_weighted_samples.rst.txt
index 66207ddb217c3..9dd5fa4eb28a4 100644
--- a/dev/_sources/auto_examples/linear_model/plot_elastic_net_precomputed_gram_matrix_with_weighted_samples.rst.txt
+++ b/dev/_sources/auto_examples/linear_model/plot_elastic_net_precomputed_gram_matrix_with_weighted_samples.rst.txt
@@ -122,7 +122,7 @@ matrix, the preprocessing code will incorrectly rescale it a second time.
 .. raw:: html
 
     <div class="output_subarea output_html rendered_html output_result">
-    <style>#sk-container-id-31 {
+    <style>#sk-container-id-32 {
       /* Definition of color scheme common for light and dark mode */
       --sklearn-color-text: #000;
       --sklearn-color-text-muted: #666;
@@ -153,15 +153,15 @@ matrix, the preprocessing code will incorrectly rescale it a second time.
       }
     }
 
-    #sk-container-id-31 {
+    #sk-container-id-32 {
       color: var(--sklearn-color-text);
     }
 
-    #sk-container-id-31 pre {
+    #sk-container-id-32 pre {
       padding: 0;
     }
 
-    #sk-container-id-31 input.sk-hidden--visually {
+    #sk-container-id-32 input.sk-hidden--visually {
       border: 0;
       clip: rect(1px 1px 1px 1px);
       clip: rect(1px, 1px, 1px, 1px);
@@ -173,7 +173,7 @@ matrix, the preprocessing code will incorrectly rescale it a second time.
       width: 1px;
     }
 
-    #sk-container-id-31 div.sk-dashed-wrapped {
+    #sk-container-id-32 div.sk-dashed-wrapped {
       border: 1px dashed var(--sklearn-color-line);
       margin: 0 0.4em 0.5em 0.4em;
       box-sizing: border-box;
@@ -181,7 +181,7 @@ matrix, the preprocessing code will incorrectly rescale it a second time.
       background-color: var(--sklearn-color-background);
     }
 
-    #sk-container-id-31 div.sk-container {
+    #sk-container-id-32 div.sk-container {
       /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
          but bootstrap.min.css set `[hidden] { display: none !important; }`
          so we also need the `!important` here to be able to override the
@@ -191,7 +191,7 @@ matrix, the preprocessing code will incorrectly rescale it a second time.
       position: relative;
     }
 
-    #sk-container-id-31 div.sk-text-repr-fallback {
+    #sk-container-id-32 div.sk-text-repr-fallback {
       display: none;
     }
 
@@ -207,14 +207,14 @@ matrix, the preprocessing code will incorrectly rescale it a second time.
 
     /* Parallel-specific style estimator block */
 
-    #sk-container-id-31 div.sk-parallel-item::after {
+    #sk-container-id-32 div.sk-parallel-item::after {
       content: "";
       width: 100%;
       border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
       flex-grow: 1;
     }
 
-    #sk-container-id-31 div.sk-parallel {
+    #sk-container-id-32 div.sk-parallel {
       display: flex;
       align-items: stretch;
       justify-content: center;
@@ -222,28 +222,28 @@ matrix, the preprocessing code will incorrectly rescale it a second time.
       position: relative;
     }
 
-    #sk-container-id-31 div.sk-parallel-item {
+    #sk-container-id-32 div.sk-parallel-item {
       display: flex;
       flex-direction: column;
     }
 
-    #sk-container-id-31 div.sk-parallel-item:first-child::after {
+    #sk-container-id-32 div.sk-parallel-item:first-child::after {
       align-self: flex-end;
       width: 50%;
     }
 
-    #sk-container-id-31 div.sk-parallel-item:last-child::after {
+    #sk-container-id-32 div.sk-parallel-item:last-child::after {
       align-self: flex-start;
       width: 50%;
     }
 
-    #sk-container-id-31 div.sk-parallel-item:only-child::after {
+    #sk-container-id-32 div.sk-parallel-item:only-child::after {
       width: 0;
     }
 
     /* Serial-specific style estimator block */
 
-    #sk-container-id-31 div.sk-serial {
+    #sk-container-id-32 div.sk-serial {
       display: flex;
       flex-direction: column;
       align-items: center;
@@ -261,14 +261,14 @@ matrix, the preprocessing code will incorrectly rescale it a second time.
 
     /* Pipeline and ColumnTransformer style (default) */
 
-    #sk-container-id-31 div.sk-toggleable {
+    #sk-container-id-32 div.sk-toggleable {
       /* Default theme specific background. It is overwritten whether we have a
       specific estimator or a Pipeline/ColumnTransformer */
       background-color: var(--sklearn-color-background);
     }
 
     /* Toggleable label */
-    #sk-container-id-31 label.sk-toggleable__label {
+    #sk-container-id-32 label.sk-toggleable__label {
       cursor: pointer;
       display: flex;
       width: 100%;
@@ -281,13 +281,13 @@ matrix, the preprocessing code will incorrectly rescale it a second time.
       gap: 0.5em;
     }
 
-    #sk-container-id-31 label.sk-toggleable__label .caption {
+    #sk-container-id-32 label.sk-toggleable__label .caption {
       font-size: 0.6rem;
       font-weight: lighter;
       color: var(--sklearn-color-text-muted);
     }
 
-    #sk-container-id-31 label.sk-toggleable__label-arrow:before {
+    #sk-container-id-32 label.sk-toggleable__label-arrow:before {
       /* Arrow on the left of the label */
       content: "▸";
       float: left;
@@ -295,13 +295,13 @@ matrix, the preprocessing code will incorrectly rescale it a second time.
       color: var(--sklearn-color-icon);
     }
 
-    #sk-container-id-31 label.sk-toggleable__label-arrow:hover:before {
+    #sk-container-id-32 label.sk-toggleable__label-arrow:hover:before {
       color: var(--sklearn-color-text);
     }
 
     /* Toggleable content - dropdown */
 
-    #sk-container-id-31 div.sk-toggleable__content {
+    #sk-container-id-32 div.sk-toggleable__content {
       max-height: 0;
       max-width: 0;
       overflow: hidden;
@@ -310,12 +310,12 @@ matrix, the preprocessing code will incorrectly rescale it a second time.
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-31 div.sk-toggleable__content.fitted {
+    #sk-container-id-32 div.sk-toggleable__content.fitted {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
-    #sk-container-id-31 div.sk-toggleable__content pre {
+    #sk-container-id-32 div.sk-toggleable__content pre {
       margin: 0.2em;
       border-radius: 0.25em;
       color: var(--sklearn-color-text);
@@ -323,79 +323,79 @@ matrix, the preprocessing code will incorrectly rescale it a second time.
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-31 div.sk-toggleable__content.fitted pre {
+    #sk-container-id-32 div.sk-toggleable__content.fitted pre {
       /* unfitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
-    #sk-container-id-31 input.sk-toggleable__control:checked~div.sk-toggleable__content {
+    #sk-container-id-32 input.sk-toggleable__control:checked~div.sk-toggleable__content {
       /* Expand drop-down */
       max-height: 200px;
       max-width: 100%;
       overflow: auto;
     }
 
-    #sk-container-id-31 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
+    #sk-container-id-32 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
       content: "▾";
     }
 
     /* Pipeline/ColumnTransformer-specific style */
 
-    #sk-container-id-31 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-32 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-31 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-32 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
     /* Estimator-specific style */
 
     /* Colorize estimator box */
-    #sk-container-id-31 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-32 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-31 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-32 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
-    #sk-container-id-31 div.sk-label label.sk-toggleable__label,
-    #sk-container-id-31 div.sk-label label {
+    #sk-container-id-32 div.sk-label label.sk-toggleable__label,
+    #sk-container-id-32 div.sk-label label {
       /* The background is the default theme color */
       color: var(--sklearn-color-text-on-default-background);
     }
 
     /* On hover, darken the color of the background */
-    #sk-container-id-31 div.sk-label:hover label.sk-toggleable__label {
+    #sk-container-id-32 div.sk-label:hover label.sk-toggleable__label {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
     /* Label box, darken color on hover, fitted */
-    #sk-container-id-31 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
+    #sk-container-id-32 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
     /* Estimator label */
 
-    #sk-container-id-31 div.sk-label label {
+    #sk-container-id-32 div.sk-label label {
       font-family: monospace;
       font-weight: bold;
       display: inline-block;
       line-height: 1.2em;
     }
 
-    #sk-container-id-31 div.sk-label-container {
+    #sk-container-id-32 div.sk-label-container {
       text-align: center;
     }
 
     /* Estimator-specific */
-    #sk-container-id-31 div.sk-estimator {
+    #sk-container-id-32 div.sk-estimator {
       font-family: monospace;
       border: 1px dotted var(--sklearn-color-border-box);
       border-radius: 0.25em;
@@ -405,18 +405,18 @@ matrix, the preprocessing code will incorrectly rescale it a second time.
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-31 div.sk-estimator.fitted {
+    #sk-container-id-32 div.sk-estimator.fitted {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
     /* on hover */
-    #sk-container-id-31 div.sk-estimator:hover {
+    #sk-container-id-32 div.sk-estimator:hover {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-31 div.sk-estimator.fitted:hover {
+    #sk-container-id-32 div.sk-estimator.fitted:hover {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-2);
     }
@@ -504,7 +504,7 @@ matrix, the preprocessing code will incorrectly rescale it a second time.
 
     /* "?"-specific style due to the `<a>` HTML tag */
 
-    #sk-container-id-31 a.estimator_doc_link {
+    #sk-container-id-32 a.estimator_doc_link {
       float: right;
       font-size: 1rem;
       line-height: 1em;
@@ -519,25 +519,25 @@ matrix, the preprocessing code will incorrectly rescale it a second time.
       border: var(--sklearn-color-unfitted-level-1) 1pt solid;
     }
 
-    #sk-container-id-31 a.estimator_doc_link.fitted {
+    #sk-container-id-32 a.estimator_doc_link.fitted {
       /* fitted */
       border: var(--sklearn-color-fitted-level-1) 1pt solid;
       color: var(--sklearn-color-fitted-level-1);
     }
 
     /* On hover */
-    #sk-container-id-31 a.estimator_doc_link:hover {
+    #sk-container-id-32 a.estimator_doc_link:hover {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-3);
       color: var(--sklearn-color-background);
       text-decoration: none;
     }
 
-    #sk-container-id-31 a.estimator_doc_link.fitted:hover {
+    #sk-container-id-32 a.estimator_doc_link.fitted:hover {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-3);
     }
-    </style><div id="sk-container-id-31" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>ElasticNet(alpha=0.01,
+    </style><div id="sk-container-id-32" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>ElasticNet(alpha=0.01,
                precompute=array([[ 9.98809919e+04, -4.48938813e+02, -1.03237920e+03, ...,
             -2.25349312e+02, -3.53959628e+02, -1.67451144e+02],
            [-4.48938813e+02,  1.00768662e+05,  1.19112072e+02, ...,
@@ -551,7 +551,7 @@ matrix, the preprocessing code will incorrectly rescale it a second time.
             -4.58195950e+02,  9.98350372e+04,  5.60836363e+02],
            [-1.67451144e+02, -5.76195467e+02,  2.65799352e+02, ...,
             -1.58667835e+02,  5.60836363e+02,  1.00911944e+05]],
-          shape=(100, 100)))</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-115" type="checkbox" checked><label for="sk-estimator-id-115" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>ElasticNet</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.linear_model.ElasticNet.html">?<span>Documentation for ElasticNet</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>ElasticNet(alpha=0.01,
+          shape=(100, 100)))</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-125" type="checkbox" checked><label for="sk-estimator-id-125" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>ElasticNet</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.linear_model.ElasticNet.html">?<span>Documentation for ElasticNet</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>ElasticNet(alpha=0.01,
                precompute=array([[ 9.98809919e+04, -4.48938813e+02, -1.03237920e+03, ...,
             -2.25349312e+02, -3.53959628e+02, -1.67451144e+02],
            [-4.48938813e+02,  1.00768662e+05,  1.19112072e+02, ...,
@@ -573,7 +573,7 @@ matrix, the preprocessing code will incorrectly rescale it a second time.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.794 seconds)
+   **Total running time of the script:** (0 minutes 1.956 seconds)
 
 
 .. _sphx_glr_download_auto_examples_linear_model_plot_elastic_net_precomputed_gram_matrix_with_weighted_samples.py:
diff --git a/dev/_sources/auto_examples/linear_model/plot_huber_vs_ridge.rst.txt b/dev/_sources/auto_examples/linear_model/plot_huber_vs_ridge.rst.txt
index 4330d72321cfd..1b4cb636d73ec 100644
--- a/dev/_sources/auto_examples/linear_model/plot_huber_vs_ridge.rst.txt
+++ b/dev/_sources/auto_examples/linear_model/plot_huber_vs_ridge.rst.txt
@@ -99,7 +99,7 @@ function approaches that of the ridge.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.099 seconds)
+   **Total running time of the script:** (0 minutes 0.119 seconds)
 
 
 .. _sphx_glr_download_auto_examples_linear_model_plot_huber_vs_ridge.py:
diff --git a/dev/_sources/auto_examples/linear_model/plot_lasso_and_elasticnet.rst.txt b/dev/_sources/auto_examples/linear_model/plot_lasso_and_elasticnet.rst.txt
index 093073c819783..a9431f575b99a 100644
--- a/dev/_sources/auto_examples/linear_model/plot_lasso_and_elasticnet.rst.txt
+++ b/dev/_sources/auto_examples/linear_model/plot_lasso_and_elasticnet.rst.txt
@@ -288,7 +288,7 @@ comparison of :class:`~sklearn.linear_model.ARDRegression` and
 
  .. code-block:: none
 
-    ARD fit done in 0.041s
+    ARD fit done in 0.043s
     ARD r^2 on test data : 0.543
 
 
@@ -437,7 +437,7 @@ References
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.433 seconds)
+   **Total running time of the script:** (0 minutes 0.467 seconds)
 
 
 .. _sphx_glr_download_auto_examples_linear_model_plot_lasso_and_elasticnet.py:
diff --git a/dev/_sources/auto_examples/linear_model/plot_lasso_dense_vs_sparse_data.rst.txt b/dev/_sources/auto_examples/linear_model/plot_lasso_dense_vs_sparse_data.rst.txt
index 8116159e97791..0a126299efac9 100644
--- a/dev/_sources/auto_examples/linear_model/plot_lasso_dense_vs_sparse_data.rst.txt
+++ b/dev/_sources/auto_examples/linear_model/plot_lasso_dense_vs_sparse_data.rst.txt
@@ -94,8 +94,8 @@ runtime with a dense data format.
 
  .. code-block:: none
 
-    Sparse Lasso done in 0.104s
-    Dense Lasso done in 0.030s
+    Sparse Lasso done in 0.103s
+    Dense Lasso done in 0.033s
     Distance between coefficients : 1.01e-13
 
 
@@ -151,8 +151,8 @@ expect the implementation that uses the sparse data format to be faster.
  .. code-block:: none
 
     Matrix density : 0.626%
-    Sparse Lasso done in 0.171s
-    Dense Lasso done in  0.668s
+    Sparse Lasso done in 0.175s
+    Dense Lasso done in  0.799s
     Distance between coefficients : 8.65e-12
 
 
@@ -161,7 +161,7 @@ expect the implementation that uses the sparse data format to be faster.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 1.039 seconds)
+   **Total running time of the script:** (0 minutes 1.178 seconds)
 
 
 .. _sphx_glr_download_auto_examples_linear_model_plot_lasso_dense_vs_sparse_data.py:
diff --git a/dev/_sources/auto_examples/linear_model/plot_lasso_lars_ic.rst.txt b/dev/_sources/auto_examples/linear_model/plot_lasso_lars_ic.rst.txt
index e5976f2174f5d..2561f6e1e7ef9 100644
--- a/dev/_sources/auto_examples/linear_model/plot_lasso_lars_ic.rst.txt
+++ b/dev/_sources/auto_examples/linear_model/plot_lasso_lars_ic.rst.txt
@@ -346,7 +346,7 @@ regularization parameter.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.097 seconds)
+   **Total running time of the script:** (0 minutes 0.145 seconds)
 
 
 .. _sphx_glr_download_auto_examples_linear_model_plot_lasso_lars_ic.py:
diff --git a/dev/_sources/auto_examples/linear_model/plot_lasso_lasso_lars_elasticnet_path.rst.txt b/dev/_sources/auto_examples/linear_model/plot_lasso_lasso_lars_elasticnet_path.rst.txt
index ea39ece2764ac..d72a087b1b1f6 100644
--- a/dev/_sources/auto_examples/linear_model/plot_lasso_lasso_lars_elasticnet_path.rst.txt
+++ b/dev/_sources/auto_examples/linear_model/plot_lasso_lasso_lars_elasticnet_path.rst.txt
@@ -220,7 +220,7 @@ under different constraints.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.789 seconds)
+   **Total running time of the script:** (0 minutes 0.843 seconds)
 
 
 .. _sphx_glr_download_auto_examples_linear_model_plot_lasso_lasso_lars_elasticnet_path.py:
diff --git a/dev/_sources/auto_examples/linear_model/plot_lasso_model_selection.rst.txt b/dev/_sources/auto_examples/linear_model/plot_lasso_model_selection.rst.txt
index 886fa975bb657..f5249706894c6 100644
--- a/dev/_sources/auto_examples/linear_model/plot_lasso_model_selection.rst.txt
+++ b/dev/_sources/auto_examples/linear_model/plot_lasso_model_selection.rst.txt
@@ -397,16 +397,16 @@ We can check which value of `alpha` leads to the minimum AIC and BIC.
 
     <div class="output_subarea output_html rendered_html output_result">
     <style type="text/css">
-    #T_3e930_row6_col1, #T_3e930_row21_col0 {
+    #T_c6871_row6_col1, #T_c6871_row21_col0 {
       font-weight: bold;
     }
     </style>
-    <table id="T_3e930">
+    <table id="T_c6871">
       <thead>
         <tr>
           <th class="blank level0" >&nbsp;</th>
-          <th id="T_3e930_level0_col0" class="col_heading level0 col0" >AIC criterion</th>
-          <th id="T_3e930_level0_col1" class="col_heading level0 col1" >BIC criterion</th>
+          <th id="T_c6871_level0_col0" class="col_heading level0 col0" >AIC criterion</th>
+          <th id="T_c6871_level0_col1" class="col_heading level0 col1" >BIC criterion</th>
         </tr>
         <tr>
           <th class="index_name level0" >alphas</th>
@@ -416,139 +416,139 @@ We can check which value of `alpha` leads to the minimum AIC and BIC.
       </thead>
       <tbody>
         <tr>
-          <th id="T_3e930_level0_row0" class="row_heading level0 row0" >45.160030</th>
-          <td id="T_3e930_row0_col0" class="data row0 col0" >5244.764779</td>
-          <td id="T_3e930_row0_col1" class="data row0 col1" >5244.764779</td>
+          <th id="T_c6871_level0_row0" class="row_heading level0 row0" >45.160030</th>
+          <td id="T_c6871_row0_col0" class="data row0 col0" >5244.764779</td>
+          <td id="T_c6871_row0_col1" class="data row0 col1" >5244.764779</td>
         </tr>
         <tr>
-          <th id="T_3e930_level0_row1" class="row_heading level0 row1" >42.300343</th>
-          <td id="T_3e930_row1_col0" class="data row1 col0" >5208.250639</td>
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+          <th id="T_c6871_level0_row23" class="row_heading level0 row23" >0.259031</th>
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-          <td id="T_3e930_row24_col1" class="data row24 col1" >4894.853846</td>
+          <th id="T_c6871_level0_row24" class="row_heading level0 row24" >0.032179</th>
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-          <td id="T_3e930_row25_col1" class="data row25 col1" >4888.752867</td>
+          <th id="T_c6871_level0_row25" class="row_heading level0 row25" >0.019069</th>
+          <td id="T_c6871_row25_col0" class="data row25 col0" >4794.652739</td>
+          <td id="T_c6871_row25_col1" class="data row25 col1" >4888.752867</td>
         </tr>
         <tr>
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-          <td id="T_3e930_row26_col1" class="data row26 col1" >4894.817724</td>
+          <th id="T_c6871_level0_row26" class="row_heading level0 row26" >0.000000</th>
+          <td id="T_c6871_row26_col0" class="data row26 col0" >4796.626286</td>
+          <td id="T_c6871_row26_col1" class="data row26 col1" >4894.817724</td>
         </tr>
       </tbody>
     </table>
@@ -685,7 +685,7 @@ Let's start by making the hyperparameter tuning using
 
 
 .. image-sg:: /auto_examples/linear_model/images/sphx_glr_plot_lasso_model_selection_002.png
-   :alt: Mean square error on each fold: coordinate descent (train time: 0.24s)
+   :alt: Mean square error on each fold: coordinate descent (train time: 0.29s)
    :srcset: /auto_examples/linear_model/images/sphx_glr_plot_lasso_model_selection_002.png
    :class: sphx-glr-single-img
 
@@ -742,7 +742,7 @@ Let's start by making the hyperparameter tuning using
 
 
 .. image-sg:: /auto_examples/linear_model/images/sphx_glr_plot_lasso_model_selection_003.png
-   :alt: Mean square error on each fold: Lars (train time: 0.07s)
+   :alt: Mean square error on each fold: Lars (train time: 0.08s)
    :srcset: /auto_examples/linear_model/images/sphx_glr_plot_lasso_model_selection_003.png
    :class: sphx-glr-single-img
 
@@ -792,7 +792,7 @@ strategy: it works in different settings.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.863 seconds)
+   **Total running time of the script:** (0 minutes 1.013 seconds)
 
 
 .. _sphx_glr_download_auto_examples_linear_model_plot_lasso_model_selection.py:
diff --git a/dev/_sources/auto_examples/linear_model/plot_logistic.rst.txt b/dev/_sources/auto_examples/linear_model/plot_logistic.rst.txt
index ff09d11da1eed..9d389027d3972 100644
--- a/dev/_sources/auto_examples/linear_model/plot_logistic.rst.txt
+++ b/dev/_sources/auto_examples/linear_model/plot_logistic.rst.txt
@@ -101,7 +101,7 @@ i.e. class one or two, using the logistic curve.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.110 seconds)
+   **Total running time of the script:** (0 minutes 0.116 seconds)
 
 
 .. _sphx_glr_download_auto_examples_linear_model_plot_logistic.py:
diff --git a/dev/_sources/auto_examples/linear_model/plot_logistic_l1_l2_sparsity.rst.txt b/dev/_sources/auto_examples/linear_model/plot_logistic_l1_l2_sparsity.rst.txt
index f34907866e926..520f4ebacc80c 100644
--- a/dev/_sources/auto_examples/linear_model/plot_logistic_l1_l2_sparsity.rst.txt
+++ b/dev/_sources/auto_examples/linear_model/plot_logistic_l1_l2_sparsity.rst.txt
@@ -153,7 +153,7 @@ The visualization shows coefficients of the models for varying C.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.430 seconds)
+   **Total running time of the script:** (0 minutes 0.470 seconds)
 
 
 .. _sphx_glr_download_auto_examples_linear_model_plot_logistic_l1_l2_sparsity.py:
diff --git a/dev/_sources/auto_examples/linear_model/plot_logistic_multinomial.rst.txt b/dev/_sources/auto_examples/linear_model/plot_logistic_multinomial.rst.txt
index 1e4defc20b984..7649d5a6711d2 100644
--- a/dev/_sources/auto_examples/linear_model/plot_logistic_multinomial.rst.txt
+++ b/dev/_sources/auto_examples/linear_model/plot_logistic_multinomial.rst.txt
@@ -303,7 +303,7 @@ a utility function would.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.569 seconds)
+   **Total running time of the script:** (0 minutes 0.745 seconds)
 
 
 .. _sphx_glr_download_auto_examples_linear_model_plot_logistic_multinomial.py:
diff --git a/dev/_sources/auto_examples/linear_model/plot_logistic_path.rst.txt b/dev/_sources/auto_examples/linear_model/plot_logistic_path.rst.txt
index d795b4a528e27..0e49bef576cd0 100644
--- a/dev/_sources/auto_examples/linear_model/plot_logistic_path.rst.txt
+++ b/dev/_sources/auto_examples/linear_model/plot_logistic_path.rst.txt
@@ -203,7 +203,7 @@ Plot regularization path
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.169 seconds)
+   **Total running time of the script:** (0 minutes 0.190 seconds)
 
 
 .. _sphx_glr_download_auto_examples_linear_model_plot_logistic_path.py:
diff --git a/dev/_sources/auto_examples/linear_model/plot_multi_task_lasso_support.rst.txt b/dev/_sources/auto_examples/linear_model/plot_multi_task_lasso_support.rst.txt
index 772c7b5f90960..614a85342545f 100644
--- a/dev/_sources/auto_examples/linear_model/plot_multi_task_lasso_support.rst.txt
+++ b/dev/_sources/auto_examples/linear_model/plot_multi_task_lasso_support.rst.txt
@@ -168,7 +168,7 @@ Plot support and time series
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.219 seconds)
+   **Total running time of the script:** (0 minutes 0.237 seconds)
 
 
 .. _sphx_glr_download_auto_examples_linear_model_plot_multi_task_lasso_support.py:
diff --git a/dev/_sources/auto_examples/linear_model/plot_nnls.rst.txt b/dev/_sources/auto_examples/linear_model/plot_nnls.rst.txt
index 43aca482aef5b..619398f9fd517 100644
--- a/dev/_sources/auto_examples/linear_model/plot_nnls.rst.txt
+++ b/dev/_sources/auto_examples/linear_model/plot_nnls.rst.txt
@@ -190,7 +190,7 @@ The Non-Negative Least squares inherently yield sparse results.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.063 seconds)
+   **Total running time of the script:** (0 minutes 0.066 seconds)
 
 
 .. _sphx_glr_download_auto_examples_linear_model_plot_nnls.py:
diff --git a/dev/_sources/auto_examples/linear_model/plot_ols_ridge.rst.txt b/dev/_sources/auto_examples/linear_model/plot_ols_ridge.rst.txt
index 885b30035e9b5..27dd47da53f88 100644
--- a/dev/_sources/auto_examples/linear_model/plot_ols_ridge.rst.txt
+++ b/dev/_sources/auto_examples/linear_model/plot_ols_ridge.rst.txt
@@ -293,7 +293,7 @@ is noisy, or sample size is small.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.417 seconds)
+   **Total running time of the script:** (0 minutes 0.457 seconds)
 
 
 .. _sphx_glr_download_auto_examples_linear_model_plot_ols_ridge.py:
diff --git a/dev/_sources/auto_examples/linear_model/plot_omp.rst.txt b/dev/_sources/auto_examples/linear_model/plot_omp.rst.txt
index 9ca3ed903096a..086c7d7c5b4d1 100644
--- a/dev/_sources/auto_examples/linear_model/plot_omp.rst.txt
+++ b/dev/_sources/auto_examples/linear_model/plot_omp.rst.txt
@@ -115,7 +115,7 @@ measurement encoded with a dictionary
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.199 seconds)
+   **Total running time of the script:** (0 minutes 0.211 seconds)
 
 
 .. _sphx_glr_download_auto_examples_linear_model_plot_omp.py:
diff --git a/dev/_sources/auto_examples/linear_model/plot_poisson_regression_non_normal_loss.rst.txt b/dev/_sources/auto_examples/linear_model/plot_poisson_regression_non_normal_loss.rst.txt
index 4ea45d3577644..be33b03f3be87 100644
--- a/dev/_sources/auto_examples/linear_model/plot_poisson_regression_non_normal_loss.rst.txt
+++ b/dev/_sources/auto_examples/linear_model/plot_poisson_regression_non_normal_loss.rst.txt
@@ -1000,7 +1000,7 @@ This plot is called a Lorenz curve and can be summarized by the Gini index:
  .. code-block:: none
 
 
-    <matplotlib.legend.Legend object at 0x7fb57b277670>
+    <matplotlib.legend.Legend object at 0x7fd8415708e0>
 
 
 
@@ -1077,7 +1077,7 @@ Main takeaways
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 19.253 seconds)
+   **Total running time of the script:** (0 minutes 20.589 seconds)
 
 
 .. _sphx_glr_download_auto_examples_linear_model_plot_poisson_regression_non_normal_loss.py:
diff --git a/dev/_sources/auto_examples/linear_model/plot_polynomial_interpolation.rst.txt b/dev/_sources/auto_examples/linear_model/plot_polynomial_interpolation.rst.txt
index c8d0e5cbc4cd4..6414715ed9c74 100644
--- a/dev/_sources/auto_examples/linear_model/plot_polynomial_interpolation.rst.txt
+++ b/dev/_sources/auto_examples/linear_model/plot_polynomial_interpolation.rst.txt
@@ -335,7 +335,7 @@ setting the knots manually.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.436 seconds)
+   **Total running time of the script:** (0 minutes 0.459 seconds)
 
 
 .. _sphx_glr_download_auto_examples_linear_model_plot_polynomial_interpolation.py:
diff --git a/dev/_sources/auto_examples/linear_model/plot_quantile_regression.rst.txt b/dev/_sources/auto_examples/linear_model/plot_quantile_regression.rst.txt
index cc905b7f66aa5..b239538def686 100644
--- a/dev/_sources/auto_examples/linear_model/plot_quantile_regression.rst.txt
+++ b/dev/_sources/auto_examples/linear_model/plot_quantile_regression.rst.txt
@@ -491,7 +491,7 @@ We reach similar conclusions on the out-of-sample evaluation.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.870 seconds)
+   **Total running time of the script:** (0 minutes 0.922 seconds)
 
 
 .. _sphx_glr_download_auto_examples_linear_model_plot_quantile_regression.py:
diff --git a/dev/_sources/auto_examples/linear_model/plot_ransac.rst.txt b/dev/_sources/auto_examples/linear_model/plot_ransac.rst.txt
index 5a7daf3d104ab..2850639ed94b7 100644
--- a/dev/_sources/auto_examples/linear_model/plot_ransac.rst.txt
+++ b/dev/_sources/auto_examples/linear_model/plot_ransac.rst.txt
@@ -126,7 +126,7 @@ and the fitted line is determined only by the identified inliers.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.092 seconds)
+   **Total running time of the script:** (0 minutes 0.097 seconds)
 
 
 .. _sphx_glr_download_auto_examples_linear_model_plot_ransac.py:
diff --git a/dev/_sources/auto_examples/linear_model/plot_ridge_coeffs.rst.txt b/dev/_sources/auto_examples/linear_model/plot_ridge_coeffs.rst.txt
index 936d1941f32d1..8c5a50b7dc1b6 100644
--- a/dev/_sources/auto_examples/linear_model/plot_ridge_coeffs.rst.txt
+++ b/dev/_sources/auto_examples/linear_model/plot_ridge_coeffs.rst.txt
@@ -266,7 +266,7 @@ the :ref:`sphx_glr_auto_examples_linear_model_plot_huber_vs_ridge.py` example.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.657 seconds)
+   **Total running time of the script:** (0 minutes 0.704 seconds)
 
 
 .. _sphx_glr_download_auto_examples_linear_model_plot_ridge_coeffs.py:
diff --git a/dev/_sources/auto_examples/linear_model/plot_ridge_path.rst.txt b/dev/_sources/auto_examples/linear_model/plot_ridge_path.rst.txt
index ed44d17a98b1d..c0fb244fe060d 100644
--- a/dev/_sources/auto_examples/linear_model/plot_ridge_path.rst.txt
+++ b/dev/_sources/auto_examples/linear_model/plot_ridge_path.rst.txt
@@ -129,7 +129,7 @@ Display results
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.334 seconds)
+   **Total running time of the script:** (0 minutes 0.340 seconds)
 
 
 .. _sphx_glr_download_auto_examples_linear_model_plot_ridge_path.py:
diff --git a/dev/_sources/auto_examples/linear_model/plot_robust_fit.rst.txt b/dev/_sources/auto_examples/linear_model/plot_robust_fit.rst.txt
index 746a6dd84ee9e..bd5a3788cd4a8 100644
--- a/dev/_sources/auto_examples/linear_model/plot_robust_fit.rst.txt
+++ b/dev/_sources/auto_examples/linear_model/plot_robust_fit.rst.txt
@@ -187,7 +187,7 @@ What we can see that:
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 1.912 seconds)
+   **Total running time of the script:** (0 minutes 1.841 seconds)
 
 
 .. _sphx_glr_download_auto_examples_linear_model_plot_robust_fit.py:
diff --git a/dev/_sources/auto_examples/linear_model/plot_sgd_early_stopping.rst.txt b/dev/_sources/auto_examples/linear_model/plot_sgd_early_stopping.rst.txt
index 9805ff1e39963..97d9937bd5bb3 100644
--- a/dev/_sources/auto_examples/linear_model/plot_sgd_early_stopping.rst.txt
+++ b/dev/_sources/auto_examples/linear_model/plot_sgd_early_stopping.rst.txt
@@ -213,7 +213,7 @@ is held out with the validation stopping criterion.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 22.640 seconds)
+   **Total running time of the script:** (0 minutes 24.961 seconds)
 
 
 .. _sphx_glr_download_auto_examples_linear_model_plot_sgd_early_stopping.py:
diff --git a/dev/_sources/auto_examples/linear_model/plot_sgd_iris.rst.txt b/dev/_sources/auto_examples/linear_model/plot_sgd_iris.rst.txt
index 212bf7ae91491..b188a5bc291a1 100644
--- a/dev/_sources/auto_examples/linear_model/plot_sgd_iris.rst.txt
+++ b/dev/_sources/auto_examples/linear_model/plot_sgd_iris.rst.txt
@@ -88,7 +88,7 @@ are represented by the dashed lines.
 
     # Plot also the training points
     for i, color in zip(clf.classes_, colors):
-        idx = np.where(y == i)
+        idx = (y == i).nonzero()
         plt.scatter(
             X[idx, 0],
             X[idx, 1],
@@ -122,7 +122,7 @@ are represented by the dashed lines.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.109 seconds)
+   **Total running time of the script:** (0 minutes 0.115 seconds)
 
 
 .. _sphx_glr_download_auto_examples_linear_model_plot_sgd_iris.py:
diff --git a/dev/_sources/auto_examples/linear_model/plot_sgd_loss_functions.rst.txt b/dev/_sources/auto_examples/linear_model/plot_sgd_loss_functions.rst.txt
index 10f6878804d87..a00a8386d7bbb 100644
--- a/dev/_sources/auto_examples/linear_model/plot_sgd_loss_functions.rst.txt
+++ b/dev/_sources/auto_examples/linear_model/plot_sgd_loss_functions.rst.txt
@@ -87,7 +87,7 @@ A plot that compares the various convex loss functions supported by
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.095 seconds)
+   **Total running time of the script:** (0 minutes 0.100 seconds)
 
 
 .. _sphx_glr_download_auto_examples_linear_model_plot_sgd_loss_functions.py:
diff --git a/dev/_sources/auto_examples/linear_model/plot_sgd_penalties.rst.txt b/dev/_sources/auto_examples/linear_model/plot_sgd_penalties.rst.txt
index ab3f93ea0c8c8..cc7d059368f5a 100644
--- a/dev/_sources/auto_examples/linear_model/plot_sgd_penalties.rst.txt
+++ b/dev/_sources/auto_examples/linear_model/plot_sgd_penalties.rst.txt
@@ -92,7 +92,7 @@ and :class:`~sklearn.linear_model.SGDRegressor`.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.249 seconds)
+   **Total running time of the script:** (0 minutes 0.262 seconds)
 
 
 .. _sphx_glr_download_auto_examples_linear_model_plot_sgd_penalties.py:
diff --git a/dev/_sources/auto_examples/linear_model/plot_sgd_weighted_samples.rst.txt b/dev/_sources/auto_examples/linear_model/plot_sgd_weighted_samples.rst.txt
index e839fe1cd2927..3baa67145d476 100644
--- a/dev/_sources/auto_examples/linear_model/plot_sgd_weighted_samples.rst.txt
+++ b/dev/_sources/auto_examples/linear_model/plot_sgd_weighted_samples.rst.txt
@@ -98,7 +98,7 @@ is proportional to its weight.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.070 seconds)
+   **Total running time of the script:** (0 minutes 0.073 seconds)
 
 
 .. _sphx_glr_download_auto_examples_linear_model_plot_sgd_weighted_samples.py:
diff --git a/dev/_sources/auto_examples/linear_model/plot_sgdocsvm_vs_ocsvm.rst.txt b/dev/_sources/auto_examples/linear_model/plot_sgdocsvm_vs_ocsvm.rst.txt
index 81584029f4997..47934753677cb 100644
--- a/dev/_sources/auto_examples/linear_model/plot_sgdocsvm_vs_ocsvm.rst.txt
+++ b/dev/_sources/auto_examples/linear_model/plot_sgdocsvm_vs_ocsvm.rst.txt
@@ -270,7 +270,7 @@ show that we obtain similar results on a toy dataset.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.404 seconds)
+   **Total running time of the script:** (0 minutes 0.421 seconds)
 
 
 .. _sphx_glr_download_auto_examples_linear_model_plot_sgdocsvm_vs_ocsvm.py:
diff --git a/dev/_sources/auto_examples/linear_model/plot_sparse_logistic_regression_20newsgroups.rst.txt b/dev/_sources/auto_examples/linear_model/plot_sparse_logistic_regression_20newsgroups.rst.txt
index 0a5d38d289d9b..e494ce646fb8b 100644
--- a/dev/_sources/auto_examples/linear_model/plot_sparse_logistic_regression_20newsgroups.rst.txt
+++ b/dev/_sources/auto_examples/linear_model/plot_sparse_logistic_regression_20newsgroups.rst.txt
@@ -61,7 +61,7 @@ traditional (l2-penalised) logistic regression model.
      0.27054655 0.62717609 0.19522393 0.30897646 0.34586917 0.28207552
      0.34125758 0.29898468 0.34279478 0.59489497 0.38353048 0.35278655
      0.19829832 0.14603365]
-    Run time (3 epochs) for model ovr:1.31
+    Run time (3 epochs) for model ovr:1.38
     [model=Multinomial, solver=saga] Number of epochs: 1
     [model=Multinomial, solver=saga] Number of epochs: 2
     [model=Multinomial, solver=saga] Number of epochs: 5
@@ -71,8 +71,8 @@ traditional (l2-penalised) logistic regression model.
      0.06686804 0.21443888 0.11528972 0.2075215  0.10914094 0.11144673
      0.13988486 0.09684337 0.26286057 0.11682692 0.55800226 0.17370318
      0.11452112 0.14603365]
-    Run time (5 epochs) for model multinomial:1.07
-    Example run in 5.403 s
+    Run time (5 epochs) for model multinomial:1.15
+    Example run in 5.787 s
 
 
 
@@ -198,7 +198,7 @@ traditional (l2-penalised) logistic regression model.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 5.471 seconds)
+   **Total running time of the script:** (0 minutes 5.861 seconds)
 
 
 .. _sphx_glr_download_auto_examples_linear_model_plot_sparse_logistic_regression_20newsgroups.py:
diff --git a/dev/_sources/auto_examples/linear_model/plot_sparse_logistic_regression_mnist.rst.txt b/dev/_sources/auto_examples/linear_model/plot_sparse_logistic_regression_mnist.rst.txt
index 05c12c02511ec..2c2e839ce5d64 100644
--- a/dev/_sources/auto_examples/linear_model/plot_sparse_logistic_regression_mnist.rst.txt
+++ b/dev/_sources/auto_examples/linear_model/plot_sparse_logistic_regression_mnist.rst.txt
@@ -50,7 +50,7 @@ multi-layer perceptron model on this dataset.
 
     Sparsity with L1 penalty: 74.57%
     Test score with L1 penalty: 0.8253
-    Example run in 10.681 s
+    Example run in 13.902 s
 
 
 
@@ -120,7 +120,7 @@ multi-layer perceptron model on this dataset.
         )
         l1_plot.set_xticks(())
         l1_plot.set_yticks(())
-        l1_plot.set_xlabel("Class %i" % i)
+        l1_plot.set_xlabel(f"Class {i}")
     plt.suptitle("Classification vector for...")
 
     run_time = time.time() - t0
@@ -130,7 +130,7 @@ multi-layer perceptron model on this dataset.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 10.741 seconds)
+   **Total running time of the script:** (0 minutes 13.966 seconds)
 
 
 .. _sphx_glr_download_auto_examples_linear_model_plot_sparse_logistic_regression_mnist.py:
diff --git a/dev/_sources/auto_examples/linear_model/plot_theilsen.rst.txt b/dev/_sources/auto_examples/linear_model/plot_theilsen.rst.txt
index 52b385f7b8217..2b18f971f6fb7 100644
--- a/dev/_sources/auto_examples/linear_model/plot_theilsen.rst.txt
+++ b/dev/_sources/auto_examples/linear_model/plot_theilsen.rst.txt
@@ -191,7 +191,7 @@ Outliers in the X direction
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.540 seconds)
+   **Total running time of the script:** (0 minutes 0.545 seconds)
 
 
 .. _sphx_glr_download_auto_examples_linear_model_plot_theilsen.py:
diff --git a/dev/_sources/auto_examples/linear_model/plot_tweedie_regression_insurance_claims.rst.txt b/dev/_sources/auto_examples/linear_model/plot_tweedie_regression_insurance_claims.rst.txt
index 06e7f415ced8a..ca173bb220f43 100644
--- a/dev/_sources/auto_examples/linear_model/plot_tweedie_regression_insurance_claims.rst.txt
+++ b/dev/_sources/auto_examples/linear_model/plot_tweedie_regression_insurance_claims.rst.txt
@@ -864,7 +864,7 @@ predicted total claim amount over the test and train subsets. We see that,
 on average, both model tend to underestimate the total claim (but this
 behavior depends on the amount of regularization).
 
-.. GENERATED FROM PYTHON SOURCE LINES 595-616
+.. GENERATED FROM PYTHON SOURCE LINES 595-617
 
 .. code-block:: Python
 
@@ -882,8 +882,9 @@ behavior depends on the amount of regularization).
                 "predicted, frequency*severity model": np.sum(
                     exposure * glm_freq.predict(X) * glm_sev.predict(X)
                 ),
-                "predicted, tweedie, power=%.2f"
-                % glm_pure_premium.power: np.sum(exposure * glm_pure_premium.predict(X)),
+                "predicted, tweedie, power=%.2f" % glm_pure_premium.power: np.sum(
+                    exposure * glm_pure_premium.predict(X)
+                ),
             }
         )
 
@@ -905,7 +906,7 @@ behavior depends on the amount of regularization).
 
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 617-646
+.. GENERATED FROM PYTHON SOURCE LINES 618-647
 
 Finally, we can compare the two models using a plot of cumulative claims: for
 each model, the policyholders are ranked from safest to riskiest based on the
@@ -937,7 +938,7 @@ directly fit on the pure premium is operationally simpler to develop and
 maintain as it consists of a single scikit-learn estimator instead of a pair
 of models, each with its own set of hyperparameters.
 
-.. GENERATED FROM PYTHON SOURCE LINES 647-700
+.. GENERATED FROM PYTHON SOURCE LINES 648-701
 
 .. code-block:: Python
 
@@ -1015,7 +1016,7 @@ of models, each with its own set of hyperparameters.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 7.625 seconds)
+   **Total running time of the script:** (0 minutes 10.268 seconds)
 
 
 .. _sphx_glr_download_auto_examples_linear_model_plot_tweedie_regression_insurance_claims.py:
diff --git a/dev/_sources/auto_examples/manifold/plot_compare_methods.rst.txt b/dev/_sources/auto_examples/manifold/plot_compare_methods.rst.txt
index 571963d18d771..74e03060b43d7 100644
--- a/dev/_sources/auto_examples/manifold/plot_compare_methods.rst.txt
+++ b/dev/_sources/auto_examples/manifold/plot_compare_methods.rst.txt
@@ -291,7 +291,7 @@ Read more in the :ref:`User Guide <multidimensional_scaling>`.
     md_scaling = manifold.MDS(
         n_components=n_components,
         max_iter=50,
-        n_init=4,
+        n_init=1,
         random_state=0,
         normalized_stress=False,
     )
@@ -386,7 +386,7 @@ different results. Read more in the :ref:`User Guide <t_sne>`.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 17.663 seconds)
+   **Total running time of the script:** (0 minutes 13.462 seconds)
 
 
 .. _sphx_glr_download_auto_examples_manifold_plot_compare_methods.py:
diff --git a/dev/_sources/auto_examples/manifold/plot_lle_digits.rst.txt b/dev/_sources/auto_examples/manifold/plot_lle_digits.rst.txt
index 98986430f7f03..2d97a34942454 100644
--- a/dev/_sources/auto_examples/manifold/plot_lle_digits.rst.txt
+++ b/dev/_sources/auto_examples/manifold/plot_lle_digits.rst.txt
@@ -24,7 +24,7 @@ Manifold learning on handwritten digits: Locally Linear Embedding, Isomap...
 
 We illustrate various embedding techniques on the digits dataset.
 
-.. GENERATED FROM PYTHON SOURCE LINES 9-14
+.. GENERATED FROM PYTHON SOURCE LINES 9-13
 
 .. code-block:: Python
 
@@ -39,14 +39,13 @@ We illustrate various embedding techniques on the digits dataset.
 
 
 
-
-.. GENERATED FROM PYTHON SOURCE LINES 15-18
+.. GENERATED FROM PYTHON SOURCE LINES 14-17
 
 Load digits dataset
 -------------------
 We will load the digits dataset and only use six first of the ten available classes.
 
-.. GENERATED FROM PYTHON SOURCE LINES 18-25
+.. GENERATED FROM PYTHON SOURCE LINES 17-24
 
 .. code-block:: Python
 
@@ -64,11 +63,11 @@ We will load the digits dataset and only use six first of the ten available clas
 
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 26-27
+.. GENERATED FROM PYTHON SOURCE LINES 25-26
 
 We can plot the first hundred digits from this data set.
 
-.. GENERATED FROM PYTHON SOURCE LINES 27-35
+.. GENERATED FROM PYTHON SOURCE LINES 26-34
 
 .. code-block:: Python
 
@@ -92,7 +91,7 @@ We can plot the first hundred digits from this data set.
 
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 36-42
+.. GENERATED FROM PYTHON SOURCE LINES 35-41
 
 Helper function to plot embedding
 ---------------------------------
@@ -101,7 +100,7 @@ the projection of the original data onto each embedding. It will allow us to
 check whether or digits are grouped together in the embedding space, or
 scattered across it.
 
-.. GENERATED FROM PYTHON SOURCE LINES 42-80
+.. GENERATED FROM PYTHON SOURCE LINES 41-79
 
 .. code-block:: Python
 
@@ -150,7 +149,7 @@ scattered across it.
 
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 81-99
+.. GENERATED FROM PYTHON SOURCE LINES 80-98
 
 Embedding techniques comparison
 -------------------------------
@@ -171,7 +170,7 @@ to note:
   generated by PCA in this example. It ensures global stability  of the embedding,
   i.e., the embedding does not depend on random initialization.
 
-.. GENERATED FROM PYTHON SOURCE LINES 99-154
+.. GENERATED FROM PYTHON SOURCE LINES 98-153
 
 .. code-block:: Python
 
@@ -210,7 +209,7 @@ to note:
         "LTSA LLE embedding": LocallyLinearEmbedding(
             n_neighbors=n_neighbors, n_components=2, method="ltsa"
         ),
-        "MDS embedding": MDS(n_components=2, n_init=1, max_iter=120, n_jobs=2),
+        "MDS embedding": MDS(n_components=2, n_init=1, max_iter=120, eps=1e-6),
         "Random Trees embedding": make_pipeline(
             RandomTreesEmbedding(n_estimators=200, max_depth=5, random_state=0),
             TruncatedSVD(n_components=2),
@@ -237,13 +236,13 @@ to note:
 
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 155-158
+.. GENERATED FROM PYTHON SOURCE LINES 154-157
 
 Once we declared all the methods of interest, we can run and perform the projection
 of the original data. We will store the projected data as well as the computational
 time needed to perform each projection.
 
-.. GENERATED FROM PYTHON SOURCE LINES 158-173
+.. GENERATED FROM PYTHON SOURCE LINES 157-172
 
 .. code-block:: Python
 
@@ -287,11 +286,11 @@ time needed to perform each projection.
 
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 174-175
+.. GENERATED FROM PYTHON SOURCE LINES 173-174
 
 Finally, we can plot the resulting projection given by each method.
 
-.. GENERATED FROM PYTHON SOURCE LINES 175-180
+.. GENERATED FROM PYTHON SOURCE LINES 174-179
 
 .. code-block:: Python
 
@@ -309,91 +308,91 @@ Finally, we can plot the resulting projection given by each method.
     *
 
       .. image-sg:: /auto_examples/manifold/images/sphx_glr_plot_lle_digits_002.png
-         :alt: Random projection embedding (time 0.002s)
+         :alt: Random projection embedding (time 0.003s)
          :srcset: /auto_examples/manifold/images/sphx_glr_plot_lle_digits_002.png
          :class: sphx-glr-multi-img
 
     *
 
       .. image-sg:: /auto_examples/manifold/images/sphx_glr_plot_lle_digits_003.png
-         :alt: Truncated SVD embedding (time 0.003s)
+         :alt: Truncated SVD embedding (time 0.004s)
          :srcset: /auto_examples/manifold/images/sphx_glr_plot_lle_digits_003.png
          :class: sphx-glr-multi-img
 
     *
 
       .. image-sg:: /auto_examples/manifold/images/sphx_glr_plot_lle_digits_004.png
-         :alt: Linear Discriminant Analysis embedding (time 0.008s)
+         :alt: Linear Discriminant Analysis embedding (time 0.010s)
          :srcset: /auto_examples/manifold/images/sphx_glr_plot_lle_digits_004.png
          :class: sphx-glr-multi-img
 
     *
 
       .. image-sg:: /auto_examples/manifold/images/sphx_glr_plot_lle_digits_005.png
-         :alt: Isomap embedding (time 0.789s)
+         :alt: Isomap embedding (time 0.822s)
          :srcset: /auto_examples/manifold/images/sphx_glr_plot_lle_digits_005.png
          :class: sphx-glr-multi-img
 
     *
 
       .. image-sg:: /auto_examples/manifold/images/sphx_glr_plot_lle_digits_006.png
-         :alt: Standard LLE embedding (time 0.164s)
+         :alt: Standard LLE embedding (time 0.227s)
          :srcset: /auto_examples/manifold/images/sphx_glr_plot_lle_digits_006.png
          :class: sphx-glr-multi-img
 
     *
 
       .. image-sg:: /auto_examples/manifold/images/sphx_glr_plot_lle_digits_007.png
-         :alt: Modified LLE embedding (time 2.676s)
+         :alt: Modified LLE embedding (time 3.006s)
          :srcset: /auto_examples/manifold/images/sphx_glr_plot_lle_digits_007.png
          :class: sphx-glr-multi-img
 
     *
 
       .. image-sg:: /auto_examples/manifold/images/sphx_glr_plot_lle_digits_008.png
-         :alt: Hessian LLE embedding (time 1.765s)
+         :alt: Hessian LLE embedding (time 2.036s)
          :srcset: /auto_examples/manifold/images/sphx_glr_plot_lle_digits_008.png
          :class: sphx-glr-multi-img
 
     *
 
       .. image-sg:: /auto_examples/manifold/images/sphx_glr_plot_lle_digits_009.png
-         :alt: LTSA LLE embedding (time 2.337s)
+         :alt: LTSA LLE embedding (time 2.413s)
          :srcset: /auto_examples/manifold/images/sphx_glr_plot_lle_digits_009.png
          :class: sphx-glr-multi-img
 
     *
 
       .. image-sg:: /auto_examples/manifold/images/sphx_glr_plot_lle_digits_010.png
-         :alt: MDS embedding (time 2.478s)
+         :alt: MDS embedding (time 2.747s)
          :srcset: /auto_examples/manifold/images/sphx_glr_plot_lle_digits_010.png
          :class: sphx-glr-multi-img
 
     *
 
       .. image-sg:: /auto_examples/manifold/images/sphx_glr_plot_lle_digits_011.png
-         :alt: Random Trees embedding (time 0.235s)
+         :alt: Random Trees embedding (time 0.209s)
          :srcset: /auto_examples/manifold/images/sphx_glr_plot_lle_digits_011.png
          :class: sphx-glr-multi-img
 
     *
 
       .. image-sg:: /auto_examples/manifold/images/sphx_glr_plot_lle_digits_012.png
-         :alt: Spectral embedding (time 0.150s)
+         :alt: Spectral embedding (time 0.153s)
          :srcset: /auto_examples/manifold/images/sphx_glr_plot_lle_digits_012.png
          :class: sphx-glr-multi-img
 
     *
 
       .. image-sg:: /auto_examples/manifold/images/sphx_glr_plot_lle_digits_013.png
-         :alt: t-SNE embedding (time 2.562s)
+         :alt: t-SNE embedding (time 2.565s)
          :srcset: /auto_examples/manifold/images/sphx_glr_plot_lle_digits_013.png
          :class: sphx-glr-multi-img
 
     *
 
       .. image-sg:: /auto_examples/manifold/images/sphx_glr_plot_lle_digits_014.png
-         :alt: NCA embedding (time 2.716s)
+         :alt: NCA embedding (time 2.912s)
          :srcset: /auto_examples/manifold/images/sphx_glr_plot_lle_digits_014.png
          :class: sphx-glr-multi-img
 
@@ -404,7 +403,7 @@ Finally, we can plot the resulting projection given by each method.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 20.354 seconds)
+   **Total running time of the script:** (0 minutes 22.763 seconds)
 
 
 .. _sphx_glr_download_auto_examples_manifold_plot_lle_digits.py:
diff --git a/dev/_sources/auto_examples/manifold/plot_manifold_sphere.rst.txt b/dev/_sources/auto_examples/manifold/plot_manifold_sphere.rst.txt
index d544870f02d36..eccd91a3c6c14 100644
--- a/dev/_sources/auto_examples/manifold/plot_manifold_sphere.rst.txt
+++ b/dev/_sources/auto_examples/manifold/plot_manifold_sphere.rst.txt
@@ -47,7 +47,7 @@ that of representing a flat map of the Earth, as with
 
 
 .. image-sg:: /auto_examples/manifold/images/sphx_glr_plot_manifold_sphere_001.png
-   :alt: Manifold Learning with 1000 points, 10 neighbors, LLE (0.055 sec), LTSA (0.85 sec), Hessian LLE (0.64 sec), Modified LLE (1.2 sec), Isomap (0.19 sec), MDS (0.68 sec), Spectral Embedding (0.04 sec), t-SNE (3.6 sec)
+   :alt: Manifold Learning with 1000 points, 10 neighbors, LLE (0.055 sec), LTSA (0.96 sec), Hessian LLE (0.65 sec), Modified LLE (1.3 sec), Isomap (0.2 sec), MDS (0.94 sec), Spectral Embedding (0.055 sec), t-SNE (3.6 sec)
    :srcset: /auto_examples/manifold/images/sphx_glr_plot_manifold_sphere_001.png
    :class: sphx-glr-single-img
 
@@ -57,12 +57,12 @@ that of representing a flat map of the Earth, as with
  .. code-block:: none
 
     standard: 0.055 sec
-    ltsa: 0.85 sec
-    hessian: 0.64 sec
-    modified: 1.2 sec
-    ISO: 0.19 sec
-    MDS: 0.68 sec
-    Spectral Embedding: 0.04 sec
+    ltsa: 0.96 sec
+    hessian: 0.65 sec
+    modified: 1.3 sec
+    ISO: 0.2 sec
+    MDS: 0.94 sec
+    Spectral Embedding: 0.055 sec
     t-SNE: 3.6 sec
 
 
@@ -100,7 +100,7 @@ that of representing a flat map of the Earth, as with
     t = random_state.rand(n_samples) * np.pi
 
     # Sever the poles from the sphere.
-    indices = (t < (np.pi - (np.pi / 8))) & (t > ((np.pi / 8)))
+    indices = (t < (np.pi - (np.pi / 8))) & (t > (np.pi / 8))
     colors = p[indices]
     x, y, z = (
         np.sin(t[indices]) * np.cos(p[indices]),
@@ -209,7 +209,7 @@ that of representing a flat map of the Earth, as with
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 7.882 seconds)
+   **Total running time of the script:** (0 minutes 8.331 seconds)
 
 
 .. _sphx_glr_download_auto_examples_manifold_plot_manifold_sphere.py:
diff --git a/dev/_sources/auto_examples/manifold/plot_mds.rst.txt b/dev/_sources/auto_examples/manifold/plot_mds.rst.txt
index 82d0fb1a0f338..c3f8ecd0f5c8a 100644
--- a/dev/_sources/auto_examples/manifold/plot_mds.rst.txt
+++ b/dev/_sources/auto_examples/manifold/plot_mds.rst.txt
@@ -24,27 +24,32 @@ Multi-dimensional scaling
 
 An illustration of the metric and non-metric MDS on generated noisy data.
 
-The reconstructed points using the metric MDS and non metric MDS are slightly
-shifted to avoid overlapping.
+.. GENERATED FROM PYTHON SOURCE LINES 9-13
 
-.. GENERATED FROM PYTHON SOURCE LINES 12-109
+.. code-block:: Python
 
 
+    # Authors: The scikit-learn developers
+    # SPDX-License-Identifier: BSD-3-Clause
 
-.. image-sg:: /auto_examples/manifold/images/sphx_glr_plot_mds_001.png
-   :alt: plot mds
-   :srcset: /auto_examples/manifold/images/sphx_glr_plot_mds_001.png
-   :class: sphx-glr-single-img
 
 
 
 
 
-.. code-block:: Python
 
 
-    # Authors: The scikit-learn developers
-    # SPDX-License-Identifier: BSD-3-Clause
+.. GENERATED FROM PYTHON SOURCE LINES 14-18
+
+Dataset preparation
+-------------------
+
+We start by uniformly generating 20 points in a 2D space.
+
+.. GENERATED FROM PYTHON SOURCE LINES 18-37
+
+.. code-block:: Python
+
 
     import numpy as np
     from matplotlib import pyplot as plt
@@ -64,6 +69,24 @@ shifted to avoid overlapping.
     # Center the data
     X_true -= X_true.mean()
 
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 38-41
+
+Now we compute pairwise distances between all points and add
+a small amount of noise to the distance matrix. We make sure
+to keep the noisy distance matrix symmetric.
+
+.. GENERATED FROM PYTHON SOURCE LINES 41-51
+
+.. code-block:: Python
+
+
     # Compute pairwise Euclidean distances
     distances = euclidean_distances(X_true)
 
@@ -73,10 +96,27 @@ shifted to avoid overlapping.
     np.fill_diagonal(noise, 0)
     distances += noise
 
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 52-53
+
+Here we compute metric and non-metric MDS of the noisy distance matrix.
+
+.. GENERATED FROM PYTHON SOURCE LINES 53-77
+
+.. code-block:: Python
+
+
     mds = manifold.MDS(
         n_components=2,
         max_iter=3000,
         eps=1e-9,
+        n_init=1,
         random_state=42,
         dissimilarity="precomputed",
         n_jobs=1,
@@ -95,10 +135,42 @@ shifted to avoid overlapping.
     )
     X_nmds = nmds.fit_transform(distances)
 
-    # Rescale the data
-    X_mds *= np.sqrt((X_true**2).sum()) / np.sqrt((X_mds**2).sum())
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 78-79
+
+Rescaling the non-metric MDS solution to match the spread of the original data.
+
+.. GENERATED FROM PYTHON SOURCE LINES 79-82
+
+.. code-block:: Python
+
+
     X_nmds *= np.sqrt((X_true**2).sum()) / np.sqrt((X_nmds**2).sum())
 
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 83-86
+
+To make the visual comparisons easier, we rotate the original data and both MDS
+solutions to their PCA axes. And flip horizontal and vertical MDS axes, if needed,
+to match the original data orientation.
+
+.. GENERATED FROM PYTHON SOURCE LINES 86-100
+
+.. code-block:: Python
+
+
     # Rotate the data
     pca = PCA(n_components=2)
     X_true = pca.fit_transform(X_true)
@@ -112,6 +184,22 @@ shifted to avoid overlapping.
         if np.corrcoef(X_nmds[:, i], X_true[:, i])[0, 1] < 0:
             X_nmds[:, i] *= -1
 
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 101-102
+
+Finally, we plot the original data and both MDS reconstructions.
+
+.. GENERATED FROM PYTHON SOURCE LINES 102-130
+
+.. code-block:: Python
+
+
     fig = plt.figure(1)
     ax = plt.axes([0.0, 0.0, 1.0, 1.0])
 
@@ -122,7 +210,7 @@ shifted to avoid overlapping.
     plt.legend(scatterpoints=1, loc="best", shadow=False)
 
     # Plot the edges
-    start_idx, end_idx = np.where(X_mds)
+    start_idx, end_idx = X_mds.nonzero()
     # a sequence of (*line0*, *line1*, *line2*), where::
     #            linen = (x0, y0), (x1, y1), ... (xm, ym)
     segments = [
@@ -141,9 +229,20 @@ shifted to avoid overlapping.
     plt.show()
 
 
+
+.. image-sg:: /auto_examples/manifold/images/sphx_glr_plot_mds_001.png
+   :alt: plot mds
+   :srcset: /auto_examples/manifold/images/sphx_glr_plot_mds_001.png
+   :class: sphx-glr-single-img
+
+
+
+
+
+
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.311 seconds)
+   **Total running time of the script:** (0 minutes 0.330 seconds)
 
 
 .. _sphx_glr_download_auto_examples_manifold_plot_mds.py:
diff --git a/dev/_sources/auto_examples/manifold/plot_swissroll.rst.txt b/dev/_sources/auto_examples/manifold/plot_swissroll.rst.txt
index 1193a50a7938d..b3546ae1b23c9 100644
--- a/dev/_sources/auto_examples/manifold/plot_swissroll.rst.txt
+++ b/dev/_sources/auto_examples/manifold/plot_swissroll.rst.txt
@@ -241,7 +241,7 @@ on real world data.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 17.003 seconds)
+   **Total running time of the script:** (0 minutes 19.192 seconds)
 
 
 .. _sphx_glr_download_auto_examples_manifold_plot_swissroll.py:
diff --git a/dev/_sources/auto_examples/manifold/plot_t_sne_perplexity.rst.txt b/dev/_sources/auto_examples/manifold/plot_t_sne_perplexity.rst.txt
index 0275c6f46696a..e37503c6679ca 100644
--- a/dev/_sources/auto_examples/manifold/plot_t_sne_perplexity.rst.txt
+++ b/dev/_sources/auto_examples/manifold/plot_t_sne_perplexity.rst.txt
@@ -57,16 +57,16 @@ those effects.
 
     circles, perplexity=5 in 0.13 sec
     circles, perplexity=30 in 0.21 sec
-    circles, perplexity=50 in 0.24 sec
+    circles, perplexity=50 in 0.25 sec
     circles, perplexity=100 in 0.23 sec
     S-curve, perplexity=5 in 0.13 sec
-    S-curve, perplexity=30 in 0.2 sec
-    S-curve, perplexity=50 in 0.24 sec
-    S-curve, perplexity=100 in 0.24 sec
+    S-curve, perplexity=30 in 0.19 sec
+    S-curve, perplexity=50 in 0.23 sec
+    S-curve, perplexity=100 in 0.23 sec
     uniform grid, perplexity=5 in 0.16 sec
     uniform grid, perplexity=30 in 0.24 sec
     uniform grid, perplexity=50 in 0.27 sec
-    uniform grid, perplexity=100 in 0.27 sec
+    uniform grid, perplexity=100 in 0.3 sec
 
 
 
@@ -202,7 +202,7 @@ those effects.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 3.096 seconds)
+   **Total running time of the script:** (0 minutes 3.158 seconds)
 
 
 .. _sphx_glr_download_auto_examples_manifold_plot_t_sne_perplexity.py:
diff --git a/dev/_sources/auto_examples/miscellaneous/plot_anomaly_comparison.rst.txt b/dev/_sources/auto_examples/miscellaneous/plot_anomaly_comparison.rst.txt
index 5164155cad23b..c6864c9f8d93e 100644
--- a/dev/_sources/auto_examples/miscellaneous/plot_anomaly_comparison.rst.txt
+++ b/dev/_sources/auto_examples/miscellaneous/plot_anomaly_comparison.rst.txt
@@ -224,7 +224,7 @@ the problem is completely unsupervised so model selection can be a challenge.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 3.359 seconds)
+   **Total running time of the script:** (0 minutes 3.511 seconds)
 
 
 .. _sphx_glr_download_auto_examples_miscellaneous_plot_anomaly_comparison.py:
diff --git a/dev/_sources/auto_examples/miscellaneous/plot_display_object_visualization.rst.txt b/dev/_sources/auto_examples/miscellaneous/plot_display_object_visualization.rst.txt
index 61ecb845c313e..8f665c83ff4ed 100644
--- a/dev/_sources/auto_examples/miscellaneous/plot_display_object_visualization.rst.txt
+++ b/dev/_sources/auto_examples/miscellaneous/plot_display_object_visualization.rst.txt
@@ -81,7 +81,7 @@ fitted with the train dataset.
 .. raw:: html
 
     <div class="output_subarea output_html rendered_html output_result">
-    <style>#sk-container-id-54 {
+    <style>#sk-container-id-55 {
       /* Definition of color scheme common for light and dark mode */
       --sklearn-color-text: #000;
       --sklearn-color-text-muted: #666;
@@ -112,15 +112,15 @@ fitted with the train dataset.
       }
     }
 
-    #sk-container-id-54 {
+    #sk-container-id-55 {
       color: var(--sklearn-color-text);
     }
 
-    #sk-container-id-54 pre {
+    #sk-container-id-55 pre {
       padding: 0;
     }
 
-    #sk-container-id-54 input.sk-hidden--visually {
+    #sk-container-id-55 input.sk-hidden--visually {
       border: 0;
       clip: rect(1px 1px 1px 1px);
       clip: rect(1px, 1px, 1px, 1px);
@@ -132,7 +132,7 @@ fitted with the train dataset.
       width: 1px;
     }
 
-    #sk-container-id-54 div.sk-dashed-wrapped {
+    #sk-container-id-55 div.sk-dashed-wrapped {
       border: 1px dashed var(--sklearn-color-line);
       margin: 0 0.4em 0.5em 0.4em;
       box-sizing: border-box;
@@ -140,7 +140,7 @@ fitted with the train dataset.
       background-color: var(--sklearn-color-background);
     }
 
-    #sk-container-id-54 div.sk-container {
+    #sk-container-id-55 div.sk-container {
       /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
          but bootstrap.min.css set `[hidden] { display: none !important; }`
          so we also need the `!important` here to be able to override the
@@ -150,7 +150,7 @@ fitted with the train dataset.
       position: relative;
     }
 
-    #sk-container-id-54 div.sk-text-repr-fallback {
+    #sk-container-id-55 div.sk-text-repr-fallback {
       display: none;
     }
 
@@ -166,14 +166,14 @@ fitted with the train dataset.
 
     /* Parallel-specific style estimator block */
 
-    #sk-container-id-54 div.sk-parallel-item::after {
+    #sk-container-id-55 div.sk-parallel-item::after {
       content: "";
       width: 100%;
       border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
       flex-grow: 1;
     }
 
-    #sk-container-id-54 div.sk-parallel {
+    #sk-container-id-55 div.sk-parallel {
       display: flex;
       align-items: stretch;
       justify-content: center;
@@ -181,28 +181,28 @@ fitted with the train dataset.
       position: relative;
     }
 
-    #sk-container-id-54 div.sk-parallel-item {
+    #sk-container-id-55 div.sk-parallel-item {
       display: flex;
       flex-direction: column;
     }
 
-    #sk-container-id-54 div.sk-parallel-item:first-child::after {
+    #sk-container-id-55 div.sk-parallel-item:first-child::after {
       align-self: flex-end;
       width: 50%;
     }
 
-    #sk-container-id-54 div.sk-parallel-item:last-child::after {
+    #sk-container-id-55 div.sk-parallel-item:last-child::after {
       align-self: flex-start;
       width: 50%;
     }
 
-    #sk-container-id-54 div.sk-parallel-item:only-child::after {
+    #sk-container-id-55 div.sk-parallel-item:only-child::after {
       width: 0;
     }
 
     /* Serial-specific style estimator block */
 
-    #sk-container-id-54 div.sk-serial {
+    #sk-container-id-55 div.sk-serial {
       display: flex;
       flex-direction: column;
       align-items: center;
@@ -220,14 +220,14 @@ fitted with the train dataset.
 
     /* Pipeline and ColumnTransformer style (default) */
 
-    #sk-container-id-54 div.sk-toggleable {
+    #sk-container-id-55 div.sk-toggleable {
       /* Default theme specific background. It is overwritten whether we have a
       specific estimator or a Pipeline/ColumnTransformer */
       background-color: var(--sklearn-color-background);
     }
 
     /* Toggleable label */
-    #sk-container-id-54 label.sk-toggleable__label {
+    #sk-container-id-55 label.sk-toggleable__label {
       cursor: pointer;
       display: flex;
       width: 100%;
@@ -240,13 +240,13 @@ fitted with the train dataset.
       gap: 0.5em;
     }
 
-    #sk-container-id-54 label.sk-toggleable__label .caption {
+    #sk-container-id-55 label.sk-toggleable__label .caption {
       font-size: 0.6rem;
       font-weight: lighter;
       color: var(--sklearn-color-text-muted);
     }
 
-    #sk-container-id-54 label.sk-toggleable__label-arrow:before {
+    #sk-container-id-55 label.sk-toggleable__label-arrow:before {
       /* Arrow on the left of the label */
       content: "▸";
       float: left;
@@ -254,13 +254,13 @@ fitted with the train dataset.
       color: var(--sklearn-color-icon);
     }
 
-    #sk-container-id-54 label.sk-toggleable__label-arrow:hover:before {
+    #sk-container-id-55 label.sk-toggleable__label-arrow:hover:before {
       color: var(--sklearn-color-text);
     }
 
     /* Toggleable content - dropdown */
 
-    #sk-container-id-54 div.sk-toggleable__content {
+    #sk-container-id-55 div.sk-toggleable__content {
       max-height: 0;
       max-width: 0;
       overflow: hidden;
@@ -269,12 +269,12 @@ fitted with the train dataset.
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-54 div.sk-toggleable__content.fitted {
+    #sk-container-id-55 div.sk-toggleable__content.fitted {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
-    #sk-container-id-54 div.sk-toggleable__content pre {
+    #sk-container-id-55 div.sk-toggleable__content pre {
       margin: 0.2em;
       border-radius: 0.25em;
       color: var(--sklearn-color-text);
@@ -282,79 +282,79 @@ fitted with the train dataset.
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-54 div.sk-toggleable__content.fitted pre {
+    #sk-container-id-55 div.sk-toggleable__content.fitted pre {
       /* unfitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
-    #sk-container-id-54 input.sk-toggleable__control:checked~div.sk-toggleable__content {
+    #sk-container-id-55 input.sk-toggleable__control:checked~div.sk-toggleable__content {
       /* Expand drop-down */
       max-height: 200px;
       max-width: 100%;
       overflow: auto;
     }
 
-    #sk-container-id-54 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
+    #sk-container-id-55 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
       content: "▾";
     }
 
     /* Pipeline/ColumnTransformer-specific style */
 
-    #sk-container-id-54 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-55 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-54 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-55 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
     /* Estimator-specific style */
 
     /* Colorize estimator box */
-    #sk-container-id-54 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-55 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-54 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-55 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
-    #sk-container-id-54 div.sk-label label.sk-toggleable__label,
-    #sk-container-id-54 div.sk-label label {
+    #sk-container-id-55 div.sk-label label.sk-toggleable__label,
+    #sk-container-id-55 div.sk-label label {
       /* The background is the default theme color */
       color: var(--sklearn-color-text-on-default-background);
     }
 
     /* On hover, darken the color of the background */
-    #sk-container-id-54 div.sk-label:hover label.sk-toggleable__label {
+    #sk-container-id-55 div.sk-label:hover label.sk-toggleable__label {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
     /* Label box, darken color on hover, fitted */
-    #sk-container-id-54 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
+    #sk-container-id-55 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
     /* Estimator label */
 
-    #sk-container-id-54 div.sk-label label {
+    #sk-container-id-55 div.sk-label label {
       font-family: monospace;
       font-weight: bold;
       display: inline-block;
       line-height: 1.2em;
     }
 
-    #sk-container-id-54 div.sk-label-container {
+    #sk-container-id-55 div.sk-label-container {
       text-align: center;
     }
 
     /* Estimator-specific */
-    #sk-container-id-54 div.sk-estimator {
+    #sk-container-id-55 div.sk-estimator {
       font-family: monospace;
       border: 1px dotted var(--sklearn-color-border-box);
       border-radius: 0.25em;
@@ -364,18 +364,18 @@ fitted with the train dataset.
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-54 div.sk-estimator.fitted {
+    #sk-container-id-55 div.sk-estimator.fitted {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
     /* on hover */
-    #sk-container-id-54 div.sk-estimator:hover {
+    #sk-container-id-55 div.sk-estimator:hover {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-54 div.sk-estimator.fitted:hover {
+    #sk-container-id-55 div.sk-estimator.fitted:hover {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-2);
     }
@@ -463,7 +463,7 @@ fitted with the train dataset.
 
     /* "?"-specific style due to the `<a>` HTML tag */
 
-    #sk-container-id-54 a.estimator_doc_link {
+    #sk-container-id-55 a.estimator_doc_link {
       float: right;
       font-size: 1rem;
       line-height: 1em;
@@ -478,27 +478,27 @@ fitted with the train dataset.
       border: var(--sklearn-color-unfitted-level-1) 1pt solid;
     }
 
-    #sk-container-id-54 a.estimator_doc_link.fitted {
+    #sk-container-id-55 a.estimator_doc_link.fitted {
       /* fitted */
       border: var(--sklearn-color-fitted-level-1) 1pt solid;
       color: var(--sklearn-color-fitted-level-1);
     }
 
     /* On hover */
-    #sk-container-id-54 a.estimator_doc_link:hover {
+    #sk-container-id-55 a.estimator_doc_link:hover {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-3);
       color: var(--sklearn-color-background);
       text-decoration: none;
     }
 
-    #sk-container-id-54 a.estimator_doc_link.fitted:hover {
+    #sk-container-id-55 a.estimator_doc_link.fitted:hover {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-3);
     }
-    </style><div id="sk-container-id-54" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),
-                    (&#x27;logisticregression&#x27;, LogisticRegression(random_state=0))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-223" type="checkbox" ><label for="sk-estimator-id-223" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>Pipeline</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.pipeline.Pipeline.html">?<span>Documentation for Pipeline</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),
-                    (&#x27;logisticregression&#x27;, LogisticRegression(random_state=0))])</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-224" type="checkbox" ><label for="sk-estimator-id-224" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>StandardScaler</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.StandardScaler.html">?<span>Documentation for StandardScaler</span></a></div></label><div class="sk-toggleable__content fitted"><pre>StandardScaler()</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-225" type="checkbox" ><label for="sk-estimator-id-225" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>LogisticRegression</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.linear_model.LogisticRegression.html">?<span>Documentation for LogisticRegression</span></a></div></label><div class="sk-toggleable__content fitted"><pre>LogisticRegression(random_state=0)</pre></div> </div></div></div></div></div></div>
+    </style><div id="sk-container-id-55" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),
+                    (&#x27;logisticregression&#x27;, LogisticRegression(random_state=0))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-237" type="checkbox" ><label for="sk-estimator-id-237" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>Pipeline</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.pipeline.Pipeline.html">?<span>Documentation for Pipeline</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),
+                    (&#x27;logisticregression&#x27;, LogisticRegression(random_state=0))])</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-238" type="checkbox" ><label for="sk-estimator-id-238" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>StandardScaler</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.StandardScaler.html">?<span>Documentation for StandardScaler</span></a></div></label><div class="sk-toggleable__content fitted"><pre>StandardScaler()</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-239" type="checkbox" ><label for="sk-estimator-id-239" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>LogisticRegression</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.linear_model.LogisticRegression.html">?<span>Documentation for LogisticRegression</span></a></div></label><div class="sk-toggleable__content fitted"><pre>LogisticRegression(random_state=0)</pre></div> </div></div></div></div></div></div>
     </div>
     <br />
     <br />
@@ -567,7 +567,7 @@ a decision function, we will use it to plot the roc curve:
 
  .. code-block:: none
 
-    /home/circleci/project/sklearn/metrics/_plot/roc_curve.py:192: UserWarning:
+    /home/circleci/project/sklearn/metrics/_plot/roc_curve.py:197: UserWarning:
 
     No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.
 
@@ -637,7 +637,7 @@ row.
 
  .. code-block:: none
 
-    /home/circleci/project/sklearn/metrics/_plot/roc_curve.py:192: UserWarning:
+    /home/circleci/project/sklearn/metrics/_plot/roc_curve.py:197: UserWarning:
 
     No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.
 
@@ -648,7 +648,7 @@ row.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.320 seconds)
+   **Total running time of the script:** (0 minutes 0.374 seconds)
 
 
 .. _sphx_glr_download_auto_examples_miscellaneous_plot_display_object_visualization.py:
diff --git a/dev/_sources/auto_examples/miscellaneous/plot_estimator_representation.rst.txt b/dev/_sources/auto_examples/miscellaneous/plot_estimator_representation.rst.txt
index 2d7c82adaf803..99c0680f448c6 100644
--- a/dev/_sources/auto_examples/miscellaneous/plot_estimator_representation.rst.txt
+++ b/dev/_sources/auto_examples/miscellaneous/plot_estimator_representation.rst.txt
@@ -113,7 +113,7 @@ this feature.
 .. raw:: html
 
     <div class="output_subarea output_html rendered_html output_result">
-    <style>#sk-container-id-45 {
+    <style>#sk-container-id-46 {
       /* Definition of color scheme common for light and dark mode */
       --sklearn-color-text: #000;
       --sklearn-color-text-muted: #666;
@@ -144,15 +144,15 @@ this feature.
       }
     }
 
-    #sk-container-id-45 {
+    #sk-container-id-46 {
       color: var(--sklearn-color-text);
     }
 
-    #sk-container-id-45 pre {
+    #sk-container-id-46 pre {
       padding: 0;
     }
 
-    #sk-container-id-45 input.sk-hidden--visually {
+    #sk-container-id-46 input.sk-hidden--visually {
       border: 0;
       clip: rect(1px 1px 1px 1px);
       clip: rect(1px, 1px, 1px, 1px);
@@ -164,7 +164,7 @@ this feature.
       width: 1px;
     }
 
-    #sk-container-id-45 div.sk-dashed-wrapped {
+    #sk-container-id-46 div.sk-dashed-wrapped {
       border: 1px dashed var(--sklearn-color-line);
       margin: 0 0.4em 0.5em 0.4em;
       box-sizing: border-box;
@@ -172,7 +172,7 @@ this feature.
       background-color: var(--sklearn-color-background);
     }
 
-    #sk-container-id-45 div.sk-container {
+    #sk-container-id-46 div.sk-container {
       /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
          but bootstrap.min.css set `[hidden] { display: none !important; }`
          so we also need the `!important` here to be able to override the
@@ -182,7 +182,7 @@ this feature.
       position: relative;
     }
 
-    #sk-container-id-45 div.sk-text-repr-fallback {
+    #sk-container-id-46 div.sk-text-repr-fallback {
       display: none;
     }
 
@@ -198,14 +198,14 @@ this feature.
 
     /* Parallel-specific style estimator block */
 
-    #sk-container-id-45 div.sk-parallel-item::after {
+    #sk-container-id-46 div.sk-parallel-item::after {
       content: "";
       width: 100%;
       border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
       flex-grow: 1;
     }
 
-    #sk-container-id-45 div.sk-parallel {
+    #sk-container-id-46 div.sk-parallel {
       display: flex;
       align-items: stretch;
       justify-content: center;
@@ -213,28 +213,28 @@ this feature.
       position: relative;
     }
 
-    #sk-container-id-45 div.sk-parallel-item {
+    #sk-container-id-46 div.sk-parallel-item {
       display: flex;
       flex-direction: column;
     }
 
-    #sk-container-id-45 div.sk-parallel-item:first-child::after {
+    #sk-container-id-46 div.sk-parallel-item:first-child::after {
       align-self: flex-end;
       width: 50%;
     }
 
-    #sk-container-id-45 div.sk-parallel-item:last-child::after {
+    #sk-container-id-46 div.sk-parallel-item:last-child::after {
       align-self: flex-start;
       width: 50%;
     }
 
-    #sk-container-id-45 div.sk-parallel-item:only-child::after {
+    #sk-container-id-46 div.sk-parallel-item:only-child::after {
       width: 0;
     }
 
     /* Serial-specific style estimator block */
 
-    #sk-container-id-45 div.sk-serial {
+    #sk-container-id-46 div.sk-serial {
       display: flex;
       flex-direction: column;
       align-items: center;
@@ -252,14 +252,14 @@ this feature.
 
     /* Pipeline and ColumnTransformer style (default) */
 
-    #sk-container-id-45 div.sk-toggleable {
+    #sk-container-id-46 div.sk-toggleable {
       /* Default theme specific background. It is overwritten whether we have a
       specific estimator or a Pipeline/ColumnTransformer */
       background-color: var(--sklearn-color-background);
     }
 
     /* Toggleable label */
-    #sk-container-id-45 label.sk-toggleable__label {
+    #sk-container-id-46 label.sk-toggleable__label {
       cursor: pointer;
       display: flex;
       width: 100%;
@@ -272,13 +272,13 @@ this feature.
       gap: 0.5em;
     }
 
-    #sk-container-id-45 label.sk-toggleable__label .caption {
+    #sk-container-id-46 label.sk-toggleable__label .caption {
       font-size: 0.6rem;
       font-weight: lighter;
       color: var(--sklearn-color-text-muted);
     }
 
-    #sk-container-id-45 label.sk-toggleable__label-arrow:before {
+    #sk-container-id-46 label.sk-toggleable__label-arrow:before {
       /* Arrow on the left of the label */
       content: "▸";
       float: left;
@@ -286,13 +286,13 @@ this feature.
       color: var(--sklearn-color-icon);
     }
 
-    #sk-container-id-45 label.sk-toggleable__label-arrow:hover:before {
+    #sk-container-id-46 label.sk-toggleable__label-arrow:hover:before {
       color: var(--sklearn-color-text);
     }
 
     /* Toggleable content - dropdown */
 
-    #sk-container-id-45 div.sk-toggleable__content {
+    #sk-container-id-46 div.sk-toggleable__content {
       max-height: 0;
       max-width: 0;
       overflow: hidden;
@@ -301,12 +301,12 @@ this feature.
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-45 div.sk-toggleable__content.fitted {
+    #sk-container-id-46 div.sk-toggleable__content.fitted {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
-    #sk-container-id-45 div.sk-toggleable__content pre {
+    #sk-container-id-46 div.sk-toggleable__content pre {
       margin: 0.2em;
       border-radius: 0.25em;
       color: var(--sklearn-color-text);
@@ -314,79 +314,79 @@ this feature.
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-45 div.sk-toggleable__content.fitted pre {
+    #sk-container-id-46 div.sk-toggleable__content.fitted pre {
       /* unfitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
-    #sk-container-id-45 input.sk-toggleable__control:checked~div.sk-toggleable__content {
+    #sk-container-id-46 input.sk-toggleable__control:checked~div.sk-toggleable__content {
       /* Expand drop-down */
       max-height: 200px;
       max-width: 100%;
       overflow: auto;
     }
 
-    #sk-container-id-45 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
+    #sk-container-id-46 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
       content: "▾";
     }
 
     /* Pipeline/ColumnTransformer-specific style */
 
-    #sk-container-id-45 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-46 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-45 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-46 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
     /* Estimator-specific style */
 
     /* Colorize estimator box */
-    #sk-container-id-45 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-46 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-45 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-46 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
-    #sk-container-id-45 div.sk-label label.sk-toggleable__label,
-    #sk-container-id-45 div.sk-label label {
+    #sk-container-id-46 div.sk-label label.sk-toggleable__label,
+    #sk-container-id-46 div.sk-label label {
       /* The background is the default theme color */
       color: var(--sklearn-color-text-on-default-background);
     }
 
     /* On hover, darken the color of the background */
-    #sk-container-id-45 div.sk-label:hover label.sk-toggleable__label {
+    #sk-container-id-46 div.sk-label:hover label.sk-toggleable__label {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
     /* Label box, darken color on hover, fitted */
-    #sk-container-id-45 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
+    #sk-container-id-46 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
     /* Estimator label */
 
-    #sk-container-id-45 div.sk-label label {
+    #sk-container-id-46 div.sk-label label {
       font-family: monospace;
       font-weight: bold;
       display: inline-block;
       line-height: 1.2em;
     }
 
-    #sk-container-id-45 div.sk-label-container {
+    #sk-container-id-46 div.sk-label-container {
       text-align: center;
     }
 
     /* Estimator-specific */
-    #sk-container-id-45 div.sk-estimator {
+    #sk-container-id-46 div.sk-estimator {
       font-family: monospace;
       border: 1px dotted var(--sklearn-color-border-box);
       border-radius: 0.25em;
@@ -396,18 +396,18 @@ this feature.
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-45 div.sk-estimator.fitted {
+    #sk-container-id-46 div.sk-estimator.fitted {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
     /* on hover */
-    #sk-container-id-45 div.sk-estimator:hover {
+    #sk-container-id-46 div.sk-estimator:hover {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-45 div.sk-estimator.fitted:hover {
+    #sk-container-id-46 div.sk-estimator.fitted:hover {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-2);
     }
@@ -495,7 +495,7 @@ this feature.
 
     /* "?"-specific style due to the `<a>` HTML tag */
 
-    #sk-container-id-45 a.estimator_doc_link {
+    #sk-container-id-46 a.estimator_doc_link {
       float: right;
       font-size: 1rem;
       line-height: 1em;
@@ -510,25 +510,25 @@ this feature.
       border: var(--sklearn-color-unfitted-level-1) 1pt solid;
     }
 
-    #sk-container-id-45 a.estimator_doc_link.fitted {
+    #sk-container-id-46 a.estimator_doc_link.fitted {
       /* fitted */
       border: var(--sklearn-color-fitted-level-1) 1pt solid;
       color: var(--sklearn-color-fitted-level-1);
     }
 
     /* On hover */
-    #sk-container-id-45 a.estimator_doc_link:hover {
+    #sk-container-id-46 a.estimator_doc_link:hover {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-3);
       color: var(--sklearn-color-background);
       text-decoration: none;
     }
 
-    #sk-container-id-45 a.estimator_doc_link.fitted:hover {
+    #sk-container-id-46 a.estimator_doc_link.fitted:hover {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-3);
     }
-    </style><div id="sk-container-id-45" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,
+    </style><div id="sk-container-id-46" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,
                      ColumnTransformer(transformers=[(&#x27;pipeline-1&#x27;,
                                                       Pipeline(steps=[(&#x27;simpleimputer&#x27;,
                                                                        SimpleImputer(strategy=&#x27;median&#x27;)),
@@ -542,7 +542,7 @@ this feature.
                                                                       (&#x27;onehotencoder&#x27;,
                                                                        OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),
                                                       (&#x27;feat0&#x27;, &#x27;feat2&#x27;))])),
-                    (&#x27;logisticregression&#x27;, LogisticRegression())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-195" type="checkbox" ><label for="sk-estimator-id-195" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>Pipeline</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.pipeline.Pipeline.html">?<span>Documentation for Pipeline</span></a><span class="sk-estimator-doc-link ">i<span>Not fitted</span></span></div></label><div class="sk-toggleable__content "><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,
+                    (&#x27;logisticregression&#x27;, LogisticRegression())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-209" type="checkbox" ><label for="sk-estimator-id-209" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>Pipeline</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.pipeline.Pipeline.html">?<span>Documentation for Pipeline</span></a><span class="sk-estimator-doc-link ">i<span>Not fitted</span></span></div></label><div class="sk-toggleable__content "><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,
                      ColumnTransformer(transformers=[(&#x27;pipeline-1&#x27;,
                                                       Pipeline(steps=[(&#x27;simpleimputer&#x27;,
                                                                        SimpleImputer(strategy=&#x27;median&#x27;)),
@@ -556,7 +556,7 @@ this feature.
                                                                       (&#x27;onehotencoder&#x27;,
                                                                        OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),
                                                       (&#x27;feat0&#x27;, &#x27;feat2&#x27;))])),
-                    (&#x27;logisticregression&#x27;, LogisticRegression())])</pre></div> </div></div><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-196" type="checkbox" ><label for="sk-estimator-id-196" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>columntransformer: ColumnTransformer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.compose.ColumnTransformer.html">?<span>Documentation for columntransformer: ColumnTransformer</span></a></div></label><div class="sk-toggleable__content "><pre>ColumnTransformer(transformers=[(&#x27;pipeline-1&#x27;,
+                    (&#x27;logisticregression&#x27;, LogisticRegression())])</pre></div> </div></div><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-210" type="checkbox" ><label for="sk-estimator-id-210" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>columntransformer: ColumnTransformer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.compose.ColumnTransformer.html">?<span>Documentation for columntransformer: ColumnTransformer</span></a></div></label><div class="sk-toggleable__content "><pre>ColumnTransformer(transformers=[(&#x27;pipeline-1&#x27;,
                                      Pipeline(steps=[(&#x27;simpleimputer&#x27;,
                                                       SimpleImputer(strategy=&#x27;median&#x27;)),
                                                      (&#x27;standardscaler&#x27;,
@@ -568,7 +568,7 @@ this feature.
                                                                     strategy=&#x27;constant&#x27;)),
                                                      (&#x27;onehotencoder&#x27;,
                                                       OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),
-                                     (&#x27;feat0&#x27;, &#x27;feat2&#x27;))])</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-197" type="checkbox" ><label for="sk-estimator-id-197" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>pipeline-1</div></div></label><div class="sk-toggleable__content "><pre>(&#x27;feat1&#x27;, &#x27;feat3&#x27;)</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-198" type="checkbox" ><label for="sk-estimator-id-198" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>SimpleImputer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.impute.SimpleImputer.html">?<span>Documentation for SimpleImputer</span></a></div></label><div class="sk-toggleable__content "><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div> </div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-199" type="checkbox" ><label for="sk-estimator-id-199" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>StandardScaler</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.StandardScaler.html">?<span>Documentation for StandardScaler</span></a></div></label><div class="sk-toggleable__content "><pre>StandardScaler()</pre></div> </div></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-200" type="checkbox" ><label for="sk-estimator-id-200" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>pipeline-2</div></div></label><div class="sk-toggleable__content "><pre>(&#x27;feat0&#x27;, &#x27;feat2&#x27;)</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-201" type="checkbox" ><label for="sk-estimator-id-201" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>SimpleImputer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.impute.SimpleImputer.html">?<span>Documentation for SimpleImputer</span></a></div></label><div class="sk-toggleable__content "><pre>SimpleImputer(fill_value=&#x27;missing&#x27;, strategy=&#x27;constant&#x27;)</pre></div> </div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-202" type="checkbox" ><label for="sk-estimator-id-202" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>OneHotEncoder</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.OneHotEncoder.html">?<span>Documentation for OneHotEncoder</span></a></div></label><div class="sk-toggleable__content "><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</pre></div> </div></div></div></div></div></div></div></div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-203" type="checkbox" ><label for="sk-estimator-id-203" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>LogisticRegression</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.linear_model.LogisticRegression.html">?<span>Documentation for LogisticRegression</span></a></div></label><div class="sk-toggleable__content "><pre>LogisticRegression()</pre></div> </div></div></div></div></div></div>
+                                     (&#x27;feat0&#x27;, &#x27;feat2&#x27;))])</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-211" type="checkbox" ><label for="sk-estimator-id-211" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>pipeline-1</div></div></label><div class="sk-toggleable__content "><pre>(&#x27;feat1&#x27;, &#x27;feat3&#x27;)</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-212" type="checkbox" ><label for="sk-estimator-id-212" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>SimpleImputer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.impute.SimpleImputer.html">?<span>Documentation for SimpleImputer</span></a></div></label><div class="sk-toggleable__content "><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div> </div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-213" type="checkbox" ><label for="sk-estimator-id-213" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>StandardScaler</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.StandardScaler.html">?<span>Documentation for StandardScaler</span></a></div></label><div class="sk-toggleable__content "><pre>StandardScaler()</pre></div> </div></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-214" type="checkbox" ><label for="sk-estimator-id-214" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>pipeline-2</div></div></label><div class="sk-toggleable__content "><pre>(&#x27;feat0&#x27;, &#x27;feat2&#x27;)</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-215" type="checkbox" ><label for="sk-estimator-id-215" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>SimpleImputer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.impute.SimpleImputer.html">?<span>Documentation for SimpleImputer</span></a></div></label><div class="sk-toggleable__content "><pre>SimpleImputer(fill_value=&#x27;missing&#x27;, strategy=&#x27;constant&#x27;)</pre></div> </div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-216" type="checkbox" ><label for="sk-estimator-id-216" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>OneHotEncoder</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.OneHotEncoder.html">?<span>Documentation for OneHotEncoder</span></a></div></label><div class="sk-toggleable__content "><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</pre></div> </div></div></div></div></div></div></div></div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-217" type="checkbox" ><label for="sk-estimator-id-217" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>LogisticRegression</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.linear_model.LogisticRegression.html">?<span>Documentation for LogisticRegression</span></a></div></label><div class="sk-toggleable__content "><pre>LogisticRegression()</pre></div> </div></div></div></div></div></div>
     </div>
     <br />
     <br />
@@ -576,7 +576,7 @@ this feature.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.029 seconds)
+   **Total running time of the script:** (0 minutes 0.028 seconds)
 
 
 .. _sphx_glr_download_auto_examples_miscellaneous_plot_estimator_representation.py:
diff --git a/dev/_sources/auto_examples/miscellaneous/plot_isotonic_regression.rst.txt b/dev/_sources/auto_examples/miscellaneous/plot_isotonic_regression.rst.txt
index 28ab9a8229c36..a84ede2f3ec92 100644
--- a/dev/_sources/auto_examples/miscellaneous/plot_isotonic_regression.rst.txt
+++ b/dev/_sources/auto_examples/miscellaneous/plot_isotonic_regression.rst.txt
@@ -87,7 +87,7 @@ Fit IsotonicRegression and LinearRegression models:
 .. raw:: html
 
     <div class="output_subarea output_html rendered_html output_result">
-    <style>#sk-container-id-48 {
+    <style>#sk-container-id-49 {
       /* Definition of color scheme common for light and dark mode */
       --sklearn-color-text: #000;
       --sklearn-color-text-muted: #666;
@@ -118,15 +118,15 @@ Fit IsotonicRegression and LinearRegression models:
       }
     }
 
-    #sk-container-id-48 {
+    #sk-container-id-49 {
       color: var(--sklearn-color-text);
     }
 
-    #sk-container-id-48 pre {
+    #sk-container-id-49 pre {
       padding: 0;
     }
 
-    #sk-container-id-48 input.sk-hidden--visually {
+    #sk-container-id-49 input.sk-hidden--visually {
       border: 0;
       clip: rect(1px 1px 1px 1px);
       clip: rect(1px, 1px, 1px, 1px);
@@ -138,7 +138,7 @@ Fit IsotonicRegression and LinearRegression models:
       width: 1px;
     }
 
-    #sk-container-id-48 div.sk-dashed-wrapped {
+    #sk-container-id-49 div.sk-dashed-wrapped {
       border: 1px dashed var(--sklearn-color-line);
       margin: 0 0.4em 0.5em 0.4em;
       box-sizing: border-box;
@@ -146,7 +146,7 @@ Fit IsotonicRegression and LinearRegression models:
       background-color: var(--sklearn-color-background);
     }
 
-    #sk-container-id-48 div.sk-container {
+    #sk-container-id-49 div.sk-container {
       /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
          but bootstrap.min.css set `[hidden] { display: none !important; }`
          so we also need the `!important` here to be able to override the
@@ -156,7 +156,7 @@ Fit IsotonicRegression and LinearRegression models:
       position: relative;
     }
 
-    #sk-container-id-48 div.sk-text-repr-fallback {
+    #sk-container-id-49 div.sk-text-repr-fallback {
       display: none;
     }
 
@@ -172,14 +172,14 @@ Fit IsotonicRegression and LinearRegression models:
 
     /* Parallel-specific style estimator block */
 
-    #sk-container-id-48 div.sk-parallel-item::after {
+    #sk-container-id-49 div.sk-parallel-item::after {
       content: "";
       width: 100%;
       border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
       flex-grow: 1;
     }
 
-    #sk-container-id-48 div.sk-parallel {
+    #sk-container-id-49 div.sk-parallel {
       display: flex;
       align-items: stretch;
       justify-content: center;
@@ -187,28 +187,28 @@ Fit IsotonicRegression and LinearRegression models:
       position: relative;
     }
 
-    #sk-container-id-48 div.sk-parallel-item {
+    #sk-container-id-49 div.sk-parallel-item {
       display: flex;
       flex-direction: column;
     }
 
-    #sk-container-id-48 div.sk-parallel-item:first-child::after {
+    #sk-container-id-49 div.sk-parallel-item:first-child::after {
       align-self: flex-end;
       width: 50%;
     }
 
-    #sk-container-id-48 div.sk-parallel-item:last-child::after {
+    #sk-container-id-49 div.sk-parallel-item:last-child::after {
       align-self: flex-start;
       width: 50%;
     }
 
-    #sk-container-id-48 div.sk-parallel-item:only-child::after {
+    #sk-container-id-49 div.sk-parallel-item:only-child::after {
       width: 0;
     }
 
     /* Serial-specific style estimator block */
 
-    #sk-container-id-48 div.sk-serial {
+    #sk-container-id-49 div.sk-serial {
       display: flex;
       flex-direction: column;
       align-items: center;
@@ -226,14 +226,14 @@ Fit IsotonicRegression and LinearRegression models:
 
     /* Pipeline and ColumnTransformer style (default) */
 
-    #sk-container-id-48 div.sk-toggleable {
+    #sk-container-id-49 div.sk-toggleable {
       /* Default theme specific background. It is overwritten whether we have a
       specific estimator or a Pipeline/ColumnTransformer */
       background-color: var(--sklearn-color-background);
     }
 
     /* Toggleable label */
-    #sk-container-id-48 label.sk-toggleable__label {
+    #sk-container-id-49 label.sk-toggleable__label {
       cursor: pointer;
       display: flex;
       width: 100%;
@@ -246,13 +246,13 @@ Fit IsotonicRegression and LinearRegression models:
       gap: 0.5em;
     }
 
-    #sk-container-id-48 label.sk-toggleable__label .caption {
+    #sk-container-id-49 label.sk-toggleable__label .caption {
       font-size: 0.6rem;
       font-weight: lighter;
       color: var(--sklearn-color-text-muted);
     }
 
-    #sk-container-id-48 label.sk-toggleable__label-arrow:before {
+    #sk-container-id-49 label.sk-toggleable__label-arrow:before {
       /* Arrow on the left of the label */
       content: "▸";
       float: left;
@@ -260,13 +260,13 @@ Fit IsotonicRegression and LinearRegression models:
       color: var(--sklearn-color-icon);
     }
 
-    #sk-container-id-48 label.sk-toggleable__label-arrow:hover:before {
+    #sk-container-id-49 label.sk-toggleable__label-arrow:hover:before {
       color: var(--sklearn-color-text);
     }
 
     /* Toggleable content - dropdown */
 
-    #sk-container-id-48 div.sk-toggleable__content {
+    #sk-container-id-49 div.sk-toggleable__content {
       max-height: 0;
       max-width: 0;
       overflow: hidden;
@@ -275,12 +275,12 @@ Fit IsotonicRegression and LinearRegression models:
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-48 div.sk-toggleable__content.fitted {
+    #sk-container-id-49 div.sk-toggleable__content.fitted {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
-    #sk-container-id-48 div.sk-toggleable__content pre {
+    #sk-container-id-49 div.sk-toggleable__content pre {
       margin: 0.2em;
       border-radius: 0.25em;
       color: var(--sklearn-color-text);
@@ -288,79 +288,79 @@ Fit IsotonicRegression and LinearRegression models:
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-48 div.sk-toggleable__content.fitted pre {
+    #sk-container-id-49 div.sk-toggleable__content.fitted pre {
       /* unfitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
-    #sk-container-id-48 input.sk-toggleable__control:checked~div.sk-toggleable__content {
+    #sk-container-id-49 input.sk-toggleable__control:checked~div.sk-toggleable__content {
       /* Expand drop-down */
       max-height: 200px;
       max-width: 100%;
       overflow: auto;
     }
 
-    #sk-container-id-48 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
+    #sk-container-id-49 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
       content: "▾";
     }
 
     /* Pipeline/ColumnTransformer-specific style */
 
-    #sk-container-id-48 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-49 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-48 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-49 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
     /* Estimator-specific style */
 
     /* Colorize estimator box */
-    #sk-container-id-48 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-49 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-48 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-49 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
-    #sk-container-id-48 div.sk-label label.sk-toggleable__label,
-    #sk-container-id-48 div.sk-label label {
+    #sk-container-id-49 div.sk-label label.sk-toggleable__label,
+    #sk-container-id-49 div.sk-label label {
       /* The background is the default theme color */
       color: var(--sklearn-color-text-on-default-background);
     }
 
     /* On hover, darken the color of the background */
-    #sk-container-id-48 div.sk-label:hover label.sk-toggleable__label {
+    #sk-container-id-49 div.sk-label:hover label.sk-toggleable__label {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
     /* Label box, darken color on hover, fitted */
-    #sk-container-id-48 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
+    #sk-container-id-49 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
     /* Estimator label */
 
-    #sk-container-id-48 div.sk-label label {
+    #sk-container-id-49 div.sk-label label {
       font-family: monospace;
       font-weight: bold;
       display: inline-block;
       line-height: 1.2em;
     }
 
-    #sk-container-id-48 div.sk-label-container {
+    #sk-container-id-49 div.sk-label-container {
       text-align: center;
     }
 
     /* Estimator-specific */
-    #sk-container-id-48 div.sk-estimator {
+    #sk-container-id-49 div.sk-estimator {
       font-family: monospace;
       border: 1px dotted var(--sklearn-color-border-box);
       border-radius: 0.25em;
@@ -370,18 +370,18 @@ Fit IsotonicRegression and LinearRegression models:
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-48 div.sk-estimator.fitted {
+    #sk-container-id-49 div.sk-estimator.fitted {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
     /* on hover */
-    #sk-container-id-48 div.sk-estimator:hover {
+    #sk-container-id-49 div.sk-estimator:hover {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-48 div.sk-estimator.fitted:hover {
+    #sk-container-id-49 div.sk-estimator.fitted:hover {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-2);
     }
@@ -469,7 +469,7 @@ Fit IsotonicRegression and LinearRegression models:
 
     /* "?"-specific style due to the `<a>` HTML tag */
 
-    #sk-container-id-48 a.estimator_doc_link {
+    #sk-container-id-49 a.estimator_doc_link {
       float: right;
       font-size: 1rem;
       line-height: 1em;
@@ -484,25 +484,25 @@ Fit IsotonicRegression and LinearRegression models:
       border: var(--sklearn-color-unfitted-level-1) 1pt solid;
     }
 
-    #sk-container-id-48 a.estimator_doc_link.fitted {
+    #sk-container-id-49 a.estimator_doc_link.fitted {
       /* fitted */
       border: var(--sklearn-color-fitted-level-1) 1pt solid;
       color: var(--sklearn-color-fitted-level-1);
     }
 
     /* On hover */
-    #sk-container-id-48 a.estimator_doc_link:hover {
+    #sk-container-id-49 a.estimator_doc_link:hover {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-3);
       color: var(--sklearn-color-background);
       text-decoration: none;
     }
 
-    #sk-container-id-48 a.estimator_doc_link.fitted:hover {
+    #sk-container-id-49 a.estimator_doc_link.fitted:hover {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-3);
     }
-    </style><div id="sk-container-id-48" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-209" type="checkbox" checked><label for="sk-estimator-id-209" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>LinearRegression</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.linear_model.LinearRegression.html">?<span>Documentation for LinearRegression</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>LinearRegression()</pre></div> </div></div></div></div>
+    </style><div id="sk-container-id-49" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-223" type="checkbox" checked><label for="sk-estimator-id-223" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>LinearRegression</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.linear_model.LinearRegression.html">?<span>Documentation for LinearRegression</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>LinearRegression()</pre></div> </div></div></div></div>
     </div>
     <br />
     <br />
@@ -559,7 +559,7 @@ be seen on the plot of the decision function on the right-hand.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.144 seconds)
+   **Total running time of the script:** (0 minutes 0.156 seconds)
 
 
 .. _sphx_glr_download_auto_examples_miscellaneous_plot_isotonic_regression.py:
diff --git a/dev/_sources/auto_examples/miscellaneous/plot_johnson_lindenstrauss_bound.rst.txt b/dev/_sources/auto_examples/miscellaneous/plot_johnson_lindenstrauss_bound.rst.txt
index 08c808786248f..21b84cf67c1dc 100644
--- a/dev/_sources/auto_examples/miscellaneous/plot_johnson_lindenstrauss_bound.rst.txt
+++ b/dev/_sources/auto_examples/miscellaneous/plot_johnson_lindenstrauss_bound.rst.txt
@@ -326,14 +326,14 @@ For each value of ``n_components``, we plot:
  .. code-block:: none
 
     Embedding 300 samples with dim 130107 using various random projections
-    Projected 300 samples from 130107 to 300 in 0.255s
-    Random matrix with size: 1.295 MB
-    Mean distances rate: 1.01 (0.17)
-    Projected 300 samples from 130107 to 1000 in 0.837s
-    Random matrix with size: 4.325 MB
+    Projected 300 samples from 130107 to 300 in 0.226s
+    Random matrix with size: 1.301 MB
+    Mean distances rate: 1.02 (0.17)
+    Projected 300 samples from 130107 to 1000 in 0.742s
+    Random matrix with size: 4.324 MB
     Mean distances rate: 1.01 (0.09)
-    Projected 300 samples from 130107 to 10000 in 7.806s
-    Random matrix with size: 43.243 MB
+    Projected 300 samples from 130107 to 10000 in 7.469s
+    Random matrix with size: 43.239 MB
     Mean distances rate: 1.01 (0.03)
 
 
@@ -365,7 +365,7 @@ pairwise distances.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 11.506 seconds)
+   **Total running time of the script:** (0 minutes 11.082 seconds)
 
 
 .. _sphx_glr_download_auto_examples_miscellaneous_plot_johnson_lindenstrauss_bound.py:
diff --git a/dev/_sources/auto_examples/miscellaneous/plot_kernel_approximation.rst.txt b/dev/_sources/auto_examples/miscellaneous/plot_kernel_approximation.rst.txt
index 30205ed17565c..95b9e71b6679b 100644
--- a/dev/_sources/auto_examples/miscellaneous/plot_kernel_approximation.rst.txt
+++ b/dev/_sources/auto_examples/miscellaneous/plot_kernel_approximation.rst.txt
@@ -326,7 +326,7 @@ in :ref:`kernel_approximation`.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 1.734 seconds)
+   **Total running time of the script:** (0 minutes 1.808 seconds)
 
 
 .. _sphx_glr_download_auto_examples_miscellaneous_plot_kernel_approximation.py:
diff --git a/dev/_sources/auto_examples/miscellaneous/plot_kernel_ridge_regression.rst.txt b/dev/_sources/auto_examples/miscellaneous/plot_kernel_ridge_regression.rst.txt
index 921f5aad96328..f772496310abd 100644
--- a/dev/_sources/auto_examples/miscellaneous/plot_kernel_ridge_regression.rst.txt
+++ b/dev/_sources/auto_examples/miscellaneous/plot_kernel_ridge_regression.rst.txt
@@ -147,12 +147,12 @@ Compare times of SVR and Kernel Ridge Regression
  .. code-block:: none
 
     Best SVR with params: {'C': 1.0, 'gamma': np.float64(0.1)} and R2 score: 0.737
-    SVR complexity and bandwidth selected and model fitted in 0.511 s
+    SVR complexity and bandwidth selected and model fitted in 0.588 s
     Best KRR with params: {'alpha': 0.1, 'gamma': np.float64(0.1)} and R2 score: 0.723
-    KRR complexity and bandwidth selected and model fitted in 0.192 s
+    KRR complexity and bandwidth selected and model fitted in 0.205 s
     Support vector ratio: 0.340
-    SVR prediction for 100000 inputs in 0.115 s
-    KRR prediction for 100000 inputs in 0.094 s
+    SVR prediction for 100000 inputs in 0.126 s
+    KRR prediction for 100000 inputs in 0.596 s
 
 
 
@@ -343,7 +343,7 @@ Visualize the learning curves
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 9.342 seconds)
+   **Total running time of the script:** (0 minutes 14.892 seconds)
 
 
 .. _sphx_glr_download_auto_examples_miscellaneous_plot_kernel_ridge_regression.py:
diff --git a/dev/_sources/auto_examples/miscellaneous/plot_metadata_routing.rst.txt b/dev/_sources/auto_examples/miscellaneous/plot_metadata_routing.rst.txt
index e75dde84737b5..85a4a1a6293c0 100644
--- a/dev/_sources/auto_examples/miscellaneous/plot_metadata_routing.rst.txt
+++ b/dev/_sources/auto_examples/miscellaneous/plot_metadata_routing.rst.txt
@@ -423,7 +423,7 @@ raised.
 .. raw:: html
 
     <div class="output_subarea output_html rendered_html output_result">
-    <style>#sk-container-id-49 {
+    <style>#sk-container-id-50 {
       /* Definition of color scheme common for light and dark mode */
       --sklearn-color-text: #000;
       --sklearn-color-text-muted: #666;
@@ -454,15 +454,15 @@ raised.
       }
     }
 
-    #sk-container-id-49 {
+    #sk-container-id-50 {
       color: var(--sklearn-color-text);
     }
 
-    #sk-container-id-49 pre {
+    #sk-container-id-50 pre {
       padding: 0;
     }
 
-    #sk-container-id-49 input.sk-hidden--visually {
+    #sk-container-id-50 input.sk-hidden--visually {
       border: 0;
       clip: rect(1px 1px 1px 1px);
       clip: rect(1px, 1px, 1px, 1px);
@@ -474,7 +474,7 @@ raised.
       width: 1px;
     }
 
-    #sk-container-id-49 div.sk-dashed-wrapped {
+    #sk-container-id-50 div.sk-dashed-wrapped {
       border: 1px dashed var(--sklearn-color-line);
       margin: 0 0.4em 0.5em 0.4em;
       box-sizing: border-box;
@@ -482,7 +482,7 @@ raised.
       background-color: var(--sklearn-color-background);
     }
 
-    #sk-container-id-49 div.sk-container {
+    #sk-container-id-50 div.sk-container {
       /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
          but bootstrap.min.css set `[hidden] { display: none !important; }`
          so we also need the `!important` here to be able to override the
@@ -492,7 +492,7 @@ raised.
       position: relative;
     }
 
-    #sk-container-id-49 div.sk-text-repr-fallback {
+    #sk-container-id-50 div.sk-text-repr-fallback {
       display: none;
     }
 
@@ -508,14 +508,14 @@ raised.
 
     /* Parallel-specific style estimator block */
 
-    #sk-container-id-49 div.sk-parallel-item::after {
+    #sk-container-id-50 div.sk-parallel-item::after {
       content: "";
       width: 100%;
       border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
       flex-grow: 1;
     }
 
-    #sk-container-id-49 div.sk-parallel {
+    #sk-container-id-50 div.sk-parallel {
       display: flex;
       align-items: stretch;
       justify-content: center;
@@ -523,28 +523,28 @@ raised.
       position: relative;
     }
 
-    #sk-container-id-49 div.sk-parallel-item {
+    #sk-container-id-50 div.sk-parallel-item {
       display: flex;
       flex-direction: column;
     }
 
-    #sk-container-id-49 div.sk-parallel-item:first-child::after {
+    #sk-container-id-50 div.sk-parallel-item:first-child::after {
       align-self: flex-end;
       width: 50%;
     }
 
-    #sk-container-id-49 div.sk-parallel-item:last-child::after {
+    #sk-container-id-50 div.sk-parallel-item:last-child::after {
       align-self: flex-start;
       width: 50%;
     }
 
-    #sk-container-id-49 div.sk-parallel-item:only-child::after {
+    #sk-container-id-50 div.sk-parallel-item:only-child::after {
       width: 0;
     }
 
     /* Serial-specific style estimator block */
 
-    #sk-container-id-49 div.sk-serial {
+    #sk-container-id-50 div.sk-serial {
       display: flex;
       flex-direction: column;
       align-items: center;
@@ -562,14 +562,14 @@ raised.
 
     /* Pipeline and ColumnTransformer style (default) */
 
-    #sk-container-id-49 div.sk-toggleable {
+    #sk-container-id-50 div.sk-toggleable {
       /* Default theme specific background. It is overwritten whether we have a
       specific estimator or a Pipeline/ColumnTransformer */
       background-color: var(--sklearn-color-background);
     }
 
     /* Toggleable label */
-    #sk-container-id-49 label.sk-toggleable__label {
+    #sk-container-id-50 label.sk-toggleable__label {
       cursor: pointer;
       display: flex;
       width: 100%;
@@ -582,13 +582,13 @@ raised.
       gap: 0.5em;
     }
 
-    #sk-container-id-49 label.sk-toggleable__label .caption {
+    #sk-container-id-50 label.sk-toggleable__label .caption {
       font-size: 0.6rem;
       font-weight: lighter;
       color: var(--sklearn-color-text-muted);
     }
 
-    #sk-container-id-49 label.sk-toggleable__label-arrow:before {
+    #sk-container-id-50 label.sk-toggleable__label-arrow:before {
       /* Arrow on the left of the label */
       content: "▸";
       float: left;
@@ -596,13 +596,13 @@ raised.
       color: var(--sklearn-color-icon);
     }
 
-    #sk-container-id-49 label.sk-toggleable__label-arrow:hover:before {
+    #sk-container-id-50 label.sk-toggleable__label-arrow:hover:before {
       color: var(--sklearn-color-text);
     }
 
     /* Toggleable content - dropdown */
 
-    #sk-container-id-49 div.sk-toggleable__content {
+    #sk-container-id-50 div.sk-toggleable__content {
       max-height: 0;
       max-width: 0;
       overflow: hidden;
@@ -611,12 +611,12 @@ raised.
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-49 div.sk-toggleable__content.fitted {
+    #sk-container-id-50 div.sk-toggleable__content.fitted {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
-    #sk-container-id-49 div.sk-toggleable__content pre {
+    #sk-container-id-50 div.sk-toggleable__content pre {
       margin: 0.2em;
       border-radius: 0.25em;
       color: var(--sklearn-color-text);
@@ -624,79 +624,79 @@ raised.
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-49 div.sk-toggleable__content.fitted pre {
+    #sk-container-id-50 div.sk-toggleable__content.fitted pre {
       /* unfitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
-    #sk-container-id-49 input.sk-toggleable__control:checked~div.sk-toggleable__content {
+    #sk-container-id-50 input.sk-toggleable__control:checked~div.sk-toggleable__content {
       /* Expand drop-down */
       max-height: 200px;
       max-width: 100%;
       overflow: auto;
     }
 
-    #sk-container-id-49 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
+    #sk-container-id-50 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
       content: "▾";
     }
 
     /* Pipeline/ColumnTransformer-specific style */
 
-    #sk-container-id-49 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-50 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-49 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-50 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
     /* Estimator-specific style */
 
     /* Colorize estimator box */
-    #sk-container-id-49 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-50 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-49 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-50 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
-    #sk-container-id-49 div.sk-label label.sk-toggleable__label,
-    #sk-container-id-49 div.sk-label label {
+    #sk-container-id-50 div.sk-label label.sk-toggleable__label,
+    #sk-container-id-50 div.sk-label label {
       /* The background is the default theme color */
       color: var(--sklearn-color-text-on-default-background);
     }
 
     /* On hover, darken the color of the background */
-    #sk-container-id-49 div.sk-label:hover label.sk-toggleable__label {
+    #sk-container-id-50 div.sk-label:hover label.sk-toggleable__label {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
     /* Label box, darken color on hover, fitted */
-    #sk-container-id-49 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
+    #sk-container-id-50 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
     /* Estimator label */
 
-    #sk-container-id-49 div.sk-label label {
+    #sk-container-id-50 div.sk-label label {
       font-family: monospace;
       font-weight: bold;
       display: inline-block;
       line-height: 1.2em;
     }
 
-    #sk-container-id-49 div.sk-label-container {
+    #sk-container-id-50 div.sk-label-container {
       text-align: center;
     }
 
     /* Estimator-specific */
-    #sk-container-id-49 div.sk-estimator {
+    #sk-container-id-50 div.sk-estimator {
       font-family: monospace;
       border: 1px dotted var(--sklearn-color-border-box);
       border-radius: 0.25em;
@@ -706,18 +706,18 @@ raised.
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-49 div.sk-estimator.fitted {
+    #sk-container-id-50 div.sk-estimator.fitted {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
     /* on hover */
-    #sk-container-id-49 div.sk-estimator:hover {
+    #sk-container-id-50 div.sk-estimator:hover {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-49 div.sk-estimator.fitted:hover {
+    #sk-container-id-50 div.sk-estimator.fitted:hover {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-2);
     }
@@ -805,7 +805,7 @@ raised.
 
     /* "?"-specific style due to the `<a>` HTML tag */
 
-    #sk-container-id-49 a.estimator_doc_link {
+    #sk-container-id-50 a.estimator_doc_link {
       float: right;
       font-size: 1rem;
       line-height: 1em;
@@ -820,25 +820,25 @@ raised.
       border: var(--sklearn-color-unfitted-level-1) 1pt solid;
     }
 
-    #sk-container-id-49 a.estimator_doc_link.fitted {
+    #sk-container-id-50 a.estimator_doc_link.fitted {
       /* fitted */
       border: var(--sklearn-color-fitted-level-1) 1pt solid;
       color: var(--sklearn-color-fitted-level-1);
     }
 
     /* On hover */
-    #sk-container-id-49 a.estimator_doc_link:hover {
+    #sk-container-id-50 a.estimator_doc_link:hover {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-3);
       color: var(--sklearn-color-background);
       text-decoration: none;
     }
 
-    #sk-container-id-49 a.estimator_doc_link.fitted:hover {
+    #sk-container-id-50 a.estimator_doc_link.fitted:hover {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-3);
     }
-    </style><div id="sk-container-id-49" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>MetaClassifier(estimator=ExampleClassifier())</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-210" type="checkbox" ><label for="sk-estimator-id-210" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>MetaClassifier</div></div><div><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>MetaClassifier(estimator=ExampleClassifier())</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-211" type="checkbox" ><label for="sk-estimator-id-211" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>estimator: ExampleClassifier</div></div></label><div class="sk-toggleable__content fitted"><pre>ExampleClassifier()</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-212" type="checkbox" ><label for="sk-estimator-id-212" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>ExampleClassifier</div></div></label><div class="sk-toggleable__content fitted"><pre>ExampleClassifier()</pre></div> </div></div></div></div></div></div></div></div></div>
+    </style><div id="sk-container-id-50" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>MetaClassifier(estimator=ExampleClassifier())</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-224" type="checkbox" ><label for="sk-estimator-id-224" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>MetaClassifier</div></div><div><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>MetaClassifier(estimator=ExampleClassifier())</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-225" type="checkbox" ><label for="sk-estimator-id-225" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>estimator: ExampleClassifier</div></div></label><div class="sk-toggleable__content fitted"><pre>ExampleClassifier()</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-226" type="checkbox" ><label for="sk-estimator-id-226" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>ExampleClassifier</div></div></label><div class="sk-toggleable__content fitted"><pre>ExampleClassifier()</pre></div> </div></div></div></div></div></div></div></div></div>
     </div>
     <br />
     <br />
@@ -871,7 +871,7 @@ following example, it would print that ``sample_weight`` is ``None``:
 .. raw:: html
 
     <div class="output_subarea output_html rendered_html output_result">
-    <style>#sk-container-id-50 {
+    <style>#sk-container-id-51 {
       /* Definition of color scheme common for light and dark mode */
       --sklearn-color-text: #000;
       --sklearn-color-text-muted: #666;
@@ -902,15 +902,15 @@ following example, it would print that ``sample_weight`` is ``None``:
       }
     }
 
-    #sk-container-id-50 {
+    #sk-container-id-51 {
       color: var(--sklearn-color-text);
     }
 
-    #sk-container-id-50 pre {
+    #sk-container-id-51 pre {
       padding: 0;
     }
 
-    #sk-container-id-50 input.sk-hidden--visually {
+    #sk-container-id-51 input.sk-hidden--visually {
       border: 0;
       clip: rect(1px 1px 1px 1px);
       clip: rect(1px, 1px, 1px, 1px);
@@ -922,7 +922,7 @@ following example, it would print that ``sample_weight`` is ``None``:
       width: 1px;
     }
 
-    #sk-container-id-50 div.sk-dashed-wrapped {
+    #sk-container-id-51 div.sk-dashed-wrapped {
       border: 1px dashed var(--sklearn-color-line);
       margin: 0 0.4em 0.5em 0.4em;
       box-sizing: border-box;
@@ -930,7 +930,7 @@ following example, it would print that ``sample_weight`` is ``None``:
       background-color: var(--sklearn-color-background);
     }
 
-    #sk-container-id-50 div.sk-container {
+    #sk-container-id-51 div.sk-container {
       /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
          but bootstrap.min.css set `[hidden] { display: none !important; }`
          so we also need the `!important` here to be able to override the
@@ -940,7 +940,7 @@ following example, it would print that ``sample_weight`` is ``None``:
       position: relative;
     }
 
-    #sk-container-id-50 div.sk-text-repr-fallback {
+    #sk-container-id-51 div.sk-text-repr-fallback {
       display: none;
     }
 
@@ -956,14 +956,14 @@ following example, it would print that ``sample_weight`` is ``None``:
 
     /* Parallel-specific style estimator block */
 
-    #sk-container-id-50 div.sk-parallel-item::after {
+    #sk-container-id-51 div.sk-parallel-item::after {
       content: "";
       width: 100%;
       border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
       flex-grow: 1;
     }
 
-    #sk-container-id-50 div.sk-parallel {
+    #sk-container-id-51 div.sk-parallel {
       display: flex;
       align-items: stretch;
       justify-content: center;
@@ -971,28 +971,28 @@ following example, it would print that ``sample_weight`` is ``None``:
       position: relative;
     }
 
-    #sk-container-id-50 div.sk-parallel-item {
+    #sk-container-id-51 div.sk-parallel-item {
       display: flex;
       flex-direction: column;
     }
 
-    #sk-container-id-50 div.sk-parallel-item:first-child::after {
+    #sk-container-id-51 div.sk-parallel-item:first-child::after {
       align-self: flex-end;
       width: 50%;
     }
 
-    #sk-container-id-50 div.sk-parallel-item:last-child::after {
+    #sk-container-id-51 div.sk-parallel-item:last-child::after {
       align-self: flex-start;
       width: 50%;
     }
 
-    #sk-container-id-50 div.sk-parallel-item:only-child::after {
+    #sk-container-id-51 div.sk-parallel-item:only-child::after {
       width: 0;
     }
 
     /* Serial-specific style estimator block */
 
-    #sk-container-id-50 div.sk-serial {
+    #sk-container-id-51 div.sk-serial {
       display: flex;
       flex-direction: column;
       align-items: center;
@@ -1010,14 +1010,14 @@ following example, it would print that ``sample_weight`` is ``None``:
 
     /* Pipeline and ColumnTransformer style (default) */
 
-    #sk-container-id-50 div.sk-toggleable {
+    #sk-container-id-51 div.sk-toggleable {
       /* Default theme specific background. It is overwritten whether we have a
       specific estimator or a Pipeline/ColumnTransformer */
       background-color: var(--sklearn-color-background);
     }
 
     /* Toggleable label */
-    #sk-container-id-50 label.sk-toggleable__label {
+    #sk-container-id-51 label.sk-toggleable__label {
       cursor: pointer;
       display: flex;
       width: 100%;
@@ -1030,13 +1030,13 @@ following example, it would print that ``sample_weight`` is ``None``:
       gap: 0.5em;
     }
 
-    #sk-container-id-50 label.sk-toggleable__label .caption {
+    #sk-container-id-51 label.sk-toggleable__label .caption {
       font-size: 0.6rem;
       font-weight: lighter;
       color: var(--sklearn-color-text-muted);
     }
 
-    #sk-container-id-50 label.sk-toggleable__label-arrow:before {
+    #sk-container-id-51 label.sk-toggleable__label-arrow:before {
       /* Arrow on the left of the label */
       content: "▸";
       float: left;
@@ -1044,13 +1044,13 @@ following example, it would print that ``sample_weight`` is ``None``:
       color: var(--sklearn-color-icon);
     }
 
-    #sk-container-id-50 label.sk-toggleable__label-arrow:hover:before {
+    #sk-container-id-51 label.sk-toggleable__label-arrow:hover:before {
       color: var(--sklearn-color-text);
     }
 
     /* Toggleable content - dropdown */
 
-    #sk-container-id-50 div.sk-toggleable__content {
+    #sk-container-id-51 div.sk-toggleable__content {
       max-height: 0;
       max-width: 0;
       overflow: hidden;
@@ -1059,12 +1059,12 @@ following example, it would print that ``sample_weight`` is ``None``:
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-50 div.sk-toggleable__content.fitted {
+    #sk-container-id-51 div.sk-toggleable__content.fitted {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
-    #sk-container-id-50 div.sk-toggleable__content pre {
+    #sk-container-id-51 div.sk-toggleable__content pre {
       margin: 0.2em;
       border-radius: 0.25em;
       color: var(--sklearn-color-text);
@@ -1072,79 +1072,79 @@ following example, it would print that ``sample_weight`` is ``None``:
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-50 div.sk-toggleable__content.fitted pre {
+    #sk-container-id-51 div.sk-toggleable__content.fitted pre {
       /* unfitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
-    #sk-container-id-50 input.sk-toggleable__control:checked~div.sk-toggleable__content {
+    #sk-container-id-51 input.sk-toggleable__control:checked~div.sk-toggleable__content {
       /* Expand drop-down */
       max-height: 200px;
       max-width: 100%;
       overflow: auto;
     }
 
-    #sk-container-id-50 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
+    #sk-container-id-51 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
       content: "▾";
     }
 
     /* Pipeline/ColumnTransformer-specific style */
 
-    #sk-container-id-50 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-51 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-50 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-51 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
     /* Estimator-specific style */
 
     /* Colorize estimator box */
-    #sk-container-id-50 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-51 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-50 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-51 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
-    #sk-container-id-50 div.sk-label label.sk-toggleable__label,
-    #sk-container-id-50 div.sk-label label {
+    #sk-container-id-51 div.sk-label label.sk-toggleable__label,
+    #sk-container-id-51 div.sk-label label {
       /* The background is the default theme color */
       color: var(--sklearn-color-text-on-default-background);
     }
 
     /* On hover, darken the color of the background */
-    #sk-container-id-50 div.sk-label:hover label.sk-toggleable__label {
+    #sk-container-id-51 div.sk-label:hover label.sk-toggleable__label {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
     /* Label box, darken color on hover, fitted */
-    #sk-container-id-50 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
+    #sk-container-id-51 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
     /* Estimator label */
 
-    #sk-container-id-50 div.sk-label label {
+    #sk-container-id-51 div.sk-label label {
       font-family: monospace;
       font-weight: bold;
       display: inline-block;
       line-height: 1.2em;
     }
 
-    #sk-container-id-50 div.sk-label-container {
+    #sk-container-id-51 div.sk-label-container {
       text-align: center;
     }
 
     /* Estimator-specific */
-    #sk-container-id-50 div.sk-estimator {
+    #sk-container-id-51 div.sk-estimator {
       font-family: monospace;
       border: 1px dotted var(--sklearn-color-border-box);
       border-radius: 0.25em;
@@ -1154,18 +1154,18 @@ following example, it would print that ``sample_weight`` is ``None``:
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-50 div.sk-estimator.fitted {
+    #sk-container-id-51 div.sk-estimator.fitted {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
     /* on hover */
-    #sk-container-id-50 div.sk-estimator:hover {
+    #sk-container-id-51 div.sk-estimator:hover {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-50 div.sk-estimator.fitted:hover {
+    #sk-container-id-51 div.sk-estimator.fitted:hover {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-2);
     }
@@ -1253,7 +1253,7 @@ following example, it would print that ``sample_weight`` is ``None``:
 
     /* "?"-specific style due to the `<a>` HTML tag */
 
-    #sk-container-id-50 a.estimator_doc_link {
+    #sk-container-id-51 a.estimator_doc_link {
       float: right;
       font-size: 1rem;
       line-height: 1em;
@@ -1268,25 +1268,25 @@ following example, it would print that ``sample_weight`` is ``None``:
       border: var(--sklearn-color-unfitted-level-1) 1pt solid;
     }
 
-    #sk-container-id-50 a.estimator_doc_link.fitted {
+    #sk-container-id-51 a.estimator_doc_link.fitted {
       /* fitted */
       border: var(--sklearn-color-fitted-level-1) 1pt solid;
       color: var(--sklearn-color-fitted-level-1);
     }
 
     /* On hover */
-    #sk-container-id-50 a.estimator_doc_link:hover {
+    #sk-container-id-51 a.estimator_doc_link:hover {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-3);
       color: var(--sklearn-color-background);
       text-decoration: none;
     }
 
-    #sk-container-id-50 a.estimator_doc_link.fitted:hover {
+    #sk-container-id-51 a.estimator_doc_link.fitted:hover {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-3);
     }
-    </style><div id="sk-container-id-50" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>MetaClassifier(estimator=ExampleClassifier())</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-213" type="checkbox" ><label for="sk-estimator-id-213" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>MetaClassifier</div></div><div><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>MetaClassifier(estimator=ExampleClassifier())</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-214" type="checkbox" ><label for="sk-estimator-id-214" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>estimator: ExampleClassifier</div></div></label><div class="sk-toggleable__content fitted"><pre>ExampleClassifier()</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-215" type="checkbox" ><label for="sk-estimator-id-215" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>ExampleClassifier</div></div></label><div class="sk-toggleable__content fitted"><pre>ExampleClassifier()</pre></div> </div></div></div></div></div></div></div></div></div>
+    </style><div id="sk-container-id-51" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>MetaClassifier(estimator=ExampleClassifier())</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-227" type="checkbox" ><label for="sk-estimator-id-227" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>MetaClassifier</div></div><div><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>MetaClassifier(estimator=ExampleClassifier())</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-228" type="checkbox" ><label for="sk-estimator-id-228" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>estimator: ExampleClassifier</div></div></label><div class="sk-toggleable__content fitted"><pre>ExampleClassifier()</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-229" type="checkbox" ><label for="sk-estimator-id-229" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>ExampleClassifier</div></div></label><div class="sk-toggleable__content fitted"><pre>ExampleClassifier()</pre></div> </div></div></div></div></div></div></div></div></div>
     </div>
     <br />
     <br />
@@ -1412,7 +1412,7 @@ meta-estimator, but the meta-estimator understands that
 .. raw:: html
 
     <div class="output_subarea output_html rendered_html output_result">
-    <style>#sk-container-id-51 {
+    <style>#sk-container-id-52 {
       /* Definition of color scheme common for light and dark mode */
       --sklearn-color-text: #000;
       --sklearn-color-text-muted: #666;
@@ -1443,15 +1443,15 @@ meta-estimator, but the meta-estimator understands that
       }
     }
 
-    #sk-container-id-51 {
+    #sk-container-id-52 {
       color: var(--sklearn-color-text);
     }
 
-    #sk-container-id-51 pre {
+    #sk-container-id-52 pre {
       padding: 0;
     }
 
-    #sk-container-id-51 input.sk-hidden--visually {
+    #sk-container-id-52 input.sk-hidden--visually {
       border: 0;
       clip: rect(1px 1px 1px 1px);
       clip: rect(1px, 1px, 1px, 1px);
@@ -1463,7 +1463,7 @@ meta-estimator, but the meta-estimator understands that
       width: 1px;
     }
 
-    #sk-container-id-51 div.sk-dashed-wrapped {
+    #sk-container-id-52 div.sk-dashed-wrapped {
       border: 1px dashed var(--sklearn-color-line);
       margin: 0 0.4em 0.5em 0.4em;
       box-sizing: border-box;
@@ -1471,7 +1471,7 @@ meta-estimator, but the meta-estimator understands that
       background-color: var(--sklearn-color-background);
     }
 
-    #sk-container-id-51 div.sk-container {
+    #sk-container-id-52 div.sk-container {
       /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
          but bootstrap.min.css set `[hidden] { display: none !important; }`
          so we also need the `!important` here to be able to override the
@@ -1481,7 +1481,7 @@ meta-estimator, but the meta-estimator understands that
       position: relative;
     }
 
-    #sk-container-id-51 div.sk-text-repr-fallback {
+    #sk-container-id-52 div.sk-text-repr-fallback {
       display: none;
     }
 
@@ -1497,14 +1497,14 @@ meta-estimator, but the meta-estimator understands that
 
     /* Parallel-specific style estimator block */
 
-    #sk-container-id-51 div.sk-parallel-item::after {
+    #sk-container-id-52 div.sk-parallel-item::after {
       content: "";
       width: 100%;
       border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
       flex-grow: 1;
     }
 
-    #sk-container-id-51 div.sk-parallel {
+    #sk-container-id-52 div.sk-parallel {
       display: flex;
       align-items: stretch;
       justify-content: center;
@@ -1512,28 +1512,28 @@ meta-estimator, but the meta-estimator understands that
       position: relative;
     }
 
-    #sk-container-id-51 div.sk-parallel-item {
+    #sk-container-id-52 div.sk-parallel-item {
       display: flex;
       flex-direction: column;
     }
 
-    #sk-container-id-51 div.sk-parallel-item:first-child::after {
+    #sk-container-id-52 div.sk-parallel-item:first-child::after {
       align-self: flex-end;
       width: 50%;
     }
 
-    #sk-container-id-51 div.sk-parallel-item:last-child::after {
+    #sk-container-id-52 div.sk-parallel-item:last-child::after {
       align-self: flex-start;
       width: 50%;
     }
 
-    #sk-container-id-51 div.sk-parallel-item:only-child::after {
+    #sk-container-id-52 div.sk-parallel-item:only-child::after {
       width: 0;
     }
 
     /* Serial-specific style estimator block */
 
-    #sk-container-id-51 div.sk-serial {
+    #sk-container-id-52 div.sk-serial {
       display: flex;
       flex-direction: column;
       align-items: center;
@@ -1551,14 +1551,14 @@ meta-estimator, but the meta-estimator understands that
 
     /* Pipeline and ColumnTransformer style (default) */
 
-    #sk-container-id-51 div.sk-toggleable {
+    #sk-container-id-52 div.sk-toggleable {
       /* Default theme specific background. It is overwritten whether we have a
       specific estimator or a Pipeline/ColumnTransformer */
       background-color: var(--sklearn-color-background);
     }
 
     /* Toggleable label */
-    #sk-container-id-51 label.sk-toggleable__label {
+    #sk-container-id-52 label.sk-toggleable__label {
       cursor: pointer;
       display: flex;
       width: 100%;
@@ -1571,13 +1571,13 @@ meta-estimator, but the meta-estimator understands that
       gap: 0.5em;
     }
 
-    #sk-container-id-51 label.sk-toggleable__label .caption {
+    #sk-container-id-52 label.sk-toggleable__label .caption {
       font-size: 0.6rem;
       font-weight: lighter;
       color: var(--sklearn-color-text-muted);
     }
 
-    #sk-container-id-51 label.sk-toggleable__label-arrow:before {
+    #sk-container-id-52 label.sk-toggleable__label-arrow:before {
       /* Arrow on the left of the label */
       content: "▸";
       float: left;
@@ -1585,13 +1585,13 @@ meta-estimator, but the meta-estimator understands that
       color: var(--sklearn-color-icon);
     }
 
-    #sk-container-id-51 label.sk-toggleable__label-arrow:hover:before {
+    #sk-container-id-52 label.sk-toggleable__label-arrow:hover:before {
       color: var(--sklearn-color-text);
     }
 
     /* Toggleable content - dropdown */
 
-    #sk-container-id-51 div.sk-toggleable__content {
+    #sk-container-id-52 div.sk-toggleable__content {
       max-height: 0;
       max-width: 0;
       overflow: hidden;
@@ -1600,12 +1600,12 @@ meta-estimator, but the meta-estimator understands that
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-51 div.sk-toggleable__content.fitted {
+    #sk-container-id-52 div.sk-toggleable__content.fitted {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
-    #sk-container-id-51 div.sk-toggleable__content pre {
+    #sk-container-id-52 div.sk-toggleable__content pre {
       margin: 0.2em;
       border-radius: 0.25em;
       color: var(--sklearn-color-text);
@@ -1613,79 +1613,79 @@ meta-estimator, but the meta-estimator understands that
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-51 div.sk-toggleable__content.fitted pre {
+    #sk-container-id-52 div.sk-toggleable__content.fitted pre {
       /* unfitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
-    #sk-container-id-51 input.sk-toggleable__control:checked~div.sk-toggleable__content {
+    #sk-container-id-52 input.sk-toggleable__control:checked~div.sk-toggleable__content {
       /* Expand drop-down */
       max-height: 200px;
       max-width: 100%;
       overflow: auto;
     }
 
-    #sk-container-id-51 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
+    #sk-container-id-52 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
       content: "▾";
     }
 
     /* Pipeline/ColumnTransformer-specific style */
 
-    #sk-container-id-51 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-52 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-51 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-52 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
     /* Estimator-specific style */
 
     /* Colorize estimator box */
-    #sk-container-id-51 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-52 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-51 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-52 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
-    #sk-container-id-51 div.sk-label label.sk-toggleable__label,
-    #sk-container-id-51 div.sk-label label {
+    #sk-container-id-52 div.sk-label label.sk-toggleable__label,
+    #sk-container-id-52 div.sk-label label {
       /* The background is the default theme color */
       color: var(--sklearn-color-text-on-default-background);
     }
 
     /* On hover, darken the color of the background */
-    #sk-container-id-51 div.sk-label:hover label.sk-toggleable__label {
+    #sk-container-id-52 div.sk-label:hover label.sk-toggleable__label {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
     /* Label box, darken color on hover, fitted */
-    #sk-container-id-51 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
+    #sk-container-id-52 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
     /* Estimator label */
 
-    #sk-container-id-51 div.sk-label label {
+    #sk-container-id-52 div.sk-label label {
       font-family: monospace;
       font-weight: bold;
       display: inline-block;
       line-height: 1.2em;
     }
 
-    #sk-container-id-51 div.sk-label-container {
+    #sk-container-id-52 div.sk-label-container {
       text-align: center;
     }
 
     /* Estimator-specific */
-    #sk-container-id-51 div.sk-estimator {
+    #sk-container-id-52 div.sk-estimator {
       font-family: monospace;
       border: 1px dotted var(--sklearn-color-border-box);
       border-radius: 0.25em;
@@ -1695,18 +1695,18 @@ meta-estimator, but the meta-estimator understands that
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-51 div.sk-estimator.fitted {
+    #sk-container-id-52 div.sk-estimator.fitted {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
     /* on hover */
-    #sk-container-id-51 div.sk-estimator:hover {
+    #sk-container-id-52 div.sk-estimator:hover {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-51 div.sk-estimator.fitted:hover {
+    #sk-container-id-52 div.sk-estimator.fitted:hover {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-2);
     }
@@ -1794,7 +1794,7 @@ meta-estimator, but the meta-estimator understands that
 
     /* "?"-specific style due to the `<a>` HTML tag */
 
-    #sk-container-id-51 a.estimator_doc_link {
+    #sk-container-id-52 a.estimator_doc_link {
       float: right;
       font-size: 1rem;
       line-height: 1em;
@@ -1809,25 +1809,25 @@ meta-estimator, but the meta-estimator understands that
       border: var(--sklearn-color-unfitted-level-1) 1pt solid;
     }
 
-    #sk-container-id-51 a.estimator_doc_link.fitted {
+    #sk-container-id-52 a.estimator_doc_link.fitted {
       /* fitted */
       border: var(--sklearn-color-fitted-level-1) 1pt solid;
       color: var(--sklearn-color-fitted-level-1);
     }
 
     /* On hover */
-    #sk-container-id-51 a.estimator_doc_link:hover {
+    #sk-container-id-52 a.estimator_doc_link:hover {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-3);
       color: var(--sklearn-color-background);
       text-decoration: none;
     }
 
-    #sk-container-id-51 a.estimator_doc_link.fitted:hover {
+    #sk-container-id-52 a.estimator_doc_link.fitted:hover {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-3);
     }
-    </style><div id="sk-container-id-51" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>MetaClassifier(estimator=ExampleClassifier())</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-216" type="checkbox" ><label for="sk-estimator-id-216" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>MetaClassifier</div></div><div><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>MetaClassifier(estimator=ExampleClassifier())</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-217" type="checkbox" ><label for="sk-estimator-id-217" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>estimator: ExampleClassifier</div></div></label><div class="sk-toggleable__content fitted"><pre>ExampleClassifier()</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-218" type="checkbox" ><label for="sk-estimator-id-218" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>ExampleClassifier</div></div></label><div class="sk-toggleable__content fitted"><pre>ExampleClassifier()</pre></div> </div></div></div></div></div></div></div></div></div>
+    </style><div id="sk-container-id-52" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>MetaClassifier(estimator=ExampleClassifier())</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-230" type="checkbox" ><label for="sk-estimator-id-230" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>MetaClassifier</div></div><div><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>MetaClassifier(estimator=ExampleClassifier())</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-231" type="checkbox" ><label for="sk-estimator-id-231" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>estimator: ExampleClassifier</div></div></label><div class="sk-toggleable__content fitted"><pre>ExampleClassifier()</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-232" type="checkbox" ><label for="sk-estimator-id-232" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>ExampleClassifier</div></div></label><div class="sk-toggleable__content fitted"><pre>ExampleClassifier()</pre></div> </div></div></div></div></div></div></div></div></div>
     </div>
     <br />
     <br />
@@ -2199,7 +2199,7 @@ it doesn't validate and route its own required metadata:
 .. raw:: html
 
     <div class="output_subarea output_html rendered_html output_result">
-    <style>#sk-container-id-52 {
+    <style>#sk-container-id-53 {
       /* Definition of color scheme common for light and dark mode */
       --sklearn-color-text: #000;
       --sklearn-color-text-muted: #666;
@@ -2230,15 +2230,15 @@ it doesn't validate and route its own required metadata:
       }
     }
 
-    #sk-container-id-52 {
+    #sk-container-id-53 {
       color: var(--sklearn-color-text);
     }
 
-    #sk-container-id-52 pre {
+    #sk-container-id-53 pre {
       padding: 0;
     }
 
-    #sk-container-id-52 input.sk-hidden--visually {
+    #sk-container-id-53 input.sk-hidden--visually {
       border: 0;
       clip: rect(1px 1px 1px 1px);
       clip: rect(1px, 1px, 1px, 1px);
@@ -2250,7 +2250,7 @@ it doesn't validate and route its own required metadata:
       width: 1px;
     }
 
-    #sk-container-id-52 div.sk-dashed-wrapped {
+    #sk-container-id-53 div.sk-dashed-wrapped {
       border: 1px dashed var(--sklearn-color-line);
       margin: 0 0.4em 0.5em 0.4em;
       box-sizing: border-box;
@@ -2258,7 +2258,7 @@ it doesn't validate and route its own required metadata:
       background-color: var(--sklearn-color-background);
     }
 
-    #sk-container-id-52 div.sk-container {
+    #sk-container-id-53 div.sk-container {
       /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
          but bootstrap.min.css set `[hidden] { display: none !important; }`
          so we also need the `!important` here to be able to override the
@@ -2268,7 +2268,7 @@ it doesn't validate and route its own required metadata:
       position: relative;
     }
 
-    #sk-container-id-52 div.sk-text-repr-fallback {
+    #sk-container-id-53 div.sk-text-repr-fallback {
       display: none;
     }
 
@@ -2284,14 +2284,14 @@ it doesn't validate and route its own required metadata:
 
     /* Parallel-specific style estimator block */
 
-    #sk-container-id-52 div.sk-parallel-item::after {
+    #sk-container-id-53 div.sk-parallel-item::after {
       content: "";
       width: 100%;
       border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
       flex-grow: 1;
     }
 
-    #sk-container-id-52 div.sk-parallel {
+    #sk-container-id-53 div.sk-parallel {
       display: flex;
       align-items: stretch;
       justify-content: center;
@@ -2299,28 +2299,28 @@ it doesn't validate and route its own required metadata:
       position: relative;
     }
 
-    #sk-container-id-52 div.sk-parallel-item {
+    #sk-container-id-53 div.sk-parallel-item {
       display: flex;
       flex-direction: column;
     }
 
-    #sk-container-id-52 div.sk-parallel-item:first-child::after {
+    #sk-container-id-53 div.sk-parallel-item:first-child::after {
       align-self: flex-end;
       width: 50%;
     }
 
-    #sk-container-id-52 div.sk-parallel-item:last-child::after {
+    #sk-container-id-53 div.sk-parallel-item:last-child::after {
       align-self: flex-start;
       width: 50%;
     }
 
-    #sk-container-id-52 div.sk-parallel-item:only-child::after {
+    #sk-container-id-53 div.sk-parallel-item:only-child::after {
       width: 0;
     }
 
     /* Serial-specific style estimator block */
 
-    #sk-container-id-52 div.sk-serial {
+    #sk-container-id-53 div.sk-serial {
       display: flex;
       flex-direction: column;
       align-items: center;
@@ -2338,14 +2338,14 @@ it doesn't validate and route its own required metadata:
 
     /* Pipeline and ColumnTransformer style (default) */
 
-    #sk-container-id-52 div.sk-toggleable {
+    #sk-container-id-53 div.sk-toggleable {
       /* Default theme specific background. It is overwritten whether we have a
       specific estimator or a Pipeline/ColumnTransformer */
       background-color: var(--sklearn-color-background);
     }
 
     /* Toggleable label */
-    #sk-container-id-52 label.sk-toggleable__label {
+    #sk-container-id-53 label.sk-toggleable__label {
       cursor: pointer;
       display: flex;
       width: 100%;
@@ -2358,13 +2358,13 @@ it doesn't validate and route its own required metadata:
       gap: 0.5em;
     }
 
-    #sk-container-id-52 label.sk-toggleable__label .caption {
+    #sk-container-id-53 label.sk-toggleable__label .caption {
       font-size: 0.6rem;
       font-weight: lighter;
       color: var(--sklearn-color-text-muted);
     }
 
-    #sk-container-id-52 label.sk-toggleable__label-arrow:before {
+    #sk-container-id-53 label.sk-toggleable__label-arrow:before {
       /* Arrow on the left of the label */
       content: "▸";
       float: left;
@@ -2372,13 +2372,13 @@ it doesn't validate and route its own required metadata:
       color: var(--sklearn-color-icon);
     }
 
-    #sk-container-id-52 label.sk-toggleable__label-arrow:hover:before {
+    #sk-container-id-53 label.sk-toggleable__label-arrow:hover:before {
       color: var(--sklearn-color-text);
     }
 
     /* Toggleable content - dropdown */
 
-    #sk-container-id-52 div.sk-toggleable__content {
+    #sk-container-id-53 div.sk-toggleable__content {
       max-height: 0;
       max-width: 0;
       overflow: hidden;
@@ -2387,12 +2387,12 @@ it doesn't validate and route its own required metadata:
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-52 div.sk-toggleable__content.fitted {
+    #sk-container-id-53 div.sk-toggleable__content.fitted {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
-    #sk-container-id-52 div.sk-toggleable__content pre {
+    #sk-container-id-53 div.sk-toggleable__content pre {
       margin: 0.2em;
       border-radius: 0.25em;
       color: var(--sklearn-color-text);
@@ -2400,79 +2400,79 @@ it doesn't validate and route its own required metadata:
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-52 div.sk-toggleable__content.fitted pre {
+    #sk-container-id-53 div.sk-toggleable__content.fitted pre {
       /* unfitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
-    #sk-container-id-52 input.sk-toggleable__control:checked~div.sk-toggleable__content {
+    #sk-container-id-53 input.sk-toggleable__control:checked~div.sk-toggleable__content {
       /* Expand drop-down */
       max-height: 200px;
       max-width: 100%;
       overflow: auto;
     }
 
-    #sk-container-id-52 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
+    #sk-container-id-53 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
       content: "▾";
     }
 
     /* Pipeline/ColumnTransformer-specific style */
 
-    #sk-container-id-52 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-53 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-52 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-53 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
     /* Estimator-specific style */
 
     /* Colorize estimator box */
-    #sk-container-id-52 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-53 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-52 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-53 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
-    #sk-container-id-52 div.sk-label label.sk-toggleable__label,
-    #sk-container-id-52 div.sk-label label {
+    #sk-container-id-53 div.sk-label label.sk-toggleable__label,
+    #sk-container-id-53 div.sk-label label {
       /* The background is the default theme color */
       color: var(--sklearn-color-text-on-default-background);
     }
 
     /* On hover, darken the color of the background */
-    #sk-container-id-52 div.sk-label:hover label.sk-toggleable__label {
+    #sk-container-id-53 div.sk-label:hover label.sk-toggleable__label {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
     /* Label box, darken color on hover, fitted */
-    #sk-container-id-52 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
+    #sk-container-id-53 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
     /* Estimator label */
 
-    #sk-container-id-52 div.sk-label label {
+    #sk-container-id-53 div.sk-label label {
       font-family: monospace;
       font-weight: bold;
       display: inline-block;
       line-height: 1.2em;
     }
 
-    #sk-container-id-52 div.sk-label-container {
+    #sk-container-id-53 div.sk-label-container {
       text-align: center;
     }
 
     /* Estimator-specific */
-    #sk-container-id-52 div.sk-estimator {
+    #sk-container-id-53 div.sk-estimator {
       font-family: monospace;
       border: 1px dotted var(--sklearn-color-border-box);
       border-radius: 0.25em;
@@ -2482,18 +2482,18 @@ it doesn't validate and route its own required metadata:
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-52 div.sk-estimator.fitted {
+    #sk-container-id-53 div.sk-estimator.fitted {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
     /* on hover */
-    #sk-container-id-52 div.sk-estimator:hover {
+    #sk-container-id-53 div.sk-estimator:hover {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-52 div.sk-estimator.fitted:hover {
+    #sk-container-id-53 div.sk-estimator.fitted:hover {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-2);
     }
@@ -2581,7 +2581,7 @@ it doesn't validate and route its own required metadata:
 
     /* "?"-specific style due to the `<a>` HTML tag */
 
-    #sk-container-id-52 a.estimator_doc_link {
+    #sk-container-id-53 a.estimator_doc_link {
       float: right;
       font-size: 1rem;
       line-height: 1em;
@@ -2596,25 +2596,25 @@ it doesn't validate and route its own required metadata:
       border: var(--sklearn-color-unfitted-level-1) 1pt solid;
     }
 
-    #sk-container-id-52 a.estimator_doc_link.fitted {
+    #sk-container-id-53 a.estimator_doc_link.fitted {
       /* fitted */
       border: var(--sklearn-color-fitted-level-1) 1pt solid;
       color: var(--sklearn-color-fitted-level-1);
     }
 
     /* On hover */
-    #sk-container-id-52 a.estimator_doc_link:hover {
+    #sk-container-id-53 a.estimator_doc_link:hover {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-3);
       color: var(--sklearn-color-background);
       text-decoration: none;
     }
 
-    #sk-container-id-52 a.estimator_doc_link.fitted:hover {
+    #sk-container-id-53 a.estimator_doc_link.fitted:hover {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-3);
     }
-    </style><div id="sk-container-id-52" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>RouterConsumerClassifier(estimator=ExampleClassifier())</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-219" type="checkbox" ><label for="sk-estimator-id-219" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>RouterConsumerClassifier</div></div><div><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>RouterConsumerClassifier(estimator=ExampleClassifier())</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-220" type="checkbox" ><label for="sk-estimator-id-220" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>estimator: ExampleClassifier</div></div></label><div class="sk-toggleable__content fitted"><pre>ExampleClassifier()</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-221" type="checkbox" ><label for="sk-estimator-id-221" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>ExampleClassifier</div></div></label><div class="sk-toggleable__content fitted"><pre>ExampleClassifier()</pre></div> </div></div></div></div></div></div></div></div></div>
+    </style><div id="sk-container-id-53" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>RouterConsumerClassifier(estimator=ExampleClassifier())</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-233" type="checkbox" ><label for="sk-estimator-id-233" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>RouterConsumerClassifier</div></div><div><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>RouterConsumerClassifier(estimator=ExampleClassifier())</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-234" type="checkbox" ><label for="sk-estimator-id-234" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>estimator: ExampleClassifier</div></div></label><div class="sk-toggleable__content fitted"><pre>ExampleClassifier()</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-235" type="checkbox" ><label for="sk-estimator-id-235" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>ExampleClassifier</div></div></label><div class="sk-toggleable__content fitted"><pre>ExampleClassifier()</pre></div> </div></div></div></div></div></div></div></div></div>
     </div>
     <br />
     <br />
@@ -3061,7 +3061,7 @@ need to modify your code at all:
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.042 seconds)
+   **Total running time of the script:** (0 minutes 0.044 seconds)
 
 
 .. _sphx_glr_download_auto_examples_miscellaneous_plot_metadata_routing.py:
diff --git a/dev/_sources/auto_examples/miscellaneous/plot_multilabel.rst.txt b/dev/_sources/auto_examples/miscellaneous/plot_multilabel.rst.txt
index baaa7471ca0a5..1145a5d4140d5 100644
--- a/dev/_sources/auto_examples/miscellaneous/plot_multilabel.rst.txt
+++ b/dev/_sources/auto_examples/miscellaneous/plot_multilabel.rst.txt
@@ -104,8 +104,8 @@ have a label.
         plt.subplot(2, 2, subplot)
         plt.title(title)
 
-        zero_class = np.where(Y[:, 0])
-        one_class = np.where(Y[:, 1])
+        zero_class = (Y[:, 0]).nonzero()
+        one_class = (Y[:, 1]).nonzero()
         plt.scatter(X[:, 0], X[:, 1], s=40, c="gray", edgecolors=(0, 0, 0))
         plt.scatter(
             X[zero_class, 0],
@@ -165,7 +165,7 @@ have a label.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.196 seconds)
+   **Total running time of the script:** (0 minutes 0.189 seconds)
 
 
 .. _sphx_glr_download_auto_examples_miscellaneous_plot_multilabel.py:
diff --git a/dev/_sources/auto_examples/miscellaneous/plot_multioutput_face_completion.rst.txt b/dev/_sources/auto_examples/miscellaneous/plot_multioutput_face_completion.rst.txt
index 75240c11fd02c..944363ed1dcba 100644
--- a/dev/_sources/auto_examples/miscellaneous/plot_multioutput_face_completion.rst.txt
+++ b/dev/_sources/auto_examples/miscellaneous/plot_multioutput_face_completion.rst.txt
@@ -133,7 +133,7 @@ regression and ridge regression complete the lower half of those faces.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 1.759 seconds)
+   **Total running time of the script:** (0 minutes 1.872 seconds)
 
 
 .. _sphx_glr_download_auto_examples_miscellaneous_plot_multioutput_face_completion.py:
diff --git a/dev/_sources/auto_examples/miscellaneous/plot_outlier_detection_bench.rst.txt b/dev/_sources/auto_examples/miscellaneous/plot_outlier_detection_bench.rst.txt
index 3f378e9862c27..b2860d036a561 100644
--- a/dev/_sources/auto_examples/miscellaneous/plot_outlier_detection_bench.rst.txt
+++ b/dev/_sources/auto_examples/miscellaneous/plot_outlier_detection_bench.rst.txt
@@ -137,12 +137,12 @@ The following `fit_predict` function returns the average outlier score of X.
         tic = perf_counter()
         if estimator[-1].__class__.__name__ == "LocalOutlierFactor":
             estimator.fit(X)
-            y_pred = estimator[-1].negative_outlier_factor_
+            y_score = estimator[-1].negative_outlier_factor_
         else:  # "IsolationForest"
-            y_pred = estimator.fit(X).decision_function(X)
+            y_score = estimator.fit(X).decision_function(X)
         toc = perf_counter()
         print(f"Duration for {model_name}: {toc - tic:.2f} s")
-        return y_pred
+        return y_score
 
 
 
@@ -215,7 +215,7 @@ The SA dataset contains 41 features out of which 3 are categorical:
 .. code-block:: Python
 
     y_true = {}
-    y_pred = {"LOF": {}, "IForest": {}}
+    y_score = {"LOF": {}, "IForest": {}}
     model_names = ["LOF", "IForest"]
     cat_columns = ["protocol_type", "service", "flag"]
 
@@ -227,7 +227,7 @@ The SA dataset contains 41 features out of which 3 are categorical:
             lof_kw={"n_neighbors": int(n_samples * anomaly_frac)},
             iforest_kw={"random_state": 42},
         )
-        y_pred[model_name]["KDDCup99 - SA"] = fit_predict(model, X)
+        y_score[model_name]["KDDCup99 - SA"] = fit_predict(model, X)
 
 
 
@@ -237,8 +237,8 @@ The SA dataset contains 41 features out of which 3 are categorical:
 
  .. code-block:: none
 
-    Duration for LOF: 1.80 s
-    Duration for IForest: 0.29 s
+    Duration for LOF: 1.90 s
+    Duration for IForest: 0.28 s
 
 
 
@@ -296,7 +296,7 @@ samples encoded with label 2 and outliers as those with label 4.
             lof_kw={"n_neighbors": int(n_samples * anomaly_frac)},
             iforest_kw={"random_state": 42},
         )
-        y_pred[model_name]["forestcover"] = fit_predict(model, X)
+        y_score[model_name]["forestcover"] = fit_predict(model, X)
 
 
 
@@ -306,8 +306,8 @@ samples encoded with label 2 and outliers as those with label 4.
 
  .. code-block:: none
 
-    Duration for LOF: 1.83 s
-    Duration for IForest: 0.24 s
+    Duration for LOF: 1.84 s
+    Duration for IForest: 0.22 s
 
 
 
@@ -403,7 +403,7 @@ a list made by hand.
             lof_kw={"n_neighbors": int(n_samples * anomaly_frac)},
             iforest_kw={"random_state": 42},
         )
-        y_pred[model_name]["ames_housing"] = fit_predict(model, X)
+        y_score[model_name]["ames_housing"] = fit_predict(model, X)
 
 
 
@@ -413,8 +413,8 @@ a list made by hand.
 
  .. code-block:: none
 
-    Duration for LOF: 0.84 s
-    Duration for IForest: 0.25 s
+    Duration for LOF: 0.82 s
+    Duration for IForest: 0.22 s
 
 
 
@@ -466,7 +466,7 @@ which are binary encoded and some are continuous.
             lof_kw={"n_neighbors": int(n_samples * anomaly_frac)},
             iforest_kw={"random_state": 42},
         )
-        y_pred[model_name]["cardiotocography"] = fit_predict(model, X)
+        y_score[model_name]["cardiotocography"] = fit_predict(model, X)
 
 
 
@@ -477,7 +477,7 @@ which are binary encoded and some are continuous.
  .. code-block:: none
 
     Duration for LOF: 0.06 s
-    Duration for IForest: 0.16 s
+    Duration for IForest: 0.14 s
 
 
 
@@ -512,7 +512,7 @@ inliers.
         for model_idx, model_name in enumerate(model_names):
             display = RocCurveDisplay.from_predictions(
                 y_true[dataset_name],
-                y_pred[model_name][dataset_name],
+                y_score[model_name][dataset_name],
                 pos_label=pos_label,
                 name=model_name,
                 ax=ax,
@@ -575,10 +575,10 @@ difficult.
     for model_idx, (linestyle, n_neighbors) in enumerate(zip(linestyles, n_neighbors_list)):
         model.set_params(localoutlierfactor__n_neighbors=n_neighbors)
         model.fit(X)
-        y_pred = model[-1].negative_outlier_factor_
+        y_score = model[-1].negative_outlier_factor_
         display = RocCurveDisplay.from_predictions(
             y,
-            y_pred,
+            y_score,
             pos_label=pos_label,
             name=f"n_neighbors = {n_neighbors}",
             ax=ax,
@@ -631,10 +631,10 @@ contamination.
     ):
         model = make_pipeline(preprocessor, lof)
         model.fit(X)
-        y_pred = model[-1].negative_outlier_factor_
+        y_score = model[-1].negative_outlier_factor_
         display = RocCurveDisplay.from_predictions(
             y,
-            y_pred,
+            y_score,
             pos_label=pos_label,
             name=str(preprocessor).split("(")[0],
             ax=ax,
@@ -699,10 +699,10 @@ Note that the optimal preprocessing depends on the dataset, as shown below:
     ):
         model = make_pipeline(preprocessor, lof)
         model.fit(X)
-        y_pred = model[-1].negative_outlier_factor_
+        y_score = model[-1].negative_outlier_factor_
         display = RocCurveDisplay.from_predictions(
             y,
-            y_pred,
+            y_score,
             pos_label=pos_label,
             name=str(preprocessor).split("(")[0],
             ax=ax,
@@ -730,7 +730,7 @@ Note that the optimal preprocessing depends on the dataset, as shown below:
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 48.298 seconds)
+   **Total running time of the script:** (0 minutes 57.950 seconds)
 
 
 .. _sphx_glr_download_auto_examples_miscellaneous_plot_outlier_detection_bench.py:
diff --git a/dev/_sources/auto_examples/miscellaneous/plot_partial_dependence_visualization_api.rst.txt b/dev/_sources/auto_examples/miscellaneous/plot_partial_dependence_visualization_api.rst.txt
index b0a0ec8659733..a2dac884f8195 100644
--- a/dev/_sources/auto_examples/miscellaneous/plot_partial_dependence_visualization_api.rst.txt
+++ b/dev/_sources/auto_examples/miscellaneous/plot_partial_dependence_visualization_api.rst.txt
@@ -88,7 +88,7 @@ dataset.
 .. raw:: html
 
     <div class="output_subarea output_html rendered_html output_result">
-    <style>#sk-container-id-39 {
+    <style>#sk-container-id-40 {
       /* Definition of color scheme common for light and dark mode */
       --sklearn-color-text: #000;
       --sklearn-color-text-muted: #666;
@@ -119,15 +119,15 @@ dataset.
       }
     }
 
-    #sk-container-id-39 {
+    #sk-container-id-40 {
       color: var(--sklearn-color-text);
     }
 
-    #sk-container-id-39 pre {
+    #sk-container-id-40 pre {
       padding: 0;
     }
 
-    #sk-container-id-39 input.sk-hidden--visually {
+    #sk-container-id-40 input.sk-hidden--visually {
       border: 0;
       clip: rect(1px 1px 1px 1px);
       clip: rect(1px, 1px, 1px, 1px);
@@ -139,7 +139,7 @@ dataset.
       width: 1px;
     }
 
-    #sk-container-id-39 div.sk-dashed-wrapped {
+    #sk-container-id-40 div.sk-dashed-wrapped {
       border: 1px dashed var(--sklearn-color-line);
       margin: 0 0.4em 0.5em 0.4em;
       box-sizing: border-box;
@@ -147,7 +147,7 @@ dataset.
       background-color: var(--sklearn-color-background);
     }
 
-    #sk-container-id-39 div.sk-container {
+    #sk-container-id-40 div.sk-container {
       /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
          but bootstrap.min.css set `[hidden] { display: none !important; }`
          so we also need the `!important` here to be able to override the
@@ -157,7 +157,7 @@ dataset.
       position: relative;
     }
 
-    #sk-container-id-39 div.sk-text-repr-fallback {
+    #sk-container-id-40 div.sk-text-repr-fallback {
       display: none;
     }
 
@@ -173,14 +173,14 @@ dataset.
 
     /* Parallel-specific style estimator block */
 
-    #sk-container-id-39 div.sk-parallel-item::after {
+    #sk-container-id-40 div.sk-parallel-item::after {
       content: "";
       width: 100%;
       border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
       flex-grow: 1;
     }
 
-    #sk-container-id-39 div.sk-parallel {
+    #sk-container-id-40 div.sk-parallel {
       display: flex;
       align-items: stretch;
       justify-content: center;
@@ -188,28 +188,28 @@ dataset.
       position: relative;
     }
 
-    #sk-container-id-39 div.sk-parallel-item {
+    #sk-container-id-40 div.sk-parallel-item {
       display: flex;
       flex-direction: column;
     }
 
-    #sk-container-id-39 div.sk-parallel-item:first-child::after {
+    #sk-container-id-40 div.sk-parallel-item:first-child::after {
       align-self: flex-end;
       width: 50%;
     }
 
-    #sk-container-id-39 div.sk-parallel-item:last-child::after {
+    #sk-container-id-40 div.sk-parallel-item:last-child::after {
       align-self: flex-start;
       width: 50%;
     }
 
-    #sk-container-id-39 div.sk-parallel-item:only-child::after {
+    #sk-container-id-40 div.sk-parallel-item:only-child::after {
       width: 0;
     }
 
     /* Serial-specific style estimator block */
 
-    #sk-container-id-39 div.sk-serial {
+    #sk-container-id-40 div.sk-serial {
       display: flex;
       flex-direction: column;
       align-items: center;
@@ -227,14 +227,14 @@ dataset.
 
     /* Pipeline and ColumnTransformer style (default) */
 
-    #sk-container-id-39 div.sk-toggleable {
+    #sk-container-id-40 div.sk-toggleable {
       /* Default theme specific background. It is overwritten whether we have a
       specific estimator or a Pipeline/ColumnTransformer */
       background-color: var(--sklearn-color-background);
     }
 
     /* Toggleable label */
-    #sk-container-id-39 label.sk-toggleable__label {
+    #sk-container-id-40 label.sk-toggleable__label {
       cursor: pointer;
       display: flex;
       width: 100%;
@@ -247,13 +247,13 @@ dataset.
       gap: 0.5em;
     }
 
-    #sk-container-id-39 label.sk-toggleable__label .caption {
+    #sk-container-id-40 label.sk-toggleable__label .caption {
       font-size: 0.6rem;
       font-weight: lighter;
       color: var(--sklearn-color-text-muted);
     }
 
-    #sk-container-id-39 label.sk-toggleable__label-arrow:before {
+    #sk-container-id-40 label.sk-toggleable__label-arrow:before {
       /* Arrow on the left of the label */
       content: "▸";
       float: left;
@@ -261,13 +261,13 @@ dataset.
       color: var(--sklearn-color-icon);
     }
 
-    #sk-container-id-39 label.sk-toggleable__label-arrow:hover:before {
+    #sk-container-id-40 label.sk-toggleable__label-arrow:hover:before {
       color: var(--sklearn-color-text);
     }
 
     /* Toggleable content - dropdown */
 
-    #sk-container-id-39 div.sk-toggleable__content {
+    #sk-container-id-40 div.sk-toggleable__content {
       max-height: 0;
       max-width: 0;
       overflow: hidden;
@@ -276,12 +276,12 @@ dataset.
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-39 div.sk-toggleable__content.fitted {
+    #sk-container-id-40 div.sk-toggleable__content.fitted {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
-    #sk-container-id-39 div.sk-toggleable__content pre {
+    #sk-container-id-40 div.sk-toggleable__content pre {
       margin: 0.2em;
       border-radius: 0.25em;
       color: var(--sklearn-color-text);
@@ -289,79 +289,79 @@ dataset.
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-39 div.sk-toggleable__content.fitted pre {
+    #sk-container-id-40 div.sk-toggleable__content.fitted pre {
       /* unfitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
-    #sk-container-id-39 input.sk-toggleable__control:checked~div.sk-toggleable__content {
+    #sk-container-id-40 input.sk-toggleable__control:checked~div.sk-toggleable__content {
       /* Expand drop-down */
       max-height: 200px;
       max-width: 100%;
       overflow: auto;
     }
 
-    #sk-container-id-39 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
+    #sk-container-id-40 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
       content: "▾";
     }
 
     /* Pipeline/ColumnTransformer-specific style */
 
-    #sk-container-id-39 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-40 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-39 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-40 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
     /* Estimator-specific style */
 
     /* Colorize estimator box */
-    #sk-container-id-39 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-40 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-39 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-40 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
-    #sk-container-id-39 div.sk-label label.sk-toggleable__label,
-    #sk-container-id-39 div.sk-label label {
+    #sk-container-id-40 div.sk-label label.sk-toggleable__label,
+    #sk-container-id-40 div.sk-label label {
       /* The background is the default theme color */
       color: var(--sklearn-color-text-on-default-background);
     }
 
     /* On hover, darken the color of the background */
-    #sk-container-id-39 div.sk-label:hover label.sk-toggleable__label {
+    #sk-container-id-40 div.sk-label:hover label.sk-toggleable__label {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
     /* Label box, darken color on hover, fitted */
-    #sk-container-id-39 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
+    #sk-container-id-40 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
     /* Estimator label */
 
-    #sk-container-id-39 div.sk-label label {
+    #sk-container-id-40 div.sk-label label {
       font-family: monospace;
       font-weight: bold;
       display: inline-block;
       line-height: 1.2em;
     }
 
-    #sk-container-id-39 div.sk-label-container {
+    #sk-container-id-40 div.sk-label-container {
       text-align: center;
     }
 
     /* Estimator-specific */
-    #sk-container-id-39 div.sk-estimator {
+    #sk-container-id-40 div.sk-estimator {
       font-family: monospace;
       border: 1px dotted var(--sklearn-color-border-box);
       border-radius: 0.25em;
@@ -371,18 +371,18 @@ dataset.
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-39 div.sk-estimator.fitted {
+    #sk-container-id-40 div.sk-estimator.fitted {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
     /* on hover */
-    #sk-container-id-39 div.sk-estimator:hover {
+    #sk-container-id-40 div.sk-estimator:hover {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-39 div.sk-estimator.fitted:hover {
+    #sk-container-id-40 div.sk-estimator.fitted:hover {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-2);
     }
@@ -470,7 +470,7 @@ dataset.
 
     /* "?"-specific style due to the `<a>` HTML tag */
 
-    #sk-container-id-39 a.estimator_doc_link {
+    #sk-container-id-40 a.estimator_doc_link {
       float: right;
       font-size: 1rem;
       line-height: 1em;
@@ -485,31 +485,31 @@ dataset.
       border: var(--sklearn-color-unfitted-level-1) 1pt solid;
     }
 
-    #sk-container-id-39 a.estimator_doc_link.fitted {
+    #sk-container-id-40 a.estimator_doc_link.fitted {
       /* fitted */
       border: var(--sklearn-color-fitted-level-1) 1pt solid;
       color: var(--sklearn-color-fitted-level-1);
     }
 
     /* On hover */
-    #sk-container-id-39 a.estimator_doc_link:hover {
+    #sk-container-id-40 a.estimator_doc_link:hover {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-3);
       color: var(--sklearn-color-background);
       text-decoration: none;
     }
 
-    #sk-container-id-39 a.estimator_doc_link.fitted:hover {
+    #sk-container-id-40 a.estimator_doc_link.fitted:hover {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-3);
     }
-    </style><div id="sk-container-id-39" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),
+    </style><div id="sk-container-id-40" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),
                     (&#x27;mlpregressor&#x27;,
                      MLPRegressor(hidden_layer_sizes=(100, 100), max_iter=500,
-                                  random_state=0, tol=0.01))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-163" type="checkbox" ><label for="sk-estimator-id-163" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>Pipeline</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.pipeline.Pipeline.html">?<span>Documentation for Pipeline</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),
+                                  random_state=0, tol=0.01))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-177" type="checkbox" ><label for="sk-estimator-id-177" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>Pipeline</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.pipeline.Pipeline.html">?<span>Documentation for Pipeline</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),
                     (&#x27;mlpregressor&#x27;,
                      MLPRegressor(hidden_layer_sizes=(100, 100), max_iter=500,
-                                  random_state=0, tol=0.01))])</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-164" type="checkbox" ><label for="sk-estimator-id-164" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>StandardScaler</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.StandardScaler.html">?<span>Documentation for StandardScaler</span></a></div></label><div class="sk-toggleable__content fitted"><pre>StandardScaler()</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-165" type="checkbox" ><label for="sk-estimator-id-165" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>MLPRegressor</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.neural_network.MLPRegressor.html">?<span>Documentation for MLPRegressor</span></a></div></label><div class="sk-toggleable__content fitted"><pre>MLPRegressor(hidden_layer_sizes=(100, 100), max_iter=500, random_state=0,
+                                  random_state=0, tol=0.01))])</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-178" type="checkbox" ><label for="sk-estimator-id-178" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>StandardScaler</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.StandardScaler.html">?<span>Documentation for StandardScaler</span></a></div></label><div class="sk-toggleable__content fitted"><pre>StandardScaler()</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-179" type="checkbox" ><label for="sk-estimator-id-179" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>MLPRegressor</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.neural_network.MLPRegressor.html">?<span>Documentation for MLPRegressor</span></a></div></label><div class="sk-toggleable__content fitted"><pre>MLPRegressor(hidden_layer_sizes=(100, 100), max_iter=500, random_state=0,
                  tol=0.01)</pre></div> </div></div></div></div></div></div>
     </div>
     <br />
@@ -659,7 +659,7 @@ The length of the axes list must be equal to the number of plots drawn.
  .. code-block:: none
 
 
-    <matplotlib.legend.Legend object at 0x7fb57b2b1750>
+    <matplotlib.legend.Legend object at 0x7fd8415567a0>
 
 
 
@@ -730,7 +730,7 @@ plot function.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 2.450 seconds)
+   **Total running time of the script:** (0 minutes 2.712 seconds)
 
 
 .. _sphx_glr_download_auto_examples_miscellaneous_plot_partial_dependence_visualization_api.py:
diff --git a/dev/_sources/auto_examples/miscellaneous/plot_pipeline_display.rst.txt b/dev/_sources/auto_examples/miscellaneous/plot_pipeline_display.rst.txt
index 5866f2a05b2e2..c1ee535ea8f6b 100644
--- a/dev/_sources/auto_examples/miscellaneous/plot_pipeline_display.rst.txt
+++ b/dev/_sources/auto_examples/miscellaneous/plot_pipeline_display.rst.txt
@@ -95,7 +95,7 @@ To visualize the diagram, the default is `display='diagram'`.
 .. raw:: html
 
     <div class="output_subarea output_html rendered_html output_result">
-    <style>#sk-container-id-40 {
+    <style>#sk-container-id-41 {
       /* Definition of color scheme common for light and dark mode */
       --sklearn-color-text: #000;
       --sklearn-color-text-muted: #666;
@@ -126,15 +126,15 @@ To visualize the diagram, the default is `display='diagram'`.
       }
     }
 
-    #sk-container-id-40 {
+    #sk-container-id-41 {
       color: var(--sklearn-color-text);
     }
 
-    #sk-container-id-40 pre {
+    #sk-container-id-41 pre {
       padding: 0;
     }
 
-    #sk-container-id-40 input.sk-hidden--visually {
+    #sk-container-id-41 input.sk-hidden--visually {
       border: 0;
       clip: rect(1px 1px 1px 1px);
       clip: rect(1px, 1px, 1px, 1px);
@@ -146,7 +146,7 @@ To visualize the diagram, the default is `display='diagram'`.
       width: 1px;
     }
 
-    #sk-container-id-40 div.sk-dashed-wrapped {
+    #sk-container-id-41 div.sk-dashed-wrapped {
       border: 1px dashed var(--sklearn-color-line);
       margin: 0 0.4em 0.5em 0.4em;
       box-sizing: border-box;
@@ -154,7 +154,7 @@ To visualize the diagram, the default is `display='diagram'`.
       background-color: var(--sklearn-color-background);
     }
 
-    #sk-container-id-40 div.sk-container {
+    #sk-container-id-41 div.sk-container {
       /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
          but bootstrap.min.css set `[hidden] { display: none !important; }`
          so we also need the `!important` here to be able to override the
@@ -164,7 +164,7 @@ To visualize the diagram, the default is `display='diagram'`.
       position: relative;
     }
 
-    #sk-container-id-40 div.sk-text-repr-fallback {
+    #sk-container-id-41 div.sk-text-repr-fallback {
       display: none;
     }
 
@@ -180,14 +180,14 @@ To visualize the diagram, the default is `display='diagram'`.
 
     /* Parallel-specific style estimator block */
 
-    #sk-container-id-40 div.sk-parallel-item::after {
+    #sk-container-id-41 div.sk-parallel-item::after {
       content: "";
       width: 100%;
       border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
       flex-grow: 1;
     }
 
-    #sk-container-id-40 div.sk-parallel {
+    #sk-container-id-41 div.sk-parallel {
       display: flex;
       align-items: stretch;
       justify-content: center;
@@ -195,28 +195,28 @@ To visualize the diagram, the default is `display='diagram'`.
       position: relative;
     }
 
-    #sk-container-id-40 div.sk-parallel-item {
+    #sk-container-id-41 div.sk-parallel-item {
       display: flex;
       flex-direction: column;
     }
 
-    #sk-container-id-40 div.sk-parallel-item:first-child::after {
+    #sk-container-id-41 div.sk-parallel-item:first-child::after {
       align-self: flex-end;
       width: 50%;
     }
 
-    #sk-container-id-40 div.sk-parallel-item:last-child::after {
+    #sk-container-id-41 div.sk-parallel-item:last-child::after {
       align-self: flex-start;
       width: 50%;
     }
 
-    #sk-container-id-40 div.sk-parallel-item:only-child::after {
+    #sk-container-id-41 div.sk-parallel-item:only-child::after {
       width: 0;
     }
 
     /* Serial-specific style estimator block */
 
-    #sk-container-id-40 div.sk-serial {
+    #sk-container-id-41 div.sk-serial {
       display: flex;
       flex-direction: column;
       align-items: center;
@@ -234,14 +234,14 @@ To visualize the diagram, the default is `display='diagram'`.
 
     /* Pipeline and ColumnTransformer style (default) */
 
-    #sk-container-id-40 div.sk-toggleable {
+    #sk-container-id-41 div.sk-toggleable {
       /* Default theme specific background. It is overwritten whether we have a
       specific estimator or a Pipeline/ColumnTransformer */
       background-color: var(--sklearn-color-background);
     }
 
     /* Toggleable label */
-    #sk-container-id-40 label.sk-toggleable__label {
+    #sk-container-id-41 label.sk-toggleable__label {
       cursor: pointer;
       display: flex;
       width: 100%;
@@ -254,13 +254,13 @@ To visualize the diagram, the default is `display='diagram'`.
       gap: 0.5em;
     }
 
-    #sk-container-id-40 label.sk-toggleable__label .caption {
+    #sk-container-id-41 label.sk-toggleable__label .caption {
       font-size: 0.6rem;
       font-weight: lighter;
       color: var(--sklearn-color-text-muted);
     }
 
-    #sk-container-id-40 label.sk-toggleable__label-arrow:before {
+    #sk-container-id-41 label.sk-toggleable__label-arrow:before {
       /* Arrow on the left of the label */
       content: "▸";
       float: left;
@@ -268,13 +268,13 @@ To visualize the diagram, the default is `display='diagram'`.
       color: var(--sklearn-color-icon);
     }
 
-    #sk-container-id-40 label.sk-toggleable__label-arrow:hover:before {
+    #sk-container-id-41 label.sk-toggleable__label-arrow:hover:before {
       color: var(--sklearn-color-text);
     }
 
     /* Toggleable content - dropdown */
 
-    #sk-container-id-40 div.sk-toggleable__content {
+    #sk-container-id-41 div.sk-toggleable__content {
       max-height: 0;
       max-width: 0;
       overflow: hidden;
@@ -283,12 +283,12 @@ To visualize the diagram, the default is `display='diagram'`.
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-40 div.sk-toggleable__content.fitted {
+    #sk-container-id-41 div.sk-toggleable__content.fitted {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
-    #sk-container-id-40 div.sk-toggleable__content pre {
+    #sk-container-id-41 div.sk-toggleable__content pre {
       margin: 0.2em;
       border-radius: 0.25em;
       color: var(--sklearn-color-text);
@@ -296,79 +296,79 @@ To visualize the diagram, the default is `display='diagram'`.
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-40 div.sk-toggleable__content.fitted pre {
+    #sk-container-id-41 div.sk-toggleable__content.fitted pre {
       /* unfitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
-    #sk-container-id-40 input.sk-toggleable__control:checked~div.sk-toggleable__content {
+    #sk-container-id-41 input.sk-toggleable__control:checked~div.sk-toggleable__content {
       /* Expand drop-down */
       max-height: 200px;
       max-width: 100%;
       overflow: auto;
     }
 
-    #sk-container-id-40 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
+    #sk-container-id-41 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
       content: "▾";
     }
 
     /* Pipeline/ColumnTransformer-specific style */
 
-    #sk-container-id-40 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-41 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-40 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-41 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
     /* Estimator-specific style */
 
     /* Colorize estimator box */
-    #sk-container-id-40 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-41 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-40 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-41 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
-    #sk-container-id-40 div.sk-label label.sk-toggleable__label,
-    #sk-container-id-40 div.sk-label label {
+    #sk-container-id-41 div.sk-label label.sk-toggleable__label,
+    #sk-container-id-41 div.sk-label label {
       /* The background is the default theme color */
       color: var(--sklearn-color-text-on-default-background);
     }
 
     /* On hover, darken the color of the background */
-    #sk-container-id-40 div.sk-label:hover label.sk-toggleable__label {
+    #sk-container-id-41 div.sk-label:hover label.sk-toggleable__label {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
     /* Label box, darken color on hover, fitted */
-    #sk-container-id-40 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
+    #sk-container-id-41 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
     /* Estimator label */
 
-    #sk-container-id-40 div.sk-label label {
+    #sk-container-id-41 div.sk-label label {
       font-family: monospace;
       font-weight: bold;
       display: inline-block;
       line-height: 1.2em;
     }
 
-    #sk-container-id-40 div.sk-label-container {
+    #sk-container-id-41 div.sk-label-container {
       text-align: center;
     }
 
     /* Estimator-specific */
-    #sk-container-id-40 div.sk-estimator {
+    #sk-container-id-41 div.sk-estimator {
       font-family: monospace;
       border: 1px dotted var(--sklearn-color-border-box);
       border-radius: 0.25em;
@@ -378,18 +378,18 @@ To visualize the diagram, the default is `display='diagram'`.
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-40 div.sk-estimator.fitted {
+    #sk-container-id-41 div.sk-estimator.fitted {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
     /* on hover */
-    #sk-container-id-40 div.sk-estimator:hover {
+    #sk-container-id-41 div.sk-estimator:hover {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-40 div.sk-estimator.fitted:hover {
+    #sk-container-id-41 div.sk-estimator.fitted:hover {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-2);
     }
@@ -477,7 +477,7 @@ To visualize the diagram, the default is `display='diagram'`.
 
     /* "?"-specific style due to the `<a>` HTML tag */
 
-    #sk-container-id-40 a.estimator_doc_link {
+    #sk-container-id-41 a.estimator_doc_link {
       float: right;
       font-size: 1rem;
       line-height: 1em;
@@ -492,27 +492,27 @@ To visualize the diagram, the default is `display='diagram'`.
       border: var(--sklearn-color-unfitted-level-1) 1pt solid;
     }
 
-    #sk-container-id-40 a.estimator_doc_link.fitted {
+    #sk-container-id-41 a.estimator_doc_link.fitted {
       /* fitted */
       border: var(--sklearn-color-fitted-level-1) 1pt solid;
       color: var(--sklearn-color-fitted-level-1);
     }
 
     /* On hover */
-    #sk-container-id-40 a.estimator_doc_link:hover {
+    #sk-container-id-41 a.estimator_doc_link:hover {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-3);
       color: var(--sklearn-color-background);
       text-decoration: none;
     }
 
-    #sk-container-id-40 a.estimator_doc_link.fitted:hover {
+    #sk-container-id-41 a.estimator_doc_link.fitted:hover {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-3);
     }
-    </style><div id="sk-container-id-40" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;preprocessing&#x27;, StandardScaler()),
-                    (&#x27;classifier&#x27;, LogisticRegression())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-166" type="checkbox" ><label for="sk-estimator-id-166" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>Pipeline</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.pipeline.Pipeline.html">?<span>Documentation for Pipeline</span></a><span class="sk-estimator-doc-link ">i<span>Not fitted</span></span></div></label><div class="sk-toggleable__content "><pre>Pipeline(steps=[(&#x27;preprocessing&#x27;, StandardScaler()),
-                    (&#x27;classifier&#x27;, LogisticRegression())])</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-167" type="checkbox" ><label for="sk-estimator-id-167" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>StandardScaler</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.StandardScaler.html">?<span>Documentation for StandardScaler</span></a></div></label><div class="sk-toggleable__content "><pre>StandardScaler()</pre></div> </div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-168" type="checkbox" ><label for="sk-estimator-id-168" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>LogisticRegression</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.linear_model.LogisticRegression.html">?<span>Documentation for LogisticRegression</span></a></div></label><div class="sk-toggleable__content "><pre>LogisticRegression()</pre></div> </div></div></div></div></div></div>
+    </style><div id="sk-container-id-41" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;preprocessing&#x27;, StandardScaler()),
+                    (&#x27;classifier&#x27;, LogisticRegression())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-180" type="checkbox" ><label for="sk-estimator-id-180" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>Pipeline</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.pipeline.Pipeline.html">?<span>Documentation for Pipeline</span></a><span class="sk-estimator-doc-link ">i<span>Not fitted</span></span></div></label><div class="sk-toggleable__content "><pre>Pipeline(steps=[(&#x27;preprocessing&#x27;, StandardScaler()),
+                    (&#x27;classifier&#x27;, LogisticRegression())])</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-181" type="checkbox" ><label for="sk-estimator-id-181" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>StandardScaler</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.StandardScaler.html">?<span>Documentation for StandardScaler</span></a></div></label><div class="sk-toggleable__content "><pre>StandardScaler()</pre></div> </div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-182" type="checkbox" ><label for="sk-estimator-id-182" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>LogisticRegression</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.linear_model.LogisticRegression.html">?<span>Documentation for LogisticRegression</span></a></div></label><div class="sk-toggleable__content "><pre>LogisticRegression()</pre></div> </div></div></div></div></div></div>
     </div>
     <br />
     <br />
@@ -594,7 +594,7 @@ representation.
 .. raw:: html
 
     <div class="output_subarea output_html rendered_html output_result">
-    <style>#sk-container-id-41 {
+    <style>#sk-container-id-42 {
       /* Definition of color scheme common for light and dark mode */
       --sklearn-color-text: #000;
       --sklearn-color-text-muted: #666;
@@ -625,15 +625,15 @@ representation.
       }
     }
 
-    #sk-container-id-41 {
+    #sk-container-id-42 {
       color: var(--sklearn-color-text);
     }
 
-    #sk-container-id-41 pre {
+    #sk-container-id-42 pre {
       padding: 0;
     }
 
-    #sk-container-id-41 input.sk-hidden--visually {
+    #sk-container-id-42 input.sk-hidden--visually {
       border: 0;
       clip: rect(1px 1px 1px 1px);
       clip: rect(1px, 1px, 1px, 1px);
@@ -645,7 +645,7 @@ representation.
       width: 1px;
     }
 
-    #sk-container-id-41 div.sk-dashed-wrapped {
+    #sk-container-id-42 div.sk-dashed-wrapped {
       border: 1px dashed var(--sklearn-color-line);
       margin: 0 0.4em 0.5em 0.4em;
       box-sizing: border-box;
@@ -653,7 +653,7 @@ representation.
       background-color: var(--sklearn-color-background);
     }
 
-    #sk-container-id-41 div.sk-container {
+    #sk-container-id-42 div.sk-container {
       /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
          but bootstrap.min.css set `[hidden] { display: none !important; }`
          so we also need the `!important` here to be able to override the
@@ -663,7 +663,7 @@ representation.
       position: relative;
     }
 
-    #sk-container-id-41 div.sk-text-repr-fallback {
+    #sk-container-id-42 div.sk-text-repr-fallback {
       display: none;
     }
 
@@ -679,14 +679,14 @@ representation.
 
     /* Parallel-specific style estimator block */
 
-    #sk-container-id-41 div.sk-parallel-item::after {
+    #sk-container-id-42 div.sk-parallel-item::after {
       content: "";
       width: 100%;
       border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
       flex-grow: 1;
     }
 
-    #sk-container-id-41 div.sk-parallel {
+    #sk-container-id-42 div.sk-parallel {
       display: flex;
       align-items: stretch;
       justify-content: center;
@@ -694,28 +694,28 @@ representation.
       position: relative;
     }
 
-    #sk-container-id-41 div.sk-parallel-item {
+    #sk-container-id-42 div.sk-parallel-item {
       display: flex;
       flex-direction: column;
     }
 
-    #sk-container-id-41 div.sk-parallel-item:first-child::after {
+    #sk-container-id-42 div.sk-parallel-item:first-child::after {
       align-self: flex-end;
       width: 50%;
     }
 
-    #sk-container-id-41 div.sk-parallel-item:last-child::after {
+    #sk-container-id-42 div.sk-parallel-item:last-child::after {
       align-self: flex-start;
       width: 50%;
     }
 
-    #sk-container-id-41 div.sk-parallel-item:only-child::after {
+    #sk-container-id-42 div.sk-parallel-item:only-child::after {
       width: 0;
     }
 
     /* Serial-specific style estimator block */
 
-    #sk-container-id-41 div.sk-serial {
+    #sk-container-id-42 div.sk-serial {
       display: flex;
       flex-direction: column;
       align-items: center;
@@ -733,14 +733,14 @@ representation.
 
     /* Pipeline and ColumnTransformer style (default) */
 
-    #sk-container-id-41 div.sk-toggleable {
+    #sk-container-id-42 div.sk-toggleable {
       /* Default theme specific background. It is overwritten whether we have a
       specific estimator or a Pipeline/ColumnTransformer */
       background-color: var(--sklearn-color-background);
     }
 
     /* Toggleable label */
-    #sk-container-id-41 label.sk-toggleable__label {
+    #sk-container-id-42 label.sk-toggleable__label {
       cursor: pointer;
       display: flex;
       width: 100%;
@@ -753,13 +753,13 @@ representation.
       gap: 0.5em;
     }
 
-    #sk-container-id-41 label.sk-toggleable__label .caption {
+    #sk-container-id-42 label.sk-toggleable__label .caption {
       font-size: 0.6rem;
       font-weight: lighter;
       color: var(--sklearn-color-text-muted);
     }
 
-    #sk-container-id-41 label.sk-toggleable__label-arrow:before {
+    #sk-container-id-42 label.sk-toggleable__label-arrow:before {
       /* Arrow on the left of the label */
       content: "▸";
       float: left;
@@ -767,13 +767,13 @@ representation.
       color: var(--sklearn-color-icon);
     }
 
-    #sk-container-id-41 label.sk-toggleable__label-arrow:hover:before {
+    #sk-container-id-42 label.sk-toggleable__label-arrow:hover:before {
       color: var(--sklearn-color-text);
     }
 
     /* Toggleable content - dropdown */
 
-    #sk-container-id-41 div.sk-toggleable__content {
+    #sk-container-id-42 div.sk-toggleable__content {
       max-height: 0;
       max-width: 0;
       overflow: hidden;
@@ -782,12 +782,12 @@ representation.
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-41 div.sk-toggleable__content.fitted {
+    #sk-container-id-42 div.sk-toggleable__content.fitted {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
-    #sk-container-id-41 div.sk-toggleable__content pre {
+    #sk-container-id-42 div.sk-toggleable__content pre {
       margin: 0.2em;
       border-radius: 0.25em;
       color: var(--sklearn-color-text);
@@ -795,79 +795,79 @@ representation.
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-41 div.sk-toggleable__content.fitted pre {
+    #sk-container-id-42 div.sk-toggleable__content.fitted pre {
       /* unfitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
-    #sk-container-id-41 input.sk-toggleable__control:checked~div.sk-toggleable__content {
+    #sk-container-id-42 input.sk-toggleable__control:checked~div.sk-toggleable__content {
       /* Expand drop-down */
       max-height: 200px;
       max-width: 100%;
       overflow: auto;
     }
 
-    #sk-container-id-41 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
+    #sk-container-id-42 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
       content: "▾";
     }
 
     /* Pipeline/ColumnTransformer-specific style */
 
-    #sk-container-id-41 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-42 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-41 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-42 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
     /* Estimator-specific style */
 
     /* Colorize estimator box */
-    #sk-container-id-41 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-42 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-41 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-42 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
-    #sk-container-id-41 div.sk-label label.sk-toggleable__label,
-    #sk-container-id-41 div.sk-label label {
+    #sk-container-id-42 div.sk-label label.sk-toggleable__label,
+    #sk-container-id-42 div.sk-label label {
       /* The background is the default theme color */
       color: var(--sklearn-color-text-on-default-background);
     }
 
     /* On hover, darken the color of the background */
-    #sk-container-id-41 div.sk-label:hover label.sk-toggleable__label {
+    #sk-container-id-42 div.sk-label:hover label.sk-toggleable__label {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
     /* Label box, darken color on hover, fitted */
-    #sk-container-id-41 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
+    #sk-container-id-42 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
     /* Estimator label */
 
-    #sk-container-id-41 div.sk-label label {
+    #sk-container-id-42 div.sk-label label {
       font-family: monospace;
       font-weight: bold;
       display: inline-block;
       line-height: 1.2em;
     }
 
-    #sk-container-id-41 div.sk-label-container {
+    #sk-container-id-42 div.sk-label-container {
       text-align: center;
     }
 
     /* Estimator-specific */
-    #sk-container-id-41 div.sk-estimator {
+    #sk-container-id-42 div.sk-estimator {
       font-family: monospace;
       border: 1px dotted var(--sklearn-color-border-box);
       border-radius: 0.25em;
@@ -877,18 +877,18 @@ representation.
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-41 div.sk-estimator.fitted {
+    #sk-container-id-42 div.sk-estimator.fitted {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
     /* on hover */
-    #sk-container-id-41 div.sk-estimator:hover {
+    #sk-container-id-42 div.sk-estimator:hover {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-41 div.sk-estimator.fitted:hover {
+    #sk-container-id-42 div.sk-estimator.fitted:hover {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-2);
     }
@@ -976,7 +976,7 @@ representation.
 
     /* "?"-specific style due to the `<a>` HTML tag */
 
-    #sk-container-id-41 a.estimator_doc_link {
+    #sk-container-id-42 a.estimator_doc_link {
       float: right;
       font-size: 1rem;
       line-height: 1em;
@@ -991,29 +991,29 @@ representation.
       border: var(--sklearn-color-unfitted-level-1) 1pt solid;
     }
 
-    #sk-container-id-41 a.estimator_doc_link.fitted {
+    #sk-container-id-42 a.estimator_doc_link.fitted {
       /* fitted */
       border: var(--sklearn-color-fitted-level-1) 1pt solid;
       color: var(--sklearn-color-fitted-level-1);
     }
 
     /* On hover */
-    #sk-container-id-41 a.estimator_doc_link:hover {
+    #sk-container-id-42 a.estimator_doc_link:hover {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-3);
       color: var(--sklearn-color-background);
       text-decoration: none;
     }
 
-    #sk-container-id-41 a.estimator_doc_link.fitted:hover {
+    #sk-container-id-42 a.estimator_doc_link.fitted:hover {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-3);
     }
-    </style><div id="sk-container-id-41" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;standard_scaler&#x27;, StandardScaler()),
+    </style><div id="sk-container-id-42" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;standard_scaler&#x27;, StandardScaler()),
                     (&#x27;polynomial&#x27;, PolynomialFeatures(degree=3)),
-                    (&#x27;classifier&#x27;, LogisticRegression(C=2.0))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-169" type="checkbox" ><label for="sk-estimator-id-169" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>Pipeline</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.pipeline.Pipeline.html">?<span>Documentation for Pipeline</span></a><span class="sk-estimator-doc-link ">i<span>Not fitted</span></span></div></label><div class="sk-toggleable__content "><pre>Pipeline(steps=[(&#x27;standard_scaler&#x27;, StandardScaler()),
+                    (&#x27;classifier&#x27;, LogisticRegression(C=2.0))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-183" type="checkbox" ><label for="sk-estimator-id-183" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>Pipeline</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.pipeline.Pipeline.html">?<span>Documentation for Pipeline</span></a><span class="sk-estimator-doc-link ">i<span>Not fitted</span></span></div></label><div class="sk-toggleable__content "><pre>Pipeline(steps=[(&#x27;standard_scaler&#x27;, StandardScaler()),
                     (&#x27;polynomial&#x27;, PolynomialFeatures(degree=3)),
-                    (&#x27;classifier&#x27;, LogisticRegression(C=2.0))])</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-170" type="checkbox" ><label for="sk-estimator-id-170" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>StandardScaler</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.StandardScaler.html">?<span>Documentation for StandardScaler</span></a></div></label><div class="sk-toggleable__content "><pre>StandardScaler()</pre></div> </div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-171" type="checkbox" ><label for="sk-estimator-id-171" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>PolynomialFeatures</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.PolynomialFeatures.html">?<span>Documentation for PolynomialFeatures</span></a></div></label><div class="sk-toggleable__content "><pre>PolynomialFeatures(degree=3)</pre></div> </div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-172" type="checkbox" ><label for="sk-estimator-id-172" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>LogisticRegression</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.linear_model.LogisticRegression.html">?<span>Documentation for LogisticRegression</span></a></div></label><div class="sk-toggleable__content "><pre>LogisticRegression(C=2.0)</pre></div> </div></div></div></div></div></div>
+                    (&#x27;classifier&#x27;, LogisticRegression(C=2.0))])</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-184" type="checkbox" ><label for="sk-estimator-id-184" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>StandardScaler</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.StandardScaler.html">?<span>Documentation for StandardScaler</span></a></div></label><div class="sk-toggleable__content "><pre>StandardScaler()</pre></div> </div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-185" type="checkbox" ><label for="sk-estimator-id-185" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>PolynomialFeatures</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.PolynomialFeatures.html">?<span>Documentation for PolynomialFeatures</span></a></div></label><div class="sk-toggleable__content "><pre>PolynomialFeatures(degree=3)</pre></div> </div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-186" type="checkbox" ><label for="sk-estimator-id-186" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>LogisticRegression</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.linear_model.LogisticRegression.html">?<span>Documentation for LogisticRegression</span></a></div></label><div class="sk-toggleable__content "><pre>LogisticRegression(C=2.0)</pre></div> </div></div></div></div></div></div>
     </div>
     <br />
     <br />
@@ -1048,7 +1048,7 @@ representation.
 .. raw:: html
 
     <div class="output_subarea output_html rendered_html output_result">
-    <style>#sk-container-id-42 {
+    <style>#sk-container-id-43 {
       /* Definition of color scheme common for light and dark mode */
       --sklearn-color-text: #000;
       --sklearn-color-text-muted: #666;
@@ -1079,15 +1079,15 @@ representation.
       }
     }
 
-    #sk-container-id-42 {
+    #sk-container-id-43 {
       color: var(--sklearn-color-text);
     }
 
-    #sk-container-id-42 pre {
+    #sk-container-id-43 pre {
       padding: 0;
     }
 
-    #sk-container-id-42 input.sk-hidden--visually {
+    #sk-container-id-43 input.sk-hidden--visually {
       border: 0;
       clip: rect(1px 1px 1px 1px);
       clip: rect(1px, 1px, 1px, 1px);
@@ -1099,7 +1099,7 @@ representation.
       width: 1px;
     }
 
-    #sk-container-id-42 div.sk-dashed-wrapped {
+    #sk-container-id-43 div.sk-dashed-wrapped {
       border: 1px dashed var(--sklearn-color-line);
       margin: 0 0.4em 0.5em 0.4em;
       box-sizing: border-box;
@@ -1107,7 +1107,7 @@ representation.
       background-color: var(--sklearn-color-background);
     }
 
-    #sk-container-id-42 div.sk-container {
+    #sk-container-id-43 div.sk-container {
       /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
          but bootstrap.min.css set `[hidden] { display: none !important; }`
          so we also need the `!important` here to be able to override the
@@ -1117,7 +1117,7 @@ representation.
       position: relative;
     }
 
-    #sk-container-id-42 div.sk-text-repr-fallback {
+    #sk-container-id-43 div.sk-text-repr-fallback {
       display: none;
     }
 
@@ -1133,14 +1133,14 @@ representation.
 
     /* Parallel-specific style estimator block */
 
-    #sk-container-id-42 div.sk-parallel-item::after {
+    #sk-container-id-43 div.sk-parallel-item::after {
       content: "";
       width: 100%;
       border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
       flex-grow: 1;
     }
 
-    #sk-container-id-42 div.sk-parallel {
+    #sk-container-id-43 div.sk-parallel {
       display: flex;
       align-items: stretch;
       justify-content: center;
@@ -1148,28 +1148,28 @@ representation.
       position: relative;
     }
 
-    #sk-container-id-42 div.sk-parallel-item {
+    #sk-container-id-43 div.sk-parallel-item {
       display: flex;
       flex-direction: column;
     }
 
-    #sk-container-id-42 div.sk-parallel-item:first-child::after {
+    #sk-container-id-43 div.sk-parallel-item:first-child::after {
       align-self: flex-end;
       width: 50%;
     }
 
-    #sk-container-id-42 div.sk-parallel-item:last-child::after {
+    #sk-container-id-43 div.sk-parallel-item:last-child::after {
       align-self: flex-start;
       width: 50%;
     }
 
-    #sk-container-id-42 div.sk-parallel-item:only-child::after {
+    #sk-container-id-43 div.sk-parallel-item:only-child::after {
       width: 0;
     }
 
     /* Serial-specific style estimator block */
 
-    #sk-container-id-42 div.sk-serial {
+    #sk-container-id-43 div.sk-serial {
       display: flex;
       flex-direction: column;
       align-items: center;
@@ -1187,14 +1187,14 @@ representation.
 
     /* Pipeline and ColumnTransformer style (default) */
 
-    #sk-container-id-42 div.sk-toggleable {
+    #sk-container-id-43 div.sk-toggleable {
       /* Default theme specific background. It is overwritten whether we have a
       specific estimator or a Pipeline/ColumnTransformer */
       background-color: var(--sklearn-color-background);
     }
 
     /* Toggleable label */
-    #sk-container-id-42 label.sk-toggleable__label {
+    #sk-container-id-43 label.sk-toggleable__label {
       cursor: pointer;
       display: flex;
       width: 100%;
@@ -1207,13 +1207,13 @@ representation.
       gap: 0.5em;
     }
 
-    #sk-container-id-42 label.sk-toggleable__label .caption {
+    #sk-container-id-43 label.sk-toggleable__label .caption {
       font-size: 0.6rem;
       font-weight: lighter;
       color: var(--sklearn-color-text-muted);
     }
 
-    #sk-container-id-42 label.sk-toggleable__label-arrow:before {
+    #sk-container-id-43 label.sk-toggleable__label-arrow:before {
       /* Arrow on the left of the label */
       content: "▸";
       float: left;
@@ -1221,13 +1221,13 @@ representation.
       color: var(--sklearn-color-icon);
     }
 
-    #sk-container-id-42 label.sk-toggleable__label-arrow:hover:before {
+    #sk-container-id-43 label.sk-toggleable__label-arrow:hover:before {
       color: var(--sklearn-color-text);
     }
 
     /* Toggleable content - dropdown */
 
-    #sk-container-id-42 div.sk-toggleable__content {
+    #sk-container-id-43 div.sk-toggleable__content {
       max-height: 0;
       max-width: 0;
       overflow: hidden;
@@ -1236,12 +1236,12 @@ representation.
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-42 div.sk-toggleable__content.fitted {
+    #sk-container-id-43 div.sk-toggleable__content.fitted {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
-    #sk-container-id-42 div.sk-toggleable__content pre {
+    #sk-container-id-43 div.sk-toggleable__content pre {
       margin: 0.2em;
       border-radius: 0.25em;
       color: var(--sklearn-color-text);
@@ -1249,79 +1249,79 @@ representation.
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-42 div.sk-toggleable__content.fitted pre {
+    #sk-container-id-43 div.sk-toggleable__content.fitted pre {
       /* unfitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
-    #sk-container-id-42 input.sk-toggleable__control:checked~div.sk-toggleable__content {
+    #sk-container-id-43 input.sk-toggleable__control:checked~div.sk-toggleable__content {
       /* Expand drop-down */
       max-height: 200px;
       max-width: 100%;
       overflow: auto;
     }
 
-    #sk-container-id-42 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
+    #sk-container-id-43 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
       content: "▾";
     }
 
     /* Pipeline/ColumnTransformer-specific style */
 
-    #sk-container-id-42 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-43 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-42 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-43 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
     /* Estimator-specific style */
 
     /* Colorize estimator box */
-    #sk-container-id-42 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-43 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-42 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-43 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
-    #sk-container-id-42 div.sk-label label.sk-toggleable__label,
-    #sk-container-id-42 div.sk-label label {
+    #sk-container-id-43 div.sk-label label.sk-toggleable__label,
+    #sk-container-id-43 div.sk-label label {
       /* The background is the default theme color */
       color: var(--sklearn-color-text-on-default-background);
     }
 
     /* On hover, darken the color of the background */
-    #sk-container-id-42 div.sk-label:hover label.sk-toggleable__label {
+    #sk-container-id-43 div.sk-label:hover label.sk-toggleable__label {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
     /* Label box, darken color on hover, fitted */
-    #sk-container-id-42 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
+    #sk-container-id-43 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
     /* Estimator label */
 
-    #sk-container-id-42 div.sk-label label {
+    #sk-container-id-43 div.sk-label label {
       font-family: monospace;
       font-weight: bold;
       display: inline-block;
       line-height: 1.2em;
     }
 
-    #sk-container-id-42 div.sk-label-container {
+    #sk-container-id-43 div.sk-label-container {
       text-align: center;
     }
 
     /* Estimator-specific */
-    #sk-container-id-42 div.sk-estimator {
+    #sk-container-id-43 div.sk-estimator {
       font-family: monospace;
       border: 1px dotted var(--sklearn-color-border-box);
       border-radius: 0.25em;
@@ -1331,18 +1331,18 @@ representation.
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-42 div.sk-estimator.fitted {
+    #sk-container-id-43 div.sk-estimator.fitted {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
     /* on hover */
-    #sk-container-id-42 div.sk-estimator:hover {
+    #sk-container-id-43 div.sk-estimator:hover {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-42 div.sk-estimator.fitted:hover {
+    #sk-container-id-43 div.sk-estimator.fitted:hover {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-2);
     }
@@ -1430,7 +1430,7 @@ representation.
 
     /* "?"-specific style due to the `<a>` HTML tag */
 
-    #sk-container-id-42 a.estimator_doc_link {
+    #sk-container-id-43 a.estimator_doc_link {
       float: right;
       font-size: 1rem;
       line-height: 1em;
@@ -1445,27 +1445,27 @@ representation.
       border: var(--sklearn-color-unfitted-level-1) 1pt solid;
     }
 
-    #sk-container-id-42 a.estimator_doc_link.fitted {
+    #sk-container-id-43 a.estimator_doc_link.fitted {
       /* fitted */
       border: var(--sklearn-color-fitted-level-1) 1pt solid;
       color: var(--sklearn-color-fitted-level-1);
     }
 
     /* On hover */
-    #sk-container-id-42 a.estimator_doc_link:hover {
+    #sk-container-id-43 a.estimator_doc_link:hover {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-3);
       color: var(--sklearn-color-background);
       text-decoration: none;
     }
 
-    #sk-container-id-42 a.estimator_doc_link.fitted:hover {
+    #sk-container-id-43 a.estimator_doc_link.fitted:hover {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-3);
     }
-    </style><div id="sk-container-id-42" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;reduce_dim&#x27;, PCA(n_components=4)),
-                    (&#x27;classifier&#x27;, SVC(kernel=&#x27;linear&#x27;))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-173" type="checkbox" ><label for="sk-estimator-id-173" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>Pipeline</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.pipeline.Pipeline.html">?<span>Documentation for Pipeline</span></a><span class="sk-estimator-doc-link ">i<span>Not fitted</span></span></div></label><div class="sk-toggleable__content "><pre>Pipeline(steps=[(&#x27;reduce_dim&#x27;, PCA(n_components=4)),
-                    (&#x27;classifier&#x27;, SVC(kernel=&#x27;linear&#x27;))])</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-174" type="checkbox" ><label for="sk-estimator-id-174" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>PCA</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.decomposition.PCA.html">?<span>Documentation for PCA</span></a></div></label><div class="sk-toggleable__content "><pre>PCA(n_components=4)</pre></div> </div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-175" type="checkbox" ><label for="sk-estimator-id-175" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>SVC</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.svm.SVC.html">?<span>Documentation for SVC</span></a></div></label><div class="sk-toggleable__content "><pre>SVC(kernel=&#x27;linear&#x27;)</pre></div> </div></div></div></div></div></div>
+    </style><div id="sk-container-id-43" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;reduce_dim&#x27;, PCA(n_components=4)),
+                    (&#x27;classifier&#x27;, SVC(kernel=&#x27;linear&#x27;))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-187" type="checkbox" ><label for="sk-estimator-id-187" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>Pipeline</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.pipeline.Pipeline.html">?<span>Documentation for Pipeline</span></a><span class="sk-estimator-doc-link ">i<span>Not fitted</span></span></div></label><div class="sk-toggleable__content "><pre>Pipeline(steps=[(&#x27;reduce_dim&#x27;, PCA(n_components=4)),
+                    (&#x27;classifier&#x27;, SVC(kernel=&#x27;linear&#x27;))])</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-188" type="checkbox" ><label for="sk-estimator-id-188" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>PCA</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.decomposition.PCA.html">?<span>Documentation for PCA</span></a></div></label><div class="sk-toggleable__content "><pre>PCA(n_components=4)</pre></div> </div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-189" type="checkbox" ><label for="sk-estimator-id-189" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>SVC</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.svm.SVC.html">?<span>Documentation for SVC</span></a></div></label><div class="sk-toggleable__content "><pre>SVC(kernel=&#x27;linear&#x27;)</pre></div> </div></div></div></div></div></div>
     </div>
     <br />
     <br />
@@ -1527,7 +1527,7 @@ representation.
 .. raw:: html
 
     <div class="output_subarea output_html rendered_html output_result">
-    <style>#sk-container-id-43 {
+    <style>#sk-container-id-44 {
       /* Definition of color scheme common for light and dark mode */
       --sklearn-color-text: #000;
       --sklearn-color-text-muted: #666;
@@ -1558,15 +1558,15 @@ representation.
       }
     }
 
-    #sk-container-id-43 {
+    #sk-container-id-44 {
       color: var(--sklearn-color-text);
     }
 
-    #sk-container-id-43 pre {
+    #sk-container-id-44 pre {
       padding: 0;
     }
 
-    #sk-container-id-43 input.sk-hidden--visually {
+    #sk-container-id-44 input.sk-hidden--visually {
       border: 0;
       clip: rect(1px 1px 1px 1px);
       clip: rect(1px, 1px, 1px, 1px);
@@ -1578,7 +1578,7 @@ representation.
       width: 1px;
     }
 
-    #sk-container-id-43 div.sk-dashed-wrapped {
+    #sk-container-id-44 div.sk-dashed-wrapped {
       border: 1px dashed var(--sklearn-color-line);
       margin: 0 0.4em 0.5em 0.4em;
       box-sizing: border-box;
@@ -1586,7 +1586,7 @@ representation.
       background-color: var(--sklearn-color-background);
     }
 
-    #sk-container-id-43 div.sk-container {
+    #sk-container-id-44 div.sk-container {
       /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
          but bootstrap.min.css set `[hidden] { display: none !important; }`
          so we also need the `!important` here to be able to override the
@@ -1596,7 +1596,7 @@ representation.
       position: relative;
     }
 
-    #sk-container-id-43 div.sk-text-repr-fallback {
+    #sk-container-id-44 div.sk-text-repr-fallback {
       display: none;
     }
 
@@ -1612,14 +1612,14 @@ representation.
 
     /* Parallel-specific style estimator block */
 
-    #sk-container-id-43 div.sk-parallel-item::after {
+    #sk-container-id-44 div.sk-parallel-item::after {
       content: "";
       width: 100%;
       border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
       flex-grow: 1;
     }
 
-    #sk-container-id-43 div.sk-parallel {
+    #sk-container-id-44 div.sk-parallel {
       display: flex;
       align-items: stretch;
       justify-content: center;
@@ -1627,28 +1627,28 @@ representation.
       position: relative;
     }
 
-    #sk-container-id-43 div.sk-parallel-item {
+    #sk-container-id-44 div.sk-parallel-item {
       display: flex;
       flex-direction: column;
     }
 
-    #sk-container-id-43 div.sk-parallel-item:first-child::after {
+    #sk-container-id-44 div.sk-parallel-item:first-child::after {
       align-self: flex-end;
       width: 50%;
     }
 
-    #sk-container-id-43 div.sk-parallel-item:last-child::after {
+    #sk-container-id-44 div.sk-parallel-item:last-child::after {
       align-self: flex-start;
       width: 50%;
     }
 
-    #sk-container-id-43 div.sk-parallel-item:only-child::after {
+    #sk-container-id-44 div.sk-parallel-item:only-child::after {
       width: 0;
     }
 
     /* Serial-specific style estimator block */
 
-    #sk-container-id-43 div.sk-serial {
+    #sk-container-id-44 div.sk-serial {
       display: flex;
       flex-direction: column;
       align-items: center;
@@ -1666,14 +1666,14 @@ representation.
 
     /* Pipeline and ColumnTransformer style (default) */
 
-    #sk-container-id-43 div.sk-toggleable {
+    #sk-container-id-44 div.sk-toggleable {
       /* Default theme specific background. It is overwritten whether we have a
       specific estimator or a Pipeline/ColumnTransformer */
       background-color: var(--sklearn-color-background);
     }
 
     /* Toggleable label */
-    #sk-container-id-43 label.sk-toggleable__label {
+    #sk-container-id-44 label.sk-toggleable__label {
       cursor: pointer;
       display: flex;
       width: 100%;
@@ -1686,13 +1686,13 @@ representation.
       gap: 0.5em;
     }
 
-    #sk-container-id-43 label.sk-toggleable__label .caption {
+    #sk-container-id-44 label.sk-toggleable__label .caption {
       font-size: 0.6rem;
       font-weight: lighter;
       color: var(--sklearn-color-text-muted);
     }
 
-    #sk-container-id-43 label.sk-toggleable__label-arrow:before {
+    #sk-container-id-44 label.sk-toggleable__label-arrow:before {
       /* Arrow on the left of the label */
       content: "▸";
       float: left;
@@ -1700,13 +1700,13 @@ representation.
       color: var(--sklearn-color-icon);
     }
 
-    #sk-container-id-43 label.sk-toggleable__label-arrow:hover:before {
+    #sk-container-id-44 label.sk-toggleable__label-arrow:hover:before {
       color: var(--sklearn-color-text);
     }
 
     /* Toggleable content - dropdown */
 
-    #sk-container-id-43 div.sk-toggleable__content {
+    #sk-container-id-44 div.sk-toggleable__content {
       max-height: 0;
       max-width: 0;
       overflow: hidden;
@@ -1715,12 +1715,12 @@ representation.
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-43 div.sk-toggleable__content.fitted {
+    #sk-container-id-44 div.sk-toggleable__content.fitted {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
-    #sk-container-id-43 div.sk-toggleable__content pre {
+    #sk-container-id-44 div.sk-toggleable__content pre {
       margin: 0.2em;
       border-radius: 0.25em;
       color: var(--sklearn-color-text);
@@ -1728,79 +1728,79 @@ representation.
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-43 div.sk-toggleable__content.fitted pre {
+    #sk-container-id-44 div.sk-toggleable__content.fitted pre {
       /* unfitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
-    #sk-container-id-43 input.sk-toggleable__control:checked~div.sk-toggleable__content {
+    #sk-container-id-44 input.sk-toggleable__control:checked~div.sk-toggleable__content {
       /* Expand drop-down */
       max-height: 200px;
       max-width: 100%;
       overflow: auto;
     }
 
-    #sk-container-id-43 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
+    #sk-container-id-44 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
       content: "▾";
     }
 
     /* Pipeline/ColumnTransformer-specific style */
 
-    #sk-container-id-43 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-44 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-43 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-44 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
     /* Estimator-specific style */
 
     /* Colorize estimator box */
-    #sk-container-id-43 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-44 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-43 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-44 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
-    #sk-container-id-43 div.sk-label label.sk-toggleable__label,
-    #sk-container-id-43 div.sk-label label {
+    #sk-container-id-44 div.sk-label label.sk-toggleable__label,
+    #sk-container-id-44 div.sk-label label {
       /* The background is the default theme color */
       color: var(--sklearn-color-text-on-default-background);
     }
 
     /* On hover, darken the color of the background */
-    #sk-container-id-43 div.sk-label:hover label.sk-toggleable__label {
+    #sk-container-id-44 div.sk-label:hover label.sk-toggleable__label {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
     /* Label box, darken color on hover, fitted */
-    #sk-container-id-43 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
+    #sk-container-id-44 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
     /* Estimator label */
 
-    #sk-container-id-43 div.sk-label label {
+    #sk-container-id-44 div.sk-label label {
       font-family: monospace;
       font-weight: bold;
       display: inline-block;
       line-height: 1.2em;
     }
 
-    #sk-container-id-43 div.sk-label-container {
+    #sk-container-id-44 div.sk-label-container {
       text-align: center;
     }
 
     /* Estimator-specific */
-    #sk-container-id-43 div.sk-estimator {
+    #sk-container-id-44 div.sk-estimator {
       font-family: monospace;
       border: 1px dotted var(--sklearn-color-border-box);
       border-radius: 0.25em;
@@ -1810,18 +1810,18 @@ representation.
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-43 div.sk-estimator.fitted {
+    #sk-container-id-44 div.sk-estimator.fitted {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
     /* on hover */
-    #sk-container-id-43 div.sk-estimator:hover {
+    #sk-container-id-44 div.sk-estimator:hover {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-43 div.sk-estimator.fitted:hover {
+    #sk-container-id-44 div.sk-estimator.fitted:hover {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-2);
     }
@@ -1909,7 +1909,7 @@ representation.
 
     /* "?"-specific style due to the `<a>` HTML tag */
 
-    #sk-container-id-43 a.estimator_doc_link {
+    #sk-container-id-44 a.estimator_doc_link {
       float: right;
       font-size: 1rem;
       line-height: 1em;
@@ -1924,25 +1924,25 @@ representation.
       border: var(--sklearn-color-unfitted-level-1) 1pt solid;
     }
 
-    #sk-container-id-43 a.estimator_doc_link.fitted {
+    #sk-container-id-44 a.estimator_doc_link.fitted {
       /* fitted */
       border: var(--sklearn-color-fitted-level-1) 1pt solid;
       color: var(--sklearn-color-fitted-level-1);
     }
 
     /* On hover */
-    #sk-container-id-43 a.estimator_doc_link:hover {
+    #sk-container-id-44 a.estimator_doc_link:hover {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-3);
       color: var(--sklearn-color-background);
       text-decoration: none;
     }
 
-    #sk-container-id-43 a.estimator_doc_link.fitted:hover {
+    #sk-container-id-44 a.estimator_doc_link.fitted:hover {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-3);
     }
-    </style><div id="sk-container-id-43" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,
+    </style><div id="sk-container-id-44" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,
                      ColumnTransformer(transformers=[(&#x27;categorical&#x27;,
                                                       Pipeline(steps=[(&#x27;imputation_constant&#x27;,
                                                                        SimpleImputer(fill_value=&#x27;missing&#x27;,
@@ -1956,7 +1956,7 @@ representation.
                                                                       (&#x27;scaler&#x27;,
                                                                        StandardScaler())]),
                                                       [&#x27;age&#x27;, &#x27;weight&#x27;])])),
-                    (&#x27;logisticregression&#x27;, LogisticRegression(max_iter=500))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-176" type="checkbox" ><label for="sk-estimator-id-176" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>Pipeline</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.pipeline.Pipeline.html">?<span>Documentation for Pipeline</span></a><span class="sk-estimator-doc-link ">i<span>Not fitted</span></span></div></label><div class="sk-toggleable__content "><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,
+                    (&#x27;logisticregression&#x27;, LogisticRegression(max_iter=500))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-190" type="checkbox" ><label for="sk-estimator-id-190" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>Pipeline</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.pipeline.Pipeline.html">?<span>Documentation for Pipeline</span></a><span class="sk-estimator-doc-link ">i<span>Not fitted</span></span></div></label><div class="sk-toggleable__content "><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,
                      ColumnTransformer(transformers=[(&#x27;categorical&#x27;,
                                                       Pipeline(steps=[(&#x27;imputation_constant&#x27;,
                                                                        SimpleImputer(fill_value=&#x27;missing&#x27;,
@@ -1970,7 +1970,7 @@ representation.
                                                                       (&#x27;scaler&#x27;,
                                                                        StandardScaler())]),
                                                       [&#x27;age&#x27;, &#x27;weight&#x27;])])),
-                    (&#x27;logisticregression&#x27;, LogisticRegression(max_iter=500))])</pre></div> </div></div><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-177" type="checkbox" ><label for="sk-estimator-id-177" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>columntransformer: ColumnTransformer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.compose.ColumnTransformer.html">?<span>Documentation for columntransformer: ColumnTransformer</span></a></div></label><div class="sk-toggleable__content "><pre>ColumnTransformer(transformers=[(&#x27;categorical&#x27;,
+                    (&#x27;logisticregression&#x27;, LogisticRegression(max_iter=500))])</pre></div> </div></div><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-191" type="checkbox" ><label for="sk-estimator-id-191" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>columntransformer: ColumnTransformer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.compose.ColumnTransformer.html">?<span>Documentation for columntransformer: ColumnTransformer</span></a></div></label><div class="sk-toggleable__content "><pre>ColumnTransformer(transformers=[(&#x27;categorical&#x27;,
                                      Pipeline(steps=[(&#x27;imputation_constant&#x27;,
                                                       SimpleImputer(fill_value=&#x27;missing&#x27;,
                                                                     strategy=&#x27;constant&#x27;)),
@@ -1981,7 +1981,7 @@ representation.
                                      Pipeline(steps=[(&#x27;imputation_mean&#x27;,
                                                       SimpleImputer()),
                                                      (&#x27;scaler&#x27;, StandardScaler())]),
-                                     [&#x27;age&#x27;, &#x27;weight&#x27;])])</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-178" type="checkbox" ><label for="sk-estimator-id-178" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>categorical</div></div></label><div class="sk-toggleable__content "><pre>[&#x27;state&#x27;, &#x27;gender&#x27;]</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-179" type="checkbox" ><label for="sk-estimator-id-179" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>SimpleImputer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.impute.SimpleImputer.html">?<span>Documentation for SimpleImputer</span></a></div></label><div class="sk-toggleable__content "><pre>SimpleImputer(fill_value=&#x27;missing&#x27;, strategy=&#x27;constant&#x27;)</pre></div> </div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-180" type="checkbox" ><label for="sk-estimator-id-180" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>OneHotEncoder</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.OneHotEncoder.html">?<span>Documentation for OneHotEncoder</span></a></div></label><div class="sk-toggleable__content "><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</pre></div> </div></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-181" type="checkbox" ><label for="sk-estimator-id-181" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>numerical</div></div></label><div class="sk-toggleable__content "><pre>[&#x27;age&#x27;, &#x27;weight&#x27;]</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-182" type="checkbox" ><label for="sk-estimator-id-182" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>SimpleImputer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.impute.SimpleImputer.html">?<span>Documentation for SimpleImputer</span></a></div></label><div class="sk-toggleable__content "><pre>SimpleImputer()</pre></div> </div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-183" type="checkbox" ><label for="sk-estimator-id-183" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>StandardScaler</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.StandardScaler.html">?<span>Documentation for StandardScaler</span></a></div></label><div class="sk-toggleable__content "><pre>StandardScaler()</pre></div> </div></div></div></div></div></div></div></div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-184" type="checkbox" ><label for="sk-estimator-id-184" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>LogisticRegression</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.linear_model.LogisticRegression.html">?<span>Documentation for LogisticRegression</span></a></div></label><div class="sk-toggleable__content "><pre>LogisticRegression(max_iter=500)</pre></div> </div></div></div></div></div></div>
+                                     [&#x27;age&#x27;, &#x27;weight&#x27;])])</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-192" type="checkbox" ><label for="sk-estimator-id-192" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>categorical</div></div></label><div class="sk-toggleable__content "><pre>[&#x27;state&#x27;, &#x27;gender&#x27;]</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-193" type="checkbox" ><label for="sk-estimator-id-193" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>SimpleImputer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.impute.SimpleImputer.html">?<span>Documentation for SimpleImputer</span></a></div></label><div class="sk-toggleable__content "><pre>SimpleImputer(fill_value=&#x27;missing&#x27;, strategy=&#x27;constant&#x27;)</pre></div> </div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-194" type="checkbox" ><label for="sk-estimator-id-194" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>OneHotEncoder</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.OneHotEncoder.html">?<span>Documentation for OneHotEncoder</span></a></div></label><div class="sk-toggleable__content "><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</pre></div> </div></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-195" type="checkbox" ><label for="sk-estimator-id-195" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>numerical</div></div></label><div class="sk-toggleable__content "><pre>[&#x27;age&#x27;, &#x27;weight&#x27;]</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-196" type="checkbox" ><label for="sk-estimator-id-196" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>SimpleImputer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.impute.SimpleImputer.html">?<span>Documentation for SimpleImputer</span></a></div></label><div class="sk-toggleable__content "><pre>SimpleImputer()</pre></div> </div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-197" type="checkbox" ><label for="sk-estimator-id-197" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>StandardScaler</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.StandardScaler.html">?<span>Documentation for StandardScaler</span></a></div></label><div class="sk-toggleable__content "><pre>StandardScaler()</pre></div> </div></div></div></div></div></div></div></div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-198" type="checkbox" ><label for="sk-estimator-id-198" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>LogisticRegression</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.linear_model.LogisticRegression.html">?<span>Documentation for LogisticRegression</span></a></div></label><div class="sk-toggleable__content "><pre>LogisticRegression(max_iter=500)</pre></div> </div></div></div></div></div></div>
     </div>
     <br />
     <br />
@@ -2054,7 +2054,7 @@ representation.
 .. raw:: html
 
     <div class="output_subarea output_html rendered_html output_result">
-    <style>#sk-container-id-44 {
+    <style>#sk-container-id-45 {
       /* Definition of color scheme common for light and dark mode */
       --sklearn-color-text: #000;
       --sklearn-color-text-muted: #666;
@@ -2085,15 +2085,15 @@ representation.
       }
     }
 
-    #sk-container-id-44 {
+    #sk-container-id-45 {
       color: var(--sklearn-color-text);
     }
 
-    #sk-container-id-44 pre {
+    #sk-container-id-45 pre {
       padding: 0;
     }
 
-    #sk-container-id-44 input.sk-hidden--visually {
+    #sk-container-id-45 input.sk-hidden--visually {
       border: 0;
       clip: rect(1px 1px 1px 1px);
       clip: rect(1px, 1px, 1px, 1px);
@@ -2105,7 +2105,7 @@ representation.
       width: 1px;
     }
 
-    #sk-container-id-44 div.sk-dashed-wrapped {
+    #sk-container-id-45 div.sk-dashed-wrapped {
       border: 1px dashed var(--sklearn-color-line);
       margin: 0 0.4em 0.5em 0.4em;
       box-sizing: border-box;
@@ -2113,7 +2113,7 @@ representation.
       background-color: var(--sklearn-color-background);
     }
 
-    #sk-container-id-44 div.sk-container {
+    #sk-container-id-45 div.sk-container {
       /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
          but bootstrap.min.css set `[hidden] { display: none !important; }`
          so we also need the `!important` here to be able to override the
@@ -2123,7 +2123,7 @@ representation.
       position: relative;
     }
 
-    #sk-container-id-44 div.sk-text-repr-fallback {
+    #sk-container-id-45 div.sk-text-repr-fallback {
       display: none;
     }
 
@@ -2139,14 +2139,14 @@ representation.
 
     /* Parallel-specific style estimator block */
 
-    #sk-container-id-44 div.sk-parallel-item::after {
+    #sk-container-id-45 div.sk-parallel-item::after {
       content: "";
       width: 100%;
       border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
       flex-grow: 1;
     }
 
-    #sk-container-id-44 div.sk-parallel {
+    #sk-container-id-45 div.sk-parallel {
       display: flex;
       align-items: stretch;
       justify-content: center;
@@ -2154,28 +2154,28 @@ representation.
       position: relative;
     }
 
-    #sk-container-id-44 div.sk-parallel-item {
+    #sk-container-id-45 div.sk-parallel-item {
       display: flex;
       flex-direction: column;
     }
 
-    #sk-container-id-44 div.sk-parallel-item:first-child::after {
+    #sk-container-id-45 div.sk-parallel-item:first-child::after {
       align-self: flex-end;
       width: 50%;
     }
 
-    #sk-container-id-44 div.sk-parallel-item:last-child::after {
+    #sk-container-id-45 div.sk-parallel-item:last-child::after {
       align-self: flex-start;
       width: 50%;
     }
 
-    #sk-container-id-44 div.sk-parallel-item:only-child::after {
+    #sk-container-id-45 div.sk-parallel-item:only-child::after {
       width: 0;
     }
 
     /* Serial-specific style estimator block */
 
-    #sk-container-id-44 div.sk-serial {
+    #sk-container-id-45 div.sk-serial {
       display: flex;
       flex-direction: column;
       align-items: center;
@@ -2193,14 +2193,14 @@ representation.
 
     /* Pipeline and ColumnTransformer style (default) */
 
-    #sk-container-id-44 div.sk-toggleable {
+    #sk-container-id-45 div.sk-toggleable {
       /* Default theme specific background. It is overwritten whether we have a
       specific estimator or a Pipeline/ColumnTransformer */
       background-color: var(--sklearn-color-background);
     }
 
     /* Toggleable label */
-    #sk-container-id-44 label.sk-toggleable__label {
+    #sk-container-id-45 label.sk-toggleable__label {
       cursor: pointer;
       display: flex;
       width: 100%;
@@ -2213,13 +2213,13 @@ representation.
       gap: 0.5em;
     }
 
-    #sk-container-id-44 label.sk-toggleable__label .caption {
+    #sk-container-id-45 label.sk-toggleable__label .caption {
       font-size: 0.6rem;
       font-weight: lighter;
       color: var(--sklearn-color-text-muted);
     }
 
-    #sk-container-id-44 label.sk-toggleable__label-arrow:before {
+    #sk-container-id-45 label.sk-toggleable__label-arrow:before {
       /* Arrow on the left of the label */
       content: "▸";
       float: left;
@@ -2227,13 +2227,13 @@ representation.
       color: var(--sklearn-color-icon);
     }
 
-    #sk-container-id-44 label.sk-toggleable__label-arrow:hover:before {
+    #sk-container-id-45 label.sk-toggleable__label-arrow:hover:before {
       color: var(--sklearn-color-text);
     }
 
     /* Toggleable content - dropdown */
 
-    #sk-container-id-44 div.sk-toggleable__content {
+    #sk-container-id-45 div.sk-toggleable__content {
       max-height: 0;
       max-width: 0;
       overflow: hidden;
@@ -2242,12 +2242,12 @@ representation.
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-44 div.sk-toggleable__content.fitted {
+    #sk-container-id-45 div.sk-toggleable__content.fitted {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
-    #sk-container-id-44 div.sk-toggleable__content pre {
+    #sk-container-id-45 div.sk-toggleable__content pre {
       margin: 0.2em;
       border-radius: 0.25em;
       color: var(--sklearn-color-text);
@@ -2255,79 +2255,79 @@ representation.
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-44 div.sk-toggleable__content.fitted pre {
+    #sk-container-id-45 div.sk-toggleable__content.fitted pre {
       /* unfitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
-    #sk-container-id-44 input.sk-toggleable__control:checked~div.sk-toggleable__content {
+    #sk-container-id-45 input.sk-toggleable__control:checked~div.sk-toggleable__content {
       /* Expand drop-down */
       max-height: 200px;
       max-width: 100%;
       overflow: auto;
     }
 
-    #sk-container-id-44 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
+    #sk-container-id-45 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
       content: "▾";
     }
 
     /* Pipeline/ColumnTransformer-specific style */
 
-    #sk-container-id-44 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-45 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-44 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-45 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
     /* Estimator-specific style */
 
     /* Colorize estimator box */
-    #sk-container-id-44 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-45 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-44 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-45 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
-    #sk-container-id-44 div.sk-label label.sk-toggleable__label,
-    #sk-container-id-44 div.sk-label label {
+    #sk-container-id-45 div.sk-label label.sk-toggleable__label,
+    #sk-container-id-45 div.sk-label label {
       /* The background is the default theme color */
       color: var(--sklearn-color-text-on-default-background);
     }
 
     /* On hover, darken the color of the background */
-    #sk-container-id-44 div.sk-label:hover label.sk-toggleable__label {
+    #sk-container-id-45 div.sk-label:hover label.sk-toggleable__label {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
     /* Label box, darken color on hover, fitted */
-    #sk-container-id-44 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
+    #sk-container-id-45 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
     /* Estimator label */
 
-    #sk-container-id-44 div.sk-label label {
+    #sk-container-id-45 div.sk-label label {
       font-family: monospace;
       font-weight: bold;
       display: inline-block;
       line-height: 1.2em;
     }
 
-    #sk-container-id-44 div.sk-label-container {
+    #sk-container-id-45 div.sk-label-container {
       text-align: center;
     }
 
     /* Estimator-specific */
-    #sk-container-id-44 div.sk-estimator {
+    #sk-container-id-45 div.sk-estimator {
       font-family: monospace;
       border: 1px dotted var(--sklearn-color-border-box);
       border-radius: 0.25em;
@@ -2337,18 +2337,18 @@ representation.
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-44 div.sk-estimator.fitted {
+    #sk-container-id-45 div.sk-estimator.fitted {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
     /* on hover */
-    #sk-container-id-44 div.sk-estimator:hover {
+    #sk-container-id-45 div.sk-estimator:hover {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-44 div.sk-estimator.fitted:hover {
+    #sk-container-id-45 div.sk-estimator.fitted:hover {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-2);
     }
@@ -2436,7 +2436,7 @@ representation.
 
     /* "?"-specific style due to the `<a>` HTML tag */
 
-    #sk-container-id-44 a.estimator_doc_link {
+    #sk-container-id-45 a.estimator_doc_link {
       float: right;
       font-size: 1rem;
       line-height: 1em;
@@ -2451,25 +2451,25 @@ representation.
       border: var(--sklearn-color-unfitted-level-1) 1pt solid;
     }
 
-    #sk-container-id-44 a.estimator_doc_link.fitted {
+    #sk-container-id-45 a.estimator_doc_link.fitted {
       /* fitted */
       border: var(--sklearn-color-fitted-level-1) 1pt solid;
       color: var(--sklearn-color-fitted-level-1);
     }
 
     /* On hover */
-    #sk-container-id-44 a.estimator_doc_link:hover {
+    #sk-container-id-45 a.estimator_doc_link:hover {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-3);
       color: var(--sklearn-color-background);
       text-decoration: none;
     }
 
-    #sk-container-id-44 a.estimator_doc_link.fitted:hover {
+    #sk-container-id-45 a.estimator_doc_link.fitted:hover {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-3);
     }
-    </style><div id="sk-container-id-44" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>GridSearchCV(estimator=Pipeline(steps=[(&#x27;preprocessor&#x27;,
+    </style><div id="sk-container-id-45" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>GridSearchCV(estimator=Pipeline(steps=[(&#x27;preprocessor&#x27;,
                                             ColumnTransformer(transformers=[(&#x27;categorical&#x27;,
                                                                              Pipeline(steps=[(&#x27;imputation_constant&#x27;,
                                                                                               SimpleImputer(fill_value=&#x27;missing&#x27;,
@@ -2491,7 +2491,7 @@ representation.
                  param_grid={&#x27;classifier__criterion&#x27;: [&#x27;gini&#x27;, &#x27;entropy&#x27;],
                              &#x27;classifier__max_depth&#x27;: [4, 5, 6, 7, 8],
                              &#x27;classifier__max_features&#x27;: [&#x27;auto&#x27;, &#x27;sqrt&#x27;, &#x27;log2&#x27;],
-                             &#x27;classifier__n_estimators&#x27;: [200, 500]})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-185" type="checkbox" ><label for="sk-estimator-id-185" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>GridSearchCV</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.model_selection.GridSearchCV.html">?<span>Documentation for GridSearchCV</span></a><span class="sk-estimator-doc-link ">i<span>Not fitted</span></span></div></label><div class="sk-toggleable__content "><pre>GridSearchCV(estimator=Pipeline(steps=[(&#x27;preprocessor&#x27;,
+                             &#x27;classifier__n_estimators&#x27;: [200, 500]})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-199" type="checkbox" ><label for="sk-estimator-id-199" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>GridSearchCV</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.model_selection.GridSearchCV.html">?<span>Documentation for GridSearchCV</span></a><span class="sk-estimator-doc-link ">i<span>Not fitted</span></span></div></label><div class="sk-toggleable__content "><pre>GridSearchCV(estimator=Pipeline(steps=[(&#x27;preprocessor&#x27;,
                                             ColumnTransformer(transformers=[(&#x27;categorical&#x27;,
                                                                              Pipeline(steps=[(&#x27;imputation_constant&#x27;,
                                                                                               SimpleImputer(fill_value=&#x27;missing&#x27;,
@@ -2513,7 +2513,7 @@ representation.
                  param_grid={&#x27;classifier__criterion&#x27;: [&#x27;gini&#x27;, &#x27;entropy&#x27;],
                              &#x27;classifier__max_depth&#x27;: [4, 5, 6, 7, 8],
                              &#x27;classifier__max_features&#x27;: [&#x27;auto&#x27;, &#x27;sqrt&#x27;, &#x27;log2&#x27;],
-                             &#x27;classifier__n_estimators&#x27;: [200, 500]})</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-186" type="checkbox" ><label for="sk-estimator-id-186" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>estimator: Pipeline</div></div></label><div class="sk-toggleable__content "><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,
+                             &#x27;classifier__n_estimators&#x27;: [200, 500]})</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-200" type="checkbox" ><label for="sk-estimator-id-200" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>estimator: Pipeline</div></div></label><div class="sk-toggleable__content "><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,
                      ColumnTransformer(transformers=[(&#x27;categorical&#x27;,
                                                       Pipeline(steps=[(&#x27;imputation_constant&#x27;,
                                                                        SimpleImputer(fill_value=&#x27;missing&#x27;,
@@ -2527,7 +2527,7 @@ representation.
                                                                       (&#x27;scaler&#x27;,
                                                                        StandardScaler())]),
                                                       [&#x27;age&#x27;, &#x27;weight&#x27;])])),
-                    (&#x27;classifier&#x27;, RandomForestClassifier())])</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-187" type="checkbox" ><label for="sk-estimator-id-187" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>preprocessor: ColumnTransformer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.compose.ColumnTransformer.html">?<span>Documentation for preprocessor: ColumnTransformer</span></a></div></label><div class="sk-toggleable__content "><pre>ColumnTransformer(transformers=[(&#x27;categorical&#x27;,
+                    (&#x27;classifier&#x27;, RandomForestClassifier())])</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-201" type="checkbox" ><label for="sk-estimator-id-201" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>preprocessor: ColumnTransformer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.compose.ColumnTransformer.html">?<span>Documentation for preprocessor: ColumnTransformer</span></a></div></label><div class="sk-toggleable__content "><pre>ColumnTransformer(transformers=[(&#x27;categorical&#x27;,
                                      Pipeline(steps=[(&#x27;imputation_constant&#x27;,
                                                       SimpleImputer(fill_value=&#x27;missing&#x27;,
                                                                     strategy=&#x27;constant&#x27;)),
@@ -2538,7 +2538,7 @@ representation.
                                      Pipeline(steps=[(&#x27;imputation_mean&#x27;,
                                                       SimpleImputer()),
                                                      (&#x27;scaler&#x27;, StandardScaler())]),
-                                     [&#x27;age&#x27;, &#x27;weight&#x27;])])</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-188" type="checkbox" ><label for="sk-estimator-id-188" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>categorical</div></div></label><div class="sk-toggleable__content "><pre>[&#x27;state&#x27;, &#x27;gender&#x27;]</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-189" type="checkbox" ><label for="sk-estimator-id-189" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>SimpleImputer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.impute.SimpleImputer.html">?<span>Documentation for SimpleImputer</span></a></div></label><div class="sk-toggleable__content "><pre>SimpleImputer(fill_value=&#x27;missing&#x27;, strategy=&#x27;constant&#x27;)</pre></div> </div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-190" type="checkbox" ><label for="sk-estimator-id-190" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>OneHotEncoder</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.OneHotEncoder.html">?<span>Documentation for OneHotEncoder</span></a></div></label><div class="sk-toggleable__content "><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</pre></div> </div></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-191" type="checkbox" ><label for="sk-estimator-id-191" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>numerical</div></div></label><div class="sk-toggleable__content "><pre>[&#x27;age&#x27;, &#x27;weight&#x27;]</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-192" type="checkbox" ><label for="sk-estimator-id-192" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>SimpleImputer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.impute.SimpleImputer.html">?<span>Documentation for SimpleImputer</span></a></div></label><div class="sk-toggleable__content "><pre>SimpleImputer()</pre></div> </div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-193" type="checkbox" ><label for="sk-estimator-id-193" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>StandardScaler</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.StandardScaler.html">?<span>Documentation for StandardScaler</span></a></div></label><div class="sk-toggleable__content "><pre>StandardScaler()</pre></div> </div></div></div></div></div></div></div></div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-194" type="checkbox" ><label for="sk-estimator-id-194" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>RandomForestClassifier</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.ensemble.RandomForestClassifier.html">?<span>Documentation for RandomForestClassifier</span></a></div></label><div class="sk-toggleable__content "><pre>RandomForestClassifier()</pre></div> </div></div></div></div></div></div></div></div></div></div></div>
+                                     [&#x27;age&#x27;, &#x27;weight&#x27;])])</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-202" type="checkbox" ><label for="sk-estimator-id-202" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>categorical</div></div></label><div class="sk-toggleable__content "><pre>[&#x27;state&#x27;, &#x27;gender&#x27;]</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-203" type="checkbox" ><label for="sk-estimator-id-203" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>SimpleImputer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.impute.SimpleImputer.html">?<span>Documentation for SimpleImputer</span></a></div></label><div class="sk-toggleable__content "><pre>SimpleImputer(fill_value=&#x27;missing&#x27;, strategy=&#x27;constant&#x27;)</pre></div> </div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-204" type="checkbox" ><label for="sk-estimator-id-204" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>OneHotEncoder</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.OneHotEncoder.html">?<span>Documentation for OneHotEncoder</span></a></div></label><div class="sk-toggleable__content "><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</pre></div> </div></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-205" type="checkbox" ><label for="sk-estimator-id-205" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>numerical</div></div></label><div class="sk-toggleable__content "><pre>[&#x27;age&#x27;, &#x27;weight&#x27;]</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-206" type="checkbox" ><label for="sk-estimator-id-206" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>SimpleImputer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.impute.SimpleImputer.html">?<span>Documentation for SimpleImputer</span></a></div></label><div class="sk-toggleable__content "><pre>SimpleImputer()</pre></div> </div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-207" type="checkbox" ><label for="sk-estimator-id-207" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>StandardScaler</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.StandardScaler.html">?<span>Documentation for StandardScaler</span></a></div></label><div class="sk-toggleable__content "><pre>StandardScaler()</pre></div> </div></div></div></div></div></div></div></div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-208" type="checkbox" ><label for="sk-estimator-id-208" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>RandomForestClassifier</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.ensemble.RandomForestClassifier.html">?<span>Documentation for RandomForestClassifier</span></a></div></label><div class="sk-toggleable__content "><pre>RandomForestClassifier()</pre></div> </div></div></div></div></div></div></div></div></div></div></div>
     </div>
     <br />
     <br />
diff --git a/dev/_sources/auto_examples/miscellaneous/plot_roc_curve_visualization_api.rst.txt b/dev/_sources/auto_examples/miscellaneous/plot_roc_curve_visualization_api.rst.txt
index 0a789deb3b070..f7b51946ce96c 100644
--- a/dev/_sources/auto_examples/miscellaneous/plot_roc_curve_visualization_api.rst.txt
+++ b/dev/_sources/auto_examples/miscellaneous/plot_roc_curve_visualization_api.rst.txt
@@ -75,7 +75,7 @@ problem. Then, we train a support vector classifier on a training dataset.
 .. raw:: html
 
     <div class="output_subarea output_html rendered_html output_result">
-    <style>#sk-container-id-53 {
+    <style>#sk-container-id-54 {
       /* Definition of color scheme common for light and dark mode */
       --sklearn-color-text: #000;
       --sklearn-color-text-muted: #666;
@@ -106,15 +106,15 @@ problem. Then, we train a support vector classifier on a training dataset.
       }
     }
 
-    #sk-container-id-53 {
+    #sk-container-id-54 {
       color: var(--sklearn-color-text);
     }
 
-    #sk-container-id-53 pre {
+    #sk-container-id-54 pre {
       padding: 0;
     }
 
-    #sk-container-id-53 input.sk-hidden--visually {
+    #sk-container-id-54 input.sk-hidden--visually {
       border: 0;
       clip: rect(1px 1px 1px 1px);
       clip: rect(1px, 1px, 1px, 1px);
@@ -126,7 +126,7 @@ problem. Then, we train a support vector classifier on a training dataset.
       width: 1px;
     }
 
-    #sk-container-id-53 div.sk-dashed-wrapped {
+    #sk-container-id-54 div.sk-dashed-wrapped {
       border: 1px dashed var(--sklearn-color-line);
       margin: 0 0.4em 0.5em 0.4em;
       box-sizing: border-box;
@@ -134,7 +134,7 @@ problem. Then, we train a support vector classifier on a training dataset.
       background-color: var(--sklearn-color-background);
     }
 
-    #sk-container-id-53 div.sk-container {
+    #sk-container-id-54 div.sk-container {
       /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
          but bootstrap.min.css set `[hidden] { display: none !important; }`
          so we also need the `!important` here to be able to override the
@@ -144,7 +144,7 @@ problem. Then, we train a support vector classifier on a training dataset.
       position: relative;
     }
 
-    #sk-container-id-53 div.sk-text-repr-fallback {
+    #sk-container-id-54 div.sk-text-repr-fallback {
       display: none;
     }
 
@@ -160,14 +160,14 @@ problem. Then, we train a support vector classifier on a training dataset.
 
     /* Parallel-specific style estimator block */
 
-    #sk-container-id-53 div.sk-parallel-item::after {
+    #sk-container-id-54 div.sk-parallel-item::after {
       content: "";
       width: 100%;
       border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
       flex-grow: 1;
     }
 
-    #sk-container-id-53 div.sk-parallel {
+    #sk-container-id-54 div.sk-parallel {
       display: flex;
       align-items: stretch;
       justify-content: center;
@@ -175,28 +175,28 @@ problem. Then, we train a support vector classifier on a training dataset.
       position: relative;
     }
 
-    #sk-container-id-53 div.sk-parallel-item {
+    #sk-container-id-54 div.sk-parallel-item {
       display: flex;
       flex-direction: column;
     }
 
-    #sk-container-id-53 div.sk-parallel-item:first-child::after {
+    #sk-container-id-54 div.sk-parallel-item:first-child::after {
       align-self: flex-end;
       width: 50%;
     }
 
-    #sk-container-id-53 div.sk-parallel-item:last-child::after {
+    #sk-container-id-54 div.sk-parallel-item:last-child::after {
       align-self: flex-start;
       width: 50%;
     }
 
-    #sk-container-id-53 div.sk-parallel-item:only-child::after {
+    #sk-container-id-54 div.sk-parallel-item:only-child::after {
       width: 0;
     }
 
     /* Serial-specific style estimator block */
 
-    #sk-container-id-53 div.sk-serial {
+    #sk-container-id-54 div.sk-serial {
       display: flex;
       flex-direction: column;
       align-items: center;
@@ -214,14 +214,14 @@ problem. Then, we train a support vector classifier on a training dataset.
 
     /* Pipeline and ColumnTransformer style (default) */
 
-    #sk-container-id-53 div.sk-toggleable {
+    #sk-container-id-54 div.sk-toggleable {
       /* Default theme specific background. It is overwritten whether we have a
       specific estimator or a Pipeline/ColumnTransformer */
       background-color: var(--sklearn-color-background);
     }
 
     /* Toggleable label */
-    #sk-container-id-53 label.sk-toggleable__label {
+    #sk-container-id-54 label.sk-toggleable__label {
       cursor: pointer;
       display: flex;
       width: 100%;
@@ -234,13 +234,13 @@ problem. Then, we train a support vector classifier on a training dataset.
       gap: 0.5em;
     }
 
-    #sk-container-id-53 label.sk-toggleable__label .caption {
+    #sk-container-id-54 label.sk-toggleable__label .caption {
       font-size: 0.6rem;
       font-weight: lighter;
       color: var(--sklearn-color-text-muted);
     }
 
-    #sk-container-id-53 label.sk-toggleable__label-arrow:before {
+    #sk-container-id-54 label.sk-toggleable__label-arrow:before {
       /* Arrow on the left of the label */
       content: "▸";
       float: left;
@@ -248,13 +248,13 @@ problem. Then, we train a support vector classifier on a training dataset.
       color: var(--sklearn-color-icon);
     }
 
-    #sk-container-id-53 label.sk-toggleable__label-arrow:hover:before {
+    #sk-container-id-54 label.sk-toggleable__label-arrow:hover:before {
       color: var(--sklearn-color-text);
     }
 
     /* Toggleable content - dropdown */
 
-    #sk-container-id-53 div.sk-toggleable__content {
+    #sk-container-id-54 div.sk-toggleable__content {
       max-height: 0;
       max-width: 0;
       overflow: hidden;
@@ -263,12 +263,12 @@ problem. Then, we train a support vector classifier on a training dataset.
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-53 div.sk-toggleable__content.fitted {
+    #sk-container-id-54 div.sk-toggleable__content.fitted {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
-    #sk-container-id-53 div.sk-toggleable__content pre {
+    #sk-container-id-54 div.sk-toggleable__content pre {
       margin: 0.2em;
       border-radius: 0.25em;
       color: var(--sklearn-color-text);
@@ -276,79 +276,79 @@ problem. Then, we train a support vector classifier on a training dataset.
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-53 div.sk-toggleable__content.fitted pre {
+    #sk-container-id-54 div.sk-toggleable__content.fitted pre {
       /* unfitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
-    #sk-container-id-53 input.sk-toggleable__control:checked~div.sk-toggleable__content {
+    #sk-container-id-54 input.sk-toggleable__control:checked~div.sk-toggleable__content {
       /* Expand drop-down */
       max-height: 200px;
       max-width: 100%;
       overflow: auto;
     }
 
-    #sk-container-id-53 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
+    #sk-container-id-54 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
       content: "▾";
     }
 
     /* Pipeline/ColumnTransformer-specific style */
 
-    #sk-container-id-53 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-54 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-53 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-54 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
     /* Estimator-specific style */
 
     /* Colorize estimator box */
-    #sk-container-id-53 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-54 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-53 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-54 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
-    #sk-container-id-53 div.sk-label label.sk-toggleable__label,
-    #sk-container-id-53 div.sk-label label {
+    #sk-container-id-54 div.sk-label label.sk-toggleable__label,
+    #sk-container-id-54 div.sk-label label {
       /* The background is the default theme color */
       color: var(--sklearn-color-text-on-default-background);
     }
 
     /* On hover, darken the color of the background */
-    #sk-container-id-53 div.sk-label:hover label.sk-toggleable__label {
+    #sk-container-id-54 div.sk-label:hover label.sk-toggleable__label {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
     /* Label box, darken color on hover, fitted */
-    #sk-container-id-53 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
+    #sk-container-id-54 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
     /* Estimator label */
 
-    #sk-container-id-53 div.sk-label label {
+    #sk-container-id-54 div.sk-label label {
       font-family: monospace;
       font-weight: bold;
       display: inline-block;
       line-height: 1.2em;
     }
 
-    #sk-container-id-53 div.sk-label-container {
+    #sk-container-id-54 div.sk-label-container {
       text-align: center;
     }
 
     /* Estimator-specific */
-    #sk-container-id-53 div.sk-estimator {
+    #sk-container-id-54 div.sk-estimator {
       font-family: monospace;
       border: 1px dotted var(--sklearn-color-border-box);
       border-radius: 0.25em;
@@ -358,18 +358,18 @@ problem. Then, we train a support vector classifier on a training dataset.
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-53 div.sk-estimator.fitted {
+    #sk-container-id-54 div.sk-estimator.fitted {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
     /* on hover */
-    #sk-container-id-53 div.sk-estimator:hover {
+    #sk-container-id-54 div.sk-estimator:hover {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-53 div.sk-estimator.fitted:hover {
+    #sk-container-id-54 div.sk-estimator.fitted:hover {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-2);
     }
@@ -457,7 +457,7 @@ problem. Then, we train a support vector classifier on a training dataset.
 
     /* "?"-specific style due to the `<a>` HTML tag */
 
-    #sk-container-id-53 a.estimator_doc_link {
+    #sk-container-id-54 a.estimator_doc_link {
       float: right;
       font-size: 1rem;
       line-height: 1em;
@@ -472,25 +472,25 @@ problem. Then, we train a support vector classifier on a training dataset.
       border: var(--sklearn-color-unfitted-level-1) 1pt solid;
     }
 
-    #sk-container-id-53 a.estimator_doc_link.fitted {
+    #sk-container-id-54 a.estimator_doc_link.fitted {
       /* fitted */
       border: var(--sklearn-color-fitted-level-1) 1pt solid;
       color: var(--sklearn-color-fitted-level-1);
     }
 
     /* On hover */
-    #sk-container-id-53 a.estimator_doc_link:hover {
+    #sk-container-id-54 a.estimator_doc_link:hover {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-3);
       color: var(--sklearn-color-background);
       text-decoration: none;
     }
 
-    #sk-container-id-53 a.estimator_doc_link.fitted:hover {
+    #sk-container-id-54 a.estimator_doc_link.fitted:hover {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-3);
     }
-    </style><div id="sk-container-id-53" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>SVC(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-222" type="checkbox" checked><label for="sk-estimator-id-222" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>SVC</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.svm.SVC.html">?<span>Documentation for SVC</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>SVC(random_state=42)</pre></div> </div></div></div></div>
+    </style><div id="sk-container-id-54" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>SVC(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-236" type="checkbox" checked><label for="sk-estimator-id-236" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>SVC</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.svm.SVC.html">?<span>Documentation for SVC</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>SVC(random_state=42)</pre></div> </div></div></div></div>
     </div>
     <br />
     <br />
@@ -559,7 +559,7 @@ curves.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.166 seconds)
+   **Total running time of the script:** (0 minutes 0.167 seconds)
 
 
 .. _sphx_glr_download_auto_examples_miscellaneous_plot_roc_curve_visualization_api.py:
diff --git a/dev/_sources/auto_examples/miscellaneous/plot_set_output.rst.txt b/dev/_sources/auto_examples/miscellaneous/plot_set_output.rst.txt
index ff1f8523c403a..12222747205b0 100644
--- a/dev/_sources/auto_examples/miscellaneous/plot_set_output.rst.txt
+++ b/dev/_sources/auto_examples/miscellaneous/plot_set_output.rst.txt
@@ -272,7 +272,7 @@ DataFrames.
 .. raw:: html
 
     <div class="output_subarea output_html rendered_html output_result">
-    <style>#sk-container-id-46 {
+    <style>#sk-container-id-47 {
       /* Definition of color scheme common for light and dark mode */
       --sklearn-color-text: #000;
       --sklearn-color-text-muted: #666;
@@ -303,15 +303,15 @@ DataFrames.
       }
     }
 
-    #sk-container-id-46 {
+    #sk-container-id-47 {
       color: var(--sklearn-color-text);
     }
 
-    #sk-container-id-46 pre {
+    #sk-container-id-47 pre {
       padding: 0;
     }
 
-    #sk-container-id-46 input.sk-hidden--visually {
+    #sk-container-id-47 input.sk-hidden--visually {
       border: 0;
       clip: rect(1px 1px 1px 1px);
       clip: rect(1px, 1px, 1px, 1px);
@@ -323,7 +323,7 @@ DataFrames.
       width: 1px;
     }
 
-    #sk-container-id-46 div.sk-dashed-wrapped {
+    #sk-container-id-47 div.sk-dashed-wrapped {
       border: 1px dashed var(--sklearn-color-line);
       margin: 0 0.4em 0.5em 0.4em;
       box-sizing: border-box;
@@ -331,7 +331,7 @@ DataFrames.
       background-color: var(--sklearn-color-background);
     }
 
-    #sk-container-id-46 div.sk-container {
+    #sk-container-id-47 div.sk-container {
       /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
          but bootstrap.min.css set `[hidden] { display: none !important; }`
          so we also need the `!important` here to be able to override the
@@ -341,7 +341,7 @@ DataFrames.
       position: relative;
     }
 
-    #sk-container-id-46 div.sk-text-repr-fallback {
+    #sk-container-id-47 div.sk-text-repr-fallback {
       display: none;
     }
 
@@ -357,14 +357,14 @@ DataFrames.
 
     /* Parallel-specific style estimator block */
 
-    #sk-container-id-46 div.sk-parallel-item::after {
+    #sk-container-id-47 div.sk-parallel-item::after {
       content: "";
       width: 100%;
       border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
       flex-grow: 1;
     }
 
-    #sk-container-id-46 div.sk-parallel {
+    #sk-container-id-47 div.sk-parallel {
       display: flex;
       align-items: stretch;
       justify-content: center;
@@ -372,28 +372,28 @@ DataFrames.
       position: relative;
     }
 
-    #sk-container-id-46 div.sk-parallel-item {
+    #sk-container-id-47 div.sk-parallel-item {
       display: flex;
       flex-direction: column;
     }
 
-    #sk-container-id-46 div.sk-parallel-item:first-child::after {
+    #sk-container-id-47 div.sk-parallel-item:first-child::after {
       align-self: flex-end;
       width: 50%;
     }
 
-    #sk-container-id-46 div.sk-parallel-item:last-child::after {
+    #sk-container-id-47 div.sk-parallel-item:last-child::after {
       align-self: flex-start;
       width: 50%;
     }
 
-    #sk-container-id-46 div.sk-parallel-item:only-child::after {
+    #sk-container-id-47 div.sk-parallel-item:only-child::after {
       width: 0;
     }
 
     /* Serial-specific style estimator block */
 
-    #sk-container-id-46 div.sk-serial {
+    #sk-container-id-47 div.sk-serial {
       display: flex;
       flex-direction: column;
       align-items: center;
@@ -411,14 +411,14 @@ DataFrames.
 
     /* Pipeline and ColumnTransformer style (default) */
 
-    #sk-container-id-46 div.sk-toggleable {
+    #sk-container-id-47 div.sk-toggleable {
       /* Default theme specific background. It is overwritten whether we have a
       specific estimator or a Pipeline/ColumnTransformer */
       background-color: var(--sklearn-color-background);
     }
 
     /* Toggleable label */
-    #sk-container-id-46 label.sk-toggleable__label {
+    #sk-container-id-47 label.sk-toggleable__label {
       cursor: pointer;
       display: flex;
       width: 100%;
@@ -431,13 +431,13 @@ DataFrames.
       gap: 0.5em;
     }
 
-    #sk-container-id-46 label.sk-toggleable__label .caption {
+    #sk-container-id-47 label.sk-toggleable__label .caption {
       font-size: 0.6rem;
       font-weight: lighter;
       color: var(--sklearn-color-text-muted);
     }
 
-    #sk-container-id-46 label.sk-toggleable__label-arrow:before {
+    #sk-container-id-47 label.sk-toggleable__label-arrow:before {
       /* Arrow on the left of the label */
       content: "▸";
       float: left;
@@ -445,13 +445,13 @@ DataFrames.
       color: var(--sklearn-color-icon);
     }
 
-    #sk-container-id-46 label.sk-toggleable__label-arrow:hover:before {
+    #sk-container-id-47 label.sk-toggleable__label-arrow:hover:before {
       color: var(--sklearn-color-text);
     }
 
     /* Toggleable content - dropdown */
 
-    #sk-container-id-46 div.sk-toggleable__content {
+    #sk-container-id-47 div.sk-toggleable__content {
       max-height: 0;
       max-width: 0;
       overflow: hidden;
@@ -460,12 +460,12 @@ DataFrames.
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-46 div.sk-toggleable__content.fitted {
+    #sk-container-id-47 div.sk-toggleable__content.fitted {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
-    #sk-container-id-46 div.sk-toggleable__content pre {
+    #sk-container-id-47 div.sk-toggleable__content pre {
       margin: 0.2em;
       border-radius: 0.25em;
       color: var(--sklearn-color-text);
@@ -473,79 +473,79 @@ DataFrames.
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-46 div.sk-toggleable__content.fitted pre {
+    #sk-container-id-47 div.sk-toggleable__content.fitted pre {
       /* unfitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
-    #sk-container-id-46 input.sk-toggleable__control:checked~div.sk-toggleable__content {
+    #sk-container-id-47 input.sk-toggleable__control:checked~div.sk-toggleable__content {
       /* Expand drop-down */
       max-height: 200px;
       max-width: 100%;
       overflow: auto;
     }
 
-    #sk-container-id-46 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
+    #sk-container-id-47 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
       content: "▾";
     }
 
     /* Pipeline/ColumnTransformer-specific style */
 
-    #sk-container-id-46 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-47 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-46 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-47 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
     /* Estimator-specific style */
 
     /* Colorize estimator box */
-    #sk-container-id-46 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-47 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-46 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-47 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
-    #sk-container-id-46 div.sk-label label.sk-toggleable__label,
-    #sk-container-id-46 div.sk-label label {
+    #sk-container-id-47 div.sk-label label.sk-toggleable__label,
+    #sk-container-id-47 div.sk-label label {
       /* The background is the default theme color */
       color: var(--sklearn-color-text-on-default-background);
     }
 
     /* On hover, darken the color of the background */
-    #sk-container-id-46 div.sk-label:hover label.sk-toggleable__label {
+    #sk-container-id-47 div.sk-label:hover label.sk-toggleable__label {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
     /* Label box, darken color on hover, fitted */
-    #sk-container-id-46 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
+    #sk-container-id-47 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
     /* Estimator label */
 
-    #sk-container-id-46 div.sk-label label {
+    #sk-container-id-47 div.sk-label label {
       font-family: monospace;
       font-weight: bold;
       display: inline-block;
       line-height: 1.2em;
     }
 
-    #sk-container-id-46 div.sk-label-container {
+    #sk-container-id-47 div.sk-label-container {
       text-align: center;
     }
 
     /* Estimator-specific */
-    #sk-container-id-46 div.sk-estimator {
+    #sk-container-id-47 div.sk-estimator {
       font-family: monospace;
       border: 1px dotted var(--sklearn-color-border-box);
       border-radius: 0.25em;
@@ -555,18 +555,18 @@ DataFrames.
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-46 div.sk-estimator.fitted {
+    #sk-container-id-47 div.sk-estimator.fitted {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
     /* on hover */
-    #sk-container-id-46 div.sk-estimator:hover {
+    #sk-container-id-47 div.sk-estimator:hover {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-46 div.sk-estimator.fitted:hover {
+    #sk-container-id-47 div.sk-estimator.fitted:hover {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-2);
     }
@@ -654,7 +654,7 @@ DataFrames.
 
     /* "?"-specific style due to the `<a>` HTML tag */
 
-    #sk-container-id-46 a.estimator_doc_link {
+    #sk-container-id-47 a.estimator_doc_link {
       float: right;
       font-size: 1rem;
       line-height: 1em;
@@ -669,29 +669,29 @@ DataFrames.
       border: var(--sklearn-color-unfitted-level-1) 1pt solid;
     }
 
-    #sk-container-id-46 a.estimator_doc_link.fitted {
+    #sk-container-id-47 a.estimator_doc_link.fitted {
       /* fitted */
       border: var(--sklearn-color-fitted-level-1) 1pt solid;
       color: var(--sklearn-color-fitted-level-1);
     }
 
     /* On hover */
-    #sk-container-id-46 a.estimator_doc_link:hover {
+    #sk-container-id-47 a.estimator_doc_link:hover {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-3);
       color: var(--sklearn-color-background);
       text-decoration: none;
     }
 
-    #sk-container-id-46 a.estimator_doc_link.fitted:hover {
+    #sk-container-id-47 a.estimator_doc_link.fitted:hover {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-3);
     }
-    </style><div id="sk-container-id-46" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),
+    </style><div id="sk-container-id-47" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),
                     (&#x27;selectpercentile&#x27;, SelectPercentile(percentile=75)),
-                    (&#x27;logisticregression&#x27;, LogisticRegression())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-204" type="checkbox" ><label for="sk-estimator-id-204" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>Pipeline</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.pipeline.Pipeline.html">?<span>Documentation for Pipeline</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),
+                    (&#x27;logisticregression&#x27;, LogisticRegression())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-218" type="checkbox" ><label for="sk-estimator-id-218" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>Pipeline</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.pipeline.Pipeline.html">?<span>Documentation for Pipeline</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),
                     (&#x27;selectpercentile&#x27;, SelectPercentile(percentile=75)),
-                    (&#x27;logisticregression&#x27;, LogisticRegression())])</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-205" type="checkbox" ><label for="sk-estimator-id-205" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>StandardScaler</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.StandardScaler.html">?<span>Documentation for StandardScaler</span></a></div></label><div class="sk-toggleable__content fitted"><pre>StandardScaler()</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-206" type="checkbox" ><label for="sk-estimator-id-206" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>SelectPercentile</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.feature_selection.SelectPercentile.html">?<span>Documentation for SelectPercentile</span></a></div></label><div class="sk-toggleable__content fitted"><pre>SelectPercentile(percentile=75)</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-207" type="checkbox" ><label for="sk-estimator-id-207" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>LogisticRegression</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.linear_model.LogisticRegression.html">?<span>Documentation for LogisticRegression</span></a></div></label><div class="sk-toggleable__content fitted"><pre>LogisticRegression()</pre></div> </div></div></div></div></div></div>
+                    (&#x27;logisticregression&#x27;, LogisticRegression())])</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-219" type="checkbox" ><label for="sk-estimator-id-219" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>StandardScaler</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.StandardScaler.html">?<span>Documentation for StandardScaler</span></a></div></label><div class="sk-toggleable__content fitted"><pre>StandardScaler()</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-220" type="checkbox" ><label for="sk-estimator-id-220" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>SelectPercentile</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.feature_selection.SelectPercentile.html">?<span>Documentation for SelectPercentile</span></a></div></label><div class="sk-toggleable__content fitted"><pre>SelectPercentile(percentile=75)</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-221" type="checkbox" ><label for="sk-estimator-id-221" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>LogisticRegression</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.linear_model.LogisticRegression.html">?<span>Documentation for LogisticRegression</span></a></div></label><div class="sk-toggleable__content fitted"><pre>LogisticRegression()</pre></div> </div></div></div></div></div></div>
     </div>
     <br />
     <br />
@@ -840,7 +840,7 @@ setting `transform_output` to `"pandas"`.
  .. code-block:: none
 
 
-    0.75
+    0.801829268292683
 
 
 
@@ -913,7 +913,7 @@ the transformer has no effect.
 .. raw:: html
 
     <div class="output_subarea output_html rendered_html output_result">
-    <style>#sk-container-id-47 {
+    <style>#sk-container-id-48 {
       /* Definition of color scheme common for light and dark mode */
       --sklearn-color-text: #000;
       --sklearn-color-text-muted: #666;
@@ -944,15 +944,15 @@ the transformer has no effect.
       }
     }
 
-    #sk-container-id-47 {
+    #sk-container-id-48 {
       color: var(--sklearn-color-text);
     }
 
-    #sk-container-id-47 pre {
+    #sk-container-id-48 pre {
       padding: 0;
     }
 
-    #sk-container-id-47 input.sk-hidden--visually {
+    #sk-container-id-48 input.sk-hidden--visually {
       border: 0;
       clip: rect(1px 1px 1px 1px);
       clip: rect(1px, 1px, 1px, 1px);
@@ -964,7 +964,7 @@ the transformer has no effect.
       width: 1px;
     }
 
-    #sk-container-id-47 div.sk-dashed-wrapped {
+    #sk-container-id-48 div.sk-dashed-wrapped {
       border: 1px dashed var(--sklearn-color-line);
       margin: 0 0.4em 0.5em 0.4em;
       box-sizing: border-box;
@@ -972,7 +972,7 @@ the transformer has no effect.
       background-color: var(--sklearn-color-background);
     }
 
-    #sk-container-id-47 div.sk-container {
+    #sk-container-id-48 div.sk-container {
       /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
          but bootstrap.min.css set `[hidden] { display: none !important; }`
          so we also need the `!important` here to be able to override the
@@ -982,7 +982,7 @@ the transformer has no effect.
       position: relative;
     }
 
-    #sk-container-id-47 div.sk-text-repr-fallback {
+    #sk-container-id-48 div.sk-text-repr-fallback {
       display: none;
     }
 
@@ -998,14 +998,14 @@ the transformer has no effect.
 
     /* Parallel-specific style estimator block */
 
-    #sk-container-id-47 div.sk-parallel-item::after {
+    #sk-container-id-48 div.sk-parallel-item::after {
       content: "";
       width: 100%;
       border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
       flex-grow: 1;
     }
 
-    #sk-container-id-47 div.sk-parallel {
+    #sk-container-id-48 div.sk-parallel {
       display: flex;
       align-items: stretch;
       justify-content: center;
@@ -1013,28 +1013,28 @@ the transformer has no effect.
       position: relative;
     }
 
-    #sk-container-id-47 div.sk-parallel-item {
+    #sk-container-id-48 div.sk-parallel-item {
       display: flex;
       flex-direction: column;
     }
 
-    #sk-container-id-47 div.sk-parallel-item:first-child::after {
+    #sk-container-id-48 div.sk-parallel-item:first-child::after {
       align-self: flex-end;
       width: 50%;
     }
 
-    #sk-container-id-47 div.sk-parallel-item:last-child::after {
+    #sk-container-id-48 div.sk-parallel-item:last-child::after {
       align-self: flex-start;
       width: 50%;
     }
 
-    #sk-container-id-47 div.sk-parallel-item:only-child::after {
+    #sk-container-id-48 div.sk-parallel-item:only-child::after {
       width: 0;
     }
 
     /* Serial-specific style estimator block */
 
-    #sk-container-id-47 div.sk-serial {
+    #sk-container-id-48 div.sk-serial {
       display: flex;
       flex-direction: column;
       align-items: center;
@@ -1052,14 +1052,14 @@ the transformer has no effect.
 
     /* Pipeline and ColumnTransformer style (default) */
 
-    #sk-container-id-47 div.sk-toggleable {
+    #sk-container-id-48 div.sk-toggleable {
       /* Default theme specific background. It is overwritten whether we have a
       specific estimator or a Pipeline/ColumnTransformer */
       background-color: var(--sklearn-color-background);
     }
 
     /* Toggleable label */
-    #sk-container-id-47 label.sk-toggleable__label {
+    #sk-container-id-48 label.sk-toggleable__label {
       cursor: pointer;
       display: flex;
       width: 100%;
@@ -1072,13 +1072,13 @@ the transformer has no effect.
       gap: 0.5em;
     }
 
-    #sk-container-id-47 label.sk-toggleable__label .caption {
+    #sk-container-id-48 label.sk-toggleable__label .caption {
       font-size: 0.6rem;
       font-weight: lighter;
       color: var(--sklearn-color-text-muted);
     }
 
-    #sk-container-id-47 label.sk-toggleable__label-arrow:before {
+    #sk-container-id-48 label.sk-toggleable__label-arrow:before {
       /* Arrow on the left of the label */
       content: "▸";
       float: left;
@@ -1086,13 +1086,13 @@ the transformer has no effect.
       color: var(--sklearn-color-icon);
     }
 
-    #sk-container-id-47 label.sk-toggleable__label-arrow:hover:before {
+    #sk-container-id-48 label.sk-toggleable__label-arrow:hover:before {
       color: var(--sklearn-color-text);
     }
 
     /* Toggleable content - dropdown */
 
-    #sk-container-id-47 div.sk-toggleable__content {
+    #sk-container-id-48 div.sk-toggleable__content {
       max-height: 0;
       max-width: 0;
       overflow: hidden;
@@ -1101,12 +1101,12 @@ the transformer has no effect.
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-47 div.sk-toggleable__content.fitted {
+    #sk-container-id-48 div.sk-toggleable__content.fitted {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
-    #sk-container-id-47 div.sk-toggleable__content pre {
+    #sk-container-id-48 div.sk-toggleable__content pre {
       margin: 0.2em;
       border-radius: 0.25em;
       color: var(--sklearn-color-text);
@@ -1114,79 +1114,79 @@ the transformer has no effect.
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-47 div.sk-toggleable__content.fitted pre {
+    #sk-container-id-48 div.sk-toggleable__content.fitted pre {
       /* unfitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
-    #sk-container-id-47 input.sk-toggleable__control:checked~div.sk-toggleable__content {
+    #sk-container-id-48 input.sk-toggleable__control:checked~div.sk-toggleable__content {
       /* Expand drop-down */
       max-height: 200px;
       max-width: 100%;
       overflow: auto;
     }
 
-    #sk-container-id-47 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
+    #sk-container-id-48 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
       content: "▾";
     }
 
     /* Pipeline/ColumnTransformer-specific style */
 
-    #sk-container-id-47 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-48 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-47 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-48 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
     /* Estimator-specific style */
 
     /* Colorize estimator box */
-    #sk-container-id-47 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-48 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-47 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-48 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
-    #sk-container-id-47 div.sk-label label.sk-toggleable__label,
-    #sk-container-id-47 div.sk-label label {
+    #sk-container-id-48 div.sk-label label.sk-toggleable__label,
+    #sk-container-id-48 div.sk-label label {
       /* The background is the default theme color */
       color: var(--sklearn-color-text-on-default-background);
     }
 
     /* On hover, darken the color of the background */
-    #sk-container-id-47 div.sk-label:hover label.sk-toggleable__label {
+    #sk-container-id-48 div.sk-label:hover label.sk-toggleable__label {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
     /* Label box, darken color on hover, fitted */
-    #sk-container-id-47 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
+    #sk-container-id-48 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
     /* Estimator label */
 
-    #sk-container-id-47 div.sk-label label {
+    #sk-container-id-48 div.sk-label label {
       font-family: monospace;
       font-weight: bold;
       display: inline-block;
       line-height: 1.2em;
     }
 
-    #sk-container-id-47 div.sk-label-container {
+    #sk-container-id-48 div.sk-label-container {
       text-align: center;
     }
 
     /* Estimator-specific */
-    #sk-container-id-47 div.sk-estimator {
+    #sk-container-id-48 div.sk-estimator {
       font-family: monospace;
       border: 1px dotted var(--sklearn-color-border-box);
       border-radius: 0.25em;
@@ -1196,18 +1196,18 @@ the transformer has no effect.
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-47 div.sk-estimator.fitted {
+    #sk-container-id-48 div.sk-estimator.fitted {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
     /* on hover */
-    #sk-container-id-47 div.sk-estimator:hover {
+    #sk-container-id-48 div.sk-estimator:hover {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-47 div.sk-estimator.fitted:hover {
+    #sk-container-id-48 div.sk-estimator.fitted:hover {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-2);
     }
@@ -1295,7 +1295,7 @@ the transformer has no effect.
 
     /* "?"-specific style due to the `<a>` HTML tag */
 
-    #sk-container-id-47 a.estimator_doc_link {
+    #sk-container-id-48 a.estimator_doc_link {
       float: right;
       font-size: 1rem;
       line-height: 1em;
@@ -1310,25 +1310,25 @@ the transformer has no effect.
       border: var(--sklearn-color-unfitted-level-1) 1pt solid;
     }
 
-    #sk-container-id-47 a.estimator_doc_link.fitted {
+    #sk-container-id-48 a.estimator_doc_link.fitted {
       /* fitted */
       border: var(--sklearn-color-fitted-level-1) 1pt solid;
       color: var(--sklearn-color-fitted-level-1);
     }
 
     /* On hover */
-    #sk-container-id-47 a.estimator_doc_link:hover {
+    #sk-container-id-48 a.estimator_doc_link:hover {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-3);
       color: var(--sklearn-color-background);
       text-decoration: none;
     }
 
-    #sk-container-id-47 a.estimator_doc_link.fitted:hover {
+    #sk-container-id-48 a.estimator_doc_link.fitted:hover {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-3);
     }
-    </style><div id="sk-container-id-47" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>StandardScaler()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-208" type="checkbox" checked><label for="sk-estimator-id-208" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>StandardScaler</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.StandardScaler.html">?<span>Documentation for StandardScaler</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>StandardScaler()</pre></div> </div></div></div></div>
+    </style><div id="sk-container-id-48" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>StandardScaler()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-222" type="checkbox" checked><label for="sk-estimator-id-222" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>StandardScaler</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.StandardScaler.html">?<span>Documentation for StandardScaler</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>StandardScaler()</pre></div> </div></div></div></div>
     </div>
     <br />
     <br />
@@ -1374,29 +1374,29 @@ the transformer has no effect.
       </thead>
       <tbody>
         <tr>
-          <th>186</th>
-          <td>1.737289</td>
-          <td>-0.109540</td>
+          <th>629</th>
+          <td>0.628306</td>
+          <td>-0.063210</td>
         </tr>
         <tr>
-          <th>103</th>
-          <td>0.573219</td>
-          <td>3.925942</td>
+          <th>688</th>
+          <td>-0.057984</td>
+          <td>-0.515704</td>
         </tr>
         <tr>
-          <th>359</th>
-          <td>-1.971758</td>
-          <td>-0.083704</td>
+          <th>439</th>
+          <td>1.314596</td>
+          <td>0.566624</td>
         </tr>
         <tr>
-          <th>935</th>
-          <td>-0.043053</td>
-          <td>-0.224579</td>
+          <th>664</th>
+          <td>-0.675645</td>
+          <td>-0.512279</td>
         </tr>
         <tr>
-          <th>408</th>
-          <td>-0.796274</td>
-          <td>-0.457352</td>
+          <th>669</th>
+          <td>-0.744274</td>
+          <td>-0.496950</td>
         </tr>
       </tbody>
     </table>
@@ -1424,18 +1424,18 @@ outside of the context manager, the output will be a NumPy array
  .. code-block:: none
 
 
-    array([[ 1.7372889 , -0.10954015],
-           [ 0.57321941,  3.92594178],
-           [-1.97175833, -0.08370392],
-           [-0.04305267, -0.22457927],
-           [-0.79627411, -0.4573518 ]])
+    array([[ 0.62830616, -0.06320955],
+           [-0.05798371, -0.51570367],
+           [ 1.31459603,  0.56662405],
+           [-0.6756446 , -0.51227857],
+           [-0.74427358, -0.49694966]])
 
 
 
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.150 seconds)
+   **Total running time of the script:** (0 minutes 0.159 seconds)
 
 
 .. _sphx_glr_download_auto_examples_miscellaneous_plot_set_output.py:
diff --git a/dev/_sources/auto_examples/mixture/plot_concentration_prior.rst.txt b/dev/_sources/auto_examples/mixture/plot_concentration_prior.rst.txt
index b1a80810dd237..4f7fc954712c5 100644
--- a/dev/_sources/auto_examples/mixture/plot_concentration_prior.rst.txt
+++ b/dev/_sources/auto_examples/mixture/plot_concentration_prior.rst.txt
@@ -211,7 +211,7 @@ tends to divide natural clusters into unnecessary sub-components.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 5.734 seconds)
+   **Total running time of the script:** (0 minutes 6.470 seconds)
 
 
 .. _sphx_glr_download_auto_examples_mixture_plot_concentration_prior.py:
diff --git a/dev/_sources/auto_examples/mixture/plot_gmm.rst.txt b/dev/_sources/auto_examples/mixture/plot_gmm.rst.txt
index 3e7ed782a909a..7a716f759771c 100644
--- a/dev/_sources/auto_examples/mixture/plot_gmm.rst.txt
+++ b/dev/_sources/auto_examples/mixture/plot_gmm.rst.txt
@@ -144,7 +144,7 @@ regularization properties of the inference algorithm.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.195 seconds)
+   **Total running time of the script:** (0 minutes 0.273 seconds)
 
 
 .. _sphx_glr_download_auto_examples_mixture_plot_gmm.py:
diff --git a/dev/_sources/auto_examples/mixture/plot_gmm_covariances.rst.txt b/dev/_sources/auto_examples/mixture/plot_gmm_covariances.rst.txt
index 634a837f87ad8..c57b4edd33655 100644
--- a/dev/_sources/auto_examples/mixture/plot_gmm_covariances.rst.txt
+++ b/dev/_sources/auto_examples/mixture/plot_gmm_covariances.rst.txt
@@ -173,7 +173,7 @@ dimensions.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.181 seconds)
+   **Total running time of the script:** (0 minutes 0.255 seconds)
 
 
 .. _sphx_glr_download_auto_examples_mixture_plot_gmm_covariances.py:
diff --git a/dev/_sources/auto_examples/mixture/plot_gmm_init.rst.txt b/dev/_sources/auto_examples/mixture/plot_gmm_init.rst.txt
index e10fb339789da..789622c84d8d8 100644
--- a/dev/_sources/auto_examples/mixture/plot_gmm_init.rst.txt
+++ b/dev/_sources/auto_examples/mixture/plot_gmm_init.rst.txt
@@ -56,7 +56,7 @@ time to initialize and low number of GaussianMixture iterations to converge.
 
 
 .. image-sg:: /auto_examples/mixture/images/sphx_glr_plot_gmm_init_001.png
-   :alt: GMM iterations and relative time taken to initialize, kmeans, Iter 8 | Init Time 1.00x, random_from_data, Iter 137 | Init Time 0.50x, k-means++, Iter 11 | Init Time 0.68x, random, Iter 47 | Init Time 0.48x
+   :alt: GMM iterations and relative time taken to initialize, kmeans, Iter 8 | Init Time 1.00x, random_from_data, Iter 137 | Init Time 0.58x, k-means++, Iter 11 | Init Time 0.85x, random, Iter 47 | Init Time 0.60x
    :srcset: /auto_examples/mixture/images/sphx_glr_plot_gmm_init_001.png
    :class: sphx-glr-single-img
 
@@ -146,7 +146,7 @@ time to initialize and low number of GaussianMixture iterations to converge.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.597 seconds)
+   **Total running time of the script:** (0 minutes 0.836 seconds)
 
 
 .. _sphx_glr_download_auto_examples_mixture_plot_gmm_init.py:
diff --git a/dev/_sources/auto_examples/mixture/plot_gmm_pdf.rst.txt b/dev/_sources/auto_examples/mixture/plot_gmm_pdf.rst.txt
index 1f2c7d463fd3b..0147b6b8c3eec 100644
--- a/dev/_sources/auto_examples/mixture/plot_gmm_pdf.rst.txt
+++ b/dev/_sources/auto_examples/mixture/plot_gmm_pdf.rst.txt
@@ -91,7 +91,7 @@ matrices.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.114 seconds)
+   **Total running time of the script:** (0 minutes 0.131 seconds)
 
 
 .. _sphx_glr_download_auto_examples_mixture_plot_gmm_pdf.py:
diff --git a/dev/_sources/auto_examples/mixture/plot_gmm_selection.rst.txt b/dev/_sources/auto_examples/mixture/plot_gmm_selection.rst.txt
index f6f503a5fc7fc..8c98dd56c42e4 100644
--- a/dev/_sources/auto_examples/mixture/plot_gmm_selection.rst.txt
+++ b/dev/_sources/auto_examples/mixture/plot_gmm_selection.rst.txt
@@ -161,7 +161,7 @@ The best set of parameters and estimator are stored in `best_parameters_` and
 .. raw:: html
 
     <div class="output_subarea output_html rendered_html output_result">
-    <style>#sk-container-id-29 {
+    <style>#sk-container-id-30 {
       /* Definition of color scheme common for light and dark mode */
       --sklearn-color-text: #000;
       --sklearn-color-text-muted: #666;
@@ -192,15 +192,15 @@ The best set of parameters and estimator are stored in `best_parameters_` and
       }
     }
 
-    #sk-container-id-29 {
+    #sk-container-id-30 {
       color: var(--sklearn-color-text);
     }
 
-    #sk-container-id-29 pre {
+    #sk-container-id-30 pre {
       padding: 0;
     }
 
-    #sk-container-id-29 input.sk-hidden--visually {
+    #sk-container-id-30 input.sk-hidden--visually {
       border: 0;
       clip: rect(1px 1px 1px 1px);
       clip: rect(1px, 1px, 1px, 1px);
@@ -212,7 +212,7 @@ The best set of parameters and estimator are stored in `best_parameters_` and
       width: 1px;
     }
 
-    #sk-container-id-29 div.sk-dashed-wrapped {
+    #sk-container-id-30 div.sk-dashed-wrapped {
       border: 1px dashed var(--sklearn-color-line);
       margin: 0 0.4em 0.5em 0.4em;
       box-sizing: border-box;
@@ -220,7 +220,7 @@ The best set of parameters and estimator are stored in `best_parameters_` and
       background-color: var(--sklearn-color-background);
     }
 
-    #sk-container-id-29 div.sk-container {
+    #sk-container-id-30 div.sk-container {
       /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
          but bootstrap.min.css set `[hidden] { display: none !important; }`
          so we also need the `!important` here to be able to override the
@@ -230,7 +230,7 @@ The best set of parameters and estimator are stored in `best_parameters_` and
       position: relative;
     }
 
-    #sk-container-id-29 div.sk-text-repr-fallback {
+    #sk-container-id-30 div.sk-text-repr-fallback {
       display: none;
     }
 
@@ -246,14 +246,14 @@ The best set of parameters and estimator are stored in `best_parameters_` and
 
     /* Parallel-specific style estimator block */
 
-    #sk-container-id-29 div.sk-parallel-item::after {
+    #sk-container-id-30 div.sk-parallel-item::after {
       content: "";
       width: 100%;
       border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
       flex-grow: 1;
     }
 
-    #sk-container-id-29 div.sk-parallel {
+    #sk-container-id-30 div.sk-parallel {
       display: flex;
       align-items: stretch;
       justify-content: center;
@@ -261,28 +261,28 @@ The best set of parameters and estimator are stored in `best_parameters_` and
       position: relative;
     }
 
-    #sk-container-id-29 div.sk-parallel-item {
+    #sk-container-id-30 div.sk-parallel-item {
       display: flex;
       flex-direction: column;
     }
 
-    #sk-container-id-29 div.sk-parallel-item:first-child::after {
+    #sk-container-id-30 div.sk-parallel-item:first-child::after {
       align-self: flex-end;
       width: 50%;
     }
 
-    #sk-container-id-29 div.sk-parallel-item:last-child::after {
+    #sk-container-id-30 div.sk-parallel-item:last-child::after {
       align-self: flex-start;
       width: 50%;
     }
 
-    #sk-container-id-29 div.sk-parallel-item:only-child::after {
+    #sk-container-id-30 div.sk-parallel-item:only-child::after {
       width: 0;
     }
 
     /* Serial-specific style estimator block */
 
-    #sk-container-id-29 div.sk-serial {
+    #sk-container-id-30 div.sk-serial {
       display: flex;
       flex-direction: column;
       align-items: center;
@@ -300,14 +300,14 @@ The best set of parameters and estimator are stored in `best_parameters_` and
 
     /* Pipeline and ColumnTransformer style (default) */
 
-    #sk-container-id-29 div.sk-toggleable {
+    #sk-container-id-30 div.sk-toggleable {
       /* Default theme specific background. It is overwritten whether we have a
       specific estimator or a Pipeline/ColumnTransformer */
       background-color: var(--sklearn-color-background);
     }
 
     /* Toggleable label */
-    #sk-container-id-29 label.sk-toggleable__label {
+    #sk-container-id-30 label.sk-toggleable__label {
       cursor: pointer;
       display: flex;
       width: 100%;
@@ -320,13 +320,13 @@ The best set of parameters and estimator are stored in `best_parameters_` and
       gap: 0.5em;
     }
 
-    #sk-container-id-29 label.sk-toggleable__label .caption {
+    #sk-container-id-30 label.sk-toggleable__label .caption {
       font-size: 0.6rem;
       font-weight: lighter;
       color: var(--sklearn-color-text-muted);
     }
 
-    #sk-container-id-29 label.sk-toggleable__label-arrow:before {
+    #sk-container-id-30 label.sk-toggleable__label-arrow:before {
       /* Arrow on the left of the label */
       content: "▸";
       float: left;
@@ -334,13 +334,13 @@ The best set of parameters and estimator are stored in `best_parameters_` and
       color: var(--sklearn-color-icon);
     }
 
-    #sk-container-id-29 label.sk-toggleable__label-arrow:hover:before {
+    #sk-container-id-30 label.sk-toggleable__label-arrow:hover:before {
       color: var(--sklearn-color-text);
     }
 
     /* Toggleable content - dropdown */
 
-    #sk-container-id-29 div.sk-toggleable__content {
+    #sk-container-id-30 div.sk-toggleable__content {
       max-height: 0;
       max-width: 0;
       overflow: hidden;
@@ -349,12 +349,12 @@ The best set of parameters and estimator are stored in `best_parameters_` and
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-29 div.sk-toggleable__content.fitted {
+    #sk-container-id-30 div.sk-toggleable__content.fitted {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
-    #sk-container-id-29 div.sk-toggleable__content pre {
+    #sk-container-id-30 div.sk-toggleable__content pre {
       margin: 0.2em;
       border-radius: 0.25em;
       color: var(--sklearn-color-text);
@@ -362,79 +362,79 @@ The best set of parameters and estimator are stored in `best_parameters_` and
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-29 div.sk-toggleable__content.fitted pre {
+    #sk-container-id-30 div.sk-toggleable__content.fitted pre {
       /* unfitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
-    #sk-container-id-29 input.sk-toggleable__control:checked~div.sk-toggleable__content {
+    #sk-container-id-30 input.sk-toggleable__control:checked~div.sk-toggleable__content {
       /* Expand drop-down */
       max-height: 200px;
       max-width: 100%;
       overflow: auto;
     }
 
-    #sk-container-id-29 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
+    #sk-container-id-30 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
       content: "▾";
     }
 
     /* Pipeline/ColumnTransformer-specific style */
 
-    #sk-container-id-29 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-30 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-29 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-30 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
     /* Estimator-specific style */
 
     /* Colorize estimator box */
-    #sk-container-id-29 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-30 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-29 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-30 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
-    #sk-container-id-29 div.sk-label label.sk-toggleable__label,
-    #sk-container-id-29 div.sk-label label {
+    #sk-container-id-30 div.sk-label label.sk-toggleable__label,
+    #sk-container-id-30 div.sk-label label {
       /* The background is the default theme color */
       color: var(--sklearn-color-text-on-default-background);
     }
 
     /* On hover, darken the color of the background */
-    #sk-container-id-29 div.sk-label:hover label.sk-toggleable__label {
+    #sk-container-id-30 div.sk-label:hover label.sk-toggleable__label {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
     /* Label box, darken color on hover, fitted */
-    #sk-container-id-29 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
+    #sk-container-id-30 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
     /* Estimator label */
 
-    #sk-container-id-29 div.sk-label label {
+    #sk-container-id-30 div.sk-label label {
       font-family: monospace;
       font-weight: bold;
       display: inline-block;
       line-height: 1.2em;
     }
 
-    #sk-container-id-29 div.sk-label-container {
+    #sk-container-id-30 div.sk-label-container {
       text-align: center;
     }
 
     /* Estimator-specific */
-    #sk-container-id-29 div.sk-estimator {
+    #sk-container-id-30 div.sk-estimator {
       font-family: monospace;
       border: 1px dotted var(--sklearn-color-border-box);
       border-radius: 0.25em;
@@ -444,18 +444,18 @@ The best set of parameters and estimator are stored in `best_parameters_` and
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-29 div.sk-estimator.fitted {
+    #sk-container-id-30 div.sk-estimator.fitted {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
     /* on hover */
-    #sk-container-id-29 div.sk-estimator:hover {
+    #sk-container-id-30 div.sk-estimator:hover {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-29 div.sk-estimator.fitted:hover {
+    #sk-container-id-30 div.sk-estimator.fitted:hover {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-2);
     }
@@ -543,7 +543,7 @@ The best set of parameters and estimator are stored in `best_parameters_` and
 
     /* "?"-specific style due to the `<a>` HTML tag */
 
-    #sk-container-id-29 a.estimator_doc_link {
+    #sk-container-id-30 a.estimator_doc_link {
       float: right;
       font-size: 1rem;
       line-height: 1em;
@@ -558,33 +558,33 @@ The best set of parameters and estimator are stored in `best_parameters_` and
       border: var(--sklearn-color-unfitted-level-1) 1pt solid;
     }
 
-    #sk-container-id-29 a.estimator_doc_link.fitted {
+    #sk-container-id-30 a.estimator_doc_link.fitted {
       /* fitted */
       border: var(--sklearn-color-fitted-level-1) 1pt solid;
       color: var(--sklearn-color-fitted-level-1);
     }
 
     /* On hover */
-    #sk-container-id-29 a.estimator_doc_link:hover {
+    #sk-container-id-30 a.estimator_doc_link:hover {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-3);
       color: var(--sklearn-color-background);
       text-decoration: none;
     }
 
-    #sk-container-id-29 a.estimator_doc_link.fitted:hover {
+    #sk-container-id-30 a.estimator_doc_link.fitted:hover {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-3);
     }
-    </style><div id="sk-container-id-29" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>GridSearchCV(estimator=GaussianMixture(),
+    </style><div id="sk-container-id-30" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>GridSearchCV(estimator=GaussianMixture(),
                  param_grid={&#x27;covariance_type&#x27;: [&#x27;spherical&#x27;, &#x27;tied&#x27;, &#x27;diag&#x27;,
                                                  &#x27;full&#x27;],
                              &#x27;n_components&#x27;: range(1, 7)},
-                 scoring=&lt;function gmm_bic_score at 0x7fb578d8f2e0&gt;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-111" type="checkbox" ><label for="sk-estimator-id-111" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>GridSearchCV</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.model_selection.GridSearchCV.html">?<span>Documentation for GridSearchCV</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>GridSearchCV(estimator=GaussianMixture(),
+                 scoring=&lt;function gmm_bic_score at 0x7fd848870700&gt;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-121" type="checkbox" ><label for="sk-estimator-id-121" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>GridSearchCV</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.model_selection.GridSearchCV.html">?<span>Documentation for GridSearchCV</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>GridSearchCV(estimator=GaussianMixture(),
                  param_grid={&#x27;covariance_type&#x27;: [&#x27;spherical&#x27;, &#x27;tied&#x27;, &#x27;diag&#x27;,
                                                  &#x27;full&#x27;],
                              &#x27;n_components&#x27;: range(1, 7)},
-                 scoring=&lt;function gmm_bic_score at 0x7fb578d8f2e0&gt;)</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-112" type="checkbox" ><label for="sk-estimator-id-112" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>best_estimator_: GaussianMixture</div></div></label><div class="sk-toggleable__content fitted"><pre>GaussianMixture(n_components=2)</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-113" type="checkbox" ><label for="sk-estimator-id-113" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>GaussianMixture</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.mixture.GaussianMixture.html">?<span>Documentation for GaussianMixture</span></a></div></label><div class="sk-toggleable__content fitted"><pre>GaussianMixture(n_components=2)</pre></div> </div></div></div></div></div></div></div></div></div>
+                 scoring=&lt;function gmm_bic_score at 0x7fd848870700&gt;)</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-122" type="checkbox" ><label for="sk-estimator-id-122" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>best_estimator_: GaussianMixture</div></div></label><div class="sk-toggleable__content fitted"><pre>GaussianMixture(n_components=2)</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-123" type="checkbox" ><label for="sk-estimator-id-123" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>GaussianMixture</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.mixture.GaussianMixture.html">?<span>Documentation for GaussianMixture</span></a></div></label><div class="sk-toggleable__content fitted"><pre>GaussianMixture(n_components=2)</pre></div> </div></div></div></div></div></div></div></div></div>
     </div>
     <br />
     <br />
@@ -787,7 +787,7 @@ on the `covariance_type`:
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 1.306 seconds)
+   **Total running time of the script:** (0 minutes 1.771 seconds)
 
 
 .. _sphx_glr_download_auto_examples_mixture_plot_gmm_selection.py:
diff --git a/dev/_sources/auto_examples/mixture/plot_gmm_sin.rst.txt b/dev/_sources/auto_examples/mixture/plot_gmm_sin.rst.txt
index 856526523ed5e..6db3999d916c9 100644
--- a/dev/_sources/auto_examples/mixture/plot_gmm_sin.rst.txt
+++ b/dev/_sources/auto_examples/mixture/plot_gmm_sin.rst.txt
@@ -225,7 +225,7 @@ number of Gaussian components instead of a continuous noisy sine curve.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.396 seconds)
+   **Total running time of the script:** (0 minutes 0.498 seconds)
 
 
 .. _sphx_glr_download_auto_examples_mixture_plot_gmm_sin.py:
diff --git a/dev/_sources/auto_examples/model_selection/plot_confusion_matrix.rst.txt b/dev/_sources/auto_examples/model_selection/plot_confusion_matrix.rst.txt
index b7b7372434317..938504622a413 100644
--- a/dev/_sources/auto_examples/model_selection/plot_confusion_matrix.rst.txt
+++ b/dev/_sources/auto_examples/model_selection/plot_confusion_matrix.rst.txt
@@ -135,7 +135,7 @@ using :ref:`grid_search`.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.177 seconds)
+   **Total running time of the script:** (0 minutes 0.200 seconds)
 
 
 .. _sphx_glr_download_auto_examples_model_selection_plot_confusion_matrix.py:
diff --git a/dev/_sources/auto_examples/model_selection/plot_cost_sensitive_learning.rst.txt b/dev/_sources/auto_examples/model_selection/plot_cost_sensitive_learning.rst.txt
index f5b77ba1919fe..7b87a5043c56c 100644
--- a/dev/_sources/auto_examples/model_selection/plot_cost_sensitive_learning.rst.txt
+++ b/dev/_sources/auto_examples/model_selection/plot_cost_sensitive_learning.rst.txt
@@ -380,7 +380,7 @@ that natively handles categorical features and missing values.
 .. raw:: html
 
     <div class="output_subarea output_html rendered_html output_result">
-    <style>#sk-container-id-57 {
+    <style>#sk-container-id-58 {
       /* Definition of color scheme common for light and dark mode */
       --sklearn-color-text: #000;
       --sklearn-color-text-muted: #666;
@@ -411,15 +411,15 @@ that natively handles categorical features and missing values.
       }
     }
 
-    #sk-container-id-57 {
+    #sk-container-id-58 {
       color: var(--sklearn-color-text);
     }
 
-    #sk-container-id-57 pre {
+    #sk-container-id-58 pre {
       padding: 0;
     }
 
-    #sk-container-id-57 input.sk-hidden--visually {
+    #sk-container-id-58 input.sk-hidden--visually {
       border: 0;
       clip: rect(1px 1px 1px 1px);
       clip: rect(1px, 1px, 1px, 1px);
@@ -431,7 +431,7 @@ that natively handles categorical features and missing values.
       width: 1px;
     }
 
-    #sk-container-id-57 div.sk-dashed-wrapped {
+    #sk-container-id-58 div.sk-dashed-wrapped {
       border: 1px dashed var(--sklearn-color-line);
       margin: 0 0.4em 0.5em 0.4em;
       box-sizing: border-box;
@@ -439,7 +439,7 @@ that natively handles categorical features and missing values.
       background-color: var(--sklearn-color-background);
     }
 
-    #sk-container-id-57 div.sk-container {
+    #sk-container-id-58 div.sk-container {
       /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
          but bootstrap.min.css set `[hidden] { display: none !important; }`
          so we also need the `!important` here to be able to override the
@@ -449,7 +449,7 @@ that natively handles categorical features and missing values.
       position: relative;
     }
 
-    #sk-container-id-57 div.sk-text-repr-fallback {
+    #sk-container-id-58 div.sk-text-repr-fallback {
       display: none;
     }
 
@@ -465,14 +465,14 @@ that natively handles categorical features and missing values.
 
     /* Parallel-specific style estimator block */
 
-    #sk-container-id-57 div.sk-parallel-item::after {
+    #sk-container-id-58 div.sk-parallel-item::after {
       content: "";
       width: 100%;
       border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
       flex-grow: 1;
     }
 
-    #sk-container-id-57 div.sk-parallel {
+    #sk-container-id-58 div.sk-parallel {
       display: flex;
       align-items: stretch;
       justify-content: center;
@@ -480,28 +480,28 @@ that natively handles categorical features and missing values.
       position: relative;
     }
 
-    #sk-container-id-57 div.sk-parallel-item {
+    #sk-container-id-58 div.sk-parallel-item {
       display: flex;
       flex-direction: column;
     }
 
-    #sk-container-id-57 div.sk-parallel-item:first-child::after {
+    #sk-container-id-58 div.sk-parallel-item:first-child::after {
       align-self: flex-end;
       width: 50%;
     }
 
-    #sk-container-id-57 div.sk-parallel-item:last-child::after {
+    #sk-container-id-58 div.sk-parallel-item:last-child::after {
       align-self: flex-start;
       width: 50%;
     }
 
-    #sk-container-id-57 div.sk-parallel-item:only-child::after {
+    #sk-container-id-58 div.sk-parallel-item:only-child::after {
       width: 0;
     }
 
     /* Serial-specific style estimator block */
 
-    #sk-container-id-57 div.sk-serial {
+    #sk-container-id-58 div.sk-serial {
       display: flex;
       flex-direction: column;
       align-items: center;
@@ -519,14 +519,14 @@ that natively handles categorical features and missing values.
 
     /* Pipeline and ColumnTransformer style (default) */
 
-    #sk-container-id-57 div.sk-toggleable {
+    #sk-container-id-58 div.sk-toggleable {
       /* Default theme specific background. It is overwritten whether we have a
       specific estimator or a Pipeline/ColumnTransformer */
       background-color: var(--sklearn-color-background);
     }
 
     /* Toggleable label */
-    #sk-container-id-57 label.sk-toggleable__label {
+    #sk-container-id-58 label.sk-toggleable__label {
       cursor: pointer;
       display: flex;
       width: 100%;
@@ -539,13 +539,13 @@ that natively handles categorical features and missing values.
       gap: 0.5em;
     }
 
-    #sk-container-id-57 label.sk-toggleable__label .caption {
+    #sk-container-id-58 label.sk-toggleable__label .caption {
       font-size: 0.6rem;
       font-weight: lighter;
       color: var(--sklearn-color-text-muted);
     }
 
-    #sk-container-id-57 label.sk-toggleable__label-arrow:before {
+    #sk-container-id-58 label.sk-toggleable__label-arrow:before {
       /* Arrow on the left of the label */
       content: "▸";
       float: left;
@@ -553,13 +553,13 @@ that natively handles categorical features and missing values.
       color: var(--sklearn-color-icon);
     }
 
-    #sk-container-id-57 label.sk-toggleable__label-arrow:hover:before {
+    #sk-container-id-58 label.sk-toggleable__label-arrow:hover:before {
       color: var(--sklearn-color-text);
     }
 
     /* Toggleable content - dropdown */
 
-    #sk-container-id-57 div.sk-toggleable__content {
+    #sk-container-id-58 div.sk-toggleable__content {
       max-height: 0;
       max-width: 0;
       overflow: hidden;
@@ -568,12 +568,12 @@ that natively handles categorical features and missing values.
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-57 div.sk-toggleable__content.fitted {
+    #sk-container-id-58 div.sk-toggleable__content.fitted {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
-    #sk-container-id-57 div.sk-toggleable__content pre {
+    #sk-container-id-58 div.sk-toggleable__content pre {
       margin: 0.2em;
       border-radius: 0.25em;
       color: var(--sklearn-color-text);
@@ -581,79 +581,79 @@ that natively handles categorical features and missing values.
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-57 div.sk-toggleable__content.fitted pre {
+    #sk-container-id-58 div.sk-toggleable__content.fitted pre {
       /* unfitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
-    #sk-container-id-57 input.sk-toggleable__control:checked~div.sk-toggleable__content {
+    #sk-container-id-58 input.sk-toggleable__control:checked~div.sk-toggleable__content {
       /* Expand drop-down */
       max-height: 200px;
       max-width: 100%;
       overflow: auto;
     }
 
-    #sk-container-id-57 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
+    #sk-container-id-58 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
       content: "▾";
     }
 
     /* Pipeline/ColumnTransformer-specific style */
 
-    #sk-container-id-57 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-58 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-57 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-58 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
     /* Estimator-specific style */
 
     /* Colorize estimator box */
-    #sk-container-id-57 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-58 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-57 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-58 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
-    #sk-container-id-57 div.sk-label label.sk-toggleable__label,
-    #sk-container-id-57 div.sk-label label {
+    #sk-container-id-58 div.sk-label label.sk-toggleable__label,
+    #sk-container-id-58 div.sk-label label {
       /* The background is the default theme color */
       color: var(--sklearn-color-text-on-default-background);
     }
 
     /* On hover, darken the color of the background */
-    #sk-container-id-57 div.sk-label:hover label.sk-toggleable__label {
+    #sk-container-id-58 div.sk-label:hover label.sk-toggleable__label {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
     /* Label box, darken color on hover, fitted */
-    #sk-container-id-57 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
+    #sk-container-id-58 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
     /* Estimator label */
 
-    #sk-container-id-57 div.sk-label label {
+    #sk-container-id-58 div.sk-label label {
       font-family: monospace;
       font-weight: bold;
       display: inline-block;
       line-height: 1.2em;
     }
 
-    #sk-container-id-57 div.sk-label-container {
+    #sk-container-id-58 div.sk-label-container {
       text-align: center;
     }
 
     /* Estimator-specific */
-    #sk-container-id-57 div.sk-estimator {
+    #sk-container-id-58 div.sk-estimator {
       font-family: monospace;
       border: 1px dotted var(--sklearn-color-border-box);
       border-radius: 0.25em;
@@ -663,18 +663,18 @@ that natively handles categorical features and missing values.
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-57 div.sk-estimator.fitted {
+    #sk-container-id-58 div.sk-estimator.fitted {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
     /* on hover */
-    #sk-container-id-57 div.sk-estimator:hover {
+    #sk-container-id-58 div.sk-estimator:hover {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-57 div.sk-estimator.fitted:hover {
+    #sk-container-id-58 div.sk-estimator.fitted:hover {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-2);
     }
@@ -762,7 +762,7 @@ that natively handles categorical features and missing values.
 
     /* "?"-specific style due to the `<a>` HTML tag */
 
-    #sk-container-id-57 a.estimator_doc_link {
+    #sk-container-id-58 a.estimator_doc_link {
       float: right;
       font-size: 1rem;
       line-height: 1em;
@@ -777,25 +777,25 @@ that natively handles categorical features and missing values.
       border: var(--sklearn-color-unfitted-level-1) 1pt solid;
     }
 
-    #sk-container-id-57 a.estimator_doc_link.fitted {
+    #sk-container-id-58 a.estimator_doc_link.fitted {
       /* fitted */
       border: var(--sklearn-color-fitted-level-1) 1pt solid;
       color: var(--sklearn-color-fitted-level-1);
     }
 
     /* On hover */
-    #sk-container-id-57 a.estimator_doc_link:hover {
+    #sk-container-id-58 a.estimator_doc_link:hover {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-3);
       color: var(--sklearn-color-background);
       text-decoration: none;
     }
 
-    #sk-container-id-57 a.estimator_doc_link.fitted:hover {
+    #sk-container-id-58 a.estimator_doc_link.fitted:hover {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-3);
     }
-    </style><div id="sk-container-id-57" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>HistGradientBoostingClassifier(random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-232" type="checkbox" checked><label for="sk-estimator-id-232" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>HistGradientBoostingClassifier</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.ensemble.HistGradientBoostingClassifier.html">?<span>Documentation for HistGradientBoostingClassifier</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>HistGradientBoostingClassifier(random_state=0)</pre></div> </div></div></div></div>
+    </style><div id="sk-container-id-58" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>HistGradientBoostingClassifier(random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-246" type="checkbox" checked><label for="sk-estimator-id-246" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>HistGradientBoostingClassifier</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.ensemble.HistGradientBoostingClassifier.html">?<span>Documentation for HistGradientBoostingClassifier</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>HistGradientBoostingClassifier(random_state=0)</pre></div> </div></div></div></div>
     </div>
     <br />
     <br />
@@ -1199,7 +1199,7 @@ option:
 .. raw:: html
 
     <div class="output_subarea output_html rendered_html output_result">
-    <style>#sk-container-id-58 {
+    <style>#sk-container-id-59 {
       /* Definition of color scheme common for light and dark mode */
       --sklearn-color-text: #000;
       --sklearn-color-text-muted: #666;
@@ -1230,15 +1230,15 @@ option:
       }
     }
 
-    #sk-container-id-58 {
+    #sk-container-id-59 {
       color: var(--sklearn-color-text);
     }
 
-    #sk-container-id-58 pre {
+    #sk-container-id-59 pre {
       padding: 0;
     }
 
-    #sk-container-id-58 input.sk-hidden--visually {
+    #sk-container-id-59 input.sk-hidden--visually {
       border: 0;
       clip: rect(1px 1px 1px 1px);
       clip: rect(1px, 1px, 1px, 1px);
@@ -1250,7 +1250,7 @@ option:
       width: 1px;
     }
 
-    #sk-container-id-58 div.sk-dashed-wrapped {
+    #sk-container-id-59 div.sk-dashed-wrapped {
       border: 1px dashed var(--sklearn-color-line);
       margin: 0 0.4em 0.5em 0.4em;
       box-sizing: border-box;
@@ -1258,7 +1258,7 @@ option:
       background-color: var(--sklearn-color-background);
     }
 
-    #sk-container-id-58 div.sk-container {
+    #sk-container-id-59 div.sk-container {
       /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
          but bootstrap.min.css set `[hidden] { display: none !important; }`
          so we also need the `!important` here to be able to override the
@@ -1268,7 +1268,7 @@ option:
       position: relative;
     }
 
-    #sk-container-id-58 div.sk-text-repr-fallback {
+    #sk-container-id-59 div.sk-text-repr-fallback {
       display: none;
     }
 
@@ -1284,14 +1284,14 @@ option:
 
     /* Parallel-specific style estimator block */
 
-    #sk-container-id-58 div.sk-parallel-item::after {
+    #sk-container-id-59 div.sk-parallel-item::after {
       content: "";
       width: 100%;
       border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
       flex-grow: 1;
     }
 
-    #sk-container-id-58 div.sk-parallel {
+    #sk-container-id-59 div.sk-parallel {
       display: flex;
       align-items: stretch;
       justify-content: center;
@@ -1299,28 +1299,28 @@ option:
       position: relative;
     }
 
-    #sk-container-id-58 div.sk-parallel-item {
+    #sk-container-id-59 div.sk-parallel-item {
       display: flex;
       flex-direction: column;
     }
 
-    #sk-container-id-58 div.sk-parallel-item:first-child::after {
+    #sk-container-id-59 div.sk-parallel-item:first-child::after {
       align-self: flex-end;
       width: 50%;
     }
 
-    #sk-container-id-58 div.sk-parallel-item:last-child::after {
+    #sk-container-id-59 div.sk-parallel-item:last-child::after {
       align-self: flex-start;
       width: 50%;
     }
 
-    #sk-container-id-58 div.sk-parallel-item:only-child::after {
+    #sk-container-id-59 div.sk-parallel-item:only-child::after {
       width: 0;
     }
 
     /* Serial-specific style estimator block */
 
-    #sk-container-id-58 div.sk-serial {
+    #sk-container-id-59 div.sk-serial {
       display: flex;
       flex-direction: column;
       align-items: center;
@@ -1338,14 +1338,14 @@ option:
 
     /* Pipeline and ColumnTransformer style (default) */
 
-    #sk-container-id-58 div.sk-toggleable {
+    #sk-container-id-59 div.sk-toggleable {
       /* Default theme specific background. It is overwritten whether we have a
       specific estimator or a Pipeline/ColumnTransformer */
       background-color: var(--sklearn-color-background);
     }
 
     /* Toggleable label */
-    #sk-container-id-58 label.sk-toggleable__label {
+    #sk-container-id-59 label.sk-toggleable__label {
       cursor: pointer;
       display: flex;
       width: 100%;
@@ -1358,13 +1358,13 @@ option:
       gap: 0.5em;
     }
 
-    #sk-container-id-58 label.sk-toggleable__label .caption {
+    #sk-container-id-59 label.sk-toggleable__label .caption {
       font-size: 0.6rem;
       font-weight: lighter;
       color: var(--sklearn-color-text-muted);
     }
 
-    #sk-container-id-58 label.sk-toggleable__label-arrow:before {
+    #sk-container-id-59 label.sk-toggleable__label-arrow:before {
       /* Arrow on the left of the label */
       content: "▸";
       float: left;
@@ -1372,13 +1372,13 @@ option:
       color: var(--sklearn-color-icon);
     }
 
-    #sk-container-id-58 label.sk-toggleable__label-arrow:hover:before {
+    #sk-container-id-59 label.sk-toggleable__label-arrow:hover:before {
       color: var(--sklearn-color-text);
     }
 
     /* Toggleable content - dropdown */
 
-    #sk-container-id-58 div.sk-toggleable__content {
+    #sk-container-id-59 div.sk-toggleable__content {
       max-height: 0;
       max-width: 0;
       overflow: hidden;
@@ -1387,12 +1387,12 @@ option:
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-58 div.sk-toggleable__content.fitted {
+    #sk-container-id-59 div.sk-toggleable__content.fitted {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
-    #sk-container-id-58 div.sk-toggleable__content pre {
+    #sk-container-id-59 div.sk-toggleable__content pre {
       margin: 0.2em;
       border-radius: 0.25em;
       color: var(--sklearn-color-text);
@@ -1400,79 +1400,79 @@ option:
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-58 div.sk-toggleable__content.fitted pre {
+    #sk-container-id-59 div.sk-toggleable__content.fitted pre {
       /* unfitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
-    #sk-container-id-58 input.sk-toggleable__control:checked~div.sk-toggleable__content {
+    #sk-container-id-59 input.sk-toggleable__control:checked~div.sk-toggleable__content {
       /* Expand drop-down */
       max-height: 200px;
       max-width: 100%;
       overflow: auto;
     }
 
-    #sk-container-id-58 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
+    #sk-container-id-59 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
       content: "▾";
     }
 
     /* Pipeline/ColumnTransformer-specific style */
 
-    #sk-container-id-58 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-59 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-58 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-59 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
     /* Estimator-specific style */
 
     /* Colorize estimator box */
-    #sk-container-id-58 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-59 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-58 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-59 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
-    #sk-container-id-58 div.sk-label label.sk-toggleable__label,
-    #sk-container-id-58 div.sk-label label {
+    #sk-container-id-59 div.sk-label label.sk-toggleable__label,
+    #sk-container-id-59 div.sk-label label {
       /* The background is the default theme color */
       color: var(--sklearn-color-text-on-default-background);
     }
 
     /* On hover, darken the color of the background */
-    #sk-container-id-58 div.sk-label:hover label.sk-toggleable__label {
+    #sk-container-id-59 div.sk-label:hover label.sk-toggleable__label {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
     /* Label box, darken color on hover, fitted */
-    #sk-container-id-58 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
+    #sk-container-id-59 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
     /* Estimator label */
 
-    #sk-container-id-58 div.sk-label label {
+    #sk-container-id-59 div.sk-label label {
       font-family: monospace;
       font-weight: bold;
       display: inline-block;
       line-height: 1.2em;
     }
 
-    #sk-container-id-58 div.sk-label-container {
+    #sk-container-id-59 div.sk-label-container {
       text-align: center;
     }
 
     /* Estimator-specific */
-    #sk-container-id-58 div.sk-estimator {
+    #sk-container-id-59 div.sk-estimator {
       font-family: monospace;
       border: 1px dotted var(--sklearn-color-border-box);
       border-radius: 0.25em;
@@ -1482,18 +1482,18 @@ option:
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-58 div.sk-estimator.fitted {
+    #sk-container-id-59 div.sk-estimator.fitted {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
     /* on hover */
-    #sk-container-id-58 div.sk-estimator:hover {
+    #sk-container-id-59 div.sk-estimator:hover {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-58 div.sk-estimator.fitted:hover {
+    #sk-container-id-59 div.sk-estimator.fitted:hover {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-2);
     }
@@ -1581,7 +1581,7 @@ option:
 
     /* "?"-specific style due to the `<a>` HTML tag */
 
-    #sk-container-id-58 a.estimator_doc_link {
+    #sk-container-id-59 a.estimator_doc_link {
       float: right;
       font-size: 1rem;
       line-height: 1em;
@@ -1596,33 +1596,33 @@ option:
       border: var(--sklearn-color-unfitted-level-1) 1pt solid;
     }
 
-    #sk-container-id-58 a.estimator_doc_link.fitted {
+    #sk-container-id-59 a.estimator_doc_link.fitted {
       /* fitted */
       border: var(--sklearn-color-fitted-level-1) 1pt solid;
       color: var(--sklearn-color-fitted-level-1);
     }
 
     /* On hover */
-    #sk-container-id-58 a.estimator_doc_link:hover {
+    #sk-container-id-59 a.estimator_doc_link:hover {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-3);
       color: var(--sklearn-color-background);
       text-decoration: none;
     }
 
-    #sk-container-id-58 a.estimator_doc_link.fitted:hover {
+    #sk-container-id-59 a.estimator_doc_link.fitted:hover {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-3);
     }
-    </style><div id="sk-container-id-58" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>TunedThresholdClassifierCV(cv=0.75,
+    </style><div id="sk-container-id-59" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>TunedThresholdClassifierCV(cv=0.75,
                                estimator=HistGradientBoostingClassifier(random_state=0),
                                refit=False,
                                scoring=make_scorer(credit_gain_score, response_method=&#x27;predict&#x27;, neg_label=good, pos_label=bad),
-                               store_cv_results=True)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-233" type="checkbox" ><label for="sk-estimator-id-233" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>TunedThresholdClassifierCV</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.model_selection.TunedThresholdClassifierCV.html">?<span>Documentation for TunedThresholdClassifierCV</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>TunedThresholdClassifierCV(cv=0.75,
+                               store_cv_results=True)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-247" type="checkbox" ><label for="sk-estimator-id-247" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>TunedThresholdClassifierCV</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.model_selection.TunedThresholdClassifierCV.html">?<span>Documentation for TunedThresholdClassifierCV</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>TunedThresholdClassifierCV(cv=0.75,
                                estimator=HistGradientBoostingClassifier(random_state=0),
                                refit=False,
                                scoring=make_scorer(credit_gain_score, response_method=&#x27;predict&#x27;, neg_label=good, pos_label=bad),
-                               store_cv_results=True)</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-234" type="checkbox" ><label for="sk-estimator-id-234" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>estimator: HistGradientBoostingClassifier</div></div></label><div class="sk-toggleable__content fitted"><pre>HistGradientBoostingClassifier(random_state=0)</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-235" type="checkbox" ><label for="sk-estimator-id-235" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>HistGradientBoostingClassifier</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.ensemble.HistGradientBoostingClassifier.html">?<span>Documentation for HistGradientBoostingClassifier</span></a></div></label><div class="sk-toggleable__content fitted"><pre>HistGradientBoostingClassifier(random_state=0)</pre></div> </div></div></div></div></div></div></div></div></div>
+                               store_cv_results=True)</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-248" type="checkbox" ><label for="sk-estimator-id-248" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>estimator: HistGradientBoostingClassifier</div></div></label><div class="sk-toggleable__content fitted"><pre>HistGradientBoostingClassifier(random_state=0)</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-249" type="checkbox" ><label for="sk-estimator-id-249" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>HistGradientBoostingClassifier</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.ensemble.HistGradientBoostingClassifier.html">?<span>Documentation for HistGradientBoostingClassifier</span></a></div></label><div class="sk-toggleable__content fitted"><pre>HistGradientBoostingClassifier(random_state=0)</pre></div> </div></div></div></div></div></div></div></div></div>
     </div>
     <br />
     <br />
@@ -2037,7 +2037,7 @@ threshold.
 .. raw:: html
 
     <div class="output_subarea output_html rendered_html output_result">
-    <style>#sk-container-id-59 {
+    <style>#sk-container-id-60 {
       /* Definition of color scheme common for light and dark mode */
       --sklearn-color-text: #000;
       --sklearn-color-text-muted: #666;
@@ -2068,15 +2068,15 @@ threshold.
       }
     }
 
-    #sk-container-id-59 {
+    #sk-container-id-60 {
       color: var(--sklearn-color-text);
     }
 
-    #sk-container-id-59 pre {
+    #sk-container-id-60 pre {
       padding: 0;
     }
 
-    #sk-container-id-59 input.sk-hidden--visually {
+    #sk-container-id-60 input.sk-hidden--visually {
       border: 0;
       clip: rect(1px 1px 1px 1px);
       clip: rect(1px, 1px, 1px, 1px);
@@ -2088,7 +2088,7 @@ threshold.
       width: 1px;
     }
 
-    #sk-container-id-59 div.sk-dashed-wrapped {
+    #sk-container-id-60 div.sk-dashed-wrapped {
       border: 1px dashed var(--sklearn-color-line);
       margin: 0 0.4em 0.5em 0.4em;
       box-sizing: border-box;
@@ -2096,7 +2096,7 @@ threshold.
       background-color: var(--sklearn-color-background);
     }
 
-    #sk-container-id-59 div.sk-container {
+    #sk-container-id-60 div.sk-container {
       /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
          but bootstrap.min.css set `[hidden] { display: none !important; }`
          so we also need the `!important` here to be able to override the
@@ -2106,7 +2106,7 @@ threshold.
       position: relative;
     }
 
-    #sk-container-id-59 div.sk-text-repr-fallback {
+    #sk-container-id-60 div.sk-text-repr-fallback {
       display: none;
     }
 
@@ -2122,14 +2122,14 @@ threshold.
 
     /* Parallel-specific style estimator block */
 
-    #sk-container-id-59 div.sk-parallel-item::after {
+    #sk-container-id-60 div.sk-parallel-item::after {
       content: "";
       width: 100%;
       border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
       flex-grow: 1;
     }
 
-    #sk-container-id-59 div.sk-parallel {
+    #sk-container-id-60 div.sk-parallel {
       display: flex;
       align-items: stretch;
       justify-content: center;
@@ -2137,28 +2137,28 @@ threshold.
       position: relative;
     }
 
-    #sk-container-id-59 div.sk-parallel-item {
+    #sk-container-id-60 div.sk-parallel-item {
       display: flex;
       flex-direction: column;
     }
 
-    #sk-container-id-59 div.sk-parallel-item:first-child::after {
+    #sk-container-id-60 div.sk-parallel-item:first-child::after {
       align-self: flex-end;
       width: 50%;
     }
 
-    #sk-container-id-59 div.sk-parallel-item:last-child::after {
+    #sk-container-id-60 div.sk-parallel-item:last-child::after {
       align-self: flex-start;
       width: 50%;
     }
 
-    #sk-container-id-59 div.sk-parallel-item:only-child::after {
+    #sk-container-id-60 div.sk-parallel-item:only-child::after {
       width: 0;
     }
 
     /* Serial-specific style estimator block */
 
-    #sk-container-id-59 div.sk-serial {
+    #sk-container-id-60 div.sk-serial {
       display: flex;
       flex-direction: column;
       align-items: center;
@@ -2176,14 +2176,14 @@ threshold.
 
     /* Pipeline and ColumnTransformer style (default) */
 
-    #sk-container-id-59 div.sk-toggleable {
+    #sk-container-id-60 div.sk-toggleable {
       /* Default theme specific background. It is overwritten whether we have a
       specific estimator or a Pipeline/ColumnTransformer */
       background-color: var(--sklearn-color-background);
     }
 
     /* Toggleable label */
-    #sk-container-id-59 label.sk-toggleable__label {
+    #sk-container-id-60 label.sk-toggleable__label {
       cursor: pointer;
       display: flex;
       width: 100%;
@@ -2196,13 +2196,13 @@ threshold.
       gap: 0.5em;
     }
 
-    #sk-container-id-59 label.sk-toggleable__label .caption {
+    #sk-container-id-60 label.sk-toggleable__label .caption {
       font-size: 0.6rem;
       font-weight: lighter;
       color: var(--sklearn-color-text-muted);
     }
 
-    #sk-container-id-59 label.sk-toggleable__label-arrow:before {
+    #sk-container-id-60 label.sk-toggleable__label-arrow:before {
       /* Arrow on the left of the label */
       content: "▸";
       float: left;
@@ -2210,13 +2210,13 @@ threshold.
       color: var(--sklearn-color-icon);
     }
 
-    #sk-container-id-59 label.sk-toggleable__label-arrow:hover:before {
+    #sk-container-id-60 label.sk-toggleable__label-arrow:hover:before {
       color: var(--sklearn-color-text);
     }
 
     /* Toggleable content - dropdown */
 
-    #sk-container-id-59 div.sk-toggleable__content {
+    #sk-container-id-60 div.sk-toggleable__content {
       max-height: 0;
       max-width: 0;
       overflow: hidden;
@@ -2225,12 +2225,12 @@ threshold.
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-59 div.sk-toggleable__content.fitted {
+    #sk-container-id-60 div.sk-toggleable__content.fitted {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
-    #sk-container-id-59 div.sk-toggleable__content pre {
+    #sk-container-id-60 div.sk-toggleable__content pre {
       margin: 0.2em;
       border-radius: 0.25em;
       color: var(--sklearn-color-text);
@@ -2238,79 +2238,79 @@ threshold.
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-59 div.sk-toggleable__content.fitted pre {
+    #sk-container-id-60 div.sk-toggleable__content.fitted pre {
       /* unfitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
-    #sk-container-id-59 input.sk-toggleable__control:checked~div.sk-toggleable__content {
+    #sk-container-id-60 input.sk-toggleable__control:checked~div.sk-toggleable__content {
       /* Expand drop-down */
       max-height: 200px;
       max-width: 100%;
       overflow: auto;
     }
 
-    #sk-container-id-59 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
+    #sk-container-id-60 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
       content: "▾";
     }
 
     /* Pipeline/ColumnTransformer-specific style */
 
-    #sk-container-id-59 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-60 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-59 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-60 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
     /* Estimator-specific style */
 
     /* Colorize estimator box */
-    #sk-container-id-59 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-60 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-59 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-60 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
-    #sk-container-id-59 div.sk-label label.sk-toggleable__label,
-    #sk-container-id-59 div.sk-label label {
+    #sk-container-id-60 div.sk-label label.sk-toggleable__label,
+    #sk-container-id-60 div.sk-label label {
       /* The background is the default theme color */
       color: var(--sklearn-color-text-on-default-background);
     }
 
     /* On hover, darken the color of the background */
-    #sk-container-id-59 div.sk-label:hover label.sk-toggleable__label {
+    #sk-container-id-60 div.sk-label:hover label.sk-toggleable__label {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
     /* Label box, darken color on hover, fitted */
-    #sk-container-id-59 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
+    #sk-container-id-60 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
     /* Estimator label */
 
-    #sk-container-id-59 div.sk-label label {
+    #sk-container-id-60 div.sk-label label {
       font-family: monospace;
       font-weight: bold;
       display: inline-block;
       line-height: 1.2em;
     }
 
-    #sk-container-id-59 div.sk-label-container {
+    #sk-container-id-60 div.sk-label-container {
       text-align: center;
     }
 
     /* Estimator-specific */
-    #sk-container-id-59 div.sk-estimator {
+    #sk-container-id-60 div.sk-estimator {
       font-family: monospace;
       border: 1px dotted var(--sklearn-color-border-box);
       border-radius: 0.25em;
@@ -2320,18 +2320,18 @@ threshold.
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-59 div.sk-estimator.fitted {
+    #sk-container-id-60 div.sk-estimator.fitted {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
     /* on hover */
-    #sk-container-id-59 div.sk-estimator:hover {
+    #sk-container-id-60 div.sk-estimator:hover {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-59 div.sk-estimator.fitted:hover {
+    #sk-container-id-60 div.sk-estimator.fitted:hover {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-2);
     }
@@ -2419,7 +2419,7 @@ threshold.
 
     /* "?"-specific style due to the `<a>` HTML tag */
 
-    #sk-container-id-59 a.estimator_doc_link {
+    #sk-container-id-60 a.estimator_doc_link {
       float: right;
       font-size: 1rem;
       line-height: 1em;
@@ -2434,37 +2434,37 @@ threshold.
       border: var(--sklearn-color-unfitted-level-1) 1pt solid;
     }
 
-    #sk-container-id-59 a.estimator_doc_link.fitted {
+    #sk-container-id-60 a.estimator_doc_link.fitted {
       /* fitted */
       border: var(--sklearn-color-fitted-level-1) 1pt solid;
       color: var(--sklearn-color-fitted-level-1);
     }
 
     /* On hover */
-    #sk-container-id-59 a.estimator_doc_link:hover {
+    #sk-container-id-60 a.estimator_doc_link:hover {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-3);
       color: var(--sklearn-color-background);
       text-decoration: none;
     }
 
-    #sk-container-id-59 a.estimator_doc_link.fitted:hover {
+    #sk-container-id-60 a.estimator_doc_link.fitted:hover {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-3);
     }
-    </style><div id="sk-container-id-59" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>GridSearchCV(estimator=Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),
+    </style><div id="sk-container-id-60" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>GridSearchCV(estimator=Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),
                                            (&#x27;logisticregression&#x27;,
                                             LogisticRegression())]),
                  param_grid={&#x27;logisticregression__C&#x27;: array([1.e-06, 1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01,
            1.e+02, 1.e+03, 1.e+04, 1.e+05, 1.e+06])},
-                 scoring=&#x27;neg_log_loss&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-236" type="checkbox" ><label for="sk-estimator-id-236" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>GridSearchCV</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.model_selection.GridSearchCV.html">?<span>Documentation for GridSearchCV</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>GridSearchCV(estimator=Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),
+                 scoring=&#x27;neg_log_loss&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-250" type="checkbox" ><label for="sk-estimator-id-250" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>GridSearchCV</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.model_selection.GridSearchCV.html">?<span>Documentation for GridSearchCV</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>GridSearchCV(estimator=Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),
                                            (&#x27;logisticregression&#x27;,
                                             LogisticRegression())]),
                  param_grid={&#x27;logisticregression__C&#x27;: array([1.e-06, 1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01,
            1.e+02, 1.e+03, 1.e+04, 1.e+05, 1.e+06])},
-                 scoring=&#x27;neg_log_loss&#x27;)</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-237" type="checkbox" ><label for="sk-estimator-id-237" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>best_estimator_: Pipeline</div></div></label><div class="sk-toggleable__content fitted"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),
+                 scoring=&#x27;neg_log_loss&#x27;)</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-251" type="checkbox" ><label for="sk-estimator-id-251" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>best_estimator_: Pipeline</div></div></label><div class="sk-toggleable__content fitted"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),
                     (&#x27;logisticregression&#x27;,
-                     LogisticRegression(C=np.float64(100.0)))])</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-238" type="checkbox" ><label for="sk-estimator-id-238" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>StandardScaler</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.StandardScaler.html">?<span>Documentation for StandardScaler</span></a></div></label><div class="sk-toggleable__content fitted"><pre>StandardScaler()</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-239" type="checkbox" ><label for="sk-estimator-id-239" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>LogisticRegression</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.linear_model.LogisticRegression.html">?<span>Documentation for LogisticRegression</span></a></div></label><div class="sk-toggleable__content fitted"><pre>LogisticRegression(C=np.float64(100.0))</pre></div> </div></div></div></div></div></div></div></div></div></div></div>
+                     LogisticRegression(C=np.float64(100.0)))])</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-252" type="checkbox" ><label for="sk-estimator-id-252" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>StandardScaler</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.StandardScaler.html">?<span>Documentation for StandardScaler</span></a></div></label><div class="sk-toggleable__content fitted"><pre>StandardScaler()</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-253" type="checkbox" ><label for="sk-estimator-id-253" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>LogisticRegression</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.linear_model.LogisticRegression.html">?<span>Documentation for LogisticRegression</span></a></div></label><div class="sk-toggleable__content fitted"><pre>LogisticRegression(C=np.float64(100.0))</pre></div> </div></div></div></div></div></div></div></div></div></div></div>
     </div>
     <br />
     <br />
@@ -2546,7 +2546,7 @@ automatically dispatching this metadata to the underlying scorer.
 .. raw:: html
 
     <div class="output_subarea output_html rendered_html output_result">
-    <style>#sk-container-id-60 {
+    <style>#sk-container-id-61 {
       /* Definition of color scheme common for light and dark mode */
       --sklearn-color-text: #000;
       --sklearn-color-text-muted: #666;
@@ -2577,15 +2577,15 @@ automatically dispatching this metadata to the underlying scorer.
       }
     }
 
-    #sk-container-id-60 {
+    #sk-container-id-61 {
       color: var(--sklearn-color-text);
     }
 
-    #sk-container-id-60 pre {
+    #sk-container-id-61 pre {
       padding: 0;
     }
 
-    #sk-container-id-60 input.sk-hidden--visually {
+    #sk-container-id-61 input.sk-hidden--visually {
       border: 0;
       clip: rect(1px 1px 1px 1px);
       clip: rect(1px, 1px, 1px, 1px);
@@ -2597,7 +2597,7 @@ automatically dispatching this metadata to the underlying scorer.
       width: 1px;
     }
 
-    #sk-container-id-60 div.sk-dashed-wrapped {
+    #sk-container-id-61 div.sk-dashed-wrapped {
       border: 1px dashed var(--sklearn-color-line);
       margin: 0 0.4em 0.5em 0.4em;
       box-sizing: border-box;
@@ -2605,7 +2605,7 @@ automatically dispatching this metadata to the underlying scorer.
       background-color: var(--sklearn-color-background);
     }
 
-    #sk-container-id-60 div.sk-container {
+    #sk-container-id-61 div.sk-container {
       /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
          but bootstrap.min.css set `[hidden] { display: none !important; }`
          so we also need the `!important` here to be able to override the
@@ -2615,7 +2615,7 @@ automatically dispatching this metadata to the underlying scorer.
       position: relative;
     }
 
-    #sk-container-id-60 div.sk-text-repr-fallback {
+    #sk-container-id-61 div.sk-text-repr-fallback {
       display: none;
     }
 
@@ -2631,14 +2631,14 @@ automatically dispatching this metadata to the underlying scorer.
 
     /* Parallel-specific style estimator block */
 
-    #sk-container-id-60 div.sk-parallel-item::after {
+    #sk-container-id-61 div.sk-parallel-item::after {
       content: "";
       width: 100%;
       border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
       flex-grow: 1;
     }
 
-    #sk-container-id-60 div.sk-parallel {
+    #sk-container-id-61 div.sk-parallel {
       display: flex;
       align-items: stretch;
       justify-content: center;
@@ -2646,28 +2646,28 @@ automatically dispatching this metadata to the underlying scorer.
       position: relative;
     }
 
-    #sk-container-id-60 div.sk-parallel-item {
+    #sk-container-id-61 div.sk-parallel-item {
       display: flex;
       flex-direction: column;
     }
 
-    #sk-container-id-60 div.sk-parallel-item:first-child::after {
+    #sk-container-id-61 div.sk-parallel-item:first-child::after {
       align-self: flex-end;
       width: 50%;
     }
 
-    #sk-container-id-60 div.sk-parallel-item:last-child::after {
+    #sk-container-id-61 div.sk-parallel-item:last-child::after {
       align-self: flex-start;
       width: 50%;
     }
 
-    #sk-container-id-60 div.sk-parallel-item:only-child::after {
+    #sk-container-id-61 div.sk-parallel-item:only-child::after {
       width: 0;
     }
 
     /* Serial-specific style estimator block */
 
-    #sk-container-id-60 div.sk-serial {
+    #sk-container-id-61 div.sk-serial {
       display: flex;
       flex-direction: column;
       align-items: center;
@@ -2685,14 +2685,14 @@ automatically dispatching this metadata to the underlying scorer.
 
     /* Pipeline and ColumnTransformer style (default) */
 
-    #sk-container-id-60 div.sk-toggleable {
+    #sk-container-id-61 div.sk-toggleable {
       /* Default theme specific background. It is overwritten whether we have a
       specific estimator or a Pipeline/ColumnTransformer */
       background-color: var(--sklearn-color-background);
     }
 
     /* Toggleable label */
-    #sk-container-id-60 label.sk-toggleable__label {
+    #sk-container-id-61 label.sk-toggleable__label {
       cursor: pointer;
       display: flex;
       width: 100%;
@@ -2705,13 +2705,13 @@ automatically dispatching this metadata to the underlying scorer.
       gap: 0.5em;
     }
 
-    #sk-container-id-60 label.sk-toggleable__label .caption {
+    #sk-container-id-61 label.sk-toggleable__label .caption {
       font-size: 0.6rem;
       font-weight: lighter;
       color: var(--sklearn-color-text-muted);
     }
 
-    #sk-container-id-60 label.sk-toggleable__label-arrow:before {
+    #sk-container-id-61 label.sk-toggleable__label-arrow:before {
       /* Arrow on the left of the label */
       content: "▸";
       float: left;
@@ -2719,13 +2719,13 @@ automatically dispatching this metadata to the underlying scorer.
       color: var(--sklearn-color-icon);
     }
 
-    #sk-container-id-60 label.sk-toggleable__label-arrow:hover:before {
+    #sk-container-id-61 label.sk-toggleable__label-arrow:hover:before {
       color: var(--sklearn-color-text);
     }
 
     /* Toggleable content - dropdown */
 
-    #sk-container-id-60 div.sk-toggleable__content {
+    #sk-container-id-61 div.sk-toggleable__content {
       max-height: 0;
       max-width: 0;
       overflow: hidden;
@@ -2734,12 +2734,12 @@ automatically dispatching this metadata to the underlying scorer.
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-60 div.sk-toggleable__content.fitted {
+    #sk-container-id-61 div.sk-toggleable__content.fitted {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
-    #sk-container-id-60 div.sk-toggleable__content pre {
+    #sk-container-id-61 div.sk-toggleable__content pre {
       margin: 0.2em;
       border-radius: 0.25em;
       color: var(--sklearn-color-text);
@@ -2747,79 +2747,79 @@ automatically dispatching this metadata to the underlying scorer.
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-60 div.sk-toggleable__content.fitted pre {
+    #sk-container-id-61 div.sk-toggleable__content.fitted pre {
       /* unfitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
-    #sk-container-id-60 input.sk-toggleable__control:checked~div.sk-toggleable__content {
+    #sk-container-id-61 input.sk-toggleable__control:checked~div.sk-toggleable__content {
       /* Expand drop-down */
       max-height: 200px;
       max-width: 100%;
       overflow: auto;
     }
 
-    #sk-container-id-60 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
+    #sk-container-id-61 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
       content: "▾";
     }
 
     /* Pipeline/ColumnTransformer-specific style */
 
-    #sk-container-id-60 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-61 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-60 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-61 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
     /* Estimator-specific style */
 
     /* Colorize estimator box */
-    #sk-container-id-60 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-61 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-60 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-61 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
-    #sk-container-id-60 div.sk-label label.sk-toggleable__label,
-    #sk-container-id-60 div.sk-label label {
+    #sk-container-id-61 div.sk-label label.sk-toggleable__label,
+    #sk-container-id-61 div.sk-label label {
       /* The background is the default theme color */
       color: var(--sklearn-color-text-on-default-background);
     }
 
     /* On hover, darken the color of the background */
-    #sk-container-id-60 div.sk-label:hover label.sk-toggleable__label {
+    #sk-container-id-61 div.sk-label:hover label.sk-toggleable__label {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
     /* Label box, darken color on hover, fitted */
-    #sk-container-id-60 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
+    #sk-container-id-61 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
     /* Estimator label */
 
-    #sk-container-id-60 div.sk-label label {
+    #sk-container-id-61 div.sk-label label {
       font-family: monospace;
       font-weight: bold;
       display: inline-block;
       line-height: 1.2em;
     }
 
-    #sk-container-id-60 div.sk-label-container {
+    #sk-container-id-61 div.sk-label-container {
       text-align: center;
     }
 
     /* Estimator-specific */
-    #sk-container-id-60 div.sk-estimator {
+    #sk-container-id-61 div.sk-estimator {
       font-family: monospace;
       border: 1px dotted var(--sklearn-color-border-box);
       border-radius: 0.25em;
@@ -2829,18 +2829,18 @@ automatically dispatching this metadata to the underlying scorer.
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-60 div.sk-estimator.fitted {
+    #sk-container-id-61 div.sk-estimator.fitted {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
     /* on hover */
-    #sk-container-id-60 div.sk-estimator:hover {
+    #sk-container-id-61 div.sk-estimator:hover {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-60 div.sk-estimator.fitted:hover {
+    #sk-container-id-61 div.sk-estimator.fitted:hover {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-2);
     }
@@ -2928,7 +2928,7 @@ automatically dispatching this metadata to the underlying scorer.
 
     /* "?"-specific style due to the `<a>` HTML tag */
 
-    #sk-container-id-60 a.estimator_doc_link {
+    #sk-container-id-61 a.estimator_doc_link {
       float: right;
       font-size: 1rem;
       line-height: 1em;
@@ -2943,37 +2943,37 @@ automatically dispatching this metadata to the underlying scorer.
       border: var(--sklearn-color-unfitted-level-1) 1pt solid;
     }
 
-    #sk-container-id-60 a.estimator_doc_link.fitted {
+    #sk-container-id-61 a.estimator_doc_link.fitted {
       /* fitted */
       border: var(--sklearn-color-fitted-level-1) 1pt solid;
       color: var(--sklearn-color-fitted-level-1);
     }
 
     /* On hover */
-    #sk-container-id-60 a.estimator_doc_link:hover {
+    #sk-container-id-61 a.estimator_doc_link:hover {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-3);
       color: var(--sklearn-color-background);
       text-decoration: none;
     }
 
-    #sk-container-id-60 a.estimator_doc_link.fitted:hover {
+    #sk-container-id-61 a.estimator_doc_link.fitted:hover {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-3);
     }
-    </style><div id="sk-container-id-60" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>TunedThresholdClassifierCV(estimator=Pipeline(steps=[(&#x27;standardscaler&#x27;,
+    </style><div id="sk-container-id-61" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>TunedThresholdClassifierCV(estimator=Pipeline(steps=[(&#x27;standardscaler&#x27;,
                                                           StandardScaler()),
                                                          (&#x27;logisticregression&#x27;,
                                                           LogisticRegression(C=np.float64(100.0)))]),
                                n_jobs=2,
-                               scoring=make_scorer(business_metric, response_method=&#x27;predict&#x27;))</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-240" type="checkbox" ><label for="sk-estimator-id-240" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>TunedThresholdClassifierCV</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.model_selection.TunedThresholdClassifierCV.html">?<span>Documentation for TunedThresholdClassifierCV</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>TunedThresholdClassifierCV(estimator=Pipeline(steps=[(&#x27;standardscaler&#x27;,
+                               scoring=make_scorer(business_metric, response_method=&#x27;predict&#x27;))</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-254" type="checkbox" ><label for="sk-estimator-id-254" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>TunedThresholdClassifierCV</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.model_selection.TunedThresholdClassifierCV.html">?<span>Documentation for TunedThresholdClassifierCV</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>TunedThresholdClassifierCV(estimator=Pipeline(steps=[(&#x27;standardscaler&#x27;,
                                                           StandardScaler()),
                                                          (&#x27;logisticregression&#x27;,
                                                           LogisticRegression(C=np.float64(100.0)))]),
                                n_jobs=2,
-                               scoring=make_scorer(business_metric, response_method=&#x27;predict&#x27;))</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-241" type="checkbox" ><label for="sk-estimator-id-241" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>estimator: Pipeline</div></div></label><div class="sk-toggleable__content fitted"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),
+                               scoring=make_scorer(business_metric, response_method=&#x27;predict&#x27;))</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-255" type="checkbox" ><label for="sk-estimator-id-255" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>estimator: Pipeline</div></div></label><div class="sk-toggleable__content fitted"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),
                     (&#x27;logisticregression&#x27;,
-                     LogisticRegression(C=np.float64(100.0)))])</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-242" type="checkbox" ><label for="sk-estimator-id-242" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>StandardScaler</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.StandardScaler.html">?<span>Documentation for StandardScaler</span></a></div></label><div class="sk-toggleable__content fitted"><pre>StandardScaler()</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-243" type="checkbox" ><label for="sk-estimator-id-243" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>LogisticRegression</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.linear_model.LogisticRegression.html">?<span>Documentation for LogisticRegression</span></a></div></label><div class="sk-toggleable__content fitted"><pre>LogisticRegression(C=np.float64(100.0))</pre></div> </div></div></div></div></div></div></div></div></div></div></div>
+                     LogisticRegression(C=np.float64(100.0)))])</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-256" type="checkbox" ><label for="sk-estimator-id-256" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>StandardScaler</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.StandardScaler.html">?<span>Documentation for StandardScaler</span></a></div></label><div class="sk-toggleable__content fitted"><pre>StandardScaler()</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-257" type="checkbox" ><label for="sk-estimator-id-257" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>LogisticRegression</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.linear_model.LogisticRegression.html">?<span>Documentation for LogisticRegression</span></a></div></label><div class="sk-toggleable__content fitted"><pre>LogisticRegression(C=np.float64(100.0))</pre></div> </div></div></div></div></div></div></div></div></div></div></div>
     </div>
     <br />
     <br />
@@ -3116,7 +3116,7 @@ beyond the scope of the scikit-learn library itself.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 25.109 seconds)
+   **Total running time of the script:** (0 minutes 28.551 seconds)
 
 
 .. _sphx_glr_download_auto_examples_model_selection_plot_cost_sensitive_learning.py:
diff --git a/dev/_sources/auto_examples/model_selection/plot_cv_indices.rst.txt b/dev/_sources/auto_examples/model_selection/plot_cv_indices.rst.txt
index 45174bf69cece..1ec3b8269c669 100644
--- a/dev/_sources/auto_examples/model_selection/plot_cv_indices.rst.txt
+++ b/dev/_sources/auto_examples/model_selection/plot_cv_indices.rst.txt
@@ -412,7 +412,7 @@ Note how some use the group/class information while others do not.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 1.282 seconds)
+   **Total running time of the script:** (0 minutes 1.322 seconds)
 
 
 .. _sphx_glr_download_auto_examples_model_selection_plot_cv_indices.py:
diff --git a/dev/_sources/auto_examples/model_selection/plot_cv_predict.rst.txt b/dev/_sources/auto_examples/model_selection/plot_cv_predict.rst.txt
index 066df4dd0bf56..7e150d6dabcb8 100644
--- a/dev/_sources/auto_examples/model_selection/plot_cv_predict.rst.txt
+++ b/dev/_sources/auto_examples/model_selection/plot_cv_predict.rst.txt
@@ -159,7 +159,7 @@ It is recommended to compute per-fold performance metrics using:
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.190 seconds)
+   **Total running time of the script:** (0 minutes 0.189 seconds)
 
 
 .. _sphx_glr_download_auto_examples_model_selection_plot_cv_predict.py:
diff --git a/dev/_sources/auto_examples/model_selection/plot_det.rst.txt b/dev/_sources/auto_examples/model_selection/plot_det.rst.txt
index 752f50403afb2..a32a88fbaefcb 100644
--- a/dev/_sources/auto_examples/model_selection/plot_det.rst.txt
+++ b/dev/_sources/auto_examples/model_selection/plot_det.rst.txt
@@ -97,21 +97,20 @@ Generate synthetic data
 
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 59-66
+.. GENERATED FROM PYTHON SOURCE LINES 59-64
 
 Define the classifiers
 ----------------------
 
 Here we define two different classifiers. The goal is to visually compare their
-statistical performance across thresholds using the ROC and DET curves. There
-is no particular reason why these classifiers are chosen other classifiers
-available in scikit-learn.
+statistical performance across thresholds using the ROC and DET curves.
 
-.. GENERATED FROM PYTHON SOURCE LINES 66-78
+.. GENERATED FROM PYTHON SOURCE LINES 64-78
 
 .. code-block:: Python
 
 
+    from sklearn.dummy import DummyClassifier
     from sklearn.ensemble import RandomForestClassifier
     from sklearn.pipeline import make_pipeline
     from sklearn.svm import LinearSVC
@@ -119,8 +118,9 @@ available in scikit-learn.
     classifiers = {
         "Linear SVM": make_pipeline(StandardScaler(), LinearSVC(C=0.025)),
         "Random Forest": RandomForestClassifier(
-            max_depth=5, n_estimators=10, max_features=1
+            max_depth=5, n_estimators=10, max_features=1, random_state=0
         ),
+        "Non-informative baseline": DummyClassifier(),
     }
 
 
@@ -132,37 +132,44 @@ available in scikit-learn.
 
 .. GENERATED FROM PYTHON SOURCE LINES 79-86
 
-Plot ROC and DET curves
------------------------
+Compare ROC and DET curves
+--------------------------
 
 DET curves are commonly plotted in normal deviate scale. To achieve this the
 DET display transforms the error rates as returned by the
 :func:`~sklearn.metrics.det_curve` and the axis scale using
 `scipy.stats.norm`.
 
-.. GENERATED FROM PYTHON SOURCE LINES 86-108
+.. GENERATED FROM PYTHON SOURCE LINES 86-115
 
 .. code-block:: Python
 
 
     import matplotlib.pyplot as plt
 
+    from sklearn.dummy import DummyClassifier
     from sklearn.metrics import DetCurveDisplay, RocCurveDisplay
 
     fig, [ax_roc, ax_det] = plt.subplots(1, 2, figsize=(11, 5))
 
-    for name, clf in classifiers.items():
-        clf.fit(X_train, y_train)
-
-        RocCurveDisplay.from_estimator(clf, X_test, y_test, ax=ax_roc, name=name)
-        DetCurveDisplay.from_estimator(clf, X_test, y_test, ax=ax_det, name=name)
-
     ax_roc.set_title("Receiver Operating Characteristic (ROC) curves")
     ax_det.set_title("Detection Error Tradeoff (DET) curves")
 
     ax_roc.grid(linestyle="--")
     ax_det.grid(linestyle="--")
 
+    for name, clf in classifiers.items():
+        (color, linestyle) = (
+            ("black", "--") if name == "Non-informative baseline" else (None, None)
+        )
+        clf.fit(X_train, y_train)
+        RocCurveDisplay.from_estimator(
+            clf, X_test, y_test, ax=ax_roc, name=name, color=color, linestyle=linestyle
+        )
+        DetCurveDisplay.from_estimator(
+            clf, X_test, y_test, ax=ax_det, name=name, color=color, linestyle=linestyle
+        )
+
     plt.legend()
     plt.show()
 
@@ -178,7 +185,7 @@ DET display transforms the error rates as returned by the
 
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 109-120
+.. GENERATED FROM PYTHON SOURCE LINES 116-159
 
 Notice that it is easier to visually assess the overall performance of
 different classification algorithms using DET curves than using ROC curves. As
@@ -192,10 +199,42 @@ DET curves give direct feedback of the detection error tradeoff to aid in
 operating point analysis. The user can then decide the FNR they are willing to
 accept at the expense of the FPR (or vice-versa).
 
+Non-informative classifier baseline for the ROC and DET curves
+--------------------------------------------------------------
+
+The diagonal black-dotted lines in the plots above correspond to a
+:class:`~sklearn.dummy.DummyClassifier` using the default "prior" strategy, to
+serve as baseline for comparison with other classifiers. This classifier makes
+constant predictions, independent of the input features in `X`, making it a
+non-informative classifier.
+
+To further understand the non-informative baseline of the ROC and DET curves,
+we recall the following mathematical definitions:
+
+:math:`\text{FPR} = \frac{\text{FP}}{\text{FP} + \text{TN}}`
+
+:math:`\text{FNR} = \frac{\text{FN}}{\text{TP} + \text{FN}}`
+
+:math:`\text{TPR} = \frac{\text{TP}}{\text{TP} + \text{FN}}`
+
+A classifier that always predict the positive class would have no true
+negatives nor false negatives, giving :math:`\text{FPR} = \text{TPR} = 1` and
+:math:`\text{FNR} = 0`, i.e.:
+
+- a single point in the upper right corner of the ROC plane,
+- a single point in the lower right corner of the DET plane.
+
+Similarly, a classifier that always predict the negative class would have no
+true positives nor false positives, thus :math:`\text{FPR} = \text{TPR} = 0`
+and :math:`\text{FNR} = 1`, i.e.:
+
+- a single point in the lower left corner of the ROC plane,
+- a single point in the upper left corner of the DET plane.
+
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.192 seconds)
+   **Total running time of the script:** (0 minutes 0.206 seconds)
 
 
 .. _sphx_glr_download_auto_examples_model_selection_plot_det.py:
diff --git a/dev/_sources/auto_examples/model_selection/plot_grid_search_digits.rst.txt b/dev/_sources/auto_examples/model_selection/plot_grid_search_digits.rst.txt
index 0f194781ee5ee..515e17e1b7ce8 100644
--- a/dev/_sources/auto_examples/model_selection/plot_grid_search_digits.rst.txt
+++ b/dev/_sources/auto_examples/model_selection/plot_grid_search_digits.rst.txt
@@ -334,21 +334,21 @@ of the hyper-parameters and create the grid-search instance:
     selected subset of best models based on precision and recall.
     Its scoring time is:
 
-    mean_score_time                                          0.006194
-    mean_test_recall                                         0.877206
-    std_test_recall                                          0.069196
-    mean_test_precision                                           1.0
-    std_test_precision                                            0.0
-    rank_test_recall                                                3
-    rank_test_precision                                             1
-    params                 {'C': 10, 'gamma': 0.001, 'kernel': 'rbf'}
-    Name: 2, dtype: object
+    mean_score_time                                           0.006381
+    mean_test_recall                                          0.877206
+    std_test_recall                                           0.069196
+    mean_test_precision                                            1.0
+    std_test_precision                                             0.0
+    rank_test_recall                                                 3
+    rank_test_precision                                              1
+    params                 {'C': 100, 'gamma': 0.001, 'kernel': 'rbf'}
+    Name: 4, dtype: object
 
 
 .. raw:: html
 
     <div class="output_subarea output_html rendered_html output_result">
-    <style>#sk-container-id-55 {
+    <style>#sk-container-id-56 {
       /* Definition of color scheme common for light and dark mode */
       --sklearn-color-text: #000;
       --sklearn-color-text-muted: #666;
@@ -379,15 +379,15 @@ of the hyper-parameters and create the grid-search instance:
       }
     }
 
-    #sk-container-id-55 {
+    #sk-container-id-56 {
       color: var(--sklearn-color-text);
     }
 
-    #sk-container-id-55 pre {
+    #sk-container-id-56 pre {
       padding: 0;
     }
 
-    #sk-container-id-55 input.sk-hidden--visually {
+    #sk-container-id-56 input.sk-hidden--visually {
       border: 0;
       clip: rect(1px 1px 1px 1px);
       clip: rect(1px, 1px, 1px, 1px);
@@ -399,7 +399,7 @@ of the hyper-parameters and create the grid-search instance:
       width: 1px;
     }
 
-    #sk-container-id-55 div.sk-dashed-wrapped {
+    #sk-container-id-56 div.sk-dashed-wrapped {
       border: 1px dashed var(--sklearn-color-line);
       margin: 0 0.4em 0.5em 0.4em;
       box-sizing: border-box;
@@ -407,7 +407,7 @@ of the hyper-parameters and create the grid-search instance:
       background-color: var(--sklearn-color-background);
     }
 
-    #sk-container-id-55 div.sk-container {
+    #sk-container-id-56 div.sk-container {
       /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
          but bootstrap.min.css set `[hidden] { display: none !important; }`
          so we also need the `!important` here to be able to override the
@@ -417,7 +417,7 @@ of the hyper-parameters and create the grid-search instance:
       position: relative;
     }
 
-    #sk-container-id-55 div.sk-text-repr-fallback {
+    #sk-container-id-56 div.sk-text-repr-fallback {
       display: none;
     }
 
@@ -433,14 +433,14 @@ of the hyper-parameters and create the grid-search instance:
 
     /* Parallel-specific style estimator block */
 
-    #sk-container-id-55 div.sk-parallel-item::after {
+    #sk-container-id-56 div.sk-parallel-item::after {
       content: "";
       width: 100%;
       border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
       flex-grow: 1;
     }
 
-    #sk-container-id-55 div.sk-parallel {
+    #sk-container-id-56 div.sk-parallel {
       display: flex;
       align-items: stretch;
       justify-content: center;
@@ -448,28 +448,28 @@ of the hyper-parameters and create the grid-search instance:
       position: relative;
     }
 
-    #sk-container-id-55 div.sk-parallel-item {
+    #sk-container-id-56 div.sk-parallel-item {
       display: flex;
       flex-direction: column;
     }
 
-    #sk-container-id-55 div.sk-parallel-item:first-child::after {
+    #sk-container-id-56 div.sk-parallel-item:first-child::after {
       align-self: flex-end;
       width: 50%;
     }
 
-    #sk-container-id-55 div.sk-parallel-item:last-child::after {
+    #sk-container-id-56 div.sk-parallel-item:last-child::after {
       align-self: flex-start;
       width: 50%;
     }
 
-    #sk-container-id-55 div.sk-parallel-item:only-child::after {
+    #sk-container-id-56 div.sk-parallel-item:only-child::after {
       width: 0;
     }
 
     /* Serial-specific style estimator block */
 
-    #sk-container-id-55 div.sk-serial {
+    #sk-container-id-56 div.sk-serial {
       display: flex;
       flex-direction: column;
       align-items: center;
@@ -487,14 +487,14 @@ of the hyper-parameters and create the grid-search instance:
 
     /* Pipeline and ColumnTransformer style (default) */
 
-    #sk-container-id-55 div.sk-toggleable {
+    #sk-container-id-56 div.sk-toggleable {
       /* Default theme specific background. It is overwritten whether we have a
       specific estimator or a Pipeline/ColumnTransformer */
       background-color: var(--sklearn-color-background);
     }
 
     /* Toggleable label */
-    #sk-container-id-55 label.sk-toggleable__label {
+    #sk-container-id-56 label.sk-toggleable__label {
       cursor: pointer;
       display: flex;
       width: 100%;
@@ -507,13 +507,13 @@ of the hyper-parameters and create the grid-search instance:
       gap: 0.5em;
     }
 
-    #sk-container-id-55 label.sk-toggleable__label .caption {
+    #sk-container-id-56 label.sk-toggleable__label .caption {
       font-size: 0.6rem;
       font-weight: lighter;
       color: var(--sklearn-color-text-muted);
     }
 
-    #sk-container-id-55 label.sk-toggleable__label-arrow:before {
+    #sk-container-id-56 label.sk-toggleable__label-arrow:before {
       /* Arrow on the left of the label */
       content: "▸";
       float: left;
@@ -521,13 +521,13 @@ of the hyper-parameters and create the grid-search instance:
       color: var(--sklearn-color-icon);
     }
 
-    #sk-container-id-55 label.sk-toggleable__label-arrow:hover:before {
+    #sk-container-id-56 label.sk-toggleable__label-arrow:hover:before {
       color: var(--sklearn-color-text);
     }
 
     /* Toggleable content - dropdown */
 
-    #sk-container-id-55 div.sk-toggleable__content {
+    #sk-container-id-56 div.sk-toggleable__content {
       max-height: 0;
       max-width: 0;
       overflow: hidden;
@@ -536,12 +536,12 @@ of the hyper-parameters and create the grid-search instance:
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-55 div.sk-toggleable__content.fitted {
+    #sk-container-id-56 div.sk-toggleable__content.fitted {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
-    #sk-container-id-55 div.sk-toggleable__content pre {
+    #sk-container-id-56 div.sk-toggleable__content pre {
       margin: 0.2em;
       border-radius: 0.25em;
       color: var(--sklearn-color-text);
@@ -549,79 +549,79 @@ of the hyper-parameters and create the grid-search instance:
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-55 div.sk-toggleable__content.fitted pre {
+    #sk-container-id-56 div.sk-toggleable__content.fitted pre {
       /* unfitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
-    #sk-container-id-55 input.sk-toggleable__control:checked~div.sk-toggleable__content {
+    #sk-container-id-56 input.sk-toggleable__control:checked~div.sk-toggleable__content {
       /* Expand drop-down */
       max-height: 200px;
       max-width: 100%;
       overflow: auto;
     }
 
-    #sk-container-id-55 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
+    #sk-container-id-56 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
       content: "▾";
     }
 
     /* Pipeline/ColumnTransformer-specific style */
 
-    #sk-container-id-55 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-56 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-55 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-56 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
     /* Estimator-specific style */
 
     /* Colorize estimator box */
-    #sk-container-id-55 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-56 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-55 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-56 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
-    #sk-container-id-55 div.sk-label label.sk-toggleable__label,
-    #sk-container-id-55 div.sk-label label {
+    #sk-container-id-56 div.sk-label label.sk-toggleable__label,
+    #sk-container-id-56 div.sk-label label {
       /* The background is the default theme color */
       color: var(--sklearn-color-text-on-default-background);
     }
 
     /* On hover, darken the color of the background */
-    #sk-container-id-55 div.sk-label:hover label.sk-toggleable__label {
+    #sk-container-id-56 div.sk-label:hover label.sk-toggleable__label {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
     /* Label box, darken color on hover, fitted */
-    #sk-container-id-55 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
+    #sk-container-id-56 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
     /* Estimator label */
 
-    #sk-container-id-55 div.sk-label label {
+    #sk-container-id-56 div.sk-label label {
       font-family: monospace;
       font-weight: bold;
       display: inline-block;
       line-height: 1.2em;
     }
 
-    #sk-container-id-55 div.sk-label-container {
+    #sk-container-id-56 div.sk-label-container {
       text-align: center;
     }
 
     /* Estimator-specific */
-    #sk-container-id-55 div.sk-estimator {
+    #sk-container-id-56 div.sk-estimator {
       font-family: monospace;
       border: 1px dotted var(--sklearn-color-border-box);
       border-radius: 0.25em;
@@ -631,18 +631,18 @@ of the hyper-parameters and create the grid-search instance:
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-55 div.sk-estimator.fitted {
+    #sk-container-id-56 div.sk-estimator.fitted {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
     /* on hover */
-    #sk-container-id-55 div.sk-estimator:hover {
+    #sk-container-id-56 div.sk-estimator:hover {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-55 div.sk-estimator.fitted:hover {
+    #sk-container-id-56 div.sk-estimator.fitted:hover {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-2);
     }
@@ -730,7 +730,7 @@ of the hyper-parameters and create the grid-search instance:
 
     /* "?"-specific style due to the `<a>` HTML tag */
 
-    #sk-container-id-55 a.estimator_doc_link {
+    #sk-container-id-56 a.estimator_doc_link {
       float: right;
       font-size: 1rem;
       line-height: 1em;
@@ -745,35 +745,35 @@ of the hyper-parameters and create the grid-search instance:
       border: var(--sklearn-color-unfitted-level-1) 1pt solid;
     }
 
-    #sk-container-id-55 a.estimator_doc_link.fitted {
+    #sk-container-id-56 a.estimator_doc_link.fitted {
       /* fitted */
       border: var(--sklearn-color-fitted-level-1) 1pt solid;
       color: var(--sklearn-color-fitted-level-1);
     }
 
     /* On hover */
-    #sk-container-id-55 a.estimator_doc_link:hover {
+    #sk-container-id-56 a.estimator_doc_link:hover {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-3);
       color: var(--sklearn-color-background);
       text-decoration: none;
     }
 
-    #sk-container-id-55 a.estimator_doc_link.fitted:hover {
+    #sk-container-id-56 a.estimator_doc_link.fitted:hover {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-3);
     }
-    </style><div id="sk-container-id-55" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>GridSearchCV(estimator=SVC(),
+    </style><div id="sk-container-id-56" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>GridSearchCV(estimator=SVC(),
                  param_grid=[{&#x27;C&#x27;: [1, 10, 100, 1000], &#x27;gamma&#x27;: [0.001, 0.0001],
                               &#x27;kernel&#x27;: [&#x27;rbf&#x27;]},
                              {&#x27;C&#x27;: [1, 10, 100, 1000], &#x27;kernel&#x27;: [&#x27;linear&#x27;]}],
-                 refit=&lt;function refit_strategy at 0x7fb585547010&gt;,
-                 scoring=[&#x27;precision&#x27;, &#x27;recall&#x27;])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-226" type="checkbox" ><label for="sk-estimator-id-226" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>GridSearchCV</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.model_selection.GridSearchCV.html">?<span>Documentation for GridSearchCV</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>GridSearchCV(estimator=SVC(),
+                 refit=&lt;function refit_strategy at 0x7fd8418536d0&gt;,
+                 scoring=[&#x27;precision&#x27;, &#x27;recall&#x27;])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-240" type="checkbox" ><label for="sk-estimator-id-240" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>GridSearchCV</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.model_selection.GridSearchCV.html">?<span>Documentation for GridSearchCV</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>GridSearchCV(estimator=SVC(),
                  param_grid=[{&#x27;C&#x27;: [1, 10, 100, 1000], &#x27;gamma&#x27;: [0.001, 0.0001],
                               &#x27;kernel&#x27;: [&#x27;rbf&#x27;]},
                              {&#x27;C&#x27;: [1, 10, 100, 1000], &#x27;kernel&#x27;: [&#x27;linear&#x27;]}],
-                 refit=&lt;function refit_strategy at 0x7fb585547010&gt;,
-                 scoring=[&#x27;precision&#x27;, &#x27;recall&#x27;])</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-227" type="checkbox" ><label for="sk-estimator-id-227" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>best_estimator_: SVC</div></div></label><div class="sk-toggleable__content fitted"><pre>SVC(C=10, gamma=0.001)</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-228" type="checkbox" ><label for="sk-estimator-id-228" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>SVC</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.svm.SVC.html">?<span>Documentation for SVC</span></a></div></label><div class="sk-toggleable__content fitted"><pre>SVC(C=10, gamma=0.001)</pre></div> </div></div></div></div></div></div></div></div></div>
+                 refit=&lt;function refit_strategy at 0x7fd8418536d0&gt;,
+                 scoring=[&#x27;precision&#x27;, &#x27;recall&#x27;])</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-241" type="checkbox" ><label for="sk-estimator-id-241" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>best_estimator_: SVC</div></div></label><div class="sk-toggleable__content fitted"><pre>SVC(C=100, gamma=0.001)</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-242" type="checkbox" ><label for="sk-estimator-id-242" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>SVC</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.svm.SVC.html">?<span>Documentation for SVC</span></a></div></label><div class="sk-toggleable__content fitted"><pre>SVC(C=100, gamma=0.001)</pre></div> </div></div></div></div></div></div></div></div></div>
     </div>
     <br />
     <br />
@@ -797,7 +797,7 @@ The parameters selected by the grid-search with our custom strategy are:
  .. code-block:: none
 
 
-    {'C': 10, 'gamma': 0.001, 'kernel': 'rbf'}
+    {'C': 100, 'gamma': 0.001, 'kernel': 'rbf'}
 
 
 
@@ -849,7 +849,7 @@ metrics on the left-out set:
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 9.971 seconds)
+   **Total running time of the script:** (0 minutes 10.263 seconds)
 
 
 .. _sphx_glr_download_auto_examples_model_selection_plot_grid_search_digits.py:
diff --git a/dev/_sources/auto_examples/model_selection/plot_grid_search_refit_callable.rst.txt b/dev/_sources/auto_examples/model_selection/plot_grid_search_refit_callable.rst.txt
index 35db7dbdbe21f..85a9c59325ea0 100644
--- a/dev/_sources/auto_examples/model_selection/plot_grid_search_refit_callable.rst.txt
+++ b/dev/_sources/auto_examples/model_selection/plot_grid_search_refit_callable.rst.txt
@@ -174,7 +174,7 @@ NY, USA: Springer New York Inc..
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 1.147 seconds)
+   **Total running time of the script:** (0 minutes 1.095 seconds)
 
 
 .. _sphx_glr_download_auto_examples_model_selection_plot_grid_search_refit_callable.py:
diff --git a/dev/_sources/auto_examples/model_selection/plot_grid_search_stats.rst.txt b/dev/_sources/auto_examples/model_selection/plot_grid_search_stats.rst.txt
index c45c1477cb57f..eea4bc6007e5f 100644
--- a/dev/_sources/auto_examples/model_selection/plot_grid_search_stats.rst.txt
+++ b/dev/_sources/auto_examples/model_selection/plot_grid_search_stats.rst.txt
@@ -116,7 +116,7 @@ data in each repetition. The performance will be evaluated using
 .. raw:: html
 
     <div class="output_subarea output_html rendered_html output_result">
-    <style>#sk-container-id-63 {
+    <style>#sk-container-id-64 {
       /* Definition of color scheme common for light and dark mode */
       --sklearn-color-text: #000;
       --sklearn-color-text-muted: #666;
@@ -147,15 +147,15 @@ data in each repetition. The performance will be evaluated using
       }
     }
 
-    #sk-container-id-63 {
+    #sk-container-id-64 {
       color: var(--sklearn-color-text);
     }
 
-    #sk-container-id-63 pre {
+    #sk-container-id-64 pre {
       padding: 0;
     }
 
-    #sk-container-id-63 input.sk-hidden--visually {
+    #sk-container-id-64 input.sk-hidden--visually {
       border: 0;
       clip: rect(1px 1px 1px 1px);
       clip: rect(1px, 1px, 1px, 1px);
@@ -167,7 +167,7 @@ data in each repetition. The performance will be evaluated using
       width: 1px;
     }
 
-    #sk-container-id-63 div.sk-dashed-wrapped {
+    #sk-container-id-64 div.sk-dashed-wrapped {
       border: 1px dashed var(--sklearn-color-line);
       margin: 0 0.4em 0.5em 0.4em;
       box-sizing: border-box;
@@ -175,7 +175,7 @@ data in each repetition. The performance will be evaluated using
       background-color: var(--sklearn-color-background);
     }
 
-    #sk-container-id-63 div.sk-container {
+    #sk-container-id-64 div.sk-container {
       /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
          but bootstrap.min.css set `[hidden] { display: none !important; }`
          so we also need the `!important` here to be able to override the
@@ -185,7 +185,7 @@ data in each repetition. The performance will be evaluated using
       position: relative;
     }
 
-    #sk-container-id-63 div.sk-text-repr-fallback {
+    #sk-container-id-64 div.sk-text-repr-fallback {
       display: none;
     }
 
@@ -201,14 +201,14 @@ data in each repetition. The performance will be evaluated using
 
     /* Parallel-specific style estimator block */
 
-    #sk-container-id-63 div.sk-parallel-item::after {
+    #sk-container-id-64 div.sk-parallel-item::after {
       content: "";
       width: 100%;
       border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
       flex-grow: 1;
     }
 
-    #sk-container-id-63 div.sk-parallel {
+    #sk-container-id-64 div.sk-parallel {
       display: flex;
       align-items: stretch;
       justify-content: center;
@@ -216,28 +216,28 @@ data in each repetition. The performance will be evaluated using
       position: relative;
     }
 
-    #sk-container-id-63 div.sk-parallel-item {
+    #sk-container-id-64 div.sk-parallel-item {
       display: flex;
       flex-direction: column;
     }
 
-    #sk-container-id-63 div.sk-parallel-item:first-child::after {
+    #sk-container-id-64 div.sk-parallel-item:first-child::after {
       align-self: flex-end;
       width: 50%;
     }
 
-    #sk-container-id-63 div.sk-parallel-item:last-child::after {
+    #sk-container-id-64 div.sk-parallel-item:last-child::after {
       align-self: flex-start;
       width: 50%;
     }
 
-    #sk-container-id-63 div.sk-parallel-item:only-child::after {
+    #sk-container-id-64 div.sk-parallel-item:only-child::after {
       width: 0;
     }
 
     /* Serial-specific style estimator block */
 
-    #sk-container-id-63 div.sk-serial {
+    #sk-container-id-64 div.sk-serial {
       display: flex;
       flex-direction: column;
       align-items: center;
@@ -255,14 +255,14 @@ data in each repetition. The performance will be evaluated using
 
     /* Pipeline and ColumnTransformer style (default) */
 
-    #sk-container-id-63 div.sk-toggleable {
+    #sk-container-id-64 div.sk-toggleable {
       /* Default theme specific background. It is overwritten whether we have a
       specific estimator or a Pipeline/ColumnTransformer */
       background-color: var(--sklearn-color-background);
     }
 
     /* Toggleable label */
-    #sk-container-id-63 label.sk-toggleable__label {
+    #sk-container-id-64 label.sk-toggleable__label {
       cursor: pointer;
       display: flex;
       width: 100%;
@@ -275,13 +275,13 @@ data in each repetition. The performance will be evaluated using
       gap: 0.5em;
     }
 
-    #sk-container-id-63 label.sk-toggleable__label .caption {
+    #sk-container-id-64 label.sk-toggleable__label .caption {
       font-size: 0.6rem;
       font-weight: lighter;
       color: var(--sklearn-color-text-muted);
     }
 
-    #sk-container-id-63 label.sk-toggleable__label-arrow:before {
+    #sk-container-id-64 label.sk-toggleable__label-arrow:before {
       /* Arrow on the left of the label */
       content: "▸";
       float: left;
@@ -289,13 +289,13 @@ data in each repetition. The performance will be evaluated using
       color: var(--sklearn-color-icon);
     }
 
-    #sk-container-id-63 label.sk-toggleable__label-arrow:hover:before {
+    #sk-container-id-64 label.sk-toggleable__label-arrow:hover:before {
       color: var(--sklearn-color-text);
     }
 
     /* Toggleable content - dropdown */
 
-    #sk-container-id-63 div.sk-toggleable__content {
+    #sk-container-id-64 div.sk-toggleable__content {
       max-height: 0;
       max-width: 0;
       overflow: hidden;
@@ -304,12 +304,12 @@ data in each repetition. The performance will be evaluated using
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-63 div.sk-toggleable__content.fitted {
+    #sk-container-id-64 div.sk-toggleable__content.fitted {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
-    #sk-container-id-63 div.sk-toggleable__content pre {
+    #sk-container-id-64 div.sk-toggleable__content pre {
       margin: 0.2em;
       border-radius: 0.25em;
       color: var(--sklearn-color-text);
@@ -317,79 +317,79 @@ data in each repetition. The performance will be evaluated using
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-63 div.sk-toggleable__content.fitted pre {
+    #sk-container-id-64 div.sk-toggleable__content.fitted pre {
       /* unfitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
-    #sk-container-id-63 input.sk-toggleable__control:checked~div.sk-toggleable__content {
+    #sk-container-id-64 input.sk-toggleable__control:checked~div.sk-toggleable__content {
       /* Expand drop-down */
       max-height: 200px;
       max-width: 100%;
       overflow: auto;
     }
 
-    #sk-container-id-63 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
+    #sk-container-id-64 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
       content: "▾";
     }
 
     /* Pipeline/ColumnTransformer-specific style */
 
-    #sk-container-id-63 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-64 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-63 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-64 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
     /* Estimator-specific style */
 
     /* Colorize estimator box */
-    #sk-container-id-63 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-64 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-63 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-64 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
-    #sk-container-id-63 div.sk-label label.sk-toggleable__label,
-    #sk-container-id-63 div.sk-label label {
+    #sk-container-id-64 div.sk-label label.sk-toggleable__label,
+    #sk-container-id-64 div.sk-label label {
       /* The background is the default theme color */
       color: var(--sklearn-color-text-on-default-background);
     }
 
     /* On hover, darken the color of the background */
-    #sk-container-id-63 div.sk-label:hover label.sk-toggleable__label {
+    #sk-container-id-64 div.sk-label:hover label.sk-toggleable__label {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
     /* Label box, darken color on hover, fitted */
-    #sk-container-id-63 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
+    #sk-container-id-64 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
     /* Estimator label */
 
-    #sk-container-id-63 div.sk-label label {
+    #sk-container-id-64 div.sk-label label {
       font-family: monospace;
       font-weight: bold;
       display: inline-block;
       line-height: 1.2em;
     }
 
-    #sk-container-id-63 div.sk-label-container {
+    #sk-container-id-64 div.sk-label-container {
       text-align: center;
     }
 
     /* Estimator-specific */
-    #sk-container-id-63 div.sk-estimator {
+    #sk-container-id-64 div.sk-estimator {
       font-family: monospace;
       border: 1px dotted var(--sklearn-color-border-box);
       border-radius: 0.25em;
@@ -399,18 +399,18 @@ data in each repetition. The performance will be evaluated using
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-63 div.sk-estimator.fitted {
+    #sk-container-id-64 div.sk-estimator.fitted {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
     /* on hover */
-    #sk-container-id-63 div.sk-estimator:hover {
+    #sk-container-id-64 div.sk-estimator:hover {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-63 div.sk-estimator.fitted:hover {
+    #sk-container-id-64 div.sk-estimator.fitted:hover {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-2);
     }
@@ -498,7 +498,7 @@ data in each repetition. The performance will be evaluated using
 
     /* "?"-specific style due to the `<a>` HTML tag */
 
-    #sk-container-id-63 a.estimator_doc_link {
+    #sk-container-id-64 a.estimator_doc_link {
       float: right;
       font-size: 1rem;
       line-height: 1em;
@@ -513,35 +513,35 @@ data in each repetition. The performance will be evaluated using
       border: var(--sklearn-color-unfitted-level-1) 1pt solid;
     }
 
-    #sk-container-id-63 a.estimator_doc_link.fitted {
+    #sk-container-id-64 a.estimator_doc_link.fitted {
       /* fitted */
       border: var(--sklearn-color-fitted-level-1) 1pt solid;
       color: var(--sklearn-color-fitted-level-1);
     }
 
     /* On hover */
-    #sk-container-id-63 a.estimator_doc_link:hover {
+    #sk-container-id-64 a.estimator_doc_link:hover {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-3);
       color: var(--sklearn-color-background);
       text-decoration: none;
     }
 
-    #sk-container-id-63 a.estimator_doc_link.fitted:hover {
+    #sk-container-id-64 a.estimator_doc_link.fitted:hover {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-3);
     }
-    </style><div id="sk-container-id-63" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>GridSearchCV(cv=RepeatedStratifiedKFold(n_repeats=10, n_splits=10, random_state=0),
+    </style><div id="sk-container-id-64" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>GridSearchCV(cv=RepeatedStratifiedKFold(n_repeats=10, n_splits=10, random_state=0),
                  estimator=SVC(random_state=0),
                  param_grid=[{&#x27;kernel&#x27;: [&#x27;linear&#x27;]},
                              {&#x27;degree&#x27;: [2, 3], &#x27;kernel&#x27;: [&#x27;poly&#x27;]},
                              {&#x27;kernel&#x27;: [&#x27;rbf&#x27;]}],
-                 scoring=&#x27;roc_auc&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-250" type="checkbox" ><label for="sk-estimator-id-250" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>GridSearchCV</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.model_selection.GridSearchCV.html">?<span>Documentation for GridSearchCV</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>GridSearchCV(cv=RepeatedStratifiedKFold(n_repeats=10, n_splits=10, random_state=0),
+                 scoring=&#x27;roc_auc&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-264" type="checkbox" ><label for="sk-estimator-id-264" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>GridSearchCV</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.model_selection.GridSearchCV.html">?<span>Documentation for GridSearchCV</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>GridSearchCV(cv=RepeatedStratifiedKFold(n_repeats=10, n_splits=10, random_state=0),
                  estimator=SVC(random_state=0),
                  param_grid=[{&#x27;kernel&#x27;: [&#x27;linear&#x27;]},
                              {&#x27;degree&#x27;: [2, 3], &#x27;kernel&#x27;: [&#x27;poly&#x27;]},
                              {&#x27;kernel&#x27;: [&#x27;rbf&#x27;]}],
-                 scoring=&#x27;roc_auc&#x27;)</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-251" type="checkbox" ><label for="sk-estimator-id-251" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>best_estimator_: SVC</div></div></label><div class="sk-toggleable__content fitted"><pre>SVC(random_state=0)</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-252" type="checkbox" ><label for="sk-estimator-id-252" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>SVC</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.svm.SVC.html">?<span>Documentation for SVC</span></a></div></label><div class="sk-toggleable__content fitted"><pre>SVC(random_state=0)</pre></div> </div></div></div></div></div></div></div></div></div>
+                 scoring=&#x27;roc_auc&#x27;)</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-265" type="checkbox" ><label for="sk-estimator-id-265" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>best_estimator_: SVC</div></div></label><div class="sk-toggleable__content fitted"><pre>SVC(random_state=0)</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-266" type="checkbox" ><label for="sk-estimator-id-266" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>SVC</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.svm.SVC.html">?<span>Documentation for SVC</span></a></div></label><div class="sk-toggleable__content fitted"><pre>SVC(random_state=0)</pre></div> </div></div></div></div></div></div></div></div></div>
     </div>
     <br />
     <br />
@@ -1581,7 +1581,7 @@ Take-home messages
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 1.629 seconds)
+   **Total running time of the script:** (0 minutes 1.604 seconds)
 
 
 .. _sphx_glr_download_auto_examples_model_selection_plot_grid_search_stats.py:
diff --git a/dev/_sources/auto_examples/model_selection/plot_grid_search_text_feature_extraction.rst.txt b/dev/_sources/auto_examples/model_selection/plot_grid_search_text_feature_extraction.rst.txt
index ac1bcae056ec0..5f48477bfdb56 100644
--- a/dev/_sources/auto_examples/model_selection/plot_grid_search_text_feature_extraction.rst.txt
+++ b/dev/_sources/auto_examples/model_selection/plot_grid_search_text_feature_extraction.rst.txt
@@ -136,7 +136,7 @@ classifier yet effective for text classification.
 .. raw:: html
 
     <div class="output_subarea output_html rendered_html output_result">
-    <style>#sk-container-id-62 {
+    <style>#sk-container-id-63 {
       /* Definition of color scheme common for light and dark mode */
       --sklearn-color-text: #000;
       --sklearn-color-text-muted: #666;
@@ -167,15 +167,15 @@ classifier yet effective for text classification.
       }
     }
 
-    #sk-container-id-62 {
+    #sk-container-id-63 {
       color: var(--sklearn-color-text);
     }
 
-    #sk-container-id-62 pre {
+    #sk-container-id-63 pre {
       padding: 0;
     }
 
-    #sk-container-id-62 input.sk-hidden--visually {
+    #sk-container-id-63 input.sk-hidden--visually {
       border: 0;
       clip: rect(1px 1px 1px 1px);
       clip: rect(1px, 1px, 1px, 1px);
@@ -187,7 +187,7 @@ classifier yet effective for text classification.
       width: 1px;
     }
 
-    #sk-container-id-62 div.sk-dashed-wrapped {
+    #sk-container-id-63 div.sk-dashed-wrapped {
       border: 1px dashed var(--sklearn-color-line);
       margin: 0 0.4em 0.5em 0.4em;
       box-sizing: border-box;
@@ -195,7 +195,7 @@ classifier yet effective for text classification.
       background-color: var(--sklearn-color-background);
     }
 
-    #sk-container-id-62 div.sk-container {
+    #sk-container-id-63 div.sk-container {
       /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
          but bootstrap.min.css set `[hidden] { display: none !important; }`
          so we also need the `!important` here to be able to override the
@@ -205,7 +205,7 @@ classifier yet effective for text classification.
       position: relative;
     }
 
-    #sk-container-id-62 div.sk-text-repr-fallback {
+    #sk-container-id-63 div.sk-text-repr-fallback {
       display: none;
     }
 
@@ -221,14 +221,14 @@ classifier yet effective for text classification.
 
     /* Parallel-specific style estimator block */
 
-    #sk-container-id-62 div.sk-parallel-item::after {
+    #sk-container-id-63 div.sk-parallel-item::after {
       content: "";
       width: 100%;
       border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
       flex-grow: 1;
     }
 
-    #sk-container-id-62 div.sk-parallel {
+    #sk-container-id-63 div.sk-parallel {
       display: flex;
       align-items: stretch;
       justify-content: center;
@@ -236,28 +236,28 @@ classifier yet effective for text classification.
       position: relative;
     }
 
-    #sk-container-id-62 div.sk-parallel-item {
+    #sk-container-id-63 div.sk-parallel-item {
       display: flex;
       flex-direction: column;
     }
 
-    #sk-container-id-62 div.sk-parallel-item:first-child::after {
+    #sk-container-id-63 div.sk-parallel-item:first-child::after {
       align-self: flex-end;
       width: 50%;
     }
 
-    #sk-container-id-62 div.sk-parallel-item:last-child::after {
+    #sk-container-id-63 div.sk-parallel-item:last-child::after {
       align-self: flex-start;
       width: 50%;
     }
 
-    #sk-container-id-62 div.sk-parallel-item:only-child::after {
+    #sk-container-id-63 div.sk-parallel-item:only-child::after {
       width: 0;
     }
 
     /* Serial-specific style estimator block */
 
-    #sk-container-id-62 div.sk-serial {
+    #sk-container-id-63 div.sk-serial {
       display: flex;
       flex-direction: column;
       align-items: center;
@@ -275,14 +275,14 @@ classifier yet effective for text classification.
 
     /* Pipeline and ColumnTransformer style (default) */
 
-    #sk-container-id-62 div.sk-toggleable {
+    #sk-container-id-63 div.sk-toggleable {
       /* Default theme specific background. It is overwritten whether we have a
       specific estimator or a Pipeline/ColumnTransformer */
       background-color: var(--sklearn-color-background);
     }
 
     /* Toggleable label */
-    #sk-container-id-62 label.sk-toggleable__label {
+    #sk-container-id-63 label.sk-toggleable__label {
       cursor: pointer;
       display: flex;
       width: 100%;
@@ -295,13 +295,13 @@ classifier yet effective for text classification.
       gap: 0.5em;
     }
 
-    #sk-container-id-62 label.sk-toggleable__label .caption {
+    #sk-container-id-63 label.sk-toggleable__label .caption {
       font-size: 0.6rem;
       font-weight: lighter;
       color: var(--sklearn-color-text-muted);
     }
 
-    #sk-container-id-62 label.sk-toggleable__label-arrow:before {
+    #sk-container-id-63 label.sk-toggleable__label-arrow:before {
       /* Arrow on the left of the label */
       content: "▸";
       float: left;
@@ -309,13 +309,13 @@ classifier yet effective for text classification.
       color: var(--sklearn-color-icon);
     }
 
-    #sk-container-id-62 label.sk-toggleable__label-arrow:hover:before {
+    #sk-container-id-63 label.sk-toggleable__label-arrow:hover:before {
       color: var(--sklearn-color-text);
     }
 
     /* Toggleable content - dropdown */
 
-    #sk-container-id-62 div.sk-toggleable__content {
+    #sk-container-id-63 div.sk-toggleable__content {
       max-height: 0;
       max-width: 0;
       overflow: hidden;
@@ -324,12 +324,12 @@ classifier yet effective for text classification.
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-62 div.sk-toggleable__content.fitted {
+    #sk-container-id-63 div.sk-toggleable__content.fitted {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
-    #sk-container-id-62 div.sk-toggleable__content pre {
+    #sk-container-id-63 div.sk-toggleable__content pre {
       margin: 0.2em;
       border-radius: 0.25em;
       color: var(--sklearn-color-text);
@@ -337,79 +337,79 @@ classifier yet effective for text classification.
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-62 div.sk-toggleable__content.fitted pre {
+    #sk-container-id-63 div.sk-toggleable__content.fitted pre {
       /* unfitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
-    #sk-container-id-62 input.sk-toggleable__control:checked~div.sk-toggleable__content {
+    #sk-container-id-63 input.sk-toggleable__control:checked~div.sk-toggleable__content {
       /* Expand drop-down */
       max-height: 200px;
       max-width: 100%;
       overflow: auto;
     }
 
-    #sk-container-id-62 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
+    #sk-container-id-63 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
       content: "▾";
     }
 
     /* Pipeline/ColumnTransformer-specific style */
 
-    #sk-container-id-62 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-63 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-62 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-63 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
     /* Estimator-specific style */
 
     /* Colorize estimator box */
-    #sk-container-id-62 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-63 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-62 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-63 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
-    #sk-container-id-62 div.sk-label label.sk-toggleable__label,
-    #sk-container-id-62 div.sk-label label {
+    #sk-container-id-63 div.sk-label label.sk-toggleable__label,
+    #sk-container-id-63 div.sk-label label {
       /* The background is the default theme color */
       color: var(--sklearn-color-text-on-default-background);
     }
 
     /* On hover, darken the color of the background */
-    #sk-container-id-62 div.sk-label:hover label.sk-toggleable__label {
+    #sk-container-id-63 div.sk-label:hover label.sk-toggleable__label {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
     /* Label box, darken color on hover, fitted */
-    #sk-container-id-62 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
+    #sk-container-id-63 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
     /* Estimator label */
 
-    #sk-container-id-62 div.sk-label label {
+    #sk-container-id-63 div.sk-label label {
       font-family: monospace;
       font-weight: bold;
       display: inline-block;
       line-height: 1.2em;
     }
 
-    #sk-container-id-62 div.sk-label-container {
+    #sk-container-id-63 div.sk-label-container {
       text-align: center;
     }
 
     /* Estimator-specific */
-    #sk-container-id-62 div.sk-estimator {
+    #sk-container-id-63 div.sk-estimator {
       font-family: monospace;
       border: 1px dotted var(--sklearn-color-border-box);
       border-radius: 0.25em;
@@ -419,18 +419,18 @@ classifier yet effective for text classification.
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-62 div.sk-estimator.fitted {
+    #sk-container-id-63 div.sk-estimator.fitted {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
     /* on hover */
-    #sk-container-id-62 div.sk-estimator:hover {
+    #sk-container-id-63 div.sk-estimator:hover {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-62 div.sk-estimator.fitted:hover {
+    #sk-container-id-63 div.sk-estimator.fitted:hover {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-2);
     }
@@ -518,7 +518,7 @@ classifier yet effective for text classification.
 
     /* "?"-specific style due to the `<a>` HTML tag */
 
-    #sk-container-id-62 a.estimator_doc_link {
+    #sk-container-id-63 a.estimator_doc_link {
       float: right;
       font-size: 1rem;
       line-height: 1em;
@@ -533,25 +533,25 @@ classifier yet effective for text classification.
       border: var(--sklearn-color-unfitted-level-1) 1pt solid;
     }
 
-    #sk-container-id-62 a.estimator_doc_link.fitted {
+    #sk-container-id-63 a.estimator_doc_link.fitted {
       /* fitted */
       border: var(--sklearn-color-fitted-level-1) 1pt solid;
       color: var(--sklearn-color-fitted-level-1);
     }
 
     /* On hover */
-    #sk-container-id-62 a.estimator_doc_link:hover {
+    #sk-container-id-63 a.estimator_doc_link:hover {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-3);
       color: var(--sklearn-color-background);
       text-decoration: none;
     }
 
-    #sk-container-id-62 a.estimator_doc_link.fitted:hover {
+    #sk-container-id-63 a.estimator_doc_link.fitted:hover {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-3);
     }
-    </style><div id="sk-container-id-62" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;vect&#x27;, TfidfVectorizer()), (&#x27;clf&#x27;, ComplementNB())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-247" type="checkbox" ><label for="sk-estimator-id-247" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>Pipeline</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.pipeline.Pipeline.html">?<span>Documentation for Pipeline</span></a><span class="sk-estimator-doc-link ">i<span>Not fitted</span></span></div></label><div class="sk-toggleable__content "><pre>Pipeline(steps=[(&#x27;vect&#x27;, TfidfVectorizer()), (&#x27;clf&#x27;, ComplementNB())])</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-248" type="checkbox" ><label for="sk-estimator-id-248" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>TfidfVectorizer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html">?<span>Documentation for TfidfVectorizer</span></a></div></label><div class="sk-toggleable__content "><pre>TfidfVectorizer()</pre></div> </div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-249" type="checkbox" ><label for="sk-estimator-id-249" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>ComplementNB</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.naive_bayes.ComplementNB.html">?<span>Documentation for ComplementNB</span></a></div></label><div class="sk-toggleable__content "><pre>ComplementNB()</pre></div> </div></div></div></div></div></div>
+    </style><div id="sk-container-id-63" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;vect&#x27;, TfidfVectorizer()), (&#x27;clf&#x27;, ComplementNB())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-261" type="checkbox" ><label for="sk-estimator-id-261" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>Pipeline</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.pipeline.Pipeline.html">?<span>Documentation for Pipeline</span></a><span class="sk-estimator-doc-link ">i<span>Not fitted</span></span></div></label><div class="sk-toggleable__content "><pre>Pipeline(steps=[(&#x27;vect&#x27;, TfidfVectorizer()), (&#x27;clf&#x27;, ComplementNB())])</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-262" type="checkbox" ><label for="sk-estimator-id-262" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>TfidfVectorizer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html">?<span>Documentation for TfidfVectorizer</span></a></div></label><div class="sk-toggleable__content "><pre>TfidfVectorizer()</pre></div> </div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-263" type="checkbox" ><label for="sk-estimator-id-263" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>ComplementNB</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.naive_bayes.ComplementNB.html">?<span>Documentation for ComplementNB</span></a></div></label><div class="sk-toggleable__content "><pre>ComplementNB()</pre></div> </div></div></div></div></div></div>
     </div>
     <br />
     <br />
@@ -661,7 +661,7 @@ via the parameter `n_jobs`.
  .. code-block:: none
 
     Fitting 5 folds for each of 40 candidates, totalling 200 fits
-    Done in 25.382s
+    Done in 26.539s
 
 
 
@@ -800,7 +800,7 @@ different folds of the cross-validation.
 
     <div class="output_subarea output_html rendered_html output_result">
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     </div>
     <br />
     <br />
@@ -898,7 +898,7 @@ and :math:`10^0`, regardless of the hyperparameter `norm`.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 27.423 seconds)
+   **Total running time of the script:** (0 minutes 28.635 seconds)
 
 
 .. _sphx_glr_download_auto_examples_model_selection_plot_grid_search_text_feature_extraction.py:
diff --git a/dev/_sources/auto_examples/model_selection/plot_learning_curve.rst.txt b/dev/_sources/auto_examples/model_selection/plot_learning_curve.rst.txt
index 029da31176862..0d4742ffd9175 100644
--- a/dev/_sources/auto_examples/model_selection/plot_learning_curve.rst.txt
+++ b/dev/_sources/auto_examples/model_selection/plot_learning_curve.rst.txt
@@ -293,7 +293,7 @@ increases.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 25.330 seconds)
+   **Total running time of the script:** (0 minutes 28.549 seconds)
 
 
 .. _sphx_glr_download_auto_examples_model_selection_plot_learning_curve.py:
diff --git a/dev/_sources/auto_examples/model_selection/plot_likelihood_ratios.rst.txt b/dev/_sources/auto_examples/model_selection/plot_likelihood_ratios.rst.txt
index 942e685c2b561..10414d1cfae9c 100644
--- a/dev/_sources/auto_examples/model_selection/plot_likelihood_ratios.rst.txt
+++ b/dev/_sources/auto_examples/model_selection/plot_likelihood_ratios.rst.txt
@@ -75,7 +75,7 @@ not carry the disease but a minority (in this case around 10%) does:
     from sklearn.datasets import make_classification
 
     X, y = make_classification(n_samples=10_000, weights=[0.9, 0.1], random_state=0)
-    print(f"Percentage of people carrying the disease: {100*y.mean():.2f}%")
+    print(f"Percentage of people carrying the disease: {100 * y.mean():.2f}%")
 
 
 
@@ -872,7 +872,7 @@ of those computed with on balanced classes.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 1.857 seconds)
+   **Total running time of the script:** (0 minutes 1.831 seconds)
 
 
 .. _sphx_glr_download_auto_examples_model_selection_plot_likelihood_ratios.py:
diff --git a/dev/_sources/auto_examples/model_selection/plot_multi_metric_evaluation.rst.txt b/dev/_sources/auto_examples/model_selection/plot_multi_metric_evaluation.rst.txt
index 0af73d31b6f8e..1749283e23686 100644
--- a/dev/_sources/auto_examples/model_selection/plot_multi_metric_evaluation.rst.txt
+++ b/dev/_sources/auto_examples/model_selection/plot_multi_metric_evaluation.rst.txt
@@ -177,7 +177,7 @@ Plotting the result
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 8.280 seconds)
+   **Total running time of the script:** (0 minutes 8.333 seconds)
 
 
 .. _sphx_glr_download_auto_examples_model_selection_plot_multi_metric_evaluation.py:
diff --git a/dev/_sources/auto_examples/model_selection/plot_nested_cross_validation_iris.rst.txt b/dev/_sources/auto_examples/model_selection/plot_nested_cross_validation_iris.rst.txt
index aaeebf41df971..404fd19fbb2f0 100644
--- a/dev/_sources/auto_examples/model_selection/plot_nested_cross_validation_iris.rst.txt
+++ b/dev/_sources/auto_examples/model_selection/plot_nested_cross_validation_iris.rst.txt
@@ -175,7 +175,7 @@ between their scores.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 7.050 seconds)
+   **Total running time of the script:** (0 minutes 6.625 seconds)
 
 
 .. _sphx_glr_download_auto_examples_model_selection_plot_nested_cross_validation_iris.py:
diff --git a/dev/_sources/auto_examples/model_selection/plot_permutation_tests_for_classification.rst.txt b/dev/_sources/auto_examples/model_selection/plot_permutation_tests_for_classification.rst.txt
index 47b6551d14405..18758ed98842d 100644
--- a/dev/_sources/auto_examples/model_selection/plot_permutation_tests_for_classification.rst.txt
+++ b/dev/_sources/auto_examples/model_selection/plot_permutation_tests_for_classification.rst.txt
@@ -251,7 +251,7 @@ if there is only weak structure in the data [1]_.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 12.772 seconds)
+   **Total running time of the script:** (0 minutes 12.552 seconds)
 
 
 .. _sphx_glr_download_auto_examples_model_selection_plot_permutation_tests_for_classification.py:
diff --git a/dev/_sources/auto_examples/model_selection/plot_precision_recall.rst.txt b/dev/_sources/auto_examples/model_selection/plot_precision_recall.rst.txt
index c2415e4900f79..5f0ae9e5f83a7 100644
--- a/dev/_sources/auto_examples/model_selection/plot_precision_recall.rst.txt
+++ b/dev/_sources/auto_examples/model_selection/plot_precision_recall.rst.txt
@@ -188,7 +188,7 @@ we will first scale the data using a
 .. raw:: html
 
     <div class="output_subarea output_html rendered_html output_result">
-    <style>#sk-container-id-61 {
+    <style>#sk-container-id-62 {
       /* Definition of color scheme common for light and dark mode */
       --sklearn-color-text: #000;
       --sklearn-color-text-muted: #666;
@@ -219,15 +219,15 @@ we will first scale the data using a
       }
     }
 
-    #sk-container-id-61 {
+    #sk-container-id-62 {
       color: var(--sklearn-color-text);
     }
 
-    #sk-container-id-61 pre {
+    #sk-container-id-62 pre {
       padding: 0;
     }
 
-    #sk-container-id-61 input.sk-hidden--visually {
+    #sk-container-id-62 input.sk-hidden--visually {
       border: 0;
       clip: rect(1px 1px 1px 1px);
       clip: rect(1px, 1px, 1px, 1px);
@@ -239,7 +239,7 @@ we will first scale the data using a
       width: 1px;
     }
 
-    #sk-container-id-61 div.sk-dashed-wrapped {
+    #sk-container-id-62 div.sk-dashed-wrapped {
       border: 1px dashed var(--sklearn-color-line);
       margin: 0 0.4em 0.5em 0.4em;
       box-sizing: border-box;
@@ -247,7 +247,7 @@ we will first scale the data using a
       background-color: var(--sklearn-color-background);
     }
 
-    #sk-container-id-61 div.sk-container {
+    #sk-container-id-62 div.sk-container {
       /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
          but bootstrap.min.css set `[hidden] { display: none !important; }`
          so we also need the `!important` here to be able to override the
@@ -257,7 +257,7 @@ we will first scale the data using a
       position: relative;
     }
 
-    #sk-container-id-61 div.sk-text-repr-fallback {
+    #sk-container-id-62 div.sk-text-repr-fallback {
       display: none;
     }
 
@@ -273,14 +273,14 @@ we will first scale the data using a
 
     /* Parallel-specific style estimator block */
 
-    #sk-container-id-61 div.sk-parallel-item::after {
+    #sk-container-id-62 div.sk-parallel-item::after {
       content: "";
       width: 100%;
       border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
       flex-grow: 1;
     }
 
-    #sk-container-id-61 div.sk-parallel {
+    #sk-container-id-62 div.sk-parallel {
       display: flex;
       align-items: stretch;
       justify-content: center;
@@ -288,28 +288,28 @@ we will first scale the data using a
       position: relative;
     }
 
-    #sk-container-id-61 div.sk-parallel-item {
+    #sk-container-id-62 div.sk-parallel-item {
       display: flex;
       flex-direction: column;
     }
 
-    #sk-container-id-61 div.sk-parallel-item:first-child::after {
+    #sk-container-id-62 div.sk-parallel-item:first-child::after {
       align-self: flex-end;
       width: 50%;
     }
 
-    #sk-container-id-61 div.sk-parallel-item:last-child::after {
+    #sk-container-id-62 div.sk-parallel-item:last-child::after {
       align-self: flex-start;
       width: 50%;
     }
 
-    #sk-container-id-61 div.sk-parallel-item:only-child::after {
+    #sk-container-id-62 div.sk-parallel-item:only-child::after {
       width: 0;
     }
 
     /* Serial-specific style estimator block */
 
-    #sk-container-id-61 div.sk-serial {
+    #sk-container-id-62 div.sk-serial {
       display: flex;
       flex-direction: column;
       align-items: center;
@@ -327,14 +327,14 @@ we will first scale the data using a
 
     /* Pipeline and ColumnTransformer style (default) */
 
-    #sk-container-id-61 div.sk-toggleable {
+    #sk-container-id-62 div.sk-toggleable {
       /* Default theme specific background. It is overwritten whether we have a
       specific estimator or a Pipeline/ColumnTransformer */
       background-color: var(--sklearn-color-background);
     }
 
     /* Toggleable label */
-    #sk-container-id-61 label.sk-toggleable__label {
+    #sk-container-id-62 label.sk-toggleable__label {
       cursor: pointer;
       display: flex;
       width: 100%;
@@ -347,13 +347,13 @@ we will first scale the data using a
       gap: 0.5em;
     }
 
-    #sk-container-id-61 label.sk-toggleable__label .caption {
+    #sk-container-id-62 label.sk-toggleable__label .caption {
       font-size: 0.6rem;
       font-weight: lighter;
       color: var(--sklearn-color-text-muted);
     }
 
-    #sk-container-id-61 label.sk-toggleable__label-arrow:before {
+    #sk-container-id-62 label.sk-toggleable__label-arrow:before {
       /* Arrow on the left of the label */
       content: "▸";
       float: left;
@@ -361,13 +361,13 @@ we will first scale the data using a
       color: var(--sklearn-color-icon);
     }
 
-    #sk-container-id-61 label.sk-toggleable__label-arrow:hover:before {
+    #sk-container-id-62 label.sk-toggleable__label-arrow:hover:before {
       color: var(--sklearn-color-text);
     }
 
     /* Toggleable content - dropdown */
 
-    #sk-container-id-61 div.sk-toggleable__content {
+    #sk-container-id-62 div.sk-toggleable__content {
       max-height: 0;
       max-width: 0;
       overflow: hidden;
@@ -376,12 +376,12 @@ we will first scale the data using a
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-61 div.sk-toggleable__content.fitted {
+    #sk-container-id-62 div.sk-toggleable__content.fitted {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
-    #sk-container-id-61 div.sk-toggleable__content pre {
+    #sk-container-id-62 div.sk-toggleable__content pre {
       margin: 0.2em;
       border-radius: 0.25em;
       color: var(--sklearn-color-text);
@@ -389,79 +389,79 @@ we will first scale the data using a
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-61 div.sk-toggleable__content.fitted pre {
+    #sk-container-id-62 div.sk-toggleable__content.fitted pre {
       /* unfitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
-    #sk-container-id-61 input.sk-toggleable__control:checked~div.sk-toggleable__content {
+    #sk-container-id-62 input.sk-toggleable__control:checked~div.sk-toggleable__content {
       /* Expand drop-down */
       max-height: 200px;
       max-width: 100%;
       overflow: auto;
     }
 
-    #sk-container-id-61 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
+    #sk-container-id-62 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
       content: "▾";
     }
 
     /* Pipeline/ColumnTransformer-specific style */
 
-    #sk-container-id-61 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-62 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-61 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-62 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
     /* Estimator-specific style */
 
     /* Colorize estimator box */
-    #sk-container-id-61 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-62 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-61 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-62 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
-    #sk-container-id-61 div.sk-label label.sk-toggleable__label,
-    #sk-container-id-61 div.sk-label label {
+    #sk-container-id-62 div.sk-label label.sk-toggleable__label,
+    #sk-container-id-62 div.sk-label label {
       /* The background is the default theme color */
       color: var(--sklearn-color-text-on-default-background);
     }
 
     /* On hover, darken the color of the background */
-    #sk-container-id-61 div.sk-label:hover label.sk-toggleable__label {
+    #sk-container-id-62 div.sk-label:hover label.sk-toggleable__label {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
     /* Label box, darken color on hover, fitted */
-    #sk-container-id-61 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
+    #sk-container-id-62 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
     /* Estimator label */
 
-    #sk-container-id-61 div.sk-label label {
+    #sk-container-id-62 div.sk-label label {
       font-family: monospace;
       font-weight: bold;
       display: inline-block;
       line-height: 1.2em;
     }
 
-    #sk-container-id-61 div.sk-label-container {
+    #sk-container-id-62 div.sk-label-container {
       text-align: center;
     }
 
     /* Estimator-specific */
-    #sk-container-id-61 div.sk-estimator {
+    #sk-container-id-62 div.sk-estimator {
       font-family: monospace;
       border: 1px dotted var(--sklearn-color-border-box);
       border-radius: 0.25em;
@@ -471,18 +471,18 @@ we will first scale the data using a
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-61 div.sk-estimator.fitted {
+    #sk-container-id-62 div.sk-estimator.fitted {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
     /* on hover */
-    #sk-container-id-61 div.sk-estimator:hover {
+    #sk-container-id-62 div.sk-estimator:hover {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-61 div.sk-estimator.fitted:hover {
+    #sk-container-id-62 div.sk-estimator.fitted:hover {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-2);
     }
@@ -570,7 +570,7 @@ we will first scale the data using a
 
     /* "?"-specific style due to the `<a>` HTML tag */
 
-    #sk-container-id-61 a.estimator_doc_link {
+    #sk-container-id-62 a.estimator_doc_link {
       float: right;
       font-size: 1rem;
       line-height: 1em;
@@ -585,29 +585,29 @@ we will first scale the data using a
       border: var(--sklearn-color-unfitted-level-1) 1pt solid;
     }
 
-    #sk-container-id-61 a.estimator_doc_link.fitted {
+    #sk-container-id-62 a.estimator_doc_link.fitted {
       /* fitted */
       border: var(--sklearn-color-fitted-level-1) 1pt solid;
       color: var(--sklearn-color-fitted-level-1);
     }
 
     /* On hover */
-    #sk-container-id-61 a.estimator_doc_link:hover {
+    #sk-container-id-62 a.estimator_doc_link:hover {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-3);
       color: var(--sklearn-color-background);
       text-decoration: none;
     }
 
-    #sk-container-id-61 a.estimator_doc_link.fitted:hover {
+    #sk-container-id-62 a.estimator_doc_link.fitted:hover {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-3);
     }
-    </style><div id="sk-container-id-61" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),
+    </style><div id="sk-container-id-62" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),
                     (&#x27;linearsvc&#x27;,
-                     LinearSVC(random_state=RandomState(MT19937) at 0x7FB58659C940))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-244" type="checkbox" ><label for="sk-estimator-id-244" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>Pipeline</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.pipeline.Pipeline.html">?<span>Documentation for Pipeline</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),
+                     LinearSVC(random_state=RandomState(MT19937) at 0x7FD843CCCF40))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-258" type="checkbox" ><label for="sk-estimator-id-258" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>Pipeline</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.pipeline.Pipeline.html">?<span>Documentation for Pipeline</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),
                     (&#x27;linearsvc&#x27;,
-                     LinearSVC(random_state=RandomState(MT19937) at 0x7FB58659C940))])</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-245" type="checkbox" ><label for="sk-estimator-id-245" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>StandardScaler</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.StandardScaler.html">?<span>Documentation for StandardScaler</span></a></div></label><div class="sk-toggleable__content fitted"><pre>StandardScaler()</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-246" type="checkbox" ><label for="sk-estimator-id-246" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>LinearSVC</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.svm.LinearSVC.html">?<span>Documentation for LinearSVC</span></a></div></label><div class="sk-toggleable__content fitted"><pre>LinearSVC(random_state=RandomState(MT19937) at 0x7FB58659C940)</pre></div> </div></div></div></div></div></div>
+                     LinearSVC(random_state=RandomState(MT19937) at 0x7FD843CCCF40))])</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-259" type="checkbox" ><label for="sk-estimator-id-259" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>StandardScaler</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.StandardScaler.html">?<span>Documentation for StandardScaler</span></a></div></label><div class="sk-toggleable__content fitted"><pre>StandardScaler()</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-260" type="checkbox" ><label for="sk-estimator-id-260" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>LinearSVC</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.svm.LinearSVC.html">?<span>Documentation for LinearSVC</span></a></div></label><div class="sk-toggleable__content fitted"><pre>LinearSVC(random_state=RandomState(MT19937) at 0x7FD843CCCF40)</pre></div> </div></div></div></div></div></div>
     </div>
     <br />
     <br />
@@ -872,7 +872,7 @@ Plot Precision-Recall curve for each class and iso-f1 curves
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.387 seconds)
+   **Total running time of the script:** (0 minutes 0.391 seconds)
 
 
 .. _sphx_glr_download_auto_examples_model_selection_plot_precision_recall.py:
diff --git a/dev/_sources/auto_examples/model_selection/plot_randomized_search.rst.txt b/dev/_sources/auto_examples/model_selection/plot_randomized_search.rst.txt
index 5a5c31070093c..6092d4f77f757 100644
--- a/dev/_sources/auto_examples/model_selection/plot_randomized_search.rst.txt
+++ b/dev/_sources/auto_examples/model_selection/plot_randomized_search.rst.txt
@@ -46,35 +46,31 @@ simultaneously using grid search, but pick only the ones deemed most important.
 
  .. code-block:: none
 
-    RandomizedSearchCV took 0.80 seconds for 15 candidates parameter settings.
+    RandomizedSearchCV took 0.59 seconds for 15 candidates parameter settings.
     Model with rank: 1
-    Mean validation score: 0.989 (std: 0.011)
-    Parameters: {'alpha': np.float64(0.05922132104755188), 'average': False, 'l1_ratio': np.float64(0.36190312127777724)}
+    Mean validation score: 0.987 (std: 0.011)
+    Parameters: {'alpha': np.float64(0.01001911984591966), 'average': False, 'l1_ratio': np.float64(0.7665012035905148)}
 
     Model with rank: 2
-    Mean validation score: 0.985 (std: 0.013)
-    Parameters: {'alpha': np.float64(0.01755014670388658), 'average': False, 'l1_ratio': np.float64(0.6752682741380909)}
+    Mean validation score: 0.987 (std: 0.011)
+    Parameters: {'alpha': np.float64(0.40134964872774576), 'average': False, 'l1_ratio': np.float64(0.05033776045421079)}
 
     Model with rank: 3
     Mean validation score: 0.983 (std: 0.011)
-    Parameters: {'alpha': np.float64(0.14212307574165603), 'average': False, 'l1_ratio': np.float64(0.09875903932772567)}
+    Parameters: {'alpha': np.float64(0.1352374671440465), 'average': False, 'l1_ratio': np.float64(0.6719936995475292)}
 
-    GridSearchCV took 3.27 seconds for 60 candidate parameter settings.
+    GridSearchCV took 3.42 seconds for 60 candidate parameter settings.
     Model with rank: 1
-    Mean validation score: 0.993 (std: 0.009)
+    Mean validation score: 0.994 (std: 0.005)
     Parameters: {'alpha': np.float64(0.1), 'average': False, 'l1_ratio': np.float64(0.1111111111111111)}
 
     Model with rank: 2
-    Mean validation score: 0.991 (std: 0.010)
-    Parameters: {'alpha': np.float64(0.01), 'average': False, 'l1_ratio': np.float64(0.5555555555555556)}
+    Mean validation score: 0.991 (std: 0.008)
+    Parameters: {'alpha': np.float64(0.1), 'average': False, 'l1_ratio': np.float64(0.0)}
 
     Model with rank: 3
-    Mean validation score: 0.989 (std: 0.009)
-    Parameters: {'alpha': np.float64(0.01), 'average': False, 'l1_ratio': np.float64(0.0)}
-
-    Model with rank: 3
-    Mean validation score: 0.989 (std: 0.007)
-    Parameters: {'alpha': np.float64(0.01), 'average': False, 'l1_ratio': np.float64(0.4444444444444444)}
+    Mean validation score: 0.989 (std: 0.018)
+    Parameters: {'alpha': np.float64(1.0), 'average': False, 'l1_ratio': np.float64(0.0)}
 
 
 
@@ -164,7 +160,7 @@ simultaneously using grid search, but pick only the ones deemed most important.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 4.081 seconds)
+   **Total running time of the script:** (0 minutes 4.027 seconds)
 
 
 .. _sphx_glr_download_auto_examples_model_selection_plot_randomized_search.py:
diff --git a/dev/_sources/auto_examples/model_selection/plot_roc.rst.txt b/dev/_sources/auto_examples/model_selection/plot_roc.rst.txt
index 11cf4f49cd359..924010b04011b 100644
--- a/dev/_sources/auto_examples/model_selection/plot_roc.rst.txt
+++ b/dev/_sources/auto_examples/model_selection/plot_roc.rst.txt
@@ -286,9 +286,9 @@ We can briefly demo the effect of :func:`numpy.ravel`:
 .. code-block:: Python
 
 
-    print(f"y_score:\n{y_score[0:2,:]}")
+    print(f"y_score:\n{y_score[0:2, :]}")
     print()
-    print(f"y_score.ravel():\n{y_score[0:2,:].ravel()}")
+    print(f"y_score.ravel():\n{y_score[0:2, :].ravel()}")
 
 
 
@@ -649,7 +649,7 @@ taking the mean of both scores.
         plt.plot(
             fpr_grid,
             mean_tpr[ix],
-            label=f"Mean {label_a} vs {label_b} (AUC = {mean_score :.2f})",
+            label=f"Mean {label_a} vs {label_b} (AUC = {mean_score:.2f})",
             linestyle=":",
             linewidth=4,
         )
@@ -832,7 +832,7 @@ depending on the desired outcome:
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.651 seconds)
+   **Total running time of the script:** (0 minutes 0.673 seconds)
 
 
 .. _sphx_glr_download_auto_examples_model_selection_plot_roc.py:
diff --git a/dev/_sources/auto_examples/model_selection/plot_roc_crossval.rst.txt b/dev/_sources/auto_examples/model_selection/plot_roc_crossval.rst.txt
index 338f8cd6008db..d7e812151079f 100644
--- a/dev/_sources/auto_examples/model_selection/plot_roc_crossval.rst.txt
+++ b/dev/_sources/auto_examples/model_selection/plot_roc_crossval.rst.txt
@@ -208,7 +208,7 @@ frequent class.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.176 seconds)
+   **Total running time of the script:** (0 minutes 0.180 seconds)
 
 
 .. _sphx_glr_download_auto_examples_model_selection_plot_roc_crossval.py:
diff --git a/dev/_sources/auto_examples/model_selection/plot_successive_halving_heatmap.rst.txt b/dev/_sources/auto_examples/model_selection/plot_successive_halving_heatmap.rst.txt
index ed7b47e8b7489..94ef90c2ba079 100644
--- a/dev/_sources/auto_examples/model_selection/plot_successive_halving_heatmap.rst.txt
+++ b/dev/_sources/auto_examples/model_selection/plot_successive_halving_heatmap.rst.txt
@@ -40,7 +40,7 @@ This example compares the parameter search performed by
     import pandas as pd
 
     from sklearn import datasets
-    from sklearn.experimental import enable_halving_search_cv  # noqa
+    from sklearn.experimental import enable_halving_search_cv  # noqa: F401
     from sklearn.model_selection import GridSearchCV, HalvingGridSearchCV
     from sklearn.svm import SVC
 
@@ -171,7 +171,7 @@ We now plot heatmaps for both search estimators.
 
 
 .. image-sg:: /auto_examples/model_selection/images/sphx_glr_plot_successive_halving_heatmap_001.png
-   :alt: Successive Halving time = 1.590s, GridSearch time = 5.597s
+   :alt: Successive Halving time = 1.522s, GridSearch time = 5.917s
    :srcset: /auto_examples/model_selection/images/sphx_glr_plot_successive_halving_heatmap_001.png
    :class: sphx-glr-single-img
 
@@ -195,7 +195,7 @@ class is able to find parameter combinations that are just as accurate as
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 7.352 seconds)
+   **Total running time of the script:** (0 minutes 7.639 seconds)
 
 
 .. _sphx_glr_download_auto_examples_model_selection_plot_successive_halving_heatmap.py:
diff --git a/dev/_sources/auto_examples/model_selection/plot_successive_halving_iterations.rst.txt b/dev/_sources/auto_examples/model_selection/plot_successive_halving_iterations.rst.txt
index 45e96f999586b..f79884aa91f61 100644
--- a/dev/_sources/auto_examples/model_selection/plot_successive_halving_iterations.rst.txt
+++ b/dev/_sources/auto_examples/model_selection/plot_successive_halving_iterations.rst.txt
@@ -42,7 +42,7 @@ multiple candidates.
 
     from sklearn import datasets
     from sklearn.ensemble import RandomForestClassifier
-    from sklearn.experimental import enable_halving_search_cv  # noqa
+    from sklearn.experimental import enable_halving_search_cv  # noqa: F401
     from sklearn.model_selection import HalvingRandomSearchCV
 
 
@@ -89,7 +89,7 @@ We first define the parameter space and train a
 .. raw:: html
 
     <div class="output_subarea output_html rendered_html output_result">
-    <style>#sk-container-id-64 {
+    <style>#sk-container-id-65 {
       /* Definition of color scheme common for light and dark mode */
       --sklearn-color-text: #000;
       --sklearn-color-text-muted: #666;
@@ -120,15 +120,15 @@ We first define the parameter space and train a
       }
     }
 
-    #sk-container-id-64 {
+    #sk-container-id-65 {
       color: var(--sklearn-color-text);
     }
 
-    #sk-container-id-64 pre {
+    #sk-container-id-65 pre {
       padding: 0;
     }
 
-    #sk-container-id-64 input.sk-hidden--visually {
+    #sk-container-id-65 input.sk-hidden--visually {
       border: 0;
       clip: rect(1px 1px 1px 1px);
       clip: rect(1px, 1px, 1px, 1px);
@@ -140,7 +140,7 @@ We first define the parameter space and train a
       width: 1px;
     }
 
-    #sk-container-id-64 div.sk-dashed-wrapped {
+    #sk-container-id-65 div.sk-dashed-wrapped {
       border: 1px dashed var(--sklearn-color-line);
       margin: 0 0.4em 0.5em 0.4em;
       box-sizing: border-box;
@@ -148,7 +148,7 @@ We first define the parameter space and train a
       background-color: var(--sklearn-color-background);
     }
 
-    #sk-container-id-64 div.sk-container {
+    #sk-container-id-65 div.sk-container {
       /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
          but bootstrap.min.css set `[hidden] { display: none !important; }`
          so we also need the `!important` here to be able to override the
@@ -158,7 +158,7 @@ We first define the parameter space and train a
       position: relative;
     }
 
-    #sk-container-id-64 div.sk-text-repr-fallback {
+    #sk-container-id-65 div.sk-text-repr-fallback {
       display: none;
     }
 
@@ -174,14 +174,14 @@ We first define the parameter space and train a
 
     /* Parallel-specific style estimator block */
 
-    #sk-container-id-64 div.sk-parallel-item::after {
+    #sk-container-id-65 div.sk-parallel-item::after {
       content: "";
       width: 100%;
       border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
       flex-grow: 1;
     }
 
-    #sk-container-id-64 div.sk-parallel {
+    #sk-container-id-65 div.sk-parallel {
       display: flex;
       align-items: stretch;
       justify-content: center;
@@ -189,28 +189,28 @@ We first define the parameter space and train a
       position: relative;
     }
 
-    #sk-container-id-64 div.sk-parallel-item {
+    #sk-container-id-65 div.sk-parallel-item {
       display: flex;
       flex-direction: column;
     }
 
-    #sk-container-id-64 div.sk-parallel-item:first-child::after {
+    #sk-container-id-65 div.sk-parallel-item:first-child::after {
       align-self: flex-end;
       width: 50%;
     }
 
-    #sk-container-id-64 div.sk-parallel-item:last-child::after {
+    #sk-container-id-65 div.sk-parallel-item:last-child::after {
       align-self: flex-start;
       width: 50%;
     }
 
-    #sk-container-id-64 div.sk-parallel-item:only-child::after {
+    #sk-container-id-65 div.sk-parallel-item:only-child::after {
       width: 0;
     }
 
     /* Serial-specific style estimator block */
 
-    #sk-container-id-64 div.sk-serial {
+    #sk-container-id-65 div.sk-serial {
       display: flex;
       flex-direction: column;
       align-items: center;
@@ -228,14 +228,14 @@ We first define the parameter space and train a
 
     /* Pipeline and ColumnTransformer style (default) */
 
-    #sk-container-id-64 div.sk-toggleable {
+    #sk-container-id-65 div.sk-toggleable {
       /* Default theme specific background. It is overwritten whether we have a
       specific estimator or a Pipeline/ColumnTransformer */
       background-color: var(--sklearn-color-background);
     }
 
     /* Toggleable label */
-    #sk-container-id-64 label.sk-toggleable__label {
+    #sk-container-id-65 label.sk-toggleable__label {
       cursor: pointer;
       display: flex;
       width: 100%;
@@ -248,13 +248,13 @@ We first define the parameter space and train a
       gap: 0.5em;
     }
 
-    #sk-container-id-64 label.sk-toggleable__label .caption {
+    #sk-container-id-65 label.sk-toggleable__label .caption {
       font-size: 0.6rem;
       font-weight: lighter;
       color: var(--sklearn-color-text-muted);
     }
 
-    #sk-container-id-64 label.sk-toggleable__label-arrow:before {
+    #sk-container-id-65 label.sk-toggleable__label-arrow:before {
       /* Arrow on the left of the label */
       content: "▸";
       float: left;
@@ -262,13 +262,13 @@ We first define the parameter space and train a
       color: var(--sklearn-color-icon);
     }
 
-    #sk-container-id-64 label.sk-toggleable__label-arrow:hover:before {
+    #sk-container-id-65 label.sk-toggleable__label-arrow:hover:before {
       color: var(--sklearn-color-text);
     }
 
     /* Toggleable content - dropdown */
 
-    #sk-container-id-64 div.sk-toggleable__content {
+    #sk-container-id-65 div.sk-toggleable__content {
       max-height: 0;
       max-width: 0;
       overflow: hidden;
@@ -277,12 +277,12 @@ We first define the parameter space and train a
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-64 div.sk-toggleable__content.fitted {
+    #sk-container-id-65 div.sk-toggleable__content.fitted {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
-    #sk-container-id-64 div.sk-toggleable__content pre {
+    #sk-container-id-65 div.sk-toggleable__content pre {
       margin: 0.2em;
       border-radius: 0.25em;
       color: var(--sklearn-color-text);
@@ -290,79 +290,79 @@ We first define the parameter space and train a
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-64 div.sk-toggleable__content.fitted pre {
+    #sk-container-id-65 div.sk-toggleable__content.fitted pre {
       /* unfitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
-    #sk-container-id-64 input.sk-toggleable__control:checked~div.sk-toggleable__content {
+    #sk-container-id-65 input.sk-toggleable__control:checked~div.sk-toggleable__content {
       /* Expand drop-down */
       max-height: 200px;
       max-width: 100%;
       overflow: auto;
     }
 
-    #sk-container-id-64 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
+    #sk-container-id-65 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
       content: "▾";
     }
 
     /* Pipeline/ColumnTransformer-specific style */
 
-    #sk-container-id-64 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-65 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-64 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-65 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
     /* Estimator-specific style */
 
     /* Colorize estimator box */
-    #sk-container-id-64 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-65 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-64 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-65 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
-    #sk-container-id-64 div.sk-label label.sk-toggleable__label,
-    #sk-container-id-64 div.sk-label label {
+    #sk-container-id-65 div.sk-label label.sk-toggleable__label,
+    #sk-container-id-65 div.sk-label label {
       /* The background is the default theme color */
       color: var(--sklearn-color-text-on-default-background);
     }
 
     /* On hover, darken the color of the background */
-    #sk-container-id-64 div.sk-label:hover label.sk-toggleable__label {
+    #sk-container-id-65 div.sk-label:hover label.sk-toggleable__label {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
     /* Label box, darken color on hover, fitted */
-    #sk-container-id-64 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
+    #sk-container-id-65 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
     /* Estimator label */
 
-    #sk-container-id-64 div.sk-label label {
+    #sk-container-id-65 div.sk-label label {
       font-family: monospace;
       font-weight: bold;
       display: inline-block;
       line-height: 1.2em;
     }
 
-    #sk-container-id-64 div.sk-label-container {
+    #sk-container-id-65 div.sk-label-container {
       text-align: center;
     }
 
     /* Estimator-specific */
-    #sk-container-id-64 div.sk-estimator {
+    #sk-container-id-65 div.sk-estimator {
       font-family: monospace;
       border: 1px dotted var(--sklearn-color-border-box);
       border-radius: 0.25em;
@@ -372,18 +372,18 @@ We first define the parameter space and train a
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-64 div.sk-estimator.fitted {
+    #sk-container-id-65 div.sk-estimator.fitted {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
     /* on hover */
-    #sk-container-id-64 div.sk-estimator:hover {
+    #sk-container-id-65 div.sk-estimator:hover {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-64 div.sk-estimator.fitted:hover {
+    #sk-container-id-65 div.sk-estimator.fitted:hover {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-2);
     }
@@ -471,7 +471,7 @@ We first define the parameter space and train a
 
     /* "?"-specific style due to the `<a>` HTML tag */
 
-    #sk-container-id-64 a.estimator_doc_link {
+    #sk-container-id-65 a.estimator_doc_link {
       float: right;
       font-size: 1rem;
       line-height: 1em;
@@ -486,45 +486,45 @@ We first define the parameter space and train a
       border: var(--sklearn-color-unfitted-level-1) 1pt solid;
     }
 
-    #sk-container-id-64 a.estimator_doc_link.fitted {
+    #sk-container-id-65 a.estimator_doc_link.fitted {
       /* fitted */
       border: var(--sklearn-color-fitted-level-1) 1pt solid;
       color: var(--sklearn-color-fitted-level-1);
     }
 
     /* On hover */
-    #sk-container-id-64 a.estimator_doc_link:hover {
+    #sk-container-id-65 a.estimator_doc_link:hover {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-3);
       color: var(--sklearn-color-background);
       text-decoration: none;
     }
 
-    #sk-container-id-64 a.estimator_doc_link.fitted:hover {
+    #sk-container-id-65 a.estimator_doc_link.fitted:hover {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-3);
     }
-    </style><div id="sk-container-id-64" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>HalvingRandomSearchCV(estimator=RandomForestClassifier(n_estimators=20,
-                                                           random_state=RandomState(MT19937) at 0x7FB57BFE7B40),
+    </style><div id="sk-container-id-65" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>HalvingRandomSearchCV(estimator=RandomForestClassifier(n_estimators=20,
+                                                           random_state=RandomState(MT19937) at 0x7FD8431B0B40),
                           factor=2,
                           param_distributions={&#x27;bootstrap&#x27;: [True, False],
                                                &#x27;criterion&#x27;: [&#x27;gini&#x27;, &#x27;entropy&#x27;],
                                                &#x27;max_depth&#x27;: [3, None],
-                                               &#x27;max_features&#x27;: &lt;scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x7fb57af9e770&gt;,
-                                               &#x27;min_samples_split&#x27;: &lt;scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x7fb57af9e170&gt;},
-                          random_state=RandomState(MT19937) at 0x7FB57BFE7B40)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-253" type="checkbox" ><label for="sk-estimator-id-253" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>HalvingRandomSearchCV</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.model_selection.HalvingRandomSearchCV.html">?<span>Documentation for HalvingRandomSearchCV</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>HalvingRandomSearchCV(estimator=RandomForestClassifier(n_estimators=20,
-                                                           random_state=RandomState(MT19937) at 0x7FB57BFE7B40),
+                                               &#x27;max_features&#x27;: &lt;scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x7fd848bb7a00&gt;,
+                                               &#x27;min_samples_split&#x27;: &lt;scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x7fd841fa62c0&gt;},
+                          random_state=RandomState(MT19937) at 0x7FD8431B0B40)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-267" type="checkbox" ><label for="sk-estimator-id-267" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>HalvingRandomSearchCV</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.model_selection.HalvingRandomSearchCV.html">?<span>Documentation for HalvingRandomSearchCV</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>HalvingRandomSearchCV(estimator=RandomForestClassifier(n_estimators=20,
+                                                           random_state=RandomState(MT19937) at 0x7FD8431B0B40),
                           factor=2,
                           param_distributions={&#x27;bootstrap&#x27;: [True, False],
                                                &#x27;criterion&#x27;: [&#x27;gini&#x27;, &#x27;entropy&#x27;],
                                                &#x27;max_depth&#x27;: [3, None],
-                                               &#x27;max_features&#x27;: &lt;scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x7fb57af9e770&gt;,
-                                               &#x27;min_samples_split&#x27;: &lt;scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x7fb57af9e170&gt;},
-                          random_state=RandomState(MT19937) at 0x7FB57BFE7B40)</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-254" type="checkbox" ><label for="sk-estimator-id-254" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>best_estimator_: RandomForestClassifier</div></div></label><div class="sk-toggleable__content fitted"><pre>RandomForestClassifier(bootstrap=False, criterion=&#x27;entropy&#x27;, max_features=4,
+                                               &#x27;max_features&#x27;: &lt;scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x7fd848bb7a00&gt;,
+                                               &#x27;min_samples_split&#x27;: &lt;scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x7fd841fa62c0&gt;},
+                          random_state=RandomState(MT19937) at 0x7FD8431B0B40)</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-268" type="checkbox" ><label for="sk-estimator-id-268" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>best_estimator_: RandomForestClassifier</div></div></label><div class="sk-toggleable__content fitted"><pre>RandomForestClassifier(bootstrap=False, criterion=&#x27;entropy&#x27;, max_features=4,
                            min_samples_split=5, n_estimators=20,
-                           random_state=RandomState(MT19937) at 0x7FB57BFE7C40)</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-255" type="checkbox" ><label for="sk-estimator-id-255" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>RandomForestClassifier</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.ensemble.RandomForestClassifier.html">?<span>Documentation for RandomForestClassifier</span></a></div></label><div class="sk-toggleable__content fitted"><pre>RandomForestClassifier(bootstrap=False, criterion=&#x27;entropy&#x27;, max_features=4,
+                           random_state=RandomState(MT19937) at 0x7FD8431B3240)</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-269" type="checkbox" ><label for="sk-estimator-id-269" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>RandomForestClassifier</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.ensemble.RandomForestClassifier.html">?<span>Documentation for RandomForestClassifier</span></a></div></label><div class="sk-toggleable__content fitted"><pre>RandomForestClassifier(bootstrap=False, criterion=&#x27;entropy&#x27;, max_features=4,
                            min_samples_split=5, n_estimators=20,
-                           random_state=RandomState(MT19937) at 0x7FB57BFE7C40)</pre></div> </div></div></div></div></div></div></div></div></div>
+                           random_state=RandomState(MT19937) at 0x7FD8431B3240)</pre></div> </div></div></div></div></div></div></div></div></div>
     </div>
     <br />
     <br />
@@ -592,7 +592,7 @@ last iteration.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 5.403 seconds)
+   **Total running time of the script:** (0 minutes 5.223 seconds)
 
 
 .. _sphx_glr_download_auto_examples_model_selection_plot_successive_halving_iterations.py:
diff --git a/dev/_sources/auto_examples/model_selection/plot_train_error_vs_test_error.rst.txt b/dev/_sources/auto_examples/model_selection/plot_train_error_vs_test_error.rst.txt
index f477717434cbc..55c6a55c0ff6c 100644
--- a/dev/_sources/auto_examples/model_selection/plot_train_error_vs_test_error.rst.txt
+++ b/dev/_sources/auto_examples/model_selection/plot_train_error_vs_test_error.rst.txt
@@ -280,7 +280,7 @@ obtained on the test set.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 4.979 seconds)
+   **Total running time of the script:** (0 minutes 4.847 seconds)
 
 
 .. _sphx_glr_download_auto_examples_model_selection_plot_train_error_vs_test_error.py:
diff --git a/dev/_sources/auto_examples/model_selection/plot_tuned_decision_threshold.rst.txt b/dev/_sources/auto_examples/model_selection/plot_tuned_decision_threshold.rst.txt
index 0d574aff5fc5b..232a8b5430887 100644
--- a/dev/_sources/auto_examples/model_selection/plot_tuned_decision_threshold.rst.txt
+++ b/dev/_sources/auto_examples/model_selection/plot_tuned_decision_threshold.rst.txt
@@ -149,7 +149,7 @@ regression classifier.
 .. raw:: html
 
     <div class="output_subarea output_html rendered_html output_result">
-    <style>#sk-container-id-56 {
+    <style>#sk-container-id-57 {
       /* Definition of color scheme common for light and dark mode */
       --sklearn-color-text: #000;
       --sklearn-color-text-muted: #666;
@@ -180,15 +180,15 @@ regression classifier.
       }
     }
 
-    #sk-container-id-56 {
+    #sk-container-id-57 {
       color: var(--sklearn-color-text);
     }
 
-    #sk-container-id-56 pre {
+    #sk-container-id-57 pre {
       padding: 0;
     }
 
-    #sk-container-id-56 input.sk-hidden--visually {
+    #sk-container-id-57 input.sk-hidden--visually {
       border: 0;
       clip: rect(1px 1px 1px 1px);
       clip: rect(1px, 1px, 1px, 1px);
@@ -200,7 +200,7 @@ regression classifier.
       width: 1px;
     }
 
-    #sk-container-id-56 div.sk-dashed-wrapped {
+    #sk-container-id-57 div.sk-dashed-wrapped {
       border: 1px dashed var(--sklearn-color-line);
       margin: 0 0.4em 0.5em 0.4em;
       box-sizing: border-box;
@@ -208,7 +208,7 @@ regression classifier.
       background-color: var(--sklearn-color-background);
     }
 
-    #sk-container-id-56 div.sk-container {
+    #sk-container-id-57 div.sk-container {
       /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
          but bootstrap.min.css set `[hidden] { display: none !important; }`
          so we also need the `!important` here to be able to override the
@@ -218,7 +218,7 @@ regression classifier.
       position: relative;
     }
 
-    #sk-container-id-56 div.sk-text-repr-fallback {
+    #sk-container-id-57 div.sk-text-repr-fallback {
       display: none;
     }
 
@@ -234,14 +234,14 @@ regression classifier.
 
     /* Parallel-specific style estimator block */
 
-    #sk-container-id-56 div.sk-parallel-item::after {
+    #sk-container-id-57 div.sk-parallel-item::after {
       content: "";
       width: 100%;
       border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
       flex-grow: 1;
     }
 
-    #sk-container-id-56 div.sk-parallel {
+    #sk-container-id-57 div.sk-parallel {
       display: flex;
       align-items: stretch;
       justify-content: center;
@@ -249,28 +249,28 @@ regression classifier.
       position: relative;
     }
 
-    #sk-container-id-56 div.sk-parallel-item {
+    #sk-container-id-57 div.sk-parallel-item {
       display: flex;
       flex-direction: column;
     }
 
-    #sk-container-id-56 div.sk-parallel-item:first-child::after {
+    #sk-container-id-57 div.sk-parallel-item:first-child::after {
       align-self: flex-end;
       width: 50%;
     }
 
-    #sk-container-id-56 div.sk-parallel-item:last-child::after {
+    #sk-container-id-57 div.sk-parallel-item:last-child::after {
       align-self: flex-start;
       width: 50%;
     }
 
-    #sk-container-id-56 div.sk-parallel-item:only-child::after {
+    #sk-container-id-57 div.sk-parallel-item:only-child::after {
       width: 0;
     }
 
     /* Serial-specific style estimator block */
 
-    #sk-container-id-56 div.sk-serial {
+    #sk-container-id-57 div.sk-serial {
       display: flex;
       flex-direction: column;
       align-items: center;
@@ -288,14 +288,14 @@ regression classifier.
 
     /* Pipeline and ColumnTransformer style (default) */
 
-    #sk-container-id-56 div.sk-toggleable {
+    #sk-container-id-57 div.sk-toggleable {
       /* Default theme specific background. It is overwritten whether we have a
       specific estimator or a Pipeline/ColumnTransformer */
       background-color: var(--sklearn-color-background);
     }
 
     /* Toggleable label */
-    #sk-container-id-56 label.sk-toggleable__label {
+    #sk-container-id-57 label.sk-toggleable__label {
       cursor: pointer;
       display: flex;
       width: 100%;
@@ -308,13 +308,13 @@ regression classifier.
       gap: 0.5em;
     }
 
-    #sk-container-id-56 label.sk-toggleable__label .caption {
+    #sk-container-id-57 label.sk-toggleable__label .caption {
       font-size: 0.6rem;
       font-weight: lighter;
       color: var(--sklearn-color-text-muted);
     }
 
-    #sk-container-id-56 label.sk-toggleable__label-arrow:before {
+    #sk-container-id-57 label.sk-toggleable__label-arrow:before {
       /* Arrow on the left of the label */
       content: "▸";
       float: left;
@@ -322,13 +322,13 @@ regression classifier.
       color: var(--sklearn-color-icon);
     }
 
-    #sk-container-id-56 label.sk-toggleable__label-arrow:hover:before {
+    #sk-container-id-57 label.sk-toggleable__label-arrow:hover:before {
       color: var(--sklearn-color-text);
     }
 
     /* Toggleable content - dropdown */
 
-    #sk-container-id-56 div.sk-toggleable__content {
+    #sk-container-id-57 div.sk-toggleable__content {
       max-height: 0;
       max-width: 0;
       overflow: hidden;
@@ -337,12 +337,12 @@ regression classifier.
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-56 div.sk-toggleable__content.fitted {
+    #sk-container-id-57 div.sk-toggleable__content.fitted {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
-    #sk-container-id-56 div.sk-toggleable__content pre {
+    #sk-container-id-57 div.sk-toggleable__content pre {
       margin: 0.2em;
       border-radius: 0.25em;
       color: var(--sklearn-color-text);
@@ -350,79 +350,79 @@ regression classifier.
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-56 div.sk-toggleable__content.fitted pre {
+    #sk-container-id-57 div.sk-toggleable__content.fitted pre {
       /* unfitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
-    #sk-container-id-56 input.sk-toggleable__control:checked~div.sk-toggleable__content {
+    #sk-container-id-57 input.sk-toggleable__control:checked~div.sk-toggleable__content {
       /* Expand drop-down */
       max-height: 200px;
       max-width: 100%;
       overflow: auto;
     }
 
-    #sk-container-id-56 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
+    #sk-container-id-57 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
       content: "▾";
     }
 
     /* Pipeline/ColumnTransformer-specific style */
 
-    #sk-container-id-56 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-57 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-56 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-57 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
     /* Estimator-specific style */
 
     /* Colorize estimator box */
-    #sk-container-id-56 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-57 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-56 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-57 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
-    #sk-container-id-56 div.sk-label label.sk-toggleable__label,
-    #sk-container-id-56 div.sk-label label {
+    #sk-container-id-57 div.sk-label label.sk-toggleable__label,
+    #sk-container-id-57 div.sk-label label {
       /* The background is the default theme color */
       color: var(--sklearn-color-text-on-default-background);
     }
 
     /* On hover, darken the color of the background */
-    #sk-container-id-56 div.sk-label:hover label.sk-toggleable__label {
+    #sk-container-id-57 div.sk-label:hover label.sk-toggleable__label {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
     /* Label box, darken color on hover, fitted */
-    #sk-container-id-56 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
+    #sk-container-id-57 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
     /* Estimator label */
 
-    #sk-container-id-56 div.sk-label label {
+    #sk-container-id-57 div.sk-label label {
       font-family: monospace;
       font-weight: bold;
       display: inline-block;
       line-height: 1.2em;
     }
 
-    #sk-container-id-56 div.sk-label-container {
+    #sk-container-id-57 div.sk-label-container {
       text-align: center;
     }
 
     /* Estimator-specific */
-    #sk-container-id-56 div.sk-estimator {
+    #sk-container-id-57 div.sk-estimator {
       font-family: monospace;
       border: 1px dotted var(--sklearn-color-border-box);
       border-radius: 0.25em;
@@ -432,18 +432,18 @@ regression classifier.
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-56 div.sk-estimator.fitted {
+    #sk-container-id-57 div.sk-estimator.fitted {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
     /* on hover */
-    #sk-container-id-56 div.sk-estimator:hover {
+    #sk-container-id-57 div.sk-estimator:hover {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-56 div.sk-estimator.fitted:hover {
+    #sk-container-id-57 div.sk-estimator.fitted:hover {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-2);
     }
@@ -531,7 +531,7 @@ regression classifier.
 
     /* "?"-specific style due to the `<a>` HTML tag */
 
-    #sk-container-id-56 a.estimator_doc_link {
+    #sk-container-id-57 a.estimator_doc_link {
       float: right;
       font-size: 1rem;
       line-height: 1em;
@@ -546,27 +546,27 @@ regression classifier.
       border: var(--sklearn-color-unfitted-level-1) 1pt solid;
     }
 
-    #sk-container-id-56 a.estimator_doc_link.fitted {
+    #sk-container-id-57 a.estimator_doc_link.fitted {
       /* fitted */
       border: var(--sklearn-color-fitted-level-1) 1pt solid;
       color: var(--sklearn-color-fitted-level-1);
     }
 
     /* On hover */
-    #sk-container-id-56 a.estimator_doc_link:hover {
+    #sk-container-id-57 a.estimator_doc_link:hover {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-3);
       color: var(--sklearn-color-background);
       text-decoration: none;
     }
 
-    #sk-container-id-56 a.estimator_doc_link.fitted:hover {
+    #sk-container-id-57 a.estimator_doc_link.fitted:hover {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-3);
     }
-    </style><div id="sk-container-id-56" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),
-                    (&#x27;logisticregression&#x27;, LogisticRegression())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-229" type="checkbox" ><label for="sk-estimator-id-229" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>Pipeline</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.pipeline.Pipeline.html">?<span>Documentation for Pipeline</span></a><span class="sk-estimator-doc-link ">i<span>Not fitted</span></span></div></label><div class="sk-toggleable__content "><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),
-                    (&#x27;logisticregression&#x27;, LogisticRegression())])</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-230" type="checkbox" ><label for="sk-estimator-id-230" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>StandardScaler</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.StandardScaler.html">?<span>Documentation for StandardScaler</span></a></div></label><div class="sk-toggleable__content "><pre>StandardScaler()</pre></div> </div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-231" type="checkbox" ><label for="sk-estimator-id-231" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>LogisticRegression</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.linear_model.LogisticRegression.html">?<span>Documentation for LogisticRegression</span></a></div></label><div class="sk-toggleable__content "><pre>LogisticRegression()</pre></div> </div></div></div></div></div></div>
+    </style><div id="sk-container-id-57" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),
+                    (&#x27;logisticregression&#x27;, LogisticRegression())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-243" type="checkbox" ><label for="sk-estimator-id-243" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>Pipeline</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.pipeline.Pipeline.html">?<span>Documentation for Pipeline</span></a><span class="sk-estimator-doc-link ">i<span>Not fitted</span></span></div></label><div class="sk-toggleable__content "><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),
+                    (&#x27;logisticregression&#x27;, LogisticRegression())])</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-244" type="checkbox" ><label for="sk-estimator-id-244" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>StandardScaler</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.StandardScaler.html">?<span>Documentation for StandardScaler</span></a></div></label><div class="sk-toggleable__content "><pre>StandardScaler()</pre></div> </div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-245" type="checkbox" ><label for="sk-estimator-id-245" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>LogisticRegression</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.linear_model.LogisticRegression.html">?<span>Documentation for LogisticRegression</span></a></div></label><div class="sk-toggleable__content "><pre>LogisticRegression()</pre></div> </div></div></div></div></div></div>
     </div>
     <br />
     <br />
@@ -868,7 +868,7 @@ for more details.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 32.343 seconds)
+   **Total running time of the script:** (0 minutes 33.154 seconds)
 
 
 .. _sphx_glr_download_auto_examples_model_selection_plot_tuned_decision_threshold.py:
diff --git a/dev/_sources/auto_examples/model_selection/plot_underfitting_overfitting.rst.txt b/dev/_sources/auto_examples/model_selection/plot_underfitting_overfitting.rst.txt
index de059a04324f8..c8140fae3f142 100644
--- a/dev/_sources/auto_examples/model_selection/plot_underfitting_overfitting.rst.txt
+++ b/dev/_sources/auto_examples/model_selection/plot_underfitting_overfitting.rst.txt
@@ -117,7 +117,7 @@ training data.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.224 seconds)
+   **Total running time of the script:** (0 minutes 0.208 seconds)
 
 
 .. _sphx_glr_download_auto_examples_model_selection_plot_underfitting_overfitting.py:
diff --git a/dev/_sources/auto_examples/multiclass/plot_multiclass_overview.rst.txt b/dev/_sources/auto_examples/multiclass/plot_multiclass_overview.rst.txt
index fca378a836296..de4e0758ea34d 100644
--- a/dev/_sources/auto_examples/multiclass/plot_multiclass_overview.rst.txt
+++ b/dev/_sources/auto_examples/multiclass/plot_multiclass_overview.rst.txt
@@ -327,7 +327,7 @@ References
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 20.396 seconds)
+   **Total running time of the script:** (0 minutes 19.729 seconds)
 
 
 .. _sphx_glr_download_auto_examples_multiclass_plot_multiclass_overview.py:
diff --git a/dev/_sources/auto_examples/multioutput/plot_classifier_chain_yeast.rst.txt b/dev/_sources/auto_examples/multioutput/plot_classifier_chain_yeast.rst.txt
index d39b27b14ea66..439f0649dc59b 100644
--- a/dev/_sources/auto_examples/multioutput/plot_classifier_chain_yeast.rst.txt
+++ b/dev/_sources/auto_examples/multioutput/plot_classifier_chain_yeast.rst.txt
@@ -243,7 +243,7 @@ There are three main takeaways from this plot:
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 1.831 seconds)
+   **Total running time of the script:** (0 minutes 2.044 seconds)
 
 
 .. _sphx_glr_download_auto_examples_multioutput_plot_classifier_chain_yeast.py:
diff --git a/dev/_sources/auto_examples/neighbors/plot_caching_nearest_neighbors.rst.txt b/dev/_sources/auto_examples/neighbors/plot_caching_nearest_neighbors.rst.txt
index d49db4bd16fc1..87d4053ea1a11 100644
--- a/dev/_sources/auto_examples/neighbors/plot_caching_nearest_neighbors.rst.txt
+++ b/dev/_sources/auto_examples/neighbors/plot_caching_nearest_neighbors.rst.txt
@@ -105,7 +105,7 @@ larger, or when the grid of parameter to search is large.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 1.227 seconds)
+   **Total running time of the script:** (0 minutes 1.298 seconds)
 
 
 .. _sphx_glr_download_auto_examples_neighbors_plot_caching_nearest_neighbors.py:
diff --git a/dev/_sources/auto_examples/neighbors/plot_classification.rst.txt b/dev/_sources/auto_examples/neighbors/plot_classification.rst.txt
index cb491b7c3b383..93a5c688e14dc 100644
--- a/dev/_sources/auto_examples/neighbors/plot_classification.rst.txt
+++ b/dev/_sources/auto_examples/neighbors/plot_classification.rst.txt
@@ -174,7 +174,7 @@ In some cases, taking the distance into account might improve the model.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.523 seconds)
+   **Total running time of the script:** (0 minutes 0.525 seconds)
 
 
 .. _sphx_glr_download_auto_examples_neighbors_plot_classification.py:
diff --git a/dev/_sources/auto_examples/neighbors/plot_digits_kde_sampling.rst.txt b/dev/_sources/auto_examples/neighbors/plot_digits_kde_sampling.rst.txt
index d6baa79fae99f..c44ac91b37348 100644
--- a/dev/_sources/auto_examples/neighbors/plot_digits_kde_sampling.rst.txt
+++ b/dev/_sources/auto_examples/neighbors/plot_digits_kde_sampling.rst.txt
@@ -112,7 +112,7 @@ of the data.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 4.081 seconds)
+   **Total running time of the script:** (0 minutes 4.554 seconds)
 
 
 .. _sphx_glr_download_auto_examples_neighbors_plot_digits_kde_sampling.py:
diff --git a/dev/_sources/auto_examples/neighbors/plot_kde_1d.rst.txt b/dev/_sources/auto_examples/neighbors/plot_kde_1d.rst.txt
index dedf811363c6d..3ca33c8a9f256 100644
--- a/dev/_sources/auto_examples/neighbors/plot_kde_1d.rst.txt
+++ b/dev/_sources/auto_examples/neighbors/plot_kde_1d.rst.txt
@@ -212,7 +212,7 @@ as well.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.527 seconds)
+   **Total running time of the script:** (0 minutes 0.629 seconds)
 
 
 .. _sphx_glr_download_auto_examples_neighbors_plot_kde_1d.py:
diff --git a/dev/_sources/auto_examples/neighbors/plot_lof_novelty_detection.rst.txt b/dev/_sources/auto_examples/neighbors/plot_lof_novelty_detection.rst.txt
index 98fb5555668c1..9a31c487e854b 100644
--- a/dev/_sources/auto_examples/neighbors/plot_lof_novelty_detection.rst.txt
+++ b/dev/_sources/auto_examples/neighbors/plot_lof_novelty_detection.rst.txt
@@ -128,7 +128,7 @@ n_neighbors=20 appears to work well in general.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.686 seconds)
+   **Total running time of the script:** (0 minutes 0.774 seconds)
 
 
 .. _sphx_glr_download_auto_examples_neighbors_plot_lof_novelty_detection.py:
diff --git a/dev/_sources/auto_examples/neighbors/plot_lof_outlier_detection.rst.txt b/dev/_sources/auto_examples/neighbors/plot_lof_outlier_detection.rst.txt
index 381976f07fd82..9a9e310670f98 100644
--- a/dev/_sources/auto_examples/neighbors/plot_lof_outlier_detection.rst.txt
+++ b/dev/_sources/auto_examples/neighbors/plot_lof_outlier_detection.rst.txt
@@ -165,7 +165,7 @@ Plot results
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.081 seconds)
+   **Total running time of the script:** (0 minutes 0.090 seconds)
 
 
 .. _sphx_glr_download_auto_examples_neighbors_plot_lof_outlier_detection.py:
diff --git a/dev/_sources/auto_examples/neighbors/plot_nca_classification.rst.txt b/dev/_sources/auto_examples/neighbors/plot_nca_classification.rst.txt
index d5fca8de6ee07..b754f50d5641f 100644
--- a/dev/_sources/auto_examples/neighbors/plot_nca_classification.rst.txt
+++ b/dev/_sources/auto_examples/neighbors/plot_nca_classification.rst.txt
@@ -144,7 +144,7 @@ training set.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.737 seconds)
+   **Total running time of the script:** (0 minutes 0.779 seconds)
 
 
 .. _sphx_glr_download_auto_examples_neighbors_plot_nca_classification.py:
diff --git a/dev/_sources/auto_examples/neighbors/plot_nca_dim_reduction.rst.txt b/dev/_sources/auto_examples/neighbors/plot_nca_dim_reduction.rst.txt
index 58af297134179..1e06b335d4614 100644
--- a/dev/_sources/auto_examples/neighbors/plot_nca_dim_reduction.rst.txt
+++ b/dev/_sources/auto_examples/neighbors/plot_nca_dim_reduction.rst.txt
@@ -153,7 +153,7 @@ meaningful despite the large reduction in dimension.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 1.630 seconds)
+   **Total running time of the script:** (0 minutes 1.856 seconds)
 
 
 .. _sphx_glr_download_auto_examples_neighbors_plot_nca_dim_reduction.py:
diff --git a/dev/_sources/auto_examples/neighbors/plot_nca_illustration.rst.txt b/dev/_sources/auto_examples/neighbors/plot_nca_illustration.rst.txt
index b231f7afc65d8..f76cc361e770d 100644
--- a/dev/_sources/auto_examples/neighbors/plot_nca_illustration.rst.txt
+++ b/dev/_sources/auto_examples/neighbors/plot_nca_illustration.rst.txt
@@ -168,7 +168,7 @@ embedding and find the nearest neighbors.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.147 seconds)
+   **Total running time of the script:** (0 minutes 0.163 seconds)
 
 
 .. _sphx_glr_download_auto_examples_neighbors_plot_nca_illustration.py:
diff --git a/dev/_sources/auto_examples/neighbors/plot_nearest_centroid.rst.txt b/dev/_sources/auto_examples/neighbors/plot_nearest_centroid.rst.txt
index 84eca529dc38b..2d0b636361e76 100644
--- a/dev/_sources/auto_examples/neighbors/plot_nearest_centroid.rst.txt
+++ b/dev/_sources/auto_examples/neighbors/plot_nearest_centroid.rst.txt
@@ -108,7 +108,7 @@ It will plot the decision boundaries for each class.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.157 seconds)
+   **Total running time of the script:** (0 minutes 0.163 seconds)
 
 
 .. _sphx_glr_download_auto_examples_neighbors_plot_nearest_centroid.py:
diff --git a/dev/_sources/auto_examples/neighbors/plot_regression.rst.txt b/dev/_sources/auto_examples/neighbors/plot_regression.rst.txt
index 66b0cd588580c..0cb18092416d0 100644
--- a/dev/_sources/auto_examples/neighbors/plot_regression.rst.txt
+++ b/dev/_sources/auto_examples/neighbors/plot_regression.rst.txt
@@ -114,7 +114,7 @@ weights in prediction effect predicted values.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.211 seconds)
+   **Total running time of the script:** (0 minutes 0.223 seconds)
 
 
 .. _sphx_glr_download_auto_examples_neighbors_plot_regression.py:
diff --git a/dev/_sources/auto_examples/neighbors/plot_species_kde.rst.txt b/dev/_sources/auto_examples/neighbors/plot_species_kde.rst.txt
index dfe1ad14d3090..c889a18dc7c73 100644
--- a/dev/_sources/auto_examples/neighbors/plot_species_kde.rst.txt
+++ b/dev/_sources/auto_examples/neighbors/plot_species_kde.rst.txt
@@ -199,7 +199,7 @@ References
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 3.390 seconds)
+   **Total running time of the script:** (0 minutes 3.670 seconds)
 
 
 .. _sphx_glr_download_auto_examples_neighbors_plot_species_kde.py:
diff --git a/dev/_sources/auto_examples/neural_networks/plot_mlp_alpha.rst.txt b/dev/_sources/auto_examples/neural_networks/plot_mlp_alpha.rst.txt
index 5cb2a2fb18282..ce14475e659c6 100644
--- a/dev/_sources/auto_examples/neural_networks/plot_mlp_alpha.rst.txt
+++ b/dev/_sources/auto_examples/neural_networks/plot_mlp_alpha.rst.txt
@@ -182,7 +182,7 @@ decision boundary.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 1.815 seconds)
+   **Total running time of the script:** (0 minutes 2.049 seconds)
 
 
 .. _sphx_glr_download_auto_examples_neural_networks_plot_mlp_alpha.py:
diff --git a/dev/_sources/auto_examples/neural_networks/plot_mlp_training_curves.rst.txt b/dev/_sources/auto_examples/neural_networks/plot_mlp_training_curves.rst.txt
index 3366ee8c1acc2..362cce2edbbdb 100644
--- a/dev/_sources/auto_examples/neural_networks/plot_mlp_training_curves.rst.txt
+++ b/dev/_sources/auto_examples/neural_networks/plot_mlp_training_curves.rst.txt
@@ -280,7 +280,7 @@ Note that those results can be highly dependent on the value of
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 2.541 seconds)
+   **Total running time of the script:** (0 minutes 3.007 seconds)
 
 
 .. _sphx_glr_download_auto_examples_neural_networks_plot_mlp_training_curves.py:
diff --git a/dev/_sources/auto_examples/neural_networks/plot_mnist_filters.rst.txt b/dev/_sources/auto_examples/neural_networks/plot_mnist_filters.rst.txt
index 7a878c7567fba..ce633ae8f5037 100644
--- a/dev/_sources/auto_examples/neural_networks/plot_mnist_filters.rst.txt
+++ b/dev/_sources/auto_examples/neural_networks/plot_mnist_filters.rst.txt
@@ -129,7 +129,7 @@ to build this documentation on a regular basis.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 6.189 seconds)
+   **Total running time of the script:** (0 minutes 10.396 seconds)
 
 
 .. _sphx_glr_download_auto_examples_neural_networks_plot_mnist_filters.py:
diff --git a/dev/_sources/auto_examples/neural_networks/plot_rbm_logistic_classification.rst.txt b/dev/_sources/auto_examples/neural_networks/plot_rbm_logistic_classification.rst.txt
index 7439cb8ee2849..2fd9444a9ea73 100644
--- a/dev/_sources/auto_examples/neural_networks/plot_rbm_logistic_classification.rst.txt
+++ b/dev/_sources/auto_examples/neural_networks/plot_rbm_logistic_classification.rst.txt
@@ -174,14 +174,14 @@ reproduced here because of runtime constraints.
 
  .. code-block:: none
 
-    [BernoulliRBM] Iteration 1, pseudo-likelihood = -25.57, time = 0.09s
+    [BernoulliRBM] Iteration 1, pseudo-likelihood = -25.57, time = 0.10s
     [BernoulliRBM] Iteration 2, pseudo-likelihood = -23.68, time = 0.13s
     [BernoulliRBM] Iteration 3, pseudo-likelihood = -22.88, time = 0.13s
-    [BernoulliRBM] Iteration 4, pseudo-likelihood = -21.91, time = 0.13s
-    [BernoulliRBM] Iteration 5, pseudo-likelihood = -21.79, time = 0.13s
-    [BernoulliRBM] Iteration 6, pseudo-likelihood = -20.96, time = 0.12s
-    [BernoulliRBM] Iteration 7, pseudo-likelihood = -20.88, time = 0.12s
-    [BernoulliRBM] Iteration 8, pseudo-likelihood = -20.50, time = 0.12s
+    [BernoulliRBM] Iteration 4, pseudo-likelihood = -21.91, time = 0.14s
+    [BernoulliRBM] Iteration 5, pseudo-likelihood = -21.79, time = 0.14s
+    [BernoulliRBM] Iteration 6, pseudo-likelihood = -20.96, time = 0.14s
+    [BernoulliRBM] Iteration 7, pseudo-likelihood = -20.88, time = 0.14s
+    [BernoulliRBM] Iteration 8, pseudo-likelihood = -20.50, time = 0.13s
     [BernoulliRBM] Iteration 9, pseudo-likelihood = -20.34, time = 0.12s
     [BernoulliRBM] Iteration 10, pseudo-likelihood = -20.21, time = 0.12s
 
@@ -189,7 +189,7 @@ reproduced here because of runtime constraints.
 .. raw:: html
 
     <div class="output_subarea output_html rendered_html output_result">
-    <style>#sk-container-id-65 {
+    <style>#sk-container-id-66 {
       /* Definition of color scheme common for light and dark mode */
       --sklearn-color-text: #000;
       --sklearn-color-text-muted: #666;
@@ -220,15 +220,15 @@ reproduced here because of runtime constraints.
       }
     }
 
-    #sk-container-id-65 {
+    #sk-container-id-66 {
       color: var(--sklearn-color-text);
     }
 
-    #sk-container-id-65 pre {
+    #sk-container-id-66 pre {
       padding: 0;
     }
 
-    #sk-container-id-65 input.sk-hidden--visually {
+    #sk-container-id-66 input.sk-hidden--visually {
       border: 0;
       clip: rect(1px 1px 1px 1px);
       clip: rect(1px, 1px, 1px, 1px);
@@ -240,7 +240,7 @@ reproduced here because of runtime constraints.
       width: 1px;
     }
 
-    #sk-container-id-65 div.sk-dashed-wrapped {
+    #sk-container-id-66 div.sk-dashed-wrapped {
       border: 1px dashed var(--sklearn-color-line);
       margin: 0 0.4em 0.5em 0.4em;
       box-sizing: border-box;
@@ -248,7 +248,7 @@ reproduced here because of runtime constraints.
       background-color: var(--sklearn-color-background);
     }
 
-    #sk-container-id-65 div.sk-container {
+    #sk-container-id-66 div.sk-container {
       /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
          but bootstrap.min.css set `[hidden] { display: none !important; }`
          so we also need the `!important` here to be able to override the
@@ -258,7 +258,7 @@ reproduced here because of runtime constraints.
       position: relative;
     }
 
-    #sk-container-id-65 div.sk-text-repr-fallback {
+    #sk-container-id-66 div.sk-text-repr-fallback {
       display: none;
     }
 
@@ -274,14 +274,14 @@ reproduced here because of runtime constraints.
 
     /* Parallel-specific style estimator block */
 
-    #sk-container-id-65 div.sk-parallel-item::after {
+    #sk-container-id-66 div.sk-parallel-item::after {
       content: "";
       width: 100%;
       border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
       flex-grow: 1;
     }
 
-    #sk-container-id-65 div.sk-parallel {
+    #sk-container-id-66 div.sk-parallel {
       display: flex;
       align-items: stretch;
       justify-content: center;
@@ -289,28 +289,28 @@ reproduced here because of runtime constraints.
       position: relative;
     }
 
-    #sk-container-id-65 div.sk-parallel-item {
+    #sk-container-id-66 div.sk-parallel-item {
       display: flex;
       flex-direction: column;
     }
 
-    #sk-container-id-65 div.sk-parallel-item:first-child::after {
+    #sk-container-id-66 div.sk-parallel-item:first-child::after {
       align-self: flex-end;
       width: 50%;
     }
 
-    #sk-container-id-65 div.sk-parallel-item:last-child::after {
+    #sk-container-id-66 div.sk-parallel-item:last-child::after {
       align-self: flex-start;
       width: 50%;
     }
 
-    #sk-container-id-65 div.sk-parallel-item:only-child::after {
+    #sk-container-id-66 div.sk-parallel-item:only-child::after {
       width: 0;
     }
 
     /* Serial-specific style estimator block */
 
-    #sk-container-id-65 div.sk-serial {
+    #sk-container-id-66 div.sk-serial {
       display: flex;
       flex-direction: column;
       align-items: center;
@@ -328,14 +328,14 @@ reproduced here because of runtime constraints.
 
     /* Pipeline and ColumnTransformer style (default) */
 
-    #sk-container-id-65 div.sk-toggleable {
+    #sk-container-id-66 div.sk-toggleable {
       /* Default theme specific background. It is overwritten whether we have a
       specific estimator or a Pipeline/ColumnTransformer */
       background-color: var(--sklearn-color-background);
     }
 
     /* Toggleable label */
-    #sk-container-id-65 label.sk-toggleable__label {
+    #sk-container-id-66 label.sk-toggleable__label {
       cursor: pointer;
       display: flex;
       width: 100%;
@@ -348,13 +348,13 @@ reproduced here because of runtime constraints.
       gap: 0.5em;
     }
 
-    #sk-container-id-65 label.sk-toggleable__label .caption {
+    #sk-container-id-66 label.sk-toggleable__label .caption {
       font-size: 0.6rem;
       font-weight: lighter;
       color: var(--sklearn-color-text-muted);
     }
 
-    #sk-container-id-65 label.sk-toggleable__label-arrow:before {
+    #sk-container-id-66 label.sk-toggleable__label-arrow:before {
       /* Arrow on the left of the label */
       content: "▸";
       float: left;
@@ -362,13 +362,13 @@ reproduced here because of runtime constraints.
       color: var(--sklearn-color-icon);
     }
 
-    #sk-container-id-65 label.sk-toggleable__label-arrow:hover:before {
+    #sk-container-id-66 label.sk-toggleable__label-arrow:hover:before {
       color: var(--sklearn-color-text);
     }
 
     /* Toggleable content - dropdown */
 
-    #sk-container-id-65 div.sk-toggleable__content {
+    #sk-container-id-66 div.sk-toggleable__content {
       max-height: 0;
       max-width: 0;
       overflow: hidden;
@@ -377,12 +377,12 @@ reproduced here because of runtime constraints.
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-65 div.sk-toggleable__content.fitted {
+    #sk-container-id-66 div.sk-toggleable__content.fitted {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
-    #sk-container-id-65 div.sk-toggleable__content pre {
+    #sk-container-id-66 div.sk-toggleable__content pre {
       margin: 0.2em;
       border-radius: 0.25em;
       color: var(--sklearn-color-text);
@@ -390,79 +390,79 @@ reproduced here because of runtime constraints.
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-65 div.sk-toggleable__content.fitted pre {
+    #sk-container-id-66 div.sk-toggleable__content.fitted pre {
       /* unfitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
-    #sk-container-id-65 input.sk-toggleable__control:checked~div.sk-toggleable__content {
+    #sk-container-id-66 input.sk-toggleable__control:checked~div.sk-toggleable__content {
       /* Expand drop-down */
       max-height: 200px;
       max-width: 100%;
       overflow: auto;
     }
 
-    #sk-container-id-65 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
+    #sk-container-id-66 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
       content: "▾";
     }
 
     /* Pipeline/ColumnTransformer-specific style */
 
-    #sk-container-id-65 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-66 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-65 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-66 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
     /* Estimator-specific style */
 
     /* Colorize estimator box */
-    #sk-container-id-65 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-66 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-65 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-66 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
-    #sk-container-id-65 div.sk-label label.sk-toggleable__label,
-    #sk-container-id-65 div.sk-label label {
+    #sk-container-id-66 div.sk-label label.sk-toggleable__label,
+    #sk-container-id-66 div.sk-label label {
       /* The background is the default theme color */
       color: var(--sklearn-color-text-on-default-background);
     }
 
     /* On hover, darken the color of the background */
-    #sk-container-id-65 div.sk-label:hover label.sk-toggleable__label {
+    #sk-container-id-66 div.sk-label:hover label.sk-toggleable__label {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
     /* Label box, darken color on hover, fitted */
-    #sk-container-id-65 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
+    #sk-container-id-66 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
     /* Estimator label */
 
-    #sk-container-id-65 div.sk-label label {
+    #sk-container-id-66 div.sk-label label {
       font-family: monospace;
       font-weight: bold;
       display: inline-block;
       line-height: 1.2em;
     }
 
-    #sk-container-id-65 div.sk-label-container {
+    #sk-container-id-66 div.sk-label-container {
       text-align: center;
     }
 
     /* Estimator-specific */
-    #sk-container-id-65 div.sk-estimator {
+    #sk-container-id-66 div.sk-estimator {
       font-family: monospace;
       border: 1px dotted var(--sklearn-color-border-box);
       border-radius: 0.25em;
@@ -472,18 +472,18 @@ reproduced here because of runtime constraints.
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-65 div.sk-estimator.fitted {
+    #sk-container-id-66 div.sk-estimator.fitted {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
     /* on hover */
-    #sk-container-id-65 div.sk-estimator:hover {
+    #sk-container-id-66 div.sk-estimator:hover {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-65 div.sk-estimator.fitted:hover {
+    #sk-container-id-66 div.sk-estimator.fitted:hover {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-2);
     }
@@ -571,7 +571,7 @@ reproduced here because of runtime constraints.
 
     /* "?"-specific style due to the `<a>` HTML tag */
 
-    #sk-container-id-65 a.estimator_doc_link {
+    #sk-container-id-66 a.estimator_doc_link {
       float: right;
       font-size: 1rem;
       line-height: 1em;
@@ -586,25 +586,25 @@ reproduced here because of runtime constraints.
       border: var(--sklearn-color-unfitted-level-1) 1pt solid;
     }
 
-    #sk-container-id-65 a.estimator_doc_link.fitted {
+    #sk-container-id-66 a.estimator_doc_link.fitted {
       /* fitted */
       border: var(--sklearn-color-fitted-level-1) 1pt solid;
       color: var(--sklearn-color-fitted-level-1);
     }
 
     /* On hover */
-    #sk-container-id-65 a.estimator_doc_link:hover {
+    #sk-container-id-66 a.estimator_doc_link:hover {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-3);
       color: var(--sklearn-color-background);
       text-decoration: none;
     }
 
-    #sk-container-id-65 a.estimator_doc_link.fitted:hover {
+    #sk-container-id-66 a.estimator_doc_link.fitted:hover {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-3);
     }
-    </style><div id="sk-container-id-65" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>LogisticRegression(C=100.0, solver=&#x27;newton-cg&#x27;, tol=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-256" type="checkbox" checked><label for="sk-estimator-id-256" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>LogisticRegression</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.linear_model.LogisticRegression.html">?<span>Documentation for LogisticRegression</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>LogisticRegression(C=100.0, solver=&#x27;newton-cg&#x27;, tol=1)</pre></div> </div></div></div></div>
+    </style><div id="sk-container-id-66" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>LogisticRegression(C=100.0, solver=&#x27;newton-cg&#x27;, tol=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-270" type="checkbox" checked><label for="sk-estimator-id-270" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>LogisticRegression</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.linear_model.LogisticRegression.html">?<span>Documentation for LogisticRegression</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>LogisticRegression(C=100.0, solver=&#x27;newton-cg&#x27;, tol=1)</pre></div> </div></div></div></div>
     </div>
     <br />
     <br />
@@ -635,15 +635,15 @@ Evaluation
 
  .. code-block:: none
 
-    /home/circleci/project/sklearn/metrics/_classification.py:1694: UndefinedMetricWarning:
+    /home/circleci/project/sklearn/metrics/_classification.py:1705: UndefinedMetricWarning:
 
     Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
 
-    /home/circleci/project/sklearn/metrics/_classification.py:1694: UndefinedMetricWarning:
+    /home/circleci/project/sklearn/metrics/_classification.py:1705: UndefinedMetricWarning:
 
     Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
 
-    /home/circleci/project/sklearn/metrics/_classification.py:1694: UndefinedMetricWarning:
+    /home/circleci/project/sklearn/metrics/_classification.py:1705: UndefinedMetricWarning:
 
     Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
 
@@ -688,15 +688,15 @@ Evaluation
 
  .. code-block:: none
 
-    /home/circleci/project/sklearn/metrics/_classification.py:1694: UndefinedMetricWarning:
+    /home/circleci/project/sklearn/metrics/_classification.py:1705: UndefinedMetricWarning:
 
     Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
 
-    /home/circleci/project/sklearn/metrics/_classification.py:1694: UndefinedMetricWarning:
+    /home/circleci/project/sklearn/metrics/_classification.py:1705: UndefinedMetricWarning:
 
     Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
 
-    /home/circleci/project/sklearn/metrics/_classification.py:1694: UndefinedMetricWarning:
+    /home/circleci/project/sklearn/metrics/_classification.py:1705: UndefinedMetricWarning:
 
     Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
 
@@ -765,7 +765,7 @@ Plotting
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 2.517 seconds)
+   **Total running time of the script:** (0 minutes 2.737 seconds)
 
 
 .. _sphx_glr_download_auto_examples_neural_networks_plot_rbm_logistic_classification.py:
diff --git a/dev/_sources/auto_examples/preprocessing/plot_all_scaling.rst.txt b/dev/_sources/auto_examples/preprocessing/plot_all_scaling.rst.txt
index e43d4a3d17167..ee63b8fd68175 100644
--- a/dev/_sources/auto_examples/preprocessing/plot_all_scaling.rst.txt
+++ b/dev/_sources/auto_examples/preprocessing/plot_all_scaling.rst.txt
@@ -633,7 +633,7 @@ case if some original features had a mix of positive and negative values.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 8.394 seconds)
+   **Total running time of the script:** (0 minutes 10.263 seconds)
 
 
 .. _sphx_glr_download_auto_examples_preprocessing_plot_all_scaling.py:
diff --git a/dev/_sources/auto_examples/preprocessing/plot_discretization.rst.txt b/dev/_sources/auto_examples/preprocessing/plot_discretization.rst.txt
index 466e8b9b97a29..f223de6e63bcc 100644
--- a/dev/_sources/auto_examples/preprocessing/plot_discretization.rst.txt
+++ b/dev/_sources/auto_examples/preprocessing/plot_discretization.rst.txt
@@ -127,7 +127,7 @@ up the data anywhere.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.248 seconds)
+   **Total running time of the script:** (0 minutes 0.236 seconds)
 
 
 .. _sphx_glr_download_auto_examples_preprocessing_plot_discretization.py:
diff --git a/dev/_sources/auto_examples/preprocessing/plot_discretization_classification.rst.txt b/dev/_sources/auto_examples/preprocessing/plot_discretization_classification.rst.txt
index 8e35d7f9eab8d..dbce1d37d4199 100644
--- a/dev/_sources/auto_examples/preprocessing/plot_discretization_classification.rst.txt
+++ b/dev/_sources/auto_examples/preprocessing/plot_discretization_classification.rst.txt
@@ -304,7 +304,7 @@ set.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 3.777 seconds)
+   **Total running time of the script:** (0 minutes 3.755 seconds)
 
 
 .. _sphx_glr_download_auto_examples_preprocessing_plot_discretization_classification.py:
diff --git a/dev/_sources/auto_examples/preprocessing/plot_discretization_strategies.rst.txt b/dev/_sources/auto_examples/preprocessing/plot_discretization_strategies.rst.txt
index 891ddb029f04e..10e80b57b8f21 100644
--- a/dev/_sources/auto_examples/preprocessing/plot_discretization_strategies.rst.txt
+++ b/dev/_sources/auto_examples/preprocessing/plot_discretization_strategies.rst.txt
@@ -143,7 +143,7 @@ The plot shows the regions where the discretized encoding is constant.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.627 seconds)
+   **Total running time of the script:** (0 minutes 0.654 seconds)
 
 
 .. _sphx_glr_download_auto_examples_preprocessing_plot_discretization_strategies.py:
diff --git a/dev/_sources/auto_examples/preprocessing/plot_map_data_to_normal.rst.txt b/dev/_sources/auto_examples/preprocessing/plot_map_data_to_normal.rst.txt
index 7a0c826367382..b4457010d4d79 100644
--- a/dev/_sources/auto_examples/preprocessing/plot_map_data_to_normal.rst.txt
+++ b/dev/_sources/auto_examples/preprocessing/plot_map_data_to_normal.rst.txt
@@ -179,7 +179,7 @@ is prone to overfitting. The use of the power transform is then recommended.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 2.124 seconds)
+   **Total running time of the script:** (0 minutes 2.091 seconds)
 
 
 .. _sphx_glr_download_auto_examples_preprocessing_plot_map_data_to_normal.py:
diff --git a/dev/_sources/auto_examples/preprocessing/plot_scaling_importance.rst.txt b/dev/_sources/auto_examples/preprocessing/plot_scaling_importance.rst.txt
index 6459995af3a6d..db175535f8ac7 100644
--- a/dev/_sources/auto_examples/preprocessing/plot_scaling_importance.rst.txt
+++ b/dev/_sources/auto_examples/preprocessing/plot_scaling_importance.rst.txt
@@ -405,7 +405,7 @@ the scaling step.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 1.848 seconds)
+   **Total running time of the script:** (0 minutes 1.785 seconds)
 
 
 .. _sphx_glr_download_auto_examples_preprocessing_plot_scaling_importance.py:
diff --git a/dev/_sources/auto_examples/preprocessing/plot_target_encoder.rst.txt b/dev/_sources/auto_examples/preprocessing/plot_target_encoder.rst.txt
index 5da1687d53d9a..f18cd2e79106c 100644
--- a/dev/_sources/auto_examples/preprocessing/plot_target_encoder.rst.txt
+++ b/dev/_sources/auto_examples/preprocessing/plot_target_encoder.rst.txt
@@ -391,7 +391,7 @@ categorical feature in gradient boosting.
 .. raw:: html
 
     <div class="output_subarea output_html rendered_html output_result">
-    <style>#sk-container-id-70 {
+    <style>#sk-container-id-71 {
       /* Definition of color scheme common for light and dark mode */
       --sklearn-color-text: #000;
       --sklearn-color-text-muted: #666;
@@ -422,15 +422,15 @@ categorical feature in gradient boosting.
       }
     }
 
-    #sk-container-id-70 {
+    #sk-container-id-71 {
       color: var(--sklearn-color-text);
     }
 
-    #sk-container-id-70 pre {
+    #sk-container-id-71 pre {
       padding: 0;
     }
 
-    #sk-container-id-70 input.sk-hidden--visually {
+    #sk-container-id-71 input.sk-hidden--visually {
       border: 0;
       clip: rect(1px 1px 1px 1px);
       clip: rect(1px, 1px, 1px, 1px);
@@ -442,7 +442,7 @@ categorical feature in gradient boosting.
       width: 1px;
     }
 
-    #sk-container-id-70 div.sk-dashed-wrapped {
+    #sk-container-id-71 div.sk-dashed-wrapped {
       border: 1px dashed var(--sklearn-color-line);
       margin: 0 0.4em 0.5em 0.4em;
       box-sizing: border-box;
@@ -450,7 +450,7 @@ categorical feature in gradient boosting.
       background-color: var(--sklearn-color-background);
     }
 
-    #sk-container-id-70 div.sk-container {
+    #sk-container-id-71 div.sk-container {
       /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
          but bootstrap.min.css set `[hidden] { display: none !important; }`
          so we also need the `!important` here to be able to override the
@@ -460,7 +460,7 @@ categorical feature in gradient boosting.
       position: relative;
     }
 
-    #sk-container-id-70 div.sk-text-repr-fallback {
+    #sk-container-id-71 div.sk-text-repr-fallback {
       display: none;
     }
 
@@ -476,14 +476,14 @@ categorical feature in gradient boosting.
 
     /* Parallel-specific style estimator block */
 
-    #sk-container-id-70 div.sk-parallel-item::after {
+    #sk-container-id-71 div.sk-parallel-item::after {
       content: "";
       width: 100%;
       border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
       flex-grow: 1;
     }
 
-    #sk-container-id-70 div.sk-parallel {
+    #sk-container-id-71 div.sk-parallel {
       display: flex;
       align-items: stretch;
       justify-content: center;
@@ -491,28 +491,28 @@ categorical feature in gradient boosting.
       position: relative;
     }
 
-    #sk-container-id-70 div.sk-parallel-item {
+    #sk-container-id-71 div.sk-parallel-item {
       display: flex;
       flex-direction: column;
     }
 
-    #sk-container-id-70 div.sk-parallel-item:first-child::after {
+    #sk-container-id-71 div.sk-parallel-item:first-child::after {
       align-self: flex-end;
       width: 50%;
     }
 
-    #sk-container-id-70 div.sk-parallel-item:last-child::after {
+    #sk-container-id-71 div.sk-parallel-item:last-child::after {
       align-self: flex-start;
       width: 50%;
     }
 
-    #sk-container-id-70 div.sk-parallel-item:only-child::after {
+    #sk-container-id-71 div.sk-parallel-item:only-child::after {
       width: 0;
     }
 
     /* Serial-specific style estimator block */
 
-    #sk-container-id-70 div.sk-serial {
+    #sk-container-id-71 div.sk-serial {
       display: flex;
       flex-direction: column;
       align-items: center;
@@ -530,14 +530,14 @@ categorical feature in gradient boosting.
 
     /* Pipeline and ColumnTransformer style (default) */
 
-    #sk-container-id-70 div.sk-toggleable {
+    #sk-container-id-71 div.sk-toggleable {
       /* Default theme specific background. It is overwritten whether we have a
       specific estimator or a Pipeline/ColumnTransformer */
       background-color: var(--sklearn-color-background);
     }
 
     /* Toggleable label */
-    #sk-container-id-70 label.sk-toggleable__label {
+    #sk-container-id-71 label.sk-toggleable__label {
       cursor: pointer;
       display: flex;
       width: 100%;
@@ -550,13 +550,13 @@ categorical feature in gradient boosting.
       gap: 0.5em;
     }
 
-    #sk-container-id-70 label.sk-toggleable__label .caption {
+    #sk-container-id-71 label.sk-toggleable__label .caption {
       font-size: 0.6rem;
       font-weight: lighter;
       color: var(--sklearn-color-text-muted);
     }
 
-    #sk-container-id-70 label.sk-toggleable__label-arrow:before {
+    #sk-container-id-71 label.sk-toggleable__label-arrow:before {
       /* Arrow on the left of the label */
       content: "▸";
       float: left;
@@ -564,13 +564,13 @@ categorical feature in gradient boosting.
       color: var(--sklearn-color-icon);
     }
 
-    #sk-container-id-70 label.sk-toggleable__label-arrow:hover:before {
+    #sk-container-id-71 label.sk-toggleable__label-arrow:hover:before {
       color: var(--sklearn-color-text);
     }
 
     /* Toggleable content - dropdown */
 
-    #sk-container-id-70 div.sk-toggleable__content {
+    #sk-container-id-71 div.sk-toggleable__content {
       max-height: 0;
       max-width: 0;
       overflow: hidden;
@@ -579,12 +579,12 @@ categorical feature in gradient boosting.
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-70 div.sk-toggleable__content.fitted {
+    #sk-container-id-71 div.sk-toggleable__content.fitted {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
-    #sk-container-id-70 div.sk-toggleable__content pre {
+    #sk-container-id-71 div.sk-toggleable__content pre {
       margin: 0.2em;
       border-radius: 0.25em;
       color: var(--sklearn-color-text);
@@ -592,79 +592,79 @@ categorical feature in gradient boosting.
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-70 div.sk-toggleable__content.fitted pre {
+    #sk-container-id-71 div.sk-toggleable__content.fitted pre {
       /* unfitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
-    #sk-container-id-70 input.sk-toggleable__control:checked~div.sk-toggleable__content {
+    #sk-container-id-71 input.sk-toggleable__control:checked~div.sk-toggleable__content {
       /* Expand drop-down */
       max-height: 200px;
       max-width: 100%;
       overflow: auto;
     }
 
-    #sk-container-id-70 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
+    #sk-container-id-71 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
       content: "▾";
     }
 
     /* Pipeline/ColumnTransformer-specific style */
 
-    #sk-container-id-70 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-71 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-70 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-71 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
     /* Estimator-specific style */
 
     /* Colorize estimator box */
-    #sk-container-id-70 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-71 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-70 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-71 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
-    #sk-container-id-70 div.sk-label label.sk-toggleable__label,
-    #sk-container-id-70 div.sk-label label {
+    #sk-container-id-71 div.sk-label label.sk-toggleable__label,
+    #sk-container-id-71 div.sk-label label {
       /* The background is the default theme color */
       color: var(--sklearn-color-text-on-default-background);
     }
 
     /* On hover, darken the color of the background */
-    #sk-container-id-70 div.sk-label:hover label.sk-toggleable__label {
+    #sk-container-id-71 div.sk-label:hover label.sk-toggleable__label {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
     /* Label box, darken color on hover, fitted */
-    #sk-container-id-70 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
+    #sk-container-id-71 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
     /* Estimator label */
 
-    #sk-container-id-70 div.sk-label label {
+    #sk-container-id-71 div.sk-label label {
       font-family: monospace;
       font-weight: bold;
       display: inline-block;
       line-height: 1.2em;
     }
 
-    #sk-container-id-70 div.sk-label-container {
+    #sk-container-id-71 div.sk-label-container {
       text-align: center;
     }
 
     /* Estimator-specific */
-    #sk-container-id-70 div.sk-estimator {
+    #sk-container-id-71 div.sk-estimator {
       font-family: monospace;
       border: 1px dotted var(--sklearn-color-border-box);
       border-radius: 0.25em;
@@ -674,18 +674,18 @@ categorical feature in gradient boosting.
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-70 div.sk-estimator.fitted {
+    #sk-container-id-71 div.sk-estimator.fitted {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
     /* on hover */
-    #sk-container-id-70 div.sk-estimator:hover {
+    #sk-container-id-71 div.sk-estimator:hover {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-70 div.sk-estimator.fitted:hover {
+    #sk-container-id-71 div.sk-estimator.fitted:hover {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-2);
     }
@@ -773,7 +773,7 @@ categorical feature in gradient boosting.
 
     /* "?"-specific style due to the `<a>` HTML tag */
 
-    #sk-container-id-70 a.estimator_doc_link {
+    #sk-container-id-71 a.estimator_doc_link {
       float: right;
       font-size: 1rem;
       line-height: 1em;
@@ -788,25 +788,25 @@ categorical feature in gradient boosting.
       border: var(--sklearn-color-unfitted-level-1) 1pt solid;
     }
 
-    #sk-container-id-70 a.estimator_doc_link.fitted {
+    #sk-container-id-71 a.estimator_doc_link.fitted {
       /* fitted */
       border: var(--sklearn-color-fitted-level-1) 1pt solid;
       color: var(--sklearn-color-fitted-level-1);
     }
 
     /* On hover */
-    #sk-container-id-70 a.estimator_doc_link:hover {
+    #sk-container-id-71 a.estimator_doc_link:hover {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-3);
       color: var(--sklearn-color-background);
       text-decoration: none;
     }
 
-    #sk-container-id-70 a.estimator_doc_link.fitted:hover {
+    #sk-container-id-71 a.estimator_doc_link.fitted:hover {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-3);
     }
-    </style><div id="sk-container-id-70" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,
+    </style><div id="sk-container-id-71" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,
                      ColumnTransformer(transformers=[(&#x27;numerical&#x27;, &#x27;passthrough&#x27;,
                                                       [&#x27;price&#x27;]),
                                                      (&#x27;high_cardinality&#x27;,
@@ -819,7 +819,7 @@ categorical feature in gradient boosting.
                                        verbose_feature_names_out=False)),
                     (&#x27;histgradientboostingregressor&#x27;,
                      HistGradientBoostingRegressor(categorical_features=Index([&#x27;country&#x27;, &#x27;region_2&#x27;], dtype=&#x27;object&#x27;),
-                                                   max_iter=20, random_state=0))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-290" type="checkbox" ><label for="sk-estimator-id-290" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>Pipeline</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.pipeline.Pipeline.html">?<span>Documentation for Pipeline</span></a><span class="sk-estimator-doc-link ">i<span>Not fitted</span></span></div></label><div class="sk-toggleable__content "><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,
+                                                   max_iter=20, random_state=0))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-304" type="checkbox" ><label for="sk-estimator-id-304" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>Pipeline</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.pipeline.Pipeline.html">?<span>Documentation for Pipeline</span></a><span class="sk-estimator-doc-link ">i<span>Not fitted</span></span></div></label><div class="sk-toggleable__content "><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,
                      ColumnTransformer(transformers=[(&#x27;numerical&#x27;, &#x27;passthrough&#x27;,
                                                       [&#x27;price&#x27;]),
                                                      (&#x27;high_cardinality&#x27;,
@@ -832,7 +832,7 @@ categorical feature in gradient boosting.
                                        verbose_feature_names_out=False)),
                     (&#x27;histgradientboostingregressor&#x27;,
                      HistGradientBoostingRegressor(categorical_features=Index([&#x27;country&#x27;, &#x27;region_2&#x27;], dtype=&#x27;object&#x27;),
-                                                   max_iter=20, random_state=0))])</pre></div> </div></div><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-291" type="checkbox" ><label for="sk-estimator-id-291" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>columntransformer: ColumnTransformer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.compose.ColumnTransformer.html">?<span>Documentation for columntransformer: ColumnTransformer</span></a></div></label><div class="sk-toggleable__content "><pre>ColumnTransformer(transformers=[(&#x27;numerical&#x27;, &#x27;passthrough&#x27;, [&#x27;price&#x27;]),
+                                                   max_iter=20, random_state=0))])</pre></div> </div></div><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-305" type="checkbox" ><label for="sk-estimator-id-305" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>columntransformer: ColumnTransformer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.compose.ColumnTransformer.html">?<span>Documentation for columntransformer: ColumnTransformer</span></a></div></label><div class="sk-toggleable__content "><pre>ColumnTransformer(transformers=[(&#x27;numerical&#x27;, &#x27;passthrough&#x27;, [&#x27;price&#x27;]),
                                     (&#x27;high_cardinality&#x27;,
                                      TargetEncoder(target_type=&#x27;continuous&#x27;),
                                      Index([&#x27;winery&#x27;, &#x27;region_1&#x27;, &#x27;variety&#x27;, &#x27;province&#x27;], dtype=&#x27;object&#x27;)),
@@ -840,7 +840,7 @@ categorical feature in gradient boosting.
                                      OrdinalEncoder(handle_unknown=&#x27;use_encoded_value&#x27;,
                                                     unknown_value=-1),
                                      Index([&#x27;country&#x27;, &#x27;region_2&#x27;], dtype=&#x27;object&#x27;))],
-                      verbose_feature_names_out=False)</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-292" type="checkbox" ><label for="sk-estimator-id-292" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>numerical</div></div></label><div class="sk-toggleable__content "><pre>[&#x27;price&#x27;]</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-293" type="checkbox" ><label for="sk-estimator-id-293" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>passthrough</div></div></label><div class="sk-toggleable__content "><pre>passthrough</pre></div> </div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-294" type="checkbox" ><label for="sk-estimator-id-294" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>high_cardinality</div></div></label><div class="sk-toggleable__content "><pre>Index([&#x27;winery&#x27;, &#x27;region_1&#x27;, &#x27;variety&#x27;, &#x27;province&#x27;], dtype=&#x27;object&#x27;)</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-295" type="checkbox" ><label for="sk-estimator-id-295" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>TargetEncoder</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.TargetEncoder.html">?<span>Documentation for TargetEncoder</span></a></div></label><div class="sk-toggleable__content "><pre>TargetEncoder(target_type=&#x27;continuous&#x27;)</pre></div> </div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-296" type="checkbox" ><label for="sk-estimator-id-296" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>low_cardinality</div></div></label><div class="sk-toggleable__content "><pre>Index([&#x27;country&#x27;, &#x27;region_2&#x27;], dtype=&#x27;object&#x27;)</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-297" type="checkbox" ><label for="sk-estimator-id-297" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>OrdinalEncoder</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.OrdinalEncoder.html">?<span>Documentation for OrdinalEncoder</span></a></div></label><div class="sk-toggleable__content "><pre>OrdinalEncoder(handle_unknown=&#x27;use_encoded_value&#x27;, unknown_value=-1)</pre></div> </div></div></div></div></div></div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-298" type="checkbox" ><label for="sk-estimator-id-298" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>HistGradientBoostingRegressor</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.ensemble.HistGradientBoostingRegressor.html">?<span>Documentation for HistGradientBoostingRegressor</span></a></div></label><div class="sk-toggleable__content "><pre>HistGradientBoostingRegressor(categorical_features=Index([&#x27;country&#x27;, &#x27;region_2&#x27;], dtype=&#x27;object&#x27;),
+                      verbose_feature_names_out=False)</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-306" type="checkbox" ><label for="sk-estimator-id-306" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>numerical</div></div></label><div class="sk-toggleable__content "><pre>[&#x27;price&#x27;]</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-307" type="checkbox" ><label for="sk-estimator-id-307" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>passthrough</div></div></label><div class="sk-toggleable__content "><pre>passthrough</pre></div> </div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-308" type="checkbox" ><label for="sk-estimator-id-308" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>high_cardinality</div></div></label><div class="sk-toggleable__content "><pre>Index([&#x27;winery&#x27;, &#x27;region_1&#x27;, &#x27;variety&#x27;, &#x27;province&#x27;], dtype=&#x27;object&#x27;)</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-309" type="checkbox" ><label for="sk-estimator-id-309" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>TargetEncoder</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.TargetEncoder.html">?<span>Documentation for TargetEncoder</span></a></div></label><div class="sk-toggleable__content "><pre>TargetEncoder(target_type=&#x27;continuous&#x27;)</pre></div> </div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-310" type="checkbox" ><label for="sk-estimator-id-310" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>low_cardinality</div></div></label><div class="sk-toggleable__content "><pre>Index([&#x27;country&#x27;, &#x27;region_2&#x27;], dtype=&#x27;object&#x27;)</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-311" type="checkbox" ><label for="sk-estimator-id-311" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>OrdinalEncoder</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.OrdinalEncoder.html">?<span>Documentation for OrdinalEncoder</span></a></div></label><div class="sk-toggleable__content "><pre>OrdinalEncoder(handle_unknown=&#x27;use_encoded_value&#x27;, unknown_value=-1)</pre></div> </div></div></div></div></div></div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-312" type="checkbox" ><label for="sk-estimator-id-312" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>HistGradientBoostingRegressor</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.ensemble.HistGradientBoostingRegressor.html">?<span>Documentation for HistGradientBoostingRegressor</span></a></div></label><div class="sk-toggleable__content "><pre>HistGradientBoostingRegressor(categorical_features=Index([&#x27;country&#x27;, &#x27;region_2&#x27;], dtype=&#x27;object&#x27;),
                                   max_iter=20, random_state=0)</pre></div> </div></div></div></div></div></div>
     </div>
     <br />
@@ -947,7 +947,7 @@ can be explained as follows:
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 22.370 seconds)
+   **Total running time of the script:** (0 minutes 27.010 seconds)
 
 
 .. _sphx_glr_download_auto_examples_preprocessing_plot_target_encoder.py:
diff --git a/dev/_sources/auto_examples/preprocessing/plot_target_encoder_cross_val.rst.txt b/dev/_sources/auto_examples/preprocessing/plot_target_encoder_cross_val.rst.txt
index 972a107855929..44fd36e4e210f 100644
--- a/dev/_sources/auto_examples/preprocessing/plot_target_encoder_cross_val.rst.txt
+++ b/dev/_sources/auto_examples/preprocessing/plot_target_encoder_cross_val.rst.txt
@@ -388,7 +388,7 @@ will correctly call :meth:`TargetEncoder.fit_transform` and use
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.331 seconds)
+   **Total running time of the script:** (0 minutes 0.333 seconds)
 
 
 .. _sphx_glr_download_auto_examples_preprocessing_plot_target_encoder_cross_val.py:
diff --git a/dev/_sources/auto_examples/release_highlights/plot_release_highlights_0_22_0.rst.txt b/dev/_sources/auto_examples/release_highlights/plot_release_highlights_0_22_0.rst.txt
index 64a8c28f0a34d..23a9083aae703 100644
--- a/dev/_sources/auto_examples/release_highlights/plot_release_highlights_0_22_0.rst.txt
+++ b/dev/_sources/auto_examples/release_highlights/plot_release_highlights_0_22_0.rst.txt
@@ -503,7 +503,7 @@ Read more in the :ref:`User Guide <roc_metrics>`.
 
  .. code-block:: none
 
-    0.9954666666666666
+    0.9940955448201825
 
 
 
@@ -511,7 +511,7 @@ Read more in the :ref:`User Guide <roc_metrics>`.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 1.368 seconds)
+   **Total running time of the script:** (0 minutes 1.705 seconds)
 
 
 .. _sphx_glr_download_auto_examples_release_highlights_plot_release_highlights_0_22_0.py:
diff --git a/dev/_sources/auto_examples/release_highlights/plot_release_highlights_0_23_0.rst.txt b/dev/_sources/auto_examples/release_highlights/plot_release_highlights_0_23_0.rst.txt
index cef96bf7f014b..44dc1b456f584 100644
--- a/dev/_sources/auto_examples/release_highlights/plot_release_highlights_0_23_0.rst.txt
+++ b/dev/_sources/auto_examples/release_highlights/plot_release_highlights_0_23_0.rst.txt
@@ -51,15 +51,16 @@ the :ref:`User Guide <Generalized_linear_regression>`. Additionally,
 :class:`~sklearn.ensemble.HistGradientBoostingRegressor` supports a new
 'poisson' loss as well.
 
-.. GENERATED FROM PYTHON SOURCE LINES 36-55
+.. GENERATED FROM PYTHON SOURCE LINES 36-56
 
 .. code-block:: Python
 
 
     import numpy as np
-    from sklearn.model_selection import train_test_split
-    from sklearn.linear_model import PoissonRegressor
+
     from sklearn.ensemble import HistGradientBoostingRegressor
+    from sklearn.linear_model import PoissonRegressor
+    from sklearn.model_selection import train_test_split
 
     n_samples, n_features = 1000, 20
     rng = np.random.RandomState(0)
@@ -88,7 +89,7 @@ the :ref:`User Guide <Generalized_linear_regression>`. Additionally,
 
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 56-64
+.. GENERATED FROM PYTHON SOURCE LINES 57-65
 
 Rich visual representation of estimators
 -----------------------------------------
@@ -99,17 +100,17 @@ provide detail.  Click on the example image below to expand Pipeline
 elements.  See :ref:`visualizing_composite_estimators` for how you can use
 this feature.
 
-.. GENERATED FROM PYTHON SOURCE LINES 64-88
+.. GENERATED FROM PYTHON SOURCE LINES 65-89
 
 .. code-block:: Python
 
 
     from sklearn import set_config
-    from sklearn.pipeline import make_pipeline
-    from sklearn.preprocessing import OneHotEncoder, StandardScaler
-    from sklearn.impute import SimpleImputer
     from sklearn.compose import make_column_transformer
+    from sklearn.impute import SimpleImputer
     from sklearn.linear_model import LogisticRegression
+    from sklearn.pipeline import make_pipeline
+    from sklearn.preprocessing import OneHotEncoder, StandardScaler
 
     set_config(display="diagram")
 
@@ -595,7 +596,7 @@ this feature.
     <br />
     <br />
 
-.. GENERATED FROM PYTHON SOURCE LINES 89-97
+.. GENERATED FROM PYTHON SOURCE LINES 90-98
 
 Scalability and stability improvements to KMeans
 ------------------------------------------------
@@ -606,16 +607,17 @@ parallelism instead of relying on joblib, so the `n_jobs` parameter has no
 effect anymore. For more details on how to control the number of threads,
 please refer to our :ref:`parallelism` notes.
 
-.. GENERATED FROM PYTHON SOURCE LINES 97-111
+.. GENERATED FROM PYTHON SOURCE LINES 98-113
 
 .. code-block:: Python
 
-    import scipy
     import numpy as np
-    from sklearn.model_selection import train_test_split
+    import scipy
+
     from sklearn.cluster import KMeans
     from sklearn.datasets import make_blobs
     from sklearn.metrics import completeness_score
+    from sklearn.model_selection import train_test_split
 
     rng = np.random.RandomState(0)
     X, y = make_blobs(random_state=rng)
@@ -632,12 +634,12 @@ please refer to our :ref:`parallelism` notes.
 
  .. code-block:: none
 
-    0.7652857744152419
+    0.7091072911505116
 
 
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 112-127
+.. GENERATED FROM PYTHON SOURCE LINES 114-129
 
 Improvements to the histogram-based Gradient Boosting estimators
 ----------------------------------------------------------------
@@ -655,17 +657,18 @@ Applying monotoinc constraints allows the prediction to capture the global
 effect of the first feature, instead of fitting the noise. For a usecase
 example, see :ref:`sphx_glr_auto_examples_ensemble_plot_hgbt_regression.py`.
 
-.. GENERATED FROM PYTHON SOURCE LINES 127-170
+.. GENERATED FROM PYTHON SOURCE LINES 129-173
 
 .. code-block:: Python
 
     import numpy as np
     from matplotlib import pyplot as plt
-    from sklearn.model_selection import train_test_split
+
+    from sklearn.ensemble import HistGradientBoostingRegressor
 
     # from sklearn.inspection import plot_partial_dependence
     from sklearn.inspection import PartialDependenceDisplay
-    from sklearn.ensemble import HistGradientBoostingRegressor
+    from sklearn.model_selection import train_test_split
 
     n_samples = 500
     rng = np.random.RandomState(0)
@@ -714,22 +717,23 @@ example, see :ref:`sphx_glr_auto_examples_ensemble_plot_hgbt_regression.py`.
 
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 171-175
+.. GENERATED FROM PYTHON SOURCE LINES 174-178
 
 Sample-weight support for Lasso and ElasticNet
 ----------------------------------------------
 The two linear regressors :class:`~sklearn.linear_model.Lasso` and
 :class:`~sklearn.linear_model.ElasticNet` now support sample weights.
 
-.. GENERATED FROM PYTHON SOURCE LINES 175-191
+.. GENERATED FROM PYTHON SOURCE LINES 178-195
 
 .. code-block:: Python
 
 
-    from sklearn.model_selection import train_test_split
+    import numpy as np
+
     from sklearn.datasets import make_regression
     from sklearn.linear_model import Lasso
-    import numpy as np
+    from sklearn.model_selection import train_test_split
 
     n_samples, n_features = 1000, 20
     rng = np.random.RandomState(0)
@@ -757,7 +761,7 @@ The two linear regressors :class:`~sklearn.linear_model.Lasso` and
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.611 seconds)
+   **Total running time of the script:** (0 minutes 0.888 seconds)
 
 
 .. _sphx_glr_download_auto_examples_release_highlights_plot_release_highlights_0_23_0.py:
diff --git a/dev/_sources/auto_examples/release_highlights/plot_release_highlights_0_24_0.rst.txt b/dev/_sources/auto_examples/release_highlights/plot_release_highlights_0_24_0.rst.txt
index cd2bd33a68c79..6dea0d292c95a 100644
--- a/dev/_sources/auto_examples/release_highlights/plot_release_highlights_0_24_0.rst.txt
+++ b/dev/_sources/auto_examples/release_highlights/plot_release_highlights_0_24_0.rst.txt
@@ -66,17 +66,18 @@ the Successive Halving estimators are still :term:`experimental
   :target: ../model_selection/plot_successive_halving_iterations.html
   :align: center
 
-.. GENERATED FROM PYTHON SOURCE LINES 51-79
+.. GENERATED FROM PYTHON SOURCE LINES 51-80
 
 .. code-block:: Python
 
 
     import numpy as np
     from scipy.stats import randint
-    from sklearn.experimental import enable_halving_search_cv  # noqa
-    from sklearn.model_selection import HalvingRandomSearchCV
-    from sklearn.ensemble import RandomForestClassifier
+
     from sklearn.datasets import make_classification
+    from sklearn.ensemble import RandomForestClassifier
+    from sklearn.experimental import enable_halving_search_cv  # noqa: F401
+    from sklearn.model_selection import HalvingRandomSearchCV
 
     rng = np.random.RandomState(0)
 
@@ -111,7 +112,7 @@ the Successive Halving estimators are still :term:`experimental
 
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 80-99
+.. GENERATED FROM PYTHON SOURCE LINES 81-100
 
 Native support for categorical features in HistGradientBoosting estimators
 --------------------------------------------------------------------------
@@ -133,7 +134,7 @@ use the new `categorical_features` parameter, it is still required to
 preprocess the data within a pipeline as demonstrated in this :ref:`example
 <sphx_glr_auto_examples_ensemble_plot_gradient_boosting_categorical.py>`.
 
-.. GENERATED FROM PYTHON SOURCE LINES 101-109
+.. GENERATED FROM PYTHON SOURCE LINES 102-110
 
 Improved performances of HistGradientBoosting estimators
 --------------------------------------------------------
@@ -144,7 +145,7 @@ done in parallel which results in slight speed improvements.
 See more in the `Benchmark page
 <https://scikit-learn.org/scikit-learn-benchmarks/>`_.
 
-.. GENERATED FROM PYTHON SOURCE LINES 111-119
+.. GENERATED FROM PYTHON SOURCE LINES 112-120
 
 New self-training meta-estimator
 --------------------------------
@@ -155,12 +156,13 @@ will behave as a
 semi-supervised classifier, allowing it to learn from unlabeled data.
 Read more in the :ref:`User guide <self_training>`.
 
-.. GENERATED FROM PYTHON SOURCE LINES 119-133
+.. GENERATED FROM PYTHON SOURCE LINES 120-135
 
 .. code-block:: Python
 
 
     import numpy as np
+
     from sklearn import datasets
     from sklearn.semi_supervised import SelfTrainingClassifier
     from sklearn.svm import SVC
@@ -601,7 +603,7 @@ Read more in the :ref:`User guide <self_training>`.
     <br />
     <br />
 
-.. GENERATED FROM PYTHON SOURCE LINES 134-142
+.. GENERATED FROM PYTHON SOURCE LINES 136-144
 
 New SequentialFeatureSelector transformer
 -----------------------------------------
@@ -612,14 +614,14 @@ selection) or remove features from the list of the available features
 (backward selection), based on a cross-validated score maximization.
 See the :ref:`User Guide <sequential_feature_selection>`.
 
-.. GENERATED FROM PYTHON SOURCE LINES 142-157
+.. GENERATED FROM PYTHON SOURCE LINES 144-159
 
 .. code-block:: Python
 
 
+    from sklearn.datasets import load_iris
     from sklearn.feature_selection import SequentialFeatureSelector
     from sklearn.neighbors import KNeighborsClassifier
-    from sklearn.datasets import load_iris
 
     X, y = load_iris(return_X_y=True, as_frame=True)
     feature_names = X.columns
@@ -644,7 +646,7 @@ See the :ref:`User Guide <sequential_feature_selection>`.
 
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 158-164
+.. GENERATED FROM PYTHON SOURCE LINES 160-166
 
 New PolynomialCountSketch kernel approximation function
 -------------------------------------------------------
@@ -653,17 +655,17 @@ approximates a polynomial expansion of a feature space when used with linear
 models, but uses much less memory than
 :class:`~sklearn.preprocessing.PolynomialFeatures`.
 
-.. GENERATED FROM PYTHON SOURCE LINES 164-183
+.. GENERATED FROM PYTHON SOURCE LINES 166-185
 
 .. code-block:: Python
 
 
     from sklearn.datasets import fetch_covtype
-    from sklearn.pipeline import make_pipeline
-    from sklearn.model_selection import train_test_split
-    from sklearn.preprocessing import MinMaxScaler
     from sklearn.kernel_approximation import PolynomialCountSketch
     from sklearn.linear_model import LogisticRegression
+    from sklearn.model_selection import train_test_split
+    from sklearn.pipeline import make_pipeline
+    from sklearn.preprocessing import MinMaxScaler
 
     X, y = fetch_covtype(return_X_y=True)
     pipe = make_pipeline(
@@ -685,15 +687,15 @@ models, but uses much less memory than
  .. code-block:: none
 
 
-    0.7389
+    0.7354
 
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 184-185
+.. GENERATED FROM PYTHON SOURCE LINES 186-187
 
 For comparison, here is the score of a linear baseline for the same data:
 
-.. GENERATED FROM PYTHON SOURCE LINES 185-189
+.. GENERATED FROM PYTHON SOURCE LINES 187-191
 
 .. code-block:: Python
 
@@ -714,7 +716,7 @@ For comparison, here is the score of a linear baseline for the same data:
 
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 190-196
+.. GENERATED FROM PYTHON SOURCE LINES 192-198
 
 Individual Conditional Expectation plots
 ----------------------------------------
@@ -723,13 +725,13 @@ Conditional Expectation (ICE) plot. ICE plots visualize the dependence of the
 prediction on a feature for each sample separately, with one line per sample.
 See the :ref:`User Guide <individual_conditional>`
 
-.. GENERATED FROM PYTHON SOURCE LINES 196-227
+.. GENERATED FROM PYTHON SOURCE LINES 198-229
 
 .. code-block:: Python
 
 
-    from sklearn.ensemble import RandomForestRegressor
     from sklearn.datasets import fetch_california_housing
+    from sklearn.ensemble import RandomForestRegressor
 
     # from sklearn.inspection import plot_partial_dependence
     from sklearn.inspection import PartialDependenceDisplay
@@ -770,7 +772,7 @@ See the :ref:`User Guide <individual_conditional>`
 
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 228-234
+.. GENERATED FROM PYTHON SOURCE LINES 230-236
 
 New Poisson splitting criterion for DecisionTreeRegressor
 ---------------------------------------------------------
@@ -779,15 +781,16 @@ The integration of Poisson regression estimation continues from version 0.23.
 splitting criterion. Setting `criterion="poisson"` might be a good choice
 if your target is a count or a frequency.
 
-.. GENERATED FROM PYTHON SOURCE LINES 234-248
+.. GENERATED FROM PYTHON SOURCE LINES 236-251
 
 .. code-block:: Python
 
 
-    from sklearn.tree import DecisionTreeRegressor
-    from sklearn.model_selection import train_test_split
     import numpy as np
 
+    from sklearn.model_selection import train_test_split
+    from sklearn.tree import DecisionTreeRegressor
+
     n_samples, n_features = 1000, 20
     rng = np.random.RandomState(0)
     X = rng.randn(n_samples, n_features)
@@ -1225,7 +1228,7 @@ if your target is a count or a frequency.
     <br />
     <br />
 
-.. GENERATED FROM PYTHON SOURCE LINES 249-265
+.. GENERATED FROM PYTHON SOURCE LINES 252-268
 
 New documentation improvements
 ------------------------------
@@ -1247,7 +1250,7 @@ to improve the understanding of machine learning practices:
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 12.891 seconds)
+   **Total running time of the script:** (0 minutes 12.848 seconds)
 
 
 .. _sphx_glr_download_auto_examples_release_highlights_plot_release_highlights_0_24_0.py:
diff --git a/dev/_sources/auto_examples/release_highlights/plot_release_highlights_1_0_0.rst.txt b/dev/_sources/auto_examples/release_highlights/plot_release_highlights_1_0_0.rst.txt
index 1dc9d98b22eed..dd23e296a9b34 100644
--- a/dev/_sources/auto_examples/release_highlights/plot_release_highlights_1_0_0.rst.txt
+++ b/dev/_sources/auto_examples/release_highlights/plot_release_highlights_1_0_0.rst.txt
@@ -106,12 +106,13 @@ parametrized by their polynomial degree and the positions of the knots. The
 The following code shows splines in action, for more information, please
 refer to the :ref:`User Guide <spline_transformer>`.
 
-.. GENERATED FROM PYTHON SOURCE LINES 90-99
+.. GENERATED FROM PYTHON SOURCE LINES 90-100
 
 .. code-block:: Python
 
 
     import numpy as np
+
     from sklearn.preprocessing import SplineTransformer
 
     X = np.arange(5).reshape(5, 1)
@@ -136,7 +137,7 @@ refer to the :ref:`User Guide <spline_transformer>`.
 
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 100-136
+.. GENERATED FROM PYTHON SOURCE LINES 101-137
 
 Quantile Regressor
 --------------------------------------------------------------------------
@@ -175,7 +176,7 @@ Guide <quantile_regression>` for more details.
    :align: center
    :scale: 50%
 
-.. GENERATED FROM PYTHON SOURCE LINES 138-150
+.. GENERATED FROM PYTHON SOURCE LINES 139-151
 
 Feature Names Support
 --------------------------------------------------------------------------
@@ -190,13 +191,14 @@ is used to check that the column names of the dataframe passed in
 non-:term:`fit`, such as :term:`predict`, are consistent with features in
 :term:`fit`:
 
-.. GENERATED FROM PYTHON SOURCE LINES 150-157
+.. GENERATED FROM PYTHON SOURCE LINES 151-159
 
 .. code-block:: Python
 
-    from sklearn.preprocessing import StandardScaler
     import pandas as pd
 
+    from sklearn.preprocessing import StandardScaler
+
     X = pd.DataFrame([[1, 2, 3], [4, 5, 6]], columns=["a", "b", "c"])
     scalar = StandardScaler().fit(X)
     scalar.feature_names_in_
@@ -214,7 +216,7 @@ non-:term:`fit`, such as :term:`predict`, are consistent with features in
 
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 158-165
+.. GENERATED FROM PYTHON SOURCE LINES 160-167
 
 The support of :term:`get_feature_names_out` is available for transformers
 that already had `get_feature_names` and transformers with a one-to-one
@@ -224,13 +226,14 @@ will be added to all other transformers in future releases. Additionally,
 :meth:`compose.ColumnTransformer.get_feature_names_out` is available to
 combine feature names of its transformers:
 
-.. GENERATED FROM PYTHON SOURCE LINES 165-180
+.. GENERATED FROM PYTHON SOURCE LINES 167-183
 
 .. code-block:: Python
 
+    import pandas as pd
+
     from sklearn.compose import ColumnTransformer
     from sklearn.preprocessing import OneHotEncoder
-    import pandas as pd
 
     X = pd.DataFrame({"pet": ["dog", "cat", "fish"], "age": [3, 7, 1]})
     preprocessor = ColumnTransformer(
@@ -256,13 +259,13 @@ combine feature names of its transformers:
 
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 181-184
+.. GENERATED FROM PYTHON SOURCE LINES 184-187
 
 When this ``preprocessor`` is used with a pipeline, the feature names used
 by the classifier are obtained by slicing and calling
 :term:`get_feature_names_out`:
 
-.. GENERATED FROM PYTHON SOURCE LINES 184-193
+.. GENERATED FROM PYTHON SOURCE LINES 187-196
 
 .. code-block:: Python
 
@@ -288,7 +291,7 @@ by the classifier are obtained by slicing and calling
 
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 194-206
+.. GENERATED FROM PYTHON SOURCE LINES 197-209
 
 A more flexible plotting API
 --------------------------------------------------------------------------
@@ -303,7 +306,7 @@ a plot given the predictions or an estimator. This means the corresponding
 <sphx_glr_auto_examples_classification_plot_digits_classification.py>` for
 how to use the new plotting functionalities.
 
-.. GENERATED FROM PYTHON SOURCE LINES 208-228
+.. GENERATED FROM PYTHON SOURCE LINES 211-231
 
 Online One-Class SVM
 --------------------------------------------------------------------------
@@ -326,7 +329,7 @@ details.
    :target: ../miscellaneous/plot_anomaly_comparison.html
    :align: center
 
-.. GENERATED FROM PYTHON SOURCE LINES 230-237
+.. GENERATED FROM PYTHON SOURCE LINES 233-240
 
 Histogram-based Gradient Boosting Models are now stable
 --------------------------------------------------------------------------
@@ -336,7 +339,7 @@ and can simply be imported and used as::
 
     from sklearn.ensemble import HistGradientBoostingClassifier
 
-.. GENERATED FROM PYTHON SOURCE LINES 239-244
+.. GENERATED FROM PYTHON SOURCE LINES 242-247
 
 New documentation improvements
 ------------------------------
@@ -347,7 +350,7 @@ documentation.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.016 seconds)
+   **Total running time of the script:** (0 minutes 0.015 seconds)
 
 
 .. _sphx_glr_download_auto_examples_release_highlights_plot_release_highlights_1_0_0.py:
diff --git a/dev/_sources/auto_examples/release_highlights/plot_release_highlights_1_1_0.rst.txt b/dev/_sources/auto_examples/release_highlights/plot_release_highlights_1_1_0.rst.txt
index c6670195cd812..96751f049d4e4 100644
--- a/dev/_sources/auto_examples/release_highlights/plot_release_highlights_1_1_0.rst.txt
+++ b/dev/_sources/auto_examples/release_highlights/plot_release_highlights_1_1_0.rst.txt
@@ -46,13 +46,14 @@ Quantile loss in :class:`~ensemble.HistGradientBoostingRegressor`
 :class:`~ensemble.HistGradientBoostingRegressor` can model quantiles with
 `loss="quantile"` and the new parameter `quantile`.
 
-.. GENERATED FROM PYTHON SOURCE LINES 31-56
+.. GENERATED FROM PYTHON SOURCE LINES 31-57
 
 .. code-block:: Python
 
-    from sklearn.ensemble import HistGradientBoostingRegressor
-    import numpy as np
     import matplotlib.pyplot as plt
+    import numpy as np
+
+    from sklearn.ensemble import HistGradientBoostingRegressor
 
     # Simple regression function for X * cos(X)
     rng = np.random.RandomState(42)
@@ -87,12 +88,12 @@ Quantile loss in :class:`~ensemble.HistGradientBoostingRegressor`
 
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 57-59
+.. GENERATED FROM PYTHON SOURCE LINES 58-60
 
 For a usecase example, see
 :ref:`sphx_glr_auto_examples_ensemble_plot_hgbt_regression.py`
 
-.. GENERATED FROM PYTHON SOURCE LINES 61-68
+.. GENERATED FROM PYTHON SOURCE LINES 62-69
 
 `get_feature_names_out` Available in all Transformers
 -----------------------------------------------------
@@ -102,17 +103,17 @@ concluding the implementation of
 This enables :class:`~pipeline.Pipeline` to construct the output feature names for
 more complex pipelines:
 
-.. GENERATED FROM PYTHON SOURCE LINES 68-98
+.. GENERATED FROM PYTHON SOURCE LINES 69-99
 
 .. code-block:: Python
 
     from sklearn.compose import ColumnTransformer
-    from sklearn.preprocessing import OneHotEncoder, StandardScaler
-    from sklearn.pipeline import make_pipeline
-    from sklearn.impute import SimpleImputer
-    from sklearn.feature_selection import SelectKBest
     from sklearn.datasets import fetch_openml
+    from sklearn.feature_selection import SelectKBest
+    from sklearn.impute import SimpleImputer
     from sklearn.linear_model import LogisticRegression
+    from sklearn.pipeline import make_pipeline
+    from sklearn.preprocessing import OneHotEncoder, StandardScaler
 
     X, y = fetch_openml(
         "titanic", version=1, as_frame=True, return_X_y=True, parser="pandas"
@@ -600,14 +601,14 @@ more complex pipelines:
     <br />
     <br />
 
-.. GENERATED FROM PYTHON SOURCE LINES 99-103
+.. GENERATED FROM PYTHON SOURCE LINES 100-104
 
 Here we slice the pipeline to include all the steps but the last one. The output
 feature names of this pipeline slice are the features put into logistic
 regression. These names correspond directly to the coefficients in the logistic
 regression:
 
-.. GENERATED FROM PYTHON SOURCE LINES 103-110
+.. GENERATED FROM PYTHON SOURCE LINES 104-111
 
 .. code-block:: Python
 
@@ -630,7 +631,7 @@ regression:
 
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 111-118
+.. GENERATED FROM PYTHON SOURCE LINES 112-119
 
 Grouping infrequent categories in :class:`~preprocessing.OneHotEncoder`
 -----------------------------------------------------------------------
@@ -640,13 +641,14 @@ the gathering of infrequent categories are `min_frequency` and
 `max_categories`. See the :ref:`User Guide <encoder_infrequent_categories>`
 for more details.
 
-.. GENERATED FROM PYTHON SOURCE LINES 118-127
+.. GENERATED FROM PYTHON SOURCE LINES 119-129
 
 .. code-block:: Python
 
-    from sklearn.preprocessing import OneHotEncoder
     import numpy as np
 
+    from sklearn.preprocessing import OneHotEncoder
+
     X = np.array(
         [["dog"] * 5 + ["cat"] * 20 + ["rabbit"] * 10 + ["snake"] * 3], dtype=object
     ).T
@@ -666,12 +668,12 @@ for more details.
 
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 128-130
+.. GENERATED FROM PYTHON SOURCE LINES 130-132
 
 Since dog and snake are infrequent categories, they are grouped together when
 transformed:
 
-.. GENERATED FROM PYTHON SOURCE LINES 130-133
+.. GENERATED FROM PYTHON SOURCE LINES 132-135
 
 .. code-block:: Python
 
@@ -741,7 +743,7 @@ transformed:
     <br />
     <br />
 
-.. GENERATED FROM PYTHON SOURCE LINES 134-174
+.. GENERATED FROM PYTHON SOURCE LINES 136-176
 
 Performance improvements
 ------------------------
@@ -784,7 +786,7 @@ Cython resulting in performance improvements for the following estimators:
 - :class:`linear_model.PoissonRegressor`
 - :class:`linear_model.TweedieRegressor`
 
-.. GENERATED FROM PYTHON SOURCE LINES 176-186
+.. GENERATED FROM PYTHON SOURCE LINES 178-188
 
 :class:`~decomposition.MiniBatchNMF`: an online version of NMF
 --------------------------------------------------------------
@@ -797,11 +799,12 @@ particular, it implements `partial_fit`, which can be used for online
 learning when the data is not readily available from the start, or when the
 data does not fit into memory.
 
-.. GENERATED FROM PYTHON SOURCE LINES 186-209
+.. GENERATED FROM PYTHON SOURCE LINES 188-212
 
 .. code-block:: Python
 
     import numpy as np
+
     from sklearn.decomposition import MiniBatchNMF
 
     rng = np.random.RandomState(0)
@@ -820,7 +823,7 @@ data does not fit into memory.
     X_reconstructed = W @ H
 
     print(
-        f"relative reconstruction error: ",
+        "relative reconstruction error: ",
         f"{np.sum((X - X_reconstructed) ** 2) / np.sum(X**2):.5f}",
     )
 
@@ -837,7 +840,7 @@ data does not fit into memory.
 
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 210-218
+.. GENERATED FROM PYTHON SOURCE LINES 213-221
 
 :class:`~cluster.BisectingKMeans`: divide and cluster
 -----------------------------------------------------
@@ -848,14 +851,15 @@ previous clustering: a cluster is split into two new clusters repeatedly
 until the target number of clusters is reached, giving a hierarchical
 structure to the clustering.
 
-.. GENERATED FROM PYTHON SOURCE LINES 218-237
+.. GENERATED FROM PYTHON SOURCE LINES 221-241
 
 .. code-block:: Python
 
-    from sklearn.datasets import make_blobs
-    from sklearn.cluster import KMeans, BisectingKMeans
     import matplotlib.pyplot as plt
 
+    from sklearn.cluster import BisectingKMeans, KMeans
+    from sklearn.datasets import make_blobs
+
     X, _ = make_blobs(n_samples=1000, centers=2, random_state=0)
 
     km = KMeans(n_clusters=5, random_state=0, n_init="auto").fit(X)
@@ -886,7 +890,7 @@ structure to the clustering.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.947 seconds)
+   **Total running time of the script:** (0 minutes 0.946 seconds)
 
 
 .. _sphx_glr_download_auto_examples_release_highlights_plot_release_highlights_1_1_0.py:
diff --git a/dev/_sources/auto_examples/release_highlights/plot_release_highlights_1_2_0.rst.txt b/dev/_sources/auto_examples/release_highlights/plot_release_highlights_1_2_0.rst.txt
index b7b384e6f3f56..178b3bf12b5ad 100644
--- a/dev/_sources/auto_examples/release_highlights/plot_release_highlights_1_2_0.rst.txt
+++ b/dev/_sources/auto_examples/release_highlights/plot_release_highlights_1_2_0.rst.txt
@@ -47,15 +47,16 @@ To learn more about the `set_output` API see the example:
 # this `video, pandas DataFrame output for scikit-learn transformers
 (some examples) <https://youtu.be/5bCg8VfX2x8>`__.
 
-.. GENERATED FROM PYTHON SOURCE LINES 32-57
+.. GENERATED FROM PYTHON SOURCE LINES 32-58
 
 .. code-block:: Python
 
 
     import numpy as np
-    from sklearn.datasets import load_iris
-    from sklearn.preprocessing import StandardScaler, KBinsDiscretizer
+
     from sklearn.compose import ColumnTransformer
+    from sklearn.datasets import load_iris
+    from sklearn.preprocessing import KBinsDiscretizer, StandardScaler
 
     X, y = load_iris(as_frame=True, return_X_y=True)
     sepal_cols = ["sepal length (cm)", "sepal width (cm)"]
@@ -151,7 +152,7 @@ To learn more about the `set_output` API see the example:
     <br />
     <br />
 
-.. GENERATED FROM PYTHON SOURCE LINES 58-65
+.. GENERATED FROM PYTHON SOURCE LINES 59-66
 
 Interaction constraints in Histogram-based Gradient Boosting Trees
 ------------------------------------------------------------------
@@ -161,7 +162,7 @@ with the `interaction_cst` parameter. For details, see the
 :ref:`User Guide <interaction_cst_hgbt>`. In the following example, features are not
 allowed to interact.
 
-.. GENERATED FROM PYTHON SOURCE LINES 65-75
+.. GENERATED FROM PYTHON SOURCE LINES 66-76
 
 .. code-block:: Python
 
@@ -607,18 +608,19 @@ allowed to interact.
     <br />
     <br />
 
-.. GENERATED FROM PYTHON SOURCE LINES 76-80
+.. GENERATED FROM PYTHON SOURCE LINES 77-81
 
 New and enhanced displays
 -------------------------
 :class:`~metrics.PredictionErrorDisplay` provides a way to analyze regression
 models in a qualitative manner.
 
-.. GENERATED FROM PYTHON SOURCE LINES 80-91
+.. GENERATED FROM PYTHON SOURCE LINES 81-93
 
 .. code-block:: Python
 
     import matplotlib.pyplot as plt
+
     from sklearn.metrics import PredictionErrorDisplay
 
     fig, axs = plt.subplots(nrows=1, ncols=2, figsize=(12, 5))
@@ -641,12 +643,12 @@ models in a qualitative manner.
 
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 92-94
+.. GENERATED FROM PYTHON SOURCE LINES 94-96
 
 :class:`~model_selection.LearningCurveDisplay` is now available to plot
 results from :func:`~model_selection.learning_curve`.
 
-.. GENERATED FROM PYTHON SOURCE LINES 94-100
+.. GENERATED FROM PYTHON SOURCE LINES 96-102
 
 .. code-block:: Python
 
@@ -668,13 +670,13 @@ results from :func:`~model_selection.learning_curve`.
 
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 101-104
+.. GENERATED FROM PYTHON SOURCE LINES 103-106
 
 :class:`~inspection.PartialDependenceDisplay` exposes a new parameter
 `categorical_features` to display partial dependence for categorical features
 using bar plots and heatmaps.
 
-.. GENERATED FROM PYTHON SOURCE LINES 104-111
+.. GENERATED FROM PYTHON SOURCE LINES 106-113
 
 .. code-block:: Python
 
@@ -692,12 +694,12 @@ using bar plots and heatmaps.
 
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 112-124
+.. GENERATED FROM PYTHON SOURCE LINES 114-126
 
 .. code-block:: Python
 
-    from sklearn.preprocessing import OrdinalEncoder
     from sklearn.pipeline import make_pipeline
+    from sklearn.preprocessing import OrdinalEncoder
 
     categorical_features = ["pclass", "sex", "embarked"]
     model = make_pipeline(
@@ -715,7 +717,7 @@ using bar plots and heatmaps.
 
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 125-136
+.. GENERATED FROM PYTHON SOURCE LINES 127-138
 
 .. code-block:: Python
 
@@ -742,7 +744,7 @@ using bar plots and heatmaps.
 
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 137-143
+.. GENERATED FROM PYTHON SOURCE LINES 139-145
 
 Faster parser in :func:`~datasets.fetch_openml`
 -----------------------------------------------
@@ -751,7 +753,7 @@ more memory and CPU efficient. In v1.4, the default will change to
 `parser="auto"` which will automatically use the `"pandas"` parser for dense
 data and `"liac-arff"` for sparse data.
 
-.. GENERATED FROM PYTHON SOURCE LINES 143-148
+.. GENERATED FROM PYTHON SOURCE LINES 145-150
 
 .. code-block:: Python
 
@@ -889,7 +891,7 @@ data and `"liac-arff"` for sparse data.
     <br />
     <br />
 
-.. GENERATED FROM PYTHON SOURCE LINES 149-156
+.. GENERATED FROM PYTHON SOURCE LINES 151-158
 
 Experimental Array API support in :class:`~discriminant_analysis.LinearDiscriminantAnalysis`
 --------------------------------------------------------------------------------------------
@@ -899,7 +901,7 @@ The estimator can now run on any Array API compliant libraries such as
 `CuPy <https://docs.cupy.dev/en/stable/overview.html>`__, a GPU-accelerated array
 library. For details, see the :ref:`User Guide <array_api>`.
 
-.. GENERATED FROM PYTHON SOURCE LINES 158-171
+.. GENERATED FROM PYTHON SOURCE LINES 160-173
 
 Improved efficiency of many estimators
 --------------------------------------
@@ -918,7 +920,7 @@ A detailed list of the impacted estimators can be found in the
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 5.228 seconds)
+   **Total running time of the script:** (0 minutes 4.844 seconds)
 
 
 .. _sphx_glr_download_auto_examples_release_highlights_plot_release_highlights_1_2_0.py:
diff --git a/dev/_sources/auto_examples/release_highlights/plot_release_highlights_1_3_0.rst.txt b/dev/_sources/auto_examples/release_highlights/plot_release_highlights_1_3_0.rst.txt
index 086ee0c385d31..3580263457e25 100644
--- a/dev/_sources/auto_examples/release_highlights/plot_release_highlights_1_3_0.rst.txt
+++ b/dev/_sources/auto_examples/release_highlights/plot_release_highlights_1_3_0.rst.txt
@@ -68,11 +68,12 @@ values simultaneously, :class:`cluster.HDBSCAN` finds clusters of varying densit
 making it more robust to parameter selection than :class:`cluster.DBSCAN`.
 More details in the :ref:`User Guide <hdbscan>`.
 
-.. GENERATED FROM PYTHON SOURCE LINES 52-66
+.. GENERATED FROM PYTHON SOURCE LINES 52-67
 
 .. code-block:: Python
 
     import numpy as np
+
     from sklearn.cluster import HDBSCAN
     from sklearn.datasets import load_digits
     from sklearn.metrics import v_measure_score
@@ -101,7 +102,7 @@ More details in the :ref:`User Guide <hdbscan>`.
 
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 67-73
+.. GENERATED FROM PYTHON SOURCE LINES 68-74
 
 TargetEncoder: a new category encoding strategy
 -----------------------------------------------
@@ -110,11 +111,12 @@ Well suited for categorical features with high cardinality,
 estimate of the average target values for observations belonging to that category.
 More details in the :ref:`User Guide <target_encoder>`.
 
-.. GENERATED FROM PYTHON SOURCE LINES 73-84
+.. GENERATED FROM PYTHON SOURCE LINES 74-86
 
 .. code-block:: Python
 
     import numpy as np
+
     from sklearn.preprocessing import TargetEncoder
 
     X = np.array([["cat"] * 30 + ["dog"] * 20 + ["snake"] * 38], dtype=object).T
@@ -138,7 +140,7 @@ More details in the :ref:`User Guide <target_encoder>`.
 
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 85-94
+.. GENERATED FROM PYTHON SOURCE LINES 87-96
 
 Missing values support in decision trees
 ----------------------------------------
@@ -150,11 +152,12 @@ See more details in the :ref:`User Guide <tree_missing_value_support>` or see
 :ref:`sphx_glr_auto_examples_ensemble_plot_hgbt_regression.py` for a usecase
 example of this feature in :class:`~ensemble.HistGradientBoostingRegressor`.
 
-.. GENERATED FROM PYTHON SOURCE LINES 94-103
+.. GENERATED FROM PYTHON SOURCE LINES 96-106
 
 .. code-block:: Python
 
     import numpy as np
+
     from sklearn.tree import DecisionTreeClassifier
 
     X = np.array([0, 1, 6, np.nan]).reshape(-1, 1)
@@ -176,14 +179,14 @@ example of this feature in :class:`~ensemble.HistGradientBoostingRegressor`.
 
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 104-108
+.. GENERATED FROM PYTHON SOURCE LINES 107-111
 
 New display :class:`~model_selection.ValidationCurveDisplay`
 ------------------------------------------------------------
 :class:`model_selection.ValidationCurveDisplay` is now available to plot results
 from :func:`model_selection.validation_curve`.
 
-.. GENERATED FROM PYTHON SOURCE LINES 108-124
+.. GENERATED FROM PYTHON SOURCE LINES 111-127
 
 .. code-block:: Python
 
@@ -215,7 +218,7 @@ from :func:`model_selection.validation_curve`.
 
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 125-130
+.. GENERATED FROM PYTHON SOURCE LINES 128-133
 
 Gamma loss for gradient boosting
 --------------------------------
@@ -223,14 +226,15 @@ The class :class:`ensemble.HistGradientBoostingRegressor` supports the
 Gamma deviance loss function via `loss="gamma"`. This loss function is useful for
 modeling strictly positive targets with a right-skewed distribution.
 
-.. GENERATED FROM PYTHON SOURCE LINES 130-143
+.. GENERATED FROM PYTHON SOURCE LINES 133-147
 
 .. code-block:: Python
 
     import numpy as np
-    from sklearn.model_selection import cross_val_score
+
     from sklearn.datasets import make_low_rank_matrix
     from sklearn.ensemble import HistGradientBoostingRegressor
+    from sklearn.model_selection import cross_val_score
 
     n_samples, n_features = 500, 10
     rng = np.random.RandomState(0)
@@ -253,7 +257,7 @@ modeling strictly positive targets with a right-skewed distribution.
 
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 144-151
+.. GENERATED FROM PYTHON SOURCE LINES 148-155
 
 Grouping infrequent categories in :class:`~preprocessing.OrdinalEncoder`
 ------------------------------------------------------------------------
@@ -263,13 +267,14 @@ into a single output for each feature. The parameters to enable the gathering of
 infrequent categories are `min_frequency` and `max_categories`.
 See the :ref:`User Guide <encoder_infrequent_categories>` for more details.
 
-.. GENERATED FROM PYTHON SOURCE LINES 151-159
+.. GENERATED FROM PYTHON SOURCE LINES 155-164
 
 .. code-block:: Python
 
-    from sklearn.preprocessing import OrdinalEncoder
     import numpy as np
 
+    from sklearn.preprocessing import OrdinalEncoder
+
     X = np.array(
         [["dog"] * 5 + ["cat"] * 20 + ["rabbit"] * 10 + ["snake"] * 3], dtype=object
     ).T
@@ -291,7 +296,7 @@ See the :ref:`User Guide <encoder_infrequent_categories>` for more details.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 1.489 seconds)
+   **Total running time of the script:** (0 minutes 1.441 seconds)
 
 
 .. _sphx_glr_download_auto_examples_release_highlights_plot_release_highlights_1_3_0.py:
diff --git a/dev/_sources/auto_examples/release_highlights/plot_release_highlights_1_4_0.rst.txt b/dev/_sources/auto_examples/release_highlights/plot_release_highlights_1_4_0.rst.txt
index a62f7821f0b00..85f0907b12e47 100644
--- a/dev/_sources/auto_examples/release_highlights/plot_release_highlights_1_4_0.rst.txt
+++ b/dev/_sources/auto_examples/release_highlights/plot_release_highlights_1_4_0.rst.txt
@@ -94,8 +94,8 @@ algorithm:
 .. code-block:: Python
 
     from sklearn.ensemble import HistGradientBoostingClassifier
-    from sklearn.model_selection import train_test_split
     from sklearn.metrics import roc_auc_score
+    from sklearn.model_selection import train_test_split
 
     X_train, X_test, y_train, y_test = train_test_split(X_adult, y_adult, random_state=0)
     hist = HistGradientBoostingClassifier(categorical_features="from_dtype")
@@ -112,7 +112,7 @@ algorithm:
 
  .. code-block:: none
 
-    ROC AUC score is 0.928592269276614
+    ROC AUC score is 0.9283255323708193
 
 
 
@@ -128,9 +128,9 @@ scikit-learn's transformers now support polars output with the `set_output` API.
 .. code-block:: Python
 
     import polars as pl
-    from sklearn.preprocessing import StandardScaler
-    from sklearn.preprocessing import OneHotEncoder
+
     from sklearn.compose import ColumnTransformer
+    from sklearn.preprocessing import OneHotEncoder, StandardScaler
 
     df = pl.DataFrame(
         {"height": [120, 140, 150, 110, 100], "pet": ["dog", "cat", "dog", "cat", "cat"]}
@@ -196,11 +196,12 @@ every individual tree, the splitter evaluates each potential threshold with the
 missing values going to the left and right nodes. More details in the
 :ref:`User Guide <tree_missing_value_support>`.
 
-.. GENERATED FROM PYTHON SOURCE LINES 89-98
+.. GENERATED FROM PYTHON SOURCE LINES 89-99
 
 .. code-block:: Python
 
     import numpy as np
+
     from sklearn.ensemble import RandomForestClassifier
 
     X = np.array([0, 1, 6, np.nan]).reshape(-1, 1)
@@ -222,7 +223,7 @@ missing values going to the left and right nodes. More details in the
 
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 99-105
+.. GENERATED FROM PYTHON SOURCE LINES 100-106
 
 Add support for monotonic constraints in tree-based models
 ----------------------------------------------------------
@@ -231,13 +232,14 @@ in scikit-learn 0.23, we now support this feature for all other tree-based model
 trees, random forests, extra-trees, and exact gradient boosting. Here, we show this
 feature for random forest on a regression problem.
 
-.. GENERATED FROM PYTHON SOURCE LINES 105-140
+.. GENERATED FROM PYTHON SOURCE LINES 106-142
 
 .. code-block:: Python
 
     import matplotlib.pyplot as plt
-    from sklearn.inspection import PartialDependenceDisplay
+
     from sklearn.ensemble import RandomForestRegressor
+    from sklearn.inspection import PartialDependenceDisplay
 
     n_samples = 500
     rng = np.random.RandomState(0)
@@ -282,13 +284,13 @@ feature for random forest on a regression problem.
 
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 141-144
+.. GENERATED FROM PYTHON SOURCE LINES 143-146
 
 Enriched estimator displays
 ---------------------------
 Estimators displays have been enriched: if we look at `forest`, defined above:
 
-.. GENERATED FROM PYTHON SOURCE LINES 144-146
+.. GENERATED FROM PYTHON SOURCE LINES 146-148
 
 .. code-block:: Python
 
@@ -722,7 +724,7 @@ Estimators displays have been enriched: if we look at `forest`, defined above:
     <br />
     <br />
 
-.. GENERATED FROM PYTHON SOURCE LINES 147-152
+.. GENERATED FROM PYTHON SOURCE LINES 149-154
 
 One can access the documentation of the estimator by clicking on the icon "?" on
 the top right corner of the diagram.
@@ -730,7 +732,7 @@ the top right corner of the diagram.
 In addition, the display changes color, from orange to blue, when the estimator is
 fitted. You can also get this information by hovering on the icon "i".
 
-.. GENERATED FROM PYTHON SOURCE LINES 152-156
+.. GENERATED FROM PYTHON SOURCE LINES 154-158
 
 .. code-block:: Python
 
@@ -1166,7 +1168,7 @@ fitted. You can also get this information by hovering on the icon "i".
     <br />
     <br />
 
-.. GENERATED FROM PYTHON SOURCE LINES 157-163
+.. GENERATED FROM PYTHON SOURCE LINES 159-165
 
 Metadata Routing Support
 ------------------------
@@ -1175,15 +1177,15 @@ routing, which are listed in the :ref:`user guide
 <metadata_routing_models>`. For instance, this is how you can do a nested
 cross-validation with sample weights and :class:`~model_selection.GroupKFold`:
 
-.. GENERATED FROM PYTHON SOURCE LINES 163-211
+.. GENERATED FROM PYTHON SOURCE LINES 165-213
 
 .. code-block:: Python
 
     import sklearn
-    from sklearn.metrics import get_scorer
     from sklearn.datasets import make_regression
     from sklearn.linear_model import Lasso
-    from sklearn.model_selection import GridSearchCV, cross_validate, GroupKFold
+    from sklearn.metrics import get_scorer
+    from sklearn.model_selection import GridSearchCV, GroupKFold, cross_validate
 
     # For now by default metadata routing is disabled, and need to be explicitly
     # enabled.
@@ -1235,12 +1237,12 @@ cross-validation with sample weights and :class:`~model_selection.GroupKFold`:
 
  .. code-block:: none
 
-    cv error on test sets: [-0.27507931 -0.22314656 -0.30565693 -0.36850513 -0.22465845]
+    cv error on test sets: [-0.34132945 -0.33254954 -0.29704428 -0.17925063 -0.35690129]
 
 
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 212-219
+.. GENERATED FROM PYTHON SOURCE LINES 214-221
 
 Improved memory and runtime efficiency for PCA on sparse data
 -------------------------------------------------------------
@@ -1250,14 +1252,16 @@ materializing large sparse matrices when performing the
 eigenvalue decomposition of the data set covariance matrix.
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 219-235
+.. GENERATED FROM PYTHON SOURCE LINES 221-239
 
 .. code-block:: Python
 
-    from sklearn.decomposition import PCA
-    import scipy.sparse as sp
     from time import time
 
+    import scipy.sparse as sp
+
+    from sklearn.decomposition import PCA
+
     X_sparse = sp.random(m=1000, n=1000, random_state=0)
     X_dense = X_sparse.toarray()
 
@@ -1278,7 +1282,7 @@ eigenvalue decomposition of the data set covariance matrix.
 
  .. code-block:: none
 
-    Speedup: 3.5x
+    Speedup: 4.1x
 
 
 
@@ -1286,7 +1290,7 @@ eigenvalue decomposition of the data set covariance matrix.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 2.090 seconds)
+   **Total running time of the script:** (0 minutes 2.142 seconds)
 
 
 .. _sphx_glr_download_auto_examples_release_highlights_plot_release_highlights_1_4_0.py:
diff --git a/dev/_sources/auto_examples/release_highlights/plot_release_highlights_1_5_0.rst.txt b/dev/_sources/auto_examples/release_highlights/plot_release_highlights_1_5_0.rst.txt
index 5e25a177fc1d9..7a3f9298c76ba 100644
--- a/dev/_sources/auto_examples/release_highlights/plot_release_highlights_1_5_0.rst.txt
+++ b/dev/_sources/auto_examples/release_highlights/plot_release_highlights_1_5_0.rst.txt
@@ -47,15 +47,14 @@ predictions. However, 0.5 is almost never the desired threshold for a given
 problem. :class:`~model_selection.FixedThresholdClassifier` allows wrapping any
 binary classifier and setting a custom decision threshold.
 
-.. GENERATED FROM PYTHON SOURCE LINES 32-44
+.. GENERATED FROM PYTHON SOURCE LINES 32-43
 
 .. code-block:: Python
 
     from sklearn.datasets import make_classification
-    from sklearn.model_selection import train_test_split
     from sklearn.linear_model import LogisticRegression
     from sklearn.metrics import ConfusionMatrixDisplay
-
+    from sklearn.model_selection import train_test_split
 
     X, y = make_classification(n_samples=10_000, weights=[0.9, 0.1], random_state=0)
     X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
@@ -75,13 +74,13 @@ binary classifier and setting a custom decision threshold.
 
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 45-48
+.. GENERATED FROM PYTHON SOURCE LINES 44-47
 
 Lowering the threshold, i.e. allowing more samples to be classified as the positive
 class, increases the number of true positives at the cost of more false positives
 (as is well known from the concavity of the ROC curve).
 
-.. GENERATED FROM PYTHON SOURCE LINES 48-54
+.. GENERATED FROM PYTHON SOURCE LINES 47-53
 
 .. code-block:: Python
 
@@ -103,7 +102,7 @@ class, increases the number of true positives at the cost of more false positive
 
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 55-75
+.. GENERATED FROM PYTHON SOURCE LINES 54-74
 
 TunedThresholdClassifierCV: Tuning the decision threshold of a binary classifier
 --------------------------------------------------------------------------------
@@ -126,7 +125,7 @@ Let's illustrate this by considering an arbitrary case where:
 Our metric quantifies the average profit per sample, which is defined by the
 following Python function:
 
-.. GENERATED FROM PYTHON SOURCE LINES 75-86
+.. GENERATED FROM PYTHON SOURCE LINES 74-85
 
 .. code-block:: Python
 
@@ -155,7 +154,7 @@ following Python function:
 
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 87-93
+.. GENERATED FROM PYTHON SOURCE LINES 86-92
 
 It is interesting to observe that the average gain per prediction is negative
 which means that this decision system is making a loss on average.
@@ -164,12 +163,12 @@ Tuning the threshold to optimize this custom metric gives a smaller threshold
 that allows more samples to be classified as the positive class. As a result,
 the average gain per prediction improves.
 
-.. GENERATED FROM PYTHON SOURCE LINES 93-106
+.. GENERATED FROM PYTHON SOURCE LINES 92-105
 
 .. code-block:: Python
 
-    from sklearn.model_selection import TunedThresholdClassifierCV
     from sklearn.metrics import make_scorer
+    from sklearn.model_selection import TunedThresholdClassifierCV
 
     custom_scorer = make_scorer(
         custom_score, response_method="predict", greater_is_better=True
@@ -195,7 +194,7 @@ the average gain per prediction improves.
 
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 107-121
+.. GENERATED FROM PYTHON SOURCE LINES 106-120
 
 We observe that tuning the decision threshold can turn a machine
 learning-based system that makes a loss on average into a beneficial one.
@@ -212,7 +211,7 @@ detailed in :ref:`Post-tuning the decision threshold for cost-sensitive
 learning
 <sphx_glr_auto_examples_model_selection_plot_cost_sensitive_learning.py>`.
 
-.. GENERATED FROM PYTHON SOURCE LINES 123-128
+.. GENERATED FROM PYTHON SOURCE LINES 122-127
 
 Performance improvements in PCA
 -------------------------------
@@ -220,7 +219,7 @@ Performance improvements in PCA
 up to an order of magnitude faster and more memory efficient than the other
 solvers for datasets with many data points and few features.
 
-.. GENERATED FROM PYTHON SOURCE LINES 128-139
+.. GENERATED FROM PYTHON SOURCE LINES 127-138
 
 .. code-block:: Python
 
@@ -248,11 +247,11 @@ solvers for datasets with many data points and few features.
 
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 140-141
+.. GENERATED FROM PYTHON SOURCE LINES 139-140
 
 The new solver also accepts sparse input data:
 
-.. GENERATED FROM PYTHON SOURCE LINES 141-148
+.. GENERATED FROM PYTHON SOURCE LINES 140-147
 
 .. code-block:: Python
 
@@ -276,7 +275,7 @@ The new solver also accepts sparse input data:
 
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 149-157
+.. GENERATED FROM PYTHON SOURCE LINES 148-156
 
 The `"full"` solver has also been improved to use less memory and allows
 faster transformation. The default `svd_solver="auto"` option takes
@@ -287,20 +286,21 @@ Similarly to most other PCA solvers, the new `"covariance_eigh"` solver can leve
 GPU computation if the input data is passed as a PyTorch or CuPy array by
 enabling the experimental support for :ref:`Array API <array_api>`.
 
-.. GENERATED FROM PYTHON SOURCE LINES 159-163
+.. GENERATED FROM PYTHON SOURCE LINES 158-162
 
 ColumnTransformer is subscriptable
 ----------------------------------
 The transformers of a :class:`~compose.ColumnTransformer` can now be directly
 accessed using indexing by name.
 
-.. GENERATED FROM PYTHON SOURCE LINES 163-177
+.. GENERATED FROM PYTHON SOURCE LINES 162-177
 
 .. code-block:: Python
 
     import numpy as np
+
     from sklearn.compose import ColumnTransformer
-    from sklearn.preprocessing import StandardScaler, OneHotEncoder
+    from sklearn.preprocessing import OneHotEncoder, StandardScaler
 
     X = np.array([[0, 1, 2], [3, 4, 5]])
     column_transformer = ColumnTransformer(
@@ -427,7 +427,7 @@ non-numeric arrays using a callable metric.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.701 seconds)
+   **Total running time of the script:** (0 minutes 0.727 seconds)
 
 
 .. _sphx_glr_download_auto_examples_release_highlights_plot_release_highlights_1_5_0.py:
diff --git a/dev/_sources/auto_examples/release_highlights/plot_release_highlights_1_6_0.rst.txt b/dev/_sources/auto_examples/release_highlights/plot_release_highlights_1_6_0.rst.txt
index a54bdf1f89f7a..f7c4bea2b2a5a 100644
--- a/dev/_sources/auto_examples/release_highlights/plot_release_highlights_1_6_0.rst.txt
+++ b/dev/_sources/auto_examples/release_highlights/plot_release_highlights_1_6_0.rst.txt
@@ -49,12 +49,13 @@ original estimator's other methods and properties are left unchanged. An interes
 use case for this is to use a pre-fitted model as a transformer step in a pipeline
 or to pass a pre-fitted model to some of the meta-estimators. Here's a short example:
 
-.. GENERATED FROM PYTHON SOURCE LINES 34-56
+.. GENERATED FROM PYTHON SOURCE LINES 34-57
 
 .. code-block:: Python
 
 
     import time
+
     from sklearn.datasets import make_classification
     from sklearn.frozen import FrozenEstimator
     from sklearn.linear_model import SGDClassifier
@@ -83,18 +84,18 @@ or to pass a pre-fitted model to some of the meta-estimators. Here's a short exa
 
  .. code-block:: none
 
-    Fitting the classifier took 3.88 milliseconds
-    Fitting the threshold classifier took 0.60 milliseconds
+    Fitting the classifier took 4.21 milliseconds
+    Fitting the threshold classifier took 0.59 milliseconds
 
 
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 57-59
+.. GENERATED FROM PYTHON SOURCE LINES 58-60
 
 Fitting the threshold classifier skipped fitting the inner `SGDClassifier`. For more
 details refer to the example :ref:`sphx_glr_auto_examples_frozen_plot_frozen_examples.py`.
 
-.. GENERATED FROM PYTHON SOURCE LINES 61-95
+.. GENERATED FROM PYTHON SOURCE LINES 62-96
 
 Transforming data other than X in a Pipeline
 --------------------------------------------
@@ -131,7 +132,7 @@ Note that at this time scikit-learn estimators have not yet been extended to acc
 user specified validation sets. This feature is released early to collect feedback
 from third-party libraries who might benefit from it.
 
-.. GENERATED FROM PYTHON SOURCE LINES 97-116
+.. GENERATED FROM PYTHON SOURCE LINES 98-117
 
 Multiclass support for `LogisticRegression(solver="newton-cholesky")`
 ---------------------------------------------------------------------
@@ -153,7 +154,7 @@ categorical features as can be seen in the `benchmark results of the
 pull-request
 <https://github.com/scikit-learn/scikit-learn/pull/28840#issuecomment-2065368727>`_.
 
-.. GENERATED FROM PYTHON SOURCE LINES 118-124
+.. GENERATED FROM PYTHON SOURCE LINES 119-125
 
 Missing value support for Extra Trees
 -------------------------------------
@@ -162,11 +163,12 @@ The classes :class:`ensemble.ExtraTreesClassifier` and
 :class:`ensemble.ExtraTreesRegressor` now support missing values. More details in the
 :ref:`User Guide <tree_missing_value_support>`.
 
-.. GENERATED FROM PYTHON SOURCE LINES 124-133
+.. GENERATED FROM PYTHON SOURCE LINES 125-135
 
 .. code-block:: Python
 
     import numpy as np
+
     from sklearn.ensemble import ExtraTreesClassifier
 
     X = np.array([0, 1, 6, np.nan]).reshape(-1, 1)
@@ -188,7 +190,7 @@ The classes :class:`ensemble.ExtraTreesClassifier` and
 
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 134-147
+.. GENERATED FROM PYTHON SOURCE LINES 136-149
 
 Download any dataset from the web
 ---------------------------------
@@ -204,7 +206,7 @@ formats.
 The downloaded file can then be loaded with generic or domain specific functions such
 as `pandas.read_csv`, `pandas.read_parquet`, etc.
 
-.. GENERATED FROM PYTHON SOURCE LINES 149-159
+.. GENERATED FROM PYTHON SOURCE LINES 151-161
 
 Array API support
 -----------------
@@ -217,7 +219,7 @@ from the :mod:`sklearn.model_selection` module and the metrics from the
 Please refer to the :ref:`array API support<array_api>` page for instructions to use
 scikit-learn with array API compatible libraries such as PyTorch or CuPy.
 
-.. GENERATED FROM PYTHON SOURCE LINES 161-167
+.. GENERATED FROM PYTHON SOURCE LINES 163-169
 
 Almost complete Metadata Routing support
 ----------------------------------------
@@ -226,7 +228,7 @@ Support for routing metadata has been added to all remaining estimators and
 functions except AdaBoost. See :ref:`Metadata Routing User Guide <metadata_routing>`
 for more details.
 
-.. GENERATED FROM PYTHON SOURCE LINES 169-184
+.. GENERATED FROM PYTHON SOURCE LINES 171-186
 
 Free-threaded CPython 3.13 support
 ----------------------------------
@@ -244,7 +246,7 @@ and `Ecosystem compatibility tracking <https://py-free-threading.github.io/track
 
 Feel free to try free-threaded CPython on your use case and report any issues!
 
-.. GENERATED FROM PYTHON SOURCE LINES 186-213
+.. GENERATED FROM PYTHON SOURCE LINES 188-215
 
 Improvements to the developer API for third party libraries
 -----------------------------------------------------------
@@ -277,7 +279,7 @@ check out.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.102 seconds)
+   **Total running time of the script:** (0 minutes 0.094 seconds)
 
 
 .. _sphx_glr_download_auto_examples_release_highlights_plot_release_highlights_1_6_0.py:
diff --git a/dev/_sources/auto_examples/semi_supervised/index.rst.txt b/dev/_sources/auto_examples/semi_supervised/index.rst.txt
index 600bec996ef3d..bab4cc8716e08 100644
--- a/dev/_sources/auto_examples/semi_supervised/index.rst.txt
+++ b/dev/_sources/auto_examples/semi_supervised/index.rst.txt
@@ -53,52 +53,52 @@ Examples concerning the :mod:`sklearn.semi_supervised` module.
 
 .. raw:: html
 
-    <div class="sphx-glr-thumbcontainer" tooltip="Demonstrates an active learning technique to learn handwritten digits using label propagation.">
+    <div class="sphx-glr-thumbcontainer" tooltip="Example of LabelPropagation learning a complex internal structure to demonstrate &quot;manifold learning&quot;. The outer circle should be labeled &quot;red&quot; and the inner circle &quot;blue&quot;. Because both label groups lie inside their own distinct shape, we can see that the labels propagate correctly around the circle.">
 
 .. only:: html
 
-  .. image:: /auto_examples/semi_supervised/images/thumb/sphx_glr_plot_label_propagation_digits_active_learning_thumb.png
+  .. image:: /auto_examples/semi_supervised/images/thumb/sphx_glr_plot_label_propagation_structure_thumb.png
     :alt:
 
-  :ref:`sphx_glr_auto_examples_semi_supervised_plot_label_propagation_digits_active_learning.py`
+  :ref:`sphx_glr_auto_examples_semi_supervised_plot_label_propagation_structure.py`
 
 .. raw:: html
 
-      <div class="sphx-glr-thumbnail-title">Label Propagation digits active learning</div>
+      <div class="sphx-glr-thumbnail-title">Label Propagation circles: Learning a complex structure</div>
     </div>
 
 
 .. raw:: html
 
-    <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates the power of semisupervised learning by training a Label Spreading model to classify handwritten digits with sets of very few labels.">
+    <div class="sphx-glr-thumbcontainer" tooltip="Demonstrates an active learning technique to learn handwritten digits using label propagation.">
 
 .. only:: html
 
-  .. image:: /auto_examples/semi_supervised/images/thumb/sphx_glr_plot_label_propagation_digits_thumb.png
+  .. image:: /auto_examples/semi_supervised/images/thumb/sphx_glr_plot_label_propagation_digits_active_learning_thumb.png
     :alt:
 
-  :ref:`sphx_glr_auto_examples_semi_supervised_plot_label_propagation_digits.py`
+  :ref:`sphx_glr_auto_examples_semi_supervised_plot_label_propagation_digits_active_learning.py`
 
 .. raw:: html
 
-      <div class="sphx-glr-thumbnail-title">Label Propagation digits: Demonstrating performance</div>
+      <div class="sphx-glr-thumbnail-title">Label Propagation digits: Active learning</div>
     </div>
 
 
 .. raw:: html
 
-    <div class="sphx-glr-thumbcontainer" tooltip="Example of LabelPropagation learning a complex internal structure to demonstrate &quot;manifold learning&quot;. The outer circle should be labeled &quot;red&quot; and the inner circle &quot;blue&quot;. Because both label groups lie inside their own distinct shape, we can see that the labels propagate correctly around the circle.">
+    <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates the power of semisupervised learning by training a Label Spreading model to classify handwritten digits with sets of very few labels.">
 
 .. only:: html
 
-  .. image:: /auto_examples/semi_supervised/images/thumb/sphx_glr_plot_label_propagation_structure_thumb.png
+  .. image:: /auto_examples/semi_supervised/images/thumb/sphx_glr_plot_label_propagation_digits_thumb.png
     :alt:
 
-  :ref:`sphx_glr_auto_examples_semi_supervised_plot_label_propagation_structure.py`
+  :ref:`sphx_glr_auto_examples_semi_supervised_plot_label_propagation_digits.py`
 
 .. raw:: html
 
-      <div class="sphx-glr-thumbnail-title">Label Propagation learning a complex structure</div>
+      <div class="sphx-glr-thumbnail-title">Label Propagation digits: Demonstrating performance</div>
     </div>
 
 
@@ -131,8 +131,8 @@ Examples concerning the :mod:`sklearn.semi_supervised` module.
 
    /auto_examples/semi_supervised/plot_semi_supervised_versus_svm_iris
    /auto_examples/semi_supervised/plot_self_training_varying_threshold
+   /auto_examples/semi_supervised/plot_label_propagation_structure
    /auto_examples/semi_supervised/plot_label_propagation_digits_active_learning
    /auto_examples/semi_supervised/plot_label_propagation_digits
-   /auto_examples/semi_supervised/plot_label_propagation_structure
    /auto_examples/semi_supervised/plot_semi_supervised_newsgroups
 
diff --git a/dev/_sources/auto_examples/semi_supervised/plot_label_propagation_digits.rst.txt b/dev/_sources/auto_examples/semi_supervised/plot_label_propagation_digits.rst.txt
index d73f0fed60159..79d57d5ee0b94 100644
--- a/dev/_sources/auto_examples/semi_supervised/plot_label_propagation_digits.rst.txt
+++ b/dev/_sources/auto_examples/semi_supervised/plot_label_propagation_digits.rst.txt
@@ -226,7 +226,7 @@ Confusion matrix
  .. code-block:: none
 
 
-    <sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay object at 0x7fb585273910>
+    <sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay object at 0x7fd8490f47c0>
 
 
 
@@ -308,7 +308,7 @@ Plot
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.350 seconds)
+   **Total running time of the script:** (0 minutes 0.314 seconds)
 
 
 .. _sphx_glr_download_auto_examples_semi_supervised_plot_label_propagation_digits.py:
diff --git a/dev/_sources/auto_examples/semi_supervised/plot_label_propagation_digits_active_learning.rst.txt b/dev/_sources/auto_examples/semi_supervised/plot_label_propagation_digits_active_learning.rst.txt
index 618808a2c913a..2198eadbb569c 100644
--- a/dev/_sources/auto_examples/semi_supervised/plot_label_propagation_digits_active_learning.rst.txt
+++ b/dev/_sources/auto_examples/semi_supervised/plot_label_propagation_digits_active_learning.rst.txt
@@ -18,9 +18,9 @@
 .. _sphx_glr_auto_examples_semi_supervised_plot_label_propagation_digits_active_learning.py:
 
 
-========================================
-Label Propagation digits active learning
-========================================
+=========================================
+Label Propagation digits: Active learning
+=========================================
 
 Demonstrates an active learning technique to learn handwritten digits
 using label propagation.
@@ -300,7 +300,7 @@ model with their true labels.
                 sub.axis("off")
 
             # labeling 5 points, remote from labeled set
-            (delete_index,) = np.where(unlabeled_indices == image_index)
+            (delete_index,) = (unlabeled_indices == image_index).nonzero()
             delete_indices = np.concatenate((delete_indices, delete_index))
 
         unlabeled_indices = np.delete(unlabeled_indices, delete_indices)
@@ -319,7 +319,7 @@ model with their true labels.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.537 seconds)
+   **Total running time of the script:** (0 minutes 0.494 seconds)
 
 
 .. _sphx_glr_download_auto_examples_semi_supervised_plot_label_propagation_digits_active_learning.py:
diff --git a/dev/_sources/auto_examples/semi_supervised/plot_label_propagation_structure.rst.txt b/dev/_sources/auto_examples/semi_supervised/plot_label_propagation_structure.rst.txt
index 06dbf2862e384..7093856ddda3f 100644
--- a/dev/_sources/auto_examples/semi_supervised/plot_label_propagation_structure.rst.txt
+++ b/dev/_sources/auto_examples/semi_supervised/plot_label_propagation_structure.rst.txt
@@ -18,9 +18,9 @@
 .. _sphx_glr_auto_examples_semi_supervised_plot_label_propagation_structure.py:
 
 
-==============================================
-Label Propagation learning a complex structure
-==============================================
+=======================================================
+Label Propagation circles: Learning a complex structure
+=======================================================
 
 Example of LabelPropagation learning a complex internal structure
 to demonstrate "manifold learning". The outer circle should be
@@ -146,7 +146,7 @@ a label to sample where the label is initially unknown.
 .. raw:: html
 
     <div class="output_subarea output_html rendered_html output_result">
-    <style>#sk-container-id-71 {
+    <style>#sk-container-id-72 {
       /* Definition of color scheme common for light and dark mode */
       --sklearn-color-text: #000;
       --sklearn-color-text-muted: #666;
@@ -177,15 +177,15 @@ a label to sample where the label is initially unknown.
       }
     }
 
-    #sk-container-id-71 {
+    #sk-container-id-72 {
       color: var(--sklearn-color-text);
     }
 
-    #sk-container-id-71 pre {
+    #sk-container-id-72 pre {
       padding: 0;
     }
 
-    #sk-container-id-71 input.sk-hidden--visually {
+    #sk-container-id-72 input.sk-hidden--visually {
       border: 0;
       clip: rect(1px 1px 1px 1px);
       clip: rect(1px, 1px, 1px, 1px);
@@ -197,7 +197,7 @@ a label to sample where the label is initially unknown.
       width: 1px;
     }
 
-    #sk-container-id-71 div.sk-dashed-wrapped {
+    #sk-container-id-72 div.sk-dashed-wrapped {
       border: 1px dashed var(--sklearn-color-line);
       margin: 0 0.4em 0.5em 0.4em;
       box-sizing: border-box;
@@ -205,7 +205,7 @@ a label to sample where the label is initially unknown.
       background-color: var(--sklearn-color-background);
     }
 
-    #sk-container-id-71 div.sk-container {
+    #sk-container-id-72 div.sk-container {
       /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
          but bootstrap.min.css set `[hidden] { display: none !important; }`
          so we also need the `!important` here to be able to override the
@@ -215,7 +215,7 @@ a label to sample where the label is initially unknown.
       position: relative;
     }
 
-    #sk-container-id-71 div.sk-text-repr-fallback {
+    #sk-container-id-72 div.sk-text-repr-fallback {
       display: none;
     }
 
@@ -231,14 +231,14 @@ a label to sample where the label is initially unknown.
 
     /* Parallel-specific style estimator block */
 
-    #sk-container-id-71 div.sk-parallel-item::after {
+    #sk-container-id-72 div.sk-parallel-item::after {
       content: "";
       width: 100%;
       border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
       flex-grow: 1;
     }
 
-    #sk-container-id-71 div.sk-parallel {
+    #sk-container-id-72 div.sk-parallel {
       display: flex;
       align-items: stretch;
       justify-content: center;
@@ -246,28 +246,28 @@ a label to sample where the label is initially unknown.
       position: relative;
     }
 
-    #sk-container-id-71 div.sk-parallel-item {
+    #sk-container-id-72 div.sk-parallel-item {
       display: flex;
       flex-direction: column;
     }
 
-    #sk-container-id-71 div.sk-parallel-item:first-child::after {
+    #sk-container-id-72 div.sk-parallel-item:first-child::after {
       align-self: flex-end;
       width: 50%;
     }
 
-    #sk-container-id-71 div.sk-parallel-item:last-child::after {
+    #sk-container-id-72 div.sk-parallel-item:last-child::after {
       align-self: flex-start;
       width: 50%;
     }
 
-    #sk-container-id-71 div.sk-parallel-item:only-child::after {
+    #sk-container-id-72 div.sk-parallel-item:only-child::after {
       width: 0;
     }
 
     /* Serial-specific style estimator block */
 
-    #sk-container-id-71 div.sk-serial {
+    #sk-container-id-72 div.sk-serial {
       display: flex;
       flex-direction: column;
       align-items: center;
@@ -285,14 +285,14 @@ a label to sample where the label is initially unknown.
 
     /* Pipeline and ColumnTransformer style (default) */
 
-    #sk-container-id-71 div.sk-toggleable {
+    #sk-container-id-72 div.sk-toggleable {
       /* Default theme specific background. It is overwritten whether we have a
       specific estimator or a Pipeline/ColumnTransformer */
       background-color: var(--sklearn-color-background);
     }
 
     /* Toggleable label */
-    #sk-container-id-71 label.sk-toggleable__label {
+    #sk-container-id-72 label.sk-toggleable__label {
       cursor: pointer;
       display: flex;
       width: 100%;
@@ -305,13 +305,13 @@ a label to sample where the label is initially unknown.
       gap: 0.5em;
     }
 
-    #sk-container-id-71 label.sk-toggleable__label .caption {
+    #sk-container-id-72 label.sk-toggleable__label .caption {
       font-size: 0.6rem;
       font-weight: lighter;
       color: var(--sklearn-color-text-muted);
     }
 
-    #sk-container-id-71 label.sk-toggleable__label-arrow:before {
+    #sk-container-id-72 label.sk-toggleable__label-arrow:before {
       /* Arrow on the left of the label */
       content: "▸";
       float: left;
@@ -319,13 +319,13 @@ a label to sample where the label is initially unknown.
       color: var(--sklearn-color-icon);
     }
 
-    #sk-container-id-71 label.sk-toggleable__label-arrow:hover:before {
+    #sk-container-id-72 label.sk-toggleable__label-arrow:hover:before {
       color: var(--sklearn-color-text);
     }
 
     /* Toggleable content - dropdown */
 
-    #sk-container-id-71 div.sk-toggleable__content {
+    #sk-container-id-72 div.sk-toggleable__content {
       max-height: 0;
       max-width: 0;
       overflow: hidden;
@@ -334,12 +334,12 @@ a label to sample where the label is initially unknown.
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-71 div.sk-toggleable__content.fitted {
+    #sk-container-id-72 div.sk-toggleable__content.fitted {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
-    #sk-container-id-71 div.sk-toggleable__content pre {
+    #sk-container-id-72 div.sk-toggleable__content pre {
       margin: 0.2em;
       border-radius: 0.25em;
       color: var(--sklearn-color-text);
@@ -347,79 +347,79 @@ a label to sample where the label is initially unknown.
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-71 div.sk-toggleable__content.fitted pre {
+    #sk-container-id-72 div.sk-toggleable__content.fitted pre {
       /* unfitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
-    #sk-container-id-71 input.sk-toggleable__control:checked~div.sk-toggleable__content {
+    #sk-container-id-72 input.sk-toggleable__control:checked~div.sk-toggleable__content {
       /* Expand drop-down */
       max-height: 200px;
       max-width: 100%;
       overflow: auto;
     }
 
-    #sk-container-id-71 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
+    #sk-container-id-72 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
       content: "▾";
     }
 
     /* Pipeline/ColumnTransformer-specific style */
 
-    #sk-container-id-71 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-72 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-71 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-72 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
     /* Estimator-specific style */
 
     /* Colorize estimator box */
-    #sk-container-id-71 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-72 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-71 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+    #sk-container-id-72 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
-    #sk-container-id-71 div.sk-label label.sk-toggleable__label,
-    #sk-container-id-71 div.sk-label label {
+    #sk-container-id-72 div.sk-label label.sk-toggleable__label,
+    #sk-container-id-72 div.sk-label label {
       /* The background is the default theme color */
       color: var(--sklearn-color-text-on-default-background);
     }
 
     /* On hover, darken the color of the background */
-    #sk-container-id-71 div.sk-label:hover label.sk-toggleable__label {
+    #sk-container-id-72 div.sk-label:hover label.sk-toggleable__label {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
     /* Label box, darken color on hover, fitted */
-    #sk-container-id-71 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
+    #sk-container-id-72 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
       color: var(--sklearn-color-text);
       background-color: var(--sklearn-color-fitted-level-2);
     }
 
     /* Estimator label */
 
-    #sk-container-id-71 div.sk-label label {
+    #sk-container-id-72 div.sk-label label {
       font-family: monospace;
       font-weight: bold;
       display: inline-block;
       line-height: 1.2em;
     }
 
-    #sk-container-id-71 div.sk-label-container {
+    #sk-container-id-72 div.sk-label-container {
       text-align: center;
     }
 
     /* Estimator-specific */
-    #sk-container-id-71 div.sk-estimator {
+    #sk-container-id-72 div.sk-estimator {
       font-family: monospace;
       border: 1px dotted var(--sklearn-color-border-box);
       border-radius: 0.25em;
@@ -429,18 +429,18 @@ a label to sample where the label is initially unknown.
       background-color: var(--sklearn-color-unfitted-level-0);
     }
 
-    #sk-container-id-71 div.sk-estimator.fitted {
+    #sk-container-id-72 div.sk-estimator.fitted {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-0);
     }
 
     /* on hover */
-    #sk-container-id-71 div.sk-estimator:hover {
+    #sk-container-id-72 div.sk-estimator:hover {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-2);
     }
 
-    #sk-container-id-71 div.sk-estimator.fitted:hover {
+    #sk-container-id-72 div.sk-estimator.fitted:hover {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-2);
     }
@@ -528,7 +528,7 @@ a label to sample where the label is initially unknown.
 
     /* "?"-specific style due to the `<a>` HTML tag */
 
-    #sk-container-id-71 a.estimator_doc_link {
+    #sk-container-id-72 a.estimator_doc_link {
       float: right;
       font-size: 1rem;
       line-height: 1em;
@@ -543,25 +543,25 @@ a label to sample where the label is initially unknown.
       border: var(--sklearn-color-unfitted-level-1) 1pt solid;
     }
 
-    #sk-container-id-71 a.estimator_doc_link.fitted {
+    #sk-container-id-72 a.estimator_doc_link.fitted {
       /* fitted */
       border: var(--sklearn-color-fitted-level-1) 1pt solid;
       color: var(--sklearn-color-fitted-level-1);
     }
 
     /* On hover */
-    #sk-container-id-71 a.estimator_doc_link:hover {
+    #sk-container-id-72 a.estimator_doc_link:hover {
       /* unfitted */
       background-color: var(--sklearn-color-unfitted-level-3);
       color: var(--sklearn-color-background);
       text-decoration: none;
     }
 
-    #sk-container-id-71 a.estimator_doc_link.fitted:hover {
+    #sk-container-id-72 a.estimator_doc_link.fitted:hover {
       /* fitted */
       background-color: var(--sklearn-color-fitted-level-3);
     }
-    </style><div id="sk-container-id-71" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>LabelSpreading(alpha=0.8, kernel=&#x27;knn&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-299" type="checkbox" checked><label for="sk-estimator-id-299" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>LabelSpreading</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.semi_supervised.LabelSpreading.html">?<span>Documentation for LabelSpreading</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>LabelSpreading(alpha=0.8, kernel=&#x27;knn&#x27;)</pre></div> </div></div></div></div>
+    </style><div id="sk-container-id-72" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>LabelSpreading(alpha=0.8, kernel=&#x27;knn&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-313" type="checkbox" checked><label for="sk-estimator-id-313" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>LabelSpreading</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.semi_supervised.LabelSpreading.html">?<span>Documentation for LabelSpreading</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>LabelSpreading(alpha=0.8, kernel=&#x27;knn&#x27;)</pre></div> </div></div></div></div>
     </div>
     <br />
     <br />
@@ -577,8 +577,8 @@ when the label was unknown.
 
     output_labels = label_spread.transduction_
     output_label_array = np.asarray(output_labels)
-    outer_numbers = np.where(output_label_array == outer)[0]
-    inner_numbers = np.where(output_label_array == inner)[0]
+    outer_numbers = (output_label_array == outer).nonzero()[0]
+    inner_numbers = (output_label_array == inner).nonzero()[0]
 
     plt.figure(figsize=(4, 4))
     plt.scatter(
@@ -617,7 +617,7 @@ when the label was unknown.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.173 seconds)
+   **Total running time of the script:** (0 minutes 0.162 seconds)
 
 
 .. _sphx_glr_download_auto_examples_semi_supervised_plot_label_propagation_structure.py:
diff --git a/dev/_sources/auto_examples/semi_supervised/plot_self_training_varying_threshold.rst.txt b/dev/_sources/auto_examples/semi_supervised/plot_self_training_varying_threshold.rst.txt
index b1101d025808b..1e193e3ded87a 100644
--- a/dev/_sources/auto_examples/semi_supervised/plot_self_training_varying_threshold.rst.txt
+++ b/dev/_sources/auto_examples/semi_supervised/plot_self_training_varying_threshold.rst.txt
@@ -154,7 +154,7 @@ around 0.7.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 5.982 seconds)
+   **Total running time of the script:** (0 minutes 5.434 seconds)
 
 
 .. _sphx_glr_download_auto_examples_semi_supervised_plot_self_training_varying_threshold.py:
diff --git a/dev/_sources/auto_examples/semi_supervised/plot_semi_supervised_newsgroups.rst.txt b/dev/_sources/auto_examples/semi_supervised/plot_semi_supervised_newsgroups.rst.txt
index 66e09aafb22dc..3925d8383d1a3 100644
--- a/dev/_sources/auto_examples/semi_supervised/plot_semi_supervised_newsgroups.rst.txt
+++ b/dev/_sources/auto_examples/semi_supervised/plot_semi_supervised_newsgroups.rst.txt
@@ -187,7 +187,7 @@ loader or setting them to `None` to get all 20 of them.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 7.667 seconds)
+   **Total running time of the script:** (0 minutes 7.178 seconds)
 
 
 .. _sphx_glr_download_auto_examples_semi_supervised_plot_semi_supervised_newsgroups.py:
diff --git a/dev/_sources/auto_examples/semi_supervised/plot_semi_supervised_versus_svm_iris.rst.txt b/dev/_sources/auto_examples/semi_supervised/plot_semi_supervised_versus_svm_iris.rst.txt
index 034a86ba0f81a..2b951e007715e 100644
--- a/dev/_sources/auto_examples/semi_supervised/plot_semi_supervised_versus_svm_iris.rst.txt
+++ b/dev/_sources/auto_examples/semi_supervised/plot_semi_supervised_versus_svm_iris.rst.txt
@@ -123,7 +123,7 @@ identical to training the SVC on 100% of the data.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 1.022 seconds)
+   **Total running time of the script:** (0 minutes 0.958 seconds)
 
 
 .. _sphx_glr_download_auto_examples_semi_supervised_plot_semi_supervised_versus_svm_iris.py:
diff --git a/dev/_sources/auto_examples/svm/plot_custom_kernel.rst.txt b/dev/_sources/auto_examples/svm/plot_custom_kernel.rst.txt
index 56281fded84f8..ef47ad4eaa6ac 100644
--- a/dev/_sources/auto_examples/svm/plot_custom_kernel.rst.txt
+++ b/dev/_sources/auto_examples/svm/plot_custom_kernel.rst.txt
@@ -95,7 +95,7 @@ plot the decision surface and the support vectors.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.103 seconds)
+   **Total running time of the script:** (0 minutes 0.098 seconds)
 
 
 .. _sphx_glr_download_auto_examples_svm_plot_custom_kernel.py:
diff --git a/dev/_sources/auto_examples/svm/plot_iris_svc.rst.txt b/dev/_sources/auto_examples/svm/plot_iris_svc.rst.txt
index 8ef9f7ab68d3d..e064bf60b898c 100644
--- a/dev/_sources/auto_examples/svm/plot_iris_svc.rst.txt
+++ b/dev/_sources/auto_examples/svm/plot_iris_svc.rst.txt
@@ -127,7 +127,7 @@ kernel and its parameters.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.211 seconds)
+   **Total running time of the script:** (0 minutes 0.198 seconds)
 
 
 .. _sphx_glr_download_auto_examples_svm_plot_iris_svc.py:
diff --git a/dev/_sources/auto_examples/svm/plot_linearsvc_support_vectors.rst.txt b/dev/_sources/auto_examples/svm/plot_linearsvc_support_vectors.rst.txt
index 07495149b111a..d7fe678c8139b 100644
--- a/dev/_sources/auto_examples/svm/plot_linearsvc_support_vectors.rst.txt
+++ b/dev/_sources/auto_examples/svm/plot_linearsvc_support_vectors.rst.txt
@@ -64,7 +64,7 @@ vectors in LinearSVC.
         # decision_function = np.dot(X, clf.coef_[0]) + clf.intercept_[0]
         # The support vectors are the samples that lie within the margin
         # boundaries, whose size is conventionally constrained to 1
-        support_vector_indices = np.where(np.abs(decision_function) <= 1 + 1e-15)[0]
+        support_vector_indices = (np.abs(decision_function) <= 1 + 1e-15).nonzero()[0]
         support_vectors = X[support_vector_indices]
 
         plt.subplot(1, 2, i + 1)
@@ -96,7 +96,7 @@ vectors in LinearSVC.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.204 seconds)
+   **Total running time of the script:** (0 minutes 0.193 seconds)
 
 
 .. _sphx_glr_download_auto_examples_svm_plot_linearsvc_support_vectors.py:
diff --git a/dev/_sources/auto_examples/svm/plot_oneclass.rst.txt b/dev/_sources/auto_examples/svm/plot_oneclass.rst.txt
index 7c06011ce20f3..edbf2424f5fbc 100644
--- a/dev/_sources/auto_examples/svm/plot_oneclass.rst.txt
+++ b/dev/_sources/auto_examples/svm/plot_oneclass.rst.txt
@@ -160,7 +160,7 @@ classifying new data as similar or different to the training set.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.160 seconds)
+   **Total running time of the script:** (0 minutes 0.143 seconds)
 
 
 .. _sphx_glr_download_auto_examples_svm_plot_oneclass.py:
diff --git a/dev/_sources/auto_examples/svm/plot_rbf_parameters.rst.txt b/dev/_sources/auto_examples/svm/plot_rbf_parameters.rst.txt
index d86fef1525d73..23e5b10bfea45 100644
--- a/dev/_sources/auto_examples/svm/plot_rbf_parameters.rst.txt
+++ b/dev/_sources/auto_examples/svm/plot_rbf_parameters.rst.txt
@@ -371,7 +371,7 @@ the same color.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 5.574 seconds)
+   **Total running time of the script:** (0 minutes 4.985 seconds)
 
 
 .. _sphx_glr_download_auto_examples_svm_plot_rbf_parameters.py:
diff --git a/dev/_sources/auto_examples/svm/plot_separating_hyperplane.rst.txt b/dev/_sources/auto_examples/svm/plot_separating_hyperplane.rst.txt
index 4f1d6b3ef90ac..5bd317e4e26ca 100644
--- a/dev/_sources/auto_examples/svm/plot_separating_hyperplane.rst.txt
+++ b/dev/_sources/auto_examples/svm/plot_separating_hyperplane.rst.txt
@@ -86,7 +86,7 @@ linear kernel.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.071 seconds)
+   **Total running time of the script:** (0 minutes 0.070 seconds)
 
 
 .. _sphx_glr_download_auto_examples_svm_plot_separating_hyperplane.py:
diff --git a/dev/_sources/auto_examples/svm/plot_separating_hyperplane_unbalanced.rst.txt b/dev/_sources/auto_examples/svm/plot_separating_hyperplane_unbalanced.rst.txt
index a4a69c577bf90..fb1b24b1b26f6 100644
--- a/dev/_sources/auto_examples/svm/plot_separating_hyperplane_unbalanced.rst.txt
+++ b/dev/_sources/auto_examples/svm/plot_separating_hyperplane_unbalanced.rst.txt
@@ -130,7 +130,7 @@ unbalanced classes.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.188 seconds)
+   **Total running time of the script:** (0 minutes 0.168 seconds)
 
 
 .. _sphx_glr_download_auto_examples_svm_plot_separating_hyperplane_unbalanced.py:
diff --git a/dev/_sources/auto_examples/svm/plot_svm_anova.rst.txt b/dev/_sources/auto_examples/svm/plot_svm_anova.rst.txt
index 50747bf90466e..65a68a006a24b 100644
--- a/dev/_sources/auto_examples/svm/plot_svm_anova.rst.txt
+++ b/dev/_sources/auto_examples/svm/plot_svm_anova.rst.txt
@@ -145,7 +145,7 @@ Plot the cross-validation score as a function of percentile of features
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.412 seconds)
+   **Total running time of the script:** (0 minutes 0.348 seconds)
 
 
 .. _sphx_glr_download_auto_examples_svm_plot_svm_anova.py:
diff --git a/dev/_sources/auto_examples/svm/plot_svm_kernels.rst.txt b/dev/_sources/auto_examples/svm/plot_svm_kernels.rst.txt
index b156bc77418fa..bccfb37cb7e73 100644
--- a/dev/_sources/auto_examples/svm/plot_svm_kernels.rst.txt
+++ b/dev/_sources/auto_examples/svm/plot_svm_kernels.rst.txt
@@ -459,7 +459,7 @@ reasonable decision boundary for the above dataset.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 1.458 seconds)
+   **Total running time of the script:** (0 minutes 1.337 seconds)
 
 
 .. _sphx_glr_download_auto_examples_svm_plot_svm_kernels.py:
diff --git a/dev/_sources/auto_examples/svm/plot_svm_margin.rst.txt b/dev/_sources/auto_examples/svm/plot_svm_margin.rst.txt
index 5af0ad019887c..702983232e238 100644
--- a/dev/_sources/auto_examples/svm/plot_svm_margin.rst.txt
+++ b/dev/_sources/auto_examples/svm/plot_svm_margin.rst.txt
@@ -137,7 +137,7 @@ the margins to be calculated using all the data in the area.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.082 seconds)
+   **Total running time of the script:** (0 minutes 0.067 seconds)
 
 
 .. _sphx_glr_download_auto_examples_svm_plot_svm_margin.py:
diff --git a/dev/_sources/auto_examples/svm/plot_svm_regression.rst.txt b/dev/_sources/auto_examples/svm/plot_svm_regression.rst.txt
index b90affa6908b0..be37745a5cb3b 100644
--- a/dev/_sources/auto_examples/svm/plot_svm_regression.rst.txt
+++ b/dev/_sources/auto_examples/svm/plot_svm_regression.rst.txt
@@ -153,7 +153,7 @@ Look at the results
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 6.081 seconds)
+   **Total running time of the script:** (0 minutes 5.534 seconds)
 
 
 .. _sphx_glr_download_auto_examples_svm_plot_svm_regression.py:
diff --git a/dev/_sources/auto_examples/svm/plot_svm_scale_c.rst.txt b/dev/_sources/auto_examples/svm/plot_svm_scale_c.rst.txt
index b9c5bba72c858..bd7f8657892c1 100644
--- a/dev/_sources/auto_examples/svm/plot_svm_scale_c.rst.txt
+++ b/dev/_sources/auto_examples/svm/plot_svm_scale_c.rst.txt
@@ -323,7 +323,7 @@ builder.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 21.737 seconds)
+   **Total running time of the script:** (0 minutes 18.652 seconds)
 
 
 .. _sphx_glr_download_auto_examples_svm_plot_svm_scale_c.py:
diff --git a/dev/_sources/auto_examples/svm/plot_svm_tie_breaking.rst.txt b/dev/_sources/auto_examples/svm/plot_svm_tie_breaking.rst.txt
index 5179f92c1312f..6ee517bdcda08 100644
--- a/dev/_sources/auto_examples/svm/plot_svm_tie_breaking.rst.txt
+++ b/dev/_sources/auto_examples/svm/plot_svm_tie_breaking.rst.txt
@@ -101,7 +101,7 @@ create a non-convex decision boundary in that area.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 1.082 seconds)
+   **Total running time of the script:** (0 minutes 0.989 seconds)
 
 
 .. _sphx_glr_download_auto_examples_svm_plot_svm_tie_breaking.py:
diff --git a/dev/_sources/auto_examples/svm/plot_weighted_samples.rst.txt b/dev/_sources/auto_examples/svm/plot_weighted_samples.rst.txt
index eceede80909c0..5b36bbce56d84 100644
--- a/dev/_sources/auto_examples/svm/plot_weighted_samples.rst.txt
+++ b/dev/_sources/auto_examples/svm/plot_weighted_samples.rst.txt
@@ -124,7 +124,7 @@ class, making the deformation of the decision boundary more visible.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.312 seconds)
+   **Total running time of the script:** (0 minutes 0.293 seconds)
 
 
 .. _sphx_glr_download_auto_examples_svm_plot_weighted_samples.py:
diff --git a/dev/_sources/auto_examples/text/plot_document_classification_20newsgroups.rst.txt b/dev/_sources/auto_examples/text/plot_document_classification_20newsgroups.rst.txt
index f64a5d6054a54..081db554b4e8d 100644
--- a/dev/_sources/auto_examples/text/plot_document_classification_20newsgroups.rst.txt
+++ b/dev/_sources/auto_examples/text/plot_document_classification_20newsgroups.rst.txt
@@ -193,9 +193,9 @@ metadata stripping.
     2034 documents - 3.98MB (training set)
     1353 documents - 2.87MB (test set)
     4 categories
-    vectorize training done in 0.446s at 8.928MB/s
+    vectorize training done in 0.377s at 10.560MB/s
     n_samples: 2034, n_features: 7831
-    vectorize testing done in 0.292s at 9.821MB/s
+    vectorize testing done in 0.245s at 11.702MB/s
     n_samples: 1353, n_features: 7831
 
 
@@ -717,8 +717,8 @@ for a demo on how such tuning can be done.
     ________________________________________________________________________________
     Training: 
     LogisticRegression(C=5, max_iter=1000)
-    train time: 0.203s
-    test time:  0.000715s
+    train time: 0.179s
+    test time:  0.000654s
     accuracy:   0.772
     dimensionality: 5316
     density: 1.0
@@ -729,8 +729,8 @@ for a demo on how such tuning can be done.
     ________________________________________________________________________________
     Training: 
     RidgeClassifier(solver='sparse_cg')
-    train time: 0.0378s
-    test time:  0.00076s
+    train time: 0.0329s
+    test time:  0.000674s
     accuracy:   0.76
     dimensionality: 5316
     density: 1.0
@@ -741,8 +741,8 @@ for a demo on how such tuning can be done.
     ________________________________________________________________________________
     Training: 
     KNeighborsClassifier(n_neighbors=100)
-    train time: 0.00143s
-    test time:  0.073s
+    train time: 0.00135s
+    test time:  0.0754s
     accuracy:   0.752
 
     ================================================================================
@@ -750,8 +750,8 @@ for a demo on how such tuning can be done.
     ________________________________________________________________________________
     Training: 
     RandomForestClassifier()
-    train time: 1.84s
-    test time:  0.0592s
+    train time: 1.71s
+    test time:  0.0588s
     accuracy:   0.7
 
     ================================================================================
@@ -759,8 +759,8 @@ for a demo on how such tuning can be done.
     ________________________________________________________________________________
     Training: 
     LinearSVC(C=0.1, dual=False)
-    train time: 0.033s
-    test time:  0.000721s
+    train time: 0.0344s
+    test time:  0.00134s
     accuracy:   0.752
     dimensionality: 5316
     density: 1.0
@@ -771,8 +771,8 @@ for a demo on how such tuning can be done.
     ________________________________________________________________________________
     Training: 
     SGDClassifier(early_stopping=True, loss='log_loss', n_iter_no_change=3)
-    train time: 0.0336s
-    test time:  0.000675s
+    train time: 0.0338s
+    test time:  0.000661s
     accuracy:   0.764
     dimensionality: 5316
     density: 1.0
@@ -783,8 +783,8 @@ for a demo on how such tuning can be done.
     ________________________________________________________________________________
     Training: 
     NearestCentroid()
-    train time: 0.167s
-    test time:  0.00166s
+    train time: 0.176s
+    test time:  0.00175s
     accuracy:   0.748
 
     ================================================================================
@@ -792,8 +792,8 @@ for a demo on how such tuning can be done.
     ________________________________________________________________________________
     Training: 
     ComplementNB(alpha=0.1)
-    train time: 0.00204s
-    test time:  0.000541s
+    train time: 0.00205s
+    test time:  0.000531s
     accuracy:   0.779
 
 
@@ -894,7 +894,7 @@ classification problems.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 7.425 seconds)
+   **Total running time of the script:** (0 minutes 7.023 seconds)
 
 
 .. _sphx_glr_download_auto_examples_text_plot_document_classification_20newsgroups.py:
diff --git a/dev/_sources/auto_examples/text/plot_document_clustering.rst.txt b/dev/_sources/auto_examples/text/plot_document_clustering.rst.txt
index d3690b606f179..c46c1c71fbcbe 100644
--- a/dev/_sources/auto_examples/text/plot_document_clustering.rst.txt
+++ b/dev/_sources/auto_examples/text/plot_document_clustering.rst.txt
@@ -267,7 +267,7 @@ IDF normalization as provided by
 
  .. code-block:: none
 
-    vectorization done in 0.502 s
+    vectorization done in 0.406 s
     n_samples: 3387, n_features: 7929
 
 
@@ -389,7 +389,7 @@ best inertia (objective function of k-means) is chosen.
 
  .. code-block:: none
 
-    clustering done in 0.22 ± 0.05 s 
+    clustering done in 0.19 ± 0.05 s 
     Homogeneity: 0.349 ± 0.010
     Completeness: 0.398 ± 0.009
     V-measure: 0.372 ± 0.009
@@ -448,7 +448,7 @@ the information retrieval and text mining literature.
 
  .. code-block:: none
 
-    LSA done in 0.419 s
+    LSA done in 0.377 s
     Explained variance of the SVD step: 18.4%
 
 
@@ -618,7 +618,7 @@ the hashed vector space.
 
  .. code-block:: none
 
-    vectorization done in 1.949 s
+    vectorization done in 1.839 s
 
 
 
@@ -650,7 +650,7 @@ hashed-lsa-reduced data:
 
  .. code-block:: none
 
-    clustering done in 0.02 ± 0.01 s 
+    clustering done in 0.02 ± 0.00 s 
     Homogeneity: 0.390 ± 0.008
     Completeness: 0.436 ± 0.012
     V-measure: 0.411 ± 0.010
@@ -678,7 +678,7 @@ hashed-lsa-reduced data:
 
  .. code-block:: none
 
-    clustering done in 0.02 ± 0.01 s 
+    clustering done in 0.02 ± 0.00 s 
     Homogeneity: 0.359 ± 0.053
     Completeness: 0.385 ± 0.059
     V-measure: 0.371 ± 0.055
@@ -772,7 +772,7 @@ quality compared to the traditional k-means algorithm.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 8.511 seconds)
+   **Total running time of the script:** (0 minutes 7.639 seconds)
 
 
 .. _sphx_glr_download_auto_examples_text_plot_document_clustering.py:
diff --git a/dev/_sources/auto_examples/text/plot_hashing_vs_dict_vectorizer.rst.txt b/dev/_sources/auto_examples/text/plot_hashing_vs_dict_vectorizer.rst.txt
index fd7c2c6d60a8a..09a9a0fe6e433 100644
--- a/dev/_sources/auto_examples/text/plot_hashing_vs_dict_vectorizer.rst.txt
+++ b/dev/_sources/auto_examples/text/plot_hashing_vs_dict_vectorizer.rst.txt
@@ -234,7 +234,7 @@ both of them receive dictionaries as input.
 
  .. code-block:: none
 
-    done in 1.337 s at 4.7 MB/s
+    done in 1.071 s at 5.8 MB/s
     Found 47928 unique terms
 
 
@@ -374,7 +374,7 @@ The default number of features for the
 
  .. code-block:: none
 
-    done in 0.722 s at 8.6 MB/s
+    done in 0.613 s at 10.2 MB/s
     Found 43873 unique tokens
 
 
@@ -414,7 +414,7 @@ majority of them is inactive.
 
  .. code-block:: none
 
-    done in 0.722 s at 8.6 MB/s
+    done in 0.622 s at 10.0 MB/s
     Found 47668 unique tokens
 
 
@@ -456,7 +456,7 @@ passing a dictionary with an implied frequency of 1 for each feature name.
 
  .. code-block:: none
 
-    done in 0.658 s at 9.5 MB/s
+    done in 0.601 s at 10.4 MB/s
     Found 43873 unique tokens
 
 
@@ -544,7 +544,7 @@ is more flexible. In particular it accepts various regex patterns through the
 
  .. code-block:: none
 
-    done in 0.802 s at 7.8 MB/s
+    done in 0.691 s at 9.0 MB/s
     Found 47885 unique terms
 
 
@@ -590,7 +590,7 @@ preprocessing and tokenization of the
 
  .. code-block:: none
 
-    done in 0.608 s at 10.3 MB/s
+    done in 0.546 s at 11.4 MB/s
 
 
 
@@ -645,7 +645,7 @@ normalizing and weighting from a
 
  .. code-block:: none
 
-    done in 0.832 s at 7.5 MB/s
+    done in 0.691 s at 9.0 MB/s
     Found 47885 unique terms
 
 
@@ -707,7 +707,7 @@ compiles a regular expression once and then reuses it for all the documents.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 6.275 seconds)
+   **Total running time of the script:** (0 minutes 5.290 seconds)
 
 
 .. _sphx_glr_download_auto_examples_text_plot_hashing_vs_dict_vectorizer.py:
diff --git a/dev/_sources/auto_examples/tree/plot_cost_complexity_pruning.rst.txt b/dev/_sources/auto_examples/tree/plot_cost_complexity_pruning.rst.txt
index f68146540ace3..295c358ca414b 100644
--- a/dev/_sources/auto_examples/tree/plot_cost_complexity_pruning.rst.txt
+++ b/dev/_sources/auto_examples/tree/plot_cost_complexity_pruning.rst.txt
@@ -233,7 +233,7 @@ In this example, setting ``ccp_alpha=0.015`` maximizes the testing accuracy.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.433 seconds)
+   **Total running time of the script:** (0 minutes 0.500 seconds)
 
 
 .. _sphx_glr_download_auto_examples_tree_plot_cost_complexity_pruning.py:
diff --git a/dev/_sources/auto_examples/tree/plot_iris_dtc.rst.txt b/dev/_sources/auto_examples/tree/plot_iris_dtc.rst.txt
index f00814973e03d..cc293a370548f 100644
--- a/dev/_sources/auto_examples/tree/plot_iris_dtc.rst.txt
+++ b/dev/_sources/auto_examples/tree/plot_iris_dtc.rst.txt
@@ -112,7 +112,7 @@ Display the decision functions of trees trained on all pairs of features.
 
         # Plot the training points
         for i, color in zip(range(n_classes), plot_colors):
-            idx = np.where(y == i)
+            idx = np.asarray(y == i).nonzero()
             plt.scatter(
                 X[idx, 0],
                 X[idx, 1],
@@ -169,7 +169,7 @@ together.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.856 seconds)
+   **Total running time of the script:** (0 minutes 0.938 seconds)
 
 
 .. _sphx_glr_download_auto_examples_tree_plot_iris_dtc.py:
diff --git a/dev/_sources/auto_examples/tree/plot_tree_regression.rst.txt b/dev/_sources/auto_examples/tree/plot_tree_regression.rst.txt
index 0e385b833437e..fcbd031046ec7 100644
--- a/dev/_sources/auto_examples/tree/plot_tree_regression.rst.txt
+++ b/dev/_sources/auto_examples/tree/plot_tree_regression.rst.txt
@@ -1135,7 +1135,7 @@ influenced by the noise.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.309 seconds)
+   **Total running time of the script:** (0 minutes 0.334 seconds)
 
 
 .. _sphx_glr_download_auto_examples_tree_plot_tree_regression.py:
diff --git a/dev/_sources/auto_examples/tree/plot_unveil_tree_structure.rst.txt b/dev/_sources/auto_examples/tree/plot_unveil_tree_structure.rst.txt
index 0c5a72dacd85a..a9688d2541e26 100644
--- a/dev/_sources/auto_examples/tree/plot_unveil_tree_structure.rst.txt
+++ b/dev/_sources/auto_examples/tree/plot_unveil_tree_structure.rst.txt
@@ -788,7 +788,7 @@ through.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.077 seconds)
+   **Total running time of the script:** (0 minutes 0.081 seconds)
 
 
 .. _sphx_glr_download_auto_examples_tree_plot_unveil_tree_structure.py:
diff --git a/dev/_sources/developers/advanced_installation.rst.txt b/dev/_sources/developers/advanced_installation.rst.txt
index 0b2aa30efb757..1a0c58de77f4e 100644
--- a/dev/_sources/developers/advanced_installation.rst.txt
+++ b/dev/_sources/developers/advanced_installation.rst.txt
@@ -159,7 +159,7 @@ to build scikit-learn Cython extensions for each supported platform.
 Windows
 -------
 
-First, download the `Build Tools for Visual Studio 2019 installer
+First, download the `Build Tools for Visual Studio installer
 <https://aka.ms/vs/17/release/vs_buildtools.exe>`_.
 
 Run the downloaded `vs_buildtools.exe` file, during the installation you will
@@ -168,39 +168,12 @@ screenshot:
 
 .. image:: ../images/visual-studio-build-tools-selection.png
 
-Secondly, find out if you are running 64-bit or 32-bit Python. The building
-command depends on the architecture of the Python interpreter. You can check
-the architecture by running the following in ``cmd`` or ``powershell``
-console:
+Build scikit-learn by running the following command in your `sklearn-env` conda environment
+or virtualenv:
 
 .. prompt:: bash $
 
-    python -c "import struct; print(struct.calcsize('P') * 8)"
-
-For 64-bit Python, configure the build environment by running the following
-commands in ``cmd`` or an Anaconda Prompt (if you use Anaconda):
-
-.. sphinx-prompt 1.3.0 (used in doc-min-dependencies CI task) does not support `batch` prompt type,
-.. so we work around by using a known prompt type and an explicit prompt text.
-..
-.. prompt:: bash C:\>
-
-    SET DISTUTILS_USE_SDK=1
-    "C:\Program Files (x86)\Microsoft Visual Studio\2019\BuildTools\VC\Auxiliary\Build\vcvarsall.bat" x64
-
-Replace ``x64`` by ``x86`` to build for 32-bit Python.
-
-Please be aware that the path above might be different from user to user. The
-aim is to point to the "vcvarsall.bat" file that will set the necessary
-environment variables in the current command prompt.
-
-Finally, build scikit-learn with this command prompt:
-
-.. prompt:: bash $
-
-    pip install --editable . \
-        --verbose --no-build-isolation \
-        --config-settings editable-verbose=true
+    pip install --editable . --verbose --no-build-isolation --config-settings editable-verbose=true
 
 .. _compiler_macos:
 
diff --git a/dev/_sources/developers/contributing.rst.txt b/dev/_sources/developers/contributing.rst.txt
index 49ec027be1388..bebeb93d86b0c 100644
--- a/dev/_sources/developers/contributing.rst.txt
+++ b/dev/_sources/developers/contributing.rst.txt
@@ -269,7 +269,7 @@ how to set up your git repository:
 
    .. prompt:: bash
 
-        pip install pytest pytest-cov ruff mypy numpydoc black==24.3.0
+        pip install pytest pytest-cov ruff==0.11.2 mypy numpydoc
 
 .. _upstream:
 
@@ -726,6 +726,19 @@ We are glad to accept any sort of documentation:
 
 .. dropdown:: Guidelines for writing docstrings
 
+  * You can use `pytest` to test docstrings, e.g. assuming the
+    `RandomForestClassifier` docstring has been modified, the following command
+    would test its docstring compliance:
+
+    .. prompt:: bash
+
+      pytest --doctest-modules sklearn/ensemble/_forest.py -k RandomForestClassifier
+
+  * The correct order of sections is: Parameters, Returns, See Also, Notes, Examples.
+    See the `numpydoc documentation
+    <https://numpydoc.readthedocs.io/en/latest/format.html#sections>`_ for
+    information on other possible sections.
+
   * When documenting the parameters and attributes, here is a list of some
     well-formatted examples
 
@@ -791,7 +804,15 @@ We are glad to accept any sort of documentation:
       SelectKBest : Select features based on the k highest scores.
       SelectFpr : Select features based on a false positive rate test.
 
-  * Add one or two snippets of code in "Example" section to show how it can be used.
+  * The "Notes" section is optional. It is meant to provide information on
+    specific behavior of a function/class/classmethod/method.
+
+  * A `Note` can also be added to an attribute, but in that case it requires
+    using the `.. rubric:: Note` directive.
+
+  * Add one or two **snippets** of code in "Example" section to show how it can
+    be used. The code should be runable as is, i.e. it should include all
+    required imports. Keep this section as brief as possible.
 
 
 .. dropdown:: Guidelines for writing the user guide and other reStructuredText documents
@@ -1565,7 +1586,7 @@ make this task easier and faster (in no particular order).
     variable) in the code base.
 
 - Configure `git blame` to ignore the commit that migrated the code style to
-  `black`.
+  `black` and then `ruff`.
 
   .. prompt:: bash
 
diff --git a/dev/_sources/metadata_routing.rst.txt b/dev/_sources/metadata_routing.rst.txt
index b7f95f3d608d7..d302b84c5de68 100644
--- a/dev/_sources/metadata_routing.rst.txt
+++ b/dev/_sources/metadata_routing.rst.txt
@@ -1,7 +1,5 @@
 .. currentmodule:: sklearn
 
-.. TODO: update doc/conftest.py once document is updated and examples run.
-
 .. _metadata_routing:
 
 Metadata Routing
diff --git a/dev/_sources/min_dependency_substitutions.rst.txt b/dev/_sources/min_dependency_substitutions.rst.txt
index 3ba2c6db33c0f..28ce2be492e29 100644
--- a/dev/_sources/min_dependency_substitutions.rst.txt
+++ b/dev/_sources/min_dependency_substitutions.rst.txt
@@ -11,8 +11,7 @@
 .. |Memory_profilerMinVersion| replace:: 0.57.0
 .. |PytestMinVersion| replace:: 7.1.2
 .. |Pytest-covMinVersion| replace:: 2.9.0
-.. |RuffMinVersion| replace:: 0.11.0
-.. |BlackMinVersion| replace:: 24.3.0
+.. |RuffMinVersion| replace:: 0.11.7
 .. |MypyMinVersion| replace:: 1.15
 .. |PyamgMinVersion| replace:: 4.2.1
 .. |PolarsMinVersion| replace:: 0.20.30
diff --git a/dev/_sources/min_dependency_table.rst.txt b/dev/_sources/min_dependency_table.rst.txt
index d56a2cfd7e286..2eb3ccd81de92 100644
--- a/dev/_sources/min_dependency_table.rst.txt
+++ b/dev/_sources/min_dependency_table.rst.txt
@@ -58,11 +58,7 @@
     - tests
 
   * - ruff
-    - 0.11.0
-    - tests
-
-  * - black
-    - 24.3.0
+    - 0.11.7
     - tests
 
   * - mypy
diff --git a/dev/_sources/modules/array_api.rst.txt b/dev/_sources/modules/array_api.rst.txt
index b4940eccec2fc..e7261ea35cc7c 100644
--- a/dev/_sources/modules/array_api.rst.txt
+++ b/dev/_sources/modules/array_api.rst.txt
@@ -111,6 +111,7 @@ Estimators
   `svd_solver="randomized"` and `power_iteration_normalizer="QR"`)
 - :class:`linear_model.Ridge` (with `solver="svd"`)
 - :class:`discriminant_analysis.LinearDiscriminantAnalysis` (with `solver="svd"`)
+- :class:`preprocessing.Binarizer`
 - :class:`preprocessing.KernelCenterer`
 - :class:`preprocessing.LabelEncoder`
 - :class:`preprocessing.MaxAbsScaler`
diff --git a/dev/_sources/modules/classification_threshold.rst.txt b/dev/_sources/modules/classification_threshold.rst.txt
index ec0963d9da9a2..ee7028f469b5f 100644
--- a/dev/_sources/modules/classification_threshold.rst.txt
+++ b/dev/_sources/modules/classification_threshold.rst.txt
@@ -38,8 +38,8 @@ probability estimates :math:`P(y|X)` and class labels::
     >>> classifier.predict_proba(X[:4])
     array([[0.94     , 0.06     ],
            [0.94     , 0.06     ],
-           [0.0416..., 0.9583...],
-           [0.0416..., 0.9583...]])
+           [0.0416, 0.9583],
+           [0.0416, 0.9583]])
     >>> classifier.predict(X[:4])
     array([0, 0, 1, 1])
 
@@ -112,10 +112,10 @@ a meaningful metric for their use case.
         >>> base_model = LogisticRegression()
         >>> model = TunedThresholdClassifierCV(base_model, scoring=scorer)
         >>> scorer(model.fit(X, y), X, y)
-        0.88...
+        0.88
         >>> # compare it with the internal score found by cross-validation
         >>> model.best_score_
-        np.float64(0.86...)
+        np.float64(0.86)
 
 Important notes regarding the internal cross-validation
 -------------------------------------------------------
diff --git a/dev/_sources/modules/clustering.rst.txt b/dev/_sources/modules/clustering.rst.txt
index 6489d8f245201..cdf8421a103e3 100644
--- a/dev/_sources/modules/clustering.rst.txt
+++ b/dev/_sources/modules/clustering.rst.txt
@@ -1310,32 +1310,32 @@ ignoring permutations::
   >>> labels_true = [0, 0, 0, 1, 1, 1]
   >>> labels_pred = [0, 0, 1, 1, 2, 2]
   >>> metrics.rand_score(labels_true, labels_pred)
-  0.66...
+  0.66
 
 The Rand index does not ensure to obtain a value close to 0.0 for a
 random labelling. The adjusted Rand index **corrects for chance** and
 will give such a baseline.
 
   >>> metrics.adjusted_rand_score(labels_true, labels_pred)
-  0.24...
+  0.24
 
 As with all clustering metrics, one can permute 0 and 1 in the predicted
 labels, rename 2 to 3, and get the same score::
 
   >>> labels_pred = [1, 1, 0, 0, 3, 3]
   >>> metrics.rand_score(labels_true, labels_pred)
-  0.66...
+  0.66
   >>> metrics.adjusted_rand_score(labels_true, labels_pred)
-  0.24...
+  0.24
 
 Furthermore, both :func:`rand_score` and :func:`adjusted_rand_score` are
 **symmetric**: swapping the argument does not change the scores. They can
 thus be used as **consensus measures**::
 
   >>> metrics.rand_score(labels_pred, labels_true)
-  0.66...
+  0.66
   >>> metrics.adjusted_rand_score(labels_pred, labels_true)
-  0.24...
+  0.24
 
 Perfect labeling is scored 1.0::
 
@@ -1353,9 +1353,9 @@ will not necessarily be close to zero::
   >>> labels_true = [0, 0, 0, 0, 0, 0, 1, 1]
   >>> labels_pred = [0, 1, 2, 3, 4, 5, 5, 6]
   >>> metrics.rand_score(labels_true, labels_pred)
-  0.39...
+  0.39
   >>> metrics.adjusted_rand_score(labels_true, labels_pred)
-  -0.07...
+  -0.072
 
 
 .. topic:: Advantages:
@@ -1466,21 +1466,21 @@ proposed more recently and is **normalized against chance**::
   >>> labels_pred = [0, 0, 1, 1, 2, 2]
 
   >>> metrics.adjusted_mutual_info_score(labels_true, labels_pred)  # doctest: +SKIP
-  0.22504...
+  0.22504
 
 One can permute 0 and 1 in the predicted labels, rename 2 to 3 and get
 the same score::
 
   >>> labels_pred = [1, 1, 0, 0, 3, 3]
   >>> metrics.adjusted_mutual_info_score(labels_true, labels_pred)  # doctest: +SKIP
-  0.22504...
+  0.22504
 
 All, :func:`mutual_info_score`, :func:`adjusted_mutual_info_score` and
 :func:`normalized_mutual_info_score` are symmetric: swapping the argument does
 not change the score. Thus they can be used as a **consensus measure**::
 
   >>> metrics.adjusted_mutual_info_score(labels_pred, labels_true)  # doctest: +SKIP
-  0.22504...
+  0.22504
 
 Perfect labeling is scored 1.0::
 
@@ -1494,14 +1494,14 @@ Perfect labeling is scored 1.0::
 This is not true for ``mutual_info_score``, which is therefore harder to judge::
 
   >>> metrics.mutual_info_score(labels_true, labels_pred)  # doctest: +SKIP
-  0.69...
+  0.69
 
 Bad (e.g. independent labelings) have non-positive scores::
 
   >>> labels_true = [0, 1, 2, 0, 3, 4, 5, 1]
   >>> labels_pred = [1, 1, 0, 0, 2, 2, 2, 2]
   >>> metrics.adjusted_mutual_info_score(labels_true, labels_pred)  # doctest: +SKIP
-  -0.10526...
+  -0.10526
 
 
 .. topic:: Advantages:
@@ -1649,16 +1649,16 @@ We can turn those concept as scores :func:`homogeneity_score` and
   >>> labels_pred = [0, 0, 1, 1, 2, 2]
 
   >>> metrics.homogeneity_score(labels_true, labels_pred)
-  0.66...
+  0.66
 
   >>> metrics.completeness_score(labels_true, labels_pred)
-  0.42...
+  0.42
 
 Their harmonic mean called **V-measure** is computed by
 :func:`v_measure_score`::
 
   >>> metrics.v_measure_score(labels_true, labels_pred)
-  0.51...
+  0.516
 
 This function's formula is as follows:
 
@@ -1667,12 +1667,12 @@ This function's formula is as follows:
 `beta` defaults to a value of 1.0, but for using a value less than 1 for beta::
 
   >>> metrics.v_measure_score(labels_true, labels_pred, beta=0.6)
-  0.54...
+  0.547
 
 more weight will be attributed to homogeneity, and using a value greater than 1::
 
   >>> metrics.v_measure_score(labels_true, labels_pred, beta=1.8)
-  0.48...
+  0.48
 
 more weight will be attributed to completeness.
 
@@ -1683,14 +1683,14 @@ Homogeneity, completeness and V-measure can be computed at once using
 :func:`homogeneity_completeness_v_measure` as follows::
 
   >>> metrics.homogeneity_completeness_v_measure(labels_true, labels_pred)
-  (0.66..., 0.42..., 0.51...)
+  (0.67, 0.42, 0.52)
 
 The following clustering assignment is slightly better, since it is
 homogeneous but not complete::
 
   >>> labels_pred = [0, 0, 0, 1, 2, 2]
   >>> metrics.homogeneity_completeness_v_measure(labels_true, labels_pred)
-  (1.0, 0.68..., 0.81...)
+  (1.0, 0.68, 0.81)
 
 .. note::
 
@@ -1820,7 +1820,7 @@ between two clusters.
   >>> labels_pred = [0, 0, 1, 1, 2, 2]
 
   >>> metrics.fowlkes_mallows_score(labels_true, labels_pred)
-  0.47140...
+  0.47140
 
 One can permute 0 and 1 in the predicted labels, rename 2 to 3 and get
 the same score::
@@ -1828,7 +1828,7 @@ the same score::
   >>> labels_pred = [1, 1, 0, 0, 3, 3]
 
   >>> metrics.fowlkes_mallows_score(labels_true, labels_pred)
-  0.47140...
+  0.47140
 
 Perfect labeling is scored 1.0::
 
@@ -1917,7 +1917,7 @@ cluster analysis.
   >>> kmeans_model = KMeans(n_clusters=3, random_state=1).fit(X)
   >>> labels = kmeans_model.labels_
   >>> metrics.silhouette_score(X, labels, metric='euclidean')
-  0.55...
+  0.55
 
 .. topic:: Advantages:
 
@@ -1974,7 +1974,7 @@ cluster analysis:
   >>> kmeans_model = KMeans(n_clusters=3, random_state=1).fit(X)
   >>> labels = kmeans_model.labels_
   >>> metrics.calinski_harabasz_score(X, labels)
-  561.59...
+  561.59
 
 
 .. topic:: Advantages:
@@ -2048,7 +2048,7 @@ cluster analysis as follows:
   >>> kmeans = KMeans(n_clusters=3, random_state=1).fit(X)
   >>> labels = kmeans.labels_
   >>> davies_bouldin_score(X, labels)
-  0.666...
+  0.666
 
 
 .. topic:: Advantages:
diff --git a/dev/_sources/modules/compose.rst.txt b/dev/_sources/modules/compose.rst.txt
index 3db1104602a5d..3ef0d94236aa6 100644
--- a/dev/_sources/modules/compose.rst.txt
+++ b/dev/_sources/modules/compose.rst.txt
@@ -504,10 +504,10 @@ on data type or column name::
   ...       OneHotEncoder(),
   ...       make_column_selector(pattern='city', dtype_include=object))])
   >>> ct.fit_transform(X)
-  array([[ 0.904...,  0.      ,  1. ,  0. ,  0. ],
-         [-1.507...,  1.414...,  1. ,  0. ,  0. ],
-         [-0.301...,  0.      ,  0. ,  1. ,  0. ],
-         [ 0.904..., -1.414...,  0. ,  0. ,  1. ]])
+  array([[ 0.904,  0.      ,  1. ,  0. ,  0. ],
+         [-1.507,  1.414,  1. ,  0. ,  0. ],
+         [-0.301,  0.      ,  0. ,  1. ,  0. ],
+         [ 0.904, -1.414,  0. ,  0. ,  1. ]])
 
 Strings can reference columns if the input is a DataFrame, integers are always
 interpreted as the positional columns.
@@ -571,9 +571,9 @@ will use the column names to select the columns::
   >>> X_new = pd.DataFrame({"expert_rating": [5, 6, 1],
   ...                       "ignored_new_col": [1.2, 0.3, -0.1]})
   >>> ct.transform(X_new)
-  array([[ 0.9...],
-         [ 2.1...],
-         [-3.9...]])
+  array([[ 0.9],
+         [ 2.1],
+         [-3.9]])
 
 .. _visualizing_composite_estimators:
 
diff --git a/dev/_sources/modules/cross_validation.rst.txt b/dev/_sources/modules/cross_validation.rst.txt
index 84a6c1a985a3d..bfdee6c8a043d 100644
--- a/dev/_sources/modules/cross_validation.rst.txt
+++ b/dev/_sources/modules/cross_validation.rst.txt
@@ -55,7 +55,7 @@ data for testing (evaluating) our classifier::
 
   >>> clf = svm.SVC(kernel='linear', C=1).fit(X_train, y_train)
   >>> clf.score(X_test, y_test)
-  0.96...
+  0.96
 
 When evaluating different settings ("hyperparameters") for estimators,
 such as the ``C`` setting that must be manually set for an SVM,
@@ -120,7 +120,7 @@ time)::
   >>> clf = svm.SVC(kernel='linear', C=1, random_state=42)
   >>> scores = cross_val_score(clf, X, y, cv=5)
   >>> scores
-  array([0.96..., 1. , 0.96..., 0.96..., 1. ])
+  array([0.96, 1. , 0.96, 0.96, 1. ])
 
 The mean score and the standard deviation are hence given by::
 
@@ -135,7 +135,7 @@ scoring parameter::
   >>> scores = cross_val_score(
   ...     clf, X, y, cv=5, scoring='f1_macro')
   >>> scores
-  array([0.96..., 1.  ..., 0.96..., 0.96..., 1.        ])
+  array([0.96, 1., 0.96, 0.96, 1.])
 
 See :ref:`scoring_parameter` for details.
 In the case of the Iris dataset, the samples are balanced across target
@@ -153,7 +153,7 @@ validation iterator instead, for instance::
   >>> n_samples = X.shape[0]
   >>> cv = ShuffleSplit(n_splits=5, test_size=0.3, random_state=0)
   >>> cross_val_score(clf, X, y, cv=cv)
-  array([0.977..., 0.977..., 1.  ..., 0.955..., 1.        ])
+  array([0.977, 0.977, 1., 0.955, 1.])
 
 Another option is to use an iterable yielding (train, test) splits as arrays of
 indices, for example::
@@ -168,7 +168,7 @@ indices, for example::
   ...
   >>> custom_cv = custom_cv_2folds(X)
   >>> cross_val_score(clf, X, y, cv=custom_cv)
-  array([1.        , 0.973...])
+  array([1.        , 0.973])
 
 .. dropdown:: Data transformation with held-out data
 
@@ -185,7 +185,7 @@ indices, for example::
     >>> clf = svm.SVC(C=1).fit(X_train_transformed, y_train)
     >>> X_test_transformed = scaler.transform(X_test)
     >>> clf.score(X_test_transformed, y_test)
-    0.9333...
+    0.9333
 
   A :class:`Pipeline <sklearn.pipeline.Pipeline>` makes it easier to compose
   estimators, providing this behavior under cross-validation::
@@ -193,7 +193,7 @@ indices, for example::
     >>> from sklearn.pipeline import make_pipeline
     >>> clf = make_pipeline(preprocessing.StandardScaler(), svm.SVC(C=1))
     >>> cross_val_score(clf, X, y, cv=cv)
-    array([0.977..., 0.933..., 0.955..., 0.933..., 0.977...])
+    array([0.977, 0.933, 0.955, 0.933, 0.977])
 
   See :ref:`combining_estimators`.
 
@@ -237,7 +237,7 @@ predefined scorer names::
     >>> sorted(scores.keys())
     ['fit_time', 'score_time', 'test_precision_macro', 'test_recall_macro']
     >>> scores['test_recall_macro']
-    array([0.96..., 1.  ..., 0.96..., 0.96..., 1.        ])
+    array([0.96, 1., 0.96, 0.96, 1.])
 
 Or as a dict mapping scorer name to a predefined or custom scoring function::
 
@@ -250,7 +250,7 @@ Or as a dict mapping scorer name to a predefined or custom scoring function::
     ['fit_time', 'score_time', 'test_prec_macro', 'test_rec_macro',
      'train_prec_macro', 'train_rec_macro']
     >>> scores['train_rec_macro']
-    array([0.97..., 0.97..., 0.99..., 0.98..., 0.98...])
+    array([0.97, 0.97, 0.99, 0.98, 0.98])
 
 Here is an example of ``cross_validate`` using a single metric::
 
diff --git a/dev/_sources/modules/decomposition.rst.txt b/dev/_sources/modules/decomposition.rst.txt
index b3be752e31e91..24fcd43a292c0 100644
--- a/dev/_sources/modules/decomposition.rst.txt
+++ b/dev/_sources/modules/decomposition.rst.txt
@@ -739,7 +739,7 @@ implemented in scikit-learn using the :class:`Fast ICA <FastICA>`
 algorithm. Typically, ICA is not used for reducing dimensionality but
 for separating superimposed signals. Since the ICA model does not include
 a noise term, for the model to be correct, whitening must be applied.
-This can be done internally using the whiten argument or manually using one
+This can be done internally using the `whiten` argument or manually using one
 of the PCA variants.
 
 It is classically used to separate mixed signals (a problem known as
diff --git a/dev/_sources/modules/ensemble.rst.txt b/dev/_sources/modules/ensemble.rst.txt
index 35ef9f6d7bbfc..f0f14c60e4867 100644
--- a/dev/_sources/modules/ensemble.rst.txt
+++ b/dev/_sources/modules/ensemble.rst.txt
@@ -241,7 +241,7 @@ The following toy example demonstrates that samples with a sample weight of zero
     >>> gb.predict([[1, 0]])
     array([1])
     >>> gb.predict_proba([[1, 0]])[0, 1]
-    np.float64(0.999...)
+    np.float64(0.999)
 
 As you can see, the `[1, 0]` is comfortably classified as `1` since the first
 two samples are ignored due to their sample weights.
@@ -513,7 +513,7 @@ parameters of these estimators are `n_estimators` and `learning_rate`.
       >>> clf = GradientBoostingClassifier(n_estimators=100, learning_rate=1.0,
       ...     max_depth=1, random_state=0).fit(X_train, y_train)
       >>> clf.score(X_test, y_test)
-      0.913...
+      0.913
 
   The number of weak learners (i.e. regression trees) is controlled by the
   parameter ``n_estimators``; :ref:`The size of each tree
@@ -556,7 +556,7 @@ parameters of these estimators are `n_estimators` and `learning_rate`.
       ...     loss='squared_error'
       ... ).fit(X_train, y_train)
       >>> mean_squared_error(y_test, est.predict(X_test))
-      5.00...
+      5.00
 
   The figure below shows the results of applying :class:`GradientBoostingRegressor`
   with least squares loss and 500 base learners to the diabetes dataset
@@ -604,11 +604,11 @@ fitted model.
   ... )
   >>> est = est.fit(X_train, y_train)  # fit with 100 trees
   >>> mean_squared_error(y_test, est.predict(X_test))
-  5.00...
+  5.00
   >>> _ = est.set_params(n_estimators=200, warm_start=True)  # set warm_start and increase num of trees
   >>> _ = est.fit(X_train, y_train) # fit additional 100 trees to est
   >>> mean_squared_error(y_test, est.predict(X_test))
-  3.84...
+  3.84
 
 .. _gradient_boosting_tree_size:
 
@@ -900,7 +900,8 @@ accessed via the ``feature_importances_`` property::
     >>> clf = GradientBoostingClassifier(n_estimators=100, learning_rate=1.0,
     ...     max_depth=1, random_state=0).fit(X, y)
     >>> clf.feature_importances_
-    array([0.10..., 0.10..., 0.11..., ...
+    array([0.107, 0.105, 0.113, 0.0987, 0.0947,
+           0.107, 0.0916, 0.0972, 0.0958, 0.0906])
 
 Note that this computation of feature importance is based on entropy, and it
 is distinct from :func:`sklearn.inspection.permutation_importance` which is
@@ -1035,13 +1036,13 @@ in bias::
     ...     random_state=0)
     >>> scores = cross_val_score(clf, X, y, cv=5)
     >>> scores.mean()
-    np.float64(0.98...)
+    np.float64(0.98)
 
     >>> clf = RandomForestClassifier(n_estimators=10, max_depth=None,
     ...     min_samples_split=2, random_state=0)
     >>> scores = cross_val_score(clf, X, y, cv=5)
     >>> scores.mean()
-    np.float64(0.999...)
+    np.float64(0.999)
 
     >>> clf = ExtraTreesClassifier(n_estimators=10, max_depth=None,
     ...     min_samples_split=2, random_state=0)
@@ -1410,40 +1411,17 @@ classifier 3      w3 * 0.3      w3 * 0.4        w3 * 0.3
 weighted average  0.37          0.4             0.23
 ================  ==========    ==========      ==========
 
-Here, the predicted class label is 2, since it has the highest average probability. See
-this example on :ref:`Visualising class probabilities in a Voting Classifier
-<sphx_glr_auto_examples_ensemble_plot_voting_probas.py>` for a detailed illustration of
-class probabilities averaged by soft voting.
+Here, the predicted class label is 2, since it has the highest average
+predicted probability. See the example on
+:ref:`sphx_glr_auto_examples_ensemble_plot_voting_decision_regions.py` for a
+demonstration of how the predicted class label can be obtained from the weighted
+average of predicted probabilities.
 
-Also, the following example illustrates how the decision regions may change
-when a soft :class:`VotingClassifier` is used based on a linear Support
-Vector Machine, a Decision Tree, and a K-nearest neighbor classifier::
+The following figure illustrates how the decision regions may change when
+a soft :class:`VotingClassifier` is trained with weights on three linear
+models:
 
-   >>> from sklearn import datasets
-   >>> from sklearn.tree import DecisionTreeClassifier
-   >>> from sklearn.neighbors import KNeighborsClassifier
-   >>> from sklearn.svm import SVC
-   >>> from itertools import product
-   >>> from sklearn.ensemble import VotingClassifier
-
-   >>> # Loading some example data
-   >>> iris = datasets.load_iris()
-   >>> X = iris.data[:, [0, 2]]
-   >>> y = iris.target
-
-   >>> # Training classifiers
-   >>> clf1 = DecisionTreeClassifier(max_depth=4)
-   >>> clf2 = KNeighborsClassifier(n_neighbors=7)
-   >>> clf3 = SVC(kernel='rbf', probability=True)
-   >>> eclf = VotingClassifier(estimators=[('dt', clf1), ('knn', clf2), ('svc', clf3)],
-   ...                         voting='soft', weights=[2, 1, 2])
-
-   >>> clf1 = clf1.fit(X, y)
-   >>> clf2 = clf2.fit(X, y)
-   >>> clf3 = clf3.fit(X, y)
-   >>> eclf = eclf.fit(X, y)
-
-.. figure:: ../auto_examples/ensemble/images/sphx_glr_plot_voting_decision_regions_001.png
+.. figure:: ../auto_examples/ensemble/images/sphx_glr_plot_voting_decision_regions_002.png
     :target: ../auto_examples/ensemble/plot_voting_decision_regions.html
     :align: center
     :scale: 75%
@@ -1601,11 +1579,11 @@ Note that it is also possible to get the output of the stacked
 `estimators` using the `transform` method::
 
   >>> reg.transform(X_test[:5])
-  array([[142..., 138..., 146...],
-         [179..., 182..., 151...],
-         [139..., 132..., 158...],
-         [286..., 292..., 225...],
-         [126..., 124..., 164...]])
+  array([[142, 138, 146],
+         [179, 182, 151],
+         [139, 132, 158],
+         [286, 292, 225],
+         [126, 124, 164]])
 
 In practice, a stacking predictor predicts as good as the best predictor of the
 base layer and even sometimes outperforms it by combining the different
@@ -1707,7 +1685,7 @@ learners::
     >>> clf = AdaBoostClassifier(n_estimators=100)
     >>> scores = cross_val_score(clf, X, y, cv=5)
     >>> scores.mean()
-    np.float64(0.9...)
+    np.float64(0.95)
 
 The number of weak learners is controlled by the parameter ``n_estimators``. The
 ``learning_rate`` parameter controls the contribution of the weak learners in
diff --git a/dev/_sources/modules/feature_extraction.rst.txt b/dev/_sources/modules/feature_extraction.rst.txt
index f7ac0979ce51e..42bcf18e1d572 100644
--- a/dev/_sources/modules/feature_extraction.rst.txt
+++ b/dev/_sources/modules/feature_extraction.rst.txt
@@ -245,7 +245,7 @@ The Bag of Words representation
 -------------------------------
 
 Text Analysis is a major application field for machine learning
-algorithms. However the raw data, a sequence of symbols cannot be fed
+algorithms. However the raw data, a sequence of symbols, cannot be fed
 directly to the algorithms themselves as most of them expect numerical
 feature vectors with a fixed size rather than the raw text documents
 with variable length.
@@ -583,7 +583,7 @@ Again please see the :ref:`reference documentation
   attribute::
 
     >>> transformer.idf_
-    array([1. ..., 2.25..., 1.84...])
+    array([1., 2.25, 1.84])
 
   As tf-idf is very often used for text features, there is also another
   class called :class:`TfidfVectorizer` that combines all the options of
@@ -1041,6 +1041,8 @@ implemented as a scikit-learn transformer, so it can be used in pipelines. See::
     >>> patches.shape
     (45, 2, 2, 3)
 
+.. _connectivity_graph_image:
+
 Connectivity graph of an image
 -------------------------------
 
diff --git a/dev/_sources/modules/feature_selection.rst.txt b/dev/_sources/modules/feature_selection.rst.txt
index aff37f466521c..ffee801f34ccc 100644
--- a/dev/_sources/modules/feature_selection.rst.txt
+++ b/dev/_sources/modules/feature_selection.rst.txt
@@ -262,7 +262,7 @@ meta-transformer)::
   >>> clf = ExtraTreesClassifier(n_estimators=50)
   >>> clf = clf.fit(X, y)
   >>> clf.feature_importances_  # doctest: +SKIP
-  array([ 0.04...,  0.05...,  0.4...,  0.4...])
+  array([ 0.04,  0.05,  0.4,  0.4])
   >>> model = SelectFromModel(clf, prefit=True)
   >>> X_new = model.transform(X)
   >>> X_new.shape               # doctest: +SKIP
diff --git a/dev/_sources/modules/gaussian_process.rst.txt b/dev/_sources/modules/gaussian_process.rst.txt
index 4990649624f18..46d04ac35d832 100644
--- a/dev/_sources/modules/gaussian_process.rst.txt
+++ b/dev/_sources/modules/gaussian_process.rst.txt
@@ -106,11 +106,11 @@ The :class:`GaussianProcessClassifier` implements Gaussian processes (GP) for
 classification purposes, more specifically for probabilistic classification,
 where test predictions take the form of class probabilities.
 GaussianProcessClassifier places a GP prior on a latent function :math:`f`,
-which is then squashed through a link function to obtain the probabilistic
+which is then squashed through a link function :math:`\pi` to obtain the probabilistic
 classification. The latent function :math:`f` is a so-called nuisance function,
 whose values are not observed and are not relevant by themselves.
 Its purpose is to allow a convenient formulation of the model, and :math:`f`
-is removed (integrated out) during prediction. GaussianProcessClassifier
+is removed (integrated out) during prediction. :class:`GaussianProcessClassifier`
 implements the logistic link function, for which the integral cannot be
 computed analytically but is easily approximated in the binary case.
 
@@ -134,6 +134,11 @@ that have been chosen randomly from the range of allowed values.
 If the initial hyperparameters should be kept fixed, `None` can be passed as
 optimizer.
 
+In some scenarios, information about the latent function :math:`f` is desired
+(i.e. the mean :math:`\bar{f_*}` and the variance :math:`\text{Var}[f_*]` described
+in Eqs. (3.21) and (3.24) of [RW2006]_). The :class:`GaussianProcessClassifier`
+provides access to these quantities via the `latent_mean_and_variance` method.
+
 :class:`GaussianProcessClassifier` supports multi-class classification
 by performing either one-versus-rest or one-versus-one based training and
 prediction.  In one-versus-rest, one binary Gaussian process classifier is
@@ -236,8 +241,10 @@ translations in the input space, while non-stationary kernels
 depend also on the specific values of the datapoints. Stationary kernels can further
 be subdivided into isotropic and anisotropic kernels, where isotropic kernels are
 also invariant to rotations in the input space. For more details, we refer to
-Chapter 4 of [RW2006]_. For guidance on how to best combine different kernels,
-we refer to [Duv2014]_.
+Chapter 4 of [RW2006]_. :ref:`This example
+<sphx_glr_auto_examples_gaussian_process_plot_gpr_on_structured_data.py>`
+shows how to define a custom kernel over discrete data. For guidance on how to best
+combine different kernels, we refer to [Duv2014]_.
 
 .. dropdown:: Gaussian Process Kernel API
 
diff --git a/dev/_sources/modules/generated/sklearn.base.BaseEstimator.rst.txt b/dev/_sources/modules/generated/sklearn.base.BaseEstimator.rst.txt
index 9314178146d21..3b9f10a999556 100644
--- a/dev/_sources/modules/generated/sklearn.base.BaseEstimator.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.base.BaseEstimator.rst.txt
@@ -8,6 +8,6 @@ BaseEstimator
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.base.BaseEstimator sklearn.base.BaseEstimator.__delattr__ sklearn.base.BaseEstimator.__dir__ sklearn.base.BaseEstimator.__eq__ sklearn.base.BaseEstimator.__format__ sklearn.base.BaseEstimator.__ge__ sklearn.base.BaseEstimator.__getattribute__ sklearn.base.BaseEstimator.__getstate__ sklearn.base.BaseEstimator.__gt__ sklearn.base.BaseEstimator.__hash__ sklearn.base.BaseEstimator.__init__ sklearn.base.BaseEstimator.__init_subclass__ sklearn.base.BaseEstimator.__le__ sklearn.base.BaseEstimator.__lt__ sklearn.base.BaseEstimator.__ne__ sklearn.base.BaseEstimator.__new__ sklearn.base.BaseEstimator.__reduce__ sklearn.base.BaseEstimator.__reduce_ex__ sklearn.base.BaseEstimator.__repr__ sklearn.base.BaseEstimator.__setattr__ sklearn.base.BaseEstimator.__setstate__ sklearn.base.BaseEstimator.__sizeof__ sklearn.base.BaseEstimator.__sklearn_clone__ sklearn.base.BaseEstimator.__sklearn_tags__ sklearn.base.BaseEstimator.__str__ sklearn.base.BaseEstimator.__subclasshook__ sklearn.base.BaseEstimator._build_request_for_signature sklearn.base.BaseEstimator._check_feature_names sklearn.base.BaseEstimator._check_n_features sklearn.base.BaseEstimator._get_default_requests sklearn.base.BaseEstimator._get_doc_link sklearn.base.BaseEstimator._get_metadata_request sklearn.base.BaseEstimator._get_param_names sklearn.base.BaseEstimator._get_tags sklearn.base.BaseEstimator._more_tags sklearn.base.BaseEstimator._repr_html_inner sklearn.base.BaseEstimator._repr_mimebundle_ sklearn.base.BaseEstimator._validate_data sklearn.base.BaseEstimator._validate_params sklearn.base.BaseEstimator.get_metadata_routing sklearn.base.BaseEstimator.get_params sklearn.base.BaseEstimator.set_params 
+.. minigallery:: sklearn.base.BaseEstimator sklearn.base.BaseEstimator.__delattr__ sklearn.base.BaseEstimator.__dir__ sklearn.base.BaseEstimator.__eq__ sklearn.base.BaseEstimator.__format__ sklearn.base.BaseEstimator.__ge__ sklearn.base.BaseEstimator.__getattribute__ sklearn.base.BaseEstimator.__getstate__ sklearn.base.BaseEstimator.__gt__ sklearn.base.BaseEstimator.__hash__ sklearn.base.BaseEstimator.__init__ sklearn.base.BaseEstimator.__init_subclass__ sklearn.base.BaseEstimator.__le__ sklearn.base.BaseEstimator.__lt__ sklearn.base.BaseEstimator.__ne__ sklearn.base.BaseEstimator.__new__ sklearn.base.BaseEstimator.__reduce__ sklearn.base.BaseEstimator.__reduce_ex__ sklearn.base.BaseEstimator.__repr__ sklearn.base.BaseEstimator.__setattr__ sklearn.base.BaseEstimator.__setstate__ sklearn.base.BaseEstimator.__sizeof__ sklearn.base.BaseEstimator.__sklearn_clone__ sklearn.base.BaseEstimator.__sklearn_tags__ sklearn.base.BaseEstimator.__str__ sklearn.base.BaseEstimator.__subclasshook__ sklearn.base.BaseEstimator._build_request_for_signature sklearn.base.BaseEstimator._get_default_requests sklearn.base.BaseEstimator._get_doc_link sklearn.base.BaseEstimator._get_metadata_request sklearn.base.BaseEstimator._get_param_names sklearn.base.BaseEstimator._repr_html_inner sklearn.base.BaseEstimator._repr_mimebundle_ sklearn.base.BaseEstimator._validate_params sklearn.base.BaseEstimator.get_metadata_routing sklearn.base.BaseEstimator.get_params sklearn.base.BaseEstimator.set_params 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.calibration.CalibratedClassifierCV.rst.txt b/dev/_sources/modules/generated/sklearn.calibration.CalibratedClassifierCV.rst.txt
index b6b594b4e6400..6fe95a4032ce1 100644
--- a/dev/_sources/modules/generated/sklearn.calibration.CalibratedClassifierCV.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.calibration.CalibratedClassifierCV.rst.txt
@@ -8,6 +8,6 @@ CalibratedClassifierCV
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.calibration.CalibratedClassifierCV sklearn.calibration.CalibratedClassifierCV.__delattr__ sklearn.calibration.CalibratedClassifierCV.__dir__ sklearn.calibration.CalibratedClassifierCV.__eq__ sklearn.calibration.CalibratedClassifierCV.__format__ sklearn.calibration.CalibratedClassifierCV.__ge__ sklearn.calibration.CalibratedClassifierCV.__getattribute__ sklearn.calibration.CalibratedClassifierCV.__getstate__ sklearn.calibration.CalibratedClassifierCV.__gt__ sklearn.calibration.CalibratedClassifierCV.__hash__ sklearn.calibration.CalibratedClassifierCV.__init__ sklearn.calibration.CalibratedClassifierCV.__init_subclass__ sklearn.calibration.CalibratedClassifierCV.__le__ sklearn.calibration.CalibratedClassifierCV.__lt__ sklearn.calibration.CalibratedClassifierCV.__ne__ sklearn.calibration.CalibratedClassifierCV.__new__ sklearn.calibration.CalibratedClassifierCV.__reduce__ sklearn.calibration.CalibratedClassifierCV.__reduce_ex__ sklearn.calibration.CalibratedClassifierCV.__repr__ sklearn.calibration.CalibratedClassifierCV.__setattr__ sklearn.calibration.CalibratedClassifierCV.__setstate__ sklearn.calibration.CalibratedClassifierCV.__sizeof__ sklearn.calibration.CalibratedClassifierCV.__sklearn_clone__ sklearn.calibration.CalibratedClassifierCV.__sklearn_tags__ sklearn.calibration.CalibratedClassifierCV.__str__ sklearn.calibration.CalibratedClassifierCV.__subclasshook__ sklearn.calibration.CalibratedClassifierCV._build_request_for_signature sklearn.calibration.CalibratedClassifierCV._check_feature_names sklearn.calibration.CalibratedClassifierCV._check_n_features sklearn.calibration.CalibratedClassifierCV._get_default_requests sklearn.calibration.CalibratedClassifierCV._get_doc_link sklearn.calibration.CalibratedClassifierCV._get_estimator sklearn.calibration.CalibratedClassifierCV._get_metadata_request sklearn.calibration.CalibratedClassifierCV._get_param_names sklearn.calibration.CalibratedClassifierCV._get_tags sklearn.calibration.CalibratedClassifierCV._more_tags sklearn.calibration.CalibratedClassifierCV._repr_html_inner sklearn.calibration.CalibratedClassifierCV._repr_mimebundle_ sklearn.calibration.CalibratedClassifierCV._validate_data sklearn.calibration.CalibratedClassifierCV._validate_params sklearn.calibration.CalibratedClassifierCV.fit sklearn.calibration.CalibratedClassifierCV.get_metadata_routing sklearn.calibration.CalibratedClassifierCV.get_params sklearn.calibration.CalibratedClassifierCV.predict sklearn.calibration.CalibratedClassifierCV.predict_proba sklearn.calibration.CalibratedClassifierCV.score sklearn.calibration.CalibratedClassifierCV.set_fit_request sklearn.calibration.CalibratedClassifierCV.set_params sklearn.calibration.CalibratedClassifierCV.set_score_request 
+.. minigallery:: sklearn.calibration.CalibratedClassifierCV sklearn.calibration.CalibratedClassifierCV.__delattr__ sklearn.calibration.CalibratedClassifierCV.__dir__ sklearn.calibration.CalibratedClassifierCV.__eq__ sklearn.calibration.CalibratedClassifierCV.__format__ sklearn.calibration.CalibratedClassifierCV.__ge__ sklearn.calibration.CalibratedClassifierCV.__getattribute__ sklearn.calibration.CalibratedClassifierCV.__getstate__ sklearn.calibration.CalibratedClassifierCV.__gt__ sklearn.calibration.CalibratedClassifierCV.__hash__ sklearn.calibration.CalibratedClassifierCV.__init__ sklearn.calibration.CalibratedClassifierCV.__init_subclass__ sklearn.calibration.CalibratedClassifierCV.__le__ sklearn.calibration.CalibratedClassifierCV.__lt__ sklearn.calibration.CalibratedClassifierCV.__ne__ sklearn.calibration.CalibratedClassifierCV.__new__ sklearn.calibration.CalibratedClassifierCV.__reduce__ sklearn.calibration.CalibratedClassifierCV.__reduce_ex__ sklearn.calibration.CalibratedClassifierCV.__repr__ sklearn.calibration.CalibratedClassifierCV.__setattr__ sklearn.calibration.CalibratedClassifierCV.__setstate__ sklearn.calibration.CalibratedClassifierCV.__sizeof__ sklearn.calibration.CalibratedClassifierCV.__sklearn_clone__ sklearn.calibration.CalibratedClassifierCV.__sklearn_tags__ sklearn.calibration.CalibratedClassifierCV.__str__ sklearn.calibration.CalibratedClassifierCV.__subclasshook__ sklearn.calibration.CalibratedClassifierCV._build_request_for_signature sklearn.calibration.CalibratedClassifierCV._get_default_requests sklearn.calibration.CalibratedClassifierCV._get_doc_link sklearn.calibration.CalibratedClassifierCV._get_estimator sklearn.calibration.CalibratedClassifierCV._get_metadata_request sklearn.calibration.CalibratedClassifierCV._get_param_names sklearn.calibration.CalibratedClassifierCV._repr_html_inner sklearn.calibration.CalibratedClassifierCV._repr_mimebundle_ sklearn.calibration.CalibratedClassifierCV._validate_params sklearn.calibration.CalibratedClassifierCV.fit sklearn.calibration.CalibratedClassifierCV.get_metadata_routing sklearn.calibration.CalibratedClassifierCV.get_params sklearn.calibration.CalibratedClassifierCV.predict sklearn.calibration.CalibratedClassifierCV.predict_proba sklearn.calibration.CalibratedClassifierCV.score sklearn.calibration.CalibratedClassifierCV.set_fit_request sklearn.calibration.CalibratedClassifierCV.set_params sklearn.calibration.CalibratedClassifierCV.set_score_request 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.cluster.AffinityPropagation.rst.txt b/dev/_sources/modules/generated/sklearn.cluster.AffinityPropagation.rst.txt
index 847d08ad3aa9d..cee4826764f69 100644
--- a/dev/_sources/modules/generated/sklearn.cluster.AffinityPropagation.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.cluster.AffinityPropagation.rst.txt
@@ -8,6 +8,6 @@ AffinityPropagation
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.cluster.AffinityPropagation sklearn.cluster.AffinityPropagation.__delattr__ sklearn.cluster.AffinityPropagation.__dir__ sklearn.cluster.AffinityPropagation.__eq__ sklearn.cluster.AffinityPropagation.__format__ sklearn.cluster.AffinityPropagation.__ge__ sklearn.cluster.AffinityPropagation.__getattribute__ sklearn.cluster.AffinityPropagation.__getstate__ sklearn.cluster.AffinityPropagation.__gt__ sklearn.cluster.AffinityPropagation.__hash__ sklearn.cluster.AffinityPropagation.__init__ sklearn.cluster.AffinityPropagation.__init_subclass__ sklearn.cluster.AffinityPropagation.__le__ sklearn.cluster.AffinityPropagation.__lt__ sklearn.cluster.AffinityPropagation.__ne__ sklearn.cluster.AffinityPropagation.__new__ sklearn.cluster.AffinityPropagation.__reduce__ sklearn.cluster.AffinityPropagation.__reduce_ex__ sklearn.cluster.AffinityPropagation.__repr__ sklearn.cluster.AffinityPropagation.__setattr__ sklearn.cluster.AffinityPropagation.__setstate__ sklearn.cluster.AffinityPropagation.__sizeof__ sklearn.cluster.AffinityPropagation.__sklearn_clone__ sklearn.cluster.AffinityPropagation.__sklearn_tags__ sklearn.cluster.AffinityPropagation.__str__ sklearn.cluster.AffinityPropagation.__subclasshook__ sklearn.cluster.AffinityPropagation._build_request_for_signature sklearn.cluster.AffinityPropagation._check_feature_names sklearn.cluster.AffinityPropagation._check_n_features sklearn.cluster.AffinityPropagation._get_default_requests sklearn.cluster.AffinityPropagation._get_doc_link sklearn.cluster.AffinityPropagation._get_metadata_request sklearn.cluster.AffinityPropagation._get_param_names sklearn.cluster.AffinityPropagation._get_tags sklearn.cluster.AffinityPropagation._more_tags sklearn.cluster.AffinityPropagation._repr_html_inner sklearn.cluster.AffinityPropagation._repr_mimebundle_ sklearn.cluster.AffinityPropagation._validate_data sklearn.cluster.AffinityPropagation._validate_params sklearn.cluster.AffinityPropagation.fit sklearn.cluster.AffinityPropagation.fit_predict sklearn.cluster.AffinityPropagation.get_metadata_routing sklearn.cluster.AffinityPropagation.get_params sklearn.cluster.AffinityPropagation.predict sklearn.cluster.AffinityPropagation.set_params 
+.. minigallery:: sklearn.cluster.AffinityPropagation sklearn.cluster.AffinityPropagation.__delattr__ sklearn.cluster.AffinityPropagation.__dir__ sklearn.cluster.AffinityPropagation.__eq__ sklearn.cluster.AffinityPropagation.__format__ sklearn.cluster.AffinityPropagation.__ge__ sklearn.cluster.AffinityPropagation.__getattribute__ sklearn.cluster.AffinityPropagation.__getstate__ sklearn.cluster.AffinityPropagation.__gt__ sklearn.cluster.AffinityPropagation.__hash__ sklearn.cluster.AffinityPropagation.__init__ sklearn.cluster.AffinityPropagation.__init_subclass__ sklearn.cluster.AffinityPropagation.__le__ sklearn.cluster.AffinityPropagation.__lt__ sklearn.cluster.AffinityPropagation.__ne__ sklearn.cluster.AffinityPropagation.__new__ sklearn.cluster.AffinityPropagation.__reduce__ sklearn.cluster.AffinityPropagation.__reduce_ex__ sklearn.cluster.AffinityPropagation.__repr__ sklearn.cluster.AffinityPropagation.__setattr__ sklearn.cluster.AffinityPropagation.__setstate__ sklearn.cluster.AffinityPropagation.__sizeof__ sklearn.cluster.AffinityPropagation.__sklearn_clone__ sklearn.cluster.AffinityPropagation.__sklearn_tags__ sklearn.cluster.AffinityPropagation.__str__ sklearn.cluster.AffinityPropagation.__subclasshook__ sklearn.cluster.AffinityPropagation._build_request_for_signature sklearn.cluster.AffinityPropagation._get_default_requests sklearn.cluster.AffinityPropagation._get_doc_link sklearn.cluster.AffinityPropagation._get_metadata_request sklearn.cluster.AffinityPropagation._get_param_names sklearn.cluster.AffinityPropagation._repr_html_inner sklearn.cluster.AffinityPropagation._repr_mimebundle_ sklearn.cluster.AffinityPropagation._validate_params sklearn.cluster.AffinityPropagation.fit sklearn.cluster.AffinityPropagation.fit_predict sklearn.cluster.AffinityPropagation.get_metadata_routing sklearn.cluster.AffinityPropagation.get_params sklearn.cluster.AffinityPropagation.predict sklearn.cluster.AffinityPropagation.set_params 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.cluster.AgglomerativeClustering.rst.txt b/dev/_sources/modules/generated/sklearn.cluster.AgglomerativeClustering.rst.txt
index c1961f9ee63a8..0fe0a5e32d41e 100644
--- a/dev/_sources/modules/generated/sklearn.cluster.AgglomerativeClustering.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.cluster.AgglomerativeClustering.rst.txt
@@ -8,6 +8,6 @@ AgglomerativeClustering
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.cluster.AgglomerativeClustering sklearn.cluster.AgglomerativeClustering.__delattr__ sklearn.cluster.AgglomerativeClustering.__dir__ sklearn.cluster.AgglomerativeClustering.__eq__ sklearn.cluster.AgglomerativeClustering.__format__ sklearn.cluster.AgglomerativeClustering.__ge__ sklearn.cluster.AgglomerativeClustering.__getattribute__ sklearn.cluster.AgglomerativeClustering.__getstate__ sklearn.cluster.AgglomerativeClustering.__gt__ sklearn.cluster.AgglomerativeClustering.__hash__ sklearn.cluster.AgglomerativeClustering.__init__ sklearn.cluster.AgglomerativeClustering.__init_subclass__ sklearn.cluster.AgglomerativeClustering.__le__ sklearn.cluster.AgglomerativeClustering.__lt__ sklearn.cluster.AgglomerativeClustering.__ne__ sklearn.cluster.AgglomerativeClustering.__new__ sklearn.cluster.AgglomerativeClustering.__reduce__ sklearn.cluster.AgglomerativeClustering.__reduce_ex__ sklearn.cluster.AgglomerativeClustering.__repr__ sklearn.cluster.AgglomerativeClustering.__setattr__ sklearn.cluster.AgglomerativeClustering.__setstate__ sklearn.cluster.AgglomerativeClustering.__sizeof__ sklearn.cluster.AgglomerativeClustering.__sklearn_clone__ sklearn.cluster.AgglomerativeClustering.__sklearn_tags__ sklearn.cluster.AgglomerativeClustering.__str__ sklearn.cluster.AgglomerativeClustering.__subclasshook__ sklearn.cluster.AgglomerativeClustering._build_request_for_signature sklearn.cluster.AgglomerativeClustering._check_feature_names sklearn.cluster.AgglomerativeClustering._check_n_features sklearn.cluster.AgglomerativeClustering._fit sklearn.cluster.AgglomerativeClustering._get_default_requests sklearn.cluster.AgglomerativeClustering._get_doc_link sklearn.cluster.AgglomerativeClustering._get_metadata_request sklearn.cluster.AgglomerativeClustering._get_param_names sklearn.cluster.AgglomerativeClustering._get_tags sklearn.cluster.AgglomerativeClustering._more_tags sklearn.cluster.AgglomerativeClustering._repr_html_inner sklearn.cluster.AgglomerativeClustering._repr_mimebundle_ sklearn.cluster.AgglomerativeClustering._validate_data sklearn.cluster.AgglomerativeClustering._validate_params sklearn.cluster.AgglomerativeClustering.fit sklearn.cluster.AgglomerativeClustering.fit_predict sklearn.cluster.AgglomerativeClustering.get_metadata_routing sklearn.cluster.AgglomerativeClustering.get_params sklearn.cluster.AgglomerativeClustering.set_params 
+.. minigallery:: sklearn.cluster.AgglomerativeClustering sklearn.cluster.AgglomerativeClustering.__delattr__ sklearn.cluster.AgglomerativeClustering.__dir__ sklearn.cluster.AgglomerativeClustering.__eq__ sklearn.cluster.AgglomerativeClustering.__format__ sklearn.cluster.AgglomerativeClustering.__ge__ sklearn.cluster.AgglomerativeClustering.__getattribute__ sklearn.cluster.AgglomerativeClustering.__getstate__ sklearn.cluster.AgglomerativeClustering.__gt__ sklearn.cluster.AgglomerativeClustering.__hash__ sklearn.cluster.AgglomerativeClustering.__init__ sklearn.cluster.AgglomerativeClustering.__init_subclass__ sklearn.cluster.AgglomerativeClustering.__le__ sklearn.cluster.AgglomerativeClustering.__lt__ sklearn.cluster.AgglomerativeClustering.__ne__ sklearn.cluster.AgglomerativeClustering.__new__ sklearn.cluster.AgglomerativeClustering.__reduce__ sklearn.cluster.AgglomerativeClustering.__reduce_ex__ sklearn.cluster.AgglomerativeClustering.__repr__ sklearn.cluster.AgglomerativeClustering.__setattr__ sklearn.cluster.AgglomerativeClustering.__setstate__ sklearn.cluster.AgglomerativeClustering.__sizeof__ sklearn.cluster.AgglomerativeClustering.__sklearn_clone__ sklearn.cluster.AgglomerativeClustering.__sklearn_tags__ sklearn.cluster.AgglomerativeClustering.__str__ sklearn.cluster.AgglomerativeClustering.__subclasshook__ sklearn.cluster.AgglomerativeClustering._build_request_for_signature sklearn.cluster.AgglomerativeClustering._fit sklearn.cluster.AgglomerativeClustering._get_default_requests sklearn.cluster.AgglomerativeClustering._get_doc_link sklearn.cluster.AgglomerativeClustering._get_metadata_request sklearn.cluster.AgglomerativeClustering._get_param_names sklearn.cluster.AgglomerativeClustering._repr_html_inner sklearn.cluster.AgglomerativeClustering._repr_mimebundle_ sklearn.cluster.AgglomerativeClustering._validate_params sklearn.cluster.AgglomerativeClustering.fit sklearn.cluster.AgglomerativeClustering.fit_predict sklearn.cluster.AgglomerativeClustering.get_metadata_routing sklearn.cluster.AgglomerativeClustering.get_params sklearn.cluster.AgglomerativeClustering.set_params 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.cluster.Birch.rst.txt b/dev/_sources/modules/generated/sklearn.cluster.Birch.rst.txt
index 2f850195f2313..b52d273f07efd 100644
--- a/dev/_sources/modules/generated/sklearn.cluster.Birch.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.cluster.Birch.rst.txt
@@ -8,6 +8,6 @@ Birch
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.cluster.Birch sklearn.cluster.Birch.__delattr__ sklearn.cluster.Birch.__dir__ sklearn.cluster.Birch.__eq__ sklearn.cluster.Birch.__format__ sklearn.cluster.Birch.__ge__ sklearn.cluster.Birch.__getattribute__ sklearn.cluster.Birch.__getstate__ sklearn.cluster.Birch.__gt__ sklearn.cluster.Birch.__hash__ sklearn.cluster.Birch.__init__ sklearn.cluster.Birch.__init_subclass__ sklearn.cluster.Birch.__le__ sklearn.cluster.Birch.__lt__ sklearn.cluster.Birch.__ne__ sklearn.cluster.Birch.__new__ sklearn.cluster.Birch.__reduce__ sklearn.cluster.Birch.__reduce_ex__ sklearn.cluster.Birch.__repr__ sklearn.cluster.Birch.__setattr__ sklearn.cluster.Birch.__setstate__ sklearn.cluster.Birch.__sizeof__ sklearn.cluster.Birch.__sklearn_clone__ sklearn.cluster.Birch.__sklearn_tags__ sklearn.cluster.Birch.__str__ sklearn.cluster.Birch.__subclasshook__ sklearn.cluster.Birch._build_request_for_signature sklearn.cluster.Birch._check_feature_names sklearn.cluster.Birch._check_n_features sklearn.cluster.Birch._fit sklearn.cluster.Birch._get_default_requests sklearn.cluster.Birch._get_doc_link sklearn.cluster.Birch._get_leaves sklearn.cluster.Birch._get_metadata_request sklearn.cluster.Birch._get_param_names sklearn.cluster.Birch._get_tags sklearn.cluster.Birch._global_clustering sklearn.cluster.Birch._more_tags sklearn.cluster.Birch._predict sklearn.cluster.Birch._repr_html_inner sklearn.cluster.Birch._repr_mimebundle_ sklearn.cluster.Birch._validate_data sklearn.cluster.Birch._validate_params sklearn.cluster.Birch.fit sklearn.cluster.Birch.fit_predict sklearn.cluster.Birch.fit_transform sklearn.cluster.Birch.get_feature_names_out sklearn.cluster.Birch.get_metadata_routing sklearn.cluster.Birch.get_params sklearn.cluster.Birch.partial_fit sklearn.cluster.Birch.predict sklearn.cluster.Birch.set_output sklearn.cluster.Birch.set_params sklearn.cluster.Birch.transform 
+.. minigallery:: sklearn.cluster.Birch sklearn.cluster.Birch.__delattr__ sklearn.cluster.Birch.__dir__ sklearn.cluster.Birch.__eq__ sklearn.cluster.Birch.__format__ sklearn.cluster.Birch.__ge__ sklearn.cluster.Birch.__getattribute__ sklearn.cluster.Birch.__getstate__ sklearn.cluster.Birch.__gt__ sklearn.cluster.Birch.__hash__ sklearn.cluster.Birch.__init__ sklearn.cluster.Birch.__init_subclass__ sklearn.cluster.Birch.__le__ sklearn.cluster.Birch.__lt__ sklearn.cluster.Birch.__ne__ sklearn.cluster.Birch.__new__ sklearn.cluster.Birch.__reduce__ sklearn.cluster.Birch.__reduce_ex__ sklearn.cluster.Birch.__repr__ sklearn.cluster.Birch.__setattr__ sklearn.cluster.Birch.__setstate__ sklearn.cluster.Birch.__sizeof__ sklearn.cluster.Birch.__sklearn_clone__ sklearn.cluster.Birch.__sklearn_tags__ sklearn.cluster.Birch.__str__ sklearn.cluster.Birch.__subclasshook__ sklearn.cluster.Birch._build_request_for_signature sklearn.cluster.Birch._fit sklearn.cluster.Birch._get_default_requests sklearn.cluster.Birch._get_doc_link sklearn.cluster.Birch._get_leaves sklearn.cluster.Birch._get_metadata_request sklearn.cluster.Birch._get_param_names sklearn.cluster.Birch._global_clustering sklearn.cluster.Birch._predict sklearn.cluster.Birch._repr_html_inner sklearn.cluster.Birch._repr_mimebundle_ sklearn.cluster.Birch._validate_params sklearn.cluster.Birch.fit sklearn.cluster.Birch.fit_predict sklearn.cluster.Birch.fit_transform sklearn.cluster.Birch.get_feature_names_out sklearn.cluster.Birch.get_metadata_routing sklearn.cluster.Birch.get_params sklearn.cluster.Birch.partial_fit sklearn.cluster.Birch.predict sklearn.cluster.Birch.set_output sklearn.cluster.Birch.set_params sklearn.cluster.Birch.transform 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.cluster.BisectingKMeans.rst.txt b/dev/_sources/modules/generated/sklearn.cluster.BisectingKMeans.rst.txt
index 760d4b6ccd2ee..247c4f749ec7d 100644
--- a/dev/_sources/modules/generated/sklearn.cluster.BisectingKMeans.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.cluster.BisectingKMeans.rst.txt
@@ -8,6 +8,6 @@ BisectingKMeans
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.cluster.BisectingKMeans sklearn.cluster.BisectingKMeans.__delattr__ sklearn.cluster.BisectingKMeans.__dir__ sklearn.cluster.BisectingKMeans.__eq__ sklearn.cluster.BisectingKMeans.__format__ sklearn.cluster.BisectingKMeans.__ge__ sklearn.cluster.BisectingKMeans.__getattribute__ sklearn.cluster.BisectingKMeans.__getstate__ sklearn.cluster.BisectingKMeans.__gt__ sklearn.cluster.BisectingKMeans.__hash__ sklearn.cluster.BisectingKMeans.__init__ sklearn.cluster.BisectingKMeans.__init_subclass__ sklearn.cluster.BisectingKMeans.__le__ sklearn.cluster.BisectingKMeans.__lt__ sklearn.cluster.BisectingKMeans.__ne__ sklearn.cluster.BisectingKMeans.__new__ sklearn.cluster.BisectingKMeans.__reduce__ sklearn.cluster.BisectingKMeans.__reduce_ex__ sklearn.cluster.BisectingKMeans.__repr__ sklearn.cluster.BisectingKMeans.__setattr__ sklearn.cluster.BisectingKMeans.__setstate__ sklearn.cluster.BisectingKMeans.__sizeof__ sklearn.cluster.BisectingKMeans.__sklearn_clone__ sklearn.cluster.BisectingKMeans.__sklearn_tags__ sklearn.cluster.BisectingKMeans.__str__ sklearn.cluster.BisectingKMeans.__subclasshook__ sklearn.cluster.BisectingKMeans._bisect sklearn.cluster.BisectingKMeans._build_request_for_signature sklearn.cluster.BisectingKMeans._check_feature_names sklearn.cluster.BisectingKMeans._check_mkl_vcomp sklearn.cluster.BisectingKMeans._check_n_features sklearn.cluster.BisectingKMeans._check_params_vs_input sklearn.cluster.BisectingKMeans._check_test_data sklearn.cluster.BisectingKMeans._get_default_requests sklearn.cluster.BisectingKMeans._get_doc_link sklearn.cluster.BisectingKMeans._get_metadata_request sklearn.cluster.BisectingKMeans._get_param_names sklearn.cluster.BisectingKMeans._get_tags sklearn.cluster.BisectingKMeans._inertia_per_cluster sklearn.cluster.BisectingKMeans._init_centroids sklearn.cluster.BisectingKMeans._more_tags sklearn.cluster.BisectingKMeans._predict_recursive sklearn.cluster.BisectingKMeans._repr_html_inner sklearn.cluster.BisectingKMeans._repr_mimebundle_ sklearn.cluster.BisectingKMeans._transform sklearn.cluster.BisectingKMeans._validate_center_shape sklearn.cluster.BisectingKMeans._validate_data sklearn.cluster.BisectingKMeans._validate_params sklearn.cluster.BisectingKMeans._warn_mkl_vcomp sklearn.cluster.BisectingKMeans.fit sklearn.cluster.BisectingKMeans.fit_predict sklearn.cluster.BisectingKMeans.fit_transform sklearn.cluster.BisectingKMeans.get_feature_names_out sklearn.cluster.BisectingKMeans.get_metadata_routing sklearn.cluster.BisectingKMeans.get_params sklearn.cluster.BisectingKMeans.predict sklearn.cluster.BisectingKMeans.score sklearn.cluster.BisectingKMeans.set_fit_request sklearn.cluster.BisectingKMeans.set_output sklearn.cluster.BisectingKMeans.set_params sklearn.cluster.BisectingKMeans.set_score_request sklearn.cluster.BisectingKMeans.transform 
+.. minigallery:: sklearn.cluster.BisectingKMeans sklearn.cluster.BisectingKMeans.__delattr__ sklearn.cluster.BisectingKMeans.__dir__ sklearn.cluster.BisectingKMeans.__eq__ sklearn.cluster.BisectingKMeans.__format__ sklearn.cluster.BisectingKMeans.__ge__ sklearn.cluster.BisectingKMeans.__getattribute__ sklearn.cluster.BisectingKMeans.__getstate__ sklearn.cluster.BisectingKMeans.__gt__ sklearn.cluster.BisectingKMeans.__hash__ sklearn.cluster.BisectingKMeans.__init__ sklearn.cluster.BisectingKMeans.__init_subclass__ sklearn.cluster.BisectingKMeans.__le__ sklearn.cluster.BisectingKMeans.__lt__ sklearn.cluster.BisectingKMeans.__ne__ sklearn.cluster.BisectingKMeans.__new__ sklearn.cluster.BisectingKMeans.__reduce__ sklearn.cluster.BisectingKMeans.__reduce_ex__ sklearn.cluster.BisectingKMeans.__repr__ sklearn.cluster.BisectingKMeans.__setattr__ sklearn.cluster.BisectingKMeans.__setstate__ sklearn.cluster.BisectingKMeans.__sizeof__ sklearn.cluster.BisectingKMeans.__sklearn_clone__ sklearn.cluster.BisectingKMeans.__sklearn_tags__ sklearn.cluster.BisectingKMeans.__str__ sklearn.cluster.BisectingKMeans.__subclasshook__ sklearn.cluster.BisectingKMeans._bisect sklearn.cluster.BisectingKMeans._build_request_for_signature sklearn.cluster.BisectingKMeans._check_mkl_vcomp sklearn.cluster.BisectingKMeans._check_params_vs_input sklearn.cluster.BisectingKMeans._check_test_data sklearn.cluster.BisectingKMeans._get_default_requests sklearn.cluster.BisectingKMeans._get_doc_link sklearn.cluster.BisectingKMeans._get_metadata_request sklearn.cluster.BisectingKMeans._get_param_names sklearn.cluster.BisectingKMeans._inertia_per_cluster sklearn.cluster.BisectingKMeans._init_centroids sklearn.cluster.BisectingKMeans._predict_recursive sklearn.cluster.BisectingKMeans._repr_html_inner sklearn.cluster.BisectingKMeans._repr_mimebundle_ sklearn.cluster.BisectingKMeans._transform sklearn.cluster.BisectingKMeans._validate_center_shape sklearn.cluster.BisectingKMeans._validate_params sklearn.cluster.BisectingKMeans._warn_mkl_vcomp sklearn.cluster.BisectingKMeans.fit sklearn.cluster.BisectingKMeans.fit_predict sklearn.cluster.BisectingKMeans.fit_transform sklearn.cluster.BisectingKMeans.get_feature_names_out sklearn.cluster.BisectingKMeans.get_metadata_routing sklearn.cluster.BisectingKMeans.get_params sklearn.cluster.BisectingKMeans.predict sklearn.cluster.BisectingKMeans.score sklearn.cluster.BisectingKMeans.set_fit_request sklearn.cluster.BisectingKMeans.set_output sklearn.cluster.BisectingKMeans.set_params sklearn.cluster.BisectingKMeans.set_score_request sklearn.cluster.BisectingKMeans.transform 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.cluster.DBSCAN.rst.txt b/dev/_sources/modules/generated/sklearn.cluster.DBSCAN.rst.txt
index 5e789cee5a1c3..20e096dd63075 100644
--- a/dev/_sources/modules/generated/sklearn.cluster.DBSCAN.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.cluster.DBSCAN.rst.txt
@@ -8,6 +8,6 @@ DBSCAN
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.cluster.DBSCAN sklearn.cluster.DBSCAN.__delattr__ sklearn.cluster.DBSCAN.__dir__ sklearn.cluster.DBSCAN.__eq__ sklearn.cluster.DBSCAN.__format__ sklearn.cluster.DBSCAN.__ge__ sklearn.cluster.DBSCAN.__getattribute__ sklearn.cluster.DBSCAN.__getstate__ sklearn.cluster.DBSCAN.__gt__ sklearn.cluster.DBSCAN.__hash__ sklearn.cluster.DBSCAN.__init__ sklearn.cluster.DBSCAN.__init_subclass__ sklearn.cluster.DBSCAN.__le__ sklearn.cluster.DBSCAN.__lt__ sklearn.cluster.DBSCAN.__ne__ sklearn.cluster.DBSCAN.__new__ sklearn.cluster.DBSCAN.__reduce__ sklearn.cluster.DBSCAN.__reduce_ex__ sklearn.cluster.DBSCAN.__repr__ sklearn.cluster.DBSCAN.__setattr__ sklearn.cluster.DBSCAN.__setstate__ sklearn.cluster.DBSCAN.__sizeof__ sklearn.cluster.DBSCAN.__sklearn_clone__ sklearn.cluster.DBSCAN.__sklearn_tags__ sklearn.cluster.DBSCAN.__str__ sklearn.cluster.DBSCAN.__subclasshook__ sklearn.cluster.DBSCAN._build_request_for_signature sklearn.cluster.DBSCAN._check_feature_names sklearn.cluster.DBSCAN._check_n_features sklearn.cluster.DBSCAN._get_default_requests sklearn.cluster.DBSCAN._get_doc_link sklearn.cluster.DBSCAN._get_metadata_request sklearn.cluster.DBSCAN._get_param_names sklearn.cluster.DBSCAN._get_tags sklearn.cluster.DBSCAN._more_tags sklearn.cluster.DBSCAN._repr_html_inner sklearn.cluster.DBSCAN._repr_mimebundle_ sklearn.cluster.DBSCAN._validate_data sklearn.cluster.DBSCAN._validate_params sklearn.cluster.DBSCAN.fit sklearn.cluster.DBSCAN.fit_predict sklearn.cluster.DBSCAN.get_metadata_routing sklearn.cluster.DBSCAN.get_params sklearn.cluster.DBSCAN.set_fit_request sklearn.cluster.DBSCAN.set_params 
+.. minigallery:: sklearn.cluster.DBSCAN sklearn.cluster.DBSCAN.__delattr__ sklearn.cluster.DBSCAN.__dir__ sklearn.cluster.DBSCAN.__eq__ sklearn.cluster.DBSCAN.__format__ sklearn.cluster.DBSCAN.__ge__ sklearn.cluster.DBSCAN.__getattribute__ sklearn.cluster.DBSCAN.__getstate__ sklearn.cluster.DBSCAN.__gt__ sklearn.cluster.DBSCAN.__hash__ sklearn.cluster.DBSCAN.__init__ sklearn.cluster.DBSCAN.__init_subclass__ sklearn.cluster.DBSCAN.__le__ sklearn.cluster.DBSCAN.__lt__ sklearn.cluster.DBSCAN.__ne__ sklearn.cluster.DBSCAN.__new__ sklearn.cluster.DBSCAN.__reduce__ sklearn.cluster.DBSCAN.__reduce_ex__ sklearn.cluster.DBSCAN.__repr__ sklearn.cluster.DBSCAN.__setattr__ sklearn.cluster.DBSCAN.__setstate__ sklearn.cluster.DBSCAN.__sizeof__ sklearn.cluster.DBSCAN.__sklearn_clone__ sklearn.cluster.DBSCAN.__sklearn_tags__ sklearn.cluster.DBSCAN.__str__ sklearn.cluster.DBSCAN.__subclasshook__ sklearn.cluster.DBSCAN._build_request_for_signature sklearn.cluster.DBSCAN._get_default_requests sklearn.cluster.DBSCAN._get_doc_link sklearn.cluster.DBSCAN._get_metadata_request sklearn.cluster.DBSCAN._get_param_names sklearn.cluster.DBSCAN._repr_html_inner sklearn.cluster.DBSCAN._repr_mimebundle_ sklearn.cluster.DBSCAN._validate_params sklearn.cluster.DBSCAN.fit sklearn.cluster.DBSCAN.fit_predict sklearn.cluster.DBSCAN.get_metadata_routing sklearn.cluster.DBSCAN.get_params sklearn.cluster.DBSCAN.set_fit_request sklearn.cluster.DBSCAN.set_params 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.cluster.FeatureAgglomeration.rst.txt b/dev/_sources/modules/generated/sklearn.cluster.FeatureAgglomeration.rst.txt
index 09a9edee8188e..5adba7489930f 100644
--- a/dev/_sources/modules/generated/sklearn.cluster.FeatureAgglomeration.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.cluster.FeatureAgglomeration.rst.txt
@@ -8,6 +8,6 @@ FeatureAgglomeration
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.cluster.FeatureAgglomeration sklearn.cluster.FeatureAgglomeration.__delattr__ sklearn.cluster.FeatureAgglomeration.__dir__ sklearn.cluster.FeatureAgglomeration.__eq__ sklearn.cluster.FeatureAgglomeration.__format__ sklearn.cluster.FeatureAgglomeration.__ge__ sklearn.cluster.FeatureAgglomeration.__getattribute__ sklearn.cluster.FeatureAgglomeration.__getstate__ sklearn.cluster.FeatureAgglomeration.__gt__ sklearn.cluster.FeatureAgglomeration.__hash__ sklearn.cluster.FeatureAgglomeration.__init__ sklearn.cluster.FeatureAgglomeration.__init_subclass__ sklearn.cluster.FeatureAgglomeration.__le__ sklearn.cluster.FeatureAgglomeration.__lt__ sklearn.cluster.FeatureAgglomeration.__ne__ sklearn.cluster.FeatureAgglomeration.__new__ sklearn.cluster.FeatureAgglomeration.__reduce__ sklearn.cluster.FeatureAgglomeration.__reduce_ex__ sklearn.cluster.FeatureAgglomeration.__repr__ sklearn.cluster.FeatureAgglomeration.__setattr__ sklearn.cluster.FeatureAgglomeration.__setstate__ sklearn.cluster.FeatureAgglomeration.__sizeof__ sklearn.cluster.FeatureAgglomeration.__sklearn_clone__ sklearn.cluster.FeatureAgglomeration.__sklearn_tags__ sklearn.cluster.FeatureAgglomeration.__str__ sklearn.cluster.FeatureAgglomeration.__subclasshook__ sklearn.cluster.FeatureAgglomeration._build_request_for_signature sklearn.cluster.FeatureAgglomeration._check_feature_names sklearn.cluster.FeatureAgglomeration._check_n_features sklearn.cluster.FeatureAgglomeration._fit sklearn.cluster.FeatureAgglomeration._get_default_requests sklearn.cluster.FeatureAgglomeration._get_doc_link sklearn.cluster.FeatureAgglomeration._get_metadata_request sklearn.cluster.FeatureAgglomeration._get_param_names sklearn.cluster.FeatureAgglomeration._get_tags sklearn.cluster.FeatureAgglomeration._more_tags sklearn.cluster.FeatureAgglomeration._repr_html_inner sklearn.cluster.FeatureAgglomeration._repr_mimebundle_ sklearn.cluster.FeatureAgglomeration._validate_data sklearn.cluster.FeatureAgglomeration._validate_params sklearn.cluster.FeatureAgglomeration.fit sklearn.cluster.FeatureAgglomeration.fit_transform sklearn.cluster.FeatureAgglomeration.get_feature_names_out sklearn.cluster.FeatureAgglomeration.get_metadata_routing sklearn.cluster.FeatureAgglomeration.get_params sklearn.cluster.FeatureAgglomeration.inverse_transform sklearn.cluster.FeatureAgglomeration.set_output sklearn.cluster.FeatureAgglomeration.set_params sklearn.cluster.FeatureAgglomeration.transform 
+.. minigallery:: sklearn.cluster.FeatureAgglomeration sklearn.cluster.FeatureAgglomeration.__delattr__ sklearn.cluster.FeatureAgglomeration.__dir__ sklearn.cluster.FeatureAgglomeration.__eq__ sklearn.cluster.FeatureAgglomeration.__format__ sklearn.cluster.FeatureAgglomeration.__ge__ sklearn.cluster.FeatureAgglomeration.__getattribute__ sklearn.cluster.FeatureAgglomeration.__getstate__ sklearn.cluster.FeatureAgglomeration.__gt__ sklearn.cluster.FeatureAgglomeration.__hash__ sklearn.cluster.FeatureAgglomeration.__init__ sklearn.cluster.FeatureAgglomeration.__init_subclass__ sklearn.cluster.FeatureAgglomeration.__le__ sklearn.cluster.FeatureAgglomeration.__lt__ sklearn.cluster.FeatureAgglomeration.__ne__ sklearn.cluster.FeatureAgglomeration.__new__ sklearn.cluster.FeatureAgglomeration.__reduce__ sklearn.cluster.FeatureAgglomeration.__reduce_ex__ sklearn.cluster.FeatureAgglomeration.__repr__ sklearn.cluster.FeatureAgglomeration.__setattr__ sklearn.cluster.FeatureAgglomeration.__setstate__ sklearn.cluster.FeatureAgglomeration.__sizeof__ sklearn.cluster.FeatureAgglomeration.__sklearn_clone__ sklearn.cluster.FeatureAgglomeration.__sklearn_tags__ sklearn.cluster.FeatureAgglomeration.__str__ sklearn.cluster.FeatureAgglomeration.__subclasshook__ sklearn.cluster.FeatureAgglomeration._build_request_for_signature sklearn.cluster.FeatureAgglomeration._fit sklearn.cluster.FeatureAgglomeration._get_default_requests sklearn.cluster.FeatureAgglomeration._get_doc_link sklearn.cluster.FeatureAgglomeration._get_metadata_request sklearn.cluster.FeatureAgglomeration._get_param_names sklearn.cluster.FeatureAgglomeration._repr_html_inner sklearn.cluster.FeatureAgglomeration._repr_mimebundle_ sklearn.cluster.FeatureAgglomeration._validate_params sklearn.cluster.FeatureAgglomeration.fit sklearn.cluster.FeatureAgglomeration.fit_transform sklearn.cluster.FeatureAgglomeration.get_feature_names_out sklearn.cluster.FeatureAgglomeration.get_metadata_routing sklearn.cluster.FeatureAgglomeration.get_params sklearn.cluster.FeatureAgglomeration.inverse_transform sklearn.cluster.FeatureAgglomeration.set_output sklearn.cluster.FeatureAgglomeration.set_params sklearn.cluster.FeatureAgglomeration.transform 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.cluster.HDBSCAN.rst.txt b/dev/_sources/modules/generated/sklearn.cluster.HDBSCAN.rst.txt
index 1045b8becfcd7..4644c7b7e436e 100644
--- a/dev/_sources/modules/generated/sklearn.cluster.HDBSCAN.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.cluster.HDBSCAN.rst.txt
@@ -8,6 +8,6 @@ HDBSCAN
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.cluster.HDBSCAN sklearn.cluster.HDBSCAN.__delattr__ sklearn.cluster.HDBSCAN.__dir__ sklearn.cluster.HDBSCAN.__eq__ sklearn.cluster.HDBSCAN.__format__ sklearn.cluster.HDBSCAN.__ge__ sklearn.cluster.HDBSCAN.__getattribute__ sklearn.cluster.HDBSCAN.__getstate__ sklearn.cluster.HDBSCAN.__gt__ sklearn.cluster.HDBSCAN.__hash__ sklearn.cluster.HDBSCAN.__init__ sklearn.cluster.HDBSCAN.__init_subclass__ sklearn.cluster.HDBSCAN.__le__ sklearn.cluster.HDBSCAN.__lt__ sklearn.cluster.HDBSCAN.__ne__ sklearn.cluster.HDBSCAN.__new__ sklearn.cluster.HDBSCAN.__reduce__ sklearn.cluster.HDBSCAN.__reduce_ex__ sklearn.cluster.HDBSCAN.__repr__ sklearn.cluster.HDBSCAN.__setattr__ sklearn.cluster.HDBSCAN.__setstate__ sklearn.cluster.HDBSCAN.__sizeof__ sklearn.cluster.HDBSCAN.__sklearn_clone__ sklearn.cluster.HDBSCAN.__sklearn_tags__ sklearn.cluster.HDBSCAN.__str__ sklearn.cluster.HDBSCAN.__subclasshook__ sklearn.cluster.HDBSCAN._build_request_for_signature sklearn.cluster.HDBSCAN._check_feature_names sklearn.cluster.HDBSCAN._check_n_features sklearn.cluster.HDBSCAN._get_default_requests sklearn.cluster.HDBSCAN._get_doc_link sklearn.cluster.HDBSCAN._get_metadata_request sklearn.cluster.HDBSCAN._get_param_names sklearn.cluster.HDBSCAN._get_tags sklearn.cluster.HDBSCAN._more_tags sklearn.cluster.HDBSCAN._repr_html_inner sklearn.cluster.HDBSCAN._repr_mimebundle_ sklearn.cluster.HDBSCAN._validate_data sklearn.cluster.HDBSCAN._validate_params sklearn.cluster.HDBSCAN._weighted_cluster_center sklearn.cluster.HDBSCAN.dbscan_clustering sklearn.cluster.HDBSCAN.fit sklearn.cluster.HDBSCAN.fit_predict sklearn.cluster.HDBSCAN.get_metadata_routing sklearn.cluster.HDBSCAN.get_params sklearn.cluster.HDBSCAN.set_params 
+.. minigallery:: sklearn.cluster.HDBSCAN sklearn.cluster.HDBSCAN.__delattr__ sklearn.cluster.HDBSCAN.__dir__ sklearn.cluster.HDBSCAN.__eq__ sklearn.cluster.HDBSCAN.__format__ sklearn.cluster.HDBSCAN.__ge__ sklearn.cluster.HDBSCAN.__getattribute__ sklearn.cluster.HDBSCAN.__getstate__ sklearn.cluster.HDBSCAN.__gt__ sklearn.cluster.HDBSCAN.__hash__ sklearn.cluster.HDBSCAN.__init__ sklearn.cluster.HDBSCAN.__init_subclass__ sklearn.cluster.HDBSCAN.__le__ sklearn.cluster.HDBSCAN.__lt__ sklearn.cluster.HDBSCAN.__ne__ sklearn.cluster.HDBSCAN.__new__ sklearn.cluster.HDBSCAN.__reduce__ sklearn.cluster.HDBSCAN.__reduce_ex__ sklearn.cluster.HDBSCAN.__repr__ sklearn.cluster.HDBSCAN.__setattr__ sklearn.cluster.HDBSCAN.__setstate__ sklearn.cluster.HDBSCAN.__sizeof__ sklearn.cluster.HDBSCAN.__sklearn_clone__ sklearn.cluster.HDBSCAN.__sklearn_tags__ sklearn.cluster.HDBSCAN.__str__ sklearn.cluster.HDBSCAN.__subclasshook__ sklearn.cluster.HDBSCAN._build_request_for_signature sklearn.cluster.HDBSCAN._get_default_requests sklearn.cluster.HDBSCAN._get_doc_link sklearn.cluster.HDBSCAN._get_metadata_request sklearn.cluster.HDBSCAN._get_param_names sklearn.cluster.HDBSCAN._repr_html_inner sklearn.cluster.HDBSCAN._repr_mimebundle_ sklearn.cluster.HDBSCAN._validate_params sklearn.cluster.HDBSCAN._weighted_cluster_center sklearn.cluster.HDBSCAN.dbscan_clustering sklearn.cluster.HDBSCAN.fit sklearn.cluster.HDBSCAN.fit_predict sklearn.cluster.HDBSCAN.get_metadata_routing sklearn.cluster.HDBSCAN.get_params sklearn.cluster.HDBSCAN.set_params 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.cluster.KMeans.rst.txt b/dev/_sources/modules/generated/sklearn.cluster.KMeans.rst.txt
index f22b44eeb21cc..7e075acbc240c 100644
--- a/dev/_sources/modules/generated/sklearn.cluster.KMeans.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.cluster.KMeans.rst.txt
@@ -8,6 +8,6 @@ KMeans
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.cluster.KMeans sklearn.cluster.KMeans.__delattr__ sklearn.cluster.KMeans.__dir__ sklearn.cluster.KMeans.__eq__ sklearn.cluster.KMeans.__format__ sklearn.cluster.KMeans.__ge__ sklearn.cluster.KMeans.__getattribute__ sklearn.cluster.KMeans.__getstate__ sklearn.cluster.KMeans.__gt__ sklearn.cluster.KMeans.__hash__ sklearn.cluster.KMeans.__init__ sklearn.cluster.KMeans.__init_subclass__ sklearn.cluster.KMeans.__le__ sklearn.cluster.KMeans.__lt__ sklearn.cluster.KMeans.__ne__ sklearn.cluster.KMeans.__new__ sklearn.cluster.KMeans.__reduce__ sklearn.cluster.KMeans.__reduce_ex__ sklearn.cluster.KMeans.__repr__ sklearn.cluster.KMeans.__setattr__ sklearn.cluster.KMeans.__setstate__ sklearn.cluster.KMeans.__sizeof__ sklearn.cluster.KMeans.__sklearn_clone__ sklearn.cluster.KMeans.__sklearn_tags__ sklearn.cluster.KMeans.__str__ sklearn.cluster.KMeans.__subclasshook__ sklearn.cluster.KMeans._build_request_for_signature sklearn.cluster.KMeans._check_feature_names sklearn.cluster.KMeans._check_mkl_vcomp sklearn.cluster.KMeans._check_n_features sklearn.cluster.KMeans._check_params_vs_input sklearn.cluster.KMeans._check_test_data sklearn.cluster.KMeans._get_default_requests sklearn.cluster.KMeans._get_doc_link sklearn.cluster.KMeans._get_metadata_request sklearn.cluster.KMeans._get_param_names sklearn.cluster.KMeans._get_tags sklearn.cluster.KMeans._init_centroids sklearn.cluster.KMeans._more_tags sklearn.cluster.KMeans._repr_html_inner sklearn.cluster.KMeans._repr_mimebundle_ sklearn.cluster.KMeans._transform sklearn.cluster.KMeans._validate_center_shape sklearn.cluster.KMeans._validate_data sklearn.cluster.KMeans._validate_params sklearn.cluster.KMeans._warn_mkl_vcomp sklearn.cluster.KMeans.fit sklearn.cluster.KMeans.fit_predict sklearn.cluster.KMeans.fit_transform sklearn.cluster.KMeans.get_feature_names_out sklearn.cluster.KMeans.get_metadata_routing sklearn.cluster.KMeans.get_params sklearn.cluster.KMeans.predict sklearn.cluster.KMeans.score sklearn.cluster.KMeans.set_fit_request sklearn.cluster.KMeans.set_output sklearn.cluster.KMeans.set_params sklearn.cluster.KMeans.set_score_request sklearn.cluster.KMeans.transform 
+.. minigallery:: sklearn.cluster.KMeans sklearn.cluster.KMeans.__delattr__ sklearn.cluster.KMeans.__dir__ sklearn.cluster.KMeans.__eq__ sklearn.cluster.KMeans.__format__ sklearn.cluster.KMeans.__ge__ sklearn.cluster.KMeans.__getattribute__ sklearn.cluster.KMeans.__getstate__ sklearn.cluster.KMeans.__gt__ sklearn.cluster.KMeans.__hash__ sklearn.cluster.KMeans.__init__ sklearn.cluster.KMeans.__init_subclass__ sklearn.cluster.KMeans.__le__ sklearn.cluster.KMeans.__lt__ sklearn.cluster.KMeans.__ne__ sklearn.cluster.KMeans.__new__ sklearn.cluster.KMeans.__reduce__ sklearn.cluster.KMeans.__reduce_ex__ sklearn.cluster.KMeans.__repr__ sklearn.cluster.KMeans.__setattr__ sklearn.cluster.KMeans.__setstate__ sklearn.cluster.KMeans.__sizeof__ sklearn.cluster.KMeans.__sklearn_clone__ sklearn.cluster.KMeans.__sklearn_tags__ sklearn.cluster.KMeans.__str__ sklearn.cluster.KMeans.__subclasshook__ sklearn.cluster.KMeans._build_request_for_signature sklearn.cluster.KMeans._check_mkl_vcomp sklearn.cluster.KMeans._check_params_vs_input sklearn.cluster.KMeans._check_test_data sklearn.cluster.KMeans._get_default_requests sklearn.cluster.KMeans._get_doc_link sklearn.cluster.KMeans._get_metadata_request sklearn.cluster.KMeans._get_param_names sklearn.cluster.KMeans._init_centroids sklearn.cluster.KMeans._repr_html_inner sklearn.cluster.KMeans._repr_mimebundle_ sklearn.cluster.KMeans._transform sklearn.cluster.KMeans._validate_center_shape sklearn.cluster.KMeans._validate_params sklearn.cluster.KMeans._warn_mkl_vcomp sklearn.cluster.KMeans.fit sklearn.cluster.KMeans.fit_predict sklearn.cluster.KMeans.fit_transform sklearn.cluster.KMeans.get_feature_names_out sklearn.cluster.KMeans.get_metadata_routing sklearn.cluster.KMeans.get_params sklearn.cluster.KMeans.predict sklearn.cluster.KMeans.score sklearn.cluster.KMeans.set_fit_request sklearn.cluster.KMeans.set_output sklearn.cluster.KMeans.set_params sklearn.cluster.KMeans.set_score_request sklearn.cluster.KMeans.transform 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.cluster.MeanShift.rst.txt b/dev/_sources/modules/generated/sklearn.cluster.MeanShift.rst.txt
index c2ec27a066ef7..80f256faba091 100644
--- a/dev/_sources/modules/generated/sklearn.cluster.MeanShift.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.cluster.MeanShift.rst.txt
@@ -8,6 +8,6 @@ MeanShift
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.cluster.MeanShift sklearn.cluster.MeanShift.__delattr__ sklearn.cluster.MeanShift.__dir__ sklearn.cluster.MeanShift.__eq__ sklearn.cluster.MeanShift.__format__ sklearn.cluster.MeanShift.__ge__ sklearn.cluster.MeanShift.__getattribute__ sklearn.cluster.MeanShift.__getstate__ sklearn.cluster.MeanShift.__gt__ sklearn.cluster.MeanShift.__hash__ sklearn.cluster.MeanShift.__init__ sklearn.cluster.MeanShift.__init_subclass__ sklearn.cluster.MeanShift.__le__ sklearn.cluster.MeanShift.__lt__ sklearn.cluster.MeanShift.__ne__ sklearn.cluster.MeanShift.__new__ sklearn.cluster.MeanShift.__reduce__ sklearn.cluster.MeanShift.__reduce_ex__ sklearn.cluster.MeanShift.__repr__ sklearn.cluster.MeanShift.__setattr__ sklearn.cluster.MeanShift.__setstate__ sklearn.cluster.MeanShift.__sizeof__ sklearn.cluster.MeanShift.__sklearn_clone__ sklearn.cluster.MeanShift.__sklearn_tags__ sklearn.cluster.MeanShift.__str__ sklearn.cluster.MeanShift.__subclasshook__ sklearn.cluster.MeanShift._build_request_for_signature sklearn.cluster.MeanShift._check_feature_names sklearn.cluster.MeanShift._check_n_features sklearn.cluster.MeanShift._get_default_requests sklearn.cluster.MeanShift._get_doc_link sklearn.cluster.MeanShift._get_metadata_request sklearn.cluster.MeanShift._get_param_names sklearn.cluster.MeanShift._get_tags sklearn.cluster.MeanShift._more_tags sklearn.cluster.MeanShift._repr_html_inner sklearn.cluster.MeanShift._repr_mimebundle_ sklearn.cluster.MeanShift._validate_data sklearn.cluster.MeanShift._validate_params sklearn.cluster.MeanShift.fit sklearn.cluster.MeanShift.fit_predict sklearn.cluster.MeanShift.get_metadata_routing sklearn.cluster.MeanShift.get_params sklearn.cluster.MeanShift.predict sklearn.cluster.MeanShift.set_params 
+.. minigallery:: sklearn.cluster.MeanShift sklearn.cluster.MeanShift.__delattr__ sklearn.cluster.MeanShift.__dir__ sklearn.cluster.MeanShift.__eq__ sklearn.cluster.MeanShift.__format__ sklearn.cluster.MeanShift.__ge__ sklearn.cluster.MeanShift.__getattribute__ sklearn.cluster.MeanShift.__getstate__ sklearn.cluster.MeanShift.__gt__ sklearn.cluster.MeanShift.__hash__ sklearn.cluster.MeanShift.__init__ sklearn.cluster.MeanShift.__init_subclass__ sklearn.cluster.MeanShift.__le__ sklearn.cluster.MeanShift.__lt__ sklearn.cluster.MeanShift.__ne__ sklearn.cluster.MeanShift.__new__ sklearn.cluster.MeanShift.__reduce__ sklearn.cluster.MeanShift.__reduce_ex__ sklearn.cluster.MeanShift.__repr__ sklearn.cluster.MeanShift.__setattr__ sklearn.cluster.MeanShift.__setstate__ sklearn.cluster.MeanShift.__sizeof__ sklearn.cluster.MeanShift.__sklearn_clone__ sklearn.cluster.MeanShift.__sklearn_tags__ sklearn.cluster.MeanShift.__str__ sklearn.cluster.MeanShift.__subclasshook__ sklearn.cluster.MeanShift._build_request_for_signature sklearn.cluster.MeanShift._get_default_requests sklearn.cluster.MeanShift._get_doc_link sklearn.cluster.MeanShift._get_metadata_request sklearn.cluster.MeanShift._get_param_names sklearn.cluster.MeanShift._repr_html_inner sklearn.cluster.MeanShift._repr_mimebundle_ sklearn.cluster.MeanShift._validate_params sklearn.cluster.MeanShift.fit sklearn.cluster.MeanShift.fit_predict sklearn.cluster.MeanShift.get_metadata_routing sklearn.cluster.MeanShift.get_params sklearn.cluster.MeanShift.predict sklearn.cluster.MeanShift.set_params 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.cluster.MiniBatchKMeans.rst.txt b/dev/_sources/modules/generated/sklearn.cluster.MiniBatchKMeans.rst.txt
index 4d5ce92abc973..3f7e509c72d7d 100644
--- a/dev/_sources/modules/generated/sklearn.cluster.MiniBatchKMeans.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.cluster.MiniBatchKMeans.rst.txt
@@ -8,6 +8,6 @@ MiniBatchKMeans
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.cluster.MiniBatchKMeans sklearn.cluster.MiniBatchKMeans.__delattr__ sklearn.cluster.MiniBatchKMeans.__dir__ sklearn.cluster.MiniBatchKMeans.__eq__ sklearn.cluster.MiniBatchKMeans.__format__ sklearn.cluster.MiniBatchKMeans.__ge__ sklearn.cluster.MiniBatchKMeans.__getattribute__ sklearn.cluster.MiniBatchKMeans.__getstate__ sklearn.cluster.MiniBatchKMeans.__gt__ sklearn.cluster.MiniBatchKMeans.__hash__ sklearn.cluster.MiniBatchKMeans.__init__ sklearn.cluster.MiniBatchKMeans.__init_subclass__ sklearn.cluster.MiniBatchKMeans.__le__ sklearn.cluster.MiniBatchKMeans.__lt__ sklearn.cluster.MiniBatchKMeans.__ne__ sklearn.cluster.MiniBatchKMeans.__new__ sklearn.cluster.MiniBatchKMeans.__reduce__ sklearn.cluster.MiniBatchKMeans.__reduce_ex__ sklearn.cluster.MiniBatchKMeans.__repr__ sklearn.cluster.MiniBatchKMeans.__setattr__ sklearn.cluster.MiniBatchKMeans.__setstate__ sklearn.cluster.MiniBatchKMeans.__sizeof__ sklearn.cluster.MiniBatchKMeans.__sklearn_clone__ sklearn.cluster.MiniBatchKMeans.__sklearn_tags__ sklearn.cluster.MiniBatchKMeans.__str__ sklearn.cluster.MiniBatchKMeans.__subclasshook__ sklearn.cluster.MiniBatchKMeans._build_request_for_signature sklearn.cluster.MiniBatchKMeans._check_feature_names sklearn.cluster.MiniBatchKMeans._check_mkl_vcomp sklearn.cluster.MiniBatchKMeans._check_n_features sklearn.cluster.MiniBatchKMeans._check_params_vs_input sklearn.cluster.MiniBatchKMeans._check_test_data sklearn.cluster.MiniBatchKMeans._get_default_requests sklearn.cluster.MiniBatchKMeans._get_doc_link sklearn.cluster.MiniBatchKMeans._get_metadata_request sklearn.cluster.MiniBatchKMeans._get_param_names sklearn.cluster.MiniBatchKMeans._get_tags sklearn.cluster.MiniBatchKMeans._init_centroids sklearn.cluster.MiniBatchKMeans._mini_batch_convergence sklearn.cluster.MiniBatchKMeans._more_tags sklearn.cluster.MiniBatchKMeans._random_reassign sklearn.cluster.MiniBatchKMeans._repr_html_inner sklearn.cluster.MiniBatchKMeans._repr_mimebundle_ sklearn.cluster.MiniBatchKMeans._transform sklearn.cluster.MiniBatchKMeans._validate_center_shape sklearn.cluster.MiniBatchKMeans._validate_data sklearn.cluster.MiniBatchKMeans._validate_params sklearn.cluster.MiniBatchKMeans._warn_mkl_vcomp sklearn.cluster.MiniBatchKMeans.fit sklearn.cluster.MiniBatchKMeans.fit_predict sklearn.cluster.MiniBatchKMeans.fit_transform sklearn.cluster.MiniBatchKMeans.get_feature_names_out sklearn.cluster.MiniBatchKMeans.get_metadata_routing sklearn.cluster.MiniBatchKMeans.get_params sklearn.cluster.MiniBatchKMeans.partial_fit sklearn.cluster.MiniBatchKMeans.predict sklearn.cluster.MiniBatchKMeans.score sklearn.cluster.MiniBatchKMeans.set_fit_request sklearn.cluster.MiniBatchKMeans.set_output sklearn.cluster.MiniBatchKMeans.set_params sklearn.cluster.MiniBatchKMeans.set_partial_fit_request sklearn.cluster.MiniBatchKMeans.set_score_request sklearn.cluster.MiniBatchKMeans.transform 
+.. minigallery:: sklearn.cluster.MiniBatchKMeans sklearn.cluster.MiniBatchKMeans.__delattr__ sklearn.cluster.MiniBatchKMeans.__dir__ sklearn.cluster.MiniBatchKMeans.__eq__ sklearn.cluster.MiniBatchKMeans.__format__ sklearn.cluster.MiniBatchKMeans.__ge__ sklearn.cluster.MiniBatchKMeans.__getattribute__ sklearn.cluster.MiniBatchKMeans.__getstate__ sklearn.cluster.MiniBatchKMeans.__gt__ sklearn.cluster.MiniBatchKMeans.__hash__ sklearn.cluster.MiniBatchKMeans.__init__ sklearn.cluster.MiniBatchKMeans.__init_subclass__ sklearn.cluster.MiniBatchKMeans.__le__ sklearn.cluster.MiniBatchKMeans.__lt__ sklearn.cluster.MiniBatchKMeans.__ne__ sklearn.cluster.MiniBatchKMeans.__new__ sklearn.cluster.MiniBatchKMeans.__reduce__ sklearn.cluster.MiniBatchKMeans.__reduce_ex__ sklearn.cluster.MiniBatchKMeans.__repr__ sklearn.cluster.MiniBatchKMeans.__setattr__ sklearn.cluster.MiniBatchKMeans.__setstate__ sklearn.cluster.MiniBatchKMeans.__sizeof__ sklearn.cluster.MiniBatchKMeans.__sklearn_clone__ sklearn.cluster.MiniBatchKMeans.__sklearn_tags__ sklearn.cluster.MiniBatchKMeans.__str__ sklearn.cluster.MiniBatchKMeans.__subclasshook__ sklearn.cluster.MiniBatchKMeans._build_request_for_signature sklearn.cluster.MiniBatchKMeans._check_mkl_vcomp sklearn.cluster.MiniBatchKMeans._check_params_vs_input sklearn.cluster.MiniBatchKMeans._check_test_data sklearn.cluster.MiniBatchKMeans._get_default_requests sklearn.cluster.MiniBatchKMeans._get_doc_link sklearn.cluster.MiniBatchKMeans._get_metadata_request sklearn.cluster.MiniBatchKMeans._get_param_names sklearn.cluster.MiniBatchKMeans._init_centroids sklearn.cluster.MiniBatchKMeans._mini_batch_convergence sklearn.cluster.MiniBatchKMeans._random_reassign sklearn.cluster.MiniBatchKMeans._repr_html_inner sklearn.cluster.MiniBatchKMeans._repr_mimebundle_ sklearn.cluster.MiniBatchKMeans._transform sklearn.cluster.MiniBatchKMeans._validate_center_shape sklearn.cluster.MiniBatchKMeans._validate_params sklearn.cluster.MiniBatchKMeans._warn_mkl_vcomp sklearn.cluster.MiniBatchKMeans.fit sklearn.cluster.MiniBatchKMeans.fit_predict sklearn.cluster.MiniBatchKMeans.fit_transform sklearn.cluster.MiniBatchKMeans.get_feature_names_out sklearn.cluster.MiniBatchKMeans.get_metadata_routing sklearn.cluster.MiniBatchKMeans.get_params sklearn.cluster.MiniBatchKMeans.partial_fit sklearn.cluster.MiniBatchKMeans.predict sklearn.cluster.MiniBatchKMeans.score sklearn.cluster.MiniBatchKMeans.set_fit_request sklearn.cluster.MiniBatchKMeans.set_output sklearn.cluster.MiniBatchKMeans.set_params sklearn.cluster.MiniBatchKMeans.set_partial_fit_request sklearn.cluster.MiniBatchKMeans.set_score_request sklearn.cluster.MiniBatchKMeans.transform 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.cluster.OPTICS.rst.txt b/dev/_sources/modules/generated/sklearn.cluster.OPTICS.rst.txt
index c90fdbee95950..215bbc86d6252 100644
--- a/dev/_sources/modules/generated/sklearn.cluster.OPTICS.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.cluster.OPTICS.rst.txt
@@ -8,6 +8,6 @@ OPTICS
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.cluster.OPTICS sklearn.cluster.OPTICS.__delattr__ sklearn.cluster.OPTICS.__dir__ sklearn.cluster.OPTICS.__eq__ sklearn.cluster.OPTICS.__format__ sklearn.cluster.OPTICS.__ge__ sklearn.cluster.OPTICS.__getattribute__ sklearn.cluster.OPTICS.__getstate__ sklearn.cluster.OPTICS.__gt__ sklearn.cluster.OPTICS.__hash__ sklearn.cluster.OPTICS.__init__ sklearn.cluster.OPTICS.__init_subclass__ sklearn.cluster.OPTICS.__le__ sklearn.cluster.OPTICS.__lt__ sklearn.cluster.OPTICS.__ne__ sklearn.cluster.OPTICS.__new__ sklearn.cluster.OPTICS.__reduce__ sklearn.cluster.OPTICS.__reduce_ex__ sklearn.cluster.OPTICS.__repr__ sklearn.cluster.OPTICS.__setattr__ sklearn.cluster.OPTICS.__setstate__ sklearn.cluster.OPTICS.__sizeof__ sklearn.cluster.OPTICS.__sklearn_clone__ sklearn.cluster.OPTICS.__sklearn_tags__ sklearn.cluster.OPTICS.__str__ sklearn.cluster.OPTICS.__subclasshook__ sklearn.cluster.OPTICS._build_request_for_signature sklearn.cluster.OPTICS._check_feature_names sklearn.cluster.OPTICS._check_n_features sklearn.cluster.OPTICS._get_default_requests sklearn.cluster.OPTICS._get_doc_link sklearn.cluster.OPTICS._get_metadata_request sklearn.cluster.OPTICS._get_param_names sklearn.cluster.OPTICS._get_tags sklearn.cluster.OPTICS._more_tags sklearn.cluster.OPTICS._repr_html_inner sklearn.cluster.OPTICS._repr_mimebundle_ sklearn.cluster.OPTICS._validate_data sklearn.cluster.OPTICS._validate_params sklearn.cluster.OPTICS.fit sklearn.cluster.OPTICS.fit_predict sklearn.cluster.OPTICS.get_metadata_routing sklearn.cluster.OPTICS.get_params sklearn.cluster.OPTICS.set_params 
+.. minigallery:: sklearn.cluster.OPTICS sklearn.cluster.OPTICS.__delattr__ sklearn.cluster.OPTICS.__dir__ sklearn.cluster.OPTICS.__eq__ sklearn.cluster.OPTICS.__format__ sklearn.cluster.OPTICS.__ge__ sklearn.cluster.OPTICS.__getattribute__ sklearn.cluster.OPTICS.__getstate__ sklearn.cluster.OPTICS.__gt__ sklearn.cluster.OPTICS.__hash__ sklearn.cluster.OPTICS.__init__ sklearn.cluster.OPTICS.__init_subclass__ sklearn.cluster.OPTICS.__le__ sklearn.cluster.OPTICS.__lt__ sklearn.cluster.OPTICS.__ne__ sklearn.cluster.OPTICS.__new__ sklearn.cluster.OPTICS.__reduce__ sklearn.cluster.OPTICS.__reduce_ex__ sklearn.cluster.OPTICS.__repr__ sklearn.cluster.OPTICS.__setattr__ sklearn.cluster.OPTICS.__setstate__ sklearn.cluster.OPTICS.__sizeof__ sklearn.cluster.OPTICS.__sklearn_clone__ sklearn.cluster.OPTICS.__sklearn_tags__ sklearn.cluster.OPTICS.__str__ sklearn.cluster.OPTICS.__subclasshook__ sklearn.cluster.OPTICS._build_request_for_signature sklearn.cluster.OPTICS._get_default_requests sklearn.cluster.OPTICS._get_doc_link sklearn.cluster.OPTICS._get_metadata_request sklearn.cluster.OPTICS._get_param_names sklearn.cluster.OPTICS._repr_html_inner sklearn.cluster.OPTICS._repr_mimebundle_ sklearn.cluster.OPTICS._validate_params sklearn.cluster.OPTICS.fit sklearn.cluster.OPTICS.fit_predict sklearn.cluster.OPTICS.get_metadata_routing sklearn.cluster.OPTICS.get_params sklearn.cluster.OPTICS.set_params 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.cluster.SpectralBiclustering.rst.txt b/dev/_sources/modules/generated/sklearn.cluster.SpectralBiclustering.rst.txt
index 21500aca7f606..195ee22534903 100644
--- a/dev/_sources/modules/generated/sklearn.cluster.SpectralBiclustering.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.cluster.SpectralBiclustering.rst.txt
@@ -8,6 +8,6 @@ SpectralBiclustering
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.cluster.SpectralBiclustering sklearn.cluster.SpectralBiclustering.__delattr__ sklearn.cluster.SpectralBiclustering.__dir__ sklearn.cluster.SpectralBiclustering.__eq__ sklearn.cluster.SpectralBiclustering.__format__ sklearn.cluster.SpectralBiclustering.__ge__ sklearn.cluster.SpectralBiclustering.__getattribute__ sklearn.cluster.SpectralBiclustering.__getstate__ sklearn.cluster.SpectralBiclustering.__gt__ sklearn.cluster.SpectralBiclustering.__hash__ sklearn.cluster.SpectralBiclustering.__init__ sklearn.cluster.SpectralBiclustering.__init_subclass__ sklearn.cluster.SpectralBiclustering.__le__ sklearn.cluster.SpectralBiclustering.__lt__ sklearn.cluster.SpectralBiclustering.__ne__ sklearn.cluster.SpectralBiclustering.__new__ sklearn.cluster.SpectralBiclustering.__reduce__ sklearn.cluster.SpectralBiclustering.__reduce_ex__ sklearn.cluster.SpectralBiclustering.__repr__ sklearn.cluster.SpectralBiclustering.__setattr__ sklearn.cluster.SpectralBiclustering.__setstate__ sklearn.cluster.SpectralBiclustering.__sizeof__ sklearn.cluster.SpectralBiclustering.__sklearn_clone__ sklearn.cluster.SpectralBiclustering.__sklearn_tags__ sklearn.cluster.SpectralBiclustering.__str__ sklearn.cluster.SpectralBiclustering.__subclasshook__ sklearn.cluster.SpectralBiclustering._build_request_for_signature sklearn.cluster.SpectralBiclustering._check_feature_names sklearn.cluster.SpectralBiclustering._check_n_features sklearn.cluster.SpectralBiclustering._check_parameters sklearn.cluster.SpectralBiclustering._fit sklearn.cluster.SpectralBiclustering._fit_best_piecewise sklearn.cluster.SpectralBiclustering._get_default_requests sklearn.cluster.SpectralBiclustering._get_doc_link sklearn.cluster.SpectralBiclustering._get_metadata_request sklearn.cluster.SpectralBiclustering._get_param_names sklearn.cluster.SpectralBiclustering._get_tags sklearn.cluster.SpectralBiclustering._k_means sklearn.cluster.SpectralBiclustering._more_tags sklearn.cluster.SpectralBiclustering._project_and_cluster sklearn.cluster.SpectralBiclustering._repr_html_inner sklearn.cluster.SpectralBiclustering._repr_mimebundle_ sklearn.cluster.SpectralBiclustering._svd sklearn.cluster.SpectralBiclustering._validate_data sklearn.cluster.SpectralBiclustering._validate_params sklearn.cluster.SpectralBiclustering.fit sklearn.cluster.SpectralBiclustering.get_indices sklearn.cluster.SpectralBiclustering.get_metadata_routing sklearn.cluster.SpectralBiclustering.get_params sklearn.cluster.SpectralBiclustering.get_shape sklearn.cluster.SpectralBiclustering.get_submatrix sklearn.cluster.SpectralBiclustering.set_params 
+.. minigallery:: sklearn.cluster.SpectralBiclustering sklearn.cluster.SpectralBiclustering.__delattr__ sklearn.cluster.SpectralBiclustering.__dir__ sklearn.cluster.SpectralBiclustering.__eq__ sklearn.cluster.SpectralBiclustering.__format__ sklearn.cluster.SpectralBiclustering.__ge__ sklearn.cluster.SpectralBiclustering.__getattribute__ sklearn.cluster.SpectralBiclustering.__getstate__ sklearn.cluster.SpectralBiclustering.__gt__ sklearn.cluster.SpectralBiclustering.__hash__ sklearn.cluster.SpectralBiclustering.__init__ sklearn.cluster.SpectralBiclustering.__init_subclass__ sklearn.cluster.SpectralBiclustering.__le__ sklearn.cluster.SpectralBiclustering.__lt__ sklearn.cluster.SpectralBiclustering.__ne__ sklearn.cluster.SpectralBiclustering.__new__ sklearn.cluster.SpectralBiclustering.__reduce__ sklearn.cluster.SpectralBiclustering.__reduce_ex__ sklearn.cluster.SpectralBiclustering.__repr__ sklearn.cluster.SpectralBiclustering.__setattr__ sklearn.cluster.SpectralBiclustering.__setstate__ sklearn.cluster.SpectralBiclustering.__sizeof__ sklearn.cluster.SpectralBiclustering.__sklearn_clone__ sklearn.cluster.SpectralBiclustering.__sklearn_tags__ sklearn.cluster.SpectralBiclustering.__str__ sklearn.cluster.SpectralBiclustering.__subclasshook__ sklearn.cluster.SpectralBiclustering._build_request_for_signature sklearn.cluster.SpectralBiclustering._check_parameters sklearn.cluster.SpectralBiclustering._fit sklearn.cluster.SpectralBiclustering._fit_best_piecewise sklearn.cluster.SpectralBiclustering._get_default_requests sklearn.cluster.SpectralBiclustering._get_doc_link sklearn.cluster.SpectralBiclustering._get_metadata_request sklearn.cluster.SpectralBiclustering._get_param_names sklearn.cluster.SpectralBiclustering._k_means sklearn.cluster.SpectralBiclustering._project_and_cluster sklearn.cluster.SpectralBiclustering._repr_html_inner sklearn.cluster.SpectralBiclustering._repr_mimebundle_ sklearn.cluster.SpectralBiclustering._svd sklearn.cluster.SpectralBiclustering._validate_params sklearn.cluster.SpectralBiclustering.fit sklearn.cluster.SpectralBiclustering.get_indices sklearn.cluster.SpectralBiclustering.get_metadata_routing sklearn.cluster.SpectralBiclustering.get_params sklearn.cluster.SpectralBiclustering.get_shape sklearn.cluster.SpectralBiclustering.get_submatrix sklearn.cluster.SpectralBiclustering.set_params 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.cluster.SpectralClustering.rst.txt b/dev/_sources/modules/generated/sklearn.cluster.SpectralClustering.rst.txt
index 70fadcf132ab3..4e3bc8e119772 100644
--- a/dev/_sources/modules/generated/sklearn.cluster.SpectralClustering.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.cluster.SpectralClustering.rst.txt
@@ -8,6 +8,6 @@ SpectralClustering
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.cluster.SpectralClustering sklearn.cluster.SpectralClustering.__delattr__ sklearn.cluster.SpectralClustering.__dir__ sklearn.cluster.SpectralClustering.__eq__ sklearn.cluster.SpectralClustering.__format__ sklearn.cluster.SpectralClustering.__ge__ sklearn.cluster.SpectralClustering.__getattribute__ sklearn.cluster.SpectralClustering.__getstate__ sklearn.cluster.SpectralClustering.__gt__ sklearn.cluster.SpectralClustering.__hash__ sklearn.cluster.SpectralClustering.__init__ sklearn.cluster.SpectralClustering.__init_subclass__ sklearn.cluster.SpectralClustering.__le__ sklearn.cluster.SpectralClustering.__lt__ sklearn.cluster.SpectralClustering.__ne__ sklearn.cluster.SpectralClustering.__new__ sklearn.cluster.SpectralClustering.__reduce__ sklearn.cluster.SpectralClustering.__reduce_ex__ sklearn.cluster.SpectralClustering.__repr__ sklearn.cluster.SpectralClustering.__setattr__ sklearn.cluster.SpectralClustering.__setstate__ sklearn.cluster.SpectralClustering.__sizeof__ sklearn.cluster.SpectralClustering.__sklearn_clone__ sklearn.cluster.SpectralClustering.__sklearn_tags__ sklearn.cluster.SpectralClustering.__str__ sklearn.cluster.SpectralClustering.__subclasshook__ sklearn.cluster.SpectralClustering._build_request_for_signature sklearn.cluster.SpectralClustering._check_feature_names sklearn.cluster.SpectralClustering._check_n_features sklearn.cluster.SpectralClustering._get_default_requests sklearn.cluster.SpectralClustering._get_doc_link sklearn.cluster.SpectralClustering._get_metadata_request sklearn.cluster.SpectralClustering._get_param_names sklearn.cluster.SpectralClustering._get_tags sklearn.cluster.SpectralClustering._more_tags sklearn.cluster.SpectralClustering._repr_html_inner sklearn.cluster.SpectralClustering._repr_mimebundle_ sklearn.cluster.SpectralClustering._validate_data sklearn.cluster.SpectralClustering._validate_params sklearn.cluster.SpectralClustering.fit sklearn.cluster.SpectralClustering.fit_predict sklearn.cluster.SpectralClustering.get_metadata_routing sklearn.cluster.SpectralClustering.get_params sklearn.cluster.SpectralClustering.set_params 
+.. minigallery:: sklearn.cluster.SpectralClustering sklearn.cluster.SpectralClustering.__delattr__ sklearn.cluster.SpectralClustering.__dir__ sklearn.cluster.SpectralClustering.__eq__ sklearn.cluster.SpectralClustering.__format__ sklearn.cluster.SpectralClustering.__ge__ sklearn.cluster.SpectralClustering.__getattribute__ sklearn.cluster.SpectralClustering.__getstate__ sklearn.cluster.SpectralClustering.__gt__ sklearn.cluster.SpectralClustering.__hash__ sklearn.cluster.SpectralClustering.__init__ sklearn.cluster.SpectralClustering.__init_subclass__ sklearn.cluster.SpectralClustering.__le__ sklearn.cluster.SpectralClustering.__lt__ sklearn.cluster.SpectralClustering.__ne__ sklearn.cluster.SpectralClustering.__new__ sklearn.cluster.SpectralClustering.__reduce__ sklearn.cluster.SpectralClustering.__reduce_ex__ sklearn.cluster.SpectralClustering.__repr__ sklearn.cluster.SpectralClustering.__setattr__ sklearn.cluster.SpectralClustering.__setstate__ sklearn.cluster.SpectralClustering.__sizeof__ sklearn.cluster.SpectralClustering.__sklearn_clone__ sklearn.cluster.SpectralClustering.__sklearn_tags__ sklearn.cluster.SpectralClustering.__str__ sklearn.cluster.SpectralClustering.__subclasshook__ sklearn.cluster.SpectralClustering._build_request_for_signature sklearn.cluster.SpectralClustering._get_default_requests sklearn.cluster.SpectralClustering._get_doc_link sklearn.cluster.SpectralClustering._get_metadata_request sklearn.cluster.SpectralClustering._get_param_names sklearn.cluster.SpectralClustering._repr_html_inner sklearn.cluster.SpectralClustering._repr_mimebundle_ sklearn.cluster.SpectralClustering._validate_params sklearn.cluster.SpectralClustering.fit sklearn.cluster.SpectralClustering.fit_predict sklearn.cluster.SpectralClustering.get_metadata_routing sklearn.cluster.SpectralClustering.get_params sklearn.cluster.SpectralClustering.set_params 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.cluster.SpectralCoclustering.rst.txt b/dev/_sources/modules/generated/sklearn.cluster.SpectralCoclustering.rst.txt
index c76ef26951440..51693ca5c5fbc 100644
--- a/dev/_sources/modules/generated/sklearn.cluster.SpectralCoclustering.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.cluster.SpectralCoclustering.rst.txt
@@ -8,6 +8,6 @@ SpectralCoclustering
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.cluster.SpectralCoclustering sklearn.cluster.SpectralCoclustering.__delattr__ sklearn.cluster.SpectralCoclustering.__dir__ sklearn.cluster.SpectralCoclustering.__eq__ sklearn.cluster.SpectralCoclustering.__format__ sklearn.cluster.SpectralCoclustering.__ge__ sklearn.cluster.SpectralCoclustering.__getattribute__ sklearn.cluster.SpectralCoclustering.__getstate__ sklearn.cluster.SpectralCoclustering.__gt__ sklearn.cluster.SpectralCoclustering.__hash__ sklearn.cluster.SpectralCoclustering.__init__ sklearn.cluster.SpectralCoclustering.__init_subclass__ sklearn.cluster.SpectralCoclustering.__le__ sklearn.cluster.SpectralCoclustering.__lt__ sklearn.cluster.SpectralCoclustering.__ne__ sklearn.cluster.SpectralCoclustering.__new__ sklearn.cluster.SpectralCoclustering.__reduce__ sklearn.cluster.SpectralCoclustering.__reduce_ex__ sklearn.cluster.SpectralCoclustering.__repr__ sklearn.cluster.SpectralCoclustering.__setattr__ sklearn.cluster.SpectralCoclustering.__setstate__ sklearn.cluster.SpectralCoclustering.__sizeof__ sklearn.cluster.SpectralCoclustering.__sklearn_clone__ sklearn.cluster.SpectralCoclustering.__sklearn_tags__ sklearn.cluster.SpectralCoclustering.__str__ sklearn.cluster.SpectralCoclustering.__subclasshook__ sklearn.cluster.SpectralCoclustering._build_request_for_signature sklearn.cluster.SpectralCoclustering._check_feature_names sklearn.cluster.SpectralCoclustering._check_n_features sklearn.cluster.SpectralCoclustering._check_parameters sklearn.cluster.SpectralCoclustering._fit sklearn.cluster.SpectralCoclustering._get_default_requests sklearn.cluster.SpectralCoclustering._get_doc_link sklearn.cluster.SpectralCoclustering._get_metadata_request sklearn.cluster.SpectralCoclustering._get_param_names sklearn.cluster.SpectralCoclustering._get_tags sklearn.cluster.SpectralCoclustering._k_means sklearn.cluster.SpectralCoclustering._more_tags sklearn.cluster.SpectralCoclustering._repr_html_inner sklearn.cluster.SpectralCoclustering._repr_mimebundle_ sklearn.cluster.SpectralCoclustering._svd sklearn.cluster.SpectralCoclustering._validate_data sklearn.cluster.SpectralCoclustering._validate_params sklearn.cluster.SpectralCoclustering.fit sklearn.cluster.SpectralCoclustering.get_indices sklearn.cluster.SpectralCoclustering.get_metadata_routing sklearn.cluster.SpectralCoclustering.get_params sklearn.cluster.SpectralCoclustering.get_shape sklearn.cluster.SpectralCoclustering.get_submatrix sklearn.cluster.SpectralCoclustering.set_params 
+.. minigallery:: sklearn.cluster.SpectralCoclustering sklearn.cluster.SpectralCoclustering.__delattr__ sklearn.cluster.SpectralCoclustering.__dir__ sklearn.cluster.SpectralCoclustering.__eq__ sklearn.cluster.SpectralCoclustering.__format__ sklearn.cluster.SpectralCoclustering.__ge__ sklearn.cluster.SpectralCoclustering.__getattribute__ sklearn.cluster.SpectralCoclustering.__getstate__ sklearn.cluster.SpectralCoclustering.__gt__ sklearn.cluster.SpectralCoclustering.__hash__ sklearn.cluster.SpectralCoclustering.__init__ sklearn.cluster.SpectralCoclustering.__init_subclass__ sklearn.cluster.SpectralCoclustering.__le__ sklearn.cluster.SpectralCoclustering.__lt__ sklearn.cluster.SpectralCoclustering.__ne__ sklearn.cluster.SpectralCoclustering.__new__ sklearn.cluster.SpectralCoclustering.__reduce__ sklearn.cluster.SpectralCoclustering.__reduce_ex__ sklearn.cluster.SpectralCoclustering.__repr__ sklearn.cluster.SpectralCoclustering.__setattr__ sklearn.cluster.SpectralCoclustering.__setstate__ sklearn.cluster.SpectralCoclustering.__sizeof__ sklearn.cluster.SpectralCoclustering.__sklearn_clone__ sklearn.cluster.SpectralCoclustering.__sklearn_tags__ sklearn.cluster.SpectralCoclustering.__str__ sklearn.cluster.SpectralCoclustering.__subclasshook__ sklearn.cluster.SpectralCoclustering._build_request_for_signature sklearn.cluster.SpectralCoclustering._check_parameters sklearn.cluster.SpectralCoclustering._fit sklearn.cluster.SpectralCoclustering._get_default_requests sklearn.cluster.SpectralCoclustering._get_doc_link sklearn.cluster.SpectralCoclustering._get_metadata_request sklearn.cluster.SpectralCoclustering._get_param_names sklearn.cluster.SpectralCoclustering._k_means sklearn.cluster.SpectralCoclustering._repr_html_inner sklearn.cluster.SpectralCoclustering._repr_mimebundle_ sklearn.cluster.SpectralCoclustering._svd sklearn.cluster.SpectralCoclustering._validate_params sklearn.cluster.SpectralCoclustering.fit sklearn.cluster.SpectralCoclustering.get_indices sklearn.cluster.SpectralCoclustering.get_metadata_routing sklearn.cluster.SpectralCoclustering.get_params sklearn.cluster.SpectralCoclustering.get_shape sklearn.cluster.SpectralCoclustering.get_submatrix sklearn.cluster.SpectralCoclustering.set_params 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.compose.ColumnTransformer.rst.txt b/dev/_sources/modules/generated/sklearn.compose.ColumnTransformer.rst.txt
index f9c492e7be5f5..566749750f0b0 100644
--- a/dev/_sources/modules/generated/sklearn.compose.ColumnTransformer.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.compose.ColumnTransformer.rst.txt
@@ -8,6 +8,6 @@ ColumnTransformer
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.compose.ColumnTransformer sklearn.compose.ColumnTransformer.__delattr__ sklearn.compose.ColumnTransformer.__dir__ sklearn.compose.ColumnTransformer.__eq__ sklearn.compose.ColumnTransformer.__format__ sklearn.compose.ColumnTransformer.__ge__ sklearn.compose.ColumnTransformer.__getattribute__ sklearn.compose.ColumnTransformer.__getitem__ sklearn.compose.ColumnTransformer.__getstate__ sklearn.compose.ColumnTransformer.__gt__ sklearn.compose.ColumnTransformer.__hash__ sklearn.compose.ColumnTransformer.__init__ sklearn.compose.ColumnTransformer.__init_subclass__ sklearn.compose.ColumnTransformer.__le__ sklearn.compose.ColumnTransformer.__lt__ sklearn.compose.ColumnTransformer.__ne__ sklearn.compose.ColumnTransformer.__new__ sklearn.compose.ColumnTransformer.__reduce__ sklearn.compose.ColumnTransformer.__reduce_ex__ sklearn.compose.ColumnTransformer.__repr__ sklearn.compose.ColumnTransformer.__setattr__ sklearn.compose.ColumnTransformer.__setstate__ sklearn.compose.ColumnTransformer.__sizeof__ sklearn.compose.ColumnTransformer.__sklearn_clone__ sklearn.compose.ColumnTransformer.__sklearn_tags__ sklearn.compose.ColumnTransformer.__str__ sklearn.compose.ColumnTransformer.__subclasshook__ sklearn.compose.ColumnTransformer._add_prefix_for_feature_names_out sklearn.compose.ColumnTransformer._build_request_for_signature sklearn.compose.ColumnTransformer._call_func_on_transformers sklearn.compose.ColumnTransformer._check_feature_names sklearn.compose.ColumnTransformer._check_n_features sklearn.compose.ColumnTransformer._get_default_requests sklearn.compose.ColumnTransformer._get_doc_link sklearn.compose.ColumnTransformer._get_empty_routing sklearn.compose.ColumnTransformer._get_feature_name_out_for_transformer sklearn.compose.ColumnTransformer._get_metadata_request sklearn.compose.ColumnTransformer._get_param_names sklearn.compose.ColumnTransformer._get_params sklearn.compose.ColumnTransformer._get_remainder_cols sklearn.compose.ColumnTransformer._get_remainder_cols_dtype sklearn.compose.ColumnTransformer._get_tags sklearn.compose.ColumnTransformer._hstack sklearn.compose.ColumnTransformer._iter sklearn.compose.ColumnTransformer._log_message sklearn.compose.ColumnTransformer._more_tags sklearn.compose.ColumnTransformer._record_output_indices sklearn.compose.ColumnTransformer._replace_estimator sklearn.compose.ColumnTransformer._repr_html_inner sklearn.compose.ColumnTransformer._repr_mimebundle_ sklearn.compose.ColumnTransformer._set_params sklearn.compose.ColumnTransformer._sk_visual_block_ sklearn.compose.ColumnTransformer._update_fitted_transformers sklearn.compose.ColumnTransformer._validate_column_callables sklearn.compose.ColumnTransformer._validate_data sklearn.compose.ColumnTransformer._validate_names sklearn.compose.ColumnTransformer._validate_output sklearn.compose.ColumnTransformer._validate_params sklearn.compose.ColumnTransformer._validate_remainder sklearn.compose.ColumnTransformer._validate_transformers sklearn.compose.ColumnTransformer.fit sklearn.compose.ColumnTransformer.fit_transform sklearn.compose.ColumnTransformer.get_feature_names_out sklearn.compose.ColumnTransformer.get_metadata_routing sklearn.compose.ColumnTransformer.get_params sklearn.compose.ColumnTransformer.set_output sklearn.compose.ColumnTransformer.set_params sklearn.compose.ColumnTransformer.transform 
+.. minigallery:: sklearn.compose.ColumnTransformer sklearn.compose.ColumnTransformer.__delattr__ sklearn.compose.ColumnTransformer.__dir__ sklearn.compose.ColumnTransformer.__eq__ sklearn.compose.ColumnTransformer.__format__ sklearn.compose.ColumnTransformer.__ge__ sklearn.compose.ColumnTransformer.__getattribute__ sklearn.compose.ColumnTransformer.__getitem__ sklearn.compose.ColumnTransformer.__getstate__ sklearn.compose.ColumnTransformer.__gt__ sklearn.compose.ColumnTransformer.__hash__ sklearn.compose.ColumnTransformer.__init__ sklearn.compose.ColumnTransformer.__init_subclass__ sklearn.compose.ColumnTransformer.__le__ sklearn.compose.ColumnTransformer.__lt__ sklearn.compose.ColumnTransformer.__ne__ sklearn.compose.ColumnTransformer.__new__ sklearn.compose.ColumnTransformer.__reduce__ sklearn.compose.ColumnTransformer.__reduce_ex__ sklearn.compose.ColumnTransformer.__repr__ sklearn.compose.ColumnTransformer.__setattr__ sklearn.compose.ColumnTransformer.__setstate__ sklearn.compose.ColumnTransformer.__sizeof__ sklearn.compose.ColumnTransformer.__sklearn_clone__ sklearn.compose.ColumnTransformer.__sklearn_tags__ sklearn.compose.ColumnTransformer.__str__ sklearn.compose.ColumnTransformer.__subclasshook__ sklearn.compose.ColumnTransformer._add_prefix_for_feature_names_out sklearn.compose.ColumnTransformer._build_request_for_signature sklearn.compose.ColumnTransformer._call_func_on_transformers sklearn.compose.ColumnTransformer._get_default_requests sklearn.compose.ColumnTransformer._get_doc_link sklearn.compose.ColumnTransformer._get_empty_routing sklearn.compose.ColumnTransformer._get_feature_name_out_for_transformer sklearn.compose.ColumnTransformer._get_metadata_request sklearn.compose.ColumnTransformer._get_param_names sklearn.compose.ColumnTransformer._get_params sklearn.compose.ColumnTransformer._get_remainder_cols sklearn.compose.ColumnTransformer._get_remainder_cols_dtype sklearn.compose.ColumnTransformer._hstack sklearn.compose.ColumnTransformer._iter sklearn.compose.ColumnTransformer._log_message sklearn.compose.ColumnTransformer._record_output_indices sklearn.compose.ColumnTransformer._replace_estimator sklearn.compose.ColumnTransformer._repr_html_inner sklearn.compose.ColumnTransformer._repr_mimebundle_ sklearn.compose.ColumnTransformer._set_params sklearn.compose.ColumnTransformer._sk_visual_block_ sklearn.compose.ColumnTransformer._update_fitted_transformers sklearn.compose.ColumnTransformer._validate_column_callables sklearn.compose.ColumnTransformer._validate_names sklearn.compose.ColumnTransformer._validate_output sklearn.compose.ColumnTransformer._validate_params sklearn.compose.ColumnTransformer._validate_remainder sklearn.compose.ColumnTransformer._validate_transformers sklearn.compose.ColumnTransformer.fit sklearn.compose.ColumnTransformer.fit_transform sklearn.compose.ColumnTransformer.get_feature_names_out sklearn.compose.ColumnTransformer.get_metadata_routing sklearn.compose.ColumnTransformer.get_params sklearn.compose.ColumnTransformer.set_output sklearn.compose.ColumnTransformer.set_params sklearn.compose.ColumnTransformer.transform 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.compose.TransformedTargetRegressor.rst.txt b/dev/_sources/modules/generated/sklearn.compose.TransformedTargetRegressor.rst.txt
index ee17d02cdd928..517e8c6c4b3d9 100644
--- a/dev/_sources/modules/generated/sklearn.compose.TransformedTargetRegressor.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.compose.TransformedTargetRegressor.rst.txt
@@ -8,6 +8,6 @@ TransformedTargetRegressor
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.compose.TransformedTargetRegressor sklearn.compose.TransformedTargetRegressor.__delattr__ sklearn.compose.TransformedTargetRegressor.__dir__ sklearn.compose.TransformedTargetRegressor.__eq__ sklearn.compose.TransformedTargetRegressor.__format__ sklearn.compose.TransformedTargetRegressor.__ge__ sklearn.compose.TransformedTargetRegressor.__getattribute__ sklearn.compose.TransformedTargetRegressor.__getstate__ sklearn.compose.TransformedTargetRegressor.__gt__ sklearn.compose.TransformedTargetRegressor.__hash__ sklearn.compose.TransformedTargetRegressor.__init__ sklearn.compose.TransformedTargetRegressor.__init_subclass__ sklearn.compose.TransformedTargetRegressor.__le__ sklearn.compose.TransformedTargetRegressor.__lt__ sklearn.compose.TransformedTargetRegressor.__ne__ sklearn.compose.TransformedTargetRegressor.__new__ sklearn.compose.TransformedTargetRegressor.__reduce__ sklearn.compose.TransformedTargetRegressor.__reduce_ex__ sklearn.compose.TransformedTargetRegressor.__repr__ sklearn.compose.TransformedTargetRegressor.__setattr__ sklearn.compose.TransformedTargetRegressor.__setstate__ sklearn.compose.TransformedTargetRegressor.__sizeof__ sklearn.compose.TransformedTargetRegressor.__sklearn_clone__ sklearn.compose.TransformedTargetRegressor.__sklearn_tags__ sklearn.compose.TransformedTargetRegressor.__str__ sklearn.compose.TransformedTargetRegressor.__subclasshook__ sklearn.compose.TransformedTargetRegressor._build_request_for_signature sklearn.compose.TransformedTargetRegressor._check_feature_names sklearn.compose.TransformedTargetRegressor._check_n_features sklearn.compose.TransformedTargetRegressor._fit_transformer sklearn.compose.TransformedTargetRegressor._get_default_requests sklearn.compose.TransformedTargetRegressor._get_doc_link sklearn.compose.TransformedTargetRegressor._get_metadata_request sklearn.compose.TransformedTargetRegressor._get_param_names sklearn.compose.TransformedTargetRegressor._get_regressor sklearn.compose.TransformedTargetRegressor._get_tags sklearn.compose.TransformedTargetRegressor._more_tags sklearn.compose.TransformedTargetRegressor._repr_html_inner sklearn.compose.TransformedTargetRegressor._repr_mimebundle_ sklearn.compose.TransformedTargetRegressor._validate_data sklearn.compose.TransformedTargetRegressor._validate_params sklearn.compose.TransformedTargetRegressor.fit sklearn.compose.TransformedTargetRegressor.get_metadata_routing sklearn.compose.TransformedTargetRegressor.get_params sklearn.compose.TransformedTargetRegressor.predict sklearn.compose.TransformedTargetRegressor.score sklearn.compose.TransformedTargetRegressor.set_params sklearn.compose.TransformedTargetRegressor.set_score_request 
+.. minigallery:: sklearn.compose.TransformedTargetRegressor sklearn.compose.TransformedTargetRegressor.__delattr__ sklearn.compose.TransformedTargetRegressor.__dir__ sklearn.compose.TransformedTargetRegressor.__eq__ sklearn.compose.TransformedTargetRegressor.__format__ sklearn.compose.TransformedTargetRegressor.__ge__ sklearn.compose.TransformedTargetRegressor.__getattribute__ sklearn.compose.TransformedTargetRegressor.__getstate__ sklearn.compose.TransformedTargetRegressor.__gt__ sklearn.compose.TransformedTargetRegressor.__hash__ sklearn.compose.TransformedTargetRegressor.__init__ sklearn.compose.TransformedTargetRegressor.__init_subclass__ sklearn.compose.TransformedTargetRegressor.__le__ sklearn.compose.TransformedTargetRegressor.__lt__ sklearn.compose.TransformedTargetRegressor.__ne__ sklearn.compose.TransformedTargetRegressor.__new__ sklearn.compose.TransformedTargetRegressor.__reduce__ sklearn.compose.TransformedTargetRegressor.__reduce_ex__ sklearn.compose.TransformedTargetRegressor.__repr__ sklearn.compose.TransformedTargetRegressor.__setattr__ sklearn.compose.TransformedTargetRegressor.__setstate__ sklearn.compose.TransformedTargetRegressor.__sizeof__ sklearn.compose.TransformedTargetRegressor.__sklearn_clone__ sklearn.compose.TransformedTargetRegressor.__sklearn_tags__ sklearn.compose.TransformedTargetRegressor.__str__ sklearn.compose.TransformedTargetRegressor.__subclasshook__ sklearn.compose.TransformedTargetRegressor._build_request_for_signature sklearn.compose.TransformedTargetRegressor._fit_transformer sklearn.compose.TransformedTargetRegressor._get_default_requests sklearn.compose.TransformedTargetRegressor._get_doc_link sklearn.compose.TransformedTargetRegressor._get_metadata_request sklearn.compose.TransformedTargetRegressor._get_param_names sklearn.compose.TransformedTargetRegressor._get_regressor sklearn.compose.TransformedTargetRegressor._repr_html_inner sklearn.compose.TransformedTargetRegressor._repr_mimebundle_ sklearn.compose.TransformedTargetRegressor._validate_params sklearn.compose.TransformedTargetRegressor.fit sklearn.compose.TransformedTargetRegressor.get_metadata_routing sklearn.compose.TransformedTargetRegressor.get_params sklearn.compose.TransformedTargetRegressor.predict sklearn.compose.TransformedTargetRegressor.score sklearn.compose.TransformedTargetRegressor.set_params sklearn.compose.TransformedTargetRegressor.set_score_request 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.covariance.EllipticEnvelope.rst.txt b/dev/_sources/modules/generated/sklearn.covariance.EllipticEnvelope.rst.txt
index 6724252ec4ed3..bb60d58f4909b 100644
--- a/dev/_sources/modules/generated/sklearn.covariance.EllipticEnvelope.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.covariance.EllipticEnvelope.rst.txt
@@ -8,6 +8,6 @@ EllipticEnvelope
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.covariance.EllipticEnvelope sklearn.covariance.EllipticEnvelope.__delattr__ sklearn.covariance.EllipticEnvelope.__dir__ sklearn.covariance.EllipticEnvelope.__eq__ sklearn.covariance.EllipticEnvelope.__format__ sklearn.covariance.EllipticEnvelope.__ge__ sklearn.covariance.EllipticEnvelope.__getattribute__ sklearn.covariance.EllipticEnvelope.__getstate__ sklearn.covariance.EllipticEnvelope.__gt__ sklearn.covariance.EllipticEnvelope.__hash__ sklearn.covariance.EllipticEnvelope.__init__ sklearn.covariance.EllipticEnvelope.__init_subclass__ sklearn.covariance.EllipticEnvelope.__le__ sklearn.covariance.EllipticEnvelope.__lt__ sklearn.covariance.EllipticEnvelope.__ne__ sklearn.covariance.EllipticEnvelope.__new__ sklearn.covariance.EllipticEnvelope.__reduce__ sklearn.covariance.EllipticEnvelope.__reduce_ex__ sklearn.covariance.EllipticEnvelope.__repr__ sklearn.covariance.EllipticEnvelope.__setattr__ sklearn.covariance.EllipticEnvelope.__setstate__ sklearn.covariance.EllipticEnvelope.__sizeof__ sklearn.covariance.EllipticEnvelope.__sklearn_clone__ sklearn.covariance.EllipticEnvelope.__sklearn_tags__ sklearn.covariance.EllipticEnvelope.__str__ sklearn.covariance.EllipticEnvelope.__subclasshook__ sklearn.covariance.EllipticEnvelope._build_request_for_signature sklearn.covariance.EllipticEnvelope._check_feature_names sklearn.covariance.EllipticEnvelope._check_n_features sklearn.covariance.EllipticEnvelope._get_default_requests sklearn.covariance.EllipticEnvelope._get_doc_link sklearn.covariance.EllipticEnvelope._get_metadata_request sklearn.covariance.EllipticEnvelope._get_param_names sklearn.covariance.EllipticEnvelope._get_tags sklearn.covariance.EllipticEnvelope._more_tags sklearn.covariance.EllipticEnvelope._nonrobust_covariance sklearn.covariance.EllipticEnvelope._repr_html_inner sklearn.covariance.EllipticEnvelope._repr_mimebundle_ sklearn.covariance.EllipticEnvelope._set_covariance sklearn.covariance.EllipticEnvelope._validate_data sklearn.covariance.EllipticEnvelope._validate_params sklearn.covariance.EllipticEnvelope.correct_covariance sklearn.covariance.EllipticEnvelope.decision_function sklearn.covariance.EllipticEnvelope.error_norm sklearn.covariance.EllipticEnvelope.fit sklearn.covariance.EllipticEnvelope.fit_predict sklearn.covariance.EllipticEnvelope.get_metadata_routing sklearn.covariance.EllipticEnvelope.get_params sklearn.covariance.EllipticEnvelope.get_precision sklearn.covariance.EllipticEnvelope.mahalanobis sklearn.covariance.EllipticEnvelope.predict sklearn.covariance.EllipticEnvelope.reweight_covariance sklearn.covariance.EllipticEnvelope.score sklearn.covariance.EllipticEnvelope.score_samples sklearn.covariance.EllipticEnvelope.set_params 
+.. minigallery:: sklearn.covariance.EllipticEnvelope sklearn.covariance.EllipticEnvelope.__delattr__ sklearn.covariance.EllipticEnvelope.__dir__ sklearn.covariance.EllipticEnvelope.__eq__ sklearn.covariance.EllipticEnvelope.__format__ sklearn.covariance.EllipticEnvelope.__ge__ sklearn.covariance.EllipticEnvelope.__getattribute__ sklearn.covariance.EllipticEnvelope.__getstate__ sklearn.covariance.EllipticEnvelope.__gt__ sklearn.covariance.EllipticEnvelope.__hash__ sklearn.covariance.EllipticEnvelope.__init__ sklearn.covariance.EllipticEnvelope.__init_subclass__ sklearn.covariance.EllipticEnvelope.__le__ sklearn.covariance.EllipticEnvelope.__lt__ sklearn.covariance.EllipticEnvelope.__ne__ sklearn.covariance.EllipticEnvelope.__new__ sklearn.covariance.EllipticEnvelope.__reduce__ sklearn.covariance.EllipticEnvelope.__reduce_ex__ sklearn.covariance.EllipticEnvelope.__repr__ sklearn.covariance.EllipticEnvelope.__setattr__ sklearn.covariance.EllipticEnvelope.__setstate__ sklearn.covariance.EllipticEnvelope.__sizeof__ sklearn.covariance.EllipticEnvelope.__sklearn_clone__ sklearn.covariance.EllipticEnvelope.__sklearn_tags__ sklearn.covariance.EllipticEnvelope.__str__ sklearn.covariance.EllipticEnvelope.__subclasshook__ sklearn.covariance.EllipticEnvelope._build_request_for_signature sklearn.covariance.EllipticEnvelope._get_default_requests sklearn.covariance.EllipticEnvelope._get_doc_link sklearn.covariance.EllipticEnvelope._get_metadata_request sklearn.covariance.EllipticEnvelope._get_param_names sklearn.covariance.EllipticEnvelope._nonrobust_covariance sklearn.covariance.EllipticEnvelope._repr_html_inner sklearn.covariance.EllipticEnvelope._repr_mimebundle_ sklearn.covariance.EllipticEnvelope._set_covariance sklearn.covariance.EllipticEnvelope._validate_params sklearn.covariance.EllipticEnvelope.correct_covariance sklearn.covariance.EllipticEnvelope.decision_function sklearn.covariance.EllipticEnvelope.error_norm sklearn.covariance.EllipticEnvelope.fit sklearn.covariance.EllipticEnvelope.fit_predict sklearn.covariance.EllipticEnvelope.get_metadata_routing sklearn.covariance.EllipticEnvelope.get_params sklearn.covariance.EllipticEnvelope.get_precision sklearn.covariance.EllipticEnvelope.mahalanobis sklearn.covariance.EllipticEnvelope.predict sklearn.covariance.EllipticEnvelope.reweight_covariance sklearn.covariance.EllipticEnvelope.score sklearn.covariance.EllipticEnvelope.score_samples sklearn.covariance.EllipticEnvelope.set_params 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.covariance.EmpiricalCovariance.rst.txt b/dev/_sources/modules/generated/sklearn.covariance.EmpiricalCovariance.rst.txt
index 047b6480a7bf8..a320b831af2b5 100644
--- a/dev/_sources/modules/generated/sklearn.covariance.EmpiricalCovariance.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.covariance.EmpiricalCovariance.rst.txt
@@ -8,6 +8,6 @@ EmpiricalCovariance
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.covariance.EmpiricalCovariance sklearn.covariance.EmpiricalCovariance.__delattr__ sklearn.covariance.EmpiricalCovariance.__dir__ sklearn.covariance.EmpiricalCovariance.__eq__ sklearn.covariance.EmpiricalCovariance.__format__ sklearn.covariance.EmpiricalCovariance.__ge__ sklearn.covariance.EmpiricalCovariance.__getattribute__ sklearn.covariance.EmpiricalCovariance.__getstate__ sklearn.covariance.EmpiricalCovariance.__gt__ sklearn.covariance.EmpiricalCovariance.__hash__ sklearn.covariance.EmpiricalCovariance.__init__ sklearn.covariance.EmpiricalCovariance.__init_subclass__ sklearn.covariance.EmpiricalCovariance.__le__ sklearn.covariance.EmpiricalCovariance.__lt__ sklearn.covariance.EmpiricalCovariance.__ne__ sklearn.covariance.EmpiricalCovariance.__new__ sklearn.covariance.EmpiricalCovariance.__reduce__ sklearn.covariance.EmpiricalCovariance.__reduce_ex__ sklearn.covariance.EmpiricalCovariance.__repr__ sklearn.covariance.EmpiricalCovariance.__setattr__ sklearn.covariance.EmpiricalCovariance.__setstate__ sklearn.covariance.EmpiricalCovariance.__sizeof__ sklearn.covariance.EmpiricalCovariance.__sklearn_clone__ sklearn.covariance.EmpiricalCovariance.__sklearn_tags__ sklearn.covariance.EmpiricalCovariance.__str__ sklearn.covariance.EmpiricalCovariance.__subclasshook__ sklearn.covariance.EmpiricalCovariance._build_request_for_signature sklearn.covariance.EmpiricalCovariance._check_feature_names sklearn.covariance.EmpiricalCovariance._check_n_features sklearn.covariance.EmpiricalCovariance._get_default_requests sklearn.covariance.EmpiricalCovariance._get_doc_link sklearn.covariance.EmpiricalCovariance._get_metadata_request sklearn.covariance.EmpiricalCovariance._get_param_names sklearn.covariance.EmpiricalCovariance._get_tags sklearn.covariance.EmpiricalCovariance._more_tags sklearn.covariance.EmpiricalCovariance._repr_html_inner sklearn.covariance.EmpiricalCovariance._repr_mimebundle_ sklearn.covariance.EmpiricalCovariance._set_covariance sklearn.covariance.EmpiricalCovariance._validate_data sklearn.covariance.EmpiricalCovariance._validate_params sklearn.covariance.EmpiricalCovariance.error_norm sklearn.covariance.EmpiricalCovariance.fit sklearn.covariance.EmpiricalCovariance.get_metadata_routing sklearn.covariance.EmpiricalCovariance.get_params sklearn.covariance.EmpiricalCovariance.get_precision sklearn.covariance.EmpiricalCovariance.mahalanobis sklearn.covariance.EmpiricalCovariance.score sklearn.covariance.EmpiricalCovariance.set_params 
+.. minigallery:: sklearn.covariance.EmpiricalCovariance sklearn.covariance.EmpiricalCovariance.__delattr__ sklearn.covariance.EmpiricalCovariance.__dir__ sklearn.covariance.EmpiricalCovariance.__eq__ sklearn.covariance.EmpiricalCovariance.__format__ sklearn.covariance.EmpiricalCovariance.__ge__ sklearn.covariance.EmpiricalCovariance.__getattribute__ sklearn.covariance.EmpiricalCovariance.__getstate__ sklearn.covariance.EmpiricalCovariance.__gt__ sklearn.covariance.EmpiricalCovariance.__hash__ sklearn.covariance.EmpiricalCovariance.__init__ sklearn.covariance.EmpiricalCovariance.__init_subclass__ sklearn.covariance.EmpiricalCovariance.__le__ sklearn.covariance.EmpiricalCovariance.__lt__ sklearn.covariance.EmpiricalCovariance.__ne__ sklearn.covariance.EmpiricalCovariance.__new__ sklearn.covariance.EmpiricalCovariance.__reduce__ sklearn.covariance.EmpiricalCovariance.__reduce_ex__ sklearn.covariance.EmpiricalCovariance.__repr__ sklearn.covariance.EmpiricalCovariance.__setattr__ sklearn.covariance.EmpiricalCovariance.__setstate__ sklearn.covariance.EmpiricalCovariance.__sizeof__ sklearn.covariance.EmpiricalCovariance.__sklearn_clone__ sklearn.covariance.EmpiricalCovariance.__sklearn_tags__ sklearn.covariance.EmpiricalCovariance.__str__ sklearn.covariance.EmpiricalCovariance.__subclasshook__ sklearn.covariance.EmpiricalCovariance._build_request_for_signature sklearn.covariance.EmpiricalCovariance._get_default_requests sklearn.covariance.EmpiricalCovariance._get_doc_link sklearn.covariance.EmpiricalCovariance._get_metadata_request sklearn.covariance.EmpiricalCovariance._get_param_names sklearn.covariance.EmpiricalCovariance._repr_html_inner sklearn.covariance.EmpiricalCovariance._repr_mimebundle_ sklearn.covariance.EmpiricalCovariance._set_covariance sklearn.covariance.EmpiricalCovariance._validate_params sklearn.covariance.EmpiricalCovariance.error_norm sklearn.covariance.EmpiricalCovariance.fit sklearn.covariance.EmpiricalCovariance.get_metadata_routing sklearn.covariance.EmpiricalCovariance.get_params sklearn.covariance.EmpiricalCovariance.get_precision sklearn.covariance.EmpiricalCovariance.mahalanobis sklearn.covariance.EmpiricalCovariance.score sklearn.covariance.EmpiricalCovariance.set_params 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.covariance.GraphicalLasso.rst.txt b/dev/_sources/modules/generated/sklearn.covariance.GraphicalLasso.rst.txt
index aa7a232eeb4f8..1353a4e72303d 100644
--- a/dev/_sources/modules/generated/sklearn.covariance.GraphicalLasso.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.covariance.GraphicalLasso.rst.txt
@@ -8,6 +8,6 @@ GraphicalLasso
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.covariance.GraphicalLasso sklearn.covariance.GraphicalLasso.__delattr__ sklearn.covariance.GraphicalLasso.__dir__ sklearn.covariance.GraphicalLasso.__eq__ sklearn.covariance.GraphicalLasso.__format__ sklearn.covariance.GraphicalLasso.__ge__ sklearn.covariance.GraphicalLasso.__getattribute__ sklearn.covariance.GraphicalLasso.__getstate__ sklearn.covariance.GraphicalLasso.__gt__ sklearn.covariance.GraphicalLasso.__hash__ sklearn.covariance.GraphicalLasso.__init__ sklearn.covariance.GraphicalLasso.__init_subclass__ sklearn.covariance.GraphicalLasso.__le__ sklearn.covariance.GraphicalLasso.__lt__ sklearn.covariance.GraphicalLasso.__ne__ sklearn.covariance.GraphicalLasso.__new__ sklearn.covariance.GraphicalLasso.__reduce__ sklearn.covariance.GraphicalLasso.__reduce_ex__ sklearn.covariance.GraphicalLasso.__repr__ sklearn.covariance.GraphicalLasso.__setattr__ sklearn.covariance.GraphicalLasso.__setstate__ sklearn.covariance.GraphicalLasso.__sizeof__ sklearn.covariance.GraphicalLasso.__sklearn_clone__ sklearn.covariance.GraphicalLasso.__sklearn_tags__ sklearn.covariance.GraphicalLasso.__str__ sklearn.covariance.GraphicalLasso.__subclasshook__ sklearn.covariance.GraphicalLasso._build_request_for_signature sklearn.covariance.GraphicalLasso._check_feature_names sklearn.covariance.GraphicalLasso._check_n_features sklearn.covariance.GraphicalLasso._get_default_requests sklearn.covariance.GraphicalLasso._get_doc_link sklearn.covariance.GraphicalLasso._get_metadata_request sklearn.covariance.GraphicalLasso._get_param_names sklearn.covariance.GraphicalLasso._get_tags sklearn.covariance.GraphicalLasso._more_tags sklearn.covariance.GraphicalLasso._repr_html_inner sklearn.covariance.GraphicalLasso._repr_mimebundle_ sklearn.covariance.GraphicalLasso._set_covariance sklearn.covariance.GraphicalLasso._validate_data sklearn.covariance.GraphicalLasso._validate_params sklearn.covariance.GraphicalLasso.error_norm sklearn.covariance.GraphicalLasso.fit sklearn.covariance.GraphicalLasso.get_metadata_routing sklearn.covariance.GraphicalLasso.get_params sklearn.covariance.GraphicalLasso.get_precision sklearn.covariance.GraphicalLasso.mahalanobis sklearn.covariance.GraphicalLasso.score sklearn.covariance.GraphicalLasso.set_params 
+.. minigallery:: sklearn.covariance.GraphicalLasso sklearn.covariance.GraphicalLasso.__delattr__ sklearn.covariance.GraphicalLasso.__dir__ sklearn.covariance.GraphicalLasso.__eq__ sklearn.covariance.GraphicalLasso.__format__ sklearn.covariance.GraphicalLasso.__ge__ sklearn.covariance.GraphicalLasso.__getattribute__ sklearn.covariance.GraphicalLasso.__getstate__ sklearn.covariance.GraphicalLasso.__gt__ sklearn.covariance.GraphicalLasso.__hash__ sklearn.covariance.GraphicalLasso.__init__ sklearn.covariance.GraphicalLasso.__init_subclass__ sklearn.covariance.GraphicalLasso.__le__ sklearn.covariance.GraphicalLasso.__lt__ sklearn.covariance.GraphicalLasso.__ne__ sklearn.covariance.GraphicalLasso.__new__ sklearn.covariance.GraphicalLasso.__reduce__ sklearn.covariance.GraphicalLasso.__reduce_ex__ sklearn.covariance.GraphicalLasso.__repr__ sklearn.covariance.GraphicalLasso.__setattr__ sklearn.covariance.GraphicalLasso.__setstate__ sklearn.covariance.GraphicalLasso.__sizeof__ sklearn.covariance.GraphicalLasso.__sklearn_clone__ sklearn.covariance.GraphicalLasso.__sklearn_tags__ sklearn.covariance.GraphicalLasso.__str__ sklearn.covariance.GraphicalLasso.__subclasshook__ sklearn.covariance.GraphicalLasso._build_request_for_signature sklearn.covariance.GraphicalLasso._get_default_requests sklearn.covariance.GraphicalLasso._get_doc_link sklearn.covariance.GraphicalLasso._get_metadata_request sklearn.covariance.GraphicalLasso._get_param_names sklearn.covariance.GraphicalLasso._repr_html_inner sklearn.covariance.GraphicalLasso._repr_mimebundle_ sklearn.covariance.GraphicalLasso._set_covariance sklearn.covariance.GraphicalLasso._validate_params sklearn.covariance.GraphicalLasso.error_norm sklearn.covariance.GraphicalLasso.fit sklearn.covariance.GraphicalLasso.get_metadata_routing sklearn.covariance.GraphicalLasso.get_params sklearn.covariance.GraphicalLasso.get_precision sklearn.covariance.GraphicalLasso.mahalanobis sklearn.covariance.GraphicalLasso.score sklearn.covariance.GraphicalLasso.set_params 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.covariance.GraphicalLassoCV.rst.txt b/dev/_sources/modules/generated/sklearn.covariance.GraphicalLassoCV.rst.txt
index 51f23fac69dc6..c27d362b0e69b 100644
--- a/dev/_sources/modules/generated/sklearn.covariance.GraphicalLassoCV.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.covariance.GraphicalLassoCV.rst.txt
@@ -8,6 +8,6 @@ GraphicalLassoCV
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.covariance.GraphicalLassoCV sklearn.covariance.GraphicalLassoCV.__delattr__ sklearn.covariance.GraphicalLassoCV.__dir__ sklearn.covariance.GraphicalLassoCV.__eq__ sklearn.covariance.GraphicalLassoCV.__format__ sklearn.covariance.GraphicalLassoCV.__ge__ sklearn.covariance.GraphicalLassoCV.__getattribute__ sklearn.covariance.GraphicalLassoCV.__getstate__ sklearn.covariance.GraphicalLassoCV.__gt__ sklearn.covariance.GraphicalLassoCV.__hash__ sklearn.covariance.GraphicalLassoCV.__init__ sklearn.covariance.GraphicalLassoCV.__init_subclass__ sklearn.covariance.GraphicalLassoCV.__le__ sklearn.covariance.GraphicalLassoCV.__lt__ sklearn.covariance.GraphicalLassoCV.__ne__ sklearn.covariance.GraphicalLassoCV.__new__ sklearn.covariance.GraphicalLassoCV.__reduce__ sklearn.covariance.GraphicalLassoCV.__reduce_ex__ sklearn.covariance.GraphicalLassoCV.__repr__ sklearn.covariance.GraphicalLassoCV.__setattr__ sklearn.covariance.GraphicalLassoCV.__setstate__ sklearn.covariance.GraphicalLassoCV.__sizeof__ sklearn.covariance.GraphicalLassoCV.__sklearn_clone__ sklearn.covariance.GraphicalLassoCV.__sklearn_tags__ sklearn.covariance.GraphicalLassoCV.__str__ sklearn.covariance.GraphicalLassoCV.__subclasshook__ sklearn.covariance.GraphicalLassoCV._build_request_for_signature sklearn.covariance.GraphicalLassoCV._check_feature_names sklearn.covariance.GraphicalLassoCV._check_n_features sklearn.covariance.GraphicalLassoCV._get_default_requests sklearn.covariance.GraphicalLassoCV._get_doc_link sklearn.covariance.GraphicalLassoCV._get_metadata_request sklearn.covariance.GraphicalLassoCV._get_param_names sklearn.covariance.GraphicalLassoCV._get_tags sklearn.covariance.GraphicalLassoCV._more_tags sklearn.covariance.GraphicalLassoCV._repr_html_inner sklearn.covariance.GraphicalLassoCV._repr_mimebundle_ sklearn.covariance.GraphicalLassoCV._set_covariance sklearn.covariance.GraphicalLassoCV._validate_data sklearn.covariance.GraphicalLassoCV._validate_params sklearn.covariance.GraphicalLassoCV.error_norm sklearn.covariance.GraphicalLassoCV.fit sklearn.covariance.GraphicalLassoCV.get_metadata_routing sklearn.covariance.GraphicalLassoCV.get_params sklearn.covariance.GraphicalLassoCV.get_precision sklearn.covariance.GraphicalLassoCV.mahalanobis sklearn.covariance.GraphicalLassoCV.score sklearn.covariance.GraphicalLassoCV.set_params 
+.. minigallery:: sklearn.covariance.GraphicalLassoCV sklearn.covariance.GraphicalLassoCV.__delattr__ sklearn.covariance.GraphicalLassoCV.__dir__ sklearn.covariance.GraphicalLassoCV.__eq__ sklearn.covariance.GraphicalLassoCV.__format__ sklearn.covariance.GraphicalLassoCV.__ge__ sklearn.covariance.GraphicalLassoCV.__getattribute__ sklearn.covariance.GraphicalLassoCV.__getstate__ sklearn.covariance.GraphicalLassoCV.__gt__ sklearn.covariance.GraphicalLassoCV.__hash__ sklearn.covariance.GraphicalLassoCV.__init__ sklearn.covariance.GraphicalLassoCV.__init_subclass__ sklearn.covariance.GraphicalLassoCV.__le__ sklearn.covariance.GraphicalLassoCV.__lt__ sklearn.covariance.GraphicalLassoCV.__ne__ sklearn.covariance.GraphicalLassoCV.__new__ sklearn.covariance.GraphicalLassoCV.__reduce__ sklearn.covariance.GraphicalLassoCV.__reduce_ex__ sklearn.covariance.GraphicalLassoCV.__repr__ sklearn.covariance.GraphicalLassoCV.__setattr__ sklearn.covariance.GraphicalLassoCV.__setstate__ sklearn.covariance.GraphicalLassoCV.__sizeof__ sklearn.covariance.GraphicalLassoCV.__sklearn_clone__ sklearn.covariance.GraphicalLassoCV.__sklearn_tags__ sklearn.covariance.GraphicalLassoCV.__str__ sklearn.covariance.GraphicalLassoCV.__subclasshook__ sklearn.covariance.GraphicalLassoCV._build_request_for_signature sklearn.covariance.GraphicalLassoCV._get_default_requests sklearn.covariance.GraphicalLassoCV._get_doc_link sklearn.covariance.GraphicalLassoCV._get_metadata_request sklearn.covariance.GraphicalLassoCV._get_param_names sklearn.covariance.GraphicalLassoCV._repr_html_inner sklearn.covariance.GraphicalLassoCV._repr_mimebundle_ sklearn.covariance.GraphicalLassoCV._set_covariance sklearn.covariance.GraphicalLassoCV._validate_params sklearn.covariance.GraphicalLassoCV.error_norm sklearn.covariance.GraphicalLassoCV.fit sklearn.covariance.GraphicalLassoCV.get_metadata_routing sklearn.covariance.GraphicalLassoCV.get_params sklearn.covariance.GraphicalLassoCV.get_precision sklearn.covariance.GraphicalLassoCV.mahalanobis sklearn.covariance.GraphicalLassoCV.score sklearn.covariance.GraphicalLassoCV.set_params 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.covariance.LedoitWolf.rst.txt b/dev/_sources/modules/generated/sklearn.covariance.LedoitWolf.rst.txt
index e80156da4e08a..ac5aae7e917a3 100644
--- a/dev/_sources/modules/generated/sklearn.covariance.LedoitWolf.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.covariance.LedoitWolf.rst.txt
@@ -8,6 +8,6 @@ LedoitWolf
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.covariance.LedoitWolf sklearn.covariance.LedoitWolf.__delattr__ sklearn.covariance.LedoitWolf.__dir__ sklearn.covariance.LedoitWolf.__eq__ sklearn.covariance.LedoitWolf.__format__ sklearn.covariance.LedoitWolf.__ge__ sklearn.covariance.LedoitWolf.__getattribute__ sklearn.covariance.LedoitWolf.__getstate__ sklearn.covariance.LedoitWolf.__gt__ sklearn.covariance.LedoitWolf.__hash__ sklearn.covariance.LedoitWolf.__init__ sklearn.covariance.LedoitWolf.__init_subclass__ sklearn.covariance.LedoitWolf.__le__ sklearn.covariance.LedoitWolf.__lt__ sklearn.covariance.LedoitWolf.__ne__ sklearn.covariance.LedoitWolf.__new__ sklearn.covariance.LedoitWolf.__reduce__ sklearn.covariance.LedoitWolf.__reduce_ex__ sklearn.covariance.LedoitWolf.__repr__ sklearn.covariance.LedoitWolf.__setattr__ sklearn.covariance.LedoitWolf.__setstate__ sklearn.covariance.LedoitWolf.__sizeof__ sklearn.covariance.LedoitWolf.__sklearn_clone__ sklearn.covariance.LedoitWolf.__sklearn_tags__ sklearn.covariance.LedoitWolf.__str__ sklearn.covariance.LedoitWolf.__subclasshook__ sklearn.covariance.LedoitWolf._build_request_for_signature sklearn.covariance.LedoitWolf._check_feature_names sklearn.covariance.LedoitWolf._check_n_features sklearn.covariance.LedoitWolf._get_default_requests sklearn.covariance.LedoitWolf._get_doc_link sklearn.covariance.LedoitWolf._get_metadata_request sklearn.covariance.LedoitWolf._get_param_names sklearn.covariance.LedoitWolf._get_tags sklearn.covariance.LedoitWolf._more_tags sklearn.covariance.LedoitWolf._repr_html_inner sklearn.covariance.LedoitWolf._repr_mimebundle_ sklearn.covariance.LedoitWolf._set_covariance sklearn.covariance.LedoitWolf._validate_data sklearn.covariance.LedoitWolf._validate_params sklearn.covariance.LedoitWolf.error_norm sklearn.covariance.LedoitWolf.fit sklearn.covariance.LedoitWolf.get_metadata_routing sklearn.covariance.LedoitWolf.get_params sklearn.covariance.LedoitWolf.get_precision sklearn.covariance.LedoitWolf.mahalanobis sklearn.covariance.LedoitWolf.score sklearn.covariance.LedoitWolf.set_params 
+.. minigallery:: sklearn.covariance.LedoitWolf sklearn.covariance.LedoitWolf.__delattr__ sklearn.covariance.LedoitWolf.__dir__ sklearn.covariance.LedoitWolf.__eq__ sklearn.covariance.LedoitWolf.__format__ sklearn.covariance.LedoitWolf.__ge__ sklearn.covariance.LedoitWolf.__getattribute__ sklearn.covariance.LedoitWolf.__getstate__ sklearn.covariance.LedoitWolf.__gt__ sklearn.covariance.LedoitWolf.__hash__ sklearn.covariance.LedoitWolf.__init__ sklearn.covariance.LedoitWolf.__init_subclass__ sklearn.covariance.LedoitWolf.__le__ sklearn.covariance.LedoitWolf.__lt__ sklearn.covariance.LedoitWolf.__ne__ sklearn.covariance.LedoitWolf.__new__ sklearn.covariance.LedoitWolf.__reduce__ sklearn.covariance.LedoitWolf.__reduce_ex__ sklearn.covariance.LedoitWolf.__repr__ sklearn.covariance.LedoitWolf.__setattr__ sklearn.covariance.LedoitWolf.__setstate__ sklearn.covariance.LedoitWolf.__sizeof__ sklearn.covariance.LedoitWolf.__sklearn_clone__ sklearn.covariance.LedoitWolf.__sklearn_tags__ sklearn.covariance.LedoitWolf.__str__ sklearn.covariance.LedoitWolf.__subclasshook__ sklearn.covariance.LedoitWolf._build_request_for_signature sklearn.covariance.LedoitWolf._get_default_requests sklearn.covariance.LedoitWolf._get_doc_link sklearn.covariance.LedoitWolf._get_metadata_request sklearn.covariance.LedoitWolf._get_param_names sklearn.covariance.LedoitWolf._repr_html_inner sklearn.covariance.LedoitWolf._repr_mimebundle_ sklearn.covariance.LedoitWolf._set_covariance sklearn.covariance.LedoitWolf._validate_params sklearn.covariance.LedoitWolf.error_norm sklearn.covariance.LedoitWolf.fit sklearn.covariance.LedoitWolf.get_metadata_routing sklearn.covariance.LedoitWolf.get_params sklearn.covariance.LedoitWolf.get_precision sklearn.covariance.LedoitWolf.mahalanobis sklearn.covariance.LedoitWolf.score sklearn.covariance.LedoitWolf.set_params 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.covariance.MinCovDet.rst.txt b/dev/_sources/modules/generated/sklearn.covariance.MinCovDet.rst.txt
index 11b4f4892c4e2..e76457c932bc2 100644
--- a/dev/_sources/modules/generated/sklearn.covariance.MinCovDet.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.covariance.MinCovDet.rst.txt
@@ -8,6 +8,6 @@ MinCovDet
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.covariance.MinCovDet sklearn.covariance.MinCovDet.__delattr__ sklearn.covariance.MinCovDet.__dir__ sklearn.covariance.MinCovDet.__eq__ sklearn.covariance.MinCovDet.__format__ sklearn.covariance.MinCovDet.__ge__ sklearn.covariance.MinCovDet.__getattribute__ sklearn.covariance.MinCovDet.__getstate__ sklearn.covariance.MinCovDet.__gt__ sklearn.covariance.MinCovDet.__hash__ sklearn.covariance.MinCovDet.__init__ sklearn.covariance.MinCovDet.__init_subclass__ sklearn.covariance.MinCovDet.__le__ sklearn.covariance.MinCovDet.__lt__ sklearn.covariance.MinCovDet.__ne__ sklearn.covariance.MinCovDet.__new__ sklearn.covariance.MinCovDet.__reduce__ sklearn.covariance.MinCovDet.__reduce_ex__ sklearn.covariance.MinCovDet.__repr__ sklearn.covariance.MinCovDet.__setattr__ sklearn.covariance.MinCovDet.__setstate__ sklearn.covariance.MinCovDet.__sizeof__ sklearn.covariance.MinCovDet.__sklearn_clone__ sklearn.covariance.MinCovDet.__sklearn_tags__ sklearn.covariance.MinCovDet.__str__ sklearn.covariance.MinCovDet.__subclasshook__ sklearn.covariance.MinCovDet._build_request_for_signature sklearn.covariance.MinCovDet._check_feature_names sklearn.covariance.MinCovDet._check_n_features sklearn.covariance.MinCovDet._get_default_requests sklearn.covariance.MinCovDet._get_doc_link sklearn.covariance.MinCovDet._get_metadata_request sklearn.covariance.MinCovDet._get_param_names sklearn.covariance.MinCovDet._get_tags sklearn.covariance.MinCovDet._more_tags sklearn.covariance.MinCovDet._nonrobust_covariance sklearn.covariance.MinCovDet._repr_html_inner sklearn.covariance.MinCovDet._repr_mimebundle_ sklearn.covariance.MinCovDet._set_covariance sklearn.covariance.MinCovDet._validate_data sklearn.covariance.MinCovDet._validate_params sklearn.covariance.MinCovDet.correct_covariance sklearn.covariance.MinCovDet.error_norm sklearn.covariance.MinCovDet.fit sklearn.covariance.MinCovDet.get_metadata_routing sklearn.covariance.MinCovDet.get_params sklearn.covariance.MinCovDet.get_precision sklearn.covariance.MinCovDet.mahalanobis sklearn.covariance.MinCovDet.reweight_covariance sklearn.covariance.MinCovDet.score sklearn.covariance.MinCovDet.set_params 
+.. minigallery:: sklearn.covariance.MinCovDet sklearn.covariance.MinCovDet.__delattr__ sklearn.covariance.MinCovDet.__dir__ sklearn.covariance.MinCovDet.__eq__ sklearn.covariance.MinCovDet.__format__ sklearn.covariance.MinCovDet.__ge__ sklearn.covariance.MinCovDet.__getattribute__ sklearn.covariance.MinCovDet.__getstate__ sklearn.covariance.MinCovDet.__gt__ sklearn.covariance.MinCovDet.__hash__ sklearn.covariance.MinCovDet.__init__ sklearn.covariance.MinCovDet.__init_subclass__ sklearn.covariance.MinCovDet.__le__ sklearn.covariance.MinCovDet.__lt__ sklearn.covariance.MinCovDet.__ne__ sklearn.covariance.MinCovDet.__new__ sklearn.covariance.MinCovDet.__reduce__ sklearn.covariance.MinCovDet.__reduce_ex__ sklearn.covariance.MinCovDet.__repr__ sklearn.covariance.MinCovDet.__setattr__ sklearn.covariance.MinCovDet.__setstate__ sklearn.covariance.MinCovDet.__sizeof__ sklearn.covariance.MinCovDet.__sklearn_clone__ sklearn.covariance.MinCovDet.__sklearn_tags__ sklearn.covariance.MinCovDet.__str__ sklearn.covariance.MinCovDet.__subclasshook__ sklearn.covariance.MinCovDet._build_request_for_signature sklearn.covariance.MinCovDet._get_default_requests sklearn.covariance.MinCovDet._get_doc_link sklearn.covariance.MinCovDet._get_metadata_request sklearn.covariance.MinCovDet._get_param_names sklearn.covariance.MinCovDet._nonrobust_covariance sklearn.covariance.MinCovDet._repr_html_inner sklearn.covariance.MinCovDet._repr_mimebundle_ sklearn.covariance.MinCovDet._set_covariance sklearn.covariance.MinCovDet._validate_params sklearn.covariance.MinCovDet.correct_covariance sklearn.covariance.MinCovDet.error_norm sklearn.covariance.MinCovDet.fit sklearn.covariance.MinCovDet.get_metadata_routing sklearn.covariance.MinCovDet.get_params sklearn.covariance.MinCovDet.get_precision sklearn.covariance.MinCovDet.mahalanobis sklearn.covariance.MinCovDet.reweight_covariance sklearn.covariance.MinCovDet.score sklearn.covariance.MinCovDet.set_params 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.covariance.OAS.rst.txt b/dev/_sources/modules/generated/sklearn.covariance.OAS.rst.txt
index 207e20c6a43a8..96281573db783 100644
--- a/dev/_sources/modules/generated/sklearn.covariance.OAS.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.covariance.OAS.rst.txt
@@ -8,6 +8,6 @@ OAS
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.covariance.OAS sklearn.covariance.OAS.__delattr__ sklearn.covariance.OAS.__dir__ sklearn.covariance.OAS.__eq__ sklearn.covariance.OAS.__format__ sklearn.covariance.OAS.__ge__ sklearn.covariance.OAS.__getattribute__ sklearn.covariance.OAS.__getstate__ sklearn.covariance.OAS.__gt__ sklearn.covariance.OAS.__hash__ sklearn.covariance.OAS.__init__ sklearn.covariance.OAS.__init_subclass__ sklearn.covariance.OAS.__le__ sklearn.covariance.OAS.__lt__ sklearn.covariance.OAS.__ne__ sklearn.covariance.OAS.__new__ sklearn.covariance.OAS.__reduce__ sklearn.covariance.OAS.__reduce_ex__ sklearn.covariance.OAS.__repr__ sklearn.covariance.OAS.__setattr__ sklearn.covariance.OAS.__setstate__ sklearn.covariance.OAS.__sizeof__ sklearn.covariance.OAS.__sklearn_clone__ sklearn.covariance.OAS.__sklearn_tags__ sklearn.covariance.OAS.__str__ sklearn.covariance.OAS.__subclasshook__ sklearn.covariance.OAS._build_request_for_signature sklearn.covariance.OAS._check_feature_names sklearn.covariance.OAS._check_n_features sklearn.covariance.OAS._get_default_requests sklearn.covariance.OAS._get_doc_link sklearn.covariance.OAS._get_metadata_request sklearn.covariance.OAS._get_param_names sklearn.covariance.OAS._get_tags sklearn.covariance.OAS._more_tags sklearn.covariance.OAS._repr_html_inner sklearn.covariance.OAS._repr_mimebundle_ sklearn.covariance.OAS._set_covariance sklearn.covariance.OAS._validate_data sklearn.covariance.OAS._validate_params sklearn.covariance.OAS.error_norm sklearn.covariance.OAS.fit sklearn.covariance.OAS.get_metadata_routing sklearn.covariance.OAS.get_params sklearn.covariance.OAS.get_precision sklearn.covariance.OAS.mahalanobis sklearn.covariance.OAS.score sklearn.covariance.OAS.set_params 
+.. minigallery:: sklearn.covariance.OAS sklearn.covariance.OAS.__delattr__ sklearn.covariance.OAS.__dir__ sklearn.covariance.OAS.__eq__ sklearn.covariance.OAS.__format__ sklearn.covariance.OAS.__ge__ sklearn.covariance.OAS.__getattribute__ sklearn.covariance.OAS.__getstate__ sklearn.covariance.OAS.__gt__ sklearn.covariance.OAS.__hash__ sklearn.covariance.OAS.__init__ sklearn.covariance.OAS.__init_subclass__ sklearn.covariance.OAS.__le__ sklearn.covariance.OAS.__lt__ sklearn.covariance.OAS.__ne__ sklearn.covariance.OAS.__new__ sklearn.covariance.OAS.__reduce__ sklearn.covariance.OAS.__reduce_ex__ sklearn.covariance.OAS.__repr__ sklearn.covariance.OAS.__setattr__ sklearn.covariance.OAS.__setstate__ sklearn.covariance.OAS.__sizeof__ sklearn.covariance.OAS.__sklearn_clone__ sklearn.covariance.OAS.__sklearn_tags__ sklearn.covariance.OAS.__str__ sklearn.covariance.OAS.__subclasshook__ sklearn.covariance.OAS._build_request_for_signature sklearn.covariance.OAS._get_default_requests sklearn.covariance.OAS._get_doc_link sklearn.covariance.OAS._get_metadata_request sklearn.covariance.OAS._get_param_names sklearn.covariance.OAS._repr_html_inner sklearn.covariance.OAS._repr_mimebundle_ sklearn.covariance.OAS._set_covariance sklearn.covariance.OAS._validate_params sklearn.covariance.OAS.error_norm sklearn.covariance.OAS.fit sklearn.covariance.OAS.get_metadata_routing sklearn.covariance.OAS.get_params sklearn.covariance.OAS.get_precision sklearn.covariance.OAS.mahalanobis sklearn.covariance.OAS.score sklearn.covariance.OAS.set_params 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.covariance.ShrunkCovariance.rst.txt b/dev/_sources/modules/generated/sklearn.covariance.ShrunkCovariance.rst.txt
index 3d2c7f22ad561..ed538078c304e 100644
--- a/dev/_sources/modules/generated/sklearn.covariance.ShrunkCovariance.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.covariance.ShrunkCovariance.rst.txt
@@ -8,6 +8,6 @@ ShrunkCovariance
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.covariance.ShrunkCovariance sklearn.covariance.ShrunkCovariance.__delattr__ sklearn.covariance.ShrunkCovariance.__dir__ sklearn.covariance.ShrunkCovariance.__eq__ sklearn.covariance.ShrunkCovariance.__format__ sklearn.covariance.ShrunkCovariance.__ge__ sklearn.covariance.ShrunkCovariance.__getattribute__ sklearn.covariance.ShrunkCovariance.__getstate__ sklearn.covariance.ShrunkCovariance.__gt__ sklearn.covariance.ShrunkCovariance.__hash__ sklearn.covariance.ShrunkCovariance.__init__ sklearn.covariance.ShrunkCovariance.__init_subclass__ sklearn.covariance.ShrunkCovariance.__le__ sklearn.covariance.ShrunkCovariance.__lt__ sklearn.covariance.ShrunkCovariance.__ne__ sklearn.covariance.ShrunkCovariance.__new__ sklearn.covariance.ShrunkCovariance.__reduce__ sklearn.covariance.ShrunkCovariance.__reduce_ex__ sklearn.covariance.ShrunkCovariance.__repr__ sklearn.covariance.ShrunkCovariance.__setattr__ sklearn.covariance.ShrunkCovariance.__setstate__ sklearn.covariance.ShrunkCovariance.__sizeof__ sklearn.covariance.ShrunkCovariance.__sklearn_clone__ sklearn.covariance.ShrunkCovariance.__sklearn_tags__ sklearn.covariance.ShrunkCovariance.__str__ sklearn.covariance.ShrunkCovariance.__subclasshook__ sklearn.covariance.ShrunkCovariance._build_request_for_signature sklearn.covariance.ShrunkCovariance._check_feature_names sklearn.covariance.ShrunkCovariance._check_n_features sklearn.covariance.ShrunkCovariance._get_default_requests sklearn.covariance.ShrunkCovariance._get_doc_link sklearn.covariance.ShrunkCovariance._get_metadata_request sklearn.covariance.ShrunkCovariance._get_param_names sklearn.covariance.ShrunkCovariance._get_tags sklearn.covariance.ShrunkCovariance._more_tags sklearn.covariance.ShrunkCovariance._repr_html_inner sklearn.covariance.ShrunkCovariance._repr_mimebundle_ sklearn.covariance.ShrunkCovariance._set_covariance sklearn.covariance.ShrunkCovariance._validate_data sklearn.covariance.ShrunkCovariance._validate_params sklearn.covariance.ShrunkCovariance.error_norm sklearn.covariance.ShrunkCovariance.fit sklearn.covariance.ShrunkCovariance.get_metadata_routing sklearn.covariance.ShrunkCovariance.get_params sklearn.covariance.ShrunkCovariance.get_precision sklearn.covariance.ShrunkCovariance.mahalanobis sklearn.covariance.ShrunkCovariance.score sklearn.covariance.ShrunkCovariance.set_params 
+.. minigallery:: sklearn.covariance.ShrunkCovariance sklearn.covariance.ShrunkCovariance.__delattr__ sklearn.covariance.ShrunkCovariance.__dir__ sklearn.covariance.ShrunkCovariance.__eq__ sklearn.covariance.ShrunkCovariance.__format__ sklearn.covariance.ShrunkCovariance.__ge__ sklearn.covariance.ShrunkCovariance.__getattribute__ sklearn.covariance.ShrunkCovariance.__getstate__ sklearn.covariance.ShrunkCovariance.__gt__ sklearn.covariance.ShrunkCovariance.__hash__ sklearn.covariance.ShrunkCovariance.__init__ sklearn.covariance.ShrunkCovariance.__init_subclass__ sklearn.covariance.ShrunkCovariance.__le__ sklearn.covariance.ShrunkCovariance.__lt__ sklearn.covariance.ShrunkCovariance.__ne__ sklearn.covariance.ShrunkCovariance.__new__ sklearn.covariance.ShrunkCovariance.__reduce__ sklearn.covariance.ShrunkCovariance.__reduce_ex__ sklearn.covariance.ShrunkCovariance.__repr__ sklearn.covariance.ShrunkCovariance.__setattr__ sklearn.covariance.ShrunkCovariance.__setstate__ sklearn.covariance.ShrunkCovariance.__sizeof__ sklearn.covariance.ShrunkCovariance.__sklearn_clone__ sklearn.covariance.ShrunkCovariance.__sklearn_tags__ sklearn.covariance.ShrunkCovariance.__str__ sklearn.covariance.ShrunkCovariance.__subclasshook__ sklearn.covariance.ShrunkCovariance._build_request_for_signature sklearn.covariance.ShrunkCovariance._get_default_requests sklearn.covariance.ShrunkCovariance._get_doc_link sklearn.covariance.ShrunkCovariance._get_metadata_request sklearn.covariance.ShrunkCovariance._get_param_names sklearn.covariance.ShrunkCovariance._repr_html_inner sklearn.covariance.ShrunkCovariance._repr_mimebundle_ sklearn.covariance.ShrunkCovariance._set_covariance sklearn.covariance.ShrunkCovariance._validate_params sklearn.covariance.ShrunkCovariance.error_norm sklearn.covariance.ShrunkCovariance.fit sklearn.covariance.ShrunkCovariance.get_metadata_routing sklearn.covariance.ShrunkCovariance.get_params sklearn.covariance.ShrunkCovariance.get_precision sklearn.covariance.ShrunkCovariance.mahalanobis sklearn.covariance.ShrunkCovariance.score sklearn.covariance.ShrunkCovariance.set_params 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.cross_decomposition.CCA.rst.txt b/dev/_sources/modules/generated/sklearn.cross_decomposition.CCA.rst.txt
index 27847976f4a73..8920742e69512 100644
--- a/dev/_sources/modules/generated/sklearn.cross_decomposition.CCA.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.cross_decomposition.CCA.rst.txt
@@ -8,6 +8,6 @@ CCA
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.cross_decomposition.CCA sklearn.cross_decomposition.CCA.__delattr__ sklearn.cross_decomposition.CCA.__dir__ sklearn.cross_decomposition.CCA.__eq__ sklearn.cross_decomposition.CCA.__format__ sklearn.cross_decomposition.CCA.__ge__ sklearn.cross_decomposition.CCA.__getattribute__ sklearn.cross_decomposition.CCA.__getstate__ sklearn.cross_decomposition.CCA.__gt__ sklearn.cross_decomposition.CCA.__hash__ sklearn.cross_decomposition.CCA.__init__ sklearn.cross_decomposition.CCA.__init_subclass__ sklearn.cross_decomposition.CCA.__le__ sklearn.cross_decomposition.CCA.__lt__ sklearn.cross_decomposition.CCA.__ne__ sklearn.cross_decomposition.CCA.__new__ sklearn.cross_decomposition.CCA.__reduce__ sklearn.cross_decomposition.CCA.__reduce_ex__ sklearn.cross_decomposition.CCA.__repr__ sklearn.cross_decomposition.CCA.__setattr__ sklearn.cross_decomposition.CCA.__setstate__ sklearn.cross_decomposition.CCA.__sizeof__ sklearn.cross_decomposition.CCA.__sklearn_clone__ sklearn.cross_decomposition.CCA.__sklearn_tags__ sklearn.cross_decomposition.CCA.__str__ sklearn.cross_decomposition.CCA.__subclasshook__ sklearn.cross_decomposition.CCA._build_request_for_signature sklearn.cross_decomposition.CCA._check_feature_names sklearn.cross_decomposition.CCA._check_n_features sklearn.cross_decomposition.CCA._get_default_requests sklearn.cross_decomposition.CCA._get_doc_link sklearn.cross_decomposition.CCA._get_metadata_request sklearn.cross_decomposition.CCA._get_param_names sklearn.cross_decomposition.CCA._get_tags sklearn.cross_decomposition.CCA._more_tags sklearn.cross_decomposition.CCA._repr_html_inner sklearn.cross_decomposition.CCA._repr_mimebundle_ sklearn.cross_decomposition.CCA._validate_data sklearn.cross_decomposition.CCA._validate_params sklearn.cross_decomposition.CCA.fit sklearn.cross_decomposition.CCA.fit_transform sklearn.cross_decomposition.CCA.get_feature_names_out sklearn.cross_decomposition.CCA.get_metadata_routing sklearn.cross_decomposition.CCA.get_params sklearn.cross_decomposition.CCA.inverse_transform sklearn.cross_decomposition.CCA.predict sklearn.cross_decomposition.CCA.score sklearn.cross_decomposition.CCA.set_output sklearn.cross_decomposition.CCA.set_params sklearn.cross_decomposition.CCA.set_predict_request sklearn.cross_decomposition.CCA.set_score_request sklearn.cross_decomposition.CCA.set_transform_request sklearn.cross_decomposition.CCA.transform 
+.. minigallery:: sklearn.cross_decomposition.CCA sklearn.cross_decomposition.CCA.__delattr__ sklearn.cross_decomposition.CCA.__dir__ sklearn.cross_decomposition.CCA.__eq__ sklearn.cross_decomposition.CCA.__format__ sklearn.cross_decomposition.CCA.__ge__ sklearn.cross_decomposition.CCA.__getattribute__ sklearn.cross_decomposition.CCA.__getstate__ sklearn.cross_decomposition.CCA.__gt__ sklearn.cross_decomposition.CCA.__hash__ sklearn.cross_decomposition.CCA.__init__ sklearn.cross_decomposition.CCA.__init_subclass__ sklearn.cross_decomposition.CCA.__le__ sklearn.cross_decomposition.CCA.__lt__ sklearn.cross_decomposition.CCA.__ne__ sklearn.cross_decomposition.CCA.__new__ sklearn.cross_decomposition.CCA.__reduce__ sklearn.cross_decomposition.CCA.__reduce_ex__ sklearn.cross_decomposition.CCA.__repr__ sklearn.cross_decomposition.CCA.__setattr__ sklearn.cross_decomposition.CCA.__setstate__ sklearn.cross_decomposition.CCA.__sizeof__ sklearn.cross_decomposition.CCA.__sklearn_clone__ sklearn.cross_decomposition.CCA.__sklearn_tags__ sklearn.cross_decomposition.CCA.__str__ sklearn.cross_decomposition.CCA.__subclasshook__ sklearn.cross_decomposition.CCA._build_request_for_signature sklearn.cross_decomposition.CCA._get_default_requests sklearn.cross_decomposition.CCA._get_doc_link sklearn.cross_decomposition.CCA._get_metadata_request sklearn.cross_decomposition.CCA._get_param_names sklearn.cross_decomposition.CCA._repr_html_inner sklearn.cross_decomposition.CCA._repr_mimebundle_ sklearn.cross_decomposition.CCA._validate_params sklearn.cross_decomposition.CCA.fit sklearn.cross_decomposition.CCA.fit_transform sklearn.cross_decomposition.CCA.get_feature_names_out sklearn.cross_decomposition.CCA.get_metadata_routing sklearn.cross_decomposition.CCA.get_params sklearn.cross_decomposition.CCA.inverse_transform sklearn.cross_decomposition.CCA.predict sklearn.cross_decomposition.CCA.score sklearn.cross_decomposition.CCA.set_output sklearn.cross_decomposition.CCA.set_params sklearn.cross_decomposition.CCA.set_predict_request sklearn.cross_decomposition.CCA.set_score_request sklearn.cross_decomposition.CCA.set_transform_request sklearn.cross_decomposition.CCA.transform 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.cross_decomposition.PLSCanonical.rst.txt b/dev/_sources/modules/generated/sklearn.cross_decomposition.PLSCanonical.rst.txt
index 3b9f639d458c0..8eba4e9e8adde 100644
--- a/dev/_sources/modules/generated/sklearn.cross_decomposition.PLSCanonical.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.cross_decomposition.PLSCanonical.rst.txt
@@ -8,6 +8,6 @@ PLSCanonical
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.cross_decomposition.PLSCanonical sklearn.cross_decomposition.PLSCanonical.__delattr__ sklearn.cross_decomposition.PLSCanonical.__dir__ sklearn.cross_decomposition.PLSCanonical.__eq__ sklearn.cross_decomposition.PLSCanonical.__format__ sklearn.cross_decomposition.PLSCanonical.__ge__ sklearn.cross_decomposition.PLSCanonical.__getattribute__ sklearn.cross_decomposition.PLSCanonical.__getstate__ sklearn.cross_decomposition.PLSCanonical.__gt__ sklearn.cross_decomposition.PLSCanonical.__hash__ sklearn.cross_decomposition.PLSCanonical.__init__ sklearn.cross_decomposition.PLSCanonical.__init_subclass__ sklearn.cross_decomposition.PLSCanonical.__le__ sklearn.cross_decomposition.PLSCanonical.__lt__ sklearn.cross_decomposition.PLSCanonical.__ne__ sklearn.cross_decomposition.PLSCanonical.__new__ sklearn.cross_decomposition.PLSCanonical.__reduce__ sklearn.cross_decomposition.PLSCanonical.__reduce_ex__ sklearn.cross_decomposition.PLSCanonical.__repr__ sklearn.cross_decomposition.PLSCanonical.__setattr__ sklearn.cross_decomposition.PLSCanonical.__setstate__ sklearn.cross_decomposition.PLSCanonical.__sizeof__ sklearn.cross_decomposition.PLSCanonical.__sklearn_clone__ sklearn.cross_decomposition.PLSCanonical.__sklearn_tags__ sklearn.cross_decomposition.PLSCanonical.__str__ sklearn.cross_decomposition.PLSCanonical.__subclasshook__ sklearn.cross_decomposition.PLSCanonical._build_request_for_signature sklearn.cross_decomposition.PLSCanonical._check_feature_names sklearn.cross_decomposition.PLSCanonical._check_n_features sklearn.cross_decomposition.PLSCanonical._get_default_requests sklearn.cross_decomposition.PLSCanonical._get_doc_link sklearn.cross_decomposition.PLSCanonical._get_metadata_request sklearn.cross_decomposition.PLSCanonical._get_param_names sklearn.cross_decomposition.PLSCanonical._get_tags sklearn.cross_decomposition.PLSCanonical._more_tags sklearn.cross_decomposition.PLSCanonical._repr_html_inner sklearn.cross_decomposition.PLSCanonical._repr_mimebundle_ sklearn.cross_decomposition.PLSCanonical._validate_data sklearn.cross_decomposition.PLSCanonical._validate_params sklearn.cross_decomposition.PLSCanonical.fit sklearn.cross_decomposition.PLSCanonical.fit_transform sklearn.cross_decomposition.PLSCanonical.get_feature_names_out sklearn.cross_decomposition.PLSCanonical.get_metadata_routing sklearn.cross_decomposition.PLSCanonical.get_params sklearn.cross_decomposition.PLSCanonical.inverse_transform sklearn.cross_decomposition.PLSCanonical.predict sklearn.cross_decomposition.PLSCanonical.score sklearn.cross_decomposition.PLSCanonical.set_output sklearn.cross_decomposition.PLSCanonical.set_params sklearn.cross_decomposition.PLSCanonical.set_predict_request sklearn.cross_decomposition.PLSCanonical.set_score_request sklearn.cross_decomposition.PLSCanonical.set_transform_request sklearn.cross_decomposition.PLSCanonical.transform 
+.. minigallery:: sklearn.cross_decomposition.PLSCanonical sklearn.cross_decomposition.PLSCanonical.__delattr__ sklearn.cross_decomposition.PLSCanonical.__dir__ sklearn.cross_decomposition.PLSCanonical.__eq__ sklearn.cross_decomposition.PLSCanonical.__format__ sklearn.cross_decomposition.PLSCanonical.__ge__ sklearn.cross_decomposition.PLSCanonical.__getattribute__ sklearn.cross_decomposition.PLSCanonical.__getstate__ sklearn.cross_decomposition.PLSCanonical.__gt__ sklearn.cross_decomposition.PLSCanonical.__hash__ sklearn.cross_decomposition.PLSCanonical.__init__ sklearn.cross_decomposition.PLSCanonical.__init_subclass__ sklearn.cross_decomposition.PLSCanonical.__le__ sklearn.cross_decomposition.PLSCanonical.__lt__ sklearn.cross_decomposition.PLSCanonical.__ne__ sklearn.cross_decomposition.PLSCanonical.__new__ sklearn.cross_decomposition.PLSCanonical.__reduce__ sklearn.cross_decomposition.PLSCanonical.__reduce_ex__ sklearn.cross_decomposition.PLSCanonical.__repr__ sklearn.cross_decomposition.PLSCanonical.__setattr__ sklearn.cross_decomposition.PLSCanonical.__setstate__ sklearn.cross_decomposition.PLSCanonical.__sizeof__ sklearn.cross_decomposition.PLSCanonical.__sklearn_clone__ sklearn.cross_decomposition.PLSCanonical.__sklearn_tags__ sklearn.cross_decomposition.PLSCanonical.__str__ sklearn.cross_decomposition.PLSCanonical.__subclasshook__ sklearn.cross_decomposition.PLSCanonical._build_request_for_signature sklearn.cross_decomposition.PLSCanonical._get_default_requests sklearn.cross_decomposition.PLSCanonical._get_doc_link sklearn.cross_decomposition.PLSCanonical._get_metadata_request sklearn.cross_decomposition.PLSCanonical._get_param_names sklearn.cross_decomposition.PLSCanonical._repr_html_inner sklearn.cross_decomposition.PLSCanonical._repr_mimebundle_ sklearn.cross_decomposition.PLSCanonical._validate_params sklearn.cross_decomposition.PLSCanonical.fit sklearn.cross_decomposition.PLSCanonical.fit_transform sklearn.cross_decomposition.PLSCanonical.get_feature_names_out sklearn.cross_decomposition.PLSCanonical.get_metadata_routing sklearn.cross_decomposition.PLSCanonical.get_params sklearn.cross_decomposition.PLSCanonical.inverse_transform sklearn.cross_decomposition.PLSCanonical.predict sklearn.cross_decomposition.PLSCanonical.score sklearn.cross_decomposition.PLSCanonical.set_output sklearn.cross_decomposition.PLSCanonical.set_params sklearn.cross_decomposition.PLSCanonical.set_predict_request sklearn.cross_decomposition.PLSCanonical.set_score_request sklearn.cross_decomposition.PLSCanonical.set_transform_request sklearn.cross_decomposition.PLSCanonical.transform 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.cross_decomposition.PLSRegression.rst.txt b/dev/_sources/modules/generated/sklearn.cross_decomposition.PLSRegression.rst.txt
index 0b6fcad3bc809..f09472d481065 100644
--- a/dev/_sources/modules/generated/sklearn.cross_decomposition.PLSRegression.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.cross_decomposition.PLSRegression.rst.txt
@@ -8,6 +8,6 @@ PLSRegression
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.cross_decomposition.PLSRegression sklearn.cross_decomposition.PLSRegression.__delattr__ sklearn.cross_decomposition.PLSRegression.__dir__ sklearn.cross_decomposition.PLSRegression.__eq__ sklearn.cross_decomposition.PLSRegression.__format__ sklearn.cross_decomposition.PLSRegression.__ge__ sklearn.cross_decomposition.PLSRegression.__getattribute__ sklearn.cross_decomposition.PLSRegression.__getstate__ sklearn.cross_decomposition.PLSRegression.__gt__ sklearn.cross_decomposition.PLSRegression.__hash__ sklearn.cross_decomposition.PLSRegression.__init__ sklearn.cross_decomposition.PLSRegression.__init_subclass__ sklearn.cross_decomposition.PLSRegression.__le__ sklearn.cross_decomposition.PLSRegression.__lt__ sklearn.cross_decomposition.PLSRegression.__ne__ sklearn.cross_decomposition.PLSRegression.__new__ sklearn.cross_decomposition.PLSRegression.__reduce__ sklearn.cross_decomposition.PLSRegression.__reduce_ex__ sklearn.cross_decomposition.PLSRegression.__repr__ sklearn.cross_decomposition.PLSRegression.__setattr__ sklearn.cross_decomposition.PLSRegression.__setstate__ sklearn.cross_decomposition.PLSRegression.__sizeof__ sklearn.cross_decomposition.PLSRegression.__sklearn_clone__ sklearn.cross_decomposition.PLSRegression.__sklearn_tags__ sklearn.cross_decomposition.PLSRegression.__str__ sklearn.cross_decomposition.PLSRegression.__subclasshook__ sklearn.cross_decomposition.PLSRegression._build_request_for_signature sklearn.cross_decomposition.PLSRegression._check_feature_names sklearn.cross_decomposition.PLSRegression._check_n_features sklearn.cross_decomposition.PLSRegression._get_default_requests sklearn.cross_decomposition.PLSRegression._get_doc_link sklearn.cross_decomposition.PLSRegression._get_metadata_request sklearn.cross_decomposition.PLSRegression._get_param_names sklearn.cross_decomposition.PLSRegression._get_tags sklearn.cross_decomposition.PLSRegression._more_tags sklearn.cross_decomposition.PLSRegression._repr_html_inner sklearn.cross_decomposition.PLSRegression._repr_mimebundle_ sklearn.cross_decomposition.PLSRegression._validate_data sklearn.cross_decomposition.PLSRegression._validate_params sklearn.cross_decomposition.PLSRegression.fit sklearn.cross_decomposition.PLSRegression.fit_transform sklearn.cross_decomposition.PLSRegression.get_feature_names_out sklearn.cross_decomposition.PLSRegression.get_metadata_routing sklearn.cross_decomposition.PLSRegression.get_params sklearn.cross_decomposition.PLSRegression.inverse_transform sklearn.cross_decomposition.PLSRegression.predict sklearn.cross_decomposition.PLSRegression.score sklearn.cross_decomposition.PLSRegression.set_output sklearn.cross_decomposition.PLSRegression.set_params sklearn.cross_decomposition.PLSRegression.set_predict_request sklearn.cross_decomposition.PLSRegression.set_score_request sklearn.cross_decomposition.PLSRegression.set_transform_request sklearn.cross_decomposition.PLSRegression.transform 
+.. minigallery:: sklearn.cross_decomposition.PLSRegression sklearn.cross_decomposition.PLSRegression.__delattr__ sklearn.cross_decomposition.PLSRegression.__dir__ sklearn.cross_decomposition.PLSRegression.__eq__ sklearn.cross_decomposition.PLSRegression.__format__ sklearn.cross_decomposition.PLSRegression.__ge__ sklearn.cross_decomposition.PLSRegression.__getattribute__ sklearn.cross_decomposition.PLSRegression.__getstate__ sklearn.cross_decomposition.PLSRegression.__gt__ sklearn.cross_decomposition.PLSRegression.__hash__ sklearn.cross_decomposition.PLSRegression.__init__ sklearn.cross_decomposition.PLSRegression.__init_subclass__ sklearn.cross_decomposition.PLSRegression.__le__ sklearn.cross_decomposition.PLSRegression.__lt__ sklearn.cross_decomposition.PLSRegression.__ne__ sklearn.cross_decomposition.PLSRegression.__new__ sklearn.cross_decomposition.PLSRegression.__reduce__ sklearn.cross_decomposition.PLSRegression.__reduce_ex__ sklearn.cross_decomposition.PLSRegression.__repr__ sklearn.cross_decomposition.PLSRegression.__setattr__ sklearn.cross_decomposition.PLSRegression.__setstate__ sklearn.cross_decomposition.PLSRegression.__sizeof__ sklearn.cross_decomposition.PLSRegression.__sklearn_clone__ sklearn.cross_decomposition.PLSRegression.__sklearn_tags__ sklearn.cross_decomposition.PLSRegression.__str__ sklearn.cross_decomposition.PLSRegression.__subclasshook__ sklearn.cross_decomposition.PLSRegression._build_request_for_signature sklearn.cross_decomposition.PLSRegression._get_default_requests sklearn.cross_decomposition.PLSRegression._get_doc_link sklearn.cross_decomposition.PLSRegression._get_metadata_request sklearn.cross_decomposition.PLSRegression._get_param_names sklearn.cross_decomposition.PLSRegression._repr_html_inner sklearn.cross_decomposition.PLSRegression._repr_mimebundle_ sklearn.cross_decomposition.PLSRegression._validate_params sklearn.cross_decomposition.PLSRegression.fit sklearn.cross_decomposition.PLSRegression.fit_transform sklearn.cross_decomposition.PLSRegression.get_feature_names_out sklearn.cross_decomposition.PLSRegression.get_metadata_routing sklearn.cross_decomposition.PLSRegression.get_params sklearn.cross_decomposition.PLSRegression.inverse_transform sklearn.cross_decomposition.PLSRegression.predict sklearn.cross_decomposition.PLSRegression.score sklearn.cross_decomposition.PLSRegression.set_output sklearn.cross_decomposition.PLSRegression.set_params sklearn.cross_decomposition.PLSRegression.set_predict_request sklearn.cross_decomposition.PLSRegression.set_score_request sklearn.cross_decomposition.PLSRegression.set_transform_request sklearn.cross_decomposition.PLSRegression.transform 
    :add-heading: Gallery examples
    :heading-level: -
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diff --git a/dev/_sources/modules/generated/sklearn.cross_decomposition.PLSSVD.rst.txt b/dev/_sources/modules/generated/sklearn.cross_decomposition.PLSSVD.rst.txt
index 925796fd0eb80..b5e2b1065b114 100644
--- a/dev/_sources/modules/generated/sklearn.cross_decomposition.PLSSVD.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.cross_decomposition.PLSSVD.rst.txt
@@ -8,6 +8,6 @@ PLSSVD
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.cross_decomposition.PLSSVD sklearn.cross_decomposition.PLSSVD.__delattr__ sklearn.cross_decomposition.PLSSVD.__dir__ sklearn.cross_decomposition.PLSSVD.__eq__ sklearn.cross_decomposition.PLSSVD.__format__ sklearn.cross_decomposition.PLSSVD.__ge__ sklearn.cross_decomposition.PLSSVD.__getattribute__ sklearn.cross_decomposition.PLSSVD.__getstate__ sklearn.cross_decomposition.PLSSVD.__gt__ sklearn.cross_decomposition.PLSSVD.__hash__ sklearn.cross_decomposition.PLSSVD.__init__ sklearn.cross_decomposition.PLSSVD.__init_subclass__ sklearn.cross_decomposition.PLSSVD.__le__ sklearn.cross_decomposition.PLSSVD.__lt__ sklearn.cross_decomposition.PLSSVD.__ne__ sklearn.cross_decomposition.PLSSVD.__new__ sklearn.cross_decomposition.PLSSVD.__reduce__ sklearn.cross_decomposition.PLSSVD.__reduce_ex__ sklearn.cross_decomposition.PLSSVD.__repr__ sklearn.cross_decomposition.PLSSVD.__setattr__ sklearn.cross_decomposition.PLSSVD.__setstate__ sklearn.cross_decomposition.PLSSVD.__sizeof__ sklearn.cross_decomposition.PLSSVD.__sklearn_clone__ sklearn.cross_decomposition.PLSSVD.__sklearn_tags__ sklearn.cross_decomposition.PLSSVD.__str__ sklearn.cross_decomposition.PLSSVD.__subclasshook__ sklearn.cross_decomposition.PLSSVD._build_request_for_signature sklearn.cross_decomposition.PLSSVD._check_feature_names sklearn.cross_decomposition.PLSSVD._check_n_features sklearn.cross_decomposition.PLSSVD._get_default_requests sklearn.cross_decomposition.PLSSVD._get_doc_link sklearn.cross_decomposition.PLSSVD._get_metadata_request sklearn.cross_decomposition.PLSSVD._get_param_names sklearn.cross_decomposition.PLSSVD._get_tags sklearn.cross_decomposition.PLSSVD._more_tags sklearn.cross_decomposition.PLSSVD._repr_html_inner sklearn.cross_decomposition.PLSSVD._repr_mimebundle_ sklearn.cross_decomposition.PLSSVD._validate_data sklearn.cross_decomposition.PLSSVD._validate_params sklearn.cross_decomposition.PLSSVD.fit sklearn.cross_decomposition.PLSSVD.fit_transform sklearn.cross_decomposition.PLSSVD.get_feature_names_out sklearn.cross_decomposition.PLSSVD.get_metadata_routing sklearn.cross_decomposition.PLSSVD.get_params sklearn.cross_decomposition.PLSSVD.set_output sklearn.cross_decomposition.PLSSVD.set_params sklearn.cross_decomposition.PLSSVD.transform 
+.. minigallery:: sklearn.cross_decomposition.PLSSVD sklearn.cross_decomposition.PLSSVD.__delattr__ sklearn.cross_decomposition.PLSSVD.__dir__ sklearn.cross_decomposition.PLSSVD.__eq__ sklearn.cross_decomposition.PLSSVD.__format__ sklearn.cross_decomposition.PLSSVD.__ge__ sklearn.cross_decomposition.PLSSVD.__getattribute__ sklearn.cross_decomposition.PLSSVD.__getstate__ sklearn.cross_decomposition.PLSSVD.__gt__ sklearn.cross_decomposition.PLSSVD.__hash__ sklearn.cross_decomposition.PLSSVD.__init__ sklearn.cross_decomposition.PLSSVD.__init_subclass__ sklearn.cross_decomposition.PLSSVD.__le__ sklearn.cross_decomposition.PLSSVD.__lt__ sklearn.cross_decomposition.PLSSVD.__ne__ sklearn.cross_decomposition.PLSSVD.__new__ sklearn.cross_decomposition.PLSSVD.__reduce__ sklearn.cross_decomposition.PLSSVD.__reduce_ex__ sklearn.cross_decomposition.PLSSVD.__repr__ sklearn.cross_decomposition.PLSSVD.__setattr__ sklearn.cross_decomposition.PLSSVD.__setstate__ sklearn.cross_decomposition.PLSSVD.__sizeof__ sklearn.cross_decomposition.PLSSVD.__sklearn_clone__ sklearn.cross_decomposition.PLSSVD.__sklearn_tags__ sklearn.cross_decomposition.PLSSVD.__str__ sklearn.cross_decomposition.PLSSVD.__subclasshook__ sklearn.cross_decomposition.PLSSVD._build_request_for_signature sklearn.cross_decomposition.PLSSVD._get_default_requests sklearn.cross_decomposition.PLSSVD._get_doc_link sklearn.cross_decomposition.PLSSVD._get_metadata_request sklearn.cross_decomposition.PLSSVD._get_param_names sklearn.cross_decomposition.PLSSVD._repr_html_inner sklearn.cross_decomposition.PLSSVD._repr_mimebundle_ sklearn.cross_decomposition.PLSSVD._validate_params sklearn.cross_decomposition.PLSSVD.fit sklearn.cross_decomposition.PLSSVD.fit_transform sklearn.cross_decomposition.PLSSVD.get_feature_names_out sklearn.cross_decomposition.PLSSVD.get_metadata_routing sklearn.cross_decomposition.PLSSVD.get_params sklearn.cross_decomposition.PLSSVD.set_output sklearn.cross_decomposition.PLSSVD.set_params sklearn.cross_decomposition.PLSSVD.transform 
    :add-heading: Gallery examples
    :heading-level: -
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diff --git a/dev/_sources/modules/generated/sklearn.decomposition.DictionaryLearning.rst.txt b/dev/_sources/modules/generated/sklearn.decomposition.DictionaryLearning.rst.txt
index 51c7ff8b9a6e7..bc1175ea41443 100644
--- a/dev/_sources/modules/generated/sklearn.decomposition.DictionaryLearning.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.decomposition.DictionaryLearning.rst.txt
@@ -8,6 +8,6 @@ DictionaryLearning
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.decomposition.DictionaryLearning sklearn.decomposition.DictionaryLearning.__delattr__ sklearn.decomposition.DictionaryLearning.__dir__ sklearn.decomposition.DictionaryLearning.__eq__ sklearn.decomposition.DictionaryLearning.__format__ sklearn.decomposition.DictionaryLearning.__ge__ sklearn.decomposition.DictionaryLearning.__getattribute__ sklearn.decomposition.DictionaryLearning.__getstate__ sklearn.decomposition.DictionaryLearning.__gt__ sklearn.decomposition.DictionaryLearning.__hash__ sklearn.decomposition.DictionaryLearning.__init__ sklearn.decomposition.DictionaryLearning.__init_subclass__ sklearn.decomposition.DictionaryLearning.__le__ sklearn.decomposition.DictionaryLearning.__lt__ sklearn.decomposition.DictionaryLearning.__ne__ sklearn.decomposition.DictionaryLearning.__new__ sklearn.decomposition.DictionaryLearning.__reduce__ sklearn.decomposition.DictionaryLearning.__reduce_ex__ sklearn.decomposition.DictionaryLearning.__repr__ sklearn.decomposition.DictionaryLearning.__setattr__ sklearn.decomposition.DictionaryLearning.__setstate__ sklearn.decomposition.DictionaryLearning.__sizeof__ sklearn.decomposition.DictionaryLearning.__sklearn_clone__ sklearn.decomposition.DictionaryLearning.__sklearn_tags__ sklearn.decomposition.DictionaryLearning.__str__ sklearn.decomposition.DictionaryLearning.__subclasshook__ sklearn.decomposition.DictionaryLearning._build_request_for_signature sklearn.decomposition.DictionaryLearning._check_feature_names sklearn.decomposition.DictionaryLearning._check_n_features sklearn.decomposition.DictionaryLearning._get_default_requests sklearn.decomposition.DictionaryLearning._get_doc_link sklearn.decomposition.DictionaryLearning._get_metadata_request sklearn.decomposition.DictionaryLearning._get_param_names sklearn.decomposition.DictionaryLearning._get_tags sklearn.decomposition.DictionaryLearning._inverse_transform sklearn.decomposition.DictionaryLearning._more_tags sklearn.decomposition.DictionaryLearning._repr_html_inner sklearn.decomposition.DictionaryLearning._repr_mimebundle_ sklearn.decomposition.DictionaryLearning._transform sklearn.decomposition.DictionaryLearning._validate_data sklearn.decomposition.DictionaryLearning._validate_params sklearn.decomposition.DictionaryLearning.fit sklearn.decomposition.DictionaryLearning.fit_transform sklearn.decomposition.DictionaryLearning.get_feature_names_out sklearn.decomposition.DictionaryLearning.get_metadata_routing sklearn.decomposition.DictionaryLearning.get_params sklearn.decomposition.DictionaryLearning.inverse_transform sklearn.decomposition.DictionaryLearning.set_output sklearn.decomposition.DictionaryLearning.set_params sklearn.decomposition.DictionaryLearning.transform 
+.. minigallery:: sklearn.decomposition.DictionaryLearning sklearn.decomposition.DictionaryLearning.__delattr__ sklearn.decomposition.DictionaryLearning.__dir__ sklearn.decomposition.DictionaryLearning.__eq__ sklearn.decomposition.DictionaryLearning.__format__ sklearn.decomposition.DictionaryLearning.__ge__ sklearn.decomposition.DictionaryLearning.__getattribute__ sklearn.decomposition.DictionaryLearning.__getstate__ sklearn.decomposition.DictionaryLearning.__gt__ sklearn.decomposition.DictionaryLearning.__hash__ sklearn.decomposition.DictionaryLearning.__init__ sklearn.decomposition.DictionaryLearning.__init_subclass__ sklearn.decomposition.DictionaryLearning.__le__ sklearn.decomposition.DictionaryLearning.__lt__ sklearn.decomposition.DictionaryLearning.__ne__ sklearn.decomposition.DictionaryLearning.__new__ sklearn.decomposition.DictionaryLearning.__reduce__ sklearn.decomposition.DictionaryLearning.__reduce_ex__ sklearn.decomposition.DictionaryLearning.__repr__ sklearn.decomposition.DictionaryLearning.__setattr__ sklearn.decomposition.DictionaryLearning.__setstate__ sklearn.decomposition.DictionaryLearning.__sizeof__ sklearn.decomposition.DictionaryLearning.__sklearn_clone__ sklearn.decomposition.DictionaryLearning.__sklearn_tags__ sklearn.decomposition.DictionaryLearning.__str__ sklearn.decomposition.DictionaryLearning.__subclasshook__ sklearn.decomposition.DictionaryLearning._build_request_for_signature sklearn.decomposition.DictionaryLearning._get_default_requests sklearn.decomposition.DictionaryLearning._get_doc_link sklearn.decomposition.DictionaryLearning._get_metadata_request sklearn.decomposition.DictionaryLearning._get_param_names sklearn.decomposition.DictionaryLearning._inverse_transform sklearn.decomposition.DictionaryLearning._repr_html_inner sklearn.decomposition.DictionaryLearning._repr_mimebundle_ sklearn.decomposition.DictionaryLearning._transform sklearn.decomposition.DictionaryLearning._validate_params sklearn.decomposition.DictionaryLearning.fit sklearn.decomposition.DictionaryLearning.fit_transform sklearn.decomposition.DictionaryLearning.get_feature_names_out sklearn.decomposition.DictionaryLearning.get_metadata_routing sklearn.decomposition.DictionaryLearning.get_params sklearn.decomposition.DictionaryLearning.inverse_transform sklearn.decomposition.DictionaryLearning.set_output sklearn.decomposition.DictionaryLearning.set_params sklearn.decomposition.DictionaryLearning.transform 
    :add-heading: Gallery examples
    :heading-level: -
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diff --git a/dev/_sources/modules/generated/sklearn.decomposition.FactorAnalysis.rst.txt b/dev/_sources/modules/generated/sklearn.decomposition.FactorAnalysis.rst.txt
index c9f16e2c8a065..f1479136c028d 100644
--- a/dev/_sources/modules/generated/sklearn.decomposition.FactorAnalysis.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.decomposition.FactorAnalysis.rst.txt
@@ -8,6 +8,6 @@ FactorAnalysis
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.decomposition.FactorAnalysis sklearn.decomposition.FactorAnalysis.__delattr__ sklearn.decomposition.FactorAnalysis.__dir__ sklearn.decomposition.FactorAnalysis.__eq__ sklearn.decomposition.FactorAnalysis.__format__ sklearn.decomposition.FactorAnalysis.__ge__ sklearn.decomposition.FactorAnalysis.__getattribute__ sklearn.decomposition.FactorAnalysis.__getstate__ sklearn.decomposition.FactorAnalysis.__gt__ sklearn.decomposition.FactorAnalysis.__hash__ sklearn.decomposition.FactorAnalysis.__init__ sklearn.decomposition.FactorAnalysis.__init_subclass__ sklearn.decomposition.FactorAnalysis.__le__ sklearn.decomposition.FactorAnalysis.__lt__ sklearn.decomposition.FactorAnalysis.__ne__ sklearn.decomposition.FactorAnalysis.__new__ sklearn.decomposition.FactorAnalysis.__reduce__ sklearn.decomposition.FactorAnalysis.__reduce_ex__ sklearn.decomposition.FactorAnalysis.__repr__ sklearn.decomposition.FactorAnalysis.__setattr__ sklearn.decomposition.FactorAnalysis.__setstate__ sklearn.decomposition.FactorAnalysis.__sizeof__ sklearn.decomposition.FactorAnalysis.__sklearn_clone__ sklearn.decomposition.FactorAnalysis.__sklearn_tags__ sklearn.decomposition.FactorAnalysis.__str__ sklearn.decomposition.FactorAnalysis.__subclasshook__ sklearn.decomposition.FactorAnalysis._build_request_for_signature sklearn.decomposition.FactorAnalysis._check_feature_names sklearn.decomposition.FactorAnalysis._check_n_features sklearn.decomposition.FactorAnalysis._get_default_requests sklearn.decomposition.FactorAnalysis._get_doc_link sklearn.decomposition.FactorAnalysis._get_metadata_request sklearn.decomposition.FactorAnalysis._get_param_names sklearn.decomposition.FactorAnalysis._get_tags sklearn.decomposition.FactorAnalysis._more_tags sklearn.decomposition.FactorAnalysis._repr_html_inner sklearn.decomposition.FactorAnalysis._repr_mimebundle_ sklearn.decomposition.FactorAnalysis._rotate sklearn.decomposition.FactorAnalysis._validate_data sklearn.decomposition.FactorAnalysis._validate_params sklearn.decomposition.FactorAnalysis.fit sklearn.decomposition.FactorAnalysis.fit_transform sklearn.decomposition.FactorAnalysis.get_covariance sklearn.decomposition.FactorAnalysis.get_feature_names_out sklearn.decomposition.FactorAnalysis.get_metadata_routing sklearn.decomposition.FactorAnalysis.get_params sklearn.decomposition.FactorAnalysis.get_precision sklearn.decomposition.FactorAnalysis.score sklearn.decomposition.FactorAnalysis.score_samples sklearn.decomposition.FactorAnalysis.set_output sklearn.decomposition.FactorAnalysis.set_params sklearn.decomposition.FactorAnalysis.transform 
+.. minigallery:: sklearn.decomposition.FactorAnalysis sklearn.decomposition.FactorAnalysis.__delattr__ sklearn.decomposition.FactorAnalysis.__dir__ sklearn.decomposition.FactorAnalysis.__eq__ sklearn.decomposition.FactorAnalysis.__format__ sklearn.decomposition.FactorAnalysis.__ge__ sklearn.decomposition.FactorAnalysis.__getattribute__ sklearn.decomposition.FactorAnalysis.__getstate__ sklearn.decomposition.FactorAnalysis.__gt__ sklearn.decomposition.FactorAnalysis.__hash__ sklearn.decomposition.FactorAnalysis.__init__ sklearn.decomposition.FactorAnalysis.__init_subclass__ sklearn.decomposition.FactorAnalysis.__le__ sklearn.decomposition.FactorAnalysis.__lt__ sklearn.decomposition.FactorAnalysis.__ne__ sklearn.decomposition.FactorAnalysis.__new__ sklearn.decomposition.FactorAnalysis.__reduce__ sklearn.decomposition.FactorAnalysis.__reduce_ex__ sklearn.decomposition.FactorAnalysis.__repr__ sklearn.decomposition.FactorAnalysis.__setattr__ sklearn.decomposition.FactorAnalysis.__setstate__ sklearn.decomposition.FactorAnalysis.__sizeof__ sklearn.decomposition.FactorAnalysis.__sklearn_clone__ sklearn.decomposition.FactorAnalysis.__sklearn_tags__ sklearn.decomposition.FactorAnalysis.__str__ sklearn.decomposition.FactorAnalysis.__subclasshook__ sklearn.decomposition.FactorAnalysis._build_request_for_signature sklearn.decomposition.FactorAnalysis._get_default_requests sklearn.decomposition.FactorAnalysis._get_doc_link sklearn.decomposition.FactorAnalysis._get_metadata_request sklearn.decomposition.FactorAnalysis._get_param_names sklearn.decomposition.FactorAnalysis._repr_html_inner sklearn.decomposition.FactorAnalysis._repr_mimebundle_ sklearn.decomposition.FactorAnalysis._rotate sklearn.decomposition.FactorAnalysis._validate_params sklearn.decomposition.FactorAnalysis.fit sklearn.decomposition.FactorAnalysis.fit_transform sklearn.decomposition.FactorAnalysis.get_covariance sklearn.decomposition.FactorAnalysis.get_feature_names_out sklearn.decomposition.FactorAnalysis.get_metadata_routing sklearn.decomposition.FactorAnalysis.get_params sklearn.decomposition.FactorAnalysis.get_precision sklearn.decomposition.FactorAnalysis.score sklearn.decomposition.FactorAnalysis.score_samples sklearn.decomposition.FactorAnalysis.set_output sklearn.decomposition.FactorAnalysis.set_params sklearn.decomposition.FactorAnalysis.transform 
    :add-heading: Gallery examples
    :heading-level: -
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diff --git a/dev/_sources/modules/generated/sklearn.decomposition.FastICA.rst.txt b/dev/_sources/modules/generated/sklearn.decomposition.FastICA.rst.txt
index f6a6ba73d6c59..8e1b09d60d67d 100644
--- a/dev/_sources/modules/generated/sklearn.decomposition.FastICA.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.decomposition.FastICA.rst.txt
@@ -8,6 +8,6 @@ FastICA
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.decomposition.FastICA sklearn.decomposition.FastICA.__delattr__ sklearn.decomposition.FastICA.__dir__ sklearn.decomposition.FastICA.__eq__ sklearn.decomposition.FastICA.__format__ sklearn.decomposition.FastICA.__ge__ sklearn.decomposition.FastICA.__getattribute__ sklearn.decomposition.FastICA.__getstate__ sklearn.decomposition.FastICA.__gt__ sklearn.decomposition.FastICA.__hash__ sklearn.decomposition.FastICA.__init__ sklearn.decomposition.FastICA.__init_subclass__ sklearn.decomposition.FastICA.__le__ sklearn.decomposition.FastICA.__lt__ sklearn.decomposition.FastICA.__ne__ sklearn.decomposition.FastICA.__new__ sklearn.decomposition.FastICA.__reduce__ sklearn.decomposition.FastICA.__reduce_ex__ sklearn.decomposition.FastICA.__repr__ sklearn.decomposition.FastICA.__setattr__ sklearn.decomposition.FastICA.__setstate__ sklearn.decomposition.FastICA.__sizeof__ sklearn.decomposition.FastICA.__sklearn_clone__ sklearn.decomposition.FastICA.__sklearn_tags__ sklearn.decomposition.FastICA.__str__ sklearn.decomposition.FastICA.__subclasshook__ sklearn.decomposition.FastICA._build_request_for_signature sklearn.decomposition.FastICA._check_feature_names sklearn.decomposition.FastICA._check_n_features sklearn.decomposition.FastICA._fit_transform sklearn.decomposition.FastICA._get_default_requests sklearn.decomposition.FastICA._get_doc_link sklearn.decomposition.FastICA._get_metadata_request sklearn.decomposition.FastICA._get_param_names sklearn.decomposition.FastICA._get_tags sklearn.decomposition.FastICA._more_tags sklearn.decomposition.FastICA._repr_html_inner sklearn.decomposition.FastICA._repr_mimebundle_ sklearn.decomposition.FastICA._validate_data sklearn.decomposition.FastICA._validate_params sklearn.decomposition.FastICA.fit sklearn.decomposition.FastICA.fit_transform sklearn.decomposition.FastICA.get_feature_names_out sklearn.decomposition.FastICA.get_metadata_routing sklearn.decomposition.FastICA.get_params sklearn.decomposition.FastICA.inverse_transform sklearn.decomposition.FastICA.set_inverse_transform_request sklearn.decomposition.FastICA.set_output sklearn.decomposition.FastICA.set_params sklearn.decomposition.FastICA.set_transform_request sklearn.decomposition.FastICA.transform 
+.. minigallery:: sklearn.decomposition.FastICA sklearn.decomposition.FastICA.__delattr__ sklearn.decomposition.FastICA.__dir__ sklearn.decomposition.FastICA.__eq__ sklearn.decomposition.FastICA.__format__ sklearn.decomposition.FastICA.__ge__ sklearn.decomposition.FastICA.__getattribute__ sklearn.decomposition.FastICA.__getstate__ sklearn.decomposition.FastICA.__gt__ sklearn.decomposition.FastICA.__hash__ sklearn.decomposition.FastICA.__init__ sklearn.decomposition.FastICA.__init_subclass__ sklearn.decomposition.FastICA.__le__ sklearn.decomposition.FastICA.__lt__ sklearn.decomposition.FastICA.__ne__ sklearn.decomposition.FastICA.__new__ sklearn.decomposition.FastICA.__reduce__ sklearn.decomposition.FastICA.__reduce_ex__ sklearn.decomposition.FastICA.__repr__ sklearn.decomposition.FastICA.__setattr__ sklearn.decomposition.FastICA.__setstate__ sklearn.decomposition.FastICA.__sizeof__ sklearn.decomposition.FastICA.__sklearn_clone__ sklearn.decomposition.FastICA.__sklearn_tags__ sklearn.decomposition.FastICA.__str__ sklearn.decomposition.FastICA.__subclasshook__ sklearn.decomposition.FastICA._build_request_for_signature sklearn.decomposition.FastICA._fit_transform sklearn.decomposition.FastICA._get_default_requests sklearn.decomposition.FastICA._get_doc_link sklearn.decomposition.FastICA._get_metadata_request sklearn.decomposition.FastICA._get_param_names sklearn.decomposition.FastICA._repr_html_inner sklearn.decomposition.FastICA._repr_mimebundle_ sklearn.decomposition.FastICA._validate_params sklearn.decomposition.FastICA.fit sklearn.decomposition.FastICA.fit_transform sklearn.decomposition.FastICA.get_feature_names_out sklearn.decomposition.FastICA.get_metadata_routing sklearn.decomposition.FastICA.get_params sklearn.decomposition.FastICA.inverse_transform sklearn.decomposition.FastICA.set_inverse_transform_request sklearn.decomposition.FastICA.set_output sklearn.decomposition.FastICA.set_params sklearn.decomposition.FastICA.set_transform_request sklearn.decomposition.FastICA.transform 
    :add-heading: Gallery examples
    :heading-level: -
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diff --git a/dev/_sources/modules/generated/sklearn.decomposition.IncrementalPCA.rst.txt b/dev/_sources/modules/generated/sklearn.decomposition.IncrementalPCA.rst.txt
index b292781f773c2..7072f564fa911 100644
--- a/dev/_sources/modules/generated/sklearn.decomposition.IncrementalPCA.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.decomposition.IncrementalPCA.rst.txt
@@ -8,6 +8,6 @@ IncrementalPCA
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.decomposition.IncrementalPCA sklearn.decomposition.IncrementalPCA.__delattr__ sklearn.decomposition.IncrementalPCA.__dir__ sklearn.decomposition.IncrementalPCA.__eq__ sklearn.decomposition.IncrementalPCA.__format__ sklearn.decomposition.IncrementalPCA.__ge__ sklearn.decomposition.IncrementalPCA.__getattribute__ sklearn.decomposition.IncrementalPCA.__getstate__ sklearn.decomposition.IncrementalPCA.__gt__ sklearn.decomposition.IncrementalPCA.__hash__ sklearn.decomposition.IncrementalPCA.__init__ sklearn.decomposition.IncrementalPCA.__init_subclass__ sklearn.decomposition.IncrementalPCA.__le__ sklearn.decomposition.IncrementalPCA.__lt__ sklearn.decomposition.IncrementalPCA.__ne__ sklearn.decomposition.IncrementalPCA.__new__ sklearn.decomposition.IncrementalPCA.__reduce__ sklearn.decomposition.IncrementalPCA.__reduce_ex__ sklearn.decomposition.IncrementalPCA.__repr__ sklearn.decomposition.IncrementalPCA.__setattr__ sklearn.decomposition.IncrementalPCA.__setstate__ sklearn.decomposition.IncrementalPCA.__sizeof__ sklearn.decomposition.IncrementalPCA.__sklearn_clone__ sklearn.decomposition.IncrementalPCA.__sklearn_tags__ sklearn.decomposition.IncrementalPCA.__str__ sklearn.decomposition.IncrementalPCA.__subclasshook__ sklearn.decomposition.IncrementalPCA._build_request_for_signature sklearn.decomposition.IncrementalPCA._check_feature_names sklearn.decomposition.IncrementalPCA._check_n_features sklearn.decomposition.IncrementalPCA._get_default_requests sklearn.decomposition.IncrementalPCA._get_doc_link sklearn.decomposition.IncrementalPCA._get_metadata_request sklearn.decomposition.IncrementalPCA._get_param_names sklearn.decomposition.IncrementalPCA._get_tags sklearn.decomposition.IncrementalPCA._more_tags sklearn.decomposition.IncrementalPCA._repr_html_inner sklearn.decomposition.IncrementalPCA._repr_mimebundle_ sklearn.decomposition.IncrementalPCA._transform sklearn.decomposition.IncrementalPCA._validate_data sklearn.decomposition.IncrementalPCA._validate_params sklearn.decomposition.IncrementalPCA.fit sklearn.decomposition.IncrementalPCA.fit_transform sklearn.decomposition.IncrementalPCA.get_covariance sklearn.decomposition.IncrementalPCA.get_feature_names_out sklearn.decomposition.IncrementalPCA.get_metadata_routing sklearn.decomposition.IncrementalPCA.get_params sklearn.decomposition.IncrementalPCA.get_precision sklearn.decomposition.IncrementalPCA.inverse_transform sklearn.decomposition.IncrementalPCA.partial_fit sklearn.decomposition.IncrementalPCA.set_output sklearn.decomposition.IncrementalPCA.set_params sklearn.decomposition.IncrementalPCA.transform 
+.. minigallery:: sklearn.decomposition.IncrementalPCA sklearn.decomposition.IncrementalPCA.__delattr__ sklearn.decomposition.IncrementalPCA.__dir__ sklearn.decomposition.IncrementalPCA.__eq__ sklearn.decomposition.IncrementalPCA.__format__ sklearn.decomposition.IncrementalPCA.__ge__ sklearn.decomposition.IncrementalPCA.__getattribute__ sklearn.decomposition.IncrementalPCA.__getstate__ sklearn.decomposition.IncrementalPCA.__gt__ sklearn.decomposition.IncrementalPCA.__hash__ sklearn.decomposition.IncrementalPCA.__init__ sklearn.decomposition.IncrementalPCA.__init_subclass__ sklearn.decomposition.IncrementalPCA.__le__ sklearn.decomposition.IncrementalPCA.__lt__ sklearn.decomposition.IncrementalPCA.__ne__ sklearn.decomposition.IncrementalPCA.__new__ sklearn.decomposition.IncrementalPCA.__reduce__ sklearn.decomposition.IncrementalPCA.__reduce_ex__ sklearn.decomposition.IncrementalPCA.__repr__ sklearn.decomposition.IncrementalPCA.__setattr__ sklearn.decomposition.IncrementalPCA.__setstate__ sklearn.decomposition.IncrementalPCA.__sizeof__ sklearn.decomposition.IncrementalPCA.__sklearn_clone__ sklearn.decomposition.IncrementalPCA.__sklearn_tags__ sklearn.decomposition.IncrementalPCA.__str__ sklearn.decomposition.IncrementalPCA.__subclasshook__ sklearn.decomposition.IncrementalPCA._build_request_for_signature sklearn.decomposition.IncrementalPCA._get_default_requests sklearn.decomposition.IncrementalPCA._get_doc_link sklearn.decomposition.IncrementalPCA._get_metadata_request sklearn.decomposition.IncrementalPCA._get_param_names sklearn.decomposition.IncrementalPCA._repr_html_inner sklearn.decomposition.IncrementalPCA._repr_mimebundle_ sklearn.decomposition.IncrementalPCA._transform sklearn.decomposition.IncrementalPCA._validate_params sklearn.decomposition.IncrementalPCA.fit sklearn.decomposition.IncrementalPCA.fit_transform sklearn.decomposition.IncrementalPCA.get_covariance sklearn.decomposition.IncrementalPCA.get_feature_names_out sklearn.decomposition.IncrementalPCA.get_metadata_routing sklearn.decomposition.IncrementalPCA.get_params sklearn.decomposition.IncrementalPCA.get_precision sklearn.decomposition.IncrementalPCA.inverse_transform sklearn.decomposition.IncrementalPCA.partial_fit sklearn.decomposition.IncrementalPCA.set_output sklearn.decomposition.IncrementalPCA.set_params sklearn.decomposition.IncrementalPCA.transform 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.decomposition.KernelPCA.rst.txt b/dev/_sources/modules/generated/sklearn.decomposition.KernelPCA.rst.txt
index e0c53bed584ef..b551e5de2ef36 100644
--- a/dev/_sources/modules/generated/sklearn.decomposition.KernelPCA.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.decomposition.KernelPCA.rst.txt
@@ -8,6 +8,6 @@ KernelPCA
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.decomposition.KernelPCA sklearn.decomposition.KernelPCA.__delattr__ sklearn.decomposition.KernelPCA.__dir__ sklearn.decomposition.KernelPCA.__eq__ sklearn.decomposition.KernelPCA.__format__ sklearn.decomposition.KernelPCA.__ge__ sklearn.decomposition.KernelPCA.__getattribute__ sklearn.decomposition.KernelPCA.__getstate__ sklearn.decomposition.KernelPCA.__gt__ sklearn.decomposition.KernelPCA.__hash__ sklearn.decomposition.KernelPCA.__init__ sklearn.decomposition.KernelPCA.__init_subclass__ sklearn.decomposition.KernelPCA.__le__ sklearn.decomposition.KernelPCA.__lt__ sklearn.decomposition.KernelPCA.__ne__ sklearn.decomposition.KernelPCA.__new__ sklearn.decomposition.KernelPCA.__reduce__ sklearn.decomposition.KernelPCA.__reduce_ex__ sklearn.decomposition.KernelPCA.__repr__ sklearn.decomposition.KernelPCA.__setattr__ sklearn.decomposition.KernelPCA.__setstate__ sklearn.decomposition.KernelPCA.__sizeof__ sklearn.decomposition.KernelPCA.__sklearn_clone__ sklearn.decomposition.KernelPCA.__sklearn_tags__ sklearn.decomposition.KernelPCA.__str__ sklearn.decomposition.KernelPCA.__subclasshook__ sklearn.decomposition.KernelPCA._build_request_for_signature sklearn.decomposition.KernelPCA._check_feature_names sklearn.decomposition.KernelPCA._check_n_features sklearn.decomposition.KernelPCA._fit_inverse_transform sklearn.decomposition.KernelPCA._fit_transform_in_place sklearn.decomposition.KernelPCA._get_default_requests sklearn.decomposition.KernelPCA._get_doc_link sklearn.decomposition.KernelPCA._get_kernel sklearn.decomposition.KernelPCA._get_metadata_request sklearn.decomposition.KernelPCA._get_param_names sklearn.decomposition.KernelPCA._get_tags sklearn.decomposition.KernelPCA._more_tags sklearn.decomposition.KernelPCA._repr_html_inner sklearn.decomposition.KernelPCA._repr_mimebundle_ sklearn.decomposition.KernelPCA._validate_data sklearn.decomposition.KernelPCA._validate_params sklearn.decomposition.KernelPCA.fit sklearn.decomposition.KernelPCA.fit_transform sklearn.decomposition.KernelPCA.get_feature_names_out sklearn.decomposition.KernelPCA.get_metadata_routing sklearn.decomposition.KernelPCA.get_params sklearn.decomposition.KernelPCA.inverse_transform sklearn.decomposition.KernelPCA.set_output sklearn.decomposition.KernelPCA.set_params sklearn.decomposition.KernelPCA.transform 
+.. minigallery:: sklearn.decomposition.KernelPCA sklearn.decomposition.KernelPCA.__delattr__ sklearn.decomposition.KernelPCA.__dir__ sklearn.decomposition.KernelPCA.__eq__ sklearn.decomposition.KernelPCA.__format__ sklearn.decomposition.KernelPCA.__ge__ sklearn.decomposition.KernelPCA.__getattribute__ sklearn.decomposition.KernelPCA.__getstate__ sklearn.decomposition.KernelPCA.__gt__ sklearn.decomposition.KernelPCA.__hash__ sklearn.decomposition.KernelPCA.__init__ sklearn.decomposition.KernelPCA.__init_subclass__ sklearn.decomposition.KernelPCA.__le__ sklearn.decomposition.KernelPCA.__lt__ sklearn.decomposition.KernelPCA.__ne__ sklearn.decomposition.KernelPCA.__new__ sklearn.decomposition.KernelPCA.__reduce__ sklearn.decomposition.KernelPCA.__reduce_ex__ sklearn.decomposition.KernelPCA.__repr__ sklearn.decomposition.KernelPCA.__setattr__ sklearn.decomposition.KernelPCA.__setstate__ sklearn.decomposition.KernelPCA.__sizeof__ sklearn.decomposition.KernelPCA.__sklearn_clone__ sklearn.decomposition.KernelPCA.__sklearn_tags__ sklearn.decomposition.KernelPCA.__str__ sklearn.decomposition.KernelPCA.__subclasshook__ sklearn.decomposition.KernelPCA._build_request_for_signature sklearn.decomposition.KernelPCA._fit_inverse_transform sklearn.decomposition.KernelPCA._fit_transform_in_place sklearn.decomposition.KernelPCA._get_default_requests sklearn.decomposition.KernelPCA._get_doc_link sklearn.decomposition.KernelPCA._get_kernel sklearn.decomposition.KernelPCA._get_metadata_request sklearn.decomposition.KernelPCA._get_param_names sklearn.decomposition.KernelPCA._repr_html_inner sklearn.decomposition.KernelPCA._repr_mimebundle_ sklearn.decomposition.KernelPCA._validate_params sklearn.decomposition.KernelPCA.fit sklearn.decomposition.KernelPCA.fit_transform sklearn.decomposition.KernelPCA.get_feature_names_out sklearn.decomposition.KernelPCA.get_metadata_routing sklearn.decomposition.KernelPCA.get_params sklearn.decomposition.KernelPCA.inverse_transform sklearn.decomposition.KernelPCA.set_output sklearn.decomposition.KernelPCA.set_params sklearn.decomposition.KernelPCA.transform 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.decomposition.LatentDirichletAllocation.rst.txt b/dev/_sources/modules/generated/sklearn.decomposition.LatentDirichletAllocation.rst.txt
index fd2c2f95abf6b..fee8c7bed621f 100644
--- a/dev/_sources/modules/generated/sklearn.decomposition.LatentDirichletAllocation.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.decomposition.LatentDirichletAllocation.rst.txt
@@ -8,6 +8,6 @@ LatentDirichletAllocation
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.decomposition.LatentDirichletAllocation sklearn.decomposition.LatentDirichletAllocation.__delattr__ sklearn.decomposition.LatentDirichletAllocation.__dir__ sklearn.decomposition.LatentDirichletAllocation.__eq__ sklearn.decomposition.LatentDirichletAllocation.__format__ sklearn.decomposition.LatentDirichletAllocation.__ge__ sklearn.decomposition.LatentDirichletAllocation.__getattribute__ sklearn.decomposition.LatentDirichletAllocation.__getstate__ sklearn.decomposition.LatentDirichletAllocation.__gt__ sklearn.decomposition.LatentDirichletAllocation.__hash__ sklearn.decomposition.LatentDirichletAllocation.__init__ sklearn.decomposition.LatentDirichletAllocation.__init_subclass__ sklearn.decomposition.LatentDirichletAllocation.__le__ sklearn.decomposition.LatentDirichletAllocation.__lt__ sklearn.decomposition.LatentDirichletAllocation.__ne__ sklearn.decomposition.LatentDirichletAllocation.__new__ sklearn.decomposition.LatentDirichletAllocation.__reduce__ sklearn.decomposition.LatentDirichletAllocation.__reduce_ex__ sklearn.decomposition.LatentDirichletAllocation.__repr__ sklearn.decomposition.LatentDirichletAllocation.__setattr__ sklearn.decomposition.LatentDirichletAllocation.__setstate__ sklearn.decomposition.LatentDirichletAllocation.__sizeof__ sklearn.decomposition.LatentDirichletAllocation.__sklearn_clone__ sklearn.decomposition.LatentDirichletAllocation.__sklearn_tags__ sklearn.decomposition.LatentDirichletAllocation.__str__ sklearn.decomposition.LatentDirichletAllocation.__subclasshook__ sklearn.decomposition.LatentDirichletAllocation._approx_bound sklearn.decomposition.LatentDirichletAllocation._build_request_for_signature sklearn.decomposition.LatentDirichletAllocation._check_feature_names sklearn.decomposition.LatentDirichletAllocation._check_n_features sklearn.decomposition.LatentDirichletAllocation._check_non_neg_array sklearn.decomposition.LatentDirichletAllocation._e_step sklearn.decomposition.LatentDirichletAllocation._em_step sklearn.decomposition.LatentDirichletAllocation._get_default_requests sklearn.decomposition.LatentDirichletAllocation._get_doc_link sklearn.decomposition.LatentDirichletAllocation._get_metadata_request sklearn.decomposition.LatentDirichletAllocation._get_param_names sklearn.decomposition.LatentDirichletAllocation._get_tags sklearn.decomposition.LatentDirichletAllocation._init_latent_vars sklearn.decomposition.LatentDirichletAllocation._more_tags sklearn.decomposition.LatentDirichletAllocation._perplexity_precomp_distr sklearn.decomposition.LatentDirichletAllocation._repr_html_inner sklearn.decomposition.LatentDirichletAllocation._repr_mimebundle_ sklearn.decomposition.LatentDirichletAllocation._unnormalized_transform sklearn.decomposition.LatentDirichletAllocation._validate_data sklearn.decomposition.LatentDirichletAllocation._validate_params sklearn.decomposition.LatentDirichletAllocation.fit sklearn.decomposition.LatentDirichletAllocation.fit_transform sklearn.decomposition.LatentDirichletAllocation.get_feature_names_out sklearn.decomposition.LatentDirichletAllocation.get_metadata_routing sklearn.decomposition.LatentDirichletAllocation.get_params sklearn.decomposition.LatentDirichletAllocation.partial_fit sklearn.decomposition.LatentDirichletAllocation.perplexity sklearn.decomposition.LatentDirichletAllocation.score sklearn.decomposition.LatentDirichletAllocation.set_output sklearn.decomposition.LatentDirichletAllocation.set_params sklearn.decomposition.LatentDirichletAllocation.set_transform_request sklearn.decomposition.LatentDirichletAllocation.transform 
+.. minigallery:: sklearn.decomposition.LatentDirichletAllocation sklearn.decomposition.LatentDirichletAllocation.__delattr__ sklearn.decomposition.LatentDirichletAllocation.__dir__ sklearn.decomposition.LatentDirichletAllocation.__eq__ sklearn.decomposition.LatentDirichletAllocation.__format__ sklearn.decomposition.LatentDirichletAllocation.__ge__ sklearn.decomposition.LatentDirichletAllocation.__getattribute__ sklearn.decomposition.LatentDirichletAllocation.__getstate__ sklearn.decomposition.LatentDirichletAllocation.__gt__ sklearn.decomposition.LatentDirichletAllocation.__hash__ sklearn.decomposition.LatentDirichletAllocation.__init__ sklearn.decomposition.LatentDirichletAllocation.__init_subclass__ sklearn.decomposition.LatentDirichletAllocation.__le__ sklearn.decomposition.LatentDirichletAllocation.__lt__ sklearn.decomposition.LatentDirichletAllocation.__ne__ sklearn.decomposition.LatentDirichletAllocation.__new__ sklearn.decomposition.LatentDirichletAllocation.__reduce__ sklearn.decomposition.LatentDirichletAllocation.__reduce_ex__ sklearn.decomposition.LatentDirichletAllocation.__repr__ sklearn.decomposition.LatentDirichletAllocation.__setattr__ sklearn.decomposition.LatentDirichletAllocation.__setstate__ sklearn.decomposition.LatentDirichletAllocation.__sizeof__ sklearn.decomposition.LatentDirichletAllocation.__sklearn_clone__ sklearn.decomposition.LatentDirichletAllocation.__sklearn_tags__ sklearn.decomposition.LatentDirichletAllocation.__str__ sklearn.decomposition.LatentDirichletAllocation.__subclasshook__ sklearn.decomposition.LatentDirichletAllocation._approx_bound sklearn.decomposition.LatentDirichletAllocation._build_request_for_signature sklearn.decomposition.LatentDirichletAllocation._check_non_neg_array sklearn.decomposition.LatentDirichletAllocation._e_step sklearn.decomposition.LatentDirichletAllocation._em_step sklearn.decomposition.LatentDirichletAllocation._get_default_requests sklearn.decomposition.LatentDirichletAllocation._get_doc_link sklearn.decomposition.LatentDirichletAllocation._get_metadata_request sklearn.decomposition.LatentDirichletAllocation._get_param_names sklearn.decomposition.LatentDirichletAllocation._init_latent_vars sklearn.decomposition.LatentDirichletAllocation._perplexity_precomp_distr sklearn.decomposition.LatentDirichletAllocation._repr_html_inner sklearn.decomposition.LatentDirichletAllocation._repr_mimebundle_ sklearn.decomposition.LatentDirichletAllocation._unnormalized_transform sklearn.decomposition.LatentDirichletAllocation._validate_params sklearn.decomposition.LatentDirichletAllocation.fit sklearn.decomposition.LatentDirichletAllocation.fit_transform sklearn.decomposition.LatentDirichletAllocation.get_feature_names_out sklearn.decomposition.LatentDirichletAllocation.get_metadata_routing sklearn.decomposition.LatentDirichletAllocation.get_params sklearn.decomposition.LatentDirichletAllocation.partial_fit sklearn.decomposition.LatentDirichletAllocation.perplexity sklearn.decomposition.LatentDirichletAllocation.score sklearn.decomposition.LatentDirichletAllocation.set_output sklearn.decomposition.LatentDirichletAllocation.set_params sklearn.decomposition.LatentDirichletAllocation.set_transform_request sklearn.decomposition.LatentDirichletAllocation.transform 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.decomposition.MiniBatchDictionaryLearning.rst.txt b/dev/_sources/modules/generated/sklearn.decomposition.MiniBatchDictionaryLearning.rst.txt
index 0d07c00192c8b..acc40518b8672 100644
--- a/dev/_sources/modules/generated/sklearn.decomposition.MiniBatchDictionaryLearning.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.decomposition.MiniBatchDictionaryLearning.rst.txt
@@ -8,6 +8,6 @@ MiniBatchDictionaryLearning
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.decomposition.MiniBatchDictionaryLearning sklearn.decomposition.MiniBatchDictionaryLearning.__delattr__ sklearn.decomposition.MiniBatchDictionaryLearning.__dir__ sklearn.decomposition.MiniBatchDictionaryLearning.__eq__ sklearn.decomposition.MiniBatchDictionaryLearning.__format__ sklearn.decomposition.MiniBatchDictionaryLearning.__ge__ sklearn.decomposition.MiniBatchDictionaryLearning.__getattribute__ sklearn.decomposition.MiniBatchDictionaryLearning.__getstate__ sklearn.decomposition.MiniBatchDictionaryLearning.__gt__ sklearn.decomposition.MiniBatchDictionaryLearning.__hash__ sklearn.decomposition.MiniBatchDictionaryLearning.__init__ sklearn.decomposition.MiniBatchDictionaryLearning.__init_subclass__ sklearn.decomposition.MiniBatchDictionaryLearning.__le__ sklearn.decomposition.MiniBatchDictionaryLearning.__lt__ sklearn.decomposition.MiniBatchDictionaryLearning.__ne__ sklearn.decomposition.MiniBatchDictionaryLearning.__new__ sklearn.decomposition.MiniBatchDictionaryLearning.__reduce__ sklearn.decomposition.MiniBatchDictionaryLearning.__reduce_ex__ sklearn.decomposition.MiniBatchDictionaryLearning.__repr__ sklearn.decomposition.MiniBatchDictionaryLearning.__setattr__ sklearn.decomposition.MiniBatchDictionaryLearning.__setstate__ sklearn.decomposition.MiniBatchDictionaryLearning.__sizeof__ sklearn.decomposition.MiniBatchDictionaryLearning.__sklearn_clone__ sklearn.decomposition.MiniBatchDictionaryLearning.__sklearn_tags__ sklearn.decomposition.MiniBatchDictionaryLearning.__str__ sklearn.decomposition.MiniBatchDictionaryLearning.__subclasshook__ sklearn.decomposition.MiniBatchDictionaryLearning._build_request_for_signature sklearn.decomposition.MiniBatchDictionaryLearning._check_convergence sklearn.decomposition.MiniBatchDictionaryLearning._check_feature_names sklearn.decomposition.MiniBatchDictionaryLearning._check_n_features sklearn.decomposition.MiniBatchDictionaryLearning._check_params sklearn.decomposition.MiniBatchDictionaryLearning._get_default_requests sklearn.decomposition.MiniBatchDictionaryLearning._get_doc_link sklearn.decomposition.MiniBatchDictionaryLearning._get_metadata_request sklearn.decomposition.MiniBatchDictionaryLearning._get_param_names sklearn.decomposition.MiniBatchDictionaryLearning._get_tags sklearn.decomposition.MiniBatchDictionaryLearning._initialize_dict sklearn.decomposition.MiniBatchDictionaryLearning._inverse_transform sklearn.decomposition.MiniBatchDictionaryLearning._minibatch_step sklearn.decomposition.MiniBatchDictionaryLearning._more_tags sklearn.decomposition.MiniBatchDictionaryLearning._repr_html_inner sklearn.decomposition.MiniBatchDictionaryLearning._repr_mimebundle_ sklearn.decomposition.MiniBatchDictionaryLearning._transform sklearn.decomposition.MiniBatchDictionaryLearning._update_inner_stats sklearn.decomposition.MiniBatchDictionaryLearning._validate_data sklearn.decomposition.MiniBatchDictionaryLearning._validate_params sklearn.decomposition.MiniBatchDictionaryLearning.fit sklearn.decomposition.MiniBatchDictionaryLearning.fit_transform sklearn.decomposition.MiniBatchDictionaryLearning.get_feature_names_out sklearn.decomposition.MiniBatchDictionaryLearning.get_metadata_routing sklearn.decomposition.MiniBatchDictionaryLearning.get_params sklearn.decomposition.MiniBatchDictionaryLearning.inverse_transform sklearn.decomposition.MiniBatchDictionaryLearning.partial_fit sklearn.decomposition.MiniBatchDictionaryLearning.set_output sklearn.decomposition.MiniBatchDictionaryLearning.set_params sklearn.decomposition.MiniBatchDictionaryLearning.transform 
+.. minigallery:: sklearn.decomposition.MiniBatchDictionaryLearning sklearn.decomposition.MiniBatchDictionaryLearning.__delattr__ sklearn.decomposition.MiniBatchDictionaryLearning.__dir__ sklearn.decomposition.MiniBatchDictionaryLearning.__eq__ sklearn.decomposition.MiniBatchDictionaryLearning.__format__ sklearn.decomposition.MiniBatchDictionaryLearning.__ge__ sklearn.decomposition.MiniBatchDictionaryLearning.__getattribute__ sklearn.decomposition.MiniBatchDictionaryLearning.__getstate__ sklearn.decomposition.MiniBatchDictionaryLearning.__gt__ sklearn.decomposition.MiniBatchDictionaryLearning.__hash__ sklearn.decomposition.MiniBatchDictionaryLearning.__init__ sklearn.decomposition.MiniBatchDictionaryLearning.__init_subclass__ sklearn.decomposition.MiniBatchDictionaryLearning.__le__ sklearn.decomposition.MiniBatchDictionaryLearning.__lt__ sklearn.decomposition.MiniBatchDictionaryLearning.__ne__ sklearn.decomposition.MiniBatchDictionaryLearning.__new__ sklearn.decomposition.MiniBatchDictionaryLearning.__reduce__ sklearn.decomposition.MiniBatchDictionaryLearning.__reduce_ex__ sklearn.decomposition.MiniBatchDictionaryLearning.__repr__ sklearn.decomposition.MiniBatchDictionaryLearning.__setattr__ sklearn.decomposition.MiniBatchDictionaryLearning.__setstate__ sklearn.decomposition.MiniBatchDictionaryLearning.__sizeof__ sklearn.decomposition.MiniBatchDictionaryLearning.__sklearn_clone__ sklearn.decomposition.MiniBatchDictionaryLearning.__sklearn_tags__ sklearn.decomposition.MiniBatchDictionaryLearning.__str__ sklearn.decomposition.MiniBatchDictionaryLearning.__subclasshook__ sklearn.decomposition.MiniBatchDictionaryLearning._build_request_for_signature sklearn.decomposition.MiniBatchDictionaryLearning._check_convergence sklearn.decomposition.MiniBatchDictionaryLearning._check_params sklearn.decomposition.MiniBatchDictionaryLearning._get_default_requests sklearn.decomposition.MiniBatchDictionaryLearning._get_doc_link sklearn.decomposition.MiniBatchDictionaryLearning._get_metadata_request sklearn.decomposition.MiniBatchDictionaryLearning._get_param_names sklearn.decomposition.MiniBatchDictionaryLearning._initialize_dict sklearn.decomposition.MiniBatchDictionaryLearning._inverse_transform sklearn.decomposition.MiniBatchDictionaryLearning._minibatch_step sklearn.decomposition.MiniBatchDictionaryLearning._repr_html_inner sklearn.decomposition.MiniBatchDictionaryLearning._repr_mimebundle_ sklearn.decomposition.MiniBatchDictionaryLearning._transform sklearn.decomposition.MiniBatchDictionaryLearning._update_inner_stats sklearn.decomposition.MiniBatchDictionaryLearning._validate_params sklearn.decomposition.MiniBatchDictionaryLearning.fit sklearn.decomposition.MiniBatchDictionaryLearning.fit_transform sklearn.decomposition.MiniBatchDictionaryLearning.get_feature_names_out sklearn.decomposition.MiniBatchDictionaryLearning.get_metadata_routing sklearn.decomposition.MiniBatchDictionaryLearning.get_params sklearn.decomposition.MiniBatchDictionaryLearning.inverse_transform sklearn.decomposition.MiniBatchDictionaryLearning.partial_fit sklearn.decomposition.MiniBatchDictionaryLearning.set_output sklearn.decomposition.MiniBatchDictionaryLearning.set_params sklearn.decomposition.MiniBatchDictionaryLearning.transform 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.decomposition.MiniBatchNMF.rst.txt b/dev/_sources/modules/generated/sklearn.decomposition.MiniBatchNMF.rst.txt
index 305cb3c4bc8dc..23c2bbf5cedc0 100644
--- a/dev/_sources/modules/generated/sklearn.decomposition.MiniBatchNMF.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.decomposition.MiniBatchNMF.rst.txt
@@ -8,6 +8,6 @@ MiniBatchNMF
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.decomposition.MiniBatchNMF sklearn.decomposition.MiniBatchNMF.__delattr__ sklearn.decomposition.MiniBatchNMF.__dir__ sklearn.decomposition.MiniBatchNMF.__eq__ sklearn.decomposition.MiniBatchNMF.__format__ sklearn.decomposition.MiniBatchNMF.__ge__ sklearn.decomposition.MiniBatchNMF.__getattribute__ sklearn.decomposition.MiniBatchNMF.__getstate__ sklearn.decomposition.MiniBatchNMF.__gt__ sklearn.decomposition.MiniBatchNMF.__hash__ sklearn.decomposition.MiniBatchNMF.__init__ sklearn.decomposition.MiniBatchNMF.__init_subclass__ sklearn.decomposition.MiniBatchNMF.__le__ sklearn.decomposition.MiniBatchNMF.__lt__ sklearn.decomposition.MiniBatchNMF.__ne__ sklearn.decomposition.MiniBatchNMF.__new__ sklearn.decomposition.MiniBatchNMF.__reduce__ sklearn.decomposition.MiniBatchNMF.__reduce_ex__ sklearn.decomposition.MiniBatchNMF.__repr__ sklearn.decomposition.MiniBatchNMF.__setattr__ sklearn.decomposition.MiniBatchNMF.__setstate__ sklearn.decomposition.MiniBatchNMF.__sizeof__ sklearn.decomposition.MiniBatchNMF.__sklearn_clone__ sklearn.decomposition.MiniBatchNMF.__sklearn_tags__ sklearn.decomposition.MiniBatchNMF.__str__ sklearn.decomposition.MiniBatchNMF.__subclasshook__ sklearn.decomposition.MiniBatchNMF._build_request_for_signature sklearn.decomposition.MiniBatchNMF._check_feature_names sklearn.decomposition.MiniBatchNMF._check_n_features sklearn.decomposition.MiniBatchNMF._check_params sklearn.decomposition.MiniBatchNMF._check_w_h sklearn.decomposition.MiniBatchNMF._compute_regularization sklearn.decomposition.MiniBatchNMF._fit_transform sklearn.decomposition.MiniBatchNMF._get_default_requests sklearn.decomposition.MiniBatchNMF._get_doc_link sklearn.decomposition.MiniBatchNMF._get_metadata_request sklearn.decomposition.MiniBatchNMF._get_param_names sklearn.decomposition.MiniBatchNMF._get_tags sklearn.decomposition.MiniBatchNMF._minibatch_convergence sklearn.decomposition.MiniBatchNMF._minibatch_step sklearn.decomposition.MiniBatchNMF._more_tags sklearn.decomposition.MiniBatchNMF._repr_html_inner sklearn.decomposition.MiniBatchNMF._repr_mimebundle_ sklearn.decomposition.MiniBatchNMF._solve_W sklearn.decomposition.MiniBatchNMF._validate_data sklearn.decomposition.MiniBatchNMF._validate_params sklearn.decomposition.MiniBatchNMF.fit sklearn.decomposition.MiniBatchNMF.fit_transform sklearn.decomposition.MiniBatchNMF.get_feature_names_out sklearn.decomposition.MiniBatchNMF.get_metadata_routing sklearn.decomposition.MiniBatchNMF.get_params sklearn.decomposition.MiniBatchNMF.inverse_transform sklearn.decomposition.MiniBatchNMF.partial_fit sklearn.decomposition.MiniBatchNMF.set_output sklearn.decomposition.MiniBatchNMF.set_params sklearn.decomposition.MiniBatchNMF.set_partial_fit_request sklearn.decomposition.MiniBatchNMF.transform 
+.. minigallery:: sklearn.decomposition.MiniBatchNMF sklearn.decomposition.MiniBatchNMF.__delattr__ sklearn.decomposition.MiniBatchNMF.__dir__ sklearn.decomposition.MiniBatchNMF.__eq__ sklearn.decomposition.MiniBatchNMF.__format__ sklearn.decomposition.MiniBatchNMF.__ge__ sklearn.decomposition.MiniBatchNMF.__getattribute__ sklearn.decomposition.MiniBatchNMF.__getstate__ sklearn.decomposition.MiniBatchNMF.__gt__ sklearn.decomposition.MiniBatchNMF.__hash__ sklearn.decomposition.MiniBatchNMF.__init__ sklearn.decomposition.MiniBatchNMF.__init_subclass__ sklearn.decomposition.MiniBatchNMF.__le__ sklearn.decomposition.MiniBatchNMF.__lt__ sklearn.decomposition.MiniBatchNMF.__ne__ sklearn.decomposition.MiniBatchNMF.__new__ sklearn.decomposition.MiniBatchNMF.__reduce__ sklearn.decomposition.MiniBatchNMF.__reduce_ex__ sklearn.decomposition.MiniBatchNMF.__repr__ sklearn.decomposition.MiniBatchNMF.__setattr__ sklearn.decomposition.MiniBatchNMF.__setstate__ sklearn.decomposition.MiniBatchNMF.__sizeof__ sklearn.decomposition.MiniBatchNMF.__sklearn_clone__ sklearn.decomposition.MiniBatchNMF.__sklearn_tags__ sklearn.decomposition.MiniBatchNMF.__str__ sklearn.decomposition.MiniBatchNMF.__subclasshook__ sklearn.decomposition.MiniBatchNMF._build_request_for_signature sklearn.decomposition.MiniBatchNMF._check_params sklearn.decomposition.MiniBatchNMF._check_w_h sklearn.decomposition.MiniBatchNMF._compute_regularization sklearn.decomposition.MiniBatchNMF._fit_transform sklearn.decomposition.MiniBatchNMF._get_default_requests sklearn.decomposition.MiniBatchNMF._get_doc_link sklearn.decomposition.MiniBatchNMF._get_metadata_request sklearn.decomposition.MiniBatchNMF._get_param_names sklearn.decomposition.MiniBatchNMF._minibatch_convergence sklearn.decomposition.MiniBatchNMF._minibatch_step sklearn.decomposition.MiniBatchNMF._repr_html_inner sklearn.decomposition.MiniBatchNMF._repr_mimebundle_ sklearn.decomposition.MiniBatchNMF._solve_W sklearn.decomposition.MiniBatchNMF._validate_params sklearn.decomposition.MiniBatchNMF.fit sklearn.decomposition.MiniBatchNMF.fit_transform sklearn.decomposition.MiniBatchNMF.get_feature_names_out sklearn.decomposition.MiniBatchNMF.get_metadata_routing sklearn.decomposition.MiniBatchNMF.get_params sklearn.decomposition.MiniBatchNMF.inverse_transform sklearn.decomposition.MiniBatchNMF.partial_fit sklearn.decomposition.MiniBatchNMF.set_output sklearn.decomposition.MiniBatchNMF.set_params sklearn.decomposition.MiniBatchNMF.set_partial_fit_request sklearn.decomposition.MiniBatchNMF.transform 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.decomposition.MiniBatchSparsePCA.rst.txt b/dev/_sources/modules/generated/sklearn.decomposition.MiniBatchSparsePCA.rst.txt
index d29d788effb48..5a6095818cd2c 100644
--- a/dev/_sources/modules/generated/sklearn.decomposition.MiniBatchSparsePCA.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.decomposition.MiniBatchSparsePCA.rst.txt
@@ -8,6 +8,6 @@ MiniBatchSparsePCA
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.decomposition.MiniBatchSparsePCA sklearn.decomposition.MiniBatchSparsePCA.__delattr__ sklearn.decomposition.MiniBatchSparsePCA.__dir__ sklearn.decomposition.MiniBatchSparsePCA.__eq__ sklearn.decomposition.MiniBatchSparsePCA.__format__ sklearn.decomposition.MiniBatchSparsePCA.__ge__ sklearn.decomposition.MiniBatchSparsePCA.__getattribute__ sklearn.decomposition.MiniBatchSparsePCA.__getstate__ sklearn.decomposition.MiniBatchSparsePCA.__gt__ sklearn.decomposition.MiniBatchSparsePCA.__hash__ sklearn.decomposition.MiniBatchSparsePCA.__init__ sklearn.decomposition.MiniBatchSparsePCA.__init_subclass__ sklearn.decomposition.MiniBatchSparsePCA.__le__ sklearn.decomposition.MiniBatchSparsePCA.__lt__ sklearn.decomposition.MiniBatchSparsePCA.__ne__ sklearn.decomposition.MiniBatchSparsePCA.__new__ sklearn.decomposition.MiniBatchSparsePCA.__reduce__ sklearn.decomposition.MiniBatchSparsePCA.__reduce_ex__ sklearn.decomposition.MiniBatchSparsePCA.__repr__ sklearn.decomposition.MiniBatchSparsePCA.__setattr__ sklearn.decomposition.MiniBatchSparsePCA.__setstate__ sklearn.decomposition.MiniBatchSparsePCA.__sizeof__ sklearn.decomposition.MiniBatchSparsePCA.__sklearn_clone__ sklearn.decomposition.MiniBatchSparsePCA.__sklearn_tags__ sklearn.decomposition.MiniBatchSparsePCA.__str__ sklearn.decomposition.MiniBatchSparsePCA.__subclasshook__ sklearn.decomposition.MiniBatchSparsePCA._build_request_for_signature sklearn.decomposition.MiniBatchSparsePCA._check_feature_names sklearn.decomposition.MiniBatchSparsePCA._check_n_features sklearn.decomposition.MiniBatchSparsePCA._fit sklearn.decomposition.MiniBatchSparsePCA._get_default_requests sklearn.decomposition.MiniBatchSparsePCA._get_doc_link sklearn.decomposition.MiniBatchSparsePCA._get_metadata_request sklearn.decomposition.MiniBatchSparsePCA._get_param_names sklearn.decomposition.MiniBatchSparsePCA._get_tags sklearn.decomposition.MiniBatchSparsePCA._more_tags sklearn.decomposition.MiniBatchSparsePCA._repr_html_inner sklearn.decomposition.MiniBatchSparsePCA._repr_mimebundle_ sklearn.decomposition.MiniBatchSparsePCA._validate_data sklearn.decomposition.MiniBatchSparsePCA._validate_params sklearn.decomposition.MiniBatchSparsePCA.fit sklearn.decomposition.MiniBatchSparsePCA.fit_transform sklearn.decomposition.MiniBatchSparsePCA.get_feature_names_out sklearn.decomposition.MiniBatchSparsePCA.get_metadata_routing sklearn.decomposition.MiniBatchSparsePCA.get_params sklearn.decomposition.MiniBatchSparsePCA.inverse_transform sklearn.decomposition.MiniBatchSparsePCA.set_output sklearn.decomposition.MiniBatchSparsePCA.set_params sklearn.decomposition.MiniBatchSparsePCA.transform 
+.. minigallery:: sklearn.decomposition.MiniBatchSparsePCA sklearn.decomposition.MiniBatchSparsePCA.__delattr__ sklearn.decomposition.MiniBatchSparsePCA.__dir__ sklearn.decomposition.MiniBatchSparsePCA.__eq__ sklearn.decomposition.MiniBatchSparsePCA.__format__ sklearn.decomposition.MiniBatchSparsePCA.__ge__ sklearn.decomposition.MiniBatchSparsePCA.__getattribute__ sklearn.decomposition.MiniBatchSparsePCA.__getstate__ sklearn.decomposition.MiniBatchSparsePCA.__gt__ sklearn.decomposition.MiniBatchSparsePCA.__hash__ sklearn.decomposition.MiniBatchSparsePCA.__init__ sklearn.decomposition.MiniBatchSparsePCA.__init_subclass__ sklearn.decomposition.MiniBatchSparsePCA.__le__ sklearn.decomposition.MiniBatchSparsePCA.__lt__ sklearn.decomposition.MiniBatchSparsePCA.__ne__ sklearn.decomposition.MiniBatchSparsePCA.__new__ sklearn.decomposition.MiniBatchSparsePCA.__reduce__ sklearn.decomposition.MiniBatchSparsePCA.__reduce_ex__ sklearn.decomposition.MiniBatchSparsePCA.__repr__ sklearn.decomposition.MiniBatchSparsePCA.__setattr__ sklearn.decomposition.MiniBatchSparsePCA.__setstate__ sklearn.decomposition.MiniBatchSparsePCA.__sizeof__ sklearn.decomposition.MiniBatchSparsePCA.__sklearn_clone__ sklearn.decomposition.MiniBatchSparsePCA.__sklearn_tags__ sklearn.decomposition.MiniBatchSparsePCA.__str__ sklearn.decomposition.MiniBatchSparsePCA.__subclasshook__ sklearn.decomposition.MiniBatchSparsePCA._build_request_for_signature sklearn.decomposition.MiniBatchSparsePCA._fit sklearn.decomposition.MiniBatchSparsePCA._get_default_requests sklearn.decomposition.MiniBatchSparsePCA._get_doc_link sklearn.decomposition.MiniBatchSparsePCA._get_metadata_request sklearn.decomposition.MiniBatchSparsePCA._get_param_names sklearn.decomposition.MiniBatchSparsePCA._repr_html_inner sklearn.decomposition.MiniBatchSparsePCA._repr_mimebundle_ sklearn.decomposition.MiniBatchSparsePCA._validate_params sklearn.decomposition.MiniBatchSparsePCA.fit sklearn.decomposition.MiniBatchSparsePCA.fit_transform sklearn.decomposition.MiniBatchSparsePCA.get_feature_names_out sklearn.decomposition.MiniBatchSparsePCA.get_metadata_routing sklearn.decomposition.MiniBatchSparsePCA.get_params sklearn.decomposition.MiniBatchSparsePCA.inverse_transform sklearn.decomposition.MiniBatchSparsePCA.set_output sklearn.decomposition.MiniBatchSparsePCA.set_params sklearn.decomposition.MiniBatchSparsePCA.transform 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.decomposition.NMF.rst.txt b/dev/_sources/modules/generated/sklearn.decomposition.NMF.rst.txt
index 97cd232a71353..4b30acb12f571 100644
--- a/dev/_sources/modules/generated/sklearn.decomposition.NMF.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.decomposition.NMF.rst.txt
@@ -8,6 +8,6 @@ NMF
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.decomposition.NMF sklearn.decomposition.NMF.__delattr__ sklearn.decomposition.NMF.__dir__ sklearn.decomposition.NMF.__eq__ sklearn.decomposition.NMF.__format__ sklearn.decomposition.NMF.__ge__ sklearn.decomposition.NMF.__getattribute__ sklearn.decomposition.NMF.__getstate__ sklearn.decomposition.NMF.__gt__ sklearn.decomposition.NMF.__hash__ sklearn.decomposition.NMF.__init__ sklearn.decomposition.NMF.__init_subclass__ sklearn.decomposition.NMF.__le__ sklearn.decomposition.NMF.__lt__ sklearn.decomposition.NMF.__ne__ sklearn.decomposition.NMF.__new__ sklearn.decomposition.NMF.__reduce__ sklearn.decomposition.NMF.__reduce_ex__ sklearn.decomposition.NMF.__repr__ sklearn.decomposition.NMF.__setattr__ sklearn.decomposition.NMF.__setstate__ sklearn.decomposition.NMF.__sizeof__ sklearn.decomposition.NMF.__sklearn_clone__ sklearn.decomposition.NMF.__sklearn_tags__ sklearn.decomposition.NMF.__str__ sklearn.decomposition.NMF.__subclasshook__ sklearn.decomposition.NMF._build_request_for_signature sklearn.decomposition.NMF._check_feature_names sklearn.decomposition.NMF._check_n_features sklearn.decomposition.NMF._check_params sklearn.decomposition.NMF._check_w_h sklearn.decomposition.NMF._compute_regularization sklearn.decomposition.NMF._fit_transform sklearn.decomposition.NMF._get_default_requests sklearn.decomposition.NMF._get_doc_link sklearn.decomposition.NMF._get_metadata_request sklearn.decomposition.NMF._get_param_names sklearn.decomposition.NMF._get_tags sklearn.decomposition.NMF._more_tags sklearn.decomposition.NMF._repr_html_inner sklearn.decomposition.NMF._repr_mimebundle_ sklearn.decomposition.NMF._validate_data sklearn.decomposition.NMF._validate_params sklearn.decomposition.NMF.fit sklearn.decomposition.NMF.fit_transform sklearn.decomposition.NMF.get_feature_names_out sklearn.decomposition.NMF.get_metadata_routing sklearn.decomposition.NMF.get_params sklearn.decomposition.NMF.inverse_transform sklearn.decomposition.NMF.set_output sklearn.decomposition.NMF.set_params sklearn.decomposition.NMF.transform 
+.. minigallery:: sklearn.decomposition.NMF sklearn.decomposition.NMF.__delattr__ sklearn.decomposition.NMF.__dir__ sklearn.decomposition.NMF.__eq__ sklearn.decomposition.NMF.__format__ sklearn.decomposition.NMF.__ge__ sklearn.decomposition.NMF.__getattribute__ sklearn.decomposition.NMF.__getstate__ sklearn.decomposition.NMF.__gt__ sklearn.decomposition.NMF.__hash__ sklearn.decomposition.NMF.__init__ sklearn.decomposition.NMF.__init_subclass__ sklearn.decomposition.NMF.__le__ sklearn.decomposition.NMF.__lt__ sklearn.decomposition.NMF.__ne__ sklearn.decomposition.NMF.__new__ sklearn.decomposition.NMF.__reduce__ sklearn.decomposition.NMF.__reduce_ex__ sklearn.decomposition.NMF.__repr__ sklearn.decomposition.NMF.__setattr__ sklearn.decomposition.NMF.__setstate__ sklearn.decomposition.NMF.__sizeof__ sklearn.decomposition.NMF.__sklearn_clone__ sklearn.decomposition.NMF.__sklearn_tags__ sklearn.decomposition.NMF.__str__ sklearn.decomposition.NMF.__subclasshook__ sklearn.decomposition.NMF._build_request_for_signature sklearn.decomposition.NMF._check_params sklearn.decomposition.NMF._check_w_h sklearn.decomposition.NMF._compute_regularization sklearn.decomposition.NMF._fit_transform sklearn.decomposition.NMF._get_default_requests sklearn.decomposition.NMF._get_doc_link sklearn.decomposition.NMF._get_metadata_request sklearn.decomposition.NMF._get_param_names sklearn.decomposition.NMF._repr_html_inner sklearn.decomposition.NMF._repr_mimebundle_ sklearn.decomposition.NMF._validate_params sklearn.decomposition.NMF.fit sklearn.decomposition.NMF.fit_transform sklearn.decomposition.NMF.get_feature_names_out sklearn.decomposition.NMF.get_metadata_routing sklearn.decomposition.NMF.get_params sklearn.decomposition.NMF.inverse_transform sklearn.decomposition.NMF.set_output sklearn.decomposition.NMF.set_params sklearn.decomposition.NMF.transform 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.decomposition.PCA.rst.txt b/dev/_sources/modules/generated/sklearn.decomposition.PCA.rst.txt
index 03d9b0d12a893..76a3a540192fc 100644
--- a/dev/_sources/modules/generated/sklearn.decomposition.PCA.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.decomposition.PCA.rst.txt
@@ -8,6 +8,6 @@ PCA
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.decomposition.PCA sklearn.decomposition.PCA.__delattr__ sklearn.decomposition.PCA.__dir__ sklearn.decomposition.PCA.__eq__ sklearn.decomposition.PCA.__format__ sklearn.decomposition.PCA.__ge__ sklearn.decomposition.PCA.__getattribute__ sklearn.decomposition.PCA.__getstate__ sklearn.decomposition.PCA.__gt__ sklearn.decomposition.PCA.__hash__ sklearn.decomposition.PCA.__init__ sklearn.decomposition.PCA.__init_subclass__ sklearn.decomposition.PCA.__le__ sklearn.decomposition.PCA.__lt__ sklearn.decomposition.PCA.__ne__ sklearn.decomposition.PCA.__new__ sklearn.decomposition.PCA.__reduce__ sklearn.decomposition.PCA.__reduce_ex__ sklearn.decomposition.PCA.__repr__ sklearn.decomposition.PCA.__setattr__ sklearn.decomposition.PCA.__setstate__ sklearn.decomposition.PCA.__sizeof__ sklearn.decomposition.PCA.__sklearn_clone__ sklearn.decomposition.PCA.__sklearn_tags__ sklearn.decomposition.PCA.__str__ sklearn.decomposition.PCA.__subclasshook__ sklearn.decomposition.PCA._build_request_for_signature sklearn.decomposition.PCA._check_feature_names sklearn.decomposition.PCA._check_n_features sklearn.decomposition.PCA._fit sklearn.decomposition.PCA._fit_full sklearn.decomposition.PCA._fit_truncated sklearn.decomposition.PCA._get_default_requests sklearn.decomposition.PCA._get_doc_link sklearn.decomposition.PCA._get_metadata_request sklearn.decomposition.PCA._get_param_names sklearn.decomposition.PCA._get_tags sklearn.decomposition.PCA._more_tags sklearn.decomposition.PCA._repr_html_inner sklearn.decomposition.PCA._repr_mimebundle_ sklearn.decomposition.PCA._transform sklearn.decomposition.PCA._validate_data sklearn.decomposition.PCA._validate_params sklearn.decomposition.PCA.fit sklearn.decomposition.PCA.fit_transform sklearn.decomposition.PCA.get_covariance sklearn.decomposition.PCA.get_feature_names_out sklearn.decomposition.PCA.get_metadata_routing sklearn.decomposition.PCA.get_params sklearn.decomposition.PCA.get_precision sklearn.decomposition.PCA.inverse_transform sklearn.decomposition.PCA.score sklearn.decomposition.PCA.score_samples sklearn.decomposition.PCA.set_output sklearn.decomposition.PCA.set_params sklearn.decomposition.PCA.transform 
+.. minigallery:: sklearn.decomposition.PCA sklearn.decomposition.PCA.__delattr__ sklearn.decomposition.PCA.__dir__ sklearn.decomposition.PCA.__eq__ sklearn.decomposition.PCA.__format__ sklearn.decomposition.PCA.__ge__ sklearn.decomposition.PCA.__getattribute__ sklearn.decomposition.PCA.__getstate__ sklearn.decomposition.PCA.__gt__ sklearn.decomposition.PCA.__hash__ sklearn.decomposition.PCA.__init__ sklearn.decomposition.PCA.__init_subclass__ sklearn.decomposition.PCA.__le__ sklearn.decomposition.PCA.__lt__ sklearn.decomposition.PCA.__ne__ sklearn.decomposition.PCA.__new__ sklearn.decomposition.PCA.__reduce__ sklearn.decomposition.PCA.__reduce_ex__ sklearn.decomposition.PCA.__repr__ sklearn.decomposition.PCA.__setattr__ sklearn.decomposition.PCA.__setstate__ sklearn.decomposition.PCA.__sizeof__ sklearn.decomposition.PCA.__sklearn_clone__ sklearn.decomposition.PCA.__sklearn_tags__ sklearn.decomposition.PCA.__str__ sklearn.decomposition.PCA.__subclasshook__ sklearn.decomposition.PCA._build_request_for_signature sklearn.decomposition.PCA._fit sklearn.decomposition.PCA._fit_full sklearn.decomposition.PCA._fit_truncated sklearn.decomposition.PCA._get_default_requests sklearn.decomposition.PCA._get_doc_link sklearn.decomposition.PCA._get_metadata_request sklearn.decomposition.PCA._get_param_names sklearn.decomposition.PCA._repr_html_inner sklearn.decomposition.PCA._repr_mimebundle_ sklearn.decomposition.PCA._transform sklearn.decomposition.PCA._validate_params sklearn.decomposition.PCA.fit sklearn.decomposition.PCA.fit_transform sklearn.decomposition.PCA.get_covariance sklearn.decomposition.PCA.get_feature_names_out sklearn.decomposition.PCA.get_metadata_routing sklearn.decomposition.PCA.get_params sklearn.decomposition.PCA.get_precision sklearn.decomposition.PCA.inverse_transform sklearn.decomposition.PCA.score sklearn.decomposition.PCA.score_samples sklearn.decomposition.PCA.set_output sklearn.decomposition.PCA.set_params sklearn.decomposition.PCA.transform 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.decomposition.SparseCoder.rst.txt b/dev/_sources/modules/generated/sklearn.decomposition.SparseCoder.rst.txt
index 74aee89eefdf5..5cc9c5a1d815c 100644
--- a/dev/_sources/modules/generated/sklearn.decomposition.SparseCoder.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.decomposition.SparseCoder.rst.txt
@@ -8,6 +8,6 @@ SparseCoder
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.decomposition.SparseCoder sklearn.decomposition.SparseCoder.__delattr__ sklearn.decomposition.SparseCoder.__dir__ sklearn.decomposition.SparseCoder.__eq__ sklearn.decomposition.SparseCoder.__format__ sklearn.decomposition.SparseCoder.__ge__ sklearn.decomposition.SparseCoder.__getattribute__ sklearn.decomposition.SparseCoder.__getstate__ sklearn.decomposition.SparseCoder.__gt__ sklearn.decomposition.SparseCoder.__hash__ sklearn.decomposition.SparseCoder.__init__ sklearn.decomposition.SparseCoder.__init_subclass__ sklearn.decomposition.SparseCoder.__le__ sklearn.decomposition.SparseCoder.__lt__ sklearn.decomposition.SparseCoder.__ne__ sklearn.decomposition.SparseCoder.__new__ sklearn.decomposition.SparseCoder.__reduce__ sklearn.decomposition.SparseCoder.__reduce_ex__ sklearn.decomposition.SparseCoder.__repr__ sklearn.decomposition.SparseCoder.__setattr__ sklearn.decomposition.SparseCoder.__setstate__ sklearn.decomposition.SparseCoder.__sizeof__ sklearn.decomposition.SparseCoder.__sklearn_clone__ sklearn.decomposition.SparseCoder.__sklearn_tags__ sklearn.decomposition.SparseCoder.__str__ sklearn.decomposition.SparseCoder.__subclasshook__ sklearn.decomposition.SparseCoder._build_request_for_signature sklearn.decomposition.SparseCoder._check_feature_names sklearn.decomposition.SparseCoder._check_n_features sklearn.decomposition.SparseCoder._get_default_requests sklearn.decomposition.SparseCoder._get_doc_link sklearn.decomposition.SparseCoder._get_metadata_request sklearn.decomposition.SparseCoder._get_param_names sklearn.decomposition.SparseCoder._get_tags sklearn.decomposition.SparseCoder._inverse_transform sklearn.decomposition.SparseCoder._more_tags sklearn.decomposition.SparseCoder._repr_html_inner sklearn.decomposition.SparseCoder._repr_mimebundle_ sklearn.decomposition.SparseCoder._transform sklearn.decomposition.SparseCoder._validate_data sklearn.decomposition.SparseCoder._validate_params sklearn.decomposition.SparseCoder.fit sklearn.decomposition.SparseCoder.fit_transform sklearn.decomposition.SparseCoder.get_feature_names_out sklearn.decomposition.SparseCoder.get_metadata_routing sklearn.decomposition.SparseCoder.get_params sklearn.decomposition.SparseCoder.inverse_transform sklearn.decomposition.SparseCoder.set_output sklearn.decomposition.SparseCoder.set_params sklearn.decomposition.SparseCoder.transform 
+.. minigallery:: sklearn.decomposition.SparseCoder sklearn.decomposition.SparseCoder.__delattr__ sklearn.decomposition.SparseCoder.__dir__ sklearn.decomposition.SparseCoder.__eq__ sklearn.decomposition.SparseCoder.__format__ sklearn.decomposition.SparseCoder.__ge__ sklearn.decomposition.SparseCoder.__getattribute__ sklearn.decomposition.SparseCoder.__getstate__ sklearn.decomposition.SparseCoder.__gt__ sklearn.decomposition.SparseCoder.__hash__ sklearn.decomposition.SparseCoder.__init__ sklearn.decomposition.SparseCoder.__init_subclass__ sklearn.decomposition.SparseCoder.__le__ sklearn.decomposition.SparseCoder.__lt__ sklearn.decomposition.SparseCoder.__ne__ sklearn.decomposition.SparseCoder.__new__ sklearn.decomposition.SparseCoder.__reduce__ sklearn.decomposition.SparseCoder.__reduce_ex__ sklearn.decomposition.SparseCoder.__repr__ sklearn.decomposition.SparseCoder.__setattr__ sklearn.decomposition.SparseCoder.__setstate__ sklearn.decomposition.SparseCoder.__sizeof__ sklearn.decomposition.SparseCoder.__sklearn_clone__ sklearn.decomposition.SparseCoder.__sklearn_tags__ sklearn.decomposition.SparseCoder.__str__ sklearn.decomposition.SparseCoder.__subclasshook__ sklearn.decomposition.SparseCoder._build_request_for_signature sklearn.decomposition.SparseCoder._get_default_requests sklearn.decomposition.SparseCoder._get_doc_link sklearn.decomposition.SparseCoder._get_metadata_request sklearn.decomposition.SparseCoder._get_param_names sklearn.decomposition.SparseCoder._inverse_transform sklearn.decomposition.SparseCoder._repr_html_inner sklearn.decomposition.SparseCoder._repr_mimebundle_ sklearn.decomposition.SparseCoder._transform sklearn.decomposition.SparseCoder._validate_params sklearn.decomposition.SparseCoder.fit sklearn.decomposition.SparseCoder.fit_transform sklearn.decomposition.SparseCoder.get_feature_names_out sklearn.decomposition.SparseCoder.get_metadata_routing sklearn.decomposition.SparseCoder.get_params sklearn.decomposition.SparseCoder.inverse_transform sklearn.decomposition.SparseCoder.set_output sklearn.decomposition.SparseCoder.set_params sklearn.decomposition.SparseCoder.transform 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.decomposition.SparsePCA.rst.txt b/dev/_sources/modules/generated/sklearn.decomposition.SparsePCA.rst.txt
index f6104b5f275f4..ff02e1f820b54 100644
--- a/dev/_sources/modules/generated/sklearn.decomposition.SparsePCA.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.decomposition.SparsePCA.rst.txt
@@ -8,6 +8,6 @@ SparsePCA
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.decomposition.SparsePCA sklearn.decomposition.SparsePCA.__delattr__ sklearn.decomposition.SparsePCA.__dir__ sklearn.decomposition.SparsePCA.__eq__ sklearn.decomposition.SparsePCA.__format__ sklearn.decomposition.SparsePCA.__ge__ sklearn.decomposition.SparsePCA.__getattribute__ sklearn.decomposition.SparsePCA.__getstate__ sklearn.decomposition.SparsePCA.__gt__ sklearn.decomposition.SparsePCA.__hash__ sklearn.decomposition.SparsePCA.__init__ sklearn.decomposition.SparsePCA.__init_subclass__ sklearn.decomposition.SparsePCA.__le__ sklearn.decomposition.SparsePCA.__lt__ sklearn.decomposition.SparsePCA.__ne__ sklearn.decomposition.SparsePCA.__new__ sklearn.decomposition.SparsePCA.__reduce__ sklearn.decomposition.SparsePCA.__reduce_ex__ sklearn.decomposition.SparsePCA.__repr__ sklearn.decomposition.SparsePCA.__setattr__ sklearn.decomposition.SparsePCA.__setstate__ sklearn.decomposition.SparsePCA.__sizeof__ sklearn.decomposition.SparsePCA.__sklearn_clone__ sklearn.decomposition.SparsePCA.__sklearn_tags__ sklearn.decomposition.SparsePCA.__str__ sklearn.decomposition.SparsePCA.__subclasshook__ sklearn.decomposition.SparsePCA._build_request_for_signature sklearn.decomposition.SparsePCA._check_feature_names sklearn.decomposition.SparsePCA._check_n_features sklearn.decomposition.SparsePCA._fit sklearn.decomposition.SparsePCA._get_default_requests sklearn.decomposition.SparsePCA._get_doc_link sklearn.decomposition.SparsePCA._get_metadata_request sklearn.decomposition.SparsePCA._get_param_names sklearn.decomposition.SparsePCA._get_tags sklearn.decomposition.SparsePCA._more_tags sklearn.decomposition.SparsePCA._repr_html_inner sklearn.decomposition.SparsePCA._repr_mimebundle_ sklearn.decomposition.SparsePCA._validate_data sklearn.decomposition.SparsePCA._validate_params sklearn.decomposition.SparsePCA.fit sklearn.decomposition.SparsePCA.fit_transform sklearn.decomposition.SparsePCA.get_feature_names_out sklearn.decomposition.SparsePCA.get_metadata_routing sklearn.decomposition.SparsePCA.get_params sklearn.decomposition.SparsePCA.inverse_transform sklearn.decomposition.SparsePCA.set_output sklearn.decomposition.SparsePCA.set_params sklearn.decomposition.SparsePCA.transform 
+.. minigallery:: sklearn.decomposition.SparsePCA sklearn.decomposition.SparsePCA.__delattr__ sklearn.decomposition.SparsePCA.__dir__ sklearn.decomposition.SparsePCA.__eq__ sklearn.decomposition.SparsePCA.__format__ sklearn.decomposition.SparsePCA.__ge__ sklearn.decomposition.SparsePCA.__getattribute__ sklearn.decomposition.SparsePCA.__getstate__ sklearn.decomposition.SparsePCA.__gt__ sklearn.decomposition.SparsePCA.__hash__ sklearn.decomposition.SparsePCA.__init__ sklearn.decomposition.SparsePCA.__init_subclass__ sklearn.decomposition.SparsePCA.__le__ sklearn.decomposition.SparsePCA.__lt__ sklearn.decomposition.SparsePCA.__ne__ sklearn.decomposition.SparsePCA.__new__ sklearn.decomposition.SparsePCA.__reduce__ sklearn.decomposition.SparsePCA.__reduce_ex__ sklearn.decomposition.SparsePCA.__repr__ sklearn.decomposition.SparsePCA.__setattr__ sklearn.decomposition.SparsePCA.__setstate__ sklearn.decomposition.SparsePCA.__sizeof__ sklearn.decomposition.SparsePCA.__sklearn_clone__ sklearn.decomposition.SparsePCA.__sklearn_tags__ sklearn.decomposition.SparsePCA.__str__ sklearn.decomposition.SparsePCA.__subclasshook__ sklearn.decomposition.SparsePCA._build_request_for_signature sklearn.decomposition.SparsePCA._fit sklearn.decomposition.SparsePCA._get_default_requests sklearn.decomposition.SparsePCA._get_doc_link sklearn.decomposition.SparsePCA._get_metadata_request sklearn.decomposition.SparsePCA._get_param_names sklearn.decomposition.SparsePCA._repr_html_inner sklearn.decomposition.SparsePCA._repr_mimebundle_ sklearn.decomposition.SparsePCA._validate_params sklearn.decomposition.SparsePCA.fit sklearn.decomposition.SparsePCA.fit_transform sklearn.decomposition.SparsePCA.get_feature_names_out sklearn.decomposition.SparsePCA.get_metadata_routing sklearn.decomposition.SparsePCA.get_params sklearn.decomposition.SparsePCA.inverse_transform sklearn.decomposition.SparsePCA.set_output sklearn.decomposition.SparsePCA.set_params sklearn.decomposition.SparsePCA.transform 
    :add-heading: Gallery examples
    :heading-level: -
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diff --git a/dev/_sources/modules/generated/sklearn.decomposition.TruncatedSVD.rst.txt b/dev/_sources/modules/generated/sklearn.decomposition.TruncatedSVD.rst.txt
index 04b4caee1650d..6e9e752cf91a3 100644
--- a/dev/_sources/modules/generated/sklearn.decomposition.TruncatedSVD.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.decomposition.TruncatedSVD.rst.txt
@@ -8,6 +8,6 @@ TruncatedSVD
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.decomposition.TruncatedSVD sklearn.decomposition.TruncatedSVD.__delattr__ sklearn.decomposition.TruncatedSVD.__dir__ sklearn.decomposition.TruncatedSVD.__eq__ sklearn.decomposition.TruncatedSVD.__format__ sklearn.decomposition.TruncatedSVD.__ge__ sklearn.decomposition.TruncatedSVD.__getattribute__ sklearn.decomposition.TruncatedSVD.__getstate__ sklearn.decomposition.TruncatedSVD.__gt__ sklearn.decomposition.TruncatedSVD.__hash__ sklearn.decomposition.TruncatedSVD.__init__ sklearn.decomposition.TruncatedSVD.__init_subclass__ sklearn.decomposition.TruncatedSVD.__le__ sklearn.decomposition.TruncatedSVD.__lt__ sklearn.decomposition.TruncatedSVD.__ne__ sklearn.decomposition.TruncatedSVD.__new__ sklearn.decomposition.TruncatedSVD.__reduce__ sklearn.decomposition.TruncatedSVD.__reduce_ex__ sklearn.decomposition.TruncatedSVD.__repr__ sklearn.decomposition.TruncatedSVD.__setattr__ sklearn.decomposition.TruncatedSVD.__setstate__ sklearn.decomposition.TruncatedSVD.__sizeof__ sklearn.decomposition.TruncatedSVD.__sklearn_clone__ sklearn.decomposition.TruncatedSVD.__sklearn_tags__ sklearn.decomposition.TruncatedSVD.__str__ sklearn.decomposition.TruncatedSVD.__subclasshook__ sklearn.decomposition.TruncatedSVD._build_request_for_signature sklearn.decomposition.TruncatedSVD._check_feature_names sklearn.decomposition.TruncatedSVD._check_n_features sklearn.decomposition.TruncatedSVD._get_default_requests sklearn.decomposition.TruncatedSVD._get_doc_link sklearn.decomposition.TruncatedSVD._get_metadata_request sklearn.decomposition.TruncatedSVD._get_param_names sklearn.decomposition.TruncatedSVD._get_tags sklearn.decomposition.TruncatedSVD._more_tags sklearn.decomposition.TruncatedSVD._repr_html_inner sklearn.decomposition.TruncatedSVD._repr_mimebundle_ sklearn.decomposition.TruncatedSVD._validate_data sklearn.decomposition.TruncatedSVD._validate_params sklearn.decomposition.TruncatedSVD.fit sklearn.decomposition.TruncatedSVD.fit_transform sklearn.decomposition.TruncatedSVD.get_feature_names_out sklearn.decomposition.TruncatedSVD.get_metadata_routing sklearn.decomposition.TruncatedSVD.get_params sklearn.decomposition.TruncatedSVD.inverse_transform sklearn.decomposition.TruncatedSVD.set_output sklearn.decomposition.TruncatedSVD.set_params sklearn.decomposition.TruncatedSVD.transform 
+.. minigallery:: sklearn.decomposition.TruncatedSVD sklearn.decomposition.TruncatedSVD.__delattr__ sklearn.decomposition.TruncatedSVD.__dir__ sklearn.decomposition.TruncatedSVD.__eq__ sklearn.decomposition.TruncatedSVD.__format__ sklearn.decomposition.TruncatedSVD.__ge__ sklearn.decomposition.TruncatedSVD.__getattribute__ sklearn.decomposition.TruncatedSVD.__getstate__ sklearn.decomposition.TruncatedSVD.__gt__ sklearn.decomposition.TruncatedSVD.__hash__ sklearn.decomposition.TruncatedSVD.__init__ sklearn.decomposition.TruncatedSVD.__init_subclass__ sklearn.decomposition.TruncatedSVD.__le__ sklearn.decomposition.TruncatedSVD.__lt__ sklearn.decomposition.TruncatedSVD.__ne__ sklearn.decomposition.TruncatedSVD.__new__ sklearn.decomposition.TruncatedSVD.__reduce__ sklearn.decomposition.TruncatedSVD.__reduce_ex__ sklearn.decomposition.TruncatedSVD.__repr__ sklearn.decomposition.TruncatedSVD.__setattr__ sklearn.decomposition.TruncatedSVD.__setstate__ sklearn.decomposition.TruncatedSVD.__sizeof__ sklearn.decomposition.TruncatedSVD.__sklearn_clone__ sklearn.decomposition.TruncatedSVD.__sklearn_tags__ sklearn.decomposition.TruncatedSVD.__str__ sklearn.decomposition.TruncatedSVD.__subclasshook__ sklearn.decomposition.TruncatedSVD._build_request_for_signature sklearn.decomposition.TruncatedSVD._get_default_requests sklearn.decomposition.TruncatedSVD._get_doc_link sklearn.decomposition.TruncatedSVD._get_metadata_request sklearn.decomposition.TruncatedSVD._get_param_names sklearn.decomposition.TruncatedSVD._repr_html_inner sklearn.decomposition.TruncatedSVD._repr_mimebundle_ sklearn.decomposition.TruncatedSVD._validate_params sklearn.decomposition.TruncatedSVD.fit sklearn.decomposition.TruncatedSVD.fit_transform sklearn.decomposition.TruncatedSVD.get_feature_names_out sklearn.decomposition.TruncatedSVD.get_metadata_routing sklearn.decomposition.TruncatedSVD.get_params sklearn.decomposition.TruncatedSVD.inverse_transform sklearn.decomposition.TruncatedSVD.set_output sklearn.decomposition.TruncatedSVD.set_params sklearn.decomposition.TruncatedSVD.transform 
    :add-heading: Gallery examples
    :heading-level: -
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diff --git a/dev/_sources/modules/generated/sklearn.discriminant_analysis.LinearDiscriminantAnalysis.rst.txt b/dev/_sources/modules/generated/sklearn.discriminant_analysis.LinearDiscriminantAnalysis.rst.txt
index a363173143fa3..9152914fc42cf 100644
--- a/dev/_sources/modules/generated/sklearn.discriminant_analysis.LinearDiscriminantAnalysis.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.discriminant_analysis.LinearDiscriminantAnalysis.rst.txt
@@ -8,6 +8,6 @@ LinearDiscriminantAnalysis
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.discriminant_analysis.LinearDiscriminantAnalysis sklearn.discriminant_analysis.LinearDiscriminantAnalysis.__delattr__ sklearn.discriminant_analysis.LinearDiscriminantAnalysis.__dir__ sklearn.discriminant_analysis.LinearDiscriminantAnalysis.__eq__ sklearn.discriminant_analysis.LinearDiscriminantAnalysis.__format__ sklearn.discriminant_analysis.LinearDiscriminantAnalysis.__ge__ sklearn.discriminant_analysis.LinearDiscriminantAnalysis.__getattribute__ sklearn.discriminant_analysis.LinearDiscriminantAnalysis.__getstate__ sklearn.discriminant_analysis.LinearDiscriminantAnalysis.__gt__ sklearn.discriminant_analysis.LinearDiscriminantAnalysis.__hash__ sklearn.discriminant_analysis.LinearDiscriminantAnalysis.__init__ sklearn.discriminant_analysis.LinearDiscriminantAnalysis.__init_subclass__ sklearn.discriminant_analysis.LinearDiscriminantAnalysis.__le__ sklearn.discriminant_analysis.LinearDiscriminantAnalysis.__lt__ sklearn.discriminant_analysis.LinearDiscriminantAnalysis.__ne__ sklearn.discriminant_analysis.LinearDiscriminantAnalysis.__new__ sklearn.discriminant_analysis.LinearDiscriminantAnalysis.__reduce__ sklearn.discriminant_analysis.LinearDiscriminantAnalysis.__reduce_ex__ sklearn.discriminant_analysis.LinearDiscriminantAnalysis.__repr__ sklearn.discriminant_analysis.LinearDiscriminantAnalysis.__setattr__ sklearn.discriminant_analysis.LinearDiscriminantAnalysis.__setstate__ sklearn.discriminant_analysis.LinearDiscriminantAnalysis.__sizeof__ sklearn.discriminant_analysis.LinearDiscriminantAnalysis.__sklearn_clone__ sklearn.discriminant_analysis.LinearDiscriminantAnalysis.__sklearn_tags__ sklearn.discriminant_analysis.LinearDiscriminantAnalysis.__str__ sklearn.discriminant_analysis.LinearDiscriminantAnalysis.__subclasshook__ sklearn.discriminant_analysis.LinearDiscriminantAnalysis._build_request_for_signature sklearn.discriminant_analysis.LinearDiscriminantAnalysis._check_feature_names sklearn.discriminant_analysis.LinearDiscriminantAnalysis._check_n_features sklearn.discriminant_analysis.LinearDiscriminantAnalysis._get_default_requests sklearn.discriminant_analysis.LinearDiscriminantAnalysis._get_doc_link sklearn.discriminant_analysis.LinearDiscriminantAnalysis._get_metadata_request sklearn.discriminant_analysis.LinearDiscriminantAnalysis._get_param_names sklearn.discriminant_analysis.LinearDiscriminantAnalysis._get_tags sklearn.discriminant_analysis.LinearDiscriminantAnalysis._more_tags sklearn.discriminant_analysis.LinearDiscriminantAnalysis._predict_proba_lr sklearn.discriminant_analysis.LinearDiscriminantAnalysis._repr_html_inner sklearn.discriminant_analysis.LinearDiscriminantAnalysis._repr_mimebundle_ sklearn.discriminant_analysis.LinearDiscriminantAnalysis._solve_eigen sklearn.discriminant_analysis.LinearDiscriminantAnalysis._solve_lstsq sklearn.discriminant_analysis.LinearDiscriminantAnalysis._solve_svd sklearn.discriminant_analysis.LinearDiscriminantAnalysis._validate_data sklearn.discriminant_analysis.LinearDiscriminantAnalysis._validate_params sklearn.discriminant_analysis.LinearDiscriminantAnalysis.decision_function sklearn.discriminant_analysis.LinearDiscriminantAnalysis.fit sklearn.discriminant_analysis.LinearDiscriminantAnalysis.fit_transform sklearn.discriminant_analysis.LinearDiscriminantAnalysis.get_feature_names_out sklearn.discriminant_analysis.LinearDiscriminantAnalysis.get_metadata_routing sklearn.discriminant_analysis.LinearDiscriminantAnalysis.get_params sklearn.discriminant_analysis.LinearDiscriminantAnalysis.predict sklearn.discriminant_analysis.LinearDiscriminantAnalysis.predict_log_proba sklearn.discriminant_analysis.LinearDiscriminantAnalysis.predict_proba sklearn.discriminant_analysis.LinearDiscriminantAnalysis.score sklearn.discriminant_analysis.LinearDiscriminantAnalysis.set_output sklearn.discriminant_analysis.LinearDiscriminantAnalysis.set_params sklearn.discriminant_analysis.LinearDiscriminantAnalysis.set_score_request sklearn.discriminant_analysis.LinearDiscriminantAnalysis.transform 
+.. minigallery:: sklearn.discriminant_analysis.LinearDiscriminantAnalysis sklearn.discriminant_analysis.LinearDiscriminantAnalysis.__delattr__ sklearn.discriminant_analysis.LinearDiscriminantAnalysis.__dir__ sklearn.discriminant_analysis.LinearDiscriminantAnalysis.__eq__ sklearn.discriminant_analysis.LinearDiscriminantAnalysis.__format__ sklearn.discriminant_analysis.LinearDiscriminantAnalysis.__ge__ sklearn.discriminant_analysis.LinearDiscriminantAnalysis.__getattribute__ sklearn.discriminant_analysis.LinearDiscriminantAnalysis.__getstate__ sklearn.discriminant_analysis.LinearDiscriminantAnalysis.__gt__ sklearn.discriminant_analysis.LinearDiscriminantAnalysis.__hash__ sklearn.discriminant_analysis.LinearDiscriminantAnalysis.__init__ sklearn.discriminant_analysis.LinearDiscriminantAnalysis.__init_subclass__ sklearn.discriminant_analysis.LinearDiscriminantAnalysis.__le__ sklearn.discriminant_analysis.LinearDiscriminantAnalysis.__lt__ sklearn.discriminant_analysis.LinearDiscriminantAnalysis.__ne__ sklearn.discriminant_analysis.LinearDiscriminantAnalysis.__new__ sklearn.discriminant_analysis.LinearDiscriminantAnalysis.__reduce__ sklearn.discriminant_analysis.LinearDiscriminantAnalysis.__reduce_ex__ sklearn.discriminant_analysis.LinearDiscriminantAnalysis.__repr__ sklearn.discriminant_analysis.LinearDiscriminantAnalysis.__setattr__ sklearn.discriminant_analysis.LinearDiscriminantAnalysis.__setstate__ sklearn.discriminant_analysis.LinearDiscriminantAnalysis.__sizeof__ sklearn.discriminant_analysis.LinearDiscriminantAnalysis.__sklearn_clone__ sklearn.discriminant_analysis.LinearDiscriminantAnalysis.__sklearn_tags__ sklearn.discriminant_analysis.LinearDiscriminantAnalysis.__str__ sklearn.discriminant_analysis.LinearDiscriminantAnalysis.__subclasshook__ sklearn.discriminant_analysis.LinearDiscriminantAnalysis._build_request_for_signature sklearn.discriminant_analysis.LinearDiscriminantAnalysis._get_default_requests sklearn.discriminant_analysis.LinearDiscriminantAnalysis._get_doc_link sklearn.discriminant_analysis.LinearDiscriminantAnalysis._get_metadata_request sklearn.discriminant_analysis.LinearDiscriminantAnalysis._get_param_names sklearn.discriminant_analysis.LinearDiscriminantAnalysis._predict_proba_lr sklearn.discriminant_analysis.LinearDiscriminantAnalysis._repr_html_inner sklearn.discriminant_analysis.LinearDiscriminantAnalysis._repr_mimebundle_ sklearn.discriminant_analysis.LinearDiscriminantAnalysis._solve_eigen sklearn.discriminant_analysis.LinearDiscriminantAnalysis._solve_lstsq sklearn.discriminant_analysis.LinearDiscriminantAnalysis._solve_svd sklearn.discriminant_analysis.LinearDiscriminantAnalysis._validate_params sklearn.discriminant_analysis.LinearDiscriminantAnalysis.decision_function sklearn.discriminant_analysis.LinearDiscriminantAnalysis.fit sklearn.discriminant_analysis.LinearDiscriminantAnalysis.fit_transform sklearn.discriminant_analysis.LinearDiscriminantAnalysis.get_feature_names_out sklearn.discriminant_analysis.LinearDiscriminantAnalysis.get_metadata_routing sklearn.discriminant_analysis.LinearDiscriminantAnalysis.get_params sklearn.discriminant_analysis.LinearDiscriminantAnalysis.predict sklearn.discriminant_analysis.LinearDiscriminantAnalysis.predict_log_proba sklearn.discriminant_analysis.LinearDiscriminantAnalysis.predict_proba sklearn.discriminant_analysis.LinearDiscriminantAnalysis.score sklearn.discriminant_analysis.LinearDiscriminantAnalysis.set_output sklearn.discriminant_analysis.LinearDiscriminantAnalysis.set_params sklearn.discriminant_analysis.LinearDiscriminantAnalysis.set_score_request sklearn.discriminant_analysis.LinearDiscriminantAnalysis.transform 
    :add-heading: Gallery examples
    :heading-level: -
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diff --git a/dev/_sources/modules/generated/sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.rst.txt b/dev/_sources/modules/generated/sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.rst.txt
index d3823cb7d73a2..86d4365ce219a 100644
--- a/dev/_sources/modules/generated/sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.rst.txt
@@ -8,6 +8,6 @@ QuadraticDiscriminantAnalysis
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.__delattr__ sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.__dir__ sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.__eq__ sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.__format__ sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.__ge__ sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.__getattribute__ sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.__getstate__ sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.__gt__ sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.__hash__ sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.__init__ sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.__init_subclass__ sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.__le__ sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.__lt__ sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.__ne__ sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.__new__ sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.__reduce__ sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.__reduce_ex__ sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.__repr__ sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.__setattr__ sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.__setstate__ sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.__sizeof__ sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.__sklearn_clone__ sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.__sklearn_tags__ sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.__str__ sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.__subclasshook__ sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis._build_request_for_signature sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis._check_feature_names sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis._check_n_features sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis._decision_function sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis._get_default_requests sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis._get_doc_link sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis._get_metadata_request sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis._get_param_names sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis._get_tags sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis._more_tags sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis._repr_html_inner sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis._repr_mimebundle_ sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis._validate_data sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis._validate_params sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.decision_function sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.fit sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.get_metadata_routing sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.get_params sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.predict sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.predict_log_proba sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.predict_proba sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.score sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.set_params sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.set_score_request 
+.. minigallery:: sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.__delattr__ sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.__dir__ sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.__eq__ sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.__format__ sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.__ge__ sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.__getattribute__ sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.__getstate__ sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.__gt__ sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.__hash__ sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.__init__ sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.__init_subclass__ sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.__le__ sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.__lt__ sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.__ne__ sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.__new__ sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.__reduce__ sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.__reduce_ex__ sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.__repr__ sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.__setattr__ sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.__setstate__ sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.__sizeof__ sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.__sklearn_clone__ sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.__sklearn_tags__ sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.__str__ sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.__subclasshook__ sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis._build_request_for_signature sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis._decision_function sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis._get_default_requests sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis._get_doc_link sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis._get_metadata_request sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis._get_param_names sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis._repr_html_inner sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis._repr_mimebundle_ sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis._validate_params sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.decision_function sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.fit sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.get_metadata_routing sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.get_params sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.predict sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.predict_log_proba sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.predict_proba sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.score sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.set_params sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.set_score_request 
    :add-heading: Gallery examples
    :heading-level: -
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diff --git a/dev/_sources/modules/generated/sklearn.dummy.DummyClassifier.rst.txt b/dev/_sources/modules/generated/sklearn.dummy.DummyClassifier.rst.txt
index 017aee80c030e..e098b06b741f2 100644
--- a/dev/_sources/modules/generated/sklearn.dummy.DummyClassifier.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.dummy.DummyClassifier.rst.txt
@@ -8,6 +8,6 @@ DummyClassifier
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.dummy.DummyClassifier sklearn.dummy.DummyClassifier.__delattr__ sklearn.dummy.DummyClassifier.__dir__ sklearn.dummy.DummyClassifier.__eq__ sklearn.dummy.DummyClassifier.__format__ sklearn.dummy.DummyClassifier.__ge__ sklearn.dummy.DummyClassifier.__getattribute__ sklearn.dummy.DummyClassifier.__getstate__ sklearn.dummy.DummyClassifier.__gt__ sklearn.dummy.DummyClassifier.__hash__ sklearn.dummy.DummyClassifier.__init__ sklearn.dummy.DummyClassifier.__init_subclass__ sklearn.dummy.DummyClassifier.__le__ sklearn.dummy.DummyClassifier.__lt__ sklearn.dummy.DummyClassifier.__ne__ sklearn.dummy.DummyClassifier.__new__ sklearn.dummy.DummyClassifier.__reduce__ sklearn.dummy.DummyClassifier.__reduce_ex__ sklearn.dummy.DummyClassifier.__repr__ sklearn.dummy.DummyClassifier.__setattr__ sklearn.dummy.DummyClassifier.__setstate__ sklearn.dummy.DummyClassifier.__sizeof__ sklearn.dummy.DummyClassifier.__sklearn_clone__ sklearn.dummy.DummyClassifier.__sklearn_tags__ sklearn.dummy.DummyClassifier.__str__ sklearn.dummy.DummyClassifier.__subclasshook__ sklearn.dummy.DummyClassifier._build_request_for_signature sklearn.dummy.DummyClassifier._check_feature_names sklearn.dummy.DummyClassifier._check_n_features sklearn.dummy.DummyClassifier._get_default_requests sklearn.dummy.DummyClassifier._get_doc_link sklearn.dummy.DummyClassifier._get_metadata_request sklearn.dummy.DummyClassifier._get_param_names sklearn.dummy.DummyClassifier._get_tags sklearn.dummy.DummyClassifier._more_tags sklearn.dummy.DummyClassifier._repr_html_inner sklearn.dummy.DummyClassifier._repr_mimebundle_ sklearn.dummy.DummyClassifier._validate_data sklearn.dummy.DummyClassifier._validate_params sklearn.dummy.DummyClassifier.fit sklearn.dummy.DummyClassifier.get_metadata_routing sklearn.dummy.DummyClassifier.get_params sklearn.dummy.DummyClassifier.predict sklearn.dummy.DummyClassifier.predict_log_proba sklearn.dummy.DummyClassifier.predict_proba sklearn.dummy.DummyClassifier.score sklearn.dummy.DummyClassifier.set_fit_request sklearn.dummy.DummyClassifier.set_params sklearn.dummy.DummyClassifier.set_score_request 
+.. minigallery:: sklearn.dummy.DummyClassifier sklearn.dummy.DummyClassifier.__delattr__ sklearn.dummy.DummyClassifier.__dir__ sklearn.dummy.DummyClassifier.__eq__ sklearn.dummy.DummyClassifier.__format__ sklearn.dummy.DummyClassifier.__ge__ sklearn.dummy.DummyClassifier.__getattribute__ sklearn.dummy.DummyClassifier.__getstate__ sklearn.dummy.DummyClassifier.__gt__ sklearn.dummy.DummyClassifier.__hash__ sklearn.dummy.DummyClassifier.__init__ sklearn.dummy.DummyClassifier.__init_subclass__ sklearn.dummy.DummyClassifier.__le__ sklearn.dummy.DummyClassifier.__lt__ sklearn.dummy.DummyClassifier.__ne__ sklearn.dummy.DummyClassifier.__new__ sklearn.dummy.DummyClassifier.__reduce__ sklearn.dummy.DummyClassifier.__reduce_ex__ sklearn.dummy.DummyClassifier.__repr__ sklearn.dummy.DummyClassifier.__setattr__ sklearn.dummy.DummyClassifier.__setstate__ sklearn.dummy.DummyClassifier.__sizeof__ sklearn.dummy.DummyClassifier.__sklearn_clone__ sklearn.dummy.DummyClassifier.__sklearn_tags__ sklearn.dummy.DummyClassifier.__str__ sklearn.dummy.DummyClassifier.__subclasshook__ sklearn.dummy.DummyClassifier._build_request_for_signature sklearn.dummy.DummyClassifier._get_default_requests sklearn.dummy.DummyClassifier._get_doc_link sklearn.dummy.DummyClassifier._get_metadata_request sklearn.dummy.DummyClassifier._get_param_names sklearn.dummy.DummyClassifier._repr_html_inner sklearn.dummy.DummyClassifier._repr_mimebundle_ sklearn.dummy.DummyClassifier._validate_params sklearn.dummy.DummyClassifier.fit sklearn.dummy.DummyClassifier.get_metadata_routing sklearn.dummy.DummyClassifier.get_params sklearn.dummy.DummyClassifier.predict sklearn.dummy.DummyClassifier.predict_log_proba sklearn.dummy.DummyClassifier.predict_proba sklearn.dummy.DummyClassifier.score sklearn.dummy.DummyClassifier.set_fit_request sklearn.dummy.DummyClassifier.set_params sklearn.dummy.DummyClassifier.set_score_request 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.dummy.DummyRegressor.rst.txt b/dev/_sources/modules/generated/sklearn.dummy.DummyRegressor.rst.txt
index 0bee3356cdab1..6c9ac205d29cd 100644
--- a/dev/_sources/modules/generated/sklearn.dummy.DummyRegressor.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.dummy.DummyRegressor.rst.txt
@@ -8,6 +8,6 @@ DummyRegressor
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.dummy.DummyRegressor sklearn.dummy.DummyRegressor.__delattr__ sklearn.dummy.DummyRegressor.__dir__ sklearn.dummy.DummyRegressor.__eq__ sklearn.dummy.DummyRegressor.__format__ sklearn.dummy.DummyRegressor.__ge__ sklearn.dummy.DummyRegressor.__getattribute__ sklearn.dummy.DummyRegressor.__getstate__ sklearn.dummy.DummyRegressor.__gt__ sklearn.dummy.DummyRegressor.__hash__ sklearn.dummy.DummyRegressor.__init__ sklearn.dummy.DummyRegressor.__init_subclass__ sklearn.dummy.DummyRegressor.__le__ sklearn.dummy.DummyRegressor.__lt__ sklearn.dummy.DummyRegressor.__ne__ sklearn.dummy.DummyRegressor.__new__ sklearn.dummy.DummyRegressor.__reduce__ sklearn.dummy.DummyRegressor.__reduce_ex__ sklearn.dummy.DummyRegressor.__repr__ sklearn.dummy.DummyRegressor.__setattr__ sklearn.dummy.DummyRegressor.__setstate__ sklearn.dummy.DummyRegressor.__sizeof__ sklearn.dummy.DummyRegressor.__sklearn_clone__ sklearn.dummy.DummyRegressor.__sklearn_tags__ sklearn.dummy.DummyRegressor.__str__ sklearn.dummy.DummyRegressor.__subclasshook__ sklearn.dummy.DummyRegressor._build_request_for_signature sklearn.dummy.DummyRegressor._check_feature_names sklearn.dummy.DummyRegressor._check_n_features sklearn.dummy.DummyRegressor._get_default_requests sklearn.dummy.DummyRegressor._get_doc_link sklearn.dummy.DummyRegressor._get_metadata_request sklearn.dummy.DummyRegressor._get_param_names sklearn.dummy.DummyRegressor._get_tags sklearn.dummy.DummyRegressor._more_tags sklearn.dummy.DummyRegressor._repr_html_inner sklearn.dummy.DummyRegressor._repr_mimebundle_ sklearn.dummy.DummyRegressor._validate_data sklearn.dummy.DummyRegressor._validate_params sklearn.dummy.DummyRegressor.fit sklearn.dummy.DummyRegressor.get_metadata_routing sklearn.dummy.DummyRegressor.get_params sklearn.dummy.DummyRegressor.predict sklearn.dummy.DummyRegressor.score sklearn.dummy.DummyRegressor.set_fit_request sklearn.dummy.DummyRegressor.set_params sklearn.dummy.DummyRegressor.set_predict_request sklearn.dummy.DummyRegressor.set_score_request 
+.. minigallery:: sklearn.dummy.DummyRegressor sklearn.dummy.DummyRegressor.__delattr__ sklearn.dummy.DummyRegressor.__dir__ sklearn.dummy.DummyRegressor.__eq__ sklearn.dummy.DummyRegressor.__format__ sklearn.dummy.DummyRegressor.__ge__ sklearn.dummy.DummyRegressor.__getattribute__ sklearn.dummy.DummyRegressor.__getstate__ sklearn.dummy.DummyRegressor.__gt__ sklearn.dummy.DummyRegressor.__hash__ sklearn.dummy.DummyRegressor.__init__ sklearn.dummy.DummyRegressor.__init_subclass__ sklearn.dummy.DummyRegressor.__le__ sklearn.dummy.DummyRegressor.__lt__ sklearn.dummy.DummyRegressor.__ne__ sklearn.dummy.DummyRegressor.__new__ sklearn.dummy.DummyRegressor.__reduce__ sklearn.dummy.DummyRegressor.__reduce_ex__ sklearn.dummy.DummyRegressor.__repr__ sklearn.dummy.DummyRegressor.__setattr__ sklearn.dummy.DummyRegressor.__setstate__ sklearn.dummy.DummyRegressor.__sizeof__ sklearn.dummy.DummyRegressor.__sklearn_clone__ sklearn.dummy.DummyRegressor.__sklearn_tags__ sklearn.dummy.DummyRegressor.__str__ sklearn.dummy.DummyRegressor.__subclasshook__ sklearn.dummy.DummyRegressor._build_request_for_signature sklearn.dummy.DummyRegressor._get_default_requests sklearn.dummy.DummyRegressor._get_doc_link sklearn.dummy.DummyRegressor._get_metadata_request sklearn.dummy.DummyRegressor._get_param_names sklearn.dummy.DummyRegressor._repr_html_inner sklearn.dummy.DummyRegressor._repr_mimebundle_ sklearn.dummy.DummyRegressor._validate_params sklearn.dummy.DummyRegressor.fit sklearn.dummy.DummyRegressor.get_metadata_routing sklearn.dummy.DummyRegressor.get_params sklearn.dummy.DummyRegressor.predict sklearn.dummy.DummyRegressor.score sklearn.dummy.DummyRegressor.set_fit_request sklearn.dummy.DummyRegressor.set_params sklearn.dummy.DummyRegressor.set_predict_request sklearn.dummy.DummyRegressor.set_score_request 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.ensemble.AdaBoostClassifier.rst.txt b/dev/_sources/modules/generated/sklearn.ensemble.AdaBoostClassifier.rst.txt
index be99bf67c3992..6022967672345 100644
--- a/dev/_sources/modules/generated/sklearn.ensemble.AdaBoostClassifier.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.ensemble.AdaBoostClassifier.rst.txt
@@ -8,6 +8,6 @@ AdaBoostClassifier
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.ensemble.AdaBoostClassifier sklearn.ensemble.AdaBoostClassifier.__delattr__ sklearn.ensemble.AdaBoostClassifier.__dir__ sklearn.ensemble.AdaBoostClassifier.__eq__ sklearn.ensemble.AdaBoostClassifier.__format__ sklearn.ensemble.AdaBoostClassifier.__ge__ sklearn.ensemble.AdaBoostClassifier.__getattribute__ sklearn.ensemble.AdaBoostClassifier.__getitem__ sklearn.ensemble.AdaBoostClassifier.__getstate__ sklearn.ensemble.AdaBoostClassifier.__gt__ sklearn.ensemble.AdaBoostClassifier.__hash__ sklearn.ensemble.AdaBoostClassifier.__init__ sklearn.ensemble.AdaBoostClassifier.__init_subclass__ sklearn.ensemble.AdaBoostClassifier.__iter__ sklearn.ensemble.AdaBoostClassifier.__le__ sklearn.ensemble.AdaBoostClassifier.__len__ sklearn.ensemble.AdaBoostClassifier.__lt__ sklearn.ensemble.AdaBoostClassifier.__ne__ sklearn.ensemble.AdaBoostClassifier.__new__ sklearn.ensemble.AdaBoostClassifier.__reduce__ sklearn.ensemble.AdaBoostClassifier.__reduce_ex__ sklearn.ensemble.AdaBoostClassifier.__repr__ sklearn.ensemble.AdaBoostClassifier.__setattr__ sklearn.ensemble.AdaBoostClassifier.__setstate__ sklearn.ensemble.AdaBoostClassifier.__sizeof__ sklearn.ensemble.AdaBoostClassifier.__sklearn_clone__ sklearn.ensemble.AdaBoostClassifier.__sklearn_tags__ sklearn.ensemble.AdaBoostClassifier.__str__ sklearn.ensemble.AdaBoostClassifier.__subclasshook__ sklearn.ensemble.AdaBoostClassifier._boost sklearn.ensemble.AdaBoostClassifier._build_request_for_signature sklearn.ensemble.AdaBoostClassifier._check_X sklearn.ensemble.AdaBoostClassifier._check_feature_names sklearn.ensemble.AdaBoostClassifier._check_n_features sklearn.ensemble.AdaBoostClassifier._compute_proba_from_decision sklearn.ensemble.AdaBoostClassifier._get_default_requests sklearn.ensemble.AdaBoostClassifier._get_doc_link sklearn.ensemble.AdaBoostClassifier._get_metadata_request sklearn.ensemble.AdaBoostClassifier._get_param_names sklearn.ensemble.AdaBoostClassifier._get_tags sklearn.ensemble.AdaBoostClassifier._make_estimator sklearn.ensemble.AdaBoostClassifier._more_tags sklearn.ensemble.AdaBoostClassifier._repr_html_inner sklearn.ensemble.AdaBoostClassifier._repr_mimebundle_ sklearn.ensemble.AdaBoostClassifier._validate_data sklearn.ensemble.AdaBoostClassifier._validate_estimator sklearn.ensemble.AdaBoostClassifier._validate_params sklearn.ensemble.AdaBoostClassifier.decision_function sklearn.ensemble.AdaBoostClassifier.fit sklearn.ensemble.AdaBoostClassifier.get_metadata_routing sklearn.ensemble.AdaBoostClassifier.get_params sklearn.ensemble.AdaBoostClassifier.predict sklearn.ensemble.AdaBoostClassifier.predict_log_proba sklearn.ensemble.AdaBoostClassifier.predict_proba sklearn.ensemble.AdaBoostClassifier.score sklearn.ensemble.AdaBoostClassifier.set_fit_request sklearn.ensemble.AdaBoostClassifier.set_params sklearn.ensemble.AdaBoostClassifier.set_score_request sklearn.ensemble.AdaBoostClassifier.staged_decision_function sklearn.ensemble.AdaBoostClassifier.staged_predict sklearn.ensemble.AdaBoostClassifier.staged_predict_proba sklearn.ensemble.AdaBoostClassifier.staged_score 
+.. minigallery:: sklearn.ensemble.AdaBoostClassifier sklearn.ensemble.AdaBoostClassifier.__delattr__ sklearn.ensemble.AdaBoostClassifier.__dir__ sklearn.ensemble.AdaBoostClassifier.__eq__ sklearn.ensemble.AdaBoostClassifier.__format__ sklearn.ensemble.AdaBoostClassifier.__ge__ sklearn.ensemble.AdaBoostClassifier.__getattribute__ sklearn.ensemble.AdaBoostClassifier.__getitem__ sklearn.ensemble.AdaBoostClassifier.__getstate__ sklearn.ensemble.AdaBoostClassifier.__gt__ sklearn.ensemble.AdaBoostClassifier.__hash__ sklearn.ensemble.AdaBoostClassifier.__init__ sklearn.ensemble.AdaBoostClassifier.__init_subclass__ sklearn.ensemble.AdaBoostClassifier.__iter__ sklearn.ensemble.AdaBoostClassifier.__le__ sklearn.ensemble.AdaBoostClassifier.__len__ sklearn.ensemble.AdaBoostClassifier.__lt__ sklearn.ensemble.AdaBoostClassifier.__ne__ sklearn.ensemble.AdaBoostClassifier.__new__ sklearn.ensemble.AdaBoostClassifier.__reduce__ sklearn.ensemble.AdaBoostClassifier.__reduce_ex__ sklearn.ensemble.AdaBoostClassifier.__repr__ sklearn.ensemble.AdaBoostClassifier.__setattr__ sklearn.ensemble.AdaBoostClassifier.__setstate__ sklearn.ensemble.AdaBoostClassifier.__sizeof__ sklearn.ensemble.AdaBoostClassifier.__sklearn_clone__ sklearn.ensemble.AdaBoostClassifier.__sklearn_tags__ sklearn.ensemble.AdaBoostClassifier.__str__ sklearn.ensemble.AdaBoostClassifier.__subclasshook__ sklearn.ensemble.AdaBoostClassifier._boost sklearn.ensemble.AdaBoostClassifier._build_request_for_signature sklearn.ensemble.AdaBoostClassifier._check_X sklearn.ensemble.AdaBoostClassifier._compute_proba_from_decision sklearn.ensemble.AdaBoostClassifier._get_default_requests sklearn.ensemble.AdaBoostClassifier._get_doc_link sklearn.ensemble.AdaBoostClassifier._get_metadata_request sklearn.ensemble.AdaBoostClassifier._get_param_names sklearn.ensemble.AdaBoostClassifier._make_estimator sklearn.ensemble.AdaBoostClassifier._repr_html_inner sklearn.ensemble.AdaBoostClassifier._repr_mimebundle_ sklearn.ensemble.AdaBoostClassifier._validate_estimator sklearn.ensemble.AdaBoostClassifier._validate_params sklearn.ensemble.AdaBoostClassifier.decision_function sklearn.ensemble.AdaBoostClassifier.fit sklearn.ensemble.AdaBoostClassifier.get_metadata_routing sklearn.ensemble.AdaBoostClassifier.get_params sklearn.ensemble.AdaBoostClassifier.predict sklearn.ensemble.AdaBoostClassifier.predict_log_proba sklearn.ensemble.AdaBoostClassifier.predict_proba sklearn.ensemble.AdaBoostClassifier.score sklearn.ensemble.AdaBoostClassifier.set_fit_request sklearn.ensemble.AdaBoostClassifier.set_params sklearn.ensemble.AdaBoostClassifier.set_score_request sklearn.ensemble.AdaBoostClassifier.staged_decision_function sklearn.ensemble.AdaBoostClassifier.staged_predict sklearn.ensemble.AdaBoostClassifier.staged_predict_proba sklearn.ensemble.AdaBoostClassifier.staged_score 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.ensemble.AdaBoostRegressor.rst.txt b/dev/_sources/modules/generated/sklearn.ensemble.AdaBoostRegressor.rst.txt
index c51926be920d3..282c675b3b334 100644
--- a/dev/_sources/modules/generated/sklearn.ensemble.AdaBoostRegressor.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.ensemble.AdaBoostRegressor.rst.txt
@@ -8,6 +8,6 @@ AdaBoostRegressor
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.ensemble.AdaBoostRegressor sklearn.ensemble.AdaBoostRegressor.__delattr__ sklearn.ensemble.AdaBoostRegressor.__dir__ sklearn.ensemble.AdaBoostRegressor.__eq__ sklearn.ensemble.AdaBoostRegressor.__format__ sklearn.ensemble.AdaBoostRegressor.__ge__ sklearn.ensemble.AdaBoostRegressor.__getattribute__ sklearn.ensemble.AdaBoostRegressor.__getitem__ sklearn.ensemble.AdaBoostRegressor.__getstate__ sklearn.ensemble.AdaBoostRegressor.__gt__ sklearn.ensemble.AdaBoostRegressor.__hash__ sklearn.ensemble.AdaBoostRegressor.__init__ sklearn.ensemble.AdaBoostRegressor.__init_subclass__ sklearn.ensemble.AdaBoostRegressor.__iter__ sklearn.ensemble.AdaBoostRegressor.__le__ sklearn.ensemble.AdaBoostRegressor.__len__ sklearn.ensemble.AdaBoostRegressor.__lt__ sklearn.ensemble.AdaBoostRegressor.__ne__ sklearn.ensemble.AdaBoostRegressor.__new__ sklearn.ensemble.AdaBoostRegressor.__reduce__ sklearn.ensemble.AdaBoostRegressor.__reduce_ex__ sklearn.ensemble.AdaBoostRegressor.__repr__ sklearn.ensemble.AdaBoostRegressor.__setattr__ sklearn.ensemble.AdaBoostRegressor.__setstate__ sklearn.ensemble.AdaBoostRegressor.__sizeof__ sklearn.ensemble.AdaBoostRegressor.__sklearn_clone__ sklearn.ensemble.AdaBoostRegressor.__sklearn_tags__ sklearn.ensemble.AdaBoostRegressor.__str__ sklearn.ensemble.AdaBoostRegressor.__subclasshook__ sklearn.ensemble.AdaBoostRegressor._boost sklearn.ensemble.AdaBoostRegressor._build_request_for_signature sklearn.ensemble.AdaBoostRegressor._check_X sklearn.ensemble.AdaBoostRegressor._check_feature_names sklearn.ensemble.AdaBoostRegressor._check_n_features sklearn.ensemble.AdaBoostRegressor._get_default_requests sklearn.ensemble.AdaBoostRegressor._get_doc_link sklearn.ensemble.AdaBoostRegressor._get_median_predict sklearn.ensemble.AdaBoostRegressor._get_metadata_request sklearn.ensemble.AdaBoostRegressor._get_param_names sklearn.ensemble.AdaBoostRegressor._get_tags sklearn.ensemble.AdaBoostRegressor._make_estimator sklearn.ensemble.AdaBoostRegressor._more_tags sklearn.ensemble.AdaBoostRegressor._repr_html_inner sklearn.ensemble.AdaBoostRegressor._repr_mimebundle_ sklearn.ensemble.AdaBoostRegressor._validate_data sklearn.ensemble.AdaBoostRegressor._validate_estimator sklearn.ensemble.AdaBoostRegressor._validate_params sklearn.ensemble.AdaBoostRegressor.fit sklearn.ensemble.AdaBoostRegressor.get_metadata_routing sklearn.ensemble.AdaBoostRegressor.get_params sklearn.ensemble.AdaBoostRegressor.predict sklearn.ensemble.AdaBoostRegressor.score sklearn.ensemble.AdaBoostRegressor.set_fit_request sklearn.ensemble.AdaBoostRegressor.set_params sklearn.ensemble.AdaBoostRegressor.set_score_request sklearn.ensemble.AdaBoostRegressor.staged_predict sklearn.ensemble.AdaBoostRegressor.staged_score 
+.. minigallery:: sklearn.ensemble.AdaBoostRegressor sklearn.ensemble.AdaBoostRegressor.__delattr__ sklearn.ensemble.AdaBoostRegressor.__dir__ sklearn.ensemble.AdaBoostRegressor.__eq__ sklearn.ensemble.AdaBoostRegressor.__format__ sklearn.ensemble.AdaBoostRegressor.__ge__ sklearn.ensemble.AdaBoostRegressor.__getattribute__ sklearn.ensemble.AdaBoostRegressor.__getitem__ sklearn.ensemble.AdaBoostRegressor.__getstate__ sklearn.ensemble.AdaBoostRegressor.__gt__ sklearn.ensemble.AdaBoostRegressor.__hash__ sklearn.ensemble.AdaBoostRegressor.__init__ sklearn.ensemble.AdaBoostRegressor.__init_subclass__ sklearn.ensemble.AdaBoostRegressor.__iter__ sklearn.ensemble.AdaBoostRegressor.__le__ sklearn.ensemble.AdaBoostRegressor.__len__ sklearn.ensemble.AdaBoostRegressor.__lt__ sklearn.ensemble.AdaBoostRegressor.__ne__ sklearn.ensemble.AdaBoostRegressor.__new__ sklearn.ensemble.AdaBoostRegressor.__reduce__ sklearn.ensemble.AdaBoostRegressor.__reduce_ex__ sklearn.ensemble.AdaBoostRegressor.__repr__ sklearn.ensemble.AdaBoostRegressor.__setattr__ sklearn.ensemble.AdaBoostRegressor.__setstate__ sklearn.ensemble.AdaBoostRegressor.__sizeof__ sklearn.ensemble.AdaBoostRegressor.__sklearn_clone__ sklearn.ensemble.AdaBoostRegressor.__sklearn_tags__ sklearn.ensemble.AdaBoostRegressor.__str__ sklearn.ensemble.AdaBoostRegressor.__subclasshook__ sklearn.ensemble.AdaBoostRegressor._boost sklearn.ensemble.AdaBoostRegressor._build_request_for_signature sklearn.ensemble.AdaBoostRegressor._check_X sklearn.ensemble.AdaBoostRegressor._get_default_requests sklearn.ensemble.AdaBoostRegressor._get_doc_link sklearn.ensemble.AdaBoostRegressor._get_median_predict sklearn.ensemble.AdaBoostRegressor._get_metadata_request sklearn.ensemble.AdaBoostRegressor._get_param_names sklearn.ensemble.AdaBoostRegressor._make_estimator sklearn.ensemble.AdaBoostRegressor._repr_html_inner sklearn.ensemble.AdaBoostRegressor._repr_mimebundle_ sklearn.ensemble.AdaBoostRegressor._validate_estimator sklearn.ensemble.AdaBoostRegressor._validate_params sklearn.ensemble.AdaBoostRegressor.fit sklearn.ensemble.AdaBoostRegressor.get_metadata_routing sklearn.ensemble.AdaBoostRegressor.get_params sklearn.ensemble.AdaBoostRegressor.predict sklearn.ensemble.AdaBoostRegressor.score sklearn.ensemble.AdaBoostRegressor.set_fit_request sklearn.ensemble.AdaBoostRegressor.set_params sklearn.ensemble.AdaBoostRegressor.set_score_request sklearn.ensemble.AdaBoostRegressor.staged_predict sklearn.ensemble.AdaBoostRegressor.staged_score 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.ensemble.BaggingClassifier.rst.txt b/dev/_sources/modules/generated/sklearn.ensemble.BaggingClassifier.rst.txt
index 1bb5ad65d20dd..8e4b23ddaaf95 100644
--- a/dev/_sources/modules/generated/sklearn.ensemble.BaggingClassifier.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.ensemble.BaggingClassifier.rst.txt
@@ -8,6 +8,6 @@ BaggingClassifier
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.ensemble.BaggingClassifier sklearn.ensemble.BaggingClassifier.__delattr__ sklearn.ensemble.BaggingClassifier.__dir__ sklearn.ensemble.BaggingClassifier.__eq__ sklearn.ensemble.BaggingClassifier.__format__ sklearn.ensemble.BaggingClassifier.__ge__ sklearn.ensemble.BaggingClassifier.__getattribute__ sklearn.ensemble.BaggingClassifier.__getitem__ sklearn.ensemble.BaggingClassifier.__getstate__ sklearn.ensemble.BaggingClassifier.__gt__ sklearn.ensemble.BaggingClassifier.__hash__ sklearn.ensemble.BaggingClassifier.__init__ sklearn.ensemble.BaggingClassifier.__init_subclass__ sklearn.ensemble.BaggingClassifier.__iter__ sklearn.ensemble.BaggingClassifier.__le__ sklearn.ensemble.BaggingClassifier.__len__ sklearn.ensemble.BaggingClassifier.__lt__ sklearn.ensemble.BaggingClassifier.__ne__ sklearn.ensemble.BaggingClassifier.__new__ sklearn.ensemble.BaggingClassifier.__reduce__ sklearn.ensemble.BaggingClassifier.__reduce_ex__ sklearn.ensemble.BaggingClassifier.__repr__ sklearn.ensemble.BaggingClassifier.__setattr__ sklearn.ensemble.BaggingClassifier.__setstate__ sklearn.ensemble.BaggingClassifier.__sizeof__ sklearn.ensemble.BaggingClassifier.__sklearn_clone__ sklearn.ensemble.BaggingClassifier.__sklearn_tags__ sklearn.ensemble.BaggingClassifier.__str__ sklearn.ensemble.BaggingClassifier.__subclasshook__ sklearn.ensemble.BaggingClassifier._build_request_for_signature sklearn.ensemble.BaggingClassifier._check_feature_names sklearn.ensemble.BaggingClassifier._check_n_features sklearn.ensemble.BaggingClassifier._fit sklearn.ensemble.BaggingClassifier._get_default_requests sklearn.ensemble.BaggingClassifier._get_doc_link sklearn.ensemble.BaggingClassifier._get_estimator sklearn.ensemble.BaggingClassifier._get_estimators_indices sklearn.ensemble.BaggingClassifier._get_metadata_request sklearn.ensemble.BaggingClassifier._get_param_names sklearn.ensemble.BaggingClassifier._get_tags sklearn.ensemble.BaggingClassifier._make_estimator sklearn.ensemble.BaggingClassifier._more_tags sklearn.ensemble.BaggingClassifier._parallel_args sklearn.ensemble.BaggingClassifier._repr_html_inner sklearn.ensemble.BaggingClassifier._repr_mimebundle_ sklearn.ensemble.BaggingClassifier._set_oob_score sklearn.ensemble.BaggingClassifier._validate_data sklearn.ensemble.BaggingClassifier._validate_estimator sklearn.ensemble.BaggingClassifier._validate_params sklearn.ensemble.BaggingClassifier._validate_y sklearn.ensemble.BaggingClassifier.decision_function sklearn.ensemble.BaggingClassifier.fit sklearn.ensemble.BaggingClassifier.get_metadata_routing sklearn.ensemble.BaggingClassifier.get_params sklearn.ensemble.BaggingClassifier.predict sklearn.ensemble.BaggingClassifier.predict_log_proba sklearn.ensemble.BaggingClassifier.predict_proba sklearn.ensemble.BaggingClassifier.score sklearn.ensemble.BaggingClassifier.set_fit_request sklearn.ensemble.BaggingClassifier.set_params sklearn.ensemble.BaggingClassifier.set_score_request 
+.. minigallery:: sklearn.ensemble.BaggingClassifier sklearn.ensemble.BaggingClassifier.__delattr__ sklearn.ensemble.BaggingClassifier.__dir__ sklearn.ensemble.BaggingClassifier.__eq__ sklearn.ensemble.BaggingClassifier.__format__ sklearn.ensemble.BaggingClassifier.__ge__ sklearn.ensemble.BaggingClassifier.__getattribute__ sklearn.ensemble.BaggingClassifier.__getitem__ sklearn.ensemble.BaggingClassifier.__getstate__ sklearn.ensemble.BaggingClassifier.__gt__ sklearn.ensemble.BaggingClassifier.__hash__ sklearn.ensemble.BaggingClassifier.__init__ sklearn.ensemble.BaggingClassifier.__init_subclass__ sklearn.ensemble.BaggingClassifier.__iter__ sklearn.ensemble.BaggingClassifier.__le__ sklearn.ensemble.BaggingClassifier.__len__ sklearn.ensemble.BaggingClassifier.__lt__ sklearn.ensemble.BaggingClassifier.__ne__ sklearn.ensemble.BaggingClassifier.__new__ sklearn.ensemble.BaggingClassifier.__reduce__ sklearn.ensemble.BaggingClassifier.__reduce_ex__ sklearn.ensemble.BaggingClassifier.__repr__ sklearn.ensemble.BaggingClassifier.__setattr__ sklearn.ensemble.BaggingClassifier.__setstate__ sklearn.ensemble.BaggingClassifier.__sizeof__ sklearn.ensemble.BaggingClassifier.__sklearn_clone__ sklearn.ensemble.BaggingClassifier.__sklearn_tags__ sklearn.ensemble.BaggingClassifier.__str__ sklearn.ensemble.BaggingClassifier.__subclasshook__ sklearn.ensemble.BaggingClassifier._build_request_for_signature sklearn.ensemble.BaggingClassifier._fit sklearn.ensemble.BaggingClassifier._get_default_requests sklearn.ensemble.BaggingClassifier._get_doc_link sklearn.ensemble.BaggingClassifier._get_estimator sklearn.ensemble.BaggingClassifier._get_estimators_indices sklearn.ensemble.BaggingClassifier._get_metadata_request sklearn.ensemble.BaggingClassifier._get_param_names sklearn.ensemble.BaggingClassifier._make_estimator sklearn.ensemble.BaggingClassifier._parallel_args sklearn.ensemble.BaggingClassifier._repr_html_inner sklearn.ensemble.BaggingClassifier._repr_mimebundle_ sklearn.ensemble.BaggingClassifier._set_oob_score sklearn.ensemble.BaggingClassifier._validate_estimator sklearn.ensemble.BaggingClassifier._validate_params sklearn.ensemble.BaggingClassifier._validate_y sklearn.ensemble.BaggingClassifier.decision_function sklearn.ensemble.BaggingClassifier.fit sklearn.ensemble.BaggingClassifier.get_metadata_routing sklearn.ensemble.BaggingClassifier.get_params sklearn.ensemble.BaggingClassifier.predict sklearn.ensemble.BaggingClassifier.predict_log_proba sklearn.ensemble.BaggingClassifier.predict_proba sklearn.ensemble.BaggingClassifier.score sklearn.ensemble.BaggingClassifier.set_fit_request sklearn.ensemble.BaggingClassifier.set_params sklearn.ensemble.BaggingClassifier.set_score_request 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.ensemble.BaggingRegressor.rst.txt b/dev/_sources/modules/generated/sklearn.ensemble.BaggingRegressor.rst.txt
index 851c779e746ba..55db6cba34c35 100644
--- a/dev/_sources/modules/generated/sklearn.ensemble.BaggingRegressor.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.ensemble.BaggingRegressor.rst.txt
@@ -8,6 +8,6 @@ BaggingRegressor
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.ensemble.BaggingRegressor sklearn.ensemble.BaggingRegressor.__delattr__ sklearn.ensemble.BaggingRegressor.__dir__ sklearn.ensemble.BaggingRegressor.__eq__ sklearn.ensemble.BaggingRegressor.__format__ sklearn.ensemble.BaggingRegressor.__ge__ sklearn.ensemble.BaggingRegressor.__getattribute__ sklearn.ensemble.BaggingRegressor.__getitem__ sklearn.ensemble.BaggingRegressor.__getstate__ sklearn.ensemble.BaggingRegressor.__gt__ sklearn.ensemble.BaggingRegressor.__hash__ sklearn.ensemble.BaggingRegressor.__init__ sklearn.ensemble.BaggingRegressor.__init_subclass__ sklearn.ensemble.BaggingRegressor.__iter__ sklearn.ensemble.BaggingRegressor.__le__ sklearn.ensemble.BaggingRegressor.__len__ sklearn.ensemble.BaggingRegressor.__lt__ sklearn.ensemble.BaggingRegressor.__ne__ sklearn.ensemble.BaggingRegressor.__new__ sklearn.ensemble.BaggingRegressor.__reduce__ sklearn.ensemble.BaggingRegressor.__reduce_ex__ sklearn.ensemble.BaggingRegressor.__repr__ sklearn.ensemble.BaggingRegressor.__setattr__ sklearn.ensemble.BaggingRegressor.__setstate__ sklearn.ensemble.BaggingRegressor.__sizeof__ sklearn.ensemble.BaggingRegressor.__sklearn_clone__ sklearn.ensemble.BaggingRegressor.__sklearn_tags__ sklearn.ensemble.BaggingRegressor.__str__ sklearn.ensemble.BaggingRegressor.__subclasshook__ sklearn.ensemble.BaggingRegressor._build_request_for_signature sklearn.ensemble.BaggingRegressor._check_feature_names sklearn.ensemble.BaggingRegressor._check_n_features sklearn.ensemble.BaggingRegressor._fit sklearn.ensemble.BaggingRegressor._get_default_requests sklearn.ensemble.BaggingRegressor._get_doc_link sklearn.ensemble.BaggingRegressor._get_estimator sklearn.ensemble.BaggingRegressor._get_estimators_indices sklearn.ensemble.BaggingRegressor._get_metadata_request sklearn.ensemble.BaggingRegressor._get_param_names sklearn.ensemble.BaggingRegressor._get_tags sklearn.ensemble.BaggingRegressor._make_estimator sklearn.ensemble.BaggingRegressor._more_tags sklearn.ensemble.BaggingRegressor._parallel_args sklearn.ensemble.BaggingRegressor._repr_html_inner sklearn.ensemble.BaggingRegressor._repr_mimebundle_ sklearn.ensemble.BaggingRegressor._set_oob_score sklearn.ensemble.BaggingRegressor._validate_data sklearn.ensemble.BaggingRegressor._validate_estimator sklearn.ensemble.BaggingRegressor._validate_params sklearn.ensemble.BaggingRegressor._validate_y sklearn.ensemble.BaggingRegressor.fit sklearn.ensemble.BaggingRegressor.get_metadata_routing sklearn.ensemble.BaggingRegressor.get_params sklearn.ensemble.BaggingRegressor.predict sklearn.ensemble.BaggingRegressor.score sklearn.ensemble.BaggingRegressor.set_fit_request sklearn.ensemble.BaggingRegressor.set_params sklearn.ensemble.BaggingRegressor.set_score_request 
+.. minigallery:: sklearn.ensemble.BaggingRegressor sklearn.ensemble.BaggingRegressor.__delattr__ sklearn.ensemble.BaggingRegressor.__dir__ sklearn.ensemble.BaggingRegressor.__eq__ sklearn.ensemble.BaggingRegressor.__format__ sklearn.ensemble.BaggingRegressor.__ge__ sklearn.ensemble.BaggingRegressor.__getattribute__ sklearn.ensemble.BaggingRegressor.__getitem__ sklearn.ensemble.BaggingRegressor.__getstate__ sklearn.ensemble.BaggingRegressor.__gt__ sklearn.ensemble.BaggingRegressor.__hash__ sklearn.ensemble.BaggingRegressor.__init__ sklearn.ensemble.BaggingRegressor.__init_subclass__ sklearn.ensemble.BaggingRegressor.__iter__ sklearn.ensemble.BaggingRegressor.__le__ sklearn.ensemble.BaggingRegressor.__len__ sklearn.ensemble.BaggingRegressor.__lt__ sklearn.ensemble.BaggingRegressor.__ne__ sklearn.ensemble.BaggingRegressor.__new__ sklearn.ensemble.BaggingRegressor.__reduce__ sklearn.ensemble.BaggingRegressor.__reduce_ex__ sklearn.ensemble.BaggingRegressor.__repr__ sklearn.ensemble.BaggingRegressor.__setattr__ sklearn.ensemble.BaggingRegressor.__setstate__ sklearn.ensemble.BaggingRegressor.__sizeof__ sklearn.ensemble.BaggingRegressor.__sklearn_clone__ sklearn.ensemble.BaggingRegressor.__sklearn_tags__ sklearn.ensemble.BaggingRegressor.__str__ sklearn.ensemble.BaggingRegressor.__subclasshook__ sklearn.ensemble.BaggingRegressor._build_request_for_signature sklearn.ensemble.BaggingRegressor._fit sklearn.ensemble.BaggingRegressor._get_default_requests sklearn.ensemble.BaggingRegressor._get_doc_link sklearn.ensemble.BaggingRegressor._get_estimator sklearn.ensemble.BaggingRegressor._get_estimators_indices sklearn.ensemble.BaggingRegressor._get_metadata_request sklearn.ensemble.BaggingRegressor._get_param_names sklearn.ensemble.BaggingRegressor._make_estimator sklearn.ensemble.BaggingRegressor._parallel_args sklearn.ensemble.BaggingRegressor._repr_html_inner sklearn.ensemble.BaggingRegressor._repr_mimebundle_ sklearn.ensemble.BaggingRegressor._set_oob_score sklearn.ensemble.BaggingRegressor._validate_estimator sklearn.ensemble.BaggingRegressor._validate_params sklearn.ensemble.BaggingRegressor._validate_y sklearn.ensemble.BaggingRegressor.fit sklearn.ensemble.BaggingRegressor.get_metadata_routing sklearn.ensemble.BaggingRegressor.get_params sklearn.ensemble.BaggingRegressor.predict sklearn.ensemble.BaggingRegressor.score sklearn.ensemble.BaggingRegressor.set_fit_request sklearn.ensemble.BaggingRegressor.set_params sklearn.ensemble.BaggingRegressor.set_score_request 
    :add-heading: Gallery examples
    :heading-level: -
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diff --git a/dev/_sources/modules/generated/sklearn.ensemble.ExtraTreesClassifier.rst.txt b/dev/_sources/modules/generated/sklearn.ensemble.ExtraTreesClassifier.rst.txt
index 8a500b4c6a670..65402d5648a55 100644
--- a/dev/_sources/modules/generated/sklearn.ensemble.ExtraTreesClassifier.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.ensemble.ExtraTreesClassifier.rst.txt
@@ -8,6 +8,6 @@ ExtraTreesClassifier
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.ensemble.ExtraTreesClassifier sklearn.ensemble.ExtraTreesClassifier.__delattr__ sklearn.ensemble.ExtraTreesClassifier.__dir__ sklearn.ensemble.ExtraTreesClassifier.__eq__ sklearn.ensemble.ExtraTreesClassifier.__format__ sklearn.ensemble.ExtraTreesClassifier.__ge__ sklearn.ensemble.ExtraTreesClassifier.__getattribute__ sklearn.ensemble.ExtraTreesClassifier.__getitem__ sklearn.ensemble.ExtraTreesClassifier.__getstate__ sklearn.ensemble.ExtraTreesClassifier.__gt__ sklearn.ensemble.ExtraTreesClassifier.__hash__ sklearn.ensemble.ExtraTreesClassifier.__init__ sklearn.ensemble.ExtraTreesClassifier.__init_subclass__ sklearn.ensemble.ExtraTreesClassifier.__iter__ sklearn.ensemble.ExtraTreesClassifier.__le__ sklearn.ensemble.ExtraTreesClassifier.__len__ sklearn.ensemble.ExtraTreesClassifier.__lt__ sklearn.ensemble.ExtraTreesClassifier.__ne__ sklearn.ensemble.ExtraTreesClassifier.__new__ sklearn.ensemble.ExtraTreesClassifier.__reduce__ sklearn.ensemble.ExtraTreesClassifier.__reduce_ex__ sklearn.ensemble.ExtraTreesClassifier.__repr__ sklearn.ensemble.ExtraTreesClassifier.__setattr__ sklearn.ensemble.ExtraTreesClassifier.__setstate__ sklearn.ensemble.ExtraTreesClassifier.__sizeof__ sklearn.ensemble.ExtraTreesClassifier.__sklearn_clone__ sklearn.ensemble.ExtraTreesClassifier.__sklearn_tags__ sklearn.ensemble.ExtraTreesClassifier.__str__ sklearn.ensemble.ExtraTreesClassifier.__subclasshook__ sklearn.ensemble.ExtraTreesClassifier._build_request_for_signature sklearn.ensemble.ExtraTreesClassifier._check_feature_names sklearn.ensemble.ExtraTreesClassifier._check_n_features sklearn.ensemble.ExtraTreesClassifier._compute_oob_predictions sklearn.ensemble.ExtraTreesClassifier._get_default_requests sklearn.ensemble.ExtraTreesClassifier._get_doc_link sklearn.ensemble.ExtraTreesClassifier._get_estimators_indices sklearn.ensemble.ExtraTreesClassifier._get_metadata_request sklearn.ensemble.ExtraTreesClassifier._get_oob_predictions sklearn.ensemble.ExtraTreesClassifier._get_param_names sklearn.ensemble.ExtraTreesClassifier._get_tags sklearn.ensemble.ExtraTreesClassifier._make_estimator sklearn.ensemble.ExtraTreesClassifier._more_tags sklearn.ensemble.ExtraTreesClassifier._repr_html_inner sklearn.ensemble.ExtraTreesClassifier._repr_mimebundle_ sklearn.ensemble.ExtraTreesClassifier._set_oob_score_and_attributes sklearn.ensemble.ExtraTreesClassifier._validate_X_predict sklearn.ensemble.ExtraTreesClassifier._validate_data sklearn.ensemble.ExtraTreesClassifier._validate_estimator sklearn.ensemble.ExtraTreesClassifier._validate_params sklearn.ensemble.ExtraTreesClassifier._validate_y_class_weight sklearn.ensemble.ExtraTreesClassifier.apply sklearn.ensemble.ExtraTreesClassifier.decision_path sklearn.ensemble.ExtraTreesClassifier.fit sklearn.ensemble.ExtraTreesClassifier.get_metadata_routing sklearn.ensemble.ExtraTreesClassifier.get_params sklearn.ensemble.ExtraTreesClassifier.predict sklearn.ensemble.ExtraTreesClassifier.predict_log_proba sklearn.ensemble.ExtraTreesClassifier.predict_proba sklearn.ensemble.ExtraTreesClassifier.score sklearn.ensemble.ExtraTreesClassifier.set_fit_request sklearn.ensemble.ExtraTreesClassifier.set_params sklearn.ensemble.ExtraTreesClassifier.set_score_request 
+.. minigallery:: sklearn.ensemble.ExtraTreesClassifier sklearn.ensemble.ExtraTreesClassifier.__delattr__ sklearn.ensemble.ExtraTreesClassifier.__dir__ sklearn.ensemble.ExtraTreesClassifier.__eq__ sklearn.ensemble.ExtraTreesClassifier.__format__ sklearn.ensemble.ExtraTreesClassifier.__ge__ sklearn.ensemble.ExtraTreesClassifier.__getattribute__ sklearn.ensemble.ExtraTreesClassifier.__getitem__ sklearn.ensemble.ExtraTreesClassifier.__getstate__ sklearn.ensemble.ExtraTreesClassifier.__gt__ sklearn.ensemble.ExtraTreesClassifier.__hash__ sklearn.ensemble.ExtraTreesClassifier.__init__ sklearn.ensemble.ExtraTreesClassifier.__init_subclass__ sklearn.ensemble.ExtraTreesClassifier.__iter__ sklearn.ensemble.ExtraTreesClassifier.__le__ sklearn.ensemble.ExtraTreesClassifier.__len__ sklearn.ensemble.ExtraTreesClassifier.__lt__ sklearn.ensemble.ExtraTreesClassifier.__ne__ sklearn.ensemble.ExtraTreesClassifier.__new__ sklearn.ensemble.ExtraTreesClassifier.__reduce__ sklearn.ensemble.ExtraTreesClassifier.__reduce_ex__ sklearn.ensemble.ExtraTreesClassifier.__repr__ sklearn.ensemble.ExtraTreesClassifier.__setattr__ sklearn.ensemble.ExtraTreesClassifier.__setstate__ sklearn.ensemble.ExtraTreesClassifier.__sizeof__ sklearn.ensemble.ExtraTreesClassifier.__sklearn_clone__ sklearn.ensemble.ExtraTreesClassifier.__sklearn_tags__ sklearn.ensemble.ExtraTreesClassifier.__str__ sklearn.ensemble.ExtraTreesClassifier.__subclasshook__ sklearn.ensemble.ExtraTreesClassifier._build_request_for_signature sklearn.ensemble.ExtraTreesClassifier._compute_oob_predictions sklearn.ensemble.ExtraTreesClassifier._get_default_requests sklearn.ensemble.ExtraTreesClassifier._get_doc_link sklearn.ensemble.ExtraTreesClassifier._get_estimators_indices sklearn.ensemble.ExtraTreesClassifier._get_metadata_request sklearn.ensemble.ExtraTreesClassifier._get_oob_predictions sklearn.ensemble.ExtraTreesClassifier._get_param_names sklearn.ensemble.ExtraTreesClassifier._make_estimator sklearn.ensemble.ExtraTreesClassifier._repr_html_inner sklearn.ensemble.ExtraTreesClassifier._repr_mimebundle_ sklearn.ensemble.ExtraTreesClassifier._set_oob_score_and_attributes sklearn.ensemble.ExtraTreesClassifier._validate_X_predict sklearn.ensemble.ExtraTreesClassifier._validate_estimator sklearn.ensemble.ExtraTreesClassifier._validate_params sklearn.ensemble.ExtraTreesClassifier._validate_y_class_weight sklearn.ensemble.ExtraTreesClassifier.apply sklearn.ensemble.ExtraTreesClassifier.decision_path sklearn.ensemble.ExtraTreesClassifier.fit sklearn.ensemble.ExtraTreesClassifier.get_metadata_routing sklearn.ensemble.ExtraTreesClassifier.get_params sklearn.ensemble.ExtraTreesClassifier.predict sklearn.ensemble.ExtraTreesClassifier.predict_log_proba sklearn.ensemble.ExtraTreesClassifier.predict_proba sklearn.ensemble.ExtraTreesClassifier.score sklearn.ensemble.ExtraTreesClassifier.set_fit_request sklearn.ensemble.ExtraTreesClassifier.set_params sklearn.ensemble.ExtraTreesClassifier.set_score_request 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.ensemble.ExtraTreesRegressor.rst.txt b/dev/_sources/modules/generated/sklearn.ensemble.ExtraTreesRegressor.rst.txt
index 075e4027cc179..c0a588e5c32ab 100644
--- a/dev/_sources/modules/generated/sklearn.ensemble.ExtraTreesRegressor.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.ensemble.ExtraTreesRegressor.rst.txt
@@ -8,6 +8,6 @@ ExtraTreesRegressor
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.ensemble.ExtraTreesRegressor sklearn.ensemble.ExtraTreesRegressor.__delattr__ sklearn.ensemble.ExtraTreesRegressor.__dir__ sklearn.ensemble.ExtraTreesRegressor.__eq__ sklearn.ensemble.ExtraTreesRegressor.__format__ sklearn.ensemble.ExtraTreesRegressor.__ge__ sklearn.ensemble.ExtraTreesRegressor.__getattribute__ sklearn.ensemble.ExtraTreesRegressor.__getitem__ sklearn.ensemble.ExtraTreesRegressor.__getstate__ sklearn.ensemble.ExtraTreesRegressor.__gt__ sklearn.ensemble.ExtraTreesRegressor.__hash__ sklearn.ensemble.ExtraTreesRegressor.__init__ sklearn.ensemble.ExtraTreesRegressor.__init_subclass__ sklearn.ensemble.ExtraTreesRegressor.__iter__ sklearn.ensemble.ExtraTreesRegressor.__le__ sklearn.ensemble.ExtraTreesRegressor.__len__ sklearn.ensemble.ExtraTreesRegressor.__lt__ sklearn.ensemble.ExtraTreesRegressor.__ne__ sklearn.ensemble.ExtraTreesRegressor.__new__ sklearn.ensemble.ExtraTreesRegressor.__reduce__ sklearn.ensemble.ExtraTreesRegressor.__reduce_ex__ sklearn.ensemble.ExtraTreesRegressor.__repr__ sklearn.ensemble.ExtraTreesRegressor.__setattr__ sklearn.ensemble.ExtraTreesRegressor.__setstate__ sklearn.ensemble.ExtraTreesRegressor.__sizeof__ sklearn.ensemble.ExtraTreesRegressor.__sklearn_clone__ sklearn.ensemble.ExtraTreesRegressor.__sklearn_tags__ sklearn.ensemble.ExtraTreesRegressor.__str__ sklearn.ensemble.ExtraTreesRegressor.__subclasshook__ sklearn.ensemble.ExtraTreesRegressor._build_request_for_signature sklearn.ensemble.ExtraTreesRegressor._check_feature_names sklearn.ensemble.ExtraTreesRegressor._check_n_features sklearn.ensemble.ExtraTreesRegressor._compute_oob_predictions sklearn.ensemble.ExtraTreesRegressor._compute_partial_dependence_recursion sklearn.ensemble.ExtraTreesRegressor._get_default_requests sklearn.ensemble.ExtraTreesRegressor._get_doc_link sklearn.ensemble.ExtraTreesRegressor._get_estimators_indices sklearn.ensemble.ExtraTreesRegressor._get_metadata_request sklearn.ensemble.ExtraTreesRegressor._get_oob_predictions sklearn.ensemble.ExtraTreesRegressor._get_param_names sklearn.ensemble.ExtraTreesRegressor._get_tags sklearn.ensemble.ExtraTreesRegressor._make_estimator sklearn.ensemble.ExtraTreesRegressor._more_tags sklearn.ensemble.ExtraTreesRegressor._repr_html_inner sklearn.ensemble.ExtraTreesRegressor._repr_mimebundle_ sklearn.ensemble.ExtraTreesRegressor._set_oob_score_and_attributes sklearn.ensemble.ExtraTreesRegressor._validate_X_predict sklearn.ensemble.ExtraTreesRegressor._validate_data sklearn.ensemble.ExtraTreesRegressor._validate_estimator sklearn.ensemble.ExtraTreesRegressor._validate_params sklearn.ensemble.ExtraTreesRegressor._validate_y_class_weight sklearn.ensemble.ExtraTreesRegressor.apply sklearn.ensemble.ExtraTreesRegressor.decision_path sklearn.ensemble.ExtraTreesRegressor.fit sklearn.ensemble.ExtraTreesRegressor.get_metadata_routing sklearn.ensemble.ExtraTreesRegressor.get_params sklearn.ensemble.ExtraTreesRegressor.predict sklearn.ensemble.ExtraTreesRegressor.score sklearn.ensemble.ExtraTreesRegressor.set_fit_request sklearn.ensemble.ExtraTreesRegressor.set_params sklearn.ensemble.ExtraTreesRegressor.set_score_request 
+.. minigallery:: sklearn.ensemble.ExtraTreesRegressor sklearn.ensemble.ExtraTreesRegressor.__delattr__ sklearn.ensemble.ExtraTreesRegressor.__dir__ sklearn.ensemble.ExtraTreesRegressor.__eq__ sklearn.ensemble.ExtraTreesRegressor.__format__ sklearn.ensemble.ExtraTreesRegressor.__ge__ sklearn.ensemble.ExtraTreesRegressor.__getattribute__ sklearn.ensemble.ExtraTreesRegressor.__getitem__ sklearn.ensemble.ExtraTreesRegressor.__getstate__ sklearn.ensemble.ExtraTreesRegressor.__gt__ sklearn.ensemble.ExtraTreesRegressor.__hash__ sklearn.ensemble.ExtraTreesRegressor.__init__ sklearn.ensemble.ExtraTreesRegressor.__init_subclass__ sklearn.ensemble.ExtraTreesRegressor.__iter__ sklearn.ensemble.ExtraTreesRegressor.__le__ sklearn.ensemble.ExtraTreesRegressor.__len__ sklearn.ensemble.ExtraTreesRegressor.__lt__ sklearn.ensemble.ExtraTreesRegressor.__ne__ sklearn.ensemble.ExtraTreesRegressor.__new__ sklearn.ensemble.ExtraTreesRegressor.__reduce__ sklearn.ensemble.ExtraTreesRegressor.__reduce_ex__ sklearn.ensemble.ExtraTreesRegressor.__repr__ sklearn.ensemble.ExtraTreesRegressor.__setattr__ sklearn.ensemble.ExtraTreesRegressor.__setstate__ sklearn.ensemble.ExtraTreesRegressor.__sizeof__ sklearn.ensemble.ExtraTreesRegressor.__sklearn_clone__ sklearn.ensemble.ExtraTreesRegressor.__sklearn_tags__ sklearn.ensemble.ExtraTreesRegressor.__str__ sklearn.ensemble.ExtraTreesRegressor.__subclasshook__ sklearn.ensemble.ExtraTreesRegressor._build_request_for_signature sklearn.ensemble.ExtraTreesRegressor._compute_oob_predictions sklearn.ensemble.ExtraTreesRegressor._compute_partial_dependence_recursion sklearn.ensemble.ExtraTreesRegressor._get_default_requests sklearn.ensemble.ExtraTreesRegressor._get_doc_link sklearn.ensemble.ExtraTreesRegressor._get_estimators_indices sklearn.ensemble.ExtraTreesRegressor._get_metadata_request sklearn.ensemble.ExtraTreesRegressor._get_oob_predictions sklearn.ensemble.ExtraTreesRegressor._get_param_names sklearn.ensemble.ExtraTreesRegressor._make_estimator sklearn.ensemble.ExtraTreesRegressor._repr_html_inner sklearn.ensemble.ExtraTreesRegressor._repr_mimebundle_ sklearn.ensemble.ExtraTreesRegressor._set_oob_score_and_attributes sklearn.ensemble.ExtraTreesRegressor._validate_X_predict sklearn.ensemble.ExtraTreesRegressor._validate_estimator sklearn.ensemble.ExtraTreesRegressor._validate_params sklearn.ensemble.ExtraTreesRegressor._validate_y_class_weight sklearn.ensemble.ExtraTreesRegressor.apply sklearn.ensemble.ExtraTreesRegressor.decision_path sklearn.ensemble.ExtraTreesRegressor.fit sklearn.ensemble.ExtraTreesRegressor.get_metadata_routing sklearn.ensemble.ExtraTreesRegressor.get_params sklearn.ensemble.ExtraTreesRegressor.predict sklearn.ensemble.ExtraTreesRegressor.score sklearn.ensemble.ExtraTreesRegressor.set_fit_request sklearn.ensemble.ExtraTreesRegressor.set_params sklearn.ensemble.ExtraTreesRegressor.set_score_request 
    :add-heading: Gallery examples
    :heading-level: -
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diff --git a/dev/_sources/modules/generated/sklearn.ensemble.GradientBoostingClassifier.rst.txt b/dev/_sources/modules/generated/sklearn.ensemble.GradientBoostingClassifier.rst.txt
index e73bc4876cec6..04bb9bfa9c872 100644
--- a/dev/_sources/modules/generated/sklearn.ensemble.GradientBoostingClassifier.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.ensemble.GradientBoostingClassifier.rst.txt
@@ -8,6 +8,6 @@ GradientBoostingClassifier
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.ensemble.GradientBoostingClassifier sklearn.ensemble.GradientBoostingClassifier.__delattr__ sklearn.ensemble.GradientBoostingClassifier.__dir__ sklearn.ensemble.GradientBoostingClassifier.__eq__ sklearn.ensemble.GradientBoostingClassifier.__format__ sklearn.ensemble.GradientBoostingClassifier.__ge__ sklearn.ensemble.GradientBoostingClassifier.__getattribute__ sklearn.ensemble.GradientBoostingClassifier.__getitem__ sklearn.ensemble.GradientBoostingClassifier.__getstate__ sklearn.ensemble.GradientBoostingClassifier.__gt__ sklearn.ensemble.GradientBoostingClassifier.__hash__ sklearn.ensemble.GradientBoostingClassifier.__init__ sklearn.ensemble.GradientBoostingClassifier.__init_subclass__ sklearn.ensemble.GradientBoostingClassifier.__iter__ sklearn.ensemble.GradientBoostingClassifier.__le__ sklearn.ensemble.GradientBoostingClassifier.__len__ sklearn.ensemble.GradientBoostingClassifier.__lt__ sklearn.ensemble.GradientBoostingClassifier.__ne__ sklearn.ensemble.GradientBoostingClassifier.__new__ sklearn.ensemble.GradientBoostingClassifier.__reduce__ sklearn.ensemble.GradientBoostingClassifier.__reduce_ex__ sklearn.ensemble.GradientBoostingClassifier.__repr__ sklearn.ensemble.GradientBoostingClassifier.__setattr__ sklearn.ensemble.GradientBoostingClassifier.__setstate__ sklearn.ensemble.GradientBoostingClassifier.__sizeof__ sklearn.ensemble.GradientBoostingClassifier.__sklearn_clone__ sklearn.ensemble.GradientBoostingClassifier.__sklearn_tags__ sklearn.ensemble.GradientBoostingClassifier.__str__ sklearn.ensemble.GradientBoostingClassifier.__subclasshook__ sklearn.ensemble.GradientBoostingClassifier._build_request_for_signature sklearn.ensemble.GradientBoostingClassifier._check_feature_names sklearn.ensemble.GradientBoostingClassifier._check_initialized sklearn.ensemble.GradientBoostingClassifier._check_n_features sklearn.ensemble.GradientBoostingClassifier._clear_state sklearn.ensemble.GradientBoostingClassifier._compute_partial_dependence_recursion sklearn.ensemble.GradientBoostingClassifier._encode_y sklearn.ensemble.GradientBoostingClassifier._fit_stage sklearn.ensemble.GradientBoostingClassifier._fit_stages sklearn.ensemble.GradientBoostingClassifier._get_default_requests sklearn.ensemble.GradientBoostingClassifier._get_doc_link sklearn.ensemble.GradientBoostingClassifier._get_loss sklearn.ensemble.GradientBoostingClassifier._get_metadata_request sklearn.ensemble.GradientBoostingClassifier._get_param_names sklearn.ensemble.GradientBoostingClassifier._get_tags sklearn.ensemble.GradientBoostingClassifier._init_state sklearn.ensemble.GradientBoostingClassifier._is_fitted sklearn.ensemble.GradientBoostingClassifier._make_estimator sklearn.ensemble.GradientBoostingClassifier._more_tags sklearn.ensemble.GradientBoostingClassifier._raw_predict sklearn.ensemble.GradientBoostingClassifier._raw_predict_init sklearn.ensemble.GradientBoostingClassifier._repr_html_inner sklearn.ensemble.GradientBoostingClassifier._repr_mimebundle_ sklearn.ensemble.GradientBoostingClassifier._resize_state sklearn.ensemble.GradientBoostingClassifier._set_max_features sklearn.ensemble.GradientBoostingClassifier._staged_raw_predict sklearn.ensemble.GradientBoostingClassifier._validate_data sklearn.ensemble.GradientBoostingClassifier._validate_estimator sklearn.ensemble.GradientBoostingClassifier._validate_params sklearn.ensemble.GradientBoostingClassifier.apply sklearn.ensemble.GradientBoostingClassifier.decision_function sklearn.ensemble.GradientBoostingClassifier.fit sklearn.ensemble.GradientBoostingClassifier.get_metadata_routing sklearn.ensemble.GradientBoostingClassifier.get_params sklearn.ensemble.GradientBoostingClassifier.predict sklearn.ensemble.GradientBoostingClassifier.predict_log_proba sklearn.ensemble.GradientBoostingClassifier.predict_proba sklearn.ensemble.GradientBoostingClassifier.score sklearn.ensemble.GradientBoostingClassifier.set_fit_request sklearn.ensemble.GradientBoostingClassifier.set_params sklearn.ensemble.GradientBoostingClassifier.set_score_request sklearn.ensemble.GradientBoostingClassifier.staged_decision_function sklearn.ensemble.GradientBoostingClassifier.staged_predict sklearn.ensemble.GradientBoostingClassifier.staged_predict_proba 
+.. minigallery:: sklearn.ensemble.GradientBoostingClassifier sklearn.ensemble.GradientBoostingClassifier.__delattr__ sklearn.ensemble.GradientBoostingClassifier.__dir__ sklearn.ensemble.GradientBoostingClassifier.__eq__ sklearn.ensemble.GradientBoostingClassifier.__format__ sklearn.ensemble.GradientBoostingClassifier.__ge__ sklearn.ensemble.GradientBoostingClassifier.__getattribute__ sklearn.ensemble.GradientBoostingClassifier.__getitem__ sklearn.ensemble.GradientBoostingClassifier.__getstate__ sklearn.ensemble.GradientBoostingClassifier.__gt__ sklearn.ensemble.GradientBoostingClassifier.__hash__ sklearn.ensemble.GradientBoostingClassifier.__init__ sklearn.ensemble.GradientBoostingClassifier.__init_subclass__ sklearn.ensemble.GradientBoostingClassifier.__iter__ sklearn.ensemble.GradientBoostingClassifier.__le__ sklearn.ensemble.GradientBoostingClassifier.__len__ sklearn.ensemble.GradientBoostingClassifier.__lt__ sklearn.ensemble.GradientBoostingClassifier.__ne__ sklearn.ensemble.GradientBoostingClassifier.__new__ sklearn.ensemble.GradientBoostingClassifier.__reduce__ sklearn.ensemble.GradientBoostingClassifier.__reduce_ex__ sklearn.ensemble.GradientBoostingClassifier.__repr__ sklearn.ensemble.GradientBoostingClassifier.__setattr__ sklearn.ensemble.GradientBoostingClassifier.__setstate__ sklearn.ensemble.GradientBoostingClassifier.__sizeof__ sklearn.ensemble.GradientBoostingClassifier.__sklearn_clone__ sklearn.ensemble.GradientBoostingClassifier.__sklearn_tags__ sklearn.ensemble.GradientBoostingClassifier.__str__ sklearn.ensemble.GradientBoostingClassifier.__subclasshook__ sklearn.ensemble.GradientBoostingClassifier._build_request_for_signature sklearn.ensemble.GradientBoostingClassifier._check_initialized sklearn.ensemble.GradientBoostingClassifier._clear_state sklearn.ensemble.GradientBoostingClassifier._compute_partial_dependence_recursion sklearn.ensemble.GradientBoostingClassifier._encode_y sklearn.ensemble.GradientBoostingClassifier._fit_stage sklearn.ensemble.GradientBoostingClassifier._fit_stages sklearn.ensemble.GradientBoostingClassifier._get_default_requests sklearn.ensemble.GradientBoostingClassifier._get_doc_link sklearn.ensemble.GradientBoostingClassifier._get_loss sklearn.ensemble.GradientBoostingClassifier._get_metadata_request sklearn.ensemble.GradientBoostingClassifier._get_param_names sklearn.ensemble.GradientBoostingClassifier._init_state sklearn.ensemble.GradientBoostingClassifier._is_fitted sklearn.ensemble.GradientBoostingClassifier._make_estimator sklearn.ensemble.GradientBoostingClassifier._raw_predict sklearn.ensemble.GradientBoostingClassifier._raw_predict_init sklearn.ensemble.GradientBoostingClassifier._repr_html_inner sklearn.ensemble.GradientBoostingClassifier._repr_mimebundle_ sklearn.ensemble.GradientBoostingClassifier._resize_state sklearn.ensemble.GradientBoostingClassifier._set_max_features sklearn.ensemble.GradientBoostingClassifier._staged_raw_predict sklearn.ensemble.GradientBoostingClassifier._validate_estimator sklearn.ensemble.GradientBoostingClassifier._validate_params sklearn.ensemble.GradientBoostingClassifier.apply sklearn.ensemble.GradientBoostingClassifier.decision_function sklearn.ensemble.GradientBoostingClassifier.fit sklearn.ensemble.GradientBoostingClassifier.get_metadata_routing sklearn.ensemble.GradientBoostingClassifier.get_params sklearn.ensemble.GradientBoostingClassifier.predict sklearn.ensemble.GradientBoostingClassifier.predict_log_proba sklearn.ensemble.GradientBoostingClassifier.predict_proba sklearn.ensemble.GradientBoostingClassifier.score sklearn.ensemble.GradientBoostingClassifier.set_fit_request sklearn.ensemble.GradientBoostingClassifier.set_params sklearn.ensemble.GradientBoostingClassifier.set_score_request sklearn.ensemble.GradientBoostingClassifier.staged_decision_function sklearn.ensemble.GradientBoostingClassifier.staged_predict sklearn.ensemble.GradientBoostingClassifier.staged_predict_proba 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.ensemble.GradientBoostingRegressor.rst.txt b/dev/_sources/modules/generated/sklearn.ensemble.GradientBoostingRegressor.rst.txt
index 4e910c5fafda2..dcc65b141cdcf 100644
--- a/dev/_sources/modules/generated/sklearn.ensemble.GradientBoostingRegressor.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.ensemble.GradientBoostingRegressor.rst.txt
@@ -8,6 +8,6 @@ GradientBoostingRegressor
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.ensemble.GradientBoostingRegressor sklearn.ensemble.GradientBoostingRegressor.__delattr__ sklearn.ensemble.GradientBoostingRegressor.__dir__ sklearn.ensemble.GradientBoostingRegressor.__eq__ sklearn.ensemble.GradientBoostingRegressor.__format__ sklearn.ensemble.GradientBoostingRegressor.__ge__ sklearn.ensemble.GradientBoostingRegressor.__getattribute__ sklearn.ensemble.GradientBoostingRegressor.__getitem__ sklearn.ensemble.GradientBoostingRegressor.__getstate__ sklearn.ensemble.GradientBoostingRegressor.__gt__ sklearn.ensemble.GradientBoostingRegressor.__hash__ sklearn.ensemble.GradientBoostingRegressor.__init__ sklearn.ensemble.GradientBoostingRegressor.__init_subclass__ sklearn.ensemble.GradientBoostingRegressor.__iter__ sklearn.ensemble.GradientBoostingRegressor.__le__ sklearn.ensemble.GradientBoostingRegressor.__len__ sklearn.ensemble.GradientBoostingRegressor.__lt__ sklearn.ensemble.GradientBoostingRegressor.__ne__ sklearn.ensemble.GradientBoostingRegressor.__new__ sklearn.ensemble.GradientBoostingRegressor.__reduce__ sklearn.ensemble.GradientBoostingRegressor.__reduce_ex__ sklearn.ensemble.GradientBoostingRegressor.__repr__ sklearn.ensemble.GradientBoostingRegressor.__setattr__ sklearn.ensemble.GradientBoostingRegressor.__setstate__ sklearn.ensemble.GradientBoostingRegressor.__sizeof__ sklearn.ensemble.GradientBoostingRegressor.__sklearn_clone__ sklearn.ensemble.GradientBoostingRegressor.__sklearn_tags__ sklearn.ensemble.GradientBoostingRegressor.__str__ sklearn.ensemble.GradientBoostingRegressor.__subclasshook__ sklearn.ensemble.GradientBoostingRegressor._build_request_for_signature sklearn.ensemble.GradientBoostingRegressor._check_feature_names sklearn.ensemble.GradientBoostingRegressor._check_initialized sklearn.ensemble.GradientBoostingRegressor._check_n_features sklearn.ensemble.GradientBoostingRegressor._clear_state sklearn.ensemble.GradientBoostingRegressor._compute_partial_dependence_recursion sklearn.ensemble.GradientBoostingRegressor._encode_y sklearn.ensemble.GradientBoostingRegressor._fit_stage sklearn.ensemble.GradientBoostingRegressor._fit_stages sklearn.ensemble.GradientBoostingRegressor._get_default_requests sklearn.ensemble.GradientBoostingRegressor._get_doc_link sklearn.ensemble.GradientBoostingRegressor._get_loss sklearn.ensemble.GradientBoostingRegressor._get_metadata_request sklearn.ensemble.GradientBoostingRegressor._get_param_names sklearn.ensemble.GradientBoostingRegressor._get_tags sklearn.ensemble.GradientBoostingRegressor._init_state sklearn.ensemble.GradientBoostingRegressor._is_fitted sklearn.ensemble.GradientBoostingRegressor._make_estimator sklearn.ensemble.GradientBoostingRegressor._more_tags sklearn.ensemble.GradientBoostingRegressor._raw_predict sklearn.ensemble.GradientBoostingRegressor._raw_predict_init sklearn.ensemble.GradientBoostingRegressor._repr_html_inner sklearn.ensemble.GradientBoostingRegressor._repr_mimebundle_ sklearn.ensemble.GradientBoostingRegressor._resize_state sklearn.ensemble.GradientBoostingRegressor._set_max_features sklearn.ensemble.GradientBoostingRegressor._staged_raw_predict sklearn.ensemble.GradientBoostingRegressor._validate_data sklearn.ensemble.GradientBoostingRegressor._validate_estimator sklearn.ensemble.GradientBoostingRegressor._validate_params sklearn.ensemble.GradientBoostingRegressor.apply sklearn.ensemble.GradientBoostingRegressor.fit sklearn.ensemble.GradientBoostingRegressor.get_metadata_routing sklearn.ensemble.GradientBoostingRegressor.get_params sklearn.ensemble.GradientBoostingRegressor.predict sklearn.ensemble.GradientBoostingRegressor.score sklearn.ensemble.GradientBoostingRegressor.set_fit_request sklearn.ensemble.GradientBoostingRegressor.set_params sklearn.ensemble.GradientBoostingRegressor.set_score_request sklearn.ensemble.GradientBoostingRegressor.staged_predict 
+.. minigallery:: sklearn.ensemble.GradientBoostingRegressor sklearn.ensemble.GradientBoostingRegressor.__delattr__ sklearn.ensemble.GradientBoostingRegressor.__dir__ sklearn.ensemble.GradientBoostingRegressor.__eq__ sklearn.ensemble.GradientBoostingRegressor.__format__ sklearn.ensemble.GradientBoostingRegressor.__ge__ sklearn.ensemble.GradientBoostingRegressor.__getattribute__ sklearn.ensemble.GradientBoostingRegressor.__getitem__ sklearn.ensemble.GradientBoostingRegressor.__getstate__ sklearn.ensemble.GradientBoostingRegressor.__gt__ sklearn.ensemble.GradientBoostingRegressor.__hash__ sklearn.ensemble.GradientBoostingRegressor.__init__ sklearn.ensemble.GradientBoostingRegressor.__init_subclass__ sklearn.ensemble.GradientBoostingRegressor.__iter__ sklearn.ensemble.GradientBoostingRegressor.__le__ sklearn.ensemble.GradientBoostingRegressor.__len__ sklearn.ensemble.GradientBoostingRegressor.__lt__ sklearn.ensemble.GradientBoostingRegressor.__ne__ sklearn.ensemble.GradientBoostingRegressor.__new__ sklearn.ensemble.GradientBoostingRegressor.__reduce__ sklearn.ensemble.GradientBoostingRegressor.__reduce_ex__ sklearn.ensemble.GradientBoostingRegressor.__repr__ sklearn.ensemble.GradientBoostingRegressor.__setattr__ sklearn.ensemble.GradientBoostingRegressor.__setstate__ sklearn.ensemble.GradientBoostingRegressor.__sizeof__ sklearn.ensemble.GradientBoostingRegressor.__sklearn_clone__ sklearn.ensemble.GradientBoostingRegressor.__sklearn_tags__ sklearn.ensemble.GradientBoostingRegressor.__str__ sklearn.ensemble.GradientBoostingRegressor.__subclasshook__ sklearn.ensemble.GradientBoostingRegressor._build_request_for_signature sklearn.ensemble.GradientBoostingRegressor._check_initialized sklearn.ensemble.GradientBoostingRegressor._clear_state sklearn.ensemble.GradientBoostingRegressor._compute_partial_dependence_recursion sklearn.ensemble.GradientBoostingRegressor._encode_y sklearn.ensemble.GradientBoostingRegressor._fit_stage sklearn.ensemble.GradientBoostingRegressor._fit_stages sklearn.ensemble.GradientBoostingRegressor._get_default_requests sklearn.ensemble.GradientBoostingRegressor._get_doc_link sklearn.ensemble.GradientBoostingRegressor._get_loss sklearn.ensemble.GradientBoostingRegressor._get_metadata_request sklearn.ensemble.GradientBoostingRegressor._get_param_names sklearn.ensemble.GradientBoostingRegressor._init_state sklearn.ensemble.GradientBoostingRegressor._is_fitted sklearn.ensemble.GradientBoostingRegressor._make_estimator sklearn.ensemble.GradientBoostingRegressor._raw_predict sklearn.ensemble.GradientBoostingRegressor._raw_predict_init sklearn.ensemble.GradientBoostingRegressor._repr_html_inner sklearn.ensemble.GradientBoostingRegressor._repr_mimebundle_ sklearn.ensemble.GradientBoostingRegressor._resize_state sklearn.ensemble.GradientBoostingRegressor._set_max_features sklearn.ensemble.GradientBoostingRegressor._staged_raw_predict sklearn.ensemble.GradientBoostingRegressor._validate_estimator sklearn.ensemble.GradientBoostingRegressor._validate_params sklearn.ensemble.GradientBoostingRegressor.apply sklearn.ensemble.GradientBoostingRegressor.fit sklearn.ensemble.GradientBoostingRegressor.get_metadata_routing sklearn.ensemble.GradientBoostingRegressor.get_params sklearn.ensemble.GradientBoostingRegressor.predict sklearn.ensemble.GradientBoostingRegressor.score sklearn.ensemble.GradientBoostingRegressor.set_fit_request sklearn.ensemble.GradientBoostingRegressor.set_params sklearn.ensemble.GradientBoostingRegressor.set_score_request sklearn.ensemble.GradientBoostingRegressor.staged_predict 
    :add-heading: Gallery examples
    :heading-level: -
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diff --git a/dev/_sources/modules/generated/sklearn.ensemble.HistGradientBoostingClassifier.rst.txt b/dev/_sources/modules/generated/sklearn.ensemble.HistGradientBoostingClassifier.rst.txt
index b9a2c0ba9f0db..e6f3122f12784 100644
--- a/dev/_sources/modules/generated/sklearn.ensemble.HistGradientBoostingClassifier.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.ensemble.HistGradientBoostingClassifier.rst.txt
@@ -8,6 +8,6 @@ HistGradientBoostingClassifier
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.ensemble.HistGradientBoostingClassifier sklearn.ensemble.HistGradientBoostingClassifier.__delattr__ sklearn.ensemble.HistGradientBoostingClassifier.__dir__ sklearn.ensemble.HistGradientBoostingClassifier.__eq__ sklearn.ensemble.HistGradientBoostingClassifier.__format__ sklearn.ensemble.HistGradientBoostingClassifier.__ge__ sklearn.ensemble.HistGradientBoostingClassifier.__getattribute__ sklearn.ensemble.HistGradientBoostingClassifier.__getstate__ sklearn.ensemble.HistGradientBoostingClassifier.__gt__ sklearn.ensemble.HistGradientBoostingClassifier.__hash__ sklearn.ensemble.HistGradientBoostingClassifier.__init__ sklearn.ensemble.HistGradientBoostingClassifier.__init_subclass__ sklearn.ensemble.HistGradientBoostingClassifier.__le__ sklearn.ensemble.HistGradientBoostingClassifier.__lt__ sklearn.ensemble.HistGradientBoostingClassifier.__ne__ sklearn.ensemble.HistGradientBoostingClassifier.__new__ sklearn.ensemble.HistGradientBoostingClassifier.__reduce__ sklearn.ensemble.HistGradientBoostingClassifier.__reduce_ex__ sklearn.ensemble.HistGradientBoostingClassifier.__repr__ sklearn.ensemble.HistGradientBoostingClassifier.__setattr__ sklearn.ensemble.HistGradientBoostingClassifier.__setstate__ sklearn.ensemble.HistGradientBoostingClassifier.__sizeof__ sklearn.ensemble.HistGradientBoostingClassifier.__sklearn_clone__ sklearn.ensemble.HistGradientBoostingClassifier.__sklearn_tags__ sklearn.ensemble.HistGradientBoostingClassifier.__str__ sklearn.ensemble.HistGradientBoostingClassifier.__subclasshook__ sklearn.ensemble.HistGradientBoostingClassifier._bin_data sklearn.ensemble.HistGradientBoostingClassifier._build_request_for_signature sklearn.ensemble.HistGradientBoostingClassifier._check_categorical_features sklearn.ensemble.HistGradientBoostingClassifier._check_categories sklearn.ensemble.HistGradientBoostingClassifier._check_early_stopping_loss sklearn.ensemble.HistGradientBoostingClassifier._check_early_stopping_scorer sklearn.ensemble.HistGradientBoostingClassifier._check_feature_names sklearn.ensemble.HistGradientBoostingClassifier._check_interaction_cst sklearn.ensemble.HistGradientBoostingClassifier._check_n_features sklearn.ensemble.HistGradientBoostingClassifier._clear_state sklearn.ensemble.HistGradientBoostingClassifier._compute_partial_dependence_recursion sklearn.ensemble.HistGradientBoostingClassifier._encode_y sklearn.ensemble.HistGradientBoostingClassifier._finalize_sample_weight sklearn.ensemble.HistGradientBoostingClassifier._get_default_requests sklearn.ensemble.HistGradientBoostingClassifier._get_doc_link sklearn.ensemble.HistGradientBoostingClassifier._get_loss sklearn.ensemble.HistGradientBoostingClassifier._get_metadata_request sklearn.ensemble.HistGradientBoostingClassifier._get_param_names sklearn.ensemble.HistGradientBoostingClassifier._get_small_trainset sklearn.ensemble.HistGradientBoostingClassifier._get_tags sklearn.ensemble.HistGradientBoostingClassifier._is_fitted sklearn.ensemble.HistGradientBoostingClassifier._more_tags sklearn.ensemble.HistGradientBoostingClassifier._predict_iterations sklearn.ensemble.HistGradientBoostingClassifier._preprocess_X sklearn.ensemble.HistGradientBoostingClassifier._print_iteration_stats sklearn.ensemble.HistGradientBoostingClassifier._raw_predict sklearn.ensemble.HistGradientBoostingClassifier._repr_html_inner sklearn.ensemble.HistGradientBoostingClassifier._repr_mimebundle_ sklearn.ensemble.HistGradientBoostingClassifier._score_with_raw_predictions sklearn.ensemble.HistGradientBoostingClassifier._should_stop sklearn.ensemble.HistGradientBoostingClassifier._staged_raw_predict sklearn.ensemble.HistGradientBoostingClassifier._validate_data sklearn.ensemble.HistGradientBoostingClassifier._validate_parameters sklearn.ensemble.HistGradientBoostingClassifier._validate_params sklearn.ensemble.HistGradientBoostingClassifier.decision_function sklearn.ensemble.HistGradientBoostingClassifier.fit sklearn.ensemble.HistGradientBoostingClassifier.get_metadata_routing sklearn.ensemble.HistGradientBoostingClassifier.get_params sklearn.ensemble.HistGradientBoostingClassifier.predict sklearn.ensemble.HistGradientBoostingClassifier.predict_proba sklearn.ensemble.HistGradientBoostingClassifier.score sklearn.ensemble.HistGradientBoostingClassifier.set_fit_request sklearn.ensemble.HistGradientBoostingClassifier.set_params sklearn.ensemble.HistGradientBoostingClassifier.set_score_request sklearn.ensemble.HistGradientBoostingClassifier.staged_decision_function sklearn.ensemble.HistGradientBoostingClassifier.staged_predict sklearn.ensemble.HistGradientBoostingClassifier.staged_predict_proba 
+.. minigallery:: sklearn.ensemble.HistGradientBoostingClassifier sklearn.ensemble.HistGradientBoostingClassifier.__delattr__ sklearn.ensemble.HistGradientBoostingClassifier.__dir__ sklearn.ensemble.HistGradientBoostingClassifier.__eq__ sklearn.ensemble.HistGradientBoostingClassifier.__format__ sklearn.ensemble.HistGradientBoostingClassifier.__ge__ sklearn.ensemble.HistGradientBoostingClassifier.__getattribute__ sklearn.ensemble.HistGradientBoostingClassifier.__getstate__ sklearn.ensemble.HistGradientBoostingClassifier.__gt__ sklearn.ensemble.HistGradientBoostingClassifier.__hash__ sklearn.ensemble.HistGradientBoostingClassifier.__init__ sklearn.ensemble.HistGradientBoostingClassifier.__init_subclass__ sklearn.ensemble.HistGradientBoostingClassifier.__le__ sklearn.ensemble.HistGradientBoostingClassifier.__lt__ sklearn.ensemble.HistGradientBoostingClassifier.__ne__ sklearn.ensemble.HistGradientBoostingClassifier.__new__ sklearn.ensemble.HistGradientBoostingClassifier.__reduce__ sklearn.ensemble.HistGradientBoostingClassifier.__reduce_ex__ sklearn.ensemble.HistGradientBoostingClassifier.__repr__ sklearn.ensemble.HistGradientBoostingClassifier.__setattr__ sklearn.ensemble.HistGradientBoostingClassifier.__setstate__ sklearn.ensemble.HistGradientBoostingClassifier.__sizeof__ sklearn.ensemble.HistGradientBoostingClassifier.__sklearn_clone__ sklearn.ensemble.HistGradientBoostingClassifier.__sklearn_tags__ sklearn.ensemble.HistGradientBoostingClassifier.__str__ sklearn.ensemble.HistGradientBoostingClassifier.__subclasshook__ sklearn.ensemble.HistGradientBoostingClassifier._bin_data sklearn.ensemble.HistGradientBoostingClassifier._build_request_for_signature sklearn.ensemble.HistGradientBoostingClassifier._check_categorical_features sklearn.ensemble.HistGradientBoostingClassifier._check_categories sklearn.ensemble.HistGradientBoostingClassifier._check_early_stopping_loss sklearn.ensemble.HistGradientBoostingClassifier._check_early_stopping_scorer sklearn.ensemble.HistGradientBoostingClassifier._check_interaction_cst sklearn.ensemble.HistGradientBoostingClassifier._clear_state sklearn.ensemble.HistGradientBoostingClassifier._compute_partial_dependence_recursion sklearn.ensemble.HistGradientBoostingClassifier._encode_y sklearn.ensemble.HistGradientBoostingClassifier._encode_y_val sklearn.ensemble.HistGradientBoostingClassifier._finalize_sample_weight sklearn.ensemble.HistGradientBoostingClassifier._get_default_requests sklearn.ensemble.HistGradientBoostingClassifier._get_doc_link sklearn.ensemble.HistGradientBoostingClassifier._get_loss sklearn.ensemble.HistGradientBoostingClassifier._get_metadata_request sklearn.ensemble.HistGradientBoostingClassifier._get_param_names sklearn.ensemble.HistGradientBoostingClassifier._get_small_trainset sklearn.ensemble.HistGradientBoostingClassifier._is_fitted sklearn.ensemble.HistGradientBoostingClassifier._predict_iterations sklearn.ensemble.HistGradientBoostingClassifier._preprocess_X sklearn.ensemble.HistGradientBoostingClassifier._print_iteration_stats sklearn.ensemble.HistGradientBoostingClassifier._raw_predict sklearn.ensemble.HistGradientBoostingClassifier._repr_html_inner sklearn.ensemble.HistGradientBoostingClassifier._repr_mimebundle_ sklearn.ensemble.HistGradientBoostingClassifier._score_with_raw_predictions sklearn.ensemble.HistGradientBoostingClassifier._should_stop sklearn.ensemble.HistGradientBoostingClassifier._staged_raw_predict sklearn.ensemble.HistGradientBoostingClassifier._validate_parameters sklearn.ensemble.HistGradientBoostingClassifier._validate_params sklearn.ensemble.HistGradientBoostingClassifier.decision_function sklearn.ensemble.HistGradientBoostingClassifier.fit sklearn.ensemble.HistGradientBoostingClassifier.get_metadata_routing sklearn.ensemble.HistGradientBoostingClassifier.get_params sklearn.ensemble.HistGradientBoostingClassifier.predict sklearn.ensemble.HistGradientBoostingClassifier.predict_proba sklearn.ensemble.HistGradientBoostingClassifier.score sklearn.ensemble.HistGradientBoostingClassifier.set_fit_request sklearn.ensemble.HistGradientBoostingClassifier.set_params sklearn.ensemble.HistGradientBoostingClassifier.set_score_request sklearn.ensemble.HistGradientBoostingClassifier.staged_decision_function sklearn.ensemble.HistGradientBoostingClassifier.staged_predict sklearn.ensemble.HistGradientBoostingClassifier.staged_predict_proba 
    :add-heading: Gallery examples
    :heading-level: -
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diff --git a/dev/_sources/modules/generated/sklearn.ensemble.HistGradientBoostingRegressor.rst.txt b/dev/_sources/modules/generated/sklearn.ensemble.HistGradientBoostingRegressor.rst.txt
index bac27c95aad24..f52aaa6ed53fc 100644
--- a/dev/_sources/modules/generated/sklearn.ensemble.HistGradientBoostingRegressor.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.ensemble.HistGradientBoostingRegressor.rst.txt
@@ -8,6 +8,6 @@ HistGradientBoostingRegressor
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.ensemble.HistGradientBoostingRegressor sklearn.ensemble.HistGradientBoostingRegressor.__delattr__ sklearn.ensemble.HistGradientBoostingRegressor.__dir__ sklearn.ensemble.HistGradientBoostingRegressor.__eq__ sklearn.ensemble.HistGradientBoostingRegressor.__format__ sklearn.ensemble.HistGradientBoostingRegressor.__ge__ sklearn.ensemble.HistGradientBoostingRegressor.__getattribute__ sklearn.ensemble.HistGradientBoostingRegressor.__getstate__ sklearn.ensemble.HistGradientBoostingRegressor.__gt__ sklearn.ensemble.HistGradientBoostingRegressor.__hash__ sklearn.ensemble.HistGradientBoostingRegressor.__init__ sklearn.ensemble.HistGradientBoostingRegressor.__init_subclass__ sklearn.ensemble.HistGradientBoostingRegressor.__le__ sklearn.ensemble.HistGradientBoostingRegressor.__lt__ sklearn.ensemble.HistGradientBoostingRegressor.__ne__ sklearn.ensemble.HistGradientBoostingRegressor.__new__ sklearn.ensemble.HistGradientBoostingRegressor.__reduce__ sklearn.ensemble.HistGradientBoostingRegressor.__reduce_ex__ sklearn.ensemble.HistGradientBoostingRegressor.__repr__ sklearn.ensemble.HistGradientBoostingRegressor.__setattr__ sklearn.ensemble.HistGradientBoostingRegressor.__setstate__ sklearn.ensemble.HistGradientBoostingRegressor.__sizeof__ sklearn.ensemble.HistGradientBoostingRegressor.__sklearn_clone__ sklearn.ensemble.HistGradientBoostingRegressor.__sklearn_tags__ sklearn.ensemble.HistGradientBoostingRegressor.__str__ sklearn.ensemble.HistGradientBoostingRegressor.__subclasshook__ sklearn.ensemble.HistGradientBoostingRegressor._bin_data sklearn.ensemble.HistGradientBoostingRegressor._build_request_for_signature sklearn.ensemble.HistGradientBoostingRegressor._check_categorical_features sklearn.ensemble.HistGradientBoostingRegressor._check_categories sklearn.ensemble.HistGradientBoostingRegressor._check_early_stopping_loss sklearn.ensemble.HistGradientBoostingRegressor._check_early_stopping_scorer sklearn.ensemble.HistGradientBoostingRegressor._check_feature_names sklearn.ensemble.HistGradientBoostingRegressor._check_interaction_cst sklearn.ensemble.HistGradientBoostingRegressor._check_n_features sklearn.ensemble.HistGradientBoostingRegressor._clear_state sklearn.ensemble.HistGradientBoostingRegressor._compute_partial_dependence_recursion sklearn.ensemble.HistGradientBoostingRegressor._encode_y sklearn.ensemble.HistGradientBoostingRegressor._finalize_sample_weight sklearn.ensemble.HistGradientBoostingRegressor._get_default_requests sklearn.ensemble.HistGradientBoostingRegressor._get_doc_link sklearn.ensemble.HistGradientBoostingRegressor._get_loss sklearn.ensemble.HistGradientBoostingRegressor._get_metadata_request sklearn.ensemble.HistGradientBoostingRegressor._get_param_names sklearn.ensemble.HistGradientBoostingRegressor._get_small_trainset sklearn.ensemble.HistGradientBoostingRegressor._get_tags sklearn.ensemble.HistGradientBoostingRegressor._is_fitted sklearn.ensemble.HistGradientBoostingRegressor._more_tags sklearn.ensemble.HistGradientBoostingRegressor._predict_iterations sklearn.ensemble.HistGradientBoostingRegressor._preprocess_X sklearn.ensemble.HistGradientBoostingRegressor._print_iteration_stats sklearn.ensemble.HistGradientBoostingRegressor._raw_predict sklearn.ensemble.HistGradientBoostingRegressor._repr_html_inner sklearn.ensemble.HistGradientBoostingRegressor._repr_mimebundle_ sklearn.ensemble.HistGradientBoostingRegressor._score_with_raw_predictions sklearn.ensemble.HistGradientBoostingRegressor._should_stop sklearn.ensemble.HistGradientBoostingRegressor._staged_raw_predict sklearn.ensemble.HistGradientBoostingRegressor._validate_data sklearn.ensemble.HistGradientBoostingRegressor._validate_parameters sklearn.ensemble.HistGradientBoostingRegressor._validate_params sklearn.ensemble.HistGradientBoostingRegressor.fit sklearn.ensemble.HistGradientBoostingRegressor.get_metadata_routing sklearn.ensemble.HistGradientBoostingRegressor.get_params sklearn.ensemble.HistGradientBoostingRegressor.predict sklearn.ensemble.HistGradientBoostingRegressor.score sklearn.ensemble.HistGradientBoostingRegressor.set_fit_request sklearn.ensemble.HistGradientBoostingRegressor.set_params sklearn.ensemble.HistGradientBoostingRegressor.set_score_request sklearn.ensemble.HistGradientBoostingRegressor.staged_predict 
+.. minigallery:: sklearn.ensemble.HistGradientBoostingRegressor sklearn.ensemble.HistGradientBoostingRegressor.__delattr__ sklearn.ensemble.HistGradientBoostingRegressor.__dir__ sklearn.ensemble.HistGradientBoostingRegressor.__eq__ sklearn.ensemble.HistGradientBoostingRegressor.__format__ sklearn.ensemble.HistGradientBoostingRegressor.__ge__ sklearn.ensemble.HistGradientBoostingRegressor.__getattribute__ sklearn.ensemble.HistGradientBoostingRegressor.__getstate__ sklearn.ensemble.HistGradientBoostingRegressor.__gt__ sklearn.ensemble.HistGradientBoostingRegressor.__hash__ sklearn.ensemble.HistGradientBoostingRegressor.__init__ sklearn.ensemble.HistGradientBoostingRegressor.__init_subclass__ sklearn.ensemble.HistGradientBoostingRegressor.__le__ sklearn.ensemble.HistGradientBoostingRegressor.__lt__ sklearn.ensemble.HistGradientBoostingRegressor.__ne__ sklearn.ensemble.HistGradientBoostingRegressor.__new__ sklearn.ensemble.HistGradientBoostingRegressor.__reduce__ sklearn.ensemble.HistGradientBoostingRegressor.__reduce_ex__ sklearn.ensemble.HistGradientBoostingRegressor.__repr__ sklearn.ensemble.HistGradientBoostingRegressor.__setattr__ sklearn.ensemble.HistGradientBoostingRegressor.__setstate__ sklearn.ensemble.HistGradientBoostingRegressor.__sizeof__ sklearn.ensemble.HistGradientBoostingRegressor.__sklearn_clone__ sklearn.ensemble.HistGradientBoostingRegressor.__sklearn_tags__ sklearn.ensemble.HistGradientBoostingRegressor.__str__ sklearn.ensemble.HistGradientBoostingRegressor.__subclasshook__ sklearn.ensemble.HistGradientBoostingRegressor._bin_data sklearn.ensemble.HistGradientBoostingRegressor._build_request_for_signature sklearn.ensemble.HistGradientBoostingRegressor._check_categorical_features sklearn.ensemble.HistGradientBoostingRegressor._check_categories sklearn.ensemble.HistGradientBoostingRegressor._check_early_stopping_loss sklearn.ensemble.HistGradientBoostingRegressor._check_early_stopping_scorer sklearn.ensemble.HistGradientBoostingRegressor._check_interaction_cst sklearn.ensemble.HistGradientBoostingRegressor._clear_state sklearn.ensemble.HistGradientBoostingRegressor._compute_partial_dependence_recursion sklearn.ensemble.HistGradientBoostingRegressor._encode_y sklearn.ensemble.HistGradientBoostingRegressor._encode_y_val sklearn.ensemble.HistGradientBoostingRegressor._finalize_sample_weight sklearn.ensemble.HistGradientBoostingRegressor._get_default_requests sklearn.ensemble.HistGradientBoostingRegressor._get_doc_link sklearn.ensemble.HistGradientBoostingRegressor._get_loss sklearn.ensemble.HistGradientBoostingRegressor._get_metadata_request sklearn.ensemble.HistGradientBoostingRegressor._get_param_names sklearn.ensemble.HistGradientBoostingRegressor._get_small_trainset sklearn.ensemble.HistGradientBoostingRegressor._is_fitted sklearn.ensemble.HistGradientBoostingRegressor._predict_iterations sklearn.ensemble.HistGradientBoostingRegressor._preprocess_X sklearn.ensemble.HistGradientBoostingRegressor._print_iteration_stats sklearn.ensemble.HistGradientBoostingRegressor._raw_predict sklearn.ensemble.HistGradientBoostingRegressor._repr_html_inner sklearn.ensemble.HistGradientBoostingRegressor._repr_mimebundle_ sklearn.ensemble.HistGradientBoostingRegressor._score_with_raw_predictions sklearn.ensemble.HistGradientBoostingRegressor._should_stop sklearn.ensemble.HistGradientBoostingRegressor._staged_raw_predict sklearn.ensemble.HistGradientBoostingRegressor._validate_parameters sklearn.ensemble.HistGradientBoostingRegressor._validate_params sklearn.ensemble.HistGradientBoostingRegressor.fit sklearn.ensemble.HistGradientBoostingRegressor.get_metadata_routing sklearn.ensemble.HistGradientBoostingRegressor.get_params sklearn.ensemble.HistGradientBoostingRegressor.predict sklearn.ensemble.HistGradientBoostingRegressor.score sklearn.ensemble.HistGradientBoostingRegressor.set_fit_request sklearn.ensemble.HistGradientBoostingRegressor.set_params sklearn.ensemble.HistGradientBoostingRegressor.set_score_request sklearn.ensemble.HistGradientBoostingRegressor.staged_predict 
    :add-heading: Gallery examples
    :heading-level: -
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diff --git a/dev/_sources/modules/generated/sklearn.ensemble.IsolationForest.rst.txt b/dev/_sources/modules/generated/sklearn.ensemble.IsolationForest.rst.txt
index 33788119ed27a..d33d3c17b155f 100644
--- a/dev/_sources/modules/generated/sklearn.ensemble.IsolationForest.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.ensemble.IsolationForest.rst.txt
@@ -8,6 +8,6 @@ IsolationForest
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.ensemble.IsolationForest sklearn.ensemble.IsolationForest.__delattr__ sklearn.ensemble.IsolationForest.__dir__ sklearn.ensemble.IsolationForest.__eq__ sklearn.ensemble.IsolationForest.__format__ sklearn.ensemble.IsolationForest.__ge__ sklearn.ensemble.IsolationForest.__getattribute__ sklearn.ensemble.IsolationForest.__getitem__ sklearn.ensemble.IsolationForest.__getstate__ sklearn.ensemble.IsolationForest.__gt__ sklearn.ensemble.IsolationForest.__hash__ sklearn.ensemble.IsolationForest.__init__ sklearn.ensemble.IsolationForest.__init_subclass__ sklearn.ensemble.IsolationForest.__iter__ sklearn.ensemble.IsolationForest.__le__ sklearn.ensemble.IsolationForest.__len__ sklearn.ensemble.IsolationForest.__lt__ sklearn.ensemble.IsolationForest.__ne__ sklearn.ensemble.IsolationForest.__new__ sklearn.ensemble.IsolationForest.__reduce__ sklearn.ensemble.IsolationForest.__reduce_ex__ sklearn.ensemble.IsolationForest.__repr__ sklearn.ensemble.IsolationForest.__setattr__ sklearn.ensemble.IsolationForest.__setstate__ sklearn.ensemble.IsolationForest.__sizeof__ sklearn.ensemble.IsolationForest.__sklearn_clone__ sklearn.ensemble.IsolationForest.__sklearn_tags__ sklearn.ensemble.IsolationForest.__str__ sklearn.ensemble.IsolationForest.__subclasshook__ sklearn.ensemble.IsolationForest._build_request_for_signature sklearn.ensemble.IsolationForest._check_feature_names sklearn.ensemble.IsolationForest._check_n_features sklearn.ensemble.IsolationForest._compute_chunked_score_samples sklearn.ensemble.IsolationForest._compute_score_samples sklearn.ensemble.IsolationForest._fit sklearn.ensemble.IsolationForest._get_default_requests sklearn.ensemble.IsolationForest._get_doc_link sklearn.ensemble.IsolationForest._get_estimator sklearn.ensemble.IsolationForest._get_estimators_indices sklearn.ensemble.IsolationForest._get_metadata_request sklearn.ensemble.IsolationForest._get_param_names sklearn.ensemble.IsolationForest._get_tags sklearn.ensemble.IsolationForest._make_estimator sklearn.ensemble.IsolationForest._more_tags sklearn.ensemble.IsolationForest._parallel_args sklearn.ensemble.IsolationForest._repr_html_inner sklearn.ensemble.IsolationForest._repr_mimebundle_ sklearn.ensemble.IsolationForest._score_samples sklearn.ensemble.IsolationForest._set_oob_score sklearn.ensemble.IsolationForest._validate_data sklearn.ensemble.IsolationForest._validate_estimator sklearn.ensemble.IsolationForest._validate_params sklearn.ensemble.IsolationForest._validate_y sklearn.ensemble.IsolationForest.decision_function sklearn.ensemble.IsolationForest.fit sklearn.ensemble.IsolationForest.fit_predict sklearn.ensemble.IsolationForest.get_metadata_routing sklearn.ensemble.IsolationForest.get_params sklearn.ensemble.IsolationForest.predict sklearn.ensemble.IsolationForest.score_samples sklearn.ensemble.IsolationForest.set_fit_request sklearn.ensemble.IsolationForest.set_params 
+.. minigallery:: sklearn.ensemble.IsolationForest sklearn.ensemble.IsolationForest.__delattr__ sklearn.ensemble.IsolationForest.__dir__ sklearn.ensemble.IsolationForest.__eq__ sklearn.ensemble.IsolationForest.__format__ sklearn.ensemble.IsolationForest.__ge__ sklearn.ensemble.IsolationForest.__getattribute__ sklearn.ensemble.IsolationForest.__getitem__ sklearn.ensemble.IsolationForest.__getstate__ sklearn.ensemble.IsolationForest.__gt__ sklearn.ensemble.IsolationForest.__hash__ sklearn.ensemble.IsolationForest.__init__ sklearn.ensemble.IsolationForest.__init_subclass__ sklearn.ensemble.IsolationForest.__iter__ sklearn.ensemble.IsolationForest.__le__ sklearn.ensemble.IsolationForest.__len__ sklearn.ensemble.IsolationForest.__lt__ sklearn.ensemble.IsolationForest.__ne__ sklearn.ensemble.IsolationForest.__new__ sklearn.ensemble.IsolationForest.__reduce__ sklearn.ensemble.IsolationForest.__reduce_ex__ sklearn.ensemble.IsolationForest.__repr__ sklearn.ensemble.IsolationForest.__setattr__ sklearn.ensemble.IsolationForest.__setstate__ sklearn.ensemble.IsolationForest.__sizeof__ sklearn.ensemble.IsolationForest.__sklearn_clone__ sklearn.ensemble.IsolationForest.__sklearn_tags__ sklearn.ensemble.IsolationForest.__str__ sklearn.ensemble.IsolationForest.__subclasshook__ sklearn.ensemble.IsolationForest._build_request_for_signature sklearn.ensemble.IsolationForest._compute_chunked_score_samples sklearn.ensemble.IsolationForest._compute_score_samples sklearn.ensemble.IsolationForest._fit sklearn.ensemble.IsolationForest._get_default_requests sklearn.ensemble.IsolationForest._get_doc_link sklearn.ensemble.IsolationForest._get_estimator sklearn.ensemble.IsolationForest._get_estimators_indices sklearn.ensemble.IsolationForest._get_metadata_request sklearn.ensemble.IsolationForest._get_param_names sklearn.ensemble.IsolationForest._make_estimator sklearn.ensemble.IsolationForest._parallel_args sklearn.ensemble.IsolationForest._repr_html_inner sklearn.ensemble.IsolationForest._repr_mimebundle_ sklearn.ensemble.IsolationForest._score_samples sklearn.ensemble.IsolationForest._set_oob_score sklearn.ensemble.IsolationForest._validate_estimator sklearn.ensemble.IsolationForest._validate_params sklearn.ensemble.IsolationForest._validate_y sklearn.ensemble.IsolationForest.decision_function sklearn.ensemble.IsolationForest.fit sklearn.ensemble.IsolationForest.fit_predict sklearn.ensemble.IsolationForest.get_metadata_routing sklearn.ensemble.IsolationForest.get_params sklearn.ensemble.IsolationForest.predict sklearn.ensemble.IsolationForest.score_samples sklearn.ensemble.IsolationForest.set_fit_request sklearn.ensemble.IsolationForest.set_params 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.ensemble.RandomForestClassifier.rst.txt b/dev/_sources/modules/generated/sklearn.ensemble.RandomForestClassifier.rst.txt
index d98d356d61d22..b0ba4f6796843 100644
--- a/dev/_sources/modules/generated/sklearn.ensemble.RandomForestClassifier.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.ensemble.RandomForestClassifier.rst.txt
@@ -8,6 +8,6 @@ RandomForestClassifier
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.ensemble.RandomForestClassifier sklearn.ensemble.RandomForestClassifier.__delattr__ sklearn.ensemble.RandomForestClassifier.__dir__ sklearn.ensemble.RandomForestClassifier.__eq__ sklearn.ensemble.RandomForestClassifier.__format__ sklearn.ensemble.RandomForestClassifier.__ge__ sklearn.ensemble.RandomForestClassifier.__getattribute__ sklearn.ensemble.RandomForestClassifier.__getitem__ sklearn.ensemble.RandomForestClassifier.__getstate__ sklearn.ensemble.RandomForestClassifier.__gt__ sklearn.ensemble.RandomForestClassifier.__hash__ sklearn.ensemble.RandomForestClassifier.__init__ sklearn.ensemble.RandomForestClassifier.__init_subclass__ sklearn.ensemble.RandomForestClassifier.__iter__ sklearn.ensemble.RandomForestClassifier.__le__ sklearn.ensemble.RandomForestClassifier.__len__ sklearn.ensemble.RandomForestClassifier.__lt__ sklearn.ensemble.RandomForestClassifier.__ne__ sklearn.ensemble.RandomForestClassifier.__new__ sklearn.ensemble.RandomForestClassifier.__reduce__ sklearn.ensemble.RandomForestClassifier.__reduce_ex__ sklearn.ensemble.RandomForestClassifier.__repr__ sklearn.ensemble.RandomForestClassifier.__setattr__ sklearn.ensemble.RandomForestClassifier.__setstate__ sklearn.ensemble.RandomForestClassifier.__sizeof__ sklearn.ensemble.RandomForestClassifier.__sklearn_clone__ sklearn.ensemble.RandomForestClassifier.__sklearn_tags__ sklearn.ensemble.RandomForestClassifier.__str__ sklearn.ensemble.RandomForestClassifier.__subclasshook__ sklearn.ensemble.RandomForestClassifier._build_request_for_signature sklearn.ensemble.RandomForestClassifier._check_feature_names sklearn.ensemble.RandomForestClassifier._check_n_features sklearn.ensemble.RandomForestClassifier._compute_oob_predictions sklearn.ensemble.RandomForestClassifier._get_default_requests sklearn.ensemble.RandomForestClassifier._get_doc_link sklearn.ensemble.RandomForestClassifier._get_estimators_indices sklearn.ensemble.RandomForestClassifier._get_metadata_request sklearn.ensemble.RandomForestClassifier._get_oob_predictions sklearn.ensemble.RandomForestClassifier._get_param_names sklearn.ensemble.RandomForestClassifier._get_tags sklearn.ensemble.RandomForestClassifier._make_estimator sklearn.ensemble.RandomForestClassifier._more_tags sklearn.ensemble.RandomForestClassifier._repr_html_inner sklearn.ensemble.RandomForestClassifier._repr_mimebundle_ sklearn.ensemble.RandomForestClassifier._set_oob_score_and_attributes sklearn.ensemble.RandomForestClassifier._validate_X_predict sklearn.ensemble.RandomForestClassifier._validate_data sklearn.ensemble.RandomForestClassifier._validate_estimator sklearn.ensemble.RandomForestClassifier._validate_params sklearn.ensemble.RandomForestClassifier._validate_y_class_weight sklearn.ensemble.RandomForestClassifier.apply sklearn.ensemble.RandomForestClassifier.decision_path sklearn.ensemble.RandomForestClassifier.fit sklearn.ensemble.RandomForestClassifier.get_metadata_routing sklearn.ensemble.RandomForestClassifier.get_params sklearn.ensemble.RandomForestClassifier.predict sklearn.ensemble.RandomForestClassifier.predict_log_proba sklearn.ensemble.RandomForestClassifier.predict_proba sklearn.ensemble.RandomForestClassifier.score sklearn.ensemble.RandomForestClassifier.set_fit_request sklearn.ensemble.RandomForestClassifier.set_params sklearn.ensemble.RandomForestClassifier.set_score_request 
+.. minigallery:: sklearn.ensemble.RandomForestClassifier sklearn.ensemble.RandomForestClassifier.__delattr__ sklearn.ensemble.RandomForestClassifier.__dir__ sklearn.ensemble.RandomForestClassifier.__eq__ sklearn.ensemble.RandomForestClassifier.__format__ sklearn.ensemble.RandomForestClassifier.__ge__ sklearn.ensemble.RandomForestClassifier.__getattribute__ sklearn.ensemble.RandomForestClassifier.__getitem__ sklearn.ensemble.RandomForestClassifier.__getstate__ sklearn.ensemble.RandomForestClassifier.__gt__ sklearn.ensemble.RandomForestClassifier.__hash__ sklearn.ensemble.RandomForestClassifier.__init__ sklearn.ensemble.RandomForestClassifier.__init_subclass__ sklearn.ensemble.RandomForestClassifier.__iter__ sklearn.ensemble.RandomForestClassifier.__le__ sklearn.ensemble.RandomForestClassifier.__len__ sklearn.ensemble.RandomForestClassifier.__lt__ sklearn.ensemble.RandomForestClassifier.__ne__ sklearn.ensemble.RandomForestClassifier.__new__ sklearn.ensemble.RandomForestClassifier.__reduce__ sklearn.ensemble.RandomForestClassifier.__reduce_ex__ sklearn.ensemble.RandomForestClassifier.__repr__ sklearn.ensemble.RandomForestClassifier.__setattr__ sklearn.ensemble.RandomForestClassifier.__setstate__ sklearn.ensemble.RandomForestClassifier.__sizeof__ sklearn.ensemble.RandomForestClassifier.__sklearn_clone__ sklearn.ensemble.RandomForestClassifier.__sklearn_tags__ sklearn.ensemble.RandomForestClassifier.__str__ sklearn.ensemble.RandomForestClassifier.__subclasshook__ sklearn.ensemble.RandomForestClassifier._build_request_for_signature sklearn.ensemble.RandomForestClassifier._compute_oob_predictions sklearn.ensemble.RandomForestClassifier._get_default_requests sklearn.ensemble.RandomForestClassifier._get_doc_link sklearn.ensemble.RandomForestClassifier._get_estimators_indices sklearn.ensemble.RandomForestClassifier._get_metadata_request sklearn.ensemble.RandomForestClassifier._get_oob_predictions sklearn.ensemble.RandomForestClassifier._get_param_names sklearn.ensemble.RandomForestClassifier._make_estimator sklearn.ensemble.RandomForestClassifier._repr_html_inner sklearn.ensemble.RandomForestClassifier._repr_mimebundle_ sklearn.ensemble.RandomForestClassifier._set_oob_score_and_attributes sklearn.ensemble.RandomForestClassifier._validate_X_predict sklearn.ensemble.RandomForestClassifier._validate_estimator sklearn.ensemble.RandomForestClassifier._validate_params sklearn.ensemble.RandomForestClassifier._validate_y_class_weight sklearn.ensemble.RandomForestClassifier.apply sklearn.ensemble.RandomForestClassifier.decision_path sklearn.ensemble.RandomForestClassifier.fit sklearn.ensemble.RandomForestClassifier.get_metadata_routing sklearn.ensemble.RandomForestClassifier.get_params sklearn.ensemble.RandomForestClassifier.predict sklearn.ensemble.RandomForestClassifier.predict_log_proba sklearn.ensemble.RandomForestClassifier.predict_proba sklearn.ensemble.RandomForestClassifier.score sklearn.ensemble.RandomForestClassifier.set_fit_request sklearn.ensemble.RandomForestClassifier.set_params sklearn.ensemble.RandomForestClassifier.set_score_request 
    :add-heading: Gallery examples
    :heading-level: -
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diff --git a/dev/_sources/modules/generated/sklearn.ensemble.RandomForestRegressor.rst.txt b/dev/_sources/modules/generated/sklearn.ensemble.RandomForestRegressor.rst.txt
index b8c7bffa4725a..a106e31593550 100644
--- a/dev/_sources/modules/generated/sklearn.ensemble.RandomForestRegressor.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.ensemble.RandomForestRegressor.rst.txt
@@ -8,6 +8,6 @@ RandomForestRegressor
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.ensemble.RandomForestRegressor sklearn.ensemble.RandomForestRegressor.__delattr__ sklearn.ensemble.RandomForestRegressor.__dir__ sklearn.ensemble.RandomForestRegressor.__eq__ sklearn.ensemble.RandomForestRegressor.__format__ sklearn.ensemble.RandomForestRegressor.__ge__ sklearn.ensemble.RandomForestRegressor.__getattribute__ sklearn.ensemble.RandomForestRegressor.__getitem__ sklearn.ensemble.RandomForestRegressor.__getstate__ sklearn.ensemble.RandomForestRegressor.__gt__ sklearn.ensemble.RandomForestRegressor.__hash__ sklearn.ensemble.RandomForestRegressor.__init__ sklearn.ensemble.RandomForestRegressor.__init_subclass__ sklearn.ensemble.RandomForestRegressor.__iter__ sklearn.ensemble.RandomForestRegressor.__le__ sklearn.ensemble.RandomForestRegressor.__len__ sklearn.ensemble.RandomForestRegressor.__lt__ sklearn.ensemble.RandomForestRegressor.__ne__ sklearn.ensemble.RandomForestRegressor.__new__ sklearn.ensemble.RandomForestRegressor.__reduce__ sklearn.ensemble.RandomForestRegressor.__reduce_ex__ sklearn.ensemble.RandomForestRegressor.__repr__ sklearn.ensemble.RandomForestRegressor.__setattr__ sklearn.ensemble.RandomForestRegressor.__setstate__ sklearn.ensemble.RandomForestRegressor.__sizeof__ sklearn.ensemble.RandomForestRegressor.__sklearn_clone__ sklearn.ensemble.RandomForestRegressor.__sklearn_tags__ sklearn.ensemble.RandomForestRegressor.__str__ sklearn.ensemble.RandomForestRegressor.__subclasshook__ sklearn.ensemble.RandomForestRegressor._build_request_for_signature sklearn.ensemble.RandomForestRegressor._check_feature_names sklearn.ensemble.RandomForestRegressor._check_n_features sklearn.ensemble.RandomForestRegressor._compute_oob_predictions sklearn.ensemble.RandomForestRegressor._compute_partial_dependence_recursion sklearn.ensemble.RandomForestRegressor._get_default_requests sklearn.ensemble.RandomForestRegressor._get_doc_link sklearn.ensemble.RandomForestRegressor._get_estimators_indices sklearn.ensemble.RandomForestRegressor._get_metadata_request sklearn.ensemble.RandomForestRegressor._get_oob_predictions sklearn.ensemble.RandomForestRegressor._get_param_names sklearn.ensemble.RandomForestRegressor._get_tags sklearn.ensemble.RandomForestRegressor._make_estimator sklearn.ensemble.RandomForestRegressor._more_tags sklearn.ensemble.RandomForestRegressor._repr_html_inner sklearn.ensemble.RandomForestRegressor._repr_mimebundle_ sklearn.ensemble.RandomForestRegressor._set_oob_score_and_attributes sklearn.ensemble.RandomForestRegressor._validate_X_predict sklearn.ensemble.RandomForestRegressor._validate_data sklearn.ensemble.RandomForestRegressor._validate_estimator sklearn.ensemble.RandomForestRegressor._validate_params sklearn.ensemble.RandomForestRegressor._validate_y_class_weight sklearn.ensemble.RandomForestRegressor.apply sklearn.ensemble.RandomForestRegressor.decision_path sklearn.ensemble.RandomForestRegressor.fit sklearn.ensemble.RandomForestRegressor.get_metadata_routing sklearn.ensemble.RandomForestRegressor.get_params sklearn.ensemble.RandomForestRegressor.predict sklearn.ensemble.RandomForestRegressor.score sklearn.ensemble.RandomForestRegressor.set_fit_request sklearn.ensemble.RandomForestRegressor.set_params sklearn.ensemble.RandomForestRegressor.set_score_request 
+.. minigallery:: sklearn.ensemble.RandomForestRegressor sklearn.ensemble.RandomForestRegressor.__delattr__ sklearn.ensemble.RandomForestRegressor.__dir__ sklearn.ensemble.RandomForestRegressor.__eq__ sklearn.ensemble.RandomForestRegressor.__format__ sklearn.ensemble.RandomForestRegressor.__ge__ sklearn.ensemble.RandomForestRegressor.__getattribute__ sklearn.ensemble.RandomForestRegressor.__getitem__ sklearn.ensemble.RandomForestRegressor.__getstate__ sklearn.ensemble.RandomForestRegressor.__gt__ sklearn.ensemble.RandomForestRegressor.__hash__ sklearn.ensemble.RandomForestRegressor.__init__ sklearn.ensemble.RandomForestRegressor.__init_subclass__ sklearn.ensemble.RandomForestRegressor.__iter__ sklearn.ensemble.RandomForestRegressor.__le__ sklearn.ensemble.RandomForestRegressor.__len__ sklearn.ensemble.RandomForestRegressor.__lt__ sklearn.ensemble.RandomForestRegressor.__ne__ sklearn.ensemble.RandomForestRegressor.__new__ sklearn.ensemble.RandomForestRegressor.__reduce__ sklearn.ensemble.RandomForestRegressor.__reduce_ex__ sklearn.ensemble.RandomForestRegressor.__repr__ sklearn.ensemble.RandomForestRegressor.__setattr__ sklearn.ensemble.RandomForestRegressor.__setstate__ sklearn.ensemble.RandomForestRegressor.__sizeof__ sklearn.ensemble.RandomForestRegressor.__sklearn_clone__ sklearn.ensemble.RandomForestRegressor.__sklearn_tags__ sklearn.ensemble.RandomForestRegressor.__str__ sklearn.ensemble.RandomForestRegressor.__subclasshook__ sklearn.ensemble.RandomForestRegressor._build_request_for_signature sklearn.ensemble.RandomForestRegressor._compute_oob_predictions sklearn.ensemble.RandomForestRegressor._compute_partial_dependence_recursion sklearn.ensemble.RandomForestRegressor._get_default_requests sklearn.ensemble.RandomForestRegressor._get_doc_link sklearn.ensemble.RandomForestRegressor._get_estimators_indices sklearn.ensemble.RandomForestRegressor._get_metadata_request sklearn.ensemble.RandomForestRegressor._get_oob_predictions sklearn.ensemble.RandomForestRegressor._get_param_names sklearn.ensemble.RandomForestRegressor._make_estimator sklearn.ensemble.RandomForestRegressor._repr_html_inner sklearn.ensemble.RandomForestRegressor._repr_mimebundle_ sklearn.ensemble.RandomForestRegressor._set_oob_score_and_attributes sklearn.ensemble.RandomForestRegressor._validate_X_predict sklearn.ensemble.RandomForestRegressor._validate_estimator sklearn.ensemble.RandomForestRegressor._validate_params sklearn.ensemble.RandomForestRegressor._validate_y_class_weight sklearn.ensemble.RandomForestRegressor.apply sklearn.ensemble.RandomForestRegressor.decision_path sklearn.ensemble.RandomForestRegressor.fit sklearn.ensemble.RandomForestRegressor.get_metadata_routing sklearn.ensemble.RandomForestRegressor.get_params sklearn.ensemble.RandomForestRegressor.predict sklearn.ensemble.RandomForestRegressor.score sklearn.ensemble.RandomForestRegressor.set_fit_request sklearn.ensemble.RandomForestRegressor.set_params sklearn.ensemble.RandomForestRegressor.set_score_request 
    :add-heading: Gallery examples
    :heading-level: -
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diff --git a/dev/_sources/modules/generated/sklearn.ensemble.RandomTreesEmbedding.rst.txt b/dev/_sources/modules/generated/sklearn.ensemble.RandomTreesEmbedding.rst.txt
index 870b0a058fa2d..a7ea4e202dbbe 100644
--- a/dev/_sources/modules/generated/sklearn.ensemble.RandomTreesEmbedding.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.ensemble.RandomTreesEmbedding.rst.txt
@@ -8,6 +8,6 @@ RandomTreesEmbedding
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.ensemble.RandomTreesEmbedding sklearn.ensemble.RandomTreesEmbedding.__delattr__ sklearn.ensemble.RandomTreesEmbedding.__dir__ sklearn.ensemble.RandomTreesEmbedding.__eq__ sklearn.ensemble.RandomTreesEmbedding.__format__ sklearn.ensemble.RandomTreesEmbedding.__ge__ sklearn.ensemble.RandomTreesEmbedding.__getattribute__ sklearn.ensemble.RandomTreesEmbedding.__getitem__ sklearn.ensemble.RandomTreesEmbedding.__getstate__ sklearn.ensemble.RandomTreesEmbedding.__gt__ sklearn.ensemble.RandomTreesEmbedding.__hash__ sklearn.ensemble.RandomTreesEmbedding.__init__ sklearn.ensemble.RandomTreesEmbedding.__init_subclass__ sklearn.ensemble.RandomTreesEmbedding.__iter__ sklearn.ensemble.RandomTreesEmbedding.__le__ sklearn.ensemble.RandomTreesEmbedding.__len__ sklearn.ensemble.RandomTreesEmbedding.__lt__ sklearn.ensemble.RandomTreesEmbedding.__ne__ sklearn.ensemble.RandomTreesEmbedding.__new__ sklearn.ensemble.RandomTreesEmbedding.__reduce__ sklearn.ensemble.RandomTreesEmbedding.__reduce_ex__ sklearn.ensemble.RandomTreesEmbedding.__repr__ sklearn.ensemble.RandomTreesEmbedding.__setattr__ sklearn.ensemble.RandomTreesEmbedding.__setstate__ sklearn.ensemble.RandomTreesEmbedding.__sizeof__ sklearn.ensemble.RandomTreesEmbedding.__sklearn_clone__ sklearn.ensemble.RandomTreesEmbedding.__sklearn_tags__ sklearn.ensemble.RandomTreesEmbedding.__str__ sklearn.ensemble.RandomTreesEmbedding.__subclasshook__ sklearn.ensemble.RandomTreesEmbedding._build_request_for_signature sklearn.ensemble.RandomTreesEmbedding._check_feature_names sklearn.ensemble.RandomTreesEmbedding._check_n_features sklearn.ensemble.RandomTreesEmbedding._compute_oob_predictions sklearn.ensemble.RandomTreesEmbedding._get_default_requests sklearn.ensemble.RandomTreesEmbedding._get_doc_link sklearn.ensemble.RandomTreesEmbedding._get_estimators_indices sklearn.ensemble.RandomTreesEmbedding._get_metadata_request sklearn.ensemble.RandomTreesEmbedding._get_param_names sklearn.ensemble.RandomTreesEmbedding._get_tags sklearn.ensemble.RandomTreesEmbedding._make_estimator sklearn.ensemble.RandomTreesEmbedding._more_tags sklearn.ensemble.RandomTreesEmbedding._repr_html_inner sklearn.ensemble.RandomTreesEmbedding._repr_mimebundle_ sklearn.ensemble.RandomTreesEmbedding._set_oob_score_and_attributes sklearn.ensemble.RandomTreesEmbedding._validate_X_predict sklearn.ensemble.RandomTreesEmbedding._validate_data sklearn.ensemble.RandomTreesEmbedding._validate_estimator sklearn.ensemble.RandomTreesEmbedding._validate_params sklearn.ensemble.RandomTreesEmbedding._validate_y_class_weight sklearn.ensemble.RandomTreesEmbedding.apply sklearn.ensemble.RandomTreesEmbedding.decision_path sklearn.ensemble.RandomTreesEmbedding.fit sklearn.ensemble.RandomTreesEmbedding.fit_transform sklearn.ensemble.RandomTreesEmbedding.get_feature_names_out sklearn.ensemble.RandomTreesEmbedding.get_metadata_routing sklearn.ensemble.RandomTreesEmbedding.get_params sklearn.ensemble.RandomTreesEmbedding.set_fit_request sklearn.ensemble.RandomTreesEmbedding.set_output sklearn.ensemble.RandomTreesEmbedding.set_params sklearn.ensemble.RandomTreesEmbedding.transform 
+.. minigallery:: sklearn.ensemble.RandomTreesEmbedding sklearn.ensemble.RandomTreesEmbedding.__delattr__ sklearn.ensemble.RandomTreesEmbedding.__dir__ sklearn.ensemble.RandomTreesEmbedding.__eq__ sklearn.ensemble.RandomTreesEmbedding.__format__ sklearn.ensemble.RandomTreesEmbedding.__ge__ sklearn.ensemble.RandomTreesEmbedding.__getattribute__ sklearn.ensemble.RandomTreesEmbedding.__getitem__ sklearn.ensemble.RandomTreesEmbedding.__getstate__ sklearn.ensemble.RandomTreesEmbedding.__gt__ sklearn.ensemble.RandomTreesEmbedding.__hash__ sklearn.ensemble.RandomTreesEmbedding.__init__ sklearn.ensemble.RandomTreesEmbedding.__init_subclass__ sklearn.ensemble.RandomTreesEmbedding.__iter__ sklearn.ensemble.RandomTreesEmbedding.__le__ sklearn.ensemble.RandomTreesEmbedding.__len__ sklearn.ensemble.RandomTreesEmbedding.__lt__ sklearn.ensemble.RandomTreesEmbedding.__ne__ sklearn.ensemble.RandomTreesEmbedding.__new__ sklearn.ensemble.RandomTreesEmbedding.__reduce__ sklearn.ensemble.RandomTreesEmbedding.__reduce_ex__ sklearn.ensemble.RandomTreesEmbedding.__repr__ sklearn.ensemble.RandomTreesEmbedding.__setattr__ sklearn.ensemble.RandomTreesEmbedding.__setstate__ sklearn.ensemble.RandomTreesEmbedding.__sizeof__ sklearn.ensemble.RandomTreesEmbedding.__sklearn_clone__ sklearn.ensemble.RandomTreesEmbedding.__sklearn_tags__ sklearn.ensemble.RandomTreesEmbedding.__str__ sklearn.ensemble.RandomTreesEmbedding.__subclasshook__ sklearn.ensemble.RandomTreesEmbedding._build_request_for_signature sklearn.ensemble.RandomTreesEmbedding._compute_oob_predictions sklearn.ensemble.RandomTreesEmbedding._get_default_requests sklearn.ensemble.RandomTreesEmbedding._get_doc_link sklearn.ensemble.RandomTreesEmbedding._get_estimators_indices sklearn.ensemble.RandomTreesEmbedding._get_metadata_request sklearn.ensemble.RandomTreesEmbedding._get_param_names sklearn.ensemble.RandomTreesEmbedding._make_estimator sklearn.ensemble.RandomTreesEmbedding._repr_html_inner sklearn.ensemble.RandomTreesEmbedding._repr_mimebundle_ sklearn.ensemble.RandomTreesEmbedding._set_oob_score_and_attributes sklearn.ensemble.RandomTreesEmbedding._validate_X_predict sklearn.ensemble.RandomTreesEmbedding._validate_estimator sklearn.ensemble.RandomTreesEmbedding._validate_params sklearn.ensemble.RandomTreesEmbedding._validate_y_class_weight sklearn.ensemble.RandomTreesEmbedding.apply sklearn.ensemble.RandomTreesEmbedding.decision_path sklearn.ensemble.RandomTreesEmbedding.fit sklearn.ensemble.RandomTreesEmbedding.fit_transform sklearn.ensemble.RandomTreesEmbedding.get_feature_names_out sklearn.ensemble.RandomTreesEmbedding.get_metadata_routing sklearn.ensemble.RandomTreesEmbedding.get_params sklearn.ensemble.RandomTreesEmbedding.set_fit_request sklearn.ensemble.RandomTreesEmbedding.set_output sklearn.ensemble.RandomTreesEmbedding.set_params sklearn.ensemble.RandomTreesEmbedding.transform 
    :add-heading: Gallery examples
    :heading-level: -
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diff --git a/dev/_sources/modules/generated/sklearn.ensemble.StackingClassifier.rst.txt b/dev/_sources/modules/generated/sklearn.ensemble.StackingClassifier.rst.txt
index 8860249073b11..2696735a80890 100644
--- a/dev/_sources/modules/generated/sklearn.ensemble.StackingClassifier.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.ensemble.StackingClassifier.rst.txt
@@ -8,6 +8,6 @@ StackingClassifier
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.ensemble.StackingClassifier sklearn.ensemble.StackingClassifier.__delattr__ sklearn.ensemble.StackingClassifier.__dir__ sklearn.ensemble.StackingClassifier.__eq__ sklearn.ensemble.StackingClassifier.__format__ sklearn.ensemble.StackingClassifier.__ge__ sklearn.ensemble.StackingClassifier.__getattribute__ sklearn.ensemble.StackingClassifier.__getstate__ sklearn.ensemble.StackingClassifier.__gt__ sklearn.ensemble.StackingClassifier.__hash__ sklearn.ensemble.StackingClassifier.__init__ sklearn.ensemble.StackingClassifier.__init_subclass__ sklearn.ensemble.StackingClassifier.__le__ sklearn.ensemble.StackingClassifier.__lt__ sklearn.ensemble.StackingClassifier.__ne__ sklearn.ensemble.StackingClassifier.__new__ sklearn.ensemble.StackingClassifier.__reduce__ sklearn.ensemble.StackingClassifier.__reduce_ex__ sklearn.ensemble.StackingClassifier.__repr__ sklearn.ensemble.StackingClassifier.__setattr__ sklearn.ensemble.StackingClassifier.__setstate__ sklearn.ensemble.StackingClassifier.__sizeof__ sklearn.ensemble.StackingClassifier.__sklearn_clone__ sklearn.ensemble.StackingClassifier.__sklearn_tags__ sklearn.ensemble.StackingClassifier.__str__ sklearn.ensemble.StackingClassifier.__subclasshook__ sklearn.ensemble.StackingClassifier._build_request_for_signature sklearn.ensemble.StackingClassifier._check_feature_names sklearn.ensemble.StackingClassifier._check_n_features sklearn.ensemble.StackingClassifier._clone_final_estimator sklearn.ensemble.StackingClassifier._concatenate_predictions sklearn.ensemble.StackingClassifier._get_default_requests sklearn.ensemble.StackingClassifier._get_doc_link sklearn.ensemble.StackingClassifier._get_metadata_request sklearn.ensemble.StackingClassifier._get_param_names sklearn.ensemble.StackingClassifier._get_params sklearn.ensemble.StackingClassifier._get_tags sklearn.ensemble.StackingClassifier._method_name sklearn.ensemble.StackingClassifier._more_tags sklearn.ensemble.StackingClassifier._replace_estimator sklearn.ensemble.StackingClassifier._repr_html_inner sklearn.ensemble.StackingClassifier._repr_mimebundle_ sklearn.ensemble.StackingClassifier._set_params sklearn.ensemble.StackingClassifier._sk_visual_block_ sklearn.ensemble.StackingClassifier._sk_visual_block_with_final_estimator sklearn.ensemble.StackingClassifier._transform sklearn.ensemble.StackingClassifier._validate_data sklearn.ensemble.StackingClassifier._validate_estimators sklearn.ensemble.StackingClassifier._validate_final_estimator sklearn.ensemble.StackingClassifier._validate_names sklearn.ensemble.StackingClassifier._validate_params sklearn.ensemble.StackingClassifier.decision_function sklearn.ensemble.StackingClassifier.fit sklearn.ensemble.StackingClassifier.fit_transform sklearn.ensemble.StackingClassifier.get_feature_names_out sklearn.ensemble.StackingClassifier.get_metadata_routing sklearn.ensemble.StackingClassifier.get_params sklearn.ensemble.StackingClassifier.predict sklearn.ensemble.StackingClassifier.predict_proba sklearn.ensemble.StackingClassifier.score sklearn.ensemble.StackingClassifier.set_fit_request sklearn.ensemble.StackingClassifier.set_output sklearn.ensemble.StackingClassifier.set_params sklearn.ensemble.StackingClassifier.set_score_request sklearn.ensemble.StackingClassifier.transform 
+.. minigallery:: sklearn.ensemble.StackingClassifier sklearn.ensemble.StackingClassifier.__delattr__ sklearn.ensemble.StackingClassifier.__dir__ sklearn.ensemble.StackingClassifier.__eq__ sklearn.ensemble.StackingClassifier.__format__ sklearn.ensemble.StackingClassifier.__ge__ sklearn.ensemble.StackingClassifier.__getattribute__ sklearn.ensemble.StackingClassifier.__getstate__ sklearn.ensemble.StackingClassifier.__gt__ sklearn.ensemble.StackingClassifier.__hash__ sklearn.ensemble.StackingClassifier.__init__ sklearn.ensemble.StackingClassifier.__init_subclass__ sklearn.ensemble.StackingClassifier.__le__ sklearn.ensemble.StackingClassifier.__lt__ sklearn.ensemble.StackingClassifier.__ne__ sklearn.ensemble.StackingClassifier.__new__ sklearn.ensemble.StackingClassifier.__reduce__ sklearn.ensemble.StackingClassifier.__reduce_ex__ sklearn.ensemble.StackingClassifier.__repr__ sklearn.ensemble.StackingClassifier.__setattr__ sklearn.ensemble.StackingClassifier.__setstate__ sklearn.ensemble.StackingClassifier.__sizeof__ sklearn.ensemble.StackingClassifier.__sklearn_clone__ sklearn.ensemble.StackingClassifier.__sklearn_tags__ sklearn.ensemble.StackingClassifier.__str__ sklearn.ensemble.StackingClassifier.__subclasshook__ sklearn.ensemble.StackingClassifier._build_request_for_signature sklearn.ensemble.StackingClassifier._clone_final_estimator sklearn.ensemble.StackingClassifier._concatenate_predictions sklearn.ensemble.StackingClassifier._get_default_requests sklearn.ensemble.StackingClassifier._get_doc_link sklearn.ensemble.StackingClassifier._get_metadata_request sklearn.ensemble.StackingClassifier._get_param_names sklearn.ensemble.StackingClassifier._get_params sklearn.ensemble.StackingClassifier._method_name sklearn.ensemble.StackingClassifier._replace_estimator sklearn.ensemble.StackingClassifier._repr_html_inner sklearn.ensemble.StackingClassifier._repr_mimebundle_ sklearn.ensemble.StackingClassifier._set_params sklearn.ensemble.StackingClassifier._sk_visual_block_ sklearn.ensemble.StackingClassifier._sk_visual_block_with_final_estimator sklearn.ensemble.StackingClassifier._transform sklearn.ensemble.StackingClassifier._validate_estimators sklearn.ensemble.StackingClassifier._validate_final_estimator sklearn.ensemble.StackingClassifier._validate_names sklearn.ensemble.StackingClassifier._validate_params sklearn.ensemble.StackingClassifier.decision_function sklearn.ensemble.StackingClassifier.fit sklearn.ensemble.StackingClassifier.fit_transform sklearn.ensemble.StackingClassifier.get_feature_names_out sklearn.ensemble.StackingClassifier.get_metadata_routing sklearn.ensemble.StackingClassifier.get_params sklearn.ensemble.StackingClassifier.predict sklearn.ensemble.StackingClassifier.predict_proba sklearn.ensemble.StackingClassifier.score sklearn.ensemble.StackingClassifier.set_output sklearn.ensemble.StackingClassifier.set_params sklearn.ensemble.StackingClassifier.set_score_request sklearn.ensemble.StackingClassifier.transform 
    :add-heading: Gallery examples
    :heading-level: -
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diff --git a/dev/_sources/modules/generated/sklearn.ensemble.StackingRegressor.rst.txt b/dev/_sources/modules/generated/sklearn.ensemble.StackingRegressor.rst.txt
index a25a9f64351f5..48f1af62eb047 100644
--- a/dev/_sources/modules/generated/sklearn.ensemble.StackingRegressor.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.ensemble.StackingRegressor.rst.txt
@@ -8,6 +8,6 @@ StackingRegressor
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.ensemble.StackingRegressor sklearn.ensemble.StackingRegressor.__delattr__ sklearn.ensemble.StackingRegressor.__dir__ sklearn.ensemble.StackingRegressor.__eq__ sklearn.ensemble.StackingRegressor.__format__ sklearn.ensemble.StackingRegressor.__ge__ sklearn.ensemble.StackingRegressor.__getattribute__ sklearn.ensemble.StackingRegressor.__getstate__ sklearn.ensemble.StackingRegressor.__gt__ sklearn.ensemble.StackingRegressor.__hash__ sklearn.ensemble.StackingRegressor.__init__ sklearn.ensemble.StackingRegressor.__init_subclass__ sklearn.ensemble.StackingRegressor.__le__ sklearn.ensemble.StackingRegressor.__lt__ sklearn.ensemble.StackingRegressor.__ne__ sklearn.ensemble.StackingRegressor.__new__ sklearn.ensemble.StackingRegressor.__reduce__ sklearn.ensemble.StackingRegressor.__reduce_ex__ sklearn.ensemble.StackingRegressor.__repr__ sklearn.ensemble.StackingRegressor.__setattr__ sklearn.ensemble.StackingRegressor.__setstate__ sklearn.ensemble.StackingRegressor.__sizeof__ sklearn.ensemble.StackingRegressor.__sklearn_clone__ sklearn.ensemble.StackingRegressor.__sklearn_tags__ sklearn.ensemble.StackingRegressor.__str__ sklearn.ensemble.StackingRegressor.__subclasshook__ sklearn.ensemble.StackingRegressor._build_request_for_signature sklearn.ensemble.StackingRegressor._check_feature_names sklearn.ensemble.StackingRegressor._check_n_features sklearn.ensemble.StackingRegressor._clone_final_estimator sklearn.ensemble.StackingRegressor._concatenate_predictions sklearn.ensemble.StackingRegressor._get_default_requests sklearn.ensemble.StackingRegressor._get_doc_link sklearn.ensemble.StackingRegressor._get_metadata_request sklearn.ensemble.StackingRegressor._get_param_names sklearn.ensemble.StackingRegressor._get_params sklearn.ensemble.StackingRegressor._get_tags sklearn.ensemble.StackingRegressor._method_name sklearn.ensemble.StackingRegressor._more_tags sklearn.ensemble.StackingRegressor._replace_estimator sklearn.ensemble.StackingRegressor._repr_html_inner sklearn.ensemble.StackingRegressor._repr_mimebundle_ sklearn.ensemble.StackingRegressor._set_params sklearn.ensemble.StackingRegressor._sk_visual_block_ sklearn.ensemble.StackingRegressor._sk_visual_block_with_final_estimator sklearn.ensemble.StackingRegressor._transform sklearn.ensemble.StackingRegressor._validate_data sklearn.ensemble.StackingRegressor._validate_estimators sklearn.ensemble.StackingRegressor._validate_final_estimator sklearn.ensemble.StackingRegressor._validate_names sklearn.ensemble.StackingRegressor._validate_params sklearn.ensemble.StackingRegressor.fit sklearn.ensemble.StackingRegressor.fit_transform sklearn.ensemble.StackingRegressor.get_feature_names_out sklearn.ensemble.StackingRegressor.get_metadata_routing sklearn.ensemble.StackingRegressor.get_params sklearn.ensemble.StackingRegressor.predict sklearn.ensemble.StackingRegressor.score sklearn.ensemble.StackingRegressor.set_fit_request sklearn.ensemble.StackingRegressor.set_output sklearn.ensemble.StackingRegressor.set_params sklearn.ensemble.StackingRegressor.set_score_request sklearn.ensemble.StackingRegressor.transform 
+.. minigallery:: sklearn.ensemble.StackingRegressor sklearn.ensemble.StackingRegressor.__delattr__ sklearn.ensemble.StackingRegressor.__dir__ sklearn.ensemble.StackingRegressor.__eq__ sklearn.ensemble.StackingRegressor.__format__ sklearn.ensemble.StackingRegressor.__ge__ sklearn.ensemble.StackingRegressor.__getattribute__ sklearn.ensemble.StackingRegressor.__getstate__ sklearn.ensemble.StackingRegressor.__gt__ sklearn.ensemble.StackingRegressor.__hash__ sklearn.ensemble.StackingRegressor.__init__ sklearn.ensemble.StackingRegressor.__init_subclass__ sklearn.ensemble.StackingRegressor.__le__ sklearn.ensemble.StackingRegressor.__lt__ sklearn.ensemble.StackingRegressor.__ne__ sklearn.ensemble.StackingRegressor.__new__ sklearn.ensemble.StackingRegressor.__reduce__ sklearn.ensemble.StackingRegressor.__reduce_ex__ sklearn.ensemble.StackingRegressor.__repr__ sklearn.ensemble.StackingRegressor.__setattr__ sklearn.ensemble.StackingRegressor.__setstate__ sklearn.ensemble.StackingRegressor.__sizeof__ sklearn.ensemble.StackingRegressor.__sklearn_clone__ sklearn.ensemble.StackingRegressor.__sklearn_tags__ sklearn.ensemble.StackingRegressor.__str__ sklearn.ensemble.StackingRegressor.__subclasshook__ sklearn.ensemble.StackingRegressor._build_request_for_signature sklearn.ensemble.StackingRegressor._clone_final_estimator sklearn.ensemble.StackingRegressor._concatenate_predictions sklearn.ensemble.StackingRegressor._get_default_requests sklearn.ensemble.StackingRegressor._get_doc_link sklearn.ensemble.StackingRegressor._get_metadata_request sklearn.ensemble.StackingRegressor._get_param_names sklearn.ensemble.StackingRegressor._get_params sklearn.ensemble.StackingRegressor._method_name sklearn.ensemble.StackingRegressor._replace_estimator sklearn.ensemble.StackingRegressor._repr_html_inner sklearn.ensemble.StackingRegressor._repr_mimebundle_ sklearn.ensemble.StackingRegressor._set_params sklearn.ensemble.StackingRegressor._sk_visual_block_ sklearn.ensemble.StackingRegressor._sk_visual_block_with_final_estimator sklearn.ensemble.StackingRegressor._transform sklearn.ensemble.StackingRegressor._validate_estimators sklearn.ensemble.StackingRegressor._validate_final_estimator sklearn.ensemble.StackingRegressor._validate_names sklearn.ensemble.StackingRegressor._validate_params sklearn.ensemble.StackingRegressor.fit sklearn.ensemble.StackingRegressor.fit_transform sklearn.ensemble.StackingRegressor.get_feature_names_out sklearn.ensemble.StackingRegressor.get_metadata_routing sklearn.ensemble.StackingRegressor.get_params sklearn.ensemble.StackingRegressor.predict sklearn.ensemble.StackingRegressor.score sklearn.ensemble.StackingRegressor.set_output sklearn.ensemble.StackingRegressor.set_params sklearn.ensemble.StackingRegressor.set_score_request sklearn.ensemble.StackingRegressor.transform 
    :add-heading: Gallery examples
    :heading-level: -
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diff --git a/dev/_sources/modules/generated/sklearn.ensemble.VotingClassifier.rst.txt b/dev/_sources/modules/generated/sklearn.ensemble.VotingClassifier.rst.txt
index 4461f1c1cd267..ebf4f61969fbe 100644
--- a/dev/_sources/modules/generated/sklearn.ensemble.VotingClassifier.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.ensemble.VotingClassifier.rst.txt
@@ -8,6 +8,6 @@ VotingClassifier
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.ensemble.VotingClassifier sklearn.ensemble.VotingClassifier.__delattr__ sklearn.ensemble.VotingClassifier.__dir__ sklearn.ensemble.VotingClassifier.__eq__ sklearn.ensemble.VotingClassifier.__format__ sklearn.ensemble.VotingClassifier.__ge__ sklearn.ensemble.VotingClassifier.__getattribute__ sklearn.ensemble.VotingClassifier.__getstate__ sklearn.ensemble.VotingClassifier.__gt__ sklearn.ensemble.VotingClassifier.__hash__ sklearn.ensemble.VotingClassifier.__init__ sklearn.ensemble.VotingClassifier.__init_subclass__ sklearn.ensemble.VotingClassifier.__le__ sklearn.ensemble.VotingClassifier.__lt__ sklearn.ensemble.VotingClassifier.__ne__ sklearn.ensemble.VotingClassifier.__new__ sklearn.ensemble.VotingClassifier.__reduce__ sklearn.ensemble.VotingClassifier.__reduce_ex__ sklearn.ensemble.VotingClassifier.__repr__ sklearn.ensemble.VotingClassifier.__setattr__ sklearn.ensemble.VotingClassifier.__setstate__ sklearn.ensemble.VotingClassifier.__sizeof__ sklearn.ensemble.VotingClassifier.__sklearn_clone__ sklearn.ensemble.VotingClassifier.__sklearn_tags__ sklearn.ensemble.VotingClassifier.__str__ sklearn.ensemble.VotingClassifier.__subclasshook__ sklearn.ensemble.VotingClassifier._build_request_for_signature sklearn.ensemble.VotingClassifier._check_feature_names sklearn.ensemble.VotingClassifier._check_n_features sklearn.ensemble.VotingClassifier._check_voting sklearn.ensemble.VotingClassifier._collect_probas sklearn.ensemble.VotingClassifier._get_default_requests sklearn.ensemble.VotingClassifier._get_doc_link sklearn.ensemble.VotingClassifier._get_metadata_request sklearn.ensemble.VotingClassifier._get_param_names sklearn.ensemble.VotingClassifier._get_params sklearn.ensemble.VotingClassifier._get_tags sklearn.ensemble.VotingClassifier._log_message sklearn.ensemble.VotingClassifier._more_tags sklearn.ensemble.VotingClassifier._predict sklearn.ensemble.VotingClassifier._replace_estimator sklearn.ensemble.VotingClassifier._repr_html_inner sklearn.ensemble.VotingClassifier._repr_mimebundle_ sklearn.ensemble.VotingClassifier._set_params sklearn.ensemble.VotingClassifier._sk_visual_block_ sklearn.ensemble.VotingClassifier._validate_data sklearn.ensemble.VotingClassifier._validate_estimators sklearn.ensemble.VotingClassifier._validate_names sklearn.ensemble.VotingClassifier._validate_params sklearn.ensemble.VotingClassifier.fit sklearn.ensemble.VotingClassifier.fit_transform sklearn.ensemble.VotingClassifier.get_feature_names_out sklearn.ensemble.VotingClassifier.get_metadata_routing sklearn.ensemble.VotingClassifier.get_params sklearn.ensemble.VotingClassifier.predict sklearn.ensemble.VotingClassifier.predict_proba sklearn.ensemble.VotingClassifier.score sklearn.ensemble.VotingClassifier.set_fit_request sklearn.ensemble.VotingClassifier.set_output sklearn.ensemble.VotingClassifier.set_params sklearn.ensemble.VotingClassifier.set_score_request sklearn.ensemble.VotingClassifier.transform 
+.. minigallery:: sklearn.ensemble.VotingClassifier sklearn.ensemble.VotingClassifier.__delattr__ sklearn.ensemble.VotingClassifier.__dir__ sklearn.ensemble.VotingClassifier.__eq__ sklearn.ensemble.VotingClassifier.__format__ sklearn.ensemble.VotingClassifier.__ge__ sklearn.ensemble.VotingClassifier.__getattribute__ sklearn.ensemble.VotingClassifier.__getstate__ sklearn.ensemble.VotingClassifier.__gt__ sklearn.ensemble.VotingClassifier.__hash__ sklearn.ensemble.VotingClassifier.__init__ sklearn.ensemble.VotingClassifier.__init_subclass__ sklearn.ensemble.VotingClassifier.__le__ sklearn.ensemble.VotingClassifier.__lt__ sklearn.ensemble.VotingClassifier.__ne__ sklearn.ensemble.VotingClassifier.__new__ sklearn.ensemble.VotingClassifier.__reduce__ sklearn.ensemble.VotingClassifier.__reduce_ex__ sklearn.ensemble.VotingClassifier.__repr__ sklearn.ensemble.VotingClassifier.__setattr__ sklearn.ensemble.VotingClassifier.__setstate__ sklearn.ensemble.VotingClassifier.__sizeof__ sklearn.ensemble.VotingClassifier.__sklearn_clone__ sklearn.ensemble.VotingClassifier.__sklearn_tags__ sklearn.ensemble.VotingClassifier.__str__ sklearn.ensemble.VotingClassifier.__subclasshook__ sklearn.ensemble.VotingClassifier._build_request_for_signature sklearn.ensemble.VotingClassifier._check_voting sklearn.ensemble.VotingClassifier._collect_probas sklearn.ensemble.VotingClassifier._get_default_requests sklearn.ensemble.VotingClassifier._get_doc_link sklearn.ensemble.VotingClassifier._get_metadata_request sklearn.ensemble.VotingClassifier._get_param_names sklearn.ensemble.VotingClassifier._get_params sklearn.ensemble.VotingClassifier._log_message sklearn.ensemble.VotingClassifier._predict sklearn.ensemble.VotingClassifier._replace_estimator sklearn.ensemble.VotingClassifier._repr_html_inner sklearn.ensemble.VotingClassifier._repr_mimebundle_ sklearn.ensemble.VotingClassifier._set_params sklearn.ensemble.VotingClassifier._sk_visual_block_ sklearn.ensemble.VotingClassifier._validate_estimators sklearn.ensemble.VotingClassifier._validate_names sklearn.ensemble.VotingClassifier._validate_params sklearn.ensemble.VotingClassifier.fit sklearn.ensemble.VotingClassifier.fit_transform sklearn.ensemble.VotingClassifier.get_feature_names_out sklearn.ensemble.VotingClassifier.get_metadata_routing sklearn.ensemble.VotingClassifier.get_params sklearn.ensemble.VotingClassifier.predict sklearn.ensemble.VotingClassifier.predict_proba sklearn.ensemble.VotingClassifier.score sklearn.ensemble.VotingClassifier.set_output sklearn.ensemble.VotingClassifier.set_params sklearn.ensemble.VotingClassifier.set_score_request sklearn.ensemble.VotingClassifier.transform 
    :add-heading: Gallery examples
    :heading-level: -
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diff --git a/dev/_sources/modules/generated/sklearn.ensemble.VotingRegressor.rst.txt b/dev/_sources/modules/generated/sklearn.ensemble.VotingRegressor.rst.txt
index b6422431a864c..89111b676add9 100644
--- a/dev/_sources/modules/generated/sklearn.ensemble.VotingRegressor.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.ensemble.VotingRegressor.rst.txt
@@ -8,6 +8,6 @@ VotingRegressor
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.ensemble.VotingRegressor sklearn.ensemble.VotingRegressor.__delattr__ sklearn.ensemble.VotingRegressor.__dir__ sklearn.ensemble.VotingRegressor.__eq__ sklearn.ensemble.VotingRegressor.__format__ sklearn.ensemble.VotingRegressor.__ge__ sklearn.ensemble.VotingRegressor.__getattribute__ sklearn.ensemble.VotingRegressor.__getstate__ sklearn.ensemble.VotingRegressor.__gt__ sklearn.ensemble.VotingRegressor.__hash__ sklearn.ensemble.VotingRegressor.__init__ sklearn.ensemble.VotingRegressor.__init_subclass__ sklearn.ensemble.VotingRegressor.__le__ sklearn.ensemble.VotingRegressor.__lt__ sklearn.ensemble.VotingRegressor.__ne__ sklearn.ensemble.VotingRegressor.__new__ sklearn.ensemble.VotingRegressor.__reduce__ sklearn.ensemble.VotingRegressor.__reduce_ex__ sklearn.ensemble.VotingRegressor.__repr__ sklearn.ensemble.VotingRegressor.__setattr__ sklearn.ensemble.VotingRegressor.__setstate__ sklearn.ensemble.VotingRegressor.__sizeof__ sklearn.ensemble.VotingRegressor.__sklearn_clone__ sklearn.ensemble.VotingRegressor.__sklearn_tags__ sklearn.ensemble.VotingRegressor.__str__ sklearn.ensemble.VotingRegressor.__subclasshook__ sklearn.ensemble.VotingRegressor._build_request_for_signature sklearn.ensemble.VotingRegressor._check_feature_names sklearn.ensemble.VotingRegressor._check_n_features sklearn.ensemble.VotingRegressor._get_default_requests sklearn.ensemble.VotingRegressor._get_doc_link sklearn.ensemble.VotingRegressor._get_metadata_request sklearn.ensemble.VotingRegressor._get_param_names sklearn.ensemble.VotingRegressor._get_params sklearn.ensemble.VotingRegressor._get_tags sklearn.ensemble.VotingRegressor._log_message sklearn.ensemble.VotingRegressor._more_tags sklearn.ensemble.VotingRegressor._predict sklearn.ensemble.VotingRegressor._replace_estimator sklearn.ensemble.VotingRegressor._repr_html_inner sklearn.ensemble.VotingRegressor._repr_mimebundle_ sklearn.ensemble.VotingRegressor._set_params sklearn.ensemble.VotingRegressor._sk_visual_block_ sklearn.ensemble.VotingRegressor._validate_data sklearn.ensemble.VotingRegressor._validate_estimators sklearn.ensemble.VotingRegressor._validate_names sklearn.ensemble.VotingRegressor._validate_params sklearn.ensemble.VotingRegressor.fit sklearn.ensemble.VotingRegressor.fit_transform sklearn.ensemble.VotingRegressor.get_feature_names_out sklearn.ensemble.VotingRegressor.get_metadata_routing sklearn.ensemble.VotingRegressor.get_params sklearn.ensemble.VotingRegressor.predict sklearn.ensemble.VotingRegressor.score sklearn.ensemble.VotingRegressor.set_fit_request sklearn.ensemble.VotingRegressor.set_output sklearn.ensemble.VotingRegressor.set_params sklearn.ensemble.VotingRegressor.set_score_request sklearn.ensemble.VotingRegressor.transform 
+.. minigallery:: sklearn.ensemble.VotingRegressor sklearn.ensemble.VotingRegressor.__delattr__ sklearn.ensemble.VotingRegressor.__dir__ sklearn.ensemble.VotingRegressor.__eq__ sklearn.ensemble.VotingRegressor.__format__ sklearn.ensemble.VotingRegressor.__ge__ sklearn.ensemble.VotingRegressor.__getattribute__ sklearn.ensemble.VotingRegressor.__getstate__ sklearn.ensemble.VotingRegressor.__gt__ sklearn.ensemble.VotingRegressor.__hash__ sklearn.ensemble.VotingRegressor.__init__ sklearn.ensemble.VotingRegressor.__init_subclass__ sklearn.ensemble.VotingRegressor.__le__ sklearn.ensemble.VotingRegressor.__lt__ sklearn.ensemble.VotingRegressor.__ne__ sklearn.ensemble.VotingRegressor.__new__ sklearn.ensemble.VotingRegressor.__reduce__ sklearn.ensemble.VotingRegressor.__reduce_ex__ sklearn.ensemble.VotingRegressor.__repr__ sklearn.ensemble.VotingRegressor.__setattr__ sklearn.ensemble.VotingRegressor.__setstate__ sklearn.ensemble.VotingRegressor.__sizeof__ sklearn.ensemble.VotingRegressor.__sklearn_clone__ sklearn.ensemble.VotingRegressor.__sklearn_tags__ sklearn.ensemble.VotingRegressor.__str__ sklearn.ensemble.VotingRegressor.__subclasshook__ sklearn.ensemble.VotingRegressor._build_request_for_signature sklearn.ensemble.VotingRegressor._get_default_requests sklearn.ensemble.VotingRegressor._get_doc_link sklearn.ensemble.VotingRegressor._get_metadata_request sklearn.ensemble.VotingRegressor._get_param_names sklearn.ensemble.VotingRegressor._get_params sklearn.ensemble.VotingRegressor._log_message sklearn.ensemble.VotingRegressor._predict sklearn.ensemble.VotingRegressor._replace_estimator sklearn.ensemble.VotingRegressor._repr_html_inner sklearn.ensemble.VotingRegressor._repr_mimebundle_ sklearn.ensemble.VotingRegressor._set_params sklearn.ensemble.VotingRegressor._sk_visual_block_ sklearn.ensemble.VotingRegressor._validate_estimators sklearn.ensemble.VotingRegressor._validate_names sklearn.ensemble.VotingRegressor._validate_params sklearn.ensemble.VotingRegressor.fit sklearn.ensemble.VotingRegressor.fit_transform sklearn.ensemble.VotingRegressor.get_feature_names_out sklearn.ensemble.VotingRegressor.get_metadata_routing sklearn.ensemble.VotingRegressor.get_params sklearn.ensemble.VotingRegressor.predict sklearn.ensemble.VotingRegressor.score sklearn.ensemble.VotingRegressor.set_output sklearn.ensemble.VotingRegressor.set_params sklearn.ensemble.VotingRegressor.set_score_request sklearn.ensemble.VotingRegressor.transform 
    :add-heading: Gallery examples
    :heading-level: -
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diff --git a/dev/_sources/modules/generated/sklearn.feature_extraction.DictVectorizer.rst.txt b/dev/_sources/modules/generated/sklearn.feature_extraction.DictVectorizer.rst.txt
index b0e516bbd27dc..cd575b0b77311 100644
--- a/dev/_sources/modules/generated/sklearn.feature_extraction.DictVectorizer.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.feature_extraction.DictVectorizer.rst.txt
@@ -8,6 +8,6 @@ DictVectorizer
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.feature_extraction.DictVectorizer sklearn.feature_extraction.DictVectorizer.__delattr__ sklearn.feature_extraction.DictVectorizer.__dir__ sklearn.feature_extraction.DictVectorizer.__eq__ sklearn.feature_extraction.DictVectorizer.__format__ sklearn.feature_extraction.DictVectorizer.__ge__ sklearn.feature_extraction.DictVectorizer.__getattribute__ sklearn.feature_extraction.DictVectorizer.__getstate__ sklearn.feature_extraction.DictVectorizer.__gt__ sklearn.feature_extraction.DictVectorizer.__hash__ sklearn.feature_extraction.DictVectorizer.__init__ sklearn.feature_extraction.DictVectorizer.__init_subclass__ sklearn.feature_extraction.DictVectorizer.__le__ sklearn.feature_extraction.DictVectorizer.__lt__ sklearn.feature_extraction.DictVectorizer.__ne__ sklearn.feature_extraction.DictVectorizer.__new__ sklearn.feature_extraction.DictVectorizer.__reduce__ sklearn.feature_extraction.DictVectorizer.__reduce_ex__ sklearn.feature_extraction.DictVectorizer.__repr__ sklearn.feature_extraction.DictVectorizer.__setattr__ sklearn.feature_extraction.DictVectorizer.__setstate__ sklearn.feature_extraction.DictVectorizer.__sizeof__ sklearn.feature_extraction.DictVectorizer.__sklearn_clone__ sklearn.feature_extraction.DictVectorizer.__sklearn_tags__ sklearn.feature_extraction.DictVectorizer.__str__ sklearn.feature_extraction.DictVectorizer.__subclasshook__ sklearn.feature_extraction.DictVectorizer._add_iterable_element sklearn.feature_extraction.DictVectorizer._build_request_for_signature sklearn.feature_extraction.DictVectorizer._check_feature_names sklearn.feature_extraction.DictVectorizer._check_n_features sklearn.feature_extraction.DictVectorizer._get_default_requests sklearn.feature_extraction.DictVectorizer._get_doc_link sklearn.feature_extraction.DictVectorizer._get_metadata_request sklearn.feature_extraction.DictVectorizer._get_param_names sklearn.feature_extraction.DictVectorizer._get_tags sklearn.feature_extraction.DictVectorizer._more_tags sklearn.feature_extraction.DictVectorizer._repr_html_inner sklearn.feature_extraction.DictVectorizer._repr_mimebundle_ sklearn.feature_extraction.DictVectorizer._transform sklearn.feature_extraction.DictVectorizer._validate_data sklearn.feature_extraction.DictVectorizer._validate_params sklearn.feature_extraction.DictVectorizer.fit sklearn.feature_extraction.DictVectorizer.fit_transform sklearn.feature_extraction.DictVectorizer.get_feature_names_out sklearn.feature_extraction.DictVectorizer.get_metadata_routing sklearn.feature_extraction.DictVectorizer.get_params sklearn.feature_extraction.DictVectorizer.inverse_transform sklearn.feature_extraction.DictVectorizer.restrict sklearn.feature_extraction.DictVectorizer.set_output sklearn.feature_extraction.DictVectorizer.set_params sklearn.feature_extraction.DictVectorizer.transform 
+.. minigallery:: sklearn.feature_extraction.DictVectorizer sklearn.feature_extraction.DictVectorizer.__delattr__ sklearn.feature_extraction.DictVectorizer.__dir__ sklearn.feature_extraction.DictVectorizer.__eq__ sklearn.feature_extraction.DictVectorizer.__format__ sklearn.feature_extraction.DictVectorizer.__ge__ sklearn.feature_extraction.DictVectorizer.__getattribute__ sklearn.feature_extraction.DictVectorizer.__getstate__ sklearn.feature_extraction.DictVectorizer.__gt__ sklearn.feature_extraction.DictVectorizer.__hash__ sklearn.feature_extraction.DictVectorizer.__init__ sklearn.feature_extraction.DictVectorizer.__init_subclass__ sklearn.feature_extraction.DictVectorizer.__le__ sklearn.feature_extraction.DictVectorizer.__lt__ sklearn.feature_extraction.DictVectorizer.__ne__ sklearn.feature_extraction.DictVectorizer.__new__ sklearn.feature_extraction.DictVectorizer.__reduce__ sklearn.feature_extraction.DictVectorizer.__reduce_ex__ sklearn.feature_extraction.DictVectorizer.__repr__ sklearn.feature_extraction.DictVectorizer.__setattr__ sklearn.feature_extraction.DictVectorizer.__setstate__ sklearn.feature_extraction.DictVectorizer.__sizeof__ sklearn.feature_extraction.DictVectorizer.__sklearn_clone__ sklearn.feature_extraction.DictVectorizer.__sklearn_tags__ sklearn.feature_extraction.DictVectorizer.__str__ sklearn.feature_extraction.DictVectorizer.__subclasshook__ sklearn.feature_extraction.DictVectorizer._add_iterable_element sklearn.feature_extraction.DictVectorizer._build_request_for_signature sklearn.feature_extraction.DictVectorizer._get_default_requests sklearn.feature_extraction.DictVectorizer._get_doc_link sklearn.feature_extraction.DictVectorizer._get_metadata_request sklearn.feature_extraction.DictVectorizer._get_param_names sklearn.feature_extraction.DictVectorizer._repr_html_inner sklearn.feature_extraction.DictVectorizer._repr_mimebundle_ sklearn.feature_extraction.DictVectorizer._transform sklearn.feature_extraction.DictVectorizer._validate_params sklearn.feature_extraction.DictVectorizer.fit sklearn.feature_extraction.DictVectorizer.fit_transform sklearn.feature_extraction.DictVectorizer.get_feature_names_out sklearn.feature_extraction.DictVectorizer.get_metadata_routing sklearn.feature_extraction.DictVectorizer.get_params sklearn.feature_extraction.DictVectorizer.inverse_transform sklearn.feature_extraction.DictVectorizer.restrict sklearn.feature_extraction.DictVectorizer.set_output sklearn.feature_extraction.DictVectorizer.set_params sklearn.feature_extraction.DictVectorizer.transform 
    :add-heading: Gallery examples
    :heading-level: -
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diff --git a/dev/_sources/modules/generated/sklearn.feature_extraction.FeatureHasher.rst.txt b/dev/_sources/modules/generated/sklearn.feature_extraction.FeatureHasher.rst.txt
index ec118d134ce3f..b1f83f161ae0e 100644
--- a/dev/_sources/modules/generated/sklearn.feature_extraction.FeatureHasher.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.feature_extraction.FeatureHasher.rst.txt
@@ -8,6 +8,6 @@ FeatureHasher
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.feature_extraction.FeatureHasher sklearn.feature_extraction.FeatureHasher.__delattr__ sklearn.feature_extraction.FeatureHasher.__dir__ sklearn.feature_extraction.FeatureHasher.__eq__ sklearn.feature_extraction.FeatureHasher.__format__ sklearn.feature_extraction.FeatureHasher.__ge__ sklearn.feature_extraction.FeatureHasher.__getattribute__ sklearn.feature_extraction.FeatureHasher.__getstate__ sklearn.feature_extraction.FeatureHasher.__gt__ sklearn.feature_extraction.FeatureHasher.__hash__ sklearn.feature_extraction.FeatureHasher.__init__ sklearn.feature_extraction.FeatureHasher.__init_subclass__ sklearn.feature_extraction.FeatureHasher.__le__ sklearn.feature_extraction.FeatureHasher.__lt__ sklearn.feature_extraction.FeatureHasher.__ne__ sklearn.feature_extraction.FeatureHasher.__new__ sklearn.feature_extraction.FeatureHasher.__reduce__ sklearn.feature_extraction.FeatureHasher.__reduce_ex__ sklearn.feature_extraction.FeatureHasher.__repr__ sklearn.feature_extraction.FeatureHasher.__setattr__ sklearn.feature_extraction.FeatureHasher.__setstate__ sklearn.feature_extraction.FeatureHasher.__sizeof__ sklearn.feature_extraction.FeatureHasher.__sklearn_clone__ sklearn.feature_extraction.FeatureHasher.__sklearn_tags__ sklearn.feature_extraction.FeatureHasher.__str__ sklearn.feature_extraction.FeatureHasher.__subclasshook__ sklearn.feature_extraction.FeatureHasher._build_request_for_signature sklearn.feature_extraction.FeatureHasher._check_feature_names sklearn.feature_extraction.FeatureHasher._check_n_features sklearn.feature_extraction.FeatureHasher._get_default_requests sklearn.feature_extraction.FeatureHasher._get_doc_link sklearn.feature_extraction.FeatureHasher._get_metadata_request sklearn.feature_extraction.FeatureHasher._get_param_names sklearn.feature_extraction.FeatureHasher._get_tags sklearn.feature_extraction.FeatureHasher._more_tags sklearn.feature_extraction.FeatureHasher._repr_html_inner sklearn.feature_extraction.FeatureHasher._repr_mimebundle_ sklearn.feature_extraction.FeatureHasher._validate_data sklearn.feature_extraction.FeatureHasher._validate_params sklearn.feature_extraction.FeatureHasher.fit sklearn.feature_extraction.FeatureHasher.fit_transform sklearn.feature_extraction.FeatureHasher.get_metadata_routing sklearn.feature_extraction.FeatureHasher.get_params sklearn.feature_extraction.FeatureHasher.set_output sklearn.feature_extraction.FeatureHasher.set_params sklearn.feature_extraction.FeatureHasher.transform 
+.. minigallery:: sklearn.feature_extraction.FeatureHasher sklearn.feature_extraction.FeatureHasher.__delattr__ sklearn.feature_extraction.FeatureHasher.__dir__ sklearn.feature_extraction.FeatureHasher.__eq__ sklearn.feature_extraction.FeatureHasher.__format__ sklearn.feature_extraction.FeatureHasher.__ge__ sklearn.feature_extraction.FeatureHasher.__getattribute__ sklearn.feature_extraction.FeatureHasher.__getstate__ sklearn.feature_extraction.FeatureHasher.__gt__ sklearn.feature_extraction.FeatureHasher.__hash__ sklearn.feature_extraction.FeatureHasher.__init__ sklearn.feature_extraction.FeatureHasher.__init_subclass__ sklearn.feature_extraction.FeatureHasher.__le__ sklearn.feature_extraction.FeatureHasher.__lt__ sklearn.feature_extraction.FeatureHasher.__ne__ sklearn.feature_extraction.FeatureHasher.__new__ sklearn.feature_extraction.FeatureHasher.__reduce__ sklearn.feature_extraction.FeatureHasher.__reduce_ex__ sklearn.feature_extraction.FeatureHasher.__repr__ sklearn.feature_extraction.FeatureHasher.__setattr__ sklearn.feature_extraction.FeatureHasher.__setstate__ sklearn.feature_extraction.FeatureHasher.__sizeof__ sklearn.feature_extraction.FeatureHasher.__sklearn_clone__ sklearn.feature_extraction.FeatureHasher.__sklearn_tags__ sklearn.feature_extraction.FeatureHasher.__str__ sklearn.feature_extraction.FeatureHasher.__subclasshook__ sklearn.feature_extraction.FeatureHasher._build_request_for_signature sklearn.feature_extraction.FeatureHasher._get_default_requests sklearn.feature_extraction.FeatureHasher._get_doc_link sklearn.feature_extraction.FeatureHasher._get_metadata_request sklearn.feature_extraction.FeatureHasher._get_param_names sklearn.feature_extraction.FeatureHasher._repr_html_inner sklearn.feature_extraction.FeatureHasher._repr_mimebundle_ sklearn.feature_extraction.FeatureHasher._validate_params sklearn.feature_extraction.FeatureHasher.fit sklearn.feature_extraction.FeatureHasher.fit_transform sklearn.feature_extraction.FeatureHasher.get_metadata_routing sklearn.feature_extraction.FeatureHasher.get_params sklearn.feature_extraction.FeatureHasher.set_output sklearn.feature_extraction.FeatureHasher.set_params sklearn.feature_extraction.FeatureHasher.transform 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.feature_extraction.image.PatchExtractor.rst.txt b/dev/_sources/modules/generated/sklearn.feature_extraction.image.PatchExtractor.rst.txt
index 739bbc24fc9fd..20bfafbb15cc3 100644
--- a/dev/_sources/modules/generated/sklearn.feature_extraction.image.PatchExtractor.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.feature_extraction.image.PatchExtractor.rst.txt
@@ -8,6 +8,6 @@ PatchExtractor
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.feature_extraction.image.PatchExtractor sklearn.feature_extraction.image.PatchExtractor.__delattr__ sklearn.feature_extraction.image.PatchExtractor.__dir__ sklearn.feature_extraction.image.PatchExtractor.__eq__ sklearn.feature_extraction.image.PatchExtractor.__format__ sklearn.feature_extraction.image.PatchExtractor.__ge__ sklearn.feature_extraction.image.PatchExtractor.__getattribute__ sklearn.feature_extraction.image.PatchExtractor.__getstate__ sklearn.feature_extraction.image.PatchExtractor.__gt__ sklearn.feature_extraction.image.PatchExtractor.__hash__ sklearn.feature_extraction.image.PatchExtractor.__init__ sklearn.feature_extraction.image.PatchExtractor.__init_subclass__ sklearn.feature_extraction.image.PatchExtractor.__le__ sklearn.feature_extraction.image.PatchExtractor.__lt__ sklearn.feature_extraction.image.PatchExtractor.__ne__ sklearn.feature_extraction.image.PatchExtractor.__new__ sklearn.feature_extraction.image.PatchExtractor.__reduce__ sklearn.feature_extraction.image.PatchExtractor.__reduce_ex__ sklearn.feature_extraction.image.PatchExtractor.__repr__ sklearn.feature_extraction.image.PatchExtractor.__setattr__ sklearn.feature_extraction.image.PatchExtractor.__setstate__ sklearn.feature_extraction.image.PatchExtractor.__sizeof__ sklearn.feature_extraction.image.PatchExtractor.__sklearn_clone__ sklearn.feature_extraction.image.PatchExtractor.__sklearn_tags__ sklearn.feature_extraction.image.PatchExtractor.__str__ sklearn.feature_extraction.image.PatchExtractor.__subclasshook__ sklearn.feature_extraction.image.PatchExtractor._build_request_for_signature sklearn.feature_extraction.image.PatchExtractor._check_feature_names sklearn.feature_extraction.image.PatchExtractor._check_n_features sklearn.feature_extraction.image.PatchExtractor._get_default_requests sklearn.feature_extraction.image.PatchExtractor._get_doc_link sklearn.feature_extraction.image.PatchExtractor._get_metadata_request sklearn.feature_extraction.image.PatchExtractor._get_param_names sklearn.feature_extraction.image.PatchExtractor._get_tags sklearn.feature_extraction.image.PatchExtractor._more_tags sklearn.feature_extraction.image.PatchExtractor._repr_html_inner sklearn.feature_extraction.image.PatchExtractor._repr_mimebundle_ sklearn.feature_extraction.image.PatchExtractor._validate_data sklearn.feature_extraction.image.PatchExtractor._validate_params sklearn.feature_extraction.image.PatchExtractor.fit sklearn.feature_extraction.image.PatchExtractor.fit_transform sklearn.feature_extraction.image.PatchExtractor.get_metadata_routing sklearn.feature_extraction.image.PatchExtractor.get_params sklearn.feature_extraction.image.PatchExtractor.set_output sklearn.feature_extraction.image.PatchExtractor.set_params sklearn.feature_extraction.image.PatchExtractor.transform 
+.. minigallery:: sklearn.feature_extraction.image.PatchExtractor sklearn.feature_extraction.image.PatchExtractor.__delattr__ sklearn.feature_extraction.image.PatchExtractor.__dir__ sklearn.feature_extraction.image.PatchExtractor.__eq__ sklearn.feature_extraction.image.PatchExtractor.__format__ sklearn.feature_extraction.image.PatchExtractor.__ge__ sklearn.feature_extraction.image.PatchExtractor.__getattribute__ sklearn.feature_extraction.image.PatchExtractor.__getstate__ sklearn.feature_extraction.image.PatchExtractor.__gt__ sklearn.feature_extraction.image.PatchExtractor.__hash__ sklearn.feature_extraction.image.PatchExtractor.__init__ sklearn.feature_extraction.image.PatchExtractor.__init_subclass__ sklearn.feature_extraction.image.PatchExtractor.__le__ sklearn.feature_extraction.image.PatchExtractor.__lt__ sklearn.feature_extraction.image.PatchExtractor.__ne__ sklearn.feature_extraction.image.PatchExtractor.__new__ sklearn.feature_extraction.image.PatchExtractor.__reduce__ sklearn.feature_extraction.image.PatchExtractor.__reduce_ex__ sklearn.feature_extraction.image.PatchExtractor.__repr__ sklearn.feature_extraction.image.PatchExtractor.__setattr__ sklearn.feature_extraction.image.PatchExtractor.__setstate__ sklearn.feature_extraction.image.PatchExtractor.__sizeof__ sklearn.feature_extraction.image.PatchExtractor.__sklearn_clone__ sklearn.feature_extraction.image.PatchExtractor.__sklearn_tags__ sklearn.feature_extraction.image.PatchExtractor.__str__ sklearn.feature_extraction.image.PatchExtractor.__subclasshook__ sklearn.feature_extraction.image.PatchExtractor._build_request_for_signature sklearn.feature_extraction.image.PatchExtractor._get_default_requests sklearn.feature_extraction.image.PatchExtractor._get_doc_link sklearn.feature_extraction.image.PatchExtractor._get_metadata_request sklearn.feature_extraction.image.PatchExtractor._get_param_names sklearn.feature_extraction.image.PatchExtractor._repr_html_inner sklearn.feature_extraction.image.PatchExtractor._repr_mimebundle_ sklearn.feature_extraction.image.PatchExtractor._validate_params sklearn.feature_extraction.image.PatchExtractor.fit sklearn.feature_extraction.image.PatchExtractor.fit_transform sklearn.feature_extraction.image.PatchExtractor.get_metadata_routing sklearn.feature_extraction.image.PatchExtractor.get_params sklearn.feature_extraction.image.PatchExtractor.set_output sklearn.feature_extraction.image.PatchExtractor.set_params sklearn.feature_extraction.image.PatchExtractor.transform 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.feature_extraction.text.CountVectorizer.rst.txt b/dev/_sources/modules/generated/sklearn.feature_extraction.text.CountVectorizer.rst.txt
index 665437d4da5b4..b5194f5e70a4f 100644
--- a/dev/_sources/modules/generated/sklearn.feature_extraction.text.CountVectorizer.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.feature_extraction.text.CountVectorizer.rst.txt
@@ -8,6 +8,6 @@ CountVectorizer
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.feature_extraction.text.CountVectorizer sklearn.feature_extraction.text.CountVectorizer.__delattr__ sklearn.feature_extraction.text.CountVectorizer.__dir__ sklearn.feature_extraction.text.CountVectorizer.__eq__ sklearn.feature_extraction.text.CountVectorizer.__format__ sklearn.feature_extraction.text.CountVectorizer.__ge__ sklearn.feature_extraction.text.CountVectorizer.__getattribute__ sklearn.feature_extraction.text.CountVectorizer.__getstate__ sklearn.feature_extraction.text.CountVectorizer.__gt__ sklearn.feature_extraction.text.CountVectorizer.__hash__ sklearn.feature_extraction.text.CountVectorizer.__init__ sklearn.feature_extraction.text.CountVectorizer.__init_subclass__ sklearn.feature_extraction.text.CountVectorizer.__le__ sklearn.feature_extraction.text.CountVectorizer.__lt__ sklearn.feature_extraction.text.CountVectorizer.__ne__ sklearn.feature_extraction.text.CountVectorizer.__new__ sklearn.feature_extraction.text.CountVectorizer.__reduce__ sklearn.feature_extraction.text.CountVectorizer.__reduce_ex__ sklearn.feature_extraction.text.CountVectorizer.__repr__ sklearn.feature_extraction.text.CountVectorizer.__setattr__ sklearn.feature_extraction.text.CountVectorizer.__setstate__ sklearn.feature_extraction.text.CountVectorizer.__sizeof__ sklearn.feature_extraction.text.CountVectorizer.__sklearn_clone__ sklearn.feature_extraction.text.CountVectorizer.__sklearn_tags__ sklearn.feature_extraction.text.CountVectorizer.__str__ sklearn.feature_extraction.text.CountVectorizer.__subclasshook__ sklearn.feature_extraction.text.CountVectorizer._build_request_for_signature sklearn.feature_extraction.text.CountVectorizer._char_ngrams sklearn.feature_extraction.text.CountVectorizer._char_wb_ngrams sklearn.feature_extraction.text.CountVectorizer._check_feature_names sklearn.feature_extraction.text.CountVectorizer._check_n_features sklearn.feature_extraction.text.CountVectorizer._check_stop_words_consistency sklearn.feature_extraction.text.CountVectorizer._check_vocabulary sklearn.feature_extraction.text.CountVectorizer._count_vocab sklearn.feature_extraction.text.CountVectorizer._get_default_requests sklearn.feature_extraction.text.CountVectorizer._get_doc_link sklearn.feature_extraction.text.CountVectorizer._get_metadata_request sklearn.feature_extraction.text.CountVectorizer._get_param_names sklearn.feature_extraction.text.CountVectorizer._get_tags sklearn.feature_extraction.text.CountVectorizer._limit_features sklearn.feature_extraction.text.CountVectorizer._more_tags sklearn.feature_extraction.text.CountVectorizer._repr_html_inner sklearn.feature_extraction.text.CountVectorizer._repr_mimebundle_ sklearn.feature_extraction.text.CountVectorizer._sort_features sklearn.feature_extraction.text.CountVectorizer._validate_data sklearn.feature_extraction.text.CountVectorizer._validate_ngram_range sklearn.feature_extraction.text.CountVectorizer._validate_params sklearn.feature_extraction.text.CountVectorizer._validate_vocabulary sklearn.feature_extraction.text.CountVectorizer._warn_for_unused_params sklearn.feature_extraction.text.CountVectorizer._word_ngrams sklearn.feature_extraction.text.CountVectorizer.build_analyzer sklearn.feature_extraction.text.CountVectorizer.build_preprocessor sklearn.feature_extraction.text.CountVectorizer.build_tokenizer sklearn.feature_extraction.text.CountVectorizer.decode sklearn.feature_extraction.text.CountVectorizer.fit sklearn.feature_extraction.text.CountVectorizer.fit_transform sklearn.feature_extraction.text.CountVectorizer.get_feature_names_out sklearn.feature_extraction.text.CountVectorizer.get_metadata_routing sklearn.feature_extraction.text.CountVectorizer.get_params sklearn.feature_extraction.text.CountVectorizer.get_stop_words sklearn.feature_extraction.text.CountVectorizer.inverse_transform sklearn.feature_extraction.text.CountVectorizer.set_params sklearn.feature_extraction.text.CountVectorizer.transform 
+.. minigallery:: sklearn.feature_extraction.text.CountVectorizer sklearn.feature_extraction.text.CountVectorizer.__delattr__ sklearn.feature_extraction.text.CountVectorizer.__dir__ sklearn.feature_extraction.text.CountVectorizer.__eq__ sklearn.feature_extraction.text.CountVectorizer.__format__ sklearn.feature_extraction.text.CountVectorizer.__ge__ sklearn.feature_extraction.text.CountVectorizer.__getattribute__ sklearn.feature_extraction.text.CountVectorizer.__getstate__ sklearn.feature_extraction.text.CountVectorizer.__gt__ sklearn.feature_extraction.text.CountVectorizer.__hash__ sklearn.feature_extraction.text.CountVectorizer.__init__ sklearn.feature_extraction.text.CountVectorizer.__init_subclass__ sklearn.feature_extraction.text.CountVectorizer.__le__ sklearn.feature_extraction.text.CountVectorizer.__lt__ sklearn.feature_extraction.text.CountVectorizer.__ne__ sklearn.feature_extraction.text.CountVectorizer.__new__ sklearn.feature_extraction.text.CountVectorizer.__reduce__ sklearn.feature_extraction.text.CountVectorizer.__reduce_ex__ sklearn.feature_extraction.text.CountVectorizer.__repr__ sklearn.feature_extraction.text.CountVectorizer.__setattr__ sklearn.feature_extraction.text.CountVectorizer.__setstate__ sklearn.feature_extraction.text.CountVectorizer.__sizeof__ sklearn.feature_extraction.text.CountVectorizer.__sklearn_clone__ sklearn.feature_extraction.text.CountVectorizer.__sklearn_tags__ sklearn.feature_extraction.text.CountVectorizer.__str__ sklearn.feature_extraction.text.CountVectorizer.__subclasshook__ sklearn.feature_extraction.text.CountVectorizer._build_request_for_signature sklearn.feature_extraction.text.CountVectorizer._char_ngrams sklearn.feature_extraction.text.CountVectorizer._char_wb_ngrams sklearn.feature_extraction.text.CountVectorizer._check_stop_words_consistency sklearn.feature_extraction.text.CountVectorizer._check_vocabulary sklearn.feature_extraction.text.CountVectorizer._count_vocab sklearn.feature_extraction.text.CountVectorizer._get_default_requests sklearn.feature_extraction.text.CountVectorizer._get_doc_link sklearn.feature_extraction.text.CountVectorizer._get_metadata_request sklearn.feature_extraction.text.CountVectorizer._get_param_names sklearn.feature_extraction.text.CountVectorizer._limit_features sklearn.feature_extraction.text.CountVectorizer._repr_html_inner sklearn.feature_extraction.text.CountVectorizer._repr_mimebundle_ sklearn.feature_extraction.text.CountVectorizer._sort_features sklearn.feature_extraction.text.CountVectorizer._validate_ngram_range sklearn.feature_extraction.text.CountVectorizer._validate_params sklearn.feature_extraction.text.CountVectorizer._validate_vocabulary sklearn.feature_extraction.text.CountVectorizer._warn_for_unused_params sklearn.feature_extraction.text.CountVectorizer._word_ngrams sklearn.feature_extraction.text.CountVectorizer.build_analyzer sklearn.feature_extraction.text.CountVectorizer.build_preprocessor sklearn.feature_extraction.text.CountVectorizer.build_tokenizer sklearn.feature_extraction.text.CountVectorizer.decode sklearn.feature_extraction.text.CountVectorizer.fit sklearn.feature_extraction.text.CountVectorizer.fit_transform sklearn.feature_extraction.text.CountVectorizer.get_feature_names_out sklearn.feature_extraction.text.CountVectorizer.get_metadata_routing sklearn.feature_extraction.text.CountVectorizer.get_params sklearn.feature_extraction.text.CountVectorizer.get_stop_words sklearn.feature_extraction.text.CountVectorizer.inverse_transform sklearn.feature_extraction.text.CountVectorizer.set_params sklearn.feature_extraction.text.CountVectorizer.transform 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.feature_extraction.text.HashingVectorizer.rst.txt b/dev/_sources/modules/generated/sklearn.feature_extraction.text.HashingVectorizer.rst.txt
index c1c4284bcad6d..49a7f15117195 100644
--- a/dev/_sources/modules/generated/sklearn.feature_extraction.text.HashingVectorizer.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.feature_extraction.text.HashingVectorizer.rst.txt
@@ -8,6 +8,6 @@ HashingVectorizer
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.feature_extraction.text.HashingVectorizer sklearn.feature_extraction.text.HashingVectorizer.__delattr__ sklearn.feature_extraction.text.HashingVectorizer.__dir__ sklearn.feature_extraction.text.HashingVectorizer.__eq__ sklearn.feature_extraction.text.HashingVectorizer.__format__ sklearn.feature_extraction.text.HashingVectorizer.__ge__ sklearn.feature_extraction.text.HashingVectorizer.__getattribute__ sklearn.feature_extraction.text.HashingVectorizer.__getstate__ sklearn.feature_extraction.text.HashingVectorizer.__gt__ sklearn.feature_extraction.text.HashingVectorizer.__hash__ sklearn.feature_extraction.text.HashingVectorizer.__init__ sklearn.feature_extraction.text.HashingVectorizer.__init_subclass__ sklearn.feature_extraction.text.HashingVectorizer.__le__ sklearn.feature_extraction.text.HashingVectorizer.__lt__ sklearn.feature_extraction.text.HashingVectorizer.__ne__ sklearn.feature_extraction.text.HashingVectorizer.__new__ sklearn.feature_extraction.text.HashingVectorizer.__reduce__ sklearn.feature_extraction.text.HashingVectorizer.__reduce_ex__ sklearn.feature_extraction.text.HashingVectorizer.__repr__ sklearn.feature_extraction.text.HashingVectorizer.__setattr__ sklearn.feature_extraction.text.HashingVectorizer.__setstate__ sklearn.feature_extraction.text.HashingVectorizer.__sizeof__ sklearn.feature_extraction.text.HashingVectorizer.__sklearn_clone__ sklearn.feature_extraction.text.HashingVectorizer.__sklearn_tags__ sklearn.feature_extraction.text.HashingVectorizer.__str__ sklearn.feature_extraction.text.HashingVectorizer.__subclasshook__ sklearn.feature_extraction.text.HashingVectorizer._build_request_for_signature sklearn.feature_extraction.text.HashingVectorizer._char_ngrams sklearn.feature_extraction.text.HashingVectorizer._char_wb_ngrams sklearn.feature_extraction.text.HashingVectorizer._check_feature_names sklearn.feature_extraction.text.HashingVectorizer._check_n_features sklearn.feature_extraction.text.HashingVectorizer._check_stop_words_consistency sklearn.feature_extraction.text.HashingVectorizer._check_vocabulary sklearn.feature_extraction.text.HashingVectorizer._get_default_requests sklearn.feature_extraction.text.HashingVectorizer._get_doc_link sklearn.feature_extraction.text.HashingVectorizer._get_hasher sklearn.feature_extraction.text.HashingVectorizer._get_metadata_request sklearn.feature_extraction.text.HashingVectorizer._get_param_names sklearn.feature_extraction.text.HashingVectorizer._get_tags sklearn.feature_extraction.text.HashingVectorizer._more_tags sklearn.feature_extraction.text.HashingVectorizer._repr_html_inner sklearn.feature_extraction.text.HashingVectorizer._repr_mimebundle_ sklearn.feature_extraction.text.HashingVectorizer._validate_data sklearn.feature_extraction.text.HashingVectorizer._validate_ngram_range sklearn.feature_extraction.text.HashingVectorizer._validate_params sklearn.feature_extraction.text.HashingVectorizer._validate_vocabulary sklearn.feature_extraction.text.HashingVectorizer._warn_for_unused_params sklearn.feature_extraction.text.HashingVectorizer._word_ngrams sklearn.feature_extraction.text.HashingVectorizer.build_analyzer sklearn.feature_extraction.text.HashingVectorizer.build_preprocessor sklearn.feature_extraction.text.HashingVectorizer.build_tokenizer sklearn.feature_extraction.text.HashingVectorizer.decode sklearn.feature_extraction.text.HashingVectorizer.fit sklearn.feature_extraction.text.HashingVectorizer.fit_transform sklearn.feature_extraction.text.HashingVectorizer.get_metadata_routing sklearn.feature_extraction.text.HashingVectorizer.get_params sklearn.feature_extraction.text.HashingVectorizer.get_stop_words sklearn.feature_extraction.text.HashingVectorizer.partial_fit sklearn.feature_extraction.text.HashingVectorizer.set_output sklearn.feature_extraction.text.HashingVectorizer.set_params sklearn.feature_extraction.text.HashingVectorizer.transform 
+.. minigallery:: sklearn.feature_extraction.text.HashingVectorizer sklearn.feature_extraction.text.HashingVectorizer.__delattr__ sklearn.feature_extraction.text.HashingVectorizer.__dir__ sklearn.feature_extraction.text.HashingVectorizer.__eq__ sklearn.feature_extraction.text.HashingVectorizer.__format__ sklearn.feature_extraction.text.HashingVectorizer.__ge__ sklearn.feature_extraction.text.HashingVectorizer.__getattribute__ sklearn.feature_extraction.text.HashingVectorizer.__getstate__ sklearn.feature_extraction.text.HashingVectorizer.__gt__ sklearn.feature_extraction.text.HashingVectorizer.__hash__ sklearn.feature_extraction.text.HashingVectorizer.__init__ sklearn.feature_extraction.text.HashingVectorizer.__init_subclass__ sklearn.feature_extraction.text.HashingVectorizer.__le__ sklearn.feature_extraction.text.HashingVectorizer.__lt__ sklearn.feature_extraction.text.HashingVectorizer.__ne__ sklearn.feature_extraction.text.HashingVectorizer.__new__ sklearn.feature_extraction.text.HashingVectorizer.__reduce__ sklearn.feature_extraction.text.HashingVectorizer.__reduce_ex__ sklearn.feature_extraction.text.HashingVectorizer.__repr__ sklearn.feature_extraction.text.HashingVectorizer.__setattr__ sklearn.feature_extraction.text.HashingVectorizer.__setstate__ sklearn.feature_extraction.text.HashingVectorizer.__sizeof__ sklearn.feature_extraction.text.HashingVectorizer.__sklearn_clone__ sklearn.feature_extraction.text.HashingVectorizer.__sklearn_tags__ sklearn.feature_extraction.text.HashingVectorizer.__str__ sklearn.feature_extraction.text.HashingVectorizer.__subclasshook__ sklearn.feature_extraction.text.HashingVectorizer._build_request_for_signature sklearn.feature_extraction.text.HashingVectorizer._char_ngrams sklearn.feature_extraction.text.HashingVectorizer._char_wb_ngrams sklearn.feature_extraction.text.HashingVectorizer._check_stop_words_consistency sklearn.feature_extraction.text.HashingVectorizer._check_vocabulary sklearn.feature_extraction.text.HashingVectorizer._get_default_requests sklearn.feature_extraction.text.HashingVectorizer._get_doc_link sklearn.feature_extraction.text.HashingVectorizer._get_hasher sklearn.feature_extraction.text.HashingVectorizer._get_metadata_request sklearn.feature_extraction.text.HashingVectorizer._get_param_names sklearn.feature_extraction.text.HashingVectorizer._repr_html_inner sklearn.feature_extraction.text.HashingVectorizer._repr_mimebundle_ sklearn.feature_extraction.text.HashingVectorizer._validate_ngram_range sklearn.feature_extraction.text.HashingVectorizer._validate_params sklearn.feature_extraction.text.HashingVectorizer._validate_vocabulary sklearn.feature_extraction.text.HashingVectorizer._warn_for_unused_params sklearn.feature_extraction.text.HashingVectorizer._word_ngrams sklearn.feature_extraction.text.HashingVectorizer.build_analyzer sklearn.feature_extraction.text.HashingVectorizer.build_preprocessor sklearn.feature_extraction.text.HashingVectorizer.build_tokenizer sklearn.feature_extraction.text.HashingVectorizer.decode sklearn.feature_extraction.text.HashingVectorizer.fit sklearn.feature_extraction.text.HashingVectorizer.fit_transform sklearn.feature_extraction.text.HashingVectorizer.get_metadata_routing sklearn.feature_extraction.text.HashingVectorizer.get_params sklearn.feature_extraction.text.HashingVectorizer.get_stop_words sklearn.feature_extraction.text.HashingVectorizer.partial_fit sklearn.feature_extraction.text.HashingVectorizer.set_output sklearn.feature_extraction.text.HashingVectorizer.set_params sklearn.feature_extraction.text.HashingVectorizer.transform 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.feature_extraction.text.TfidfTransformer.rst.txt b/dev/_sources/modules/generated/sklearn.feature_extraction.text.TfidfTransformer.rst.txt
index 86287205a71e2..d90b8545a577c 100644
--- a/dev/_sources/modules/generated/sklearn.feature_extraction.text.TfidfTransformer.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.feature_extraction.text.TfidfTransformer.rst.txt
@@ -8,6 +8,6 @@ TfidfTransformer
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.feature_extraction.text.TfidfTransformer sklearn.feature_extraction.text.TfidfTransformer.__delattr__ sklearn.feature_extraction.text.TfidfTransformer.__dir__ sklearn.feature_extraction.text.TfidfTransformer.__eq__ sklearn.feature_extraction.text.TfidfTransformer.__format__ sklearn.feature_extraction.text.TfidfTransformer.__ge__ sklearn.feature_extraction.text.TfidfTransformer.__getattribute__ sklearn.feature_extraction.text.TfidfTransformer.__getstate__ sklearn.feature_extraction.text.TfidfTransformer.__gt__ sklearn.feature_extraction.text.TfidfTransformer.__hash__ sklearn.feature_extraction.text.TfidfTransformer.__init__ sklearn.feature_extraction.text.TfidfTransformer.__init_subclass__ sklearn.feature_extraction.text.TfidfTransformer.__le__ sklearn.feature_extraction.text.TfidfTransformer.__lt__ sklearn.feature_extraction.text.TfidfTransformer.__ne__ sklearn.feature_extraction.text.TfidfTransformer.__new__ sklearn.feature_extraction.text.TfidfTransformer.__reduce__ sklearn.feature_extraction.text.TfidfTransformer.__reduce_ex__ sklearn.feature_extraction.text.TfidfTransformer.__repr__ sklearn.feature_extraction.text.TfidfTransformer.__setattr__ sklearn.feature_extraction.text.TfidfTransformer.__setstate__ sklearn.feature_extraction.text.TfidfTransformer.__sizeof__ sklearn.feature_extraction.text.TfidfTransformer.__sklearn_clone__ sklearn.feature_extraction.text.TfidfTransformer.__sklearn_tags__ sklearn.feature_extraction.text.TfidfTransformer.__str__ sklearn.feature_extraction.text.TfidfTransformer.__subclasshook__ sklearn.feature_extraction.text.TfidfTransformer._build_request_for_signature sklearn.feature_extraction.text.TfidfTransformer._check_feature_names sklearn.feature_extraction.text.TfidfTransformer._check_n_features sklearn.feature_extraction.text.TfidfTransformer._get_default_requests sklearn.feature_extraction.text.TfidfTransformer._get_doc_link sklearn.feature_extraction.text.TfidfTransformer._get_metadata_request sklearn.feature_extraction.text.TfidfTransformer._get_param_names sklearn.feature_extraction.text.TfidfTransformer._get_tags sklearn.feature_extraction.text.TfidfTransformer._more_tags sklearn.feature_extraction.text.TfidfTransformer._repr_html_inner sklearn.feature_extraction.text.TfidfTransformer._repr_mimebundle_ sklearn.feature_extraction.text.TfidfTransformer._validate_data sklearn.feature_extraction.text.TfidfTransformer._validate_params sklearn.feature_extraction.text.TfidfTransformer.fit sklearn.feature_extraction.text.TfidfTransformer.fit_transform sklearn.feature_extraction.text.TfidfTransformer.get_feature_names_out sklearn.feature_extraction.text.TfidfTransformer.get_metadata_routing sklearn.feature_extraction.text.TfidfTransformer.get_params sklearn.feature_extraction.text.TfidfTransformer.set_output sklearn.feature_extraction.text.TfidfTransformer.set_params sklearn.feature_extraction.text.TfidfTransformer.set_transform_request sklearn.feature_extraction.text.TfidfTransformer.transform 
+.. minigallery:: sklearn.feature_extraction.text.TfidfTransformer sklearn.feature_extraction.text.TfidfTransformer.__delattr__ sklearn.feature_extraction.text.TfidfTransformer.__dir__ sklearn.feature_extraction.text.TfidfTransformer.__eq__ sklearn.feature_extraction.text.TfidfTransformer.__format__ sklearn.feature_extraction.text.TfidfTransformer.__ge__ sklearn.feature_extraction.text.TfidfTransformer.__getattribute__ sklearn.feature_extraction.text.TfidfTransformer.__getstate__ sklearn.feature_extraction.text.TfidfTransformer.__gt__ sklearn.feature_extraction.text.TfidfTransformer.__hash__ sklearn.feature_extraction.text.TfidfTransformer.__init__ sklearn.feature_extraction.text.TfidfTransformer.__init_subclass__ sklearn.feature_extraction.text.TfidfTransformer.__le__ sklearn.feature_extraction.text.TfidfTransformer.__lt__ sklearn.feature_extraction.text.TfidfTransformer.__ne__ sklearn.feature_extraction.text.TfidfTransformer.__new__ sklearn.feature_extraction.text.TfidfTransformer.__reduce__ sklearn.feature_extraction.text.TfidfTransformer.__reduce_ex__ sklearn.feature_extraction.text.TfidfTransformer.__repr__ sklearn.feature_extraction.text.TfidfTransformer.__setattr__ sklearn.feature_extraction.text.TfidfTransformer.__setstate__ sklearn.feature_extraction.text.TfidfTransformer.__sizeof__ sklearn.feature_extraction.text.TfidfTransformer.__sklearn_clone__ sklearn.feature_extraction.text.TfidfTransformer.__sklearn_tags__ sklearn.feature_extraction.text.TfidfTransformer.__str__ sklearn.feature_extraction.text.TfidfTransformer.__subclasshook__ sklearn.feature_extraction.text.TfidfTransformer._build_request_for_signature sklearn.feature_extraction.text.TfidfTransformer._get_default_requests sklearn.feature_extraction.text.TfidfTransformer._get_doc_link sklearn.feature_extraction.text.TfidfTransformer._get_metadata_request sklearn.feature_extraction.text.TfidfTransformer._get_param_names sklearn.feature_extraction.text.TfidfTransformer._repr_html_inner sklearn.feature_extraction.text.TfidfTransformer._repr_mimebundle_ sklearn.feature_extraction.text.TfidfTransformer._validate_params sklearn.feature_extraction.text.TfidfTransformer.fit sklearn.feature_extraction.text.TfidfTransformer.fit_transform sklearn.feature_extraction.text.TfidfTransformer.get_feature_names_out sklearn.feature_extraction.text.TfidfTransformer.get_metadata_routing sklearn.feature_extraction.text.TfidfTransformer.get_params sklearn.feature_extraction.text.TfidfTransformer.set_output sklearn.feature_extraction.text.TfidfTransformer.set_params sklearn.feature_extraction.text.TfidfTransformer.set_transform_request sklearn.feature_extraction.text.TfidfTransformer.transform 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.rst.txt b/dev/_sources/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.rst.txt
index 4924c17759001..c4852cb409357 100644
--- a/dev/_sources/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.rst.txt
@@ -8,6 +8,6 @@ TfidfVectorizer
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.feature_extraction.text.TfidfVectorizer sklearn.feature_extraction.text.TfidfVectorizer.__delattr__ sklearn.feature_extraction.text.TfidfVectorizer.__dir__ sklearn.feature_extraction.text.TfidfVectorizer.__eq__ sklearn.feature_extraction.text.TfidfVectorizer.__format__ sklearn.feature_extraction.text.TfidfVectorizer.__ge__ sklearn.feature_extraction.text.TfidfVectorizer.__getattribute__ sklearn.feature_extraction.text.TfidfVectorizer.__getstate__ sklearn.feature_extraction.text.TfidfVectorizer.__gt__ sklearn.feature_extraction.text.TfidfVectorizer.__hash__ sklearn.feature_extraction.text.TfidfVectorizer.__init__ sklearn.feature_extraction.text.TfidfVectorizer.__init_subclass__ sklearn.feature_extraction.text.TfidfVectorizer.__le__ sklearn.feature_extraction.text.TfidfVectorizer.__lt__ sklearn.feature_extraction.text.TfidfVectorizer.__ne__ sklearn.feature_extraction.text.TfidfVectorizer.__new__ sklearn.feature_extraction.text.TfidfVectorizer.__reduce__ sklearn.feature_extraction.text.TfidfVectorizer.__reduce_ex__ sklearn.feature_extraction.text.TfidfVectorizer.__repr__ sklearn.feature_extraction.text.TfidfVectorizer.__setattr__ sklearn.feature_extraction.text.TfidfVectorizer.__setstate__ sklearn.feature_extraction.text.TfidfVectorizer.__sizeof__ sklearn.feature_extraction.text.TfidfVectorizer.__sklearn_clone__ sklearn.feature_extraction.text.TfidfVectorizer.__sklearn_tags__ sklearn.feature_extraction.text.TfidfVectorizer.__str__ sklearn.feature_extraction.text.TfidfVectorizer.__subclasshook__ sklearn.feature_extraction.text.TfidfVectorizer._build_request_for_signature sklearn.feature_extraction.text.TfidfVectorizer._char_ngrams sklearn.feature_extraction.text.TfidfVectorizer._char_wb_ngrams sklearn.feature_extraction.text.TfidfVectorizer._check_feature_names sklearn.feature_extraction.text.TfidfVectorizer._check_n_features sklearn.feature_extraction.text.TfidfVectorizer._check_params sklearn.feature_extraction.text.TfidfVectorizer._check_stop_words_consistency sklearn.feature_extraction.text.TfidfVectorizer._check_vocabulary sklearn.feature_extraction.text.TfidfVectorizer._count_vocab sklearn.feature_extraction.text.TfidfVectorizer._get_default_requests sklearn.feature_extraction.text.TfidfVectorizer._get_doc_link sklearn.feature_extraction.text.TfidfVectorizer._get_metadata_request sklearn.feature_extraction.text.TfidfVectorizer._get_param_names sklearn.feature_extraction.text.TfidfVectorizer._get_tags sklearn.feature_extraction.text.TfidfVectorizer._limit_features sklearn.feature_extraction.text.TfidfVectorizer._more_tags sklearn.feature_extraction.text.TfidfVectorizer._repr_html_inner sklearn.feature_extraction.text.TfidfVectorizer._repr_mimebundle_ sklearn.feature_extraction.text.TfidfVectorizer._sort_features sklearn.feature_extraction.text.TfidfVectorizer._validate_data sklearn.feature_extraction.text.TfidfVectorizer._validate_ngram_range sklearn.feature_extraction.text.TfidfVectorizer._validate_params sklearn.feature_extraction.text.TfidfVectorizer._validate_vocabulary sklearn.feature_extraction.text.TfidfVectorizer._warn_for_unused_params sklearn.feature_extraction.text.TfidfVectorizer._word_ngrams sklearn.feature_extraction.text.TfidfVectorizer.build_analyzer sklearn.feature_extraction.text.TfidfVectorizer.build_preprocessor sklearn.feature_extraction.text.TfidfVectorizer.build_tokenizer sklearn.feature_extraction.text.TfidfVectorizer.decode sklearn.feature_extraction.text.TfidfVectorizer.fit sklearn.feature_extraction.text.TfidfVectorizer.fit_transform sklearn.feature_extraction.text.TfidfVectorizer.get_feature_names_out sklearn.feature_extraction.text.TfidfVectorizer.get_metadata_routing sklearn.feature_extraction.text.TfidfVectorizer.get_params sklearn.feature_extraction.text.TfidfVectorizer.get_stop_words sklearn.feature_extraction.text.TfidfVectorizer.inverse_transform sklearn.feature_extraction.text.TfidfVectorizer.set_params sklearn.feature_extraction.text.TfidfVectorizer.transform 
+.. minigallery:: sklearn.feature_extraction.text.TfidfVectorizer sklearn.feature_extraction.text.TfidfVectorizer.__delattr__ sklearn.feature_extraction.text.TfidfVectorizer.__dir__ sklearn.feature_extraction.text.TfidfVectorizer.__eq__ sklearn.feature_extraction.text.TfidfVectorizer.__format__ sklearn.feature_extraction.text.TfidfVectorizer.__ge__ sklearn.feature_extraction.text.TfidfVectorizer.__getattribute__ sklearn.feature_extraction.text.TfidfVectorizer.__getstate__ sklearn.feature_extraction.text.TfidfVectorizer.__gt__ sklearn.feature_extraction.text.TfidfVectorizer.__hash__ sklearn.feature_extraction.text.TfidfVectorizer.__init__ sklearn.feature_extraction.text.TfidfVectorizer.__init_subclass__ sklearn.feature_extraction.text.TfidfVectorizer.__le__ sklearn.feature_extraction.text.TfidfVectorizer.__lt__ sklearn.feature_extraction.text.TfidfVectorizer.__ne__ sklearn.feature_extraction.text.TfidfVectorizer.__new__ sklearn.feature_extraction.text.TfidfVectorizer.__reduce__ sklearn.feature_extraction.text.TfidfVectorizer.__reduce_ex__ sklearn.feature_extraction.text.TfidfVectorizer.__repr__ sklearn.feature_extraction.text.TfidfVectorizer.__setattr__ sklearn.feature_extraction.text.TfidfVectorizer.__setstate__ sklearn.feature_extraction.text.TfidfVectorizer.__sizeof__ sklearn.feature_extraction.text.TfidfVectorizer.__sklearn_clone__ sklearn.feature_extraction.text.TfidfVectorizer.__sklearn_tags__ sklearn.feature_extraction.text.TfidfVectorizer.__str__ sklearn.feature_extraction.text.TfidfVectorizer.__subclasshook__ sklearn.feature_extraction.text.TfidfVectorizer._build_request_for_signature sklearn.feature_extraction.text.TfidfVectorizer._char_ngrams sklearn.feature_extraction.text.TfidfVectorizer._char_wb_ngrams sklearn.feature_extraction.text.TfidfVectorizer._check_params sklearn.feature_extraction.text.TfidfVectorizer._check_stop_words_consistency sklearn.feature_extraction.text.TfidfVectorizer._check_vocabulary sklearn.feature_extraction.text.TfidfVectorizer._count_vocab sklearn.feature_extraction.text.TfidfVectorizer._get_default_requests sklearn.feature_extraction.text.TfidfVectorizer._get_doc_link sklearn.feature_extraction.text.TfidfVectorizer._get_metadata_request sklearn.feature_extraction.text.TfidfVectorizer._get_param_names sklearn.feature_extraction.text.TfidfVectorizer._limit_features sklearn.feature_extraction.text.TfidfVectorizer._repr_html_inner sklearn.feature_extraction.text.TfidfVectorizer._repr_mimebundle_ sklearn.feature_extraction.text.TfidfVectorizer._sort_features sklearn.feature_extraction.text.TfidfVectorizer._validate_ngram_range sklearn.feature_extraction.text.TfidfVectorizer._validate_params sklearn.feature_extraction.text.TfidfVectorizer._validate_vocabulary sklearn.feature_extraction.text.TfidfVectorizer._warn_for_unused_params sklearn.feature_extraction.text.TfidfVectorizer._word_ngrams sklearn.feature_extraction.text.TfidfVectorizer.build_analyzer sklearn.feature_extraction.text.TfidfVectorizer.build_preprocessor sklearn.feature_extraction.text.TfidfVectorizer.build_tokenizer sklearn.feature_extraction.text.TfidfVectorizer.decode sklearn.feature_extraction.text.TfidfVectorizer.fit sklearn.feature_extraction.text.TfidfVectorizer.fit_transform sklearn.feature_extraction.text.TfidfVectorizer.get_feature_names_out sklearn.feature_extraction.text.TfidfVectorizer.get_metadata_routing sklearn.feature_extraction.text.TfidfVectorizer.get_params sklearn.feature_extraction.text.TfidfVectorizer.get_stop_words sklearn.feature_extraction.text.TfidfVectorizer.inverse_transform sklearn.feature_extraction.text.TfidfVectorizer.set_params sklearn.feature_extraction.text.TfidfVectorizer.transform 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.feature_selection.GenericUnivariateSelect.rst.txt b/dev/_sources/modules/generated/sklearn.feature_selection.GenericUnivariateSelect.rst.txt
index f02c88654e33c..20c6fcfb11536 100644
--- a/dev/_sources/modules/generated/sklearn.feature_selection.GenericUnivariateSelect.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.feature_selection.GenericUnivariateSelect.rst.txt
@@ -8,6 +8,6 @@ GenericUnivariateSelect
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.feature_selection.GenericUnivariateSelect sklearn.feature_selection.GenericUnivariateSelect.__delattr__ sklearn.feature_selection.GenericUnivariateSelect.__dir__ sklearn.feature_selection.GenericUnivariateSelect.__eq__ sklearn.feature_selection.GenericUnivariateSelect.__format__ sklearn.feature_selection.GenericUnivariateSelect.__ge__ sklearn.feature_selection.GenericUnivariateSelect.__getattribute__ sklearn.feature_selection.GenericUnivariateSelect.__getstate__ sklearn.feature_selection.GenericUnivariateSelect.__gt__ sklearn.feature_selection.GenericUnivariateSelect.__hash__ sklearn.feature_selection.GenericUnivariateSelect.__init__ sklearn.feature_selection.GenericUnivariateSelect.__init_subclass__ sklearn.feature_selection.GenericUnivariateSelect.__le__ sklearn.feature_selection.GenericUnivariateSelect.__lt__ sklearn.feature_selection.GenericUnivariateSelect.__ne__ sklearn.feature_selection.GenericUnivariateSelect.__new__ sklearn.feature_selection.GenericUnivariateSelect.__reduce__ sklearn.feature_selection.GenericUnivariateSelect.__reduce_ex__ sklearn.feature_selection.GenericUnivariateSelect.__repr__ sklearn.feature_selection.GenericUnivariateSelect.__setattr__ sklearn.feature_selection.GenericUnivariateSelect.__setstate__ sklearn.feature_selection.GenericUnivariateSelect.__sizeof__ sklearn.feature_selection.GenericUnivariateSelect.__sklearn_clone__ sklearn.feature_selection.GenericUnivariateSelect.__sklearn_tags__ sklearn.feature_selection.GenericUnivariateSelect.__str__ sklearn.feature_selection.GenericUnivariateSelect.__subclasshook__ sklearn.feature_selection.GenericUnivariateSelect._build_request_for_signature sklearn.feature_selection.GenericUnivariateSelect._check_feature_names sklearn.feature_selection.GenericUnivariateSelect._check_n_features sklearn.feature_selection.GenericUnivariateSelect._check_params sklearn.feature_selection.GenericUnivariateSelect._get_default_requests sklearn.feature_selection.GenericUnivariateSelect._get_doc_link sklearn.feature_selection.GenericUnivariateSelect._get_metadata_request sklearn.feature_selection.GenericUnivariateSelect._get_param_names sklearn.feature_selection.GenericUnivariateSelect._get_support_mask sklearn.feature_selection.GenericUnivariateSelect._get_tags sklearn.feature_selection.GenericUnivariateSelect._make_selector sklearn.feature_selection.GenericUnivariateSelect._more_tags sklearn.feature_selection.GenericUnivariateSelect._repr_html_inner sklearn.feature_selection.GenericUnivariateSelect._repr_mimebundle_ sklearn.feature_selection.GenericUnivariateSelect._transform sklearn.feature_selection.GenericUnivariateSelect._validate_data sklearn.feature_selection.GenericUnivariateSelect._validate_params sklearn.feature_selection.GenericUnivariateSelect.fit sklearn.feature_selection.GenericUnivariateSelect.fit_transform sklearn.feature_selection.GenericUnivariateSelect.get_feature_names_out sklearn.feature_selection.GenericUnivariateSelect.get_metadata_routing sklearn.feature_selection.GenericUnivariateSelect.get_params sklearn.feature_selection.GenericUnivariateSelect.get_support sklearn.feature_selection.GenericUnivariateSelect.inverse_transform sklearn.feature_selection.GenericUnivariateSelect.set_output sklearn.feature_selection.GenericUnivariateSelect.set_params sklearn.feature_selection.GenericUnivariateSelect.transform 
+.. minigallery:: sklearn.feature_selection.GenericUnivariateSelect sklearn.feature_selection.GenericUnivariateSelect.__delattr__ sklearn.feature_selection.GenericUnivariateSelect.__dir__ sklearn.feature_selection.GenericUnivariateSelect.__eq__ sklearn.feature_selection.GenericUnivariateSelect.__format__ sklearn.feature_selection.GenericUnivariateSelect.__ge__ sklearn.feature_selection.GenericUnivariateSelect.__getattribute__ sklearn.feature_selection.GenericUnivariateSelect.__getstate__ sklearn.feature_selection.GenericUnivariateSelect.__gt__ sklearn.feature_selection.GenericUnivariateSelect.__hash__ sklearn.feature_selection.GenericUnivariateSelect.__init__ sklearn.feature_selection.GenericUnivariateSelect.__init_subclass__ sklearn.feature_selection.GenericUnivariateSelect.__le__ sklearn.feature_selection.GenericUnivariateSelect.__lt__ sklearn.feature_selection.GenericUnivariateSelect.__ne__ sklearn.feature_selection.GenericUnivariateSelect.__new__ sklearn.feature_selection.GenericUnivariateSelect.__reduce__ sklearn.feature_selection.GenericUnivariateSelect.__reduce_ex__ sklearn.feature_selection.GenericUnivariateSelect.__repr__ sklearn.feature_selection.GenericUnivariateSelect.__setattr__ sklearn.feature_selection.GenericUnivariateSelect.__setstate__ sklearn.feature_selection.GenericUnivariateSelect.__sizeof__ sklearn.feature_selection.GenericUnivariateSelect.__sklearn_clone__ sklearn.feature_selection.GenericUnivariateSelect.__sklearn_tags__ sklearn.feature_selection.GenericUnivariateSelect.__str__ sklearn.feature_selection.GenericUnivariateSelect.__subclasshook__ sklearn.feature_selection.GenericUnivariateSelect._build_request_for_signature sklearn.feature_selection.GenericUnivariateSelect._check_params sklearn.feature_selection.GenericUnivariateSelect._get_default_requests sklearn.feature_selection.GenericUnivariateSelect._get_doc_link sklearn.feature_selection.GenericUnivariateSelect._get_metadata_request sklearn.feature_selection.GenericUnivariateSelect._get_param_names sklearn.feature_selection.GenericUnivariateSelect._get_support_mask sklearn.feature_selection.GenericUnivariateSelect._make_selector sklearn.feature_selection.GenericUnivariateSelect._repr_html_inner sklearn.feature_selection.GenericUnivariateSelect._repr_mimebundle_ sklearn.feature_selection.GenericUnivariateSelect._transform sklearn.feature_selection.GenericUnivariateSelect._validate_params sklearn.feature_selection.GenericUnivariateSelect.fit sklearn.feature_selection.GenericUnivariateSelect.fit_transform sklearn.feature_selection.GenericUnivariateSelect.get_feature_names_out sklearn.feature_selection.GenericUnivariateSelect.get_metadata_routing sklearn.feature_selection.GenericUnivariateSelect.get_params sklearn.feature_selection.GenericUnivariateSelect.get_support sklearn.feature_selection.GenericUnivariateSelect.inverse_transform sklearn.feature_selection.GenericUnivariateSelect.set_output sklearn.feature_selection.GenericUnivariateSelect.set_params sklearn.feature_selection.GenericUnivariateSelect.transform 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.feature_selection.RFE.rst.txt b/dev/_sources/modules/generated/sklearn.feature_selection.RFE.rst.txt
index 199de894e5f55..0907bc0da194e 100644
--- a/dev/_sources/modules/generated/sklearn.feature_selection.RFE.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.feature_selection.RFE.rst.txt
@@ -8,6 +8,6 @@ RFE
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.feature_selection.RFE sklearn.feature_selection.RFE.__delattr__ sklearn.feature_selection.RFE.__dir__ sklearn.feature_selection.RFE.__eq__ sklearn.feature_selection.RFE.__format__ sklearn.feature_selection.RFE.__ge__ sklearn.feature_selection.RFE.__getattribute__ sklearn.feature_selection.RFE.__getstate__ sklearn.feature_selection.RFE.__gt__ sklearn.feature_selection.RFE.__hash__ sklearn.feature_selection.RFE.__init__ sklearn.feature_selection.RFE.__init_subclass__ sklearn.feature_selection.RFE.__le__ sklearn.feature_selection.RFE.__lt__ sklearn.feature_selection.RFE.__ne__ sklearn.feature_selection.RFE.__new__ sklearn.feature_selection.RFE.__reduce__ sklearn.feature_selection.RFE.__reduce_ex__ sklearn.feature_selection.RFE.__repr__ sklearn.feature_selection.RFE.__setattr__ sklearn.feature_selection.RFE.__setstate__ sklearn.feature_selection.RFE.__sizeof__ sklearn.feature_selection.RFE.__sklearn_clone__ sklearn.feature_selection.RFE.__sklearn_tags__ sklearn.feature_selection.RFE.__str__ sklearn.feature_selection.RFE.__subclasshook__ sklearn.feature_selection.RFE._build_request_for_signature sklearn.feature_selection.RFE._check_feature_names sklearn.feature_selection.RFE._check_n_features sklearn.feature_selection.RFE._fit sklearn.feature_selection.RFE._get_default_requests sklearn.feature_selection.RFE._get_doc_link sklearn.feature_selection.RFE._get_metadata_request sklearn.feature_selection.RFE._get_param_names sklearn.feature_selection.RFE._get_support_mask sklearn.feature_selection.RFE._get_tags sklearn.feature_selection.RFE._more_tags sklearn.feature_selection.RFE._repr_html_inner sklearn.feature_selection.RFE._repr_mimebundle_ sklearn.feature_selection.RFE._transform sklearn.feature_selection.RFE._validate_data sklearn.feature_selection.RFE._validate_params sklearn.feature_selection.RFE.decision_function sklearn.feature_selection.RFE.fit sklearn.feature_selection.RFE.fit_transform sklearn.feature_selection.RFE.get_feature_names_out sklearn.feature_selection.RFE.get_metadata_routing sklearn.feature_selection.RFE.get_params sklearn.feature_selection.RFE.get_support sklearn.feature_selection.RFE.inverse_transform sklearn.feature_selection.RFE.predict sklearn.feature_selection.RFE.predict_log_proba sklearn.feature_selection.RFE.predict_proba sklearn.feature_selection.RFE.score sklearn.feature_selection.RFE.set_output sklearn.feature_selection.RFE.set_params sklearn.feature_selection.RFE.transform 
+.. minigallery:: sklearn.feature_selection.RFE sklearn.feature_selection.RFE.__delattr__ sklearn.feature_selection.RFE.__dir__ sklearn.feature_selection.RFE.__eq__ sklearn.feature_selection.RFE.__format__ sklearn.feature_selection.RFE.__ge__ sklearn.feature_selection.RFE.__getattribute__ sklearn.feature_selection.RFE.__getstate__ sklearn.feature_selection.RFE.__gt__ sklearn.feature_selection.RFE.__hash__ sklearn.feature_selection.RFE.__init__ sklearn.feature_selection.RFE.__init_subclass__ sklearn.feature_selection.RFE.__le__ sklearn.feature_selection.RFE.__lt__ sklearn.feature_selection.RFE.__ne__ sklearn.feature_selection.RFE.__new__ sklearn.feature_selection.RFE.__reduce__ sklearn.feature_selection.RFE.__reduce_ex__ sklearn.feature_selection.RFE.__repr__ sklearn.feature_selection.RFE.__setattr__ sklearn.feature_selection.RFE.__setstate__ sklearn.feature_selection.RFE.__sizeof__ sklearn.feature_selection.RFE.__sklearn_clone__ sklearn.feature_selection.RFE.__sklearn_tags__ sklearn.feature_selection.RFE.__str__ sklearn.feature_selection.RFE.__subclasshook__ sklearn.feature_selection.RFE._build_request_for_signature sklearn.feature_selection.RFE._fit sklearn.feature_selection.RFE._get_default_requests sklearn.feature_selection.RFE._get_doc_link sklearn.feature_selection.RFE._get_metadata_request sklearn.feature_selection.RFE._get_param_names sklearn.feature_selection.RFE._get_support_mask sklearn.feature_selection.RFE._repr_html_inner sklearn.feature_selection.RFE._repr_mimebundle_ sklearn.feature_selection.RFE._transform sklearn.feature_selection.RFE._validate_params sklearn.feature_selection.RFE.decision_function sklearn.feature_selection.RFE.fit sklearn.feature_selection.RFE.fit_transform sklearn.feature_selection.RFE.get_feature_names_out sklearn.feature_selection.RFE.get_metadata_routing sklearn.feature_selection.RFE.get_params sklearn.feature_selection.RFE.get_support sklearn.feature_selection.RFE.inverse_transform sklearn.feature_selection.RFE.predict sklearn.feature_selection.RFE.predict_log_proba sklearn.feature_selection.RFE.predict_proba sklearn.feature_selection.RFE.score sklearn.feature_selection.RFE.set_output sklearn.feature_selection.RFE.set_params sklearn.feature_selection.RFE.transform 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.feature_selection.RFECV.rst.txt b/dev/_sources/modules/generated/sklearn.feature_selection.RFECV.rst.txt
index fde6560d690e6..65aca79bf7233 100644
--- a/dev/_sources/modules/generated/sklearn.feature_selection.RFECV.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.feature_selection.RFECV.rst.txt
@@ -8,6 +8,6 @@ RFECV
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.feature_selection.RFECV sklearn.feature_selection.RFECV.__delattr__ sklearn.feature_selection.RFECV.__dir__ sklearn.feature_selection.RFECV.__eq__ sklearn.feature_selection.RFECV.__format__ sklearn.feature_selection.RFECV.__ge__ sklearn.feature_selection.RFECV.__getattribute__ sklearn.feature_selection.RFECV.__getstate__ sklearn.feature_selection.RFECV.__gt__ sklearn.feature_selection.RFECV.__hash__ sklearn.feature_selection.RFECV.__init__ sklearn.feature_selection.RFECV.__init_subclass__ sklearn.feature_selection.RFECV.__le__ sklearn.feature_selection.RFECV.__lt__ sklearn.feature_selection.RFECV.__ne__ sklearn.feature_selection.RFECV.__new__ sklearn.feature_selection.RFECV.__reduce__ sklearn.feature_selection.RFECV.__reduce_ex__ sklearn.feature_selection.RFECV.__repr__ sklearn.feature_selection.RFECV.__setattr__ sklearn.feature_selection.RFECV.__setstate__ sklearn.feature_selection.RFECV.__sizeof__ sklearn.feature_selection.RFECV.__sklearn_clone__ sklearn.feature_selection.RFECV.__sklearn_tags__ sklearn.feature_selection.RFECV.__str__ sklearn.feature_selection.RFECV.__subclasshook__ sklearn.feature_selection.RFECV._build_request_for_signature sklearn.feature_selection.RFECV._check_feature_names sklearn.feature_selection.RFECV._check_n_features sklearn.feature_selection.RFECV._fit sklearn.feature_selection.RFECV._get_default_requests sklearn.feature_selection.RFECV._get_doc_link sklearn.feature_selection.RFECV._get_metadata_request sklearn.feature_selection.RFECV._get_param_names sklearn.feature_selection.RFECV._get_scorer sklearn.feature_selection.RFECV._get_support_mask sklearn.feature_selection.RFECV._get_tags sklearn.feature_selection.RFECV._more_tags sklearn.feature_selection.RFECV._repr_html_inner sklearn.feature_selection.RFECV._repr_mimebundle_ sklearn.feature_selection.RFECV._transform sklearn.feature_selection.RFECV._validate_data sklearn.feature_selection.RFECV._validate_params sklearn.feature_selection.RFECV.decision_function sklearn.feature_selection.RFECV.fit sklearn.feature_selection.RFECV.fit_transform sklearn.feature_selection.RFECV.get_feature_names_out sklearn.feature_selection.RFECV.get_metadata_routing sklearn.feature_selection.RFECV.get_params sklearn.feature_selection.RFECV.get_support sklearn.feature_selection.RFECV.inverse_transform sklearn.feature_selection.RFECV.predict sklearn.feature_selection.RFECV.predict_log_proba sklearn.feature_selection.RFECV.predict_proba sklearn.feature_selection.RFECV.score sklearn.feature_selection.RFECV.set_output sklearn.feature_selection.RFECV.set_params sklearn.feature_selection.RFECV.transform 
+.. minigallery:: sklearn.feature_selection.RFECV sklearn.feature_selection.RFECV.__delattr__ sklearn.feature_selection.RFECV.__dir__ sklearn.feature_selection.RFECV.__eq__ sklearn.feature_selection.RFECV.__format__ sklearn.feature_selection.RFECV.__ge__ sklearn.feature_selection.RFECV.__getattribute__ sklearn.feature_selection.RFECV.__getstate__ sklearn.feature_selection.RFECV.__gt__ sklearn.feature_selection.RFECV.__hash__ sklearn.feature_selection.RFECV.__init__ sklearn.feature_selection.RFECV.__init_subclass__ sklearn.feature_selection.RFECV.__le__ sklearn.feature_selection.RFECV.__lt__ sklearn.feature_selection.RFECV.__ne__ sklearn.feature_selection.RFECV.__new__ sklearn.feature_selection.RFECV.__reduce__ sklearn.feature_selection.RFECV.__reduce_ex__ sklearn.feature_selection.RFECV.__repr__ sklearn.feature_selection.RFECV.__setattr__ sklearn.feature_selection.RFECV.__setstate__ sklearn.feature_selection.RFECV.__sizeof__ sklearn.feature_selection.RFECV.__sklearn_clone__ sklearn.feature_selection.RFECV.__sklearn_tags__ sklearn.feature_selection.RFECV.__str__ sklearn.feature_selection.RFECV.__subclasshook__ sklearn.feature_selection.RFECV._build_request_for_signature sklearn.feature_selection.RFECV._fit sklearn.feature_selection.RFECV._get_default_requests sklearn.feature_selection.RFECV._get_doc_link sklearn.feature_selection.RFECV._get_metadata_request sklearn.feature_selection.RFECV._get_param_names sklearn.feature_selection.RFECV._get_scorer sklearn.feature_selection.RFECV._get_support_mask sklearn.feature_selection.RFECV._repr_html_inner sklearn.feature_selection.RFECV._repr_mimebundle_ sklearn.feature_selection.RFECV._transform sklearn.feature_selection.RFECV._validate_params sklearn.feature_selection.RFECV.decision_function sklearn.feature_selection.RFECV.fit sklearn.feature_selection.RFECV.fit_transform sklearn.feature_selection.RFECV.get_feature_names_out sklearn.feature_selection.RFECV.get_metadata_routing sklearn.feature_selection.RFECV.get_params sklearn.feature_selection.RFECV.get_support sklearn.feature_selection.RFECV.inverse_transform sklearn.feature_selection.RFECV.predict sklearn.feature_selection.RFECV.predict_log_proba sklearn.feature_selection.RFECV.predict_proba sklearn.feature_selection.RFECV.score sklearn.feature_selection.RFECV.set_output sklearn.feature_selection.RFECV.set_params sklearn.feature_selection.RFECV.transform 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.feature_selection.SelectFdr.rst.txt b/dev/_sources/modules/generated/sklearn.feature_selection.SelectFdr.rst.txt
index 9504e1574e9f9..8c07e0fb134a4 100644
--- a/dev/_sources/modules/generated/sklearn.feature_selection.SelectFdr.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.feature_selection.SelectFdr.rst.txt
@@ -8,6 +8,6 @@ SelectFdr
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.feature_selection.SelectFdr sklearn.feature_selection.SelectFdr.__delattr__ sklearn.feature_selection.SelectFdr.__dir__ sklearn.feature_selection.SelectFdr.__eq__ sklearn.feature_selection.SelectFdr.__format__ sklearn.feature_selection.SelectFdr.__ge__ sklearn.feature_selection.SelectFdr.__getattribute__ sklearn.feature_selection.SelectFdr.__getstate__ sklearn.feature_selection.SelectFdr.__gt__ sklearn.feature_selection.SelectFdr.__hash__ sklearn.feature_selection.SelectFdr.__init__ sklearn.feature_selection.SelectFdr.__init_subclass__ sklearn.feature_selection.SelectFdr.__le__ sklearn.feature_selection.SelectFdr.__lt__ sklearn.feature_selection.SelectFdr.__ne__ sklearn.feature_selection.SelectFdr.__new__ sklearn.feature_selection.SelectFdr.__reduce__ sklearn.feature_selection.SelectFdr.__reduce_ex__ sklearn.feature_selection.SelectFdr.__repr__ sklearn.feature_selection.SelectFdr.__setattr__ sklearn.feature_selection.SelectFdr.__setstate__ sklearn.feature_selection.SelectFdr.__sizeof__ sklearn.feature_selection.SelectFdr.__sklearn_clone__ sklearn.feature_selection.SelectFdr.__sklearn_tags__ sklearn.feature_selection.SelectFdr.__str__ sklearn.feature_selection.SelectFdr.__subclasshook__ sklearn.feature_selection.SelectFdr._build_request_for_signature sklearn.feature_selection.SelectFdr._check_feature_names sklearn.feature_selection.SelectFdr._check_n_features sklearn.feature_selection.SelectFdr._check_params sklearn.feature_selection.SelectFdr._get_default_requests sklearn.feature_selection.SelectFdr._get_doc_link sklearn.feature_selection.SelectFdr._get_metadata_request sklearn.feature_selection.SelectFdr._get_param_names sklearn.feature_selection.SelectFdr._get_support_mask sklearn.feature_selection.SelectFdr._get_tags sklearn.feature_selection.SelectFdr._more_tags sklearn.feature_selection.SelectFdr._repr_html_inner sklearn.feature_selection.SelectFdr._repr_mimebundle_ sklearn.feature_selection.SelectFdr._transform sklearn.feature_selection.SelectFdr._validate_data sklearn.feature_selection.SelectFdr._validate_params sklearn.feature_selection.SelectFdr.fit sklearn.feature_selection.SelectFdr.fit_transform sklearn.feature_selection.SelectFdr.get_feature_names_out sklearn.feature_selection.SelectFdr.get_metadata_routing sklearn.feature_selection.SelectFdr.get_params sklearn.feature_selection.SelectFdr.get_support sklearn.feature_selection.SelectFdr.inverse_transform sklearn.feature_selection.SelectFdr.set_output sklearn.feature_selection.SelectFdr.set_params sklearn.feature_selection.SelectFdr.transform 
+.. minigallery:: sklearn.feature_selection.SelectFdr sklearn.feature_selection.SelectFdr.__delattr__ sklearn.feature_selection.SelectFdr.__dir__ sklearn.feature_selection.SelectFdr.__eq__ sklearn.feature_selection.SelectFdr.__format__ sklearn.feature_selection.SelectFdr.__ge__ sklearn.feature_selection.SelectFdr.__getattribute__ sklearn.feature_selection.SelectFdr.__getstate__ sklearn.feature_selection.SelectFdr.__gt__ sklearn.feature_selection.SelectFdr.__hash__ sklearn.feature_selection.SelectFdr.__init__ sklearn.feature_selection.SelectFdr.__init_subclass__ sklearn.feature_selection.SelectFdr.__le__ sklearn.feature_selection.SelectFdr.__lt__ sklearn.feature_selection.SelectFdr.__ne__ sklearn.feature_selection.SelectFdr.__new__ sklearn.feature_selection.SelectFdr.__reduce__ sklearn.feature_selection.SelectFdr.__reduce_ex__ sklearn.feature_selection.SelectFdr.__repr__ sklearn.feature_selection.SelectFdr.__setattr__ sklearn.feature_selection.SelectFdr.__setstate__ sklearn.feature_selection.SelectFdr.__sizeof__ sklearn.feature_selection.SelectFdr.__sklearn_clone__ sklearn.feature_selection.SelectFdr.__sklearn_tags__ sklearn.feature_selection.SelectFdr.__str__ sklearn.feature_selection.SelectFdr.__subclasshook__ sklearn.feature_selection.SelectFdr._build_request_for_signature sklearn.feature_selection.SelectFdr._check_params sklearn.feature_selection.SelectFdr._get_default_requests sklearn.feature_selection.SelectFdr._get_doc_link sklearn.feature_selection.SelectFdr._get_metadata_request sklearn.feature_selection.SelectFdr._get_param_names sklearn.feature_selection.SelectFdr._get_support_mask sklearn.feature_selection.SelectFdr._repr_html_inner sklearn.feature_selection.SelectFdr._repr_mimebundle_ sklearn.feature_selection.SelectFdr._transform sklearn.feature_selection.SelectFdr._validate_params sklearn.feature_selection.SelectFdr.fit sklearn.feature_selection.SelectFdr.fit_transform sklearn.feature_selection.SelectFdr.get_feature_names_out sklearn.feature_selection.SelectFdr.get_metadata_routing sklearn.feature_selection.SelectFdr.get_params sklearn.feature_selection.SelectFdr.get_support sklearn.feature_selection.SelectFdr.inverse_transform sklearn.feature_selection.SelectFdr.set_output sklearn.feature_selection.SelectFdr.set_params sklearn.feature_selection.SelectFdr.transform 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.feature_selection.SelectFpr.rst.txt b/dev/_sources/modules/generated/sklearn.feature_selection.SelectFpr.rst.txt
index b7d56c5ec97e7..9b8e2b3676598 100644
--- a/dev/_sources/modules/generated/sklearn.feature_selection.SelectFpr.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.feature_selection.SelectFpr.rst.txt
@@ -8,6 +8,6 @@ SelectFpr
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.feature_selection.SelectFpr sklearn.feature_selection.SelectFpr.__delattr__ sklearn.feature_selection.SelectFpr.__dir__ sklearn.feature_selection.SelectFpr.__eq__ sklearn.feature_selection.SelectFpr.__format__ sklearn.feature_selection.SelectFpr.__ge__ sklearn.feature_selection.SelectFpr.__getattribute__ sklearn.feature_selection.SelectFpr.__getstate__ sklearn.feature_selection.SelectFpr.__gt__ sklearn.feature_selection.SelectFpr.__hash__ sklearn.feature_selection.SelectFpr.__init__ sklearn.feature_selection.SelectFpr.__init_subclass__ sklearn.feature_selection.SelectFpr.__le__ sklearn.feature_selection.SelectFpr.__lt__ sklearn.feature_selection.SelectFpr.__ne__ sklearn.feature_selection.SelectFpr.__new__ sklearn.feature_selection.SelectFpr.__reduce__ sklearn.feature_selection.SelectFpr.__reduce_ex__ sklearn.feature_selection.SelectFpr.__repr__ sklearn.feature_selection.SelectFpr.__setattr__ sklearn.feature_selection.SelectFpr.__setstate__ sklearn.feature_selection.SelectFpr.__sizeof__ sklearn.feature_selection.SelectFpr.__sklearn_clone__ sklearn.feature_selection.SelectFpr.__sklearn_tags__ sklearn.feature_selection.SelectFpr.__str__ sklearn.feature_selection.SelectFpr.__subclasshook__ sklearn.feature_selection.SelectFpr._build_request_for_signature sklearn.feature_selection.SelectFpr._check_feature_names sklearn.feature_selection.SelectFpr._check_n_features sklearn.feature_selection.SelectFpr._check_params sklearn.feature_selection.SelectFpr._get_default_requests sklearn.feature_selection.SelectFpr._get_doc_link sklearn.feature_selection.SelectFpr._get_metadata_request sklearn.feature_selection.SelectFpr._get_param_names sklearn.feature_selection.SelectFpr._get_support_mask sklearn.feature_selection.SelectFpr._get_tags sklearn.feature_selection.SelectFpr._more_tags sklearn.feature_selection.SelectFpr._repr_html_inner sklearn.feature_selection.SelectFpr._repr_mimebundle_ sklearn.feature_selection.SelectFpr._transform sklearn.feature_selection.SelectFpr._validate_data sklearn.feature_selection.SelectFpr._validate_params sklearn.feature_selection.SelectFpr.fit sklearn.feature_selection.SelectFpr.fit_transform sklearn.feature_selection.SelectFpr.get_feature_names_out sklearn.feature_selection.SelectFpr.get_metadata_routing sklearn.feature_selection.SelectFpr.get_params sklearn.feature_selection.SelectFpr.get_support sklearn.feature_selection.SelectFpr.inverse_transform sklearn.feature_selection.SelectFpr.set_output sklearn.feature_selection.SelectFpr.set_params sklearn.feature_selection.SelectFpr.transform 
+.. minigallery:: sklearn.feature_selection.SelectFpr sklearn.feature_selection.SelectFpr.__delattr__ sklearn.feature_selection.SelectFpr.__dir__ sklearn.feature_selection.SelectFpr.__eq__ sklearn.feature_selection.SelectFpr.__format__ sklearn.feature_selection.SelectFpr.__ge__ sklearn.feature_selection.SelectFpr.__getattribute__ sklearn.feature_selection.SelectFpr.__getstate__ sklearn.feature_selection.SelectFpr.__gt__ sklearn.feature_selection.SelectFpr.__hash__ sklearn.feature_selection.SelectFpr.__init__ sklearn.feature_selection.SelectFpr.__init_subclass__ sklearn.feature_selection.SelectFpr.__le__ sklearn.feature_selection.SelectFpr.__lt__ sklearn.feature_selection.SelectFpr.__ne__ sklearn.feature_selection.SelectFpr.__new__ sklearn.feature_selection.SelectFpr.__reduce__ sklearn.feature_selection.SelectFpr.__reduce_ex__ sklearn.feature_selection.SelectFpr.__repr__ sklearn.feature_selection.SelectFpr.__setattr__ sklearn.feature_selection.SelectFpr.__setstate__ sklearn.feature_selection.SelectFpr.__sizeof__ sklearn.feature_selection.SelectFpr.__sklearn_clone__ sklearn.feature_selection.SelectFpr.__sklearn_tags__ sklearn.feature_selection.SelectFpr.__str__ sklearn.feature_selection.SelectFpr.__subclasshook__ sklearn.feature_selection.SelectFpr._build_request_for_signature sklearn.feature_selection.SelectFpr._check_params sklearn.feature_selection.SelectFpr._get_default_requests sklearn.feature_selection.SelectFpr._get_doc_link sklearn.feature_selection.SelectFpr._get_metadata_request sklearn.feature_selection.SelectFpr._get_param_names sklearn.feature_selection.SelectFpr._get_support_mask sklearn.feature_selection.SelectFpr._repr_html_inner sklearn.feature_selection.SelectFpr._repr_mimebundle_ sklearn.feature_selection.SelectFpr._transform sklearn.feature_selection.SelectFpr._validate_params sklearn.feature_selection.SelectFpr.fit sklearn.feature_selection.SelectFpr.fit_transform sklearn.feature_selection.SelectFpr.get_feature_names_out sklearn.feature_selection.SelectFpr.get_metadata_routing sklearn.feature_selection.SelectFpr.get_params sklearn.feature_selection.SelectFpr.get_support sklearn.feature_selection.SelectFpr.inverse_transform sklearn.feature_selection.SelectFpr.set_output sklearn.feature_selection.SelectFpr.set_params sklearn.feature_selection.SelectFpr.transform 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.feature_selection.SelectFromModel.rst.txt b/dev/_sources/modules/generated/sklearn.feature_selection.SelectFromModel.rst.txt
index d469cb65805f2..3156bf4072d53 100644
--- a/dev/_sources/modules/generated/sklearn.feature_selection.SelectFromModel.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.feature_selection.SelectFromModel.rst.txt
@@ -8,6 +8,6 @@ SelectFromModel
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.feature_selection.SelectFromModel sklearn.feature_selection.SelectFromModel.__delattr__ sklearn.feature_selection.SelectFromModel.__dir__ sklearn.feature_selection.SelectFromModel.__eq__ sklearn.feature_selection.SelectFromModel.__format__ sklearn.feature_selection.SelectFromModel.__ge__ sklearn.feature_selection.SelectFromModel.__getattribute__ sklearn.feature_selection.SelectFromModel.__getstate__ sklearn.feature_selection.SelectFromModel.__gt__ sklearn.feature_selection.SelectFromModel.__hash__ sklearn.feature_selection.SelectFromModel.__init__ sklearn.feature_selection.SelectFromModel.__init_subclass__ sklearn.feature_selection.SelectFromModel.__le__ sklearn.feature_selection.SelectFromModel.__lt__ sklearn.feature_selection.SelectFromModel.__ne__ sklearn.feature_selection.SelectFromModel.__new__ sklearn.feature_selection.SelectFromModel.__reduce__ sklearn.feature_selection.SelectFromModel.__reduce_ex__ sklearn.feature_selection.SelectFromModel.__repr__ sklearn.feature_selection.SelectFromModel.__setattr__ sklearn.feature_selection.SelectFromModel.__setstate__ sklearn.feature_selection.SelectFromModel.__sizeof__ sklearn.feature_selection.SelectFromModel.__sklearn_clone__ sklearn.feature_selection.SelectFromModel.__sklearn_tags__ sklearn.feature_selection.SelectFromModel.__str__ sklearn.feature_selection.SelectFromModel.__subclasshook__ sklearn.feature_selection.SelectFromModel._build_request_for_signature sklearn.feature_selection.SelectFromModel._check_feature_names sklearn.feature_selection.SelectFromModel._check_max_features sklearn.feature_selection.SelectFromModel._check_n_features sklearn.feature_selection.SelectFromModel._get_default_requests sklearn.feature_selection.SelectFromModel._get_doc_link sklearn.feature_selection.SelectFromModel._get_metadata_request sklearn.feature_selection.SelectFromModel._get_param_names sklearn.feature_selection.SelectFromModel._get_support_mask sklearn.feature_selection.SelectFromModel._get_tags sklearn.feature_selection.SelectFromModel._more_tags sklearn.feature_selection.SelectFromModel._repr_html_inner sklearn.feature_selection.SelectFromModel._repr_mimebundle_ sklearn.feature_selection.SelectFromModel._transform sklearn.feature_selection.SelectFromModel._validate_data sklearn.feature_selection.SelectFromModel._validate_params sklearn.feature_selection.SelectFromModel.fit sklearn.feature_selection.SelectFromModel.fit_transform sklearn.feature_selection.SelectFromModel.get_feature_names_out sklearn.feature_selection.SelectFromModel.get_metadata_routing sklearn.feature_selection.SelectFromModel.get_params sklearn.feature_selection.SelectFromModel.get_support sklearn.feature_selection.SelectFromModel.inverse_transform sklearn.feature_selection.SelectFromModel.partial_fit sklearn.feature_selection.SelectFromModel.set_output sklearn.feature_selection.SelectFromModel.set_params sklearn.feature_selection.SelectFromModel.transform 
+.. minigallery:: sklearn.feature_selection.SelectFromModel sklearn.feature_selection.SelectFromModel.__delattr__ sklearn.feature_selection.SelectFromModel.__dir__ sklearn.feature_selection.SelectFromModel.__eq__ sklearn.feature_selection.SelectFromModel.__format__ sklearn.feature_selection.SelectFromModel.__ge__ sklearn.feature_selection.SelectFromModel.__getattribute__ sklearn.feature_selection.SelectFromModel.__getstate__ sklearn.feature_selection.SelectFromModel.__gt__ sklearn.feature_selection.SelectFromModel.__hash__ sklearn.feature_selection.SelectFromModel.__init__ sklearn.feature_selection.SelectFromModel.__init_subclass__ sklearn.feature_selection.SelectFromModel.__le__ sklearn.feature_selection.SelectFromModel.__lt__ sklearn.feature_selection.SelectFromModel.__ne__ sklearn.feature_selection.SelectFromModel.__new__ sklearn.feature_selection.SelectFromModel.__reduce__ sklearn.feature_selection.SelectFromModel.__reduce_ex__ sklearn.feature_selection.SelectFromModel.__repr__ sklearn.feature_selection.SelectFromModel.__setattr__ sklearn.feature_selection.SelectFromModel.__setstate__ sklearn.feature_selection.SelectFromModel.__sizeof__ sklearn.feature_selection.SelectFromModel.__sklearn_clone__ sklearn.feature_selection.SelectFromModel.__sklearn_tags__ sklearn.feature_selection.SelectFromModel.__str__ sklearn.feature_selection.SelectFromModel.__subclasshook__ sklearn.feature_selection.SelectFromModel._build_request_for_signature sklearn.feature_selection.SelectFromModel._check_max_features sklearn.feature_selection.SelectFromModel._get_default_requests sklearn.feature_selection.SelectFromModel._get_doc_link sklearn.feature_selection.SelectFromModel._get_metadata_request sklearn.feature_selection.SelectFromModel._get_param_names sklearn.feature_selection.SelectFromModel._get_support_mask sklearn.feature_selection.SelectFromModel._repr_html_inner sklearn.feature_selection.SelectFromModel._repr_mimebundle_ sklearn.feature_selection.SelectFromModel._transform sklearn.feature_selection.SelectFromModel._validate_params sklearn.feature_selection.SelectFromModel.fit sklearn.feature_selection.SelectFromModel.fit_transform sklearn.feature_selection.SelectFromModel.get_feature_names_out sklearn.feature_selection.SelectFromModel.get_metadata_routing sklearn.feature_selection.SelectFromModel.get_params sklearn.feature_selection.SelectFromModel.get_support sklearn.feature_selection.SelectFromModel.inverse_transform sklearn.feature_selection.SelectFromModel.partial_fit sklearn.feature_selection.SelectFromModel.set_output sklearn.feature_selection.SelectFromModel.set_params sklearn.feature_selection.SelectFromModel.transform 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.feature_selection.SelectFwe.rst.txt b/dev/_sources/modules/generated/sklearn.feature_selection.SelectFwe.rst.txt
index 5a2b5f0daa71a..cba48634c6436 100644
--- a/dev/_sources/modules/generated/sklearn.feature_selection.SelectFwe.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.feature_selection.SelectFwe.rst.txt
@@ -8,6 +8,6 @@ SelectFwe
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.feature_selection.SelectFwe sklearn.feature_selection.SelectFwe.__delattr__ sklearn.feature_selection.SelectFwe.__dir__ sklearn.feature_selection.SelectFwe.__eq__ sklearn.feature_selection.SelectFwe.__format__ sklearn.feature_selection.SelectFwe.__ge__ sklearn.feature_selection.SelectFwe.__getattribute__ sklearn.feature_selection.SelectFwe.__getstate__ sklearn.feature_selection.SelectFwe.__gt__ sklearn.feature_selection.SelectFwe.__hash__ sklearn.feature_selection.SelectFwe.__init__ sklearn.feature_selection.SelectFwe.__init_subclass__ sklearn.feature_selection.SelectFwe.__le__ sklearn.feature_selection.SelectFwe.__lt__ sklearn.feature_selection.SelectFwe.__ne__ sklearn.feature_selection.SelectFwe.__new__ sklearn.feature_selection.SelectFwe.__reduce__ sklearn.feature_selection.SelectFwe.__reduce_ex__ sklearn.feature_selection.SelectFwe.__repr__ sklearn.feature_selection.SelectFwe.__setattr__ sklearn.feature_selection.SelectFwe.__setstate__ sklearn.feature_selection.SelectFwe.__sizeof__ sklearn.feature_selection.SelectFwe.__sklearn_clone__ sklearn.feature_selection.SelectFwe.__sklearn_tags__ sklearn.feature_selection.SelectFwe.__str__ sklearn.feature_selection.SelectFwe.__subclasshook__ sklearn.feature_selection.SelectFwe._build_request_for_signature sklearn.feature_selection.SelectFwe._check_feature_names sklearn.feature_selection.SelectFwe._check_n_features sklearn.feature_selection.SelectFwe._check_params sklearn.feature_selection.SelectFwe._get_default_requests sklearn.feature_selection.SelectFwe._get_doc_link sklearn.feature_selection.SelectFwe._get_metadata_request sklearn.feature_selection.SelectFwe._get_param_names sklearn.feature_selection.SelectFwe._get_support_mask sklearn.feature_selection.SelectFwe._get_tags sklearn.feature_selection.SelectFwe._more_tags sklearn.feature_selection.SelectFwe._repr_html_inner sklearn.feature_selection.SelectFwe._repr_mimebundle_ sklearn.feature_selection.SelectFwe._transform sklearn.feature_selection.SelectFwe._validate_data sklearn.feature_selection.SelectFwe._validate_params sklearn.feature_selection.SelectFwe.fit sklearn.feature_selection.SelectFwe.fit_transform sklearn.feature_selection.SelectFwe.get_feature_names_out sklearn.feature_selection.SelectFwe.get_metadata_routing sklearn.feature_selection.SelectFwe.get_params sklearn.feature_selection.SelectFwe.get_support sklearn.feature_selection.SelectFwe.inverse_transform sklearn.feature_selection.SelectFwe.set_output sklearn.feature_selection.SelectFwe.set_params sklearn.feature_selection.SelectFwe.transform 
+.. minigallery:: sklearn.feature_selection.SelectFwe sklearn.feature_selection.SelectFwe.__delattr__ sklearn.feature_selection.SelectFwe.__dir__ sklearn.feature_selection.SelectFwe.__eq__ sklearn.feature_selection.SelectFwe.__format__ sklearn.feature_selection.SelectFwe.__ge__ sklearn.feature_selection.SelectFwe.__getattribute__ sklearn.feature_selection.SelectFwe.__getstate__ sklearn.feature_selection.SelectFwe.__gt__ sklearn.feature_selection.SelectFwe.__hash__ sklearn.feature_selection.SelectFwe.__init__ sklearn.feature_selection.SelectFwe.__init_subclass__ sklearn.feature_selection.SelectFwe.__le__ sklearn.feature_selection.SelectFwe.__lt__ sklearn.feature_selection.SelectFwe.__ne__ sklearn.feature_selection.SelectFwe.__new__ sklearn.feature_selection.SelectFwe.__reduce__ sklearn.feature_selection.SelectFwe.__reduce_ex__ sklearn.feature_selection.SelectFwe.__repr__ sklearn.feature_selection.SelectFwe.__setattr__ sklearn.feature_selection.SelectFwe.__setstate__ sklearn.feature_selection.SelectFwe.__sizeof__ sklearn.feature_selection.SelectFwe.__sklearn_clone__ sklearn.feature_selection.SelectFwe.__sklearn_tags__ sklearn.feature_selection.SelectFwe.__str__ sklearn.feature_selection.SelectFwe.__subclasshook__ sklearn.feature_selection.SelectFwe._build_request_for_signature sklearn.feature_selection.SelectFwe._check_params sklearn.feature_selection.SelectFwe._get_default_requests sklearn.feature_selection.SelectFwe._get_doc_link sklearn.feature_selection.SelectFwe._get_metadata_request sklearn.feature_selection.SelectFwe._get_param_names sklearn.feature_selection.SelectFwe._get_support_mask sklearn.feature_selection.SelectFwe._repr_html_inner sklearn.feature_selection.SelectFwe._repr_mimebundle_ sklearn.feature_selection.SelectFwe._transform sklearn.feature_selection.SelectFwe._validate_params sklearn.feature_selection.SelectFwe.fit sklearn.feature_selection.SelectFwe.fit_transform sklearn.feature_selection.SelectFwe.get_feature_names_out sklearn.feature_selection.SelectFwe.get_metadata_routing sklearn.feature_selection.SelectFwe.get_params sklearn.feature_selection.SelectFwe.get_support sklearn.feature_selection.SelectFwe.inverse_transform sklearn.feature_selection.SelectFwe.set_output sklearn.feature_selection.SelectFwe.set_params sklearn.feature_selection.SelectFwe.transform 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.feature_selection.SelectKBest.rst.txt b/dev/_sources/modules/generated/sklearn.feature_selection.SelectKBest.rst.txt
index 1f43f7db0d1a2..9749d47ccdf31 100644
--- a/dev/_sources/modules/generated/sklearn.feature_selection.SelectKBest.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.feature_selection.SelectKBest.rst.txt
@@ -8,6 +8,6 @@ SelectKBest
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.feature_selection.SelectKBest sklearn.feature_selection.SelectKBest.__delattr__ sklearn.feature_selection.SelectKBest.__dir__ sklearn.feature_selection.SelectKBest.__eq__ sklearn.feature_selection.SelectKBest.__format__ sklearn.feature_selection.SelectKBest.__ge__ sklearn.feature_selection.SelectKBest.__getattribute__ sklearn.feature_selection.SelectKBest.__getstate__ sklearn.feature_selection.SelectKBest.__gt__ sklearn.feature_selection.SelectKBest.__hash__ sklearn.feature_selection.SelectKBest.__init__ sklearn.feature_selection.SelectKBest.__init_subclass__ sklearn.feature_selection.SelectKBest.__le__ sklearn.feature_selection.SelectKBest.__lt__ sklearn.feature_selection.SelectKBest.__ne__ sklearn.feature_selection.SelectKBest.__new__ sklearn.feature_selection.SelectKBest.__reduce__ sklearn.feature_selection.SelectKBest.__reduce_ex__ sklearn.feature_selection.SelectKBest.__repr__ sklearn.feature_selection.SelectKBest.__setattr__ sklearn.feature_selection.SelectKBest.__setstate__ sklearn.feature_selection.SelectKBest.__sizeof__ sklearn.feature_selection.SelectKBest.__sklearn_clone__ sklearn.feature_selection.SelectKBest.__sklearn_tags__ sklearn.feature_selection.SelectKBest.__str__ sklearn.feature_selection.SelectKBest.__subclasshook__ sklearn.feature_selection.SelectKBest._build_request_for_signature sklearn.feature_selection.SelectKBest._check_feature_names sklearn.feature_selection.SelectKBest._check_n_features sklearn.feature_selection.SelectKBest._check_params sklearn.feature_selection.SelectKBest._get_default_requests sklearn.feature_selection.SelectKBest._get_doc_link sklearn.feature_selection.SelectKBest._get_metadata_request sklearn.feature_selection.SelectKBest._get_param_names sklearn.feature_selection.SelectKBest._get_support_mask sklearn.feature_selection.SelectKBest._get_tags sklearn.feature_selection.SelectKBest._more_tags sklearn.feature_selection.SelectKBest._repr_html_inner sklearn.feature_selection.SelectKBest._repr_mimebundle_ sklearn.feature_selection.SelectKBest._transform sklearn.feature_selection.SelectKBest._validate_data sklearn.feature_selection.SelectKBest._validate_params sklearn.feature_selection.SelectKBest.fit sklearn.feature_selection.SelectKBest.fit_transform sklearn.feature_selection.SelectKBest.get_feature_names_out sklearn.feature_selection.SelectKBest.get_metadata_routing sklearn.feature_selection.SelectKBest.get_params sklearn.feature_selection.SelectKBest.get_support sklearn.feature_selection.SelectKBest.inverse_transform sklearn.feature_selection.SelectKBest.set_output sklearn.feature_selection.SelectKBest.set_params sklearn.feature_selection.SelectKBest.transform 
+.. minigallery:: sklearn.feature_selection.SelectKBest sklearn.feature_selection.SelectKBest.__delattr__ sklearn.feature_selection.SelectKBest.__dir__ sklearn.feature_selection.SelectKBest.__eq__ sklearn.feature_selection.SelectKBest.__format__ sklearn.feature_selection.SelectKBest.__ge__ sklearn.feature_selection.SelectKBest.__getattribute__ sklearn.feature_selection.SelectKBest.__getstate__ sklearn.feature_selection.SelectKBest.__gt__ sklearn.feature_selection.SelectKBest.__hash__ sklearn.feature_selection.SelectKBest.__init__ sklearn.feature_selection.SelectKBest.__init_subclass__ sklearn.feature_selection.SelectKBest.__le__ sklearn.feature_selection.SelectKBest.__lt__ sklearn.feature_selection.SelectKBest.__ne__ sklearn.feature_selection.SelectKBest.__new__ sklearn.feature_selection.SelectKBest.__reduce__ sklearn.feature_selection.SelectKBest.__reduce_ex__ sklearn.feature_selection.SelectKBest.__repr__ sklearn.feature_selection.SelectKBest.__setattr__ sklearn.feature_selection.SelectKBest.__setstate__ sklearn.feature_selection.SelectKBest.__sizeof__ sklearn.feature_selection.SelectKBest.__sklearn_clone__ sklearn.feature_selection.SelectKBest.__sklearn_tags__ sklearn.feature_selection.SelectKBest.__str__ sklearn.feature_selection.SelectKBest.__subclasshook__ sklearn.feature_selection.SelectKBest._build_request_for_signature sklearn.feature_selection.SelectKBest._check_params sklearn.feature_selection.SelectKBest._get_default_requests sklearn.feature_selection.SelectKBest._get_doc_link sklearn.feature_selection.SelectKBest._get_metadata_request sklearn.feature_selection.SelectKBest._get_param_names sklearn.feature_selection.SelectKBest._get_support_mask sklearn.feature_selection.SelectKBest._repr_html_inner sklearn.feature_selection.SelectKBest._repr_mimebundle_ sklearn.feature_selection.SelectKBest._transform sklearn.feature_selection.SelectKBest._validate_params sklearn.feature_selection.SelectKBest.fit sklearn.feature_selection.SelectKBest.fit_transform sklearn.feature_selection.SelectKBest.get_feature_names_out sklearn.feature_selection.SelectKBest.get_metadata_routing sklearn.feature_selection.SelectKBest.get_params sklearn.feature_selection.SelectKBest.get_support sklearn.feature_selection.SelectKBest.inverse_transform sklearn.feature_selection.SelectKBest.set_output sklearn.feature_selection.SelectKBest.set_params sklearn.feature_selection.SelectKBest.transform 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.feature_selection.SelectPercentile.rst.txt b/dev/_sources/modules/generated/sklearn.feature_selection.SelectPercentile.rst.txt
index 1f63f19625535..9f30f5321e9c7 100644
--- a/dev/_sources/modules/generated/sklearn.feature_selection.SelectPercentile.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.feature_selection.SelectPercentile.rst.txt
@@ -8,6 +8,6 @@ SelectPercentile
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.feature_selection.SelectPercentile sklearn.feature_selection.SelectPercentile.__delattr__ sklearn.feature_selection.SelectPercentile.__dir__ sklearn.feature_selection.SelectPercentile.__eq__ sklearn.feature_selection.SelectPercentile.__format__ sklearn.feature_selection.SelectPercentile.__ge__ sklearn.feature_selection.SelectPercentile.__getattribute__ sklearn.feature_selection.SelectPercentile.__getstate__ sklearn.feature_selection.SelectPercentile.__gt__ sklearn.feature_selection.SelectPercentile.__hash__ sklearn.feature_selection.SelectPercentile.__init__ sklearn.feature_selection.SelectPercentile.__init_subclass__ sklearn.feature_selection.SelectPercentile.__le__ sklearn.feature_selection.SelectPercentile.__lt__ sklearn.feature_selection.SelectPercentile.__ne__ sklearn.feature_selection.SelectPercentile.__new__ sklearn.feature_selection.SelectPercentile.__reduce__ sklearn.feature_selection.SelectPercentile.__reduce_ex__ sklearn.feature_selection.SelectPercentile.__repr__ sklearn.feature_selection.SelectPercentile.__setattr__ sklearn.feature_selection.SelectPercentile.__setstate__ sklearn.feature_selection.SelectPercentile.__sizeof__ sklearn.feature_selection.SelectPercentile.__sklearn_clone__ sklearn.feature_selection.SelectPercentile.__sklearn_tags__ sklearn.feature_selection.SelectPercentile.__str__ sklearn.feature_selection.SelectPercentile.__subclasshook__ sklearn.feature_selection.SelectPercentile._build_request_for_signature sklearn.feature_selection.SelectPercentile._check_feature_names sklearn.feature_selection.SelectPercentile._check_n_features sklearn.feature_selection.SelectPercentile._check_params sklearn.feature_selection.SelectPercentile._get_default_requests sklearn.feature_selection.SelectPercentile._get_doc_link sklearn.feature_selection.SelectPercentile._get_metadata_request sklearn.feature_selection.SelectPercentile._get_param_names sklearn.feature_selection.SelectPercentile._get_support_mask sklearn.feature_selection.SelectPercentile._get_tags sklearn.feature_selection.SelectPercentile._more_tags sklearn.feature_selection.SelectPercentile._repr_html_inner sklearn.feature_selection.SelectPercentile._repr_mimebundle_ sklearn.feature_selection.SelectPercentile._transform sklearn.feature_selection.SelectPercentile._validate_data sklearn.feature_selection.SelectPercentile._validate_params sklearn.feature_selection.SelectPercentile.fit sklearn.feature_selection.SelectPercentile.fit_transform sklearn.feature_selection.SelectPercentile.get_feature_names_out sklearn.feature_selection.SelectPercentile.get_metadata_routing sklearn.feature_selection.SelectPercentile.get_params sklearn.feature_selection.SelectPercentile.get_support sklearn.feature_selection.SelectPercentile.inverse_transform sklearn.feature_selection.SelectPercentile.set_output sklearn.feature_selection.SelectPercentile.set_params sklearn.feature_selection.SelectPercentile.transform 
+.. minigallery:: sklearn.feature_selection.SelectPercentile sklearn.feature_selection.SelectPercentile.__delattr__ sklearn.feature_selection.SelectPercentile.__dir__ sklearn.feature_selection.SelectPercentile.__eq__ sklearn.feature_selection.SelectPercentile.__format__ sklearn.feature_selection.SelectPercentile.__ge__ sklearn.feature_selection.SelectPercentile.__getattribute__ sklearn.feature_selection.SelectPercentile.__getstate__ sklearn.feature_selection.SelectPercentile.__gt__ sklearn.feature_selection.SelectPercentile.__hash__ sklearn.feature_selection.SelectPercentile.__init__ sklearn.feature_selection.SelectPercentile.__init_subclass__ sklearn.feature_selection.SelectPercentile.__le__ sklearn.feature_selection.SelectPercentile.__lt__ sklearn.feature_selection.SelectPercentile.__ne__ sklearn.feature_selection.SelectPercentile.__new__ sklearn.feature_selection.SelectPercentile.__reduce__ sklearn.feature_selection.SelectPercentile.__reduce_ex__ sklearn.feature_selection.SelectPercentile.__repr__ sklearn.feature_selection.SelectPercentile.__setattr__ sklearn.feature_selection.SelectPercentile.__setstate__ sklearn.feature_selection.SelectPercentile.__sizeof__ sklearn.feature_selection.SelectPercentile.__sklearn_clone__ sklearn.feature_selection.SelectPercentile.__sklearn_tags__ sklearn.feature_selection.SelectPercentile.__str__ sklearn.feature_selection.SelectPercentile.__subclasshook__ sklearn.feature_selection.SelectPercentile._build_request_for_signature sklearn.feature_selection.SelectPercentile._check_params sklearn.feature_selection.SelectPercentile._get_default_requests sklearn.feature_selection.SelectPercentile._get_doc_link sklearn.feature_selection.SelectPercentile._get_metadata_request sklearn.feature_selection.SelectPercentile._get_param_names sklearn.feature_selection.SelectPercentile._get_support_mask sklearn.feature_selection.SelectPercentile._repr_html_inner sklearn.feature_selection.SelectPercentile._repr_mimebundle_ sklearn.feature_selection.SelectPercentile._transform sklearn.feature_selection.SelectPercentile._validate_params sklearn.feature_selection.SelectPercentile.fit sklearn.feature_selection.SelectPercentile.fit_transform sklearn.feature_selection.SelectPercentile.get_feature_names_out sklearn.feature_selection.SelectPercentile.get_metadata_routing sklearn.feature_selection.SelectPercentile.get_params sklearn.feature_selection.SelectPercentile.get_support sklearn.feature_selection.SelectPercentile.inverse_transform sklearn.feature_selection.SelectPercentile.set_output sklearn.feature_selection.SelectPercentile.set_params sklearn.feature_selection.SelectPercentile.transform 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.feature_selection.SequentialFeatureSelector.rst.txt b/dev/_sources/modules/generated/sklearn.feature_selection.SequentialFeatureSelector.rst.txt
index 9b59e1af4e0ec..38b370cb2f468 100644
--- a/dev/_sources/modules/generated/sklearn.feature_selection.SequentialFeatureSelector.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.feature_selection.SequentialFeatureSelector.rst.txt
@@ -8,6 +8,6 @@ SequentialFeatureSelector
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.feature_selection.SequentialFeatureSelector sklearn.feature_selection.SequentialFeatureSelector.__delattr__ sklearn.feature_selection.SequentialFeatureSelector.__dir__ sklearn.feature_selection.SequentialFeatureSelector.__eq__ sklearn.feature_selection.SequentialFeatureSelector.__format__ sklearn.feature_selection.SequentialFeatureSelector.__ge__ sklearn.feature_selection.SequentialFeatureSelector.__getattribute__ sklearn.feature_selection.SequentialFeatureSelector.__getstate__ sklearn.feature_selection.SequentialFeatureSelector.__gt__ sklearn.feature_selection.SequentialFeatureSelector.__hash__ sklearn.feature_selection.SequentialFeatureSelector.__init__ sklearn.feature_selection.SequentialFeatureSelector.__init_subclass__ sklearn.feature_selection.SequentialFeatureSelector.__le__ sklearn.feature_selection.SequentialFeatureSelector.__lt__ sklearn.feature_selection.SequentialFeatureSelector.__ne__ sklearn.feature_selection.SequentialFeatureSelector.__new__ sklearn.feature_selection.SequentialFeatureSelector.__reduce__ sklearn.feature_selection.SequentialFeatureSelector.__reduce_ex__ sklearn.feature_selection.SequentialFeatureSelector.__repr__ sklearn.feature_selection.SequentialFeatureSelector.__setattr__ sklearn.feature_selection.SequentialFeatureSelector.__setstate__ sklearn.feature_selection.SequentialFeatureSelector.__sizeof__ sklearn.feature_selection.SequentialFeatureSelector.__sklearn_clone__ sklearn.feature_selection.SequentialFeatureSelector.__sklearn_tags__ sklearn.feature_selection.SequentialFeatureSelector.__str__ sklearn.feature_selection.SequentialFeatureSelector.__subclasshook__ sklearn.feature_selection.SequentialFeatureSelector._build_request_for_signature sklearn.feature_selection.SequentialFeatureSelector._check_feature_names sklearn.feature_selection.SequentialFeatureSelector._check_n_features sklearn.feature_selection.SequentialFeatureSelector._get_best_new_feature_score sklearn.feature_selection.SequentialFeatureSelector._get_default_requests sklearn.feature_selection.SequentialFeatureSelector._get_doc_link sklearn.feature_selection.SequentialFeatureSelector._get_metadata_request sklearn.feature_selection.SequentialFeatureSelector._get_param_names sklearn.feature_selection.SequentialFeatureSelector._get_support_mask sklearn.feature_selection.SequentialFeatureSelector._get_tags sklearn.feature_selection.SequentialFeatureSelector._more_tags sklearn.feature_selection.SequentialFeatureSelector._repr_html_inner sklearn.feature_selection.SequentialFeatureSelector._repr_mimebundle_ sklearn.feature_selection.SequentialFeatureSelector._transform sklearn.feature_selection.SequentialFeatureSelector._validate_data sklearn.feature_selection.SequentialFeatureSelector._validate_params sklearn.feature_selection.SequentialFeatureSelector.fit sklearn.feature_selection.SequentialFeatureSelector.fit_transform sklearn.feature_selection.SequentialFeatureSelector.get_feature_names_out sklearn.feature_selection.SequentialFeatureSelector.get_metadata_routing sklearn.feature_selection.SequentialFeatureSelector.get_params sklearn.feature_selection.SequentialFeatureSelector.get_support sklearn.feature_selection.SequentialFeatureSelector.inverse_transform sklearn.feature_selection.SequentialFeatureSelector.set_output sklearn.feature_selection.SequentialFeatureSelector.set_params sklearn.feature_selection.SequentialFeatureSelector.transform 
+.. minigallery:: sklearn.feature_selection.SequentialFeatureSelector sklearn.feature_selection.SequentialFeatureSelector.__delattr__ sklearn.feature_selection.SequentialFeatureSelector.__dir__ sklearn.feature_selection.SequentialFeatureSelector.__eq__ sklearn.feature_selection.SequentialFeatureSelector.__format__ sklearn.feature_selection.SequentialFeatureSelector.__ge__ sklearn.feature_selection.SequentialFeatureSelector.__getattribute__ sklearn.feature_selection.SequentialFeatureSelector.__getstate__ sklearn.feature_selection.SequentialFeatureSelector.__gt__ sklearn.feature_selection.SequentialFeatureSelector.__hash__ sklearn.feature_selection.SequentialFeatureSelector.__init__ sklearn.feature_selection.SequentialFeatureSelector.__init_subclass__ sklearn.feature_selection.SequentialFeatureSelector.__le__ sklearn.feature_selection.SequentialFeatureSelector.__lt__ sklearn.feature_selection.SequentialFeatureSelector.__ne__ sklearn.feature_selection.SequentialFeatureSelector.__new__ sklearn.feature_selection.SequentialFeatureSelector.__reduce__ sklearn.feature_selection.SequentialFeatureSelector.__reduce_ex__ sklearn.feature_selection.SequentialFeatureSelector.__repr__ sklearn.feature_selection.SequentialFeatureSelector.__setattr__ sklearn.feature_selection.SequentialFeatureSelector.__setstate__ sklearn.feature_selection.SequentialFeatureSelector.__sizeof__ sklearn.feature_selection.SequentialFeatureSelector.__sklearn_clone__ sklearn.feature_selection.SequentialFeatureSelector.__sklearn_tags__ sklearn.feature_selection.SequentialFeatureSelector.__str__ sklearn.feature_selection.SequentialFeatureSelector.__subclasshook__ sklearn.feature_selection.SequentialFeatureSelector._build_request_for_signature sklearn.feature_selection.SequentialFeatureSelector._get_best_new_feature_score sklearn.feature_selection.SequentialFeatureSelector._get_default_requests sklearn.feature_selection.SequentialFeatureSelector._get_doc_link sklearn.feature_selection.SequentialFeatureSelector._get_metadata_request sklearn.feature_selection.SequentialFeatureSelector._get_param_names sklearn.feature_selection.SequentialFeatureSelector._get_support_mask sklearn.feature_selection.SequentialFeatureSelector._repr_html_inner sklearn.feature_selection.SequentialFeatureSelector._repr_mimebundle_ sklearn.feature_selection.SequentialFeatureSelector._transform sklearn.feature_selection.SequentialFeatureSelector._validate_params sklearn.feature_selection.SequentialFeatureSelector.fit sklearn.feature_selection.SequentialFeatureSelector.fit_transform sklearn.feature_selection.SequentialFeatureSelector.get_feature_names_out sklearn.feature_selection.SequentialFeatureSelector.get_metadata_routing sklearn.feature_selection.SequentialFeatureSelector.get_params sklearn.feature_selection.SequentialFeatureSelector.get_support sklearn.feature_selection.SequentialFeatureSelector.inverse_transform sklearn.feature_selection.SequentialFeatureSelector.set_output sklearn.feature_selection.SequentialFeatureSelector.set_params sklearn.feature_selection.SequentialFeatureSelector.transform 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.feature_selection.VarianceThreshold.rst.txt b/dev/_sources/modules/generated/sklearn.feature_selection.VarianceThreshold.rst.txt
index c3313ce6a2c7a..f41a9b378a352 100644
--- a/dev/_sources/modules/generated/sklearn.feature_selection.VarianceThreshold.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.feature_selection.VarianceThreshold.rst.txt
@@ -8,6 +8,6 @@ VarianceThreshold
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.feature_selection.VarianceThreshold sklearn.feature_selection.VarianceThreshold.__delattr__ sklearn.feature_selection.VarianceThreshold.__dir__ sklearn.feature_selection.VarianceThreshold.__eq__ sklearn.feature_selection.VarianceThreshold.__format__ sklearn.feature_selection.VarianceThreshold.__ge__ sklearn.feature_selection.VarianceThreshold.__getattribute__ sklearn.feature_selection.VarianceThreshold.__getstate__ sklearn.feature_selection.VarianceThreshold.__gt__ sklearn.feature_selection.VarianceThreshold.__hash__ sklearn.feature_selection.VarianceThreshold.__init__ sklearn.feature_selection.VarianceThreshold.__init_subclass__ sklearn.feature_selection.VarianceThreshold.__le__ sklearn.feature_selection.VarianceThreshold.__lt__ sklearn.feature_selection.VarianceThreshold.__ne__ sklearn.feature_selection.VarianceThreshold.__new__ sklearn.feature_selection.VarianceThreshold.__reduce__ sklearn.feature_selection.VarianceThreshold.__reduce_ex__ sklearn.feature_selection.VarianceThreshold.__repr__ sklearn.feature_selection.VarianceThreshold.__setattr__ sklearn.feature_selection.VarianceThreshold.__setstate__ sklearn.feature_selection.VarianceThreshold.__sizeof__ sklearn.feature_selection.VarianceThreshold.__sklearn_clone__ sklearn.feature_selection.VarianceThreshold.__sklearn_tags__ sklearn.feature_selection.VarianceThreshold.__str__ sklearn.feature_selection.VarianceThreshold.__subclasshook__ sklearn.feature_selection.VarianceThreshold._build_request_for_signature sklearn.feature_selection.VarianceThreshold._check_feature_names sklearn.feature_selection.VarianceThreshold._check_n_features sklearn.feature_selection.VarianceThreshold._get_default_requests sklearn.feature_selection.VarianceThreshold._get_doc_link sklearn.feature_selection.VarianceThreshold._get_metadata_request sklearn.feature_selection.VarianceThreshold._get_param_names sklearn.feature_selection.VarianceThreshold._get_support_mask sklearn.feature_selection.VarianceThreshold._get_tags sklearn.feature_selection.VarianceThreshold._more_tags sklearn.feature_selection.VarianceThreshold._repr_html_inner sklearn.feature_selection.VarianceThreshold._repr_mimebundle_ sklearn.feature_selection.VarianceThreshold._transform sklearn.feature_selection.VarianceThreshold._validate_data sklearn.feature_selection.VarianceThreshold._validate_params sklearn.feature_selection.VarianceThreshold.fit sklearn.feature_selection.VarianceThreshold.fit_transform sklearn.feature_selection.VarianceThreshold.get_feature_names_out sklearn.feature_selection.VarianceThreshold.get_metadata_routing sklearn.feature_selection.VarianceThreshold.get_params sklearn.feature_selection.VarianceThreshold.get_support sklearn.feature_selection.VarianceThreshold.inverse_transform sklearn.feature_selection.VarianceThreshold.set_output sklearn.feature_selection.VarianceThreshold.set_params sklearn.feature_selection.VarianceThreshold.transform 
+.. minigallery:: sklearn.feature_selection.VarianceThreshold sklearn.feature_selection.VarianceThreshold.__delattr__ sklearn.feature_selection.VarianceThreshold.__dir__ sklearn.feature_selection.VarianceThreshold.__eq__ sklearn.feature_selection.VarianceThreshold.__format__ sklearn.feature_selection.VarianceThreshold.__ge__ sklearn.feature_selection.VarianceThreshold.__getattribute__ sklearn.feature_selection.VarianceThreshold.__getstate__ sklearn.feature_selection.VarianceThreshold.__gt__ sklearn.feature_selection.VarianceThreshold.__hash__ sklearn.feature_selection.VarianceThreshold.__init__ sklearn.feature_selection.VarianceThreshold.__init_subclass__ sklearn.feature_selection.VarianceThreshold.__le__ sklearn.feature_selection.VarianceThreshold.__lt__ sklearn.feature_selection.VarianceThreshold.__ne__ sklearn.feature_selection.VarianceThreshold.__new__ sklearn.feature_selection.VarianceThreshold.__reduce__ sklearn.feature_selection.VarianceThreshold.__reduce_ex__ sklearn.feature_selection.VarianceThreshold.__repr__ sklearn.feature_selection.VarianceThreshold.__setattr__ sklearn.feature_selection.VarianceThreshold.__setstate__ sklearn.feature_selection.VarianceThreshold.__sizeof__ sklearn.feature_selection.VarianceThreshold.__sklearn_clone__ sklearn.feature_selection.VarianceThreshold.__sklearn_tags__ sklearn.feature_selection.VarianceThreshold.__str__ sklearn.feature_selection.VarianceThreshold.__subclasshook__ sklearn.feature_selection.VarianceThreshold._build_request_for_signature sklearn.feature_selection.VarianceThreshold._get_default_requests sklearn.feature_selection.VarianceThreshold._get_doc_link sklearn.feature_selection.VarianceThreshold._get_metadata_request sklearn.feature_selection.VarianceThreshold._get_param_names sklearn.feature_selection.VarianceThreshold._get_support_mask sklearn.feature_selection.VarianceThreshold._repr_html_inner sklearn.feature_selection.VarianceThreshold._repr_mimebundle_ sklearn.feature_selection.VarianceThreshold._transform sklearn.feature_selection.VarianceThreshold._validate_params sklearn.feature_selection.VarianceThreshold.fit sklearn.feature_selection.VarianceThreshold.fit_transform sklearn.feature_selection.VarianceThreshold.get_feature_names_out sklearn.feature_selection.VarianceThreshold.get_metadata_routing sklearn.feature_selection.VarianceThreshold.get_params sklearn.feature_selection.VarianceThreshold.get_support sklearn.feature_selection.VarianceThreshold.inverse_transform sklearn.feature_selection.VarianceThreshold.set_output sklearn.feature_selection.VarianceThreshold.set_params sklearn.feature_selection.VarianceThreshold.transform 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.frozen.FrozenEstimator.rst.txt b/dev/_sources/modules/generated/sklearn.frozen.FrozenEstimator.rst.txt
index 884c231834560..0d2379c39010c 100644
--- a/dev/_sources/modules/generated/sklearn.frozen.FrozenEstimator.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.frozen.FrozenEstimator.rst.txt
@@ -8,6 +8,6 @@ FrozenEstimator
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.frozen.FrozenEstimator sklearn.frozen.FrozenEstimator.__delattr__ sklearn.frozen.FrozenEstimator.__dir__ sklearn.frozen.FrozenEstimator.__eq__ sklearn.frozen.FrozenEstimator.__format__ sklearn.frozen.FrozenEstimator.__ge__ sklearn.frozen.FrozenEstimator.__getattr__ sklearn.frozen.FrozenEstimator.__getattribute__ sklearn.frozen.FrozenEstimator.__getitem__ sklearn.frozen.FrozenEstimator.__getstate__ sklearn.frozen.FrozenEstimator.__gt__ sklearn.frozen.FrozenEstimator.__hash__ sklearn.frozen.FrozenEstimator.__init__ sklearn.frozen.FrozenEstimator.__init_subclass__ sklearn.frozen.FrozenEstimator.__le__ sklearn.frozen.FrozenEstimator.__lt__ sklearn.frozen.FrozenEstimator.__ne__ sklearn.frozen.FrozenEstimator.__new__ sklearn.frozen.FrozenEstimator.__reduce__ sklearn.frozen.FrozenEstimator.__reduce_ex__ sklearn.frozen.FrozenEstimator.__repr__ sklearn.frozen.FrozenEstimator.__setattr__ sklearn.frozen.FrozenEstimator.__setstate__ sklearn.frozen.FrozenEstimator.__sizeof__ sklearn.frozen.FrozenEstimator.__sklearn_clone__ sklearn.frozen.FrozenEstimator.__sklearn_is_fitted__ sklearn.frozen.FrozenEstimator.__sklearn_tags__ sklearn.frozen.FrozenEstimator.__str__ sklearn.frozen.FrozenEstimator.__subclasshook__ sklearn.frozen.FrozenEstimator._build_request_for_signature sklearn.frozen.FrozenEstimator._check_feature_names sklearn.frozen.FrozenEstimator._check_n_features sklearn.frozen.FrozenEstimator._get_default_requests sklearn.frozen.FrozenEstimator._get_doc_link sklearn.frozen.FrozenEstimator._get_metadata_request sklearn.frozen.FrozenEstimator._get_param_names sklearn.frozen.FrozenEstimator._get_tags sklearn.frozen.FrozenEstimator._more_tags sklearn.frozen.FrozenEstimator._repr_html_inner sklearn.frozen.FrozenEstimator._repr_mimebundle_ sklearn.frozen.FrozenEstimator._validate_data sklearn.frozen.FrozenEstimator._validate_params sklearn.frozen.FrozenEstimator.fit sklearn.frozen.FrozenEstimator.get_metadata_routing sklearn.frozen.FrozenEstimator.get_params sklearn.frozen.FrozenEstimator.set_params 
+.. minigallery:: sklearn.frozen.FrozenEstimator sklearn.frozen.FrozenEstimator.__delattr__ sklearn.frozen.FrozenEstimator.__dir__ sklearn.frozen.FrozenEstimator.__eq__ sklearn.frozen.FrozenEstimator.__format__ sklearn.frozen.FrozenEstimator.__ge__ sklearn.frozen.FrozenEstimator.__getattr__ sklearn.frozen.FrozenEstimator.__getattribute__ sklearn.frozen.FrozenEstimator.__getitem__ sklearn.frozen.FrozenEstimator.__getstate__ sklearn.frozen.FrozenEstimator.__gt__ sklearn.frozen.FrozenEstimator.__hash__ sklearn.frozen.FrozenEstimator.__init__ sklearn.frozen.FrozenEstimator.__init_subclass__ sklearn.frozen.FrozenEstimator.__le__ sklearn.frozen.FrozenEstimator.__lt__ sklearn.frozen.FrozenEstimator.__ne__ sklearn.frozen.FrozenEstimator.__new__ sklearn.frozen.FrozenEstimator.__reduce__ sklearn.frozen.FrozenEstimator.__reduce_ex__ sklearn.frozen.FrozenEstimator.__repr__ sklearn.frozen.FrozenEstimator.__setattr__ sklearn.frozen.FrozenEstimator.__setstate__ sklearn.frozen.FrozenEstimator.__sizeof__ sklearn.frozen.FrozenEstimator.__sklearn_clone__ sklearn.frozen.FrozenEstimator.__sklearn_is_fitted__ sklearn.frozen.FrozenEstimator.__sklearn_tags__ sklearn.frozen.FrozenEstimator.__str__ sklearn.frozen.FrozenEstimator.__subclasshook__ sklearn.frozen.FrozenEstimator._build_request_for_signature sklearn.frozen.FrozenEstimator._get_default_requests sklearn.frozen.FrozenEstimator._get_doc_link sklearn.frozen.FrozenEstimator._get_metadata_request sklearn.frozen.FrozenEstimator._get_param_names sklearn.frozen.FrozenEstimator._repr_html_inner sklearn.frozen.FrozenEstimator._repr_mimebundle_ sklearn.frozen.FrozenEstimator._validate_params sklearn.frozen.FrozenEstimator.fit sklearn.frozen.FrozenEstimator.get_metadata_routing sklearn.frozen.FrozenEstimator.get_params sklearn.frozen.FrozenEstimator.set_params 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.gaussian_process.GaussianProcessClassifier.rst.txt b/dev/_sources/modules/generated/sklearn.gaussian_process.GaussianProcessClassifier.rst.txt
index fa6a7ee82b494..2deff8fbde209 100644
--- a/dev/_sources/modules/generated/sklearn.gaussian_process.GaussianProcessClassifier.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.gaussian_process.GaussianProcessClassifier.rst.txt
@@ -8,6 +8,6 @@ GaussianProcessClassifier
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.gaussian_process.GaussianProcessClassifier sklearn.gaussian_process.GaussianProcessClassifier.__delattr__ sklearn.gaussian_process.GaussianProcessClassifier.__dir__ sklearn.gaussian_process.GaussianProcessClassifier.__eq__ sklearn.gaussian_process.GaussianProcessClassifier.__format__ sklearn.gaussian_process.GaussianProcessClassifier.__ge__ sklearn.gaussian_process.GaussianProcessClassifier.__getattribute__ sklearn.gaussian_process.GaussianProcessClassifier.__getstate__ sklearn.gaussian_process.GaussianProcessClassifier.__gt__ sklearn.gaussian_process.GaussianProcessClassifier.__hash__ sklearn.gaussian_process.GaussianProcessClassifier.__init__ sklearn.gaussian_process.GaussianProcessClassifier.__init_subclass__ sklearn.gaussian_process.GaussianProcessClassifier.__le__ sklearn.gaussian_process.GaussianProcessClassifier.__lt__ sklearn.gaussian_process.GaussianProcessClassifier.__ne__ sklearn.gaussian_process.GaussianProcessClassifier.__new__ sklearn.gaussian_process.GaussianProcessClassifier.__reduce__ sklearn.gaussian_process.GaussianProcessClassifier.__reduce_ex__ sklearn.gaussian_process.GaussianProcessClassifier.__repr__ sklearn.gaussian_process.GaussianProcessClassifier.__setattr__ sklearn.gaussian_process.GaussianProcessClassifier.__setstate__ sklearn.gaussian_process.GaussianProcessClassifier.__sizeof__ sklearn.gaussian_process.GaussianProcessClassifier.__sklearn_clone__ sklearn.gaussian_process.GaussianProcessClassifier.__sklearn_tags__ sklearn.gaussian_process.GaussianProcessClassifier.__str__ sklearn.gaussian_process.GaussianProcessClassifier.__subclasshook__ sklearn.gaussian_process.GaussianProcessClassifier._build_request_for_signature sklearn.gaussian_process.GaussianProcessClassifier._check_feature_names sklearn.gaussian_process.GaussianProcessClassifier._check_n_features sklearn.gaussian_process.GaussianProcessClassifier._get_default_requests sklearn.gaussian_process.GaussianProcessClassifier._get_doc_link sklearn.gaussian_process.GaussianProcessClassifier._get_metadata_request sklearn.gaussian_process.GaussianProcessClassifier._get_param_names sklearn.gaussian_process.GaussianProcessClassifier._get_tags sklearn.gaussian_process.GaussianProcessClassifier._more_tags sklearn.gaussian_process.GaussianProcessClassifier._repr_html_inner sklearn.gaussian_process.GaussianProcessClassifier._repr_mimebundle_ sklearn.gaussian_process.GaussianProcessClassifier._validate_data sklearn.gaussian_process.GaussianProcessClassifier._validate_params sklearn.gaussian_process.GaussianProcessClassifier.fit sklearn.gaussian_process.GaussianProcessClassifier.get_metadata_routing sklearn.gaussian_process.GaussianProcessClassifier.get_params sklearn.gaussian_process.GaussianProcessClassifier.log_marginal_likelihood sklearn.gaussian_process.GaussianProcessClassifier.predict sklearn.gaussian_process.GaussianProcessClassifier.predict_proba sklearn.gaussian_process.GaussianProcessClassifier.score sklearn.gaussian_process.GaussianProcessClassifier.set_params sklearn.gaussian_process.GaussianProcessClassifier.set_score_request 
+.. minigallery:: sklearn.gaussian_process.GaussianProcessClassifier sklearn.gaussian_process.GaussianProcessClassifier.__delattr__ sklearn.gaussian_process.GaussianProcessClassifier.__dir__ sklearn.gaussian_process.GaussianProcessClassifier.__eq__ sklearn.gaussian_process.GaussianProcessClassifier.__format__ sklearn.gaussian_process.GaussianProcessClassifier.__ge__ sklearn.gaussian_process.GaussianProcessClassifier.__getattribute__ sklearn.gaussian_process.GaussianProcessClassifier.__getstate__ sklearn.gaussian_process.GaussianProcessClassifier.__gt__ sklearn.gaussian_process.GaussianProcessClassifier.__hash__ sklearn.gaussian_process.GaussianProcessClassifier.__init__ sklearn.gaussian_process.GaussianProcessClassifier.__init_subclass__ sklearn.gaussian_process.GaussianProcessClassifier.__le__ sklearn.gaussian_process.GaussianProcessClassifier.__lt__ sklearn.gaussian_process.GaussianProcessClassifier.__ne__ sklearn.gaussian_process.GaussianProcessClassifier.__new__ sklearn.gaussian_process.GaussianProcessClassifier.__reduce__ sklearn.gaussian_process.GaussianProcessClassifier.__reduce_ex__ sklearn.gaussian_process.GaussianProcessClassifier.__repr__ sklearn.gaussian_process.GaussianProcessClassifier.__setattr__ sklearn.gaussian_process.GaussianProcessClassifier.__setstate__ sklearn.gaussian_process.GaussianProcessClassifier.__sizeof__ sklearn.gaussian_process.GaussianProcessClassifier.__sklearn_clone__ sklearn.gaussian_process.GaussianProcessClassifier.__sklearn_tags__ sklearn.gaussian_process.GaussianProcessClassifier.__str__ sklearn.gaussian_process.GaussianProcessClassifier.__subclasshook__ sklearn.gaussian_process.GaussianProcessClassifier._build_request_for_signature sklearn.gaussian_process.GaussianProcessClassifier._get_default_requests sklearn.gaussian_process.GaussianProcessClassifier._get_doc_link sklearn.gaussian_process.GaussianProcessClassifier._get_metadata_request sklearn.gaussian_process.GaussianProcessClassifier._get_param_names sklearn.gaussian_process.GaussianProcessClassifier._repr_html_inner sklearn.gaussian_process.GaussianProcessClassifier._repr_mimebundle_ sklearn.gaussian_process.GaussianProcessClassifier._validate_params sklearn.gaussian_process.GaussianProcessClassifier.fit sklearn.gaussian_process.GaussianProcessClassifier.get_metadata_routing sklearn.gaussian_process.GaussianProcessClassifier.get_params sklearn.gaussian_process.GaussianProcessClassifier.latent_mean_and_variance sklearn.gaussian_process.GaussianProcessClassifier.log_marginal_likelihood sklearn.gaussian_process.GaussianProcessClassifier.predict sklearn.gaussian_process.GaussianProcessClassifier.predict_proba sklearn.gaussian_process.GaussianProcessClassifier.score sklearn.gaussian_process.GaussianProcessClassifier.set_params sklearn.gaussian_process.GaussianProcessClassifier.set_score_request 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.gaussian_process.GaussianProcessRegressor.rst.txt b/dev/_sources/modules/generated/sklearn.gaussian_process.GaussianProcessRegressor.rst.txt
index 37a1557f41458..5b0e099ba91f2 100644
--- a/dev/_sources/modules/generated/sklearn.gaussian_process.GaussianProcessRegressor.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.gaussian_process.GaussianProcessRegressor.rst.txt
@@ -8,6 +8,6 @@ GaussianProcessRegressor
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.gaussian_process.GaussianProcessRegressor sklearn.gaussian_process.GaussianProcessRegressor.__delattr__ sklearn.gaussian_process.GaussianProcessRegressor.__dir__ sklearn.gaussian_process.GaussianProcessRegressor.__eq__ sklearn.gaussian_process.GaussianProcessRegressor.__format__ sklearn.gaussian_process.GaussianProcessRegressor.__ge__ sklearn.gaussian_process.GaussianProcessRegressor.__getattribute__ sklearn.gaussian_process.GaussianProcessRegressor.__getstate__ sklearn.gaussian_process.GaussianProcessRegressor.__gt__ sklearn.gaussian_process.GaussianProcessRegressor.__hash__ sklearn.gaussian_process.GaussianProcessRegressor.__init__ sklearn.gaussian_process.GaussianProcessRegressor.__init_subclass__ sklearn.gaussian_process.GaussianProcessRegressor.__le__ sklearn.gaussian_process.GaussianProcessRegressor.__lt__ sklearn.gaussian_process.GaussianProcessRegressor.__ne__ sklearn.gaussian_process.GaussianProcessRegressor.__new__ sklearn.gaussian_process.GaussianProcessRegressor.__reduce__ sklearn.gaussian_process.GaussianProcessRegressor.__reduce_ex__ sklearn.gaussian_process.GaussianProcessRegressor.__repr__ sklearn.gaussian_process.GaussianProcessRegressor.__setattr__ sklearn.gaussian_process.GaussianProcessRegressor.__setstate__ sklearn.gaussian_process.GaussianProcessRegressor.__sizeof__ sklearn.gaussian_process.GaussianProcessRegressor.__sklearn_clone__ sklearn.gaussian_process.GaussianProcessRegressor.__sklearn_tags__ sklearn.gaussian_process.GaussianProcessRegressor.__str__ sklearn.gaussian_process.GaussianProcessRegressor.__subclasshook__ sklearn.gaussian_process.GaussianProcessRegressor._build_request_for_signature sklearn.gaussian_process.GaussianProcessRegressor._check_feature_names sklearn.gaussian_process.GaussianProcessRegressor._check_n_features sklearn.gaussian_process.GaussianProcessRegressor._constrained_optimization sklearn.gaussian_process.GaussianProcessRegressor._get_default_requests sklearn.gaussian_process.GaussianProcessRegressor._get_doc_link sklearn.gaussian_process.GaussianProcessRegressor._get_metadata_request sklearn.gaussian_process.GaussianProcessRegressor._get_param_names sklearn.gaussian_process.GaussianProcessRegressor._get_tags sklearn.gaussian_process.GaussianProcessRegressor._more_tags sklearn.gaussian_process.GaussianProcessRegressor._repr_html_inner sklearn.gaussian_process.GaussianProcessRegressor._repr_mimebundle_ sklearn.gaussian_process.GaussianProcessRegressor._validate_data sklearn.gaussian_process.GaussianProcessRegressor._validate_params sklearn.gaussian_process.GaussianProcessRegressor.fit sklearn.gaussian_process.GaussianProcessRegressor.get_metadata_routing sklearn.gaussian_process.GaussianProcessRegressor.get_params sklearn.gaussian_process.GaussianProcessRegressor.log_marginal_likelihood sklearn.gaussian_process.GaussianProcessRegressor.predict sklearn.gaussian_process.GaussianProcessRegressor.sample_y sklearn.gaussian_process.GaussianProcessRegressor.score sklearn.gaussian_process.GaussianProcessRegressor.set_params sklearn.gaussian_process.GaussianProcessRegressor.set_predict_request sklearn.gaussian_process.GaussianProcessRegressor.set_score_request 
+.. minigallery:: sklearn.gaussian_process.GaussianProcessRegressor sklearn.gaussian_process.GaussianProcessRegressor.__delattr__ sklearn.gaussian_process.GaussianProcessRegressor.__dir__ sklearn.gaussian_process.GaussianProcessRegressor.__eq__ sklearn.gaussian_process.GaussianProcessRegressor.__format__ sklearn.gaussian_process.GaussianProcessRegressor.__ge__ sklearn.gaussian_process.GaussianProcessRegressor.__getattribute__ sklearn.gaussian_process.GaussianProcessRegressor.__getstate__ sklearn.gaussian_process.GaussianProcessRegressor.__gt__ sklearn.gaussian_process.GaussianProcessRegressor.__hash__ sklearn.gaussian_process.GaussianProcessRegressor.__init__ sklearn.gaussian_process.GaussianProcessRegressor.__init_subclass__ sklearn.gaussian_process.GaussianProcessRegressor.__le__ sklearn.gaussian_process.GaussianProcessRegressor.__lt__ sklearn.gaussian_process.GaussianProcessRegressor.__ne__ sklearn.gaussian_process.GaussianProcessRegressor.__new__ sklearn.gaussian_process.GaussianProcessRegressor.__reduce__ sklearn.gaussian_process.GaussianProcessRegressor.__reduce_ex__ sklearn.gaussian_process.GaussianProcessRegressor.__repr__ sklearn.gaussian_process.GaussianProcessRegressor.__setattr__ sklearn.gaussian_process.GaussianProcessRegressor.__setstate__ sklearn.gaussian_process.GaussianProcessRegressor.__sizeof__ sklearn.gaussian_process.GaussianProcessRegressor.__sklearn_clone__ sklearn.gaussian_process.GaussianProcessRegressor.__sklearn_tags__ sklearn.gaussian_process.GaussianProcessRegressor.__str__ sklearn.gaussian_process.GaussianProcessRegressor.__subclasshook__ sklearn.gaussian_process.GaussianProcessRegressor._build_request_for_signature sklearn.gaussian_process.GaussianProcessRegressor._constrained_optimization sklearn.gaussian_process.GaussianProcessRegressor._get_default_requests sklearn.gaussian_process.GaussianProcessRegressor._get_doc_link sklearn.gaussian_process.GaussianProcessRegressor._get_metadata_request sklearn.gaussian_process.GaussianProcessRegressor._get_param_names sklearn.gaussian_process.GaussianProcessRegressor._repr_html_inner sklearn.gaussian_process.GaussianProcessRegressor._repr_mimebundle_ sklearn.gaussian_process.GaussianProcessRegressor._validate_params sklearn.gaussian_process.GaussianProcessRegressor.fit sklearn.gaussian_process.GaussianProcessRegressor.get_metadata_routing sklearn.gaussian_process.GaussianProcessRegressor.get_params sklearn.gaussian_process.GaussianProcessRegressor.log_marginal_likelihood sklearn.gaussian_process.GaussianProcessRegressor.predict sklearn.gaussian_process.GaussianProcessRegressor.sample_y sklearn.gaussian_process.GaussianProcessRegressor.score sklearn.gaussian_process.GaussianProcessRegressor.set_params sklearn.gaussian_process.GaussianProcessRegressor.set_predict_request sklearn.gaussian_process.GaussianProcessRegressor.set_score_request 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.impute.IterativeImputer.rst.txt b/dev/_sources/modules/generated/sklearn.impute.IterativeImputer.rst.txt
index 3e07baf211c91..170ccf1eb7c77 100644
--- a/dev/_sources/modules/generated/sklearn.impute.IterativeImputer.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.impute.IterativeImputer.rst.txt
@@ -8,6 +8,6 @@ IterativeImputer
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.impute.IterativeImputer sklearn.impute.IterativeImputer.__delattr__ sklearn.impute.IterativeImputer.__dir__ sklearn.impute.IterativeImputer.__eq__ sklearn.impute.IterativeImputer.__format__ sklearn.impute.IterativeImputer.__ge__ sklearn.impute.IterativeImputer.__getattribute__ sklearn.impute.IterativeImputer.__getstate__ sklearn.impute.IterativeImputer.__gt__ sklearn.impute.IterativeImputer.__hash__ sklearn.impute.IterativeImputer.__init__ sklearn.impute.IterativeImputer.__init_subclass__ sklearn.impute.IterativeImputer.__le__ sklearn.impute.IterativeImputer.__lt__ sklearn.impute.IterativeImputer.__ne__ sklearn.impute.IterativeImputer.__new__ sklearn.impute.IterativeImputer.__reduce__ sklearn.impute.IterativeImputer.__reduce_ex__ sklearn.impute.IterativeImputer.__repr__ sklearn.impute.IterativeImputer.__setattr__ sklearn.impute.IterativeImputer.__setstate__ sklearn.impute.IterativeImputer.__sizeof__ sklearn.impute.IterativeImputer.__sklearn_clone__ sklearn.impute.IterativeImputer.__sklearn_tags__ sklearn.impute.IterativeImputer.__str__ sklearn.impute.IterativeImputer.__subclasshook__ sklearn.impute.IterativeImputer._build_request_for_signature sklearn.impute.IterativeImputer._check_feature_names sklearn.impute.IterativeImputer._check_n_features sklearn.impute.IterativeImputer._concatenate_indicator sklearn.impute.IterativeImputer._concatenate_indicator_feature_names_out sklearn.impute.IterativeImputer._fit_indicator sklearn.impute.IterativeImputer._get_abs_corr_mat sklearn.impute.IterativeImputer._get_default_requests sklearn.impute.IterativeImputer._get_doc_link sklearn.impute.IterativeImputer._get_metadata_request sklearn.impute.IterativeImputer._get_neighbor_feat_idx sklearn.impute.IterativeImputer._get_ordered_idx sklearn.impute.IterativeImputer._get_param_names sklearn.impute.IterativeImputer._get_tags sklearn.impute.IterativeImputer._impute_one_feature sklearn.impute.IterativeImputer._initial_imputation sklearn.impute.IterativeImputer._more_tags sklearn.impute.IterativeImputer._repr_html_inner sklearn.impute.IterativeImputer._repr_mimebundle_ sklearn.impute.IterativeImputer._transform_indicator sklearn.impute.IterativeImputer._validate_data sklearn.impute.IterativeImputer._validate_limit sklearn.impute.IterativeImputer._validate_params sklearn.impute.IterativeImputer.fit sklearn.impute.IterativeImputer.fit_transform sklearn.impute.IterativeImputer.get_feature_names_out sklearn.impute.IterativeImputer.get_metadata_routing sklearn.impute.IterativeImputer.get_params sklearn.impute.IterativeImputer.set_output sklearn.impute.IterativeImputer.set_params sklearn.impute.IterativeImputer.transform 
+.. minigallery:: sklearn.impute.IterativeImputer sklearn.impute.IterativeImputer.__delattr__ sklearn.impute.IterativeImputer.__dir__ sklearn.impute.IterativeImputer.__eq__ sklearn.impute.IterativeImputer.__format__ sklearn.impute.IterativeImputer.__ge__ sklearn.impute.IterativeImputer.__getattribute__ sklearn.impute.IterativeImputer.__getstate__ sklearn.impute.IterativeImputer.__gt__ sklearn.impute.IterativeImputer.__hash__ sklearn.impute.IterativeImputer.__init__ sklearn.impute.IterativeImputer.__init_subclass__ sklearn.impute.IterativeImputer.__le__ sklearn.impute.IterativeImputer.__lt__ sklearn.impute.IterativeImputer.__ne__ sklearn.impute.IterativeImputer.__new__ sklearn.impute.IterativeImputer.__reduce__ sklearn.impute.IterativeImputer.__reduce_ex__ sklearn.impute.IterativeImputer.__repr__ sklearn.impute.IterativeImputer.__setattr__ sklearn.impute.IterativeImputer.__setstate__ sklearn.impute.IterativeImputer.__sizeof__ sklearn.impute.IterativeImputer.__sklearn_clone__ sklearn.impute.IterativeImputer.__sklearn_tags__ sklearn.impute.IterativeImputer.__str__ sklearn.impute.IterativeImputer.__subclasshook__ sklearn.impute.IterativeImputer._build_request_for_signature sklearn.impute.IterativeImputer._concatenate_indicator sklearn.impute.IterativeImputer._concatenate_indicator_feature_names_out sklearn.impute.IterativeImputer._fit_indicator sklearn.impute.IterativeImputer._get_abs_corr_mat sklearn.impute.IterativeImputer._get_default_requests sklearn.impute.IterativeImputer._get_doc_link sklearn.impute.IterativeImputer._get_metadata_request sklearn.impute.IterativeImputer._get_neighbor_feat_idx sklearn.impute.IterativeImputer._get_ordered_idx sklearn.impute.IterativeImputer._get_param_names sklearn.impute.IterativeImputer._impute_one_feature sklearn.impute.IterativeImputer._initial_imputation sklearn.impute.IterativeImputer._repr_html_inner sklearn.impute.IterativeImputer._repr_mimebundle_ sklearn.impute.IterativeImputer._transform_indicator sklearn.impute.IterativeImputer._validate_limit sklearn.impute.IterativeImputer._validate_params sklearn.impute.IterativeImputer.fit sklearn.impute.IterativeImputer.fit_transform sklearn.impute.IterativeImputer.get_feature_names_out sklearn.impute.IterativeImputer.get_metadata_routing sklearn.impute.IterativeImputer.get_params sklearn.impute.IterativeImputer.set_output sklearn.impute.IterativeImputer.set_params sklearn.impute.IterativeImputer.transform 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.impute.KNNImputer.rst.txt b/dev/_sources/modules/generated/sklearn.impute.KNNImputer.rst.txt
index 880fa7c188555..d46d396ef44a7 100644
--- a/dev/_sources/modules/generated/sklearn.impute.KNNImputer.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.impute.KNNImputer.rst.txt
@@ -8,6 +8,6 @@ KNNImputer
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.impute.KNNImputer sklearn.impute.KNNImputer.__delattr__ sklearn.impute.KNNImputer.__dir__ sklearn.impute.KNNImputer.__eq__ sklearn.impute.KNNImputer.__format__ sklearn.impute.KNNImputer.__ge__ sklearn.impute.KNNImputer.__getattribute__ sklearn.impute.KNNImputer.__getstate__ sklearn.impute.KNNImputer.__gt__ sklearn.impute.KNNImputer.__hash__ sklearn.impute.KNNImputer.__init__ sklearn.impute.KNNImputer.__init_subclass__ sklearn.impute.KNNImputer.__le__ sklearn.impute.KNNImputer.__lt__ sklearn.impute.KNNImputer.__ne__ sklearn.impute.KNNImputer.__new__ sklearn.impute.KNNImputer.__reduce__ sklearn.impute.KNNImputer.__reduce_ex__ sklearn.impute.KNNImputer.__repr__ sklearn.impute.KNNImputer.__setattr__ sklearn.impute.KNNImputer.__setstate__ sklearn.impute.KNNImputer.__sizeof__ sklearn.impute.KNNImputer.__sklearn_clone__ sklearn.impute.KNNImputer.__sklearn_tags__ sklearn.impute.KNNImputer.__str__ sklearn.impute.KNNImputer.__subclasshook__ sklearn.impute.KNNImputer._build_request_for_signature sklearn.impute.KNNImputer._calc_impute sklearn.impute.KNNImputer._check_feature_names sklearn.impute.KNNImputer._check_n_features sklearn.impute.KNNImputer._concatenate_indicator sklearn.impute.KNNImputer._concatenate_indicator_feature_names_out sklearn.impute.KNNImputer._fit_indicator sklearn.impute.KNNImputer._get_default_requests sklearn.impute.KNNImputer._get_doc_link sklearn.impute.KNNImputer._get_metadata_request sklearn.impute.KNNImputer._get_param_names sklearn.impute.KNNImputer._get_tags sklearn.impute.KNNImputer._more_tags sklearn.impute.KNNImputer._repr_html_inner sklearn.impute.KNNImputer._repr_mimebundle_ sklearn.impute.KNNImputer._transform_indicator sklearn.impute.KNNImputer._validate_data sklearn.impute.KNNImputer._validate_params sklearn.impute.KNNImputer.fit sklearn.impute.KNNImputer.fit_transform sklearn.impute.KNNImputer.get_feature_names_out sklearn.impute.KNNImputer.get_metadata_routing sklearn.impute.KNNImputer.get_params sklearn.impute.KNNImputer.set_output sklearn.impute.KNNImputer.set_params sklearn.impute.KNNImputer.transform 
+.. minigallery:: sklearn.impute.KNNImputer sklearn.impute.KNNImputer.__delattr__ sklearn.impute.KNNImputer.__dir__ sklearn.impute.KNNImputer.__eq__ sklearn.impute.KNNImputer.__format__ sklearn.impute.KNNImputer.__ge__ sklearn.impute.KNNImputer.__getattribute__ sklearn.impute.KNNImputer.__getstate__ sklearn.impute.KNNImputer.__gt__ sklearn.impute.KNNImputer.__hash__ sklearn.impute.KNNImputer.__init__ sklearn.impute.KNNImputer.__init_subclass__ sklearn.impute.KNNImputer.__le__ sklearn.impute.KNNImputer.__lt__ sklearn.impute.KNNImputer.__ne__ sklearn.impute.KNNImputer.__new__ sklearn.impute.KNNImputer.__reduce__ sklearn.impute.KNNImputer.__reduce_ex__ sklearn.impute.KNNImputer.__repr__ sklearn.impute.KNNImputer.__setattr__ sklearn.impute.KNNImputer.__setstate__ sklearn.impute.KNNImputer.__sizeof__ sklearn.impute.KNNImputer.__sklearn_clone__ sklearn.impute.KNNImputer.__sklearn_tags__ sklearn.impute.KNNImputer.__str__ sklearn.impute.KNNImputer.__subclasshook__ sklearn.impute.KNNImputer._build_request_for_signature sklearn.impute.KNNImputer._calc_impute sklearn.impute.KNNImputer._concatenate_indicator sklearn.impute.KNNImputer._concatenate_indicator_feature_names_out sklearn.impute.KNNImputer._fit_indicator sklearn.impute.KNNImputer._get_default_requests sklearn.impute.KNNImputer._get_doc_link sklearn.impute.KNNImputer._get_metadata_request sklearn.impute.KNNImputer._get_param_names sklearn.impute.KNNImputer._repr_html_inner sklearn.impute.KNNImputer._repr_mimebundle_ sklearn.impute.KNNImputer._transform_indicator sklearn.impute.KNNImputer._validate_params sklearn.impute.KNNImputer.fit sklearn.impute.KNNImputer.fit_transform sklearn.impute.KNNImputer.get_feature_names_out sklearn.impute.KNNImputer.get_metadata_routing sklearn.impute.KNNImputer.get_params sklearn.impute.KNNImputer.set_output sklearn.impute.KNNImputer.set_params sklearn.impute.KNNImputer.transform 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.impute.MissingIndicator.rst.txt b/dev/_sources/modules/generated/sklearn.impute.MissingIndicator.rst.txt
index 43efced94b360..cd11b20278b35 100644
--- a/dev/_sources/modules/generated/sklearn.impute.MissingIndicator.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.impute.MissingIndicator.rst.txt
@@ -8,6 +8,6 @@ MissingIndicator
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.impute.MissingIndicator sklearn.impute.MissingIndicator.__delattr__ sklearn.impute.MissingIndicator.__dir__ sklearn.impute.MissingIndicator.__eq__ sklearn.impute.MissingIndicator.__format__ sklearn.impute.MissingIndicator.__ge__ sklearn.impute.MissingIndicator.__getattribute__ sklearn.impute.MissingIndicator.__getstate__ sklearn.impute.MissingIndicator.__gt__ sklearn.impute.MissingIndicator.__hash__ sklearn.impute.MissingIndicator.__init__ sklearn.impute.MissingIndicator.__init_subclass__ sklearn.impute.MissingIndicator.__le__ sklearn.impute.MissingIndicator.__lt__ sklearn.impute.MissingIndicator.__ne__ sklearn.impute.MissingIndicator.__new__ sklearn.impute.MissingIndicator.__reduce__ sklearn.impute.MissingIndicator.__reduce_ex__ sklearn.impute.MissingIndicator.__repr__ sklearn.impute.MissingIndicator.__setattr__ sklearn.impute.MissingIndicator.__setstate__ sklearn.impute.MissingIndicator.__sizeof__ sklearn.impute.MissingIndicator.__sklearn_clone__ sklearn.impute.MissingIndicator.__sklearn_tags__ sklearn.impute.MissingIndicator.__str__ sklearn.impute.MissingIndicator.__subclasshook__ sklearn.impute.MissingIndicator._build_request_for_signature sklearn.impute.MissingIndicator._check_feature_names sklearn.impute.MissingIndicator._check_n_features sklearn.impute.MissingIndicator._fit sklearn.impute.MissingIndicator._get_default_requests sklearn.impute.MissingIndicator._get_doc_link sklearn.impute.MissingIndicator._get_metadata_request sklearn.impute.MissingIndicator._get_missing_features_info sklearn.impute.MissingIndicator._get_param_names sklearn.impute.MissingIndicator._get_tags sklearn.impute.MissingIndicator._more_tags sklearn.impute.MissingIndicator._repr_html_inner sklearn.impute.MissingIndicator._repr_mimebundle_ sklearn.impute.MissingIndicator._validate_data sklearn.impute.MissingIndicator._validate_input sklearn.impute.MissingIndicator._validate_params sklearn.impute.MissingIndicator.fit sklearn.impute.MissingIndicator.fit_transform sklearn.impute.MissingIndicator.get_feature_names_out sklearn.impute.MissingIndicator.get_metadata_routing sklearn.impute.MissingIndicator.get_params sklearn.impute.MissingIndicator.set_output sklearn.impute.MissingIndicator.set_params sklearn.impute.MissingIndicator.transform 
+.. minigallery:: sklearn.impute.MissingIndicator sklearn.impute.MissingIndicator.__delattr__ sklearn.impute.MissingIndicator.__dir__ sklearn.impute.MissingIndicator.__eq__ sklearn.impute.MissingIndicator.__format__ sklearn.impute.MissingIndicator.__ge__ sklearn.impute.MissingIndicator.__getattribute__ sklearn.impute.MissingIndicator.__getstate__ sklearn.impute.MissingIndicator.__gt__ sklearn.impute.MissingIndicator.__hash__ sklearn.impute.MissingIndicator.__init__ sklearn.impute.MissingIndicator.__init_subclass__ sklearn.impute.MissingIndicator.__le__ sklearn.impute.MissingIndicator.__lt__ sklearn.impute.MissingIndicator.__ne__ sklearn.impute.MissingIndicator.__new__ sklearn.impute.MissingIndicator.__reduce__ sklearn.impute.MissingIndicator.__reduce_ex__ sklearn.impute.MissingIndicator.__repr__ sklearn.impute.MissingIndicator.__setattr__ sklearn.impute.MissingIndicator.__setstate__ sklearn.impute.MissingIndicator.__sizeof__ sklearn.impute.MissingIndicator.__sklearn_clone__ sklearn.impute.MissingIndicator.__sklearn_tags__ sklearn.impute.MissingIndicator.__str__ sklearn.impute.MissingIndicator.__subclasshook__ sklearn.impute.MissingIndicator._build_request_for_signature sklearn.impute.MissingIndicator._fit sklearn.impute.MissingIndicator._get_default_requests sklearn.impute.MissingIndicator._get_doc_link sklearn.impute.MissingIndicator._get_metadata_request sklearn.impute.MissingIndicator._get_missing_features_info sklearn.impute.MissingIndicator._get_param_names sklearn.impute.MissingIndicator._repr_html_inner sklearn.impute.MissingIndicator._repr_mimebundle_ sklearn.impute.MissingIndicator._validate_input sklearn.impute.MissingIndicator._validate_params sklearn.impute.MissingIndicator.fit sklearn.impute.MissingIndicator.fit_transform sklearn.impute.MissingIndicator.get_feature_names_out sklearn.impute.MissingIndicator.get_metadata_routing sklearn.impute.MissingIndicator.get_params sklearn.impute.MissingIndicator.set_output sklearn.impute.MissingIndicator.set_params sklearn.impute.MissingIndicator.transform 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.impute.SimpleImputer.rst.txt b/dev/_sources/modules/generated/sklearn.impute.SimpleImputer.rst.txt
index 0539614847b40..f989aaf0b6142 100644
--- a/dev/_sources/modules/generated/sklearn.impute.SimpleImputer.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.impute.SimpleImputer.rst.txt
@@ -8,6 +8,6 @@ SimpleImputer
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.impute.SimpleImputer sklearn.impute.SimpleImputer.__delattr__ sklearn.impute.SimpleImputer.__dir__ sklearn.impute.SimpleImputer.__eq__ sklearn.impute.SimpleImputer.__format__ sklearn.impute.SimpleImputer.__ge__ sklearn.impute.SimpleImputer.__getattribute__ sklearn.impute.SimpleImputer.__getstate__ sklearn.impute.SimpleImputer.__gt__ sklearn.impute.SimpleImputer.__hash__ sklearn.impute.SimpleImputer.__init__ sklearn.impute.SimpleImputer.__init_subclass__ sklearn.impute.SimpleImputer.__le__ sklearn.impute.SimpleImputer.__lt__ sklearn.impute.SimpleImputer.__ne__ sklearn.impute.SimpleImputer.__new__ sklearn.impute.SimpleImputer.__reduce__ sklearn.impute.SimpleImputer.__reduce_ex__ sklearn.impute.SimpleImputer.__repr__ sklearn.impute.SimpleImputer.__setattr__ sklearn.impute.SimpleImputer.__setstate__ sklearn.impute.SimpleImputer.__sizeof__ sklearn.impute.SimpleImputer.__sklearn_clone__ sklearn.impute.SimpleImputer.__sklearn_tags__ sklearn.impute.SimpleImputer.__str__ sklearn.impute.SimpleImputer.__subclasshook__ sklearn.impute.SimpleImputer._build_request_for_signature sklearn.impute.SimpleImputer._check_feature_names sklearn.impute.SimpleImputer._check_n_features sklearn.impute.SimpleImputer._concatenate_indicator sklearn.impute.SimpleImputer._concatenate_indicator_feature_names_out sklearn.impute.SimpleImputer._dense_fit sklearn.impute.SimpleImputer._fit_indicator sklearn.impute.SimpleImputer._get_default_requests sklearn.impute.SimpleImputer._get_doc_link sklearn.impute.SimpleImputer._get_metadata_request sklearn.impute.SimpleImputer._get_param_names sklearn.impute.SimpleImputer._get_tags sklearn.impute.SimpleImputer._more_tags sklearn.impute.SimpleImputer._repr_html_inner sklearn.impute.SimpleImputer._repr_mimebundle_ sklearn.impute.SimpleImputer._sparse_fit sklearn.impute.SimpleImputer._transform_indicator sklearn.impute.SimpleImputer._validate_data sklearn.impute.SimpleImputer._validate_input sklearn.impute.SimpleImputer._validate_params sklearn.impute.SimpleImputer.fit sklearn.impute.SimpleImputer.fit_transform sklearn.impute.SimpleImputer.get_feature_names_out sklearn.impute.SimpleImputer.get_metadata_routing sklearn.impute.SimpleImputer.get_params sklearn.impute.SimpleImputer.inverse_transform sklearn.impute.SimpleImputer.set_output sklearn.impute.SimpleImputer.set_params sklearn.impute.SimpleImputer.transform 
+.. minigallery:: sklearn.impute.SimpleImputer sklearn.impute.SimpleImputer.__delattr__ sklearn.impute.SimpleImputer.__dir__ sklearn.impute.SimpleImputer.__eq__ sklearn.impute.SimpleImputer.__format__ sklearn.impute.SimpleImputer.__ge__ sklearn.impute.SimpleImputer.__getattribute__ sklearn.impute.SimpleImputer.__getstate__ sklearn.impute.SimpleImputer.__gt__ sklearn.impute.SimpleImputer.__hash__ sklearn.impute.SimpleImputer.__init__ sklearn.impute.SimpleImputer.__init_subclass__ sklearn.impute.SimpleImputer.__le__ sklearn.impute.SimpleImputer.__lt__ sklearn.impute.SimpleImputer.__ne__ sklearn.impute.SimpleImputer.__new__ sklearn.impute.SimpleImputer.__reduce__ sklearn.impute.SimpleImputer.__reduce_ex__ sklearn.impute.SimpleImputer.__repr__ sklearn.impute.SimpleImputer.__setattr__ sklearn.impute.SimpleImputer.__setstate__ sklearn.impute.SimpleImputer.__sizeof__ sklearn.impute.SimpleImputer.__sklearn_clone__ sklearn.impute.SimpleImputer.__sklearn_tags__ sklearn.impute.SimpleImputer.__str__ sklearn.impute.SimpleImputer.__subclasshook__ sklearn.impute.SimpleImputer._build_request_for_signature sklearn.impute.SimpleImputer._concatenate_indicator sklearn.impute.SimpleImputer._concatenate_indicator_feature_names_out sklearn.impute.SimpleImputer._dense_fit sklearn.impute.SimpleImputer._fit_indicator sklearn.impute.SimpleImputer._get_default_requests sklearn.impute.SimpleImputer._get_doc_link sklearn.impute.SimpleImputer._get_metadata_request sklearn.impute.SimpleImputer._get_param_names sklearn.impute.SimpleImputer._repr_html_inner sklearn.impute.SimpleImputer._repr_mimebundle_ sklearn.impute.SimpleImputer._sparse_fit sklearn.impute.SimpleImputer._transform_indicator sklearn.impute.SimpleImputer._validate_input sklearn.impute.SimpleImputer._validate_params sklearn.impute.SimpleImputer.fit sklearn.impute.SimpleImputer.fit_transform sklearn.impute.SimpleImputer.get_feature_names_out sklearn.impute.SimpleImputer.get_metadata_routing sklearn.impute.SimpleImputer.get_params sklearn.impute.SimpleImputer.inverse_transform sklearn.impute.SimpleImputer.set_output sklearn.impute.SimpleImputer.set_params sklearn.impute.SimpleImputer.transform 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.isotonic.IsotonicRegression.rst.txt b/dev/_sources/modules/generated/sklearn.isotonic.IsotonicRegression.rst.txt
index 180e215875b89..d15f5636f9e33 100644
--- a/dev/_sources/modules/generated/sklearn.isotonic.IsotonicRegression.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.isotonic.IsotonicRegression.rst.txt
@@ -8,6 +8,6 @@ IsotonicRegression
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.isotonic.IsotonicRegression sklearn.isotonic.IsotonicRegression.__delattr__ sklearn.isotonic.IsotonicRegression.__dir__ sklearn.isotonic.IsotonicRegression.__eq__ sklearn.isotonic.IsotonicRegression.__format__ sklearn.isotonic.IsotonicRegression.__ge__ sklearn.isotonic.IsotonicRegression.__getattribute__ sklearn.isotonic.IsotonicRegression.__getstate__ sklearn.isotonic.IsotonicRegression.__gt__ sklearn.isotonic.IsotonicRegression.__hash__ sklearn.isotonic.IsotonicRegression.__init__ sklearn.isotonic.IsotonicRegression.__init_subclass__ sklearn.isotonic.IsotonicRegression.__le__ sklearn.isotonic.IsotonicRegression.__lt__ sklearn.isotonic.IsotonicRegression.__ne__ sklearn.isotonic.IsotonicRegression.__new__ sklearn.isotonic.IsotonicRegression.__reduce__ sklearn.isotonic.IsotonicRegression.__reduce_ex__ sklearn.isotonic.IsotonicRegression.__repr__ sklearn.isotonic.IsotonicRegression.__setattr__ sklearn.isotonic.IsotonicRegression.__setstate__ sklearn.isotonic.IsotonicRegression.__sizeof__ sklearn.isotonic.IsotonicRegression.__sklearn_clone__ sklearn.isotonic.IsotonicRegression.__sklearn_tags__ sklearn.isotonic.IsotonicRegression.__str__ sklearn.isotonic.IsotonicRegression.__subclasshook__ sklearn.isotonic.IsotonicRegression._build_f sklearn.isotonic.IsotonicRegression._build_request_for_signature sklearn.isotonic.IsotonicRegression._build_y sklearn.isotonic.IsotonicRegression._check_feature_names sklearn.isotonic.IsotonicRegression._check_input_data_shape sklearn.isotonic.IsotonicRegression._check_n_features sklearn.isotonic.IsotonicRegression._get_default_requests sklearn.isotonic.IsotonicRegression._get_doc_link sklearn.isotonic.IsotonicRegression._get_metadata_request sklearn.isotonic.IsotonicRegression._get_param_names sklearn.isotonic.IsotonicRegression._get_tags sklearn.isotonic.IsotonicRegression._more_tags sklearn.isotonic.IsotonicRegression._repr_html_inner sklearn.isotonic.IsotonicRegression._repr_mimebundle_ sklearn.isotonic.IsotonicRegression._transform sklearn.isotonic.IsotonicRegression._validate_data sklearn.isotonic.IsotonicRegression._validate_params sklearn.isotonic.IsotonicRegression.fit sklearn.isotonic.IsotonicRegression.fit_transform sklearn.isotonic.IsotonicRegression.get_feature_names_out sklearn.isotonic.IsotonicRegression.get_metadata_routing sklearn.isotonic.IsotonicRegression.get_params sklearn.isotonic.IsotonicRegression.predict sklearn.isotonic.IsotonicRegression.score sklearn.isotonic.IsotonicRegression.set_fit_request sklearn.isotonic.IsotonicRegression.set_output sklearn.isotonic.IsotonicRegression.set_params sklearn.isotonic.IsotonicRegression.set_score_request sklearn.isotonic.IsotonicRegression.transform 
+.. minigallery:: sklearn.isotonic.IsotonicRegression sklearn.isotonic.IsotonicRegression.__delattr__ sklearn.isotonic.IsotonicRegression.__dir__ sklearn.isotonic.IsotonicRegression.__eq__ sklearn.isotonic.IsotonicRegression.__format__ sklearn.isotonic.IsotonicRegression.__ge__ sklearn.isotonic.IsotonicRegression.__getattribute__ sklearn.isotonic.IsotonicRegression.__getstate__ sklearn.isotonic.IsotonicRegression.__gt__ sklearn.isotonic.IsotonicRegression.__hash__ sklearn.isotonic.IsotonicRegression.__init__ sklearn.isotonic.IsotonicRegression.__init_subclass__ sklearn.isotonic.IsotonicRegression.__le__ sklearn.isotonic.IsotonicRegression.__lt__ sklearn.isotonic.IsotonicRegression.__ne__ sklearn.isotonic.IsotonicRegression.__new__ sklearn.isotonic.IsotonicRegression.__reduce__ sklearn.isotonic.IsotonicRegression.__reduce_ex__ sklearn.isotonic.IsotonicRegression.__repr__ sklearn.isotonic.IsotonicRegression.__setattr__ sklearn.isotonic.IsotonicRegression.__setstate__ sklearn.isotonic.IsotonicRegression.__sizeof__ sklearn.isotonic.IsotonicRegression.__sklearn_clone__ sklearn.isotonic.IsotonicRegression.__sklearn_tags__ sklearn.isotonic.IsotonicRegression.__str__ sklearn.isotonic.IsotonicRegression.__subclasshook__ sklearn.isotonic.IsotonicRegression._build_f sklearn.isotonic.IsotonicRegression._build_request_for_signature sklearn.isotonic.IsotonicRegression._build_y sklearn.isotonic.IsotonicRegression._check_input_data_shape sklearn.isotonic.IsotonicRegression._get_default_requests sklearn.isotonic.IsotonicRegression._get_doc_link sklearn.isotonic.IsotonicRegression._get_metadata_request sklearn.isotonic.IsotonicRegression._get_param_names sklearn.isotonic.IsotonicRegression._repr_html_inner sklearn.isotonic.IsotonicRegression._repr_mimebundle_ sklearn.isotonic.IsotonicRegression._transform sklearn.isotonic.IsotonicRegression._validate_params sklearn.isotonic.IsotonicRegression.fit sklearn.isotonic.IsotonicRegression.fit_transform sklearn.isotonic.IsotonicRegression.get_feature_names_out sklearn.isotonic.IsotonicRegression.get_metadata_routing sklearn.isotonic.IsotonicRegression.get_params sklearn.isotonic.IsotonicRegression.predict sklearn.isotonic.IsotonicRegression.score sklearn.isotonic.IsotonicRegression.set_fit_request sklearn.isotonic.IsotonicRegression.set_output sklearn.isotonic.IsotonicRegression.set_params sklearn.isotonic.IsotonicRegression.set_score_request sklearn.isotonic.IsotonicRegression.transform 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.kernel_approximation.AdditiveChi2Sampler.rst.txt b/dev/_sources/modules/generated/sklearn.kernel_approximation.AdditiveChi2Sampler.rst.txt
index 9ef2b43fa5a85..46d48cf93c150 100644
--- a/dev/_sources/modules/generated/sklearn.kernel_approximation.AdditiveChi2Sampler.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.kernel_approximation.AdditiveChi2Sampler.rst.txt
@@ -8,6 +8,6 @@ AdditiveChi2Sampler
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.kernel_approximation.AdditiveChi2Sampler sklearn.kernel_approximation.AdditiveChi2Sampler.__delattr__ sklearn.kernel_approximation.AdditiveChi2Sampler.__dir__ sklearn.kernel_approximation.AdditiveChi2Sampler.__eq__ sklearn.kernel_approximation.AdditiveChi2Sampler.__format__ sklearn.kernel_approximation.AdditiveChi2Sampler.__ge__ sklearn.kernel_approximation.AdditiveChi2Sampler.__getattribute__ sklearn.kernel_approximation.AdditiveChi2Sampler.__getstate__ sklearn.kernel_approximation.AdditiveChi2Sampler.__gt__ sklearn.kernel_approximation.AdditiveChi2Sampler.__hash__ sklearn.kernel_approximation.AdditiveChi2Sampler.__init__ sklearn.kernel_approximation.AdditiveChi2Sampler.__init_subclass__ sklearn.kernel_approximation.AdditiveChi2Sampler.__le__ sklearn.kernel_approximation.AdditiveChi2Sampler.__lt__ sklearn.kernel_approximation.AdditiveChi2Sampler.__ne__ sklearn.kernel_approximation.AdditiveChi2Sampler.__new__ sklearn.kernel_approximation.AdditiveChi2Sampler.__reduce__ sklearn.kernel_approximation.AdditiveChi2Sampler.__reduce_ex__ sklearn.kernel_approximation.AdditiveChi2Sampler.__repr__ sklearn.kernel_approximation.AdditiveChi2Sampler.__setattr__ sklearn.kernel_approximation.AdditiveChi2Sampler.__setstate__ sklearn.kernel_approximation.AdditiveChi2Sampler.__sizeof__ sklearn.kernel_approximation.AdditiveChi2Sampler.__sklearn_clone__ sklearn.kernel_approximation.AdditiveChi2Sampler.__sklearn_tags__ sklearn.kernel_approximation.AdditiveChi2Sampler.__str__ sklearn.kernel_approximation.AdditiveChi2Sampler.__subclasshook__ sklearn.kernel_approximation.AdditiveChi2Sampler._build_request_for_signature sklearn.kernel_approximation.AdditiveChi2Sampler._check_feature_names sklearn.kernel_approximation.AdditiveChi2Sampler._check_n_features sklearn.kernel_approximation.AdditiveChi2Sampler._get_default_requests sklearn.kernel_approximation.AdditiveChi2Sampler._get_doc_link sklearn.kernel_approximation.AdditiveChi2Sampler._get_metadata_request sklearn.kernel_approximation.AdditiveChi2Sampler._get_param_names sklearn.kernel_approximation.AdditiveChi2Sampler._get_tags sklearn.kernel_approximation.AdditiveChi2Sampler._more_tags sklearn.kernel_approximation.AdditiveChi2Sampler._repr_html_inner sklearn.kernel_approximation.AdditiveChi2Sampler._repr_mimebundle_ sklearn.kernel_approximation.AdditiveChi2Sampler._transform_dense sklearn.kernel_approximation.AdditiveChi2Sampler._transform_sparse sklearn.kernel_approximation.AdditiveChi2Sampler._validate_data sklearn.kernel_approximation.AdditiveChi2Sampler._validate_params sklearn.kernel_approximation.AdditiveChi2Sampler.fit sklearn.kernel_approximation.AdditiveChi2Sampler.fit_transform sklearn.kernel_approximation.AdditiveChi2Sampler.get_feature_names_out sklearn.kernel_approximation.AdditiveChi2Sampler.get_metadata_routing sklearn.kernel_approximation.AdditiveChi2Sampler.get_params sklearn.kernel_approximation.AdditiveChi2Sampler.set_output sklearn.kernel_approximation.AdditiveChi2Sampler.set_params sklearn.kernel_approximation.AdditiveChi2Sampler.transform 
+.. minigallery:: sklearn.kernel_approximation.AdditiveChi2Sampler sklearn.kernel_approximation.AdditiveChi2Sampler.__delattr__ sklearn.kernel_approximation.AdditiveChi2Sampler.__dir__ sklearn.kernel_approximation.AdditiveChi2Sampler.__eq__ sklearn.kernel_approximation.AdditiveChi2Sampler.__format__ sklearn.kernel_approximation.AdditiveChi2Sampler.__ge__ sklearn.kernel_approximation.AdditiveChi2Sampler.__getattribute__ sklearn.kernel_approximation.AdditiveChi2Sampler.__getstate__ sklearn.kernel_approximation.AdditiveChi2Sampler.__gt__ sklearn.kernel_approximation.AdditiveChi2Sampler.__hash__ sklearn.kernel_approximation.AdditiveChi2Sampler.__init__ sklearn.kernel_approximation.AdditiveChi2Sampler.__init_subclass__ sklearn.kernel_approximation.AdditiveChi2Sampler.__le__ sklearn.kernel_approximation.AdditiveChi2Sampler.__lt__ sklearn.kernel_approximation.AdditiveChi2Sampler.__ne__ sklearn.kernel_approximation.AdditiveChi2Sampler.__new__ sklearn.kernel_approximation.AdditiveChi2Sampler.__reduce__ sklearn.kernel_approximation.AdditiveChi2Sampler.__reduce_ex__ sklearn.kernel_approximation.AdditiveChi2Sampler.__repr__ sklearn.kernel_approximation.AdditiveChi2Sampler.__setattr__ sklearn.kernel_approximation.AdditiveChi2Sampler.__setstate__ sklearn.kernel_approximation.AdditiveChi2Sampler.__sizeof__ sklearn.kernel_approximation.AdditiveChi2Sampler.__sklearn_clone__ sklearn.kernel_approximation.AdditiveChi2Sampler.__sklearn_tags__ sklearn.kernel_approximation.AdditiveChi2Sampler.__str__ sklearn.kernel_approximation.AdditiveChi2Sampler.__subclasshook__ sklearn.kernel_approximation.AdditiveChi2Sampler._build_request_for_signature sklearn.kernel_approximation.AdditiveChi2Sampler._get_default_requests sklearn.kernel_approximation.AdditiveChi2Sampler._get_doc_link sklearn.kernel_approximation.AdditiveChi2Sampler._get_metadata_request sklearn.kernel_approximation.AdditiveChi2Sampler._get_param_names sklearn.kernel_approximation.AdditiveChi2Sampler._repr_html_inner sklearn.kernel_approximation.AdditiveChi2Sampler._repr_mimebundle_ sklearn.kernel_approximation.AdditiveChi2Sampler._transform_dense sklearn.kernel_approximation.AdditiveChi2Sampler._transform_sparse sklearn.kernel_approximation.AdditiveChi2Sampler._validate_params sklearn.kernel_approximation.AdditiveChi2Sampler.fit sklearn.kernel_approximation.AdditiveChi2Sampler.fit_transform sklearn.kernel_approximation.AdditiveChi2Sampler.get_feature_names_out sklearn.kernel_approximation.AdditiveChi2Sampler.get_metadata_routing sklearn.kernel_approximation.AdditiveChi2Sampler.get_params sklearn.kernel_approximation.AdditiveChi2Sampler.set_output sklearn.kernel_approximation.AdditiveChi2Sampler.set_params sklearn.kernel_approximation.AdditiveChi2Sampler.transform 
    :add-heading: Gallery examples
    :heading-level: -
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diff --git a/dev/_sources/modules/generated/sklearn.kernel_approximation.Nystroem.rst.txt b/dev/_sources/modules/generated/sklearn.kernel_approximation.Nystroem.rst.txt
index 389daec15d0eb..45ef2611e8627 100644
--- a/dev/_sources/modules/generated/sklearn.kernel_approximation.Nystroem.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.kernel_approximation.Nystroem.rst.txt
@@ -8,6 +8,6 @@ Nystroem
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.kernel_approximation.Nystroem sklearn.kernel_approximation.Nystroem.__delattr__ sklearn.kernel_approximation.Nystroem.__dir__ sklearn.kernel_approximation.Nystroem.__eq__ sklearn.kernel_approximation.Nystroem.__format__ sklearn.kernel_approximation.Nystroem.__ge__ sklearn.kernel_approximation.Nystroem.__getattribute__ sklearn.kernel_approximation.Nystroem.__getstate__ sklearn.kernel_approximation.Nystroem.__gt__ sklearn.kernel_approximation.Nystroem.__hash__ sklearn.kernel_approximation.Nystroem.__init__ sklearn.kernel_approximation.Nystroem.__init_subclass__ sklearn.kernel_approximation.Nystroem.__le__ sklearn.kernel_approximation.Nystroem.__lt__ sklearn.kernel_approximation.Nystroem.__ne__ sklearn.kernel_approximation.Nystroem.__new__ sklearn.kernel_approximation.Nystroem.__reduce__ sklearn.kernel_approximation.Nystroem.__reduce_ex__ sklearn.kernel_approximation.Nystroem.__repr__ sklearn.kernel_approximation.Nystroem.__setattr__ sklearn.kernel_approximation.Nystroem.__setstate__ sklearn.kernel_approximation.Nystroem.__sizeof__ sklearn.kernel_approximation.Nystroem.__sklearn_clone__ sklearn.kernel_approximation.Nystroem.__sklearn_tags__ sklearn.kernel_approximation.Nystroem.__str__ sklearn.kernel_approximation.Nystroem.__subclasshook__ sklearn.kernel_approximation.Nystroem._build_request_for_signature sklearn.kernel_approximation.Nystroem._check_feature_names sklearn.kernel_approximation.Nystroem._check_n_features sklearn.kernel_approximation.Nystroem._get_default_requests sklearn.kernel_approximation.Nystroem._get_doc_link sklearn.kernel_approximation.Nystroem._get_kernel_params sklearn.kernel_approximation.Nystroem._get_metadata_request sklearn.kernel_approximation.Nystroem._get_param_names sklearn.kernel_approximation.Nystroem._get_tags sklearn.kernel_approximation.Nystroem._more_tags sklearn.kernel_approximation.Nystroem._repr_html_inner sklearn.kernel_approximation.Nystroem._repr_mimebundle_ sklearn.kernel_approximation.Nystroem._validate_data sklearn.kernel_approximation.Nystroem._validate_params sklearn.kernel_approximation.Nystroem.fit sklearn.kernel_approximation.Nystroem.fit_transform sklearn.kernel_approximation.Nystroem.get_feature_names_out sklearn.kernel_approximation.Nystroem.get_metadata_routing sklearn.kernel_approximation.Nystroem.get_params sklearn.kernel_approximation.Nystroem.set_output sklearn.kernel_approximation.Nystroem.set_params sklearn.kernel_approximation.Nystroem.transform 
+.. minigallery:: sklearn.kernel_approximation.Nystroem sklearn.kernel_approximation.Nystroem.__delattr__ sklearn.kernel_approximation.Nystroem.__dir__ sklearn.kernel_approximation.Nystroem.__eq__ sklearn.kernel_approximation.Nystroem.__format__ sklearn.kernel_approximation.Nystroem.__ge__ sklearn.kernel_approximation.Nystroem.__getattribute__ sklearn.kernel_approximation.Nystroem.__getstate__ sklearn.kernel_approximation.Nystroem.__gt__ sklearn.kernel_approximation.Nystroem.__hash__ sklearn.kernel_approximation.Nystroem.__init__ sklearn.kernel_approximation.Nystroem.__init_subclass__ sklearn.kernel_approximation.Nystroem.__le__ sklearn.kernel_approximation.Nystroem.__lt__ sklearn.kernel_approximation.Nystroem.__ne__ sklearn.kernel_approximation.Nystroem.__new__ sklearn.kernel_approximation.Nystroem.__reduce__ sklearn.kernel_approximation.Nystroem.__reduce_ex__ sklearn.kernel_approximation.Nystroem.__repr__ sklearn.kernel_approximation.Nystroem.__setattr__ sklearn.kernel_approximation.Nystroem.__setstate__ sklearn.kernel_approximation.Nystroem.__sizeof__ sklearn.kernel_approximation.Nystroem.__sklearn_clone__ sklearn.kernel_approximation.Nystroem.__sklearn_tags__ sklearn.kernel_approximation.Nystroem.__str__ sklearn.kernel_approximation.Nystroem.__subclasshook__ sklearn.kernel_approximation.Nystroem._build_request_for_signature sklearn.kernel_approximation.Nystroem._get_default_requests sklearn.kernel_approximation.Nystroem._get_doc_link sklearn.kernel_approximation.Nystroem._get_kernel_params sklearn.kernel_approximation.Nystroem._get_metadata_request sklearn.kernel_approximation.Nystroem._get_param_names sklearn.kernel_approximation.Nystroem._repr_html_inner sklearn.kernel_approximation.Nystroem._repr_mimebundle_ sklearn.kernel_approximation.Nystroem._validate_params sklearn.kernel_approximation.Nystroem.fit sklearn.kernel_approximation.Nystroem.fit_transform sklearn.kernel_approximation.Nystroem.get_feature_names_out sklearn.kernel_approximation.Nystroem.get_metadata_routing sklearn.kernel_approximation.Nystroem.get_params sklearn.kernel_approximation.Nystroem.set_output sklearn.kernel_approximation.Nystroem.set_params sklearn.kernel_approximation.Nystroem.transform 
    :add-heading: Gallery examples
    :heading-level: -
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diff --git a/dev/_sources/modules/generated/sklearn.kernel_approximation.PolynomialCountSketch.rst.txt b/dev/_sources/modules/generated/sklearn.kernel_approximation.PolynomialCountSketch.rst.txt
index c3fa549d7b376..2f21541683e26 100644
--- a/dev/_sources/modules/generated/sklearn.kernel_approximation.PolynomialCountSketch.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.kernel_approximation.PolynomialCountSketch.rst.txt
@@ -8,6 +8,6 @@ PolynomialCountSketch
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.kernel_approximation.PolynomialCountSketch sklearn.kernel_approximation.PolynomialCountSketch.__delattr__ sklearn.kernel_approximation.PolynomialCountSketch.__dir__ sklearn.kernel_approximation.PolynomialCountSketch.__eq__ sklearn.kernel_approximation.PolynomialCountSketch.__format__ sklearn.kernel_approximation.PolynomialCountSketch.__ge__ sklearn.kernel_approximation.PolynomialCountSketch.__getattribute__ sklearn.kernel_approximation.PolynomialCountSketch.__getstate__ sklearn.kernel_approximation.PolynomialCountSketch.__gt__ sklearn.kernel_approximation.PolynomialCountSketch.__hash__ sklearn.kernel_approximation.PolynomialCountSketch.__init__ sklearn.kernel_approximation.PolynomialCountSketch.__init_subclass__ sklearn.kernel_approximation.PolynomialCountSketch.__le__ sklearn.kernel_approximation.PolynomialCountSketch.__lt__ sklearn.kernel_approximation.PolynomialCountSketch.__ne__ sklearn.kernel_approximation.PolynomialCountSketch.__new__ sklearn.kernel_approximation.PolynomialCountSketch.__reduce__ sklearn.kernel_approximation.PolynomialCountSketch.__reduce_ex__ sklearn.kernel_approximation.PolynomialCountSketch.__repr__ sklearn.kernel_approximation.PolynomialCountSketch.__setattr__ sklearn.kernel_approximation.PolynomialCountSketch.__setstate__ sklearn.kernel_approximation.PolynomialCountSketch.__sizeof__ sklearn.kernel_approximation.PolynomialCountSketch.__sklearn_clone__ sklearn.kernel_approximation.PolynomialCountSketch.__sklearn_tags__ sklearn.kernel_approximation.PolynomialCountSketch.__str__ sklearn.kernel_approximation.PolynomialCountSketch.__subclasshook__ sklearn.kernel_approximation.PolynomialCountSketch._build_request_for_signature sklearn.kernel_approximation.PolynomialCountSketch._check_feature_names sklearn.kernel_approximation.PolynomialCountSketch._check_n_features sklearn.kernel_approximation.PolynomialCountSketch._get_default_requests sklearn.kernel_approximation.PolynomialCountSketch._get_doc_link sklearn.kernel_approximation.PolynomialCountSketch._get_metadata_request sklearn.kernel_approximation.PolynomialCountSketch._get_param_names sklearn.kernel_approximation.PolynomialCountSketch._get_tags sklearn.kernel_approximation.PolynomialCountSketch._more_tags sklearn.kernel_approximation.PolynomialCountSketch._repr_html_inner sklearn.kernel_approximation.PolynomialCountSketch._repr_mimebundle_ sklearn.kernel_approximation.PolynomialCountSketch._validate_data sklearn.kernel_approximation.PolynomialCountSketch._validate_params sklearn.kernel_approximation.PolynomialCountSketch.fit sklearn.kernel_approximation.PolynomialCountSketch.fit_transform sklearn.kernel_approximation.PolynomialCountSketch.get_feature_names_out sklearn.kernel_approximation.PolynomialCountSketch.get_metadata_routing sklearn.kernel_approximation.PolynomialCountSketch.get_params sklearn.kernel_approximation.PolynomialCountSketch.set_output sklearn.kernel_approximation.PolynomialCountSketch.set_params sklearn.kernel_approximation.PolynomialCountSketch.transform 
+.. minigallery:: sklearn.kernel_approximation.PolynomialCountSketch sklearn.kernel_approximation.PolynomialCountSketch.__delattr__ sklearn.kernel_approximation.PolynomialCountSketch.__dir__ sklearn.kernel_approximation.PolynomialCountSketch.__eq__ sklearn.kernel_approximation.PolynomialCountSketch.__format__ sklearn.kernel_approximation.PolynomialCountSketch.__ge__ sklearn.kernel_approximation.PolynomialCountSketch.__getattribute__ sklearn.kernel_approximation.PolynomialCountSketch.__getstate__ sklearn.kernel_approximation.PolynomialCountSketch.__gt__ sklearn.kernel_approximation.PolynomialCountSketch.__hash__ sklearn.kernel_approximation.PolynomialCountSketch.__init__ sklearn.kernel_approximation.PolynomialCountSketch.__init_subclass__ sklearn.kernel_approximation.PolynomialCountSketch.__le__ sklearn.kernel_approximation.PolynomialCountSketch.__lt__ sklearn.kernel_approximation.PolynomialCountSketch.__ne__ sklearn.kernel_approximation.PolynomialCountSketch.__new__ sklearn.kernel_approximation.PolynomialCountSketch.__reduce__ sklearn.kernel_approximation.PolynomialCountSketch.__reduce_ex__ sklearn.kernel_approximation.PolynomialCountSketch.__repr__ sklearn.kernel_approximation.PolynomialCountSketch.__setattr__ sklearn.kernel_approximation.PolynomialCountSketch.__setstate__ sklearn.kernel_approximation.PolynomialCountSketch.__sizeof__ sklearn.kernel_approximation.PolynomialCountSketch.__sklearn_clone__ sklearn.kernel_approximation.PolynomialCountSketch.__sklearn_tags__ sklearn.kernel_approximation.PolynomialCountSketch.__str__ sklearn.kernel_approximation.PolynomialCountSketch.__subclasshook__ sklearn.kernel_approximation.PolynomialCountSketch._build_request_for_signature sklearn.kernel_approximation.PolynomialCountSketch._get_default_requests sklearn.kernel_approximation.PolynomialCountSketch._get_doc_link sklearn.kernel_approximation.PolynomialCountSketch._get_metadata_request sklearn.kernel_approximation.PolynomialCountSketch._get_param_names sklearn.kernel_approximation.PolynomialCountSketch._repr_html_inner sklearn.kernel_approximation.PolynomialCountSketch._repr_mimebundle_ sklearn.kernel_approximation.PolynomialCountSketch._validate_params sklearn.kernel_approximation.PolynomialCountSketch.fit sklearn.kernel_approximation.PolynomialCountSketch.fit_transform sklearn.kernel_approximation.PolynomialCountSketch.get_feature_names_out sklearn.kernel_approximation.PolynomialCountSketch.get_metadata_routing sklearn.kernel_approximation.PolynomialCountSketch.get_params sklearn.kernel_approximation.PolynomialCountSketch.set_output sklearn.kernel_approximation.PolynomialCountSketch.set_params sklearn.kernel_approximation.PolynomialCountSketch.transform 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.kernel_approximation.RBFSampler.rst.txt b/dev/_sources/modules/generated/sklearn.kernel_approximation.RBFSampler.rst.txt
index 1f0f8b967eaad..0a0095bbc62e4 100644
--- a/dev/_sources/modules/generated/sklearn.kernel_approximation.RBFSampler.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.kernel_approximation.RBFSampler.rst.txt
@@ -8,6 +8,6 @@ RBFSampler
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.kernel_approximation.RBFSampler sklearn.kernel_approximation.RBFSampler.__delattr__ sklearn.kernel_approximation.RBFSampler.__dir__ sklearn.kernel_approximation.RBFSampler.__eq__ sklearn.kernel_approximation.RBFSampler.__format__ sklearn.kernel_approximation.RBFSampler.__ge__ sklearn.kernel_approximation.RBFSampler.__getattribute__ sklearn.kernel_approximation.RBFSampler.__getstate__ sklearn.kernel_approximation.RBFSampler.__gt__ sklearn.kernel_approximation.RBFSampler.__hash__ sklearn.kernel_approximation.RBFSampler.__init__ sklearn.kernel_approximation.RBFSampler.__init_subclass__ sklearn.kernel_approximation.RBFSampler.__le__ sklearn.kernel_approximation.RBFSampler.__lt__ sklearn.kernel_approximation.RBFSampler.__ne__ sklearn.kernel_approximation.RBFSampler.__new__ sklearn.kernel_approximation.RBFSampler.__reduce__ sklearn.kernel_approximation.RBFSampler.__reduce_ex__ sklearn.kernel_approximation.RBFSampler.__repr__ sklearn.kernel_approximation.RBFSampler.__setattr__ sklearn.kernel_approximation.RBFSampler.__setstate__ sklearn.kernel_approximation.RBFSampler.__sizeof__ sklearn.kernel_approximation.RBFSampler.__sklearn_clone__ sklearn.kernel_approximation.RBFSampler.__sklearn_tags__ sklearn.kernel_approximation.RBFSampler.__str__ sklearn.kernel_approximation.RBFSampler.__subclasshook__ sklearn.kernel_approximation.RBFSampler._build_request_for_signature sklearn.kernel_approximation.RBFSampler._check_feature_names sklearn.kernel_approximation.RBFSampler._check_n_features sklearn.kernel_approximation.RBFSampler._get_default_requests sklearn.kernel_approximation.RBFSampler._get_doc_link sklearn.kernel_approximation.RBFSampler._get_metadata_request sklearn.kernel_approximation.RBFSampler._get_param_names sklearn.kernel_approximation.RBFSampler._get_tags sklearn.kernel_approximation.RBFSampler._more_tags sklearn.kernel_approximation.RBFSampler._repr_html_inner sklearn.kernel_approximation.RBFSampler._repr_mimebundle_ sklearn.kernel_approximation.RBFSampler._validate_data sklearn.kernel_approximation.RBFSampler._validate_params sklearn.kernel_approximation.RBFSampler.fit sklearn.kernel_approximation.RBFSampler.fit_transform sklearn.kernel_approximation.RBFSampler.get_feature_names_out sklearn.kernel_approximation.RBFSampler.get_metadata_routing sklearn.kernel_approximation.RBFSampler.get_params sklearn.kernel_approximation.RBFSampler.set_output sklearn.kernel_approximation.RBFSampler.set_params sklearn.kernel_approximation.RBFSampler.transform 
+.. minigallery:: sklearn.kernel_approximation.RBFSampler sklearn.kernel_approximation.RBFSampler.__delattr__ sklearn.kernel_approximation.RBFSampler.__dir__ sklearn.kernel_approximation.RBFSampler.__eq__ sklearn.kernel_approximation.RBFSampler.__format__ sklearn.kernel_approximation.RBFSampler.__ge__ sklearn.kernel_approximation.RBFSampler.__getattribute__ sklearn.kernel_approximation.RBFSampler.__getstate__ sklearn.kernel_approximation.RBFSampler.__gt__ sklearn.kernel_approximation.RBFSampler.__hash__ sklearn.kernel_approximation.RBFSampler.__init__ sklearn.kernel_approximation.RBFSampler.__init_subclass__ sklearn.kernel_approximation.RBFSampler.__le__ sklearn.kernel_approximation.RBFSampler.__lt__ sklearn.kernel_approximation.RBFSampler.__ne__ sklearn.kernel_approximation.RBFSampler.__new__ sklearn.kernel_approximation.RBFSampler.__reduce__ sklearn.kernel_approximation.RBFSampler.__reduce_ex__ sklearn.kernel_approximation.RBFSampler.__repr__ sklearn.kernel_approximation.RBFSampler.__setattr__ sklearn.kernel_approximation.RBFSampler.__setstate__ sklearn.kernel_approximation.RBFSampler.__sizeof__ sklearn.kernel_approximation.RBFSampler.__sklearn_clone__ sklearn.kernel_approximation.RBFSampler.__sklearn_tags__ sklearn.kernel_approximation.RBFSampler.__str__ sklearn.kernel_approximation.RBFSampler.__subclasshook__ sklearn.kernel_approximation.RBFSampler._build_request_for_signature sklearn.kernel_approximation.RBFSampler._get_default_requests sklearn.kernel_approximation.RBFSampler._get_doc_link sklearn.kernel_approximation.RBFSampler._get_metadata_request sklearn.kernel_approximation.RBFSampler._get_param_names sklearn.kernel_approximation.RBFSampler._repr_html_inner sklearn.kernel_approximation.RBFSampler._repr_mimebundle_ sklearn.kernel_approximation.RBFSampler._validate_params sklearn.kernel_approximation.RBFSampler.fit sklearn.kernel_approximation.RBFSampler.fit_transform sklearn.kernel_approximation.RBFSampler.get_feature_names_out sklearn.kernel_approximation.RBFSampler.get_metadata_routing sklearn.kernel_approximation.RBFSampler.get_params sklearn.kernel_approximation.RBFSampler.set_output sklearn.kernel_approximation.RBFSampler.set_params sklearn.kernel_approximation.RBFSampler.transform 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.kernel_approximation.SkewedChi2Sampler.rst.txt b/dev/_sources/modules/generated/sklearn.kernel_approximation.SkewedChi2Sampler.rst.txt
index 07ef3558f422f..a4c0887e39cbf 100644
--- a/dev/_sources/modules/generated/sklearn.kernel_approximation.SkewedChi2Sampler.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.kernel_approximation.SkewedChi2Sampler.rst.txt
@@ -8,6 +8,6 @@ SkewedChi2Sampler
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.kernel_approximation.SkewedChi2Sampler sklearn.kernel_approximation.SkewedChi2Sampler.__delattr__ sklearn.kernel_approximation.SkewedChi2Sampler.__dir__ sklearn.kernel_approximation.SkewedChi2Sampler.__eq__ sklearn.kernel_approximation.SkewedChi2Sampler.__format__ sklearn.kernel_approximation.SkewedChi2Sampler.__ge__ sklearn.kernel_approximation.SkewedChi2Sampler.__getattribute__ sklearn.kernel_approximation.SkewedChi2Sampler.__getstate__ sklearn.kernel_approximation.SkewedChi2Sampler.__gt__ sklearn.kernel_approximation.SkewedChi2Sampler.__hash__ sklearn.kernel_approximation.SkewedChi2Sampler.__init__ sklearn.kernel_approximation.SkewedChi2Sampler.__init_subclass__ sklearn.kernel_approximation.SkewedChi2Sampler.__le__ sklearn.kernel_approximation.SkewedChi2Sampler.__lt__ sklearn.kernel_approximation.SkewedChi2Sampler.__ne__ sklearn.kernel_approximation.SkewedChi2Sampler.__new__ sklearn.kernel_approximation.SkewedChi2Sampler.__reduce__ sklearn.kernel_approximation.SkewedChi2Sampler.__reduce_ex__ sklearn.kernel_approximation.SkewedChi2Sampler.__repr__ sklearn.kernel_approximation.SkewedChi2Sampler.__setattr__ sklearn.kernel_approximation.SkewedChi2Sampler.__setstate__ sklearn.kernel_approximation.SkewedChi2Sampler.__sizeof__ sklearn.kernel_approximation.SkewedChi2Sampler.__sklearn_clone__ sklearn.kernel_approximation.SkewedChi2Sampler.__sklearn_tags__ sklearn.kernel_approximation.SkewedChi2Sampler.__str__ sklearn.kernel_approximation.SkewedChi2Sampler.__subclasshook__ sklearn.kernel_approximation.SkewedChi2Sampler._build_request_for_signature sklearn.kernel_approximation.SkewedChi2Sampler._check_feature_names sklearn.kernel_approximation.SkewedChi2Sampler._check_n_features sklearn.kernel_approximation.SkewedChi2Sampler._get_default_requests sklearn.kernel_approximation.SkewedChi2Sampler._get_doc_link sklearn.kernel_approximation.SkewedChi2Sampler._get_metadata_request sklearn.kernel_approximation.SkewedChi2Sampler._get_param_names sklearn.kernel_approximation.SkewedChi2Sampler._get_tags sklearn.kernel_approximation.SkewedChi2Sampler._more_tags sklearn.kernel_approximation.SkewedChi2Sampler._repr_html_inner sklearn.kernel_approximation.SkewedChi2Sampler._repr_mimebundle_ sklearn.kernel_approximation.SkewedChi2Sampler._validate_data sklearn.kernel_approximation.SkewedChi2Sampler._validate_params sklearn.kernel_approximation.SkewedChi2Sampler.fit sklearn.kernel_approximation.SkewedChi2Sampler.fit_transform sklearn.kernel_approximation.SkewedChi2Sampler.get_feature_names_out sklearn.kernel_approximation.SkewedChi2Sampler.get_metadata_routing sklearn.kernel_approximation.SkewedChi2Sampler.get_params sklearn.kernel_approximation.SkewedChi2Sampler.set_output sklearn.kernel_approximation.SkewedChi2Sampler.set_params sklearn.kernel_approximation.SkewedChi2Sampler.transform 
+.. minigallery:: sklearn.kernel_approximation.SkewedChi2Sampler sklearn.kernel_approximation.SkewedChi2Sampler.__delattr__ sklearn.kernel_approximation.SkewedChi2Sampler.__dir__ sklearn.kernel_approximation.SkewedChi2Sampler.__eq__ sklearn.kernel_approximation.SkewedChi2Sampler.__format__ sklearn.kernel_approximation.SkewedChi2Sampler.__ge__ sklearn.kernel_approximation.SkewedChi2Sampler.__getattribute__ sklearn.kernel_approximation.SkewedChi2Sampler.__getstate__ sklearn.kernel_approximation.SkewedChi2Sampler.__gt__ sklearn.kernel_approximation.SkewedChi2Sampler.__hash__ sklearn.kernel_approximation.SkewedChi2Sampler.__init__ sklearn.kernel_approximation.SkewedChi2Sampler.__init_subclass__ sklearn.kernel_approximation.SkewedChi2Sampler.__le__ sklearn.kernel_approximation.SkewedChi2Sampler.__lt__ sklearn.kernel_approximation.SkewedChi2Sampler.__ne__ sklearn.kernel_approximation.SkewedChi2Sampler.__new__ sklearn.kernel_approximation.SkewedChi2Sampler.__reduce__ sklearn.kernel_approximation.SkewedChi2Sampler.__reduce_ex__ sklearn.kernel_approximation.SkewedChi2Sampler.__repr__ sklearn.kernel_approximation.SkewedChi2Sampler.__setattr__ sklearn.kernel_approximation.SkewedChi2Sampler.__setstate__ sklearn.kernel_approximation.SkewedChi2Sampler.__sizeof__ sklearn.kernel_approximation.SkewedChi2Sampler.__sklearn_clone__ sklearn.kernel_approximation.SkewedChi2Sampler.__sklearn_tags__ sklearn.kernel_approximation.SkewedChi2Sampler.__str__ sklearn.kernel_approximation.SkewedChi2Sampler.__subclasshook__ sklearn.kernel_approximation.SkewedChi2Sampler._build_request_for_signature sklearn.kernel_approximation.SkewedChi2Sampler._get_default_requests sklearn.kernel_approximation.SkewedChi2Sampler._get_doc_link sklearn.kernel_approximation.SkewedChi2Sampler._get_metadata_request sklearn.kernel_approximation.SkewedChi2Sampler._get_param_names sklearn.kernel_approximation.SkewedChi2Sampler._repr_html_inner sklearn.kernel_approximation.SkewedChi2Sampler._repr_mimebundle_ sklearn.kernel_approximation.SkewedChi2Sampler._validate_params sklearn.kernel_approximation.SkewedChi2Sampler.fit sklearn.kernel_approximation.SkewedChi2Sampler.fit_transform sklearn.kernel_approximation.SkewedChi2Sampler.get_feature_names_out sklearn.kernel_approximation.SkewedChi2Sampler.get_metadata_routing sklearn.kernel_approximation.SkewedChi2Sampler.get_params sklearn.kernel_approximation.SkewedChi2Sampler.set_output sklearn.kernel_approximation.SkewedChi2Sampler.set_params sklearn.kernel_approximation.SkewedChi2Sampler.transform 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.kernel_ridge.KernelRidge.rst.txt b/dev/_sources/modules/generated/sklearn.kernel_ridge.KernelRidge.rst.txt
index a521740dd56e8..50d4b6d665cf0 100644
--- a/dev/_sources/modules/generated/sklearn.kernel_ridge.KernelRidge.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.kernel_ridge.KernelRidge.rst.txt
@@ -8,6 +8,6 @@ KernelRidge
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.kernel_ridge.KernelRidge sklearn.kernel_ridge.KernelRidge.__delattr__ sklearn.kernel_ridge.KernelRidge.__dir__ sklearn.kernel_ridge.KernelRidge.__eq__ sklearn.kernel_ridge.KernelRidge.__format__ sklearn.kernel_ridge.KernelRidge.__ge__ sklearn.kernel_ridge.KernelRidge.__getattribute__ sklearn.kernel_ridge.KernelRidge.__getstate__ sklearn.kernel_ridge.KernelRidge.__gt__ sklearn.kernel_ridge.KernelRidge.__hash__ sklearn.kernel_ridge.KernelRidge.__init__ sklearn.kernel_ridge.KernelRidge.__init_subclass__ sklearn.kernel_ridge.KernelRidge.__le__ sklearn.kernel_ridge.KernelRidge.__lt__ sklearn.kernel_ridge.KernelRidge.__ne__ sklearn.kernel_ridge.KernelRidge.__new__ sklearn.kernel_ridge.KernelRidge.__reduce__ sklearn.kernel_ridge.KernelRidge.__reduce_ex__ sklearn.kernel_ridge.KernelRidge.__repr__ sklearn.kernel_ridge.KernelRidge.__setattr__ sklearn.kernel_ridge.KernelRidge.__setstate__ sklearn.kernel_ridge.KernelRidge.__sizeof__ sklearn.kernel_ridge.KernelRidge.__sklearn_clone__ sklearn.kernel_ridge.KernelRidge.__sklearn_tags__ sklearn.kernel_ridge.KernelRidge.__str__ sklearn.kernel_ridge.KernelRidge.__subclasshook__ sklearn.kernel_ridge.KernelRidge._build_request_for_signature sklearn.kernel_ridge.KernelRidge._check_feature_names sklearn.kernel_ridge.KernelRidge._check_n_features sklearn.kernel_ridge.KernelRidge._get_default_requests sklearn.kernel_ridge.KernelRidge._get_doc_link sklearn.kernel_ridge.KernelRidge._get_kernel sklearn.kernel_ridge.KernelRidge._get_metadata_request sklearn.kernel_ridge.KernelRidge._get_param_names sklearn.kernel_ridge.KernelRidge._get_tags sklearn.kernel_ridge.KernelRidge._more_tags sklearn.kernel_ridge.KernelRidge._repr_html_inner sklearn.kernel_ridge.KernelRidge._repr_mimebundle_ sklearn.kernel_ridge.KernelRidge._validate_data sklearn.kernel_ridge.KernelRidge._validate_params sklearn.kernel_ridge.KernelRidge.fit sklearn.kernel_ridge.KernelRidge.get_metadata_routing sklearn.kernel_ridge.KernelRidge.get_params sklearn.kernel_ridge.KernelRidge.predict sklearn.kernel_ridge.KernelRidge.score sklearn.kernel_ridge.KernelRidge.set_fit_request sklearn.kernel_ridge.KernelRidge.set_params sklearn.kernel_ridge.KernelRidge.set_score_request 
+.. minigallery:: sklearn.kernel_ridge.KernelRidge sklearn.kernel_ridge.KernelRidge.__delattr__ sklearn.kernel_ridge.KernelRidge.__dir__ sklearn.kernel_ridge.KernelRidge.__eq__ sklearn.kernel_ridge.KernelRidge.__format__ sklearn.kernel_ridge.KernelRidge.__ge__ sklearn.kernel_ridge.KernelRidge.__getattribute__ sklearn.kernel_ridge.KernelRidge.__getstate__ sklearn.kernel_ridge.KernelRidge.__gt__ sklearn.kernel_ridge.KernelRidge.__hash__ sklearn.kernel_ridge.KernelRidge.__init__ sklearn.kernel_ridge.KernelRidge.__init_subclass__ sklearn.kernel_ridge.KernelRidge.__le__ sklearn.kernel_ridge.KernelRidge.__lt__ sklearn.kernel_ridge.KernelRidge.__ne__ sklearn.kernel_ridge.KernelRidge.__new__ sklearn.kernel_ridge.KernelRidge.__reduce__ sklearn.kernel_ridge.KernelRidge.__reduce_ex__ sklearn.kernel_ridge.KernelRidge.__repr__ sklearn.kernel_ridge.KernelRidge.__setattr__ sklearn.kernel_ridge.KernelRidge.__setstate__ sklearn.kernel_ridge.KernelRidge.__sizeof__ sklearn.kernel_ridge.KernelRidge.__sklearn_clone__ sklearn.kernel_ridge.KernelRidge.__sklearn_tags__ sklearn.kernel_ridge.KernelRidge.__str__ sklearn.kernel_ridge.KernelRidge.__subclasshook__ sklearn.kernel_ridge.KernelRidge._build_request_for_signature sklearn.kernel_ridge.KernelRidge._get_default_requests sklearn.kernel_ridge.KernelRidge._get_doc_link sklearn.kernel_ridge.KernelRidge._get_kernel sklearn.kernel_ridge.KernelRidge._get_metadata_request sklearn.kernel_ridge.KernelRidge._get_param_names sklearn.kernel_ridge.KernelRidge._repr_html_inner sklearn.kernel_ridge.KernelRidge._repr_mimebundle_ sklearn.kernel_ridge.KernelRidge._validate_params sklearn.kernel_ridge.KernelRidge.fit sklearn.kernel_ridge.KernelRidge.get_metadata_routing sklearn.kernel_ridge.KernelRidge.get_params sklearn.kernel_ridge.KernelRidge.predict sklearn.kernel_ridge.KernelRidge.score sklearn.kernel_ridge.KernelRidge.set_fit_request sklearn.kernel_ridge.KernelRidge.set_params sklearn.kernel_ridge.KernelRidge.set_score_request 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.linear_model.ARDRegression.rst.txt b/dev/_sources/modules/generated/sklearn.linear_model.ARDRegression.rst.txt
index ab08bf159b36d..ffaed9208d81e 100644
--- a/dev/_sources/modules/generated/sklearn.linear_model.ARDRegression.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.linear_model.ARDRegression.rst.txt
@@ -8,6 +8,6 @@ ARDRegression
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.linear_model.ARDRegression sklearn.linear_model.ARDRegression.__delattr__ sklearn.linear_model.ARDRegression.__dir__ sklearn.linear_model.ARDRegression.__eq__ sklearn.linear_model.ARDRegression.__format__ sklearn.linear_model.ARDRegression.__ge__ sklearn.linear_model.ARDRegression.__getattribute__ sklearn.linear_model.ARDRegression.__getstate__ sklearn.linear_model.ARDRegression.__gt__ sklearn.linear_model.ARDRegression.__hash__ sklearn.linear_model.ARDRegression.__init__ sklearn.linear_model.ARDRegression.__init_subclass__ sklearn.linear_model.ARDRegression.__le__ sklearn.linear_model.ARDRegression.__lt__ sklearn.linear_model.ARDRegression.__ne__ sklearn.linear_model.ARDRegression.__new__ sklearn.linear_model.ARDRegression.__reduce__ sklearn.linear_model.ARDRegression.__reduce_ex__ sklearn.linear_model.ARDRegression.__repr__ sklearn.linear_model.ARDRegression.__setattr__ sklearn.linear_model.ARDRegression.__setstate__ sklearn.linear_model.ARDRegression.__sizeof__ sklearn.linear_model.ARDRegression.__sklearn_clone__ sklearn.linear_model.ARDRegression.__sklearn_tags__ sklearn.linear_model.ARDRegression.__str__ sklearn.linear_model.ARDRegression.__subclasshook__ sklearn.linear_model.ARDRegression._build_request_for_signature sklearn.linear_model.ARDRegression._check_feature_names sklearn.linear_model.ARDRegression._check_n_features sklearn.linear_model.ARDRegression._decision_function sklearn.linear_model.ARDRegression._get_default_requests sklearn.linear_model.ARDRegression._get_doc_link sklearn.linear_model.ARDRegression._get_metadata_request sklearn.linear_model.ARDRegression._get_param_names sklearn.linear_model.ARDRegression._get_tags sklearn.linear_model.ARDRegression._more_tags sklearn.linear_model.ARDRegression._repr_html_inner sklearn.linear_model.ARDRegression._repr_mimebundle_ sklearn.linear_model.ARDRegression._set_intercept sklearn.linear_model.ARDRegression._update_sigma sklearn.linear_model.ARDRegression._update_sigma_woodbury sklearn.linear_model.ARDRegression._validate_data sklearn.linear_model.ARDRegression._validate_params sklearn.linear_model.ARDRegression.fit sklearn.linear_model.ARDRegression.get_metadata_routing sklearn.linear_model.ARDRegression.get_params sklearn.linear_model.ARDRegression.predict sklearn.linear_model.ARDRegression.score sklearn.linear_model.ARDRegression.set_params sklearn.linear_model.ARDRegression.set_predict_request sklearn.linear_model.ARDRegression.set_score_request 
+.. minigallery:: sklearn.linear_model.ARDRegression sklearn.linear_model.ARDRegression.__delattr__ sklearn.linear_model.ARDRegression.__dir__ sklearn.linear_model.ARDRegression.__eq__ sklearn.linear_model.ARDRegression.__format__ sklearn.linear_model.ARDRegression.__ge__ sklearn.linear_model.ARDRegression.__getattribute__ sklearn.linear_model.ARDRegression.__getstate__ sklearn.linear_model.ARDRegression.__gt__ sklearn.linear_model.ARDRegression.__hash__ sklearn.linear_model.ARDRegression.__init__ sklearn.linear_model.ARDRegression.__init_subclass__ sklearn.linear_model.ARDRegression.__le__ sklearn.linear_model.ARDRegression.__lt__ sklearn.linear_model.ARDRegression.__ne__ sklearn.linear_model.ARDRegression.__new__ sklearn.linear_model.ARDRegression.__reduce__ sklearn.linear_model.ARDRegression.__reduce_ex__ sklearn.linear_model.ARDRegression.__repr__ sklearn.linear_model.ARDRegression.__setattr__ sklearn.linear_model.ARDRegression.__setstate__ sklearn.linear_model.ARDRegression.__sizeof__ sklearn.linear_model.ARDRegression.__sklearn_clone__ sklearn.linear_model.ARDRegression.__sklearn_tags__ sklearn.linear_model.ARDRegression.__str__ sklearn.linear_model.ARDRegression.__subclasshook__ sklearn.linear_model.ARDRegression._build_request_for_signature sklearn.linear_model.ARDRegression._decision_function sklearn.linear_model.ARDRegression._get_default_requests sklearn.linear_model.ARDRegression._get_doc_link sklearn.linear_model.ARDRegression._get_metadata_request sklearn.linear_model.ARDRegression._get_param_names sklearn.linear_model.ARDRegression._repr_html_inner sklearn.linear_model.ARDRegression._repr_mimebundle_ sklearn.linear_model.ARDRegression._set_intercept sklearn.linear_model.ARDRegression._update_sigma sklearn.linear_model.ARDRegression._update_sigma_woodbury sklearn.linear_model.ARDRegression._validate_params sklearn.linear_model.ARDRegression.fit sklearn.linear_model.ARDRegression.get_metadata_routing sklearn.linear_model.ARDRegression.get_params sklearn.linear_model.ARDRegression.predict sklearn.linear_model.ARDRegression.score sklearn.linear_model.ARDRegression.set_params sklearn.linear_model.ARDRegression.set_predict_request sklearn.linear_model.ARDRegression.set_score_request 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.linear_model.BayesianRidge.rst.txt b/dev/_sources/modules/generated/sklearn.linear_model.BayesianRidge.rst.txt
index 1ca1212ad2b6d..3fbc9d1982994 100644
--- a/dev/_sources/modules/generated/sklearn.linear_model.BayesianRidge.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.linear_model.BayesianRidge.rst.txt
@@ -8,6 +8,6 @@ BayesianRidge
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.linear_model.BayesianRidge sklearn.linear_model.BayesianRidge.__delattr__ sklearn.linear_model.BayesianRidge.__dir__ sklearn.linear_model.BayesianRidge.__eq__ sklearn.linear_model.BayesianRidge.__format__ sklearn.linear_model.BayesianRidge.__ge__ sklearn.linear_model.BayesianRidge.__getattribute__ sklearn.linear_model.BayesianRidge.__getstate__ sklearn.linear_model.BayesianRidge.__gt__ sklearn.linear_model.BayesianRidge.__hash__ sklearn.linear_model.BayesianRidge.__init__ sklearn.linear_model.BayesianRidge.__init_subclass__ sklearn.linear_model.BayesianRidge.__le__ sklearn.linear_model.BayesianRidge.__lt__ sklearn.linear_model.BayesianRidge.__ne__ sklearn.linear_model.BayesianRidge.__new__ sklearn.linear_model.BayesianRidge.__reduce__ sklearn.linear_model.BayesianRidge.__reduce_ex__ sklearn.linear_model.BayesianRidge.__repr__ sklearn.linear_model.BayesianRidge.__setattr__ sklearn.linear_model.BayesianRidge.__setstate__ sklearn.linear_model.BayesianRidge.__sizeof__ sklearn.linear_model.BayesianRidge.__sklearn_clone__ sklearn.linear_model.BayesianRidge.__sklearn_tags__ sklearn.linear_model.BayesianRidge.__str__ sklearn.linear_model.BayesianRidge.__subclasshook__ sklearn.linear_model.BayesianRidge._build_request_for_signature sklearn.linear_model.BayesianRidge._check_feature_names sklearn.linear_model.BayesianRidge._check_n_features sklearn.linear_model.BayesianRidge._decision_function sklearn.linear_model.BayesianRidge._get_default_requests sklearn.linear_model.BayesianRidge._get_doc_link sklearn.linear_model.BayesianRidge._get_metadata_request sklearn.linear_model.BayesianRidge._get_param_names sklearn.linear_model.BayesianRidge._get_tags sklearn.linear_model.BayesianRidge._log_marginal_likelihood sklearn.linear_model.BayesianRidge._more_tags sklearn.linear_model.BayesianRidge._repr_html_inner sklearn.linear_model.BayesianRidge._repr_mimebundle_ sklearn.linear_model.BayesianRidge._set_intercept sklearn.linear_model.BayesianRidge._update_coef_ sklearn.linear_model.BayesianRidge._validate_data sklearn.linear_model.BayesianRidge._validate_params sklearn.linear_model.BayesianRidge.fit sklearn.linear_model.BayesianRidge.get_metadata_routing sklearn.linear_model.BayesianRidge.get_params sklearn.linear_model.BayesianRidge.predict sklearn.linear_model.BayesianRidge.score sklearn.linear_model.BayesianRidge.set_fit_request sklearn.linear_model.BayesianRidge.set_params sklearn.linear_model.BayesianRidge.set_predict_request sklearn.linear_model.BayesianRidge.set_score_request 
+.. minigallery:: sklearn.linear_model.BayesianRidge sklearn.linear_model.BayesianRidge.__delattr__ sklearn.linear_model.BayesianRidge.__dir__ sklearn.linear_model.BayesianRidge.__eq__ sklearn.linear_model.BayesianRidge.__format__ sklearn.linear_model.BayesianRidge.__ge__ sklearn.linear_model.BayesianRidge.__getattribute__ sklearn.linear_model.BayesianRidge.__getstate__ sklearn.linear_model.BayesianRidge.__gt__ sklearn.linear_model.BayesianRidge.__hash__ sklearn.linear_model.BayesianRidge.__init__ sklearn.linear_model.BayesianRidge.__init_subclass__ sklearn.linear_model.BayesianRidge.__le__ sklearn.linear_model.BayesianRidge.__lt__ sklearn.linear_model.BayesianRidge.__ne__ sklearn.linear_model.BayesianRidge.__new__ sklearn.linear_model.BayesianRidge.__reduce__ sklearn.linear_model.BayesianRidge.__reduce_ex__ sklearn.linear_model.BayesianRidge.__repr__ sklearn.linear_model.BayesianRidge.__setattr__ sklearn.linear_model.BayesianRidge.__setstate__ sklearn.linear_model.BayesianRidge.__sizeof__ sklearn.linear_model.BayesianRidge.__sklearn_clone__ sklearn.linear_model.BayesianRidge.__sklearn_tags__ sklearn.linear_model.BayesianRidge.__str__ sklearn.linear_model.BayesianRidge.__subclasshook__ sklearn.linear_model.BayesianRidge._build_request_for_signature sklearn.linear_model.BayesianRidge._decision_function sklearn.linear_model.BayesianRidge._get_default_requests sklearn.linear_model.BayesianRidge._get_doc_link sklearn.linear_model.BayesianRidge._get_metadata_request sklearn.linear_model.BayesianRidge._get_param_names sklearn.linear_model.BayesianRidge._log_marginal_likelihood sklearn.linear_model.BayesianRidge._repr_html_inner sklearn.linear_model.BayesianRidge._repr_mimebundle_ sklearn.linear_model.BayesianRidge._set_intercept sklearn.linear_model.BayesianRidge._update_coef_ sklearn.linear_model.BayesianRidge._validate_params sklearn.linear_model.BayesianRidge.fit sklearn.linear_model.BayesianRidge.get_metadata_routing sklearn.linear_model.BayesianRidge.get_params sklearn.linear_model.BayesianRidge.predict sklearn.linear_model.BayesianRidge.score sklearn.linear_model.BayesianRidge.set_fit_request sklearn.linear_model.BayesianRidge.set_params sklearn.linear_model.BayesianRidge.set_predict_request sklearn.linear_model.BayesianRidge.set_score_request 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.linear_model.ElasticNet.rst.txt b/dev/_sources/modules/generated/sklearn.linear_model.ElasticNet.rst.txt
index d0150c18844f9..a0099696c2ee8 100644
--- a/dev/_sources/modules/generated/sklearn.linear_model.ElasticNet.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.linear_model.ElasticNet.rst.txt
@@ -8,6 +8,6 @@ ElasticNet
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.linear_model.ElasticNet sklearn.linear_model.ElasticNet.__delattr__ sklearn.linear_model.ElasticNet.__dir__ sklearn.linear_model.ElasticNet.__eq__ sklearn.linear_model.ElasticNet.__format__ sklearn.linear_model.ElasticNet.__ge__ sklearn.linear_model.ElasticNet.__getattribute__ sklearn.linear_model.ElasticNet.__getstate__ sklearn.linear_model.ElasticNet.__gt__ sklearn.linear_model.ElasticNet.__hash__ sklearn.linear_model.ElasticNet.__init__ sklearn.linear_model.ElasticNet.__init_subclass__ sklearn.linear_model.ElasticNet.__le__ sklearn.linear_model.ElasticNet.__lt__ sklearn.linear_model.ElasticNet.__ne__ sklearn.linear_model.ElasticNet.__new__ sklearn.linear_model.ElasticNet.__reduce__ sklearn.linear_model.ElasticNet.__reduce_ex__ sklearn.linear_model.ElasticNet.__repr__ sklearn.linear_model.ElasticNet.__setattr__ sklearn.linear_model.ElasticNet.__setstate__ sklearn.linear_model.ElasticNet.__sizeof__ sklearn.linear_model.ElasticNet.__sklearn_clone__ sklearn.linear_model.ElasticNet.__sklearn_tags__ sklearn.linear_model.ElasticNet.__str__ sklearn.linear_model.ElasticNet.__subclasshook__ sklearn.linear_model.ElasticNet._build_request_for_signature sklearn.linear_model.ElasticNet._check_feature_names sklearn.linear_model.ElasticNet._check_n_features sklearn.linear_model.ElasticNet._decision_function sklearn.linear_model.ElasticNet._get_default_requests sklearn.linear_model.ElasticNet._get_doc_link sklearn.linear_model.ElasticNet._get_metadata_request sklearn.linear_model.ElasticNet._get_param_names sklearn.linear_model.ElasticNet._get_tags sklearn.linear_model.ElasticNet._more_tags sklearn.linear_model.ElasticNet._repr_html_inner sklearn.linear_model.ElasticNet._repr_mimebundle_ sklearn.linear_model.ElasticNet._set_intercept sklearn.linear_model.ElasticNet._validate_data sklearn.linear_model.ElasticNet._validate_params sklearn.linear_model.ElasticNet.fit sklearn.linear_model.ElasticNet.get_metadata_routing sklearn.linear_model.ElasticNet.get_params sklearn.linear_model.ElasticNet.path sklearn.linear_model.ElasticNet.predict sklearn.linear_model.ElasticNet.score sklearn.linear_model.ElasticNet.set_fit_request sklearn.linear_model.ElasticNet.set_params sklearn.linear_model.ElasticNet.set_score_request 
+.. minigallery:: sklearn.linear_model.ElasticNet sklearn.linear_model.ElasticNet.__delattr__ sklearn.linear_model.ElasticNet.__dir__ sklearn.linear_model.ElasticNet.__eq__ sklearn.linear_model.ElasticNet.__format__ sklearn.linear_model.ElasticNet.__ge__ sklearn.linear_model.ElasticNet.__getattribute__ sklearn.linear_model.ElasticNet.__getstate__ sklearn.linear_model.ElasticNet.__gt__ sklearn.linear_model.ElasticNet.__hash__ sklearn.linear_model.ElasticNet.__init__ sklearn.linear_model.ElasticNet.__init_subclass__ sklearn.linear_model.ElasticNet.__le__ sklearn.linear_model.ElasticNet.__lt__ sklearn.linear_model.ElasticNet.__ne__ sklearn.linear_model.ElasticNet.__new__ sklearn.linear_model.ElasticNet.__reduce__ sklearn.linear_model.ElasticNet.__reduce_ex__ sklearn.linear_model.ElasticNet.__repr__ sklearn.linear_model.ElasticNet.__setattr__ sklearn.linear_model.ElasticNet.__setstate__ sklearn.linear_model.ElasticNet.__sizeof__ sklearn.linear_model.ElasticNet.__sklearn_clone__ sklearn.linear_model.ElasticNet.__sklearn_tags__ sklearn.linear_model.ElasticNet.__str__ sklearn.linear_model.ElasticNet.__subclasshook__ sklearn.linear_model.ElasticNet._build_request_for_signature sklearn.linear_model.ElasticNet._decision_function sklearn.linear_model.ElasticNet._get_default_requests sklearn.linear_model.ElasticNet._get_doc_link sklearn.linear_model.ElasticNet._get_metadata_request sklearn.linear_model.ElasticNet._get_param_names sklearn.linear_model.ElasticNet._repr_html_inner sklearn.linear_model.ElasticNet._repr_mimebundle_ sklearn.linear_model.ElasticNet._set_intercept sklearn.linear_model.ElasticNet._validate_params sklearn.linear_model.ElasticNet.fit sklearn.linear_model.ElasticNet.get_metadata_routing sklearn.linear_model.ElasticNet.get_params sklearn.linear_model.ElasticNet.path sklearn.linear_model.ElasticNet.predict sklearn.linear_model.ElasticNet.score sklearn.linear_model.ElasticNet.set_fit_request sklearn.linear_model.ElasticNet.set_params sklearn.linear_model.ElasticNet.set_score_request 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.linear_model.ElasticNetCV.rst.txt b/dev/_sources/modules/generated/sklearn.linear_model.ElasticNetCV.rst.txt
index 11b93563cb74a..1b4111eb0b2c6 100644
--- a/dev/_sources/modules/generated/sklearn.linear_model.ElasticNetCV.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.linear_model.ElasticNetCV.rst.txt
@@ -8,6 +8,6 @@ ElasticNetCV
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.linear_model.ElasticNetCV sklearn.linear_model.ElasticNetCV.__delattr__ sklearn.linear_model.ElasticNetCV.__dir__ sklearn.linear_model.ElasticNetCV.__eq__ sklearn.linear_model.ElasticNetCV.__format__ sklearn.linear_model.ElasticNetCV.__ge__ sklearn.linear_model.ElasticNetCV.__getattribute__ sklearn.linear_model.ElasticNetCV.__getstate__ sklearn.linear_model.ElasticNetCV.__gt__ sklearn.linear_model.ElasticNetCV.__hash__ sklearn.linear_model.ElasticNetCV.__init__ sklearn.linear_model.ElasticNetCV.__init_subclass__ sklearn.linear_model.ElasticNetCV.__le__ sklearn.linear_model.ElasticNetCV.__lt__ sklearn.linear_model.ElasticNetCV.__ne__ sklearn.linear_model.ElasticNetCV.__new__ sklearn.linear_model.ElasticNetCV.__reduce__ sklearn.linear_model.ElasticNetCV.__reduce_ex__ sklearn.linear_model.ElasticNetCV.__repr__ sklearn.linear_model.ElasticNetCV.__setattr__ sklearn.linear_model.ElasticNetCV.__setstate__ sklearn.linear_model.ElasticNetCV.__sizeof__ sklearn.linear_model.ElasticNetCV.__sklearn_clone__ sklearn.linear_model.ElasticNetCV.__sklearn_tags__ sklearn.linear_model.ElasticNetCV.__str__ sklearn.linear_model.ElasticNetCV.__subclasshook__ sklearn.linear_model.ElasticNetCV._build_request_for_signature sklearn.linear_model.ElasticNetCV._check_feature_names sklearn.linear_model.ElasticNetCV._check_n_features sklearn.linear_model.ElasticNetCV._decision_function sklearn.linear_model.ElasticNetCV._get_default_requests sklearn.linear_model.ElasticNetCV._get_doc_link sklearn.linear_model.ElasticNetCV._get_estimator sklearn.linear_model.ElasticNetCV._get_metadata_request sklearn.linear_model.ElasticNetCV._get_param_names sklearn.linear_model.ElasticNetCV._get_tags sklearn.linear_model.ElasticNetCV._is_multitask sklearn.linear_model.ElasticNetCV._more_tags sklearn.linear_model.ElasticNetCV._repr_html_inner sklearn.linear_model.ElasticNetCV._repr_mimebundle_ sklearn.linear_model.ElasticNetCV._set_intercept sklearn.linear_model.ElasticNetCV._validate_data sklearn.linear_model.ElasticNetCV._validate_params sklearn.linear_model.ElasticNetCV.fit sklearn.linear_model.ElasticNetCV.get_metadata_routing sklearn.linear_model.ElasticNetCV.get_params sklearn.linear_model.ElasticNetCV.path sklearn.linear_model.ElasticNetCV.predict sklearn.linear_model.ElasticNetCV.score sklearn.linear_model.ElasticNetCV.set_fit_request sklearn.linear_model.ElasticNetCV.set_params sklearn.linear_model.ElasticNetCV.set_score_request 
+.. minigallery:: sklearn.linear_model.ElasticNetCV sklearn.linear_model.ElasticNetCV.__delattr__ sklearn.linear_model.ElasticNetCV.__dir__ sklearn.linear_model.ElasticNetCV.__eq__ sklearn.linear_model.ElasticNetCV.__format__ sklearn.linear_model.ElasticNetCV.__ge__ sklearn.linear_model.ElasticNetCV.__getattribute__ sklearn.linear_model.ElasticNetCV.__getstate__ sklearn.linear_model.ElasticNetCV.__gt__ sklearn.linear_model.ElasticNetCV.__hash__ sklearn.linear_model.ElasticNetCV.__init__ sklearn.linear_model.ElasticNetCV.__init_subclass__ sklearn.linear_model.ElasticNetCV.__le__ sklearn.linear_model.ElasticNetCV.__lt__ sklearn.linear_model.ElasticNetCV.__ne__ sklearn.linear_model.ElasticNetCV.__new__ sklearn.linear_model.ElasticNetCV.__reduce__ sklearn.linear_model.ElasticNetCV.__reduce_ex__ sklearn.linear_model.ElasticNetCV.__repr__ sklearn.linear_model.ElasticNetCV.__setattr__ sklearn.linear_model.ElasticNetCV.__setstate__ sklearn.linear_model.ElasticNetCV.__sizeof__ sklearn.linear_model.ElasticNetCV.__sklearn_clone__ sklearn.linear_model.ElasticNetCV.__sklearn_tags__ sklearn.linear_model.ElasticNetCV.__str__ sklearn.linear_model.ElasticNetCV.__subclasshook__ sklearn.linear_model.ElasticNetCV._build_request_for_signature sklearn.linear_model.ElasticNetCV._decision_function sklearn.linear_model.ElasticNetCV._get_default_requests sklearn.linear_model.ElasticNetCV._get_doc_link sklearn.linear_model.ElasticNetCV._get_estimator sklearn.linear_model.ElasticNetCV._get_metadata_request sklearn.linear_model.ElasticNetCV._get_param_names sklearn.linear_model.ElasticNetCV._is_multitask sklearn.linear_model.ElasticNetCV._repr_html_inner sklearn.linear_model.ElasticNetCV._repr_mimebundle_ sklearn.linear_model.ElasticNetCV._set_intercept sklearn.linear_model.ElasticNetCV._validate_params sklearn.linear_model.ElasticNetCV.fit sklearn.linear_model.ElasticNetCV.get_metadata_routing sklearn.linear_model.ElasticNetCV.get_params sklearn.linear_model.ElasticNetCV.path sklearn.linear_model.ElasticNetCV.predict sklearn.linear_model.ElasticNetCV.score sklearn.linear_model.ElasticNetCV.set_fit_request sklearn.linear_model.ElasticNetCV.set_params sklearn.linear_model.ElasticNetCV.set_score_request 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.linear_model.GammaRegressor.rst.txt b/dev/_sources/modules/generated/sklearn.linear_model.GammaRegressor.rst.txt
index 66e685dd04f16..1be91dd6d0a7f 100644
--- a/dev/_sources/modules/generated/sklearn.linear_model.GammaRegressor.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.linear_model.GammaRegressor.rst.txt
@@ -8,6 +8,6 @@ GammaRegressor
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.linear_model.GammaRegressor sklearn.linear_model.GammaRegressor.__delattr__ sklearn.linear_model.GammaRegressor.__dir__ sklearn.linear_model.GammaRegressor.__eq__ sklearn.linear_model.GammaRegressor.__format__ sklearn.linear_model.GammaRegressor.__ge__ sklearn.linear_model.GammaRegressor.__getattribute__ sklearn.linear_model.GammaRegressor.__getstate__ sklearn.linear_model.GammaRegressor.__gt__ sklearn.linear_model.GammaRegressor.__hash__ sklearn.linear_model.GammaRegressor.__init__ sklearn.linear_model.GammaRegressor.__init_subclass__ sklearn.linear_model.GammaRegressor.__le__ sklearn.linear_model.GammaRegressor.__lt__ sklearn.linear_model.GammaRegressor.__ne__ sklearn.linear_model.GammaRegressor.__new__ sklearn.linear_model.GammaRegressor.__reduce__ sklearn.linear_model.GammaRegressor.__reduce_ex__ sklearn.linear_model.GammaRegressor.__repr__ sklearn.linear_model.GammaRegressor.__setattr__ sklearn.linear_model.GammaRegressor.__setstate__ sklearn.linear_model.GammaRegressor.__sizeof__ sklearn.linear_model.GammaRegressor.__sklearn_clone__ sklearn.linear_model.GammaRegressor.__sklearn_tags__ sklearn.linear_model.GammaRegressor.__str__ sklearn.linear_model.GammaRegressor.__subclasshook__ sklearn.linear_model.GammaRegressor._build_request_for_signature sklearn.linear_model.GammaRegressor._check_feature_names sklearn.linear_model.GammaRegressor._check_n_features sklearn.linear_model.GammaRegressor._get_default_requests sklearn.linear_model.GammaRegressor._get_doc_link sklearn.linear_model.GammaRegressor._get_loss sklearn.linear_model.GammaRegressor._get_metadata_request sklearn.linear_model.GammaRegressor._get_param_names sklearn.linear_model.GammaRegressor._get_tags sklearn.linear_model.GammaRegressor._linear_predictor sklearn.linear_model.GammaRegressor._more_tags sklearn.linear_model.GammaRegressor._repr_html_inner sklearn.linear_model.GammaRegressor._repr_mimebundle_ sklearn.linear_model.GammaRegressor._validate_data sklearn.linear_model.GammaRegressor._validate_params sklearn.linear_model.GammaRegressor.fit sklearn.linear_model.GammaRegressor.get_metadata_routing sklearn.linear_model.GammaRegressor.get_params sklearn.linear_model.GammaRegressor.predict sklearn.linear_model.GammaRegressor.score sklearn.linear_model.GammaRegressor.set_fit_request sklearn.linear_model.GammaRegressor.set_params sklearn.linear_model.GammaRegressor.set_score_request 
+.. minigallery:: sklearn.linear_model.GammaRegressor sklearn.linear_model.GammaRegressor.__delattr__ sklearn.linear_model.GammaRegressor.__dir__ sklearn.linear_model.GammaRegressor.__eq__ sklearn.linear_model.GammaRegressor.__format__ sklearn.linear_model.GammaRegressor.__ge__ sklearn.linear_model.GammaRegressor.__getattribute__ sklearn.linear_model.GammaRegressor.__getstate__ sklearn.linear_model.GammaRegressor.__gt__ sklearn.linear_model.GammaRegressor.__hash__ sklearn.linear_model.GammaRegressor.__init__ sklearn.linear_model.GammaRegressor.__init_subclass__ sklearn.linear_model.GammaRegressor.__le__ sklearn.linear_model.GammaRegressor.__lt__ sklearn.linear_model.GammaRegressor.__ne__ sklearn.linear_model.GammaRegressor.__new__ sklearn.linear_model.GammaRegressor.__reduce__ sklearn.linear_model.GammaRegressor.__reduce_ex__ sklearn.linear_model.GammaRegressor.__repr__ sklearn.linear_model.GammaRegressor.__setattr__ sklearn.linear_model.GammaRegressor.__setstate__ sklearn.linear_model.GammaRegressor.__sizeof__ sklearn.linear_model.GammaRegressor.__sklearn_clone__ sklearn.linear_model.GammaRegressor.__sklearn_tags__ sklearn.linear_model.GammaRegressor.__str__ sklearn.linear_model.GammaRegressor.__subclasshook__ sklearn.linear_model.GammaRegressor._build_request_for_signature sklearn.linear_model.GammaRegressor._get_default_requests sklearn.linear_model.GammaRegressor._get_doc_link sklearn.linear_model.GammaRegressor._get_loss sklearn.linear_model.GammaRegressor._get_metadata_request sklearn.linear_model.GammaRegressor._get_param_names sklearn.linear_model.GammaRegressor._linear_predictor sklearn.linear_model.GammaRegressor._repr_html_inner sklearn.linear_model.GammaRegressor._repr_mimebundle_ sklearn.linear_model.GammaRegressor._validate_params sklearn.linear_model.GammaRegressor.fit sklearn.linear_model.GammaRegressor.get_metadata_routing sklearn.linear_model.GammaRegressor.get_params sklearn.linear_model.GammaRegressor.predict sklearn.linear_model.GammaRegressor.score sklearn.linear_model.GammaRegressor.set_fit_request sklearn.linear_model.GammaRegressor.set_params sklearn.linear_model.GammaRegressor.set_score_request 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.linear_model.HuberRegressor.rst.txt b/dev/_sources/modules/generated/sklearn.linear_model.HuberRegressor.rst.txt
index ec2f6baaba400..c5878a5d9dd12 100644
--- a/dev/_sources/modules/generated/sklearn.linear_model.HuberRegressor.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.linear_model.HuberRegressor.rst.txt
@@ -8,6 +8,6 @@ HuberRegressor
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.linear_model.HuberRegressor sklearn.linear_model.HuberRegressor.__delattr__ sklearn.linear_model.HuberRegressor.__dir__ sklearn.linear_model.HuberRegressor.__eq__ sklearn.linear_model.HuberRegressor.__format__ sklearn.linear_model.HuberRegressor.__ge__ sklearn.linear_model.HuberRegressor.__getattribute__ sklearn.linear_model.HuberRegressor.__getstate__ sklearn.linear_model.HuberRegressor.__gt__ sklearn.linear_model.HuberRegressor.__hash__ sklearn.linear_model.HuberRegressor.__init__ sklearn.linear_model.HuberRegressor.__init_subclass__ sklearn.linear_model.HuberRegressor.__le__ sklearn.linear_model.HuberRegressor.__lt__ sklearn.linear_model.HuberRegressor.__ne__ sklearn.linear_model.HuberRegressor.__new__ sklearn.linear_model.HuberRegressor.__reduce__ sklearn.linear_model.HuberRegressor.__reduce_ex__ sklearn.linear_model.HuberRegressor.__repr__ sklearn.linear_model.HuberRegressor.__setattr__ sklearn.linear_model.HuberRegressor.__setstate__ sklearn.linear_model.HuberRegressor.__sizeof__ sklearn.linear_model.HuberRegressor.__sklearn_clone__ sklearn.linear_model.HuberRegressor.__sklearn_tags__ sklearn.linear_model.HuberRegressor.__str__ sklearn.linear_model.HuberRegressor.__subclasshook__ sklearn.linear_model.HuberRegressor._build_request_for_signature sklearn.linear_model.HuberRegressor._check_feature_names sklearn.linear_model.HuberRegressor._check_n_features sklearn.linear_model.HuberRegressor._decision_function sklearn.linear_model.HuberRegressor._get_default_requests sklearn.linear_model.HuberRegressor._get_doc_link sklearn.linear_model.HuberRegressor._get_metadata_request sklearn.linear_model.HuberRegressor._get_param_names sklearn.linear_model.HuberRegressor._get_tags sklearn.linear_model.HuberRegressor._more_tags sklearn.linear_model.HuberRegressor._repr_html_inner sklearn.linear_model.HuberRegressor._repr_mimebundle_ sklearn.linear_model.HuberRegressor._set_intercept sklearn.linear_model.HuberRegressor._validate_data sklearn.linear_model.HuberRegressor._validate_params sklearn.linear_model.HuberRegressor.fit sklearn.linear_model.HuberRegressor.get_metadata_routing sklearn.linear_model.HuberRegressor.get_params sklearn.linear_model.HuberRegressor.predict sklearn.linear_model.HuberRegressor.score sklearn.linear_model.HuberRegressor.set_fit_request sklearn.linear_model.HuberRegressor.set_params sklearn.linear_model.HuberRegressor.set_score_request 
+.. minigallery:: sklearn.linear_model.HuberRegressor sklearn.linear_model.HuberRegressor.__delattr__ sklearn.linear_model.HuberRegressor.__dir__ sklearn.linear_model.HuberRegressor.__eq__ sklearn.linear_model.HuberRegressor.__format__ sklearn.linear_model.HuberRegressor.__ge__ sklearn.linear_model.HuberRegressor.__getattribute__ sklearn.linear_model.HuberRegressor.__getstate__ sklearn.linear_model.HuberRegressor.__gt__ sklearn.linear_model.HuberRegressor.__hash__ sklearn.linear_model.HuberRegressor.__init__ sklearn.linear_model.HuberRegressor.__init_subclass__ sklearn.linear_model.HuberRegressor.__le__ sklearn.linear_model.HuberRegressor.__lt__ sklearn.linear_model.HuberRegressor.__ne__ sklearn.linear_model.HuberRegressor.__new__ sklearn.linear_model.HuberRegressor.__reduce__ sklearn.linear_model.HuberRegressor.__reduce_ex__ sklearn.linear_model.HuberRegressor.__repr__ sklearn.linear_model.HuberRegressor.__setattr__ sklearn.linear_model.HuberRegressor.__setstate__ sklearn.linear_model.HuberRegressor.__sizeof__ sklearn.linear_model.HuberRegressor.__sklearn_clone__ sklearn.linear_model.HuberRegressor.__sklearn_tags__ sklearn.linear_model.HuberRegressor.__str__ sklearn.linear_model.HuberRegressor.__subclasshook__ sklearn.linear_model.HuberRegressor._build_request_for_signature sklearn.linear_model.HuberRegressor._decision_function sklearn.linear_model.HuberRegressor._get_default_requests sklearn.linear_model.HuberRegressor._get_doc_link sklearn.linear_model.HuberRegressor._get_metadata_request sklearn.linear_model.HuberRegressor._get_param_names sklearn.linear_model.HuberRegressor._repr_html_inner sklearn.linear_model.HuberRegressor._repr_mimebundle_ sklearn.linear_model.HuberRegressor._set_intercept sklearn.linear_model.HuberRegressor._validate_params sklearn.linear_model.HuberRegressor.fit sklearn.linear_model.HuberRegressor.get_metadata_routing sklearn.linear_model.HuberRegressor.get_params sklearn.linear_model.HuberRegressor.predict sklearn.linear_model.HuberRegressor.score sklearn.linear_model.HuberRegressor.set_fit_request sklearn.linear_model.HuberRegressor.set_params sklearn.linear_model.HuberRegressor.set_score_request 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.linear_model.Lars.rst.txt b/dev/_sources/modules/generated/sklearn.linear_model.Lars.rst.txt
index 57fec4009fb61..a4d5e140ea3c4 100644
--- a/dev/_sources/modules/generated/sklearn.linear_model.Lars.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.linear_model.Lars.rst.txt
@@ -8,6 +8,6 @@ Lars
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.linear_model.Lars sklearn.linear_model.Lars.__delattr__ sklearn.linear_model.Lars.__dir__ sklearn.linear_model.Lars.__eq__ sklearn.linear_model.Lars.__format__ sklearn.linear_model.Lars.__ge__ sklearn.linear_model.Lars.__getattribute__ sklearn.linear_model.Lars.__getstate__ sklearn.linear_model.Lars.__gt__ sklearn.linear_model.Lars.__hash__ sklearn.linear_model.Lars.__init__ sklearn.linear_model.Lars.__init_subclass__ sklearn.linear_model.Lars.__le__ sklearn.linear_model.Lars.__lt__ sklearn.linear_model.Lars.__ne__ sklearn.linear_model.Lars.__new__ sklearn.linear_model.Lars.__reduce__ sklearn.linear_model.Lars.__reduce_ex__ sklearn.linear_model.Lars.__repr__ sklearn.linear_model.Lars.__setattr__ sklearn.linear_model.Lars.__setstate__ sklearn.linear_model.Lars.__sizeof__ sklearn.linear_model.Lars.__sklearn_clone__ sklearn.linear_model.Lars.__sklearn_tags__ sklearn.linear_model.Lars.__str__ sklearn.linear_model.Lars.__subclasshook__ sklearn.linear_model.Lars._build_request_for_signature sklearn.linear_model.Lars._check_feature_names sklearn.linear_model.Lars._check_n_features sklearn.linear_model.Lars._decision_function sklearn.linear_model.Lars._fit sklearn.linear_model.Lars._get_default_requests sklearn.linear_model.Lars._get_doc_link sklearn.linear_model.Lars._get_gram sklearn.linear_model.Lars._get_metadata_request sklearn.linear_model.Lars._get_param_names sklearn.linear_model.Lars._get_tags sklearn.linear_model.Lars._more_tags sklearn.linear_model.Lars._repr_html_inner sklearn.linear_model.Lars._repr_mimebundle_ sklearn.linear_model.Lars._set_intercept sklearn.linear_model.Lars._validate_data sklearn.linear_model.Lars._validate_params sklearn.linear_model.Lars.fit sklearn.linear_model.Lars.get_metadata_routing sklearn.linear_model.Lars.get_params sklearn.linear_model.Lars.predict sklearn.linear_model.Lars.score sklearn.linear_model.Lars.set_fit_request sklearn.linear_model.Lars.set_params sklearn.linear_model.Lars.set_score_request 
+.. minigallery:: sklearn.linear_model.Lars sklearn.linear_model.Lars.__delattr__ sklearn.linear_model.Lars.__dir__ sklearn.linear_model.Lars.__eq__ sklearn.linear_model.Lars.__format__ sklearn.linear_model.Lars.__ge__ sklearn.linear_model.Lars.__getattribute__ sklearn.linear_model.Lars.__getstate__ sklearn.linear_model.Lars.__gt__ sklearn.linear_model.Lars.__hash__ sklearn.linear_model.Lars.__init__ sklearn.linear_model.Lars.__init_subclass__ sklearn.linear_model.Lars.__le__ sklearn.linear_model.Lars.__lt__ sklearn.linear_model.Lars.__ne__ sklearn.linear_model.Lars.__new__ sklearn.linear_model.Lars.__reduce__ sklearn.linear_model.Lars.__reduce_ex__ sklearn.linear_model.Lars.__repr__ sklearn.linear_model.Lars.__setattr__ sklearn.linear_model.Lars.__setstate__ sklearn.linear_model.Lars.__sizeof__ sklearn.linear_model.Lars.__sklearn_clone__ sklearn.linear_model.Lars.__sklearn_tags__ sklearn.linear_model.Lars.__str__ sklearn.linear_model.Lars.__subclasshook__ sklearn.linear_model.Lars._build_request_for_signature sklearn.linear_model.Lars._decision_function sklearn.linear_model.Lars._fit sklearn.linear_model.Lars._get_default_requests sklearn.linear_model.Lars._get_doc_link sklearn.linear_model.Lars._get_gram sklearn.linear_model.Lars._get_metadata_request sklearn.linear_model.Lars._get_param_names sklearn.linear_model.Lars._repr_html_inner sklearn.linear_model.Lars._repr_mimebundle_ sklearn.linear_model.Lars._set_intercept sklearn.linear_model.Lars._validate_params sklearn.linear_model.Lars.fit sklearn.linear_model.Lars.get_metadata_routing sklearn.linear_model.Lars.get_params sklearn.linear_model.Lars.predict sklearn.linear_model.Lars.score sklearn.linear_model.Lars.set_fit_request sklearn.linear_model.Lars.set_params sklearn.linear_model.Lars.set_score_request 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.linear_model.LarsCV.rst.txt b/dev/_sources/modules/generated/sklearn.linear_model.LarsCV.rst.txt
index 1333bcd3ebe15..5178261414a4b 100644
--- a/dev/_sources/modules/generated/sklearn.linear_model.LarsCV.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.linear_model.LarsCV.rst.txt
@@ -8,6 +8,6 @@ LarsCV
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.linear_model.LarsCV sklearn.linear_model.LarsCV.__delattr__ sklearn.linear_model.LarsCV.__dir__ sklearn.linear_model.LarsCV.__eq__ sklearn.linear_model.LarsCV.__format__ sklearn.linear_model.LarsCV.__ge__ sklearn.linear_model.LarsCV.__getattribute__ sklearn.linear_model.LarsCV.__getstate__ sklearn.linear_model.LarsCV.__gt__ sklearn.linear_model.LarsCV.__hash__ sklearn.linear_model.LarsCV.__init__ sklearn.linear_model.LarsCV.__init_subclass__ sklearn.linear_model.LarsCV.__le__ sklearn.linear_model.LarsCV.__lt__ sklearn.linear_model.LarsCV.__ne__ sklearn.linear_model.LarsCV.__new__ sklearn.linear_model.LarsCV.__reduce__ sklearn.linear_model.LarsCV.__reduce_ex__ sklearn.linear_model.LarsCV.__repr__ sklearn.linear_model.LarsCV.__setattr__ sklearn.linear_model.LarsCV.__setstate__ sklearn.linear_model.LarsCV.__sizeof__ sklearn.linear_model.LarsCV.__sklearn_clone__ sklearn.linear_model.LarsCV.__sklearn_tags__ sklearn.linear_model.LarsCV.__str__ sklearn.linear_model.LarsCV.__subclasshook__ sklearn.linear_model.LarsCV._build_request_for_signature sklearn.linear_model.LarsCV._check_feature_names sklearn.linear_model.LarsCV._check_n_features sklearn.linear_model.LarsCV._decision_function sklearn.linear_model.LarsCV._fit sklearn.linear_model.LarsCV._get_default_requests sklearn.linear_model.LarsCV._get_doc_link sklearn.linear_model.LarsCV._get_gram sklearn.linear_model.LarsCV._get_metadata_request sklearn.linear_model.LarsCV._get_param_names sklearn.linear_model.LarsCV._get_tags sklearn.linear_model.LarsCV._more_tags sklearn.linear_model.LarsCV._repr_html_inner sklearn.linear_model.LarsCV._repr_mimebundle_ sklearn.linear_model.LarsCV._set_intercept sklearn.linear_model.LarsCV._validate_data sklearn.linear_model.LarsCV._validate_params sklearn.linear_model.LarsCV.fit sklearn.linear_model.LarsCV.get_metadata_routing sklearn.linear_model.LarsCV.get_params sklearn.linear_model.LarsCV.predict sklearn.linear_model.LarsCV.score sklearn.linear_model.LarsCV.set_fit_request sklearn.linear_model.LarsCV.set_params sklearn.linear_model.LarsCV.set_score_request 
+.. minigallery:: sklearn.linear_model.LarsCV sklearn.linear_model.LarsCV.__delattr__ sklearn.linear_model.LarsCV.__dir__ sklearn.linear_model.LarsCV.__eq__ sklearn.linear_model.LarsCV.__format__ sklearn.linear_model.LarsCV.__ge__ sklearn.linear_model.LarsCV.__getattribute__ sklearn.linear_model.LarsCV.__getstate__ sklearn.linear_model.LarsCV.__gt__ sklearn.linear_model.LarsCV.__hash__ sklearn.linear_model.LarsCV.__init__ sklearn.linear_model.LarsCV.__init_subclass__ sklearn.linear_model.LarsCV.__le__ sklearn.linear_model.LarsCV.__lt__ sklearn.linear_model.LarsCV.__ne__ sklearn.linear_model.LarsCV.__new__ sklearn.linear_model.LarsCV.__reduce__ sklearn.linear_model.LarsCV.__reduce_ex__ sklearn.linear_model.LarsCV.__repr__ sklearn.linear_model.LarsCV.__setattr__ sklearn.linear_model.LarsCV.__setstate__ sklearn.linear_model.LarsCV.__sizeof__ sklearn.linear_model.LarsCV.__sklearn_clone__ sklearn.linear_model.LarsCV.__sklearn_tags__ sklearn.linear_model.LarsCV.__str__ sklearn.linear_model.LarsCV.__subclasshook__ sklearn.linear_model.LarsCV._build_request_for_signature sklearn.linear_model.LarsCV._decision_function sklearn.linear_model.LarsCV._fit sklearn.linear_model.LarsCV._get_default_requests sklearn.linear_model.LarsCV._get_doc_link sklearn.linear_model.LarsCV._get_gram sklearn.linear_model.LarsCV._get_metadata_request sklearn.linear_model.LarsCV._get_param_names sklearn.linear_model.LarsCV._repr_html_inner sklearn.linear_model.LarsCV._repr_mimebundle_ sklearn.linear_model.LarsCV._set_intercept sklearn.linear_model.LarsCV._validate_params sklearn.linear_model.LarsCV.fit sklearn.linear_model.LarsCV.get_metadata_routing sklearn.linear_model.LarsCV.get_params sklearn.linear_model.LarsCV.predict sklearn.linear_model.LarsCV.score sklearn.linear_model.LarsCV.set_fit_request sklearn.linear_model.LarsCV.set_params sklearn.linear_model.LarsCV.set_score_request 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.linear_model.Lasso.rst.txt b/dev/_sources/modules/generated/sklearn.linear_model.Lasso.rst.txt
index 562758d74f420..46b60418d6a31 100644
--- a/dev/_sources/modules/generated/sklearn.linear_model.Lasso.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.linear_model.Lasso.rst.txt
@@ -8,6 +8,6 @@ Lasso
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.linear_model.Lasso sklearn.linear_model.Lasso.__delattr__ sklearn.linear_model.Lasso.__dir__ sklearn.linear_model.Lasso.__eq__ sklearn.linear_model.Lasso.__format__ sklearn.linear_model.Lasso.__ge__ sklearn.linear_model.Lasso.__getattribute__ sklearn.linear_model.Lasso.__getstate__ sklearn.linear_model.Lasso.__gt__ sklearn.linear_model.Lasso.__hash__ sklearn.linear_model.Lasso.__init__ sklearn.linear_model.Lasso.__init_subclass__ sklearn.linear_model.Lasso.__le__ sklearn.linear_model.Lasso.__lt__ sklearn.linear_model.Lasso.__ne__ sklearn.linear_model.Lasso.__new__ sklearn.linear_model.Lasso.__reduce__ sklearn.linear_model.Lasso.__reduce_ex__ sklearn.linear_model.Lasso.__repr__ sklearn.linear_model.Lasso.__setattr__ sklearn.linear_model.Lasso.__setstate__ sklearn.linear_model.Lasso.__sizeof__ sklearn.linear_model.Lasso.__sklearn_clone__ sklearn.linear_model.Lasso.__sklearn_tags__ sklearn.linear_model.Lasso.__str__ sklearn.linear_model.Lasso.__subclasshook__ sklearn.linear_model.Lasso._build_request_for_signature sklearn.linear_model.Lasso._check_feature_names sklearn.linear_model.Lasso._check_n_features sklearn.linear_model.Lasso._decision_function sklearn.linear_model.Lasso._get_default_requests sklearn.linear_model.Lasso._get_doc_link sklearn.linear_model.Lasso._get_metadata_request sklearn.linear_model.Lasso._get_param_names sklearn.linear_model.Lasso._get_tags sklearn.linear_model.Lasso._more_tags sklearn.linear_model.Lasso._repr_html_inner sklearn.linear_model.Lasso._repr_mimebundle_ sklearn.linear_model.Lasso._set_intercept sklearn.linear_model.Lasso._validate_data sklearn.linear_model.Lasso._validate_params sklearn.linear_model.Lasso.fit sklearn.linear_model.Lasso.get_metadata_routing sklearn.linear_model.Lasso.get_params sklearn.linear_model.Lasso.path sklearn.linear_model.Lasso.predict sklearn.linear_model.Lasso.score sklearn.linear_model.Lasso.set_fit_request sklearn.linear_model.Lasso.set_params sklearn.linear_model.Lasso.set_score_request 
+.. minigallery:: sklearn.linear_model.Lasso sklearn.linear_model.Lasso.__delattr__ sklearn.linear_model.Lasso.__dir__ sklearn.linear_model.Lasso.__eq__ sklearn.linear_model.Lasso.__format__ sklearn.linear_model.Lasso.__ge__ sklearn.linear_model.Lasso.__getattribute__ sklearn.linear_model.Lasso.__getstate__ sklearn.linear_model.Lasso.__gt__ sklearn.linear_model.Lasso.__hash__ sklearn.linear_model.Lasso.__init__ sklearn.linear_model.Lasso.__init_subclass__ sklearn.linear_model.Lasso.__le__ sklearn.linear_model.Lasso.__lt__ sklearn.linear_model.Lasso.__ne__ sklearn.linear_model.Lasso.__new__ sklearn.linear_model.Lasso.__reduce__ sklearn.linear_model.Lasso.__reduce_ex__ sklearn.linear_model.Lasso.__repr__ sklearn.linear_model.Lasso.__setattr__ sklearn.linear_model.Lasso.__setstate__ sklearn.linear_model.Lasso.__sizeof__ sklearn.linear_model.Lasso.__sklearn_clone__ sklearn.linear_model.Lasso.__sklearn_tags__ sklearn.linear_model.Lasso.__str__ sklearn.linear_model.Lasso.__subclasshook__ sklearn.linear_model.Lasso._build_request_for_signature sklearn.linear_model.Lasso._decision_function sklearn.linear_model.Lasso._get_default_requests sklearn.linear_model.Lasso._get_doc_link sklearn.linear_model.Lasso._get_metadata_request sklearn.linear_model.Lasso._get_param_names sklearn.linear_model.Lasso._repr_html_inner sklearn.linear_model.Lasso._repr_mimebundle_ sklearn.linear_model.Lasso._set_intercept sklearn.linear_model.Lasso._validate_params sklearn.linear_model.Lasso.fit sklearn.linear_model.Lasso.get_metadata_routing sklearn.linear_model.Lasso.get_params sklearn.linear_model.Lasso.path sklearn.linear_model.Lasso.predict sklearn.linear_model.Lasso.score sklearn.linear_model.Lasso.set_fit_request sklearn.linear_model.Lasso.set_params sklearn.linear_model.Lasso.set_score_request 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.linear_model.LassoCV.rst.txt b/dev/_sources/modules/generated/sklearn.linear_model.LassoCV.rst.txt
index c75f088bb2a60..e9aa85b9078ee 100644
--- a/dev/_sources/modules/generated/sklearn.linear_model.LassoCV.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.linear_model.LassoCV.rst.txt
@@ -8,6 +8,6 @@ LassoCV
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.linear_model.LassoCV sklearn.linear_model.LassoCV.__delattr__ sklearn.linear_model.LassoCV.__dir__ sklearn.linear_model.LassoCV.__eq__ sklearn.linear_model.LassoCV.__format__ sklearn.linear_model.LassoCV.__ge__ sklearn.linear_model.LassoCV.__getattribute__ sklearn.linear_model.LassoCV.__getstate__ sklearn.linear_model.LassoCV.__gt__ sklearn.linear_model.LassoCV.__hash__ sklearn.linear_model.LassoCV.__init__ sklearn.linear_model.LassoCV.__init_subclass__ sklearn.linear_model.LassoCV.__le__ sklearn.linear_model.LassoCV.__lt__ sklearn.linear_model.LassoCV.__ne__ sklearn.linear_model.LassoCV.__new__ sklearn.linear_model.LassoCV.__reduce__ sklearn.linear_model.LassoCV.__reduce_ex__ sklearn.linear_model.LassoCV.__repr__ sklearn.linear_model.LassoCV.__setattr__ sklearn.linear_model.LassoCV.__setstate__ sklearn.linear_model.LassoCV.__sizeof__ sklearn.linear_model.LassoCV.__sklearn_clone__ sklearn.linear_model.LassoCV.__sklearn_tags__ sklearn.linear_model.LassoCV.__str__ sklearn.linear_model.LassoCV.__subclasshook__ sklearn.linear_model.LassoCV._build_request_for_signature sklearn.linear_model.LassoCV._check_feature_names sklearn.linear_model.LassoCV._check_n_features sklearn.linear_model.LassoCV._decision_function sklearn.linear_model.LassoCV._get_default_requests sklearn.linear_model.LassoCV._get_doc_link sklearn.linear_model.LassoCV._get_estimator sklearn.linear_model.LassoCV._get_metadata_request sklearn.linear_model.LassoCV._get_param_names sklearn.linear_model.LassoCV._get_tags sklearn.linear_model.LassoCV._is_multitask sklearn.linear_model.LassoCV._more_tags sklearn.linear_model.LassoCV._repr_html_inner sklearn.linear_model.LassoCV._repr_mimebundle_ sklearn.linear_model.LassoCV._set_intercept sklearn.linear_model.LassoCV._validate_data sklearn.linear_model.LassoCV._validate_params sklearn.linear_model.LassoCV.fit sklearn.linear_model.LassoCV.get_metadata_routing sklearn.linear_model.LassoCV.get_params sklearn.linear_model.LassoCV.path sklearn.linear_model.LassoCV.predict sklearn.linear_model.LassoCV.score sklearn.linear_model.LassoCV.set_fit_request sklearn.linear_model.LassoCV.set_params sklearn.linear_model.LassoCV.set_score_request 
+.. minigallery:: sklearn.linear_model.LassoCV sklearn.linear_model.LassoCV.__delattr__ sklearn.linear_model.LassoCV.__dir__ sklearn.linear_model.LassoCV.__eq__ sklearn.linear_model.LassoCV.__format__ sklearn.linear_model.LassoCV.__ge__ sklearn.linear_model.LassoCV.__getattribute__ sklearn.linear_model.LassoCV.__getstate__ sklearn.linear_model.LassoCV.__gt__ sklearn.linear_model.LassoCV.__hash__ sklearn.linear_model.LassoCV.__init__ sklearn.linear_model.LassoCV.__init_subclass__ sklearn.linear_model.LassoCV.__le__ sklearn.linear_model.LassoCV.__lt__ sklearn.linear_model.LassoCV.__ne__ sklearn.linear_model.LassoCV.__new__ sklearn.linear_model.LassoCV.__reduce__ sklearn.linear_model.LassoCV.__reduce_ex__ sklearn.linear_model.LassoCV.__repr__ sklearn.linear_model.LassoCV.__setattr__ sklearn.linear_model.LassoCV.__setstate__ sklearn.linear_model.LassoCV.__sizeof__ sklearn.linear_model.LassoCV.__sklearn_clone__ sklearn.linear_model.LassoCV.__sklearn_tags__ sklearn.linear_model.LassoCV.__str__ sklearn.linear_model.LassoCV.__subclasshook__ sklearn.linear_model.LassoCV._build_request_for_signature sklearn.linear_model.LassoCV._decision_function sklearn.linear_model.LassoCV._get_default_requests sklearn.linear_model.LassoCV._get_doc_link sklearn.linear_model.LassoCV._get_estimator sklearn.linear_model.LassoCV._get_metadata_request sklearn.linear_model.LassoCV._get_param_names sklearn.linear_model.LassoCV._is_multitask sklearn.linear_model.LassoCV._repr_html_inner sklearn.linear_model.LassoCV._repr_mimebundle_ sklearn.linear_model.LassoCV._set_intercept sklearn.linear_model.LassoCV._validate_params sklearn.linear_model.LassoCV.fit sklearn.linear_model.LassoCV.get_metadata_routing sklearn.linear_model.LassoCV.get_params sklearn.linear_model.LassoCV.path sklearn.linear_model.LassoCV.predict sklearn.linear_model.LassoCV.score sklearn.linear_model.LassoCV.set_fit_request sklearn.linear_model.LassoCV.set_params sklearn.linear_model.LassoCV.set_score_request 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.linear_model.LassoLars.rst.txt b/dev/_sources/modules/generated/sklearn.linear_model.LassoLars.rst.txt
index c4238949b0192..366f42e01da93 100644
--- a/dev/_sources/modules/generated/sklearn.linear_model.LassoLars.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.linear_model.LassoLars.rst.txt
@@ -8,6 +8,6 @@ LassoLars
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.linear_model.LassoLars sklearn.linear_model.LassoLars.__delattr__ sklearn.linear_model.LassoLars.__dir__ sklearn.linear_model.LassoLars.__eq__ sklearn.linear_model.LassoLars.__format__ sklearn.linear_model.LassoLars.__ge__ sklearn.linear_model.LassoLars.__getattribute__ sklearn.linear_model.LassoLars.__getstate__ sklearn.linear_model.LassoLars.__gt__ sklearn.linear_model.LassoLars.__hash__ sklearn.linear_model.LassoLars.__init__ sklearn.linear_model.LassoLars.__init_subclass__ sklearn.linear_model.LassoLars.__le__ sklearn.linear_model.LassoLars.__lt__ sklearn.linear_model.LassoLars.__ne__ sklearn.linear_model.LassoLars.__new__ sklearn.linear_model.LassoLars.__reduce__ sklearn.linear_model.LassoLars.__reduce_ex__ sklearn.linear_model.LassoLars.__repr__ sklearn.linear_model.LassoLars.__setattr__ sklearn.linear_model.LassoLars.__setstate__ sklearn.linear_model.LassoLars.__sizeof__ sklearn.linear_model.LassoLars.__sklearn_clone__ sklearn.linear_model.LassoLars.__sklearn_tags__ sklearn.linear_model.LassoLars.__str__ sklearn.linear_model.LassoLars.__subclasshook__ sklearn.linear_model.LassoLars._build_request_for_signature sklearn.linear_model.LassoLars._check_feature_names sklearn.linear_model.LassoLars._check_n_features sklearn.linear_model.LassoLars._decision_function sklearn.linear_model.LassoLars._fit sklearn.linear_model.LassoLars._get_default_requests sklearn.linear_model.LassoLars._get_doc_link sklearn.linear_model.LassoLars._get_gram sklearn.linear_model.LassoLars._get_metadata_request sklearn.linear_model.LassoLars._get_param_names sklearn.linear_model.LassoLars._get_tags sklearn.linear_model.LassoLars._more_tags sklearn.linear_model.LassoLars._repr_html_inner sklearn.linear_model.LassoLars._repr_mimebundle_ sklearn.linear_model.LassoLars._set_intercept sklearn.linear_model.LassoLars._validate_data sklearn.linear_model.LassoLars._validate_params sklearn.linear_model.LassoLars.fit sklearn.linear_model.LassoLars.get_metadata_routing sklearn.linear_model.LassoLars.get_params sklearn.linear_model.LassoLars.predict sklearn.linear_model.LassoLars.score sklearn.linear_model.LassoLars.set_fit_request sklearn.linear_model.LassoLars.set_params sklearn.linear_model.LassoLars.set_score_request 
+.. minigallery:: sklearn.linear_model.LassoLars sklearn.linear_model.LassoLars.__delattr__ sklearn.linear_model.LassoLars.__dir__ sklearn.linear_model.LassoLars.__eq__ sklearn.linear_model.LassoLars.__format__ sklearn.linear_model.LassoLars.__ge__ sklearn.linear_model.LassoLars.__getattribute__ sklearn.linear_model.LassoLars.__getstate__ sklearn.linear_model.LassoLars.__gt__ sklearn.linear_model.LassoLars.__hash__ sklearn.linear_model.LassoLars.__init__ sklearn.linear_model.LassoLars.__init_subclass__ sklearn.linear_model.LassoLars.__le__ sklearn.linear_model.LassoLars.__lt__ sklearn.linear_model.LassoLars.__ne__ sklearn.linear_model.LassoLars.__new__ sklearn.linear_model.LassoLars.__reduce__ sklearn.linear_model.LassoLars.__reduce_ex__ sklearn.linear_model.LassoLars.__repr__ sklearn.linear_model.LassoLars.__setattr__ sklearn.linear_model.LassoLars.__setstate__ sklearn.linear_model.LassoLars.__sizeof__ sklearn.linear_model.LassoLars.__sklearn_clone__ sklearn.linear_model.LassoLars.__sklearn_tags__ sklearn.linear_model.LassoLars.__str__ sklearn.linear_model.LassoLars.__subclasshook__ sklearn.linear_model.LassoLars._build_request_for_signature sklearn.linear_model.LassoLars._decision_function sklearn.linear_model.LassoLars._fit sklearn.linear_model.LassoLars._get_default_requests sklearn.linear_model.LassoLars._get_doc_link sklearn.linear_model.LassoLars._get_gram sklearn.linear_model.LassoLars._get_metadata_request sklearn.linear_model.LassoLars._get_param_names sklearn.linear_model.LassoLars._repr_html_inner sklearn.linear_model.LassoLars._repr_mimebundle_ sklearn.linear_model.LassoLars._set_intercept sklearn.linear_model.LassoLars._validate_params sklearn.linear_model.LassoLars.fit sklearn.linear_model.LassoLars.get_metadata_routing sklearn.linear_model.LassoLars.get_params sklearn.linear_model.LassoLars.predict sklearn.linear_model.LassoLars.score sklearn.linear_model.LassoLars.set_fit_request sklearn.linear_model.LassoLars.set_params sklearn.linear_model.LassoLars.set_score_request 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.linear_model.LassoLarsCV.rst.txt b/dev/_sources/modules/generated/sklearn.linear_model.LassoLarsCV.rst.txt
index 4e07282658a6c..a2c9066f4c1a0 100644
--- a/dev/_sources/modules/generated/sklearn.linear_model.LassoLarsCV.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.linear_model.LassoLarsCV.rst.txt
@@ -8,6 +8,6 @@ LassoLarsCV
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.linear_model.LassoLarsCV sklearn.linear_model.LassoLarsCV.__delattr__ sklearn.linear_model.LassoLarsCV.__dir__ sklearn.linear_model.LassoLarsCV.__eq__ sklearn.linear_model.LassoLarsCV.__format__ sklearn.linear_model.LassoLarsCV.__ge__ sklearn.linear_model.LassoLarsCV.__getattribute__ sklearn.linear_model.LassoLarsCV.__getstate__ sklearn.linear_model.LassoLarsCV.__gt__ sklearn.linear_model.LassoLarsCV.__hash__ sklearn.linear_model.LassoLarsCV.__init__ sklearn.linear_model.LassoLarsCV.__init_subclass__ sklearn.linear_model.LassoLarsCV.__le__ sklearn.linear_model.LassoLarsCV.__lt__ sklearn.linear_model.LassoLarsCV.__ne__ sklearn.linear_model.LassoLarsCV.__new__ sklearn.linear_model.LassoLarsCV.__reduce__ sklearn.linear_model.LassoLarsCV.__reduce_ex__ sklearn.linear_model.LassoLarsCV.__repr__ sklearn.linear_model.LassoLarsCV.__setattr__ sklearn.linear_model.LassoLarsCV.__setstate__ sklearn.linear_model.LassoLarsCV.__sizeof__ sklearn.linear_model.LassoLarsCV.__sklearn_clone__ sklearn.linear_model.LassoLarsCV.__sklearn_tags__ sklearn.linear_model.LassoLarsCV.__str__ sklearn.linear_model.LassoLarsCV.__subclasshook__ sklearn.linear_model.LassoLarsCV._build_request_for_signature sklearn.linear_model.LassoLarsCV._check_feature_names sklearn.linear_model.LassoLarsCV._check_n_features sklearn.linear_model.LassoLarsCV._decision_function sklearn.linear_model.LassoLarsCV._fit sklearn.linear_model.LassoLarsCV._get_default_requests sklearn.linear_model.LassoLarsCV._get_doc_link sklearn.linear_model.LassoLarsCV._get_gram sklearn.linear_model.LassoLarsCV._get_metadata_request sklearn.linear_model.LassoLarsCV._get_param_names sklearn.linear_model.LassoLarsCV._get_tags sklearn.linear_model.LassoLarsCV._more_tags sklearn.linear_model.LassoLarsCV._repr_html_inner sklearn.linear_model.LassoLarsCV._repr_mimebundle_ sklearn.linear_model.LassoLarsCV._set_intercept sklearn.linear_model.LassoLarsCV._validate_data sklearn.linear_model.LassoLarsCV._validate_params sklearn.linear_model.LassoLarsCV.fit sklearn.linear_model.LassoLarsCV.get_metadata_routing sklearn.linear_model.LassoLarsCV.get_params sklearn.linear_model.LassoLarsCV.predict sklearn.linear_model.LassoLarsCV.score sklearn.linear_model.LassoLarsCV.set_fit_request sklearn.linear_model.LassoLarsCV.set_params sklearn.linear_model.LassoLarsCV.set_score_request 
+.. minigallery:: sklearn.linear_model.LassoLarsCV sklearn.linear_model.LassoLarsCV.__delattr__ sklearn.linear_model.LassoLarsCV.__dir__ sklearn.linear_model.LassoLarsCV.__eq__ sklearn.linear_model.LassoLarsCV.__format__ sklearn.linear_model.LassoLarsCV.__ge__ sklearn.linear_model.LassoLarsCV.__getattribute__ sklearn.linear_model.LassoLarsCV.__getstate__ sklearn.linear_model.LassoLarsCV.__gt__ sklearn.linear_model.LassoLarsCV.__hash__ sklearn.linear_model.LassoLarsCV.__init__ sklearn.linear_model.LassoLarsCV.__init_subclass__ sklearn.linear_model.LassoLarsCV.__le__ sklearn.linear_model.LassoLarsCV.__lt__ sklearn.linear_model.LassoLarsCV.__ne__ sklearn.linear_model.LassoLarsCV.__new__ sklearn.linear_model.LassoLarsCV.__reduce__ sklearn.linear_model.LassoLarsCV.__reduce_ex__ sklearn.linear_model.LassoLarsCV.__repr__ sklearn.linear_model.LassoLarsCV.__setattr__ sklearn.linear_model.LassoLarsCV.__setstate__ sklearn.linear_model.LassoLarsCV.__sizeof__ sklearn.linear_model.LassoLarsCV.__sklearn_clone__ sklearn.linear_model.LassoLarsCV.__sklearn_tags__ sklearn.linear_model.LassoLarsCV.__str__ sklearn.linear_model.LassoLarsCV.__subclasshook__ sklearn.linear_model.LassoLarsCV._build_request_for_signature sklearn.linear_model.LassoLarsCV._decision_function sklearn.linear_model.LassoLarsCV._fit sklearn.linear_model.LassoLarsCV._get_default_requests sklearn.linear_model.LassoLarsCV._get_doc_link sklearn.linear_model.LassoLarsCV._get_gram sklearn.linear_model.LassoLarsCV._get_metadata_request sklearn.linear_model.LassoLarsCV._get_param_names sklearn.linear_model.LassoLarsCV._repr_html_inner sklearn.linear_model.LassoLarsCV._repr_mimebundle_ sklearn.linear_model.LassoLarsCV._set_intercept sklearn.linear_model.LassoLarsCV._validate_params sklearn.linear_model.LassoLarsCV.fit sklearn.linear_model.LassoLarsCV.get_metadata_routing sklearn.linear_model.LassoLarsCV.get_params sklearn.linear_model.LassoLarsCV.predict sklearn.linear_model.LassoLarsCV.score sklearn.linear_model.LassoLarsCV.set_fit_request sklearn.linear_model.LassoLarsCV.set_params sklearn.linear_model.LassoLarsCV.set_score_request 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.linear_model.LassoLarsIC.rst.txt b/dev/_sources/modules/generated/sklearn.linear_model.LassoLarsIC.rst.txt
index 0817f1175e841..0fc8e0b6de399 100644
--- a/dev/_sources/modules/generated/sklearn.linear_model.LassoLarsIC.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.linear_model.LassoLarsIC.rst.txt
@@ -8,6 +8,6 @@ LassoLarsIC
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.linear_model.LassoLarsIC sklearn.linear_model.LassoLarsIC.__delattr__ sklearn.linear_model.LassoLarsIC.__dir__ sklearn.linear_model.LassoLarsIC.__eq__ sklearn.linear_model.LassoLarsIC.__format__ sklearn.linear_model.LassoLarsIC.__ge__ sklearn.linear_model.LassoLarsIC.__getattribute__ sklearn.linear_model.LassoLarsIC.__getstate__ sklearn.linear_model.LassoLarsIC.__gt__ sklearn.linear_model.LassoLarsIC.__hash__ sklearn.linear_model.LassoLarsIC.__init__ sklearn.linear_model.LassoLarsIC.__init_subclass__ sklearn.linear_model.LassoLarsIC.__le__ sklearn.linear_model.LassoLarsIC.__lt__ sklearn.linear_model.LassoLarsIC.__ne__ sklearn.linear_model.LassoLarsIC.__new__ sklearn.linear_model.LassoLarsIC.__reduce__ sklearn.linear_model.LassoLarsIC.__reduce_ex__ sklearn.linear_model.LassoLarsIC.__repr__ sklearn.linear_model.LassoLarsIC.__setattr__ sklearn.linear_model.LassoLarsIC.__setstate__ sklearn.linear_model.LassoLarsIC.__sizeof__ sklearn.linear_model.LassoLarsIC.__sklearn_clone__ sklearn.linear_model.LassoLarsIC.__sklearn_tags__ sklearn.linear_model.LassoLarsIC.__str__ sklearn.linear_model.LassoLarsIC.__subclasshook__ sklearn.linear_model.LassoLarsIC._build_request_for_signature sklearn.linear_model.LassoLarsIC._check_feature_names sklearn.linear_model.LassoLarsIC._check_n_features sklearn.linear_model.LassoLarsIC._decision_function sklearn.linear_model.LassoLarsIC._estimate_noise_variance sklearn.linear_model.LassoLarsIC._fit sklearn.linear_model.LassoLarsIC._get_default_requests sklearn.linear_model.LassoLarsIC._get_doc_link sklearn.linear_model.LassoLarsIC._get_gram sklearn.linear_model.LassoLarsIC._get_metadata_request sklearn.linear_model.LassoLarsIC._get_param_names sklearn.linear_model.LassoLarsIC._get_tags sklearn.linear_model.LassoLarsIC._more_tags sklearn.linear_model.LassoLarsIC._repr_html_inner sklearn.linear_model.LassoLarsIC._repr_mimebundle_ sklearn.linear_model.LassoLarsIC._set_intercept sklearn.linear_model.LassoLarsIC._validate_data sklearn.linear_model.LassoLarsIC._validate_params sklearn.linear_model.LassoLarsIC.fit sklearn.linear_model.LassoLarsIC.get_metadata_routing sklearn.linear_model.LassoLarsIC.get_params sklearn.linear_model.LassoLarsIC.predict sklearn.linear_model.LassoLarsIC.score sklearn.linear_model.LassoLarsIC.set_fit_request sklearn.linear_model.LassoLarsIC.set_params sklearn.linear_model.LassoLarsIC.set_score_request 
+.. minigallery:: sklearn.linear_model.LassoLarsIC sklearn.linear_model.LassoLarsIC.__delattr__ sklearn.linear_model.LassoLarsIC.__dir__ sklearn.linear_model.LassoLarsIC.__eq__ sklearn.linear_model.LassoLarsIC.__format__ sklearn.linear_model.LassoLarsIC.__ge__ sklearn.linear_model.LassoLarsIC.__getattribute__ sklearn.linear_model.LassoLarsIC.__getstate__ sklearn.linear_model.LassoLarsIC.__gt__ sklearn.linear_model.LassoLarsIC.__hash__ sklearn.linear_model.LassoLarsIC.__init__ sklearn.linear_model.LassoLarsIC.__init_subclass__ sklearn.linear_model.LassoLarsIC.__le__ sklearn.linear_model.LassoLarsIC.__lt__ sklearn.linear_model.LassoLarsIC.__ne__ sklearn.linear_model.LassoLarsIC.__new__ sklearn.linear_model.LassoLarsIC.__reduce__ sklearn.linear_model.LassoLarsIC.__reduce_ex__ sklearn.linear_model.LassoLarsIC.__repr__ sklearn.linear_model.LassoLarsIC.__setattr__ sklearn.linear_model.LassoLarsIC.__setstate__ sklearn.linear_model.LassoLarsIC.__sizeof__ sklearn.linear_model.LassoLarsIC.__sklearn_clone__ sklearn.linear_model.LassoLarsIC.__sklearn_tags__ sklearn.linear_model.LassoLarsIC.__str__ sklearn.linear_model.LassoLarsIC.__subclasshook__ sklearn.linear_model.LassoLarsIC._build_request_for_signature sklearn.linear_model.LassoLarsIC._decision_function sklearn.linear_model.LassoLarsIC._estimate_noise_variance sklearn.linear_model.LassoLarsIC._fit sklearn.linear_model.LassoLarsIC._get_default_requests sklearn.linear_model.LassoLarsIC._get_doc_link sklearn.linear_model.LassoLarsIC._get_gram sklearn.linear_model.LassoLarsIC._get_metadata_request sklearn.linear_model.LassoLarsIC._get_param_names sklearn.linear_model.LassoLarsIC._repr_html_inner sklearn.linear_model.LassoLarsIC._repr_mimebundle_ sklearn.linear_model.LassoLarsIC._set_intercept sklearn.linear_model.LassoLarsIC._validate_params sklearn.linear_model.LassoLarsIC.fit sklearn.linear_model.LassoLarsIC.get_metadata_routing sklearn.linear_model.LassoLarsIC.get_params sklearn.linear_model.LassoLarsIC.predict sklearn.linear_model.LassoLarsIC.score sklearn.linear_model.LassoLarsIC.set_fit_request sklearn.linear_model.LassoLarsIC.set_params sklearn.linear_model.LassoLarsIC.set_score_request 
    :add-heading: Gallery examples
    :heading-level: -
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diff --git a/dev/_sources/modules/generated/sklearn.linear_model.LinearRegression.rst.txt b/dev/_sources/modules/generated/sklearn.linear_model.LinearRegression.rst.txt
index f1b82e48a67c4..10aa2f77d48b3 100644
--- a/dev/_sources/modules/generated/sklearn.linear_model.LinearRegression.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.linear_model.LinearRegression.rst.txt
@@ -8,6 +8,6 @@ LinearRegression
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.linear_model.LinearRegression sklearn.linear_model.LinearRegression.__delattr__ sklearn.linear_model.LinearRegression.__dir__ sklearn.linear_model.LinearRegression.__eq__ sklearn.linear_model.LinearRegression.__format__ sklearn.linear_model.LinearRegression.__ge__ sklearn.linear_model.LinearRegression.__getattribute__ sklearn.linear_model.LinearRegression.__getstate__ sklearn.linear_model.LinearRegression.__gt__ sklearn.linear_model.LinearRegression.__hash__ sklearn.linear_model.LinearRegression.__init__ sklearn.linear_model.LinearRegression.__init_subclass__ sklearn.linear_model.LinearRegression.__le__ sklearn.linear_model.LinearRegression.__lt__ sklearn.linear_model.LinearRegression.__ne__ sklearn.linear_model.LinearRegression.__new__ sklearn.linear_model.LinearRegression.__reduce__ sklearn.linear_model.LinearRegression.__reduce_ex__ sklearn.linear_model.LinearRegression.__repr__ sklearn.linear_model.LinearRegression.__setattr__ sklearn.linear_model.LinearRegression.__setstate__ sklearn.linear_model.LinearRegression.__sizeof__ sklearn.linear_model.LinearRegression.__sklearn_clone__ sklearn.linear_model.LinearRegression.__sklearn_tags__ sklearn.linear_model.LinearRegression.__str__ sklearn.linear_model.LinearRegression.__subclasshook__ sklearn.linear_model.LinearRegression._build_request_for_signature sklearn.linear_model.LinearRegression._check_feature_names sklearn.linear_model.LinearRegression._check_n_features sklearn.linear_model.LinearRegression._decision_function sklearn.linear_model.LinearRegression._get_default_requests sklearn.linear_model.LinearRegression._get_doc_link sklearn.linear_model.LinearRegression._get_metadata_request sklearn.linear_model.LinearRegression._get_param_names sklearn.linear_model.LinearRegression._get_tags sklearn.linear_model.LinearRegression._more_tags sklearn.linear_model.LinearRegression._repr_html_inner sklearn.linear_model.LinearRegression._repr_mimebundle_ sklearn.linear_model.LinearRegression._set_intercept sklearn.linear_model.LinearRegression._validate_data sklearn.linear_model.LinearRegression._validate_params sklearn.linear_model.LinearRegression.fit sklearn.linear_model.LinearRegression.get_metadata_routing sklearn.linear_model.LinearRegression.get_params sklearn.linear_model.LinearRegression.predict sklearn.linear_model.LinearRegression.score sklearn.linear_model.LinearRegression.set_fit_request sklearn.linear_model.LinearRegression.set_params sklearn.linear_model.LinearRegression.set_score_request 
+.. minigallery:: sklearn.linear_model.LinearRegression sklearn.linear_model.LinearRegression.__delattr__ sklearn.linear_model.LinearRegression.__dir__ sklearn.linear_model.LinearRegression.__eq__ sklearn.linear_model.LinearRegression.__format__ sklearn.linear_model.LinearRegression.__ge__ sklearn.linear_model.LinearRegression.__getattribute__ sklearn.linear_model.LinearRegression.__getstate__ sklearn.linear_model.LinearRegression.__gt__ sklearn.linear_model.LinearRegression.__hash__ sklearn.linear_model.LinearRegression.__init__ sklearn.linear_model.LinearRegression.__init_subclass__ sklearn.linear_model.LinearRegression.__le__ sklearn.linear_model.LinearRegression.__lt__ sklearn.linear_model.LinearRegression.__ne__ sklearn.linear_model.LinearRegression.__new__ sklearn.linear_model.LinearRegression.__reduce__ sklearn.linear_model.LinearRegression.__reduce_ex__ sklearn.linear_model.LinearRegression.__repr__ sklearn.linear_model.LinearRegression.__setattr__ sklearn.linear_model.LinearRegression.__setstate__ sklearn.linear_model.LinearRegression.__sizeof__ sklearn.linear_model.LinearRegression.__sklearn_clone__ sklearn.linear_model.LinearRegression.__sklearn_tags__ sklearn.linear_model.LinearRegression.__str__ sklearn.linear_model.LinearRegression.__subclasshook__ sklearn.linear_model.LinearRegression._build_request_for_signature sklearn.linear_model.LinearRegression._decision_function sklearn.linear_model.LinearRegression._get_default_requests sklearn.linear_model.LinearRegression._get_doc_link sklearn.linear_model.LinearRegression._get_metadata_request sklearn.linear_model.LinearRegression._get_param_names sklearn.linear_model.LinearRegression._repr_html_inner sklearn.linear_model.LinearRegression._repr_mimebundle_ sklearn.linear_model.LinearRegression._set_intercept sklearn.linear_model.LinearRegression._validate_params sklearn.linear_model.LinearRegression.fit sklearn.linear_model.LinearRegression.get_metadata_routing sklearn.linear_model.LinearRegression.get_params sklearn.linear_model.LinearRegression.predict sklearn.linear_model.LinearRegression.score sklearn.linear_model.LinearRegression.set_fit_request sklearn.linear_model.LinearRegression.set_params sklearn.linear_model.LinearRegression.set_score_request 
    :add-heading: Gallery examples
    :heading-level: -
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diff --git a/dev/_sources/modules/generated/sklearn.linear_model.LogisticRegression.rst.txt b/dev/_sources/modules/generated/sklearn.linear_model.LogisticRegression.rst.txt
index 61a01bbaf45ca..165e1fc7a5606 100644
--- a/dev/_sources/modules/generated/sklearn.linear_model.LogisticRegression.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.linear_model.LogisticRegression.rst.txt
@@ -8,6 +8,6 @@ LogisticRegression
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.linear_model.LogisticRegression sklearn.linear_model.LogisticRegression.__delattr__ sklearn.linear_model.LogisticRegression.__dir__ sklearn.linear_model.LogisticRegression.__eq__ sklearn.linear_model.LogisticRegression.__format__ sklearn.linear_model.LogisticRegression.__ge__ sklearn.linear_model.LogisticRegression.__getattribute__ sklearn.linear_model.LogisticRegression.__getstate__ sklearn.linear_model.LogisticRegression.__gt__ sklearn.linear_model.LogisticRegression.__hash__ sklearn.linear_model.LogisticRegression.__init__ sklearn.linear_model.LogisticRegression.__init_subclass__ sklearn.linear_model.LogisticRegression.__le__ sklearn.linear_model.LogisticRegression.__lt__ sklearn.linear_model.LogisticRegression.__ne__ sklearn.linear_model.LogisticRegression.__new__ sklearn.linear_model.LogisticRegression.__reduce__ sklearn.linear_model.LogisticRegression.__reduce_ex__ sklearn.linear_model.LogisticRegression.__repr__ sklearn.linear_model.LogisticRegression.__setattr__ sklearn.linear_model.LogisticRegression.__setstate__ sklearn.linear_model.LogisticRegression.__sizeof__ sklearn.linear_model.LogisticRegression.__sklearn_clone__ sklearn.linear_model.LogisticRegression.__sklearn_tags__ sklearn.linear_model.LogisticRegression.__str__ sklearn.linear_model.LogisticRegression.__subclasshook__ sklearn.linear_model.LogisticRegression._build_request_for_signature sklearn.linear_model.LogisticRegression._check_feature_names sklearn.linear_model.LogisticRegression._check_n_features sklearn.linear_model.LogisticRegression._get_default_requests sklearn.linear_model.LogisticRegression._get_doc_link sklearn.linear_model.LogisticRegression._get_metadata_request sklearn.linear_model.LogisticRegression._get_param_names sklearn.linear_model.LogisticRegression._get_tags sklearn.linear_model.LogisticRegression._more_tags sklearn.linear_model.LogisticRegression._predict_proba_lr sklearn.linear_model.LogisticRegression._repr_html_inner sklearn.linear_model.LogisticRegression._repr_mimebundle_ sklearn.linear_model.LogisticRegression._validate_data sklearn.linear_model.LogisticRegression._validate_params sklearn.linear_model.LogisticRegression.decision_function sklearn.linear_model.LogisticRegression.densify sklearn.linear_model.LogisticRegression.fit sklearn.linear_model.LogisticRegression.get_metadata_routing sklearn.linear_model.LogisticRegression.get_params sklearn.linear_model.LogisticRegression.predict sklearn.linear_model.LogisticRegression.predict_log_proba sklearn.linear_model.LogisticRegression.predict_proba sklearn.linear_model.LogisticRegression.score sklearn.linear_model.LogisticRegression.set_fit_request sklearn.linear_model.LogisticRegression.set_params sklearn.linear_model.LogisticRegression.set_score_request sklearn.linear_model.LogisticRegression.sparsify 
+.. minigallery:: sklearn.linear_model.LogisticRegression sklearn.linear_model.LogisticRegression.__delattr__ sklearn.linear_model.LogisticRegression.__dir__ sklearn.linear_model.LogisticRegression.__eq__ sklearn.linear_model.LogisticRegression.__format__ sklearn.linear_model.LogisticRegression.__ge__ sklearn.linear_model.LogisticRegression.__getattribute__ sklearn.linear_model.LogisticRegression.__getstate__ sklearn.linear_model.LogisticRegression.__gt__ sklearn.linear_model.LogisticRegression.__hash__ sklearn.linear_model.LogisticRegression.__init__ sklearn.linear_model.LogisticRegression.__init_subclass__ sklearn.linear_model.LogisticRegression.__le__ sklearn.linear_model.LogisticRegression.__lt__ sklearn.linear_model.LogisticRegression.__ne__ sklearn.linear_model.LogisticRegression.__new__ sklearn.linear_model.LogisticRegression.__reduce__ sklearn.linear_model.LogisticRegression.__reduce_ex__ sklearn.linear_model.LogisticRegression.__repr__ sklearn.linear_model.LogisticRegression.__setattr__ sklearn.linear_model.LogisticRegression.__setstate__ sklearn.linear_model.LogisticRegression.__sizeof__ sklearn.linear_model.LogisticRegression.__sklearn_clone__ sklearn.linear_model.LogisticRegression.__sklearn_tags__ sklearn.linear_model.LogisticRegression.__str__ sklearn.linear_model.LogisticRegression.__subclasshook__ sklearn.linear_model.LogisticRegression._build_request_for_signature sklearn.linear_model.LogisticRegression._get_default_requests sklearn.linear_model.LogisticRegression._get_doc_link sklearn.linear_model.LogisticRegression._get_metadata_request sklearn.linear_model.LogisticRegression._get_param_names sklearn.linear_model.LogisticRegression._predict_proba_lr sklearn.linear_model.LogisticRegression._repr_html_inner sklearn.linear_model.LogisticRegression._repr_mimebundle_ sklearn.linear_model.LogisticRegression._validate_params sklearn.linear_model.LogisticRegression.decision_function sklearn.linear_model.LogisticRegression.densify sklearn.linear_model.LogisticRegression.fit sklearn.linear_model.LogisticRegression.get_metadata_routing sklearn.linear_model.LogisticRegression.get_params sklearn.linear_model.LogisticRegression.predict sklearn.linear_model.LogisticRegression.predict_log_proba sklearn.linear_model.LogisticRegression.predict_proba sklearn.linear_model.LogisticRegression.score sklearn.linear_model.LogisticRegression.set_fit_request sklearn.linear_model.LogisticRegression.set_params sklearn.linear_model.LogisticRegression.set_score_request sklearn.linear_model.LogisticRegression.sparsify 
    :add-heading: Gallery examples
    :heading-level: -
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diff --git a/dev/_sources/modules/generated/sklearn.linear_model.LogisticRegressionCV.rst.txt b/dev/_sources/modules/generated/sklearn.linear_model.LogisticRegressionCV.rst.txt
index 8e0e3af6219f6..120c535f9defa 100644
--- a/dev/_sources/modules/generated/sklearn.linear_model.LogisticRegressionCV.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.linear_model.LogisticRegressionCV.rst.txt
@@ -8,6 +8,6 @@ LogisticRegressionCV
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.linear_model.LogisticRegressionCV sklearn.linear_model.LogisticRegressionCV.__delattr__ sklearn.linear_model.LogisticRegressionCV.__dir__ sklearn.linear_model.LogisticRegressionCV.__eq__ sklearn.linear_model.LogisticRegressionCV.__format__ sklearn.linear_model.LogisticRegressionCV.__ge__ sklearn.linear_model.LogisticRegressionCV.__getattribute__ sklearn.linear_model.LogisticRegressionCV.__getstate__ sklearn.linear_model.LogisticRegressionCV.__gt__ sklearn.linear_model.LogisticRegressionCV.__hash__ sklearn.linear_model.LogisticRegressionCV.__init__ sklearn.linear_model.LogisticRegressionCV.__init_subclass__ sklearn.linear_model.LogisticRegressionCV.__le__ sklearn.linear_model.LogisticRegressionCV.__lt__ sklearn.linear_model.LogisticRegressionCV.__ne__ sklearn.linear_model.LogisticRegressionCV.__new__ sklearn.linear_model.LogisticRegressionCV.__reduce__ sklearn.linear_model.LogisticRegressionCV.__reduce_ex__ sklearn.linear_model.LogisticRegressionCV.__repr__ sklearn.linear_model.LogisticRegressionCV.__setattr__ sklearn.linear_model.LogisticRegressionCV.__setstate__ sklearn.linear_model.LogisticRegressionCV.__sizeof__ sklearn.linear_model.LogisticRegressionCV.__sklearn_clone__ sklearn.linear_model.LogisticRegressionCV.__sklearn_tags__ sklearn.linear_model.LogisticRegressionCV.__str__ sklearn.linear_model.LogisticRegressionCV.__subclasshook__ sklearn.linear_model.LogisticRegressionCV._build_request_for_signature sklearn.linear_model.LogisticRegressionCV._check_feature_names sklearn.linear_model.LogisticRegressionCV._check_n_features sklearn.linear_model.LogisticRegressionCV._get_default_requests sklearn.linear_model.LogisticRegressionCV._get_doc_link sklearn.linear_model.LogisticRegressionCV._get_metadata_request sklearn.linear_model.LogisticRegressionCV._get_param_names sklearn.linear_model.LogisticRegressionCV._get_scorer sklearn.linear_model.LogisticRegressionCV._get_tags sklearn.linear_model.LogisticRegressionCV._more_tags sklearn.linear_model.LogisticRegressionCV._predict_proba_lr sklearn.linear_model.LogisticRegressionCV._repr_html_inner sklearn.linear_model.LogisticRegressionCV._repr_mimebundle_ sklearn.linear_model.LogisticRegressionCV._validate_data sklearn.linear_model.LogisticRegressionCV._validate_params sklearn.linear_model.LogisticRegressionCV.decision_function sklearn.linear_model.LogisticRegressionCV.densify sklearn.linear_model.LogisticRegressionCV.fit sklearn.linear_model.LogisticRegressionCV.get_metadata_routing sklearn.linear_model.LogisticRegressionCV.get_params sklearn.linear_model.LogisticRegressionCV.predict sklearn.linear_model.LogisticRegressionCV.predict_log_proba sklearn.linear_model.LogisticRegressionCV.predict_proba sklearn.linear_model.LogisticRegressionCV.score sklearn.linear_model.LogisticRegressionCV.set_fit_request sklearn.linear_model.LogisticRegressionCV.set_params sklearn.linear_model.LogisticRegressionCV.set_score_request sklearn.linear_model.LogisticRegressionCV.sparsify 
+.. minigallery:: sklearn.linear_model.LogisticRegressionCV sklearn.linear_model.LogisticRegressionCV.__delattr__ sklearn.linear_model.LogisticRegressionCV.__dir__ sklearn.linear_model.LogisticRegressionCV.__eq__ sklearn.linear_model.LogisticRegressionCV.__format__ sklearn.linear_model.LogisticRegressionCV.__ge__ sklearn.linear_model.LogisticRegressionCV.__getattribute__ sklearn.linear_model.LogisticRegressionCV.__getstate__ sklearn.linear_model.LogisticRegressionCV.__gt__ sklearn.linear_model.LogisticRegressionCV.__hash__ sklearn.linear_model.LogisticRegressionCV.__init__ sklearn.linear_model.LogisticRegressionCV.__init_subclass__ sklearn.linear_model.LogisticRegressionCV.__le__ sklearn.linear_model.LogisticRegressionCV.__lt__ sklearn.linear_model.LogisticRegressionCV.__ne__ sklearn.linear_model.LogisticRegressionCV.__new__ sklearn.linear_model.LogisticRegressionCV.__reduce__ sklearn.linear_model.LogisticRegressionCV.__reduce_ex__ sklearn.linear_model.LogisticRegressionCV.__repr__ sklearn.linear_model.LogisticRegressionCV.__setattr__ sklearn.linear_model.LogisticRegressionCV.__setstate__ sklearn.linear_model.LogisticRegressionCV.__sizeof__ sklearn.linear_model.LogisticRegressionCV.__sklearn_clone__ sklearn.linear_model.LogisticRegressionCV.__sklearn_tags__ sklearn.linear_model.LogisticRegressionCV.__str__ sklearn.linear_model.LogisticRegressionCV.__subclasshook__ sklearn.linear_model.LogisticRegressionCV._build_request_for_signature sklearn.linear_model.LogisticRegressionCV._get_default_requests sklearn.linear_model.LogisticRegressionCV._get_doc_link sklearn.linear_model.LogisticRegressionCV._get_metadata_request sklearn.linear_model.LogisticRegressionCV._get_param_names sklearn.linear_model.LogisticRegressionCV._get_scorer sklearn.linear_model.LogisticRegressionCV._predict_proba_lr sklearn.linear_model.LogisticRegressionCV._repr_html_inner sklearn.linear_model.LogisticRegressionCV._repr_mimebundle_ sklearn.linear_model.LogisticRegressionCV._validate_params sklearn.linear_model.LogisticRegressionCV.decision_function sklearn.linear_model.LogisticRegressionCV.densify sklearn.linear_model.LogisticRegressionCV.fit sklearn.linear_model.LogisticRegressionCV.get_metadata_routing sklearn.linear_model.LogisticRegressionCV.get_params sklearn.linear_model.LogisticRegressionCV.predict sklearn.linear_model.LogisticRegressionCV.predict_log_proba sklearn.linear_model.LogisticRegressionCV.predict_proba sklearn.linear_model.LogisticRegressionCV.score sklearn.linear_model.LogisticRegressionCV.set_fit_request sklearn.linear_model.LogisticRegressionCV.set_params sklearn.linear_model.LogisticRegressionCV.set_score_request sklearn.linear_model.LogisticRegressionCV.sparsify 
    :add-heading: Gallery examples
    :heading-level: -
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diff --git a/dev/_sources/modules/generated/sklearn.linear_model.MultiTaskElasticNet.rst.txt b/dev/_sources/modules/generated/sklearn.linear_model.MultiTaskElasticNet.rst.txt
index 9deddf75ff06e..e837096c350d5 100644
--- a/dev/_sources/modules/generated/sklearn.linear_model.MultiTaskElasticNet.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.linear_model.MultiTaskElasticNet.rst.txt
@@ -8,6 +8,6 @@ MultiTaskElasticNet
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.linear_model.MultiTaskElasticNet sklearn.linear_model.MultiTaskElasticNet.__delattr__ sklearn.linear_model.MultiTaskElasticNet.__dir__ sklearn.linear_model.MultiTaskElasticNet.__eq__ sklearn.linear_model.MultiTaskElasticNet.__format__ sklearn.linear_model.MultiTaskElasticNet.__ge__ sklearn.linear_model.MultiTaskElasticNet.__getattribute__ sklearn.linear_model.MultiTaskElasticNet.__getstate__ sklearn.linear_model.MultiTaskElasticNet.__gt__ sklearn.linear_model.MultiTaskElasticNet.__hash__ sklearn.linear_model.MultiTaskElasticNet.__init__ sklearn.linear_model.MultiTaskElasticNet.__init_subclass__ sklearn.linear_model.MultiTaskElasticNet.__le__ sklearn.linear_model.MultiTaskElasticNet.__lt__ sklearn.linear_model.MultiTaskElasticNet.__ne__ sklearn.linear_model.MultiTaskElasticNet.__new__ sklearn.linear_model.MultiTaskElasticNet.__reduce__ sklearn.linear_model.MultiTaskElasticNet.__reduce_ex__ sklearn.linear_model.MultiTaskElasticNet.__repr__ sklearn.linear_model.MultiTaskElasticNet.__setattr__ sklearn.linear_model.MultiTaskElasticNet.__setstate__ sklearn.linear_model.MultiTaskElasticNet.__sizeof__ sklearn.linear_model.MultiTaskElasticNet.__sklearn_clone__ sklearn.linear_model.MultiTaskElasticNet.__sklearn_tags__ sklearn.linear_model.MultiTaskElasticNet.__str__ sklearn.linear_model.MultiTaskElasticNet.__subclasshook__ sklearn.linear_model.MultiTaskElasticNet._build_request_for_signature sklearn.linear_model.MultiTaskElasticNet._check_feature_names sklearn.linear_model.MultiTaskElasticNet._check_n_features sklearn.linear_model.MultiTaskElasticNet._decision_function sklearn.linear_model.MultiTaskElasticNet._get_default_requests sklearn.linear_model.MultiTaskElasticNet._get_doc_link sklearn.linear_model.MultiTaskElasticNet._get_metadata_request sklearn.linear_model.MultiTaskElasticNet._get_param_names sklearn.linear_model.MultiTaskElasticNet._get_tags sklearn.linear_model.MultiTaskElasticNet._more_tags sklearn.linear_model.MultiTaskElasticNet._repr_html_inner sklearn.linear_model.MultiTaskElasticNet._repr_mimebundle_ sklearn.linear_model.MultiTaskElasticNet._set_intercept sklearn.linear_model.MultiTaskElasticNet._validate_data sklearn.linear_model.MultiTaskElasticNet._validate_params sklearn.linear_model.MultiTaskElasticNet.fit sklearn.linear_model.MultiTaskElasticNet.get_metadata_routing sklearn.linear_model.MultiTaskElasticNet.get_params sklearn.linear_model.MultiTaskElasticNet.path sklearn.linear_model.MultiTaskElasticNet.predict sklearn.linear_model.MultiTaskElasticNet.score sklearn.linear_model.MultiTaskElasticNet.set_fit_request sklearn.linear_model.MultiTaskElasticNet.set_params sklearn.linear_model.MultiTaskElasticNet.set_score_request 
+.. minigallery:: sklearn.linear_model.MultiTaskElasticNet sklearn.linear_model.MultiTaskElasticNet.__delattr__ sklearn.linear_model.MultiTaskElasticNet.__dir__ sklearn.linear_model.MultiTaskElasticNet.__eq__ sklearn.linear_model.MultiTaskElasticNet.__format__ sklearn.linear_model.MultiTaskElasticNet.__ge__ sklearn.linear_model.MultiTaskElasticNet.__getattribute__ sklearn.linear_model.MultiTaskElasticNet.__getstate__ sklearn.linear_model.MultiTaskElasticNet.__gt__ sklearn.linear_model.MultiTaskElasticNet.__hash__ sklearn.linear_model.MultiTaskElasticNet.__init__ sklearn.linear_model.MultiTaskElasticNet.__init_subclass__ sklearn.linear_model.MultiTaskElasticNet.__le__ sklearn.linear_model.MultiTaskElasticNet.__lt__ sklearn.linear_model.MultiTaskElasticNet.__ne__ sklearn.linear_model.MultiTaskElasticNet.__new__ sklearn.linear_model.MultiTaskElasticNet.__reduce__ sklearn.linear_model.MultiTaskElasticNet.__reduce_ex__ sklearn.linear_model.MultiTaskElasticNet.__repr__ sklearn.linear_model.MultiTaskElasticNet.__setattr__ sklearn.linear_model.MultiTaskElasticNet.__setstate__ sklearn.linear_model.MultiTaskElasticNet.__sizeof__ sklearn.linear_model.MultiTaskElasticNet.__sklearn_clone__ sklearn.linear_model.MultiTaskElasticNet.__sklearn_tags__ sklearn.linear_model.MultiTaskElasticNet.__str__ sklearn.linear_model.MultiTaskElasticNet.__subclasshook__ sklearn.linear_model.MultiTaskElasticNet._build_request_for_signature sklearn.linear_model.MultiTaskElasticNet._decision_function sklearn.linear_model.MultiTaskElasticNet._get_default_requests sklearn.linear_model.MultiTaskElasticNet._get_doc_link sklearn.linear_model.MultiTaskElasticNet._get_metadata_request sklearn.linear_model.MultiTaskElasticNet._get_param_names sklearn.linear_model.MultiTaskElasticNet._repr_html_inner sklearn.linear_model.MultiTaskElasticNet._repr_mimebundle_ sklearn.linear_model.MultiTaskElasticNet._set_intercept sklearn.linear_model.MultiTaskElasticNet._validate_params sklearn.linear_model.MultiTaskElasticNet.fit sklearn.linear_model.MultiTaskElasticNet.get_metadata_routing sklearn.linear_model.MultiTaskElasticNet.get_params sklearn.linear_model.MultiTaskElasticNet.path sklearn.linear_model.MultiTaskElasticNet.predict sklearn.linear_model.MultiTaskElasticNet.score sklearn.linear_model.MultiTaskElasticNet.set_fit_request sklearn.linear_model.MultiTaskElasticNet.set_params sklearn.linear_model.MultiTaskElasticNet.set_score_request 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.linear_model.MultiTaskElasticNetCV.rst.txt b/dev/_sources/modules/generated/sklearn.linear_model.MultiTaskElasticNetCV.rst.txt
index edd7d13588c21..c149c92d8c28c 100644
--- a/dev/_sources/modules/generated/sklearn.linear_model.MultiTaskElasticNetCV.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.linear_model.MultiTaskElasticNetCV.rst.txt
@@ -8,6 +8,6 @@ MultiTaskElasticNetCV
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.linear_model.MultiTaskElasticNetCV sklearn.linear_model.MultiTaskElasticNetCV.__delattr__ sklearn.linear_model.MultiTaskElasticNetCV.__dir__ sklearn.linear_model.MultiTaskElasticNetCV.__eq__ sklearn.linear_model.MultiTaskElasticNetCV.__format__ sklearn.linear_model.MultiTaskElasticNetCV.__ge__ sklearn.linear_model.MultiTaskElasticNetCV.__getattribute__ sklearn.linear_model.MultiTaskElasticNetCV.__getstate__ sklearn.linear_model.MultiTaskElasticNetCV.__gt__ sklearn.linear_model.MultiTaskElasticNetCV.__hash__ sklearn.linear_model.MultiTaskElasticNetCV.__init__ sklearn.linear_model.MultiTaskElasticNetCV.__init_subclass__ sklearn.linear_model.MultiTaskElasticNetCV.__le__ sklearn.linear_model.MultiTaskElasticNetCV.__lt__ sklearn.linear_model.MultiTaskElasticNetCV.__ne__ sklearn.linear_model.MultiTaskElasticNetCV.__new__ sklearn.linear_model.MultiTaskElasticNetCV.__reduce__ sklearn.linear_model.MultiTaskElasticNetCV.__reduce_ex__ sklearn.linear_model.MultiTaskElasticNetCV.__repr__ sklearn.linear_model.MultiTaskElasticNetCV.__setattr__ sklearn.linear_model.MultiTaskElasticNetCV.__setstate__ sklearn.linear_model.MultiTaskElasticNetCV.__sizeof__ sklearn.linear_model.MultiTaskElasticNetCV.__sklearn_clone__ sklearn.linear_model.MultiTaskElasticNetCV.__sklearn_tags__ sklearn.linear_model.MultiTaskElasticNetCV.__str__ sklearn.linear_model.MultiTaskElasticNetCV.__subclasshook__ sklearn.linear_model.MultiTaskElasticNetCV._build_request_for_signature sklearn.linear_model.MultiTaskElasticNetCV._check_feature_names sklearn.linear_model.MultiTaskElasticNetCV._check_n_features sklearn.linear_model.MultiTaskElasticNetCV._decision_function sklearn.linear_model.MultiTaskElasticNetCV._get_default_requests sklearn.linear_model.MultiTaskElasticNetCV._get_doc_link sklearn.linear_model.MultiTaskElasticNetCV._get_estimator sklearn.linear_model.MultiTaskElasticNetCV._get_metadata_request sklearn.linear_model.MultiTaskElasticNetCV._get_param_names sklearn.linear_model.MultiTaskElasticNetCV._get_tags sklearn.linear_model.MultiTaskElasticNetCV._is_multitask sklearn.linear_model.MultiTaskElasticNetCV._more_tags sklearn.linear_model.MultiTaskElasticNetCV._repr_html_inner sklearn.linear_model.MultiTaskElasticNetCV._repr_mimebundle_ sklearn.linear_model.MultiTaskElasticNetCV._set_intercept sklearn.linear_model.MultiTaskElasticNetCV._validate_data sklearn.linear_model.MultiTaskElasticNetCV._validate_params sklearn.linear_model.MultiTaskElasticNetCV.fit sklearn.linear_model.MultiTaskElasticNetCV.get_metadata_routing sklearn.linear_model.MultiTaskElasticNetCV.get_params sklearn.linear_model.MultiTaskElasticNetCV.path sklearn.linear_model.MultiTaskElasticNetCV.predict sklearn.linear_model.MultiTaskElasticNetCV.score sklearn.linear_model.MultiTaskElasticNetCV.set_fit_request sklearn.linear_model.MultiTaskElasticNetCV.set_params sklearn.linear_model.MultiTaskElasticNetCV.set_score_request 
+.. minigallery:: sklearn.linear_model.MultiTaskElasticNetCV sklearn.linear_model.MultiTaskElasticNetCV.__delattr__ sklearn.linear_model.MultiTaskElasticNetCV.__dir__ sklearn.linear_model.MultiTaskElasticNetCV.__eq__ sklearn.linear_model.MultiTaskElasticNetCV.__format__ sklearn.linear_model.MultiTaskElasticNetCV.__ge__ sklearn.linear_model.MultiTaskElasticNetCV.__getattribute__ sklearn.linear_model.MultiTaskElasticNetCV.__getstate__ sklearn.linear_model.MultiTaskElasticNetCV.__gt__ sklearn.linear_model.MultiTaskElasticNetCV.__hash__ sklearn.linear_model.MultiTaskElasticNetCV.__init__ sklearn.linear_model.MultiTaskElasticNetCV.__init_subclass__ sklearn.linear_model.MultiTaskElasticNetCV.__le__ sklearn.linear_model.MultiTaskElasticNetCV.__lt__ sklearn.linear_model.MultiTaskElasticNetCV.__ne__ sklearn.linear_model.MultiTaskElasticNetCV.__new__ sklearn.linear_model.MultiTaskElasticNetCV.__reduce__ sklearn.linear_model.MultiTaskElasticNetCV.__reduce_ex__ sklearn.linear_model.MultiTaskElasticNetCV.__repr__ sklearn.linear_model.MultiTaskElasticNetCV.__setattr__ sklearn.linear_model.MultiTaskElasticNetCV.__setstate__ sklearn.linear_model.MultiTaskElasticNetCV.__sizeof__ sklearn.linear_model.MultiTaskElasticNetCV.__sklearn_clone__ sklearn.linear_model.MultiTaskElasticNetCV.__sklearn_tags__ sklearn.linear_model.MultiTaskElasticNetCV.__str__ sklearn.linear_model.MultiTaskElasticNetCV.__subclasshook__ sklearn.linear_model.MultiTaskElasticNetCV._build_request_for_signature sklearn.linear_model.MultiTaskElasticNetCV._decision_function sklearn.linear_model.MultiTaskElasticNetCV._get_default_requests sklearn.linear_model.MultiTaskElasticNetCV._get_doc_link sklearn.linear_model.MultiTaskElasticNetCV._get_estimator sklearn.linear_model.MultiTaskElasticNetCV._get_metadata_request sklearn.linear_model.MultiTaskElasticNetCV._get_param_names sklearn.linear_model.MultiTaskElasticNetCV._is_multitask sklearn.linear_model.MultiTaskElasticNetCV._repr_html_inner sklearn.linear_model.MultiTaskElasticNetCV._repr_mimebundle_ sklearn.linear_model.MultiTaskElasticNetCV._set_intercept sklearn.linear_model.MultiTaskElasticNetCV._validate_params sklearn.linear_model.MultiTaskElasticNetCV.fit sklearn.linear_model.MultiTaskElasticNetCV.get_metadata_routing sklearn.linear_model.MultiTaskElasticNetCV.get_params sklearn.linear_model.MultiTaskElasticNetCV.path sklearn.linear_model.MultiTaskElasticNetCV.predict sklearn.linear_model.MultiTaskElasticNetCV.score sklearn.linear_model.MultiTaskElasticNetCV.set_fit_request sklearn.linear_model.MultiTaskElasticNetCV.set_params sklearn.linear_model.MultiTaskElasticNetCV.set_score_request 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.linear_model.MultiTaskLasso.rst.txt b/dev/_sources/modules/generated/sklearn.linear_model.MultiTaskLasso.rst.txt
index 1c83d31c6819f..a12b4fe20bd04 100644
--- a/dev/_sources/modules/generated/sklearn.linear_model.MultiTaskLasso.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.linear_model.MultiTaskLasso.rst.txt
@@ -8,6 +8,6 @@ MultiTaskLasso
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.linear_model.MultiTaskLasso sklearn.linear_model.MultiTaskLasso.__delattr__ sklearn.linear_model.MultiTaskLasso.__dir__ sklearn.linear_model.MultiTaskLasso.__eq__ sklearn.linear_model.MultiTaskLasso.__format__ sklearn.linear_model.MultiTaskLasso.__ge__ sklearn.linear_model.MultiTaskLasso.__getattribute__ sklearn.linear_model.MultiTaskLasso.__getstate__ sklearn.linear_model.MultiTaskLasso.__gt__ sklearn.linear_model.MultiTaskLasso.__hash__ sklearn.linear_model.MultiTaskLasso.__init__ sklearn.linear_model.MultiTaskLasso.__init_subclass__ sklearn.linear_model.MultiTaskLasso.__le__ sklearn.linear_model.MultiTaskLasso.__lt__ sklearn.linear_model.MultiTaskLasso.__ne__ sklearn.linear_model.MultiTaskLasso.__new__ sklearn.linear_model.MultiTaskLasso.__reduce__ sklearn.linear_model.MultiTaskLasso.__reduce_ex__ sklearn.linear_model.MultiTaskLasso.__repr__ sklearn.linear_model.MultiTaskLasso.__setattr__ sklearn.linear_model.MultiTaskLasso.__setstate__ sklearn.linear_model.MultiTaskLasso.__sizeof__ sklearn.linear_model.MultiTaskLasso.__sklearn_clone__ sklearn.linear_model.MultiTaskLasso.__sklearn_tags__ sklearn.linear_model.MultiTaskLasso.__str__ sklearn.linear_model.MultiTaskLasso.__subclasshook__ sklearn.linear_model.MultiTaskLasso._build_request_for_signature sklearn.linear_model.MultiTaskLasso._check_feature_names sklearn.linear_model.MultiTaskLasso._check_n_features sklearn.linear_model.MultiTaskLasso._decision_function sklearn.linear_model.MultiTaskLasso._get_default_requests sklearn.linear_model.MultiTaskLasso._get_doc_link sklearn.linear_model.MultiTaskLasso._get_metadata_request sklearn.linear_model.MultiTaskLasso._get_param_names sklearn.linear_model.MultiTaskLasso._get_tags sklearn.linear_model.MultiTaskLasso._more_tags sklearn.linear_model.MultiTaskLasso._repr_html_inner sklearn.linear_model.MultiTaskLasso._repr_mimebundle_ sklearn.linear_model.MultiTaskLasso._set_intercept sklearn.linear_model.MultiTaskLasso._validate_data sklearn.linear_model.MultiTaskLasso._validate_params sklearn.linear_model.MultiTaskLasso.fit sklearn.linear_model.MultiTaskLasso.get_metadata_routing sklearn.linear_model.MultiTaskLasso.get_params sklearn.linear_model.MultiTaskLasso.path sklearn.linear_model.MultiTaskLasso.predict sklearn.linear_model.MultiTaskLasso.score sklearn.linear_model.MultiTaskLasso.set_fit_request sklearn.linear_model.MultiTaskLasso.set_params sklearn.linear_model.MultiTaskLasso.set_score_request 
+.. minigallery:: sklearn.linear_model.MultiTaskLasso sklearn.linear_model.MultiTaskLasso.__delattr__ sklearn.linear_model.MultiTaskLasso.__dir__ sklearn.linear_model.MultiTaskLasso.__eq__ sklearn.linear_model.MultiTaskLasso.__format__ sklearn.linear_model.MultiTaskLasso.__ge__ sklearn.linear_model.MultiTaskLasso.__getattribute__ sklearn.linear_model.MultiTaskLasso.__getstate__ sklearn.linear_model.MultiTaskLasso.__gt__ sklearn.linear_model.MultiTaskLasso.__hash__ sklearn.linear_model.MultiTaskLasso.__init__ sklearn.linear_model.MultiTaskLasso.__init_subclass__ sklearn.linear_model.MultiTaskLasso.__le__ sklearn.linear_model.MultiTaskLasso.__lt__ sklearn.linear_model.MultiTaskLasso.__ne__ sklearn.linear_model.MultiTaskLasso.__new__ sklearn.linear_model.MultiTaskLasso.__reduce__ sklearn.linear_model.MultiTaskLasso.__reduce_ex__ sklearn.linear_model.MultiTaskLasso.__repr__ sklearn.linear_model.MultiTaskLasso.__setattr__ sklearn.linear_model.MultiTaskLasso.__setstate__ sklearn.linear_model.MultiTaskLasso.__sizeof__ sklearn.linear_model.MultiTaskLasso.__sklearn_clone__ sklearn.linear_model.MultiTaskLasso.__sklearn_tags__ sklearn.linear_model.MultiTaskLasso.__str__ sklearn.linear_model.MultiTaskLasso.__subclasshook__ sklearn.linear_model.MultiTaskLasso._build_request_for_signature sklearn.linear_model.MultiTaskLasso._decision_function sklearn.linear_model.MultiTaskLasso._get_default_requests sklearn.linear_model.MultiTaskLasso._get_doc_link sklearn.linear_model.MultiTaskLasso._get_metadata_request sklearn.linear_model.MultiTaskLasso._get_param_names sklearn.linear_model.MultiTaskLasso._repr_html_inner sklearn.linear_model.MultiTaskLasso._repr_mimebundle_ sklearn.linear_model.MultiTaskLasso._set_intercept sklearn.linear_model.MultiTaskLasso._validate_params sklearn.linear_model.MultiTaskLasso.fit sklearn.linear_model.MultiTaskLasso.get_metadata_routing sklearn.linear_model.MultiTaskLasso.get_params sklearn.linear_model.MultiTaskLasso.path sklearn.linear_model.MultiTaskLasso.predict sklearn.linear_model.MultiTaskLasso.score sklearn.linear_model.MultiTaskLasso.set_fit_request sklearn.linear_model.MultiTaskLasso.set_params sklearn.linear_model.MultiTaskLasso.set_score_request 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.linear_model.MultiTaskLassoCV.rst.txt b/dev/_sources/modules/generated/sklearn.linear_model.MultiTaskLassoCV.rst.txt
index 491996d67c043..893019cdbff9b 100644
--- a/dev/_sources/modules/generated/sklearn.linear_model.MultiTaskLassoCV.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.linear_model.MultiTaskLassoCV.rst.txt
@@ -8,6 +8,6 @@ MultiTaskLassoCV
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.linear_model.MultiTaskLassoCV sklearn.linear_model.MultiTaskLassoCV.__delattr__ sklearn.linear_model.MultiTaskLassoCV.__dir__ sklearn.linear_model.MultiTaskLassoCV.__eq__ sklearn.linear_model.MultiTaskLassoCV.__format__ sklearn.linear_model.MultiTaskLassoCV.__ge__ sklearn.linear_model.MultiTaskLassoCV.__getattribute__ sklearn.linear_model.MultiTaskLassoCV.__getstate__ sklearn.linear_model.MultiTaskLassoCV.__gt__ sklearn.linear_model.MultiTaskLassoCV.__hash__ sklearn.linear_model.MultiTaskLassoCV.__init__ sklearn.linear_model.MultiTaskLassoCV.__init_subclass__ sklearn.linear_model.MultiTaskLassoCV.__le__ sklearn.linear_model.MultiTaskLassoCV.__lt__ sklearn.linear_model.MultiTaskLassoCV.__ne__ sklearn.linear_model.MultiTaskLassoCV.__new__ sklearn.linear_model.MultiTaskLassoCV.__reduce__ sklearn.linear_model.MultiTaskLassoCV.__reduce_ex__ sklearn.linear_model.MultiTaskLassoCV.__repr__ sklearn.linear_model.MultiTaskLassoCV.__setattr__ sklearn.linear_model.MultiTaskLassoCV.__setstate__ sklearn.linear_model.MultiTaskLassoCV.__sizeof__ sklearn.linear_model.MultiTaskLassoCV.__sklearn_clone__ sklearn.linear_model.MultiTaskLassoCV.__sklearn_tags__ sklearn.linear_model.MultiTaskLassoCV.__str__ sklearn.linear_model.MultiTaskLassoCV.__subclasshook__ sklearn.linear_model.MultiTaskLassoCV._build_request_for_signature sklearn.linear_model.MultiTaskLassoCV._check_feature_names sklearn.linear_model.MultiTaskLassoCV._check_n_features sklearn.linear_model.MultiTaskLassoCV._decision_function sklearn.linear_model.MultiTaskLassoCV._get_default_requests sklearn.linear_model.MultiTaskLassoCV._get_doc_link sklearn.linear_model.MultiTaskLassoCV._get_estimator sklearn.linear_model.MultiTaskLassoCV._get_metadata_request sklearn.linear_model.MultiTaskLassoCV._get_param_names sklearn.linear_model.MultiTaskLassoCV._get_tags sklearn.linear_model.MultiTaskLassoCV._is_multitask sklearn.linear_model.MultiTaskLassoCV._more_tags sklearn.linear_model.MultiTaskLassoCV._repr_html_inner sklearn.linear_model.MultiTaskLassoCV._repr_mimebundle_ sklearn.linear_model.MultiTaskLassoCV._set_intercept sklearn.linear_model.MultiTaskLassoCV._validate_data sklearn.linear_model.MultiTaskLassoCV._validate_params sklearn.linear_model.MultiTaskLassoCV.fit sklearn.linear_model.MultiTaskLassoCV.get_metadata_routing sklearn.linear_model.MultiTaskLassoCV.get_params sklearn.linear_model.MultiTaskLassoCV.path sklearn.linear_model.MultiTaskLassoCV.predict sklearn.linear_model.MultiTaskLassoCV.score sklearn.linear_model.MultiTaskLassoCV.set_fit_request sklearn.linear_model.MultiTaskLassoCV.set_params sklearn.linear_model.MultiTaskLassoCV.set_score_request 
+.. minigallery:: sklearn.linear_model.MultiTaskLassoCV sklearn.linear_model.MultiTaskLassoCV.__delattr__ sklearn.linear_model.MultiTaskLassoCV.__dir__ sklearn.linear_model.MultiTaskLassoCV.__eq__ sklearn.linear_model.MultiTaskLassoCV.__format__ sklearn.linear_model.MultiTaskLassoCV.__ge__ sklearn.linear_model.MultiTaskLassoCV.__getattribute__ sklearn.linear_model.MultiTaskLassoCV.__getstate__ sklearn.linear_model.MultiTaskLassoCV.__gt__ sklearn.linear_model.MultiTaskLassoCV.__hash__ sklearn.linear_model.MultiTaskLassoCV.__init__ sklearn.linear_model.MultiTaskLassoCV.__init_subclass__ sklearn.linear_model.MultiTaskLassoCV.__le__ sklearn.linear_model.MultiTaskLassoCV.__lt__ sklearn.linear_model.MultiTaskLassoCV.__ne__ sklearn.linear_model.MultiTaskLassoCV.__new__ sklearn.linear_model.MultiTaskLassoCV.__reduce__ sklearn.linear_model.MultiTaskLassoCV.__reduce_ex__ sklearn.linear_model.MultiTaskLassoCV.__repr__ sklearn.linear_model.MultiTaskLassoCV.__setattr__ sklearn.linear_model.MultiTaskLassoCV.__setstate__ sklearn.linear_model.MultiTaskLassoCV.__sizeof__ sklearn.linear_model.MultiTaskLassoCV.__sklearn_clone__ sklearn.linear_model.MultiTaskLassoCV.__sklearn_tags__ sklearn.linear_model.MultiTaskLassoCV.__str__ sklearn.linear_model.MultiTaskLassoCV.__subclasshook__ sklearn.linear_model.MultiTaskLassoCV._build_request_for_signature sklearn.linear_model.MultiTaskLassoCV._decision_function sklearn.linear_model.MultiTaskLassoCV._get_default_requests sklearn.linear_model.MultiTaskLassoCV._get_doc_link sklearn.linear_model.MultiTaskLassoCV._get_estimator sklearn.linear_model.MultiTaskLassoCV._get_metadata_request sklearn.linear_model.MultiTaskLassoCV._get_param_names sklearn.linear_model.MultiTaskLassoCV._is_multitask sklearn.linear_model.MultiTaskLassoCV._repr_html_inner sklearn.linear_model.MultiTaskLassoCV._repr_mimebundle_ sklearn.linear_model.MultiTaskLassoCV._set_intercept sklearn.linear_model.MultiTaskLassoCV._validate_params sklearn.linear_model.MultiTaskLassoCV.fit sklearn.linear_model.MultiTaskLassoCV.get_metadata_routing sklearn.linear_model.MultiTaskLassoCV.get_params sklearn.linear_model.MultiTaskLassoCV.path sklearn.linear_model.MultiTaskLassoCV.predict sklearn.linear_model.MultiTaskLassoCV.score sklearn.linear_model.MultiTaskLassoCV.set_fit_request sklearn.linear_model.MultiTaskLassoCV.set_params sklearn.linear_model.MultiTaskLassoCV.set_score_request 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.linear_model.OrthogonalMatchingPursuit.rst.txt b/dev/_sources/modules/generated/sklearn.linear_model.OrthogonalMatchingPursuit.rst.txt
index a00c3ca597e10..a38e1258a7cd2 100644
--- a/dev/_sources/modules/generated/sklearn.linear_model.OrthogonalMatchingPursuit.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.linear_model.OrthogonalMatchingPursuit.rst.txt
@@ -8,6 +8,6 @@ OrthogonalMatchingPursuit
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.linear_model.OrthogonalMatchingPursuit sklearn.linear_model.OrthogonalMatchingPursuit.__delattr__ sklearn.linear_model.OrthogonalMatchingPursuit.__dir__ sklearn.linear_model.OrthogonalMatchingPursuit.__eq__ sklearn.linear_model.OrthogonalMatchingPursuit.__format__ sklearn.linear_model.OrthogonalMatchingPursuit.__ge__ sklearn.linear_model.OrthogonalMatchingPursuit.__getattribute__ sklearn.linear_model.OrthogonalMatchingPursuit.__getstate__ sklearn.linear_model.OrthogonalMatchingPursuit.__gt__ sklearn.linear_model.OrthogonalMatchingPursuit.__hash__ sklearn.linear_model.OrthogonalMatchingPursuit.__init__ sklearn.linear_model.OrthogonalMatchingPursuit.__init_subclass__ sklearn.linear_model.OrthogonalMatchingPursuit.__le__ sklearn.linear_model.OrthogonalMatchingPursuit.__lt__ sklearn.linear_model.OrthogonalMatchingPursuit.__ne__ sklearn.linear_model.OrthogonalMatchingPursuit.__new__ sklearn.linear_model.OrthogonalMatchingPursuit.__reduce__ sklearn.linear_model.OrthogonalMatchingPursuit.__reduce_ex__ sklearn.linear_model.OrthogonalMatchingPursuit.__repr__ sklearn.linear_model.OrthogonalMatchingPursuit.__setattr__ sklearn.linear_model.OrthogonalMatchingPursuit.__setstate__ sklearn.linear_model.OrthogonalMatchingPursuit.__sizeof__ sklearn.linear_model.OrthogonalMatchingPursuit.__sklearn_clone__ sklearn.linear_model.OrthogonalMatchingPursuit.__sklearn_tags__ sklearn.linear_model.OrthogonalMatchingPursuit.__str__ sklearn.linear_model.OrthogonalMatchingPursuit.__subclasshook__ sklearn.linear_model.OrthogonalMatchingPursuit._build_request_for_signature sklearn.linear_model.OrthogonalMatchingPursuit._check_feature_names sklearn.linear_model.OrthogonalMatchingPursuit._check_n_features sklearn.linear_model.OrthogonalMatchingPursuit._decision_function sklearn.linear_model.OrthogonalMatchingPursuit._get_default_requests sklearn.linear_model.OrthogonalMatchingPursuit._get_doc_link sklearn.linear_model.OrthogonalMatchingPursuit._get_metadata_request sklearn.linear_model.OrthogonalMatchingPursuit._get_param_names sklearn.linear_model.OrthogonalMatchingPursuit._get_tags sklearn.linear_model.OrthogonalMatchingPursuit._more_tags sklearn.linear_model.OrthogonalMatchingPursuit._repr_html_inner sklearn.linear_model.OrthogonalMatchingPursuit._repr_mimebundle_ sklearn.linear_model.OrthogonalMatchingPursuit._set_intercept sklearn.linear_model.OrthogonalMatchingPursuit._validate_data sklearn.linear_model.OrthogonalMatchingPursuit._validate_params sklearn.linear_model.OrthogonalMatchingPursuit.fit sklearn.linear_model.OrthogonalMatchingPursuit.get_metadata_routing sklearn.linear_model.OrthogonalMatchingPursuit.get_params sklearn.linear_model.OrthogonalMatchingPursuit.predict sklearn.linear_model.OrthogonalMatchingPursuit.score sklearn.linear_model.OrthogonalMatchingPursuit.set_params sklearn.linear_model.OrthogonalMatchingPursuit.set_score_request 
+.. minigallery:: sklearn.linear_model.OrthogonalMatchingPursuit sklearn.linear_model.OrthogonalMatchingPursuit.__delattr__ sklearn.linear_model.OrthogonalMatchingPursuit.__dir__ sklearn.linear_model.OrthogonalMatchingPursuit.__eq__ sklearn.linear_model.OrthogonalMatchingPursuit.__format__ sklearn.linear_model.OrthogonalMatchingPursuit.__ge__ sklearn.linear_model.OrthogonalMatchingPursuit.__getattribute__ sklearn.linear_model.OrthogonalMatchingPursuit.__getstate__ sklearn.linear_model.OrthogonalMatchingPursuit.__gt__ sklearn.linear_model.OrthogonalMatchingPursuit.__hash__ sklearn.linear_model.OrthogonalMatchingPursuit.__init__ sklearn.linear_model.OrthogonalMatchingPursuit.__init_subclass__ sklearn.linear_model.OrthogonalMatchingPursuit.__le__ sklearn.linear_model.OrthogonalMatchingPursuit.__lt__ sklearn.linear_model.OrthogonalMatchingPursuit.__ne__ sklearn.linear_model.OrthogonalMatchingPursuit.__new__ sklearn.linear_model.OrthogonalMatchingPursuit.__reduce__ sklearn.linear_model.OrthogonalMatchingPursuit.__reduce_ex__ sklearn.linear_model.OrthogonalMatchingPursuit.__repr__ sklearn.linear_model.OrthogonalMatchingPursuit.__setattr__ sklearn.linear_model.OrthogonalMatchingPursuit.__setstate__ sklearn.linear_model.OrthogonalMatchingPursuit.__sizeof__ sklearn.linear_model.OrthogonalMatchingPursuit.__sklearn_clone__ sklearn.linear_model.OrthogonalMatchingPursuit.__sklearn_tags__ sklearn.linear_model.OrthogonalMatchingPursuit.__str__ sklearn.linear_model.OrthogonalMatchingPursuit.__subclasshook__ sklearn.linear_model.OrthogonalMatchingPursuit._build_request_for_signature sklearn.linear_model.OrthogonalMatchingPursuit._decision_function sklearn.linear_model.OrthogonalMatchingPursuit._get_default_requests sklearn.linear_model.OrthogonalMatchingPursuit._get_doc_link sklearn.linear_model.OrthogonalMatchingPursuit._get_metadata_request sklearn.linear_model.OrthogonalMatchingPursuit._get_param_names sklearn.linear_model.OrthogonalMatchingPursuit._repr_html_inner sklearn.linear_model.OrthogonalMatchingPursuit._repr_mimebundle_ sklearn.linear_model.OrthogonalMatchingPursuit._set_intercept sklearn.linear_model.OrthogonalMatchingPursuit._validate_params sklearn.linear_model.OrthogonalMatchingPursuit.fit sklearn.linear_model.OrthogonalMatchingPursuit.get_metadata_routing sklearn.linear_model.OrthogonalMatchingPursuit.get_params sklearn.linear_model.OrthogonalMatchingPursuit.predict sklearn.linear_model.OrthogonalMatchingPursuit.score sklearn.linear_model.OrthogonalMatchingPursuit.set_params sklearn.linear_model.OrthogonalMatchingPursuit.set_score_request 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.linear_model.OrthogonalMatchingPursuitCV.rst.txt b/dev/_sources/modules/generated/sklearn.linear_model.OrthogonalMatchingPursuitCV.rst.txt
index a5de0560be34d..1b652cab995d5 100644
--- a/dev/_sources/modules/generated/sklearn.linear_model.OrthogonalMatchingPursuitCV.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.linear_model.OrthogonalMatchingPursuitCV.rst.txt
@@ -8,6 +8,6 @@ OrthogonalMatchingPursuitCV
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.linear_model.OrthogonalMatchingPursuitCV sklearn.linear_model.OrthogonalMatchingPursuitCV.__delattr__ sklearn.linear_model.OrthogonalMatchingPursuitCV.__dir__ sklearn.linear_model.OrthogonalMatchingPursuitCV.__eq__ sklearn.linear_model.OrthogonalMatchingPursuitCV.__format__ sklearn.linear_model.OrthogonalMatchingPursuitCV.__ge__ sklearn.linear_model.OrthogonalMatchingPursuitCV.__getattribute__ sklearn.linear_model.OrthogonalMatchingPursuitCV.__getstate__ sklearn.linear_model.OrthogonalMatchingPursuitCV.__gt__ sklearn.linear_model.OrthogonalMatchingPursuitCV.__hash__ sklearn.linear_model.OrthogonalMatchingPursuitCV.__init__ sklearn.linear_model.OrthogonalMatchingPursuitCV.__init_subclass__ sklearn.linear_model.OrthogonalMatchingPursuitCV.__le__ sklearn.linear_model.OrthogonalMatchingPursuitCV.__lt__ sklearn.linear_model.OrthogonalMatchingPursuitCV.__ne__ sklearn.linear_model.OrthogonalMatchingPursuitCV.__new__ sklearn.linear_model.OrthogonalMatchingPursuitCV.__reduce__ sklearn.linear_model.OrthogonalMatchingPursuitCV.__reduce_ex__ sklearn.linear_model.OrthogonalMatchingPursuitCV.__repr__ sklearn.linear_model.OrthogonalMatchingPursuitCV.__setattr__ sklearn.linear_model.OrthogonalMatchingPursuitCV.__setstate__ sklearn.linear_model.OrthogonalMatchingPursuitCV.__sizeof__ sklearn.linear_model.OrthogonalMatchingPursuitCV.__sklearn_clone__ sklearn.linear_model.OrthogonalMatchingPursuitCV.__sklearn_tags__ sklearn.linear_model.OrthogonalMatchingPursuitCV.__str__ sklearn.linear_model.OrthogonalMatchingPursuitCV.__subclasshook__ sklearn.linear_model.OrthogonalMatchingPursuitCV._build_request_for_signature sklearn.linear_model.OrthogonalMatchingPursuitCV._check_feature_names sklearn.linear_model.OrthogonalMatchingPursuitCV._check_n_features sklearn.linear_model.OrthogonalMatchingPursuitCV._decision_function sklearn.linear_model.OrthogonalMatchingPursuitCV._get_default_requests sklearn.linear_model.OrthogonalMatchingPursuitCV._get_doc_link sklearn.linear_model.OrthogonalMatchingPursuitCV._get_metadata_request sklearn.linear_model.OrthogonalMatchingPursuitCV._get_param_names sklearn.linear_model.OrthogonalMatchingPursuitCV._get_tags sklearn.linear_model.OrthogonalMatchingPursuitCV._more_tags sklearn.linear_model.OrthogonalMatchingPursuitCV._repr_html_inner sklearn.linear_model.OrthogonalMatchingPursuitCV._repr_mimebundle_ sklearn.linear_model.OrthogonalMatchingPursuitCV._set_intercept sklearn.linear_model.OrthogonalMatchingPursuitCV._validate_data sklearn.linear_model.OrthogonalMatchingPursuitCV._validate_params sklearn.linear_model.OrthogonalMatchingPursuitCV.fit sklearn.linear_model.OrthogonalMatchingPursuitCV.get_metadata_routing sklearn.linear_model.OrthogonalMatchingPursuitCV.get_params sklearn.linear_model.OrthogonalMatchingPursuitCV.predict sklearn.linear_model.OrthogonalMatchingPursuitCV.score sklearn.linear_model.OrthogonalMatchingPursuitCV.set_params sklearn.linear_model.OrthogonalMatchingPursuitCV.set_score_request 
+.. minigallery:: sklearn.linear_model.OrthogonalMatchingPursuitCV sklearn.linear_model.OrthogonalMatchingPursuitCV.__delattr__ sklearn.linear_model.OrthogonalMatchingPursuitCV.__dir__ sklearn.linear_model.OrthogonalMatchingPursuitCV.__eq__ sklearn.linear_model.OrthogonalMatchingPursuitCV.__format__ sklearn.linear_model.OrthogonalMatchingPursuitCV.__ge__ sklearn.linear_model.OrthogonalMatchingPursuitCV.__getattribute__ sklearn.linear_model.OrthogonalMatchingPursuitCV.__getstate__ sklearn.linear_model.OrthogonalMatchingPursuitCV.__gt__ sklearn.linear_model.OrthogonalMatchingPursuitCV.__hash__ sklearn.linear_model.OrthogonalMatchingPursuitCV.__init__ sklearn.linear_model.OrthogonalMatchingPursuitCV.__init_subclass__ sklearn.linear_model.OrthogonalMatchingPursuitCV.__le__ sklearn.linear_model.OrthogonalMatchingPursuitCV.__lt__ sklearn.linear_model.OrthogonalMatchingPursuitCV.__ne__ sklearn.linear_model.OrthogonalMatchingPursuitCV.__new__ sklearn.linear_model.OrthogonalMatchingPursuitCV.__reduce__ sklearn.linear_model.OrthogonalMatchingPursuitCV.__reduce_ex__ sklearn.linear_model.OrthogonalMatchingPursuitCV.__repr__ sklearn.linear_model.OrthogonalMatchingPursuitCV.__setattr__ sklearn.linear_model.OrthogonalMatchingPursuitCV.__setstate__ sklearn.linear_model.OrthogonalMatchingPursuitCV.__sizeof__ sklearn.linear_model.OrthogonalMatchingPursuitCV.__sklearn_clone__ sklearn.linear_model.OrthogonalMatchingPursuitCV.__sklearn_tags__ sklearn.linear_model.OrthogonalMatchingPursuitCV.__str__ sklearn.linear_model.OrthogonalMatchingPursuitCV.__subclasshook__ sklearn.linear_model.OrthogonalMatchingPursuitCV._build_request_for_signature sklearn.linear_model.OrthogonalMatchingPursuitCV._decision_function sklearn.linear_model.OrthogonalMatchingPursuitCV._get_default_requests sklearn.linear_model.OrthogonalMatchingPursuitCV._get_doc_link sklearn.linear_model.OrthogonalMatchingPursuitCV._get_metadata_request sklearn.linear_model.OrthogonalMatchingPursuitCV._get_param_names sklearn.linear_model.OrthogonalMatchingPursuitCV._repr_html_inner sklearn.linear_model.OrthogonalMatchingPursuitCV._repr_mimebundle_ sklearn.linear_model.OrthogonalMatchingPursuitCV._set_intercept sklearn.linear_model.OrthogonalMatchingPursuitCV._validate_params sklearn.linear_model.OrthogonalMatchingPursuitCV.fit sklearn.linear_model.OrthogonalMatchingPursuitCV.get_metadata_routing sklearn.linear_model.OrthogonalMatchingPursuitCV.get_params sklearn.linear_model.OrthogonalMatchingPursuitCV.predict sklearn.linear_model.OrthogonalMatchingPursuitCV.score sklearn.linear_model.OrthogonalMatchingPursuitCV.set_params sklearn.linear_model.OrthogonalMatchingPursuitCV.set_score_request 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.linear_model.PassiveAggressiveClassifier.rst.txt b/dev/_sources/modules/generated/sklearn.linear_model.PassiveAggressiveClassifier.rst.txt
index a40371bc6fad8..6e44b91c7e6ca 100644
--- a/dev/_sources/modules/generated/sklearn.linear_model.PassiveAggressiveClassifier.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.linear_model.PassiveAggressiveClassifier.rst.txt
@@ -8,6 +8,6 @@ PassiveAggressiveClassifier
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.linear_model.PassiveAggressiveClassifier sklearn.linear_model.PassiveAggressiveClassifier.__delattr__ sklearn.linear_model.PassiveAggressiveClassifier.__dir__ sklearn.linear_model.PassiveAggressiveClassifier.__eq__ sklearn.linear_model.PassiveAggressiveClassifier.__format__ sklearn.linear_model.PassiveAggressiveClassifier.__ge__ sklearn.linear_model.PassiveAggressiveClassifier.__getattribute__ sklearn.linear_model.PassiveAggressiveClassifier.__getstate__ sklearn.linear_model.PassiveAggressiveClassifier.__gt__ sklearn.linear_model.PassiveAggressiveClassifier.__hash__ sklearn.linear_model.PassiveAggressiveClassifier.__init__ sklearn.linear_model.PassiveAggressiveClassifier.__init_subclass__ sklearn.linear_model.PassiveAggressiveClassifier.__le__ sklearn.linear_model.PassiveAggressiveClassifier.__lt__ sklearn.linear_model.PassiveAggressiveClassifier.__ne__ sklearn.linear_model.PassiveAggressiveClassifier.__new__ sklearn.linear_model.PassiveAggressiveClassifier.__reduce__ sklearn.linear_model.PassiveAggressiveClassifier.__reduce_ex__ sklearn.linear_model.PassiveAggressiveClassifier.__repr__ sklearn.linear_model.PassiveAggressiveClassifier.__setattr__ sklearn.linear_model.PassiveAggressiveClassifier.__setstate__ sklearn.linear_model.PassiveAggressiveClassifier.__sizeof__ sklearn.linear_model.PassiveAggressiveClassifier.__sklearn_clone__ sklearn.linear_model.PassiveAggressiveClassifier.__sklearn_tags__ sklearn.linear_model.PassiveAggressiveClassifier.__str__ sklearn.linear_model.PassiveAggressiveClassifier.__subclasshook__ sklearn.linear_model.PassiveAggressiveClassifier._allocate_parameter_mem sklearn.linear_model.PassiveAggressiveClassifier._build_request_for_signature sklearn.linear_model.PassiveAggressiveClassifier._check_feature_names sklearn.linear_model.PassiveAggressiveClassifier._check_n_features sklearn.linear_model.PassiveAggressiveClassifier._fit sklearn.linear_model.PassiveAggressiveClassifier._fit_binary sklearn.linear_model.PassiveAggressiveClassifier._fit_multiclass sklearn.linear_model.PassiveAggressiveClassifier._get_default_requests sklearn.linear_model.PassiveAggressiveClassifier._get_doc_link sklearn.linear_model.PassiveAggressiveClassifier._get_learning_rate_type sklearn.linear_model.PassiveAggressiveClassifier._get_loss_function sklearn.linear_model.PassiveAggressiveClassifier._get_metadata_request sklearn.linear_model.PassiveAggressiveClassifier._get_param_names sklearn.linear_model.PassiveAggressiveClassifier._get_penalty_type sklearn.linear_model.PassiveAggressiveClassifier._get_tags sklearn.linear_model.PassiveAggressiveClassifier._make_validation_score_cb sklearn.linear_model.PassiveAggressiveClassifier._make_validation_split sklearn.linear_model.PassiveAggressiveClassifier._more_tags sklearn.linear_model.PassiveAggressiveClassifier._more_validate_params sklearn.linear_model.PassiveAggressiveClassifier._partial_fit sklearn.linear_model.PassiveAggressiveClassifier._predict_proba_lr sklearn.linear_model.PassiveAggressiveClassifier._repr_html_inner sklearn.linear_model.PassiveAggressiveClassifier._repr_mimebundle_ sklearn.linear_model.PassiveAggressiveClassifier._validate_data sklearn.linear_model.PassiveAggressiveClassifier._validate_params sklearn.linear_model.PassiveAggressiveClassifier.decision_function sklearn.linear_model.PassiveAggressiveClassifier.densify sklearn.linear_model.PassiveAggressiveClassifier.fit sklearn.linear_model.PassiveAggressiveClassifier.get_metadata_routing sklearn.linear_model.PassiveAggressiveClassifier.get_params sklearn.linear_model.PassiveAggressiveClassifier.partial_fit sklearn.linear_model.PassiveAggressiveClassifier.predict sklearn.linear_model.PassiveAggressiveClassifier.score sklearn.linear_model.PassiveAggressiveClassifier.set_fit_request sklearn.linear_model.PassiveAggressiveClassifier.set_params sklearn.linear_model.PassiveAggressiveClassifier.set_partial_fit_request sklearn.linear_model.PassiveAggressiveClassifier.set_score_request sklearn.linear_model.PassiveAggressiveClassifier.sparsify 
+.. minigallery:: sklearn.linear_model.PassiveAggressiveClassifier sklearn.linear_model.PassiveAggressiveClassifier.__delattr__ sklearn.linear_model.PassiveAggressiveClassifier.__dir__ sklearn.linear_model.PassiveAggressiveClassifier.__eq__ sklearn.linear_model.PassiveAggressiveClassifier.__format__ sklearn.linear_model.PassiveAggressiveClassifier.__ge__ sklearn.linear_model.PassiveAggressiveClassifier.__getattribute__ sklearn.linear_model.PassiveAggressiveClassifier.__getstate__ sklearn.linear_model.PassiveAggressiveClassifier.__gt__ sklearn.linear_model.PassiveAggressiveClassifier.__hash__ sklearn.linear_model.PassiveAggressiveClassifier.__init__ sklearn.linear_model.PassiveAggressiveClassifier.__init_subclass__ sklearn.linear_model.PassiveAggressiveClassifier.__le__ sklearn.linear_model.PassiveAggressiveClassifier.__lt__ sklearn.linear_model.PassiveAggressiveClassifier.__ne__ sklearn.linear_model.PassiveAggressiveClassifier.__new__ sklearn.linear_model.PassiveAggressiveClassifier.__reduce__ sklearn.linear_model.PassiveAggressiveClassifier.__reduce_ex__ sklearn.linear_model.PassiveAggressiveClassifier.__repr__ sklearn.linear_model.PassiveAggressiveClassifier.__setattr__ sklearn.linear_model.PassiveAggressiveClassifier.__setstate__ sklearn.linear_model.PassiveAggressiveClassifier.__sizeof__ sklearn.linear_model.PassiveAggressiveClassifier.__sklearn_clone__ sklearn.linear_model.PassiveAggressiveClassifier.__sklearn_tags__ sklearn.linear_model.PassiveAggressiveClassifier.__str__ sklearn.linear_model.PassiveAggressiveClassifier.__subclasshook__ sklearn.linear_model.PassiveAggressiveClassifier._allocate_parameter_mem sklearn.linear_model.PassiveAggressiveClassifier._build_request_for_signature sklearn.linear_model.PassiveAggressiveClassifier._fit sklearn.linear_model.PassiveAggressiveClassifier._fit_binary sklearn.linear_model.PassiveAggressiveClassifier._fit_multiclass sklearn.linear_model.PassiveAggressiveClassifier._get_default_requests sklearn.linear_model.PassiveAggressiveClassifier._get_doc_link sklearn.linear_model.PassiveAggressiveClassifier._get_l1_ratio sklearn.linear_model.PassiveAggressiveClassifier._get_learning_rate_type sklearn.linear_model.PassiveAggressiveClassifier._get_loss_function sklearn.linear_model.PassiveAggressiveClassifier._get_metadata_request sklearn.linear_model.PassiveAggressiveClassifier._get_param_names sklearn.linear_model.PassiveAggressiveClassifier._get_penalty_type sklearn.linear_model.PassiveAggressiveClassifier._make_validation_score_cb sklearn.linear_model.PassiveAggressiveClassifier._make_validation_split sklearn.linear_model.PassiveAggressiveClassifier._more_validate_params sklearn.linear_model.PassiveAggressiveClassifier._partial_fit sklearn.linear_model.PassiveAggressiveClassifier._predict_proba_lr sklearn.linear_model.PassiveAggressiveClassifier._repr_html_inner sklearn.linear_model.PassiveAggressiveClassifier._repr_mimebundle_ sklearn.linear_model.PassiveAggressiveClassifier._validate_params sklearn.linear_model.PassiveAggressiveClassifier.decision_function sklearn.linear_model.PassiveAggressiveClassifier.densify sklearn.linear_model.PassiveAggressiveClassifier.fit sklearn.linear_model.PassiveAggressiveClassifier.get_metadata_routing sklearn.linear_model.PassiveAggressiveClassifier.get_params sklearn.linear_model.PassiveAggressiveClassifier.partial_fit sklearn.linear_model.PassiveAggressiveClassifier.predict sklearn.linear_model.PassiveAggressiveClassifier.score sklearn.linear_model.PassiveAggressiveClassifier.set_fit_request sklearn.linear_model.PassiveAggressiveClassifier.set_params sklearn.linear_model.PassiveAggressiveClassifier.set_partial_fit_request sklearn.linear_model.PassiveAggressiveClassifier.set_score_request sklearn.linear_model.PassiveAggressiveClassifier.sparsify 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.linear_model.PassiveAggressiveRegressor.rst.txt b/dev/_sources/modules/generated/sklearn.linear_model.PassiveAggressiveRegressor.rst.txt
index c0129c558dfd1..64eb5a8a9e4b4 100644
--- a/dev/_sources/modules/generated/sklearn.linear_model.PassiveAggressiveRegressor.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.linear_model.PassiveAggressiveRegressor.rst.txt
@@ -8,6 +8,6 @@ PassiveAggressiveRegressor
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.linear_model.PassiveAggressiveRegressor sklearn.linear_model.PassiveAggressiveRegressor.__delattr__ sklearn.linear_model.PassiveAggressiveRegressor.__dir__ sklearn.linear_model.PassiveAggressiveRegressor.__eq__ sklearn.linear_model.PassiveAggressiveRegressor.__format__ sklearn.linear_model.PassiveAggressiveRegressor.__ge__ sklearn.linear_model.PassiveAggressiveRegressor.__getattribute__ sklearn.linear_model.PassiveAggressiveRegressor.__getstate__ sklearn.linear_model.PassiveAggressiveRegressor.__gt__ sklearn.linear_model.PassiveAggressiveRegressor.__hash__ sklearn.linear_model.PassiveAggressiveRegressor.__init__ sklearn.linear_model.PassiveAggressiveRegressor.__init_subclass__ sklearn.linear_model.PassiveAggressiveRegressor.__le__ sklearn.linear_model.PassiveAggressiveRegressor.__lt__ sklearn.linear_model.PassiveAggressiveRegressor.__ne__ sklearn.linear_model.PassiveAggressiveRegressor.__new__ sklearn.linear_model.PassiveAggressiveRegressor.__reduce__ sklearn.linear_model.PassiveAggressiveRegressor.__reduce_ex__ sklearn.linear_model.PassiveAggressiveRegressor.__repr__ sklearn.linear_model.PassiveAggressiveRegressor.__setattr__ sklearn.linear_model.PassiveAggressiveRegressor.__setstate__ sklearn.linear_model.PassiveAggressiveRegressor.__sizeof__ sklearn.linear_model.PassiveAggressiveRegressor.__sklearn_clone__ sklearn.linear_model.PassiveAggressiveRegressor.__sklearn_tags__ sklearn.linear_model.PassiveAggressiveRegressor.__str__ sklearn.linear_model.PassiveAggressiveRegressor.__subclasshook__ sklearn.linear_model.PassiveAggressiveRegressor._allocate_parameter_mem sklearn.linear_model.PassiveAggressiveRegressor._build_request_for_signature sklearn.linear_model.PassiveAggressiveRegressor._check_feature_names sklearn.linear_model.PassiveAggressiveRegressor._check_n_features sklearn.linear_model.PassiveAggressiveRegressor._decision_function sklearn.linear_model.PassiveAggressiveRegressor._fit sklearn.linear_model.PassiveAggressiveRegressor._fit_regressor sklearn.linear_model.PassiveAggressiveRegressor._get_default_requests sklearn.linear_model.PassiveAggressiveRegressor._get_doc_link sklearn.linear_model.PassiveAggressiveRegressor._get_learning_rate_type sklearn.linear_model.PassiveAggressiveRegressor._get_loss_function sklearn.linear_model.PassiveAggressiveRegressor._get_metadata_request sklearn.linear_model.PassiveAggressiveRegressor._get_param_names sklearn.linear_model.PassiveAggressiveRegressor._get_penalty_type sklearn.linear_model.PassiveAggressiveRegressor._get_tags sklearn.linear_model.PassiveAggressiveRegressor._make_validation_score_cb sklearn.linear_model.PassiveAggressiveRegressor._make_validation_split sklearn.linear_model.PassiveAggressiveRegressor._more_tags sklearn.linear_model.PassiveAggressiveRegressor._more_validate_params sklearn.linear_model.PassiveAggressiveRegressor._partial_fit sklearn.linear_model.PassiveAggressiveRegressor._repr_html_inner sklearn.linear_model.PassiveAggressiveRegressor._repr_mimebundle_ sklearn.linear_model.PassiveAggressiveRegressor._validate_data sklearn.linear_model.PassiveAggressiveRegressor._validate_params sklearn.linear_model.PassiveAggressiveRegressor.densify sklearn.linear_model.PassiveAggressiveRegressor.fit sklearn.linear_model.PassiveAggressiveRegressor.get_metadata_routing sklearn.linear_model.PassiveAggressiveRegressor.get_params sklearn.linear_model.PassiveAggressiveRegressor.partial_fit sklearn.linear_model.PassiveAggressiveRegressor.predict sklearn.linear_model.PassiveAggressiveRegressor.score sklearn.linear_model.PassiveAggressiveRegressor.set_fit_request sklearn.linear_model.PassiveAggressiveRegressor.set_params sklearn.linear_model.PassiveAggressiveRegressor.set_partial_fit_request sklearn.linear_model.PassiveAggressiveRegressor.set_score_request sklearn.linear_model.PassiveAggressiveRegressor.sparsify 
+.. minigallery:: sklearn.linear_model.PassiveAggressiveRegressor sklearn.linear_model.PassiveAggressiveRegressor.__delattr__ sklearn.linear_model.PassiveAggressiveRegressor.__dir__ sklearn.linear_model.PassiveAggressiveRegressor.__eq__ sklearn.linear_model.PassiveAggressiveRegressor.__format__ sklearn.linear_model.PassiveAggressiveRegressor.__ge__ sklearn.linear_model.PassiveAggressiveRegressor.__getattribute__ sklearn.linear_model.PassiveAggressiveRegressor.__getstate__ sklearn.linear_model.PassiveAggressiveRegressor.__gt__ sklearn.linear_model.PassiveAggressiveRegressor.__hash__ sklearn.linear_model.PassiveAggressiveRegressor.__init__ sklearn.linear_model.PassiveAggressiveRegressor.__init_subclass__ sklearn.linear_model.PassiveAggressiveRegressor.__le__ sklearn.linear_model.PassiveAggressiveRegressor.__lt__ sklearn.linear_model.PassiveAggressiveRegressor.__ne__ sklearn.linear_model.PassiveAggressiveRegressor.__new__ sklearn.linear_model.PassiveAggressiveRegressor.__reduce__ sklearn.linear_model.PassiveAggressiveRegressor.__reduce_ex__ sklearn.linear_model.PassiveAggressiveRegressor.__repr__ sklearn.linear_model.PassiveAggressiveRegressor.__setattr__ sklearn.linear_model.PassiveAggressiveRegressor.__setstate__ sklearn.linear_model.PassiveAggressiveRegressor.__sizeof__ sklearn.linear_model.PassiveAggressiveRegressor.__sklearn_clone__ sklearn.linear_model.PassiveAggressiveRegressor.__sklearn_tags__ sklearn.linear_model.PassiveAggressiveRegressor.__str__ sklearn.linear_model.PassiveAggressiveRegressor.__subclasshook__ sklearn.linear_model.PassiveAggressiveRegressor._allocate_parameter_mem sklearn.linear_model.PassiveAggressiveRegressor._build_request_for_signature sklearn.linear_model.PassiveAggressiveRegressor._decision_function sklearn.linear_model.PassiveAggressiveRegressor._fit sklearn.linear_model.PassiveAggressiveRegressor._fit_regressor sklearn.linear_model.PassiveAggressiveRegressor._get_default_requests sklearn.linear_model.PassiveAggressiveRegressor._get_doc_link sklearn.linear_model.PassiveAggressiveRegressor._get_l1_ratio sklearn.linear_model.PassiveAggressiveRegressor._get_learning_rate_type sklearn.linear_model.PassiveAggressiveRegressor._get_loss_function sklearn.linear_model.PassiveAggressiveRegressor._get_metadata_request sklearn.linear_model.PassiveAggressiveRegressor._get_param_names sklearn.linear_model.PassiveAggressiveRegressor._get_penalty_type sklearn.linear_model.PassiveAggressiveRegressor._make_validation_score_cb sklearn.linear_model.PassiveAggressiveRegressor._make_validation_split sklearn.linear_model.PassiveAggressiveRegressor._more_validate_params sklearn.linear_model.PassiveAggressiveRegressor._partial_fit sklearn.linear_model.PassiveAggressiveRegressor._repr_html_inner sklearn.linear_model.PassiveAggressiveRegressor._repr_mimebundle_ sklearn.linear_model.PassiveAggressiveRegressor._validate_params sklearn.linear_model.PassiveAggressiveRegressor.densify sklearn.linear_model.PassiveAggressiveRegressor.fit sklearn.linear_model.PassiveAggressiveRegressor.get_metadata_routing sklearn.linear_model.PassiveAggressiveRegressor.get_params sklearn.linear_model.PassiveAggressiveRegressor.partial_fit sklearn.linear_model.PassiveAggressiveRegressor.predict sklearn.linear_model.PassiveAggressiveRegressor.score sklearn.linear_model.PassiveAggressiveRegressor.set_fit_request sklearn.linear_model.PassiveAggressiveRegressor.set_params sklearn.linear_model.PassiveAggressiveRegressor.set_partial_fit_request sklearn.linear_model.PassiveAggressiveRegressor.set_score_request sklearn.linear_model.PassiveAggressiveRegressor.sparsify 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.linear_model.Perceptron.rst.txt b/dev/_sources/modules/generated/sklearn.linear_model.Perceptron.rst.txt
index fa53ecd89e2af..1ba5cf03904f0 100644
--- a/dev/_sources/modules/generated/sklearn.linear_model.Perceptron.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.linear_model.Perceptron.rst.txt
@@ -8,6 +8,6 @@ Perceptron
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.linear_model.Perceptron sklearn.linear_model.Perceptron.__delattr__ sklearn.linear_model.Perceptron.__dir__ sklearn.linear_model.Perceptron.__eq__ sklearn.linear_model.Perceptron.__format__ sklearn.linear_model.Perceptron.__ge__ sklearn.linear_model.Perceptron.__getattribute__ sklearn.linear_model.Perceptron.__getstate__ sklearn.linear_model.Perceptron.__gt__ sklearn.linear_model.Perceptron.__hash__ sklearn.linear_model.Perceptron.__init__ sklearn.linear_model.Perceptron.__init_subclass__ sklearn.linear_model.Perceptron.__le__ sklearn.linear_model.Perceptron.__lt__ sklearn.linear_model.Perceptron.__ne__ sklearn.linear_model.Perceptron.__new__ sklearn.linear_model.Perceptron.__reduce__ sklearn.linear_model.Perceptron.__reduce_ex__ sklearn.linear_model.Perceptron.__repr__ sklearn.linear_model.Perceptron.__setattr__ sklearn.linear_model.Perceptron.__setstate__ sklearn.linear_model.Perceptron.__sizeof__ sklearn.linear_model.Perceptron.__sklearn_clone__ sklearn.linear_model.Perceptron.__sklearn_tags__ sklearn.linear_model.Perceptron.__str__ sklearn.linear_model.Perceptron.__subclasshook__ sklearn.linear_model.Perceptron._allocate_parameter_mem sklearn.linear_model.Perceptron._build_request_for_signature sklearn.linear_model.Perceptron._check_feature_names sklearn.linear_model.Perceptron._check_n_features sklearn.linear_model.Perceptron._fit sklearn.linear_model.Perceptron._fit_binary sklearn.linear_model.Perceptron._fit_multiclass sklearn.linear_model.Perceptron._get_default_requests sklearn.linear_model.Perceptron._get_doc_link sklearn.linear_model.Perceptron._get_learning_rate_type sklearn.linear_model.Perceptron._get_loss_function sklearn.linear_model.Perceptron._get_metadata_request sklearn.linear_model.Perceptron._get_param_names sklearn.linear_model.Perceptron._get_penalty_type sklearn.linear_model.Perceptron._get_tags sklearn.linear_model.Perceptron._make_validation_score_cb sklearn.linear_model.Perceptron._make_validation_split sklearn.linear_model.Perceptron._more_tags sklearn.linear_model.Perceptron._more_validate_params sklearn.linear_model.Perceptron._partial_fit sklearn.linear_model.Perceptron._predict_proba_lr sklearn.linear_model.Perceptron._repr_html_inner sklearn.linear_model.Perceptron._repr_mimebundle_ sklearn.linear_model.Perceptron._validate_data sklearn.linear_model.Perceptron._validate_params sklearn.linear_model.Perceptron.decision_function sklearn.linear_model.Perceptron.densify sklearn.linear_model.Perceptron.fit sklearn.linear_model.Perceptron.get_metadata_routing sklearn.linear_model.Perceptron.get_params sklearn.linear_model.Perceptron.partial_fit sklearn.linear_model.Perceptron.predict sklearn.linear_model.Perceptron.score sklearn.linear_model.Perceptron.set_fit_request sklearn.linear_model.Perceptron.set_params sklearn.linear_model.Perceptron.set_partial_fit_request sklearn.linear_model.Perceptron.set_score_request sklearn.linear_model.Perceptron.sparsify 
+.. minigallery:: sklearn.linear_model.Perceptron sklearn.linear_model.Perceptron.__delattr__ sklearn.linear_model.Perceptron.__dir__ sklearn.linear_model.Perceptron.__eq__ sklearn.linear_model.Perceptron.__format__ sklearn.linear_model.Perceptron.__ge__ sklearn.linear_model.Perceptron.__getattribute__ sklearn.linear_model.Perceptron.__getstate__ sklearn.linear_model.Perceptron.__gt__ sklearn.linear_model.Perceptron.__hash__ sklearn.linear_model.Perceptron.__init__ sklearn.linear_model.Perceptron.__init_subclass__ sklearn.linear_model.Perceptron.__le__ sklearn.linear_model.Perceptron.__lt__ sklearn.linear_model.Perceptron.__ne__ sklearn.linear_model.Perceptron.__new__ sklearn.linear_model.Perceptron.__reduce__ sklearn.linear_model.Perceptron.__reduce_ex__ sklearn.linear_model.Perceptron.__repr__ sklearn.linear_model.Perceptron.__setattr__ sklearn.linear_model.Perceptron.__setstate__ sklearn.linear_model.Perceptron.__sizeof__ sklearn.linear_model.Perceptron.__sklearn_clone__ sklearn.linear_model.Perceptron.__sklearn_tags__ sklearn.linear_model.Perceptron.__str__ sklearn.linear_model.Perceptron.__subclasshook__ sklearn.linear_model.Perceptron._allocate_parameter_mem sklearn.linear_model.Perceptron._build_request_for_signature sklearn.linear_model.Perceptron._fit sklearn.linear_model.Perceptron._fit_binary sklearn.linear_model.Perceptron._fit_multiclass sklearn.linear_model.Perceptron._get_default_requests sklearn.linear_model.Perceptron._get_doc_link sklearn.linear_model.Perceptron._get_l1_ratio sklearn.linear_model.Perceptron._get_learning_rate_type sklearn.linear_model.Perceptron._get_loss_function sklearn.linear_model.Perceptron._get_metadata_request sklearn.linear_model.Perceptron._get_param_names sklearn.linear_model.Perceptron._get_penalty_type sklearn.linear_model.Perceptron._make_validation_score_cb sklearn.linear_model.Perceptron._make_validation_split sklearn.linear_model.Perceptron._more_validate_params sklearn.linear_model.Perceptron._partial_fit sklearn.linear_model.Perceptron._predict_proba_lr sklearn.linear_model.Perceptron._repr_html_inner sklearn.linear_model.Perceptron._repr_mimebundle_ sklearn.linear_model.Perceptron._validate_params sklearn.linear_model.Perceptron.decision_function sklearn.linear_model.Perceptron.densify sklearn.linear_model.Perceptron.fit sklearn.linear_model.Perceptron.get_metadata_routing sklearn.linear_model.Perceptron.get_params sklearn.linear_model.Perceptron.partial_fit sklearn.linear_model.Perceptron.predict sklearn.linear_model.Perceptron.score sklearn.linear_model.Perceptron.set_fit_request sklearn.linear_model.Perceptron.set_params sklearn.linear_model.Perceptron.set_partial_fit_request sklearn.linear_model.Perceptron.set_score_request sklearn.linear_model.Perceptron.sparsify 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.linear_model.PoissonRegressor.rst.txt b/dev/_sources/modules/generated/sklearn.linear_model.PoissonRegressor.rst.txt
index 7fe29a17bf2d3..01dabaf8536a2 100644
--- a/dev/_sources/modules/generated/sklearn.linear_model.PoissonRegressor.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.linear_model.PoissonRegressor.rst.txt
@@ -8,6 +8,6 @@ PoissonRegressor
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.linear_model.PoissonRegressor sklearn.linear_model.PoissonRegressor.__delattr__ sklearn.linear_model.PoissonRegressor.__dir__ sklearn.linear_model.PoissonRegressor.__eq__ sklearn.linear_model.PoissonRegressor.__format__ sklearn.linear_model.PoissonRegressor.__ge__ sklearn.linear_model.PoissonRegressor.__getattribute__ sklearn.linear_model.PoissonRegressor.__getstate__ sklearn.linear_model.PoissonRegressor.__gt__ sklearn.linear_model.PoissonRegressor.__hash__ sklearn.linear_model.PoissonRegressor.__init__ sklearn.linear_model.PoissonRegressor.__init_subclass__ sklearn.linear_model.PoissonRegressor.__le__ sklearn.linear_model.PoissonRegressor.__lt__ sklearn.linear_model.PoissonRegressor.__ne__ sklearn.linear_model.PoissonRegressor.__new__ sklearn.linear_model.PoissonRegressor.__reduce__ sklearn.linear_model.PoissonRegressor.__reduce_ex__ sklearn.linear_model.PoissonRegressor.__repr__ sklearn.linear_model.PoissonRegressor.__setattr__ sklearn.linear_model.PoissonRegressor.__setstate__ sklearn.linear_model.PoissonRegressor.__sizeof__ sklearn.linear_model.PoissonRegressor.__sklearn_clone__ sklearn.linear_model.PoissonRegressor.__sklearn_tags__ sklearn.linear_model.PoissonRegressor.__str__ sklearn.linear_model.PoissonRegressor.__subclasshook__ sklearn.linear_model.PoissonRegressor._build_request_for_signature sklearn.linear_model.PoissonRegressor._check_feature_names sklearn.linear_model.PoissonRegressor._check_n_features sklearn.linear_model.PoissonRegressor._get_default_requests sklearn.linear_model.PoissonRegressor._get_doc_link sklearn.linear_model.PoissonRegressor._get_loss sklearn.linear_model.PoissonRegressor._get_metadata_request sklearn.linear_model.PoissonRegressor._get_param_names sklearn.linear_model.PoissonRegressor._get_tags sklearn.linear_model.PoissonRegressor._linear_predictor sklearn.linear_model.PoissonRegressor._more_tags sklearn.linear_model.PoissonRegressor._repr_html_inner sklearn.linear_model.PoissonRegressor._repr_mimebundle_ sklearn.linear_model.PoissonRegressor._validate_data sklearn.linear_model.PoissonRegressor._validate_params sklearn.linear_model.PoissonRegressor.fit sklearn.linear_model.PoissonRegressor.get_metadata_routing sklearn.linear_model.PoissonRegressor.get_params sklearn.linear_model.PoissonRegressor.predict sklearn.linear_model.PoissonRegressor.score sklearn.linear_model.PoissonRegressor.set_fit_request sklearn.linear_model.PoissonRegressor.set_params sklearn.linear_model.PoissonRegressor.set_score_request 
+.. minigallery:: sklearn.linear_model.PoissonRegressor sklearn.linear_model.PoissonRegressor.__delattr__ sklearn.linear_model.PoissonRegressor.__dir__ sklearn.linear_model.PoissonRegressor.__eq__ sklearn.linear_model.PoissonRegressor.__format__ sklearn.linear_model.PoissonRegressor.__ge__ sklearn.linear_model.PoissonRegressor.__getattribute__ sklearn.linear_model.PoissonRegressor.__getstate__ sklearn.linear_model.PoissonRegressor.__gt__ sklearn.linear_model.PoissonRegressor.__hash__ sklearn.linear_model.PoissonRegressor.__init__ sklearn.linear_model.PoissonRegressor.__init_subclass__ sklearn.linear_model.PoissonRegressor.__le__ sklearn.linear_model.PoissonRegressor.__lt__ sklearn.linear_model.PoissonRegressor.__ne__ sklearn.linear_model.PoissonRegressor.__new__ sklearn.linear_model.PoissonRegressor.__reduce__ sklearn.linear_model.PoissonRegressor.__reduce_ex__ sklearn.linear_model.PoissonRegressor.__repr__ sklearn.linear_model.PoissonRegressor.__setattr__ sklearn.linear_model.PoissonRegressor.__setstate__ sklearn.linear_model.PoissonRegressor.__sizeof__ sklearn.linear_model.PoissonRegressor.__sklearn_clone__ sklearn.linear_model.PoissonRegressor.__sklearn_tags__ sklearn.linear_model.PoissonRegressor.__str__ sklearn.linear_model.PoissonRegressor.__subclasshook__ sklearn.linear_model.PoissonRegressor._build_request_for_signature sklearn.linear_model.PoissonRegressor._get_default_requests sklearn.linear_model.PoissonRegressor._get_doc_link sklearn.linear_model.PoissonRegressor._get_loss sklearn.linear_model.PoissonRegressor._get_metadata_request sklearn.linear_model.PoissonRegressor._get_param_names sklearn.linear_model.PoissonRegressor._linear_predictor sklearn.linear_model.PoissonRegressor._repr_html_inner sklearn.linear_model.PoissonRegressor._repr_mimebundle_ sklearn.linear_model.PoissonRegressor._validate_params sklearn.linear_model.PoissonRegressor.fit sklearn.linear_model.PoissonRegressor.get_metadata_routing sklearn.linear_model.PoissonRegressor.get_params sklearn.linear_model.PoissonRegressor.predict sklearn.linear_model.PoissonRegressor.score sklearn.linear_model.PoissonRegressor.set_fit_request sklearn.linear_model.PoissonRegressor.set_params sklearn.linear_model.PoissonRegressor.set_score_request 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.linear_model.QuantileRegressor.rst.txt b/dev/_sources/modules/generated/sklearn.linear_model.QuantileRegressor.rst.txt
index b6bd3279b933d..200211e00ae02 100644
--- a/dev/_sources/modules/generated/sklearn.linear_model.QuantileRegressor.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.linear_model.QuantileRegressor.rst.txt
@@ -8,6 +8,6 @@ QuantileRegressor
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.linear_model.QuantileRegressor sklearn.linear_model.QuantileRegressor.__delattr__ sklearn.linear_model.QuantileRegressor.__dir__ sklearn.linear_model.QuantileRegressor.__eq__ sklearn.linear_model.QuantileRegressor.__format__ sklearn.linear_model.QuantileRegressor.__ge__ sklearn.linear_model.QuantileRegressor.__getattribute__ sklearn.linear_model.QuantileRegressor.__getstate__ sklearn.linear_model.QuantileRegressor.__gt__ sklearn.linear_model.QuantileRegressor.__hash__ sklearn.linear_model.QuantileRegressor.__init__ sklearn.linear_model.QuantileRegressor.__init_subclass__ sklearn.linear_model.QuantileRegressor.__le__ sklearn.linear_model.QuantileRegressor.__lt__ sklearn.linear_model.QuantileRegressor.__ne__ sklearn.linear_model.QuantileRegressor.__new__ sklearn.linear_model.QuantileRegressor.__reduce__ sklearn.linear_model.QuantileRegressor.__reduce_ex__ sklearn.linear_model.QuantileRegressor.__repr__ sklearn.linear_model.QuantileRegressor.__setattr__ sklearn.linear_model.QuantileRegressor.__setstate__ sklearn.linear_model.QuantileRegressor.__sizeof__ sklearn.linear_model.QuantileRegressor.__sklearn_clone__ sklearn.linear_model.QuantileRegressor.__sklearn_tags__ sklearn.linear_model.QuantileRegressor.__str__ sklearn.linear_model.QuantileRegressor.__subclasshook__ sklearn.linear_model.QuantileRegressor._build_request_for_signature sklearn.linear_model.QuantileRegressor._check_feature_names sklearn.linear_model.QuantileRegressor._check_n_features sklearn.linear_model.QuantileRegressor._decision_function sklearn.linear_model.QuantileRegressor._get_default_requests sklearn.linear_model.QuantileRegressor._get_doc_link sklearn.linear_model.QuantileRegressor._get_metadata_request sklearn.linear_model.QuantileRegressor._get_param_names sklearn.linear_model.QuantileRegressor._get_tags sklearn.linear_model.QuantileRegressor._more_tags sklearn.linear_model.QuantileRegressor._repr_html_inner sklearn.linear_model.QuantileRegressor._repr_mimebundle_ sklearn.linear_model.QuantileRegressor._set_intercept sklearn.linear_model.QuantileRegressor._validate_data sklearn.linear_model.QuantileRegressor._validate_params sklearn.linear_model.QuantileRegressor.fit sklearn.linear_model.QuantileRegressor.get_metadata_routing sklearn.linear_model.QuantileRegressor.get_params sklearn.linear_model.QuantileRegressor.predict sklearn.linear_model.QuantileRegressor.score sklearn.linear_model.QuantileRegressor.set_fit_request sklearn.linear_model.QuantileRegressor.set_params sklearn.linear_model.QuantileRegressor.set_score_request 
+.. minigallery:: sklearn.linear_model.QuantileRegressor sklearn.linear_model.QuantileRegressor.__delattr__ sklearn.linear_model.QuantileRegressor.__dir__ sklearn.linear_model.QuantileRegressor.__eq__ sklearn.linear_model.QuantileRegressor.__format__ sklearn.linear_model.QuantileRegressor.__ge__ sklearn.linear_model.QuantileRegressor.__getattribute__ sklearn.linear_model.QuantileRegressor.__getstate__ sklearn.linear_model.QuantileRegressor.__gt__ sklearn.linear_model.QuantileRegressor.__hash__ sklearn.linear_model.QuantileRegressor.__init__ sklearn.linear_model.QuantileRegressor.__init_subclass__ sklearn.linear_model.QuantileRegressor.__le__ sklearn.linear_model.QuantileRegressor.__lt__ sklearn.linear_model.QuantileRegressor.__ne__ sklearn.linear_model.QuantileRegressor.__new__ sklearn.linear_model.QuantileRegressor.__reduce__ sklearn.linear_model.QuantileRegressor.__reduce_ex__ sklearn.linear_model.QuantileRegressor.__repr__ sklearn.linear_model.QuantileRegressor.__setattr__ sklearn.linear_model.QuantileRegressor.__setstate__ sklearn.linear_model.QuantileRegressor.__sizeof__ sklearn.linear_model.QuantileRegressor.__sklearn_clone__ sklearn.linear_model.QuantileRegressor.__sklearn_tags__ sklearn.linear_model.QuantileRegressor.__str__ sklearn.linear_model.QuantileRegressor.__subclasshook__ sklearn.linear_model.QuantileRegressor._build_request_for_signature sklearn.linear_model.QuantileRegressor._decision_function sklearn.linear_model.QuantileRegressor._get_default_requests sklearn.linear_model.QuantileRegressor._get_doc_link sklearn.linear_model.QuantileRegressor._get_metadata_request sklearn.linear_model.QuantileRegressor._get_param_names sklearn.linear_model.QuantileRegressor._repr_html_inner sklearn.linear_model.QuantileRegressor._repr_mimebundle_ sklearn.linear_model.QuantileRegressor._set_intercept sklearn.linear_model.QuantileRegressor._validate_params sklearn.linear_model.QuantileRegressor.fit sklearn.linear_model.QuantileRegressor.get_metadata_routing sklearn.linear_model.QuantileRegressor.get_params sklearn.linear_model.QuantileRegressor.predict sklearn.linear_model.QuantileRegressor.score sklearn.linear_model.QuantileRegressor.set_fit_request sklearn.linear_model.QuantileRegressor.set_params sklearn.linear_model.QuantileRegressor.set_score_request 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.linear_model.RANSACRegressor.rst.txt b/dev/_sources/modules/generated/sklearn.linear_model.RANSACRegressor.rst.txt
index cd53058d6a45a..ad948f6cac6c1 100644
--- a/dev/_sources/modules/generated/sklearn.linear_model.RANSACRegressor.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.linear_model.RANSACRegressor.rst.txt
@@ -8,6 +8,6 @@ RANSACRegressor
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.linear_model.RANSACRegressor sklearn.linear_model.RANSACRegressor.__delattr__ sklearn.linear_model.RANSACRegressor.__dir__ sklearn.linear_model.RANSACRegressor.__eq__ sklearn.linear_model.RANSACRegressor.__format__ sklearn.linear_model.RANSACRegressor.__ge__ sklearn.linear_model.RANSACRegressor.__getattribute__ sklearn.linear_model.RANSACRegressor.__getstate__ sklearn.linear_model.RANSACRegressor.__gt__ sklearn.linear_model.RANSACRegressor.__hash__ sklearn.linear_model.RANSACRegressor.__init__ sklearn.linear_model.RANSACRegressor.__init_subclass__ sklearn.linear_model.RANSACRegressor.__le__ sklearn.linear_model.RANSACRegressor.__lt__ sklearn.linear_model.RANSACRegressor.__ne__ sklearn.linear_model.RANSACRegressor.__new__ sklearn.linear_model.RANSACRegressor.__reduce__ sklearn.linear_model.RANSACRegressor.__reduce_ex__ sklearn.linear_model.RANSACRegressor.__repr__ sklearn.linear_model.RANSACRegressor.__setattr__ sklearn.linear_model.RANSACRegressor.__setstate__ sklearn.linear_model.RANSACRegressor.__sizeof__ sklearn.linear_model.RANSACRegressor.__sklearn_clone__ sklearn.linear_model.RANSACRegressor.__sklearn_tags__ sklearn.linear_model.RANSACRegressor.__str__ sklearn.linear_model.RANSACRegressor.__subclasshook__ sklearn.linear_model.RANSACRegressor._build_request_for_signature sklearn.linear_model.RANSACRegressor._check_feature_names sklearn.linear_model.RANSACRegressor._check_n_features sklearn.linear_model.RANSACRegressor._get_default_requests sklearn.linear_model.RANSACRegressor._get_doc_link sklearn.linear_model.RANSACRegressor._get_metadata_request sklearn.linear_model.RANSACRegressor._get_param_names sklearn.linear_model.RANSACRegressor._get_tags sklearn.linear_model.RANSACRegressor._more_tags sklearn.linear_model.RANSACRegressor._repr_html_inner sklearn.linear_model.RANSACRegressor._repr_mimebundle_ sklearn.linear_model.RANSACRegressor._validate_data sklearn.linear_model.RANSACRegressor._validate_params sklearn.linear_model.RANSACRegressor.fit sklearn.linear_model.RANSACRegressor.get_metadata_routing sklearn.linear_model.RANSACRegressor.get_params sklearn.linear_model.RANSACRegressor.predict sklearn.linear_model.RANSACRegressor.score sklearn.linear_model.RANSACRegressor.set_fit_request sklearn.linear_model.RANSACRegressor.set_params 
+.. minigallery:: sklearn.linear_model.RANSACRegressor sklearn.linear_model.RANSACRegressor.__delattr__ sklearn.linear_model.RANSACRegressor.__dir__ sklearn.linear_model.RANSACRegressor.__eq__ sklearn.linear_model.RANSACRegressor.__format__ sklearn.linear_model.RANSACRegressor.__ge__ sklearn.linear_model.RANSACRegressor.__getattribute__ sklearn.linear_model.RANSACRegressor.__getstate__ sklearn.linear_model.RANSACRegressor.__gt__ sklearn.linear_model.RANSACRegressor.__hash__ sklearn.linear_model.RANSACRegressor.__init__ sklearn.linear_model.RANSACRegressor.__init_subclass__ sklearn.linear_model.RANSACRegressor.__le__ sklearn.linear_model.RANSACRegressor.__lt__ sklearn.linear_model.RANSACRegressor.__ne__ sklearn.linear_model.RANSACRegressor.__new__ sklearn.linear_model.RANSACRegressor.__reduce__ sklearn.linear_model.RANSACRegressor.__reduce_ex__ sklearn.linear_model.RANSACRegressor.__repr__ sklearn.linear_model.RANSACRegressor.__setattr__ sklearn.linear_model.RANSACRegressor.__setstate__ sklearn.linear_model.RANSACRegressor.__sizeof__ sklearn.linear_model.RANSACRegressor.__sklearn_clone__ sklearn.linear_model.RANSACRegressor.__sklearn_tags__ sklearn.linear_model.RANSACRegressor.__str__ sklearn.linear_model.RANSACRegressor.__subclasshook__ sklearn.linear_model.RANSACRegressor._build_request_for_signature sklearn.linear_model.RANSACRegressor._get_default_requests sklearn.linear_model.RANSACRegressor._get_doc_link sklearn.linear_model.RANSACRegressor._get_metadata_request sklearn.linear_model.RANSACRegressor._get_param_names sklearn.linear_model.RANSACRegressor._repr_html_inner sklearn.linear_model.RANSACRegressor._repr_mimebundle_ sklearn.linear_model.RANSACRegressor._validate_params sklearn.linear_model.RANSACRegressor.fit sklearn.linear_model.RANSACRegressor.get_metadata_routing sklearn.linear_model.RANSACRegressor.get_params sklearn.linear_model.RANSACRegressor.predict sklearn.linear_model.RANSACRegressor.score sklearn.linear_model.RANSACRegressor.set_fit_request sklearn.linear_model.RANSACRegressor.set_params 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.linear_model.Ridge.rst.txt b/dev/_sources/modules/generated/sklearn.linear_model.Ridge.rst.txt
index 8dd29a7b507bd..375f31f6257b1 100644
--- a/dev/_sources/modules/generated/sklearn.linear_model.Ridge.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.linear_model.Ridge.rst.txt
@@ -8,6 +8,6 @@ Ridge
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.linear_model.Ridge sklearn.linear_model.Ridge.__delattr__ sklearn.linear_model.Ridge.__dir__ sklearn.linear_model.Ridge.__eq__ sklearn.linear_model.Ridge.__format__ sklearn.linear_model.Ridge.__ge__ sklearn.linear_model.Ridge.__getattribute__ sklearn.linear_model.Ridge.__getstate__ sklearn.linear_model.Ridge.__gt__ sklearn.linear_model.Ridge.__hash__ sklearn.linear_model.Ridge.__init__ sklearn.linear_model.Ridge.__init_subclass__ sklearn.linear_model.Ridge.__le__ sklearn.linear_model.Ridge.__lt__ sklearn.linear_model.Ridge.__ne__ sklearn.linear_model.Ridge.__new__ sklearn.linear_model.Ridge.__reduce__ sklearn.linear_model.Ridge.__reduce_ex__ sklearn.linear_model.Ridge.__repr__ sklearn.linear_model.Ridge.__setattr__ sklearn.linear_model.Ridge.__setstate__ sklearn.linear_model.Ridge.__sizeof__ sklearn.linear_model.Ridge.__sklearn_clone__ sklearn.linear_model.Ridge.__sklearn_tags__ sklearn.linear_model.Ridge.__str__ sklearn.linear_model.Ridge.__subclasshook__ sklearn.linear_model.Ridge._build_request_for_signature sklearn.linear_model.Ridge._check_feature_names sklearn.linear_model.Ridge._check_n_features sklearn.linear_model.Ridge._decision_function sklearn.linear_model.Ridge._get_default_requests sklearn.linear_model.Ridge._get_doc_link sklearn.linear_model.Ridge._get_metadata_request sklearn.linear_model.Ridge._get_param_names sklearn.linear_model.Ridge._get_tags sklearn.linear_model.Ridge._more_tags sklearn.linear_model.Ridge._repr_html_inner sklearn.linear_model.Ridge._repr_mimebundle_ sklearn.linear_model.Ridge._set_intercept sklearn.linear_model.Ridge._validate_data sklearn.linear_model.Ridge._validate_params sklearn.linear_model.Ridge.fit sklearn.linear_model.Ridge.get_metadata_routing sklearn.linear_model.Ridge.get_params sklearn.linear_model.Ridge.predict sklearn.linear_model.Ridge.score sklearn.linear_model.Ridge.set_fit_request sklearn.linear_model.Ridge.set_params sklearn.linear_model.Ridge.set_score_request 
+.. minigallery:: sklearn.linear_model.Ridge sklearn.linear_model.Ridge.__delattr__ sklearn.linear_model.Ridge.__dir__ sklearn.linear_model.Ridge.__eq__ sklearn.linear_model.Ridge.__format__ sklearn.linear_model.Ridge.__ge__ sklearn.linear_model.Ridge.__getattribute__ sklearn.linear_model.Ridge.__getstate__ sklearn.linear_model.Ridge.__gt__ sklearn.linear_model.Ridge.__hash__ sklearn.linear_model.Ridge.__init__ sklearn.linear_model.Ridge.__init_subclass__ sklearn.linear_model.Ridge.__le__ sklearn.linear_model.Ridge.__lt__ sklearn.linear_model.Ridge.__ne__ sklearn.linear_model.Ridge.__new__ sklearn.linear_model.Ridge.__reduce__ sklearn.linear_model.Ridge.__reduce_ex__ sklearn.linear_model.Ridge.__repr__ sklearn.linear_model.Ridge.__setattr__ sklearn.linear_model.Ridge.__setstate__ sklearn.linear_model.Ridge.__sizeof__ sklearn.linear_model.Ridge.__sklearn_clone__ sklearn.linear_model.Ridge.__sklearn_tags__ sklearn.linear_model.Ridge.__str__ sklearn.linear_model.Ridge.__subclasshook__ sklearn.linear_model.Ridge._build_request_for_signature sklearn.linear_model.Ridge._decision_function sklearn.linear_model.Ridge._get_default_requests sklearn.linear_model.Ridge._get_doc_link sklearn.linear_model.Ridge._get_metadata_request sklearn.linear_model.Ridge._get_param_names sklearn.linear_model.Ridge._repr_html_inner sklearn.linear_model.Ridge._repr_mimebundle_ sklearn.linear_model.Ridge._set_intercept sklearn.linear_model.Ridge._validate_params sklearn.linear_model.Ridge.fit sklearn.linear_model.Ridge.get_metadata_routing sklearn.linear_model.Ridge.get_params sklearn.linear_model.Ridge.predict sklearn.linear_model.Ridge.score sklearn.linear_model.Ridge.set_fit_request sklearn.linear_model.Ridge.set_params sklearn.linear_model.Ridge.set_score_request 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.linear_model.RidgeCV.rst.txt b/dev/_sources/modules/generated/sklearn.linear_model.RidgeCV.rst.txt
index 647b7a1f98fac..1e87f8faf89ae 100644
--- a/dev/_sources/modules/generated/sklearn.linear_model.RidgeCV.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.linear_model.RidgeCV.rst.txt
@@ -8,6 +8,6 @@ RidgeCV
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.linear_model.RidgeCV sklearn.linear_model.RidgeCV.__delattr__ sklearn.linear_model.RidgeCV.__dir__ sklearn.linear_model.RidgeCV.__eq__ sklearn.linear_model.RidgeCV.__format__ sklearn.linear_model.RidgeCV.__ge__ sklearn.linear_model.RidgeCV.__getattribute__ sklearn.linear_model.RidgeCV.__getstate__ sklearn.linear_model.RidgeCV.__gt__ sklearn.linear_model.RidgeCV.__hash__ sklearn.linear_model.RidgeCV.__init__ sklearn.linear_model.RidgeCV.__init_subclass__ sklearn.linear_model.RidgeCV.__le__ sklearn.linear_model.RidgeCV.__lt__ sklearn.linear_model.RidgeCV.__ne__ sklearn.linear_model.RidgeCV.__new__ sklearn.linear_model.RidgeCV.__reduce__ sklearn.linear_model.RidgeCV.__reduce_ex__ sklearn.linear_model.RidgeCV.__repr__ sklearn.linear_model.RidgeCV.__setattr__ sklearn.linear_model.RidgeCV.__setstate__ sklearn.linear_model.RidgeCV.__sizeof__ sklearn.linear_model.RidgeCV.__sklearn_clone__ sklearn.linear_model.RidgeCV.__sklearn_tags__ sklearn.linear_model.RidgeCV.__str__ sklearn.linear_model.RidgeCV.__subclasshook__ sklearn.linear_model.RidgeCV._build_request_for_signature sklearn.linear_model.RidgeCV._check_feature_names sklearn.linear_model.RidgeCV._check_n_features sklearn.linear_model.RidgeCV._decision_function sklearn.linear_model.RidgeCV._get_default_requests sklearn.linear_model.RidgeCV._get_doc_link sklearn.linear_model.RidgeCV._get_metadata_request sklearn.linear_model.RidgeCV._get_param_names sklearn.linear_model.RidgeCV._get_scorer sklearn.linear_model.RidgeCV._get_tags sklearn.linear_model.RidgeCV._more_tags sklearn.linear_model.RidgeCV._repr_html_inner sklearn.linear_model.RidgeCV._repr_mimebundle_ sklearn.linear_model.RidgeCV._set_intercept sklearn.linear_model.RidgeCV._validate_data sklearn.linear_model.RidgeCV._validate_params sklearn.linear_model.RidgeCV.fit sklearn.linear_model.RidgeCV.get_metadata_routing sklearn.linear_model.RidgeCV.get_params sklearn.linear_model.RidgeCV.predict sklearn.linear_model.RidgeCV.score sklearn.linear_model.RidgeCV.set_fit_request sklearn.linear_model.RidgeCV.set_params sklearn.linear_model.RidgeCV.set_score_request 
+.. minigallery:: sklearn.linear_model.RidgeCV sklearn.linear_model.RidgeCV.__delattr__ sklearn.linear_model.RidgeCV.__dir__ sklearn.linear_model.RidgeCV.__eq__ sklearn.linear_model.RidgeCV.__format__ sklearn.linear_model.RidgeCV.__ge__ sklearn.linear_model.RidgeCV.__getattribute__ sklearn.linear_model.RidgeCV.__getstate__ sklearn.linear_model.RidgeCV.__gt__ sklearn.linear_model.RidgeCV.__hash__ sklearn.linear_model.RidgeCV.__init__ sklearn.linear_model.RidgeCV.__init_subclass__ sklearn.linear_model.RidgeCV.__le__ sklearn.linear_model.RidgeCV.__lt__ sklearn.linear_model.RidgeCV.__ne__ sklearn.linear_model.RidgeCV.__new__ sklearn.linear_model.RidgeCV.__reduce__ sklearn.linear_model.RidgeCV.__reduce_ex__ sklearn.linear_model.RidgeCV.__repr__ sklearn.linear_model.RidgeCV.__setattr__ sklearn.linear_model.RidgeCV.__setstate__ sklearn.linear_model.RidgeCV.__sizeof__ sklearn.linear_model.RidgeCV.__sklearn_clone__ sklearn.linear_model.RidgeCV.__sklearn_tags__ sklearn.linear_model.RidgeCV.__str__ sklearn.linear_model.RidgeCV.__subclasshook__ sklearn.linear_model.RidgeCV._build_request_for_signature sklearn.linear_model.RidgeCV._decision_function sklearn.linear_model.RidgeCV._get_default_requests sklearn.linear_model.RidgeCV._get_doc_link sklearn.linear_model.RidgeCV._get_metadata_request sklearn.linear_model.RidgeCV._get_param_names sklearn.linear_model.RidgeCV._get_scorer sklearn.linear_model.RidgeCV._repr_html_inner sklearn.linear_model.RidgeCV._repr_mimebundle_ sklearn.linear_model.RidgeCV._set_intercept sklearn.linear_model.RidgeCV._validate_params sklearn.linear_model.RidgeCV.fit sklearn.linear_model.RidgeCV.get_metadata_routing sklearn.linear_model.RidgeCV.get_params sklearn.linear_model.RidgeCV.predict sklearn.linear_model.RidgeCV.score sklearn.linear_model.RidgeCV.set_fit_request sklearn.linear_model.RidgeCV.set_params sklearn.linear_model.RidgeCV.set_score_request 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.linear_model.RidgeClassifier.rst.txt b/dev/_sources/modules/generated/sklearn.linear_model.RidgeClassifier.rst.txt
index fb93f02b002bc..69b708527bf6f 100644
--- a/dev/_sources/modules/generated/sklearn.linear_model.RidgeClassifier.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.linear_model.RidgeClassifier.rst.txt
@@ -8,6 +8,6 @@ RidgeClassifier
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.linear_model.RidgeClassifier sklearn.linear_model.RidgeClassifier.__delattr__ sklearn.linear_model.RidgeClassifier.__dir__ sklearn.linear_model.RidgeClassifier.__eq__ sklearn.linear_model.RidgeClassifier.__format__ sklearn.linear_model.RidgeClassifier.__ge__ sklearn.linear_model.RidgeClassifier.__getattribute__ sklearn.linear_model.RidgeClassifier.__getstate__ sklearn.linear_model.RidgeClassifier.__gt__ sklearn.linear_model.RidgeClassifier.__hash__ sklearn.linear_model.RidgeClassifier.__init__ sklearn.linear_model.RidgeClassifier.__init_subclass__ sklearn.linear_model.RidgeClassifier.__le__ sklearn.linear_model.RidgeClassifier.__lt__ sklearn.linear_model.RidgeClassifier.__ne__ sklearn.linear_model.RidgeClassifier.__new__ sklearn.linear_model.RidgeClassifier.__reduce__ sklearn.linear_model.RidgeClassifier.__reduce_ex__ sklearn.linear_model.RidgeClassifier.__repr__ sklearn.linear_model.RidgeClassifier.__setattr__ sklearn.linear_model.RidgeClassifier.__setstate__ sklearn.linear_model.RidgeClassifier.__sizeof__ sklearn.linear_model.RidgeClassifier.__sklearn_clone__ sklearn.linear_model.RidgeClassifier.__sklearn_tags__ sklearn.linear_model.RidgeClassifier.__str__ sklearn.linear_model.RidgeClassifier.__subclasshook__ sklearn.linear_model.RidgeClassifier._build_request_for_signature sklearn.linear_model.RidgeClassifier._check_feature_names sklearn.linear_model.RidgeClassifier._check_n_features sklearn.linear_model.RidgeClassifier._decision_function sklearn.linear_model.RidgeClassifier._get_default_requests sklearn.linear_model.RidgeClassifier._get_doc_link sklearn.linear_model.RidgeClassifier._get_metadata_request sklearn.linear_model.RidgeClassifier._get_param_names sklearn.linear_model.RidgeClassifier._get_tags sklearn.linear_model.RidgeClassifier._more_tags sklearn.linear_model.RidgeClassifier._predict_proba_lr sklearn.linear_model.RidgeClassifier._prepare_data sklearn.linear_model.RidgeClassifier._repr_html_inner sklearn.linear_model.RidgeClassifier._repr_mimebundle_ sklearn.linear_model.RidgeClassifier._set_intercept sklearn.linear_model.RidgeClassifier._validate_data sklearn.linear_model.RidgeClassifier._validate_params sklearn.linear_model.RidgeClassifier.decision_function sklearn.linear_model.RidgeClassifier.fit sklearn.linear_model.RidgeClassifier.get_metadata_routing sklearn.linear_model.RidgeClassifier.get_params sklearn.linear_model.RidgeClassifier.predict sklearn.linear_model.RidgeClassifier.score sklearn.linear_model.RidgeClassifier.set_fit_request sklearn.linear_model.RidgeClassifier.set_params sklearn.linear_model.RidgeClassifier.set_score_request 
+.. minigallery:: sklearn.linear_model.RidgeClassifier sklearn.linear_model.RidgeClassifier.__delattr__ sklearn.linear_model.RidgeClassifier.__dir__ sklearn.linear_model.RidgeClassifier.__eq__ sklearn.linear_model.RidgeClassifier.__format__ sklearn.linear_model.RidgeClassifier.__ge__ sklearn.linear_model.RidgeClassifier.__getattribute__ sklearn.linear_model.RidgeClassifier.__getstate__ sklearn.linear_model.RidgeClassifier.__gt__ sklearn.linear_model.RidgeClassifier.__hash__ sklearn.linear_model.RidgeClassifier.__init__ sklearn.linear_model.RidgeClassifier.__init_subclass__ sklearn.linear_model.RidgeClassifier.__le__ sklearn.linear_model.RidgeClassifier.__lt__ sklearn.linear_model.RidgeClassifier.__ne__ sklearn.linear_model.RidgeClassifier.__new__ sklearn.linear_model.RidgeClassifier.__reduce__ sklearn.linear_model.RidgeClassifier.__reduce_ex__ sklearn.linear_model.RidgeClassifier.__repr__ sklearn.linear_model.RidgeClassifier.__setattr__ sklearn.linear_model.RidgeClassifier.__setstate__ sklearn.linear_model.RidgeClassifier.__sizeof__ sklearn.linear_model.RidgeClassifier.__sklearn_clone__ sklearn.linear_model.RidgeClassifier.__sklearn_tags__ sklearn.linear_model.RidgeClassifier.__str__ sklearn.linear_model.RidgeClassifier.__subclasshook__ sklearn.linear_model.RidgeClassifier._build_request_for_signature sklearn.linear_model.RidgeClassifier._decision_function sklearn.linear_model.RidgeClassifier._get_default_requests sklearn.linear_model.RidgeClassifier._get_doc_link sklearn.linear_model.RidgeClassifier._get_metadata_request sklearn.linear_model.RidgeClassifier._get_param_names sklearn.linear_model.RidgeClassifier._predict_proba_lr sklearn.linear_model.RidgeClassifier._prepare_data sklearn.linear_model.RidgeClassifier._repr_html_inner sklearn.linear_model.RidgeClassifier._repr_mimebundle_ sklearn.linear_model.RidgeClassifier._set_intercept sklearn.linear_model.RidgeClassifier._validate_params sklearn.linear_model.RidgeClassifier.decision_function sklearn.linear_model.RidgeClassifier.fit sklearn.linear_model.RidgeClassifier.get_metadata_routing sklearn.linear_model.RidgeClassifier.get_params sklearn.linear_model.RidgeClassifier.predict sklearn.linear_model.RidgeClassifier.score sklearn.linear_model.RidgeClassifier.set_fit_request sklearn.linear_model.RidgeClassifier.set_params sklearn.linear_model.RidgeClassifier.set_score_request 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.linear_model.RidgeClassifierCV.rst.txt b/dev/_sources/modules/generated/sklearn.linear_model.RidgeClassifierCV.rst.txt
index c9430b03a7485..ae3485fd137e7 100644
--- a/dev/_sources/modules/generated/sklearn.linear_model.RidgeClassifierCV.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.linear_model.RidgeClassifierCV.rst.txt
@@ -8,6 +8,6 @@ RidgeClassifierCV
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.linear_model.RidgeClassifierCV sklearn.linear_model.RidgeClassifierCV.__delattr__ sklearn.linear_model.RidgeClassifierCV.__dir__ sklearn.linear_model.RidgeClassifierCV.__eq__ sklearn.linear_model.RidgeClassifierCV.__format__ sklearn.linear_model.RidgeClassifierCV.__ge__ sklearn.linear_model.RidgeClassifierCV.__getattribute__ sklearn.linear_model.RidgeClassifierCV.__getstate__ sklearn.linear_model.RidgeClassifierCV.__gt__ sklearn.linear_model.RidgeClassifierCV.__hash__ sklearn.linear_model.RidgeClassifierCV.__init__ sklearn.linear_model.RidgeClassifierCV.__init_subclass__ sklearn.linear_model.RidgeClassifierCV.__le__ sklearn.linear_model.RidgeClassifierCV.__lt__ sklearn.linear_model.RidgeClassifierCV.__ne__ sklearn.linear_model.RidgeClassifierCV.__new__ sklearn.linear_model.RidgeClassifierCV.__reduce__ sklearn.linear_model.RidgeClassifierCV.__reduce_ex__ sklearn.linear_model.RidgeClassifierCV.__repr__ sklearn.linear_model.RidgeClassifierCV.__setattr__ sklearn.linear_model.RidgeClassifierCV.__setstate__ sklearn.linear_model.RidgeClassifierCV.__sizeof__ sklearn.linear_model.RidgeClassifierCV.__sklearn_clone__ sklearn.linear_model.RidgeClassifierCV.__sklearn_tags__ sklearn.linear_model.RidgeClassifierCV.__str__ sklearn.linear_model.RidgeClassifierCV.__subclasshook__ sklearn.linear_model.RidgeClassifierCV._build_request_for_signature sklearn.linear_model.RidgeClassifierCV._check_feature_names sklearn.linear_model.RidgeClassifierCV._check_n_features sklearn.linear_model.RidgeClassifierCV._decision_function sklearn.linear_model.RidgeClassifierCV._get_default_requests sklearn.linear_model.RidgeClassifierCV._get_doc_link sklearn.linear_model.RidgeClassifierCV._get_metadata_request sklearn.linear_model.RidgeClassifierCV._get_param_names sklearn.linear_model.RidgeClassifierCV._get_scorer sklearn.linear_model.RidgeClassifierCV._get_tags sklearn.linear_model.RidgeClassifierCV._more_tags sklearn.linear_model.RidgeClassifierCV._predict_proba_lr sklearn.linear_model.RidgeClassifierCV._prepare_data sklearn.linear_model.RidgeClassifierCV._repr_html_inner sklearn.linear_model.RidgeClassifierCV._repr_mimebundle_ sklearn.linear_model.RidgeClassifierCV._set_intercept sklearn.linear_model.RidgeClassifierCV._validate_data sklearn.linear_model.RidgeClassifierCV._validate_params sklearn.linear_model.RidgeClassifierCV.decision_function sklearn.linear_model.RidgeClassifierCV.fit sklearn.linear_model.RidgeClassifierCV.get_metadata_routing sklearn.linear_model.RidgeClassifierCV.get_params sklearn.linear_model.RidgeClassifierCV.predict sklearn.linear_model.RidgeClassifierCV.score sklearn.linear_model.RidgeClassifierCV.set_fit_request sklearn.linear_model.RidgeClassifierCV.set_params sklearn.linear_model.RidgeClassifierCV.set_score_request 
+.. minigallery:: sklearn.linear_model.RidgeClassifierCV sklearn.linear_model.RidgeClassifierCV.__delattr__ sklearn.linear_model.RidgeClassifierCV.__dir__ sklearn.linear_model.RidgeClassifierCV.__eq__ sklearn.linear_model.RidgeClassifierCV.__format__ sklearn.linear_model.RidgeClassifierCV.__ge__ sklearn.linear_model.RidgeClassifierCV.__getattribute__ sklearn.linear_model.RidgeClassifierCV.__getstate__ sklearn.linear_model.RidgeClassifierCV.__gt__ sklearn.linear_model.RidgeClassifierCV.__hash__ sklearn.linear_model.RidgeClassifierCV.__init__ sklearn.linear_model.RidgeClassifierCV.__init_subclass__ sklearn.linear_model.RidgeClassifierCV.__le__ sklearn.linear_model.RidgeClassifierCV.__lt__ sklearn.linear_model.RidgeClassifierCV.__ne__ sklearn.linear_model.RidgeClassifierCV.__new__ sklearn.linear_model.RidgeClassifierCV.__reduce__ sklearn.linear_model.RidgeClassifierCV.__reduce_ex__ sklearn.linear_model.RidgeClassifierCV.__repr__ sklearn.linear_model.RidgeClassifierCV.__setattr__ sklearn.linear_model.RidgeClassifierCV.__setstate__ sklearn.linear_model.RidgeClassifierCV.__sizeof__ sklearn.linear_model.RidgeClassifierCV.__sklearn_clone__ sklearn.linear_model.RidgeClassifierCV.__sklearn_tags__ sklearn.linear_model.RidgeClassifierCV.__str__ sklearn.linear_model.RidgeClassifierCV.__subclasshook__ sklearn.linear_model.RidgeClassifierCV._build_request_for_signature sklearn.linear_model.RidgeClassifierCV._decision_function sklearn.linear_model.RidgeClassifierCV._get_default_requests sklearn.linear_model.RidgeClassifierCV._get_doc_link sklearn.linear_model.RidgeClassifierCV._get_metadata_request sklearn.linear_model.RidgeClassifierCV._get_param_names sklearn.linear_model.RidgeClassifierCV._get_scorer sklearn.linear_model.RidgeClassifierCV._predict_proba_lr sklearn.linear_model.RidgeClassifierCV._prepare_data sklearn.linear_model.RidgeClassifierCV._repr_html_inner sklearn.linear_model.RidgeClassifierCV._repr_mimebundle_ sklearn.linear_model.RidgeClassifierCV._set_intercept sklearn.linear_model.RidgeClassifierCV._validate_params sklearn.linear_model.RidgeClassifierCV.decision_function sklearn.linear_model.RidgeClassifierCV.fit sklearn.linear_model.RidgeClassifierCV.get_metadata_routing sklearn.linear_model.RidgeClassifierCV.get_params sklearn.linear_model.RidgeClassifierCV.predict sklearn.linear_model.RidgeClassifierCV.score sklearn.linear_model.RidgeClassifierCV.set_fit_request sklearn.linear_model.RidgeClassifierCV.set_params sklearn.linear_model.RidgeClassifierCV.set_score_request 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.linear_model.SGDClassifier.rst.txt b/dev/_sources/modules/generated/sklearn.linear_model.SGDClassifier.rst.txt
index 4986c2e7ec132..68f57df53213c 100644
--- a/dev/_sources/modules/generated/sklearn.linear_model.SGDClassifier.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.linear_model.SGDClassifier.rst.txt
@@ -8,6 +8,6 @@ SGDClassifier
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.linear_model.SGDClassifier sklearn.linear_model.SGDClassifier.__delattr__ sklearn.linear_model.SGDClassifier.__dir__ sklearn.linear_model.SGDClassifier.__eq__ sklearn.linear_model.SGDClassifier.__format__ sklearn.linear_model.SGDClassifier.__ge__ sklearn.linear_model.SGDClassifier.__getattribute__ sklearn.linear_model.SGDClassifier.__getstate__ sklearn.linear_model.SGDClassifier.__gt__ sklearn.linear_model.SGDClassifier.__hash__ sklearn.linear_model.SGDClassifier.__init__ sklearn.linear_model.SGDClassifier.__init_subclass__ sklearn.linear_model.SGDClassifier.__le__ sklearn.linear_model.SGDClassifier.__lt__ sklearn.linear_model.SGDClassifier.__ne__ sklearn.linear_model.SGDClassifier.__new__ sklearn.linear_model.SGDClassifier.__reduce__ sklearn.linear_model.SGDClassifier.__reduce_ex__ sklearn.linear_model.SGDClassifier.__repr__ sklearn.linear_model.SGDClassifier.__setattr__ sklearn.linear_model.SGDClassifier.__setstate__ sklearn.linear_model.SGDClassifier.__sizeof__ sklearn.linear_model.SGDClassifier.__sklearn_clone__ sklearn.linear_model.SGDClassifier.__sklearn_tags__ sklearn.linear_model.SGDClassifier.__str__ sklearn.linear_model.SGDClassifier.__subclasshook__ sklearn.linear_model.SGDClassifier._allocate_parameter_mem sklearn.linear_model.SGDClassifier._build_request_for_signature sklearn.linear_model.SGDClassifier._check_feature_names sklearn.linear_model.SGDClassifier._check_n_features sklearn.linear_model.SGDClassifier._check_proba sklearn.linear_model.SGDClassifier._fit sklearn.linear_model.SGDClassifier._fit_binary sklearn.linear_model.SGDClassifier._fit_multiclass sklearn.linear_model.SGDClassifier._get_default_requests sklearn.linear_model.SGDClassifier._get_doc_link sklearn.linear_model.SGDClassifier._get_learning_rate_type sklearn.linear_model.SGDClassifier._get_loss_function sklearn.linear_model.SGDClassifier._get_metadata_request sklearn.linear_model.SGDClassifier._get_param_names sklearn.linear_model.SGDClassifier._get_penalty_type sklearn.linear_model.SGDClassifier._get_tags sklearn.linear_model.SGDClassifier._make_validation_score_cb sklearn.linear_model.SGDClassifier._make_validation_split sklearn.linear_model.SGDClassifier._more_tags sklearn.linear_model.SGDClassifier._more_validate_params sklearn.linear_model.SGDClassifier._partial_fit sklearn.linear_model.SGDClassifier._predict_proba_lr sklearn.linear_model.SGDClassifier._repr_html_inner sklearn.linear_model.SGDClassifier._repr_mimebundle_ sklearn.linear_model.SGDClassifier._validate_data sklearn.linear_model.SGDClassifier._validate_params sklearn.linear_model.SGDClassifier.decision_function sklearn.linear_model.SGDClassifier.densify sklearn.linear_model.SGDClassifier.fit sklearn.linear_model.SGDClassifier.get_metadata_routing sklearn.linear_model.SGDClassifier.get_params sklearn.linear_model.SGDClassifier.partial_fit sklearn.linear_model.SGDClassifier.predict sklearn.linear_model.SGDClassifier.predict_log_proba sklearn.linear_model.SGDClassifier.predict_proba sklearn.linear_model.SGDClassifier.score sklearn.linear_model.SGDClassifier.set_fit_request sklearn.linear_model.SGDClassifier.set_params sklearn.linear_model.SGDClassifier.set_partial_fit_request sklearn.linear_model.SGDClassifier.set_score_request sklearn.linear_model.SGDClassifier.sparsify 
+.. minigallery:: sklearn.linear_model.SGDClassifier sklearn.linear_model.SGDClassifier.__delattr__ sklearn.linear_model.SGDClassifier.__dir__ sklearn.linear_model.SGDClassifier.__eq__ sklearn.linear_model.SGDClassifier.__format__ sklearn.linear_model.SGDClassifier.__ge__ sklearn.linear_model.SGDClassifier.__getattribute__ sklearn.linear_model.SGDClassifier.__getstate__ sklearn.linear_model.SGDClassifier.__gt__ sklearn.linear_model.SGDClassifier.__hash__ sklearn.linear_model.SGDClassifier.__init__ sklearn.linear_model.SGDClassifier.__init_subclass__ sklearn.linear_model.SGDClassifier.__le__ sklearn.linear_model.SGDClassifier.__lt__ sklearn.linear_model.SGDClassifier.__ne__ sklearn.linear_model.SGDClassifier.__new__ sklearn.linear_model.SGDClassifier.__reduce__ sklearn.linear_model.SGDClassifier.__reduce_ex__ sklearn.linear_model.SGDClassifier.__repr__ sklearn.linear_model.SGDClassifier.__setattr__ sklearn.linear_model.SGDClassifier.__setstate__ sklearn.linear_model.SGDClassifier.__sizeof__ sklearn.linear_model.SGDClassifier.__sklearn_clone__ sklearn.linear_model.SGDClassifier.__sklearn_tags__ sklearn.linear_model.SGDClassifier.__str__ sklearn.linear_model.SGDClassifier.__subclasshook__ sklearn.linear_model.SGDClassifier._allocate_parameter_mem sklearn.linear_model.SGDClassifier._build_request_for_signature sklearn.linear_model.SGDClassifier._check_proba sklearn.linear_model.SGDClassifier._fit sklearn.linear_model.SGDClassifier._fit_binary sklearn.linear_model.SGDClassifier._fit_multiclass sklearn.linear_model.SGDClassifier._get_default_requests sklearn.linear_model.SGDClassifier._get_doc_link sklearn.linear_model.SGDClassifier._get_l1_ratio sklearn.linear_model.SGDClassifier._get_learning_rate_type sklearn.linear_model.SGDClassifier._get_loss_function sklearn.linear_model.SGDClassifier._get_metadata_request sklearn.linear_model.SGDClassifier._get_param_names sklearn.linear_model.SGDClassifier._get_penalty_type sklearn.linear_model.SGDClassifier._make_validation_score_cb sklearn.linear_model.SGDClassifier._make_validation_split sklearn.linear_model.SGDClassifier._more_validate_params sklearn.linear_model.SGDClassifier._partial_fit sklearn.linear_model.SGDClassifier._predict_proba_lr sklearn.linear_model.SGDClassifier._repr_html_inner sklearn.linear_model.SGDClassifier._repr_mimebundle_ sklearn.linear_model.SGDClassifier._validate_params sklearn.linear_model.SGDClassifier.decision_function sklearn.linear_model.SGDClassifier.densify sklearn.linear_model.SGDClassifier.fit sklearn.linear_model.SGDClassifier.get_metadata_routing sklearn.linear_model.SGDClassifier.get_params sklearn.linear_model.SGDClassifier.partial_fit sklearn.linear_model.SGDClassifier.predict sklearn.linear_model.SGDClassifier.predict_log_proba sklearn.linear_model.SGDClassifier.predict_proba sklearn.linear_model.SGDClassifier.score sklearn.linear_model.SGDClassifier.set_fit_request sklearn.linear_model.SGDClassifier.set_params sklearn.linear_model.SGDClassifier.set_partial_fit_request sklearn.linear_model.SGDClassifier.set_score_request sklearn.linear_model.SGDClassifier.sparsify 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.linear_model.SGDOneClassSVM.rst.txt b/dev/_sources/modules/generated/sklearn.linear_model.SGDOneClassSVM.rst.txt
index 62da014dfc1e9..0e4ce4d052b17 100644
--- a/dev/_sources/modules/generated/sklearn.linear_model.SGDOneClassSVM.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.linear_model.SGDOneClassSVM.rst.txt
@@ -8,6 +8,6 @@ SGDOneClassSVM
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.linear_model.SGDOneClassSVM sklearn.linear_model.SGDOneClassSVM.__delattr__ sklearn.linear_model.SGDOneClassSVM.__dir__ sklearn.linear_model.SGDOneClassSVM.__eq__ sklearn.linear_model.SGDOneClassSVM.__format__ sklearn.linear_model.SGDOneClassSVM.__ge__ sklearn.linear_model.SGDOneClassSVM.__getattribute__ sklearn.linear_model.SGDOneClassSVM.__getstate__ sklearn.linear_model.SGDOneClassSVM.__gt__ sklearn.linear_model.SGDOneClassSVM.__hash__ sklearn.linear_model.SGDOneClassSVM.__init__ sklearn.linear_model.SGDOneClassSVM.__init_subclass__ sklearn.linear_model.SGDOneClassSVM.__le__ sklearn.linear_model.SGDOneClassSVM.__lt__ sklearn.linear_model.SGDOneClassSVM.__ne__ sklearn.linear_model.SGDOneClassSVM.__new__ sklearn.linear_model.SGDOneClassSVM.__reduce__ sklearn.linear_model.SGDOneClassSVM.__reduce_ex__ sklearn.linear_model.SGDOneClassSVM.__repr__ sklearn.linear_model.SGDOneClassSVM.__setattr__ sklearn.linear_model.SGDOneClassSVM.__setstate__ sklearn.linear_model.SGDOneClassSVM.__sizeof__ sklearn.linear_model.SGDOneClassSVM.__sklearn_clone__ sklearn.linear_model.SGDOneClassSVM.__sklearn_tags__ sklearn.linear_model.SGDOneClassSVM.__str__ sklearn.linear_model.SGDOneClassSVM.__subclasshook__ sklearn.linear_model.SGDOneClassSVM._allocate_parameter_mem sklearn.linear_model.SGDOneClassSVM._build_request_for_signature sklearn.linear_model.SGDOneClassSVM._check_feature_names sklearn.linear_model.SGDOneClassSVM._check_n_features sklearn.linear_model.SGDOneClassSVM._fit sklearn.linear_model.SGDOneClassSVM._fit_one_class sklearn.linear_model.SGDOneClassSVM._get_default_requests sklearn.linear_model.SGDOneClassSVM._get_doc_link sklearn.linear_model.SGDOneClassSVM._get_learning_rate_type sklearn.linear_model.SGDOneClassSVM._get_loss_function sklearn.linear_model.SGDOneClassSVM._get_metadata_request sklearn.linear_model.SGDOneClassSVM._get_param_names sklearn.linear_model.SGDOneClassSVM._get_penalty_type sklearn.linear_model.SGDOneClassSVM._get_tags sklearn.linear_model.SGDOneClassSVM._make_validation_score_cb sklearn.linear_model.SGDOneClassSVM._make_validation_split sklearn.linear_model.SGDOneClassSVM._more_tags sklearn.linear_model.SGDOneClassSVM._more_validate_params sklearn.linear_model.SGDOneClassSVM._partial_fit sklearn.linear_model.SGDOneClassSVM._repr_html_inner sklearn.linear_model.SGDOneClassSVM._repr_mimebundle_ sklearn.linear_model.SGDOneClassSVM._validate_data sklearn.linear_model.SGDOneClassSVM._validate_params sklearn.linear_model.SGDOneClassSVM.decision_function sklearn.linear_model.SGDOneClassSVM.densify sklearn.linear_model.SGDOneClassSVM.fit sklearn.linear_model.SGDOneClassSVM.fit_predict sklearn.linear_model.SGDOneClassSVM.get_metadata_routing sklearn.linear_model.SGDOneClassSVM.get_params sklearn.linear_model.SGDOneClassSVM.partial_fit sklearn.linear_model.SGDOneClassSVM.predict sklearn.linear_model.SGDOneClassSVM.score_samples sklearn.linear_model.SGDOneClassSVM.set_fit_request sklearn.linear_model.SGDOneClassSVM.set_params sklearn.linear_model.SGDOneClassSVM.set_partial_fit_request sklearn.linear_model.SGDOneClassSVM.sparsify 
+.. minigallery:: sklearn.linear_model.SGDOneClassSVM sklearn.linear_model.SGDOneClassSVM.__delattr__ sklearn.linear_model.SGDOneClassSVM.__dir__ sklearn.linear_model.SGDOneClassSVM.__eq__ sklearn.linear_model.SGDOneClassSVM.__format__ sklearn.linear_model.SGDOneClassSVM.__ge__ sklearn.linear_model.SGDOneClassSVM.__getattribute__ sklearn.linear_model.SGDOneClassSVM.__getstate__ sklearn.linear_model.SGDOneClassSVM.__gt__ sklearn.linear_model.SGDOneClassSVM.__hash__ sklearn.linear_model.SGDOneClassSVM.__init__ sklearn.linear_model.SGDOneClassSVM.__init_subclass__ sklearn.linear_model.SGDOneClassSVM.__le__ sklearn.linear_model.SGDOneClassSVM.__lt__ sklearn.linear_model.SGDOneClassSVM.__ne__ sklearn.linear_model.SGDOneClassSVM.__new__ sklearn.linear_model.SGDOneClassSVM.__reduce__ sklearn.linear_model.SGDOneClassSVM.__reduce_ex__ sklearn.linear_model.SGDOneClassSVM.__repr__ sklearn.linear_model.SGDOneClassSVM.__setattr__ sklearn.linear_model.SGDOneClassSVM.__setstate__ sklearn.linear_model.SGDOneClassSVM.__sizeof__ sklearn.linear_model.SGDOneClassSVM.__sklearn_clone__ sklearn.linear_model.SGDOneClassSVM.__sklearn_tags__ sklearn.linear_model.SGDOneClassSVM.__str__ sklearn.linear_model.SGDOneClassSVM.__subclasshook__ sklearn.linear_model.SGDOneClassSVM._allocate_parameter_mem sklearn.linear_model.SGDOneClassSVM._build_request_for_signature sklearn.linear_model.SGDOneClassSVM._fit sklearn.linear_model.SGDOneClassSVM._fit_one_class sklearn.linear_model.SGDOneClassSVM._get_default_requests sklearn.linear_model.SGDOneClassSVM._get_doc_link sklearn.linear_model.SGDOneClassSVM._get_l1_ratio sklearn.linear_model.SGDOneClassSVM._get_learning_rate_type sklearn.linear_model.SGDOneClassSVM._get_loss_function sklearn.linear_model.SGDOneClassSVM._get_metadata_request sklearn.linear_model.SGDOneClassSVM._get_param_names sklearn.linear_model.SGDOneClassSVM._get_penalty_type sklearn.linear_model.SGDOneClassSVM._make_validation_score_cb sklearn.linear_model.SGDOneClassSVM._make_validation_split sklearn.linear_model.SGDOneClassSVM._more_validate_params sklearn.linear_model.SGDOneClassSVM._partial_fit sklearn.linear_model.SGDOneClassSVM._repr_html_inner sklearn.linear_model.SGDOneClassSVM._repr_mimebundle_ sklearn.linear_model.SGDOneClassSVM._validate_params sklearn.linear_model.SGDOneClassSVM.decision_function sklearn.linear_model.SGDOneClassSVM.densify sklearn.linear_model.SGDOneClassSVM.fit sklearn.linear_model.SGDOneClassSVM.fit_predict sklearn.linear_model.SGDOneClassSVM.get_metadata_routing sklearn.linear_model.SGDOneClassSVM.get_params sklearn.linear_model.SGDOneClassSVM.partial_fit sklearn.linear_model.SGDOneClassSVM.predict sklearn.linear_model.SGDOneClassSVM.score_samples sklearn.linear_model.SGDOneClassSVM.set_fit_request sklearn.linear_model.SGDOneClassSVM.set_params sklearn.linear_model.SGDOneClassSVM.set_partial_fit_request sklearn.linear_model.SGDOneClassSVM.sparsify 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.linear_model.SGDRegressor.rst.txt b/dev/_sources/modules/generated/sklearn.linear_model.SGDRegressor.rst.txt
index 61999f9cf9a74..f02e16b830a14 100644
--- a/dev/_sources/modules/generated/sklearn.linear_model.SGDRegressor.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.linear_model.SGDRegressor.rst.txt
@@ -8,6 +8,6 @@ SGDRegressor
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.linear_model.SGDRegressor sklearn.linear_model.SGDRegressor.__delattr__ sklearn.linear_model.SGDRegressor.__dir__ sklearn.linear_model.SGDRegressor.__eq__ sklearn.linear_model.SGDRegressor.__format__ sklearn.linear_model.SGDRegressor.__ge__ sklearn.linear_model.SGDRegressor.__getattribute__ sklearn.linear_model.SGDRegressor.__getstate__ sklearn.linear_model.SGDRegressor.__gt__ sklearn.linear_model.SGDRegressor.__hash__ sklearn.linear_model.SGDRegressor.__init__ sklearn.linear_model.SGDRegressor.__init_subclass__ sklearn.linear_model.SGDRegressor.__le__ sklearn.linear_model.SGDRegressor.__lt__ sklearn.linear_model.SGDRegressor.__ne__ sklearn.linear_model.SGDRegressor.__new__ sklearn.linear_model.SGDRegressor.__reduce__ sklearn.linear_model.SGDRegressor.__reduce_ex__ sklearn.linear_model.SGDRegressor.__repr__ sklearn.linear_model.SGDRegressor.__setattr__ sklearn.linear_model.SGDRegressor.__setstate__ sklearn.linear_model.SGDRegressor.__sizeof__ sklearn.linear_model.SGDRegressor.__sklearn_clone__ sklearn.linear_model.SGDRegressor.__sklearn_tags__ sklearn.linear_model.SGDRegressor.__str__ sklearn.linear_model.SGDRegressor.__subclasshook__ sklearn.linear_model.SGDRegressor._allocate_parameter_mem sklearn.linear_model.SGDRegressor._build_request_for_signature sklearn.linear_model.SGDRegressor._check_feature_names sklearn.linear_model.SGDRegressor._check_n_features sklearn.linear_model.SGDRegressor._decision_function sklearn.linear_model.SGDRegressor._fit sklearn.linear_model.SGDRegressor._fit_regressor sklearn.linear_model.SGDRegressor._get_default_requests sklearn.linear_model.SGDRegressor._get_doc_link sklearn.linear_model.SGDRegressor._get_learning_rate_type sklearn.linear_model.SGDRegressor._get_loss_function sklearn.linear_model.SGDRegressor._get_metadata_request sklearn.linear_model.SGDRegressor._get_param_names sklearn.linear_model.SGDRegressor._get_penalty_type sklearn.linear_model.SGDRegressor._get_tags sklearn.linear_model.SGDRegressor._make_validation_score_cb sklearn.linear_model.SGDRegressor._make_validation_split sklearn.linear_model.SGDRegressor._more_tags sklearn.linear_model.SGDRegressor._more_validate_params sklearn.linear_model.SGDRegressor._partial_fit sklearn.linear_model.SGDRegressor._repr_html_inner sklearn.linear_model.SGDRegressor._repr_mimebundle_ sklearn.linear_model.SGDRegressor._validate_data sklearn.linear_model.SGDRegressor._validate_params sklearn.linear_model.SGDRegressor.densify sklearn.linear_model.SGDRegressor.fit sklearn.linear_model.SGDRegressor.get_metadata_routing sklearn.linear_model.SGDRegressor.get_params sklearn.linear_model.SGDRegressor.partial_fit sklearn.linear_model.SGDRegressor.predict sklearn.linear_model.SGDRegressor.score sklearn.linear_model.SGDRegressor.set_fit_request sklearn.linear_model.SGDRegressor.set_params sklearn.linear_model.SGDRegressor.set_partial_fit_request sklearn.linear_model.SGDRegressor.set_score_request sklearn.linear_model.SGDRegressor.sparsify 
+.. minigallery:: sklearn.linear_model.SGDRegressor sklearn.linear_model.SGDRegressor.__delattr__ sklearn.linear_model.SGDRegressor.__dir__ sklearn.linear_model.SGDRegressor.__eq__ sklearn.linear_model.SGDRegressor.__format__ sklearn.linear_model.SGDRegressor.__ge__ sklearn.linear_model.SGDRegressor.__getattribute__ sklearn.linear_model.SGDRegressor.__getstate__ sklearn.linear_model.SGDRegressor.__gt__ sklearn.linear_model.SGDRegressor.__hash__ sklearn.linear_model.SGDRegressor.__init__ sklearn.linear_model.SGDRegressor.__init_subclass__ sklearn.linear_model.SGDRegressor.__le__ sklearn.linear_model.SGDRegressor.__lt__ sklearn.linear_model.SGDRegressor.__ne__ sklearn.linear_model.SGDRegressor.__new__ sklearn.linear_model.SGDRegressor.__reduce__ sklearn.linear_model.SGDRegressor.__reduce_ex__ sklearn.linear_model.SGDRegressor.__repr__ sklearn.linear_model.SGDRegressor.__setattr__ sklearn.linear_model.SGDRegressor.__setstate__ sklearn.linear_model.SGDRegressor.__sizeof__ sklearn.linear_model.SGDRegressor.__sklearn_clone__ sklearn.linear_model.SGDRegressor.__sklearn_tags__ sklearn.linear_model.SGDRegressor.__str__ sklearn.linear_model.SGDRegressor.__subclasshook__ sklearn.linear_model.SGDRegressor._allocate_parameter_mem sklearn.linear_model.SGDRegressor._build_request_for_signature sklearn.linear_model.SGDRegressor._decision_function sklearn.linear_model.SGDRegressor._fit sklearn.linear_model.SGDRegressor._fit_regressor sklearn.linear_model.SGDRegressor._get_default_requests sklearn.linear_model.SGDRegressor._get_doc_link sklearn.linear_model.SGDRegressor._get_l1_ratio sklearn.linear_model.SGDRegressor._get_learning_rate_type sklearn.linear_model.SGDRegressor._get_loss_function sklearn.linear_model.SGDRegressor._get_metadata_request sklearn.linear_model.SGDRegressor._get_param_names sklearn.linear_model.SGDRegressor._get_penalty_type sklearn.linear_model.SGDRegressor._make_validation_score_cb sklearn.linear_model.SGDRegressor._make_validation_split sklearn.linear_model.SGDRegressor._more_validate_params sklearn.linear_model.SGDRegressor._partial_fit sklearn.linear_model.SGDRegressor._repr_html_inner sklearn.linear_model.SGDRegressor._repr_mimebundle_ sklearn.linear_model.SGDRegressor._validate_params sklearn.linear_model.SGDRegressor.densify sklearn.linear_model.SGDRegressor.fit sklearn.linear_model.SGDRegressor.get_metadata_routing sklearn.linear_model.SGDRegressor.get_params sklearn.linear_model.SGDRegressor.partial_fit sklearn.linear_model.SGDRegressor.predict sklearn.linear_model.SGDRegressor.score sklearn.linear_model.SGDRegressor.set_fit_request sklearn.linear_model.SGDRegressor.set_params sklearn.linear_model.SGDRegressor.set_partial_fit_request sklearn.linear_model.SGDRegressor.set_score_request sklearn.linear_model.SGDRegressor.sparsify 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.linear_model.TheilSenRegressor.rst.txt b/dev/_sources/modules/generated/sklearn.linear_model.TheilSenRegressor.rst.txt
index dff795069f898..098fc6c54006e 100644
--- a/dev/_sources/modules/generated/sklearn.linear_model.TheilSenRegressor.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.linear_model.TheilSenRegressor.rst.txt
@@ -8,6 +8,6 @@ TheilSenRegressor
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.linear_model.TheilSenRegressor sklearn.linear_model.TheilSenRegressor.__delattr__ sklearn.linear_model.TheilSenRegressor.__dir__ sklearn.linear_model.TheilSenRegressor.__eq__ sklearn.linear_model.TheilSenRegressor.__format__ sklearn.linear_model.TheilSenRegressor.__ge__ sklearn.linear_model.TheilSenRegressor.__getattribute__ sklearn.linear_model.TheilSenRegressor.__getstate__ sklearn.linear_model.TheilSenRegressor.__gt__ sklearn.linear_model.TheilSenRegressor.__hash__ sklearn.linear_model.TheilSenRegressor.__init__ sklearn.linear_model.TheilSenRegressor.__init_subclass__ sklearn.linear_model.TheilSenRegressor.__le__ sklearn.linear_model.TheilSenRegressor.__lt__ sklearn.linear_model.TheilSenRegressor.__ne__ sklearn.linear_model.TheilSenRegressor.__new__ sklearn.linear_model.TheilSenRegressor.__reduce__ sklearn.linear_model.TheilSenRegressor.__reduce_ex__ sklearn.linear_model.TheilSenRegressor.__repr__ sklearn.linear_model.TheilSenRegressor.__setattr__ sklearn.linear_model.TheilSenRegressor.__setstate__ sklearn.linear_model.TheilSenRegressor.__sizeof__ sklearn.linear_model.TheilSenRegressor.__sklearn_clone__ sklearn.linear_model.TheilSenRegressor.__sklearn_tags__ sklearn.linear_model.TheilSenRegressor.__str__ sklearn.linear_model.TheilSenRegressor.__subclasshook__ sklearn.linear_model.TheilSenRegressor._build_request_for_signature sklearn.linear_model.TheilSenRegressor._check_feature_names sklearn.linear_model.TheilSenRegressor._check_n_features sklearn.linear_model.TheilSenRegressor._check_subparams sklearn.linear_model.TheilSenRegressor._decision_function sklearn.linear_model.TheilSenRegressor._get_default_requests sklearn.linear_model.TheilSenRegressor._get_doc_link sklearn.linear_model.TheilSenRegressor._get_metadata_request sklearn.linear_model.TheilSenRegressor._get_param_names sklearn.linear_model.TheilSenRegressor._get_tags sklearn.linear_model.TheilSenRegressor._more_tags sklearn.linear_model.TheilSenRegressor._repr_html_inner sklearn.linear_model.TheilSenRegressor._repr_mimebundle_ sklearn.linear_model.TheilSenRegressor._set_intercept sklearn.linear_model.TheilSenRegressor._validate_data sklearn.linear_model.TheilSenRegressor._validate_params sklearn.linear_model.TheilSenRegressor.fit sklearn.linear_model.TheilSenRegressor.get_metadata_routing sklearn.linear_model.TheilSenRegressor.get_params sklearn.linear_model.TheilSenRegressor.predict sklearn.linear_model.TheilSenRegressor.score sklearn.linear_model.TheilSenRegressor.set_params sklearn.linear_model.TheilSenRegressor.set_score_request 
+.. minigallery:: sklearn.linear_model.TheilSenRegressor sklearn.linear_model.TheilSenRegressor.__delattr__ sklearn.linear_model.TheilSenRegressor.__dir__ sklearn.linear_model.TheilSenRegressor.__eq__ sklearn.linear_model.TheilSenRegressor.__format__ sklearn.linear_model.TheilSenRegressor.__ge__ sklearn.linear_model.TheilSenRegressor.__getattribute__ sklearn.linear_model.TheilSenRegressor.__getstate__ sklearn.linear_model.TheilSenRegressor.__gt__ sklearn.linear_model.TheilSenRegressor.__hash__ sklearn.linear_model.TheilSenRegressor.__init__ sklearn.linear_model.TheilSenRegressor.__init_subclass__ sklearn.linear_model.TheilSenRegressor.__le__ sklearn.linear_model.TheilSenRegressor.__lt__ sklearn.linear_model.TheilSenRegressor.__ne__ sklearn.linear_model.TheilSenRegressor.__new__ sklearn.linear_model.TheilSenRegressor.__reduce__ sklearn.linear_model.TheilSenRegressor.__reduce_ex__ sklearn.linear_model.TheilSenRegressor.__repr__ sklearn.linear_model.TheilSenRegressor.__setattr__ sklearn.linear_model.TheilSenRegressor.__setstate__ sklearn.linear_model.TheilSenRegressor.__sizeof__ sklearn.linear_model.TheilSenRegressor.__sklearn_clone__ sklearn.linear_model.TheilSenRegressor.__sklearn_tags__ sklearn.linear_model.TheilSenRegressor.__str__ sklearn.linear_model.TheilSenRegressor.__subclasshook__ sklearn.linear_model.TheilSenRegressor._build_request_for_signature sklearn.linear_model.TheilSenRegressor._check_subparams sklearn.linear_model.TheilSenRegressor._decision_function sklearn.linear_model.TheilSenRegressor._get_default_requests sklearn.linear_model.TheilSenRegressor._get_doc_link sklearn.linear_model.TheilSenRegressor._get_metadata_request sklearn.linear_model.TheilSenRegressor._get_param_names sklearn.linear_model.TheilSenRegressor._repr_html_inner sklearn.linear_model.TheilSenRegressor._repr_mimebundle_ sklearn.linear_model.TheilSenRegressor._set_intercept sklearn.linear_model.TheilSenRegressor._validate_params sklearn.linear_model.TheilSenRegressor.fit sklearn.linear_model.TheilSenRegressor.get_metadata_routing sklearn.linear_model.TheilSenRegressor.get_params sklearn.linear_model.TheilSenRegressor.predict sklearn.linear_model.TheilSenRegressor.score sklearn.linear_model.TheilSenRegressor.set_params sklearn.linear_model.TheilSenRegressor.set_score_request 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.linear_model.TweedieRegressor.rst.txt b/dev/_sources/modules/generated/sklearn.linear_model.TweedieRegressor.rst.txt
index 049efe288cc33..2a79bc23a98aa 100644
--- a/dev/_sources/modules/generated/sklearn.linear_model.TweedieRegressor.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.linear_model.TweedieRegressor.rst.txt
@@ -8,6 +8,6 @@ TweedieRegressor
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.linear_model.TweedieRegressor sklearn.linear_model.TweedieRegressor.__delattr__ sklearn.linear_model.TweedieRegressor.__dir__ sklearn.linear_model.TweedieRegressor.__eq__ sklearn.linear_model.TweedieRegressor.__format__ sklearn.linear_model.TweedieRegressor.__ge__ sklearn.linear_model.TweedieRegressor.__getattribute__ sklearn.linear_model.TweedieRegressor.__getstate__ sklearn.linear_model.TweedieRegressor.__gt__ sklearn.linear_model.TweedieRegressor.__hash__ sklearn.linear_model.TweedieRegressor.__init__ sklearn.linear_model.TweedieRegressor.__init_subclass__ sklearn.linear_model.TweedieRegressor.__le__ sklearn.linear_model.TweedieRegressor.__lt__ sklearn.linear_model.TweedieRegressor.__ne__ sklearn.linear_model.TweedieRegressor.__new__ sklearn.linear_model.TweedieRegressor.__reduce__ sklearn.linear_model.TweedieRegressor.__reduce_ex__ sklearn.linear_model.TweedieRegressor.__repr__ sklearn.linear_model.TweedieRegressor.__setattr__ sklearn.linear_model.TweedieRegressor.__setstate__ sklearn.linear_model.TweedieRegressor.__sizeof__ sklearn.linear_model.TweedieRegressor.__sklearn_clone__ sklearn.linear_model.TweedieRegressor.__sklearn_tags__ sklearn.linear_model.TweedieRegressor.__str__ sklearn.linear_model.TweedieRegressor.__subclasshook__ sklearn.linear_model.TweedieRegressor._build_request_for_signature sklearn.linear_model.TweedieRegressor._check_feature_names sklearn.linear_model.TweedieRegressor._check_n_features sklearn.linear_model.TweedieRegressor._get_default_requests sklearn.linear_model.TweedieRegressor._get_doc_link sklearn.linear_model.TweedieRegressor._get_loss sklearn.linear_model.TweedieRegressor._get_metadata_request sklearn.linear_model.TweedieRegressor._get_param_names sklearn.linear_model.TweedieRegressor._get_tags sklearn.linear_model.TweedieRegressor._linear_predictor sklearn.linear_model.TweedieRegressor._more_tags sklearn.linear_model.TweedieRegressor._repr_html_inner sklearn.linear_model.TweedieRegressor._repr_mimebundle_ sklearn.linear_model.TweedieRegressor._validate_data sklearn.linear_model.TweedieRegressor._validate_params sklearn.linear_model.TweedieRegressor.fit sklearn.linear_model.TweedieRegressor.get_metadata_routing sklearn.linear_model.TweedieRegressor.get_params sklearn.linear_model.TweedieRegressor.predict sklearn.linear_model.TweedieRegressor.score sklearn.linear_model.TweedieRegressor.set_fit_request sklearn.linear_model.TweedieRegressor.set_params sklearn.linear_model.TweedieRegressor.set_score_request 
+.. minigallery:: sklearn.linear_model.TweedieRegressor sklearn.linear_model.TweedieRegressor.__delattr__ sklearn.linear_model.TweedieRegressor.__dir__ sklearn.linear_model.TweedieRegressor.__eq__ sklearn.linear_model.TweedieRegressor.__format__ sklearn.linear_model.TweedieRegressor.__ge__ sklearn.linear_model.TweedieRegressor.__getattribute__ sklearn.linear_model.TweedieRegressor.__getstate__ sklearn.linear_model.TweedieRegressor.__gt__ sklearn.linear_model.TweedieRegressor.__hash__ sklearn.linear_model.TweedieRegressor.__init__ sklearn.linear_model.TweedieRegressor.__init_subclass__ sklearn.linear_model.TweedieRegressor.__le__ sklearn.linear_model.TweedieRegressor.__lt__ sklearn.linear_model.TweedieRegressor.__ne__ sklearn.linear_model.TweedieRegressor.__new__ sklearn.linear_model.TweedieRegressor.__reduce__ sklearn.linear_model.TweedieRegressor.__reduce_ex__ sklearn.linear_model.TweedieRegressor.__repr__ sklearn.linear_model.TweedieRegressor.__setattr__ sklearn.linear_model.TweedieRegressor.__setstate__ sklearn.linear_model.TweedieRegressor.__sizeof__ sklearn.linear_model.TweedieRegressor.__sklearn_clone__ sklearn.linear_model.TweedieRegressor.__sklearn_tags__ sklearn.linear_model.TweedieRegressor.__str__ sklearn.linear_model.TweedieRegressor.__subclasshook__ sklearn.linear_model.TweedieRegressor._build_request_for_signature sklearn.linear_model.TweedieRegressor._get_default_requests sklearn.linear_model.TweedieRegressor._get_doc_link sklearn.linear_model.TweedieRegressor._get_loss sklearn.linear_model.TweedieRegressor._get_metadata_request sklearn.linear_model.TweedieRegressor._get_param_names sklearn.linear_model.TweedieRegressor._linear_predictor sklearn.linear_model.TweedieRegressor._repr_html_inner sklearn.linear_model.TweedieRegressor._repr_mimebundle_ sklearn.linear_model.TweedieRegressor._validate_params sklearn.linear_model.TweedieRegressor.fit sklearn.linear_model.TweedieRegressor.get_metadata_routing sklearn.linear_model.TweedieRegressor.get_params sklearn.linear_model.TweedieRegressor.predict sklearn.linear_model.TweedieRegressor.score sklearn.linear_model.TweedieRegressor.set_fit_request sklearn.linear_model.TweedieRegressor.set_params sklearn.linear_model.TweedieRegressor.set_score_request 
    :add-heading: Gallery examples
    :heading-level: -
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diff --git a/dev/_sources/modules/generated/sklearn.manifold.Isomap.rst.txt b/dev/_sources/modules/generated/sklearn.manifold.Isomap.rst.txt
index cef05c2c26dcc..81b8d1cb3d803 100644
--- a/dev/_sources/modules/generated/sklearn.manifold.Isomap.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.manifold.Isomap.rst.txt
@@ -8,6 +8,6 @@ Isomap
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.manifold.Isomap sklearn.manifold.Isomap.__delattr__ sklearn.manifold.Isomap.__dir__ sklearn.manifold.Isomap.__eq__ sklearn.manifold.Isomap.__format__ sklearn.manifold.Isomap.__ge__ sklearn.manifold.Isomap.__getattribute__ sklearn.manifold.Isomap.__getstate__ sklearn.manifold.Isomap.__gt__ sklearn.manifold.Isomap.__hash__ sklearn.manifold.Isomap.__init__ sklearn.manifold.Isomap.__init_subclass__ sklearn.manifold.Isomap.__le__ sklearn.manifold.Isomap.__lt__ sklearn.manifold.Isomap.__ne__ sklearn.manifold.Isomap.__new__ sklearn.manifold.Isomap.__reduce__ sklearn.manifold.Isomap.__reduce_ex__ sklearn.manifold.Isomap.__repr__ sklearn.manifold.Isomap.__setattr__ sklearn.manifold.Isomap.__setstate__ sklearn.manifold.Isomap.__sizeof__ sklearn.manifold.Isomap.__sklearn_clone__ sklearn.manifold.Isomap.__sklearn_tags__ sklearn.manifold.Isomap.__str__ sklearn.manifold.Isomap.__subclasshook__ sklearn.manifold.Isomap._build_request_for_signature sklearn.manifold.Isomap._check_feature_names sklearn.manifold.Isomap._check_n_features sklearn.manifold.Isomap._fit_transform sklearn.manifold.Isomap._get_default_requests sklearn.manifold.Isomap._get_doc_link sklearn.manifold.Isomap._get_metadata_request sklearn.manifold.Isomap._get_param_names sklearn.manifold.Isomap._get_tags sklearn.manifold.Isomap._more_tags sklearn.manifold.Isomap._repr_html_inner sklearn.manifold.Isomap._repr_mimebundle_ sklearn.manifold.Isomap._validate_data sklearn.manifold.Isomap._validate_params sklearn.manifold.Isomap.fit sklearn.manifold.Isomap.fit_transform sklearn.manifold.Isomap.get_feature_names_out sklearn.manifold.Isomap.get_metadata_routing sklearn.manifold.Isomap.get_params sklearn.manifold.Isomap.reconstruction_error sklearn.manifold.Isomap.set_output sklearn.manifold.Isomap.set_params sklearn.manifold.Isomap.transform 
+.. minigallery:: sklearn.manifold.Isomap sklearn.manifold.Isomap.__delattr__ sklearn.manifold.Isomap.__dir__ sklearn.manifold.Isomap.__eq__ sklearn.manifold.Isomap.__format__ sklearn.manifold.Isomap.__ge__ sklearn.manifold.Isomap.__getattribute__ sklearn.manifold.Isomap.__getstate__ sklearn.manifold.Isomap.__gt__ sklearn.manifold.Isomap.__hash__ sklearn.manifold.Isomap.__init__ sklearn.manifold.Isomap.__init_subclass__ sklearn.manifold.Isomap.__le__ sklearn.manifold.Isomap.__lt__ sklearn.manifold.Isomap.__ne__ sklearn.manifold.Isomap.__new__ sklearn.manifold.Isomap.__reduce__ sklearn.manifold.Isomap.__reduce_ex__ sklearn.manifold.Isomap.__repr__ sklearn.manifold.Isomap.__setattr__ sklearn.manifold.Isomap.__setstate__ sklearn.manifold.Isomap.__sizeof__ sklearn.manifold.Isomap.__sklearn_clone__ sklearn.manifold.Isomap.__sklearn_tags__ sklearn.manifold.Isomap.__str__ sklearn.manifold.Isomap.__subclasshook__ sklearn.manifold.Isomap._build_request_for_signature sklearn.manifold.Isomap._fit_transform sklearn.manifold.Isomap._get_default_requests sklearn.manifold.Isomap._get_doc_link sklearn.manifold.Isomap._get_metadata_request sklearn.manifold.Isomap._get_param_names sklearn.manifold.Isomap._repr_html_inner sklearn.manifold.Isomap._repr_mimebundle_ sklearn.manifold.Isomap._validate_params sklearn.manifold.Isomap.fit sklearn.manifold.Isomap.fit_transform sklearn.manifold.Isomap.get_feature_names_out sklearn.manifold.Isomap.get_metadata_routing sklearn.manifold.Isomap.get_params sklearn.manifold.Isomap.reconstruction_error sklearn.manifold.Isomap.set_output sklearn.manifold.Isomap.set_params sklearn.manifold.Isomap.transform 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.manifold.LocallyLinearEmbedding.rst.txt b/dev/_sources/modules/generated/sklearn.manifold.LocallyLinearEmbedding.rst.txt
index 3b703181d60ab..00e973022236f 100644
--- a/dev/_sources/modules/generated/sklearn.manifold.LocallyLinearEmbedding.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.manifold.LocallyLinearEmbedding.rst.txt
@@ -8,6 +8,6 @@ LocallyLinearEmbedding
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.manifold.LocallyLinearEmbedding sklearn.manifold.LocallyLinearEmbedding.__delattr__ sklearn.manifold.LocallyLinearEmbedding.__dir__ sklearn.manifold.LocallyLinearEmbedding.__eq__ sklearn.manifold.LocallyLinearEmbedding.__format__ sklearn.manifold.LocallyLinearEmbedding.__ge__ sklearn.manifold.LocallyLinearEmbedding.__getattribute__ sklearn.manifold.LocallyLinearEmbedding.__getstate__ sklearn.manifold.LocallyLinearEmbedding.__gt__ sklearn.manifold.LocallyLinearEmbedding.__hash__ sklearn.manifold.LocallyLinearEmbedding.__init__ sklearn.manifold.LocallyLinearEmbedding.__init_subclass__ sklearn.manifold.LocallyLinearEmbedding.__le__ sklearn.manifold.LocallyLinearEmbedding.__lt__ sklearn.manifold.LocallyLinearEmbedding.__ne__ sklearn.manifold.LocallyLinearEmbedding.__new__ sklearn.manifold.LocallyLinearEmbedding.__reduce__ sklearn.manifold.LocallyLinearEmbedding.__reduce_ex__ sklearn.manifold.LocallyLinearEmbedding.__repr__ sklearn.manifold.LocallyLinearEmbedding.__setattr__ sklearn.manifold.LocallyLinearEmbedding.__setstate__ sklearn.manifold.LocallyLinearEmbedding.__sizeof__ sklearn.manifold.LocallyLinearEmbedding.__sklearn_clone__ sklearn.manifold.LocallyLinearEmbedding.__sklearn_tags__ sklearn.manifold.LocallyLinearEmbedding.__str__ sklearn.manifold.LocallyLinearEmbedding.__subclasshook__ sklearn.manifold.LocallyLinearEmbedding._build_request_for_signature sklearn.manifold.LocallyLinearEmbedding._check_feature_names sklearn.manifold.LocallyLinearEmbedding._check_n_features sklearn.manifold.LocallyLinearEmbedding._fit_transform sklearn.manifold.LocallyLinearEmbedding._get_default_requests sklearn.manifold.LocallyLinearEmbedding._get_doc_link sklearn.manifold.LocallyLinearEmbedding._get_metadata_request sklearn.manifold.LocallyLinearEmbedding._get_param_names sklearn.manifold.LocallyLinearEmbedding._get_tags sklearn.manifold.LocallyLinearEmbedding._more_tags sklearn.manifold.LocallyLinearEmbedding._repr_html_inner sklearn.manifold.LocallyLinearEmbedding._repr_mimebundle_ sklearn.manifold.LocallyLinearEmbedding._validate_data sklearn.manifold.LocallyLinearEmbedding._validate_params sklearn.manifold.LocallyLinearEmbedding.fit sklearn.manifold.LocallyLinearEmbedding.fit_transform sklearn.manifold.LocallyLinearEmbedding.get_feature_names_out sklearn.manifold.LocallyLinearEmbedding.get_metadata_routing sklearn.manifold.LocallyLinearEmbedding.get_params sklearn.manifold.LocallyLinearEmbedding.set_output sklearn.manifold.LocallyLinearEmbedding.set_params sklearn.manifold.LocallyLinearEmbedding.transform 
+.. minigallery:: sklearn.manifold.LocallyLinearEmbedding sklearn.manifold.LocallyLinearEmbedding.__delattr__ sklearn.manifold.LocallyLinearEmbedding.__dir__ sklearn.manifold.LocallyLinearEmbedding.__eq__ sklearn.manifold.LocallyLinearEmbedding.__format__ sklearn.manifold.LocallyLinearEmbedding.__ge__ sklearn.manifold.LocallyLinearEmbedding.__getattribute__ sklearn.manifold.LocallyLinearEmbedding.__getstate__ sklearn.manifold.LocallyLinearEmbedding.__gt__ sklearn.manifold.LocallyLinearEmbedding.__hash__ sklearn.manifold.LocallyLinearEmbedding.__init__ sklearn.manifold.LocallyLinearEmbedding.__init_subclass__ sklearn.manifold.LocallyLinearEmbedding.__le__ sklearn.manifold.LocallyLinearEmbedding.__lt__ sklearn.manifold.LocallyLinearEmbedding.__ne__ sklearn.manifold.LocallyLinearEmbedding.__new__ sklearn.manifold.LocallyLinearEmbedding.__reduce__ sklearn.manifold.LocallyLinearEmbedding.__reduce_ex__ sklearn.manifold.LocallyLinearEmbedding.__repr__ sklearn.manifold.LocallyLinearEmbedding.__setattr__ sklearn.manifold.LocallyLinearEmbedding.__setstate__ sklearn.manifold.LocallyLinearEmbedding.__sizeof__ sklearn.manifold.LocallyLinearEmbedding.__sklearn_clone__ sklearn.manifold.LocallyLinearEmbedding.__sklearn_tags__ sklearn.manifold.LocallyLinearEmbedding.__str__ sklearn.manifold.LocallyLinearEmbedding.__subclasshook__ sklearn.manifold.LocallyLinearEmbedding._build_request_for_signature sklearn.manifold.LocallyLinearEmbedding._fit_transform sklearn.manifold.LocallyLinearEmbedding._get_default_requests sklearn.manifold.LocallyLinearEmbedding._get_doc_link sklearn.manifold.LocallyLinearEmbedding._get_metadata_request sklearn.manifold.LocallyLinearEmbedding._get_param_names sklearn.manifold.LocallyLinearEmbedding._repr_html_inner sklearn.manifold.LocallyLinearEmbedding._repr_mimebundle_ sklearn.manifold.LocallyLinearEmbedding._validate_params sklearn.manifold.LocallyLinearEmbedding.fit sklearn.manifold.LocallyLinearEmbedding.fit_transform sklearn.manifold.LocallyLinearEmbedding.get_feature_names_out sklearn.manifold.LocallyLinearEmbedding.get_metadata_routing sklearn.manifold.LocallyLinearEmbedding.get_params sklearn.manifold.LocallyLinearEmbedding.set_output sklearn.manifold.LocallyLinearEmbedding.set_params sklearn.manifold.LocallyLinearEmbedding.transform 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.manifold.MDS.rst.txt b/dev/_sources/modules/generated/sklearn.manifold.MDS.rst.txt
index 1a0ab334c77a5..2be0abbf977e7 100644
--- a/dev/_sources/modules/generated/sklearn.manifold.MDS.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.manifold.MDS.rst.txt
@@ -8,6 +8,6 @@ MDS
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.manifold.MDS sklearn.manifold.MDS.__delattr__ sklearn.manifold.MDS.__dir__ sklearn.manifold.MDS.__eq__ sklearn.manifold.MDS.__format__ sklearn.manifold.MDS.__ge__ sklearn.manifold.MDS.__getattribute__ sklearn.manifold.MDS.__getstate__ sklearn.manifold.MDS.__gt__ sklearn.manifold.MDS.__hash__ sklearn.manifold.MDS.__init__ sklearn.manifold.MDS.__init_subclass__ sklearn.manifold.MDS.__le__ sklearn.manifold.MDS.__lt__ sklearn.manifold.MDS.__ne__ sklearn.manifold.MDS.__new__ sklearn.manifold.MDS.__reduce__ sklearn.manifold.MDS.__reduce_ex__ sklearn.manifold.MDS.__repr__ sklearn.manifold.MDS.__setattr__ sklearn.manifold.MDS.__setstate__ sklearn.manifold.MDS.__sizeof__ sklearn.manifold.MDS.__sklearn_clone__ sklearn.manifold.MDS.__sklearn_tags__ sklearn.manifold.MDS.__str__ sklearn.manifold.MDS.__subclasshook__ sklearn.manifold.MDS._build_request_for_signature sklearn.manifold.MDS._check_feature_names sklearn.manifold.MDS._check_n_features sklearn.manifold.MDS._get_default_requests sklearn.manifold.MDS._get_doc_link sklearn.manifold.MDS._get_metadata_request sklearn.manifold.MDS._get_param_names sklearn.manifold.MDS._get_tags sklearn.manifold.MDS._more_tags sklearn.manifold.MDS._repr_html_inner sklearn.manifold.MDS._repr_mimebundle_ sklearn.manifold.MDS._validate_data sklearn.manifold.MDS._validate_params sklearn.manifold.MDS.fit sklearn.manifold.MDS.fit_transform sklearn.manifold.MDS.get_metadata_routing sklearn.manifold.MDS.get_params sklearn.manifold.MDS.set_fit_request sklearn.manifold.MDS.set_params 
+.. minigallery:: sklearn.manifold.MDS sklearn.manifold.MDS.__delattr__ sklearn.manifold.MDS.__dir__ sklearn.manifold.MDS.__eq__ sklearn.manifold.MDS.__format__ sklearn.manifold.MDS.__ge__ sklearn.manifold.MDS.__getattribute__ sklearn.manifold.MDS.__getstate__ sklearn.manifold.MDS.__gt__ sklearn.manifold.MDS.__hash__ sklearn.manifold.MDS.__init__ sklearn.manifold.MDS.__init_subclass__ sklearn.manifold.MDS.__le__ sklearn.manifold.MDS.__lt__ sklearn.manifold.MDS.__ne__ sklearn.manifold.MDS.__new__ sklearn.manifold.MDS.__reduce__ sklearn.manifold.MDS.__reduce_ex__ sklearn.manifold.MDS.__repr__ sklearn.manifold.MDS.__setattr__ sklearn.manifold.MDS.__setstate__ sklearn.manifold.MDS.__sizeof__ sklearn.manifold.MDS.__sklearn_clone__ sklearn.manifold.MDS.__sklearn_tags__ sklearn.manifold.MDS.__str__ sklearn.manifold.MDS.__subclasshook__ sklearn.manifold.MDS._build_request_for_signature sklearn.manifold.MDS._get_default_requests sklearn.manifold.MDS._get_doc_link sklearn.manifold.MDS._get_metadata_request sklearn.manifold.MDS._get_param_names sklearn.manifold.MDS._repr_html_inner sklearn.manifold.MDS._repr_mimebundle_ sklearn.manifold.MDS._validate_params sklearn.manifold.MDS.fit sklearn.manifold.MDS.fit_transform sklearn.manifold.MDS.get_metadata_routing sklearn.manifold.MDS.get_params sklearn.manifold.MDS.set_fit_request sklearn.manifold.MDS.set_params 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.manifold.SpectralEmbedding.rst.txt b/dev/_sources/modules/generated/sklearn.manifold.SpectralEmbedding.rst.txt
index 966726a5f1eb6..415f6a82434af 100644
--- a/dev/_sources/modules/generated/sklearn.manifold.SpectralEmbedding.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.manifold.SpectralEmbedding.rst.txt
@@ -8,6 +8,6 @@ SpectralEmbedding
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.manifold.SpectralEmbedding sklearn.manifold.SpectralEmbedding.__delattr__ sklearn.manifold.SpectralEmbedding.__dir__ sklearn.manifold.SpectralEmbedding.__eq__ sklearn.manifold.SpectralEmbedding.__format__ sklearn.manifold.SpectralEmbedding.__ge__ sklearn.manifold.SpectralEmbedding.__getattribute__ sklearn.manifold.SpectralEmbedding.__getstate__ sklearn.manifold.SpectralEmbedding.__gt__ sklearn.manifold.SpectralEmbedding.__hash__ sklearn.manifold.SpectralEmbedding.__init__ sklearn.manifold.SpectralEmbedding.__init_subclass__ sklearn.manifold.SpectralEmbedding.__le__ sklearn.manifold.SpectralEmbedding.__lt__ sklearn.manifold.SpectralEmbedding.__ne__ sklearn.manifold.SpectralEmbedding.__new__ sklearn.manifold.SpectralEmbedding.__reduce__ sklearn.manifold.SpectralEmbedding.__reduce_ex__ sklearn.manifold.SpectralEmbedding.__repr__ sklearn.manifold.SpectralEmbedding.__setattr__ sklearn.manifold.SpectralEmbedding.__setstate__ sklearn.manifold.SpectralEmbedding.__sizeof__ sklearn.manifold.SpectralEmbedding.__sklearn_clone__ sklearn.manifold.SpectralEmbedding.__sklearn_tags__ sklearn.manifold.SpectralEmbedding.__str__ sklearn.manifold.SpectralEmbedding.__subclasshook__ sklearn.manifold.SpectralEmbedding._build_request_for_signature sklearn.manifold.SpectralEmbedding._check_feature_names sklearn.manifold.SpectralEmbedding._check_n_features sklearn.manifold.SpectralEmbedding._get_affinity_matrix sklearn.manifold.SpectralEmbedding._get_default_requests sklearn.manifold.SpectralEmbedding._get_doc_link sklearn.manifold.SpectralEmbedding._get_metadata_request sklearn.manifold.SpectralEmbedding._get_param_names sklearn.manifold.SpectralEmbedding._get_tags sklearn.manifold.SpectralEmbedding._more_tags sklearn.manifold.SpectralEmbedding._repr_html_inner sklearn.manifold.SpectralEmbedding._repr_mimebundle_ sklearn.manifold.SpectralEmbedding._validate_data sklearn.manifold.SpectralEmbedding._validate_params sklearn.manifold.SpectralEmbedding.fit sklearn.manifold.SpectralEmbedding.fit_transform sklearn.manifold.SpectralEmbedding.get_metadata_routing sklearn.manifold.SpectralEmbedding.get_params sklearn.manifold.SpectralEmbedding.set_params 
+.. minigallery:: sklearn.manifold.SpectralEmbedding sklearn.manifold.SpectralEmbedding.__delattr__ sklearn.manifold.SpectralEmbedding.__dir__ sklearn.manifold.SpectralEmbedding.__eq__ sklearn.manifold.SpectralEmbedding.__format__ sklearn.manifold.SpectralEmbedding.__ge__ sklearn.manifold.SpectralEmbedding.__getattribute__ sklearn.manifold.SpectralEmbedding.__getstate__ sklearn.manifold.SpectralEmbedding.__gt__ sklearn.manifold.SpectralEmbedding.__hash__ sklearn.manifold.SpectralEmbedding.__init__ sklearn.manifold.SpectralEmbedding.__init_subclass__ sklearn.manifold.SpectralEmbedding.__le__ sklearn.manifold.SpectralEmbedding.__lt__ sklearn.manifold.SpectralEmbedding.__ne__ sklearn.manifold.SpectralEmbedding.__new__ sklearn.manifold.SpectralEmbedding.__reduce__ sklearn.manifold.SpectralEmbedding.__reduce_ex__ sklearn.manifold.SpectralEmbedding.__repr__ sklearn.manifold.SpectralEmbedding.__setattr__ sklearn.manifold.SpectralEmbedding.__setstate__ sklearn.manifold.SpectralEmbedding.__sizeof__ sklearn.manifold.SpectralEmbedding.__sklearn_clone__ sklearn.manifold.SpectralEmbedding.__sklearn_tags__ sklearn.manifold.SpectralEmbedding.__str__ sklearn.manifold.SpectralEmbedding.__subclasshook__ sklearn.manifold.SpectralEmbedding._build_request_for_signature sklearn.manifold.SpectralEmbedding._get_affinity_matrix sklearn.manifold.SpectralEmbedding._get_default_requests sklearn.manifold.SpectralEmbedding._get_doc_link sklearn.manifold.SpectralEmbedding._get_metadata_request sklearn.manifold.SpectralEmbedding._get_param_names sklearn.manifold.SpectralEmbedding._repr_html_inner sklearn.manifold.SpectralEmbedding._repr_mimebundle_ sklearn.manifold.SpectralEmbedding._validate_params sklearn.manifold.SpectralEmbedding.fit sklearn.manifold.SpectralEmbedding.fit_transform sklearn.manifold.SpectralEmbedding.get_metadata_routing sklearn.manifold.SpectralEmbedding.get_params sklearn.manifold.SpectralEmbedding.set_params 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.manifold.TSNE.rst.txt b/dev/_sources/modules/generated/sklearn.manifold.TSNE.rst.txt
index 9a151569e4e5c..5d993898fd1cb 100644
--- a/dev/_sources/modules/generated/sklearn.manifold.TSNE.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.manifold.TSNE.rst.txt
@@ -8,6 +8,6 @@ TSNE
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.manifold.TSNE sklearn.manifold.TSNE.__delattr__ sklearn.manifold.TSNE.__dir__ sklearn.manifold.TSNE.__eq__ sklearn.manifold.TSNE.__format__ sklearn.manifold.TSNE.__ge__ sklearn.manifold.TSNE.__getattribute__ sklearn.manifold.TSNE.__getstate__ sklearn.manifold.TSNE.__gt__ sklearn.manifold.TSNE.__hash__ sklearn.manifold.TSNE.__init__ sklearn.manifold.TSNE.__init_subclass__ sklearn.manifold.TSNE.__le__ sklearn.manifold.TSNE.__lt__ sklearn.manifold.TSNE.__ne__ sklearn.manifold.TSNE.__new__ sklearn.manifold.TSNE.__reduce__ sklearn.manifold.TSNE.__reduce_ex__ sklearn.manifold.TSNE.__repr__ sklearn.manifold.TSNE.__setattr__ sklearn.manifold.TSNE.__setstate__ sklearn.manifold.TSNE.__sizeof__ sklearn.manifold.TSNE.__sklearn_clone__ sklearn.manifold.TSNE.__sklearn_tags__ sklearn.manifold.TSNE.__str__ sklearn.manifold.TSNE.__subclasshook__ sklearn.manifold.TSNE._build_request_for_signature sklearn.manifold.TSNE._check_feature_names sklearn.manifold.TSNE._check_n_features sklearn.manifold.TSNE._check_params_vs_input sklearn.manifold.TSNE._fit sklearn.manifold.TSNE._get_default_requests sklearn.manifold.TSNE._get_doc_link sklearn.manifold.TSNE._get_metadata_request sklearn.manifold.TSNE._get_param_names sklearn.manifold.TSNE._get_tags sklearn.manifold.TSNE._more_tags sklearn.manifold.TSNE._repr_html_inner sklearn.manifold.TSNE._repr_mimebundle_ sklearn.manifold.TSNE._tsne sklearn.manifold.TSNE._validate_data sklearn.manifold.TSNE._validate_params sklearn.manifold.TSNE.fit sklearn.manifold.TSNE.fit_transform sklearn.manifold.TSNE.get_feature_names_out sklearn.manifold.TSNE.get_metadata_routing sklearn.manifold.TSNE.get_params sklearn.manifold.TSNE.set_output sklearn.manifold.TSNE.set_params 
+.. minigallery:: sklearn.manifold.TSNE sklearn.manifold.TSNE.__delattr__ sklearn.manifold.TSNE.__dir__ sklearn.manifold.TSNE.__eq__ sklearn.manifold.TSNE.__format__ sklearn.manifold.TSNE.__ge__ sklearn.manifold.TSNE.__getattribute__ sklearn.manifold.TSNE.__getstate__ sklearn.manifold.TSNE.__gt__ sklearn.manifold.TSNE.__hash__ sklearn.manifold.TSNE.__init__ sklearn.manifold.TSNE.__init_subclass__ sklearn.manifold.TSNE.__le__ sklearn.manifold.TSNE.__lt__ sklearn.manifold.TSNE.__ne__ sklearn.manifold.TSNE.__new__ sklearn.manifold.TSNE.__reduce__ sklearn.manifold.TSNE.__reduce_ex__ sklearn.manifold.TSNE.__repr__ sklearn.manifold.TSNE.__setattr__ sklearn.manifold.TSNE.__setstate__ sklearn.manifold.TSNE.__sizeof__ sklearn.manifold.TSNE.__sklearn_clone__ sklearn.manifold.TSNE.__sklearn_tags__ sklearn.manifold.TSNE.__str__ sklearn.manifold.TSNE.__subclasshook__ sklearn.manifold.TSNE._build_request_for_signature sklearn.manifold.TSNE._check_params_vs_input sklearn.manifold.TSNE._fit sklearn.manifold.TSNE._get_default_requests sklearn.manifold.TSNE._get_doc_link sklearn.manifold.TSNE._get_metadata_request sklearn.manifold.TSNE._get_param_names sklearn.manifold.TSNE._repr_html_inner sklearn.manifold.TSNE._repr_mimebundle_ sklearn.manifold.TSNE._tsne sklearn.manifold.TSNE._validate_params sklearn.manifold.TSNE.fit sklearn.manifold.TSNE.fit_transform sklearn.manifold.TSNE.get_feature_names_out sklearn.manifold.TSNE.get_metadata_routing sklearn.manifold.TSNE.get_params sklearn.manifold.TSNE.set_output sklearn.manifold.TSNE.set_params 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.mixture.BayesianGaussianMixture.rst.txt b/dev/_sources/modules/generated/sklearn.mixture.BayesianGaussianMixture.rst.txt
index bb82a22f17d7c..5fff38ff7e995 100644
--- a/dev/_sources/modules/generated/sklearn.mixture.BayesianGaussianMixture.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.mixture.BayesianGaussianMixture.rst.txt
@@ -8,6 +8,6 @@ BayesianGaussianMixture
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.mixture.BayesianGaussianMixture sklearn.mixture.BayesianGaussianMixture.__delattr__ sklearn.mixture.BayesianGaussianMixture.__dir__ sklearn.mixture.BayesianGaussianMixture.__eq__ sklearn.mixture.BayesianGaussianMixture.__format__ sklearn.mixture.BayesianGaussianMixture.__ge__ sklearn.mixture.BayesianGaussianMixture.__getattribute__ sklearn.mixture.BayesianGaussianMixture.__getstate__ sklearn.mixture.BayesianGaussianMixture.__gt__ sklearn.mixture.BayesianGaussianMixture.__hash__ sklearn.mixture.BayesianGaussianMixture.__init__ sklearn.mixture.BayesianGaussianMixture.__init_subclass__ sklearn.mixture.BayesianGaussianMixture.__le__ sklearn.mixture.BayesianGaussianMixture.__lt__ sklearn.mixture.BayesianGaussianMixture.__ne__ sklearn.mixture.BayesianGaussianMixture.__new__ sklearn.mixture.BayesianGaussianMixture.__reduce__ sklearn.mixture.BayesianGaussianMixture.__reduce_ex__ sklearn.mixture.BayesianGaussianMixture.__repr__ sklearn.mixture.BayesianGaussianMixture.__setattr__ sklearn.mixture.BayesianGaussianMixture.__setstate__ sklearn.mixture.BayesianGaussianMixture.__sizeof__ sklearn.mixture.BayesianGaussianMixture.__sklearn_clone__ sklearn.mixture.BayesianGaussianMixture.__sklearn_tags__ sklearn.mixture.BayesianGaussianMixture.__str__ sklearn.mixture.BayesianGaussianMixture.__subclasshook__ sklearn.mixture.BayesianGaussianMixture._build_request_for_signature sklearn.mixture.BayesianGaussianMixture._check_feature_names sklearn.mixture.BayesianGaussianMixture._check_means_parameters sklearn.mixture.BayesianGaussianMixture._check_n_features sklearn.mixture.BayesianGaussianMixture._check_parameters sklearn.mixture.BayesianGaussianMixture._check_precision_parameters sklearn.mixture.BayesianGaussianMixture._check_weights_parameters sklearn.mixture.BayesianGaussianMixture._checkcovariance_prior_parameter sklearn.mixture.BayesianGaussianMixture._compute_lower_bound sklearn.mixture.BayesianGaussianMixture._e_step sklearn.mixture.BayesianGaussianMixture._estimate_log_prob sklearn.mixture.BayesianGaussianMixture._estimate_log_prob_resp sklearn.mixture.BayesianGaussianMixture._estimate_log_weights sklearn.mixture.BayesianGaussianMixture._estimate_means sklearn.mixture.BayesianGaussianMixture._estimate_precisions sklearn.mixture.BayesianGaussianMixture._estimate_weighted_log_prob sklearn.mixture.BayesianGaussianMixture._estimate_weights sklearn.mixture.BayesianGaussianMixture._estimate_wishart_diag sklearn.mixture.BayesianGaussianMixture._estimate_wishart_full sklearn.mixture.BayesianGaussianMixture._estimate_wishart_spherical sklearn.mixture.BayesianGaussianMixture._estimate_wishart_tied sklearn.mixture.BayesianGaussianMixture._get_default_requests sklearn.mixture.BayesianGaussianMixture._get_doc_link sklearn.mixture.BayesianGaussianMixture._get_metadata_request sklearn.mixture.BayesianGaussianMixture._get_param_names sklearn.mixture.BayesianGaussianMixture._get_parameters sklearn.mixture.BayesianGaussianMixture._get_tags sklearn.mixture.BayesianGaussianMixture._initialize sklearn.mixture.BayesianGaussianMixture._initialize_parameters sklearn.mixture.BayesianGaussianMixture._m_step sklearn.mixture.BayesianGaussianMixture._more_tags sklearn.mixture.BayesianGaussianMixture._print_verbose_msg_init_beg sklearn.mixture.BayesianGaussianMixture._print_verbose_msg_init_end sklearn.mixture.BayesianGaussianMixture._print_verbose_msg_iter_end sklearn.mixture.BayesianGaussianMixture._repr_html_inner sklearn.mixture.BayesianGaussianMixture._repr_mimebundle_ sklearn.mixture.BayesianGaussianMixture._set_parameters sklearn.mixture.BayesianGaussianMixture._validate_data sklearn.mixture.BayesianGaussianMixture._validate_params sklearn.mixture.BayesianGaussianMixture.fit sklearn.mixture.BayesianGaussianMixture.fit_predict sklearn.mixture.BayesianGaussianMixture.get_metadata_routing sklearn.mixture.BayesianGaussianMixture.get_params sklearn.mixture.BayesianGaussianMixture.predict sklearn.mixture.BayesianGaussianMixture.predict_proba sklearn.mixture.BayesianGaussianMixture.sample sklearn.mixture.BayesianGaussianMixture.score sklearn.mixture.BayesianGaussianMixture.score_samples sklearn.mixture.BayesianGaussianMixture.set_params 
+.. minigallery:: sklearn.mixture.BayesianGaussianMixture sklearn.mixture.BayesianGaussianMixture.__delattr__ sklearn.mixture.BayesianGaussianMixture.__dir__ sklearn.mixture.BayesianGaussianMixture.__eq__ sklearn.mixture.BayesianGaussianMixture.__format__ sklearn.mixture.BayesianGaussianMixture.__ge__ sklearn.mixture.BayesianGaussianMixture.__getattribute__ sklearn.mixture.BayesianGaussianMixture.__getstate__ sklearn.mixture.BayesianGaussianMixture.__gt__ sklearn.mixture.BayesianGaussianMixture.__hash__ sklearn.mixture.BayesianGaussianMixture.__init__ sklearn.mixture.BayesianGaussianMixture.__init_subclass__ sklearn.mixture.BayesianGaussianMixture.__le__ sklearn.mixture.BayesianGaussianMixture.__lt__ sklearn.mixture.BayesianGaussianMixture.__ne__ sklearn.mixture.BayesianGaussianMixture.__new__ sklearn.mixture.BayesianGaussianMixture.__reduce__ sklearn.mixture.BayesianGaussianMixture.__reduce_ex__ sklearn.mixture.BayesianGaussianMixture.__repr__ sklearn.mixture.BayesianGaussianMixture.__setattr__ sklearn.mixture.BayesianGaussianMixture.__setstate__ sklearn.mixture.BayesianGaussianMixture.__sizeof__ sklearn.mixture.BayesianGaussianMixture.__sklearn_clone__ sklearn.mixture.BayesianGaussianMixture.__sklearn_tags__ sklearn.mixture.BayesianGaussianMixture.__str__ sklearn.mixture.BayesianGaussianMixture.__subclasshook__ sklearn.mixture.BayesianGaussianMixture._build_request_for_signature sklearn.mixture.BayesianGaussianMixture._check_means_parameters sklearn.mixture.BayesianGaussianMixture._check_parameters sklearn.mixture.BayesianGaussianMixture._check_precision_parameters sklearn.mixture.BayesianGaussianMixture._check_weights_parameters sklearn.mixture.BayesianGaussianMixture._checkcovariance_prior_parameter sklearn.mixture.BayesianGaussianMixture._compute_lower_bound sklearn.mixture.BayesianGaussianMixture._e_step sklearn.mixture.BayesianGaussianMixture._estimate_log_prob sklearn.mixture.BayesianGaussianMixture._estimate_log_prob_resp sklearn.mixture.BayesianGaussianMixture._estimate_log_weights sklearn.mixture.BayesianGaussianMixture._estimate_means sklearn.mixture.BayesianGaussianMixture._estimate_precisions sklearn.mixture.BayesianGaussianMixture._estimate_weighted_log_prob sklearn.mixture.BayesianGaussianMixture._estimate_weights sklearn.mixture.BayesianGaussianMixture._estimate_wishart_diag sklearn.mixture.BayesianGaussianMixture._estimate_wishart_full sklearn.mixture.BayesianGaussianMixture._estimate_wishart_spherical sklearn.mixture.BayesianGaussianMixture._estimate_wishart_tied sklearn.mixture.BayesianGaussianMixture._get_default_requests sklearn.mixture.BayesianGaussianMixture._get_doc_link sklearn.mixture.BayesianGaussianMixture._get_metadata_request sklearn.mixture.BayesianGaussianMixture._get_param_names sklearn.mixture.BayesianGaussianMixture._get_parameters sklearn.mixture.BayesianGaussianMixture._initialize sklearn.mixture.BayesianGaussianMixture._initialize_parameters sklearn.mixture.BayesianGaussianMixture._m_step sklearn.mixture.BayesianGaussianMixture._print_verbose_msg_init_beg sklearn.mixture.BayesianGaussianMixture._print_verbose_msg_init_end sklearn.mixture.BayesianGaussianMixture._print_verbose_msg_iter_end sklearn.mixture.BayesianGaussianMixture._repr_html_inner sklearn.mixture.BayesianGaussianMixture._repr_mimebundle_ sklearn.mixture.BayesianGaussianMixture._set_parameters sklearn.mixture.BayesianGaussianMixture._validate_params sklearn.mixture.BayesianGaussianMixture.fit sklearn.mixture.BayesianGaussianMixture.fit_predict sklearn.mixture.BayesianGaussianMixture.get_metadata_routing sklearn.mixture.BayesianGaussianMixture.get_params sklearn.mixture.BayesianGaussianMixture.predict sklearn.mixture.BayesianGaussianMixture.predict_proba sklearn.mixture.BayesianGaussianMixture.sample sklearn.mixture.BayesianGaussianMixture.score sklearn.mixture.BayesianGaussianMixture.score_samples sklearn.mixture.BayesianGaussianMixture.set_params 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.mixture.GaussianMixture.rst.txt b/dev/_sources/modules/generated/sklearn.mixture.GaussianMixture.rst.txt
index 131f1c622df30..e4ce19a3be4bb 100644
--- a/dev/_sources/modules/generated/sklearn.mixture.GaussianMixture.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.mixture.GaussianMixture.rst.txt
@@ -8,6 +8,6 @@ GaussianMixture
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.mixture.GaussianMixture sklearn.mixture.GaussianMixture.__delattr__ sklearn.mixture.GaussianMixture.__dir__ sklearn.mixture.GaussianMixture.__eq__ sklearn.mixture.GaussianMixture.__format__ sklearn.mixture.GaussianMixture.__ge__ sklearn.mixture.GaussianMixture.__getattribute__ sklearn.mixture.GaussianMixture.__getstate__ sklearn.mixture.GaussianMixture.__gt__ sklearn.mixture.GaussianMixture.__hash__ sklearn.mixture.GaussianMixture.__init__ sklearn.mixture.GaussianMixture.__init_subclass__ sklearn.mixture.GaussianMixture.__le__ sklearn.mixture.GaussianMixture.__lt__ sklearn.mixture.GaussianMixture.__ne__ sklearn.mixture.GaussianMixture.__new__ sklearn.mixture.GaussianMixture.__reduce__ sklearn.mixture.GaussianMixture.__reduce_ex__ sklearn.mixture.GaussianMixture.__repr__ sklearn.mixture.GaussianMixture.__setattr__ sklearn.mixture.GaussianMixture.__setstate__ sklearn.mixture.GaussianMixture.__sizeof__ sklearn.mixture.GaussianMixture.__sklearn_clone__ sklearn.mixture.GaussianMixture.__sklearn_tags__ sklearn.mixture.GaussianMixture.__str__ sklearn.mixture.GaussianMixture.__subclasshook__ sklearn.mixture.GaussianMixture._build_request_for_signature sklearn.mixture.GaussianMixture._check_feature_names sklearn.mixture.GaussianMixture._check_n_features sklearn.mixture.GaussianMixture._check_parameters sklearn.mixture.GaussianMixture._compute_lower_bound sklearn.mixture.GaussianMixture._e_step sklearn.mixture.GaussianMixture._estimate_log_prob sklearn.mixture.GaussianMixture._estimate_log_prob_resp sklearn.mixture.GaussianMixture._estimate_log_weights sklearn.mixture.GaussianMixture._estimate_weighted_log_prob sklearn.mixture.GaussianMixture._get_default_requests sklearn.mixture.GaussianMixture._get_doc_link sklearn.mixture.GaussianMixture._get_metadata_request sklearn.mixture.GaussianMixture._get_param_names sklearn.mixture.GaussianMixture._get_parameters sklearn.mixture.GaussianMixture._get_tags sklearn.mixture.GaussianMixture._initialize sklearn.mixture.GaussianMixture._initialize_parameters sklearn.mixture.GaussianMixture._m_step sklearn.mixture.GaussianMixture._more_tags sklearn.mixture.GaussianMixture._n_parameters sklearn.mixture.GaussianMixture._print_verbose_msg_init_beg sklearn.mixture.GaussianMixture._print_verbose_msg_init_end sklearn.mixture.GaussianMixture._print_verbose_msg_iter_end sklearn.mixture.GaussianMixture._repr_html_inner sklearn.mixture.GaussianMixture._repr_mimebundle_ sklearn.mixture.GaussianMixture._set_parameters sklearn.mixture.GaussianMixture._validate_data sklearn.mixture.GaussianMixture._validate_params sklearn.mixture.GaussianMixture.aic sklearn.mixture.GaussianMixture.bic sklearn.mixture.GaussianMixture.fit sklearn.mixture.GaussianMixture.fit_predict sklearn.mixture.GaussianMixture.get_metadata_routing sklearn.mixture.GaussianMixture.get_params sklearn.mixture.GaussianMixture.predict sklearn.mixture.GaussianMixture.predict_proba sklearn.mixture.GaussianMixture.sample sklearn.mixture.GaussianMixture.score sklearn.mixture.GaussianMixture.score_samples sklearn.mixture.GaussianMixture.set_params 
+.. minigallery:: sklearn.mixture.GaussianMixture sklearn.mixture.GaussianMixture.__delattr__ sklearn.mixture.GaussianMixture.__dir__ sklearn.mixture.GaussianMixture.__eq__ sklearn.mixture.GaussianMixture.__format__ sklearn.mixture.GaussianMixture.__ge__ sklearn.mixture.GaussianMixture.__getattribute__ sklearn.mixture.GaussianMixture.__getstate__ sklearn.mixture.GaussianMixture.__gt__ sklearn.mixture.GaussianMixture.__hash__ sklearn.mixture.GaussianMixture.__init__ sklearn.mixture.GaussianMixture.__init_subclass__ sklearn.mixture.GaussianMixture.__le__ sklearn.mixture.GaussianMixture.__lt__ sklearn.mixture.GaussianMixture.__ne__ sklearn.mixture.GaussianMixture.__new__ sklearn.mixture.GaussianMixture.__reduce__ sklearn.mixture.GaussianMixture.__reduce_ex__ sklearn.mixture.GaussianMixture.__repr__ sklearn.mixture.GaussianMixture.__setattr__ sklearn.mixture.GaussianMixture.__setstate__ sklearn.mixture.GaussianMixture.__sizeof__ sklearn.mixture.GaussianMixture.__sklearn_clone__ sklearn.mixture.GaussianMixture.__sklearn_tags__ sklearn.mixture.GaussianMixture.__str__ sklearn.mixture.GaussianMixture.__subclasshook__ sklearn.mixture.GaussianMixture._build_request_for_signature sklearn.mixture.GaussianMixture._check_parameters sklearn.mixture.GaussianMixture._compute_lower_bound sklearn.mixture.GaussianMixture._e_step sklearn.mixture.GaussianMixture._estimate_log_prob sklearn.mixture.GaussianMixture._estimate_log_prob_resp sklearn.mixture.GaussianMixture._estimate_log_weights sklearn.mixture.GaussianMixture._estimate_weighted_log_prob sklearn.mixture.GaussianMixture._get_default_requests sklearn.mixture.GaussianMixture._get_doc_link sklearn.mixture.GaussianMixture._get_metadata_request sklearn.mixture.GaussianMixture._get_param_names sklearn.mixture.GaussianMixture._get_parameters sklearn.mixture.GaussianMixture._initialize sklearn.mixture.GaussianMixture._initialize_parameters sklearn.mixture.GaussianMixture._m_step sklearn.mixture.GaussianMixture._n_parameters sklearn.mixture.GaussianMixture._print_verbose_msg_init_beg sklearn.mixture.GaussianMixture._print_verbose_msg_init_end sklearn.mixture.GaussianMixture._print_verbose_msg_iter_end sklearn.mixture.GaussianMixture._repr_html_inner sklearn.mixture.GaussianMixture._repr_mimebundle_ sklearn.mixture.GaussianMixture._set_parameters sklearn.mixture.GaussianMixture._validate_params sklearn.mixture.GaussianMixture.aic sklearn.mixture.GaussianMixture.bic sklearn.mixture.GaussianMixture.fit sklearn.mixture.GaussianMixture.fit_predict sklearn.mixture.GaussianMixture.get_metadata_routing sklearn.mixture.GaussianMixture.get_params sklearn.mixture.GaussianMixture.predict sklearn.mixture.GaussianMixture.predict_proba sklearn.mixture.GaussianMixture.sample sklearn.mixture.GaussianMixture.score sklearn.mixture.GaussianMixture.score_samples sklearn.mixture.GaussianMixture.set_params 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.model_selection.FixedThresholdClassifier.rst.txt b/dev/_sources/modules/generated/sklearn.model_selection.FixedThresholdClassifier.rst.txt
index 30bee5161cfaf..b8c4612d01bad 100644
--- a/dev/_sources/modules/generated/sklearn.model_selection.FixedThresholdClassifier.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.model_selection.FixedThresholdClassifier.rst.txt
@@ -8,6 +8,6 @@ FixedThresholdClassifier
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.model_selection.FixedThresholdClassifier sklearn.model_selection.FixedThresholdClassifier.__delattr__ sklearn.model_selection.FixedThresholdClassifier.__dir__ sklearn.model_selection.FixedThresholdClassifier.__eq__ sklearn.model_selection.FixedThresholdClassifier.__format__ sklearn.model_selection.FixedThresholdClassifier.__ge__ sklearn.model_selection.FixedThresholdClassifier.__getattribute__ sklearn.model_selection.FixedThresholdClassifier.__getstate__ sklearn.model_selection.FixedThresholdClassifier.__gt__ sklearn.model_selection.FixedThresholdClassifier.__hash__ sklearn.model_selection.FixedThresholdClassifier.__init__ sklearn.model_selection.FixedThresholdClassifier.__init_subclass__ sklearn.model_selection.FixedThresholdClassifier.__le__ sklearn.model_selection.FixedThresholdClassifier.__lt__ sklearn.model_selection.FixedThresholdClassifier.__ne__ sklearn.model_selection.FixedThresholdClassifier.__new__ sklearn.model_selection.FixedThresholdClassifier.__reduce__ sklearn.model_selection.FixedThresholdClassifier.__reduce_ex__ sklearn.model_selection.FixedThresholdClassifier.__repr__ sklearn.model_selection.FixedThresholdClassifier.__setattr__ sklearn.model_selection.FixedThresholdClassifier.__setstate__ sklearn.model_selection.FixedThresholdClassifier.__sizeof__ sklearn.model_selection.FixedThresholdClassifier.__sklearn_clone__ sklearn.model_selection.FixedThresholdClassifier.__sklearn_tags__ sklearn.model_selection.FixedThresholdClassifier.__str__ sklearn.model_selection.FixedThresholdClassifier.__subclasshook__ sklearn.model_selection.FixedThresholdClassifier._build_request_for_signature sklearn.model_selection.FixedThresholdClassifier._check_feature_names sklearn.model_selection.FixedThresholdClassifier._check_n_features sklearn.model_selection.FixedThresholdClassifier._fit sklearn.model_selection.FixedThresholdClassifier._get_default_requests sklearn.model_selection.FixedThresholdClassifier._get_doc_link sklearn.model_selection.FixedThresholdClassifier._get_metadata_request sklearn.model_selection.FixedThresholdClassifier._get_param_names sklearn.model_selection.FixedThresholdClassifier._get_response_method sklearn.model_selection.FixedThresholdClassifier._get_tags sklearn.model_selection.FixedThresholdClassifier._more_tags sklearn.model_selection.FixedThresholdClassifier._repr_html_inner sklearn.model_selection.FixedThresholdClassifier._repr_mimebundle_ sklearn.model_selection.FixedThresholdClassifier._validate_data sklearn.model_selection.FixedThresholdClassifier._validate_params sklearn.model_selection.FixedThresholdClassifier.decision_function sklearn.model_selection.FixedThresholdClassifier.fit sklearn.model_selection.FixedThresholdClassifier.get_metadata_routing sklearn.model_selection.FixedThresholdClassifier.get_params sklearn.model_selection.FixedThresholdClassifier.predict sklearn.model_selection.FixedThresholdClassifier.predict_log_proba sklearn.model_selection.FixedThresholdClassifier.predict_proba sklearn.model_selection.FixedThresholdClassifier.score sklearn.model_selection.FixedThresholdClassifier.set_params sklearn.model_selection.FixedThresholdClassifier.set_score_request 
+.. minigallery:: sklearn.model_selection.FixedThresholdClassifier sklearn.model_selection.FixedThresholdClassifier.__delattr__ sklearn.model_selection.FixedThresholdClassifier.__dir__ sklearn.model_selection.FixedThresholdClassifier.__eq__ sklearn.model_selection.FixedThresholdClassifier.__format__ sklearn.model_selection.FixedThresholdClassifier.__ge__ sklearn.model_selection.FixedThresholdClassifier.__getattribute__ sklearn.model_selection.FixedThresholdClassifier.__getstate__ sklearn.model_selection.FixedThresholdClassifier.__gt__ sklearn.model_selection.FixedThresholdClassifier.__hash__ sklearn.model_selection.FixedThresholdClassifier.__init__ sklearn.model_selection.FixedThresholdClassifier.__init_subclass__ sklearn.model_selection.FixedThresholdClassifier.__le__ sklearn.model_selection.FixedThresholdClassifier.__lt__ sklearn.model_selection.FixedThresholdClassifier.__ne__ sklearn.model_selection.FixedThresholdClassifier.__new__ sklearn.model_selection.FixedThresholdClassifier.__reduce__ sklearn.model_selection.FixedThresholdClassifier.__reduce_ex__ sklearn.model_selection.FixedThresholdClassifier.__repr__ sklearn.model_selection.FixedThresholdClassifier.__setattr__ sklearn.model_selection.FixedThresholdClassifier.__setstate__ sklearn.model_selection.FixedThresholdClassifier.__sizeof__ sklearn.model_selection.FixedThresholdClassifier.__sklearn_clone__ sklearn.model_selection.FixedThresholdClassifier.__sklearn_tags__ sklearn.model_selection.FixedThresholdClassifier.__str__ sklearn.model_selection.FixedThresholdClassifier.__subclasshook__ sklearn.model_selection.FixedThresholdClassifier._build_request_for_signature sklearn.model_selection.FixedThresholdClassifier._fit sklearn.model_selection.FixedThresholdClassifier._get_default_requests sklearn.model_selection.FixedThresholdClassifier._get_doc_link sklearn.model_selection.FixedThresholdClassifier._get_metadata_request sklearn.model_selection.FixedThresholdClassifier._get_param_names sklearn.model_selection.FixedThresholdClassifier._get_response_method sklearn.model_selection.FixedThresholdClassifier._repr_html_inner sklearn.model_selection.FixedThresholdClassifier._repr_mimebundle_ sklearn.model_selection.FixedThresholdClassifier._validate_params sklearn.model_selection.FixedThresholdClassifier.decision_function sklearn.model_selection.FixedThresholdClassifier.fit sklearn.model_selection.FixedThresholdClassifier.get_metadata_routing sklearn.model_selection.FixedThresholdClassifier.get_params sklearn.model_selection.FixedThresholdClassifier.predict sklearn.model_selection.FixedThresholdClassifier.predict_log_proba sklearn.model_selection.FixedThresholdClassifier.predict_proba sklearn.model_selection.FixedThresholdClassifier.score sklearn.model_selection.FixedThresholdClassifier.set_params sklearn.model_selection.FixedThresholdClassifier.set_score_request 
    :add-heading: Gallery examples
    :heading-level: -
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diff --git a/dev/_sources/modules/generated/sklearn.model_selection.GridSearchCV.rst.txt b/dev/_sources/modules/generated/sklearn.model_selection.GridSearchCV.rst.txt
index aad0564fe613d..d1735cf2b3eb5 100644
--- a/dev/_sources/modules/generated/sklearn.model_selection.GridSearchCV.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.model_selection.GridSearchCV.rst.txt
@@ -8,6 +8,6 @@ GridSearchCV
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.model_selection.GridSearchCV sklearn.model_selection.GridSearchCV.__delattr__ sklearn.model_selection.GridSearchCV.__dir__ sklearn.model_selection.GridSearchCV.__eq__ sklearn.model_selection.GridSearchCV.__format__ sklearn.model_selection.GridSearchCV.__ge__ sklearn.model_selection.GridSearchCV.__getattribute__ sklearn.model_selection.GridSearchCV.__getstate__ sklearn.model_selection.GridSearchCV.__gt__ sklearn.model_selection.GridSearchCV.__hash__ sklearn.model_selection.GridSearchCV.__init__ sklearn.model_selection.GridSearchCV.__init_subclass__ sklearn.model_selection.GridSearchCV.__le__ sklearn.model_selection.GridSearchCV.__lt__ sklearn.model_selection.GridSearchCV.__ne__ sklearn.model_selection.GridSearchCV.__new__ sklearn.model_selection.GridSearchCV.__reduce__ sklearn.model_selection.GridSearchCV.__reduce_ex__ sklearn.model_selection.GridSearchCV.__repr__ sklearn.model_selection.GridSearchCV.__setattr__ sklearn.model_selection.GridSearchCV.__setstate__ sklearn.model_selection.GridSearchCV.__sizeof__ sklearn.model_selection.GridSearchCV.__sklearn_clone__ sklearn.model_selection.GridSearchCV.__sklearn_tags__ sklearn.model_selection.GridSearchCV.__str__ sklearn.model_selection.GridSearchCV.__subclasshook__ sklearn.model_selection.GridSearchCV._build_request_for_signature sklearn.model_selection.GridSearchCV._check_feature_names sklearn.model_selection.GridSearchCV._check_n_features sklearn.model_selection.GridSearchCV._check_refit_for_multimetric sklearn.model_selection.GridSearchCV._check_scorers_accept_sample_weight sklearn.model_selection.GridSearchCV._format_results sklearn.model_selection.GridSearchCV._get_default_requests sklearn.model_selection.GridSearchCV._get_doc_link sklearn.model_selection.GridSearchCV._get_metadata_request sklearn.model_selection.GridSearchCV._get_param_names sklearn.model_selection.GridSearchCV._get_routed_params_for_fit sklearn.model_selection.GridSearchCV._get_scorers sklearn.model_selection.GridSearchCV._get_tags sklearn.model_selection.GridSearchCV._more_tags sklearn.model_selection.GridSearchCV._repr_html_inner sklearn.model_selection.GridSearchCV._repr_mimebundle_ sklearn.model_selection.GridSearchCV._run_search sklearn.model_selection.GridSearchCV._select_best_index sklearn.model_selection.GridSearchCV._sk_visual_block_ sklearn.model_selection.GridSearchCV._validate_data sklearn.model_selection.GridSearchCV._validate_params sklearn.model_selection.GridSearchCV.decision_function sklearn.model_selection.GridSearchCV.fit sklearn.model_selection.GridSearchCV.get_metadata_routing sklearn.model_selection.GridSearchCV.get_params sklearn.model_selection.GridSearchCV.inverse_transform sklearn.model_selection.GridSearchCV.predict sklearn.model_selection.GridSearchCV.predict_log_proba sklearn.model_selection.GridSearchCV.predict_proba sklearn.model_selection.GridSearchCV.score sklearn.model_selection.GridSearchCV.score_samples sklearn.model_selection.GridSearchCV.set_params sklearn.model_selection.GridSearchCV.transform 
+.. minigallery:: sklearn.model_selection.GridSearchCV sklearn.model_selection.GridSearchCV.__delattr__ sklearn.model_selection.GridSearchCV.__dir__ sklearn.model_selection.GridSearchCV.__eq__ sklearn.model_selection.GridSearchCV.__format__ sklearn.model_selection.GridSearchCV.__ge__ sklearn.model_selection.GridSearchCV.__getattribute__ sklearn.model_selection.GridSearchCV.__getstate__ sklearn.model_selection.GridSearchCV.__gt__ sklearn.model_selection.GridSearchCV.__hash__ sklearn.model_selection.GridSearchCV.__init__ sklearn.model_selection.GridSearchCV.__init_subclass__ sklearn.model_selection.GridSearchCV.__le__ sklearn.model_selection.GridSearchCV.__lt__ sklearn.model_selection.GridSearchCV.__ne__ sklearn.model_selection.GridSearchCV.__new__ sklearn.model_selection.GridSearchCV.__reduce__ sklearn.model_selection.GridSearchCV.__reduce_ex__ sklearn.model_selection.GridSearchCV.__repr__ sklearn.model_selection.GridSearchCV.__setattr__ sklearn.model_selection.GridSearchCV.__setstate__ sklearn.model_selection.GridSearchCV.__sizeof__ sklearn.model_selection.GridSearchCV.__sklearn_clone__ sklearn.model_selection.GridSearchCV.__sklearn_tags__ sklearn.model_selection.GridSearchCV.__str__ sklearn.model_selection.GridSearchCV.__subclasshook__ sklearn.model_selection.GridSearchCV._build_request_for_signature sklearn.model_selection.GridSearchCV._check_refit_for_multimetric sklearn.model_selection.GridSearchCV._check_scorers_accept_sample_weight sklearn.model_selection.GridSearchCV._format_results sklearn.model_selection.GridSearchCV._get_default_requests sklearn.model_selection.GridSearchCV._get_doc_link sklearn.model_selection.GridSearchCV._get_metadata_request sklearn.model_selection.GridSearchCV._get_param_names sklearn.model_selection.GridSearchCV._get_routed_params_for_fit sklearn.model_selection.GridSearchCV._get_scorers sklearn.model_selection.GridSearchCV._repr_html_inner sklearn.model_selection.GridSearchCV._repr_mimebundle_ sklearn.model_selection.GridSearchCV._run_search sklearn.model_selection.GridSearchCV._select_best_index sklearn.model_selection.GridSearchCV._sk_visual_block_ sklearn.model_selection.GridSearchCV._validate_params sklearn.model_selection.GridSearchCV.decision_function sklearn.model_selection.GridSearchCV.fit sklearn.model_selection.GridSearchCV.get_metadata_routing sklearn.model_selection.GridSearchCV.get_params sklearn.model_selection.GridSearchCV.inverse_transform sklearn.model_selection.GridSearchCV.predict sklearn.model_selection.GridSearchCV.predict_log_proba sklearn.model_selection.GridSearchCV.predict_proba sklearn.model_selection.GridSearchCV.score sklearn.model_selection.GridSearchCV.score_samples sklearn.model_selection.GridSearchCV.set_params sklearn.model_selection.GridSearchCV.transform 
    :add-heading: Gallery examples
    :heading-level: -
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diff --git a/dev/_sources/modules/generated/sklearn.model_selection.HalvingGridSearchCV.rst.txt b/dev/_sources/modules/generated/sklearn.model_selection.HalvingGridSearchCV.rst.txt
index 7679d5904c729..1134825745424 100644
--- a/dev/_sources/modules/generated/sklearn.model_selection.HalvingGridSearchCV.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.model_selection.HalvingGridSearchCV.rst.txt
@@ -8,6 +8,6 @@ HalvingGridSearchCV
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.model_selection.HalvingGridSearchCV sklearn.model_selection.HalvingGridSearchCV.__delattr__ sklearn.model_selection.HalvingGridSearchCV.__dir__ sklearn.model_selection.HalvingGridSearchCV.__eq__ sklearn.model_selection.HalvingGridSearchCV.__format__ sklearn.model_selection.HalvingGridSearchCV.__ge__ sklearn.model_selection.HalvingGridSearchCV.__getattribute__ sklearn.model_selection.HalvingGridSearchCV.__getstate__ sklearn.model_selection.HalvingGridSearchCV.__gt__ sklearn.model_selection.HalvingGridSearchCV.__hash__ sklearn.model_selection.HalvingGridSearchCV.__init__ sklearn.model_selection.HalvingGridSearchCV.__init_subclass__ sklearn.model_selection.HalvingGridSearchCV.__le__ sklearn.model_selection.HalvingGridSearchCV.__lt__ sklearn.model_selection.HalvingGridSearchCV.__ne__ sklearn.model_selection.HalvingGridSearchCV.__new__ sklearn.model_selection.HalvingGridSearchCV.__reduce__ sklearn.model_selection.HalvingGridSearchCV.__reduce_ex__ sklearn.model_selection.HalvingGridSearchCV.__repr__ sklearn.model_selection.HalvingGridSearchCV.__setattr__ sklearn.model_selection.HalvingGridSearchCV.__setstate__ sklearn.model_selection.HalvingGridSearchCV.__sizeof__ sklearn.model_selection.HalvingGridSearchCV.__sklearn_clone__ sklearn.model_selection.HalvingGridSearchCV.__sklearn_tags__ sklearn.model_selection.HalvingGridSearchCV.__str__ sklearn.model_selection.HalvingGridSearchCV.__subclasshook__ sklearn.model_selection.HalvingGridSearchCV._build_request_for_signature sklearn.model_selection.HalvingGridSearchCV._check_feature_names sklearn.model_selection.HalvingGridSearchCV._check_input_parameters sklearn.model_selection.HalvingGridSearchCV._check_n_features sklearn.model_selection.HalvingGridSearchCV._check_refit_for_multimetric sklearn.model_selection.HalvingGridSearchCV._check_scorers_accept_sample_weight sklearn.model_selection.HalvingGridSearchCV._format_results sklearn.model_selection.HalvingGridSearchCV._generate_candidate_params sklearn.model_selection.HalvingGridSearchCV._get_default_requests sklearn.model_selection.HalvingGridSearchCV._get_doc_link sklearn.model_selection.HalvingGridSearchCV._get_metadata_request sklearn.model_selection.HalvingGridSearchCV._get_param_names sklearn.model_selection.HalvingGridSearchCV._get_routed_params_for_fit sklearn.model_selection.HalvingGridSearchCV._get_scorers sklearn.model_selection.HalvingGridSearchCV._get_tags sklearn.model_selection.HalvingGridSearchCV._more_tags sklearn.model_selection.HalvingGridSearchCV._repr_html_inner sklearn.model_selection.HalvingGridSearchCV._repr_mimebundle_ sklearn.model_selection.HalvingGridSearchCV._run_search sklearn.model_selection.HalvingGridSearchCV._select_best_index sklearn.model_selection.HalvingGridSearchCV._sk_visual_block_ sklearn.model_selection.HalvingGridSearchCV._validate_data sklearn.model_selection.HalvingGridSearchCV._validate_params sklearn.model_selection.HalvingGridSearchCV.decision_function sklearn.model_selection.HalvingGridSearchCV.fit sklearn.model_selection.HalvingGridSearchCV.get_metadata_routing sklearn.model_selection.HalvingGridSearchCV.get_params sklearn.model_selection.HalvingGridSearchCV.inverse_transform sklearn.model_selection.HalvingGridSearchCV.predict sklearn.model_selection.HalvingGridSearchCV.predict_log_proba sklearn.model_selection.HalvingGridSearchCV.predict_proba sklearn.model_selection.HalvingGridSearchCV.score sklearn.model_selection.HalvingGridSearchCV.score_samples sklearn.model_selection.HalvingGridSearchCV.set_params sklearn.model_selection.HalvingGridSearchCV.transform 
+.. minigallery:: sklearn.model_selection.HalvingGridSearchCV sklearn.model_selection.HalvingGridSearchCV.__delattr__ sklearn.model_selection.HalvingGridSearchCV.__dir__ sklearn.model_selection.HalvingGridSearchCV.__eq__ sklearn.model_selection.HalvingGridSearchCV.__format__ sklearn.model_selection.HalvingGridSearchCV.__ge__ sklearn.model_selection.HalvingGridSearchCV.__getattribute__ sklearn.model_selection.HalvingGridSearchCV.__getstate__ sklearn.model_selection.HalvingGridSearchCV.__gt__ sklearn.model_selection.HalvingGridSearchCV.__hash__ sklearn.model_selection.HalvingGridSearchCV.__init__ sklearn.model_selection.HalvingGridSearchCV.__init_subclass__ sklearn.model_selection.HalvingGridSearchCV.__le__ sklearn.model_selection.HalvingGridSearchCV.__lt__ sklearn.model_selection.HalvingGridSearchCV.__ne__ sklearn.model_selection.HalvingGridSearchCV.__new__ sklearn.model_selection.HalvingGridSearchCV.__reduce__ sklearn.model_selection.HalvingGridSearchCV.__reduce_ex__ sklearn.model_selection.HalvingGridSearchCV.__repr__ sklearn.model_selection.HalvingGridSearchCV.__setattr__ sklearn.model_selection.HalvingGridSearchCV.__setstate__ sklearn.model_selection.HalvingGridSearchCV.__sizeof__ sklearn.model_selection.HalvingGridSearchCV.__sklearn_clone__ sklearn.model_selection.HalvingGridSearchCV.__sklearn_tags__ sklearn.model_selection.HalvingGridSearchCV.__str__ sklearn.model_selection.HalvingGridSearchCV.__subclasshook__ sklearn.model_selection.HalvingGridSearchCV._build_request_for_signature sklearn.model_selection.HalvingGridSearchCV._check_input_parameters sklearn.model_selection.HalvingGridSearchCV._check_refit_for_multimetric sklearn.model_selection.HalvingGridSearchCV._check_scorers_accept_sample_weight sklearn.model_selection.HalvingGridSearchCV._format_results sklearn.model_selection.HalvingGridSearchCV._generate_candidate_params sklearn.model_selection.HalvingGridSearchCV._get_default_requests sklearn.model_selection.HalvingGridSearchCV._get_doc_link sklearn.model_selection.HalvingGridSearchCV._get_metadata_request sklearn.model_selection.HalvingGridSearchCV._get_param_names sklearn.model_selection.HalvingGridSearchCV._get_routed_params_for_fit sklearn.model_selection.HalvingGridSearchCV._get_scorers sklearn.model_selection.HalvingGridSearchCV._repr_html_inner sklearn.model_selection.HalvingGridSearchCV._repr_mimebundle_ sklearn.model_selection.HalvingGridSearchCV._run_search sklearn.model_selection.HalvingGridSearchCV._select_best_index sklearn.model_selection.HalvingGridSearchCV._sk_visual_block_ sklearn.model_selection.HalvingGridSearchCV._validate_params sklearn.model_selection.HalvingGridSearchCV.decision_function sklearn.model_selection.HalvingGridSearchCV.fit sklearn.model_selection.HalvingGridSearchCV.get_metadata_routing sklearn.model_selection.HalvingGridSearchCV.get_params sklearn.model_selection.HalvingGridSearchCV.inverse_transform sklearn.model_selection.HalvingGridSearchCV.predict sklearn.model_selection.HalvingGridSearchCV.predict_log_proba sklearn.model_selection.HalvingGridSearchCV.predict_proba sklearn.model_selection.HalvingGridSearchCV.score sklearn.model_selection.HalvingGridSearchCV.score_samples sklearn.model_selection.HalvingGridSearchCV.set_params sklearn.model_selection.HalvingGridSearchCV.transform 
    :add-heading: Gallery examples
    :heading-level: -
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diff --git a/dev/_sources/modules/generated/sklearn.model_selection.HalvingRandomSearchCV.rst.txt b/dev/_sources/modules/generated/sklearn.model_selection.HalvingRandomSearchCV.rst.txt
index 92a7f9a0172f3..14d7ff6780759 100644
--- a/dev/_sources/modules/generated/sklearn.model_selection.HalvingRandomSearchCV.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.model_selection.HalvingRandomSearchCV.rst.txt
@@ -8,6 +8,6 @@ HalvingRandomSearchCV
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.model_selection.HalvingRandomSearchCV sklearn.model_selection.HalvingRandomSearchCV.__delattr__ sklearn.model_selection.HalvingRandomSearchCV.__dir__ sklearn.model_selection.HalvingRandomSearchCV.__eq__ sklearn.model_selection.HalvingRandomSearchCV.__format__ sklearn.model_selection.HalvingRandomSearchCV.__ge__ sklearn.model_selection.HalvingRandomSearchCV.__getattribute__ sklearn.model_selection.HalvingRandomSearchCV.__getstate__ sklearn.model_selection.HalvingRandomSearchCV.__gt__ sklearn.model_selection.HalvingRandomSearchCV.__hash__ sklearn.model_selection.HalvingRandomSearchCV.__init__ sklearn.model_selection.HalvingRandomSearchCV.__init_subclass__ sklearn.model_selection.HalvingRandomSearchCV.__le__ sklearn.model_selection.HalvingRandomSearchCV.__lt__ sklearn.model_selection.HalvingRandomSearchCV.__ne__ sklearn.model_selection.HalvingRandomSearchCV.__new__ sklearn.model_selection.HalvingRandomSearchCV.__reduce__ sklearn.model_selection.HalvingRandomSearchCV.__reduce_ex__ sklearn.model_selection.HalvingRandomSearchCV.__repr__ sklearn.model_selection.HalvingRandomSearchCV.__setattr__ sklearn.model_selection.HalvingRandomSearchCV.__setstate__ sklearn.model_selection.HalvingRandomSearchCV.__sizeof__ sklearn.model_selection.HalvingRandomSearchCV.__sklearn_clone__ sklearn.model_selection.HalvingRandomSearchCV.__sklearn_tags__ sklearn.model_selection.HalvingRandomSearchCV.__str__ sklearn.model_selection.HalvingRandomSearchCV.__subclasshook__ sklearn.model_selection.HalvingRandomSearchCV._build_request_for_signature sklearn.model_selection.HalvingRandomSearchCV._check_feature_names sklearn.model_selection.HalvingRandomSearchCV._check_input_parameters sklearn.model_selection.HalvingRandomSearchCV._check_n_features sklearn.model_selection.HalvingRandomSearchCV._check_refit_for_multimetric sklearn.model_selection.HalvingRandomSearchCV._check_scorers_accept_sample_weight sklearn.model_selection.HalvingRandomSearchCV._format_results sklearn.model_selection.HalvingRandomSearchCV._generate_candidate_params sklearn.model_selection.HalvingRandomSearchCV._get_default_requests sklearn.model_selection.HalvingRandomSearchCV._get_doc_link sklearn.model_selection.HalvingRandomSearchCV._get_metadata_request sklearn.model_selection.HalvingRandomSearchCV._get_param_names sklearn.model_selection.HalvingRandomSearchCV._get_routed_params_for_fit sklearn.model_selection.HalvingRandomSearchCV._get_scorers sklearn.model_selection.HalvingRandomSearchCV._get_tags sklearn.model_selection.HalvingRandomSearchCV._more_tags sklearn.model_selection.HalvingRandomSearchCV._repr_html_inner sklearn.model_selection.HalvingRandomSearchCV._repr_mimebundle_ sklearn.model_selection.HalvingRandomSearchCV._run_search sklearn.model_selection.HalvingRandomSearchCV._select_best_index sklearn.model_selection.HalvingRandomSearchCV._sk_visual_block_ sklearn.model_selection.HalvingRandomSearchCV._validate_data sklearn.model_selection.HalvingRandomSearchCV._validate_params sklearn.model_selection.HalvingRandomSearchCV.decision_function sklearn.model_selection.HalvingRandomSearchCV.fit sklearn.model_selection.HalvingRandomSearchCV.get_metadata_routing sklearn.model_selection.HalvingRandomSearchCV.get_params sklearn.model_selection.HalvingRandomSearchCV.inverse_transform sklearn.model_selection.HalvingRandomSearchCV.predict sklearn.model_selection.HalvingRandomSearchCV.predict_log_proba sklearn.model_selection.HalvingRandomSearchCV.predict_proba sklearn.model_selection.HalvingRandomSearchCV.score sklearn.model_selection.HalvingRandomSearchCV.score_samples sklearn.model_selection.HalvingRandomSearchCV.set_params sklearn.model_selection.HalvingRandomSearchCV.transform 
+.. minigallery:: sklearn.model_selection.HalvingRandomSearchCV sklearn.model_selection.HalvingRandomSearchCV.__delattr__ sklearn.model_selection.HalvingRandomSearchCV.__dir__ sklearn.model_selection.HalvingRandomSearchCV.__eq__ sklearn.model_selection.HalvingRandomSearchCV.__format__ sklearn.model_selection.HalvingRandomSearchCV.__ge__ sklearn.model_selection.HalvingRandomSearchCV.__getattribute__ sklearn.model_selection.HalvingRandomSearchCV.__getstate__ sklearn.model_selection.HalvingRandomSearchCV.__gt__ sklearn.model_selection.HalvingRandomSearchCV.__hash__ sklearn.model_selection.HalvingRandomSearchCV.__init__ sklearn.model_selection.HalvingRandomSearchCV.__init_subclass__ sklearn.model_selection.HalvingRandomSearchCV.__le__ sklearn.model_selection.HalvingRandomSearchCV.__lt__ sklearn.model_selection.HalvingRandomSearchCV.__ne__ sklearn.model_selection.HalvingRandomSearchCV.__new__ sklearn.model_selection.HalvingRandomSearchCV.__reduce__ sklearn.model_selection.HalvingRandomSearchCV.__reduce_ex__ sklearn.model_selection.HalvingRandomSearchCV.__repr__ sklearn.model_selection.HalvingRandomSearchCV.__setattr__ sklearn.model_selection.HalvingRandomSearchCV.__setstate__ sklearn.model_selection.HalvingRandomSearchCV.__sizeof__ sklearn.model_selection.HalvingRandomSearchCV.__sklearn_clone__ sklearn.model_selection.HalvingRandomSearchCV.__sklearn_tags__ sklearn.model_selection.HalvingRandomSearchCV.__str__ sklearn.model_selection.HalvingRandomSearchCV.__subclasshook__ sklearn.model_selection.HalvingRandomSearchCV._build_request_for_signature sklearn.model_selection.HalvingRandomSearchCV._check_input_parameters sklearn.model_selection.HalvingRandomSearchCV._check_refit_for_multimetric sklearn.model_selection.HalvingRandomSearchCV._check_scorers_accept_sample_weight sklearn.model_selection.HalvingRandomSearchCV._format_results sklearn.model_selection.HalvingRandomSearchCV._generate_candidate_params sklearn.model_selection.HalvingRandomSearchCV._get_default_requests sklearn.model_selection.HalvingRandomSearchCV._get_doc_link sklearn.model_selection.HalvingRandomSearchCV._get_metadata_request sklearn.model_selection.HalvingRandomSearchCV._get_param_names sklearn.model_selection.HalvingRandomSearchCV._get_routed_params_for_fit sklearn.model_selection.HalvingRandomSearchCV._get_scorers sklearn.model_selection.HalvingRandomSearchCV._repr_html_inner sklearn.model_selection.HalvingRandomSearchCV._repr_mimebundle_ sklearn.model_selection.HalvingRandomSearchCV._run_search sklearn.model_selection.HalvingRandomSearchCV._select_best_index sklearn.model_selection.HalvingRandomSearchCV._sk_visual_block_ sklearn.model_selection.HalvingRandomSearchCV._validate_params sklearn.model_selection.HalvingRandomSearchCV.decision_function sklearn.model_selection.HalvingRandomSearchCV.fit sklearn.model_selection.HalvingRandomSearchCV.get_metadata_routing sklearn.model_selection.HalvingRandomSearchCV.get_params sklearn.model_selection.HalvingRandomSearchCV.inverse_transform sklearn.model_selection.HalvingRandomSearchCV.predict sklearn.model_selection.HalvingRandomSearchCV.predict_log_proba sklearn.model_selection.HalvingRandomSearchCV.predict_proba sklearn.model_selection.HalvingRandomSearchCV.score sklearn.model_selection.HalvingRandomSearchCV.score_samples sklearn.model_selection.HalvingRandomSearchCV.set_params sklearn.model_selection.HalvingRandomSearchCV.transform 
    :add-heading: Gallery examples
    :heading-level: -
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diff --git a/dev/_sources/modules/generated/sklearn.model_selection.RandomizedSearchCV.rst.txt b/dev/_sources/modules/generated/sklearn.model_selection.RandomizedSearchCV.rst.txt
index 65991621cb623..2fa51121b1ba4 100644
--- a/dev/_sources/modules/generated/sklearn.model_selection.RandomizedSearchCV.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.model_selection.RandomizedSearchCV.rst.txt
@@ -8,6 +8,6 @@ RandomizedSearchCV
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.model_selection.RandomizedSearchCV sklearn.model_selection.RandomizedSearchCV.__delattr__ sklearn.model_selection.RandomizedSearchCV.__dir__ sklearn.model_selection.RandomizedSearchCV.__eq__ sklearn.model_selection.RandomizedSearchCV.__format__ sklearn.model_selection.RandomizedSearchCV.__ge__ sklearn.model_selection.RandomizedSearchCV.__getattribute__ sklearn.model_selection.RandomizedSearchCV.__getstate__ sklearn.model_selection.RandomizedSearchCV.__gt__ sklearn.model_selection.RandomizedSearchCV.__hash__ sklearn.model_selection.RandomizedSearchCV.__init__ sklearn.model_selection.RandomizedSearchCV.__init_subclass__ sklearn.model_selection.RandomizedSearchCV.__le__ sklearn.model_selection.RandomizedSearchCV.__lt__ sklearn.model_selection.RandomizedSearchCV.__ne__ sklearn.model_selection.RandomizedSearchCV.__new__ sklearn.model_selection.RandomizedSearchCV.__reduce__ sklearn.model_selection.RandomizedSearchCV.__reduce_ex__ sklearn.model_selection.RandomizedSearchCV.__repr__ sklearn.model_selection.RandomizedSearchCV.__setattr__ sklearn.model_selection.RandomizedSearchCV.__setstate__ sklearn.model_selection.RandomizedSearchCV.__sizeof__ sklearn.model_selection.RandomizedSearchCV.__sklearn_clone__ sklearn.model_selection.RandomizedSearchCV.__sklearn_tags__ sklearn.model_selection.RandomizedSearchCV.__str__ sklearn.model_selection.RandomizedSearchCV.__subclasshook__ sklearn.model_selection.RandomizedSearchCV._build_request_for_signature sklearn.model_selection.RandomizedSearchCV._check_feature_names sklearn.model_selection.RandomizedSearchCV._check_n_features sklearn.model_selection.RandomizedSearchCV._check_refit_for_multimetric sklearn.model_selection.RandomizedSearchCV._check_scorers_accept_sample_weight sklearn.model_selection.RandomizedSearchCV._format_results sklearn.model_selection.RandomizedSearchCV._get_default_requests sklearn.model_selection.RandomizedSearchCV._get_doc_link sklearn.model_selection.RandomizedSearchCV._get_metadata_request sklearn.model_selection.RandomizedSearchCV._get_param_names sklearn.model_selection.RandomizedSearchCV._get_routed_params_for_fit sklearn.model_selection.RandomizedSearchCV._get_scorers sklearn.model_selection.RandomizedSearchCV._get_tags sklearn.model_selection.RandomizedSearchCV._more_tags sklearn.model_selection.RandomizedSearchCV._repr_html_inner sklearn.model_selection.RandomizedSearchCV._repr_mimebundle_ sklearn.model_selection.RandomizedSearchCV._run_search sklearn.model_selection.RandomizedSearchCV._select_best_index sklearn.model_selection.RandomizedSearchCV._sk_visual_block_ sklearn.model_selection.RandomizedSearchCV._validate_data sklearn.model_selection.RandomizedSearchCV._validate_params sklearn.model_selection.RandomizedSearchCV.decision_function sklearn.model_selection.RandomizedSearchCV.fit sklearn.model_selection.RandomizedSearchCV.get_metadata_routing sklearn.model_selection.RandomizedSearchCV.get_params sklearn.model_selection.RandomizedSearchCV.inverse_transform sklearn.model_selection.RandomizedSearchCV.predict sklearn.model_selection.RandomizedSearchCV.predict_log_proba sklearn.model_selection.RandomizedSearchCV.predict_proba sklearn.model_selection.RandomizedSearchCV.score sklearn.model_selection.RandomizedSearchCV.score_samples sklearn.model_selection.RandomizedSearchCV.set_params sklearn.model_selection.RandomizedSearchCV.transform 
+.. minigallery:: sklearn.model_selection.RandomizedSearchCV sklearn.model_selection.RandomizedSearchCV.__delattr__ sklearn.model_selection.RandomizedSearchCV.__dir__ sklearn.model_selection.RandomizedSearchCV.__eq__ sklearn.model_selection.RandomizedSearchCV.__format__ sklearn.model_selection.RandomizedSearchCV.__ge__ sklearn.model_selection.RandomizedSearchCV.__getattribute__ sklearn.model_selection.RandomizedSearchCV.__getstate__ sklearn.model_selection.RandomizedSearchCV.__gt__ sklearn.model_selection.RandomizedSearchCV.__hash__ sklearn.model_selection.RandomizedSearchCV.__init__ sklearn.model_selection.RandomizedSearchCV.__init_subclass__ sklearn.model_selection.RandomizedSearchCV.__le__ sklearn.model_selection.RandomizedSearchCV.__lt__ sklearn.model_selection.RandomizedSearchCV.__ne__ sklearn.model_selection.RandomizedSearchCV.__new__ sklearn.model_selection.RandomizedSearchCV.__reduce__ sklearn.model_selection.RandomizedSearchCV.__reduce_ex__ sklearn.model_selection.RandomizedSearchCV.__repr__ sklearn.model_selection.RandomizedSearchCV.__setattr__ sklearn.model_selection.RandomizedSearchCV.__setstate__ sklearn.model_selection.RandomizedSearchCV.__sizeof__ sklearn.model_selection.RandomizedSearchCV.__sklearn_clone__ sklearn.model_selection.RandomizedSearchCV.__sklearn_tags__ sklearn.model_selection.RandomizedSearchCV.__str__ sklearn.model_selection.RandomizedSearchCV.__subclasshook__ sklearn.model_selection.RandomizedSearchCV._build_request_for_signature sklearn.model_selection.RandomizedSearchCV._check_refit_for_multimetric sklearn.model_selection.RandomizedSearchCV._check_scorers_accept_sample_weight sklearn.model_selection.RandomizedSearchCV._format_results sklearn.model_selection.RandomizedSearchCV._get_default_requests sklearn.model_selection.RandomizedSearchCV._get_doc_link sklearn.model_selection.RandomizedSearchCV._get_metadata_request sklearn.model_selection.RandomizedSearchCV._get_param_names sklearn.model_selection.RandomizedSearchCV._get_routed_params_for_fit sklearn.model_selection.RandomizedSearchCV._get_scorers sklearn.model_selection.RandomizedSearchCV._repr_html_inner sklearn.model_selection.RandomizedSearchCV._repr_mimebundle_ sklearn.model_selection.RandomizedSearchCV._run_search sklearn.model_selection.RandomizedSearchCV._select_best_index sklearn.model_selection.RandomizedSearchCV._sk_visual_block_ sklearn.model_selection.RandomizedSearchCV._validate_params sklearn.model_selection.RandomizedSearchCV.decision_function sklearn.model_selection.RandomizedSearchCV.fit sklearn.model_selection.RandomizedSearchCV.get_metadata_routing sklearn.model_selection.RandomizedSearchCV.get_params sklearn.model_selection.RandomizedSearchCV.inverse_transform sklearn.model_selection.RandomizedSearchCV.predict sklearn.model_selection.RandomizedSearchCV.predict_log_proba sklearn.model_selection.RandomizedSearchCV.predict_proba sklearn.model_selection.RandomizedSearchCV.score sklearn.model_selection.RandomizedSearchCV.score_samples sklearn.model_selection.RandomizedSearchCV.set_params sklearn.model_selection.RandomizedSearchCV.transform 
    :add-heading: Gallery examples
    :heading-level: -
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diff --git a/dev/_sources/modules/generated/sklearn.model_selection.TunedThresholdClassifierCV.rst.txt b/dev/_sources/modules/generated/sklearn.model_selection.TunedThresholdClassifierCV.rst.txt
index b9ce7218ee679..8ba38f7465d97 100644
--- a/dev/_sources/modules/generated/sklearn.model_selection.TunedThresholdClassifierCV.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.model_selection.TunedThresholdClassifierCV.rst.txt
@@ -8,6 +8,6 @@ TunedThresholdClassifierCV
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.model_selection.TunedThresholdClassifierCV sklearn.model_selection.TunedThresholdClassifierCV.__delattr__ sklearn.model_selection.TunedThresholdClassifierCV.__dir__ sklearn.model_selection.TunedThresholdClassifierCV.__eq__ sklearn.model_selection.TunedThresholdClassifierCV.__format__ sklearn.model_selection.TunedThresholdClassifierCV.__ge__ sklearn.model_selection.TunedThresholdClassifierCV.__getattribute__ sklearn.model_selection.TunedThresholdClassifierCV.__getstate__ sklearn.model_selection.TunedThresholdClassifierCV.__gt__ sklearn.model_selection.TunedThresholdClassifierCV.__hash__ sklearn.model_selection.TunedThresholdClassifierCV.__init__ sklearn.model_selection.TunedThresholdClassifierCV.__init_subclass__ sklearn.model_selection.TunedThresholdClassifierCV.__le__ sklearn.model_selection.TunedThresholdClassifierCV.__lt__ sklearn.model_selection.TunedThresholdClassifierCV.__ne__ sklearn.model_selection.TunedThresholdClassifierCV.__new__ sklearn.model_selection.TunedThresholdClassifierCV.__reduce__ sklearn.model_selection.TunedThresholdClassifierCV.__reduce_ex__ sklearn.model_selection.TunedThresholdClassifierCV.__repr__ sklearn.model_selection.TunedThresholdClassifierCV.__setattr__ sklearn.model_selection.TunedThresholdClassifierCV.__setstate__ sklearn.model_selection.TunedThresholdClassifierCV.__sizeof__ sklearn.model_selection.TunedThresholdClassifierCV.__sklearn_clone__ sklearn.model_selection.TunedThresholdClassifierCV.__sklearn_tags__ sklearn.model_selection.TunedThresholdClassifierCV.__str__ sklearn.model_selection.TunedThresholdClassifierCV.__subclasshook__ sklearn.model_selection.TunedThresholdClassifierCV._build_request_for_signature sklearn.model_selection.TunedThresholdClassifierCV._check_feature_names sklearn.model_selection.TunedThresholdClassifierCV._check_n_features sklearn.model_selection.TunedThresholdClassifierCV._fit sklearn.model_selection.TunedThresholdClassifierCV._get_curve_scorer sklearn.model_selection.TunedThresholdClassifierCV._get_default_requests sklearn.model_selection.TunedThresholdClassifierCV._get_doc_link sklearn.model_selection.TunedThresholdClassifierCV._get_metadata_request sklearn.model_selection.TunedThresholdClassifierCV._get_param_names sklearn.model_selection.TunedThresholdClassifierCV._get_response_method sklearn.model_selection.TunedThresholdClassifierCV._get_tags sklearn.model_selection.TunedThresholdClassifierCV._more_tags sklearn.model_selection.TunedThresholdClassifierCV._repr_html_inner sklearn.model_selection.TunedThresholdClassifierCV._repr_mimebundle_ sklearn.model_selection.TunedThresholdClassifierCV._validate_data sklearn.model_selection.TunedThresholdClassifierCV._validate_params sklearn.model_selection.TunedThresholdClassifierCV.decision_function sklearn.model_selection.TunedThresholdClassifierCV.fit sklearn.model_selection.TunedThresholdClassifierCV.get_metadata_routing sklearn.model_selection.TunedThresholdClassifierCV.get_params sklearn.model_selection.TunedThresholdClassifierCV.predict sklearn.model_selection.TunedThresholdClassifierCV.predict_log_proba sklearn.model_selection.TunedThresholdClassifierCV.predict_proba sklearn.model_selection.TunedThresholdClassifierCV.score sklearn.model_selection.TunedThresholdClassifierCV.set_params sklearn.model_selection.TunedThresholdClassifierCV.set_score_request 
+.. minigallery:: sklearn.model_selection.TunedThresholdClassifierCV sklearn.model_selection.TunedThresholdClassifierCV.__delattr__ sklearn.model_selection.TunedThresholdClassifierCV.__dir__ sklearn.model_selection.TunedThresholdClassifierCV.__eq__ sklearn.model_selection.TunedThresholdClassifierCV.__format__ sklearn.model_selection.TunedThresholdClassifierCV.__ge__ sklearn.model_selection.TunedThresholdClassifierCV.__getattribute__ sklearn.model_selection.TunedThresholdClassifierCV.__getstate__ sklearn.model_selection.TunedThresholdClassifierCV.__gt__ sklearn.model_selection.TunedThresholdClassifierCV.__hash__ sklearn.model_selection.TunedThresholdClassifierCV.__init__ sklearn.model_selection.TunedThresholdClassifierCV.__init_subclass__ sklearn.model_selection.TunedThresholdClassifierCV.__le__ sklearn.model_selection.TunedThresholdClassifierCV.__lt__ sklearn.model_selection.TunedThresholdClassifierCV.__ne__ sklearn.model_selection.TunedThresholdClassifierCV.__new__ sklearn.model_selection.TunedThresholdClassifierCV.__reduce__ sklearn.model_selection.TunedThresholdClassifierCV.__reduce_ex__ sklearn.model_selection.TunedThresholdClassifierCV.__repr__ sklearn.model_selection.TunedThresholdClassifierCV.__setattr__ sklearn.model_selection.TunedThresholdClassifierCV.__setstate__ sklearn.model_selection.TunedThresholdClassifierCV.__sizeof__ sklearn.model_selection.TunedThresholdClassifierCV.__sklearn_clone__ sklearn.model_selection.TunedThresholdClassifierCV.__sklearn_tags__ sklearn.model_selection.TunedThresholdClassifierCV.__str__ sklearn.model_selection.TunedThresholdClassifierCV.__subclasshook__ sklearn.model_selection.TunedThresholdClassifierCV._build_request_for_signature sklearn.model_selection.TunedThresholdClassifierCV._fit sklearn.model_selection.TunedThresholdClassifierCV._get_curve_scorer sklearn.model_selection.TunedThresholdClassifierCV._get_default_requests sklearn.model_selection.TunedThresholdClassifierCV._get_doc_link sklearn.model_selection.TunedThresholdClassifierCV._get_metadata_request sklearn.model_selection.TunedThresholdClassifierCV._get_param_names sklearn.model_selection.TunedThresholdClassifierCV._get_response_method sklearn.model_selection.TunedThresholdClassifierCV._repr_html_inner sklearn.model_selection.TunedThresholdClassifierCV._repr_mimebundle_ sklearn.model_selection.TunedThresholdClassifierCV._validate_params sklearn.model_selection.TunedThresholdClassifierCV.decision_function sklearn.model_selection.TunedThresholdClassifierCV.fit sklearn.model_selection.TunedThresholdClassifierCV.get_metadata_routing sklearn.model_selection.TunedThresholdClassifierCV.get_params sklearn.model_selection.TunedThresholdClassifierCV.predict sklearn.model_selection.TunedThresholdClassifierCV.predict_log_proba sklearn.model_selection.TunedThresholdClassifierCV.predict_proba sklearn.model_selection.TunedThresholdClassifierCV.score sklearn.model_selection.TunedThresholdClassifierCV.set_params sklearn.model_selection.TunedThresholdClassifierCV.set_score_request 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.multiclass.OneVsOneClassifier.rst.txt b/dev/_sources/modules/generated/sklearn.multiclass.OneVsOneClassifier.rst.txt
index 65c2ea2c2418c..04781c434397c 100644
--- a/dev/_sources/modules/generated/sklearn.multiclass.OneVsOneClassifier.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.multiclass.OneVsOneClassifier.rst.txt
@@ -8,6 +8,6 @@ OneVsOneClassifier
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.multiclass.OneVsOneClassifier sklearn.multiclass.OneVsOneClassifier.__delattr__ sklearn.multiclass.OneVsOneClassifier.__dir__ sklearn.multiclass.OneVsOneClassifier.__eq__ sklearn.multiclass.OneVsOneClassifier.__format__ sklearn.multiclass.OneVsOneClassifier.__ge__ sklearn.multiclass.OneVsOneClassifier.__getattribute__ sklearn.multiclass.OneVsOneClassifier.__getstate__ sklearn.multiclass.OneVsOneClassifier.__gt__ sklearn.multiclass.OneVsOneClassifier.__hash__ sklearn.multiclass.OneVsOneClassifier.__init__ sklearn.multiclass.OneVsOneClassifier.__init_subclass__ sklearn.multiclass.OneVsOneClassifier.__le__ sklearn.multiclass.OneVsOneClassifier.__lt__ sklearn.multiclass.OneVsOneClassifier.__ne__ sklearn.multiclass.OneVsOneClassifier.__new__ sklearn.multiclass.OneVsOneClassifier.__reduce__ sklearn.multiclass.OneVsOneClassifier.__reduce_ex__ sklearn.multiclass.OneVsOneClassifier.__repr__ sklearn.multiclass.OneVsOneClassifier.__setattr__ sklearn.multiclass.OneVsOneClassifier.__setstate__ sklearn.multiclass.OneVsOneClassifier.__sizeof__ sklearn.multiclass.OneVsOneClassifier.__sklearn_clone__ sklearn.multiclass.OneVsOneClassifier.__sklearn_tags__ sklearn.multiclass.OneVsOneClassifier.__str__ sklearn.multiclass.OneVsOneClassifier.__subclasshook__ sklearn.multiclass.OneVsOneClassifier._build_request_for_signature sklearn.multiclass.OneVsOneClassifier._check_feature_names sklearn.multiclass.OneVsOneClassifier._check_n_features sklearn.multiclass.OneVsOneClassifier._get_default_requests sklearn.multiclass.OneVsOneClassifier._get_doc_link sklearn.multiclass.OneVsOneClassifier._get_metadata_request sklearn.multiclass.OneVsOneClassifier._get_param_names sklearn.multiclass.OneVsOneClassifier._get_tags sklearn.multiclass.OneVsOneClassifier._more_tags sklearn.multiclass.OneVsOneClassifier._repr_html_inner sklearn.multiclass.OneVsOneClassifier._repr_mimebundle_ sklearn.multiclass.OneVsOneClassifier._validate_data sklearn.multiclass.OneVsOneClassifier._validate_params sklearn.multiclass.OneVsOneClassifier.decision_function sklearn.multiclass.OneVsOneClassifier.fit sklearn.multiclass.OneVsOneClassifier.get_metadata_routing sklearn.multiclass.OneVsOneClassifier.get_params sklearn.multiclass.OneVsOneClassifier.partial_fit sklearn.multiclass.OneVsOneClassifier.predict sklearn.multiclass.OneVsOneClassifier.score sklearn.multiclass.OneVsOneClassifier.set_params sklearn.multiclass.OneVsOneClassifier.set_partial_fit_request sklearn.multiclass.OneVsOneClassifier.set_score_request 
+.. minigallery:: sklearn.multiclass.OneVsOneClassifier sklearn.multiclass.OneVsOneClassifier.__delattr__ sklearn.multiclass.OneVsOneClassifier.__dir__ sklearn.multiclass.OneVsOneClassifier.__eq__ sklearn.multiclass.OneVsOneClassifier.__format__ sklearn.multiclass.OneVsOneClassifier.__ge__ sklearn.multiclass.OneVsOneClassifier.__getattribute__ sklearn.multiclass.OneVsOneClassifier.__getstate__ sklearn.multiclass.OneVsOneClassifier.__gt__ sklearn.multiclass.OneVsOneClassifier.__hash__ sklearn.multiclass.OneVsOneClassifier.__init__ sklearn.multiclass.OneVsOneClassifier.__init_subclass__ sklearn.multiclass.OneVsOneClassifier.__le__ sklearn.multiclass.OneVsOneClassifier.__lt__ sklearn.multiclass.OneVsOneClassifier.__ne__ sklearn.multiclass.OneVsOneClassifier.__new__ sklearn.multiclass.OneVsOneClassifier.__reduce__ sklearn.multiclass.OneVsOneClassifier.__reduce_ex__ sklearn.multiclass.OneVsOneClassifier.__repr__ sklearn.multiclass.OneVsOneClassifier.__setattr__ sklearn.multiclass.OneVsOneClassifier.__setstate__ sklearn.multiclass.OneVsOneClassifier.__sizeof__ sklearn.multiclass.OneVsOneClassifier.__sklearn_clone__ sklearn.multiclass.OneVsOneClassifier.__sklearn_tags__ sklearn.multiclass.OneVsOneClassifier.__str__ sklearn.multiclass.OneVsOneClassifier.__subclasshook__ sklearn.multiclass.OneVsOneClassifier._build_request_for_signature sklearn.multiclass.OneVsOneClassifier._get_default_requests sklearn.multiclass.OneVsOneClassifier._get_doc_link sklearn.multiclass.OneVsOneClassifier._get_metadata_request sklearn.multiclass.OneVsOneClassifier._get_param_names sklearn.multiclass.OneVsOneClassifier._repr_html_inner sklearn.multiclass.OneVsOneClassifier._repr_mimebundle_ sklearn.multiclass.OneVsOneClassifier._validate_params sklearn.multiclass.OneVsOneClassifier.decision_function sklearn.multiclass.OneVsOneClassifier.fit sklearn.multiclass.OneVsOneClassifier.get_metadata_routing sklearn.multiclass.OneVsOneClassifier.get_params sklearn.multiclass.OneVsOneClassifier.partial_fit sklearn.multiclass.OneVsOneClassifier.predict sklearn.multiclass.OneVsOneClassifier.score sklearn.multiclass.OneVsOneClassifier.set_params sklearn.multiclass.OneVsOneClassifier.set_partial_fit_request sklearn.multiclass.OneVsOneClassifier.set_score_request 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.multiclass.OneVsRestClassifier.rst.txt b/dev/_sources/modules/generated/sklearn.multiclass.OneVsRestClassifier.rst.txt
index 52b1b65469521..e734c6e63f316 100644
--- a/dev/_sources/modules/generated/sklearn.multiclass.OneVsRestClassifier.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.multiclass.OneVsRestClassifier.rst.txt
@@ -8,6 +8,6 @@ OneVsRestClassifier
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.multiclass.OneVsRestClassifier sklearn.multiclass.OneVsRestClassifier.__delattr__ sklearn.multiclass.OneVsRestClassifier.__dir__ sklearn.multiclass.OneVsRestClassifier.__eq__ sklearn.multiclass.OneVsRestClassifier.__format__ sklearn.multiclass.OneVsRestClassifier.__ge__ sklearn.multiclass.OneVsRestClassifier.__getattribute__ sklearn.multiclass.OneVsRestClassifier.__getstate__ sklearn.multiclass.OneVsRestClassifier.__gt__ sklearn.multiclass.OneVsRestClassifier.__hash__ sklearn.multiclass.OneVsRestClassifier.__init__ sklearn.multiclass.OneVsRestClassifier.__init_subclass__ sklearn.multiclass.OneVsRestClassifier.__le__ sklearn.multiclass.OneVsRestClassifier.__lt__ sklearn.multiclass.OneVsRestClassifier.__ne__ sklearn.multiclass.OneVsRestClassifier.__new__ sklearn.multiclass.OneVsRestClassifier.__reduce__ sklearn.multiclass.OneVsRestClassifier.__reduce_ex__ sklearn.multiclass.OneVsRestClassifier.__repr__ sklearn.multiclass.OneVsRestClassifier.__setattr__ sklearn.multiclass.OneVsRestClassifier.__setstate__ sklearn.multiclass.OneVsRestClassifier.__sizeof__ sklearn.multiclass.OneVsRestClassifier.__sklearn_clone__ sklearn.multiclass.OneVsRestClassifier.__sklearn_tags__ sklearn.multiclass.OneVsRestClassifier.__str__ sklearn.multiclass.OneVsRestClassifier.__subclasshook__ sklearn.multiclass.OneVsRestClassifier._build_request_for_signature sklearn.multiclass.OneVsRestClassifier._check_feature_names sklearn.multiclass.OneVsRestClassifier._check_n_features sklearn.multiclass.OneVsRestClassifier._get_default_requests sklearn.multiclass.OneVsRestClassifier._get_doc_link sklearn.multiclass.OneVsRestClassifier._get_metadata_request sklearn.multiclass.OneVsRestClassifier._get_param_names sklearn.multiclass.OneVsRestClassifier._get_tags sklearn.multiclass.OneVsRestClassifier._more_tags sklearn.multiclass.OneVsRestClassifier._repr_html_inner sklearn.multiclass.OneVsRestClassifier._repr_mimebundle_ sklearn.multiclass.OneVsRestClassifier._validate_data sklearn.multiclass.OneVsRestClassifier._validate_params sklearn.multiclass.OneVsRestClassifier.decision_function sklearn.multiclass.OneVsRestClassifier.fit sklearn.multiclass.OneVsRestClassifier.get_metadata_routing sklearn.multiclass.OneVsRestClassifier.get_params sklearn.multiclass.OneVsRestClassifier.partial_fit sklearn.multiclass.OneVsRestClassifier.predict sklearn.multiclass.OneVsRestClassifier.predict_proba sklearn.multiclass.OneVsRestClassifier.score sklearn.multiclass.OneVsRestClassifier.set_params sklearn.multiclass.OneVsRestClassifier.set_partial_fit_request sklearn.multiclass.OneVsRestClassifier.set_score_request 
+.. minigallery:: sklearn.multiclass.OneVsRestClassifier sklearn.multiclass.OneVsRestClassifier.__delattr__ sklearn.multiclass.OneVsRestClassifier.__dir__ sklearn.multiclass.OneVsRestClassifier.__eq__ sklearn.multiclass.OneVsRestClassifier.__format__ sklearn.multiclass.OneVsRestClassifier.__ge__ sklearn.multiclass.OneVsRestClassifier.__getattribute__ sklearn.multiclass.OneVsRestClassifier.__getstate__ sklearn.multiclass.OneVsRestClassifier.__gt__ sklearn.multiclass.OneVsRestClassifier.__hash__ sklearn.multiclass.OneVsRestClassifier.__init__ sklearn.multiclass.OneVsRestClassifier.__init_subclass__ sklearn.multiclass.OneVsRestClassifier.__le__ sklearn.multiclass.OneVsRestClassifier.__lt__ sklearn.multiclass.OneVsRestClassifier.__ne__ sklearn.multiclass.OneVsRestClassifier.__new__ sklearn.multiclass.OneVsRestClassifier.__reduce__ sklearn.multiclass.OneVsRestClassifier.__reduce_ex__ sklearn.multiclass.OneVsRestClassifier.__repr__ sklearn.multiclass.OneVsRestClassifier.__setattr__ sklearn.multiclass.OneVsRestClassifier.__setstate__ sklearn.multiclass.OneVsRestClassifier.__sizeof__ sklearn.multiclass.OneVsRestClassifier.__sklearn_clone__ sklearn.multiclass.OneVsRestClassifier.__sklearn_tags__ sklearn.multiclass.OneVsRestClassifier.__str__ sklearn.multiclass.OneVsRestClassifier.__subclasshook__ sklearn.multiclass.OneVsRestClassifier._build_request_for_signature sklearn.multiclass.OneVsRestClassifier._get_default_requests sklearn.multiclass.OneVsRestClassifier._get_doc_link sklearn.multiclass.OneVsRestClassifier._get_metadata_request sklearn.multiclass.OneVsRestClassifier._get_param_names sklearn.multiclass.OneVsRestClassifier._repr_html_inner sklearn.multiclass.OneVsRestClassifier._repr_mimebundle_ sklearn.multiclass.OneVsRestClassifier._validate_params sklearn.multiclass.OneVsRestClassifier.decision_function sklearn.multiclass.OneVsRestClassifier.fit sklearn.multiclass.OneVsRestClassifier.get_metadata_routing sklearn.multiclass.OneVsRestClassifier.get_params sklearn.multiclass.OneVsRestClassifier.partial_fit sklearn.multiclass.OneVsRestClassifier.predict sklearn.multiclass.OneVsRestClassifier.predict_proba sklearn.multiclass.OneVsRestClassifier.score sklearn.multiclass.OneVsRestClassifier.set_params sklearn.multiclass.OneVsRestClassifier.set_partial_fit_request sklearn.multiclass.OneVsRestClassifier.set_score_request 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.multiclass.OutputCodeClassifier.rst.txt b/dev/_sources/modules/generated/sklearn.multiclass.OutputCodeClassifier.rst.txt
index 1088634743bbe..40ad47e9ef65b 100644
--- a/dev/_sources/modules/generated/sklearn.multiclass.OutputCodeClassifier.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.multiclass.OutputCodeClassifier.rst.txt
@@ -8,6 +8,6 @@ OutputCodeClassifier
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.multiclass.OutputCodeClassifier sklearn.multiclass.OutputCodeClassifier.__delattr__ sklearn.multiclass.OutputCodeClassifier.__dir__ sklearn.multiclass.OutputCodeClassifier.__eq__ sklearn.multiclass.OutputCodeClassifier.__format__ sklearn.multiclass.OutputCodeClassifier.__ge__ sklearn.multiclass.OutputCodeClassifier.__getattribute__ sklearn.multiclass.OutputCodeClassifier.__getstate__ sklearn.multiclass.OutputCodeClassifier.__gt__ sklearn.multiclass.OutputCodeClassifier.__hash__ sklearn.multiclass.OutputCodeClassifier.__init__ sklearn.multiclass.OutputCodeClassifier.__init_subclass__ sklearn.multiclass.OutputCodeClassifier.__le__ sklearn.multiclass.OutputCodeClassifier.__lt__ sklearn.multiclass.OutputCodeClassifier.__ne__ sklearn.multiclass.OutputCodeClassifier.__new__ sklearn.multiclass.OutputCodeClassifier.__reduce__ sklearn.multiclass.OutputCodeClassifier.__reduce_ex__ sklearn.multiclass.OutputCodeClassifier.__repr__ sklearn.multiclass.OutputCodeClassifier.__setattr__ sklearn.multiclass.OutputCodeClassifier.__setstate__ sklearn.multiclass.OutputCodeClassifier.__sizeof__ sklearn.multiclass.OutputCodeClassifier.__sklearn_clone__ sklearn.multiclass.OutputCodeClassifier.__sklearn_tags__ sklearn.multiclass.OutputCodeClassifier.__str__ sklearn.multiclass.OutputCodeClassifier.__subclasshook__ sklearn.multiclass.OutputCodeClassifier._build_request_for_signature sklearn.multiclass.OutputCodeClassifier._check_feature_names sklearn.multiclass.OutputCodeClassifier._check_n_features sklearn.multiclass.OutputCodeClassifier._get_default_requests sklearn.multiclass.OutputCodeClassifier._get_doc_link sklearn.multiclass.OutputCodeClassifier._get_metadata_request sklearn.multiclass.OutputCodeClassifier._get_param_names sklearn.multiclass.OutputCodeClassifier._get_tags sklearn.multiclass.OutputCodeClassifier._more_tags sklearn.multiclass.OutputCodeClassifier._repr_html_inner sklearn.multiclass.OutputCodeClassifier._repr_mimebundle_ sklearn.multiclass.OutputCodeClassifier._validate_data sklearn.multiclass.OutputCodeClassifier._validate_params sklearn.multiclass.OutputCodeClassifier.fit sklearn.multiclass.OutputCodeClassifier.get_metadata_routing sklearn.multiclass.OutputCodeClassifier.get_params sklearn.multiclass.OutputCodeClassifier.predict sklearn.multiclass.OutputCodeClassifier.score sklearn.multiclass.OutputCodeClassifier.set_params sklearn.multiclass.OutputCodeClassifier.set_score_request 
+.. minigallery:: sklearn.multiclass.OutputCodeClassifier sklearn.multiclass.OutputCodeClassifier.__delattr__ sklearn.multiclass.OutputCodeClassifier.__dir__ sklearn.multiclass.OutputCodeClassifier.__eq__ sklearn.multiclass.OutputCodeClassifier.__format__ sklearn.multiclass.OutputCodeClassifier.__ge__ sklearn.multiclass.OutputCodeClassifier.__getattribute__ sklearn.multiclass.OutputCodeClassifier.__getstate__ sklearn.multiclass.OutputCodeClassifier.__gt__ sklearn.multiclass.OutputCodeClassifier.__hash__ sklearn.multiclass.OutputCodeClassifier.__init__ sklearn.multiclass.OutputCodeClassifier.__init_subclass__ sklearn.multiclass.OutputCodeClassifier.__le__ sklearn.multiclass.OutputCodeClassifier.__lt__ sklearn.multiclass.OutputCodeClassifier.__ne__ sklearn.multiclass.OutputCodeClassifier.__new__ sklearn.multiclass.OutputCodeClassifier.__reduce__ sklearn.multiclass.OutputCodeClassifier.__reduce_ex__ sklearn.multiclass.OutputCodeClassifier.__repr__ sklearn.multiclass.OutputCodeClassifier.__setattr__ sklearn.multiclass.OutputCodeClassifier.__setstate__ sklearn.multiclass.OutputCodeClassifier.__sizeof__ sklearn.multiclass.OutputCodeClassifier.__sklearn_clone__ sklearn.multiclass.OutputCodeClassifier.__sklearn_tags__ sklearn.multiclass.OutputCodeClassifier.__str__ sklearn.multiclass.OutputCodeClassifier.__subclasshook__ sklearn.multiclass.OutputCodeClassifier._build_request_for_signature sklearn.multiclass.OutputCodeClassifier._get_default_requests sklearn.multiclass.OutputCodeClassifier._get_doc_link sklearn.multiclass.OutputCodeClassifier._get_metadata_request sklearn.multiclass.OutputCodeClassifier._get_param_names sklearn.multiclass.OutputCodeClassifier._repr_html_inner sklearn.multiclass.OutputCodeClassifier._repr_mimebundle_ sklearn.multiclass.OutputCodeClassifier._validate_params sklearn.multiclass.OutputCodeClassifier.fit sklearn.multiclass.OutputCodeClassifier.get_metadata_routing sklearn.multiclass.OutputCodeClassifier.get_params sklearn.multiclass.OutputCodeClassifier.predict sklearn.multiclass.OutputCodeClassifier.score sklearn.multiclass.OutputCodeClassifier.set_params sklearn.multiclass.OutputCodeClassifier.set_score_request 
    :add-heading: Gallery examples
    :heading-level: -
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diff --git a/dev/_sources/modules/generated/sklearn.multioutput.ClassifierChain.rst.txt b/dev/_sources/modules/generated/sklearn.multioutput.ClassifierChain.rst.txt
index edfdb565897b7..c27d51b533d66 100644
--- a/dev/_sources/modules/generated/sklearn.multioutput.ClassifierChain.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.multioutput.ClassifierChain.rst.txt
@@ -8,6 +8,6 @@ ClassifierChain
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.multioutput.ClassifierChain sklearn.multioutput.ClassifierChain.__delattr__ sklearn.multioutput.ClassifierChain.__dir__ sklearn.multioutput.ClassifierChain.__eq__ sklearn.multioutput.ClassifierChain.__format__ sklearn.multioutput.ClassifierChain.__ge__ sklearn.multioutput.ClassifierChain.__getattribute__ sklearn.multioutput.ClassifierChain.__getstate__ sklearn.multioutput.ClassifierChain.__gt__ sklearn.multioutput.ClassifierChain.__hash__ sklearn.multioutput.ClassifierChain.__init__ sklearn.multioutput.ClassifierChain.__init_subclass__ sklearn.multioutput.ClassifierChain.__le__ sklearn.multioutput.ClassifierChain.__lt__ sklearn.multioutput.ClassifierChain.__ne__ sklearn.multioutput.ClassifierChain.__new__ sklearn.multioutput.ClassifierChain.__reduce__ sklearn.multioutput.ClassifierChain.__reduce_ex__ sklearn.multioutput.ClassifierChain.__repr__ sklearn.multioutput.ClassifierChain.__setattr__ sklearn.multioutput.ClassifierChain.__setstate__ sklearn.multioutput.ClassifierChain.__sizeof__ sklearn.multioutput.ClassifierChain.__sklearn_clone__ sklearn.multioutput.ClassifierChain.__sklearn_tags__ sklearn.multioutput.ClassifierChain.__str__ sklearn.multioutput.ClassifierChain.__subclasshook__ sklearn.multioutput.ClassifierChain._build_request_for_signature sklearn.multioutput.ClassifierChain._check_feature_names sklearn.multioutput.ClassifierChain._check_n_features sklearn.multioutput.ClassifierChain._get_default_requests sklearn.multioutput.ClassifierChain._get_doc_link sklearn.multioutput.ClassifierChain._get_estimator sklearn.multioutput.ClassifierChain._get_metadata_request sklearn.multioutput.ClassifierChain._get_param_names sklearn.multioutput.ClassifierChain._get_predictions sklearn.multioutput.ClassifierChain._get_tags sklearn.multioutput.ClassifierChain._log_message sklearn.multioutput.ClassifierChain._more_tags sklearn.multioutput.ClassifierChain._repr_html_inner sklearn.multioutput.ClassifierChain._repr_mimebundle_ sklearn.multioutput.ClassifierChain._validate_data sklearn.multioutput.ClassifierChain._validate_params sklearn.multioutput.ClassifierChain.decision_function sklearn.multioutput.ClassifierChain.fit sklearn.multioutput.ClassifierChain.get_metadata_routing sklearn.multioutput.ClassifierChain.get_params sklearn.multioutput.ClassifierChain.predict sklearn.multioutput.ClassifierChain.predict_log_proba sklearn.multioutput.ClassifierChain.predict_proba sklearn.multioutput.ClassifierChain.score sklearn.multioutput.ClassifierChain.set_params sklearn.multioutput.ClassifierChain.set_score_request 
+.. minigallery:: sklearn.multioutput.ClassifierChain sklearn.multioutput.ClassifierChain.__delattr__ sklearn.multioutput.ClassifierChain.__dir__ sklearn.multioutput.ClassifierChain.__eq__ sklearn.multioutput.ClassifierChain.__format__ sklearn.multioutput.ClassifierChain.__ge__ sklearn.multioutput.ClassifierChain.__getattribute__ sklearn.multioutput.ClassifierChain.__getstate__ sklearn.multioutput.ClassifierChain.__gt__ sklearn.multioutput.ClassifierChain.__hash__ sklearn.multioutput.ClassifierChain.__init__ sklearn.multioutput.ClassifierChain.__init_subclass__ sklearn.multioutput.ClassifierChain.__le__ sklearn.multioutput.ClassifierChain.__lt__ sklearn.multioutput.ClassifierChain.__ne__ sklearn.multioutput.ClassifierChain.__new__ sklearn.multioutput.ClassifierChain.__reduce__ sklearn.multioutput.ClassifierChain.__reduce_ex__ sklearn.multioutput.ClassifierChain.__repr__ sklearn.multioutput.ClassifierChain.__setattr__ sklearn.multioutput.ClassifierChain.__setstate__ sklearn.multioutput.ClassifierChain.__sizeof__ sklearn.multioutput.ClassifierChain.__sklearn_clone__ sklearn.multioutput.ClassifierChain.__sklearn_tags__ sklearn.multioutput.ClassifierChain.__str__ sklearn.multioutput.ClassifierChain.__subclasshook__ sklearn.multioutput.ClassifierChain._build_request_for_signature sklearn.multioutput.ClassifierChain._get_default_requests sklearn.multioutput.ClassifierChain._get_doc_link sklearn.multioutput.ClassifierChain._get_estimator sklearn.multioutput.ClassifierChain._get_metadata_request sklearn.multioutput.ClassifierChain._get_param_names sklearn.multioutput.ClassifierChain._get_predictions sklearn.multioutput.ClassifierChain._log_message sklearn.multioutput.ClassifierChain._repr_html_inner sklearn.multioutput.ClassifierChain._repr_mimebundle_ sklearn.multioutput.ClassifierChain._validate_params sklearn.multioutput.ClassifierChain.decision_function sklearn.multioutput.ClassifierChain.fit sklearn.multioutput.ClassifierChain.get_metadata_routing sklearn.multioutput.ClassifierChain.get_params sklearn.multioutput.ClassifierChain.predict sklearn.multioutput.ClassifierChain.predict_log_proba sklearn.multioutput.ClassifierChain.predict_proba sklearn.multioutput.ClassifierChain.score sklearn.multioutput.ClassifierChain.set_params sklearn.multioutput.ClassifierChain.set_score_request 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.multioutput.MultiOutputClassifier.rst.txt b/dev/_sources/modules/generated/sklearn.multioutput.MultiOutputClassifier.rst.txt
index c4144c378a0a6..2469c5ad9e7fc 100644
--- a/dev/_sources/modules/generated/sklearn.multioutput.MultiOutputClassifier.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.multioutput.MultiOutputClassifier.rst.txt
@@ -8,6 +8,6 @@ MultiOutputClassifier
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.multioutput.MultiOutputClassifier sklearn.multioutput.MultiOutputClassifier.__delattr__ sklearn.multioutput.MultiOutputClassifier.__dir__ sklearn.multioutput.MultiOutputClassifier.__eq__ sklearn.multioutput.MultiOutputClassifier.__format__ sklearn.multioutput.MultiOutputClassifier.__ge__ sklearn.multioutput.MultiOutputClassifier.__getattribute__ sklearn.multioutput.MultiOutputClassifier.__getstate__ sklearn.multioutput.MultiOutputClassifier.__gt__ sklearn.multioutput.MultiOutputClassifier.__hash__ sklearn.multioutput.MultiOutputClassifier.__init__ sklearn.multioutput.MultiOutputClassifier.__init_subclass__ sklearn.multioutput.MultiOutputClassifier.__le__ sklearn.multioutput.MultiOutputClassifier.__lt__ sklearn.multioutput.MultiOutputClassifier.__ne__ sklearn.multioutput.MultiOutputClassifier.__new__ sklearn.multioutput.MultiOutputClassifier.__reduce__ sklearn.multioutput.MultiOutputClassifier.__reduce_ex__ sklearn.multioutput.MultiOutputClassifier.__repr__ sklearn.multioutput.MultiOutputClassifier.__setattr__ sklearn.multioutput.MultiOutputClassifier.__setstate__ sklearn.multioutput.MultiOutputClassifier.__sizeof__ sklearn.multioutput.MultiOutputClassifier.__sklearn_clone__ sklearn.multioutput.MultiOutputClassifier.__sklearn_tags__ sklearn.multioutput.MultiOutputClassifier.__str__ sklearn.multioutput.MultiOutputClassifier.__subclasshook__ sklearn.multioutput.MultiOutputClassifier._build_request_for_signature sklearn.multioutput.MultiOutputClassifier._check_feature_names sklearn.multioutput.MultiOutputClassifier._check_n_features sklearn.multioutput.MultiOutputClassifier._check_predict_proba sklearn.multioutput.MultiOutputClassifier._get_default_requests sklearn.multioutput.MultiOutputClassifier._get_doc_link sklearn.multioutput.MultiOutputClassifier._get_metadata_request sklearn.multioutput.MultiOutputClassifier._get_param_names sklearn.multioutput.MultiOutputClassifier._get_tags sklearn.multioutput.MultiOutputClassifier._more_tags sklearn.multioutput.MultiOutputClassifier._repr_html_inner sklearn.multioutput.MultiOutputClassifier._repr_mimebundle_ sklearn.multioutput.MultiOutputClassifier._validate_data sklearn.multioutput.MultiOutputClassifier._validate_params sklearn.multioutput.MultiOutputClassifier.fit sklearn.multioutput.MultiOutputClassifier.get_metadata_routing sklearn.multioutput.MultiOutputClassifier.get_params sklearn.multioutput.MultiOutputClassifier.partial_fit sklearn.multioutput.MultiOutputClassifier.predict sklearn.multioutput.MultiOutputClassifier.predict_proba sklearn.multioutput.MultiOutputClassifier.score sklearn.multioutput.MultiOutputClassifier.set_fit_request sklearn.multioutput.MultiOutputClassifier.set_params sklearn.multioutput.MultiOutputClassifier.set_partial_fit_request 
+.. minigallery:: sklearn.multioutput.MultiOutputClassifier sklearn.multioutput.MultiOutputClassifier.__delattr__ sklearn.multioutput.MultiOutputClassifier.__dir__ sklearn.multioutput.MultiOutputClassifier.__eq__ sklearn.multioutput.MultiOutputClassifier.__format__ sklearn.multioutput.MultiOutputClassifier.__ge__ sklearn.multioutput.MultiOutputClassifier.__getattribute__ sklearn.multioutput.MultiOutputClassifier.__getstate__ sklearn.multioutput.MultiOutputClassifier.__gt__ sklearn.multioutput.MultiOutputClassifier.__hash__ sklearn.multioutput.MultiOutputClassifier.__init__ sklearn.multioutput.MultiOutputClassifier.__init_subclass__ sklearn.multioutput.MultiOutputClassifier.__le__ sklearn.multioutput.MultiOutputClassifier.__lt__ sklearn.multioutput.MultiOutputClassifier.__ne__ sklearn.multioutput.MultiOutputClassifier.__new__ sklearn.multioutput.MultiOutputClassifier.__reduce__ sklearn.multioutput.MultiOutputClassifier.__reduce_ex__ sklearn.multioutput.MultiOutputClassifier.__repr__ sklearn.multioutput.MultiOutputClassifier.__setattr__ sklearn.multioutput.MultiOutputClassifier.__setstate__ sklearn.multioutput.MultiOutputClassifier.__sizeof__ sklearn.multioutput.MultiOutputClassifier.__sklearn_clone__ sklearn.multioutput.MultiOutputClassifier.__sklearn_tags__ sklearn.multioutput.MultiOutputClassifier.__str__ sklearn.multioutput.MultiOutputClassifier.__subclasshook__ sklearn.multioutput.MultiOutputClassifier._build_request_for_signature sklearn.multioutput.MultiOutputClassifier._check_predict_proba sklearn.multioutput.MultiOutputClassifier._get_default_requests sklearn.multioutput.MultiOutputClassifier._get_doc_link sklearn.multioutput.MultiOutputClassifier._get_metadata_request sklearn.multioutput.MultiOutputClassifier._get_param_names sklearn.multioutput.MultiOutputClassifier._repr_html_inner sklearn.multioutput.MultiOutputClassifier._repr_mimebundle_ sklearn.multioutput.MultiOutputClassifier._validate_params sklearn.multioutput.MultiOutputClassifier.fit sklearn.multioutput.MultiOutputClassifier.get_metadata_routing sklearn.multioutput.MultiOutputClassifier.get_params sklearn.multioutput.MultiOutputClassifier.partial_fit sklearn.multioutput.MultiOutputClassifier.predict sklearn.multioutput.MultiOutputClassifier.predict_proba sklearn.multioutput.MultiOutputClassifier.score sklearn.multioutput.MultiOutputClassifier.set_fit_request sklearn.multioutput.MultiOutputClassifier.set_params sklearn.multioutput.MultiOutputClassifier.set_partial_fit_request 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.multioutput.MultiOutputRegressor.rst.txt b/dev/_sources/modules/generated/sklearn.multioutput.MultiOutputRegressor.rst.txt
index 7e852fcc97ee6..dfcd894baf589 100644
--- a/dev/_sources/modules/generated/sklearn.multioutput.MultiOutputRegressor.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.multioutput.MultiOutputRegressor.rst.txt
@@ -8,6 +8,6 @@ MultiOutputRegressor
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.multioutput.MultiOutputRegressor sklearn.multioutput.MultiOutputRegressor.__delattr__ sklearn.multioutput.MultiOutputRegressor.__dir__ sklearn.multioutput.MultiOutputRegressor.__eq__ sklearn.multioutput.MultiOutputRegressor.__format__ sklearn.multioutput.MultiOutputRegressor.__ge__ sklearn.multioutput.MultiOutputRegressor.__getattribute__ sklearn.multioutput.MultiOutputRegressor.__getstate__ sklearn.multioutput.MultiOutputRegressor.__gt__ sklearn.multioutput.MultiOutputRegressor.__hash__ sklearn.multioutput.MultiOutputRegressor.__init__ sklearn.multioutput.MultiOutputRegressor.__init_subclass__ sklearn.multioutput.MultiOutputRegressor.__le__ sklearn.multioutput.MultiOutputRegressor.__lt__ sklearn.multioutput.MultiOutputRegressor.__ne__ sklearn.multioutput.MultiOutputRegressor.__new__ sklearn.multioutput.MultiOutputRegressor.__reduce__ sklearn.multioutput.MultiOutputRegressor.__reduce_ex__ sklearn.multioutput.MultiOutputRegressor.__repr__ sklearn.multioutput.MultiOutputRegressor.__setattr__ sklearn.multioutput.MultiOutputRegressor.__setstate__ sklearn.multioutput.MultiOutputRegressor.__sizeof__ sklearn.multioutput.MultiOutputRegressor.__sklearn_clone__ sklearn.multioutput.MultiOutputRegressor.__sklearn_tags__ sklearn.multioutput.MultiOutputRegressor.__str__ sklearn.multioutput.MultiOutputRegressor.__subclasshook__ sklearn.multioutput.MultiOutputRegressor._build_request_for_signature sklearn.multioutput.MultiOutputRegressor._check_feature_names sklearn.multioutput.MultiOutputRegressor._check_n_features sklearn.multioutput.MultiOutputRegressor._get_default_requests sklearn.multioutput.MultiOutputRegressor._get_doc_link sklearn.multioutput.MultiOutputRegressor._get_metadata_request sklearn.multioutput.MultiOutputRegressor._get_param_names sklearn.multioutput.MultiOutputRegressor._get_tags sklearn.multioutput.MultiOutputRegressor._more_tags sklearn.multioutput.MultiOutputRegressor._repr_html_inner sklearn.multioutput.MultiOutputRegressor._repr_mimebundle_ sklearn.multioutput.MultiOutputRegressor._validate_data sklearn.multioutput.MultiOutputRegressor._validate_params sklearn.multioutput.MultiOutputRegressor.fit sklearn.multioutput.MultiOutputRegressor.get_metadata_routing sklearn.multioutput.MultiOutputRegressor.get_params sklearn.multioutput.MultiOutputRegressor.partial_fit sklearn.multioutput.MultiOutputRegressor.predict sklearn.multioutput.MultiOutputRegressor.score sklearn.multioutput.MultiOutputRegressor.set_fit_request sklearn.multioutput.MultiOutputRegressor.set_params sklearn.multioutput.MultiOutputRegressor.set_partial_fit_request sklearn.multioutput.MultiOutputRegressor.set_score_request 
+.. minigallery:: sklearn.multioutput.MultiOutputRegressor sklearn.multioutput.MultiOutputRegressor.__delattr__ sklearn.multioutput.MultiOutputRegressor.__dir__ sklearn.multioutput.MultiOutputRegressor.__eq__ sklearn.multioutput.MultiOutputRegressor.__format__ sklearn.multioutput.MultiOutputRegressor.__ge__ sklearn.multioutput.MultiOutputRegressor.__getattribute__ sklearn.multioutput.MultiOutputRegressor.__getstate__ sklearn.multioutput.MultiOutputRegressor.__gt__ sklearn.multioutput.MultiOutputRegressor.__hash__ sklearn.multioutput.MultiOutputRegressor.__init__ sklearn.multioutput.MultiOutputRegressor.__init_subclass__ sklearn.multioutput.MultiOutputRegressor.__le__ sklearn.multioutput.MultiOutputRegressor.__lt__ sklearn.multioutput.MultiOutputRegressor.__ne__ sklearn.multioutput.MultiOutputRegressor.__new__ sklearn.multioutput.MultiOutputRegressor.__reduce__ sklearn.multioutput.MultiOutputRegressor.__reduce_ex__ sklearn.multioutput.MultiOutputRegressor.__repr__ sklearn.multioutput.MultiOutputRegressor.__setattr__ sklearn.multioutput.MultiOutputRegressor.__setstate__ sklearn.multioutput.MultiOutputRegressor.__sizeof__ sklearn.multioutput.MultiOutputRegressor.__sklearn_clone__ sklearn.multioutput.MultiOutputRegressor.__sklearn_tags__ sklearn.multioutput.MultiOutputRegressor.__str__ sklearn.multioutput.MultiOutputRegressor.__subclasshook__ sklearn.multioutput.MultiOutputRegressor._build_request_for_signature sklearn.multioutput.MultiOutputRegressor._get_default_requests sklearn.multioutput.MultiOutputRegressor._get_doc_link sklearn.multioutput.MultiOutputRegressor._get_metadata_request sklearn.multioutput.MultiOutputRegressor._get_param_names sklearn.multioutput.MultiOutputRegressor._repr_html_inner sklearn.multioutput.MultiOutputRegressor._repr_mimebundle_ sklearn.multioutput.MultiOutputRegressor._validate_params sklearn.multioutput.MultiOutputRegressor.fit sklearn.multioutput.MultiOutputRegressor.get_metadata_routing sklearn.multioutput.MultiOutputRegressor.get_params sklearn.multioutput.MultiOutputRegressor.partial_fit sklearn.multioutput.MultiOutputRegressor.predict sklearn.multioutput.MultiOutputRegressor.score sklearn.multioutput.MultiOutputRegressor.set_fit_request sklearn.multioutput.MultiOutputRegressor.set_params sklearn.multioutput.MultiOutputRegressor.set_partial_fit_request sklearn.multioutput.MultiOutputRegressor.set_score_request 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.multioutput.RegressorChain.rst.txt b/dev/_sources/modules/generated/sklearn.multioutput.RegressorChain.rst.txt
index 6d0780fb33b2e..41cd8a2766b2e 100644
--- a/dev/_sources/modules/generated/sklearn.multioutput.RegressorChain.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.multioutput.RegressorChain.rst.txt
@@ -8,6 +8,6 @@ RegressorChain
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.multioutput.RegressorChain sklearn.multioutput.RegressorChain.__delattr__ sklearn.multioutput.RegressorChain.__dir__ sklearn.multioutput.RegressorChain.__eq__ sklearn.multioutput.RegressorChain.__format__ sklearn.multioutput.RegressorChain.__ge__ sklearn.multioutput.RegressorChain.__getattribute__ sklearn.multioutput.RegressorChain.__getstate__ sklearn.multioutput.RegressorChain.__gt__ sklearn.multioutput.RegressorChain.__hash__ sklearn.multioutput.RegressorChain.__init__ sklearn.multioutput.RegressorChain.__init_subclass__ sklearn.multioutput.RegressorChain.__le__ sklearn.multioutput.RegressorChain.__lt__ sklearn.multioutput.RegressorChain.__ne__ sklearn.multioutput.RegressorChain.__new__ sklearn.multioutput.RegressorChain.__reduce__ sklearn.multioutput.RegressorChain.__reduce_ex__ sklearn.multioutput.RegressorChain.__repr__ sklearn.multioutput.RegressorChain.__setattr__ sklearn.multioutput.RegressorChain.__setstate__ sklearn.multioutput.RegressorChain.__sizeof__ sklearn.multioutput.RegressorChain.__sklearn_clone__ sklearn.multioutput.RegressorChain.__sklearn_tags__ sklearn.multioutput.RegressorChain.__str__ sklearn.multioutput.RegressorChain.__subclasshook__ sklearn.multioutput.RegressorChain._build_request_for_signature sklearn.multioutput.RegressorChain._check_feature_names sklearn.multioutput.RegressorChain._check_n_features sklearn.multioutput.RegressorChain._get_default_requests sklearn.multioutput.RegressorChain._get_doc_link sklearn.multioutput.RegressorChain._get_estimator sklearn.multioutput.RegressorChain._get_metadata_request sklearn.multioutput.RegressorChain._get_param_names sklearn.multioutput.RegressorChain._get_predictions sklearn.multioutput.RegressorChain._get_tags sklearn.multioutput.RegressorChain._log_message sklearn.multioutput.RegressorChain._more_tags sklearn.multioutput.RegressorChain._repr_html_inner sklearn.multioutput.RegressorChain._repr_mimebundle_ sklearn.multioutput.RegressorChain._validate_data sklearn.multioutput.RegressorChain._validate_params sklearn.multioutput.RegressorChain.fit sklearn.multioutput.RegressorChain.get_metadata_routing sklearn.multioutput.RegressorChain.get_params sklearn.multioutput.RegressorChain.predict sklearn.multioutput.RegressorChain.score sklearn.multioutput.RegressorChain.set_params sklearn.multioutput.RegressorChain.set_score_request 
+.. minigallery:: sklearn.multioutput.RegressorChain sklearn.multioutput.RegressorChain.__delattr__ sklearn.multioutput.RegressorChain.__dir__ sklearn.multioutput.RegressorChain.__eq__ sklearn.multioutput.RegressorChain.__format__ sklearn.multioutput.RegressorChain.__ge__ sklearn.multioutput.RegressorChain.__getattribute__ sklearn.multioutput.RegressorChain.__getstate__ sklearn.multioutput.RegressorChain.__gt__ sklearn.multioutput.RegressorChain.__hash__ sklearn.multioutput.RegressorChain.__init__ sklearn.multioutput.RegressorChain.__init_subclass__ sklearn.multioutput.RegressorChain.__le__ sklearn.multioutput.RegressorChain.__lt__ sklearn.multioutput.RegressorChain.__ne__ sklearn.multioutput.RegressorChain.__new__ sklearn.multioutput.RegressorChain.__reduce__ sklearn.multioutput.RegressorChain.__reduce_ex__ sklearn.multioutput.RegressorChain.__repr__ sklearn.multioutput.RegressorChain.__setattr__ sklearn.multioutput.RegressorChain.__setstate__ sklearn.multioutput.RegressorChain.__sizeof__ sklearn.multioutput.RegressorChain.__sklearn_clone__ sklearn.multioutput.RegressorChain.__sklearn_tags__ sklearn.multioutput.RegressorChain.__str__ sklearn.multioutput.RegressorChain.__subclasshook__ sklearn.multioutput.RegressorChain._build_request_for_signature sklearn.multioutput.RegressorChain._get_default_requests sklearn.multioutput.RegressorChain._get_doc_link sklearn.multioutput.RegressorChain._get_estimator sklearn.multioutput.RegressorChain._get_metadata_request sklearn.multioutput.RegressorChain._get_param_names sklearn.multioutput.RegressorChain._get_predictions sklearn.multioutput.RegressorChain._log_message sklearn.multioutput.RegressorChain._repr_html_inner sklearn.multioutput.RegressorChain._repr_mimebundle_ sklearn.multioutput.RegressorChain._validate_params sklearn.multioutput.RegressorChain.fit sklearn.multioutput.RegressorChain.get_metadata_routing sklearn.multioutput.RegressorChain.get_params sklearn.multioutput.RegressorChain.predict sklearn.multioutput.RegressorChain.score sklearn.multioutput.RegressorChain.set_params sklearn.multioutput.RegressorChain.set_score_request 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.naive_bayes.BernoulliNB.rst.txt b/dev/_sources/modules/generated/sklearn.naive_bayes.BernoulliNB.rst.txt
index a9dbb033b66f9..cb69b4c650d5a 100644
--- a/dev/_sources/modules/generated/sklearn.naive_bayes.BernoulliNB.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.naive_bayes.BernoulliNB.rst.txt
@@ -8,6 +8,6 @@ BernoulliNB
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.naive_bayes.BernoulliNB sklearn.naive_bayes.BernoulliNB.__delattr__ sklearn.naive_bayes.BernoulliNB.__dir__ sklearn.naive_bayes.BernoulliNB.__eq__ sklearn.naive_bayes.BernoulliNB.__format__ sklearn.naive_bayes.BernoulliNB.__ge__ sklearn.naive_bayes.BernoulliNB.__getattribute__ sklearn.naive_bayes.BernoulliNB.__getstate__ sklearn.naive_bayes.BernoulliNB.__gt__ sklearn.naive_bayes.BernoulliNB.__hash__ sklearn.naive_bayes.BernoulliNB.__init__ sklearn.naive_bayes.BernoulliNB.__init_subclass__ sklearn.naive_bayes.BernoulliNB.__le__ sklearn.naive_bayes.BernoulliNB.__lt__ sklearn.naive_bayes.BernoulliNB.__ne__ sklearn.naive_bayes.BernoulliNB.__new__ sklearn.naive_bayes.BernoulliNB.__reduce__ sklearn.naive_bayes.BernoulliNB.__reduce_ex__ sklearn.naive_bayes.BernoulliNB.__repr__ sklearn.naive_bayes.BernoulliNB.__setattr__ sklearn.naive_bayes.BernoulliNB.__setstate__ sklearn.naive_bayes.BernoulliNB.__sizeof__ sklearn.naive_bayes.BernoulliNB.__sklearn_clone__ sklearn.naive_bayes.BernoulliNB.__sklearn_tags__ sklearn.naive_bayes.BernoulliNB.__str__ sklearn.naive_bayes.BernoulliNB.__subclasshook__ sklearn.naive_bayes.BernoulliNB._build_request_for_signature sklearn.naive_bayes.BernoulliNB._check_X sklearn.naive_bayes.BernoulliNB._check_X_y sklearn.naive_bayes.BernoulliNB._check_alpha sklearn.naive_bayes.BernoulliNB._check_feature_names sklearn.naive_bayes.BernoulliNB._check_n_features sklearn.naive_bayes.BernoulliNB._count sklearn.naive_bayes.BernoulliNB._get_default_requests sklearn.naive_bayes.BernoulliNB._get_doc_link sklearn.naive_bayes.BernoulliNB._get_metadata_request sklearn.naive_bayes.BernoulliNB._get_param_names sklearn.naive_bayes.BernoulliNB._get_tags sklearn.naive_bayes.BernoulliNB._init_counters sklearn.naive_bayes.BernoulliNB._joint_log_likelihood sklearn.naive_bayes.BernoulliNB._more_tags sklearn.naive_bayes.BernoulliNB._repr_html_inner sklearn.naive_bayes.BernoulliNB._repr_mimebundle_ sklearn.naive_bayes.BernoulliNB._update_class_log_prior sklearn.naive_bayes.BernoulliNB._update_feature_log_prob sklearn.naive_bayes.BernoulliNB._validate_data sklearn.naive_bayes.BernoulliNB._validate_params sklearn.naive_bayes.BernoulliNB.fit sklearn.naive_bayes.BernoulliNB.get_metadata_routing sklearn.naive_bayes.BernoulliNB.get_params sklearn.naive_bayes.BernoulliNB.partial_fit sklearn.naive_bayes.BernoulliNB.predict sklearn.naive_bayes.BernoulliNB.predict_joint_log_proba sklearn.naive_bayes.BernoulliNB.predict_log_proba sklearn.naive_bayes.BernoulliNB.predict_proba sklearn.naive_bayes.BernoulliNB.score sklearn.naive_bayes.BernoulliNB.set_fit_request sklearn.naive_bayes.BernoulliNB.set_params sklearn.naive_bayes.BernoulliNB.set_partial_fit_request sklearn.naive_bayes.BernoulliNB.set_score_request 
+.. minigallery:: sklearn.naive_bayes.BernoulliNB sklearn.naive_bayes.BernoulliNB.__delattr__ sklearn.naive_bayes.BernoulliNB.__dir__ sklearn.naive_bayes.BernoulliNB.__eq__ sklearn.naive_bayes.BernoulliNB.__format__ sklearn.naive_bayes.BernoulliNB.__ge__ sklearn.naive_bayes.BernoulliNB.__getattribute__ sklearn.naive_bayes.BernoulliNB.__getstate__ sklearn.naive_bayes.BernoulliNB.__gt__ sklearn.naive_bayes.BernoulliNB.__hash__ sklearn.naive_bayes.BernoulliNB.__init__ sklearn.naive_bayes.BernoulliNB.__init_subclass__ sklearn.naive_bayes.BernoulliNB.__le__ sklearn.naive_bayes.BernoulliNB.__lt__ sklearn.naive_bayes.BernoulliNB.__ne__ sklearn.naive_bayes.BernoulliNB.__new__ sklearn.naive_bayes.BernoulliNB.__reduce__ sklearn.naive_bayes.BernoulliNB.__reduce_ex__ sklearn.naive_bayes.BernoulliNB.__repr__ sklearn.naive_bayes.BernoulliNB.__setattr__ sklearn.naive_bayes.BernoulliNB.__setstate__ sklearn.naive_bayes.BernoulliNB.__sizeof__ sklearn.naive_bayes.BernoulliNB.__sklearn_clone__ sklearn.naive_bayes.BernoulliNB.__sklearn_tags__ sklearn.naive_bayes.BernoulliNB.__str__ sklearn.naive_bayes.BernoulliNB.__subclasshook__ sklearn.naive_bayes.BernoulliNB._build_request_for_signature sklearn.naive_bayes.BernoulliNB._check_X sklearn.naive_bayes.BernoulliNB._check_X_y sklearn.naive_bayes.BernoulliNB._check_alpha sklearn.naive_bayes.BernoulliNB._count sklearn.naive_bayes.BernoulliNB._get_default_requests sklearn.naive_bayes.BernoulliNB._get_doc_link sklearn.naive_bayes.BernoulliNB._get_metadata_request sklearn.naive_bayes.BernoulliNB._get_param_names sklearn.naive_bayes.BernoulliNB._init_counters sklearn.naive_bayes.BernoulliNB._joint_log_likelihood sklearn.naive_bayes.BernoulliNB._repr_html_inner sklearn.naive_bayes.BernoulliNB._repr_mimebundle_ sklearn.naive_bayes.BernoulliNB._update_class_log_prior sklearn.naive_bayes.BernoulliNB._update_feature_log_prob sklearn.naive_bayes.BernoulliNB._validate_params sklearn.naive_bayes.BernoulliNB.fit sklearn.naive_bayes.BernoulliNB.get_metadata_routing sklearn.naive_bayes.BernoulliNB.get_params sklearn.naive_bayes.BernoulliNB.partial_fit sklearn.naive_bayes.BernoulliNB.predict sklearn.naive_bayes.BernoulliNB.predict_joint_log_proba sklearn.naive_bayes.BernoulliNB.predict_log_proba sklearn.naive_bayes.BernoulliNB.predict_proba sklearn.naive_bayes.BernoulliNB.score sklearn.naive_bayes.BernoulliNB.set_fit_request sklearn.naive_bayes.BernoulliNB.set_params sklearn.naive_bayes.BernoulliNB.set_partial_fit_request sklearn.naive_bayes.BernoulliNB.set_score_request 
    :add-heading: Gallery examples
    :heading-level: -
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diff --git a/dev/_sources/modules/generated/sklearn.naive_bayes.CategoricalNB.rst.txt b/dev/_sources/modules/generated/sklearn.naive_bayes.CategoricalNB.rst.txt
index ac5112b44bb98..6223a7ba8fe19 100644
--- a/dev/_sources/modules/generated/sklearn.naive_bayes.CategoricalNB.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.naive_bayes.CategoricalNB.rst.txt
@@ -8,6 +8,6 @@ CategoricalNB
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.naive_bayes.CategoricalNB sklearn.naive_bayes.CategoricalNB.__delattr__ sklearn.naive_bayes.CategoricalNB.__dir__ sklearn.naive_bayes.CategoricalNB.__eq__ sklearn.naive_bayes.CategoricalNB.__format__ sklearn.naive_bayes.CategoricalNB.__ge__ sklearn.naive_bayes.CategoricalNB.__getattribute__ sklearn.naive_bayes.CategoricalNB.__getstate__ sklearn.naive_bayes.CategoricalNB.__gt__ sklearn.naive_bayes.CategoricalNB.__hash__ sklearn.naive_bayes.CategoricalNB.__init__ sklearn.naive_bayes.CategoricalNB.__init_subclass__ sklearn.naive_bayes.CategoricalNB.__le__ sklearn.naive_bayes.CategoricalNB.__lt__ sklearn.naive_bayes.CategoricalNB.__ne__ sklearn.naive_bayes.CategoricalNB.__new__ sklearn.naive_bayes.CategoricalNB.__reduce__ sklearn.naive_bayes.CategoricalNB.__reduce_ex__ sklearn.naive_bayes.CategoricalNB.__repr__ sklearn.naive_bayes.CategoricalNB.__setattr__ sklearn.naive_bayes.CategoricalNB.__setstate__ sklearn.naive_bayes.CategoricalNB.__sizeof__ sklearn.naive_bayes.CategoricalNB.__sklearn_clone__ sklearn.naive_bayes.CategoricalNB.__sklearn_tags__ sklearn.naive_bayes.CategoricalNB.__str__ sklearn.naive_bayes.CategoricalNB.__subclasshook__ sklearn.naive_bayes.CategoricalNB._build_request_for_signature sklearn.naive_bayes.CategoricalNB._check_X sklearn.naive_bayes.CategoricalNB._check_X_y sklearn.naive_bayes.CategoricalNB._check_alpha sklearn.naive_bayes.CategoricalNB._check_feature_names sklearn.naive_bayes.CategoricalNB._check_n_features sklearn.naive_bayes.CategoricalNB._count sklearn.naive_bayes.CategoricalNB._get_default_requests sklearn.naive_bayes.CategoricalNB._get_doc_link sklearn.naive_bayes.CategoricalNB._get_metadata_request sklearn.naive_bayes.CategoricalNB._get_param_names sklearn.naive_bayes.CategoricalNB._get_tags sklearn.naive_bayes.CategoricalNB._init_counters sklearn.naive_bayes.CategoricalNB._joint_log_likelihood sklearn.naive_bayes.CategoricalNB._more_tags sklearn.naive_bayes.CategoricalNB._repr_html_inner sklearn.naive_bayes.CategoricalNB._repr_mimebundle_ sklearn.naive_bayes.CategoricalNB._update_class_log_prior sklearn.naive_bayes.CategoricalNB._update_feature_log_prob sklearn.naive_bayes.CategoricalNB._validate_data sklearn.naive_bayes.CategoricalNB._validate_n_categories sklearn.naive_bayes.CategoricalNB._validate_params sklearn.naive_bayes.CategoricalNB.fit sklearn.naive_bayes.CategoricalNB.get_metadata_routing sklearn.naive_bayes.CategoricalNB.get_params sklearn.naive_bayes.CategoricalNB.partial_fit sklearn.naive_bayes.CategoricalNB.predict sklearn.naive_bayes.CategoricalNB.predict_joint_log_proba sklearn.naive_bayes.CategoricalNB.predict_log_proba sklearn.naive_bayes.CategoricalNB.predict_proba sklearn.naive_bayes.CategoricalNB.score sklearn.naive_bayes.CategoricalNB.set_fit_request sklearn.naive_bayes.CategoricalNB.set_params sklearn.naive_bayes.CategoricalNB.set_partial_fit_request sklearn.naive_bayes.CategoricalNB.set_score_request 
+.. minigallery:: sklearn.naive_bayes.CategoricalNB sklearn.naive_bayes.CategoricalNB.__delattr__ sklearn.naive_bayes.CategoricalNB.__dir__ sklearn.naive_bayes.CategoricalNB.__eq__ sklearn.naive_bayes.CategoricalNB.__format__ sklearn.naive_bayes.CategoricalNB.__ge__ sklearn.naive_bayes.CategoricalNB.__getattribute__ sklearn.naive_bayes.CategoricalNB.__getstate__ sklearn.naive_bayes.CategoricalNB.__gt__ sklearn.naive_bayes.CategoricalNB.__hash__ sklearn.naive_bayes.CategoricalNB.__init__ sklearn.naive_bayes.CategoricalNB.__init_subclass__ sklearn.naive_bayes.CategoricalNB.__le__ sklearn.naive_bayes.CategoricalNB.__lt__ sklearn.naive_bayes.CategoricalNB.__ne__ sklearn.naive_bayes.CategoricalNB.__new__ sklearn.naive_bayes.CategoricalNB.__reduce__ sklearn.naive_bayes.CategoricalNB.__reduce_ex__ sklearn.naive_bayes.CategoricalNB.__repr__ sklearn.naive_bayes.CategoricalNB.__setattr__ sklearn.naive_bayes.CategoricalNB.__setstate__ sklearn.naive_bayes.CategoricalNB.__sizeof__ sklearn.naive_bayes.CategoricalNB.__sklearn_clone__ sklearn.naive_bayes.CategoricalNB.__sklearn_tags__ sklearn.naive_bayes.CategoricalNB.__str__ sklearn.naive_bayes.CategoricalNB.__subclasshook__ sklearn.naive_bayes.CategoricalNB._build_request_for_signature sklearn.naive_bayes.CategoricalNB._check_X sklearn.naive_bayes.CategoricalNB._check_X_y sklearn.naive_bayes.CategoricalNB._check_alpha sklearn.naive_bayes.CategoricalNB._count sklearn.naive_bayes.CategoricalNB._get_default_requests sklearn.naive_bayes.CategoricalNB._get_doc_link sklearn.naive_bayes.CategoricalNB._get_metadata_request sklearn.naive_bayes.CategoricalNB._get_param_names sklearn.naive_bayes.CategoricalNB._init_counters sklearn.naive_bayes.CategoricalNB._joint_log_likelihood sklearn.naive_bayes.CategoricalNB._repr_html_inner sklearn.naive_bayes.CategoricalNB._repr_mimebundle_ sklearn.naive_bayes.CategoricalNB._update_class_log_prior sklearn.naive_bayes.CategoricalNB._update_feature_log_prob sklearn.naive_bayes.CategoricalNB._validate_n_categories sklearn.naive_bayes.CategoricalNB._validate_params sklearn.naive_bayes.CategoricalNB.fit sklearn.naive_bayes.CategoricalNB.get_metadata_routing sklearn.naive_bayes.CategoricalNB.get_params sklearn.naive_bayes.CategoricalNB.partial_fit sklearn.naive_bayes.CategoricalNB.predict sklearn.naive_bayes.CategoricalNB.predict_joint_log_proba sklearn.naive_bayes.CategoricalNB.predict_log_proba sklearn.naive_bayes.CategoricalNB.predict_proba sklearn.naive_bayes.CategoricalNB.score sklearn.naive_bayes.CategoricalNB.set_fit_request sklearn.naive_bayes.CategoricalNB.set_params sklearn.naive_bayes.CategoricalNB.set_partial_fit_request sklearn.naive_bayes.CategoricalNB.set_score_request 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.naive_bayes.ComplementNB.rst.txt b/dev/_sources/modules/generated/sklearn.naive_bayes.ComplementNB.rst.txt
index 424a3ef87d6e8..3f0de934312f4 100644
--- a/dev/_sources/modules/generated/sklearn.naive_bayes.ComplementNB.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.naive_bayes.ComplementNB.rst.txt
@@ -8,6 +8,6 @@ ComplementNB
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.naive_bayes.ComplementNB sklearn.naive_bayes.ComplementNB.__delattr__ sklearn.naive_bayes.ComplementNB.__dir__ sklearn.naive_bayes.ComplementNB.__eq__ sklearn.naive_bayes.ComplementNB.__format__ sklearn.naive_bayes.ComplementNB.__ge__ sklearn.naive_bayes.ComplementNB.__getattribute__ sklearn.naive_bayes.ComplementNB.__getstate__ sklearn.naive_bayes.ComplementNB.__gt__ sklearn.naive_bayes.ComplementNB.__hash__ sklearn.naive_bayes.ComplementNB.__init__ sklearn.naive_bayes.ComplementNB.__init_subclass__ sklearn.naive_bayes.ComplementNB.__le__ sklearn.naive_bayes.ComplementNB.__lt__ sklearn.naive_bayes.ComplementNB.__ne__ sklearn.naive_bayes.ComplementNB.__new__ sklearn.naive_bayes.ComplementNB.__reduce__ sklearn.naive_bayes.ComplementNB.__reduce_ex__ sklearn.naive_bayes.ComplementNB.__repr__ sklearn.naive_bayes.ComplementNB.__setattr__ sklearn.naive_bayes.ComplementNB.__setstate__ sklearn.naive_bayes.ComplementNB.__sizeof__ sklearn.naive_bayes.ComplementNB.__sklearn_clone__ sklearn.naive_bayes.ComplementNB.__sklearn_tags__ sklearn.naive_bayes.ComplementNB.__str__ sklearn.naive_bayes.ComplementNB.__subclasshook__ sklearn.naive_bayes.ComplementNB._build_request_for_signature sklearn.naive_bayes.ComplementNB._check_X sklearn.naive_bayes.ComplementNB._check_X_y sklearn.naive_bayes.ComplementNB._check_alpha sklearn.naive_bayes.ComplementNB._check_feature_names sklearn.naive_bayes.ComplementNB._check_n_features sklearn.naive_bayes.ComplementNB._count sklearn.naive_bayes.ComplementNB._get_default_requests sklearn.naive_bayes.ComplementNB._get_doc_link sklearn.naive_bayes.ComplementNB._get_metadata_request sklearn.naive_bayes.ComplementNB._get_param_names sklearn.naive_bayes.ComplementNB._get_tags sklearn.naive_bayes.ComplementNB._init_counters sklearn.naive_bayes.ComplementNB._joint_log_likelihood sklearn.naive_bayes.ComplementNB._more_tags sklearn.naive_bayes.ComplementNB._repr_html_inner sklearn.naive_bayes.ComplementNB._repr_mimebundle_ sklearn.naive_bayes.ComplementNB._update_class_log_prior sklearn.naive_bayes.ComplementNB._update_feature_log_prob sklearn.naive_bayes.ComplementNB._validate_data sklearn.naive_bayes.ComplementNB._validate_params sklearn.naive_bayes.ComplementNB.fit sklearn.naive_bayes.ComplementNB.get_metadata_routing sklearn.naive_bayes.ComplementNB.get_params sklearn.naive_bayes.ComplementNB.partial_fit sklearn.naive_bayes.ComplementNB.predict sklearn.naive_bayes.ComplementNB.predict_joint_log_proba sklearn.naive_bayes.ComplementNB.predict_log_proba sklearn.naive_bayes.ComplementNB.predict_proba sklearn.naive_bayes.ComplementNB.score sklearn.naive_bayes.ComplementNB.set_fit_request sklearn.naive_bayes.ComplementNB.set_params sklearn.naive_bayes.ComplementNB.set_partial_fit_request sklearn.naive_bayes.ComplementNB.set_score_request 
+.. minigallery:: sklearn.naive_bayes.ComplementNB sklearn.naive_bayes.ComplementNB.__delattr__ sklearn.naive_bayes.ComplementNB.__dir__ sklearn.naive_bayes.ComplementNB.__eq__ sklearn.naive_bayes.ComplementNB.__format__ sklearn.naive_bayes.ComplementNB.__ge__ sklearn.naive_bayes.ComplementNB.__getattribute__ sklearn.naive_bayes.ComplementNB.__getstate__ sklearn.naive_bayes.ComplementNB.__gt__ sklearn.naive_bayes.ComplementNB.__hash__ sklearn.naive_bayes.ComplementNB.__init__ sklearn.naive_bayes.ComplementNB.__init_subclass__ sklearn.naive_bayes.ComplementNB.__le__ sklearn.naive_bayes.ComplementNB.__lt__ sklearn.naive_bayes.ComplementNB.__ne__ sklearn.naive_bayes.ComplementNB.__new__ sklearn.naive_bayes.ComplementNB.__reduce__ sklearn.naive_bayes.ComplementNB.__reduce_ex__ sklearn.naive_bayes.ComplementNB.__repr__ sklearn.naive_bayes.ComplementNB.__setattr__ sklearn.naive_bayes.ComplementNB.__setstate__ sklearn.naive_bayes.ComplementNB.__sizeof__ sklearn.naive_bayes.ComplementNB.__sklearn_clone__ sklearn.naive_bayes.ComplementNB.__sklearn_tags__ sklearn.naive_bayes.ComplementNB.__str__ sklearn.naive_bayes.ComplementNB.__subclasshook__ sklearn.naive_bayes.ComplementNB._build_request_for_signature sklearn.naive_bayes.ComplementNB._check_X sklearn.naive_bayes.ComplementNB._check_X_y sklearn.naive_bayes.ComplementNB._check_alpha sklearn.naive_bayes.ComplementNB._count sklearn.naive_bayes.ComplementNB._get_default_requests sklearn.naive_bayes.ComplementNB._get_doc_link sklearn.naive_bayes.ComplementNB._get_metadata_request sklearn.naive_bayes.ComplementNB._get_param_names sklearn.naive_bayes.ComplementNB._init_counters sklearn.naive_bayes.ComplementNB._joint_log_likelihood sklearn.naive_bayes.ComplementNB._repr_html_inner sklearn.naive_bayes.ComplementNB._repr_mimebundle_ sklearn.naive_bayes.ComplementNB._update_class_log_prior sklearn.naive_bayes.ComplementNB._update_feature_log_prob sklearn.naive_bayes.ComplementNB._validate_params sklearn.naive_bayes.ComplementNB.fit sklearn.naive_bayes.ComplementNB.get_metadata_routing sklearn.naive_bayes.ComplementNB.get_params sklearn.naive_bayes.ComplementNB.partial_fit sklearn.naive_bayes.ComplementNB.predict sklearn.naive_bayes.ComplementNB.predict_joint_log_proba sklearn.naive_bayes.ComplementNB.predict_log_proba sklearn.naive_bayes.ComplementNB.predict_proba sklearn.naive_bayes.ComplementNB.score sklearn.naive_bayes.ComplementNB.set_fit_request sklearn.naive_bayes.ComplementNB.set_params sklearn.naive_bayes.ComplementNB.set_partial_fit_request sklearn.naive_bayes.ComplementNB.set_score_request 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.naive_bayes.GaussianNB.rst.txt b/dev/_sources/modules/generated/sklearn.naive_bayes.GaussianNB.rst.txt
index 99375e00248fb..905a63612062d 100644
--- a/dev/_sources/modules/generated/sklearn.naive_bayes.GaussianNB.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.naive_bayes.GaussianNB.rst.txt
@@ -8,6 +8,6 @@ GaussianNB
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.naive_bayes.GaussianNB sklearn.naive_bayes.GaussianNB.__delattr__ sklearn.naive_bayes.GaussianNB.__dir__ sklearn.naive_bayes.GaussianNB.__eq__ sklearn.naive_bayes.GaussianNB.__format__ sklearn.naive_bayes.GaussianNB.__ge__ sklearn.naive_bayes.GaussianNB.__getattribute__ sklearn.naive_bayes.GaussianNB.__getstate__ sklearn.naive_bayes.GaussianNB.__gt__ sklearn.naive_bayes.GaussianNB.__hash__ sklearn.naive_bayes.GaussianNB.__init__ sklearn.naive_bayes.GaussianNB.__init_subclass__ sklearn.naive_bayes.GaussianNB.__le__ sklearn.naive_bayes.GaussianNB.__lt__ sklearn.naive_bayes.GaussianNB.__ne__ sklearn.naive_bayes.GaussianNB.__new__ sklearn.naive_bayes.GaussianNB.__reduce__ sklearn.naive_bayes.GaussianNB.__reduce_ex__ sklearn.naive_bayes.GaussianNB.__repr__ sklearn.naive_bayes.GaussianNB.__setattr__ sklearn.naive_bayes.GaussianNB.__setstate__ sklearn.naive_bayes.GaussianNB.__sizeof__ sklearn.naive_bayes.GaussianNB.__sklearn_clone__ sklearn.naive_bayes.GaussianNB.__sklearn_tags__ sklearn.naive_bayes.GaussianNB.__str__ sklearn.naive_bayes.GaussianNB.__subclasshook__ sklearn.naive_bayes.GaussianNB._build_request_for_signature sklearn.naive_bayes.GaussianNB._check_X sklearn.naive_bayes.GaussianNB._check_feature_names sklearn.naive_bayes.GaussianNB._check_n_features sklearn.naive_bayes.GaussianNB._get_default_requests sklearn.naive_bayes.GaussianNB._get_doc_link sklearn.naive_bayes.GaussianNB._get_metadata_request sklearn.naive_bayes.GaussianNB._get_param_names sklearn.naive_bayes.GaussianNB._get_tags sklearn.naive_bayes.GaussianNB._joint_log_likelihood sklearn.naive_bayes.GaussianNB._more_tags sklearn.naive_bayes.GaussianNB._partial_fit sklearn.naive_bayes.GaussianNB._repr_html_inner sklearn.naive_bayes.GaussianNB._repr_mimebundle_ sklearn.naive_bayes.GaussianNB._update_mean_variance sklearn.naive_bayes.GaussianNB._validate_data sklearn.naive_bayes.GaussianNB._validate_params sklearn.naive_bayes.GaussianNB.fit sklearn.naive_bayes.GaussianNB.get_metadata_routing sklearn.naive_bayes.GaussianNB.get_params sklearn.naive_bayes.GaussianNB.partial_fit sklearn.naive_bayes.GaussianNB.predict sklearn.naive_bayes.GaussianNB.predict_joint_log_proba sklearn.naive_bayes.GaussianNB.predict_log_proba sklearn.naive_bayes.GaussianNB.predict_proba sklearn.naive_bayes.GaussianNB.score sklearn.naive_bayes.GaussianNB.set_fit_request sklearn.naive_bayes.GaussianNB.set_params sklearn.naive_bayes.GaussianNB.set_partial_fit_request sklearn.naive_bayes.GaussianNB.set_score_request 
+.. minigallery:: sklearn.naive_bayes.GaussianNB sklearn.naive_bayes.GaussianNB.__delattr__ sklearn.naive_bayes.GaussianNB.__dir__ sklearn.naive_bayes.GaussianNB.__eq__ sklearn.naive_bayes.GaussianNB.__format__ sklearn.naive_bayes.GaussianNB.__ge__ sklearn.naive_bayes.GaussianNB.__getattribute__ sklearn.naive_bayes.GaussianNB.__getstate__ sklearn.naive_bayes.GaussianNB.__gt__ sklearn.naive_bayes.GaussianNB.__hash__ sklearn.naive_bayes.GaussianNB.__init__ sklearn.naive_bayes.GaussianNB.__init_subclass__ sklearn.naive_bayes.GaussianNB.__le__ sklearn.naive_bayes.GaussianNB.__lt__ sklearn.naive_bayes.GaussianNB.__ne__ sklearn.naive_bayes.GaussianNB.__new__ sklearn.naive_bayes.GaussianNB.__reduce__ sklearn.naive_bayes.GaussianNB.__reduce_ex__ sklearn.naive_bayes.GaussianNB.__repr__ sklearn.naive_bayes.GaussianNB.__setattr__ sklearn.naive_bayes.GaussianNB.__setstate__ sklearn.naive_bayes.GaussianNB.__sizeof__ sklearn.naive_bayes.GaussianNB.__sklearn_clone__ sklearn.naive_bayes.GaussianNB.__sklearn_tags__ sklearn.naive_bayes.GaussianNB.__str__ sklearn.naive_bayes.GaussianNB.__subclasshook__ sklearn.naive_bayes.GaussianNB._build_request_for_signature sklearn.naive_bayes.GaussianNB._check_X sklearn.naive_bayes.GaussianNB._get_default_requests sklearn.naive_bayes.GaussianNB._get_doc_link sklearn.naive_bayes.GaussianNB._get_metadata_request sklearn.naive_bayes.GaussianNB._get_param_names sklearn.naive_bayes.GaussianNB._joint_log_likelihood sklearn.naive_bayes.GaussianNB._partial_fit sklearn.naive_bayes.GaussianNB._repr_html_inner sklearn.naive_bayes.GaussianNB._repr_mimebundle_ sklearn.naive_bayes.GaussianNB._update_mean_variance sklearn.naive_bayes.GaussianNB._validate_params sklearn.naive_bayes.GaussianNB.fit sklearn.naive_bayes.GaussianNB.get_metadata_routing sklearn.naive_bayes.GaussianNB.get_params sklearn.naive_bayes.GaussianNB.partial_fit sklearn.naive_bayes.GaussianNB.predict sklearn.naive_bayes.GaussianNB.predict_joint_log_proba sklearn.naive_bayes.GaussianNB.predict_log_proba sklearn.naive_bayes.GaussianNB.predict_proba sklearn.naive_bayes.GaussianNB.score sklearn.naive_bayes.GaussianNB.set_fit_request sklearn.naive_bayes.GaussianNB.set_params sklearn.naive_bayes.GaussianNB.set_partial_fit_request sklearn.naive_bayes.GaussianNB.set_score_request 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.naive_bayes.MultinomialNB.rst.txt b/dev/_sources/modules/generated/sklearn.naive_bayes.MultinomialNB.rst.txt
index 82b5c052c9cac..79d7ccbde864b 100644
--- a/dev/_sources/modules/generated/sklearn.naive_bayes.MultinomialNB.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.naive_bayes.MultinomialNB.rst.txt
@@ -8,6 +8,6 @@ MultinomialNB
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.naive_bayes.MultinomialNB sklearn.naive_bayes.MultinomialNB.__delattr__ sklearn.naive_bayes.MultinomialNB.__dir__ sklearn.naive_bayes.MultinomialNB.__eq__ sklearn.naive_bayes.MultinomialNB.__format__ sklearn.naive_bayes.MultinomialNB.__ge__ sklearn.naive_bayes.MultinomialNB.__getattribute__ sklearn.naive_bayes.MultinomialNB.__getstate__ sklearn.naive_bayes.MultinomialNB.__gt__ sklearn.naive_bayes.MultinomialNB.__hash__ sklearn.naive_bayes.MultinomialNB.__init__ sklearn.naive_bayes.MultinomialNB.__init_subclass__ sklearn.naive_bayes.MultinomialNB.__le__ sklearn.naive_bayes.MultinomialNB.__lt__ sklearn.naive_bayes.MultinomialNB.__ne__ sklearn.naive_bayes.MultinomialNB.__new__ sklearn.naive_bayes.MultinomialNB.__reduce__ sklearn.naive_bayes.MultinomialNB.__reduce_ex__ sklearn.naive_bayes.MultinomialNB.__repr__ sklearn.naive_bayes.MultinomialNB.__setattr__ sklearn.naive_bayes.MultinomialNB.__setstate__ sklearn.naive_bayes.MultinomialNB.__sizeof__ sklearn.naive_bayes.MultinomialNB.__sklearn_clone__ sklearn.naive_bayes.MultinomialNB.__sklearn_tags__ sklearn.naive_bayes.MultinomialNB.__str__ sklearn.naive_bayes.MultinomialNB.__subclasshook__ sklearn.naive_bayes.MultinomialNB._build_request_for_signature sklearn.naive_bayes.MultinomialNB._check_X sklearn.naive_bayes.MultinomialNB._check_X_y sklearn.naive_bayes.MultinomialNB._check_alpha sklearn.naive_bayes.MultinomialNB._check_feature_names sklearn.naive_bayes.MultinomialNB._check_n_features sklearn.naive_bayes.MultinomialNB._count sklearn.naive_bayes.MultinomialNB._get_default_requests sklearn.naive_bayes.MultinomialNB._get_doc_link sklearn.naive_bayes.MultinomialNB._get_metadata_request sklearn.naive_bayes.MultinomialNB._get_param_names sklearn.naive_bayes.MultinomialNB._get_tags sklearn.naive_bayes.MultinomialNB._init_counters sklearn.naive_bayes.MultinomialNB._joint_log_likelihood sklearn.naive_bayes.MultinomialNB._more_tags sklearn.naive_bayes.MultinomialNB._repr_html_inner sklearn.naive_bayes.MultinomialNB._repr_mimebundle_ sklearn.naive_bayes.MultinomialNB._update_class_log_prior sklearn.naive_bayes.MultinomialNB._update_feature_log_prob sklearn.naive_bayes.MultinomialNB._validate_data sklearn.naive_bayes.MultinomialNB._validate_params sklearn.naive_bayes.MultinomialNB.fit sklearn.naive_bayes.MultinomialNB.get_metadata_routing sklearn.naive_bayes.MultinomialNB.get_params sklearn.naive_bayes.MultinomialNB.partial_fit sklearn.naive_bayes.MultinomialNB.predict sklearn.naive_bayes.MultinomialNB.predict_joint_log_proba sklearn.naive_bayes.MultinomialNB.predict_log_proba sklearn.naive_bayes.MultinomialNB.predict_proba sklearn.naive_bayes.MultinomialNB.score sklearn.naive_bayes.MultinomialNB.set_fit_request sklearn.naive_bayes.MultinomialNB.set_params sklearn.naive_bayes.MultinomialNB.set_partial_fit_request sklearn.naive_bayes.MultinomialNB.set_score_request 
+.. minigallery:: sklearn.naive_bayes.MultinomialNB sklearn.naive_bayes.MultinomialNB.__delattr__ sklearn.naive_bayes.MultinomialNB.__dir__ sklearn.naive_bayes.MultinomialNB.__eq__ sklearn.naive_bayes.MultinomialNB.__format__ sklearn.naive_bayes.MultinomialNB.__ge__ sklearn.naive_bayes.MultinomialNB.__getattribute__ sklearn.naive_bayes.MultinomialNB.__getstate__ sklearn.naive_bayes.MultinomialNB.__gt__ sklearn.naive_bayes.MultinomialNB.__hash__ sklearn.naive_bayes.MultinomialNB.__init__ sklearn.naive_bayes.MultinomialNB.__init_subclass__ sklearn.naive_bayes.MultinomialNB.__le__ sklearn.naive_bayes.MultinomialNB.__lt__ sklearn.naive_bayes.MultinomialNB.__ne__ sklearn.naive_bayes.MultinomialNB.__new__ sklearn.naive_bayes.MultinomialNB.__reduce__ sklearn.naive_bayes.MultinomialNB.__reduce_ex__ sklearn.naive_bayes.MultinomialNB.__repr__ sklearn.naive_bayes.MultinomialNB.__setattr__ sklearn.naive_bayes.MultinomialNB.__setstate__ sklearn.naive_bayes.MultinomialNB.__sizeof__ sklearn.naive_bayes.MultinomialNB.__sklearn_clone__ sklearn.naive_bayes.MultinomialNB.__sklearn_tags__ sklearn.naive_bayes.MultinomialNB.__str__ sklearn.naive_bayes.MultinomialNB.__subclasshook__ sklearn.naive_bayes.MultinomialNB._build_request_for_signature sklearn.naive_bayes.MultinomialNB._check_X sklearn.naive_bayes.MultinomialNB._check_X_y sklearn.naive_bayes.MultinomialNB._check_alpha sklearn.naive_bayes.MultinomialNB._count sklearn.naive_bayes.MultinomialNB._get_default_requests sklearn.naive_bayes.MultinomialNB._get_doc_link sklearn.naive_bayes.MultinomialNB._get_metadata_request sklearn.naive_bayes.MultinomialNB._get_param_names sklearn.naive_bayes.MultinomialNB._init_counters sklearn.naive_bayes.MultinomialNB._joint_log_likelihood sklearn.naive_bayes.MultinomialNB._repr_html_inner sklearn.naive_bayes.MultinomialNB._repr_mimebundle_ sklearn.naive_bayes.MultinomialNB._update_class_log_prior sklearn.naive_bayes.MultinomialNB._update_feature_log_prob sklearn.naive_bayes.MultinomialNB._validate_params sklearn.naive_bayes.MultinomialNB.fit sklearn.naive_bayes.MultinomialNB.get_metadata_routing sklearn.naive_bayes.MultinomialNB.get_params sklearn.naive_bayes.MultinomialNB.partial_fit sklearn.naive_bayes.MultinomialNB.predict sklearn.naive_bayes.MultinomialNB.predict_joint_log_proba sklearn.naive_bayes.MultinomialNB.predict_log_proba sklearn.naive_bayes.MultinomialNB.predict_proba sklearn.naive_bayes.MultinomialNB.score sklearn.naive_bayes.MultinomialNB.set_fit_request sklearn.naive_bayes.MultinomialNB.set_params sklearn.naive_bayes.MultinomialNB.set_partial_fit_request sklearn.naive_bayes.MultinomialNB.set_score_request 
    :add-heading: Gallery examples
    :heading-level: -
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diff --git a/dev/_sources/modules/generated/sklearn.neighbors.KNeighborsClassifier.rst.txt b/dev/_sources/modules/generated/sklearn.neighbors.KNeighborsClassifier.rst.txt
index 7536fc1fdb8ac..7e00f4790c1ae 100644
--- a/dev/_sources/modules/generated/sklearn.neighbors.KNeighborsClassifier.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.neighbors.KNeighborsClassifier.rst.txt
@@ -8,6 +8,6 @@ KNeighborsClassifier
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.neighbors.KNeighborsClassifier sklearn.neighbors.KNeighborsClassifier.__delattr__ sklearn.neighbors.KNeighborsClassifier.__dir__ sklearn.neighbors.KNeighborsClassifier.__eq__ sklearn.neighbors.KNeighborsClassifier.__format__ sklearn.neighbors.KNeighborsClassifier.__ge__ sklearn.neighbors.KNeighborsClassifier.__getattribute__ sklearn.neighbors.KNeighborsClassifier.__getstate__ sklearn.neighbors.KNeighborsClassifier.__gt__ sklearn.neighbors.KNeighborsClassifier.__hash__ sklearn.neighbors.KNeighborsClassifier.__init__ sklearn.neighbors.KNeighborsClassifier.__init_subclass__ sklearn.neighbors.KNeighborsClassifier.__le__ sklearn.neighbors.KNeighborsClassifier.__lt__ sklearn.neighbors.KNeighborsClassifier.__ne__ sklearn.neighbors.KNeighborsClassifier.__new__ sklearn.neighbors.KNeighborsClassifier.__reduce__ sklearn.neighbors.KNeighborsClassifier.__reduce_ex__ sklearn.neighbors.KNeighborsClassifier.__repr__ sklearn.neighbors.KNeighborsClassifier.__setattr__ sklearn.neighbors.KNeighborsClassifier.__setstate__ sklearn.neighbors.KNeighborsClassifier.__sizeof__ sklearn.neighbors.KNeighborsClassifier.__sklearn_clone__ sklearn.neighbors.KNeighborsClassifier.__sklearn_tags__ sklearn.neighbors.KNeighborsClassifier.__str__ sklearn.neighbors.KNeighborsClassifier.__subclasshook__ sklearn.neighbors.KNeighborsClassifier._build_request_for_signature sklearn.neighbors.KNeighborsClassifier._check_algorithm_metric sklearn.neighbors.KNeighborsClassifier._check_feature_names sklearn.neighbors.KNeighborsClassifier._check_n_features sklearn.neighbors.KNeighborsClassifier._fit sklearn.neighbors.KNeighborsClassifier._get_default_requests sklearn.neighbors.KNeighborsClassifier._get_doc_link sklearn.neighbors.KNeighborsClassifier._get_metadata_request sklearn.neighbors.KNeighborsClassifier._get_param_names sklearn.neighbors.KNeighborsClassifier._get_tags sklearn.neighbors.KNeighborsClassifier._kneighbors_reduce_func sklearn.neighbors.KNeighborsClassifier._more_tags sklearn.neighbors.KNeighborsClassifier._repr_html_inner sklearn.neighbors.KNeighborsClassifier._repr_mimebundle_ sklearn.neighbors.KNeighborsClassifier._validate_data sklearn.neighbors.KNeighborsClassifier._validate_params sklearn.neighbors.KNeighborsClassifier.fit sklearn.neighbors.KNeighborsClassifier.get_metadata_routing sklearn.neighbors.KNeighborsClassifier.get_params sklearn.neighbors.KNeighborsClassifier.kneighbors sklearn.neighbors.KNeighborsClassifier.kneighbors_graph sklearn.neighbors.KNeighborsClassifier.predict sklearn.neighbors.KNeighborsClassifier.predict_proba sklearn.neighbors.KNeighborsClassifier.score sklearn.neighbors.KNeighborsClassifier.set_params sklearn.neighbors.KNeighborsClassifier.set_score_request 
+.. minigallery:: sklearn.neighbors.KNeighborsClassifier sklearn.neighbors.KNeighborsClassifier.__delattr__ sklearn.neighbors.KNeighborsClassifier.__dir__ sklearn.neighbors.KNeighborsClassifier.__eq__ sklearn.neighbors.KNeighborsClassifier.__format__ sklearn.neighbors.KNeighborsClassifier.__ge__ sklearn.neighbors.KNeighborsClassifier.__getattribute__ sklearn.neighbors.KNeighborsClassifier.__getstate__ sklearn.neighbors.KNeighborsClassifier.__gt__ sklearn.neighbors.KNeighborsClassifier.__hash__ sklearn.neighbors.KNeighborsClassifier.__init__ sklearn.neighbors.KNeighborsClassifier.__init_subclass__ sklearn.neighbors.KNeighborsClassifier.__le__ sklearn.neighbors.KNeighborsClassifier.__lt__ sklearn.neighbors.KNeighborsClassifier.__ne__ sklearn.neighbors.KNeighborsClassifier.__new__ sklearn.neighbors.KNeighborsClassifier.__reduce__ sklearn.neighbors.KNeighborsClassifier.__reduce_ex__ sklearn.neighbors.KNeighborsClassifier.__repr__ sklearn.neighbors.KNeighborsClassifier.__setattr__ sklearn.neighbors.KNeighborsClassifier.__setstate__ sklearn.neighbors.KNeighborsClassifier.__sizeof__ sklearn.neighbors.KNeighborsClassifier.__sklearn_clone__ sklearn.neighbors.KNeighborsClassifier.__sklearn_tags__ sklearn.neighbors.KNeighborsClassifier.__str__ sklearn.neighbors.KNeighborsClassifier.__subclasshook__ sklearn.neighbors.KNeighborsClassifier._build_request_for_signature sklearn.neighbors.KNeighborsClassifier._check_algorithm_metric sklearn.neighbors.KNeighborsClassifier._fit sklearn.neighbors.KNeighborsClassifier._get_default_requests sklearn.neighbors.KNeighborsClassifier._get_doc_link sklearn.neighbors.KNeighborsClassifier._get_metadata_request sklearn.neighbors.KNeighborsClassifier._get_param_names sklearn.neighbors.KNeighborsClassifier._kneighbors_reduce_func sklearn.neighbors.KNeighborsClassifier._repr_html_inner sklearn.neighbors.KNeighborsClassifier._repr_mimebundle_ sklearn.neighbors.KNeighborsClassifier._validate_params sklearn.neighbors.KNeighborsClassifier.fit sklearn.neighbors.KNeighborsClassifier.get_metadata_routing sklearn.neighbors.KNeighborsClassifier.get_params sklearn.neighbors.KNeighborsClassifier.kneighbors sklearn.neighbors.KNeighborsClassifier.kneighbors_graph sklearn.neighbors.KNeighborsClassifier.predict sklearn.neighbors.KNeighborsClassifier.predict_proba sklearn.neighbors.KNeighborsClassifier.score sklearn.neighbors.KNeighborsClassifier.set_params sklearn.neighbors.KNeighborsClassifier.set_score_request 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.neighbors.KNeighborsRegressor.rst.txt b/dev/_sources/modules/generated/sklearn.neighbors.KNeighborsRegressor.rst.txt
index 8a9737a5113d9..f3ab634fb2ee1 100644
--- a/dev/_sources/modules/generated/sklearn.neighbors.KNeighborsRegressor.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.neighbors.KNeighborsRegressor.rst.txt
@@ -8,6 +8,6 @@ KNeighborsRegressor
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.neighbors.KNeighborsRegressor sklearn.neighbors.KNeighborsRegressor.__delattr__ sklearn.neighbors.KNeighborsRegressor.__dir__ sklearn.neighbors.KNeighborsRegressor.__eq__ sklearn.neighbors.KNeighborsRegressor.__format__ sklearn.neighbors.KNeighborsRegressor.__ge__ sklearn.neighbors.KNeighborsRegressor.__getattribute__ sklearn.neighbors.KNeighborsRegressor.__getstate__ sklearn.neighbors.KNeighborsRegressor.__gt__ sklearn.neighbors.KNeighborsRegressor.__hash__ sklearn.neighbors.KNeighborsRegressor.__init__ sklearn.neighbors.KNeighborsRegressor.__init_subclass__ sklearn.neighbors.KNeighborsRegressor.__le__ sklearn.neighbors.KNeighborsRegressor.__lt__ sklearn.neighbors.KNeighborsRegressor.__ne__ sklearn.neighbors.KNeighborsRegressor.__new__ sklearn.neighbors.KNeighborsRegressor.__reduce__ sklearn.neighbors.KNeighborsRegressor.__reduce_ex__ sklearn.neighbors.KNeighborsRegressor.__repr__ sklearn.neighbors.KNeighborsRegressor.__setattr__ sklearn.neighbors.KNeighborsRegressor.__setstate__ sklearn.neighbors.KNeighborsRegressor.__sizeof__ sklearn.neighbors.KNeighborsRegressor.__sklearn_clone__ sklearn.neighbors.KNeighborsRegressor.__sklearn_tags__ sklearn.neighbors.KNeighborsRegressor.__str__ sklearn.neighbors.KNeighborsRegressor.__subclasshook__ sklearn.neighbors.KNeighborsRegressor._build_request_for_signature sklearn.neighbors.KNeighborsRegressor._check_algorithm_metric sklearn.neighbors.KNeighborsRegressor._check_feature_names sklearn.neighbors.KNeighborsRegressor._check_n_features sklearn.neighbors.KNeighborsRegressor._fit sklearn.neighbors.KNeighborsRegressor._get_default_requests sklearn.neighbors.KNeighborsRegressor._get_doc_link sklearn.neighbors.KNeighborsRegressor._get_metadata_request sklearn.neighbors.KNeighborsRegressor._get_param_names sklearn.neighbors.KNeighborsRegressor._get_tags sklearn.neighbors.KNeighborsRegressor._kneighbors_reduce_func sklearn.neighbors.KNeighborsRegressor._more_tags sklearn.neighbors.KNeighborsRegressor._repr_html_inner sklearn.neighbors.KNeighborsRegressor._repr_mimebundle_ sklearn.neighbors.KNeighborsRegressor._validate_data sklearn.neighbors.KNeighborsRegressor._validate_params sklearn.neighbors.KNeighborsRegressor.fit sklearn.neighbors.KNeighborsRegressor.get_metadata_routing sklearn.neighbors.KNeighborsRegressor.get_params sklearn.neighbors.KNeighborsRegressor.kneighbors sklearn.neighbors.KNeighborsRegressor.kneighbors_graph sklearn.neighbors.KNeighborsRegressor.predict sklearn.neighbors.KNeighborsRegressor.score sklearn.neighbors.KNeighborsRegressor.set_params sklearn.neighbors.KNeighborsRegressor.set_score_request 
+.. minigallery:: sklearn.neighbors.KNeighborsRegressor sklearn.neighbors.KNeighborsRegressor.__delattr__ sklearn.neighbors.KNeighborsRegressor.__dir__ sklearn.neighbors.KNeighborsRegressor.__eq__ sklearn.neighbors.KNeighborsRegressor.__format__ sklearn.neighbors.KNeighborsRegressor.__ge__ sklearn.neighbors.KNeighborsRegressor.__getattribute__ sklearn.neighbors.KNeighborsRegressor.__getstate__ sklearn.neighbors.KNeighborsRegressor.__gt__ sklearn.neighbors.KNeighborsRegressor.__hash__ sklearn.neighbors.KNeighborsRegressor.__init__ sklearn.neighbors.KNeighborsRegressor.__init_subclass__ sklearn.neighbors.KNeighborsRegressor.__le__ sklearn.neighbors.KNeighborsRegressor.__lt__ sklearn.neighbors.KNeighborsRegressor.__ne__ sklearn.neighbors.KNeighborsRegressor.__new__ sklearn.neighbors.KNeighborsRegressor.__reduce__ sklearn.neighbors.KNeighborsRegressor.__reduce_ex__ sklearn.neighbors.KNeighborsRegressor.__repr__ sklearn.neighbors.KNeighborsRegressor.__setattr__ sklearn.neighbors.KNeighborsRegressor.__setstate__ sklearn.neighbors.KNeighborsRegressor.__sizeof__ sklearn.neighbors.KNeighborsRegressor.__sklearn_clone__ sklearn.neighbors.KNeighborsRegressor.__sklearn_tags__ sklearn.neighbors.KNeighborsRegressor.__str__ sklearn.neighbors.KNeighborsRegressor.__subclasshook__ sklearn.neighbors.KNeighborsRegressor._build_request_for_signature sklearn.neighbors.KNeighborsRegressor._check_algorithm_metric sklearn.neighbors.KNeighborsRegressor._fit sklearn.neighbors.KNeighborsRegressor._get_default_requests sklearn.neighbors.KNeighborsRegressor._get_doc_link sklearn.neighbors.KNeighborsRegressor._get_metadata_request sklearn.neighbors.KNeighborsRegressor._get_param_names sklearn.neighbors.KNeighborsRegressor._kneighbors_reduce_func sklearn.neighbors.KNeighborsRegressor._repr_html_inner sklearn.neighbors.KNeighborsRegressor._repr_mimebundle_ sklearn.neighbors.KNeighborsRegressor._validate_params sklearn.neighbors.KNeighborsRegressor.fit sklearn.neighbors.KNeighborsRegressor.get_metadata_routing sklearn.neighbors.KNeighborsRegressor.get_params sklearn.neighbors.KNeighborsRegressor.kneighbors sklearn.neighbors.KNeighborsRegressor.kneighbors_graph sklearn.neighbors.KNeighborsRegressor.predict sklearn.neighbors.KNeighborsRegressor.score sklearn.neighbors.KNeighborsRegressor.set_params sklearn.neighbors.KNeighborsRegressor.set_score_request 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.neighbors.KNeighborsTransformer.rst.txt b/dev/_sources/modules/generated/sklearn.neighbors.KNeighborsTransformer.rst.txt
index 64c34699a7b11..6d952c0075a56 100644
--- a/dev/_sources/modules/generated/sklearn.neighbors.KNeighborsTransformer.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.neighbors.KNeighborsTransformer.rst.txt
@@ -8,6 +8,6 @@ KNeighborsTransformer
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.neighbors.KNeighborsTransformer sklearn.neighbors.KNeighborsTransformer.__delattr__ sklearn.neighbors.KNeighborsTransformer.__dir__ sklearn.neighbors.KNeighborsTransformer.__eq__ sklearn.neighbors.KNeighborsTransformer.__format__ sklearn.neighbors.KNeighborsTransformer.__ge__ sklearn.neighbors.KNeighborsTransformer.__getattribute__ sklearn.neighbors.KNeighborsTransformer.__getstate__ sklearn.neighbors.KNeighborsTransformer.__gt__ sklearn.neighbors.KNeighborsTransformer.__hash__ sklearn.neighbors.KNeighborsTransformer.__init__ sklearn.neighbors.KNeighborsTransformer.__init_subclass__ sklearn.neighbors.KNeighborsTransformer.__le__ sklearn.neighbors.KNeighborsTransformer.__lt__ sklearn.neighbors.KNeighborsTransformer.__ne__ sklearn.neighbors.KNeighborsTransformer.__new__ sklearn.neighbors.KNeighborsTransformer.__reduce__ sklearn.neighbors.KNeighborsTransformer.__reduce_ex__ sklearn.neighbors.KNeighborsTransformer.__repr__ sklearn.neighbors.KNeighborsTransformer.__setattr__ sklearn.neighbors.KNeighborsTransformer.__setstate__ sklearn.neighbors.KNeighborsTransformer.__sizeof__ sklearn.neighbors.KNeighborsTransformer.__sklearn_clone__ sklearn.neighbors.KNeighborsTransformer.__sklearn_tags__ sklearn.neighbors.KNeighborsTransformer.__str__ sklearn.neighbors.KNeighborsTransformer.__subclasshook__ sklearn.neighbors.KNeighborsTransformer._build_request_for_signature sklearn.neighbors.KNeighborsTransformer._check_algorithm_metric sklearn.neighbors.KNeighborsTransformer._check_feature_names sklearn.neighbors.KNeighborsTransformer._check_n_features sklearn.neighbors.KNeighborsTransformer._fit sklearn.neighbors.KNeighborsTransformer._get_default_requests sklearn.neighbors.KNeighborsTransformer._get_doc_link sklearn.neighbors.KNeighborsTransformer._get_metadata_request sklearn.neighbors.KNeighborsTransformer._get_param_names sklearn.neighbors.KNeighborsTransformer._get_tags sklearn.neighbors.KNeighborsTransformer._kneighbors_reduce_func sklearn.neighbors.KNeighborsTransformer._more_tags sklearn.neighbors.KNeighborsTransformer._repr_html_inner sklearn.neighbors.KNeighborsTransformer._repr_mimebundle_ sklearn.neighbors.KNeighborsTransformer._validate_data sklearn.neighbors.KNeighborsTransformer._validate_params sklearn.neighbors.KNeighborsTransformer.fit sklearn.neighbors.KNeighborsTransformer.fit_transform sklearn.neighbors.KNeighborsTransformer.get_feature_names_out sklearn.neighbors.KNeighborsTransformer.get_metadata_routing sklearn.neighbors.KNeighborsTransformer.get_params sklearn.neighbors.KNeighborsTransformer.kneighbors sklearn.neighbors.KNeighborsTransformer.kneighbors_graph sklearn.neighbors.KNeighborsTransformer.set_output sklearn.neighbors.KNeighborsTransformer.set_params sklearn.neighbors.KNeighborsTransformer.transform 
+.. minigallery:: sklearn.neighbors.KNeighborsTransformer sklearn.neighbors.KNeighborsTransformer.__delattr__ sklearn.neighbors.KNeighborsTransformer.__dir__ sklearn.neighbors.KNeighborsTransformer.__eq__ sklearn.neighbors.KNeighborsTransformer.__format__ sklearn.neighbors.KNeighborsTransformer.__ge__ sklearn.neighbors.KNeighborsTransformer.__getattribute__ sklearn.neighbors.KNeighborsTransformer.__getstate__ sklearn.neighbors.KNeighborsTransformer.__gt__ sklearn.neighbors.KNeighborsTransformer.__hash__ sklearn.neighbors.KNeighborsTransformer.__init__ sklearn.neighbors.KNeighborsTransformer.__init_subclass__ sklearn.neighbors.KNeighborsTransformer.__le__ sklearn.neighbors.KNeighborsTransformer.__lt__ sklearn.neighbors.KNeighborsTransformer.__ne__ sklearn.neighbors.KNeighborsTransformer.__new__ sklearn.neighbors.KNeighborsTransformer.__reduce__ sklearn.neighbors.KNeighborsTransformer.__reduce_ex__ sklearn.neighbors.KNeighborsTransformer.__repr__ sklearn.neighbors.KNeighborsTransformer.__setattr__ sklearn.neighbors.KNeighborsTransformer.__setstate__ sklearn.neighbors.KNeighborsTransformer.__sizeof__ sklearn.neighbors.KNeighborsTransformer.__sklearn_clone__ sklearn.neighbors.KNeighborsTransformer.__sklearn_tags__ sklearn.neighbors.KNeighborsTransformer.__str__ sklearn.neighbors.KNeighborsTransformer.__subclasshook__ sklearn.neighbors.KNeighborsTransformer._build_request_for_signature sklearn.neighbors.KNeighborsTransformer._check_algorithm_metric sklearn.neighbors.KNeighborsTransformer._fit sklearn.neighbors.KNeighborsTransformer._get_default_requests sklearn.neighbors.KNeighborsTransformer._get_doc_link sklearn.neighbors.KNeighborsTransformer._get_metadata_request sklearn.neighbors.KNeighborsTransformer._get_param_names sklearn.neighbors.KNeighborsTransformer._kneighbors_reduce_func sklearn.neighbors.KNeighborsTransformer._repr_html_inner sklearn.neighbors.KNeighborsTransformer._repr_mimebundle_ sklearn.neighbors.KNeighborsTransformer._validate_params sklearn.neighbors.KNeighborsTransformer.fit sklearn.neighbors.KNeighborsTransformer.fit_transform sklearn.neighbors.KNeighborsTransformer.get_feature_names_out sklearn.neighbors.KNeighborsTransformer.get_metadata_routing sklearn.neighbors.KNeighborsTransformer.get_params sklearn.neighbors.KNeighborsTransformer.kneighbors sklearn.neighbors.KNeighborsTransformer.kneighbors_graph sklearn.neighbors.KNeighborsTransformer.set_output sklearn.neighbors.KNeighborsTransformer.set_params sklearn.neighbors.KNeighborsTransformer.transform 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.neighbors.KernelDensity.rst.txt b/dev/_sources/modules/generated/sklearn.neighbors.KernelDensity.rst.txt
index e4981dcbbcfe5..263dab2251244 100644
--- a/dev/_sources/modules/generated/sklearn.neighbors.KernelDensity.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.neighbors.KernelDensity.rst.txt
@@ -8,6 +8,6 @@ KernelDensity
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.neighbors.KernelDensity sklearn.neighbors.KernelDensity.__delattr__ sklearn.neighbors.KernelDensity.__dir__ sklearn.neighbors.KernelDensity.__eq__ sklearn.neighbors.KernelDensity.__format__ sklearn.neighbors.KernelDensity.__ge__ sklearn.neighbors.KernelDensity.__getattribute__ sklearn.neighbors.KernelDensity.__getstate__ sklearn.neighbors.KernelDensity.__gt__ sklearn.neighbors.KernelDensity.__hash__ sklearn.neighbors.KernelDensity.__init__ sklearn.neighbors.KernelDensity.__init_subclass__ sklearn.neighbors.KernelDensity.__le__ sklearn.neighbors.KernelDensity.__lt__ sklearn.neighbors.KernelDensity.__ne__ sklearn.neighbors.KernelDensity.__new__ sklearn.neighbors.KernelDensity.__reduce__ sklearn.neighbors.KernelDensity.__reduce_ex__ sklearn.neighbors.KernelDensity.__repr__ sklearn.neighbors.KernelDensity.__setattr__ sklearn.neighbors.KernelDensity.__setstate__ sklearn.neighbors.KernelDensity.__sizeof__ sklearn.neighbors.KernelDensity.__sklearn_clone__ sklearn.neighbors.KernelDensity.__sklearn_tags__ sklearn.neighbors.KernelDensity.__str__ sklearn.neighbors.KernelDensity.__subclasshook__ sklearn.neighbors.KernelDensity._build_request_for_signature sklearn.neighbors.KernelDensity._check_feature_names sklearn.neighbors.KernelDensity._check_n_features sklearn.neighbors.KernelDensity._choose_algorithm sklearn.neighbors.KernelDensity._get_default_requests sklearn.neighbors.KernelDensity._get_doc_link sklearn.neighbors.KernelDensity._get_metadata_request sklearn.neighbors.KernelDensity._get_param_names sklearn.neighbors.KernelDensity._get_tags sklearn.neighbors.KernelDensity._more_tags sklearn.neighbors.KernelDensity._repr_html_inner sklearn.neighbors.KernelDensity._repr_mimebundle_ sklearn.neighbors.KernelDensity._validate_data sklearn.neighbors.KernelDensity._validate_params sklearn.neighbors.KernelDensity.fit sklearn.neighbors.KernelDensity.get_metadata_routing sklearn.neighbors.KernelDensity.get_params sklearn.neighbors.KernelDensity.sample sklearn.neighbors.KernelDensity.score sklearn.neighbors.KernelDensity.score_samples sklearn.neighbors.KernelDensity.set_fit_request sklearn.neighbors.KernelDensity.set_params 
+.. minigallery:: sklearn.neighbors.KernelDensity sklearn.neighbors.KernelDensity.__delattr__ sklearn.neighbors.KernelDensity.__dir__ sklearn.neighbors.KernelDensity.__eq__ sklearn.neighbors.KernelDensity.__format__ sklearn.neighbors.KernelDensity.__ge__ sklearn.neighbors.KernelDensity.__getattribute__ sklearn.neighbors.KernelDensity.__getstate__ sklearn.neighbors.KernelDensity.__gt__ sklearn.neighbors.KernelDensity.__hash__ sklearn.neighbors.KernelDensity.__init__ sklearn.neighbors.KernelDensity.__init_subclass__ sklearn.neighbors.KernelDensity.__le__ sklearn.neighbors.KernelDensity.__lt__ sklearn.neighbors.KernelDensity.__ne__ sklearn.neighbors.KernelDensity.__new__ sklearn.neighbors.KernelDensity.__reduce__ sklearn.neighbors.KernelDensity.__reduce_ex__ sklearn.neighbors.KernelDensity.__repr__ sklearn.neighbors.KernelDensity.__setattr__ sklearn.neighbors.KernelDensity.__setstate__ sklearn.neighbors.KernelDensity.__sizeof__ sklearn.neighbors.KernelDensity.__sklearn_clone__ sklearn.neighbors.KernelDensity.__sklearn_tags__ sklearn.neighbors.KernelDensity.__str__ sklearn.neighbors.KernelDensity.__subclasshook__ sklearn.neighbors.KernelDensity._build_request_for_signature sklearn.neighbors.KernelDensity._choose_algorithm sklearn.neighbors.KernelDensity._get_default_requests sklearn.neighbors.KernelDensity._get_doc_link sklearn.neighbors.KernelDensity._get_metadata_request sklearn.neighbors.KernelDensity._get_param_names sklearn.neighbors.KernelDensity._repr_html_inner sklearn.neighbors.KernelDensity._repr_mimebundle_ sklearn.neighbors.KernelDensity._validate_params sklearn.neighbors.KernelDensity.fit sklearn.neighbors.KernelDensity.get_metadata_routing sklearn.neighbors.KernelDensity.get_params sklearn.neighbors.KernelDensity.sample sklearn.neighbors.KernelDensity.score sklearn.neighbors.KernelDensity.score_samples sklearn.neighbors.KernelDensity.set_fit_request sklearn.neighbors.KernelDensity.set_params 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.neighbors.LocalOutlierFactor.rst.txt b/dev/_sources/modules/generated/sklearn.neighbors.LocalOutlierFactor.rst.txt
index 87395ac51528f..d478f9ed077c8 100644
--- a/dev/_sources/modules/generated/sklearn.neighbors.LocalOutlierFactor.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.neighbors.LocalOutlierFactor.rst.txt
@@ -8,6 +8,6 @@ LocalOutlierFactor
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.neighbors.LocalOutlierFactor sklearn.neighbors.LocalOutlierFactor.__delattr__ sklearn.neighbors.LocalOutlierFactor.__dir__ sklearn.neighbors.LocalOutlierFactor.__eq__ sklearn.neighbors.LocalOutlierFactor.__format__ sklearn.neighbors.LocalOutlierFactor.__ge__ sklearn.neighbors.LocalOutlierFactor.__getattribute__ sklearn.neighbors.LocalOutlierFactor.__getstate__ sklearn.neighbors.LocalOutlierFactor.__gt__ sklearn.neighbors.LocalOutlierFactor.__hash__ sklearn.neighbors.LocalOutlierFactor.__init__ sklearn.neighbors.LocalOutlierFactor.__init_subclass__ sklearn.neighbors.LocalOutlierFactor.__le__ sklearn.neighbors.LocalOutlierFactor.__lt__ sklearn.neighbors.LocalOutlierFactor.__ne__ sklearn.neighbors.LocalOutlierFactor.__new__ sklearn.neighbors.LocalOutlierFactor.__reduce__ sklearn.neighbors.LocalOutlierFactor.__reduce_ex__ sklearn.neighbors.LocalOutlierFactor.__repr__ sklearn.neighbors.LocalOutlierFactor.__setattr__ sklearn.neighbors.LocalOutlierFactor.__setstate__ sklearn.neighbors.LocalOutlierFactor.__sizeof__ sklearn.neighbors.LocalOutlierFactor.__sklearn_clone__ sklearn.neighbors.LocalOutlierFactor.__sklearn_tags__ sklearn.neighbors.LocalOutlierFactor.__str__ sklearn.neighbors.LocalOutlierFactor.__subclasshook__ sklearn.neighbors.LocalOutlierFactor._build_request_for_signature sklearn.neighbors.LocalOutlierFactor._check_algorithm_metric sklearn.neighbors.LocalOutlierFactor._check_feature_names sklearn.neighbors.LocalOutlierFactor._check_n_features sklearn.neighbors.LocalOutlierFactor._check_novelty_decision_function sklearn.neighbors.LocalOutlierFactor._check_novelty_fit_predict sklearn.neighbors.LocalOutlierFactor._check_novelty_predict sklearn.neighbors.LocalOutlierFactor._check_novelty_score_samples sklearn.neighbors.LocalOutlierFactor._fit sklearn.neighbors.LocalOutlierFactor._get_default_requests sklearn.neighbors.LocalOutlierFactor._get_doc_link sklearn.neighbors.LocalOutlierFactor._get_metadata_request sklearn.neighbors.LocalOutlierFactor._get_param_names sklearn.neighbors.LocalOutlierFactor._get_tags sklearn.neighbors.LocalOutlierFactor._kneighbors_reduce_func sklearn.neighbors.LocalOutlierFactor._local_reachability_density sklearn.neighbors.LocalOutlierFactor._more_tags sklearn.neighbors.LocalOutlierFactor._predict sklearn.neighbors.LocalOutlierFactor._repr_html_inner sklearn.neighbors.LocalOutlierFactor._repr_mimebundle_ sklearn.neighbors.LocalOutlierFactor._validate_data sklearn.neighbors.LocalOutlierFactor._validate_params sklearn.neighbors.LocalOutlierFactor.decision_function sklearn.neighbors.LocalOutlierFactor.fit sklearn.neighbors.LocalOutlierFactor.fit_predict sklearn.neighbors.LocalOutlierFactor.get_metadata_routing sklearn.neighbors.LocalOutlierFactor.get_params sklearn.neighbors.LocalOutlierFactor.kneighbors sklearn.neighbors.LocalOutlierFactor.kneighbors_graph sklearn.neighbors.LocalOutlierFactor.predict sklearn.neighbors.LocalOutlierFactor.score_samples sklearn.neighbors.LocalOutlierFactor.set_params 
+.. minigallery:: sklearn.neighbors.LocalOutlierFactor sklearn.neighbors.LocalOutlierFactor.__delattr__ sklearn.neighbors.LocalOutlierFactor.__dir__ sklearn.neighbors.LocalOutlierFactor.__eq__ sklearn.neighbors.LocalOutlierFactor.__format__ sklearn.neighbors.LocalOutlierFactor.__ge__ sklearn.neighbors.LocalOutlierFactor.__getattribute__ sklearn.neighbors.LocalOutlierFactor.__getstate__ sklearn.neighbors.LocalOutlierFactor.__gt__ sklearn.neighbors.LocalOutlierFactor.__hash__ sklearn.neighbors.LocalOutlierFactor.__init__ sklearn.neighbors.LocalOutlierFactor.__init_subclass__ sklearn.neighbors.LocalOutlierFactor.__le__ sklearn.neighbors.LocalOutlierFactor.__lt__ sklearn.neighbors.LocalOutlierFactor.__ne__ sklearn.neighbors.LocalOutlierFactor.__new__ sklearn.neighbors.LocalOutlierFactor.__reduce__ sklearn.neighbors.LocalOutlierFactor.__reduce_ex__ sklearn.neighbors.LocalOutlierFactor.__repr__ sklearn.neighbors.LocalOutlierFactor.__setattr__ sklearn.neighbors.LocalOutlierFactor.__setstate__ sklearn.neighbors.LocalOutlierFactor.__sizeof__ sklearn.neighbors.LocalOutlierFactor.__sklearn_clone__ sklearn.neighbors.LocalOutlierFactor.__sklearn_tags__ sklearn.neighbors.LocalOutlierFactor.__str__ sklearn.neighbors.LocalOutlierFactor.__subclasshook__ sklearn.neighbors.LocalOutlierFactor._build_request_for_signature sklearn.neighbors.LocalOutlierFactor._check_algorithm_metric sklearn.neighbors.LocalOutlierFactor._check_novelty_decision_function sklearn.neighbors.LocalOutlierFactor._check_novelty_fit_predict sklearn.neighbors.LocalOutlierFactor._check_novelty_predict sklearn.neighbors.LocalOutlierFactor._check_novelty_score_samples sklearn.neighbors.LocalOutlierFactor._fit sklearn.neighbors.LocalOutlierFactor._get_default_requests sklearn.neighbors.LocalOutlierFactor._get_doc_link sklearn.neighbors.LocalOutlierFactor._get_metadata_request sklearn.neighbors.LocalOutlierFactor._get_param_names sklearn.neighbors.LocalOutlierFactor._kneighbors_reduce_func sklearn.neighbors.LocalOutlierFactor._local_reachability_density sklearn.neighbors.LocalOutlierFactor._predict sklearn.neighbors.LocalOutlierFactor._repr_html_inner sklearn.neighbors.LocalOutlierFactor._repr_mimebundle_ sklearn.neighbors.LocalOutlierFactor._validate_params sklearn.neighbors.LocalOutlierFactor.decision_function sklearn.neighbors.LocalOutlierFactor.fit sklearn.neighbors.LocalOutlierFactor.fit_predict sklearn.neighbors.LocalOutlierFactor.get_metadata_routing sklearn.neighbors.LocalOutlierFactor.get_params sklearn.neighbors.LocalOutlierFactor.kneighbors sklearn.neighbors.LocalOutlierFactor.kneighbors_graph sklearn.neighbors.LocalOutlierFactor.predict sklearn.neighbors.LocalOutlierFactor.score_samples sklearn.neighbors.LocalOutlierFactor.set_params 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.neighbors.NearestCentroid.rst.txt b/dev/_sources/modules/generated/sklearn.neighbors.NearestCentroid.rst.txt
index d3355b76a64eb..64c805496fc31 100644
--- a/dev/_sources/modules/generated/sklearn.neighbors.NearestCentroid.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.neighbors.NearestCentroid.rst.txt
@@ -8,6 +8,6 @@ NearestCentroid
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.neighbors.NearestCentroid sklearn.neighbors.NearestCentroid.__delattr__ sklearn.neighbors.NearestCentroid.__dir__ sklearn.neighbors.NearestCentroid.__eq__ sklearn.neighbors.NearestCentroid.__format__ sklearn.neighbors.NearestCentroid.__ge__ sklearn.neighbors.NearestCentroid.__getattribute__ sklearn.neighbors.NearestCentroid.__getstate__ sklearn.neighbors.NearestCentroid.__gt__ sklearn.neighbors.NearestCentroid.__hash__ sklearn.neighbors.NearestCentroid.__init__ sklearn.neighbors.NearestCentroid.__init_subclass__ sklearn.neighbors.NearestCentroid.__le__ sklearn.neighbors.NearestCentroid.__lt__ sklearn.neighbors.NearestCentroid.__ne__ sklearn.neighbors.NearestCentroid.__new__ sklearn.neighbors.NearestCentroid.__reduce__ sklearn.neighbors.NearestCentroid.__reduce_ex__ sklearn.neighbors.NearestCentroid.__repr__ sklearn.neighbors.NearestCentroid.__setattr__ sklearn.neighbors.NearestCentroid.__setstate__ sklearn.neighbors.NearestCentroid.__sizeof__ sklearn.neighbors.NearestCentroid.__sklearn_clone__ sklearn.neighbors.NearestCentroid.__sklearn_tags__ sklearn.neighbors.NearestCentroid.__str__ sklearn.neighbors.NearestCentroid.__subclasshook__ sklearn.neighbors.NearestCentroid._build_request_for_signature sklearn.neighbors.NearestCentroid._check_euclidean_metric sklearn.neighbors.NearestCentroid._check_feature_names sklearn.neighbors.NearestCentroid._check_n_features sklearn.neighbors.NearestCentroid._decision_function sklearn.neighbors.NearestCentroid._get_default_requests sklearn.neighbors.NearestCentroid._get_doc_link sklearn.neighbors.NearestCentroid._get_metadata_request sklearn.neighbors.NearestCentroid._get_param_names sklearn.neighbors.NearestCentroid._get_tags sklearn.neighbors.NearestCentroid._more_tags sklearn.neighbors.NearestCentroid._repr_html_inner sklearn.neighbors.NearestCentroid._repr_mimebundle_ sklearn.neighbors.NearestCentroid._validate_data sklearn.neighbors.NearestCentroid._validate_params sklearn.neighbors.NearestCentroid.decision_function sklearn.neighbors.NearestCentroid.fit sklearn.neighbors.NearestCentroid.get_metadata_routing sklearn.neighbors.NearestCentroid.get_params sklearn.neighbors.NearestCentroid.predict sklearn.neighbors.NearestCentroid.predict_log_proba sklearn.neighbors.NearestCentroid.predict_proba sklearn.neighbors.NearestCentroid.score sklearn.neighbors.NearestCentroid.set_params sklearn.neighbors.NearestCentroid.set_score_request 
+.. minigallery:: sklearn.neighbors.NearestCentroid sklearn.neighbors.NearestCentroid.__delattr__ sklearn.neighbors.NearestCentroid.__dir__ sklearn.neighbors.NearestCentroid.__eq__ sklearn.neighbors.NearestCentroid.__format__ sklearn.neighbors.NearestCentroid.__ge__ sklearn.neighbors.NearestCentroid.__getattribute__ sklearn.neighbors.NearestCentroid.__getstate__ sklearn.neighbors.NearestCentroid.__gt__ sklearn.neighbors.NearestCentroid.__hash__ sklearn.neighbors.NearestCentroid.__init__ sklearn.neighbors.NearestCentroid.__init_subclass__ sklearn.neighbors.NearestCentroid.__le__ sklearn.neighbors.NearestCentroid.__lt__ sklearn.neighbors.NearestCentroid.__ne__ sklearn.neighbors.NearestCentroid.__new__ sklearn.neighbors.NearestCentroid.__reduce__ sklearn.neighbors.NearestCentroid.__reduce_ex__ sklearn.neighbors.NearestCentroid.__repr__ sklearn.neighbors.NearestCentroid.__setattr__ sklearn.neighbors.NearestCentroid.__setstate__ sklearn.neighbors.NearestCentroid.__sizeof__ sklearn.neighbors.NearestCentroid.__sklearn_clone__ sklearn.neighbors.NearestCentroid.__sklearn_tags__ sklearn.neighbors.NearestCentroid.__str__ sklearn.neighbors.NearestCentroid.__subclasshook__ sklearn.neighbors.NearestCentroid._build_request_for_signature sklearn.neighbors.NearestCentroid._check_euclidean_metric sklearn.neighbors.NearestCentroid._decision_function sklearn.neighbors.NearestCentroid._get_default_requests sklearn.neighbors.NearestCentroid._get_doc_link sklearn.neighbors.NearestCentroid._get_metadata_request sklearn.neighbors.NearestCentroid._get_param_names sklearn.neighbors.NearestCentroid._repr_html_inner sklearn.neighbors.NearestCentroid._repr_mimebundle_ sklearn.neighbors.NearestCentroid._validate_params sklearn.neighbors.NearestCentroid.decision_function sklearn.neighbors.NearestCentroid.fit sklearn.neighbors.NearestCentroid.get_metadata_routing sklearn.neighbors.NearestCentroid.get_params sklearn.neighbors.NearestCentroid.predict sklearn.neighbors.NearestCentroid.predict_log_proba sklearn.neighbors.NearestCentroid.predict_proba sklearn.neighbors.NearestCentroid.score sklearn.neighbors.NearestCentroid.set_params sklearn.neighbors.NearestCentroid.set_score_request 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.neighbors.NearestNeighbors.rst.txt b/dev/_sources/modules/generated/sklearn.neighbors.NearestNeighbors.rst.txt
index a761f6a3b6126..a409ddd0a183a 100644
--- a/dev/_sources/modules/generated/sklearn.neighbors.NearestNeighbors.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.neighbors.NearestNeighbors.rst.txt
@@ -8,6 +8,6 @@ NearestNeighbors
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.neighbors.NearestNeighbors sklearn.neighbors.NearestNeighbors.__delattr__ sklearn.neighbors.NearestNeighbors.__dir__ sklearn.neighbors.NearestNeighbors.__eq__ sklearn.neighbors.NearestNeighbors.__format__ sklearn.neighbors.NearestNeighbors.__ge__ sklearn.neighbors.NearestNeighbors.__getattribute__ sklearn.neighbors.NearestNeighbors.__getstate__ sklearn.neighbors.NearestNeighbors.__gt__ sklearn.neighbors.NearestNeighbors.__hash__ sklearn.neighbors.NearestNeighbors.__init__ sklearn.neighbors.NearestNeighbors.__init_subclass__ sklearn.neighbors.NearestNeighbors.__le__ sklearn.neighbors.NearestNeighbors.__lt__ sklearn.neighbors.NearestNeighbors.__ne__ sklearn.neighbors.NearestNeighbors.__new__ sklearn.neighbors.NearestNeighbors.__reduce__ sklearn.neighbors.NearestNeighbors.__reduce_ex__ sklearn.neighbors.NearestNeighbors.__repr__ sklearn.neighbors.NearestNeighbors.__setattr__ sklearn.neighbors.NearestNeighbors.__setstate__ sklearn.neighbors.NearestNeighbors.__sizeof__ sklearn.neighbors.NearestNeighbors.__sklearn_clone__ sklearn.neighbors.NearestNeighbors.__sklearn_tags__ sklearn.neighbors.NearestNeighbors.__str__ sklearn.neighbors.NearestNeighbors.__subclasshook__ sklearn.neighbors.NearestNeighbors._build_request_for_signature sklearn.neighbors.NearestNeighbors._check_algorithm_metric sklearn.neighbors.NearestNeighbors._check_feature_names sklearn.neighbors.NearestNeighbors._check_n_features sklearn.neighbors.NearestNeighbors._fit sklearn.neighbors.NearestNeighbors._get_default_requests sklearn.neighbors.NearestNeighbors._get_doc_link sklearn.neighbors.NearestNeighbors._get_metadata_request sklearn.neighbors.NearestNeighbors._get_param_names sklearn.neighbors.NearestNeighbors._get_tags sklearn.neighbors.NearestNeighbors._kneighbors_reduce_func sklearn.neighbors.NearestNeighbors._more_tags sklearn.neighbors.NearestNeighbors._radius_neighbors_reduce_func sklearn.neighbors.NearestNeighbors._repr_html_inner sklearn.neighbors.NearestNeighbors._repr_mimebundle_ sklearn.neighbors.NearestNeighbors._validate_data sklearn.neighbors.NearestNeighbors._validate_params sklearn.neighbors.NearestNeighbors.fit sklearn.neighbors.NearestNeighbors.get_metadata_routing sklearn.neighbors.NearestNeighbors.get_params sklearn.neighbors.NearestNeighbors.kneighbors sklearn.neighbors.NearestNeighbors.kneighbors_graph sklearn.neighbors.NearestNeighbors.radius_neighbors sklearn.neighbors.NearestNeighbors.radius_neighbors_graph sklearn.neighbors.NearestNeighbors.set_params 
+.. minigallery:: sklearn.neighbors.NearestNeighbors sklearn.neighbors.NearestNeighbors.__delattr__ sklearn.neighbors.NearestNeighbors.__dir__ sklearn.neighbors.NearestNeighbors.__eq__ sklearn.neighbors.NearestNeighbors.__format__ sklearn.neighbors.NearestNeighbors.__ge__ sklearn.neighbors.NearestNeighbors.__getattribute__ sklearn.neighbors.NearestNeighbors.__getstate__ sklearn.neighbors.NearestNeighbors.__gt__ sklearn.neighbors.NearestNeighbors.__hash__ sklearn.neighbors.NearestNeighbors.__init__ sklearn.neighbors.NearestNeighbors.__init_subclass__ sklearn.neighbors.NearestNeighbors.__le__ sklearn.neighbors.NearestNeighbors.__lt__ sklearn.neighbors.NearestNeighbors.__ne__ sklearn.neighbors.NearestNeighbors.__new__ sklearn.neighbors.NearestNeighbors.__reduce__ sklearn.neighbors.NearestNeighbors.__reduce_ex__ sklearn.neighbors.NearestNeighbors.__repr__ sklearn.neighbors.NearestNeighbors.__setattr__ sklearn.neighbors.NearestNeighbors.__setstate__ sklearn.neighbors.NearestNeighbors.__sizeof__ sklearn.neighbors.NearestNeighbors.__sklearn_clone__ sklearn.neighbors.NearestNeighbors.__sklearn_tags__ sklearn.neighbors.NearestNeighbors.__str__ sklearn.neighbors.NearestNeighbors.__subclasshook__ sklearn.neighbors.NearestNeighbors._build_request_for_signature sklearn.neighbors.NearestNeighbors._check_algorithm_metric sklearn.neighbors.NearestNeighbors._fit sklearn.neighbors.NearestNeighbors._get_default_requests sklearn.neighbors.NearestNeighbors._get_doc_link sklearn.neighbors.NearestNeighbors._get_metadata_request sklearn.neighbors.NearestNeighbors._get_param_names sklearn.neighbors.NearestNeighbors._kneighbors_reduce_func sklearn.neighbors.NearestNeighbors._radius_neighbors_reduce_func sklearn.neighbors.NearestNeighbors._repr_html_inner sklearn.neighbors.NearestNeighbors._repr_mimebundle_ sklearn.neighbors.NearestNeighbors._validate_params sklearn.neighbors.NearestNeighbors.fit sklearn.neighbors.NearestNeighbors.get_metadata_routing sklearn.neighbors.NearestNeighbors.get_params sklearn.neighbors.NearestNeighbors.kneighbors sklearn.neighbors.NearestNeighbors.kneighbors_graph sklearn.neighbors.NearestNeighbors.radius_neighbors sklearn.neighbors.NearestNeighbors.radius_neighbors_graph sklearn.neighbors.NearestNeighbors.set_params 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.neighbors.NeighborhoodComponentsAnalysis.rst.txt b/dev/_sources/modules/generated/sklearn.neighbors.NeighborhoodComponentsAnalysis.rst.txt
index f39afd0603c71..cb45294cd924d 100644
--- a/dev/_sources/modules/generated/sklearn.neighbors.NeighborhoodComponentsAnalysis.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.neighbors.NeighborhoodComponentsAnalysis.rst.txt
@@ -8,6 +8,6 @@ NeighborhoodComponentsAnalysis
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.neighbors.NeighborhoodComponentsAnalysis sklearn.neighbors.NeighborhoodComponentsAnalysis.__delattr__ sklearn.neighbors.NeighborhoodComponentsAnalysis.__dir__ sklearn.neighbors.NeighborhoodComponentsAnalysis.__eq__ sklearn.neighbors.NeighborhoodComponentsAnalysis.__format__ sklearn.neighbors.NeighborhoodComponentsAnalysis.__ge__ sklearn.neighbors.NeighborhoodComponentsAnalysis.__getattribute__ sklearn.neighbors.NeighborhoodComponentsAnalysis.__getstate__ sklearn.neighbors.NeighborhoodComponentsAnalysis.__gt__ sklearn.neighbors.NeighborhoodComponentsAnalysis.__hash__ sklearn.neighbors.NeighborhoodComponentsAnalysis.__init__ sklearn.neighbors.NeighborhoodComponentsAnalysis.__init_subclass__ sklearn.neighbors.NeighborhoodComponentsAnalysis.__le__ sklearn.neighbors.NeighborhoodComponentsAnalysis.__lt__ sklearn.neighbors.NeighborhoodComponentsAnalysis.__ne__ sklearn.neighbors.NeighborhoodComponentsAnalysis.__new__ sklearn.neighbors.NeighborhoodComponentsAnalysis.__reduce__ sklearn.neighbors.NeighborhoodComponentsAnalysis.__reduce_ex__ sklearn.neighbors.NeighborhoodComponentsAnalysis.__repr__ sklearn.neighbors.NeighborhoodComponentsAnalysis.__setattr__ sklearn.neighbors.NeighborhoodComponentsAnalysis.__setstate__ sklearn.neighbors.NeighborhoodComponentsAnalysis.__sizeof__ sklearn.neighbors.NeighborhoodComponentsAnalysis.__sklearn_clone__ sklearn.neighbors.NeighborhoodComponentsAnalysis.__sklearn_tags__ sklearn.neighbors.NeighborhoodComponentsAnalysis.__str__ sklearn.neighbors.NeighborhoodComponentsAnalysis.__subclasshook__ sklearn.neighbors.NeighborhoodComponentsAnalysis._build_request_for_signature sklearn.neighbors.NeighborhoodComponentsAnalysis._callback sklearn.neighbors.NeighborhoodComponentsAnalysis._check_feature_names sklearn.neighbors.NeighborhoodComponentsAnalysis._check_n_features sklearn.neighbors.NeighborhoodComponentsAnalysis._get_default_requests sklearn.neighbors.NeighborhoodComponentsAnalysis._get_doc_link sklearn.neighbors.NeighborhoodComponentsAnalysis._get_metadata_request sklearn.neighbors.NeighborhoodComponentsAnalysis._get_param_names sklearn.neighbors.NeighborhoodComponentsAnalysis._get_tags sklearn.neighbors.NeighborhoodComponentsAnalysis._initialize sklearn.neighbors.NeighborhoodComponentsAnalysis._loss_grad_lbfgs sklearn.neighbors.NeighborhoodComponentsAnalysis._more_tags sklearn.neighbors.NeighborhoodComponentsAnalysis._repr_html_inner sklearn.neighbors.NeighborhoodComponentsAnalysis._repr_mimebundle_ sklearn.neighbors.NeighborhoodComponentsAnalysis._validate_data sklearn.neighbors.NeighborhoodComponentsAnalysis._validate_params sklearn.neighbors.NeighborhoodComponentsAnalysis.fit sklearn.neighbors.NeighborhoodComponentsAnalysis.fit_transform sklearn.neighbors.NeighborhoodComponentsAnalysis.get_feature_names_out sklearn.neighbors.NeighborhoodComponentsAnalysis.get_metadata_routing sklearn.neighbors.NeighborhoodComponentsAnalysis.get_params sklearn.neighbors.NeighborhoodComponentsAnalysis.set_output sklearn.neighbors.NeighborhoodComponentsAnalysis.set_params sklearn.neighbors.NeighborhoodComponentsAnalysis.transform 
+.. minigallery:: sklearn.neighbors.NeighborhoodComponentsAnalysis sklearn.neighbors.NeighborhoodComponentsAnalysis.__delattr__ sklearn.neighbors.NeighborhoodComponentsAnalysis.__dir__ sklearn.neighbors.NeighborhoodComponentsAnalysis.__eq__ sklearn.neighbors.NeighborhoodComponentsAnalysis.__format__ sklearn.neighbors.NeighborhoodComponentsAnalysis.__ge__ sklearn.neighbors.NeighborhoodComponentsAnalysis.__getattribute__ sklearn.neighbors.NeighborhoodComponentsAnalysis.__getstate__ sklearn.neighbors.NeighborhoodComponentsAnalysis.__gt__ sklearn.neighbors.NeighborhoodComponentsAnalysis.__hash__ sklearn.neighbors.NeighborhoodComponentsAnalysis.__init__ sklearn.neighbors.NeighborhoodComponentsAnalysis.__init_subclass__ sklearn.neighbors.NeighborhoodComponentsAnalysis.__le__ sklearn.neighbors.NeighborhoodComponentsAnalysis.__lt__ sklearn.neighbors.NeighborhoodComponentsAnalysis.__ne__ sklearn.neighbors.NeighborhoodComponentsAnalysis.__new__ sklearn.neighbors.NeighborhoodComponentsAnalysis.__reduce__ sklearn.neighbors.NeighborhoodComponentsAnalysis.__reduce_ex__ sklearn.neighbors.NeighborhoodComponentsAnalysis.__repr__ sklearn.neighbors.NeighborhoodComponentsAnalysis.__setattr__ sklearn.neighbors.NeighborhoodComponentsAnalysis.__setstate__ sklearn.neighbors.NeighborhoodComponentsAnalysis.__sizeof__ sklearn.neighbors.NeighborhoodComponentsAnalysis.__sklearn_clone__ sklearn.neighbors.NeighborhoodComponentsAnalysis.__sklearn_tags__ sklearn.neighbors.NeighborhoodComponentsAnalysis.__str__ sklearn.neighbors.NeighborhoodComponentsAnalysis.__subclasshook__ sklearn.neighbors.NeighborhoodComponentsAnalysis._build_request_for_signature sklearn.neighbors.NeighborhoodComponentsAnalysis._callback sklearn.neighbors.NeighborhoodComponentsAnalysis._get_default_requests sklearn.neighbors.NeighborhoodComponentsAnalysis._get_doc_link sklearn.neighbors.NeighborhoodComponentsAnalysis._get_metadata_request sklearn.neighbors.NeighborhoodComponentsAnalysis._get_param_names sklearn.neighbors.NeighborhoodComponentsAnalysis._initialize sklearn.neighbors.NeighborhoodComponentsAnalysis._loss_grad_lbfgs sklearn.neighbors.NeighborhoodComponentsAnalysis._repr_html_inner sklearn.neighbors.NeighborhoodComponentsAnalysis._repr_mimebundle_ sklearn.neighbors.NeighborhoodComponentsAnalysis._validate_params sklearn.neighbors.NeighborhoodComponentsAnalysis.fit sklearn.neighbors.NeighborhoodComponentsAnalysis.fit_transform sklearn.neighbors.NeighborhoodComponentsAnalysis.get_feature_names_out sklearn.neighbors.NeighborhoodComponentsAnalysis.get_metadata_routing sklearn.neighbors.NeighborhoodComponentsAnalysis.get_params sklearn.neighbors.NeighborhoodComponentsAnalysis.set_output sklearn.neighbors.NeighborhoodComponentsAnalysis.set_params sklearn.neighbors.NeighborhoodComponentsAnalysis.transform 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.neighbors.RadiusNeighborsClassifier.rst.txt b/dev/_sources/modules/generated/sklearn.neighbors.RadiusNeighborsClassifier.rst.txt
index e38251f68d885..b1a4dfff9d749 100644
--- a/dev/_sources/modules/generated/sklearn.neighbors.RadiusNeighborsClassifier.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.neighbors.RadiusNeighborsClassifier.rst.txt
@@ -8,6 +8,6 @@ RadiusNeighborsClassifier
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.neighbors.RadiusNeighborsClassifier sklearn.neighbors.RadiusNeighborsClassifier.__delattr__ sklearn.neighbors.RadiusNeighborsClassifier.__dir__ sklearn.neighbors.RadiusNeighborsClassifier.__eq__ sklearn.neighbors.RadiusNeighborsClassifier.__format__ sklearn.neighbors.RadiusNeighborsClassifier.__ge__ sklearn.neighbors.RadiusNeighborsClassifier.__getattribute__ sklearn.neighbors.RadiusNeighborsClassifier.__getstate__ sklearn.neighbors.RadiusNeighborsClassifier.__gt__ sklearn.neighbors.RadiusNeighborsClassifier.__hash__ sklearn.neighbors.RadiusNeighborsClassifier.__init__ sklearn.neighbors.RadiusNeighborsClassifier.__init_subclass__ sklearn.neighbors.RadiusNeighborsClassifier.__le__ sklearn.neighbors.RadiusNeighborsClassifier.__lt__ sklearn.neighbors.RadiusNeighborsClassifier.__ne__ sklearn.neighbors.RadiusNeighborsClassifier.__new__ sklearn.neighbors.RadiusNeighborsClassifier.__reduce__ sklearn.neighbors.RadiusNeighborsClassifier.__reduce_ex__ sklearn.neighbors.RadiusNeighborsClassifier.__repr__ sklearn.neighbors.RadiusNeighborsClassifier.__setattr__ sklearn.neighbors.RadiusNeighborsClassifier.__setstate__ sklearn.neighbors.RadiusNeighborsClassifier.__sizeof__ sklearn.neighbors.RadiusNeighborsClassifier.__sklearn_clone__ sklearn.neighbors.RadiusNeighborsClassifier.__sklearn_tags__ sklearn.neighbors.RadiusNeighborsClassifier.__str__ sklearn.neighbors.RadiusNeighborsClassifier.__subclasshook__ sklearn.neighbors.RadiusNeighborsClassifier._build_request_for_signature sklearn.neighbors.RadiusNeighborsClassifier._check_algorithm_metric sklearn.neighbors.RadiusNeighborsClassifier._check_feature_names sklearn.neighbors.RadiusNeighborsClassifier._check_n_features sklearn.neighbors.RadiusNeighborsClassifier._fit sklearn.neighbors.RadiusNeighborsClassifier._get_default_requests sklearn.neighbors.RadiusNeighborsClassifier._get_doc_link sklearn.neighbors.RadiusNeighborsClassifier._get_metadata_request sklearn.neighbors.RadiusNeighborsClassifier._get_param_names sklearn.neighbors.RadiusNeighborsClassifier._get_tags sklearn.neighbors.RadiusNeighborsClassifier._more_tags sklearn.neighbors.RadiusNeighborsClassifier._radius_neighbors_reduce_func sklearn.neighbors.RadiusNeighborsClassifier._repr_html_inner sklearn.neighbors.RadiusNeighborsClassifier._repr_mimebundle_ sklearn.neighbors.RadiusNeighborsClassifier._validate_data sklearn.neighbors.RadiusNeighborsClassifier._validate_params sklearn.neighbors.RadiusNeighborsClassifier.fit sklearn.neighbors.RadiusNeighborsClassifier.get_metadata_routing sklearn.neighbors.RadiusNeighborsClassifier.get_params sklearn.neighbors.RadiusNeighborsClassifier.predict sklearn.neighbors.RadiusNeighborsClassifier.predict_proba sklearn.neighbors.RadiusNeighborsClassifier.radius_neighbors sklearn.neighbors.RadiusNeighborsClassifier.radius_neighbors_graph sklearn.neighbors.RadiusNeighborsClassifier.score sklearn.neighbors.RadiusNeighborsClassifier.set_params sklearn.neighbors.RadiusNeighborsClassifier.set_score_request 
+.. minigallery:: sklearn.neighbors.RadiusNeighborsClassifier sklearn.neighbors.RadiusNeighborsClassifier.__delattr__ sklearn.neighbors.RadiusNeighborsClassifier.__dir__ sklearn.neighbors.RadiusNeighborsClassifier.__eq__ sklearn.neighbors.RadiusNeighborsClassifier.__format__ sklearn.neighbors.RadiusNeighborsClassifier.__ge__ sklearn.neighbors.RadiusNeighborsClassifier.__getattribute__ sklearn.neighbors.RadiusNeighborsClassifier.__getstate__ sklearn.neighbors.RadiusNeighborsClassifier.__gt__ sklearn.neighbors.RadiusNeighborsClassifier.__hash__ sklearn.neighbors.RadiusNeighborsClassifier.__init__ sklearn.neighbors.RadiusNeighborsClassifier.__init_subclass__ sklearn.neighbors.RadiusNeighborsClassifier.__le__ sklearn.neighbors.RadiusNeighborsClassifier.__lt__ sklearn.neighbors.RadiusNeighborsClassifier.__ne__ sklearn.neighbors.RadiusNeighborsClassifier.__new__ sklearn.neighbors.RadiusNeighborsClassifier.__reduce__ sklearn.neighbors.RadiusNeighborsClassifier.__reduce_ex__ sklearn.neighbors.RadiusNeighborsClassifier.__repr__ sklearn.neighbors.RadiusNeighborsClassifier.__setattr__ sklearn.neighbors.RadiusNeighborsClassifier.__setstate__ sklearn.neighbors.RadiusNeighborsClassifier.__sizeof__ sklearn.neighbors.RadiusNeighborsClassifier.__sklearn_clone__ sklearn.neighbors.RadiusNeighborsClassifier.__sklearn_tags__ sklearn.neighbors.RadiusNeighborsClassifier.__str__ sklearn.neighbors.RadiusNeighborsClassifier.__subclasshook__ sklearn.neighbors.RadiusNeighborsClassifier._build_request_for_signature sklearn.neighbors.RadiusNeighborsClassifier._check_algorithm_metric sklearn.neighbors.RadiusNeighborsClassifier._fit sklearn.neighbors.RadiusNeighborsClassifier._get_default_requests sklearn.neighbors.RadiusNeighborsClassifier._get_doc_link sklearn.neighbors.RadiusNeighborsClassifier._get_metadata_request sklearn.neighbors.RadiusNeighborsClassifier._get_param_names sklearn.neighbors.RadiusNeighborsClassifier._radius_neighbors_reduce_func sklearn.neighbors.RadiusNeighborsClassifier._repr_html_inner sklearn.neighbors.RadiusNeighborsClassifier._repr_mimebundle_ sklearn.neighbors.RadiusNeighborsClassifier._validate_params sklearn.neighbors.RadiusNeighborsClassifier.fit sklearn.neighbors.RadiusNeighborsClassifier.get_metadata_routing sklearn.neighbors.RadiusNeighborsClassifier.get_params sklearn.neighbors.RadiusNeighborsClassifier.predict sklearn.neighbors.RadiusNeighborsClassifier.predict_proba sklearn.neighbors.RadiusNeighborsClassifier.radius_neighbors sklearn.neighbors.RadiusNeighborsClassifier.radius_neighbors_graph sklearn.neighbors.RadiusNeighborsClassifier.score sklearn.neighbors.RadiusNeighborsClassifier.set_params sklearn.neighbors.RadiusNeighborsClassifier.set_score_request 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.neighbors.RadiusNeighborsRegressor.rst.txt b/dev/_sources/modules/generated/sklearn.neighbors.RadiusNeighborsRegressor.rst.txt
index a39cbdd1e4a5a..4690c3f2f5383 100644
--- a/dev/_sources/modules/generated/sklearn.neighbors.RadiusNeighborsRegressor.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.neighbors.RadiusNeighborsRegressor.rst.txt
@@ -8,6 +8,6 @@ RadiusNeighborsRegressor
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.neighbors.RadiusNeighborsRegressor sklearn.neighbors.RadiusNeighborsRegressor.__delattr__ sklearn.neighbors.RadiusNeighborsRegressor.__dir__ sklearn.neighbors.RadiusNeighborsRegressor.__eq__ sklearn.neighbors.RadiusNeighborsRegressor.__format__ sklearn.neighbors.RadiusNeighborsRegressor.__ge__ sklearn.neighbors.RadiusNeighborsRegressor.__getattribute__ sklearn.neighbors.RadiusNeighborsRegressor.__getstate__ sklearn.neighbors.RadiusNeighborsRegressor.__gt__ sklearn.neighbors.RadiusNeighborsRegressor.__hash__ sklearn.neighbors.RadiusNeighborsRegressor.__init__ sklearn.neighbors.RadiusNeighborsRegressor.__init_subclass__ sklearn.neighbors.RadiusNeighborsRegressor.__le__ sklearn.neighbors.RadiusNeighborsRegressor.__lt__ sklearn.neighbors.RadiusNeighborsRegressor.__ne__ sklearn.neighbors.RadiusNeighborsRegressor.__new__ sklearn.neighbors.RadiusNeighborsRegressor.__reduce__ sklearn.neighbors.RadiusNeighborsRegressor.__reduce_ex__ sklearn.neighbors.RadiusNeighborsRegressor.__repr__ sklearn.neighbors.RadiusNeighborsRegressor.__setattr__ sklearn.neighbors.RadiusNeighborsRegressor.__setstate__ sklearn.neighbors.RadiusNeighborsRegressor.__sizeof__ sklearn.neighbors.RadiusNeighborsRegressor.__sklearn_clone__ sklearn.neighbors.RadiusNeighborsRegressor.__sklearn_tags__ sklearn.neighbors.RadiusNeighborsRegressor.__str__ sklearn.neighbors.RadiusNeighborsRegressor.__subclasshook__ sklearn.neighbors.RadiusNeighborsRegressor._build_request_for_signature sklearn.neighbors.RadiusNeighborsRegressor._check_algorithm_metric sklearn.neighbors.RadiusNeighborsRegressor._check_feature_names sklearn.neighbors.RadiusNeighborsRegressor._check_n_features sklearn.neighbors.RadiusNeighborsRegressor._fit sklearn.neighbors.RadiusNeighborsRegressor._get_default_requests sklearn.neighbors.RadiusNeighborsRegressor._get_doc_link sklearn.neighbors.RadiusNeighborsRegressor._get_metadata_request sklearn.neighbors.RadiusNeighborsRegressor._get_param_names sklearn.neighbors.RadiusNeighborsRegressor._get_tags sklearn.neighbors.RadiusNeighborsRegressor._more_tags sklearn.neighbors.RadiusNeighborsRegressor._radius_neighbors_reduce_func sklearn.neighbors.RadiusNeighborsRegressor._repr_html_inner sklearn.neighbors.RadiusNeighborsRegressor._repr_mimebundle_ sklearn.neighbors.RadiusNeighborsRegressor._validate_data sklearn.neighbors.RadiusNeighborsRegressor._validate_params sklearn.neighbors.RadiusNeighborsRegressor.fit sklearn.neighbors.RadiusNeighborsRegressor.get_metadata_routing sklearn.neighbors.RadiusNeighborsRegressor.get_params sklearn.neighbors.RadiusNeighborsRegressor.predict sklearn.neighbors.RadiusNeighborsRegressor.radius_neighbors sklearn.neighbors.RadiusNeighborsRegressor.radius_neighbors_graph sklearn.neighbors.RadiusNeighborsRegressor.score sklearn.neighbors.RadiusNeighborsRegressor.set_params sklearn.neighbors.RadiusNeighborsRegressor.set_score_request 
+.. minigallery:: sklearn.neighbors.RadiusNeighborsRegressor sklearn.neighbors.RadiusNeighborsRegressor.__delattr__ sklearn.neighbors.RadiusNeighborsRegressor.__dir__ sklearn.neighbors.RadiusNeighborsRegressor.__eq__ sklearn.neighbors.RadiusNeighborsRegressor.__format__ sklearn.neighbors.RadiusNeighborsRegressor.__ge__ sklearn.neighbors.RadiusNeighborsRegressor.__getattribute__ sklearn.neighbors.RadiusNeighborsRegressor.__getstate__ sklearn.neighbors.RadiusNeighborsRegressor.__gt__ sklearn.neighbors.RadiusNeighborsRegressor.__hash__ sklearn.neighbors.RadiusNeighborsRegressor.__init__ sklearn.neighbors.RadiusNeighborsRegressor.__init_subclass__ sklearn.neighbors.RadiusNeighborsRegressor.__le__ sklearn.neighbors.RadiusNeighborsRegressor.__lt__ sklearn.neighbors.RadiusNeighborsRegressor.__ne__ sklearn.neighbors.RadiusNeighborsRegressor.__new__ sklearn.neighbors.RadiusNeighborsRegressor.__reduce__ sklearn.neighbors.RadiusNeighborsRegressor.__reduce_ex__ sklearn.neighbors.RadiusNeighborsRegressor.__repr__ sklearn.neighbors.RadiusNeighborsRegressor.__setattr__ sklearn.neighbors.RadiusNeighborsRegressor.__setstate__ sklearn.neighbors.RadiusNeighborsRegressor.__sizeof__ sklearn.neighbors.RadiusNeighborsRegressor.__sklearn_clone__ sklearn.neighbors.RadiusNeighborsRegressor.__sklearn_tags__ sklearn.neighbors.RadiusNeighborsRegressor.__str__ sklearn.neighbors.RadiusNeighborsRegressor.__subclasshook__ sklearn.neighbors.RadiusNeighborsRegressor._build_request_for_signature sklearn.neighbors.RadiusNeighborsRegressor._check_algorithm_metric sklearn.neighbors.RadiusNeighborsRegressor._fit sklearn.neighbors.RadiusNeighborsRegressor._get_default_requests sklearn.neighbors.RadiusNeighborsRegressor._get_doc_link sklearn.neighbors.RadiusNeighborsRegressor._get_metadata_request sklearn.neighbors.RadiusNeighborsRegressor._get_param_names sklearn.neighbors.RadiusNeighborsRegressor._radius_neighbors_reduce_func sklearn.neighbors.RadiusNeighborsRegressor._repr_html_inner sklearn.neighbors.RadiusNeighborsRegressor._repr_mimebundle_ sklearn.neighbors.RadiusNeighborsRegressor._validate_params sklearn.neighbors.RadiusNeighborsRegressor.fit sklearn.neighbors.RadiusNeighborsRegressor.get_metadata_routing sklearn.neighbors.RadiusNeighborsRegressor.get_params sklearn.neighbors.RadiusNeighborsRegressor.predict sklearn.neighbors.RadiusNeighborsRegressor.radius_neighbors sklearn.neighbors.RadiusNeighborsRegressor.radius_neighbors_graph sklearn.neighbors.RadiusNeighborsRegressor.score sklearn.neighbors.RadiusNeighborsRegressor.set_params sklearn.neighbors.RadiusNeighborsRegressor.set_score_request 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.neighbors.RadiusNeighborsTransformer.rst.txt b/dev/_sources/modules/generated/sklearn.neighbors.RadiusNeighborsTransformer.rst.txt
index f1881b802d0fe..7b080bd2d9043 100644
--- a/dev/_sources/modules/generated/sklearn.neighbors.RadiusNeighborsTransformer.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.neighbors.RadiusNeighborsTransformer.rst.txt
@@ -8,6 +8,6 @@ RadiusNeighborsTransformer
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.neighbors.RadiusNeighborsTransformer sklearn.neighbors.RadiusNeighborsTransformer.__delattr__ sklearn.neighbors.RadiusNeighborsTransformer.__dir__ sklearn.neighbors.RadiusNeighborsTransformer.__eq__ sklearn.neighbors.RadiusNeighborsTransformer.__format__ sklearn.neighbors.RadiusNeighborsTransformer.__ge__ sklearn.neighbors.RadiusNeighborsTransformer.__getattribute__ sklearn.neighbors.RadiusNeighborsTransformer.__getstate__ sklearn.neighbors.RadiusNeighborsTransformer.__gt__ sklearn.neighbors.RadiusNeighborsTransformer.__hash__ sklearn.neighbors.RadiusNeighborsTransformer.__init__ sklearn.neighbors.RadiusNeighborsTransformer.__init_subclass__ sklearn.neighbors.RadiusNeighborsTransformer.__le__ sklearn.neighbors.RadiusNeighborsTransformer.__lt__ sklearn.neighbors.RadiusNeighborsTransformer.__ne__ sklearn.neighbors.RadiusNeighborsTransformer.__new__ sklearn.neighbors.RadiusNeighborsTransformer.__reduce__ sklearn.neighbors.RadiusNeighborsTransformer.__reduce_ex__ sklearn.neighbors.RadiusNeighborsTransformer.__repr__ sklearn.neighbors.RadiusNeighborsTransformer.__setattr__ sklearn.neighbors.RadiusNeighborsTransformer.__setstate__ sklearn.neighbors.RadiusNeighborsTransformer.__sizeof__ sklearn.neighbors.RadiusNeighborsTransformer.__sklearn_clone__ sklearn.neighbors.RadiusNeighborsTransformer.__sklearn_tags__ sklearn.neighbors.RadiusNeighborsTransformer.__str__ sklearn.neighbors.RadiusNeighborsTransformer.__subclasshook__ sklearn.neighbors.RadiusNeighborsTransformer._build_request_for_signature sklearn.neighbors.RadiusNeighborsTransformer._check_algorithm_metric sklearn.neighbors.RadiusNeighborsTransformer._check_feature_names sklearn.neighbors.RadiusNeighborsTransformer._check_n_features sklearn.neighbors.RadiusNeighborsTransformer._fit sklearn.neighbors.RadiusNeighborsTransformer._get_default_requests sklearn.neighbors.RadiusNeighborsTransformer._get_doc_link sklearn.neighbors.RadiusNeighborsTransformer._get_metadata_request sklearn.neighbors.RadiusNeighborsTransformer._get_param_names sklearn.neighbors.RadiusNeighborsTransformer._get_tags sklearn.neighbors.RadiusNeighborsTransformer._more_tags sklearn.neighbors.RadiusNeighborsTransformer._radius_neighbors_reduce_func sklearn.neighbors.RadiusNeighborsTransformer._repr_html_inner sklearn.neighbors.RadiusNeighborsTransformer._repr_mimebundle_ sklearn.neighbors.RadiusNeighborsTransformer._validate_data sklearn.neighbors.RadiusNeighborsTransformer._validate_params sklearn.neighbors.RadiusNeighborsTransformer.fit sklearn.neighbors.RadiusNeighborsTransformer.fit_transform sklearn.neighbors.RadiusNeighborsTransformer.get_feature_names_out sklearn.neighbors.RadiusNeighborsTransformer.get_metadata_routing sklearn.neighbors.RadiusNeighborsTransformer.get_params sklearn.neighbors.RadiusNeighborsTransformer.radius_neighbors sklearn.neighbors.RadiusNeighborsTransformer.radius_neighbors_graph sklearn.neighbors.RadiusNeighborsTransformer.set_output sklearn.neighbors.RadiusNeighborsTransformer.set_params sklearn.neighbors.RadiusNeighborsTransformer.transform 
+.. minigallery:: sklearn.neighbors.RadiusNeighborsTransformer sklearn.neighbors.RadiusNeighborsTransformer.__delattr__ sklearn.neighbors.RadiusNeighborsTransformer.__dir__ sklearn.neighbors.RadiusNeighborsTransformer.__eq__ sklearn.neighbors.RadiusNeighborsTransformer.__format__ sklearn.neighbors.RadiusNeighborsTransformer.__ge__ sklearn.neighbors.RadiusNeighborsTransformer.__getattribute__ sklearn.neighbors.RadiusNeighborsTransformer.__getstate__ sklearn.neighbors.RadiusNeighborsTransformer.__gt__ sklearn.neighbors.RadiusNeighborsTransformer.__hash__ sklearn.neighbors.RadiusNeighborsTransformer.__init__ sklearn.neighbors.RadiusNeighborsTransformer.__init_subclass__ sklearn.neighbors.RadiusNeighborsTransformer.__le__ sklearn.neighbors.RadiusNeighborsTransformer.__lt__ sklearn.neighbors.RadiusNeighborsTransformer.__ne__ sklearn.neighbors.RadiusNeighborsTransformer.__new__ sklearn.neighbors.RadiusNeighborsTransformer.__reduce__ sklearn.neighbors.RadiusNeighborsTransformer.__reduce_ex__ sklearn.neighbors.RadiusNeighborsTransformer.__repr__ sklearn.neighbors.RadiusNeighborsTransformer.__setattr__ sklearn.neighbors.RadiusNeighborsTransformer.__setstate__ sklearn.neighbors.RadiusNeighborsTransformer.__sizeof__ sklearn.neighbors.RadiusNeighborsTransformer.__sklearn_clone__ sklearn.neighbors.RadiusNeighborsTransformer.__sklearn_tags__ sklearn.neighbors.RadiusNeighborsTransformer.__str__ sklearn.neighbors.RadiusNeighborsTransformer.__subclasshook__ sklearn.neighbors.RadiusNeighborsTransformer._build_request_for_signature sklearn.neighbors.RadiusNeighborsTransformer._check_algorithm_metric sklearn.neighbors.RadiusNeighborsTransformer._fit sklearn.neighbors.RadiusNeighborsTransformer._get_default_requests sklearn.neighbors.RadiusNeighborsTransformer._get_doc_link sklearn.neighbors.RadiusNeighborsTransformer._get_metadata_request sklearn.neighbors.RadiusNeighborsTransformer._get_param_names sklearn.neighbors.RadiusNeighborsTransformer._radius_neighbors_reduce_func sklearn.neighbors.RadiusNeighborsTransformer._repr_html_inner sklearn.neighbors.RadiusNeighborsTransformer._repr_mimebundle_ sklearn.neighbors.RadiusNeighborsTransformer._validate_params sklearn.neighbors.RadiusNeighborsTransformer.fit sklearn.neighbors.RadiusNeighborsTransformer.fit_transform sklearn.neighbors.RadiusNeighborsTransformer.get_feature_names_out sklearn.neighbors.RadiusNeighborsTransformer.get_metadata_routing sklearn.neighbors.RadiusNeighborsTransformer.get_params sklearn.neighbors.RadiusNeighborsTransformer.radius_neighbors sklearn.neighbors.RadiusNeighborsTransformer.radius_neighbors_graph sklearn.neighbors.RadiusNeighborsTransformer.set_output sklearn.neighbors.RadiusNeighborsTransformer.set_params sklearn.neighbors.RadiusNeighborsTransformer.transform 
    :add-heading: Gallery examples
    :heading-level: -
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diff --git a/dev/_sources/modules/generated/sklearn.neural_network.BernoulliRBM.rst.txt b/dev/_sources/modules/generated/sklearn.neural_network.BernoulliRBM.rst.txt
index 18cac03255b66..695340aac68fb 100644
--- a/dev/_sources/modules/generated/sklearn.neural_network.BernoulliRBM.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.neural_network.BernoulliRBM.rst.txt
@@ -8,6 +8,6 @@ BernoulliRBM
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.neural_network.BernoulliRBM sklearn.neural_network.BernoulliRBM.__delattr__ sklearn.neural_network.BernoulliRBM.__dir__ sklearn.neural_network.BernoulliRBM.__eq__ sklearn.neural_network.BernoulliRBM.__format__ sklearn.neural_network.BernoulliRBM.__ge__ sklearn.neural_network.BernoulliRBM.__getattribute__ sklearn.neural_network.BernoulliRBM.__getstate__ sklearn.neural_network.BernoulliRBM.__gt__ sklearn.neural_network.BernoulliRBM.__hash__ sklearn.neural_network.BernoulliRBM.__init__ sklearn.neural_network.BernoulliRBM.__init_subclass__ sklearn.neural_network.BernoulliRBM.__le__ sklearn.neural_network.BernoulliRBM.__lt__ sklearn.neural_network.BernoulliRBM.__ne__ sklearn.neural_network.BernoulliRBM.__new__ sklearn.neural_network.BernoulliRBM.__reduce__ sklearn.neural_network.BernoulliRBM.__reduce_ex__ sklearn.neural_network.BernoulliRBM.__repr__ sklearn.neural_network.BernoulliRBM.__setattr__ sklearn.neural_network.BernoulliRBM.__setstate__ sklearn.neural_network.BernoulliRBM.__sizeof__ sklearn.neural_network.BernoulliRBM.__sklearn_clone__ sklearn.neural_network.BernoulliRBM.__sklearn_tags__ sklearn.neural_network.BernoulliRBM.__str__ sklearn.neural_network.BernoulliRBM.__subclasshook__ sklearn.neural_network.BernoulliRBM._build_request_for_signature sklearn.neural_network.BernoulliRBM._check_feature_names sklearn.neural_network.BernoulliRBM._check_n_features sklearn.neural_network.BernoulliRBM._fit sklearn.neural_network.BernoulliRBM._free_energy sklearn.neural_network.BernoulliRBM._get_default_requests sklearn.neural_network.BernoulliRBM._get_doc_link sklearn.neural_network.BernoulliRBM._get_metadata_request sklearn.neural_network.BernoulliRBM._get_param_names sklearn.neural_network.BernoulliRBM._get_tags sklearn.neural_network.BernoulliRBM._mean_hiddens sklearn.neural_network.BernoulliRBM._more_tags sklearn.neural_network.BernoulliRBM._repr_html_inner sklearn.neural_network.BernoulliRBM._repr_mimebundle_ sklearn.neural_network.BernoulliRBM._sample_hiddens sklearn.neural_network.BernoulliRBM._sample_visibles sklearn.neural_network.BernoulliRBM._validate_data sklearn.neural_network.BernoulliRBM._validate_params sklearn.neural_network.BernoulliRBM.fit sklearn.neural_network.BernoulliRBM.fit_transform sklearn.neural_network.BernoulliRBM.get_feature_names_out sklearn.neural_network.BernoulliRBM.get_metadata_routing sklearn.neural_network.BernoulliRBM.get_params sklearn.neural_network.BernoulliRBM.gibbs sklearn.neural_network.BernoulliRBM.partial_fit sklearn.neural_network.BernoulliRBM.score_samples sklearn.neural_network.BernoulliRBM.set_output sklearn.neural_network.BernoulliRBM.set_params sklearn.neural_network.BernoulliRBM.transform 
+.. minigallery:: sklearn.neural_network.BernoulliRBM sklearn.neural_network.BernoulliRBM.__delattr__ sklearn.neural_network.BernoulliRBM.__dir__ sklearn.neural_network.BernoulliRBM.__eq__ sklearn.neural_network.BernoulliRBM.__format__ sklearn.neural_network.BernoulliRBM.__ge__ sklearn.neural_network.BernoulliRBM.__getattribute__ sklearn.neural_network.BernoulliRBM.__getstate__ sklearn.neural_network.BernoulliRBM.__gt__ sklearn.neural_network.BernoulliRBM.__hash__ sklearn.neural_network.BernoulliRBM.__init__ sklearn.neural_network.BernoulliRBM.__init_subclass__ sklearn.neural_network.BernoulliRBM.__le__ sklearn.neural_network.BernoulliRBM.__lt__ sklearn.neural_network.BernoulliRBM.__ne__ sklearn.neural_network.BernoulliRBM.__new__ sklearn.neural_network.BernoulliRBM.__reduce__ sklearn.neural_network.BernoulliRBM.__reduce_ex__ sklearn.neural_network.BernoulliRBM.__repr__ sklearn.neural_network.BernoulliRBM.__setattr__ sklearn.neural_network.BernoulliRBM.__setstate__ sklearn.neural_network.BernoulliRBM.__sizeof__ sklearn.neural_network.BernoulliRBM.__sklearn_clone__ sklearn.neural_network.BernoulliRBM.__sklearn_tags__ sklearn.neural_network.BernoulliRBM.__str__ sklearn.neural_network.BernoulliRBM.__subclasshook__ sklearn.neural_network.BernoulliRBM._build_request_for_signature sklearn.neural_network.BernoulliRBM._fit sklearn.neural_network.BernoulliRBM._free_energy sklearn.neural_network.BernoulliRBM._get_default_requests sklearn.neural_network.BernoulliRBM._get_doc_link sklearn.neural_network.BernoulliRBM._get_metadata_request sklearn.neural_network.BernoulliRBM._get_param_names sklearn.neural_network.BernoulliRBM._mean_hiddens sklearn.neural_network.BernoulliRBM._repr_html_inner sklearn.neural_network.BernoulliRBM._repr_mimebundle_ sklearn.neural_network.BernoulliRBM._sample_hiddens sklearn.neural_network.BernoulliRBM._sample_visibles sklearn.neural_network.BernoulliRBM._validate_params sklearn.neural_network.BernoulliRBM.fit sklearn.neural_network.BernoulliRBM.fit_transform sklearn.neural_network.BernoulliRBM.get_feature_names_out sklearn.neural_network.BernoulliRBM.get_metadata_routing sklearn.neural_network.BernoulliRBM.get_params sklearn.neural_network.BernoulliRBM.gibbs sklearn.neural_network.BernoulliRBM.partial_fit sklearn.neural_network.BernoulliRBM.score_samples sklearn.neural_network.BernoulliRBM.set_output sklearn.neural_network.BernoulliRBM.set_params sklearn.neural_network.BernoulliRBM.transform 
    :add-heading: Gallery examples
    :heading-level: -
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diff --git a/dev/_sources/modules/generated/sklearn.neural_network.MLPClassifier.rst.txt b/dev/_sources/modules/generated/sklearn.neural_network.MLPClassifier.rst.txt
index 00e48f17d28f8..59e05315c30c0 100644
--- a/dev/_sources/modules/generated/sklearn.neural_network.MLPClassifier.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.neural_network.MLPClassifier.rst.txt
@@ -8,6 +8,6 @@ MLPClassifier
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.neural_network.MLPClassifier sklearn.neural_network.MLPClassifier.__delattr__ sklearn.neural_network.MLPClassifier.__dir__ sklearn.neural_network.MLPClassifier.__eq__ sklearn.neural_network.MLPClassifier.__format__ sklearn.neural_network.MLPClassifier.__ge__ sklearn.neural_network.MLPClassifier.__getattribute__ sklearn.neural_network.MLPClassifier.__getstate__ sklearn.neural_network.MLPClassifier.__gt__ sklearn.neural_network.MLPClassifier.__hash__ sklearn.neural_network.MLPClassifier.__init__ sklearn.neural_network.MLPClassifier.__init_subclass__ sklearn.neural_network.MLPClassifier.__le__ sklearn.neural_network.MLPClassifier.__lt__ sklearn.neural_network.MLPClassifier.__ne__ sklearn.neural_network.MLPClassifier.__new__ sklearn.neural_network.MLPClassifier.__reduce__ sklearn.neural_network.MLPClassifier.__reduce_ex__ sklearn.neural_network.MLPClassifier.__repr__ sklearn.neural_network.MLPClassifier.__setattr__ sklearn.neural_network.MLPClassifier.__setstate__ sklearn.neural_network.MLPClassifier.__sizeof__ sklearn.neural_network.MLPClassifier.__sklearn_clone__ sklearn.neural_network.MLPClassifier.__sklearn_tags__ sklearn.neural_network.MLPClassifier.__str__ sklearn.neural_network.MLPClassifier.__subclasshook__ sklearn.neural_network.MLPClassifier._backprop sklearn.neural_network.MLPClassifier._build_request_for_signature sklearn.neural_network.MLPClassifier._check_feature_names sklearn.neural_network.MLPClassifier._check_n_features sklearn.neural_network.MLPClassifier._check_solver sklearn.neural_network.MLPClassifier._compute_loss_grad sklearn.neural_network.MLPClassifier._fit sklearn.neural_network.MLPClassifier._fit_lbfgs sklearn.neural_network.MLPClassifier._fit_stochastic sklearn.neural_network.MLPClassifier._forward_pass sklearn.neural_network.MLPClassifier._forward_pass_fast sklearn.neural_network.MLPClassifier._get_default_requests sklearn.neural_network.MLPClassifier._get_doc_link sklearn.neural_network.MLPClassifier._get_metadata_request sklearn.neural_network.MLPClassifier._get_param_names sklearn.neural_network.MLPClassifier._get_tags sklearn.neural_network.MLPClassifier._init_coef sklearn.neural_network.MLPClassifier._initialize sklearn.neural_network.MLPClassifier._loss_grad_lbfgs sklearn.neural_network.MLPClassifier._more_tags sklearn.neural_network.MLPClassifier._predict sklearn.neural_network.MLPClassifier._repr_html_inner sklearn.neural_network.MLPClassifier._repr_mimebundle_ sklearn.neural_network.MLPClassifier._score sklearn.neural_network.MLPClassifier._score_with_function sklearn.neural_network.MLPClassifier._unpack sklearn.neural_network.MLPClassifier._update_no_improvement_count sklearn.neural_network.MLPClassifier._validate_data sklearn.neural_network.MLPClassifier._validate_input sklearn.neural_network.MLPClassifier._validate_params sklearn.neural_network.MLPClassifier.fit sklearn.neural_network.MLPClassifier.get_metadata_routing sklearn.neural_network.MLPClassifier.get_params sklearn.neural_network.MLPClassifier.partial_fit sklearn.neural_network.MLPClassifier.predict sklearn.neural_network.MLPClassifier.predict_log_proba sklearn.neural_network.MLPClassifier.predict_proba sklearn.neural_network.MLPClassifier.score sklearn.neural_network.MLPClassifier.set_fit_request sklearn.neural_network.MLPClassifier.set_params sklearn.neural_network.MLPClassifier.set_partial_fit_request sklearn.neural_network.MLPClassifier.set_score_request 
+.. minigallery:: sklearn.neural_network.MLPClassifier sklearn.neural_network.MLPClassifier.__delattr__ sklearn.neural_network.MLPClassifier.__dir__ sklearn.neural_network.MLPClassifier.__eq__ sklearn.neural_network.MLPClassifier.__format__ sklearn.neural_network.MLPClassifier.__ge__ sklearn.neural_network.MLPClassifier.__getattribute__ sklearn.neural_network.MLPClassifier.__getstate__ sklearn.neural_network.MLPClassifier.__gt__ sklearn.neural_network.MLPClassifier.__hash__ sklearn.neural_network.MLPClassifier.__init__ sklearn.neural_network.MLPClassifier.__init_subclass__ sklearn.neural_network.MLPClassifier.__le__ sklearn.neural_network.MLPClassifier.__lt__ sklearn.neural_network.MLPClassifier.__ne__ sklearn.neural_network.MLPClassifier.__new__ sklearn.neural_network.MLPClassifier.__reduce__ sklearn.neural_network.MLPClassifier.__reduce_ex__ sklearn.neural_network.MLPClassifier.__repr__ sklearn.neural_network.MLPClassifier.__setattr__ sklearn.neural_network.MLPClassifier.__setstate__ sklearn.neural_network.MLPClassifier.__sizeof__ sklearn.neural_network.MLPClassifier.__sklearn_clone__ sklearn.neural_network.MLPClassifier.__sklearn_tags__ sklearn.neural_network.MLPClassifier.__str__ sklearn.neural_network.MLPClassifier.__subclasshook__ sklearn.neural_network.MLPClassifier._backprop sklearn.neural_network.MLPClassifier._build_request_for_signature sklearn.neural_network.MLPClassifier._check_solver sklearn.neural_network.MLPClassifier._compute_loss_grad sklearn.neural_network.MLPClassifier._fit sklearn.neural_network.MLPClassifier._fit_lbfgs sklearn.neural_network.MLPClassifier._fit_stochastic sklearn.neural_network.MLPClassifier._forward_pass sklearn.neural_network.MLPClassifier._forward_pass_fast sklearn.neural_network.MLPClassifier._get_default_requests sklearn.neural_network.MLPClassifier._get_doc_link sklearn.neural_network.MLPClassifier._get_metadata_request sklearn.neural_network.MLPClassifier._get_param_names sklearn.neural_network.MLPClassifier._init_coef sklearn.neural_network.MLPClassifier._initialize sklearn.neural_network.MLPClassifier._loss_grad_lbfgs sklearn.neural_network.MLPClassifier._predict sklearn.neural_network.MLPClassifier._repr_html_inner sklearn.neural_network.MLPClassifier._repr_mimebundle_ sklearn.neural_network.MLPClassifier._score sklearn.neural_network.MLPClassifier._score_with_function sklearn.neural_network.MLPClassifier._unpack sklearn.neural_network.MLPClassifier._update_no_improvement_count sklearn.neural_network.MLPClassifier._validate_input sklearn.neural_network.MLPClassifier._validate_params sklearn.neural_network.MLPClassifier.fit sklearn.neural_network.MLPClassifier.get_metadata_routing sklearn.neural_network.MLPClassifier.get_params sklearn.neural_network.MLPClassifier.partial_fit sklearn.neural_network.MLPClassifier.predict sklearn.neural_network.MLPClassifier.predict_log_proba sklearn.neural_network.MLPClassifier.predict_proba sklearn.neural_network.MLPClassifier.score sklearn.neural_network.MLPClassifier.set_fit_request sklearn.neural_network.MLPClassifier.set_params sklearn.neural_network.MLPClassifier.set_partial_fit_request sklearn.neural_network.MLPClassifier.set_score_request 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.neural_network.MLPRegressor.rst.txt b/dev/_sources/modules/generated/sklearn.neural_network.MLPRegressor.rst.txt
index ade8e0dad89ed..e3497c3937717 100644
--- a/dev/_sources/modules/generated/sklearn.neural_network.MLPRegressor.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.neural_network.MLPRegressor.rst.txt
@@ -8,6 +8,6 @@ MLPRegressor
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.neural_network.MLPRegressor sklearn.neural_network.MLPRegressor.__delattr__ sklearn.neural_network.MLPRegressor.__dir__ sklearn.neural_network.MLPRegressor.__eq__ sklearn.neural_network.MLPRegressor.__format__ sklearn.neural_network.MLPRegressor.__ge__ sklearn.neural_network.MLPRegressor.__getattribute__ sklearn.neural_network.MLPRegressor.__getstate__ sklearn.neural_network.MLPRegressor.__gt__ sklearn.neural_network.MLPRegressor.__hash__ sklearn.neural_network.MLPRegressor.__init__ sklearn.neural_network.MLPRegressor.__init_subclass__ sklearn.neural_network.MLPRegressor.__le__ sklearn.neural_network.MLPRegressor.__lt__ sklearn.neural_network.MLPRegressor.__ne__ sklearn.neural_network.MLPRegressor.__new__ sklearn.neural_network.MLPRegressor.__reduce__ sklearn.neural_network.MLPRegressor.__reduce_ex__ sklearn.neural_network.MLPRegressor.__repr__ sklearn.neural_network.MLPRegressor.__setattr__ sklearn.neural_network.MLPRegressor.__setstate__ sklearn.neural_network.MLPRegressor.__sizeof__ sklearn.neural_network.MLPRegressor.__sklearn_clone__ sklearn.neural_network.MLPRegressor.__sklearn_tags__ sklearn.neural_network.MLPRegressor.__str__ sklearn.neural_network.MLPRegressor.__subclasshook__ sklearn.neural_network.MLPRegressor._backprop sklearn.neural_network.MLPRegressor._build_request_for_signature sklearn.neural_network.MLPRegressor._check_feature_names sklearn.neural_network.MLPRegressor._check_n_features sklearn.neural_network.MLPRegressor._check_solver sklearn.neural_network.MLPRegressor._compute_loss_grad sklearn.neural_network.MLPRegressor._fit sklearn.neural_network.MLPRegressor._fit_lbfgs sklearn.neural_network.MLPRegressor._fit_stochastic sklearn.neural_network.MLPRegressor._forward_pass sklearn.neural_network.MLPRegressor._forward_pass_fast sklearn.neural_network.MLPRegressor._get_default_requests sklearn.neural_network.MLPRegressor._get_doc_link sklearn.neural_network.MLPRegressor._get_metadata_request sklearn.neural_network.MLPRegressor._get_param_names sklearn.neural_network.MLPRegressor._get_tags sklearn.neural_network.MLPRegressor._init_coef sklearn.neural_network.MLPRegressor._initialize sklearn.neural_network.MLPRegressor._loss_grad_lbfgs sklearn.neural_network.MLPRegressor._more_tags sklearn.neural_network.MLPRegressor._predict sklearn.neural_network.MLPRegressor._repr_html_inner sklearn.neural_network.MLPRegressor._repr_mimebundle_ sklearn.neural_network.MLPRegressor._score sklearn.neural_network.MLPRegressor._score_with_function sklearn.neural_network.MLPRegressor._unpack sklearn.neural_network.MLPRegressor._update_no_improvement_count sklearn.neural_network.MLPRegressor._validate_data sklearn.neural_network.MLPRegressor._validate_input sklearn.neural_network.MLPRegressor._validate_params sklearn.neural_network.MLPRegressor.fit sklearn.neural_network.MLPRegressor.get_metadata_routing sklearn.neural_network.MLPRegressor.get_params sklearn.neural_network.MLPRegressor.partial_fit sklearn.neural_network.MLPRegressor.predict sklearn.neural_network.MLPRegressor.score sklearn.neural_network.MLPRegressor.set_fit_request sklearn.neural_network.MLPRegressor.set_params sklearn.neural_network.MLPRegressor.set_partial_fit_request sklearn.neural_network.MLPRegressor.set_score_request 
+.. minigallery:: sklearn.neural_network.MLPRegressor sklearn.neural_network.MLPRegressor.__delattr__ sklearn.neural_network.MLPRegressor.__dir__ sklearn.neural_network.MLPRegressor.__eq__ sklearn.neural_network.MLPRegressor.__format__ sklearn.neural_network.MLPRegressor.__ge__ sklearn.neural_network.MLPRegressor.__getattribute__ sklearn.neural_network.MLPRegressor.__getstate__ sklearn.neural_network.MLPRegressor.__gt__ sklearn.neural_network.MLPRegressor.__hash__ sklearn.neural_network.MLPRegressor.__init__ sklearn.neural_network.MLPRegressor.__init_subclass__ sklearn.neural_network.MLPRegressor.__le__ sklearn.neural_network.MLPRegressor.__lt__ sklearn.neural_network.MLPRegressor.__ne__ sklearn.neural_network.MLPRegressor.__new__ sklearn.neural_network.MLPRegressor.__reduce__ sklearn.neural_network.MLPRegressor.__reduce_ex__ sklearn.neural_network.MLPRegressor.__repr__ sklearn.neural_network.MLPRegressor.__setattr__ sklearn.neural_network.MLPRegressor.__setstate__ sklearn.neural_network.MLPRegressor.__sizeof__ sklearn.neural_network.MLPRegressor.__sklearn_clone__ sklearn.neural_network.MLPRegressor.__sklearn_tags__ sklearn.neural_network.MLPRegressor.__str__ sklearn.neural_network.MLPRegressor.__subclasshook__ sklearn.neural_network.MLPRegressor._backprop sklearn.neural_network.MLPRegressor._build_request_for_signature sklearn.neural_network.MLPRegressor._check_solver sklearn.neural_network.MLPRegressor._compute_loss_grad sklearn.neural_network.MLPRegressor._fit sklearn.neural_network.MLPRegressor._fit_lbfgs sklearn.neural_network.MLPRegressor._fit_stochastic sklearn.neural_network.MLPRegressor._forward_pass sklearn.neural_network.MLPRegressor._forward_pass_fast sklearn.neural_network.MLPRegressor._get_default_requests sklearn.neural_network.MLPRegressor._get_doc_link sklearn.neural_network.MLPRegressor._get_metadata_request sklearn.neural_network.MLPRegressor._get_param_names sklearn.neural_network.MLPRegressor._init_coef sklearn.neural_network.MLPRegressor._initialize sklearn.neural_network.MLPRegressor._loss_grad_lbfgs sklearn.neural_network.MLPRegressor._predict sklearn.neural_network.MLPRegressor._repr_html_inner sklearn.neural_network.MLPRegressor._repr_mimebundle_ sklearn.neural_network.MLPRegressor._score sklearn.neural_network.MLPRegressor._score_with_function sklearn.neural_network.MLPRegressor._unpack sklearn.neural_network.MLPRegressor._update_no_improvement_count sklearn.neural_network.MLPRegressor._validate_input sklearn.neural_network.MLPRegressor._validate_params sklearn.neural_network.MLPRegressor.fit sklearn.neural_network.MLPRegressor.get_metadata_routing sklearn.neural_network.MLPRegressor.get_params sklearn.neural_network.MLPRegressor.partial_fit sklearn.neural_network.MLPRegressor.predict sklearn.neural_network.MLPRegressor.score sklearn.neural_network.MLPRegressor.set_fit_request sklearn.neural_network.MLPRegressor.set_params sklearn.neural_network.MLPRegressor.set_partial_fit_request sklearn.neural_network.MLPRegressor.set_score_request 
    :add-heading: Gallery examples
    :heading-level: -
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diff --git a/dev/_sources/modules/generated/sklearn.pipeline.FeatureUnion.rst.txt b/dev/_sources/modules/generated/sklearn.pipeline.FeatureUnion.rst.txt
index 277fbf693c6fa..b8b1c8e843136 100644
--- a/dev/_sources/modules/generated/sklearn.pipeline.FeatureUnion.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.pipeline.FeatureUnion.rst.txt
@@ -8,6 +8,6 @@ FeatureUnion
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.pipeline.FeatureUnion sklearn.pipeline.FeatureUnion.__delattr__ sklearn.pipeline.FeatureUnion.__dir__ sklearn.pipeline.FeatureUnion.__eq__ sklearn.pipeline.FeatureUnion.__format__ sklearn.pipeline.FeatureUnion.__ge__ sklearn.pipeline.FeatureUnion.__getattribute__ sklearn.pipeline.FeatureUnion.__getitem__ sklearn.pipeline.FeatureUnion.__getstate__ sklearn.pipeline.FeatureUnion.__gt__ sklearn.pipeline.FeatureUnion.__hash__ sklearn.pipeline.FeatureUnion.__init__ sklearn.pipeline.FeatureUnion.__init_subclass__ sklearn.pipeline.FeatureUnion.__le__ sklearn.pipeline.FeatureUnion.__lt__ sklearn.pipeline.FeatureUnion.__ne__ sklearn.pipeline.FeatureUnion.__new__ sklearn.pipeline.FeatureUnion.__reduce__ sklearn.pipeline.FeatureUnion.__reduce_ex__ sklearn.pipeline.FeatureUnion.__repr__ sklearn.pipeline.FeatureUnion.__setattr__ sklearn.pipeline.FeatureUnion.__setstate__ sklearn.pipeline.FeatureUnion.__sizeof__ sklearn.pipeline.FeatureUnion.__sklearn_clone__ sklearn.pipeline.FeatureUnion.__sklearn_is_fitted__ sklearn.pipeline.FeatureUnion.__sklearn_tags__ sklearn.pipeline.FeatureUnion.__str__ sklearn.pipeline.FeatureUnion.__subclasshook__ sklearn.pipeline.FeatureUnion._add_prefix_for_feature_names_out sklearn.pipeline.FeatureUnion._build_request_for_signature sklearn.pipeline.FeatureUnion._check_feature_names sklearn.pipeline.FeatureUnion._check_n_features sklearn.pipeline.FeatureUnion._get_default_requests sklearn.pipeline.FeatureUnion._get_doc_link sklearn.pipeline.FeatureUnion._get_metadata_request sklearn.pipeline.FeatureUnion._get_param_names sklearn.pipeline.FeatureUnion._get_params sklearn.pipeline.FeatureUnion._get_tags sklearn.pipeline.FeatureUnion._hstack sklearn.pipeline.FeatureUnion._iter sklearn.pipeline.FeatureUnion._log_message sklearn.pipeline.FeatureUnion._more_tags sklearn.pipeline.FeatureUnion._parallel_func sklearn.pipeline.FeatureUnion._replace_estimator sklearn.pipeline.FeatureUnion._repr_html_inner sklearn.pipeline.FeatureUnion._repr_mimebundle_ sklearn.pipeline.FeatureUnion._set_params sklearn.pipeline.FeatureUnion._sk_visual_block_ sklearn.pipeline.FeatureUnion._update_transformer_list sklearn.pipeline.FeatureUnion._validate_data sklearn.pipeline.FeatureUnion._validate_names sklearn.pipeline.FeatureUnion._validate_params sklearn.pipeline.FeatureUnion._validate_transformer_weights sklearn.pipeline.FeatureUnion._validate_transformers sklearn.pipeline.FeatureUnion.fit sklearn.pipeline.FeatureUnion.fit_transform sklearn.pipeline.FeatureUnion.get_feature_names_out sklearn.pipeline.FeatureUnion.get_metadata_routing sklearn.pipeline.FeatureUnion.get_params sklearn.pipeline.FeatureUnion.set_output sklearn.pipeline.FeatureUnion.set_params sklearn.pipeline.FeatureUnion.transform 
+.. minigallery:: sklearn.pipeline.FeatureUnion sklearn.pipeline.FeatureUnion.__delattr__ sklearn.pipeline.FeatureUnion.__dir__ sklearn.pipeline.FeatureUnion.__eq__ sklearn.pipeline.FeatureUnion.__format__ sklearn.pipeline.FeatureUnion.__ge__ sklearn.pipeline.FeatureUnion.__getattribute__ sklearn.pipeline.FeatureUnion.__getitem__ sklearn.pipeline.FeatureUnion.__getstate__ sklearn.pipeline.FeatureUnion.__gt__ sklearn.pipeline.FeatureUnion.__hash__ sklearn.pipeline.FeatureUnion.__init__ sklearn.pipeline.FeatureUnion.__init_subclass__ sklearn.pipeline.FeatureUnion.__le__ sklearn.pipeline.FeatureUnion.__lt__ sklearn.pipeline.FeatureUnion.__ne__ sklearn.pipeline.FeatureUnion.__new__ sklearn.pipeline.FeatureUnion.__reduce__ sklearn.pipeline.FeatureUnion.__reduce_ex__ sklearn.pipeline.FeatureUnion.__repr__ sklearn.pipeline.FeatureUnion.__setattr__ sklearn.pipeline.FeatureUnion.__setstate__ sklearn.pipeline.FeatureUnion.__sizeof__ sklearn.pipeline.FeatureUnion.__sklearn_clone__ sklearn.pipeline.FeatureUnion.__sklearn_is_fitted__ sklearn.pipeline.FeatureUnion.__sklearn_tags__ sklearn.pipeline.FeatureUnion.__str__ sklearn.pipeline.FeatureUnion.__subclasshook__ sklearn.pipeline.FeatureUnion._add_prefix_for_feature_names_out sklearn.pipeline.FeatureUnion._build_request_for_signature sklearn.pipeline.FeatureUnion._get_default_requests sklearn.pipeline.FeatureUnion._get_doc_link sklearn.pipeline.FeatureUnion._get_metadata_request sklearn.pipeline.FeatureUnion._get_param_names sklearn.pipeline.FeatureUnion._get_params sklearn.pipeline.FeatureUnion._hstack sklearn.pipeline.FeatureUnion._iter sklearn.pipeline.FeatureUnion._log_message sklearn.pipeline.FeatureUnion._parallel_func sklearn.pipeline.FeatureUnion._replace_estimator sklearn.pipeline.FeatureUnion._repr_html_inner sklearn.pipeline.FeatureUnion._repr_mimebundle_ sklearn.pipeline.FeatureUnion._set_params sklearn.pipeline.FeatureUnion._sk_visual_block_ sklearn.pipeline.FeatureUnion._update_transformer_list sklearn.pipeline.FeatureUnion._validate_names sklearn.pipeline.FeatureUnion._validate_params sklearn.pipeline.FeatureUnion._validate_transformer_weights sklearn.pipeline.FeatureUnion._validate_transformers sklearn.pipeline.FeatureUnion.fit sklearn.pipeline.FeatureUnion.fit_transform sklearn.pipeline.FeatureUnion.get_feature_names_out sklearn.pipeline.FeatureUnion.get_metadata_routing sklearn.pipeline.FeatureUnion.get_params sklearn.pipeline.FeatureUnion.set_output sklearn.pipeline.FeatureUnion.set_params sklearn.pipeline.FeatureUnion.transform 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.pipeline.Pipeline.rst.txt b/dev/_sources/modules/generated/sklearn.pipeline.Pipeline.rst.txt
index 85dcd06ebe2c4..73c111f108824 100644
--- a/dev/_sources/modules/generated/sklearn.pipeline.Pipeline.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.pipeline.Pipeline.rst.txt
@@ -8,6 +8,6 @@ Pipeline
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.pipeline.Pipeline sklearn.pipeline.Pipeline.__delattr__ sklearn.pipeline.Pipeline.__dir__ sklearn.pipeline.Pipeline.__eq__ sklearn.pipeline.Pipeline.__format__ sklearn.pipeline.Pipeline.__ge__ sklearn.pipeline.Pipeline.__getattribute__ sklearn.pipeline.Pipeline.__getitem__ sklearn.pipeline.Pipeline.__getstate__ sklearn.pipeline.Pipeline.__gt__ sklearn.pipeline.Pipeline.__hash__ sklearn.pipeline.Pipeline.__init__ sklearn.pipeline.Pipeline.__init_subclass__ sklearn.pipeline.Pipeline.__le__ sklearn.pipeline.Pipeline.__len__ sklearn.pipeline.Pipeline.__lt__ sklearn.pipeline.Pipeline.__ne__ sklearn.pipeline.Pipeline.__new__ sklearn.pipeline.Pipeline.__reduce__ sklearn.pipeline.Pipeline.__reduce_ex__ sklearn.pipeline.Pipeline.__repr__ sklearn.pipeline.Pipeline.__setattr__ sklearn.pipeline.Pipeline.__setstate__ sklearn.pipeline.Pipeline.__sizeof__ sklearn.pipeline.Pipeline.__sklearn_clone__ sklearn.pipeline.Pipeline.__sklearn_is_fitted__ sklearn.pipeline.Pipeline.__sklearn_tags__ sklearn.pipeline.Pipeline.__str__ sklearn.pipeline.Pipeline.__subclasshook__ sklearn.pipeline.Pipeline._build_request_for_signature sklearn.pipeline.Pipeline._can_fit_transform sklearn.pipeline.Pipeline._can_inverse_transform sklearn.pipeline.Pipeline._can_transform sklearn.pipeline.Pipeline._check_feature_names sklearn.pipeline.Pipeline._check_method_params sklearn.pipeline.Pipeline._check_n_features sklearn.pipeline.Pipeline._fit sklearn.pipeline.Pipeline._get_default_requests sklearn.pipeline.Pipeline._get_doc_link sklearn.pipeline.Pipeline._get_metadata_for_step sklearn.pipeline.Pipeline._get_metadata_request sklearn.pipeline.Pipeline._get_param_names sklearn.pipeline.Pipeline._get_params sklearn.pipeline.Pipeline._get_tags sklearn.pipeline.Pipeline._iter sklearn.pipeline.Pipeline._log_message sklearn.pipeline.Pipeline._more_tags sklearn.pipeline.Pipeline._replace_estimator sklearn.pipeline.Pipeline._repr_html_inner sklearn.pipeline.Pipeline._repr_mimebundle_ sklearn.pipeline.Pipeline._set_params sklearn.pipeline.Pipeline._sk_visual_block_ sklearn.pipeline.Pipeline._validate_data sklearn.pipeline.Pipeline._validate_names sklearn.pipeline.Pipeline._validate_params sklearn.pipeline.Pipeline._validate_steps sklearn.pipeline.Pipeline.decision_function sklearn.pipeline.Pipeline.fit sklearn.pipeline.Pipeline.fit_predict sklearn.pipeline.Pipeline.fit_transform sklearn.pipeline.Pipeline.get_feature_names_out sklearn.pipeline.Pipeline.get_metadata_routing sklearn.pipeline.Pipeline.get_params sklearn.pipeline.Pipeline.inverse_transform sklearn.pipeline.Pipeline.predict sklearn.pipeline.Pipeline.predict_log_proba sklearn.pipeline.Pipeline.predict_proba sklearn.pipeline.Pipeline.score sklearn.pipeline.Pipeline.score_samples sklearn.pipeline.Pipeline.set_output sklearn.pipeline.Pipeline.set_params sklearn.pipeline.Pipeline.set_score_request sklearn.pipeline.Pipeline.transform 
+.. minigallery:: sklearn.pipeline.Pipeline sklearn.pipeline.Pipeline.__delattr__ sklearn.pipeline.Pipeline.__dir__ sklearn.pipeline.Pipeline.__eq__ sklearn.pipeline.Pipeline.__format__ sklearn.pipeline.Pipeline.__ge__ sklearn.pipeline.Pipeline.__getattribute__ sklearn.pipeline.Pipeline.__getitem__ sklearn.pipeline.Pipeline.__getstate__ sklearn.pipeline.Pipeline.__gt__ sklearn.pipeline.Pipeline.__hash__ sklearn.pipeline.Pipeline.__init__ sklearn.pipeline.Pipeline.__init_subclass__ sklearn.pipeline.Pipeline.__le__ sklearn.pipeline.Pipeline.__len__ sklearn.pipeline.Pipeline.__lt__ sklearn.pipeline.Pipeline.__ne__ sklearn.pipeline.Pipeline.__new__ sklearn.pipeline.Pipeline.__reduce__ sklearn.pipeline.Pipeline.__reduce_ex__ sklearn.pipeline.Pipeline.__repr__ sklearn.pipeline.Pipeline.__setattr__ sklearn.pipeline.Pipeline.__setstate__ sklearn.pipeline.Pipeline.__sizeof__ sklearn.pipeline.Pipeline.__sklearn_clone__ sklearn.pipeline.Pipeline.__sklearn_is_fitted__ sklearn.pipeline.Pipeline.__sklearn_tags__ sklearn.pipeline.Pipeline.__str__ sklearn.pipeline.Pipeline.__subclasshook__ sklearn.pipeline.Pipeline._build_request_for_signature sklearn.pipeline.Pipeline._can_fit_transform sklearn.pipeline.Pipeline._can_inverse_transform sklearn.pipeline.Pipeline._can_transform sklearn.pipeline.Pipeline._check_method_params sklearn.pipeline.Pipeline._fit sklearn.pipeline.Pipeline._get_default_requests sklearn.pipeline.Pipeline._get_doc_link sklearn.pipeline.Pipeline._get_metadata_for_step sklearn.pipeline.Pipeline._get_metadata_request sklearn.pipeline.Pipeline._get_param_names sklearn.pipeline.Pipeline._get_params sklearn.pipeline.Pipeline._iter sklearn.pipeline.Pipeline._log_message sklearn.pipeline.Pipeline._replace_estimator sklearn.pipeline.Pipeline._repr_html_inner sklearn.pipeline.Pipeline._repr_mimebundle_ sklearn.pipeline.Pipeline._set_params sklearn.pipeline.Pipeline._sk_visual_block_ sklearn.pipeline.Pipeline._validate_names sklearn.pipeline.Pipeline._validate_params sklearn.pipeline.Pipeline._validate_steps sklearn.pipeline.Pipeline.decision_function sklearn.pipeline.Pipeline.fit sklearn.pipeline.Pipeline.fit_predict sklearn.pipeline.Pipeline.fit_transform sklearn.pipeline.Pipeline.get_feature_names_out sklearn.pipeline.Pipeline.get_metadata_routing sklearn.pipeline.Pipeline.get_params sklearn.pipeline.Pipeline.inverse_transform sklearn.pipeline.Pipeline.predict sklearn.pipeline.Pipeline.predict_log_proba sklearn.pipeline.Pipeline.predict_proba sklearn.pipeline.Pipeline.score sklearn.pipeline.Pipeline.score_samples sklearn.pipeline.Pipeline.set_output sklearn.pipeline.Pipeline.set_params sklearn.pipeline.Pipeline.set_score_request sklearn.pipeline.Pipeline.transform 
    :add-heading: Gallery examples
    :heading-level: -
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diff --git a/dev/_sources/modules/generated/sklearn.preprocessing.Binarizer.rst.txt b/dev/_sources/modules/generated/sklearn.preprocessing.Binarizer.rst.txt
index 2d0110d88b86a..4c837e5777476 100644
--- a/dev/_sources/modules/generated/sklearn.preprocessing.Binarizer.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.preprocessing.Binarizer.rst.txt
@@ -8,6 +8,6 @@ Binarizer
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.preprocessing.Binarizer sklearn.preprocessing.Binarizer.__delattr__ sklearn.preprocessing.Binarizer.__dir__ sklearn.preprocessing.Binarizer.__eq__ sklearn.preprocessing.Binarizer.__format__ sklearn.preprocessing.Binarizer.__ge__ sklearn.preprocessing.Binarizer.__getattribute__ sklearn.preprocessing.Binarizer.__getstate__ sklearn.preprocessing.Binarizer.__gt__ sklearn.preprocessing.Binarizer.__hash__ sklearn.preprocessing.Binarizer.__init__ sklearn.preprocessing.Binarizer.__init_subclass__ sklearn.preprocessing.Binarizer.__le__ sklearn.preprocessing.Binarizer.__lt__ sklearn.preprocessing.Binarizer.__ne__ sklearn.preprocessing.Binarizer.__new__ sklearn.preprocessing.Binarizer.__reduce__ sklearn.preprocessing.Binarizer.__reduce_ex__ sklearn.preprocessing.Binarizer.__repr__ sklearn.preprocessing.Binarizer.__setattr__ sklearn.preprocessing.Binarizer.__setstate__ sklearn.preprocessing.Binarizer.__sizeof__ sklearn.preprocessing.Binarizer.__sklearn_clone__ sklearn.preprocessing.Binarizer.__sklearn_tags__ sklearn.preprocessing.Binarizer.__str__ sklearn.preprocessing.Binarizer.__subclasshook__ sklearn.preprocessing.Binarizer._build_request_for_signature sklearn.preprocessing.Binarizer._check_feature_names sklearn.preprocessing.Binarizer._check_n_features sklearn.preprocessing.Binarizer._get_default_requests sklearn.preprocessing.Binarizer._get_doc_link sklearn.preprocessing.Binarizer._get_metadata_request sklearn.preprocessing.Binarizer._get_param_names sklearn.preprocessing.Binarizer._get_tags sklearn.preprocessing.Binarizer._more_tags sklearn.preprocessing.Binarizer._repr_html_inner sklearn.preprocessing.Binarizer._repr_mimebundle_ sklearn.preprocessing.Binarizer._validate_data sklearn.preprocessing.Binarizer._validate_params sklearn.preprocessing.Binarizer.fit sklearn.preprocessing.Binarizer.fit_transform sklearn.preprocessing.Binarizer.get_feature_names_out sklearn.preprocessing.Binarizer.get_metadata_routing sklearn.preprocessing.Binarizer.get_params sklearn.preprocessing.Binarizer.set_output sklearn.preprocessing.Binarizer.set_params sklearn.preprocessing.Binarizer.set_transform_request sklearn.preprocessing.Binarizer.transform 
+.. minigallery:: sklearn.preprocessing.Binarizer sklearn.preprocessing.Binarizer.__delattr__ sklearn.preprocessing.Binarizer.__dir__ sklearn.preprocessing.Binarizer.__eq__ sklearn.preprocessing.Binarizer.__format__ sklearn.preprocessing.Binarizer.__ge__ sklearn.preprocessing.Binarizer.__getattribute__ sklearn.preprocessing.Binarizer.__getstate__ sklearn.preprocessing.Binarizer.__gt__ sklearn.preprocessing.Binarizer.__hash__ sklearn.preprocessing.Binarizer.__init__ sklearn.preprocessing.Binarizer.__init_subclass__ sklearn.preprocessing.Binarizer.__le__ sklearn.preprocessing.Binarizer.__lt__ sklearn.preprocessing.Binarizer.__ne__ sklearn.preprocessing.Binarizer.__new__ sklearn.preprocessing.Binarizer.__reduce__ sklearn.preprocessing.Binarizer.__reduce_ex__ sklearn.preprocessing.Binarizer.__repr__ sklearn.preprocessing.Binarizer.__setattr__ sklearn.preprocessing.Binarizer.__setstate__ sklearn.preprocessing.Binarizer.__sizeof__ sklearn.preprocessing.Binarizer.__sklearn_clone__ sklearn.preprocessing.Binarizer.__sklearn_tags__ sklearn.preprocessing.Binarizer.__str__ sklearn.preprocessing.Binarizer.__subclasshook__ sklearn.preprocessing.Binarizer._build_request_for_signature sklearn.preprocessing.Binarizer._get_default_requests sklearn.preprocessing.Binarizer._get_doc_link sklearn.preprocessing.Binarizer._get_metadata_request sklearn.preprocessing.Binarizer._get_param_names sklearn.preprocessing.Binarizer._repr_html_inner sklearn.preprocessing.Binarizer._repr_mimebundle_ sklearn.preprocessing.Binarizer._validate_params sklearn.preprocessing.Binarizer.fit sklearn.preprocessing.Binarizer.fit_transform sklearn.preprocessing.Binarizer.get_feature_names_out sklearn.preprocessing.Binarizer.get_metadata_routing sklearn.preprocessing.Binarizer.get_params sklearn.preprocessing.Binarizer.set_output sklearn.preprocessing.Binarizer.set_params sklearn.preprocessing.Binarizer.set_transform_request sklearn.preprocessing.Binarizer.transform 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.preprocessing.FunctionTransformer.rst.txt b/dev/_sources/modules/generated/sklearn.preprocessing.FunctionTransformer.rst.txt
index 201d6dfccdcea..c72ab19159097 100644
--- a/dev/_sources/modules/generated/sklearn.preprocessing.FunctionTransformer.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.preprocessing.FunctionTransformer.rst.txt
@@ -8,6 +8,6 @@ FunctionTransformer
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.preprocessing.FunctionTransformer sklearn.preprocessing.FunctionTransformer.__delattr__ sklearn.preprocessing.FunctionTransformer.__dir__ sklearn.preprocessing.FunctionTransformer.__eq__ sklearn.preprocessing.FunctionTransformer.__format__ sklearn.preprocessing.FunctionTransformer.__ge__ sklearn.preprocessing.FunctionTransformer.__getattribute__ sklearn.preprocessing.FunctionTransformer.__getstate__ sklearn.preprocessing.FunctionTransformer.__gt__ sklearn.preprocessing.FunctionTransformer.__hash__ sklearn.preprocessing.FunctionTransformer.__init__ sklearn.preprocessing.FunctionTransformer.__init_subclass__ sklearn.preprocessing.FunctionTransformer.__le__ sklearn.preprocessing.FunctionTransformer.__lt__ sklearn.preprocessing.FunctionTransformer.__ne__ sklearn.preprocessing.FunctionTransformer.__new__ sklearn.preprocessing.FunctionTransformer.__reduce__ sklearn.preprocessing.FunctionTransformer.__reduce_ex__ sklearn.preprocessing.FunctionTransformer.__repr__ sklearn.preprocessing.FunctionTransformer.__setattr__ sklearn.preprocessing.FunctionTransformer.__setstate__ sklearn.preprocessing.FunctionTransformer.__sizeof__ sklearn.preprocessing.FunctionTransformer.__sklearn_clone__ sklearn.preprocessing.FunctionTransformer.__sklearn_is_fitted__ sklearn.preprocessing.FunctionTransformer.__sklearn_tags__ sklearn.preprocessing.FunctionTransformer.__str__ sklearn.preprocessing.FunctionTransformer.__subclasshook__ sklearn.preprocessing.FunctionTransformer._build_request_for_signature sklearn.preprocessing.FunctionTransformer._check_feature_names sklearn.preprocessing.FunctionTransformer._check_input sklearn.preprocessing.FunctionTransformer._check_inverse_transform sklearn.preprocessing.FunctionTransformer._check_n_features sklearn.preprocessing.FunctionTransformer._get_default_requests sklearn.preprocessing.FunctionTransformer._get_doc_link sklearn.preprocessing.FunctionTransformer._get_function_name sklearn.preprocessing.FunctionTransformer._get_metadata_request sklearn.preprocessing.FunctionTransformer._get_param_names sklearn.preprocessing.FunctionTransformer._get_tags sklearn.preprocessing.FunctionTransformer._more_tags sklearn.preprocessing.FunctionTransformer._repr_html_inner sklearn.preprocessing.FunctionTransformer._repr_mimebundle_ sklearn.preprocessing.FunctionTransformer._sk_visual_block_ sklearn.preprocessing.FunctionTransformer._transform sklearn.preprocessing.FunctionTransformer._validate_data sklearn.preprocessing.FunctionTransformer._validate_params sklearn.preprocessing.FunctionTransformer.fit sklearn.preprocessing.FunctionTransformer.fit_transform sklearn.preprocessing.FunctionTransformer.get_feature_names_out sklearn.preprocessing.FunctionTransformer.get_metadata_routing sklearn.preprocessing.FunctionTransformer.get_params sklearn.preprocessing.FunctionTransformer.inverse_transform sklearn.preprocessing.FunctionTransformer.set_output sklearn.preprocessing.FunctionTransformer.set_params sklearn.preprocessing.FunctionTransformer.transform 
+.. minigallery:: sklearn.preprocessing.FunctionTransformer sklearn.preprocessing.FunctionTransformer.__delattr__ sklearn.preprocessing.FunctionTransformer.__dir__ sklearn.preprocessing.FunctionTransformer.__eq__ sklearn.preprocessing.FunctionTransformer.__format__ sklearn.preprocessing.FunctionTransformer.__ge__ sklearn.preprocessing.FunctionTransformer.__getattribute__ sklearn.preprocessing.FunctionTransformer.__getstate__ sklearn.preprocessing.FunctionTransformer.__gt__ sklearn.preprocessing.FunctionTransformer.__hash__ sklearn.preprocessing.FunctionTransformer.__init__ sklearn.preprocessing.FunctionTransformer.__init_subclass__ sklearn.preprocessing.FunctionTransformer.__le__ sklearn.preprocessing.FunctionTransformer.__lt__ sklearn.preprocessing.FunctionTransformer.__ne__ sklearn.preprocessing.FunctionTransformer.__new__ sklearn.preprocessing.FunctionTransformer.__reduce__ sklearn.preprocessing.FunctionTransformer.__reduce_ex__ sklearn.preprocessing.FunctionTransformer.__repr__ sklearn.preprocessing.FunctionTransformer.__setattr__ sklearn.preprocessing.FunctionTransformer.__setstate__ sklearn.preprocessing.FunctionTransformer.__sizeof__ sklearn.preprocessing.FunctionTransformer.__sklearn_clone__ sklearn.preprocessing.FunctionTransformer.__sklearn_is_fitted__ sklearn.preprocessing.FunctionTransformer.__sklearn_tags__ sklearn.preprocessing.FunctionTransformer.__str__ sklearn.preprocessing.FunctionTransformer.__subclasshook__ sklearn.preprocessing.FunctionTransformer._build_request_for_signature sklearn.preprocessing.FunctionTransformer._check_input sklearn.preprocessing.FunctionTransformer._check_inverse_transform sklearn.preprocessing.FunctionTransformer._get_default_requests sklearn.preprocessing.FunctionTransformer._get_doc_link sklearn.preprocessing.FunctionTransformer._get_function_name sklearn.preprocessing.FunctionTransformer._get_metadata_request sklearn.preprocessing.FunctionTransformer._get_param_names sklearn.preprocessing.FunctionTransformer._repr_html_inner sklearn.preprocessing.FunctionTransformer._repr_mimebundle_ sklearn.preprocessing.FunctionTransformer._sk_visual_block_ sklearn.preprocessing.FunctionTransformer._transform sklearn.preprocessing.FunctionTransformer._validate_params sklearn.preprocessing.FunctionTransformer.fit sklearn.preprocessing.FunctionTransformer.fit_transform sklearn.preprocessing.FunctionTransformer.get_feature_names_out sklearn.preprocessing.FunctionTransformer.get_metadata_routing sklearn.preprocessing.FunctionTransformer.get_params sklearn.preprocessing.FunctionTransformer.inverse_transform sklearn.preprocessing.FunctionTransformer.set_output sklearn.preprocessing.FunctionTransformer.set_params sklearn.preprocessing.FunctionTransformer.transform 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.preprocessing.KBinsDiscretizer.rst.txt b/dev/_sources/modules/generated/sklearn.preprocessing.KBinsDiscretizer.rst.txt
index 8688317f722fa..4e23bb65879f4 100644
--- a/dev/_sources/modules/generated/sklearn.preprocessing.KBinsDiscretizer.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.preprocessing.KBinsDiscretizer.rst.txt
@@ -8,6 +8,6 @@ KBinsDiscretizer
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.preprocessing.KBinsDiscretizer sklearn.preprocessing.KBinsDiscretizer.__delattr__ sklearn.preprocessing.KBinsDiscretizer.__dir__ sklearn.preprocessing.KBinsDiscretizer.__eq__ sklearn.preprocessing.KBinsDiscretizer.__format__ sklearn.preprocessing.KBinsDiscretizer.__ge__ sklearn.preprocessing.KBinsDiscretizer.__getattribute__ sklearn.preprocessing.KBinsDiscretizer.__getstate__ sklearn.preprocessing.KBinsDiscretizer.__gt__ sklearn.preprocessing.KBinsDiscretizer.__hash__ sklearn.preprocessing.KBinsDiscretizer.__init__ sklearn.preprocessing.KBinsDiscretizer.__init_subclass__ sklearn.preprocessing.KBinsDiscretizer.__le__ sklearn.preprocessing.KBinsDiscretizer.__lt__ sklearn.preprocessing.KBinsDiscretizer.__ne__ sklearn.preprocessing.KBinsDiscretizer.__new__ sklearn.preprocessing.KBinsDiscretizer.__reduce__ sklearn.preprocessing.KBinsDiscretizer.__reduce_ex__ sklearn.preprocessing.KBinsDiscretizer.__repr__ sklearn.preprocessing.KBinsDiscretizer.__setattr__ sklearn.preprocessing.KBinsDiscretizer.__setstate__ sklearn.preprocessing.KBinsDiscretizer.__sizeof__ sklearn.preprocessing.KBinsDiscretizer.__sklearn_clone__ sklearn.preprocessing.KBinsDiscretizer.__sklearn_tags__ sklearn.preprocessing.KBinsDiscretizer.__str__ sklearn.preprocessing.KBinsDiscretizer.__subclasshook__ sklearn.preprocessing.KBinsDiscretizer._build_request_for_signature sklearn.preprocessing.KBinsDiscretizer._check_feature_names sklearn.preprocessing.KBinsDiscretizer._check_n_features sklearn.preprocessing.KBinsDiscretizer._get_default_requests sklearn.preprocessing.KBinsDiscretizer._get_doc_link sklearn.preprocessing.KBinsDiscretizer._get_metadata_request sklearn.preprocessing.KBinsDiscretizer._get_param_names sklearn.preprocessing.KBinsDiscretizer._get_tags sklearn.preprocessing.KBinsDiscretizer._more_tags sklearn.preprocessing.KBinsDiscretizer._repr_html_inner sklearn.preprocessing.KBinsDiscretizer._repr_mimebundle_ sklearn.preprocessing.KBinsDiscretizer._validate_data sklearn.preprocessing.KBinsDiscretizer._validate_n_bins sklearn.preprocessing.KBinsDiscretizer._validate_params sklearn.preprocessing.KBinsDiscretizer.fit sklearn.preprocessing.KBinsDiscretizer.fit_transform sklearn.preprocessing.KBinsDiscretizer.get_feature_names_out sklearn.preprocessing.KBinsDiscretizer.get_metadata_routing sklearn.preprocessing.KBinsDiscretizer.get_params sklearn.preprocessing.KBinsDiscretizer.inverse_transform sklearn.preprocessing.KBinsDiscretizer.set_fit_request sklearn.preprocessing.KBinsDiscretizer.set_output sklearn.preprocessing.KBinsDiscretizer.set_params sklearn.preprocessing.KBinsDiscretizer.transform 
+.. minigallery:: sklearn.preprocessing.KBinsDiscretizer sklearn.preprocessing.KBinsDiscretizer.__delattr__ sklearn.preprocessing.KBinsDiscretizer.__dir__ sklearn.preprocessing.KBinsDiscretizer.__eq__ sklearn.preprocessing.KBinsDiscretizer.__format__ sklearn.preprocessing.KBinsDiscretizer.__ge__ sklearn.preprocessing.KBinsDiscretizer.__getattribute__ sklearn.preprocessing.KBinsDiscretizer.__getstate__ sklearn.preprocessing.KBinsDiscretizer.__gt__ sklearn.preprocessing.KBinsDiscretizer.__hash__ sklearn.preprocessing.KBinsDiscretizer.__init__ sklearn.preprocessing.KBinsDiscretizer.__init_subclass__ sklearn.preprocessing.KBinsDiscretizer.__le__ sklearn.preprocessing.KBinsDiscretizer.__lt__ sklearn.preprocessing.KBinsDiscretizer.__ne__ sklearn.preprocessing.KBinsDiscretizer.__new__ sklearn.preprocessing.KBinsDiscretizer.__reduce__ sklearn.preprocessing.KBinsDiscretizer.__reduce_ex__ sklearn.preprocessing.KBinsDiscretizer.__repr__ sklearn.preprocessing.KBinsDiscretizer.__setattr__ sklearn.preprocessing.KBinsDiscretizer.__setstate__ sklearn.preprocessing.KBinsDiscretizer.__sizeof__ sklearn.preprocessing.KBinsDiscretizer.__sklearn_clone__ sklearn.preprocessing.KBinsDiscretizer.__sklearn_tags__ sklearn.preprocessing.KBinsDiscretizer.__str__ sklearn.preprocessing.KBinsDiscretizer.__subclasshook__ sklearn.preprocessing.KBinsDiscretizer._build_request_for_signature sklearn.preprocessing.KBinsDiscretizer._get_default_requests sklearn.preprocessing.KBinsDiscretizer._get_doc_link sklearn.preprocessing.KBinsDiscretizer._get_metadata_request sklearn.preprocessing.KBinsDiscretizer._get_param_names sklearn.preprocessing.KBinsDiscretizer._repr_html_inner sklearn.preprocessing.KBinsDiscretizer._repr_mimebundle_ sklearn.preprocessing.KBinsDiscretizer._validate_n_bins sklearn.preprocessing.KBinsDiscretizer._validate_params sklearn.preprocessing.KBinsDiscretizer.fit sklearn.preprocessing.KBinsDiscretizer.fit_transform sklearn.preprocessing.KBinsDiscretizer.get_feature_names_out sklearn.preprocessing.KBinsDiscretizer.get_metadata_routing sklearn.preprocessing.KBinsDiscretizer.get_params sklearn.preprocessing.KBinsDiscretizer.inverse_transform sklearn.preprocessing.KBinsDiscretizer.set_fit_request sklearn.preprocessing.KBinsDiscretizer.set_output sklearn.preprocessing.KBinsDiscretizer.set_params sklearn.preprocessing.KBinsDiscretizer.transform 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.preprocessing.KernelCenterer.rst.txt b/dev/_sources/modules/generated/sklearn.preprocessing.KernelCenterer.rst.txt
index ac2940ddf3a4f..923a192feb3d9 100644
--- a/dev/_sources/modules/generated/sklearn.preprocessing.KernelCenterer.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.preprocessing.KernelCenterer.rst.txt
@@ -8,6 +8,6 @@ KernelCenterer
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.preprocessing.KernelCenterer sklearn.preprocessing.KernelCenterer.__delattr__ sklearn.preprocessing.KernelCenterer.__dir__ sklearn.preprocessing.KernelCenterer.__eq__ sklearn.preprocessing.KernelCenterer.__format__ sklearn.preprocessing.KernelCenterer.__ge__ sklearn.preprocessing.KernelCenterer.__getattribute__ sklearn.preprocessing.KernelCenterer.__getstate__ sklearn.preprocessing.KernelCenterer.__gt__ sklearn.preprocessing.KernelCenterer.__hash__ sklearn.preprocessing.KernelCenterer.__init__ sklearn.preprocessing.KernelCenterer.__init_subclass__ sklearn.preprocessing.KernelCenterer.__le__ sklearn.preprocessing.KernelCenterer.__lt__ sklearn.preprocessing.KernelCenterer.__ne__ sklearn.preprocessing.KernelCenterer.__new__ sklearn.preprocessing.KernelCenterer.__reduce__ sklearn.preprocessing.KernelCenterer.__reduce_ex__ sklearn.preprocessing.KernelCenterer.__repr__ sklearn.preprocessing.KernelCenterer.__setattr__ sklearn.preprocessing.KernelCenterer.__setstate__ sklearn.preprocessing.KernelCenterer.__sizeof__ sklearn.preprocessing.KernelCenterer.__sklearn_clone__ sklearn.preprocessing.KernelCenterer.__sklearn_tags__ sklearn.preprocessing.KernelCenterer.__str__ sklearn.preprocessing.KernelCenterer.__subclasshook__ sklearn.preprocessing.KernelCenterer._build_request_for_signature sklearn.preprocessing.KernelCenterer._check_feature_names sklearn.preprocessing.KernelCenterer._check_n_features sklearn.preprocessing.KernelCenterer._get_default_requests sklearn.preprocessing.KernelCenterer._get_doc_link sklearn.preprocessing.KernelCenterer._get_metadata_request sklearn.preprocessing.KernelCenterer._get_param_names sklearn.preprocessing.KernelCenterer._get_tags sklearn.preprocessing.KernelCenterer._more_tags sklearn.preprocessing.KernelCenterer._repr_html_inner sklearn.preprocessing.KernelCenterer._repr_mimebundle_ sklearn.preprocessing.KernelCenterer._validate_data sklearn.preprocessing.KernelCenterer._validate_params sklearn.preprocessing.KernelCenterer.fit sklearn.preprocessing.KernelCenterer.fit_transform sklearn.preprocessing.KernelCenterer.get_feature_names_out sklearn.preprocessing.KernelCenterer.get_metadata_routing sklearn.preprocessing.KernelCenterer.get_params sklearn.preprocessing.KernelCenterer.set_output sklearn.preprocessing.KernelCenterer.set_params sklearn.preprocessing.KernelCenterer.set_transform_request sklearn.preprocessing.KernelCenterer.transform 
+.. minigallery:: sklearn.preprocessing.KernelCenterer sklearn.preprocessing.KernelCenterer.__delattr__ sklearn.preprocessing.KernelCenterer.__dir__ sklearn.preprocessing.KernelCenterer.__eq__ sklearn.preprocessing.KernelCenterer.__format__ sklearn.preprocessing.KernelCenterer.__ge__ sklearn.preprocessing.KernelCenterer.__getattribute__ sklearn.preprocessing.KernelCenterer.__getstate__ sklearn.preprocessing.KernelCenterer.__gt__ sklearn.preprocessing.KernelCenterer.__hash__ sklearn.preprocessing.KernelCenterer.__init__ sklearn.preprocessing.KernelCenterer.__init_subclass__ sklearn.preprocessing.KernelCenterer.__le__ sklearn.preprocessing.KernelCenterer.__lt__ sklearn.preprocessing.KernelCenterer.__ne__ sklearn.preprocessing.KernelCenterer.__new__ sklearn.preprocessing.KernelCenterer.__reduce__ sklearn.preprocessing.KernelCenterer.__reduce_ex__ sklearn.preprocessing.KernelCenterer.__repr__ sklearn.preprocessing.KernelCenterer.__setattr__ sklearn.preprocessing.KernelCenterer.__setstate__ sklearn.preprocessing.KernelCenterer.__sizeof__ sklearn.preprocessing.KernelCenterer.__sklearn_clone__ sklearn.preprocessing.KernelCenterer.__sklearn_tags__ sklearn.preprocessing.KernelCenterer.__str__ sklearn.preprocessing.KernelCenterer.__subclasshook__ sklearn.preprocessing.KernelCenterer._build_request_for_signature sklearn.preprocessing.KernelCenterer._get_default_requests sklearn.preprocessing.KernelCenterer._get_doc_link sklearn.preprocessing.KernelCenterer._get_metadata_request sklearn.preprocessing.KernelCenterer._get_param_names sklearn.preprocessing.KernelCenterer._repr_html_inner sklearn.preprocessing.KernelCenterer._repr_mimebundle_ sklearn.preprocessing.KernelCenterer._validate_params sklearn.preprocessing.KernelCenterer.fit sklearn.preprocessing.KernelCenterer.fit_transform sklearn.preprocessing.KernelCenterer.get_feature_names_out sklearn.preprocessing.KernelCenterer.get_metadata_routing sklearn.preprocessing.KernelCenterer.get_params sklearn.preprocessing.KernelCenterer.set_output sklearn.preprocessing.KernelCenterer.set_params sklearn.preprocessing.KernelCenterer.set_transform_request sklearn.preprocessing.KernelCenterer.transform 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.preprocessing.LabelBinarizer.rst.txt b/dev/_sources/modules/generated/sklearn.preprocessing.LabelBinarizer.rst.txt
index c6f73ab9212ad..968492577e9b0 100644
--- a/dev/_sources/modules/generated/sklearn.preprocessing.LabelBinarizer.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.preprocessing.LabelBinarizer.rst.txt
@@ -8,6 +8,6 @@ LabelBinarizer
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.preprocessing.LabelBinarizer sklearn.preprocessing.LabelBinarizer.__delattr__ sklearn.preprocessing.LabelBinarizer.__dir__ sklearn.preprocessing.LabelBinarizer.__eq__ sklearn.preprocessing.LabelBinarizer.__format__ sklearn.preprocessing.LabelBinarizer.__ge__ sklearn.preprocessing.LabelBinarizer.__getattribute__ sklearn.preprocessing.LabelBinarizer.__getstate__ sklearn.preprocessing.LabelBinarizer.__gt__ sklearn.preprocessing.LabelBinarizer.__hash__ sklearn.preprocessing.LabelBinarizer.__init__ sklearn.preprocessing.LabelBinarizer.__init_subclass__ sklearn.preprocessing.LabelBinarizer.__le__ sklearn.preprocessing.LabelBinarizer.__lt__ sklearn.preprocessing.LabelBinarizer.__ne__ sklearn.preprocessing.LabelBinarizer.__new__ sklearn.preprocessing.LabelBinarizer.__reduce__ sklearn.preprocessing.LabelBinarizer.__reduce_ex__ sklearn.preprocessing.LabelBinarizer.__repr__ sklearn.preprocessing.LabelBinarizer.__setattr__ sklearn.preprocessing.LabelBinarizer.__setstate__ sklearn.preprocessing.LabelBinarizer.__sizeof__ sklearn.preprocessing.LabelBinarizer.__sklearn_clone__ sklearn.preprocessing.LabelBinarizer.__sklearn_tags__ sklearn.preprocessing.LabelBinarizer.__str__ sklearn.preprocessing.LabelBinarizer.__subclasshook__ sklearn.preprocessing.LabelBinarizer._build_request_for_signature sklearn.preprocessing.LabelBinarizer._check_feature_names sklearn.preprocessing.LabelBinarizer._check_n_features sklearn.preprocessing.LabelBinarizer._get_default_requests sklearn.preprocessing.LabelBinarizer._get_doc_link sklearn.preprocessing.LabelBinarizer._get_metadata_request sklearn.preprocessing.LabelBinarizer._get_param_names sklearn.preprocessing.LabelBinarizer._get_tags sklearn.preprocessing.LabelBinarizer._more_tags sklearn.preprocessing.LabelBinarizer._repr_html_inner sklearn.preprocessing.LabelBinarizer._repr_mimebundle_ sklearn.preprocessing.LabelBinarizer._validate_data sklearn.preprocessing.LabelBinarizer._validate_params sklearn.preprocessing.LabelBinarizer.fit sklearn.preprocessing.LabelBinarizer.fit_transform sklearn.preprocessing.LabelBinarizer.get_metadata_routing sklearn.preprocessing.LabelBinarizer.get_params sklearn.preprocessing.LabelBinarizer.inverse_transform sklearn.preprocessing.LabelBinarizer.set_inverse_transform_request sklearn.preprocessing.LabelBinarizer.set_output sklearn.preprocessing.LabelBinarizer.set_params sklearn.preprocessing.LabelBinarizer.transform 
+.. minigallery:: sklearn.preprocessing.LabelBinarizer sklearn.preprocessing.LabelBinarizer.__delattr__ sklearn.preprocessing.LabelBinarizer.__dir__ sklearn.preprocessing.LabelBinarizer.__eq__ sklearn.preprocessing.LabelBinarizer.__format__ sklearn.preprocessing.LabelBinarizer.__ge__ sklearn.preprocessing.LabelBinarizer.__getattribute__ sklearn.preprocessing.LabelBinarizer.__getstate__ sklearn.preprocessing.LabelBinarizer.__gt__ sklearn.preprocessing.LabelBinarizer.__hash__ sklearn.preprocessing.LabelBinarizer.__init__ sklearn.preprocessing.LabelBinarizer.__init_subclass__ sklearn.preprocessing.LabelBinarizer.__le__ sklearn.preprocessing.LabelBinarizer.__lt__ sklearn.preprocessing.LabelBinarizer.__ne__ sklearn.preprocessing.LabelBinarizer.__new__ sklearn.preprocessing.LabelBinarizer.__reduce__ sklearn.preprocessing.LabelBinarizer.__reduce_ex__ sklearn.preprocessing.LabelBinarizer.__repr__ sklearn.preprocessing.LabelBinarizer.__setattr__ sklearn.preprocessing.LabelBinarizer.__setstate__ sklearn.preprocessing.LabelBinarizer.__sizeof__ sklearn.preprocessing.LabelBinarizer.__sklearn_clone__ sklearn.preprocessing.LabelBinarizer.__sklearn_tags__ sklearn.preprocessing.LabelBinarizer.__str__ sklearn.preprocessing.LabelBinarizer.__subclasshook__ sklearn.preprocessing.LabelBinarizer._build_request_for_signature sklearn.preprocessing.LabelBinarizer._get_default_requests sklearn.preprocessing.LabelBinarizer._get_doc_link sklearn.preprocessing.LabelBinarizer._get_metadata_request sklearn.preprocessing.LabelBinarizer._get_param_names sklearn.preprocessing.LabelBinarizer._repr_html_inner sklearn.preprocessing.LabelBinarizer._repr_mimebundle_ sklearn.preprocessing.LabelBinarizer._validate_params sklearn.preprocessing.LabelBinarizer.fit sklearn.preprocessing.LabelBinarizer.fit_transform sklearn.preprocessing.LabelBinarizer.get_metadata_routing sklearn.preprocessing.LabelBinarizer.get_params sklearn.preprocessing.LabelBinarizer.inverse_transform sklearn.preprocessing.LabelBinarizer.set_inverse_transform_request sklearn.preprocessing.LabelBinarizer.set_output sklearn.preprocessing.LabelBinarizer.set_params sklearn.preprocessing.LabelBinarizer.transform 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.preprocessing.LabelEncoder.rst.txt b/dev/_sources/modules/generated/sklearn.preprocessing.LabelEncoder.rst.txt
index f8a1022fe2e99..0565f3a7a5acb 100644
--- a/dev/_sources/modules/generated/sklearn.preprocessing.LabelEncoder.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.preprocessing.LabelEncoder.rst.txt
@@ -8,6 +8,6 @@ LabelEncoder
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.preprocessing.LabelEncoder sklearn.preprocessing.LabelEncoder.__delattr__ sklearn.preprocessing.LabelEncoder.__dir__ sklearn.preprocessing.LabelEncoder.__eq__ sklearn.preprocessing.LabelEncoder.__format__ sklearn.preprocessing.LabelEncoder.__ge__ sklearn.preprocessing.LabelEncoder.__getattribute__ sklearn.preprocessing.LabelEncoder.__getstate__ sklearn.preprocessing.LabelEncoder.__gt__ sklearn.preprocessing.LabelEncoder.__hash__ sklearn.preprocessing.LabelEncoder.__init__ sklearn.preprocessing.LabelEncoder.__init_subclass__ sklearn.preprocessing.LabelEncoder.__le__ sklearn.preprocessing.LabelEncoder.__lt__ sklearn.preprocessing.LabelEncoder.__ne__ sklearn.preprocessing.LabelEncoder.__new__ sklearn.preprocessing.LabelEncoder.__reduce__ sklearn.preprocessing.LabelEncoder.__reduce_ex__ sklearn.preprocessing.LabelEncoder.__repr__ sklearn.preprocessing.LabelEncoder.__setattr__ sklearn.preprocessing.LabelEncoder.__setstate__ sklearn.preprocessing.LabelEncoder.__sizeof__ sklearn.preprocessing.LabelEncoder.__sklearn_clone__ sklearn.preprocessing.LabelEncoder.__sklearn_tags__ sklearn.preprocessing.LabelEncoder.__str__ sklearn.preprocessing.LabelEncoder.__subclasshook__ sklearn.preprocessing.LabelEncoder._build_request_for_signature sklearn.preprocessing.LabelEncoder._check_feature_names sklearn.preprocessing.LabelEncoder._check_n_features sklearn.preprocessing.LabelEncoder._get_default_requests sklearn.preprocessing.LabelEncoder._get_doc_link sklearn.preprocessing.LabelEncoder._get_metadata_request sklearn.preprocessing.LabelEncoder._get_param_names sklearn.preprocessing.LabelEncoder._get_tags sklearn.preprocessing.LabelEncoder._more_tags sklearn.preprocessing.LabelEncoder._repr_html_inner sklearn.preprocessing.LabelEncoder._repr_mimebundle_ sklearn.preprocessing.LabelEncoder._validate_data sklearn.preprocessing.LabelEncoder._validate_params sklearn.preprocessing.LabelEncoder.fit sklearn.preprocessing.LabelEncoder.fit_transform sklearn.preprocessing.LabelEncoder.get_metadata_routing sklearn.preprocessing.LabelEncoder.get_params sklearn.preprocessing.LabelEncoder.inverse_transform sklearn.preprocessing.LabelEncoder.set_output sklearn.preprocessing.LabelEncoder.set_params sklearn.preprocessing.LabelEncoder.transform 
+.. minigallery:: sklearn.preprocessing.LabelEncoder sklearn.preprocessing.LabelEncoder.__delattr__ sklearn.preprocessing.LabelEncoder.__dir__ sklearn.preprocessing.LabelEncoder.__eq__ sklearn.preprocessing.LabelEncoder.__format__ sklearn.preprocessing.LabelEncoder.__ge__ sklearn.preprocessing.LabelEncoder.__getattribute__ sklearn.preprocessing.LabelEncoder.__getstate__ sklearn.preprocessing.LabelEncoder.__gt__ sklearn.preprocessing.LabelEncoder.__hash__ sklearn.preprocessing.LabelEncoder.__init__ sklearn.preprocessing.LabelEncoder.__init_subclass__ sklearn.preprocessing.LabelEncoder.__le__ sklearn.preprocessing.LabelEncoder.__lt__ sklearn.preprocessing.LabelEncoder.__ne__ sklearn.preprocessing.LabelEncoder.__new__ sklearn.preprocessing.LabelEncoder.__reduce__ sklearn.preprocessing.LabelEncoder.__reduce_ex__ sklearn.preprocessing.LabelEncoder.__repr__ sklearn.preprocessing.LabelEncoder.__setattr__ sklearn.preprocessing.LabelEncoder.__setstate__ sklearn.preprocessing.LabelEncoder.__sizeof__ sklearn.preprocessing.LabelEncoder.__sklearn_clone__ sklearn.preprocessing.LabelEncoder.__sklearn_tags__ sklearn.preprocessing.LabelEncoder.__str__ sklearn.preprocessing.LabelEncoder.__subclasshook__ sklearn.preprocessing.LabelEncoder._build_request_for_signature sklearn.preprocessing.LabelEncoder._get_default_requests sklearn.preprocessing.LabelEncoder._get_doc_link sklearn.preprocessing.LabelEncoder._get_metadata_request sklearn.preprocessing.LabelEncoder._get_param_names sklearn.preprocessing.LabelEncoder._repr_html_inner sklearn.preprocessing.LabelEncoder._repr_mimebundle_ sklearn.preprocessing.LabelEncoder._validate_params sklearn.preprocessing.LabelEncoder.fit sklearn.preprocessing.LabelEncoder.fit_transform sklearn.preprocessing.LabelEncoder.get_metadata_routing sklearn.preprocessing.LabelEncoder.get_params sklearn.preprocessing.LabelEncoder.inverse_transform sklearn.preprocessing.LabelEncoder.set_output sklearn.preprocessing.LabelEncoder.set_params sklearn.preprocessing.LabelEncoder.transform 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.preprocessing.MaxAbsScaler.rst.txt b/dev/_sources/modules/generated/sklearn.preprocessing.MaxAbsScaler.rst.txt
index 33d712a2415ed..2a35cdcf6c144 100644
--- a/dev/_sources/modules/generated/sklearn.preprocessing.MaxAbsScaler.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.preprocessing.MaxAbsScaler.rst.txt
@@ -8,6 +8,6 @@ MaxAbsScaler
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.preprocessing.MaxAbsScaler sklearn.preprocessing.MaxAbsScaler.__delattr__ sklearn.preprocessing.MaxAbsScaler.__dir__ sklearn.preprocessing.MaxAbsScaler.__eq__ sklearn.preprocessing.MaxAbsScaler.__format__ sklearn.preprocessing.MaxAbsScaler.__ge__ sklearn.preprocessing.MaxAbsScaler.__getattribute__ sklearn.preprocessing.MaxAbsScaler.__getstate__ sklearn.preprocessing.MaxAbsScaler.__gt__ sklearn.preprocessing.MaxAbsScaler.__hash__ sklearn.preprocessing.MaxAbsScaler.__init__ sklearn.preprocessing.MaxAbsScaler.__init_subclass__ sklearn.preprocessing.MaxAbsScaler.__le__ sklearn.preprocessing.MaxAbsScaler.__lt__ sklearn.preprocessing.MaxAbsScaler.__ne__ sklearn.preprocessing.MaxAbsScaler.__new__ sklearn.preprocessing.MaxAbsScaler.__reduce__ sklearn.preprocessing.MaxAbsScaler.__reduce_ex__ sklearn.preprocessing.MaxAbsScaler.__repr__ sklearn.preprocessing.MaxAbsScaler.__setattr__ sklearn.preprocessing.MaxAbsScaler.__setstate__ sklearn.preprocessing.MaxAbsScaler.__sizeof__ sklearn.preprocessing.MaxAbsScaler.__sklearn_clone__ sklearn.preprocessing.MaxAbsScaler.__sklearn_tags__ sklearn.preprocessing.MaxAbsScaler.__str__ sklearn.preprocessing.MaxAbsScaler.__subclasshook__ sklearn.preprocessing.MaxAbsScaler._build_request_for_signature sklearn.preprocessing.MaxAbsScaler._check_feature_names sklearn.preprocessing.MaxAbsScaler._check_n_features sklearn.preprocessing.MaxAbsScaler._get_default_requests sklearn.preprocessing.MaxAbsScaler._get_doc_link sklearn.preprocessing.MaxAbsScaler._get_metadata_request sklearn.preprocessing.MaxAbsScaler._get_param_names sklearn.preprocessing.MaxAbsScaler._get_tags sklearn.preprocessing.MaxAbsScaler._more_tags sklearn.preprocessing.MaxAbsScaler._repr_html_inner sklearn.preprocessing.MaxAbsScaler._repr_mimebundle_ sklearn.preprocessing.MaxAbsScaler._reset sklearn.preprocessing.MaxAbsScaler._validate_data sklearn.preprocessing.MaxAbsScaler._validate_params sklearn.preprocessing.MaxAbsScaler.fit sklearn.preprocessing.MaxAbsScaler.fit_transform sklearn.preprocessing.MaxAbsScaler.get_feature_names_out sklearn.preprocessing.MaxAbsScaler.get_metadata_routing sklearn.preprocessing.MaxAbsScaler.get_params sklearn.preprocessing.MaxAbsScaler.inverse_transform sklearn.preprocessing.MaxAbsScaler.partial_fit sklearn.preprocessing.MaxAbsScaler.set_output sklearn.preprocessing.MaxAbsScaler.set_params sklearn.preprocessing.MaxAbsScaler.transform 
+.. minigallery:: sklearn.preprocessing.MaxAbsScaler sklearn.preprocessing.MaxAbsScaler.__delattr__ sklearn.preprocessing.MaxAbsScaler.__dir__ sklearn.preprocessing.MaxAbsScaler.__eq__ sklearn.preprocessing.MaxAbsScaler.__format__ sklearn.preprocessing.MaxAbsScaler.__ge__ sklearn.preprocessing.MaxAbsScaler.__getattribute__ sklearn.preprocessing.MaxAbsScaler.__getstate__ sklearn.preprocessing.MaxAbsScaler.__gt__ sklearn.preprocessing.MaxAbsScaler.__hash__ sklearn.preprocessing.MaxAbsScaler.__init__ sklearn.preprocessing.MaxAbsScaler.__init_subclass__ sklearn.preprocessing.MaxAbsScaler.__le__ sklearn.preprocessing.MaxAbsScaler.__lt__ sklearn.preprocessing.MaxAbsScaler.__ne__ sklearn.preprocessing.MaxAbsScaler.__new__ sklearn.preprocessing.MaxAbsScaler.__reduce__ sklearn.preprocessing.MaxAbsScaler.__reduce_ex__ sklearn.preprocessing.MaxAbsScaler.__repr__ sklearn.preprocessing.MaxAbsScaler.__setattr__ sklearn.preprocessing.MaxAbsScaler.__setstate__ sklearn.preprocessing.MaxAbsScaler.__sizeof__ sklearn.preprocessing.MaxAbsScaler.__sklearn_clone__ sklearn.preprocessing.MaxAbsScaler.__sklearn_tags__ sklearn.preprocessing.MaxAbsScaler.__str__ sklearn.preprocessing.MaxAbsScaler.__subclasshook__ sklearn.preprocessing.MaxAbsScaler._build_request_for_signature sklearn.preprocessing.MaxAbsScaler._get_default_requests sklearn.preprocessing.MaxAbsScaler._get_doc_link sklearn.preprocessing.MaxAbsScaler._get_metadata_request sklearn.preprocessing.MaxAbsScaler._get_param_names sklearn.preprocessing.MaxAbsScaler._repr_html_inner sklearn.preprocessing.MaxAbsScaler._repr_mimebundle_ sklearn.preprocessing.MaxAbsScaler._reset sklearn.preprocessing.MaxAbsScaler._validate_params sklearn.preprocessing.MaxAbsScaler.fit sklearn.preprocessing.MaxAbsScaler.fit_transform sklearn.preprocessing.MaxAbsScaler.get_feature_names_out sklearn.preprocessing.MaxAbsScaler.get_metadata_routing sklearn.preprocessing.MaxAbsScaler.get_params sklearn.preprocessing.MaxAbsScaler.inverse_transform sklearn.preprocessing.MaxAbsScaler.partial_fit sklearn.preprocessing.MaxAbsScaler.set_output sklearn.preprocessing.MaxAbsScaler.set_params sklearn.preprocessing.MaxAbsScaler.transform 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.preprocessing.MinMaxScaler.rst.txt b/dev/_sources/modules/generated/sklearn.preprocessing.MinMaxScaler.rst.txt
index 33a06a2cfc7de..6bb2d18cf7fb7 100644
--- a/dev/_sources/modules/generated/sklearn.preprocessing.MinMaxScaler.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.preprocessing.MinMaxScaler.rst.txt
@@ -8,6 +8,6 @@ MinMaxScaler
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.preprocessing.MinMaxScaler sklearn.preprocessing.MinMaxScaler.__delattr__ sklearn.preprocessing.MinMaxScaler.__dir__ sklearn.preprocessing.MinMaxScaler.__eq__ sklearn.preprocessing.MinMaxScaler.__format__ sklearn.preprocessing.MinMaxScaler.__ge__ sklearn.preprocessing.MinMaxScaler.__getattribute__ sklearn.preprocessing.MinMaxScaler.__getstate__ sklearn.preprocessing.MinMaxScaler.__gt__ sklearn.preprocessing.MinMaxScaler.__hash__ sklearn.preprocessing.MinMaxScaler.__init__ sklearn.preprocessing.MinMaxScaler.__init_subclass__ sklearn.preprocessing.MinMaxScaler.__le__ sklearn.preprocessing.MinMaxScaler.__lt__ sklearn.preprocessing.MinMaxScaler.__ne__ sklearn.preprocessing.MinMaxScaler.__new__ sklearn.preprocessing.MinMaxScaler.__reduce__ sklearn.preprocessing.MinMaxScaler.__reduce_ex__ sklearn.preprocessing.MinMaxScaler.__repr__ sklearn.preprocessing.MinMaxScaler.__setattr__ sklearn.preprocessing.MinMaxScaler.__setstate__ sklearn.preprocessing.MinMaxScaler.__sizeof__ sklearn.preprocessing.MinMaxScaler.__sklearn_clone__ sklearn.preprocessing.MinMaxScaler.__sklearn_tags__ sklearn.preprocessing.MinMaxScaler.__str__ sklearn.preprocessing.MinMaxScaler.__subclasshook__ sklearn.preprocessing.MinMaxScaler._build_request_for_signature sklearn.preprocessing.MinMaxScaler._check_feature_names sklearn.preprocessing.MinMaxScaler._check_n_features sklearn.preprocessing.MinMaxScaler._get_default_requests sklearn.preprocessing.MinMaxScaler._get_doc_link sklearn.preprocessing.MinMaxScaler._get_metadata_request sklearn.preprocessing.MinMaxScaler._get_param_names sklearn.preprocessing.MinMaxScaler._get_tags sklearn.preprocessing.MinMaxScaler._more_tags sklearn.preprocessing.MinMaxScaler._repr_html_inner sklearn.preprocessing.MinMaxScaler._repr_mimebundle_ sklearn.preprocessing.MinMaxScaler._reset sklearn.preprocessing.MinMaxScaler._validate_data sklearn.preprocessing.MinMaxScaler._validate_params sklearn.preprocessing.MinMaxScaler.fit sklearn.preprocessing.MinMaxScaler.fit_transform sklearn.preprocessing.MinMaxScaler.get_feature_names_out sklearn.preprocessing.MinMaxScaler.get_metadata_routing sklearn.preprocessing.MinMaxScaler.get_params sklearn.preprocessing.MinMaxScaler.inverse_transform sklearn.preprocessing.MinMaxScaler.partial_fit sklearn.preprocessing.MinMaxScaler.set_output sklearn.preprocessing.MinMaxScaler.set_params sklearn.preprocessing.MinMaxScaler.transform 
+.. minigallery:: sklearn.preprocessing.MinMaxScaler sklearn.preprocessing.MinMaxScaler.__delattr__ sklearn.preprocessing.MinMaxScaler.__dir__ sklearn.preprocessing.MinMaxScaler.__eq__ sklearn.preprocessing.MinMaxScaler.__format__ sklearn.preprocessing.MinMaxScaler.__ge__ sklearn.preprocessing.MinMaxScaler.__getattribute__ sklearn.preprocessing.MinMaxScaler.__getstate__ sklearn.preprocessing.MinMaxScaler.__gt__ sklearn.preprocessing.MinMaxScaler.__hash__ sklearn.preprocessing.MinMaxScaler.__init__ sklearn.preprocessing.MinMaxScaler.__init_subclass__ sklearn.preprocessing.MinMaxScaler.__le__ sklearn.preprocessing.MinMaxScaler.__lt__ sklearn.preprocessing.MinMaxScaler.__ne__ sklearn.preprocessing.MinMaxScaler.__new__ sklearn.preprocessing.MinMaxScaler.__reduce__ sklearn.preprocessing.MinMaxScaler.__reduce_ex__ sklearn.preprocessing.MinMaxScaler.__repr__ sklearn.preprocessing.MinMaxScaler.__setattr__ sklearn.preprocessing.MinMaxScaler.__setstate__ sklearn.preprocessing.MinMaxScaler.__sizeof__ sklearn.preprocessing.MinMaxScaler.__sklearn_clone__ sklearn.preprocessing.MinMaxScaler.__sklearn_tags__ sklearn.preprocessing.MinMaxScaler.__str__ sklearn.preprocessing.MinMaxScaler.__subclasshook__ sklearn.preprocessing.MinMaxScaler._build_request_for_signature sklearn.preprocessing.MinMaxScaler._get_default_requests sklearn.preprocessing.MinMaxScaler._get_doc_link sklearn.preprocessing.MinMaxScaler._get_metadata_request sklearn.preprocessing.MinMaxScaler._get_param_names sklearn.preprocessing.MinMaxScaler._repr_html_inner sklearn.preprocessing.MinMaxScaler._repr_mimebundle_ sklearn.preprocessing.MinMaxScaler._reset sklearn.preprocessing.MinMaxScaler._validate_params sklearn.preprocessing.MinMaxScaler.fit sklearn.preprocessing.MinMaxScaler.fit_transform sklearn.preprocessing.MinMaxScaler.get_feature_names_out sklearn.preprocessing.MinMaxScaler.get_metadata_routing sklearn.preprocessing.MinMaxScaler.get_params sklearn.preprocessing.MinMaxScaler.inverse_transform sklearn.preprocessing.MinMaxScaler.partial_fit sklearn.preprocessing.MinMaxScaler.set_output sklearn.preprocessing.MinMaxScaler.set_params sklearn.preprocessing.MinMaxScaler.transform 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.preprocessing.MultiLabelBinarizer.rst.txt b/dev/_sources/modules/generated/sklearn.preprocessing.MultiLabelBinarizer.rst.txt
index fd583cc706834..d139ed0b5b475 100644
--- a/dev/_sources/modules/generated/sklearn.preprocessing.MultiLabelBinarizer.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.preprocessing.MultiLabelBinarizer.rst.txt
@@ -8,6 +8,6 @@ MultiLabelBinarizer
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.preprocessing.MultiLabelBinarizer sklearn.preprocessing.MultiLabelBinarizer.__delattr__ sklearn.preprocessing.MultiLabelBinarizer.__dir__ sklearn.preprocessing.MultiLabelBinarizer.__eq__ sklearn.preprocessing.MultiLabelBinarizer.__format__ sklearn.preprocessing.MultiLabelBinarizer.__ge__ sklearn.preprocessing.MultiLabelBinarizer.__getattribute__ sklearn.preprocessing.MultiLabelBinarizer.__getstate__ sklearn.preprocessing.MultiLabelBinarizer.__gt__ sklearn.preprocessing.MultiLabelBinarizer.__hash__ sklearn.preprocessing.MultiLabelBinarizer.__init__ sklearn.preprocessing.MultiLabelBinarizer.__init_subclass__ sklearn.preprocessing.MultiLabelBinarizer.__le__ sklearn.preprocessing.MultiLabelBinarizer.__lt__ sklearn.preprocessing.MultiLabelBinarizer.__ne__ sklearn.preprocessing.MultiLabelBinarizer.__new__ sklearn.preprocessing.MultiLabelBinarizer.__reduce__ sklearn.preprocessing.MultiLabelBinarizer.__reduce_ex__ sklearn.preprocessing.MultiLabelBinarizer.__repr__ sklearn.preprocessing.MultiLabelBinarizer.__setattr__ sklearn.preprocessing.MultiLabelBinarizer.__setstate__ sklearn.preprocessing.MultiLabelBinarizer.__sizeof__ sklearn.preprocessing.MultiLabelBinarizer.__sklearn_clone__ sklearn.preprocessing.MultiLabelBinarizer.__sklearn_tags__ sklearn.preprocessing.MultiLabelBinarizer.__str__ sklearn.preprocessing.MultiLabelBinarizer.__subclasshook__ sklearn.preprocessing.MultiLabelBinarizer._build_cache sklearn.preprocessing.MultiLabelBinarizer._build_request_for_signature sklearn.preprocessing.MultiLabelBinarizer._check_feature_names sklearn.preprocessing.MultiLabelBinarizer._check_n_features sklearn.preprocessing.MultiLabelBinarizer._get_default_requests sklearn.preprocessing.MultiLabelBinarizer._get_doc_link sklearn.preprocessing.MultiLabelBinarizer._get_metadata_request sklearn.preprocessing.MultiLabelBinarizer._get_param_names sklearn.preprocessing.MultiLabelBinarizer._get_tags sklearn.preprocessing.MultiLabelBinarizer._more_tags sklearn.preprocessing.MultiLabelBinarizer._repr_html_inner sklearn.preprocessing.MultiLabelBinarizer._repr_mimebundle_ sklearn.preprocessing.MultiLabelBinarizer._transform sklearn.preprocessing.MultiLabelBinarizer._validate_data sklearn.preprocessing.MultiLabelBinarizer._validate_params sklearn.preprocessing.MultiLabelBinarizer.fit sklearn.preprocessing.MultiLabelBinarizer.fit_transform sklearn.preprocessing.MultiLabelBinarizer.get_metadata_routing sklearn.preprocessing.MultiLabelBinarizer.get_params sklearn.preprocessing.MultiLabelBinarizer.inverse_transform sklearn.preprocessing.MultiLabelBinarizer.set_output sklearn.preprocessing.MultiLabelBinarizer.set_params sklearn.preprocessing.MultiLabelBinarizer.transform 
+.. minigallery:: sklearn.preprocessing.MultiLabelBinarizer sklearn.preprocessing.MultiLabelBinarizer.__delattr__ sklearn.preprocessing.MultiLabelBinarizer.__dir__ sklearn.preprocessing.MultiLabelBinarizer.__eq__ sklearn.preprocessing.MultiLabelBinarizer.__format__ sklearn.preprocessing.MultiLabelBinarizer.__ge__ sklearn.preprocessing.MultiLabelBinarizer.__getattribute__ sklearn.preprocessing.MultiLabelBinarizer.__getstate__ sklearn.preprocessing.MultiLabelBinarizer.__gt__ sklearn.preprocessing.MultiLabelBinarizer.__hash__ sklearn.preprocessing.MultiLabelBinarizer.__init__ sklearn.preprocessing.MultiLabelBinarizer.__init_subclass__ sklearn.preprocessing.MultiLabelBinarizer.__le__ sklearn.preprocessing.MultiLabelBinarizer.__lt__ sklearn.preprocessing.MultiLabelBinarizer.__ne__ sklearn.preprocessing.MultiLabelBinarizer.__new__ sklearn.preprocessing.MultiLabelBinarizer.__reduce__ sklearn.preprocessing.MultiLabelBinarizer.__reduce_ex__ sklearn.preprocessing.MultiLabelBinarizer.__repr__ sklearn.preprocessing.MultiLabelBinarizer.__setattr__ sklearn.preprocessing.MultiLabelBinarizer.__setstate__ sklearn.preprocessing.MultiLabelBinarizer.__sizeof__ sklearn.preprocessing.MultiLabelBinarizer.__sklearn_clone__ sklearn.preprocessing.MultiLabelBinarizer.__sklearn_tags__ sklearn.preprocessing.MultiLabelBinarizer.__str__ sklearn.preprocessing.MultiLabelBinarizer.__subclasshook__ sklearn.preprocessing.MultiLabelBinarizer._build_cache sklearn.preprocessing.MultiLabelBinarizer._build_request_for_signature sklearn.preprocessing.MultiLabelBinarizer._get_default_requests sklearn.preprocessing.MultiLabelBinarizer._get_doc_link sklearn.preprocessing.MultiLabelBinarizer._get_metadata_request sklearn.preprocessing.MultiLabelBinarizer._get_param_names sklearn.preprocessing.MultiLabelBinarizer._repr_html_inner sklearn.preprocessing.MultiLabelBinarizer._repr_mimebundle_ sklearn.preprocessing.MultiLabelBinarizer._transform sklearn.preprocessing.MultiLabelBinarizer._validate_params sklearn.preprocessing.MultiLabelBinarizer.fit sklearn.preprocessing.MultiLabelBinarizer.fit_transform sklearn.preprocessing.MultiLabelBinarizer.get_metadata_routing sklearn.preprocessing.MultiLabelBinarizer.get_params sklearn.preprocessing.MultiLabelBinarizer.inverse_transform sklearn.preprocessing.MultiLabelBinarizer.set_output sklearn.preprocessing.MultiLabelBinarizer.set_params sklearn.preprocessing.MultiLabelBinarizer.transform 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.preprocessing.Normalizer.rst.txt b/dev/_sources/modules/generated/sklearn.preprocessing.Normalizer.rst.txt
index 2dd32ea28b570..b0b675b5dc8e9 100644
--- a/dev/_sources/modules/generated/sklearn.preprocessing.Normalizer.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.preprocessing.Normalizer.rst.txt
@@ -8,6 +8,6 @@ Normalizer
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.preprocessing.Normalizer sklearn.preprocessing.Normalizer.__delattr__ sklearn.preprocessing.Normalizer.__dir__ sklearn.preprocessing.Normalizer.__eq__ sklearn.preprocessing.Normalizer.__format__ sklearn.preprocessing.Normalizer.__ge__ sklearn.preprocessing.Normalizer.__getattribute__ sklearn.preprocessing.Normalizer.__getstate__ sklearn.preprocessing.Normalizer.__gt__ sklearn.preprocessing.Normalizer.__hash__ sklearn.preprocessing.Normalizer.__init__ sklearn.preprocessing.Normalizer.__init_subclass__ sklearn.preprocessing.Normalizer.__le__ sklearn.preprocessing.Normalizer.__lt__ sklearn.preprocessing.Normalizer.__ne__ sklearn.preprocessing.Normalizer.__new__ sklearn.preprocessing.Normalizer.__reduce__ sklearn.preprocessing.Normalizer.__reduce_ex__ sklearn.preprocessing.Normalizer.__repr__ sklearn.preprocessing.Normalizer.__setattr__ sklearn.preprocessing.Normalizer.__setstate__ sklearn.preprocessing.Normalizer.__sizeof__ sklearn.preprocessing.Normalizer.__sklearn_clone__ sklearn.preprocessing.Normalizer.__sklearn_tags__ sklearn.preprocessing.Normalizer.__str__ sklearn.preprocessing.Normalizer.__subclasshook__ sklearn.preprocessing.Normalizer._build_request_for_signature sklearn.preprocessing.Normalizer._check_feature_names sklearn.preprocessing.Normalizer._check_n_features sklearn.preprocessing.Normalizer._get_default_requests sklearn.preprocessing.Normalizer._get_doc_link sklearn.preprocessing.Normalizer._get_metadata_request sklearn.preprocessing.Normalizer._get_param_names sklearn.preprocessing.Normalizer._get_tags sklearn.preprocessing.Normalizer._more_tags sklearn.preprocessing.Normalizer._repr_html_inner sklearn.preprocessing.Normalizer._repr_mimebundle_ sklearn.preprocessing.Normalizer._validate_data sklearn.preprocessing.Normalizer._validate_params sklearn.preprocessing.Normalizer.fit sklearn.preprocessing.Normalizer.fit_transform sklearn.preprocessing.Normalizer.get_feature_names_out sklearn.preprocessing.Normalizer.get_metadata_routing sklearn.preprocessing.Normalizer.get_params sklearn.preprocessing.Normalizer.set_output sklearn.preprocessing.Normalizer.set_params sklearn.preprocessing.Normalizer.set_transform_request sklearn.preprocessing.Normalizer.transform 
+.. minigallery:: sklearn.preprocessing.Normalizer sklearn.preprocessing.Normalizer.__delattr__ sklearn.preprocessing.Normalizer.__dir__ sklearn.preprocessing.Normalizer.__eq__ sklearn.preprocessing.Normalizer.__format__ sklearn.preprocessing.Normalizer.__ge__ sklearn.preprocessing.Normalizer.__getattribute__ sklearn.preprocessing.Normalizer.__getstate__ sklearn.preprocessing.Normalizer.__gt__ sklearn.preprocessing.Normalizer.__hash__ sklearn.preprocessing.Normalizer.__init__ sklearn.preprocessing.Normalizer.__init_subclass__ sklearn.preprocessing.Normalizer.__le__ sklearn.preprocessing.Normalizer.__lt__ sklearn.preprocessing.Normalizer.__ne__ sklearn.preprocessing.Normalizer.__new__ sklearn.preprocessing.Normalizer.__reduce__ sklearn.preprocessing.Normalizer.__reduce_ex__ sklearn.preprocessing.Normalizer.__repr__ sklearn.preprocessing.Normalizer.__setattr__ sklearn.preprocessing.Normalizer.__setstate__ sklearn.preprocessing.Normalizer.__sizeof__ sklearn.preprocessing.Normalizer.__sklearn_clone__ sklearn.preprocessing.Normalizer.__sklearn_tags__ sklearn.preprocessing.Normalizer.__str__ sklearn.preprocessing.Normalizer.__subclasshook__ sklearn.preprocessing.Normalizer._build_request_for_signature sklearn.preprocessing.Normalizer._get_default_requests sklearn.preprocessing.Normalizer._get_doc_link sklearn.preprocessing.Normalizer._get_metadata_request sklearn.preprocessing.Normalizer._get_param_names sklearn.preprocessing.Normalizer._repr_html_inner sklearn.preprocessing.Normalizer._repr_mimebundle_ sklearn.preprocessing.Normalizer._validate_params sklearn.preprocessing.Normalizer.fit sklearn.preprocessing.Normalizer.fit_transform sklearn.preprocessing.Normalizer.get_feature_names_out sklearn.preprocessing.Normalizer.get_metadata_routing sklearn.preprocessing.Normalizer.get_params sklearn.preprocessing.Normalizer.set_output sklearn.preprocessing.Normalizer.set_params sklearn.preprocessing.Normalizer.set_transform_request sklearn.preprocessing.Normalizer.transform 
    :add-heading: Gallery examples
    :heading-level: -
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diff --git a/dev/_sources/modules/generated/sklearn.preprocessing.OneHotEncoder.rst.txt b/dev/_sources/modules/generated/sklearn.preprocessing.OneHotEncoder.rst.txt
index e7c55268a60d1..2722f2cb20c7e 100644
--- a/dev/_sources/modules/generated/sklearn.preprocessing.OneHotEncoder.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.preprocessing.OneHotEncoder.rst.txt
@@ -8,6 +8,6 @@ OneHotEncoder
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.preprocessing.OneHotEncoder sklearn.preprocessing.OneHotEncoder.__delattr__ sklearn.preprocessing.OneHotEncoder.__dir__ sklearn.preprocessing.OneHotEncoder.__eq__ sklearn.preprocessing.OneHotEncoder.__format__ sklearn.preprocessing.OneHotEncoder.__ge__ sklearn.preprocessing.OneHotEncoder.__getattribute__ sklearn.preprocessing.OneHotEncoder.__getstate__ sklearn.preprocessing.OneHotEncoder.__gt__ sklearn.preprocessing.OneHotEncoder.__hash__ sklearn.preprocessing.OneHotEncoder.__init__ sklearn.preprocessing.OneHotEncoder.__init_subclass__ sklearn.preprocessing.OneHotEncoder.__le__ sklearn.preprocessing.OneHotEncoder.__lt__ sklearn.preprocessing.OneHotEncoder.__ne__ sklearn.preprocessing.OneHotEncoder.__new__ sklearn.preprocessing.OneHotEncoder.__reduce__ sklearn.preprocessing.OneHotEncoder.__reduce_ex__ sklearn.preprocessing.OneHotEncoder.__repr__ sklearn.preprocessing.OneHotEncoder.__setattr__ sklearn.preprocessing.OneHotEncoder.__setstate__ sklearn.preprocessing.OneHotEncoder.__sizeof__ sklearn.preprocessing.OneHotEncoder.__sklearn_clone__ sklearn.preprocessing.OneHotEncoder.__sklearn_tags__ sklearn.preprocessing.OneHotEncoder.__str__ sklearn.preprocessing.OneHotEncoder.__subclasshook__ sklearn.preprocessing.OneHotEncoder._build_request_for_signature sklearn.preprocessing.OneHotEncoder._check_X sklearn.preprocessing.OneHotEncoder._check_feature_names sklearn.preprocessing.OneHotEncoder._check_get_feature_name_combiner sklearn.preprocessing.OneHotEncoder._check_infrequent_enabled sklearn.preprocessing.OneHotEncoder._check_n_features sklearn.preprocessing.OneHotEncoder._compute_n_features_outs sklearn.preprocessing.OneHotEncoder._compute_transformed_categories sklearn.preprocessing.OneHotEncoder._fit sklearn.preprocessing.OneHotEncoder._fit_infrequent_category_mapping sklearn.preprocessing.OneHotEncoder._get_default_requests sklearn.preprocessing.OneHotEncoder._get_doc_link sklearn.preprocessing.OneHotEncoder._get_metadata_request sklearn.preprocessing.OneHotEncoder._get_param_names sklearn.preprocessing.OneHotEncoder._get_tags sklearn.preprocessing.OneHotEncoder._identify_infrequent sklearn.preprocessing.OneHotEncoder._map_drop_idx_to_infrequent sklearn.preprocessing.OneHotEncoder._map_infrequent_categories sklearn.preprocessing.OneHotEncoder._more_tags sklearn.preprocessing.OneHotEncoder._remove_dropped_categories sklearn.preprocessing.OneHotEncoder._repr_html_inner sklearn.preprocessing.OneHotEncoder._repr_mimebundle_ sklearn.preprocessing.OneHotEncoder._set_drop_idx sklearn.preprocessing.OneHotEncoder._transform sklearn.preprocessing.OneHotEncoder._validate_data sklearn.preprocessing.OneHotEncoder._validate_params sklearn.preprocessing.OneHotEncoder.fit sklearn.preprocessing.OneHotEncoder.fit_transform sklearn.preprocessing.OneHotEncoder.get_feature_names_out sklearn.preprocessing.OneHotEncoder.get_metadata_routing sklearn.preprocessing.OneHotEncoder.get_params sklearn.preprocessing.OneHotEncoder.inverse_transform sklearn.preprocessing.OneHotEncoder.set_output sklearn.preprocessing.OneHotEncoder.set_params sklearn.preprocessing.OneHotEncoder.transform 
+.. minigallery:: sklearn.preprocessing.OneHotEncoder sklearn.preprocessing.OneHotEncoder.__delattr__ sklearn.preprocessing.OneHotEncoder.__dir__ sklearn.preprocessing.OneHotEncoder.__eq__ sklearn.preprocessing.OneHotEncoder.__format__ sklearn.preprocessing.OneHotEncoder.__ge__ sklearn.preprocessing.OneHotEncoder.__getattribute__ sklearn.preprocessing.OneHotEncoder.__getstate__ sklearn.preprocessing.OneHotEncoder.__gt__ sklearn.preprocessing.OneHotEncoder.__hash__ sklearn.preprocessing.OneHotEncoder.__init__ sklearn.preprocessing.OneHotEncoder.__init_subclass__ sklearn.preprocessing.OneHotEncoder.__le__ sklearn.preprocessing.OneHotEncoder.__lt__ sklearn.preprocessing.OneHotEncoder.__ne__ sklearn.preprocessing.OneHotEncoder.__new__ sklearn.preprocessing.OneHotEncoder.__reduce__ sklearn.preprocessing.OneHotEncoder.__reduce_ex__ sklearn.preprocessing.OneHotEncoder.__repr__ sklearn.preprocessing.OneHotEncoder.__setattr__ sklearn.preprocessing.OneHotEncoder.__setstate__ sklearn.preprocessing.OneHotEncoder.__sizeof__ sklearn.preprocessing.OneHotEncoder.__sklearn_clone__ sklearn.preprocessing.OneHotEncoder.__sklearn_tags__ sklearn.preprocessing.OneHotEncoder.__str__ sklearn.preprocessing.OneHotEncoder.__subclasshook__ sklearn.preprocessing.OneHotEncoder._build_request_for_signature sklearn.preprocessing.OneHotEncoder._check_X sklearn.preprocessing.OneHotEncoder._check_get_feature_name_combiner sklearn.preprocessing.OneHotEncoder._check_infrequent_enabled sklearn.preprocessing.OneHotEncoder._compute_n_features_outs sklearn.preprocessing.OneHotEncoder._compute_transformed_categories sklearn.preprocessing.OneHotEncoder._fit sklearn.preprocessing.OneHotEncoder._fit_infrequent_category_mapping sklearn.preprocessing.OneHotEncoder._get_default_requests sklearn.preprocessing.OneHotEncoder._get_doc_link sklearn.preprocessing.OneHotEncoder._get_metadata_request sklearn.preprocessing.OneHotEncoder._get_param_names sklearn.preprocessing.OneHotEncoder._identify_infrequent sklearn.preprocessing.OneHotEncoder._map_drop_idx_to_infrequent sklearn.preprocessing.OneHotEncoder._map_infrequent_categories sklearn.preprocessing.OneHotEncoder._remove_dropped_categories sklearn.preprocessing.OneHotEncoder._repr_html_inner sklearn.preprocessing.OneHotEncoder._repr_mimebundle_ sklearn.preprocessing.OneHotEncoder._set_drop_idx sklearn.preprocessing.OneHotEncoder._transform sklearn.preprocessing.OneHotEncoder._validate_params sklearn.preprocessing.OneHotEncoder.fit sklearn.preprocessing.OneHotEncoder.fit_transform sklearn.preprocessing.OneHotEncoder.get_feature_names_out sklearn.preprocessing.OneHotEncoder.get_metadata_routing sklearn.preprocessing.OneHotEncoder.get_params sklearn.preprocessing.OneHotEncoder.inverse_transform sklearn.preprocessing.OneHotEncoder.set_output sklearn.preprocessing.OneHotEncoder.set_params sklearn.preprocessing.OneHotEncoder.transform 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.preprocessing.OrdinalEncoder.rst.txt b/dev/_sources/modules/generated/sklearn.preprocessing.OrdinalEncoder.rst.txt
index bf7d907781942..00484b791da71 100644
--- a/dev/_sources/modules/generated/sklearn.preprocessing.OrdinalEncoder.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.preprocessing.OrdinalEncoder.rst.txt
@@ -8,6 +8,6 @@ OrdinalEncoder
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.preprocessing.OrdinalEncoder sklearn.preprocessing.OrdinalEncoder.__delattr__ sklearn.preprocessing.OrdinalEncoder.__dir__ sklearn.preprocessing.OrdinalEncoder.__eq__ sklearn.preprocessing.OrdinalEncoder.__format__ sklearn.preprocessing.OrdinalEncoder.__ge__ sklearn.preprocessing.OrdinalEncoder.__getattribute__ sklearn.preprocessing.OrdinalEncoder.__getstate__ sklearn.preprocessing.OrdinalEncoder.__gt__ sklearn.preprocessing.OrdinalEncoder.__hash__ sklearn.preprocessing.OrdinalEncoder.__init__ sklearn.preprocessing.OrdinalEncoder.__init_subclass__ sklearn.preprocessing.OrdinalEncoder.__le__ sklearn.preprocessing.OrdinalEncoder.__lt__ sklearn.preprocessing.OrdinalEncoder.__ne__ sklearn.preprocessing.OrdinalEncoder.__new__ sklearn.preprocessing.OrdinalEncoder.__reduce__ sklearn.preprocessing.OrdinalEncoder.__reduce_ex__ sklearn.preprocessing.OrdinalEncoder.__repr__ sklearn.preprocessing.OrdinalEncoder.__setattr__ sklearn.preprocessing.OrdinalEncoder.__setstate__ sklearn.preprocessing.OrdinalEncoder.__sizeof__ sklearn.preprocessing.OrdinalEncoder.__sklearn_clone__ sklearn.preprocessing.OrdinalEncoder.__sklearn_tags__ sklearn.preprocessing.OrdinalEncoder.__str__ sklearn.preprocessing.OrdinalEncoder.__subclasshook__ sklearn.preprocessing.OrdinalEncoder._build_request_for_signature sklearn.preprocessing.OrdinalEncoder._check_X sklearn.preprocessing.OrdinalEncoder._check_feature_names sklearn.preprocessing.OrdinalEncoder._check_infrequent_enabled sklearn.preprocessing.OrdinalEncoder._check_n_features sklearn.preprocessing.OrdinalEncoder._fit sklearn.preprocessing.OrdinalEncoder._fit_infrequent_category_mapping sklearn.preprocessing.OrdinalEncoder._get_default_requests sklearn.preprocessing.OrdinalEncoder._get_doc_link sklearn.preprocessing.OrdinalEncoder._get_metadata_request sklearn.preprocessing.OrdinalEncoder._get_param_names sklearn.preprocessing.OrdinalEncoder._get_tags sklearn.preprocessing.OrdinalEncoder._identify_infrequent sklearn.preprocessing.OrdinalEncoder._map_infrequent_categories sklearn.preprocessing.OrdinalEncoder._more_tags sklearn.preprocessing.OrdinalEncoder._repr_html_inner sklearn.preprocessing.OrdinalEncoder._repr_mimebundle_ sklearn.preprocessing.OrdinalEncoder._transform sklearn.preprocessing.OrdinalEncoder._validate_data sklearn.preprocessing.OrdinalEncoder._validate_params sklearn.preprocessing.OrdinalEncoder.fit sklearn.preprocessing.OrdinalEncoder.fit_transform sklearn.preprocessing.OrdinalEncoder.get_feature_names_out sklearn.preprocessing.OrdinalEncoder.get_metadata_routing sklearn.preprocessing.OrdinalEncoder.get_params sklearn.preprocessing.OrdinalEncoder.inverse_transform sklearn.preprocessing.OrdinalEncoder.set_output sklearn.preprocessing.OrdinalEncoder.set_params sklearn.preprocessing.OrdinalEncoder.transform 
+.. minigallery:: sklearn.preprocessing.OrdinalEncoder sklearn.preprocessing.OrdinalEncoder.__delattr__ sklearn.preprocessing.OrdinalEncoder.__dir__ sklearn.preprocessing.OrdinalEncoder.__eq__ sklearn.preprocessing.OrdinalEncoder.__format__ sklearn.preprocessing.OrdinalEncoder.__ge__ sklearn.preprocessing.OrdinalEncoder.__getattribute__ sklearn.preprocessing.OrdinalEncoder.__getstate__ sklearn.preprocessing.OrdinalEncoder.__gt__ sklearn.preprocessing.OrdinalEncoder.__hash__ sklearn.preprocessing.OrdinalEncoder.__init__ sklearn.preprocessing.OrdinalEncoder.__init_subclass__ sklearn.preprocessing.OrdinalEncoder.__le__ sklearn.preprocessing.OrdinalEncoder.__lt__ sklearn.preprocessing.OrdinalEncoder.__ne__ sklearn.preprocessing.OrdinalEncoder.__new__ sklearn.preprocessing.OrdinalEncoder.__reduce__ sklearn.preprocessing.OrdinalEncoder.__reduce_ex__ sklearn.preprocessing.OrdinalEncoder.__repr__ sklearn.preprocessing.OrdinalEncoder.__setattr__ sklearn.preprocessing.OrdinalEncoder.__setstate__ sklearn.preprocessing.OrdinalEncoder.__sizeof__ sklearn.preprocessing.OrdinalEncoder.__sklearn_clone__ sklearn.preprocessing.OrdinalEncoder.__sklearn_tags__ sklearn.preprocessing.OrdinalEncoder.__str__ sklearn.preprocessing.OrdinalEncoder.__subclasshook__ sklearn.preprocessing.OrdinalEncoder._build_request_for_signature sklearn.preprocessing.OrdinalEncoder._check_X sklearn.preprocessing.OrdinalEncoder._check_infrequent_enabled sklearn.preprocessing.OrdinalEncoder._fit sklearn.preprocessing.OrdinalEncoder._fit_infrequent_category_mapping sklearn.preprocessing.OrdinalEncoder._get_default_requests sklearn.preprocessing.OrdinalEncoder._get_doc_link sklearn.preprocessing.OrdinalEncoder._get_metadata_request sklearn.preprocessing.OrdinalEncoder._get_param_names sklearn.preprocessing.OrdinalEncoder._identify_infrequent sklearn.preprocessing.OrdinalEncoder._map_infrequent_categories sklearn.preprocessing.OrdinalEncoder._repr_html_inner sklearn.preprocessing.OrdinalEncoder._repr_mimebundle_ sklearn.preprocessing.OrdinalEncoder._transform sklearn.preprocessing.OrdinalEncoder._validate_params sklearn.preprocessing.OrdinalEncoder.fit sklearn.preprocessing.OrdinalEncoder.fit_transform sklearn.preprocessing.OrdinalEncoder.get_feature_names_out sklearn.preprocessing.OrdinalEncoder.get_metadata_routing sklearn.preprocessing.OrdinalEncoder.get_params sklearn.preprocessing.OrdinalEncoder.inverse_transform sklearn.preprocessing.OrdinalEncoder.set_output sklearn.preprocessing.OrdinalEncoder.set_params sklearn.preprocessing.OrdinalEncoder.transform 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.preprocessing.PolynomialFeatures.rst.txt b/dev/_sources/modules/generated/sklearn.preprocessing.PolynomialFeatures.rst.txt
index 7b18080a6b45d..dc5a182eb7804 100644
--- a/dev/_sources/modules/generated/sklearn.preprocessing.PolynomialFeatures.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.preprocessing.PolynomialFeatures.rst.txt
@@ -8,6 +8,6 @@ PolynomialFeatures
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.preprocessing.PolynomialFeatures sklearn.preprocessing.PolynomialFeatures.__delattr__ sklearn.preprocessing.PolynomialFeatures.__dir__ sklearn.preprocessing.PolynomialFeatures.__eq__ sklearn.preprocessing.PolynomialFeatures.__format__ sklearn.preprocessing.PolynomialFeatures.__ge__ sklearn.preprocessing.PolynomialFeatures.__getattribute__ sklearn.preprocessing.PolynomialFeatures.__getstate__ sklearn.preprocessing.PolynomialFeatures.__gt__ sklearn.preprocessing.PolynomialFeatures.__hash__ sklearn.preprocessing.PolynomialFeatures.__init__ sklearn.preprocessing.PolynomialFeatures.__init_subclass__ sklearn.preprocessing.PolynomialFeatures.__le__ sklearn.preprocessing.PolynomialFeatures.__lt__ sklearn.preprocessing.PolynomialFeatures.__ne__ sklearn.preprocessing.PolynomialFeatures.__new__ sklearn.preprocessing.PolynomialFeatures.__reduce__ sklearn.preprocessing.PolynomialFeatures.__reduce_ex__ sklearn.preprocessing.PolynomialFeatures.__repr__ sklearn.preprocessing.PolynomialFeatures.__setattr__ sklearn.preprocessing.PolynomialFeatures.__setstate__ sklearn.preprocessing.PolynomialFeatures.__sizeof__ sklearn.preprocessing.PolynomialFeatures.__sklearn_clone__ sklearn.preprocessing.PolynomialFeatures.__sklearn_tags__ sklearn.preprocessing.PolynomialFeatures.__str__ sklearn.preprocessing.PolynomialFeatures.__subclasshook__ sklearn.preprocessing.PolynomialFeatures._build_request_for_signature sklearn.preprocessing.PolynomialFeatures._check_feature_names sklearn.preprocessing.PolynomialFeatures._check_n_features sklearn.preprocessing.PolynomialFeatures._combinations sklearn.preprocessing.PolynomialFeatures._get_default_requests sklearn.preprocessing.PolynomialFeatures._get_doc_link sklearn.preprocessing.PolynomialFeatures._get_metadata_request sklearn.preprocessing.PolynomialFeatures._get_param_names sklearn.preprocessing.PolynomialFeatures._get_tags sklearn.preprocessing.PolynomialFeatures._more_tags sklearn.preprocessing.PolynomialFeatures._num_combinations sklearn.preprocessing.PolynomialFeatures._repr_html_inner sklearn.preprocessing.PolynomialFeatures._repr_mimebundle_ sklearn.preprocessing.PolynomialFeatures._validate_data sklearn.preprocessing.PolynomialFeatures._validate_params sklearn.preprocessing.PolynomialFeatures.fit sklearn.preprocessing.PolynomialFeatures.fit_transform sklearn.preprocessing.PolynomialFeatures.get_feature_names_out sklearn.preprocessing.PolynomialFeatures.get_metadata_routing sklearn.preprocessing.PolynomialFeatures.get_params sklearn.preprocessing.PolynomialFeatures.set_output sklearn.preprocessing.PolynomialFeatures.set_params sklearn.preprocessing.PolynomialFeatures.transform 
+.. minigallery:: sklearn.preprocessing.PolynomialFeatures sklearn.preprocessing.PolynomialFeatures.__delattr__ sklearn.preprocessing.PolynomialFeatures.__dir__ sklearn.preprocessing.PolynomialFeatures.__eq__ sklearn.preprocessing.PolynomialFeatures.__format__ sklearn.preprocessing.PolynomialFeatures.__ge__ sklearn.preprocessing.PolynomialFeatures.__getattribute__ sklearn.preprocessing.PolynomialFeatures.__getstate__ sklearn.preprocessing.PolynomialFeatures.__gt__ sklearn.preprocessing.PolynomialFeatures.__hash__ sklearn.preprocessing.PolynomialFeatures.__init__ sklearn.preprocessing.PolynomialFeatures.__init_subclass__ sklearn.preprocessing.PolynomialFeatures.__le__ sklearn.preprocessing.PolynomialFeatures.__lt__ sklearn.preprocessing.PolynomialFeatures.__ne__ sklearn.preprocessing.PolynomialFeatures.__new__ sklearn.preprocessing.PolynomialFeatures.__reduce__ sklearn.preprocessing.PolynomialFeatures.__reduce_ex__ sklearn.preprocessing.PolynomialFeatures.__repr__ sklearn.preprocessing.PolynomialFeatures.__setattr__ sklearn.preprocessing.PolynomialFeatures.__setstate__ sklearn.preprocessing.PolynomialFeatures.__sizeof__ sklearn.preprocessing.PolynomialFeatures.__sklearn_clone__ sklearn.preprocessing.PolynomialFeatures.__sklearn_tags__ sklearn.preprocessing.PolynomialFeatures.__str__ sklearn.preprocessing.PolynomialFeatures.__subclasshook__ sklearn.preprocessing.PolynomialFeatures._build_request_for_signature sklearn.preprocessing.PolynomialFeatures._combinations sklearn.preprocessing.PolynomialFeatures._get_default_requests sklearn.preprocessing.PolynomialFeatures._get_doc_link sklearn.preprocessing.PolynomialFeatures._get_metadata_request sklearn.preprocessing.PolynomialFeatures._get_param_names sklearn.preprocessing.PolynomialFeatures._num_combinations sklearn.preprocessing.PolynomialFeatures._repr_html_inner sklearn.preprocessing.PolynomialFeatures._repr_mimebundle_ sklearn.preprocessing.PolynomialFeatures._validate_params sklearn.preprocessing.PolynomialFeatures.fit sklearn.preprocessing.PolynomialFeatures.fit_transform sklearn.preprocessing.PolynomialFeatures.get_feature_names_out sklearn.preprocessing.PolynomialFeatures.get_metadata_routing sklearn.preprocessing.PolynomialFeatures.get_params sklearn.preprocessing.PolynomialFeatures.set_output sklearn.preprocessing.PolynomialFeatures.set_params sklearn.preprocessing.PolynomialFeatures.transform 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.preprocessing.PowerTransformer.rst.txt b/dev/_sources/modules/generated/sklearn.preprocessing.PowerTransformer.rst.txt
index da7c29338d8a7..21db47d9675fd 100644
--- a/dev/_sources/modules/generated/sklearn.preprocessing.PowerTransformer.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.preprocessing.PowerTransformer.rst.txt
@@ -8,6 +8,6 @@ PowerTransformer
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.preprocessing.PowerTransformer sklearn.preprocessing.PowerTransformer.__delattr__ sklearn.preprocessing.PowerTransformer.__dir__ sklearn.preprocessing.PowerTransformer.__eq__ sklearn.preprocessing.PowerTransformer.__format__ sklearn.preprocessing.PowerTransformer.__ge__ sklearn.preprocessing.PowerTransformer.__getattribute__ sklearn.preprocessing.PowerTransformer.__getstate__ sklearn.preprocessing.PowerTransformer.__gt__ sklearn.preprocessing.PowerTransformer.__hash__ sklearn.preprocessing.PowerTransformer.__init__ sklearn.preprocessing.PowerTransformer.__init_subclass__ sklearn.preprocessing.PowerTransformer.__le__ sklearn.preprocessing.PowerTransformer.__lt__ sklearn.preprocessing.PowerTransformer.__ne__ sklearn.preprocessing.PowerTransformer.__new__ sklearn.preprocessing.PowerTransformer.__reduce__ sklearn.preprocessing.PowerTransformer.__reduce_ex__ sklearn.preprocessing.PowerTransformer.__repr__ sklearn.preprocessing.PowerTransformer.__setattr__ sklearn.preprocessing.PowerTransformer.__setstate__ sklearn.preprocessing.PowerTransformer.__sizeof__ sklearn.preprocessing.PowerTransformer.__sklearn_clone__ sklearn.preprocessing.PowerTransformer.__sklearn_tags__ sklearn.preprocessing.PowerTransformer.__str__ sklearn.preprocessing.PowerTransformer.__subclasshook__ sklearn.preprocessing.PowerTransformer._box_cox_optimize sklearn.preprocessing.PowerTransformer._build_request_for_signature sklearn.preprocessing.PowerTransformer._check_feature_names sklearn.preprocessing.PowerTransformer._check_input sklearn.preprocessing.PowerTransformer._check_n_features sklearn.preprocessing.PowerTransformer._fit sklearn.preprocessing.PowerTransformer._get_default_requests sklearn.preprocessing.PowerTransformer._get_doc_link sklearn.preprocessing.PowerTransformer._get_metadata_request sklearn.preprocessing.PowerTransformer._get_param_names sklearn.preprocessing.PowerTransformer._get_tags sklearn.preprocessing.PowerTransformer._more_tags sklearn.preprocessing.PowerTransformer._repr_html_inner sklearn.preprocessing.PowerTransformer._repr_mimebundle_ sklearn.preprocessing.PowerTransformer._validate_data sklearn.preprocessing.PowerTransformer._validate_params sklearn.preprocessing.PowerTransformer._yeo_johnson_inverse_transform sklearn.preprocessing.PowerTransformer._yeo_johnson_optimize sklearn.preprocessing.PowerTransformer._yeo_johnson_transform sklearn.preprocessing.PowerTransformer.fit sklearn.preprocessing.PowerTransformer.fit_transform sklearn.preprocessing.PowerTransformer.get_feature_names_out sklearn.preprocessing.PowerTransformer.get_metadata_routing sklearn.preprocessing.PowerTransformer.get_params sklearn.preprocessing.PowerTransformer.inverse_transform sklearn.preprocessing.PowerTransformer.set_output sklearn.preprocessing.PowerTransformer.set_params sklearn.preprocessing.PowerTransformer.transform 
+.. minigallery:: sklearn.preprocessing.PowerTransformer sklearn.preprocessing.PowerTransformer.__delattr__ sklearn.preprocessing.PowerTransformer.__dir__ sklearn.preprocessing.PowerTransformer.__eq__ sklearn.preprocessing.PowerTransformer.__format__ sklearn.preprocessing.PowerTransformer.__ge__ sklearn.preprocessing.PowerTransformer.__getattribute__ sklearn.preprocessing.PowerTransformer.__getstate__ sklearn.preprocessing.PowerTransformer.__gt__ sklearn.preprocessing.PowerTransformer.__hash__ sklearn.preprocessing.PowerTransformer.__init__ sklearn.preprocessing.PowerTransformer.__init_subclass__ sklearn.preprocessing.PowerTransformer.__le__ sklearn.preprocessing.PowerTransformer.__lt__ sklearn.preprocessing.PowerTransformer.__ne__ sklearn.preprocessing.PowerTransformer.__new__ sklearn.preprocessing.PowerTransformer.__reduce__ sklearn.preprocessing.PowerTransformer.__reduce_ex__ sklearn.preprocessing.PowerTransformer.__repr__ sklearn.preprocessing.PowerTransformer.__setattr__ sklearn.preprocessing.PowerTransformer.__setstate__ sklearn.preprocessing.PowerTransformer.__sizeof__ sklearn.preprocessing.PowerTransformer.__sklearn_clone__ sklearn.preprocessing.PowerTransformer.__sklearn_tags__ sklearn.preprocessing.PowerTransformer.__str__ sklearn.preprocessing.PowerTransformer.__subclasshook__ sklearn.preprocessing.PowerTransformer._box_cox_optimize sklearn.preprocessing.PowerTransformer._build_request_for_signature sklearn.preprocessing.PowerTransformer._check_input sklearn.preprocessing.PowerTransformer._fit sklearn.preprocessing.PowerTransformer._get_default_requests sklearn.preprocessing.PowerTransformer._get_doc_link sklearn.preprocessing.PowerTransformer._get_metadata_request sklearn.preprocessing.PowerTransformer._get_param_names sklearn.preprocessing.PowerTransformer._repr_html_inner sklearn.preprocessing.PowerTransformer._repr_mimebundle_ sklearn.preprocessing.PowerTransformer._validate_params sklearn.preprocessing.PowerTransformer._yeo_johnson_inverse_transform sklearn.preprocessing.PowerTransformer._yeo_johnson_optimize sklearn.preprocessing.PowerTransformer._yeo_johnson_transform sklearn.preprocessing.PowerTransformer.fit sklearn.preprocessing.PowerTransformer.fit_transform sklearn.preprocessing.PowerTransformer.get_feature_names_out sklearn.preprocessing.PowerTransformer.get_metadata_routing sklearn.preprocessing.PowerTransformer.get_params sklearn.preprocessing.PowerTransformer.inverse_transform sklearn.preprocessing.PowerTransformer.set_output sklearn.preprocessing.PowerTransformer.set_params sklearn.preprocessing.PowerTransformer.transform 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.preprocessing.QuantileTransformer.rst.txt b/dev/_sources/modules/generated/sklearn.preprocessing.QuantileTransformer.rst.txt
index f48e00d4b5263..6b48121449ffd 100644
--- a/dev/_sources/modules/generated/sklearn.preprocessing.QuantileTransformer.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.preprocessing.QuantileTransformer.rst.txt
@@ -8,6 +8,6 @@ QuantileTransformer
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.preprocessing.QuantileTransformer sklearn.preprocessing.QuantileTransformer.__delattr__ sklearn.preprocessing.QuantileTransformer.__dir__ sklearn.preprocessing.QuantileTransformer.__eq__ sklearn.preprocessing.QuantileTransformer.__format__ sklearn.preprocessing.QuantileTransformer.__ge__ sklearn.preprocessing.QuantileTransformer.__getattribute__ sklearn.preprocessing.QuantileTransformer.__getstate__ sklearn.preprocessing.QuantileTransformer.__gt__ sklearn.preprocessing.QuantileTransformer.__hash__ sklearn.preprocessing.QuantileTransformer.__init__ sklearn.preprocessing.QuantileTransformer.__init_subclass__ sklearn.preprocessing.QuantileTransformer.__le__ sklearn.preprocessing.QuantileTransformer.__lt__ sklearn.preprocessing.QuantileTransformer.__ne__ sklearn.preprocessing.QuantileTransformer.__new__ sklearn.preprocessing.QuantileTransformer.__reduce__ sklearn.preprocessing.QuantileTransformer.__reduce_ex__ sklearn.preprocessing.QuantileTransformer.__repr__ sklearn.preprocessing.QuantileTransformer.__setattr__ sklearn.preprocessing.QuantileTransformer.__setstate__ sklearn.preprocessing.QuantileTransformer.__sizeof__ sklearn.preprocessing.QuantileTransformer.__sklearn_clone__ sklearn.preprocessing.QuantileTransformer.__sklearn_tags__ sklearn.preprocessing.QuantileTransformer.__str__ sklearn.preprocessing.QuantileTransformer.__subclasshook__ sklearn.preprocessing.QuantileTransformer._build_request_for_signature sklearn.preprocessing.QuantileTransformer._check_feature_names sklearn.preprocessing.QuantileTransformer._check_inputs sklearn.preprocessing.QuantileTransformer._check_n_features sklearn.preprocessing.QuantileTransformer._dense_fit sklearn.preprocessing.QuantileTransformer._get_default_requests sklearn.preprocessing.QuantileTransformer._get_doc_link sklearn.preprocessing.QuantileTransformer._get_metadata_request sklearn.preprocessing.QuantileTransformer._get_param_names sklearn.preprocessing.QuantileTransformer._get_tags sklearn.preprocessing.QuantileTransformer._more_tags sklearn.preprocessing.QuantileTransformer._repr_html_inner sklearn.preprocessing.QuantileTransformer._repr_mimebundle_ sklearn.preprocessing.QuantileTransformer._sparse_fit sklearn.preprocessing.QuantileTransformer._transform sklearn.preprocessing.QuantileTransformer._transform_col sklearn.preprocessing.QuantileTransformer._validate_data sklearn.preprocessing.QuantileTransformer._validate_params sklearn.preprocessing.QuantileTransformer.fit sklearn.preprocessing.QuantileTransformer.fit_transform sklearn.preprocessing.QuantileTransformer.get_feature_names_out sklearn.preprocessing.QuantileTransformer.get_metadata_routing sklearn.preprocessing.QuantileTransformer.get_params sklearn.preprocessing.QuantileTransformer.inverse_transform sklearn.preprocessing.QuantileTransformer.set_output sklearn.preprocessing.QuantileTransformer.set_params sklearn.preprocessing.QuantileTransformer.transform 
+.. minigallery:: sklearn.preprocessing.QuantileTransformer sklearn.preprocessing.QuantileTransformer.__delattr__ sklearn.preprocessing.QuantileTransformer.__dir__ sklearn.preprocessing.QuantileTransformer.__eq__ sklearn.preprocessing.QuantileTransformer.__format__ sklearn.preprocessing.QuantileTransformer.__ge__ sklearn.preprocessing.QuantileTransformer.__getattribute__ sklearn.preprocessing.QuantileTransformer.__getstate__ sklearn.preprocessing.QuantileTransformer.__gt__ sklearn.preprocessing.QuantileTransformer.__hash__ sklearn.preprocessing.QuantileTransformer.__init__ sklearn.preprocessing.QuantileTransformer.__init_subclass__ sklearn.preprocessing.QuantileTransformer.__le__ sklearn.preprocessing.QuantileTransformer.__lt__ sklearn.preprocessing.QuantileTransformer.__ne__ sklearn.preprocessing.QuantileTransformer.__new__ sklearn.preprocessing.QuantileTransformer.__reduce__ sklearn.preprocessing.QuantileTransformer.__reduce_ex__ sklearn.preprocessing.QuantileTransformer.__repr__ sklearn.preprocessing.QuantileTransformer.__setattr__ sklearn.preprocessing.QuantileTransformer.__setstate__ sklearn.preprocessing.QuantileTransformer.__sizeof__ sklearn.preprocessing.QuantileTransformer.__sklearn_clone__ sklearn.preprocessing.QuantileTransformer.__sklearn_tags__ sklearn.preprocessing.QuantileTransformer.__str__ sklearn.preprocessing.QuantileTransformer.__subclasshook__ sklearn.preprocessing.QuantileTransformer._build_request_for_signature sklearn.preprocessing.QuantileTransformer._check_inputs sklearn.preprocessing.QuantileTransformer._dense_fit sklearn.preprocessing.QuantileTransformer._get_default_requests sklearn.preprocessing.QuantileTransformer._get_doc_link sklearn.preprocessing.QuantileTransformer._get_metadata_request sklearn.preprocessing.QuantileTransformer._get_param_names sklearn.preprocessing.QuantileTransformer._repr_html_inner sklearn.preprocessing.QuantileTransformer._repr_mimebundle_ sklearn.preprocessing.QuantileTransformer._sparse_fit sklearn.preprocessing.QuantileTransformer._transform sklearn.preprocessing.QuantileTransformer._transform_col sklearn.preprocessing.QuantileTransformer._validate_params sklearn.preprocessing.QuantileTransformer.fit sklearn.preprocessing.QuantileTransformer.fit_transform sklearn.preprocessing.QuantileTransformer.get_feature_names_out sklearn.preprocessing.QuantileTransformer.get_metadata_routing sklearn.preprocessing.QuantileTransformer.get_params sklearn.preprocessing.QuantileTransformer.inverse_transform sklearn.preprocessing.QuantileTransformer.set_output sklearn.preprocessing.QuantileTransformer.set_params sklearn.preprocessing.QuantileTransformer.transform 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.preprocessing.RobustScaler.rst.txt b/dev/_sources/modules/generated/sklearn.preprocessing.RobustScaler.rst.txt
index 0c8ca9d61cbfd..a7e97333647a3 100644
--- a/dev/_sources/modules/generated/sklearn.preprocessing.RobustScaler.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.preprocessing.RobustScaler.rst.txt
@@ -8,6 +8,6 @@ RobustScaler
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.preprocessing.RobustScaler sklearn.preprocessing.RobustScaler.__delattr__ sklearn.preprocessing.RobustScaler.__dir__ sklearn.preprocessing.RobustScaler.__eq__ sklearn.preprocessing.RobustScaler.__format__ sklearn.preprocessing.RobustScaler.__ge__ sklearn.preprocessing.RobustScaler.__getattribute__ sklearn.preprocessing.RobustScaler.__getstate__ sklearn.preprocessing.RobustScaler.__gt__ sklearn.preprocessing.RobustScaler.__hash__ sklearn.preprocessing.RobustScaler.__init__ sklearn.preprocessing.RobustScaler.__init_subclass__ sklearn.preprocessing.RobustScaler.__le__ sklearn.preprocessing.RobustScaler.__lt__ sklearn.preprocessing.RobustScaler.__ne__ sklearn.preprocessing.RobustScaler.__new__ sklearn.preprocessing.RobustScaler.__reduce__ sklearn.preprocessing.RobustScaler.__reduce_ex__ sklearn.preprocessing.RobustScaler.__repr__ sklearn.preprocessing.RobustScaler.__setattr__ sklearn.preprocessing.RobustScaler.__setstate__ sklearn.preprocessing.RobustScaler.__sizeof__ sklearn.preprocessing.RobustScaler.__sklearn_clone__ sklearn.preprocessing.RobustScaler.__sklearn_tags__ sklearn.preprocessing.RobustScaler.__str__ sklearn.preprocessing.RobustScaler.__subclasshook__ sklearn.preprocessing.RobustScaler._build_request_for_signature sklearn.preprocessing.RobustScaler._check_feature_names sklearn.preprocessing.RobustScaler._check_n_features sklearn.preprocessing.RobustScaler._get_default_requests sklearn.preprocessing.RobustScaler._get_doc_link sklearn.preprocessing.RobustScaler._get_metadata_request sklearn.preprocessing.RobustScaler._get_param_names sklearn.preprocessing.RobustScaler._get_tags sklearn.preprocessing.RobustScaler._more_tags sklearn.preprocessing.RobustScaler._repr_html_inner sklearn.preprocessing.RobustScaler._repr_mimebundle_ sklearn.preprocessing.RobustScaler._validate_data sklearn.preprocessing.RobustScaler._validate_params sklearn.preprocessing.RobustScaler.fit sklearn.preprocessing.RobustScaler.fit_transform sklearn.preprocessing.RobustScaler.get_feature_names_out sklearn.preprocessing.RobustScaler.get_metadata_routing sklearn.preprocessing.RobustScaler.get_params sklearn.preprocessing.RobustScaler.inverse_transform sklearn.preprocessing.RobustScaler.set_output sklearn.preprocessing.RobustScaler.set_params sklearn.preprocessing.RobustScaler.transform 
+.. minigallery:: sklearn.preprocessing.RobustScaler sklearn.preprocessing.RobustScaler.__delattr__ sklearn.preprocessing.RobustScaler.__dir__ sklearn.preprocessing.RobustScaler.__eq__ sklearn.preprocessing.RobustScaler.__format__ sklearn.preprocessing.RobustScaler.__ge__ sklearn.preprocessing.RobustScaler.__getattribute__ sklearn.preprocessing.RobustScaler.__getstate__ sklearn.preprocessing.RobustScaler.__gt__ sklearn.preprocessing.RobustScaler.__hash__ sklearn.preprocessing.RobustScaler.__init__ sklearn.preprocessing.RobustScaler.__init_subclass__ sklearn.preprocessing.RobustScaler.__le__ sklearn.preprocessing.RobustScaler.__lt__ sklearn.preprocessing.RobustScaler.__ne__ sklearn.preprocessing.RobustScaler.__new__ sklearn.preprocessing.RobustScaler.__reduce__ sklearn.preprocessing.RobustScaler.__reduce_ex__ sklearn.preprocessing.RobustScaler.__repr__ sklearn.preprocessing.RobustScaler.__setattr__ sklearn.preprocessing.RobustScaler.__setstate__ sklearn.preprocessing.RobustScaler.__sizeof__ sklearn.preprocessing.RobustScaler.__sklearn_clone__ sklearn.preprocessing.RobustScaler.__sklearn_tags__ sklearn.preprocessing.RobustScaler.__str__ sklearn.preprocessing.RobustScaler.__subclasshook__ sklearn.preprocessing.RobustScaler._build_request_for_signature sklearn.preprocessing.RobustScaler._get_default_requests sklearn.preprocessing.RobustScaler._get_doc_link sklearn.preprocessing.RobustScaler._get_metadata_request sklearn.preprocessing.RobustScaler._get_param_names sklearn.preprocessing.RobustScaler._repr_html_inner sklearn.preprocessing.RobustScaler._repr_mimebundle_ sklearn.preprocessing.RobustScaler._validate_params sklearn.preprocessing.RobustScaler.fit sklearn.preprocessing.RobustScaler.fit_transform sklearn.preprocessing.RobustScaler.get_feature_names_out sklearn.preprocessing.RobustScaler.get_metadata_routing sklearn.preprocessing.RobustScaler.get_params sklearn.preprocessing.RobustScaler.inverse_transform sklearn.preprocessing.RobustScaler.set_output sklearn.preprocessing.RobustScaler.set_params sklearn.preprocessing.RobustScaler.transform 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.preprocessing.SplineTransformer.rst.txt b/dev/_sources/modules/generated/sklearn.preprocessing.SplineTransformer.rst.txt
index 4869c43cc997b..618d3c7fff6c7 100644
--- a/dev/_sources/modules/generated/sklearn.preprocessing.SplineTransformer.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.preprocessing.SplineTransformer.rst.txt
@@ -8,6 +8,6 @@ SplineTransformer
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.preprocessing.SplineTransformer sklearn.preprocessing.SplineTransformer.__delattr__ sklearn.preprocessing.SplineTransformer.__dir__ sklearn.preprocessing.SplineTransformer.__eq__ sklearn.preprocessing.SplineTransformer.__format__ sklearn.preprocessing.SplineTransformer.__ge__ sklearn.preprocessing.SplineTransformer.__getattribute__ sklearn.preprocessing.SplineTransformer.__getstate__ sklearn.preprocessing.SplineTransformer.__gt__ sklearn.preprocessing.SplineTransformer.__hash__ sklearn.preprocessing.SplineTransformer.__init__ sklearn.preprocessing.SplineTransformer.__init_subclass__ sklearn.preprocessing.SplineTransformer.__le__ sklearn.preprocessing.SplineTransformer.__lt__ sklearn.preprocessing.SplineTransformer.__ne__ sklearn.preprocessing.SplineTransformer.__new__ sklearn.preprocessing.SplineTransformer.__reduce__ sklearn.preprocessing.SplineTransformer.__reduce_ex__ sklearn.preprocessing.SplineTransformer.__repr__ sklearn.preprocessing.SplineTransformer.__setattr__ sklearn.preprocessing.SplineTransformer.__setstate__ sklearn.preprocessing.SplineTransformer.__sizeof__ sklearn.preprocessing.SplineTransformer.__sklearn_clone__ sklearn.preprocessing.SplineTransformer.__sklearn_tags__ sklearn.preprocessing.SplineTransformer.__str__ sklearn.preprocessing.SplineTransformer.__subclasshook__ sklearn.preprocessing.SplineTransformer._build_request_for_signature sklearn.preprocessing.SplineTransformer._check_feature_names sklearn.preprocessing.SplineTransformer._check_n_features sklearn.preprocessing.SplineTransformer._get_base_knot_positions sklearn.preprocessing.SplineTransformer._get_default_requests sklearn.preprocessing.SplineTransformer._get_doc_link sklearn.preprocessing.SplineTransformer._get_metadata_request sklearn.preprocessing.SplineTransformer._get_param_names sklearn.preprocessing.SplineTransformer._get_tags sklearn.preprocessing.SplineTransformer._more_tags sklearn.preprocessing.SplineTransformer._repr_html_inner sklearn.preprocessing.SplineTransformer._repr_mimebundle_ sklearn.preprocessing.SplineTransformer._validate_data sklearn.preprocessing.SplineTransformer._validate_params sklearn.preprocessing.SplineTransformer.fit sklearn.preprocessing.SplineTransformer.fit_transform sklearn.preprocessing.SplineTransformer.get_feature_names_out sklearn.preprocessing.SplineTransformer.get_metadata_routing sklearn.preprocessing.SplineTransformer.get_params sklearn.preprocessing.SplineTransformer.set_fit_request sklearn.preprocessing.SplineTransformer.set_output sklearn.preprocessing.SplineTransformer.set_params sklearn.preprocessing.SplineTransformer.transform 
+.. minigallery:: sklearn.preprocessing.SplineTransformer sklearn.preprocessing.SplineTransformer.__delattr__ sklearn.preprocessing.SplineTransformer.__dir__ sklearn.preprocessing.SplineTransformer.__eq__ sklearn.preprocessing.SplineTransformer.__format__ sklearn.preprocessing.SplineTransformer.__ge__ sklearn.preprocessing.SplineTransformer.__getattribute__ sklearn.preprocessing.SplineTransformer.__getstate__ sklearn.preprocessing.SplineTransformer.__gt__ sklearn.preprocessing.SplineTransformer.__hash__ sklearn.preprocessing.SplineTransformer.__init__ sklearn.preprocessing.SplineTransformer.__init_subclass__ sklearn.preprocessing.SplineTransformer.__le__ sklearn.preprocessing.SplineTransformer.__lt__ sklearn.preprocessing.SplineTransformer.__ne__ sklearn.preprocessing.SplineTransformer.__new__ sklearn.preprocessing.SplineTransformer.__reduce__ sklearn.preprocessing.SplineTransformer.__reduce_ex__ sklearn.preprocessing.SplineTransformer.__repr__ sklearn.preprocessing.SplineTransformer.__setattr__ sklearn.preprocessing.SplineTransformer.__setstate__ sklearn.preprocessing.SplineTransformer.__sizeof__ sklearn.preprocessing.SplineTransformer.__sklearn_clone__ sklearn.preprocessing.SplineTransformer.__sklearn_tags__ sklearn.preprocessing.SplineTransformer.__str__ sklearn.preprocessing.SplineTransformer.__subclasshook__ sklearn.preprocessing.SplineTransformer._build_request_for_signature sklearn.preprocessing.SplineTransformer._get_base_knot_positions sklearn.preprocessing.SplineTransformer._get_default_requests sklearn.preprocessing.SplineTransformer._get_doc_link sklearn.preprocessing.SplineTransformer._get_metadata_request sklearn.preprocessing.SplineTransformer._get_param_names sklearn.preprocessing.SplineTransformer._repr_html_inner sklearn.preprocessing.SplineTransformer._repr_mimebundle_ sklearn.preprocessing.SplineTransformer._validate_params sklearn.preprocessing.SplineTransformer.fit sklearn.preprocessing.SplineTransformer.fit_transform sklearn.preprocessing.SplineTransformer.get_feature_names_out sklearn.preprocessing.SplineTransformer.get_metadata_routing sklearn.preprocessing.SplineTransformer.get_params sklearn.preprocessing.SplineTransformer.set_fit_request sklearn.preprocessing.SplineTransformer.set_output sklearn.preprocessing.SplineTransformer.set_params sklearn.preprocessing.SplineTransformer.transform 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.preprocessing.StandardScaler.rst.txt b/dev/_sources/modules/generated/sklearn.preprocessing.StandardScaler.rst.txt
index bb856d1207703..3e51dae72b3ab 100644
--- a/dev/_sources/modules/generated/sklearn.preprocessing.StandardScaler.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.preprocessing.StandardScaler.rst.txt
@@ -8,6 +8,6 @@ StandardScaler
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.preprocessing.StandardScaler sklearn.preprocessing.StandardScaler.__delattr__ sklearn.preprocessing.StandardScaler.__dir__ sklearn.preprocessing.StandardScaler.__eq__ sklearn.preprocessing.StandardScaler.__format__ sklearn.preprocessing.StandardScaler.__ge__ sklearn.preprocessing.StandardScaler.__getattribute__ sklearn.preprocessing.StandardScaler.__getstate__ sklearn.preprocessing.StandardScaler.__gt__ sklearn.preprocessing.StandardScaler.__hash__ sklearn.preprocessing.StandardScaler.__init__ sklearn.preprocessing.StandardScaler.__init_subclass__ sklearn.preprocessing.StandardScaler.__le__ sklearn.preprocessing.StandardScaler.__lt__ sklearn.preprocessing.StandardScaler.__ne__ sklearn.preprocessing.StandardScaler.__new__ sklearn.preprocessing.StandardScaler.__reduce__ sklearn.preprocessing.StandardScaler.__reduce_ex__ sklearn.preprocessing.StandardScaler.__repr__ sklearn.preprocessing.StandardScaler.__setattr__ sklearn.preprocessing.StandardScaler.__setstate__ sklearn.preprocessing.StandardScaler.__sizeof__ sklearn.preprocessing.StandardScaler.__sklearn_clone__ sklearn.preprocessing.StandardScaler.__sklearn_tags__ sklearn.preprocessing.StandardScaler.__str__ sklearn.preprocessing.StandardScaler.__subclasshook__ sklearn.preprocessing.StandardScaler._build_request_for_signature sklearn.preprocessing.StandardScaler._check_feature_names sklearn.preprocessing.StandardScaler._check_n_features sklearn.preprocessing.StandardScaler._get_default_requests sklearn.preprocessing.StandardScaler._get_doc_link sklearn.preprocessing.StandardScaler._get_metadata_request sklearn.preprocessing.StandardScaler._get_param_names sklearn.preprocessing.StandardScaler._get_tags sklearn.preprocessing.StandardScaler._more_tags sklearn.preprocessing.StandardScaler._repr_html_inner sklearn.preprocessing.StandardScaler._repr_mimebundle_ sklearn.preprocessing.StandardScaler._reset sklearn.preprocessing.StandardScaler._validate_data sklearn.preprocessing.StandardScaler._validate_params sklearn.preprocessing.StandardScaler.fit sklearn.preprocessing.StandardScaler.fit_transform sklearn.preprocessing.StandardScaler.get_feature_names_out sklearn.preprocessing.StandardScaler.get_metadata_routing sklearn.preprocessing.StandardScaler.get_params sklearn.preprocessing.StandardScaler.inverse_transform sklearn.preprocessing.StandardScaler.partial_fit sklearn.preprocessing.StandardScaler.set_fit_request sklearn.preprocessing.StandardScaler.set_inverse_transform_request sklearn.preprocessing.StandardScaler.set_output sklearn.preprocessing.StandardScaler.set_params sklearn.preprocessing.StandardScaler.set_partial_fit_request sklearn.preprocessing.StandardScaler.set_transform_request sklearn.preprocessing.StandardScaler.transform 
+.. minigallery:: sklearn.preprocessing.StandardScaler sklearn.preprocessing.StandardScaler.__delattr__ sklearn.preprocessing.StandardScaler.__dir__ sklearn.preprocessing.StandardScaler.__eq__ sklearn.preprocessing.StandardScaler.__format__ sklearn.preprocessing.StandardScaler.__ge__ sklearn.preprocessing.StandardScaler.__getattribute__ sklearn.preprocessing.StandardScaler.__getstate__ sklearn.preprocessing.StandardScaler.__gt__ sklearn.preprocessing.StandardScaler.__hash__ sklearn.preprocessing.StandardScaler.__init__ sklearn.preprocessing.StandardScaler.__init_subclass__ sklearn.preprocessing.StandardScaler.__le__ sklearn.preprocessing.StandardScaler.__lt__ sklearn.preprocessing.StandardScaler.__ne__ sklearn.preprocessing.StandardScaler.__new__ sklearn.preprocessing.StandardScaler.__reduce__ sklearn.preprocessing.StandardScaler.__reduce_ex__ sklearn.preprocessing.StandardScaler.__repr__ sklearn.preprocessing.StandardScaler.__setattr__ sklearn.preprocessing.StandardScaler.__setstate__ sklearn.preprocessing.StandardScaler.__sizeof__ sklearn.preprocessing.StandardScaler.__sklearn_clone__ sklearn.preprocessing.StandardScaler.__sklearn_tags__ sklearn.preprocessing.StandardScaler.__str__ sklearn.preprocessing.StandardScaler.__subclasshook__ sklearn.preprocessing.StandardScaler._build_request_for_signature sklearn.preprocessing.StandardScaler._get_default_requests sklearn.preprocessing.StandardScaler._get_doc_link sklearn.preprocessing.StandardScaler._get_metadata_request sklearn.preprocessing.StandardScaler._get_param_names sklearn.preprocessing.StandardScaler._repr_html_inner sklearn.preprocessing.StandardScaler._repr_mimebundle_ sklearn.preprocessing.StandardScaler._reset sklearn.preprocessing.StandardScaler._validate_params sklearn.preprocessing.StandardScaler.fit sklearn.preprocessing.StandardScaler.fit_transform sklearn.preprocessing.StandardScaler.get_feature_names_out sklearn.preprocessing.StandardScaler.get_metadata_routing sklearn.preprocessing.StandardScaler.get_params sklearn.preprocessing.StandardScaler.inverse_transform sklearn.preprocessing.StandardScaler.partial_fit sklearn.preprocessing.StandardScaler.set_fit_request sklearn.preprocessing.StandardScaler.set_inverse_transform_request sklearn.preprocessing.StandardScaler.set_output sklearn.preprocessing.StandardScaler.set_params sklearn.preprocessing.StandardScaler.set_partial_fit_request sklearn.preprocessing.StandardScaler.set_transform_request sklearn.preprocessing.StandardScaler.transform 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.preprocessing.TargetEncoder.rst.txt b/dev/_sources/modules/generated/sklearn.preprocessing.TargetEncoder.rst.txt
index 94f47408f627f..c33fdc1950dcd 100644
--- a/dev/_sources/modules/generated/sklearn.preprocessing.TargetEncoder.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.preprocessing.TargetEncoder.rst.txt
@@ -8,6 +8,6 @@ TargetEncoder
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.preprocessing.TargetEncoder sklearn.preprocessing.TargetEncoder.__delattr__ sklearn.preprocessing.TargetEncoder.__dir__ sklearn.preprocessing.TargetEncoder.__eq__ sklearn.preprocessing.TargetEncoder.__format__ sklearn.preprocessing.TargetEncoder.__ge__ sklearn.preprocessing.TargetEncoder.__getattribute__ sklearn.preprocessing.TargetEncoder.__getstate__ sklearn.preprocessing.TargetEncoder.__gt__ sklearn.preprocessing.TargetEncoder.__hash__ sklearn.preprocessing.TargetEncoder.__init__ sklearn.preprocessing.TargetEncoder.__init_subclass__ sklearn.preprocessing.TargetEncoder.__le__ sklearn.preprocessing.TargetEncoder.__lt__ sklearn.preprocessing.TargetEncoder.__ne__ sklearn.preprocessing.TargetEncoder.__new__ sklearn.preprocessing.TargetEncoder.__reduce__ sklearn.preprocessing.TargetEncoder.__reduce_ex__ sklearn.preprocessing.TargetEncoder.__repr__ sklearn.preprocessing.TargetEncoder.__setattr__ sklearn.preprocessing.TargetEncoder.__setstate__ sklearn.preprocessing.TargetEncoder.__sizeof__ sklearn.preprocessing.TargetEncoder.__sklearn_clone__ sklearn.preprocessing.TargetEncoder.__sklearn_tags__ sklearn.preprocessing.TargetEncoder.__str__ sklearn.preprocessing.TargetEncoder.__subclasshook__ sklearn.preprocessing.TargetEncoder._build_request_for_signature sklearn.preprocessing.TargetEncoder._check_X sklearn.preprocessing.TargetEncoder._check_feature_names sklearn.preprocessing.TargetEncoder._check_infrequent_enabled sklearn.preprocessing.TargetEncoder._check_n_features sklearn.preprocessing.TargetEncoder._fit sklearn.preprocessing.TargetEncoder._fit_encoding_binary_or_continuous sklearn.preprocessing.TargetEncoder._fit_encoding_multiclass sklearn.preprocessing.TargetEncoder._fit_encodings_all sklearn.preprocessing.TargetEncoder._fit_infrequent_category_mapping sklearn.preprocessing.TargetEncoder._get_default_requests sklearn.preprocessing.TargetEncoder._get_doc_link sklearn.preprocessing.TargetEncoder._get_metadata_request sklearn.preprocessing.TargetEncoder._get_param_names sklearn.preprocessing.TargetEncoder._get_tags sklearn.preprocessing.TargetEncoder._identify_infrequent sklearn.preprocessing.TargetEncoder._map_infrequent_categories sklearn.preprocessing.TargetEncoder._more_tags sklearn.preprocessing.TargetEncoder._repr_html_inner sklearn.preprocessing.TargetEncoder._repr_mimebundle_ sklearn.preprocessing.TargetEncoder._transform sklearn.preprocessing.TargetEncoder._transform_X_ordinal sklearn.preprocessing.TargetEncoder._validate_data sklearn.preprocessing.TargetEncoder._validate_params sklearn.preprocessing.TargetEncoder.fit sklearn.preprocessing.TargetEncoder.fit_transform sklearn.preprocessing.TargetEncoder.get_feature_names_out sklearn.preprocessing.TargetEncoder.get_metadata_routing sklearn.preprocessing.TargetEncoder.get_params sklearn.preprocessing.TargetEncoder.set_output sklearn.preprocessing.TargetEncoder.set_params sklearn.preprocessing.TargetEncoder.transform 
+.. minigallery:: sklearn.preprocessing.TargetEncoder sklearn.preprocessing.TargetEncoder.__delattr__ sklearn.preprocessing.TargetEncoder.__dir__ sklearn.preprocessing.TargetEncoder.__eq__ sklearn.preprocessing.TargetEncoder.__format__ sklearn.preprocessing.TargetEncoder.__ge__ sklearn.preprocessing.TargetEncoder.__getattribute__ sklearn.preprocessing.TargetEncoder.__getstate__ sklearn.preprocessing.TargetEncoder.__gt__ sklearn.preprocessing.TargetEncoder.__hash__ sklearn.preprocessing.TargetEncoder.__init__ sklearn.preprocessing.TargetEncoder.__init_subclass__ sklearn.preprocessing.TargetEncoder.__le__ sklearn.preprocessing.TargetEncoder.__lt__ sklearn.preprocessing.TargetEncoder.__ne__ sklearn.preprocessing.TargetEncoder.__new__ sklearn.preprocessing.TargetEncoder.__reduce__ sklearn.preprocessing.TargetEncoder.__reduce_ex__ sklearn.preprocessing.TargetEncoder.__repr__ sklearn.preprocessing.TargetEncoder.__setattr__ sklearn.preprocessing.TargetEncoder.__setstate__ sklearn.preprocessing.TargetEncoder.__sizeof__ sklearn.preprocessing.TargetEncoder.__sklearn_clone__ sklearn.preprocessing.TargetEncoder.__sklearn_tags__ sklearn.preprocessing.TargetEncoder.__str__ sklearn.preprocessing.TargetEncoder.__subclasshook__ sklearn.preprocessing.TargetEncoder._build_request_for_signature sklearn.preprocessing.TargetEncoder._check_X sklearn.preprocessing.TargetEncoder._check_infrequent_enabled sklearn.preprocessing.TargetEncoder._fit sklearn.preprocessing.TargetEncoder._fit_encoding_binary_or_continuous sklearn.preprocessing.TargetEncoder._fit_encoding_multiclass sklearn.preprocessing.TargetEncoder._fit_encodings_all sklearn.preprocessing.TargetEncoder._fit_infrequent_category_mapping sklearn.preprocessing.TargetEncoder._get_default_requests sklearn.preprocessing.TargetEncoder._get_doc_link sklearn.preprocessing.TargetEncoder._get_metadata_request sklearn.preprocessing.TargetEncoder._get_param_names sklearn.preprocessing.TargetEncoder._identify_infrequent sklearn.preprocessing.TargetEncoder._map_infrequent_categories sklearn.preprocessing.TargetEncoder._repr_html_inner sklearn.preprocessing.TargetEncoder._repr_mimebundle_ sklearn.preprocessing.TargetEncoder._transform sklearn.preprocessing.TargetEncoder._transform_X_ordinal sklearn.preprocessing.TargetEncoder._validate_params sklearn.preprocessing.TargetEncoder.fit sklearn.preprocessing.TargetEncoder.fit_transform sklearn.preprocessing.TargetEncoder.get_feature_names_out sklearn.preprocessing.TargetEncoder.get_metadata_routing sklearn.preprocessing.TargetEncoder.get_params sklearn.preprocessing.TargetEncoder.set_output sklearn.preprocessing.TargetEncoder.set_params sklearn.preprocessing.TargetEncoder.transform 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.random_projection.GaussianRandomProjection.rst.txt b/dev/_sources/modules/generated/sklearn.random_projection.GaussianRandomProjection.rst.txt
index 9d7e69732f756..f45a3cc1b2df1 100644
--- a/dev/_sources/modules/generated/sklearn.random_projection.GaussianRandomProjection.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.random_projection.GaussianRandomProjection.rst.txt
@@ -8,6 +8,6 @@ GaussianRandomProjection
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.random_projection.GaussianRandomProjection sklearn.random_projection.GaussianRandomProjection.__delattr__ sklearn.random_projection.GaussianRandomProjection.__dir__ sklearn.random_projection.GaussianRandomProjection.__eq__ sklearn.random_projection.GaussianRandomProjection.__format__ sklearn.random_projection.GaussianRandomProjection.__ge__ sklearn.random_projection.GaussianRandomProjection.__getattribute__ sklearn.random_projection.GaussianRandomProjection.__getstate__ sklearn.random_projection.GaussianRandomProjection.__gt__ sklearn.random_projection.GaussianRandomProjection.__hash__ sklearn.random_projection.GaussianRandomProjection.__init__ sklearn.random_projection.GaussianRandomProjection.__init_subclass__ sklearn.random_projection.GaussianRandomProjection.__le__ sklearn.random_projection.GaussianRandomProjection.__lt__ sklearn.random_projection.GaussianRandomProjection.__ne__ sklearn.random_projection.GaussianRandomProjection.__new__ sklearn.random_projection.GaussianRandomProjection.__reduce__ sklearn.random_projection.GaussianRandomProjection.__reduce_ex__ sklearn.random_projection.GaussianRandomProjection.__repr__ sklearn.random_projection.GaussianRandomProjection.__setattr__ sklearn.random_projection.GaussianRandomProjection.__setstate__ sklearn.random_projection.GaussianRandomProjection.__sizeof__ sklearn.random_projection.GaussianRandomProjection.__sklearn_clone__ sklearn.random_projection.GaussianRandomProjection.__sklearn_tags__ sklearn.random_projection.GaussianRandomProjection.__str__ sklearn.random_projection.GaussianRandomProjection.__subclasshook__ sklearn.random_projection.GaussianRandomProjection._build_request_for_signature sklearn.random_projection.GaussianRandomProjection._check_feature_names sklearn.random_projection.GaussianRandomProjection._check_n_features sklearn.random_projection.GaussianRandomProjection._compute_inverse_components sklearn.random_projection.GaussianRandomProjection._get_default_requests sklearn.random_projection.GaussianRandomProjection._get_doc_link sklearn.random_projection.GaussianRandomProjection._get_metadata_request sklearn.random_projection.GaussianRandomProjection._get_param_names sklearn.random_projection.GaussianRandomProjection._get_tags sklearn.random_projection.GaussianRandomProjection._make_random_matrix sklearn.random_projection.GaussianRandomProjection._more_tags sklearn.random_projection.GaussianRandomProjection._repr_html_inner sklearn.random_projection.GaussianRandomProjection._repr_mimebundle_ sklearn.random_projection.GaussianRandomProjection._validate_data sklearn.random_projection.GaussianRandomProjection._validate_params sklearn.random_projection.GaussianRandomProjection.fit sklearn.random_projection.GaussianRandomProjection.fit_transform sklearn.random_projection.GaussianRandomProjection.get_feature_names_out sklearn.random_projection.GaussianRandomProjection.get_metadata_routing sklearn.random_projection.GaussianRandomProjection.get_params sklearn.random_projection.GaussianRandomProjection.inverse_transform sklearn.random_projection.GaussianRandomProjection.set_output sklearn.random_projection.GaussianRandomProjection.set_params sklearn.random_projection.GaussianRandomProjection.transform 
+.. minigallery:: sklearn.random_projection.GaussianRandomProjection sklearn.random_projection.GaussianRandomProjection.__delattr__ sklearn.random_projection.GaussianRandomProjection.__dir__ sklearn.random_projection.GaussianRandomProjection.__eq__ sklearn.random_projection.GaussianRandomProjection.__format__ sklearn.random_projection.GaussianRandomProjection.__ge__ sklearn.random_projection.GaussianRandomProjection.__getattribute__ sklearn.random_projection.GaussianRandomProjection.__getstate__ sklearn.random_projection.GaussianRandomProjection.__gt__ sklearn.random_projection.GaussianRandomProjection.__hash__ sklearn.random_projection.GaussianRandomProjection.__init__ sklearn.random_projection.GaussianRandomProjection.__init_subclass__ sklearn.random_projection.GaussianRandomProjection.__le__ sklearn.random_projection.GaussianRandomProjection.__lt__ sklearn.random_projection.GaussianRandomProjection.__ne__ sklearn.random_projection.GaussianRandomProjection.__new__ sklearn.random_projection.GaussianRandomProjection.__reduce__ sklearn.random_projection.GaussianRandomProjection.__reduce_ex__ sklearn.random_projection.GaussianRandomProjection.__repr__ sklearn.random_projection.GaussianRandomProjection.__setattr__ sklearn.random_projection.GaussianRandomProjection.__setstate__ sklearn.random_projection.GaussianRandomProjection.__sizeof__ sklearn.random_projection.GaussianRandomProjection.__sklearn_clone__ sklearn.random_projection.GaussianRandomProjection.__sklearn_tags__ sklearn.random_projection.GaussianRandomProjection.__str__ sklearn.random_projection.GaussianRandomProjection.__subclasshook__ sklearn.random_projection.GaussianRandomProjection._build_request_for_signature sklearn.random_projection.GaussianRandomProjection._compute_inverse_components sklearn.random_projection.GaussianRandomProjection._get_default_requests sklearn.random_projection.GaussianRandomProjection._get_doc_link sklearn.random_projection.GaussianRandomProjection._get_metadata_request sklearn.random_projection.GaussianRandomProjection._get_param_names sklearn.random_projection.GaussianRandomProjection._make_random_matrix sklearn.random_projection.GaussianRandomProjection._repr_html_inner sklearn.random_projection.GaussianRandomProjection._repr_mimebundle_ sklearn.random_projection.GaussianRandomProjection._validate_params sklearn.random_projection.GaussianRandomProjection.fit sklearn.random_projection.GaussianRandomProjection.fit_transform sklearn.random_projection.GaussianRandomProjection.get_feature_names_out sklearn.random_projection.GaussianRandomProjection.get_metadata_routing sklearn.random_projection.GaussianRandomProjection.get_params sklearn.random_projection.GaussianRandomProjection.inverse_transform sklearn.random_projection.GaussianRandomProjection.set_output sklearn.random_projection.GaussianRandomProjection.set_params sklearn.random_projection.GaussianRandomProjection.transform 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.random_projection.SparseRandomProjection.rst.txt b/dev/_sources/modules/generated/sklearn.random_projection.SparseRandomProjection.rst.txt
index 738561149071c..1dbdf4708cb44 100644
--- a/dev/_sources/modules/generated/sklearn.random_projection.SparseRandomProjection.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.random_projection.SparseRandomProjection.rst.txt
@@ -8,6 +8,6 @@ SparseRandomProjection
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.random_projection.SparseRandomProjection sklearn.random_projection.SparseRandomProjection.__delattr__ sklearn.random_projection.SparseRandomProjection.__dir__ sklearn.random_projection.SparseRandomProjection.__eq__ sklearn.random_projection.SparseRandomProjection.__format__ sklearn.random_projection.SparseRandomProjection.__ge__ sklearn.random_projection.SparseRandomProjection.__getattribute__ sklearn.random_projection.SparseRandomProjection.__getstate__ sklearn.random_projection.SparseRandomProjection.__gt__ sklearn.random_projection.SparseRandomProjection.__hash__ sklearn.random_projection.SparseRandomProjection.__init__ sklearn.random_projection.SparseRandomProjection.__init_subclass__ sklearn.random_projection.SparseRandomProjection.__le__ sklearn.random_projection.SparseRandomProjection.__lt__ sklearn.random_projection.SparseRandomProjection.__ne__ sklearn.random_projection.SparseRandomProjection.__new__ sklearn.random_projection.SparseRandomProjection.__reduce__ sklearn.random_projection.SparseRandomProjection.__reduce_ex__ sklearn.random_projection.SparseRandomProjection.__repr__ sklearn.random_projection.SparseRandomProjection.__setattr__ sklearn.random_projection.SparseRandomProjection.__setstate__ sklearn.random_projection.SparseRandomProjection.__sizeof__ sklearn.random_projection.SparseRandomProjection.__sklearn_clone__ sklearn.random_projection.SparseRandomProjection.__sklearn_tags__ sklearn.random_projection.SparseRandomProjection.__str__ sklearn.random_projection.SparseRandomProjection.__subclasshook__ sklearn.random_projection.SparseRandomProjection._build_request_for_signature sklearn.random_projection.SparseRandomProjection._check_feature_names sklearn.random_projection.SparseRandomProjection._check_n_features sklearn.random_projection.SparseRandomProjection._compute_inverse_components sklearn.random_projection.SparseRandomProjection._get_default_requests sklearn.random_projection.SparseRandomProjection._get_doc_link sklearn.random_projection.SparseRandomProjection._get_metadata_request sklearn.random_projection.SparseRandomProjection._get_param_names sklearn.random_projection.SparseRandomProjection._get_tags sklearn.random_projection.SparseRandomProjection._make_random_matrix sklearn.random_projection.SparseRandomProjection._more_tags sklearn.random_projection.SparseRandomProjection._repr_html_inner sklearn.random_projection.SparseRandomProjection._repr_mimebundle_ sklearn.random_projection.SparseRandomProjection._validate_data sklearn.random_projection.SparseRandomProjection._validate_params sklearn.random_projection.SparseRandomProjection.fit sklearn.random_projection.SparseRandomProjection.fit_transform sklearn.random_projection.SparseRandomProjection.get_feature_names_out sklearn.random_projection.SparseRandomProjection.get_metadata_routing sklearn.random_projection.SparseRandomProjection.get_params sklearn.random_projection.SparseRandomProjection.inverse_transform sklearn.random_projection.SparseRandomProjection.set_output sklearn.random_projection.SparseRandomProjection.set_params sklearn.random_projection.SparseRandomProjection.transform 
+.. minigallery:: sklearn.random_projection.SparseRandomProjection sklearn.random_projection.SparseRandomProjection.__delattr__ sklearn.random_projection.SparseRandomProjection.__dir__ sklearn.random_projection.SparseRandomProjection.__eq__ sklearn.random_projection.SparseRandomProjection.__format__ sklearn.random_projection.SparseRandomProjection.__ge__ sklearn.random_projection.SparseRandomProjection.__getattribute__ sklearn.random_projection.SparseRandomProjection.__getstate__ sklearn.random_projection.SparseRandomProjection.__gt__ sklearn.random_projection.SparseRandomProjection.__hash__ sklearn.random_projection.SparseRandomProjection.__init__ sklearn.random_projection.SparseRandomProjection.__init_subclass__ sklearn.random_projection.SparseRandomProjection.__le__ sklearn.random_projection.SparseRandomProjection.__lt__ sklearn.random_projection.SparseRandomProjection.__ne__ sklearn.random_projection.SparseRandomProjection.__new__ sklearn.random_projection.SparseRandomProjection.__reduce__ sklearn.random_projection.SparseRandomProjection.__reduce_ex__ sklearn.random_projection.SparseRandomProjection.__repr__ sklearn.random_projection.SparseRandomProjection.__setattr__ sklearn.random_projection.SparseRandomProjection.__setstate__ sklearn.random_projection.SparseRandomProjection.__sizeof__ sklearn.random_projection.SparseRandomProjection.__sklearn_clone__ sklearn.random_projection.SparseRandomProjection.__sklearn_tags__ sklearn.random_projection.SparseRandomProjection.__str__ sklearn.random_projection.SparseRandomProjection.__subclasshook__ sklearn.random_projection.SparseRandomProjection._build_request_for_signature sklearn.random_projection.SparseRandomProjection._compute_inverse_components sklearn.random_projection.SparseRandomProjection._get_default_requests sklearn.random_projection.SparseRandomProjection._get_doc_link sklearn.random_projection.SparseRandomProjection._get_metadata_request sklearn.random_projection.SparseRandomProjection._get_param_names sklearn.random_projection.SparseRandomProjection._make_random_matrix sklearn.random_projection.SparseRandomProjection._repr_html_inner sklearn.random_projection.SparseRandomProjection._repr_mimebundle_ sklearn.random_projection.SparseRandomProjection._validate_params sklearn.random_projection.SparseRandomProjection.fit sklearn.random_projection.SparseRandomProjection.fit_transform sklearn.random_projection.SparseRandomProjection.get_feature_names_out sklearn.random_projection.SparseRandomProjection.get_metadata_routing sklearn.random_projection.SparseRandomProjection.get_params sklearn.random_projection.SparseRandomProjection.inverse_transform sklearn.random_projection.SparseRandomProjection.set_output sklearn.random_projection.SparseRandomProjection.set_params sklearn.random_projection.SparseRandomProjection.transform 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.semi_supervised.LabelPropagation.rst.txt b/dev/_sources/modules/generated/sklearn.semi_supervised.LabelPropagation.rst.txt
index ebf2a70f71e32..45ac3fc96991a 100644
--- a/dev/_sources/modules/generated/sklearn.semi_supervised.LabelPropagation.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.semi_supervised.LabelPropagation.rst.txt
@@ -8,6 +8,6 @@ LabelPropagation
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.semi_supervised.LabelPropagation sklearn.semi_supervised.LabelPropagation.__delattr__ sklearn.semi_supervised.LabelPropagation.__dir__ sklearn.semi_supervised.LabelPropagation.__eq__ sklearn.semi_supervised.LabelPropagation.__format__ sklearn.semi_supervised.LabelPropagation.__ge__ sklearn.semi_supervised.LabelPropagation.__getattribute__ sklearn.semi_supervised.LabelPropagation.__getstate__ sklearn.semi_supervised.LabelPropagation.__gt__ sklearn.semi_supervised.LabelPropagation.__hash__ sklearn.semi_supervised.LabelPropagation.__init__ sklearn.semi_supervised.LabelPropagation.__init_subclass__ sklearn.semi_supervised.LabelPropagation.__le__ sklearn.semi_supervised.LabelPropagation.__lt__ sklearn.semi_supervised.LabelPropagation.__ne__ sklearn.semi_supervised.LabelPropagation.__new__ sklearn.semi_supervised.LabelPropagation.__reduce__ sklearn.semi_supervised.LabelPropagation.__reduce_ex__ sklearn.semi_supervised.LabelPropagation.__repr__ sklearn.semi_supervised.LabelPropagation.__setattr__ sklearn.semi_supervised.LabelPropagation.__setstate__ sklearn.semi_supervised.LabelPropagation.__sizeof__ sklearn.semi_supervised.LabelPropagation.__sklearn_clone__ sklearn.semi_supervised.LabelPropagation.__sklearn_tags__ sklearn.semi_supervised.LabelPropagation.__str__ sklearn.semi_supervised.LabelPropagation.__subclasshook__ sklearn.semi_supervised.LabelPropagation._build_graph sklearn.semi_supervised.LabelPropagation._build_request_for_signature sklearn.semi_supervised.LabelPropagation._check_feature_names sklearn.semi_supervised.LabelPropagation._check_n_features sklearn.semi_supervised.LabelPropagation._get_default_requests sklearn.semi_supervised.LabelPropagation._get_doc_link sklearn.semi_supervised.LabelPropagation._get_kernel sklearn.semi_supervised.LabelPropagation._get_metadata_request sklearn.semi_supervised.LabelPropagation._get_param_names sklearn.semi_supervised.LabelPropagation._get_tags sklearn.semi_supervised.LabelPropagation._more_tags sklearn.semi_supervised.LabelPropagation._repr_html_inner sklearn.semi_supervised.LabelPropagation._repr_mimebundle_ sklearn.semi_supervised.LabelPropagation._validate_data sklearn.semi_supervised.LabelPropagation._validate_params sklearn.semi_supervised.LabelPropagation.fit sklearn.semi_supervised.LabelPropagation.get_metadata_routing sklearn.semi_supervised.LabelPropagation.get_params sklearn.semi_supervised.LabelPropagation.predict sklearn.semi_supervised.LabelPropagation.predict_proba sklearn.semi_supervised.LabelPropagation.score sklearn.semi_supervised.LabelPropagation.set_params sklearn.semi_supervised.LabelPropagation.set_score_request 
+.. minigallery:: sklearn.semi_supervised.LabelPropagation sklearn.semi_supervised.LabelPropagation.__delattr__ sklearn.semi_supervised.LabelPropagation.__dir__ sklearn.semi_supervised.LabelPropagation.__eq__ sklearn.semi_supervised.LabelPropagation.__format__ sklearn.semi_supervised.LabelPropagation.__ge__ sklearn.semi_supervised.LabelPropagation.__getattribute__ sklearn.semi_supervised.LabelPropagation.__getstate__ sklearn.semi_supervised.LabelPropagation.__gt__ sklearn.semi_supervised.LabelPropagation.__hash__ sklearn.semi_supervised.LabelPropagation.__init__ sklearn.semi_supervised.LabelPropagation.__init_subclass__ sklearn.semi_supervised.LabelPropagation.__le__ sklearn.semi_supervised.LabelPropagation.__lt__ sklearn.semi_supervised.LabelPropagation.__ne__ sklearn.semi_supervised.LabelPropagation.__new__ sklearn.semi_supervised.LabelPropagation.__reduce__ sklearn.semi_supervised.LabelPropagation.__reduce_ex__ sklearn.semi_supervised.LabelPropagation.__repr__ sklearn.semi_supervised.LabelPropagation.__setattr__ sklearn.semi_supervised.LabelPropagation.__setstate__ sklearn.semi_supervised.LabelPropagation.__sizeof__ sklearn.semi_supervised.LabelPropagation.__sklearn_clone__ sklearn.semi_supervised.LabelPropagation.__sklearn_tags__ sklearn.semi_supervised.LabelPropagation.__str__ sklearn.semi_supervised.LabelPropagation.__subclasshook__ sklearn.semi_supervised.LabelPropagation._build_graph sklearn.semi_supervised.LabelPropagation._build_request_for_signature sklearn.semi_supervised.LabelPropagation._get_default_requests sklearn.semi_supervised.LabelPropagation._get_doc_link sklearn.semi_supervised.LabelPropagation._get_kernel sklearn.semi_supervised.LabelPropagation._get_metadata_request sklearn.semi_supervised.LabelPropagation._get_param_names sklearn.semi_supervised.LabelPropagation._repr_html_inner sklearn.semi_supervised.LabelPropagation._repr_mimebundle_ sklearn.semi_supervised.LabelPropagation._validate_params sklearn.semi_supervised.LabelPropagation.fit sklearn.semi_supervised.LabelPropagation.get_metadata_routing sklearn.semi_supervised.LabelPropagation.get_params sklearn.semi_supervised.LabelPropagation.predict sklearn.semi_supervised.LabelPropagation.predict_proba sklearn.semi_supervised.LabelPropagation.score sklearn.semi_supervised.LabelPropagation.set_params sklearn.semi_supervised.LabelPropagation.set_score_request 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.semi_supervised.LabelSpreading.rst.txt b/dev/_sources/modules/generated/sklearn.semi_supervised.LabelSpreading.rst.txt
index e6130efe50efe..2be455f63dc9a 100644
--- a/dev/_sources/modules/generated/sklearn.semi_supervised.LabelSpreading.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.semi_supervised.LabelSpreading.rst.txt
@@ -8,6 +8,6 @@ LabelSpreading
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.semi_supervised.LabelSpreading sklearn.semi_supervised.LabelSpreading.__delattr__ sklearn.semi_supervised.LabelSpreading.__dir__ sklearn.semi_supervised.LabelSpreading.__eq__ sklearn.semi_supervised.LabelSpreading.__format__ sklearn.semi_supervised.LabelSpreading.__ge__ sklearn.semi_supervised.LabelSpreading.__getattribute__ sklearn.semi_supervised.LabelSpreading.__getstate__ sklearn.semi_supervised.LabelSpreading.__gt__ sklearn.semi_supervised.LabelSpreading.__hash__ sklearn.semi_supervised.LabelSpreading.__init__ sklearn.semi_supervised.LabelSpreading.__init_subclass__ sklearn.semi_supervised.LabelSpreading.__le__ sklearn.semi_supervised.LabelSpreading.__lt__ sklearn.semi_supervised.LabelSpreading.__ne__ sklearn.semi_supervised.LabelSpreading.__new__ sklearn.semi_supervised.LabelSpreading.__reduce__ sklearn.semi_supervised.LabelSpreading.__reduce_ex__ sklearn.semi_supervised.LabelSpreading.__repr__ sklearn.semi_supervised.LabelSpreading.__setattr__ sklearn.semi_supervised.LabelSpreading.__setstate__ sklearn.semi_supervised.LabelSpreading.__sizeof__ sklearn.semi_supervised.LabelSpreading.__sklearn_clone__ sklearn.semi_supervised.LabelSpreading.__sklearn_tags__ sklearn.semi_supervised.LabelSpreading.__str__ sklearn.semi_supervised.LabelSpreading.__subclasshook__ sklearn.semi_supervised.LabelSpreading._build_graph sklearn.semi_supervised.LabelSpreading._build_request_for_signature sklearn.semi_supervised.LabelSpreading._check_feature_names sklearn.semi_supervised.LabelSpreading._check_n_features sklearn.semi_supervised.LabelSpreading._get_default_requests sklearn.semi_supervised.LabelSpreading._get_doc_link sklearn.semi_supervised.LabelSpreading._get_kernel sklearn.semi_supervised.LabelSpreading._get_metadata_request sklearn.semi_supervised.LabelSpreading._get_param_names sklearn.semi_supervised.LabelSpreading._get_tags sklearn.semi_supervised.LabelSpreading._more_tags sklearn.semi_supervised.LabelSpreading._repr_html_inner sklearn.semi_supervised.LabelSpreading._repr_mimebundle_ sklearn.semi_supervised.LabelSpreading._validate_data sklearn.semi_supervised.LabelSpreading._validate_params sklearn.semi_supervised.LabelSpreading.fit sklearn.semi_supervised.LabelSpreading.get_metadata_routing sklearn.semi_supervised.LabelSpreading.get_params sklearn.semi_supervised.LabelSpreading.predict sklearn.semi_supervised.LabelSpreading.predict_proba sklearn.semi_supervised.LabelSpreading.score sklearn.semi_supervised.LabelSpreading.set_params sklearn.semi_supervised.LabelSpreading.set_score_request 
+.. minigallery:: sklearn.semi_supervised.LabelSpreading sklearn.semi_supervised.LabelSpreading.__delattr__ sklearn.semi_supervised.LabelSpreading.__dir__ sklearn.semi_supervised.LabelSpreading.__eq__ sklearn.semi_supervised.LabelSpreading.__format__ sklearn.semi_supervised.LabelSpreading.__ge__ sklearn.semi_supervised.LabelSpreading.__getattribute__ sklearn.semi_supervised.LabelSpreading.__getstate__ sklearn.semi_supervised.LabelSpreading.__gt__ sklearn.semi_supervised.LabelSpreading.__hash__ sklearn.semi_supervised.LabelSpreading.__init__ sklearn.semi_supervised.LabelSpreading.__init_subclass__ sklearn.semi_supervised.LabelSpreading.__le__ sklearn.semi_supervised.LabelSpreading.__lt__ sklearn.semi_supervised.LabelSpreading.__ne__ sklearn.semi_supervised.LabelSpreading.__new__ sklearn.semi_supervised.LabelSpreading.__reduce__ sklearn.semi_supervised.LabelSpreading.__reduce_ex__ sklearn.semi_supervised.LabelSpreading.__repr__ sklearn.semi_supervised.LabelSpreading.__setattr__ sklearn.semi_supervised.LabelSpreading.__setstate__ sklearn.semi_supervised.LabelSpreading.__sizeof__ sklearn.semi_supervised.LabelSpreading.__sklearn_clone__ sklearn.semi_supervised.LabelSpreading.__sklearn_tags__ sklearn.semi_supervised.LabelSpreading.__str__ sklearn.semi_supervised.LabelSpreading.__subclasshook__ sklearn.semi_supervised.LabelSpreading._build_graph sklearn.semi_supervised.LabelSpreading._build_request_for_signature sklearn.semi_supervised.LabelSpreading._get_default_requests sklearn.semi_supervised.LabelSpreading._get_doc_link sklearn.semi_supervised.LabelSpreading._get_kernel sklearn.semi_supervised.LabelSpreading._get_metadata_request sklearn.semi_supervised.LabelSpreading._get_param_names sklearn.semi_supervised.LabelSpreading._repr_html_inner sklearn.semi_supervised.LabelSpreading._repr_mimebundle_ sklearn.semi_supervised.LabelSpreading._validate_params sklearn.semi_supervised.LabelSpreading.fit sklearn.semi_supervised.LabelSpreading.get_metadata_routing sklearn.semi_supervised.LabelSpreading.get_params sklearn.semi_supervised.LabelSpreading.predict sklearn.semi_supervised.LabelSpreading.predict_proba sklearn.semi_supervised.LabelSpreading.score sklearn.semi_supervised.LabelSpreading.set_params sklearn.semi_supervised.LabelSpreading.set_score_request 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.semi_supervised.SelfTrainingClassifier.rst.txt b/dev/_sources/modules/generated/sklearn.semi_supervised.SelfTrainingClassifier.rst.txt
index 49e7c9b78a403..4d474ed61619b 100644
--- a/dev/_sources/modules/generated/sklearn.semi_supervised.SelfTrainingClassifier.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.semi_supervised.SelfTrainingClassifier.rst.txt
@@ -8,6 +8,6 @@ SelfTrainingClassifier
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.semi_supervised.SelfTrainingClassifier sklearn.semi_supervised.SelfTrainingClassifier.__delattr__ sklearn.semi_supervised.SelfTrainingClassifier.__dir__ sklearn.semi_supervised.SelfTrainingClassifier.__eq__ sklearn.semi_supervised.SelfTrainingClassifier.__format__ sklearn.semi_supervised.SelfTrainingClassifier.__ge__ sklearn.semi_supervised.SelfTrainingClassifier.__getattribute__ sklearn.semi_supervised.SelfTrainingClassifier.__getstate__ sklearn.semi_supervised.SelfTrainingClassifier.__gt__ sklearn.semi_supervised.SelfTrainingClassifier.__hash__ sklearn.semi_supervised.SelfTrainingClassifier.__init__ sklearn.semi_supervised.SelfTrainingClassifier.__init_subclass__ sklearn.semi_supervised.SelfTrainingClassifier.__le__ sklearn.semi_supervised.SelfTrainingClassifier.__lt__ sklearn.semi_supervised.SelfTrainingClassifier.__ne__ sklearn.semi_supervised.SelfTrainingClassifier.__new__ sklearn.semi_supervised.SelfTrainingClassifier.__reduce__ sklearn.semi_supervised.SelfTrainingClassifier.__reduce_ex__ sklearn.semi_supervised.SelfTrainingClassifier.__repr__ sklearn.semi_supervised.SelfTrainingClassifier.__setattr__ sklearn.semi_supervised.SelfTrainingClassifier.__setstate__ sklearn.semi_supervised.SelfTrainingClassifier.__sizeof__ sklearn.semi_supervised.SelfTrainingClassifier.__sklearn_clone__ sklearn.semi_supervised.SelfTrainingClassifier.__sklearn_tags__ sklearn.semi_supervised.SelfTrainingClassifier.__str__ sklearn.semi_supervised.SelfTrainingClassifier.__subclasshook__ sklearn.semi_supervised.SelfTrainingClassifier._build_request_for_signature sklearn.semi_supervised.SelfTrainingClassifier._check_feature_names sklearn.semi_supervised.SelfTrainingClassifier._check_n_features sklearn.semi_supervised.SelfTrainingClassifier._get_default_requests sklearn.semi_supervised.SelfTrainingClassifier._get_doc_link sklearn.semi_supervised.SelfTrainingClassifier._get_estimator sklearn.semi_supervised.SelfTrainingClassifier._get_metadata_request sklearn.semi_supervised.SelfTrainingClassifier._get_param_names sklearn.semi_supervised.SelfTrainingClassifier._get_tags sklearn.semi_supervised.SelfTrainingClassifier._more_tags sklearn.semi_supervised.SelfTrainingClassifier._repr_html_inner sklearn.semi_supervised.SelfTrainingClassifier._repr_mimebundle_ sklearn.semi_supervised.SelfTrainingClassifier._validate_data sklearn.semi_supervised.SelfTrainingClassifier._validate_params sklearn.semi_supervised.SelfTrainingClassifier.decision_function sklearn.semi_supervised.SelfTrainingClassifier.fit sklearn.semi_supervised.SelfTrainingClassifier.get_metadata_routing sklearn.semi_supervised.SelfTrainingClassifier.get_params sklearn.semi_supervised.SelfTrainingClassifier.predict sklearn.semi_supervised.SelfTrainingClassifier.predict_log_proba sklearn.semi_supervised.SelfTrainingClassifier.predict_proba sklearn.semi_supervised.SelfTrainingClassifier.score sklearn.semi_supervised.SelfTrainingClassifier.set_params 
+.. minigallery:: sklearn.semi_supervised.SelfTrainingClassifier sklearn.semi_supervised.SelfTrainingClassifier.__delattr__ sklearn.semi_supervised.SelfTrainingClassifier.__dir__ sklearn.semi_supervised.SelfTrainingClassifier.__eq__ sklearn.semi_supervised.SelfTrainingClassifier.__format__ sklearn.semi_supervised.SelfTrainingClassifier.__ge__ sklearn.semi_supervised.SelfTrainingClassifier.__getattribute__ sklearn.semi_supervised.SelfTrainingClassifier.__getstate__ sklearn.semi_supervised.SelfTrainingClassifier.__gt__ sklearn.semi_supervised.SelfTrainingClassifier.__hash__ sklearn.semi_supervised.SelfTrainingClassifier.__init__ sklearn.semi_supervised.SelfTrainingClassifier.__init_subclass__ sklearn.semi_supervised.SelfTrainingClassifier.__le__ sklearn.semi_supervised.SelfTrainingClassifier.__lt__ sklearn.semi_supervised.SelfTrainingClassifier.__ne__ sklearn.semi_supervised.SelfTrainingClassifier.__new__ sklearn.semi_supervised.SelfTrainingClassifier.__reduce__ sklearn.semi_supervised.SelfTrainingClassifier.__reduce_ex__ sklearn.semi_supervised.SelfTrainingClassifier.__repr__ sklearn.semi_supervised.SelfTrainingClassifier.__setattr__ sklearn.semi_supervised.SelfTrainingClassifier.__setstate__ sklearn.semi_supervised.SelfTrainingClassifier.__sizeof__ sklearn.semi_supervised.SelfTrainingClassifier.__sklearn_clone__ sklearn.semi_supervised.SelfTrainingClassifier.__sklearn_tags__ sklearn.semi_supervised.SelfTrainingClassifier.__str__ sklearn.semi_supervised.SelfTrainingClassifier.__subclasshook__ sklearn.semi_supervised.SelfTrainingClassifier._build_request_for_signature sklearn.semi_supervised.SelfTrainingClassifier._get_default_requests sklearn.semi_supervised.SelfTrainingClassifier._get_doc_link sklearn.semi_supervised.SelfTrainingClassifier._get_estimator sklearn.semi_supervised.SelfTrainingClassifier._get_metadata_request sklearn.semi_supervised.SelfTrainingClassifier._get_param_names sklearn.semi_supervised.SelfTrainingClassifier._repr_html_inner sklearn.semi_supervised.SelfTrainingClassifier._repr_mimebundle_ sklearn.semi_supervised.SelfTrainingClassifier._validate_params sklearn.semi_supervised.SelfTrainingClassifier.decision_function sklearn.semi_supervised.SelfTrainingClassifier.fit sklearn.semi_supervised.SelfTrainingClassifier.get_metadata_routing sklearn.semi_supervised.SelfTrainingClassifier.get_params sklearn.semi_supervised.SelfTrainingClassifier.predict sklearn.semi_supervised.SelfTrainingClassifier.predict_log_proba sklearn.semi_supervised.SelfTrainingClassifier.predict_proba sklearn.semi_supervised.SelfTrainingClassifier.score sklearn.semi_supervised.SelfTrainingClassifier.set_params 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.svm.LinearSVC.rst.txt b/dev/_sources/modules/generated/sklearn.svm.LinearSVC.rst.txt
index 74cc2761579e7..177448af27f7d 100644
--- a/dev/_sources/modules/generated/sklearn.svm.LinearSVC.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.svm.LinearSVC.rst.txt
@@ -8,6 +8,6 @@ LinearSVC
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.svm.LinearSVC sklearn.svm.LinearSVC.__delattr__ sklearn.svm.LinearSVC.__dir__ sklearn.svm.LinearSVC.__eq__ sklearn.svm.LinearSVC.__format__ sklearn.svm.LinearSVC.__ge__ sklearn.svm.LinearSVC.__getattribute__ sklearn.svm.LinearSVC.__getstate__ sklearn.svm.LinearSVC.__gt__ sklearn.svm.LinearSVC.__hash__ sklearn.svm.LinearSVC.__init__ sklearn.svm.LinearSVC.__init_subclass__ sklearn.svm.LinearSVC.__le__ sklearn.svm.LinearSVC.__lt__ sklearn.svm.LinearSVC.__ne__ sklearn.svm.LinearSVC.__new__ sklearn.svm.LinearSVC.__reduce__ sklearn.svm.LinearSVC.__reduce_ex__ sklearn.svm.LinearSVC.__repr__ sklearn.svm.LinearSVC.__setattr__ sklearn.svm.LinearSVC.__setstate__ sklearn.svm.LinearSVC.__sizeof__ sklearn.svm.LinearSVC.__sklearn_clone__ sklearn.svm.LinearSVC.__sklearn_tags__ sklearn.svm.LinearSVC.__str__ sklearn.svm.LinearSVC.__subclasshook__ sklearn.svm.LinearSVC._build_request_for_signature sklearn.svm.LinearSVC._check_feature_names sklearn.svm.LinearSVC._check_n_features sklearn.svm.LinearSVC._get_default_requests sklearn.svm.LinearSVC._get_doc_link sklearn.svm.LinearSVC._get_metadata_request sklearn.svm.LinearSVC._get_param_names sklearn.svm.LinearSVC._get_tags sklearn.svm.LinearSVC._more_tags sklearn.svm.LinearSVC._predict_proba_lr sklearn.svm.LinearSVC._repr_html_inner sklearn.svm.LinearSVC._repr_mimebundle_ sklearn.svm.LinearSVC._validate_data sklearn.svm.LinearSVC._validate_params sklearn.svm.LinearSVC.decision_function sklearn.svm.LinearSVC.densify sklearn.svm.LinearSVC.fit sklearn.svm.LinearSVC.get_metadata_routing sklearn.svm.LinearSVC.get_params sklearn.svm.LinearSVC.predict sklearn.svm.LinearSVC.score sklearn.svm.LinearSVC.set_fit_request sklearn.svm.LinearSVC.set_params sklearn.svm.LinearSVC.set_score_request sklearn.svm.LinearSVC.sparsify 
+.. minigallery:: sklearn.svm.LinearSVC sklearn.svm.LinearSVC.__delattr__ sklearn.svm.LinearSVC.__dir__ sklearn.svm.LinearSVC.__eq__ sklearn.svm.LinearSVC.__format__ sklearn.svm.LinearSVC.__ge__ sklearn.svm.LinearSVC.__getattribute__ sklearn.svm.LinearSVC.__getstate__ sklearn.svm.LinearSVC.__gt__ sklearn.svm.LinearSVC.__hash__ sklearn.svm.LinearSVC.__init__ sklearn.svm.LinearSVC.__init_subclass__ sklearn.svm.LinearSVC.__le__ sklearn.svm.LinearSVC.__lt__ sklearn.svm.LinearSVC.__ne__ sklearn.svm.LinearSVC.__new__ sklearn.svm.LinearSVC.__reduce__ sklearn.svm.LinearSVC.__reduce_ex__ sklearn.svm.LinearSVC.__repr__ sklearn.svm.LinearSVC.__setattr__ sklearn.svm.LinearSVC.__setstate__ sklearn.svm.LinearSVC.__sizeof__ sklearn.svm.LinearSVC.__sklearn_clone__ sklearn.svm.LinearSVC.__sklearn_tags__ sklearn.svm.LinearSVC.__str__ sklearn.svm.LinearSVC.__subclasshook__ sklearn.svm.LinearSVC._build_request_for_signature sklearn.svm.LinearSVC._get_default_requests sklearn.svm.LinearSVC._get_doc_link sklearn.svm.LinearSVC._get_metadata_request sklearn.svm.LinearSVC._get_param_names sklearn.svm.LinearSVC._predict_proba_lr sklearn.svm.LinearSVC._repr_html_inner sklearn.svm.LinearSVC._repr_mimebundle_ sklearn.svm.LinearSVC._validate_params sklearn.svm.LinearSVC.decision_function sklearn.svm.LinearSVC.densify sklearn.svm.LinearSVC.fit sklearn.svm.LinearSVC.get_metadata_routing sklearn.svm.LinearSVC.get_params sklearn.svm.LinearSVC.predict sklearn.svm.LinearSVC.score sklearn.svm.LinearSVC.set_fit_request sklearn.svm.LinearSVC.set_params sklearn.svm.LinearSVC.set_score_request sklearn.svm.LinearSVC.sparsify 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.svm.LinearSVR.rst.txt b/dev/_sources/modules/generated/sklearn.svm.LinearSVR.rst.txt
index ce5171a5f6e5f..4aa1709621c4c 100644
--- a/dev/_sources/modules/generated/sklearn.svm.LinearSVR.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.svm.LinearSVR.rst.txt
@@ -8,6 +8,6 @@ LinearSVR
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.svm.LinearSVR sklearn.svm.LinearSVR.__delattr__ sklearn.svm.LinearSVR.__dir__ sklearn.svm.LinearSVR.__eq__ sklearn.svm.LinearSVR.__format__ sklearn.svm.LinearSVR.__ge__ sklearn.svm.LinearSVR.__getattribute__ sklearn.svm.LinearSVR.__getstate__ sklearn.svm.LinearSVR.__gt__ sklearn.svm.LinearSVR.__hash__ sklearn.svm.LinearSVR.__init__ sklearn.svm.LinearSVR.__init_subclass__ sklearn.svm.LinearSVR.__le__ sklearn.svm.LinearSVR.__lt__ sklearn.svm.LinearSVR.__ne__ sklearn.svm.LinearSVR.__new__ sklearn.svm.LinearSVR.__reduce__ sklearn.svm.LinearSVR.__reduce_ex__ sklearn.svm.LinearSVR.__repr__ sklearn.svm.LinearSVR.__setattr__ sklearn.svm.LinearSVR.__setstate__ sklearn.svm.LinearSVR.__sizeof__ sklearn.svm.LinearSVR.__sklearn_clone__ sklearn.svm.LinearSVR.__sklearn_tags__ sklearn.svm.LinearSVR.__str__ sklearn.svm.LinearSVR.__subclasshook__ sklearn.svm.LinearSVR._build_request_for_signature sklearn.svm.LinearSVR._check_feature_names sklearn.svm.LinearSVR._check_n_features sklearn.svm.LinearSVR._decision_function sklearn.svm.LinearSVR._get_default_requests sklearn.svm.LinearSVR._get_doc_link sklearn.svm.LinearSVR._get_metadata_request sklearn.svm.LinearSVR._get_param_names sklearn.svm.LinearSVR._get_tags sklearn.svm.LinearSVR._more_tags sklearn.svm.LinearSVR._repr_html_inner sklearn.svm.LinearSVR._repr_mimebundle_ sklearn.svm.LinearSVR._set_intercept sklearn.svm.LinearSVR._validate_data sklearn.svm.LinearSVR._validate_params sklearn.svm.LinearSVR.fit sklearn.svm.LinearSVR.get_metadata_routing sklearn.svm.LinearSVR.get_params sklearn.svm.LinearSVR.predict sklearn.svm.LinearSVR.score sklearn.svm.LinearSVR.set_fit_request sklearn.svm.LinearSVR.set_params sklearn.svm.LinearSVR.set_score_request 
+.. minigallery:: sklearn.svm.LinearSVR sklearn.svm.LinearSVR.__delattr__ sklearn.svm.LinearSVR.__dir__ sklearn.svm.LinearSVR.__eq__ sklearn.svm.LinearSVR.__format__ sklearn.svm.LinearSVR.__ge__ sklearn.svm.LinearSVR.__getattribute__ sklearn.svm.LinearSVR.__getstate__ sklearn.svm.LinearSVR.__gt__ sklearn.svm.LinearSVR.__hash__ sklearn.svm.LinearSVR.__init__ sklearn.svm.LinearSVR.__init_subclass__ sklearn.svm.LinearSVR.__le__ sklearn.svm.LinearSVR.__lt__ sklearn.svm.LinearSVR.__ne__ sklearn.svm.LinearSVR.__new__ sklearn.svm.LinearSVR.__reduce__ sklearn.svm.LinearSVR.__reduce_ex__ sklearn.svm.LinearSVR.__repr__ sklearn.svm.LinearSVR.__setattr__ sklearn.svm.LinearSVR.__setstate__ sklearn.svm.LinearSVR.__sizeof__ sklearn.svm.LinearSVR.__sklearn_clone__ sklearn.svm.LinearSVR.__sklearn_tags__ sklearn.svm.LinearSVR.__str__ sklearn.svm.LinearSVR.__subclasshook__ sklearn.svm.LinearSVR._build_request_for_signature sklearn.svm.LinearSVR._decision_function sklearn.svm.LinearSVR._get_default_requests sklearn.svm.LinearSVR._get_doc_link sklearn.svm.LinearSVR._get_metadata_request sklearn.svm.LinearSVR._get_param_names sklearn.svm.LinearSVR._repr_html_inner sklearn.svm.LinearSVR._repr_mimebundle_ sklearn.svm.LinearSVR._set_intercept sklearn.svm.LinearSVR._validate_params sklearn.svm.LinearSVR.fit sklearn.svm.LinearSVR.get_metadata_routing sklearn.svm.LinearSVR.get_params sklearn.svm.LinearSVR.predict sklearn.svm.LinearSVR.score sklearn.svm.LinearSVR.set_fit_request sklearn.svm.LinearSVR.set_params sklearn.svm.LinearSVR.set_score_request 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.svm.NuSVC.rst.txt b/dev/_sources/modules/generated/sklearn.svm.NuSVC.rst.txt
index 40802fde5cfb6..fcac6a5d2f1a3 100644
--- a/dev/_sources/modules/generated/sklearn.svm.NuSVC.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.svm.NuSVC.rst.txt
@@ -8,6 +8,6 @@ NuSVC
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.svm.NuSVC sklearn.svm.NuSVC.__delattr__ sklearn.svm.NuSVC.__dir__ sklearn.svm.NuSVC.__eq__ sklearn.svm.NuSVC.__format__ sklearn.svm.NuSVC.__ge__ sklearn.svm.NuSVC.__getattribute__ sklearn.svm.NuSVC.__getstate__ sklearn.svm.NuSVC.__gt__ sklearn.svm.NuSVC.__hash__ sklearn.svm.NuSVC.__init__ sklearn.svm.NuSVC.__init_subclass__ sklearn.svm.NuSVC.__le__ sklearn.svm.NuSVC.__lt__ sklearn.svm.NuSVC.__ne__ sklearn.svm.NuSVC.__new__ sklearn.svm.NuSVC.__reduce__ sklearn.svm.NuSVC.__reduce_ex__ sklearn.svm.NuSVC.__repr__ sklearn.svm.NuSVC.__setattr__ sklearn.svm.NuSVC.__setstate__ sklearn.svm.NuSVC.__sizeof__ sklearn.svm.NuSVC.__sklearn_clone__ sklearn.svm.NuSVC.__sklearn_tags__ sklearn.svm.NuSVC.__str__ sklearn.svm.NuSVC.__subclasshook__ sklearn.svm.NuSVC._build_request_for_signature sklearn.svm.NuSVC._check_feature_names sklearn.svm.NuSVC._check_n_features sklearn.svm.NuSVC._check_proba sklearn.svm.NuSVC._compute_kernel sklearn.svm.NuSVC._decision_function sklearn.svm.NuSVC._dense_decision_function sklearn.svm.NuSVC._dense_fit sklearn.svm.NuSVC._dense_predict sklearn.svm.NuSVC._dense_predict_proba sklearn.svm.NuSVC._get_coef sklearn.svm.NuSVC._get_default_requests sklearn.svm.NuSVC._get_doc_link sklearn.svm.NuSVC._get_metadata_request sklearn.svm.NuSVC._get_param_names sklearn.svm.NuSVC._get_tags sklearn.svm.NuSVC._more_tags sklearn.svm.NuSVC._repr_html_inner sklearn.svm.NuSVC._repr_mimebundle_ sklearn.svm.NuSVC._sparse_decision_function sklearn.svm.NuSVC._sparse_fit sklearn.svm.NuSVC._sparse_predict sklearn.svm.NuSVC._sparse_predict_proba sklearn.svm.NuSVC._validate_data sklearn.svm.NuSVC._validate_for_predict sklearn.svm.NuSVC._validate_params sklearn.svm.NuSVC._validate_targets sklearn.svm.NuSVC._warn_from_fit_status sklearn.svm.NuSVC.decision_function sklearn.svm.NuSVC.fit sklearn.svm.NuSVC.get_metadata_routing sklearn.svm.NuSVC.get_params sklearn.svm.NuSVC.predict sklearn.svm.NuSVC.predict_log_proba sklearn.svm.NuSVC.predict_proba sklearn.svm.NuSVC.score sklearn.svm.NuSVC.set_fit_request sklearn.svm.NuSVC.set_params sklearn.svm.NuSVC.set_score_request 
+.. minigallery:: sklearn.svm.NuSVC sklearn.svm.NuSVC.__delattr__ sklearn.svm.NuSVC.__dir__ sklearn.svm.NuSVC.__eq__ sklearn.svm.NuSVC.__format__ sklearn.svm.NuSVC.__ge__ sklearn.svm.NuSVC.__getattribute__ sklearn.svm.NuSVC.__getstate__ sklearn.svm.NuSVC.__gt__ sklearn.svm.NuSVC.__hash__ sklearn.svm.NuSVC.__init__ sklearn.svm.NuSVC.__init_subclass__ sklearn.svm.NuSVC.__le__ sklearn.svm.NuSVC.__lt__ sklearn.svm.NuSVC.__ne__ sklearn.svm.NuSVC.__new__ sklearn.svm.NuSVC.__reduce__ sklearn.svm.NuSVC.__reduce_ex__ sklearn.svm.NuSVC.__repr__ sklearn.svm.NuSVC.__setattr__ sklearn.svm.NuSVC.__setstate__ sklearn.svm.NuSVC.__sizeof__ sklearn.svm.NuSVC.__sklearn_clone__ sklearn.svm.NuSVC.__sklearn_tags__ sklearn.svm.NuSVC.__str__ sklearn.svm.NuSVC.__subclasshook__ sklearn.svm.NuSVC._build_request_for_signature sklearn.svm.NuSVC._check_proba sklearn.svm.NuSVC._compute_kernel sklearn.svm.NuSVC._decision_function sklearn.svm.NuSVC._dense_decision_function sklearn.svm.NuSVC._dense_fit sklearn.svm.NuSVC._dense_predict sklearn.svm.NuSVC._dense_predict_proba sklearn.svm.NuSVC._get_coef sklearn.svm.NuSVC._get_default_requests sklearn.svm.NuSVC._get_doc_link sklearn.svm.NuSVC._get_metadata_request sklearn.svm.NuSVC._get_param_names sklearn.svm.NuSVC._repr_html_inner sklearn.svm.NuSVC._repr_mimebundle_ sklearn.svm.NuSVC._sparse_decision_function sklearn.svm.NuSVC._sparse_fit sklearn.svm.NuSVC._sparse_predict sklearn.svm.NuSVC._sparse_predict_proba sklearn.svm.NuSVC._validate_for_predict sklearn.svm.NuSVC._validate_params sklearn.svm.NuSVC._validate_targets sklearn.svm.NuSVC._warn_from_fit_status sklearn.svm.NuSVC.decision_function sklearn.svm.NuSVC.fit sklearn.svm.NuSVC.get_metadata_routing sklearn.svm.NuSVC.get_params sklearn.svm.NuSVC.predict sklearn.svm.NuSVC.predict_log_proba sklearn.svm.NuSVC.predict_proba sklearn.svm.NuSVC.score sklearn.svm.NuSVC.set_fit_request sklearn.svm.NuSVC.set_params sklearn.svm.NuSVC.set_score_request 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.svm.NuSVR.rst.txt b/dev/_sources/modules/generated/sklearn.svm.NuSVR.rst.txt
index 5296510bc803f..b9392b61ec16f 100644
--- a/dev/_sources/modules/generated/sklearn.svm.NuSVR.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.svm.NuSVR.rst.txt
@@ -8,6 +8,6 @@ NuSVR
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.svm.NuSVR sklearn.svm.NuSVR.__delattr__ sklearn.svm.NuSVR.__dir__ sklearn.svm.NuSVR.__eq__ sklearn.svm.NuSVR.__format__ sklearn.svm.NuSVR.__ge__ sklearn.svm.NuSVR.__getattribute__ sklearn.svm.NuSVR.__getstate__ sklearn.svm.NuSVR.__gt__ sklearn.svm.NuSVR.__hash__ sklearn.svm.NuSVR.__init__ sklearn.svm.NuSVR.__init_subclass__ sklearn.svm.NuSVR.__le__ sklearn.svm.NuSVR.__lt__ sklearn.svm.NuSVR.__ne__ sklearn.svm.NuSVR.__new__ sklearn.svm.NuSVR.__reduce__ sklearn.svm.NuSVR.__reduce_ex__ sklearn.svm.NuSVR.__repr__ sklearn.svm.NuSVR.__setattr__ sklearn.svm.NuSVR.__setstate__ sklearn.svm.NuSVR.__sizeof__ sklearn.svm.NuSVR.__sklearn_clone__ sklearn.svm.NuSVR.__sklearn_tags__ sklearn.svm.NuSVR.__str__ sklearn.svm.NuSVR.__subclasshook__ sklearn.svm.NuSVR._build_request_for_signature sklearn.svm.NuSVR._check_feature_names sklearn.svm.NuSVR._check_n_features sklearn.svm.NuSVR._compute_kernel sklearn.svm.NuSVR._decision_function sklearn.svm.NuSVR._dense_decision_function sklearn.svm.NuSVR._dense_fit sklearn.svm.NuSVR._dense_predict sklearn.svm.NuSVR._get_coef sklearn.svm.NuSVR._get_default_requests sklearn.svm.NuSVR._get_doc_link sklearn.svm.NuSVR._get_metadata_request sklearn.svm.NuSVR._get_param_names sklearn.svm.NuSVR._get_tags sklearn.svm.NuSVR._more_tags sklearn.svm.NuSVR._repr_html_inner sklearn.svm.NuSVR._repr_mimebundle_ sklearn.svm.NuSVR._sparse_decision_function sklearn.svm.NuSVR._sparse_fit sklearn.svm.NuSVR._sparse_predict sklearn.svm.NuSVR._validate_data sklearn.svm.NuSVR._validate_for_predict sklearn.svm.NuSVR._validate_params sklearn.svm.NuSVR._validate_targets sklearn.svm.NuSVR._warn_from_fit_status sklearn.svm.NuSVR.fit sklearn.svm.NuSVR.get_metadata_routing sklearn.svm.NuSVR.get_params sklearn.svm.NuSVR.predict sklearn.svm.NuSVR.score sklearn.svm.NuSVR.set_fit_request sklearn.svm.NuSVR.set_params sklearn.svm.NuSVR.set_score_request 
+.. minigallery:: sklearn.svm.NuSVR sklearn.svm.NuSVR.__delattr__ sklearn.svm.NuSVR.__dir__ sklearn.svm.NuSVR.__eq__ sklearn.svm.NuSVR.__format__ sklearn.svm.NuSVR.__ge__ sklearn.svm.NuSVR.__getattribute__ sklearn.svm.NuSVR.__getstate__ sklearn.svm.NuSVR.__gt__ sklearn.svm.NuSVR.__hash__ sklearn.svm.NuSVR.__init__ sklearn.svm.NuSVR.__init_subclass__ sklearn.svm.NuSVR.__le__ sklearn.svm.NuSVR.__lt__ sklearn.svm.NuSVR.__ne__ sklearn.svm.NuSVR.__new__ sklearn.svm.NuSVR.__reduce__ sklearn.svm.NuSVR.__reduce_ex__ sklearn.svm.NuSVR.__repr__ sklearn.svm.NuSVR.__setattr__ sklearn.svm.NuSVR.__setstate__ sklearn.svm.NuSVR.__sizeof__ sklearn.svm.NuSVR.__sklearn_clone__ sklearn.svm.NuSVR.__sklearn_tags__ sklearn.svm.NuSVR.__str__ sklearn.svm.NuSVR.__subclasshook__ sklearn.svm.NuSVR._build_request_for_signature sklearn.svm.NuSVR._compute_kernel sklearn.svm.NuSVR._decision_function sklearn.svm.NuSVR._dense_decision_function sklearn.svm.NuSVR._dense_fit sklearn.svm.NuSVR._dense_predict sklearn.svm.NuSVR._get_coef sklearn.svm.NuSVR._get_default_requests sklearn.svm.NuSVR._get_doc_link sklearn.svm.NuSVR._get_metadata_request sklearn.svm.NuSVR._get_param_names sklearn.svm.NuSVR._repr_html_inner sklearn.svm.NuSVR._repr_mimebundle_ sklearn.svm.NuSVR._sparse_decision_function sklearn.svm.NuSVR._sparse_fit sklearn.svm.NuSVR._sparse_predict sklearn.svm.NuSVR._validate_for_predict sklearn.svm.NuSVR._validate_params sklearn.svm.NuSVR._validate_targets sklearn.svm.NuSVR._warn_from_fit_status sklearn.svm.NuSVR.fit sklearn.svm.NuSVR.get_metadata_routing sklearn.svm.NuSVR.get_params sklearn.svm.NuSVR.predict sklearn.svm.NuSVR.score sklearn.svm.NuSVR.set_fit_request sklearn.svm.NuSVR.set_params sklearn.svm.NuSVR.set_score_request 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.svm.OneClassSVM.rst.txt b/dev/_sources/modules/generated/sklearn.svm.OneClassSVM.rst.txt
index d793fb269e3b2..6998ecc2eb22c 100644
--- a/dev/_sources/modules/generated/sklearn.svm.OneClassSVM.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.svm.OneClassSVM.rst.txt
@@ -8,6 +8,6 @@ OneClassSVM
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.svm.OneClassSVM sklearn.svm.OneClassSVM.__delattr__ sklearn.svm.OneClassSVM.__dir__ sklearn.svm.OneClassSVM.__eq__ sklearn.svm.OneClassSVM.__format__ sklearn.svm.OneClassSVM.__ge__ sklearn.svm.OneClassSVM.__getattribute__ sklearn.svm.OneClassSVM.__getstate__ sklearn.svm.OneClassSVM.__gt__ sklearn.svm.OneClassSVM.__hash__ sklearn.svm.OneClassSVM.__init__ sklearn.svm.OneClassSVM.__init_subclass__ sklearn.svm.OneClassSVM.__le__ sklearn.svm.OneClassSVM.__lt__ sklearn.svm.OneClassSVM.__ne__ sklearn.svm.OneClassSVM.__new__ sklearn.svm.OneClassSVM.__reduce__ sklearn.svm.OneClassSVM.__reduce_ex__ sklearn.svm.OneClassSVM.__repr__ sklearn.svm.OneClassSVM.__setattr__ sklearn.svm.OneClassSVM.__setstate__ sklearn.svm.OneClassSVM.__sizeof__ sklearn.svm.OneClassSVM.__sklearn_clone__ sklearn.svm.OneClassSVM.__sklearn_tags__ sklearn.svm.OneClassSVM.__str__ sklearn.svm.OneClassSVM.__subclasshook__ sklearn.svm.OneClassSVM._build_request_for_signature sklearn.svm.OneClassSVM._check_feature_names sklearn.svm.OneClassSVM._check_n_features sklearn.svm.OneClassSVM._compute_kernel sklearn.svm.OneClassSVM._decision_function sklearn.svm.OneClassSVM._dense_decision_function sklearn.svm.OneClassSVM._dense_fit sklearn.svm.OneClassSVM._dense_predict sklearn.svm.OneClassSVM._get_coef sklearn.svm.OneClassSVM._get_default_requests sklearn.svm.OneClassSVM._get_doc_link sklearn.svm.OneClassSVM._get_metadata_request sklearn.svm.OneClassSVM._get_param_names sklearn.svm.OneClassSVM._get_tags sklearn.svm.OneClassSVM._more_tags sklearn.svm.OneClassSVM._repr_html_inner sklearn.svm.OneClassSVM._repr_mimebundle_ sklearn.svm.OneClassSVM._sparse_decision_function sklearn.svm.OneClassSVM._sparse_fit sklearn.svm.OneClassSVM._sparse_predict sklearn.svm.OneClassSVM._validate_data sklearn.svm.OneClassSVM._validate_for_predict sklearn.svm.OneClassSVM._validate_params sklearn.svm.OneClassSVM._validate_targets sklearn.svm.OneClassSVM._warn_from_fit_status sklearn.svm.OneClassSVM.decision_function sklearn.svm.OneClassSVM.fit sklearn.svm.OneClassSVM.fit_predict sklearn.svm.OneClassSVM.get_metadata_routing sklearn.svm.OneClassSVM.get_params sklearn.svm.OneClassSVM.predict sklearn.svm.OneClassSVM.score_samples sklearn.svm.OneClassSVM.set_fit_request sklearn.svm.OneClassSVM.set_params 
+.. minigallery:: sklearn.svm.OneClassSVM sklearn.svm.OneClassSVM.__delattr__ sklearn.svm.OneClassSVM.__dir__ sklearn.svm.OneClassSVM.__eq__ sklearn.svm.OneClassSVM.__format__ sklearn.svm.OneClassSVM.__ge__ sklearn.svm.OneClassSVM.__getattribute__ sklearn.svm.OneClassSVM.__getstate__ sklearn.svm.OneClassSVM.__gt__ sklearn.svm.OneClassSVM.__hash__ sklearn.svm.OneClassSVM.__init__ sklearn.svm.OneClassSVM.__init_subclass__ sklearn.svm.OneClassSVM.__le__ sklearn.svm.OneClassSVM.__lt__ sklearn.svm.OneClassSVM.__ne__ sklearn.svm.OneClassSVM.__new__ sklearn.svm.OneClassSVM.__reduce__ sklearn.svm.OneClassSVM.__reduce_ex__ sklearn.svm.OneClassSVM.__repr__ sklearn.svm.OneClassSVM.__setattr__ sklearn.svm.OneClassSVM.__setstate__ sklearn.svm.OneClassSVM.__sizeof__ sklearn.svm.OneClassSVM.__sklearn_clone__ sklearn.svm.OneClassSVM.__sklearn_tags__ sklearn.svm.OneClassSVM.__str__ sklearn.svm.OneClassSVM.__subclasshook__ sklearn.svm.OneClassSVM._build_request_for_signature sklearn.svm.OneClassSVM._compute_kernel sklearn.svm.OneClassSVM._decision_function sklearn.svm.OneClassSVM._dense_decision_function sklearn.svm.OneClassSVM._dense_fit sklearn.svm.OneClassSVM._dense_predict sklearn.svm.OneClassSVM._get_coef sklearn.svm.OneClassSVM._get_default_requests sklearn.svm.OneClassSVM._get_doc_link sklearn.svm.OneClassSVM._get_metadata_request sklearn.svm.OneClassSVM._get_param_names sklearn.svm.OneClassSVM._repr_html_inner sklearn.svm.OneClassSVM._repr_mimebundle_ sklearn.svm.OneClassSVM._sparse_decision_function sklearn.svm.OneClassSVM._sparse_fit sklearn.svm.OneClassSVM._sparse_predict sklearn.svm.OneClassSVM._validate_for_predict sklearn.svm.OneClassSVM._validate_params sklearn.svm.OneClassSVM._validate_targets sklearn.svm.OneClassSVM._warn_from_fit_status sklearn.svm.OneClassSVM.decision_function sklearn.svm.OneClassSVM.fit sklearn.svm.OneClassSVM.fit_predict sklearn.svm.OneClassSVM.get_metadata_routing sklearn.svm.OneClassSVM.get_params sklearn.svm.OneClassSVM.predict sklearn.svm.OneClassSVM.score_samples sklearn.svm.OneClassSVM.set_fit_request sklearn.svm.OneClassSVM.set_params 
    :add-heading: Gallery examples
    :heading-level: -
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diff --git a/dev/_sources/modules/generated/sklearn.svm.SVC.rst.txt b/dev/_sources/modules/generated/sklearn.svm.SVC.rst.txt
index dbd34cc8b9f48..6c86b2ac34eb5 100644
--- a/dev/_sources/modules/generated/sklearn.svm.SVC.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.svm.SVC.rst.txt
@@ -8,6 +8,6 @@ SVC
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.svm.SVC sklearn.svm.SVC.__delattr__ sklearn.svm.SVC.__dir__ sklearn.svm.SVC.__eq__ sklearn.svm.SVC.__format__ sklearn.svm.SVC.__ge__ sklearn.svm.SVC.__getattribute__ sklearn.svm.SVC.__getstate__ sklearn.svm.SVC.__gt__ sklearn.svm.SVC.__hash__ sklearn.svm.SVC.__init__ sklearn.svm.SVC.__init_subclass__ sklearn.svm.SVC.__le__ sklearn.svm.SVC.__lt__ sklearn.svm.SVC.__ne__ sklearn.svm.SVC.__new__ sklearn.svm.SVC.__reduce__ sklearn.svm.SVC.__reduce_ex__ sklearn.svm.SVC.__repr__ sklearn.svm.SVC.__setattr__ sklearn.svm.SVC.__setstate__ sklearn.svm.SVC.__sizeof__ sklearn.svm.SVC.__sklearn_clone__ sklearn.svm.SVC.__sklearn_tags__ sklearn.svm.SVC.__str__ sklearn.svm.SVC.__subclasshook__ sklearn.svm.SVC._build_request_for_signature sklearn.svm.SVC._check_feature_names sklearn.svm.SVC._check_n_features sklearn.svm.SVC._check_proba sklearn.svm.SVC._compute_kernel sklearn.svm.SVC._decision_function sklearn.svm.SVC._dense_decision_function sklearn.svm.SVC._dense_fit sklearn.svm.SVC._dense_predict sklearn.svm.SVC._dense_predict_proba sklearn.svm.SVC._get_coef sklearn.svm.SVC._get_default_requests sklearn.svm.SVC._get_doc_link sklearn.svm.SVC._get_metadata_request sklearn.svm.SVC._get_param_names sklearn.svm.SVC._get_tags sklearn.svm.SVC._more_tags sklearn.svm.SVC._repr_html_inner sklearn.svm.SVC._repr_mimebundle_ sklearn.svm.SVC._sparse_decision_function sklearn.svm.SVC._sparse_fit sklearn.svm.SVC._sparse_predict sklearn.svm.SVC._sparse_predict_proba sklearn.svm.SVC._validate_data sklearn.svm.SVC._validate_for_predict sklearn.svm.SVC._validate_params sklearn.svm.SVC._validate_targets sklearn.svm.SVC._warn_from_fit_status sklearn.svm.SVC.decision_function sklearn.svm.SVC.fit sklearn.svm.SVC.get_metadata_routing sklearn.svm.SVC.get_params sklearn.svm.SVC.predict sklearn.svm.SVC.predict_log_proba sklearn.svm.SVC.predict_proba sklearn.svm.SVC.score sklearn.svm.SVC.set_fit_request sklearn.svm.SVC.set_params sklearn.svm.SVC.set_score_request 
+.. minigallery:: sklearn.svm.SVC sklearn.svm.SVC.__delattr__ sklearn.svm.SVC.__dir__ sklearn.svm.SVC.__eq__ sklearn.svm.SVC.__format__ sklearn.svm.SVC.__ge__ sklearn.svm.SVC.__getattribute__ sklearn.svm.SVC.__getstate__ sklearn.svm.SVC.__gt__ sklearn.svm.SVC.__hash__ sklearn.svm.SVC.__init__ sklearn.svm.SVC.__init_subclass__ sklearn.svm.SVC.__le__ sklearn.svm.SVC.__lt__ sklearn.svm.SVC.__ne__ sklearn.svm.SVC.__new__ sklearn.svm.SVC.__reduce__ sklearn.svm.SVC.__reduce_ex__ sklearn.svm.SVC.__repr__ sklearn.svm.SVC.__setattr__ sklearn.svm.SVC.__setstate__ sklearn.svm.SVC.__sizeof__ sklearn.svm.SVC.__sklearn_clone__ sklearn.svm.SVC.__sklearn_tags__ sklearn.svm.SVC.__str__ sklearn.svm.SVC.__subclasshook__ sklearn.svm.SVC._build_request_for_signature sklearn.svm.SVC._check_proba sklearn.svm.SVC._compute_kernel sklearn.svm.SVC._decision_function sklearn.svm.SVC._dense_decision_function sklearn.svm.SVC._dense_fit sklearn.svm.SVC._dense_predict sklearn.svm.SVC._dense_predict_proba sklearn.svm.SVC._get_coef sklearn.svm.SVC._get_default_requests sklearn.svm.SVC._get_doc_link sklearn.svm.SVC._get_metadata_request sklearn.svm.SVC._get_param_names sklearn.svm.SVC._repr_html_inner sklearn.svm.SVC._repr_mimebundle_ sklearn.svm.SVC._sparse_decision_function sklearn.svm.SVC._sparse_fit sklearn.svm.SVC._sparse_predict sklearn.svm.SVC._sparse_predict_proba sklearn.svm.SVC._validate_for_predict sklearn.svm.SVC._validate_params sklearn.svm.SVC._validate_targets sklearn.svm.SVC._warn_from_fit_status sklearn.svm.SVC.decision_function sklearn.svm.SVC.fit sklearn.svm.SVC.get_metadata_routing sklearn.svm.SVC.get_params sklearn.svm.SVC.predict sklearn.svm.SVC.predict_log_proba sklearn.svm.SVC.predict_proba sklearn.svm.SVC.score sklearn.svm.SVC.set_fit_request sklearn.svm.SVC.set_params sklearn.svm.SVC.set_score_request 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.svm.SVR.rst.txt b/dev/_sources/modules/generated/sklearn.svm.SVR.rst.txt
index 46d3331cd54a1..66f4ef36ae558 100644
--- a/dev/_sources/modules/generated/sklearn.svm.SVR.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.svm.SVR.rst.txt
@@ -8,6 +8,6 @@ SVR
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.svm.SVR sklearn.svm.SVR.__delattr__ sklearn.svm.SVR.__dir__ sklearn.svm.SVR.__eq__ sklearn.svm.SVR.__format__ sklearn.svm.SVR.__ge__ sklearn.svm.SVR.__getattribute__ sklearn.svm.SVR.__getstate__ sklearn.svm.SVR.__gt__ sklearn.svm.SVR.__hash__ sklearn.svm.SVR.__init__ sklearn.svm.SVR.__init_subclass__ sklearn.svm.SVR.__le__ sklearn.svm.SVR.__lt__ sklearn.svm.SVR.__ne__ sklearn.svm.SVR.__new__ sklearn.svm.SVR.__reduce__ sklearn.svm.SVR.__reduce_ex__ sklearn.svm.SVR.__repr__ sklearn.svm.SVR.__setattr__ sklearn.svm.SVR.__setstate__ sklearn.svm.SVR.__sizeof__ sklearn.svm.SVR.__sklearn_clone__ sklearn.svm.SVR.__sklearn_tags__ sklearn.svm.SVR.__str__ sklearn.svm.SVR.__subclasshook__ sklearn.svm.SVR._build_request_for_signature sklearn.svm.SVR._check_feature_names sklearn.svm.SVR._check_n_features sklearn.svm.SVR._compute_kernel sklearn.svm.SVR._decision_function sklearn.svm.SVR._dense_decision_function sklearn.svm.SVR._dense_fit sklearn.svm.SVR._dense_predict sklearn.svm.SVR._get_coef sklearn.svm.SVR._get_default_requests sklearn.svm.SVR._get_doc_link sklearn.svm.SVR._get_metadata_request sklearn.svm.SVR._get_param_names sklearn.svm.SVR._get_tags sklearn.svm.SVR._more_tags sklearn.svm.SVR._repr_html_inner sklearn.svm.SVR._repr_mimebundle_ sklearn.svm.SVR._sparse_decision_function sklearn.svm.SVR._sparse_fit sklearn.svm.SVR._sparse_predict sklearn.svm.SVR._validate_data sklearn.svm.SVR._validate_for_predict sklearn.svm.SVR._validate_params sklearn.svm.SVR._validate_targets sklearn.svm.SVR._warn_from_fit_status sklearn.svm.SVR.fit sklearn.svm.SVR.get_metadata_routing sklearn.svm.SVR.get_params sklearn.svm.SVR.predict sklearn.svm.SVR.score sklearn.svm.SVR.set_fit_request sklearn.svm.SVR.set_params sklearn.svm.SVR.set_score_request 
+.. minigallery:: sklearn.svm.SVR sklearn.svm.SVR.__delattr__ sklearn.svm.SVR.__dir__ sklearn.svm.SVR.__eq__ sklearn.svm.SVR.__format__ sklearn.svm.SVR.__ge__ sklearn.svm.SVR.__getattribute__ sklearn.svm.SVR.__getstate__ sklearn.svm.SVR.__gt__ sklearn.svm.SVR.__hash__ sklearn.svm.SVR.__init__ sklearn.svm.SVR.__init_subclass__ sklearn.svm.SVR.__le__ sklearn.svm.SVR.__lt__ sklearn.svm.SVR.__ne__ sklearn.svm.SVR.__new__ sklearn.svm.SVR.__reduce__ sklearn.svm.SVR.__reduce_ex__ sklearn.svm.SVR.__repr__ sklearn.svm.SVR.__setattr__ sklearn.svm.SVR.__setstate__ sklearn.svm.SVR.__sizeof__ sklearn.svm.SVR.__sklearn_clone__ sklearn.svm.SVR.__sklearn_tags__ sklearn.svm.SVR.__str__ sklearn.svm.SVR.__subclasshook__ sklearn.svm.SVR._build_request_for_signature sklearn.svm.SVR._compute_kernel sklearn.svm.SVR._decision_function sklearn.svm.SVR._dense_decision_function sklearn.svm.SVR._dense_fit sklearn.svm.SVR._dense_predict sklearn.svm.SVR._get_coef sklearn.svm.SVR._get_default_requests sklearn.svm.SVR._get_doc_link sklearn.svm.SVR._get_metadata_request sklearn.svm.SVR._get_param_names sklearn.svm.SVR._repr_html_inner sklearn.svm.SVR._repr_mimebundle_ sklearn.svm.SVR._sparse_decision_function sklearn.svm.SVR._sparse_fit sklearn.svm.SVR._sparse_predict sklearn.svm.SVR._validate_for_predict sklearn.svm.SVR._validate_params sklearn.svm.SVR._validate_targets sklearn.svm.SVR._warn_from_fit_status sklearn.svm.SVR.fit sklearn.svm.SVR.get_metadata_routing sklearn.svm.SVR.get_params sklearn.svm.SVR.predict sklearn.svm.SVR.score sklearn.svm.SVR.set_fit_request sklearn.svm.SVR.set_params sklearn.svm.SVR.set_score_request 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.tree.DecisionTreeClassifier.rst.txt b/dev/_sources/modules/generated/sklearn.tree.DecisionTreeClassifier.rst.txt
index 67678ee56513e..b6a4af2745ffa 100644
--- a/dev/_sources/modules/generated/sklearn.tree.DecisionTreeClassifier.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.tree.DecisionTreeClassifier.rst.txt
@@ -8,6 +8,6 @@ DecisionTreeClassifier
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.tree.DecisionTreeClassifier sklearn.tree.DecisionTreeClassifier.__delattr__ sklearn.tree.DecisionTreeClassifier.__dir__ sklearn.tree.DecisionTreeClassifier.__eq__ sklearn.tree.DecisionTreeClassifier.__format__ sklearn.tree.DecisionTreeClassifier.__ge__ sklearn.tree.DecisionTreeClassifier.__getattribute__ sklearn.tree.DecisionTreeClassifier.__getstate__ sklearn.tree.DecisionTreeClassifier.__gt__ sklearn.tree.DecisionTreeClassifier.__hash__ sklearn.tree.DecisionTreeClassifier.__init__ sklearn.tree.DecisionTreeClassifier.__init_subclass__ sklearn.tree.DecisionTreeClassifier.__le__ sklearn.tree.DecisionTreeClassifier.__lt__ sklearn.tree.DecisionTreeClassifier.__ne__ sklearn.tree.DecisionTreeClassifier.__new__ sklearn.tree.DecisionTreeClassifier.__reduce__ sklearn.tree.DecisionTreeClassifier.__reduce_ex__ sklearn.tree.DecisionTreeClassifier.__repr__ sklearn.tree.DecisionTreeClassifier.__setattr__ sklearn.tree.DecisionTreeClassifier.__setstate__ sklearn.tree.DecisionTreeClassifier.__sizeof__ sklearn.tree.DecisionTreeClassifier.__sklearn_clone__ sklearn.tree.DecisionTreeClassifier.__sklearn_tags__ sklearn.tree.DecisionTreeClassifier.__str__ sklearn.tree.DecisionTreeClassifier.__subclasshook__ sklearn.tree.DecisionTreeClassifier._build_request_for_signature sklearn.tree.DecisionTreeClassifier._check_feature_names sklearn.tree.DecisionTreeClassifier._check_n_features sklearn.tree.DecisionTreeClassifier._compute_missing_values_in_feature_mask sklearn.tree.DecisionTreeClassifier._fit sklearn.tree.DecisionTreeClassifier._get_default_requests sklearn.tree.DecisionTreeClassifier._get_doc_link sklearn.tree.DecisionTreeClassifier._get_metadata_request sklearn.tree.DecisionTreeClassifier._get_param_names sklearn.tree.DecisionTreeClassifier._get_tags sklearn.tree.DecisionTreeClassifier._more_tags sklearn.tree.DecisionTreeClassifier._prune_tree sklearn.tree.DecisionTreeClassifier._repr_html_inner sklearn.tree.DecisionTreeClassifier._repr_mimebundle_ sklearn.tree.DecisionTreeClassifier._support_missing_values sklearn.tree.DecisionTreeClassifier._validate_X_predict sklearn.tree.DecisionTreeClassifier._validate_data sklearn.tree.DecisionTreeClassifier._validate_params sklearn.tree.DecisionTreeClassifier.apply sklearn.tree.DecisionTreeClassifier.cost_complexity_pruning_path sklearn.tree.DecisionTreeClassifier.decision_path sklearn.tree.DecisionTreeClassifier.fit sklearn.tree.DecisionTreeClassifier.get_depth sklearn.tree.DecisionTreeClassifier.get_metadata_routing sklearn.tree.DecisionTreeClassifier.get_n_leaves sklearn.tree.DecisionTreeClassifier.get_params sklearn.tree.DecisionTreeClassifier.predict sklearn.tree.DecisionTreeClassifier.predict_log_proba sklearn.tree.DecisionTreeClassifier.predict_proba sklearn.tree.DecisionTreeClassifier.score sklearn.tree.DecisionTreeClassifier.set_fit_request sklearn.tree.DecisionTreeClassifier.set_params sklearn.tree.DecisionTreeClassifier.set_score_request 
+.. minigallery:: sklearn.tree.DecisionTreeClassifier sklearn.tree.DecisionTreeClassifier.__delattr__ sklearn.tree.DecisionTreeClassifier.__dir__ sklearn.tree.DecisionTreeClassifier.__eq__ sklearn.tree.DecisionTreeClassifier.__format__ sklearn.tree.DecisionTreeClassifier.__ge__ sklearn.tree.DecisionTreeClassifier.__getattribute__ sklearn.tree.DecisionTreeClassifier.__getstate__ sklearn.tree.DecisionTreeClassifier.__gt__ sklearn.tree.DecisionTreeClassifier.__hash__ sklearn.tree.DecisionTreeClassifier.__init__ sklearn.tree.DecisionTreeClassifier.__init_subclass__ sklearn.tree.DecisionTreeClassifier.__le__ sklearn.tree.DecisionTreeClassifier.__lt__ sklearn.tree.DecisionTreeClassifier.__ne__ sklearn.tree.DecisionTreeClassifier.__new__ sklearn.tree.DecisionTreeClassifier.__reduce__ sklearn.tree.DecisionTreeClassifier.__reduce_ex__ sklearn.tree.DecisionTreeClassifier.__repr__ sklearn.tree.DecisionTreeClassifier.__setattr__ sklearn.tree.DecisionTreeClassifier.__setstate__ sklearn.tree.DecisionTreeClassifier.__sizeof__ sklearn.tree.DecisionTreeClassifier.__sklearn_clone__ sklearn.tree.DecisionTreeClassifier.__sklearn_tags__ sklearn.tree.DecisionTreeClassifier.__str__ sklearn.tree.DecisionTreeClassifier.__subclasshook__ sklearn.tree.DecisionTreeClassifier._build_request_for_signature sklearn.tree.DecisionTreeClassifier._compute_missing_values_in_feature_mask sklearn.tree.DecisionTreeClassifier._fit sklearn.tree.DecisionTreeClassifier._get_default_requests sklearn.tree.DecisionTreeClassifier._get_doc_link sklearn.tree.DecisionTreeClassifier._get_metadata_request sklearn.tree.DecisionTreeClassifier._get_param_names sklearn.tree.DecisionTreeClassifier._prune_tree sklearn.tree.DecisionTreeClassifier._repr_html_inner sklearn.tree.DecisionTreeClassifier._repr_mimebundle_ sklearn.tree.DecisionTreeClassifier._support_missing_values sklearn.tree.DecisionTreeClassifier._validate_X_predict sklearn.tree.DecisionTreeClassifier._validate_params sklearn.tree.DecisionTreeClassifier.apply sklearn.tree.DecisionTreeClassifier.cost_complexity_pruning_path sklearn.tree.DecisionTreeClassifier.decision_path sklearn.tree.DecisionTreeClassifier.fit sklearn.tree.DecisionTreeClassifier.get_depth sklearn.tree.DecisionTreeClassifier.get_metadata_routing sklearn.tree.DecisionTreeClassifier.get_n_leaves sklearn.tree.DecisionTreeClassifier.get_params sklearn.tree.DecisionTreeClassifier.predict sklearn.tree.DecisionTreeClassifier.predict_log_proba sklearn.tree.DecisionTreeClassifier.predict_proba sklearn.tree.DecisionTreeClassifier.score sklearn.tree.DecisionTreeClassifier.set_fit_request sklearn.tree.DecisionTreeClassifier.set_params sklearn.tree.DecisionTreeClassifier.set_score_request 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.tree.DecisionTreeRegressor.rst.txt b/dev/_sources/modules/generated/sklearn.tree.DecisionTreeRegressor.rst.txt
index 681db88f52e9f..4370db4221408 100644
--- a/dev/_sources/modules/generated/sklearn.tree.DecisionTreeRegressor.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.tree.DecisionTreeRegressor.rst.txt
@@ -8,6 +8,6 @@ DecisionTreeRegressor
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.tree.DecisionTreeRegressor sklearn.tree.DecisionTreeRegressor.__delattr__ sklearn.tree.DecisionTreeRegressor.__dir__ sklearn.tree.DecisionTreeRegressor.__eq__ sklearn.tree.DecisionTreeRegressor.__format__ sklearn.tree.DecisionTreeRegressor.__ge__ sklearn.tree.DecisionTreeRegressor.__getattribute__ sklearn.tree.DecisionTreeRegressor.__getstate__ sklearn.tree.DecisionTreeRegressor.__gt__ sklearn.tree.DecisionTreeRegressor.__hash__ sklearn.tree.DecisionTreeRegressor.__init__ sklearn.tree.DecisionTreeRegressor.__init_subclass__ sklearn.tree.DecisionTreeRegressor.__le__ sklearn.tree.DecisionTreeRegressor.__lt__ sklearn.tree.DecisionTreeRegressor.__ne__ sklearn.tree.DecisionTreeRegressor.__new__ sklearn.tree.DecisionTreeRegressor.__reduce__ sklearn.tree.DecisionTreeRegressor.__reduce_ex__ sklearn.tree.DecisionTreeRegressor.__repr__ sklearn.tree.DecisionTreeRegressor.__setattr__ sklearn.tree.DecisionTreeRegressor.__setstate__ sklearn.tree.DecisionTreeRegressor.__sizeof__ sklearn.tree.DecisionTreeRegressor.__sklearn_clone__ sklearn.tree.DecisionTreeRegressor.__sklearn_tags__ sklearn.tree.DecisionTreeRegressor.__str__ sklearn.tree.DecisionTreeRegressor.__subclasshook__ sklearn.tree.DecisionTreeRegressor._build_request_for_signature sklearn.tree.DecisionTreeRegressor._check_feature_names sklearn.tree.DecisionTreeRegressor._check_n_features sklearn.tree.DecisionTreeRegressor._compute_missing_values_in_feature_mask sklearn.tree.DecisionTreeRegressor._compute_partial_dependence_recursion sklearn.tree.DecisionTreeRegressor._fit sklearn.tree.DecisionTreeRegressor._get_default_requests sklearn.tree.DecisionTreeRegressor._get_doc_link sklearn.tree.DecisionTreeRegressor._get_metadata_request sklearn.tree.DecisionTreeRegressor._get_param_names sklearn.tree.DecisionTreeRegressor._get_tags sklearn.tree.DecisionTreeRegressor._more_tags sklearn.tree.DecisionTreeRegressor._prune_tree sklearn.tree.DecisionTreeRegressor._repr_html_inner sklearn.tree.DecisionTreeRegressor._repr_mimebundle_ sklearn.tree.DecisionTreeRegressor._support_missing_values sklearn.tree.DecisionTreeRegressor._validate_X_predict sklearn.tree.DecisionTreeRegressor._validate_data sklearn.tree.DecisionTreeRegressor._validate_params sklearn.tree.DecisionTreeRegressor.apply sklearn.tree.DecisionTreeRegressor.cost_complexity_pruning_path sklearn.tree.DecisionTreeRegressor.decision_path sklearn.tree.DecisionTreeRegressor.fit sklearn.tree.DecisionTreeRegressor.get_depth sklearn.tree.DecisionTreeRegressor.get_metadata_routing sklearn.tree.DecisionTreeRegressor.get_n_leaves sklearn.tree.DecisionTreeRegressor.get_params sklearn.tree.DecisionTreeRegressor.predict sklearn.tree.DecisionTreeRegressor.score sklearn.tree.DecisionTreeRegressor.set_fit_request sklearn.tree.DecisionTreeRegressor.set_params sklearn.tree.DecisionTreeRegressor.set_score_request 
+.. minigallery:: sklearn.tree.DecisionTreeRegressor sklearn.tree.DecisionTreeRegressor.__delattr__ sklearn.tree.DecisionTreeRegressor.__dir__ sklearn.tree.DecisionTreeRegressor.__eq__ sklearn.tree.DecisionTreeRegressor.__format__ sklearn.tree.DecisionTreeRegressor.__ge__ sklearn.tree.DecisionTreeRegressor.__getattribute__ sklearn.tree.DecisionTreeRegressor.__getstate__ sklearn.tree.DecisionTreeRegressor.__gt__ sklearn.tree.DecisionTreeRegressor.__hash__ sklearn.tree.DecisionTreeRegressor.__init__ sklearn.tree.DecisionTreeRegressor.__init_subclass__ sklearn.tree.DecisionTreeRegressor.__le__ sklearn.tree.DecisionTreeRegressor.__lt__ sklearn.tree.DecisionTreeRegressor.__ne__ sklearn.tree.DecisionTreeRegressor.__new__ sklearn.tree.DecisionTreeRegressor.__reduce__ sklearn.tree.DecisionTreeRegressor.__reduce_ex__ sklearn.tree.DecisionTreeRegressor.__repr__ sklearn.tree.DecisionTreeRegressor.__setattr__ sklearn.tree.DecisionTreeRegressor.__setstate__ sklearn.tree.DecisionTreeRegressor.__sizeof__ sklearn.tree.DecisionTreeRegressor.__sklearn_clone__ sklearn.tree.DecisionTreeRegressor.__sklearn_tags__ sklearn.tree.DecisionTreeRegressor.__str__ sklearn.tree.DecisionTreeRegressor.__subclasshook__ sklearn.tree.DecisionTreeRegressor._build_request_for_signature sklearn.tree.DecisionTreeRegressor._compute_missing_values_in_feature_mask sklearn.tree.DecisionTreeRegressor._compute_partial_dependence_recursion sklearn.tree.DecisionTreeRegressor._fit sklearn.tree.DecisionTreeRegressor._get_default_requests sklearn.tree.DecisionTreeRegressor._get_doc_link sklearn.tree.DecisionTreeRegressor._get_metadata_request sklearn.tree.DecisionTreeRegressor._get_param_names sklearn.tree.DecisionTreeRegressor._prune_tree sklearn.tree.DecisionTreeRegressor._repr_html_inner sklearn.tree.DecisionTreeRegressor._repr_mimebundle_ sklearn.tree.DecisionTreeRegressor._support_missing_values sklearn.tree.DecisionTreeRegressor._validate_X_predict sklearn.tree.DecisionTreeRegressor._validate_params sklearn.tree.DecisionTreeRegressor.apply sklearn.tree.DecisionTreeRegressor.cost_complexity_pruning_path sklearn.tree.DecisionTreeRegressor.decision_path sklearn.tree.DecisionTreeRegressor.fit sklearn.tree.DecisionTreeRegressor.get_depth sklearn.tree.DecisionTreeRegressor.get_metadata_routing sklearn.tree.DecisionTreeRegressor.get_n_leaves sklearn.tree.DecisionTreeRegressor.get_params sklearn.tree.DecisionTreeRegressor.predict sklearn.tree.DecisionTreeRegressor.score sklearn.tree.DecisionTreeRegressor.set_fit_request sklearn.tree.DecisionTreeRegressor.set_params sklearn.tree.DecisionTreeRegressor.set_score_request 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.tree.ExtraTreeClassifier.rst.txt b/dev/_sources/modules/generated/sklearn.tree.ExtraTreeClassifier.rst.txt
index 897a32f213c3a..23ea31be062f5 100644
--- a/dev/_sources/modules/generated/sklearn.tree.ExtraTreeClassifier.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.tree.ExtraTreeClassifier.rst.txt
@@ -8,6 +8,6 @@ ExtraTreeClassifier
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.tree.ExtraTreeClassifier sklearn.tree.ExtraTreeClassifier.__delattr__ sklearn.tree.ExtraTreeClassifier.__dir__ sklearn.tree.ExtraTreeClassifier.__eq__ sklearn.tree.ExtraTreeClassifier.__format__ sklearn.tree.ExtraTreeClassifier.__ge__ sklearn.tree.ExtraTreeClassifier.__getattribute__ sklearn.tree.ExtraTreeClassifier.__getstate__ sklearn.tree.ExtraTreeClassifier.__gt__ sklearn.tree.ExtraTreeClassifier.__hash__ sklearn.tree.ExtraTreeClassifier.__init__ sklearn.tree.ExtraTreeClassifier.__init_subclass__ sklearn.tree.ExtraTreeClassifier.__le__ sklearn.tree.ExtraTreeClassifier.__lt__ sklearn.tree.ExtraTreeClassifier.__ne__ sklearn.tree.ExtraTreeClassifier.__new__ sklearn.tree.ExtraTreeClassifier.__reduce__ sklearn.tree.ExtraTreeClassifier.__reduce_ex__ sklearn.tree.ExtraTreeClassifier.__repr__ sklearn.tree.ExtraTreeClassifier.__setattr__ sklearn.tree.ExtraTreeClassifier.__setstate__ sklearn.tree.ExtraTreeClassifier.__sizeof__ sklearn.tree.ExtraTreeClassifier.__sklearn_clone__ sklearn.tree.ExtraTreeClassifier.__sklearn_tags__ sklearn.tree.ExtraTreeClassifier.__str__ sklearn.tree.ExtraTreeClassifier.__subclasshook__ sklearn.tree.ExtraTreeClassifier._build_request_for_signature sklearn.tree.ExtraTreeClassifier._check_feature_names sklearn.tree.ExtraTreeClassifier._check_n_features sklearn.tree.ExtraTreeClassifier._compute_missing_values_in_feature_mask sklearn.tree.ExtraTreeClassifier._fit sklearn.tree.ExtraTreeClassifier._get_default_requests sklearn.tree.ExtraTreeClassifier._get_doc_link sklearn.tree.ExtraTreeClassifier._get_metadata_request sklearn.tree.ExtraTreeClassifier._get_param_names sklearn.tree.ExtraTreeClassifier._get_tags sklearn.tree.ExtraTreeClassifier._more_tags sklearn.tree.ExtraTreeClassifier._prune_tree sklearn.tree.ExtraTreeClassifier._repr_html_inner sklearn.tree.ExtraTreeClassifier._repr_mimebundle_ sklearn.tree.ExtraTreeClassifier._support_missing_values sklearn.tree.ExtraTreeClassifier._validate_X_predict sklearn.tree.ExtraTreeClassifier._validate_data sklearn.tree.ExtraTreeClassifier._validate_params sklearn.tree.ExtraTreeClassifier.apply sklearn.tree.ExtraTreeClassifier.cost_complexity_pruning_path sklearn.tree.ExtraTreeClassifier.decision_path sklearn.tree.ExtraTreeClassifier.fit sklearn.tree.ExtraTreeClassifier.get_depth sklearn.tree.ExtraTreeClassifier.get_metadata_routing sklearn.tree.ExtraTreeClassifier.get_n_leaves sklearn.tree.ExtraTreeClassifier.get_params sklearn.tree.ExtraTreeClassifier.predict sklearn.tree.ExtraTreeClassifier.predict_log_proba sklearn.tree.ExtraTreeClassifier.predict_proba sklearn.tree.ExtraTreeClassifier.score sklearn.tree.ExtraTreeClassifier.set_fit_request sklearn.tree.ExtraTreeClassifier.set_params sklearn.tree.ExtraTreeClassifier.set_score_request 
+.. minigallery:: sklearn.tree.ExtraTreeClassifier sklearn.tree.ExtraTreeClassifier.__delattr__ sklearn.tree.ExtraTreeClassifier.__dir__ sklearn.tree.ExtraTreeClassifier.__eq__ sklearn.tree.ExtraTreeClassifier.__format__ sklearn.tree.ExtraTreeClassifier.__ge__ sklearn.tree.ExtraTreeClassifier.__getattribute__ sklearn.tree.ExtraTreeClassifier.__getstate__ sklearn.tree.ExtraTreeClassifier.__gt__ sklearn.tree.ExtraTreeClassifier.__hash__ sklearn.tree.ExtraTreeClassifier.__init__ sklearn.tree.ExtraTreeClassifier.__init_subclass__ sklearn.tree.ExtraTreeClassifier.__le__ sklearn.tree.ExtraTreeClassifier.__lt__ sklearn.tree.ExtraTreeClassifier.__ne__ sklearn.tree.ExtraTreeClassifier.__new__ sklearn.tree.ExtraTreeClassifier.__reduce__ sklearn.tree.ExtraTreeClassifier.__reduce_ex__ sklearn.tree.ExtraTreeClassifier.__repr__ sklearn.tree.ExtraTreeClassifier.__setattr__ sklearn.tree.ExtraTreeClassifier.__setstate__ sklearn.tree.ExtraTreeClassifier.__sizeof__ sklearn.tree.ExtraTreeClassifier.__sklearn_clone__ sklearn.tree.ExtraTreeClassifier.__sklearn_tags__ sklearn.tree.ExtraTreeClassifier.__str__ sklearn.tree.ExtraTreeClassifier.__subclasshook__ sklearn.tree.ExtraTreeClassifier._build_request_for_signature sklearn.tree.ExtraTreeClassifier._compute_missing_values_in_feature_mask sklearn.tree.ExtraTreeClassifier._fit sklearn.tree.ExtraTreeClassifier._get_default_requests sklearn.tree.ExtraTreeClassifier._get_doc_link sklearn.tree.ExtraTreeClassifier._get_metadata_request sklearn.tree.ExtraTreeClassifier._get_param_names sklearn.tree.ExtraTreeClassifier._prune_tree sklearn.tree.ExtraTreeClassifier._repr_html_inner sklearn.tree.ExtraTreeClassifier._repr_mimebundle_ sklearn.tree.ExtraTreeClassifier._support_missing_values sklearn.tree.ExtraTreeClassifier._validate_X_predict sklearn.tree.ExtraTreeClassifier._validate_params sklearn.tree.ExtraTreeClassifier.apply sklearn.tree.ExtraTreeClassifier.cost_complexity_pruning_path sklearn.tree.ExtraTreeClassifier.decision_path sklearn.tree.ExtraTreeClassifier.fit sklearn.tree.ExtraTreeClassifier.get_depth sklearn.tree.ExtraTreeClassifier.get_metadata_routing sklearn.tree.ExtraTreeClassifier.get_n_leaves sklearn.tree.ExtraTreeClassifier.get_params sklearn.tree.ExtraTreeClassifier.predict sklearn.tree.ExtraTreeClassifier.predict_log_proba sklearn.tree.ExtraTreeClassifier.predict_proba sklearn.tree.ExtraTreeClassifier.score sklearn.tree.ExtraTreeClassifier.set_fit_request sklearn.tree.ExtraTreeClassifier.set_params sklearn.tree.ExtraTreeClassifier.set_score_request 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.tree.ExtraTreeRegressor.rst.txt b/dev/_sources/modules/generated/sklearn.tree.ExtraTreeRegressor.rst.txt
index ea3c8226d20a7..f193a1e8cf167 100644
--- a/dev/_sources/modules/generated/sklearn.tree.ExtraTreeRegressor.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.tree.ExtraTreeRegressor.rst.txt
@@ -8,6 +8,6 @@ ExtraTreeRegressor
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.tree.ExtraTreeRegressor sklearn.tree.ExtraTreeRegressor.__delattr__ sklearn.tree.ExtraTreeRegressor.__dir__ sklearn.tree.ExtraTreeRegressor.__eq__ sklearn.tree.ExtraTreeRegressor.__format__ sklearn.tree.ExtraTreeRegressor.__ge__ sklearn.tree.ExtraTreeRegressor.__getattribute__ sklearn.tree.ExtraTreeRegressor.__getstate__ sklearn.tree.ExtraTreeRegressor.__gt__ sklearn.tree.ExtraTreeRegressor.__hash__ sklearn.tree.ExtraTreeRegressor.__init__ sklearn.tree.ExtraTreeRegressor.__init_subclass__ sklearn.tree.ExtraTreeRegressor.__le__ sklearn.tree.ExtraTreeRegressor.__lt__ sklearn.tree.ExtraTreeRegressor.__ne__ sklearn.tree.ExtraTreeRegressor.__new__ sklearn.tree.ExtraTreeRegressor.__reduce__ sklearn.tree.ExtraTreeRegressor.__reduce_ex__ sklearn.tree.ExtraTreeRegressor.__repr__ sklearn.tree.ExtraTreeRegressor.__setattr__ sklearn.tree.ExtraTreeRegressor.__setstate__ sklearn.tree.ExtraTreeRegressor.__sizeof__ sklearn.tree.ExtraTreeRegressor.__sklearn_clone__ sklearn.tree.ExtraTreeRegressor.__sklearn_tags__ sklearn.tree.ExtraTreeRegressor.__str__ sklearn.tree.ExtraTreeRegressor.__subclasshook__ sklearn.tree.ExtraTreeRegressor._build_request_for_signature sklearn.tree.ExtraTreeRegressor._check_feature_names sklearn.tree.ExtraTreeRegressor._check_n_features sklearn.tree.ExtraTreeRegressor._compute_missing_values_in_feature_mask sklearn.tree.ExtraTreeRegressor._compute_partial_dependence_recursion sklearn.tree.ExtraTreeRegressor._fit sklearn.tree.ExtraTreeRegressor._get_default_requests sklearn.tree.ExtraTreeRegressor._get_doc_link sklearn.tree.ExtraTreeRegressor._get_metadata_request sklearn.tree.ExtraTreeRegressor._get_param_names sklearn.tree.ExtraTreeRegressor._get_tags sklearn.tree.ExtraTreeRegressor._more_tags sklearn.tree.ExtraTreeRegressor._prune_tree sklearn.tree.ExtraTreeRegressor._repr_html_inner sklearn.tree.ExtraTreeRegressor._repr_mimebundle_ sklearn.tree.ExtraTreeRegressor._support_missing_values sklearn.tree.ExtraTreeRegressor._validate_X_predict sklearn.tree.ExtraTreeRegressor._validate_data sklearn.tree.ExtraTreeRegressor._validate_params sklearn.tree.ExtraTreeRegressor.apply sklearn.tree.ExtraTreeRegressor.cost_complexity_pruning_path sklearn.tree.ExtraTreeRegressor.decision_path sklearn.tree.ExtraTreeRegressor.fit sklearn.tree.ExtraTreeRegressor.get_depth sklearn.tree.ExtraTreeRegressor.get_metadata_routing sklearn.tree.ExtraTreeRegressor.get_n_leaves sklearn.tree.ExtraTreeRegressor.get_params sklearn.tree.ExtraTreeRegressor.predict sklearn.tree.ExtraTreeRegressor.score sklearn.tree.ExtraTreeRegressor.set_fit_request sklearn.tree.ExtraTreeRegressor.set_params sklearn.tree.ExtraTreeRegressor.set_score_request 
+.. minigallery:: sklearn.tree.ExtraTreeRegressor sklearn.tree.ExtraTreeRegressor.__delattr__ sklearn.tree.ExtraTreeRegressor.__dir__ sklearn.tree.ExtraTreeRegressor.__eq__ sklearn.tree.ExtraTreeRegressor.__format__ sklearn.tree.ExtraTreeRegressor.__ge__ sklearn.tree.ExtraTreeRegressor.__getattribute__ sklearn.tree.ExtraTreeRegressor.__getstate__ sklearn.tree.ExtraTreeRegressor.__gt__ sklearn.tree.ExtraTreeRegressor.__hash__ sklearn.tree.ExtraTreeRegressor.__init__ sklearn.tree.ExtraTreeRegressor.__init_subclass__ sklearn.tree.ExtraTreeRegressor.__le__ sklearn.tree.ExtraTreeRegressor.__lt__ sklearn.tree.ExtraTreeRegressor.__ne__ sklearn.tree.ExtraTreeRegressor.__new__ sklearn.tree.ExtraTreeRegressor.__reduce__ sklearn.tree.ExtraTreeRegressor.__reduce_ex__ sklearn.tree.ExtraTreeRegressor.__repr__ sklearn.tree.ExtraTreeRegressor.__setattr__ sklearn.tree.ExtraTreeRegressor.__setstate__ sklearn.tree.ExtraTreeRegressor.__sizeof__ sklearn.tree.ExtraTreeRegressor.__sklearn_clone__ sklearn.tree.ExtraTreeRegressor.__sklearn_tags__ sklearn.tree.ExtraTreeRegressor.__str__ sklearn.tree.ExtraTreeRegressor.__subclasshook__ sklearn.tree.ExtraTreeRegressor._build_request_for_signature sklearn.tree.ExtraTreeRegressor._compute_missing_values_in_feature_mask sklearn.tree.ExtraTreeRegressor._compute_partial_dependence_recursion sklearn.tree.ExtraTreeRegressor._fit sklearn.tree.ExtraTreeRegressor._get_default_requests sklearn.tree.ExtraTreeRegressor._get_doc_link sklearn.tree.ExtraTreeRegressor._get_metadata_request sklearn.tree.ExtraTreeRegressor._get_param_names sklearn.tree.ExtraTreeRegressor._prune_tree sklearn.tree.ExtraTreeRegressor._repr_html_inner sklearn.tree.ExtraTreeRegressor._repr_mimebundle_ sklearn.tree.ExtraTreeRegressor._support_missing_values sklearn.tree.ExtraTreeRegressor._validate_X_predict sklearn.tree.ExtraTreeRegressor._validate_params sklearn.tree.ExtraTreeRegressor.apply sklearn.tree.ExtraTreeRegressor.cost_complexity_pruning_path sklearn.tree.ExtraTreeRegressor.decision_path sklearn.tree.ExtraTreeRegressor.fit sklearn.tree.ExtraTreeRegressor.get_depth sklearn.tree.ExtraTreeRegressor.get_metadata_routing sklearn.tree.ExtraTreeRegressor.get_n_leaves sklearn.tree.ExtraTreeRegressor.get_params sklearn.tree.ExtraTreeRegressor.predict sklearn.tree.ExtraTreeRegressor.score sklearn.tree.ExtraTreeRegressor.set_fit_request sklearn.tree.ExtraTreeRegressor.set_params sklearn.tree.ExtraTreeRegressor.set_score_request 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/generated/sklearn.utils.parallel.Parallel.rst.txt b/dev/_sources/modules/generated/sklearn.utils.parallel.Parallel.rst.txt
index 5601587bc5615..6c3a6f752b3cd 100644
--- a/dev/_sources/modules/generated/sklearn.utils.parallel.Parallel.rst.txt
+++ b/dev/_sources/modules/generated/sklearn.utils.parallel.Parallel.rst.txt
@@ -8,6 +8,6 @@ Parallel
    :inherited-members:
    :special-members: __call__
 
-.. minigallery:: sklearn.utils.parallel.Parallel sklearn.utils.parallel.Parallel.__call__ sklearn.utils.parallel.Parallel.__delattr__ sklearn.utils.parallel.Parallel.__dir__ sklearn.utils.parallel.Parallel.__enter__ sklearn.utils.parallel.Parallel.__eq__ sklearn.utils.parallel.Parallel.__exit__ sklearn.utils.parallel.Parallel.__format__ sklearn.utils.parallel.Parallel.__ge__ sklearn.utils.parallel.Parallel.__getattribute__ sklearn.utils.parallel.Parallel.__gt__ sklearn.utils.parallel.Parallel.__hash__ sklearn.utils.parallel.Parallel.__init__ sklearn.utils.parallel.Parallel.__init_subclass__ sklearn.utils.parallel.Parallel.__le__ sklearn.utils.parallel.Parallel.__lt__ sklearn.utils.parallel.Parallel.__ne__ sklearn.utils.parallel.Parallel.__new__ sklearn.utils.parallel.Parallel.__reduce__ sklearn.utils.parallel.Parallel.__reduce_ex__ sklearn.utils.parallel.Parallel.__repr__ sklearn.utils.parallel.Parallel.__setattr__ sklearn.utils.parallel.Parallel.__sizeof__ sklearn.utils.parallel.Parallel.__str__ sklearn.utils.parallel.Parallel.__subclasshook__ sklearn.utils.parallel.Parallel._abort sklearn.utils.parallel.Parallel._dispatch sklearn.utils.parallel.Parallel._effective_n_jobs sklearn.utils.parallel.Parallel._get_batch_size sklearn.utils.parallel.Parallel._get_outputs sklearn.utils.parallel.Parallel._get_sequential_output sklearn.utils.parallel.Parallel._initialize_backend sklearn.utils.parallel.Parallel._is_completed sklearn.utils.parallel.Parallel._print sklearn.utils.parallel.Parallel._raise_error_fast sklearn.utils.parallel.Parallel._reset_run_tracking sklearn.utils.parallel.Parallel._retrieve sklearn.utils.parallel.Parallel._start sklearn.utils.parallel.Parallel._terminate_and_reset sklearn.utils.parallel.Parallel._wait_retrieval sklearn.utils.parallel.Parallel._warn_exit_early sklearn.utils.parallel.Parallel.debug sklearn.utils.parallel.Parallel.dispatch_next sklearn.utils.parallel.Parallel.dispatch_one_batch sklearn.utils.parallel.Parallel.format sklearn.utils.parallel.Parallel.info sklearn.utils.parallel.Parallel.print_progress sklearn.utils.parallel.Parallel.warn 
+.. minigallery:: sklearn.utils.parallel.Parallel sklearn.utils.parallel.Parallel.__call__ sklearn.utils.parallel.Parallel.__delattr__ sklearn.utils.parallel.Parallel.__dir__ sklearn.utils.parallel.Parallel.__enter__ sklearn.utils.parallel.Parallel.__eq__ sklearn.utils.parallel.Parallel.__exit__ sklearn.utils.parallel.Parallel.__format__ sklearn.utils.parallel.Parallel.__ge__ sklearn.utils.parallel.Parallel.__getattribute__ sklearn.utils.parallel.Parallel.__gt__ sklearn.utils.parallel.Parallel.__hash__ sklearn.utils.parallel.Parallel.__init__ sklearn.utils.parallel.Parallel.__init_subclass__ sklearn.utils.parallel.Parallel.__le__ sklearn.utils.parallel.Parallel.__lt__ sklearn.utils.parallel.Parallel.__ne__ sklearn.utils.parallel.Parallel.__new__ sklearn.utils.parallel.Parallel.__reduce__ sklearn.utils.parallel.Parallel.__reduce_ex__ sklearn.utils.parallel.Parallel.__repr__ sklearn.utils.parallel.Parallel.__setattr__ sklearn.utils.parallel.Parallel.__sizeof__ sklearn.utils.parallel.Parallel.__str__ sklearn.utils.parallel.Parallel.__subclasshook__ sklearn.utils.parallel.Parallel._abort sklearn.utils.parallel.Parallel._dispatch sklearn.utils.parallel.Parallel._effective_n_jobs sklearn.utils.parallel.Parallel._get_batch_size sklearn.utils.parallel.Parallel._get_outputs sklearn.utils.parallel.Parallel._get_sequential_output sklearn.utils.parallel.Parallel._initialize_backend sklearn.utils.parallel.Parallel._is_completed sklearn.utils.parallel.Parallel._print sklearn.utils.parallel.Parallel._raise_error_fast sklearn.utils.parallel.Parallel._register_new_job sklearn.utils.parallel.Parallel._reset_run_tracking sklearn.utils.parallel.Parallel._retrieve sklearn.utils.parallel.Parallel._start sklearn.utils.parallel.Parallel._terminate_and_reset sklearn.utils.parallel.Parallel._wait_retrieval sklearn.utils.parallel.Parallel._warn_exit_early sklearn.utils.parallel.Parallel.debug sklearn.utils.parallel.Parallel.dispatch_next sklearn.utils.parallel.Parallel.dispatch_one_batch sklearn.utils.parallel.Parallel.format sklearn.utils.parallel.Parallel.info sklearn.utils.parallel.Parallel.print_progress sklearn.utils.parallel.Parallel.warn 
    :add-heading: Gallery examples
    :heading-level: -
\ No newline at end of file
diff --git a/dev/_sources/modules/impute.rst.txt b/dev/_sources/modules/impute.rst.txt
index d26492402274f..59367b647dd58 100644
--- a/dev/_sources/modules/impute.rst.txt
+++ b/dev/_sources/modules/impute.rst.txt
@@ -50,7 +50,7 @@ that contain the missing values::
     >>> X = [[np.nan, 2], [6, np.nan], [7, 6]]
     >>> print(imp.transform(X))
     [[4.          2.        ]
-     [6.          3.666...]
+     [6.          3.666]
      [7.          6.        ]]
 
 The :class:`SimpleImputer` class also supports sparse matrices::
diff --git a/dev/_sources/modules/learning_curve.rst.txt b/dev/_sources/modules/learning_curve.rst.txt
index 77c627d189f2a..6dca0a29af7cb 100644
--- a/dev/_sources/modules/learning_curve.rst.txt
+++ b/dev/_sources/modules/learning_curve.rst.txt
@@ -83,13 +83,13 @@ The function :func:`validation_curve` can help in this case::
   ...     SVC(kernel="linear"), X, y, param_name="C", param_range=np.logspace(-7, 3, 3),
   ... )
   >>> train_scores
-  array([[0.90..., 0.94..., 0.91..., 0.89..., 0.92...],
-         [0.9... , 0.92..., 0.93..., 0.92..., 0.93...],
-         [0.97..., 1...   , 0.98..., 0.97..., 0.99...]])
+  array([[0.90, 0.94, 0.91, 0.89, 0.92],
+         [0.9 , 0.92, 0.93, 0.92, 0.93],
+         [0.97, 1   , 0.98, 0.97, 0.99]])
   >>> valid_scores
-  array([[0.9..., 0.9... , 0.9... , 0.96..., 0.9... ],
-         [0.9..., 0.83..., 0.96..., 0.96..., 0.93...],
-         [1.... , 0.93..., 1....  , 1....  , 0.9... ]])
+  array([[0.9, 0.9 , 0.9 , 0.96, 0.9 ],
+         [0.9, 0.83, 0.96, 0.96, 0.93],
+         [1. , 0.93, 1   , 1   , 0.9 ]])
 
 If you intend to plot the validation curves only, the class
 :class:`~sklearn.model_selection.ValidationCurveDisplay` is more direct than
@@ -154,13 +154,13 @@ average scores on the validation sets)::
   >>> train_sizes
   array([ 50, 80, 110])
   >>> train_scores
-  array([[0.98..., 0.98 , 0.98..., 0.98..., 0.98...],
-         [0.98..., 1.   , 0.98..., 0.98..., 0.98...],
-         [0.98..., 1.   , 0.98..., 0.98..., 0.99...]])
+  array([[0.98, 0.98 , 0.98, 0.98, 0.98],
+         [0.98, 1.   , 0.98, 0.98, 0.98],
+         [0.98, 1.   , 0.98, 0.98, 0.99]])
   >>> valid_scores
-  array([[1. ,  0.93...,  1. ,  1. ,  0.96...],
-         [1. ,  0.96...,  1. ,  1. ,  0.96...],
-         [1. ,  0.96...,  1. ,  1. ,  0.96...]])
+  array([[1. ,  0.93,  1. ,  1. ,  0.96],
+         [1. ,  0.96,  1. ,  1. ,  0.96],
+         [1. ,  0.96,  1. ,  1. ,  0.96]])
 
 If you intend to plot the learning curves only, the class
 :class:`~sklearn.model_selection.LearningCurveDisplay` will be easier to use.
diff --git a/dev/_sources/modules/linear_model.rst.txt b/dev/_sources/modules/linear_model.rst.txt
index 2a06bc5d1ff91..69a2bf9b7f477 100644
--- a/dev/_sources/modules/linear_model.rst.txt
+++ b/dev/_sources/modules/linear_model.rst.txt
@@ -126,7 +126,7 @@ its ``coef_`` member::
     >>> reg.coef_
     array([0.34545455, 0.34545455])
     >>> reg.intercept_
-    np.float64(0.13636...)
+    np.float64(0.13636)
 
 Note that the class :class:`Ridge` allows for the user to specify that the
 solver be automatically chosen by setting `solver="auto"`. When this option
@@ -627,7 +627,7 @@ function of the norm of its coefficients.
    >>> reg.fit([[0, 0], [1, 1]], [0, 1])
    LassoLars(alpha=0.1)
    >>> reg.coef_
-   array([0.6..., 0.        ])
+   array([0.6, 0.        ])
 
 .. rubric:: Examples
 
@@ -1282,9 +1282,9 @@ Usage example::
     >>> reg.fit([[0, 0], [0, 1], [2, 2]], [0, 1, 2])
     TweedieRegressor(alpha=0.5, link='log', power=1)
     >>> reg.coef_
-    array([0.2463..., 0.4337...])
+    array([0.2463, 0.4337])
     >>> reg.intercept_
-    np.float64(-0.7638...)
+    np.float64(-0.7638)
 
 
 .. rubric:: Examples
diff --git a/dev/_sources/modules/manifold.rst.txt b/dev/_sources/modules/manifold.rst.txt
index 19694ff0cb422..fec6e96153323 100644
--- a/dev/_sources/modules/manifold.rst.txt
+++ b/dev/_sources/modules/manifold.rst.txt
@@ -112,6 +112,9 @@ from the data itself, without the use of predetermined classifications.
   using manifold learning to map the stock market structure based on historical stock
   prices.
 
+* See :ref:`sphx_glr_auto_examples_manifold_plot_manifold_sphere.py` for an example of
+  manifold learning techniques applied to a spherical data-set.
+
 The manifold learning implementations available in scikit-learn are
 summarized below
 
diff --git a/dev/_sources/modules/model_evaluation.rst.txt b/dev/_sources/modules/model_evaluation.rst.txt
index a1ae46e66b048..cf168295a6024 100644
--- a/dev/_sources/modules/model_evaluation.rst.txt
+++ b/dev/_sources/modules/model_evaluation.rst.txt
@@ -268,7 +268,7 @@ Usage examples:
     >>> X, y = datasets.load_iris(return_X_y=True)
     >>> clf = svm.SVC(random_state=0)
     >>> cross_val_score(clf, X, y, cv=5, scoring='recall_macro')
-    array([0.96..., 0.96..., 0.96..., 0.93..., 1.        ])
+    array([0.96, 0.96, 0.96, 0.93, 1.        ])
 
 .. note::
 
@@ -389,9 +389,9 @@ You can create your own custom scorer object using
       >>> clf = DummyClassifier(strategy='most_frequent', random_state=0)
       >>> clf = clf.fit(X, y)
       >>> my_custom_loss_func(y, clf.predict(X))
-      0.69...
+      0.69
       >>> score(clf, X, y)
-      -0.69...
+      -0.69
 
 .. dropdown:: Custom scorer objects from scratch
 
@@ -1091,15 +1091,15 @@ Here are some small examples in binary classification::
   >>> metrics.recall_score(y_true, y_pred)
   0.5
   >>> metrics.f1_score(y_true, y_pred)
-  0.66...
+  0.66
   >>> metrics.fbeta_score(y_true, y_pred, beta=0.5)
-  0.83...
+  0.83
   >>> metrics.fbeta_score(y_true, y_pred, beta=1)
-  0.66...
+  0.66
   >>> metrics.fbeta_score(y_true, y_pred, beta=2)
-  0.55...
+  0.55
   >>> metrics.precision_recall_fscore_support(y_true, y_pred, beta=0.5)
-  (array([0.66..., 1.        ]), array([1. , 0.5]), array([0.71..., 0.83...]), array([2, 2]))
+  (array([0.66, 1.        ]), array([1. , 0.5]), array([0.71, 0.83]), array([2, 2]))
 
 
   >>> import numpy as np
@@ -1109,13 +1109,13 @@ Here are some small examples in binary classification::
   >>> y_scores = np.array([0.1, 0.4, 0.35, 0.8])
   >>> precision, recall, threshold = precision_recall_curve(y_true, y_scores)
   >>> precision
-  array([0.5       , 0.66..., 0.5       , 1.        , 1.        ])
+  array([0.5       , 0.66, 0.5       , 1.        , 1.        ])
   >>> recall
   array([1. , 1. , 0.5, 0.5, 0. ])
   >>> threshold
   array([0.1 , 0.35, 0.4 , 0.8 ])
   >>> average_precision_score(y_true, y_scores)
-  0.83...
+  0.83
 
 
 
@@ -1178,15 +1178,15 @@ Then the metrics are defined as:
   >>> y_true = [0, 1, 2, 0, 1, 2]
   >>> y_pred = [0, 2, 1, 0, 0, 1]
   >>> metrics.precision_score(y_true, y_pred, average='macro')
-  0.22...
+  0.22
   >>> metrics.recall_score(y_true, y_pred, average='micro')
-  0.33...
+  0.33
   >>> metrics.f1_score(y_true, y_pred, average='weighted')
-  0.26...
+  0.267
   >>> metrics.fbeta_score(y_true, y_pred, average='macro', beta=0.5)
-  0.23...
+  0.238
   >>> metrics.precision_recall_fscore_support(y_true, y_pred, beta=0.5, average=None)
-  (array([0.66..., 0.        , 0.        ]), array([1., 0., 0.]), array([0.71..., 0.        , 0.        ]), array([2, 2, 2]...))
+  (array([0.667, 0., 0.]), array([1., 0., 0.]), array([0.714, 0., 0.]), array([2, 2, 2]))
 
 For multiclass classification with a "negative class", it is possible to exclude some labels:
 
@@ -1197,7 +1197,7 @@ For multiclass classification with a "negative class", it is possible to exclude
 Similarly, labels not present in the data sample may be accounted for in macro-averaging.
 
   >>> metrics.precision_score(y_true, y_pred, labels=[0, 1, 2, 3], average='macro')
-  0.166...
+  0.166
 
 .. rubric:: References
 
@@ -1234,7 +1234,7 @@ In the binary case::
   >>> y_pred = np.array([[1, 1, 1],
   ...                    [1, 0, 0]])
   >>> jaccard_score(y_true[0], y_pred[0])
-  0.6666...
+  0.6666
 
 In the 2D comparison case (e.g. image similarity):
 
@@ -1244,9 +1244,9 @@ In the 2D comparison case (e.g. image similarity):
 In the multilabel case with binary label indicators::
 
   >>> jaccard_score(y_true, y_pred, average='samples')
-  0.5833...
+  0.5833
   >>> jaccard_score(y_true, y_pred, average='macro')
-  0.6666...
+  0.6666
   >>> jaccard_score(y_true, y_pred, average=None)
   array([0.5, 0.5, 1. ])
 
@@ -1256,11 +1256,11 @@ multilabel problem::
   >>> y_pred = [0, 2, 1, 2]
   >>> y_true = [0, 1, 2, 2]
   >>> jaccard_score(y_true, y_pred, average=None)
-  array([1. , 0. , 0.33...])
+  array([1. , 0. , 0.33])
   >>> jaccard_score(y_true, y_pred, average='macro')
-  0.44...
+  0.44
   >>> jaccard_score(y_true, y_pred, average='micro')
-  0.33...
+  0.33
 
 .. _hinge_loss:
 
@@ -1313,9 +1313,9 @@ with a svm classifier in a binary class problem::
   LinearSVC(random_state=0)
   >>> pred_decision = est.decision_function([[-2], [3], [0.5]])
   >>> pred_decision
-  array([-2.18...,  2.36...,  0.09...])
+  array([-2.18,  2.36,  0.09])
   >>> hinge_loss([-1, 1, 1], pred_decision)
-  0.3...
+  0.3
 
 Here is an example demonstrating the use of the :func:`hinge_loss` function
 with a svm classifier in a multiclass problem::
@@ -1329,7 +1329,7 @@ with a svm classifier in a multiclass problem::
   >>> pred_decision = est.decision_function([[-1], [2], [3]])
   >>> y_true = [0, 2, 3]
   >>> hinge_loss(y_true, pred_decision, labels=labels)
-  0.56...
+  0.56
 
 .. _log_loss:
 
@@ -1379,7 +1379,7 @@ method.
     >>> y_true = [0, 0, 1, 1]
     >>> y_pred = [[.9, .1], [.8, .2], [.3, .7], [.01, .99]]
     >>> log_loss(y_true, y_pred)
-    0.1738...
+    0.1738
 
 The first ``[.9, .1]`` in ``y_pred`` denotes 90% probability that the first
 sample has label 0.  The log loss is non-negative.
@@ -1445,7 +1445,7 @@ function:
     >>> y_true = [+1, +1, +1, -1]
     >>> y_pred = [+1, -1, +1, +1]
     >>> matthews_corrcoef(y_true, y_pred)
-    -0.33...
+    -0.33
 
 .. rubric:: References
 
@@ -1632,7 +1632,7 @@ Therefore, the `y_score` parameter is of size (n_samples,).
   >>> from sklearn.linear_model import LogisticRegression
   >>> from sklearn.metrics import roc_auc_score
   >>> X, y = load_breast_cancer(return_X_y=True)
-  >>> clf = LogisticRegression(solver="liblinear").fit(X, y)
+  >>> clf = LogisticRegression().fit(X, y)
   >>> clf.classes_
   array([0, 1])
 
@@ -1640,12 +1640,12 @@ We can use the probability estimates corresponding to `clf.classes_[1]`.
 
   >>> y_score = clf.predict_proba(X)[:, 1]
   >>> roc_auc_score(y, y_score)
-  0.99...
+  0.99
 
 Otherwise, we can use the non-thresholded decision values
 
   >>> roc_auc_score(y, clf.decision_function(X))
-  0.99...
+  0.99
 
 .. _roc_auc_multiclass:
 
@@ -1728,11 +1728,11 @@ class with the greater label for each output.
   >>> from sklearn.datasets import make_multilabel_classification
   >>> from sklearn.multioutput import MultiOutputClassifier
   >>> X, y = make_multilabel_classification(random_state=0)
-  >>> inner_clf = LogisticRegression(solver="liblinear", random_state=0)
+  >>> inner_clf = LogisticRegression(random_state=0)
   >>> clf = MultiOutputClassifier(inner_clf).fit(X, y)
   >>> y_score = np.transpose([y_pred[:, 1] for y_pred in clf.predict_proba(X)])
   >>> roc_auc_score(y, y_score, average=None)
-  array([0.82..., 0.86..., 0.94..., 0.85... , 0.94...])
+  array([0.828, 0.851, 0.94, 0.87, 0.95])
 
 And the decision values do not require such processing.
 
@@ -1740,7 +1740,7 @@ And the decision values do not require such processing.
   >>> clf = RidgeClassifierCV().fit(X, y)
   >>> y_score = clf.decision_function(X)
   >>> roc_auc_score(y, y_score, average=None)
-  array([0.81..., 0.84... , 0.93..., 0.87..., 0.94...])
+  array([0.82, 0.85, 0.93, 0.87, 0.94])
 
 .. rubric:: Examples
 
@@ -1980,7 +1980,7 @@ two above definitions to follow.
     ...    [[0.8, 0.1, 0.1], [0.2, 0.7, 0.1], [0.2, 0.2, 0.6]],
     ...    labels=["eggs", "ham", "spam"],
     ... )
-    0.146...
+    0.146
 
 The Brier score can be used to assess how well a classifier is calibrated.
 However, a lower Brier score loss does not always mean a better calibration.
@@ -2199,7 +2199,7 @@ of 0.0.
     ...     [0.01, 0.01, 0.98],
     ... ]
     >>> d2_log_loss_score(y_true, y_pred)
-    0.981...
+    0.981
     >>> y_true = [1, 2, 3]
     >>> y_pred = [
     ...     [0.1, 0.6, 0.3],
@@ -2207,7 +2207,7 @@ of 0.0.
     ...     [0.4, 0.5, 0.1],
     ... ]
     >>> d2_log_loss_score(y_true, y_pred)
-    -0.552...
+    -0.552
 
 
 .. _multilabel_ranking_metrics:
@@ -2306,7 +2306,7 @@ Here is a small example of usage of this function::
     >>> y_true = np.array([[1, 0, 0], [0, 0, 1]])
     >>> y_score = np.array([[0.75, 0.5, 1], [1, 0.2, 0.1]])
     >>> label_ranking_average_precision_score(y_true, y_score)
-    0.416...
+    0.416
 
 .. _label_ranking_loss:
 
@@ -2341,7 +2341,7 @@ Here is a small example of usage of this function::
     >>> y_true = np.array([[1, 0, 0], [0, 0, 1]])
     >>> y_score = np.array([[0.75, 0.5, 1], [1, 0.2, 0.1]])
     >>> label_ranking_loss(y_true, y_score)
-    0.75...
+    0.75
     >>> # With the following prediction, we have perfect and minimal loss
     >>> y_score = np.array([[1.0, 0.1, 0.2], [0.1, 0.2, 0.9]])
     >>> label_ranking_loss(y_true, y_score)
@@ -2372,7 +2372,7 @@ engine algorithms or related applications. Using a graded relevance scale of
 documents in a search-engine result set, DCG measures the usefulness, or gain,
 of a document based on its position in the result list. The gain is accumulated
 from the top of the result list to the bottom, with the gain of each result
-discounted at lower ranks"
+discounted at lower ranks."
 
 DCG orders the true targets (e.g. relevance of query answers) in the predicted
 order, then multiplies them by a logarithmic decay and sums the result. The sum
@@ -2499,19 +2499,19 @@ Here is a small example of usage of the :func:`r2_score` function::
   >>> y_true = [3, -0.5, 2, 7]
   >>> y_pred = [2.5, 0.0, 2, 8]
   >>> r2_score(y_true, y_pred)
-  0.948...
+  0.948
   >>> y_true = [[0.5, 1], [-1, 1], [7, -6]]
   >>> y_pred = [[0, 2], [-1, 2], [8, -5]]
   >>> r2_score(y_true, y_pred, multioutput='variance_weighted')
-  0.938...
+  0.938
   >>> y_true = [[0.5, 1], [-1, 1], [7, -6]]
   >>> y_pred = [[0, 2], [-1, 2], [8, -5]]
   >>> r2_score(y_true, y_pred, multioutput='uniform_average')
-  0.936...
+  0.936
   >>> r2_score(y_true, y_pred, multioutput='raw_values')
-  array([0.965..., 0.908...])
+  array([0.965, 0.908])
   >>> r2_score(y_true, y_pred, multioutput=[0.3, 0.7])
-  0.925...
+  0.925
   >>> y_true = [-2, -2, -2]
   >>> y_pred = [-2, -2, -2]
   >>> r2_score(y_true, y_pred)
@@ -2563,7 +2563,7 @@ Here is a small example of usage of the :func:`mean_absolute_error` function::
   >>> mean_absolute_error(y_true, y_pred, multioutput='raw_values')
   array([0.5, 1. ])
   >>> mean_absolute_error(y_true, y_pred, multioutput=[0.3, 0.7])
-  0.85...
+  0.85
 
 .. _mean_squared_error:
 
@@ -2594,7 +2594,7 @@ function::
   >>> y_true = [[0.5, 1], [-1, 1], [7, -6]]
   >>> y_pred = [[0, 2], [-1, 2], [8, -5]]
   >>> mean_squared_error(y_true, y_pred)
-  0.7083...
+  0.7083
 
 .. rubric:: Examples
 
@@ -2636,11 +2636,11 @@ function::
   >>> y_true = [3, 5, 2.5, 7]
   >>> y_pred = [2.5, 5, 4, 8]
   >>> mean_squared_log_error(y_true, y_pred)
-  0.039...
+  0.0397
   >>> y_true = [[0.5, 1], [1, 2], [7, 6]]
   >>> y_pred = [[0.5, 2], [1, 2.5], [8, 8]]
   >>> mean_squared_log_error(y_true, y_pred)
-  0.044...
+  0.044
 
 The root mean squared logarithmic error (RMSLE) is available through the
 :func:`root_mean_squared_log_error` function.
@@ -2674,7 +2674,7 @@ function::
   >>> y_true = [1, 10, 1e6]
   >>> y_pred = [0.9, 15, 1.2e6]
   >>> mean_absolute_percentage_error(y_true, y_pred)
-  0.2666...
+  0.2666
 
 In above example, if we had used `mean_absolute_error`, it would have ignored
 the small magnitude values and only reflected the error in prediction of highest
@@ -2802,13 +2802,13 @@ function::
     >>> y_true = [3, -0.5, 2, 7]
     >>> y_pred = [2.5, 0.0, 2, 8]
     >>> explained_variance_score(y_true, y_pred)
-    0.957...
+    0.957
     >>> y_true = [[0.5, 1], [-1, 1], [7, -6]]
     >>> y_pred = [[0, 2], [-1, 2], [8, -5]]
     >>> explained_variance_score(y_true, y_pred, multioutput='raw_values')
-    array([0.967..., 1.        ])
+    array([0.967, 1.        ])
     >>> explained_variance_score(y_true, y_pred, multioutput=[0.3, 0.7])
-    0.990...
+    0.990
     >>> y_true = [-2, -2, -2]
     >>> y_pred = [-2, -2, -2]
     >>> explained_variance_score(y_true, y_pred)
@@ -2880,16 +2880,16 @@ prediction difference of the second point,::
 If we increase ``power`` to 1,::
 
     >>> mean_tweedie_deviance([1.0], [1.5], power=1)
-    0.18...
+    0.189
     >>> mean_tweedie_deviance([100.], [150.], power=1)
-    18.9...
+    18.9
 
 the difference in errors decreases. Finally, by setting, ``power=2``::
 
     >>> mean_tweedie_deviance([1.0], [1.5], power=2)
-    0.14...
+    0.144
     >>> mean_tweedie_deviance([100.], [150.], power=2)
-    0.14...
+    0.144
 
 we would get identical errors. The deviance when ``power=2`` is thus only
 sensitive to relative errors.
@@ -2916,13 +2916,13 @@ Here is a small example of usage of the :func:`mean_pinball_loss` function::
   >>> from sklearn.metrics import mean_pinball_loss
   >>> y_true = [1, 2, 3]
   >>> mean_pinball_loss(y_true, [0, 2, 3], alpha=0.1)
-  0.03...
+  0.033
   >>> mean_pinball_loss(y_true, [1, 2, 4], alpha=0.1)
-  0.3...
+  0.3
   >>> mean_pinball_loss(y_true, [0, 2, 3], alpha=0.9)
-  0.3...
+  0.3
   >>> mean_pinball_loss(y_true, [1, 2, 4], alpha=0.9)
-  0.03...
+  0.033
   >>> mean_pinball_loss(y_true, y_true, alpha=0.1)
   0.0
   >>> mean_pinball_loss(y_true, y_true, alpha=0.9)
@@ -2947,7 +2947,7 @@ quantile regressor via cross-validation:
   ...     random_state=0,
   ... )
   >>> cross_val_score(estimator, X, y, cv=5, scoring=mean_pinball_loss_95p)
-  array([13.6..., 9.7..., 23.3..., 9.5..., 10.4...])
+  array([13.6, 9.7, 23.3, 9.5, 10.4])
 
 It is also possible to build scorer objects for hyper-parameter tuning. The
 sign of the loss must be switched to ensure that greater means better as
@@ -3034,7 +3034,7 @@ of 0.0.
     >>> y_true = [3, -0.5, 2, 7]
     >>> y_pred = [2.5, 0.0, 2, 8]
     >>> d2_absolute_error_score(y_true, y_pred)
-    0.764...
+    0.764
     >>> y_true = [1, 2, 3]
     >>> y_pred = [1, 2, 3]
     >>> d2_absolute_error_score(y_true, y_pred)
@@ -3172,19 +3172,19 @@ Next, let's compare the accuracy of ``SVC`` and ``most_frequent``::
   >>> from sklearn.svm import SVC
   >>> clf = SVC(kernel='linear', C=1).fit(X_train, y_train)
   >>> clf.score(X_test, y_test)
-  0.63...
+  0.63
   >>> clf = DummyClassifier(strategy='most_frequent', random_state=0)
   >>> clf.fit(X_train, y_train)
   DummyClassifier(random_state=0, strategy='most_frequent')
   >>> clf.score(X_test, y_test)
-  0.57...
+  0.579
 
 We see that ``SVC`` doesn't do much better than a dummy classifier. Now, let's
 change the kernel::
 
   >>> clf = SVC(kernel='rbf', C=1).fit(X_train, y_train)
   >>> clf.score(X_test, y_test)
-  0.94...
+  0.94
 
 We see that the accuracy was boosted to almost 100%.  A cross validation
 strategy is recommended for a better estimate of the accuracy, if it
diff --git a/dev/_sources/modules/neural_networks_supervised.rst.txt b/dev/_sources/modules/neural_networks_supervised.rst.txt
index 1c0802f0ac92f..13611b7f52775 100644
--- a/dev/_sources/modules/neural_networks_supervised.rst.txt
+++ b/dev/_sources/modules/neural_networks_supervised.rst.txt
@@ -116,8 +116,8 @@ classification, it minimizes the Cross-Entropy loss function, giving a vector
 of probability estimates :math:`P(y|x)` per sample :math:`x`::
 
     >>> clf.predict_proba([[2., 2.], [1., 2.]])
-    array([[1.967...e-04, 9.998...-01],
-           [1.967...e-04, 9.998...-01]])
+    array([[1.967e-04, 9.998e-01],
+           [1.967e-04, 9.998e-01]])
 
 :class:`MLPClassifier` supports multi-class classification by
 applying `Softmax <https://en.wikipedia.org/wiki/Softmax_activation_function>`_
diff --git a/dev/_sources/modules/outlier_detection.rst.txt b/dev/_sources/modules/outlier_detection.rst.txt
index 4875b9807b30c..7de2da4f1818e 100644
--- a/dev/_sources/modules/outlier_detection.rst.txt
+++ b/dev/_sources/modules/outlier_detection.rst.txt
@@ -340,16 +340,14 @@ average local density of its k-nearest neighbors, and its own local density:
 a normal instance is expected to have a local density similar to that of its
 neighbors, while abnormal data are expected to have much smaller local density.
 
-The number k of neighbors considered, (alias parameter n_neighbors) is typically
-chosen 1) greater than the minimum number of objects a cluster has to contain,
-so that other objects can be local outliers relative to this cluster, and 2)
-smaller than the maximum number of close by objects that can potentially be
-local outliers.
-In practice, such information is generally not available, and taking
-n_neighbors=20 appears to work well in general.
-When the proportion of outliers is high (i.e. greater than 10 \%, as in the
-example below), n_neighbors should be greater (n_neighbors=35 in the example
-below).
+The number k of neighbors considered, (alias parameter `n_neighbors`) is
+typically chosen 1) greater than the minimum number of objects a cluster has to
+contain, so that other objects can be local outliers relative to this cluster,
+and 2) smaller than the maximum number of close by objects that can potentially
+be local outliers. In practice, such information is generally not available, and
+taking `n_neighbors=20` appears to work well in general. When the proportion of
+outliers is high (i.e. greater than 10 \%, as in the example below),
+`n_neighbors` should be greater (`n_neighbors=35` in the example below).
 
 The strength of the LOF algorithm is that it takes both local and global
 properties of datasets into consideration: it can perform well even in datasets
diff --git a/dev/_sources/modules/preprocessing.rst.txt b/dev/_sources/modules/preprocessing.rst.txt
index 835aead4f8836..69dff95518c41 100644
--- a/dev/_sources/modules/preprocessing.rst.txt
+++ b/dev/_sources/modules/preprocessing.rst.txt
@@ -57,16 +57,16 @@ dataset::
   StandardScaler()
 
   >>> scaler.mean_
-  array([1. ..., 0. ..., 0.33...])
+  array([1., 0., 0.33])
 
   >>> scaler.scale_
-  array([0.81..., 0.81..., 1.24...])
+  array([0.81, 0.81, 1.24])
 
   >>> X_scaled = scaler.transform(X_train)
   >>> X_scaled
-  array([[ 0.  ..., -1.22...,  1.33...],
-         [ 1.22...,  0.  ..., -0.26...],
-         [-1.22...,  1.22..., -1.06...]])
+  array([[ 0.  , -1.22,  1.33 ],
+         [ 1.22,  0.  , -0.267],
+         [-1.22,  1.22, -1.06 ]])
 
 ..
         >>> import numpy as np
@@ -147,10 +147,10 @@ It is possible to introspect the scaler attributes to find about the exact
 nature of the transformation learned on the training data::
 
   >>> min_max_scaler.scale_
-  array([0.5       , 0.5       , 0.33...])
+  array([0.5       , 0.5       , 0.33])
 
   >>> min_max_scaler.min_
-  array([0.        , 0.5       , 0.33...])
+  array([0.        , 0.5       , 0.33])
 
 If :class:`MinMaxScaler` is given an explicit ``feature_range=(min, max)`` the
 full formula is::
@@ -351,7 +351,7 @@ previously defined::
 
   >>> np.percentile(X_train_trans[:, 0], [0, 25, 50, 75, 100])
   ... # doctest: +SKIP
-  array([ 0.00... ,  0.24...,  0.49...,  0.73...,  0.99... ])
+  array([ 0.00 ,  0.24,  0.49,  0.73,  0.99 ])
 
 This can be confirmed on an independent testing set with similar remarks::
 
@@ -360,7 +360,7 @@ This can be confirmed on an independent testing set with similar remarks::
   array([ 4.4  ,  5.125,  5.75 ,  6.175,  7.3  ])
   >>> np.percentile(X_test_trans[:, 0], [0, 25, 50, 75, 100])
   ... # doctest: +SKIP
-  array([ 0.01...,  0.25...,  0.46...,  0.60... ,  0.94...])
+  array([ 0.01,  0.25,  0.46,  0.60 ,  0.94])
 
 Mapping to a Gaussian distribution
 ----------------------------------
@@ -401,13 +401,13 @@ the Yeo-Johnson transform and the Box-Cox transform.
     >>> pt = preprocessing.PowerTransformer(method='box-cox', standardize=False)
     >>> X_lognormal = np.random.RandomState(616).lognormal(size=(3, 3))
     >>> X_lognormal
-    array([[1.28..., 1.18..., 0.84...],
-          [0.94..., 1.60..., 0.38...],
-          [1.35..., 0.21..., 1.09...]])
+    array([[1.28, 1.18 , 0.84 ],
+           [0.94, 1.60 , 0.388],
+           [1.35, 0.217, 1.09 ]])
     >>> pt.fit_transform(X_lognormal)
-    array([[ 0.49...,  0.17..., -0.15...],
-          [-0.05...,  0.58..., -0.57...],
-          [ 0.69..., -0.84...,  0.10...]])
+    array([[ 0.49 ,  0.179, -0.156],
+           [-0.051,  0.589, -0.576],
+           [ 0.69 , -0.849,  0.101]])
 
   While the above example sets the `standardize` option to `False`,
   :class:`PowerTransformer` will apply zero-mean, unit-variance normalization
@@ -470,9 +470,9 @@ operation on a single array-like dataset, either using the ``l1``, ``l2``, or
   >>> X_normalized = preprocessing.normalize(X, norm='l2')
 
   >>> X_normalized
-  array([[ 0.40..., -0.40...,  0.81...],
-         [ 1.  ...,  0.  ...,  0.  ...],
-         [ 0.  ...,  0.70..., -0.70...]])
+  array([[ 0.408, -0.408,  0.812],
+         [ 1.   ,  0.   ,  0.   ],
+         [ 0.   ,  0.707, -0.707]])
 
 The ``preprocessing`` module further provides a utility class
 :class:`Normalizer` that implements the same operation using the
@@ -490,12 +490,12 @@ This class is hence suitable for use in the early steps of a
 The normalizer instance can then be used on sample vectors as any transformer::
 
   >>> normalizer.transform(X)
-  array([[ 0.40..., -0.40...,  0.81...],
-         [ 1.  ...,  0.  ...,  0.  ...],
-         [ 0.  ...,  0.70..., -0.70...]])
+  array([[ 0.408, -0.408,  0.812],
+         [ 1.   ,  0.   ,  0.   ],
+         [ 0.   ,  0.707, -0.707]])
 
   >>> normalizer.transform([[-1.,  1., 0.]])
-  array([[-0.70...,  0.70...,  0.  ...]])
+  array([[-0.707,  0.707,  0.]])
 
 
 Note: L2 normalization is also known as spatial sign preprocessing.
@@ -757,8 +757,8 @@ enable the gathering of infrequent categories are `min_frequency` and
    input feature. `max_categories` includes the feature that combines
    infrequent categories.
 
-In the following example with :class:`OrdinalEncoder`, the categories `'dog'` and
-`'snake'` are considered infrequent::
+In the following example with :class:`OrdinalEncoder`, the categories `'dog'`
+and `'snake'` are considered infrequent::
 
    >>> X = np.array([['dog'] * 5 + ['cat'] * 20 + ['rabbit'] * 10 +
    ...               ['snake'] * 3], dtype=object).T
diff --git a/dev/_sources/modules/sgd.rst.txt b/dev/_sources/modules/sgd.rst.txt
index b97c6d135dcfe..103ae205387e3 100644
--- a/dev/_sources/modules/sgd.rst.txt
+++ b/dev/_sources/modules/sgd.rst.txt
@@ -91,12 +91,12 @@ SGD fits a linear model to the training data. The ``coef_`` attribute holds
 the model parameters::
 
     >>> clf.coef_
-    array([[9.9..., 9.9...]])
+    array([[9.9, 9.9]])
 
 The ``intercept_`` attribute holds the intercept (aka offset or bias)::
 
     >>> clf.intercept_
-    array([-9.9...])
+    array([-9.9])
 
 Whether or not the model should use an intercept, i.e. a biased
 hyperplane, is controlled by the parameter ``fit_intercept``.
@@ -106,7 +106,7 @@ the coefficients and the input sample, plus the intercept) is given by
 :meth:`SGDClassifier.decision_function`::
 
     >>> clf.decision_function([[2., 2.]])
-    array([29.6...])
+    array([29.6])
 
 The concrete loss function can be set via the ``loss``
 parameter. :class:`SGDClassifier` supports the following loss functions:
@@ -131,7 +131,7 @@ Using ``loss="log_loss"`` or ``loss="modified_huber"`` enables the
 
     >>> clf = SGDClassifier(loss="log_loss", max_iter=5).fit(X, y)
     >>> clf.predict_proba([[1., 1.]]) # doctest: +SKIP
-    array([[0.00..., 0.99...]])
+    array([[0.00, 0.99]])
 
 The concrete penalty can be set via the ``penalty`` parameter.
 SGD supports the following penalties:
diff --git a/dev/_sources/sg_execution_times.rst.txt b/dev/_sources/sg_execution_times.rst.txt
index 5c50eda707ac9..21bc18d049cb9 100644
--- a/dev/_sources/sg_execution_times.rst.txt
+++ b/dev/_sources/sg_execution_times.rst.txt
@@ -6,7 +6,7 @@
 
 Computation times
 =================
-**20:11.455** total execution time for 279 files **from all galleries**:
+**21:57.480** total execution time for 278 files **from all galleries**:
 
 .. container::
 
@@ -33,832 +33,829 @@ Computation times
      - Time
      - Mem (MB)
    * - :ref:`sphx_glr_auto_examples_ensemble_plot_forest_hist_grad_boosting_comparison.py` (``../examples/ensemble/plot_forest_hist_grad_boosting_comparison.py``)
-     - 00:54.339
+     - 01:01.135
      - 0.0
    * - :ref:`sphx_glr_auto_examples_feature_selection_plot_select_from_model_diabetes.py` (``../examples/feature_selection/plot_select_from_model_diabetes.py``)
-     - 00:50.137
+     - 01:00.575
      - 0.0
    * - :ref:`sphx_glr_auto_examples_miscellaneous_plot_outlier_detection_bench.py` (``../examples/miscellaneous/plot_outlier_detection_bench.py``)
-     - 00:48.298
+     - 00:57.950
      - 0.0
    * - :ref:`sphx_glr_auto_examples_compose_plot_compare_reduction.py` (``../examples/compose/plot_compare_reduction.py``)
-     - 00:44.030
+     - 00:50.514
      - 0.0
    * - :ref:`sphx_glr_auto_examples_kernel_approximation_plot_scalable_poly_kernels.py` (``../examples/kernel_approximation/plot_scalable_poly_kernels.py``)
-     - 00:34.642
+     - 00:35.296
      - 0.0
    * - :ref:`sphx_glr_auto_examples_model_selection_plot_tuned_decision_threshold.py` (``../examples/model_selection/plot_tuned_decision_threshold.py``)
-     - 00:32.343
+     - 00:33.154
      - 0.0
    * - :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_text_feature_extraction.py` (``../examples/model_selection/plot_grid_search_text_feature_extraction.py``)
-     - 00:27.423
+     - 00:28.635
      - 0.0
-   * - :ref:`sphx_glr_auto_examples_decomposition_plot_image_denoising.py` (``../examples/decomposition/plot_image_denoising.py``)
-     - 00:26.263
-     - 0.0
-   * - :ref:`sphx_glr_auto_examples_inspection_plot_partial_dependence.py` (``../examples/inspection/plot_partial_dependence.py``)
-     - 00:25.670
+   * - :ref:`sphx_glr_auto_examples_model_selection_plot_cost_sensitive_learning.py` (``../examples/model_selection/plot_cost_sensitive_learning.py``)
+     - 00:28.551
      - 0.0
    * - :ref:`sphx_glr_auto_examples_model_selection_plot_learning_curve.py` (``../examples/model_selection/plot_learning_curve.py``)
-     - 00:25.330
+     - 00:28.549
      - 0.0
-   * - :ref:`sphx_glr_auto_examples_model_selection_plot_cost_sensitive_learning.py` (``../examples/model_selection/plot_cost_sensitive_learning.py``)
-     - 00:25.109
-     - 0.0
-   * - :ref:`sphx_glr_auto_examples_ensemble_plot_stack_predictors.py` (``../examples/ensemble/plot_stack_predictors.py``)
-     - 00:22.643
+   * - :ref:`sphx_glr_auto_examples_preprocessing_plot_target_encoder.py` (``../examples/preprocessing/plot_target_encoder.py``)
+     - 00:27.010
      - 0.0
-   * - :ref:`sphx_glr_auto_examples_linear_model_plot_sgd_early_stopping.py` (``../examples/linear_model/plot_sgd_early_stopping.py``)
-     - 00:22.640
+   * - :ref:`sphx_glr_auto_examples_decomposition_plot_image_denoising.py` (``../examples/decomposition/plot_image_denoising.py``)
+     - 00:26.305
      - 0.0
-   * - :ref:`sphx_glr_auto_examples_preprocessing_plot_target_encoder.py` (``../examples/preprocessing/plot_target_encoder.py``)
-     - 00:22.370
+   * - :ref:`sphx_glr_auto_examples_inspection_plot_partial_dependence.py` (``../examples/inspection/plot_partial_dependence.py``)
+     - 00:25.682
      - 0.0
    * - :ref:`sphx_glr_auto_examples_ensemble_plot_hgbt_regression.py` (``../examples/ensemble/plot_hgbt_regression.py``)
-     - 00:22.240
+     - 00:25.355
      - 0.0
-   * - :ref:`sphx_glr_auto_examples_svm_plot_svm_scale_c.py` (``../examples/svm/plot_svm_scale_c.py``)
-     - 00:21.737
+   * - :ref:`sphx_glr_auto_examples_linear_model_plot_sgd_early_stopping.py` (``../examples/linear_model/plot_sgd_early_stopping.py``)
+     - 00:24.961
      - 0.0
-   * - :ref:`sphx_glr_auto_examples_multiclass_plot_multiclass_overview.py` (``../examples/multiclass/plot_multiclass_overview.py``)
-     - 00:20.396
+   * - :ref:`sphx_glr_auto_examples_ensemble_plot_stack_predictors.py` (``../examples/ensemble/plot_stack_predictors.py``)
+     - 00:23.230
      - 0.0
    * - :ref:`sphx_glr_auto_examples_manifold_plot_lle_digits.py` (``../examples/manifold/plot_lle_digits.py``)
-     - 00:20.354
+     - 00:22.763
      - 0.0
    * - :ref:`sphx_glr_auto_examples_linear_model_plot_poisson_regression_non_normal_loss.py` (``../examples/linear_model/plot_poisson_regression_non_normal_loss.py``)
-     - 00:19.253
-     - 0.0
-   * - :ref:`sphx_glr_auto_examples_manifold_plot_compare_methods.py` (``../examples/manifold/plot_compare_methods.py``)
-     - 00:17.663
+     - 00:20.589
      - 0.0
-   * - :ref:`sphx_glr_auto_examples_applications_plot_prediction_latency.py` (``../examples/applications/plot_prediction_latency.py``)
-     - 00:17.477
+   * - :ref:`sphx_glr_auto_examples_multiclass_plot_multiclass_overview.py` (``../examples/multiclass/plot_multiclass_overview.py``)
+     - 00:19.729
      - 0.0
    * - :ref:`sphx_glr_auto_examples_manifold_plot_swissroll.py` (``../examples/manifold/plot_swissroll.py``)
-     - 00:17.003
+     - 00:19.192
      - 0.0
-   * - :ref:`sphx_glr_auto_examples_cluster_plot_hdbscan.py` (``../examples/cluster/plot_hdbscan.py``)
-     - 00:14.100
+   * - :ref:`sphx_glr_auto_examples_svm_plot_svm_scale_c.py` (``../examples/svm/plot_svm_scale_c.py``)
+     - 00:18.652
      - 0.0
-   * - :ref:`sphx_glr_auto_examples_release_highlights_plot_release_highlights_0_24_0.py` (``../examples/release_highlights/plot_release_highlights_0_24_0.py``)
-     - 00:12.891
+   * - :ref:`sphx_glr_auto_examples_applications_plot_prediction_latency.py` (``../examples/applications/plot_prediction_latency.py``)
+     - 00:18.169
      - 0.0
-   * - :ref:`sphx_glr_auto_examples_inspection_plot_linear_model_coefficient_interpretation.py` (``../examples/inspection/plot_linear_model_coefficient_interpretation.py``)
-     - 00:12.806
+   * - :ref:`sphx_glr_auto_examples_cluster_plot_hdbscan.py` (``../examples/cluster/plot_hdbscan.py``)
+     - 00:15.012
      - 0.0
-   * - :ref:`sphx_glr_auto_examples_model_selection_plot_permutation_tests_for_classification.py` (``../examples/model_selection/plot_permutation_tests_for_classification.py``)
-     - 00:12.772
+   * - :ref:`sphx_glr_auto_examples_miscellaneous_plot_kernel_ridge_regression.py` (``../examples/miscellaneous/plot_kernel_ridge_regression.py``)
+     - 00:14.892
      - 0.0
    * - :ref:`sphx_glr_auto_examples_applications_plot_cyclical_feature_engineering.py` (``../examples/applications/plot_cyclical_feature_engineering.py``)
-     - 00:12.148
+     - 00:14.675
      - 0.0
-   * - :ref:`sphx_glr_auto_examples_miscellaneous_plot_johnson_lindenstrauss_bound.py` (``../examples/miscellaneous/plot_johnson_lindenstrauss_bound.py``)
-     - 00:11.506
+   * - :ref:`sphx_glr_auto_examples_linear_model_plot_sparse_logistic_regression_mnist.py` (``../examples/linear_model/plot_sparse_logistic_regression_mnist.py``)
+     - 00:13.966
      - 0.0
-   * - :ref:`sphx_glr_auto_examples_applications_plot_topics_extraction_with_nmf_lda.py` (``../examples/applications/plot_topics_extraction_with_nmf_lda.py``)
-     - 00:11.217
+   * - :ref:`sphx_glr_auto_examples_manifold_plot_compare_methods.py` (``../examples/manifold/plot_compare_methods.py``)
+     - 00:13.462
      - 0.0
-   * - :ref:`sphx_glr_auto_examples_linear_model_plot_sparse_logistic_regression_mnist.py` (``../examples/linear_model/plot_sparse_logistic_regression_mnist.py``)
-     - 00:10.741
+   * - :ref:`sphx_glr_auto_examples_release_highlights_plot_release_highlights_0_24_0.py` (``../examples/release_highlights/plot_release_highlights_0_24_0.py``)
+     - 00:12.848
      - 0.0
-   * - :ref:`sphx_glr_auto_examples_ensemble_plot_gradient_boosting_oob.py` (``../examples/ensemble/plot_gradient_boosting_oob.py``)
-     - 00:10.376
+   * - :ref:`sphx_glr_auto_examples_inspection_plot_linear_model_coefficient_interpretation.py` (``../examples/inspection/plot_linear_model_coefficient_interpretation.py``)
+     - 00:12.624
      - 0.0
-   * - :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_digits.py` (``../examples/model_selection/plot_grid_search_digits.py``)
-     - 00:09.971
+   * - :ref:`sphx_glr_auto_examples_applications_plot_topics_extraction_with_nmf_lda.py` (``../examples/applications/plot_topics_extraction_with_nmf_lda.py``)
+     - 00:12.584
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_model_selection_plot_permutation_tests_for_classification.py` (``../examples/model_selection/plot_permutation_tests_for_classification.py``)
+     - 00:12.552
      - 0.0
    * - :ref:`sphx_glr_auto_examples_ensemble_plot_gradient_boosting_quantile.py` (``../examples/ensemble/plot_gradient_boosting_quantile.py``)
-     - 00:09.901
+     - 00:11.467
      - 0.0
-   * - :ref:`sphx_glr_auto_examples_applications_plot_tomography_l1_reconstruction.py` (``../examples/applications/plot_tomography_l1_reconstruction.py``)
-     - 00:09.552
+   * - :ref:`sphx_glr_auto_examples_miscellaneous_plot_johnson_lindenstrauss_bound.py` (``../examples/miscellaneous/plot_johnson_lindenstrauss_bound.py``)
+     - 00:11.082
      - 0.0
-   * - :ref:`sphx_glr_auto_examples_impute_plot_missing_values.py` (``../examples/impute/plot_missing_values.py``)
-     - 00:09.536
+   * - :ref:`sphx_glr_auto_examples_ensemble_plot_gradient_boosting_oob.py` (``../examples/ensemble/plot_gradient_boosting_oob.py``)
+     - 00:10.543
      - 0.0
-   * - :ref:`sphx_glr_auto_examples_applications_plot_time_series_lagged_features.py` (``../examples/applications/plot_time_series_lagged_features.py``)
-     - 00:09.360
+   * - :ref:`sphx_glr_auto_examples_neural_networks_plot_mnist_filters.py` (``../examples/neural_networks/plot_mnist_filters.py``)
+     - 00:10.396
      - 0.0
-   * - :ref:`sphx_glr_auto_examples_miscellaneous_plot_kernel_ridge_regression.py` (``../examples/miscellaneous/plot_kernel_ridge_regression.py``)
-     - 00:09.342
+   * - :ref:`sphx_glr_auto_examples_applications_plot_time_series_lagged_features.py` (``../examples/applications/plot_time_series_lagged_features.py``)
+     - 00:10.327
      - 0.0
-   * - :ref:`sphx_glr_auto_examples_text_plot_document_clustering.py` (``../examples/text/plot_document_clustering.py``)
-     - 00:08.511
+   * - :ref:`sphx_glr_auto_examples_linear_model_plot_tweedie_regression_insurance_claims.py` (``../examples/linear_model/plot_tweedie_regression_insurance_claims.py``)
+     - 00:10.268
      - 0.0
    * - :ref:`sphx_glr_auto_examples_preprocessing_plot_all_scaling.py` (``../examples/preprocessing/plot_all_scaling.py``)
-     - 00:08.394
+     - 00:10.263
      - 0.0
-   * - :ref:`sphx_glr_auto_examples_ensemble_plot_gradient_boosting_regularization.py` (``../examples/ensemble/plot_gradient_boosting_regularization.py``)
-     - 00:08.336
+   * - :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_digits.py` (``../examples/model_selection/plot_grid_search_digits.py``)
+     - 00:10.263
      - 0.0
-   * - :ref:`sphx_glr_auto_examples_model_selection_plot_multi_metric_evaluation.py` (``../examples/model_selection/plot_multi_metric_evaluation.py``)
-     - 00:08.280
+   * - :ref:`sphx_glr_auto_examples_applications_plot_tomography_l1_reconstruction.py` (``../examples/applications/plot_tomography_l1_reconstruction.py``)
+     - 00:09.836
      - 0.0
    * - :ref:`sphx_glr_auto_examples_applications_plot_out_of_core_classification.py` (``../examples/applications/plot_out_of_core_classification.py``)
-     - 00:08.066
+     - 00:09.139
      - 0.0
-   * - :ref:`sphx_glr_auto_examples_classification_plot_lda.py` (``../examples/classification/plot_lda.py``)
-     - 00:07.973
+   * - :ref:`sphx_glr_auto_examples_impute_plot_missing_values.py` (``../examples/impute/plot_missing_values.py``)
+     - 00:08.855
      - 0.0
-   * - :ref:`sphx_glr_auto_examples_manifold_plot_manifold_sphere.py` (``../examples/manifold/plot_manifold_sphere.py``)
-     - 00:07.882
+   * - :ref:`sphx_glr_auto_examples_ensemble_plot_gradient_boosting_regularization.py` (``../examples/ensemble/plot_gradient_boosting_regularization.py``)
+     - 00:08.808
      - 0.0
-   * - :ref:`sphx_glr_auto_examples_semi_supervised_plot_semi_supervised_newsgroups.py` (``../examples/semi_supervised/plot_semi_supervised_newsgroups.py``)
-     - 00:07.667
+   * - :ref:`sphx_glr_auto_examples_applications_plot_species_distribution_modeling.py` (``../examples/applications/plot_species_distribution_modeling.py``)
+     - 00:08.461
      - 0.0
-   * - :ref:`sphx_glr_auto_examples_linear_model_plot_tweedie_regression_insurance_claims.py` (``../examples/linear_model/plot_tweedie_regression_insurance_claims.py``)
-     - 00:07.625
+   * - :ref:`sphx_glr_auto_examples_model_selection_plot_multi_metric_evaluation.py` (``../examples/model_selection/plot_multi_metric_evaluation.py``)
+     - 00:08.333
      - 0.0
-   * - :ref:`sphx_glr_auto_examples_text_plot_document_classification_20newsgroups.py` (``../examples/text/plot_document_classification_20newsgroups.py``)
-     - 00:07.425
+   * - :ref:`sphx_glr_auto_examples_manifold_plot_manifold_sphere.py` (``../examples/manifold/plot_manifold_sphere.py``)
+     - 00:08.331
      - 0.0
    * - :ref:`sphx_glr_auto_examples_decomposition_plot_faces_decomposition.py` (``../examples/decomposition/plot_faces_decomposition.py``)
-     - 00:07.399
+     - 00:08.319
      - 0.0
-   * - :ref:`sphx_glr_auto_examples_model_selection_plot_successive_halving_heatmap.py` (``../examples/model_selection/plot_successive_halving_heatmap.py``)
-     - 00:07.352
-     - 0.0
-   * - :ref:`sphx_glr_auto_examples_model_selection_plot_nested_cross_validation_iris.py` (``../examples/model_selection/plot_nested_cross_validation_iris.py``)
-     - 00:07.050
+   * - :ref:`sphx_glr_auto_examples_classification_plot_lda.py` (``../examples/classification/plot_lda.py``)
+     - 00:07.778
      - 0.0
-   * - :ref:`sphx_glr_auto_examples_applications_plot_digits_denoising.py` (``../examples/applications/plot_digits_denoising.py``)
-     - 00:06.435
+   * - :ref:`sphx_glr_auto_examples_text_plot_document_clustering.py` (``../examples/text/plot_document_clustering.py``)
+     - 00:07.639
      - 0.0
-   * - :ref:`sphx_glr_auto_examples_text_plot_hashing_vs_dict_vectorizer.py` (``../examples/text/plot_hashing_vs_dict_vectorizer.py``)
-     - 00:06.275
+   * - :ref:`sphx_glr_auto_examples_model_selection_plot_successive_halving_heatmap.py` (``../examples/model_selection/plot_successive_halving_heatmap.py``)
+     - 00:07.639
      - 0.0
    * - :ref:`sphx_glr_auto_examples_ensemble_plot_forest_iris.py` (``../examples/ensemble/plot_forest_iris.py``)
-     - 00:06.220
-     - 0.0
-   * - :ref:`sphx_glr_auto_examples_neural_networks_plot_mnist_filters.py` (``../examples/neural_networks/plot_mnist_filters.py``)
-     - 00:06.189
+     - 00:07.610
      - 0.0
    * - :ref:`sphx_glr_auto_examples_bicluster_plot_bicluster_newsgroups.py` (``../examples/bicluster/plot_bicluster_newsgroups.py``)
-     - 00:06.088
+     - 00:07.279
      - 0.0
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-     - 00:00.474
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-   * - :ref:`sphx_glr_auto_examples_release_highlights_plot_release_highlights_1_6_0.py` (``../examples/release_highlights/plot_release_highlights_1_6_0.py``)
-     - 00:00.102
+   * - :ref:`sphx_glr_auto_examples_linear_model_plot_sgd_loss_functions.py` (``../examples/linear_model/plot_sgd_loss_functions.py``)
+     - 00:00.100
      - 0.0
    * - :ref:`sphx_glr_auto_examples_miscellaneous_plot_pipeline_display.py` (``../examples/miscellaneous/plot_pipeline_display.py``)
      - 00:00.100
      - 0.0
-   * - :ref:`sphx_glr_auto_examples_linear_model_plot_huber_vs_ridge.py` (``../examples/linear_model/plot_huber_vs_ridge.py``)
+   * - :ref:`sphx_glr_auto_examples_cluster_plot_agglomerative_dendrogram.py` (``../examples/cluster/plot_agglomerative_dendrogram.py``)
      - 00:00.099
      - 0.0
-   * - :ref:`sphx_glr_auto_examples_linear_model_plot_lasso_lars_ic.py` (``../examples/linear_model/plot_lasso_lars_ic.py``)
-     - 00:00.097
-     - 0.0
-   * - :ref:`sphx_glr_auto_examples_linear_model_plot_sgd_loss_functions.py` (``../examples/linear_model/plot_sgd_loss_functions.py``)
-     - 00:00.095
+   * - :ref:`sphx_glr_auto_examples_svm_plot_custom_kernel.py` (``../examples/svm/plot_custom_kernel.py``)
+     - 00:00.098
      - 0.0
    * - :ref:`sphx_glr_auto_examples_linear_model_plot_ransac.py` (``../examples/linear_model/plot_ransac.py``)
-     - 00:00.092
-     - 0.0
-   * - :ref:`sphx_glr_auto_examples_cluster_plot_agglomerative_dendrogram.py` (``../examples/cluster/plot_agglomerative_dendrogram.py``)
-     - 00:00.088
+     - 00:00.097
      - 0.0
-   * - :ref:`sphx_glr_auto_examples_svm_plot_svm_margin.py` (``../examples/svm/plot_svm_margin.py``)
-     - 00:00.082
+   * - :ref:`sphx_glr_auto_examples_release_highlights_plot_release_highlights_1_6_0.py` (``../examples/release_highlights/plot_release_highlights_1_6_0.py``)
+     - 00:00.094
      - 0.0
    * - :ref:`sphx_glr_auto_examples_neighbors_plot_lof_outlier_detection.py` (``../examples/neighbors/plot_lof_outlier_detection.py``)
-     - 00:00.081
+     - 00:00.090
      - 0.0
    * - :ref:`sphx_glr_auto_examples_tree_plot_unveil_tree_structure.py` (``../examples/tree/plot_unveil_tree_structure.py``)
-     - 00:00.077
+     - 00:00.081
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_linear_model_plot_sgd_weighted_samples.py` (``../examples/linear_model/plot_sgd_weighted_samples.py``)
+     - 00:00.073
      - 0.0
    * - :ref:`sphx_glr_auto_examples_linear_model_plot_sgd_separating_hyperplane.py` (``../examples/linear_model/plot_sgd_separating_hyperplane.py``)
      - 00:00.072
      - 0.0
    * - :ref:`sphx_glr_auto_examples_svm_plot_separating_hyperplane.py` (``../examples/svm/plot_separating_hyperplane.py``)
-     - 00:00.071
-     - 0.0
-   * - :ref:`sphx_glr_auto_examples_linear_model_plot_sgd_weighted_samples.py` (``../examples/linear_model/plot_sgd_weighted_samples.py``)
      - 00:00.070
      - 0.0
+   * - :ref:`sphx_glr_auto_examples_svm_plot_svm_margin.py` (``../examples/svm/plot_svm_margin.py``)
+     - 00:00.067
+     - 0.0
    * - :ref:`sphx_glr_auto_examples_linear_model_plot_nnls.py` (``../examples/linear_model/plot_nnls.py``)
-     - 00:00.063
+     - 00:00.066
      - 0.0
    * - :ref:`sphx_glr_auto_examples_cluster_plot_kmeans_plusplus.py` (``../examples/cluster/plot_kmeans_plusplus.py``)
-     - 00:00.057
+     - 00:00.059
      - 0.0
    * - :ref:`sphx_glr_auto_examples_miscellaneous_plot_metadata_routing.py` (``../examples/miscellaneous/plot_metadata_routing.py``)
-     - 00:00.042
+     - 00:00.044
      - 0.0
    * - :ref:`sphx_glr_auto_examples_miscellaneous_plot_estimator_representation.py` (``../examples/miscellaneous/plot_estimator_representation.py``)
-     - 00:00.029
+     - 00:00.028
      - 0.0
    * - :ref:`sphx_glr_auto_examples_frozen_plot_frozen_examples.py` (``../examples/frozen/plot_frozen_examples.py``)
-     - 00:00.017
+     - 00:00.018
      - 0.0
    * - :ref:`sphx_glr_auto_examples_release_highlights_plot_release_highlights_1_0_0.py` (``../examples/release_highlights/plot_release_highlights_1_0_0.py``)
-     - 00:00.016
+     - 00:00.015
      - 0.0
    * - :ref:`sphx_glr_auto_examples_feature_selection_plot_feature_selection_pipeline.py` (``../examples/feature_selection/plot_feature_selection_pipeline.py``)
-     - 00:00.013
+     - 00:00.014
      - 0.0
    * - :ref:`sphx_glr_auto_examples_applications_wikipedia_principal_eigenvector.py` (``../examples/applications/wikipedia_principal_eigenvector.py``)
      - 00:00.000
diff --git a/dev/_sources/user_guide.rst.txt b/dev/_sources/user_guide.rst.txt
index 81ce774a5155e..0c1a6ee66ebf9 100644
--- a/dev/_sources/user_guide.rst.txt
+++ b/dev/_sources/user_guide.rst.txt
@@ -11,6 +11,7 @@ User Guide
    supervised_learning.rst
    unsupervised_learning.rst
    model_selection.rst
+   metadata_routing.rst
    inspection.rst
    visualizations.rst
    data_transforms.rst
@@ -21,12 +22,3 @@ User Guide
    dispatching.rst
    machine_learning_map.rst
    presentations.rst
-
-Under Development
------------------
-
-.. toctree::
-   :numbered:
-   :maxdepth: 1
-
-   metadata_routing.rst
diff --git a/dev/_sources/versions.rst.txt b/dev/_sources/versions.rst.txt
index 38d6674dbc813..b332d54c7e3c1 100644
--- a/dev/_sources/versions.rst.txt
+++ b/dev/_sources/versions.rst.txt
@@ -5,7 +5,7 @@ Available documentation for scikit-learn
 
 Web-based documentation is available for versions listed below:
 
-* `scikit-learn 1.7.dev0 (dev) documentation <https://scikit-learn.org/dev/>`_ (`ZIP 90.0 MB <https://scikit-learn.org/dev//_downloads/scikit-learn-docs.zip>`_)
+* `scikit-learn 1.7.dev0 (dev) documentation <https://scikit-learn.org/dev/>`_ (`ZIP 90.3 MB <https://scikit-learn.org/dev//_downloads/scikit-learn-docs.zip>`_)
 * `scikit-learn 1.6.1 (stable) documentation <https://scikit-learn.org/stable/>`_ (`ZIP 91.2 MB <https://scikit-learn.org/stable//_downloads/scikit-learn-docs.zip>`_)
 * `scikit-learn 1.5.2 documentation <https://scikit-learn.org/1.5/>`_ (`ZIP 90.8 MB <https://scikit-learn.org/1.5//_downloads/scikit-learn-docs.zip>`_)
 * `scikit-learn 1.4.2 documentation <https://scikit-learn.org/1.4/>`_ (`ZIP 74.4 MB <https://scikit-learn.org/1.4//_downloads/scikit-learn-docs.zip>`_)
diff --git a/dev/_sources/whats_new/v1.7.rst.txt b/dev/_sources/whats_new/v1.7.rst.txt
index 1f88eb72eac30..686a13159d55b 100644
--- a/dev/_sources/whats_new/v1.7.rst.txt
+++ b/dev/_sources/whats_new/v1.7.rst.txt
@@ -31,7 +31,7 @@ Version 1.7
 Version 1.7.dev0
 ================
 
-**April 2025**
+**May 2025**
 
 Changes impacting many modules
 ------------------------------
@@ -65,9 +65,15 @@ See :ref:`array_api` for more details.
   :func:`sklearn.metrics.recall_score` now support Array API compatible inputs.
   By :user:`Omar Salman <OmarManzoor>` :pr:`30395`
 
+- |Feature| :func:`sklearn.utils.extmath.randomized_svd` now support Array API compatible inputs.
+  By :user:`Connor Lane <clane9>` and :user:`Jérémie du Boisberranger <jeremiedbb>`. :pr:`30819`
+
 - |Feature| :func:`sklearn.metrics.hamming_loss` now support Array API compatible inputs.
   By :user:`Thomas Li <lithomas1>` :pr:`30838`
 
+- |Feature| :class:`preprocessing.Binarizer` now supports Array API compatible inputs.
+  By :user:`Yaroslav Korobko <Tialo>`, :user:`Olivier Grisel <ogrisel>`, and :user:`Thomas Li <lithomas1>`. :pr:`31190`
+
 - array-api-compat and array-api-extra are now vendored within the
   scikit-learn source. Users of the experimental array API standard
   support no longer need to install array-api-compat in their environment.
@@ -95,6 +101,14 @@ more details.
   base estimator that outputs `float32`-dtyped predictions. By :user:`Olivier
   Grisel <ogrisel>` :pr:`30873`
 
+:mod:`sklearn.compose`
+----------------------
+
+- |API| The `force_int_remainder_cols` parameter of :class:`compose.ColumnTransformer` and
+  :func:`compose.make_column_transformer` is deprecated and will be removed in 1.9.
+  It has no effect.
+  By :user:`Jérémie du Boisberranger <jeremiedbb>` :pr:`31167`
+
 :mod:`sklearn.covariance`
 -------------------------
 
@@ -119,9 +133,33 @@ more details.
 :mod:`sklearn.ensemble`
 -----------------------
 
+- |Feature| :class:`ensemble.HistGradientBoostingClassifier` and
+  :class:`ensemble.HistGradientBoostingRegressor` allow for more control over the
+  validation set used for early stopping. You can now pass data to be used for
+  validation directly to `fit` via the arguments `X_val`, `y_val` and
+  `sample_weight_val`.
+  By :user:`Christian Lorentzen <lorentzenchr>`. :pr:`27124`
+
 - |Fix| :class:`ensemble.VotingClassifier` and :class:`ensemble.VotingRegressor`
   validate `estimators` to make sure it is a list of tuples. By `Thomas Fan`_. :pr:`30649`
 
+:mod:`sklearn.feature_selection`
+--------------------------------
+
+- |Enhancement| :class:`feature_selection.RFECV` now gives access to the ranking and support in each
+  iteration and cv step of feature selection.
+  By :user:`Marie S. <MarieSacksick>` :pr:`30179`
+
+- |Fix| :class:`feature_selection.SelectFromModel` now correctly works when the estimator
+  is an instance of :class:`linear_model.ElasticNetCV` with its `l1_ratio` parameter
+  being an array-like.
+  By :user:`Vasco Pereira <vasco-s-pereira>`. :pr:`31107`
+
+:mod:`sklearn.gaussian_process`
+-------------------------------
+
+- |Enhancement| :class:`gaussian_process.GaussianProcessClassifier` now includes a `latent_mean_and_variance` method that exposes the mean and the variance of the latent function, :math:`f`, used in the Laplace approximation. By :user:`Miguel González Duque <miguelgondu>` :pr:`22227`
+
 :mod:`sklearn.inspection`
 -------------------------
 
@@ -136,6 +174,11 @@ more details.
   'decision_function', 'predict_proba' or 'auto'.
   By :user:`Lucy Liu <lucyleeow>` :pr:`29797`
 
+- |Fix| :func:`inspection.partial_dependence` now raises an informative error when passing
+  an empty list as the `categorical_features` parameter. `None` should be used instead
+  to indicate that no categorical features are present.
+  By :user:`Pedro Lopes <pedroL0pes>`. :pr:`31146`
+
 - |API| :func:`inspection.partial_dependence` does no longer accept integer dtype for
   numerical feature columns. Explicit conversion to floating point values is
   now required before calling this tool (and preferably even before fitting the
@@ -145,29 +188,65 @@ more details.
 :mod:`sklearn.linear_model`
 ---------------------------
 
+- |Enhancement| :class:`linear_model.SGDClassifier` and :class:`linear_model.SGDRegressor` now accept
+  `l1_ratio=None` when `penalty` is not `"elasticnet"`.
+  By :user:`Marc Bresson <MarcBresson>`. :pr:`30730`
+
 - |Fix| :class:`linear_model.LogisticRegression` and
   :class:`linear_model.LogisticRegressionCV` now properly pass sample weights to
   :func:`utils.class_weight.compute_class_weight` when fit with
   `class_weight="balanced"`.
   By :user:`Shruti Nath <snath-xoc>` and :user:`Olivier Grisel <ogrisel>` :pr:`30057`
 
-- |Fix|  |Enhancement| Added a new parameter `tol` to
-   :class:`linear_model.LinearRegression` that determines the precision of the
-   solution `coef_` when fitting on sparse data.
-   By :user:`Success Moses <SuccessMoses>` :pr:`30521`
-
-- |Fix|  The update and initialization of the hyperparameters now properly handle
-   sample weights in :class:`linear_model.BayesianRidge`.
-   By :user:`Antoine Baker <antoinebaker>`. :pr:`30644`
+- |Fix| Added a new parameter `tol` to
+  :class:`linear_model.LinearRegression` that determines the precision of the
+  solution `coef_` when fitting on sparse data.
+  By :user:`Success Moses <SuccessMoses>` :pr:`30521`
+
+- |Fix| The update and initialization of the hyperparameters now properly handle
+  sample weights in :class:`linear_model.BayesianRidge`.
+  By :user:`Antoine Baker <antoinebaker>`. :pr:`30644`
+
+- |Fix| :class:`linear_model.BayesianRidge` now uses the full SVD to correctly estimate
+  the posterior covariance matrix `sigma_` when `n_samples < n_features`.
+  By :user:`Antoine Baker <antoinebaker>` :pr:`31094`
+
+- |API| The parameter `n_alphas` has been deprecated in the following classes:
+  :class:`linear_model.ElasticNetCV` and :class:`linear_model.LassoCV`
+  and :class:`linear_model.MultiTaskElasticNetCV`
+  and :class:`linear_model.MultiTaskLassoCV`, and will be removed in 1.9. The parameter
+  `alphas` now supports both integers and array-likes, removing the need for `n_alphas`.
+  From now on, only `alphas` should be set to either indicate the number of alphas to
+  automatically generate (int) or to provide a list of alphas (array-like) to test along
+  the regularization path.
+  By :user:`Siddharth Bansal <KANNAHWORLD >`. :pr:`30616`
+
+- |API| Using the `"liblinear"` solver for multiclass classification with a one-versus-rest
+  scheme in :class:`linear_model.LogisticRegression` and
+  :class:`linear_model.LogisticRegressionCV` is deprecated and will raise an error in
+  version 1.8. Either use a solver which supports the multinomial loss or wrap the
+  estimator in a :class:`sklearn.multiclass.OneVsRestClassifier` to keep applying a
+  one-versus-rest scheme.
+  By :user:`Jérémie du Boisberranger <jeremiedbb>`. :pr:`31241`
 
 :mod:`sklearn.manifold`
 -----------------------
 
+- |Enhancement| :class:`manifold.MDS` will switch to use `n_init=1` by default,
+  starting from version 1.9.
+  By :user:`Dmitry Kobak <dkobak>` :pr:`31117`
+
 - |Fix| :class:`manifold.MDS` now correctly handles non-metric MDS. Furthermore,
   the returned stress value now corresponds to the returned embedding and
   normalized stress is now allowed for metric MDS.
   By :user:`Dmitry Kobak <dkobak>` :pr:`30514`
 
+- |Fix| :class:`manifold.MDS` now uses `eps=1e-6` by default and the convergence
+  criterion was adjusted to make sense for both metric and non-metric MDS
+  and to follow the reference R implementation. The formula for normalized
+  stress was adjusted to follow the original definition by Kruskal.
+  By :user:`Dmitry Kobak <dkobak>` :pr:`31117`
+
 :mod:`sklearn.metrics`
 ----------------------
 
@@ -178,6 +257,13 @@ more details.
   :user:`Varun Aggarwal <aggvarun01>`, :user:`Olivier Grisel <ogrisel>` and
   :user:`Antoine Baker <antoinebaker>`. :pr:`22046`
 
+- |Enhancement| :func:`metrics.det_curve`, :class:`metrics.DetCurveDisplay.from_estimator`,
+  and :class:`metrics.DetCurveDisplay.from_estimator` now accept a
+  `drop_intermediate` option to drop thresholds where true positives (tp) do not
+  change from the previous or subsequent thresholds. All points with the same tp
+  value have the same `fnr` and thus same y coordinate in a DET curve.
+  By :user:`Arturo Amor <ArturoAmorQ>` :pr:`29151`
+
 - |Enhancement| :func:`~metrics.class_likelihood_ratios` now has a `replace_undefined_by` param.
   When there is a division by zero, the metric is undefined and the set values are
   returned for `LR+` and `LR-`.
@@ -187,6 +273,11 @@ more details.
   are missing in `labels`. By :user:`Varun Aggarwal <aggvarun01>`,
   :user:`Olivier Grisel <ogrisel>` and :user:`Antoine Baker <antoinebaker>`. :pr:`22046`
 
+- |Fix| :func:`metrics.det_curve` and :class:`metrics.DetCurveDisplay` now return an
+  extra threshold at infinity where the classifier always predicts the negative
+  class i.e. tps = fps = 0.
+  By :user:`Arturo Amor <ArturoAmorQ>` :pr:`29151`
+
 - |Fix| :func:`~metrics.class_likelihood_ratios` now raises `UndefinedMetricWarning` instead
   of `UserWarning` when a division by zero occurs.
   By :user:`Stefanie Senger <StefanieSenger>` :pr:`29288`
@@ -195,10 +286,28 @@ more details.
   `label` is `None` in both the `line_kwargs` and the `chance_level_kw`.
   By :user:`Arturo Amor <ArturoAmorQ>` :pr:`29727`
 
+- |Fix| :func:`~metrics.d2_log_loss_score` now properly handles the case when `labels` is
+  passed and not all of the labels are present in `y_true`.
+  By :user:`Vassilis Margonis <vmargonis>` :pr:`30903`
+
 - |Fix| Fix :func:`metrics.adjusted_mutual_info_score` numerical issue when number of
   classes and samples is low.
   By :user:`Hleb Levitski <glevv>` :pr:`31065`
 
+- |API| The `sparse` parameter of :func:`metrics.fowlkes_mallows_score` is deprecated and
+  will be removed in 1.9. It has no effect.
+  By :user:`Luc Rocher <cynddl>`. :pr:`28981`
+
+- |API| The `raise_warning` parameter of :func:`metrics.class_likelihood_ratios` is deprecated
+  and will be removed in 1.9. An `UndefinedMetricWarning` will always be raised in case
+  of a division by zero.
+  By :user:`Stefanie Senger <StefanieSenger>`. :pr:`29288`
+
+- |API| In :meth:`sklearn.metrics.RocCurveDisplay.from_predictions`,
+  the argument `y_pred` has been renamed to `y_score` to better reflect its purpose.
+  `y_pred` will be removed in 1.9.
+  By :user:`Bagus Tris Atmaja <bagustris>` in :pr:`29865`
+
 :mod:`sklearn.mixture`
 ----------------------
 
@@ -226,6 +335,15 @@ more details.
   now forward `sample_weight` to the scorer even when metadata routing is not enabled.
   By :user:`Antoine Baker <antoinebaker>` :pr:`30743`
 
+:mod:`sklearn.multiclass`
+-------------------------
+
+- |Fix| The `predict_proba` method of :class:`sklearn.multiclass.OneVsRestClassifier` now
+  returns zero for all classes when all inner estimators never predict their positive
+  class.
+  By :user:`Luis M. B. Varona <Luis-Varona>`, :user:`Marc Bresson <MarcBresson>`, and
+  :user:`Jérémie du Boisberranger <jeremiedbb>`. :pr:`31228`
+
 :mod:`sklearn.multioutput`
 --------------------------
 
@@ -244,6 +362,10 @@ more details.
   `"squared_error"` (default) and `"poisson"` (new).
   By :user:`Christian Lorentzen <lorentzenchr>` :pr:`30712`
 
+- |Fix| :class:`neural_network.MLPRegressor` now raises an informative error when
+  `early_stopping` is set and the computed validation set is too small.
+  By :user:`David Shumway <davidshumway>`. :pr:`24788`
+
 :mod:`sklearn.pipeline`
 -----------------------
 
@@ -258,17 +380,24 @@ more details.
 - |Enhancement| :class:`preprocessing.KBinsDiscretizer` with `strategy="uniform"` now
   accepts `sample_weight`. Additionally with `strategy="quantile"` the
   `quantile_method` can now be specified (in the future
-  `quantile_method="averaged_inverted_cdf"` will become the default)
-  :pr:`29907` by :user:`Shruti Nath <snath-xoc>` and :user:`Olivier Grisel
+  `quantile_method="averaged_inverted_cdf"` will become the default).
+  By :user:`Shruti Nath <snath-xoc>` and :user:`Olivier Grisel
   <ogrisel>` :pr:`29907`
 
 - |Fix| :class:`preprocessing.KBinsDiscretizer` now uses weighted resampling when
   sample weights are given and subsampling is used. This may change results
   even when not using sample weights, although in absolute and not in terms
   of statistical properties.
-  :pr:`29907` by :user:`Shruti Nath <snath-xoc>` and :user:`Jérémie du Boisberranger
+  By :user:`Shruti Nath <snath-xoc>` and :user:`Jérémie du Boisberranger
   <jeremiedbb>` :pr:`29907`
 
+- |Fix| Now using ``scipy.stats.yeojohnson`` instead of our own implementation of the Yeo-Johnson transform.
+  Fixed numerical stability (mostly overflows) of the Yeo-Johnson transform with
+  `PowerTransformer(method="yeo-johnson")` when scipy version is `>= 1.12`.
+  Initial PR by :user:`Xuefeng Xu <xuefeng-xu>` completed by :user:`Mohamed Yaich <yaichm>`,
+  :user:`Oussama Er-rabie <eroussama>`, :user:`Mohammed Yaslam Dlimi <Dlimim>`,
+  :user:`Hamza Zaroual <HamzaLuffy>`, :user:`Amine Hannoun <AmineHannoun>` and :user:`Sylvain Marié <smarie>`. :pr:`31227`
+
 :mod:`sklearn.svm`
 ------------------
 
@@ -280,6 +409,16 @@ more details.
 :mod:`sklearn.utils`
 --------------------
 
+- |Enhancement| :func:`utils.multiclass.type_of_target` raises a warning when the number
+  of unique classes is greater than 50% of the number of samples. This warning is raised
+  only if `y` has more than 20 samples.
+  By :user:`Rahil Parikh <rprkh>`. :pr:`26335`
+
+- |Enhancement| :func: `resample` now handles sample weights which allows
+  weighted resampling.
+  By :user:`Shruti Nath <snath-xoc>` and :user:`Olivier Grisel
+  <ogrisel>` :pr:`29907`
+
 - |Enhancement| :func:`utils.class_weight.compute_class_weight` now properly accounts for
   sample weights when using strategy "balanced" to calculate class weights.
   By :user:`Shruti Nath <snath-xoc>` :pr:`30057`
@@ -287,17 +426,21 @@ more details.
 - |Enhancement| Warning filters from the main process are propagated to joblib workers.
   By `Thomas Fan`_ :pr:`30380`
 
-
- |Enhancement|- :func: `resample` now handles sample weights which allows
-  weighted resampling.
-  :pr:`29907` by :user:`Shruti Nath <snath-xoc>` and :user:`Olivier Grisel
-  <ogrisel>` :pr:`29907`
-
-- |Fix|  In :mod:`utils.estimator_checks` we now enforce for binary classifiers a
-   binary `y` by taking the minimum as the negative class instead of the first
-   element, which makes it robust to `y` shuffling. It prevents two checks from
-   wrongly failing on binary classifiers.
-   By :user:`Antoine Baker <antoinebaker>`. :pr:`30775`
+- |Enhancement| The private helper function :func:`utils._safe_indexing` now officially supports
+  pyarrow data. For instance, passing a pyarrow `Table` as `X` in a
+  :class:`compose.ColumnTransformer` is now possible.
+  By :user:`Christian Lorentzen <lorentzenchr>` :pr:`31040`
+
+- |Fix| In :mod:`utils.estimator_checks` we now enforce for binary classifiers a
+  binary `y` by taking the minimum as the negative class instead of the first
+  element, which makes it robust to `y` shuffling. It prevents two checks from
+  wrongly failing on binary classifiers.
+  By :user:`Antoine Baker <antoinebaker>`. :pr:`30775`
+
+- |Fix| :func:`utils.extmath.randomized_svd` and :func:`utils.extmath.randomized_range_finder`
+  now validate their input array to fail early with an informative error message on
+  invalid input.
+  By :user:`Connor Lane <clane9>`. :pr:`30819`
 
 .. rubric:: Code and documentation contributors
 
diff --git a/dev/_static/jupyterlite_sphinx.css b/dev/_static/jupyterlite_sphinx.css
index 41db7f9ea7309..8919aa0596e53 100644
--- a/dev/_static/jupyterlite_sphinx.css
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+  box-shadow: 0 0.2rem 0.5rem rgba(19, 23, 29, 0.4);
   margin-bottom: 1.5rem;
 }
 
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   white-space: nowrap;
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+  color: #13171d;
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   font-family: vibur;
   font-size: larger;
-  box-shadow: 0 0.2rem 0.5rem #d8d8d8;
+  box-shadow: 0 0.2rem 0.5rem rgba(19, 23, 29, 0.4);
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 .try_examples_outer_container {
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index 6b71b1276bdc0..2a0706e6d9272 100644
--- a/dev/about.html
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index 5d6947a5356ae..c29f2f666e92a 100644
--- a/dev/api/index.html
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-    <link rel="prev" title="1. Metadata Routing" href="http://github.com/metadata_routing.html" />
+    <link rel="prev" title="14. External Resources, Videos and Talks" href="http://github.com/presentations.html" />
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 <td><div class="sk-apisearch-desc docutils">
-<p>Compute error rates for different probability thresholds.</p>
+<p>Compute Detection Error Tradeoff (DET) for different probability thresholds.</p>
 <div class="caption docutils">
 <p><a class="reference internal" href="http://github.com/sklearn.metrics.html#module-sklearn.metrics" title="sklearn.metrics"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.metrics</span></code></a></p>
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 <td><div class="sk-apisearch-desc docutils">
-<p>Compute the distance matrix between each pair from a vector array X and Y.</p>
+<p>Compute the distance matrix between each pair from a feature array X and Y.</p>
 <div class="caption docutils">
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 <td><div class="sk-apisearch-desc docutils">
-<p>Compute the distance matrix from a vector array X and optional Y.</p>
+<p>Compute the distance matrix from a feature array X and optional Y.</p>
 <div class="caption docutils">
 <p><a class="reference internal" href="http://github.com/sklearn.metrics.html#module-sklearn.metrics" title="sklearn.metrics"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.metrics</span></code></a></p>
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 <td><div class="sk-apisearch-desc docutils">
-<p>DET curve visualization.</p>
+<p>Detection Error Tradeoff (DET) curve visualization.</p>
 <div class="caption docutils">
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 <td><div class="sk-apisearch-desc docutils">
-<p>Tweak of <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.Parallel.html#joblib.Parallel" title="(in joblib v1.5.dev0)"><code class="xref py py-class docutils literal notranslate"><span class="pre">joblib.Parallel</span></code></a> that propagates the scikit-learn configuration.</p>
+<p>Tweak of <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.Parallel.html#joblib.Parallel" title="(in joblib v1.6.dev0)"><code class="xref py py-class docutils literal notranslate"><span class="pre">joblib.Parallel</span></code></a> that propagates the scikit-learn configuration.</p>
 <div class="caption docutils">
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 </div>
@@ -6486,12 +6486,12 @@
     <div class="footer-article-item">
 <div class="prev-next-area">
     <a class="left-prev"
-       href="http://github.com/metadata_routing.html"
+       href="http://github.com/presentations.html"
        title="previous page">
       <i class="fa-solid fa-angle-left"></i>
       <div class="prev-next-info">
         <p class="prev-next-subtitle">previous</p>
-        <p class="prev-next-title"><span class="section-number">1. </span>Metadata Routing</p>
+        <p class="prev-next-title"><span class="section-number">14. </span>External Resources, Videos and Talks</p>
       </div>
     </a>
     <a class="right-next"
diff --git a/dev/api/sklearn.base.html b/dev/api/sklearn.base.html
index 4de54758e54c6..9558264b54e89 100644
--- a/dev/api/sklearn.base.html
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index 106879668dfb1..fb5d28a32eef7 100644
--- a/dev/api/sklearn.calibration.html
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index dbc62bc440592..6099680c78143 100644
--- a/dev/api/sklearn.cluster.html
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index 77777f928767f..08a6413cb7048 100644
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index b78530986d201..97816b6bcad1f 100644
--- a/dev/api/sklearn.covariance.html
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index f6734f174c9e3..f841c32e7c7ae 100644
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index 843dd6bd31e59..2a1ecf00fcf79 100644
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index 0528279e6c40a..a1e2956ea11e7 100644
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index f07f0b4da21bc..09fafaf84d090 100644
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diff --git a/dev/api/sklearn.metrics.html b/dev/api/sklearn.metrics.html
index c99b7e763a255..222f6f599aaba 100644
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@@ -1360,7 +1360,7 @@
 <td><p>Compute Discounted Cumulative Gain.</p></td>
 </tr>
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-<td><p>Compute error rates for different probability thresholds.</p></td>
+<td><p>Compute Detection Error Tradeoff (DET) for different probability thresholds.</p></td>
 </tr>
 <tr class="row-odd"><td><p><a class="reference internal" href="http://github.com/modules/generated/sklearn.metrics.f1_score.html#sklearn.metrics.f1_score" title="sklearn.metrics.f1_score"><code class="xref py py-obj docutils literal notranslate"><span class="pre">f1_score</span></code></a></p></td>
 <td><p>Compute the F1 score, also known as balanced F-score or F-measure.</p></td>
@@ -1603,7 +1603,7 @@
 <td><p>Valid metrics for pairwise_distances.</p></td>
 </tr>
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-<td><p>Compute the distance matrix between each pair from a vector array X and Y.</p></td>
+<td><p>Compute the distance matrix between each pair from a feature array X and Y.</p></td>
 </tr>
 <tr class="row-odd"><td><p><a class="reference internal" href="http://github.com/modules/generated/sklearn.metrics.pairwise.haversine_distances.html#sklearn.metrics.pairwise.haversine_distances" title="sklearn.metrics.pairwise.haversine_distances"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pairwise.haversine_distances</span></code></a></p></td>
 <td><p>Compute the Haversine distance between samples in X and Y.</p></td>
@@ -1648,7 +1648,7 @@
 <td><p>Compute the sigmoid kernel between X and Y.</p></td>
 </tr>
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-<td><p>Compute the distance matrix from a vector array X and optional Y.</p></td>
+<td><p>Compute the distance matrix from a feature array X and optional Y.</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="reference internal" href="http://github.com/modules/generated/sklearn.metrics.pairwise_distances_argmin.html#sklearn.metrics.pairwise_distances_argmin" title="sklearn.metrics.pairwise_distances_argmin"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pairwise_distances_argmin</span></code></a></p></td>
 <td><p>Compute minimum distances between one point and a set of points.</p></td>
@@ -1672,7 +1672,7 @@
 <td><p>Confusion Matrix visualization.</p></td>
 </tr>
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-<td><p>DET curve visualization.</p></td>
+<td><p>Detection Error Tradeoff (DET) curve visualization.</p></td>
 </tr>
 <tr class="row-odd"><td><p><a class="reference internal" href="http://github.com/modules/generated/sklearn.metrics.PrecisionRecallDisplay.html#sklearn.metrics.PrecisionRecallDisplay" title="sklearn.metrics.PrecisionRecallDisplay"><code class="xref py py-obj docutils literal notranslate"><span class="pre">PrecisionRecallDisplay</span></code></a></p></td>
 <td><p>Precision Recall visualization.</p></td>
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 </section>
 <section id="module-sklearn.utils.parallel">
 <span id="parallel-computing"></span><span id="utils-ref-parallel-computing"></span><h2>Parallel computing<a class="headerlink" href="#module-sklearn.utils.parallel" title="Link to this heading">#</a></h2>
-<p>Customizations of <a class="reference external" href="https://joblib.readthedocs.io/en/latest/index.html#module-joblib" title="(in joblib v1.5.dev0)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">joblib</span></code></a> and <code class="xref py py-mod docutils literal notranslate"><span class="pre">threadpoolctl</span></code> tools for scikit-learn
+<p>Customizations of <a class="reference external" href="https://joblib.readthedocs.io/en/latest/index.html#module-joblib" title="(in joblib v1.6.dev0)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">joblib</span></code></a> and <code class="xref py py-mod docutils literal notranslate"><span class="pre">threadpoolctl</span></code> tools for scikit-learn
 usage.</p>
 <div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
 <tbody>
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-<td><p>Tweak of <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.Parallel.html#joblib.Parallel" title="(in joblib v1.5.dev0)"><code class="xref py py-class docutils literal notranslate"><span class="pre">joblib.Parallel</span></code></a> that propagates the scikit-learn configuration.</p></td>
+<td><p>Tweak of <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.Parallel.html#joblib.Parallel" title="(in joblib v1.6.dev0)"><code class="xref py py-class docutils literal notranslate"><span class="pre">joblib.Parallel</span></code></a> that propagates the scikit-learn configuration.</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="reference internal" href="http://github.com/modules/generated/sklearn.utils.parallel.delayed.html#sklearn.utils.parallel.delayed" title="sklearn.utils.parallel.delayed"><code class="xref py py-obj docutils literal notranslate"><span class="pre">parallel.delayed</span></code></a></p></td>
 <td><p>Decorator used to capture the arguments of a function.</p></td>
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     <link rel="next" title="Compressive sensing: tomography reconstruction with L1 prior (Lasso)" href="http://github.com/plot_tomography_l1_reconstruction.html" />
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
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+        <p class="prev-next-title">Visualizing the probabilistic predictions of a VotingClassifier</p>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
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                    precision    recall  f1-score   support
 
      Ariel Sharon       0.75      0.69      0.72        13
@@ -1170,7 +1169,7 @@
 convolutional neural networks but this family of models is outside of the scope of
 the scikit-learn library. Interested readers should instead try to use pytorch or
 tensorflow to implement such models.</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 5.528 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 6.882 seconds)</p>
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 <div class="binder-badge docutils container">
 <a class="reference external image-reference" href="https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/applications/plot_face_recognition.ipynb"><img alt="Launch binder" src="http://github.com/_images/binder_badge_logo.svg" style="width: 150px;" />
diff --git a/dev/auto_examples/applications/plot_model_complexity_influence.html b/dev/auto_examples/applications/plot_model_complexity_influence.html
index 05cac53acbdb3..e83e034487467 100644
--- a/dev/auto_examples/applications/plot_model_complexity_influence.html
+++ b/dev/auto_examples/applications/plot_model_complexity_influence.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
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@@ -679,13 +679,12 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 current active has-children"><a class="reference internal" href="http://github.com/index.html">Examples based on real world datasets</a><details open="open"><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul class="current">
@@ -902,9 +901,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
@@ -1263,49 +1262,49 @@ <h2>Run the code and plot the results<a class="headerlink" href="#run-the-code-a
 </ul>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Benchmarking SGDClassifier(alpha=0.001, l1_ratio=0.25, loss=&#39;modified_huber&#39;,
               n_iter_no_change=2, penalty=&#39;elasticnet&#39;, tol=0.1)
-Complexity: 4948 | Hamming Loss (Misclassification Ratio): 0.2675 | Pred. Time: 0.057344s
+Complexity: 4948 | Hamming Loss (Misclassification Ratio): 0.2675 | Pred. Time: 0.059860s
 
 Benchmarking SGDClassifier(alpha=0.001, l1_ratio=0.5, loss=&#39;modified_huber&#39;,
               n_iter_no_change=2, penalty=&#39;elasticnet&#39;, tol=0.1)
-Complexity: 1847 | Hamming Loss (Misclassification Ratio): 0.3264 | Pred. Time: 0.043617s
+Complexity: 1847 | Hamming Loss (Misclassification Ratio): 0.3264 | Pred. Time: 0.045086s
 
 Benchmarking SGDClassifier(alpha=0.001, l1_ratio=0.75, loss=&#39;modified_huber&#39;,
               n_iter_no_change=2, penalty=&#39;elasticnet&#39;, tol=0.1)
-Complexity: 997 | Hamming Loss (Misclassification Ratio): 0.3383 | Pred. Time: 0.035609s
+Complexity: 997 | Hamming Loss (Misclassification Ratio): 0.3383 | Pred. Time: 0.037784s
 
 Benchmarking SGDClassifier(alpha=0.001, l1_ratio=0.9, loss=&#39;modified_huber&#39;,
               n_iter_no_change=2, penalty=&#39;elasticnet&#39;, tol=0.1)
-Complexity: 802 | Hamming Loss (Misclassification Ratio): 0.3582 | Pred. Time: 0.032368s
+Complexity: 802 | Hamming Loss (Misclassification Ratio): 0.3582 | Pred. Time: 0.034099s
 
 Benchmarking NuSVR(C=1000.0, gamma=3.0517578125e-05, nu=0.05)
-Complexity: 18 | MSE: 5558.7313 | Pred. Time: 0.000218s
+Complexity: 18 | MSE: 5558.7313 | Pred. Time: 0.000187s
 
 Benchmarking NuSVR(C=1000.0, gamma=3.0517578125e-05, nu=0.1)
-Complexity: 36 | MSE: 5289.8022 | Pred. Time: 0.000286s
+Complexity: 36 | MSE: 5289.8022 | Pred. Time: 0.000260s
 
 Benchmarking NuSVR(C=1000.0, gamma=3.0517578125e-05, nu=0.2)
-Complexity: 72 | MSE: 5193.8353 | Pred. Time: 0.000440s
+Complexity: 72 | MSE: 5193.8353 | Pred. Time: 0.000412s
 
 Benchmarking NuSVR(C=1000.0, gamma=3.0517578125e-05, nu=0.35)
-Complexity: 124 | MSE: 5131.3279 | Pred. Time: 0.000640s
+Complexity: 124 | MSE: 5131.3279 | Pred. Time: 0.000643s
 
 Benchmarking NuSVR(C=1000.0, gamma=3.0517578125e-05)
-Complexity: 178 | MSE: 5149.0779 | Pred. Time: 0.000869s
+Complexity: 178 | MSE: 5149.0779 | Pred. Time: 0.000876s
 
 Benchmarking GradientBoostingRegressor(learning_rate=0.05, max_depth=2, n_estimators=10)
-Complexity: 10 | MSE: 4066.4812 | Pred. Time: 0.000263s
+Complexity: 10 | MSE: 4066.4812 | Pred. Time: 0.000178s
 
 Benchmarking GradientBoostingRegressor(learning_rate=0.05, max_depth=2, n_estimators=25)
-Complexity: 25 | MSE: 3551.1723 | Pred. Time: 0.000286s
+Complexity: 25 | MSE: 3551.1723 | Pred. Time: 0.000207s
 
 Benchmarking GradientBoostingRegressor(learning_rate=0.05, max_depth=2, n_estimators=50)
-Complexity: 50 | MSE: 3445.2171 | Pred. Time: 0.000322s
+Complexity: 50 | MSE: 3445.2171 | Pred. Time: 0.000240s
 
 Benchmarking GradientBoostingRegressor(learning_rate=0.05, max_depth=2, n_estimators=75)
-Complexity: 75 | MSE: 3433.0358 | Pred. Time: 0.000360s
+Complexity: 75 | MSE: 3433.0358 | Pred. Time: 0.000279s
 
 Benchmarking GradientBoostingRegressor(learning_rate=0.05, max_depth=2)
-Complexity: 100 | MSE: 3456.0602 | Pred. Time: 0.000391s
+Complexity: 100 | MSE: 3456.0602 | Pred. Time: 0.000306s
 </pre></div>
 </div>
 </section>
@@ -1319,7 +1318,7 @@ <h2>Conclusion<a class="headerlink" href="#conclusion" title="Link to this headi
 </ul>
 <p>These aspects are related to model generalization and avoiding model
 under-fitting or over-fitting.</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 4.715 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 5.231 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-applications-plot-model-complexity-influence-py">
 <div class="binder-badge docutils container">
 <a class="reference external image-reference" href="https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/applications/plot_model_complexity_influence.ipynb"><img alt="Launch binder" src="http://github.com/_images/binder_badge_logo.svg" style="width: 150px;" />
@@ -1346,12 +1345,12 @@ <h2>Conclusion<a class="headerlink" href="#conclusion" title="Link to this headi
 </div><div class="sphx-glr-thumbcontainer" tooltip="Gradient Boosting is an ensemble technique that combines multiple weak learners, typically decision trees, to create a robust and powerful predictive model. It does so in an iterative fashion, where each new stage (tree) corrects the errors of the previous ones."><img alt="" src="http://github.com/_images/sphx_glr_plot_gradient_boosting_early_stopping_thumb.png" />
 <p><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_early_stopping.html#sphx-glr-auto-examples-ensemble-plot-gradient-boosting-early-stopping-py"><span class="std std-ref">Early stopping in Gradient Boosting</span></a></p>
   <div class="sphx-glr-thumbnail-title">Early stopping in Gradient Boosting</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This example illustrates the effect of the parameters gamma and C of the Radial Basis Function (RBF) kernel SVM."><img alt="" src="http://github.com/_images/sphx_glr_plot_rbf_parameters_thumb.png" />
-<p><a class="reference internal" href="http://github.com/svm/plot_rbf_parameters.html#sphx-glr-auto-examples-svm-plot-rbf-parameters-py"><span class="std std-ref">RBF SVM parameters</span></a></p>
-  <div class="sphx-glr-thumbnail-title">RBF SVM parameters</div>
 </div><div class="sphx-glr-thumbcontainer" tooltip="This example plots the ellipsoids obtained from a toy dataset (mixture of three Gaussians) fitted by the BayesianGaussianMixture class models with a Dirichlet distribution prior (``weight_concentration_prior_type=&#x27;dirichlet_distribution&#x27;``) and a Dirichlet process prior (``weight_concentration_prior_type=&#x27;dirichlet_process&#x27;``). On each figure, we plot the results for three different values of the weight concentration prior."><img alt="" src="http://github.com/_images/sphx_glr_plot_concentration_prior_thumb.png" />
 <p><a class="reference internal" href="http://github.com/mixture/plot_concentration_prior.html#sphx-glr-auto-examples-mixture-plot-concentration-prior-py"><span class="std std-ref">Concentration Prior Type Analysis of Variation Bayesian Gaussian Mixture</span></a></p>
   <div class="sphx-glr-thumbnail-title">Concentration Prior Type Analysis of Variation Bayesian Gaussian Mixture</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example illustrates the effect of the parameters gamma and C of the Radial Basis Function (RBF) kernel SVM."><img alt="" src="http://github.com/_images/sphx_glr_plot_rbf_parameters_thumb.png" />
+<p><a class="reference internal" href="http://github.com/svm/plot_rbf_parameters.html#sphx-glr-auto-examples-svm-plot-rbf-parameters-py"><span class="std std-ref">RBF SVM parameters</span></a></p>
+  <div class="sphx-glr-thumbnail-title">RBF SVM parameters</div>
 </div></div><p class="sphx-glr-signature"><a class="reference external" href="https://sphinx-gallery.github.io/">Gallery generated by Sphinx-Gallery</a></p>
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 </section>
diff --git a/dev/auto_examples/applications/plot_out_of_core_classification.html b/dev/auto_examples/applications/plot_out_of_core_classification.html
index f21efb9b99315..8a8ff78cfa92a 100644
--- a/dev/auto_examples/applications/plot_out_of_core_classification.html
+++ b/dev/auto_examples/applications/plot_out_of_core_classification.html
@@ -42,7 +42,7 @@
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     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
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@@ -679,13 +679,12 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 current active has-children"><a class="reference internal" href="http://github.com/index.html">Examples based on real world datasets</a><details open="open"><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul class="current">
@@ -902,9 +901,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
@@ -1126,10 +1125,7 @@ <h2>Reuters Dataset related routines<a class="headerlink" href="#reuters-dataset
 
 <span class="sd">    &quot;&quot;&quot;</span>
 
-    <span class="n">DOWNLOAD_URL</span> <span class="o">=</span> <span class="p">(</span>
-        <span class="s2">&quot;http://archive.ics.uci.edu/ml/machine-learning-databases/&quot;</span>
-        <span class="s2">&quot;reuters21578-mld/reuters21578.tar.gz&quot;</span>
-    <span class="p">)</span>
+    <span class="n">DOWNLOAD_URL</span> <span class="o">=</span> <span class="s2">&quot;https://kdd.ics.uci.edu/databases/reuters21578/reuters21578.tar.gz&quot;</span>
     <span class="n">ARCHIVE_SHA256</span> <span class="o">=</span> <span class="s2">&quot;3bae43c9b14e387f76a61b6d82bf98a4fb5d3ef99ef7e7075ff2ccbcf59f9d30&quot;</span>
     <span class="n">ARCHIVE_FILENAME</span> <span class="o">=</span> <span class="s2">&quot;reuters21578.tar.gz&quot;</span>
 
@@ -1309,46 +1305,46 @@ <h2>Main<a class="headerlink" href="#main" title="Link to this heading">#</a></h
 </pre></div>
 </div>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Test set is 878 documents (108 positive)
-                 SGD classifier :          962 train docs (   132 positive)    878 test docs (   108 positive) accuracy: 0.915 in 0.62s ( 1552 docs/s)
-          Perceptron classifier :          962 train docs (   132 positive)    878 test docs (   108 positive) accuracy: 0.855 in 0.62s ( 1545 docs/s)
-      NB Multinomial classifier :          962 train docs (   132 positive)    878 test docs (   108 positive) accuracy: 0.877 in 0.63s ( 1525 docs/s)
-  Passive-Aggressive classifier :          962 train docs (   132 positive)    878 test docs (   108 positive) accuracy: 0.933 in 0.63s ( 1517 docs/s)
+                 SGD classifier :          962 train docs (   132 positive)    878 test docs (   108 positive) accuracy: 0.915 in 0.75s ( 1282 docs/s)
+          Perceptron classifier :          962 train docs (   132 positive)    878 test docs (   108 positive) accuracy: 0.855 in 0.75s ( 1276 docs/s)
+      NB Multinomial classifier :          962 train docs (   132 positive)    878 test docs (   108 positive) accuracy: 0.877 in 0.77s ( 1254 docs/s)
+  Passive-Aggressive classifier :          962 train docs (   132 positive)    878 test docs (   108 positive) accuracy: 0.933 in 0.77s ( 1246 docs/s)
 
 
-                 SGD classifier :         3911 train docs (   517 positive)    878 test docs (   108 positive) accuracy: 0.938 in 1.74s ( 2245 docs/s)
-          Perceptron classifier :         3911 train docs (   517 positive)    878 test docs (   108 positive) accuracy: 0.936 in 1.74s ( 2241 docs/s)
-      NB Multinomial classifier :         3911 train docs (   517 positive)    878 test docs (   108 positive) accuracy: 0.885 in 1.75s ( 2232 docs/s)
-  Passive-Aggressive classifier :         3911 train docs (   517 positive)    878 test docs (   108 positive) accuracy: 0.941 in 1.75s ( 2229 docs/s)
+                 SGD classifier :         3911 train docs (   517 positive)    878 test docs (   108 positive) accuracy: 0.938 in 2.21s ( 1769 docs/s)
+          Perceptron classifier :         3911 train docs (   517 positive)    878 test docs (   108 positive) accuracy: 0.936 in 2.21s ( 1766 docs/s)
+      NB Multinomial classifier :         3911 train docs (   517 positive)    878 test docs (   108 positive) accuracy: 0.885 in 2.22s ( 1758 docs/s)
+  Passive-Aggressive classifier :         3911 train docs (   517 positive)    878 test docs (   108 positive) accuracy: 0.941 in 2.23s ( 1755 docs/s)
 
 
-                 SGD classifier :         6821 train docs (   891 positive)    878 test docs (   108 positive) accuracy: 0.952 in 2.84s ( 2399 docs/s)
-          Perceptron classifier :         6821 train docs (   891 positive)    878 test docs (   108 positive) accuracy: 0.952 in 2.85s ( 2397 docs/s)
-      NB Multinomial classifier :         6821 train docs (   891 positive)    878 test docs (   108 positive) accuracy: 0.900 in 2.85s ( 2391 docs/s)
-  Passive-Aggressive classifier :         6821 train docs (   891 positive)    878 test docs (   108 positive) accuracy: 0.953 in 2.86s ( 2389 docs/s)
+                 SGD classifier :         6821 train docs (   891 positive)    878 test docs (   108 positive) accuracy: 0.952 in 3.46s ( 1970 docs/s)
+          Perceptron classifier :         6821 train docs (   891 positive)    878 test docs (   108 positive) accuracy: 0.952 in 3.47s ( 1968 docs/s)
+      NB Multinomial classifier :         6821 train docs (   891 positive)    878 test docs (   108 positive) accuracy: 0.900 in 3.48s ( 1962 docs/s)
+  Passive-Aggressive classifier :         6821 train docs (   891 positive)    878 test docs (   108 positive) accuracy: 0.953 in 3.48s ( 1960 docs/s)
 
 
-                 SGD classifier :         9759 train docs (  1276 positive)    878 test docs (   108 positive) accuracy: 0.949 in 3.96s ( 2466 docs/s)
-          Perceptron classifier :         9759 train docs (  1276 positive)    878 test docs (   108 positive) accuracy: 0.953 in 3.96s ( 2465 docs/s)
-      NB Multinomial classifier :         9759 train docs (  1276 positive)    878 test docs (   108 positive) accuracy: 0.909 in 3.97s ( 2460 docs/s)
-  Passive-Aggressive classifier :         9759 train docs (  1276 positive)    878 test docs (   108 positive) accuracy: 0.958 in 3.97s ( 2459 docs/s)
+                 SGD classifier :         9759 train docs (  1276 positive)    878 test docs (   108 positive) accuracy: 0.949 in 4.72s ( 2068 docs/s)
+          Perceptron classifier :         9759 train docs (  1276 positive)    878 test docs (   108 positive) accuracy: 0.953 in 4.72s ( 2066 docs/s)
+      NB Multinomial classifier :         9759 train docs (  1276 positive)    878 test docs (   108 positive) accuracy: 0.909 in 4.73s ( 2062 docs/s)
+  Passive-Aggressive classifier :         9759 train docs (  1276 positive)    878 test docs (   108 positive) accuracy: 0.958 in 4.73s ( 2061 docs/s)
 
 
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-  Passive-Aggressive classifier :        11680 train docs (  1499 positive)    878 test docs (   108 positive) accuracy: 0.950 in 4.94s ( 2366 docs/s)
+                 SGD classifier :        11680 train docs (  1499 positive)    878 test docs (   108 positive) accuracy: 0.944 in 5.88s ( 1986 docs/s)
+          Perceptron classifier :        11680 train docs (  1499 positive)    878 test docs (   108 positive) accuracy: 0.956 in 5.88s ( 1985 docs/s)
+      NB Multinomial classifier :        11680 train docs (  1499 positive)    878 test docs (   108 positive) accuracy: 0.915 in 5.89s ( 1983 docs/s)
+  Passive-Aggressive classifier :        11680 train docs (  1499 positive)    878 test docs (   108 positive) accuracy: 0.950 in 5.89s ( 1982 docs/s)
 
 
-                 SGD classifier :        14625 train docs (  1865 positive)    878 test docs (   108 positive) accuracy: 0.965 in 6.06s ( 2412 docs/s)
-          Perceptron classifier :        14625 train docs (  1865 positive)    878 test docs (   108 positive) accuracy: 0.903 in 6.06s ( 2411 docs/s)
-      NB Multinomial classifier :        14625 train docs (  1865 positive)    878 test docs (   108 positive) accuracy: 0.924 in 6.07s ( 2408 docs/s)
-  Passive-Aggressive classifier :        14625 train docs (  1865 positive)    878 test docs (   108 positive) accuracy: 0.957 in 6.07s ( 2407 docs/s)
+                 SGD classifier :        14625 train docs (  1865 positive)    878 test docs (   108 positive) accuracy: 0.965 in 7.05s ( 2075 docs/s)
+          Perceptron classifier :        14625 train docs (  1865 positive)    878 test docs (   108 positive) accuracy: 0.903 in 7.05s ( 2074 docs/s)
+      NB Multinomial classifier :        14625 train docs (  1865 positive)    878 test docs (   108 positive) accuracy: 0.924 in 7.06s ( 2072 docs/s)
+  Passive-Aggressive classifier :        14625 train docs (  1865 positive)    878 test docs (   108 positive) accuracy: 0.957 in 7.06s ( 2071 docs/s)
 
 
-                 SGD classifier :        17360 train docs (  2179 positive)    878 test docs (   108 positive) accuracy: 0.957 in 7.09s ( 2447 docs/s)
-          Perceptron classifier :        17360 train docs (  2179 positive)    878 test docs (   108 positive) accuracy: 0.933 in 7.10s ( 2446 docs/s)
-      NB Multinomial classifier :        17360 train docs (  2179 positive)    878 test docs (   108 positive) accuracy: 0.932 in 7.10s ( 2443 docs/s)
-  Passive-Aggressive classifier :        17360 train docs (  2179 positive)    878 test docs (   108 positive) accuracy: 0.952 in 7.11s ( 2442 docs/s)
+                 SGD classifier :        17360 train docs (  2179 positive)    878 test docs (   108 positive) accuracy: 0.957 in 8.14s ( 2132 docs/s)
+          Perceptron classifier :        17360 train docs (  2179 positive)    878 test docs (   108 positive) accuracy: 0.933 in 8.14s ( 2132 docs/s)
+      NB Multinomial classifier :        17360 train docs (  2179 positive)    878 test docs (   108 positive) accuracy: 0.932 in 8.15s ( 2129 docs/s)
+  Passive-Aggressive classifier :        17360 train docs (  2179 positive)    878 test docs (   108 positive) accuracy: 0.952 in 8.15s ( 2129 docs/s)
 </pre></div>
 </div>
 </section>
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 <li><img src="http://github.com/_images/sphx_glr_plot_out_of_core_classification_003.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_out_of_core_classification_003.png " alt="Training Times" class = "sphx-glr-multi-img"/></li>
 <li><img src="http://github.com/_images/sphx_glr_plot_out_of_core_classification_004.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_out_of_core_classification_004.png " alt="Prediction Times (1000 instances)" class = "sphx-glr-multi-img"/></li>
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diff --git a/dev/auto_examples/applications/plot_outlier_detection_wine.html b/dev/auto_examples/applications/plot_outlier_detection_wine.html
index ac0d098e52533..14614ca1d4cd1 100644
--- a/dev/auto_examples/applications/plot_outlier_detection_wine.html
+++ b/dev/auto_examples/applications/plot_outlier_detection_wine.html
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
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 <li class="toctree-l1 current active has-children"><a class="reference internal" href="http://github.com/index.html">Examples based on real world datasets</a><details open="open"><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul class="current">
@@ -902,9 +901,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
@@ -1123,7 +1122,7 @@ <h2>Second example<a class="headerlink" href="#second-example" title="Link to th
 <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
 </pre></div>
 </div>
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+<img src="http://github.com/_images/sphx_glr_plot_outlier_detection_wine_002.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_outlier_detection_wine_002.png " alt="Outlier detection on a real data set (wine recognition)" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 0.377 seconds)</p>
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diff --git a/dev/auto_examples/applications/plot_prediction_latency.html b/dev/auto_examples/applications/plot_prediction_latency.html
index cf8ba70478e6b..baf36d4e2e920 100644
--- a/dev/auto_examples/applications/plot_prediction_latency.html
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
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@@ -902,9 +901,9 @@
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
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@@ -1344,7 +1343,7 @@ <h2>Benchmark throughput<a class="headerlink" href="#benchmark-throughput" title
 <span class="n">plot_benchmark_throughput</span><span class="p">(</span><span class="n">throughputs</span><span class="p">,</span> <span class="n">configuration</span><span class="p">)</span>
 </pre></div>
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+<img src="http://github.com/_images/sphx_glr_plot_prediction_latency_004.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_prediction_latency_004.png " alt="Prediction Throughput for different estimators (100 features)" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 18.169 seconds)</p>
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diff --git a/dev/auto_examples/applications/plot_species_distribution_modeling.html b/dev/auto_examples/applications/plot_species_distribution_modeling.html
index 0825e93a4d405..1e6814b662abf 100644
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 current active has-children"><a class="reference internal" href="http://github.com/index.html">Examples based on real world datasets</a><details open="open"><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul class="current">
@@ -902,9 +901,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
@@ -1035,7 +1034,7 @@ <h2>References<a class="headerlink" href="#references" title="Link to this headi
 
  Area under the ROC curve : 0.993919
 
-time elapsed: 5.87s
+time elapsed: 8.29s
 </pre></div>
 </div>
 <div class="line-block">
@@ -1114,7 +1113,7 @@ <h2>References<a class="headerlink" href="#references" title="Link to this headi
 
 
 <span class="k">def</span><span class="w"> </span><span class="nf">plot_species_distribution</span><span class="p">(</span>
-    <span class="n">species</span><span class="o">=</span><span class="p">(</span><span class="s2">&quot;bradypus_variegatus_0&quot;</span><span class="p">,</span> <span class="s2">&quot;microryzomys_minutus_0&quot;</span><span class="p">)</span>
+    <span class="n">species</span><span class="o">=</span><span class="p">(</span><span class="s2">&quot;bradypus_variegatus_0&quot;</span><span class="p">,</span> <span class="s2">&quot;microryzomys_minutus_0&quot;</span><span class="p">),</span>
 <span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Plot the species distribution.</span>
@@ -1199,7 +1198,7 @@ <h2>References<a class="headerlink" href="#references" title="Link to this headi
         <span class="n">Z</span> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ones.html#numpy.ones" title="numpy.ones" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">ones</span></a><span class="p">((</span><span class="n">data</span><span class="o">.</span><span class="n">Ny</span><span class="p">,</span> <span class="n">data</span><span class="o">.</span><span class="n">Nx</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><a href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.float64" title="numpy.float64" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-attribute"><span class="n">np</span><span class="o">.</span><span class="n">float64</span></a><span class="p">)</span>
 
         <span class="c1"># We&#39;ll predict only for the land points.</span>
-        <span class="n">idx</span> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.where.html#numpy.where" title="numpy.where" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">where</span></a><span class="p">(</span><span class="n">land_reference</span> <span class="o">&gt;</span> <span class="o">-</span><span class="mi">9999</span><span class="p">)</span>
+        <span class="n">idx</span> <span class="o">=</span> <span class="p">(</span><span class="n">land_reference</span> <span class="o">&gt;</span> <span class="o">-</span><span class="mi">9999</span><span class="p">)</span><span class="o">.</span><span class="n">nonzero</span><span class="p">()</span>
         <span class="n">coverages_land</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">coverages</span><span class="p">[:,</span> <span class="n">idx</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">idx</span><span class="p">[</span><span class="mi">1</span><span class="p">]]</span><span class="o">.</span><span class="n">T</span>
 
         <span class="n">pred</span> <span class="o">=</span> <span class="n">clf</span><span class="o">.</span><span class="n">decision_function</span><span class="p">((</span><span class="n">coverages_land</span> <span class="o">-</span> <span class="n">mean</span><span class="p">)</span> <span class="o">/</span> <span class="n">std</span><span class="p">)</span>
@@ -1251,7 +1250,7 @@ <h2>References<a class="headerlink" href="#references" title="Link to this headi
 <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
 </pre></div>
 </div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 6.021 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 8.461 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-applications-plot-species-distribution-modeling-py">
 <div class="binder-badge docutils container">
 <a class="reference external image-reference" href="https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/applications/plot_species_distribution_modeling.ipynb"><img alt="Launch binder" src="http://github.com/_images/binder_badge_logo.svg" style="width: 150px;" />
diff --git a/dev/auto_examples/applications/plot_stock_market.html b/dev/auto_examples/applications/plot_stock_market.html
index 161679f8ee027..dd88dec665e47 100644
--- a/dev/auto_examples/applications/plot_stock_market.html
+++ b/dev/auto_examples/applications/plot_stock_market.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -679,13 +679,12 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 current active has-children"><a class="reference internal" href="http://github.com/index.html">Examples based on real world datasets</a><details open="open"><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul class="current">
@@ -902,9 +901,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
@@ -1172,7 +1171,7 @@ <h2>Retrieve the data from Internet<a class="headerlink" href="#retrieve-the-dat
 </pre></div>
 </div>
 <div class="output_subarea output_html rendered_html output_result">
-<style>#sk-container-id-27 {
+<style>#sk-container-id-28 {
   /* Definition of color scheme common for light and dark mode */
   --sklearn-color-text: #000;
   --sklearn-color-text-muted: #666;
@@ -1203,15 +1202,15 @@ <h2>Retrieve the data from Internet<a class="headerlink" href="#retrieve-the-dat
   }
 }
 
-#sk-container-id-27 {
+#sk-container-id-28 {
   color: var(--sklearn-color-text);
 }
 
-#sk-container-id-27 pre {
+#sk-container-id-28 pre {
   padding: 0;
 }
 
-#sk-container-id-27 input.sk-hidden--visually {
+#sk-container-id-28 input.sk-hidden--visually {
   border: 0;
   clip: rect(1px 1px 1px 1px);
   clip: rect(1px, 1px, 1px, 1px);
@@ -1223,7 +1222,7 @@ <h2>Retrieve the data from Internet<a class="headerlink" href="#retrieve-the-dat
   width: 1px;
 }
 
-#sk-container-id-27 div.sk-dashed-wrapped {
+#sk-container-id-28 div.sk-dashed-wrapped {
   border: 1px dashed var(--sklearn-color-line);
   margin: 0 0.4em 0.5em 0.4em;
   box-sizing: border-box;
@@ -1231,7 +1230,7 @@ <h2>Retrieve the data from Internet<a class="headerlink" href="#retrieve-the-dat
   background-color: var(--sklearn-color-background);
 }
 
-#sk-container-id-27 div.sk-container {
+#sk-container-id-28 div.sk-container {
   /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
      but bootstrap.min.css set `[hidden] { display: none !important; }`
      so we also need the `!important` here to be able to override the
@@ -1241,7 +1240,7 @@ <h2>Retrieve the data from Internet<a class="headerlink" href="#retrieve-the-dat
   position: relative;
 }
 
-#sk-container-id-27 div.sk-text-repr-fallback {
+#sk-container-id-28 div.sk-text-repr-fallback {
   display: none;
 }
 
@@ -1257,14 +1256,14 @@ <h2>Retrieve the data from Internet<a class="headerlink" href="#retrieve-the-dat
 
 /* Parallel-specific style estimator block */
 
-#sk-container-id-27 div.sk-parallel-item::after {
+#sk-container-id-28 div.sk-parallel-item::after {
   content: "";
   width: 100%;
   border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
   flex-grow: 1;
 }
 
-#sk-container-id-27 div.sk-parallel {
+#sk-container-id-28 div.sk-parallel {
   display: flex;
   align-items: stretch;
   justify-content: center;
@@ -1272,28 +1271,28 @@ <h2>Retrieve the data from Internet<a class="headerlink" href="#retrieve-the-dat
   position: relative;
 }
 
-#sk-container-id-27 div.sk-parallel-item {
+#sk-container-id-28 div.sk-parallel-item {
   display: flex;
   flex-direction: column;
 }
 
-#sk-container-id-27 div.sk-parallel-item:first-child::after {
+#sk-container-id-28 div.sk-parallel-item:first-child::after {
   align-self: flex-end;
   width: 50%;
 }
 
-#sk-container-id-27 div.sk-parallel-item:last-child::after {
+#sk-container-id-28 div.sk-parallel-item:last-child::after {
   align-self: flex-start;
   width: 50%;
 }
 
-#sk-container-id-27 div.sk-parallel-item:only-child::after {
+#sk-container-id-28 div.sk-parallel-item:only-child::after {
   width: 0;
 }
 
 /* Serial-specific style estimator block */
 
-#sk-container-id-27 div.sk-serial {
+#sk-container-id-28 div.sk-serial {
   display: flex;
   flex-direction: column;
   align-items: center;
@@ -1311,14 +1310,14 @@ <h2>Retrieve the data from Internet<a class="headerlink" href="#retrieve-the-dat
 
 /* Pipeline and ColumnTransformer style (default) */
 
-#sk-container-id-27 div.sk-toggleable {
+#sk-container-id-28 div.sk-toggleable {
   /* Default theme specific background. It is overwritten whether we have a
   specific estimator or a Pipeline/ColumnTransformer */
   background-color: var(--sklearn-color-background);
 }
 
 /* Toggleable label */
-#sk-container-id-27 label.sk-toggleable__label {
+#sk-container-id-28 label.sk-toggleable__label {
   cursor: pointer;
   display: flex;
   width: 100%;
@@ -1331,13 +1330,13 @@ <h2>Retrieve the data from Internet<a class="headerlink" href="#retrieve-the-dat
   gap: 0.5em;
 }
 
-#sk-container-id-27 label.sk-toggleable__label .caption {
+#sk-container-id-28 label.sk-toggleable__label .caption {
   font-size: 0.6rem;
   font-weight: lighter;
   color: var(--sklearn-color-text-muted);
 }
 
-#sk-container-id-27 label.sk-toggleable__label-arrow:before {
+#sk-container-id-28 label.sk-toggleable__label-arrow:before {
   /* Arrow on the left of the label */
   content: "▸";
   float: left;
@@ -1345,13 +1344,13 @@ <h2>Retrieve the data from Internet<a class="headerlink" href="#retrieve-the-dat
   color: var(--sklearn-color-icon);
 }
 
-#sk-container-id-27 label.sk-toggleable__label-arrow:hover:before {
+#sk-container-id-28 label.sk-toggleable__label-arrow:hover:before {
   color: var(--sklearn-color-text);
 }
 
 /* Toggleable content - dropdown */
 
-#sk-container-id-27 div.sk-toggleable__content {
+#sk-container-id-28 div.sk-toggleable__content {
   max-height: 0;
   max-width: 0;
   overflow: hidden;
@@ -1360,12 +1359,12 @@ <h2>Retrieve the data from Internet<a class="headerlink" href="#retrieve-the-dat
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-27 div.sk-toggleable__content.fitted {
+#sk-container-id-28 div.sk-toggleable__content.fitted {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
-#sk-container-id-27 div.sk-toggleable__content pre {
+#sk-container-id-28 div.sk-toggleable__content pre {
   margin: 0.2em;
   border-radius: 0.25em;
   color: var(--sklearn-color-text);
@@ -1373,79 +1372,79 @@ <h2>Retrieve the data from Internet<a class="headerlink" href="#retrieve-the-dat
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-27 div.sk-toggleable__content.fitted pre {
+#sk-container-id-28 div.sk-toggleable__content.fitted pre {
   /* unfitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
-#sk-container-id-27 input.sk-toggleable__control:checked~div.sk-toggleable__content {
+#sk-container-id-28 input.sk-toggleable__control:checked~div.sk-toggleable__content {
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   max-width: 100%;
   overflow: auto;
 }
 
-#sk-container-id-27 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
+#sk-container-id-28 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
   content: "▾";
 }
 
 /* Pipeline/ColumnTransformer-specific style */
 
-#sk-container-id-27 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-28 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
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   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-27 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-28 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
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 }
 
 /* Estimator-specific style */
 
 /* Colorize estimator box */
-#sk-container-id-27 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-28 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
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-#sk-container-id-27 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
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   background-color: var(--sklearn-color-fitted-level-2);
 }
 
-#sk-container-id-27 div.sk-label label.sk-toggleable__label,
-#sk-container-id-27 div.sk-label label {
+#sk-container-id-28 div.sk-label label.sk-toggleable__label,
+#sk-container-id-28 div.sk-label label {
   /* The background is the default theme color */
   color: var(--sklearn-color-text-on-default-background);
 }
 
 /* On hover, darken the color of the background */
-#sk-container-id-27 div.sk-label:hover label.sk-toggleable__label {
+#sk-container-id-28 div.sk-label:hover label.sk-toggleable__label {
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 }
 
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-#sk-container-id-27 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
+#sk-container-id-28 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
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   background-color: var(--sklearn-color-fitted-level-2);
 }
 
 /* Estimator label */
 
-#sk-container-id-27 div.sk-label label {
+#sk-container-id-28 div.sk-label label {
   font-family: monospace;
   font-weight: bold;
   display: inline-block;
   line-height: 1.2em;
 }
 
-#sk-container-id-27 div.sk-label-container {
+#sk-container-id-28 div.sk-label-container {
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 }
 
 /* Estimator-specific */
-#sk-container-id-27 div.sk-estimator {
+#sk-container-id-28 div.sk-estimator {
   font-family: monospace;
   border: 1px dotted var(--sklearn-color-border-box);
   border-radius: 0.25em;
@@ -1455,18 +1454,18 @@ <h2>Retrieve the data from Internet<a class="headerlink" href="#retrieve-the-dat
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-27 div.sk-estimator.fitted {
+#sk-container-id-28 div.sk-estimator.fitted {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
 /* on hover */
-#sk-container-id-27 div.sk-estimator:hover {
+#sk-container-id-28 div.sk-estimator:hover {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-27 div.sk-estimator.fitted:hover {
+#sk-container-id-28 div.sk-estimator.fitted:hover {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-2);
 }
@@ -1554,7 +1553,7 @@ <h2>Retrieve the data from Internet<a class="headerlink" href="#retrieve-the-dat
 
 /* "?"-specific style due to the `<a>` HTML tag */
 
-#sk-container-id-27 a.estimator_doc_link {
+#sk-container-id-28 a.estimator_doc_link {
   float: right;
   font-size: 1rem;
   line-height: 1em;
@@ -1569,26 +1568,26 @@ <h2>Retrieve the data from Internet<a class="headerlink" href="#retrieve-the-dat
   border: var(--sklearn-color-unfitted-level-1) 1pt solid;
 }
 
-#sk-container-id-27 a.estimator_doc_link.fitted {
+#sk-container-id-28 a.estimator_doc_link.fitted {
   /* fitted */
   border: var(--sklearn-color-fitted-level-1) 1pt solid;
   color: var(--sklearn-color-fitted-level-1);
 }
 
 /* On hover */
-#sk-container-id-27 a.estimator_doc_link:hover {
+#sk-container-id-28 a.estimator_doc_link:hover {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-3);
   color: var(--sklearn-color-background);
   text-decoration: none;
 }
 
-#sk-container-id-27 a.estimator_doc_link.fitted:hover {
+#sk-container-id-28 a.estimator_doc_link.fitted:hover {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-3);
 }
-</style><div id="sk-container-id-27" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>GraphicalLassoCV(alphas=array([ 0.03162278,  0.05994843,  0.11364637,  0.21544347,  0.40842387,
-        0.77426368,  1.46779927,  2.7825594 ,  5.27499706, 10.        ]))</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-107" type="checkbox" checked><label for="sk-estimator-id-107" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>GraphicalLassoCV</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.covariance.GraphicalLassoCV.html">?<span>Documentation for GraphicalLassoCV</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>GraphicalLassoCV(alphas=array([ 0.03162278,  0.05994843,  0.11364637,  0.21544347,  0.40842387,
+</style><div id="sk-container-id-28" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>GraphicalLassoCV(alphas=array([ 0.03162278,  0.05994843,  0.11364637,  0.21544347,  0.40842387,
+        0.77426368,  1.46779927,  2.7825594 ,  5.27499706, 10.        ]))</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-117" type="checkbox" checked><label for="sk-estimator-id-117" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>GraphicalLassoCV</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.covariance.GraphicalLassoCV.html">?<span>Documentation for GraphicalLassoCV</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>GraphicalLassoCV(alphas=array([ 0.03162278,  0.05994843,  0.11364637,  0.21544347,  0.40842387,
         0.77426368,  1.46779927,  2.7825594 ,  5.27499706, 10.        ]))</pre></div> </div></div></div></div>
 </div>
 <br />
@@ -1683,7 +1682,7 @@ <h2>Visualization<a class="headerlink" href="#visualization" title="Link to this
 <span class="p">)</span>
 
 <span class="c1"># Plot the edges</span>
-<span class="n">start_idx</span><span class="p">,</span> <span class="n">end_idx</span> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.where.html#numpy.where" title="numpy.where" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">where</span></a><span class="p">(</span><span class="n">non_zero</span><span class="p">)</span>
+<span class="n">start_idx</span><span class="p">,</span> <span class="n">end_idx</span> <span class="o">=</span> <span class="n">non_zero</span><span class="o">.</span><span class="n">nonzero</span><span class="p">()</span>
 <span class="c1"># a sequence of (*line0*, *line1*, *line2*), where::</span>
 <span class="c1">#            linen = (x0, y0), (x1, y1), ... (xm, ym)</span>
 <span class="n">segments</span> <span class="o">=</span> <span class="p">[</span>
@@ -1744,7 +1743,7 @@ <h2>Visualization<a class="headerlink" href="#visualization" title="Link to this
 <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
 </pre></div>
 </div>
-<img src="http://github.com/_images/sphx_glr_plot_stock_market_001.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_stock_market_001.png " alt="plot stock market" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 2.321 seconds)</p>
+<img src="http://github.com/_images/sphx_glr_plot_stock_market_001.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_stock_market_001.png " alt="plot stock market" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 7.065 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-applications-plot-stock-market-py">
 <div class="binder-badge docutils container">
 <a class="reference external image-reference" href="https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/applications/plot_stock_market.ipynb"><img alt="Launch binder" src="http://github.com/_images/binder_badge_logo.svg" style="width: 150px;" />
diff --git a/dev/auto_examples/applications/plot_time_series_lagged_features.html b/dev/auto_examples/applications/plot_time_series_lagged_features.html
index 9f6263de2035b..e604a55121ca4 100644
--- a/dev/auto_examples/applications/plot_time_series_lagged_features.html
+++ b/dev/auto_examples/applications/plot_time_series_lagged_features.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -680,13 +680,12 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 current active has-children"><a class="reference internal" href="http://github.com/index.html">Examples based on real world datasets</a><details open="open"><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul class="current">
@@ -903,9 +902,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
@@ -1075,7 +1074,7 @@ <h2>Analyzing the Bike Sharing Demand dataset<a class="headerlink" href="#analyz
   white-space: pre-wrap;
 }
 </style>
-<small>shape: (4, 2)</small><table border="1" class="dataframe"><thead><tr><th>season</th><th>count</th></tr><tr><td>cat</td><td>u32</td></tr></thead><tbody><tr><td>&quot;2&quot;</td><td>4409</td></tr><tr><td>&quot;1&quot;</td><td>4242</td></tr><tr><td>&quot;3&quot;</td><td>4232</td></tr><tr><td>&quot;0&quot;</td><td>4496</td></tr></tbody></table></div>
+<small>shape: (4, 2)</small><table border="1" class="dataframe"><thead><tr><th>season</th><th>count</th></tr><tr><td>cat</td><td>u32</td></tr></thead><tbody><tr><td>&quot;3&quot;</td><td>4232</td></tr><tr><td>&quot;1&quot;</td><td>4242</td></tr><tr><td>&quot;2&quot;</td><td>4409</td></tr><tr><td>&quot;0&quot;</td><td>4496</td></tr></tbody></table></div>
 </div>
 <br />
 <br /></section>
@@ -1311,7 +1310,7 @@ <h2>Modeling predictive uncertainty via quantile regression<a class="headerlink"
     <span class="n">time</span> <span class="o">=</span> <span class="n">cv_results</span><span class="p">[</span><span class="s2">&quot;fit_time&quot;</span><span class="p">]</span>
     <span class="n">scores</span><span class="p">[</span><span class="s2">&quot;fit_time&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">time</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2"> ± </span><span class="si">{</span><span class="n">time</span><span class="o">.</span><span class="n">std</span><span class="p">()</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2"> s&quot;</span><span class="p">)</span>
 
-    <span class="n">scores</span><span class="p">[</span><span class="s2">&quot;loss&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;quantile </span><span class="si">{</span><span class="nb">int</span><span class="p">(</span><span class="n">quantile</span><span class="o">*</span><span class="mi">100</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+    <span class="n">scores</span><span class="p">[</span><span class="s2">&quot;loss&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;quantile </span><span class="si">{</span><span class="nb">int</span><span class="p">(</span><span class="n">quantile</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="mi">100</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
     <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">cv_results</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
         <span class="k">if</span> <span class="n">key</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s2">&quot;test_&quot;</span><span class="p">):</span>
             <span class="n">metric</span> <span class="o">=</span> <span class="n">key</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;test_&quot;</span><span class="p">)[</span><span class="mi">1</span><span class="p">]</span>
@@ -1329,7 +1328,7 @@ <h2>Modeling predictive uncertainty via quantile regression<a class="headerlink"
   white-space: pre-wrap;
 }
 </style>
-<small>shape: (6, 8)</small><table border="1" class="dataframe"><thead><tr><th>loss</th><th>fit_time</th><th>MAPE</th><th>RMSE</th><th>MAE</th><th>pinball_loss_05</th><th>pinball_loss_50</th><th>pinball_loss_95</th></tr><tr><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td></tr></thead><tbody><tr><td>&quot;squared_error&quot;</td><td>&quot;0.32 ± 0.01 s&quot;</td><td>&quot;0.36 ± 0.07&quot;</td><td>&quot;62.3 ± 3.5&quot;</td><td>&quot;39.1 ± 2.3&quot;</td><td>&quot;17.7 ± 1.3&quot;</td><td>&quot;19.5 ± 1.1&quot;</td><td>&quot;21.4 ± 2.4&quot;</td></tr><tr><td>&quot;poisson&quot;</td><td>&quot;0.34 ± 0.01 s&quot;</td><td>&quot;0.32 ± 0.07&quot;</td><td>&quot;64.2 ± 4.0&quot;</td><td>&quot;39.3 ± 2.8&quot;</td><td>&quot;16.7 ± 1.5&quot;</td><td>&quot;19.7 ± 1.4&quot;</td><td>&quot;22.6 ± 3.0&quot;</td></tr><tr><td>&quot;absolute_error&quot;</td><td>&quot;0.43 ± 0.01 s&quot;</td><td>&quot;0.32 ± 0.06&quot;</td><td>&quot;64.6 ± 3.8&quot;</td><td>&quot;39.9 ± 3.2&quot;</td><td>&quot;17.1 ± 1.1&quot;</td><td>&quot;19.9 ± 1.6&quot;</td><td>&quot;22.7 ± 3.1&quot;</td></tr><tr><td>&quot;quantile 5&quot;</td><td>&quot;0.56 ± 0.00 s&quot;</td><td>&quot;0.41 ± 0.01&quot;</td><td>&quot;145.6 ± 20.9&quot;</td><td>&quot;92.5 ± 16.2&quot;</td><td>&quot;5.9 ± 0.9&quot;</td><td>&quot;46.2 ± 8.1&quot;</td><td>&quot;86.6 ± 15.3&quot;</td></tr><tr><td>&quot;quantile 50&quot;</td><td>&quot;0.58 ± 0.02 s&quot;</td><td>&quot;0.32 ± 0.06&quot;</td><td>&quot;64.6 ± 3.8&quot;</td><td>&quot;39.9 ± 3.2&quot;</td><td>&quot;17.1 ± 1.1&quot;</td><td>&quot;19.9 ± 1.6&quot;</td><td>&quot;22.7 ± 3.1&quot;</td></tr><tr><td>&quot;quantile 95&quot;</td><td>&quot;0.56 ± 0.00 s&quot;</td><td>&quot;1.07 ± 0.27&quot;</td><td>&quot;99.6 ± 8.7&quot;</td><td>&quot;72.0 ± 6.1&quot;</td><td>&quot;62.9 ± 7.4&quot;</td><td>&quot;36.0 ± 3.1&quot;</td><td>&quot;9.1 ± 1.3&quot;</td></tr></tbody></table></div>
+<small>shape: (6, 8)</small><table border="1" class="dataframe"><thead><tr><th>loss</th><th>fit_time</th><th>MAPE</th><th>RMSE</th><th>MAE</th><th>pinball_loss_05</th><th>pinball_loss_50</th><th>pinball_loss_95</th></tr><tr><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td></tr></thead><tbody><tr><td>&quot;squared_error&quot;</td><td>&quot;0.36 ± 0.02 s&quot;</td><td>&quot;0.36 ± 0.07&quot;</td><td>&quot;62.3 ± 3.5&quot;</td><td>&quot;39.1 ± 2.3&quot;</td><td>&quot;17.7 ± 1.3&quot;</td><td>&quot;19.5 ± 1.1&quot;</td><td>&quot;21.4 ± 2.4&quot;</td></tr><tr><td>&quot;poisson&quot;</td><td>&quot;0.38 ± 0.01 s&quot;</td><td>&quot;0.32 ± 0.07&quot;</td><td>&quot;64.2 ± 4.0&quot;</td><td>&quot;39.3 ± 2.8&quot;</td><td>&quot;16.7 ± 1.5&quot;</td><td>&quot;19.7 ± 1.4&quot;</td><td>&quot;22.6 ± 3.0&quot;</td></tr><tr><td>&quot;absolute_error&quot;</td><td>&quot;0.49 ± 0.02 s&quot;</td><td>&quot;0.32 ± 0.06&quot;</td><td>&quot;64.6 ± 3.8&quot;</td><td>&quot;39.9 ± 3.2&quot;</td><td>&quot;17.1 ± 1.1&quot;</td><td>&quot;19.9 ± 1.6&quot;</td><td>&quot;22.7 ± 3.1&quot;</td></tr><tr><td>&quot;quantile 5&quot;</td><td>&quot;0.61 ± 0.05 s&quot;</td><td>&quot;0.41 ± 0.01&quot;</td><td>&quot;145.6 ± 20.9&quot;</td><td>&quot;92.5 ± 16.2&quot;</td><td>&quot;5.9 ± 0.9&quot;</td><td>&quot;46.2 ± 8.1&quot;</td><td>&quot;86.6 ± 15.3&quot;</td></tr><tr><td>&quot;quantile 50&quot;</td><td>&quot;0.66 ± 0.03 s&quot;</td><td>&quot;0.32 ± 0.06&quot;</td><td>&quot;64.6 ± 3.8&quot;</td><td>&quot;39.9 ± 3.2&quot;</td><td>&quot;17.1 ± 1.1&quot;</td><td>&quot;19.9 ± 1.6&quot;</td><td>&quot;22.7 ± 3.1&quot;</td></tr><tr><td>&quot;quantile 95&quot;</td><td>&quot;0.68 ± 0.05 s&quot;</td><td>&quot;1.07 ± 0.27&quot;</td><td>&quot;99.6 ± 8.7&quot;</td><td>&quot;72.0 ± 6.1&quot;</td><td>&quot;62.9 ± 7.4&quot;</td><td>&quot;36.0 ± 3.1&quot;</td><td>&quot;9.1 ± 1.3&quot;</td></tr></tbody></table></div>
 </div>
 <br />
 <br /><p>Let us take a look at the losses that minimise each metric.</p>
@@ -1504,7 +1503,7 @@ <h2>Conclusion<a class="headerlink" href="#conclusion" title="Link to this headi
 by <a class="reference external" href="https://www.sktime.net/en/latest/users.html">sktime</a>
 can be used to extend scikit-learn estimators by making use of recursive time
 series forecasting, that enables dynamic predictions of future values.</p>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 current active has-children"><a class="reference internal" href="http://github.com/index.html">Examples based on real world datasets</a><details open="open"><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul class="current">
@@ -902,9 +901,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
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+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 9.836 seconds)</p>
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diff --git a/dev/auto_examples/applications/plot_topics_extraction_with_nmf_lda.html b/dev/auto_examples/applications/plot_topics_extraction_with_nmf_lda.html
index 338f069108f53..8e9ddbb04d722 100644
--- a/dev/auto_examples/applications/plot_topics_extraction_with_nmf_lda.html
+++ b/dev/auto_examples/applications/plot_topics_extraction_with_nmf_lda.html
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
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@@ -902,9 +901,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
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@@ -1015,30 +1014,30 @@
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 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Loading dataset...
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 Extracting tf-idf features for NMF...
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 Extracting tf features for LDA...
-done in 0.235s.
+done in 0.310s.
 
 Fitting the NMF model (Frobenius norm) with tf-idf features, n_samples=2000 and n_features=1000...
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+done in 0.080s.
 
 
  Fitting the NMF model (generalized Kullback-Leibler divergence) with tf-idf features, n_samples=2000 and n_features=1000...
-done in 1.295s.
+done in 1.452s.
 
 
  Fitting the MiniBatchNMF model (Frobenius norm) with tf-idf features, n_samples=2000 and n_features=1000, batch_size=128...
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+done in 0.084s.
 
 
  Fitting the MiniBatchNMF model (generalized Kullback-Leibler divergence) with tf-idf features, n_samples=2000 and n_features=1000, batch_size=128...
-done in 0.217s.
+done in 0.229s.
 
 
  Fitting LDA models with tf features, n_samples=2000 and n_features=1000...
-done in 2.572s.
+done in 2.577s.
 </pre></div>
 </div>
 <div class="line-block">
@@ -1073,7 +1072,7 @@
 
         <span class="n">ax</span> <span class="o">=</span> <span class="n">axes</span><span class="p">[</span><span class="n">topic_idx</span><span class="p">]</span>
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+        <span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Topic </span><span class="si">{</span><span class="n">topic_idx</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="mi">1</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span> <span class="n">fontdict</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;fontsize&quot;</span><span class="p">:</span> <span class="mi">30</span><span class="p">})</span>
         <span class="n">ax</span><span class="o">.</span><span class="n">tick_params</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="s2">&quot;both&quot;</span><span class="p">,</span> <span class="n">which</span><span class="o">=</span><span class="s2">&quot;major&quot;</span><span class="p">,</span> <span class="n">labelsize</span><span class="o">=</span><span class="mi">20</span><span class="p">)</span>
         <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="s2">&quot;top right left&quot;</span><span class="o">.</span><span class="n">split</span><span class="p">():</span>
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@@ -1247,7 +1246,7 @@
 <span class="n">plot_top_words</span><span class="p">(</span><span class="n">lda</span><span class="p">,</span> <span class="n">tf_feature_names</span><span class="p">,</span> <span class="n">n_top_words</span><span class="p">,</span> <span class="s2">&quot;Topics in LDA model&quot;</span><span class="p">)</span>
 </pre></div>
 </div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 11.217 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 12.584 seconds)</p>
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diff --git a/dev/auto_examples/applications/wikipedia_principal_eigenvector.html b/dev/auto_examples/applications/wikipedia_principal_eigenvector.html
index 8af56757e9a00..9deb06c6affd3 100644
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+++ b/dev/auto_examples/applications/wikipedia_principal_eigenvector.html
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@@ -679,13 +679,12 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 current active has-children"><a class="reference internal" href="http://github.com/index.html">Examples based on real world datasets</a><details open="open"><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul class="current">
@@ -902,9 +901,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
@@ -1233,12 +1232,12 @@ <h2>Computing Centrality scores<a class="headerlink" href="#computing-centrality
 </div>
 </div>
 <p class="rubric">Related examples</p>
-<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This example shows a well known decomposition technique known as Principal Component Analysis (PCA) on the Iris dataset."><img alt="" src="http://github.com/_images/sphx_glr_plot_pca_iris_thumb.png" />
-<p><a class="reference internal" href="http://github.com/decomposition/plot_pca_iris.html#sphx-glr-auto-examples-decomposition-plot-pca-iris-py"><span class="std std-ref">Principal Component Analysis (PCA) on Iris Dataset</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Principal Component Analysis (PCA) on Iris Dataset</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows the reconstruction of an image from a set of parallel projections, acquired along different angles. Such a dataset is acquired in computed tomography (CT)."><img alt="" src="http://github.com/_images/sphx_glr_plot_tomography_l1_reconstruction_thumb.png" />
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This example shows the reconstruction of an image from a set of parallel projections, acquired along different angles. Such a dataset is acquired in computed tomography (CT)."><img alt="" src="http://github.com/_images/sphx_glr_plot_tomography_l1_reconstruction_thumb.png" />
 <p><a class="reference internal" href="http://github.com/plot_tomography_l1_reconstruction.html#sphx-glr-auto-examples-applications-plot-tomography-l1-reconstruction-py"><span class="std std-ref">Compressive sensing: tomography reconstruction with L1 prior (Lasso)</span></a></p>
   <div class="sphx-glr-thumbnail-title">Compressive sensing: tomography reconstruction with L1 prior (Lasso)</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows a well known decomposition technique known as Principal Component Analysis (PCA) on the Iris dataset."><img alt="" src="http://github.com/_images/sphx_glr_plot_pca_iris_thumb.png" />
+<p><a class="reference internal" href="http://github.com/decomposition/plot_pca_iris.html#sphx-glr-auto-examples-decomposition-plot-pca-iris-py"><span class="std std-ref">Principal Component Analysis (PCA) on Iris Dataset</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Principal Component Analysis (PCA) on Iris Dataset</div>
 </div><div class="sphx-glr-thumbcontainer" tooltip="This example uses spectral_clustering on a graph created from voxel-to-voxel difference on an image to break this image into multiple partly-homogeneous regions."><img alt="" src="http://github.com/_images/sphx_glr_plot_coin_segmentation_thumb.png" />
 <p><a class="reference internal" href="http://github.com/cluster/plot_coin_segmentation.html#sphx-glr-auto-examples-cluster-plot-coin-segmentation-py"><span class="std std-ref">Segmenting the picture of greek coins in regions</span></a></p>
   <div class="sphx-glr-thumbnail-title">Segmenting the picture of greek coins in regions</div>
diff --git a/dev/auto_examples/bicluster/index.html b/dev/auto_examples/bicluster/index.html
index cfff8f9939276..4f67685f0f653 100644
--- a/dev/auto_examples/bicluster/index.html
+++ b/dev/auto_examples/bicluster/index.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -675,13 +675,12 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -898,9 +897,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
diff --git a/dev/auto_examples/bicluster/plot_bicluster_newsgroups.html b/dev/auto_examples/bicluster/plot_bicluster_newsgroups.html
index 58038271cfaab..7ef041b050ce0 100644
--- a/dev/auto_examples/bicluster/plot_bicluster_newsgroups.html
+++ b/dev/auto_examples/bicluster/plot_bicluster_newsgroups.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
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     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -679,13 +679,12 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -902,9 +901,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
@@ -1010,9 +1009,9 @@
 achieve a better V-measure than clusters found by MiniBatchKMeans.</p>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Vectorizing...
 Coclustering...
-Done in 1.23s. V-measure: 0.4415
+Done in 1.35s. V-measure: 0.4415
 MiniBatchKMeans...
-Done in 2.19s. V-measure: 0.3015
+Done in 2.71s. V-measure: 0.3015
 
 Best biclusters:
 ----------------
@@ -1164,7 +1163,7 @@
 
     <span class="c1"># words</span>
     <span class="n">out_of_cluster_docs</span> <span class="o">=</span> <span class="n">cocluster</span><span class="o">.</span><span class="n">row_labels_</span> <span class="o">!=</span> <span class="n">cluster</span>
-    <span class="n">out_of_cluster_docs</span> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.where.html#numpy.where" title="numpy.where" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">where</span></a><span class="p">(</span><span class="n">out_of_cluster_docs</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
+    <span class="n">out_of_cluster_docs</span> <span class="o">=</span> <span class="n">out_of_cluster_docs</span><span class="o">.</span><span class="n">nonzero</span><span class="p">()[</span><span class="mi">0</span><span class="p">]</span>
     <span class="n">word_col</span> <span class="o">=</span> <span class="n">X</span><span class="p">[:,</span> <span class="n">cluster_words</span><span class="p">]</span>
     <span class="n">word_scores</span> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.array.html#numpy.array" title="numpy.array" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">array</span></a><span class="p">(</span>
         <span class="n">word_col</span><span class="p">[</span><span class="n">cluster_docs</span><span class="p">,</span> <span class="p">:]</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
@@ -1180,7 +1179,7 @@
     <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;words        : </span><span class="si">{</span><span class="s1">&#39;, &#39;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">important_words</span><span class="p">)</span><span class="si">}</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">)</span>
 </pre></div>
 </div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 6.088 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 7.279 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-bicluster-plot-bicluster-newsgroups-py">
 <div class="binder-badge docutils container">
 <a class="reference external image-reference" href="https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/bicluster/plot_bicluster_newsgroups.ipynb"><img alt="Launch binder" src="http://github.com/_images/binder_badge_logo1.svg" style="width: 150px;" />
diff --git a/dev/auto_examples/bicluster/plot_spectral_biclustering.html b/dev/auto_examples/bicluster/plot_spectral_biclustering.html
index 6977cf7d4917f..ac0c7d5cea158 100644
--- a/dev/auto_examples/bicluster/plot_spectral_biclustering.html
+++ b/dev/auto_examples/bicluster/plot_spectral_biclustering.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -679,13 +679,12 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -902,9 +901,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
@@ -1110,7 +1109,7 @@ <h2>Plotting results<a class="headerlink" href="#plotting-results" title="Link t
 <img src="http://github.com/_images/sphx_glr_plot_spectral_biclustering_004.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_spectral_biclustering_004.png " alt="Checkerboard structure of rearranged data" class = "sphx-glr-single-img"/><p>The outer product of the row and column label vectors shows a representation
 of the checkerboard structure, where different combinations of row and column
 labels are represented by different shades of blue.</p>
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 </ul>
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 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
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@@ -1470,7 +1469,7 @@ <h2>References<a class="headerlink" href="#references" title="Link to this headi
 A. Niculescu-Mizil &amp; R. Caruana, ICML 2005</p>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
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 classes will change after calibration.</p>
 <p>All in all, the One-vs-Rest multiclass-calibration strategy implemented in
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
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@@ -1295,7 +1294,7 @@ <h2>Analysis<a class="headerlink" href="#analysis" title="Link to this heading">
 various non-linear models. However, for this particular dataset, using the
 raw features is enough to train a competitive model. This would not
 necessarily the case for other datasets.</p>
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@@ -1316,7 +1315,10 @@ <h2>Analysis<a class="headerlink" href="#analysis" title="Link to this heading">
 </div>
 </div>
 <p class="rubric">Related examples</p>
-<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="For greyscale image data where pixel values can be interpreted as degrees of blackness on a white background, like handwritten digit recognition, the Bernoulli Restricted Boltzmann machine model (BernoulliRBM) can perform effective non-linear feature extraction."><img alt="" src="http://github.com/_images/sphx_glr_plot_rbm_logistic_classification_thumb.png" />
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="Plot the predicted class probabilities in a toy dataset predicted by three different classifiers and averaged by the VotingClassifier."><img alt="" src="http://github.com/_images/sphx_glr_plot_voting_decision_regions_thumb.png" />
+<p><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html#sphx-glr-auto-examples-ensemble-plot-voting-decision-regions-py"><span class="std std-ref">Visualizing the probabilistic predictions of a VotingClassifier</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Visualizing the probabilistic predictions of a VotingClassifier</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="For greyscale image data where pixel values can be interpreted as degrees of blackness on a white background, like handwritten digit recognition, the Bernoulli Restricted Boltzmann machine model (BernoulliRBM) can perform effective non-linear feature extraction."><img alt="" src="http://github.com/_images/sphx_glr_plot_rbm_logistic_classification_thumb.png" />
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
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diff --git a/dev/auto_examples/classification/plot_digits_classification.html b/dev/auto_examples/classification/plot_digits_classification.html
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -902,9 +901,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
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 </details></li>
@@ -1180,7 +1179,900 @@ <h2>Second experiment: varying number of classes and clusters<a class="headerlin
 <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
 </pre></div>
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+The number of unique classes is greater than 50% of the number of samples.
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+The number of unique classes is greater than 50% of the number of samples.
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+The number of unique classes is greater than 50% of the number of samples.
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+The number of unique classes is greater than 50% of the number of samples.
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+The number of unique classes is greater than 50% of the number of samples.
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+
+The number of unique classes is greater than 50% of the number of samples.
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+
+The number of unique classes is greater than 50% of the number of samples.
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+The number of unique classes is greater than 50% of the number of samples.
+</pre></div>
+</div>
+<p>We observe similar results as for the first experiment: adjusted for chance
 metrics stay constantly near zero while other metrics tend to get larger with
 finer-grained labelings. The mean V-measure of random labeling increases
 significantly as the number of clusters is closer to the total number of
@@ -1197,7 +2089,7 @@ <h2>Second experiment: varying number of classes and clusters<a class="headerlin
 random. In particular, such non-adjusted metrics should not be used to compare
 the results of different clustering algorithms that output a different number
 of clusters.</p>
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diff --git a/dev/auto_examples/cluster/plot_affinity_propagation.html b/dev/auto_examples/cluster/plot_affinity_propagation.html
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
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+<img src="http://github.com/_images/sphx_glr_plot_affinity_propagation_001.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_affinity_propagation_001.png " alt="Estimated number of clusters: 3" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 0.298 seconds)</p>
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 158 clusters to 100 clusters.</p>
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 n_clusters : 158
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+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
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@@ -1092,16 +1091,16 @@
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 </div>
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-<li><img src="http://github.com/_images/sphx_glr_plot_coin_segmentation_002.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_coin_segmentation_002.png " alt="Spectral clustering: discretize, 1.46s" class = "sphx-glr-multi-img"/></li>
-<li><img src="http://github.com/_images/sphx_glr_plot_coin_segmentation_003.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_coin_segmentation_003.png " alt="Spectral clustering: cluster_qr, 1.45s" class = "sphx-glr-multi-img"/></li>
+<li><img src="http://github.com/_images/sphx_glr_plot_coin_segmentation_001.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_coin_segmentation_001.png " alt="Spectral clustering: kmeans, 1.99s" class = "sphx-glr-multi-img"/></li>
+<li><img src="http://github.com/_images/sphx_glr_plot_coin_segmentation_002.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_coin_segmentation_002.png " alt="Spectral clustering: discretize, 1.89s" class = "sphx-glr-multi-img"/></li>
+<li><img src="http://github.com/_images/sphx_glr_plot_coin_segmentation_003.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_coin_segmentation_003.png " alt="Spectral clustering: cluster_qr, 1.81s" class = "sphx-glr-multi-img"/></li>
 </ul>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Spectral clustering: kmeans, 1.58s
-Spectral clustering: discretize, 1.46s
-Spectral clustering: cluster_qr, 1.45s
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Spectral clustering: kmeans, 1.99s
+Spectral clustering: discretize, 1.89s
+Spectral clustering: cluster_qr, 1.81s
 </pre></div>
 </div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 4.852 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 6.140 seconds)</p>
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diff --git a/dev/auto_examples/cluster/plot_coin_ward_segmentation.html b/dev/auto_examples/cluster/plot_coin_ward_segmentation.html
index 294f34a98dca6..a329bca82f43f 100644
--- a/dev/auto_examples/cluster/plot_coin_ward_segmentation.html
+++ b/dev/auto_examples/cluster/plot_coin_ward_segmentation.html
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -902,9 +901,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
@@ -1056,7 +1055,7 @@ <h2>Compute clustering<a class="headerlink" href="#compute-clustering" title="Li
 </pre></div>
 </div>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Compute structured hierarchical clustering...
-Elapsed time: 0.159s
+Elapsed time: 0.157s
 Number of pixels: 4697
 Number of clusters: 27
 </pre></div>
@@ -1082,7 +1081,7 @@ <h2>Plot the results on an image<a class="headerlink" href="#plot-the-results-on
 <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
 </pre></div>
 </div>
-<img src="http://github.com/_images/sphx_glr_plot_coin_ward_segmentation_001.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_coin_ward_segmentation_001.png " alt="plot coin ward segmentation" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 0.327 seconds)</p>
+<img src="http://github.com/_images/sphx_glr_plot_coin_ward_segmentation_001.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_coin_ward_segmentation_001.png " alt="plot coin ward segmentation" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 0.326 seconds)</p>
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diff --git a/dev/auto_examples/cluster/plot_dbscan.html b/dev/auto_examples/cluster/plot_dbscan.html
index 49f62caaf46d6..beb715a224cfd 100644
--- a/dev/auto_examples/cluster/plot_dbscan.html
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@@ -680,13 +680,12 @@
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
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 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -903,9 +902,9 @@
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
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@@ -1124,7 +1123,7 @@ <h2>Plot results<a class="headerlink" href="#plot-results" title="Link to this h
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 </pre></div>
 </div>
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+<img src="http://github.com/_images/sphx_glr_plot_dbscan_002.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_dbscan_002.png " alt="Estimated number of clusters: 3" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 0.166 seconds)</p>
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diff --git a/dev/auto_examples/cluster/plot_dict_face_patches.html b/dev/auto_examples/cluster/plot_dict_face_patches.html
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
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@@ -902,9 +901,9 @@
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
@@ -1088,7 +1087,7 @@ <h2>Learn the dictionary of images<a class="headerlink" href="#learn-the-diction
 Partial fit of 2200 out of 2400
 Partial fit of 2300 out of 2400
 Partial fit of 2400 out of 2400
-done in 1.17s.
+done in 1.37s.
 </pre></div>
 </div>
 </section>
@@ -1113,7 +1112,7 @@ <h2>Plot the results<a class="headerlink" href="#plot-the-results" title="Link t
 <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
 </pre></div>
 </div>
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+<img src="http://github.com/_images/sphx_glr_plot_dict_face_patches_001.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_dict_face_patches_001.png " alt="Patches of faces Train time 1.4s on 3200 patches" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 2.558 seconds)</p>
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@@ -1134,12 +1133,12 @@ <h2>Plot the results<a class="headerlink" href="#plot-the-results" title="Link t
 </div>
 </div>
 <p class="rubric">Related examples</p>
-<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="An example comparing the effect of reconstructing noisy fragments of a raccoon face image using firstly online DictionaryLearning and various transform methods."><img alt="" src="http://github.com/_images/sphx_glr_plot_image_denoising_thumb.png" />
-<p><a class="reference internal" href="http://github.com/decomposition/plot_image_denoising.html#sphx-glr-auto-examples-decomposition-plot-image-denoising-py"><span class="std std-ref">Image denoising using dictionary learning</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Image denoising using dictionary learning</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This example applies to olivetti_faces_dataset different unsupervised matrix decomposition (dimension reduction) methods from the module sklearn.decomposition (see the documentation chapter decompositions)."><img alt="" src="http://github.com/_images/sphx_glr_plot_faces_decomposition_thumb.png" />
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This example applies to olivetti_faces_dataset different unsupervised matrix decomposition (dimension reduction) methods from the module sklearn.decomposition (see the documentation chapter decompositions)."><img alt="" src="http://github.com/_images/sphx_glr_plot_faces_decomposition_thumb.png" />
 <p><a class="reference internal" href="http://github.com/decomposition/plot_faces_decomposition.html#sphx-glr-auto-examples-decomposition-plot-faces-decomposition-py"><span class="std std-ref">Faces dataset decompositions</span></a></p>
   <div class="sphx-glr-thumbnail-title">Faces dataset decompositions</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="An example comparing the effect of reconstructing noisy fragments of a raccoon face image using firstly online DictionaryLearning and various transform methods."><img alt="" src="http://github.com/_images/sphx_glr_plot_image_denoising_thumb.png" />
+<p><a class="reference internal" href="http://github.com/decomposition/plot_image_denoising.html#sphx-glr-auto-examples-decomposition-plot-image-denoising-py"><span class="std std-ref">Image denoising using dictionary learning</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Image denoising using dictionary learning</div>
 </div><div class="sphx-glr-thumbcontainer" tooltip="These images show how similar features are merged together using feature agglomeration."><img alt="" src="http://github.com/_images/sphx_glr_plot_digits_agglomeration_thumb.png" />
 <p><a class="reference internal" href="http://github.com/plot_digits_agglomeration.html#sphx-glr-auto-examples-cluster-plot-digits-agglomeration-py"><span class="std std-ref">Feature agglomeration</span></a></p>
   <div class="sphx-glr-thumbnail-title">Feature agglomeration</div>
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index a39a71ab97a12..fc5bd5da45c4f 100644
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+<meta name="description" content="These images show how similar features are merged together using feature agglomeration. Total running time of the script:(0 minutes 0.129 seconds) Launch binder Launch JupyterLite Download Jupyter ..." />
 
     <title>Feature agglomeration &#8212; scikit-learn 1.7.dev0 documentation</title>
   
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -902,9 +901,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
@@ -1046,7 +1045,7 @@
 <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
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+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 0.129 seconds)</p>
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 <p><a class="reference internal" href="http://github.com/plot_dict_face_patches.html#sphx-glr-auto-examples-cluster-plot-dict-face-patches-py"><span class="std std-ref">Online learning of a dictionary of parts of faces</span></a></p>
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-  <div class="sphx-glr-thumbnail-title">Label Propagation digits active learning</div>
+<p><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html#sphx-glr-auto-examples-semi-supervised-plot-label-propagation-digits-active-learning-py"><span class="std std-ref">Label Propagation digits: Active learning</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Label Propagation digits: Active learning</div>
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diff --git a/dev/auto_examples/cluster/plot_digits_linkage.html b/dev/auto_examples/cluster/plot_digits_linkage.html
index 4288efbca71d1..20cf7260a2c0f 100644
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@@ -679,13 +679,12 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -902,9 +901,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
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diff --git a/dev/auto_examples/cluster/plot_face_compress.html b/dev/auto_examples/cluster/plot_face_compress.html
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
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 and are not too sparse, the k-means algorithm is quite effective and is one of
 the fastest clustering algorithms available. This advantage is lost if one has
 to restart it several times to avoid convergence to a local minimum.</p>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
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+random          0.037s  69735   0.681   0.723   0.701   0.574   0.698   0.173
+PCA-based       0.013s  69513   0.600   0.647   0.622   0.468   0.618   0.162
 __________________________________________________________________________________
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
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 <p><a class="reference internal" href="http://github.com/plot_mean_shift.html#sphx-glr-auto-examples-cluster-plot-mean-shift-py"><span class="std std-ref">A demo of the mean-shift clustering algorithm</span></a></p>
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-  <div class="sphx-glr-thumbnail-title">Demo of affinity propagation clustering algorithm</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="In this example we compare the various initialization strategies for K-means in terms of runtime and quality of the results."><img alt="" src="http://github.com/_images/sphx_glr_plot_kmeans_digits_thumb.png" />
+<p><a class="reference internal" href="http://github.com/plot_kmeans_digits.html#sphx-glr-auto-examples-cluster-plot-kmeans-digits-py"><span class="std std-ref">A demo of K-Means clustering on the handwritten digits data</span></a></p>
+  <div class="sphx-glr-thumbnail-title">A demo of K-Means clustering on the handwritten digits data</div>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
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+<img src="http://github.com/_images/sphx_glr_plot_segmentation_toy_002.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_segmentation_toy_002.png " alt="plot segmentation toy" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 0.496 seconds)</p>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
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+<img src="http://github.com/_images/sphx_glr_plot_ward_structured_vs_unstructured_002.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_ward_structured_vs_unstructured_002.png " alt="With connectivity constraints (time 0.06s)" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 0.397 seconds)</p>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
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 <div class="binder-badge docutils container">
 <a class="reference external image-reference" href="https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/compose/plot_column_transformer.ipynb"><img alt="Launch binder" src="http://github.com/_images/binder_badge_logo5.svg" style="width: 150px;" />
diff --git a/dev/auto_examples/compose/plot_column_transformer_mixed_types.html b/dev/auto_examples/compose/plot_column_transformer_mixed_types.html
index b7ae97d44b96b..5dba04eff9c60 100644
--- a/dev/auto_examples/compose/plot_column_transformer_mixed_types.html
+++ b/dev/auto_examples/compose/plot_column_transformer_mixed_types.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -679,13 +679,12 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -902,9 +901,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
@@ -1092,7 +1091,7 @@
 </pre></div>
 </div>
 <div class="output_subarea output_html rendered_html output_result">
-<style>#sk-container-id-66 {
+<style>#sk-container-id-67 {
   /* Definition of color scheme common for light and dark mode */
   --sklearn-color-text: #000;
   --sklearn-color-text-muted: #666;
@@ -1123,15 +1122,15 @@
   }
 }
 
-#sk-container-id-66 {
+#sk-container-id-67 {
   color: var(--sklearn-color-text);
 }
 
-#sk-container-id-66 pre {
+#sk-container-id-67 pre {
   padding: 0;
 }
 
-#sk-container-id-66 input.sk-hidden--visually {
+#sk-container-id-67 input.sk-hidden--visually {
   border: 0;
   clip: rect(1px 1px 1px 1px);
   clip: rect(1px, 1px, 1px, 1px);
@@ -1143,7 +1142,7 @@
   width: 1px;
 }
 
-#sk-container-id-66 div.sk-dashed-wrapped {
+#sk-container-id-67 div.sk-dashed-wrapped {
   border: 1px dashed var(--sklearn-color-line);
   margin: 0 0.4em 0.5em 0.4em;
   box-sizing: border-box;
@@ -1151,7 +1150,7 @@
   background-color: var(--sklearn-color-background);
 }
 
-#sk-container-id-66 div.sk-container {
+#sk-container-id-67 div.sk-container {
   /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
      but bootstrap.min.css set `[hidden] { display: none !important; }`
      so we also need the `!important` here to be able to override the
@@ -1161,7 +1160,7 @@
   position: relative;
 }
 
-#sk-container-id-66 div.sk-text-repr-fallback {
+#sk-container-id-67 div.sk-text-repr-fallback {
   display: none;
 }
 
@@ -1177,14 +1176,14 @@
 
 /* Parallel-specific style estimator block */
 
-#sk-container-id-66 div.sk-parallel-item::after {
+#sk-container-id-67 div.sk-parallel-item::after {
   content: "";
   width: 100%;
   border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
   flex-grow: 1;
 }
 
-#sk-container-id-66 div.sk-parallel {
+#sk-container-id-67 div.sk-parallel {
   display: flex;
   align-items: stretch;
   justify-content: center;
@@ -1192,28 +1191,28 @@
   position: relative;
 }
 
-#sk-container-id-66 div.sk-parallel-item {
+#sk-container-id-67 div.sk-parallel-item {
   display: flex;
   flex-direction: column;
 }
 
-#sk-container-id-66 div.sk-parallel-item:first-child::after {
+#sk-container-id-67 div.sk-parallel-item:first-child::after {
   align-self: flex-end;
   width: 50%;
 }
 
-#sk-container-id-66 div.sk-parallel-item:last-child::after {
+#sk-container-id-67 div.sk-parallel-item:last-child::after {
   align-self: flex-start;
   width: 50%;
 }
 
-#sk-container-id-66 div.sk-parallel-item:only-child::after {
+#sk-container-id-67 div.sk-parallel-item:only-child::after {
   width: 0;
 }
 
 /* Serial-specific style estimator block */
 
-#sk-container-id-66 div.sk-serial {
+#sk-container-id-67 div.sk-serial {
   display: flex;
   flex-direction: column;
   align-items: center;
@@ -1231,14 +1230,14 @@
 
 /* Pipeline and ColumnTransformer style (default) */
 
-#sk-container-id-66 div.sk-toggleable {
+#sk-container-id-67 div.sk-toggleable {
   /* Default theme specific background. It is overwritten whether we have a
   specific estimator or a Pipeline/ColumnTransformer */
   background-color: var(--sklearn-color-background);
 }
 
 /* Toggleable label */
-#sk-container-id-66 label.sk-toggleable__label {
+#sk-container-id-67 label.sk-toggleable__label {
   cursor: pointer;
   display: flex;
   width: 100%;
@@ -1251,13 +1250,13 @@
   gap: 0.5em;
 }
 
-#sk-container-id-66 label.sk-toggleable__label .caption {
+#sk-container-id-67 label.sk-toggleable__label .caption {
   font-size: 0.6rem;
   font-weight: lighter;
   color: var(--sklearn-color-text-muted);
 }
 
-#sk-container-id-66 label.sk-toggleable__label-arrow:before {
+#sk-container-id-67 label.sk-toggleable__label-arrow:before {
   /* Arrow on the left of the label */
   content: "▸";
   float: left;
@@ -1265,13 +1264,13 @@
   color: var(--sklearn-color-icon);
 }
 
-#sk-container-id-66 label.sk-toggleable__label-arrow:hover:before {
+#sk-container-id-67 label.sk-toggleable__label-arrow:hover:before {
   color: var(--sklearn-color-text);
 }
 
 /* Toggleable content - dropdown */
 
-#sk-container-id-66 div.sk-toggleable__content {
+#sk-container-id-67 div.sk-toggleable__content {
   max-height: 0;
   max-width: 0;
   overflow: hidden;
@@ -1280,12 +1279,12 @@
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-66 div.sk-toggleable__content.fitted {
+#sk-container-id-67 div.sk-toggleable__content.fitted {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
-#sk-container-id-66 div.sk-toggleable__content pre {
+#sk-container-id-67 div.sk-toggleable__content pre {
   margin: 0.2em;
   border-radius: 0.25em;
   color: var(--sklearn-color-text);
@@ -1293,79 +1292,79 @@
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-66 div.sk-toggleable__content.fitted pre {
+#sk-container-id-67 div.sk-toggleable__content.fitted pre {
   /* unfitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
-#sk-container-id-66 input.sk-toggleable__control:checked~div.sk-toggleable__content {
+#sk-container-id-67 input.sk-toggleable__control:checked~div.sk-toggleable__content {
   /* Expand drop-down */
   max-height: 200px;
   max-width: 100%;
   overflow: auto;
 }
 
-#sk-container-id-66 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
+#sk-container-id-67 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
   content: "▾";
 }
 
 /* Pipeline/ColumnTransformer-specific style */
 
-#sk-container-id-66 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-67 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-66 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-67 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
 /* Estimator-specific style */
 
 /* Colorize estimator box */
-#sk-container-id-66 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-67 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-66 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-67 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
-#sk-container-id-66 div.sk-label label.sk-toggleable__label,
-#sk-container-id-66 div.sk-label label {
+#sk-container-id-67 div.sk-label label.sk-toggleable__label,
+#sk-container-id-67 div.sk-label label {
   /* The background is the default theme color */
   color: var(--sklearn-color-text-on-default-background);
 }
 
 /* On hover, darken the color of the background */
-#sk-container-id-66 div.sk-label:hover label.sk-toggleable__label {
+#sk-container-id-67 div.sk-label:hover label.sk-toggleable__label {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
 /* Label box, darken color on hover, fitted */
-#sk-container-id-66 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
+#sk-container-id-67 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
 /* Estimator label */
 
-#sk-container-id-66 div.sk-label label {
+#sk-container-id-67 div.sk-label label {
   font-family: monospace;
   font-weight: bold;
   display: inline-block;
   line-height: 1.2em;
 }
 
-#sk-container-id-66 div.sk-label-container {
+#sk-container-id-67 div.sk-label-container {
   text-align: center;
 }
 
 /* Estimator-specific */
-#sk-container-id-66 div.sk-estimator {
+#sk-container-id-67 div.sk-estimator {
   font-family: monospace;
   border: 1px dotted var(--sklearn-color-border-box);
   border-radius: 0.25em;
@@ -1375,18 +1374,18 @@
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-66 div.sk-estimator.fitted {
+#sk-container-id-67 div.sk-estimator.fitted {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
 /* on hover */
-#sk-container-id-66 div.sk-estimator:hover {
+#sk-container-id-67 div.sk-estimator:hover {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-66 div.sk-estimator.fitted:hover {
+#sk-container-id-67 div.sk-estimator.fitted:hover {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-2);
 }
@@ -1474,7 +1473,7 @@
 
 /* "?"-specific style due to the `<a>` HTML tag */
 
-#sk-container-id-66 a.estimator_doc_link {
+#sk-container-id-67 a.estimator_doc_link {
   float: right;
   font-size: 1rem;
   line-height: 1em;
@@ -1489,25 +1488,25 @@
   border: var(--sklearn-color-unfitted-level-1) 1pt solid;
 }
 
-#sk-container-id-66 a.estimator_doc_link.fitted {
+#sk-container-id-67 a.estimator_doc_link.fitted {
   /* fitted */
   border: var(--sklearn-color-fitted-level-1) 1pt solid;
   color: var(--sklearn-color-fitted-level-1);
 }
 
 /* On hover */
-#sk-container-id-66 a.estimator_doc_link:hover {
+#sk-container-id-67 a.estimator_doc_link:hover {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-3);
   color: var(--sklearn-color-background);
   text-decoration: none;
 }
 
-#sk-container-id-66 a.estimator_doc_link.fitted:hover {
+#sk-container-id-67 a.estimator_doc_link.fitted:hover {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-3);
 }
-</style><div id="sk-container-id-66" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,
+</style><div id="sk-container-id-67" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,
                  ColumnTransformer(transformers=[(&#x27;num&#x27;,
                                                   Pipeline(steps=[(&#x27;imputer&#x27;,
                                                                    SimpleImputer(strategy=&#x27;median&#x27;)),
@@ -1519,10 +1518,10 @@
                                                                    OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)),
                                                                   (&#x27;selector&#x27;,
                                                                    SelectPercentile(percentile=50,
-                                                                                    score_func=&lt;function chi2 at 0x7fb58f5c1cf0&gt;))]),
+                                                                                    score_func=&lt;function chi2 at 0x7fd851052050&gt;))]),
                                                   [&#x27;embarked&#x27;, &#x27;sex&#x27;,
                                                    &#x27;pclass&#x27;])])),
-                (&#x27;classifier&#x27;, LogisticRegression())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-257" type="checkbox" ><label for="sk-estimator-id-257" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>Pipeline</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.pipeline.Pipeline.html">?<span>Documentation for Pipeline</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,
+                (&#x27;classifier&#x27;, LogisticRegression())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-271" type="checkbox" ><label for="sk-estimator-id-271" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>Pipeline</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.pipeline.Pipeline.html">?<span>Documentation for Pipeline</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,
                  ColumnTransformer(transformers=[(&#x27;num&#x27;,
                                                   Pipeline(steps=[(&#x27;imputer&#x27;,
                                                                    SimpleImputer(strategy=&#x27;median&#x27;)),
@@ -1534,10 +1533,10 @@
                                                                    OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)),
                                                                   (&#x27;selector&#x27;,
                                                                    SelectPercentile(percentile=50,
-                                                                                    score_func=&lt;function chi2 at 0x7fb58f5c1cf0&gt;))]),
+                                                                                    score_func=&lt;function chi2 at 0x7fd851052050&gt;))]),
                                                   [&#x27;embarked&#x27;, &#x27;sex&#x27;,
                                                    &#x27;pclass&#x27;])])),
-                (&#x27;classifier&#x27;, LogisticRegression())])</pre></div> </div></div><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-258" type="checkbox" ><label for="sk-estimator-id-258" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>preprocessor: ColumnTransformer</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.compose.ColumnTransformer.html">?<span>Documentation for preprocessor: ColumnTransformer</span></a></div></label><div class="sk-toggleable__content fitted"><pre>ColumnTransformer(transformers=[(&#x27;num&#x27;,
+                (&#x27;classifier&#x27;, LogisticRegression())])</pre></div> </div></div><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-272" type="checkbox" ><label for="sk-estimator-id-272" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>preprocessor: ColumnTransformer</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.compose.ColumnTransformer.html">?<span>Documentation for preprocessor: ColumnTransformer</span></a></div></label><div class="sk-toggleable__content fitted"><pre>ColumnTransformer(transformers=[(&#x27;num&#x27;,
                                  Pipeline(steps=[(&#x27;imputer&#x27;,
                                                   SimpleImputer(strategy=&#x27;median&#x27;)),
                                                  (&#x27;scaler&#x27;, StandardScaler())]),
@@ -1547,8 +1546,8 @@
                                                   OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)),
                                                  (&#x27;selector&#x27;,
                                                   SelectPercentile(percentile=50,
-                                                                   score_func=&lt;function chi2 at 0x7fb58f5c1cf0&gt;))]),
-                                 [&#x27;embarked&#x27;, &#x27;sex&#x27;, &#x27;pclass&#x27;])])</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-259" type="checkbox" ><label for="sk-estimator-id-259" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>num</div></div></label><div class="sk-toggleable__content fitted"><pre>[&#x27;age&#x27;, &#x27;fare&#x27;]</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-260" type="checkbox" ><label for="sk-estimator-id-260" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>SimpleImputer</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.impute.SimpleImputer.html">?<span>Documentation for SimpleImputer</span></a></div></label><div class="sk-toggleable__content fitted"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-261" type="checkbox" ><label for="sk-estimator-id-261" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>StandardScaler</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.StandardScaler.html">?<span>Documentation for StandardScaler</span></a></div></label><div class="sk-toggleable__content fitted"><pre>StandardScaler()</pre></div> </div></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-262" type="checkbox" ><label for="sk-estimator-id-262" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>cat</div></div></label><div class="sk-toggleable__content fitted"><pre>[&#x27;embarked&#x27;, &#x27;sex&#x27;, &#x27;pclass&#x27;]</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-263" type="checkbox" ><label for="sk-estimator-id-263" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>OneHotEncoder</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.OneHotEncoder.html">?<span>Documentation for OneHotEncoder</span></a></div></label><div class="sk-toggleable__content fitted"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-264" type="checkbox" ><label for="sk-estimator-id-264" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>SelectPercentile</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.feature_selection.SelectPercentile.html">?<span>Documentation for SelectPercentile</span></a></div></label><div class="sk-toggleable__content fitted"><pre>SelectPercentile(percentile=50, score_func=&lt;function chi2 at 0x7fb58f5c1cf0&gt;)</pre></div> </div></div></div></div></div></div></div></div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-265" type="checkbox" ><label for="sk-estimator-id-265" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>LogisticRegression</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.linear_model.LogisticRegression.html">?<span>Documentation for LogisticRegression</span></a></div></label><div class="sk-toggleable__content fitted"><pre>LogisticRegression()</pre></div> </div></div></div></div></div></div>
+                                                                   score_func=&lt;function chi2 at 0x7fd851052050&gt;))]),
+                                 [&#x27;embarked&#x27;, &#x27;sex&#x27;, &#x27;pclass&#x27;])])</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-273" type="checkbox" ><label for="sk-estimator-id-273" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>num</div></div></label><div class="sk-toggleable__content fitted"><pre>[&#x27;age&#x27;, &#x27;fare&#x27;]</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-274" type="checkbox" ><label for="sk-estimator-id-274" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>SimpleImputer</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.impute.SimpleImputer.html">?<span>Documentation for SimpleImputer</span></a></div></label><div class="sk-toggleable__content fitted"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-275" type="checkbox" ><label for="sk-estimator-id-275" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>StandardScaler</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.StandardScaler.html">?<span>Documentation for StandardScaler</span></a></div></label><div class="sk-toggleable__content fitted"><pre>StandardScaler()</pre></div> </div></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-276" type="checkbox" ><label for="sk-estimator-id-276" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>cat</div></div></label><div class="sk-toggleable__content fitted"><pre>[&#x27;embarked&#x27;, &#x27;sex&#x27;, &#x27;pclass&#x27;]</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-277" type="checkbox" ><label for="sk-estimator-id-277" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>OneHotEncoder</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.OneHotEncoder.html">?<span>Documentation for OneHotEncoder</span></a></div></label><div class="sk-toggleable__content fitted"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-278" type="checkbox" ><label for="sk-estimator-id-278" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>SelectPercentile</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.feature_selection.SelectPercentile.html">?<span>Documentation for SelectPercentile</span></a></div></label><div class="sk-toggleable__content fitted"><pre>SelectPercentile(percentile=50, score_func=&lt;function chi2 at 0x7fd851052050&gt;)</pre></div> </div></div></div></div></div></div></div></div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-279" type="checkbox" ><label for="sk-estimator-id-279" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>LogisticRegression</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.linear_model.LogisticRegression.html">?<span>Documentation for LogisticRegression</span></a></div></label><div class="sk-toggleable__content fitted"><pre>LogisticRegression()</pre></div> </div></div></div></div></div></div>
 </div>
 <br />
 <br /><p>Use <code class="docutils literal notranslate"><span class="pre">ColumnTransformer</span></code> by selecting column by data types</p>
@@ -1614,7 +1613,7 @@
 </pre></div>
 </div>
 <div class="output_subarea output_html rendered_html output_result">
-<style>#sk-container-id-67 {
+<style>#sk-container-id-68 {
   /* Definition of color scheme common for light and dark mode */
   --sklearn-color-text: #000;
   --sklearn-color-text-muted: #666;
@@ -1645,15 +1644,15 @@
   }
 }
 
-#sk-container-id-67 {
+#sk-container-id-68 {
   color: var(--sklearn-color-text);
 }
 
-#sk-container-id-67 pre {
+#sk-container-id-68 pre {
   padding: 0;
 }
 
-#sk-container-id-67 input.sk-hidden--visually {
+#sk-container-id-68 input.sk-hidden--visually {
   border: 0;
   clip: rect(1px 1px 1px 1px);
   clip: rect(1px, 1px, 1px, 1px);
@@ -1665,7 +1664,7 @@
   width: 1px;
 }
 
-#sk-container-id-67 div.sk-dashed-wrapped {
+#sk-container-id-68 div.sk-dashed-wrapped {
   border: 1px dashed var(--sklearn-color-line);
   margin: 0 0.4em 0.5em 0.4em;
   box-sizing: border-box;
@@ -1673,7 +1672,7 @@
   background-color: var(--sklearn-color-background);
 }
 
-#sk-container-id-67 div.sk-container {
+#sk-container-id-68 div.sk-container {
   /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
      but bootstrap.min.css set `[hidden] { display: none !important; }`
      so we also need the `!important` here to be able to override the
@@ -1683,7 +1682,7 @@
   position: relative;
 }
 
-#sk-container-id-67 div.sk-text-repr-fallback {
+#sk-container-id-68 div.sk-text-repr-fallback {
   display: none;
 }
 
@@ -1699,14 +1698,14 @@
 
 /* Parallel-specific style estimator block */
 
-#sk-container-id-67 div.sk-parallel-item::after {
+#sk-container-id-68 div.sk-parallel-item::after {
   content: "";
   width: 100%;
   border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
   flex-grow: 1;
 }
 
-#sk-container-id-67 div.sk-parallel {
+#sk-container-id-68 div.sk-parallel {
   display: flex;
   align-items: stretch;
   justify-content: center;
@@ -1714,28 +1713,28 @@
   position: relative;
 }
 
-#sk-container-id-67 div.sk-parallel-item {
+#sk-container-id-68 div.sk-parallel-item {
   display: flex;
   flex-direction: column;
 }
 
-#sk-container-id-67 div.sk-parallel-item:first-child::after {
+#sk-container-id-68 div.sk-parallel-item:first-child::after {
   align-self: flex-end;
   width: 50%;
 }
 
-#sk-container-id-67 div.sk-parallel-item:last-child::after {
+#sk-container-id-68 div.sk-parallel-item:last-child::after {
   align-self: flex-start;
   width: 50%;
 }
 
-#sk-container-id-67 div.sk-parallel-item:only-child::after {
+#sk-container-id-68 div.sk-parallel-item:only-child::after {
   width: 0;
 }
 
 /* Serial-specific style estimator block */
 
-#sk-container-id-67 div.sk-serial {
+#sk-container-id-68 div.sk-serial {
   display: flex;
   flex-direction: column;
   align-items: center;
@@ -1753,14 +1752,14 @@
 
 /* Pipeline and ColumnTransformer style (default) */
 
-#sk-container-id-67 div.sk-toggleable {
+#sk-container-id-68 div.sk-toggleable {
   /* Default theme specific background. It is overwritten whether we have a
   specific estimator or a Pipeline/ColumnTransformer */
   background-color: var(--sklearn-color-background);
 }
 
 /* Toggleable label */
-#sk-container-id-67 label.sk-toggleable__label {
+#sk-container-id-68 label.sk-toggleable__label {
   cursor: pointer;
   display: flex;
   width: 100%;
@@ -1773,13 +1772,13 @@
   gap: 0.5em;
 }
 
-#sk-container-id-67 label.sk-toggleable__label .caption {
+#sk-container-id-68 label.sk-toggleable__label .caption {
   font-size: 0.6rem;
   font-weight: lighter;
   color: var(--sklearn-color-text-muted);
 }
 
-#sk-container-id-67 label.sk-toggleable__label-arrow:before {
+#sk-container-id-68 label.sk-toggleable__label-arrow:before {
   /* Arrow on the left of the label */
   content: "▸";
   float: left;
@@ -1787,13 +1786,13 @@
   color: var(--sklearn-color-icon);
 }
 
-#sk-container-id-67 label.sk-toggleable__label-arrow:hover:before {
+#sk-container-id-68 label.sk-toggleable__label-arrow:hover:before {
   color: var(--sklearn-color-text);
 }
 
 /* Toggleable content - dropdown */
 
-#sk-container-id-67 div.sk-toggleable__content {
+#sk-container-id-68 div.sk-toggleable__content {
   max-height: 0;
   max-width: 0;
   overflow: hidden;
@@ -1802,12 +1801,12 @@
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-67 div.sk-toggleable__content.fitted {
+#sk-container-id-68 div.sk-toggleable__content.fitted {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
-#sk-container-id-67 div.sk-toggleable__content pre {
+#sk-container-id-68 div.sk-toggleable__content pre {
   margin: 0.2em;
   border-radius: 0.25em;
   color: var(--sklearn-color-text);
@@ -1815,79 +1814,79 @@
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-67 div.sk-toggleable__content.fitted pre {
+#sk-container-id-68 div.sk-toggleable__content.fitted pre {
   /* unfitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
-#sk-container-id-67 input.sk-toggleable__control:checked~div.sk-toggleable__content {
+#sk-container-id-68 input.sk-toggleable__control:checked~div.sk-toggleable__content {
   /* Expand drop-down */
   max-height: 200px;
   max-width: 100%;
   overflow: auto;
 }
 
-#sk-container-id-67 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
+#sk-container-id-68 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
   content: "▾";
 }
 
 /* Pipeline/ColumnTransformer-specific style */
 
-#sk-container-id-67 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-68 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-67 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-68 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
 /* Estimator-specific style */
 
 /* Colorize estimator box */
-#sk-container-id-67 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-68 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-67 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-68 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
-#sk-container-id-67 div.sk-label label.sk-toggleable__label,
-#sk-container-id-67 div.sk-label label {
+#sk-container-id-68 div.sk-label label.sk-toggleable__label,
+#sk-container-id-68 div.sk-label label {
   /* The background is the default theme color */
   color: var(--sklearn-color-text-on-default-background);
 }
 
 /* On hover, darken the color of the background */
-#sk-container-id-67 div.sk-label:hover label.sk-toggleable__label {
+#sk-container-id-68 div.sk-label:hover label.sk-toggleable__label {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
 /* Label box, darken color on hover, fitted */
-#sk-container-id-67 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
+#sk-container-id-68 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
 /* Estimator label */
 
-#sk-container-id-67 div.sk-label label {
+#sk-container-id-68 div.sk-label label {
   font-family: monospace;
   font-weight: bold;
   display: inline-block;
   line-height: 1.2em;
 }
 
-#sk-container-id-67 div.sk-label-container {
+#sk-container-id-68 div.sk-label-container {
   text-align: center;
 }
 
 /* Estimator-specific */
-#sk-container-id-67 div.sk-estimator {
+#sk-container-id-68 div.sk-estimator {
   font-family: monospace;
   border: 1px dotted var(--sklearn-color-border-box);
   border-radius: 0.25em;
@@ -1897,18 +1896,18 @@
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-67 div.sk-estimator.fitted {
+#sk-container-id-68 div.sk-estimator.fitted {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
 /* on hover */
-#sk-container-id-67 div.sk-estimator:hover {
+#sk-container-id-68 div.sk-estimator:hover {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-67 div.sk-estimator.fitted:hover {
+#sk-container-id-68 div.sk-estimator.fitted:hover {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-2);
 }
@@ -1996,7 +1995,7 @@
 
 /* "?"-specific style due to the `<a>` HTML tag */
 
-#sk-container-id-67 a.estimator_doc_link {
+#sk-container-id-68 a.estimator_doc_link {
   float: right;
   font-size: 1rem;
   line-height: 1em;
@@ -2011,64 +2010,64 @@
   border: var(--sklearn-color-unfitted-level-1) 1pt solid;
 }
 
-#sk-container-id-67 a.estimator_doc_link.fitted {
+#sk-container-id-68 a.estimator_doc_link.fitted {
   /* fitted */
   border: var(--sklearn-color-fitted-level-1) 1pt solid;
   color: var(--sklearn-color-fitted-level-1);
 }
 
 /* On hover */
-#sk-container-id-67 a.estimator_doc_link:hover {
+#sk-container-id-68 a.estimator_doc_link:hover {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-3);
   color: var(--sklearn-color-background);
   text-decoration: none;
 }
 
-#sk-container-id-67 a.estimator_doc_link.fitted:hover {
+#sk-container-id-68 a.estimator_doc_link.fitted:hover {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-3);
 }
-</style><div id="sk-container-id-67" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,
+</style><div id="sk-container-id-68" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,
                  ColumnTransformer(transformers=[(&#x27;num&#x27;,
                                                   Pipeline(steps=[(&#x27;imputer&#x27;,
                                                                    SimpleImputer(strategy=&#x27;median&#x27;)),
                                                                   (&#x27;scaler&#x27;,
                                                                    StandardScaler())]),
-                                                  &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb5845dccd0&gt;),
+                                                  &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd8413c07c0&gt;),
                                                  (&#x27;cat&#x27;,
                                                   Pipeline(steps=[(&#x27;encoder&#x27;,
                                                                    OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)),
                                                                   (&#x27;selector&#x27;,
                                                                    SelectPercentile(percentile=50,
-                                                                                    score_func=&lt;function chi2 at 0x7fb58f5c1cf0&gt;))]),
-                                                  &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb5845ddc30&gt;)])),
-                (&#x27;classifier&#x27;, LogisticRegression())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-266" type="checkbox" ><label for="sk-estimator-id-266" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>Pipeline</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.pipeline.Pipeline.html">?<span>Documentation for Pipeline</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,
+                                                                                    score_func=&lt;function chi2 at 0x7fd851052050&gt;))]),
+                                                  &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd8413c3040&gt;)])),
+                (&#x27;classifier&#x27;, LogisticRegression())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-280" type="checkbox" ><label for="sk-estimator-id-280" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>Pipeline</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.pipeline.Pipeline.html">?<span>Documentation for Pipeline</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,
                  ColumnTransformer(transformers=[(&#x27;num&#x27;,
                                                   Pipeline(steps=[(&#x27;imputer&#x27;,
                                                                    SimpleImputer(strategy=&#x27;median&#x27;)),
                                                                   (&#x27;scaler&#x27;,
                                                                    StandardScaler())]),
-                                                  &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb5845dccd0&gt;),
+                                                  &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd8413c07c0&gt;),
                                                  (&#x27;cat&#x27;,
                                                   Pipeline(steps=[(&#x27;encoder&#x27;,
                                                                    OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)),
                                                                   (&#x27;selector&#x27;,
                                                                    SelectPercentile(percentile=50,
-                                                                                    score_func=&lt;function chi2 at 0x7fb58f5c1cf0&gt;))]),
-                                                  &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb5845ddc30&gt;)])),
-                (&#x27;classifier&#x27;, LogisticRegression())])</pre></div> </div></div><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-267" type="checkbox" ><label for="sk-estimator-id-267" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>preprocessor: ColumnTransformer</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.compose.ColumnTransformer.html">?<span>Documentation for preprocessor: ColumnTransformer</span></a></div></label><div class="sk-toggleable__content fitted"><pre>ColumnTransformer(transformers=[(&#x27;num&#x27;,
+                                                                                    score_func=&lt;function chi2 at 0x7fd851052050&gt;))]),
+                                                  &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd8413c3040&gt;)])),
+                (&#x27;classifier&#x27;, LogisticRegression())])</pre></div> </div></div><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-281" type="checkbox" ><label for="sk-estimator-id-281" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>preprocessor: ColumnTransformer</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.compose.ColumnTransformer.html">?<span>Documentation for preprocessor: ColumnTransformer</span></a></div></label><div class="sk-toggleable__content fitted"><pre>ColumnTransformer(transformers=[(&#x27;num&#x27;,
                                  Pipeline(steps=[(&#x27;imputer&#x27;,
                                                   SimpleImputer(strategy=&#x27;median&#x27;)),
                                                  (&#x27;scaler&#x27;, StandardScaler())]),
-                                 &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb5845dccd0&gt;),
+                                 &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd8413c07c0&gt;),
                                 (&#x27;cat&#x27;,
                                  Pipeline(steps=[(&#x27;encoder&#x27;,
                                                   OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)),
                                                  (&#x27;selector&#x27;,
                                                   SelectPercentile(percentile=50,
-                                                                   score_func=&lt;function chi2 at 0x7fb58f5c1cf0&gt;))]),
-                                 &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb5845ddc30&gt;)])</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-268" type="checkbox" ><label for="sk-estimator-id-268" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>num</div></div></label><div class="sk-toggleable__content fitted"><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb5845dccd0&gt;</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-269" type="checkbox" ><label for="sk-estimator-id-269" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>SimpleImputer</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.impute.SimpleImputer.html">?<span>Documentation for SimpleImputer</span></a></div></label><div class="sk-toggleable__content fitted"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-270" type="checkbox" ><label for="sk-estimator-id-270" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>StandardScaler</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.StandardScaler.html">?<span>Documentation for StandardScaler</span></a></div></label><div class="sk-toggleable__content fitted"><pre>StandardScaler()</pre></div> </div></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-271" type="checkbox" ><label for="sk-estimator-id-271" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>cat</div></div></label><div class="sk-toggleable__content fitted"><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb5845ddc30&gt;</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-272" type="checkbox" ><label for="sk-estimator-id-272" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>OneHotEncoder</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.OneHotEncoder.html">?<span>Documentation for OneHotEncoder</span></a></div></label><div class="sk-toggleable__content fitted"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-273" type="checkbox" ><label for="sk-estimator-id-273" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>SelectPercentile</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.feature_selection.SelectPercentile.html">?<span>Documentation for SelectPercentile</span></a></div></label><div class="sk-toggleable__content fitted"><pre>SelectPercentile(percentile=50, score_func=&lt;function chi2 at 0x7fb58f5c1cf0&gt;)</pre></div> </div></div></div></div></div></div></div></div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-274" type="checkbox" ><label for="sk-estimator-id-274" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>LogisticRegression</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.linear_model.LogisticRegression.html">?<span>Documentation for LogisticRegression</span></a></div></label><div class="sk-toggleable__content fitted"><pre>LogisticRegression()</pre></div> </div></div></div></div></div></div>
+                                                                   score_func=&lt;function chi2 at 0x7fd851052050&gt;))]),
+                                 &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd8413c3040&gt;)])</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-282" type="checkbox" ><label for="sk-estimator-id-282" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>num</div></div></label><div class="sk-toggleable__content fitted"><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd8413c07c0&gt;</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-283" type="checkbox" ><label for="sk-estimator-id-283" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>SimpleImputer</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.impute.SimpleImputer.html">?<span>Documentation for SimpleImputer</span></a></div></label><div class="sk-toggleable__content fitted"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-284" type="checkbox" ><label for="sk-estimator-id-284" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>StandardScaler</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.StandardScaler.html">?<span>Documentation for StandardScaler</span></a></div></label><div class="sk-toggleable__content fitted"><pre>StandardScaler()</pre></div> </div></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-285" type="checkbox" ><label for="sk-estimator-id-285" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>cat</div></div></label><div class="sk-toggleable__content fitted"><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd8413c3040&gt;</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-286" type="checkbox" ><label for="sk-estimator-id-286" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>OneHotEncoder</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.OneHotEncoder.html">?<span>Documentation for OneHotEncoder</span></a></div></label><div class="sk-toggleable__content fitted"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-287" type="checkbox" ><label for="sk-estimator-id-287" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>SelectPercentile</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.feature_selection.SelectPercentile.html">?<span>Documentation for SelectPercentile</span></a></div></label><div class="sk-toggleable__content fitted"><pre>SelectPercentile(percentile=50, score_func=&lt;function chi2 at 0x7fd851052050&gt;)</pre></div> </div></div></div></div></div></div></div></div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-288" type="checkbox" ><label for="sk-estimator-id-288" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>LogisticRegression</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.linear_model.LogisticRegression.html">?<span>Documentation for LogisticRegression</span></a></div></label><div class="sk-toggleable__content fitted"><pre>LogisticRegression()</pre></div> </div></div></div></div></div></div>
 </div>
 <br />
 <br /><p>The resulting score is not exactly the same as the one from the previous
@@ -2108,7 +2107,7 @@
 </pre></div>
 </div>
 <div class="output_subarea output_html rendered_html output_result">
-<style>#sk-container-id-68 {
+<style>#sk-container-id-69 {
   /* Definition of color scheme common for light and dark mode */
   --sklearn-color-text: #000;
   --sklearn-color-text-muted: #666;
@@ -2139,15 +2138,15 @@
   }
 }
 
-#sk-container-id-68 {
+#sk-container-id-69 {
   color: var(--sklearn-color-text);
 }
 
-#sk-container-id-68 pre {
+#sk-container-id-69 pre {
   padding: 0;
 }
 
-#sk-container-id-68 input.sk-hidden--visually {
+#sk-container-id-69 input.sk-hidden--visually {
   border: 0;
   clip: rect(1px 1px 1px 1px);
   clip: rect(1px, 1px, 1px, 1px);
@@ -2159,7 +2158,7 @@
   width: 1px;
 }
 
-#sk-container-id-68 div.sk-dashed-wrapped {
+#sk-container-id-69 div.sk-dashed-wrapped {
   border: 1px dashed var(--sklearn-color-line);
   margin: 0 0.4em 0.5em 0.4em;
   box-sizing: border-box;
@@ -2167,7 +2166,7 @@
   background-color: var(--sklearn-color-background);
 }
 
-#sk-container-id-68 div.sk-container {
+#sk-container-id-69 div.sk-container {
   /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
      but bootstrap.min.css set `[hidden] { display: none !important; }`
      so we also need the `!important` here to be able to override the
@@ -2177,7 +2176,7 @@
   position: relative;
 }
 
-#sk-container-id-68 div.sk-text-repr-fallback {
+#sk-container-id-69 div.sk-text-repr-fallback {
   display: none;
 }
 
@@ -2193,14 +2192,14 @@
 
 /* Parallel-specific style estimator block */
 
-#sk-container-id-68 div.sk-parallel-item::after {
+#sk-container-id-69 div.sk-parallel-item::after {
   content: "";
   width: 100%;
   border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
   flex-grow: 1;
 }
 
-#sk-container-id-68 div.sk-parallel {
+#sk-container-id-69 div.sk-parallel {
   display: flex;
   align-items: stretch;
   justify-content: center;
@@ -2208,28 +2207,28 @@
   position: relative;
 }
 
-#sk-container-id-68 div.sk-parallel-item {
+#sk-container-id-69 div.sk-parallel-item {
   display: flex;
   flex-direction: column;
 }
 
-#sk-container-id-68 div.sk-parallel-item:first-child::after {
+#sk-container-id-69 div.sk-parallel-item:first-child::after {
   align-self: flex-end;
   width: 50%;
 }
 
-#sk-container-id-68 div.sk-parallel-item:last-child::after {
+#sk-container-id-69 div.sk-parallel-item:last-child::after {
   align-self: flex-start;
   width: 50%;
 }
 
-#sk-container-id-68 div.sk-parallel-item:only-child::after {
+#sk-container-id-69 div.sk-parallel-item:only-child::after {
   width: 0;
 }
 
 /* Serial-specific style estimator block */
 
-#sk-container-id-68 div.sk-serial {
+#sk-container-id-69 div.sk-serial {
   display: flex;
   flex-direction: column;
   align-items: center;
@@ -2247,14 +2246,14 @@
 
 /* Pipeline and ColumnTransformer style (default) */
 
-#sk-container-id-68 div.sk-toggleable {
+#sk-container-id-69 div.sk-toggleable {
   /* Default theme specific background. It is overwritten whether we have a
   specific estimator or a Pipeline/ColumnTransformer */
   background-color: var(--sklearn-color-background);
 }
 
 /* Toggleable label */
-#sk-container-id-68 label.sk-toggleable__label {
+#sk-container-id-69 label.sk-toggleable__label {
   cursor: pointer;
   display: flex;
   width: 100%;
@@ -2267,13 +2266,13 @@
   gap: 0.5em;
 }
 
-#sk-container-id-68 label.sk-toggleable__label .caption {
+#sk-container-id-69 label.sk-toggleable__label .caption {
   font-size: 0.6rem;
   font-weight: lighter;
   color: var(--sklearn-color-text-muted);
 }
 
-#sk-container-id-68 label.sk-toggleable__label-arrow:before {
+#sk-container-id-69 label.sk-toggleable__label-arrow:before {
   /* Arrow on the left of the label */
   content: "▸";
   float: left;
@@ -2281,13 +2280,13 @@
   color: var(--sklearn-color-icon);
 }
 
-#sk-container-id-68 label.sk-toggleable__label-arrow:hover:before {
+#sk-container-id-69 label.sk-toggleable__label-arrow:hover:before {
   color: var(--sklearn-color-text);
 }
 
 /* Toggleable content - dropdown */
 
-#sk-container-id-68 div.sk-toggleable__content {
+#sk-container-id-69 div.sk-toggleable__content {
   max-height: 0;
   max-width: 0;
   overflow: hidden;
@@ -2296,12 +2295,12 @@
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-68 div.sk-toggleable__content.fitted {
+#sk-container-id-69 div.sk-toggleable__content.fitted {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
-#sk-container-id-68 div.sk-toggleable__content pre {
+#sk-container-id-69 div.sk-toggleable__content pre {
   margin: 0.2em;
   border-radius: 0.25em;
   color: var(--sklearn-color-text);
@@ -2309,79 +2308,79 @@
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-68 div.sk-toggleable__content.fitted pre {
+#sk-container-id-69 div.sk-toggleable__content.fitted pre {
   /* unfitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
-#sk-container-id-68 input.sk-toggleable__control:checked~div.sk-toggleable__content {
+#sk-container-id-69 input.sk-toggleable__control:checked~div.sk-toggleable__content {
   /* Expand drop-down */
   max-height: 200px;
   max-width: 100%;
   overflow: auto;
 }
 
-#sk-container-id-68 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
+#sk-container-id-69 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
   content: "▾";
 }
 
 /* Pipeline/ColumnTransformer-specific style */
 
-#sk-container-id-68 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-69 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-68 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-69 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
 /* Estimator-specific style */
 
 /* Colorize estimator box */
-#sk-container-id-68 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-69 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-68 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-69 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
-#sk-container-id-68 div.sk-label label.sk-toggleable__label,
-#sk-container-id-68 div.sk-label label {
+#sk-container-id-69 div.sk-label label.sk-toggleable__label,
+#sk-container-id-69 div.sk-label label {
   /* The background is the default theme color */
   color: var(--sklearn-color-text-on-default-background);
 }
 
 /* On hover, darken the color of the background */
-#sk-container-id-68 div.sk-label:hover label.sk-toggleable__label {
+#sk-container-id-69 div.sk-label:hover label.sk-toggleable__label {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
 /* Label box, darken color on hover, fitted */
-#sk-container-id-68 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
+#sk-container-id-69 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
 /* Estimator label */
 
-#sk-container-id-68 div.sk-label label {
+#sk-container-id-69 div.sk-label label {
   font-family: monospace;
   font-weight: bold;
   display: inline-block;
   line-height: 1.2em;
 }
 
-#sk-container-id-68 div.sk-label-container {
+#sk-container-id-69 div.sk-label-container {
   text-align: center;
 }
 
 /* Estimator-specific */
-#sk-container-id-68 div.sk-estimator {
+#sk-container-id-69 div.sk-estimator {
   font-family: monospace;
   border: 1px dotted var(--sklearn-color-border-box);
   border-radius: 0.25em;
@@ -2391,18 +2390,18 @@
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-68 div.sk-estimator.fitted {
+#sk-container-id-69 div.sk-estimator.fitted {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
 /* on hover */
-#sk-container-id-68 div.sk-estimator:hover {
+#sk-container-id-69 div.sk-estimator:hover {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-68 div.sk-estimator.fitted:hover {
+#sk-container-id-69 div.sk-estimator.fitted:hover {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-2);
 }
@@ -2490,7 +2489,7 @@
 
 /* "?"-specific style due to the `<a>` HTML tag */
 
-#sk-container-id-68 a.estimator_doc_link {
+#sk-container-id-69 a.estimator_doc_link {
   float: right;
   font-size: 1rem;
   line-height: 1em;
@@ -2505,37 +2504,37 @@
   border: var(--sklearn-color-unfitted-level-1) 1pt solid;
 }
 
-#sk-container-id-68 a.estimator_doc_link.fitted {
+#sk-container-id-69 a.estimator_doc_link.fitted {
   /* fitted */
   border: var(--sklearn-color-fitted-level-1) 1pt solid;
   color: var(--sklearn-color-fitted-level-1);
 }
 
 /* On hover */
-#sk-container-id-68 a.estimator_doc_link:hover {
+#sk-container-id-69 a.estimator_doc_link:hover {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-3);
   color: var(--sklearn-color-background);
   text-decoration: none;
 }
 
-#sk-container-id-68 a.estimator_doc_link.fitted:hover {
+#sk-container-id-69 a.estimator_doc_link.fitted:hover {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-3);
 }
-</style><div id="sk-container-id-68" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>RandomizedSearchCV(estimator=Pipeline(steps=[(&#x27;preprocessor&#x27;,
+</style><div id="sk-container-id-69" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>RandomizedSearchCV(estimator=Pipeline(steps=[(&#x27;preprocessor&#x27;,
                                               ColumnTransformer(transformers=[(&#x27;num&#x27;,
                                                                                Pipeline(steps=[(&#x27;imputer&#x27;,
                                                                                                 SimpleImputer(strategy=&#x27;median&#x27;)),
                                                                                                (&#x27;scaler&#x27;,
                                                                                                 StandardScaler())]),
-                                                                               &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb5845dccd0&gt;),
+                                                                               &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd8413c07c0&gt;),
                                                                               (&#x27;cat&#x27;,
                                                                                Pipeline(steps=[(&#x27;encoder&#x27;,
                                                                                                 OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)),
                                                                                                (&#x27;s...
-                                                                                                                 score_func=&lt;function chi2 at 0x7fb58f5c1cf0&gt;))]),
-                                                                               &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb5845ddc30&gt;)])),
+                                                                                                                 score_func=&lt;function chi2 at 0x7fd851052050&gt;))]),
+                                                                               &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd8413c3040&gt;)])),
                                              (&#x27;classifier&#x27;,
                                               LogisticRegression())]),
                    param_distributions={&#x27;classifier__C&#x27;: [0.1, 1.0, 10, 100],
@@ -2545,19 +2544,19 @@
                                                                                     70],
                                         &#x27;preprocessor__num__imputer__strategy&#x27;: [&#x27;mean&#x27;,
                                                                                  &#x27;median&#x27;]},
-                   random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-275" type="checkbox" ><label for="sk-estimator-id-275" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>RandomizedSearchCV</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.model_selection.RandomizedSearchCV.html">?<span>Documentation for RandomizedSearchCV</span></a><span class="sk-estimator-doc-link ">i<span>Not fitted</span></span></div></label><div class="sk-toggleable__content "><pre>RandomizedSearchCV(estimator=Pipeline(steps=[(&#x27;preprocessor&#x27;,
+                   random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-289" type="checkbox" ><label for="sk-estimator-id-289" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>RandomizedSearchCV</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.model_selection.RandomizedSearchCV.html">?<span>Documentation for RandomizedSearchCV</span></a><span class="sk-estimator-doc-link ">i<span>Not fitted</span></span></div></label><div class="sk-toggleable__content "><pre>RandomizedSearchCV(estimator=Pipeline(steps=[(&#x27;preprocessor&#x27;,
                                               ColumnTransformer(transformers=[(&#x27;num&#x27;,
                                                                                Pipeline(steps=[(&#x27;imputer&#x27;,
                                                                                                 SimpleImputer(strategy=&#x27;median&#x27;)),
                                                                                                (&#x27;scaler&#x27;,
                                                                                                 StandardScaler())]),
-                                                                               &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb5845dccd0&gt;),
+                                                                               &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd8413c07c0&gt;),
                                                                               (&#x27;cat&#x27;,
                                                                                Pipeline(steps=[(&#x27;encoder&#x27;,
                                                                                                 OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)),
                                                                                                (&#x27;s...
-                                                                                                                 score_func=&lt;function chi2 at 0x7fb58f5c1cf0&gt;))]),
-                                                                               &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb5845ddc30&gt;)])),
+                                                                                                                 score_func=&lt;function chi2 at 0x7fd851052050&gt;))]),
+                                                                               &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd8413c3040&gt;)])),
                                              (&#x27;classifier&#x27;,
                                               LogisticRegression())]),
                    param_distributions={&#x27;classifier__C&#x27;: [0.1, 1.0, 10, 100],
@@ -2567,32 +2566,32 @@
                                                                                     70],
                                         &#x27;preprocessor__num__imputer__strategy&#x27;: [&#x27;mean&#x27;,
                                                                                  &#x27;median&#x27;]},
-                   random_state=0)</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-276" type="checkbox" ><label for="sk-estimator-id-276" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>estimator: Pipeline</div></div></label><div class="sk-toggleable__content "><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,
+                   random_state=0)</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-290" type="checkbox" ><label for="sk-estimator-id-290" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>estimator: Pipeline</div></div></label><div class="sk-toggleable__content "><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,
                  ColumnTransformer(transformers=[(&#x27;num&#x27;,
                                                   Pipeline(steps=[(&#x27;imputer&#x27;,
                                                                    SimpleImputer(strategy=&#x27;median&#x27;)),
                                                                   (&#x27;scaler&#x27;,
                                                                    StandardScaler())]),
-                                                  &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb5845dccd0&gt;),
+                                                  &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd8413c07c0&gt;),
                                                  (&#x27;cat&#x27;,
                                                   Pipeline(steps=[(&#x27;encoder&#x27;,
                                                                    OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)),
                                                                   (&#x27;selector&#x27;,
                                                                    SelectPercentile(percentile=50,
-                                                                                    score_func=&lt;function chi2 at 0x7fb58f5c1cf0&gt;))]),
-                                                  &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb5845ddc30&gt;)])),
-                (&#x27;classifier&#x27;, LogisticRegression())])</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-277" type="checkbox" ><label for="sk-estimator-id-277" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>preprocessor: ColumnTransformer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.compose.ColumnTransformer.html">?<span>Documentation for preprocessor: ColumnTransformer</span></a></div></label><div class="sk-toggleable__content "><pre>ColumnTransformer(transformers=[(&#x27;num&#x27;,
+                                                                                    score_func=&lt;function chi2 at 0x7fd851052050&gt;))]),
+                                                  &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd8413c3040&gt;)])),
+                (&#x27;classifier&#x27;, LogisticRegression())])</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-291" type="checkbox" ><label for="sk-estimator-id-291" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>preprocessor: ColumnTransformer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.compose.ColumnTransformer.html">?<span>Documentation for preprocessor: ColumnTransformer</span></a></div></label><div class="sk-toggleable__content "><pre>ColumnTransformer(transformers=[(&#x27;num&#x27;,
                                  Pipeline(steps=[(&#x27;imputer&#x27;,
                                                   SimpleImputer(strategy=&#x27;median&#x27;)),
                                                  (&#x27;scaler&#x27;, StandardScaler())]),
-                                 &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb5845dccd0&gt;),
+                                 &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd8413c07c0&gt;),
                                 (&#x27;cat&#x27;,
                                  Pipeline(steps=[(&#x27;encoder&#x27;,
                                                   OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)),
                                                  (&#x27;selector&#x27;,
                                                   SelectPercentile(percentile=50,
-                                                                   score_func=&lt;function chi2 at 0x7fb58f5c1cf0&gt;))]),
-                                 &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb5845ddc30&gt;)])</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-278" type="checkbox" ><label for="sk-estimator-id-278" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>num</div></div></label><div class="sk-toggleable__content "><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb5845dccd0&gt;</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-279" type="checkbox" ><label for="sk-estimator-id-279" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>SimpleImputer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.impute.SimpleImputer.html">?<span>Documentation for SimpleImputer</span></a></div></label><div class="sk-toggleable__content "><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div> </div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-280" type="checkbox" ><label for="sk-estimator-id-280" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>StandardScaler</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.StandardScaler.html">?<span>Documentation for StandardScaler</span></a></div></label><div class="sk-toggleable__content "><pre>StandardScaler()</pre></div> </div></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-281" type="checkbox" ><label for="sk-estimator-id-281" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>cat</div></div></label><div class="sk-toggleable__content "><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb5845ddc30&gt;</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-282" type="checkbox" ><label for="sk-estimator-id-282" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>OneHotEncoder</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.OneHotEncoder.html">?<span>Documentation for OneHotEncoder</span></a></div></label><div class="sk-toggleable__content "><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</pre></div> </div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-283" type="checkbox" ><label for="sk-estimator-id-283" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>SelectPercentile</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.feature_selection.SelectPercentile.html">?<span>Documentation for SelectPercentile</span></a></div></label><div class="sk-toggleable__content "><pre>SelectPercentile(percentile=50, score_func=&lt;function chi2 at 0x7fb58f5c1cf0&gt;)</pre></div> </div></div></div></div></div></div></div></div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-284" type="checkbox" ><label for="sk-estimator-id-284" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>LogisticRegression</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.linear_model.LogisticRegression.html">?<span>Documentation for LogisticRegression</span></a></div></label><div class="sk-toggleable__content "><pre>LogisticRegression()</pre></div> </div></div></div></div></div></div></div></div></div></div></div>
+                                                                   score_func=&lt;function chi2 at 0x7fd851052050&gt;))]),
+                                 &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd8413c3040&gt;)])</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-292" type="checkbox" ><label for="sk-estimator-id-292" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>num</div></div></label><div class="sk-toggleable__content "><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd8413c07c0&gt;</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-293" type="checkbox" ><label for="sk-estimator-id-293" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>SimpleImputer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.impute.SimpleImputer.html">?<span>Documentation for SimpleImputer</span></a></div></label><div class="sk-toggleable__content "><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div> </div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-294" type="checkbox" ><label for="sk-estimator-id-294" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>StandardScaler</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.StandardScaler.html">?<span>Documentation for StandardScaler</span></a></div></label><div class="sk-toggleable__content "><pre>StandardScaler()</pre></div> </div></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-295" type="checkbox" ><label for="sk-estimator-id-295" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>cat</div></div></label><div class="sk-toggleable__content "><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd8413c3040&gt;</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-296" type="checkbox" ><label for="sk-estimator-id-296" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>OneHotEncoder</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.OneHotEncoder.html">?<span>Documentation for OneHotEncoder</span></a></div></label><div class="sk-toggleable__content "><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</pre></div> </div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-297" type="checkbox" ><label for="sk-estimator-id-297" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>SelectPercentile</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.feature_selection.SelectPercentile.html">?<span>Documentation for SelectPercentile</span></a></div></label><div class="sk-toggleable__content "><pre>SelectPercentile(percentile=50, score_func=&lt;function chi2 at 0x7fd851052050&gt;)</pre></div> </div></div></div></div></div></div></div></div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-298" type="checkbox" ><label for="sk-estimator-id-298" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>LogisticRegression</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.linear_model.LogisticRegression.html">?<span>Documentation for LogisticRegression</span></a></div></label><div class="sk-toggleable__content "><pre>LogisticRegression()</pre></div> </div></div></div></div></div></div></div></div></div></div></div>
 </div>
 <br />
 <br /><p>Calling ‘fit’ triggers the cross-validated search for the best
@@ -2714,7 +2713,7 @@
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>accuracy of the best model from randomized search: 0.798
 </pre></div>
 </div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 1.210 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 1.460 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-compose-plot-column-transformer-mixed-types-py">
 <div class="binder-badge docutils container">
 <a class="reference external image-reference" href="https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/compose/plot_column_transformer_mixed_types.ipynb"><img alt="Launch binder" src="http://github.com/_images/binder_badge_logo5.svg" style="width: 150px;" />
diff --git a/dev/auto_examples/compose/plot_compare_reduction.html b/dev/auto_examples/compose/plot_compare_reduction.html
index f6dd0d674c94b..1543f8e46cac5 100644
--- a/dev/auto_examples/compose/plot_compare_reduction.html
+++ b/dev/auto_examples/compose/plot_compare_reduction.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -679,13 +679,12 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -902,9 +901,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
@@ -1055,7 +1054,7 @@ <h2>Illustration of <code class="docutils literal notranslate"><span class="pre"
 </pre></div>
 </div>
 <div class="output_subarea output_html rendered_html output_result">
-<style>#sk-container-id-69 {
+<style>#sk-container-id-70 {
   /* Definition of color scheme common for light and dark mode */
   --sklearn-color-text: #000;
   --sklearn-color-text-muted: #666;
@@ -1086,15 +1085,15 @@ <h2>Illustration of <code class="docutils literal notranslate"><span class="pre"
   }
 }
 
-#sk-container-id-69 {
+#sk-container-id-70 {
   color: var(--sklearn-color-text);
 }
 
-#sk-container-id-69 pre {
+#sk-container-id-70 pre {
   padding: 0;
 }
 
-#sk-container-id-69 input.sk-hidden--visually {
+#sk-container-id-70 input.sk-hidden--visually {
   border: 0;
   clip: rect(1px 1px 1px 1px);
   clip: rect(1px, 1px, 1px, 1px);
@@ -1106,7 +1105,7 @@ <h2>Illustration of <code class="docutils literal notranslate"><span class="pre"
   width: 1px;
 }
 
-#sk-container-id-69 div.sk-dashed-wrapped {
+#sk-container-id-70 div.sk-dashed-wrapped {
   border: 1px dashed var(--sklearn-color-line);
   margin: 0 0.4em 0.5em 0.4em;
   box-sizing: border-box;
@@ -1114,7 +1113,7 @@ <h2>Illustration of <code class="docutils literal notranslate"><span class="pre"
   background-color: var(--sklearn-color-background);
 }
 
-#sk-container-id-69 div.sk-container {
+#sk-container-id-70 div.sk-container {
   /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
      but bootstrap.min.css set `[hidden] { display: none !important; }`
      so we also need the `!important` here to be able to override the
@@ -1124,7 +1123,7 @@ <h2>Illustration of <code class="docutils literal notranslate"><span class="pre"
   position: relative;
 }
 
-#sk-container-id-69 div.sk-text-repr-fallback {
+#sk-container-id-70 div.sk-text-repr-fallback {
   display: none;
 }
 
@@ -1140,14 +1139,14 @@ <h2>Illustration of <code class="docutils literal notranslate"><span class="pre"
 
 /* Parallel-specific style estimator block */
 
-#sk-container-id-69 div.sk-parallel-item::after {
+#sk-container-id-70 div.sk-parallel-item::after {
   content: "";
   width: 100%;
   border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
   flex-grow: 1;
 }
 
-#sk-container-id-69 div.sk-parallel {
+#sk-container-id-70 div.sk-parallel {
   display: flex;
   align-items: stretch;
   justify-content: center;
@@ -1155,28 +1154,28 @@ <h2>Illustration of <code class="docutils literal notranslate"><span class="pre"
   position: relative;
 }
 
-#sk-container-id-69 div.sk-parallel-item {
+#sk-container-id-70 div.sk-parallel-item {
   display: flex;
   flex-direction: column;
 }
 
-#sk-container-id-69 div.sk-parallel-item:first-child::after {
+#sk-container-id-70 div.sk-parallel-item:first-child::after {
   align-self: flex-end;
   width: 50%;
 }
 
-#sk-container-id-69 div.sk-parallel-item:last-child::after {
+#sk-container-id-70 div.sk-parallel-item:last-child::after {
   align-self: flex-start;
   width: 50%;
 }
 
-#sk-container-id-69 div.sk-parallel-item:only-child::after {
+#sk-container-id-70 div.sk-parallel-item:only-child::after {
   width: 0;
 }
 
 /* Serial-specific style estimator block */
 
-#sk-container-id-69 div.sk-serial {
+#sk-container-id-70 div.sk-serial {
   display: flex;
   flex-direction: column;
   align-items: center;
@@ -1194,14 +1193,14 @@ <h2>Illustration of <code class="docutils literal notranslate"><span class="pre"
 
 /* Pipeline and ColumnTransformer style (default) */
 
-#sk-container-id-69 div.sk-toggleable {
+#sk-container-id-70 div.sk-toggleable {
   /* Default theme specific background. It is overwritten whether we have a
   specific estimator or a Pipeline/ColumnTransformer */
   background-color: var(--sklearn-color-background);
 }
 
 /* Toggleable label */
-#sk-container-id-69 label.sk-toggleable__label {
+#sk-container-id-70 label.sk-toggleable__label {
   cursor: pointer;
   display: flex;
   width: 100%;
@@ -1214,13 +1213,13 @@ <h2>Illustration of <code class="docutils literal notranslate"><span class="pre"
   gap: 0.5em;
 }
 
-#sk-container-id-69 label.sk-toggleable__label .caption {
+#sk-container-id-70 label.sk-toggleable__label .caption {
   font-size: 0.6rem;
   font-weight: lighter;
   color: var(--sklearn-color-text-muted);
 }
 
-#sk-container-id-69 label.sk-toggleable__label-arrow:before {
+#sk-container-id-70 label.sk-toggleable__label-arrow:before {
   /* Arrow on the left of the label */
   content: "▸";
   float: left;
@@ -1228,13 +1227,13 @@ <h2>Illustration of <code class="docutils literal notranslate"><span class="pre"
   color: var(--sklearn-color-icon);
 }
 
-#sk-container-id-69 label.sk-toggleable__label-arrow:hover:before {
+#sk-container-id-70 label.sk-toggleable__label-arrow:hover:before {
   color: var(--sklearn-color-text);
 }
 
 /* Toggleable content - dropdown */
 
-#sk-container-id-69 div.sk-toggleable__content {
+#sk-container-id-70 div.sk-toggleable__content {
   max-height: 0;
   max-width: 0;
   overflow: hidden;
@@ -1243,12 +1242,12 @@ <h2>Illustration of <code class="docutils literal notranslate"><span class="pre"
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-69 div.sk-toggleable__content.fitted {
+#sk-container-id-70 div.sk-toggleable__content.fitted {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
-#sk-container-id-69 div.sk-toggleable__content pre {
+#sk-container-id-70 div.sk-toggleable__content pre {
   margin: 0.2em;
   border-radius: 0.25em;
   color: var(--sklearn-color-text);
@@ -1256,79 +1255,79 @@ <h2>Illustration of <code class="docutils literal notranslate"><span class="pre"
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-69 div.sk-toggleable__content.fitted pre {
+#sk-container-id-70 div.sk-toggleable__content.fitted pre {
   /* unfitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
-#sk-container-id-69 input.sk-toggleable__control:checked~div.sk-toggleable__content {
+#sk-container-id-70 input.sk-toggleable__control:checked~div.sk-toggleable__content {
   /* Expand drop-down */
   max-height: 200px;
   max-width: 100%;
   overflow: auto;
 }
 
-#sk-container-id-69 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
+#sk-container-id-70 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
   content: "▾";
 }
 
 /* Pipeline/ColumnTransformer-specific style */
 
-#sk-container-id-69 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-70 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-69 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-70 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
 /* Estimator-specific style */
 
 /* Colorize estimator box */
-#sk-container-id-69 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-70 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-69 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-70 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
-#sk-container-id-69 div.sk-label label.sk-toggleable__label,
-#sk-container-id-69 div.sk-label label {
+#sk-container-id-70 div.sk-label label.sk-toggleable__label,
+#sk-container-id-70 div.sk-label label {
   /* The background is the default theme color */
   color: var(--sklearn-color-text-on-default-background);
 }
 
 /* On hover, darken the color of the background */
-#sk-container-id-69 div.sk-label:hover label.sk-toggleable__label {
+#sk-container-id-70 div.sk-label:hover label.sk-toggleable__label {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
 /* Label box, darken color on hover, fitted */
-#sk-container-id-69 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
+#sk-container-id-70 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
 /* Estimator label */
 
-#sk-container-id-69 div.sk-label label {
+#sk-container-id-70 div.sk-label label {
   font-family: monospace;
   font-weight: bold;
   display: inline-block;
   line-height: 1.2em;
 }
 
-#sk-container-id-69 div.sk-label-container {
+#sk-container-id-70 div.sk-label-container {
   text-align: center;
 }
 
 /* Estimator-specific */
-#sk-container-id-69 div.sk-estimator {
+#sk-container-id-70 div.sk-estimator {
   font-family: monospace;
   border: 1px dotted var(--sklearn-color-border-box);
   border-radius: 0.25em;
@@ -1338,18 +1337,18 @@ <h2>Illustration of <code class="docutils literal notranslate"><span class="pre"
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-69 div.sk-estimator.fitted {
+#sk-container-id-70 div.sk-estimator.fitted {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
 /* on hover */
-#sk-container-id-69 div.sk-estimator:hover {
+#sk-container-id-70 div.sk-estimator:hover {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-69 div.sk-estimator.fitted:hover {
+#sk-container-id-70 div.sk-estimator.fitted:hover {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-2);
 }
@@ -1437,7 +1436,7 @@ <h2>Illustration of <code class="docutils literal notranslate"><span class="pre"
 
 /* "?"-specific style due to the `<a>` HTML tag */
 
-#sk-container-id-69 a.estimator_doc_link {
+#sk-container-id-70 a.estimator_doc_link {
   float: right;
   font-size: 1rem;
   line-height: 1em;
@@ -1452,25 +1451,25 @@ <h2>Illustration of <code class="docutils literal notranslate"><span class="pre"
   border: var(--sklearn-color-unfitted-level-1) 1pt solid;
 }
 
-#sk-container-id-69 a.estimator_doc_link.fitted {
+#sk-container-id-70 a.estimator_doc_link.fitted {
   /* fitted */
   border: var(--sklearn-color-fitted-level-1) 1pt solid;
   color: var(--sklearn-color-fitted-level-1);
 }
 
 /* On hover */
-#sk-container-id-69 a.estimator_doc_link:hover {
+#sk-container-id-70 a.estimator_doc_link:hover {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-3);
   color: var(--sklearn-color-background);
   text-decoration: none;
 }
 
-#sk-container-id-69 a.estimator_doc_link.fitted:hover {
+#sk-container-id-70 a.estimator_doc_link.fitted:hover {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-3);
 }
-</style><div id="sk-container-id-69" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>GridSearchCV(estimator=Pipeline(steps=[(&#x27;scaling&#x27;, MinMaxScaler()),
+</style><div id="sk-container-id-70" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>GridSearchCV(estimator=Pipeline(steps=[(&#x27;scaling&#x27;, MinMaxScaler()),
                                        (&#x27;reduce_dim&#x27;, &#x27;passthrough&#x27;),
                                        (&#x27;classify&#x27;,
                                         LinearSVC(dual=False,
@@ -1481,8 +1480,8 @@ <h2>Illustration of <code class="docutils literal notranslate"><span class="pre"
                                          NMF(max_iter=1000)],
                           &#x27;reduce_dim__n_components&#x27;: [2, 4, 8]},
                          {&#x27;classify__C&#x27;: [1, 10, 100, 1000],
-                          &#x27;reduce_dim&#x27;: [SelectKBest(score_func=&lt;function mutual_info_classif at 0x7fb58f5c0160&gt;)],
-                          &#x27;reduce_dim__k&#x27;: [2, 4, 8]}])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-285" type="checkbox" ><label for="sk-estimator-id-285" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>GridSearchCV</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.model_selection.GridSearchCV.html">?<span>Documentation for GridSearchCV</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>GridSearchCV(estimator=Pipeline(steps=[(&#x27;scaling&#x27;, MinMaxScaler()),
+                          &#x27;reduce_dim&#x27;: [SelectKBest(score_func=&lt;function mutual_info_classif at 0x7fd8510504c0&gt;)],
+                          &#x27;reduce_dim__k&#x27;: [2, 4, 8]}])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-299" type="checkbox" ><label for="sk-estimator-id-299" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>GridSearchCV</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.model_selection.GridSearchCV.html">?<span>Documentation for GridSearchCV</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>GridSearchCV(estimator=Pipeline(steps=[(&#x27;scaling&#x27;, MinMaxScaler()),
                                        (&#x27;reduce_dim&#x27;, &#x27;passthrough&#x27;),
                                        (&#x27;classify&#x27;,
                                         LinearSVC(dual=False,
@@ -1493,10 +1492,10 @@ <h2>Illustration of <code class="docutils literal notranslate"><span class="pre"
                                          NMF(max_iter=1000)],
                           &#x27;reduce_dim__n_components&#x27;: [2, 4, 8]},
                          {&#x27;classify__C&#x27;: [1, 10, 100, 1000],
-                          &#x27;reduce_dim&#x27;: [SelectKBest(score_func=&lt;function mutual_info_classif at 0x7fb58f5c0160&gt;)],
-                          &#x27;reduce_dim__k&#x27;: [2, 4, 8]}])</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-286" type="checkbox" ><label for="sk-estimator-id-286" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>best_estimator_: Pipeline</div></div></label><div class="sk-toggleable__content fitted"><pre>Pipeline(steps=[(&#x27;scaling&#x27;, MinMaxScaler()),
+                          &#x27;reduce_dim&#x27;: [SelectKBest(score_func=&lt;function mutual_info_classif at 0x7fd8510504c0&gt;)],
+                          &#x27;reduce_dim__k&#x27;: [2, 4, 8]}])</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-300" type="checkbox" ><label for="sk-estimator-id-300" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>best_estimator_: Pipeline</div></div></label><div class="sk-toggleable__content fitted"><pre>Pipeline(steps=[(&#x27;scaling&#x27;, MinMaxScaler()),
                 (&#x27;reduce_dim&#x27;, PCA(iterated_power=7, n_components=8)),
-                (&#x27;classify&#x27;, LinearSVC(C=1, dual=False, max_iter=10000))])</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-287" type="checkbox" ><label for="sk-estimator-id-287" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>MinMaxScaler</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.MinMaxScaler.html">?<span>Documentation for MinMaxScaler</span></a></div></label><div class="sk-toggleable__content fitted"><pre>MinMaxScaler()</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-288" type="checkbox" ><label for="sk-estimator-id-288" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>PCA</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.decomposition.PCA.html">?<span>Documentation for PCA</span></a></div></label><div class="sk-toggleable__content fitted"><pre>PCA(iterated_power=7, n_components=8)</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-289" type="checkbox" ><label for="sk-estimator-id-289" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>LinearSVC</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.svm.LinearSVC.html">?<span>Documentation for LinearSVC</span></a></div></label><div class="sk-toggleable__content fitted"><pre>LinearSVC(C=1, dual=False, max_iter=10000)</pre></div> </div></div></div></div></div></div></div></div></div></div></div>
+                (&#x27;classify&#x27;, LinearSVC(C=1, dual=False, max_iter=10000))])</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-301" type="checkbox" ><label for="sk-estimator-id-301" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>MinMaxScaler</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.MinMaxScaler.html">?<span>Documentation for MinMaxScaler</span></a></div></label><div class="sk-toggleable__content fitted"><pre>MinMaxScaler()</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-302" type="checkbox" ><label for="sk-estimator-id-302" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>PCA</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.decomposition.PCA.html">?<span>Documentation for PCA</span></a></div></label><div class="sk-toggleable__content fitted"><pre>PCA(iterated_power=7, n_components=8)</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-303" type="checkbox" ><label for="sk-estimator-id-303" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>LinearSVC</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.svm.LinearSVC.html">?<span>Documentation for LinearSVC</span></a></div></label><div class="sk-toggleable__content fitted"><pre>LinearSVC(C=1, dual=False, max_iter=10000)</pre></div> </div></div></div></div></div></div></div></div></div></div></div>
 </div>
 <br />
 <br /><div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">pandas</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pd</span>
@@ -1560,7 +1559,7 @@ <h2>Caching transformers within a <code class="docutils literal notranslate"><sp
 estimator data, leading to save processing time. Therefore, the use of
 caching the pipeline using <code class="docutils literal notranslate"><span class="pre">memory</span></code> is highly beneficial when fitting
 a transformer is costly.</p>
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+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 50.514 seconds)</p>
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@@ -1587,12 +1586,12 @@ <h2>Caching transformers within a <code class="docutils literal notranslate"><sp
 </div><div class="sphx-glr-thumbcontainer" tooltip="This example balances model complexity and cross-validated score by finding a decent accuracy within 1 standard deviation of the best accuracy score while minimising the number of PCA components [1]."><img alt="" src="http://github.com/_images/sphx_glr_plot_grid_search_refit_callable_thumb.png" />
 <p><a class="reference internal" href="http://github.com/model_selection/plot_grid_search_refit_callable.html#sphx-glr-auto-examples-model-selection-plot-grid-search-refit-callable-py"><span class="std std-ref">Balance model complexity and cross-validated score</span></a></p>
   <div class="sphx-glr-thumbnail-title">Balance model complexity and cross-validated score</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how to precompute the k nearest neighbors before using them in KNeighborsClassifier. KNeighborsClassifier can compute the nearest neighbors internally, but precomputing them can have several benefits, such as finer parameter control, caching for multiple use, or custom implementations."><img alt="" src="http://github.com/_images/sphx_glr_plot_caching_nearest_neighbors_thumb.png" />
-<p><a class="reference internal" href="http://github.com/neighbors/plot_caching_nearest_neighbors.html#sphx-glr-auto-examples-neighbors-plot-caching-nearest-neighbors-py"><span class="std std-ref">Caching nearest neighbors</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Caching nearest neighbors</div>
 </div><div class="sphx-glr-thumbcontainer" tooltip="In many real-world examples, there are many ways to extract features from a dataset. Often it is beneficial to combine several methods to obtain good performance. This example shows how to use FeatureUnion to combine features obtained by PCA and univariate selection."><img alt="" src="http://github.com/_images/sphx_glr_plot_feature_union_thumb.png" />
 <p><a class="reference internal" href="http://github.com/plot_feature_union.html#sphx-glr-auto-examples-compose-plot-feature-union-py"><span class="std std-ref">Concatenating multiple feature extraction methods</span></a></p>
   <div class="sphx-glr-thumbnail-title">Concatenating multiple feature extraction methods</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example compares 2 dimensionality reduction strategies:"><img alt="" src="http://github.com/_images/sphx_glr_plot_feature_agglomeration_vs_univariate_selection_thumb.png" />
+<p><a class="reference internal" href="http://github.com/cluster/plot_feature_agglomeration_vs_univariate_selection.html#sphx-glr-auto-examples-cluster-plot-feature-agglomeration-vs-univariate-selection-py"><span class="std std-ref">Feature agglomeration vs. univariate selection</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Feature agglomeration vs. univariate selection</div>
 </div></div><p class="sphx-glr-signature"><a class="reference external" href="https://sphinx-gallery.github.io/">Gallery generated by Sphinx-Gallery</a></p>
 </section>
 </section>
diff --git a/dev/auto_examples/compose/plot_digits_pipe.html b/dev/auto_examples/compose/plot_digits_pipe.html
index a8ac7af72561c..b0faaceeaaf58 100644
--- a/dev/auto_examples/compose/plot_digits_pipe.html
+++ b/dev/auto_examples/compose/plot_digits_pipe.html
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     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
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     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -679,13 +679,12 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -902,9 +901,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
@@ -1079,7 +1078,7 @@
 <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
 </pre></div>
 </div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 1.111 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 1.470 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-compose-plot-digits-pipe-py">
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diff --git a/dev/auto_examples/compose/plot_feature_union.html b/dev/auto_examples/compose/plot_feature_union.html
index 3a1564f69068f..f2c3061a057c0 100644
--- a/dev/auto_examples/compose/plot_feature_union.html
+++ b/dev/auto_examples/compose/plot_feature_union.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
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-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -902,9 +901,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
@@ -1239,7 +1238,7 @@
 <span class="nb">print</span><span class="p">(</span><span class="n">grid_search</span><span class="o">.</span><span class="n">best_estimator_</span><span class="p">)</span>
 </pre></div>
 </div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 0.459 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 0.466 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-compose-plot-feature-union-py">
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diff --git a/dev/auto_examples/compose/plot_transformed_target.html b/dev/auto_examples/compose/plot_transformed_target.html
index 4f3f6c0d44609..a0fe08c139753 100644
--- a/dev/auto_examples/compose/plot_transformed_target.html
+++ b/dev/auto_examples/compose/plot_transformed_target.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
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     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
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@@ -680,13 +680,12 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
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 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -902,9 +901,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
@@ -1138,7 +1137,7 @@
     <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">%s</span><span class="se">\t</span><span class="si">%0.2f</span><span class="se">\t</span><span class="si">%0.2f</span><span class="se">\t</span><span class="si">%0.2f</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="n">p</span><span class="p">,</span> <span class="n">p_w</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">p_w</span><span class="p">[</span><span class="mi">1</span><span class="p">]))</span>
 </pre></div>
 </div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 0.128 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 0.183 seconds)</p>
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diff --git a/dev/auto_examples/decomposition/index.html b/dev/auto_examples/decomposition/index.html
index 62f82c3e1e56f..a380792d28c4d 100644
--- a/dev/auto_examples/decomposition/index.html
+++ b/dev/auto_examples/decomposition/index.html
@@ -42,7 +42,7 @@
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     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
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@@ -676,13 +676,12 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
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 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -899,9 +898,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
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index ece53e9834280..fcdb7ad00ee4b 100644
--- a/dev/auto_examples/decomposition/plot_faces_decomposition.html
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-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
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@@ -680,13 +680,12 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
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@@ -903,9 +902,9 @@
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
@@ -1295,7 +1294,7 @@ <h3>Dictionary learning - positive dictionary &amp; code<a class="headerlink" hr
 <span class="p">)</span>
 </pre></div>
 </div>
-<img src="http://github.com/_images/sphx_glr_plot_faces_decomposition_013.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_faces_decomposition_013.png " alt="Dictionary learning - positive dictionary & code" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 7.399 seconds)</p>
+<img src="http://github.com/_images/sphx_glr_plot_faces_decomposition_013.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_faces_decomposition_013.png " alt="Dictionary learning - positive dictionary & code" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 8.319 seconds)</p>
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diff --git a/dev/auto_examples/decomposition/plot_ica_blind_source_separation.html b/dev/auto_examples/decomposition/plot_ica_blind_source_separation.html
index c1bf319abd102..f90aeb412aa43 100644
--- a/dev/auto_examples/decomposition/plot_ica_blind_source_separation.html
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-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
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@@ -679,13 +679,12 @@
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
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@@ -902,9 +901,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
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@@ -1071,7 +1070,7 @@ <h2>Plot results<a class="headerlink" href="#plot-results" title="Link to this h
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+<img src="http://github.com/_images/sphx_glr_plot_ica_blind_source_separation_001.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_ica_blind_source_separation_001.png " alt="Observations (mixed signal), True Sources, ICA recovered signals, PCA recovered signals" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 0.431 seconds)</p>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -902,9 +901,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
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+<img src="http://github.com/_images/sphx_glr_plot_ica_vs_pca_001.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_ica_vs_pca_001.png " alt="True Independent Sources, Observations, PCA recovered signals, ICA recovered signals" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 0.413 seconds)</p>
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@@ -679,13 +679,12 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
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 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
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@@ -1138,8 +1137,8 @@ <h2>Learn the dictionary from reference patches<a class="headerlink" href="#lear
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+<img src="http://github.com/_images/sphx_glr_plot_image_denoising_002.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_image_denoising_002.png " alt="Dictionary learned from face patches Train time 14.9s on 22692 patches" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Learning the dictionary...
+2.0 iterations / 125 steps in 14.86.
 </pre></div>
 </div>
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 <li><img src="http://github.com/_images/sphx_glr_plot_image_denoising_003.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_image_denoising_003.png " alt="Orthogonal Matching Pursuit 1 atom (time: 0.5s), Image, Difference (norm: 10.70)" class = "sphx-glr-multi-img"/></li>
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-<li><img src="http://github.com/_images/sphx_glr_plot_image_denoising_005.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_image_denoising_005.png " alt="Least-angle regression 4 atoms (time: 8.6s), Image, Difference (norm: 13.35)" class = "sphx-glr-multi-img"/></li>
+<li><img src="http://github.com/_images/sphx_glr_plot_image_denoising_005.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_image_denoising_005.png " alt="Least-angle regression 4 atoms (time: 8.5s), Image, Difference (norm: 13.35)" class = "sphx-glr-multi-img"/></li>
 <li><img src="http://github.com/_images/sphx_glr_plot_image_denoising_006.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_image_denoising_006.png " alt="Thresholding  alpha=0.1 (time: 0.1s), Image, Difference (norm: 14.26)" class = "sphx-glr-multi-img"/></li>
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 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Extracting noisy patches...
 done in 0.00s.
 Orthogonal Matching Pursuit
 1 atom...
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+done in 0.53s.
 Orthogonal Matching Pursuit
 2 atoms...
-done in 1.12s.
+done in 1.11s.
 Least-angle regression
 4 atoms...
-done in 8.57s.
+done in 8.52s.
 Thresholding
  alpha=0.1...
 done in 0.09s.
 </pre></div>
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diff --git a/dev/auto_examples/decomposition/plot_incremental_pca.html b/dev/auto_examples/decomposition/plot_incremental_pca.html
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@@ -679,13 +679,12 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
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@@ -902,9 +901,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -898,9 +897,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
diff --git a/dev/auto_examples/developing_estimators/sklearn_is_fitted.html b/dev/auto_examples/developing_estimators/sklearn_is_fitted.html
index 230ada42345cd..f2bebe3b180e6 100644
--- a/dev/auto_examples/developing_estimators/sklearn_is_fitted.html
+++ b/dev/auto_examples/developing_estimators/sklearn_is_fitted.html
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@@ -679,13 +679,12 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -902,9 +901,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
diff --git a/dev/auto_examples/ensemble/index.html b/dev/auto_examples/ensemble/index.html
index 6c0efdc506c3c..be4eacbaf6629 100644
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@@ -676,13 +676,12 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -899,9 +898,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
@@ -1035,15 +1034,9 @@
 </div><div class="sphx-glr-thumbcontainer" tooltip="The RandomForestClassifier is trained using bootstrap aggregation, where each new tree is fit from a bootstrap sample of the training observations z_i = (x_i, y_i). The out-of-bag (OOB) error is the average error for each z_i calculated using predictions from the trees that do not contain z_i in their respective bootstrap sample. This allows the RandomForestClassifier to be fit and validated whilst being trained [1]_."><img alt="" src="http://github.com/_images/sphx_glr_plot_ensemble_oob_thumb.png" />
 <p><a class="reference internal" href="http://github.com/plot_ensemble_oob.html#sphx-glr-auto-examples-ensemble-plot-ensemble-oob-py"><span class="std std-ref">OOB Errors for Random Forests</span></a></p>
   <div class="sphx-glr-thumbnail-title">OOB Errors for Random Forests</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="Plot the class probabilities of the first sample in a toy dataset predicted by three different classifiers and averaged by the VotingClassifier."><img alt="" src="http://github.com/_images/sphx_glr_plot_voting_probas_thumb.png" />
-<p><a class="reference internal" href="http://github.com/plot_voting_probas.html#sphx-glr-auto-examples-ensemble-plot-voting-probas-py"><span class="std std-ref">Plot class probabilities calculated by the VotingClassifier</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Plot class probabilities calculated by the VotingClassifier</div>
 </div><div class="sphx-glr-thumbcontainer" tooltip="A voting regressor is an ensemble meta-estimator that fits several base regressors, each on the whole dataset. Then it averages the individual predictions to form a final prediction. We will use three different regressors to predict the data: GradientBoostingRegressor, RandomForestRegressor, and LinearRegression). Then the above 3 regressors will be used for the VotingRegressor."><img alt="" src="http://github.com/_images/sphx_glr_plot_voting_regressor_thumb.png" />
 <p><a class="reference internal" href="http://github.com/plot_voting_regressor.html#sphx-glr-auto-examples-ensemble-plot-voting-regressor-py"><span class="std std-ref">Plot individual and voting regression predictions</span></a></p>
   <div class="sphx-glr-thumbnail-title">Plot individual and voting regression predictions</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="Plot the decision boundaries of a VotingClassifier for two features of the Iris dataset."><img alt="" src="http://github.com/_images/sphx_glr_plot_voting_decision_regions_thumb.png" />
-<p><a class="reference internal" href="http://github.com/plot_voting_decision_regions.html#sphx-glr-auto-examples-ensemble-plot-voting-decision-regions-py"><span class="std std-ref">Plot the decision boundaries of a VotingClassifier</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Plot the decision boundaries of a VotingClassifier</div>
 </div><div class="sphx-glr-thumbcontainer" tooltip="Plot the decision surfaces of forests of randomized trees trained on pairs of features of the iris dataset."><img alt="" src="http://github.com/_images/sphx_glr_plot_forest_iris_thumb.png" />
 <p><a class="reference internal" href="http://github.com/plot_forest_iris.html#sphx-glr-auto-examples-ensemble-plot-forest-iris-py"><span class="std std-ref">Plot the decision surfaces of ensembles of trees on the iris dataset</span></a></p>
   <div class="sphx-glr-thumbnail-title">Plot the decision surfaces of ensembles of trees on the iris dataset</div>
@@ -1056,6 +1049,9 @@
 </div><div class="sphx-glr-thumbcontainer" tooltip="This example fits an AdaBoosted decision stump on a non-linearly separable classification dataset composed of two &quot;Gaussian quantiles&quot; clusters (see sklearn.datasets.make_gaussian_quantiles) and plots the decision boundary and decision scores. The distributions of decision scores are shown separately for samples of class A and B. The predicted class label for each sample is determined by the sign of the decision score. Samples with decision scores greater than zero are classified as B, and are otherwise classified as A. The magnitude of a decision score determines the degree of likeness with the predicted class label. Additionally, a new dataset could be constructed containing a desired purity of class B, for example, by only selecting samples with a decision score above some value."><img alt="" src="http://github.com/_images/sphx_glr_plot_adaboost_twoclass_thumb.png" />
 <p><a class="reference internal" href="http://github.com/plot_adaboost_twoclass.html#sphx-glr-auto-examples-ensemble-plot-adaboost-twoclass-py"><span class="std std-ref">Two-class AdaBoost</span></a></p>
   <div class="sphx-glr-thumbnail-title">Two-class AdaBoost</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="Plot the predicted class probabilities in a toy dataset predicted by three different classifiers and averaged by the VotingClassifier."><img alt="" src="http://github.com/_images/sphx_glr_plot_voting_decision_regions_thumb.png" />
+<p><a class="reference internal" href="http://github.com/plot_voting_decision_regions.html#sphx-glr-auto-examples-ensemble-plot-voting-decision-regions-py"><span class="std std-ref">Visualizing the probabilistic predictions of a VotingClassifier</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Visualizing the probabilistic predictions of a VotingClassifier</div>
 </div></div><div class="toctree-wrapper compound">
 </div>
 </section>
diff --git a/dev/auto_examples/ensemble/plot_adaboost_multiclass.html b/dev/auto_examples/ensemble/plot_adaboost_multiclass.html
index 1ffca18a0802e..93da0bb668156 100644
--- a/dev/auto_examples/ensemble/plot_adaboost_multiclass.html
+++ b/dev/auto_examples/ensemble/plot_adaboost_multiclass.html
@@ -42,7 +42,7 @@
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-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
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@@ -680,13 +680,12 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2 current active"><a class="current reference internal" href="#">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -903,9 +902,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
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        title="next page">
       <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title">Examples based on real world datasets</p>
+        <p class="prev-next-title">Visualizing the probabilistic predictions of a VotingClassifier</p>
       </div>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
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@@ -1170,8 +1169,8 @@ <h2>References<a class="headerlink" href="#references" title="Link to this headi
 
     <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplot.html#matplotlib.pyplot.subplot" title="matplotlib.pyplot.subplot" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">subplot</span></a><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="n">n_estimators</span><span class="p">,</span> <span class="n">n_estimators</span> <span class="o">+</span> <span class="n">n</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span>
     <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.plot.html#matplotlib.pyplot.plot" title="matplotlib.pyplot.plot" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">plot</span></a><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_error</span><span class="p">,</span> <span class="s2">&quot;r&quot;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;$error(x)$&quot;</span><span class="p">)</span>
-    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.plot.html#matplotlib.pyplot.plot" title="matplotlib.pyplot.plot" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">plot</span></a><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_bias</span><span class="p">,</span> <span class="s2">&quot;b&quot;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;$bias^2(x)$&quot;</span><span class="p">),</span>
-    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.plot.html#matplotlib.pyplot.plot" title="matplotlib.pyplot.plot" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">plot</span></a><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_var</span><span class="p">,</span> <span class="s2">&quot;g&quot;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;$variance(x)$&quot;</span><span class="p">),</span>
+    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.plot.html#matplotlib.pyplot.plot" title="matplotlib.pyplot.plot" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">plot</span></a><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_bias</span><span class="p">,</span> <span class="s2">&quot;b&quot;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;$bias^2(x)$&quot;</span><span class="p">)</span>
+    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.plot.html#matplotlib.pyplot.plot" title="matplotlib.pyplot.plot" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">plot</span></a><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_var</span><span class="p">,</span> <span class="s2">&quot;g&quot;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;$variance(x)$&quot;</span><span class="p">)</span>
     <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.plot.html#matplotlib.pyplot.plot" title="matplotlib.pyplot.plot" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">plot</span></a><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_noise</span><span class="p">,</span> <span class="s2">&quot;c&quot;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;$noise(x)$&quot;</span><span class="p">)</span>
 
     <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.xlim.html#matplotlib.pyplot.xlim" title="matplotlib.pyplot.xlim" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">xlim</span></a><span class="p">([</span><span class="o">-</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">])</span>
@@ -1184,7 +1183,7 @@ <h2>References<a class="headerlink" href="#references" title="Link to this headi
 <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
 </pre></div>
 </div>
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 <a class="reference external image-reference" href="https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/ensemble/plot_bias_variance.ipynb"><img alt="Launch binder" src="http://github.com/_images/binder_badge_logo11.svg" style="width: 150px;" />
diff --git a/dev/auto_examples/ensemble/plot_ensemble_oob.html b/dev/auto_examples/ensemble/plot_ensemble_oob.html
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     <link rel="search" title="Search" href="http://github.com/search.html" />
-    <link rel="next" title="Plot class probabilities calculated by the VotingClassifier" href="http://github.com/plot_voting_probas.html" />
+    <link rel="next" title="Plot individual and voting regression predictions" href="http://github.com/plot_voting_regressor.html" />
     <link rel="prev" title="Multi-class AdaBoosted Decision Trees" href="http://github.com/plot_adaboost_multiclass.html" />
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2 current active"><a class="current reference internal" href="#">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -903,9 +902,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
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 </details></li>
@@ -1094,7 +1093,7 @@
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@@ -1150,11 +1149,11 @@
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-       href="http://github.com/plot_voting_probas.html"
+       href="http://github.com/plot_voting_regressor.html"
        title="next page">
       <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title">Plot class probabilities calculated by the VotingClassifier</p>
+        <p class="prev-next-title">Plot individual and voting regression predictions</p>
       </div>
       <i class="fa-solid fa-angle-right"></i>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -902,9 +901,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
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@@ -1939,18 +1938,18 @@
   background-color: var(--sklearn-color-fitted-level-3);
 }
 </style><div id="sk-container-id-21" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;functiontransformer&#x27;,
-                 FunctionTransformer(func=&lt;function rf_apply at 0x7fb5860d4700&gt;,
+                 FunctionTransformer(func=&lt;function rf_apply at 0x7fd843d2d7e0&gt;,
                                      kw_args={&#x27;model&#x27;: RandomForestClassifier(max_depth=3,
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                 (&#x27;onehotencoder&#x27;, OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)),
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-                 FunctionTransformer(func=&lt;function rf_apply at 0x7fb5860d4700&gt;,
+                 FunctionTransformer(func=&lt;function rf_apply at 0x7fd843d2d7e0&gt;,
                                      kw_args={&#x27;model&#x27;: RandomForestClassifier(max_depth=3,
                                                                               n_estimators=10,
                                                                               random_state=10)})),
                 (&#x27;onehotencoder&#x27;, OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)),
-                (&#x27;logisticregression&#x27;, LogisticRegression(max_iter=1000))])</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-93" type="checkbox" ><label for="sk-estimator-id-93" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>rf_apply</div><div class="caption">FunctionTransformer</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.FunctionTransformer.html">?<span>Documentation for FunctionTransformer</span></a></div></label><div class="sk-toggleable__content fitted"><pre>FunctionTransformer(func=&lt;function rf_apply at 0x7fb5860d4700&gt;,
+                (&#x27;logisticregression&#x27;, LogisticRegression(max_iter=1000))])</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-93" type="checkbox" ><label for="sk-estimator-id-93" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>rf_apply</div><div class="caption">FunctionTransformer</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.FunctionTransformer.html">?<span>Documentation for FunctionTransformer</span></a></div></label><div class="sk-toggleable__content fitted"><pre>FunctionTransformer(func=&lt;function rf_apply at 0x7fd843d2d7e0&gt;,
                     kw_args={&#x27;model&#x27;: RandomForestClassifier(max_depth=3,
                                                              n_estimators=10,
                                                              random_state=10)})</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-94" type="checkbox" ><label for="sk-estimator-id-94" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>OneHotEncoder</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.OneHotEncoder.html">?<span>Documentation for OneHotEncoder</span></a></div></label><div class="sk-toggleable__content fitted"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-95" type="checkbox" ><label for="sk-estimator-id-95" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>LogisticRegression</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.linear_model.LogisticRegression.html">?<span>Documentation for LogisticRegression</span></a></div></label><div class="sk-toggleable__content fitted"><pre>LogisticRegression(max_iter=1000)</pre></div> </div></div></div></div></div></div>
@@ -2389,16 +2388,16 @@
   background-color: var(--sklearn-color-fitted-level-3);
 }
 </style><div id="sk-container-id-22" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;functiontransformer&#x27;,
-                 FunctionTransformer(func=&lt;function gbdt_apply at 0x7fb57bf40820&gt;,
+                 FunctionTransformer(func=&lt;function gbdt_apply at 0x7fd8436de4d0&gt;,
                                      kw_args={&#x27;model&#x27;: GradientBoostingClassifier(n_estimators=10,
                                                                                   random_state=10)})),
                 (&#x27;onehotencoder&#x27;, OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)),
                 (&#x27;logisticregression&#x27;, LogisticRegression(max_iter=1000))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-96" type="checkbox" ><label for="sk-estimator-id-96" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>Pipeline</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.pipeline.Pipeline.html">?<span>Documentation for Pipeline</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>Pipeline(steps=[(&#x27;functiontransformer&#x27;,
-                 FunctionTransformer(func=&lt;function gbdt_apply at 0x7fb57bf40820&gt;,
+                 FunctionTransformer(func=&lt;function gbdt_apply at 0x7fd8436de4d0&gt;,
                                      kw_args={&#x27;model&#x27;: GradientBoostingClassifier(n_estimators=10,
                                                                                   random_state=10)})),
                 (&#x27;onehotencoder&#x27;, OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)),
-                (&#x27;logisticregression&#x27;, LogisticRegression(max_iter=1000))])</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-97" type="checkbox" ><label for="sk-estimator-id-97" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>gbdt_apply</div><div class="caption">FunctionTransformer</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.FunctionTransformer.html">?<span>Documentation for FunctionTransformer</span></a></div></label><div class="sk-toggleable__content fitted"><pre>FunctionTransformer(func=&lt;function gbdt_apply at 0x7fb57bf40820&gt;,
+                (&#x27;logisticregression&#x27;, LogisticRegression(max_iter=1000))])</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-97" type="checkbox" ><label for="sk-estimator-id-97" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>gbdt_apply</div><div class="caption">FunctionTransformer</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.FunctionTransformer.html">?<span>Documentation for FunctionTransformer</span></a></div></label><div class="sk-toggleable__content fitted"><pre>FunctionTransformer(func=&lt;function gbdt_apply at 0x7fd8436de4d0&gt;,
                     kw_args={&#x27;model&#x27;: GradientBoostingClassifier(n_estimators=10,
                                                                  random_state=10)})</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-98" type="checkbox" ><label for="sk-estimator-id-98" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>OneHotEncoder</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.OneHotEncoder.html">?<span>Documentation for OneHotEncoder</span></a></div></label><div class="sk-toggleable__content fitted"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-99" type="checkbox" ><label for="sk-estimator-id-99" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>LogisticRegression</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.linear_model.LogisticRegression.html">?<span>Documentation for LogisticRegression</span></a></div></label><div class="sk-toggleable__content fitted"><pre>LogisticRegression(max_iter=1000)</pre></div> </div></div></div></div></div></div>
 </div>
@@ -2435,7 +2434,7 @@
 <span class="n">_</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;ROC curve (zoomed in at top left)&quot;</span><span class="p">)</span>
 </pre></div>
 </div>
-<img src="http://github.com/_images/sphx_glr_plot_feature_transformation_002.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_feature_transformation_002.png " alt="ROC curve (zoomed in at top left)" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 2.698 seconds)</p>
+<img src="http://github.com/_images/sphx_glr_plot_feature_transformation_002.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_feature_transformation_002.png " alt="ROC curve (zoomed in at top left)" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 3.462 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-ensemble-plot-feature-transformation-py">
 <div class="binder-badge docutils container">
 <a class="reference external image-reference" href="https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/ensemble/plot_feature_transformation.ipynb"><img alt="Launch binder" src="http://github.com/_images/binder_badge_logo11.svg" style="width: 150px;" />
@@ -2465,9 +2464,9 @@
 </div><div class="sphx-glr-thumbcontainer" tooltip="We are pleased to announce the release of scikit-learn 0.22, which comes with many bug fixes and new features! We detail below a few of the major features of this release. For an exhaustive list of all the changes, please refer to the release notes &lt;release_notes_0_22&gt;."><img alt="" src="http://github.com/_images/sphx_glr_plot_release_highlights_0_22_0_thumb.png" />
 <p><a class="reference internal" href="http://github.com/release_highlights/plot_release_highlights_0_22_0.html#sphx-glr-auto-examples-release-highlights-plot-release-highlights-0-22-0-py"><span class="std std-ref">Release Highlights for scikit-learn 0.22</span></a></p>
   <div class="sphx-glr-thumbnail-title">Release Highlights for scikit-learn 0.22</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="ROC Curve with Visualization API"><img alt="" src="http://github.com/_images/sphx_glr_plot_roc_curve_visualization_api_thumb.png" />
-<p><a class="reference internal" href="http://github.com/miscellaneous/plot_roc_curve_visualization_api.html#sphx-glr-auto-examples-miscellaneous-plot-roc-curve-visualization-api-py"><span class="std std-ref">ROC Curve with Visualization API</span></a></p>
-  <div class="sphx-glr-thumbnail-title">ROC Curve with Visualization API</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="In this example we compare the performance of Random Forest (RF) and Histogram Gradient Boosting (HGBT) models in terms of score and computation time for a regression dataset, though all the concepts here presented apply to classification as well."><img alt="" src="http://github.com/_images/sphx_glr_plot_forest_hist_grad_boosting_comparison_thumb.png" />
+<p><a class="reference internal" href="http://github.com/plot_forest_hist_grad_boosting_comparison.html#sphx-glr-auto-examples-ensemble-plot-forest-hist-grad-boosting-comparison-py"><span class="std std-ref">Comparing Random Forests and Histogram Gradient Boosting models</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Comparing Random Forests and Histogram Gradient Boosting models</div>
 </div></div><p class="sphx-glr-signature"><a class="reference external" href="https://sphinx-gallery.github.io/">Gallery generated by Sphinx-Gallery</a></p>
 </section>
 
diff --git a/dev/auto_examples/ensemble/plot_forest_hist_grad_boosting_comparison.html b/dev/auto_examples/ensemble/plot_forest_hist_grad_boosting_comparison.html
index 2c284a5c9325d..b1e63ce54f92d 100644
--- a/dev/auto_examples/ensemble/plot_forest_hist_grad_boosting_comparison.html
+++ b/dev/auto_examples/ensemble/plot_forest_hist_grad_boosting_comparison.html
@@ -42,7 +42,7 @@
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+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -902,9 +901,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
@@ -1188,7 +1187,7 @@ <h2>Plot results<a class="headerlink" href="#plot-results" title="Link to this h
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 <br />
 <br /><p>Both HGBT and RF models improve when increasing the number of trees in the
@@ -1213,7 +1212,7 @@ <h2>Plot results<a class="headerlink" href="#plot-results" title="Link to this h
 fits internally one-tree per class at each boosting iteration while the trees
 used by the RF models are naturally multiclass which should improve the speed
 accuracy trade-off of the RF models in this case.</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 54.339 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (1 minutes 1.135 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-ensemble-plot-forest-hist-grad-boosting-comparison-py">
 <div class="binder-badge docutils container">
 <a class="reference external image-reference" href="https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/ensemble/plot_forest_hist_grad_boosting_comparison.ipynb"><img alt="Launch binder" src="http://github.com/_images/binder_badge_logo11.svg" style="width: 150px;" />
@@ -1240,12 +1239,12 @@ <h2>Plot results<a class="headerlink" href="#plot-results" title="Link to this h
 </div><div class="sphx-glr-thumbcontainer" tooltip="This example balances model complexity and cross-validated score by finding a decent accuracy within 1 standard deviation of the best accuracy score while minimising the number of PCA components [1]."><img alt="" src="http://github.com/_images/sphx_glr_plot_grid_search_refit_callable_thumb.png" />
 <p><a class="reference internal" href="http://github.com/model_selection/plot_grid_search_refit_callable.html#sphx-glr-auto-examples-model-selection-plot-grid-search-refit-callable-py"><span class="std std-ref">Balance model complexity and cross-validated score</span></a></p>
   <div class="sphx-glr-thumbnail-title">Balance model complexity and cross-validated score</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="histogram_based_gradient_boosting (HGBT) models may be one of the most useful supervised learning models in scikit-learn. They are based on a modern gradient boosting implementation comparable to LightGBM and XGBoost. As such, HGBT models are more feature rich than and often outperform alternative models like random forests, especially when the number of samples is larger than some ten thousands (see sphx_glr_auto_examples_ensemble_plot_forest_hist_grad_boosting_comparison.py)."><img alt="" src="http://github.com/_images/sphx_glr_plot_hgbt_regression_thumb.png" />
-<p><a class="reference internal" href="http://github.com/plot_hgbt_regression.html#sphx-glr-auto-examples-ensemble-plot-hgbt-regression-py"><span class="std std-ref">Features in Histogram Gradient Boosting Trees</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Features in Histogram Gradient Boosting Trees</div>
 </div><div class="sphx-glr-thumbcontainer" tooltip="Transform your features into a higher dimensional, sparse space. Then train a linear model on these features."><img alt="" src="http://github.com/_images/sphx_glr_plot_feature_transformation_thumb.png" />
 <p><a class="reference internal" href="http://github.com/plot_feature_transformation.html#sphx-glr-auto-examples-ensemble-plot-feature-transformation-py"><span class="std std-ref">Feature transformations with ensembles of trees</span></a></p>
   <div class="sphx-glr-thumbnail-title">Feature transformations with ensembles of trees</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="histogram_based_gradient_boosting (HGBT) models may be one of the most useful supervised learning models in scikit-learn. They are based on a modern gradient boosting implementation comparable to LightGBM and XGBoost. As such, HGBT models are more feature rich than and often outperform alternative models like random forests, especially when the number of samples is larger than some ten thousands (see sphx_glr_auto_examples_ensemble_plot_forest_hist_grad_boosting_comparison.py)."><img alt="" src="http://github.com/_images/sphx_glr_plot_hgbt_regression_thumb.png" />
+<p><a class="reference internal" href="http://github.com/plot_hgbt_regression.html#sphx-glr-auto-examples-ensemble-plot-hgbt-regression-py"><span class="std std-ref">Features in Histogram Gradient Boosting Trees</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Features in Histogram Gradient Boosting Trees</div>
 </div></div><p class="sphx-glr-signature"><a class="reference external" href="https://sphinx-gallery.github.io/">Gallery generated by Sphinx-Gallery</a></p>
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diff --git a/dev/auto_examples/ensemble/plot_forest_importances.html b/dev/auto_examples/ensemble/plot_forest_importances.html
index 8fd2df747b051..6053b9ef0f970 100644
--- a/dev/auto_examples/ensemble/plot_forest_importances.html
+++ b/dev/auto_examples/ensemble/plot_forest_importances.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
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@@ -679,13 +679,12 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -902,9 +901,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
@@ -1479,7 +1478,7 @@ <h2>Feature importance based on mean decrease in impurity<a class="headerlink" h
 <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Elapsed time to compute the importances: </span><span class="si">{</span><span class="n">elapsed_time</span><span class="si">:</span><span class="s2">.3f</span><span class="si">}</span><span class="s2"> seconds&quot;</span><span class="p">)</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Elapsed time to compute the importances: 0.009 seconds
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Elapsed time to compute the importances: 0.007 seconds
 </pre></div>
 </div>
 <p>Let’s plot the impurity-based importance.</p>
@@ -1513,7 +1512,7 @@ <h2>Feature importance based on feature permutation<a class="headerlink" href="#
 <span class="n">forest_importances</span> <span class="o">=</span> <a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="pandas.Series" class="sphx-glr-backref-module-pandas sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">pd</span><span class="o">.</span><span class="n">Series</span></a><span class="p">(</span><span class="n">result</span><span class="o">.</span><span class="n">importances_mean</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="n">feature_names</span><span class="p">)</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Elapsed time to compute the importances: 0.503 seconds
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Elapsed time to compute the importances: 0.630 seconds
 </pre></div>
 </div>
 <p>The computation for full permutation importance is more costly. Features are
@@ -1531,7 +1530,7 @@ <h2>Feature importance based on feature permutation<a class="headerlink" href="#
 <img src="http://github.com/_images/sphx_glr_plot_forest_importances_002.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_forest_importances_002.png " alt="Feature importances using permutation on full model" class = "sphx-glr-single-img"/><p>The same features are detected as most important using both methods. Although
 the relative importances vary. As seen on the plots, MDI is less likely than
 permutation importance to fully omit a feature.</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 1.013 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 1.273 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-ensemble-plot-forest-importances-py">
 <div class="binder-badge docutils container">
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diff --git a/dev/auto_examples/ensemble/plot_forest_iris.html b/dev/auto_examples/ensemble/plot_forest_iris.html
index 622d491c3db04..9589b798cd1f7 100644
--- a/dev/auto_examples/ensemble/plot_forest_iris.html
+++ b/dev/auto_examples/ensemble/plot_forest_iris.html
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
@@ -1139,7 +1138,7 @@
 <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
 </pre></div>
 </div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 10.376 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 10.543 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-ensemble-plot-gradient-boosting-oob-py">
 <div class="binder-badge docutils container">
 <a class="reference external image-reference" href="https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/ensemble/plot_gradient_boosting_oob.ipynb"><img alt="Launch binder" src="http://github.com/_images/binder_badge_logo11.svg" style="width: 150px;" />
diff --git a/dev/auto_examples/ensemble/plot_gradient_boosting_quantile.html b/dev/auto_examples/ensemble/plot_gradient_boosting_quantile.html
index 3592d2dc8b7e2..2886e90974b46 100644
--- a/dev/auto_examples/ensemble/plot_gradient_boosting_quantile.html
+++ b/dev/auto_examples/ensemble/plot_gradient_boosting_quantile.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
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@@ -679,13 +679,12 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2 current active"><a class="current reference internal" href="#">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -902,9 +901,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
@@ -1135,18 +1134,18 @@ <h2>Analysis of the error metrics<a class="headerlink" href="#analysis-of-the-er
 </div>
 <div class="output_subarea output_html rendered_html output_result">
 <style type="text/css">
-#T_5e273_row0_col3, #T_5e273_row1_col0, #T_5e273_row2_col1, #T_5e273_row3_col2 {
+#T_e9b5c_row0_col3, #T_e9b5c_row1_col0, #T_e9b5c_row2_col1, #T_e9b5c_row3_col2 {
   font-weight: bold;
 }
 </style>
-<table id="T_5e273">
+<table id="T_e9b5c">
   <thead>
     <tr>
       <th class="blank level0" >&nbsp;</th>
-      <th id="T_5e273_level0_col0" class="col_heading level0 col0" >pbl=0.05</th>
-      <th id="T_5e273_level0_col1" class="col_heading level0 col1" >pbl=0.50</th>
-      <th id="T_5e273_level0_col2" class="col_heading level0 col2" >pbl=0.95</th>
-      <th id="T_5e273_level0_col3" class="col_heading level0 col3" >MSE</th>
+      <th id="T_e9b5c_level0_col0" class="col_heading level0 col0" >pbl=0.05</th>
+      <th id="T_e9b5c_level0_col1" class="col_heading level0 col1" >pbl=0.50</th>
+      <th id="T_e9b5c_level0_col2" class="col_heading level0 col2" >pbl=0.95</th>
+      <th id="T_e9b5c_level0_col3" class="col_heading level0 col3" >MSE</th>
     </tr>
     <tr>
       <th class="index_name level0" >model</th>
@@ -1158,32 +1157,32 @@ <h2>Analysis of the error metrics<a class="headerlink" href="#analysis-of-the-er
   </thead>
   <tbody>
     <tr>
-      <th id="T_5e273_level0_row0" class="row_heading level0 row0" >mse</th>
-      <td id="T_5e273_row0_col0" class="data row0 col0" >0.715413</td>
-      <td id="T_5e273_row0_col1" class="data row0 col1" >0.715413</td>
-      <td id="T_5e273_row0_col2" class="data row0 col2" >0.715413</td>
-      <td id="T_5e273_row0_col3" class="data row0 col3" >7.750348</td>
+      <th id="T_e9b5c_level0_row0" class="row_heading level0 row0" >mse</th>
+      <td id="T_e9b5c_row0_col0" class="data row0 col0" >0.715413</td>
+      <td id="T_e9b5c_row0_col1" class="data row0 col1" >0.715413</td>
+      <td id="T_e9b5c_row0_col2" class="data row0 col2" >0.715413</td>
+      <td id="T_e9b5c_row0_col3" class="data row0 col3" >7.750348</td>
     </tr>
     <tr>
-      <th id="T_5e273_level0_row1" class="row_heading level0 row1" >q 0.05</th>
-      <td id="T_5e273_row1_col0" class="data row1 col0" >0.127128</td>
-      <td id="T_5e273_row1_col1" class="data row1 col1" >1.253445</td>
-      <td id="T_5e273_row1_col2" class="data row1 col2" >2.379763</td>
-      <td id="T_5e273_row1_col3" class="data row1 col3" >18.933253</td>
+      <th id="T_e9b5c_level0_row1" class="row_heading level0 row1" >q 0.05</th>
+      <td id="T_e9b5c_row1_col0" class="data row1 col0" >0.127128</td>
+      <td id="T_e9b5c_row1_col1" class="data row1 col1" >1.253445</td>
+      <td id="T_e9b5c_row1_col2" class="data row1 col2" >2.379763</td>
+      <td id="T_e9b5c_row1_col3" class="data row1 col3" >18.933253</td>
     </tr>
     <tr>
-      <th id="T_5e273_level0_row2" class="row_heading level0 row2" >q 0.50</th>
-      <td id="T_5e273_row2_col0" class="data row2 col0" >0.305438</td>
-      <td id="T_5e273_row2_col1" class="data row2 col1" >0.622811</td>
-      <td id="T_5e273_row2_col2" class="data row2 col2" >0.940184</td>
-      <td id="T_5e273_row2_col3" class="data row2 col3" >9.827917</td>
+      <th id="T_e9b5c_level0_row2" class="row_heading level0 row2" >q 0.50</th>
+      <td id="T_e9b5c_row2_col0" class="data row2 col0" >0.305438</td>
+      <td id="T_e9b5c_row2_col1" class="data row2 col1" >0.622811</td>
+      <td id="T_e9b5c_row2_col2" class="data row2 col2" >0.940184</td>
+      <td id="T_e9b5c_row2_col3" class="data row2 col3" >9.827917</td>
     </tr>
     <tr>
-      <th id="T_5e273_level0_row3" class="row_heading level0 row3" >q 0.95</th>
-      <td id="T_5e273_row3_col0" class="data row3 col0" >3.909909</td>
-      <td id="T_5e273_row3_col1" class="data row3 col1" >2.145957</td>
-      <td id="T_5e273_row3_col2" class="data row3 col2" >0.382005</td>
-      <td id="T_5e273_row3_col3" class="data row3 col3" >28.667219</td>
+      <th id="T_e9b5c_level0_row3" class="row_heading level0 row3" >q 0.95</th>
+      <td id="T_e9b5c_row3_col0" class="data row3 col0" >3.909909</td>
+      <td id="T_e9b5c_row3_col1" class="data row3 col1" >2.145957</td>
+      <td id="T_e9b5c_row3_col2" class="data row3 col2" >0.382005</td>
+      <td id="T_e9b5c_row3_col3" class="data row3 col3" >28.667219</td>
     </tr>
   </tbody>
 </table>
@@ -1216,18 +1215,18 @@ <h2>Analysis of the error metrics<a class="headerlink" href="#analysis-of-the-er
 </div>
 <div class="output_subarea output_html rendered_html output_result">
 <style type="text/css">
-#T_447f7_row1_col0, #T_447f7_row2_col1, #T_447f7_row2_col3, #T_447f7_row3_col2 {
+#T_8ab45_row1_col0, #T_8ab45_row2_col1, #T_8ab45_row2_col3, #T_8ab45_row3_col2 {
   font-weight: bold;
 }
 </style>
-<table id="T_447f7">
+<table id="T_8ab45">
   <thead>
     <tr>
       <th class="blank level0" >&nbsp;</th>
-      <th id="T_447f7_level0_col0" class="col_heading level0 col0" >pbl=0.05</th>
-      <th id="T_447f7_level0_col1" class="col_heading level0 col1" >pbl=0.50</th>
-      <th id="T_447f7_level0_col2" class="col_heading level0 col2" >pbl=0.95</th>
-      <th id="T_447f7_level0_col3" class="col_heading level0 col3" >MSE</th>
+      <th id="T_8ab45_level0_col0" class="col_heading level0 col0" >pbl=0.05</th>
+      <th id="T_8ab45_level0_col1" class="col_heading level0 col1" >pbl=0.50</th>
+      <th id="T_8ab45_level0_col2" class="col_heading level0 col2" >pbl=0.95</th>
+      <th id="T_8ab45_level0_col3" class="col_heading level0 col3" >MSE</th>
     </tr>
     <tr>
       <th class="index_name level0" >model</th>
@@ -1239,32 +1238,32 @@ <h2>Analysis of the error metrics<a class="headerlink" href="#analysis-of-the-er
   </thead>
   <tbody>
     <tr>
-      <th id="T_447f7_level0_row0" class="row_heading level0 row0" >mse</th>
-      <td id="T_447f7_row0_col0" class="data row0 col0" >0.917281</td>
-      <td id="T_447f7_row0_col1" class="data row0 col1" >0.767498</td>
-      <td id="T_447f7_row0_col2" class="data row0 col2" >0.617715</td>
-      <td id="T_447f7_row0_col3" class="data row0 col3" >6.692901</td>
+      <th id="T_8ab45_level0_row0" class="row_heading level0 row0" >mse</th>
+      <td id="T_8ab45_row0_col0" class="data row0 col0" >0.917281</td>
+      <td id="T_8ab45_row0_col1" class="data row0 col1" >0.767498</td>
+      <td id="T_8ab45_row0_col2" class="data row0 col2" >0.617715</td>
+      <td id="T_8ab45_row0_col3" class="data row0 col3" >6.692901</td>
     </tr>
     <tr>
-      <th id="T_447f7_level0_row1" class="row_heading level0 row1" >q 0.05</th>
-      <td id="T_447f7_row1_col0" class="data row1 col0" >0.144204</td>
-      <td id="T_447f7_row1_col1" class="data row1 col1" >1.245961</td>
-      <td id="T_447f7_row1_col2" class="data row1 col2" >2.347717</td>
-      <td id="T_447f7_row1_col3" class="data row1 col3" >15.648026</td>
+      <th id="T_8ab45_level0_row1" class="row_heading level0 row1" >q 0.05</th>
+      <td id="T_8ab45_row1_col0" class="data row1 col0" >0.144204</td>
+      <td id="T_8ab45_row1_col1" class="data row1 col1" >1.245961</td>
+      <td id="T_8ab45_row1_col2" class="data row1 col2" >2.347717</td>
+      <td id="T_8ab45_row1_col3" class="data row1 col3" >15.648026</td>
     </tr>
     <tr>
-      <th id="T_447f7_level0_row2" class="row_heading level0 row2" >q 0.50</th>
-      <td id="T_447f7_row2_col0" class="data row2 col0" >0.412021</td>
-      <td id="T_447f7_row2_col1" class="data row2 col1" >0.607752</td>
-      <td id="T_447f7_row2_col2" class="data row2 col2" >0.803483</td>
-      <td id="T_447f7_row2_col3" class="data row2 col3" >5.874771</td>
+      <th id="T_8ab45_level0_row2" class="row_heading level0 row2" >q 0.50</th>
+      <td id="T_8ab45_row2_col0" class="data row2 col0" >0.412021</td>
+      <td id="T_8ab45_row2_col1" class="data row2 col1" >0.607752</td>
+      <td id="T_8ab45_row2_col2" class="data row2 col2" >0.803483</td>
+      <td id="T_8ab45_row2_col3" class="data row2 col3" >5.874771</td>
     </tr>
     <tr>
-      <th id="T_447f7_level0_row3" class="row_heading level0 row3" >q 0.95</th>
-      <td id="T_447f7_row3_col0" class="data row3 col0" >4.354394</td>
-      <td id="T_447f7_row3_col1" class="data row3 col1" >2.355445</td>
-      <td id="T_447f7_row3_col2" class="data row3 col2" >0.356497</td>
-      <td id="T_447f7_row3_col3" class="data row3 col3" >34.852774</td>
+      <th id="T_8ab45_level0_row3" class="row_heading level0 row3" >q 0.95</th>
+      <td id="T_8ab45_row3_col0" class="data row3 col0" >4.354394</td>
+      <td id="T_8ab45_row3_col1" class="data row3 col1" >2.355445</td>
+      <td id="T_8ab45_row3_col2" class="data row3 col2" >0.356497</td>
+      <td id="T_8ab45_row3_col3" class="data row3 col3" >34.852774</td>
     </tr>
   </tbody>
 </table>
@@ -1326,10 +1325,11 @@ <h2>Tuning the hyper-parameters of the quantile regressors<a class="headerlink"
 <p>To confirm this hypothesis, we tune the hyper-parameters of a new regressor
 of the 5th percentile by selecting the best model parameters by
 cross-validation on the pinball loss with alpha=0.05:</p>
-<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.experimental</span><span class="w"> </span><span class="kn">import</span> <span class="n">enable_halving_search_cv</span>  <span class="c1"># noqa</span>
-<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.model_selection</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.model_selection.HalvingRandomSearchCV.html#sklearn.model_selection.HalvingRandomSearchCV" title="sklearn.model_selection.HalvingRandomSearchCV" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">HalvingRandomSearchCV</span></a>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">pprint</span><span class="w"> </span><span class="kn">import</span> <a href="https://docs.python.org/3/library/pprint.html#pprint.pprint" title="pprint.pprint" class="sphx-glr-backref-module-pprint sphx-glr-backref-type-py-function"><span class="n">pprint</span></a>
+
+<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.experimental</span><span class="w"> </span><span class="kn">import</span> <span class="n">enable_halving_search_cv</span>  <span class="c1"># noqa: F401</span>
 <span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.metrics</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.metrics.make_scorer.html#sklearn.metrics.make_scorer" title="sklearn.metrics.make_scorer" class="sphx-glr-backref-module-sklearn-metrics sphx-glr-backref-type-py-function"><span class="n">make_scorer</span></a>
-<span class="kn">from</span><span class="w"> </span><span class="nn">pprint</span><span class="w"> </span><span class="kn">import</span> <a href="https://docs.python.org/3/library/pprint.html#pprint.pprint" title="pprint.pprint" class="sphx-glr-backref-module-pprint sphx-glr-backref-type-py-function"><span class="n">pprint</span></a>
+<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.model_selection</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.model_selection.HalvingRandomSearchCV.html#sklearn.model_selection.HalvingRandomSearchCV" title="sklearn.model_selection.HalvingRandomSearchCV" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">HalvingRandomSearchCV</span></a>
 
 <span class="n">param_grid</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span>
     <span class="n">learning_rate</span><span class="o">=</span><span class="p">[</span><span class="mf">0.05</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">],</span>
@@ -1439,7 +1439,7 @@ <h2>Tuning the hyper-parameters of the quantile regressors<a class="headerlink"
 width of the estimated confidence interval is still too narrow.</p>
 <p>Again, we would need to wrap this study in a cross-validation loop to
 better assess the variability of those estimates.</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 9.901 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 11.467 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-ensemble-plot-gradient-boosting-quantile-py">
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 <a class="reference external image-reference" href="https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/ensemble/plot_gradient_boosting_quantile.ipynb"><img alt="Launch binder" src="http://github.com/_images/binder_badge_logo11.svg" style="width: 150px;" />
diff --git a/dev/auto_examples/ensemble/plot_gradient_boosting_regression.html b/dev/auto_examples/ensemble/plot_gradient_boosting_regression.html
index 3035a5dc05e4a..5f08add9d9461 100644
--- a/dev/auto_examples/ensemble/plot_gradient_boosting_regression.html
+++ b/dev/auto_examples/ensemble/plot_gradient_boosting_regression.html
@@ -42,7 +42,7 @@
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
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 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
@@ -1146,7 +1145,7 @@ <h2>Plot feature importance<a class="headerlink" href="#plot-feature-importance"
 <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
 </pre></div>
 </div>
-<img src="http://github.com/_images/sphx_glr_plot_gradient_boosting_regression_002.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_gradient_boosting_regression_002.png " alt="Feature Importance (MDI), Permutation Importance (test set)" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 1.388 seconds)</p>
+<img src="http://github.com/_images/sphx_glr_plot_gradient_boosting_regression_002.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_gradient_boosting_regression_002.png " alt="Feature Importance (MDI), Permutation Importance (test set)" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 1.520 seconds)</p>
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diff --git a/dev/auto_examples/ensemble/plot_gradient_boosting_regularization.html b/dev/auto_examples/ensemble/plot_gradient_boosting_regularization.html
index 5ccaf6985a05f..846a3e7c2374a 100644
--- a/dev/auto_examples/ensemble/plot_gradient_boosting_regularization.html
+++ b/dev/auto_examples/ensemble/plot_gradient_boosting_regularization.html
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -902,9 +901,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
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 </pre></div>
 </div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 8.336 seconds)</p>
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index 0079281c4739a..6be018d553d40 100644
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -902,9 +901,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
@@ -1411,7 +1410,7 @@ <h2>Monotonic constraints<a class="headerlink" href="#monotonic-constraints" tit
 <p>That being said, notice the comparison is between two different models that
 may be optimized by a different combination of hyperparameters. That is the
 reason why we do no use the <code class="docutils literal notranslate"><span class="pre">common_params</span></code> in this section as done before.</p>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
@@ -1081,7 +1080,7 @@
 <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
 </pre></div>
 </div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 0.563 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 0.561 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-ensemble-plot-random-forest-regression-multioutput-py">
 <div class="binder-badge docutils container">
 <a class="reference external image-reference" href="https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/ensemble/plot_random_forest_regression_multioutput.ipynb"><img alt="Launch binder" src="http://github.com/_images/binder_badge_logo11.svg" style="width: 150px;" />
diff --git a/dev/auto_examples/ensemble/plot_stack_predictors.html b/dev/auto_examples/ensemble/plot_stack_predictors.html
index cdc0c7b8fc70f..f2fc122139a1b 100644
--- a/dev/auto_examples/ensemble/plot_stack_predictors.html
+++ b/dev/auto_examples/ensemble/plot_stack_predictors.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
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@@ -679,13 +679,12 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -902,9 +901,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
@@ -1527,19 +1526,19 @@ <h2>Make pipeline to preprocess the data<a class="headerlink" href="#make-pipeli
 }
 </style><div id="sk-container-id-13" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>ColumnTransformer(transformers=[(&#x27;simpleimputer&#x27;,
                                  SimpleImputer(add_indicator=True),
-                                 &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb58512ebf0&gt;),
+                                 &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd840886200&gt;),
                                 (&#x27;ordinalencoder&#x27;,
                                  OrdinalEncoder(encoded_missing_value=-2,
                                                 handle_unknown=&#x27;use_encoded_value&#x27;,
                                                 unknown_value=-1),
-                                 &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb58e4f58a0&gt;)])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-34" type="checkbox" ><label for="sk-estimator-id-34" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>ColumnTransformer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.compose.ColumnTransformer.html">?<span>Documentation for ColumnTransformer</span></a><span class="sk-estimator-doc-link ">i<span>Not fitted</span></span></div></label><div class="sk-toggleable__content "><pre>ColumnTransformer(transformers=[(&#x27;simpleimputer&#x27;,
+                                 &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd840886cb0&gt;)])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-34" type="checkbox" ><label for="sk-estimator-id-34" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>ColumnTransformer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.compose.ColumnTransformer.html">?<span>Documentation for ColumnTransformer</span></a><span class="sk-estimator-doc-link ">i<span>Not fitted</span></span></div></label><div class="sk-toggleable__content "><pre>ColumnTransformer(transformers=[(&#x27;simpleimputer&#x27;,
                                  SimpleImputer(add_indicator=True),
-                                 &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb58512ebf0&gt;),
+                                 &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd840886200&gt;),
                                 (&#x27;ordinalencoder&#x27;,
                                  OrdinalEncoder(encoded_missing_value=-2,
                                                 handle_unknown=&#x27;use_encoded_value&#x27;,
                                                 unknown_value=-1),
-                                 &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb58e4f58a0&gt;)])</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-35" type="checkbox" ><label for="sk-estimator-id-35" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>simpleimputer</div></div></label><div class="sk-toggleable__content "><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb58512ebf0&gt;</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-36" type="checkbox" ><label for="sk-estimator-id-36" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>SimpleImputer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.impute.SimpleImputer.html">?<span>Documentation for SimpleImputer</span></a></div></label><div class="sk-toggleable__content "><pre>SimpleImputer(add_indicator=True)</pre></div> </div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-37" type="checkbox" ><label for="sk-estimator-id-37" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>ordinalencoder</div></div></label><div class="sk-toggleable__content "><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb58e4f58a0&gt;</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-38" type="checkbox" ><label for="sk-estimator-id-38" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>OrdinalEncoder</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.OrdinalEncoder.html">?<span>Documentation for OrdinalEncoder</span></a></div></label><div class="sk-toggleable__content "><pre>OrdinalEncoder(encoded_missing_value=-2, handle_unknown=&#x27;use_encoded_value&#x27;,
+                                 &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd840886cb0&gt;)])</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-35" type="checkbox" ><label for="sk-estimator-id-35" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>simpleimputer</div></div></label><div class="sk-toggleable__content "><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd840886200&gt;</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-36" type="checkbox" ><label for="sk-estimator-id-36" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>SimpleImputer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.impute.SimpleImputer.html">?<span>Documentation for SimpleImputer</span></a></div></label><div class="sk-toggleable__content "><pre>SimpleImputer(add_indicator=True)</pre></div> </div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-37" type="checkbox" ><label for="sk-estimator-id-37" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>ordinalencoder</div></div></label><div class="sk-toggleable__content "><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd840886cb0&gt;</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-38" type="checkbox" ><label for="sk-estimator-id-38" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>OrdinalEncoder</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.OrdinalEncoder.html">?<span>Documentation for OrdinalEncoder</span></a></div></label><div class="sk-toggleable__content "><pre>OrdinalEncoder(encoded_missing_value=-2, handle_unknown=&#x27;use_encoded_value&#x27;,
                unknown_value=-1)</pre></div> </div></div></div></div></div></div></div></div></div>
 </div>
 <br />
@@ -1979,18 +1978,18 @@ <h2>Make pipeline to preprocess the data<a class="headerlink" href="#make-pipeli
                                                   StandardScaler()),
                                                  (&#x27;simpleimputer&#x27;,
                                                   SimpleImputer(add_indicator=True))]),
-                                 &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb58512ebf0&gt;),
+                                 &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd840886200&gt;),
                                 (&#x27;onehotencoder&#x27;,
                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;),
-                                 &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb58e4f58a0&gt;)])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-39" type="checkbox" ><label for="sk-estimator-id-39" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>ColumnTransformer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.compose.ColumnTransformer.html">?<span>Documentation for ColumnTransformer</span></a><span class="sk-estimator-doc-link ">i<span>Not fitted</span></span></div></label><div class="sk-toggleable__content "><pre>ColumnTransformer(transformers=[(&#x27;pipeline&#x27;,
+                                 &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd840886cb0&gt;)])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-39" type="checkbox" ><label for="sk-estimator-id-39" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>ColumnTransformer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.compose.ColumnTransformer.html">?<span>Documentation for ColumnTransformer</span></a><span class="sk-estimator-doc-link ">i<span>Not fitted</span></span></div></label><div class="sk-toggleable__content "><pre>ColumnTransformer(transformers=[(&#x27;pipeline&#x27;,
                                  Pipeline(steps=[(&#x27;standardscaler&#x27;,
                                                   StandardScaler()),
                                                  (&#x27;simpleimputer&#x27;,
                                                   SimpleImputer(add_indicator=True))]),
-                                 &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb58512ebf0&gt;),
+                                 &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd840886200&gt;),
                                 (&#x27;onehotencoder&#x27;,
                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;),
-                                 &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb58e4f58a0&gt;)])</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-40" type="checkbox" ><label for="sk-estimator-id-40" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>pipeline</div></div></label><div class="sk-toggleable__content "><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb58512ebf0&gt;</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-41" type="checkbox" ><label for="sk-estimator-id-41" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>StandardScaler</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.StandardScaler.html">?<span>Documentation for StandardScaler</span></a></div></label><div class="sk-toggleable__content "><pre>StandardScaler()</pre></div> </div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-42" type="checkbox" ><label for="sk-estimator-id-42" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>SimpleImputer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.impute.SimpleImputer.html">?<span>Documentation for SimpleImputer</span></a></div></label><div class="sk-toggleable__content "><pre>SimpleImputer(add_indicator=True)</pre></div> </div></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-43" type="checkbox" ><label for="sk-estimator-id-43" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>onehotencoder</div></div></label><div class="sk-toggleable__content "><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb58e4f58a0&gt;</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-44" type="checkbox" ><label for="sk-estimator-id-44" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>OneHotEncoder</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.OneHotEncoder.html">?<span>Documentation for OneHotEncoder</span></a></div></label><div class="sk-toggleable__content "><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</pre></div> </div></div></div></div></div></div></div></div></div>
+                                 &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd840886cb0&gt;)])</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-40" type="checkbox" ><label for="sk-estimator-id-40" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>pipeline</div></div></label><div class="sk-toggleable__content "><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd840886200&gt;</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-41" type="checkbox" ><label for="sk-estimator-id-41" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>StandardScaler</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.StandardScaler.html">?<span>Documentation for StandardScaler</span></a></div></label><div class="sk-toggleable__content "><pre>StandardScaler()</pre></div> </div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-42" type="checkbox" ><label for="sk-estimator-id-42" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>SimpleImputer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.impute.SimpleImputer.html">?<span>Documentation for SimpleImputer</span></a></div></label><div class="sk-toggleable__content "><pre>SimpleImputer(add_indicator=True)</pre></div> </div></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-43" type="checkbox" ><label for="sk-estimator-id-43" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>onehotencoder</div></div></label><div class="sk-toggleable__content "><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd840886cb0&gt;</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-44" type="checkbox" ><label for="sk-estimator-id-44" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>OneHotEncoder</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.OneHotEncoder.html">?<span>Documentation for OneHotEncoder</span></a></div></label><div class="sk-toggleable__content "><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</pre></div> </div></div></div></div></div></div></div></div></div>
 </div>
 <br />
 <br /></section>
@@ -2439,29 +2438,29 @@ <h2>Stack of predictors on a single data set<a class="headerlink" href="#stack-o
                                                                    StandardScaler()),
                                                                   (&#x27;simpleimputer&#x27;,
                                                                    SimpleImputer(add_indicator=True))]),
-                                                  &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb58512ebf0&gt;),
+                                                  &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd840886200&gt;),
                                                  (&#x27;onehotencoder&#x27;,
                                                   OneHotEncoder(handle_unknown=&#x27;ignore&#x27;),
-                                                  &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb58e4f58a0&gt;)])),
+                                                  &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd840886cb0&gt;)])),
                 (&#x27;lassocv&#x27;, LassoCV())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-45" type="checkbox" ><label for="sk-estimator-id-45" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>Pipeline</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.pipeline.Pipeline.html">?<span>Documentation for Pipeline</span></a><span class="sk-estimator-doc-link ">i<span>Not fitted</span></span></div></label><div class="sk-toggleable__content "><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,
                  ColumnTransformer(transformers=[(&#x27;pipeline&#x27;,
                                                   Pipeline(steps=[(&#x27;standardscaler&#x27;,
                                                                    StandardScaler()),
                                                                   (&#x27;simpleimputer&#x27;,
                                                                    SimpleImputer(add_indicator=True))]),
-                                                  &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb58512ebf0&gt;),
+                                                  &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd840886200&gt;),
                                                  (&#x27;onehotencoder&#x27;,
                                                   OneHotEncoder(handle_unknown=&#x27;ignore&#x27;),
-                                                  &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb58e4f58a0&gt;)])),
+                                                  &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd840886cb0&gt;)])),
                 (&#x27;lassocv&#x27;, LassoCV())])</pre></div> </div></div><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-46" type="checkbox" ><label for="sk-estimator-id-46" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>columntransformer: ColumnTransformer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.compose.ColumnTransformer.html">?<span>Documentation for columntransformer: ColumnTransformer</span></a></div></label><div class="sk-toggleable__content "><pre>ColumnTransformer(transformers=[(&#x27;pipeline&#x27;,
                                  Pipeline(steps=[(&#x27;standardscaler&#x27;,
                                                   StandardScaler()),
                                                  (&#x27;simpleimputer&#x27;,
                                                   SimpleImputer(add_indicator=True))]),
-                                 &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb58512ebf0&gt;),
+                                 &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd840886200&gt;),
                                 (&#x27;onehotencoder&#x27;,
                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;),
-                                 &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb58e4f58a0&gt;)])</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-47" type="checkbox" ><label for="sk-estimator-id-47" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>pipeline</div></div></label><div class="sk-toggleable__content "><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb58512ebf0&gt;</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-48" type="checkbox" ><label for="sk-estimator-id-48" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>StandardScaler</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.StandardScaler.html">?<span>Documentation for StandardScaler</span></a></div></label><div class="sk-toggleable__content "><pre>StandardScaler()</pre></div> </div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-49" type="checkbox" ><label for="sk-estimator-id-49" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>SimpleImputer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.impute.SimpleImputer.html">?<span>Documentation for SimpleImputer</span></a></div></label><div class="sk-toggleable__content "><pre>SimpleImputer(add_indicator=True)</pre></div> </div></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-50" type="checkbox" ><label for="sk-estimator-id-50" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>onehotencoder</div></div></label><div class="sk-toggleable__content "><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb58e4f58a0&gt;</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-51" type="checkbox" ><label for="sk-estimator-id-51" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>OneHotEncoder</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.OneHotEncoder.html">?<span>Documentation for OneHotEncoder</span></a></div></label><div class="sk-toggleable__content "><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</pre></div> </div></div></div></div></div></div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-52" type="checkbox" ><label for="sk-estimator-id-52" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>LassoCV</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.linear_model.LassoCV.html">?<span>Documentation for LassoCV</span></a></div></label><div class="sk-toggleable__content "><pre>LassoCV()</pre></div> </div></div></div></div></div></div>
+                                 &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd840886cb0&gt;)])</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-47" type="checkbox" ><label for="sk-estimator-id-47" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>pipeline</div></div></label><div class="sk-toggleable__content "><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd840886200&gt;</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-48" type="checkbox" ><label for="sk-estimator-id-48" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>StandardScaler</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.StandardScaler.html">?<span>Documentation for StandardScaler</span></a></div></label><div class="sk-toggleable__content "><pre>StandardScaler()</pre></div> </div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-49" type="checkbox" ><label for="sk-estimator-id-49" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>SimpleImputer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.impute.SimpleImputer.html">?<span>Documentation for SimpleImputer</span></a></div></label><div class="sk-toggleable__content "><pre>SimpleImputer(add_indicator=True)</pre></div> </div></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-50" type="checkbox" ><label for="sk-estimator-id-50" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>onehotencoder</div></div></label><div class="sk-toggleable__content "><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd840886cb0&gt;</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-51" type="checkbox" ><label for="sk-estimator-id-51" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>OneHotEncoder</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.OneHotEncoder.html">?<span>Documentation for OneHotEncoder</span></a></div></label><div class="sk-toggleable__content "><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</pre></div> </div></div></div></div></div></div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-52" type="checkbox" ><label for="sk-estimator-id-52" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>LassoCV</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.linear_model.LassoCV.html">?<span>Documentation for LassoCV</span></a></div></label><div class="sk-toggleable__content "><pre>LassoCV()</pre></div> </div></div></div></div></div></div>
 </div>
 <br />
 <br /><div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.ensemble</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.ensemble.RandomForestRegressor.html#sklearn.ensemble.RandomForestRegressor" title="sklearn.ensemble.RandomForestRegressor" class="sphx-glr-backref-module-sklearn-ensemble sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">RandomForestRegressor</span></a>
@@ -2889,31 +2888,31 @@ <h2>Stack of predictors on a single data set<a class="headerlink" href="#stack-o
 </style><div id="sk-container-id-16" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,
                  ColumnTransformer(transformers=[(&#x27;simpleimputer&#x27;,
                                                   SimpleImputer(add_indicator=True),
-                                                  &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb58512ebf0&gt;),
+                                                  &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd840886200&gt;),
                                                  (&#x27;ordinalencoder&#x27;,
                                                   OrdinalEncoder(encoded_missing_value=-2,
                                                                  handle_unknown=&#x27;use_encoded_value&#x27;,
                                                                  unknown_value=-1),
-                                                  &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb58e4f58a0&gt;)])),
+                                                  &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd840886cb0&gt;)])),
                 (&#x27;randomforestregressor&#x27;,
                  RandomForestRegressor(random_state=42))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-53" type="checkbox" ><label for="sk-estimator-id-53" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>Pipeline</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.pipeline.Pipeline.html">?<span>Documentation for Pipeline</span></a><span class="sk-estimator-doc-link ">i<span>Not fitted</span></span></div></label><div class="sk-toggleable__content "><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,
                  ColumnTransformer(transformers=[(&#x27;simpleimputer&#x27;,
                                                   SimpleImputer(add_indicator=True),
-                                                  &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb58512ebf0&gt;),
+                                                  &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd840886200&gt;),
                                                  (&#x27;ordinalencoder&#x27;,
                                                   OrdinalEncoder(encoded_missing_value=-2,
                                                                  handle_unknown=&#x27;use_encoded_value&#x27;,
                                                                  unknown_value=-1),
-                                                  &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb58e4f58a0&gt;)])),
+                                                  &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd840886cb0&gt;)])),
                 (&#x27;randomforestregressor&#x27;,
                  RandomForestRegressor(random_state=42))])</pre></div> </div></div><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-54" type="checkbox" ><label for="sk-estimator-id-54" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>columntransformer: ColumnTransformer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.compose.ColumnTransformer.html">?<span>Documentation for columntransformer: ColumnTransformer</span></a></div></label><div class="sk-toggleable__content "><pre>ColumnTransformer(transformers=[(&#x27;simpleimputer&#x27;,
                                  SimpleImputer(add_indicator=True),
-                                 &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb58512ebf0&gt;),
+                                 &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd840886200&gt;),
                                 (&#x27;ordinalencoder&#x27;,
                                  OrdinalEncoder(encoded_missing_value=-2,
                                                 handle_unknown=&#x27;use_encoded_value&#x27;,
                                                 unknown_value=-1),
-                                 &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb58e4f58a0&gt;)])</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-55" type="checkbox" ><label for="sk-estimator-id-55" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>simpleimputer</div></div></label><div class="sk-toggleable__content "><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb58512ebf0&gt;</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-56" type="checkbox" ><label for="sk-estimator-id-56" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>SimpleImputer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.impute.SimpleImputer.html">?<span>Documentation for SimpleImputer</span></a></div></label><div class="sk-toggleable__content "><pre>SimpleImputer(add_indicator=True)</pre></div> </div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-57" type="checkbox" ><label for="sk-estimator-id-57" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>ordinalencoder</div></div></label><div class="sk-toggleable__content "><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb58e4f58a0&gt;</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-58" type="checkbox" ><label for="sk-estimator-id-58" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>OrdinalEncoder</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.OrdinalEncoder.html">?<span>Documentation for OrdinalEncoder</span></a></div></label><div class="sk-toggleable__content "><pre>OrdinalEncoder(encoded_missing_value=-2, handle_unknown=&#x27;use_encoded_value&#x27;,
+                                 &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd840886cb0&gt;)])</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-55" type="checkbox" ><label for="sk-estimator-id-55" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>simpleimputer</div></div></label><div class="sk-toggleable__content "><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd840886200&gt;</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-56" type="checkbox" ><label for="sk-estimator-id-56" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>SimpleImputer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.impute.SimpleImputer.html">?<span>Documentation for SimpleImputer</span></a></div></label><div class="sk-toggleable__content "><pre>SimpleImputer(add_indicator=True)</pre></div> </div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-57" type="checkbox" ><label for="sk-estimator-id-57" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>ordinalencoder</div></div></label><div class="sk-toggleable__content "><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd840886cb0&gt;</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-58" type="checkbox" ><label for="sk-estimator-id-58" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>OrdinalEncoder</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.OrdinalEncoder.html">?<span>Documentation for OrdinalEncoder</span></a></div></label><div class="sk-toggleable__content "><pre>OrdinalEncoder(encoded_missing_value=-2, handle_unknown=&#x27;use_encoded_value&#x27;,
                unknown_value=-1)</pre></div> </div></div></div></div></div></div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-59" type="checkbox" ><label for="sk-estimator-id-59" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>RandomForestRegressor</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.ensemble.RandomForestRegressor.html">?<span>Documentation for RandomForestRegressor</span></a></div></label><div class="sk-toggleable__content "><pre>RandomForestRegressor(random_state=42)</pre></div> </div></div></div></div></div></div>
 </div>
 <br />
@@ -3344,31 +3343,31 @@ <h2>Stack of predictors on a single data set<a class="headerlink" href="#stack-o
 </style><div id="sk-container-id-17" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,
                  ColumnTransformer(transformers=[(&#x27;simpleimputer&#x27;,
                                                   SimpleImputer(add_indicator=True),
-                                                  &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb58512ebf0&gt;),
+                                                  &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd840886200&gt;),
                                                  (&#x27;ordinalencoder&#x27;,
                                                   OrdinalEncoder(encoded_missing_value=-2,
                                                                  handle_unknown=&#x27;use_encoded_value&#x27;,
                                                                  unknown_value=-1),
-                                                  &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb58e4f58a0&gt;)])),
+                                                  &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd840886cb0&gt;)])),
                 (&#x27;histgradientboostingregressor&#x27;,
                  HistGradientBoostingRegressor(random_state=0))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-60" type="checkbox" ><label for="sk-estimator-id-60" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>Pipeline</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.pipeline.Pipeline.html">?<span>Documentation for Pipeline</span></a><span class="sk-estimator-doc-link ">i<span>Not fitted</span></span></div></label><div class="sk-toggleable__content "><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,
                  ColumnTransformer(transformers=[(&#x27;simpleimputer&#x27;,
                                                   SimpleImputer(add_indicator=True),
-                                                  &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb58512ebf0&gt;),
+                                                  &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd840886200&gt;),
                                                  (&#x27;ordinalencoder&#x27;,
                                                   OrdinalEncoder(encoded_missing_value=-2,
                                                                  handle_unknown=&#x27;use_encoded_value&#x27;,
                                                                  unknown_value=-1),
-                                                  &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb58e4f58a0&gt;)])),
+                                                  &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd840886cb0&gt;)])),
                 (&#x27;histgradientboostingregressor&#x27;,
                  HistGradientBoostingRegressor(random_state=0))])</pre></div> </div></div><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-61" type="checkbox" ><label for="sk-estimator-id-61" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>columntransformer: ColumnTransformer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.compose.ColumnTransformer.html">?<span>Documentation for columntransformer: ColumnTransformer</span></a></div></label><div class="sk-toggleable__content "><pre>ColumnTransformer(transformers=[(&#x27;simpleimputer&#x27;,
                                  SimpleImputer(add_indicator=True),
-                                 &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb58512ebf0&gt;),
+                                 &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd840886200&gt;),
                                 (&#x27;ordinalencoder&#x27;,
                                  OrdinalEncoder(encoded_missing_value=-2,
                                                 handle_unknown=&#x27;use_encoded_value&#x27;,
                                                 unknown_value=-1),
-                                 &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb58e4f58a0&gt;)])</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-62" type="checkbox" ><label for="sk-estimator-id-62" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>simpleimputer</div></div></label><div class="sk-toggleable__content "><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb58512ebf0&gt;</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-63" type="checkbox" ><label for="sk-estimator-id-63" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>SimpleImputer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.impute.SimpleImputer.html">?<span>Documentation for SimpleImputer</span></a></div></label><div class="sk-toggleable__content "><pre>SimpleImputer(add_indicator=True)</pre></div> </div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-64" type="checkbox" ><label for="sk-estimator-id-64" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>ordinalencoder</div></div></label><div class="sk-toggleable__content "><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb58e4f58a0&gt;</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-65" type="checkbox" ><label for="sk-estimator-id-65" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>OrdinalEncoder</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.OrdinalEncoder.html">?<span>Documentation for OrdinalEncoder</span></a></div></label><div class="sk-toggleable__content "><pre>OrdinalEncoder(encoded_missing_value=-2, handle_unknown=&#x27;use_encoded_value&#x27;,
+                                 &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd840886cb0&gt;)])</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-62" type="checkbox" ><label for="sk-estimator-id-62" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>simpleimputer</div></div></label><div class="sk-toggleable__content "><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd840886200&gt;</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-63" type="checkbox" ><label for="sk-estimator-id-63" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>SimpleImputer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.impute.SimpleImputer.html">?<span>Documentation for SimpleImputer</span></a></div></label><div class="sk-toggleable__content "><pre>SimpleImputer(add_indicator=True)</pre></div> </div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-64" type="checkbox" ><label for="sk-estimator-id-64" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>ordinalencoder</div></div></label><div class="sk-toggleable__content "><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd840886cb0&gt;</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-65" type="checkbox" ><label for="sk-estimator-id-65" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>OrdinalEncoder</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.OrdinalEncoder.html">?<span>Documentation for OrdinalEncoder</span></a></div></label><div class="sk-toggleable__content "><pre>OrdinalEncoder(encoded_missing_value=-2, handle_unknown=&#x27;use_encoded_value&#x27;,
                unknown_value=-1)</pre></div> </div></div></div></div></div></div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-66" type="checkbox" ><label for="sk-estimator-id-66" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>HistGradientBoostingRegressor</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.ensemble.HistGradientBoostingRegressor.html">?<span>Documentation for HistGradientBoostingRegressor</span></a></div></label><div class="sk-toggleable__content "><pre>HistGradientBoostingRegressor(random_state=0)</pre></div> </div></div></div></div></div></div>
 </div>
 <br />
@@ -3805,60 +3804,60 @@ <h2>Stack of predictors on a single data set<a class="headerlink" href="#stack-o
                                Pipeline(steps=[(&#x27;columntransformer&#x27;,
                                                 ColumnTransformer(transformers=[(&#x27;simpleimputer&#x27;,
                                                                                  SimpleImputer(add_indicator=True),
-                                                                                 &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb58512ebf0&gt;),
+                                                                                 &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd840886200&gt;),
                                                                                 (&#x27;ordinalencoder&#x27;,
                                                                                  OrdinalEncoder(encoded_missing_value=-2,
                                                                                                 handle_unknown=&#x27;use_encoded_value&#x27;,
                                                                                                 unknown_v...
-                                                                                 &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb58512ebf0&gt;),
+                                                                                 &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd840886200&gt;),
                                                                                 (&#x27;ordinalencoder&#x27;,
                                                                                  OrdinalEncoder(encoded_missing_value=-2,
                                                                                                 handle_unknown=&#x27;use_encoded_value&#x27;,
                                                                                                 unknown_value=-1),
-                                                                                 &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb58e4f58a0&gt;)])),
+                                                                                 &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd840886cb0&gt;)])),
                                                (&#x27;histgradientboostingregressor&#x27;,
                                                 HistGradientBoostingRegressor(random_state=0))]))],
                   final_estimator=RidgeCV())</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-67" type="checkbox" ><label for="sk-estimator-id-67" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>StackingRegressor</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.ensemble.StackingRegressor.html">?<span>Documentation for StackingRegressor</span></a><span class="sk-estimator-doc-link ">i<span>Not fitted</span></span></div></label><div class="sk-toggleable__content "><pre>StackingRegressor(estimators=[(&#x27;Random Forest&#x27;,
                                Pipeline(steps=[(&#x27;columntransformer&#x27;,
                                                 ColumnTransformer(transformers=[(&#x27;simpleimputer&#x27;,
                                                                                  SimpleImputer(add_indicator=True),
-                                                                                 &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb58512ebf0&gt;),
+                                                                                 &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd840886200&gt;),
                                                                                 (&#x27;ordinalencoder&#x27;,
                                                                                  OrdinalEncoder(encoded_missing_value=-2,
                                                                                                 handle_unknown=&#x27;use_encoded_value&#x27;,
                                                                                                 unknown_v...
-                                                                                 &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb58512ebf0&gt;),
+                                                                                 &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd840886200&gt;),
                                                                                 (&#x27;ordinalencoder&#x27;,
                                                                                  OrdinalEncoder(encoded_missing_value=-2,
                                                                                                 handle_unknown=&#x27;use_encoded_value&#x27;,
                                                                                                 unknown_value=-1),
-                                                                                 &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb58e4f58a0&gt;)])),
+                                                                                 &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd840886cb0&gt;)])),
                                                (&#x27;histgradientboostingregressor&#x27;,
                                                 HistGradientBoostingRegressor(random_state=0))]))],
                   final_estimator=RidgeCV())</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><label>Random Forest</label></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-68" type="checkbox" ><label for="sk-estimator-id-68" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>columntransformer: ColumnTransformer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.compose.ColumnTransformer.html">?<span>Documentation for columntransformer: ColumnTransformer</span></a></div></label><div class="sk-toggleable__content "><pre>ColumnTransformer(transformers=[(&#x27;simpleimputer&#x27;,
                                  SimpleImputer(add_indicator=True),
-                                 &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb58512ebf0&gt;),
+                                 &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd840886200&gt;),
                                 (&#x27;ordinalencoder&#x27;,
                                  OrdinalEncoder(encoded_missing_value=-2,
                                                 handle_unknown=&#x27;use_encoded_value&#x27;,
                                                 unknown_value=-1),
-                                 &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb58e4f58a0&gt;)])</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-69" type="checkbox" ><label for="sk-estimator-id-69" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>simpleimputer</div></div></label><div class="sk-toggleable__content "><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb58512ebf0&gt;</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-70" type="checkbox" ><label for="sk-estimator-id-70" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>SimpleImputer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.impute.SimpleImputer.html">?<span>Documentation for SimpleImputer</span></a></div></label><div class="sk-toggleable__content "><pre>SimpleImputer(add_indicator=True)</pre></div> </div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-71" type="checkbox" ><label for="sk-estimator-id-71" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>ordinalencoder</div></div></label><div class="sk-toggleable__content "><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb58e4f58a0&gt;</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-72" type="checkbox" ><label for="sk-estimator-id-72" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>OrdinalEncoder</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.OrdinalEncoder.html">?<span>Documentation for OrdinalEncoder</span></a></div></label><div class="sk-toggleable__content "><pre>OrdinalEncoder(encoded_missing_value=-2, handle_unknown=&#x27;use_encoded_value&#x27;,
+                                 &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd840886cb0&gt;)])</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-69" type="checkbox" ><label for="sk-estimator-id-69" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>simpleimputer</div></div></label><div class="sk-toggleable__content "><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd840886200&gt;</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-70" type="checkbox" ><label for="sk-estimator-id-70" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>SimpleImputer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.impute.SimpleImputer.html">?<span>Documentation for SimpleImputer</span></a></div></label><div class="sk-toggleable__content "><pre>SimpleImputer(add_indicator=True)</pre></div> </div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-71" type="checkbox" ><label for="sk-estimator-id-71" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>ordinalencoder</div></div></label><div class="sk-toggleable__content "><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd840886cb0&gt;</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-72" type="checkbox" ><label for="sk-estimator-id-72" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>OrdinalEncoder</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.OrdinalEncoder.html">?<span>Documentation for OrdinalEncoder</span></a></div></label><div class="sk-toggleable__content "><pre>OrdinalEncoder(encoded_missing_value=-2, handle_unknown=&#x27;use_encoded_value&#x27;,
                unknown_value=-1)</pre></div> </div></div></div></div></div></div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-73" type="checkbox" ><label for="sk-estimator-id-73" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>RandomForestRegressor</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.ensemble.RandomForestRegressor.html">?<span>Documentation for RandomForestRegressor</span></a></div></label><div class="sk-toggleable__content "><pre>RandomForestRegressor(random_state=42)</pre></div> </div></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><label>Lasso</label></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-74" type="checkbox" ><label for="sk-estimator-id-74" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>columntransformer: ColumnTransformer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.compose.ColumnTransformer.html">?<span>Documentation for columntransformer: ColumnTransformer</span></a></div></label><div class="sk-toggleable__content "><pre>ColumnTransformer(transformers=[(&#x27;pipeline&#x27;,
                                  Pipeline(steps=[(&#x27;standardscaler&#x27;,
                                                   StandardScaler()),
                                                  (&#x27;simpleimputer&#x27;,
                                                   SimpleImputer(add_indicator=True))]),
-                                 &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb58512ebf0&gt;),
+                                 &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd840886200&gt;),
                                 (&#x27;onehotencoder&#x27;,
                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;),
-                                 &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb58e4f58a0&gt;)])</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-75" type="checkbox" ><label for="sk-estimator-id-75" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>pipeline</div></div></label><div class="sk-toggleable__content "><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb58512ebf0&gt;</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-76" type="checkbox" ><label for="sk-estimator-id-76" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>StandardScaler</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.StandardScaler.html">?<span>Documentation for StandardScaler</span></a></div></label><div class="sk-toggleable__content "><pre>StandardScaler()</pre></div> </div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-77" type="checkbox" ><label for="sk-estimator-id-77" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>SimpleImputer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.impute.SimpleImputer.html">?<span>Documentation for SimpleImputer</span></a></div></label><div class="sk-toggleable__content "><pre>SimpleImputer(add_indicator=True)</pre></div> </div></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-78" type="checkbox" ><label for="sk-estimator-id-78" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>onehotencoder</div></div></label><div class="sk-toggleable__content "><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb58e4f58a0&gt;</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-79" type="checkbox" ><label for="sk-estimator-id-79" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>OneHotEncoder</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.OneHotEncoder.html">?<span>Documentation for OneHotEncoder</span></a></div></label><div class="sk-toggleable__content "><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</pre></div> </div></div></div></div></div></div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-80" type="checkbox" ><label for="sk-estimator-id-80" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>LassoCV</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.linear_model.LassoCV.html">?<span>Documentation for LassoCV</span></a></div></label><div class="sk-toggleable__content "><pre>LassoCV()</pre></div> </div></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><label>Gradient Boosting</label></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-81" type="checkbox" ><label for="sk-estimator-id-81" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>columntransformer: ColumnTransformer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.compose.ColumnTransformer.html">?<span>Documentation for columntransformer: ColumnTransformer</span></a></div></label><div class="sk-toggleable__content "><pre>ColumnTransformer(transformers=[(&#x27;simpleimputer&#x27;,
+                                 &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd840886cb0&gt;)])</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-75" type="checkbox" ><label for="sk-estimator-id-75" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>pipeline</div></div></label><div class="sk-toggleable__content "><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd840886200&gt;</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-76" type="checkbox" ><label for="sk-estimator-id-76" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>StandardScaler</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.StandardScaler.html">?<span>Documentation for StandardScaler</span></a></div></label><div class="sk-toggleable__content "><pre>StandardScaler()</pre></div> </div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-77" type="checkbox" ><label for="sk-estimator-id-77" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>SimpleImputer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.impute.SimpleImputer.html">?<span>Documentation for SimpleImputer</span></a></div></label><div class="sk-toggleable__content "><pre>SimpleImputer(add_indicator=True)</pre></div> </div></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-78" type="checkbox" ><label for="sk-estimator-id-78" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>onehotencoder</div></div></label><div class="sk-toggleable__content "><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd840886cb0&gt;</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-79" type="checkbox" ><label for="sk-estimator-id-79" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>OneHotEncoder</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.OneHotEncoder.html">?<span>Documentation for OneHotEncoder</span></a></div></label><div class="sk-toggleable__content "><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</pre></div> </div></div></div></div></div></div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-80" type="checkbox" ><label for="sk-estimator-id-80" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>LassoCV</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.linear_model.LassoCV.html">?<span>Documentation for LassoCV</span></a></div></label><div class="sk-toggleable__content "><pre>LassoCV()</pre></div> </div></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><label>Gradient Boosting</label></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-81" type="checkbox" ><label for="sk-estimator-id-81" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>columntransformer: ColumnTransformer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.compose.ColumnTransformer.html">?<span>Documentation for columntransformer: ColumnTransformer</span></a></div></label><div class="sk-toggleable__content "><pre>ColumnTransformer(transformers=[(&#x27;simpleimputer&#x27;,
                                  SimpleImputer(add_indicator=True),
-                                 &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb58512ebf0&gt;),
+                                 &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd840886200&gt;),
                                 (&#x27;ordinalencoder&#x27;,
                                  OrdinalEncoder(encoded_missing_value=-2,
                                                 handle_unknown=&#x27;use_encoded_value&#x27;,
                                                 unknown_value=-1),
-                                 &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb58e4f58a0&gt;)])</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-82" type="checkbox" ><label for="sk-estimator-id-82" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>simpleimputer</div></div></label><div class="sk-toggleable__content "><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb58512ebf0&gt;</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-83" type="checkbox" ><label for="sk-estimator-id-83" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>SimpleImputer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.impute.SimpleImputer.html">?<span>Documentation for SimpleImputer</span></a></div></label><div class="sk-toggleable__content "><pre>SimpleImputer(add_indicator=True)</pre></div> </div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-84" type="checkbox" ><label for="sk-estimator-id-84" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>ordinalencoder</div></div></label><div class="sk-toggleable__content "><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fb58e4f58a0&gt;</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-85" type="checkbox" ><label for="sk-estimator-id-85" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>OrdinalEncoder</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.OrdinalEncoder.html">?<span>Documentation for OrdinalEncoder</span></a></div></label><div class="sk-toggleable__content "><pre>OrdinalEncoder(encoded_missing_value=-2, handle_unknown=&#x27;use_encoded_value&#x27;,
+                                 &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd840886cb0&gt;)])</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-82" type="checkbox" ><label for="sk-estimator-id-82" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>simpleimputer</div></div></label><div class="sk-toggleable__content "><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd840886200&gt;</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-83" type="checkbox" ><label for="sk-estimator-id-83" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>SimpleImputer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.impute.SimpleImputer.html">?<span>Documentation for SimpleImputer</span></a></div></label><div class="sk-toggleable__content "><pre>SimpleImputer(add_indicator=True)</pre></div> </div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-84" type="checkbox" ><label for="sk-estimator-id-84" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>ordinalencoder</div></div></label><div class="sk-toggleable__content "><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fd840886cb0&gt;</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-85" type="checkbox" ><label for="sk-estimator-id-85" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>OrdinalEncoder</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.OrdinalEncoder.html">?<span>Documentation for OrdinalEncoder</span></a></div></label><div class="sk-toggleable__content "><pre>OrdinalEncoder(encoded_missing_value=-2, handle_unknown=&#x27;use_encoded_value&#x27;,
                unknown_value=-1)</pre></div> </div></div></div></div></div></div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-86" type="checkbox" ><label for="sk-estimator-id-86" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>HistGradientBoostingRegressor</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.ensemble.HistGradientBoostingRegressor.html">?<span>Documentation for HistGradientBoostingRegressor</span></a></div></label><div class="sk-toggleable__content "><pre>HistGradientBoostingRegressor(random_state=0)</pre></div> </div></div></div></div></div></div></div></div></div><div class="sk-item"><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><label>final_estimator</label></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-87" type="checkbox" ><label for="sk-estimator-id-87" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>RidgeCV</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.linear_model.RidgeCV.html">?<span>Documentation for RidgeCV</span></a></div></label><div class="sk-toggleable__content "><pre>RidgeCV()</pre></div> </div></div></div></div></div></div></div></div></div></div></div>
 </div>
 <br />
@@ -3918,10 +3917,10 @@ <h2>Measure and plot the results<a class="headerlink" href="#measure-and-plot-th
 <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
 </pre></div>
 </div>
-<img src="http://github.com/_images/sphx_glr_plot_stack_predictors_001.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_stack_predictors_001.png " alt="Single predictors versus stacked predictors, Random Forest Evaluation in 1.12 seconds, Lasso Evaluation in 0.23 seconds, Gradient Boosting Evaluation in 0.47 seconds, Stacking Regressor Evaluation in 9.30 seconds" class = "sphx-glr-single-img"/><p>The stacked regressor will combine the strengths of the different regressors.
+<img src="http://github.com/_images/sphx_glr_plot_stack_predictors_001.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_stack_predictors_001.png " alt="Single predictors versus stacked predictors, Random Forest Evaluation in 1.09 seconds, Lasso Evaluation in 0.23 seconds, Gradient Boosting Evaluation in 0.49 seconds, Stacking Regressor Evaluation in 9.43 seconds" class = "sphx-glr-single-img"/><p>The stacked regressor will combine the strengths of the different regressors.
 However, we also see that training the stacked regressor is much more
 computationally expensive.</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 22.643 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 23.230 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-ensemble-plot-stack-predictors-py">
 <div class="binder-badge docutils container">
 <a class="reference external image-reference" href="https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/ensemble/plot_stack_predictors.ipynb"><img alt="Launch binder" src="http://github.com/_images/binder_badge_logo11.svg" style="width: 150px;" />
diff --git a/dev/auto_examples/ensemble/plot_voting_decision_regions.html b/dev/auto_examples/ensemble/plot_voting_decision_regions.html
index fd9ddf3ff77c2..3e035b36986c3 100644
--- a/dev/auto_examples/ensemble/plot_voting_decision_regions.html
+++ b/dev/auto_examples/ensemble/plot_voting_decision_regions.html
@@ -7,16 +7,16 @@
   <head>
     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="viewport" content="width=device-width, initial-scale=1" />
-<meta property="og:title" content="Plot the decision boundaries of a VotingClassifier" />
+<meta property="og:title" content="Visualizing the probabilistic predictions of a VotingClassifier" />
 <meta property="og:type" content="website" />
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 <meta property="og:site_name" content="scikit-learn" />
-<meta property="og:description" content="Plot the decision boundaries of a VotingClassifier for two features of the Iris dataset. Plot the class probabilities of the first sample in a toy dataset predicted by three different classifiers a..." />
+<meta property="og:description" content="Plot the predicted class probabilities in a toy dataset predicted by three different classifiers and averaged by the VotingClassifier. First, three linear classifiers are initialized. Two are splin..." />
 <meta property="og:image" content="https://scikit-learn.org/stable/_static/scikit-learn-logo-small.png" />
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-<meta name="description" content="Plot the decision boundaries of a VotingClassifier for two features of the Iris dataset. Plot the class probabilities of the first sample in a toy dataset predicted by three different classifiers a..." />
+<meta name="description" content="Plot the predicted class probabilities in a toy dataset predicted by three different classifiers and averaged by the VotingClassifier. First, three linear classifiers are initialized. Two are splin..." />
 
-    <title>Plot the decision boundaries of a VotingClassifier &#8212; scikit-learn 1.7.dev0 documentation</title>
+    <title>Visualizing the probabilistic predictions of a VotingClassifier &#8212; scikit-learn 1.7.dev0 documentation</title>
   
   
   
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
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-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
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@@ -80,8 +80,8 @@
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     <link rel="author" title="About these documents" href="http://github.com/about.html" />
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-    <link rel="next" title="Plot the decision surfaces of ensembles of trees on the iris dataset" href="http://github.com/plot_forest_iris.html" />
-    <link rel="prev" title="Plot individual and voting regression predictions" href="http://github.com/plot_voting_regressor.html" />
+    <link rel="next" title="Examples based on real world datasets" href="http://github.com/applications/index.html" />
+    <link rel="prev" title="Two-class AdaBoost" href="http://github.com/plot_adaboost_twoclass.html" />
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@@ -679,13 +679,12 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2 current active"><a class="current reference internal" href="#">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2 current active"><a class="current reference internal" href="#">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -902,9 +901,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
@@ -970,7 +969,7 @@
     
     <li class="breadcrumb-item"><a href="http://github.com/index.html" class="nav-link">Ensemble methods</a></li>
     
-    <li class="breadcrumb-item active" aria-current="page"><span class="ellipsis">Plot the decision boundaries of a VotingClassifier</span></li>
+    <li class="breadcrumb-item active" aria-current="page"><span class="ellipsis">Visualizing the probabilistic predictions of a VotingClassifier</span></li>
   </ul>
 </nav>
 </div>
@@ -992,72 +991,695 @@
 <p><a class="reference internal" href="#sphx-glr-download-auto-examples-ensemble-plot-voting-decision-regions-py"><span class="std std-ref">Go to the end</span></a>
 to download the full example code. or to run this example in your browser via JupyterLite or Binder</p>
 </div>
-<section class="sphx-glr-example-title" id="plot-the-decision-boundaries-of-a-votingclassifier">
-<span id="sphx-glr-auto-examples-ensemble-plot-voting-decision-regions-py"></span><h1>Plot the decision boundaries of a VotingClassifier<a class="headerlink" href="#plot-the-decision-boundaries-of-a-votingclassifier" title="Link to this heading">#</a></h1>
-<p>Plot the decision boundaries of a <a class="reference internal" href="http://github.com/modules/generated/sklearn.ensemble.VotingClassifier.html#sklearn.ensemble.VotingClassifier" title="sklearn.ensemble.VotingClassifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">VotingClassifier</span></code></a> for two
-features of the Iris dataset.</p>
-<p>Plot the class probabilities of the first sample in a toy dataset predicted by
-three different classifiers and averaged by the
-<a class="reference internal" href="http://github.com/modules/generated/sklearn.ensemble.VotingClassifier.html#sklearn.ensemble.VotingClassifier" title="sklearn.ensemble.VotingClassifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">VotingClassifier</span></code></a>.</p>
-<p>First, three exemplary classifiers are initialized
-(<a class="reference internal" href="http://github.com/modules/generated/sklearn.tree.DecisionTreeClassifier.html#sklearn.tree.DecisionTreeClassifier" title="sklearn.tree.DecisionTreeClassifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">DecisionTreeClassifier</span></code></a>,
-<a class="reference internal" href="http://github.com/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#sklearn.neighbors.KNeighborsClassifier" title="sklearn.neighbors.KNeighborsClassifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">KNeighborsClassifier</span></code></a>, and <a class="reference internal" href="http://github.com/modules/generated/sklearn.svm.SVC.html#sklearn.svm.SVC" title="sklearn.svm.SVC"><code class="xref py py-class docutils literal notranslate"><span class="pre">SVC</span></code></a>) and used to
-initialize a soft-voting <a class="reference internal" href="http://github.com/modules/generated/sklearn.ensemble.VotingClassifier.html#sklearn.ensemble.VotingClassifier" title="sklearn.ensemble.VotingClassifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">VotingClassifier</span></code></a> with weights <code class="docutils literal notranslate"><span class="pre">[2,</span>
-<span class="pre">1,</span> <span class="pre">2]</span></code>, which means that the predicted probabilities of the
-<a class="reference internal" href="http://github.com/modules/generated/sklearn.tree.DecisionTreeClassifier.html#sklearn.tree.DecisionTreeClassifier" title="sklearn.tree.DecisionTreeClassifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">DecisionTreeClassifier</span></code></a> and <a class="reference internal" href="http://github.com/modules/generated/sklearn.svm.SVC.html#sklearn.svm.SVC" title="sklearn.svm.SVC"><code class="xref py py-class docutils literal notranslate"><span class="pre">SVC</span></code></a> each count 2 times
-as much as the weights of the <a class="reference internal" href="http://github.com/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#sklearn.neighbors.KNeighborsClassifier" title="sklearn.neighbors.KNeighborsClassifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">KNeighborsClassifier</span></code></a>
-classifier when the averaged probability is calculated.</p>
-<img src="http://github.com/_images/sphx_glr_plot_voting_decision_regions_001.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_voting_decision_regions_001.png " alt="Decision Tree (depth=4), KNN (k=7), Kernel SVM, Soft Voting" class = "sphx-glr-single-img"/><div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Authors: The scikit-learn developers</span>
+<section class="sphx-glr-example-title" id="visualizing-the-probabilistic-predictions-of-a-votingclassifier">
+<span id="sphx-glr-auto-examples-ensemble-plot-voting-decision-regions-py"></span><h1>Visualizing the probabilistic predictions of a VotingClassifier<a class="headerlink" href="#visualizing-the-probabilistic-predictions-of-a-votingclassifier" title="Link to this heading">#</a></h1>
+<p>Plot the predicted class probabilities in a toy dataset predicted by three
+different classifiers and averaged by the <a class="reference internal" href="http://github.com/modules/generated/sklearn.ensemble.VotingClassifier.html#sklearn.ensemble.VotingClassifier" title="sklearn.ensemble.VotingClassifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">VotingClassifier</span></code></a>.</p>
+<p>First, three linear classifiers are initialized. Two are spline models with
+interaction terms, one using constant extrapolation and the other using periodic
+extrapolation. The third classifier is a <a class="reference internal" href="http://github.com/modules/generated/sklearn.kernel_approximation.Nystroem.html#sklearn.kernel_approximation.Nystroem" title="sklearn.kernel_approximation.Nystroem"><code class="xref py py-class docutils literal notranslate"><span class="pre">Nystroem</span></code></a>
+with the default “rbf” kernel.</p>
+<p>In the first part of this example, these three classifiers are used to
+demonstrate soft-voting using <a class="reference internal" href="http://github.com/modules/generated/sklearn.ensemble.VotingClassifier.html#sklearn.ensemble.VotingClassifier" title="sklearn.ensemble.VotingClassifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">VotingClassifier</span></code></a> with weighted
+average. We set <code class="docutils literal notranslate"><span class="pre">weights=[2,</span> <span class="pre">1,</span> <span class="pre">3]</span></code>, meaning the constant extrapolation spline
+model’s predictions are weighted twice as much as the periodic spline model’s,
+and the Nystroem model’s predictions are weighted three times as much as the
+periodic spline.</p>
+<p>The second part demonstrates how soft predictions can be converted into hard
+predictions.</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Authors: The scikit-learn developers</span>
 <span class="c1"># SPDX-License-Identifier: BSD-3-Clause</span>
+</pre></div>
+</div>
+<p>We first generate a noisy XOR dataset, which is a binary classification task.</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">matplotlib.pyplot</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">plt</span>
+<span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
+<span class="kn">import</span><span class="w"> </span><span class="nn">pandas</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pd</span>
+<span class="kn">from</span><span class="w"> </span><span class="nn">matplotlib.colors</span><span class="w"> </span><span class="kn">import</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.colors.ListedColormap.html#matplotlib.colors.ListedColormap" title="matplotlib.colors.ListedColormap" class="sphx-glr-backref-module-matplotlib-colors sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ListedColormap</span></a>
+
+<span class="n">n_samples</span> <span class="o">=</span> <span class="mi">500</span>
+<span class="n">rng</span> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.default_rng" title="numpy.random.default_rng" class="sphx-glr-backref-module-numpy-random sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">default_rng</span></a><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+<span class="n">feature_names</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;Feature #0&quot;</span><span class="p">,</span> <span class="s2">&quot;Feature #1&quot;</span><span class="p">]</span>
+<span class="n">common_scatter_plot_params</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span>
+    <span class="n">cmap</span><span class="o">=</span><a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.colors.ListedColormap.html#matplotlib.colors.ListedColormap" title="matplotlib.colors.ListedColormap" class="sphx-glr-backref-module-matplotlib-colors sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ListedColormap</span></a><span class="p">([</span><span class="s2">&quot;tab:red&quot;</span><span class="p">,</span> <span class="s2">&quot;tab:blue&quot;</span><span class="p">]),</span>
+    <span class="n">edgecolor</span><span class="o">=</span><span class="s2">&quot;white&quot;</span><span class="p">,</span>
+    <span class="n">linewidth</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+<span class="p">)</span>
 
-<span class="kn">from</span><span class="w"> </span><span class="nn">itertools</span><span class="w"> </span><span class="kn">import</span> <a href="https://docs.python.org/3/library/itertools.html#itertools.product" title="itertools.product" class="sphx-glr-backref-module-itertools sphx-glr-backref-type-py-function"><span class="n">product</span></a>
+<span class="n">xor</span> <span class="o">=</span> <a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="pandas.DataFrame" class="sphx-glr-backref-module-pandas sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span></a><span class="p">(</span>
+    <a href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="numpy.random.RandomState" class="sphx-glr-backref-module-numpy-random sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">RandomState</span></a><span class="p">(</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="n">low</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span> <span class="n">high</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="p">(</span><span class="n">n_samples</span><span class="p">,</span> <span class="mi">2</span><span class="p">)),</span>
+    <span class="n">columns</span><span class="o">=</span><span class="n">feature_names</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="n">noise</span> <span class="o">=</span> <span class="n">rng</span><span class="o">.</span><span class="n">normal</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="p">(</span><span class="n">n_samples</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span>
+<span class="n">target_xor</span> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.logical_xor.html#numpy.logical_xor" title="numpy.logical_xor" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-data"><span class="n">np</span><span class="o">.</span><span class="n">logical_xor</span></a><span class="p">(</span>
+    <span class="n">xor</span><span class="p">[</span><span class="s2">&quot;Feature #0&quot;</span><span class="p">]</span> <span class="o">+</span> <span class="n">noise</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">,</span> <span class="n">xor</span><span class="p">[</span><span class="s2">&quot;Feature #1&quot;</span><span class="p">]</span> <span class="o">+</span> <span class="n">noise</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mi">0</span>
+<span class="p">)</span>
 
-<span class="kn">import</span><span class="w"> </span><span class="nn">matplotlib.pyplot</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">plt</span>
+<span class="n">X</span> <span class="o">=</span> <span class="n">xor</span><span class="p">[</span><span class="n">feature_names</span><span class="p">]</span>
+<span class="n">y</span> <span class="o">=</span> <span class="n">target_xor</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.int32" title="numpy.int32" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-attribute"><span class="n">np</span><span class="o">.</span><span class="n">int32</span></a><span class="p">)</span>
 
-<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn</span><span class="w"> </span><span class="kn">import</span> <span class="n">datasets</span>
-<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.ensemble</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.ensemble.VotingClassifier.html#sklearn.ensemble.VotingClassifier" title="sklearn.ensemble.VotingClassifier" class="sphx-glr-backref-module-sklearn-ensemble sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">VotingClassifier</span></a>
-<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.inspection</span><span class="w"> </span><span class="kn">import</span> <span class="n">DecisionBoundaryDisplay</span>
-<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.neighbors</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#sklearn.neighbors.KNeighborsClassifier" title="sklearn.neighbors.KNeighborsClassifier" class="sphx-glr-backref-module-sklearn-neighbors sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">KNeighborsClassifier</span></a>
-<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.svm</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.svm.SVC.html#sklearn.svm.SVC" title="sklearn.svm.SVC" class="sphx-glr-backref-module-sklearn-svm sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">SVC</span></a>
-<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.tree</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.tree.DecisionTreeClassifier.html#sklearn.tree.DecisionTreeClassifier" title="sklearn.tree.DecisionTreeClassifier" class="sphx-glr-backref-module-sklearn-tree sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">DecisionTreeClassifier</span></a>
-
-<span class="c1"># Loading some example data</span>
-<span class="n">iris</span> <span class="o">=</span> <a href="http://github.com/modules/generated/sklearn.datasets.load_iris.html#sklearn.datasets.load_iris" title="sklearn.datasets.load_iris" class="sphx-glr-backref-module-sklearn-datasets sphx-glr-backref-type-py-function"><span class="n">datasets</span><span class="o">.</span><span class="n">load_iris</span></a><span class="p">()</span>
-<span class="n">X</span> <span class="o">=</span> <span class="n">iris</span><span class="o">.</span><span class="n">data</span><span class="p">[:,</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">]]</span>
-<span class="n">y</span> <span class="o">=</span> <span class="n">iris</span><span class="o">.</span><span class="n">target</span>
-
-<span class="c1"># Training classifiers</span>
-<span class="n">clf1</span> <span class="o">=</span> <a href="http://github.com/modules/generated/sklearn.tree.DecisionTreeClassifier.html#sklearn.tree.DecisionTreeClassifier" title="sklearn.tree.DecisionTreeClassifier" class="sphx-glr-backref-module-sklearn-tree sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">DecisionTreeClassifier</span></a><span class="p">(</span><span class="n">max_depth</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span>
-<span class="n">clf2</span> <span class="o">=</span> <a href="http://github.com/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#sklearn.neighbors.KNeighborsClassifier" title="sklearn.neighbors.KNeighborsClassifier" class="sphx-glr-backref-module-sklearn-neighbors sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">KNeighborsClassifier</span></a><span class="p">(</span><span class="n">n_neighbors</span><span class="o">=</span><span class="mi">7</span><span class="p">)</span>
-<span class="n">clf3</span> <span class="o">=</span> <a href="http://github.com/modules/generated/sklearn.svm.SVC.html#sklearn.svm.SVC" title="sklearn.svm.SVC" class="sphx-glr-backref-module-sklearn-svm sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">SVC</span></a><span class="p">(</span><span class="n">gamma</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">kernel</span><span class="o">=</span><span class="s2">&quot;rbf&quot;</span><span class="p">,</span> <span class="n">probability</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="matplotlib.pyplot.subplots" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">subplots</span></a><span class="p">()</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X</span><span class="p">[</span><span class="s2">&quot;Feature #0&quot;</span><span class="p">],</span> <span class="n">X</span><span class="p">[</span><span class="s2">&quot;Feature #1&quot;</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="n">y</span><span class="p">,</span> <span class="o">**</span><span class="n">common_scatter_plot_params</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;The XOR dataset&quot;</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
+</pre></div>
+</div>
+<img src="http://github.com/_images/sphx_glr_plot_voting_decision_regions_001.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_voting_decision_regions_001.png " alt="The XOR dataset" class = "sphx-glr-single-img"/><p>Due to the inherent non-linear separability of the XOR dataset, tree-based
+models would often be preferred. However, appropriate feature engineering
+combined with a linear model can yield effective results, with the added
+benefit of producing better-calibrated probabilities for samples located in
+the transition regions affected by noise.</p>
+<p>We define and fit the models on the whole dataset.</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.ensemble</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.ensemble.VotingClassifier.html#sklearn.ensemble.VotingClassifier" title="sklearn.ensemble.VotingClassifier" class="sphx-glr-backref-module-sklearn-ensemble sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">VotingClassifier</span></a>
+<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.kernel_approximation</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.kernel_approximation.Nystroem.html#sklearn.kernel_approximation.Nystroem" title="sklearn.kernel_approximation.Nystroem" class="sphx-glr-backref-module-sklearn-kernel_approximation sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">Nystroem</span></a>
+<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.linear_model</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.linear_model.LogisticRegression.html#sklearn.linear_model.LogisticRegression" title="sklearn.linear_model.LogisticRegression" class="sphx-glr-backref-module-sklearn-linear_model sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">LogisticRegression</span></a>
+<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.pipeline</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.pipeline.make_pipeline.html#sklearn.pipeline.make_pipeline" title="sklearn.pipeline.make_pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-function"><span class="n">make_pipeline</span></a>
+<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.preprocessing</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.preprocessing.PolynomialFeatures.html#sklearn.preprocessing.PolynomialFeatures" title="sklearn.preprocessing.PolynomialFeatures" class="sphx-glr-backref-module-sklearn-preprocessing sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">PolynomialFeatures</span></a><span class="p">,</span> <a href="http://github.com/modules/generated/sklearn.preprocessing.SplineTransformer.html#sklearn.preprocessing.SplineTransformer" title="sklearn.preprocessing.SplineTransformer" class="sphx-glr-backref-module-sklearn-preprocessing sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">SplineTransformer</span></a><span class="p">,</span> <a href="http://github.com/modules/generated/sklearn.preprocessing.StandardScaler.html#sklearn.preprocessing.StandardScaler" title="sklearn.preprocessing.StandardScaler" class="sphx-glr-backref-module-sklearn-preprocessing sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">StandardScaler</span></a>
+
+<span class="n">clf1</span> <span class="o">=</span> <a href="http://github.com/modules/generated/sklearn.pipeline.make_pipeline.html#sklearn.pipeline.make_pipeline" title="sklearn.pipeline.make_pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-function"><span class="n">make_pipeline</span></a><span class="p">(</span>
+    <a href="http://github.com/modules/generated/sklearn.preprocessing.SplineTransformer.html#sklearn.preprocessing.SplineTransformer" title="sklearn.preprocessing.SplineTransformer" class="sphx-glr-backref-module-sklearn-preprocessing sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">SplineTransformer</span></a><span class="p">(</span><span class="n">degree</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">n_knots</span><span class="o">=</span><span class="mi">2</span><span class="p">),</span>
+    <a href="http://github.com/modules/generated/sklearn.preprocessing.PolynomialFeatures.html#sklearn.preprocessing.PolynomialFeatures" title="sklearn.preprocessing.PolynomialFeatures" class="sphx-glr-backref-module-sklearn-preprocessing sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">PolynomialFeatures</span></a><span class="p">(</span><span class="n">interaction_only</span><span class="o">=</span><span class="kc">True</span><span class="p">),</span>
+    <a href="http://github.com/modules/generated/sklearn.linear_model.LogisticRegression.html#sklearn.linear_model.LogisticRegression" title="sklearn.linear_model.LogisticRegression" class="sphx-glr-backref-module-sklearn-linear_model sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">LogisticRegression</span></a><span class="p">(</span><span class="n">C</span><span class="o">=</span><span class="mi">10</span><span class="p">),</span>
+<span class="p">)</span>
+<span class="n">clf2</span> <span class="o">=</span> <a href="http://github.com/modules/generated/sklearn.pipeline.make_pipeline.html#sklearn.pipeline.make_pipeline" title="sklearn.pipeline.make_pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-function"><span class="n">make_pipeline</span></a><span class="p">(</span>
+    <a href="http://github.com/modules/generated/sklearn.preprocessing.SplineTransformer.html#sklearn.preprocessing.SplineTransformer" title="sklearn.preprocessing.SplineTransformer" class="sphx-glr-backref-module-sklearn-preprocessing sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">SplineTransformer</span></a><span class="p">(</span>
+        <span class="n">degree</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
+        <span class="n">n_knots</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span>
+        <span class="n">extrapolation</span><span class="o">=</span><span class="s2">&quot;periodic&quot;</span><span class="p">,</span>
+        <span class="n">include_bias</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="p">),</span>
+    <a href="http://github.com/modules/generated/sklearn.preprocessing.PolynomialFeatures.html#sklearn.preprocessing.PolynomialFeatures" title="sklearn.preprocessing.PolynomialFeatures" class="sphx-glr-backref-module-sklearn-preprocessing sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">PolynomialFeatures</span></a><span class="p">(</span><span class="n">interaction_only</span><span class="o">=</span><span class="kc">True</span><span class="p">),</span>
+    <a href="http://github.com/modules/generated/sklearn.linear_model.LogisticRegression.html#sklearn.linear_model.LogisticRegression" title="sklearn.linear_model.LogisticRegression" class="sphx-glr-backref-module-sklearn-linear_model sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">LogisticRegression</span></a><span class="p">(</span><span class="n">C</span><span class="o">=</span><span class="mi">10</span><span class="p">),</span>
+<span class="p">)</span>
+<span class="n">clf3</span> <span class="o">=</span> <a href="http://github.com/modules/generated/sklearn.pipeline.make_pipeline.html#sklearn.pipeline.make_pipeline" title="sklearn.pipeline.make_pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-function"><span class="n">make_pipeline</span></a><span class="p">(</span>
+    <a href="http://github.com/modules/generated/sklearn.preprocessing.StandardScaler.html#sklearn.preprocessing.StandardScaler" title="sklearn.preprocessing.StandardScaler" class="sphx-glr-backref-module-sklearn-preprocessing sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">StandardScaler</span></a><span class="p">(),</span>
+    <a href="http://github.com/modules/generated/sklearn.kernel_approximation.Nystroem.html#sklearn.kernel_approximation.Nystroem" title="sklearn.kernel_approximation.Nystroem" class="sphx-glr-backref-module-sklearn-kernel_approximation sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">Nystroem</span></a><span class="p">(</span><span class="n">gamma</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">),</span>
+    <a href="http://github.com/modules/generated/sklearn.linear_model.LogisticRegression.html#sklearn.linear_model.LogisticRegression" title="sklearn.linear_model.LogisticRegression" class="sphx-glr-backref-module-sklearn-linear_model sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">LogisticRegression</span></a><span class="p">(</span><span class="n">C</span><span class="o">=</span><span class="mi">10</span><span class="p">),</span>
+<span class="p">)</span>
+<span class="n">weights</span> <span class="o">=</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">]</span>
 <span class="n">eclf</span> <span class="o">=</span> <a href="http://github.com/modules/generated/sklearn.ensemble.VotingClassifier.html#sklearn.ensemble.VotingClassifier" title="sklearn.ensemble.VotingClassifier" class="sphx-glr-backref-module-sklearn-ensemble sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">VotingClassifier</span></a><span class="p">(</span>
-    <span class="n">estimators</span><span class="o">=</span><span class="p">[(</span><span class="s2">&quot;dt&quot;</span><span class="p">,</span> <span class="n">clf1</span><span class="p">),</span> <span class="p">(</span><span class="s2">&quot;knn&quot;</span><span class="p">,</span> <span class="n">clf2</span><span class="p">),</span> <span class="p">(</span><span class="s2">&quot;svc&quot;</span><span class="p">,</span> <span class="n">clf3</span><span class="p">)],</span>
+    <span class="n">estimators</span><span class="o">=</span><span class="p">[</span>
+        <span class="p">(</span><span class="s2">&quot;constant splines model&quot;</span><span class="p">,</span> <span class="n">clf1</span><span class="p">),</span>
+        <span class="p">(</span><span class="s2">&quot;periodic splines model&quot;</span><span class="p">,</span> <span class="n">clf2</span><span class="p">),</span>
+        <span class="p">(</span><span class="s2">&quot;nystroem model&quot;</span><span class="p">,</span> <span class="n">clf3</span><span class="p">),</span>
+    <span class="p">],</span>
     <span class="n">voting</span><span class="o">=</span><span class="s2">&quot;soft&quot;</span><span class="p">,</span>
-    <span class="n">weights</span><span class="o">=</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span>
+    <span class="n">weights</span><span class="o">=</span><span class="n">weights</span><span class="p">,</span>
 <span class="p">)</span>
 
 <span class="n">clf1</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
 <span class="n">clf2</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
 <span class="n">clf3</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
 <span class="n">eclf</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="output_subarea output_html rendered_html output_result">
+<style>#sk-container-id-27 {
+  /* Definition of color scheme common for light and dark mode */
+  --sklearn-color-text: #000;
+  --sklearn-color-text-muted: #666;
+  --sklearn-color-line: gray;
+  /* Definition of color scheme for unfitted estimators */
+  --sklearn-color-unfitted-level-0: #fff5e6;
+  --sklearn-color-unfitted-level-1: #f6e4d2;
+  --sklearn-color-unfitted-level-2: #ffe0b3;
+  --sklearn-color-unfitted-level-3: chocolate;
+  /* Definition of color scheme for fitted estimators */
+  --sklearn-color-fitted-level-0: #f0f8ff;
+  --sklearn-color-fitted-level-1: #d4ebff;
+  --sklearn-color-fitted-level-2: #b3dbfd;
+  --sklearn-color-fitted-level-3: cornflowerblue;
+
+  /* Specific color for light theme */
+  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));
+  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));
+  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));
+  --sklearn-color-icon: #696969;
+
+  @media (prefers-color-scheme: dark) {
+    /* Redefinition of color scheme for dark theme */
+    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));
+    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));
+    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));
+    --sklearn-color-icon: #878787;
+  }
+}
+
+#sk-container-id-27 {
+  color: var(--sklearn-color-text);
+}
+
+#sk-container-id-27 pre {
+  padding: 0;
+}
+
+#sk-container-id-27 input.sk-hidden--visually {
+  border: 0;
+  clip: rect(1px 1px 1px 1px);
+  clip: rect(1px, 1px, 1px, 1px);
+  height: 1px;
+  margin: -1px;
+  overflow: hidden;
+  padding: 0;
+  position: absolute;
+  width: 1px;
+}
+
+#sk-container-id-27 div.sk-dashed-wrapped {
+  border: 1px dashed var(--sklearn-color-line);
+  margin: 0 0.4em 0.5em 0.4em;
+  box-sizing: border-box;
+  padding-bottom: 0.4em;
+  background-color: var(--sklearn-color-background);
+}
+
+#sk-container-id-27 div.sk-container {
+  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
+     but bootstrap.min.css set `[hidden] { display: none !important; }`
+     so we also need the `!important` here to be able to override the
+     default hidden behavior on the sphinx rendered scikit-learn.org.
+     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */
+  display: inline-block !important;
+  position: relative;
+}
+
+#sk-container-id-27 div.sk-text-repr-fallback {
+  display: none;
+}
+
+div.sk-parallel-item,
+div.sk-serial,
+div.sk-item {
+  /* draw centered vertical line to link estimators */
+  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));
+  background-size: 2px 100%;
+  background-repeat: no-repeat;
+  background-position: center center;
+}
+
+/* Parallel-specific style estimator block */
+
+#sk-container-id-27 div.sk-parallel-item::after {
+  content: "";
+  width: 100%;
+  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
+  flex-grow: 1;
+}
+
+#sk-container-id-27 div.sk-parallel {
+  display: flex;
+  align-items: stretch;
+  justify-content: center;
+  background-color: var(--sklearn-color-background);
+  position: relative;
+}
+
+#sk-container-id-27 div.sk-parallel-item {
+  display: flex;
+  flex-direction: column;
+}
+
+#sk-container-id-27 div.sk-parallel-item:first-child::after {
+  align-self: flex-end;
+  width: 50%;
+}
+
+#sk-container-id-27 div.sk-parallel-item:last-child::after {
+  align-self: flex-start;
+  width: 50%;
+}
+
+#sk-container-id-27 div.sk-parallel-item:only-child::after {
+  width: 0;
+}
+
+/* Serial-specific style estimator block */
+
+#sk-container-id-27 div.sk-serial {
+  display: flex;
+  flex-direction: column;
+  align-items: center;
+  background-color: var(--sklearn-color-background);
+  padding-right: 1em;
+  padding-left: 1em;
+}
+
+
+/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is
+clickable and can be expanded/collapsed.
+- Pipeline and ColumnTransformer use this feature and define the default style
+- Estimators will overwrite some part of the style using the `sk-estimator` class
+*/
+
+/* Pipeline and ColumnTransformer style (default) */
+
+#sk-container-id-27 div.sk-toggleable {
+  /* Default theme specific background. It is overwritten whether we have a
+  specific estimator or a Pipeline/ColumnTransformer */
+  background-color: var(--sklearn-color-background);
+}
+
+/* Toggleable label */
+#sk-container-id-27 label.sk-toggleable__label {
+  cursor: pointer;
+  display: flex;
+  width: 100%;
+  margin-bottom: 0;
+  padding: 0.5em;
+  box-sizing: border-box;
+  text-align: center;
+  align-items: start;
+  justify-content: space-between;
+  gap: 0.5em;
+}
+
+#sk-container-id-27 label.sk-toggleable__label .caption {
+  font-size: 0.6rem;
+  font-weight: lighter;
+  color: var(--sklearn-color-text-muted);
+}
+
+#sk-container-id-27 label.sk-toggleable__label-arrow:before {
+  /* Arrow on the left of the label */
+  content: "▸";
+  float: left;
+  margin-right: 0.25em;
+  color: var(--sklearn-color-icon);
+}
+
+#sk-container-id-27 label.sk-toggleable__label-arrow:hover:before {
+  color: var(--sklearn-color-text);
+}
+
+/* Toggleable content - dropdown */
+
+#sk-container-id-27 div.sk-toggleable__content {
+  max-height: 0;
+  max-width: 0;
+  overflow: hidden;
+  text-align: left;
+  /* unfitted */
+  background-color: var(--sklearn-color-unfitted-level-0);
+}
+
+#sk-container-id-27 div.sk-toggleable__content.fitted {
+  /* fitted */
+  background-color: var(--sklearn-color-fitted-level-0);
+}
+
+#sk-container-id-27 div.sk-toggleable__content pre {
+  margin: 0.2em;
+  border-radius: 0.25em;
+  color: var(--sklearn-color-text);
+  /* unfitted */
+  background-color: var(--sklearn-color-unfitted-level-0);
+}
+
+#sk-container-id-27 div.sk-toggleable__content.fitted pre {
+  /* unfitted */
+  background-color: var(--sklearn-color-fitted-level-0);
+}
+
+#sk-container-id-27 input.sk-toggleable__control:checked~div.sk-toggleable__content {
+  /* Expand drop-down */
+  max-height: 200px;
+  max-width: 100%;
+  overflow: auto;
+}
+
+#sk-container-id-27 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
+  content: "▾";
+}
+
+/* Pipeline/ColumnTransformer-specific style */
+
+#sk-container-id-27 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
+  color: var(--sklearn-color-text);
+  background-color: var(--sklearn-color-unfitted-level-2);
+}
+
+#sk-container-id-27 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+  background-color: var(--sklearn-color-fitted-level-2);
+}
+
+/* Estimator-specific style */
+
+/* Colorize estimator box */
+#sk-container-id-27 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
+  /* unfitted */
+  background-color: var(--sklearn-color-unfitted-level-2);
+}
+
+#sk-container-id-27 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+  /* fitted */
+  background-color: var(--sklearn-color-fitted-level-2);
+}
+
+#sk-container-id-27 div.sk-label label.sk-toggleable__label,
+#sk-container-id-27 div.sk-label label {
+  /* The background is the default theme color */
+  color: var(--sklearn-color-text-on-default-background);
+}
+
+/* On hover, darken the color of the background */
+#sk-container-id-27 div.sk-label:hover label.sk-toggleable__label {
+  color: var(--sklearn-color-text);
+  background-color: var(--sklearn-color-unfitted-level-2);
+}
+
+/* Label box, darken color on hover, fitted */
+#sk-container-id-27 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
+  color: var(--sklearn-color-text);
+  background-color: var(--sklearn-color-fitted-level-2);
+}
+
+/* Estimator label */
+
+#sk-container-id-27 div.sk-label label {
+  font-family: monospace;
+  font-weight: bold;
+  display: inline-block;
+  line-height: 1.2em;
+}
+
+#sk-container-id-27 div.sk-label-container {
+  text-align: center;
+}
+
+/* Estimator-specific */
+#sk-container-id-27 div.sk-estimator {
+  font-family: monospace;
+  border: 1px dotted var(--sklearn-color-border-box);
+  border-radius: 0.25em;
+  box-sizing: border-box;
+  margin-bottom: 0.5em;
+  /* unfitted */
+  background-color: var(--sklearn-color-unfitted-level-0);
+}
+
+#sk-container-id-27 div.sk-estimator.fitted {
+  /* fitted */
+  background-color: var(--sklearn-color-fitted-level-0);
+}
+
+/* on hover */
+#sk-container-id-27 div.sk-estimator:hover {
+  /* unfitted */
+  background-color: var(--sklearn-color-unfitted-level-2);
+}
+
+#sk-container-id-27 div.sk-estimator.fitted:hover {
+  /* fitted */
+  background-color: var(--sklearn-color-fitted-level-2);
+}
+
+/* Specification for estimator info (e.g. "i" and "?") */
+
+/* Common style for "i" and "?" */
+
+.sk-estimator-doc-link,
+a:link.sk-estimator-doc-link,
+a:visited.sk-estimator-doc-link {
+  float: right;
+  font-size: smaller;
+  line-height: 1em;
+  font-family: monospace;
+  background-color: var(--sklearn-color-background);
+  border-radius: 1em;
+  height: 1em;
+  width: 1em;
+  text-decoration: none !important;
+  margin-left: 0.5em;
+  text-align: center;
+  /* unfitted */
+  border: var(--sklearn-color-unfitted-level-1) 1pt solid;
+  color: var(--sklearn-color-unfitted-level-1);
+}
+
+.sk-estimator-doc-link.fitted,
+a:link.sk-estimator-doc-link.fitted,
+a:visited.sk-estimator-doc-link.fitted {
+  /* fitted */
+  border: var(--sklearn-color-fitted-level-1) 1pt solid;
+  color: var(--sklearn-color-fitted-level-1);
+}
+
+/* On hover */
+div.sk-estimator:hover .sk-estimator-doc-link:hover,
+.sk-estimator-doc-link:hover,
+div.sk-label-container:hover .sk-estimator-doc-link:hover,
+.sk-estimator-doc-link:hover {
+  /* unfitted */
+  background-color: var(--sklearn-color-unfitted-level-3);
+  color: var(--sklearn-color-background);
+  text-decoration: none;
+}
+
+div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,
+.sk-estimator-doc-link.fitted:hover,
+div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,
+.sk-estimator-doc-link.fitted:hover {
+  /* fitted */
+  background-color: var(--sklearn-color-fitted-level-3);
+  color: var(--sklearn-color-background);
+  text-decoration: none;
+}
+
+/* Span, style for the box shown on hovering the info icon */
+.sk-estimator-doc-link span {
+  display: none;
+  z-index: 9999;
+  position: relative;
+  font-weight: normal;
+  right: .2ex;
+  padding: .5ex;
+  margin: .5ex;
+  width: min-content;
+  min-width: 20ex;
+  max-width: 50ex;
+  color: var(--sklearn-color-text);
+  box-shadow: 2pt 2pt 4pt #999;
+  /* unfitted */
+  background: var(--sklearn-color-unfitted-level-0);
+  border: .5pt solid var(--sklearn-color-unfitted-level-3);
+}
+
+.sk-estimator-doc-link.fitted span {
+  /* fitted */
+  background: var(--sklearn-color-fitted-level-0);
+  border: var(--sklearn-color-fitted-level-3);
+}
+
+.sk-estimator-doc-link:hover span {
+  display: block;
+}
+
+/* "?"-specific style due to the `<a>` HTML tag */
+
+#sk-container-id-27 a.estimator_doc_link {
+  float: right;
+  font-size: 1rem;
+  line-height: 1em;
+  font-family: monospace;
+  background-color: var(--sklearn-color-background);
+  border-radius: 1rem;
+  height: 1rem;
+  width: 1rem;
+  text-decoration: none;
+  /* unfitted */
+  color: var(--sklearn-color-unfitted-level-1);
+  border: var(--sklearn-color-unfitted-level-1) 1pt solid;
+}
+
+#sk-container-id-27 a.estimator_doc_link.fitted {
+  /* fitted */
+  border: var(--sklearn-color-fitted-level-1) 1pt solid;
+  color: var(--sklearn-color-fitted-level-1);
+}
+
+/* On hover */
+#sk-container-id-27 a.estimator_doc_link:hover {
+  /* unfitted */
+  background-color: var(--sklearn-color-unfitted-level-3);
+  color: var(--sklearn-color-background);
+  text-decoration: none;
+}
+
+#sk-container-id-27 a.estimator_doc_link.fitted:hover {
+  /* fitted */
+  background-color: var(--sklearn-color-fitted-level-3);
+}
+</style><div id="sk-container-id-27" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>VotingClassifier(estimators=[(&#x27;constant splines model&#x27;,
+                              Pipeline(steps=[(&#x27;splinetransformer&#x27;,
+                                               SplineTransformer(degree=2,
+                                                                 n_knots=2)),
+                                              (&#x27;polynomialfeatures&#x27;,
+                                               PolynomialFeatures(interaction_only=True)),
+                                              (&#x27;logisticregression&#x27;,
+                                               LogisticRegression(C=10))])),
+                             (&#x27;periodic splines model&#x27;,
+                              Pipeline(steps=[(&#x27;splinetransformer&#x27;,
+                                               SplineTransformer(degree=2,
+                                                                 extrapolation=&#x27;periodic&#x27;,
+                                                                 n_knots=4)),
+                                              (&#x27;polynomialfeatures&#x27;,
+                                               PolynomialFeatures(interaction_only=True)),
+                                              (&#x27;logisticregression&#x27;,
+                                               LogisticRegression(C=10))])),
+                             (&#x27;nystroem model&#x27;,
+                              Pipeline(steps=[(&#x27;standardscaler&#x27;,
+                                               StandardScaler()),
+                                              (&#x27;nystroem&#x27;,
+                                               Nystroem(gamma=2,
+                                                        random_state=0)),
+                                              (&#x27;logisticregression&#x27;,
+                                               LogisticRegression(C=10))]))],
+                 voting=&#x27;soft&#x27;, weights=[2, 1, 3])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-107" type="checkbox" ><label for="sk-estimator-id-107" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>VotingClassifier</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.ensemble.VotingClassifier.html">?<span>Documentation for VotingClassifier</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>VotingClassifier(estimators=[(&#x27;constant splines model&#x27;,
+                              Pipeline(steps=[(&#x27;splinetransformer&#x27;,
+                                               SplineTransformer(degree=2,
+                                                                 n_knots=2)),
+                                              (&#x27;polynomialfeatures&#x27;,
+                                               PolynomialFeatures(interaction_only=True)),
+                                              (&#x27;logisticregression&#x27;,
+                                               LogisticRegression(C=10))])),
+                             (&#x27;periodic splines model&#x27;,
+                              Pipeline(steps=[(&#x27;splinetransformer&#x27;,
+                                               SplineTransformer(degree=2,
+                                                                 extrapolation=&#x27;periodic&#x27;,
+                                                                 n_knots=4)),
+                                              (&#x27;polynomialfeatures&#x27;,
+                                               PolynomialFeatures(interaction_only=True)),
+                                              (&#x27;logisticregression&#x27;,
+                                               LogisticRegression(C=10))])),
+                             (&#x27;nystroem model&#x27;,
+                              Pipeline(steps=[(&#x27;standardscaler&#x27;,
+                                               StandardScaler()),
+                                              (&#x27;nystroem&#x27;,
+                                               Nystroem(gamma=2,
+                                                        random_state=0)),
+                                              (&#x27;logisticregression&#x27;,
+                                               LogisticRegression(C=10))]))],
+                 voting=&#x27;soft&#x27;, weights=[2, 1, 3])</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><label>constant splines model</label></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-108" type="checkbox" ><label for="sk-estimator-id-108" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>SplineTransformer</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.SplineTransformer.html">?<span>Documentation for SplineTransformer</span></a></div></label><div class="sk-toggleable__content fitted"><pre>SplineTransformer(degree=2, n_knots=2)</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-109" type="checkbox" ><label for="sk-estimator-id-109" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>PolynomialFeatures</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.PolynomialFeatures.html">?<span>Documentation for PolynomialFeatures</span></a></div></label><div class="sk-toggleable__content fitted"><pre>PolynomialFeatures(interaction_only=True)</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-110" type="checkbox" ><label for="sk-estimator-id-110" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>LogisticRegression</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.linear_model.LogisticRegression.html">?<span>Documentation for LogisticRegression</span></a></div></label><div class="sk-toggleable__content fitted"><pre>LogisticRegression(C=10)</pre></div> </div></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><label>periodic splines model</label></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-111" type="checkbox" ><label for="sk-estimator-id-111" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>SplineTransformer</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.SplineTransformer.html">?<span>Documentation for SplineTransformer</span></a></div></label><div class="sk-toggleable__content fitted"><pre>SplineTransformer(degree=2, extrapolation=&#x27;periodic&#x27;, n_knots=4)</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-112" type="checkbox" ><label for="sk-estimator-id-112" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>PolynomialFeatures</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.PolynomialFeatures.html">?<span>Documentation for PolynomialFeatures</span></a></div></label><div class="sk-toggleable__content fitted"><pre>PolynomialFeatures(interaction_only=True)</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-113" type="checkbox" ><label for="sk-estimator-id-113" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>LogisticRegression</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.linear_model.LogisticRegression.html">?<span>Documentation for LogisticRegression</span></a></div></label><div class="sk-toggleable__content fitted"><pre>LogisticRegression(C=10)</pre></div> </div></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><label>nystroem model</label></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-114" type="checkbox" ><label for="sk-estimator-id-114" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>StandardScaler</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.StandardScaler.html">?<span>Documentation for StandardScaler</span></a></div></label><div class="sk-toggleable__content fitted"><pre>StandardScaler()</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-115" type="checkbox" ><label for="sk-estimator-id-115" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>Nystroem</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.kernel_approximation.Nystroem.html">?<span>Documentation for Nystroem</span></a></div></label><div class="sk-toggleable__content fitted"><pre>Nystroem(gamma=2, random_state=0)</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-116" type="checkbox" ><label for="sk-estimator-id-116" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>LogisticRegression</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.linear_model.LogisticRegression.html">?<span>Documentation for LogisticRegression</span></a></div></label><div class="sk-toggleable__content fitted"><pre>LogisticRegression(C=10)</pre></div> </div></div></div></div></div></div></div></div></div></div></div>
+</div>
+<br />
+<br /><p>Finally we use <a class="reference internal" href="http://github.com/modules/generated/sklearn.inspection.DecisionBoundaryDisplay.html#sklearn.inspection.DecisionBoundaryDisplay" title="sklearn.inspection.DecisionBoundaryDisplay"><code class="xref py py-class docutils literal notranslate"><span class="pre">DecisionBoundaryDisplay</span></code></a> to plot the
+predicted probabilities. By using a diverging colormap (such as <code class="docutils literal notranslate"><span class="pre">&quot;RdBu&quot;</span></code>), we
+can ensure that darker colors correspond to <code class="docutils literal notranslate"><span class="pre">predict_proba</span></code> close to either 0
+or 1, and white corresponds to <code class="docutils literal notranslate"><span class="pre">predict_proba</span></code> of 0.5.</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">itertools</span><span class="w"> </span><span class="kn">import</span> <a href="https://docs.python.org/3/library/itertools.html#itertools.product" title="itertools.product" class="sphx-glr-backref-module-itertools sphx-glr-backref-type-py-function"><span class="n">product</span></a>
 
-<span class="c1"># Plotting decision regions</span>
-<span class="n">f</span><span class="p">,</span> <span class="n">axarr</span> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="matplotlib.pyplot.subplots" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">subplots</span></a><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">sharex</span><span class="o">=</span><span class="s2">&quot;col&quot;</span><span class="p">,</span> <span class="n">sharey</span><span class="o">=</span><span class="s2">&quot;row&quot;</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">8</span><span class="p">))</span>
-<span class="k">for</span> <span class="n">idx</span><span class="p">,</span> <span class="n">clf</span><span class="p">,</span> <span class="n">tt</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span>
+<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.inspection</span><span class="w"> </span><span class="kn">import</span> <span class="n">DecisionBoundaryDisplay</span>
+
+<span class="n">fig</span><span class="p">,</span> <span class="n">axarr</span> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="matplotlib.pyplot.subplots" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">subplots</span></a><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">sharex</span><span class="o">=</span><span class="s2">&quot;col&quot;</span><span class="p">,</span> <span class="n">sharey</span><span class="o">=</span><span class="s2">&quot;row&quot;</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">8</span><span class="p">))</span>
+<span class="k">for</span> <span class="n">idx</span><span class="p">,</span> <span class="n">clf</span><span class="p">,</span> <span class="n">title</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span>
     <a href="https://docs.python.org/3/library/itertools.html#itertools.product" title="itertools.product" class="sphx-glr-backref-module-itertools sphx-glr-backref-type-py-function"><span class="n">product</span></a><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]),</span>
     <span class="p">[</span><span class="n">clf1</span><span class="p">,</span> <span class="n">clf2</span><span class="p">,</span> <span class="n">clf3</span><span class="p">,</span> <span class="n">eclf</span><span class="p">],</span>
-    <span class="p">[</span><span class="s2">&quot;Decision Tree (depth=4)&quot;</span><span class="p">,</span> <span class="s2">&quot;KNN (k=7)&quot;</span><span class="p">,</span> <span class="s2">&quot;Kernel SVM&quot;</span><span class="p">,</span> <span class="s2">&quot;Soft Voting&quot;</span><span class="p">],</span>
+    <span class="p">[</span>
+        <span class="s2">&quot;Splines with</span><span class="se">\n</span><span class="s2">constant extrapolation&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Splines with</span><span class="se">\n</span><span class="s2">periodic extrapolation&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;RBF Nystroem&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Soft Voting&quot;</span><span class="p">,</span>
+    <span class="p">],</span>
 <span class="p">):</span>
-    <a href="http://github.com/modules/generated/sklearn.inspection.DecisionBoundaryDisplay.html#sklearn.inspection.DecisionBoundaryDisplay.from_estimator" title="sklearn.inspection.DecisionBoundaryDisplay.from_estimator" class="sphx-glr-backref-module-sklearn-inspection-DecisionBoundaryDisplay sphx-glr-backref-type-py-method"><span class="n">DecisionBoundaryDisplay</span><span class="o">.</span><span class="n">from_estimator</span></a><span class="p">(</span>
-        <span class="n">clf</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.4</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">axarr</span><span class="p">[</span><span class="n">idx</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">idx</span><span class="p">[</span><span class="mi">1</span><span class="p">]],</span> <span class="n">response_method</span><span class="o">=</span><span class="s2">&quot;predict&quot;</span>
+    <span class="n">disp</span> <span class="o">=</span> <a href="http://github.com/modules/generated/sklearn.inspection.DecisionBoundaryDisplay.html#sklearn.inspection.DecisionBoundaryDisplay.from_estimator" title="sklearn.inspection.DecisionBoundaryDisplay.from_estimator" class="sphx-glr-backref-module-sklearn-inspection-DecisionBoundaryDisplay sphx-glr-backref-type-py-method"><span class="n">DecisionBoundaryDisplay</span><span class="o">.</span><span class="n">from_estimator</span></a><span class="p">(</span>
+        <span class="n">clf</span><span class="p">,</span>
+        <span class="n">X</span><span class="p">,</span>
+        <span class="n">response_method</span><span class="o">=</span><span class="s2">&quot;predict_proba&quot;</span><span class="p">,</span>
+        <span class="n">plot_method</span><span class="o">=</span><span class="s2">&quot;pcolormesh&quot;</span><span class="p">,</span>
+        <span class="n">cmap</span><span class="o">=</span><span class="s2">&quot;RdBu&quot;</span><span class="p">,</span>
+        <span class="n">alpha</span><span class="o">=</span><span class="mf">0.8</span><span class="p">,</span>
+        <span class="n">ax</span><span class="o">=</span><span class="n">axarr</span><span class="p">[</span><span class="n">idx</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">idx</span><span class="p">[</span><span class="mi">1</span><span class="p">]],</span>
+    <span class="p">)</span>
+    <span class="n">axarr</span><span class="p">[</span><span class="n">idx</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">idx</span><span class="p">[</span><span class="mi">1</span><span class="p">]]</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span>
+        <span class="n">X</span><span class="p">[</span><span class="s2">&quot;Feature #0&quot;</span><span class="p">],</span>
+        <span class="n">X</span><span class="p">[</span><span class="s2">&quot;Feature #1&quot;</span><span class="p">],</span>
+        <span class="n">c</span><span class="o">=</span><span class="n">y</span><span class="p">,</span>
+        <span class="o">**</span><span class="n">common_scatter_plot_params</span><span class="p">,</span>
     <span class="p">)</span>
-    <span class="n">axarr</span><span class="p">[</span><span class="n">idx</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">idx</span><span class="p">[</span><span class="mi">1</span><span class="p">]]</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">X</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="n">y</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="n">edgecolor</span><span class="o">=</span><span class="s2">&quot;k&quot;</span><span class="p">)</span>
-    <span class="n">axarr</span><span class="p">[</span><span class="n">idx</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">idx</span><span class="p">[</span><span class="mi">1</span><span class="p">]]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="n">tt</span><span class="p">)</span>
+    <span class="n">axarr</span><span class="p">[</span><span class="n">idx</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">idx</span><span class="p">[</span><span class="mi">1</span><span class="p">]]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="n">title</span><span class="p">)</span>
+    <span class="n">fig</span><span class="o">.</span><span class="n">colorbar</span><span class="p">(</span><span class="n">disp</span><span class="o">.</span><span class="n">surface_</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">axarr</span><span class="p">[</span><span class="n">idx</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">idx</span><span class="p">[</span><span class="mi">1</span><span class="p">]],</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;Probability estimate&quot;</span><span class="p">)</span>
 
 <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
 </pre></div>
 </div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 0.665 seconds)</p>
+<img src="http://github.com/_images/sphx_glr_plot_voting_decision_regions_002.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_voting_decision_regions_002.png " alt="Splines with constant extrapolation, Splines with periodic extrapolation, RBF Nystroem, Soft Voting" class = "sphx-glr-single-img"/><p>As a sanity check, we can verify for a given sample that the probability
+predicted by the <a class="reference internal" href="http://github.com/modules/generated/sklearn.ensemble.VotingClassifier.html#sklearn.ensemble.VotingClassifier" title="sklearn.ensemble.VotingClassifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">VotingClassifier</span></code></a> is indeed the weighted
+average of the individual classifiers’ soft-predictions.</p>
+<p>In the case of binary classification such as in the present example, the
+<a class="reference internal" href="http://github.com/glossary.html#term-predict_proba"><span class="xref std std-term">predict_proba</span></a> arrays contain the probability of belonging to class 0
+(here in red) as the first entry, and the probability of belonging to class 1
+(here in blue) as the second entry.</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">test_sample</span> <span class="o">=</span> <a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="pandas.DataFrame" class="sphx-glr-backref-module-pandas sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span></a><span class="p">({</span><span class="s2">&quot;Feature #0&quot;</span><span class="p">:</span> <span class="p">[</span><span class="o">-</span><span class="mf">0.5</span><span class="p">],</span> <span class="s2">&quot;Feature #1&quot;</span><span class="p">:</span> <span class="p">[</span><span class="mf">1.5</span><span class="p">]})</span>
+<span class="n">predict_probas</span> <span class="o">=</span> <span class="p">[</span><span class="n">est</span><span class="o">.</span><span class="n">predict_proba</span><span class="p">(</span><span class="n">test_sample</span><span class="p">)</span><span class="o">.</span><span class="n">ravel</span><span class="p">()</span> <span class="k">for</span> <span class="n">est</span> <span class="ow">in</span> <span class="n">eclf</span><span class="o">.</span><span class="n">estimators_</span><span class="p">]</span>
+<span class="k">for</span> <span class="p">(</span><span class="n">est_name</span><span class="p">,</span> <span class="n">_</span><span class="p">),</span> <span class="n">est_probas</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">eclf</span><span class="o">.</span><span class="n">estimators</span><span class="p">,</span> <span class="n">predict_probas</span><span class="p">):</span>
+    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">est_name</span><span class="si">}</span><span class="s2">&#39;s predicted probabilities: </span><span class="si">{</span><span class="n">est_probas</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>constant splines model&#39;s predicted probabilities: [0.11272662 0.88727338]
+periodic splines model&#39;s predicted probabilities: [0.99726573 0.00273427]
+nystroem model&#39;s predicted probabilities: [0.3185838 0.6814162]
+</pre></div>
+</div>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span>
+    <span class="s2">&quot;Weighted average of soft-predictions: &quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.dot.html#numpy.dot" title="numpy.dot" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">dot</span></a><span class="p">(</span><span class="n">weights</span><span class="p">,</span><span class="w"> </span><span class="n">predict_probas</span><span class="p">)</span><span class="w"> </span><span class="o">/</span><span class="w"> </span><a href="https://numpy.org/doc/stable/reference/generated/numpy.sum.html#numpy.sum" title="numpy.sum" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">sum</span></a><span class="p">(</span><span class="n">weights</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Weighted average of soft-predictions: [0.3630784 0.6369216]
+</pre></div>
+</div>
+<p>We can see that manual calculation of predicted probabilities above is
+equivalent to that produced by the <code class="docutils literal notranslate"><span class="pre">VotingClassifier</span></code>:</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span>
+    <span class="s2">&quot;Predicted probability of VotingClassifier: &quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">eclf</span><span class="o">.</span><span class="n">predict_proba</span><span class="p">(</span><span class="n">test_sample</span><span class="p">)</span><span class="o">.</span><span class="n">ravel</span><span class="p">()</span><span class="si">}</span><span class="s2">&quot;</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Predicted probability of VotingClassifier: [0.3630784 0.6369216]
+</pre></div>
+</div>
+<p>To convert soft predictions into hard predictions when weights are provided,
+the weighted average predicted probabilities are computed for each class.
+Then, the final class label is then derived from the class label with the
+highest average probability, which corresponds to the default threshold at
+<code class="docutils literal notranslate"><span class="pre">predict_proba=0.5</span></code> in the case of binary classification.</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span>
+    <span class="s2">&quot;Class with the highest weighted average of soft-predictions: &quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.argmax.html#numpy.argmax" title="numpy.argmax" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">argmax</span></a><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.dot.html#numpy.dot" title="numpy.dot" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">dot</span></a><span class="p">(</span><span class="n">weights</span><span class="p">,</span><span class="w"> </span><span class="n">predict_probas</span><span class="p">)</span><span class="w"> </span><span class="o">/</span><span class="w"> </span><a href="https://numpy.org/doc/stable/reference/generated/numpy.sum.html#numpy.sum" title="numpy.sum" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">sum</span></a><span class="p">(</span><span class="n">weights</span><span class="p">))</span><span class="si">}</span><span class="s2">&quot;</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Class with the highest weighted average of soft-predictions: 1
+</pre></div>
+</div>
+<p>This is equivalent to the output of <code class="docutils literal notranslate"><span class="pre">VotingClassifier</span></code>’s <code class="docutils literal notranslate"><span class="pre">predict</span></code> method:</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Predicted class of VotingClassifier: </span><span class="si">{</span><span class="n">eclf</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">test_sample</span><span class="p">)</span><span class="o">.</span><span class="n">ravel</span><span class="p">()</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Predicted class of VotingClassifier: [1]
+</pre></div>
+</div>
+<p>Soft votes can be thresholded as for any other probabilistic classifier. This
+allows you to set a threshold probability at which the positive class will be
+predicted, instead of simply selecting the class with the highest predicted
+probability.</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.model_selection</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.model_selection.FixedThresholdClassifier.html#sklearn.model_selection.FixedThresholdClassifier" title="sklearn.model_selection.FixedThresholdClassifier" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">FixedThresholdClassifier</span></a>
+
+<span class="n">eclf_other_threshold</span> <span class="o">=</span> <a href="http://github.com/modules/generated/sklearn.model_selection.FixedThresholdClassifier.html#sklearn.model_selection.FixedThresholdClassifier" title="sklearn.model_selection.FixedThresholdClassifier" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">FixedThresholdClassifier</span></a><span class="p">(</span>
+    <span class="n">eclf</span><span class="p">,</span> <span class="n">threshold</span><span class="o">=</span><span class="mf">0.7</span><span class="p">,</span> <span class="n">response_method</span><span class="o">=</span><span class="s2">&quot;predict_proba&quot;</span>
+<span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span>
+    <span class="s2">&quot;Predicted class of thresholded VotingClassifier: &quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">eclf_other_threshold</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">test_sample</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Predicted class of thresholded VotingClassifier: [0]
+</pre></div>
+</div>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 0.702 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-ensemble-plot-voting-decision-regions-py">
 <div class="binder-badge docutils container">
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@@ -1078,18 +1700,18 @@
 </div>
 </div>
 <p class="rubric">Related examples</p>
-<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="Plot the class probabilities of the first sample in a toy dataset predicted by three different classifiers and averaged by the VotingClassifier."><img alt="" src="http://github.com/_images/sphx_glr_plot_voting_probas_thumb.png" />
-<p><a class="reference internal" href="http://github.com/plot_voting_probas.html#sphx-glr-auto-examples-ensemble-plot-voting-probas-py"><span class="std std-ref">Plot class probabilities calculated by the VotingClassifier</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Plot class probabilities calculated by the VotingClassifier</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="Plot the decision surface of a decision tree trained on pairs of features of the iris dataset."><img alt="" src="http://github.com/_images/sphx_glr_plot_iris_dtc_thumb.png" />
-<p><a class="reference internal" href="http://github.com/tree/plot_iris_dtc.html#sphx-glr-auto-examples-tree-plot-iris-dtc-py"><span class="std std-ref">Plot the decision surface of decision trees trained on the iris dataset</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Plot the decision surface of decision trees trained on the iris dataset</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="Plot decision surface of multi-class SGD on iris dataset. The hyperplanes corresponding to the three one-versus-all (OVA) classifiers are represented by the dashed lines."><img alt="" src="http://github.com/_images/sphx_glr_plot_sgd_iris_thumb.png" />
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-  <div class="sphx-glr-thumbnail-title">Plot multi-class SGD on the iris dataset</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="Comparison of different linear SVM classifiers on a 2D projection of the iris dataset. We only consider the first 2 features of this dataset:"><img alt="" src="http://github.com/_images/sphx_glr_plot_iris_svc_thumb.png" />
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-  <div class="sphx-glr-thumbnail-title">Plot different SVM classifiers in the iris dataset</div>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This example illustrates the use of sklearn.inspection.DecisionBoundaryDisplay to plot the predicted class probabilities of various classifiers in a 2D feature space, mostly for didactic purposes."><img alt="" src="http://github.com/_images/sphx_glr_plot_classification_probability_thumb.png" />
+<p><a class="reference internal" href="http://github.com/classification/plot_classification_probability.html#sphx-glr-auto-examples-classification-plot-classification-probability-py"><span class="std std-ref">Plot classification probability</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Plot classification probability</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how to approximate a function with polynomials up to degree degree by using ridge regression. We show two different ways given n_samples of 1d points x_i:"><img alt="" src="http://github.com/_images/sphx_glr_plot_polynomial_interpolation_thumb.png" />
+<p><a class="reference internal" href="http://github.com/linear_model/plot_polynomial_interpolation.html#sphx-glr-auto-examples-linear-model-plot-polynomial-interpolation-py"><span class="std std-ref">Polynomial and Spline interpolation</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Polynomial and Spline interpolation</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="Well calibrated classifiers are probabilistic classifiers for which the output of predict_proba can be directly interpreted as a confidence level. For instance, a well calibrated (binary) classifier should classify the samples such that for the samples to which it gave a predict_proba value close to 0.8, approximately 80% actually belong to the positive class."><img alt="" src="http://github.com/_images/sphx_glr_plot_compare_calibration_thumb.png" />
+<p><a class="reference internal" href="http://github.com/calibration/plot_compare_calibration.html#sphx-glr-auto-examples-calibration-plot-compare-calibration-py"><span class="std std-ref">Comparison of Calibration of Classifiers</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Comparison of Calibration of Classifiers</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This examples showcases some use cases of FrozenEstimator."><img alt="" src="http://github.com/_images/sphx_glr_plot_frozen_examples_thumb.png" />
+<p><a class="reference internal" href="http://github.com/frozen/plot_frozen_examples.html#sphx-glr-auto-examples-frozen-plot-frozen-examples-py"><span class="std std-ref">Examples of Using FrozenEstimator</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Examples of Using FrozenEstimator</div>
 </div></div><p class="sphx-glr-signature"><a class="reference external" href="https://sphinx-gallery.github.io/">Gallery generated by Sphinx-Gallery</a></p>
 </section>
 
@@ -1104,20 +1726,20 @@
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-        <p class="prev-next-title">Plot individual and voting regression predictions</p>
+        <p class="prev-next-title">Two-class AdaBoost</p>
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-        <p class="prev-next-title">Plot the decision surfaces of ensembles of trees on the iris dataset</p>
+        <p class="prev-next-title">Examples based on real world datasets</p>
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diff --git a/dev/auto_examples/ensemble/plot_voting_probas.html b/dev/auto_examples/ensemble/plot_voting_probas.html
index 7b9ca2423161e..281dbd2fc541c 100644
--- a/dev/auto_examples/ensemble/plot_voting_probas.html
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_random_forest_embedding.html">Hashing feature transformation using Totally Random Trees</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_isolation_forest.html">IsolationForest example</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2 current active"><a class="current reference internal" href="#">Plot class probabilities calculated by the VotingClassifier</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
-</ul>
-</details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/applications/plot_tomography_l1_reconstruction.html">Compressive sensing: tomography reconstruction with L1 prior (Lasso)</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/applications/plot_face_recognition.html">Faces recognition example using eigenfaces and SVMs</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/applications/plot_digits_denoising.html">Image denoising using kernel PCA</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/applications/plot_time_series_lagged_features.html">Lagged features for time series forecasting</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/applications/plot_model_complexity_influence.html">Model Complexity Influence</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/applications/plot_out_of_core_classification.html">Out-of-core classification of text documents</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/applications/plot_outlier_detection_wine.html">Outlier detection on a real data set</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/applications/plot_prediction_latency.html">Prediction Latency</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/applications/plot_species_distribution_modeling.html">Species distribution modeling</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/applications/plot_cyclical_feature_engineering.html">Time-related feature engineering</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/applications/plot_topics_extraction_with_nmf_lda.html">Topic extraction with Non-negative Matrix Factorization and Latent Dirichlet Allocation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/applications/plot_stock_market.html">Visualizing the stock market structure</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/applications/wikipedia_principal_eigenvector.html">Wikipedia principal eigenvector</a></li>
-</ul>
-</details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/feature_selection/index.html">Feature Selection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/feature_selection/plot_f_test_vs_mi.html">Comparison of F-test and mutual information</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/feature_selection/plot_select_from_model_diabetes.html">Model-based and sequential feature selection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/feature_selection/plot_feature_selection_pipeline.html">Pipeline ANOVA SVM</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/feature_selection/plot_rfe_digits.html">Recursive feature elimination</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/feature_selection/plot_rfe_with_cross_validation.html">Recursive feature elimination with cross-validation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/feature_selection/plot_feature_selection.html">Univariate Feature Selection</a></li>
-</ul>
-</details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/frozen/index.html">Frozen Estimators</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/frozen/plot_frozen_examples.html">Examples of Using <code class="docutils literal notranslate"><span class="pre">FrozenEstimator</span></code></a></li>
-</ul>
-</details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/mixture/index.html">Gaussian Mixture Models</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/mixture/plot_concentration_prior.html">Concentration Prior Type Analysis of Variation Bayesian Gaussian Mixture</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/mixture/plot_gmm_pdf.html">Density Estimation for a Gaussian mixture</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/mixture/plot_gmm_init.html">GMM Initialization Methods</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/mixture/plot_gmm_covariances.html">GMM covariances</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/mixture/plot_gmm.html">Gaussian Mixture Model Ellipsoids</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/mixture/plot_gmm_selection.html">Gaussian Mixture Model Selection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/mixture/plot_gmm_sin.html">Gaussian Mixture Model Sine Curve</a></li>
-</ul>
-</details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/gaussian_process/index.html">Gaussian Process for Machine Learning</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/gaussian_process/plot_gpr_noisy.html">Ability of Gaussian process regression (GPR) to estimate data noise-level</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/gaussian_process/plot_compare_gpr_krr.html">Comparison of kernel ridge and Gaussian process regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/gaussian_process/plot_gpr_co2.html">Forecasting of CO2 level on Mona Loa dataset using Gaussian process regression (GPR)</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/gaussian_process/plot_gpr_noisy_targets.html">Gaussian Processes regression: basic introductory example</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/gaussian_process/plot_gpc_iris.html">Gaussian process classification (GPC) on iris dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/gaussian_process/plot_gpr_on_structured_data.html">Gaussian processes on discrete data structures</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/gaussian_process/plot_gpc_xor.html">Illustration of Gaussian process classification (GPC) on the XOR dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/gaussian_process/plot_gpr_prior_posterior.html">Illustration of prior and posterior Gaussian process for different kernels</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/gaussian_process/plot_gpc_isoprobability.html">Iso-probability lines for Gaussian Processes classification (GPC)</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/gaussian_process/plot_gpc.html">Probabilistic predictions with Gaussian process classification (GPC)</a></li>
-</ul>
-</details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/linear_model/index.html">Generalized Linear Models</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/linear_model/plot_ard.html">Comparing Linear Bayesian Regressors</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/linear_model/plot_bayesian_ridge_curvefit.html">Curve Fitting with Bayesian Ridge Regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/linear_model/plot_logistic_multinomial.html">Decision Boundaries of Multinomial and One-vs-Rest Logistic Regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/linear_model/plot_sgd_early_stopping.html">Early stopping of Stochastic Gradient Descent</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/linear_model/plot_elastic_net_precomputed_gram_matrix_with_weighted_samples.html">Fitting an Elastic Net with a precomputed Gram Matrix and Weighted Samples</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/linear_model/plot_huber_vs_ridge.html">HuberRegressor vs Ridge on dataset with strong outliers</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/linear_model/plot_multi_task_lasso_support.html">Joint feature selection with multi-task Lasso</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/linear_model/plot_logistic_l1_l2_sparsity.html">L1 Penalty and Sparsity in Logistic Regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/linear_model/plot_lasso_and_elasticnet.html">L1-based models for Sparse Signals</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/linear_model/plot_lasso_lars_ic.html">Lasso model selection via information criteria</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/linear_model/plot_lasso_model_selection.html">Lasso model selection: AIC-BIC / cross-validation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/linear_model/plot_lasso_dense_vs_sparse_data.html">Lasso on dense and sparse data</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/linear_model/plot_lasso_lasso_lars_elasticnet_path.html">Lasso, Lasso-LARS, and Elastic Net paths</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/linear_model/plot_logistic.html">Logistic function</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/linear_model/plot_sparse_logistic_regression_mnist.html">MNIST classification using multinomial logistic + L1</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/linear_model/plot_sparse_logistic_regression_20newsgroups.html">Multiclass sparse logistic regression on 20newgroups</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/linear_model/plot_nnls.html">Non-negative least squares</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/linear_model/plot_sgdocsvm_vs_ocsvm.html">One-Class SVM versus One-Class SVM using Stochastic Gradient Descent</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/linear_model/plot_ols_ridge.html">Ordinary Least Squares and Ridge Regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/linear_model/plot_omp.html">Orthogonal Matching Pursuit</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/linear_model/plot_ridge_path.html">Plot Ridge coefficients as a function of the regularization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/linear_model/plot_sgd_iris.html">Plot multi-class SGD on the iris dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/linear_model/plot_poisson_regression_non_normal_loss.html">Poisson regression and non-normal loss</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/linear_model/plot_polynomial_interpolation.html">Polynomial and Spline interpolation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/linear_model/plot_quantile_regression.html">Quantile regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/linear_model/plot_logistic_path.html">Regularization path of L1- Logistic Regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/linear_model/plot_ridge_coeffs.html">Ridge coefficients as a function of the L2 Regularization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/linear_model/plot_robust_fit.html">Robust linear estimator fitting</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/linear_model/plot_ransac.html">Robust linear model estimation using RANSAC</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/linear_model/plot_sgd_separating_hyperplane.html">SGD: Maximum margin separating hyperplane</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/linear_model/plot_sgd_penalties.html">SGD: Penalties</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/linear_model/plot_sgd_weighted_samples.html">SGD: Weighted samples</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/linear_model/plot_sgd_loss_functions.html">SGD: convex loss functions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/linear_model/plot_theilsen.html">Theil-Sen Regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/linear_model/plot_tweedie_regression_insurance_claims.html">Tweedie regression on insurance claims</a></li>
-</ul>
-</details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/inspection/index.html">Inspection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/inspection/plot_linear_model_coefficient_interpretation.html">Common pitfalls in the interpretation of coefficients of linear models</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/inspection/plot_causal_interpretation.html">Failure of Machine Learning to infer causal effects</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/inspection/plot_partial_dependence.html">Partial Dependence and Individual Conditional Expectation Plots</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/inspection/plot_permutation_importance.html">Permutation Importance vs Random Forest Feature Importance (MDI)</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/inspection/plot_permutation_importance_multicollinear.html">Permutation Importance with Multicollinear or Correlated Features</a></li>
-</ul>
-</details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/kernel_approximation/index.html">Kernel Approximation</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/kernel_approximation/plot_scalable_poly_kernels.html">Scalable learning with polynomial kernel approximation</a></li>
-</ul>
-</details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/manifold/index.html">Manifold learning</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/manifold/plot_compare_methods.html">Comparison of Manifold Learning methods</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/manifold/plot_manifold_sphere.html">Manifold Learning methods on a severed sphere</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/manifold/plot_lle_digits.html">Manifold learning on handwritten digits: Locally Linear Embedding, Isomap…</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/manifold/plot_mds.html">Multi-dimensional scaling</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/manifold/plot_swissroll.html">Swiss Roll And Swiss-Hole Reduction</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/manifold/plot_t_sne_perplexity.html">t-SNE: The effect of various perplexity values on the shape</a></li>
-</ul>
-</details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/miscellaneous/index.html">Miscellaneous</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/miscellaneous/plot_partial_dependence_visualization_api.html">Advanced Plotting With Partial Dependence</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/miscellaneous/plot_anomaly_comparison.html">Comparing anomaly detection algorithms for outlier detection on toy datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/miscellaneous/plot_kernel_ridge_regression.html">Comparison of kernel ridge regression and SVR</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/miscellaneous/plot_pipeline_display.html">Displaying Pipelines</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/miscellaneous/plot_estimator_representation.html">Displaying estimators and complex pipelines</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/miscellaneous/plot_outlier_detection_bench.html">Evaluation of outlier detection estimators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/miscellaneous/plot_kernel_approximation.html">Explicit feature map approximation for RBF kernels</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/miscellaneous/plot_multioutput_face_completion.html">Face completion with a multi-output estimators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/miscellaneous/plot_set_output.html">Introducing the <code class="docutils literal notranslate"><span class="pre">set_output</span></code> API</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/miscellaneous/plot_isotonic_regression.html">Isotonic Regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/miscellaneous/plot_metadata_routing.html">Metadata Routing</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/miscellaneous/plot_multilabel.html">Multilabel classification</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/miscellaneous/plot_roc_curve_visualization_api.html">ROC Curve with Visualization API</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/miscellaneous/plot_johnson_lindenstrauss_bound.html">The Johnson-Lindenstrauss bound for embedding with random projections</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/miscellaneous/plot_display_object_visualization.html">Visualizations with Display Objects</a></li>
-</ul>
-</details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/impute/index.html">Missing Value Imputation</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/impute/plot_missing_values.html">Imputing missing values before building an estimator</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/impute/plot_iterative_imputer_variants_comparison.html">Imputing missing values with variants of IterativeImputer</a></li>
-</ul>
-</details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/model_selection/index.html">Model Selection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/model_selection/plot_grid_search_refit_callable.html">Balance model complexity and cross-validated score</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/model_selection/plot_likelihood_ratios.html">Class Likelihood Ratios to measure classification performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/model_selection/plot_randomized_search.html">Comparing randomized search and grid search for hyperparameter estimation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/model_selection/plot_successive_halving_heatmap.html">Comparison between grid search and successive halving</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/model_selection/plot_confusion_matrix.html">Confusion matrix</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/model_selection/plot_multi_metric_evaluation.html">Demonstration of multi-metric evaluation on cross_val_score and GridSearchCV</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/model_selection/plot_det.html">Detection error tradeoff (DET) curve</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/model_selection/plot_train_error_vs_test_error.html">Effect of model regularization on training and test error</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/model_selection/plot_roc.html">Multiclass Receiver Operating Characteristic (ROC)</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/model_selection/plot_nested_cross_validation_iris.html">Nested versus non-nested cross-validation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/model_selection/plot_cv_predict.html">Plotting Cross-Validated Predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/model_selection/plot_learning_curve.html">Plotting Learning Curves and Checking Models’ Scalability</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/model_selection/plot_tuned_decision_threshold.html">Post-hoc tuning the cut-off point of decision function</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/model_selection/plot_cost_sensitive_learning.html">Post-tuning the decision threshold for cost-sensitive learning</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/model_selection/plot_precision_recall.html">Precision-Recall</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/model_selection/plot_roc_crossval.html">Receiver Operating Characteristic (ROC) with cross validation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/model_selection/plot_grid_search_text_feature_extraction.html">Sample pipeline for text feature extraction and evaluation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/model_selection/plot_grid_search_stats.html">Statistical comparison of models using grid search</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/neighbors/plot_regression.html">Nearest Neighbors regression</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/neighbors/plot_lof_outlier_detection.html">Outlier detection with Local Outlier Factor (LOF)</a></li>
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-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
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-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/svm/index.html">Support Vector Machines</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/svm/plot_oneclass.html">One-class SVM with non-linear kernel (RBF)</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/svm/plot_svm_kernels.html">Plot classification boundaries with different SVM Kernels</a></li>
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-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/text/index.html">Working with text documents</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
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-  <div class="sphx-glr-download-link-note admonition note">
-<p class="admonition-title">Note</p>
-<p><a class="reference internal" href="#sphx-glr-download-auto-examples-ensemble-plot-voting-probas-py"><span class="std std-ref">Go to the end</span></a>
-to download the full example code. or to run this example in your browser via JupyterLite or Binder</p>
-</div>
-<section class="sphx-glr-example-title" id="plot-class-probabilities-calculated-by-the-votingclassifier">
-<span id="sphx-glr-auto-examples-ensemble-plot-voting-probas-py"></span><h1>Plot class probabilities calculated by the VotingClassifier<a class="headerlink" href="#plot-class-probabilities-calculated-by-the-votingclassifier" title="Link to this heading">#</a></h1>
-<p>Plot the class probabilities of the first sample in a toy dataset predicted by
-three different classifiers and averaged by the
-<a class="reference internal" href="http://github.com/modules/generated/sklearn.ensemble.VotingClassifier.html#sklearn.ensemble.VotingClassifier" title="sklearn.ensemble.VotingClassifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">VotingClassifier</span></code></a>.</p>
-<p>First, three exemplary classifiers are initialized
-(<a class="reference internal" href="http://github.com/modules/generated/sklearn.linear_model.LogisticRegression.html#sklearn.linear_model.LogisticRegression" title="sklearn.linear_model.LogisticRegression"><code class="xref py py-class docutils literal notranslate"><span class="pre">LogisticRegression</span></code></a>, <a class="reference internal" href="http://github.com/modules/generated/sklearn.naive_bayes.GaussianNB.html#sklearn.naive_bayes.GaussianNB" title="sklearn.naive_bayes.GaussianNB"><code class="xref py py-class docutils literal notranslate"><span class="pre">GaussianNB</span></code></a>,
-and <a class="reference internal" href="http://github.com/modules/generated/sklearn.ensemble.RandomForestClassifier.html#sklearn.ensemble.RandomForestClassifier" title="sklearn.ensemble.RandomForestClassifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">RandomForestClassifier</span></code></a>) and used to initialize a
-soft-voting <a class="reference internal" href="http://github.com/modules/generated/sklearn.ensemble.VotingClassifier.html#sklearn.ensemble.VotingClassifier" title="sklearn.ensemble.VotingClassifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">VotingClassifier</span></code></a> with weights <code class="docutils literal notranslate"><span class="pre">[1,</span> <span class="pre">1,</span> <span class="pre">5]</span></code>, which
-means that the predicted probabilities of the
-<a class="reference internal" href="http://github.com/modules/generated/sklearn.ensemble.RandomForestClassifier.html#sklearn.ensemble.RandomForestClassifier" title="sklearn.ensemble.RandomForestClassifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">RandomForestClassifier</span></code></a> count 5 times as much as the weights
-of the other classifiers when the averaged probability is calculated.</p>
-<p>To visualize the probability weighting, we fit each classifier on the training
-set and plot the predicted class probabilities for the first sample in this
-example dataset.</p>
-<img src="http://github.com/_images/sphx_glr_plot_voting_probas_001.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_voting_probas_001.png " alt="Class probabilities for sample 1 by different classifiers" class = "sphx-glr-single-img"/><div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Authors: The scikit-learn developers</span>
-<span class="c1"># SPDX-License-Identifier: BSD-3-Clause</span>
-
-<span class="kn">import</span><span class="w"> </span><span class="nn">matplotlib.pyplot</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">plt</span>
-<span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
-
-<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.ensemble</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.ensemble.RandomForestClassifier.html#sklearn.ensemble.RandomForestClassifier" title="sklearn.ensemble.RandomForestClassifier" class="sphx-glr-backref-module-sklearn-ensemble sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">RandomForestClassifier</span></a><span class="p">,</span> <a href="http://github.com/modules/generated/sklearn.ensemble.VotingClassifier.html#sklearn.ensemble.VotingClassifier" title="sklearn.ensemble.VotingClassifier" class="sphx-glr-backref-module-sklearn-ensemble sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">VotingClassifier</span></a>
-<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.linear_model</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.linear_model.LogisticRegression.html#sklearn.linear_model.LogisticRegression" title="sklearn.linear_model.LogisticRegression" class="sphx-glr-backref-module-sklearn-linear_model sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">LogisticRegression</span></a>
-<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.naive_bayes</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.naive_bayes.GaussianNB.html#sklearn.naive_bayes.GaussianNB" title="sklearn.naive_bayes.GaussianNB" class="sphx-glr-backref-module-sklearn-naive_bayes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">GaussianNB</span></a>
-
-<span class="n">clf1</span> <span class="o">=</span> <a href="http://github.com/modules/generated/sklearn.linear_model.LogisticRegression.html#sklearn.linear_model.LogisticRegression" title="sklearn.linear_model.LogisticRegression" class="sphx-glr-backref-module-sklearn-linear_model sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">LogisticRegression</span></a><span class="p">(</span><span class="n">max_iter</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">123</span><span class="p">)</span>
-<span class="n">clf2</span> <span class="o">=</span> <a href="http://github.com/modules/generated/sklearn.ensemble.RandomForestClassifier.html#sklearn.ensemble.RandomForestClassifier" title="sklearn.ensemble.RandomForestClassifier" class="sphx-glr-backref-module-sklearn-ensemble sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">RandomForestClassifier</span></a><span class="p">(</span><span class="n">n_estimators</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">123</span><span class="p">)</span>
-<span class="n">clf3</span> <span class="o">=</span> <a href="http://github.com/modules/generated/sklearn.naive_bayes.GaussianNB.html#sklearn.naive_bayes.GaussianNB" title="sklearn.naive_bayes.GaussianNB" class="sphx-glr-backref-module-sklearn-naive_bayes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">GaussianNB</span></a><span class="p">()</span>
-<span class="n">X</span> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.array.html#numpy.array" title="numpy.array" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">array</span></a><span class="p">([[</span><span class="o">-</span><span class="mf">1.0</span><span class="p">,</span> <span class="o">-</span><span class="mf">1.0</span><span class="p">],</span> <span class="p">[</span><span class="o">-</span><span class="mf">1.2</span><span class="p">,</span> <span class="o">-</span><span class="mf">1.4</span><span class="p">],</span> <span class="p">[</span><span class="o">-</span><span class="mf">3.4</span><span class="p">,</span> <span class="o">-</span><span class="mf">2.2</span><span class="p">],</span> <span class="p">[</span><span class="mf">1.1</span><span class="p">,</span> <span class="mf">1.2</span><span class="p">]])</span>
-<span class="n">y</span> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.array.html#numpy.array" title="numpy.array" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">array</span></a><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">])</span>
-
-<span class="n">eclf</span> <span class="o">=</span> <a href="http://github.com/modules/generated/sklearn.ensemble.VotingClassifier.html#sklearn.ensemble.VotingClassifier" title="sklearn.ensemble.VotingClassifier" class="sphx-glr-backref-module-sklearn-ensemble sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">VotingClassifier</span></a><span class="p">(</span>
-    <span class="n">estimators</span><span class="o">=</span><span class="p">[(</span><span class="s2">&quot;lr&quot;</span><span class="p">,</span> <span class="n">clf1</span><span class="p">),</span> <span class="p">(</span><span class="s2">&quot;rf&quot;</span><span class="p">,</span> <span class="n">clf2</span><span class="p">),</span> <span class="p">(</span><span class="s2">&quot;gnb&quot;</span><span class="p">,</span> <span class="n">clf3</span><span class="p">)],</span>
-    <span class="n">voting</span><span class="o">=</span><span class="s2">&quot;soft&quot;</span><span class="p">,</span>
-    <span class="n">weights</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">5</span><span class="p">],</span>
-<span class="p">)</span>
-
-<span class="c1"># predict class probabilities for all classifiers</span>
-<span class="n">probas</span> <span class="o">=</span> <span class="p">[</span><span class="n">c</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span><span class="o">.</span><span class="n">predict_proba</span><span class="p">(</span><span class="n">X</span><span class="p">)</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="p">(</span><span class="n">clf1</span><span class="p">,</span> <span class="n">clf2</span><span class="p">,</span> <span class="n">clf3</span><span class="p">,</span> <span class="n">eclf</span><span class="p">)]</span>
-
-<span class="c1"># get class probabilities for the first sample in the dataset</span>
-<span class="n">class1_1</span> <span class="o">=</span> <span class="p">[</span><span class="n">pr</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">pr</span> <span class="ow">in</span> <span class="n">probas</span><span class="p">]</span>
-<span class="n">class2_1</span> <span class="o">=</span> <span class="p">[</span><span class="n">pr</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">pr</span> <span class="ow">in</span> <span class="n">probas</span><span class="p">]</span>
-
-
-<span class="c1"># plotting</span>
-
-<span class="n">N</span> <span class="o">=</span> <span class="mi">4</span>  <span class="c1"># number of groups</span>
-<span class="n">ind</span> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.arange.html#numpy.arange" title="numpy.arange" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">arange</span></a><span class="p">(</span><span class="n">N</span><span class="p">)</span>  <span class="c1"># group positions</span>
-<span class="n">width</span> <span class="o">=</span> <span class="mf">0.35</span>  <span class="c1"># bar width</span>
-
-<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="matplotlib.pyplot.subplots" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">subplots</span></a><span class="p">()</span>
-
-<span class="c1"># bars for classifier 1-3</span>
-<span class="n">p1</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">bar</span><span class="p">(</span><span class="n">ind</span><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.hstack.html#numpy.hstack" title="numpy.hstack" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">hstack</span></a><span class="p">(([</span><span class="n">class1_1</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">]])),</span> <span class="n">width</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;green&quot;</span><span class="p">,</span> <span class="n">edgecolor</span><span class="o">=</span><span class="s2">&quot;k&quot;</span><span class="p">)</span>
-<span class="n">p2</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">bar</span><span class="p">(</span>
-    <span class="n">ind</span> <span class="o">+</span> <span class="n">width</span><span class="p">,</span>
-    <a href="https://numpy.org/doc/stable/reference/generated/numpy.hstack.html#numpy.hstack" title="numpy.hstack" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">hstack</span></a><span class="p">(([</span><span class="n">class2_1</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">]])),</span>
-    <span class="n">width</span><span class="p">,</span>
-    <span class="n">color</span><span class="o">=</span><span class="s2">&quot;lightgreen&quot;</span><span class="p">,</span>
-    <span class="n">edgecolor</span><span class="o">=</span><span class="s2">&quot;k&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-
-<span class="c1"># bars for VotingClassifier</span>
-<span class="n">p3</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">bar</span><span class="p">(</span><span class="n">ind</span><span class="p">,</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="n">class1_1</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]],</span> <span class="n">width</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;blue&quot;</span><span class="p">,</span> <span class="n">edgecolor</span><span class="o">=</span><span class="s2">&quot;k&quot;</span><span class="p">)</span>
-<span class="n">p4</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">bar</span><span class="p">(</span>
-    <span class="n">ind</span> <span class="o">+</span> <span class="n">width</span><span class="p">,</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="n">class2_1</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]],</span> <span class="n">width</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;steelblue&quot;</span><span class="p">,</span> <span class="n">edgecolor</span><span class="o">=</span><span class="s2">&quot;k&quot;</span>
-<span class="p">)</span>
-
-<span class="c1"># plot annotations</span>
-<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.axvline.html#matplotlib.pyplot.axvline" title="matplotlib.pyplot.axvline" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">axvline</span></a><span class="p">(</span><span class="mf">2.8</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;k&quot;</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s2">&quot;dashed&quot;</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_xticks</span><span class="p">(</span><span class="n">ind</span> <span class="o">+</span> <span class="n">width</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_xticklabels</span><span class="p">(</span>
-    <span class="p">[</span>
-        <span class="s2">&quot;LogisticRegression</span><span class="se">\n</span><span class="s2">weight 1&quot;</span><span class="p">,</span>
-        <span class="s2">&quot;GaussianNB</span><span class="se">\n</span><span class="s2">weight 1&quot;</span><span class="p">,</span>
-        <span class="s2">&quot;RandomForestClassifier</span><span class="se">\n</span><span class="s2">weight 5&quot;</span><span class="p">,</span>
-        <span class="s2">&quot;VotingClassifier</span><span class="se">\n</span><span class="s2">(average probabilities)&quot;</span><span class="p">,</span>
-    <span class="p">],</span>
-    <span class="n">rotation</span><span class="o">=</span><span class="mi">40</span><span class="p">,</span>
-    <span class="n">ha</span><span class="o">=</span><span class="s2">&quot;right&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.ylim.html#matplotlib.pyplot.ylim" title="matplotlib.pyplot.ylim" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">ylim</span></a><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>
-<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.title.html#matplotlib.pyplot.title" title="matplotlib.pyplot.title" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">title</span></a><span class="p">(</span><span class="s2">&quot;Class probabilities for sample 1 by different classifiers&quot;</span><span class="p">)</span>
-<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.legend.html#matplotlib.pyplot.legend" title="matplotlib.pyplot.legend" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">legend</span></a><span class="p">([</span><span class="n">p1</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">p2</span><span class="p">[</span><span class="mi">0</span><span class="p">]],</span> <span class="p">[</span><span class="s2">&quot;class 1&quot;</span><span class="p">,</span> <span class="s2">&quot;class 2&quot;</span><span class="p">],</span> <span class="n">loc</span><span class="o">=</span><span class="s2">&quot;upper left&quot;</span><span class="p">)</span>
-<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.tight_layout.html#matplotlib.pyplot.tight_layout" title="matplotlib.pyplot.tight_layout" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span></a><span class="p">()</span>
-<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
-</pre></div>
-</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 0.366 seconds)</p>
-<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-ensemble-plot-voting-probas-py">
-<div class="binder-badge docutils container">
-<a class="reference external image-reference" href="https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/ensemble/plot_voting_probas.ipynb"><img alt="Launch binder" src="http://github.com/_images/binder_badge_logo11.svg" style="width: 150px;" />
-</a>
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-<div class="lite-badge docutils container">
-<a class="reference external image-reference" href="http://github.com/lite/lab/index.html?path=auto_examples/ensemble/plot_voting_probas.ipynb"><img alt="Launch JupyterLite" src="http://github.com/_images/jupyterlite_badge_logo11.svg" style="width: 150px;" />
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-<p><a class="reference download internal" download="" href="http://github.com/_downloads/7011de1f31ecdc52f138d7e582a6a455/plot_voting_probas.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_voting_probas.ipynb</span></code></a></p>
-</div>
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-<p><a class="reference download internal" download="" href="http://github.com/_downloads/93cd12369459b2e432d0a2665e19ef8a/plot_voting_probas.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_voting_probas.py</span></code></a></p>
-</div>
-<div class="sphx-glr-download sphx-glr-download-zip docutils container">
-<p><a class="reference download internal" download="" href="http://github.com/_downloads/f1fbe67eb1c0d4cc6f257fe17aeecd87/plot_voting_probas.zip"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">zipped:</span> <span class="pre">plot_voting_probas.zip</span></code></a></p>
-</div>
-</div>
-<p class="rubric">Related examples</p>
-<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="Plot the decision boundaries of a VotingClassifier for two features of the Iris dataset."><img alt="" src="http://github.com/_images/sphx_glr_plot_voting_decision_regions_thumb.png" />
-<p><a class="reference internal" href="http://github.com/plot_voting_decision_regions.html#sphx-glr-auto-examples-ensemble-plot-voting-decision-regions-py"><span class="std std-ref">Plot the decision boundaries of a VotingClassifier</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Plot the decision boundaries of a VotingClassifier</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="Well calibrated classifiers are probabilistic classifiers for which the output of predict_proba can be directly interpreted as a confidence level. For instance, a well calibrated (binary) classifier should classify the samples such that for the samples to which it gave a predict_proba value close to 0.8, approximately 80% actually belong to the positive class."><img alt="" src="http://github.com/_images/sphx_glr_plot_compare_calibration_thumb.png" />
-<p><a class="reference internal" href="http://github.com/calibration/plot_compare_calibration.html#sphx-glr-auto-examples-calibration-plot-compare-calibration-py"><span class="std std-ref">Comparison of Calibration of Classifiers</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Comparison of Calibration of Classifiers</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This example illustrates how sigmoid calibration changes predicted probabilities for a 3-class classification problem. Illustrated is the standard 2-simplex, where the three corners correspond to the three classes. Arrows point from the probability vectors predicted by an uncalibrated classifier to the probability vectors predicted by the same classifier after sigmoid calibration on a hold-out validation set. Colors indicate the true class of an instance (red: class 1, green: class 2, blue: class 3)."><img alt="" src="http://github.com/_images/sphx_glr_plot_calibration_multiclass_thumb.png" />
-<p><a class="reference internal" href="http://github.com/calibration/plot_calibration_multiclass.html#sphx-glr-auto-examples-calibration-plot-calibration-multiclass-py"><span class="std std-ref">Probability Calibration for 3-class classification</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Probability Calibration for 3-class classification</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="When performing classification you often want to predict not only the class label, but also the associated probability. This probability gives you some kind of confidence on the prediction. However, not all classifiers provide well-calibrated probabilities, some being over-confident while others being under-confident. Thus, a separate calibration of predicted probabilities is often desirable as a postprocessing. This example illustrates two different methods for this calibration and evaluates the quality of the returned probabilities using Brier&#x27;s score (see https://en.wikipedia.org/wiki/Brier_score)."><img alt="" src="http://github.com/_images/sphx_glr_plot_calibration_thumb.png" />
-<p><a class="reference internal" href="http://github.com/calibration/plot_calibration.html#sphx-glr-auto-examples-calibration-plot-calibration-py"><span class="std std-ref">Probability calibration of classifiers</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Probability calibration of classifiers</div>
-</div></div><p class="sphx-glr-signature"><a class="reference external" href="https://sphinx-gallery.github.io/">Gallery generated by Sphinx-Gallery</a></p>
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+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
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@@ -1036,7 +1035,7 @@
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diff --git a/dev/auto_examples/feature_selection/plot_feature_selection.html b/dev/auto_examples/feature_selection/plot_feature_selection.html
index b08dc0cadc887..811fac7e7ead0 100644
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@@ -679,13 +679,12 @@
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
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@@ -902,9 +901,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
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 </details></li>
@@ -1121,7 +1120,7 @@ <h2>Compare with SVMs<a class="headerlink" href="#compare-with-svms" title="Link
 non-informative features. Applying univariate feature selection before
 the SVM increases the SVM weight attributed to the significant features,
 and will thus improve classification.</p>
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 </div>
 <div class="output_subarea output_html rendered_html output_result">
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@@ -1097,7 +1096,7 @@
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-#sk-container-id-28 div.sk-container {
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 /* Serial-specific style estimator block */
 
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 /* Pipeline and ColumnTransformer style (default) */
 
-#sk-container-id-28 div.sk-toggleable {
+#sk-container-id-29 div.sk-toggleable {
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@@ -1211,13 +1210,13 @@
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 }
 
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 }
 
 /* Toggleable content - dropdown */
 
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@@ -1226,12 +1225,12 @@
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 }
 
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 }
 
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@@ -1239,79 +1238,79 @@
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 }
 
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   overflow: auto;
 }
 
-#sk-container-id-28 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
+#sk-container-id-29 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
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 }
 
 /* Pipeline/ColumnTransformer-specific style */
 
-#sk-container-id-28 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-29 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
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 }
 
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 }
 
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 /* Colorize estimator box */
-#sk-container-id-28 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-29 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
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 }
 
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 }
 
-#sk-container-id-28 div.sk-label label.sk-toggleable__label,
-#sk-container-id-28 div.sk-label label {
+#sk-container-id-29 div.sk-label label.sk-toggleable__label,
+#sk-container-id-29 div.sk-label label {
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 }
 
 /* On hover, darken the color of the background */
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+#sk-container-id-29 div.sk-label:hover label.sk-toggleable__label {
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 }
 
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   background-color: var(--sklearn-color-fitted-level-2);
 }
 
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-#sk-container-id-28 div.sk-label label {
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 }
 
-#sk-container-id-28 div.sk-label-container {
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 }
 
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+#sk-container-id-29 div.sk-estimator {
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@@ -1321,18 +1320,18 @@
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+#sk-container-id-29 div.sk-estimator.fitted {
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 }
 
 /* on hover */
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+#sk-container-id-29 div.sk-estimator:hover {
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 }
 
-#sk-container-id-28 div.sk-estimator.fitted:hover {
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-</style><div id="sk-container-id-28" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;selectkbest&#x27;, SelectKBest(k=3)), (&#x27;linearsvc&#x27;, LinearSVC())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-108" type="checkbox" ><label for="sk-estimator-id-108" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>Pipeline</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.pipeline.Pipeline.html">?<span>Documentation for Pipeline</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>Pipeline(steps=[(&#x27;selectkbest&#x27;, SelectKBest(k=3)), (&#x27;linearsvc&#x27;, LinearSVC())])</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-109" type="checkbox" ><label for="sk-estimator-id-109" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>SelectKBest</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.feature_selection.SelectKBest.html">?<span>Documentation for SelectKBest</span></a></div></label><div class="sk-toggleable__content fitted"><pre>SelectKBest(k=3)</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-110" type="checkbox" ><label for="sk-estimator-id-110" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>LinearSVC</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.svm.LinearSVC.html">?<span>Documentation for LinearSVC</span></a></div></label><div class="sk-toggleable__content fitted"><pre>LinearSVC()</pre></div> </div></div></div></div></div></div>
+</style><div id="sk-container-id-29" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;selectkbest&#x27;, SelectKBest(k=3)), (&#x27;linearsvc&#x27;, LinearSVC())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-118" type="checkbox" ><label for="sk-estimator-id-118" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>Pipeline</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.pipeline.Pipeline.html">?<span>Documentation for Pipeline</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>Pipeline(steps=[(&#x27;selectkbest&#x27;, SelectKBest(k=3)), (&#x27;linearsvc&#x27;, LinearSVC())])</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-119" type="checkbox" ><label for="sk-estimator-id-119" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>SelectKBest</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.feature_selection.SelectKBest.html">?<span>Documentation for SelectKBest</span></a></div></label><div class="sk-toggleable__content fitted"><pre>SelectKBest(k=3)</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-120" type="checkbox" ><label for="sk-estimator-id-120" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>LinearSVC</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.svm.LinearSVC.html">?<span>Documentation for LinearSVC</span></a></div></label><div class="sk-toggleable__content fitted"><pre>LinearSVC()</pre></div> </div></div></div></div></div></div>
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 <br />
 <br /><p>Once the training is complete, we can predict on new unseen samples. In this
@@ -1501,7 +1500,7 @@
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 features by the first step.</p>
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+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="A Recursive Feature Elimination (RFE) example with automatic tuning of the number of features selected with cross-validation."><img alt="" src="http://github.com/_images/sphx_glr_plot_rfe_with_cross_validation_thumb.png" />
+<p><a class="reference internal" href="http://github.com/plot_rfe_with_cross_validation.html#sphx-glr-auto-examples-feature-selection-plot-rfe-with-cross-validation-py"><span class="std std-ref">Recursive feature elimination with cross-validation</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Recursive feature elimination with cross-validation</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example illustrates and compares two approaches for feature selection: SelectFromModel which is based on feature importance, and SequentialFeatureSelector which relies on a greedy approach."><img alt="" src="http://github.com/_images/sphx_glr_plot_select_from_model_diabetes_thumb.png" />
 <p><a class="reference internal" href="http://github.com/plot_select_from_model_diabetes.html#sphx-glr-auto-examples-feature-selection-plot-select-from-model-diabetes-py"><span class="std std-ref">Model-based and sequential feature selection</span></a></p>
   <div class="sphx-glr-thumbnail-title">Model-based and sequential feature selection</div>
 </div><div class="sphx-glr-thumbcontainer" tooltip="This examples shows how a classifier is optimized by cross-validation, which is done using the GridSearchCV object on a development set that comprises only half of the available labeled data."><img alt="" src="http://github.com/_images/sphx_glr_plot_grid_search_digits_thumb.png" />
 <p><a class="reference internal" href="http://github.com/model_selection/plot_grid_search_digits.html#sphx-glr-auto-examples-model-selection-plot-grid-search-digits-py"><span class="std std-ref">Custom refit strategy of a grid search with cross-validation</span></a></p>
   <div class="sphx-glr-thumbnail-title">Custom refit strategy of a grid search with cross-validation</div>
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-<p><a class="reference internal" href="http://github.com/plot_rfe_with_cross_validation.html#sphx-glr-auto-examples-feature-selection-plot-rfe-with-cross-validation-py"><span class="std std-ref">Recursive feature elimination with cross-validation</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Recursive feature elimination with cross-validation</div>
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 <p><a class="reference internal" href="http://github.com/plot_feature_selection.html#sphx-glr-auto-examples-feature-selection-plot-feature-selection-py"><span class="std std-ref">Univariate Feature Selection</span></a></p>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
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@@ -902,9 +901,9 @@
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -902,9 +901,9 @@
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
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 </details></li>
@@ -1008,9 +1007,12 @@ <h2>Data generation<a class="headerlink" href="#data-generation" title="Link to
 features are non-informative as they are drawn at random.</p>
 <div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.datasets</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.datasets.make_classification.html#sklearn.datasets.make_classification" title="sklearn.datasets.make_classification" class="sphx-glr-backref-module-sklearn-datasets sphx-glr-backref-type-py-function"><span class="n">make_classification</span></a>
 
+<span class="n">n_features</span> <span class="o">=</span> <span class="mi">15</span>
+<span class="n">feat_names</span> <span class="o">=</span> <span class="p">[</span><span class="sa">f</span><span class="s2">&quot;feature_</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2">&quot;</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">15</span><span class="p">)]</span>
+
 <span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <a href="http://github.com/modules/generated/sklearn.datasets.make_classification.html#sklearn.datasets.make_classification" title="sklearn.datasets.make_classification" class="sphx-glr-backref-module-sklearn-datasets sphx-glr-backref-type-py-function"><span class="n">make_classification</span></a><span class="p">(</span>
     <span class="n">n_samples</span><span class="o">=</span><span class="mi">500</span><span class="p">,</span>
-    <span class="n">n_features</span><span class="o">=</span><span class="mi">15</span><span class="p">,</span>
+    <span class="n">n_features</span><span class="o">=</span><span class="n">n_features</span><span class="p">,</span>
     <span class="n">n_informative</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span>
     <span class="n">n_redundant</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
     <span class="n">n_repeated</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
@@ -1058,7 +1060,12 @@ <h2>Plot number of features VS. cross-validation scores<a class="headerlink" hre
 <div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">matplotlib.pyplot</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">plt</span>
 <span class="kn">import</span><span class="w"> </span><span class="nn">pandas</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pd</span>
 
-<span class="n">cv_results</span> <span class="o">=</span> <a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="pandas.DataFrame" class="sphx-glr-backref-module-pandas sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span></a><span class="p">(</span><span class="n">rfecv</span><span class="o">.</span><span class="n">cv_results_</span><span class="p">)</span>
+<span class="n">data</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="n">key</span><span class="p">:</span> <span class="n">value</span>
+    <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">rfecv</span><span class="o">.</span><span class="n">cv_results_</span><span class="o">.</span><span class="n">items</span><span class="p">()</span>
+    <span class="k">if</span> <span class="n">key</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;n_features&quot;</span><span class="p">,</span> <span class="s2">&quot;mean_test_score&quot;</span><span class="p">,</span> <span class="s2">&quot;std_test_score&quot;</span><span class="p">]</span>
+<span class="p">}</span>
+<span class="n">cv_results</span> <span class="o">=</span> <a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="pandas.DataFrame" class="sphx-glr-backref-module-pandas sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span></a><span class="p">(</span><span class="n">data</span><span class="p">)</span>
 <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.figure.html#matplotlib.pyplot.figure" title="matplotlib.pyplot.figure" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">figure</span></a><span class="p">()</span>
 <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.xlabel.html#matplotlib.pyplot.xlabel" title="matplotlib.pyplot.xlabel" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span></a><span class="p">(</span><span class="s2">&quot;Number of features selected&quot;</span><span class="p">)</span>
 <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.ylabel.html#matplotlib.pyplot.ylabel" title="matplotlib.pyplot.ylabel" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span></a><span class="p">(</span><span class="s2">&quot;Mean test accuracy&quot;</span><span class="p">)</span>
@@ -1078,7 +1085,27 @@ <h2>Plot number of features VS. cross-validation scores<a class="headerlink" hre
 cross-validation technique. The test accuracy decreases above 5 selected
 features, this is, keeping non-informative features leads to over-fitting and
 is therefore detrimental for the statistical performance of the models.</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 0.674 seconds)</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
+
+<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">cv</span><span class="o">.</span><span class="n">n_splits</span><span class="p">):</span>
+    <span class="n">mask</span> <span class="o">=</span> <span class="n">rfecv</span><span class="o">.</span><span class="n">cv_results_</span><span class="p">[</span><span class="sa">f</span><span class="s2">&quot;split</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2">_support&quot;</span><span class="p">][</span>
+        <span class="n">rfecv</span><span class="o">.</span><span class="n">n_features_</span>
+    <span class="p">]</span>  <span class="c1"># mask of features selected by the RFE</span>
+    <span class="n">features_selected</span> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ma.compressed.html#numpy.ma.compressed" title="numpy.ma.compressed" class="sphx-glr-backref-module-numpy-ma sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">ma</span><span class="o">.</span><span class="n">compressed</span></a><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ma.masked_array.html#numpy.ma.masked_array" title="numpy.ma.masked_array" class="sphx-glr-backref-module-numpy-ma sphx-glr-backref-type-py-attribute"><span class="n">np</span><span class="o">.</span><span class="n">ma</span><span class="o">.</span><span class="n">masked_array</span></a><span class="p">(</span><span class="n">feat_names</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="mi">1</span> <span class="o">-</span> <span class="n">mask</span><span class="p">))</span>
+    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Features selected in fold </span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2">: </span><span class="si">{</span><span class="n">features_selected</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Features selected in fold 0: [&#39;feature_3&#39; &#39;feature_4&#39; &#39;feature_8&#39; &#39;feature_14&#39;]
+Features selected in fold 1: [&#39;feature_3&#39; &#39;feature_4&#39; &#39;feature_8&#39; &#39;feature_14&#39;]
+Features selected in fold 2: [&#39;feature_3&#39; &#39;feature_4&#39; &#39;feature_8&#39; &#39;feature_14&#39;]
+Features selected in fold 3: [&#39;feature_3&#39; &#39;feature_4&#39; &#39;feature_8&#39; &#39;feature_14&#39;]
+Features selected in fold 4: [&#39;feature_3&#39; &#39;feature_4&#39; &#39;feature_8&#39; &#39;feature_14&#39;]
+</pre></div>
+</div>
+<p>In the five folds, the selected features are consistent. This is good news,
+it means that the selection is stable across folds, and it confirms that
+these features are the most informative ones.</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 0.838 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-feature-selection-plot-rfe-with-cross-validation-py">
 <div class="binder-badge docutils container">
 <a class="reference external image-reference" href="https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/feature_selection/plot_rfe_with_cross_validation.ipynb"><img alt="Launch binder" src="http://github.com/_images/binder_badge_logo12.svg" style="width: 150px;" />
@@ -1102,12 +1129,12 @@ <h2>Plot number of features VS. cross-validation scores<a class="headerlink" hre
 <div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This example balances model complexity and cross-validated score by finding a decent accuracy within 1 standard deviation of the best accuracy score while minimising the number of PCA components [1]."><img alt="" src="http://github.com/_images/sphx_glr_plot_grid_search_refit_callable_thumb.png" />
 <p><a class="reference internal" href="http://github.com/model_selection/plot_grid_search_refit_callable.html#sphx-glr-auto-examples-model-selection-plot-grid-search-refit-callable-py"><span class="std std-ref">Balance model complexity and cross-validated score</span></a></p>
   <div class="sphx-glr-thumbnail-title">Balance model complexity and cross-validated score</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This examples shows how a classifier is optimized by cross-validation, which is done using the GridSearchCV object on a development set that comprises only half of the available labeled data."><img alt="" src="http://github.com/_images/sphx_glr_plot_grid_search_digits_thumb.png" />
-<p><a class="reference internal" href="http://github.com/model_selection/plot_grid_search_digits.html#sphx-glr-auto-examples-model-selection-plot-grid-search-digits-py"><span class="std std-ref">Custom refit strategy of a grid search with cross-validation</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Custom refit strategy of a grid search with cross-validation</div>
 </div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how a feature selection can be easily integrated within a machine learning pipeline."><img alt="" src="http://github.com/_images/sphx_glr_plot_feature_selection_pipeline_thumb.png" />
 <p><a class="reference internal" href="http://github.com/plot_feature_selection_pipeline.html#sphx-glr-auto-examples-feature-selection-plot-feature-selection-pipeline-py"><span class="std std-ref">Pipeline ANOVA SVM</span></a></p>
   <div class="sphx-glr-thumbnail-title">Pipeline ANOVA SVM</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This examples shows how a classifier is optimized by cross-validation, which is done using the GridSearchCV object on a development set that comprises only half of the available labeled data."><img alt="" src="http://github.com/_images/sphx_glr_plot_grid_search_digits_thumb.png" />
+<p><a class="reference internal" href="http://github.com/model_selection/plot_grid_search_digits.html#sphx-glr-auto-examples-model-selection-plot-grid-search-digits-py"><span class="std std-ref">Custom refit strategy of a grid search with cross-validation</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Custom refit strategy of a grid search with cross-validation</div>
 </div><div class="sphx-glr-thumbcontainer" tooltip="Once a binary classifier is trained, the predict method outputs class label predictions corresponding to a thresholding of either the decision_function or the predict_proba output. The default threshold is defined as a posterior probability estimate of 0.5 or a decision score of 0.0. However, this default strategy may not be optimal for the task at hand."><img alt="" src="http://github.com/_images/sphx_glr_plot_tuned_decision_threshold_thumb.png" />
 <p><a class="reference internal" href="http://github.com/model_selection/plot_tuned_decision_threshold.html#sphx-glr-auto-examples-model-selection-plot-tuned-decision-threshold-py"><span class="std std-ref">Post-hoc tuning the cut-off point of decision function</span></a></p>
   <div class="sphx-glr-thumbnail-title">Post-hoc tuning the cut-off point of decision function</div>
diff --git a/dev/auto_examples/feature_selection/plot_select_from_model_diabetes.html b/dev/auto_examples/feature_selection/plot_select_from_model_diabetes.html
index 2152747c220c6..6a9980fbcde1a 100644
--- a/dev/auto_examples/feature_selection/plot_select_from_model_diabetes.html
+++ b/dev/auto_examples/feature_selection/plot_select_from_model_diabetes.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -679,13 +679,12 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -902,9 +901,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
@@ -1105,7 +1104,7 @@ <h2>Selecting features based on importance<a class="headerlink" href="#selecting
 </pre></div>
 </div>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Features selected by SelectFromModel: [&#39;s1&#39; &#39;s5&#39;]
-Done in 0.002s
+Done in 0.003s
 </pre></div>
 </div>
 </section>
@@ -1148,9 +1147,9 @@ <h2>Selecting features with Sequential Feature Selection<a class="headerlink" hr
 </pre></div>
 </div>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Features selected by forward sequential selection: [&#39;bmi&#39; &#39;s5&#39;]
-Done in 0.197s
+Done in 0.270s
 Features selected by backward sequential selection: [&#39;bmi&#39; &#39;s5&#39;]
-Done in 0.561s
+Done in 0.709s
 </pre></div>
 </div>
 <p>Interestingly, forward and backward selection have selected the same set of
@@ -1340,13 +1339,13 @@ <h2>Using negative tolerance values<a class="headerlink" href="#using-negative-t
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>tol: -0.01
 Features selected: [&#39;worst perimeter&#39;]
 ROC AUC score: 0.975
-Done in 17.527s
+Done in 22.408s
 
 tol: -0.001
 Features selected: [&#39;radius error&#39; &#39;fractal dimension error&#39; &#39;worst texture&#39;
  &#39;worst perimeter&#39; &#39;worst concave points&#39;]
 ROC AUC score: 0.997
-Done in 17.086s
+Done in 19.988s
 
 tol: -0.0001
 Features selected: [&#39;mean compactness&#39; &#39;mean concavity&#39; &#39;mean concave points&#39; &#39;radius error&#39;
@@ -1354,13 +1353,13 @@ <h2>Using negative tolerance values<a class="headerlink" href="#using-negative-t
  &#39;fractal dimension error&#39; &#39;worst texture&#39; &#39;worst perimeter&#39; &#39;worst area&#39;
  &#39;worst concave points&#39; &#39;worst symmetry&#39;]
 ROC AUC score: 0.998
-Done in 14.632s
+Done in 17.049s
 </pre></div>
 </div>
 <p>We can see that the number of features selected tend to increase as negative
 values of <code class="docutils literal notranslate"><span class="pre">tol</span></code> approach to zero. The time taken for feature selection also
 decreases as the values of <code class="docutils literal notranslate"><span class="pre">tol</span></code> come closer to zero.</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 50.137 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (1 minutes 0.575 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-feature-selection-plot-select-from-model-diabetes-py">
 <div class="binder-badge docutils container">
 <a class="reference external image-reference" href="https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/feature_selection/plot_select_from_model_diabetes.ipynb"><img alt="Launch binder" src="http://github.com/_images/binder_badge_logo12.svg" style="width: 150px;" />
diff --git a/dev/auto_examples/frozen/index.html b/dev/auto_examples/frozen/index.html
index 86f73383b8548..3ad4370849d1a 100644
--- a/dev/auto_examples/frozen/index.html
+++ b/dev/auto_examples/frozen/index.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
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-<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="We are pleased to announce the release of scikit-learn 1.5! Many bug fixes and improvements were added, as well as some key new features. Below we detail the highlights of this release. For an exhaustive list of all the changes, please refer to the release notes &lt;release_notes_1_5&gt;."><img alt="" src="http://github.com/_images/sphx_glr_plot_release_highlights_1_5_0_thumb.png" />
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+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="When performing classification you often want to predict not only the class label, but also the associated probability. This probability gives you some kind of confidence on the prediction. However, not all classifiers provide well-calibrated probabilities, some being over-confident while others being under-confident. Thus, a separate calibration of predicted probabilities is often desirable as a postprocessing. This example illustrates two different methods for this calibration and evaluates the quality of the returned probabilities using Brier&#x27;s score (see https://en.wikipedia.org/wiki/Brier_score)."><img alt="" src="http://github.com/_images/sphx_glr_plot_calibration_thumb.png" />
 <p><a class="reference internal" href="http://github.com/calibration/plot_calibration.html#sphx-glr-auto-examples-calibration-plot-calibration-py"><span class="std std-ref">Probability calibration of classifiers</span></a></p>
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-  <div class="sphx-glr-thumbnail-title">Post-hoc tuning the cut-off point of decision function</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="We are pleased to announce the release of scikit-learn 1.5! Many bug fixes and improvements were added, as well as some key new features. Below we detail the highlights of this release. For an exhaustive list of all the changes, please refer to the release notes &lt;release_notes_1_5&gt;."><img alt="" src="http://github.com/_images/sphx_glr_plot_release_highlights_1_5_0_thumb.png" />
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+  <div class="sphx-glr-thumbnail-title">Release Highlights for scikit-learn 1.5</div>
 </div><div class="sphx-glr-thumbcontainer" tooltip="When performing classification one often wants to predict not only the class label, but also the associated probability. This probability gives some kind of confidence on the prediction. This example demonstrates how to visualize how well calibrated the predicted probabilities are using calibration curves, also known as reliability diagrams. Calibration of an uncalibrated classifier will also be demonstrated."><img alt="" src="http://github.com/_images/sphx_glr_plot_calibration_curve_thumb.png" />
 <p><a class="reference internal" href="http://github.com/calibration/plot_calibration_curve.html#sphx-glr-auto-examples-calibration-plot-calibration-curve-py"><span class="std std-ref">Probability Calibration curves</span></a></p>
   <div class="sphx-glr-thumbnail-title">Probability Calibration curves</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="Once a binary classifier is trained, the predict method outputs class label predictions corresponding to a thresholding of either the decision_function or the predict_proba output. The default threshold is defined as a posterior probability estimate of 0.5 or a decision score of 0.0. However, this default strategy may not be optimal for the task at hand."><img alt="" src="http://github.com/_images/sphx_glr_plot_tuned_decision_threshold_thumb.png" />
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+  <div class="sphx-glr-thumbnail-title">Post-hoc tuning the cut-off point of decision function</div>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
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 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -903,9 +902,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
@@ -1181,7 +1180,7 @@ <h3>Kernel ridge<a class="headerlink" href="#kernel-ridge" title="Link to this h
 <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Time for KernelRidge fitting: </span><span class="si">{</span><a href="https://docs.python.org/3/library/time.html#time.time" title="time.time" class="sphx-glr-backref-module-time sphx-glr-backref-type-py-function"><span class="n">time</span><span class="o">.</span><span class="n">time</span></a><span class="p">()</span><span class="w"> </span><span class="o">-</span><span class="w"> </span><span class="n">start_time</span><span class="si">:</span><span class="s2">.3f</span><span class="si">}</span><span class="s2"> seconds&quot;</span><span class="p">)</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Time for KernelRidge fitting: 3.685 seconds
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Time for KernelRidge fitting: 3.839 seconds
 </pre></div>
 </div>
 <p>Fitting the model is now more computationally expensive since we have to try
@@ -1254,7 +1253,7 @@ <h3>Gaussian process regression<a class="headerlink" href="#gaussian-process-reg
 <span class="p">)</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Time for GaussianProcessRegressor fitting: 0.028 seconds
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Time for GaussianProcessRegressor fitting: 0.031 seconds
 </pre></div>
 </div>
 <p>The computation cost of training a Gaussian process is much less than the
@@ -1394,7 +1393,7 @@ <h2>Final conclusion<a class="headerlink" href="#final-conclusion" title="Link t
 As testing samples get further away from the training ones, predictions
 are converging towards their mean and their standard deviation
 also increases.</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 4.285 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 4.481 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-gaussian-process-plot-compare-gpr-krr-py">
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diff --git a/dev/auto_examples/gaussian_process/plot_gpc.html b/dev/auto_examples/gaussian_process/plot_gpc.html
index dd95565d74266..c6118d6361ea0 100644
--- a/dev/auto_examples/gaussian_process/plot_gpc.html
+++ b/dev/auto_examples/gaussian_process/plot_gpc.html
@@ -42,7 +42,7 @@
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-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
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@@ -679,13 +679,12 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -902,9 +901,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
@@ -1126,7 +1125,7 @@
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 </pre></div>
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-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 1.891 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 2.138 seconds)</p>
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diff --git a/dev/auto_examples/gaussian_process/plot_gpc_iris.html b/dev/auto_examples/gaussian_process/plot_gpc_iris.html
index f71a2db54e531..39b4e0f5c643e 100644
--- a/dev/auto_examples/gaussian_process/plot_gpc_iris.html
+++ b/dev/auto_examples/gaussian_process/plot_gpc_iris.html
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-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
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@@ -679,13 +679,12 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -902,9 +901,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
@@ -1054,7 +1053,7 @@
 <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
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-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 2.348 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 5.657 seconds)</p>
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diff --git a/dev/auto_examples/gaussian_process/plot_gpc_isoprobability.html b/dev/auto_examples/gaussian_process/plot_gpc_isoprobability.html
index f030dceb7a82a..a68f46175de77 100644
--- a/dev/auto_examples/gaussian_process/plot_gpc_isoprobability.html
+++ b/dev/auto_examples/gaussian_process/plot_gpc_isoprobability.html
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+<meta property="og:description" content="A two-dimensional classification example showing iso-probability lines for the predicted probabilities., Total running time of the script:(0 minutes 0.152 seconds) Launch binder Launch JupyterLite ..." />
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-<meta name="description" content="A two-dimensional classification example showing iso-probability lines for the predicted probabilities., Total running time of the script:(0 minutes 0.126 seconds) Launch binder Launch JupyterLite ..." />
+<meta name="description" content="A two-dimensional classification example showing iso-probability lines for the predicted probabilities., Total running time of the script:(0 minutes 0.152 seconds) Launch binder Launch JupyterLite ..." />
 
     <title>Iso-probability lines for Gaussian Processes classification (GPC) &#8212; scikit-learn 1.7.dev0 documentation</title>
   
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-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
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@@ -679,13 +679,12 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
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-#sk-container-id-30 div.sk-label label.sk-toggleable__label,
-#sk-container-id-30 div.sk-label label {
+#sk-container-id-31 div.sk-label label.sk-toggleable__label,
+#sk-container-id-31 div.sk-label label {
   /* The background is the default theme color */
   color: var(--sklearn-color-text-on-default-background);
 }
 
 /* On hover, darken the color of the background */
-#sk-container-id-30 div.sk-label:hover label.sk-toggleable__label {
+#sk-container-id-31 div.sk-label:hover label.sk-toggleable__label {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
 /* Label box, darken color on hover, fitted */
-#sk-container-id-30 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
+#sk-container-id-31 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
 /* Estimator label */
 
-#sk-container-id-30 div.sk-label label {
+#sk-container-id-31 div.sk-label label {
   font-family: monospace;
   font-weight: bold;
   display: inline-block;
   line-height: 1.2em;
 }
 
-#sk-container-id-30 div.sk-label-container {
+#sk-container-id-31 div.sk-label-container {
   text-align: center;
 }
 
 /* Estimator-specific */
-#sk-container-id-30 div.sk-estimator {
+#sk-container-id-31 div.sk-estimator {
   font-family: monospace;
   border: 1px dotted var(--sklearn-color-border-box);
   border-radius: 0.25em;
@@ -1546,18 +1545,18 @@ <h2>Model fitting and extrapolation<a class="headerlink" href="#model-fitting-an
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-30 div.sk-estimator.fitted {
+#sk-container-id-31 div.sk-estimator.fitted {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
 /* on hover */
-#sk-container-id-30 div.sk-estimator:hover {
+#sk-container-id-31 div.sk-estimator:hover {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-30 div.sk-estimator.fitted:hover {
+#sk-container-id-31 div.sk-estimator.fitted:hover {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-2);
 }
@@ -1645,7 +1644,7 @@ <h2>Model fitting and extrapolation<a class="headerlink" href="#model-fitting-an
 
 /* "?"-specific style due to the `<a>` HTML tag */
 
-#sk-container-id-30 a.estimator_doc_link {
+#sk-container-id-31 a.estimator_doc_link {
   float: right;
   font-size: 1rem;
   line-height: 1em;
@@ -1660,25 +1659,25 @@ <h2>Model fitting and extrapolation<a class="headerlink" href="#model-fitting-an
   border: var(--sklearn-color-unfitted-level-1) 1pt solid;
 }
 
-#sk-container-id-30 a.estimator_doc_link.fitted {
+#sk-container-id-31 a.estimator_doc_link.fitted {
   /* fitted */
   border: var(--sklearn-color-fitted-level-1) 1pt solid;
   color: var(--sklearn-color-fitted-level-1);
 }
 
 /* On hover */
-#sk-container-id-30 a.estimator_doc_link:hover {
+#sk-container-id-31 a.estimator_doc_link:hover {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-3);
   color: var(--sklearn-color-background);
   text-decoration: none;
 }
 
-#sk-container-id-30 a.estimator_doc_link.fitted:hover {
+#sk-container-id-31 a.estimator_doc_link.fitted:hover {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-3);
 }
-</style><div id="sk-container-id-30" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>GaussianProcessRegressor(kernel=50**2 * RBF(length_scale=50) + 2**2 * RBF(length_scale=100) * ExpSineSquared(length_scale=1, periodicity=1) + 0.5**2 * RationalQuadratic(alpha=1, length_scale=1) + 0.1**2 * RBF(length_scale=0.1) + WhiteKernel(noise_level=0.01))</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-114" type="checkbox" checked><label for="sk-estimator-id-114" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>GaussianProcessRegressor</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.gaussian_process.GaussianProcessRegressor.html">?<span>Documentation for GaussianProcessRegressor</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>GaussianProcessRegressor(kernel=50**2 * RBF(length_scale=50) + 2**2 * RBF(length_scale=100) * ExpSineSquared(length_scale=1, periodicity=1) + 0.5**2 * RationalQuadratic(alpha=1, length_scale=1) + 0.1**2 * RBF(length_scale=0.1) + WhiteKernel(noise_level=0.01))</pre></div> </div></div></div></div>
+</style><div id="sk-container-id-31" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>GaussianProcessRegressor(kernel=50**2 * RBF(length_scale=50) + 2**2 * RBF(length_scale=100) * ExpSineSquared(length_scale=1, periodicity=1) + 0.5**2 * RationalQuadratic(alpha=1, length_scale=1) + 0.1**2 * RBF(length_scale=0.1) + WhiteKernel(noise_level=0.01))</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-124" type="checkbox" checked><label for="sk-estimator-id-124" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>GaussianProcessRegressor</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.gaussian_process.GaussianProcessRegressor.html">?<span>Documentation for GaussianProcessRegressor</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>GaussianProcessRegressor(kernel=50**2 * RBF(length_scale=50) + 2**2 * RBF(length_scale=100) * ExpSineSquared(length_scale=1, periodicity=1) + 0.5**2 * RationalQuadratic(alpha=1, length_scale=1) + 0.1**2 * RBF(length_scale=0.1) + WhiteKernel(noise_level=0.01))</pre></div> </div></div></div></div>
 </div>
 <br />
 <br /><p>Now, we will use the Gaussian process to predict on:</p>
@@ -1736,7 +1735,7 @@ <h2>Interpretation of kernel hyperparameters<a class="headerlink" href="#interpr
 amplitude of ~0.2 ppm with a length scale of ~0.12 years and a white-noise
 contribution of ~0.04 ppm. Thus, the overall noise level is very small,
 indicating that the data can be very well explained by the model.</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 3.924 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 4.609 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-gaussian-process-plot-gpr-co2-py">
 <div class="binder-badge docutils container">
 <a class="reference external image-reference" href="https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/gaussian_process/plot_gpr_co2.ipynb"><img alt="Launch binder" src="http://github.com/_images/binder_badge_logo14.svg" style="width: 150px;" />
diff --git a/dev/auto_examples/gaussian_process/plot_gpr_noisy.html b/dev/auto_examples/gaussian_process/plot_gpr_noisy.html
index 4b2318a6e75f4..e29f0ee34d51a 100644
--- a/dev/auto_examples/gaussian_process/plot_gpr_noisy.html
+++ b/dev/auto_examples/gaussian_process/plot_gpr_noisy.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
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@@ -679,13 +679,12 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -902,9 +901,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
@@ -1208,7 +1207,7 @@ <h2>Optimisation of kernel hyperparameters in GPR<a class="headerlink" href="#op
 </div>
 <img src="http://github.com/_images/sphx_glr_plot_gpr_noisy_006.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_gpr_noisy_006.png " alt="Initial: 1**2 * RBF(length_scale=10) + WhiteKernel(noise_level=1) Optimum: 1.05**2 * RBF(length_scale=0.569) + WhiteKernel(noise_level=0.134) Log-Marginal-Likelihood: -18.429732528970845" class = "sphx-glr-single-img"/><p>As we hoped, random restarts allow the optimization to find the best set
 of hyperparameters despite the bad initial values.</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 5.434 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 5.948 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-gaussian-process-plot-gpr-noisy-py">
 <div class="binder-badge docutils container">
 <a class="reference external image-reference" href="https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/gaussian_process/plot_gpr_noisy.ipynb"><img alt="Launch binder" src="http://github.com/_images/binder_badge_logo14.svg" style="width: 150px;" />
diff --git a/dev/auto_examples/gaussian_process/plot_gpr_noisy_targets.html b/dev/auto_examples/gaussian_process/plot_gpr_noisy_targets.html
index 81676cd9d2b25..47b5bddcbc00b 100644
--- a/dev/auto_examples/gaussian_process/plot_gpr_noisy_targets.html
+++ b/dev/auto_examples/gaussian_process/plot_gpr_noisy_targets.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
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@@ -680,13 +680,12 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -903,9 +902,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
@@ -1135,7 +1134,7 @@ <h2>Example with noisy targets<a class="headerlink" href="#example-with-noisy-ta
 predictive uncertainty near to the training samples is larger because we
 explicitly model a given level target noise independent of the input
 variable.</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 0.458 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 0.551 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-gaussian-process-plot-gpr-noisy-targets-py">
 <div class="binder-badge docutils container">
 <a class="reference external image-reference" href="https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/gaussian_process/plot_gpr_noisy_targets.ipynb"><img alt="Launch binder" src="http://github.com/_images/binder_badge_logo14.svg" style="width: 150px;" />
diff --git a/dev/auto_examples/gaussian_process/plot_gpr_on_structured_data.html b/dev/auto_examples/gaussian_process/plot_gpr_on_structured_data.html
index 9ff1a0325bf5f..8487db89b1dbd 100644
--- a/dev/auto_examples/gaussian_process/plot_gpr_on_structured_data.html
+++ b/dev/auto_examples/gaussian_process/plot_gpr_on_structured_data.html
@@ -42,7 +42,7 @@
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-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
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@@ -679,13 +679,12 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -902,9 +901,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
@@ -1180,7 +1179,7 @@ <h2>Classification<a class="headerlink" href="#classification" title="Link to th
 The optimal value found for dimension 0 of parameter baseline_similarity is close to the specified lower bound 1e-05. Decreasing the bound and calling fit again may find a better value.
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
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 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -902,9 +901,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
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 </details></li>
@@ -1280,7 +1279,7 @@ <h3>Matérn kernel<a class="headerlink" href="#matern-kernel" title="Link to thi
 Log-likelihood: -1.185
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
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 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
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diff --git a/dev/auto_examples/impute/plot_iterative_imputer_variants_comparison.html b/dev/auto_examples/impute/plot_iterative_imputer_variants_comparison.html
index be650a7bb580f..c0f6a28b1037e 100644
--- a/dev/auto_examples/impute/plot_iterative_imputer_variants_comparison.html
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -902,9 +901,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
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 <span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.ensemble</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.ensemble.RandomForestRegressor.html#sklearn.ensemble.RandomForestRegressor" title="sklearn.ensemble.RandomForestRegressor" class="sphx-glr-backref-module-sklearn-ensemble sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">RandomForestRegressor</span></a>
 
 <span class="c1"># To use this experimental feature, we need to explicitly ask for it:</span>
-<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.experimental</span><span class="w"> </span><span class="kn">import</span> <span class="n">enable_iterative_imputer</span>  <span class="c1"># noqa</span>
+<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.experimental</span><span class="w"> </span><span class="kn">import</span> <span class="n">enable_iterative_imputer</span>  <span class="c1"># noqa: F401</span>
 <span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.impute</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.impute.IterativeImputer.html#sklearn.impute.IterativeImputer" title="sklearn.impute.IterativeImputer" class="sphx-glr-backref-module-sklearn-impute sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">IterativeImputer</span></a><span class="p">,</span> <a href="http://github.com/modules/generated/sklearn.impute.SimpleImputer.html#sklearn.impute.SimpleImputer" title="sklearn.impute.SimpleImputer" class="sphx-glr-backref-module-sklearn-impute sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">SimpleImputer</span></a>
 <span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.kernel_approximation</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.kernel_approximation.Nystroem.html#sklearn.kernel_approximation.Nystroem" title="sklearn.kernel_approximation.Nystroem" class="sphx-glr-backref-module-sklearn-kernel_approximation sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">Nystroem</span></a>
 <span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.linear_model</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.linear_model.BayesianRidge.html#sklearn.linear_model.BayesianRidge" title="sklearn.linear_model.BayesianRidge" class="sphx-glr-backref-module-sklearn-linear_model sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">BayesianRidge</span></a><span class="p">,</span> <a href="http://github.com/modules/generated/sklearn.linear_model.Ridge.html#sklearn.linear_model.Ridge" title="sklearn.linear_model.Ridge" class="sphx-glr-backref-module-sklearn-linear_model sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">Ridge</span></a>
@@ -1137,7 +1136,7 @@
 <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
 </pre></div>
 </div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 5.772 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 5.621 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-impute-plot-iterative-imputer-variants-comparison-py">
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 <a class="reference external image-reference" href="https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/impute/plot_iterative_imputer_variants_comparison.ipynb"><img alt="Launch binder" src="http://github.com/_images/binder_badge_logo15.svg" style="width: 150px;" />
diff --git a/dev/auto_examples/impute/plot_missing_values.html b/dev/auto_examples/impute/plot_missing_values.html
index 8ba516c058c43..d21a4eb4b60ed 100644
--- a/dev/auto_examples/impute/plot_missing_values.html
+++ b/dev/auto_examples/impute/plot_missing_values.html
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-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
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@@ -679,13 +679,12 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -902,9 +901,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
@@ -1073,7 +1072,7 @@ <h2>Impute the missing data and score<a class="headerlink" href="#impute-the-mis
 <span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.ensemble</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.ensemble.RandomForestRegressor.html#sklearn.ensemble.RandomForestRegressor" title="sklearn.ensemble.RandomForestRegressor" class="sphx-glr-backref-module-sklearn-ensemble sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">RandomForestRegressor</span></a>
 
 <span class="c1"># To use the experimental IterativeImputer, we need to explicitly ask for it:</span>
-<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.experimental</span><span class="w"> </span><span class="kn">import</span> <span class="n">enable_iterative_imputer</span>  <span class="c1"># noqa</span>
+<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.experimental</span><span class="w"> </span><span class="kn">import</span> <span class="n">enable_iterative_imputer</span>  <span class="c1"># noqa: F401</span>
 <span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.impute</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.impute.IterativeImputer.html#sklearn.impute.IterativeImputer" title="sklearn.impute.IterativeImputer" class="sphx-glr-backref-module-sklearn-impute sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">IterativeImputer</span></a><span class="p">,</span> <a href="http://github.com/modules/generated/sklearn.impute.KNNImputer.html#sklearn.impute.KNNImputer" title="sklearn.impute.KNNImputer" class="sphx-glr-backref-module-sklearn-impute sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">KNNImputer</span></a><span class="p">,</span> <a href="http://github.com/modules/generated/sklearn.impute.SimpleImputer.html#sklearn.impute.SimpleImputer" title="sklearn.impute.SimpleImputer" class="sphx-glr-backref-module-sklearn-impute sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">SimpleImputer</span></a>
 <span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.model_selection</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.model_selection.cross_val_score.html#sklearn.model_selection.cross_val_score" title="sklearn.model_selection.cross_val_score" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-function"><span class="n">cross_val_score</span></a>
 <span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.pipeline</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.pipeline.make_pipeline.html#sklearn.pipeline.make_pipeline" title="sklearn.pipeline.make_pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-function"><span class="n">make_pipeline</span></a>
@@ -1267,7 +1266,7 @@ <h2>Plot the results<a class="headerlink" href="#plot-the-results" title="Link t
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 robust estimator for data with high magnitude variables which could dominate
 results (otherwise known as a ‘long tail’).</p>
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+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 8.855 seconds)</p>
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@@ -1294,12 +1293,12 @@ <h2>Plot the results<a class="headerlink" href="#plot-the-results" title="Link t
 </div><div class="sphx-glr-thumbcontainer" tooltip="We are pleased to announce the release of scikit-learn 0.22, which comes with many bug fixes and new features! We detail below a few of the major features of this release. For an exhaustive list of all the changes, please refer to the release notes &lt;release_notes_0_22&gt;."><img alt="" src="http://github.com/_images/sphx_glr_plot_release_highlights_0_22_0_thumb.png" />
 <p><a class="reference internal" href="http://github.com/release_highlights/plot_release_highlights_0_22_0.html#sphx-glr-auto-examples-release-highlights-plot-release-highlights-0-22-0-py"><span class="std std-ref">Release Highlights for scikit-learn 0.22</span></a></p>
   <div class="sphx-glr-thumbnail-title">Release Highlights for scikit-learn 0.22</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="We are pleased to announce the release of scikit-learn 1.5! Many bug fixes and improvements were added, as well as some key new features. Below we detail the highlights of this release. For an exhaustive list of all the changes, please refer to the release notes &lt;release_notes_1_5&gt;."><img alt="" src="http://github.com/_images/sphx_glr_plot_release_highlights_1_5_0_thumb.png" />
-<p><a class="reference internal" href="http://github.com/release_highlights/plot_release_highlights_1_5_0.html#sphx-glr-auto-examples-release-highlights-plot-release-highlights-1-5-0-py"><span class="std std-ref">Release Highlights for scikit-learn 1.5</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Release Highlights for scikit-learn 1.5</div>
 </div><div class="sphx-glr-thumbcontainer" tooltip="This example plots the ellipsoids obtained from a toy dataset (mixture of three Gaussians) fitted by the BayesianGaussianMixture class models with a Dirichlet distribution prior (``weight_concentration_prior_type=&#x27;dirichlet_distribution&#x27;``) and a Dirichlet process prior (``weight_concentration_prior_type=&#x27;dirichlet_process&#x27;``). On each figure, we plot the results for three different values of the weight concentration prior."><img alt="" src="http://github.com/_images/sphx_glr_plot_concentration_prior_thumb.png" />
 <p><a class="reference internal" href="http://github.com/mixture/plot_concentration_prior.html#sphx-glr-auto-examples-mixture-plot-concentration-prior-py"><span class="std std-ref">Concentration Prior Type Analysis of Variation Bayesian Gaussian Mixture</span></a></p>
   <div class="sphx-glr-thumbnail-title">Concentration Prior Type Analysis of Variation Bayesian Gaussian Mixture</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="We are pleased to announce the release of scikit-learn 1.5! Many bug fixes and improvements were added, as well as some key new features. Below we detail the highlights of this release. For an exhaustive list of all the changes, please refer to the release notes &lt;release_notes_1_5&gt;."><img alt="" src="http://github.com/_images/sphx_glr_plot_release_highlights_1_5_0_thumb.png" />
+<p><a class="reference internal" href="http://github.com/release_highlights/plot_release_highlights_1_5_0.html#sphx-glr-auto-examples-release-highlights-plot-release-highlights-1-5-0-py"><span class="std std-ref">Release Highlights for scikit-learn 1.5</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Release Highlights for scikit-learn 1.5</div>
 </div></div><p class="sphx-glr-signature"><a class="reference external" href="https://sphinx-gallery.github.io/">Gallery generated by Sphinx-Gallery</a></p>
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diff --git a/dev/auto_examples/index.html b/dev/auto_examples/index.html
index 27ac14ea2296f..35547c4a3cca0 100644
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@@ -676,13 +676,12 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -899,9 +898,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
@@ -1307,15 +1306,9 @@ <h2>Ensemble methods<a class="headerlink" href="#ensemble-methods" title="Link t
 </div><div class="sphx-glr-thumbcontainer" tooltip="The RandomForestClassifier is trained using bootstrap aggregation, where each new tree is fit from a bootstrap sample of the training observations z_i = (x_i, y_i). The out-of-bag (OOB) error is the average error for each z_i calculated using predictions from the trees that do not contain z_i in their respective bootstrap sample. This allows the RandomForestClassifier to be fit and validated whilst being trained [1]_."><img alt="" src="http://github.com/_images/sphx_glr_plot_ensemble_oob_thumb.png" />
 <p><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html#sphx-glr-auto-examples-ensemble-plot-ensemble-oob-py"><span class="std std-ref">OOB Errors for Random Forests</span></a></p>
   <div class="sphx-glr-thumbnail-title">OOB Errors for Random Forests</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="Plot the class probabilities of the first sample in a toy dataset predicted by three different classifiers and averaged by the VotingClassifier."><img alt="" src="http://github.com/_images/sphx_glr_plot_voting_probas_thumb.png" />
-<p><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html#sphx-glr-auto-examples-ensemble-plot-voting-probas-py"><span class="std std-ref">Plot class probabilities calculated by the VotingClassifier</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Plot class probabilities calculated by the VotingClassifier</div>
 </div><div class="sphx-glr-thumbcontainer" tooltip="A voting regressor is an ensemble meta-estimator that fits several base regressors, each on the whole dataset. Then it averages the individual predictions to form a final prediction. We will use three different regressors to predict the data: GradientBoostingRegressor, RandomForestRegressor, and LinearRegression). Then the above 3 regressors will be used for the VotingRegressor."><img alt="" src="http://github.com/_images/sphx_glr_plot_voting_regressor_thumb.png" />
 <p><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html#sphx-glr-auto-examples-ensemble-plot-voting-regressor-py"><span class="std std-ref">Plot individual and voting regression predictions</span></a></p>
   <div class="sphx-glr-thumbnail-title">Plot individual and voting regression predictions</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="Plot the decision boundaries of a VotingClassifier for two features of the Iris dataset."><img alt="" src="http://github.com/_images/sphx_glr_plot_voting_decision_regions_thumb.png" />
-<p><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html#sphx-glr-auto-examples-ensemble-plot-voting-decision-regions-py"><span class="std std-ref">Plot the decision boundaries of a VotingClassifier</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Plot the decision boundaries of a VotingClassifier</div>
 </div><div class="sphx-glr-thumbcontainer" tooltip="Plot the decision surfaces of forests of randomized trees trained on pairs of features of the iris dataset."><img alt="" src="http://github.com/_images/sphx_glr_plot_forest_iris_thumb.png" />
 <p><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html#sphx-glr-auto-examples-ensemble-plot-forest-iris-py"><span class="std std-ref">Plot the decision surfaces of ensembles of trees on the iris dataset</span></a></p>
   <div class="sphx-glr-thumbnail-title">Plot the decision surfaces of ensembles of trees on the iris dataset</div>
@@ -1328,6 +1321,9 @@ <h2>Ensemble methods<a class="headerlink" href="#ensemble-methods" title="Link t
 </div><div class="sphx-glr-thumbcontainer" tooltip="This example fits an AdaBoosted decision stump on a non-linearly separable classification dataset composed of two &quot;Gaussian quantiles&quot; clusters (see sklearn.datasets.make_gaussian_quantiles) and plots the decision boundary and decision scores. The distributions of decision scores are shown separately for samples of class A and B. The predicted class label for each sample is determined by the sign of the decision score. Samples with decision scores greater than zero are classified as B, and are otherwise classified as A. The magnitude of a decision score determines the degree of likeness with the predicted class label. Additionally, a new dataset could be constructed containing a desired purity of class B, for example, by only selecting samples with a decision score above some value."><img alt="" src="http://github.com/_images/sphx_glr_plot_adaboost_twoclass_thumb.png" />
 <p><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html#sphx-glr-auto-examples-ensemble-plot-adaboost-twoclass-py"><span class="std std-ref">Two-class AdaBoost</span></a></p>
   <div class="sphx-glr-thumbnail-title">Two-class AdaBoost</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="Plot the predicted class probabilities in a toy dataset predicted by three different classifiers and averaged by the VotingClassifier."><img alt="" src="http://github.com/_images/sphx_glr_plot_voting_decision_regions_thumb.png" />
+<p><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html#sphx-glr-auto-examples-ensemble-plot-voting-decision-regions-py"><span class="std std-ref">Visualizing the probabilistic predictions of a VotingClassifier</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Visualizing the probabilistic predictions of a VotingClassifier</div>
 </div></div></section>
 <section id="examples-based-on-real-world-datasets">
 <h2>Examples based on real world datasets<a class="headerlink" href="#examples-based-on-real-world-datasets" title="Link to this heading">#</a></h2>
@@ -1882,15 +1878,15 @@ <h2>Semi Supervised Classification<a class="headerlink" href="#semi-supervised-c
 </div><div class="sphx-glr-thumbcontainer" tooltip="This example illustrates the effect of a varying threshold on self-training. The breast_cancer dataset is loaded, and labels are deleted such that only 50 out of 569 samples have labels. A SelfTrainingClassifier is fitted on this dataset, with varying thresholds."><img alt="" src="http://github.com/_images/sphx_glr_plot_self_training_varying_threshold_thumb.png" />
 <p><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html#sphx-glr-auto-examples-semi-supervised-plot-self-training-varying-threshold-py"><span class="std std-ref">Effect of varying threshold for self-training</span></a></p>
   <div class="sphx-glr-thumbnail-title">Effect of varying threshold for self-training</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="Example of LabelPropagation learning a complex internal structure to demonstrate &quot;manifold learning&quot;. The outer circle should be labeled &quot;red&quot; and the inner circle &quot;blue&quot;. Because both label groups lie inside their own distinct shape, we can see that the labels propagate correctly around the circle."><img alt="" src="http://github.com/_images/sphx_glr_plot_label_propagation_structure_thumb.png" />
+<p><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html#sphx-glr-auto-examples-semi-supervised-plot-label-propagation-structure-py"><span class="std std-ref">Label Propagation circles: Learning a complex structure</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Label Propagation circles: Learning a complex structure</div>
 </div><div class="sphx-glr-thumbcontainer" tooltip="Demonstrates an active learning technique to learn handwritten digits using label propagation."><img alt="" src="http://github.com/_images/sphx_glr_plot_label_propagation_digits_active_learning_thumb.png" />
-<p><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html#sphx-glr-auto-examples-semi-supervised-plot-label-propagation-digits-active-learning-py"><span class="std std-ref">Label Propagation digits active learning</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Label Propagation digits active learning</div>
+<p><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html#sphx-glr-auto-examples-semi-supervised-plot-label-propagation-digits-active-learning-py"><span class="std std-ref">Label Propagation digits: Active learning</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Label Propagation digits: Active learning</div>
 </div><div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates the power of semisupervised learning by training a Label Spreading model to classify handwritten digits with sets of very few labels."><img alt="" src="http://github.com/_images/sphx_glr_plot_label_propagation_digits_thumb.png" />
 <p><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html#sphx-glr-auto-examples-semi-supervised-plot-label-propagation-digits-py"><span class="std std-ref">Label Propagation digits: Demonstrating performance</span></a></p>
   <div class="sphx-glr-thumbnail-title">Label Propagation digits: Demonstrating performance</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="Example of LabelPropagation learning a complex internal structure to demonstrate &quot;manifold learning&quot;. The outer circle should be labeled &quot;red&quot; and the inner circle &quot;blue&quot;. Because both label groups lie inside their own distinct shape, we can see that the labels propagate correctly around the circle."><img alt="" src="http://github.com/_images/sphx_glr_plot_label_propagation_structure_thumb.png" />
-<p><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html#sphx-glr-auto-examples-semi-supervised-plot-label-propagation-structure-py"><span class="std std-ref">Label Propagation learning a complex structure</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Label Propagation learning a complex structure</div>
 </div><div class="sphx-glr-thumbcontainer" tooltip="In this example, semi-supervised classifiers are trained on the 20 newsgroups dataset (which will be automatically downloaded)."><img alt="" src="http://github.com/_images/sphx_glr_plot_semi_supervised_newsgroups_thumb.png" />
 <p><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html#sphx-glr-auto-examples-semi-supervised-plot-semi-supervised-newsgroups-py"><span class="std std-ref">Semi-supervised Classification on a Text Dataset</span></a></p>
   <div class="sphx-glr-thumbnail-title">Semi-supervised Classification on a Text Dataset</div>
diff --git a/dev/auto_examples/inspection/index.html b/dev/auto_examples/inspection/index.html
index 2d8b72fe4f9b8..1aa412702533d 100644
--- a/dev/auto_examples/inspection/index.html
+++ b/dev/auto_examples/inspection/index.html
@@ -42,7 +42,7 @@
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     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -676,13 +676,12 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -899,9 +898,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
diff --git a/dev/auto_examples/inspection/plot_causal_interpretation.html b/dev/auto_examples/inspection/plot_causal_interpretation.html
index d9b77ab0ce961..d69fc9487fda3 100644
--- a/dev/auto_examples/inspection/plot_causal_interpretation.html
+++ b/dev/auto_examples/inspection/plot_causal_interpretation.html
@@ -42,7 +42,7 @@
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     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
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@@ -679,13 +679,12 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -902,9 +901,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
@@ -1175,7 +1174,7 @@ <h2>Lessons learned<a class="headerlink" href="#lessons-learned" title="Link to
 or unethical, researchers can sometimes use other causal inference techniques
 such as <a class="reference external" href="https://en.wikipedia.org/wiki/Instrumental_variables_estimation">Instrumental Variables</a> (IV)
 estimations.</p>
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diff --git a/dev/auto_examples/inspection/plot_linear_model_coefficient_interpretation.html b/dev/auto_examples/inspection/plot_linear_model_coefficient_interpretation.html
index efc33d08393e1..a53d8eef7f733 100644
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+++ b/dev/auto_examples/inspection/plot_linear_model_coefficient_interpretation.html
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     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
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@@ -680,13 +680,12 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -903,9 +902,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
@@ -1450,7 +1449,7 @@ <h2>Processing the dataset<a class="headerlink" href="#processing-the-dataset" t
 </pre></div>
 </div>
 <div class="output_subarea output_html rendered_html output_result">
-<style>#sk-container-id-32 {
+<style>#sk-container-id-33 {
   /* Definition of color scheme common for light and dark mode */
   --sklearn-color-text: #000;
   --sklearn-color-text-muted: #666;
@@ -1481,15 +1480,15 @@ <h2>Processing the dataset<a class="headerlink" href="#processing-the-dataset" t
   }
 }
 
-#sk-container-id-32 {
+#sk-container-id-33 {
   color: var(--sklearn-color-text);
 }
 
-#sk-container-id-32 pre {
+#sk-container-id-33 pre {
   padding: 0;
 }
 
-#sk-container-id-32 input.sk-hidden--visually {
+#sk-container-id-33 input.sk-hidden--visually {
   border: 0;
   clip: rect(1px 1px 1px 1px);
   clip: rect(1px, 1px, 1px, 1px);
@@ -1501,7 +1500,7 @@ <h2>Processing the dataset<a class="headerlink" href="#processing-the-dataset" t
   width: 1px;
 }
 
-#sk-container-id-32 div.sk-dashed-wrapped {
+#sk-container-id-33 div.sk-dashed-wrapped {
   border: 1px dashed var(--sklearn-color-line);
   margin: 0 0.4em 0.5em 0.4em;
   box-sizing: border-box;
@@ -1509,7 +1508,7 @@ <h2>Processing the dataset<a class="headerlink" href="#processing-the-dataset" t
   background-color: var(--sklearn-color-background);
 }
 
-#sk-container-id-32 div.sk-container {
+#sk-container-id-33 div.sk-container {
   /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
      but bootstrap.min.css set `[hidden] { display: none !important; }`
      so we also need the `!important` here to be able to override the
@@ -1519,7 +1518,7 @@ <h2>Processing the dataset<a class="headerlink" href="#processing-the-dataset" t
   position: relative;
 }
 
-#sk-container-id-32 div.sk-text-repr-fallback {
+#sk-container-id-33 div.sk-text-repr-fallback {
   display: none;
 }
 
@@ -1535,14 +1534,14 @@ <h2>Processing the dataset<a class="headerlink" href="#processing-the-dataset" t
 
 /* Parallel-specific style estimator block */
 
-#sk-container-id-32 div.sk-parallel-item::after {
+#sk-container-id-33 div.sk-parallel-item::after {
   content: "";
   width: 100%;
   border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
   flex-grow: 1;
 }
 
-#sk-container-id-32 div.sk-parallel {
+#sk-container-id-33 div.sk-parallel {
   display: flex;
   align-items: stretch;
   justify-content: center;
@@ -1550,28 +1549,28 @@ <h2>Processing the dataset<a class="headerlink" href="#processing-the-dataset" t
   position: relative;
 }
 
-#sk-container-id-32 div.sk-parallel-item {
+#sk-container-id-33 div.sk-parallel-item {
   display: flex;
   flex-direction: column;
 }
 
-#sk-container-id-32 div.sk-parallel-item:first-child::after {
+#sk-container-id-33 div.sk-parallel-item:first-child::after {
   align-self: flex-end;
   width: 50%;
 }
 
-#sk-container-id-32 div.sk-parallel-item:last-child::after {
+#sk-container-id-33 div.sk-parallel-item:last-child::after {
   align-self: flex-start;
   width: 50%;
 }
 
-#sk-container-id-32 div.sk-parallel-item:only-child::after {
+#sk-container-id-33 div.sk-parallel-item:only-child::after {
   width: 0;
 }
 
 /* Serial-specific style estimator block */
 
-#sk-container-id-32 div.sk-serial {
+#sk-container-id-33 div.sk-serial {
   display: flex;
   flex-direction: column;
   align-items: center;
@@ -1589,14 +1588,14 @@ <h2>Processing the dataset<a class="headerlink" href="#processing-the-dataset" t
 
 /* Pipeline and ColumnTransformer style (default) */
 
-#sk-container-id-32 div.sk-toggleable {
+#sk-container-id-33 div.sk-toggleable {
   /* Default theme specific background. It is overwritten whether we have a
   specific estimator or a Pipeline/ColumnTransformer */
   background-color: var(--sklearn-color-background);
 }
 
 /* Toggleable label */
-#sk-container-id-32 label.sk-toggleable__label {
+#sk-container-id-33 label.sk-toggleable__label {
   cursor: pointer;
   display: flex;
   width: 100%;
@@ -1609,13 +1608,13 @@ <h2>Processing the dataset<a class="headerlink" href="#processing-the-dataset" t
   gap: 0.5em;
 }
 
-#sk-container-id-32 label.sk-toggleable__label .caption {
+#sk-container-id-33 label.sk-toggleable__label .caption {
   font-size: 0.6rem;
   font-weight: lighter;
   color: var(--sklearn-color-text-muted);
 }
 
-#sk-container-id-32 label.sk-toggleable__label-arrow:before {
+#sk-container-id-33 label.sk-toggleable__label-arrow:before {
   /* Arrow on the left of the label */
   content: "▸";
   float: left;
@@ -1623,13 +1622,13 @@ <h2>Processing the dataset<a class="headerlink" href="#processing-the-dataset" t
   color: var(--sklearn-color-icon);
 }
 
-#sk-container-id-32 label.sk-toggleable__label-arrow:hover:before {
+#sk-container-id-33 label.sk-toggleable__label-arrow:hover:before {
   color: var(--sklearn-color-text);
 }
 
 /* Toggleable content - dropdown */
 
-#sk-container-id-32 div.sk-toggleable__content {
+#sk-container-id-33 div.sk-toggleable__content {
   max-height: 0;
   max-width: 0;
   overflow: hidden;
@@ -1638,12 +1637,12 @@ <h2>Processing the dataset<a class="headerlink" href="#processing-the-dataset" t
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-32 div.sk-toggleable__content.fitted {
+#sk-container-id-33 div.sk-toggleable__content.fitted {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
-#sk-container-id-32 div.sk-toggleable__content pre {
+#sk-container-id-33 div.sk-toggleable__content pre {
   margin: 0.2em;
   border-radius: 0.25em;
   color: var(--sklearn-color-text);
@@ -1651,79 +1650,79 @@ <h2>Processing the dataset<a class="headerlink" href="#processing-the-dataset" t
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-32 div.sk-toggleable__content.fitted pre {
+#sk-container-id-33 div.sk-toggleable__content.fitted pre {
   /* unfitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
-#sk-container-id-32 input.sk-toggleable__control:checked~div.sk-toggleable__content {
+#sk-container-id-33 input.sk-toggleable__control:checked~div.sk-toggleable__content {
   /* Expand drop-down */
   max-height: 200px;
   max-width: 100%;
   overflow: auto;
 }
 
-#sk-container-id-32 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
+#sk-container-id-33 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
   content: "▾";
 }
 
 /* Pipeline/ColumnTransformer-specific style */
 
-#sk-container-id-32 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-33 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-32 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-33 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
 /* Estimator-specific style */
 
 /* Colorize estimator box */
-#sk-container-id-32 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-33 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-32 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-33 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
-#sk-container-id-32 div.sk-label label.sk-toggleable__label,
-#sk-container-id-32 div.sk-label label {
+#sk-container-id-33 div.sk-label label.sk-toggleable__label,
+#sk-container-id-33 div.sk-label label {
   /* The background is the default theme color */
   color: var(--sklearn-color-text-on-default-background);
 }
 
 /* On hover, darken the color of the background */
-#sk-container-id-32 div.sk-label:hover label.sk-toggleable__label {
+#sk-container-id-33 div.sk-label:hover label.sk-toggleable__label {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
 /* Label box, darken color on hover, fitted */
-#sk-container-id-32 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
+#sk-container-id-33 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
 /* Estimator label */
 
-#sk-container-id-32 div.sk-label label {
+#sk-container-id-33 div.sk-label label {
   font-family: monospace;
   font-weight: bold;
   display: inline-block;
   line-height: 1.2em;
 }
 
-#sk-container-id-32 div.sk-label-container {
+#sk-container-id-33 div.sk-label-container {
   text-align: center;
 }
 
 /* Estimator-specific */
-#sk-container-id-32 div.sk-estimator {
+#sk-container-id-33 div.sk-estimator {
   font-family: monospace;
   border: 1px dotted var(--sklearn-color-border-box);
   border-radius: 0.25em;
@@ -1733,18 +1732,18 @@ <h2>Processing the dataset<a class="headerlink" href="#processing-the-dataset" t
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-32 div.sk-estimator.fitted {
+#sk-container-id-33 div.sk-estimator.fitted {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
 /* on hover */
-#sk-container-id-32 div.sk-estimator:hover {
+#sk-container-id-33 div.sk-estimator:hover {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-32 div.sk-estimator.fitted:hover {
+#sk-container-id-33 div.sk-estimator.fitted:hover {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-2);
 }
@@ -1832,7 +1831,7 @@ <h2>Processing the dataset<a class="headerlink" href="#processing-the-dataset" t
 
 /* "?"-specific style due to the `<a>` HTML tag */
 
-#sk-container-id-32 a.estimator_doc_link {
+#sk-container-id-33 a.estimator_doc_link {
   float: right;
   font-size: 1rem;
   line-height: 1em;
@@ -1847,25 +1846,25 @@ <h2>Processing the dataset<a class="headerlink" href="#processing-the-dataset" t
   border: var(--sklearn-color-unfitted-level-1) 1pt solid;
 }
 
-#sk-container-id-32 a.estimator_doc_link.fitted {
+#sk-container-id-33 a.estimator_doc_link.fitted {
   /* fitted */
   border: var(--sklearn-color-fitted-level-1) 1pt solid;
   color: var(--sklearn-color-fitted-level-1);
 }
 
 /* On hover */
-#sk-container-id-32 a.estimator_doc_link:hover {
+#sk-container-id-33 a.estimator_doc_link:hover {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-3);
   color: var(--sklearn-color-background);
   text-decoration: none;
 }
 
-#sk-container-id-32 a.estimator_doc_link.fitted:hover {
+#sk-container-id-33 a.estimator_doc_link.fitted:hover {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-3);
 }
-</style><div id="sk-container-id-32" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,
+</style><div id="sk-container-id-33" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,
                  ColumnTransformer(remainder=&#x27;passthrough&#x27;,
                                    transformers=[(&#x27;onehotencoder&#x27;,
                                                   OneHotEncoder(drop=&#x27;if_binary&#x27;),
@@ -1876,7 +1875,7 @@ <h2>Processing the dataset<a class="headerlink" href="#processing-the-dataset" t
                 (&#x27;transformedtargetregressor&#x27;,
                  TransformedTargetRegressor(func=&lt;ufunc &#x27;log10&#x27;&gt;,
                                             inverse_func=&lt;ufunc &#x27;exp10&#x27;&gt;,
-                                            regressor=Ridge(alpha=1e-10)))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-116" type="checkbox" ><label for="sk-estimator-id-116" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>Pipeline</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.pipeline.Pipeline.html">?<span>Documentation for Pipeline</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,
+                                            regressor=Ridge(alpha=1e-10)))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-126" type="checkbox" ><label for="sk-estimator-id-126" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>Pipeline</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.pipeline.Pipeline.html">?<span>Documentation for Pipeline</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,
                  ColumnTransformer(remainder=&#x27;passthrough&#x27;,
                                    transformers=[(&#x27;onehotencoder&#x27;,
                                                   OneHotEncoder(drop=&#x27;if_binary&#x27;),
@@ -1887,13 +1886,13 @@ <h2>Processing the dataset<a class="headerlink" href="#processing-the-dataset" t
                 (&#x27;transformedtargetregressor&#x27;,
                  TransformedTargetRegressor(func=&lt;ufunc &#x27;log10&#x27;&gt;,
                                             inverse_func=&lt;ufunc &#x27;exp10&#x27;&gt;,
-                                            regressor=Ridge(alpha=1e-10)))])</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-117" type="checkbox" ><label for="sk-estimator-id-117" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>ColumnTransformer</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.compose.ColumnTransformer.html">?<span>Documentation for ColumnTransformer</span></a></div></label><div class="sk-toggleable__content fitted"><pre>ColumnTransformer(remainder=&#x27;passthrough&#x27;,
+                                            regressor=Ridge(alpha=1e-10)))])</pre></div> </div></div><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-127" type="checkbox" ><label for="sk-estimator-id-127" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>columntransformer: ColumnTransformer</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.compose.ColumnTransformer.html">?<span>Documentation for columntransformer: ColumnTransformer</span></a></div></label><div class="sk-toggleable__content fitted"><pre>ColumnTransformer(remainder=&#x27;passthrough&#x27;,
                   transformers=[(&#x27;onehotencoder&#x27;,
                                  OneHotEncoder(drop=&#x27;if_binary&#x27;),
                                  [&#x27;RACE&#x27;, &#x27;OCCUPATION&#x27;, &#x27;SECTOR&#x27;, &#x27;MARR&#x27;,
                                   &#x27;UNION&#x27;, &#x27;SEX&#x27;, &#x27;SOUTH&#x27;])],
-                  verbose_feature_names_out=False)</pre></div> </div></div><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-118" type="checkbox" ><label for="sk-estimator-id-118" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>transformedtargetregressor: TransformedTargetRegressor</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.compose.TransformedTargetRegressor.html">?<span>Documentation for transformedtargetregressor: TransformedTargetRegressor</span></a></div></label><div class="sk-toggleable__content fitted"><pre>TransformedTargetRegressor(func=&lt;ufunc &#x27;log10&#x27;&gt;, inverse_func=&lt;ufunc &#x27;exp10&#x27;&gt;,
-                           regressor=Ridge(alpha=1e-10))</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-119" type="checkbox" ><label for="sk-estimator-id-119" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>regressor: Ridge</div></div></label><div class="sk-toggleable__content fitted"><pre>Ridge(alpha=1e-10)</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-120" type="checkbox" ><label for="sk-estimator-id-120" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>Ridge</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.linear_model.Ridge.html">?<span>Documentation for Ridge</span></a></div></label><div class="sk-toggleable__content fitted"><pre>Ridge(alpha=1e-10)</pre></div> </div></div></div></div></div></div></div></div></div></div></div>
+                  verbose_feature_names_out=False)</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-128" type="checkbox" ><label for="sk-estimator-id-128" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>onehotencoder</div></div></label><div class="sk-toggleable__content fitted"><pre>[&#x27;RACE&#x27;, &#x27;OCCUPATION&#x27;, &#x27;SECTOR&#x27;, &#x27;MARR&#x27;, &#x27;UNION&#x27;, &#x27;SEX&#x27;, &#x27;SOUTH&#x27;]</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-129" type="checkbox" ><label for="sk-estimator-id-129" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>OneHotEncoder</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.OneHotEncoder.html">?<span>Documentation for OneHotEncoder</span></a></div></label><div class="sk-toggleable__content fitted"><pre>OneHotEncoder(drop=&#x27;if_binary&#x27;)</pre></div> </div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-130" type="checkbox" ><label for="sk-estimator-id-130" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>remainder</div></div></label><div class="sk-toggleable__content fitted"><pre>[&#x27;EDUCATION&#x27;, &#x27;EXPERIENCE&#x27;, &#x27;AGE&#x27;]</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-131" type="checkbox" ><label for="sk-estimator-id-131" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>passthrough</div></div></label><div class="sk-toggleable__content fitted"><pre>passthrough</pre></div> </div></div></div></div></div></div></div><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-132" type="checkbox" ><label for="sk-estimator-id-132" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>transformedtargetregressor: TransformedTargetRegressor</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.compose.TransformedTargetRegressor.html">?<span>Documentation for transformedtargetregressor: TransformedTargetRegressor</span></a></div></label><div class="sk-toggleable__content fitted"><pre>TransformedTargetRegressor(func=&lt;ufunc &#x27;log10&#x27;&gt;, inverse_func=&lt;ufunc &#x27;exp10&#x27;&gt;,
+                           regressor=Ridge(alpha=1e-10))</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-133" type="checkbox" ><label for="sk-estimator-id-133" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>regressor: Ridge</div></div></label><div class="sk-toggleable__content fitted"><pre>Ridge(alpha=1e-10)</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-134" type="checkbox" ><label for="sk-estimator-id-134" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>Ridge</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.linear_model.Ridge.html">?<span>Documentation for Ridge</span></a></div></label><div class="sk-toggleable__content fitted"><pre>Ridge(alpha=1e-10)</pre></div> </div></div></div></div></div></div></div></div></div></div></div>
 </div>
 <br />
 <br /><p>Then we check the performance of the computed model plotting its predictions
@@ -2269,7 +2268,7 @@ <h2>The problem of correlated variables<a class="headerlink" href="#the-problem-
 </pre></div>
 </div>
 <div class="output_subarea output_html rendered_html output_result">
-<style>#sk-container-id-33 {
+<style>#sk-container-id-34 {
   /* Definition of color scheme common for light and dark mode */
   --sklearn-color-text: #000;
   --sklearn-color-text-muted: #666;
@@ -2300,15 +2299,15 @@ <h2>The problem of correlated variables<a class="headerlink" href="#the-problem-
   }
 }
 
-#sk-container-id-33 {
+#sk-container-id-34 {
   color: var(--sklearn-color-text);
 }
 
-#sk-container-id-33 pre {
+#sk-container-id-34 pre {
   padding: 0;
 }
 
-#sk-container-id-33 input.sk-hidden--visually {
+#sk-container-id-34 input.sk-hidden--visually {
   border: 0;
   clip: rect(1px 1px 1px 1px);
   clip: rect(1px, 1px, 1px, 1px);
@@ -2320,7 +2319,7 @@ <h2>The problem of correlated variables<a class="headerlink" href="#the-problem-
   width: 1px;
 }
 
-#sk-container-id-33 div.sk-dashed-wrapped {
+#sk-container-id-34 div.sk-dashed-wrapped {
   border: 1px dashed var(--sklearn-color-line);
   margin: 0 0.4em 0.5em 0.4em;
   box-sizing: border-box;
@@ -2328,7 +2327,7 @@ <h2>The problem of correlated variables<a class="headerlink" href="#the-problem-
   background-color: var(--sklearn-color-background);
 }
 
-#sk-container-id-33 div.sk-container {
+#sk-container-id-34 div.sk-container {
   /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
      but bootstrap.min.css set `[hidden] { display: none !important; }`
      so we also need the `!important` here to be able to override the
@@ -2338,7 +2337,7 @@ <h2>The problem of correlated variables<a class="headerlink" href="#the-problem-
   position: relative;
 }
 
-#sk-container-id-33 div.sk-text-repr-fallback {
+#sk-container-id-34 div.sk-text-repr-fallback {
   display: none;
 }
 
@@ -2354,14 +2353,14 @@ <h2>The problem of correlated variables<a class="headerlink" href="#the-problem-
 
 /* Parallel-specific style estimator block */
 
-#sk-container-id-33 div.sk-parallel-item::after {
+#sk-container-id-34 div.sk-parallel-item::after {
   content: "";
   width: 100%;
   border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
   flex-grow: 1;
 }
 
-#sk-container-id-33 div.sk-parallel {
+#sk-container-id-34 div.sk-parallel {
   display: flex;
   align-items: stretch;
   justify-content: center;
@@ -2369,28 +2368,28 @@ <h2>The problem of correlated variables<a class="headerlink" href="#the-problem-
   position: relative;
 }
 
-#sk-container-id-33 div.sk-parallel-item {
+#sk-container-id-34 div.sk-parallel-item {
   display: flex;
   flex-direction: column;
 }
 
-#sk-container-id-33 div.sk-parallel-item:first-child::after {
+#sk-container-id-34 div.sk-parallel-item:first-child::after {
   align-self: flex-end;
   width: 50%;
 }
 
-#sk-container-id-33 div.sk-parallel-item:last-child::after {
+#sk-container-id-34 div.sk-parallel-item:last-child::after {
   align-self: flex-start;
   width: 50%;
 }
 
-#sk-container-id-33 div.sk-parallel-item:only-child::after {
+#sk-container-id-34 div.sk-parallel-item:only-child::after {
   width: 0;
 }
 
 /* Serial-specific style estimator block */
 
-#sk-container-id-33 div.sk-serial {
+#sk-container-id-34 div.sk-serial {
   display: flex;
   flex-direction: column;
   align-items: center;
@@ -2408,14 +2407,14 @@ <h2>The problem of correlated variables<a class="headerlink" href="#the-problem-
 
 /* Pipeline and ColumnTransformer style (default) */
 
-#sk-container-id-33 div.sk-toggleable {
+#sk-container-id-34 div.sk-toggleable {
   /* Default theme specific background. It is overwritten whether we have a
   specific estimator or a Pipeline/ColumnTransformer */
   background-color: var(--sklearn-color-background);
 }
 
 /* Toggleable label */
-#sk-container-id-33 label.sk-toggleable__label {
+#sk-container-id-34 label.sk-toggleable__label {
   cursor: pointer;
   display: flex;
   width: 100%;
@@ -2428,13 +2427,13 @@ <h2>The problem of correlated variables<a class="headerlink" href="#the-problem-
   gap: 0.5em;
 }
 
-#sk-container-id-33 label.sk-toggleable__label .caption {
+#sk-container-id-34 label.sk-toggleable__label .caption {
   font-size: 0.6rem;
   font-weight: lighter;
   color: var(--sklearn-color-text-muted);
 }
 
-#sk-container-id-33 label.sk-toggleable__label-arrow:before {
+#sk-container-id-34 label.sk-toggleable__label-arrow:before {
   /* Arrow on the left of the label */
   content: "▸";
   float: left;
@@ -2442,13 +2441,13 @@ <h2>The problem of correlated variables<a class="headerlink" href="#the-problem-
   color: var(--sklearn-color-icon);
 }
 
-#sk-container-id-33 label.sk-toggleable__label-arrow:hover:before {
+#sk-container-id-34 label.sk-toggleable__label-arrow:hover:before {
   color: var(--sklearn-color-text);
 }
 
 /* Toggleable content - dropdown */
 
-#sk-container-id-33 div.sk-toggleable__content {
+#sk-container-id-34 div.sk-toggleable__content {
   max-height: 0;
   max-width: 0;
   overflow: hidden;
@@ -2457,12 +2456,12 @@ <h2>The problem of correlated variables<a class="headerlink" href="#the-problem-
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-33 div.sk-toggleable__content.fitted {
+#sk-container-id-34 div.sk-toggleable__content.fitted {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
-#sk-container-id-33 div.sk-toggleable__content pre {
+#sk-container-id-34 div.sk-toggleable__content pre {
   margin: 0.2em;
   border-radius: 0.25em;
   color: var(--sklearn-color-text);
@@ -2470,79 +2469,79 @@ <h2>The problem of correlated variables<a class="headerlink" href="#the-problem-
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-33 div.sk-toggleable__content.fitted pre {
+#sk-container-id-34 div.sk-toggleable__content.fitted pre {
   /* unfitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
-#sk-container-id-33 input.sk-toggleable__control:checked~div.sk-toggleable__content {
+#sk-container-id-34 input.sk-toggleable__control:checked~div.sk-toggleable__content {
   /* Expand drop-down */
   max-height: 200px;
   max-width: 100%;
   overflow: auto;
 }
 
-#sk-container-id-33 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
+#sk-container-id-34 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
   content: "▾";
 }
 
 /* Pipeline/ColumnTransformer-specific style */
 
-#sk-container-id-33 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-34 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-33 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-34 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
 /* Estimator-specific style */
 
 /* Colorize estimator box */
-#sk-container-id-33 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-34 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-33 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-34 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
-#sk-container-id-33 div.sk-label label.sk-toggleable__label,
-#sk-container-id-33 div.sk-label label {
+#sk-container-id-34 div.sk-label label.sk-toggleable__label,
+#sk-container-id-34 div.sk-label label {
   /* The background is the default theme color */
   color: var(--sklearn-color-text-on-default-background);
 }
 
 /* On hover, darken the color of the background */
-#sk-container-id-33 div.sk-label:hover label.sk-toggleable__label {
+#sk-container-id-34 div.sk-label:hover label.sk-toggleable__label {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
 /* Label box, darken color on hover, fitted */
-#sk-container-id-33 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
+#sk-container-id-34 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
 /* Estimator label */
 
-#sk-container-id-33 div.sk-label label {
+#sk-container-id-34 div.sk-label label {
   font-family: monospace;
   font-weight: bold;
   display: inline-block;
   line-height: 1.2em;
 }
 
-#sk-container-id-33 div.sk-label-container {
+#sk-container-id-34 div.sk-label-container {
   text-align: center;
 }
 
 /* Estimator-specific */
-#sk-container-id-33 div.sk-estimator {
+#sk-container-id-34 div.sk-estimator {
   font-family: monospace;
   border: 1px dotted var(--sklearn-color-border-box);
   border-radius: 0.25em;
@@ -2552,18 +2551,18 @@ <h2>The problem of correlated variables<a class="headerlink" href="#the-problem-
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-33 div.sk-estimator.fitted {
+#sk-container-id-34 div.sk-estimator.fitted {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
 /* on hover */
-#sk-container-id-33 div.sk-estimator:hover {
+#sk-container-id-34 div.sk-estimator:hover {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-33 div.sk-estimator.fitted:hover {
+#sk-container-id-34 div.sk-estimator.fitted:hover {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-2);
 }
@@ -2651,7 +2650,7 @@ <h2>The problem of correlated variables<a class="headerlink" href="#the-problem-
 
 /* "?"-specific style due to the `<a>` HTML tag */
 
-#sk-container-id-33 a.estimator_doc_link {
+#sk-container-id-34 a.estimator_doc_link {
   float: right;
   font-size: 1rem;
   line-height: 1em;
@@ -2666,25 +2665,25 @@ <h2>The problem of correlated variables<a class="headerlink" href="#the-problem-
   border: var(--sklearn-color-unfitted-level-1) 1pt solid;
 }
 
-#sk-container-id-33 a.estimator_doc_link.fitted {
+#sk-container-id-34 a.estimator_doc_link.fitted {
   /* fitted */
   border: var(--sklearn-color-fitted-level-1) 1pt solid;
   color: var(--sklearn-color-fitted-level-1);
 }
 
 /* On hover */
-#sk-container-id-33 a.estimator_doc_link:hover {
+#sk-container-id-34 a.estimator_doc_link:hover {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-3);
   color: var(--sklearn-color-background);
   text-decoration: none;
 }
 
-#sk-container-id-33 a.estimator_doc_link.fitted:hover {
+#sk-container-id-34 a.estimator_doc_link.fitted:hover {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-3);
 }
-</style><div id="sk-container-id-33" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,
+</style><div id="sk-container-id-34" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,
                  ColumnTransformer(transformers=[(&#x27;onehotencoder&#x27;,
                                                   OneHotEncoder(drop=&#x27;if_binary&#x27;),
                                                   [&#x27;RACE&#x27;, &#x27;OCCUPATION&#x27;,
@@ -2697,7 +2696,7 @@ <h2>The problem of correlated variables<a class="headerlink" href="#the-problem-
                 (&#x27;transformedtargetregressor&#x27;,
                  TransformedTargetRegressor(func=&lt;ufunc &#x27;log10&#x27;&gt;,
                                             inverse_func=&lt;ufunc &#x27;exp10&#x27;&gt;,
-                                            regressor=Ridge(alpha=1e-10)))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-121" type="checkbox" ><label for="sk-estimator-id-121" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>Pipeline</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.pipeline.Pipeline.html">?<span>Documentation for Pipeline</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,
+                                            regressor=Ridge(alpha=1e-10)))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-135" type="checkbox" ><label for="sk-estimator-id-135" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>Pipeline</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.pipeline.Pipeline.html">?<span>Documentation for Pipeline</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,
                  ColumnTransformer(transformers=[(&#x27;onehotencoder&#x27;,
                                                   OneHotEncoder(drop=&#x27;if_binary&#x27;),
                                                   [&#x27;RACE&#x27;, &#x27;OCCUPATION&#x27;,
@@ -2710,13 +2709,13 @@ <h2>The problem of correlated variables<a class="headerlink" href="#the-problem-
                 (&#x27;transformedtargetregressor&#x27;,
                  TransformedTargetRegressor(func=&lt;ufunc &#x27;log10&#x27;&gt;,
                                             inverse_func=&lt;ufunc &#x27;exp10&#x27;&gt;,
-                                            regressor=Ridge(alpha=1e-10)))])</pre></div> </div></div><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-122" type="checkbox" ><label for="sk-estimator-id-122" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>columntransformer: ColumnTransformer</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.compose.ColumnTransformer.html">?<span>Documentation for columntransformer: ColumnTransformer</span></a></div></label><div class="sk-toggleable__content fitted"><pre>ColumnTransformer(transformers=[(&#x27;onehotencoder&#x27;,
+                                            regressor=Ridge(alpha=1e-10)))])</pre></div> </div></div><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-136" type="checkbox" ><label for="sk-estimator-id-136" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>columntransformer: ColumnTransformer</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.compose.ColumnTransformer.html">?<span>Documentation for columntransformer: ColumnTransformer</span></a></div></label><div class="sk-toggleable__content fitted"><pre>ColumnTransformer(transformers=[(&#x27;onehotencoder&#x27;,
                                  OneHotEncoder(drop=&#x27;if_binary&#x27;),
                                  [&#x27;RACE&#x27;, &#x27;OCCUPATION&#x27;, &#x27;SECTOR&#x27;, &#x27;MARR&#x27;,
                                   &#x27;UNION&#x27;, &#x27;SEX&#x27;, &#x27;SOUTH&#x27;]),
                                 (&#x27;standardscaler&#x27;, StandardScaler(),
-                                 [&#x27;EDUCATION&#x27;, &#x27;EXPERIENCE&#x27;, &#x27;AGE&#x27;])])</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-123" type="checkbox" ><label for="sk-estimator-id-123" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>onehotencoder</div></div></label><div class="sk-toggleable__content fitted"><pre>[&#x27;RACE&#x27;, &#x27;OCCUPATION&#x27;, &#x27;SECTOR&#x27;, &#x27;MARR&#x27;, &#x27;UNION&#x27;, &#x27;SEX&#x27;, &#x27;SOUTH&#x27;]</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-124" type="checkbox" ><label for="sk-estimator-id-124" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>OneHotEncoder</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.OneHotEncoder.html">?<span>Documentation for OneHotEncoder</span></a></div></label><div class="sk-toggleable__content fitted"><pre>OneHotEncoder(drop=&#x27;if_binary&#x27;)</pre></div> </div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-125" type="checkbox" ><label for="sk-estimator-id-125" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>standardscaler</div></div></label><div class="sk-toggleable__content fitted"><pre>[&#x27;EDUCATION&#x27;, &#x27;EXPERIENCE&#x27;, &#x27;AGE&#x27;]</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-126" type="checkbox" ><label for="sk-estimator-id-126" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>StandardScaler</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.StandardScaler.html">?<span>Documentation for StandardScaler</span></a></div></label><div class="sk-toggleable__content fitted"><pre>StandardScaler()</pre></div> </div></div></div></div></div></div></div><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-127" type="checkbox" ><label for="sk-estimator-id-127" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>transformedtargetregressor: TransformedTargetRegressor</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.compose.TransformedTargetRegressor.html">?<span>Documentation for transformedtargetregressor: TransformedTargetRegressor</span></a></div></label><div class="sk-toggleable__content fitted"><pre>TransformedTargetRegressor(func=&lt;ufunc &#x27;log10&#x27;&gt;, inverse_func=&lt;ufunc &#x27;exp10&#x27;&gt;,
-                           regressor=Ridge(alpha=1e-10))</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-128" type="checkbox" ><label for="sk-estimator-id-128" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>regressor: Ridge</div></div></label><div class="sk-toggleable__content fitted"><pre>Ridge(alpha=1e-10)</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-129" type="checkbox" ><label for="sk-estimator-id-129" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>Ridge</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.linear_model.Ridge.html">?<span>Documentation for Ridge</span></a></div></label><div class="sk-toggleable__content fitted"><pre>Ridge(alpha=1e-10)</pre></div> </div></div></div></div></div></div></div></div></div></div></div>
+                                 [&#x27;EDUCATION&#x27;, &#x27;EXPERIENCE&#x27;, &#x27;AGE&#x27;])])</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-137" type="checkbox" ><label for="sk-estimator-id-137" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>onehotencoder</div></div></label><div class="sk-toggleable__content fitted"><pre>[&#x27;RACE&#x27;, &#x27;OCCUPATION&#x27;, &#x27;SECTOR&#x27;, &#x27;MARR&#x27;, &#x27;UNION&#x27;, &#x27;SEX&#x27;, &#x27;SOUTH&#x27;]</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-138" type="checkbox" ><label for="sk-estimator-id-138" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>OneHotEncoder</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.OneHotEncoder.html">?<span>Documentation for OneHotEncoder</span></a></div></label><div class="sk-toggleable__content fitted"><pre>OneHotEncoder(drop=&#x27;if_binary&#x27;)</pre></div> </div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-139" type="checkbox" ><label for="sk-estimator-id-139" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>standardscaler</div></div></label><div class="sk-toggleable__content fitted"><pre>[&#x27;EDUCATION&#x27;, &#x27;EXPERIENCE&#x27;, &#x27;AGE&#x27;]</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-140" type="checkbox" ><label for="sk-estimator-id-140" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>StandardScaler</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.StandardScaler.html">?<span>Documentation for StandardScaler</span></a></div></label><div class="sk-toggleable__content fitted"><pre>StandardScaler()</pre></div> </div></div></div></div></div></div></div><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-141" type="checkbox" ><label for="sk-estimator-id-141" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>transformedtargetregressor: TransformedTargetRegressor</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.compose.TransformedTargetRegressor.html">?<span>Documentation for transformedtargetregressor: TransformedTargetRegressor</span></a></div></label><div class="sk-toggleable__content fitted"><pre>TransformedTargetRegressor(func=&lt;ufunc &#x27;log10&#x27;&gt;, inverse_func=&lt;ufunc &#x27;exp10&#x27;&gt;,
+                           regressor=Ridge(alpha=1e-10))</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-142" type="checkbox" ><label for="sk-estimator-id-142" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>regressor: Ridge</div></div></label><div class="sk-toggleable__content fitted"><pre>Ridge(alpha=1e-10)</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-143" type="checkbox" ><label for="sk-estimator-id-143" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>Ridge</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.linear_model.Ridge.html">?<span>Documentation for Ridge</span></a></div></label><div class="sk-toggleable__content fitted"><pre>Ridge(alpha=1e-10)</pre></div> </div></div></div></div></div></div></div></div></div></div></div>
 </div>
 <br />
 <br /><p>Again, we check the performance of the computed
@@ -2806,7 +2805,7 @@ <h2>Linear models with regularization<a class="headerlink" href="#linear-models-
 </pre></div>
 </div>
 <div class="output_subarea output_html rendered_html output_result">
-<style>#sk-container-id-34 {
+<style>#sk-container-id-35 {
   /* Definition of color scheme common for light and dark mode */
   --sklearn-color-text: #000;
   --sklearn-color-text-muted: #666;
@@ -2837,15 +2836,15 @@ <h2>Linear models with regularization<a class="headerlink" href="#linear-models-
   }
 }
 
-#sk-container-id-34 {
+#sk-container-id-35 {
   color: var(--sklearn-color-text);
 }
 
-#sk-container-id-34 pre {
+#sk-container-id-35 pre {
   padding: 0;
 }
 
-#sk-container-id-34 input.sk-hidden--visually {
+#sk-container-id-35 input.sk-hidden--visually {
   border: 0;
   clip: rect(1px 1px 1px 1px);
   clip: rect(1px, 1px, 1px, 1px);
@@ -2857,7 +2856,7 @@ <h2>Linear models with regularization<a class="headerlink" href="#linear-models-
   width: 1px;
 }
 
-#sk-container-id-34 div.sk-dashed-wrapped {
+#sk-container-id-35 div.sk-dashed-wrapped {
   border: 1px dashed var(--sklearn-color-line);
   margin: 0 0.4em 0.5em 0.4em;
   box-sizing: border-box;
@@ -2865,7 +2864,7 @@ <h2>Linear models with regularization<a class="headerlink" href="#linear-models-
   background-color: var(--sklearn-color-background);
 }
 
-#sk-container-id-34 div.sk-container {
+#sk-container-id-35 div.sk-container {
   /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
      but bootstrap.min.css set `[hidden] { display: none !important; }`
      so we also need the `!important` here to be able to override the
@@ -2875,7 +2874,7 @@ <h2>Linear models with regularization<a class="headerlink" href="#linear-models-
   position: relative;
 }
 
-#sk-container-id-34 div.sk-text-repr-fallback {
+#sk-container-id-35 div.sk-text-repr-fallback {
   display: none;
 }
 
@@ -2891,14 +2890,14 @@ <h2>Linear models with regularization<a class="headerlink" href="#linear-models-
 
 /* Parallel-specific style estimator block */
 
-#sk-container-id-34 div.sk-parallel-item::after {
+#sk-container-id-35 div.sk-parallel-item::after {
   content: "";
   width: 100%;
   border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
   flex-grow: 1;
 }
 
-#sk-container-id-34 div.sk-parallel {
+#sk-container-id-35 div.sk-parallel {
   display: flex;
   align-items: stretch;
   justify-content: center;
@@ -2906,28 +2905,28 @@ <h2>Linear models with regularization<a class="headerlink" href="#linear-models-
   position: relative;
 }
 
-#sk-container-id-34 div.sk-parallel-item {
+#sk-container-id-35 div.sk-parallel-item {
   display: flex;
   flex-direction: column;
 }
 
-#sk-container-id-34 div.sk-parallel-item:first-child::after {
+#sk-container-id-35 div.sk-parallel-item:first-child::after {
   align-self: flex-end;
   width: 50%;
 }
 
-#sk-container-id-34 div.sk-parallel-item:last-child::after {
+#sk-container-id-35 div.sk-parallel-item:last-child::after {
   align-self: flex-start;
   width: 50%;
 }
 
-#sk-container-id-34 div.sk-parallel-item:only-child::after {
+#sk-container-id-35 div.sk-parallel-item:only-child::after {
   width: 0;
 }
 
 /* Serial-specific style estimator block */
 
-#sk-container-id-34 div.sk-serial {
+#sk-container-id-35 div.sk-serial {
   display: flex;
   flex-direction: column;
   align-items: center;
@@ -2945,14 +2944,14 @@ <h2>Linear models with regularization<a class="headerlink" href="#linear-models-
 
 /* Pipeline and ColumnTransformer style (default) */
 
-#sk-container-id-34 div.sk-toggleable {
+#sk-container-id-35 div.sk-toggleable {
   /* Default theme specific background. It is overwritten whether we have a
   specific estimator or a Pipeline/ColumnTransformer */
   background-color: var(--sklearn-color-background);
 }
 
 /* Toggleable label */
-#sk-container-id-34 label.sk-toggleable__label {
+#sk-container-id-35 label.sk-toggleable__label {
   cursor: pointer;
   display: flex;
   width: 100%;
@@ -2965,13 +2964,13 @@ <h2>Linear models with regularization<a class="headerlink" href="#linear-models-
   gap: 0.5em;
 }
 
-#sk-container-id-34 label.sk-toggleable__label .caption {
+#sk-container-id-35 label.sk-toggleable__label .caption {
   font-size: 0.6rem;
   font-weight: lighter;
   color: var(--sklearn-color-text-muted);
 }
 
-#sk-container-id-34 label.sk-toggleable__label-arrow:before {
+#sk-container-id-35 label.sk-toggleable__label-arrow:before {
   /* Arrow on the left of the label */
   content: "▸";
   float: left;
@@ -2979,13 +2978,13 @@ <h2>Linear models with regularization<a class="headerlink" href="#linear-models-
   color: var(--sklearn-color-icon);
 }
 
-#sk-container-id-34 label.sk-toggleable__label-arrow:hover:before {
+#sk-container-id-35 label.sk-toggleable__label-arrow:hover:before {
   color: var(--sklearn-color-text);
 }
 
 /* Toggleable content - dropdown */
 
-#sk-container-id-34 div.sk-toggleable__content {
+#sk-container-id-35 div.sk-toggleable__content {
   max-height: 0;
   max-width: 0;
   overflow: hidden;
@@ -2994,12 +2993,12 @@ <h2>Linear models with regularization<a class="headerlink" href="#linear-models-
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-34 div.sk-toggleable__content.fitted {
+#sk-container-id-35 div.sk-toggleable__content.fitted {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
-#sk-container-id-34 div.sk-toggleable__content pre {
+#sk-container-id-35 div.sk-toggleable__content pre {
   margin: 0.2em;
   border-radius: 0.25em;
   color: var(--sklearn-color-text);
@@ -3007,79 +3006,79 @@ <h2>Linear models with regularization<a class="headerlink" href="#linear-models-
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-34 div.sk-toggleable__content.fitted pre {
+#sk-container-id-35 div.sk-toggleable__content.fitted pre {
   /* unfitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
-#sk-container-id-34 input.sk-toggleable__control:checked~div.sk-toggleable__content {
+#sk-container-id-35 input.sk-toggleable__control:checked~div.sk-toggleable__content {
   /* Expand drop-down */
   max-height: 200px;
   max-width: 100%;
   overflow: auto;
 }
 
-#sk-container-id-34 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
+#sk-container-id-35 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
   content: "▾";
 }
 
 /* Pipeline/ColumnTransformer-specific style */
 
-#sk-container-id-34 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-35 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-34 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-35 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
 /* Estimator-specific style */
 
 /* Colorize estimator box */
-#sk-container-id-34 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-35 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-34 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-35 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
-#sk-container-id-34 div.sk-label label.sk-toggleable__label,
-#sk-container-id-34 div.sk-label label {
+#sk-container-id-35 div.sk-label label.sk-toggleable__label,
+#sk-container-id-35 div.sk-label label {
   /* The background is the default theme color */
   color: var(--sklearn-color-text-on-default-background);
 }
 
 /* On hover, darken the color of the background */
-#sk-container-id-34 div.sk-label:hover label.sk-toggleable__label {
+#sk-container-id-35 div.sk-label:hover label.sk-toggleable__label {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
 /* Label box, darken color on hover, fitted */
-#sk-container-id-34 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
+#sk-container-id-35 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
 /* Estimator label */
 
-#sk-container-id-34 div.sk-label label {
+#sk-container-id-35 div.sk-label label {
   font-family: monospace;
   font-weight: bold;
   display: inline-block;
   line-height: 1.2em;
 }
 
-#sk-container-id-34 div.sk-label-container {
+#sk-container-id-35 div.sk-label-container {
   text-align: center;
 }
 
 /* Estimator-specific */
-#sk-container-id-34 div.sk-estimator {
+#sk-container-id-35 div.sk-estimator {
   font-family: monospace;
   border: 1px dotted var(--sklearn-color-border-box);
   border-radius: 0.25em;
@@ -3089,18 +3088,18 @@ <h2>Linear models with regularization<a class="headerlink" href="#linear-models-
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-34 div.sk-estimator.fitted {
+#sk-container-id-35 div.sk-estimator.fitted {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
 /* on hover */
-#sk-container-id-34 div.sk-estimator:hover {
+#sk-container-id-35 div.sk-estimator:hover {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-34 div.sk-estimator.fitted:hover {
+#sk-container-id-35 div.sk-estimator.fitted:hover {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-2);
 }
@@ -3188,7 +3187,7 @@ <h2>Linear models with regularization<a class="headerlink" href="#linear-models-
 
 /* "?"-specific style due to the `<a>` HTML tag */
 
-#sk-container-id-34 a.estimator_doc_link {
+#sk-container-id-35 a.estimator_doc_link {
   float: right;
   font-size: 1rem;
   line-height: 1em;
@@ -3203,25 +3202,25 @@ <h2>Linear models with regularization<a class="headerlink" href="#linear-models-
   border: var(--sklearn-color-unfitted-level-1) 1pt solid;
 }
 
-#sk-container-id-34 a.estimator_doc_link.fitted {
+#sk-container-id-35 a.estimator_doc_link.fitted {
   /* fitted */
   border: var(--sklearn-color-fitted-level-1) 1pt solid;
   color: var(--sklearn-color-fitted-level-1);
 }
 
 /* On hover */
-#sk-container-id-34 a.estimator_doc_link:hover {
+#sk-container-id-35 a.estimator_doc_link:hover {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-3);
   color: var(--sklearn-color-background);
   text-decoration: none;
 }
 
-#sk-container-id-34 a.estimator_doc_link.fitted:hover {
+#sk-container-id-35 a.estimator_doc_link.fitted:hover {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-3);
 }
-</style><div id="sk-container-id-34" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,
+</style><div id="sk-container-id-35" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,
                  ColumnTransformer(transformers=[(&#x27;onehotencoder&#x27;,
                                                   OneHotEncoder(drop=&#x27;if_binary&#x27;),
                                                   [&#x27;RACE&#x27;, &#x27;OCCUPATION&#x27;,
@@ -3236,7 +3235,7 @@ <h2>Linear models with regularization<a class="headerlink" href="#linear-models-
                                             inverse_func=&lt;ufunc &#x27;exp10&#x27;&gt;,
                                             regressor=RidgeCV(alphas=array([1.e-10, 1.e-09, 1.e-08, 1.e-07, 1.e-06, 1.e-05, 1.e-04, 1.e-03,
        1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04, 1.e+05,
-       1.e+06, 1.e+07, 1.e+08, 1.e+09, 1.e+10]))))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-130" type="checkbox" ><label for="sk-estimator-id-130" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>Pipeline</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.pipeline.Pipeline.html">?<span>Documentation for Pipeline</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,
+       1.e+06, 1.e+07, 1.e+08, 1.e+09, 1.e+10]))))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-144" type="checkbox" ><label for="sk-estimator-id-144" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>Pipeline</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.pipeline.Pipeline.html">?<span>Documentation for Pipeline</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,
                  ColumnTransformer(transformers=[(&#x27;onehotencoder&#x27;,
                                                   OneHotEncoder(drop=&#x27;if_binary&#x27;),
                                                   [&#x27;RACE&#x27;, &#x27;OCCUPATION&#x27;,
@@ -3251,17 +3250,17 @@ <h2>Linear models with regularization<a class="headerlink" href="#linear-models-
                                             inverse_func=&lt;ufunc &#x27;exp10&#x27;&gt;,
                                             regressor=RidgeCV(alphas=array([1.e-10, 1.e-09, 1.e-08, 1.e-07, 1.e-06, 1.e-05, 1.e-04, 1.e-03,
        1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04, 1.e+05,
-       1.e+06, 1.e+07, 1.e+08, 1.e+09, 1.e+10]))))])</pre></div> </div></div><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-131" type="checkbox" ><label for="sk-estimator-id-131" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>columntransformer: ColumnTransformer</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.compose.ColumnTransformer.html">?<span>Documentation for columntransformer: ColumnTransformer</span></a></div></label><div class="sk-toggleable__content fitted"><pre>ColumnTransformer(transformers=[(&#x27;onehotencoder&#x27;,
+       1.e+06, 1.e+07, 1.e+08, 1.e+09, 1.e+10]))))])</pre></div> </div></div><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-145" type="checkbox" ><label for="sk-estimator-id-145" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>columntransformer: ColumnTransformer</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.compose.ColumnTransformer.html">?<span>Documentation for columntransformer: ColumnTransformer</span></a></div></label><div class="sk-toggleable__content fitted"><pre>ColumnTransformer(transformers=[(&#x27;onehotencoder&#x27;,
                                  OneHotEncoder(drop=&#x27;if_binary&#x27;),
                                  [&#x27;RACE&#x27;, &#x27;OCCUPATION&#x27;, &#x27;SECTOR&#x27;, &#x27;MARR&#x27;,
                                   &#x27;UNION&#x27;, &#x27;SEX&#x27;, &#x27;SOUTH&#x27;]),
                                 (&#x27;standardscaler&#x27;, StandardScaler(),
-                                 [&#x27;EDUCATION&#x27;, &#x27;EXPERIENCE&#x27;, &#x27;AGE&#x27;])])</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-132" type="checkbox" ><label for="sk-estimator-id-132" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>onehotencoder</div></div></label><div class="sk-toggleable__content fitted"><pre>[&#x27;RACE&#x27;, &#x27;OCCUPATION&#x27;, &#x27;SECTOR&#x27;, &#x27;MARR&#x27;, &#x27;UNION&#x27;, &#x27;SEX&#x27;, &#x27;SOUTH&#x27;]</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-133" type="checkbox" ><label for="sk-estimator-id-133" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>OneHotEncoder</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.OneHotEncoder.html">?<span>Documentation for OneHotEncoder</span></a></div></label><div class="sk-toggleable__content fitted"><pre>OneHotEncoder(drop=&#x27;if_binary&#x27;)</pre></div> </div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-134" type="checkbox" ><label for="sk-estimator-id-134" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>standardscaler</div></div></label><div class="sk-toggleable__content fitted"><pre>[&#x27;EDUCATION&#x27;, &#x27;EXPERIENCE&#x27;, &#x27;AGE&#x27;]</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-135" type="checkbox" ><label for="sk-estimator-id-135" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>StandardScaler</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.StandardScaler.html">?<span>Documentation for StandardScaler</span></a></div></label><div class="sk-toggleable__content fitted"><pre>StandardScaler()</pre></div> </div></div></div></div></div></div></div><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-136" type="checkbox" ><label for="sk-estimator-id-136" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>transformedtargetregressor: TransformedTargetRegressor</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.compose.TransformedTargetRegressor.html">?<span>Documentation for transformedtargetregressor: TransformedTargetRegressor</span></a></div></label><div class="sk-toggleable__content fitted"><pre>TransformedTargetRegressor(func=&lt;ufunc &#x27;log10&#x27;&gt;, inverse_func=&lt;ufunc &#x27;exp10&#x27;&gt;,
+                                 [&#x27;EDUCATION&#x27;, &#x27;EXPERIENCE&#x27;, &#x27;AGE&#x27;])])</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-146" type="checkbox" ><label for="sk-estimator-id-146" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>onehotencoder</div></div></label><div class="sk-toggleable__content fitted"><pre>[&#x27;RACE&#x27;, &#x27;OCCUPATION&#x27;, &#x27;SECTOR&#x27;, &#x27;MARR&#x27;, &#x27;UNION&#x27;, &#x27;SEX&#x27;, &#x27;SOUTH&#x27;]</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-147" type="checkbox" ><label for="sk-estimator-id-147" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>OneHotEncoder</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.OneHotEncoder.html">?<span>Documentation for OneHotEncoder</span></a></div></label><div class="sk-toggleable__content fitted"><pre>OneHotEncoder(drop=&#x27;if_binary&#x27;)</pre></div> </div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-148" type="checkbox" ><label for="sk-estimator-id-148" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>standardscaler</div></div></label><div class="sk-toggleable__content fitted"><pre>[&#x27;EDUCATION&#x27;, &#x27;EXPERIENCE&#x27;, &#x27;AGE&#x27;]</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-149" type="checkbox" ><label for="sk-estimator-id-149" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>StandardScaler</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.StandardScaler.html">?<span>Documentation for StandardScaler</span></a></div></label><div class="sk-toggleable__content fitted"><pre>StandardScaler()</pre></div> </div></div></div></div></div></div></div><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-150" type="checkbox" ><label for="sk-estimator-id-150" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>transformedtargetregressor: TransformedTargetRegressor</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.compose.TransformedTargetRegressor.html">?<span>Documentation for transformedtargetregressor: TransformedTargetRegressor</span></a></div></label><div class="sk-toggleable__content fitted"><pre>TransformedTargetRegressor(func=&lt;ufunc &#x27;log10&#x27;&gt;, inverse_func=&lt;ufunc &#x27;exp10&#x27;&gt;,
                            regressor=RidgeCV(alphas=array([1.e-10, 1.e-09, 1.e-08, 1.e-07, 1.e-06, 1.e-05, 1.e-04, 1.e-03,
        1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04, 1.e+05,
-       1.e+06, 1.e+07, 1.e+08, 1.e+09, 1.e+10])))</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-137" type="checkbox" ><label for="sk-estimator-id-137" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>regressor: RidgeCV</div></div></label><div class="sk-toggleable__content fitted"><pre>RidgeCV(alphas=array([1.e-10, 1.e-09, 1.e-08, 1.e-07, 1.e-06, 1.e-05, 1.e-04, 1.e-03,
+       1.e+06, 1.e+07, 1.e+08, 1.e+09, 1.e+10])))</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-151" type="checkbox" ><label for="sk-estimator-id-151" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>regressor: RidgeCV</div></div></label><div class="sk-toggleable__content fitted"><pre>RidgeCV(alphas=array([1.e-10, 1.e-09, 1.e-08, 1.e-07, 1.e-06, 1.e-05, 1.e-04, 1.e-03,
        1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04, 1.e+05,
-       1.e+06, 1.e+07, 1.e+08, 1.e+09, 1.e+10]))</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-138" type="checkbox" ><label for="sk-estimator-id-138" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>RidgeCV</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.linear_model.RidgeCV.html">?<span>Documentation for RidgeCV</span></a></div></label><div class="sk-toggleable__content fitted"><pre>RidgeCV(alphas=array([1.e-10, 1.e-09, 1.e-08, 1.e-07, 1.e-06, 1.e-05, 1.e-04, 1.e-03,
+       1.e+06, 1.e+07, 1.e+08, 1.e+09, 1.e+10]))</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-152" type="checkbox" ><label for="sk-estimator-id-152" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>RidgeCV</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.linear_model.RidgeCV.html">?<span>Documentation for RidgeCV</span></a></div></label><div class="sk-toggleable__content fitted"><pre>RidgeCV(alphas=array([1.e-10, 1.e-09, 1.e-08, 1.e-07, 1.e-06, 1.e-05, 1.e-04, 1.e-03,
        1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04, 1.e+05,
        1.e+06, 1.e+07, 1.e+08, 1.e+09, 1.e+10]))</pre></div> </div></div></div></div></div></div></div></div></div></div></div>
 </div>
@@ -3480,7 +3479,7 @@ <h2>Lessons learned<a class="headerlink" href="#lessons-learned" title="Link to
 <li><p>Inspection tools may not necessarily provide insights on the true
 data generating process.</p></li>
 </ul>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 12.806 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 12.624 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-inspection-plot-linear-model-coefficient-interpretation-py">
 <div class="binder-badge docutils container">
 <a class="reference external image-reference" href="https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/inspection/plot_linear_model_coefficient_interpretation.ipynb"><img alt="Launch binder" src="http://github.com/_images/binder_badge_logo16.svg" style="width: 150px;" />
diff --git a/dev/auto_examples/inspection/plot_partial_dependence.html b/dev/auto_examples/inspection/plot_partial_dependence.html
index 5a9b11a61119e..cc40bef0c17ee 100644
--- a/dev/auto_examples/inspection/plot_partial_dependence.html
+++ b/dev/auto_examples/inspection/plot_partial_dependence.html
@@ -42,7 +42,7 @@
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     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
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@@ -679,13 +679,12 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -902,9 +901,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
@@ -1201,7 +1200,7 @@ <h3>Preprocessor for the neural network model<a class="headerlink" href="#prepro
 </pre></div>
 </div>
 <div class="output_subarea output_html rendered_html output_result">
-<style>#sk-container-id-35 {
+<style>#sk-container-id-36 {
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   --sklearn-color-text: #000;
   --sklearn-color-text-muted: #666;
@@ -1232,15 +1231,15 @@ <h3>Preprocessor for the neural network model<a class="headerlink" href="#prepro
   }
 }
 
-#sk-container-id-35 {
+#sk-container-id-36 {
   color: var(--sklearn-color-text);
 }
 
-#sk-container-id-35 pre {
+#sk-container-id-36 pre {
   padding: 0;
 }
 
-#sk-container-id-35 input.sk-hidden--visually {
+#sk-container-id-36 input.sk-hidden--visually {
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   clip: rect(1px 1px 1px 1px);
   clip: rect(1px, 1px, 1px, 1px);
@@ -1252,7 +1251,7 @@ <h3>Preprocessor for the neural network model<a class="headerlink" href="#prepro
   width: 1px;
 }
 
-#sk-container-id-35 div.sk-dashed-wrapped {
+#sk-container-id-36 div.sk-dashed-wrapped {
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   box-sizing: border-box;
@@ -1260,7 +1259,7 @@ <h3>Preprocessor for the neural network model<a class="headerlink" href="#prepro
   background-color: var(--sklearn-color-background);
 }
 
-#sk-container-id-35 div.sk-container {
+#sk-container-id-36 div.sk-container {
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      but bootstrap.min.css set `[hidden] { display: none !important; }`
      so we also need the `!important` here to be able to override the
@@ -1270,7 +1269,7 @@ <h3>Preprocessor for the neural network model<a class="headerlink" href="#prepro
   position: relative;
 }
 
-#sk-container-id-35 div.sk-text-repr-fallback {
+#sk-container-id-36 div.sk-text-repr-fallback {
   display: none;
 }
 
@@ -1286,14 +1285,14 @@ <h3>Preprocessor for the neural network model<a class="headerlink" href="#prepro
 
 /* Parallel-specific style estimator block */
 
-#sk-container-id-35 div.sk-parallel-item::after {
+#sk-container-id-36 div.sk-parallel-item::after {
   content: "";
   width: 100%;
   border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
   flex-grow: 1;
 }
 
-#sk-container-id-35 div.sk-parallel {
+#sk-container-id-36 div.sk-parallel {
   display: flex;
   align-items: stretch;
   justify-content: center;
@@ -1301,28 +1300,28 @@ <h3>Preprocessor for the neural network model<a class="headerlink" href="#prepro
   position: relative;
 }
 
-#sk-container-id-35 div.sk-parallel-item {
+#sk-container-id-36 div.sk-parallel-item {
   display: flex;
   flex-direction: column;
 }
 
-#sk-container-id-35 div.sk-parallel-item:first-child::after {
+#sk-container-id-36 div.sk-parallel-item:first-child::after {
   align-self: flex-end;
   width: 50%;
 }
 
-#sk-container-id-35 div.sk-parallel-item:last-child::after {
+#sk-container-id-36 div.sk-parallel-item:last-child::after {
   align-self: flex-start;
   width: 50%;
 }
 
-#sk-container-id-35 div.sk-parallel-item:only-child::after {
+#sk-container-id-36 div.sk-parallel-item:only-child::after {
   width: 0;
 }
 
 /* Serial-specific style estimator block */
 
-#sk-container-id-35 div.sk-serial {
+#sk-container-id-36 div.sk-serial {
   display: flex;
   flex-direction: column;
   align-items: center;
@@ -1340,14 +1339,14 @@ <h3>Preprocessor for the neural network model<a class="headerlink" href="#prepro
 
 /* Pipeline and ColumnTransformer style (default) */
 
-#sk-container-id-35 div.sk-toggleable {
+#sk-container-id-36 div.sk-toggleable {
   /* Default theme specific background. It is overwritten whether we have a
   specific estimator or a Pipeline/ColumnTransformer */
   background-color: var(--sklearn-color-background);
 }
 
 /* Toggleable label */
-#sk-container-id-35 label.sk-toggleable__label {
+#sk-container-id-36 label.sk-toggleable__label {
   cursor: pointer;
   display: flex;
   width: 100%;
@@ -1360,13 +1359,13 @@ <h3>Preprocessor for the neural network model<a class="headerlink" href="#prepro
   gap: 0.5em;
 }
 
-#sk-container-id-35 label.sk-toggleable__label .caption {
+#sk-container-id-36 label.sk-toggleable__label .caption {
   font-size: 0.6rem;
   font-weight: lighter;
   color: var(--sklearn-color-text-muted);
 }
 
-#sk-container-id-35 label.sk-toggleable__label-arrow:before {
+#sk-container-id-36 label.sk-toggleable__label-arrow:before {
   /* Arrow on the left of the label */
   content: "▸";
   float: left;
@@ -1374,13 +1373,13 @@ <h3>Preprocessor for the neural network model<a class="headerlink" href="#prepro
   color: var(--sklearn-color-icon);
 }
 
-#sk-container-id-35 label.sk-toggleable__label-arrow:hover:before {
+#sk-container-id-36 label.sk-toggleable__label-arrow:hover:before {
   color: var(--sklearn-color-text);
 }
 
 /* Toggleable content - dropdown */
 
-#sk-container-id-35 div.sk-toggleable__content {
+#sk-container-id-36 div.sk-toggleable__content {
   max-height: 0;
   max-width: 0;
   overflow: hidden;
@@ -1389,12 +1388,12 @@ <h3>Preprocessor for the neural network model<a class="headerlink" href="#prepro
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-35 div.sk-toggleable__content.fitted {
+#sk-container-id-36 div.sk-toggleable__content.fitted {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
-#sk-container-id-35 div.sk-toggleable__content pre {
+#sk-container-id-36 div.sk-toggleable__content pre {
   margin: 0.2em;
   border-radius: 0.25em;
   color: var(--sklearn-color-text);
@@ -1402,79 +1401,79 @@ <h3>Preprocessor for the neural network model<a class="headerlink" href="#prepro
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-35 div.sk-toggleable__content.fitted pre {
+#sk-container-id-36 div.sk-toggleable__content.fitted pre {
   /* unfitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
-#sk-container-id-35 input.sk-toggleable__control:checked~div.sk-toggleable__content {
+#sk-container-id-36 input.sk-toggleable__control:checked~div.sk-toggleable__content {
   /* Expand drop-down */
   max-height: 200px;
   max-width: 100%;
   overflow: auto;
 }
 
-#sk-container-id-35 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
+#sk-container-id-36 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
   content: "▾";
 }
 
 /* Pipeline/ColumnTransformer-specific style */
 
-#sk-container-id-35 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-36 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-35 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-36 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
 /* Estimator-specific style */
 
 /* Colorize estimator box */
-#sk-container-id-35 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-36 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-35 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-36 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
-#sk-container-id-35 div.sk-label label.sk-toggleable__label,
-#sk-container-id-35 div.sk-label label {
+#sk-container-id-36 div.sk-label label.sk-toggleable__label,
+#sk-container-id-36 div.sk-label label {
   /* The background is the default theme color */
   color: var(--sklearn-color-text-on-default-background);
 }
 
 /* On hover, darken the color of the background */
-#sk-container-id-35 div.sk-label:hover label.sk-toggleable__label {
+#sk-container-id-36 div.sk-label:hover label.sk-toggleable__label {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
 /* Label box, darken color on hover, fitted */
-#sk-container-id-35 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
+#sk-container-id-36 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
 /* Estimator label */
 
-#sk-container-id-35 div.sk-label label {
+#sk-container-id-36 div.sk-label label {
   font-family: monospace;
   font-weight: bold;
   display: inline-block;
   line-height: 1.2em;
 }
 
-#sk-container-id-35 div.sk-label-container {
+#sk-container-id-36 div.sk-label-container {
   text-align: center;
 }
 
 /* Estimator-specific */
-#sk-container-id-35 div.sk-estimator {
+#sk-container-id-36 div.sk-estimator {
   font-family: monospace;
   border: 1px dotted var(--sklearn-color-border-box);
   border-radius: 0.25em;
@@ -1484,18 +1483,18 @@ <h3>Preprocessor for the neural network model<a class="headerlink" href="#prepro
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-35 div.sk-estimator.fitted {
+#sk-container-id-36 div.sk-estimator.fitted {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
 /* on hover */
-#sk-container-id-35 div.sk-estimator:hover {
+#sk-container-id-36 div.sk-estimator:hover {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-35 div.sk-estimator.fitted:hover {
+#sk-container-id-36 div.sk-estimator.fitted:hover {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-2);
 }
@@ -1583,7 +1582,7 @@ <h3>Preprocessor for the neural network model<a class="headerlink" href="#prepro
 
 /* "?"-specific style due to the `<a>` HTML tag */
 
-#sk-container-id-35 a.estimator_doc_link {
+#sk-container-id-36 a.estimator_doc_link {
   float: right;
   font-size: 1rem;
   line-height: 1em;
@@ -1598,39 +1597,39 @@ <h3>Preprocessor for the neural network model<a class="headerlink" href="#prepro
   border: var(--sklearn-color-unfitted-level-1) 1pt solid;
 }
 
-#sk-container-id-35 a.estimator_doc_link.fitted {
+#sk-container-id-36 a.estimator_doc_link.fitted {
   /* fitted */
   border: var(--sklearn-color-fitted-level-1) 1pt solid;
   color: var(--sklearn-color-fitted-level-1);
 }
 
 /* On hover */
-#sk-container-id-35 a.estimator_doc_link:hover {
+#sk-container-id-36 a.estimator_doc_link:hover {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-3);
   color: var(--sklearn-color-background);
   text-decoration: none;
 }
 
-#sk-container-id-35 a.estimator_doc_link.fitted:hover {
+#sk-container-id-36 a.estimator_doc_link.fitted:hover {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-3);
 }
-</style><div id="sk-container-id-35" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>ColumnTransformer(transformers=[(&#x27;num&#x27;, QuantileTransformer(n_quantiles=100),
+</style><div id="sk-container-id-36" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>ColumnTransformer(transformers=[(&#x27;num&#x27;, QuantileTransformer(n_quantiles=100),
                                  [&#x27;temp&#x27;, &#x27;feel_temp&#x27;, &#x27;humidity&#x27;,
                                   &#x27;windspeed&#x27;]),
                                 (&#x27;cat&#x27;, OneHotEncoder(handle_unknown=&#x27;ignore&#x27;),
                                  Index([&#x27;season&#x27;, &#x27;month&#x27;, &#x27;hour&#x27;, &#x27;holiday&#x27;, &#x27;weekday&#x27;, &#x27;workingday&#x27;,
        &#x27;weather&#x27;],
-      dtype=&#x27;object&#x27;))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-139" type="checkbox" ><label for="sk-estimator-id-139" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>ColumnTransformer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.compose.ColumnTransformer.html">?<span>Documentation for ColumnTransformer</span></a><span class="sk-estimator-doc-link ">i<span>Not fitted</span></span></div></label><div class="sk-toggleable__content "><pre>ColumnTransformer(transformers=[(&#x27;num&#x27;, QuantileTransformer(n_quantiles=100),
+      dtype=&#x27;object&#x27;))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-153" type="checkbox" ><label for="sk-estimator-id-153" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>ColumnTransformer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.compose.ColumnTransformer.html">?<span>Documentation for ColumnTransformer</span></a><span class="sk-estimator-doc-link ">i<span>Not fitted</span></span></div></label><div class="sk-toggleable__content "><pre>ColumnTransformer(transformers=[(&#x27;num&#x27;, QuantileTransformer(n_quantiles=100),
                                  [&#x27;temp&#x27;, &#x27;feel_temp&#x27;, &#x27;humidity&#x27;,
                                   &#x27;windspeed&#x27;]),
                                 (&#x27;cat&#x27;, OneHotEncoder(handle_unknown=&#x27;ignore&#x27;),
                                  Index([&#x27;season&#x27;, &#x27;month&#x27;, &#x27;hour&#x27;, &#x27;holiday&#x27;, &#x27;weekday&#x27;, &#x27;workingday&#x27;,
        &#x27;weather&#x27;],
-      dtype=&#x27;object&#x27;))])</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-140" type="checkbox" ><label for="sk-estimator-id-140" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>num</div></div></label><div class="sk-toggleable__content "><pre>[&#x27;temp&#x27;, &#x27;feel_temp&#x27;, &#x27;humidity&#x27;, &#x27;windspeed&#x27;]</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-141" type="checkbox" ><label for="sk-estimator-id-141" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>QuantileTransformer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.QuantileTransformer.html">?<span>Documentation for QuantileTransformer</span></a></div></label><div class="sk-toggleable__content "><pre>QuantileTransformer(n_quantiles=100)</pre></div> </div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-142" type="checkbox" ><label for="sk-estimator-id-142" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>cat</div></div></label><div class="sk-toggleable__content "><pre>Index([&#x27;season&#x27;, &#x27;month&#x27;, &#x27;hour&#x27;, &#x27;holiday&#x27;, &#x27;weekday&#x27;, &#x27;workingday&#x27;,
+      dtype=&#x27;object&#x27;))])</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-154" type="checkbox" ><label for="sk-estimator-id-154" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>num</div></div></label><div class="sk-toggleable__content "><pre>[&#x27;temp&#x27;, &#x27;feel_temp&#x27;, &#x27;humidity&#x27;, &#x27;windspeed&#x27;]</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-155" type="checkbox" ><label for="sk-estimator-id-155" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>QuantileTransformer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.QuantileTransformer.html">?<span>Documentation for QuantileTransformer</span></a></div></label><div class="sk-toggleable__content "><pre>QuantileTransformer(n_quantiles=100)</pre></div> </div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-156" type="checkbox" ><label for="sk-estimator-id-156" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>cat</div></div></label><div class="sk-toggleable__content "><pre>Index([&#x27;season&#x27;, &#x27;month&#x27;, &#x27;hour&#x27;, &#x27;holiday&#x27;, &#x27;weekday&#x27;, &#x27;workingday&#x27;,
        &#x27;weather&#x27;],
-      dtype=&#x27;object&#x27;)</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-143" type="checkbox" ><label for="sk-estimator-id-143" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>OneHotEncoder</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.OneHotEncoder.html">?<span>Documentation for OneHotEncoder</span></a></div></label><div class="sk-toggleable__content "><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</pre></div> </div></div></div></div></div></div></div></div></div>
+      dtype=&#x27;object&#x27;)</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-157" type="checkbox" ><label for="sk-estimator-id-157" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>OneHotEncoder</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.OneHotEncoder.html">?<span>Documentation for OneHotEncoder</span></a></div></label><div class="sk-toggleable__content "><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</pre></div> </div></div></div></div></div></div></div></div></div>
 </div>
 <br />
 <br /></section>
@@ -1653,7 +1652,7 @@ <h3>Preprocessor for the gradient boosting model<a class="headerlink" href="#pre
 </pre></div>
 </div>
 <div class="output_subarea output_html rendered_html output_result">
-<style>#sk-container-id-36 {
+<style>#sk-container-id-37 {
   /* Definition of color scheme common for light and dark mode */
   --sklearn-color-text: #000;
   --sklearn-color-text-muted: #666;
@@ -1684,15 +1683,15 @@ <h3>Preprocessor for the gradient boosting model<a class="headerlink" href="#pre
   }
 }
 
-#sk-container-id-36 {
+#sk-container-id-37 {
   color: var(--sklearn-color-text);
 }
 
-#sk-container-id-36 pre {
+#sk-container-id-37 pre {
   padding: 0;
 }
 
-#sk-container-id-36 input.sk-hidden--visually {
+#sk-container-id-37 input.sk-hidden--visually {
   border: 0;
   clip: rect(1px 1px 1px 1px);
   clip: rect(1px, 1px, 1px, 1px);
@@ -1704,7 +1703,7 @@ <h3>Preprocessor for the gradient boosting model<a class="headerlink" href="#pre
   width: 1px;
 }
 
-#sk-container-id-36 div.sk-dashed-wrapped {
+#sk-container-id-37 div.sk-dashed-wrapped {
   border: 1px dashed var(--sklearn-color-line);
   margin: 0 0.4em 0.5em 0.4em;
   box-sizing: border-box;
@@ -1712,7 +1711,7 @@ <h3>Preprocessor for the gradient boosting model<a class="headerlink" href="#pre
   background-color: var(--sklearn-color-background);
 }
 
-#sk-container-id-36 div.sk-container {
+#sk-container-id-37 div.sk-container {
   /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
      but bootstrap.min.css set `[hidden] { display: none !important; }`
      so we also need the `!important` here to be able to override the
@@ -1722,7 +1721,7 @@ <h3>Preprocessor for the gradient boosting model<a class="headerlink" href="#pre
   position: relative;
 }
 
-#sk-container-id-36 div.sk-text-repr-fallback {
+#sk-container-id-37 div.sk-text-repr-fallback {
   display: none;
 }
 
@@ -1738,14 +1737,14 @@ <h3>Preprocessor for the gradient boosting model<a class="headerlink" href="#pre
 
 /* Parallel-specific style estimator block */
 
-#sk-container-id-36 div.sk-parallel-item::after {
+#sk-container-id-37 div.sk-parallel-item::after {
   content: "";
   width: 100%;
   border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
   flex-grow: 1;
 }
 
-#sk-container-id-36 div.sk-parallel {
+#sk-container-id-37 div.sk-parallel {
   display: flex;
   align-items: stretch;
   justify-content: center;
@@ -1753,28 +1752,28 @@ <h3>Preprocessor for the gradient boosting model<a class="headerlink" href="#pre
   position: relative;
 }
 
-#sk-container-id-36 div.sk-parallel-item {
+#sk-container-id-37 div.sk-parallel-item {
   display: flex;
   flex-direction: column;
 }
 
-#sk-container-id-36 div.sk-parallel-item:first-child::after {
+#sk-container-id-37 div.sk-parallel-item:first-child::after {
   align-self: flex-end;
   width: 50%;
 }
 
-#sk-container-id-36 div.sk-parallel-item:last-child::after {
+#sk-container-id-37 div.sk-parallel-item:last-child::after {
   align-self: flex-start;
   width: 50%;
 }
 
-#sk-container-id-36 div.sk-parallel-item:only-child::after {
+#sk-container-id-37 div.sk-parallel-item:only-child::after {
   width: 0;
 }
 
 /* Serial-specific style estimator block */
 
-#sk-container-id-36 div.sk-serial {
+#sk-container-id-37 div.sk-serial {
   display: flex;
   flex-direction: column;
   align-items: center;
@@ -1792,14 +1791,14 @@ <h3>Preprocessor for the gradient boosting model<a class="headerlink" href="#pre
 
 /* Pipeline and ColumnTransformer style (default) */
 
-#sk-container-id-36 div.sk-toggleable {
+#sk-container-id-37 div.sk-toggleable {
   /* Default theme specific background. It is overwritten whether we have a
   specific estimator or a Pipeline/ColumnTransformer */
   background-color: var(--sklearn-color-background);
 }
 
 /* Toggleable label */
-#sk-container-id-36 label.sk-toggleable__label {
+#sk-container-id-37 label.sk-toggleable__label {
   cursor: pointer;
   display: flex;
   width: 100%;
@@ -1812,13 +1811,13 @@ <h3>Preprocessor for the gradient boosting model<a class="headerlink" href="#pre
   gap: 0.5em;
 }
 
-#sk-container-id-36 label.sk-toggleable__label .caption {
+#sk-container-id-37 label.sk-toggleable__label .caption {
   font-size: 0.6rem;
   font-weight: lighter;
   color: var(--sklearn-color-text-muted);
 }
 
-#sk-container-id-36 label.sk-toggleable__label-arrow:before {
+#sk-container-id-37 label.sk-toggleable__label-arrow:before {
   /* Arrow on the left of the label */
   content: "▸";
   float: left;
@@ -1826,13 +1825,13 @@ <h3>Preprocessor for the gradient boosting model<a class="headerlink" href="#pre
   color: var(--sklearn-color-icon);
 }
 
-#sk-container-id-36 label.sk-toggleable__label-arrow:hover:before {
+#sk-container-id-37 label.sk-toggleable__label-arrow:hover:before {
   color: var(--sklearn-color-text);
 }
 
 /* Toggleable content - dropdown */
 
-#sk-container-id-36 div.sk-toggleable__content {
+#sk-container-id-37 div.sk-toggleable__content {
   max-height: 0;
   max-width: 0;
   overflow: hidden;
@@ -1841,12 +1840,12 @@ <h3>Preprocessor for the gradient boosting model<a class="headerlink" href="#pre
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-36 div.sk-toggleable__content.fitted {
+#sk-container-id-37 div.sk-toggleable__content.fitted {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
-#sk-container-id-36 div.sk-toggleable__content pre {
+#sk-container-id-37 div.sk-toggleable__content pre {
   margin: 0.2em;
   border-radius: 0.25em;
   color: var(--sklearn-color-text);
@@ -1854,79 +1853,79 @@ <h3>Preprocessor for the gradient boosting model<a class="headerlink" href="#pre
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-36 div.sk-toggleable__content.fitted pre {
+#sk-container-id-37 div.sk-toggleable__content.fitted pre {
   /* unfitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
-#sk-container-id-36 input.sk-toggleable__control:checked~div.sk-toggleable__content {
+#sk-container-id-37 input.sk-toggleable__control:checked~div.sk-toggleable__content {
   /* Expand drop-down */
   max-height: 200px;
   max-width: 100%;
   overflow: auto;
 }
 
-#sk-container-id-36 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
+#sk-container-id-37 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
   content: "▾";
 }
 
 /* Pipeline/ColumnTransformer-specific style */
 
-#sk-container-id-36 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-37 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-36 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-37 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
 /* Estimator-specific style */
 
 /* Colorize estimator box */
-#sk-container-id-36 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-37 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-36 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-37 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
-#sk-container-id-36 div.sk-label label.sk-toggleable__label,
-#sk-container-id-36 div.sk-label label {
+#sk-container-id-37 div.sk-label label.sk-toggleable__label,
+#sk-container-id-37 div.sk-label label {
   /* The background is the default theme color */
   color: var(--sklearn-color-text-on-default-background);
 }
 
 /* On hover, darken the color of the background */
-#sk-container-id-36 div.sk-label:hover label.sk-toggleable__label {
+#sk-container-id-37 div.sk-label:hover label.sk-toggleable__label {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
 /* Label box, darken color on hover, fitted */
-#sk-container-id-36 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
+#sk-container-id-37 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
 /* Estimator label */
 
-#sk-container-id-36 div.sk-label label {
+#sk-container-id-37 div.sk-label label {
   font-family: monospace;
   font-weight: bold;
   display: inline-block;
   line-height: 1.2em;
 }
 
-#sk-container-id-36 div.sk-label-container {
+#sk-container-id-37 div.sk-label-container {
   text-align: center;
 }
 
 /* Estimator-specific */
-#sk-container-id-36 div.sk-estimator {
+#sk-container-id-37 div.sk-estimator {
   font-family: monospace;
   border: 1px dotted var(--sklearn-color-border-box);
   border-radius: 0.25em;
@@ -1936,18 +1935,18 @@ <h3>Preprocessor for the gradient boosting model<a class="headerlink" href="#pre
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-36 div.sk-estimator.fitted {
+#sk-container-id-37 div.sk-estimator.fitted {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
 /* on hover */
-#sk-container-id-36 div.sk-estimator:hover {
+#sk-container-id-37 div.sk-estimator:hover {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-36 div.sk-estimator.fitted:hover {
+#sk-container-id-37 div.sk-estimator.fitted:hover {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-2);
 }
@@ -2035,7 +2034,7 @@ <h3>Preprocessor for the gradient boosting model<a class="headerlink" href="#pre
 
 /* "?"-specific style due to the `<a>` HTML tag */
 
-#sk-container-id-36 a.estimator_doc_link {
+#sk-container-id-37 a.estimator_doc_link {
   float: right;
   font-size: 1rem;
   line-height: 1em;
@@ -2050,25 +2049,25 @@ <h3>Preprocessor for the gradient boosting model<a class="headerlink" href="#pre
   border: var(--sklearn-color-unfitted-level-1) 1pt solid;
 }
 
-#sk-container-id-36 a.estimator_doc_link.fitted {
+#sk-container-id-37 a.estimator_doc_link.fitted {
   /* fitted */
   border: var(--sklearn-color-fitted-level-1) 1pt solid;
   color: var(--sklearn-color-fitted-level-1);
 }
 
 /* On hover */
-#sk-container-id-36 a.estimator_doc_link:hover {
+#sk-container-id-37 a.estimator_doc_link:hover {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-3);
   color: var(--sklearn-color-background);
   text-decoration: none;
 }
 
-#sk-container-id-36 a.estimator_doc_link.fitted:hover {
+#sk-container-id-37 a.estimator_doc_link.fitted:hover {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-3);
 }
-</style><div id="sk-container-id-36" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>ColumnTransformer(sparse_threshold=1,
+</style><div id="sk-container-id-37" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>ColumnTransformer(sparse_threshold=1,
                   transformers=[(&#x27;cat&#x27;, OrdinalEncoder(),
                                  Index([&#x27;season&#x27;, &#x27;month&#x27;, &#x27;hour&#x27;, &#x27;holiday&#x27;, &#x27;weekday&#x27;, &#x27;workingday&#x27;,
        &#x27;weather&#x27;],
@@ -2076,7 +2075,7 @@ <h3>Preprocessor for the gradient boosting model<a class="headerlink" href="#pre
                                 (&#x27;num&#x27;, &#x27;passthrough&#x27;,
                                  [&#x27;temp&#x27;, &#x27;feel_temp&#x27;, &#x27;humidity&#x27;,
                                   &#x27;windspeed&#x27;])],
-                  verbose_feature_names_out=False)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-144" type="checkbox" ><label for="sk-estimator-id-144" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>ColumnTransformer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.compose.ColumnTransformer.html">?<span>Documentation for ColumnTransformer</span></a><span class="sk-estimator-doc-link ">i<span>Not fitted</span></span></div></label><div class="sk-toggleable__content "><pre>ColumnTransformer(sparse_threshold=1,
+                  verbose_feature_names_out=False)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-158" type="checkbox" ><label for="sk-estimator-id-158" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>ColumnTransformer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.compose.ColumnTransformer.html">?<span>Documentation for ColumnTransformer</span></a><span class="sk-estimator-doc-link ">i<span>Not fitted</span></span></div></label><div class="sk-toggleable__content "><pre>ColumnTransformer(sparse_threshold=1,
                   transformers=[(&#x27;cat&#x27;, OrdinalEncoder(),
                                  Index([&#x27;season&#x27;, &#x27;month&#x27;, &#x27;hour&#x27;, &#x27;holiday&#x27;, &#x27;weekday&#x27;, &#x27;workingday&#x27;,
        &#x27;weather&#x27;],
@@ -2084,9 +2083,9 @@ <h3>Preprocessor for the gradient boosting model<a class="headerlink" href="#pre
                                 (&#x27;num&#x27;, &#x27;passthrough&#x27;,
                                  [&#x27;temp&#x27;, &#x27;feel_temp&#x27;, &#x27;humidity&#x27;,
                                   &#x27;windspeed&#x27;])],
-                  verbose_feature_names_out=False)</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-145" type="checkbox" ><label for="sk-estimator-id-145" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>cat</div></div></label><div class="sk-toggleable__content "><pre>Index([&#x27;season&#x27;, &#x27;month&#x27;, &#x27;hour&#x27;, &#x27;holiday&#x27;, &#x27;weekday&#x27;, &#x27;workingday&#x27;,
+                  verbose_feature_names_out=False)</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-159" type="checkbox" ><label for="sk-estimator-id-159" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>cat</div></div></label><div class="sk-toggleable__content "><pre>Index([&#x27;season&#x27;, &#x27;month&#x27;, &#x27;hour&#x27;, &#x27;holiday&#x27;, &#x27;weekday&#x27;, &#x27;workingday&#x27;,
        &#x27;weather&#x27;],
-      dtype=&#x27;object&#x27;)</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-146" type="checkbox" ><label for="sk-estimator-id-146" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>OrdinalEncoder</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.OrdinalEncoder.html">?<span>Documentation for OrdinalEncoder</span></a></div></label><div class="sk-toggleable__content "><pre>OrdinalEncoder()</pre></div> </div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-147" type="checkbox" ><label for="sk-estimator-id-147" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>num</div></div></label><div class="sk-toggleable__content "><pre>[&#x27;temp&#x27;, &#x27;feel_temp&#x27;, &#x27;humidity&#x27;, &#x27;windspeed&#x27;]</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-148" type="checkbox" ><label for="sk-estimator-id-148" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>passthrough</div></div></label><div class="sk-toggleable__content "><pre>passthrough</pre></div> </div></div></div></div></div></div></div></div></div>
+      dtype=&#x27;object&#x27;)</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-160" type="checkbox" ><label for="sk-estimator-id-160" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>OrdinalEncoder</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.OrdinalEncoder.html">?<span>Documentation for OrdinalEncoder</span></a></div></label><div class="sk-toggleable__content "><pre>OrdinalEncoder()</pre></div> </div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-161" type="checkbox" ><label for="sk-estimator-id-161" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>num</div></div></label><div class="sk-toggleable__content "><pre>[&#x27;temp&#x27;, &#x27;feel_temp&#x27;, &#x27;humidity&#x27;, &#x27;windspeed&#x27;]</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-162" type="checkbox" ><label for="sk-estimator-id-162" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>passthrough</div></div></label><div class="sk-toggleable__content "><pre>passthrough</pre></div> </div></div></div></div></div></div></div></div></div>
 </div>
 <br />
 <br /></section>
@@ -2124,7 +2123,7 @@ <h3>Multi-layer perceptron<a class="headerlink" href="#multi-layer-perceptron" t
 </pre></div>
 </div>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Training MLPRegressor...
-done in 0.607s
+done in 0.658s
 Test R2 score: 0.61
 </pre></div>
 </div>
@@ -2183,7 +2182,7 @@ <h3>Multi-layer perceptron<a class="headerlink" href="#multi-layer-perceptron" t
 </pre></div>
 </div>
 <img src="http://github.com/_images/sphx_glr_plot_partial_dependence_002.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_partial_dependence_002.png " alt="Partial dependence of the number of bike rentals for the bike rental dataset with an MLPRegressor" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Computing partial dependence plots...
-done in 0.649s
+done in 0.543s
 </pre></div>
 </div>
 </section>
@@ -2210,7 +2209,7 @@ <h3>Gradient boosting<a class="headerlink" href="#gradient-boosting" title="Link
 </pre></div>
 </div>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Training HistGradientBoostingRegressor...
-done in 0.132s
+done in 0.131s
 Test R2 score: 0.62
 </pre></div>
 </div>
@@ -2244,7 +2243,7 @@ <h3>Gradient boosting<a class="headerlink" href="#gradient-boosting" title="Link
 </pre></div>
 </div>
 <img src="http://github.com/_images/sphx_glr_plot_partial_dependence_003.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_partial_dependence_003.png " alt="Partial dependence of the number of bike rentals for the bike rental dataset with a gradient boosting" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Computing partial dependence plots...
-done in 1.198s
+done in 1.118s
 </pre></div>
 </div>
 </section>
@@ -2295,7 +2294,7 @@ <h3>Analysis of the plots<a class="headerlink" href="#analysis-of-the-plots" tit
 </pre></div>
 </div>
 <img src="http://github.com/_images/sphx_glr_plot_partial_dependence_004.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_partial_dependence_004.png " alt="ICE and PDP representations" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Computing partial dependence plots and individual conditional expectation...
-done in 0.537s
+done in 0.508s
 </pre></div>
 </div>
 <p>We see that the ICE for the temperature feature gives us some additional information:
@@ -2364,7 +2363,7 @@ <h2>2D interaction plots<a class="headerlink" href="#d-interaction-plots" title=
 </pre></div>
 </div>
 <img src="http://github.com/_images/sphx_glr_plot_partial_dependence_006.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_partial_dependence_006.png " alt="1-way vs 2-way of numerical PDP using gradient boosting" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Computing partial dependence plots...
-done in 8.215s
+done in 8.146s
 </pre></div>
 </div>
 <p>The two-way partial dependence plot shows the dependence of the number of bike rentals
@@ -2401,7 +2400,7 @@ <h2>2D interaction plots<a class="headerlink" href="#d-interaction-plots" title=
 </pre></div>
 </div>
 <img src="http://github.com/_images/sphx_glr_plot_partial_dependence_007.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_partial_dependence_007.png " alt="1-way vs 2-way of numerical PDP using gradient boosting" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Computing partial dependence plots...
-done in 7.819s
+done in 7.991s
 </pre></div>
 </div>
 <p>The 1D partial dependence plots for the model constrained to not model feature
@@ -2445,7 +2444,7 @@ <h2>2D interaction plots<a class="headerlink" href="#d-interaction-plots" title=
 </pre></div>
 </div>
 <img src="http://github.com/_images/sphx_glr_plot_partial_dependence_008.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_partial_dependence_008.png " alt="1-way vs 2-way PDP of categorical features using gradient boosting" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Computing partial dependence plots...
-done in 0.382s
+done in 0.374s
 </pre></div>
 </div>
 <section id="d-representation">
@@ -2525,10 +2524,10 @@ <h3>3D representation<a class="headerlink" href="#d-representation" title="Link
 </pre></div>
 </div>
 <img src="http://github.com/_images/sphx_glr_plot_partial_dependence_010.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_partial_dependence_010.png " alt="Partial dependence of the number of bike rentals for the bike rental dataset with a gradient boosting" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Computing partial dependence plots with custom evaluation values...
-done in 0.479s
+done in 0.555s
 </pre></div>
 </div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 25.670 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 25.682 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-inspection-plot-partial-dependence-py">
 <div class="binder-badge docutils container">
 <a class="reference external image-reference" href="https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/inspection/plot_partial_dependence.ipynb"><img alt="Launch binder" src="http://github.com/_images/binder_badge_logo16.svg" style="width: 150px;" />
diff --git a/dev/auto_examples/inspection/plot_permutation_importance.html b/dev/auto_examples/inspection/plot_permutation_importance.html
index 7dc904528a96c..be9991f22b005 100644
--- a/dev/auto_examples/inspection/plot_permutation_importance.html
+++ b/dev/auto_examples/inspection/plot_permutation_importance.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -679,13 +679,12 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -902,9 +901,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
@@ -1081,7 +1080,7 @@ <h2>Data Loading and Feature Engineering<a class="headerlink" href="#data-loadin
 </pre></div>
 </div>
 <div class="output_subarea output_html rendered_html output_result">
-<style>#sk-container-id-37 {
+<style>#sk-container-id-38 {
   /* Definition of color scheme common for light and dark mode */
   --sklearn-color-text: #000;
   --sklearn-color-text-muted: #666;
@@ -1112,15 +1111,15 @@ <h2>Data Loading and Feature Engineering<a class="headerlink" href="#data-loadin
   }
 }
 
-#sk-container-id-37 {
+#sk-container-id-38 {
   color: var(--sklearn-color-text);
 }
 
-#sk-container-id-37 pre {
+#sk-container-id-38 pre {
   padding: 0;
 }
 
-#sk-container-id-37 input.sk-hidden--visually {
+#sk-container-id-38 input.sk-hidden--visually {
   border: 0;
   clip: rect(1px 1px 1px 1px);
   clip: rect(1px, 1px, 1px, 1px);
@@ -1132,7 +1131,7 @@ <h2>Data Loading and Feature Engineering<a class="headerlink" href="#data-loadin
   width: 1px;
 }
 
-#sk-container-id-37 div.sk-dashed-wrapped {
+#sk-container-id-38 div.sk-dashed-wrapped {
   border: 1px dashed var(--sklearn-color-line);
   margin: 0 0.4em 0.5em 0.4em;
   box-sizing: border-box;
@@ -1140,7 +1139,7 @@ <h2>Data Loading and Feature Engineering<a class="headerlink" href="#data-loadin
   background-color: var(--sklearn-color-background);
 }
 
-#sk-container-id-37 div.sk-container {
+#sk-container-id-38 div.sk-container {
   /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
      but bootstrap.min.css set `[hidden] { display: none !important; }`
      so we also need the `!important` here to be able to override the
@@ -1150,7 +1149,7 @@ <h2>Data Loading and Feature Engineering<a class="headerlink" href="#data-loadin
   position: relative;
 }
 
-#sk-container-id-37 div.sk-text-repr-fallback {
+#sk-container-id-38 div.sk-text-repr-fallback {
   display: none;
 }
 
@@ -1166,14 +1165,14 @@ <h2>Data Loading and Feature Engineering<a class="headerlink" href="#data-loadin
 
 /* Parallel-specific style estimator block */
 
-#sk-container-id-37 div.sk-parallel-item::after {
+#sk-container-id-38 div.sk-parallel-item::after {
   content: "";
   width: 100%;
   border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
   flex-grow: 1;
 }
 
-#sk-container-id-37 div.sk-parallel {
+#sk-container-id-38 div.sk-parallel {
   display: flex;
   align-items: stretch;
   justify-content: center;
@@ -1181,28 +1180,28 @@ <h2>Data Loading and Feature Engineering<a class="headerlink" href="#data-loadin
   position: relative;
 }
 
-#sk-container-id-37 div.sk-parallel-item {
+#sk-container-id-38 div.sk-parallel-item {
   display: flex;
   flex-direction: column;
 }
 
-#sk-container-id-37 div.sk-parallel-item:first-child::after {
+#sk-container-id-38 div.sk-parallel-item:first-child::after {
   align-self: flex-end;
   width: 50%;
 }
 
-#sk-container-id-37 div.sk-parallel-item:last-child::after {
+#sk-container-id-38 div.sk-parallel-item:last-child::after {
   align-self: flex-start;
   width: 50%;
 }
 
-#sk-container-id-37 div.sk-parallel-item:only-child::after {
+#sk-container-id-38 div.sk-parallel-item:only-child::after {
   width: 0;
 }
 
 /* Serial-specific style estimator block */
 
-#sk-container-id-37 div.sk-serial {
+#sk-container-id-38 div.sk-serial {
   display: flex;
   flex-direction: column;
   align-items: center;
@@ -1220,14 +1219,14 @@ <h2>Data Loading and Feature Engineering<a class="headerlink" href="#data-loadin
 
 /* Pipeline and ColumnTransformer style (default) */
 
-#sk-container-id-37 div.sk-toggleable {
+#sk-container-id-38 div.sk-toggleable {
   /* Default theme specific background. It is overwritten whether we have a
   specific estimator or a Pipeline/ColumnTransformer */
   background-color: var(--sklearn-color-background);
 }
 
 /* Toggleable label */
-#sk-container-id-37 label.sk-toggleable__label {
+#sk-container-id-38 label.sk-toggleable__label {
   cursor: pointer;
   display: flex;
   width: 100%;
@@ -1240,13 +1239,13 @@ <h2>Data Loading and Feature Engineering<a class="headerlink" href="#data-loadin
   gap: 0.5em;
 }
 
-#sk-container-id-37 label.sk-toggleable__label .caption {
+#sk-container-id-38 label.sk-toggleable__label .caption {
   font-size: 0.6rem;
   font-weight: lighter;
   color: var(--sklearn-color-text-muted);
 }
 
-#sk-container-id-37 label.sk-toggleable__label-arrow:before {
+#sk-container-id-38 label.sk-toggleable__label-arrow:before {
   /* Arrow on the left of the label */
   content: "▸";
   float: left;
@@ -1254,13 +1253,13 @@ <h2>Data Loading and Feature Engineering<a class="headerlink" href="#data-loadin
   color: var(--sklearn-color-icon);
 }
 
-#sk-container-id-37 label.sk-toggleable__label-arrow:hover:before {
+#sk-container-id-38 label.sk-toggleable__label-arrow:hover:before {
   color: var(--sklearn-color-text);
 }
 
 /* Toggleable content - dropdown */
 
-#sk-container-id-37 div.sk-toggleable__content {
+#sk-container-id-38 div.sk-toggleable__content {
   max-height: 0;
   max-width: 0;
   overflow: hidden;
@@ -1269,12 +1268,12 @@ <h2>Data Loading and Feature Engineering<a class="headerlink" href="#data-loadin
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-37 div.sk-toggleable__content.fitted {
+#sk-container-id-38 div.sk-toggleable__content.fitted {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
-#sk-container-id-37 div.sk-toggleable__content pre {
+#sk-container-id-38 div.sk-toggleable__content pre {
   margin: 0.2em;
   border-radius: 0.25em;
   color: var(--sklearn-color-text);
@@ -1282,79 +1281,79 @@ <h2>Data Loading and Feature Engineering<a class="headerlink" href="#data-loadin
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-37 div.sk-toggleable__content.fitted pre {
+#sk-container-id-38 div.sk-toggleable__content.fitted pre {
   /* unfitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
-#sk-container-id-37 input.sk-toggleable__control:checked~div.sk-toggleable__content {
+#sk-container-id-38 input.sk-toggleable__control:checked~div.sk-toggleable__content {
   /* Expand drop-down */
   max-height: 200px;
   max-width: 100%;
   overflow: auto;
 }
 
-#sk-container-id-37 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
+#sk-container-id-38 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
   content: "▾";
 }
 
 /* Pipeline/ColumnTransformer-specific style */
 
-#sk-container-id-37 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-38 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-37 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-38 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
 /* Estimator-specific style */
 
 /* Colorize estimator box */
-#sk-container-id-37 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-38 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-37 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-38 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
-#sk-container-id-37 div.sk-label label.sk-toggleable__label,
-#sk-container-id-37 div.sk-label label {
+#sk-container-id-38 div.sk-label label.sk-toggleable__label,
+#sk-container-id-38 div.sk-label label {
   /* The background is the default theme color */
   color: var(--sklearn-color-text-on-default-background);
 }
 
 /* On hover, darken the color of the background */
-#sk-container-id-37 div.sk-label:hover label.sk-toggleable__label {
+#sk-container-id-38 div.sk-label:hover label.sk-toggleable__label {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
 /* Label box, darken color on hover, fitted */
-#sk-container-id-37 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
+#sk-container-id-38 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
 /* Estimator label */
 
-#sk-container-id-37 div.sk-label label {
+#sk-container-id-38 div.sk-label label {
   font-family: monospace;
   font-weight: bold;
   display: inline-block;
   line-height: 1.2em;
 }
 
-#sk-container-id-37 div.sk-label-container {
+#sk-container-id-38 div.sk-label-container {
   text-align: center;
 }
 
 /* Estimator-specific */
-#sk-container-id-37 div.sk-estimator {
+#sk-container-id-38 div.sk-estimator {
   font-family: monospace;
   border: 1px dotted var(--sklearn-color-border-box);
   border-radius: 0.25em;
@@ -1364,18 +1363,18 @@ <h2>Data Loading and Feature Engineering<a class="headerlink" href="#data-loadin
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-37 div.sk-estimator.fitted {
+#sk-container-id-38 div.sk-estimator.fitted {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
 /* on hover */
-#sk-container-id-37 div.sk-estimator:hover {
+#sk-container-id-38 div.sk-estimator:hover {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-37 div.sk-estimator.fitted:hover {
+#sk-container-id-38 div.sk-estimator.fitted:hover {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-2);
 }
@@ -1463,7 +1462,7 @@ <h2>Data Loading and Feature Engineering<a class="headerlink" href="#data-loadin
 
 /* "?"-specific style due to the `<a>` HTML tag */
 
-#sk-container-id-37 a.estimator_doc_link {
+#sk-container-id-38 a.estimator_doc_link {
   float: right;
   font-size: 1rem;
   line-height: 1em;
@@ -1478,25 +1477,25 @@ <h2>Data Loading and Feature Engineering<a class="headerlink" href="#data-loadin
   border: var(--sklearn-color-unfitted-level-1) 1pt solid;
 }
 
-#sk-container-id-37 a.estimator_doc_link.fitted {
+#sk-container-id-38 a.estimator_doc_link.fitted {
   /* fitted */
   border: var(--sklearn-color-fitted-level-1) 1pt solid;
   color: var(--sklearn-color-fitted-level-1);
 }
 
 /* On hover */
-#sk-container-id-37 a.estimator_doc_link:hover {
+#sk-container-id-38 a.estimator_doc_link:hover {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-3);
   color: var(--sklearn-color-background);
   text-decoration: none;
 }
 
-#sk-container-id-37 a.estimator_doc_link.fitted:hover {
+#sk-container-id-38 a.estimator_doc_link.fitted:hover {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-3);
 }
-</style><div id="sk-container-id-37" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;preprocess&#x27;,
+</style><div id="sk-container-id-38" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;preprocess&#x27;,
                  ColumnTransformer(transformers=[(&#x27;cat&#x27;,
                                                   OrdinalEncoder(encoded_missing_value=-1,
                                                                  handle_unknown=&#x27;use_encoded_value&#x27;,
@@ -1507,7 +1506,7 @@ <h2>Data Loading and Feature Engineering<a class="headerlink" href="#data-loadin
                                                   [&#x27;age&#x27;, &#x27;sibsp&#x27;, &#x27;parch&#x27;,
                                                    &#x27;fare&#x27;, &#x27;random_num&#x27;])],
                                    verbose_feature_names_out=False)),
-                (&#x27;classifier&#x27;, RandomForestClassifier(random_state=42))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-149" type="checkbox" ><label for="sk-estimator-id-149" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>Pipeline</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.pipeline.Pipeline.html">?<span>Documentation for Pipeline</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>Pipeline(steps=[(&#x27;preprocess&#x27;,
+                (&#x27;classifier&#x27;, RandomForestClassifier(random_state=42))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-163" type="checkbox" ><label for="sk-estimator-id-163" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>Pipeline</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.pipeline.Pipeline.html">?<span>Documentation for Pipeline</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>Pipeline(steps=[(&#x27;preprocess&#x27;,
                  ColumnTransformer(transformers=[(&#x27;cat&#x27;,
                                                   OrdinalEncoder(encoded_missing_value=-1,
                                                                  handle_unknown=&#x27;use_encoded_value&#x27;,
@@ -1518,7 +1517,7 @@ <h2>Data Loading and Feature Engineering<a class="headerlink" href="#data-loadin
                                                   [&#x27;age&#x27;, &#x27;sibsp&#x27;, &#x27;parch&#x27;,
                                                    &#x27;fare&#x27;, &#x27;random_num&#x27;])],
                                    verbose_feature_names_out=False)),
-                (&#x27;classifier&#x27;, RandomForestClassifier(random_state=42))])</pre></div> </div></div><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-150" type="checkbox" ><label for="sk-estimator-id-150" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>preprocess: ColumnTransformer</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.compose.ColumnTransformer.html">?<span>Documentation for preprocess: ColumnTransformer</span></a></div></label><div class="sk-toggleable__content fitted"><pre>ColumnTransformer(transformers=[(&#x27;cat&#x27;,
+                (&#x27;classifier&#x27;, RandomForestClassifier(random_state=42))])</pre></div> </div></div><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-164" type="checkbox" ><label for="sk-estimator-id-164" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>preprocess: ColumnTransformer</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.compose.ColumnTransformer.html">?<span>Documentation for preprocess: ColumnTransformer</span></a></div></label><div class="sk-toggleable__content fitted"><pre>ColumnTransformer(transformers=[(&#x27;cat&#x27;,
                                  OrdinalEncoder(encoded_missing_value=-1,
                                                 handle_unknown=&#x27;use_encoded_value&#x27;,
                                                 unknown_value=-1),
@@ -1526,8 +1525,8 @@ <h2>Data Loading and Feature Engineering<a class="headerlink" href="#data-loadin
                                 (&#x27;num&#x27;, SimpleImputer(),
                                  [&#x27;age&#x27;, &#x27;sibsp&#x27;, &#x27;parch&#x27;, &#x27;fare&#x27;,
                                   &#x27;random_num&#x27;])],
-                  verbose_feature_names_out=False)</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-151" type="checkbox" ><label for="sk-estimator-id-151" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>cat</div></div></label><div class="sk-toggleable__content fitted"><pre>[&#x27;pclass&#x27;, &#x27;sex&#x27;, &#x27;embarked&#x27;, &#x27;random_cat&#x27;]</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-152" type="checkbox" ><label for="sk-estimator-id-152" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>OrdinalEncoder</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.OrdinalEncoder.html">?<span>Documentation for OrdinalEncoder</span></a></div></label><div class="sk-toggleable__content fitted"><pre>OrdinalEncoder(encoded_missing_value=-1, handle_unknown=&#x27;use_encoded_value&#x27;,
-               unknown_value=-1)</pre></div> </div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-153" type="checkbox" ><label for="sk-estimator-id-153" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>num</div></div></label><div class="sk-toggleable__content fitted"><pre>[&#x27;age&#x27;, &#x27;sibsp&#x27;, &#x27;parch&#x27;, &#x27;fare&#x27;, &#x27;random_num&#x27;]</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-154" type="checkbox" ><label for="sk-estimator-id-154" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>SimpleImputer</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.impute.SimpleImputer.html">?<span>Documentation for SimpleImputer</span></a></div></label><div class="sk-toggleable__content fitted"><pre>SimpleImputer()</pre></div> </div></div></div></div></div></div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-155" type="checkbox" ><label for="sk-estimator-id-155" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>RandomForestClassifier</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.ensemble.RandomForestClassifier.html">?<span>Documentation for RandomForestClassifier</span></a></div></label><div class="sk-toggleable__content fitted"><pre>RandomForestClassifier(random_state=42)</pre></div> </div></div></div></div></div></div>
+                  verbose_feature_names_out=False)</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-165" type="checkbox" ><label for="sk-estimator-id-165" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>cat</div></div></label><div class="sk-toggleable__content fitted"><pre>[&#x27;pclass&#x27;, &#x27;sex&#x27;, &#x27;embarked&#x27;, &#x27;random_cat&#x27;]</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-166" type="checkbox" ><label for="sk-estimator-id-166" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>OrdinalEncoder</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.OrdinalEncoder.html">?<span>Documentation for OrdinalEncoder</span></a></div></label><div class="sk-toggleable__content fitted"><pre>OrdinalEncoder(encoded_missing_value=-1, handle_unknown=&#x27;use_encoded_value&#x27;,
+               unknown_value=-1)</pre></div> </div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-167" type="checkbox" ><label for="sk-estimator-id-167" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>num</div></div></label><div class="sk-toggleable__content fitted"><pre>[&#x27;age&#x27;, &#x27;sibsp&#x27;, &#x27;parch&#x27;, &#x27;fare&#x27;, &#x27;random_num&#x27;]</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-168" type="checkbox" ><label for="sk-estimator-id-168" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>SimpleImputer</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.impute.SimpleImputer.html">?<span>Documentation for SimpleImputer</span></a></div></label><div class="sk-toggleable__content fitted"><pre>SimpleImputer()</pre></div> </div></div></div></div></div></div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-169" type="checkbox" ><label for="sk-estimator-id-169" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>RandomForestClassifier</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.ensemble.RandomForestClassifier.html">?<span>Documentation for RandomForestClassifier</span></a></div></label><div class="sk-toggleable__content fitted"><pre>RandomForestClassifier(random_state=42)</pre></div> </div></div></div></div></div></div>
 </div>
 <br />
 <br /></section>
@@ -1642,7 +1641,7 @@ <h2>Tree’s Feature Importance from Mean Decrease in Impurity (MDI)<a class="he
 </pre></div>
 </div>
 <div class="output_subarea output_html rendered_html output_result">
-<style>#sk-container-id-38 {
+<style>#sk-container-id-39 {
   /* Definition of color scheme common for light and dark mode */
   --sklearn-color-text: #000;
   --sklearn-color-text-muted: #666;
@@ -1673,15 +1672,15 @@ <h2>Tree’s Feature Importance from Mean Decrease in Impurity (MDI)<a class="he
   }
 }
 
-#sk-container-id-38 {
+#sk-container-id-39 {
   color: var(--sklearn-color-text);
 }
 
-#sk-container-id-38 pre {
+#sk-container-id-39 pre {
   padding: 0;
 }
 
-#sk-container-id-38 input.sk-hidden--visually {
+#sk-container-id-39 input.sk-hidden--visually {
   border: 0;
   clip: rect(1px 1px 1px 1px);
   clip: rect(1px, 1px, 1px, 1px);
@@ -1693,7 +1692,7 @@ <h2>Tree’s Feature Importance from Mean Decrease in Impurity (MDI)<a class="he
   width: 1px;
 }
 
-#sk-container-id-38 div.sk-dashed-wrapped {
+#sk-container-id-39 div.sk-dashed-wrapped {
   border: 1px dashed var(--sklearn-color-line);
   margin: 0 0.4em 0.5em 0.4em;
   box-sizing: border-box;
@@ -1701,7 +1700,7 @@ <h2>Tree’s Feature Importance from Mean Decrease in Impurity (MDI)<a class="he
   background-color: var(--sklearn-color-background);
 }
 
-#sk-container-id-38 div.sk-container {
+#sk-container-id-39 div.sk-container {
   /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
      but bootstrap.min.css set `[hidden] { display: none !important; }`
      so we also need the `!important` here to be able to override the
@@ -1711,7 +1710,7 @@ <h2>Tree’s Feature Importance from Mean Decrease in Impurity (MDI)<a class="he
   position: relative;
 }
 
-#sk-container-id-38 div.sk-text-repr-fallback {
+#sk-container-id-39 div.sk-text-repr-fallback {
   display: none;
 }
 
@@ -1727,14 +1726,14 @@ <h2>Tree’s Feature Importance from Mean Decrease in Impurity (MDI)<a class="he
 
 /* Parallel-specific style estimator block */
 
-#sk-container-id-38 div.sk-parallel-item::after {
+#sk-container-id-39 div.sk-parallel-item::after {
   content: "";
   width: 100%;
   border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
   flex-grow: 1;
 }
 
-#sk-container-id-38 div.sk-parallel {
+#sk-container-id-39 div.sk-parallel {
   display: flex;
   align-items: stretch;
   justify-content: center;
@@ -1742,28 +1741,28 @@ <h2>Tree’s Feature Importance from Mean Decrease in Impurity (MDI)<a class="he
   position: relative;
 }
 
-#sk-container-id-38 div.sk-parallel-item {
+#sk-container-id-39 div.sk-parallel-item {
   display: flex;
   flex-direction: column;
 }
 
-#sk-container-id-38 div.sk-parallel-item:first-child::after {
+#sk-container-id-39 div.sk-parallel-item:first-child::after {
   align-self: flex-end;
   width: 50%;
 }
 
-#sk-container-id-38 div.sk-parallel-item:last-child::after {
+#sk-container-id-39 div.sk-parallel-item:last-child::after {
   align-self: flex-start;
   width: 50%;
 }
 
-#sk-container-id-38 div.sk-parallel-item:only-child::after {
+#sk-container-id-39 div.sk-parallel-item:only-child::after {
   width: 0;
 }
 
 /* Serial-specific style estimator block */
 
-#sk-container-id-38 div.sk-serial {
+#sk-container-id-39 div.sk-serial {
   display: flex;
   flex-direction: column;
   align-items: center;
@@ -1781,14 +1780,14 @@ <h2>Tree’s Feature Importance from Mean Decrease in Impurity (MDI)<a class="he
 
 /* Pipeline and ColumnTransformer style (default) */
 
-#sk-container-id-38 div.sk-toggleable {
+#sk-container-id-39 div.sk-toggleable {
   /* Default theme specific background. It is overwritten whether we have a
   specific estimator or a Pipeline/ColumnTransformer */
   background-color: var(--sklearn-color-background);
 }
 
 /* Toggleable label */
-#sk-container-id-38 label.sk-toggleable__label {
+#sk-container-id-39 label.sk-toggleable__label {
   cursor: pointer;
   display: flex;
   width: 100%;
@@ -1801,13 +1800,13 @@ <h2>Tree’s Feature Importance from Mean Decrease in Impurity (MDI)<a class="he
   gap: 0.5em;
 }
 
-#sk-container-id-38 label.sk-toggleable__label .caption {
+#sk-container-id-39 label.sk-toggleable__label .caption {
   font-size: 0.6rem;
   font-weight: lighter;
   color: var(--sklearn-color-text-muted);
 }
 
-#sk-container-id-38 label.sk-toggleable__label-arrow:before {
+#sk-container-id-39 label.sk-toggleable__label-arrow:before {
   /* Arrow on the left of the label */
   content: "▸";
   float: left;
@@ -1815,13 +1814,13 @@ <h2>Tree’s Feature Importance from Mean Decrease in Impurity (MDI)<a class="he
   color: var(--sklearn-color-icon);
 }
 
-#sk-container-id-38 label.sk-toggleable__label-arrow:hover:before {
+#sk-container-id-39 label.sk-toggleable__label-arrow:hover:before {
   color: var(--sklearn-color-text);
 }
 
 /* Toggleable content - dropdown */
 
-#sk-container-id-38 div.sk-toggleable__content {
+#sk-container-id-39 div.sk-toggleable__content {
   max-height: 0;
   max-width: 0;
   overflow: hidden;
@@ -1830,12 +1829,12 @@ <h2>Tree’s Feature Importance from Mean Decrease in Impurity (MDI)<a class="he
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-38 div.sk-toggleable__content.fitted {
+#sk-container-id-39 div.sk-toggleable__content.fitted {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
-#sk-container-id-38 div.sk-toggleable__content pre {
+#sk-container-id-39 div.sk-toggleable__content pre {
   margin: 0.2em;
   border-radius: 0.25em;
   color: var(--sklearn-color-text);
@@ -1843,79 +1842,79 @@ <h2>Tree’s Feature Importance from Mean Decrease in Impurity (MDI)<a class="he
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-38 div.sk-toggleable__content.fitted pre {
+#sk-container-id-39 div.sk-toggleable__content.fitted pre {
   /* unfitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
-#sk-container-id-38 input.sk-toggleable__control:checked~div.sk-toggleable__content {
+#sk-container-id-39 input.sk-toggleable__control:checked~div.sk-toggleable__content {
   /* Expand drop-down */
   max-height: 200px;
   max-width: 100%;
   overflow: auto;
 }
 
-#sk-container-id-38 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
+#sk-container-id-39 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
   content: "▾";
 }
 
 /* Pipeline/ColumnTransformer-specific style */
 
-#sk-container-id-38 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-39 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-38 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-39 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
 /* Estimator-specific style */
 
 /* Colorize estimator box */
-#sk-container-id-38 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-39 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-38 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-39 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
-#sk-container-id-38 div.sk-label label.sk-toggleable__label,
-#sk-container-id-38 div.sk-label label {
+#sk-container-id-39 div.sk-label label.sk-toggleable__label,
+#sk-container-id-39 div.sk-label label {
   /* The background is the default theme color */
   color: var(--sklearn-color-text-on-default-background);
 }
 
 /* On hover, darken the color of the background */
-#sk-container-id-38 div.sk-label:hover label.sk-toggleable__label {
+#sk-container-id-39 div.sk-label:hover label.sk-toggleable__label {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
 /* Label box, darken color on hover, fitted */
-#sk-container-id-38 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
+#sk-container-id-39 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
 /* Estimator label */
 
-#sk-container-id-38 div.sk-label label {
+#sk-container-id-39 div.sk-label label {
   font-family: monospace;
   font-weight: bold;
   display: inline-block;
   line-height: 1.2em;
 }
 
-#sk-container-id-38 div.sk-label-container {
+#sk-container-id-39 div.sk-label-container {
   text-align: center;
 }
 
 /* Estimator-specific */
-#sk-container-id-38 div.sk-estimator {
+#sk-container-id-39 div.sk-estimator {
   font-family: monospace;
   border: 1px dotted var(--sklearn-color-border-box);
   border-radius: 0.25em;
@@ -1925,18 +1924,18 @@ <h2>Tree’s Feature Importance from Mean Decrease in Impurity (MDI)<a class="he
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-38 div.sk-estimator.fitted {
+#sk-container-id-39 div.sk-estimator.fitted {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
 /* on hover */
-#sk-container-id-38 div.sk-estimator:hover {
+#sk-container-id-39 div.sk-estimator:hover {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-38 div.sk-estimator.fitted:hover {
+#sk-container-id-39 div.sk-estimator.fitted:hover {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-2);
 }
@@ -2024,7 +2023,7 @@ <h2>Tree’s Feature Importance from Mean Decrease in Impurity (MDI)<a class="he
 
 /* "?"-specific style due to the `<a>` HTML tag */
 
-#sk-container-id-38 a.estimator_doc_link {
+#sk-container-id-39 a.estimator_doc_link {
   float: right;
   font-size: 1rem;
   line-height: 1em;
@@ -2039,25 +2038,25 @@ <h2>Tree’s Feature Importance from Mean Decrease in Impurity (MDI)<a class="he
   border: var(--sklearn-color-unfitted-level-1) 1pt solid;
 }
 
-#sk-container-id-38 a.estimator_doc_link.fitted {
+#sk-container-id-39 a.estimator_doc_link.fitted {
   /* fitted */
   border: var(--sklearn-color-fitted-level-1) 1pt solid;
   color: var(--sklearn-color-fitted-level-1);
 }
 
 /* On hover */
-#sk-container-id-38 a.estimator_doc_link:hover {
+#sk-container-id-39 a.estimator_doc_link:hover {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-3);
   color: var(--sklearn-color-background);
   text-decoration: none;
 }
 
-#sk-container-id-38 a.estimator_doc_link.fitted:hover {
+#sk-container-id-39 a.estimator_doc_link.fitted:hover {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-3);
 }
-</style><div id="sk-container-id-38" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;preprocess&#x27;,
+</style><div id="sk-container-id-39" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;preprocess&#x27;,
                  ColumnTransformer(transformers=[(&#x27;cat&#x27;,
                                                   OrdinalEncoder(encoded_missing_value=-1,
                                                                  handle_unknown=&#x27;use_encoded_value&#x27;,
@@ -2069,7 +2068,7 @@ <h2>Tree’s Feature Importance from Mean Decrease in Impurity (MDI)<a class="he
                                                    &#x27;fare&#x27;, &#x27;random_num&#x27;])],
                                    verbose_feature_names_out=False)),
                 (&#x27;classifier&#x27;,
-                 RandomForestClassifier(min_samples_leaf=20, random_state=42))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-156" type="checkbox" ><label for="sk-estimator-id-156" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>Pipeline</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.pipeline.Pipeline.html">?<span>Documentation for Pipeline</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>Pipeline(steps=[(&#x27;preprocess&#x27;,
+                 RandomForestClassifier(min_samples_leaf=20, random_state=42))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-170" type="checkbox" ><label for="sk-estimator-id-170" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>Pipeline</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.pipeline.Pipeline.html">?<span>Documentation for Pipeline</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>Pipeline(steps=[(&#x27;preprocess&#x27;,
                  ColumnTransformer(transformers=[(&#x27;cat&#x27;,
                                                   OrdinalEncoder(encoded_missing_value=-1,
                                                                  handle_unknown=&#x27;use_encoded_value&#x27;,
@@ -2081,7 +2080,7 @@ <h2>Tree’s Feature Importance from Mean Decrease in Impurity (MDI)<a class="he
                                                    &#x27;fare&#x27;, &#x27;random_num&#x27;])],
                                    verbose_feature_names_out=False)),
                 (&#x27;classifier&#x27;,
-                 RandomForestClassifier(min_samples_leaf=20, random_state=42))])</pre></div> </div></div><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-157" type="checkbox" ><label for="sk-estimator-id-157" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>preprocess: ColumnTransformer</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.compose.ColumnTransformer.html">?<span>Documentation for preprocess: ColumnTransformer</span></a></div></label><div class="sk-toggleable__content fitted"><pre>ColumnTransformer(transformers=[(&#x27;cat&#x27;,
+                 RandomForestClassifier(min_samples_leaf=20, random_state=42))])</pre></div> </div></div><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-171" type="checkbox" ><label for="sk-estimator-id-171" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>preprocess: ColumnTransformer</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.compose.ColumnTransformer.html">?<span>Documentation for preprocess: ColumnTransformer</span></a></div></label><div class="sk-toggleable__content fitted"><pre>ColumnTransformer(transformers=[(&#x27;cat&#x27;,
                                  OrdinalEncoder(encoded_missing_value=-1,
                                                 handle_unknown=&#x27;use_encoded_value&#x27;,
                                                 unknown_value=-1),
@@ -2089,8 +2088,8 @@ <h2>Tree’s Feature Importance from Mean Decrease in Impurity (MDI)<a class="he
                                 (&#x27;num&#x27;, SimpleImputer(),
                                  [&#x27;age&#x27;, &#x27;sibsp&#x27;, &#x27;parch&#x27;, &#x27;fare&#x27;,
                                   &#x27;random_num&#x27;])],
-                  verbose_feature_names_out=False)</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-158" type="checkbox" ><label for="sk-estimator-id-158" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>cat</div></div></label><div class="sk-toggleable__content fitted"><pre>[&#x27;pclass&#x27;, &#x27;sex&#x27;, &#x27;embarked&#x27;, &#x27;random_cat&#x27;]</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-159" type="checkbox" ><label for="sk-estimator-id-159" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>OrdinalEncoder</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.OrdinalEncoder.html">?<span>Documentation for OrdinalEncoder</span></a></div></label><div class="sk-toggleable__content fitted"><pre>OrdinalEncoder(encoded_missing_value=-1, handle_unknown=&#x27;use_encoded_value&#x27;,
-               unknown_value=-1)</pre></div> </div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-160" type="checkbox" ><label for="sk-estimator-id-160" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>num</div></div></label><div class="sk-toggleable__content fitted"><pre>[&#x27;age&#x27;, &#x27;sibsp&#x27;, &#x27;parch&#x27;, &#x27;fare&#x27;, &#x27;random_num&#x27;]</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-161" type="checkbox" ><label for="sk-estimator-id-161" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>SimpleImputer</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.impute.SimpleImputer.html">?<span>Documentation for SimpleImputer</span></a></div></label><div class="sk-toggleable__content fitted"><pre>SimpleImputer()</pre></div> </div></div></div></div></div></div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-162" type="checkbox" ><label for="sk-estimator-id-162" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>RandomForestClassifier</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.ensemble.RandomForestClassifier.html">?<span>Documentation for RandomForestClassifier</span></a></div></label><div class="sk-toggleable__content fitted"><pre>RandomForestClassifier(min_samples_leaf=20, random_state=42)</pre></div> </div></div></div></div></div></div>
+                  verbose_feature_names_out=False)</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-172" type="checkbox" ><label for="sk-estimator-id-172" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>cat</div></div></label><div class="sk-toggleable__content fitted"><pre>[&#x27;pclass&#x27;, &#x27;sex&#x27;, &#x27;embarked&#x27;, &#x27;random_cat&#x27;]</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-173" type="checkbox" ><label for="sk-estimator-id-173" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>OrdinalEncoder</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.OrdinalEncoder.html">?<span>Documentation for OrdinalEncoder</span></a></div></label><div class="sk-toggleable__content fitted"><pre>OrdinalEncoder(encoded_missing_value=-1, handle_unknown=&#x27;use_encoded_value&#x27;,
+               unknown_value=-1)</pre></div> </div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-174" type="checkbox" ><label for="sk-estimator-id-174" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>num</div></div></label><div class="sk-toggleable__content fitted"><pre>[&#x27;age&#x27;, &#x27;sibsp&#x27;, &#x27;parch&#x27;, &#x27;fare&#x27;, &#x27;random_num&#x27;]</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-175" type="checkbox" ><label for="sk-estimator-id-175" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>SimpleImputer</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.impute.SimpleImputer.html">?<span>Documentation for SimpleImputer</span></a></div></label><div class="sk-toggleable__content fitted"><pre>SimpleImputer()</pre></div> </div></div></div></div></div></div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-176" type="checkbox" ><label for="sk-estimator-id-176" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>RandomForestClassifier</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.ensemble.RandomForestClassifier.html">?<span>Documentation for RandomForestClassifier</span></a></div></label><div class="sk-toggleable__content fitted"><pre>RandomForestClassifier(min_samples_leaf=20, random_state=42)</pre></div> </div></div></div></div></div></div>
 </div>
 <br />
 <br /><p>Observing the accuracy score on the training and testing set, we observe that
@@ -2139,7 +2138,7 @@ <h2>Tree’s Feature Importance from Mean Decrease in Impurity (MDI)<a class="he
 features have a lower importance compared to the overfitting random forest.
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
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 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
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 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
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diff --git a/dev/auto_examples/linear_model/plot_elastic_net_precomputed_gram_matrix_with_weighted_samples.html b/dev/auto_examples/linear_model/plot_elastic_net_precomputed_gram_matrix_with_weighted_samples.html
index 05a5355099923..c545251843db3 100644
--- a/dev/auto_examples/linear_model/plot_elastic_net_precomputed_gram_matrix_with_weighted_samples.html
+++ b/dev/auto_examples/linear_model/plot_elastic_net_precomputed_gram_matrix_with_weighted_samples.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
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     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -679,13 +679,12 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -902,9 +901,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
@@ -1046,7 +1045,7 @@
 </pre></div>
 </div>
 <div class="output_subarea output_html rendered_html output_result">
-<style>#sk-container-id-31 {
+<style>#sk-container-id-32 {
   /* Definition of color scheme common for light and dark mode */
   --sklearn-color-text: #000;
   --sklearn-color-text-muted: #666;
@@ -1077,15 +1076,15 @@
   }
 }
 
-#sk-container-id-31 {
+#sk-container-id-32 {
   color: var(--sklearn-color-text);
 }
 
-#sk-container-id-31 pre {
+#sk-container-id-32 pre {
   padding: 0;
 }
 
-#sk-container-id-31 input.sk-hidden--visually {
+#sk-container-id-32 input.sk-hidden--visually {
   border: 0;
   clip: rect(1px 1px 1px 1px);
   clip: rect(1px, 1px, 1px, 1px);
@@ -1097,7 +1096,7 @@
   width: 1px;
 }
 
-#sk-container-id-31 div.sk-dashed-wrapped {
+#sk-container-id-32 div.sk-dashed-wrapped {
   border: 1px dashed var(--sklearn-color-line);
   margin: 0 0.4em 0.5em 0.4em;
   box-sizing: border-box;
@@ -1105,7 +1104,7 @@
   background-color: var(--sklearn-color-background);
 }
 
-#sk-container-id-31 div.sk-container {
+#sk-container-id-32 div.sk-container {
   /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
      but bootstrap.min.css set `[hidden] { display: none !important; }`
      so we also need the `!important` here to be able to override the
@@ -1115,7 +1114,7 @@
   position: relative;
 }
 
-#sk-container-id-31 div.sk-text-repr-fallback {
+#sk-container-id-32 div.sk-text-repr-fallback {
   display: none;
 }
 
@@ -1131,14 +1130,14 @@
 
 /* Parallel-specific style estimator block */
 
-#sk-container-id-31 div.sk-parallel-item::after {
+#sk-container-id-32 div.sk-parallel-item::after {
   content: "";
   width: 100%;
   border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
   flex-grow: 1;
 }
 
-#sk-container-id-31 div.sk-parallel {
+#sk-container-id-32 div.sk-parallel {
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   align-items: stretch;
   justify-content: center;
@@ -1146,28 +1145,28 @@
   position: relative;
 }
 
-#sk-container-id-31 div.sk-parallel-item {
+#sk-container-id-32 div.sk-parallel-item {
   display: flex;
   flex-direction: column;
 }
 
-#sk-container-id-31 div.sk-parallel-item:first-child::after {
+#sk-container-id-32 div.sk-parallel-item:first-child::after {
   align-self: flex-end;
   width: 50%;
 }
 
-#sk-container-id-31 div.sk-parallel-item:last-child::after {
+#sk-container-id-32 div.sk-parallel-item:last-child::after {
   align-self: flex-start;
   width: 50%;
 }
 
-#sk-container-id-31 div.sk-parallel-item:only-child::after {
+#sk-container-id-32 div.sk-parallel-item:only-child::after {
   width: 0;
 }
 
 /* Serial-specific style estimator block */
 
-#sk-container-id-31 div.sk-serial {
+#sk-container-id-32 div.sk-serial {
   display: flex;
   flex-direction: column;
   align-items: center;
@@ -1185,14 +1184,14 @@
 
 /* Pipeline and ColumnTransformer style (default) */
 
-#sk-container-id-31 div.sk-toggleable {
+#sk-container-id-32 div.sk-toggleable {
   /* Default theme specific background. It is overwritten whether we have a
   specific estimator or a Pipeline/ColumnTransformer */
   background-color: var(--sklearn-color-background);
 }
 
 /* Toggleable label */
-#sk-container-id-31 label.sk-toggleable__label {
+#sk-container-id-32 label.sk-toggleable__label {
   cursor: pointer;
   display: flex;
   width: 100%;
@@ -1205,13 +1204,13 @@
   gap: 0.5em;
 }
 
-#sk-container-id-31 label.sk-toggleable__label .caption {
+#sk-container-id-32 label.sk-toggleable__label .caption {
   font-size: 0.6rem;
   font-weight: lighter;
   color: var(--sklearn-color-text-muted);
 }
 
-#sk-container-id-31 label.sk-toggleable__label-arrow:before {
+#sk-container-id-32 label.sk-toggleable__label-arrow:before {
   /* Arrow on the left of the label */
   content: "▸";
   float: left;
@@ -1219,13 +1218,13 @@
   color: var(--sklearn-color-icon);
 }
 
-#sk-container-id-31 label.sk-toggleable__label-arrow:hover:before {
+#sk-container-id-32 label.sk-toggleable__label-arrow:hover:before {
   color: var(--sklearn-color-text);
 }
 
 /* Toggleable content - dropdown */
 
-#sk-container-id-31 div.sk-toggleable__content {
+#sk-container-id-32 div.sk-toggleable__content {
   max-height: 0;
   max-width: 0;
   overflow: hidden;
@@ -1234,12 +1233,12 @@
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-31 div.sk-toggleable__content.fitted {
+#sk-container-id-32 div.sk-toggleable__content.fitted {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
-#sk-container-id-31 div.sk-toggleable__content pre {
+#sk-container-id-32 div.sk-toggleable__content pre {
   margin: 0.2em;
   border-radius: 0.25em;
   color: var(--sklearn-color-text);
@@ -1247,79 +1246,79 @@
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-31 div.sk-toggleable__content.fitted pre {
+#sk-container-id-32 div.sk-toggleable__content.fitted pre {
   /* unfitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
-#sk-container-id-31 input.sk-toggleable__control:checked~div.sk-toggleable__content {
+#sk-container-id-32 input.sk-toggleable__control:checked~div.sk-toggleable__content {
   /* Expand drop-down */
   max-height: 200px;
   max-width: 100%;
   overflow: auto;
 }
 
-#sk-container-id-31 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
+#sk-container-id-32 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
   content: "▾";
 }
 
 /* Pipeline/ColumnTransformer-specific style */
 
-#sk-container-id-31 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-32 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-31 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-32 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
 /* Estimator-specific style */
 
 /* Colorize estimator box */
-#sk-container-id-31 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-32 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-31 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-32 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
-#sk-container-id-31 div.sk-label label.sk-toggleable__label,
-#sk-container-id-31 div.sk-label label {
+#sk-container-id-32 div.sk-label label.sk-toggleable__label,
+#sk-container-id-32 div.sk-label label {
   /* The background is the default theme color */
   color: var(--sklearn-color-text-on-default-background);
 }
 
 /* On hover, darken the color of the background */
-#sk-container-id-31 div.sk-label:hover label.sk-toggleable__label {
+#sk-container-id-32 div.sk-label:hover label.sk-toggleable__label {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
 /* Label box, darken color on hover, fitted */
-#sk-container-id-31 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
+#sk-container-id-32 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
 /* Estimator label */
 
-#sk-container-id-31 div.sk-label label {
+#sk-container-id-32 div.sk-label label {
   font-family: monospace;
   font-weight: bold;
   display: inline-block;
   line-height: 1.2em;
 }
 
-#sk-container-id-31 div.sk-label-container {
+#sk-container-id-32 div.sk-label-container {
   text-align: center;
 }
 
 /* Estimator-specific */
-#sk-container-id-31 div.sk-estimator {
+#sk-container-id-32 div.sk-estimator {
   font-family: monospace;
   border: 1px dotted var(--sklearn-color-border-box);
   border-radius: 0.25em;
@@ -1329,18 +1328,18 @@
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-31 div.sk-estimator.fitted {
+#sk-container-id-32 div.sk-estimator.fitted {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
 /* on hover */
-#sk-container-id-31 div.sk-estimator:hover {
+#sk-container-id-32 div.sk-estimator:hover {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-31 div.sk-estimator.fitted:hover {
+#sk-container-id-32 div.sk-estimator.fitted:hover {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-2);
 }
@@ -1428,7 +1427,7 @@
 
 /* "?"-specific style due to the `<a>` HTML tag */
 
-#sk-container-id-31 a.estimator_doc_link {
+#sk-container-id-32 a.estimator_doc_link {
   float: right;
   font-size: 1rem;
   line-height: 1em;
@@ -1443,25 +1442,25 @@
   border: var(--sklearn-color-unfitted-level-1) 1pt solid;
 }
 
-#sk-container-id-31 a.estimator_doc_link.fitted {
+#sk-container-id-32 a.estimator_doc_link.fitted {
   /* fitted */
   border: var(--sklearn-color-fitted-level-1) 1pt solid;
   color: var(--sklearn-color-fitted-level-1);
 }
 
 /* On hover */
-#sk-container-id-31 a.estimator_doc_link:hover {
+#sk-container-id-32 a.estimator_doc_link:hover {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-3);
   color: var(--sklearn-color-background);
   text-decoration: none;
 }
 
-#sk-container-id-31 a.estimator_doc_link.fitted:hover {
+#sk-container-id-32 a.estimator_doc_link.fitted:hover {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-3);
 }
-</style><div id="sk-container-id-31" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>ElasticNet(alpha=0.01,
+</style><div id="sk-container-id-32" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>ElasticNet(alpha=0.01,
            precompute=array([[ 9.98809919e+04, -4.48938813e+02, -1.03237920e+03, ...,
         -2.25349312e+02, -3.53959628e+02, -1.67451144e+02],
        [-4.48938813e+02,  1.00768662e+05,  1.19112072e+02, ...,
@@ -1475,7 +1474,7 @@
         -4.58195950e+02,  9.98350372e+04,  5.60836363e+02],
        [-1.67451144e+02, -5.76195467e+02,  2.65799352e+02, ...,
         -1.58667835e+02,  5.60836363e+02,  1.00911944e+05]],
-      shape=(100, 100)))</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-115" type="checkbox" checked><label for="sk-estimator-id-115" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>ElasticNet</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.linear_model.ElasticNet.html">?<span>Documentation for ElasticNet</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>ElasticNet(alpha=0.01,
+      shape=(100, 100)))</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-125" type="checkbox" checked><label for="sk-estimator-id-125" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>ElasticNet</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.linear_model.ElasticNet.html">?<span>Documentation for ElasticNet</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>ElasticNet(alpha=0.01,
            precompute=array([[ 9.98809919e+04, -4.48938813e+02, -1.03237920e+03, ...,
         -2.25349312e+02, -3.53959628e+02, -1.67451144e+02],
        [-4.48938813e+02,  1.00768662e+05,  1.19112072e+02, ...,
@@ -1492,7 +1491,7 @@
       shape=(100, 100)))</pre></div> </div></div></div></div>
 </div>
 <br />
-<br /><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 0.794 seconds)</p>
+<br /><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 1.956 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-linear-model-plot-elastic-net-precomputed-gram-matrix-with-weighted-samples-py">
 <div class="binder-badge docutils container">
 <a class="reference external image-reference" href="https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/linear_model/plot_elastic_net_precomputed_gram_matrix_with_weighted_samples.ipynb"><img alt="Launch binder" src="http://github.com/_images/binder_badge_logo18.svg" style="width: 150px;" />
diff --git a/dev/auto_examples/linear_model/plot_huber_vs_ridge.html b/dev/auto_examples/linear_model/plot_huber_vs_ridge.html
index 1ea786f2096d5..b53b15dda5213 100644
--- a/dev/auto_examples/linear_model/plot_huber_vs_ridge.html
+++ b/dev/auto_examples/linear_model/plot_huber_vs_ridge.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -679,13 +679,12 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -902,9 +901,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
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@@ -1051,7 +1050,7 @@
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+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 0.119 seconds)</p>
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diff --git a/dev/auto_examples/linear_model/plot_lasso_and_elasticnet.html b/dev/auto_examples/linear_model/plot_lasso_and_elasticnet.html
index cf02256210504..9f48a9ecd5316 100644
--- a/dev/auto_examples/linear_model/plot_lasso_and_elasticnet.html
+++ b/dev/auto_examples/linear_model/plot_lasso_and_elasticnet.html
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@@ -680,13 +680,12 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
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 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -903,9 +902,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
@@ -1136,7 +1135,7 @@ <h2>Automatic Relevance Determination (ARD)<a class="headerlink" href="#automati
 <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;ARD r^2 on test data : </span><span class="si">{</span><span class="n">r2_score_ard</span><span class="si">:</span><span class="s2">.3f</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>ARD fit done in 0.041s
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>ARD fit done in 0.043s
 ARD r^2 on test data : 0.543
 </pre></div>
 </div>
@@ -1241,7 +1240,7 @@ <h2>References<a class="headerlink" href="#references" title="Link to this headi
 2009, Vol. 37, No. 1, 246-270</a></p>
 </aside>
 </aside>
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index 081a36e3d3e6c..3d20e95b46b9a 100644
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
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 </details></li>
@@ -1039,8 +1038,8 @@ <h2>Comparing the two Lasso implementations on Dense data<a class="headerlink" h
 <span class="c1">#</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Sparse Lasso done in 0.104s
-Dense Lasso done in 0.030s
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Sparse Lasso done in 0.103s
+Dense Lasso done in 0.033s
 Distance between coefficients : 1.01e-13
 </pre></div>
 </div>
@@ -1079,12 +1078,12 @@ <h2>Comparing the two Lasso implementations on Sparse data<a class="headerlink"
 </pre></div>
 </div>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Matrix density : 0.626%
-Sparse Lasso done in 0.171s
-Dense Lasso done in  0.668s
+Sparse Lasso done in 0.175s
+Dense Lasso done in  0.799s
 Distance between coefficients : 8.65e-12
 </pre></div>
 </div>
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index 28c10e4ae3cc0..36b4e83c0338c 100644
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@@ -679,13 +679,12 @@
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
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 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -902,9 +901,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
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@@ -1515,7 +1514,7 @@ <h3>Lasso via coordinate descent<a class="headerlink" href="#lasso-via-coordinat
 <span class="p">)</span>
 </pre></div>
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 <section id="lasso-via-least-angle-regression">
 <h3>Lasso via least angle regression<a class="headerlink" href="#lasso-via-least-angle-regression" title="Link to this heading">#</a></h3>
 <p>Let’s start by making the hyperparameter tuning using
@@ -1545,7 +1544,7 @@ <h3>Lasso via least angle regression<a class="headerlink" href="#lasso-via-least
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 <section id="summary-of-cross-validation-approach">
 <h3>Summary of cross-validation approach<a class="headerlink" href="#summary-of-cross-validation-approach" title="Link to this heading">#</a></h3>
 <p>Both algorithms give roughly the same results.</p>
@@ -1579,7 +1578,7 @@ <h2>Conclusion<a class="headerlink" href="#conclusion" title="Link to this headi
 enough compared to the number of features.</p>
 <p>That’s why hyperparameter optimization via cross-validation is a safe
 strategy: it works in different settings.</p>
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diff --git a/dev/auto_examples/linear_model/plot_logistic.html b/dev/auto_examples/linear_model/plot_logistic.html
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@@ -679,13 +679,12 @@
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
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@@ -902,9 +901,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
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 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -902,9 +901,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
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@@ -1211,7 +1210,10 @@ <h2>Periodic Splines<a class="headerlink" href="#periodic-splines" title="Link t
 </div>
 </div>
 <p class="rubric">Related examples</p>
-<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This example compares two different bayesian regressors:"><img alt="" src="http://github.com/_images/sphx_glr_plot_ard_thumb.png" />
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="Plot the predicted class probabilities in a toy dataset predicted by three different classifiers and averaged by the VotingClassifier."><img alt="" src="http://github.com/_images/sphx_glr_plot_voting_decision_regions_thumb.png" />
+<p><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html#sphx-glr-auto-examples-ensemble-plot-voting-decision-regions-py"><span class="std std-ref">Visualizing the probabilistic predictions of a VotingClassifier</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Visualizing the probabilistic predictions of a VotingClassifier</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example compares two different bayesian regressors:"><img alt="" src="http://github.com/_images/sphx_glr_plot_ard_thumb.png" />
 <p><a class="reference internal" href="http://github.com/plot_ard.html#sphx-glr-auto-examples-linear-model-plot-ard-py"><span class="std std-ref">Comparing Linear Bayesian Regressors</span></a></p>
   <div class="sphx-glr-thumbnail-title">Comparing Linear Bayesian Regressors</div>
 </div><div class="sphx-glr-thumbcontainer" tooltip="This notebook introduces different strategies to leverage time-related features for a bike sharing demand regression task that is highly dependent on business cycles (days, weeks, months) and yearly season cycles."><img alt="" src="http://github.com/_images/sphx_glr_plot_cyclical_feature_engineering_thumb.png" />
@@ -1220,9 +1222,6 @@ <h2>Periodic Splines<a class="headerlink" href="#periodic-splines" title="Link t
 </div><div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates the problems of underfitting and overfitting and how we can use linear regression with polynomial features to approximate nonlinear functions. The plot shows the function that we want to approximate, which is a part of the cosine function. In addition, the samples from the real function and the approximations of different models are displayed. The models have polynomial features of different degrees. We can see that a linear function (polynomial with degree 1) is not sufficient to fit the training samples. This is called underfitting. A polynomial of degree 4 approximates the true function almost perfectly. However, for higher degrees the model will overfit the training data, i.e. it learns the noise of the training data. We evaluate quantitatively overfitting / underfitting by using cross-validation. We calculate the mean squared error (MSE) on the validation set, the higher, the less likely the model generalizes correctly from the training data."><img alt="" src="http://github.com/_images/sphx_glr_plot_underfitting_overfitting_thumb.png" />
 <p><a class="reference internal" href="http://github.com/model_selection/plot_underfitting_overfitting.html#sphx-glr-auto-examples-model-selection-plot-underfitting-overfitting-py"><span class="std std-ref">Underfitting vs. Overfitting</span></a></p>
   <div class="sphx-glr-thumbnail-title">Underfitting vs. Overfitting</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="We are very pleased to announce the release of scikit-learn 1.0! The library has been stable for quite some time, releasing version 1.0 is recognizing that and signalling it to our users. This release does not include any breaking changes apart from the usual two-release deprecation cycle. For the future, we do our best to keep this pattern."><img alt="" src="http://github.com/_images/sphx_glr_plot_release_highlights_1_0_0_thumb.png" />
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-  <div class="sphx-glr-thumbnail-title">Release Highlights for scikit-learn 1.0</div>
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diff --git a/dev/auto_examples/linear_model/plot_quantile_regression.html b/dev/auto_examples/linear_model/plot_quantile_regression.html
index bae3acae245eb..5b41e9751e46b 100644
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+++ b/dev/auto_examples/linear_model/plot_quantile_regression.html
@@ -42,7 +42,7 @@
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -902,9 +901,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
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 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
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 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
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diff --git a/dev/auto_examples/linear_model/plot_sgd_early_stopping.html b/dev/auto_examples/linear_model/plot_sgd_early_stopping.html
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -902,9 +901,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
@@ -1150,7 +1149,7 @@
 <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
 </pre></div>
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+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 24.961 seconds)</p>
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 <a class="reference external image-reference" href="https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/linear_model/plot_sgd_early_stopping.ipynb"><img alt="Launch binder" src="http://github.com/_images/binder_badge_logo18.svg" style="width: 150px;" />
@@ -1180,9 +1179,9 @@
 </div><div class="sphx-glr-thumbcontainer" tooltip="histogram_based_gradient_boosting (HGBT) models may be one of the most useful supervised learning models in scikit-learn. They are based on a modern gradient boosting implementation comparable to LightGBM and XGBoost. As such, HGBT models are more feature rich than and often outperform alternative models like random forests, especially when the number of samples is larger than some ten thousands (see sphx_glr_auto_examples_ensemble_plot_forest_hist_grad_boosting_comparison.py)."><img alt="" src="http://github.com/_images/sphx_glr_plot_hgbt_regression_thumb.png" />
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   <div class="sphx-glr-thumbnail-title">Features in Histogram Gradient Boosting Trees</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This example reproduces the example of Fig. 2 of [ZHT2007]_. A LassoLarsIC estimator is fit on a diabetes dataset and the AIC and the BIC criteria are used to select the best model."><img alt="" src="http://github.com/_images/sphx_glr_plot_lasso_lars_ic_thumb.png" />
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-  <div class="sphx-glr-thumbnail-title">Lasso model selection via information criteria</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="Gradient Boosting Out-of-Bag estimates"><img alt="" src="http://github.com/_images/sphx_glr_plot_gradient_boosting_oob_thumb.png" />
+<p><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_oob.html#sphx-glr-auto-examples-ensemble-plot-gradient-boosting-oob-py"><span class="std std-ref">Gradient Boosting Out-of-Bag estimates</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Gradient Boosting Out-of-Bag estimates</div>
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diff --git a/dev/auto_examples/linear_model/plot_sgd_iris.html b/dev/auto_examples/linear_model/plot_sgd_iris.html
index b07d471dd9a09..7d8f59b6fefc6 100644
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+++ b/dev/auto_examples/linear_model/plot_sgd_iris.html
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -902,9 +901,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
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@@ -1043,7 +1042,7 @@
 
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 <p><a class="reference internal" href="http://github.com/decomposition/plot_pca_iris.html#sphx-glr-auto-examples-decomposition-plot-pca-iris-py"><span class="std std-ref">Principal Component Analysis (PCA) on Iris Dataset</span></a></p>
   <div class="sphx-glr-thumbnail-title">Principal Component Analysis (PCA) on Iris Dataset</div>
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-  <div class="sphx-glr-thumbnail-title">Plot the decision boundaries of a VotingClassifier</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="Incremental principal component analysis (IPCA) is typically used as a replacement for principal component analysis (PCA) when the dataset to be decomposed is too large to fit in memory. IPCA builds a low-rank approximation for the input data using an amount of memory which is independent of the number of input data samples. It is still dependent on the input data features, but changing the batch size allows for control of memory usage."><img alt="" src="http://github.com/_images/sphx_glr_plot_incremental_pca_thumb.png" />
+<p><a class="reference internal" href="http://github.com/decomposition/plot_incremental_pca.html#sphx-glr-auto-examples-decomposition-plot-incremental-pca-py"><span class="std std-ref">Incremental PCA</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Incremental PCA</div>
 </div><div class="sphx-glr-thumbcontainer" tooltip="Simple usage of Support Vector Machines to classify a sample. It will plot the decision surface and the support vectors."><img alt="" src="http://github.com/_images/sphx_glr_plot_custom_kernel_thumb.png" />
 <p><a class="reference internal" href="http://github.com/svm/plot_custom_kernel.html#sphx-glr-auto-examples-svm-plot-custom-kernel-py"><span class="std std-ref">SVM with custom kernel</span></a></p>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -902,9 +901,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
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diff --git a/dev/auto_examples/linear_model/plot_sgd_penalties.html b/dev/auto_examples/linear_model/plot_sgd_penalties.html
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
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@@ -902,9 +901,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
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@@ -1071,12 +1070,12 @@
 </div><div class="sphx-glr-thumbcontainer" tooltip="Comparison of the sparsity (percentage of zero coefficients) of solutions when L1, L2 and Elastic-Net penalty are used for different values of C. We can see that large values of C give more freedom to the model.  Conversely, smaller values of C constrain the model more. In the L1 penalty case, this leads to sparser solutions. As expected, the Elastic-Net penalty sparsity is between that of L1 and L2."><img alt="" src="http://github.com/_images/sphx_glr_plot_logistic_l1_l2_sparsity_thumb.png" />
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   <div class="sphx-glr-thumbnail-title">L1 Penalty and Sparsity in Logistic Regression</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This example illustrates GPC on XOR data. Compared are a stationary, isotropic kernel (RBF) and a non-stationary kernel (DotProduct). On this particular dataset, the DotProduct kernel obtains considerably better results because the class-boundaries are linear and coincide with the coordinate axes. In general, stationary kernels often obtain better results."><img alt="" src="http://github.com/_images/sphx_glr_plot_gpc_xor_thumb.png" />
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 <p><a class="reference internal" href="http://github.com/plot_sgd_weighted_samples.html#sphx-glr-auto-examples-linear-model-plot-sgd-weighted-samples-py"><span class="std std-ref">SGD: Weighted samples</span></a></p>
   <div class="sphx-glr-thumbnail-title">SGD: Weighted samples</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example illustrates GPC on XOR data. Compared are a stationary, isotropic kernel (RBF) and a non-stationary kernel (DotProduct). On this particular dataset, the DotProduct kernel obtains considerably better results because the class-boundaries are linear and coincide with the coordinate axes. In general, stationary kernels often obtain better results."><img alt="" src="http://github.com/_images/sphx_glr_plot_gpc_xor_thumb.png" />
+<p><a class="reference internal" href="http://github.com/gaussian_process/plot_gpc_xor.html#sphx-glr-auto-examples-gaussian-process-plot-gpc-xor-py"><span class="std std-ref">Illustration of Gaussian process classification (GPC) on the XOR dataset</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Illustration of Gaussian process classification (GPC) on the XOR dataset</div>
 </div></div><p class="sphx-glr-signature"><a class="reference external" href="https://sphinx-gallery.github.io/">Gallery generated by Sphinx-Gallery</a></p>
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diff --git a/dev/auto_examples/linear_model/plot_sgd_separating_hyperplane.html b/dev/auto_examples/linear_model/plot_sgd_separating_hyperplane.html
index f549ba3dd0d67..f19fc27a556ea 100644
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@@ -679,13 +679,12 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -902,9 +901,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
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 multi-layer perceptron model on this dataset.</p>
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+            <span class="s2">&quot;predicted, tweedie, power=</span><span class="si">%.2f</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="n">glm_pure_premium</span><span class="o">.</span><span class="n">power</span><span class="p">:</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.sum.html#numpy.sum" title="numpy.sum" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">sum</span></a><span class="p">(</span>
+                <span class="n">exposure</span> <span class="o">*</span> <span class="n">glm_pure_premium</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
+            <span class="p">),</span>
         <span class="p">}</span>
     <span class="p">)</span>
 
@@ -1742,7 +1742,7 @@ <h2>Pure Premium Modeling via a Product Model vs single TweedieRegressor<a class
 <img src="http://github.com/_images/sphx_glr_plot_tweedie_regression_insurance_claims_003.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_tweedie_regression_insurance_claims_003.png " alt="Lorenz Curves" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[]
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@@ -1772,9 +1772,9 @@ <h2>Pure Premium Modeling via a Product Model vs single TweedieRegressor<a class
 </div><div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates the use of the Box-Cox and Yeo-Johnson transforms through PowerTransformer to map data from various distributions to a normal distribution."><img alt="" src="http://github.com/_images/sphx_glr_plot_map_data_to_normal_thumb.png" />
 <p><a class="reference internal" href="http://github.com/preprocessing/plot_map_data_to_normal.html#sphx-glr-auto-examples-preprocessing-plot-map-data-to-normal-py"><span class="std std-ref">Map data to a normal distribution</span></a></p>
   <div class="sphx-glr-thumbnail-title">Map data to a normal distribution</div>
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-  <div class="sphx-glr-thumbnail-title">Statistical comparison of models using grid search</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to use cross_val_predict together with PredictionErrorDisplay to visualize prediction errors."><img alt="" src="http://github.com/_images/sphx_glr_plot_cv_predict_thumb.png" />
+<p><a class="reference internal" href="http://github.com/model_selection/plot_cv_predict.html#sphx-glr-auto-examples-model-selection-plot-cv-predict-py"><span class="std std-ref">Plotting Cross-Validated Predictions</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Plotting Cross-Validated Predictions</div>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -898,9 +897,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
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diff --git a/dev/auto_examples/manifold/plot_compare_methods.html b/dev/auto_examples/manifold/plot_compare_methods.html
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
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@@ -1141,7 +1140,7 @@ <h3>Multidimensional scaling<a class="headerlink" href="#multidimensional-scalin
 <div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">md_scaling</span> <span class="o">=</span> <a href="http://github.com/modules/generated/sklearn.manifold.MDS.html#sklearn.manifold.MDS" title="sklearn.manifold.MDS" class="sphx-glr-backref-module-sklearn-manifold sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">manifold</span><span class="o">.</span><span class="n">MDS</span></a><span class="p">(</span>
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+    <span class="n">n_init</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
     <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
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 <span class="p">)</span>
@@ -1184,7 +1183,7 @@ <h3>T-distributed Stochastic Neighbor Embedding<a class="headerlink" href="#t-di
 <span class="n">plot_2d</span><span class="p">(</span><span class="n">S_t_sne</span><span class="p">,</span> <span class="n">S_color</span><span class="p">,</span> <span class="s2">&quot;T-distributed Stochastic  </span><span class="se">\n</span><span class="s2"> Neighbor Embedding&quot;</span><span class="p">)</span>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
@@ -1119,7 +1118,7 @@ <h2>Embedding techniques comparison<a class="headerlink" href="#embedding-techni
     <span class="s2">&quot;LTSA LLE embedding&quot;</span><span class="p">:</span> <a href="http://github.com/modules/generated/sklearn.manifold.LocallyLinearEmbedding.html#sklearn.manifold.LocallyLinearEmbedding" title="sklearn.manifold.LocallyLinearEmbedding" class="sphx-glr-backref-module-sklearn-manifold sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">LocallyLinearEmbedding</span></a><span class="p">(</span>
         <span class="n">n_neighbors</span><span class="o">=</span><span class="n">n_neighbors</span><span class="p">,</span> <span class="n">n_components</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s2">&quot;ltsa&quot;</span>
     <span class="p">),</span>
-    <span class="s2">&quot;MDS embedding&quot;</span><span class="p">:</span> <a href="http://github.com/modules/generated/sklearn.manifold.MDS.html#sklearn.manifold.MDS" title="sklearn.manifold.MDS" class="sphx-glr-backref-module-sklearn-manifold sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">MDS</span></a><span class="p">(</span><span class="n">n_components</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">n_init</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">max_iter</span><span class="o">=</span><span class="mi">120</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="mi">2</span><span class="p">),</span>
+    <span class="s2">&quot;MDS embedding&quot;</span><span class="p">:</span> <a href="http://github.com/modules/generated/sklearn.manifold.MDS.html#sklearn.manifold.MDS" title="sklearn.manifold.MDS" class="sphx-glr-backref-module-sklearn-manifold sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">MDS</span></a><span class="p">(</span><span class="n">n_components</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">n_init</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">max_iter</span><span class="o">=</span><span class="mi">120</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="mf">1e-6</span><span class="p">),</span>
     <span class="s2">&quot;Random Trees embedding&quot;</span><span class="p">:</span> <a href="http://github.com/modules/generated/sklearn.pipeline.make_pipeline.html#sklearn.pipeline.make_pipeline" title="sklearn.pipeline.make_pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-function"><span class="n">make_pipeline</span></a><span class="p">(</span>
         <a href="http://github.com/modules/generated/sklearn.ensemble.RandomTreesEmbedding.html#sklearn.ensemble.RandomTreesEmbedding" title="sklearn.ensemble.RandomTreesEmbedding" class="sphx-glr-backref-module-sklearn-ensemble sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">RandomTreesEmbedding</span></a><span class="p">(</span><span class="n">n_estimators</span><span class="o">=</span><span class="mi">200</span><span class="p">,</span> <span class="n">max_depth</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">),</span>
         <a href="http://github.com/modules/generated/sklearn.decomposition.TruncatedSVD.html#sklearn.decomposition.TruncatedSVD" title="sklearn.decomposition.TruncatedSVD" class="sphx-glr-backref-module-sklearn-decomposition sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">TruncatedSVD</span></a><span class="p">(</span><span class="n">n_components</span><span class="o">=</span><span class="mi">2</span><span class="p">),</span>
@@ -1183,21 +1182,21 @@ <h2>Embedding techniques comparison<a class="headerlink" href="#embedding-techni
 </pre></div>
 </div>
 <ul class="sphx-glr-horizontal">
-<li><img src="http://github.com/_images/sphx_glr_plot_lle_digits_002.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_lle_digits_002.png " alt="Random projection embedding (time 0.002s)" class = "sphx-glr-multi-img"/></li>
-<li><img src="http://github.com/_images/sphx_glr_plot_lle_digits_003.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_lle_digits_003.png " alt="Truncated SVD embedding (time 0.003s)" class = "sphx-glr-multi-img"/></li>
-<li><img src="http://github.com/_images/sphx_glr_plot_lle_digits_004.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_lle_digits_004.png " alt="Linear Discriminant Analysis embedding (time 0.008s)" class = "sphx-glr-multi-img"/></li>
-<li><img src="http://github.com/_images/sphx_glr_plot_lle_digits_005.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_lle_digits_005.png " alt="Isomap embedding (time 0.789s)" class = "sphx-glr-multi-img"/></li>
-<li><img src="http://github.com/_images/sphx_glr_plot_lle_digits_006.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_lle_digits_006.png " alt="Standard LLE embedding (time 0.164s)" class = "sphx-glr-multi-img"/></li>
-<li><img src="http://github.com/_images/sphx_glr_plot_lle_digits_007.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_lle_digits_007.png " alt="Modified LLE embedding (time 2.676s)" class = "sphx-glr-multi-img"/></li>
-<li><img src="http://github.com/_images/sphx_glr_plot_lle_digits_008.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_lle_digits_008.png " alt="Hessian LLE embedding (time 1.765s)" class = "sphx-glr-multi-img"/></li>
-<li><img src="http://github.com/_images/sphx_glr_plot_lle_digits_009.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_lle_digits_009.png " alt="LTSA LLE embedding (time 2.337s)" class = "sphx-glr-multi-img"/></li>
-<li><img src="http://github.com/_images/sphx_glr_plot_lle_digits_010.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_lle_digits_010.png " alt="MDS embedding (time 2.478s)" class = "sphx-glr-multi-img"/></li>
-<li><img src="http://github.com/_images/sphx_glr_plot_lle_digits_011.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_lle_digits_011.png " alt="Random Trees embedding (time 0.235s)" class = "sphx-glr-multi-img"/></li>
-<li><img src="http://github.com/_images/sphx_glr_plot_lle_digits_012.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_lle_digits_012.png " alt="Spectral embedding (time 0.150s)" class = "sphx-glr-multi-img"/></li>
-<li><img src="http://github.com/_images/sphx_glr_plot_lle_digits_013.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_lle_digits_013.png " alt="t-SNE embedding (time 2.562s)" class = "sphx-glr-multi-img"/></li>
-<li><img src="http://github.com/_images/sphx_glr_plot_lle_digits_014.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_lle_digits_014.png " alt="NCA embedding (time 2.716s)" class = "sphx-glr-multi-img"/></li>
+<li><img src="http://github.com/_images/sphx_glr_plot_lle_digits_002.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_lle_digits_002.png " alt="Random projection embedding (time 0.003s)" class = "sphx-glr-multi-img"/></li>
+<li><img src="http://github.com/_images/sphx_glr_plot_lle_digits_003.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_lle_digits_003.png " alt="Truncated SVD embedding (time 0.004s)" class = "sphx-glr-multi-img"/></li>
+<li><img src="http://github.com/_images/sphx_glr_plot_lle_digits_004.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_lle_digits_004.png " alt="Linear Discriminant Analysis embedding (time 0.010s)" class = "sphx-glr-multi-img"/></li>
+<li><img src="http://github.com/_images/sphx_glr_plot_lle_digits_005.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_lle_digits_005.png " alt="Isomap embedding (time 0.822s)" class = "sphx-glr-multi-img"/></li>
+<li><img src="http://github.com/_images/sphx_glr_plot_lle_digits_006.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_lle_digits_006.png " alt="Standard LLE embedding (time 0.227s)" class = "sphx-glr-multi-img"/></li>
+<li><img src="http://github.com/_images/sphx_glr_plot_lle_digits_007.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_lle_digits_007.png " alt="Modified LLE embedding (time 3.006s)" class = "sphx-glr-multi-img"/></li>
+<li><img src="http://github.com/_images/sphx_glr_plot_lle_digits_008.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_lle_digits_008.png " alt="Hessian LLE embedding (time 2.036s)" class = "sphx-glr-multi-img"/></li>
+<li><img src="http://github.com/_images/sphx_glr_plot_lle_digits_009.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_lle_digits_009.png " alt="LTSA LLE embedding (time 2.413s)" class = "sphx-glr-multi-img"/></li>
+<li><img src="http://github.com/_images/sphx_glr_plot_lle_digits_010.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_lle_digits_010.png " alt="MDS embedding (time 2.747s)" class = "sphx-glr-multi-img"/></li>
+<li><img src="http://github.com/_images/sphx_glr_plot_lle_digits_011.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_lle_digits_011.png " alt="Random Trees embedding (time 0.209s)" class = "sphx-glr-multi-img"/></li>
+<li><img src="http://github.com/_images/sphx_glr_plot_lle_digits_012.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_lle_digits_012.png " alt="Spectral embedding (time 0.153s)" class = "sphx-glr-multi-img"/></li>
+<li><img src="http://github.com/_images/sphx_glr_plot_lle_digits_013.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_lle_digits_013.png " alt="t-SNE embedding (time 2.565s)" class = "sphx-glr-multi-img"/></li>
+<li><img src="http://github.com/_images/sphx_glr_plot_lle_digits_014.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_lle_digits_014.png " alt="NCA embedding (time 2.912s)" class = "sphx-glr-multi-img"/></li>
 </ul>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 20.354 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 22.763 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-manifold-plot-lle-digits-py">
 <div class="binder-badge docutils container">
 <a class="reference external image-reference" href="https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/manifold/plot_lle_digits.ipynb"><img alt="Launch binder" src="http://github.com/_images/binder_badge_logo19.svg" style="width: 150px;" />
diff --git a/dev/auto_examples/manifold/plot_manifold_sphere.html b/dev/auto_examples/manifold/plot_manifold_sphere.html
index f8bc12c027cb9..d46de649d68af 100644
--- a/dev/auto_examples/manifold/plot_manifold_sphere.html
+++ b/dev/auto_examples/manifold/plot_manifold_sphere.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -679,13 +679,12 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -902,9 +901,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
@@ -1011,13 +1010,13 @@
 the low-dimensional space. Here the manifold problem matches fairly
 that of representing a flat map of the Earth, as with
 <a class="reference external" href="https://en.wikipedia.org/wiki/Map_projection">map projection</a></p>
-<img src="http://github.com/_images/sphx_glr_plot_manifold_sphere_001.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_manifold_sphere_001.png " alt="Manifold Learning with 1000 points, 10 neighbors, LLE (0.055 sec), LTSA (0.85 sec), Hessian LLE (0.64 sec), Modified LLE (1.2 sec), Isomap (0.19 sec), MDS (0.68 sec), Spectral Embedding (0.04 sec), t-SNE (3.6 sec)" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>standard: 0.055 sec
-ltsa: 0.85 sec
-hessian: 0.64 sec
-modified: 1.2 sec
-ISO: 0.19 sec
-MDS: 0.68 sec
-Spectral Embedding: 0.04 sec
+<img src="http://github.com/_images/sphx_glr_plot_manifold_sphere_001.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_manifold_sphere_001.png " alt="Manifold Learning with 1000 points, 10 neighbors, LLE (0.055 sec), LTSA (0.96 sec), Hessian LLE (0.65 sec), Modified LLE (1.3 sec), Isomap (0.2 sec), MDS (0.94 sec), Spectral Embedding (0.055 sec), t-SNE (3.6 sec)" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>standard: 0.055 sec
+ltsa: 0.96 sec
+hessian: 0.65 sec
+modified: 1.3 sec
+ISO: 0.2 sec
+MDS: 0.94 sec
+Spectral Embedding: 0.055 sec
 t-SNE: 3.6 sec
 </pre></div>
 </div>
@@ -1049,7 +1048,7 @@
 <span class="n">t</span> <span class="o">=</span> <span class="n">random_state</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="n">n_samples</span><span class="p">)</span> <span class="o">*</span> <a href="https://numpy.org/doc/stable/reference/constants.html#numpy.pi" title="numpy.pi" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-data"><span class="n">np</span><span class="o">.</span><span class="n">pi</span></a>
 
 <span class="c1"># Sever the poles from the sphere.</span>
-<span class="n">indices</span> <span class="o">=</span> <span class="p">(</span><span class="n">t</span> <span class="o">&lt;</span> <span class="p">(</span><a href="https://numpy.org/doc/stable/reference/constants.html#numpy.pi" title="numpy.pi" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-data"><span class="n">np</span><span class="o">.</span><span class="n">pi</span></a> <span class="o">-</span> <span class="p">(</span><a href="https://numpy.org/doc/stable/reference/constants.html#numpy.pi" title="numpy.pi" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-data"><span class="n">np</span><span class="o">.</span><span class="n">pi</span></a> <span class="o">/</span> <span class="mi">8</span><span class="p">)))</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">t</span> <span class="o">&gt;</span> <span class="p">((</span><a href="https://numpy.org/doc/stable/reference/constants.html#numpy.pi" title="numpy.pi" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-data"><span class="n">np</span><span class="o">.</span><span class="n">pi</span></a> <span class="o">/</span> <span class="mi">8</span><span class="p">)))</span>
+<span class="n">indices</span> <span class="o">=</span> <span class="p">(</span><span class="n">t</span> <span class="o">&lt;</span> <span class="p">(</span><a href="https://numpy.org/doc/stable/reference/constants.html#numpy.pi" title="numpy.pi" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-data"><span class="n">np</span><span class="o">.</span><span class="n">pi</span></a> <span class="o">-</span> <span class="p">(</span><a href="https://numpy.org/doc/stable/reference/constants.html#numpy.pi" title="numpy.pi" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-data"><span class="n">np</span><span class="o">.</span><span class="n">pi</span></a> <span class="o">/</span> <span class="mi">8</span><span class="p">)))</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">t</span> <span class="o">&gt;</span> <span class="p">(</span><a href="https://numpy.org/doc/stable/reference/constants.html#numpy.pi" title="numpy.pi" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-data"><span class="n">np</span><span class="o">.</span><span class="n">pi</span></a> <span class="o">/</span> <span class="mi">8</span><span class="p">))</span>
 <span class="n">colors</span> <span class="o">=</span> <span class="n">p</span><span class="p">[</span><span class="n">indices</span><span class="p">]</span>
 <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">z</span> <span class="o">=</span> <span class="p">(</span>
     <a href="https://numpy.org/doc/stable/reference/generated/numpy.sin.html#numpy.sin" title="numpy.sin" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-data"><span class="n">np</span><span class="o">.</span><span class="n">sin</span></a><span class="p">(</span><span class="n">t</span><span class="p">[</span><span class="n">indices</span><span class="p">])</span> <span class="o">*</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.cos.html#numpy.cos" title="numpy.cos" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-data"><span class="n">np</span><span class="o">.</span><span class="n">cos</span></a><span class="p">(</span><span class="n">p</span><span class="p">[</span><span class="n">indices</span><span class="p">]),</span>
@@ -1156,7 +1155,7 @@
 <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
 </pre></div>
 </div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 7.882 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 8.331 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-manifold-plot-manifold-sphere-py">
 <div class="binder-badge docutils container">
 <a class="reference external image-reference" href="https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/manifold/plot_manifold_sphere.ipynb"><img alt="Launch binder" src="http://github.com/_images/binder_badge_logo19.svg" style="width: 150px;" />
diff --git a/dev/auto_examples/manifold/plot_mds.html b/dev/auto_examples/manifold/plot_mds.html
index 099d5d309f663..2c0cdb7411254 100644
--- a/dev/auto_examples/manifold/plot_mds.html
+++ b/dev/auto_examples/manifold/plot_mds.html
@@ -11,10 +11,10 @@
 <meta property="og:type" content="website" />
 <meta property="og:url" content="https://scikit-learn/stable/auto_examples/manifold/plot_mds.html" />
 <meta property="og:site_name" content="scikit-learn" />
-<meta property="og:description" content="An illustration of the metric and non-metric MDS on generated noisy data. The reconstructed points using the metric MDS and non metric MDS are slightly shifted to avoid overlapping. Total running t..." />
+<meta property="og:description" content="An illustration of the metric and non-metric MDS on generated noisy data. Dataset preparation: We start by uniformly generating 20 points in a 2D space. Now we compute pairwise distances between al..." />
 <meta property="og:image" content="https://scikit-learn.org/stable/_static/scikit-learn-logo-small.png" />
 <meta property="og:image:alt" content="scikit-learn" />
-<meta name="description" content="An illustration of the metric and non-metric MDS on generated noisy data. The reconstructed points using the metric MDS and non metric MDS are slightly shifted to avoid overlapping. Total running t..." />
+<meta name="description" content="An illustration of the metric and non-metric MDS on generated noisy data. Dataset preparation: We start by uniformly generating 20 points in a 2D space. Now we compute pairwise distances between al..." />
 
     <title>Multi-dimensional scaling &#8212; scikit-learn 1.7.dev0 documentation</title>
   
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -679,13 +679,12 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -902,9 +901,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
@@ -995,12 +994,14 @@
 <section class="sphx-glr-example-title" id="multi-dimensional-scaling">
 <span id="sphx-glr-auto-examples-manifold-plot-mds-py"></span><h1>Multi-dimensional scaling<a class="headerlink" href="#multi-dimensional-scaling" title="Link to this heading">#</a></h1>
 <p>An illustration of the metric and non-metric MDS on generated noisy data.</p>
-<p>The reconstructed points using the metric MDS and non metric MDS are slightly
-shifted to avoid overlapping.</p>
-<img src="http://github.com/_images/sphx_glr_plot_mds_001.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_mds_001.png " alt="plot mds" class = "sphx-glr-single-img"/><div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Authors: The scikit-learn developers</span>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Authors: The scikit-learn developers</span>
 <span class="c1"># SPDX-License-Identifier: BSD-3-Clause</span>
-
-<span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
+</pre></div>
+</div>
+<section id="dataset-preparation">
+<h2>Dataset preparation<a class="headerlink" href="#dataset-preparation" title="Link to this heading">#</a></h2>
+<p>We start by uniformly generating 20 points in a 2D space.</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
 <span class="kn">from</span><span class="w"> </span><span class="nn">matplotlib</span><span class="w"> </span><span class="kn">import</span> <span class="n">pyplot</span> <span class="k">as</span> <span class="n">plt</span>
 <span class="kn">from</span><span class="w"> </span><span class="nn">matplotlib.collections</span><span class="w"> </span><span class="kn">import</span> <a href="https://matplotlib.org/stable/api/collections_api.html#matplotlib.collections.LineCollection" title="matplotlib.collections.LineCollection" class="sphx-glr-backref-module-matplotlib-collections sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">LineCollection</span></a>
 
@@ -1017,8 +1018,12 @@
 
 <span class="c1"># Center the data</span>
 <span class="n">X_true</span> <span class="o">-=</span> <span class="n">X_true</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
-
-<span class="c1"># Compute pairwise Euclidean distances</span>
+</pre></div>
+</div>
+<p>Now we compute pairwise distances between all points and add
+a small amount of noise to the distance matrix. We make sure
+to keep the noisy distance matrix symmetric.</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Compute pairwise Euclidean distances</span>
 <span class="n">distances</span> <span class="o">=</span> <span class="n">euclidean_distances</span><span class="p">(</span><span class="n">X_true</span><span class="p">)</span>
 
 <span class="c1"># Add noise to the distances</span>
@@ -1026,11 +1031,14 @@
 <span class="n">noise</span> <span class="o">=</span> <span class="n">noise</span> <span class="o">+</span> <span class="n">noise</span><span class="o">.</span><span class="n">T</span>
 <a href="https://numpy.org/doc/stable/reference/generated/numpy.fill_diagonal.html#numpy.fill_diagonal" title="numpy.fill_diagonal" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">fill_diagonal</span></a><span class="p">(</span><span class="n">noise</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
 <span class="n">distances</span> <span class="o">+=</span> <span class="n">noise</span>
-
-<span class="n">mds</span> <span class="o">=</span> <a href="http://github.com/modules/generated/sklearn.manifold.MDS.html#sklearn.manifold.MDS" title="sklearn.manifold.MDS" class="sphx-glr-backref-module-sklearn-manifold sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">manifold</span><span class="o">.</span><span class="n">MDS</span></a><span class="p">(</span>
+</pre></div>
+</div>
+<p>Here we compute metric and non-metric MDS of the noisy distance matrix.</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">mds</span> <span class="o">=</span> <a href="http://github.com/modules/generated/sklearn.manifold.MDS.html#sklearn.manifold.MDS" title="sklearn.manifold.MDS" class="sphx-glr-backref-module-sklearn-manifold sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">manifold</span><span class="o">.</span><span class="n">MDS</span></a><span class="p">(</span>
     <span class="n">n_components</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
     <span class="n">max_iter</span><span class="o">=</span><span class="mi">3000</span><span class="p">,</span>
     <span class="n">eps</span><span class="o">=</span><span class="mf">1e-9</span><span class="p">,</span>
+    <span class="n">n_init</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
     <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">,</span>
     <span class="n">dissimilarity</span><span class="o">=</span><span class="s2">&quot;precomputed&quot;</span><span class="p">,</span>
     <span class="n">n_jobs</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
@@ -1048,12 +1056,16 @@
     <span class="n">n_init</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
 <span class="p">)</span>
 <span class="n">X_nmds</span> <span class="o">=</span> <span class="n">nmds</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">distances</span><span class="p">)</span>
-
-<span class="c1"># Rescale the data</span>
-<span class="n">X_mds</span> <span class="o">*=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.sqrt.html#numpy.sqrt" title="numpy.sqrt" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-data"><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span></a><span class="p">((</span><span class="n">X_true</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">())</span> <span class="o">/</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.sqrt.html#numpy.sqrt" title="numpy.sqrt" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-data"><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span></a><span class="p">((</span><span class="n">X_mds</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">())</span>
-<span class="n">X_nmds</span> <span class="o">*=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.sqrt.html#numpy.sqrt" title="numpy.sqrt" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-data"><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span></a><span class="p">((</span><span class="n">X_true</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">())</span> <span class="o">/</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.sqrt.html#numpy.sqrt" title="numpy.sqrt" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-data"><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span></a><span class="p">((</span><span class="n">X_nmds</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">())</span>
-
-<span class="c1"># Rotate the data</span>
+</pre></div>
+</div>
+<p>Rescaling the non-metric MDS solution to match the spread of the original data.</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">X_nmds</span> <span class="o">*=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.sqrt.html#numpy.sqrt" title="numpy.sqrt" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-data"><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span></a><span class="p">((</span><span class="n">X_true</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">())</span> <span class="o">/</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.sqrt.html#numpy.sqrt" title="numpy.sqrt" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-data"><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span></a><span class="p">((</span><span class="n">X_nmds</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">())</span>
+</pre></div>
+</div>
+<p>To make the visual comparisons easier, we rotate the original data and both MDS
+solutions to their PCA axes. And flip horizontal and vertical MDS axes, if needed,
+to match the original data orientation.</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Rotate the data</span>
 <span class="n">pca</span> <span class="o">=</span> <a href="http://github.com/modules/generated/sklearn.decomposition.PCA.html#sklearn.decomposition.PCA" title="sklearn.decomposition.PCA" class="sphx-glr-backref-module-sklearn-decomposition sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">PCA</span></a><span class="p">(</span><span class="n">n_components</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
 <span class="n">X_true</span> <span class="o">=</span> <span class="n">pca</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">X_true</span><span class="p">)</span>
 <span class="n">X_mds</span> <span class="o">=</span> <span class="n">pca</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">X_mds</span><span class="p">)</span>
@@ -1065,8 +1077,10 @@
         <span class="n">X_mds</span><span class="p">[:,</span> <span class="n">i</span><span class="p">]</span> <span class="o">*=</span> <span class="o">-</span><span class="mi">1</span>
     <span class="k">if</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.corrcoef.html#numpy.corrcoef" title="numpy.corrcoef" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">corrcoef</span></a><span class="p">(</span><span class="n">X_nmds</span><span class="p">[:,</span> <span class="n">i</span><span class="p">],</span> <span class="n">X_true</span><span class="p">[:,</span> <span class="n">i</span><span class="p">])[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">&lt;</span> <span class="mi">0</span><span class="p">:</span>
         <span class="n">X_nmds</span><span class="p">[:,</span> <span class="n">i</span><span class="p">]</span> <span class="o">*=</span> <span class="o">-</span><span class="mi">1</span>
-
-<span class="n">fig</span> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.figure.html#matplotlib.pyplot.figure" title="matplotlib.pyplot.figure" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">figure</span></a><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>Finally, we plot the original data and both MDS reconstructions.</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">fig</span> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.figure.html#matplotlib.pyplot.figure" title="matplotlib.pyplot.figure" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">figure</span></a><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
 <span class="n">ax</span> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.axes.html#matplotlib.pyplot.axes" title="matplotlib.pyplot.axes" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">axes</span></a><span class="p">([</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">])</span>
 
 <span class="n">s</span> <span class="o">=</span> <span class="mi">100</span>
@@ -1076,7 +1090,7 @@
 <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.legend.html#matplotlib.pyplot.legend" title="matplotlib.pyplot.legend" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">legend</span></a><span class="p">(</span><span class="n">scatterpoints</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="s2">&quot;best&quot;</span><span class="p">,</span> <span class="n">shadow</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
 
 <span class="c1"># Plot the edges</span>
-<span class="n">start_idx</span><span class="p">,</span> <span class="n">end_idx</span> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.where.html#numpy.where" title="numpy.where" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">where</span></a><span class="p">(</span><span class="n">X_mds</span><span class="p">)</span>
+<span class="n">start_idx</span><span class="p">,</span> <span class="n">end_idx</span> <span class="o">=</span> <span class="n">X_mds</span><span class="o">.</span><span class="n">nonzero</span><span class="p">()</span>
 <span class="c1"># a sequence of (*line0*, *line1*, *line2*), where::</span>
 <span class="c1">#            linen = (x0, y0), (x1, y1), ... (xm, ym)</span>
 <span class="n">segments</span> <span class="o">=</span> <span class="p">[</span>
@@ -1095,7 +1109,7 @@
 <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
 </pre></div>
 </div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 0.311 seconds)</p>
+<img src="http://github.com/_images/sphx_glr_plot_mds_001.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_mds_001.png " alt="plot mds" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 0.330 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-manifold-plot-mds-py">
 <div class="binder-badge docutils container">
 <a class="reference external image-reference" href="https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/manifold/plot_mds.ipynb"><img alt="Launch binder" src="http://github.com/_images/binder_badge_logo19.svg" style="width: 150px;" />
@@ -1119,16 +1133,17 @@
 <div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="An illustration of dimensionality reduction on the S-curve dataset with various manifold learning methods."><img alt="" src="http://github.com/_images/sphx_glr_plot_compare_methods_thumb.png" />
 <p><a class="reference internal" href="http://github.com/plot_compare_methods.html#sphx-glr-auto-examples-manifold-plot-compare-methods-py"><span class="std std-ref">Comparison of Manifold Learning methods</span></a></p>
   <div class="sphx-glr-thumbnail-title">Comparison of Manifold Learning methods</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip=" The `Johnson-Lindenstrauss lemma`_ states that any high dimensional dataset can be randomly projected into a lower dimensional Euclidean space while controlling the distortion in the pairwise distances."><img alt="" src="http://github.com/_images/sphx_glr_plot_johnson_lindenstrauss_bound_thumb.png" />
-<p><a class="reference internal" href="http://github.com/miscellaneous/plot_johnson_lindenstrauss_bound.html#sphx-glr-auto-examples-miscellaneous-plot-johnson-lindenstrauss-bound-py"><span class="std std-ref">The Johnson-Lindenstrauss bound for embedding with random projections</span></a></p>
-  <div class="sphx-glr-thumbnail-title">The Johnson-Lindenstrauss bound for embedding with random projections</div>
 </div><div class="sphx-glr-thumbcontainer" tooltip="Demonstrates the effect of different metrics on the hierarchical clustering."><img alt="" src="http://github.com/_images/sphx_glr_plot_agglomerative_clustering_metrics_thumb.png" />
 <p><a class="reference internal" href="http://github.com/cluster/plot_agglomerative_clustering_metrics.html#sphx-glr-auto-examples-cluster-plot-agglomerative-clustering-metrics-py"><span class="std std-ref">Agglomerative clustering with different metrics</span></a></p>
   <div class="sphx-glr-thumbnail-title">Agglomerative clustering with different metrics</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip=" The `Johnson-Lindenstrauss lemma`_ states that any high dimensional dataset can be randomly projected into a lower dimensional Euclidean space while controlling the distortion in the pairwise distances."><img alt="" src="http://github.com/_images/sphx_glr_plot_johnson_lindenstrauss_bound_thumb.png" />
+<p><a class="reference internal" href="http://github.com/miscellaneous/plot_johnson_lindenstrauss_bound.html#sphx-glr-auto-examples-miscellaneous-plot-johnson-lindenstrauss-bound-py"><span class="std std-ref">The Johnson-Lindenstrauss bound for embedding with random projections</span></a></p>
+  <div class="sphx-glr-thumbnail-title">The Johnson-Lindenstrauss bound for embedding with random projections</div>
 </div><div class="sphx-glr-thumbcontainer" tooltip="An application of the different manifold techniques on a spherical data-set. Here one can see the use of dimensionality reduction in order to gain some intuition regarding the manifold learning methods. Regarding the dataset, the poles are cut from the sphere, as well as a thin slice down its side. This enables the manifold learning techniques to &#x27;spread it open&#x27; whilst projecting it onto two dimensions."><img alt="" src="http://github.com/_images/sphx_glr_plot_manifold_sphere_thumb.png" />
 <p><a class="reference internal" href="http://github.com/plot_manifold_sphere.html#sphx-glr-auto-examples-manifold-plot-manifold-sphere-py"><span class="std std-ref">Manifold Learning methods on a severed sphere</span></a></p>
   <div class="sphx-glr-thumbnail-title">Manifold Learning methods on a severed sphere</div>
 </div></div><p class="sphx-glr-signature"><a class="reference external" href="https://sphinx-gallery.github.io/">Gallery generated by Sphinx-Gallery</a></p>
+</section>
 </section>
 
 
@@ -1174,6 +1189,18 @@
                 <div id="pst-secondary-sidebar" class="bd-sidebar-secondary bd-toc"><div class="sidebar-secondary-items sidebar-secondary__inner">
 
 
+  <div class="sidebar-secondary-item">
+<div
+    id="pst-page-navigation-heading-2"
+    class="page-toc tocsection onthispage">
+    <i class="fa-solid fa-list"></i> On this page
+  </div>
+  <nav class="bd-toc-nav page-toc" aria-labelledby="pst-page-navigation-heading-2">
+    <ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#dataset-preparation">Dataset preparation</a></li>
+</ul>
+  </nav></div>
+
   <div class="sidebar-secondary-item">
   <div role="note" aria-label="source link">
     <h3>This Page</h3>
diff --git a/dev/auto_examples/manifold/plot_swissroll.html b/dev/auto_examples/manifold/plot_swissroll.html
index ff8a053ca6414..289f73466f4fa 100644
--- a/dev/auto_examples/manifold/plot_swissroll.html
+++ b/dev/auto_examples/manifold/plot_swissroll.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
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     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -679,13 +679,12 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -902,9 +901,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
@@ -1101,7 +1100,7 @@ <h2>Concluding remarks<a class="headerlink" href="#concluding-remarks" title="Li
 <p>We observe that, as seen in the “Manifold learning on
 handwritten digits” example, t-SNE generally performs better than LLE
 on real world data.</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 17.003 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 19.192 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-manifold-plot-swissroll-py">
 <div class="binder-badge docutils container">
 <a class="reference external image-reference" href="https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/manifold/plot_swissroll.ipynb"><img alt="Launch binder" src="http://github.com/_images/binder_badge_logo19.svg" style="width: 150px;" />
diff --git a/dev/auto_examples/manifold/plot_t_sne_perplexity.html b/dev/auto_examples/manifold/plot_t_sne_perplexity.html
index c7bc26a284f9d..cfa3099b545cd 100644
--- a/dev/auto_examples/manifold/plot_t_sne_perplexity.html
+++ b/dev/auto_examples/manifold/plot_t_sne_perplexity.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
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@@ -679,13 +679,12 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -902,9 +901,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
@@ -1010,16 +1009,16 @@
 those effects.</p>
 <img src="http://github.com/_images/sphx_glr_plot_t_sne_perplexity_001.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_t_sne_perplexity_001.png " alt="Perplexity=5, Perplexity=30, Perplexity=50, Perplexity=100, Perplexity=5, Perplexity=30, Perplexity=50, Perplexity=100, Perplexity=5, Perplexity=30, Perplexity=50, Perplexity=100" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>circles, perplexity=5 in 0.13 sec
 circles, perplexity=30 in 0.21 sec
-circles, perplexity=50 in 0.24 sec
+circles, perplexity=50 in 0.25 sec
 circles, perplexity=100 in 0.23 sec
 S-curve, perplexity=5 in 0.13 sec
-S-curve, perplexity=30 in 0.2 sec
-S-curve, perplexity=50 in 0.24 sec
-S-curve, perplexity=100 in 0.24 sec
+S-curve, perplexity=30 in 0.19 sec
+S-curve, perplexity=50 in 0.23 sec
+S-curve, perplexity=100 in 0.23 sec
 uniform grid, perplexity=5 in 0.16 sec
 uniform grid, perplexity=30 in 0.24 sec
 uniform grid, perplexity=50 in 0.27 sec
-uniform grid, perplexity=100 in 0.27 sec
+uniform grid, perplexity=100 in 0.3 sec
 </pre></div>
 </div>
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@@ -1147,7 +1146,7 @@
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+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
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 </pre></div>
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 }
 
-#sk-container-id-54 label.sk-toggleable__label-arrow:hover:before {
+#sk-container-id-55 label.sk-toggleable__label-arrow:hover:before {
   color: var(--sklearn-color-text);
 }
 
 /* Toggleable content - dropdown */
 
-#sk-container-id-54 div.sk-toggleable__content {
+#sk-container-id-55 div.sk-toggleable__content {
   max-height: 0;
   max-width: 0;
   overflow: hidden;
@@ -1214,12 +1213,12 @@ <h2>Load Data and train model<a class="headerlink" href="#load-data-and-train-mo
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-54 div.sk-toggleable__content.fitted {
+#sk-container-id-55 div.sk-toggleable__content.fitted {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
-#sk-container-id-54 div.sk-toggleable__content pre {
+#sk-container-id-55 div.sk-toggleable__content pre {
   margin: 0.2em;
   border-radius: 0.25em;
   color: var(--sklearn-color-text);
@@ -1227,79 +1226,79 @@ <h2>Load Data and train model<a class="headerlink" href="#load-data-and-train-mo
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-54 div.sk-toggleable__content.fitted pre {
+#sk-container-id-55 div.sk-toggleable__content.fitted pre {
   /* unfitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
-#sk-container-id-54 input.sk-toggleable__control:checked~div.sk-toggleable__content {
+#sk-container-id-55 input.sk-toggleable__control:checked~div.sk-toggleable__content {
   /* Expand drop-down */
   max-height: 200px;
   max-width: 100%;
   overflow: auto;
 }
 
-#sk-container-id-54 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
+#sk-container-id-55 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
   content: "▾";
 }
 
 /* Pipeline/ColumnTransformer-specific style */
 
-#sk-container-id-54 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-55 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-54 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-55 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
 /* Estimator-specific style */
 
 /* Colorize estimator box */
-#sk-container-id-54 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-55 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-54 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-55 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
-#sk-container-id-54 div.sk-label label.sk-toggleable__label,
-#sk-container-id-54 div.sk-label label {
+#sk-container-id-55 div.sk-label label.sk-toggleable__label,
+#sk-container-id-55 div.sk-label label {
   /* The background is the default theme color */
   color: var(--sklearn-color-text-on-default-background);
 }
 
 /* On hover, darken the color of the background */
-#sk-container-id-54 div.sk-label:hover label.sk-toggleable__label {
+#sk-container-id-55 div.sk-label:hover label.sk-toggleable__label {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
 /* Label box, darken color on hover, fitted */
-#sk-container-id-54 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
+#sk-container-id-55 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
 /* Estimator label */
 
-#sk-container-id-54 div.sk-label label {
+#sk-container-id-55 div.sk-label label {
   font-family: monospace;
   font-weight: bold;
   display: inline-block;
   line-height: 1.2em;
 }
 
-#sk-container-id-54 div.sk-label-container {
+#sk-container-id-55 div.sk-label-container {
   text-align: center;
 }
 
 /* Estimator-specific */
-#sk-container-id-54 div.sk-estimator {
+#sk-container-id-55 div.sk-estimator {
   font-family: monospace;
   border: 1px dotted var(--sklearn-color-border-box);
   border-radius: 0.25em;
@@ -1309,18 +1308,18 @@ <h2>Load Data and train model<a class="headerlink" href="#load-data-and-train-mo
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-54 div.sk-estimator.fitted {
+#sk-container-id-55 div.sk-estimator.fitted {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
 /* on hover */
-#sk-container-id-54 div.sk-estimator:hover {
+#sk-container-id-55 div.sk-estimator:hover {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-54 div.sk-estimator.fitted:hover {
+#sk-container-id-55 div.sk-estimator.fitted:hover {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-2);
 }
@@ -1408,7 +1407,7 @@ <h2>Load Data and train model<a class="headerlink" href="#load-data-and-train-mo
 
 /* "?"-specific style due to the `<a>` HTML tag */
 
-#sk-container-id-54 a.estimator_doc_link {
+#sk-container-id-55 a.estimator_doc_link {
   float: right;
   font-size: 1rem;
   line-height: 1em;
@@ -1423,27 +1422,27 @@ <h2>Load Data and train model<a class="headerlink" href="#load-data-and-train-mo
   border: var(--sklearn-color-unfitted-level-1) 1pt solid;
 }
 
-#sk-container-id-54 a.estimator_doc_link.fitted {
+#sk-container-id-55 a.estimator_doc_link.fitted {
   /* fitted */
   border: var(--sklearn-color-fitted-level-1) 1pt solid;
   color: var(--sklearn-color-fitted-level-1);
 }
 
 /* On hover */
-#sk-container-id-54 a.estimator_doc_link:hover {
+#sk-container-id-55 a.estimator_doc_link:hover {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-3);
   color: var(--sklearn-color-background);
   text-decoration: none;
 }
 
-#sk-container-id-54 a.estimator_doc_link.fitted:hover {
+#sk-container-id-55 a.estimator_doc_link.fitted:hover {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-3);
 }
-</style><div id="sk-container-id-54" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),
-                (&#x27;logisticregression&#x27;, LogisticRegression(random_state=0))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-223" type="checkbox" ><label for="sk-estimator-id-223" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>Pipeline</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.pipeline.Pipeline.html">?<span>Documentation for Pipeline</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),
-                (&#x27;logisticregression&#x27;, LogisticRegression(random_state=0))])</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-224" type="checkbox" ><label for="sk-estimator-id-224" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>StandardScaler</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.StandardScaler.html">?<span>Documentation for StandardScaler</span></a></div></label><div class="sk-toggleable__content fitted"><pre>StandardScaler()</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-225" type="checkbox" ><label for="sk-estimator-id-225" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>LogisticRegression</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.linear_model.LogisticRegression.html">?<span>Documentation for LogisticRegression</span></a></div></label><div class="sk-toggleable__content fitted"><pre>LogisticRegression(random_state=0)</pre></div> </div></div></div></div></div></div>
+</style><div id="sk-container-id-55" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),
+                (&#x27;logisticregression&#x27;, LogisticRegression(random_state=0))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-237" type="checkbox" ><label for="sk-estimator-id-237" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>Pipeline</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.pipeline.Pipeline.html">?<span>Documentation for Pipeline</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),
+                (&#x27;logisticregression&#x27;, LogisticRegression(random_state=0))])</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-238" type="checkbox" ><label for="sk-estimator-id-238" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>StandardScaler</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.StandardScaler.html">?<span>Documentation for StandardScaler</span></a></div></label><div class="sk-toggleable__content fitted"><pre>StandardScaler()</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-239" type="checkbox" ><label for="sk-estimator-id-239" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>LogisticRegression</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.linear_model.LogisticRegression.html">?<span>Documentation for LogisticRegression</span></a></div></label><div class="sk-toggleable__content fitted"><pre>LogisticRegression(random_state=0)</pre></div> </div></div></div></div></div></div>
 </div>
 <br />
 <br /><section id="create-confusionmatrixdisplay">
@@ -1473,7 +1472,7 @@ <h3>Create <a class="reference internal" href="../../modules/generated/sklearn.m
 <span class="n">roc_display</span> <span class="o">=</span> <a href="http://github.com/modules/generated/sklearn.metrics.RocCurveDisplay.html#sklearn.metrics.RocCurveDisplay" title="sklearn.metrics.RocCurveDisplay" class="sphx-glr-backref-module-sklearn-metrics sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">RocCurveDisplay</span></a><span class="p">(</span><span class="n">fpr</span><span class="o">=</span><span class="n">fpr</span><span class="p">,</span> <span class="n">tpr</span><span class="o">=</span><span class="n">tpr</span><span class="p">)</span><span class="o">.</span><span class="n">plot</span><span class="p">()</span>
 </pre></div>
 </div>
-<img src="http://github.com/_images/sphx_glr_plot_display_object_visualization_002.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_display_object_visualization_002.png " alt="plot display object visualization" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/home/circleci/project/sklearn/metrics/_plot/roc_curve.py:192: UserWarning:
+<img src="http://github.com/_images/sphx_glr_plot_display_object_visualization_002.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_display_object_visualization_002.png " alt="plot display object visualization" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/home/circleci/project/sklearn/metrics/_plot/roc_curve.py:197: UserWarning:
 
 No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.
 </pre></div>
@@ -1505,12 +1504,12 @@ <h3>Combining the display objects into a single plot<a class="headerlink" href="
 <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
 </pre></div>
 </div>
-<img src="http://github.com/_images/sphx_glr_plot_display_object_visualization_004.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_display_object_visualization_004.png " alt="plot display object visualization" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/home/circleci/project/sklearn/metrics/_plot/roc_curve.py:192: UserWarning:
+<img src="http://github.com/_images/sphx_glr_plot_display_object_visualization_004.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_display_object_visualization_004.png " alt="plot display object visualization" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/home/circleci/project/sklearn/metrics/_plot/roc_curve.py:197: UserWarning:
 
 No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.
 </pre></div>
 </div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 0.320 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 0.374 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-miscellaneous-plot-display-object-visualization-py">
 <div class="binder-badge docutils container">
 <a class="reference external image-reference" href="https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/miscellaneous/plot_display_object_visualization.ipynb"><img alt="Launch binder" src="http://github.com/_images/binder_badge_logo20.svg" style="width: 150px;" />
diff --git a/dev/auto_examples/miscellaneous/plot_estimator_representation.html b/dev/auto_examples/miscellaneous/plot_estimator_representation.html
index 2bed2f0a123e0..4bc4d96b6a599 100644
--- a/dev/auto_examples/miscellaneous/plot_estimator_representation.html
+++ b/dev/auto_examples/miscellaneous/plot_estimator_representation.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -679,13 +679,12 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -902,9 +901,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
@@ -1043,7 +1042,7 @@ <h2>Rich HTML representation<a class="headerlink" href="#rich-html-representatio
 </pre></div>
 </div>
 <div class="output_subarea output_html rendered_html output_result">
-<style>#sk-container-id-45 {
+<style>#sk-container-id-46 {
   /* Definition of color scheme common for light and dark mode */
   --sklearn-color-text: #000;
   --sklearn-color-text-muted: #666;
@@ -1074,15 +1073,15 @@ <h2>Rich HTML representation<a class="headerlink" href="#rich-html-representatio
   }
 }
 
-#sk-container-id-45 {
+#sk-container-id-46 {
   color: var(--sklearn-color-text);
 }
 
-#sk-container-id-45 pre {
+#sk-container-id-46 pre {
   padding: 0;
 }
 
-#sk-container-id-45 input.sk-hidden--visually {
+#sk-container-id-46 input.sk-hidden--visually {
   border: 0;
   clip: rect(1px 1px 1px 1px);
   clip: rect(1px, 1px, 1px, 1px);
@@ -1094,7 +1093,7 @@ <h2>Rich HTML representation<a class="headerlink" href="#rich-html-representatio
   width: 1px;
 }
 
-#sk-container-id-45 div.sk-dashed-wrapped {
+#sk-container-id-46 div.sk-dashed-wrapped {
   border: 1px dashed var(--sklearn-color-line);
   margin: 0 0.4em 0.5em 0.4em;
   box-sizing: border-box;
@@ -1102,7 +1101,7 @@ <h2>Rich HTML representation<a class="headerlink" href="#rich-html-representatio
   background-color: var(--sklearn-color-background);
 }
 
-#sk-container-id-45 div.sk-container {
+#sk-container-id-46 div.sk-container {
   /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
      but bootstrap.min.css set `[hidden] { display: none !important; }`
      so we also need the `!important` here to be able to override the
@@ -1112,7 +1111,7 @@ <h2>Rich HTML representation<a class="headerlink" href="#rich-html-representatio
   position: relative;
 }
 
-#sk-container-id-45 div.sk-text-repr-fallback {
+#sk-container-id-46 div.sk-text-repr-fallback {
   display: none;
 }
 
@@ -1128,14 +1127,14 @@ <h2>Rich HTML representation<a class="headerlink" href="#rich-html-representatio
 
 /* Parallel-specific style estimator block */
 
-#sk-container-id-45 div.sk-parallel-item::after {
+#sk-container-id-46 div.sk-parallel-item::after {
   content: "";
   width: 100%;
   border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
   flex-grow: 1;
 }
 
-#sk-container-id-45 div.sk-parallel {
+#sk-container-id-46 div.sk-parallel {
   display: flex;
   align-items: stretch;
   justify-content: center;
@@ -1143,28 +1142,28 @@ <h2>Rich HTML representation<a class="headerlink" href="#rich-html-representatio
   position: relative;
 }
 
-#sk-container-id-45 div.sk-parallel-item {
+#sk-container-id-46 div.sk-parallel-item {
   display: flex;
   flex-direction: column;
 }
 
-#sk-container-id-45 div.sk-parallel-item:first-child::after {
+#sk-container-id-46 div.sk-parallel-item:first-child::after {
   align-self: flex-end;
   width: 50%;
 }
 
-#sk-container-id-45 div.sk-parallel-item:last-child::after {
+#sk-container-id-46 div.sk-parallel-item:last-child::after {
   align-self: flex-start;
   width: 50%;
 }
 
-#sk-container-id-45 div.sk-parallel-item:only-child::after {
+#sk-container-id-46 div.sk-parallel-item:only-child::after {
   width: 0;
 }
 
 /* Serial-specific style estimator block */
 
-#sk-container-id-45 div.sk-serial {
+#sk-container-id-46 div.sk-serial {
   display: flex;
   flex-direction: column;
   align-items: center;
@@ -1182,14 +1181,14 @@ <h2>Rich HTML representation<a class="headerlink" href="#rich-html-representatio
 
 /* Pipeline and ColumnTransformer style (default) */
 
-#sk-container-id-45 div.sk-toggleable {
+#sk-container-id-46 div.sk-toggleable {
   /* Default theme specific background. It is overwritten whether we have a
   specific estimator or a Pipeline/ColumnTransformer */
   background-color: var(--sklearn-color-background);
 }
 
 /* Toggleable label */
-#sk-container-id-45 label.sk-toggleable__label {
+#sk-container-id-46 label.sk-toggleable__label {
   cursor: pointer;
   display: flex;
   width: 100%;
@@ -1202,13 +1201,13 @@ <h2>Rich HTML representation<a class="headerlink" href="#rich-html-representatio
   gap: 0.5em;
 }
 
-#sk-container-id-45 label.sk-toggleable__label .caption {
+#sk-container-id-46 label.sk-toggleable__label .caption {
   font-size: 0.6rem;
   font-weight: lighter;
   color: var(--sklearn-color-text-muted);
 }
 
-#sk-container-id-45 label.sk-toggleable__label-arrow:before {
+#sk-container-id-46 label.sk-toggleable__label-arrow:before {
   /* Arrow on the left of the label */
   content: "▸";
   float: left;
@@ -1216,13 +1215,13 @@ <h2>Rich HTML representation<a class="headerlink" href="#rich-html-representatio
   color: var(--sklearn-color-icon);
 }
 
-#sk-container-id-45 label.sk-toggleable__label-arrow:hover:before {
+#sk-container-id-46 label.sk-toggleable__label-arrow:hover:before {
   color: var(--sklearn-color-text);
 }
 
 /* Toggleable content - dropdown */
 
-#sk-container-id-45 div.sk-toggleable__content {
+#sk-container-id-46 div.sk-toggleable__content {
   max-height: 0;
   max-width: 0;
   overflow: hidden;
@@ -1231,12 +1230,12 @@ <h2>Rich HTML representation<a class="headerlink" href="#rich-html-representatio
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-45 div.sk-toggleable__content.fitted {
+#sk-container-id-46 div.sk-toggleable__content.fitted {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
-#sk-container-id-45 div.sk-toggleable__content pre {
+#sk-container-id-46 div.sk-toggleable__content pre {
   margin: 0.2em;
   border-radius: 0.25em;
   color: var(--sklearn-color-text);
@@ -1244,79 +1243,79 @@ <h2>Rich HTML representation<a class="headerlink" href="#rich-html-representatio
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-45 div.sk-toggleable__content.fitted pre {
+#sk-container-id-46 div.sk-toggleable__content.fitted pre {
   /* unfitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
-#sk-container-id-45 input.sk-toggleable__control:checked~div.sk-toggleable__content {
+#sk-container-id-46 input.sk-toggleable__control:checked~div.sk-toggleable__content {
   /* Expand drop-down */
   max-height: 200px;
   max-width: 100%;
   overflow: auto;
 }
 
-#sk-container-id-45 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
+#sk-container-id-46 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
   content: "▾";
 }
 
 /* Pipeline/ColumnTransformer-specific style */
 
-#sk-container-id-45 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-46 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-45 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-46 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
 /* Estimator-specific style */
 
 /* Colorize estimator box */
-#sk-container-id-45 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-46 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-45 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-46 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
-#sk-container-id-45 div.sk-label label.sk-toggleable__label,
-#sk-container-id-45 div.sk-label label {
+#sk-container-id-46 div.sk-label label.sk-toggleable__label,
+#sk-container-id-46 div.sk-label label {
   /* The background is the default theme color */
   color: var(--sklearn-color-text-on-default-background);
 }
 
 /* On hover, darken the color of the background */
-#sk-container-id-45 div.sk-label:hover label.sk-toggleable__label {
+#sk-container-id-46 div.sk-label:hover label.sk-toggleable__label {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
 /* Label box, darken color on hover, fitted */
-#sk-container-id-45 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
+#sk-container-id-46 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
 /* Estimator label */
 
-#sk-container-id-45 div.sk-label label {
+#sk-container-id-46 div.sk-label label {
   font-family: monospace;
   font-weight: bold;
   display: inline-block;
   line-height: 1.2em;
 }
 
-#sk-container-id-45 div.sk-label-container {
+#sk-container-id-46 div.sk-label-container {
   text-align: center;
 }
 
 /* Estimator-specific */
-#sk-container-id-45 div.sk-estimator {
+#sk-container-id-46 div.sk-estimator {
   font-family: monospace;
   border: 1px dotted var(--sklearn-color-border-box);
   border-radius: 0.25em;
@@ -1326,18 +1325,18 @@ <h2>Rich HTML representation<a class="headerlink" href="#rich-html-representatio
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-45 div.sk-estimator.fitted {
+#sk-container-id-46 div.sk-estimator.fitted {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
 /* on hover */
-#sk-container-id-45 div.sk-estimator:hover {
+#sk-container-id-46 div.sk-estimator:hover {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-45 div.sk-estimator.fitted:hover {
+#sk-container-id-46 div.sk-estimator.fitted:hover {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-2);
 }
@@ -1425,7 +1424,7 @@ <h2>Rich HTML representation<a class="headerlink" href="#rich-html-representatio
 
 /* "?"-specific style due to the `<a>` HTML tag */
 
-#sk-container-id-45 a.estimator_doc_link {
+#sk-container-id-46 a.estimator_doc_link {
   float: right;
   font-size: 1rem;
   line-height: 1em;
@@ -1440,25 +1439,25 @@ <h2>Rich HTML representation<a class="headerlink" href="#rich-html-representatio
   border: var(--sklearn-color-unfitted-level-1) 1pt solid;
 }
 
-#sk-container-id-45 a.estimator_doc_link.fitted {
+#sk-container-id-46 a.estimator_doc_link.fitted {
   /* fitted */
   border: var(--sklearn-color-fitted-level-1) 1pt solid;
   color: var(--sklearn-color-fitted-level-1);
 }
 
 /* On hover */
-#sk-container-id-45 a.estimator_doc_link:hover {
+#sk-container-id-46 a.estimator_doc_link:hover {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-3);
   color: var(--sklearn-color-background);
   text-decoration: none;
 }
 
-#sk-container-id-45 a.estimator_doc_link.fitted:hover {
+#sk-container-id-46 a.estimator_doc_link.fitted:hover {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-3);
 }
-</style><div id="sk-container-id-45" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,
+</style><div id="sk-container-id-46" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,
                  ColumnTransformer(transformers=[(&#x27;pipeline-1&#x27;,
                                                   Pipeline(steps=[(&#x27;simpleimputer&#x27;,
                                                                    SimpleImputer(strategy=&#x27;median&#x27;)),
@@ -1472,7 +1471,7 @@ <h2>Rich HTML representation<a class="headerlink" href="#rich-html-representatio
                                                                   (&#x27;onehotencoder&#x27;,
                                                                    OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),
                                                   (&#x27;feat0&#x27;, &#x27;feat2&#x27;))])),
-                (&#x27;logisticregression&#x27;, LogisticRegression())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-195" type="checkbox" ><label for="sk-estimator-id-195" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>Pipeline</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.pipeline.Pipeline.html">?<span>Documentation for Pipeline</span></a><span class="sk-estimator-doc-link ">i<span>Not fitted</span></span></div></label><div class="sk-toggleable__content "><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,
+                (&#x27;logisticregression&#x27;, LogisticRegression())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-209" type="checkbox" ><label for="sk-estimator-id-209" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>Pipeline</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.pipeline.Pipeline.html">?<span>Documentation for Pipeline</span></a><span class="sk-estimator-doc-link ">i<span>Not fitted</span></span></div></label><div class="sk-toggleable__content "><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,
                  ColumnTransformer(transformers=[(&#x27;pipeline-1&#x27;,
                                                   Pipeline(steps=[(&#x27;simpleimputer&#x27;,
                                                                    SimpleImputer(strategy=&#x27;median&#x27;)),
@@ -1486,7 +1485,7 @@ <h2>Rich HTML representation<a class="headerlink" href="#rich-html-representatio
                                                                   (&#x27;onehotencoder&#x27;,
                                                                    OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),
                                                   (&#x27;feat0&#x27;, &#x27;feat2&#x27;))])),
-                (&#x27;logisticregression&#x27;, LogisticRegression())])</pre></div> </div></div><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-196" type="checkbox" ><label for="sk-estimator-id-196" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>columntransformer: ColumnTransformer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.compose.ColumnTransformer.html">?<span>Documentation for columntransformer: ColumnTransformer</span></a></div></label><div class="sk-toggleable__content "><pre>ColumnTransformer(transformers=[(&#x27;pipeline-1&#x27;,
+                (&#x27;logisticregression&#x27;, LogisticRegression())])</pre></div> </div></div><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-210" type="checkbox" ><label for="sk-estimator-id-210" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>columntransformer: ColumnTransformer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.compose.ColumnTransformer.html">?<span>Documentation for columntransformer: ColumnTransformer</span></a></div></label><div class="sk-toggleable__content "><pre>ColumnTransformer(transformers=[(&#x27;pipeline-1&#x27;,
                                  Pipeline(steps=[(&#x27;simpleimputer&#x27;,
                                                   SimpleImputer(strategy=&#x27;median&#x27;)),
                                                  (&#x27;standardscaler&#x27;,
@@ -1498,10 +1497,10 @@ <h2>Rich HTML representation<a class="headerlink" href="#rich-html-representatio
                                                                 strategy=&#x27;constant&#x27;)),
                                                  (&#x27;onehotencoder&#x27;,
                                                   OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),
-                                 (&#x27;feat0&#x27;, &#x27;feat2&#x27;))])</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-197" type="checkbox" ><label for="sk-estimator-id-197" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>pipeline-1</div></div></label><div class="sk-toggleable__content "><pre>(&#x27;feat1&#x27;, &#x27;feat3&#x27;)</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-198" type="checkbox" ><label for="sk-estimator-id-198" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>SimpleImputer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.impute.SimpleImputer.html">?<span>Documentation for SimpleImputer</span></a></div></label><div class="sk-toggleable__content "><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div> </div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-199" type="checkbox" ><label for="sk-estimator-id-199" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>StandardScaler</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.StandardScaler.html">?<span>Documentation for StandardScaler</span></a></div></label><div class="sk-toggleable__content "><pre>StandardScaler()</pre></div> </div></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-200" type="checkbox" ><label for="sk-estimator-id-200" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>pipeline-2</div></div></label><div class="sk-toggleable__content "><pre>(&#x27;feat0&#x27;, &#x27;feat2&#x27;)</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-201" type="checkbox" ><label for="sk-estimator-id-201" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>SimpleImputer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.impute.SimpleImputer.html">?<span>Documentation for SimpleImputer</span></a></div></label><div class="sk-toggleable__content "><pre>SimpleImputer(fill_value=&#x27;missing&#x27;, strategy=&#x27;constant&#x27;)</pre></div> </div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-202" type="checkbox" ><label for="sk-estimator-id-202" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>OneHotEncoder</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.OneHotEncoder.html">?<span>Documentation for OneHotEncoder</span></a></div></label><div class="sk-toggleable__content "><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</pre></div> </div></div></div></div></div></div></div></div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-203" type="checkbox" ><label for="sk-estimator-id-203" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>LogisticRegression</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.linear_model.LogisticRegression.html">?<span>Documentation for LogisticRegression</span></a></div></label><div class="sk-toggleable__content "><pre>LogisticRegression()</pre></div> </div></div></div></div></div></div>
+                                 (&#x27;feat0&#x27;, &#x27;feat2&#x27;))])</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-211" type="checkbox" ><label for="sk-estimator-id-211" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>pipeline-1</div></div></label><div class="sk-toggleable__content "><pre>(&#x27;feat1&#x27;, &#x27;feat3&#x27;)</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-212" type="checkbox" ><label for="sk-estimator-id-212" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>SimpleImputer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.impute.SimpleImputer.html">?<span>Documentation for SimpleImputer</span></a></div></label><div class="sk-toggleable__content "><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div> </div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-213" type="checkbox" ><label for="sk-estimator-id-213" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>StandardScaler</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.StandardScaler.html">?<span>Documentation for StandardScaler</span></a></div></label><div class="sk-toggleable__content "><pre>StandardScaler()</pre></div> </div></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-214" type="checkbox" ><label for="sk-estimator-id-214" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>pipeline-2</div></div></label><div class="sk-toggleable__content "><pre>(&#x27;feat0&#x27;, &#x27;feat2&#x27;)</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-215" type="checkbox" ><label for="sk-estimator-id-215" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>SimpleImputer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.impute.SimpleImputer.html">?<span>Documentation for SimpleImputer</span></a></div></label><div class="sk-toggleable__content "><pre>SimpleImputer(fill_value=&#x27;missing&#x27;, strategy=&#x27;constant&#x27;)</pre></div> </div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-216" type="checkbox" ><label for="sk-estimator-id-216" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>OneHotEncoder</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.OneHotEncoder.html">?<span>Documentation for OneHotEncoder</span></a></div></label><div class="sk-toggleable__content "><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</pre></div> </div></div></div></div></div></div></div></div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-217" type="checkbox" ><label for="sk-estimator-id-217" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>LogisticRegression</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.linear_model.LogisticRegression.html">?<span>Documentation for LogisticRegression</span></a></div></label><div class="sk-toggleable__content "><pre>LogisticRegression()</pre></div> </div></div></div></div></div></div>
 </div>
 <br />
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 <div class="binder-badge docutils container">
 <a class="reference external image-reference" href="https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/miscellaneous/plot_estimator_representation.ipynb"><img alt="Launch binder" src="http://github.com/_images/binder_badge_logo20.svg" style="width: 150px;" />
diff --git a/dev/auto_examples/miscellaneous/plot_isotonic_regression.html b/dev/auto_examples/miscellaneous/plot_isotonic_regression.html
index 2990e01e891b2..d3c771ab59385 100644
--- a/dev/auto_examples/miscellaneous/plot_isotonic_regression.html
+++ b/dev/auto_examples/miscellaneous/plot_isotonic_regression.html
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     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
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@@ -679,13 +679,12 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -902,9 +901,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
@@ -1031,7 +1030,7 @@
 </pre></div>
 </div>
 <div class="output_subarea output_html rendered_html output_result">
-<style>#sk-container-id-48 {
+<style>#sk-container-id-49 {
   /* Definition of color scheme common for light and dark mode */
   --sklearn-color-text: #000;
   --sklearn-color-text-muted: #666;
@@ -1062,15 +1061,15 @@
   }
 }
 
-#sk-container-id-48 {
+#sk-container-id-49 {
   color: var(--sklearn-color-text);
 }
 
-#sk-container-id-48 pre {
+#sk-container-id-49 pre {
   padding: 0;
 }
 
-#sk-container-id-48 input.sk-hidden--visually {
+#sk-container-id-49 input.sk-hidden--visually {
   border: 0;
   clip: rect(1px 1px 1px 1px);
   clip: rect(1px, 1px, 1px, 1px);
@@ -1082,7 +1081,7 @@
   width: 1px;
 }
 
-#sk-container-id-48 div.sk-dashed-wrapped {
+#sk-container-id-49 div.sk-dashed-wrapped {
   border: 1px dashed var(--sklearn-color-line);
   margin: 0 0.4em 0.5em 0.4em;
   box-sizing: border-box;
@@ -1090,7 +1089,7 @@
   background-color: var(--sklearn-color-background);
 }
 
-#sk-container-id-48 div.sk-container {
+#sk-container-id-49 div.sk-container {
   /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
      but bootstrap.min.css set `[hidden] { display: none !important; }`
      so we also need the `!important` here to be able to override the
@@ -1100,7 +1099,7 @@
   position: relative;
 }
 
-#sk-container-id-48 div.sk-text-repr-fallback {
+#sk-container-id-49 div.sk-text-repr-fallback {
   display: none;
 }
 
@@ -1116,14 +1115,14 @@
 
 /* Parallel-specific style estimator block */
 
-#sk-container-id-48 div.sk-parallel-item::after {
+#sk-container-id-49 div.sk-parallel-item::after {
   content: "";
   width: 100%;
   border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
   flex-grow: 1;
 }
 
-#sk-container-id-48 div.sk-parallel {
+#sk-container-id-49 div.sk-parallel {
   display: flex;
   align-items: stretch;
   justify-content: center;
@@ -1131,28 +1130,28 @@
   position: relative;
 }
 
-#sk-container-id-48 div.sk-parallel-item {
+#sk-container-id-49 div.sk-parallel-item {
   display: flex;
   flex-direction: column;
 }
 
-#sk-container-id-48 div.sk-parallel-item:first-child::after {
+#sk-container-id-49 div.sk-parallel-item:first-child::after {
   align-self: flex-end;
   width: 50%;
 }
 
-#sk-container-id-48 div.sk-parallel-item:last-child::after {
+#sk-container-id-49 div.sk-parallel-item:last-child::after {
   align-self: flex-start;
   width: 50%;
 }
 
-#sk-container-id-48 div.sk-parallel-item:only-child::after {
+#sk-container-id-49 div.sk-parallel-item:only-child::after {
   width: 0;
 }
 
 /* Serial-specific style estimator block */
 
-#sk-container-id-48 div.sk-serial {
+#sk-container-id-49 div.sk-serial {
   display: flex;
   flex-direction: column;
   align-items: center;
@@ -1170,14 +1169,14 @@
 
 /* Pipeline and ColumnTransformer style (default) */
 
-#sk-container-id-48 div.sk-toggleable {
+#sk-container-id-49 div.sk-toggleable {
   /* Default theme specific background. It is overwritten whether we have a
   specific estimator or a Pipeline/ColumnTransformer */
   background-color: var(--sklearn-color-background);
 }
 
 /* Toggleable label */
-#sk-container-id-48 label.sk-toggleable__label {
+#sk-container-id-49 label.sk-toggleable__label {
   cursor: pointer;
   display: flex;
   width: 100%;
@@ -1190,13 +1189,13 @@
   gap: 0.5em;
 }
 
-#sk-container-id-48 label.sk-toggleable__label .caption {
+#sk-container-id-49 label.sk-toggleable__label .caption {
   font-size: 0.6rem;
   font-weight: lighter;
   color: var(--sklearn-color-text-muted);
 }
 
-#sk-container-id-48 label.sk-toggleable__label-arrow:before {
+#sk-container-id-49 label.sk-toggleable__label-arrow:before {
   /* Arrow on the left of the label */
   content: "▸";
   float: left;
@@ -1204,13 +1203,13 @@
   color: var(--sklearn-color-icon);
 }
 
-#sk-container-id-48 label.sk-toggleable__label-arrow:hover:before {
+#sk-container-id-49 label.sk-toggleable__label-arrow:hover:before {
   color: var(--sklearn-color-text);
 }
 
 /* Toggleable content - dropdown */
 
-#sk-container-id-48 div.sk-toggleable__content {
+#sk-container-id-49 div.sk-toggleable__content {
   max-height: 0;
   max-width: 0;
   overflow: hidden;
@@ -1219,12 +1218,12 @@
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-48 div.sk-toggleable__content.fitted {
+#sk-container-id-49 div.sk-toggleable__content.fitted {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
-#sk-container-id-48 div.sk-toggleable__content pre {
+#sk-container-id-49 div.sk-toggleable__content pre {
   margin: 0.2em;
   border-radius: 0.25em;
   color: var(--sklearn-color-text);
@@ -1232,79 +1231,79 @@
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-48 div.sk-toggleable__content.fitted pre {
+#sk-container-id-49 div.sk-toggleable__content.fitted pre {
   /* unfitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
-#sk-container-id-48 input.sk-toggleable__control:checked~div.sk-toggleable__content {
+#sk-container-id-49 input.sk-toggleable__control:checked~div.sk-toggleable__content {
   /* Expand drop-down */
   max-height: 200px;
   max-width: 100%;
   overflow: auto;
 }
 
-#sk-container-id-48 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
+#sk-container-id-49 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
   content: "▾";
 }
 
 /* Pipeline/ColumnTransformer-specific style */
 
-#sk-container-id-48 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-49 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-48 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-49 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
 /* Estimator-specific style */
 
 /* Colorize estimator box */
-#sk-container-id-48 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-49 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-48 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-49 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
-#sk-container-id-48 div.sk-label label.sk-toggleable__label,
-#sk-container-id-48 div.sk-label label {
+#sk-container-id-49 div.sk-label label.sk-toggleable__label,
+#sk-container-id-49 div.sk-label label {
   /* The background is the default theme color */
   color: var(--sklearn-color-text-on-default-background);
 }
 
 /* On hover, darken the color of the background */
-#sk-container-id-48 div.sk-label:hover label.sk-toggleable__label {
+#sk-container-id-49 div.sk-label:hover label.sk-toggleable__label {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
 /* Label box, darken color on hover, fitted */
-#sk-container-id-48 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
+#sk-container-id-49 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
 /* Estimator label */
 
-#sk-container-id-48 div.sk-label label {
+#sk-container-id-49 div.sk-label label {
   font-family: monospace;
   font-weight: bold;
   display: inline-block;
   line-height: 1.2em;
 }
 
-#sk-container-id-48 div.sk-label-container {
+#sk-container-id-49 div.sk-label-container {
   text-align: center;
 }
 
 /* Estimator-specific */
-#sk-container-id-48 div.sk-estimator {
+#sk-container-id-49 div.sk-estimator {
   font-family: monospace;
   border: 1px dotted var(--sklearn-color-border-box);
   border-radius: 0.25em;
@@ -1314,18 +1313,18 @@
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-48 div.sk-estimator.fitted {
+#sk-container-id-49 div.sk-estimator.fitted {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
 /* on hover */
-#sk-container-id-48 div.sk-estimator:hover {
+#sk-container-id-49 div.sk-estimator:hover {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-48 div.sk-estimator.fitted:hover {
+#sk-container-id-49 div.sk-estimator.fitted:hover {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-2);
 }
@@ -1413,7 +1412,7 @@
 
 /* "?"-specific style due to the `<a>` HTML tag */
 
-#sk-container-id-48 a.estimator_doc_link {
+#sk-container-id-49 a.estimator_doc_link {
   float: right;
   font-size: 1rem;
   line-height: 1em;
@@ -1428,25 +1427,25 @@
   border: var(--sklearn-color-unfitted-level-1) 1pt solid;
 }
 
-#sk-container-id-48 a.estimator_doc_link.fitted {
+#sk-container-id-49 a.estimator_doc_link.fitted {
   /* fitted */
   border: var(--sklearn-color-fitted-level-1) 1pt solid;
   color: var(--sklearn-color-fitted-level-1);
 }
 
 /* On hover */
-#sk-container-id-48 a.estimator_doc_link:hover {
+#sk-container-id-49 a.estimator_doc_link:hover {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-3);
   color: var(--sklearn-color-background);
   text-decoration: none;
 }
 
-#sk-container-id-48 a.estimator_doc_link.fitted:hover {
+#sk-container-id-49 a.estimator_doc_link.fitted:hover {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-3);
 }
-</style><div id="sk-container-id-48" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-209" type="checkbox" checked><label for="sk-estimator-id-209" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>LinearRegression</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.linear_model.LinearRegression.html">?<span>Documentation for LinearRegression</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>LinearRegression()</pre></div> </div></div></div></div>
+</style><div id="sk-container-id-49" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-223" type="checkbox" checked><label for="sk-estimator-id-223" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>LinearRegression</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.linear_model.LinearRegression.html">?<span>Documentation for LinearRegression</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>LinearRegression()</pre></div> </div></div></div></div>
 </div>
 <br />
 <br /><p>Plot results:</p>
@@ -1476,7 +1475,7 @@
 <code class="docutils literal notranslate"><span class="pre">IsotonicRegression</span></code> to control the way the model extrapolates outside of the
 range of data observed in the training set. This “clipping” extrapolation can
 be seen on the plot of the decision function on the right-hand.</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 0.144 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 0.156 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-miscellaneous-plot-isotonic-regression-py">
 <div class="binder-badge docutils container">
 <a class="reference external image-reference" href="https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/miscellaneous/plot_isotonic_regression.ipynb"><img alt="Launch binder" src="http://github.com/_images/binder_badge_logo20.svg" style="width: 150px;" />
diff --git a/dev/auto_examples/miscellaneous/plot_johnson_lindenstrauss_bound.html b/dev/auto_examples/miscellaneous/plot_johnson_lindenstrauss_bound.html
index 7a7644d76a2a4..e1c206a411089 100644
--- a/dev/auto_examples/miscellaneous/plot_johnson_lindenstrauss_bound.html
+++ b/dev/auto_examples/miscellaneous/plot_johnson_lindenstrauss_bound.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -680,13 +680,12 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -903,9 +902,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
@@ -1170,14 +1169,14 @@ <h2>Empirical validation<a class="headerlink" href="#empirical-validation" title
 <li><img src="http://github.com/_images/sphx_glr_plot_johnson_lindenstrauss_bound_008.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_johnson_lindenstrauss_bound_008.png " alt="Histogram of pairwise distance rates for n_components=10000" class = "sphx-glr-multi-img"/></li>
 </ul>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Embedding 300 samples with dim 130107 using various random projections
-Projected 300 samples from 130107 to 300 in 0.255s
-Random matrix with size: 1.295 MB
-Mean distances rate: 1.01 (0.17)
-Projected 300 samples from 130107 to 1000 in 0.837s
-Random matrix with size: 4.325 MB
+Projected 300 samples from 130107 to 300 in 0.226s
+Random matrix with size: 1.301 MB
+Mean distances rate: 1.02 (0.17)
+Projected 300 samples from 130107 to 1000 in 0.742s
+Random matrix with size: 4.324 MB
 Mean distances rate: 1.01 (0.09)
-Projected 300 samples from 130107 to 10000 in 7.806s
-Random matrix with size: 43.243 MB
+Projected 300 samples from 130107 to 10000 in 7.469s
+Random matrix with size: 43.239 MB
 Mean distances rate: 1.01 (0.03)
 </pre></div>
 </div>
@@ -1198,7 +1197,7 @@ <h2>Remarks<a class="headerlink" href="#remarks" title="Link to this heading">#<
 <p>On the twenty newsgroups on the other hand the dimensionality can be
 decreased from 56,436 down to 10,000 while reasonably preserving
 pairwise distances.</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 11.506 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 11.082 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-miscellaneous-plot-johnson-lindenstrauss-bound-py">
 <div class="binder-badge docutils container">
 <a class="reference external image-reference" href="https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/miscellaneous/plot_johnson_lindenstrauss_bound.ipynb"><img alt="Launch binder" src="http://github.com/_images/binder_badge_logo20.svg" style="width: 150px;" />
@@ -1228,9 +1227,9 @@ <h2>Remarks<a class="headerlink" href="#remarks" title="Link to this heading">#<
 </div><div class="sphx-glr-thumbcontainer" tooltip="The usual covariance maximum likelihood estimate can be regularized using shrinkage. Ledoit and Wolf proposed a close formula to compute the asymptotically optimal shrinkage parameter (minimizing a MSE criterion), yielding the Ledoit-Wolf covariance estimate."><img alt="" src="http://github.com/_images/sphx_glr_plot_lw_vs_oas_thumb.png" />
 <p><a class="reference internal" href="http://github.com/covariance/plot_lw_vs_oas.html#sphx-glr-auto-examples-covariance-plot-lw-vs-oas-py"><span class="std std-ref">Ledoit-Wolf vs OAS estimation</span></a></p>
   <div class="sphx-glr-thumbnail-title">Ledoit-Wolf vs OAS estimation</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="An illustration of various linkage option for agglomerative clustering on a 2D embedding of the digits dataset."><img alt="" src="http://github.com/_images/sphx_glr_plot_digits_linkage_thumb.png" />
-<p><a class="reference internal" href="http://github.com/cluster/plot_digits_linkage.html#sphx-glr-auto-examples-cluster-plot-digits-linkage-py"><span class="std std-ref">Various Agglomerative Clustering on a 2D embedding of digits</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Various Agglomerative Clustering on a 2D embedding of digits</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="An illustration of the metric and non-metric MDS on generated noisy data."><img alt="" src="http://github.com/_images/sphx_glr_plot_mds_thumb.png" />
+<p><a class="reference internal" href="http://github.com/manifold/plot_mds.html#sphx-glr-auto-examples-manifold-plot-mds-py"><span class="std std-ref">Multi-dimensional scaling</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Multi-dimensional scaling</div>
 </div></div><p class="sphx-glr-signature"><a class="reference external" href="https://sphinx-gallery.github.io/">Gallery generated by Sphinx-Gallery</a></p>
 </section>
 </section>
diff --git a/dev/auto_examples/miscellaneous/plot_kernel_approximation.html b/dev/auto_examples/miscellaneous/plot_kernel_approximation.html
index df5b8588ec210..2f4ef106ddef6 100644
--- a/dev/auto_examples/miscellaneous/plot_kernel_approximation.html
+++ b/dev/auto_examples/miscellaneous/plot_kernel_approximation.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -679,13 +679,12 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -902,9 +901,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
@@ -1238,7 +1237,7 @@ <h2>Decision Surfaces of RBF Kernel SVM and Linear SVM<a class="headerlink" href
 <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
 </pre></div>
 </div>
-<img src="http://github.com/_images/sphx_glr_plot_kernel_approximation_002.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_kernel_approximation_002.png " alt="SVC with rbf kernel, SVC (linear kernel)  with Fourier rbf feature map n_components=100, SVC (linear kernel)  with Nystroem rbf feature map n_components=100" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 1.734 seconds)</p>
+<img src="http://github.com/_images/sphx_glr_plot_kernel_approximation_002.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_kernel_approximation_002.png " alt="SVC with rbf kernel, SVC (linear kernel)  with Fourier rbf feature map n_components=100, SVC (linear kernel)  with Nystroem rbf feature map n_components=100" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 1.808 seconds)</p>
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 <div class="binder-badge docutils container">
 <a class="reference external image-reference" href="https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/miscellaneous/plot_kernel_approximation.ipynb"><img alt="Launch binder" src="http://github.com/_images/binder_badge_logo20.svg" style="width: 150px;" />
diff --git a/dev/auto_examples/miscellaneous/plot_kernel_ridge_regression.html b/dev/auto_examples/miscellaneous/plot_kernel_ridge_regression.html
index e04bb3ce8aa99..017f1c2ac0d90 100644
--- a/dev/auto_examples/miscellaneous/plot_kernel_ridge_regression.html
+++ b/dev/auto_examples/miscellaneous/plot_kernel_ridge_regression.html
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     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -679,13 +679,12 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -902,9 +901,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
@@ -1074,12 +1073,12 @@ <h2>Compare times of SVR and Kernel Ridge Regression<a class="headerlink" href="
 </pre></div>
 </div>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Best SVR with params: {&#39;C&#39;: 1.0, &#39;gamma&#39;: np.float64(0.1)} and R2 score: 0.737
-SVR complexity and bandwidth selected and model fitted in 0.511 s
+SVR complexity and bandwidth selected and model fitted in 0.588 s
 Best KRR with params: {&#39;alpha&#39;: 0.1, &#39;gamma&#39;: np.float64(0.1)} and R2 score: 0.723
-KRR complexity and bandwidth selected and model fitted in 0.192 s
+KRR complexity and bandwidth selected and model fitted in 0.205 s
 Support vector ratio: 0.340
-SVR prediction for 100000 inputs in 0.115 s
-KRR prediction for 100000 inputs in 0.094 s
+SVR prediction for 100000 inputs in 0.126 s
+KRR prediction for 100000 inputs in 0.596 s
 </pre></div>
 </div>
 </section>
@@ -1206,7 +1205,7 @@ <h2>Visualize the learning curves<a class="headerlink" href="#visualize-the-lear
 <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
 </pre></div>
 </div>
-<img src="http://github.com/_images/sphx_glr_plot_kernel_ridge_regression_003.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_kernel_ridge_regression_003.png " alt="Learning curves" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 9.342 seconds)</p>
+<img src="http://github.com/_images/sphx_glr_plot_kernel_ridge_regression_003.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_kernel_ridge_regression_003.png " alt="Learning curves" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 14.892 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-miscellaneous-plot-kernel-ridge-regression-py">
 <div class="binder-badge docutils container">
 <a class="reference external image-reference" href="https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/miscellaneous/plot_kernel_ridge_regression.ipynb"><img alt="Launch binder" src="http://github.com/_images/binder_badge_logo20.svg" style="width: 150px;" />
diff --git a/dev/auto_examples/miscellaneous/plot_metadata_routing.html b/dev/auto_examples/miscellaneous/plot_metadata_routing.html
index 408fb0badda95..1694d66c5f57e 100644
--- a/dev/auto_examples/miscellaneous/plot_metadata_routing.html
+++ b/dev/auto_examples/miscellaneous/plot_metadata_routing.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -679,13 +679,12 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -902,9 +901,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
@@ -1223,7 +1222,7 @@ <h2>Routing Meta-Estimator<a class="headerlink" href="#routing-meta-estimator" t
 </pre></div>
 </div>
 <div class="output_subarea output_html rendered_html output_result">
-<style>#sk-container-id-49 {
+<style>#sk-container-id-50 {
   /* Definition of color scheme common for light and dark mode */
   --sklearn-color-text: #000;
   --sklearn-color-text-muted: #666;
@@ -1254,15 +1253,15 @@ <h2>Routing Meta-Estimator<a class="headerlink" href="#routing-meta-estimator" t
   }
 }
 
-#sk-container-id-49 {
+#sk-container-id-50 {
   color: var(--sklearn-color-text);
 }
 
-#sk-container-id-49 pre {
+#sk-container-id-50 pre {
   padding: 0;
 }
 
-#sk-container-id-49 input.sk-hidden--visually {
+#sk-container-id-50 input.sk-hidden--visually {
   border: 0;
   clip: rect(1px 1px 1px 1px);
   clip: rect(1px, 1px, 1px, 1px);
@@ -1274,7 +1273,7 @@ <h2>Routing Meta-Estimator<a class="headerlink" href="#routing-meta-estimator" t
   width: 1px;
 }
 
-#sk-container-id-49 div.sk-dashed-wrapped {
+#sk-container-id-50 div.sk-dashed-wrapped {
   border: 1px dashed var(--sklearn-color-line);
   margin: 0 0.4em 0.5em 0.4em;
   box-sizing: border-box;
@@ -1282,7 +1281,7 @@ <h2>Routing Meta-Estimator<a class="headerlink" href="#routing-meta-estimator" t
   background-color: var(--sklearn-color-background);
 }
 
-#sk-container-id-49 div.sk-container {
+#sk-container-id-50 div.sk-container {
   /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
      but bootstrap.min.css set `[hidden] { display: none !important; }`
      so we also need the `!important` here to be able to override the
@@ -1292,7 +1291,7 @@ <h2>Routing Meta-Estimator<a class="headerlink" href="#routing-meta-estimator" t
   position: relative;
 }
 
-#sk-container-id-49 div.sk-text-repr-fallback {
+#sk-container-id-50 div.sk-text-repr-fallback {
   display: none;
 }
 
@@ -1308,14 +1307,14 @@ <h2>Routing Meta-Estimator<a class="headerlink" href="#routing-meta-estimator" t
 
 /* Parallel-specific style estimator block */
 
-#sk-container-id-49 div.sk-parallel-item::after {
+#sk-container-id-50 div.sk-parallel-item::after {
   content: "";
   width: 100%;
   border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
   flex-grow: 1;
 }
 
-#sk-container-id-49 div.sk-parallel {
+#sk-container-id-50 div.sk-parallel {
   display: flex;
   align-items: stretch;
   justify-content: center;
@@ -1323,28 +1322,28 @@ <h2>Routing Meta-Estimator<a class="headerlink" href="#routing-meta-estimator" t
   position: relative;
 }
 
-#sk-container-id-49 div.sk-parallel-item {
+#sk-container-id-50 div.sk-parallel-item {
   display: flex;
   flex-direction: column;
 }
 
-#sk-container-id-49 div.sk-parallel-item:first-child::after {
+#sk-container-id-50 div.sk-parallel-item:first-child::after {
   align-self: flex-end;
   width: 50%;
 }
 
-#sk-container-id-49 div.sk-parallel-item:last-child::after {
+#sk-container-id-50 div.sk-parallel-item:last-child::after {
   align-self: flex-start;
   width: 50%;
 }
 
-#sk-container-id-49 div.sk-parallel-item:only-child::after {
+#sk-container-id-50 div.sk-parallel-item:only-child::after {
   width: 0;
 }
 
 /* Serial-specific style estimator block */
 
-#sk-container-id-49 div.sk-serial {
+#sk-container-id-50 div.sk-serial {
   display: flex;
   flex-direction: column;
   align-items: center;
@@ -1362,14 +1361,14 @@ <h2>Routing Meta-Estimator<a class="headerlink" href="#routing-meta-estimator" t
 
 /* Pipeline and ColumnTransformer style (default) */
 
-#sk-container-id-49 div.sk-toggleable {
+#sk-container-id-50 div.sk-toggleable {
   /* Default theme specific background. It is overwritten whether we have a
   specific estimator or a Pipeline/ColumnTransformer */
   background-color: var(--sklearn-color-background);
 }
 
 /* Toggleable label */
-#sk-container-id-49 label.sk-toggleable__label {
+#sk-container-id-50 label.sk-toggleable__label {
   cursor: pointer;
   display: flex;
   width: 100%;
@@ -1382,13 +1381,13 @@ <h2>Routing Meta-Estimator<a class="headerlink" href="#routing-meta-estimator" t
   gap: 0.5em;
 }
 
-#sk-container-id-49 label.sk-toggleable__label .caption {
+#sk-container-id-50 label.sk-toggleable__label .caption {
   font-size: 0.6rem;
   font-weight: lighter;
   color: var(--sklearn-color-text-muted);
 }
 
-#sk-container-id-49 label.sk-toggleable__label-arrow:before {
+#sk-container-id-50 label.sk-toggleable__label-arrow:before {
   /* Arrow on the left of the label */
   content: "▸";
   float: left;
@@ -1396,13 +1395,13 @@ <h2>Routing Meta-Estimator<a class="headerlink" href="#routing-meta-estimator" t
   color: var(--sklearn-color-icon);
 }
 
-#sk-container-id-49 label.sk-toggleable__label-arrow:hover:before {
+#sk-container-id-50 label.sk-toggleable__label-arrow:hover:before {
   color: var(--sklearn-color-text);
 }
 
 /* Toggleable content - dropdown */
 
-#sk-container-id-49 div.sk-toggleable__content {
+#sk-container-id-50 div.sk-toggleable__content {
   max-height: 0;
   max-width: 0;
   overflow: hidden;
@@ -1411,12 +1410,12 @@ <h2>Routing Meta-Estimator<a class="headerlink" href="#routing-meta-estimator" t
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-49 div.sk-toggleable__content.fitted {
+#sk-container-id-50 div.sk-toggleable__content.fitted {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
-#sk-container-id-49 div.sk-toggleable__content pre {
+#sk-container-id-50 div.sk-toggleable__content pre {
   margin: 0.2em;
   border-radius: 0.25em;
   color: var(--sklearn-color-text);
@@ -1424,79 +1423,79 @@ <h2>Routing Meta-Estimator<a class="headerlink" href="#routing-meta-estimator" t
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-49 div.sk-toggleable__content.fitted pre {
+#sk-container-id-50 div.sk-toggleable__content.fitted pre {
   /* unfitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
-#sk-container-id-49 input.sk-toggleable__control:checked~div.sk-toggleable__content {
+#sk-container-id-50 input.sk-toggleable__control:checked~div.sk-toggleable__content {
   /* Expand drop-down */
   max-height: 200px;
   max-width: 100%;
   overflow: auto;
 }
 
-#sk-container-id-49 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
+#sk-container-id-50 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
   content: "▾";
 }
 
 /* Pipeline/ColumnTransformer-specific style */
 
-#sk-container-id-49 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-50 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-49 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-50 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
 /* Estimator-specific style */
 
 /* Colorize estimator box */
-#sk-container-id-49 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-50 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-49 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-50 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
-#sk-container-id-49 div.sk-label label.sk-toggleable__label,
-#sk-container-id-49 div.sk-label label {
+#sk-container-id-50 div.sk-label label.sk-toggleable__label,
+#sk-container-id-50 div.sk-label label {
   /* The background is the default theme color */
   color: var(--sklearn-color-text-on-default-background);
 }
 
 /* On hover, darken the color of the background */
-#sk-container-id-49 div.sk-label:hover label.sk-toggleable__label {
+#sk-container-id-50 div.sk-label:hover label.sk-toggleable__label {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
 /* Label box, darken color on hover, fitted */
-#sk-container-id-49 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
+#sk-container-id-50 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
 /* Estimator label */
 
-#sk-container-id-49 div.sk-label label {
+#sk-container-id-50 div.sk-label label {
   font-family: monospace;
   font-weight: bold;
   display: inline-block;
   line-height: 1.2em;
 }
 
-#sk-container-id-49 div.sk-label-container {
+#sk-container-id-50 div.sk-label-container {
   text-align: center;
 }
 
 /* Estimator-specific */
-#sk-container-id-49 div.sk-estimator {
+#sk-container-id-50 div.sk-estimator {
   font-family: monospace;
   border: 1px dotted var(--sklearn-color-border-box);
   border-radius: 0.25em;
@@ -1506,18 +1505,18 @@ <h2>Routing Meta-Estimator<a class="headerlink" href="#routing-meta-estimator" t
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-49 div.sk-estimator.fitted {
+#sk-container-id-50 div.sk-estimator.fitted {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
 /* on hover */
-#sk-container-id-49 div.sk-estimator:hover {
+#sk-container-id-50 div.sk-estimator:hover {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-49 div.sk-estimator.fitted:hover {
+#sk-container-id-50 div.sk-estimator.fitted:hover {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-2);
 }
@@ -1605,7 +1604,7 @@ <h2>Routing Meta-Estimator<a class="headerlink" href="#routing-meta-estimator" t
 
 /* "?"-specific style due to the `<a>` HTML tag */
 
-#sk-container-id-49 a.estimator_doc_link {
+#sk-container-id-50 a.estimator_doc_link {
   float: right;
   font-size: 1rem;
   line-height: 1em;
@@ -1620,25 +1619,25 @@ <h2>Routing Meta-Estimator<a class="headerlink" href="#routing-meta-estimator" t
   border: var(--sklearn-color-unfitted-level-1) 1pt solid;
 }
 
-#sk-container-id-49 a.estimator_doc_link.fitted {
+#sk-container-id-50 a.estimator_doc_link.fitted {
   /* fitted */
   border: var(--sklearn-color-fitted-level-1) 1pt solid;
   color: var(--sklearn-color-fitted-level-1);
 }
 
 /* On hover */
-#sk-container-id-49 a.estimator_doc_link:hover {
+#sk-container-id-50 a.estimator_doc_link:hover {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-3);
   color: var(--sklearn-color-background);
   text-decoration: none;
 }
 
-#sk-container-id-49 a.estimator_doc_link.fitted:hover {
+#sk-container-id-50 a.estimator_doc_link.fitted:hover {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-3);
 }
-</style><div id="sk-container-id-49" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>MetaClassifier(estimator=ExampleClassifier())</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-210" type="checkbox" ><label for="sk-estimator-id-210" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>MetaClassifier</div></div><div><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>MetaClassifier(estimator=ExampleClassifier())</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-211" type="checkbox" ><label for="sk-estimator-id-211" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>estimator: ExampleClassifier</div></div></label><div class="sk-toggleable__content fitted"><pre>ExampleClassifier()</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-212" type="checkbox" ><label for="sk-estimator-id-212" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>ExampleClassifier</div></div></label><div class="sk-toggleable__content fitted"><pre>ExampleClassifier()</pre></div> </div></div></div></div></div></div></div></div></div>
+</style><div id="sk-container-id-50" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>MetaClassifier(estimator=ExampleClassifier())</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-224" type="checkbox" ><label for="sk-estimator-id-224" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>MetaClassifier</div></div><div><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>MetaClassifier(estimator=ExampleClassifier())</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-225" type="checkbox" ><label for="sk-estimator-id-225" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>estimator: ExampleClassifier</div></div></label><div class="sk-toggleable__content fitted"><pre>ExampleClassifier()</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-226" type="checkbox" ><label for="sk-estimator-id-226" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>ExampleClassifier</div></div></label><div class="sk-toggleable__content fitted"><pre>ExampleClassifier()</pre></div> </div></div></div></div></div></div></div></div></div>
 </div>
 <br />
 <br /><p>Note that the above example is calling our utility function
@@ -1652,7 +1651,7 @@ <h2>Routing Meta-Estimator<a class="headerlink" href="#routing-meta-estimator" t
 </pre></div>
 </div>
 <div class="output_subarea output_html rendered_html output_result">
-<style>#sk-container-id-50 {
+<style>#sk-container-id-51 {
   /* Definition of color scheme common for light and dark mode */
   --sklearn-color-text: #000;
   --sklearn-color-text-muted: #666;
@@ -1683,15 +1682,15 @@ <h2>Routing Meta-Estimator<a class="headerlink" href="#routing-meta-estimator" t
   }
 }
 
-#sk-container-id-50 {
+#sk-container-id-51 {
   color: var(--sklearn-color-text);
 }
 
-#sk-container-id-50 pre {
+#sk-container-id-51 pre {
   padding: 0;
 }
 
-#sk-container-id-50 input.sk-hidden--visually {
+#sk-container-id-51 input.sk-hidden--visually {
   border: 0;
   clip: rect(1px 1px 1px 1px);
   clip: rect(1px, 1px, 1px, 1px);
@@ -1703,7 +1702,7 @@ <h2>Routing Meta-Estimator<a class="headerlink" href="#routing-meta-estimator" t
   width: 1px;
 }
 
-#sk-container-id-50 div.sk-dashed-wrapped {
+#sk-container-id-51 div.sk-dashed-wrapped {
   border: 1px dashed var(--sklearn-color-line);
   margin: 0 0.4em 0.5em 0.4em;
   box-sizing: border-box;
@@ -1711,7 +1710,7 @@ <h2>Routing Meta-Estimator<a class="headerlink" href="#routing-meta-estimator" t
   background-color: var(--sklearn-color-background);
 }
 
-#sk-container-id-50 div.sk-container {
+#sk-container-id-51 div.sk-container {
   /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
      but bootstrap.min.css set `[hidden] { display: none !important; }`
      so we also need the `!important` here to be able to override the
@@ -1721,7 +1720,7 @@ <h2>Routing Meta-Estimator<a class="headerlink" href="#routing-meta-estimator" t
   position: relative;
 }
 
-#sk-container-id-50 div.sk-text-repr-fallback {
+#sk-container-id-51 div.sk-text-repr-fallback {
   display: none;
 }
 
@@ -1737,14 +1736,14 @@ <h2>Routing Meta-Estimator<a class="headerlink" href="#routing-meta-estimator" t
 
 /* Parallel-specific style estimator block */
 
-#sk-container-id-50 div.sk-parallel-item::after {
+#sk-container-id-51 div.sk-parallel-item::after {
   content: "";
   width: 100%;
   border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
   flex-grow: 1;
 }
 
-#sk-container-id-50 div.sk-parallel {
+#sk-container-id-51 div.sk-parallel {
   display: flex;
   align-items: stretch;
   justify-content: center;
@@ -1752,28 +1751,28 @@ <h2>Routing Meta-Estimator<a class="headerlink" href="#routing-meta-estimator" t
   position: relative;
 }
 
-#sk-container-id-50 div.sk-parallel-item {
+#sk-container-id-51 div.sk-parallel-item {
   display: flex;
   flex-direction: column;
 }
 
-#sk-container-id-50 div.sk-parallel-item:first-child::after {
+#sk-container-id-51 div.sk-parallel-item:first-child::after {
   align-self: flex-end;
   width: 50%;
 }
 
-#sk-container-id-50 div.sk-parallel-item:last-child::after {
+#sk-container-id-51 div.sk-parallel-item:last-child::after {
   align-self: flex-start;
   width: 50%;
 }
 
-#sk-container-id-50 div.sk-parallel-item:only-child::after {
+#sk-container-id-51 div.sk-parallel-item:only-child::after {
   width: 0;
 }
 
 /* Serial-specific style estimator block */
 
-#sk-container-id-50 div.sk-serial {
+#sk-container-id-51 div.sk-serial {
   display: flex;
   flex-direction: column;
   align-items: center;
@@ -1791,14 +1790,14 @@ <h2>Routing Meta-Estimator<a class="headerlink" href="#routing-meta-estimator" t
 
 /* Pipeline and ColumnTransformer style (default) */
 
-#sk-container-id-50 div.sk-toggleable {
+#sk-container-id-51 div.sk-toggleable {
   /* Default theme specific background. It is overwritten whether we have a
   specific estimator or a Pipeline/ColumnTransformer */
   background-color: var(--sklearn-color-background);
 }
 
 /* Toggleable label */
-#sk-container-id-50 label.sk-toggleable__label {
+#sk-container-id-51 label.sk-toggleable__label {
   cursor: pointer;
   display: flex;
   width: 100%;
@@ -1811,13 +1810,13 @@ <h2>Routing Meta-Estimator<a class="headerlink" href="#routing-meta-estimator" t
   gap: 0.5em;
 }
 
-#sk-container-id-50 label.sk-toggleable__label .caption {
+#sk-container-id-51 label.sk-toggleable__label .caption {
   font-size: 0.6rem;
   font-weight: lighter;
   color: var(--sklearn-color-text-muted);
 }
 
-#sk-container-id-50 label.sk-toggleable__label-arrow:before {
+#sk-container-id-51 label.sk-toggleable__label-arrow:before {
   /* Arrow on the left of the label */
   content: "▸";
   float: left;
@@ -1825,13 +1824,13 @@ <h2>Routing Meta-Estimator<a class="headerlink" href="#routing-meta-estimator" t
   color: var(--sklearn-color-icon);
 }
 
-#sk-container-id-50 label.sk-toggleable__label-arrow:hover:before {
+#sk-container-id-51 label.sk-toggleable__label-arrow:hover:before {
   color: var(--sklearn-color-text);
 }
 
 /* Toggleable content - dropdown */
 
-#sk-container-id-50 div.sk-toggleable__content {
+#sk-container-id-51 div.sk-toggleable__content {
   max-height: 0;
   max-width: 0;
   overflow: hidden;
@@ -1840,12 +1839,12 @@ <h2>Routing Meta-Estimator<a class="headerlink" href="#routing-meta-estimator" t
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-50 div.sk-toggleable__content.fitted {
+#sk-container-id-51 div.sk-toggleable__content.fitted {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
-#sk-container-id-50 div.sk-toggleable__content pre {
+#sk-container-id-51 div.sk-toggleable__content pre {
   margin: 0.2em;
   border-radius: 0.25em;
   color: var(--sklearn-color-text);
@@ -1853,79 +1852,79 @@ <h2>Routing Meta-Estimator<a class="headerlink" href="#routing-meta-estimator" t
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-50 div.sk-toggleable__content.fitted pre {
+#sk-container-id-51 div.sk-toggleable__content.fitted pre {
   /* unfitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
-#sk-container-id-50 input.sk-toggleable__control:checked~div.sk-toggleable__content {
+#sk-container-id-51 input.sk-toggleable__control:checked~div.sk-toggleable__content {
   /* Expand drop-down */
   max-height: 200px;
   max-width: 100%;
   overflow: auto;
 }
 
-#sk-container-id-50 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
+#sk-container-id-51 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
   content: "▾";
 }
 
 /* Pipeline/ColumnTransformer-specific style */
 
-#sk-container-id-50 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-51 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-50 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-51 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
 /* Estimator-specific style */
 
 /* Colorize estimator box */
-#sk-container-id-50 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-51 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-50 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-51 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
-#sk-container-id-50 div.sk-label label.sk-toggleable__label,
-#sk-container-id-50 div.sk-label label {
+#sk-container-id-51 div.sk-label label.sk-toggleable__label,
+#sk-container-id-51 div.sk-label label {
   /* The background is the default theme color */
   color: var(--sklearn-color-text-on-default-background);
 }
 
 /* On hover, darken the color of the background */
-#sk-container-id-50 div.sk-label:hover label.sk-toggleable__label {
+#sk-container-id-51 div.sk-label:hover label.sk-toggleable__label {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
 /* Label box, darken color on hover, fitted */
-#sk-container-id-50 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
+#sk-container-id-51 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
 /* Estimator label */
 
-#sk-container-id-50 div.sk-label label {
+#sk-container-id-51 div.sk-label label {
   font-family: monospace;
   font-weight: bold;
   display: inline-block;
   line-height: 1.2em;
 }
 
-#sk-container-id-50 div.sk-label-container {
+#sk-container-id-51 div.sk-label-container {
   text-align: center;
 }
 
 /* Estimator-specific */
-#sk-container-id-50 div.sk-estimator {
+#sk-container-id-51 div.sk-estimator {
   font-family: monospace;
   border: 1px dotted var(--sklearn-color-border-box);
   border-radius: 0.25em;
@@ -1935,18 +1934,18 @@ <h2>Routing Meta-Estimator<a class="headerlink" href="#routing-meta-estimator" t
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-50 div.sk-estimator.fitted {
+#sk-container-id-51 div.sk-estimator.fitted {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
 /* on hover */
-#sk-container-id-50 div.sk-estimator:hover {
+#sk-container-id-51 div.sk-estimator:hover {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-50 div.sk-estimator.fitted:hover {
+#sk-container-id-51 div.sk-estimator.fitted:hover {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-2);
 }
@@ -2034,7 +2033,7 @@ <h2>Routing Meta-Estimator<a class="headerlink" href="#routing-meta-estimator" t
 
 /* "?"-specific style due to the `<a>` HTML tag */
 
-#sk-container-id-50 a.estimator_doc_link {
+#sk-container-id-51 a.estimator_doc_link {
   float: right;
   font-size: 1rem;
   line-height: 1em;
@@ -2049,25 +2048,25 @@ <h2>Routing Meta-Estimator<a class="headerlink" href="#routing-meta-estimator" t
   border: var(--sklearn-color-unfitted-level-1) 1pt solid;
 }
 
-#sk-container-id-50 a.estimator_doc_link.fitted {
+#sk-container-id-51 a.estimator_doc_link.fitted {
   /* fitted */
   border: var(--sklearn-color-fitted-level-1) 1pt solid;
   color: var(--sklearn-color-fitted-level-1);
 }
 
 /* On hover */
-#sk-container-id-50 a.estimator_doc_link:hover {
+#sk-container-id-51 a.estimator_doc_link:hover {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-3);
   color: var(--sklearn-color-background);
   text-decoration: none;
 }
 
-#sk-container-id-50 a.estimator_doc_link.fitted:hover {
+#sk-container-id-51 a.estimator_doc_link.fitted:hover {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-3);
 }
-</style><div id="sk-container-id-50" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>MetaClassifier(estimator=ExampleClassifier())</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-213" type="checkbox" ><label for="sk-estimator-id-213" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>MetaClassifier</div></div><div><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>MetaClassifier(estimator=ExampleClassifier())</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-214" type="checkbox" ><label for="sk-estimator-id-214" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>estimator: ExampleClassifier</div></div></label><div class="sk-toggleable__content fitted"><pre>ExampleClassifier()</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-215" type="checkbox" ><label for="sk-estimator-id-215" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>ExampleClassifier</div></div></label><div class="sk-toggleable__content fitted"><pre>ExampleClassifier()</pre></div> </div></div></div></div></div></div></div></div></div>
+</style><div id="sk-container-id-51" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>MetaClassifier(estimator=ExampleClassifier())</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-227" type="checkbox" ><label for="sk-estimator-id-227" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>MetaClassifier</div></div><div><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>MetaClassifier(estimator=ExampleClassifier())</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-228" type="checkbox" ><label for="sk-estimator-id-228" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>estimator: ExampleClassifier</div></div></label><div class="sk-toggleable__content fitted"><pre>ExampleClassifier()</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-229" type="checkbox" ><label for="sk-estimator-id-229" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>ExampleClassifier</div></div></label><div class="sk-toggleable__content fitted"><pre>ExampleClassifier()</pre></div> </div></div></div></div></div></div></div></div></div>
 </div>
 <br />
 <br /><p>If we pass an unknown metadata, an error is raised:</p>
@@ -2127,7 +2126,7 @@ <h2>Routing Meta-Estimator<a class="headerlink" href="#routing-meta-estimator" t
 </pre></div>
 </div>
 <div class="output_subarea output_html rendered_html output_result">
-<style>#sk-container-id-51 {
+<style>#sk-container-id-52 {
   /* Definition of color scheme common for light and dark mode */
   --sklearn-color-text: #000;
   --sklearn-color-text-muted: #666;
@@ -2158,15 +2157,15 @@ <h2>Routing Meta-Estimator<a class="headerlink" href="#routing-meta-estimator" t
   }
 }
 
-#sk-container-id-51 {
+#sk-container-id-52 {
   color: var(--sklearn-color-text);
 }
 
-#sk-container-id-51 pre {
+#sk-container-id-52 pre {
   padding: 0;
 }
 
-#sk-container-id-51 input.sk-hidden--visually {
+#sk-container-id-52 input.sk-hidden--visually {
   border: 0;
   clip: rect(1px 1px 1px 1px);
   clip: rect(1px, 1px, 1px, 1px);
@@ -2178,7 +2177,7 @@ <h2>Routing Meta-Estimator<a class="headerlink" href="#routing-meta-estimator" t
   width: 1px;
 }
 
-#sk-container-id-51 div.sk-dashed-wrapped {
+#sk-container-id-52 div.sk-dashed-wrapped {
   border: 1px dashed var(--sklearn-color-line);
   margin: 0 0.4em 0.5em 0.4em;
   box-sizing: border-box;
@@ -2186,7 +2185,7 @@ <h2>Routing Meta-Estimator<a class="headerlink" href="#routing-meta-estimator" t
   background-color: var(--sklearn-color-background);
 }
 
-#sk-container-id-51 div.sk-container {
+#sk-container-id-52 div.sk-container {
   /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
      but bootstrap.min.css set `[hidden] { display: none !important; }`
      so we also need the `!important` here to be able to override the
@@ -2196,7 +2195,7 @@ <h2>Routing Meta-Estimator<a class="headerlink" href="#routing-meta-estimator" t
   position: relative;
 }
 
-#sk-container-id-51 div.sk-text-repr-fallback {
+#sk-container-id-52 div.sk-text-repr-fallback {
   display: none;
 }
 
@@ -2212,14 +2211,14 @@ <h2>Routing Meta-Estimator<a class="headerlink" href="#routing-meta-estimator" t
 
 /* Parallel-specific style estimator block */
 
-#sk-container-id-51 div.sk-parallel-item::after {
+#sk-container-id-52 div.sk-parallel-item::after {
   content: "";
   width: 100%;
   border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
   flex-grow: 1;
 }
 
-#sk-container-id-51 div.sk-parallel {
+#sk-container-id-52 div.sk-parallel {
   display: flex;
   align-items: stretch;
   justify-content: center;
@@ -2227,28 +2226,28 @@ <h2>Routing Meta-Estimator<a class="headerlink" href="#routing-meta-estimator" t
   position: relative;
 }
 
-#sk-container-id-51 div.sk-parallel-item {
+#sk-container-id-52 div.sk-parallel-item {
   display: flex;
   flex-direction: column;
 }
 
-#sk-container-id-51 div.sk-parallel-item:first-child::after {
+#sk-container-id-52 div.sk-parallel-item:first-child::after {
   align-self: flex-end;
   width: 50%;
 }
 
-#sk-container-id-51 div.sk-parallel-item:last-child::after {
+#sk-container-id-52 div.sk-parallel-item:last-child::after {
   align-self: flex-start;
   width: 50%;
 }
 
-#sk-container-id-51 div.sk-parallel-item:only-child::after {
+#sk-container-id-52 div.sk-parallel-item:only-child::after {
   width: 0;
 }
 
 /* Serial-specific style estimator block */
 
-#sk-container-id-51 div.sk-serial {
+#sk-container-id-52 div.sk-serial {
   display: flex;
   flex-direction: column;
   align-items: center;
@@ -2266,14 +2265,14 @@ <h2>Routing Meta-Estimator<a class="headerlink" href="#routing-meta-estimator" t
 
 /* Pipeline and ColumnTransformer style (default) */
 
-#sk-container-id-51 div.sk-toggleable {
+#sk-container-id-52 div.sk-toggleable {
   /* Default theme specific background. It is overwritten whether we have a
   specific estimator or a Pipeline/ColumnTransformer */
   background-color: var(--sklearn-color-background);
 }
 
 /* Toggleable label */
-#sk-container-id-51 label.sk-toggleable__label {
+#sk-container-id-52 label.sk-toggleable__label {
   cursor: pointer;
   display: flex;
   width: 100%;
@@ -2286,13 +2285,13 @@ <h2>Routing Meta-Estimator<a class="headerlink" href="#routing-meta-estimator" t
   gap: 0.5em;
 }
 
-#sk-container-id-51 label.sk-toggleable__label .caption {
+#sk-container-id-52 label.sk-toggleable__label .caption {
   font-size: 0.6rem;
   font-weight: lighter;
   color: var(--sklearn-color-text-muted);
 }
 
-#sk-container-id-51 label.sk-toggleable__label-arrow:before {
+#sk-container-id-52 label.sk-toggleable__label-arrow:before {
   /* Arrow on the left of the label */
   content: "▸";
   float: left;
@@ -2300,13 +2299,13 @@ <h2>Routing Meta-Estimator<a class="headerlink" href="#routing-meta-estimator" t
   color: var(--sklearn-color-icon);
 }
 
-#sk-container-id-51 label.sk-toggleable__label-arrow:hover:before {
+#sk-container-id-52 label.sk-toggleable__label-arrow:hover:before {
   color: var(--sklearn-color-text);
 }
 
 /* Toggleable content - dropdown */
 
-#sk-container-id-51 div.sk-toggleable__content {
+#sk-container-id-52 div.sk-toggleable__content {
   max-height: 0;
   max-width: 0;
   overflow: hidden;
@@ -2315,12 +2314,12 @@ <h2>Routing Meta-Estimator<a class="headerlink" href="#routing-meta-estimator" t
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-51 div.sk-toggleable__content.fitted {
+#sk-container-id-52 div.sk-toggleable__content.fitted {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
-#sk-container-id-51 div.sk-toggleable__content pre {
+#sk-container-id-52 div.sk-toggleable__content pre {
   margin: 0.2em;
   border-radius: 0.25em;
   color: var(--sklearn-color-text);
@@ -2328,79 +2327,79 @@ <h2>Routing Meta-Estimator<a class="headerlink" href="#routing-meta-estimator" t
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-51 div.sk-toggleable__content.fitted pre {
+#sk-container-id-52 div.sk-toggleable__content.fitted pre {
   /* unfitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
-#sk-container-id-51 input.sk-toggleable__control:checked~div.sk-toggleable__content {
+#sk-container-id-52 input.sk-toggleable__control:checked~div.sk-toggleable__content {
   /* Expand drop-down */
   max-height: 200px;
   max-width: 100%;
   overflow: auto;
 }
 
-#sk-container-id-51 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
+#sk-container-id-52 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
   content: "▾";
 }
 
 /* Pipeline/ColumnTransformer-specific style */
 
-#sk-container-id-51 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-52 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-51 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-52 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
 /* Estimator-specific style */
 
 /* Colorize estimator box */
-#sk-container-id-51 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-52 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-51 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-52 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
-#sk-container-id-51 div.sk-label label.sk-toggleable__label,
-#sk-container-id-51 div.sk-label label {
+#sk-container-id-52 div.sk-label label.sk-toggleable__label,
+#sk-container-id-52 div.sk-label label {
   /* The background is the default theme color */
   color: var(--sklearn-color-text-on-default-background);
 }
 
 /* On hover, darken the color of the background */
-#sk-container-id-51 div.sk-label:hover label.sk-toggleable__label {
+#sk-container-id-52 div.sk-label:hover label.sk-toggleable__label {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
 /* Label box, darken color on hover, fitted */
-#sk-container-id-51 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
+#sk-container-id-52 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
 /* Estimator label */
 
-#sk-container-id-51 div.sk-label label {
+#sk-container-id-52 div.sk-label label {
   font-family: monospace;
   font-weight: bold;
   display: inline-block;
   line-height: 1.2em;
 }
 
-#sk-container-id-51 div.sk-label-container {
+#sk-container-id-52 div.sk-label-container {
   text-align: center;
 }
 
 /* Estimator-specific */
-#sk-container-id-51 div.sk-estimator {
+#sk-container-id-52 div.sk-estimator {
   font-family: monospace;
   border: 1px dotted var(--sklearn-color-border-box);
   border-radius: 0.25em;
@@ -2410,18 +2409,18 @@ <h2>Routing Meta-Estimator<a class="headerlink" href="#routing-meta-estimator" t
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-51 div.sk-estimator.fitted {
+#sk-container-id-52 div.sk-estimator.fitted {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
 /* on hover */
-#sk-container-id-51 div.sk-estimator:hover {
+#sk-container-id-52 div.sk-estimator:hover {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-51 div.sk-estimator.fitted:hover {
+#sk-container-id-52 div.sk-estimator.fitted:hover {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-2);
 }
@@ -2509,7 +2508,7 @@ <h2>Routing Meta-Estimator<a class="headerlink" href="#routing-meta-estimator" t
 
 /* "?"-specific style due to the `<a>` HTML tag */
 
-#sk-container-id-51 a.estimator_doc_link {
+#sk-container-id-52 a.estimator_doc_link {
   float: right;
   font-size: 1rem;
   line-height: 1em;
@@ -2524,25 +2523,25 @@ <h2>Routing Meta-Estimator<a class="headerlink" href="#routing-meta-estimator" t
   border: var(--sklearn-color-unfitted-level-1) 1pt solid;
 }
 
-#sk-container-id-51 a.estimator_doc_link.fitted {
+#sk-container-id-52 a.estimator_doc_link.fitted {
   /* fitted */
   border: var(--sklearn-color-fitted-level-1) 1pt solid;
   color: var(--sklearn-color-fitted-level-1);
 }
 
 /* On hover */
-#sk-container-id-51 a.estimator_doc_link:hover {
+#sk-container-id-52 a.estimator_doc_link:hover {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-3);
   color: var(--sklearn-color-background);
   text-decoration: none;
 }
 
-#sk-container-id-51 a.estimator_doc_link.fitted:hover {
+#sk-container-id-52 a.estimator_doc_link.fitted:hover {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-3);
 }
-</style><div id="sk-container-id-51" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>MetaClassifier(estimator=ExampleClassifier())</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-216" type="checkbox" ><label for="sk-estimator-id-216" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>MetaClassifier</div></div><div><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>MetaClassifier(estimator=ExampleClassifier())</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-217" type="checkbox" ><label for="sk-estimator-id-217" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>estimator: ExampleClassifier</div></div></label><div class="sk-toggleable__content fitted"><pre>ExampleClassifier()</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-218" type="checkbox" ><label for="sk-estimator-id-218" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>ExampleClassifier</div></div></label><div class="sk-toggleable__content fitted"><pre>ExampleClassifier()</pre></div> </div></div></div></div></div></div></div></div></div>
+</style><div id="sk-container-id-52" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>MetaClassifier(estimator=ExampleClassifier())</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-230" type="checkbox" ><label for="sk-estimator-id-230" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>MetaClassifier</div></div><div><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>MetaClassifier(estimator=ExampleClassifier())</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-231" type="checkbox" ><label for="sk-estimator-id-231" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>estimator: ExampleClassifier</div></div></label><div class="sk-toggleable__content fitted"><pre>ExampleClassifier()</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-232" type="checkbox" ><label for="sk-estimator-id-232" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>ExampleClassifier</div></div></label><div class="sk-toggleable__content fitted"><pre>ExampleClassifier()</pre></div> </div></div></div></div></div></div></div></div></div>
 </div>
 <br />
 <br /><p>Passing <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> here will fail since it is requested with an
@@ -2765,7 +2764,7 @@ <h2>Consuming and routing Meta-Estimator<a class="headerlink" href="#consuming-a
 </pre></div>
 </div>
 <div class="output_subarea output_html rendered_html output_result">
-<style>#sk-container-id-52 {
+<style>#sk-container-id-53 {
   /* Definition of color scheme common for light and dark mode */
   --sklearn-color-text: #000;
   --sklearn-color-text-muted: #666;
@@ -2796,15 +2795,15 @@ <h2>Consuming and routing Meta-Estimator<a class="headerlink" href="#consuming-a
   }
 }
 
-#sk-container-id-52 {
+#sk-container-id-53 {
   color: var(--sklearn-color-text);
 }
 
-#sk-container-id-52 pre {
+#sk-container-id-53 pre {
   padding: 0;
 }
 
-#sk-container-id-52 input.sk-hidden--visually {
+#sk-container-id-53 input.sk-hidden--visually {
   border: 0;
   clip: rect(1px 1px 1px 1px);
   clip: rect(1px, 1px, 1px, 1px);
@@ -2816,7 +2815,7 @@ <h2>Consuming and routing Meta-Estimator<a class="headerlink" href="#consuming-a
   width: 1px;
 }
 
-#sk-container-id-52 div.sk-dashed-wrapped {
+#sk-container-id-53 div.sk-dashed-wrapped {
   border: 1px dashed var(--sklearn-color-line);
   margin: 0 0.4em 0.5em 0.4em;
   box-sizing: border-box;
@@ -2824,7 +2823,7 @@ <h2>Consuming and routing Meta-Estimator<a class="headerlink" href="#consuming-a
   background-color: var(--sklearn-color-background);
 }
 
-#sk-container-id-52 div.sk-container {
+#sk-container-id-53 div.sk-container {
   /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
      but bootstrap.min.css set `[hidden] { display: none !important; }`
      so we also need the `!important` here to be able to override the
@@ -2834,7 +2833,7 @@ <h2>Consuming and routing Meta-Estimator<a class="headerlink" href="#consuming-a
   position: relative;
 }
 
-#sk-container-id-52 div.sk-text-repr-fallback {
+#sk-container-id-53 div.sk-text-repr-fallback {
   display: none;
 }
 
@@ -2850,14 +2849,14 @@ <h2>Consuming and routing Meta-Estimator<a class="headerlink" href="#consuming-a
 
 /* Parallel-specific style estimator block */
 
-#sk-container-id-52 div.sk-parallel-item::after {
+#sk-container-id-53 div.sk-parallel-item::after {
   content: "";
   width: 100%;
   border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
   flex-grow: 1;
 }
 
-#sk-container-id-52 div.sk-parallel {
+#sk-container-id-53 div.sk-parallel {
   display: flex;
   align-items: stretch;
   justify-content: center;
@@ -2865,28 +2864,28 @@ <h2>Consuming and routing Meta-Estimator<a class="headerlink" href="#consuming-a
   position: relative;
 }
 
-#sk-container-id-52 div.sk-parallel-item {
+#sk-container-id-53 div.sk-parallel-item {
   display: flex;
   flex-direction: column;
 }
 
-#sk-container-id-52 div.sk-parallel-item:first-child::after {
+#sk-container-id-53 div.sk-parallel-item:first-child::after {
   align-self: flex-end;
   width: 50%;
 }
 
-#sk-container-id-52 div.sk-parallel-item:last-child::after {
+#sk-container-id-53 div.sk-parallel-item:last-child::after {
   align-self: flex-start;
   width: 50%;
 }
 
-#sk-container-id-52 div.sk-parallel-item:only-child::after {
+#sk-container-id-53 div.sk-parallel-item:only-child::after {
   width: 0;
 }
 
 /* Serial-specific style estimator block */
 
-#sk-container-id-52 div.sk-serial {
+#sk-container-id-53 div.sk-serial {
   display: flex;
   flex-direction: column;
   align-items: center;
@@ -2904,14 +2903,14 @@ <h2>Consuming and routing Meta-Estimator<a class="headerlink" href="#consuming-a
 
 /* Pipeline and ColumnTransformer style (default) */
 
-#sk-container-id-52 div.sk-toggleable {
+#sk-container-id-53 div.sk-toggleable {
   /* Default theme specific background. It is overwritten whether we have a
   specific estimator or a Pipeline/ColumnTransformer */
   background-color: var(--sklearn-color-background);
 }
 
 /* Toggleable label */
-#sk-container-id-52 label.sk-toggleable__label {
+#sk-container-id-53 label.sk-toggleable__label {
   cursor: pointer;
   display: flex;
   width: 100%;
@@ -2924,13 +2923,13 @@ <h2>Consuming and routing Meta-Estimator<a class="headerlink" href="#consuming-a
   gap: 0.5em;
 }
 
-#sk-container-id-52 label.sk-toggleable__label .caption {
+#sk-container-id-53 label.sk-toggleable__label .caption {
   font-size: 0.6rem;
   font-weight: lighter;
   color: var(--sklearn-color-text-muted);
 }
 
-#sk-container-id-52 label.sk-toggleable__label-arrow:before {
+#sk-container-id-53 label.sk-toggleable__label-arrow:before {
   /* Arrow on the left of the label */
   content: "▸";
   float: left;
@@ -2938,13 +2937,13 @@ <h2>Consuming and routing Meta-Estimator<a class="headerlink" href="#consuming-a
   color: var(--sklearn-color-icon);
 }
 
-#sk-container-id-52 label.sk-toggleable__label-arrow:hover:before {
+#sk-container-id-53 label.sk-toggleable__label-arrow:hover:before {
   color: var(--sklearn-color-text);
 }
 
 /* Toggleable content - dropdown */
 
-#sk-container-id-52 div.sk-toggleable__content {
+#sk-container-id-53 div.sk-toggleable__content {
   max-height: 0;
   max-width: 0;
   overflow: hidden;
@@ -2953,12 +2952,12 @@ <h2>Consuming and routing Meta-Estimator<a class="headerlink" href="#consuming-a
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-52 div.sk-toggleable__content.fitted {
+#sk-container-id-53 div.sk-toggleable__content.fitted {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
-#sk-container-id-52 div.sk-toggleable__content pre {
+#sk-container-id-53 div.sk-toggleable__content pre {
   margin: 0.2em;
   border-radius: 0.25em;
   color: var(--sklearn-color-text);
@@ -2966,79 +2965,79 @@ <h2>Consuming and routing Meta-Estimator<a class="headerlink" href="#consuming-a
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-52 div.sk-toggleable__content.fitted pre {
+#sk-container-id-53 div.sk-toggleable__content.fitted pre {
   /* unfitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
-#sk-container-id-52 input.sk-toggleable__control:checked~div.sk-toggleable__content {
+#sk-container-id-53 input.sk-toggleable__control:checked~div.sk-toggleable__content {
   /* Expand drop-down */
   max-height: 200px;
   max-width: 100%;
   overflow: auto;
 }
 
-#sk-container-id-52 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
+#sk-container-id-53 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
   content: "▾";
 }
 
 /* Pipeline/ColumnTransformer-specific style */
 
-#sk-container-id-52 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-53 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-52 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-53 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
 /* Estimator-specific style */
 
 /* Colorize estimator box */
-#sk-container-id-52 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-53 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-52 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-53 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
-#sk-container-id-52 div.sk-label label.sk-toggleable__label,
-#sk-container-id-52 div.sk-label label {
+#sk-container-id-53 div.sk-label label.sk-toggleable__label,
+#sk-container-id-53 div.sk-label label {
   /* The background is the default theme color */
   color: var(--sklearn-color-text-on-default-background);
 }
 
 /* On hover, darken the color of the background */
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-</style><div id="sk-container-id-52" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>RouterConsumerClassifier(estimator=ExampleClassifier())</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-219" type="checkbox" ><label for="sk-estimator-id-219" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>RouterConsumerClassifier</div></div><div><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>RouterConsumerClassifier(estimator=ExampleClassifier())</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-220" type="checkbox" ><label for="sk-estimator-id-220" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>estimator: ExampleClassifier</div></div></label><div class="sk-toggleable__content fitted"><pre>ExampleClassifier()</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-221" type="checkbox" ><label for="sk-estimator-id-221" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>ExampleClassifier</div></div></label><div class="sk-toggleable__content fitted"><pre>ExampleClassifier()</pre></div> </div></div></div></div></div></div></div></div></div>
+</style><div id="sk-container-id-53" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>RouterConsumerClassifier(estimator=ExampleClassifier())</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-233" type="checkbox" ><label for="sk-estimator-id-233" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>RouterConsumerClassifier</div></div><div><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>RouterConsumerClassifier(estimator=ExampleClassifier())</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-234" type="checkbox" ><label for="sk-estimator-id-234" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>estimator: ExampleClassifier</div></div></label><div class="sk-toggleable__content fitted"><pre>ExampleClassifier()</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-235" type="checkbox" ><label for="sk-estimator-id-235" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>ExampleClassifier</div></div></label><div class="sk-toggleable__content fitted"><pre>ExampleClassifier()</pre></div> </div></div></div></div></div></div></div></div></div>
 </div>
 <br />
 <br /><ul class="simple">
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+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
@@ -1085,7 +1084,7 @@
 <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
 </pre></div>
 </div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 1.759 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 1.872 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-miscellaneous-plot-multioutput-face-completion-py">
 <div class="binder-badge docutils container">
 <a class="reference external image-reference" href="https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/miscellaneous/plot_multioutput_face_completion.ipynb"><img alt="Launch binder" src="http://github.com/_images/binder_badge_logo20.svg" style="width: 150px;" />
diff --git a/dev/auto_examples/miscellaneous/plot_outlier_detection_bench.html b/dev/auto_examples/miscellaneous/plot_outlier_detection_bench.html
index 54a2461f293e1..69308b6c9c624 100644
--- a/dev/auto_examples/miscellaneous/plot_outlier_detection_bench.html
+++ b/dev/auto_examples/miscellaneous/plot_outlier_detection_bench.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -679,13 +679,12 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -902,9 +901,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
@@ -1071,12 +1070,12 @@ <h2>Dataset preprocessing and model training<a class="headerlink" href="#dataset
     <span class="n">tic</span> <span class="o">=</span> <a href="https://docs.python.org/3/library/time.html#time.perf_counter" title="time.perf_counter" class="sphx-glr-backref-module-time sphx-glr-backref-type-py-function"><span class="n">perf_counter</span></a><span class="p">()</span>
     <span class="k">if</span> <span class="n">estimator</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span> <span class="o">==</span> <span class="s2">&quot;LocalOutlierFactor&quot;</span><span class="p">:</span>
         <span class="n">estimator</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
-        <span class="n">y_pred</span> <span class="o">=</span> <span class="n">estimator</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">negative_outlier_factor_</span>
+        <span class="n">y_score</span> <span class="o">=</span> <span class="n">estimator</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">negative_outlier_factor_</span>
     <span class="k">else</span><span class="p">:</span>  <span class="c1"># &quot;IsolationForest&quot;</span>
-        <span class="n">y_pred</span> <span class="o">=</span> <span class="n">estimator</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">)</span><span class="o">.</span><span class="n">decision_function</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
+        <span class="n">y_score</span> <span class="o">=</span> <span class="n">estimator</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">)</span><span class="o">.</span><span class="n">decision_function</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
     <span class="n">toc</span> <span class="o">=</span> <a href="https://docs.python.org/3/library/time.html#time.perf_counter" title="time.perf_counter" class="sphx-glr-backref-module-time sphx-glr-backref-type-py-function"><span class="n">perf_counter</span></a><span class="p">()</span>
     <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Duration for </span><span class="si">{</span><span class="n">model_name</span><span class="si">}</span><span class="s2">: </span><span class="si">{</span><span class="n">toc</span><span class="w"> </span><span class="o">-</span><span class="w"> </span><span class="n">tic</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2"> s&quot;</span><span class="p">)</span>
-    <span class="k">return</span> <span class="n">y_pred</span>
+    <span class="k">return</span> <span class="n">y_score</span>
 </pre></div>
 </div>
 <p>On the rest of the example we process one dataset per section. After loading
@@ -1116,7 +1115,7 @@ <h3>KDDCup99 - SA dataset<a class="headerlink" href="#kddcup99-sa-dataset" title
 <p>The SA dataset contains 41 features out of which 3 are categorical:
 “protocol_type”, “service” and “flag”.</p>
 <div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">y_true</span> <span class="o">=</span> <span class="p">{}</span>
-<span class="n">y_pred</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;LOF&quot;</span><span class="p">:</span> <span class="p">{},</span> <span class="s2">&quot;IForest&quot;</span><span class="p">:</span> <span class="p">{}}</span>
+<span class="n">y_score</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;LOF&quot;</span><span class="p">:</span> <span class="p">{},</span> <span class="s2">&quot;IForest&quot;</span><span class="p">:</span> <span class="p">{}}</span>
 <span class="n">model_names</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;LOF&quot;</span><span class="p">,</span> <span class="s2">&quot;IForest&quot;</span><span class="p">]</span>
 <span class="n">cat_columns</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;protocol_type&quot;</span><span class="p">,</span> <span class="s2">&quot;service&quot;</span><span class="p">,</span> <span class="s2">&quot;flag&quot;</span><span class="p">]</span>
 
@@ -1128,11 +1127,11 @@ <h3>KDDCup99 - SA dataset<a class="headerlink" href="#kddcup99-sa-dataset" title
         <span class="n">lof_kw</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;n_neighbors&quot;</span><span class="p">:</span> <span class="nb">int</span><span class="p">(</span><span class="n">n_samples</span> <span class="o">*</span> <span class="n">anomaly_frac</span><span class="p">)},</span>
         <span class="n">iforest_kw</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;random_state&quot;</span><span class="p">:</span> <span class="mi">42</span><span class="p">},</span>
     <span class="p">)</span>
-    <span class="n">y_pred</span><span class="p">[</span><span class="n">model_name</span><span class="p">][</span><span class="s2">&quot;KDDCup99 - SA&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">fit_predict</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">X</span><span class="p">)</span>
+    <span class="n">y_score</span><span class="p">[</span><span class="n">model_name</span><span class="p">][</span><span class="s2">&quot;KDDCup99 - SA&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">fit_predict</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">X</span><span class="p">)</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Duration for LOF: 1.80 s
-Duration for IForest: 0.29 s
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Duration for LOF: 1.90 s
+Duration for IForest: 0.28 s
 </pre></div>
 </div>
 </section>
@@ -1168,11 +1167,11 @@ <h3>Forest covertypes dataset<a class="headerlink" href="#forest-covertypes-data
         <span class="n">lof_kw</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;n_neighbors&quot;</span><span class="p">:</span> <span class="nb">int</span><span class="p">(</span><span class="n">n_samples</span> <span class="o">*</span> <span class="n">anomaly_frac</span><span class="p">)},</span>
         <span class="n">iforest_kw</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;random_state&quot;</span><span class="p">:</span> <span class="mi">42</span><span class="p">},</span>
     <span class="p">)</span>
-    <span class="n">y_pred</span><span class="p">[</span><span class="n">model_name</span><span class="p">][</span><span class="s2">&quot;forestcover&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">fit_predict</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">X</span><span class="p">)</span>
+    <span class="n">y_score</span><span class="p">[</span><span class="n">model_name</span><span class="p">][</span><span class="s2">&quot;forestcover&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">fit_predict</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">X</span><span class="p">)</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Duration for LOF: 1.83 s
-Duration for IForest: 0.24 s
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Duration for LOF: 1.84 s
+Duration for IForest: 0.22 s
 </pre></div>
 </div>
 </section>
@@ -1228,11 +1227,11 @@ <h3>Ames Housing dataset<a class="headerlink" href="#ames-housing-dataset" title
         <span class="n">lof_kw</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;n_neighbors&quot;</span><span class="p">:</span> <span class="nb">int</span><span class="p">(</span><span class="n">n_samples</span> <span class="o">*</span> <span class="n">anomaly_frac</span><span class="p">)},</span>
         <span class="n">iforest_kw</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;random_state&quot;</span><span class="p">:</span> <span class="mi">42</span><span class="p">},</span>
     <span class="p">)</span>
-    <span class="n">y_pred</span><span class="p">[</span><span class="n">model_name</span><span class="p">][</span><span class="s2">&quot;ames_housing&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">fit_predict</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">X</span><span class="p">)</span>
+    <span class="n">y_score</span><span class="p">[</span><span class="n">model_name</span><span class="p">][</span><span class="s2">&quot;ames_housing&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">fit_predict</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">X</span><span class="p">)</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Duration for LOF: 0.84 s
-Duration for IForest: 0.25 s
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Duration for LOF: 0.82 s
+Duration for IForest: 0.22 s
 </pre></div>
 </div>
 </section>
@@ -1262,11 +1261,11 @@ <h3>Cardiotocography dataset<a class="headerlink" href="#cardiotocography-datase
         <span class="n">lof_kw</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;n_neighbors&quot;</span><span class="p">:</span> <span class="nb">int</span><span class="p">(</span><span class="n">n_samples</span> <span class="o">*</span> <span class="n">anomaly_frac</span><span class="p">)},</span>
         <span class="n">iforest_kw</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;random_state&quot;</span><span class="p">:</span> <span class="mi">42</span><span class="p">},</span>
     <span class="p">)</span>
-    <span class="n">y_pred</span><span class="p">[</span><span class="n">model_name</span><span class="p">][</span><span class="s2">&quot;cardiotocography&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">fit_predict</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">X</span><span class="p">)</span>
+    <span class="n">y_score</span><span class="p">[</span><span class="n">model_name</span><span class="p">][</span><span class="s2">&quot;cardiotocography&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">fit_predict</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">X</span><span class="p">)</span>
 </pre></div>
 </div>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Duration for LOF: 0.06 s
-Duration for IForest: 0.16 s
+Duration for IForest: 0.14 s
 </pre></div>
 </div>
 </section>
@@ -1293,7 +1292,7 @@ <h2>Plot and interpret results<a class="headerlink" href="#plot-and-interpret-re
     <span class="k">for</span> <span class="n">model_idx</span><span class="p">,</span> <span class="n">model_name</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">model_names</span><span class="p">):</span>
         <span class="n">display</span> <span class="o">=</span> <a href="http://github.com/modules/generated/sklearn.metrics.RocCurveDisplay.html#sklearn.metrics.RocCurveDisplay.from_predictions" title="sklearn.metrics.RocCurveDisplay.from_predictions" class="sphx-glr-backref-module-sklearn-metrics-RocCurveDisplay sphx-glr-backref-type-py-method"><span class="n">RocCurveDisplay</span><span class="o">.</span><span class="n">from_predictions</span></a><span class="p">(</span>
             <span class="n">y_true</span><span class="p">[</span><span class="n">dataset_name</span><span class="p">],</span>
-            <span class="n">y_pred</span><span class="p">[</span><span class="n">model_name</span><span class="p">][</span><span class="n">dataset_name</span><span class="p">],</span>
+            <span class="n">y_score</span><span class="p">[</span><span class="n">model_name</span><span class="p">][</span><span class="n">dataset_name</span><span class="p">],</span>
             <span class="n">pos_label</span><span class="o">=</span><span class="n">pos_label</span><span class="p">,</span>
             <span class="n">name</span><span class="o">=</span><span class="n">model_name</span><span class="p">,</span>
             <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span>
@@ -1336,10 +1335,10 @@ <h2>Ablation study<a class="headerlink" href="#ablation-study" title="Link to th
 <span class="k">for</span> <span class="n">model_idx</span><span class="p">,</span> <span class="p">(</span><span class="n">linestyle</span><span class="p">,</span> <span class="n">n_neighbors</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">linestyles</span><span class="p">,</span> <span class="n">n_neighbors_list</span><span class="p">)):</span>
     <span class="n">model</span><span class="o">.</span><span class="n">set_params</span><span class="p">(</span><span class="n">localoutlierfactor__n_neighbors</span><span class="o">=</span><span class="n">n_neighbors</span><span class="p">)</span>
     <span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
-    <span class="n">y_pred</span> <span class="o">=</span> <span class="n">model</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">negative_outlier_factor_</span>
+    <span class="n">y_score</span> <span class="o">=</span> <span class="n">model</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">negative_outlier_factor_</span>
     <span class="n">display</span> <span class="o">=</span> <a href="http://github.com/modules/generated/sklearn.metrics.RocCurveDisplay.html#sklearn.metrics.RocCurveDisplay.from_predictions" title="sklearn.metrics.RocCurveDisplay.from_predictions" class="sphx-glr-backref-module-sklearn-metrics-RocCurveDisplay sphx-glr-backref-type-py-method"><span class="n">RocCurveDisplay</span><span class="o">.</span><span class="n">from_predictions</span></a><span class="p">(</span>
         <span class="n">y</span><span class="p">,</span>
-        <span class="n">y_pred</span><span class="p">,</span>
+        <span class="n">y_score</span><span class="p">,</span>
         <span class="n">pos_label</span><span class="o">=</span><span class="n">pos_label</span><span class="p">,</span>
         <span class="n">name</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;n_neighbors = </span><span class="si">{</span><span class="n">n_neighbors</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span>
         <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span>
@@ -1374,10 +1373,10 @@ <h2>Ablation study<a class="headerlink" href="#ablation-study" title="Link to th
 <span class="p">):</span>
     <span class="n">model</span> <span class="o">=</span> <a href="http://github.com/modules/generated/sklearn.pipeline.make_pipeline.html#sklearn.pipeline.make_pipeline" title="sklearn.pipeline.make_pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-function"><span class="n">make_pipeline</span></a><span class="p">(</span><span class="n">preprocessor</span><span class="p">,</span> <span class="n">lof</span><span class="p">)</span>
     <span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
-    <span class="n">y_pred</span> <span class="o">=</span> <span class="n">model</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">negative_outlier_factor_</span>
+    <span class="n">y_score</span> <span class="o">=</span> <span class="n">model</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">negative_outlier_factor_</span>
     <span class="n">display</span> <span class="o">=</span> <a href="http://github.com/modules/generated/sklearn.metrics.RocCurveDisplay.html#sklearn.metrics.RocCurveDisplay.from_predictions" title="sklearn.metrics.RocCurveDisplay.from_predictions" class="sphx-glr-backref-module-sklearn-metrics-RocCurveDisplay sphx-glr-backref-type-py-method"><span class="n">RocCurveDisplay</span><span class="o">.</span><span class="n">from_predictions</span></a><span class="p">(</span>
         <span class="n">y</span><span class="p">,</span>
-        <span class="n">y_pred</span><span class="p">,</span>
+        <span class="n">y_score</span><span class="p">,</span>
         <span class="n">pos_label</span><span class="o">=</span><span class="n">pos_label</span><span class="p">,</span>
         <span class="n">name</span><span class="o">=</span><span class="nb">str</span><span class="p">(</span><span class="n">preprocessor</span><span class="p">)</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;(&quot;</span><span class="p">)[</span><span class="mi">0</span><span class="p">],</span>
         <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span>
@@ -1421,10 +1420,10 @@ <h2>Ablation study<a class="headerlink" href="#ablation-study" title="Link to th
 <span class="p">):</span>
     <span class="n">model</span> <span class="o">=</span> <a href="http://github.com/modules/generated/sklearn.pipeline.make_pipeline.html#sklearn.pipeline.make_pipeline" title="sklearn.pipeline.make_pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-function"><span class="n">make_pipeline</span></a><span class="p">(</span><span class="n">preprocessor</span><span class="p">,</span> <span class="n">lof</span><span class="p">)</span>
     <span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
-    <span class="n">y_pred</span> <span class="o">=</span> <span class="n">model</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">negative_outlier_factor_</span>
+    <span class="n">y_score</span> <span class="o">=</span> <span class="n">model</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">negative_outlier_factor_</span>
     <span class="n">display</span> <span class="o">=</span> <a href="http://github.com/modules/generated/sklearn.metrics.RocCurveDisplay.html#sklearn.metrics.RocCurveDisplay.from_predictions" title="sklearn.metrics.RocCurveDisplay.from_predictions" class="sphx-glr-backref-module-sklearn-metrics-RocCurveDisplay sphx-glr-backref-type-py-method"><span class="n">RocCurveDisplay</span><span class="o">.</span><span class="n">from_predictions</span></a><span class="p">(</span>
         <span class="n">y</span><span class="p">,</span>
-        <span class="n">y_pred</span><span class="p">,</span>
+        <span class="n">y_score</span><span class="p">,</span>
         <span class="n">pos_label</span><span class="o">=</span><span class="n">pos_label</span><span class="p">,</span>
         <span class="n">name</span><span class="o">=</span><span class="nb">str</span><span class="p">(</span><span class="n">preprocessor</span><span class="p">)</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;(&quot;</span><span class="p">)[</span><span class="mi">0</span><span class="p">],</span>
         <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span>
@@ -1439,7 +1438,7 @@ <h2>Ablation study<a class="headerlink" href="#ablation-study" title="Link to th
 <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
 </pre></div>
 </div>
-<img src="http://github.com/_images/sphx_glr_plot_outlier_detection_bench_005.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_outlier_detection_bench_005.png " alt="Fixed n_neighbors with varying preprocessing on cardiotocography dataset" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 48.298 seconds)</p>
+<img src="http://github.com/_images/sphx_glr_plot_outlier_detection_bench_005.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_outlier_detection_bench_005.png " alt="Fixed n_neighbors with varying preprocessing on cardiotocography dataset" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 57.950 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-miscellaneous-plot-outlier-detection-bench-py">
 <div class="binder-badge docutils container">
 <a class="reference external image-reference" href="https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/miscellaneous/plot_outlier_detection_bench.ipynb"><img alt="Launch binder" src="http://github.com/_images/binder_badge_logo20.svg" style="width: 150px;" />
diff --git a/dev/auto_examples/miscellaneous/plot_partial_dependence_visualization_api.html b/dev/auto_examples/miscellaneous/plot_partial_dependence_visualization_api.html
index 0de16f2d16b9e..772fcd7146faf 100644
--- a/dev/auto_examples/miscellaneous/plot_partial_dependence_visualization_api.html
+++ b/dev/auto_examples/miscellaneous/plot_partial_dependence_visualization_api.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -679,13 +679,12 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -902,9 +901,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
@@ -1034,7 +1033,7 @@ <h2>Train models on the diabetes dataset<a class="headerlink" href="#train-model
 </pre></div>
 </div>
 <div class="output_subarea output_html rendered_html output_result">
-<style>#sk-container-id-39 {
+<style>#sk-container-id-40 {
   /* Definition of color scheme common for light and dark mode */
   --sklearn-color-text: #000;
   --sklearn-color-text-muted: #666;
@@ -1065,15 +1064,15 @@ <h2>Train models on the diabetes dataset<a class="headerlink" href="#train-model
   }
 }
 
-#sk-container-id-39 {
+#sk-container-id-40 {
   color: var(--sklearn-color-text);
 }
 
-#sk-container-id-39 pre {
+#sk-container-id-40 pre {
   padding: 0;
 }
 
-#sk-container-id-39 input.sk-hidden--visually {
+#sk-container-id-40 input.sk-hidden--visually {
   border: 0;
   clip: rect(1px 1px 1px 1px);
   clip: rect(1px, 1px, 1px, 1px);
@@ -1085,7 +1084,7 @@ <h2>Train models on the diabetes dataset<a class="headerlink" href="#train-model
   width: 1px;
 }
 
-#sk-container-id-39 div.sk-dashed-wrapped {
+#sk-container-id-40 div.sk-dashed-wrapped {
   border: 1px dashed var(--sklearn-color-line);
   margin: 0 0.4em 0.5em 0.4em;
   box-sizing: border-box;
@@ -1093,7 +1092,7 @@ <h2>Train models on the diabetes dataset<a class="headerlink" href="#train-model
   background-color: var(--sklearn-color-background);
 }
 
-#sk-container-id-39 div.sk-container {
+#sk-container-id-40 div.sk-container {
   /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
      but bootstrap.min.css set `[hidden] { display: none !important; }`
      so we also need the `!important` here to be able to override the
@@ -1103,7 +1102,7 @@ <h2>Train models on the diabetes dataset<a class="headerlink" href="#train-model
   position: relative;
 }
 
-#sk-container-id-39 div.sk-text-repr-fallback {
+#sk-container-id-40 div.sk-text-repr-fallback {
   display: none;
 }
 
@@ -1119,14 +1118,14 @@ <h2>Train models on the diabetes dataset<a class="headerlink" href="#train-model
 
 /* Parallel-specific style estimator block */
 
-#sk-container-id-39 div.sk-parallel-item::after {
+#sk-container-id-40 div.sk-parallel-item::after {
   content: "";
   width: 100%;
   border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
   flex-grow: 1;
 }
 
-#sk-container-id-39 div.sk-parallel {
+#sk-container-id-40 div.sk-parallel {
   display: flex;
   align-items: stretch;
   justify-content: center;
@@ -1134,28 +1133,28 @@ <h2>Train models on the diabetes dataset<a class="headerlink" href="#train-model
   position: relative;
 }
 
-#sk-container-id-39 div.sk-parallel-item {
+#sk-container-id-40 div.sk-parallel-item {
   display: flex;
   flex-direction: column;
 }
 
-#sk-container-id-39 div.sk-parallel-item:first-child::after {
+#sk-container-id-40 div.sk-parallel-item:first-child::after {
   align-self: flex-end;
   width: 50%;
 }
 
-#sk-container-id-39 div.sk-parallel-item:last-child::after {
+#sk-container-id-40 div.sk-parallel-item:last-child::after {
   align-self: flex-start;
   width: 50%;
 }
 
-#sk-container-id-39 div.sk-parallel-item:only-child::after {
+#sk-container-id-40 div.sk-parallel-item:only-child::after {
   width: 0;
 }
 
 /* Serial-specific style estimator block */
 
-#sk-container-id-39 div.sk-serial {
+#sk-container-id-40 div.sk-serial {
   display: flex;
   flex-direction: column;
   align-items: center;
@@ -1173,14 +1172,14 @@ <h2>Train models on the diabetes dataset<a class="headerlink" href="#train-model
 
 /* Pipeline and ColumnTransformer style (default) */
 
-#sk-container-id-39 div.sk-toggleable {
+#sk-container-id-40 div.sk-toggleable {
   /* Default theme specific background. It is overwritten whether we have a
   specific estimator or a Pipeline/ColumnTransformer */
   background-color: var(--sklearn-color-background);
 }
 
 /* Toggleable label */
-#sk-container-id-39 label.sk-toggleable__label {
+#sk-container-id-40 label.sk-toggleable__label {
   cursor: pointer;
   display: flex;
   width: 100%;
@@ -1193,13 +1192,13 @@ <h2>Train models on the diabetes dataset<a class="headerlink" href="#train-model
   gap: 0.5em;
 }
 
-#sk-container-id-39 label.sk-toggleable__label .caption {
+#sk-container-id-40 label.sk-toggleable__label .caption {
   font-size: 0.6rem;
   font-weight: lighter;
   color: var(--sklearn-color-text-muted);
 }
 
-#sk-container-id-39 label.sk-toggleable__label-arrow:before {
+#sk-container-id-40 label.sk-toggleable__label-arrow:before {
   /* Arrow on the left of the label */
   content: "▸";
   float: left;
@@ -1207,13 +1206,13 @@ <h2>Train models on the diabetes dataset<a class="headerlink" href="#train-model
   color: var(--sklearn-color-icon);
 }
 
-#sk-container-id-39 label.sk-toggleable__label-arrow:hover:before {
+#sk-container-id-40 label.sk-toggleable__label-arrow:hover:before {
   color: var(--sklearn-color-text);
 }
 
 /* Toggleable content - dropdown */
 
-#sk-container-id-39 div.sk-toggleable__content {
+#sk-container-id-40 div.sk-toggleable__content {
   max-height: 0;
   max-width: 0;
   overflow: hidden;
@@ -1222,12 +1221,12 @@ <h2>Train models on the diabetes dataset<a class="headerlink" href="#train-model
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-39 div.sk-toggleable__content.fitted {
+#sk-container-id-40 div.sk-toggleable__content.fitted {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
-#sk-container-id-39 div.sk-toggleable__content pre {
+#sk-container-id-40 div.sk-toggleable__content pre {
   margin: 0.2em;
   border-radius: 0.25em;
   color: var(--sklearn-color-text);
@@ -1235,79 +1234,79 @@ <h2>Train models on the diabetes dataset<a class="headerlink" href="#train-model
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-39 div.sk-toggleable__content.fitted pre {
+#sk-container-id-40 div.sk-toggleable__content.fitted pre {
   /* unfitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
-#sk-container-id-39 input.sk-toggleable__control:checked~div.sk-toggleable__content {
+#sk-container-id-40 input.sk-toggleable__control:checked~div.sk-toggleable__content {
   /* Expand drop-down */
   max-height: 200px;
   max-width: 100%;
   overflow: auto;
 }
 
-#sk-container-id-39 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
+#sk-container-id-40 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
   content: "▾";
 }
 
 /* Pipeline/ColumnTransformer-specific style */
 
-#sk-container-id-39 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-40 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-39 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-40 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
 /* Estimator-specific style */
 
 /* Colorize estimator box */
-#sk-container-id-39 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-40 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-39 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-40 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
-#sk-container-id-39 div.sk-label label.sk-toggleable__label,
-#sk-container-id-39 div.sk-label label {
+#sk-container-id-40 div.sk-label label.sk-toggleable__label,
+#sk-container-id-40 div.sk-label label {
   /* The background is the default theme color */
   color: var(--sklearn-color-text-on-default-background);
 }
 
 /* On hover, darken the color of the background */
-#sk-container-id-39 div.sk-label:hover label.sk-toggleable__label {
+#sk-container-id-40 div.sk-label:hover label.sk-toggleable__label {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
 /* Label box, darken color on hover, fitted */
-#sk-container-id-39 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
+#sk-container-id-40 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
 /* Estimator label */
 
-#sk-container-id-39 div.sk-label label {
+#sk-container-id-40 div.sk-label label {
   font-family: monospace;
   font-weight: bold;
   display: inline-block;
   line-height: 1.2em;
 }
 
-#sk-container-id-39 div.sk-label-container {
+#sk-container-id-40 div.sk-label-container {
   text-align: center;
 }
 
 /* Estimator-specific */
-#sk-container-id-39 div.sk-estimator {
+#sk-container-id-40 div.sk-estimator {
   font-family: monospace;
   border: 1px dotted var(--sklearn-color-border-box);
   border-radius: 0.25em;
@@ -1317,18 +1316,18 @@ <h2>Train models on the diabetes dataset<a class="headerlink" href="#train-model
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-39 div.sk-estimator.fitted {
+#sk-container-id-40 div.sk-estimator.fitted {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
 /* on hover */
-#sk-container-id-39 div.sk-estimator:hover {
+#sk-container-id-40 div.sk-estimator:hover {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-39 div.sk-estimator.fitted:hover {
+#sk-container-id-40 div.sk-estimator.fitted:hover {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-2);
 }
@@ -1416,7 +1415,7 @@ <h2>Train models on the diabetes dataset<a class="headerlink" href="#train-model
 
 /* "?"-specific style due to the `<a>` HTML tag */
 
-#sk-container-id-39 a.estimator_doc_link {
+#sk-container-id-40 a.estimator_doc_link {
   float: right;
   font-size: 1rem;
   line-height: 1em;
@@ -1431,31 +1430,31 @@ <h2>Train models on the diabetes dataset<a class="headerlink" href="#train-model
   border: var(--sklearn-color-unfitted-level-1) 1pt solid;
 }
 
-#sk-container-id-39 a.estimator_doc_link.fitted {
+#sk-container-id-40 a.estimator_doc_link.fitted {
   /* fitted */
   border: var(--sklearn-color-fitted-level-1) 1pt solid;
   color: var(--sklearn-color-fitted-level-1);
 }
 
 /* On hover */
-#sk-container-id-39 a.estimator_doc_link:hover {
+#sk-container-id-40 a.estimator_doc_link:hover {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-3);
   color: var(--sklearn-color-background);
   text-decoration: none;
 }
 
-#sk-container-id-39 a.estimator_doc_link.fitted:hover {
+#sk-container-id-40 a.estimator_doc_link.fitted:hover {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-3);
 }
-</style><div id="sk-container-id-39" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),
+</style><div id="sk-container-id-40" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),
                 (&#x27;mlpregressor&#x27;,
                  MLPRegressor(hidden_layer_sizes=(100, 100), max_iter=500,
-                              random_state=0, tol=0.01))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-163" type="checkbox" ><label for="sk-estimator-id-163" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>Pipeline</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.pipeline.Pipeline.html">?<span>Documentation for Pipeline</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),
+                              random_state=0, tol=0.01))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-177" type="checkbox" ><label for="sk-estimator-id-177" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>Pipeline</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.pipeline.Pipeline.html">?<span>Documentation for Pipeline</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),
                 (&#x27;mlpregressor&#x27;,
                  MLPRegressor(hidden_layer_sizes=(100, 100), max_iter=500,
-                              random_state=0, tol=0.01))])</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-164" type="checkbox" ><label for="sk-estimator-id-164" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>StandardScaler</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.StandardScaler.html">?<span>Documentation for StandardScaler</span></a></div></label><div class="sk-toggleable__content fitted"><pre>StandardScaler()</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-165" type="checkbox" ><label for="sk-estimator-id-165" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>MLPRegressor</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.neural_network.MLPRegressor.html">?<span>Documentation for MLPRegressor</span></a></div></label><div class="sk-toggleable__content fitted"><pre>MLPRegressor(hidden_layer_sizes=(100, 100), max_iter=500, random_state=0,
+                              random_state=0, tol=0.01))])</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-178" type="checkbox" ><label for="sk-estimator-id-178" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>StandardScaler</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.StandardScaler.html">?<span>Documentation for StandardScaler</span></a></div></label><div class="sk-toggleable__content fitted"><pre>StandardScaler()</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-179" type="checkbox" ><label for="sk-estimator-id-179" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>MLPRegressor</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.neural_network.MLPRegressor.html">?<span>Documentation for MLPRegressor</span></a></div></label><div class="sk-toggleable__content fitted"><pre>MLPRegressor(hidden_layer_sizes=(100, 100), max_iter=500, random_state=0,
              tol=0.01)</pre></div> </div></div></div></div></div></div>
 </div>
 <br />
@@ -1523,7 +1522,7 @@ <h2>Plotting partial dependence of the two models together<a class="headerlink"
 <span class="n">ax2</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
 </pre></div>
 </div>
-<img src="http://github.com/_images/sphx_glr_plot_partial_dependence_visualization_api_004.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_partial_dependence_visualization_api_004.png " alt="plot partial dependence visualization api" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>&lt;matplotlib.legend.Legend object at 0x7fb57b2b1750&gt;
+<img src="http://github.com/_images/sphx_glr_plot_partial_dependence_visualization_api_004.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_partial_dependence_visualization_api_004.png " alt="plot partial dependence visualization api" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>&lt;matplotlib.legend.Legend object at 0x7fd8415567a0&gt;
 </pre></div>
 </div>
 <p><code class="docutils literal notranslate"><span class="pre">tree_disp.axes_</span></code> is a numpy array container the axes used to draw the
@@ -1554,7 +1553,7 @@ <h2>Plotting partial dependence for one feature<a class="headerlink" href="#plot
 <span class="p">)</span>
 </pre></div>
 </div>
-<img src="http://github.com/_images/sphx_glr_plot_partial_dependence_visualization_api_006.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_partial_dependence_visualization_api_006.png " alt="plot partial dependence visualization api" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 2.450 seconds)</p>
+<img src="http://github.com/_images/sphx_glr_plot_partial_dependence_visualization_api_006.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_partial_dependence_visualization_api_006.png " alt="plot partial dependence visualization api" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 2.712 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-miscellaneous-plot-partial-dependence-visualization-api-py">
 <div class="binder-badge docutils container">
 <a class="reference external image-reference" href="https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/miscellaneous/plot_partial_dependence_visualization_api.ipynb"><img alt="Launch binder" src="http://github.com/_images/binder_badge_logo20.svg" style="width: 150px;" />
diff --git a/dev/auto_examples/miscellaneous/plot_pipeline_display.html b/dev/auto_examples/miscellaneous/plot_pipeline_display.html
index 9b367f4b42157..cb1be35cb0ac6 100644
--- a/dev/auto_examples/miscellaneous/plot_pipeline_display.html
+++ b/dev/auto_examples/miscellaneous/plot_pipeline_display.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -679,13 +679,12 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -902,9 +901,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
@@ -1027,7 +1026,7 @@ <h2>Displaying a Pipeline with a Preprocessing Step and Classifier<a class="head
 </pre></div>
 </div>
 <div class="output_subarea output_html rendered_html output_result">
-<style>#sk-container-id-40 {
+<style>#sk-container-id-41 {
   /* Definition of color scheme common for light and dark mode */
   --sklearn-color-text: #000;
   --sklearn-color-text-muted: #666;
@@ -1058,15 +1057,15 @@ <h2>Displaying a Pipeline with a Preprocessing Step and Classifier<a class="head
   }
 }
 
-#sk-container-id-40 {
+#sk-container-id-41 {
   color: var(--sklearn-color-text);
 }
 
-#sk-container-id-40 pre {
+#sk-container-id-41 pre {
   padding: 0;
 }
 
-#sk-container-id-40 input.sk-hidden--visually {
+#sk-container-id-41 input.sk-hidden--visually {
   border: 0;
   clip: rect(1px 1px 1px 1px);
   clip: rect(1px, 1px, 1px, 1px);
@@ -1078,7 +1077,7 @@ <h2>Displaying a Pipeline with a Preprocessing Step and Classifier<a class="head
   width: 1px;
 }
 
-#sk-container-id-40 div.sk-dashed-wrapped {
+#sk-container-id-41 div.sk-dashed-wrapped {
   border: 1px dashed var(--sklearn-color-line);
   margin: 0 0.4em 0.5em 0.4em;
   box-sizing: border-box;
@@ -1086,7 +1085,7 @@ <h2>Displaying a Pipeline with a Preprocessing Step and Classifier<a class="head
   background-color: var(--sklearn-color-background);
 }
 
-#sk-container-id-40 div.sk-container {
+#sk-container-id-41 div.sk-container {
   /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
      but bootstrap.min.css set `[hidden] { display: none !important; }`
      so we also need the `!important` here to be able to override the
@@ -1096,7 +1095,7 @@ <h2>Displaying a Pipeline with a Preprocessing Step and Classifier<a class="head
   position: relative;
 }
 
-#sk-container-id-40 div.sk-text-repr-fallback {
+#sk-container-id-41 div.sk-text-repr-fallback {
   display: none;
 }
 
@@ -1112,14 +1111,14 @@ <h2>Displaying a Pipeline with a Preprocessing Step and Classifier<a class="head
 
 /* Parallel-specific style estimator block */
 
-#sk-container-id-40 div.sk-parallel-item::after {
+#sk-container-id-41 div.sk-parallel-item::after {
   content: "";
   width: 100%;
   border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
   flex-grow: 1;
 }
 
-#sk-container-id-40 div.sk-parallel {
+#sk-container-id-41 div.sk-parallel {
   display: flex;
   align-items: stretch;
   justify-content: center;
@@ -1127,28 +1126,28 @@ <h2>Displaying a Pipeline with a Preprocessing Step and Classifier<a class="head
   position: relative;
 }
 
-#sk-container-id-40 div.sk-parallel-item {
+#sk-container-id-41 div.sk-parallel-item {
   display: flex;
   flex-direction: column;
 }
 
-#sk-container-id-40 div.sk-parallel-item:first-child::after {
+#sk-container-id-41 div.sk-parallel-item:first-child::after {
   align-self: flex-end;
   width: 50%;
 }
 
-#sk-container-id-40 div.sk-parallel-item:last-child::after {
+#sk-container-id-41 div.sk-parallel-item:last-child::after {
   align-self: flex-start;
   width: 50%;
 }
 
-#sk-container-id-40 div.sk-parallel-item:only-child::after {
+#sk-container-id-41 div.sk-parallel-item:only-child::after {
   width: 0;
 }
 
 /* Serial-specific style estimator block */
 
-#sk-container-id-40 div.sk-serial {
+#sk-container-id-41 div.sk-serial {
   display: flex;
   flex-direction: column;
   align-items: center;
@@ -1166,14 +1165,14 @@ <h2>Displaying a Pipeline with a Preprocessing Step and Classifier<a class="head
 
 /* Pipeline and ColumnTransformer style (default) */
 
-#sk-container-id-40 div.sk-toggleable {
+#sk-container-id-41 div.sk-toggleable {
   /* Default theme specific background. It is overwritten whether we have a
   specific estimator or a Pipeline/ColumnTransformer */
   background-color: var(--sklearn-color-background);
 }
 
 /* Toggleable label */
-#sk-container-id-40 label.sk-toggleable__label {
+#sk-container-id-41 label.sk-toggleable__label {
   cursor: pointer;
   display: flex;
   width: 100%;
@@ -1186,13 +1185,13 @@ <h2>Displaying a Pipeline with a Preprocessing Step and Classifier<a class="head
   gap: 0.5em;
 }
 
-#sk-container-id-40 label.sk-toggleable__label .caption {
+#sk-container-id-41 label.sk-toggleable__label .caption {
   font-size: 0.6rem;
   font-weight: lighter;
   color: var(--sklearn-color-text-muted);
 }
 
-#sk-container-id-40 label.sk-toggleable__label-arrow:before {
+#sk-container-id-41 label.sk-toggleable__label-arrow:before {
   /* Arrow on the left of the label */
   content: "▸";
   float: left;
@@ -1200,13 +1199,13 @@ <h2>Displaying a Pipeline with a Preprocessing Step and Classifier<a class="head
   color: var(--sklearn-color-icon);
 }
 
-#sk-container-id-40 label.sk-toggleable__label-arrow:hover:before {
+#sk-container-id-41 label.sk-toggleable__label-arrow:hover:before {
   color: var(--sklearn-color-text);
 }
 
 /* Toggleable content - dropdown */
 
-#sk-container-id-40 div.sk-toggleable__content {
+#sk-container-id-41 div.sk-toggleable__content {
   max-height: 0;
   max-width: 0;
   overflow: hidden;
@@ -1215,12 +1214,12 @@ <h2>Displaying a Pipeline with a Preprocessing Step and Classifier<a class="head
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-40 div.sk-toggleable__content.fitted {
+#sk-container-id-41 div.sk-toggleable__content.fitted {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
-#sk-container-id-40 div.sk-toggleable__content pre {
+#sk-container-id-41 div.sk-toggleable__content pre {
   margin: 0.2em;
   border-radius: 0.25em;
   color: var(--sklearn-color-text);
@@ -1228,79 +1227,79 @@ <h2>Displaying a Pipeline with a Preprocessing Step and Classifier<a class="head
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-40 div.sk-toggleable__content.fitted pre {
+#sk-container-id-41 div.sk-toggleable__content.fitted pre {
   /* unfitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
-#sk-container-id-40 input.sk-toggleable__control:checked~div.sk-toggleable__content {
+#sk-container-id-41 input.sk-toggleable__control:checked~div.sk-toggleable__content {
   /* Expand drop-down */
   max-height: 200px;
   max-width: 100%;
   overflow: auto;
 }
 
-#sk-container-id-40 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
+#sk-container-id-41 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
   content: "▾";
 }
 
 /* Pipeline/ColumnTransformer-specific style */
 
-#sk-container-id-40 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-41 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-40 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-41 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
 /* Estimator-specific style */
 
 /* Colorize estimator box */
-#sk-container-id-40 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-41 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-40 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-41 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
-#sk-container-id-40 div.sk-label label.sk-toggleable__label,
-#sk-container-id-40 div.sk-label label {
+#sk-container-id-41 div.sk-label label.sk-toggleable__label,
+#sk-container-id-41 div.sk-label label {
   /* The background is the default theme color */
   color: var(--sklearn-color-text-on-default-background);
 }
 
 /* On hover, darken the color of the background */
-#sk-container-id-40 div.sk-label:hover label.sk-toggleable__label {
+#sk-container-id-41 div.sk-label:hover label.sk-toggleable__label {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
 /* Label box, darken color on hover, fitted */
-#sk-container-id-40 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
+#sk-container-id-41 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
 /* Estimator label */
 
-#sk-container-id-40 div.sk-label label {
+#sk-container-id-41 div.sk-label label {
   font-family: monospace;
   font-weight: bold;
   display: inline-block;
   line-height: 1.2em;
 }
 
-#sk-container-id-40 div.sk-label-container {
+#sk-container-id-41 div.sk-label-container {
   text-align: center;
 }
 
 /* Estimator-specific */
-#sk-container-id-40 div.sk-estimator {
+#sk-container-id-41 div.sk-estimator {
   font-family: monospace;
   border: 1px dotted var(--sklearn-color-border-box);
   border-radius: 0.25em;
@@ -1310,18 +1309,18 @@ <h2>Displaying a Pipeline with a Preprocessing Step and Classifier<a class="head
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-40 div.sk-estimator.fitted {
+#sk-container-id-41 div.sk-estimator.fitted {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
 /* on hover */
-#sk-container-id-40 div.sk-estimator:hover {
+#sk-container-id-41 div.sk-estimator:hover {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-40 div.sk-estimator.fitted:hover {
+#sk-container-id-41 div.sk-estimator.fitted:hover {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-2);
 }
@@ -1409,7 +1408,7 @@ <h2>Displaying a Pipeline with a Preprocessing Step and Classifier<a class="head
 
 /* "?"-specific style due to the `<a>` HTML tag */
 
-#sk-container-id-40 a.estimator_doc_link {
+#sk-container-id-41 a.estimator_doc_link {
   float: right;
   font-size: 1rem;
   line-height: 1em;
@@ -1424,27 +1423,27 @@ <h2>Displaying a Pipeline with a Preprocessing Step and Classifier<a class="head
   border: var(--sklearn-color-unfitted-level-1) 1pt solid;
 }
 
-#sk-container-id-40 a.estimator_doc_link.fitted {
+#sk-container-id-41 a.estimator_doc_link.fitted {
   /* fitted */
   border: var(--sklearn-color-fitted-level-1) 1pt solid;
   color: var(--sklearn-color-fitted-level-1);
 }
 
 /* On hover */
-#sk-container-id-40 a.estimator_doc_link:hover {
+#sk-container-id-41 a.estimator_doc_link:hover {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-3);
   color: var(--sklearn-color-background);
   text-decoration: none;
 }
 
-#sk-container-id-40 a.estimator_doc_link.fitted:hover {
+#sk-container-id-41 a.estimator_doc_link.fitted:hover {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-3);
 }
-</style><div id="sk-container-id-40" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;preprocessing&#x27;, StandardScaler()),
-                (&#x27;classifier&#x27;, LogisticRegression())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-166" type="checkbox" ><label for="sk-estimator-id-166" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>Pipeline</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.pipeline.Pipeline.html">?<span>Documentation for Pipeline</span></a><span class="sk-estimator-doc-link ">i<span>Not fitted</span></span></div></label><div class="sk-toggleable__content "><pre>Pipeline(steps=[(&#x27;preprocessing&#x27;, StandardScaler()),
-                (&#x27;classifier&#x27;, LogisticRegression())])</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-167" type="checkbox" ><label for="sk-estimator-id-167" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>StandardScaler</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.StandardScaler.html">?<span>Documentation for StandardScaler</span></a></div></label><div class="sk-toggleable__content "><pre>StandardScaler()</pre></div> </div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-168" type="checkbox" ><label for="sk-estimator-id-168" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>LogisticRegression</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.linear_model.LogisticRegression.html">?<span>Documentation for LogisticRegression</span></a></div></label><div class="sk-toggleable__content "><pre>LogisticRegression()</pre></div> </div></div></div></div></div></div>
+</style><div id="sk-container-id-41" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;preprocessing&#x27;, StandardScaler()),
+                (&#x27;classifier&#x27;, LogisticRegression())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-180" type="checkbox" ><label for="sk-estimator-id-180" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>Pipeline</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.pipeline.Pipeline.html">?<span>Documentation for Pipeline</span></a><span class="sk-estimator-doc-link ">i<span>Not fitted</span></span></div></label><div class="sk-toggleable__content "><pre>Pipeline(steps=[(&#x27;preprocessing&#x27;, StandardScaler()),
+                (&#x27;classifier&#x27;, LogisticRegression())])</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-181" type="checkbox" ><label for="sk-estimator-id-181" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>StandardScaler</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.StandardScaler.html">?<span>Documentation for StandardScaler</span></a></div></label><div class="sk-toggleable__content "><pre>StandardScaler()</pre></div> </div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-182" type="checkbox" ><label for="sk-estimator-id-182" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>LogisticRegression</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.linear_model.LogisticRegression.html">?<span>Documentation for LogisticRegression</span></a></div></label><div class="sk-toggleable__content "><pre>LogisticRegression()</pre></div> </div></div></div></div></div></div>
 </div>
 <br />
 <br /><p>To view the text pipeline, change to <code class="docutils literal notranslate"><span class="pre">display='text'</span></code>.</p>
@@ -1482,7 +1481,7 @@ <h2>Displaying a Pipeline Chaining Multiple Preprocessing Steps &amp; Classifier
 </pre></div>
 </div>
 <div class="output_subarea output_html rendered_html output_result">
-<style>#sk-container-id-41 {
+<style>#sk-container-id-42 {
   /* Definition of color scheme common for light and dark mode */
   --sklearn-color-text: #000;
   --sklearn-color-text-muted: #666;
@@ -1513,15 +1512,15 @@ <h2>Displaying a Pipeline Chaining Multiple Preprocessing Steps &amp; Classifier
   }
 }
 
-#sk-container-id-41 {
+#sk-container-id-42 {
   color: var(--sklearn-color-text);
 }
 
-#sk-container-id-41 pre {
+#sk-container-id-42 pre {
   padding: 0;
 }
 
-#sk-container-id-41 input.sk-hidden--visually {
+#sk-container-id-42 input.sk-hidden--visually {
   border: 0;
   clip: rect(1px 1px 1px 1px);
   clip: rect(1px, 1px, 1px, 1px);
@@ -1533,7 +1532,7 @@ <h2>Displaying a Pipeline Chaining Multiple Preprocessing Steps &amp; Classifier
   width: 1px;
 }
 
-#sk-container-id-41 div.sk-dashed-wrapped {
+#sk-container-id-42 div.sk-dashed-wrapped {
   border: 1px dashed var(--sklearn-color-line);
   margin: 0 0.4em 0.5em 0.4em;
   box-sizing: border-box;
@@ -1541,7 +1540,7 @@ <h2>Displaying a Pipeline Chaining Multiple Preprocessing Steps &amp; Classifier
   background-color: var(--sklearn-color-background);
 }
 
-#sk-container-id-41 div.sk-container {
+#sk-container-id-42 div.sk-container {
   /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
      but bootstrap.min.css set `[hidden] { display: none !important; }`
      so we also need the `!important` here to be able to override the
@@ -1551,7 +1550,7 @@ <h2>Displaying a Pipeline Chaining Multiple Preprocessing Steps &amp; Classifier
   position: relative;
 }
 
-#sk-container-id-41 div.sk-text-repr-fallback {
+#sk-container-id-42 div.sk-text-repr-fallback {
   display: none;
 }
 
@@ -1567,14 +1566,14 @@ <h2>Displaying a Pipeline Chaining Multiple Preprocessing Steps &amp; Classifier
 
 /* Parallel-specific style estimator block */
 
-#sk-container-id-41 div.sk-parallel-item::after {
+#sk-container-id-42 div.sk-parallel-item::after {
   content: "";
   width: 100%;
   border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
   flex-grow: 1;
 }
 
-#sk-container-id-41 div.sk-parallel {
+#sk-container-id-42 div.sk-parallel {
   display: flex;
   align-items: stretch;
   justify-content: center;
@@ -1582,28 +1581,28 @@ <h2>Displaying a Pipeline Chaining Multiple Preprocessing Steps &amp; Classifier
   position: relative;
 }
 
-#sk-container-id-41 div.sk-parallel-item {
+#sk-container-id-42 div.sk-parallel-item {
   display: flex;
   flex-direction: column;
 }
 
-#sk-container-id-41 div.sk-parallel-item:first-child::after {
+#sk-container-id-42 div.sk-parallel-item:first-child::after {
   align-self: flex-end;
   width: 50%;
 }
 
-#sk-container-id-41 div.sk-parallel-item:last-child::after {
+#sk-container-id-42 div.sk-parallel-item:last-child::after {
   align-self: flex-start;
   width: 50%;
 }
 
-#sk-container-id-41 div.sk-parallel-item:only-child::after {
+#sk-container-id-42 div.sk-parallel-item:only-child::after {
   width: 0;
 }
 
 /* Serial-specific style estimator block */
 
-#sk-container-id-41 div.sk-serial {
+#sk-container-id-42 div.sk-serial {
   display: flex;
   flex-direction: column;
   align-items: center;
@@ -1621,14 +1620,14 @@ <h2>Displaying a Pipeline Chaining Multiple Preprocessing Steps &amp; Classifier
 
 /* Pipeline and ColumnTransformer style (default) */
 
-#sk-container-id-41 div.sk-toggleable {
+#sk-container-id-42 div.sk-toggleable {
   /* Default theme specific background. It is overwritten whether we have a
   specific estimator or a Pipeline/ColumnTransformer */
   background-color: var(--sklearn-color-background);
 }
 
 /* Toggleable label */
-#sk-container-id-41 label.sk-toggleable__label {
+#sk-container-id-42 label.sk-toggleable__label {
   cursor: pointer;
   display: flex;
   width: 100%;
@@ -1641,13 +1640,13 @@ <h2>Displaying a Pipeline Chaining Multiple Preprocessing Steps &amp; Classifier
   gap: 0.5em;
 }
 
-#sk-container-id-41 label.sk-toggleable__label .caption {
+#sk-container-id-42 label.sk-toggleable__label .caption {
   font-size: 0.6rem;
   font-weight: lighter;
   color: var(--sklearn-color-text-muted);
 }
 
-#sk-container-id-41 label.sk-toggleable__label-arrow:before {
+#sk-container-id-42 label.sk-toggleable__label-arrow:before {
   /* Arrow on the left of the label */
   content: "▸";
   float: left;
@@ -1655,13 +1654,13 @@ <h2>Displaying a Pipeline Chaining Multiple Preprocessing Steps &amp; Classifier
   color: var(--sklearn-color-icon);
 }
 
-#sk-container-id-41 label.sk-toggleable__label-arrow:hover:before {
+#sk-container-id-42 label.sk-toggleable__label-arrow:hover:before {
   color: var(--sklearn-color-text);
 }
 
 /* Toggleable content - dropdown */
 
-#sk-container-id-41 div.sk-toggleable__content {
+#sk-container-id-42 div.sk-toggleable__content {
   max-height: 0;
   max-width: 0;
   overflow: hidden;
@@ -1670,12 +1669,12 @@ <h2>Displaying a Pipeline Chaining Multiple Preprocessing Steps &amp; Classifier
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-41 div.sk-toggleable__content.fitted {
+#sk-container-id-42 div.sk-toggleable__content.fitted {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
-#sk-container-id-41 div.sk-toggleable__content pre {
+#sk-container-id-42 div.sk-toggleable__content pre {
   margin: 0.2em;
   border-radius: 0.25em;
   color: var(--sklearn-color-text);
@@ -1683,79 +1682,79 @@ <h2>Displaying a Pipeline Chaining Multiple Preprocessing Steps &amp; Classifier
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-41 div.sk-toggleable__content.fitted pre {
+#sk-container-id-42 div.sk-toggleable__content.fitted pre {
   /* unfitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
-#sk-container-id-41 input.sk-toggleable__control:checked~div.sk-toggleable__content {
+#sk-container-id-42 input.sk-toggleable__control:checked~div.sk-toggleable__content {
   /* Expand drop-down */
   max-height: 200px;
   max-width: 100%;
   overflow: auto;
 }
 
-#sk-container-id-41 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
+#sk-container-id-42 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
   content: "▾";
 }
 
 /* Pipeline/ColumnTransformer-specific style */
 
-#sk-container-id-41 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-42 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-41 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-42 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
 /* Estimator-specific style */
 
 /* Colorize estimator box */
-#sk-container-id-41 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-42 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-41 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-42 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
-#sk-container-id-41 div.sk-label label.sk-toggleable__label,
-#sk-container-id-41 div.sk-label label {
+#sk-container-id-42 div.sk-label label.sk-toggleable__label,
+#sk-container-id-42 div.sk-label label {
   /* The background is the default theme color */
   color: var(--sklearn-color-text-on-default-background);
 }
 
 /* On hover, darken the color of the background */
-#sk-container-id-41 div.sk-label:hover label.sk-toggleable__label {
+#sk-container-id-42 div.sk-label:hover label.sk-toggleable__label {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
 /* Label box, darken color on hover, fitted */
-#sk-container-id-41 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
+#sk-container-id-42 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
 /* Estimator label */
 
-#sk-container-id-41 div.sk-label label {
+#sk-container-id-42 div.sk-label label {
   font-family: monospace;
   font-weight: bold;
   display: inline-block;
   line-height: 1.2em;
 }
 
-#sk-container-id-41 div.sk-label-container {
+#sk-container-id-42 div.sk-label-container {
   text-align: center;
 }
 
 /* Estimator-specific */
-#sk-container-id-41 div.sk-estimator {
+#sk-container-id-42 div.sk-estimator {
   font-family: monospace;
   border: 1px dotted var(--sklearn-color-border-box);
   border-radius: 0.25em;
@@ -1765,18 +1764,18 @@ <h2>Displaying a Pipeline Chaining Multiple Preprocessing Steps &amp; Classifier
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-41 div.sk-estimator.fitted {
+#sk-container-id-42 div.sk-estimator.fitted {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
 /* on hover */
-#sk-container-id-41 div.sk-estimator:hover {
+#sk-container-id-42 div.sk-estimator:hover {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-41 div.sk-estimator.fitted:hover {
+#sk-container-id-42 div.sk-estimator.fitted:hover {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-2);
 }
@@ -1864,7 +1863,7 @@ <h2>Displaying a Pipeline Chaining Multiple Preprocessing Steps &amp; Classifier
 
 /* "?"-specific style due to the `<a>` HTML tag */
 
-#sk-container-id-41 a.estimator_doc_link {
+#sk-container-id-42 a.estimator_doc_link {
   float: right;
   font-size: 1rem;
   line-height: 1em;
@@ -1879,29 +1878,29 @@ <h2>Displaying a Pipeline Chaining Multiple Preprocessing Steps &amp; Classifier
   border: var(--sklearn-color-unfitted-level-1) 1pt solid;
 }
 
-#sk-container-id-41 a.estimator_doc_link.fitted {
+#sk-container-id-42 a.estimator_doc_link.fitted {
   /* fitted */
   border: var(--sklearn-color-fitted-level-1) 1pt solid;
   color: var(--sklearn-color-fitted-level-1);
 }
 
 /* On hover */
-#sk-container-id-41 a.estimator_doc_link:hover {
+#sk-container-id-42 a.estimator_doc_link:hover {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-3);
   color: var(--sklearn-color-background);
   text-decoration: none;
 }
 
-#sk-container-id-41 a.estimator_doc_link.fitted:hover {
+#sk-container-id-42 a.estimator_doc_link.fitted:hover {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-3);
 }
-</style><div id="sk-container-id-41" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;standard_scaler&#x27;, StandardScaler()),
+</style><div id="sk-container-id-42" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;standard_scaler&#x27;, StandardScaler()),
                 (&#x27;polynomial&#x27;, PolynomialFeatures(degree=3)),
-                (&#x27;classifier&#x27;, LogisticRegression(C=2.0))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-169" type="checkbox" ><label for="sk-estimator-id-169" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>Pipeline</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.pipeline.Pipeline.html">?<span>Documentation for Pipeline</span></a><span class="sk-estimator-doc-link ">i<span>Not fitted</span></span></div></label><div class="sk-toggleable__content "><pre>Pipeline(steps=[(&#x27;standard_scaler&#x27;, StandardScaler()),
+                (&#x27;classifier&#x27;, LogisticRegression(C=2.0))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-183" type="checkbox" ><label for="sk-estimator-id-183" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>Pipeline</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.pipeline.Pipeline.html">?<span>Documentation for Pipeline</span></a><span class="sk-estimator-doc-link ">i<span>Not fitted</span></span></div></label><div class="sk-toggleable__content "><pre>Pipeline(steps=[(&#x27;standard_scaler&#x27;, StandardScaler()),
                 (&#x27;polynomial&#x27;, PolynomialFeatures(degree=3)),
-                (&#x27;classifier&#x27;, LogisticRegression(C=2.0))])</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-170" type="checkbox" ><label for="sk-estimator-id-170" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>StandardScaler</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.StandardScaler.html">?<span>Documentation for StandardScaler</span></a></div></label><div class="sk-toggleable__content "><pre>StandardScaler()</pre></div> </div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-171" type="checkbox" ><label for="sk-estimator-id-171" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>PolynomialFeatures</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.PolynomialFeatures.html">?<span>Documentation for PolynomialFeatures</span></a></div></label><div class="sk-toggleable__content "><pre>PolynomialFeatures(degree=3)</pre></div> </div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-172" type="checkbox" ><label for="sk-estimator-id-172" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>LogisticRegression</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.linear_model.LogisticRegression.html">?<span>Documentation for LogisticRegression</span></a></div></label><div class="sk-toggleable__content "><pre>LogisticRegression(C=2.0)</pre></div> </div></div></div></div></div></div>
+                (&#x27;classifier&#x27;, LogisticRegression(C=2.0))])</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-184" type="checkbox" ><label for="sk-estimator-id-184" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>StandardScaler</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.StandardScaler.html">?<span>Documentation for StandardScaler</span></a></div></label><div class="sk-toggleable__content "><pre>StandardScaler()</pre></div> </div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-185" type="checkbox" ><label for="sk-estimator-id-185" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>PolynomialFeatures</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.PolynomialFeatures.html">?<span>Documentation for PolynomialFeatures</span></a></div></label><div class="sk-toggleable__content "><pre>PolynomialFeatures(degree=3)</pre></div> </div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-186" type="checkbox" ><label for="sk-estimator-id-186" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>LogisticRegression</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.linear_model.LogisticRegression.html">?<span>Documentation for LogisticRegression</span></a></div></label><div class="sk-toggleable__content "><pre>LogisticRegression(C=2.0)</pre></div> </div></div></div></div></div></div>
 </div>
 <br />
 <br /></section>
@@ -1921,7 +1920,7 @@ <h2>Displaying a Pipeline and Dimensionality Reduction and Classifier<a class="h
 </pre></div>
 </div>
 <div class="output_subarea output_html rendered_html output_result">
-<style>#sk-container-id-42 {
+<style>#sk-container-id-43 {
   /* Definition of color scheme common for light and dark mode */
   --sklearn-color-text: #000;
   --sklearn-color-text-muted: #666;
@@ -1952,15 +1951,15 @@ <h2>Displaying a Pipeline and Dimensionality Reduction and Classifier<a class="h
   }
 }
 
-#sk-container-id-42 {
+#sk-container-id-43 {
   color: var(--sklearn-color-text);
 }
 
-#sk-container-id-42 pre {
+#sk-container-id-43 pre {
   padding: 0;
 }
 
-#sk-container-id-42 input.sk-hidden--visually {
+#sk-container-id-43 input.sk-hidden--visually {
   border: 0;
   clip: rect(1px 1px 1px 1px);
   clip: rect(1px, 1px, 1px, 1px);
@@ -1972,7 +1971,7 @@ <h2>Displaying a Pipeline and Dimensionality Reduction and Classifier<a class="h
   width: 1px;
 }
 
-#sk-container-id-42 div.sk-dashed-wrapped {
+#sk-container-id-43 div.sk-dashed-wrapped {
   border: 1px dashed var(--sklearn-color-line);
   margin: 0 0.4em 0.5em 0.4em;
   box-sizing: border-box;
@@ -1980,7 +1979,7 @@ <h2>Displaying a Pipeline and Dimensionality Reduction and Classifier<a class="h
   background-color: var(--sklearn-color-background);
 }
 
-#sk-container-id-42 div.sk-container {
+#sk-container-id-43 div.sk-container {
   /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
      but bootstrap.min.css set `[hidden] { display: none !important; }`
      so we also need the `!important` here to be able to override the
@@ -1990,7 +1989,7 @@ <h2>Displaying a Pipeline and Dimensionality Reduction and Classifier<a class="h
   position: relative;
 }
 
-#sk-container-id-42 div.sk-text-repr-fallback {
+#sk-container-id-43 div.sk-text-repr-fallback {
   display: none;
 }
 
@@ -2006,14 +2005,14 @@ <h2>Displaying a Pipeline and Dimensionality Reduction and Classifier<a class="h
 
 /* Parallel-specific style estimator block */
 
-#sk-container-id-42 div.sk-parallel-item::after {
+#sk-container-id-43 div.sk-parallel-item::after {
   content: "";
   width: 100%;
   border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
   flex-grow: 1;
 }
 
-#sk-container-id-42 div.sk-parallel {
+#sk-container-id-43 div.sk-parallel {
   display: flex;
   align-items: stretch;
   justify-content: center;
@@ -2021,28 +2020,28 @@ <h2>Displaying a Pipeline and Dimensionality Reduction and Classifier<a class="h
   position: relative;
 }
 
-#sk-container-id-42 div.sk-parallel-item {
+#sk-container-id-43 div.sk-parallel-item {
   display: flex;
   flex-direction: column;
 }
 
-#sk-container-id-42 div.sk-parallel-item:first-child::after {
+#sk-container-id-43 div.sk-parallel-item:first-child::after {
   align-self: flex-end;
   width: 50%;
 }
 
-#sk-container-id-42 div.sk-parallel-item:last-child::after {
+#sk-container-id-43 div.sk-parallel-item:last-child::after {
   align-self: flex-start;
   width: 50%;
 }
 
-#sk-container-id-42 div.sk-parallel-item:only-child::after {
+#sk-container-id-43 div.sk-parallel-item:only-child::after {
   width: 0;
 }
 
 /* Serial-specific style estimator block */
 
-#sk-container-id-42 div.sk-serial {
+#sk-container-id-43 div.sk-serial {
   display: flex;
   flex-direction: column;
   align-items: center;
@@ -2060,14 +2059,14 @@ <h2>Displaying a Pipeline and Dimensionality Reduction and Classifier<a class="h
 
 /* Pipeline and ColumnTransformer style (default) */
 
-#sk-container-id-42 div.sk-toggleable {
+#sk-container-id-43 div.sk-toggleable {
   /* Default theme specific background. It is overwritten whether we have a
   specific estimator or a Pipeline/ColumnTransformer */
   background-color: var(--sklearn-color-background);
 }
 
 /* Toggleable label */
-#sk-container-id-42 label.sk-toggleable__label {
+#sk-container-id-43 label.sk-toggleable__label {
   cursor: pointer;
   display: flex;
   width: 100%;
@@ -2080,13 +2079,13 @@ <h2>Displaying a Pipeline and Dimensionality Reduction and Classifier<a class="h
   gap: 0.5em;
 }
 
-#sk-container-id-42 label.sk-toggleable__label .caption {
+#sk-container-id-43 label.sk-toggleable__label .caption {
   font-size: 0.6rem;
   font-weight: lighter;
   color: var(--sklearn-color-text-muted);
 }
 
-#sk-container-id-42 label.sk-toggleable__label-arrow:before {
+#sk-container-id-43 label.sk-toggleable__label-arrow:before {
   /* Arrow on the left of the label */
   content: "▸";
   float: left;
@@ -2094,13 +2093,13 @@ <h2>Displaying a Pipeline and Dimensionality Reduction and Classifier<a class="h
   color: var(--sklearn-color-icon);
 }
 
-#sk-container-id-42 label.sk-toggleable__label-arrow:hover:before {
+#sk-container-id-43 label.sk-toggleable__label-arrow:hover:before {
   color: var(--sklearn-color-text);
 }
 
 /* Toggleable content - dropdown */
 
-#sk-container-id-42 div.sk-toggleable__content {
+#sk-container-id-43 div.sk-toggleable__content {
   max-height: 0;
   max-width: 0;
   overflow: hidden;
@@ -2109,12 +2108,12 @@ <h2>Displaying a Pipeline and Dimensionality Reduction and Classifier<a class="h
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-42 div.sk-toggleable__content.fitted {
+#sk-container-id-43 div.sk-toggleable__content.fitted {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
-#sk-container-id-42 div.sk-toggleable__content pre {
+#sk-container-id-43 div.sk-toggleable__content pre {
   margin: 0.2em;
   border-radius: 0.25em;
   color: var(--sklearn-color-text);
@@ -2122,79 +2121,79 @@ <h2>Displaying a Pipeline and Dimensionality Reduction and Classifier<a class="h
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-42 div.sk-toggleable__content.fitted pre {
+#sk-container-id-43 div.sk-toggleable__content.fitted pre {
   /* unfitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
-#sk-container-id-42 input.sk-toggleable__control:checked~div.sk-toggleable__content {
+#sk-container-id-43 input.sk-toggleable__control:checked~div.sk-toggleable__content {
   /* Expand drop-down */
   max-height: 200px;
   max-width: 100%;
   overflow: auto;
 }
 
-#sk-container-id-42 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
+#sk-container-id-43 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
   content: "▾";
 }
 
 /* Pipeline/ColumnTransformer-specific style */
 
-#sk-container-id-42 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-43 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-42 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-43 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
 /* Estimator-specific style */
 
 /* Colorize estimator box */
-#sk-container-id-42 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-43 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-42 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-43 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
-#sk-container-id-42 div.sk-label label.sk-toggleable__label,
-#sk-container-id-42 div.sk-label label {
+#sk-container-id-43 div.sk-label label.sk-toggleable__label,
+#sk-container-id-43 div.sk-label label {
   /* The background is the default theme color */
   color: var(--sklearn-color-text-on-default-background);
 }
 
 /* On hover, darken the color of the background */
-#sk-container-id-42 div.sk-label:hover label.sk-toggleable__label {
+#sk-container-id-43 div.sk-label:hover label.sk-toggleable__label {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
 /* Label box, darken color on hover, fitted */
-#sk-container-id-42 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
+#sk-container-id-43 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
 /* Estimator label */
 
-#sk-container-id-42 div.sk-label label {
+#sk-container-id-43 div.sk-label label {
   font-family: monospace;
   font-weight: bold;
   display: inline-block;
   line-height: 1.2em;
 }
 
-#sk-container-id-42 div.sk-label-container {
+#sk-container-id-43 div.sk-label-container {
   text-align: center;
 }
 
 /* Estimator-specific */
-#sk-container-id-42 div.sk-estimator {
+#sk-container-id-43 div.sk-estimator {
   font-family: monospace;
   border: 1px dotted var(--sklearn-color-border-box);
   border-radius: 0.25em;
@@ -2204,18 +2203,18 @@ <h2>Displaying a Pipeline and Dimensionality Reduction and Classifier<a class="h
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-42 div.sk-estimator.fitted {
+#sk-container-id-43 div.sk-estimator.fitted {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
 /* on hover */
-#sk-container-id-42 div.sk-estimator:hover {
+#sk-container-id-43 div.sk-estimator:hover {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-42 div.sk-estimator.fitted:hover {
+#sk-container-id-43 div.sk-estimator.fitted:hover {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-2);
 }
@@ -2303,7 +2302,7 @@ <h2>Displaying a Pipeline and Dimensionality Reduction and Classifier<a class="h
 
 /* "?"-specific style due to the `<a>` HTML tag */
 
-#sk-container-id-42 a.estimator_doc_link {
+#sk-container-id-43 a.estimator_doc_link {
   float: right;
   font-size: 1rem;
   line-height: 1em;
@@ -2318,27 +2317,27 @@ <h2>Displaying a Pipeline and Dimensionality Reduction and Classifier<a class="h
   border: var(--sklearn-color-unfitted-level-1) 1pt solid;
 }
 
-#sk-container-id-42 a.estimator_doc_link.fitted {
+#sk-container-id-43 a.estimator_doc_link.fitted {
   /* fitted */
   border: var(--sklearn-color-fitted-level-1) 1pt solid;
   color: var(--sklearn-color-fitted-level-1);
 }
 
 /* On hover */
-#sk-container-id-42 a.estimator_doc_link:hover {
+#sk-container-id-43 a.estimator_doc_link:hover {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-3);
   color: var(--sklearn-color-background);
   text-decoration: none;
 }
 
-#sk-container-id-42 a.estimator_doc_link.fitted:hover {
+#sk-container-id-43 a.estimator_doc_link.fitted:hover {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-3);
 }
-</style><div id="sk-container-id-42" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;reduce_dim&#x27;, PCA(n_components=4)),
-                (&#x27;classifier&#x27;, SVC(kernel=&#x27;linear&#x27;))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-173" type="checkbox" ><label for="sk-estimator-id-173" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>Pipeline</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.pipeline.Pipeline.html">?<span>Documentation for Pipeline</span></a><span class="sk-estimator-doc-link ">i<span>Not fitted</span></span></div></label><div class="sk-toggleable__content "><pre>Pipeline(steps=[(&#x27;reduce_dim&#x27;, PCA(n_components=4)),
-                (&#x27;classifier&#x27;, SVC(kernel=&#x27;linear&#x27;))])</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-174" type="checkbox" ><label for="sk-estimator-id-174" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>PCA</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.decomposition.PCA.html">?<span>Documentation for PCA</span></a></div></label><div class="sk-toggleable__content "><pre>PCA(n_components=4)</pre></div> </div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-175" type="checkbox" ><label for="sk-estimator-id-175" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>SVC</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.svm.SVC.html">?<span>Documentation for SVC</span></a></div></label><div class="sk-toggleable__content "><pre>SVC(kernel=&#x27;linear&#x27;)</pre></div> </div></div></div></div></div></div>
+</style><div id="sk-container-id-43" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;reduce_dim&#x27;, PCA(n_components=4)),
+                (&#x27;classifier&#x27;, SVC(kernel=&#x27;linear&#x27;))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-187" type="checkbox" ><label for="sk-estimator-id-187" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>Pipeline</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.pipeline.Pipeline.html">?<span>Documentation for Pipeline</span></a><span class="sk-estimator-doc-link ">i<span>Not fitted</span></span></div></label><div class="sk-toggleable__content "><pre>Pipeline(steps=[(&#x27;reduce_dim&#x27;, PCA(n_components=4)),
+                (&#x27;classifier&#x27;, SVC(kernel=&#x27;linear&#x27;))])</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-188" type="checkbox" ><label for="sk-estimator-id-188" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>PCA</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.decomposition.PCA.html">?<span>Documentation for PCA</span></a></div></label><div class="sk-toggleable__content "><pre>PCA(n_components=4)</pre></div> </div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-189" type="checkbox" ><label for="sk-estimator-id-189" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>SVC</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.svm.SVC.html">?<span>Documentation for SVC</span></a></div></label><div class="sk-toggleable__content "><pre>SVC(kernel=&#x27;linear&#x27;)</pre></div> </div></div></div></div></div></div>
 </div>
 <br />
 <br /></section>
@@ -2385,7 +2384,7 @@ <h2>Displaying a Complex Pipeline Chaining a Column Transformer<a class="headerl
 </pre></div>
 </div>
 <div class="output_subarea output_html rendered_html output_result">
-<style>#sk-container-id-43 {
+<style>#sk-container-id-44 {
   /* Definition of color scheme common for light and dark mode */
   --sklearn-color-text: #000;
   --sklearn-color-text-muted: #666;
@@ -2416,15 +2415,15 @@ <h2>Displaying a Complex Pipeline Chaining a Column Transformer<a class="headerl
   }
 }
 
-#sk-container-id-43 {
+#sk-container-id-44 {
   color: var(--sklearn-color-text);
 }
 
-#sk-container-id-43 pre {
+#sk-container-id-44 pre {
   padding: 0;
 }
 
-#sk-container-id-43 input.sk-hidden--visually {
+#sk-container-id-44 input.sk-hidden--visually {
   border: 0;
   clip: rect(1px 1px 1px 1px);
   clip: rect(1px, 1px, 1px, 1px);
@@ -2436,7 +2435,7 @@ <h2>Displaying a Complex Pipeline Chaining a Column Transformer<a class="headerl
   width: 1px;
 }
 
-#sk-container-id-43 div.sk-dashed-wrapped {
+#sk-container-id-44 div.sk-dashed-wrapped {
   border: 1px dashed var(--sklearn-color-line);
   margin: 0 0.4em 0.5em 0.4em;
   box-sizing: border-box;
@@ -2444,7 +2443,7 @@ <h2>Displaying a Complex Pipeline Chaining a Column Transformer<a class="headerl
   background-color: var(--sklearn-color-background);
 }
 
-#sk-container-id-43 div.sk-container {
+#sk-container-id-44 div.sk-container {
   /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
      but bootstrap.min.css set `[hidden] { display: none !important; }`
      so we also need the `!important` here to be able to override the
@@ -2454,7 +2453,7 @@ <h2>Displaying a Complex Pipeline Chaining a Column Transformer<a class="headerl
   position: relative;
 }
 
-#sk-container-id-43 div.sk-text-repr-fallback {
+#sk-container-id-44 div.sk-text-repr-fallback {
   display: none;
 }
 
@@ -2470,14 +2469,14 @@ <h2>Displaying a Complex Pipeline Chaining a Column Transformer<a class="headerl
 
 /* Parallel-specific style estimator block */
 
-#sk-container-id-43 div.sk-parallel-item::after {
+#sk-container-id-44 div.sk-parallel-item::after {
   content: "";
   width: 100%;
   border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
   flex-grow: 1;
 }
 
-#sk-container-id-43 div.sk-parallel {
+#sk-container-id-44 div.sk-parallel {
   display: flex;
   align-items: stretch;
   justify-content: center;
@@ -2485,28 +2484,28 @@ <h2>Displaying a Complex Pipeline Chaining a Column Transformer<a class="headerl
   position: relative;
 }
 
-#sk-container-id-43 div.sk-parallel-item {
+#sk-container-id-44 div.sk-parallel-item {
   display: flex;
   flex-direction: column;
 }
 
-#sk-container-id-43 div.sk-parallel-item:first-child::after {
+#sk-container-id-44 div.sk-parallel-item:first-child::after {
   align-self: flex-end;
   width: 50%;
 }
 
-#sk-container-id-43 div.sk-parallel-item:last-child::after {
+#sk-container-id-44 div.sk-parallel-item:last-child::after {
   align-self: flex-start;
   width: 50%;
 }
 
-#sk-container-id-43 div.sk-parallel-item:only-child::after {
+#sk-container-id-44 div.sk-parallel-item:only-child::after {
   width: 0;
 }
 
 /* Serial-specific style estimator block */
 
-#sk-container-id-43 div.sk-serial {
+#sk-container-id-44 div.sk-serial {
   display: flex;
   flex-direction: column;
   align-items: center;
@@ -2524,14 +2523,14 @@ <h2>Displaying a Complex Pipeline Chaining a Column Transformer<a class="headerl
 
 /* Pipeline and ColumnTransformer style (default) */
 
-#sk-container-id-43 div.sk-toggleable {
+#sk-container-id-44 div.sk-toggleable {
   /* Default theme specific background. It is overwritten whether we have a
   specific estimator or a Pipeline/ColumnTransformer */
   background-color: var(--sklearn-color-background);
 }
 
 /* Toggleable label */
-#sk-container-id-43 label.sk-toggleable__label {
+#sk-container-id-44 label.sk-toggleable__label {
   cursor: pointer;
   display: flex;
   width: 100%;
@@ -2544,13 +2543,13 @@ <h2>Displaying a Complex Pipeline Chaining a Column Transformer<a class="headerl
   gap: 0.5em;
 }
 
-#sk-container-id-43 label.sk-toggleable__label .caption {
+#sk-container-id-44 label.sk-toggleable__label .caption {
   font-size: 0.6rem;
   font-weight: lighter;
   color: var(--sklearn-color-text-muted);
 }
 
-#sk-container-id-43 label.sk-toggleable__label-arrow:before {
+#sk-container-id-44 label.sk-toggleable__label-arrow:before {
   /* Arrow on the left of the label */
   content: "▸";
   float: left;
@@ -2558,13 +2557,13 @@ <h2>Displaying a Complex Pipeline Chaining a Column Transformer<a class="headerl
   color: var(--sklearn-color-icon);
 }
 
-#sk-container-id-43 label.sk-toggleable__label-arrow:hover:before {
+#sk-container-id-44 label.sk-toggleable__label-arrow:hover:before {
   color: var(--sklearn-color-text);
 }
 
 /* Toggleable content - dropdown */
 
-#sk-container-id-43 div.sk-toggleable__content {
+#sk-container-id-44 div.sk-toggleable__content {
   max-height: 0;
   max-width: 0;
   overflow: hidden;
@@ -2573,12 +2572,12 @@ <h2>Displaying a Complex Pipeline Chaining a Column Transformer<a class="headerl
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-43 div.sk-toggleable__content.fitted {
+#sk-container-id-44 div.sk-toggleable__content.fitted {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
-#sk-container-id-43 div.sk-toggleable__content pre {
+#sk-container-id-44 div.sk-toggleable__content pre {
   margin: 0.2em;
   border-radius: 0.25em;
   color: var(--sklearn-color-text);
@@ -2586,79 +2585,79 @@ <h2>Displaying a Complex Pipeline Chaining a Column Transformer<a class="headerl
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-43 div.sk-toggleable__content.fitted pre {
+#sk-container-id-44 div.sk-toggleable__content.fitted pre {
   /* unfitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
-#sk-container-id-43 input.sk-toggleable__control:checked~div.sk-toggleable__content {
+#sk-container-id-44 input.sk-toggleable__control:checked~div.sk-toggleable__content {
   /* Expand drop-down */
   max-height: 200px;
   max-width: 100%;
   overflow: auto;
 }
 
-#sk-container-id-43 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
+#sk-container-id-44 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
   content: "▾";
 }
 
 /* Pipeline/ColumnTransformer-specific style */
 
-#sk-container-id-43 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-44 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-43 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-44 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
 /* Estimator-specific style */
 
 /* Colorize estimator box */
-#sk-container-id-43 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-44 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-43 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-44 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
-#sk-container-id-43 div.sk-label label.sk-toggleable__label,
-#sk-container-id-43 div.sk-label label {
+#sk-container-id-44 div.sk-label label.sk-toggleable__label,
+#sk-container-id-44 div.sk-label label {
   /* The background is the default theme color */
   color: var(--sklearn-color-text-on-default-background);
 }
 
 /* On hover, darken the color of the background */
-#sk-container-id-43 div.sk-label:hover label.sk-toggleable__label {
+#sk-container-id-44 div.sk-label:hover label.sk-toggleable__label {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
 /* Label box, darken color on hover, fitted */
-#sk-container-id-43 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
+#sk-container-id-44 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
 /* Estimator label */
 
-#sk-container-id-43 div.sk-label label {
+#sk-container-id-44 div.sk-label label {
   font-family: monospace;
   font-weight: bold;
   display: inline-block;
   line-height: 1.2em;
 }
 
-#sk-container-id-43 div.sk-label-container {
+#sk-container-id-44 div.sk-label-container {
   text-align: center;
 }
 
 /* Estimator-specific */
-#sk-container-id-43 div.sk-estimator {
+#sk-container-id-44 div.sk-estimator {
   font-family: monospace;
   border: 1px dotted var(--sklearn-color-border-box);
   border-radius: 0.25em;
@@ -2668,18 +2667,18 @@ <h2>Displaying a Complex Pipeline Chaining a Column Transformer<a class="headerl
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-43 div.sk-estimator.fitted {
+#sk-container-id-44 div.sk-estimator.fitted {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
 /* on hover */
-#sk-container-id-43 div.sk-estimator:hover {
+#sk-container-id-44 div.sk-estimator:hover {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-43 div.sk-estimator.fitted:hover {
+#sk-container-id-44 div.sk-estimator.fitted:hover {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-2);
 }
@@ -2767,7 +2766,7 @@ <h2>Displaying a Complex Pipeline Chaining a Column Transformer<a class="headerl
 
 /* "?"-specific style due to the `<a>` HTML tag */
 
-#sk-container-id-43 a.estimator_doc_link {
+#sk-container-id-44 a.estimator_doc_link {
   float: right;
   font-size: 1rem;
   line-height: 1em;
@@ -2782,25 +2781,25 @@ <h2>Displaying a Complex Pipeline Chaining a Column Transformer<a class="headerl
   border: var(--sklearn-color-unfitted-level-1) 1pt solid;
 }
 
-#sk-container-id-43 a.estimator_doc_link.fitted {
+#sk-container-id-44 a.estimator_doc_link.fitted {
   /* fitted */
   border: var(--sklearn-color-fitted-level-1) 1pt solid;
   color: var(--sklearn-color-fitted-level-1);
 }
 
 /* On hover */
-#sk-container-id-43 a.estimator_doc_link:hover {
+#sk-container-id-44 a.estimator_doc_link:hover {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-3);
   color: var(--sklearn-color-background);
   text-decoration: none;
 }
 
-#sk-container-id-43 a.estimator_doc_link.fitted:hover {
+#sk-container-id-44 a.estimator_doc_link.fitted:hover {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-3);
 }
-</style><div id="sk-container-id-43" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,
+</style><div id="sk-container-id-44" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,
                  ColumnTransformer(transformers=[(&#x27;categorical&#x27;,
                                                   Pipeline(steps=[(&#x27;imputation_constant&#x27;,
                                                                    SimpleImputer(fill_value=&#x27;missing&#x27;,
@@ -2814,7 +2813,7 @@ <h2>Displaying a Complex Pipeline Chaining a Column Transformer<a class="headerl
                                                                   (&#x27;scaler&#x27;,
                                                                    StandardScaler())]),
                                                   [&#x27;age&#x27;, &#x27;weight&#x27;])])),
-                (&#x27;logisticregression&#x27;, LogisticRegression(max_iter=500))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-176" type="checkbox" ><label for="sk-estimator-id-176" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>Pipeline</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.pipeline.Pipeline.html">?<span>Documentation for Pipeline</span></a><span class="sk-estimator-doc-link ">i<span>Not fitted</span></span></div></label><div class="sk-toggleable__content "><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,
+                (&#x27;logisticregression&#x27;, LogisticRegression(max_iter=500))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-190" type="checkbox" ><label for="sk-estimator-id-190" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>Pipeline</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.pipeline.Pipeline.html">?<span>Documentation for Pipeline</span></a><span class="sk-estimator-doc-link ">i<span>Not fitted</span></span></div></label><div class="sk-toggleable__content "><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,
                  ColumnTransformer(transformers=[(&#x27;categorical&#x27;,
                                                   Pipeline(steps=[(&#x27;imputation_constant&#x27;,
                                                                    SimpleImputer(fill_value=&#x27;missing&#x27;,
@@ -2828,7 +2827,7 @@ <h2>Displaying a Complex Pipeline Chaining a Column Transformer<a class="headerl
                                                                   (&#x27;scaler&#x27;,
                                                                    StandardScaler())]),
                                                   [&#x27;age&#x27;, &#x27;weight&#x27;])])),
-                (&#x27;logisticregression&#x27;, LogisticRegression(max_iter=500))])</pre></div> </div></div><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-177" type="checkbox" ><label for="sk-estimator-id-177" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>columntransformer: ColumnTransformer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.compose.ColumnTransformer.html">?<span>Documentation for columntransformer: ColumnTransformer</span></a></div></label><div class="sk-toggleable__content "><pre>ColumnTransformer(transformers=[(&#x27;categorical&#x27;,
+                (&#x27;logisticregression&#x27;, LogisticRegression(max_iter=500))])</pre></div> </div></div><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-191" type="checkbox" ><label for="sk-estimator-id-191" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>columntransformer: ColumnTransformer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.compose.ColumnTransformer.html">?<span>Documentation for columntransformer: ColumnTransformer</span></a></div></label><div class="sk-toggleable__content "><pre>ColumnTransformer(transformers=[(&#x27;categorical&#x27;,
                                  Pipeline(steps=[(&#x27;imputation_constant&#x27;,
                                                   SimpleImputer(fill_value=&#x27;missing&#x27;,
                                                                 strategy=&#x27;constant&#x27;)),
@@ -2839,7 +2838,7 @@ <h2>Displaying a Complex Pipeline Chaining a Column Transformer<a class="headerl
                                  Pipeline(steps=[(&#x27;imputation_mean&#x27;,
                                                   SimpleImputer()),
                                                  (&#x27;scaler&#x27;, StandardScaler())]),
-                                 [&#x27;age&#x27;, &#x27;weight&#x27;])])</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-178" type="checkbox" ><label for="sk-estimator-id-178" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>categorical</div></div></label><div class="sk-toggleable__content "><pre>[&#x27;state&#x27;, &#x27;gender&#x27;]</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-179" type="checkbox" ><label for="sk-estimator-id-179" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>SimpleImputer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.impute.SimpleImputer.html">?<span>Documentation for SimpleImputer</span></a></div></label><div class="sk-toggleable__content "><pre>SimpleImputer(fill_value=&#x27;missing&#x27;, strategy=&#x27;constant&#x27;)</pre></div> </div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-180" type="checkbox" ><label for="sk-estimator-id-180" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>OneHotEncoder</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.OneHotEncoder.html">?<span>Documentation for OneHotEncoder</span></a></div></label><div class="sk-toggleable__content "><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</pre></div> </div></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-181" type="checkbox" ><label for="sk-estimator-id-181" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>numerical</div></div></label><div class="sk-toggleable__content "><pre>[&#x27;age&#x27;, &#x27;weight&#x27;]</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-182" type="checkbox" ><label for="sk-estimator-id-182" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>SimpleImputer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.impute.SimpleImputer.html">?<span>Documentation for SimpleImputer</span></a></div></label><div class="sk-toggleable__content "><pre>SimpleImputer()</pre></div> </div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-183" type="checkbox" ><label for="sk-estimator-id-183" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>StandardScaler</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.StandardScaler.html">?<span>Documentation for StandardScaler</span></a></div></label><div class="sk-toggleable__content "><pre>StandardScaler()</pre></div> </div></div></div></div></div></div></div></div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-184" type="checkbox" ><label for="sk-estimator-id-184" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>LogisticRegression</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.linear_model.LogisticRegression.html">?<span>Documentation for LogisticRegression</span></a></div></label><div class="sk-toggleable__content "><pre>LogisticRegression(max_iter=500)</pre></div> </div></div></div></div></div></div>
+                                 [&#x27;age&#x27;, &#x27;weight&#x27;])])</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-192" type="checkbox" ><label for="sk-estimator-id-192" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>categorical</div></div></label><div class="sk-toggleable__content "><pre>[&#x27;state&#x27;, &#x27;gender&#x27;]</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-193" type="checkbox" ><label for="sk-estimator-id-193" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>SimpleImputer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.impute.SimpleImputer.html">?<span>Documentation for SimpleImputer</span></a></div></label><div class="sk-toggleable__content "><pre>SimpleImputer(fill_value=&#x27;missing&#x27;, strategy=&#x27;constant&#x27;)</pre></div> </div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-194" type="checkbox" ><label for="sk-estimator-id-194" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>OneHotEncoder</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.OneHotEncoder.html">?<span>Documentation for OneHotEncoder</span></a></div></label><div class="sk-toggleable__content "><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</pre></div> </div></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-195" type="checkbox" ><label for="sk-estimator-id-195" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>numerical</div></div></label><div class="sk-toggleable__content "><pre>[&#x27;age&#x27;, &#x27;weight&#x27;]</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-196" type="checkbox" ><label for="sk-estimator-id-196" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>SimpleImputer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.impute.SimpleImputer.html">?<span>Documentation for SimpleImputer</span></a></div></label><div class="sk-toggleable__content "><pre>SimpleImputer()</pre></div> </div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-197" type="checkbox" ><label for="sk-estimator-id-197" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>StandardScaler</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.StandardScaler.html">?<span>Documentation for StandardScaler</span></a></div></label><div class="sk-toggleable__content "><pre>StandardScaler()</pre></div> </div></div></div></div></div></div></div></div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-198" type="checkbox" ><label for="sk-estimator-id-198" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>LogisticRegression</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.linear_model.LogisticRegression.html">?<span>Documentation for LogisticRegression</span></a></div></label><div class="sk-toggleable__content "><pre>LogisticRegression(max_iter=500)</pre></div> </div></div></div></div></div></div>
 </div>
 <br />
 <br /></section>
@@ -2898,7 +2897,7 @@ <h2>Displaying a Grid Search over a Pipeline with a Classifier<a class="headerli
 </pre></div>
 </div>
 <div class="output_subarea output_html rendered_html output_result">
-<style>#sk-container-id-44 {
+<style>#sk-container-id-45 {
   /* Definition of color scheme common for light and dark mode */
   --sklearn-color-text: #000;
   --sklearn-color-text-muted: #666;
@@ -2929,15 +2928,15 @@ <h2>Displaying a Grid Search over a Pipeline with a Classifier<a class="headerli
   }
 }
 
-#sk-container-id-44 {
+#sk-container-id-45 {
   color: var(--sklearn-color-text);
 }
 
-#sk-container-id-44 pre {
+#sk-container-id-45 pre {
   padding: 0;
 }
 
-#sk-container-id-44 input.sk-hidden--visually {
+#sk-container-id-45 input.sk-hidden--visually {
   border: 0;
   clip: rect(1px 1px 1px 1px);
   clip: rect(1px, 1px, 1px, 1px);
@@ -2949,7 +2948,7 @@ <h2>Displaying a Grid Search over a Pipeline with a Classifier<a class="headerli
   width: 1px;
 }
 
-#sk-container-id-44 div.sk-dashed-wrapped {
+#sk-container-id-45 div.sk-dashed-wrapped {
   border: 1px dashed var(--sklearn-color-line);
   margin: 0 0.4em 0.5em 0.4em;
   box-sizing: border-box;
@@ -2957,7 +2956,7 @@ <h2>Displaying a Grid Search over a Pipeline with a Classifier<a class="headerli
   background-color: var(--sklearn-color-background);
 }
 
-#sk-container-id-44 div.sk-container {
+#sk-container-id-45 div.sk-container {
   /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
      but bootstrap.min.css set `[hidden] { display: none !important; }`
      so we also need the `!important` here to be able to override the
@@ -2967,7 +2966,7 @@ <h2>Displaying a Grid Search over a Pipeline with a Classifier<a class="headerli
   position: relative;
 }
 
-#sk-container-id-44 div.sk-text-repr-fallback {
+#sk-container-id-45 div.sk-text-repr-fallback {
   display: none;
 }
 
@@ -2983,14 +2982,14 @@ <h2>Displaying a Grid Search over a Pipeline with a Classifier<a class="headerli
 
 /* Parallel-specific style estimator block */
 
-#sk-container-id-44 div.sk-parallel-item::after {
+#sk-container-id-45 div.sk-parallel-item::after {
   content: "";
   width: 100%;
   border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
   flex-grow: 1;
 }
 
-#sk-container-id-44 div.sk-parallel {
+#sk-container-id-45 div.sk-parallel {
   display: flex;
   align-items: stretch;
   justify-content: center;
@@ -2998,28 +2997,28 @@ <h2>Displaying a Grid Search over a Pipeline with a Classifier<a class="headerli
   position: relative;
 }
 
-#sk-container-id-44 div.sk-parallel-item {
+#sk-container-id-45 div.sk-parallel-item {
   display: flex;
   flex-direction: column;
 }
 
-#sk-container-id-44 div.sk-parallel-item:first-child::after {
+#sk-container-id-45 div.sk-parallel-item:first-child::after {
   align-self: flex-end;
   width: 50%;
 }
 
-#sk-container-id-44 div.sk-parallel-item:last-child::after {
+#sk-container-id-45 div.sk-parallel-item:last-child::after {
   align-self: flex-start;
   width: 50%;
 }
 
-#sk-container-id-44 div.sk-parallel-item:only-child::after {
+#sk-container-id-45 div.sk-parallel-item:only-child::after {
   width: 0;
 }
 
 /* Serial-specific style estimator block */
 
-#sk-container-id-44 div.sk-serial {
+#sk-container-id-45 div.sk-serial {
   display: flex;
   flex-direction: column;
   align-items: center;
@@ -3037,14 +3036,14 @@ <h2>Displaying a Grid Search over a Pipeline with a Classifier<a class="headerli
 
 /* Pipeline and ColumnTransformer style (default) */
 
-#sk-container-id-44 div.sk-toggleable {
+#sk-container-id-45 div.sk-toggleable {
   /* Default theme specific background. It is overwritten whether we have a
   specific estimator or a Pipeline/ColumnTransformer */
   background-color: var(--sklearn-color-background);
 }
 
 /* Toggleable label */
-#sk-container-id-44 label.sk-toggleable__label {
+#sk-container-id-45 label.sk-toggleable__label {
   cursor: pointer;
   display: flex;
   width: 100%;
@@ -3057,13 +3056,13 @@ <h2>Displaying a Grid Search over a Pipeline with a Classifier<a class="headerli
   gap: 0.5em;
 }
 
-#sk-container-id-44 label.sk-toggleable__label .caption {
+#sk-container-id-45 label.sk-toggleable__label .caption {
   font-size: 0.6rem;
   font-weight: lighter;
   color: var(--sklearn-color-text-muted);
 }
 
-#sk-container-id-44 label.sk-toggleable__label-arrow:before {
+#sk-container-id-45 label.sk-toggleable__label-arrow:before {
   /* Arrow on the left of the label */
   content: "▸";
   float: left;
@@ -3071,13 +3070,13 @@ <h2>Displaying a Grid Search over a Pipeline with a Classifier<a class="headerli
   color: var(--sklearn-color-icon);
 }
 
-#sk-container-id-44 label.sk-toggleable__label-arrow:hover:before {
+#sk-container-id-45 label.sk-toggleable__label-arrow:hover:before {
   color: var(--sklearn-color-text);
 }
 
 /* Toggleable content - dropdown */
 
-#sk-container-id-44 div.sk-toggleable__content {
+#sk-container-id-45 div.sk-toggleable__content {
   max-height: 0;
   max-width: 0;
   overflow: hidden;
@@ -3086,12 +3085,12 @@ <h2>Displaying a Grid Search over a Pipeline with a Classifier<a class="headerli
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-44 div.sk-toggleable__content.fitted {
+#sk-container-id-45 div.sk-toggleable__content.fitted {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
-#sk-container-id-44 div.sk-toggleable__content pre {
+#sk-container-id-45 div.sk-toggleable__content pre {
   margin: 0.2em;
   border-radius: 0.25em;
   color: var(--sklearn-color-text);
@@ -3099,79 +3098,79 @@ <h2>Displaying a Grid Search over a Pipeline with a Classifier<a class="headerli
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-44 div.sk-toggleable__content.fitted pre {
+#sk-container-id-45 div.sk-toggleable__content.fitted pre {
   /* unfitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
-#sk-container-id-44 input.sk-toggleable__control:checked~div.sk-toggleable__content {
+#sk-container-id-45 input.sk-toggleable__control:checked~div.sk-toggleable__content {
   /* Expand drop-down */
   max-height: 200px;
   max-width: 100%;
   overflow: auto;
 }
 
-#sk-container-id-44 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
+#sk-container-id-45 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
   content: "▾";
 }
 
 /* Pipeline/ColumnTransformer-specific style */
 
-#sk-container-id-44 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-45 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-44 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-45 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
 /* Estimator-specific style */
 
 /* Colorize estimator box */
-#sk-container-id-44 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-45 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-44 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-45 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
-#sk-container-id-44 div.sk-label label.sk-toggleable__label,
-#sk-container-id-44 div.sk-label label {
+#sk-container-id-45 div.sk-label label.sk-toggleable__label,
+#sk-container-id-45 div.sk-label label {
   /* The background is the default theme color */
   color: var(--sklearn-color-text-on-default-background);
 }
 
 /* On hover, darken the color of the background */
-#sk-container-id-44 div.sk-label:hover label.sk-toggleable__label {
+#sk-container-id-45 div.sk-label:hover label.sk-toggleable__label {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
 /* Label box, darken color on hover, fitted */
-#sk-container-id-44 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
+#sk-container-id-45 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
 /* Estimator label */
 
-#sk-container-id-44 div.sk-label label {
+#sk-container-id-45 div.sk-label label {
   font-family: monospace;
   font-weight: bold;
   display: inline-block;
   line-height: 1.2em;
 }
 
-#sk-container-id-44 div.sk-label-container {
+#sk-container-id-45 div.sk-label-container {
   text-align: center;
 }
 
 /* Estimator-specific */
-#sk-container-id-44 div.sk-estimator {
+#sk-container-id-45 div.sk-estimator {
   font-family: monospace;
   border: 1px dotted var(--sklearn-color-border-box);
   border-radius: 0.25em;
@@ -3181,18 +3180,18 @@ <h2>Displaying a Grid Search over a Pipeline with a Classifier<a class="headerli
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-44 div.sk-estimator.fitted {
+#sk-container-id-45 div.sk-estimator.fitted {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
 /* on hover */
-#sk-container-id-44 div.sk-estimator:hover {
+#sk-container-id-45 div.sk-estimator:hover {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-44 div.sk-estimator.fitted:hover {
+#sk-container-id-45 div.sk-estimator.fitted:hover {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-2);
 }
@@ -3280,7 +3279,7 @@ <h2>Displaying a Grid Search over a Pipeline with a Classifier<a class="headerli
 
 /* "?"-specific style due to the `<a>` HTML tag */
 
-#sk-container-id-44 a.estimator_doc_link {
+#sk-container-id-45 a.estimator_doc_link {
   float: right;
   font-size: 1rem;
   line-height: 1em;
@@ -3295,25 +3294,25 @@ <h2>Displaying a Grid Search over a Pipeline with a Classifier<a class="headerli
   border: var(--sklearn-color-unfitted-level-1) 1pt solid;
 }
 
-#sk-container-id-44 a.estimator_doc_link.fitted {
+#sk-container-id-45 a.estimator_doc_link.fitted {
   /* fitted */
   border: var(--sklearn-color-fitted-level-1) 1pt solid;
   color: var(--sklearn-color-fitted-level-1);
 }
 
 /* On hover */
-#sk-container-id-44 a.estimator_doc_link:hover {
+#sk-container-id-45 a.estimator_doc_link:hover {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-3);
   color: var(--sklearn-color-background);
   text-decoration: none;
 }
 
-#sk-container-id-44 a.estimator_doc_link.fitted:hover {
+#sk-container-id-45 a.estimator_doc_link.fitted:hover {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-3);
 }
-</style><div id="sk-container-id-44" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>GridSearchCV(estimator=Pipeline(steps=[(&#x27;preprocessor&#x27;,
+</style><div id="sk-container-id-45" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>GridSearchCV(estimator=Pipeline(steps=[(&#x27;preprocessor&#x27;,
                                         ColumnTransformer(transformers=[(&#x27;categorical&#x27;,
                                                                          Pipeline(steps=[(&#x27;imputation_constant&#x27;,
                                                                                           SimpleImputer(fill_value=&#x27;missing&#x27;,
@@ -3335,7 +3334,7 @@ <h2>Displaying a Grid Search over a Pipeline with a Classifier<a class="headerli
              param_grid={&#x27;classifier__criterion&#x27;: [&#x27;gini&#x27;, &#x27;entropy&#x27;],
                          &#x27;classifier__max_depth&#x27;: [4, 5, 6, 7, 8],
                          &#x27;classifier__max_features&#x27;: [&#x27;auto&#x27;, &#x27;sqrt&#x27;, &#x27;log2&#x27;],
-                         &#x27;classifier__n_estimators&#x27;: [200, 500]})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-185" type="checkbox" ><label for="sk-estimator-id-185" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>GridSearchCV</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.model_selection.GridSearchCV.html">?<span>Documentation for GridSearchCV</span></a><span class="sk-estimator-doc-link ">i<span>Not fitted</span></span></div></label><div class="sk-toggleable__content "><pre>GridSearchCV(estimator=Pipeline(steps=[(&#x27;preprocessor&#x27;,
+                         &#x27;classifier__n_estimators&#x27;: [200, 500]})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-199" type="checkbox" ><label for="sk-estimator-id-199" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>GridSearchCV</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.model_selection.GridSearchCV.html">?<span>Documentation for GridSearchCV</span></a><span class="sk-estimator-doc-link ">i<span>Not fitted</span></span></div></label><div class="sk-toggleable__content "><pre>GridSearchCV(estimator=Pipeline(steps=[(&#x27;preprocessor&#x27;,
                                         ColumnTransformer(transformers=[(&#x27;categorical&#x27;,
                                                                          Pipeline(steps=[(&#x27;imputation_constant&#x27;,
                                                                                           SimpleImputer(fill_value=&#x27;missing&#x27;,
@@ -3357,7 +3356,7 @@ <h2>Displaying a Grid Search over a Pipeline with a Classifier<a class="headerli
              param_grid={&#x27;classifier__criterion&#x27;: [&#x27;gini&#x27;, &#x27;entropy&#x27;],
                          &#x27;classifier__max_depth&#x27;: [4, 5, 6, 7, 8],
                          &#x27;classifier__max_features&#x27;: [&#x27;auto&#x27;, &#x27;sqrt&#x27;, &#x27;log2&#x27;],
-                         &#x27;classifier__n_estimators&#x27;: [200, 500]})</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-186" type="checkbox" ><label for="sk-estimator-id-186" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>estimator: Pipeline</div></div></label><div class="sk-toggleable__content "><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,
+                         &#x27;classifier__n_estimators&#x27;: [200, 500]})</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-200" type="checkbox" ><label for="sk-estimator-id-200" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>estimator: Pipeline</div></div></label><div class="sk-toggleable__content "><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,
                  ColumnTransformer(transformers=[(&#x27;categorical&#x27;,
                                                   Pipeline(steps=[(&#x27;imputation_constant&#x27;,
                                                                    SimpleImputer(fill_value=&#x27;missing&#x27;,
@@ -3371,7 +3370,7 @@ <h2>Displaying a Grid Search over a Pipeline with a Classifier<a class="headerli
                                                                   (&#x27;scaler&#x27;,
                                                                    StandardScaler())]),
                                                   [&#x27;age&#x27;, &#x27;weight&#x27;])])),
-                (&#x27;classifier&#x27;, RandomForestClassifier())])</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-187" type="checkbox" ><label for="sk-estimator-id-187" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>preprocessor: ColumnTransformer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.compose.ColumnTransformer.html">?<span>Documentation for preprocessor: ColumnTransformer</span></a></div></label><div class="sk-toggleable__content "><pre>ColumnTransformer(transformers=[(&#x27;categorical&#x27;,
+                (&#x27;classifier&#x27;, RandomForestClassifier())])</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-201" type="checkbox" ><label for="sk-estimator-id-201" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>preprocessor: ColumnTransformer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.compose.ColumnTransformer.html">?<span>Documentation for preprocessor: ColumnTransformer</span></a></div></label><div class="sk-toggleable__content "><pre>ColumnTransformer(transformers=[(&#x27;categorical&#x27;,
                                  Pipeline(steps=[(&#x27;imputation_constant&#x27;,
                                                   SimpleImputer(fill_value=&#x27;missing&#x27;,
                                                                 strategy=&#x27;constant&#x27;)),
@@ -3382,7 +3381,7 @@ <h2>Displaying a Grid Search over a Pipeline with a Classifier<a class="headerli
                                  Pipeline(steps=[(&#x27;imputation_mean&#x27;,
                                                   SimpleImputer()),
                                                  (&#x27;scaler&#x27;, StandardScaler())]),
-                                 [&#x27;age&#x27;, &#x27;weight&#x27;])])</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-188" type="checkbox" ><label for="sk-estimator-id-188" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>categorical</div></div></label><div class="sk-toggleable__content "><pre>[&#x27;state&#x27;, &#x27;gender&#x27;]</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-189" type="checkbox" ><label for="sk-estimator-id-189" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>SimpleImputer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.impute.SimpleImputer.html">?<span>Documentation for SimpleImputer</span></a></div></label><div class="sk-toggleable__content "><pre>SimpleImputer(fill_value=&#x27;missing&#x27;, strategy=&#x27;constant&#x27;)</pre></div> </div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-190" type="checkbox" ><label for="sk-estimator-id-190" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>OneHotEncoder</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.OneHotEncoder.html">?<span>Documentation for OneHotEncoder</span></a></div></label><div class="sk-toggleable__content "><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</pre></div> </div></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-191" type="checkbox" ><label for="sk-estimator-id-191" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>numerical</div></div></label><div class="sk-toggleable__content "><pre>[&#x27;age&#x27;, &#x27;weight&#x27;]</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-192" type="checkbox" ><label for="sk-estimator-id-192" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>SimpleImputer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.impute.SimpleImputer.html">?<span>Documentation for SimpleImputer</span></a></div></label><div class="sk-toggleable__content "><pre>SimpleImputer()</pre></div> </div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-193" type="checkbox" ><label for="sk-estimator-id-193" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>StandardScaler</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.StandardScaler.html">?<span>Documentation for StandardScaler</span></a></div></label><div class="sk-toggleable__content "><pre>StandardScaler()</pre></div> </div></div></div></div></div></div></div></div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-194" type="checkbox" ><label for="sk-estimator-id-194" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>RandomForestClassifier</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.ensemble.RandomForestClassifier.html">?<span>Documentation for RandomForestClassifier</span></a></div></label><div class="sk-toggleable__content "><pre>RandomForestClassifier()</pre></div> </div></div></div></div></div></div></div></div></div></div></div>
+                                 [&#x27;age&#x27;, &#x27;weight&#x27;])])</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-202" type="checkbox" ><label for="sk-estimator-id-202" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>categorical</div></div></label><div class="sk-toggleable__content "><pre>[&#x27;state&#x27;, &#x27;gender&#x27;]</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-203" type="checkbox" ><label for="sk-estimator-id-203" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>SimpleImputer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.impute.SimpleImputer.html">?<span>Documentation for SimpleImputer</span></a></div></label><div class="sk-toggleable__content "><pre>SimpleImputer(fill_value=&#x27;missing&#x27;, strategy=&#x27;constant&#x27;)</pre></div> </div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-204" type="checkbox" ><label for="sk-estimator-id-204" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>OneHotEncoder</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.OneHotEncoder.html">?<span>Documentation for OneHotEncoder</span></a></div></label><div class="sk-toggleable__content "><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</pre></div> </div></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-205" type="checkbox" ><label for="sk-estimator-id-205" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>numerical</div></div></label><div class="sk-toggleable__content "><pre>[&#x27;age&#x27;, &#x27;weight&#x27;]</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-206" type="checkbox" ><label for="sk-estimator-id-206" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>SimpleImputer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.impute.SimpleImputer.html">?<span>Documentation for SimpleImputer</span></a></div></label><div class="sk-toggleable__content "><pre>SimpleImputer()</pre></div> </div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-207" type="checkbox" ><label for="sk-estimator-id-207" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>StandardScaler</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.StandardScaler.html">?<span>Documentation for StandardScaler</span></a></div></label><div class="sk-toggleable__content "><pre>StandardScaler()</pre></div> </div></div></div></div></div></div></div></div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-208" type="checkbox" ><label for="sk-estimator-id-208" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>RandomForestClassifier</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.ensemble.RandomForestClassifier.html">?<span>Documentation for RandomForestClassifier</span></a></div></label><div class="sk-toggleable__content "><pre>RandomForestClassifier()</pre></div> </div></div></div></div></div></div></div></div></div></div></div>
 </div>
 <br />
 <br /><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 0.100 seconds)</p>
diff --git a/dev/auto_examples/miscellaneous/plot_roc_curve_visualization_api.html b/dev/auto_examples/miscellaneous/plot_roc_curve_visualization_api.html
index 881a63eec65c1..12169ffa12554 100644
--- a/dev/auto_examples/miscellaneous/plot_roc_curve_visualization_api.html
+++ b/dev/auto_examples/miscellaneous/plot_roc_curve_visualization_api.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -679,13 +679,12 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -902,9 +901,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
@@ -1023,7 +1022,7 @@ <h2>Load Data and Train a SVC<a class="headerlink" href="#load-data-and-train-a-
 </pre></div>
 </div>
 <div class="output_subarea output_html rendered_html output_result">
-<style>#sk-container-id-53 {
+<style>#sk-container-id-54 {
   /* Definition of color scheme common for light and dark mode */
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   --sklearn-color-text-muted: #666;
@@ -1054,15 +1053,15 @@ <h2>Load Data and Train a SVC<a class="headerlink" href="#load-data-and-train-a-
   }
 }
 
-#sk-container-id-53 {
+#sk-container-id-54 {
   color: var(--sklearn-color-text);
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-#sk-container-id-53 pre {
+#sk-container-id-54 pre {
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-#sk-container-id-53 input.sk-hidden--visually {
+#sk-container-id-54 input.sk-hidden--visually {
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   clip: rect(1px 1px 1px 1px);
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@@ -1074,7 +1073,7 @@ <h2>Load Data and Train a SVC<a class="headerlink" href="#load-data-and-train-a-
   width: 1px;
 }
 
-#sk-container-id-53 div.sk-dashed-wrapped {
+#sk-container-id-54 div.sk-dashed-wrapped {
   border: 1px dashed var(--sklearn-color-line);
   margin: 0 0.4em 0.5em 0.4em;
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@@ -1082,7 +1081,7 @@ <h2>Load Data and Train a SVC<a class="headerlink" href="#load-data-and-train-a-
   background-color: var(--sklearn-color-background);
 }
 
-#sk-container-id-53 div.sk-container {
+#sk-container-id-54 div.sk-container {
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      but bootstrap.min.css set `[hidden] { display: none !important; }`
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@@ -1092,7 +1091,7 @@ <h2>Load Data and Train a SVC<a class="headerlink" href="#load-data-and-train-a-
   position: relative;
 }
 
-#sk-container-id-53 div.sk-text-repr-fallback {
+#sk-container-id-54 div.sk-text-repr-fallback {
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 }
 
@@ -1108,14 +1107,14 @@ <h2>Load Data and Train a SVC<a class="headerlink" href="#load-data-and-train-a-
 
 /* Parallel-specific style estimator block */
 
-#sk-container-id-53 div.sk-parallel-item::after {
+#sk-container-id-54 div.sk-parallel-item::after {
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   width: 100%;
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 }
 
-#sk-container-id-53 div.sk-parallel {
+#sk-container-id-54 div.sk-parallel {
   display: flex;
   align-items: stretch;
   justify-content: center;
@@ -1123,28 +1122,28 @@ <h2>Load Data and Train a SVC<a class="headerlink" href="#load-data-and-train-a-
   position: relative;
 }
 
-#sk-container-id-53 div.sk-parallel-item {
+#sk-container-id-54 div.sk-parallel-item {
   display: flex;
   flex-direction: column;
 }
 
-#sk-container-id-53 div.sk-parallel-item:first-child::after {
+#sk-container-id-54 div.sk-parallel-item:first-child::after {
   align-self: flex-end;
   width: 50%;
 }
 
-#sk-container-id-53 div.sk-parallel-item:last-child::after {
+#sk-container-id-54 div.sk-parallel-item:last-child::after {
   align-self: flex-start;
   width: 50%;
 }
 
-#sk-container-id-53 div.sk-parallel-item:only-child::after {
+#sk-container-id-54 div.sk-parallel-item:only-child::after {
   width: 0;
 }
 
 /* Serial-specific style estimator block */
 
-#sk-container-id-53 div.sk-serial {
+#sk-container-id-54 div.sk-serial {
   display: flex;
   flex-direction: column;
   align-items: center;
@@ -1162,14 +1161,14 @@ <h2>Load Data and Train a SVC<a class="headerlink" href="#load-data-and-train-a-
 
 /* Pipeline and ColumnTransformer style (default) */
 
-#sk-container-id-53 div.sk-toggleable {
+#sk-container-id-54 div.sk-toggleable {
   /* Default theme specific background. It is overwritten whether we have a
   specific estimator or a Pipeline/ColumnTransformer */
   background-color: var(--sklearn-color-background);
 }
 
 /* Toggleable label */
-#sk-container-id-53 label.sk-toggleable__label {
+#sk-container-id-54 label.sk-toggleable__label {
   cursor: pointer;
   display: flex;
   width: 100%;
@@ -1182,13 +1181,13 @@ <h2>Load Data and Train a SVC<a class="headerlink" href="#load-data-and-train-a-
   gap: 0.5em;
 }
 
-#sk-container-id-53 label.sk-toggleable__label .caption {
+#sk-container-id-54 label.sk-toggleable__label .caption {
   font-size: 0.6rem;
   font-weight: lighter;
   color: var(--sklearn-color-text-muted);
 }
 
-#sk-container-id-53 label.sk-toggleable__label-arrow:before {
+#sk-container-id-54 label.sk-toggleable__label-arrow:before {
   /* Arrow on the left of the label */
   content: "▸";
   float: left;
@@ -1196,13 +1195,13 @@ <h2>Load Data and Train a SVC<a class="headerlink" href="#load-data-and-train-a-
   color: var(--sklearn-color-icon);
 }
 
-#sk-container-id-53 label.sk-toggleable__label-arrow:hover:before {
+#sk-container-id-54 label.sk-toggleable__label-arrow:hover:before {
   color: var(--sklearn-color-text);
 }
 
 /* Toggleable content - dropdown */
 
-#sk-container-id-53 div.sk-toggleable__content {
+#sk-container-id-54 div.sk-toggleable__content {
   max-height: 0;
   max-width: 0;
   overflow: hidden;
@@ -1211,12 +1210,12 @@ <h2>Load Data and Train a SVC<a class="headerlink" href="#load-data-and-train-a-
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-53 div.sk-toggleable__content.fitted {
+#sk-container-id-54 div.sk-toggleable__content.fitted {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
-#sk-container-id-53 div.sk-toggleable__content pre {
+#sk-container-id-54 div.sk-toggleable__content pre {
   margin: 0.2em;
   border-radius: 0.25em;
   color: var(--sklearn-color-text);
@@ -1224,79 +1223,79 @@ <h2>Load Data and Train a SVC<a class="headerlink" href="#load-data-and-train-a-
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-53 div.sk-toggleable__content.fitted pre {
+#sk-container-id-54 div.sk-toggleable__content.fitted pre {
   /* unfitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
-#sk-container-id-53 input.sk-toggleable__control:checked~div.sk-toggleable__content {
+#sk-container-id-54 input.sk-toggleable__control:checked~div.sk-toggleable__content {
   /* Expand drop-down */
   max-height: 200px;
   max-width: 100%;
   overflow: auto;
 }
 
-#sk-container-id-53 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
+#sk-container-id-54 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
   content: "▾";
 }
 
 /* Pipeline/ColumnTransformer-specific style */
 
-#sk-container-id-53 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-54 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-53 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-54 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
 /* Estimator-specific style */
 
 /* Colorize estimator box */
-#sk-container-id-53 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-54 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-53 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-54 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
-#sk-container-id-53 div.sk-label label.sk-toggleable__label,
-#sk-container-id-53 div.sk-label label {
+#sk-container-id-54 div.sk-label label.sk-toggleable__label,
+#sk-container-id-54 div.sk-label label {
   /* The background is the default theme color */
   color: var(--sklearn-color-text-on-default-background);
 }
 
 /* On hover, darken the color of the background */
-#sk-container-id-53 div.sk-label:hover label.sk-toggleable__label {
+#sk-container-id-54 div.sk-label:hover label.sk-toggleable__label {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
 /* Label box, darken color on hover, fitted */
-#sk-container-id-53 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
+#sk-container-id-54 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
 /* Estimator label */
 
-#sk-container-id-53 div.sk-label label {
+#sk-container-id-54 div.sk-label label {
   font-family: monospace;
   font-weight: bold;
   display: inline-block;
   line-height: 1.2em;
 }
 
-#sk-container-id-53 div.sk-label-container {
+#sk-container-id-54 div.sk-label-container {
   text-align: center;
 }
 
 /* Estimator-specific */
-#sk-container-id-53 div.sk-estimator {
+#sk-container-id-54 div.sk-estimator {
   font-family: monospace;
   border: 1px dotted var(--sklearn-color-border-box);
   border-radius: 0.25em;
@@ -1306,18 +1305,18 @@ <h2>Load Data and Train a SVC<a class="headerlink" href="#load-data-and-train-a-
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-53 div.sk-estimator.fitted {
+#sk-container-id-54 div.sk-estimator.fitted {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
 /* on hover */
-#sk-container-id-53 div.sk-estimator:hover {
+#sk-container-id-54 div.sk-estimator:hover {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-53 div.sk-estimator.fitted:hover {
+#sk-container-id-54 div.sk-estimator.fitted:hover {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-2);
 }
@@ -1405,7 +1404,7 @@ <h2>Load Data and Train a SVC<a class="headerlink" href="#load-data-and-train-a-
 
 /* "?"-specific style due to the `<a>` HTML tag */
 
-#sk-container-id-53 a.estimator_doc_link {
+#sk-container-id-54 a.estimator_doc_link {
   float: right;
   font-size: 1rem;
   line-height: 1em;
@@ -1420,25 +1419,25 @@ <h2>Load Data and Train a SVC<a class="headerlink" href="#load-data-and-train-a-
   border: var(--sklearn-color-unfitted-level-1) 1pt solid;
 }
 
-#sk-container-id-53 a.estimator_doc_link.fitted {
+#sk-container-id-54 a.estimator_doc_link.fitted {
   /* fitted */
   border: var(--sklearn-color-fitted-level-1) 1pt solid;
   color: var(--sklearn-color-fitted-level-1);
 }
 
 /* On hover */
-#sk-container-id-53 a.estimator_doc_link:hover {
+#sk-container-id-54 a.estimator_doc_link:hover {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-3);
   color: var(--sklearn-color-background);
   text-decoration: none;
 }
 
-#sk-container-id-53 a.estimator_doc_link.fitted:hover {
+#sk-container-id-54 a.estimator_doc_link.fitted:hover {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-3);
 }
-</style><div id="sk-container-id-53" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>SVC(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-222" type="checkbox" checked><label for="sk-estimator-id-222" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>SVC</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.svm.SVC.html">?<span>Documentation for SVC</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>SVC(random_state=42)</pre></div> </div></div></div></div>
+</style><div id="sk-container-id-54" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>SVC(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-236" type="checkbox" checked><label for="sk-estimator-id-236" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>SVC</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.svm.SVC.html">?<span>Documentation for SVC</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>SVC(random_state=42)</pre></div> </div></div></div></div>
 </div>
 <br />
 <br /></section>
@@ -1469,7 +1468,7 @@ <h2>Training a Random Forest and Plotting the ROC Curve<a class="headerlink" hre
 <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
 </pre></div>
 </div>
-<img src="http://github.com/_images/sphx_glr_plot_roc_curve_visualization_api_002.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_roc_curve_visualization_api_002.png " alt="plot roc curve visualization api" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 0.166 seconds)</p>
+<img src="http://github.com/_images/sphx_glr_plot_roc_curve_visualization_api_002.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_roc_curve_visualization_api_002.png " alt="plot roc curve visualization api" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 0.167 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-miscellaneous-plot-roc-curve-visualization-api-py">
 <div class="binder-badge docutils container">
 <a class="reference external image-reference" href="https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/miscellaneous/plot_roc_curve_visualization_api.ipynb"><img alt="Launch binder" src="http://github.com/_images/binder_badge_logo20.svg" style="width: 150px;" />
@@ -1493,15 +1492,15 @@ <h2>Training a Random Forest and Plotting the ROC Curve<a class="headerlink" hre
 <div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This example presents how to estimate and visualize the variance of the Receiver Operating Characteristic (ROC) metric using cross-validation."><img alt="" src="http://github.com/_images/sphx_glr_plot_roc_crossval_thumb.png" />
 <p><a class="reference internal" href="http://github.com/model_selection/plot_roc_crossval.html#sphx-glr-auto-examples-model-selection-plot-roc-crossval-py"><span class="std std-ref">Receiver Operating Characteristic (ROC) with cross validation</span></a></p>
   <div class="sphx-glr-thumbnail-title">Receiver Operating Characteristic (ROC) with cross validation</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we compare two binary classification multi-threshold metrics: the Receiver Operating Characteristic (ROC) and the Detection Error Tradeoff (DET). For such purpose, we evaluate two different classifiers for the same classification task."><img alt="" src="http://github.com/_images/sphx_glr_plot_det_thumb.png" />
-<p><a class="reference internal" href="http://github.com/model_selection/plot_det.html#sphx-glr-auto-examples-model-selection-plot-det-py"><span class="std std-ref">Detection error tradeoff (DET) curve</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Detection error tradeoff (DET) curve</div>
 </div><div class="sphx-glr-thumbcontainer" tooltip="We are pleased to announce the release of scikit-learn 0.22, which comes with many bug fixes and new features! We detail below a few of the major features of this release. For an exhaustive list of all the changes, please refer to the release notes &lt;release_notes_0_22&gt;."><img alt="" src="http://github.com/_images/sphx_glr_plot_release_highlights_0_22_0_thumb.png" />
 <p><a class="reference internal" href="http://github.com/release_highlights/plot_release_highlights_0_22_0.html#sphx-glr-auto-examples-release-highlights-plot-release-highlights-0-22-0-py"><span class="std std-ref">Release Highlights for scikit-learn 0.22</span></a></p>
   <div class="sphx-glr-thumbnail-title">Release Highlights for scikit-learn 0.22</div>
 </div><div class="sphx-glr-thumbcontainer" tooltip="This example describes the use of the Receiver Operating Characteristic (ROC) metric to evaluate the quality of multiclass classifiers."><img alt="" src="http://github.com/_images/sphx_glr_plot_roc_thumb.png" />
 <p><a class="reference internal" href="http://github.com/model_selection/plot_roc.html#sphx-glr-auto-examples-model-selection-plot-roc-py"><span class="std std-ref">Multiclass Receiver Operating Characteristic (ROC)</span></a></p>
   <div class="sphx-glr-thumbnail-title">Multiclass Receiver Operating Characteristic (ROC)</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we compare two binary classification multi-threshold metrics: the Receiver Operating Characteristic (ROC) and the Detection Error Tradeoff (DET). For such purpose, we evaluate two different classifiers for the same classification task."><img alt="" src="http://github.com/_images/sphx_glr_plot_det_thumb.png" />
+<p><a class="reference internal" href="http://github.com/model_selection/plot_det.html#sphx-glr-auto-examples-model-selection-plot-det-py"><span class="std std-ref">Detection error tradeoff (DET) curve</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Detection error tradeoff (DET) curve</div>
 </div></div><p class="sphx-glr-signature"><a class="reference external" href="https://sphinx-gallery.github.io/">Gallery generated by Sphinx-Gallery</a></p>
 </section>
 </section>
diff --git a/dev/auto_examples/miscellaneous/plot_set_output.html b/dev/auto_examples/miscellaneous/plot_set_output.html
index b6df8554f256a..9aa8c3212f300 100644
--- a/dev/auto_examples/miscellaneous/plot_set_output.html
+++ b/dev/auto_examples/miscellaneous/plot_set_output.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -679,13 +679,12 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -902,9 +901,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
@@ -1181,7 +1180,7 @@
 </pre></div>
 </div>
 <div class="output_subarea output_html rendered_html output_result">
-<style>#sk-container-id-46 {
+<style>#sk-container-id-47 {
   /* Definition of color scheme common for light and dark mode */
   --sklearn-color-text: #000;
   --sklearn-color-text-muted: #666;
@@ -1212,15 +1211,15 @@
   }
 }
 
-#sk-container-id-46 {
+#sk-container-id-47 {
   color: var(--sklearn-color-text);
 }
 
-#sk-container-id-46 pre {
+#sk-container-id-47 pre {
   padding: 0;
 }
 
-#sk-container-id-46 input.sk-hidden--visually {
+#sk-container-id-47 input.sk-hidden--visually {
   border: 0;
   clip: rect(1px 1px 1px 1px);
   clip: rect(1px, 1px, 1px, 1px);
@@ -1232,7 +1231,7 @@
   width: 1px;
 }
 
-#sk-container-id-46 div.sk-dashed-wrapped {
+#sk-container-id-47 div.sk-dashed-wrapped {
   border: 1px dashed var(--sklearn-color-line);
   margin: 0 0.4em 0.5em 0.4em;
   box-sizing: border-box;
@@ -1240,7 +1239,7 @@
   background-color: var(--sklearn-color-background);
 }
 
-#sk-container-id-46 div.sk-container {
+#sk-container-id-47 div.sk-container {
   /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
      but bootstrap.min.css set `[hidden] { display: none !important; }`
      so we also need the `!important` here to be able to override the
@@ -1250,7 +1249,7 @@
   position: relative;
 }
 
-#sk-container-id-46 div.sk-text-repr-fallback {
+#sk-container-id-47 div.sk-text-repr-fallback {
   display: none;
 }
 
@@ -1266,14 +1265,14 @@
 
 /* Parallel-specific style estimator block */
 
-#sk-container-id-46 div.sk-parallel-item::after {
+#sk-container-id-47 div.sk-parallel-item::after {
   content: "";
   width: 100%;
   border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
   flex-grow: 1;
 }
 
-#sk-container-id-46 div.sk-parallel {
+#sk-container-id-47 div.sk-parallel {
   display: flex;
   align-items: stretch;
   justify-content: center;
@@ -1281,28 +1280,28 @@
   position: relative;
 }
 
-#sk-container-id-46 div.sk-parallel-item {
+#sk-container-id-47 div.sk-parallel-item {
   display: flex;
   flex-direction: column;
 }
 
-#sk-container-id-46 div.sk-parallel-item:first-child::after {
+#sk-container-id-47 div.sk-parallel-item:first-child::after {
   align-self: flex-end;
   width: 50%;
 }
 
-#sk-container-id-46 div.sk-parallel-item:last-child::after {
+#sk-container-id-47 div.sk-parallel-item:last-child::after {
   align-self: flex-start;
   width: 50%;
 }
 
-#sk-container-id-46 div.sk-parallel-item:only-child::after {
+#sk-container-id-47 div.sk-parallel-item:only-child::after {
   width: 0;
 }
 
 /* Serial-specific style estimator block */
 
-#sk-container-id-46 div.sk-serial {
+#sk-container-id-47 div.sk-serial {
   display: flex;
   flex-direction: column;
   align-items: center;
@@ -1320,14 +1319,14 @@
 
 /* Pipeline and ColumnTransformer style (default) */
 
-#sk-container-id-46 div.sk-toggleable {
+#sk-container-id-47 div.sk-toggleable {
   /* Default theme specific background. It is overwritten whether we have a
   specific estimator or a Pipeline/ColumnTransformer */
   background-color: var(--sklearn-color-background);
 }
 
 /* Toggleable label */
-#sk-container-id-46 label.sk-toggleable__label {
+#sk-container-id-47 label.sk-toggleable__label {
   cursor: pointer;
   display: flex;
   width: 100%;
@@ -1340,13 +1339,13 @@
   gap: 0.5em;
 }
 
-#sk-container-id-46 label.sk-toggleable__label .caption {
+#sk-container-id-47 label.sk-toggleable__label .caption {
   font-size: 0.6rem;
   font-weight: lighter;
   color: var(--sklearn-color-text-muted);
 }
 
-#sk-container-id-46 label.sk-toggleable__label-arrow:before {
+#sk-container-id-47 label.sk-toggleable__label-arrow:before {
   /* Arrow on the left of the label */
   content: "▸";
   float: left;
@@ -1354,13 +1353,13 @@
   color: var(--sklearn-color-icon);
 }
 
-#sk-container-id-46 label.sk-toggleable__label-arrow:hover:before {
+#sk-container-id-47 label.sk-toggleable__label-arrow:hover:before {
   color: var(--sklearn-color-text);
 }
 
 /* Toggleable content - dropdown */
 
-#sk-container-id-46 div.sk-toggleable__content {
+#sk-container-id-47 div.sk-toggleable__content {
   max-height: 0;
   max-width: 0;
   overflow: hidden;
@@ -1369,12 +1368,12 @@
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-46 div.sk-toggleable__content.fitted {
+#sk-container-id-47 div.sk-toggleable__content.fitted {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
-#sk-container-id-46 div.sk-toggleable__content pre {
+#sk-container-id-47 div.sk-toggleable__content pre {
   margin: 0.2em;
   border-radius: 0.25em;
   color: var(--sklearn-color-text);
@@ -1382,79 +1381,79 @@
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-46 div.sk-toggleable__content.fitted pre {
+#sk-container-id-47 div.sk-toggleable__content.fitted pre {
   /* unfitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
-#sk-container-id-46 input.sk-toggleable__control:checked~div.sk-toggleable__content {
+#sk-container-id-47 input.sk-toggleable__control:checked~div.sk-toggleable__content {
   /* Expand drop-down */
   max-height: 200px;
   max-width: 100%;
   overflow: auto;
 }
 
-#sk-container-id-46 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
+#sk-container-id-47 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
   content: "▾";
 }
 
 /* Pipeline/ColumnTransformer-specific style */
 
-#sk-container-id-46 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-47 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-46 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-47 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
 /* Estimator-specific style */
 
 /* Colorize estimator box */
-#sk-container-id-46 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-47 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-46 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-47 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
-#sk-container-id-46 div.sk-label label.sk-toggleable__label,
-#sk-container-id-46 div.sk-label label {
+#sk-container-id-47 div.sk-label label.sk-toggleable__label,
+#sk-container-id-47 div.sk-label label {
   /* The background is the default theme color */
   color: var(--sklearn-color-text-on-default-background);
 }
 
 /* On hover, darken the color of the background */
-#sk-container-id-46 div.sk-label:hover label.sk-toggleable__label {
+#sk-container-id-47 div.sk-label:hover label.sk-toggleable__label {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
 /* Label box, darken color on hover, fitted */
-#sk-container-id-46 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
+#sk-container-id-47 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
 /* Estimator label */
 
-#sk-container-id-46 div.sk-label label {
+#sk-container-id-47 div.sk-label label {
   font-family: monospace;
   font-weight: bold;
   display: inline-block;
   line-height: 1.2em;
 }
 
-#sk-container-id-46 div.sk-label-container {
+#sk-container-id-47 div.sk-label-container {
   text-align: center;
 }
 
 /* Estimator-specific */
-#sk-container-id-46 div.sk-estimator {
+#sk-container-id-47 div.sk-estimator {
   font-family: monospace;
   border: 1px dotted var(--sklearn-color-border-box);
   border-radius: 0.25em;
@@ -1464,18 +1463,18 @@
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-46 div.sk-estimator.fitted {
+#sk-container-id-47 div.sk-estimator.fitted {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
 /* on hover */
-#sk-container-id-46 div.sk-estimator:hover {
+#sk-container-id-47 div.sk-estimator:hover {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-46 div.sk-estimator.fitted:hover {
+#sk-container-id-47 div.sk-estimator.fitted:hover {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-2);
 }
@@ -1563,7 +1562,7 @@
 
 /* "?"-specific style due to the `<a>` HTML tag */
 
-#sk-container-id-46 a.estimator_doc_link {
+#sk-container-id-47 a.estimator_doc_link {
   float: right;
   font-size: 1rem;
   line-height: 1em;
@@ -1578,29 +1577,29 @@
   border: var(--sklearn-color-unfitted-level-1) 1pt solid;
 }
 
-#sk-container-id-46 a.estimator_doc_link.fitted {
+#sk-container-id-47 a.estimator_doc_link.fitted {
   /* fitted */
   border: var(--sklearn-color-fitted-level-1) 1pt solid;
   color: var(--sklearn-color-fitted-level-1);
 }
 
 /* On hover */
-#sk-container-id-46 a.estimator_doc_link:hover {
+#sk-container-id-47 a.estimator_doc_link:hover {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-3);
   color: var(--sklearn-color-background);
   text-decoration: none;
 }
 
-#sk-container-id-46 a.estimator_doc_link.fitted:hover {
+#sk-container-id-47 a.estimator_doc_link.fitted:hover {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-3);
 }
-</style><div id="sk-container-id-46" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),
+</style><div id="sk-container-id-47" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),
                 (&#x27;selectpercentile&#x27;, SelectPercentile(percentile=75)),
-                (&#x27;logisticregression&#x27;, LogisticRegression())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-204" type="checkbox" ><label for="sk-estimator-id-204" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>Pipeline</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.pipeline.Pipeline.html">?<span>Documentation for Pipeline</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),
+                (&#x27;logisticregression&#x27;, LogisticRegression())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-218" type="checkbox" ><label for="sk-estimator-id-218" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>Pipeline</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.pipeline.Pipeline.html">?<span>Documentation for Pipeline</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),
                 (&#x27;selectpercentile&#x27;, SelectPercentile(percentile=75)),
-                (&#x27;logisticregression&#x27;, LogisticRegression())])</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-205" type="checkbox" ><label for="sk-estimator-id-205" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>StandardScaler</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.StandardScaler.html">?<span>Documentation for StandardScaler</span></a></div></label><div class="sk-toggleable__content fitted"><pre>StandardScaler()</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-206" type="checkbox" ><label for="sk-estimator-id-206" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>SelectPercentile</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.feature_selection.SelectPercentile.html">?<span>Documentation for SelectPercentile</span></a></div></label><div class="sk-toggleable__content fitted"><pre>SelectPercentile(percentile=75)</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-207" type="checkbox" ><label for="sk-estimator-id-207" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>LogisticRegression</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.linear_model.LogisticRegression.html">?<span>Documentation for LogisticRegression</span></a></div></label><div class="sk-toggleable__content fitted"><pre>LogisticRegression()</pre></div> </div></div></div></div></div></div>
+                (&#x27;logisticregression&#x27;, LogisticRegression())])</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-219" type="checkbox" ><label for="sk-estimator-id-219" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>StandardScaler</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.StandardScaler.html">?<span>Documentation for StandardScaler</span></a></div></label><div class="sk-toggleable__content fitted"><pre>StandardScaler()</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-220" type="checkbox" ><label for="sk-estimator-id-220" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>SelectPercentile</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.feature_selection.SelectPercentile.html">?<span>Documentation for SelectPercentile</span></a></div></label><div class="sk-toggleable__content fitted"><pre>SelectPercentile(percentile=75)</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-221" type="checkbox" ><label for="sk-estimator-id-221" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>LogisticRegression</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.linear_model.LogisticRegression.html">?<span>Documentation for LogisticRegression</span></a></div></label><div class="sk-toggleable__content fitted"><pre>LogisticRegression()</pre></div> </div></div></div></div></div></div>
 </div>
 <br />
 <br /><p>Each transformer in the pipeline is configured to return DataFrames. This
@@ -1674,7 +1673,7 @@
 <span class="n">clf</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>0.75
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>0.801829268292683
 </pre></div>
 </div>
 <p>With the global configuration, all transformers output DataFrames. This allows us to
@@ -1702,7 +1701,7 @@
 </pre></div>
 </div>
 <div class="output_subarea output_html rendered_html output_result">
-<style>#sk-container-id-47 {
+<style>#sk-container-id-48 {
   /* Definition of color scheme common for light and dark mode */
   --sklearn-color-text: #000;
   --sklearn-color-text-muted: #666;
@@ -1733,15 +1732,15 @@
   }
 }
 
-#sk-container-id-47 {
+#sk-container-id-48 {
   color: var(--sklearn-color-text);
 }
 
-#sk-container-id-47 pre {
+#sk-container-id-48 pre {
   padding: 0;
 }
 
-#sk-container-id-47 input.sk-hidden--visually {
+#sk-container-id-48 input.sk-hidden--visually {
   border: 0;
   clip: rect(1px 1px 1px 1px);
   clip: rect(1px, 1px, 1px, 1px);
@@ -1753,7 +1752,7 @@
   width: 1px;
 }
 
-#sk-container-id-47 div.sk-dashed-wrapped {
+#sk-container-id-48 div.sk-dashed-wrapped {
   border: 1px dashed var(--sklearn-color-line);
   margin: 0 0.4em 0.5em 0.4em;
   box-sizing: border-box;
@@ -1761,7 +1760,7 @@
   background-color: var(--sklearn-color-background);
 }
 
-#sk-container-id-47 div.sk-container {
+#sk-container-id-48 div.sk-container {
   /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
      but bootstrap.min.css set `[hidden] { display: none !important; }`
      so we also need the `!important` here to be able to override the
@@ -1771,7 +1770,7 @@
   position: relative;
 }
 
-#sk-container-id-47 div.sk-text-repr-fallback {
+#sk-container-id-48 div.sk-text-repr-fallback {
   display: none;
 }
 
@@ -1787,14 +1786,14 @@
 
 /* Parallel-specific style estimator block */
 
-#sk-container-id-47 div.sk-parallel-item::after {
+#sk-container-id-48 div.sk-parallel-item::after {
   content: "";
   width: 100%;
   border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
   flex-grow: 1;
 }
 
-#sk-container-id-47 div.sk-parallel {
+#sk-container-id-48 div.sk-parallel {
   display: flex;
   align-items: stretch;
   justify-content: center;
@@ -1802,28 +1801,28 @@
   position: relative;
 }
 
-#sk-container-id-47 div.sk-parallel-item {
+#sk-container-id-48 div.sk-parallel-item {
   display: flex;
   flex-direction: column;
 }
 
-#sk-container-id-47 div.sk-parallel-item:first-child::after {
+#sk-container-id-48 div.sk-parallel-item:first-child::after {
   align-self: flex-end;
   width: 50%;
 }
 
-#sk-container-id-47 div.sk-parallel-item:last-child::after {
+#sk-container-id-48 div.sk-parallel-item:last-child::after {
   align-self: flex-start;
   width: 50%;
 }
 
-#sk-container-id-47 div.sk-parallel-item:only-child::after {
+#sk-container-id-48 div.sk-parallel-item:only-child::after {
   width: 0;
 }
 
 /* Serial-specific style estimator block */
 
-#sk-container-id-47 div.sk-serial {
+#sk-container-id-48 div.sk-serial {
   display: flex;
   flex-direction: column;
   align-items: center;
@@ -1841,14 +1840,14 @@
 
 /* Pipeline and ColumnTransformer style (default) */
 
-#sk-container-id-47 div.sk-toggleable {
+#sk-container-id-48 div.sk-toggleable {
   /* Default theme specific background. It is overwritten whether we have a
   specific estimator or a Pipeline/ColumnTransformer */
   background-color: var(--sklearn-color-background);
 }
 
 /* Toggleable label */
-#sk-container-id-47 label.sk-toggleable__label {
+#sk-container-id-48 label.sk-toggleable__label {
   cursor: pointer;
   display: flex;
   width: 100%;
@@ -1861,13 +1860,13 @@
   gap: 0.5em;
 }
 
-#sk-container-id-47 label.sk-toggleable__label .caption {
+#sk-container-id-48 label.sk-toggleable__label .caption {
   font-size: 0.6rem;
   font-weight: lighter;
   color: var(--sklearn-color-text-muted);
 }
 
-#sk-container-id-47 label.sk-toggleable__label-arrow:before {
+#sk-container-id-48 label.sk-toggleable__label-arrow:before {
   /* Arrow on the left of the label */
   content: "▸";
   float: left;
@@ -1875,13 +1874,13 @@
   color: var(--sklearn-color-icon);
 }
 
-#sk-container-id-47 label.sk-toggleable__label-arrow:hover:before {
+#sk-container-id-48 label.sk-toggleable__label-arrow:hover:before {
   color: var(--sklearn-color-text);
 }
 
 /* Toggleable content - dropdown */
 
-#sk-container-id-47 div.sk-toggleable__content {
+#sk-container-id-48 div.sk-toggleable__content {
   max-height: 0;
   max-width: 0;
   overflow: hidden;
@@ -1890,12 +1889,12 @@
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-47 div.sk-toggleable__content.fitted {
+#sk-container-id-48 div.sk-toggleable__content.fitted {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
-#sk-container-id-47 div.sk-toggleable__content pre {
+#sk-container-id-48 div.sk-toggleable__content pre {
   margin: 0.2em;
   border-radius: 0.25em;
   color: var(--sklearn-color-text);
@@ -1903,79 +1902,79 @@
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-47 div.sk-toggleable__content.fitted pre {
+#sk-container-id-48 div.sk-toggleable__content.fitted pre {
   /* unfitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
-#sk-container-id-47 input.sk-toggleable__control:checked~div.sk-toggleable__content {
+#sk-container-id-48 input.sk-toggleable__control:checked~div.sk-toggleable__content {
   /* Expand drop-down */
   max-height: 200px;
   max-width: 100%;
   overflow: auto;
 }
 
-#sk-container-id-47 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
+#sk-container-id-48 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
   content: "▾";
 }
 
 /* Pipeline/ColumnTransformer-specific style */
 
-#sk-container-id-47 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-48 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-47 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-48 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
 /* Estimator-specific style */
 
 /* Colorize estimator box */
-#sk-container-id-47 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-48 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-47 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-48 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
-#sk-container-id-47 div.sk-label label.sk-toggleable__label,
-#sk-container-id-47 div.sk-label label {
+#sk-container-id-48 div.sk-label label.sk-toggleable__label,
+#sk-container-id-48 div.sk-label label {
   /* The background is the default theme color */
   color: var(--sklearn-color-text-on-default-background);
 }
 
 /* On hover, darken the color of the background */
-#sk-container-id-47 div.sk-label:hover label.sk-toggleable__label {
+#sk-container-id-48 div.sk-label:hover label.sk-toggleable__label {
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   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
 /* Label box, darken color on hover, fitted */
-#sk-container-id-47 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
+#sk-container-id-48 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
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   background-color: var(--sklearn-color-fitted-level-2);
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 /* Estimator label */
 
-#sk-container-id-47 div.sk-label label {
+#sk-container-id-48 div.sk-label label {
   font-family: monospace;
   font-weight: bold;
   display: inline-block;
   line-height: 1.2em;
 }
 
-#sk-container-id-47 div.sk-label-container {
+#sk-container-id-48 div.sk-label-container {
   text-align: center;
 }
 
 /* Estimator-specific */
-#sk-container-id-47 div.sk-estimator {
+#sk-container-id-48 div.sk-estimator {
   font-family: monospace;
   border: 1px dotted var(--sklearn-color-border-box);
   border-radius: 0.25em;
@@ -1985,18 +1984,18 @@
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-47 div.sk-estimator.fitted {
+#sk-container-id-48 div.sk-estimator.fitted {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
 /* on hover */
-#sk-container-id-47 div.sk-estimator:hover {
+#sk-container-id-48 div.sk-estimator:hover {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-47 div.sk-estimator.fitted:hover {
+#sk-container-id-48 div.sk-estimator.fitted:hover {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-2);
 }
@@ -2084,7 +2083,7 @@
 
 /* "?"-specific style due to the `<a>` HTML tag */
 
-#sk-container-id-47 a.estimator_doc_link {
+#sk-container-id-48 a.estimator_doc_link {
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   font-size: 1rem;
   line-height: 1em;
@@ -2099,25 +2098,25 @@
   border: var(--sklearn-color-unfitted-level-1) 1pt solid;
 }
 
-#sk-container-id-47 a.estimator_doc_link.fitted {
+#sk-container-id-48 a.estimator_doc_link.fitted {
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 /* On hover */
-#sk-container-id-47 a.estimator_doc_link:hover {
+#sk-container-id-48 a.estimator_doc_link:hover {
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   background-color: var(--sklearn-color-unfitted-level-3);
   color: var(--sklearn-color-background);
   text-decoration: none;
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-#sk-container-id-47 a.estimator_doc_link.fitted:hover {
+#sk-container-id-48 a.estimator_doc_link.fitted:hover {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-3);
 }
-</style><div id="sk-container-id-47" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>StandardScaler()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-208" type="checkbox" checked><label for="sk-estimator-id-208" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>StandardScaler</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.StandardScaler.html">?<span>Documentation for StandardScaler</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>StandardScaler()</pre></div> </div></div></div></div>
+</style><div id="sk-container-id-48" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>StandardScaler()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-222" type="checkbox" checked><label for="sk-estimator-id-222" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>StandardScaler</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.StandardScaler.html">?<span>Documentation for StandardScaler</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>StandardScaler()</pre></div> </div></div></div></div>
 </div>
 <br />
 <br /><div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="k">with</span> <a href="http://github.com/modules/generated/sklearn.config_context.html#sklearn.config_context" title="sklearn.config_context" class="sphx-glr-backref-module-sklearn sphx-glr-backref-type-py-function"><span class="n">config_context</span></a><span class="p">(</span><span class="n">transform_output</span><span class="o">=</span><span class="s2">&quot;pandas&quot;</span><span class="p">):</span>
@@ -2151,29 +2150,29 @@
   </thead>
   <tbody>
     <tr>
-      <th>186</th>
-      <td>1.737289</td>
-      <td>-0.109540</td>
+      <th>629</th>
+      <td>0.628306</td>
+      <td>-0.063210</td>
     </tr>
     <tr>
-      <th>103</th>
-      <td>0.573219</td>
-      <td>3.925942</td>
+      <th>688</th>
+      <td>-0.057984</td>
+      <td>-0.515704</td>
     </tr>
     <tr>
-      <th>359</th>
-      <td>-1.971758</td>
-      <td>-0.083704</td>
+      <th>439</th>
+      <td>1.314596</td>
+      <td>0.566624</td>
     </tr>
     <tr>
-      <th>935</th>
-      <td>-0.043053</td>
-      <td>-0.224579</td>
+      <th>664</th>
+      <td>-0.675645</td>
+      <td>-0.512279</td>
     </tr>
     <tr>
-      <th>408</th>
-      <td>-0.796274</td>
-      <td>-0.457352</td>
+      <th>669</th>
+      <td>-0.744274</td>
+      <td>-0.496950</td>
     </tr>
   </tbody>
 </table>
@@ -2185,14 +2184,14 @@
 <span class="n">X_test_scaled</span><span class="p">[:</span><span class="mi">5</span><span class="p">]</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>array([[ 1.7372889 , -0.10954015],
-       [ 0.57321941,  3.92594178],
-       [-1.97175833, -0.08370392],
-       [-0.04305267, -0.22457927],
-       [-0.79627411, -0.4573518 ]])
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>array([[ 0.62830616, -0.06320955],
+       [-0.05798371, -0.51570367],
+       [ 1.31459603,  0.56662405],
+       [-0.6756446 , -0.51227857],
+       [-0.74427358, -0.49694966]])
 </pre></div>
 </div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 0.150 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 0.159 seconds)</p>
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 <div class="binder-badge docutils container">
 <a class="reference external image-reference" href="https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/miscellaneous/plot_set_output.ipynb"><img alt="Launch binder" src="http://github.com/_images/binder_badge_logo20.svg" style="width: 150px;" />
diff --git a/dev/auto_examples/mixture/index.html b/dev/auto_examples/mixture/index.html
index 83b0f5b349d10..6704a3b481cd0 100644
--- a/dev/auto_examples/mixture/index.html
+++ b/dev/auto_examples/mixture/index.html
@@ -42,7 +42,7 @@
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-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -898,9 +897,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
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diff --git a/dev/auto_examples/mixture/plot_concentration_prior.html b/dev/auto_examples/mixture/plot_concentration_prior.html
index 55425e3ee0a3f..7b13e29aff9ef 100644
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+++ b/dev/auto_examples/mixture/plot_concentration_prior.html
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -902,9 +901,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
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 </details></li>
@@ -1153,7 +1152,7 @@
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 </pre></div>
 </div>
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+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 6.470 seconds)</p>
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@@ -1174,12 +1173,12 @@
 </div>
 </div>
 <p class="rubric">Related examples</p>
-<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates the behavior of Gaussian mixture models fit on data that was not sampled from a mixture of Gaussian random variables. The dataset is formed by 100 points loosely spaced following a noisy sine curve. There is therefore no ground truth value for the number of Gaussian components."><img alt="" src="http://github.com/_images/sphx_glr_plot_gmm_sin_thumb.png" />
-<p><a class="reference internal" href="http://github.com/plot_gmm_sin.html#sphx-glr-auto-examples-mixture-plot-gmm-sin-py"><span class="std std-ref">Gaussian Mixture Model Sine Curve</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Gaussian Mixture Model Sine Curve</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="Plot the confidence ellipsoids of a mixture of two Gaussians obtained with Expectation Maximisation (``GaussianMixture`` class) and Variational Inference (``BayesianGaussianMixture`` class models with a Dirichlet process prior)."><img alt="" src="http://github.com/_images/sphx_glr_plot_gmm_thumb.png" />
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="Plot the confidence ellipsoids of a mixture of two Gaussians obtained with Expectation Maximisation (``GaussianMixture`` class) and Variational Inference (``BayesianGaussianMixture`` class models with a Dirichlet process prior)."><img alt="" src="http://github.com/_images/sphx_glr_plot_gmm_thumb.png" />
 <p><a class="reference internal" href="http://github.com/plot_gmm.html#sphx-glr-auto-examples-mixture-plot-gmm-py"><span class="std std-ref">Gaussian Mixture Model Ellipsoids</span></a></p>
   <div class="sphx-glr-thumbnail-title">Gaussian Mixture Model Ellipsoids</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates the behavior of Gaussian mixture models fit on data that was not sampled from a mixture of Gaussian random variables. The dataset is formed by 100 points loosely spaced following a noisy sine curve. There is therefore no ground truth value for the number of Gaussian components."><img alt="" src="http://github.com/_images/sphx_glr_plot_gmm_sin_thumb.png" />
+<p><a class="reference internal" href="http://github.com/plot_gmm_sin.html#sphx-glr-auto-examples-mixture-plot-gmm-sin-py"><span class="std std-ref">Gaussian Mixture Model Sine Curve</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Gaussian Mixture Model Sine Curve</div>
 </div><div class="sphx-glr-thumbcontainer" tooltip="Plot the density estimation of a mixture of two Gaussians. Data is generated from two Gaussians with different centers and covariance matrices."><img alt="" src="http://github.com/_images/sphx_glr_plot_gmm_pdf_thumb.png" />
 <p><a class="reference internal" href="http://github.com/plot_gmm_pdf.html#sphx-glr-auto-examples-mixture-plot-gmm-pdf-py"><span class="std std-ref">Density Estimation for a Gaussian mixture</span></a></p>
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diff --git a/dev/auto_examples/mixture/plot_gmm.html b/dev/auto_examples/mixture/plot_gmm.html
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -902,9 +901,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -902,9 +901,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
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diff --git a/dev/auto_examples/mixture/plot_gmm_init.html b/dev/auto_examples/mixture/plot_gmm_init.html
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -902,9 +901,9 @@
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
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@@ -1015,7 +1014,7 @@
 <p>In this example, when initialized with <em>random_from_data</em> or <em>random</em> the model takes
 more iterations to converge. Here <em>k-means++</em> does a good job of both low
 time to initialize and low number of GaussianMixture iterations to converge.</p>
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+<img src="http://github.com/_images/sphx_glr_plot_gmm_init_001.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_gmm_init_001.png " alt="GMM iterations and relative time taken to initialize, kmeans, Iter 8 | Init Time 1.00x, random_from_data, Iter 137 | Init Time 0.58x, k-means++, Iter 11 | Init Time 0.85x, random, Iter 47 | Init Time 0.60x" class = "sphx-glr-single-img"/><div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Authors: The scikit-learn developers</span>
 <span class="c1"># SPDX-License-Identifier: BSD-3-Clause</span>
 
 <span class="kn">from</span><span class="w"> </span><span class="nn">timeit</span><span class="w"> </span><span class="kn">import</span> <span class="n">default_timer</span> <span class="k">as</span> <a href="https://docs.python.org/3/library/timeit.html#timeit.default_timer" title="timeit.default_timer" class="sphx-glr-backref-module-timeit sphx-glr-backref-type-py-function"><span class="n">timer</span></a>
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--- a/dev/auto_examples/mixture/plot_gmm_pdf.html
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-<meta name="description" content="Plot the density estimation of a mixture of two Gaussians. Data is generated from two Gaussians with different centers and covariance matrices. Total running time of the script:(0 minutes 0.114 sec..." />
+<meta name="description" content="Plot the density estimation of a mixture of two Gaussians. Data is generated from two Gaussians with different centers and covariance matrices. Total running time of the script:(0 minutes 0.131 sec..." />
 
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@@ -679,13 +679,12 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
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 </ul>
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 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -902,9 +901,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
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diff --git a/dev/auto_examples/mixture/plot_gmm_selection.html b/dev/auto_examples/mixture/plot_gmm_selection.html
index 6f538689ad4bb..301b6e59587cc 100644
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
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 </div>
 <div class="output_subarea output_html rendered_html output_result">
-<style>#sk-container-id-29 {
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 }
 
-#sk-container-id-29 label.sk-toggleable__label-arrow:before {
+#sk-container-id-30 label.sk-toggleable__label-arrow:before {
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@@ -1244,13 +1243,13 @@ <h2>Model training and selection<a class="headerlink" href="#model-training-and-
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 }
 
-#sk-container-id-29 label.sk-toggleable__label-arrow:hover:before {
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-#sk-container-id-29 div.sk-label label.sk-toggleable__label,
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 }
 
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+#sk-container-id-30 div.sk-estimator {
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@@ -1354,18 +1353,18 @@ <h2>Model training and selection<a class="headerlink" href="#model-training-and-
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+#sk-container-id-30 div.sk-estimator:hover {
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   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-29 div.sk-estimator.fitted:hover {
+#sk-container-id-30 div.sk-estimator.fitted:hover {
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 }
@@ -1453,7 +1452,7 @@ <h2>Model training and selection<a class="headerlink" href="#model-training-and-
 
 /* "?"-specific style due to the `<a>` HTML tag */
 
-#sk-container-id-29 a.estimator_doc_link {
+#sk-container-id-30 a.estimator_doc_link {
   float: right;
   font-size: 1rem;
   line-height: 1em;
@@ -1468,33 +1467,33 @@ <h2>Model training and selection<a class="headerlink" href="#model-training-and-
   border: var(--sklearn-color-unfitted-level-1) 1pt solid;
 }
 
-#sk-container-id-29 a.estimator_doc_link.fitted {
+#sk-container-id-30 a.estimator_doc_link.fitted {
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+#sk-container-id-30 a.estimator_doc_link:hover {
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   background-color: var(--sklearn-color-unfitted-level-3);
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 }
-</style><div id="sk-container-id-29" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>GridSearchCV(estimator=GaussianMixture(),
+</style><div id="sk-container-id-30" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>GridSearchCV(estimator=GaussianMixture(),
              param_grid={&#x27;covariance_type&#x27;: [&#x27;spherical&#x27;, &#x27;tied&#x27;, &#x27;diag&#x27;,
                                              &#x27;full&#x27;],
                          &#x27;n_components&#x27;: range(1, 7)},
-             scoring=&lt;function gmm_bic_score at 0x7fb578d8f2e0&gt;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-111" type="checkbox" ><label for="sk-estimator-id-111" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>GridSearchCV</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.model_selection.GridSearchCV.html">?<span>Documentation for GridSearchCV</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>GridSearchCV(estimator=GaussianMixture(),
+             scoring=&lt;function gmm_bic_score at 0x7fd848870700&gt;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-121" type="checkbox" ><label for="sk-estimator-id-121" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>GridSearchCV</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.model_selection.GridSearchCV.html">?<span>Documentation for GridSearchCV</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>GridSearchCV(estimator=GaussianMixture(),
              param_grid={&#x27;covariance_type&#x27;: [&#x27;spherical&#x27;, &#x27;tied&#x27;, &#x27;diag&#x27;,
                                              &#x27;full&#x27;],
                          &#x27;n_components&#x27;: range(1, 7)},
-             scoring=&lt;function gmm_bic_score at 0x7fb578d8f2e0&gt;)</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-112" type="checkbox" ><label for="sk-estimator-id-112" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>best_estimator_: GaussianMixture</div></div></label><div class="sk-toggleable__content fitted"><pre>GaussianMixture(n_components=2)</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-113" type="checkbox" ><label for="sk-estimator-id-113" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>GaussianMixture</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.mixture.GaussianMixture.html">?<span>Documentation for GaussianMixture</span></a></div></label><div class="sk-toggleable__content fitted"><pre>GaussianMixture(n_components=2)</pre></div> </div></div></div></div></div></div></div></div></div>
+             scoring=&lt;function gmm_bic_score at 0x7fd848870700&gt;)</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-122" type="checkbox" ><label for="sk-estimator-id-122" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>best_estimator_: GaussianMixture</div></div></label><div class="sk-toggleable__content fitted"><pre>GaussianMixture(n_components=2)</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-123" type="checkbox" ><label for="sk-estimator-id-123" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>GaussianMixture</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.mixture.GaussianMixture.html">?<span>Documentation for GaussianMixture</span></a></div></label><div class="sk-toggleable__content fitted"><pre>GaussianMixture(n_components=2)</pre></div> </div></div></div></div></div></div></div></div></div>
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diff --git a/dev/auto_examples/mixture/plot_gmm_sin.html b/dev/auto_examples/mixture/plot_gmm_sin.html
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@@ -679,13 +679,12 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -902,9 +901,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
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@@ -1173,7 +1172,7 @@
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 </pre></div>
 </div>
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+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 0.498 seconds)</p>
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diff --git a/dev/auto_examples/model_selection/index.html b/dev/auto_examples/model_selection/index.html
index 32bf46f571d35..e517b7e71aa98 100644
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
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@@ -899,9 +898,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -902,9 +901,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
@@ -1074,7 +1073,7 @@
 <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
 </pre></div>
 </div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 0.177 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 0.200 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-model-selection-plot-confusion-matrix-py">
 <div class="binder-badge docutils container">
 <a class="reference external image-reference" href="https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/model_selection/plot_confusion_matrix.ipynb"><img alt="Launch binder" src="http://github.com/_images/binder_badge_logo22.svg" style="width: 150px;" />
@@ -1101,12 +1100,12 @@
 </div><div class="sphx-glr-thumbcontainer" tooltip="This example shows a well known decomposition technique known as Principal Component Analysis (PCA) on the Iris dataset."><img alt="" src="http://github.com/_images/sphx_glr_plot_pca_iris_thumb.png" />
 <p><a class="reference internal" href="http://github.com/decomposition/plot_pca_iris.html#sphx-glr-auto-examples-decomposition-plot-pca-iris-py"><span class="std std-ref">Principal Component Analysis (PCA) on Iris Dataset</span></a></p>
   <div class="sphx-glr-thumbnail-title">Principal Component Analysis (PCA) on Iris Dataset</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to use KNeighborsClassifier. We train such a classifier on the iris dataset and observe the difference of the decision boundary obtained with regards to the parameter weights."><img alt="" src="http://github.com/_images/sphx_glr_plot_classification_thumb.png" />
-<p><a class="reference internal" href="http://github.com/neighbors/plot_classification.html#sphx-glr-auto-examples-neighbors-plot-classification-py"><span class="std std-ref">Nearest Neighbors Classification</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Nearest Neighbors Classification</div>
 </div><div class="sphx-glr-thumbcontainer" tooltip="Comparison of different linear SVM classifiers on a 2D projection of the iris dataset. We only consider the first 2 features of this dataset:"><img alt="" src="http://github.com/_images/sphx_glr_plot_iris_svc_thumb.png" />
 <p><a class="reference internal" href="http://github.com/svm/plot_iris_svc.html#sphx-glr-auto-examples-svm-plot-iris-svc-py"><span class="std std-ref">Plot different SVM classifiers in the iris dataset</span></a></p>
   <div class="sphx-glr-thumbnail-title">Plot different SVM classifiers in the iris dataset</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to use KNeighborsClassifier. We train such a classifier on the iris dataset and observe the difference of the decision boundary obtained with regards to the parameter weights."><img alt="" src="http://github.com/_images/sphx_glr_plot_classification_thumb.png" />
+<p><a class="reference internal" href="http://github.com/neighbors/plot_classification.html#sphx-glr-auto-examples-neighbors-plot-classification-py"><span class="std std-ref">Nearest Neighbors Classification</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Nearest Neighbors Classification</div>
 </div></div><p class="sphx-glr-signature"><a class="reference external" href="https://sphinx-gallery.github.io/">Gallery generated by Sphinx-Gallery</a></p>
 </section>
 
diff --git a/dev/auto_examples/model_selection/plot_cost_sensitive_learning.html b/dev/auto_examples/model_selection/plot_cost_sensitive_learning.html
index a2908fc833858..95dabbc965524 100644
--- a/dev/auto_examples/model_selection/plot_cost_sensitive_learning.html
+++ b/dev/auto_examples/model_selection/plot_cost_sensitive_learning.html
@@ -42,7 +42,7 @@
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     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -679,13 +679,12 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -902,9 +901,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
@@ -1212,7 +1211,7 @@ <h3>Vanilla predictive model<a class="headerlink" href="#vanilla-predictive-mode
 </pre></div>
 </div>
 <div class="output_subarea output_html rendered_html output_result">
-<style>#sk-container-id-57 {
+<style>#sk-container-id-58 {
   /* Definition of color scheme common for light and dark mode */
   --sklearn-color-text: #000;
   --sklearn-color-text-muted: #666;
@@ -1243,15 +1242,15 @@ <h3>Vanilla predictive model<a class="headerlink" href="#vanilla-predictive-mode
   }
 }
 
-#sk-container-id-57 {
+#sk-container-id-58 {
   color: var(--sklearn-color-text);
 }
 
-#sk-container-id-57 pre {
+#sk-container-id-58 pre {
   padding: 0;
 }
 
-#sk-container-id-57 input.sk-hidden--visually {
+#sk-container-id-58 input.sk-hidden--visually {
   border: 0;
   clip: rect(1px 1px 1px 1px);
   clip: rect(1px, 1px, 1px, 1px);
@@ -1263,7 +1262,7 @@ <h3>Vanilla predictive model<a class="headerlink" href="#vanilla-predictive-mode
   width: 1px;
 }
 
-#sk-container-id-57 div.sk-dashed-wrapped {
+#sk-container-id-58 div.sk-dashed-wrapped {
   border: 1px dashed var(--sklearn-color-line);
   margin: 0 0.4em 0.5em 0.4em;
   box-sizing: border-box;
@@ -1271,7 +1270,7 @@ <h3>Vanilla predictive model<a class="headerlink" href="#vanilla-predictive-mode
   background-color: var(--sklearn-color-background);
 }
 
-#sk-container-id-57 div.sk-container {
+#sk-container-id-58 div.sk-container {
   /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
      but bootstrap.min.css set `[hidden] { display: none !important; }`
      so we also need the `!important` here to be able to override the
@@ -1281,7 +1280,7 @@ <h3>Vanilla predictive model<a class="headerlink" href="#vanilla-predictive-mode
   position: relative;
 }
 
-#sk-container-id-57 div.sk-text-repr-fallback {
+#sk-container-id-58 div.sk-text-repr-fallback {
   display: none;
 }
 
@@ -1297,14 +1296,14 @@ <h3>Vanilla predictive model<a class="headerlink" href="#vanilla-predictive-mode
 
 /* Parallel-specific style estimator block */
 
-#sk-container-id-57 div.sk-parallel-item::after {
+#sk-container-id-58 div.sk-parallel-item::after {
   content: "";
   width: 100%;
   border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
   flex-grow: 1;
 }
 
-#sk-container-id-57 div.sk-parallel {
+#sk-container-id-58 div.sk-parallel {
   display: flex;
   align-items: stretch;
   justify-content: center;
@@ -1312,28 +1311,28 @@ <h3>Vanilla predictive model<a class="headerlink" href="#vanilla-predictive-mode
   position: relative;
 }
 
-#sk-container-id-57 div.sk-parallel-item {
+#sk-container-id-58 div.sk-parallel-item {
   display: flex;
   flex-direction: column;
 }
 
-#sk-container-id-57 div.sk-parallel-item:first-child::after {
+#sk-container-id-58 div.sk-parallel-item:first-child::after {
   align-self: flex-end;
   width: 50%;
 }
 
-#sk-container-id-57 div.sk-parallel-item:last-child::after {
+#sk-container-id-58 div.sk-parallel-item:last-child::after {
   align-self: flex-start;
   width: 50%;
 }
 
-#sk-container-id-57 div.sk-parallel-item:only-child::after {
+#sk-container-id-58 div.sk-parallel-item:only-child::after {
   width: 0;
 }
 
 /* Serial-specific style estimator block */
 
-#sk-container-id-57 div.sk-serial {
+#sk-container-id-58 div.sk-serial {
   display: flex;
   flex-direction: column;
   align-items: center;
@@ -1351,14 +1350,14 @@ <h3>Vanilla predictive model<a class="headerlink" href="#vanilla-predictive-mode
 
 /* Pipeline and ColumnTransformer style (default) */
 
-#sk-container-id-57 div.sk-toggleable {
+#sk-container-id-58 div.sk-toggleable {
   /* Default theme specific background. It is overwritten whether we have a
   specific estimator or a Pipeline/ColumnTransformer */
   background-color: var(--sklearn-color-background);
 }
 
 /* Toggleable label */
-#sk-container-id-57 label.sk-toggleable__label {
+#sk-container-id-58 label.sk-toggleable__label {
   cursor: pointer;
   display: flex;
   width: 100%;
@@ -1371,13 +1370,13 @@ <h3>Vanilla predictive model<a class="headerlink" href="#vanilla-predictive-mode
   gap: 0.5em;
 }
 
-#sk-container-id-57 label.sk-toggleable__label .caption {
+#sk-container-id-58 label.sk-toggleable__label .caption {
   font-size: 0.6rem;
   font-weight: lighter;
   color: var(--sklearn-color-text-muted);
 }
 
-#sk-container-id-57 label.sk-toggleable__label-arrow:before {
+#sk-container-id-58 label.sk-toggleable__label-arrow:before {
   /* Arrow on the left of the label */
   content: "▸";
   float: left;
@@ -1385,13 +1384,13 @@ <h3>Vanilla predictive model<a class="headerlink" href="#vanilla-predictive-mode
   color: var(--sklearn-color-icon);
 }
 
-#sk-container-id-57 label.sk-toggleable__label-arrow:hover:before {
+#sk-container-id-58 label.sk-toggleable__label-arrow:hover:before {
   color: var(--sklearn-color-text);
 }
 
 /* Toggleable content - dropdown */
 
-#sk-container-id-57 div.sk-toggleable__content {
+#sk-container-id-58 div.sk-toggleable__content {
   max-height: 0;
   max-width: 0;
   overflow: hidden;
@@ -1400,12 +1399,12 @@ <h3>Vanilla predictive model<a class="headerlink" href="#vanilla-predictive-mode
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-57 div.sk-toggleable__content.fitted {
+#sk-container-id-58 div.sk-toggleable__content.fitted {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
-#sk-container-id-57 div.sk-toggleable__content pre {
+#sk-container-id-58 div.sk-toggleable__content pre {
   margin: 0.2em;
   border-radius: 0.25em;
   color: var(--sklearn-color-text);
@@ -1413,79 +1412,79 @@ <h3>Vanilla predictive model<a class="headerlink" href="#vanilla-predictive-mode
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-57 div.sk-toggleable__content.fitted pre {
+#sk-container-id-58 div.sk-toggleable__content.fitted pre {
   /* unfitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
-#sk-container-id-57 input.sk-toggleable__control:checked~div.sk-toggleable__content {
+#sk-container-id-58 input.sk-toggleable__control:checked~div.sk-toggleable__content {
   /* Expand drop-down */
   max-height: 200px;
   max-width: 100%;
   overflow: auto;
 }
 
-#sk-container-id-57 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
+#sk-container-id-58 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
   content: "▾";
 }
 
 /* Pipeline/ColumnTransformer-specific style */
 
-#sk-container-id-57 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-58 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-57 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-58 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
 /* Estimator-specific style */
 
 /* Colorize estimator box */
-#sk-container-id-57 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-58 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-57 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-58 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
-#sk-container-id-57 div.sk-label label.sk-toggleable__label,
-#sk-container-id-57 div.sk-label label {
+#sk-container-id-58 div.sk-label label.sk-toggleable__label,
+#sk-container-id-58 div.sk-label label {
   /* The background is the default theme color */
   color: var(--sklearn-color-text-on-default-background);
 }
 
 /* On hover, darken the color of the background */
-#sk-container-id-57 div.sk-label:hover label.sk-toggleable__label {
+#sk-container-id-58 div.sk-label:hover label.sk-toggleable__label {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
 /* Label box, darken color on hover, fitted */
-#sk-container-id-57 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
+#sk-container-id-58 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
 /* Estimator label */
 
-#sk-container-id-57 div.sk-label label {
+#sk-container-id-58 div.sk-label label {
   font-family: monospace;
   font-weight: bold;
   display: inline-block;
   line-height: 1.2em;
 }
 
-#sk-container-id-57 div.sk-label-container {
+#sk-container-id-58 div.sk-label-container {
   text-align: center;
 }
 
 /* Estimator-specific */
-#sk-container-id-57 div.sk-estimator {
+#sk-container-id-58 div.sk-estimator {
   font-family: monospace;
   border: 1px dotted var(--sklearn-color-border-box);
   border-radius: 0.25em;
@@ -1495,18 +1494,18 @@ <h3>Vanilla predictive model<a class="headerlink" href="#vanilla-predictive-mode
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-57 div.sk-estimator.fitted {
+#sk-container-id-58 div.sk-estimator.fitted {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
 /* on hover */
-#sk-container-id-57 div.sk-estimator:hover {
+#sk-container-id-58 div.sk-estimator:hover {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-57 div.sk-estimator.fitted:hover {
+#sk-container-id-58 div.sk-estimator.fitted:hover {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-2);
 }
@@ -1594,7 +1593,7 @@ <h3>Vanilla predictive model<a class="headerlink" href="#vanilla-predictive-mode
 
 /* "?"-specific style due to the `<a>` HTML tag */
 
-#sk-container-id-57 a.estimator_doc_link {
+#sk-container-id-58 a.estimator_doc_link {
   float: right;
   font-size: 1rem;
   line-height: 1em;
@@ -1609,25 +1608,25 @@ <h3>Vanilla predictive model<a class="headerlink" href="#vanilla-predictive-mode
   border: var(--sklearn-color-unfitted-level-1) 1pt solid;
 }
 
-#sk-container-id-57 a.estimator_doc_link.fitted {
+#sk-container-id-58 a.estimator_doc_link.fitted {
   /* fitted */
   border: var(--sklearn-color-fitted-level-1) 1pt solid;
   color: var(--sklearn-color-fitted-level-1);
 }
 
 /* On hover */
-#sk-container-id-57 a.estimator_doc_link:hover {
+#sk-container-id-58 a.estimator_doc_link:hover {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-3);
   color: var(--sklearn-color-background);
   text-decoration: none;
 }
 
-#sk-container-id-57 a.estimator_doc_link.fitted:hover {
+#sk-container-id-58 a.estimator_doc_link.fitted:hover {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-3);
 }
-</style><div id="sk-container-id-57" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>HistGradientBoostingClassifier(random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-232" type="checkbox" checked><label for="sk-estimator-id-232" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>HistGradientBoostingClassifier</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.ensemble.HistGradientBoostingClassifier.html">?<span>Documentation for HistGradientBoostingClassifier</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>HistGradientBoostingClassifier(random_state=0)</pre></div> </div></div></div></div>
+</style><div id="sk-container-id-58" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>HistGradientBoostingClassifier(random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-246" type="checkbox" checked><label for="sk-estimator-id-246" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>HistGradientBoostingClassifier</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.ensemble.HistGradientBoostingClassifier.html">?<span>Documentation for HistGradientBoostingClassifier</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>HistGradientBoostingClassifier(random_state=0)</pre></div> </div></div></div></div>
 </div>
 <br />
 <br /><p>We evaluate the performance of our predictive model using the ROC and Precision-Recall
@@ -1871,7 +1870,7 @@ <h3>Vanilla predictive model<a class="headerlink" href="#vanilla-predictive-mode
 </pre></div>
 </div>
 <div class="output_subarea output_html rendered_html output_result">
-<style>#sk-container-id-58 {
+<style>#sk-container-id-59 {
   /* Definition of color scheme common for light and dark mode */
   --sklearn-color-text: #000;
   --sklearn-color-text-muted: #666;
@@ -1902,15 +1901,15 @@ <h3>Vanilla predictive model<a class="headerlink" href="#vanilla-predictive-mode
   }
 }
 
-#sk-container-id-58 {
+#sk-container-id-59 {
   color: var(--sklearn-color-text);
 }
 
-#sk-container-id-58 pre {
+#sk-container-id-59 pre {
   padding: 0;
 }
 
-#sk-container-id-58 input.sk-hidden--visually {
+#sk-container-id-59 input.sk-hidden--visually {
   border: 0;
   clip: rect(1px 1px 1px 1px);
   clip: rect(1px, 1px, 1px, 1px);
@@ -1922,7 +1921,7 @@ <h3>Vanilla predictive model<a class="headerlink" href="#vanilla-predictive-mode
   width: 1px;
 }
 
-#sk-container-id-58 div.sk-dashed-wrapped {
+#sk-container-id-59 div.sk-dashed-wrapped {
   border: 1px dashed var(--sklearn-color-line);
   margin: 0 0.4em 0.5em 0.4em;
   box-sizing: border-box;
@@ -1930,7 +1929,7 @@ <h3>Vanilla predictive model<a class="headerlink" href="#vanilla-predictive-mode
   background-color: var(--sklearn-color-background);
 }
 
-#sk-container-id-58 div.sk-container {
+#sk-container-id-59 div.sk-container {
   /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
      but bootstrap.min.css set `[hidden] { display: none !important; }`
      so we also need the `!important` here to be able to override the
@@ -1940,7 +1939,7 @@ <h3>Vanilla predictive model<a class="headerlink" href="#vanilla-predictive-mode
   position: relative;
 }
 
-#sk-container-id-58 div.sk-text-repr-fallback {
+#sk-container-id-59 div.sk-text-repr-fallback {
   display: none;
 }
 
@@ -1956,14 +1955,14 @@ <h3>Vanilla predictive model<a class="headerlink" href="#vanilla-predictive-mode
 
 /* Parallel-specific style estimator block */
 
-#sk-container-id-58 div.sk-parallel-item::after {
+#sk-container-id-59 div.sk-parallel-item::after {
   content: "";
   width: 100%;
   border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
   flex-grow: 1;
 }
 
-#sk-container-id-58 div.sk-parallel {
+#sk-container-id-59 div.sk-parallel {
   display: flex;
   align-items: stretch;
   justify-content: center;
@@ -1971,28 +1970,28 @@ <h3>Vanilla predictive model<a class="headerlink" href="#vanilla-predictive-mode
   position: relative;
 }
 
-#sk-container-id-58 div.sk-parallel-item {
+#sk-container-id-59 div.sk-parallel-item {
   display: flex;
   flex-direction: column;
 }
 
-#sk-container-id-58 div.sk-parallel-item:first-child::after {
+#sk-container-id-59 div.sk-parallel-item:first-child::after {
   align-self: flex-end;
   width: 50%;
 }
 
-#sk-container-id-58 div.sk-parallel-item:last-child::after {
+#sk-container-id-59 div.sk-parallel-item:last-child::after {
   align-self: flex-start;
   width: 50%;
 }
 
-#sk-container-id-58 div.sk-parallel-item:only-child::after {
+#sk-container-id-59 div.sk-parallel-item:only-child::after {
   width: 0;
 }
 
 /* Serial-specific style estimator block */
 
-#sk-container-id-58 div.sk-serial {
+#sk-container-id-59 div.sk-serial {
   display: flex;
   flex-direction: column;
   align-items: center;
@@ -2010,14 +2009,14 @@ <h3>Vanilla predictive model<a class="headerlink" href="#vanilla-predictive-mode
 
 /* Pipeline and ColumnTransformer style (default) */
 
-#sk-container-id-58 div.sk-toggleable {
+#sk-container-id-59 div.sk-toggleable {
   /* Default theme specific background. It is overwritten whether we have a
   specific estimator or a Pipeline/ColumnTransformer */
   background-color: var(--sklearn-color-background);
 }
 
 /* Toggleable label */
-#sk-container-id-58 label.sk-toggleable__label {
+#sk-container-id-59 label.sk-toggleable__label {
   cursor: pointer;
   display: flex;
   width: 100%;
@@ -2030,13 +2029,13 @@ <h3>Vanilla predictive model<a class="headerlink" href="#vanilla-predictive-mode
   gap: 0.5em;
 }
 
-#sk-container-id-58 label.sk-toggleable__label .caption {
+#sk-container-id-59 label.sk-toggleable__label .caption {
   font-size: 0.6rem;
   font-weight: lighter;
   color: var(--sklearn-color-text-muted);
 }
 
-#sk-container-id-58 label.sk-toggleable__label-arrow:before {
+#sk-container-id-59 label.sk-toggleable__label-arrow:before {
   /* Arrow on the left of the label */
   content: "▸";
   float: left;
@@ -2044,13 +2043,13 @@ <h3>Vanilla predictive model<a class="headerlink" href="#vanilla-predictive-mode
   color: var(--sklearn-color-icon);
 }
 
-#sk-container-id-58 label.sk-toggleable__label-arrow:hover:before {
+#sk-container-id-59 label.sk-toggleable__label-arrow:hover:before {
   color: var(--sklearn-color-text);
 }
 
 /* Toggleable content - dropdown */
 
-#sk-container-id-58 div.sk-toggleable__content {
+#sk-container-id-59 div.sk-toggleable__content {
   max-height: 0;
   max-width: 0;
   overflow: hidden;
@@ -2059,12 +2058,12 @@ <h3>Vanilla predictive model<a class="headerlink" href="#vanilla-predictive-mode
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-58 div.sk-toggleable__content.fitted {
+#sk-container-id-59 div.sk-toggleable__content.fitted {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
-#sk-container-id-58 div.sk-toggleable__content pre {
+#sk-container-id-59 div.sk-toggleable__content pre {
   margin: 0.2em;
   border-radius: 0.25em;
   color: var(--sklearn-color-text);
@@ -2072,79 +2071,79 @@ <h3>Vanilla predictive model<a class="headerlink" href="#vanilla-predictive-mode
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-58 div.sk-toggleable__content.fitted pre {
+#sk-container-id-59 div.sk-toggleable__content.fitted pre {
   /* unfitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
-#sk-container-id-58 input.sk-toggleable__control:checked~div.sk-toggleable__content {
+#sk-container-id-59 input.sk-toggleable__control:checked~div.sk-toggleable__content {
   /* Expand drop-down */
   max-height: 200px;
   max-width: 100%;
   overflow: auto;
 }
 
-#sk-container-id-58 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
+#sk-container-id-59 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
   content: "▾";
 }
 
 /* Pipeline/ColumnTransformer-specific style */
 
-#sk-container-id-58 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-59 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-58 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-59 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
 /* Estimator-specific style */
 
 /* Colorize estimator box */
-#sk-container-id-58 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-59 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-58 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-59 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
-#sk-container-id-58 div.sk-label label.sk-toggleable__label,
-#sk-container-id-58 div.sk-label label {
+#sk-container-id-59 div.sk-label label.sk-toggleable__label,
+#sk-container-id-59 div.sk-label label {
   /* The background is the default theme color */
   color: var(--sklearn-color-text-on-default-background);
 }
 
 /* On hover, darken the color of the background */
-#sk-container-id-58 div.sk-label:hover label.sk-toggleable__label {
+#sk-container-id-59 div.sk-label:hover label.sk-toggleable__label {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
 /* Label box, darken color on hover, fitted */
-#sk-container-id-58 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
+#sk-container-id-59 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
 /* Estimator label */
 
-#sk-container-id-58 div.sk-label label {
+#sk-container-id-59 div.sk-label label {
   font-family: monospace;
   font-weight: bold;
   display: inline-block;
   line-height: 1.2em;
 }
 
-#sk-container-id-58 div.sk-label-container {
+#sk-container-id-59 div.sk-label-container {
   text-align: center;
 }
 
 /* Estimator-specific */
-#sk-container-id-58 div.sk-estimator {
+#sk-container-id-59 div.sk-estimator {
   font-family: monospace;
   border: 1px dotted var(--sklearn-color-border-box);
   border-radius: 0.25em;
@@ -2154,18 +2153,18 @@ <h3>Vanilla predictive model<a class="headerlink" href="#vanilla-predictive-mode
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-58 div.sk-estimator.fitted {
+#sk-container-id-59 div.sk-estimator.fitted {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
 /* on hover */
-#sk-container-id-58 div.sk-estimator:hover {
+#sk-container-id-59 div.sk-estimator:hover {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-58 div.sk-estimator.fitted:hover {
+#sk-container-id-59 div.sk-estimator.fitted:hover {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-2);
 }
@@ -2253,7 +2252,7 @@ <h3>Vanilla predictive model<a class="headerlink" href="#vanilla-predictive-mode
 
 /* "?"-specific style due to the `<a>` HTML tag */
 
-#sk-container-id-58 a.estimator_doc_link {
+#sk-container-id-59 a.estimator_doc_link {
   float: right;
   font-size: 1rem;
   line-height: 1em;
@@ -2268,33 +2267,33 @@ <h3>Vanilla predictive model<a class="headerlink" href="#vanilla-predictive-mode
   border: var(--sklearn-color-unfitted-level-1) 1pt solid;
 }
 
-#sk-container-id-58 a.estimator_doc_link.fitted {
+#sk-container-id-59 a.estimator_doc_link.fitted {
   /* fitted */
   border: var(--sklearn-color-fitted-level-1) 1pt solid;
   color: var(--sklearn-color-fitted-level-1);
 }
 
 /* On hover */
-#sk-container-id-58 a.estimator_doc_link:hover {
+#sk-container-id-59 a.estimator_doc_link:hover {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-3);
   color: var(--sklearn-color-background);
   text-decoration: none;
 }
 
-#sk-container-id-58 a.estimator_doc_link.fitted:hover {
+#sk-container-id-59 a.estimator_doc_link.fitted:hover {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-3);
 }
-</style><div id="sk-container-id-58" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>TunedThresholdClassifierCV(cv=0.75,
+</style><div id="sk-container-id-59" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>TunedThresholdClassifierCV(cv=0.75,
                            estimator=HistGradientBoostingClassifier(random_state=0),
                            refit=False,
                            scoring=make_scorer(credit_gain_score, response_method=&#x27;predict&#x27;, neg_label=good, pos_label=bad),
-                           store_cv_results=True)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-233" type="checkbox" ><label for="sk-estimator-id-233" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>TunedThresholdClassifierCV</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.model_selection.TunedThresholdClassifierCV.html">?<span>Documentation for TunedThresholdClassifierCV</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>TunedThresholdClassifierCV(cv=0.75,
+                           store_cv_results=True)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-247" type="checkbox" ><label for="sk-estimator-id-247" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>TunedThresholdClassifierCV</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.model_selection.TunedThresholdClassifierCV.html">?<span>Documentation for TunedThresholdClassifierCV</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>TunedThresholdClassifierCV(cv=0.75,
                            estimator=HistGradientBoostingClassifier(random_state=0),
                            refit=False,
                            scoring=make_scorer(credit_gain_score, response_method=&#x27;predict&#x27;, neg_label=good, pos_label=bad),
-                           store_cv_results=True)</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-234" type="checkbox" ><label for="sk-estimator-id-234" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>estimator: HistGradientBoostingClassifier</div></div></label><div class="sk-toggleable__content fitted"><pre>HistGradientBoostingClassifier(random_state=0)</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-235" type="checkbox" ><label for="sk-estimator-id-235" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>HistGradientBoostingClassifier</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.ensemble.HistGradientBoostingClassifier.html">?<span>Documentation for HistGradientBoostingClassifier</span></a></div></label><div class="sk-toggleable__content fitted"><pre>HistGradientBoostingClassifier(random_state=0)</pre></div> </div></div></div></div></div></div></div></div></div>
+                           store_cv_results=True)</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-248" type="checkbox" ><label for="sk-estimator-id-248" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>estimator: HistGradientBoostingClassifier</div></div></label><div class="sk-toggleable__content fitted"><pre>HistGradientBoostingClassifier(random_state=0)</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-249" type="checkbox" ><label for="sk-estimator-id-249" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>HistGradientBoostingClassifier</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.ensemble.HistGradientBoostingClassifier.html">?<span>Documentation for HistGradientBoostingClassifier</span></a></div></label><div class="sk-toggleable__content fitted"><pre>HistGradientBoostingClassifier(random_state=0)</pre></div> </div></div></div></div></div></div></div></div></div>
 </div>
 <br />
 <br /><div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">title</span> <span class="o">=</span> <span class="s2">&quot;Tuned GBDT model without refitting and using the entire dataset&quot;</span>
@@ -2511,7 +2510,7 @@ <h3>Addressing the problem with a business metric<a class="headerlink" href="#ad
 </pre></div>
 </div>
 <div class="output_subarea output_html rendered_html output_result">
-<style>#sk-container-id-59 {
+<style>#sk-container-id-60 {
   /* Definition of color scheme common for light and dark mode */
   --sklearn-color-text: #000;
   --sklearn-color-text-muted: #666;
@@ -2542,15 +2541,15 @@ <h3>Addressing the problem with a business metric<a class="headerlink" href="#ad
   }
 }
 
-#sk-container-id-59 {
+#sk-container-id-60 {
   color: var(--sklearn-color-text);
 }
 
-#sk-container-id-59 pre {
+#sk-container-id-60 pre {
   padding: 0;
 }
 
-#sk-container-id-59 input.sk-hidden--visually {
+#sk-container-id-60 input.sk-hidden--visually {
   border: 0;
   clip: rect(1px 1px 1px 1px);
   clip: rect(1px, 1px, 1px, 1px);
@@ -2562,7 +2561,7 @@ <h3>Addressing the problem with a business metric<a class="headerlink" href="#ad
   width: 1px;
 }
 
-#sk-container-id-59 div.sk-dashed-wrapped {
+#sk-container-id-60 div.sk-dashed-wrapped {
   border: 1px dashed var(--sklearn-color-line);
   margin: 0 0.4em 0.5em 0.4em;
   box-sizing: border-box;
@@ -2570,7 +2569,7 @@ <h3>Addressing the problem with a business metric<a class="headerlink" href="#ad
   background-color: var(--sklearn-color-background);
 }
 
-#sk-container-id-59 div.sk-container {
+#sk-container-id-60 div.sk-container {
   /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
      but bootstrap.min.css set `[hidden] { display: none !important; }`
      so we also need the `!important` here to be able to override the
@@ -2580,7 +2579,7 @@ <h3>Addressing the problem with a business metric<a class="headerlink" href="#ad
   position: relative;
 }
 
-#sk-container-id-59 div.sk-text-repr-fallback {
+#sk-container-id-60 div.sk-text-repr-fallback {
   display: none;
 }
 
@@ -2596,14 +2595,14 @@ <h3>Addressing the problem with a business metric<a class="headerlink" href="#ad
 
 /* Parallel-specific style estimator block */
 
-#sk-container-id-59 div.sk-parallel-item::after {
+#sk-container-id-60 div.sk-parallel-item::after {
   content: "";
   width: 100%;
   border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
   flex-grow: 1;
 }
 
-#sk-container-id-59 div.sk-parallel {
+#sk-container-id-60 div.sk-parallel {
   display: flex;
   align-items: stretch;
   justify-content: center;
@@ -2611,28 +2610,28 @@ <h3>Addressing the problem with a business metric<a class="headerlink" href="#ad
   position: relative;
 }
 
-#sk-container-id-59 div.sk-parallel-item {
+#sk-container-id-60 div.sk-parallel-item {
   display: flex;
   flex-direction: column;
 }
 
-#sk-container-id-59 div.sk-parallel-item:first-child::after {
+#sk-container-id-60 div.sk-parallel-item:first-child::after {
   align-self: flex-end;
   width: 50%;
 }
 
-#sk-container-id-59 div.sk-parallel-item:last-child::after {
+#sk-container-id-60 div.sk-parallel-item:last-child::after {
   align-self: flex-start;
   width: 50%;
 }
 
-#sk-container-id-59 div.sk-parallel-item:only-child::after {
+#sk-container-id-60 div.sk-parallel-item:only-child::after {
   width: 0;
 }
 
 /* Serial-specific style estimator block */
 
-#sk-container-id-59 div.sk-serial {
+#sk-container-id-60 div.sk-serial {
   display: flex;
   flex-direction: column;
   align-items: center;
@@ -2650,14 +2649,14 @@ <h3>Addressing the problem with a business metric<a class="headerlink" href="#ad
 
 /* Pipeline and ColumnTransformer style (default) */
 
-#sk-container-id-59 div.sk-toggleable {
+#sk-container-id-60 div.sk-toggleable {
   /* Default theme specific background. It is overwritten whether we have a
   specific estimator or a Pipeline/ColumnTransformer */
   background-color: var(--sklearn-color-background);
 }
 
 /* Toggleable label */
-#sk-container-id-59 label.sk-toggleable__label {
+#sk-container-id-60 label.sk-toggleable__label {
   cursor: pointer;
   display: flex;
   width: 100%;
@@ -2670,13 +2669,13 @@ <h3>Addressing the problem with a business metric<a class="headerlink" href="#ad
   gap: 0.5em;
 }
 
-#sk-container-id-59 label.sk-toggleable__label .caption {
+#sk-container-id-60 label.sk-toggleable__label .caption {
   font-size: 0.6rem;
   font-weight: lighter;
   color: var(--sklearn-color-text-muted);
 }
 
-#sk-container-id-59 label.sk-toggleable__label-arrow:before {
+#sk-container-id-60 label.sk-toggleable__label-arrow:before {
   /* Arrow on the left of the label */
   content: "▸";
   float: left;
@@ -2684,13 +2683,13 @@ <h3>Addressing the problem with a business metric<a class="headerlink" href="#ad
   color: var(--sklearn-color-icon);
 }
 
-#sk-container-id-59 label.sk-toggleable__label-arrow:hover:before {
+#sk-container-id-60 label.sk-toggleable__label-arrow:hover:before {
   color: var(--sklearn-color-text);
 }
 
 /* Toggleable content - dropdown */
 
-#sk-container-id-59 div.sk-toggleable__content {
+#sk-container-id-60 div.sk-toggleable__content {
   max-height: 0;
   max-width: 0;
   overflow: hidden;
@@ -2699,12 +2698,12 @@ <h3>Addressing the problem with a business metric<a class="headerlink" href="#ad
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-59 div.sk-toggleable__content.fitted {
+#sk-container-id-60 div.sk-toggleable__content.fitted {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
-#sk-container-id-59 div.sk-toggleable__content pre {
+#sk-container-id-60 div.sk-toggleable__content pre {
   margin: 0.2em;
   border-radius: 0.25em;
   color: var(--sklearn-color-text);
@@ -2712,79 +2711,79 @@ <h3>Addressing the problem with a business metric<a class="headerlink" href="#ad
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-59 div.sk-toggleable__content.fitted pre {
+#sk-container-id-60 div.sk-toggleable__content.fitted pre {
   /* unfitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
-#sk-container-id-59 input.sk-toggleable__control:checked~div.sk-toggleable__content {
+#sk-container-id-60 input.sk-toggleable__control:checked~div.sk-toggleable__content {
   /* Expand drop-down */
   max-height: 200px;
   max-width: 100%;
   overflow: auto;
 }
 
-#sk-container-id-59 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
+#sk-container-id-60 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
   content: "▾";
 }
 
 /* Pipeline/ColumnTransformer-specific style */
 
-#sk-container-id-59 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-60 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-59 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-60 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
 /* Estimator-specific style */
 
 /* Colorize estimator box */
-#sk-container-id-59 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-60 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-59 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-60 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
-#sk-container-id-59 div.sk-label label.sk-toggleable__label,
-#sk-container-id-59 div.sk-label label {
+#sk-container-id-60 div.sk-label label.sk-toggleable__label,
+#sk-container-id-60 div.sk-label label {
   /* The background is the default theme color */
   color: var(--sklearn-color-text-on-default-background);
 }
 
 /* On hover, darken the color of the background */
-#sk-container-id-59 div.sk-label:hover label.sk-toggleable__label {
+#sk-container-id-60 div.sk-label:hover label.sk-toggleable__label {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
 /* Label box, darken color on hover, fitted */
-#sk-container-id-59 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
+#sk-container-id-60 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
 /* Estimator label */
 
-#sk-container-id-59 div.sk-label label {
+#sk-container-id-60 div.sk-label label {
   font-family: monospace;
   font-weight: bold;
   display: inline-block;
   line-height: 1.2em;
 }
 
-#sk-container-id-59 div.sk-label-container {
+#sk-container-id-60 div.sk-label-container {
   text-align: center;
 }
 
 /* Estimator-specific */
-#sk-container-id-59 div.sk-estimator {
+#sk-container-id-60 div.sk-estimator {
   font-family: monospace;
   border: 1px dotted var(--sklearn-color-border-box);
   border-radius: 0.25em;
@@ -2794,18 +2793,18 @@ <h3>Addressing the problem with a business metric<a class="headerlink" href="#ad
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-59 div.sk-estimator.fitted {
+#sk-container-id-60 div.sk-estimator.fitted {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
 /* on hover */
-#sk-container-id-59 div.sk-estimator:hover {
+#sk-container-id-60 div.sk-estimator:hover {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-59 div.sk-estimator.fitted:hover {
+#sk-container-id-60 div.sk-estimator.fitted:hover {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-2);
 }
@@ -2893,7 +2892,7 @@ <h3>Addressing the problem with a business metric<a class="headerlink" href="#ad
 
 /* "?"-specific style due to the `<a>` HTML tag */
 
-#sk-container-id-59 a.estimator_doc_link {
+#sk-container-id-60 a.estimator_doc_link {
   float: right;
   font-size: 1rem;
   line-height: 1em;
@@ -2908,37 +2907,37 @@ <h3>Addressing the problem with a business metric<a class="headerlink" href="#ad
   border: var(--sklearn-color-unfitted-level-1) 1pt solid;
 }
 
-#sk-container-id-59 a.estimator_doc_link.fitted {
+#sk-container-id-60 a.estimator_doc_link.fitted {
   /* fitted */
   border: var(--sklearn-color-fitted-level-1) 1pt solid;
   color: var(--sklearn-color-fitted-level-1);
 }
 
 /* On hover */
-#sk-container-id-59 a.estimator_doc_link:hover {
+#sk-container-id-60 a.estimator_doc_link:hover {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-3);
   color: var(--sklearn-color-background);
   text-decoration: none;
 }
 
-#sk-container-id-59 a.estimator_doc_link.fitted:hover {
+#sk-container-id-60 a.estimator_doc_link.fitted:hover {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-3);
 }
-</style><div id="sk-container-id-59" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>GridSearchCV(estimator=Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),
+</style><div id="sk-container-id-60" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>GridSearchCV(estimator=Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),
                                        (&#x27;logisticregression&#x27;,
                                         LogisticRegression())]),
              param_grid={&#x27;logisticregression__C&#x27;: array([1.e-06, 1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01,
        1.e+02, 1.e+03, 1.e+04, 1.e+05, 1.e+06])},
-             scoring=&#x27;neg_log_loss&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-236" type="checkbox" ><label for="sk-estimator-id-236" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>GridSearchCV</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.model_selection.GridSearchCV.html">?<span>Documentation for GridSearchCV</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>GridSearchCV(estimator=Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),
+             scoring=&#x27;neg_log_loss&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-250" type="checkbox" ><label for="sk-estimator-id-250" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>GridSearchCV</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.model_selection.GridSearchCV.html">?<span>Documentation for GridSearchCV</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>GridSearchCV(estimator=Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),
                                        (&#x27;logisticregression&#x27;,
                                         LogisticRegression())]),
              param_grid={&#x27;logisticregression__C&#x27;: array([1.e-06, 1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01,
        1.e+02, 1.e+03, 1.e+04, 1.e+05, 1.e+06])},
-             scoring=&#x27;neg_log_loss&#x27;)</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-237" type="checkbox" ><label for="sk-estimator-id-237" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>best_estimator_: Pipeline</div></div></label><div class="sk-toggleable__content fitted"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),
+             scoring=&#x27;neg_log_loss&#x27;)</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-251" type="checkbox" ><label for="sk-estimator-id-251" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>best_estimator_: Pipeline</div></div></label><div class="sk-toggleable__content fitted"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),
                 (&#x27;logisticregression&#x27;,
-                 LogisticRegression(C=np.float64(100.0)))])</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-238" type="checkbox" ><label for="sk-estimator-id-238" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>StandardScaler</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.StandardScaler.html">?<span>Documentation for StandardScaler</span></a></div></label><div class="sk-toggleable__content fitted"><pre>StandardScaler()</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-239" type="checkbox" ><label for="sk-estimator-id-239" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>LogisticRegression</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.linear_model.LogisticRegression.html">?<span>Documentation for LogisticRegression</span></a></div></label><div class="sk-toggleable__content fitted"><pre>LogisticRegression(C=np.float64(100.0))</pre></div> </div></div></div></div></div></div></div></div></div></div></div>
+                 LogisticRegression(C=np.float64(100.0)))])</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-252" type="checkbox" ><label for="sk-estimator-id-252" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>StandardScaler</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.StandardScaler.html">?<span>Documentation for StandardScaler</span></a></div></label><div class="sk-toggleable__content fitted"><pre>StandardScaler()</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-253" type="checkbox" ><label for="sk-estimator-id-253" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>LogisticRegression</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.linear_model.LogisticRegression.html">?<span>Documentation for LogisticRegression</span></a></div></label><div class="sk-toggleable__content fitted"><pre>LogisticRegression(C=np.float64(100.0))</pre></div> </div></div></div></div></div></div></div></div></div></div></div>
 </div>
 <br />
 <br /><div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span>
@@ -2978,7 +2977,7 @@ <h3>Tuning the decision threshold<a class="headerlink" href="#tuning-the-decisio
 </pre></div>
 </div>
 <div class="output_subarea output_html rendered_html output_result">
-<style>#sk-container-id-60 {
+<style>#sk-container-id-61 {
   /* Definition of color scheme common for light and dark mode */
   --sklearn-color-text: #000;
   --sklearn-color-text-muted: #666;
@@ -3009,15 +3008,15 @@ <h3>Tuning the decision threshold<a class="headerlink" href="#tuning-the-decisio
   }
 }
 
-#sk-container-id-60 {
+#sk-container-id-61 {
   color: var(--sklearn-color-text);
 }
 
-#sk-container-id-60 pre {
+#sk-container-id-61 pre {
   padding: 0;
 }
 
-#sk-container-id-60 input.sk-hidden--visually {
+#sk-container-id-61 input.sk-hidden--visually {
   border: 0;
   clip: rect(1px 1px 1px 1px);
   clip: rect(1px, 1px, 1px, 1px);
@@ -3029,7 +3028,7 @@ <h3>Tuning the decision threshold<a class="headerlink" href="#tuning-the-decisio
   width: 1px;
 }
 
-#sk-container-id-60 div.sk-dashed-wrapped {
+#sk-container-id-61 div.sk-dashed-wrapped {
   border: 1px dashed var(--sklearn-color-line);
   margin: 0 0.4em 0.5em 0.4em;
   box-sizing: border-box;
@@ -3037,7 +3036,7 @@ <h3>Tuning the decision threshold<a class="headerlink" href="#tuning-the-decisio
   background-color: var(--sklearn-color-background);
 }
 
-#sk-container-id-60 div.sk-container {
+#sk-container-id-61 div.sk-container {
   /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
      but bootstrap.min.css set `[hidden] { display: none !important; }`
      so we also need the `!important` here to be able to override the
@@ -3047,7 +3046,7 @@ <h3>Tuning the decision threshold<a class="headerlink" href="#tuning-the-decisio
   position: relative;
 }
 
-#sk-container-id-60 div.sk-text-repr-fallback {
+#sk-container-id-61 div.sk-text-repr-fallback {
   display: none;
 }
 
@@ -3063,14 +3062,14 @@ <h3>Tuning the decision threshold<a class="headerlink" href="#tuning-the-decisio
 
 /* Parallel-specific style estimator block */
 
-#sk-container-id-60 div.sk-parallel-item::after {
+#sk-container-id-61 div.sk-parallel-item::after {
   content: "";
   width: 100%;
   border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
   flex-grow: 1;
 }
 
-#sk-container-id-60 div.sk-parallel {
+#sk-container-id-61 div.sk-parallel {
   display: flex;
   align-items: stretch;
   justify-content: center;
@@ -3078,28 +3077,28 @@ <h3>Tuning the decision threshold<a class="headerlink" href="#tuning-the-decisio
   position: relative;
 }
 
-#sk-container-id-60 div.sk-parallel-item {
+#sk-container-id-61 div.sk-parallel-item {
   display: flex;
   flex-direction: column;
 }
 
-#sk-container-id-60 div.sk-parallel-item:first-child::after {
+#sk-container-id-61 div.sk-parallel-item:first-child::after {
   align-self: flex-end;
   width: 50%;
 }
 
-#sk-container-id-60 div.sk-parallel-item:last-child::after {
+#sk-container-id-61 div.sk-parallel-item:last-child::after {
   align-self: flex-start;
   width: 50%;
 }
 
-#sk-container-id-60 div.sk-parallel-item:only-child::after {
+#sk-container-id-61 div.sk-parallel-item:only-child::after {
   width: 0;
 }
 
 /* Serial-specific style estimator block */
 
-#sk-container-id-60 div.sk-serial {
+#sk-container-id-61 div.sk-serial {
   display: flex;
   flex-direction: column;
   align-items: center;
@@ -3117,14 +3116,14 @@ <h3>Tuning the decision threshold<a class="headerlink" href="#tuning-the-decisio
 
 /* Pipeline and ColumnTransformer style (default) */
 
-#sk-container-id-60 div.sk-toggleable {
+#sk-container-id-61 div.sk-toggleable {
   /* Default theme specific background. It is overwritten whether we have a
   specific estimator or a Pipeline/ColumnTransformer */
   background-color: var(--sklearn-color-background);
 }
 
 /* Toggleable label */
-#sk-container-id-60 label.sk-toggleable__label {
+#sk-container-id-61 label.sk-toggleable__label {
   cursor: pointer;
   display: flex;
   width: 100%;
@@ -3137,13 +3136,13 @@ <h3>Tuning the decision threshold<a class="headerlink" href="#tuning-the-decisio
   gap: 0.5em;
 }
 
-#sk-container-id-60 label.sk-toggleable__label .caption {
+#sk-container-id-61 label.sk-toggleable__label .caption {
   font-size: 0.6rem;
   font-weight: lighter;
   color: var(--sklearn-color-text-muted);
 }
 
-#sk-container-id-60 label.sk-toggleable__label-arrow:before {
+#sk-container-id-61 label.sk-toggleable__label-arrow:before {
   /* Arrow on the left of the label */
   content: "▸";
   float: left;
@@ -3151,13 +3150,13 @@ <h3>Tuning the decision threshold<a class="headerlink" href="#tuning-the-decisio
   color: var(--sklearn-color-icon);
 }
 
-#sk-container-id-60 label.sk-toggleable__label-arrow:hover:before {
+#sk-container-id-61 label.sk-toggleable__label-arrow:hover:before {
   color: var(--sklearn-color-text);
 }
 
 /* Toggleable content - dropdown */
 
-#sk-container-id-60 div.sk-toggleable__content {
+#sk-container-id-61 div.sk-toggleable__content {
   max-height: 0;
   max-width: 0;
   overflow: hidden;
@@ -3166,12 +3165,12 @@ <h3>Tuning the decision threshold<a class="headerlink" href="#tuning-the-decisio
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-60 div.sk-toggleable__content.fitted {
+#sk-container-id-61 div.sk-toggleable__content.fitted {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
-#sk-container-id-60 div.sk-toggleable__content pre {
+#sk-container-id-61 div.sk-toggleable__content pre {
   margin: 0.2em;
   border-radius: 0.25em;
   color: var(--sklearn-color-text);
@@ -3179,79 +3178,79 @@ <h3>Tuning the decision threshold<a class="headerlink" href="#tuning-the-decisio
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-60 div.sk-toggleable__content.fitted pre {
+#sk-container-id-61 div.sk-toggleable__content.fitted pre {
   /* unfitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
-#sk-container-id-60 input.sk-toggleable__control:checked~div.sk-toggleable__content {
+#sk-container-id-61 input.sk-toggleable__control:checked~div.sk-toggleable__content {
   /* Expand drop-down */
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   max-width: 100%;
   overflow: auto;
 }
 
-#sk-container-id-60 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
+#sk-container-id-61 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
   content: "▾";
 }
 
 /* Pipeline/ColumnTransformer-specific style */
 
-#sk-container-id-60 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-61 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-60 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-61 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
 /* Estimator-specific style */
 
 /* Colorize estimator box */
-#sk-container-id-60 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-61 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-60 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-61 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
-#sk-container-id-60 div.sk-label label.sk-toggleable__label,
-#sk-container-id-60 div.sk-label label {
+#sk-container-id-61 div.sk-label label.sk-toggleable__label,
+#sk-container-id-61 div.sk-label label {
   /* The background is the default theme color */
   color: var(--sklearn-color-text-on-default-background);
 }
 
 /* On hover, darken the color of the background */
-#sk-container-id-60 div.sk-label:hover label.sk-toggleable__label {
+#sk-container-id-61 div.sk-label:hover label.sk-toggleable__label {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
 /* Label box, darken color on hover, fitted */
-#sk-container-id-60 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
+#sk-container-id-61 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
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   background-color: var(--sklearn-color-fitted-level-2);
 }
 
 /* Estimator label */
 
-#sk-container-id-60 div.sk-label label {
+#sk-container-id-61 div.sk-label label {
   font-family: monospace;
   font-weight: bold;
   display: inline-block;
   line-height: 1.2em;
 }
 
-#sk-container-id-60 div.sk-label-container {
+#sk-container-id-61 div.sk-label-container {
   text-align: center;
 }
 
 /* Estimator-specific */
-#sk-container-id-60 div.sk-estimator {
+#sk-container-id-61 div.sk-estimator {
   font-family: monospace;
   border: 1px dotted var(--sklearn-color-border-box);
   border-radius: 0.25em;
@@ -3261,18 +3260,18 @@ <h3>Tuning the decision threshold<a class="headerlink" href="#tuning-the-decisio
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-60 div.sk-estimator.fitted {
+#sk-container-id-61 div.sk-estimator.fitted {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
 /* on hover */
-#sk-container-id-60 div.sk-estimator:hover {
+#sk-container-id-61 div.sk-estimator:hover {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-60 div.sk-estimator.fitted:hover {
+#sk-container-id-61 div.sk-estimator.fitted:hover {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-2);
 }
@@ -3360,7 +3359,7 @@ <h3>Tuning the decision threshold<a class="headerlink" href="#tuning-the-decisio
 
 /* "?"-specific style due to the `<a>` HTML tag */
 
-#sk-container-id-60 a.estimator_doc_link {
+#sk-container-id-61 a.estimator_doc_link {
   float: right;
   font-size: 1rem;
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@@ -3375,37 +3374,37 @@ <h3>Tuning the decision threshold<a class="headerlink" href="#tuning-the-decisio
   border: var(--sklearn-color-unfitted-level-1) 1pt solid;
 }
 
-#sk-container-id-60 a.estimator_doc_link.fitted {
+#sk-container-id-61 a.estimator_doc_link.fitted {
   /* fitted */
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   color: var(--sklearn-color-fitted-level-1);
 }
 
 /* On hover */
-#sk-container-id-60 a.estimator_doc_link:hover {
+#sk-container-id-61 a.estimator_doc_link:hover {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-3);
   color: var(--sklearn-color-background);
   text-decoration: none;
 }
 
-#sk-container-id-60 a.estimator_doc_link.fitted:hover {
+#sk-container-id-61 a.estimator_doc_link.fitted:hover {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-3);
 }
-</style><div id="sk-container-id-60" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>TunedThresholdClassifierCV(estimator=Pipeline(steps=[(&#x27;standardscaler&#x27;,
+</style><div id="sk-container-id-61" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>TunedThresholdClassifierCV(estimator=Pipeline(steps=[(&#x27;standardscaler&#x27;,
                                                       StandardScaler()),
                                                      (&#x27;logisticregression&#x27;,
                                                       LogisticRegression(C=np.float64(100.0)))]),
                            n_jobs=2,
-                           scoring=make_scorer(business_metric, response_method=&#x27;predict&#x27;))</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-240" type="checkbox" ><label for="sk-estimator-id-240" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>TunedThresholdClassifierCV</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.model_selection.TunedThresholdClassifierCV.html">?<span>Documentation for TunedThresholdClassifierCV</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>TunedThresholdClassifierCV(estimator=Pipeline(steps=[(&#x27;standardscaler&#x27;,
+                           scoring=make_scorer(business_metric, response_method=&#x27;predict&#x27;))</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-254" type="checkbox" ><label for="sk-estimator-id-254" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>TunedThresholdClassifierCV</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.model_selection.TunedThresholdClassifierCV.html">?<span>Documentation for TunedThresholdClassifierCV</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>TunedThresholdClassifierCV(estimator=Pipeline(steps=[(&#x27;standardscaler&#x27;,
                                                       StandardScaler()),
                                                      (&#x27;logisticregression&#x27;,
                                                       LogisticRegression(C=np.float64(100.0)))]),
                            n_jobs=2,
-                           scoring=make_scorer(business_metric, response_method=&#x27;predict&#x27;))</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-241" type="checkbox" ><label for="sk-estimator-id-241" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>estimator: Pipeline</div></div></label><div class="sk-toggleable__content fitted"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),
+                           scoring=make_scorer(business_metric, response_method=&#x27;predict&#x27;))</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-255" type="checkbox" ><label for="sk-estimator-id-255" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>estimator: Pipeline</div></div></label><div class="sk-toggleable__content fitted"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),
                 (&#x27;logisticregression&#x27;,
-                 LogisticRegression(C=np.float64(100.0)))])</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-242" type="checkbox" ><label for="sk-estimator-id-242" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>StandardScaler</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.StandardScaler.html">?<span>Documentation for StandardScaler</span></a></div></label><div class="sk-toggleable__content fitted"><pre>StandardScaler()</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-243" type="checkbox" ><label for="sk-estimator-id-243" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>LogisticRegression</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.linear_model.LogisticRegression.html">?<span>Documentation for LogisticRegression</span></a></div></label><div class="sk-toggleable__content fitted"><pre>LogisticRegression(C=np.float64(100.0))</pre></div> </div></div></div></div></div></div></div></div></div></div></div>
+                 LogisticRegression(C=np.float64(100.0)))])</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-256" type="checkbox" ><label for="sk-estimator-id-256" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>StandardScaler</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.StandardScaler.html">?<span>Documentation for StandardScaler</span></a></div></label><div class="sk-toggleable__content fitted"><pre>StandardScaler()</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-257" type="checkbox" ><label for="sk-estimator-id-257" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>LogisticRegression</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.linear_model.LogisticRegression.html">?<span>Documentation for LogisticRegression</span></a></div></label><div class="sk-toggleable__content fitted"><pre>LogisticRegression(C=np.float64(100.0))</pre></div> </div></div></div></div></div></div></div></div></div></div></div>
 </div>
 <br />
 <br /><p>We observe that the tuned decision threshold is far away from the default 0.5:</p>
@@ -3471,7 +3470,7 @@ <h3>Manually setting the decision threshold instead of tuning it<a class="header
 <a href="http://github.com/modules/generated/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config" class="sphx-glr-backref-module-sklearn sphx-glr-backref-type-py-function"><span class="n">sklearn</span><span class="o">.</span><span class="n">set_config</span></a><span class="p">(</span><span class="n">enable_metadata_routing</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
 </pre></div>
 </div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 25.109 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 28.551 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-model-selection-plot-cost-sensitive-learning-py">
 <div class="binder-badge docutils container">
 <a class="reference external image-reference" href="https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/model_selection/plot_cost_sensitive_learning.ipynb"><img alt="Launch binder" src="http://github.com/_images/binder_badge_logo22.svg" style="width: 150px;" />
diff --git a/dev/auto_examples/model_selection/plot_cv_indices.html b/dev/auto_examples/model_selection/plot_cv_indices.html
index f3bf3aed36f2c..4ebac7819c542 100644
--- a/dev/auto_examples/model_selection/plot_cv_indices.html
+++ b/dev/auto_examples/model_selection/plot_cv_indices.html
@@ -42,7 +42,7 @@
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     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -679,13 +679,12 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -902,9 +901,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
@@ -1214,7 +1213,7 @@ <h2>Visualize cross-validation indices for many CV objects<a class="headerlink"
 <li><img src="http://github.com/_images/sphx_glr_plot_cv_indices_012.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_cv_indices_012.png " alt="StratifiedShuffleSplit" class = "sphx-glr-multi-img"/></li>
 <li><img src="http://github.com/_images/sphx_glr_plot_cv_indices_013.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_cv_indices_013.png " alt="TimeSeriesSplit" class = "sphx-glr-multi-img"/></li>
 </ul>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 1.282 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 1.322 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-model-selection-plot-cv-indices-py">
 <div class="binder-badge docutils container">
 <a class="reference external image-reference" href="https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/model_selection/plot_cv_indices.ipynb"><img alt="Launch binder" src="http://github.com/_images/binder_badge_logo22.svg" style="width: 150px;" />
@@ -1235,12 +1234,12 @@ <h2>Visualize cross-validation indices for many CV objects<a class="headerlink"
 </div>
 </div>
 <p class="rubric">Related examples</p>
-<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This example compares non-nested and nested cross-validation strategies on a classifier of the iris data set. Nested cross-validation (CV) is often used to train a model in which hyperparameters also need to be optimized. Nested CV estimates the generalization error of the underlying model and its (hyper)parameter search. Choosing the parameters that maximize non-nested CV biases the model to the dataset, yielding an overly-optimistic score."><img alt="" src="http://github.com/_images/sphx_glr_plot_nested_cross_validation_iris_thumb.png" />
-<p><a class="reference internal" href="http://github.com/plot_nested_cross_validation_iris.html#sphx-glr-auto-examples-model-selection-plot-nested-cross-validation-iris-py"><span class="std std-ref">Nested versus non-nested cross-validation</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Nested versus non-nested cross-validation</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="A Recursive Feature Elimination (RFE) example with automatic tuning of the number of features selected with cross-validation."><img alt="" src="http://github.com/_images/sphx_glr_plot_rfe_with_cross_validation_thumb.png" />
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="A Recursive Feature Elimination (RFE) example with automatic tuning of the number of features selected with cross-validation."><img alt="" src="http://github.com/_images/sphx_glr_plot_rfe_with_cross_validation_thumb.png" />
 <p><a class="reference internal" href="http://github.com/feature_selection/plot_rfe_with_cross_validation.html#sphx-glr-auto-examples-feature-selection-plot-rfe-with-cross-validation-py"><span class="std std-ref">Recursive feature elimination with cross-validation</span></a></p>
   <div class="sphx-glr-thumbnail-title">Recursive feature elimination with cross-validation</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example compares non-nested and nested cross-validation strategies on a classifier of the iris data set. Nested cross-validation (CV) is often used to train a model in which hyperparameters also need to be optimized. Nested CV estimates the generalization error of the underlying model and its (hyper)parameter search. Choosing the parameters that maximize non-nested CV biases the model to the dataset, yielding an overly-optimistic score."><img alt="" src="http://github.com/_images/sphx_glr_plot_nested_cross_validation_iris_thumb.png" />
+<p><a class="reference internal" href="http://github.com/plot_nested_cross_validation_iris.html#sphx-glr-auto-examples-model-selection-plot-nested-cross-validation-iris-py"><span class="std std-ref">Nested versus non-nested cross-validation</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Nested versus non-nested cross-validation</div>
 </div><div class="sphx-glr-thumbcontainer" tooltip="This example presents how to estimate and visualize the variance of the Receiver Operating Characteristic (ROC) metric using cross-validation."><img alt="" src="http://github.com/_images/sphx_glr_plot_roc_crossval_thumb.png" />
 <p><a class="reference internal" href="http://github.com/plot_roc_crossval.html#sphx-glr-auto-examples-model-selection-plot-roc-crossval-py"><span class="std std-ref">Receiver Operating Characteristic (ROC) with cross validation</span></a></p>
   <div class="sphx-glr-thumbnail-title">Receiver Operating Characteristic (ROC) with cross validation</div>
diff --git a/dev/auto_examples/model_selection/plot_cv_predict.html b/dev/auto_examples/model_selection/plot_cv_predict.html
index 65653d03d8e51..576f589c7f78d 100644
--- a/dev/auto_examples/model_selection/plot_cv_predict.html
+++ b/dev/auto_examples/model_selection/plot_cv_predict.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -680,13 +680,12 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -903,9 +902,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
@@ -1067,7 +1066,7 @@
 <p>It is recommended to compute per-fold performance metrics using:
 <a class="reference internal" href="http://github.com/modules/generated/sklearn.model_selection.cross_val_score.html#sklearn.model_selection.cross_val_score" title="sklearn.model_selection.cross_val_score"><code class="xref py py-func docutils literal notranslate"><span class="pre">cross_val_score</span></code></a> or
 <a class="reference internal" href="http://github.com/modules/generated/sklearn.model_selection.cross_validate.html#sklearn.model_selection.cross_validate" title="sklearn.model_selection.cross_validate"><code class="xref py py-func docutils literal notranslate"><span class="pre">cross_validate</span></code></a> instead.</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 0.190 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 0.189 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-model-selection-plot-cv-predict-py">
 <div class="binder-badge docutils container">
 <a class="reference external image-reference" href="https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/model_selection/plot_cv_predict.ipynb"><img alt="Launch binder" src="http://github.com/_images/binder_badge_logo22.svg" style="width: 150px;" />
diff --git a/dev/auto_examples/model_selection/plot_det.html b/dev/auto_examples/model_selection/plot_det.html
index 381530a55b549..65427fd6bca25 100644
--- a/dev/auto_examples/model_selection/plot_det.html
+++ b/dev/auto_examples/model_selection/plot_det.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -65,6 +65,7 @@
     <script src="http://github.com/_static/jupyterlite_sphinx.js?v=96e329c5"></script>
     <script src="http://github.com/_static/design-tabs.js?v=f930bc37"></script>
     <script data-domain="scikit-learn.org" defer="defer" src="https://views.scientific-python.org/js/script.js"></script>
+    <script async="async" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-chtml.js"></script>
     <script>DOCUMENTATION_OPTIONS.pagename = 'auto_examples/model_selection/plot_det';</script>
     <script>
         DOCUMENTATION_OPTIONS.theme_version = '0.16.1';
@@ -679,13 +680,12 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -902,9 +902,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
@@ -1044,46 +1044,53 @@ <h2>Generate synthetic data<a class="headerlink" href="#generate-synthetic-data"
 <section id="define-the-classifiers">
 <h2>Define the classifiers<a class="headerlink" href="#define-the-classifiers" title="Link to this heading">#</a></h2>
 <p>Here we define two different classifiers. The goal is to visually compare their
-statistical performance across thresholds using the ROC and DET curves. There
-is no particular reason why these classifiers are chosen other classifiers
-available in scikit-learn.</p>
-<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.ensemble</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.ensemble.RandomForestClassifier.html#sklearn.ensemble.RandomForestClassifier" title="sklearn.ensemble.RandomForestClassifier" class="sphx-glr-backref-module-sklearn-ensemble sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">RandomForestClassifier</span></a>
+statistical performance across thresholds using the ROC and DET curves.</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.dummy</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.dummy.DummyClassifier.html#sklearn.dummy.DummyClassifier" title="sklearn.dummy.DummyClassifier" class="sphx-glr-backref-module-sklearn-dummy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">DummyClassifier</span></a>
+<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.ensemble</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.ensemble.RandomForestClassifier.html#sklearn.ensemble.RandomForestClassifier" title="sklearn.ensemble.RandomForestClassifier" class="sphx-glr-backref-module-sklearn-ensemble sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">RandomForestClassifier</span></a>
 <span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.pipeline</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.pipeline.make_pipeline.html#sklearn.pipeline.make_pipeline" title="sklearn.pipeline.make_pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-function"><span class="n">make_pipeline</span></a>
 <span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.svm</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.svm.LinearSVC.html#sklearn.svm.LinearSVC" title="sklearn.svm.LinearSVC" class="sphx-glr-backref-module-sklearn-svm sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">LinearSVC</span></a>
 
 <span class="n">classifiers</span> <span class="o">=</span> <span class="p">{</span>
     <span class="s2">&quot;Linear SVM&quot;</span><span class="p">:</span> <a href="http://github.com/modules/generated/sklearn.pipeline.make_pipeline.html#sklearn.pipeline.make_pipeline" title="sklearn.pipeline.make_pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-function"><span class="n">make_pipeline</span></a><span class="p">(</span><a href="http://github.com/modules/generated/sklearn.preprocessing.StandardScaler.html#sklearn.preprocessing.StandardScaler" title="sklearn.preprocessing.StandardScaler" class="sphx-glr-backref-module-sklearn-preprocessing sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">StandardScaler</span></a><span class="p">(),</span> <a href="http://github.com/modules/generated/sklearn.svm.LinearSVC.html#sklearn.svm.LinearSVC" title="sklearn.svm.LinearSVC" class="sphx-glr-backref-module-sklearn-svm sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">LinearSVC</span></a><span class="p">(</span><span class="n">C</span><span class="o">=</span><span class="mf">0.025</span><span class="p">)),</span>
     <span class="s2">&quot;Random Forest&quot;</span><span class="p">:</span> <a href="http://github.com/modules/generated/sklearn.ensemble.RandomForestClassifier.html#sklearn.ensemble.RandomForestClassifier" title="sklearn.ensemble.RandomForestClassifier" class="sphx-glr-backref-module-sklearn-ensemble sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">RandomForestClassifier</span></a><span class="p">(</span>
-        <span class="n">max_depth</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">n_estimators</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">max_features</span><span class="o">=</span><span class="mi">1</span>
+        <span class="n">max_depth</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">n_estimators</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">max_features</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span>
     <span class="p">),</span>
+    <span class="s2">&quot;Non-informative baseline&quot;</span><span class="p">:</span> <a href="http://github.com/modules/generated/sklearn.dummy.DummyClassifier.html#sklearn.dummy.DummyClassifier" title="sklearn.dummy.DummyClassifier" class="sphx-glr-backref-module-sklearn-dummy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">DummyClassifier</span></a><span class="p">(),</span>
 <span class="p">}</span>
 </pre></div>
 </div>
 </section>
-<section id="plot-roc-and-det-curves">
-<h2>Plot ROC and DET curves<a class="headerlink" href="#plot-roc-and-det-curves" title="Link to this heading">#</a></h2>
+<section id="compare-roc-and-det-curves">
+<h2>Compare ROC and DET curves<a class="headerlink" href="#compare-roc-and-det-curves" title="Link to this heading">#</a></h2>
 <p>DET curves are commonly plotted in normal deviate scale. To achieve this the
 DET display transforms the error rates as returned by the
 <a class="reference internal" href="http://github.com/modules/generated/sklearn.metrics.det_curve.html#sklearn.metrics.det_curve" title="sklearn.metrics.det_curve"><code class="xref py py-func docutils literal notranslate"><span class="pre">det_curve</span></code></a> and the axis scale using
 <code class="docutils literal notranslate"><span class="pre">scipy.stats.norm</span></code>.</p>
 <div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">matplotlib.pyplot</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">plt</span>
 
+<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.dummy</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.dummy.DummyClassifier.html#sklearn.dummy.DummyClassifier" title="sklearn.dummy.DummyClassifier" class="sphx-glr-backref-module-sklearn-dummy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">DummyClassifier</span></a>
 <span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.metrics</span><span class="w"> </span><span class="kn">import</span> <span class="n">DetCurveDisplay</span><span class="p">,</span> <span class="n">RocCurveDisplay</span>
 
 <span class="n">fig</span><span class="p">,</span> <span class="p">[</span><span class="n">ax_roc</span><span class="p">,</span> <span class="n">ax_det</span><span class="p">]</span> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="matplotlib.pyplot.subplots" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">subplots</span></a><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">11</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
 
-<span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">clf</span> <span class="ow">in</span> <span class="n">classifiers</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
-    <span class="n">clf</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
-
-    <a href="http://github.com/modules/generated/sklearn.metrics.RocCurveDisplay.html#sklearn.metrics.RocCurveDisplay.from_estimator" title="sklearn.metrics.RocCurveDisplay.from_estimator" class="sphx-glr-backref-module-sklearn-metrics-RocCurveDisplay sphx-glr-backref-type-py-method"><span class="n">RocCurveDisplay</span><span class="o">.</span><span class="n">from_estimator</span></a><span class="p">(</span><span class="n">clf</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax_roc</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="n">name</span><span class="p">)</span>
-    <a href="http://github.com/modules/generated/sklearn.metrics.DetCurveDisplay.html#sklearn.metrics.DetCurveDisplay.from_estimator" title="sklearn.metrics.DetCurveDisplay.from_estimator" class="sphx-glr-backref-module-sklearn-metrics-DetCurveDisplay sphx-glr-backref-type-py-method"><span class="n">DetCurveDisplay</span><span class="o">.</span><span class="n">from_estimator</span></a><span class="p">(</span><span class="n">clf</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax_det</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="n">name</span><span class="p">)</span>
-
 <span class="n">ax_roc</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;Receiver Operating Characteristic (ROC) curves&quot;</span><span class="p">)</span>
 <span class="n">ax_det</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;Detection Error Tradeoff (DET) curves&quot;</span><span class="p">)</span>
 
 <span class="n">ax_roc</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="n">linestyle</span><span class="o">=</span><span class="s2">&quot;--&quot;</span><span class="p">)</span>
 <span class="n">ax_det</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="n">linestyle</span><span class="o">=</span><span class="s2">&quot;--&quot;</span><span class="p">)</span>
 
+<span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">clf</span> <span class="ow">in</span> <span class="n">classifiers</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+    <span class="p">(</span><span class="n">color</span><span class="p">,</span> <span class="n">linestyle</span><span class="p">)</span> <span class="o">=</span> <span class="p">(</span>
+        <span class="p">(</span><span class="s2">&quot;black&quot;</span><span class="p">,</span> <span class="s2">&quot;--&quot;</span><span class="p">)</span> <span class="k">if</span> <span class="n">name</span> <span class="o">==</span> <span class="s2">&quot;Non-informative baseline&quot;</span> <span class="k">else</span> <span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+    <span class="p">)</span>
+    <span class="n">clf</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
+    <a href="http://github.com/modules/generated/sklearn.metrics.RocCurveDisplay.html#sklearn.metrics.RocCurveDisplay.from_estimator" title="sklearn.metrics.RocCurveDisplay.from_estimator" class="sphx-glr-backref-module-sklearn-metrics-RocCurveDisplay sphx-glr-backref-type-py-method"><span class="n">RocCurveDisplay</span><span class="o">.</span><span class="n">from_estimator</span></a><span class="p">(</span>
+        <span class="n">clf</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax_roc</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="n">name</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">color</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="n">linestyle</span>
+    <span class="p">)</span>
+    <a href="http://github.com/modules/generated/sklearn.metrics.DetCurveDisplay.html#sklearn.metrics.DetCurveDisplay.from_estimator" title="sklearn.metrics.DetCurveDisplay.from_estimator" class="sphx-glr-backref-module-sklearn-metrics-DetCurveDisplay sphx-glr-backref-type-py-method"><span class="n">DetCurveDisplay</span><span class="o">.</span><span class="n">from_estimator</span></a><span class="p">(</span>
+        <span class="n">clf</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax_det</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="n">name</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">color</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="n">linestyle</span>
+    <span class="p">)</span>
+
 <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.legend.html#matplotlib.pyplot.legend" title="matplotlib.pyplot.legend" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">legend</span></a><span class="p">()</span>
 <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
 </pre></div>
@@ -1098,7 +1105,34 @@ <h2>Plot ROC and DET curves<a class="headerlink" href="#plot-roc-and-det-curves"
 <p>DET curves give direct feedback of the detection error tradeoff to aid in
 operating point analysis. The user can then decide the FNR they are willing to
 accept at the expense of the FPR (or vice-versa).</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 0.192 seconds)</p>
+</section>
+<section id="non-informative-classifier-baseline-for-the-roc-and-det-curves">
+<h2>Non-informative classifier baseline for the ROC and DET curves<a class="headerlink" href="#non-informative-classifier-baseline-for-the-roc-and-det-curves" title="Link to this heading">#</a></h2>
+<p>The diagonal black-dotted lines in the plots above correspond to a
+<a class="reference internal" href="http://github.com/modules/generated/sklearn.dummy.DummyClassifier.html#sklearn.dummy.DummyClassifier" title="sklearn.dummy.DummyClassifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">DummyClassifier</span></code></a> using the default “prior” strategy, to
+serve as baseline for comparison with other classifiers. This classifier makes
+constant predictions, independent of the input features in <code class="docutils literal notranslate"><span class="pre">X</span></code>, making it a
+non-informative classifier.</p>
+<p>To further understand the non-informative baseline of the ROC and DET curves,
+we recall the following mathematical definitions:</p>
+<p><span class="math notranslate nohighlight">\(\text{FPR} = \frac{\text{FP}}{\text{FP} + \text{TN}}\)</span></p>
+<p><span class="math notranslate nohighlight">\(\text{FNR} = \frac{\text{FN}}{\text{TP} + \text{FN}}\)</span></p>
+<p><span class="math notranslate nohighlight">\(\text{TPR} = \frac{\text{TP}}{\text{TP} + \text{FN}}\)</span></p>
+<p>A classifier that always predict the positive class would have no true
+negatives nor false negatives, giving <span class="math notranslate nohighlight">\(\text{FPR} = \text{TPR} = 1\)</span> and
+<span class="math notranslate nohighlight">\(\text{FNR} = 0\)</span>, i.e.:</p>
+<ul class="simple">
+<li><p>a single point in the upper right corner of the ROC plane,</p></li>
+<li><p>a single point in the lower right corner of the DET plane.</p></li>
+</ul>
+<p>Similarly, a classifier that always predict the negative class would have no
+true positives nor false positives, thus <span class="math notranslate nohighlight">\(\text{FPR} = \text{TPR} = 0\)</span>
+and <span class="math notranslate nohighlight">\(\text{FNR} = 1\)</span>, i.e.:</p>
+<ul class="simple">
+<li><p>a single point in the lower left corner of the ROC plane,</p></li>
+<li><p>a single point in the upper left corner of the DET plane.</p></li>
+</ul>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 0.206 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-model-selection-plot-det-py">
 <div class="binder-badge docutils container">
 <a class="reference external image-reference" href="https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/model_selection/plot_det.ipynb"><img alt="Launch binder" src="http://github.com/_images/binder_badge_logo22.svg" style="width: 150px;" />
@@ -1122,15 +1156,15 @@ <h2>Plot ROC and DET curves<a class="headerlink" href="#plot-roc-and-det-curves"
 <div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This example presents how to estimate and visualize the variance of the Receiver Operating Characteristic (ROC) metric using cross-validation."><img alt="" src="http://github.com/_images/sphx_glr_plot_roc_crossval_thumb.png" />
 <p><a class="reference internal" href="http://github.com/plot_roc_crossval.html#sphx-glr-auto-examples-model-selection-plot-roc-crossval-py"><span class="std std-ref">Receiver Operating Characteristic (ROC) with cross validation</span></a></p>
   <div class="sphx-glr-thumbnail-title">Receiver Operating Characteristic (ROC) with cross validation</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="ROC Curve with Visualization API"><img alt="" src="http://github.com/_images/sphx_glr_plot_roc_curve_visualization_api_thumb.png" />
-<p><a class="reference internal" href="http://github.com/miscellaneous/plot_roc_curve_visualization_api.html#sphx-glr-auto-examples-miscellaneous-plot-roc-curve-visualization-api-py"><span class="std std-ref">ROC Curve with Visualization API</span></a></p>
-  <div class="sphx-glr-thumbnail-title">ROC Curve with Visualization API</div>
 </div><div class="sphx-glr-thumbcontainer" tooltip="This example describes the use of the Receiver Operating Characteristic (ROC) metric to evaluate the quality of multiclass classifiers."><img alt="" src="http://github.com/_images/sphx_glr_plot_roc_thumb.png" />
 <p><a class="reference internal" href="http://github.com/plot_roc.html#sphx-glr-auto-examples-model-selection-plot-roc-py"><span class="std std-ref">Multiclass Receiver Operating Characteristic (ROC)</span></a></p>
   <div class="sphx-glr-thumbnail-title">Multiclass Receiver Operating Characteristic (ROC)</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="Transform your features into a higher dimensional, sparse space. Then train a linear model on these features."><img alt="" src="http://github.com/_images/sphx_glr_plot_feature_transformation_thumb.png" />
-<p><a class="reference internal" href="http://github.com/ensemble/plot_feature_transformation.html#sphx-glr-auto-examples-ensemble-plot-feature-transformation-py"><span class="std std-ref">Feature transformations with ensembles of trees</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Feature transformations with ensembles of trees</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="ROC Curve with Visualization API"><img alt="" src="http://github.com/_images/sphx_glr_plot_roc_curve_visualization_api_thumb.png" />
+<p><a class="reference internal" href="http://github.com/miscellaneous/plot_roc_curve_visualization_api.html#sphx-glr-auto-examples-miscellaneous-plot-roc-curve-visualization-api-py"><span class="std std-ref">ROC Curve with Visualization API</span></a></p>
+  <div class="sphx-glr-thumbnail-title">ROC Curve with Visualization API</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we will construct display objects, ConfusionMatrixDisplay, RocCurveDisplay, and PrecisionRecallDisplay directly from their respective metrics. This is an alternative to using their corresponding plot functions when a model&#x27;s predictions are already computed or expensive to compute. Note that this is advanced usage, and in general we recommend using their respective plot functions."><img alt="" src="http://github.com/_images/sphx_glr_plot_display_object_visualization_thumb.png" />
+<p><a class="reference internal" href="http://github.com/miscellaneous/plot_display_object_visualization.html#sphx-glr-auto-examples-miscellaneous-plot-display-object-visualization-py"><span class="std std-ref">Visualizations with Display Objects</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Visualizations with Display Objects</div>
 </div></div><p class="sphx-glr-signature"><a class="reference external" href="https://sphinx-gallery.github.io/">Gallery generated by Sphinx-Gallery</a></p>
 </section>
 </section>
@@ -1188,7 +1222,8 @@ <h2>Plot ROC and DET curves<a class="headerlink" href="#plot-roc-and-det-curves"
     <ul class="visible nav section-nav flex-column">
 <li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#generate-synthetic-data">Generate synthetic data</a></li>
 <li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#define-the-classifiers">Define the classifiers</a></li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#plot-roc-and-det-curves">Plot ROC and DET curves</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#compare-roc-and-det-curves">Compare ROC and DET curves</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#non-informative-classifier-baseline-for-the-roc-and-det-curves">Non-informative classifier baseline for the ROC and DET curves</a></li>
 </ul>
   </nav></div>
 
diff --git a/dev/auto_examples/model_selection/plot_grid_search_digits.html b/dev/auto_examples/model_selection/plot_grid_search_digits.html
index 871e9c2c00a14..441c206778759 100644
--- a/dev/auto_examples/model_selection/plot_grid_search_digits.html
+++ b/dev/auto_examples/model_selection/plot_grid_search_digits.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -679,13 +679,12 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -902,9 +901,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
@@ -1205,19 +1204,19 @@ <h2>Tuning hyper-parameters<a class="headerlink" href="#tuning-hyper-parameters"
 selected subset of best models based on precision and recall.
 Its scoring time is:
 
-mean_score_time                                          0.006194
-mean_test_recall                                         0.877206
-std_test_recall                                          0.069196
-mean_test_precision                                           1.0
-std_test_precision                                            0.0
-rank_test_recall                                                3
-rank_test_precision                                             1
-params                 {&#39;C&#39;: 10, &#39;gamma&#39;: 0.001, &#39;kernel&#39;: &#39;rbf&#39;}
-Name: 2, dtype: object
+mean_score_time                                           0.006381
+mean_test_recall                                          0.877206
+std_test_recall                                           0.069196
+mean_test_precision                                            1.0
+std_test_precision                                             0.0
+rank_test_recall                                                 3
+rank_test_precision                                              1
+params                 {&#39;C&#39;: 100, &#39;gamma&#39;: 0.001, &#39;kernel&#39;: &#39;rbf&#39;}
+Name: 4, dtype: object
 </pre></div>
 </div>
 <div class="output_subarea output_html rendered_html output_result">
-<style>#sk-container-id-55 {
+<style>#sk-container-id-56 {
   /* Definition of color scheme common for light and dark mode */
   --sklearn-color-text: #000;
   --sklearn-color-text-muted: #666;
@@ -1248,15 +1247,15 @@ <h2>Tuning hyper-parameters<a class="headerlink" href="#tuning-hyper-parameters"
   }
 }
 
-#sk-container-id-55 {
+#sk-container-id-56 {
   color: var(--sklearn-color-text);
 }
 
-#sk-container-id-55 pre {
+#sk-container-id-56 pre {
   padding: 0;
 }
 
-#sk-container-id-55 input.sk-hidden--visually {
+#sk-container-id-56 input.sk-hidden--visually {
   border: 0;
   clip: rect(1px 1px 1px 1px);
   clip: rect(1px, 1px, 1px, 1px);
@@ -1268,7 +1267,7 @@ <h2>Tuning hyper-parameters<a class="headerlink" href="#tuning-hyper-parameters"
   width: 1px;
 }
 
-#sk-container-id-55 div.sk-dashed-wrapped {
+#sk-container-id-56 div.sk-dashed-wrapped {
   border: 1px dashed var(--sklearn-color-line);
   margin: 0 0.4em 0.5em 0.4em;
   box-sizing: border-box;
@@ -1276,7 +1275,7 @@ <h2>Tuning hyper-parameters<a class="headerlink" href="#tuning-hyper-parameters"
   background-color: var(--sklearn-color-background);
 }
 
-#sk-container-id-55 div.sk-container {
+#sk-container-id-56 div.sk-container {
   /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
      but bootstrap.min.css set `[hidden] { display: none !important; }`
      so we also need the `!important` here to be able to override the
@@ -1286,7 +1285,7 @@ <h2>Tuning hyper-parameters<a class="headerlink" href="#tuning-hyper-parameters"
   position: relative;
 }
 
-#sk-container-id-55 div.sk-text-repr-fallback {
+#sk-container-id-56 div.sk-text-repr-fallback {
   display: none;
 }
 
@@ -1302,14 +1301,14 @@ <h2>Tuning hyper-parameters<a class="headerlink" href="#tuning-hyper-parameters"
 
 /* Parallel-specific style estimator block */
 
-#sk-container-id-55 div.sk-parallel-item::after {
+#sk-container-id-56 div.sk-parallel-item::after {
   content: "";
   width: 100%;
   border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
   flex-grow: 1;
 }
 
-#sk-container-id-55 div.sk-parallel {
+#sk-container-id-56 div.sk-parallel {
   display: flex;
   align-items: stretch;
   justify-content: center;
@@ -1317,28 +1316,28 @@ <h2>Tuning hyper-parameters<a class="headerlink" href="#tuning-hyper-parameters"
   position: relative;
 }
 
-#sk-container-id-55 div.sk-parallel-item {
+#sk-container-id-56 div.sk-parallel-item {
   display: flex;
   flex-direction: column;
 }
 
-#sk-container-id-55 div.sk-parallel-item:first-child::after {
+#sk-container-id-56 div.sk-parallel-item:first-child::after {
   align-self: flex-end;
   width: 50%;
 }
 
-#sk-container-id-55 div.sk-parallel-item:last-child::after {
+#sk-container-id-56 div.sk-parallel-item:last-child::after {
   align-self: flex-start;
   width: 50%;
 }
 
-#sk-container-id-55 div.sk-parallel-item:only-child::after {
+#sk-container-id-56 div.sk-parallel-item:only-child::after {
   width: 0;
 }
 
 /* Serial-specific style estimator block */
 
-#sk-container-id-55 div.sk-serial {
+#sk-container-id-56 div.sk-serial {
   display: flex;
   flex-direction: column;
   align-items: center;
@@ -1356,14 +1355,14 @@ <h2>Tuning hyper-parameters<a class="headerlink" href="#tuning-hyper-parameters"
 
 /* Pipeline and ColumnTransformer style (default) */
 
-#sk-container-id-55 div.sk-toggleable {
+#sk-container-id-56 div.sk-toggleable {
   /* Default theme specific background. It is overwritten whether we have a
   specific estimator or a Pipeline/ColumnTransformer */
   background-color: var(--sklearn-color-background);
 }
 
 /* Toggleable label */
-#sk-container-id-55 label.sk-toggleable__label {
+#sk-container-id-56 label.sk-toggleable__label {
   cursor: pointer;
   display: flex;
   width: 100%;
@@ -1376,13 +1375,13 @@ <h2>Tuning hyper-parameters<a class="headerlink" href="#tuning-hyper-parameters"
   gap: 0.5em;
 }
 
-#sk-container-id-55 label.sk-toggleable__label .caption {
+#sk-container-id-56 label.sk-toggleable__label .caption {
   font-size: 0.6rem;
   font-weight: lighter;
   color: var(--sklearn-color-text-muted);
 }
 
-#sk-container-id-55 label.sk-toggleable__label-arrow:before {
+#sk-container-id-56 label.sk-toggleable__label-arrow:before {
   /* Arrow on the left of the label */
   content: "▸";
   float: left;
@@ -1390,13 +1389,13 @@ <h2>Tuning hyper-parameters<a class="headerlink" href="#tuning-hyper-parameters"
   color: var(--sklearn-color-icon);
 }
 
-#sk-container-id-55 label.sk-toggleable__label-arrow:hover:before {
+#sk-container-id-56 label.sk-toggleable__label-arrow:hover:before {
   color: var(--sklearn-color-text);
 }
 
 /* Toggleable content - dropdown */
 
-#sk-container-id-55 div.sk-toggleable__content {
+#sk-container-id-56 div.sk-toggleable__content {
   max-height: 0;
   max-width: 0;
   overflow: hidden;
@@ -1405,12 +1404,12 @@ <h2>Tuning hyper-parameters<a class="headerlink" href="#tuning-hyper-parameters"
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-55 div.sk-toggleable__content.fitted {
+#sk-container-id-56 div.sk-toggleable__content.fitted {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
-#sk-container-id-55 div.sk-toggleable__content pre {
+#sk-container-id-56 div.sk-toggleable__content pre {
   margin: 0.2em;
   border-radius: 0.25em;
   color: var(--sklearn-color-text);
@@ -1418,79 +1417,79 @@ <h2>Tuning hyper-parameters<a class="headerlink" href="#tuning-hyper-parameters"
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-55 div.sk-toggleable__content.fitted pre {
+#sk-container-id-56 div.sk-toggleable__content.fitted pre {
   /* unfitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
-#sk-container-id-55 input.sk-toggleable__control:checked~div.sk-toggleable__content {
+#sk-container-id-56 input.sk-toggleable__control:checked~div.sk-toggleable__content {
   /* Expand drop-down */
   max-height: 200px;
   max-width: 100%;
   overflow: auto;
 }
 
-#sk-container-id-55 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
+#sk-container-id-56 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
   content: "▾";
 }
 
 /* Pipeline/ColumnTransformer-specific style */
 
-#sk-container-id-55 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-56 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-55 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-56 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
 /* Estimator-specific style */
 
 /* Colorize estimator box */
-#sk-container-id-55 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-56 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-55 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-56 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
-#sk-container-id-55 div.sk-label label.sk-toggleable__label,
-#sk-container-id-55 div.sk-label label {
+#sk-container-id-56 div.sk-label label.sk-toggleable__label,
+#sk-container-id-56 div.sk-label label {
   /* The background is the default theme color */
   color: var(--sklearn-color-text-on-default-background);
 }
 
 /* On hover, darken the color of the background */
-#sk-container-id-55 div.sk-label:hover label.sk-toggleable__label {
+#sk-container-id-56 div.sk-label:hover label.sk-toggleable__label {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
 /* Label box, darken color on hover, fitted */
-#sk-container-id-55 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
+#sk-container-id-56 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
 /* Estimator label */
 
-#sk-container-id-55 div.sk-label label {
+#sk-container-id-56 div.sk-label label {
   font-family: monospace;
   font-weight: bold;
   display: inline-block;
   line-height: 1.2em;
 }
 
-#sk-container-id-55 div.sk-label-container {
+#sk-container-id-56 div.sk-label-container {
   text-align: center;
 }
 
 /* Estimator-specific */
-#sk-container-id-55 div.sk-estimator {
+#sk-container-id-56 div.sk-estimator {
   font-family: monospace;
   border: 1px dotted var(--sklearn-color-border-box);
   border-radius: 0.25em;
@@ -1500,18 +1499,18 @@ <h2>Tuning hyper-parameters<a class="headerlink" href="#tuning-hyper-parameters"
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-55 div.sk-estimator.fitted {
+#sk-container-id-56 div.sk-estimator.fitted {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
 /* on hover */
-#sk-container-id-55 div.sk-estimator:hover {
+#sk-container-id-56 div.sk-estimator:hover {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-55 div.sk-estimator.fitted:hover {
+#sk-container-id-56 div.sk-estimator.fitted:hover {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-2);
 }
@@ -1599,7 +1598,7 @@ <h2>Tuning hyper-parameters<a class="headerlink" href="#tuning-hyper-parameters"
 
 /* "?"-specific style due to the `<a>` HTML tag */
 
-#sk-container-id-55 a.estimator_doc_link {
+#sk-container-id-56 a.estimator_doc_link {
   float: right;
   font-size: 1rem;
   line-height: 1em;
@@ -1614,42 +1613,42 @@ <h2>Tuning hyper-parameters<a class="headerlink" href="#tuning-hyper-parameters"
   border: var(--sklearn-color-unfitted-level-1) 1pt solid;
 }
 
-#sk-container-id-55 a.estimator_doc_link.fitted {
+#sk-container-id-56 a.estimator_doc_link.fitted {
   /* fitted */
   border: var(--sklearn-color-fitted-level-1) 1pt solid;
   color: var(--sklearn-color-fitted-level-1);
 }
 
 /* On hover */
-#sk-container-id-55 a.estimator_doc_link:hover {
+#sk-container-id-56 a.estimator_doc_link:hover {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-3);
   color: var(--sklearn-color-background);
   text-decoration: none;
 }
 
-#sk-container-id-55 a.estimator_doc_link.fitted:hover {
+#sk-container-id-56 a.estimator_doc_link.fitted:hover {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-3);
 }
-</style><div id="sk-container-id-55" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>GridSearchCV(estimator=SVC(),
+</style><div id="sk-container-id-56" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>GridSearchCV(estimator=SVC(),
              param_grid=[{&#x27;C&#x27;: [1, 10, 100, 1000], &#x27;gamma&#x27;: [0.001, 0.0001],
                           &#x27;kernel&#x27;: [&#x27;rbf&#x27;]},
                          {&#x27;C&#x27;: [1, 10, 100, 1000], &#x27;kernel&#x27;: [&#x27;linear&#x27;]}],
-             refit=&lt;function refit_strategy at 0x7fb585547010&gt;,
-             scoring=[&#x27;precision&#x27;, &#x27;recall&#x27;])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-226" type="checkbox" ><label for="sk-estimator-id-226" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>GridSearchCV</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.model_selection.GridSearchCV.html">?<span>Documentation for GridSearchCV</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>GridSearchCV(estimator=SVC(),
+             refit=&lt;function refit_strategy at 0x7fd8418536d0&gt;,
+             scoring=[&#x27;precision&#x27;, &#x27;recall&#x27;])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-240" type="checkbox" ><label for="sk-estimator-id-240" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>GridSearchCV</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.model_selection.GridSearchCV.html">?<span>Documentation for GridSearchCV</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>GridSearchCV(estimator=SVC(),
              param_grid=[{&#x27;C&#x27;: [1, 10, 100, 1000], &#x27;gamma&#x27;: [0.001, 0.0001],
                           &#x27;kernel&#x27;: [&#x27;rbf&#x27;]},
                          {&#x27;C&#x27;: [1, 10, 100, 1000], &#x27;kernel&#x27;: [&#x27;linear&#x27;]}],
-             refit=&lt;function refit_strategy at 0x7fb585547010&gt;,
-             scoring=[&#x27;precision&#x27;, &#x27;recall&#x27;])</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-227" type="checkbox" ><label for="sk-estimator-id-227" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>best_estimator_: SVC</div></div></label><div class="sk-toggleable__content fitted"><pre>SVC(C=10, gamma=0.001)</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-228" type="checkbox" ><label for="sk-estimator-id-228" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>SVC</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.svm.SVC.html">?<span>Documentation for SVC</span></a></div></label><div class="sk-toggleable__content fitted"><pre>SVC(C=10, gamma=0.001)</pre></div> </div></div></div></div></div></div></div></div></div>
+             refit=&lt;function refit_strategy at 0x7fd8418536d0&gt;,
+             scoring=[&#x27;precision&#x27;, &#x27;recall&#x27;])</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-241" type="checkbox" ><label for="sk-estimator-id-241" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>best_estimator_: SVC</div></div></label><div class="sk-toggleable__content fitted"><pre>SVC(C=100, gamma=0.001)</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-242" type="checkbox" ><label for="sk-estimator-id-242" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>SVC</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.svm.SVC.html">?<span>Documentation for SVC</span></a></div></label><div class="sk-toggleable__content fitted"><pre>SVC(C=100, gamma=0.001)</pre></div> </div></div></div></div></div></div></div></div></div>
 </div>
 <br />
 <br /><p>The parameters selected by the grid-search with our custom strategy are:</p>
 <div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">grid_search</span><span class="o">.</span><span class="n">best_params_</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>{&#39;C&#39;: 10, &#39;gamma&#39;: 0.001, &#39;kernel&#39;: &#39;rbf&#39;}
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>{&#39;C&#39;: 100, &#39;gamma&#39;: 0.001, &#39;kernel&#39;: &#39;rbf&#39;}
 </pre></div>
 </div>
 <p>Finally, we evaluate the fine-tuned model on the left-out evaluation set: the
@@ -1678,7 +1677,7 @@ <h2>Tuning hyper-parameters<a class="headerlink" href="#tuning-hyper-parameters"
 <p>The problem is too easy: the hyperparameter plateau is too flat and the
 output model is the same for precision and recall with ties in quality.</p>
 </div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 9.971 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 10.263 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-model-selection-plot-grid-search-digits-py">
 <div class="binder-badge docutils container">
 <a class="reference external image-reference" href="https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/model_selection/plot_grid_search_digits.ipynb"><img alt="Launch binder" src="http://github.com/_images/binder_badge_logo22.svg" style="width: 150px;" />
diff --git a/dev/auto_examples/model_selection/plot_grid_search_refit_callable.html b/dev/auto_examples/model_selection/plot_grid_search_refit_callable.html
index 10e92f9d86f23..0cae830d00408 100644
--- a/dev/auto_examples/model_selection/plot_grid_search_refit_callable.html
+++ b/dev/auto_examples/model_selection/plot_grid_search_refit_callable.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -679,13 +679,12 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -902,9 +901,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
@@ -1121,7 +1120,7 @@
 <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
 </pre></div>
 </div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 1.147 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 1.095 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-model-selection-plot-grid-search-refit-callable-py">
 <div class="binder-badge docutils container">
 <a class="reference external image-reference" href="https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/model_selection/plot_grid_search_refit_callable.ipynb"><img alt="Launch binder" src="http://github.com/_images/binder_badge_logo22.svg" style="width: 150px;" />
diff --git a/dev/auto_examples/model_selection/plot_grid_search_stats.html b/dev/auto_examples/model_selection/plot_grid_search_stats.html
index 0f4c5e693cde4..f61032715b864 100644
--- a/dev/auto_examples/model_selection/plot_grid_search_stats.html
+++ b/dev/auto_examples/model_selection/plot_grid_search_stats.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -680,13 +680,12 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -903,9 +902,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
@@ -1044,7 +1043,7 @@
 </pre></div>
 </div>
 <div class="output_subarea output_html rendered_html output_result">
-<style>#sk-container-id-63 {
+<style>#sk-container-id-64 {
   /* Definition of color scheme common for light and dark mode */
   --sklearn-color-text: #000;
   --sklearn-color-text-muted: #666;
@@ -1075,15 +1074,15 @@
   }
 }
 
-#sk-container-id-63 {
+#sk-container-id-64 {
   color: var(--sklearn-color-text);
 }
 
-#sk-container-id-63 pre {
+#sk-container-id-64 pre {
   padding: 0;
 }
 
-#sk-container-id-63 input.sk-hidden--visually {
+#sk-container-id-64 input.sk-hidden--visually {
   border: 0;
   clip: rect(1px 1px 1px 1px);
   clip: rect(1px, 1px, 1px, 1px);
@@ -1095,7 +1094,7 @@
   width: 1px;
 }
 
-#sk-container-id-63 div.sk-dashed-wrapped {
+#sk-container-id-64 div.sk-dashed-wrapped {
   border: 1px dashed var(--sklearn-color-line);
   margin: 0 0.4em 0.5em 0.4em;
   box-sizing: border-box;
@@ -1103,7 +1102,7 @@
   background-color: var(--sklearn-color-background);
 }
 
-#sk-container-id-63 div.sk-container {
+#sk-container-id-64 div.sk-container {
   /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
      but bootstrap.min.css set `[hidden] { display: none !important; }`
      so we also need the `!important` here to be able to override the
@@ -1113,7 +1112,7 @@
   position: relative;
 }
 
-#sk-container-id-63 div.sk-text-repr-fallback {
+#sk-container-id-64 div.sk-text-repr-fallback {
   display: none;
 }
 
@@ -1129,14 +1128,14 @@
 
 /* Parallel-specific style estimator block */
 
-#sk-container-id-63 div.sk-parallel-item::after {
+#sk-container-id-64 div.sk-parallel-item::after {
   content: "";
   width: 100%;
   border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
   flex-grow: 1;
 }
 
-#sk-container-id-63 div.sk-parallel {
+#sk-container-id-64 div.sk-parallel {
   display: flex;
   align-items: stretch;
   justify-content: center;
@@ -1144,28 +1143,28 @@
   position: relative;
 }
 
-#sk-container-id-63 div.sk-parallel-item {
+#sk-container-id-64 div.sk-parallel-item {
   display: flex;
   flex-direction: column;
 }
 
-#sk-container-id-63 div.sk-parallel-item:first-child::after {
+#sk-container-id-64 div.sk-parallel-item:first-child::after {
   align-self: flex-end;
   width: 50%;
 }
 
-#sk-container-id-63 div.sk-parallel-item:last-child::after {
+#sk-container-id-64 div.sk-parallel-item:last-child::after {
   align-self: flex-start;
   width: 50%;
 }
 
-#sk-container-id-63 div.sk-parallel-item:only-child::after {
+#sk-container-id-64 div.sk-parallel-item:only-child::after {
   width: 0;
 }
 
 /* Serial-specific style estimator block */
 
-#sk-container-id-63 div.sk-serial {
+#sk-container-id-64 div.sk-serial {
   display: flex;
   flex-direction: column;
   align-items: center;
@@ -1183,14 +1182,14 @@
 
 /* Pipeline and ColumnTransformer style (default) */
 
-#sk-container-id-63 div.sk-toggleable {
+#sk-container-id-64 div.sk-toggleable {
   /* Default theme specific background. It is overwritten whether we have a
   specific estimator or a Pipeline/ColumnTransformer */
   background-color: var(--sklearn-color-background);
 }
 
 /* Toggleable label */
-#sk-container-id-63 label.sk-toggleable__label {
+#sk-container-id-64 label.sk-toggleable__label {
   cursor: pointer;
   display: flex;
   width: 100%;
@@ -1203,13 +1202,13 @@
   gap: 0.5em;
 }
 
-#sk-container-id-63 label.sk-toggleable__label .caption {
+#sk-container-id-64 label.sk-toggleable__label .caption {
   font-size: 0.6rem;
   font-weight: lighter;
   color: var(--sklearn-color-text-muted);
 }
 
-#sk-container-id-63 label.sk-toggleable__label-arrow:before {
+#sk-container-id-64 label.sk-toggleable__label-arrow:before {
   /* Arrow on the left of the label */
   content: "▸";
   float: left;
@@ -1217,13 +1216,13 @@
   color: var(--sklearn-color-icon);
 }
 
-#sk-container-id-63 label.sk-toggleable__label-arrow:hover:before {
+#sk-container-id-64 label.sk-toggleable__label-arrow:hover:before {
   color: var(--sklearn-color-text);
 }
 
 /* Toggleable content - dropdown */
 
-#sk-container-id-63 div.sk-toggleable__content {
+#sk-container-id-64 div.sk-toggleable__content {
   max-height: 0;
   max-width: 0;
   overflow: hidden;
@@ -1232,12 +1231,12 @@
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-63 div.sk-toggleable__content.fitted {
+#sk-container-id-64 div.sk-toggleable__content.fitted {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
-#sk-container-id-63 div.sk-toggleable__content pre {
+#sk-container-id-64 div.sk-toggleable__content pre {
   margin: 0.2em;
   border-radius: 0.25em;
   color: var(--sklearn-color-text);
@@ -1245,79 +1244,79 @@
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-63 div.sk-toggleable__content.fitted pre {
+#sk-container-id-64 div.sk-toggleable__content.fitted pre {
   /* unfitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
-#sk-container-id-63 input.sk-toggleable__control:checked~div.sk-toggleable__content {
+#sk-container-id-64 input.sk-toggleable__control:checked~div.sk-toggleable__content {
   /* Expand drop-down */
   max-height: 200px;
   max-width: 100%;
   overflow: auto;
 }
 
-#sk-container-id-63 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
+#sk-container-id-64 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
   content: "▾";
 }
 
 /* Pipeline/ColumnTransformer-specific style */
 
-#sk-container-id-63 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-64 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-63 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-64 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
 /* Estimator-specific style */
 
 /* Colorize estimator box */
-#sk-container-id-63 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-64 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-63 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-64 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
-#sk-container-id-63 div.sk-label label.sk-toggleable__label,
-#sk-container-id-63 div.sk-label label {
+#sk-container-id-64 div.sk-label label.sk-toggleable__label,
+#sk-container-id-64 div.sk-label label {
   /* The background is the default theme color */
   color: var(--sklearn-color-text-on-default-background);
 }
 
 /* On hover, darken the color of the background */
-#sk-container-id-63 div.sk-label:hover label.sk-toggleable__label {
+#sk-container-id-64 div.sk-label:hover label.sk-toggleable__label {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
 /* Label box, darken color on hover, fitted */
-#sk-container-id-63 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
+#sk-container-id-64 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
 /* Estimator label */
 
-#sk-container-id-63 div.sk-label label {
+#sk-container-id-64 div.sk-label label {
   font-family: monospace;
   font-weight: bold;
   display: inline-block;
   line-height: 1.2em;
 }
 
-#sk-container-id-63 div.sk-label-container {
+#sk-container-id-64 div.sk-label-container {
   text-align: center;
 }
 
 /* Estimator-specific */
-#sk-container-id-63 div.sk-estimator {
+#sk-container-id-64 div.sk-estimator {
   font-family: monospace;
   border: 1px dotted var(--sklearn-color-border-box);
   border-radius: 0.25em;
@@ -1327,18 +1326,18 @@
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-63 div.sk-estimator.fitted {
+#sk-container-id-64 div.sk-estimator.fitted {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
 /* on hover */
-#sk-container-id-63 div.sk-estimator:hover {
+#sk-container-id-64 div.sk-estimator:hover {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-63 div.sk-estimator.fitted:hover {
+#sk-container-id-64 div.sk-estimator.fitted:hover {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-2);
 }
@@ -1426,7 +1425,7 @@
 
 /* "?"-specific style due to the `<a>` HTML tag */
 
-#sk-container-id-63 a.estimator_doc_link {
+#sk-container-id-64 a.estimator_doc_link {
   float: right;
   font-size: 1rem;
   line-height: 1em;
@@ -1441,35 +1440,35 @@
   border: var(--sklearn-color-unfitted-level-1) 1pt solid;
 }
 
-#sk-container-id-63 a.estimator_doc_link.fitted {
+#sk-container-id-64 a.estimator_doc_link.fitted {
   /* fitted */
   border: var(--sklearn-color-fitted-level-1) 1pt solid;
   color: var(--sklearn-color-fitted-level-1);
 }
 
 /* On hover */
-#sk-container-id-63 a.estimator_doc_link:hover {
+#sk-container-id-64 a.estimator_doc_link:hover {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-3);
   color: var(--sklearn-color-background);
   text-decoration: none;
 }
 
-#sk-container-id-63 a.estimator_doc_link.fitted:hover {
+#sk-container-id-64 a.estimator_doc_link.fitted:hover {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-3);
 }
-</style><div id="sk-container-id-63" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>GridSearchCV(cv=RepeatedStratifiedKFold(n_repeats=10, n_splits=10, random_state=0),
+</style><div id="sk-container-id-64" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>GridSearchCV(cv=RepeatedStratifiedKFold(n_repeats=10, n_splits=10, random_state=0),
              estimator=SVC(random_state=0),
              param_grid=[{&#x27;kernel&#x27;: [&#x27;linear&#x27;]},
                          {&#x27;degree&#x27;: [2, 3], &#x27;kernel&#x27;: [&#x27;poly&#x27;]},
                          {&#x27;kernel&#x27;: [&#x27;rbf&#x27;]}],
-             scoring=&#x27;roc_auc&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-250" type="checkbox" ><label for="sk-estimator-id-250" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>GridSearchCV</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.model_selection.GridSearchCV.html">?<span>Documentation for GridSearchCV</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>GridSearchCV(cv=RepeatedStratifiedKFold(n_repeats=10, n_splits=10, random_state=0),
+             scoring=&#x27;roc_auc&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-264" type="checkbox" ><label for="sk-estimator-id-264" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>GridSearchCV</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.model_selection.GridSearchCV.html">?<span>Documentation for GridSearchCV</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>GridSearchCV(cv=RepeatedStratifiedKFold(n_repeats=10, n_splits=10, random_state=0),
              estimator=SVC(random_state=0),
              param_grid=[{&#x27;kernel&#x27;: [&#x27;linear&#x27;]},
                          {&#x27;degree&#x27;: [2, 3], &#x27;kernel&#x27;: [&#x27;poly&#x27;]},
                          {&#x27;kernel&#x27;: [&#x27;rbf&#x27;]}],
-             scoring=&#x27;roc_auc&#x27;)</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-251" type="checkbox" ><label for="sk-estimator-id-251" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>best_estimator_: SVC</div></div></label><div class="sk-toggleable__content fitted"><pre>SVC(random_state=0)</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-252" type="checkbox" ><label for="sk-estimator-id-252" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>SVC</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.svm.SVC.html">?<span>Documentation for SVC</span></a></div></label><div class="sk-toggleable__content fitted"><pre>SVC(random_state=0)</pre></div> </div></div></div></div></div></div></div></div></div>
+             scoring=&#x27;roc_auc&#x27;)</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-265" type="checkbox" ><label for="sk-estimator-id-265" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>best_estimator_: SVC</div></div></label><div class="sk-toggleable__content fitted"><pre>SVC(random_state=0)</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-266" type="checkbox" ><label for="sk-estimator-id-266" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>SVC</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.svm.SVC.html">?<span>Documentation for SVC</span></a></div></label><div class="sk-toggleable__content fitted"><pre>SVC(random_state=0)</pre></div> </div></div></div></div></div></div></div></div></div>
 </div>
 <br />
 <br /><p>We can now inspect the results of our search, sorted by their
@@ -2245,7 +2244,7 @@ <h2>Take-home messages<a class="headerlink" href="#take-home-messages" title="Li
 Journal of Business &amp; economic statistics, 20(1), 134-144.</p>
 </aside>
 </aside>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 1.629 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 1.604 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-model-selection-plot-grid-search-stats-py">
 <div class="binder-badge docutils container">
 <a class="reference external image-reference" href="https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/model_selection/plot_grid_search_stats.ipynb"><img alt="Launch binder" src="http://github.com/_images/binder_badge_logo22.svg" style="width: 150px;" />
diff --git a/dev/auto_examples/model_selection/plot_grid_search_text_feature_extraction.html b/dev/auto_examples/model_selection/plot_grid_search_text_feature_extraction.html
index e013c6bdcd6fa..1ba280f67153a 100644
--- a/dev/auto_examples/model_selection/plot_grid_search_text_feature_extraction.html
+++ b/dev/auto_examples/model_selection/plot_grid_search_text_feature_extraction.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -680,13 +680,12 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -903,9 +902,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
@@ -1065,7 +1064,7 @@ <h2>Pipeline with hyperparameter tuning<a class="headerlink" href="#pipeline-wit
 </pre></div>
 </div>
 <div class="output_subarea output_html rendered_html output_result">
-<style>#sk-container-id-62 {
+<style>#sk-container-id-63 {
   /* Definition of color scheme common for light and dark mode */
   --sklearn-color-text: #000;
   --sklearn-color-text-muted: #666;
@@ -1096,15 +1095,15 @@ <h2>Pipeline with hyperparameter tuning<a class="headerlink" href="#pipeline-wit
   }
 }
 
-#sk-container-id-62 {
+#sk-container-id-63 {
   color: var(--sklearn-color-text);
 }
 
-#sk-container-id-62 pre {
+#sk-container-id-63 pre {
   padding: 0;
 }
 
-#sk-container-id-62 input.sk-hidden--visually {
+#sk-container-id-63 input.sk-hidden--visually {
   border: 0;
   clip: rect(1px 1px 1px 1px);
   clip: rect(1px, 1px, 1px, 1px);
@@ -1116,7 +1115,7 @@ <h2>Pipeline with hyperparameter tuning<a class="headerlink" href="#pipeline-wit
   width: 1px;
 }
 
-#sk-container-id-62 div.sk-dashed-wrapped {
+#sk-container-id-63 div.sk-dashed-wrapped {
   border: 1px dashed var(--sklearn-color-line);
   margin: 0 0.4em 0.5em 0.4em;
   box-sizing: border-box;
@@ -1124,7 +1123,7 @@ <h2>Pipeline with hyperparameter tuning<a class="headerlink" href="#pipeline-wit
   background-color: var(--sklearn-color-background);
 }
 
-#sk-container-id-62 div.sk-container {
+#sk-container-id-63 div.sk-container {
   /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
      but bootstrap.min.css set `[hidden] { display: none !important; }`
      so we also need the `!important` here to be able to override the
@@ -1134,7 +1133,7 @@ <h2>Pipeline with hyperparameter tuning<a class="headerlink" href="#pipeline-wit
   position: relative;
 }
 
-#sk-container-id-62 div.sk-text-repr-fallback {
+#sk-container-id-63 div.sk-text-repr-fallback {
   display: none;
 }
 
@@ -1150,14 +1149,14 @@ <h2>Pipeline with hyperparameter tuning<a class="headerlink" href="#pipeline-wit
 
 /* Parallel-specific style estimator block */
 
-#sk-container-id-62 div.sk-parallel-item::after {
+#sk-container-id-63 div.sk-parallel-item::after {
   content: "";
   width: 100%;
   border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
   flex-grow: 1;
 }
 
-#sk-container-id-62 div.sk-parallel {
+#sk-container-id-63 div.sk-parallel {
   display: flex;
   align-items: stretch;
   justify-content: center;
@@ -1165,28 +1164,28 @@ <h2>Pipeline with hyperparameter tuning<a class="headerlink" href="#pipeline-wit
   position: relative;
 }
 
-#sk-container-id-62 div.sk-parallel-item {
+#sk-container-id-63 div.sk-parallel-item {
   display: flex;
   flex-direction: column;
 }
 
-#sk-container-id-62 div.sk-parallel-item:first-child::after {
+#sk-container-id-63 div.sk-parallel-item:first-child::after {
   align-self: flex-end;
   width: 50%;
 }
 
-#sk-container-id-62 div.sk-parallel-item:last-child::after {
+#sk-container-id-63 div.sk-parallel-item:last-child::after {
   align-self: flex-start;
   width: 50%;
 }
 
-#sk-container-id-62 div.sk-parallel-item:only-child::after {
+#sk-container-id-63 div.sk-parallel-item:only-child::after {
   width: 0;
 }
 
 /* Serial-specific style estimator block */
 
-#sk-container-id-62 div.sk-serial {
+#sk-container-id-63 div.sk-serial {
   display: flex;
   flex-direction: column;
   align-items: center;
@@ -1204,14 +1203,14 @@ <h2>Pipeline with hyperparameter tuning<a class="headerlink" href="#pipeline-wit
 
 /* Pipeline and ColumnTransformer style (default) */
 
-#sk-container-id-62 div.sk-toggleable {
+#sk-container-id-63 div.sk-toggleable {
   /* Default theme specific background. It is overwritten whether we have a
   specific estimator or a Pipeline/ColumnTransformer */
   background-color: var(--sklearn-color-background);
 }
 
 /* Toggleable label */
-#sk-container-id-62 label.sk-toggleable__label {
+#sk-container-id-63 label.sk-toggleable__label {
   cursor: pointer;
   display: flex;
   width: 100%;
@@ -1224,13 +1223,13 @@ <h2>Pipeline with hyperparameter tuning<a class="headerlink" href="#pipeline-wit
   gap: 0.5em;
 }
 
-#sk-container-id-62 label.sk-toggleable__label .caption {
+#sk-container-id-63 label.sk-toggleable__label .caption {
   font-size: 0.6rem;
   font-weight: lighter;
   color: var(--sklearn-color-text-muted);
 }
 
-#sk-container-id-62 label.sk-toggleable__label-arrow:before {
+#sk-container-id-63 label.sk-toggleable__label-arrow:before {
   /* Arrow on the left of the label */
   content: "▸";
   float: left;
@@ -1238,13 +1237,13 @@ <h2>Pipeline with hyperparameter tuning<a class="headerlink" href="#pipeline-wit
   color: var(--sklearn-color-icon);
 }
 
-#sk-container-id-62 label.sk-toggleable__label-arrow:hover:before {
+#sk-container-id-63 label.sk-toggleable__label-arrow:hover:before {
   color: var(--sklearn-color-text);
 }
 
 /* Toggleable content - dropdown */
 
-#sk-container-id-62 div.sk-toggleable__content {
+#sk-container-id-63 div.sk-toggleable__content {
   max-height: 0;
   max-width: 0;
   overflow: hidden;
@@ -1253,12 +1252,12 @@ <h2>Pipeline with hyperparameter tuning<a class="headerlink" href="#pipeline-wit
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-62 div.sk-toggleable__content.fitted {
+#sk-container-id-63 div.sk-toggleable__content.fitted {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
-#sk-container-id-62 div.sk-toggleable__content pre {
+#sk-container-id-63 div.sk-toggleable__content pre {
   margin: 0.2em;
   border-radius: 0.25em;
   color: var(--sklearn-color-text);
@@ -1266,79 +1265,79 @@ <h2>Pipeline with hyperparameter tuning<a class="headerlink" href="#pipeline-wit
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-62 div.sk-toggleable__content.fitted pre {
+#sk-container-id-63 div.sk-toggleable__content.fitted pre {
   /* unfitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
-#sk-container-id-62 input.sk-toggleable__control:checked~div.sk-toggleable__content {
+#sk-container-id-63 input.sk-toggleable__control:checked~div.sk-toggleable__content {
   /* Expand drop-down */
   max-height: 200px;
   max-width: 100%;
   overflow: auto;
 }
 
-#sk-container-id-62 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
+#sk-container-id-63 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
   content: "▾";
 }
 
 /* Pipeline/ColumnTransformer-specific style */
 
-#sk-container-id-62 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-63 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-62 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-63 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
 /* Estimator-specific style */
 
 /* Colorize estimator box */
-#sk-container-id-62 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-63 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-62 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-63 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
-#sk-container-id-62 div.sk-label label.sk-toggleable__label,
-#sk-container-id-62 div.sk-label label {
+#sk-container-id-63 div.sk-label label.sk-toggleable__label,
+#sk-container-id-63 div.sk-label label {
   /* The background is the default theme color */
   color: var(--sklearn-color-text-on-default-background);
 }
 
 /* On hover, darken the color of the background */
-#sk-container-id-62 div.sk-label:hover label.sk-toggleable__label {
+#sk-container-id-63 div.sk-label:hover label.sk-toggleable__label {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
 /* Label box, darken color on hover, fitted */
-#sk-container-id-62 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
+#sk-container-id-63 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
 /* Estimator label */
 
-#sk-container-id-62 div.sk-label label {
+#sk-container-id-63 div.sk-label label {
   font-family: monospace;
   font-weight: bold;
   display: inline-block;
   line-height: 1.2em;
 }
 
-#sk-container-id-62 div.sk-label-container {
+#sk-container-id-63 div.sk-label-container {
   text-align: center;
 }
 
 /* Estimator-specific */
-#sk-container-id-62 div.sk-estimator {
+#sk-container-id-63 div.sk-estimator {
   font-family: monospace;
   border: 1px dotted var(--sklearn-color-border-box);
   border-radius: 0.25em;
@@ -1348,18 +1347,18 @@ <h2>Pipeline with hyperparameter tuning<a class="headerlink" href="#pipeline-wit
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-62 div.sk-estimator.fitted {
+#sk-container-id-63 div.sk-estimator.fitted {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
 /* on hover */
-#sk-container-id-62 div.sk-estimator:hover {
+#sk-container-id-63 div.sk-estimator:hover {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-62 div.sk-estimator.fitted:hover {
+#sk-container-id-63 div.sk-estimator.fitted:hover {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-2);
 }
@@ -1447,7 +1446,7 @@ <h2>Pipeline with hyperparameter tuning<a class="headerlink" href="#pipeline-wit
 
 /* "?"-specific style due to the `<a>` HTML tag */
 
-#sk-container-id-62 a.estimator_doc_link {
+#sk-container-id-63 a.estimator_doc_link {
   float: right;
   font-size: 1rem;
   line-height: 1em;
@@ -1462,25 +1461,25 @@ <h2>Pipeline with hyperparameter tuning<a class="headerlink" href="#pipeline-wit
   border: var(--sklearn-color-unfitted-level-1) 1pt solid;
 }
 
-#sk-container-id-62 a.estimator_doc_link.fitted {
+#sk-container-id-63 a.estimator_doc_link.fitted {
   /* fitted */
   border: var(--sklearn-color-fitted-level-1) 1pt solid;
   color: var(--sklearn-color-fitted-level-1);
 }
 
 /* On hover */
-#sk-container-id-62 a.estimator_doc_link:hover {
+#sk-container-id-63 a.estimator_doc_link:hover {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-3);
   color: var(--sklearn-color-background);
   text-decoration: none;
 }
 
-#sk-container-id-62 a.estimator_doc_link.fitted:hover {
+#sk-container-id-63 a.estimator_doc_link.fitted:hover {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-3);
 }
-</style><div id="sk-container-id-62" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;vect&#x27;, TfidfVectorizer()), (&#x27;clf&#x27;, ComplementNB())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-247" type="checkbox" ><label for="sk-estimator-id-247" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>Pipeline</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.pipeline.Pipeline.html">?<span>Documentation for Pipeline</span></a><span class="sk-estimator-doc-link ">i<span>Not fitted</span></span></div></label><div class="sk-toggleable__content "><pre>Pipeline(steps=[(&#x27;vect&#x27;, TfidfVectorizer()), (&#x27;clf&#x27;, ComplementNB())])</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-248" type="checkbox" ><label for="sk-estimator-id-248" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>TfidfVectorizer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html">?<span>Documentation for TfidfVectorizer</span></a></div></label><div class="sk-toggleable__content "><pre>TfidfVectorizer()</pre></div> </div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-249" type="checkbox" ><label for="sk-estimator-id-249" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>ComplementNB</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.naive_bayes.ComplementNB.html">?<span>Documentation for ComplementNB</span></a></div></label><div class="sk-toggleable__content "><pre>ComplementNB()</pre></div> </div></div></div></div></div></div>
+</style><div id="sk-container-id-63" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;vect&#x27;, TfidfVectorizer()), (&#x27;clf&#x27;, ComplementNB())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-261" type="checkbox" ><label for="sk-estimator-id-261" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>Pipeline</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.pipeline.Pipeline.html">?<span>Documentation for Pipeline</span></a><span class="sk-estimator-doc-link ">i<span>Not fitted</span></span></div></label><div class="sk-toggleable__content "><pre>Pipeline(steps=[(&#x27;vect&#x27;, TfidfVectorizer()), (&#x27;clf&#x27;, ComplementNB())])</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-262" type="checkbox" ><label for="sk-estimator-id-262" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>TfidfVectorizer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html">?<span>Documentation for TfidfVectorizer</span></a></div></label><div class="sk-toggleable__content "><pre>TfidfVectorizer()</pre></div> </div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-263" type="checkbox" ><label for="sk-estimator-id-263" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>ComplementNB</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.naive_bayes.ComplementNB.html">?<span>Documentation for ComplementNB</span></a></div></label><div class="sk-toggleable__content "><pre>ComplementNB()</pre></div> </div></div></div></div></div></div>
 </div>
 <br />
 <br /><p>We define a grid of hyperparameters to be explored by the
@@ -1546,7 +1545,7 @@ <h2>Pipeline with hyperparameter tuning<a class="headerlink" href="#pipeline-wit
 </pre></div>
 </div>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Fitting 5 folds for each of 40 candidates, totalling 200 fits
-Done in 25.382s
+Done in 26.539s
 </pre></div>
 </div>
 <div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Best parameters combination found:&quot;</span><span class="p">)</span>
@@ -1628,7 +1627,7 @@ <h2>Pipeline with hyperparameter tuning<a class="headerlink" href="#pipeline-wit
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 <br />
 <br /><p>The parallel coordinates plot displays the values of the hyperparameters on
@@ -1699,7 +1698,7 @@ <h2>Pipeline with hyperparameter tuning<a class="headerlink" href="#pipeline-wit
 less features require less regularization and less scoring time.</p>
 <p>The best accuracy scores are obtained when <code class="docutils literal notranslate"><span class="pre">alpha</span></code> is between <span class="math notranslate nohighlight">\(10^{-6}\)</span>
 and <span class="math notranslate nohighlight">\(10^0\)</span>, regardless of the hyperparameter <code class="docutils literal notranslate"><span class="pre">norm</span></code>.</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 27.423 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 28.635 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-model-selection-plot-grid-search-text-feature-extraction-py">
 <div class="binder-badge docutils container">
 <a class="reference external image-reference" href="https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/model_selection/plot_grid_search_text_feature_extraction.ipynb"><img alt="Launch binder" src="http://github.com/_images/binder_badge_logo22.svg" style="width: 150px;" />
diff --git a/dev/auto_examples/model_selection/plot_learning_curve.html b/dev/auto_examples/model_selection/plot_learning_curve.html
index 0263511bc71ed..d716791955aa9 100644
--- a/dev/auto_examples/model_selection/plot_learning_curve.html
+++ b/dev/auto_examples/model_selection/plot_learning_curve.html
@@ -42,7 +42,7 @@
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@@ -679,13 +679,12 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -902,9 +901,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
@@ -1163,7 +1162,7 @@ <h2>Complexity analysis<a class="headerlink" href="#complexity-analysis" title="
 <img src="http://github.com/_images/sphx_glr_plot_learning_curve_003.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_learning_curve_003.png " alt="Performance of the GaussianNB classifier, Performance of the SVC classifier" class = "sphx-glr-single-img"/><p>In these plots, we can look for the inflection point for which the
 cross-validation score does not increase anymore and only the training time
 increases.</p>
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+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 28.549 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-model-selection-plot-learning-curve-py">
 <div class="binder-badge docutils container">
 <a class="reference external image-reference" href="https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/model_selection/plot_learning_curve.ipynb"><img alt="Launch binder" src="http://github.com/_images/binder_badge_logo22.svg" style="width: 150px;" />
@@ -1193,9 +1192,9 @@ <h2>Complexity analysis<a class="headerlink" href="#complexity-analysis" title="
 </div><div class="sphx-glr-thumbcontainer" tooltip="ROC Curve with Visualization API"><img alt="" src="http://github.com/_images/sphx_glr_plot_roc_curve_visualization_api_thumb.png" />
 <p><a class="reference internal" href="http://github.com/miscellaneous/plot_roc_curve_visualization_api.html#sphx-glr-auto-examples-miscellaneous-plot-roc-curve-visualization-api-py"><span class="std std-ref">ROC Curve with Visualization API</span></a></p>
   <div class="sphx-glr-thumbnail-title">ROC Curve with Visualization API</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This example presents how to estimate and visualize the variance of the Receiver Operating Characteristic (ROC) metric using cross-validation."><img alt="" src="http://github.com/_images/sphx_glr_plot_roc_crossval_thumb.png" />
-<p><a class="reference internal" href="http://github.com/plot_roc_crossval.html#sphx-glr-auto-examples-model-selection-plot-roc-crossval-py"><span class="std std-ref">Receiver Operating Characteristic (ROC) with cross validation</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Receiver Operating Characteristic (ROC) with cross validation</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="Comparison of different linear SVM classifiers on a 2D projection of the iris dataset. We only consider the first 2 features of this dataset:"><img alt="" src="http://github.com/_images/sphx_glr_plot_iris_svc_thumb.png" />
+<p><a class="reference internal" href="http://github.com/svm/plot_iris_svc.html#sphx-glr-auto-examples-svm-plot-iris-svc-py"><span class="std std-ref">Plot different SVM classifiers in the iris dataset</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Plot different SVM classifiers in the iris dataset</div>
 </div></div><p class="sphx-glr-signature"><a class="reference external" href="https://sphinx-gallery.github.io/">Gallery generated by Sphinx-Gallery</a></p>
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index 097bc049e04cb..a18e60a92bc7d 100644
--- a/dev/auto_examples/model_selection/plot_likelihood_ratios.html
+++ b/dev/auto_examples/model_selection/plot_likelihood_ratios.html
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -902,9 +901,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
@@ -1024,7 +1023,7 @@ <h2>Pre-test vs. post-test analysis<a class="headerlink" href="#pre-test-vs-post
 <div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.datasets</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.datasets.make_classification.html#sklearn.datasets.make_classification" title="sklearn.datasets.make_classification" class="sphx-glr-backref-module-sklearn-datasets sphx-glr-backref-type-py-function"><span class="n">make_classification</span></a>
 
 <span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <a href="http://github.com/modules/generated/sklearn.datasets.make_classification.html#sklearn.datasets.make_classification" title="sklearn.datasets.make_classification" class="sphx-glr-backref-module-sklearn-datasets sphx-glr-backref-type-py-function"><span class="n">make_classification</span></a><span class="p">(</span><span class="n">n_samples</span><span class="o">=</span><span class="mi">10_000</span><span class="p">,</span> <span class="n">weights</span><span class="o">=</span><span class="p">[</span><span class="mf">0.9</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">],</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Percentage of people carrying the disease: </span><span class="si">{</span><span class="mi">100</span><span class="o">*</span><span class="n">y</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">%&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Percentage of people carrying the disease: </span><span class="si">{</span><span class="mi">100</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">y</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">%&quot;</span><span class="p">)</span>
 </pre></div>
 </div>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Percentage of people carrying the disease: 10.37%
@@ -1603,7 +1602,7 @@ <h2>Invariance with respect to prevalence<a class="headerlink" href="#invariance
 <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
 </pre></div>
 </div>
-<img src="http://github.com/_images/sphx_glr_plot_likelihood_ratios_002.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_likelihood_ratios_002.png " alt="Positive likelihood ratio, Negative likelihood ratio" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 1.857 seconds)</p>
+<img src="http://github.com/_images/sphx_glr_plot_likelihood_ratios_002.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_likelihood_ratios_002.png " alt="Positive likelihood ratio, Negative likelihood ratio" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 1.831 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-model-selection-plot-likelihood-ratios-py">
 <div class="binder-badge docutils container">
 <a class="reference external image-reference" href="https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/model_selection/plot_likelihood_ratios.ipynb"><img alt="Launch binder" src="http://github.com/_images/binder_badge_logo22.svg" style="width: 150px;" />
@@ -1633,9 +1632,9 @@ <h2>Invariance with respect to prevalence<a class="headerlink" href="#invariance
 </div><div class="sphx-glr-thumbcontainer" tooltip="Feature scaling through standardization, also called Z-score normalization, is an important preprocessing step for many machine learning algorithms. It involves rescaling each feature such that it has a standard deviation of 1 and a mean of 0."><img alt="" src="http://github.com/_images/sphx_glr_plot_scaling_importance_thumb.png" />
 <p><a class="reference internal" href="http://github.com/preprocessing/plot_scaling_importance.html#sphx-glr-auto-examples-preprocessing-plot-scaling-importance-py"><span class="std std-ref">Importance of Feature Scaling</span></a></p>
   <div class="sphx-glr-thumbnail-title">Importance of Feature Scaling</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="A Recursive Feature Elimination (RFE) example with automatic tuning of the number of features selected with cross-validation."><img alt="" src="http://github.com/_images/sphx_glr_plot_rfe_with_cross_validation_thumb.png" />
-<p><a class="reference internal" href="http://github.com/feature_selection/plot_rfe_with_cross_validation.html#sphx-glr-auto-examples-feature-selection-plot-rfe-with-cross-validation-py"><span class="std std-ref">Recursive feature elimination with cross-validation</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Recursive feature elimination with cross-validation</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example compares two different bayesian regressors:"><img alt="" src="http://github.com/_images/sphx_glr_plot_ard_thumb.png" />
+<p><a class="reference internal" href="http://github.com/linear_model/plot_ard.html#sphx-glr-auto-examples-linear-model-plot-ard-py"><span class="std std-ref">Comparing Linear Bayesian Regressors</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Comparing Linear Bayesian Regressors</div>
 </div></div><p class="sphx-glr-signature"><a class="reference external" href="https://sphinx-gallery.github.io/">Gallery generated by Sphinx-Gallery</a></p>
 </section>
 </section>
diff --git a/dev/auto_examples/model_selection/plot_multi_metric_evaluation.html b/dev/auto_examples/model_selection/plot_multi_metric_evaluation.html
index 402c47f2854fd..d0d19160e0308 100644
--- a/dev/auto_examples/model_selection/plot_multi_metric_evaluation.html
+++ b/dev/auto_examples/model_selection/plot_multi_metric_evaluation.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -679,13 +679,12 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -902,9 +901,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
@@ -1100,7 +1099,7 @@ <h2>Plotting the result<a class="headerlink" href="#plotting-the-result" title="
 <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
 </pre></div>
 </div>
-<img src="http://github.com/_images/sphx_glr_plot_multi_metric_evaluation_001.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_multi_metric_evaluation_001.png " alt="GridSearchCV evaluating using multiple scorers simultaneously" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 8.280 seconds)</p>
+<img src="http://github.com/_images/sphx_glr_plot_multi_metric_evaluation_001.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_multi_metric_evaluation_001.png " alt="GridSearchCV evaluating using multiple scorers simultaneously" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 8.333 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-model-selection-plot-multi-metric-evaluation-py">
 <div class="binder-badge docutils container">
 <a class="reference external image-reference" href="https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/model_selection/plot_multi_metric_evaluation.ipynb"><img alt="Launch binder" src="http://github.com/_images/binder_badge_logo22.svg" style="width: 150px;" />
diff --git a/dev/auto_examples/model_selection/plot_nested_cross_validation_iris.html b/dev/auto_examples/model_selection/plot_nested_cross_validation_iris.html
index 99112822b1cdb..f5117248950b0 100644
--- a/dev/auto_examples/model_selection/plot_nested_cross_validation_iris.html
+++ b/dev/auto_examples/model_selection/plot_nested_cross_validation_iris.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
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@@ -679,13 +679,12 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -902,9 +901,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
@@ -1124,7 +1123,7 @@
 <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
 </pre></div>
 </div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 7.050 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 6.625 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-model-selection-plot-nested-cross-validation-iris-py">
 <div class="binder-badge docutils container">
 <a class="reference external image-reference" href="https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/model_selection/plot_nested_cross_validation_iris.ipynb"><img alt="Launch binder" src="http://github.com/_images/binder_badge_logo22.svg" style="width: 150px;" />
@@ -1154,9 +1153,9 @@
 </div><div class="sphx-glr-thumbcontainer" tooltip="Choosing the right cross-validation object is a crucial part of fitting a model properly. There are many ways to split data into training and test sets in order to avoid model overfitting, to standardize the number of groups in test sets, etc."><img alt="" src="http://github.com/_images/sphx_glr_plot_cv_indices_thumb.png" />
 <p><a class="reference internal" href="http://github.com/plot_cv_indices.html#sphx-glr-auto-examples-model-selection-plot-cv-indices-py"><span class="std std-ref">Visualizing cross-validation behavior in scikit-learn</span></a></p>
   <div class="sphx-glr-thumbnail-title">Visualizing cross-validation behavior in scikit-learn</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="A Recursive Feature Elimination (RFE) example with automatic tuning of the number of features selected with cross-validation."><img alt="" src="http://github.com/_images/sphx_glr_plot_rfe_with_cross_validation_thumb.png" />
-<p><a class="reference internal" href="http://github.com/feature_selection/plot_rfe_with_cross_validation.html#sphx-glr-auto-examples-feature-selection-plot-rfe-with-cross-validation-py"><span class="std std-ref">Recursive feature elimination with cross-validation</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Recursive feature elimination with cross-validation</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example illustrates the effect of the parameters gamma and C of the Radial Basis Function (RBF) kernel SVM."><img alt="" src="http://github.com/_images/sphx_glr_plot_rbf_parameters_thumb.png" />
+<p><a class="reference internal" href="http://github.com/svm/plot_rbf_parameters.html#sphx-glr-auto-examples-svm-plot-rbf-parameters-py"><span class="std std-ref">RBF SVM parameters</span></a></p>
+  <div class="sphx-glr-thumbnail-title">RBF SVM parameters</div>
 </div></div><p class="sphx-glr-signature"><a class="reference external" href="https://sphinx-gallery.github.io/">Gallery generated by Sphinx-Gallery</a></p>
 </section>
 
diff --git a/dev/auto_examples/model_selection/plot_permutation_tests_for_classification.html b/dev/auto_examples/model_selection/plot_permutation_tests_for_classification.html
index 4967149d778cf..3f40a19cff451 100644
--- a/dev/auto_examples/model_selection/plot_permutation_tests_for_classification.html
+++ b/dev/auto_examples/model_selection/plot_permutation_tests_for_classification.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
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@@ -679,13 +679,12 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -902,9 +901,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
@@ -1122,7 +1121,7 @@ <h3>Random data<a class="headerlink" href="#random-data" title="Link to this hea
 Journal of Machine Learning Research (2010) vol. 11</p>
 </aside>
 </aside>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 12.772 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 12.552 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-model-selection-plot-permutation-tests-for-classification-py">
 <div class="binder-badge docutils container">
 <a class="reference external image-reference" href="https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/model_selection/plot_permutation_tests_for_classification.ipynb"><img alt="Launch binder" src="http://github.com/_images/binder_badge_logo22.svg" style="width: 150px;" />
diff --git a/dev/auto_examples/model_selection/plot_precision_recall.html b/dev/auto_examples/model_selection/plot_precision_recall.html
index 1fb2329cbf2b4..133bdbb3ebedb 100644
--- a/dev/auto_examples/model_selection/plot_precision_recall.html
+++ b/dev/auto_examples/model_selection/plot_precision_recall.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -680,13 +680,12 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -903,9 +902,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
@@ -1108,7 +1107,7 @@ <h3>Dataset and model<a class="headerlink" href="#dataset-and-model" title="Link
 </pre></div>
 </div>
 <div class="output_subarea output_html rendered_html output_result">
-<style>#sk-container-id-61 {
+<style>#sk-container-id-62 {
   /* Definition of color scheme common for light and dark mode */
   --sklearn-color-text: #000;
   --sklearn-color-text-muted: #666;
@@ -1139,15 +1138,15 @@ <h3>Dataset and model<a class="headerlink" href="#dataset-and-model" title="Link
   }
 }
 
-#sk-container-id-61 {
+#sk-container-id-62 {
   color: var(--sklearn-color-text);
 }
 
-#sk-container-id-61 pre {
+#sk-container-id-62 pre {
   padding: 0;
 }
 
-#sk-container-id-61 input.sk-hidden--visually {
+#sk-container-id-62 input.sk-hidden--visually {
   border: 0;
   clip: rect(1px 1px 1px 1px);
   clip: rect(1px, 1px, 1px, 1px);
@@ -1159,7 +1158,7 @@ <h3>Dataset and model<a class="headerlink" href="#dataset-and-model" title="Link
   width: 1px;
 }
 
-#sk-container-id-61 div.sk-dashed-wrapped {
+#sk-container-id-62 div.sk-dashed-wrapped {
   border: 1px dashed var(--sklearn-color-line);
   margin: 0 0.4em 0.5em 0.4em;
   box-sizing: border-box;
@@ -1167,7 +1166,7 @@ <h3>Dataset and model<a class="headerlink" href="#dataset-and-model" title="Link
   background-color: var(--sklearn-color-background);
 }
 
-#sk-container-id-61 div.sk-container {
+#sk-container-id-62 div.sk-container {
   /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
      but bootstrap.min.css set `[hidden] { display: none !important; }`
      so we also need the `!important` here to be able to override the
@@ -1177,7 +1176,7 @@ <h3>Dataset and model<a class="headerlink" href="#dataset-and-model" title="Link
   position: relative;
 }
 
-#sk-container-id-61 div.sk-text-repr-fallback {
+#sk-container-id-62 div.sk-text-repr-fallback {
   display: none;
 }
 
@@ -1193,14 +1192,14 @@ <h3>Dataset and model<a class="headerlink" href="#dataset-and-model" title="Link
 
 /* Parallel-specific style estimator block */
 
-#sk-container-id-61 div.sk-parallel-item::after {
+#sk-container-id-62 div.sk-parallel-item::after {
   content: "";
   width: 100%;
   border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
   flex-grow: 1;
 }
 
-#sk-container-id-61 div.sk-parallel {
+#sk-container-id-62 div.sk-parallel {
   display: flex;
   align-items: stretch;
   justify-content: center;
@@ -1208,28 +1207,28 @@ <h3>Dataset and model<a class="headerlink" href="#dataset-and-model" title="Link
   position: relative;
 }
 
-#sk-container-id-61 div.sk-parallel-item {
+#sk-container-id-62 div.sk-parallel-item {
   display: flex;
   flex-direction: column;
 }
 
-#sk-container-id-61 div.sk-parallel-item:first-child::after {
+#sk-container-id-62 div.sk-parallel-item:first-child::after {
   align-self: flex-end;
   width: 50%;
 }
 
-#sk-container-id-61 div.sk-parallel-item:last-child::after {
+#sk-container-id-62 div.sk-parallel-item:last-child::after {
   align-self: flex-start;
   width: 50%;
 }
 
-#sk-container-id-61 div.sk-parallel-item:only-child::after {
+#sk-container-id-62 div.sk-parallel-item:only-child::after {
   width: 0;
 }
 
 /* Serial-specific style estimator block */
 
-#sk-container-id-61 div.sk-serial {
+#sk-container-id-62 div.sk-serial {
   display: flex;
   flex-direction: column;
   align-items: center;
@@ -1247,14 +1246,14 @@ <h3>Dataset and model<a class="headerlink" href="#dataset-and-model" title="Link
 
 /* Pipeline and ColumnTransformer style (default) */
 
-#sk-container-id-61 div.sk-toggleable {
+#sk-container-id-62 div.sk-toggleable {
   /* Default theme specific background. It is overwritten whether we have a
   specific estimator or a Pipeline/ColumnTransformer */
   background-color: var(--sklearn-color-background);
 }
 
 /* Toggleable label */
-#sk-container-id-61 label.sk-toggleable__label {
+#sk-container-id-62 label.sk-toggleable__label {
   cursor: pointer;
   display: flex;
   width: 100%;
@@ -1267,13 +1266,13 @@ <h3>Dataset and model<a class="headerlink" href="#dataset-and-model" title="Link
   gap: 0.5em;
 }
 
-#sk-container-id-61 label.sk-toggleable__label .caption {
+#sk-container-id-62 label.sk-toggleable__label .caption {
   font-size: 0.6rem;
   font-weight: lighter;
   color: var(--sklearn-color-text-muted);
 }
 
-#sk-container-id-61 label.sk-toggleable__label-arrow:before {
+#sk-container-id-62 label.sk-toggleable__label-arrow:before {
   /* Arrow on the left of the label */
   content: "▸";
   float: left;
@@ -1281,13 +1280,13 @@ <h3>Dataset and model<a class="headerlink" href="#dataset-and-model" title="Link
   color: var(--sklearn-color-icon);
 }
 
-#sk-container-id-61 label.sk-toggleable__label-arrow:hover:before {
+#sk-container-id-62 label.sk-toggleable__label-arrow:hover:before {
   color: var(--sklearn-color-text);
 }
 
 /* Toggleable content - dropdown */
 
-#sk-container-id-61 div.sk-toggleable__content {
+#sk-container-id-62 div.sk-toggleable__content {
   max-height: 0;
   max-width: 0;
   overflow: hidden;
@@ -1296,12 +1295,12 @@ <h3>Dataset and model<a class="headerlink" href="#dataset-and-model" title="Link
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-61 div.sk-toggleable__content.fitted {
+#sk-container-id-62 div.sk-toggleable__content.fitted {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
-#sk-container-id-61 div.sk-toggleable__content pre {
+#sk-container-id-62 div.sk-toggleable__content pre {
   margin: 0.2em;
   border-radius: 0.25em;
   color: var(--sklearn-color-text);
@@ -1309,79 +1308,79 @@ <h3>Dataset and model<a class="headerlink" href="#dataset-and-model" title="Link
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-61 div.sk-toggleable__content.fitted pre {
+#sk-container-id-62 div.sk-toggleable__content.fitted pre {
   /* unfitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
-#sk-container-id-61 input.sk-toggleable__control:checked~div.sk-toggleable__content {
+#sk-container-id-62 input.sk-toggleable__control:checked~div.sk-toggleable__content {
   /* Expand drop-down */
   max-height: 200px;
   max-width: 100%;
   overflow: auto;
 }
 
-#sk-container-id-61 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
+#sk-container-id-62 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
   content: "▾";
 }
 
 /* Pipeline/ColumnTransformer-specific style */
 
-#sk-container-id-61 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-62 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-61 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-62 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
 /* Estimator-specific style */
 
 /* Colorize estimator box */
-#sk-container-id-61 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-62 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-61 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-62 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
-#sk-container-id-61 div.sk-label label.sk-toggleable__label,
-#sk-container-id-61 div.sk-label label {
+#sk-container-id-62 div.sk-label label.sk-toggleable__label,
+#sk-container-id-62 div.sk-label label {
   /* The background is the default theme color */
   color: var(--sklearn-color-text-on-default-background);
 }
 
 /* On hover, darken the color of the background */
-#sk-container-id-61 div.sk-label:hover label.sk-toggleable__label {
+#sk-container-id-62 div.sk-label:hover label.sk-toggleable__label {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
 /* Label box, darken color on hover, fitted */
-#sk-container-id-61 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
+#sk-container-id-62 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
 /* Estimator label */
 
-#sk-container-id-61 div.sk-label label {
+#sk-container-id-62 div.sk-label label {
   font-family: monospace;
   font-weight: bold;
   display: inline-block;
   line-height: 1.2em;
 }
 
-#sk-container-id-61 div.sk-label-container {
+#sk-container-id-62 div.sk-label-container {
   text-align: center;
 }
 
 /* Estimator-specific */
-#sk-container-id-61 div.sk-estimator {
+#sk-container-id-62 div.sk-estimator {
   font-family: monospace;
   border: 1px dotted var(--sklearn-color-border-box);
   border-radius: 0.25em;
@@ -1391,18 +1390,18 @@ <h3>Dataset and model<a class="headerlink" href="#dataset-and-model" title="Link
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-61 div.sk-estimator.fitted {
+#sk-container-id-62 div.sk-estimator.fitted {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
 /* on hover */
-#sk-container-id-61 div.sk-estimator:hover {
+#sk-container-id-62 div.sk-estimator:hover {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-61 div.sk-estimator.fitted:hover {
+#sk-container-id-62 div.sk-estimator.fitted:hover {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-2);
 }
@@ -1490,7 +1489,7 @@ <h3>Dataset and model<a class="headerlink" href="#dataset-and-model" title="Link
 
 /* "?"-specific style due to the `<a>` HTML tag */
 
-#sk-container-id-61 a.estimator_doc_link {
+#sk-container-id-62 a.estimator_doc_link {
   float: right;
   font-size: 1rem;
   line-height: 1em;
@@ -1505,29 +1504,29 @@ <h3>Dataset and model<a class="headerlink" href="#dataset-and-model" title="Link
   border: var(--sklearn-color-unfitted-level-1) 1pt solid;
 }
 
-#sk-container-id-61 a.estimator_doc_link.fitted {
+#sk-container-id-62 a.estimator_doc_link.fitted {
   /* fitted */
   border: var(--sklearn-color-fitted-level-1) 1pt solid;
   color: var(--sklearn-color-fitted-level-1);
 }
 
 /* On hover */
-#sk-container-id-61 a.estimator_doc_link:hover {
+#sk-container-id-62 a.estimator_doc_link:hover {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-3);
   color: var(--sklearn-color-background);
   text-decoration: none;
 }
 
-#sk-container-id-61 a.estimator_doc_link.fitted:hover {
+#sk-container-id-62 a.estimator_doc_link.fitted:hover {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-3);
 }
-</style><div id="sk-container-id-61" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),
+</style><div id="sk-container-id-62" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),
                 (&#x27;linearsvc&#x27;,
-                 LinearSVC(random_state=RandomState(MT19937) at 0x7FB58659C940))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-244" type="checkbox" ><label for="sk-estimator-id-244" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>Pipeline</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.pipeline.Pipeline.html">?<span>Documentation for Pipeline</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),
+                 LinearSVC(random_state=RandomState(MT19937) at 0x7FD843CCCF40))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-258" type="checkbox" ><label for="sk-estimator-id-258" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>Pipeline</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.pipeline.Pipeline.html">?<span>Documentation for Pipeline</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),
                 (&#x27;linearsvc&#x27;,
-                 LinearSVC(random_state=RandomState(MT19937) at 0x7FB58659C940))])</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-245" type="checkbox" ><label for="sk-estimator-id-245" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>StandardScaler</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.StandardScaler.html">?<span>Documentation for StandardScaler</span></a></div></label><div class="sk-toggleable__content fitted"><pre>StandardScaler()</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-246" type="checkbox" ><label for="sk-estimator-id-246" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>LinearSVC</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.svm.LinearSVC.html">?<span>Documentation for LinearSVC</span></a></div></label><div class="sk-toggleable__content fitted"><pre>LinearSVC(random_state=RandomState(MT19937) at 0x7FB58659C940)</pre></div> </div></div></div></div></div></div>
+                 LinearSVC(random_state=RandomState(MT19937) at 0x7FD843CCCF40))])</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-259" type="checkbox" ><label for="sk-estimator-id-259" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>StandardScaler</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.StandardScaler.html">?<span>Documentation for StandardScaler</span></a></div></label><div class="sk-toggleable__content fitted"><pre>StandardScaler()</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-260" type="checkbox" ><label for="sk-estimator-id-260" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>LinearSVC</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.svm.LinearSVC.html">?<span>Documentation for LinearSVC</span></a></div></label><div class="sk-toggleable__content fitted"><pre>LinearSVC(random_state=RandomState(MT19937) at 0x7FD843CCCF40)</pre></div> </div></div></div></div></div></div>
 </div>
 <br />
 <br /></section>
@@ -1676,7 +1675,7 @@ <h3>Plot Precision-Recall curve for each class and iso-f1 curves<a class="header
 <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
 </pre></div>
 </div>
-<img src="http://github.com/_images/sphx_glr_plot_precision_recall_004.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_precision_recall_004.png " alt="Extension of Precision-Recall curve to multi-class" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 0.387 seconds)</p>
+<img src="http://github.com/_images/sphx_glr_plot_precision_recall_004.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_precision_recall_004.png " alt="Extension of Precision-Recall curve to multi-class" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 0.391 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-model-selection-plot-precision-recall-py">
 <div class="binder-badge docutils container">
 <a class="reference external image-reference" href="https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/model_selection/plot_precision_recall.ipynb"><img alt="Launch binder" src="http://github.com/_images/binder_badge_logo22.svg" style="width: 150px;" />
diff --git a/dev/auto_examples/model_selection/plot_randomized_search.html b/dev/auto_examples/model_selection/plot_randomized_search.html
index 39fc9e8bdb846..92292efec5bf7 100644
--- a/dev/auto_examples/model_selection/plot_randomized_search.html
+++ b/dev/auto_examples/model_selection/plot_randomized_search.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -679,13 +679,12 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -902,9 +901,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
@@ -1005,35 +1004,31 @@
 due to a noise effect and would not carry over to a held-out test set.</p>
 <p>Note that in practice, one would not search over this many different parameters
 simultaneously using grid search, but pick only the ones deemed most important.</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>RandomizedSearchCV took 0.80 seconds for 15 candidates parameter settings.
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>RandomizedSearchCV took 0.59 seconds for 15 candidates parameter settings.
 Model with rank: 1
-Mean validation score: 0.989 (std: 0.011)
-Parameters: {&#39;alpha&#39;: np.float64(0.05922132104755188), &#39;average&#39;: False, &#39;l1_ratio&#39;: np.float64(0.36190312127777724)}
+Mean validation score: 0.987 (std: 0.011)
+Parameters: {&#39;alpha&#39;: np.float64(0.01001911984591966), &#39;average&#39;: False, &#39;l1_ratio&#39;: np.float64(0.7665012035905148)}
 
 Model with rank: 2
-Mean validation score: 0.985 (std: 0.013)
-Parameters: {&#39;alpha&#39;: np.float64(0.01755014670388658), &#39;average&#39;: False, &#39;l1_ratio&#39;: np.float64(0.6752682741380909)}
+Mean validation score: 0.987 (std: 0.011)
+Parameters: {&#39;alpha&#39;: np.float64(0.40134964872774576), &#39;average&#39;: False, &#39;l1_ratio&#39;: np.float64(0.05033776045421079)}
 
 Model with rank: 3
 Mean validation score: 0.983 (std: 0.011)
-Parameters: {&#39;alpha&#39;: np.float64(0.14212307574165603), &#39;average&#39;: False, &#39;l1_ratio&#39;: np.float64(0.09875903932772567)}
+Parameters: {&#39;alpha&#39;: np.float64(0.1352374671440465), &#39;average&#39;: False, &#39;l1_ratio&#39;: np.float64(0.6719936995475292)}
 
-GridSearchCV took 3.27 seconds for 60 candidate parameter settings.
+GridSearchCV took 3.42 seconds for 60 candidate parameter settings.
 Model with rank: 1
-Mean validation score: 0.993 (std: 0.009)
+Mean validation score: 0.994 (std: 0.005)
 Parameters: {&#39;alpha&#39;: np.float64(0.1), &#39;average&#39;: False, &#39;l1_ratio&#39;: np.float64(0.1111111111111111)}
 
 Model with rank: 2
-Mean validation score: 0.991 (std: 0.010)
-Parameters: {&#39;alpha&#39;: np.float64(0.01), &#39;average&#39;: False, &#39;l1_ratio&#39;: np.float64(0.5555555555555556)}
+Mean validation score: 0.991 (std: 0.008)
+Parameters: {&#39;alpha&#39;: np.float64(0.1), &#39;average&#39;: False, &#39;l1_ratio&#39;: np.float64(0.0)}
 
 Model with rank: 3
-Mean validation score: 0.989 (std: 0.009)
-Parameters: {&#39;alpha&#39;: np.float64(0.01), &#39;average&#39;: False, &#39;l1_ratio&#39;: np.float64(0.0)}
-
-Model with rank: 3
-Mean validation score: 0.989 (std: 0.007)
-Parameters: {&#39;alpha&#39;: np.float64(0.01), &#39;average&#39;: False, &#39;l1_ratio&#39;: np.float64(0.4444444444444444)}
+Mean validation score: 0.989 (std: 0.018)
+Parameters: {&#39;alpha&#39;: np.float64(1.0), &#39;average&#39;: False, &#39;l1_ratio&#39;: np.float64(0.0)}
 </pre></div>
 </div>
 <div class="line-block">
@@ -1114,7 +1109,7 @@
 <span class="n">report</span><span class="p">(</span><span class="n">grid_search</span><span class="o">.</span><span class="n">cv_results_</span><span class="p">)</span>
 </pre></div>
 </div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 4.081 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 4.027 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-model-selection-plot-randomized-search-py">
 <div class="binder-badge docutils container">
 <a class="reference external image-reference" href="https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/model_selection/plot_randomized_search.ipynb"><img alt="Launch binder" src="http://github.com/_images/binder_badge_logo22.svg" style="width: 150px;" />
diff --git a/dev/auto_examples/model_selection/plot_roc.html b/dev/auto_examples/model_selection/plot_roc.html
index 88e7f6e4c1fd5..6038f019583a7 100644
--- a/dev/auto_examples/model_selection/plot_roc.html
+++ b/dev/auto_examples/model_selection/plot_roc.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -680,13 +680,12 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -903,9 +902,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
@@ -1138,9 +1137,9 @@ <h3>ROC curve using micro-averaged OvR<a class="headerlink" href="#roc-curve-usi
 <p><span class="math notranslate nohighlight">\(TPR=\frac{\sum_{c}TP_c}{\sum_{c}(TP_c + FN_c)}\)</span> ;</p>
 <p><span class="math notranslate nohighlight">\(FPR=\frac{\sum_{c}FP_c}{\sum_{c}(FP_c + TN_c)}\)</span> .</p>
 <p>We can briefly demo the effect of <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ravel.html#numpy.ravel" title="(in NumPy v2.2)"><code class="xref py py-func docutils literal notranslate"><span class="pre">numpy.ravel</span></code></a>:</p>
-<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;y_score:</span><span class="se">\n</span><span class="si">{</span><span class="n">y_score</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="mi">2</span><span class="p">,:]</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;y_score:</span><span class="se">\n</span><span class="si">{</span><span class="n">y_score</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="mi">2</span><span class="p">,</span><span class="w"> </span><span class="p">:]</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
 <span class="nb">print</span><span class="p">()</span>
-<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;y_score.ravel():</span><span class="se">\n</span><span class="si">{</span><span class="n">y_score</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="mi">2</span><span class="p">,:]</span><span class="o">.</span><span class="n">ravel</span><span class="p">()</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;y_score.ravel():</span><span class="se">\n</span><span class="si">{</span><span class="n">y_score</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="mi">2</span><span class="p">,</span><span class="w"> </span><span class="p">:]</span><span class="o">.</span><span class="n">ravel</span><span class="p">()</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
 </pre></div>
 </div>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>y_score:
@@ -1363,7 +1362,7 @@ <h3>ROC curve using the OvO macro-average<a class="headerlink" href="#roc-curve-
     <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.plot.html#matplotlib.pyplot.plot" title="matplotlib.pyplot.plot" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">plot</span></a><span class="p">(</span>
         <span class="n">fpr_grid</span><span class="p">,</span>
         <span class="n">mean_tpr</span><span class="p">[</span><span class="n">ix</span><span class="p">],</span>
-        <span class="n">label</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;Mean </span><span class="si">{</span><span class="n">label_a</span><span class="si">}</span><span class="s2"> vs </span><span class="si">{</span><span class="n">label_b</span><span class="si">}</span><span class="s2"> (AUC = </span><span class="si">{</span><span class="n">mean_score</span><span class="w"> </span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">)&quot;</span><span class="p">,</span>
+        <span class="n">label</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;Mean </span><span class="si">{</span><span class="n">label_a</span><span class="si">}</span><span class="s2"> vs </span><span class="si">{</span><span class="n">label_b</span><span class="si">}</span><span class="s2"> (AUC = </span><span class="si">{</span><span class="n">mean_score</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">)&quot;</span><span class="p">,</span>
         <span class="n">linestyle</span><span class="o">=</span><span class="s2">&quot;:&quot;</span><span class="p">,</span>
         <span class="n">linewidth</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span>
     <span class="p">)</span>
@@ -1475,7 +1474,7 @@ <h3>Plot all OvO ROC curves together<a class="headerlink" href="#plot-all-ovo-ro
 when you want under-represented classes to be considered as important as highly
 populated classes.</p></li>
 </ul>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 0.651 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 0.673 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-model-selection-plot-roc-py">
 <div class="binder-badge docutils container">
 <a class="reference external image-reference" href="https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/model_selection/plot_roc.ipynb"><img alt="Launch binder" src="http://github.com/_images/binder_badge_logo22.svg" style="width: 150px;" />
diff --git a/dev/auto_examples/model_selection/plot_roc_crossval.html b/dev/auto_examples/model_selection/plot_roc_crossval.html
index 1775e6cb3da0f..dc091edd0d315 100644
--- a/dev/auto_examples/model_selection/plot_roc_crossval.html
+++ b/dev/auto_examples/model_selection/plot_roc_crossval.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -679,13 +679,12 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -902,9 +901,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
@@ -1115,7 +1114,7 @@ <h3>Classification and ROC analysis<a class="headerlink" href="#classification-a
 <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
 </pre></div>
 </div>
-<img src="http://github.com/_images/sphx_glr_plot_roc_crossval_001.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_roc_crossval_001.png " alt="Mean ROC curve with variability (Positive label 'versicolor')" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 0.176 seconds)</p>
+<img src="http://github.com/_images/sphx_glr_plot_roc_crossval_001.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_roc_crossval_001.png " alt="Mean ROC curve with variability (Positive label 'versicolor')" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 0.180 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-model-selection-plot-roc-crossval-py">
 <div class="binder-badge docutils container">
 <a class="reference external image-reference" href="https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/model_selection/plot_roc_crossval.ipynb"><img alt="Launch binder" src="http://github.com/_images/binder_badge_logo22.svg" style="width: 150px;" />
diff --git a/dev/auto_examples/model_selection/plot_successive_halving_heatmap.html b/dev/auto_examples/model_selection/plot_successive_halving_heatmap.html
index cacc7b445a200..a9200098af552 100644
--- a/dev/auto_examples/model_selection/plot_successive_halving_heatmap.html
+++ b/dev/auto_examples/model_selection/plot_successive_halving_heatmap.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -679,13 +679,12 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -902,9 +901,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
@@ -1007,7 +1006,7 @@
 <span class="kn">import</span><span class="w"> </span><span class="nn">pandas</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pd</span>
 
 <span class="kn">from</span><span class="w"> </span><span class="nn">sklearn</span><span class="w"> </span><span class="kn">import</span> <span class="n">datasets</span>
-<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.experimental</span><span class="w"> </span><span class="kn">import</span> <span class="n">enable_halving_search_cv</span>  <span class="c1"># noqa</span>
+<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.experimental</span><span class="w"> </span><span class="kn">import</span> <span class="n">enable_halving_search_cv</span>  <span class="c1"># noqa: F401</span>
 <span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.model_selection</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.model_selection.GridSearchCV.html#sklearn.model_selection.GridSearchCV" title="sklearn.model_selection.GridSearchCV" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">GridSearchCV</span></a><span class="p">,</span> <a href="http://github.com/modules/generated/sklearn.model_selection.HalvingGridSearchCV.html#sklearn.model_selection.HalvingGridSearchCV" title="sklearn.model_selection.HalvingGridSearchCV" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">HalvingGridSearchCV</span></a>
 <span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.svm</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.svm.SVC.html#sklearn.svm.SVC" title="sklearn.svm.SVC" class="sphx-glr-backref-module-sklearn-svm sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">SVC</span></a>
 </pre></div>
@@ -1106,7 +1105,7 @@
 <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
 </pre></div>
 </div>
-<img src="http://github.com/_images/sphx_glr_plot_successive_halving_heatmap_001.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_successive_halving_heatmap_001.png " alt="Successive Halving time = 1.590s, GridSearch time = 5.597s" class = "sphx-glr-single-img"/><p>The heatmaps show the mean test score of the parameter combinations for an
+<img src="http://github.com/_images/sphx_glr_plot_successive_halving_heatmap_001.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_successive_halving_heatmap_001.png " alt="Successive Halving time = 1.522s, GridSearch time = 5.917s" class = "sphx-glr-single-img"/><p>The heatmaps show the mean test score of the parameter combinations for an
 <a class="reference internal" href="http://github.com/modules/generated/sklearn.svm.SVC.html#sklearn.svm.SVC" title="sklearn.svm.SVC"><code class="xref py py-class docutils literal notranslate"><span class="pre">SVC</span></code></a> instance. The
 <a class="reference internal" href="http://github.com/modules/generated/sklearn.model_selection.HalvingGridSearchCV.html#sklearn.model_selection.HalvingGridSearchCV" title="sklearn.model_selection.HalvingGridSearchCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">HalvingGridSearchCV</span></code></a> also shows the
 iteration at which the combinations where last used. The combinations marked
@@ -1115,7 +1114,7 @@
 <p>We can see that the <a class="reference internal" href="http://github.com/modules/generated/sklearn.model_selection.HalvingGridSearchCV.html#sklearn.model_selection.HalvingGridSearchCV" title="sklearn.model_selection.HalvingGridSearchCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">HalvingGridSearchCV</span></code></a>
 class is able to find parameter combinations that are just as accurate as
 <a class="reference internal" href="http://github.com/modules/generated/sklearn.model_selection.GridSearchCV.html#sklearn.model_selection.GridSearchCV" title="sklearn.model_selection.GridSearchCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">GridSearchCV</span></code></a>, in much less time.</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 7.352 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 7.639 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-model-selection-plot-successive-halving-heatmap-py">
 <div class="binder-badge docutils container">
 <a class="reference external image-reference" href="https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/model_selection/plot_successive_halving_heatmap.ipynb"><img alt="Launch binder" src="http://github.com/_images/binder_badge_logo22.svg" style="width: 150px;" />
diff --git a/dev/auto_examples/model_selection/plot_successive_halving_iterations.html b/dev/auto_examples/model_selection/plot_successive_halving_iterations.html
index 3b9d0e15bd2f9..f0e2213a9002b 100644
--- a/dev/auto_examples/model_selection/plot_successive_halving_iterations.html
+++ b/dev/auto_examples/model_selection/plot_successive_halving_iterations.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -679,13 +679,12 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -902,9 +901,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
@@ -1009,7 +1008,7 @@
 
 <span class="kn">from</span><span class="w"> </span><span class="nn">sklearn</span><span class="w"> </span><span class="kn">import</span> <span class="n">datasets</span>
 <span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.ensemble</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.ensemble.RandomForestClassifier.html#sklearn.ensemble.RandomForestClassifier" title="sklearn.ensemble.RandomForestClassifier" class="sphx-glr-backref-module-sklearn-ensemble sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">RandomForestClassifier</span></a>
-<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.experimental</span><span class="w"> </span><span class="kn">import</span> <span class="n">enable_halving_search_cv</span>  <span class="c1"># noqa</span>
+<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.experimental</span><span class="w"> </span><span class="kn">import</span> <span class="n">enable_halving_search_cv</span>  <span class="c1"># noqa: F401</span>
 <span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.model_selection</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.model_selection.HalvingRandomSearchCV.html#sklearn.model_selection.HalvingRandomSearchCV" title="sklearn.model_selection.HalvingRandomSearchCV" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">HalvingRandomSearchCV</span></a>
 </pre></div>
 </div>
@@ -1036,7 +1035,7 @@
 </pre></div>
 </div>
 <div class="output_subarea output_html rendered_html output_result">
-<style>#sk-container-id-64 {
+<style>#sk-container-id-65 {
   /* Definition of color scheme common for light and dark mode */
   --sklearn-color-text: #000;
   --sklearn-color-text-muted: #666;
@@ -1067,15 +1066,15 @@
   }
 }
 
-#sk-container-id-64 {
+#sk-container-id-65 {
   color: var(--sklearn-color-text);
 }
 
-#sk-container-id-64 pre {
+#sk-container-id-65 pre {
   padding: 0;
 }
 
-#sk-container-id-64 input.sk-hidden--visually {
+#sk-container-id-65 input.sk-hidden--visually {
   border: 0;
   clip: rect(1px 1px 1px 1px);
   clip: rect(1px, 1px, 1px, 1px);
@@ -1087,7 +1086,7 @@
   width: 1px;
 }
 
-#sk-container-id-64 div.sk-dashed-wrapped {
+#sk-container-id-65 div.sk-dashed-wrapped {
   border: 1px dashed var(--sklearn-color-line);
   margin: 0 0.4em 0.5em 0.4em;
   box-sizing: border-box;
@@ -1095,7 +1094,7 @@
   background-color: var(--sklearn-color-background);
 }
 
-#sk-container-id-64 div.sk-container {
+#sk-container-id-65 div.sk-container {
   /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
      but bootstrap.min.css set `[hidden] { display: none !important; }`
      so we also need the `!important` here to be able to override the
@@ -1105,7 +1104,7 @@
   position: relative;
 }
 
-#sk-container-id-64 div.sk-text-repr-fallback {
+#sk-container-id-65 div.sk-text-repr-fallback {
   display: none;
 }
 
@@ -1121,14 +1120,14 @@
 
 /* Parallel-specific style estimator block */
 
-#sk-container-id-64 div.sk-parallel-item::after {
+#sk-container-id-65 div.sk-parallel-item::after {
   content: "";
   width: 100%;
   border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
   flex-grow: 1;
 }
 
-#sk-container-id-64 div.sk-parallel {
+#sk-container-id-65 div.sk-parallel {
   display: flex;
   align-items: stretch;
   justify-content: center;
@@ -1136,28 +1135,28 @@
   position: relative;
 }
 
-#sk-container-id-64 div.sk-parallel-item {
+#sk-container-id-65 div.sk-parallel-item {
   display: flex;
   flex-direction: column;
 }
 
-#sk-container-id-64 div.sk-parallel-item:first-child::after {
+#sk-container-id-65 div.sk-parallel-item:first-child::after {
   align-self: flex-end;
   width: 50%;
 }
 
-#sk-container-id-64 div.sk-parallel-item:last-child::after {
+#sk-container-id-65 div.sk-parallel-item:last-child::after {
   align-self: flex-start;
   width: 50%;
 }
 
-#sk-container-id-64 div.sk-parallel-item:only-child::after {
+#sk-container-id-65 div.sk-parallel-item:only-child::after {
   width: 0;
 }
 
 /* Serial-specific style estimator block */
 
-#sk-container-id-64 div.sk-serial {
+#sk-container-id-65 div.sk-serial {
   display: flex;
   flex-direction: column;
   align-items: center;
@@ -1175,14 +1174,14 @@
 
 /* Pipeline and ColumnTransformer style (default) */
 
-#sk-container-id-64 div.sk-toggleable {
+#sk-container-id-65 div.sk-toggleable {
   /* Default theme specific background. It is overwritten whether we have a
   specific estimator or a Pipeline/ColumnTransformer */
   background-color: var(--sklearn-color-background);
 }
 
 /* Toggleable label */
-#sk-container-id-64 label.sk-toggleable__label {
+#sk-container-id-65 label.sk-toggleable__label {
   cursor: pointer;
   display: flex;
   width: 100%;
@@ -1195,13 +1194,13 @@
   gap: 0.5em;
 }
 
-#sk-container-id-64 label.sk-toggleable__label .caption {
+#sk-container-id-65 label.sk-toggleable__label .caption {
   font-size: 0.6rem;
   font-weight: lighter;
   color: var(--sklearn-color-text-muted);
 }
 
-#sk-container-id-64 label.sk-toggleable__label-arrow:before {
+#sk-container-id-65 label.sk-toggleable__label-arrow:before {
   /* Arrow on the left of the label */
   content: "▸";
   float: left;
@@ -1209,13 +1208,13 @@
   color: var(--sklearn-color-icon);
 }
 
-#sk-container-id-64 label.sk-toggleable__label-arrow:hover:before {
+#sk-container-id-65 label.sk-toggleable__label-arrow:hover:before {
   color: var(--sklearn-color-text);
 }
 
 /* Toggleable content - dropdown */
 
-#sk-container-id-64 div.sk-toggleable__content {
+#sk-container-id-65 div.sk-toggleable__content {
   max-height: 0;
   max-width: 0;
   overflow: hidden;
@@ -1224,12 +1223,12 @@
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-64 div.sk-toggleable__content.fitted {
+#sk-container-id-65 div.sk-toggleable__content.fitted {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
-#sk-container-id-64 div.sk-toggleable__content pre {
+#sk-container-id-65 div.sk-toggleable__content pre {
   margin: 0.2em;
   border-radius: 0.25em;
   color: var(--sklearn-color-text);
@@ -1237,79 +1236,79 @@
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-64 div.sk-toggleable__content.fitted pre {
+#sk-container-id-65 div.sk-toggleable__content.fitted pre {
   /* unfitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
-#sk-container-id-64 input.sk-toggleable__control:checked~div.sk-toggleable__content {
+#sk-container-id-65 input.sk-toggleable__control:checked~div.sk-toggleable__content {
   /* Expand drop-down */
   max-height: 200px;
   max-width: 100%;
   overflow: auto;
 }
 
-#sk-container-id-64 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
+#sk-container-id-65 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
   content: "▾";
 }
 
 /* Pipeline/ColumnTransformer-specific style */
 
-#sk-container-id-64 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-65 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-64 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-65 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
 /* Estimator-specific style */
 
 /* Colorize estimator box */
-#sk-container-id-64 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-65 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-64 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-65 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
-#sk-container-id-64 div.sk-label label.sk-toggleable__label,
-#sk-container-id-64 div.sk-label label {
+#sk-container-id-65 div.sk-label label.sk-toggleable__label,
+#sk-container-id-65 div.sk-label label {
   /* The background is the default theme color */
   color: var(--sklearn-color-text-on-default-background);
 }
 
 /* On hover, darken the color of the background */
-#sk-container-id-64 div.sk-label:hover label.sk-toggleable__label {
+#sk-container-id-65 div.sk-label:hover label.sk-toggleable__label {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
 /* Label box, darken color on hover, fitted */
-#sk-container-id-64 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
+#sk-container-id-65 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
 /* Estimator label */
 
-#sk-container-id-64 div.sk-label label {
+#sk-container-id-65 div.sk-label label {
   font-family: monospace;
   font-weight: bold;
   display: inline-block;
   line-height: 1.2em;
 }
 
-#sk-container-id-64 div.sk-label-container {
+#sk-container-id-65 div.sk-label-container {
   text-align: center;
 }
 
 /* Estimator-specific */
-#sk-container-id-64 div.sk-estimator {
+#sk-container-id-65 div.sk-estimator {
   font-family: monospace;
   border: 1px dotted var(--sklearn-color-border-box);
   border-radius: 0.25em;
@@ -1319,18 +1318,18 @@
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-64 div.sk-estimator.fitted {
+#sk-container-id-65 div.sk-estimator.fitted {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
 /* on hover */
-#sk-container-id-64 div.sk-estimator:hover {
+#sk-container-id-65 div.sk-estimator:hover {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-64 div.sk-estimator.fitted:hover {
+#sk-container-id-65 div.sk-estimator.fitted:hover {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-2);
 }
@@ -1418,7 +1417,7 @@
 
 /* "?"-specific style due to the `<a>` HTML tag */
 
-#sk-container-id-64 a.estimator_doc_link {
+#sk-container-id-65 a.estimator_doc_link {
   float: right;
   font-size: 1rem;
   line-height: 1em;
@@ -1433,45 +1432,45 @@
   border: var(--sklearn-color-unfitted-level-1) 1pt solid;
 }
 
-#sk-container-id-64 a.estimator_doc_link.fitted {
+#sk-container-id-65 a.estimator_doc_link.fitted {
   /* fitted */
   border: var(--sklearn-color-fitted-level-1) 1pt solid;
   color: var(--sklearn-color-fitted-level-1);
 }
 
 /* On hover */
-#sk-container-id-64 a.estimator_doc_link:hover {
+#sk-container-id-65 a.estimator_doc_link:hover {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-3);
   color: var(--sklearn-color-background);
   text-decoration: none;
 }
 
-#sk-container-id-64 a.estimator_doc_link.fitted:hover {
+#sk-container-id-65 a.estimator_doc_link.fitted:hover {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-3);
 }
-</style><div id="sk-container-id-64" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>HalvingRandomSearchCV(estimator=RandomForestClassifier(n_estimators=20,
-                                                       random_state=RandomState(MT19937) at 0x7FB57BFE7B40),
+</style><div id="sk-container-id-65" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>HalvingRandomSearchCV(estimator=RandomForestClassifier(n_estimators=20,
+                                                       random_state=RandomState(MT19937) at 0x7FD8431B0B40),
                       factor=2,
                       param_distributions={&#x27;bootstrap&#x27;: [True, False],
                                            &#x27;criterion&#x27;: [&#x27;gini&#x27;, &#x27;entropy&#x27;],
                                            &#x27;max_depth&#x27;: [3, None],
-                                           &#x27;max_features&#x27;: &lt;scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x7fb57af9e770&gt;,
-                                           &#x27;min_samples_split&#x27;: &lt;scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x7fb57af9e170&gt;},
-                      random_state=RandomState(MT19937) at 0x7FB57BFE7B40)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-253" type="checkbox" ><label for="sk-estimator-id-253" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>HalvingRandomSearchCV</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.model_selection.HalvingRandomSearchCV.html">?<span>Documentation for HalvingRandomSearchCV</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>HalvingRandomSearchCV(estimator=RandomForestClassifier(n_estimators=20,
-                                                       random_state=RandomState(MT19937) at 0x7FB57BFE7B40),
+                                           &#x27;max_features&#x27;: &lt;scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x7fd848bb7a00&gt;,
+                                           &#x27;min_samples_split&#x27;: &lt;scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x7fd841fa62c0&gt;},
+                      random_state=RandomState(MT19937) at 0x7FD8431B0B40)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-267" type="checkbox" ><label for="sk-estimator-id-267" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>HalvingRandomSearchCV</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.model_selection.HalvingRandomSearchCV.html">?<span>Documentation for HalvingRandomSearchCV</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>HalvingRandomSearchCV(estimator=RandomForestClassifier(n_estimators=20,
+                                                       random_state=RandomState(MT19937) at 0x7FD8431B0B40),
                       factor=2,
                       param_distributions={&#x27;bootstrap&#x27;: [True, False],
                                            &#x27;criterion&#x27;: [&#x27;gini&#x27;, &#x27;entropy&#x27;],
                                            &#x27;max_depth&#x27;: [3, None],
-                                           &#x27;max_features&#x27;: &lt;scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x7fb57af9e770&gt;,
-                                           &#x27;min_samples_split&#x27;: &lt;scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x7fb57af9e170&gt;},
-                      random_state=RandomState(MT19937) at 0x7FB57BFE7B40)</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-254" type="checkbox" ><label for="sk-estimator-id-254" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>best_estimator_: RandomForestClassifier</div></div></label><div class="sk-toggleable__content fitted"><pre>RandomForestClassifier(bootstrap=False, criterion=&#x27;entropy&#x27;, max_features=4,
+                                           &#x27;max_features&#x27;: &lt;scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x7fd848bb7a00&gt;,
+                                           &#x27;min_samples_split&#x27;: &lt;scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x7fd841fa62c0&gt;},
+                      random_state=RandomState(MT19937) at 0x7FD8431B0B40)</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-268" type="checkbox" ><label for="sk-estimator-id-268" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>best_estimator_: RandomForestClassifier</div></div></label><div class="sk-toggleable__content fitted"><pre>RandomForestClassifier(bootstrap=False, criterion=&#x27;entropy&#x27;, max_features=4,
                        min_samples_split=5, n_estimators=20,
-                       random_state=RandomState(MT19937) at 0x7FB57BFE7C40)</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-255" type="checkbox" ><label for="sk-estimator-id-255" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>RandomForestClassifier</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.ensemble.RandomForestClassifier.html">?<span>Documentation for RandomForestClassifier</span></a></div></label><div class="sk-toggleable__content fitted"><pre>RandomForestClassifier(bootstrap=False, criterion=&#x27;entropy&#x27;, max_features=4,
+                       random_state=RandomState(MT19937) at 0x7FD8431B3240)</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-269" type="checkbox" ><label for="sk-estimator-id-269" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>RandomForestClassifier</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.ensemble.RandomForestClassifier.html">?<span>Documentation for RandomForestClassifier</span></a></div></label><div class="sk-toggleable__content fitted"><pre>RandomForestClassifier(bootstrap=False, criterion=&#x27;entropy&#x27;, max_features=4,
                        min_samples_split=5, n_estimators=20,
-                       random_state=RandomState(MT19937) at 0x7FB57BFE7C40)</pre></div> </div></div></div></div></div></div></div></div></div>
+                       random_state=RandomState(MT19937) at 0x7FD8431B3240)</pre></div> </div></div></div></div></div></div></div></div></div>
 </div>
 <br />
 <br /><p>We can now use the <code class="docutils literal notranslate"><span class="pre">cv_results_</span></code> attribute of the search estimator to inspect
@@ -1509,7 +1508,7 @@ <h2>Number of candidates and amount of resource at each iteration<a class="heade
 <p>This process is repeated until the last iteration, where only 2 candidates
 are left. The best candidate is the candidate that has the best score at the
 last iteration.</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 5.403 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 5.223 seconds)</p>
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diff --git a/dev/auto_examples/model_selection/plot_train_error_vs_test_error.html b/dev/auto_examples/model_selection/plot_train_error_vs_test_error.html
index bd0198ae2a556..9ea00ed6b0d06 100644
--- a/dev/auto_examples/model_selection/plot_train_error_vs_test_error.html
+++ b/dev/auto_examples/model_selection/plot_train_error_vs_test_error.html
@@ -42,7 +42,7 @@
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     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
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@@ -679,13 +679,12 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -902,9 +901,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
@@ -1145,7 +1144,7 @@ <h2>Coefficients comparison<a class="headerlink" href="#coefficients-comparison"
 the true generative model, our model shrinks them towards zero. So we don’t recover
 the true coefficients, but we get a sensible outcome in line with the performance
 obtained on the test set.</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 4.979 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 4.847 seconds)</p>
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diff --git a/dev/auto_examples/model_selection/plot_tuned_decision_threshold.html b/dev/auto_examples/model_selection/plot_tuned_decision_threshold.html
index 45d340a96751c..c9d74858fad6c 100644
--- a/dev/auto_examples/model_selection/plot_tuned_decision_threshold.html
+++ b/dev/auto_examples/model_selection/plot_tuned_decision_threshold.html
@@ -42,7 +42,7 @@
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-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
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@@ -679,13 +679,12 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -902,9 +901,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
@@ -1051,7 +1050,7 @@ <h2>Our vanilla classifier<a class="headerlink" href="#our-vanilla-classifier" t
 </pre></div>
 </div>
 <div class="output_subarea output_html rendered_html output_result">
-<style>#sk-container-id-56 {
+<style>#sk-container-id-57 {
   /* Definition of color scheme common for light and dark mode */
   --sklearn-color-text: #000;
   --sklearn-color-text-muted: #666;
@@ -1082,15 +1081,15 @@ <h2>Our vanilla classifier<a class="headerlink" href="#our-vanilla-classifier" t
   }
 }
 
-#sk-container-id-56 {
+#sk-container-id-57 {
   color: var(--sklearn-color-text);
 }
 
-#sk-container-id-56 pre {
+#sk-container-id-57 pre {
   padding: 0;
 }
 
-#sk-container-id-56 input.sk-hidden--visually {
+#sk-container-id-57 input.sk-hidden--visually {
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   clip: rect(1px 1px 1px 1px);
   clip: rect(1px, 1px, 1px, 1px);
@@ -1102,7 +1101,7 @@ <h2>Our vanilla classifier<a class="headerlink" href="#our-vanilla-classifier" t
   width: 1px;
 }
 
-#sk-container-id-56 div.sk-dashed-wrapped {
+#sk-container-id-57 div.sk-dashed-wrapped {
   border: 1px dashed var(--sklearn-color-line);
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   box-sizing: border-box;
@@ -1110,7 +1109,7 @@ <h2>Our vanilla classifier<a class="headerlink" href="#our-vanilla-classifier" t
   background-color: var(--sklearn-color-background);
 }
 
-#sk-container-id-56 div.sk-container {
+#sk-container-id-57 div.sk-container {
   /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
      but bootstrap.min.css set `[hidden] { display: none !important; }`
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@@ -1120,7 +1119,7 @@ <h2>Our vanilla classifier<a class="headerlink" href="#our-vanilla-classifier" t
   position: relative;
 }
 
-#sk-container-id-56 div.sk-text-repr-fallback {
+#sk-container-id-57 div.sk-text-repr-fallback {
   display: none;
 }
 
@@ -1136,14 +1135,14 @@ <h2>Our vanilla classifier<a class="headerlink" href="#our-vanilla-classifier" t
 
 /* Parallel-specific style estimator block */
 
-#sk-container-id-56 div.sk-parallel-item::after {
+#sk-container-id-57 div.sk-parallel-item::after {
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   width: 100%;
   border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
   flex-grow: 1;
 }
 
-#sk-container-id-56 div.sk-parallel {
+#sk-container-id-57 div.sk-parallel {
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@@ -1151,28 +1150,28 @@ <h2>Our vanilla classifier<a class="headerlink" href="#our-vanilla-classifier" t
   position: relative;
 }
 
-#sk-container-id-56 div.sk-parallel-item {
+#sk-container-id-57 div.sk-parallel-item {
   display: flex;
   flex-direction: column;
 }
 
-#sk-container-id-56 div.sk-parallel-item:first-child::after {
+#sk-container-id-57 div.sk-parallel-item:first-child::after {
   align-self: flex-end;
   width: 50%;
 }
 
-#sk-container-id-56 div.sk-parallel-item:last-child::after {
+#sk-container-id-57 div.sk-parallel-item:last-child::after {
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   width: 50%;
 }
 
-#sk-container-id-56 div.sk-parallel-item:only-child::after {
+#sk-container-id-57 div.sk-parallel-item:only-child::after {
   width: 0;
 }
 
 /* Serial-specific style estimator block */
 
-#sk-container-id-56 div.sk-serial {
+#sk-container-id-57 div.sk-serial {
   display: flex;
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@@ -1190,14 +1189,14 @@ <h2>Our vanilla classifier<a class="headerlink" href="#our-vanilla-classifier" t
 
 /* Pipeline and ColumnTransformer style (default) */
 
-#sk-container-id-56 div.sk-toggleable {
+#sk-container-id-57 div.sk-toggleable {
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   specific estimator or a Pipeline/ColumnTransformer */
   background-color: var(--sklearn-color-background);
 }
 
 /* Toggleable label */
-#sk-container-id-56 label.sk-toggleable__label {
+#sk-container-id-57 label.sk-toggleable__label {
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   display: flex;
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@@ -1210,13 +1209,13 @@ <h2>Our vanilla classifier<a class="headerlink" href="#our-vanilla-classifier" t
   gap: 0.5em;
 }
 
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+#sk-container-id-57 label.sk-toggleable__label .caption {
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   font-weight: lighter;
   color: var(--sklearn-color-text-muted);
 }
 
-#sk-container-id-56 label.sk-toggleable__label-arrow:before {
+#sk-container-id-57 label.sk-toggleable__label-arrow:before {
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   float: left;
@@ -1224,13 +1223,13 @@ <h2>Our vanilla classifier<a class="headerlink" href="#our-vanilla-classifier" t
   color: var(--sklearn-color-icon);
 }
 
-#sk-container-id-56 label.sk-toggleable__label-arrow:hover:before {
+#sk-container-id-57 label.sk-toggleable__label-arrow:hover:before {
   color: var(--sklearn-color-text);
 }
 
 /* Toggleable content - dropdown */
 
-#sk-container-id-56 div.sk-toggleable__content {
+#sk-container-id-57 div.sk-toggleable__content {
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@@ -1239,12 +1238,12 @@ <h2>Our vanilla classifier<a class="headerlink" href="#our-vanilla-classifier" t
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-56 div.sk-toggleable__content.fitted {
+#sk-container-id-57 div.sk-toggleable__content.fitted {
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 }
 
-#sk-container-id-56 div.sk-toggleable__content pre {
+#sk-container-id-57 div.sk-toggleable__content pre {
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   color: var(--sklearn-color-text);
@@ -1252,79 +1251,79 @@ <h2>Our vanilla classifier<a class="headerlink" href="#our-vanilla-classifier" t
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-56 div.sk-toggleable__content.fitted pre {
+#sk-container-id-57 div.sk-toggleable__content.fitted pre {
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 }
 
-#sk-container-id-56 input.sk-toggleable__control:checked~div.sk-toggleable__content {
+#sk-container-id-57 input.sk-toggleable__control:checked~div.sk-toggleable__content {
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   max-width: 100%;
   overflow: auto;
 }
 
-#sk-container-id-56 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
+#sk-container-id-57 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
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 }
 
 /* Pipeline/ColumnTransformer-specific style */
 
-#sk-container-id-56 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-57 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
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   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-56 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-57 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
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 }
 
 /* Estimator-specific style */
 
 /* Colorize estimator box */
-#sk-container-id-56 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-57 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
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-#sk-container-id-56 div.sk-label label.sk-toggleable__label,
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-</style><div id="sk-container-id-56" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),
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-                (&#x27;logisticregression&#x27;, LogisticRegression())])</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-230" type="checkbox" ><label for="sk-estimator-id-230" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>StandardScaler</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.StandardScaler.html">?<span>Documentation for StandardScaler</span></a></div></label><div class="sk-toggleable__content "><pre>StandardScaler()</pre></div> </div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-231" type="checkbox" ><label for="sk-estimator-id-231" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>LogisticRegression</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.linear_model.LogisticRegression.html">?<span>Documentation for LogisticRegression</span></a></div></label><div class="sk-toggleable__content "><pre>LogisticRegression()</pre></div> </div></div></div></div></div></div>
+</style><div id="sk-container-id-57" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),
+                (&#x27;logisticregression&#x27;, LogisticRegression())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-243" type="checkbox" ><label for="sk-estimator-id-243" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>Pipeline</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.pipeline.Pipeline.html">?<span>Documentation for Pipeline</span></a><span class="sk-estimator-doc-link ">i<span>Not fitted</span></span></div></label><div class="sk-toggleable__content "><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),
+                (&#x27;logisticregression&#x27;, LogisticRegression())])</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-244" type="checkbox" ><label for="sk-estimator-id-244" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>StandardScaler</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.StandardScaler.html">?<span>Documentation for StandardScaler</span></a></div></label><div class="sk-toggleable__content "><pre>StandardScaler()</pre></div> </div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-245" type="checkbox" ><label for="sk-estimator-id-245" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>LogisticRegression</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.linear_model.LogisticRegression.html">?<span>Documentation for LogisticRegression</span></a></div></label><div class="sk-toggleable__content "><pre>LogisticRegression()</pre></div> </div></div></div></div></div></div>
 </div>
 <br />
 <br /><p>We evaluate our model using cross-validation. We use the accuracy and the balanced
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
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 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -902,9 +901,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
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 </details></li>
@@ -1199,7 +1198,7 @@ <h2>References<a class="headerlink" href="#references" title="Link to this headi
 Journal of Machine Learning Research. 5 Jan (2004): 101-141.</a></p>
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 <a class="reference external image-reference" href="https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/multiclass/plot_multiclass_overview.ipynb"><img alt="Launch binder" src="http://github.com/_images/binder_badge_logo23.svg" style="width: 150px;" />
@@ -1223,12 +1222,12 @@ <h2>References<a class="headerlink" href="#references" title="Link to this headi
 <div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This example compares non-nested and nested cross-validation strategies on a classifier of the iris data set. Nested cross-validation (CV) is often used to train a model in which hyperparameters also need to be optimized. Nested CV estimates the generalization error of the underlying model and its (hyper)parameter search. Choosing the parameters that maximize non-nested CV biases the model to the dataset, yielding an overly-optimistic score."><img alt="" src="http://github.com/_images/sphx_glr_plot_nested_cross_validation_iris_thumb.png" />
 <p><a class="reference internal" href="http://github.com/model_selection/plot_nested_cross_validation_iris.html#sphx-glr-auto-examples-model-selection-plot-nested-cross-validation-iris-py"><span class="std std-ref">Nested versus non-nested cross-validation</span></a></p>
   <div class="sphx-glr-thumbnail-title">Nested versus non-nested cross-validation</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="The most naive strategy to solve such a task is to independently train a binary classifier on each label (i.e. each column of the target variable). At prediction time, the ensemble of binary classifiers is used to assemble multitask prediction."><img alt="" src="http://github.com/_images/sphx_glr_plot_classifier_chain_yeast_thumb.png" />
-<p><a class="reference internal" href="http://github.com/multioutput/plot_classifier_chain_yeast.html#sphx-glr-auto-examples-multioutput-plot-classifier-chain-yeast-py"><span class="std std-ref">Multilabel classification using a classifier chain</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Multilabel classification using a classifier chain</div>
 </div><div class="sphx-glr-thumbcontainer" tooltip="Once a binary classifier is trained, the predict method outputs class label predictions corresponding to a thresholding of either the decision_function or the predict_proba output. The default threshold is defined as a posterior probability estimate of 0.5 or a decision score of 0.0. However, this default strategy may not be optimal for the task at hand."><img alt="" src="http://github.com/_images/sphx_glr_plot_tuned_decision_threshold_thumb.png" />
 <p><a class="reference internal" href="http://github.com/model_selection/plot_tuned_decision_threshold.html#sphx-glr-auto-examples-model-selection-plot-tuned-decision-threshold-py"><span class="std std-ref">Post-hoc tuning the cut-off point of decision function</span></a></p>
   <div class="sphx-glr-thumbnail-title">Post-hoc tuning the cut-off point of decision function</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="The most naive strategy to solve such a task is to independently train a binary classifier on each label (i.e. each column of the target variable). At prediction time, the ensemble of binary classifiers is used to assemble multitask prediction."><img alt="" src="http://github.com/_images/sphx_glr_plot_classifier_chain_yeast_thumb.png" />
+<p><a class="reference internal" href="http://github.com/multioutput/plot_classifier_chain_yeast.html#sphx-glr-auto-examples-multioutput-plot-classifier-chain-yeast-py"><span class="std std-ref">Multilabel classification using a classifier chain</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Multilabel classification using a classifier chain</div>
 </div><div class="sphx-glr-thumbcontainer" tooltip="This example compares decision boundaries of multinomial and one-vs-rest logistic regression on a 2D dataset with three classes."><img alt="" src="http://github.com/_images/sphx_glr_plot_logistic_multinomial_thumb.png" />
 <p><a class="reference internal" href="http://github.com/linear_model/plot_logistic_multinomial.html#sphx-glr-auto-examples-linear-model-plot-logistic-multinomial-py"><span class="std std-ref">Decision Boundaries of Multinomial and One-vs-Rest Logistic Regression</span></a></p>
   <div class="sphx-glr-thumbnail-title">Decision Boundaries of Multinomial and One-vs-Rest Logistic Regression</div>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -898,9 +897,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
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 </ul>
 </details></li>
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@@ -902,9 +901,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
@@ -1138,7 +1137,7 @@ <h2>Results interpretation<a class="headerlink" href="#results-interpretation" t
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
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 <p><a class="reference internal" href="http://github.com/miscellaneous/plot_anomaly_comparison.html#sphx-glr-auto-examples-miscellaneous-plot-anomaly-comparison-py"><span class="std std-ref">Comparing anomaly detection algorithms for outlier detection on toy datasets</span></a></p>
   <div class="sphx-glr-thumbnail-title">Comparing anomaly detection algorithms for outlier detection on toy datasets</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This example compares two outlier detection algorithms, namely local_outlier_factor (LOF) and isolation_forest (IForest), on real-world datasets available in sklearn.datasets. The goal is to show that different algorithms perform well on different datasets and contrast their training speed and sensitivity to hyperparameters."><img alt="" src="http://github.com/_images/sphx_glr_plot_outlier_detection_bench_thumb.png" />
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 </div><div class="sphx-glr-thumbcontainer" tooltip="This example illustrates the need for robust covariance estimation on a real data set. It is useful both for outlier detection and for a better understanding of the data structure."><img alt="" src="http://github.com/_images/sphx_glr_plot_outlier_detection_wine_thumb.png" />
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   <div class="sphx-glr-thumbnail-title">Outlier detection on a real data set</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example compares two outlier detection algorithms, namely local_outlier_factor (LOF) and isolation_forest (IForest), on real-world datasets available in sklearn.datasets. The goal is to show that different algorithms perform well on different datasets and contrast their training speed and sensitivity to hyperparameters."><img alt="" src="http://github.com/_images/sphx_glr_plot_outlier_detection_bench_thumb.png" />
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
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 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
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-<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="The Iris dataset represents 3 kind of Iris flowers (Setosa, Versicolour and Virginica) with 4 attributes: sepal length, sepal width, petal length and petal width."><img alt="" src="http://github.com/_images/sphx_glr_plot_pca_vs_lda_thumb.png" />
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-  <div class="sphx-glr-thumbnail-title">Comparison of LDA and PCA 2D projection of Iris dataset</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="An example comparing nearest neighbors classification with and without Neighborhood Components Analysis."><img alt="" src="http://github.com/_images/sphx_glr_plot_nca_classification_thumb.png" />
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="An example comparing nearest neighbors classification with and without Neighborhood Components Analysis."><img alt="" src="http://github.com/_images/sphx_glr_plot_nca_classification_thumb.png" />
 <p><a class="reference internal" href="http://github.com/plot_nca_classification.html#sphx-glr-auto-examples-neighbors-plot-nca-classification-py"><span class="std std-ref">Comparing Nearest Neighbors with and without Neighborhood Components Analysis</span></a></p>
   <div class="sphx-glr-thumbnail-title">Comparing Nearest Neighbors with and without Neighborhood Components Analysis</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="The Iris dataset represents 3 kind of Iris flowers (Setosa, Versicolour and Virginica) with 4 attributes: sepal length, sepal width, petal length and petal width."><img alt="" src="http://github.com/_images/sphx_glr_plot_pca_vs_lda_thumb.png" />
+<p><a class="reference internal" href="http://github.com/decomposition/plot_pca_vs_lda.html#sphx-glr-auto-examples-decomposition-plot-pca-vs-lda-py"><span class="std std-ref">Comparison of LDA and PCA 2D projection of Iris dataset</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Comparison of LDA and PCA 2D projection of Iris dataset</div>
 </div><div class="sphx-glr-thumbcontainer" tooltip="This example illustrates how the Ledoit-Wolf and Oracle Approximating Shrinkage (OAS) estimators of covariance can improve classification."><img alt="" src="http://github.com/_images/sphx_glr_plot_lda_thumb.png" />
 <p><a class="reference internal" href="http://github.com/classification/plot_lda.html#sphx-glr-auto-examples-classification-plot-lda-py"><span class="std std-ref">Normal, Ledoit-Wolf and OAS Linear Discriminant Analysis for classification</span></a></p>
   <div class="sphx-glr-thumbnail-title">Normal, Ledoit-Wolf and OAS Linear Discriminant Analysis for classification</div>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -902,9 +901,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
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+<meta name="description" content="Sample usage of Nearest Centroid classification. It will plot the decision boundaries for each class.,., Total running time of the script:(0 minutes 0.163 seconds) Launch binder Launch JupyterLite ..." />
 
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
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@@ -1257,9 +1256,9 @@
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-</div><div class="sphx-glr-thumbcontainer" tooltip="Comparison of multinomial logistic L1 vs one-versus-rest L1 logistic regression to classify documents from the newgroups20 dataset. Multinomial logistic regression yields more accurate results and is faster to train on the larger scale dataset."><img alt="" src="http://github.com/_images/sphx_glr_plot_sparse_logistic_regression_20newsgroups_thumb.png" />
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-  <div class="sphx-glr-thumbnail-title">Multiclass sparse logistic regression on 20newgroups</div>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -902,9 +901,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
@@ -1075,7 +1074,7 @@
 <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
 </pre></div>
 </div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 6.189 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 10.396 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-neural-networks-plot-mnist-filters-py">
 <div class="binder-badge docutils container">
 <a class="reference external image-reference" href="https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/neural_networks/plot_mnist_filters.ipynb"><img alt="Launch binder" src="http://github.com/_images/binder_badge_logo26.svg" style="width: 150px;" />
diff --git a/dev/auto_examples/neural_networks/plot_rbm_logistic_classification.html b/dev/auto_examples/neural_networks/plot_rbm_logistic_classification.html
index b27a15eb95ab4..9e0c65e7b64f9 100644
--- a/dev/auto_examples/neural_networks/plot_rbm_logistic_classification.html
+++ b/dev/auto_examples/neural_networks/plot_rbm_logistic_classification.html
@@ -42,7 +42,7 @@
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-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
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@@ -679,13 +679,12 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -902,9 +901,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
@@ -1089,20 +1088,20 @@ <h2>Training<a class="headerlink" href="#training" title="Link to this heading">
 <span class="n">raw_pixel_classifier</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">Y_train</span><span class="p">)</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[BernoulliRBM] Iteration 1, pseudo-likelihood = -25.57, time = 0.09s
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[BernoulliRBM] Iteration 1, pseudo-likelihood = -25.57, time = 0.10s
 [BernoulliRBM] Iteration 2, pseudo-likelihood = -23.68, time = 0.13s
 [BernoulliRBM] Iteration 3, pseudo-likelihood = -22.88, time = 0.13s
-[BernoulliRBM] Iteration 4, pseudo-likelihood = -21.91, time = 0.13s
-[BernoulliRBM] Iteration 5, pseudo-likelihood = -21.79, time = 0.13s
-[BernoulliRBM] Iteration 6, pseudo-likelihood = -20.96, time = 0.12s
-[BernoulliRBM] Iteration 7, pseudo-likelihood = -20.88, time = 0.12s
-[BernoulliRBM] Iteration 8, pseudo-likelihood = -20.50, time = 0.12s
+[BernoulliRBM] Iteration 4, pseudo-likelihood = -21.91, time = 0.14s
+[BernoulliRBM] Iteration 5, pseudo-likelihood = -21.79, time = 0.14s
+[BernoulliRBM] Iteration 6, pseudo-likelihood = -20.96, time = 0.14s
+[BernoulliRBM] Iteration 7, pseudo-likelihood = -20.88, time = 0.14s
+[BernoulliRBM] Iteration 8, pseudo-likelihood = -20.50, time = 0.13s
 [BernoulliRBM] Iteration 9, pseudo-likelihood = -20.34, time = 0.12s
 [BernoulliRBM] Iteration 10, pseudo-likelihood = -20.21, time = 0.12s
 </pre></div>
 </div>
 <div class="output_subarea output_html rendered_html output_result">
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+<style>#sk-container-id-66 {
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@@ -1133,15 +1132,15 @@ <h2>Training<a class="headerlink" href="#training" title="Link to this heading">
   }
 }
 
-#sk-container-id-65 {
+#sk-container-id-66 {
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   width: 1px;
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-#sk-container-id-65 div.sk-dashed-wrapped {
+#sk-container-id-66 div.sk-dashed-wrapped {
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@@ -1161,7 +1160,7 @@ <h2>Training<a class="headerlink" href="#training" title="Link to this heading">
   background-color: var(--sklearn-color-background);
 }
 
-#sk-container-id-65 div.sk-container {
+#sk-container-id-66 div.sk-container {
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   position: relative;
 }
 
-#sk-container-id-65 div.sk-text-repr-fallback {
+#sk-container-id-66 div.sk-text-repr-fallback {
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@@ -1187,14 +1186,14 @@ <h2>Training<a class="headerlink" href="#training" title="Link to this heading">
 
 /* Parallel-specific style estimator block */
 
-#sk-container-id-65 div.sk-parallel-item::after {
+#sk-container-id-66 div.sk-parallel-item::after {
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   width: 100%;
   border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
   flex-grow: 1;
 }
 
-#sk-container-id-65 div.sk-parallel {
+#sk-container-id-66 div.sk-parallel {
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@@ -1202,28 +1201,28 @@ <h2>Training<a class="headerlink" href="#training" title="Link to this heading">
   position: relative;
 }
 
-#sk-container-id-65 div.sk-parallel-item {
+#sk-container-id-66 div.sk-parallel-item {
   display: flex;
   flex-direction: column;
 }
 
-#sk-container-id-65 div.sk-parallel-item:first-child::after {
+#sk-container-id-66 div.sk-parallel-item:first-child::after {
   align-self: flex-end;
   width: 50%;
 }
 
-#sk-container-id-65 div.sk-parallel-item:last-child::after {
+#sk-container-id-66 div.sk-parallel-item:last-child::after {
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 }
 
-#sk-container-id-65 div.sk-parallel-item:only-child::after {
+#sk-container-id-66 div.sk-parallel-item:only-child::after {
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 }
 
 /* Serial-specific style estimator block */
 
-#sk-container-id-65 div.sk-serial {
+#sk-container-id-66 div.sk-serial {
   display: flex;
   flex-direction: column;
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@@ -1241,14 +1240,14 @@ <h2>Training<a class="headerlink" href="#training" title="Link to this heading">
 
 /* Pipeline and ColumnTransformer style (default) */
 
-#sk-container-id-65 div.sk-toggleable {
+#sk-container-id-66 div.sk-toggleable {
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   specific estimator or a Pipeline/ColumnTransformer */
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 }
 
 /* Toggleable label */
-#sk-container-id-65 label.sk-toggleable__label {
+#sk-container-id-66 label.sk-toggleable__label {
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   display: flex;
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@@ -1261,13 +1260,13 @@ <h2>Training<a class="headerlink" href="#training" title="Link to this heading">
   gap: 0.5em;
 }
 
-#sk-container-id-65 label.sk-toggleable__label .caption {
+#sk-container-id-66 label.sk-toggleable__label .caption {
   font-size: 0.6rem;
   font-weight: lighter;
   color: var(--sklearn-color-text-muted);
 }
 
-#sk-container-id-65 label.sk-toggleable__label-arrow:before {
+#sk-container-id-66 label.sk-toggleable__label-arrow:before {
   /* Arrow on the left of the label */
   content: "▸";
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@@ -1275,13 +1274,13 @@ <h2>Training<a class="headerlink" href="#training" title="Link to this heading">
   color: var(--sklearn-color-icon);
 }
 
-#sk-container-id-65 label.sk-toggleable__label-arrow:hover:before {
+#sk-container-id-66 label.sk-toggleable__label-arrow:hover:before {
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 }
 
 /* Toggleable content - dropdown */
 
-#sk-container-id-65 div.sk-toggleable__content {
+#sk-container-id-66 div.sk-toggleable__content {
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@@ -1290,12 +1289,12 @@ <h2>Training<a class="headerlink" href="#training" title="Link to this heading">
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-65 div.sk-toggleable__content.fitted {
+#sk-container-id-66 div.sk-toggleable__content.fitted {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
-#sk-container-id-65 div.sk-toggleable__content pre {
+#sk-container-id-66 div.sk-toggleable__content pre {
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@@ -1303,79 +1302,79 @@ <h2>Training<a class="headerlink" href="#training" title="Link to this heading">
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-65 div.sk-toggleable__content.fitted pre {
+#sk-container-id-66 div.sk-toggleable__content.fitted pre {
   /* unfitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
-#sk-container-id-65 input.sk-toggleable__control:checked~div.sk-toggleable__content {
+#sk-container-id-66 input.sk-toggleable__control:checked~div.sk-toggleable__content {
   /* Expand drop-down */
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   max-width: 100%;
   overflow: auto;
 }
 
-#sk-container-id-65 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
+#sk-container-id-66 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
   content: "▾";
 }
 
 /* Pipeline/ColumnTransformer-specific style */
 
-#sk-container-id-65 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-66 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
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   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-65 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-66 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
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 }
 
 /* Estimator-specific style */
 
 /* Colorize estimator box */
-#sk-container-id-65 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-66 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
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 }
 
-#sk-container-id-65 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-66 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
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 }
 
-#sk-container-id-65 div.sk-label label.sk-toggleable__label,
-#sk-container-id-65 div.sk-label label {
+#sk-container-id-66 div.sk-label label.sk-toggleable__label,
+#sk-container-id-66 div.sk-label label {
   /* The background is the default theme color */
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 }
 
 /* On hover, darken the color of the background */
-#sk-container-id-65 div.sk-label:hover label.sk-toggleable__label {
+#sk-container-id-66 div.sk-label:hover label.sk-toggleable__label {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
 /* Label box, darken color on hover, fitted */
-#sk-container-id-65 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
+#sk-container-id-66 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
 /* Estimator label */
 
-#sk-container-id-65 div.sk-label label {
+#sk-container-id-66 div.sk-label label {
   font-family: monospace;
   font-weight: bold;
   display: inline-block;
   line-height: 1.2em;
 }
 
-#sk-container-id-65 div.sk-label-container {
+#sk-container-id-66 div.sk-label-container {
   text-align: center;
 }
 
 /* Estimator-specific */
-#sk-container-id-65 div.sk-estimator {
+#sk-container-id-66 div.sk-estimator {
   font-family: monospace;
   border: 1px dotted var(--sklearn-color-border-box);
   border-radius: 0.25em;
@@ -1385,18 +1384,18 @@ <h2>Training<a class="headerlink" href="#training" title="Link to this heading">
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-65 div.sk-estimator.fitted {
+#sk-container-id-66 div.sk-estimator.fitted {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
 /* on hover */
-#sk-container-id-65 div.sk-estimator:hover {
+#sk-container-id-66 div.sk-estimator:hover {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-65 div.sk-estimator.fitted:hover {
+#sk-container-id-66 div.sk-estimator.fitted:hover {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-2);
 }
@@ -1484,7 +1483,7 @@ <h2>Training<a class="headerlink" href="#training" title="Link to this heading">
 
 /* "?"-specific style due to the `<a>` HTML tag */
 
-#sk-container-id-65 a.estimator_doc_link {
+#sk-container-id-66 a.estimator_doc_link {
   float: right;
   font-size: 1rem;
   line-height: 1em;
@@ -1499,25 +1498,25 @@ <h2>Training<a class="headerlink" href="#training" title="Link to this heading">
   border: var(--sklearn-color-unfitted-level-1) 1pt solid;
 }
 
-#sk-container-id-65 a.estimator_doc_link.fitted {
+#sk-container-id-66 a.estimator_doc_link.fitted {
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     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -679,13 +679,12 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -902,9 +901,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
@@ -1240,7 +1239,7 @@ <h2>Effect of rescaling on model’s performance<a class="headerlink" href="#eff
 increase overfitting.</p>
 <p>Last but not least, we observe that one achieves a lower log-loss by means of
 the scaling step.</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 1.848 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 1.785 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-preprocessing-plot-scaling-importance-py">
 <div class="binder-badge docutils container">
 <a class="reference external image-reference" href="https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/preprocessing/plot_scaling_importance.ipynb"><img alt="Launch binder" src="http://github.com/_images/binder_badge_logo27.svg" style="width: 150px;" />
diff --git a/dev/auto_examples/preprocessing/plot_target_encoder.html b/dev/auto_examples/preprocessing/plot_target_encoder.html
index a752b0ad39d3c..2584056c8dc79 100644
--- a/dev/auto_examples/preprocessing/plot_target_encoder.html
+++ b/dev/auto_examples/preprocessing/plot_target_encoder.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -679,13 +679,12 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -902,9 +901,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
@@ -1263,7 +1262,7 @@ <h2>Native Categorical Feature Support<a class="headerlink" href="#native-catego
 </pre></div>
 </div>
 <div class="output_subarea output_html rendered_html output_result">
-<style>#sk-container-id-70 {
+<style>#sk-container-id-71 {
   /* Definition of color scheme common for light and dark mode */
   --sklearn-color-text: #000;
   --sklearn-color-text-muted: #666;
@@ -1294,15 +1293,15 @@ <h2>Native Categorical Feature Support<a class="headerlink" href="#native-catego
   }
 }
 
-#sk-container-id-70 {
+#sk-container-id-71 {
   color: var(--sklearn-color-text);
 }
 
-#sk-container-id-70 pre {
+#sk-container-id-71 pre {
   padding: 0;
 }
 
-#sk-container-id-70 input.sk-hidden--visually {
+#sk-container-id-71 input.sk-hidden--visually {
   border: 0;
   clip: rect(1px 1px 1px 1px);
   clip: rect(1px, 1px, 1px, 1px);
@@ -1314,7 +1313,7 @@ <h2>Native Categorical Feature Support<a class="headerlink" href="#native-catego
   width: 1px;
 }
 
-#sk-container-id-70 div.sk-dashed-wrapped {
+#sk-container-id-71 div.sk-dashed-wrapped {
   border: 1px dashed var(--sklearn-color-line);
   margin: 0 0.4em 0.5em 0.4em;
   box-sizing: border-box;
@@ -1322,7 +1321,7 @@ <h2>Native Categorical Feature Support<a class="headerlink" href="#native-catego
   background-color: var(--sklearn-color-background);
 }
 
-#sk-container-id-70 div.sk-container {
+#sk-container-id-71 div.sk-container {
   /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
      but bootstrap.min.css set `[hidden] { display: none !important; }`
      so we also need the `!important` here to be able to override the
@@ -1332,7 +1331,7 @@ <h2>Native Categorical Feature Support<a class="headerlink" href="#native-catego
   position: relative;
 }
 
-#sk-container-id-70 div.sk-text-repr-fallback {
+#sk-container-id-71 div.sk-text-repr-fallback {
   display: none;
 }
 
@@ -1348,14 +1347,14 @@ <h2>Native Categorical Feature Support<a class="headerlink" href="#native-catego
 
 /* Parallel-specific style estimator block */
 
-#sk-container-id-70 div.sk-parallel-item::after {
+#sk-container-id-71 div.sk-parallel-item::after {
   content: "";
   width: 100%;
   border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
   flex-grow: 1;
 }
 
-#sk-container-id-70 div.sk-parallel {
+#sk-container-id-71 div.sk-parallel {
   display: flex;
   align-items: stretch;
   justify-content: center;
@@ -1363,28 +1362,28 @@ <h2>Native Categorical Feature Support<a class="headerlink" href="#native-catego
   position: relative;
 }
 
-#sk-container-id-70 div.sk-parallel-item {
+#sk-container-id-71 div.sk-parallel-item {
   display: flex;
   flex-direction: column;
 }
 
-#sk-container-id-70 div.sk-parallel-item:first-child::after {
+#sk-container-id-71 div.sk-parallel-item:first-child::after {
   align-self: flex-end;
   width: 50%;
 }
 
-#sk-container-id-70 div.sk-parallel-item:last-child::after {
+#sk-container-id-71 div.sk-parallel-item:last-child::after {
   align-self: flex-start;
   width: 50%;
 }
 
-#sk-container-id-70 div.sk-parallel-item:only-child::after {
+#sk-container-id-71 div.sk-parallel-item:only-child::after {
   width: 0;
 }
 
 /* Serial-specific style estimator block */
 
-#sk-container-id-70 div.sk-serial {
+#sk-container-id-71 div.sk-serial {
   display: flex;
   flex-direction: column;
   align-items: center;
@@ -1402,14 +1401,14 @@ <h2>Native Categorical Feature Support<a class="headerlink" href="#native-catego
 
 /* Pipeline and ColumnTransformer style (default) */
 
-#sk-container-id-70 div.sk-toggleable {
+#sk-container-id-71 div.sk-toggleable {
   /* Default theme specific background. It is overwritten whether we have a
   specific estimator or a Pipeline/ColumnTransformer */
   background-color: var(--sklearn-color-background);
 }
 
 /* Toggleable label */
-#sk-container-id-70 label.sk-toggleable__label {
+#sk-container-id-71 label.sk-toggleable__label {
   cursor: pointer;
   display: flex;
   width: 100%;
@@ -1422,13 +1421,13 @@ <h2>Native Categorical Feature Support<a class="headerlink" href="#native-catego
   gap: 0.5em;
 }
 
-#sk-container-id-70 label.sk-toggleable__label .caption {
+#sk-container-id-71 label.sk-toggleable__label .caption {
   font-size: 0.6rem;
   font-weight: lighter;
   color: var(--sklearn-color-text-muted);
 }
 
-#sk-container-id-70 label.sk-toggleable__label-arrow:before {
+#sk-container-id-71 label.sk-toggleable__label-arrow:before {
   /* Arrow on the left of the label */
   content: "▸";
   float: left;
@@ -1436,13 +1435,13 @@ <h2>Native Categorical Feature Support<a class="headerlink" href="#native-catego
   color: var(--sklearn-color-icon);
 }
 
-#sk-container-id-70 label.sk-toggleable__label-arrow:hover:before {
+#sk-container-id-71 label.sk-toggleable__label-arrow:hover:before {
   color: var(--sklearn-color-text);
 }
 
 /* Toggleable content - dropdown */
 
-#sk-container-id-70 div.sk-toggleable__content {
+#sk-container-id-71 div.sk-toggleable__content {
   max-height: 0;
   max-width: 0;
   overflow: hidden;
@@ -1451,12 +1450,12 @@ <h2>Native Categorical Feature Support<a class="headerlink" href="#native-catego
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-70 div.sk-toggleable__content.fitted {
+#sk-container-id-71 div.sk-toggleable__content.fitted {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
-#sk-container-id-70 div.sk-toggleable__content pre {
+#sk-container-id-71 div.sk-toggleable__content pre {
   margin: 0.2em;
   border-radius: 0.25em;
   color: var(--sklearn-color-text);
@@ -1464,79 +1463,79 @@ <h2>Native Categorical Feature Support<a class="headerlink" href="#native-catego
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-70 div.sk-toggleable__content.fitted pre {
+#sk-container-id-71 div.sk-toggleable__content.fitted pre {
   /* unfitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
-#sk-container-id-70 input.sk-toggleable__control:checked~div.sk-toggleable__content {
+#sk-container-id-71 input.sk-toggleable__control:checked~div.sk-toggleable__content {
   /* Expand drop-down */
   max-height: 200px;
   max-width: 100%;
   overflow: auto;
 }
 
-#sk-container-id-70 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
+#sk-container-id-71 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
   content: "▾";
 }
 
 /* Pipeline/ColumnTransformer-specific style */
 
-#sk-container-id-70 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-71 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-70 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-71 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
 /* Estimator-specific style */
 
 /* Colorize estimator box */
-#sk-container-id-70 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-71 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-70 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-71 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
-#sk-container-id-70 div.sk-label label.sk-toggleable__label,
-#sk-container-id-70 div.sk-label label {
+#sk-container-id-71 div.sk-label label.sk-toggleable__label,
+#sk-container-id-71 div.sk-label label {
   /* The background is the default theme color */
   color: var(--sklearn-color-text-on-default-background);
 }
 
 /* On hover, darken the color of the background */
-#sk-container-id-70 div.sk-label:hover label.sk-toggleable__label {
+#sk-container-id-71 div.sk-label:hover label.sk-toggleable__label {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
 /* Label box, darken color on hover, fitted */
-#sk-container-id-70 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
+#sk-container-id-71 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
 /* Estimator label */
 
-#sk-container-id-70 div.sk-label label {
+#sk-container-id-71 div.sk-label label {
   font-family: monospace;
   font-weight: bold;
   display: inline-block;
   line-height: 1.2em;
 }
 
-#sk-container-id-70 div.sk-label-container {
+#sk-container-id-71 div.sk-label-container {
   text-align: center;
 }
 
 /* Estimator-specific */
-#sk-container-id-70 div.sk-estimator {
+#sk-container-id-71 div.sk-estimator {
   font-family: monospace;
   border: 1px dotted var(--sklearn-color-border-box);
   border-radius: 0.25em;
@@ -1546,18 +1545,18 @@ <h2>Native Categorical Feature Support<a class="headerlink" href="#native-catego
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-70 div.sk-estimator.fitted {
+#sk-container-id-71 div.sk-estimator.fitted {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
 /* on hover */
-#sk-container-id-70 div.sk-estimator:hover {
+#sk-container-id-71 div.sk-estimator:hover {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-70 div.sk-estimator.fitted:hover {
+#sk-container-id-71 div.sk-estimator.fitted:hover {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-2);
 }
@@ -1645,7 +1644,7 @@ <h2>Native Categorical Feature Support<a class="headerlink" href="#native-catego
 
 /* "?"-specific style due to the `<a>` HTML tag */
 
-#sk-container-id-70 a.estimator_doc_link {
+#sk-container-id-71 a.estimator_doc_link {
   float: right;
   font-size: 1rem;
   line-height: 1em;
@@ -1660,25 +1659,25 @@ <h2>Native Categorical Feature Support<a class="headerlink" href="#native-catego
   border: var(--sklearn-color-unfitted-level-1) 1pt solid;
 }
 
-#sk-container-id-70 a.estimator_doc_link.fitted {
+#sk-container-id-71 a.estimator_doc_link.fitted {
   /* fitted */
   border: var(--sklearn-color-fitted-level-1) 1pt solid;
   color: var(--sklearn-color-fitted-level-1);
 }
 
 /* On hover */
-#sk-container-id-70 a.estimator_doc_link:hover {
+#sk-container-id-71 a.estimator_doc_link:hover {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-3);
   color: var(--sklearn-color-background);
   text-decoration: none;
 }
 
-#sk-container-id-70 a.estimator_doc_link.fitted:hover {
+#sk-container-id-71 a.estimator_doc_link.fitted:hover {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-3);
 }
-</style><div id="sk-container-id-70" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,
+</style><div id="sk-container-id-71" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,
                  ColumnTransformer(transformers=[(&#x27;numerical&#x27;, &#x27;passthrough&#x27;,
                                                   [&#x27;price&#x27;]),
                                                  (&#x27;high_cardinality&#x27;,
@@ -1691,7 +1690,7 @@ <h2>Native Categorical Feature Support<a class="headerlink" href="#native-catego
                                    verbose_feature_names_out=False)),
                 (&#x27;histgradientboostingregressor&#x27;,
                  HistGradientBoostingRegressor(categorical_features=Index([&#x27;country&#x27;, &#x27;region_2&#x27;], dtype=&#x27;object&#x27;),
-                                               max_iter=20, random_state=0))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-290" type="checkbox" ><label for="sk-estimator-id-290" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>Pipeline</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.pipeline.Pipeline.html">?<span>Documentation for Pipeline</span></a><span class="sk-estimator-doc-link ">i<span>Not fitted</span></span></div></label><div class="sk-toggleable__content "><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,
+                                               max_iter=20, random_state=0))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-304" type="checkbox" ><label for="sk-estimator-id-304" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>Pipeline</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.pipeline.Pipeline.html">?<span>Documentation for Pipeline</span></a><span class="sk-estimator-doc-link ">i<span>Not fitted</span></span></div></label><div class="sk-toggleable__content "><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,
                  ColumnTransformer(transformers=[(&#x27;numerical&#x27;, &#x27;passthrough&#x27;,
                                                   [&#x27;price&#x27;]),
                                                  (&#x27;high_cardinality&#x27;,
@@ -1704,7 +1703,7 @@ <h2>Native Categorical Feature Support<a class="headerlink" href="#native-catego
                                    verbose_feature_names_out=False)),
                 (&#x27;histgradientboostingregressor&#x27;,
                  HistGradientBoostingRegressor(categorical_features=Index([&#x27;country&#x27;, &#x27;region_2&#x27;], dtype=&#x27;object&#x27;),
-                                               max_iter=20, random_state=0))])</pre></div> </div></div><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-291" type="checkbox" ><label for="sk-estimator-id-291" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>columntransformer: ColumnTransformer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.compose.ColumnTransformer.html">?<span>Documentation for columntransformer: ColumnTransformer</span></a></div></label><div class="sk-toggleable__content "><pre>ColumnTransformer(transformers=[(&#x27;numerical&#x27;, &#x27;passthrough&#x27;, [&#x27;price&#x27;]),
+                                               max_iter=20, random_state=0))])</pre></div> </div></div><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-305" type="checkbox" ><label for="sk-estimator-id-305" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>columntransformer: ColumnTransformer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.compose.ColumnTransformer.html">?<span>Documentation for columntransformer: ColumnTransformer</span></a></div></label><div class="sk-toggleable__content "><pre>ColumnTransformer(transformers=[(&#x27;numerical&#x27;, &#x27;passthrough&#x27;, [&#x27;price&#x27;]),
                                 (&#x27;high_cardinality&#x27;,
                                  TargetEncoder(target_type=&#x27;continuous&#x27;),
                                  Index([&#x27;winery&#x27;, &#x27;region_1&#x27;, &#x27;variety&#x27;, &#x27;province&#x27;], dtype=&#x27;object&#x27;)),
@@ -1712,7 +1711,7 @@ <h2>Native Categorical Feature Support<a class="headerlink" href="#native-catego
                                  OrdinalEncoder(handle_unknown=&#x27;use_encoded_value&#x27;,
                                                 unknown_value=-1),
                                  Index([&#x27;country&#x27;, &#x27;region_2&#x27;], dtype=&#x27;object&#x27;))],
-                  verbose_feature_names_out=False)</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-292" type="checkbox" ><label for="sk-estimator-id-292" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>numerical</div></div></label><div class="sk-toggleable__content "><pre>[&#x27;price&#x27;]</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-293" type="checkbox" ><label for="sk-estimator-id-293" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>passthrough</div></div></label><div class="sk-toggleable__content "><pre>passthrough</pre></div> </div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-294" type="checkbox" ><label for="sk-estimator-id-294" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>high_cardinality</div></div></label><div class="sk-toggleable__content "><pre>Index([&#x27;winery&#x27;, &#x27;region_1&#x27;, &#x27;variety&#x27;, &#x27;province&#x27;], dtype=&#x27;object&#x27;)</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-295" type="checkbox" ><label for="sk-estimator-id-295" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>TargetEncoder</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.TargetEncoder.html">?<span>Documentation for TargetEncoder</span></a></div></label><div class="sk-toggleable__content "><pre>TargetEncoder(target_type=&#x27;continuous&#x27;)</pre></div> </div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-296" type="checkbox" ><label for="sk-estimator-id-296" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>low_cardinality</div></div></label><div class="sk-toggleable__content "><pre>Index([&#x27;country&#x27;, &#x27;region_2&#x27;], dtype=&#x27;object&#x27;)</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-297" type="checkbox" ><label for="sk-estimator-id-297" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>OrdinalEncoder</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.OrdinalEncoder.html">?<span>Documentation for OrdinalEncoder</span></a></div></label><div class="sk-toggleable__content "><pre>OrdinalEncoder(handle_unknown=&#x27;use_encoded_value&#x27;, unknown_value=-1)</pre></div> </div></div></div></div></div></div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-298" type="checkbox" ><label for="sk-estimator-id-298" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>HistGradientBoostingRegressor</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.ensemble.HistGradientBoostingRegressor.html">?<span>Documentation for HistGradientBoostingRegressor</span></a></div></label><div class="sk-toggleable__content "><pre>HistGradientBoostingRegressor(categorical_features=Index([&#x27;country&#x27;, &#x27;region_2&#x27;], dtype=&#x27;object&#x27;),
+                  verbose_feature_names_out=False)</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-306" type="checkbox" ><label for="sk-estimator-id-306" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>numerical</div></div></label><div class="sk-toggleable__content "><pre>[&#x27;price&#x27;]</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-307" type="checkbox" ><label for="sk-estimator-id-307" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>passthrough</div></div></label><div class="sk-toggleable__content "><pre>passthrough</pre></div> </div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-308" type="checkbox" ><label for="sk-estimator-id-308" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>high_cardinality</div></div></label><div class="sk-toggleable__content "><pre>Index([&#x27;winery&#x27;, &#x27;region_1&#x27;, &#x27;variety&#x27;, &#x27;province&#x27;], dtype=&#x27;object&#x27;)</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-309" type="checkbox" ><label for="sk-estimator-id-309" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>TargetEncoder</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.TargetEncoder.html">?<span>Documentation for TargetEncoder</span></a></div></label><div class="sk-toggleable__content "><pre>TargetEncoder(target_type=&#x27;continuous&#x27;)</pre></div> </div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-310" type="checkbox" ><label for="sk-estimator-id-310" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>low_cardinality</div></div></label><div class="sk-toggleable__content "><pre>Index([&#x27;country&#x27;, &#x27;region_2&#x27;], dtype=&#x27;object&#x27;)</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-311" type="checkbox" ><label for="sk-estimator-id-311" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>OrdinalEncoder</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.OrdinalEncoder.html">?<span>Documentation for OrdinalEncoder</span></a></div></label><div class="sk-toggleable__content "><pre>OrdinalEncoder(handle_unknown=&#x27;use_encoded_value&#x27;, unknown_value=-1)</pre></div> </div></div></div></div></div></div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-312" type="checkbox" ><label for="sk-estimator-id-312" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>HistGradientBoostingRegressor</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.ensemble.HistGradientBoostingRegressor.html">?<span>Documentation for HistGradientBoostingRegressor</span></a></div></label><div class="sk-toggleable__content "><pre>HistGradientBoostingRegressor(categorical_features=Index([&#x27;country&#x27;, &#x27;region_2&#x27;], dtype=&#x27;object&#x27;),
                               max_iter=20, random_state=0)</pre></div> </div></div></div></div></div></div>
 </div>
 <br />
@@ -1782,7 +1781,7 @@ <h2>Plotting the Results<a class="headerlink" href="#plotting-the-results" title
 encoding and binning works as a good regularizing strategy against
 overfitting while not limiting the expressiveness of the pipeline too much.</p></li>
 </ul>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
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 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -899,9 +898,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
diff --git a/dev/auto_examples/release_highlights/plot_release_highlights_0_22_0.html b/dev/auto_examples/release_highlights/plot_release_highlights_0_22_0.html
index 458daf23a85af..ac740be109665 100644
--- a/dev/auto_examples/release_highlights/plot_release_highlights_0_22_0.html
+++ b/dev/auto_examples/release_highlights/plot_release_highlights_0_22_0.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -679,13 +679,12 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -902,9 +901,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
@@ -1295,10 +1294,10 @@ <h2>ROC AUC now supports multiclass classification<a class="headerlink" href="#r
 <span class="nb">print</span><span class="p">(</span><a href="http://github.com/modules/generated/sklearn.metrics.roc_auc_score.html#sklearn.metrics.roc_auc_score" title="sklearn.metrics.roc_auc_score" class="sphx-glr-backref-module-sklearn-metrics sphx-glr-backref-type-py-function"><span class="n">roc_auc_score</span></a><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">clf</span><span class="o">.</span><span class="n">predict_proba</span><span class="p">(</span><span class="n">X</span><span class="p">),</span> <span class="n">multi_class</span><span class="o">=</span><span class="s2">&quot;ovo&quot;</span><span class="p">))</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>0.9954666666666666
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>0.9940955448201825
 </pre></div>
 </div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 1.368 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 1.705 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-release-highlights-plot-release-highlights-0-22-0-py">
 <div class="binder-badge docutils container">
 <a class="reference external image-reference" href="https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/release_highlights/plot_release_highlights_0_22_0.ipynb"><img alt="Launch binder" src="http://github.com/_images/binder_badge_logo28.svg" style="width: 150px;" />
diff --git a/dev/auto_examples/release_highlights/plot_release_highlights_0_23_0.html b/dev/auto_examples/release_highlights/plot_release_highlights_0_23_0.html
index be78b5a382d60..8de276f16fea4 100644
--- a/dev/auto_examples/release_highlights/plot_release_highlights_0_23_0.html
+++ b/dev/auto_examples/release_highlights/plot_release_highlights_0_23_0.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -679,13 +679,12 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -902,9 +901,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
@@ -1018,9 +1017,10 @@ <h2>Generalized Linear Models, and Poisson loss for gradient boosting<a class="h
 <a class="reference internal" href="http://github.com/modules/generated/sklearn.ensemble.HistGradientBoostingRegressor.html#sklearn.ensemble.HistGradientBoostingRegressor" title="sklearn.ensemble.HistGradientBoostingRegressor"><code class="xref py py-class docutils literal notranslate"><span class="pre">HistGradientBoostingRegressor</span></code></a> supports a new
 ‘poisson’ loss as well.</p>
 <div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
-<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.model_selection</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.model_selection.train_test_split.html#sklearn.model_selection.train_test_split" title="sklearn.model_selection.train_test_split" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-function"><span class="n">train_test_split</span></a>
-<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.linear_model</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.linear_model.PoissonRegressor.html#sklearn.linear_model.PoissonRegressor" title="sklearn.linear_model.PoissonRegressor" class="sphx-glr-backref-module-sklearn-linear_model sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">PoissonRegressor</span></a>
+
 <span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.ensemble</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.ensemble.HistGradientBoostingRegressor.html#sklearn.ensemble.HistGradientBoostingRegressor" title="sklearn.ensemble.HistGradientBoostingRegressor" class="sphx-glr-backref-module-sklearn-ensemble sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">HistGradientBoostingRegressor</span></a>
+<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.linear_model</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.linear_model.PoissonRegressor.html#sklearn.linear_model.PoissonRegressor" title="sklearn.linear_model.PoissonRegressor" class="sphx-glr-backref-module-sklearn-linear_model sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">PoissonRegressor</span></a>
+<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.model_selection</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.model_selection.train_test_split.html#sklearn.model_selection.train_test_split" title="sklearn.model_selection.train_test_split" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-function"><span class="n">train_test_split</span></a>
 
 <span class="n">n_samples</span><span class="p">,</span> <span class="n">n_features</span> <span class="o">=</span> <span class="mi">1000</span><span class="p">,</span> <span class="mi">20</span>
 <span class="n">rng</span> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="numpy.random.RandomState" class="sphx-glr-backref-module-numpy-random sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">RandomState</span></a><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
@@ -1050,11 +1050,11 @@ <h2>Rich visual representation of estimators<a class="headerlink" href="#rich-vi
 elements.  See <a class="reference internal" href="http://github.com/modules/compose.html#visualizing-composite-estimators"><span class="std std-ref">Visualizing Composite Estimators</span></a> for how you can use
 this feature.</p>
 <div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config" class="sphx-glr-backref-module-sklearn sphx-glr-backref-type-py-function"><span class="n">set_config</span></a>
-<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.pipeline</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.pipeline.make_pipeline.html#sklearn.pipeline.make_pipeline" title="sklearn.pipeline.make_pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-function"><span class="n">make_pipeline</span></a>
-<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.preprocessing</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.preprocessing.OneHotEncoder.html#sklearn.preprocessing.OneHotEncoder" title="sklearn.preprocessing.OneHotEncoder" class="sphx-glr-backref-module-sklearn-preprocessing sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">OneHotEncoder</span></a><span class="p">,</span> <a href="http://github.com/modules/generated/sklearn.preprocessing.StandardScaler.html#sklearn.preprocessing.StandardScaler" title="sklearn.preprocessing.StandardScaler" class="sphx-glr-backref-module-sklearn-preprocessing sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">StandardScaler</span></a>
-<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.impute</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.impute.SimpleImputer.html#sklearn.impute.SimpleImputer" title="sklearn.impute.SimpleImputer" class="sphx-glr-backref-module-sklearn-impute sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">SimpleImputer</span></a>
 <span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.compose</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.compose.make_column_transformer.html#sklearn.compose.make_column_transformer" title="sklearn.compose.make_column_transformer" class="sphx-glr-backref-module-sklearn-compose sphx-glr-backref-type-py-function"><span class="n">make_column_transformer</span></a>
+<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.impute</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.impute.SimpleImputer.html#sklearn.impute.SimpleImputer" title="sklearn.impute.SimpleImputer" class="sphx-glr-backref-module-sklearn-impute sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">SimpleImputer</span></a>
 <span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.linear_model</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.linear_model.LogisticRegression.html#sklearn.linear_model.LogisticRegression" title="sklearn.linear_model.LogisticRegression" class="sphx-glr-backref-module-sklearn-linear_model sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">LogisticRegression</span></a>
+<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.pipeline</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.pipeline.make_pipeline.html#sklearn.pipeline.make_pipeline" title="sklearn.pipeline.make_pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-function"><span class="n">make_pipeline</span></a>
+<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.preprocessing</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.preprocessing.OneHotEncoder.html#sklearn.preprocessing.OneHotEncoder" title="sklearn.preprocessing.OneHotEncoder" class="sphx-glr-backref-module-sklearn-preprocessing sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">OneHotEncoder</span></a><span class="p">,</span> <a href="http://github.com/modules/generated/sklearn.preprocessing.StandardScaler.html#sklearn.preprocessing.StandardScaler" title="sklearn.preprocessing.StandardScaler" class="sphx-glr-backref-module-sklearn-preprocessing sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">StandardScaler</span></a>
 
 <a href="http://github.com/modules/generated/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config" class="sphx-glr-backref-module-sklearn sphx-glr-backref-type-py-function"><span class="n">set_config</span></a><span class="p">(</span><span class="n">display</span><span class="o">=</span><span class="s2">&quot;diagram&quot;</span><span class="p">)</span>
 
@@ -1541,12 +1541,13 @@ <h2>Scalability and stability improvements to KMeans<a class="headerlink" href="
 parallelism instead of relying on joblib, so the <code class="docutils literal notranslate"><span class="pre">n_jobs</span></code> parameter has no
 effect anymore. For more details on how to control the number of threads,
 please refer to our <a class="reference internal" href="http://github.com/computing/parallelism.html#parallelism"><span class="std std-ref">Parallelism</span></a> notes.</p>
-<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">scipy</span>
-<span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
-<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.model_selection</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.model_selection.train_test_split.html#sklearn.model_selection.train_test_split" title="sklearn.model_selection.train_test_split" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-function"><span class="n">train_test_split</span></a>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
+<span class="kn">import</span><span class="w"> </span><span class="nn">scipy</span>
+
 <span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.cluster</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.cluster.KMeans.html#sklearn.cluster.KMeans" title="sklearn.cluster.KMeans" class="sphx-glr-backref-module-sklearn-cluster sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">KMeans</span></a>
 <span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.datasets</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.datasets.make_blobs.html#sklearn.datasets.make_blobs" title="sklearn.datasets.make_blobs" class="sphx-glr-backref-module-sklearn-datasets sphx-glr-backref-type-py-function"><span class="n">make_blobs</span></a>
 <span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.metrics</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.metrics.completeness_score.html#sklearn.metrics.completeness_score" title="sklearn.metrics.completeness_score" class="sphx-glr-backref-module-sklearn-metrics sphx-glr-backref-type-py-function"><span class="n">completeness_score</span></a>
+<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.model_selection</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.model_selection.train_test_split.html#sklearn.model_selection.train_test_split" title="sklearn.model_selection.train_test_split" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-function"><span class="n">train_test_split</span></a>
 
 <span class="n">rng</span> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="numpy.random.RandomState" class="sphx-glr-backref-module-numpy-random sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">RandomState</span></a><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
 <span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <a href="http://github.com/modules/generated/sklearn.datasets.make_blobs.html#sklearn.datasets.make_blobs" title="sklearn.datasets.make_blobs" class="sphx-glr-backref-module-sklearn-datasets sphx-glr-backref-type-py-function"><span class="n">make_blobs</span></a><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="n">rng</span><span class="p">)</span>
@@ -1556,7 +1557,7 @@ <h2>Scalability and stability improvements to KMeans<a class="headerlink" href="
 <span class="nb">print</span><span class="p">(</span><a href="http://github.com/modules/generated/sklearn.metrics.completeness_score.html#sklearn.metrics.completeness_score" title="sklearn.metrics.completeness_score" class="sphx-glr-backref-module-sklearn-metrics sphx-glr-backref-type-py-function"><span class="n">completeness_score</span></a><span class="p">(</span><span class="n">kmeans</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">),</span> <span class="n">y_test</span><span class="p">))</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>0.7652857744152419
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>0.7091072911505116
 </pre></div>
 </div>
 </section>
@@ -1576,11 +1577,12 @@ <h2>Improvements to the histogram-based Gradient Boosting estimators<a class="he
 example, see <a class="reference internal" href="http://github.com/ensemble/plot_hgbt_regression.html#sphx-glr-auto-examples-ensemble-plot-hgbt-regression-py"><span class="std std-ref">Features in Histogram Gradient Boosting Trees</span></a>.</p>
 <div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
 <span class="kn">from</span><span class="w"> </span><span class="nn">matplotlib</span><span class="w"> </span><span class="kn">import</span> <span class="n">pyplot</span> <span class="k">as</span> <span class="n">plt</span>
-<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.model_selection</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.model_selection.train_test_split.html#sklearn.model_selection.train_test_split" title="sklearn.model_selection.train_test_split" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-function"><span class="n">train_test_split</span></a>
+
+<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.ensemble</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.ensemble.HistGradientBoostingRegressor.html#sklearn.ensemble.HistGradientBoostingRegressor" title="sklearn.ensemble.HistGradientBoostingRegressor" class="sphx-glr-backref-module-sklearn-ensemble sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">HistGradientBoostingRegressor</span></a>
 
 <span class="c1"># from sklearn.inspection import plot_partial_dependence</span>
 <span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.inspection</span><span class="w"> </span><span class="kn">import</span> <span class="n">PartialDependenceDisplay</span>
-<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.ensemble</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.ensemble.HistGradientBoostingRegressor.html#sklearn.ensemble.HistGradientBoostingRegressor" title="sklearn.ensemble.HistGradientBoostingRegressor" class="sphx-glr-backref-module-sklearn-ensemble sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">HistGradientBoostingRegressor</span></a>
+<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.model_selection</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.model_selection.train_test_split.html#sklearn.model_selection.train_test_split" title="sklearn.model_selection.train_test_split" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-function"><span class="n">train_test_split</span></a>
 
 <span class="n">n_samples</span> <span class="o">=</span> <span class="mi">500</span>
 <span class="n">rng</span> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="numpy.random.RandomState" class="sphx-glr-backref-module-numpy-random sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">RandomState</span></a><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
@@ -1623,10 +1625,11 @@ <h2>Improvements to the histogram-based Gradient Boosting estimators<a class="he
 <h2>Sample-weight support for Lasso and ElasticNet<a class="headerlink" href="#sample-weight-support-for-lasso-and-elasticnet" title="Link to this heading">#</a></h2>
 <p>The two linear regressors <a class="reference internal" href="http://github.com/modules/generated/sklearn.linear_model.Lasso.html#sklearn.linear_model.Lasso" title="sklearn.linear_model.Lasso"><code class="xref py py-class docutils literal notranslate"><span class="pre">Lasso</span></code></a> and
 <a class="reference internal" href="http://github.com/modules/generated/sklearn.linear_model.ElasticNet.html#sklearn.linear_model.ElasticNet" title="sklearn.linear_model.ElasticNet"><code class="xref py py-class docutils literal notranslate"><span class="pre">ElasticNet</span></code></a> now support sample weights.</p>
-<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.model_selection</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.model_selection.train_test_split.html#sklearn.model_selection.train_test_split" title="sklearn.model_selection.train_test_split" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-function"><span class="n">train_test_split</span></a>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
+
 <span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.datasets</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.datasets.make_regression.html#sklearn.datasets.make_regression" title="sklearn.datasets.make_regression" class="sphx-glr-backref-module-sklearn-datasets sphx-glr-backref-type-py-function"><span class="n">make_regression</span></a>
 <span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.linear_model</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.linear_model.Lasso.html#sklearn.linear_model.Lasso" title="sklearn.linear_model.Lasso" class="sphx-glr-backref-module-sklearn-linear_model sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">Lasso</span></a>
-<span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
+<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.model_selection</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.model_selection.train_test_split.html#sklearn.model_selection.train_test_split" title="sklearn.model_selection.train_test_split" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-function"><span class="n">train_test_split</span></a>
 
 <span class="n">n_samples</span><span class="p">,</span> <span class="n">n_features</span> <span class="o">=</span> <span class="mi">1000</span><span class="p">,</span> <span class="mi">20</span>
 <span class="n">rng</span> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="numpy.random.RandomState" class="sphx-glr-backref-module-numpy-random sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">RandomState</span></a><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
@@ -1643,7 +1646,7 @@ <h2>Sample-weight support for Lasso and ElasticNet<a class="headerlink" href="#s
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>0.999791942438998
 </pre></div>
 </div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 0.611 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 0.888 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-release-highlights-plot-release-highlights-0-23-0-py">
 <div class="binder-badge docutils container">
 <a class="reference external image-reference" href="https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/release_highlights/plot_release_highlights_0_23_0.ipynb"><img alt="Launch binder" src="http://github.com/_images/binder_badge_logo28.svg" style="width: 150px;" />
diff --git a/dev/auto_examples/release_highlights/plot_release_highlights_0_24_0.html b/dev/auto_examples/release_highlights/plot_release_highlights_0_24_0.html
index aeed33e281d9a..78b367d42efcb 100644
--- a/dev/auto_examples/release_highlights/plot_release_highlights_0_24_0.html
+++ b/dev/auto_examples/release_highlights/plot_release_highlights_0_24_0.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -679,13 +679,12 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -902,9 +901,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
@@ -1032,10 +1031,11 @@ <h2>Successive Halving estimators for tuning hyper-parameters<a class="headerlin
 </figure>
 <div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
 <span class="kn">from</span><span class="w"> </span><span class="nn">scipy.stats</span><span class="w"> </span><span class="kn">import</span> <a href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.randint.html#scipy.stats.randint" title="scipy.stats.randint" class="sphx-glr-backref-module-scipy-stats sphx-glr-backref-type-py-data"><span class="n">randint</span></a>
-<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.experimental</span><span class="w"> </span><span class="kn">import</span> <span class="n">enable_halving_search_cv</span>  <span class="c1"># noqa</span>
-<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.model_selection</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.model_selection.HalvingRandomSearchCV.html#sklearn.model_selection.HalvingRandomSearchCV" title="sklearn.model_selection.HalvingRandomSearchCV" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">HalvingRandomSearchCV</span></a>
-<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.ensemble</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.ensemble.RandomForestClassifier.html#sklearn.ensemble.RandomForestClassifier" title="sklearn.ensemble.RandomForestClassifier" class="sphx-glr-backref-module-sklearn-ensemble sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">RandomForestClassifier</span></a>
+
 <span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.datasets</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.datasets.make_classification.html#sklearn.datasets.make_classification" title="sklearn.datasets.make_classification" class="sphx-glr-backref-module-sklearn-datasets sphx-glr-backref-type-py-function"><span class="n">make_classification</span></a>
+<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.ensemble</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.ensemble.RandomForestClassifier.html#sklearn.ensemble.RandomForestClassifier" title="sklearn.ensemble.RandomForestClassifier" class="sphx-glr-backref-module-sklearn-ensemble sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">RandomForestClassifier</span></a>
+<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.experimental</span><span class="w"> </span><span class="kn">import</span> <span class="n">enable_halving_search_cv</span>  <span class="c1"># noqa: F401</span>
+<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.model_selection</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.model_selection.HalvingRandomSearchCV.html#sklearn.model_selection.HalvingRandomSearchCV" title="sklearn.model_selection.HalvingRandomSearchCV" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">HalvingRandomSearchCV</span></a>
 
 <span class="n">rng</span> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="numpy.random.RandomState" class="sphx-glr-backref-module-numpy-random sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">RandomState</span></a><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
 
@@ -1095,6 +1095,7 @@ <h2>New self-training meta-estimator<a class="headerlink" href="#new-self-traini
 semi-supervised classifier, allowing it to learn from unlabeled data.
 Read more in the <a class="reference internal" href="http://github.com/modules/semi_supervised.html#self-training"><span class="std std-ref">User guide</span></a>.</p>
 <div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
+
 <span class="kn">from</span><span class="w"> </span><span class="nn">sklearn</span><span class="w"> </span><span class="kn">import</span> <span class="n">datasets</span>
 <span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.semi_supervised</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.semi_supervised.SelfTrainingClassifier.html#sklearn.semi_supervised.SelfTrainingClassifier" title="sklearn.semi_supervised.SelfTrainingClassifier" class="sphx-glr-backref-module-sklearn-semi_supervised sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">SelfTrainingClassifier</span></a>
 <span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.svm</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.svm.SVC.html#sklearn.svm.SVC" title="sklearn.svm.SVC" class="sphx-glr-backref-module-sklearn-svm sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">SVC</span></a>
@@ -1536,9 +1537,9 @@ <h2>New SequentialFeatureSelector transformer<a class="headerlink" href="#new-se
 selection) or remove features from the list of the available features
 (backward selection), based on a cross-validated score maximization.
 See the <a class="reference internal" href="http://github.com/modules/feature_selection.html#sequential-feature-selection"><span class="std std-ref">User Guide</span></a>.</p>
-<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.feature_selection</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.feature_selection.SequentialFeatureSelector.html#sklearn.feature_selection.SequentialFeatureSelector" title="sklearn.feature_selection.SequentialFeatureSelector" class="sphx-glr-backref-module-sklearn-feature_selection sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">SequentialFeatureSelector</span></a>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.datasets</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.datasets.load_iris.html#sklearn.datasets.load_iris" title="sklearn.datasets.load_iris" class="sphx-glr-backref-module-sklearn-datasets sphx-glr-backref-type-py-function"><span class="n">load_iris</span></a>
+<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.feature_selection</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.feature_selection.SequentialFeatureSelector.html#sklearn.feature_selection.SequentialFeatureSelector" title="sklearn.feature_selection.SequentialFeatureSelector" class="sphx-glr-backref-module-sklearn-feature_selection sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">SequentialFeatureSelector</span></a>
 <span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.neighbors</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#sklearn.neighbors.KNeighborsClassifier" title="sklearn.neighbors.KNeighborsClassifier" class="sphx-glr-backref-module-sklearn-neighbors sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">KNeighborsClassifier</span></a>
-<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.datasets</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.datasets.load_iris.html#sklearn.datasets.load_iris" title="sklearn.datasets.load_iris" class="sphx-glr-backref-module-sklearn-datasets sphx-glr-backref-type-py-function"><span class="n">load_iris</span></a>
 
 <span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <a href="http://github.com/modules/generated/sklearn.datasets.load_iris.html#sklearn.datasets.load_iris" title="sklearn.datasets.load_iris" class="sphx-glr-backref-module-sklearn-datasets sphx-glr-backref-type-py-function"><span class="n">load_iris</span></a><span class="p">(</span><span class="n">return_X_y</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">as_frame</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
 <span class="n">feature_names</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">columns</span>
@@ -1562,11 +1563,11 @@ <h2>New PolynomialCountSketch kernel approximation function<a class="headerlink"
 models, but uses much less memory than
 <a class="reference internal" href="http://github.com/modules/generated/sklearn.preprocessing.PolynomialFeatures.html#sklearn.preprocessing.PolynomialFeatures" title="sklearn.preprocessing.PolynomialFeatures"><code class="xref py py-class docutils literal notranslate"><span class="pre">PolynomialFeatures</span></code></a>.</p>
 <div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.datasets</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.datasets.fetch_covtype.html#sklearn.datasets.fetch_covtype" title="sklearn.datasets.fetch_covtype" class="sphx-glr-backref-module-sklearn-datasets sphx-glr-backref-type-py-function"><span class="n">fetch_covtype</span></a>
-<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.pipeline</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.pipeline.make_pipeline.html#sklearn.pipeline.make_pipeline" title="sklearn.pipeline.make_pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-function"><span class="n">make_pipeline</span></a>
-<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.model_selection</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.model_selection.train_test_split.html#sklearn.model_selection.train_test_split" title="sklearn.model_selection.train_test_split" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-function"><span class="n">train_test_split</span></a>
-<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.preprocessing</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.preprocessing.MinMaxScaler.html#sklearn.preprocessing.MinMaxScaler" title="sklearn.preprocessing.MinMaxScaler" class="sphx-glr-backref-module-sklearn-preprocessing sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">MinMaxScaler</span></a>
 <span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.kernel_approximation</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.kernel_approximation.PolynomialCountSketch.html#sklearn.kernel_approximation.PolynomialCountSketch" title="sklearn.kernel_approximation.PolynomialCountSketch" class="sphx-glr-backref-module-sklearn-kernel_approximation sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">PolynomialCountSketch</span></a>
 <span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.linear_model</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.linear_model.LogisticRegression.html#sklearn.linear_model.LogisticRegression" title="sklearn.linear_model.LogisticRegression" class="sphx-glr-backref-module-sklearn-linear_model sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">LogisticRegression</span></a>
+<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.model_selection</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.model_selection.train_test_split.html#sklearn.model_selection.train_test_split" title="sklearn.model_selection.train_test_split" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-function"><span class="n">train_test_split</span></a>
+<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.pipeline</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.pipeline.make_pipeline.html#sklearn.pipeline.make_pipeline" title="sklearn.pipeline.make_pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-function"><span class="n">make_pipeline</span></a>
+<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.preprocessing</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.preprocessing.MinMaxScaler.html#sklearn.preprocessing.MinMaxScaler" title="sklearn.preprocessing.MinMaxScaler" class="sphx-glr-backref-module-sklearn-preprocessing sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">MinMaxScaler</span></a>
 
 <span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <a href="http://github.com/modules/generated/sklearn.datasets.fetch_covtype.html#sklearn.datasets.fetch_covtype" title="sklearn.datasets.fetch_covtype" class="sphx-glr-backref-module-sklearn-datasets sphx-glr-backref-type-py-function"><span class="n">fetch_covtype</span></a><span class="p">(</span><span class="n">return_X_y</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
 <span class="n">pipe</span> <span class="o">=</span> <a href="http://github.com/modules/generated/sklearn.pipeline.make_pipeline.html#sklearn.pipeline.make_pipeline" title="sklearn.pipeline.make_pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-function"><span class="n">make_pipeline</span></a><span class="p">(</span>
@@ -1580,7 +1581,7 @@ <h2>New PolynomialCountSketch kernel approximation function<a class="headerlink"
 <span class="n">pipe</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>0.7389
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>0.7354
 </pre></div>
 </div>
 <p>For comparison, here is the score of a linear baseline for the same data:</p>
@@ -1598,8 +1599,8 @@ <h2>Individual Conditional Expectation plots<a class="headerlink" href="#individ
 Conditional Expectation (ICE) plot. ICE plots visualize the dependence of the
 prediction on a feature for each sample separately, with one line per sample.
 See the <a class="reference internal" href="http://github.com/modules/partial_dependence.html#individual-conditional"><span class="std std-ref">User Guide</span></a></p>
-<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.ensemble</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.ensemble.RandomForestRegressor.html#sklearn.ensemble.RandomForestRegressor" title="sklearn.ensemble.RandomForestRegressor" class="sphx-glr-backref-module-sklearn-ensemble sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">RandomForestRegressor</span></a>
-<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.datasets</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.datasets.fetch_california_housing.html#sklearn.datasets.fetch_california_housing" title="sklearn.datasets.fetch_california_housing" class="sphx-glr-backref-module-sklearn-datasets sphx-glr-backref-type-py-function"><span class="n">fetch_california_housing</span></a>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.datasets</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.datasets.fetch_california_housing.html#sklearn.datasets.fetch_california_housing" title="sklearn.datasets.fetch_california_housing" class="sphx-glr-backref-module-sklearn-datasets sphx-glr-backref-type-py-function"><span class="n">fetch_california_housing</span></a>
+<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.ensemble</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.ensemble.RandomForestRegressor.html#sklearn.ensemble.RandomForestRegressor" title="sklearn.ensemble.RandomForestRegressor" class="sphx-glr-backref-module-sklearn-ensemble sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">RandomForestRegressor</span></a>
 
 <span class="c1"># from sklearn.inspection import plot_partial_dependence</span>
 <span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.inspection</span><span class="w"> </span><span class="kn">import</span> <span class="n">PartialDependenceDisplay</span>
@@ -1636,9 +1637,10 @@ <h2>New Poisson splitting criterion for DecisionTreeRegressor<a class="headerlin
 <a class="reference internal" href="http://github.com/modules/generated/sklearn.tree.DecisionTreeRegressor.html#sklearn.tree.DecisionTreeRegressor" title="sklearn.tree.DecisionTreeRegressor"><code class="xref py py-class docutils literal notranslate"><span class="pre">DecisionTreeRegressor</span></code></a> now supports a new <code class="docutils literal notranslate"><span class="pre">'poisson'</span></code>
 splitting criterion. Setting <code class="docutils literal notranslate"><span class="pre">criterion=&quot;poisson&quot;</span></code> might be a good choice
 if your target is a count or a frequency.</p>
-<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.tree</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.tree.DecisionTreeRegressor.html#sklearn.tree.DecisionTreeRegressor" title="sklearn.tree.DecisionTreeRegressor" class="sphx-glr-backref-module-sklearn-tree sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">DecisionTreeRegressor</span></a>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
+
 <span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.model_selection</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.model_selection.train_test_split.html#sklearn.model_selection.train_test_split" title="sklearn.model_selection.train_test_split" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-function"><span class="n">train_test_split</span></a>
-<span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
+<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.tree</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.tree.DecisionTreeRegressor.html#sklearn.tree.DecisionTreeRegressor" title="sklearn.tree.DecisionTreeRegressor" class="sphx-glr-backref-module-sklearn-tree sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">DecisionTreeRegressor</span></a>
 
 <span class="n">n_samples</span><span class="p">,</span> <span class="n">n_features</span> <span class="o">=</span> <span class="mi">1000</span><span class="p">,</span> <span class="mi">20</span>
 <span class="n">rng</span> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="numpy.random.RandomState" class="sphx-glr-backref-module-numpy-random sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">RandomState</span></a><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
@@ -2084,7 +2086,7 @@ <h2>New documentation improvements<a class="headerlink" href="#new-documentation
 <li><p>an <a class="reference internal" href="http://github.com/cross_decomposition/plot_pcr_vs_pls.html#sphx-glr-auto-examples-cross-decomposition-plot-pcr-vs-pls-py"><span class="std std-ref">example</span></a>
 comparing Principal Component Regression and Partial Least Squares.</p></li>
 </ul>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 12.891 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 12.848 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-release-highlights-plot-release-highlights-0-24-0-py">
 <div class="binder-badge docutils container">
 <a class="reference external image-reference" href="https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/release_highlights/plot_release_highlights_0_24_0.ipynb"><img alt="Launch binder" src="http://github.com/_images/binder_badge_logo28.svg" style="width: 150px;" />
diff --git a/dev/auto_examples/release_highlights/plot_release_highlights_1_0_0.html b/dev/auto_examples/release_highlights/plot_release_highlights_1_0_0.html
index e70a6008f5561..961282fb14408 100644
--- a/dev/auto_examples/release_highlights/plot_release_highlights_1_0_0.html
+++ b/dev/auto_examples/release_highlights/plot_release_highlights_1_0_0.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -680,13 +680,12 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -903,9 +902,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
@@ -1070,6 +1069,7 @@ <h2>Spline Transformers<a class="headerlink" href="#spline-transformers" title="
 <p>The following code shows splines in action, for more information, please
 refer to the <a class="reference internal" href="http://github.com/modules/preprocessing.html#spline-transformer"><span class="std std-ref">User Guide</span></a>.</p>
 <div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
+
 <span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.preprocessing</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.preprocessing.SplineTransformer.html#sklearn.preprocessing.SplineTransformer" title="sklearn.preprocessing.SplineTransformer" class="sphx-glr-backref-module-sklearn-preprocessing sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">SplineTransformer</span></a>
 
 <span class="n">X</span> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.arange.html#numpy.arange" title="numpy.arange" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">arange</span></a><span class="p">(</span><span class="mi">5</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
@@ -1126,8 +1126,9 @@ <h2>Feature Names Support<a class="headerlink" href="#feature-names-support" tit
 is used to check that the column names of the dataframe passed in
 non-<a class="reference internal" href="http://github.com/glossary.html#term-fit"><span class="xref std std-term">fit</span></a>, such as <a class="reference internal" href="http://github.com/glossary.html#term-predict"><span class="xref std std-term">predict</span></a>, are consistent with features in
 <a class="reference internal" href="http://github.com/glossary.html#term-fit"><span class="xref std std-term">fit</span></a>:</p>
-<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.preprocessing</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.preprocessing.StandardScaler.html#sklearn.preprocessing.StandardScaler" title="sklearn.preprocessing.StandardScaler" class="sphx-glr-backref-module-sklearn-preprocessing sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">StandardScaler</span></a>
-<span class="kn">import</span><span class="w"> </span><span class="nn">pandas</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pd</span>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">pandas</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pd</span>
+
+<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.preprocessing</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.preprocessing.StandardScaler.html#sklearn.preprocessing.StandardScaler" title="sklearn.preprocessing.StandardScaler" class="sphx-glr-backref-module-sklearn-preprocessing sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">StandardScaler</span></a>
 
 <span class="n">X</span> <span class="o">=</span> <a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="pandas.DataFrame" class="sphx-glr-backref-module-pandas sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span></a><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">]],</span> <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;a&quot;</span><span class="p">,</span> <span class="s2">&quot;b&quot;</span><span class="p">,</span> <span class="s2">&quot;c&quot;</span><span class="p">])</span>
 <span class="n">scalar</span> <span class="o">=</span> <a href="http://github.com/modules/generated/sklearn.preprocessing.StandardScaler.html#sklearn.preprocessing.StandardScaler" title="sklearn.preprocessing.StandardScaler" class="sphx-glr-backref-module-sklearn-preprocessing sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">StandardScaler</span></a><span class="p">()</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
@@ -1144,9 +1145,10 @@ <h2>Feature Names Support<a class="headerlink" href="#feature-names-support" tit
 will be added to all other transformers in future releases. Additionally,
 <a class="reference internal" href="http://github.com/modules/generated/sklearn.compose.ColumnTransformer.html#sklearn.compose.ColumnTransformer.get_feature_names_out" title="sklearn.compose.ColumnTransformer.get_feature_names_out"><code class="xref py py-meth docutils literal notranslate"><span class="pre">compose.ColumnTransformer.get_feature_names_out</span></code></a> is available to
 combine feature names of its transformers:</p>
-<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.compose</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.compose.ColumnTransformer.html#sklearn.compose.ColumnTransformer" title="sklearn.compose.ColumnTransformer" class="sphx-glr-backref-module-sklearn-compose sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ColumnTransformer</span></a>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">pandas</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pd</span>
+
+<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.compose</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.compose.ColumnTransformer.html#sklearn.compose.ColumnTransformer" title="sklearn.compose.ColumnTransformer" class="sphx-glr-backref-module-sklearn-compose sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ColumnTransformer</span></a>
 <span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.preprocessing</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.preprocessing.OneHotEncoder.html#sklearn.preprocessing.OneHotEncoder" title="sklearn.preprocessing.OneHotEncoder" class="sphx-glr-backref-module-sklearn-preprocessing sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">OneHotEncoder</span></a>
-<span class="kn">import</span><span class="w"> </span><span class="nn">pandas</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pd</span>
 
 <span class="n">X</span> <span class="o">=</span> <a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="pandas.DataFrame" class="sphx-glr-backref-module-pandas sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span></a><span class="p">({</span><span class="s2">&quot;pet&quot;</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;dog&quot;</span><span class="p">,</span> <span class="s2">&quot;cat&quot;</span><span class="p">,</span> <span class="s2">&quot;fish&quot;</span><span class="p">],</span> <span class="s2">&quot;age&quot;</span><span class="p">:</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">7</span><span class="p">,</span> <span class="mi">1</span><span class="p">]})</span>
 <span class="n">preprocessor</span> <span class="o">=</span> <a href="http://github.com/modules/generated/sklearn.compose.ColumnTransformer.html#sklearn.compose.ColumnTransformer" title="sklearn.compose.ColumnTransformer" class="sphx-glr-backref-module-sklearn-compose sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ColumnTransformer</span></a><span class="p">(</span>
@@ -1224,7 +1226,7 @@ <h2>New documentation improvements<a class="headerlink" href="#new-documentation
 <p>This release includes many documentation improvements. Out of over 2100
 merged pull requests, about 800 of them are improvements to our
 documentation.</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 0.016 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 0.015 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-release-highlights-plot-release-highlights-1-0-0-py">
 <div class="binder-badge docutils container">
 <a class="reference external image-reference" href="https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/release_highlights/plot_release_highlights_1_0_0.ipynb"><img alt="Launch binder" src="http://github.com/_images/binder_badge_logo28.svg" style="width: 150px;" />
diff --git a/dev/auto_examples/release_highlights/plot_release_highlights_1_1_0.html b/dev/auto_examples/release_highlights/plot_release_highlights_1_1_0.html
index 022cf379e22b2..3878ee03ea99a 100644
--- a/dev/auto_examples/release_highlights/plot_release_highlights_1_1_0.html
+++ b/dev/auto_examples/release_highlights/plot_release_highlights_1_1_0.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -679,13 +679,12 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -902,9 +901,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
@@ -1010,9 +1009,10 @@
 <span id="quantile-support-hgbdt"></span><h2>Quantile loss in <a class="reference internal" href="http://github.com/modules/generated/sklearn.ensemble.HistGradientBoostingRegressor.html#sklearn.ensemble.HistGradientBoostingRegressor" title="sklearn.ensemble.HistGradientBoostingRegressor"><code class="xref py py-class docutils literal notranslate"><span class="pre">HistGradientBoostingRegressor</span></code></a><a class="headerlink" href="#quantile-loss-in-histgradientboostingregressor" title="Link to this heading">#</a></h2>
 <p><a class="reference internal" href="http://github.com/modules/generated/sklearn.ensemble.HistGradientBoostingRegressor.html#sklearn.ensemble.HistGradientBoostingRegressor" title="sklearn.ensemble.HistGradientBoostingRegressor"><code class="xref py py-class docutils literal notranslate"><span class="pre">HistGradientBoostingRegressor</span></code></a> can model quantiles with
 <code class="docutils literal notranslate"><span class="pre">loss=&quot;quantile&quot;</span></code> and the new parameter <code class="docutils literal notranslate"><span class="pre">quantile</span></code>.</p>
-<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.ensemble</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.ensemble.HistGradientBoostingRegressor.html#sklearn.ensemble.HistGradientBoostingRegressor" title="sklearn.ensemble.HistGradientBoostingRegressor" class="sphx-glr-backref-module-sklearn-ensemble sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">HistGradientBoostingRegressor</span></a>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">matplotlib.pyplot</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">plt</span>
 <span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
-<span class="kn">import</span><span class="w"> </span><span class="nn">matplotlib.pyplot</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">plt</span>
+
+<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.ensemble</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.ensemble.HistGradientBoostingRegressor.html#sklearn.ensemble.HistGradientBoostingRegressor" title="sklearn.ensemble.HistGradientBoostingRegressor" class="sphx-glr-backref-module-sklearn-ensemble sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">HistGradientBoostingRegressor</span></a>
 
 <span class="c1"># Simple regression function for X * cos(X)</span>
 <span class="n">rng</span> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="numpy.random.RandomState" class="sphx-glr-backref-module-numpy-random sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">RandomState</span></a><span class="p">(</span><span class="mi">42</span><span class="p">)</span>
@@ -1047,12 +1047,12 @@ <h2><code class="docutils literal notranslate"><span class="pre">get_feature_nam
 This enables <a class="reference internal" href="http://github.com/modules/generated/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a> to construct the output feature names for
 more complex pipelines:</p>
 <div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.compose</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.compose.ColumnTransformer.html#sklearn.compose.ColumnTransformer" title="sklearn.compose.ColumnTransformer" class="sphx-glr-backref-module-sklearn-compose sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ColumnTransformer</span></a>
-<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.preprocessing</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.preprocessing.OneHotEncoder.html#sklearn.preprocessing.OneHotEncoder" title="sklearn.preprocessing.OneHotEncoder" class="sphx-glr-backref-module-sklearn-preprocessing sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">OneHotEncoder</span></a><span class="p">,</span> <a href="http://github.com/modules/generated/sklearn.preprocessing.StandardScaler.html#sklearn.preprocessing.StandardScaler" title="sklearn.preprocessing.StandardScaler" class="sphx-glr-backref-module-sklearn-preprocessing sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">StandardScaler</span></a>
-<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.pipeline</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.pipeline.make_pipeline.html#sklearn.pipeline.make_pipeline" title="sklearn.pipeline.make_pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-function"><span class="n">make_pipeline</span></a>
-<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.impute</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.impute.SimpleImputer.html#sklearn.impute.SimpleImputer" title="sklearn.impute.SimpleImputer" class="sphx-glr-backref-module-sklearn-impute sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">SimpleImputer</span></a>
-<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.feature_selection</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.feature_selection.SelectKBest.html#sklearn.feature_selection.SelectKBest" title="sklearn.feature_selection.SelectKBest" class="sphx-glr-backref-module-sklearn-feature_selection sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">SelectKBest</span></a>
 <span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.datasets</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.datasets.fetch_openml.html#sklearn.datasets.fetch_openml" title="sklearn.datasets.fetch_openml" class="sphx-glr-backref-module-sklearn-datasets sphx-glr-backref-type-py-function"><span class="n">fetch_openml</span></a>
+<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.feature_selection</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.feature_selection.SelectKBest.html#sklearn.feature_selection.SelectKBest" title="sklearn.feature_selection.SelectKBest" class="sphx-glr-backref-module-sklearn-feature_selection sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">SelectKBest</span></a>
+<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.impute</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.impute.SimpleImputer.html#sklearn.impute.SimpleImputer" title="sklearn.impute.SimpleImputer" class="sphx-glr-backref-module-sklearn-impute sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">SimpleImputer</span></a>
 <span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.linear_model</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.linear_model.LogisticRegression.html#sklearn.linear_model.LogisticRegression" title="sklearn.linear_model.LogisticRegression" class="sphx-glr-backref-module-sklearn-linear_model sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">LogisticRegression</span></a>
+<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.pipeline</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.pipeline.make_pipeline.html#sklearn.pipeline.make_pipeline" title="sklearn.pipeline.make_pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-function"><span class="n">make_pipeline</span></a>
+<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.preprocessing</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.preprocessing.OneHotEncoder.html#sklearn.preprocessing.OneHotEncoder" title="sklearn.preprocessing.OneHotEncoder" class="sphx-glr-backref-module-sklearn-preprocessing sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">OneHotEncoder</span></a><span class="p">,</span> <a href="http://github.com/modules/generated/sklearn.preprocessing.StandardScaler.html#sklearn.preprocessing.StandardScaler" title="sklearn.preprocessing.StandardScaler" class="sphx-glr-backref-module-sklearn-preprocessing sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">StandardScaler</span></a>
 
 <span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <a href="http://github.com/modules/generated/sklearn.datasets.fetch_openml.html#sklearn.datasets.fetch_openml" title="sklearn.datasets.fetch_openml" class="sphx-glr-backref-module-sklearn-datasets sphx-glr-backref-type-py-function"><span class="n">fetch_openml</span></a><span class="p">(</span>
     <span class="s2">&quot;titanic&quot;</span><span class="p">,</span> <span class="n">version</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">as_frame</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">return_X_y</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">parser</span><span class="o">=</span><span class="s2">&quot;pandas&quot;</span>
@@ -1550,8 +1550,9 @@ <h2>Grouping infrequent categories in <a class="reference internal" href="../../
 the gathering of infrequent categories are <code class="docutils literal notranslate"><span class="pre">min_frequency</span></code> and
 <code class="docutils literal notranslate"><span class="pre">max_categories</span></code>. See the <a class="reference internal" href="http://github.com/modules/preprocessing.html#encoder-infrequent-categories"><span class="std std-ref">User Guide</span></a>
 for more details.</p>
-<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.preprocessing</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.preprocessing.OneHotEncoder.html#sklearn.preprocessing.OneHotEncoder" title="sklearn.preprocessing.OneHotEncoder" class="sphx-glr-backref-module-sklearn-preprocessing sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">OneHotEncoder</span></a>
-<span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
+
+<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.preprocessing</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.preprocessing.OneHotEncoder.html#sklearn.preprocessing.OneHotEncoder" title="sklearn.preprocessing.OneHotEncoder" class="sphx-glr-backref-module-sklearn-preprocessing sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">OneHotEncoder</span></a>
 
 <span class="n">X</span> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.array.html#numpy.array" title="numpy.array" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">array</span></a><span class="p">(</span>
     <span class="p">[[</span><span class="s2">&quot;dog&quot;</span><span class="p">]</span> <span class="o">*</span> <span class="mi">5</span> <span class="o">+</span> <span class="p">[</span><span class="s2">&quot;cat&quot;</span><span class="p">]</span> <span class="o">*</span> <span class="mi">20</span> <span class="o">+</span> <span class="p">[</span><span class="s2">&quot;rabbit&quot;</span><span class="p">]</span> <span class="o">*</span> <span class="mi">10</span> <span class="o">+</span> <span class="p">[</span><span class="s2">&quot;snake&quot;</span><span class="p">]</span> <span class="o">*</span> <span class="mi">3</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">object</span>
@@ -1676,6 +1677,7 @@ <h2><a class="reference internal" href="../../modules/generated/sklearn.decompos
 learning when the data is not readily available from the start, or when the
 data does not fit into memory.</p>
 <div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
+
 <span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.decomposition</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.decomposition.MiniBatchNMF.html#sklearn.decomposition.MiniBatchNMF" title="sklearn.decomposition.MiniBatchNMF" class="sphx-glr-backref-module-sklearn-decomposition sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">MiniBatchNMF</span></a>
 
 <span class="n">rng</span> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="numpy.random.RandomState" class="sphx-glr-backref-module-numpy-random sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">RandomState</span></a><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
@@ -1694,7 +1696,7 @@ <h2><a class="reference internal" href="../../modules/generated/sklearn.decompos
 <span class="n">X_reconstructed</span> <span class="o">=</span> <span class="n">W</span> <span class="o">@</span> <span class="n">H</span>
 
 <span class="nb">print</span><span class="p">(</span>
-    <span class="sa">f</span><span class="s2">&quot;relative reconstruction error: &quot;</span><span class="p">,</span>
+    <span class="s2">&quot;relative reconstruction error: &quot;</span><span class="p">,</span>
     <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.sum.html#numpy.sum" title="numpy.sum" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">sum</span></a><span class="p">((</span><span class="n">X</span><span class="w"> </span><span class="o">-</span><span class="w"> </span><span class="n">X_reconstructed</span><span class="p">)</span><span class="w"> </span><span class="o">**</span><span class="w"> </span><span class="mi">2</span><span class="p">)</span><span class="w"> </span><span class="o">/</span><span class="w"> </span><a href="https://numpy.org/doc/stable/reference/generated/numpy.sum.html#numpy.sum" title="numpy.sum" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">sum</span></a><span class="p">(</span><span class="n">X</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span><span class="si">:</span><span class="s2">.5f</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span>
 <span class="p">)</span>
 </pre></div>
@@ -1711,9 +1713,10 @@ <h2><a class="reference internal" href="../../modules/generated/sklearn.cluster.
 previous clustering: a cluster is split into two new clusters repeatedly
 until the target number of clusters is reached, giving a hierarchical
 structure to the clustering.</p>
-<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.datasets</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.datasets.make_blobs.html#sklearn.datasets.make_blobs" title="sklearn.datasets.make_blobs" class="sphx-glr-backref-module-sklearn-datasets sphx-glr-backref-type-py-function"><span class="n">make_blobs</span></a>
-<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.cluster</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.cluster.KMeans.html#sklearn.cluster.KMeans" title="sklearn.cluster.KMeans" class="sphx-glr-backref-module-sklearn-cluster sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">KMeans</span></a><span class="p">,</span> <a href="http://github.com/modules/generated/sklearn.cluster.BisectingKMeans.html#sklearn.cluster.BisectingKMeans" title="sklearn.cluster.BisectingKMeans" class="sphx-glr-backref-module-sklearn-cluster sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">BisectingKMeans</span></a>
-<span class="kn">import</span><span class="w"> </span><span class="nn">matplotlib.pyplot</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">plt</span>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">matplotlib.pyplot</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">plt</span>
+
+<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.cluster</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.cluster.BisectingKMeans.html#sklearn.cluster.BisectingKMeans" title="sklearn.cluster.BisectingKMeans" class="sphx-glr-backref-module-sklearn-cluster sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">BisectingKMeans</span></a><span class="p">,</span> <a href="http://github.com/modules/generated/sklearn.cluster.KMeans.html#sklearn.cluster.KMeans" title="sklearn.cluster.KMeans" class="sphx-glr-backref-module-sklearn-cluster sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">KMeans</span></a>
+<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.datasets</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.datasets.make_blobs.html#sklearn.datasets.make_blobs" title="sklearn.datasets.make_blobs" class="sphx-glr-backref-module-sklearn-datasets sphx-glr-backref-type-py-function"><span class="n">make_blobs</span></a>
 
 <span class="n">X</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <a href="http://github.com/modules/generated/sklearn.datasets.make_blobs.html#sklearn.datasets.make_blobs" title="sklearn.datasets.make_blobs" class="sphx-glr-backref-module-sklearn-datasets sphx-glr-backref-type-py-function"><span class="n">make_blobs</span></a><span class="p">(</span><span class="n">n_samples</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">centers</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
 
@@ -1732,7 +1735,7 @@ <h2><a class="reference internal" href="../../modules/generated/sklearn.cluster.
 <span class="n">_</span> <span class="o">=</span> <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;BisectingKMeans&quot;</span><span class="p">)</span>
 </pre></div>
 </div>
-<img src="http://github.com/_images/sphx_glr_plot_release_highlights_1_1_0_003.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_release_highlights_1_1_0_003.png " alt="KMeans, BisectingKMeans" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 0.947 seconds)</p>
+<img src="http://github.com/_images/sphx_glr_plot_release_highlights_1_1_0_003.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_release_highlights_1_1_0_003.png " alt="KMeans, BisectingKMeans" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 0.946 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-release-highlights-plot-release-highlights-1-1-0-py">
 <div class="binder-badge docutils container">
 <a class="reference external image-reference" href="https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/release_highlights/plot_release_highlights_1_1_0.ipynb"><img alt="Launch binder" src="http://github.com/_images/binder_badge_logo28.svg" style="width: 150px;" />
diff --git a/dev/auto_examples/release_highlights/plot_release_highlights_1_2_0.html b/dev/auto_examples/release_highlights/plot_release_highlights_1_2_0.html
index 30997ecb42d8c..0240bc79daada 100644
--- a/dev/auto_examples/release_highlights/plot_release_highlights_1_2_0.html
+++ b/dev/auto_examples/release_highlights/plot_release_highlights_1_2_0.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -679,13 +679,12 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -902,9 +901,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
@@ -1014,9 +1013,10 @@ <h2>Pandas output with <code class="docutils literal notranslate"><span class="p
 # this <a class="reference external" href="https://youtu.be/5bCg8VfX2x8">video, pandas DataFrame output for scikit-learn transformers
 (some examples)</a>.</p>
 <div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
-<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.datasets</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.datasets.load_iris.html#sklearn.datasets.load_iris" title="sklearn.datasets.load_iris" class="sphx-glr-backref-module-sklearn-datasets sphx-glr-backref-type-py-function"><span class="n">load_iris</span></a>
-<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.preprocessing</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.preprocessing.StandardScaler.html#sklearn.preprocessing.StandardScaler" title="sklearn.preprocessing.StandardScaler" class="sphx-glr-backref-module-sklearn-preprocessing sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">StandardScaler</span></a><span class="p">,</span> <a href="http://github.com/modules/generated/sklearn.preprocessing.KBinsDiscretizer.html#sklearn.preprocessing.KBinsDiscretizer" title="sklearn.preprocessing.KBinsDiscretizer" class="sphx-glr-backref-module-sklearn-preprocessing sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">KBinsDiscretizer</span></a>
+
 <span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.compose</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.compose.ColumnTransformer.html#sklearn.compose.ColumnTransformer" title="sklearn.compose.ColumnTransformer" class="sphx-glr-backref-module-sklearn-compose sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ColumnTransformer</span></a>
+<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.datasets</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.datasets.load_iris.html#sklearn.datasets.load_iris" title="sklearn.datasets.load_iris" class="sphx-glr-backref-module-sklearn-datasets sphx-glr-backref-type-py-function"><span class="n">load_iris</span></a>
+<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.preprocessing</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.preprocessing.KBinsDiscretizer.html#sklearn.preprocessing.KBinsDiscretizer" title="sklearn.preprocessing.KBinsDiscretizer" class="sphx-glr-backref-module-sklearn-preprocessing sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">KBinsDiscretizer</span></a><span class="p">,</span> <a href="http://github.com/modules/generated/sklearn.preprocessing.StandardScaler.html#sklearn.preprocessing.StandardScaler" title="sklearn.preprocessing.StandardScaler" class="sphx-glr-backref-module-sklearn-preprocessing sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">StandardScaler</span></a>
 
 <span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <a href="http://github.com/modules/generated/sklearn.datasets.load_iris.html#sklearn.datasets.load_iris" title="sklearn.datasets.load_iris" class="sphx-glr-backref-module-sklearn-datasets sphx-glr-backref-type-py-function"><span class="n">load_iris</span></a><span class="p">(</span><span class="n">as_frame</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">return_X_y</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
 <span class="n">sepal_cols</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;sepal length (cm)&quot;</span><span class="p">,</span> <span class="s2">&quot;sepal width (cm)&quot;</span><span class="p">]</span>
@@ -1552,6 +1552,7 @@ <h2>New and enhanced displays<a class="headerlink" href="#new-and-enhanced-displ
 <p><a class="reference internal" href="http://github.com/modules/generated/sklearn.metrics.PredictionErrorDisplay.html#sklearn.metrics.PredictionErrorDisplay" title="sklearn.metrics.PredictionErrorDisplay"><code class="xref py py-class docutils literal notranslate"><span class="pre">PredictionErrorDisplay</span></code></a> provides a way to analyze regression
 models in a qualitative manner.</p>
 <div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">matplotlib.pyplot</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">plt</span>
+
 <span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.metrics</span><span class="w"> </span><span class="kn">import</span> <span class="n">PredictionErrorDisplay</span>
 
 <span class="n">fig</span><span class="p">,</span> <span class="n">axs</span> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="matplotlib.pyplot.subplots" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">subplots</span></a><span class="p">(</span><span class="n">nrows</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">ncols</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
@@ -1583,8 +1584,8 @@ <h2>New and enhanced displays<a class="headerlink" href="#new-and-enhanced-displ
 <span class="n">X</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">select_dtypes</span><span class="p">([</span><span class="s2">&quot;number&quot;</span><span class="p">,</span> <span class="s2">&quot;category&quot;</span><span class="p">])</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;body&quot;</span><span class="p">])</span>
 </pre></div>
 </div>
-<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.preprocessing</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.preprocessing.OrdinalEncoder.html#sklearn.preprocessing.OrdinalEncoder" title="sklearn.preprocessing.OrdinalEncoder" class="sphx-glr-backref-module-sklearn-preprocessing sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">OrdinalEncoder</span></a>
-<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.pipeline</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.pipeline.make_pipeline.html#sklearn.pipeline.make_pipeline" title="sklearn.pipeline.make_pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-function"><span class="n">make_pipeline</span></a>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.pipeline</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.pipeline.make_pipeline.html#sklearn.pipeline.make_pipeline" title="sklearn.pipeline.make_pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-function"><span class="n">make_pipeline</span></a>
+<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.preprocessing</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.preprocessing.OrdinalEncoder.html#sklearn.preprocessing.OrdinalEncoder" title="sklearn.preprocessing.OrdinalEncoder" class="sphx-glr-backref-module-sklearn-preprocessing sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">OrdinalEncoder</span></a>
 
 <span class="n">categorical_features</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;pclass&quot;</span><span class="p">,</span> <span class="s2">&quot;sex&quot;</span><span class="p">,</span> <span class="s2">&quot;embarked&quot;</span><span class="p">]</span>
 <span class="n">model</span> <span class="o">=</span> <a href="http://github.com/modules/generated/sklearn.pipeline.make_pipeline.html#sklearn.pipeline.make_pipeline" title="sklearn.pipeline.make_pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-function"><span class="n">make_pipeline</span></a><span class="p">(</span>
@@ -1763,7 +1764,7 @@ <h2>Improved efficiency of many estimators<a class="headerlink" href="#improved-
 Euclidean Distance metrics.
 A detailed list of the impacted estimators can be found in the
 <a class="reference internal" href="http://github.com/whats_new/v1.2.html#release-notes-1-2"><span class="std std-ref">changelog</span></a>.</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 5.228 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 4.844 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-release-highlights-plot-release-highlights-1-2-0-py">
 <div class="binder-badge docutils container">
 <a class="reference external image-reference" href="https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/release_highlights/plot_release_highlights_1_2_0.ipynb"><img alt="Launch binder" src="http://github.com/_images/binder_badge_logo28.svg" style="width: 150px;" />
diff --git a/dev/auto_examples/release_highlights/plot_release_highlights_1_3_0.html b/dev/auto_examples/release_highlights/plot_release_highlights_1_3_0.html
index 39de8e53538ec..4c9b0b07d16a1 100644
--- a/dev/auto_examples/release_highlights/plot_release_highlights_1_3_0.html
+++ b/dev/auto_examples/release_highlights/plot_release_highlights_1_3_0.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -679,13 +679,12 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -902,9 +901,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
@@ -1030,6 +1029,7 @@ <h2>HDBSCAN: hierarchical density-based clustering<a class="headerlink" href="#h
 making it more robust to parameter selection than <a class="reference internal" href="http://github.com/modules/generated/sklearn.cluster.DBSCAN.html#sklearn.cluster.DBSCAN" title="sklearn.cluster.DBSCAN"><code class="xref py py-class docutils literal notranslate"><span class="pre">cluster.DBSCAN</span></code></a>.
 More details in the <a class="reference internal" href="http://github.com/modules/clustering.html#hdbscan"><span class="std std-ref">User Guide</span></a>.</p>
 <div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
+
 <span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.cluster</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.cluster.HDBSCAN.html#sklearn.cluster.HDBSCAN" title="sklearn.cluster.HDBSCAN" class="sphx-glr-backref-module-sklearn-cluster sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">HDBSCAN</span></a>
 <span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.datasets</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.datasets.load_digits.html#sklearn.datasets.load_digits" title="sklearn.datasets.load_digits" class="sphx-glr-backref-module-sklearn-datasets sphx-glr-backref-type-py-function"><span class="n">load_digits</span></a>
 <span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.metrics</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.metrics.v_measure_score.html#sklearn.metrics.v_measure_score" title="sklearn.metrics.v_measure_score" class="sphx-glr-backref-module-sklearn-metrics sphx-glr-backref-type-py-function"><span class="n">v_measure_score</span></a>
@@ -1057,6 +1057,7 @@ <h2>TargetEncoder: a new category encoding strategy<a class="headerlink" href="#
 estimate of the average target values for observations belonging to that category.
 More details in the <a class="reference internal" href="http://github.com/modules/preprocessing.html#target-encoder"><span class="std std-ref">User Guide</span></a>.</p>
 <div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
+
 <span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.preprocessing</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.preprocessing.TargetEncoder.html#sklearn.preprocessing.TargetEncoder" title="sklearn.preprocessing.TargetEncoder" class="sphx-glr-backref-module-sklearn-preprocessing sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">TargetEncoder</span></a>
 
 <span class="n">X</span> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.array.html#numpy.array" title="numpy.array" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">array</span></a><span class="p">([[</span><span class="s2">&quot;cat&quot;</span><span class="p">]</span> <span class="o">*</span> <span class="mi">30</span> <span class="o">+</span> <span class="p">[</span><span class="s2">&quot;dog&quot;</span><span class="p">]</span> <span class="o">*</span> <span class="mi">20</span> <span class="o">+</span> <span class="p">[</span><span class="s2">&quot;snake&quot;</span><span class="p">]</span> <span class="o">*</span> <span class="mi">38</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">object</span><span class="p">)</span><span class="o">.</span><span class="n">T</span>
@@ -1082,6 +1083,7 @@ <h2>Missing values support in decision trees<a class="headerlink" href="#missing
 <a class="reference internal" href="http://github.com/ensemble/plot_hgbt_regression.html#sphx-glr-auto-examples-ensemble-plot-hgbt-regression-py"><span class="std std-ref">Features in Histogram Gradient Boosting Trees</span></a> for a usecase
 example of this feature in <a class="reference internal" href="http://github.com/modules/generated/sklearn.ensemble.HistGradientBoostingRegressor.html#sklearn.ensemble.HistGradientBoostingRegressor" title="sklearn.ensemble.HistGradientBoostingRegressor"><code class="xref py py-class docutils literal notranslate"><span class="pre">HistGradientBoostingRegressor</span></code></a>.</p>
 <div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
+
 <span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.tree</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.tree.DecisionTreeClassifier.html#sklearn.tree.DecisionTreeClassifier" title="sklearn.tree.DecisionTreeClassifier" class="sphx-glr-backref-module-sklearn-tree sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">DecisionTreeClassifier</span></a>
 
 <span class="n">X</span> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.array.html#numpy.array" title="numpy.array" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">array</span></a><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/constants.html#numpy.nan" title="numpy.nan" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-data"><span class="n">np</span><span class="o">.</span><span class="n">nan</span></a><span class="p">])</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
@@ -1123,9 +1125,10 @@ <h2>Gamma loss for gradient boosting<a class="headerlink" href="#gamma-loss-for-
 Gamma deviance loss function via <code class="docutils literal notranslate"><span class="pre">loss=&quot;gamma&quot;</span></code>. This loss function is useful for
 modeling strictly positive targets with a right-skewed distribution.</p>
 <div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
-<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.model_selection</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.model_selection.cross_val_score.html#sklearn.model_selection.cross_val_score" title="sklearn.model_selection.cross_val_score" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-function"><span class="n">cross_val_score</span></a>
+
 <span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.datasets</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.datasets.make_low_rank_matrix.html#sklearn.datasets.make_low_rank_matrix" title="sklearn.datasets.make_low_rank_matrix" class="sphx-glr-backref-module-sklearn-datasets sphx-glr-backref-type-py-function"><span class="n">make_low_rank_matrix</span></a>
 <span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.ensemble</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.ensemble.HistGradientBoostingRegressor.html#sklearn.ensemble.HistGradientBoostingRegressor" title="sklearn.ensemble.HistGradientBoostingRegressor" class="sphx-glr-backref-module-sklearn-ensemble sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">HistGradientBoostingRegressor</span></a>
+<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.model_selection</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.model_selection.cross_val_score.html#sklearn.model_selection.cross_val_score" title="sklearn.model_selection.cross_val_score" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-function"><span class="n">cross_val_score</span></a>
 
 <span class="n">n_samples</span><span class="p">,</span> <span class="n">n_features</span> <span class="o">=</span> <span class="mi">500</span><span class="p">,</span> <span class="mi">10</span>
 <span class="n">rng</span> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="numpy.random.RandomState" class="sphx-glr-backref-module-numpy-random sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">RandomState</span></a><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
@@ -1147,8 +1150,9 @@ <h2>Grouping infrequent categories in <a class="reference internal" href="../../
 into a single output for each feature. The parameters to enable the gathering of
 infrequent categories are <code class="docutils literal notranslate"><span class="pre">min_frequency</span></code> and <code class="docutils literal notranslate"><span class="pre">max_categories</span></code>.
 See the <a class="reference internal" href="http://github.com/modules/preprocessing.html#encoder-infrequent-categories"><span class="std std-ref">User Guide</span></a> for more details.</p>
-<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.preprocessing</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.preprocessing.OrdinalEncoder.html#sklearn.preprocessing.OrdinalEncoder" title="sklearn.preprocessing.OrdinalEncoder" class="sphx-glr-backref-module-sklearn-preprocessing sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">OrdinalEncoder</span></a>
-<span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
+
+<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.preprocessing</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.preprocessing.OrdinalEncoder.html#sklearn.preprocessing.OrdinalEncoder" title="sklearn.preprocessing.OrdinalEncoder" class="sphx-glr-backref-module-sklearn-preprocessing sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">OrdinalEncoder</span></a>
 
 <span class="n">X</span> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.array.html#numpy.array" title="numpy.array" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">array</span></a><span class="p">(</span>
     <span class="p">[[</span><span class="s2">&quot;dog&quot;</span><span class="p">]</span> <span class="o">*</span> <span class="mi">5</span> <span class="o">+</span> <span class="p">[</span><span class="s2">&quot;cat&quot;</span><span class="p">]</span> <span class="o">*</span> <span class="mi">20</span> <span class="o">+</span> <span class="p">[</span><span class="s2">&quot;rabbit&quot;</span><span class="p">]</span> <span class="o">*</span> <span class="mi">10</span> <span class="o">+</span> <span class="p">[</span><span class="s2">&quot;snake&quot;</span><span class="p">]</span> <span class="o">*</span> <span class="mi">3</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">object</span>
@@ -1160,7 +1164,7 @@ <h2>Grouping infrequent categories in <a class="reference internal" href="../../
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[array([&#39;dog&#39;, &#39;snake&#39;], dtype=object)]
 </pre></div>
 </div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 1.489 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 1.441 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-release-highlights-plot-release-highlights-1-3-0-py">
 <div class="binder-badge docutils container">
 <a class="reference external image-reference" href="https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/release_highlights/plot_release_highlights_1_3_0.ipynb"><img alt="Launch binder" src="http://github.com/_images/binder_badge_logo28.svg" style="width: 150px;" />
diff --git a/dev/auto_examples/release_highlights/plot_release_highlights_1_4_0.html b/dev/auto_examples/release_highlights/plot_release_highlights_1_4_0.html
index 3fc10185ae1ed..ef4351d349b1b 100644
--- a/dev/auto_examples/release_highlights/plot_release_highlights_1_4_0.html
+++ b/dev/auto_examples/release_highlights/plot_release_highlights_1_4_0.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -679,13 +679,12 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -902,9 +901,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
@@ -1040,8 +1039,8 @@ <h2>HistGradientBoosting Natively Supports Categorical DTypes in DataFrames<a cl
 treats the columns with categorical dtypes as categorical features in the
 algorithm:</p>
 <div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.ensemble</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.ensemble.HistGradientBoostingClassifier.html#sklearn.ensemble.HistGradientBoostingClassifier" title="sklearn.ensemble.HistGradientBoostingClassifier" class="sphx-glr-backref-module-sklearn-ensemble sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">HistGradientBoostingClassifier</span></a>
-<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.model_selection</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.model_selection.train_test_split.html#sklearn.model_selection.train_test_split" title="sklearn.model_selection.train_test_split" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-function"><span class="n">train_test_split</span></a>
 <span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.metrics</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.metrics.roc_auc_score.html#sklearn.metrics.roc_auc_score" title="sklearn.metrics.roc_auc_score" class="sphx-glr-backref-module-sklearn-metrics sphx-glr-backref-type-py-function"><span class="n">roc_auc_score</span></a>
+<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.model_selection</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.model_selection.train_test_split.html#sklearn.model_selection.train_test_split" title="sklearn.model_selection.train_test_split" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-function"><span class="n">train_test_split</span></a>
 
 <span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <a href="http://github.com/modules/generated/sklearn.model_selection.train_test_split.html#sklearn.model_selection.train_test_split" title="sklearn.model_selection.train_test_split" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-function"><span class="n">train_test_split</span></a><span class="p">(</span><span class="n">X_adult</span><span class="p">,</span> <span class="n">y_adult</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
 <span class="n">hist</span> <span class="o">=</span> <a href="http://github.com/modules/generated/sklearn.ensemble.HistGradientBoostingClassifier.html#sklearn.ensemble.HistGradientBoostingClassifier" title="sklearn.ensemble.HistGradientBoostingClassifier" class="sphx-glr-backref-module-sklearn-ensemble sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">HistGradientBoostingClassifier</span></a><span class="p">(</span><span class="n">categorical_features</span><span class="o">=</span><span class="s2">&quot;from_dtype&quot;</span><span class="p">)</span>
@@ -1051,7 +1050,7 @@ <h2>HistGradientBoosting Natively Supports Categorical DTypes in DataFrames<a cl
 <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;ROC AUC score is </span><span class="si">{</span><a href="http://github.com/modules/generated/sklearn.metrics.roc_auc_score.html#sklearn.metrics.roc_auc_score" title="sklearn.metrics.roc_auc_score" class="sphx-glr-backref-module-sklearn-metrics sphx-glr-backref-type-py-function"><span class="n">roc_auc_score</span></a><span class="p">(</span><span class="n">y_test</span><span class="p">,</span><span class="w"> </span><span class="n">y_decision</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>ROC AUC score is 0.928592269276614
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>ROC AUC score is 0.9283255323708193
 </pre></div>
 </div>
 </section>
@@ -1059,9 +1058,9 @@ <h2>HistGradientBoosting Natively Supports Categorical DTypes in DataFrames<a cl
 <h2>Polars output in <code class="docutils literal notranslate"><span class="pre">set_output</span></code><a class="headerlink" href="#polars-output-in-set-output" title="Link to this heading">#</a></h2>
 <p>scikit-learn’s transformers now support polars output with the <code class="docutils literal notranslate"><span class="pre">set_output</span></code> API.</p>
 <div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">polars</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pl</span>
-<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.preprocessing</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.preprocessing.StandardScaler.html#sklearn.preprocessing.StandardScaler" title="sklearn.preprocessing.StandardScaler" class="sphx-glr-backref-module-sklearn-preprocessing sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">StandardScaler</span></a>
-<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.preprocessing</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.preprocessing.OneHotEncoder.html#sklearn.preprocessing.OneHotEncoder" title="sklearn.preprocessing.OneHotEncoder" class="sphx-glr-backref-module-sklearn-preprocessing sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">OneHotEncoder</span></a>
+
 <span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.compose</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.compose.ColumnTransformer.html#sklearn.compose.ColumnTransformer" title="sklearn.compose.ColumnTransformer" class="sphx-glr-backref-module-sklearn-compose sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ColumnTransformer</span></a>
+<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.preprocessing</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.preprocessing.OneHotEncoder.html#sklearn.preprocessing.OneHotEncoder" title="sklearn.preprocessing.OneHotEncoder" class="sphx-glr-backref-module-sklearn-preprocessing sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">OneHotEncoder</span></a><span class="p">,</span> <a href="http://github.com/modules/generated/sklearn.preprocessing.StandardScaler.html#sklearn.preprocessing.StandardScaler" title="sklearn.preprocessing.StandardScaler" class="sphx-glr-backref-module-sklearn-preprocessing sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">StandardScaler</span></a>
 
 <span class="n">df</span> <span class="o">=</span> <span class="n">pl</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span>
     <span class="p">{</span><span class="s2">&quot;height&quot;</span><span class="p">:</span> <span class="p">[</span><span class="mi">120</span><span class="p">,</span> <span class="mi">140</span><span class="p">,</span> <span class="mi">150</span><span class="p">,</span> <span class="mi">110</span><span class="p">,</span> <span class="mi">100</span><span class="p">],</span> <span class="s2">&quot;pet&quot;</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;dog&quot;</span><span class="p">,</span> <span class="s2">&quot;cat&quot;</span><span class="p">,</span> <span class="s2">&quot;dog&quot;</span><span class="p">,</span> <span class="s2">&quot;cat&quot;</span><span class="p">,</span> <span class="s2">&quot;cat&quot;</span><span class="p">]}</span>
@@ -1105,6 +1104,7 @@ <h2>Missing value support for Random Forest<a class="headerlink" href="#missing-
 missing values going to the left and right nodes. More details in the
 <a class="reference internal" href="http://github.com/modules/tree.html#tree-missing-value-support"><span class="std std-ref">User Guide</span></a>.</p>
 <div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
+
 <span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.ensemble</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.ensemble.RandomForestClassifier.html#sklearn.ensemble.RandomForestClassifier" title="sklearn.ensemble.RandomForestClassifier" class="sphx-glr-backref-module-sklearn-ensemble sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">RandomForestClassifier</span></a>
 
 <span class="n">X</span> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.array.html#numpy.array" title="numpy.array" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">array</span></a><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/constants.html#numpy.nan" title="numpy.nan" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-data"><span class="n">np</span><span class="o">.</span><span class="n">nan</span></a><span class="p">])</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
@@ -1125,8 +1125,9 @@ <h2>Add support for monotonic constraints in tree-based models<a class="headerli
 trees, random forests, extra-trees, and exact gradient boosting. Here, we show this
 feature for random forest on a regression problem.</p>
 <div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">matplotlib.pyplot</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">plt</span>
-<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.inspection</span><span class="w"> </span><span class="kn">import</span> <span class="n">PartialDependenceDisplay</span>
+
 <span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.ensemble</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.ensemble.RandomForestRegressor.html#sklearn.ensemble.RandomForestRegressor" title="sklearn.ensemble.RandomForestRegressor" class="sphx-glr-backref-module-sklearn-ensemble sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">RandomForestRegressor</span></a>
+<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.inspection</span><span class="w"> </span><span class="kn">import</span> <span class="n">PartialDependenceDisplay</span>
 
 <span class="n">n_samples</span> <span class="o">=</span> <span class="mi">500</span>
 <span class="n">rng</span> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="numpy.random.RandomState" class="sphx-glr-backref-module-numpy-random sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">RandomState</span></a><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
@@ -2021,10 +2022,10 @@ <h2>Metadata Routing Support<a class="headerlink" href="#metadata-routing-suppor
 routing, which are listed in the <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing-models"><span class="std std-ref">user guide</span></a>. For instance, this is how you can do a nested
 cross-validation with sample weights and <a class="reference internal" href="http://github.com/modules/generated/sklearn.model_selection.GroupKFold.html#sklearn.model_selection.GroupKFold" title="sklearn.model_selection.GroupKFold"><code class="xref py py-class docutils literal notranslate"><span class="pre">GroupKFold</span></code></a>:</p>
 <div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">sklearn</span>
-<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.metrics</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.metrics.get_scorer.html#sklearn.metrics.get_scorer" title="sklearn.metrics.get_scorer" class="sphx-glr-backref-module-sklearn-metrics sphx-glr-backref-type-py-function"><span class="n">get_scorer</span></a>
 <span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.datasets</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.datasets.make_regression.html#sklearn.datasets.make_regression" title="sklearn.datasets.make_regression" class="sphx-glr-backref-module-sklearn-datasets sphx-glr-backref-type-py-function"><span class="n">make_regression</span></a>
 <span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.linear_model</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.linear_model.Lasso.html#sklearn.linear_model.Lasso" title="sklearn.linear_model.Lasso" class="sphx-glr-backref-module-sklearn-linear_model sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">Lasso</span></a>
-<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.model_selection</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.model_selection.GridSearchCV.html#sklearn.model_selection.GridSearchCV" title="sklearn.model_selection.GridSearchCV" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">GridSearchCV</span></a><span class="p">,</span> <a href="http://github.com/modules/generated/sklearn.model_selection.cross_validate.html#sklearn.model_selection.cross_validate" title="sklearn.model_selection.cross_validate" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-function"><span class="n">cross_validate</span></a><span class="p">,</span> <a href="http://github.com/modules/generated/sklearn.model_selection.GroupKFold.html#sklearn.model_selection.GroupKFold" title="sklearn.model_selection.GroupKFold" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">GroupKFold</span></a>
+<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.metrics</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.metrics.get_scorer.html#sklearn.metrics.get_scorer" title="sklearn.metrics.get_scorer" class="sphx-glr-backref-module-sklearn-metrics sphx-glr-backref-type-py-function"><span class="n">get_scorer</span></a>
+<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.model_selection</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.model_selection.GridSearchCV.html#sklearn.model_selection.GridSearchCV" title="sklearn.model_selection.GridSearchCV" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">GridSearchCV</span></a><span class="p">,</span> <a href="http://github.com/modules/generated/sklearn.model_selection.GroupKFold.html#sklearn.model_selection.GroupKFold" title="sklearn.model_selection.GroupKFold" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">GroupKFold</span></a><span class="p">,</span> <a href="http://github.com/modules/generated/sklearn.model_selection.cross_validate.html#sklearn.model_selection.cross_validate" title="sklearn.model_selection.cross_validate" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-function"><span class="n">cross_validate</span></a>
 
 <span class="c1"># For now by default metadata routing is disabled, and need to be explicitly</span>
 <span class="c1"># enabled.</span>
@@ -2069,7 +2070,7 @@ <h2>Metadata Routing Support<a class="headerlink" href="#metadata-routing-suppor
 <a href="http://github.com/modules/generated/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config" class="sphx-glr-backref-module-sklearn sphx-glr-backref-type-py-function"><span class="n">sklearn</span><span class="o">.</span><span class="n">set_config</span></a><span class="p">(</span><span class="n">enable_metadata_routing</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>cv error on test sets: [-0.27507931 -0.22314656 -0.30565693 -0.36850513 -0.22465845]
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>cv error on test sets: [-0.34132945 -0.33254954 -0.29704428 -0.17925063 -0.35690129]
 </pre></div>
 </div>
 </section>
@@ -2079,9 +2080,11 @@ <h2>Improved memory and runtime efficiency for PCA on sparse data<a class="heade
 solver by levaraging <code class="docutils literal notranslate"><span class="pre">scipy.sparse.linalg.LinearOperator</span></code> to avoid
 materializing large sparse matrices when performing the
 eigenvalue decomposition of the data set covariance matrix.</p>
-<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.decomposition</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.decomposition.PCA.html#sklearn.decomposition.PCA" title="sklearn.decomposition.PCA" class="sphx-glr-backref-module-sklearn-decomposition sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">PCA</span></a>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">time</span><span class="w"> </span><span class="kn">import</span> <a href="https://docs.python.org/3/library/time.html#time.time" title="time.time" class="sphx-glr-backref-module-time sphx-glr-backref-type-py-function"><span class="n">time</span></a>
+
 <span class="kn">import</span><span class="w"> </span><span class="nn">scipy.sparse</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">sp</span>
-<span class="kn">from</span><span class="w"> </span><span class="nn">time</span><span class="w"> </span><span class="kn">import</span> <a href="https://docs.python.org/3/library/time.html#time.time" title="time.time" class="sphx-glr-backref-module-time sphx-glr-backref-type-py-function"><span class="n">time</span></a>
+
+<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.decomposition</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.decomposition.PCA.html#sklearn.decomposition.PCA" title="sklearn.decomposition.PCA" class="sphx-glr-backref-module-sklearn-decomposition sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">PCA</span></a>
 
 <span class="n">X_sparse</span> <span class="o">=</span> <a href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.random.html#scipy.sparse.random" title="scipy.sparse.random" class="sphx-glr-backref-module-scipy-sparse sphx-glr-backref-type-py-function"><span class="n">sp</span><span class="o">.</span><span class="n">random</span></a><span class="p">(</span><span class="n">m</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">n</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
 <span class="n">X_dense</span> <span class="o">=</span> <span class="n">X_sparse</span><span class="o">.</span><span class="n">toarray</span><span class="p">()</span>
@@ -2097,10 +2100,10 @@ <h2>Improved memory and runtime efficiency for PCA on sparse data<a class="heade
 <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Speedup: </span><span class="si">{</span><span class="n">time_dense</span><span class="w"> </span><span class="o">/</span><span class="w"> </span><span class="n">time_sparse</span><span class="si">:</span><span class="s2">.1f</span><span class="si">}</span><span class="s2">x&quot;</span><span class="p">)</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Speedup: 3.5x
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Speedup: 4.1x
 </pre></div>
 </div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 2.090 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 2.142 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-release-highlights-plot-release-highlights-1-4-0-py">
 <div class="binder-badge docutils container">
 <a class="reference external image-reference" href="https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/release_highlights/plot_release_highlights_1_4_0.ipynb"><img alt="Launch binder" src="http://github.com/_images/binder_badge_logo28.svg" style="width: 150px;" />
diff --git a/dev/auto_examples/release_highlights/plot_release_highlights_1_5_0.html b/dev/auto_examples/release_highlights/plot_release_highlights_1_5_0.html
index a0bc06686d66f..a24e5bc47232e 100644
--- a/dev/auto_examples/release_highlights/plot_release_highlights_1_5_0.html
+++ b/dev/auto_examples/release_highlights/plot_release_highlights_1_5_0.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -679,13 +679,12 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -902,9 +901,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
@@ -1014,10 +1013,9 @@ <h2>FixedThresholdClassifier: Setting the decision threshold of a binary classif
 problem. <a class="reference internal" href="http://github.com/modules/generated/sklearn.model_selection.FixedThresholdClassifier.html#sklearn.model_selection.FixedThresholdClassifier" title="sklearn.model_selection.FixedThresholdClassifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">FixedThresholdClassifier</span></code></a> allows wrapping any
 binary classifier and setting a custom decision threshold.</p>
 <div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.datasets</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.datasets.make_classification.html#sklearn.datasets.make_classification" title="sklearn.datasets.make_classification" class="sphx-glr-backref-module-sklearn-datasets sphx-glr-backref-type-py-function"><span class="n">make_classification</span></a>
-<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.model_selection</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.model_selection.train_test_split.html#sklearn.model_selection.train_test_split" title="sklearn.model_selection.train_test_split" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-function"><span class="n">train_test_split</span></a>
 <span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.linear_model</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.linear_model.LogisticRegression.html#sklearn.linear_model.LogisticRegression" title="sklearn.linear_model.LogisticRegression" class="sphx-glr-backref-module-sklearn-linear_model sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">LogisticRegression</span></a>
 <span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.metrics</span><span class="w"> </span><span class="kn">import</span> <span class="n">ConfusionMatrixDisplay</span>
-
+<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.model_selection</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.model_selection.train_test_split.html#sklearn.model_selection.train_test_split" title="sklearn.model_selection.train_test_split" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-function"><span class="n">train_test_split</span></a>
 
 <span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <a href="http://github.com/modules/generated/sklearn.datasets.make_classification.html#sklearn.datasets.make_classification" title="sklearn.datasets.make_classification" class="sphx-glr-backref-module-sklearn-datasets sphx-glr-backref-type-py-function"><span class="n">make_classification</span></a><span class="p">(</span><span class="n">n_samples</span><span class="o">=</span><span class="mi">10_000</span><span class="p">,</span> <span class="n">weights</span><span class="o">=</span><span class="p">[</span><span class="mf">0.9</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">],</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
 <span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <a href="http://github.com/modules/generated/sklearn.model_selection.train_test_split.html#sklearn.model_selection.train_test_split" title="sklearn.model_selection.train_test_split" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-function"><span class="n">train_test_split</span></a><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
@@ -1076,8 +1074,8 @@ <h2>TunedThresholdClassifierCV: Tuning the decision threshold of a binary classi
 <p>Tuning the threshold to optimize this custom metric gives a smaller threshold
 that allows more samples to be classified as the positive class. As a result,
 the average gain per prediction improves.</p>
-<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.model_selection</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.model_selection.TunedThresholdClassifierCV.html#sklearn.model_selection.TunedThresholdClassifierCV" title="sklearn.model_selection.TunedThresholdClassifierCV" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">TunedThresholdClassifierCV</span></a>
-<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.metrics</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.metrics.make_scorer.html#sklearn.metrics.make_scorer" title="sklearn.metrics.make_scorer" class="sphx-glr-backref-module-sklearn-metrics sphx-glr-backref-type-py-function"><span class="n">make_scorer</span></a>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.metrics</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.metrics.make_scorer.html#sklearn.metrics.make_scorer" title="sklearn.metrics.make_scorer" class="sphx-glr-backref-module-sklearn-metrics sphx-glr-backref-type-py-function"><span class="n">make_scorer</span></a>
+<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.model_selection</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.model_selection.TunedThresholdClassifierCV.html#sklearn.model_selection.TunedThresholdClassifierCV" title="sklearn.model_selection.TunedThresholdClassifierCV" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">TunedThresholdClassifierCV</span></a>
 
 <span class="n">custom_scorer</span> <span class="o">=</span> <a href="http://github.com/modules/generated/sklearn.metrics.make_scorer.html#sklearn.metrics.make_scorer" title="sklearn.metrics.make_scorer" class="sphx-glr-backref-module-sklearn-metrics sphx-glr-backref-type-py-function"><span class="n">make_scorer</span></a><span class="p">(</span>
     <span class="n">custom_score</span><span class="p">,</span> <span class="n">response_method</span><span class="o">=</span><span class="s2">&quot;predict&quot;</span><span class="p">,</span> <span class="n">greater_is_better</span><span class="o">=</span><span class="kc">True</span>
@@ -1150,8 +1148,9 @@ <h2>ColumnTransformer is subscriptable<a class="headerlink" href="#columntransfo
 <p>The transformers of a <a class="reference internal" href="http://github.com/modules/generated/sklearn.compose.ColumnTransformer.html#sklearn.compose.ColumnTransformer" title="sklearn.compose.ColumnTransformer"><code class="xref py py-class docutils literal notranslate"><span class="pre">ColumnTransformer</span></code></a> can now be directly
 accessed using indexing by name.</p>
 <div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
+
 <span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.compose</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.compose.ColumnTransformer.html#sklearn.compose.ColumnTransformer" title="sklearn.compose.ColumnTransformer" class="sphx-glr-backref-module-sklearn-compose sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ColumnTransformer</span></a>
-<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.preprocessing</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.preprocessing.StandardScaler.html#sklearn.preprocessing.StandardScaler" title="sklearn.preprocessing.StandardScaler" class="sphx-glr-backref-module-sklearn-preprocessing sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">StandardScaler</span></a><span class="p">,</span> <a href="http://github.com/modules/generated/sklearn.preprocessing.OneHotEncoder.html#sklearn.preprocessing.OneHotEncoder" title="sklearn.preprocessing.OneHotEncoder" class="sphx-glr-backref-module-sklearn-preprocessing sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">OneHotEncoder</span></a>
+<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.preprocessing</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.preprocessing.OneHotEncoder.html#sklearn.preprocessing.OneHotEncoder" title="sklearn.preprocessing.OneHotEncoder" class="sphx-glr-backref-module-sklearn-preprocessing sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">OneHotEncoder</span></a><span class="p">,</span> <a href="http://github.com/modules/generated/sklearn.preprocessing.StandardScaler.html#sklearn.preprocessing.StandardScaler" title="sklearn.preprocessing.StandardScaler" class="sphx-glr-backref-module-sklearn-preprocessing sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">StandardScaler</span></a>
 
 <span class="n">X</span> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.array.html#numpy.array" title="numpy.array" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">array</span></a><span class="p">([[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">]])</span>
 <span class="n">column_transformer</span> <span class="o">=</span> <a href="http://github.com/modules/generated/sklearn.compose.ColumnTransformer.html#sklearn.compose.ColumnTransformer" title="sklearn.compose.ColumnTransformer" class="sphx-glr-backref-module-sklearn-compose sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ColumnTransformer</span></a><span class="p">(</span>
@@ -1237,7 +1236,7 @@ <h2>Pairwise distances with non-numeric arrays<a class="headerlink" href="#pairw
        [3., 2.]])
 </pre></div>
 </div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 0.701 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 0.727 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-release-highlights-plot-release-highlights-1-5-0-py">
 <div class="binder-badge docutils container">
 <a class="reference external image-reference" href="https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/release_highlights/plot_release_highlights_1_5_0.ipynb"><img alt="Launch binder" src="http://github.com/_images/binder_badge_logo28.svg" style="width: 150px;" />
@@ -1264,12 +1263,12 @@ <h2>Pairwise distances with non-numeric arrays<a class="headerlink" href="#pairw
 </div><div class="sphx-glr-thumbcontainer" tooltip="Once a classifier is trained, the output of the predict method outputs class label predictions corresponding to a thresholding of either the decision_function or the predict_proba output. For a binary classifier, the default threshold is defined as a posterior probability estimate of 0.5 or a decision score of 0.0."><img alt="" src="http://github.com/_images/sphx_glr_plot_cost_sensitive_learning_thumb.png" />
 <p><a class="reference internal" href="http://github.com/model_selection/plot_cost_sensitive_learning.html#sphx-glr-auto-examples-model-selection-plot-cost-sensitive-learning-py"><span class="std std-ref">Post-tuning the decision threshold for cost-sensitive learning</span></a></p>
   <div class="sphx-glr-thumbnail-title">Post-tuning the decision threshold for cost-sensitive learning</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This examples showcases some use cases of FrozenEstimator."><img alt="" src="http://github.com/_images/sphx_glr_plot_frozen_examples_thumb.png" />
-<p><a class="reference internal" href="http://github.com/frozen/plot_frozen_examples.html#sphx-glr-auto-examples-frozen-plot-frozen-examples-py"><span class="std std-ref">Examples of Using FrozenEstimator</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Examples of Using FrozenEstimator</div>
 </div><div class="sphx-glr-thumbcontainer" tooltip="We are pleased to announce the release of scikit-learn 1.6! Many bug fixes and improvements were added, as well as some key new features. Below we detail the highlights of this release. For an exhaustive list of all the changes, please refer to the release notes &lt;release_notes_1_6&gt;."><img alt="" src="http://github.com/_images/sphx_glr_plot_release_highlights_1_6_0_thumb.png" />
 <p><a class="reference internal" href="http://github.com/plot_release_highlights_1_6_0.html#sphx-glr-auto-examples-release-highlights-plot-release-highlights-1-6-0-py"><span class="std std-ref">Release Highlights for scikit-learn 1.6</span></a></p>
   <div class="sphx-glr-thumbnail-title">Release Highlights for scikit-learn 1.6</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This examples showcases some use cases of FrozenEstimator."><img alt="" src="http://github.com/_images/sphx_glr_plot_frozen_examples_thumb.png" />
+<p><a class="reference internal" href="http://github.com/frozen/plot_frozen_examples.html#sphx-glr-auto-examples-frozen-plot-frozen-examples-py"><span class="std std-ref">Examples of Using FrozenEstimator</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Examples of Using FrozenEstimator</div>
 </div></div><p class="sphx-glr-signature"><a class="reference external" href="https://sphinx-gallery.github.io/">Gallery generated by Sphinx-Gallery</a></p>
 </section>
 </section>
diff --git a/dev/auto_examples/release_highlights/plot_release_highlights_1_6_0.html b/dev/auto_examples/release_highlights/plot_release_highlights_1_6_0.html
index 53a9814c5ce9e..1620dd2442171 100644
--- a/dev/auto_examples/release_highlights/plot_release_highlights_1_6_0.html
+++ b/dev/auto_examples/release_highlights/plot_release_highlights_1_6_0.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -679,13 +679,12 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -902,9 +901,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
@@ -1015,6 +1014,7 @@ <h2>FrozenEstimator: Freezing an estimator<a class="headerlink" href="#frozenest
 use case for this is to use a pre-fitted model as a transformer step in a pipeline
 or to pass a pre-fitted model to some of the meta-estimators. Here’s a short example:</p>
 <div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">time</span>
+
 <span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.datasets</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.datasets.make_classification.html#sklearn.datasets.make_classification" title="sklearn.datasets.make_classification" class="sphx-glr-backref-module-sklearn-datasets sphx-glr-backref-type-py-function"><span class="n">make_classification</span></a>
 <span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.frozen</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.frozen.FrozenEstimator.html#sklearn.frozen.FrozenEstimator" title="sklearn.frozen.FrozenEstimator" class="sphx-glr-backref-module-sklearn-frozen sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">FrozenEstimator</span></a>
 <span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.linear_model</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.linear_model.SGDClassifier.html#sklearn.linear_model.SGDClassifier" title="sklearn.linear_model.SGDClassifier" class="sphx-glr-backref-module-sklearn-linear_model sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">SGDClassifier</span></a>
@@ -1036,8 +1036,8 @@ <h2>FrozenEstimator: Freezing an estimator<a class="headerlink" href="#frozenest
 <span class="p">)</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Fitting the classifier took 3.88 milliseconds
-Fitting the threshold classifier took 0.60 milliseconds
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Fitting the classifier took 4.21 milliseconds
+Fitting the threshold classifier took 0.59 milliseconds
 </pre></div>
 </div>
 <p>Fitting the threshold classifier skipped fitting the inner <code class="docutils literal notranslate"><span class="pre">SGDClassifier</span></code>. For more
@@ -1097,6 +1097,7 @@ <h2>Missing value support for Extra Trees<a class="headerlink" href="#missing-va
 <a class="reference internal" href="http://github.com/modules/generated/sklearn.ensemble.ExtraTreesRegressor.html#sklearn.ensemble.ExtraTreesRegressor" title="sklearn.ensemble.ExtraTreesRegressor"><code class="xref py py-class docutils literal notranslate"><span class="pre">ensemble.ExtraTreesRegressor</span></code></a> now support missing values. More details in the
 <a class="reference internal" href="http://github.com/modules/tree.html#tree-missing-value-support"><span class="std std-ref">User Guide</span></a>.</p>
 <div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
+
 <span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.ensemble</span><span class="w"> </span><span class="kn">import</span> <a href="http://github.com/modules/generated/sklearn.ensemble.ExtraTreesClassifier.html#sklearn.ensemble.ExtraTreesClassifier" title="sklearn.ensemble.ExtraTreesClassifier" class="sphx-glr-backref-module-sklearn-ensemble sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ExtraTreesClassifier</span></a>
 
 <span class="n">X</span> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.array.html#numpy.array" title="numpy.array" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">array</span></a><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/constants.html#numpy.nan" title="numpy.nan" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-data"><span class="n">np</span><span class="o">.</span><span class="n">nan</span></a><span class="p">])</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
@@ -1174,7 +1175,7 @@ <h2>Improvements to the developer API for third party libraries<a class="headerl
 </ul>
 <p>An updated version of our <a class="reference internal" href="http://github.com/developers/develop.html#develop"><span class="std std-ref">Developing scikit-learn estimators</span></a> is also available, which we recommend you
 check out.</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 0.102 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 0.094 seconds)</p>
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diff --git a/dev/auto_examples/semi_supervised/index.html b/dev/auto_examples/semi_supervised/index.html
index aef2e4ee50bc1..94ae46e59c671 100644
--- a/dev/auto_examples/semi_supervised/index.html
+++ b/dev/auto_examples/semi_supervised/index.html
@@ -42,7 +42,7 @@
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@@ -675,13 +675,12 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -898,9 +897,9 @@
 <li class="toctree-l1 current active has-children"><a class="current reference internal" href="#">Semi Supervised Classification</a><details open="open"><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
@@ -989,15 +988,15 @@
 </div><div class="sphx-glr-thumbcontainer" tooltip="This example illustrates the effect of a varying threshold on self-training. The breast_cancer dataset is loaded, and labels are deleted such that only 50 out of 569 samples have labels. A SelfTrainingClassifier is fitted on this dataset, with varying thresholds."><img alt="" src="http://github.com/_images/sphx_glr_plot_self_training_varying_threshold_thumb.png" />
 <p><a class="reference internal" href="http://github.com/plot_self_training_varying_threshold.html#sphx-glr-auto-examples-semi-supervised-plot-self-training-varying-threshold-py"><span class="std std-ref">Effect of varying threshold for self-training</span></a></p>
   <div class="sphx-glr-thumbnail-title">Effect of varying threshold for self-training</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="Example of LabelPropagation learning a complex internal structure to demonstrate &quot;manifold learning&quot;. The outer circle should be labeled &quot;red&quot; and the inner circle &quot;blue&quot;. Because both label groups lie inside their own distinct shape, we can see that the labels propagate correctly around the circle."><img alt="" src="http://github.com/_images/sphx_glr_plot_label_propagation_structure_thumb.png" />
+<p><a class="reference internal" href="http://github.com/plot_label_propagation_structure.html#sphx-glr-auto-examples-semi-supervised-plot-label-propagation-structure-py"><span class="std std-ref">Label Propagation circles: Learning a complex structure</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Label Propagation circles: Learning a complex structure</div>
 </div><div class="sphx-glr-thumbcontainer" tooltip="Demonstrates an active learning technique to learn handwritten digits using label propagation."><img alt="" src="http://github.com/_images/sphx_glr_plot_label_propagation_digits_active_learning_thumb.png" />
-<p><a class="reference internal" href="http://github.com/plot_label_propagation_digits_active_learning.html#sphx-glr-auto-examples-semi-supervised-plot-label-propagation-digits-active-learning-py"><span class="std std-ref">Label Propagation digits active learning</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Label Propagation digits active learning</div>
+<p><a class="reference internal" href="http://github.com/plot_label_propagation_digits_active_learning.html#sphx-glr-auto-examples-semi-supervised-plot-label-propagation-digits-active-learning-py"><span class="std std-ref">Label Propagation digits: Active learning</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Label Propagation digits: Active learning</div>
 </div><div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates the power of semisupervised learning by training a Label Spreading model to classify handwritten digits with sets of very few labels."><img alt="" src="http://github.com/_images/sphx_glr_plot_label_propagation_digits_thumb.png" />
 <p><a class="reference internal" href="http://github.com/plot_label_propagation_digits.html#sphx-glr-auto-examples-semi-supervised-plot-label-propagation-digits-py"><span class="std std-ref">Label Propagation digits: Demonstrating performance</span></a></p>
   <div class="sphx-glr-thumbnail-title">Label Propagation digits: Demonstrating performance</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="Example of LabelPropagation learning a complex internal structure to demonstrate &quot;manifold learning&quot;. The outer circle should be labeled &quot;red&quot; and the inner circle &quot;blue&quot;. Because both label groups lie inside their own distinct shape, we can see that the labels propagate correctly around the circle."><img alt="" src="http://github.com/_images/sphx_glr_plot_label_propagation_structure_thumb.png" />
-<p><a class="reference internal" href="http://github.com/plot_label_propagation_structure.html#sphx-glr-auto-examples-semi-supervised-plot-label-propagation-structure-py"><span class="std std-ref">Label Propagation learning a complex structure</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Label Propagation learning a complex structure</div>
 </div><div class="sphx-glr-thumbcontainer" tooltip="In this example, semi-supervised classifiers are trained on the 20 newsgroups dataset (which will be automatically downloaded)."><img alt="" src="http://github.com/_images/sphx_glr_plot_semi_supervised_newsgroups_thumb.png" />
 <p><a class="reference internal" href="http://github.com/plot_semi_supervised_newsgroups.html#sphx-glr-auto-examples-semi-supervised-plot-semi-supervised-newsgroups-py"><span class="std std-ref">Semi-supervised Classification on a Text Dataset</span></a></p>
   <div class="sphx-glr-thumbnail-title">Semi-supervised Classification on a Text Dataset</div>
diff --git a/dev/auto_examples/semi_supervised/plot_label_propagation_digits.html b/dev/auto_examples/semi_supervised/plot_label_propagation_digits.html
index 8b847c9fc2868..39339fbd9e2a4 100644
--- a/dev/auto_examples/semi_supervised/plot_label_propagation_digits.html
+++ b/dev/auto_examples/semi_supervised/plot_label_propagation_digits.html
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-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
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@@ -80,8 +80,8 @@
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-    <link rel="next" title="Label Propagation learning a complex structure" href="http://github.com/plot_label_propagation_structure.html" />
-    <link rel="prev" title="Label Propagation digits active learning" href="http://github.com/plot_label_propagation_digits_active_learning.html" />
+    <link rel="next" title="Semi-supervised Classification on a Text Dataset" href="http://github.com/plot_semi_supervised_newsgroups.html" />
+    <link rel="prev" title="Label Propagation digits: Active learning" href="http://github.com/plot_label_propagation_digits_active_learning.html" />
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@@ -679,13 +679,12 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -902,9 +901,9 @@
 <li class="toctree-l1 current active has-children"><a class="reference internal" href="http://github.com/index.html">Semi Supervised Classification</a><details open="open"><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul class="current">
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2 current active"><a class="current reference internal" href="#">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
@@ -1091,7 +1090,7 @@ <h2>Semi-supervised learning<a class="headerlink" href="#semi-supervised-learnin
 <span class="p">)</span>
 </pre></div>
 </div>
-<img src="http://github.com/_images/sphx_glr_plot_label_propagation_digits_001.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_label_propagation_digits_001.png " alt="plot label propagation digits" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>&lt;sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay object at 0x7fb585273910&gt;
+<img src="http://github.com/_images/sphx_glr_plot_label_propagation_digits_001.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_label_propagation_digits_001.png " alt="plot label propagation digits" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>&lt;sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay object at 0x7fd8490f47c0&gt;
 </pre></div>
 </div>
 </section>
@@ -1126,7 +1125,7 @@ <h2>Plot the most uncertain predictions<a class="headerlink" href="#plot-the-mos
 <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
 </pre></div>
 </div>
-<img src="http://github.com/_images/sphx_glr_plot_label_propagation_digits_002.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_label_propagation_digits_002.png " alt="Learning with small amount of labeled data, predict: 1 true: 2, predict: 2 true: 2, predict: 8 true: 8, predict: 1 true: 8, predict: 1 true: 8, predict: 1 true: 8, predict: 3 true: 3, predict: 8 true: 8, predict: 2 true: 2, predict: 7 true: 2" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 0.350 seconds)</p>
+<img src="http://github.com/_images/sphx_glr_plot_label_propagation_digits_002.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_label_propagation_digits_002.png " alt="Learning with small amount of labeled data, predict: 1 true: 2, predict: 2 true: 2, predict: 8 true: 8, predict: 1 true: 8, predict: 1 true: 8, predict: 1 true: 8, predict: 3 true: 3, predict: 8 true: 8, predict: 2 true: 2, predict: 7 true: 2" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 0.314 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-semi-supervised-plot-label-propagation-digits-py">
 <div class="binder-badge docutils container">
 <a class="reference external image-reference" href="https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/semi_supervised/plot_label_propagation_digits.ipynb"><img alt="Launch binder" src="http://github.com/_images/binder_badge_logo29.svg" style="width: 150px;" />
@@ -1148,8 +1147,8 @@ <h2>Plot the most uncertain predictions<a class="headerlink" href="#plot-the-mos
 </div>
 <p class="rubric">Related examples</p>
 <div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="Demonstrates an active learning technique to learn handwritten digits using label propagation."><img alt="" src="http://github.com/_images/sphx_glr_plot_label_propagation_digits_active_learning_thumb.png" />
-<p><a class="reference internal" href="http://github.com/plot_label_propagation_digits_active_learning.html#sphx-glr-auto-examples-semi-supervised-plot-label-propagation-digits-active-learning-py"><span class="std std-ref">Label Propagation digits active learning</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Label Propagation digits active learning</div>
+<p><a class="reference internal" href="http://github.com/plot_label_propagation_digits_active_learning.html#sphx-glr-auto-examples-semi-supervised-plot-label-propagation-digits-active-learning-py"><span class="std std-ref">Label Propagation digits: Active learning</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Label Propagation digits: Active learning</div>
 </div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how scikit-learn can be used to recognize images of hand-written digits, from 0-9."><img alt="" src="http://github.com/_images/sphx_glr_plot_digits_classification_thumb.png" />
 <p><a class="reference internal" href="http://github.com/classification/plot_digits_classification.html#sphx-glr-auto-examples-classification-plot-digits-classification-py"><span class="std std-ref">Recognizing hand-written digits</span></a></p>
   <div class="sphx-glr-thumbnail-title">Recognizing hand-written digits</div>
@@ -1179,15 +1178,15 @@ <h2>Plot the most uncertain predictions<a class="headerlink" href="#plot-the-mos
       <i class="fa-solid fa-angle-left"></i>
       <div class="prev-next-info">
         <p class="prev-next-subtitle">previous</p>
-        <p class="prev-next-title">Label Propagation digits active learning</p>
+        <p class="prev-next-title">Label Propagation digits: Active learning</p>
       </div>
     </a>
     <a class="right-next"
-       href="http://github.com/plot_label_propagation_structure.html"
+       href="http://github.com/plot_semi_supervised_newsgroups.html"
        title="next page">
       <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title">Label Propagation learning a complex structure</p>
+        <p class="prev-next-title">Semi-supervised Classification on a Text Dataset</p>
       </div>
       <i class="fa-solid fa-angle-right"></i>
     </a>
diff --git a/dev/auto_examples/semi_supervised/plot_label_propagation_digits_active_learning.html b/dev/auto_examples/semi_supervised/plot_label_propagation_digits_active_learning.html
index 85f462227381d..293134ba10f11 100644
--- a/dev/auto_examples/semi_supervised/plot_label_propagation_digits_active_learning.html
+++ b/dev/auto_examples/semi_supervised/plot_label_propagation_digits_active_learning.html
@@ -7,7 +7,7 @@
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     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="viewport" content="width=device-width, initial-scale=1" />
-<meta property="og:title" content="Label Propagation digits active learning" />
+<meta property="og:title" content="Label Propagation digits: Active learning" />
 <meta property="og:type" content="website" />
 <meta property="og:url" content="https://scikit-learn/stable/auto_examples/semi_supervised/plot_label_propagation_digits_active_learning.html" />
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@@ -16,7 +16,7 @@
 <meta property="og:image:alt" content="scikit-learn" />
 <meta name="description" content="Demonstrates an active learning technique to learn handwritten digits using label propagation. We start by training a label propagation model with only 10 labeled points, then we select the top fiv..." />
 
-    <title>Label Propagation digits active learning &#8212; scikit-learn 1.7.dev0 documentation</title>
+    <title>Label Propagation digits: Active learning &#8212; scikit-learn 1.7.dev0 documentation</title>
   
   
   
@@ -42,7 +42,7 @@
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     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
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@@ -81,7 +81,7 @@
     <link rel="author" title="About these documents" href="http://github.com/about.html" />
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     <link rel="next" title="Label Propagation digits: Demonstrating performance" href="http://github.com/plot_label_propagation_digits.html" />
-    <link rel="prev" title="Effect of varying threshold for self-training" href="http://github.com/plot_self_training_varying_threshold.html" />
+    <link rel="prev" title="Label Propagation circles: Learning a complex structure" href="http://github.com/plot_label_propagation_structure.html" />
   <meta name="viewport" content="width=device-width, initial-scale=1"/>
   <meta name="docsearch:language" content="en"/>
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@@ -679,13 +679,12 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -902,9 +901,9 @@
 <li class="toctree-l1 current active has-children"><a class="reference internal" href="http://github.com/index.html">Semi Supervised Classification</a><details open="open"><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul class="current">
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2 current active"><a class="current reference internal" href="#">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2 current active"><a class="current reference internal" href="#">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
@@ -970,7 +969,7 @@
     
     <li class="breadcrumb-item"><a href="http://github.com/index.html" class="nav-link">Semi Supervised Classification</a></li>
     
-    <li class="breadcrumb-item active" aria-current="page"><span class="ellipsis">Label Propagation digits active learning</span></li>
+    <li class="breadcrumb-item active" aria-current="page"><span class="ellipsis">Label Propagation digits: Active learning</span></li>
   </ul>
 </nav>
 </div>
@@ -993,7 +992,7 @@
 to download the full example code. or to run this example in your browser via JupyterLite or Binder</p>
 </div>
 <section class="sphx-glr-example-title" id="label-propagation-digits-active-learning">
-<span id="sphx-glr-auto-examples-semi-supervised-plot-label-propagation-digits-active-learning-py"></span><h1>Label Propagation digits active learning<a class="headerlink" href="#label-propagation-digits-active-learning" title="Link to this heading">#</a></h1>
+<span id="sphx-glr-auto-examples-semi-supervised-plot-label-propagation-digits-active-learning-py"></span><h1>Label Propagation digits: Active learning<a class="headerlink" href="#label-propagation-digits-active-learning" title="Link to this heading">#</a></h1>
 <p>Demonstrates an active learning technique to learn handwritten digits
 using label propagation.</p>
 <p>We start by training a label propagation model with only 10 labeled points,
@@ -1249,7 +1248,7 @@
             <span class="n">sub</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s2">&quot;off&quot;</span><span class="p">)</span>
 
         <span class="c1"># labeling 5 points, remote from labeled set</span>
-        <span class="p">(</span><span class="n">delete_index</span><span class="p">,)</span> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.where.html#numpy.where" title="numpy.where" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">where</span></a><span class="p">(</span><span class="n">unlabeled_indices</span> <span class="o">==</span> <span class="n">image_index</span><span class="p">)</span>
+        <span class="p">(</span><span class="n">delete_index</span><span class="p">,)</span> <span class="o">=</span> <span class="p">(</span><span class="n">unlabeled_indices</span> <span class="o">==</span> <span class="n">image_index</span><span class="p">)</span><span class="o">.</span><span class="n">nonzero</span><span class="p">()</span>
         <span class="n">delete_indices</span> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.concatenate.html#numpy.concatenate" title="numpy.concatenate" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">concatenate</span></a><span class="p">((</span><span class="n">delete_indices</span><span class="p">,</span> <span class="n">delete_index</span><span class="p">))</span>
 
     <span class="n">unlabeled_indices</span> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.delete.html#numpy.delete" title="numpy.delete" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">delete</span></a><span class="p">(</span><span class="n">unlabeled_indices</span><span class="p">,</span> <span class="n">delete_indices</span><span class="p">)</span>
@@ -1266,7 +1265,7 @@
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 </pre></div>
 </div>
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+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 0.494 seconds)</p>
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 <a class="reference external image-reference" href="https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/semi_supervised/plot_label_propagation_digits_active_learning.ipynb"><img alt="Launch binder" src="http://github.com/_images/binder_badge_logo29.svg" style="width: 150px;" />
@@ -1313,12 +1312,12 @@
     <div class="footer-article-item">
 <div class="prev-next-area">
     <a class="left-prev"
-       href="http://github.com/plot_self_training_varying_threshold.html"
+       href="http://github.com/plot_label_propagation_structure.html"
        title="previous page">
       <i class="fa-solid fa-angle-left"></i>
       <div class="prev-next-info">
         <p class="prev-next-subtitle">previous</p>
-        <p class="prev-next-title">Effect of varying threshold for self-training</p>
+        <p class="prev-next-title">Label Propagation circles: Learning a complex structure</p>
       </div>
     </a>
     <a class="right-next"
diff --git a/dev/auto_examples/semi_supervised/plot_label_propagation_structure.html b/dev/auto_examples/semi_supervised/plot_label_propagation_structure.html
index 164617e4f84fb..63530585a3d37 100644
--- a/dev/auto_examples/semi_supervised/plot_label_propagation_structure.html
+++ b/dev/auto_examples/semi_supervised/plot_label_propagation_structure.html
@@ -7,7 +7,7 @@
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     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="viewport" content="width=device-width, initial-scale=1" />
-<meta property="og:title" content="Label Propagation learning a complex structure" />
+<meta property="og:title" content="Label Propagation circles: Learning a complex structure" />
 <meta property="og:type" content="website" />
 <meta property="og:url" content="https://scikit-learn/stable/auto_examples/semi_supervised/plot_label_propagation_structure.html" />
 <meta property="og:site_name" content="scikit-learn" />
@@ -16,7 +16,7 @@
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 <meta name="description" content="Example of LabelPropagation learning a complex internal structure to demonstrate “manifold learning”. The outer circle should be labeled “red” and the inner circle “blue”. Because both label groups..." />
 
-    <title>Label Propagation learning a complex structure &#8212; scikit-learn 1.7.dev0 documentation</title>
+    <title>Label Propagation circles: Learning a complex structure &#8212; scikit-learn 1.7.dev0 documentation</title>
   
   
   
@@ -42,7 +42,7 @@
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-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
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     <link rel="author" title="About these documents" href="http://github.com/about.html" />
     <link rel="search" title="Search" href="http://github.com/search.html" />
-    <link rel="next" title="Semi-supervised Classification on a Text Dataset" href="http://github.com/plot_semi_supervised_newsgroups.html" />
-    <link rel="prev" title="Label Propagation digits: Demonstrating performance" href="http://github.com/plot_label_propagation_digits.html" />
+    <link rel="next" title="Label Propagation digits: Active learning" href="http://github.com/plot_label_propagation_digits_active_learning.html" />
+    <link rel="prev" title="Effect of varying threshold for self-training" href="http://github.com/plot_self_training_varying_threshold.html" />
   <meta name="viewport" content="width=device-width, initial-scale=1"/>
   <meta name="docsearch:language" content="en"/>
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@@ -679,13 +679,12 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -902,9 +901,9 @@
 <li class="toctree-l1 current active has-children"><a class="reference internal" href="http://github.com/index.html">Semi Supervised Classification</a><details open="open"><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul class="current">
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2 current active"><a class="current reference internal" href="#">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2 current active"><a class="current reference internal" href="#">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
@@ -970,7 +969,7 @@
     
     <li class="breadcrumb-item"><a href="http://github.com/index.html" class="nav-link">Semi Supervised Classification</a></li>
     
-    <li class="breadcrumb-item active" aria-current="page"><span class="ellipsis">Label Propagation learning a complex structure</span></li>
+    <li class="breadcrumb-item active" aria-current="page"><span class="ellipsis">Label Propagation circles: Learning a complex structure</span></li>
   </ul>
 </nav>
 </div>
@@ -992,8 +991,8 @@
 <p><a class="reference internal" href="#sphx-glr-download-auto-examples-semi-supervised-plot-label-propagation-structure-py"><span class="std std-ref">Go to the end</span></a>
 to download the full example code. or to run this example in your browser via JupyterLite or Binder</p>
 </div>
-<section class="sphx-glr-example-title" id="label-propagation-learning-a-complex-structure">
-<span id="sphx-glr-auto-examples-semi-supervised-plot-label-propagation-structure-py"></span><h1>Label Propagation learning a complex structure<a class="headerlink" href="#label-propagation-learning-a-complex-structure" title="Link to this heading">#</a></h1>
+<section class="sphx-glr-example-title" id="label-propagation-circles-learning-a-complex-structure">
+<span id="sphx-glr-auto-examples-semi-supervised-plot-label-propagation-structure-py"></span><h1>Label Propagation circles: Learning a complex structure<a class="headerlink" href="#label-propagation-circles-learning-a-complex-structure" title="Link to this heading">#</a></h1>
 <p>Example of LabelPropagation learning a complex internal structure
 to demonstrate “manifold learning”. The outer circle should be
 labeled “red” and the inner circle “blue”. Because both label groups
@@ -1061,7 +1060,7 @@
 </pre></div>
 </div>
 <div class="output_subarea output_html rendered_html output_result">
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@@ -1161,28 +1160,28 @@
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-#sk-container-id-71 div.sk-parallel-item:last-child::after {
+#sk-container-id-72 div.sk-parallel-item:last-child::after {
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-#sk-container-id-71 div.sk-parallel-item:only-child::after {
+#sk-container-id-72 div.sk-parallel-item:only-child::after {
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@@ -1200,14 +1199,14 @@
 
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-#sk-container-id-71 div.sk-toggleable {
+#sk-container-id-72 div.sk-toggleable {
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+#sk-container-id-72 label.sk-toggleable__label {
   cursor: pointer;
   display: flex;
   width: 100%;
@@ -1220,13 +1219,13 @@
   gap: 0.5em;
 }
 
-#sk-container-id-71 label.sk-toggleable__label .caption {
+#sk-container-id-72 label.sk-toggleable__label .caption {
   font-size: 0.6rem;
   font-weight: lighter;
   color: var(--sklearn-color-text-muted);
 }
 
-#sk-container-id-71 label.sk-toggleable__label-arrow:before {
+#sk-container-id-72 label.sk-toggleable__label-arrow:before {
   /* Arrow on the left of the label */
   content: "▸";
   float: left;
@@ -1234,13 +1233,13 @@
   color: var(--sklearn-color-icon);
 }
 
-#sk-container-id-71 label.sk-toggleable__label-arrow:hover:before {
+#sk-container-id-72 label.sk-toggleable__label-arrow:hover:before {
   color: var(--sklearn-color-text);
 }
 
 /* Toggleable content - dropdown */
 
-#sk-container-id-71 div.sk-toggleable__content {
+#sk-container-id-72 div.sk-toggleable__content {
   max-height: 0;
   max-width: 0;
   overflow: hidden;
@@ -1249,12 +1248,12 @@
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-71 div.sk-toggleable__content.fitted {
+#sk-container-id-72 div.sk-toggleable__content.fitted {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
-#sk-container-id-71 div.sk-toggleable__content pre {
+#sk-container-id-72 div.sk-toggleable__content pre {
   margin: 0.2em;
   border-radius: 0.25em;
   color: var(--sklearn-color-text);
@@ -1262,79 +1261,79 @@
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-71 div.sk-toggleable__content.fitted pre {
+#sk-container-id-72 div.sk-toggleable__content.fitted pre {
   /* unfitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
-#sk-container-id-71 input.sk-toggleable__control:checked~div.sk-toggleable__content {
+#sk-container-id-72 input.sk-toggleable__control:checked~div.sk-toggleable__content {
   /* Expand drop-down */
   max-height: 200px;
   max-width: 100%;
   overflow: auto;
 }
 
-#sk-container-id-71 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
+#sk-container-id-72 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
   content: "▾";
 }
 
 /* Pipeline/ColumnTransformer-specific style */
 
-#sk-container-id-71 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-72 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-71 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-72 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
 /* Estimator-specific style */
 
 /* Colorize estimator box */
-#sk-container-id-71 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-72 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-71 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+#sk-container-id-72 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
-#sk-container-id-71 div.sk-label label.sk-toggleable__label,
-#sk-container-id-71 div.sk-label label {
+#sk-container-id-72 div.sk-label label.sk-toggleable__label,
+#sk-container-id-72 div.sk-label label {
   /* The background is the default theme color */
   color: var(--sklearn-color-text-on-default-background);
 }
 
 /* On hover, darken the color of the background */
-#sk-container-id-71 div.sk-label:hover label.sk-toggleable__label {
+#sk-container-id-72 div.sk-label:hover label.sk-toggleable__label {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
 /* Label box, darken color on hover, fitted */
-#sk-container-id-71 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
+#sk-container-id-72 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
   color: var(--sklearn-color-text);
   background-color: var(--sklearn-color-fitted-level-2);
 }
 
 /* Estimator label */
 
-#sk-container-id-71 div.sk-label label {
+#sk-container-id-72 div.sk-label label {
   font-family: monospace;
   font-weight: bold;
   display: inline-block;
   line-height: 1.2em;
 }
 
-#sk-container-id-71 div.sk-label-container {
+#sk-container-id-72 div.sk-label-container {
   text-align: center;
 }
 
 /* Estimator-specific */
-#sk-container-id-71 div.sk-estimator {
+#sk-container-id-72 div.sk-estimator {
   font-family: monospace;
   border: 1px dotted var(--sklearn-color-border-box);
   border-radius: 0.25em;
@@ -1344,18 +1343,18 @@
   background-color: var(--sklearn-color-unfitted-level-0);
 }
 
-#sk-container-id-71 div.sk-estimator.fitted {
+#sk-container-id-72 div.sk-estimator.fitted {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-0);
 }
 
 /* on hover */
-#sk-container-id-71 div.sk-estimator:hover {
+#sk-container-id-72 div.sk-estimator:hover {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-2);
 }
 
-#sk-container-id-71 div.sk-estimator.fitted:hover {
+#sk-container-id-72 div.sk-estimator.fitted:hover {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-2);
 }
@@ -1443,7 +1442,7 @@
 
 /* "?"-specific style due to the `<a>` HTML tag */
 
-#sk-container-id-71 a.estimator_doc_link {
+#sk-container-id-72 a.estimator_doc_link {
   float: right;
   font-size: 1rem;
   line-height: 1em;
@@ -1458,33 +1457,33 @@
   border: var(--sklearn-color-unfitted-level-1) 1pt solid;
 }
 
-#sk-container-id-71 a.estimator_doc_link.fitted {
+#sk-container-id-72 a.estimator_doc_link.fitted {
   /* fitted */
   border: var(--sklearn-color-fitted-level-1) 1pt solid;
   color: var(--sklearn-color-fitted-level-1);
 }
 
 /* On hover */
-#sk-container-id-71 a.estimator_doc_link:hover {
+#sk-container-id-72 a.estimator_doc_link:hover {
   /* unfitted */
   background-color: var(--sklearn-color-unfitted-level-3);
   color: var(--sklearn-color-background);
   text-decoration: none;
 }
 
-#sk-container-id-71 a.estimator_doc_link.fitted:hover {
+#sk-container-id-72 a.estimator_doc_link.fitted:hover {
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-3);
 }
-</style><div id="sk-container-id-71" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>LabelSpreading(alpha=0.8, kernel=&#x27;knn&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-299" type="checkbox" checked><label for="sk-estimator-id-299" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>LabelSpreading</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.semi_supervised.LabelSpreading.html">?<span>Documentation for LabelSpreading</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>LabelSpreading(alpha=0.8, kernel=&#x27;knn&#x27;)</pre></div> </div></div></div></div>
+</style><div id="sk-container-id-72" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>LabelSpreading(alpha=0.8, kernel=&#x27;knn&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-313" type="checkbox" checked><label for="sk-estimator-id-313" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>LabelSpreading</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.semi_supervised.LabelSpreading.html">?<span>Documentation for LabelSpreading</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>LabelSpreading(alpha=0.8, kernel=&#x27;knn&#x27;)</pre></div> </div></div></div></div>
 </div>
 <br />
 <br /><p>Now, we can check which labels have been associated with each sample
 when the label was unknown.</p>
 <div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">output_labels</span> <span class="o">=</span> <span class="n">label_spread</span><span class="o">.</span><span class="n">transduction_</span>
 <span class="n">output_label_array</span> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.asarray.html#numpy.asarray" title="numpy.asarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">asarray</span></a><span class="p">(</span><span class="n">output_labels</span><span class="p">)</span>
-<span class="n">outer_numbers</span> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.where.html#numpy.where" title="numpy.where" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">where</span></a><span class="p">(</span><span class="n">output_label_array</span> <span class="o">==</span> <span class="n">outer</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
-<span class="n">inner_numbers</span> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.where.html#numpy.where" title="numpy.where" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">where</span></a><span class="p">(</span><span class="n">output_label_array</span> <span class="o">==</span> <span class="n">inner</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
+<span class="n">outer_numbers</span> <span class="o">=</span> <span class="p">(</span><span class="n">output_label_array</span> <span class="o">==</span> <span class="n">outer</span><span class="p">)</span><span class="o">.</span><span class="n">nonzero</span><span class="p">()[</span><span class="mi">0</span><span class="p">]</span>
+<span class="n">inner_numbers</span> <span class="o">=</span> <span class="p">(</span><span class="n">output_label_array</span> <span class="o">==</span> <span class="n">inner</span><span class="p">)</span><span class="o">.</span><span class="n">nonzero</span><span class="p">()[</span><span class="mi">0</span><span class="p">]</span>
 
 <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.figure.html#matplotlib.pyplot.figure" title="matplotlib.pyplot.figure" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">figure</span></a><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">))</span>
 <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.scatter.html#matplotlib.pyplot.scatter" title="matplotlib.pyplot.scatter" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">scatter</span></a><span class="p">(</span>
@@ -1510,7 +1509,7 @@
 <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
 </pre></div>
 </div>
-<img src="http://github.com/_images/sphx_glr_plot_label_propagation_structure_002.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_label_propagation_structure_002.png " alt="Labels learned with Label Spreading (KNN)" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 0.173 seconds)</p>
+<img src="http://github.com/_images/sphx_glr_plot_label_propagation_structure_002.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_label_propagation_structure_002.png " alt="Labels learned with Label Spreading (KNN)" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 0.162 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-semi-supervised-plot-label-propagation-structure-py">
 <div class="binder-badge docutils container">
 <a class="reference external image-reference" href="https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/semi_supervised/plot_label_propagation_structure.ipynb"><img alt="Launch binder" src="http://github.com/_images/binder_badge_logo29.svg" style="width: 150px;" />
@@ -1557,20 +1556,20 @@
     <div class="footer-article-item">
 <div class="prev-next-area">
     <a class="left-prev"
-       href="http://github.com/plot_label_propagation_digits.html"
+       href="http://github.com/plot_self_training_varying_threshold.html"
        title="previous page">
       <i class="fa-solid fa-angle-left"></i>
       <div class="prev-next-info">
         <p class="prev-next-subtitle">previous</p>
-        <p class="prev-next-title">Label Propagation digits: Demonstrating performance</p>
+        <p class="prev-next-title">Effect of varying threshold for self-training</p>
       </div>
     </a>
     <a class="right-next"
-       href="http://github.com/plot_semi_supervised_newsgroups.html"
+       href="http://github.com/plot_label_propagation_digits_active_learning.html"
        title="next page">
       <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title">Semi-supervised Classification on a Text Dataset</p>
+        <p class="prev-next-title">Label Propagation digits: Active learning</p>
       </div>
       <i class="fa-solid fa-angle-right"></i>
     </a>
diff --git a/dev/auto_examples/semi_supervised/plot_self_training_varying_threshold.html b/dev/auto_examples/semi_supervised/plot_self_training_varying_threshold.html
index 340c87cedd9c9..803cf4bb65856 100644
--- a/dev/auto_examples/semi_supervised/plot_self_training_varying_threshold.html
+++ b/dev/auto_examples/semi_supervised/plot_self_training_varying_threshold.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -80,7 +80,7 @@
     <link rel="icon" href="http://github.com/_static/favicon.ico"/>
     <link rel="author" title="About these documents" href="http://github.com/about.html" />
     <link rel="search" title="Search" href="http://github.com/search.html" />
-    <link rel="next" title="Label Propagation digits active learning" href="http://github.com/plot_label_propagation_digits_active_learning.html" />
+    <link rel="next" title="Label Propagation circles: Learning a complex structure" href="http://github.com/plot_label_propagation_structure.html" />
     <link rel="prev" title="Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset" href="http://github.com/plot_semi_supervised_versus_svm_iris.html" />
   <meta name="viewport" content="width=device-width, initial-scale=1"/>
   <meta name="docsearch:language" content="en"/>
@@ -679,13 +679,12 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -902,9 +901,9 @@
 <li class="toctree-l1 current active has-children"><a class="reference internal" href="http://github.com/index.html">Semi Supervised Classification</a><details open="open"><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul class="current">
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2 current active"><a class="current reference internal" href="#">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
@@ -1103,7 +1102,7 @@
 <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
 </pre></div>
 </div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 5.982 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 5.434 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-semi-supervised-plot-self-training-varying-threshold-py">
 <div class="binder-badge docutils container">
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@@ -1159,11 +1158,11 @@
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -902,9 +901,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
@@ -1048,7 +1047,7 @@
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+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 0.098 seconds)</p>
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diff --git a/dev/auto_examples/svm/plot_iris_svc.html b/dev/auto_examples/svm/plot_iris_svc.html
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -902,9 +901,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
@@ -1080,7 +1079,7 @@
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@@ -1110,9 +1109,9 @@
 </div><div class="sphx-glr-thumbcontainer" tooltip="Unlike SVC (based on LIBSVM), LinearSVC (based on LIBLINEAR) does not provide the support vectors. This example demonstrates how to obtain the support vectors in LinearSVC."><img alt="" src="http://github.com/_images/sphx_glr_plot_linearsvc_support_vectors_thumb.png" />
 <p><a class="reference internal" href="http://github.com/plot_linearsvc_support_vectors.html#sphx-glr-auto-examples-svm-plot-linearsvc-support-vectors-py"><span class="std std-ref">Plot the support vectors in LinearSVC</span></a></p>
   <div class="sphx-glr-thumbnail-title">Plot the support vectors in LinearSVC</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="Plot the decision boundaries of a VotingClassifier for two features of the Iris dataset."><img alt="" src="http://github.com/_images/sphx_glr_plot_voting_decision_regions_thumb.png" />
-<p><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html#sphx-glr-auto-examples-ensemble-plot-voting-decision-regions-py"><span class="std std-ref">Plot the decision boundaries of a VotingClassifier</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Plot the decision boundaries of a VotingClassifier</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows a well known decomposition technique known as Principal Component Analysis (PCA) on the Iris dataset."><img alt="" src="http://github.com/_images/sphx_glr_plot_pca_iris_thumb.png" />
+<p><a class="reference internal" href="http://github.com/decomposition/plot_pca_iris.html#sphx-glr-auto-examples-decomposition-plot-pca-iris-py"><span class="std std-ref">Principal Component Analysis (PCA) on Iris Dataset</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Principal Component Analysis (PCA) on Iris Dataset</div>
 </div></div><p class="sphx-glr-signature"><a class="reference external" href="https://sphinx-gallery.github.io/">Gallery generated by Sphinx-Gallery</a></p>
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diff --git a/dev/auto_examples/svm/plot_linearsvc_support_vectors.html b/dev/auto_examples/svm/plot_linearsvc_support_vectors.html
index 0f37e7ebde13b..b4074e36dd7c8 100644
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+++ b/dev/auto_examples/svm/plot_linearsvc_support_vectors.html
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -902,9 +901,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
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@@ -1019,7 +1018,7 @@
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+    <span class="n">support_vector_indices</span> <span class="o">=</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">decision_function</span><span class="p">)</span> <span class="o">&lt;=</span> <span class="mi">1</span> <span class="o">+</span> <span class="mf">1e-15</span><span class="p">)</span><span class="o">.</span><span class="n">nonzero</span><span class="p">()[</span><span class="mi">0</span><span class="p">]</span>
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diff --git a/dev/auto_examples/svm/plot_oneclass.html b/dev/auto_examples/svm/plot_oneclass.html
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
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@@ -902,9 +901,9 @@
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
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 ================================================================================
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 ================================================================================
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 ================================================================================
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 ________________________________________________________________________________
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 accuracy:   0.779
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
@@ -1165,7 +1164,7 @@ <h3>Feature Extraction using TfidfVectorizer<a class="headerlink" href="#feature
 <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;n_samples: </span><span class="si">{</span><span class="n">X_tfidf</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="si">}</span><span class="s2">, n_features: </span><span class="si">{</span><span class="n">X_tfidf</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>vectorization done in 0.502 s
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>vectorization done in 0.406 s
 n_samples: 3387, n_features: 7929
 </pre></div>
 </div>
@@ -1232,7 +1231,7 @@ <h3>Feature Extraction using TfidfVectorizer<a class="headerlink" href="#feature
 <span class="n">fit_and_evaluate</span><span class="p">(</span><span class="n">kmeans</span><span class="p">,</span> <span class="n">X_tfidf</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s2">&quot;KMeans</span><span class="se">\n</span><span class="s2">on tf-idf vectors&quot;</span><span class="p">)</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>clustering done in 0.22 ± 0.05 s
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>clustering done in 0.19 ± 0.05 s
 Homogeneity: 0.349 ± 0.010
 Completeness: 0.398 ± 0.009
 V-measure: 0.372 ± 0.009
@@ -1272,7 +1271,7 @@ <h3>Performing dimensionality reduction using LSA<a class="headerlink" href="#pe
 <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Explained variance of the SVD step: </span><span class="si">{</span><span class="n">explained_variance</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="mi">100</span><span class="si">:</span><span class="s2">.1f</span><span class="si">}</span><span class="s2">%&quot;</span><span class="p">)</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>LSA done in 0.419 s
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>LSA done in 0.377 s
 Explained variance of the SVD step: 18.4%
 </pre></div>
 </div>
@@ -1375,7 +1374,7 @@ <h3>HashingVectorizer<a class="headerlink" href="#hashingvectorizer" title="Link
 <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;vectorization done in </span><span class="si">{</span><a href="https://docs.python.org/3/library/time.html#time.time" title="time.time" class="sphx-glr-backref-module-time sphx-glr-backref-type-py-function"><span class="n">time</span></a><span class="p">()</span><span class="w"> </span><span class="o">-</span><span class="w"> </span><span class="n">t0</span><span class="si">:</span><span class="s2">.3f</span><span class="si">}</span><span class="s2"> s&quot;</span><span class="p">)</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>vectorization done in 1.949 s
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>vectorization done in 1.839 s
 </pre></div>
 </div>
 <p>One can observe that the LSA step takes a relatively long time to fit,
@@ -1389,7 +1388,7 @@ <h3>HashingVectorizer<a class="headerlink" href="#hashingvectorizer" title="Link
 <div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">fit_and_evaluate</span><span class="p">(</span><span class="n">kmeans</span><span class="p">,</span> <span class="n">X_hashed_lsa</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s2">&quot;KMeans</span><span class="se">\n</span><span class="s2">with LSA on hashed vectors&quot;</span><span class="p">)</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>clustering done in 0.02 ± 0.01 s
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>clustering done in 0.02 ± 0.00 s
 Homogeneity: 0.390 ± 0.008
 Completeness: 0.436 ± 0.012
 V-measure: 0.411 ± 0.010
@@ -1404,7 +1403,7 @@ <h3>HashingVectorizer<a class="headerlink" href="#hashingvectorizer" title="Link
 <span class="p">)</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>clustering done in 0.02 ± 0.01 s
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>clustering done in 0.02 ± 0.00 s
 Homogeneity: 0.359 ± 0.053
 Completeness: 0.385 ± 0.059
 V-measure: 0.371 ± 0.055
@@ -1468,7 +1467,7 @@ <h2>Clustering evaluation summary<a class="headerlink" href="#clustering-evaluat
 dataset. It is more interesting to use when the number of samples is much
 bigger, but it can come at the expense of a small degradation in clustering
 quality compared to the traditional k-means algorithm.</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 8.511 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 7.639 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-text-plot-document-clustering-py">
 <div class="binder-badge docutils container">
 <a class="reference external image-reference" href="https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/text/plot_document_clustering.ipynb"><img alt="Launch binder" src="http://github.com/_images/binder_badge_logo31.svg" style="width: 150px;" />
diff --git a/dev/auto_examples/text/plot_hashing_vs_dict_vectorizer.html b/dev/auto_examples/text/plot_hashing_vs_dict_vectorizer.html
index 486155a17f5d2..95b82a5c2b71f 100644
--- a/dev/auto_examples/text/plot_hashing_vs_dict_vectorizer.html
+++ b/dev/auto_examples/text/plot_hashing_vs_dict_vectorizer.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -679,13 +679,12 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_bias_variance.html">Single estimator versus bagging: bias-variance decomposition</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Visualizing the probabilistic predictions of a VotingClassifier</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/applications/index.html">Examples based on real world datasets</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
@@ -902,9 +901,9 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/semi_supervised/index.html">Semi Supervised Classification</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_versus_svm_iris.html">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_self_training_varying_threshold.html">Effect of varying threshold for self-training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits active learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits.html">Label Propagation digits: Demonstrating performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation learning a complex structure</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
 </ul>
 </details></li>
@@ -1120,7 +1119,7 @@ <h2>DictVectorizer<a class="headerlink" href="#dictvectorizer" title="Link to th
 <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Found </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">vectorizer</span><span class="o">.</span><span class="n">get_feature_names_out</span><span class="p">())</span><span class="si">}</span><span class="s2"> unique terms&quot;</span><span class="p">)</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>done in 1.337 s at 4.7 MB/s
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>done in 1.071 s at 5.8 MB/s
 Found 47928 unique terms
 </pre></div>
 </div>
@@ -1187,7 +1186,7 @@ <h2>FeatureHasher<a class="headerlink" href="#featurehasher" title="Link to this
 <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Found </span><span class="si">{</span><span class="n">n_nonzero_columns</span><span class="p">(</span><span class="n">X</span><span class="p">)</span><span class="si">}</span><span class="s2"> unique tokens&quot;</span><span class="p">)</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>done in 0.722 s at 8.6 MB/s
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>done in 0.613 s at 10.2 MB/s
 Found 43873 unique tokens
 </pre></div>
 </div>
@@ -1209,7 +1208,7 @@ <h2>FeatureHasher<a class="headerlink" href="#featurehasher" title="Link to this
 <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Found </span><span class="si">{</span><span class="n">n_nonzero_columns</span><span class="p">(</span><span class="n">X</span><span class="p">)</span><span class="si">}</span><span class="s2"> unique tokens&quot;</span><span class="p">)</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>done in 0.722 s at 8.6 MB/s
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>done in 0.622 s at 10.0 MB/s
 Found 47668 unique tokens
 </pre></div>
 </div>
@@ -1232,7 +1231,7 @@ <h2>FeatureHasher<a class="headerlink" href="#featurehasher" title="Link to this
 <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Found </span><span class="si">{</span><span class="n">n_nonzero_columns</span><span class="p">(</span><span class="n">X</span><span class="p">)</span><span class="si">}</span><span class="s2"> unique tokens&quot;</span><span class="p">)</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>done in 0.658 s at 9.5 MB/s
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>done in 0.601 s at 10.4 MB/s
 Found 43873 unique tokens
 </pre></div>
 </div>
@@ -1282,7 +1281,7 @@ <h2>Comparison with special purpose text vectorizers<a class="headerlink" href="
 <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Found </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">vectorizer</span><span class="o">.</span><span class="n">get_feature_names_out</span><span class="p">())</span><span class="si">}</span><span class="s2"> unique terms&quot;</span><span class="p">)</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>done in 0.802 s at 7.8 MB/s
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>done in 0.691 s at 9.0 MB/s
 Found 47885 unique terms
 </pre></div>
 </div>
@@ -1310,7 +1309,7 @@ <h2>Comparison with special purpose text vectorizers<a class="headerlink" href="
 <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;done in </span><span class="si">{</span><span class="n">duration</span><span class="si">:</span><span class="s2">.3f</span><span class="si">}</span><span class="s2"> s at </span><span class="si">{</span><span class="n">data_size_mb</span><span class="w"> </span><span class="o">/</span><span class="w"> </span><span class="n">duration</span><span class="si">:</span><span class="s2">.1f</span><span class="si">}</span><span class="s2"> MB/s&quot;</span><span class="p">)</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>done in 0.608 s at 10.3 MB/s
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>done in 0.546 s at 11.4 MB/s
 </pre></div>
 </div>
 <p>We can observe that this is the fastest text tokenization strategy so far,
@@ -1346,7 +1345,7 @@ <h2>TfidfVectorizer<a class="headerlink" href="#tfidfvectorizer" title="Link to
 <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Found </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">vectorizer</span><span class="o">.</span><span class="n">get_feature_names_out</span><span class="p">())</span><span class="si">}</span><span class="s2"> unique terms&quot;</span><span class="p">)</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>done in 0.832 s at 7.5 MB/s
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>done in 0.691 s at 9.0 MB/s
 Found 47885 unique terms
 </pre></div>
 </div>
@@ -1380,7 +1379,7 @@ <h2>Summary<a class="headerlink" href="#summary" title="Link to this heading">#<
 <a class="reference internal" href="http://github.com/modules/generated/sklearn.feature_extraction.FeatureHasher.html#sklearn.feature_extraction.FeatureHasher" title="sklearn.feature_extraction.FeatureHasher"><code class="xref py py-func docutils literal notranslate"><span class="pre">FeatureHasher</span></code></a> on manually tokenized
 documents since the internal tokenization step of the former vectorizers
 compiles a regular expression once and then reuses it for all the documents.</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 6.275 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 5.290 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-text-plot-hashing-vs-dict-vectorizer-py">
 <div class="binder-badge docutils container">
 <a class="reference external image-reference" href="https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/text/plot_hashing_vs_dict_vectorizer.ipynb"><img alt="Launch binder" src="http://github.com/_images/binder_badge_logo31.svg" style="width: 150px;" />
diff --git a/dev/auto_examples/tree/index.html b/dev/auto_examples/tree/index.html
index 9cefabd601fa3..efff7e96fd1c4 100644
--- a/dev/auto_examples/tree/index.html
+++ b/dev/auto_examples/tree/index.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -675,13 +675,12 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_monotonic_constraints.html">Monotonic Constraints</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_multiclass.html">Multi-class AdaBoosted Decision Trees</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_ensemble_oob.html">OOB Errors for Random Forests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_probas.html">Plot class probabilities calculated by the VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_regressor.html">Plot individual and voting regression predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_voting_decision_regions.html">Plot the decision boundaries of a VotingClassifier</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_forest_iris.html">Plot the decision surfaces of ensembles of trees on the iris dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_gradient_boosting_quantile.html">Prediction Intervals for Gradient Boosting Regression</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_semi_supervised_newsgroups.html">Semi-supervised Classification on a Text Dataset</a></li>
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+<img src="http://github.com/_images/sphx_glr_plot_cost_complexity_pruning_003.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_cost_complexity_pruning_003.png " alt="Accuracy vs alpha for training and testing sets" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 0.500 seconds)</p>
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+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_structure.html">Label Propagation circles: Learning a complex structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/semi_supervised/plot_label_propagation_digits_active_learning.html">Label Propagation digits: Active learning</a></li>
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+<img src="http://github.com/_images/sphx_glr_plot_iris_dtc_002.png" srcset="http://github.com/service/http://github.com/_images/sphx_glr_plot_iris_dtc_002.png " alt="Decision tree trained on all the iris features" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 0.938 seconds)</p>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/ensemble/plot_adaboost_twoclass.html">Two-class AdaBoost</a></li>
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@@ -902,9 +901,9 @@
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-    <title>10. Common pitfalls and recommended practices &#8212; scikit-learn 1.7.dev0 documentation</title>
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-    <link rel="prev" title="9. Model persistence" href="http://github.com/model_persistence.html" />
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-    <li class="breadcrumb-item active" aria-current="page"><span class="ellipsis"><span class="section-number">10. </span>Common pitfalls and recommended practices</span></li>
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@@ -686,13 +687,13 @@
                 <article class="bd-article">
                   
   <section id="common-pitfalls-and-recommended-practices">
-<span id="common-pitfalls"></span><h1><span class="section-number">10. </span>Common pitfalls and recommended practices<a class="headerlink" href="#common-pitfalls-and-recommended-practices" title="Link to this heading">#</a></h1>
+<span id="common-pitfalls"></span><h1><span class="section-number">11. </span>Common pitfalls and recommended practices<a class="headerlink" href="#common-pitfalls-and-recommended-practices" title="Link to this heading">#</a></h1>
 <p>The purpose of this chapter is to illustrate some common pitfalls and
 anti-patterns that occur when using scikit-learn. It provides
 examples of what <strong>not</strong> to do, along with a corresponding correct
 example.</p>
 <section id="inconsistent-preprocessing">
-<h2><span class="section-number">10.1. </span>Inconsistent preprocessing<a class="headerlink" href="#inconsistent-preprocessing" title="Link to this heading">#</a></h2>
+<h2><span class="section-number">11.1. </span>Inconsistent preprocessing<a class="headerlink" href="#inconsistent-preprocessing" title="Link to this heading">#</a></h2>
 <p>scikit-learn provides a library of <a class="reference internal" href="http://github.com/data_transforms.html#data-transforms"><span class="std std-ref">Dataset transformations</span></a>, which
 may clean (see <a class="reference internal" href="http://github.com/modules/preprocessing.html#preprocessing"><span class="std std-ref">Preprocessing data</span></a>), reduce
 (see <a class="reference internal" href="http://github.com/modules/unsupervised_reduction.html#data-reduction"><span class="std std-ref">Unsupervised dimensionality reduction</span></a>), expand (see <a class="reference internal" href="http://github.com/modules/kernel_approximation.html#kernel-approximation"><span class="std std-ref">Kernel Approximation</span></a>)
@@ -750,7 +751,7 @@ <h2><span class="section-number">10.1. </span>Inconsistent preprocessing<a class
 into the training data.</p>
 </section>
 <section id="data-leakage">
-<span id="id1"></span><h2><span class="section-number">10.2. </span>Data leakage<a class="headerlink" href="#data-leakage" title="Link to this heading">#</a></h2>
+<span id="id1"></span><h2><span class="section-number">11.2. </span>Data leakage<a class="headerlink" href="#data-leakage" title="Link to this heading">#</a></h2>
 <p>Data leakage occurs when information that would not be available at prediction
 time is used when building the model. This results in overly optimistic
 performance estimates, for example from <a class="reference internal" href="http://github.com/modules/cross_validation.html#cross-validation"><span class="std std-ref">cross-validation</span></a>, and thus poorer performance when the model is used
@@ -769,7 +770,7 @@ <h2><span class="section-number">10.1. </span>Inconsistent preprocessing<a class
 test subset is included in the average calculation, information from the test
 subset is influencing the model.</p>
 <section id="how-to-avoid-data-leakage">
-<h3><span class="section-number">10.2.1. </span>How to avoid data leakage<a class="headerlink" href="#how-to-avoid-data-leakage" title="Link to this heading">#</a></h3>
+<h3><span class="section-number">11.2.1. </span>How to avoid data leakage<a class="headerlink" href="#how-to-avoid-data-leakage" title="Link to this heading">#</a></h3>
 <p>Below are some tips on avoiding data leakage:</p>
 <ul>
 <li><p>Always split the data into train and test subsets first, particularly
@@ -790,7 +791,7 @@ <h3><span class="section-number">10.2.1. </span>How to avoid data leakage<a clas
 <p>An example of data leakage during preprocessing is detailed below.</p>
 </section>
 <section id="data-leakage-during-pre-processing">
-<h3><span class="section-number">10.2.2. </span>Data leakage during pre-processing<a class="headerlink" href="#data-leakage-during-pre-processing" title="Link to this heading">#</a></h3>
+<h3><span class="section-number">11.2.2. </span>Data leakage during pre-processing<a class="headerlink" href="#data-leakage-during-pre-processing" title="Link to this heading">#</a></h3>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
 <p>We here choose to illustrate data leakage with a feature selection step.
@@ -896,7 +897,7 @@ <h3><span class="section-number">10.2.2. </span>Data leakage during pre-processi
 </section>
 </section>
 <section id="controlling-randomness">
-<span id="randomness"></span><h2><span class="section-number">10.3. </span>Controlling randomness<a class="headerlink" href="#controlling-randomness" title="Link to this heading">#</a></h2>
+<span id="randomness"></span><h2><span class="section-number">11.3. </span>Controlling randomness<a class="headerlink" href="#controlling-randomness" title="Link to this heading">#</a></h2>
 <p>Some scikit-learn objects are inherently random. These are usually estimators
 (e.g. <a class="reference internal" href="http://github.com/modules/generated/sklearn.ensemble.RandomForestClassifier.html#sklearn.ensemble.RandomForestClassifier" title="sklearn.ensemble.RandomForestClassifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">RandomForestClassifier</span></code></a>) and cross-validation
 splitters (e.g. <a class="reference internal" href="http://github.com/modules/generated/sklearn.model_selection.KFold.html#sklearn.model_selection.KFold" title="sklearn.model_selection.KFold"><code class="xref py py-class docutils literal notranslate"><span class="pre">KFold</span></code></a>). The randomness of
@@ -920,7 +921,7 @@ <h3><span class="section-number">10.2.2. </span>Data leakage during pre-processi
 <code class="docutils literal notranslate"><span class="pre">random_state=None</span></code>.</p>
 </div>
 <section id="using-none-or-randomstate-instances-and-repeated-calls-to-fit-and-split">
-<h3><span class="section-number">10.3.1. </span>Using <code class="docutils literal notranslate"><span class="pre">None</span></code> or <code class="docutils literal notranslate"><span class="pre">RandomState</span></code> instances, and repeated calls to <code class="docutils literal notranslate"><span class="pre">fit</span></code> and <code class="docutils literal notranslate"><span class="pre">split</span></code><a class="headerlink" href="#using-none-or-randomstate-instances-and-repeated-calls-to-fit-and-split" title="Link to this heading">#</a></h3>
+<h3><span class="section-number">11.3.1. </span>Using <code class="docutils literal notranslate"><span class="pre">None</span></code> or <code class="docutils literal notranslate"><span class="pre">RandomState</span></code> instances, and repeated calls to <code class="docutils literal notranslate"><span class="pre">fit</span></code> and <code class="docutils literal notranslate"><span class="pre">split</span></code><a class="headerlink" href="#using-none-or-randomstate-instances-and-repeated-calls-to-fit-and-split" title="Link to this heading">#</a></h3>
 <p>The <code class="docutils literal notranslate"><span class="pre">random_state</span></code> parameter determines whether multiple calls to <a class="reference internal" href="http://github.com/glossary.html#term-fit"><span class="xref std std-term">fit</span></a>
 (for estimators) or to <a class="reference internal" href="http://github.com/glossary.html#term-split"><span class="xref std std-term">split</span></a> (for CV splitters) will produce the same
 results, according to these rules:</p>
@@ -942,7 +943,7 @@ <h3><span class="section-number">10.3.1. </span>Using <code class="docutils lite
 <code class="docutils literal notranslate"><span class="pre">None</span></code>.</p>
 </div>
 <section id="estimators">
-<h4><span class="section-number">10.3.1.1. </span>Estimators<a class="headerlink" href="#estimators" title="Link to this heading">#</a></h4>
+<h4><span class="section-number">11.3.1.1. </span>Estimators<a class="headerlink" href="#estimators" title="Link to this heading">#</a></h4>
 <p>Passing instances means that calling <code class="docutils literal notranslate"><span class="pre">fit</span></code> multiple times will not yield the
 same results, even if the estimator is fitted on the same data and with the
 same hyper-parameters:</p>
@@ -978,7 +979,7 @@ <h4><span class="section-number">10.3.1.1. </span>Estimators<a class="headerlink
 its original state at the beginning of <code class="docutils literal notranslate"><span class="pre">fit</span></code>.</p>
 </section>
 <section id="cv-splitters">
-<h4><span class="section-number">10.3.1.2. </span>CV splitters<a class="headerlink" href="#cv-splitters" title="Link to this heading">#</a></h4>
+<h4><span class="section-number">11.3.1.2. </span>CV splitters<a class="headerlink" href="#cv-splitters" title="Link to this heading">#</a></h4>
 <p>Randomized CV splitters have a similar behavior when a <code class="docutils literal notranslate"><span class="pre">RandomState</span></code>
 instance is passed; calling <code class="docutils literal notranslate"><span class="pre">split</span></code> multiple times yields different data
 splits:</p>
@@ -1007,12 +1008,12 @@ <h4><span class="section-number">10.3.1.2. </span>CV splitters<a class="headerli
 </section>
 </section>
 <section id="common-pitfalls-and-subtleties">
-<h3><span class="section-number">10.3.2. </span>Common pitfalls and subtleties<a class="headerlink" href="#common-pitfalls-and-subtleties" title="Link to this heading">#</a></h3>
+<h3><span class="section-number">11.3.2. </span>Common pitfalls and subtleties<a class="headerlink" href="#common-pitfalls-and-subtleties" title="Link to this heading">#</a></h3>
 <p>While the rules that govern the <code class="docutils literal notranslate"><span class="pre">random_state</span></code> parameter are seemingly simple,
 they do however have some subtle implications. In some cases, this can even
 lead to wrong conclusions.</p>
 <section id="id2">
-<h4><span class="section-number">10.3.2.1. </span>Estimators<a class="headerlink" href="#id2" title="Link to this heading">#</a></h4>
+<h4><span class="section-number">11.3.2.1. </span>Estimators<a class="headerlink" href="#id2" title="Link to this heading">#</a></h4>
 <p><strong>Different `random_state` types lead to different cross-validation
 procedures</strong></p>
 <p>Depending on the type of the <code class="docutils literal notranslate"><span class="pre">random_state</span></code> parameter, estimators will behave
@@ -1101,7 +1102,7 @@ <h4><span class="section-number">10.3.2.1. </span>Estimators<a class="headerlink
 </div>
 </details></section>
 <section id="id3">
-<h4><span class="section-number">10.3.2.2. </span>CV splitters<a class="headerlink" href="#id3" title="Link to this heading">#</a></h4>
+<h4><span class="section-number">11.3.2.2. </span>CV splitters<a class="headerlink" href="#id3" title="Link to this heading">#</a></h4>
 <p>When passed a <code class="docutils literal notranslate"><span class="pre">RandomState</span></code> instance, CV splitters yield different splits
 each time <code class="docutils literal notranslate"><span class="pre">split</span></code> is called. When comparing different estimators, this can
 lead to overestimating the variance of the difference in performance between
@@ -1161,9 +1162,9 @@ <h4><span class="section-number">10.3.2.2. </span>CV splitters<a class="headerli
 </section>
 </section>
 <section id="general-recommendations">
-<h3><span class="section-number">10.3.3. </span>General recommendations<a class="headerlink" href="#general-recommendations" title="Link to this heading">#</a></h3>
+<h3><span class="section-number">11.3.3. </span>General recommendations<a class="headerlink" href="#general-recommendations" title="Link to this heading">#</a></h3>
 <section id="getting-reproducible-results-across-multiple-executions">
-<h4><span class="section-number">10.3.3.1. </span>Getting reproducible results across multiple executions<a class="headerlink" href="#getting-reproducible-results-across-multiple-executions" title="Link to this heading">#</a></h4>
+<h4><span class="section-number">11.3.3.1. </span>Getting reproducible results across multiple executions<a class="headerlink" href="#getting-reproducible-results-across-multiple-executions" title="Link to this heading">#</a></h4>
 <p>In order to obtain reproducible (i.e. constant) results across multiple
 <em>program executions</em>, we need to remove all uses of <code class="docutils literal notranslate"><span class="pre">random_state=None</span></code>, which
 is the default. The recommended way is to declare a <code class="docutils literal notranslate"><span class="pre">rng</span></code> variable at the top
@@ -1197,7 +1198,7 @@ <h4><span class="section-number">10.3.3.1. </span>Getting reproducible results a
 </div>
 </section>
 <section id="robustness-of-cross-validation-results">
-<h4><span class="section-number">10.3.3.2. </span>Robustness of cross-validation results<a class="headerlink" href="#robustness-of-cross-validation-results" title="Link to this heading">#</a></h4>
+<h4><span class="section-number">11.3.3.2. </span>Robustness of cross-validation results<a class="headerlink" href="#robustness-of-cross-validation-results" title="Link to this heading">#</a></h4>
 <p>When we evaluate a randomized estimator performance by cross-validation, we
 want to make sure that the estimator can yield accurate predictions for new
 data, but we also want to make sure that the estimator is robust w.r.t. its
@@ -1243,7 +1244,7 @@ <h4><span class="section-number">10.3.3.2. </span>Robustness of cross-validation
       <i class="fa-solid fa-angle-left"></i>
       <div class="prev-next-info">
         <p class="prev-next-subtitle">previous</p>
-        <p class="prev-next-title"><span class="section-number">9. </span>Model persistence</p>
+        <p class="prev-next-title"><span class="section-number">10. </span>Model persistence</p>
       </div>
     </a>
     <a class="right-next"
@@ -1251,7 +1252,7 @@ <h4><span class="section-number">10.3.3.2. </span>Robustness of cross-validation
        title="next page">
       <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">11. </span>Dispatching</p>
+        <p class="prev-next-title"><span class="section-number">12. </span>Dispatching</p>
       </div>
       <i class="fa-solid fa-angle-right"></i>
     </a>
@@ -1278,26 +1279,26 @@ <h4><span class="section-number">10.3.3.2. </span>Robustness of cross-validation
   </div>
   <nav class="bd-toc-nav page-toc" aria-labelledby="pst-page-navigation-heading-2">
     <ul class="visible nav section-nav flex-column">
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#inconsistent-preprocessing">10.1. Inconsistent preprocessing</a></li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#data-leakage">10.2. Data leakage</a><ul class="nav section-nav flex-column">
-<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#how-to-avoid-data-leakage">10.2.1. How to avoid data leakage</a></li>
-<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#data-leakage-during-pre-processing">10.2.2. Data leakage during pre-processing</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#inconsistent-preprocessing">11.1. Inconsistent preprocessing</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#data-leakage">11.2. Data leakage</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#how-to-avoid-data-leakage">11.2.1. How to avoid data leakage</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#data-leakage-during-pre-processing">11.2.2. Data leakage during pre-processing</a></li>
 </ul>
 </li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#controlling-randomness">10.3. Controlling randomness</a><ul class="nav section-nav flex-column">
-<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#using-none-or-randomstate-instances-and-repeated-calls-to-fit-and-split">10.3.1. Using <code class="docutils literal notranslate"><span class="pre">None</span></code> or <code class="docutils literal notranslate"><span class="pre">RandomState</span></code> instances, and repeated calls to <code class="docutils literal notranslate"><span class="pre">fit</span></code> and <code class="docutils literal notranslate"><span class="pre">split</span></code></a><ul class="nav section-nav flex-column">
-<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#estimators">10.3.1.1. Estimators</a></li>
-<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#cv-splitters">10.3.1.2. CV splitters</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#controlling-randomness">11.3. Controlling randomness</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#using-none-or-randomstate-instances-and-repeated-calls-to-fit-and-split">11.3.1. Using <code class="docutils literal notranslate"><span class="pre">None</span></code> or <code class="docutils literal notranslate"><span class="pre">RandomState</span></code> instances, and repeated calls to <code class="docutils literal notranslate"><span class="pre">fit</span></code> and <code class="docutils literal notranslate"><span class="pre">split</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#estimators">11.3.1.1. Estimators</a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#cv-splitters">11.3.1.2. CV splitters</a></li>
 </ul>
 </li>
-<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#common-pitfalls-and-subtleties">10.3.2. Common pitfalls and subtleties</a><ul class="nav section-nav flex-column">
-<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#id2">10.3.2.1. Estimators</a></li>
-<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#id3">10.3.2.2. CV splitters</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#common-pitfalls-and-subtleties">11.3.2. Common pitfalls and subtleties</a><ul class="nav section-nav flex-column">
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#id2">11.3.2.1. Estimators</a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#id3">11.3.2.2. CV splitters</a></li>
 </ul>
 </li>
-<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#general-recommendations">10.3.3. General recommendations</a><ul class="nav section-nav flex-column">
-<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#getting-reproducible-results-across-multiple-executions">10.3.3.1. Getting reproducible results across multiple executions</a></li>
-<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#robustness-of-cross-validation-results">10.3.3.2. Robustness of cross-validation results</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#general-recommendations">11.3.3. General recommendations</a><ul class="nav section-nav flex-column">
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#getting-reproducible-results-across-multiple-executions">11.3.3.1. Getting reproducible results across multiple executions</a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#robustness-of-cross-validation-results">11.3.3.2. Robustness of cross-validation results</a></li>
 </ul>
 </li>
 </ul>
diff --git a/dev/communication_team.html b/dev/communication_team.html
index e6246cc5f90ab..a48d7b0005229 100644
--- a/dev/communication_team.html
+++ b/dev/communication_team.html
@@ -40,7 +40,7 @@
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     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
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diff --git a/dev/communication_team_emeritus.html b/dev/communication_team_emeritus.html
index f2619cb647d92..483ee95c4db4c 100644
--- a/dev/communication_team_emeritus.html
+++ b/dev/communication_team_emeritus.html
@@ -42,7 +42,7 @@
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-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
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diff --git a/dev/computing.html b/dev/computing.html
index c386dc5ae9997..7cd7db5c0fb18 100644
--- a/dev/computing.html
+++ b/dev/computing.html
@@ -7,7 +7,7 @@
   <head>
     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="viewport" content="width=device-width, initial-scale=1" />
-<meta property="og:title" content="8. Computing with scikit-learn" />
+<meta property="og:title" content="9. Computing with scikit-learn" />
 <meta property="og:type" content="website" />
 <meta property="og:url" content="https://scikit-learn/stable/computing.html" />
 <meta property="og:site_name" content="scikit-learn" />
@@ -16,7 +16,7 @@
 <meta property="og:image:alt" content="scikit-learn" />
 <meta name="description" content="Strategies to scale computationally: bigger data- Scaling with instances using out-of-core learning., Computational Performance- Prediction Latency, Prediction Throughput, Tips and Tricks., Paralle..." />
 
-    <title>8. Computing with scikit-learn &#8212; scikit-learn 1.7.dev0 documentation</title>
+    <title>9. Computing with scikit-learn &#8212; scikit-learn 1.7.dev0 documentation</title>
   
   
   
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
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-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
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@@ -81,8 +81,8 @@
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-    <link rel="prev" title="7.4. Loading other datasets" href="http://github.com/datasets/loading_other_datasets.html" />
+    <link rel="next" title="9.1. Strategies to scale computationally: bigger data" href="http://github.com/computing/scaling_strategies.html" />
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@@ -594,45 +594,46 @@
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-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/inspection.html">4. Inspection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/partial_dependence.html">4.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/permutation_importance.html">4.2. Permutation feature importance</a></li>
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+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/inspection.html">5. Inspection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/partial_dependence.html">5.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/permutation_importance.html">5.2. Permutation feature importance</a></li>
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-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/data_transforms.html">6. Dataset transformations</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/compose.html">6.1. Pipelines and composite estimators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/feature_extraction.html">6.2. Feature extraction</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="http://github.com/visualizations.html">6. Visualizations</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/data_transforms.html">7. Dataset transformations</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/compose.html">7.1. Pipelines and composite estimators</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/feature_extraction.html">7.2. Feature extraction</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/preprocessing_targets.html">7.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html">7.4. Loading other datasets</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/datasets.html">8. Dataset loading utilities</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html">8.1. Toy datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html">8.2. Real world datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html">8.3. Generated datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html">8.4. Loading other datasets</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 current active has-children"><a class="current reference internal" href="#">8. Computing with scikit-learn</a><details open="open"><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/scaling_strategies.html">8.1. Strategies to scale computationally: bigger data</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/computational_performance.html">8.2. Computational Performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/parallelism.html">8.3. Parallelism, resource management, and configuration</a></li>
+<li class="toctree-l1 current active has-children"><a class="current reference internal" href="#">9. Computing with scikit-learn</a><details open="open"><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/scaling_strategies.html">9.1. Strategies to scale computationally: bigger data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/computational_performance.html">9.2. Computational Performance</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/parallelism.html">9.3. Parallelism, resource management, and configuration</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/array_api.html">11.1. Array API support (experimental)</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="http://github.com/common_pitfalls.html">11. Common pitfalls and recommended practices</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/dispatching.html">12. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
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-<li class="toctree-l1"><a class="reference internal" href="http://github.com/machine_learning_map.html">12. Choosing the right estimator</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="http://github.com/presentations.html">14. External Resources, Videos and Talks</a></li>
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@@ -669,7 +670,7 @@
     
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-    <li class="breadcrumb-item active" aria-current="page"><span class="ellipsis"><span class="section-number">8. </span>Computing with scikit-learn</span></li>
+    <li class="breadcrumb-item active" aria-current="page"><span class="ellipsis"><span class="section-number">9. </span>Computing with scikit-learn</span></li>
   </ul>
 </nav>
 </div>
@@ -687,22 +688,22 @@
                 <article class="bd-article">
                   
   <section id="computing-with-scikit-learn">
-<h1><span class="section-number">8. </span>Computing with scikit-learn<a class="headerlink" href="#computing-with-scikit-learn" title="Link to this heading">#</a></h1>
+<h1><span class="section-number">9. </span>Computing with scikit-learn<a class="headerlink" href="#computing-with-scikit-learn" title="Link to this heading">#</a></h1>
 <div class="toctree-wrapper compound">
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/computing/scaling_strategies.html">8.1. Strategies to scale computationally: bigger data</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/scaling_strategies.html#scaling-with-instances-using-out-of-core-learning">8.1.1. Scaling with instances using out-of-core learning</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/computing/scaling_strategies.html">9.1. Strategies to scale computationally: bigger data</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/scaling_strategies.html#scaling-with-instances-using-out-of-core-learning">9.1.1. Scaling with instances using out-of-core learning</a></li>
 </ul>
 </li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/computing/computational_performance.html">8.2. Computational Performance</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/computational_performance.html#prediction-latency">8.2.1. Prediction Latency</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/computational_performance.html#prediction-throughput">8.2.2. Prediction Throughput</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/computational_performance.html#tips-and-tricks">8.2.3. Tips and Tricks</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/computing/computational_performance.html">9.2. Computational Performance</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/computational_performance.html#prediction-latency">9.2.1. Prediction Latency</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/computational_performance.html#prediction-throughput">9.2.2. Prediction Throughput</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/computational_performance.html#tips-and-tricks">9.2.3. Tips and Tricks</a></li>
 </ul>
 </li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/computing/parallelism.html">8.3. Parallelism, resource management, and configuration</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/parallelism.html#parallelism">8.3.1. Parallelism</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/parallelism.html#configuration-switches">8.3.2. Configuration switches</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/computing/parallelism.html">9.3. Parallelism, resource management, and configuration</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/parallelism.html#parallelism">9.3.1. Parallelism</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/parallelism.html#configuration-switches">9.3.2. Configuration switches</a></li>
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 </li>
 </ul>
@@ -725,7 +726,7 @@ <h1><span class="section-number">8. </span>Computing with scikit-learn<a class="
       <i class="fa-solid fa-angle-left"></i>
       <div class="prev-next-info">
         <p class="prev-next-subtitle">previous</p>
-        <p class="prev-next-title"><span class="section-number">7.4. </span>Loading other datasets</p>
+        <p class="prev-next-title"><span class="section-number">8.4. </span>Loading other datasets</p>
       </div>
     </a>
     <a class="right-next"
@@ -733,7 +734,7 @@ <h1><span class="section-number">8. </span>Computing with scikit-learn<a class="
        title="next page">
       <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">8.1. </span>Strategies to scale computationally: bigger data</p>
+        <p class="prev-next-title"><span class="section-number">9.1. </span>Strategies to scale computationally: bigger data</p>
       </div>
       <i class="fa-solid fa-angle-right"></i>
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diff --git a/dev/computing/computational_performance.html b/dev/computing/computational_performance.html
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-<meta property="og:title" content="8.2. Computational Performance" />
+<meta property="og:title" content="9.2. Computational Performance" />
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@@ -16,7 +16,7 @@
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 <meta name="description" content="For some applications the performance (mainly latency and throughput at prediction time) of estimators is crucial. It may also be of interest to consider the training throughput but this is often l..." />
 
-    <title>8.2. Computational Performance &#8212; scikit-learn 1.7.dev0 documentation</title>
+    <title>9.2. Computational Performance &#8212; scikit-learn 1.7.dev0 documentation</title>
   
   
   
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-    <link rel="next" title="8.3. Parallelism, resource management, and configuration" href="http://github.com/parallelism.html" />
-    <link rel="prev" title="8.1. Strategies to scale computationally: bigger data" href="http://github.com/scaling_strategies.html" />
+    <link rel="next" title="9.3. Parallelism, resource management, and configuration" href="http://github.com/parallelism.html" />
+    <link rel="prev" title="9.1. Strategies to scale computationally: bigger data" href="http://github.com/scaling_strategies.html" />
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@@ -594,45 +594,46 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/learning_curve.html">3.5. Validation curves: plotting scores to evaluate models</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/inspection.html">4. Inspection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/partial_dependence.html">4.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/permutation_importance.html">4.2. Permutation feature importance</a></li>
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+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/inspection.html">5. Inspection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/partial_dependence.html">5.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/permutation_importance.html">5.2. Permutation feature importance</a></li>
 </ul>
 </details></li>
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-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/data_transforms.html">6. Dataset transformations</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/compose.html">6.1. Pipelines and composite estimators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/feature_extraction.html">6.2. Feature extraction</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/preprocessing_targets.html">6.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/visualizations.html">6. Visualizations</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/data_transforms.html">7. Dataset transformations</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/compose.html">7.1. Pipelines and composite estimators</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/feature_extraction.html">7.2. Feature extraction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/preprocessing.html">7.3. Preprocessing data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/impute.html">7.4. Imputation of missing values</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/unsupervised_reduction.html">7.5. Unsupervised dimensionality reduction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/random_projection.html">7.6. Random Projection</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/kernel_approximation.html">7.7. Kernel Approximation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/metrics.html">7.8. Pairwise metrics, Affinities and Kernels</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/preprocessing_targets.html">7.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/datasets.html">7. Dataset loading utilities</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html">7.1. Toy datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html">7.2. Real world datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html">7.3. Generated datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html">7.4. Loading other datasets</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/datasets.html">8. Dataset loading utilities</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html">8.1. Toy datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html">8.2. Real world datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html">8.3. Generated datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html">8.4. Loading other datasets</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 current active has-children"><a class="reference internal" href="http://github.com/computing.html">8. Computing with scikit-learn</a><details open="open"><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul class="current">
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/scaling_strategies.html">8.1. Strategies to scale computationally: bigger data</a></li>
-<li class="toctree-l2 current active"><a class="current reference internal" href="#">8.2. Computational Performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/parallelism.html">8.3. Parallelism, resource management, and configuration</a></li>
+<li class="toctree-l1 current active has-children"><a class="reference internal" href="http://github.com/computing.html">9. Computing with scikit-learn</a><details open="open"><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/scaling_strategies.html">9.1. Strategies to scale computationally: bigger data</a></li>
+<li class="toctree-l2 current active"><a class="current reference internal" href="#">9.2. Computational Performance</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/parallelism.html">9.3. Parallelism, resource management, and configuration</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/model_persistence.html">9. Model persistence</a></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/common_pitfalls.html">10. Common pitfalls and recommended practices</a></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/dispatching.html">11. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/array_api.html">11.1. Array API support (experimental)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/model_persistence.html">10. Model persistence</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/common_pitfalls.html">11. Common pitfalls and recommended practices</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/dispatching.html">12. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/array_api.html">12.1. Array API support (experimental)</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/machine_learning_map.html">12. Choosing the right estimator</a></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/presentations.html">13. External Resources, Videos and Talks</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/machine_learning_map.html">13. Choosing the right estimator</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/presentations.html">14. External Resources, Videos and Talks</a></li>
 </ul>
 </div>
 </nav></div>
@@ -670,9 +671,9 @@
     <li class="breadcrumb-item"><a href="http://github.com/user_guide.html" class="nav-link">User Guide</a></li>
     
     
-    <li class="breadcrumb-item"><a href="http://github.com/computing.html" class="nav-link"><span class="section-number">8. </span>Computing with scikit-learn</a></li>
+    <li class="breadcrumb-item"><a href="http://github.com/computing.html" class="nav-link"><span class="section-number">9. </span>Computing with scikit-learn</a></li>
     
-    <li class="breadcrumb-item active" aria-current="page"><span class="ellipsis"><span class="section-number">8.2. </span>Computational Performance</span></li>
+    <li class="breadcrumb-item active" aria-current="page"><span class="ellipsis"><span class="section-number">9.2. </span>Computational Performance</span></li>
   </ul>
 </nav>
 </div>
@@ -690,7 +691,7 @@
                 <article class="bd-article">
                   
   <section id="computational-performance">
-<span id="id1"></span><h1><span class="section-number">8.2. </span>Computational Performance<a class="headerlink" href="#computational-performance" title="Link to this heading">#</a></h1>
+<span id="id1"></span><h1><span class="section-number">9.2. </span>Computational Performance<a class="headerlink" href="#computational-performance" title="Link to this heading">#</a></h1>
 <p>For some applications the performance (mainly latency and throughput at
 prediction time) of estimators is crucial. It may also be of interest to
 consider the training throughput but this is often less important in a
@@ -709,7 +710,7 @@
 non-linear, or with fewer parameters) often run faster but are not always able
 to take into account the same exact properties of the data as more complex ones.</p>
 <section id="prediction-latency">
-<h2><span class="section-number">8.2.1. </span>Prediction Latency<a class="headerlink" href="#prediction-latency" title="Link to this heading">#</a></h2>
+<h2><span class="section-number">9.2.1. </span>Prediction Latency<a class="headerlink" href="#prediction-latency" title="Link to this heading">#</a></h2>
 <p>One of the most straightforward concerns one may have when using/choosing a
 machine learning toolkit is the latency at which predictions can be made in a
 production environment.</p>
@@ -723,7 +724,7 @@ <h2><span class="section-number">8.2.1. </span>Prediction Latency<a class="heade
 <p>A last major parameter is also the possibility to do predictions in bulk or
 one-at-a-time mode.</p>
 <section id="bulk-versus-atomic-mode">
-<h3><span class="section-number">8.2.1.1. </span>Bulk versus Atomic mode<a class="headerlink" href="#bulk-versus-atomic-mode" title="Link to this heading">#</a></h3>
+<h3><span class="section-number">9.2.1.1. </span>Bulk versus Atomic mode<a class="headerlink" href="#bulk-versus-atomic-mode" title="Link to this heading">#</a></h3>
 <p>In general doing predictions in bulk (many instances at the same time) is
 more efficient for a number of reasons (branching predictability, CPU cache,
 linear algebra libraries optimizations etc.). Here we see on a setting
@@ -737,7 +738,7 @@ <h3><span class="section-number">8.2.1.1. </span>Bulk versus Atomic mode<a class
 you an estimate of the order of magnitude of the prediction latency.</p>
 </section>
 <section id="configuring-scikit-learn-for-reduced-validation-overhead">
-<h3><span class="section-number">8.2.1.2. </span>Configuring Scikit-learn for reduced validation overhead<a class="headerlink" href="#configuring-scikit-learn-for-reduced-validation-overhead" title="Link to this heading">#</a></h3>
+<h3><span class="section-number">9.2.1.2. </span>Configuring Scikit-learn for reduced validation overhead<a class="headerlink" href="#configuring-scikit-learn-for-reduced-validation-overhead" title="Link to this heading">#</a></h3>
 <p>Scikit-learn does some validation on data that increases the overhead per
 call to <code class="docutils literal notranslate"><span class="pre">predict</span></code> and similar functions. In particular, checking that
 features are finite (not NaN or infinite) involves a full pass over the
@@ -756,7 +757,7 @@ <h3><span class="section-number">8.2.1.2. </span>Configuring Scikit-learn for re
 <a class="reference internal" href="http://github.com/modules/generated/sklearn.utils.assert_all_finite.html#sklearn.utils.assert_all_finite" title="sklearn.utils.assert_all_finite"><code class="xref py py-func docutils literal notranslate"><span class="pre">assert_all_finite</span></code></a> within the context.</p>
 </section>
 <section id="influence-of-the-number-of-features">
-<h3><span class="section-number">8.2.1.3. </span>Influence of the Number of Features<a class="headerlink" href="#influence-of-the-number-of-features" title="Link to this heading">#</a></h3>
+<h3><span class="section-number">9.2.1.3. </span>Influence of the Number of Features<a class="headerlink" href="#influence-of-the-number-of-features" title="Link to this heading">#</a></h3>
 <p>Obviously when the number of features increases so does the memory
 consumption of each example. Indeed, for a matrix of <span class="math notranslate nohighlight">\(M\)</span> instances
 with <span class="math notranslate nohighlight">\(N\)</span> features, the space complexity is in <span class="math notranslate nohighlight">\(O(NM)\)</span>.
@@ -770,7 +771,7 @@ <h3><span class="section-number">8.2.1.3. </span>Influence of the Number of Feat
 memory footprint and estimator).</p>
 </section>
 <section id="influence-of-the-input-data-representation">
-<h3><span class="section-number">8.2.1.4. </span>Influence of the Input Data Representation<a class="headerlink" href="#influence-of-the-input-data-representation" title="Link to this heading">#</a></h3>
+<h3><span class="section-number">9.2.1.4. </span>Influence of the Input Data Representation<a class="headerlink" href="#influence-of-the-input-data-representation" title="Link to this heading">#</a></h3>
 <p>Scipy provides sparse matrix data structures which are optimized for storing
 sparse data. The main feature of sparse formats is that you don’t store zeros
 so if your data is sparse then you use much less memory. A non-zero value in
@@ -800,7 +801,7 @@ <h3><span class="section-number">8.2.1.4. </span>Influence of the Input Data Rep
 formats. Most of the time the <code class="docutils literal notranslate"><span class="pre">CSR</span></code> and <code class="docutils literal notranslate"><span class="pre">CSC</span></code> formats work best.</p>
 </section>
 <section id="influence-of-the-model-complexity">
-<h3><span class="section-number">8.2.1.5. </span>Influence of the Model Complexity<a class="headerlink" href="#influence-of-the-model-complexity" title="Link to this heading">#</a></h3>
+<h3><span class="section-number">9.2.1.5. </span>Influence of the Model Complexity<a class="headerlink" href="#influence-of-the-model-complexity" title="Link to this heading">#</a></h3>
 <p>Generally speaking, when model complexity increases, predictive power and
 latency are supposed to increase. Increasing predictive power is usually
 interesting, but for many applications we would better not increase
@@ -843,7 +844,7 @@ <h3><span class="section-number">8.2.1.5. </span>Influence of the Model Complexi
 the process.</p>
 </section>
 <section id="feature-extraction-latency">
-<h3><span class="section-number">8.2.1.6. </span>Feature Extraction Latency<a class="headerlink" href="#feature-extraction-latency" title="Link to this heading">#</a></h3>
+<h3><span class="section-number">9.2.1.6. </span>Feature Extraction Latency<a class="headerlink" href="#feature-extraction-latency" title="Link to this heading">#</a></h3>
 <p>Most scikit-learn models are usually pretty fast as they are implemented
 either with compiled Cython extensions or optimized computing libraries.
 On the other hand, in many real world applications the feature extraction
@@ -860,7 +861,7 @@ <h3><span class="section-number">8.2.1.6. </span>Feature Extraction Latency<a cl
 </section>
 </section>
 <section id="prediction-throughput">
-<h2><span class="section-number">8.2.2. </span>Prediction Throughput<a class="headerlink" href="#prediction-throughput" title="Link to this heading">#</a></h2>
+<h2><span class="section-number">9.2.2. </span>Prediction Throughput<a class="headerlink" href="#prediction-throughput" title="Link to this heading">#</a></h2>
 <p>Another important metric to care about when sizing production systems is the
 throughput i.e. the number of predictions you can make in a given amount of
 time. Here is a benchmark from the
@@ -876,9 +877,9 @@ <h2><span class="section-number">8.2.2. </span>Prediction Throughput<a class="he
 though.</p>
 </section>
 <section id="tips-and-tricks">
-<h2><span class="section-number">8.2.3. </span>Tips and Tricks<a class="headerlink" href="#tips-and-tricks" title="Link to this heading">#</a></h2>
+<h2><span class="section-number">9.2.3. </span>Tips and Tricks<a class="headerlink" href="#tips-and-tricks" title="Link to this heading">#</a></h2>
 <section id="linear-algebra-libraries">
-<h3><span class="section-number">8.2.3.1. </span>Linear algebra libraries<a class="headerlink" href="#linear-algebra-libraries" title="Link to this heading">#</a></h3>
+<h3><span class="section-number">9.2.3.1. </span>Linear algebra libraries<a class="headerlink" href="#linear-algebra-libraries" title="Link to this heading">#</a></h3>
 <p>As scikit-learn relies heavily on Numpy/Scipy and linear algebra in general it
 makes sense to take explicit care of the versions of these libraries.
 Basically, you ought to make sure that Numpy is built using an optimized <a class="reference external" href="https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms">BLAS</a> /
@@ -909,7 +910,7 @@ <h3><span class="section-number">8.2.3.1. </span>Linear algebra libraries<a clas
 Debian / Ubuntu.</p>
 </section>
 <section id="limiting-working-memory">
-<span id="working-memory"></span><h3><span class="section-number">8.2.3.2. </span>Limiting Working Memory<a class="headerlink" href="#limiting-working-memory" title="Link to this heading">#</a></h3>
+<span id="working-memory"></span><h3><span class="section-number">9.2.3.2. </span>Limiting Working Memory<a class="headerlink" href="#limiting-working-memory" title="Link to this heading">#</a></h3>
 <p>Some calculations when implemented using standard numpy vectorized operations
 involve using a large amount of temporary memory.  This may potentially exhaust
 system memory.  Where computations can be performed in fixed-memory chunks, we
@@ -927,7 +928,7 @@ <h3><span class="section-number">8.2.3.1. </span>Linear algebra libraries<a clas
 row-wise reductions of a pairwise distance matrix.</p>
 </section>
 <section id="model-compression">
-<h3><span class="section-number">8.2.3.3. </span>Model Compression<a class="headerlink" href="#model-compression" title="Link to this heading">#</a></h3>
+<h3><span class="section-number">9.2.3.3. </span>Model Compression<a class="headerlink" href="#model-compression" title="Link to this heading">#</a></h3>
 <p>Model compression in scikit-learn only concerns linear models for the moment.
 In this context it means that we want to control the model sparsity (i.e. the
 number of non-zero coordinates in the model vectors). It is generally a good
@@ -951,7 +952,7 @@ <h3><span class="section-number">8.2.3.3. </span>Model Compression<a class="head
 predictive models deployed on production servers.</p>
 </section>
 <section id="model-reshaping">
-<h3><span class="section-number">8.2.3.4. </span>Model Reshaping<a class="headerlink" href="#model-reshaping" title="Link to this heading">#</a></h3>
+<h3><span class="section-number">9.2.3.4. </span>Model Reshaping<a class="headerlink" href="#model-reshaping" title="Link to this heading">#</a></h3>
 <p>Model reshaping consists in selecting only a portion of the available features
 to fit a model. In other words, if a model discards features during the
 learning phase we can then strip those from the input. This has several
@@ -969,7 +970,7 @@ <h3><span class="section-number">8.2.3.4. </span>Model Reshaping<a class="header
 sufficient to not generate the relevant features, leaving their columns empty.</p>
 </section>
 <section id="links">
-<h3><span class="section-number">8.2.3.5. </span>Links<a class="headerlink" href="#links" title="Link to this heading">#</a></h3>
+<h3><span class="section-number">9.2.3.5. </span>Links<a class="headerlink" href="#links" title="Link to this heading">#</a></h3>
 <ul class="simple">
 <li><p><a class="reference internal" href="http://github.com/developers/performance.html#performance-howto"><span class="std std-ref">scikit-learn developer performance documentation</span></a></p></li>
 <li><p><a class="reference external" href="https://docs.scipy.org/doc/scipy/reference/sparse.html">Scipy sparse matrix formats documentation</a></p></li>
@@ -994,7 +995,7 @@ <h3><span class="section-number">8.2.3.5. </span>Links<a class="headerlink" href
       <i class="fa-solid fa-angle-left"></i>
       <div class="prev-next-info">
         <p class="prev-next-subtitle">previous</p>
-        <p class="prev-next-title"><span class="section-number">8.1. </span>Strategies to scale computationally: bigger data</p>
+        <p class="prev-next-title"><span class="section-number">9.1. </span>Strategies to scale computationally: bigger data</p>
       </div>
     </a>
     <a class="right-next"
@@ -1002,7 +1003,7 @@ <h3><span class="section-number">8.2.3.5. </span>Links<a class="headerlink" href
        title="next page">
       <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">8.3. </span>Parallelism, resource management, and configuration</p>
+        <p class="prev-next-title"><span class="section-number">9.3. </span>Parallelism, resource management, and configuration</p>
       </div>
       <i class="fa-solid fa-angle-right"></i>
     </a>
@@ -1029,22 +1030,22 @@ <h3><span class="section-number">8.2.3.5. </span>Links<a class="headerlink" href
   </div>
   <nav class="bd-toc-nav page-toc" aria-labelledby="pst-page-navigation-heading-2">
     <ul class="visible nav section-nav flex-column">
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#prediction-latency">8.2.1. Prediction Latency</a><ul class="nav section-nav flex-column">
-<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#bulk-versus-atomic-mode">8.2.1.1. Bulk versus Atomic mode</a></li>
-<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#configuring-scikit-learn-for-reduced-validation-overhead">8.2.1.2. Configuring Scikit-learn for reduced validation overhead</a></li>
-<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#influence-of-the-number-of-features">8.2.1.3. Influence of the Number of Features</a></li>
-<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#influence-of-the-input-data-representation">8.2.1.4. Influence of the Input Data Representation</a></li>
-<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#influence-of-the-model-complexity">8.2.1.5. Influence of the Model Complexity</a></li>
-<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#feature-extraction-latency">8.2.1.6. Feature Extraction Latency</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#prediction-latency">9.2.1. Prediction Latency</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#bulk-versus-atomic-mode">9.2.1.1. Bulk versus Atomic mode</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#configuring-scikit-learn-for-reduced-validation-overhead">9.2.1.2. Configuring Scikit-learn for reduced validation overhead</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#influence-of-the-number-of-features">9.2.1.3. Influence of the Number of Features</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#influence-of-the-input-data-representation">9.2.1.4. Influence of the Input Data Representation</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#influence-of-the-model-complexity">9.2.1.5. Influence of the Model Complexity</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#feature-extraction-latency">9.2.1.6. Feature Extraction Latency</a></li>
 </ul>
 </li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#prediction-throughput">8.2.2. Prediction Throughput</a></li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#tips-and-tricks">8.2.3. Tips and Tricks</a><ul class="nav section-nav flex-column">
-<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#linear-algebra-libraries">8.2.3.1. Linear algebra libraries</a></li>
-<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#limiting-working-memory">8.2.3.2. Limiting Working Memory</a></li>
-<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#model-compression">8.2.3.3. Model Compression</a></li>
-<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#model-reshaping">8.2.3.4. Model Reshaping</a></li>
-<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#links">8.2.3.5. Links</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#prediction-throughput">9.2.2. Prediction Throughput</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#tips-and-tricks">9.2.3. Tips and Tricks</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#linear-algebra-libraries">9.2.3.1. Linear algebra libraries</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#limiting-working-memory">9.2.3.2. Limiting Working Memory</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#model-compression">9.2.3.3. Model Compression</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#model-reshaping">9.2.3.4. Model Reshaping</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#links">9.2.3.5. Links</a></li>
 </ul>
 </li>
 </ul>
diff --git a/dev/computing/parallelism.html b/dev/computing/parallelism.html
index 537db01305b19..d3cbbcb39c27a 100644
--- a/dev/computing/parallelism.html
+++ b/dev/computing/parallelism.html
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     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="viewport" content="width=device-width, initial-scale=1" />
-<meta property="og:title" content="8.3. Parallelism, resource management, and configuration" />
+<meta property="og:title" content="9.3. Parallelism, resource management, and configuration" />
 <meta property="og:type" content="website" />
 <meta property="og:url" content="https://scikit-learn/stable/computing/parallelism.html" />
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@@ -16,7 +16,7 @@
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 <meta name="description" content="Parallelism: Some scikit-learn estimators and utilities parallelize costly operations using multiple CPU cores. Depending on the type of estimator and sometimes the values of the constructor parame..." />
 
-    <title>8.3. Parallelism, resource management, and configuration &#8212; scikit-learn 1.7.dev0 documentation</title>
+    <title>9.3. Parallelism, resource management, and configuration &#8212; scikit-learn 1.7.dev0 documentation</title>
   
   
   
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
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-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
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     <link rel="author" title="About these documents" href="http://github.com/about.html" />
     <link rel="search" title="Search" href="http://github.com/search.html" />
-    <link rel="next" title="9. Model persistence" href="http://github.com/model_persistence.html" />
-    <link rel="prev" title="8.2. Computational Performance" href="http://github.com/computational_performance.html" />
+    <link rel="next" title="10. Model persistence" href="http://github.com/model_persistence.html" />
+    <link rel="prev" title="9.2. Computational Performance" href="http://github.com/computational_performance.html" />
   <meta name="viewport" content="width=device-width, initial-scale=1"/>
   <meta name="docsearch:language" content="en"/>
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@@ -593,45 +593,46 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/learning_curve.html">3.5. Validation curves: plotting scores to evaluate models</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/inspection.html">4. Inspection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/partial_dependence.html">4.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/permutation_importance.html">4.2. Permutation feature importance</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/metadata_routing.html">4. Metadata Routing</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/inspection.html">5. Inspection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/partial_dependence.html">5.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/permutation_importance.html">5.2. Permutation feature importance</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/visualizations.html">5. Visualizations</a></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/data_transforms.html">6. Dataset transformations</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/compose.html">6.1. Pipelines and composite estimators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/feature_extraction.html">6.2. Feature extraction</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/preprocessing.html">6.3. Preprocessing data</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/impute.html">6.4. Imputation of missing values</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/unsupervised_reduction.html">6.5. Unsupervised dimensionality reduction</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/random_projection.html">6.6. Random Projection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/kernel_approximation.html">6.7. Kernel Approximation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/metrics.html">6.8. Pairwise metrics, Affinities and Kernels</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/preprocessing_targets.html">6.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/visualizations.html">6. Visualizations</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/data_transforms.html">7. Dataset transformations</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/compose.html">7.1. Pipelines and composite estimators</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/feature_extraction.html">7.2. Feature extraction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/preprocessing.html">7.3. Preprocessing data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/impute.html">7.4. Imputation of missing values</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/unsupervised_reduction.html">7.5. Unsupervised dimensionality reduction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/random_projection.html">7.6. Random Projection</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/kernel_approximation.html">7.7. Kernel Approximation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/metrics.html">7.8. Pairwise metrics, Affinities and Kernels</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/preprocessing_targets.html">7.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/datasets.html">7. Dataset loading utilities</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html">7.1. Toy datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html">7.2. Real world datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html">7.3. Generated datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html">7.4. Loading other datasets</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/datasets.html">8. Dataset loading utilities</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html">8.1. Toy datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html">8.2. Real world datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html">8.3. Generated datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html">8.4. Loading other datasets</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 current active has-children"><a class="reference internal" href="http://github.com/computing.html">8. Computing with scikit-learn</a><details open="open"><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul class="current">
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/scaling_strategies.html">8.1. Strategies to scale computationally: bigger data</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computational_performance.html">8.2. Computational Performance</a></li>
-<li class="toctree-l2 current active"><a class="current reference internal" href="#">8.3. Parallelism, resource management, and configuration</a></li>
+<li class="toctree-l1 current active has-children"><a class="reference internal" href="http://github.com/computing.html">9. Computing with scikit-learn</a><details open="open"><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/scaling_strategies.html">9.1. Strategies to scale computationally: bigger data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computational_performance.html">9.2. Computational Performance</a></li>
+<li class="toctree-l2 current active"><a class="current reference internal" href="#">9.3. Parallelism, resource management, and configuration</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/model_persistence.html">9. Model persistence</a></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/common_pitfalls.html">10. Common pitfalls and recommended practices</a></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/dispatching.html">11. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/array_api.html">11.1. Array API support (experimental)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/model_persistence.html">10. Model persistence</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/common_pitfalls.html">11. Common pitfalls and recommended practices</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/dispatching.html">12. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/array_api.html">12.1. Array API support (experimental)</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/machine_learning_map.html">12. Choosing the right estimator</a></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/presentations.html">13. External Resources, Videos and Talks</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/machine_learning_map.html">13. Choosing the right estimator</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/presentations.html">14. External Resources, Videos and Talks</a></li>
 </ul>
 </div>
 </nav></div>
@@ -669,9 +670,9 @@
     <li class="breadcrumb-item"><a href="http://github.com/user_guide.html" class="nav-link">User Guide</a></li>
     
     
-    <li class="breadcrumb-item"><a href="http://github.com/computing.html" class="nav-link"><span class="section-number">8. </span>Computing with scikit-learn</a></li>
+    <li class="breadcrumb-item"><a href="http://github.com/computing.html" class="nav-link"><span class="section-number">9. </span>Computing with scikit-learn</a></li>
     
-    <li class="breadcrumb-item active" aria-current="page"><span class="ellipsis"><span class="section-number">8.3. </span>Parallelism, resource management, and configuration</span></li>
+    <li class="breadcrumb-item active" aria-current="page"><span class="ellipsis"><span class="section-number">9.3. </span>Parallelism, resource management, and configuration</span></li>
   </ul>
 </nav>
 </div>
@@ -689,9 +690,9 @@
                 <article class="bd-article">
                   
   <section id="parallelism-resource-management-and-configuration">
-<h1><span class="section-number">8.3. </span>Parallelism, resource management, and configuration<a class="headerlink" href="#parallelism-resource-management-and-configuration" title="Link to this heading">#</a></h1>
+<h1><span class="section-number">9.3. </span>Parallelism, resource management, and configuration<a class="headerlink" href="#parallelism-resource-management-and-configuration" title="Link to this heading">#</a></h1>
 <section id="parallelism">
-<span id="id1"></span><h2><span class="section-number">8.3.1. </span>Parallelism<a class="headerlink" href="#parallelism" title="Link to this heading">#</a></h2>
+<span id="id1"></span><h2><span class="section-number">9.3.1. </span>Parallelism<a class="headerlink" href="#parallelism" title="Link to this heading">#</a></h2>
 <p>Some scikit-learn estimators and utilities parallelize costly operations
 using multiple CPU cores.</p>
 <p>Depending on the type of estimator and sometimes the values of the
@@ -711,7 +712,7 @@ <h1><span class="section-number">8.3. </span>Parallelism, resource management, a
 points of their training and prediction methods.</p>
 <p>We describe these 3 types of parallelism in the following subsections in more details.</p>
 <section id="higher-level-parallelism-with-joblib">
-<h3><span class="section-number">8.3.1.1. </span>Higher-level parallelism with joblib<a class="headerlink" href="#higher-level-parallelism-with-joblib" title="Link to this heading">#</a></h3>
+<h3><span class="section-number">9.3.1.1. </span>Higher-level parallelism with joblib<a class="headerlink" href="#higher-level-parallelism-with-joblib" title="Link to this heading">#</a></h3>
 <p>When the underlying implementation uses joblib, the number of workers
 (threads or processes) that are spawned in parallel can be controlled via the
 <code class="docutils literal notranslate"><span class="pre">n_jobs</span></code> parameter.</p>
@@ -749,7 +750,7 @@ <h3><span class="section-number">8.3.1.1. </span>Higher-level parallelism with j
 estimators or functions in parallel (see <a class="reference internal" href="#oversubscription"><span class="std std-ref">oversubscription</span></a> below).</p>
 </section>
 <section id="lower-level-parallelism-with-openmp">
-<h3><span class="section-number">8.3.1.2. </span>Lower-level parallelism with OpenMP<a class="headerlink" href="#lower-level-parallelism-with-openmp" title="Link to this heading">#</a></h3>
+<h3><span class="section-number">9.3.1.2. </span>Lower-level parallelism with OpenMP<a class="headerlink" href="#lower-level-parallelism-with-openmp" title="Link to this heading">#</a></h3>
 <p>OpenMP is used to parallelize code written in Cython or C, relying on
 multi-threading exclusively. By default, the implementations using OpenMP
 will use as many threads as possible, i.e. as many threads as logical cores.</p>
@@ -767,7 +768,7 @@ <h3><span class="section-number">8.3.1.2. </span>Lower-level parallelism with Op
 </ul>
 </section>
 <section id="parallel-numpy-and-scipy-routines-from-numerical-libraries">
-<h3><span class="section-number">8.3.1.3. </span>Parallel NumPy and SciPy routines from numerical libraries<a class="headerlink" href="#parallel-numpy-and-scipy-routines-from-numerical-libraries" title="Link to this heading">#</a></h3>
+<h3><span class="section-number">9.3.1.3. </span>Parallel NumPy and SciPy routines from numerical libraries<a class="headerlink" href="#parallel-numpy-and-scipy-routines-from-numerical-libraries" title="Link to this heading">#</a></h3>
 <p>scikit-learn relies heavily on NumPy and SciPy, which internally call
 multi-threaded linear algebra routines (BLAS &amp; LAPACK) implemented in libraries
 such as MKL, OpenBLAS or BLIS.</p>
@@ -796,7 +797,7 @@ <h3><span class="section-number">8.3.1.3. </span>Parallel NumPy and SciPy routin
 </div>
 </section>
 <section id="oversubscription-spawning-too-many-threads">
-<span id="oversubscription"></span><h3><span class="section-number">8.3.1.4. </span>Oversubscription: spawning too many threads<a class="headerlink" href="#oversubscription-spawning-too-many-threads" title="Link to this heading">#</a></h3>
+<span id="oversubscription"></span><h3><span class="section-number">9.3.1.4. </span>Oversubscription: spawning too many threads<a class="headerlink" href="#oversubscription-spawning-too-many-threads" title="Link to this heading">#</a></h3>
 <p>It is generally recommended to avoid using significantly more processes or
 threads than the number of CPUs on a machine. Over-subscription happens when
 a program is running too many threads at the same time.</p>
@@ -845,27 +846,27 @@ <h3><span class="section-number">8.3.1.3. </span>Parallel NumPy and SciPy routin
 </section>
 </section>
 <section id="configuration-switches">
-<h2><span class="section-number">8.3.2. </span>Configuration switches<a class="headerlink" href="#configuration-switches" title="Link to this heading">#</a></h2>
+<h2><span class="section-number">9.3.2. </span>Configuration switches<a class="headerlink" href="#configuration-switches" title="Link to this heading">#</a></h2>
 <section id="python-api">
-<h3><span class="section-number">8.3.2.1. </span>Python API<a class="headerlink" href="#python-api" title="Link to this heading">#</a></h3>
+<h3><span class="section-number">9.3.2.1. </span>Python API<a class="headerlink" href="#python-api" title="Link to this heading">#</a></h3>
 <p><a class="reference internal" href="http://github.com/modules/generated/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a> and <a class="reference internal" href="http://github.com/modules/generated/sklearn.config_context.html#sklearn.config_context" title="sklearn.config_context"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.config_context</span></code></a> can be used to change
 parameters of the configuration which control aspect of parallelism.</p>
 </section>
 <section id="environment-variables">
-<span id="environment-variable"></span><h3><span class="section-number">8.3.2.2. </span>Environment variables<a class="headerlink" href="#environment-variables" title="Link to this heading">#</a></h3>
+<span id="environment-variable"></span><h3><span class="section-number">9.3.2.2. </span>Environment variables<a class="headerlink" href="#environment-variables" title="Link to this heading">#</a></h3>
 <p>These environment variables should be set before importing scikit-learn.</p>
 <section id="sklearn-assume-finite">
-<h4><span class="section-number">8.3.2.2.1. </span><code class="docutils literal notranslate"><span class="pre">SKLEARN_ASSUME_FINITE</span></code><a class="headerlink" href="#sklearn-assume-finite" title="Link to this heading">#</a></h4>
+<h4><span class="section-number">9.3.2.2.1. </span><code class="docutils literal notranslate"><span class="pre">SKLEARN_ASSUME_FINITE</span></code><a class="headerlink" href="#sklearn-assume-finite" title="Link to this heading">#</a></h4>
 <p>Sets the default value for the <code class="docutils literal notranslate"><span class="pre">assume_finite</span></code> argument of
 <a class="reference internal" href="http://github.com/modules/generated/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>.</p>
 </section>
 <section id="sklearn-working-memory">
-<h4><span class="section-number">8.3.2.2.2. </span><code class="docutils literal notranslate"><span class="pre">SKLEARN_WORKING_MEMORY</span></code><a class="headerlink" href="#sklearn-working-memory" title="Link to this heading">#</a></h4>
+<h4><span class="section-number">9.3.2.2.2. </span><code class="docutils literal notranslate"><span class="pre">SKLEARN_WORKING_MEMORY</span></code><a class="headerlink" href="#sklearn-working-memory" title="Link to this heading">#</a></h4>
 <p>Sets the default value for the <code class="docutils literal notranslate"><span class="pre">working_memory</span></code> argument of
 <a class="reference internal" href="http://github.com/modules/generated/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>.</p>
 </section>
 <section id="sklearn-seed">
-<h4><span class="section-number">8.3.2.2.3. </span><code class="docutils literal notranslate"><span class="pre">SKLEARN_SEED</span></code><a class="headerlink" href="#sklearn-seed" title="Link to this heading">#</a></h4>
+<h4><span class="section-number">9.3.2.2.3. </span><code class="docutils literal notranslate"><span class="pre">SKLEARN_SEED</span></code><a class="headerlink" href="#sklearn-seed" title="Link to this heading">#</a></h4>
 <p>Sets the seed of the global random generator when running the tests, for
 reproducibility.</p>
 <p>Note that scikit-learn tests are expected to run deterministically with
@@ -876,7 +877,7 @@ <h4><span class="section-number">8.3.2.2.3. </span><code class="docutils literal
 state of the aforementioned singletons.</p>
 </section>
 <section id="sklearn-tests-global-random-seed">
-<h4><span class="section-number">8.3.2.2.4. </span><code class="docutils literal notranslate"><span class="pre">SKLEARN_TESTS_GLOBAL_RANDOM_SEED</span></code><a class="headerlink" href="#sklearn-tests-global-random-seed" title="Link to this heading">#</a></h4>
+<h4><span class="section-number">9.3.2.2.4. </span><code class="docutils literal notranslate"><span class="pre">SKLEARN_TESTS_GLOBAL_RANDOM_SEED</span></code><a class="headerlink" href="#sklearn-tests-global-random-seed" title="Link to this heading">#</a></h4>
 <p>Controls the seeding of the random number generator used in tests that rely on
 the <code class="docutils literal notranslate"><span class="pre">global_random_seed</span></code> fixture.</p>
 <p>All tests that use this fixture accept the contract that they should
@@ -913,32 +914,32 @@ <h4><span class="section-number">8.3.2.2.4. </span><code class="docutils literal
 <div class="highlight-default notranslate"><div class="highlight"><pre><span class="prompt1"><span class="nv">SKLEARN_TESTS_GLOBAL_RANDOM_SEED</span><span class="o">=</span><span class="s2">&quot;all&quot;</span><span class="w"> </span>pytest<span class="w"> </span>-v<span class="w"> </span>-k<span class="w"> </span>test_your_test_name</span>
 </pre></div></div></section>
 <section id="sklearn-skip-network-tests">
-<h4><span class="section-number">8.3.2.2.5. </span><code class="docutils literal notranslate"><span class="pre">SKLEARN_SKIP_NETWORK_TESTS</span></code><a class="headerlink" href="#sklearn-skip-network-tests" title="Link to this heading">#</a></h4>
+<h4><span class="section-number">9.3.2.2.5. </span><code class="docutils literal notranslate"><span class="pre">SKLEARN_SKIP_NETWORK_TESTS</span></code><a class="headerlink" href="#sklearn-skip-network-tests" title="Link to this heading">#</a></h4>
 <p>When this environment variable is set to a non zero value, the tests that need
 network access are skipped. When this environment variable is not set then
 network tests are skipped.</p>
 </section>
 <section id="sklearn-run-float32-tests">
-<h4><span class="section-number">8.3.2.2.6. </span><code class="docutils literal notranslate"><span class="pre">SKLEARN_RUN_FLOAT32_TESTS</span></code><a class="headerlink" href="#sklearn-run-float32-tests" title="Link to this heading">#</a></h4>
+<h4><span class="section-number">9.3.2.2.6. </span><code class="docutils literal notranslate"><span class="pre">SKLEARN_RUN_FLOAT32_TESTS</span></code><a class="headerlink" href="#sklearn-run-float32-tests" title="Link to this heading">#</a></h4>
 <p>When this environment variable is set to ‘1’, the tests using the
 <code class="docutils literal notranslate"><span class="pre">global_dtype</span></code> fixture are also run on float32 data.
 When this environment variable is not set, the tests are only run on
 float64 data.</p>
 </section>
 <section id="sklearn-enable-debug-cython-directives">
-<h4><span class="section-number">8.3.2.2.7. </span><code class="docutils literal notranslate"><span class="pre">SKLEARN_ENABLE_DEBUG_CYTHON_DIRECTIVES</span></code><a class="headerlink" href="#sklearn-enable-debug-cython-directives" title="Link to this heading">#</a></h4>
+<h4><span class="section-number">9.3.2.2.7. </span><code class="docutils literal notranslate"><span class="pre">SKLEARN_ENABLE_DEBUG_CYTHON_DIRECTIVES</span></code><a class="headerlink" href="#sklearn-enable-debug-cython-directives" title="Link to this heading">#</a></h4>
 <p>When this environment variable is set to a non zero value, the <code class="docutils literal notranslate"><span class="pre">Cython</span></code>
 derivative, <code class="docutils literal notranslate"><span class="pre">boundscheck</span></code> is set to <code class="docutils literal notranslate"><span class="pre">True</span></code>. This is useful for finding
 segfaults.</p>
 </section>
 <section id="sklearn-build-enable-debug-symbols">
-<h4><span class="section-number">8.3.2.2.8. </span><code class="docutils literal notranslate"><span class="pre">SKLEARN_BUILD_ENABLE_DEBUG_SYMBOLS</span></code><a class="headerlink" href="#sklearn-build-enable-debug-symbols" title="Link to this heading">#</a></h4>
+<h4><span class="section-number">9.3.2.2.8. </span><code class="docutils literal notranslate"><span class="pre">SKLEARN_BUILD_ENABLE_DEBUG_SYMBOLS</span></code><a class="headerlink" href="#sklearn-build-enable-debug-symbols" title="Link to this heading">#</a></h4>
 <p>When this environment variable is set to a non zero value, the debug symbols
 will be included in the compiled C extensions. Only debug symbols for POSIX
 systems are configured.</p>
 </section>
 <section id="sklearn-pairwise-dist-chunk-size">
-<h4><span class="section-number">8.3.2.2.9. </span><code class="docutils literal notranslate"><span class="pre">SKLEARN_PAIRWISE_DIST_CHUNK_SIZE</span></code><a class="headerlink" href="#sklearn-pairwise-dist-chunk-size" title="Link to this heading">#</a></h4>
+<h4><span class="section-number">9.3.2.2.9. </span><code class="docutils literal notranslate"><span class="pre">SKLEARN_PAIRWISE_DIST_CHUNK_SIZE</span></code><a class="headerlink" href="#sklearn-pairwise-dist-chunk-size" title="Link to this heading">#</a></h4>
 <p>This sets the size of chunk to be used by the underlying <code class="docutils literal notranslate"><span class="pre">PairwiseDistancesReductions</span></code>
 implementations. The default value is <code class="docutils literal notranslate"><span class="pre">256</span></code> which has been showed to be adequate on
 most machines.</p>
@@ -947,7 +948,7 @@ <h4><span class="section-number">8.3.2.2.9. </span><code class="docutils literal
 especially with respect to their caches’ sizes.</p>
 </section>
 <section id="sklearn-warnings-as-errors">
-<h4><span class="section-number">8.3.2.2.10. </span><code class="docutils literal notranslate"><span class="pre">SKLEARN_WARNINGS_AS_ERRORS</span></code><a class="headerlink" href="#sklearn-warnings-as-errors" title="Link to this heading">#</a></h4>
+<h4><span class="section-number">9.3.2.2.10. </span><code class="docutils literal notranslate"><span class="pre">SKLEARN_WARNINGS_AS_ERRORS</span></code><a class="headerlink" href="#sklearn-warnings-as-errors" title="Link to this heading">#</a></h4>
 <p>This environment variable is used to turn warnings into errors in tests and
 documentation build.</p>
 <p>Some CI (Continuous Integration) builds set <code class="docutils literal notranslate"><span class="pre">SKLEARN_WARNINGS_AS_ERRORS=1</span></code>, for
@@ -986,7 +987,7 @@ <h4><span class="section-number">8.3.2.2.10. </span><code class="docutils litera
       <i class="fa-solid fa-angle-left"></i>
       <div class="prev-next-info">
         <p class="prev-next-subtitle">previous</p>
-        <p class="prev-next-title"><span class="section-number">8.2. </span>Computational Performance</p>
+        <p class="prev-next-title"><span class="section-number">9.2. </span>Computational Performance</p>
       </div>
     </a>
     <a class="right-next"
@@ -994,7 +995,7 @@ <h4><span class="section-number">8.3.2.2.10. </span><code class="docutils litera
        title="next page">
       <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">9. </span>Model persistence</p>
+        <p class="prev-next-title"><span class="section-number">10. </span>Model persistence</p>
       </div>
       <i class="fa-solid fa-angle-right"></i>
     </a>
@@ -1021,26 +1022,26 @@ <h4><span class="section-number">8.3.2.2.10. </span><code class="docutils litera
   </div>
   <nav class="bd-toc-nav page-toc" aria-labelledby="pst-page-navigation-heading-2">
     <ul class="visible nav section-nav flex-column">
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#parallelism">8.3.1. Parallelism</a><ul class="nav section-nav flex-column">
-<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#higher-level-parallelism-with-joblib">8.3.1.1. Higher-level parallelism with joblib</a></li>
-<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#lower-level-parallelism-with-openmp">8.3.1.2. Lower-level parallelism with OpenMP</a></li>
-<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#parallel-numpy-and-scipy-routines-from-numerical-libraries">8.3.1.3. Parallel NumPy and SciPy routines from numerical libraries</a></li>
-<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#oversubscription-spawning-too-many-threads">8.3.1.4. Oversubscription: spawning too many threads</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#parallelism">9.3.1. Parallelism</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#higher-level-parallelism-with-joblib">9.3.1.1. Higher-level parallelism with joblib</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#lower-level-parallelism-with-openmp">9.3.1.2. Lower-level parallelism with OpenMP</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#parallel-numpy-and-scipy-routines-from-numerical-libraries">9.3.1.3. Parallel NumPy and SciPy routines from numerical libraries</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#oversubscription-spawning-too-many-threads">9.3.1.4. Oversubscription: spawning too many threads</a></li>
 </ul>
 </li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#configuration-switches">8.3.2. Configuration switches</a><ul class="nav section-nav flex-column">
-<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#python-api">8.3.2.1. Python API</a></li>
-<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#environment-variables">8.3.2.2. Environment variables</a><ul class="nav section-nav flex-column">
-<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn-assume-finite">8.3.2.2.1. <code class="docutils literal notranslate"><span class="pre">SKLEARN_ASSUME_FINITE</span></code></a></li>
-<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn-working-memory">8.3.2.2.2. <code class="docutils literal notranslate"><span class="pre">SKLEARN_WORKING_MEMORY</span></code></a></li>
-<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn-seed">8.3.2.2.3. <code class="docutils literal notranslate"><span class="pre">SKLEARN_SEED</span></code></a></li>
-<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn-tests-global-random-seed">8.3.2.2.4. <code class="docutils literal notranslate"><span class="pre">SKLEARN_TESTS_GLOBAL_RANDOM_SEED</span></code></a></li>
-<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn-skip-network-tests">8.3.2.2.5. <code class="docutils literal notranslate"><span class="pre">SKLEARN_SKIP_NETWORK_TESTS</span></code></a></li>
-<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn-run-float32-tests">8.3.2.2.6. <code class="docutils literal notranslate"><span class="pre">SKLEARN_RUN_FLOAT32_TESTS</span></code></a></li>
-<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn-enable-debug-cython-directives">8.3.2.2.7. <code class="docutils literal notranslate"><span class="pre">SKLEARN_ENABLE_DEBUG_CYTHON_DIRECTIVES</span></code></a></li>
-<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn-build-enable-debug-symbols">8.3.2.2.8. <code class="docutils literal notranslate"><span class="pre">SKLEARN_BUILD_ENABLE_DEBUG_SYMBOLS</span></code></a></li>
-<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn-pairwise-dist-chunk-size">8.3.2.2.9. <code class="docutils literal notranslate"><span class="pre">SKLEARN_PAIRWISE_DIST_CHUNK_SIZE</span></code></a></li>
-<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn-warnings-as-errors">8.3.2.2.10. <code class="docutils literal notranslate"><span class="pre">SKLEARN_WARNINGS_AS_ERRORS</span></code></a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#configuration-switches">9.3.2. Configuration switches</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#python-api">9.3.2.1. Python API</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#environment-variables">9.3.2.2. Environment variables</a><ul class="nav section-nav flex-column">
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn-assume-finite">9.3.2.2.1. <code class="docutils literal notranslate"><span class="pre">SKLEARN_ASSUME_FINITE</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn-working-memory">9.3.2.2.2. <code class="docutils literal notranslate"><span class="pre">SKLEARN_WORKING_MEMORY</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn-seed">9.3.2.2.3. <code class="docutils literal notranslate"><span class="pre">SKLEARN_SEED</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn-tests-global-random-seed">9.3.2.2.4. <code class="docutils literal notranslate"><span class="pre">SKLEARN_TESTS_GLOBAL_RANDOM_SEED</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn-skip-network-tests">9.3.2.2.5. <code class="docutils literal notranslate"><span class="pre">SKLEARN_SKIP_NETWORK_TESTS</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn-run-float32-tests">9.3.2.2.6. <code class="docutils literal notranslate"><span class="pre">SKLEARN_RUN_FLOAT32_TESTS</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn-enable-debug-cython-directives">9.3.2.2.7. <code class="docutils literal notranslate"><span class="pre">SKLEARN_ENABLE_DEBUG_CYTHON_DIRECTIVES</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn-build-enable-debug-symbols">9.3.2.2.8. <code class="docutils literal notranslate"><span class="pre">SKLEARN_BUILD_ENABLE_DEBUG_SYMBOLS</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn-pairwise-dist-chunk-size">9.3.2.2.9. <code class="docutils literal notranslate"><span class="pre">SKLEARN_PAIRWISE_DIST_CHUNK_SIZE</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn-warnings-as-errors">9.3.2.2.10. <code class="docutils literal notranslate"><span class="pre">SKLEARN_WARNINGS_AS_ERRORS</span></code></a></li>
 </ul>
 </li>
 </ul>
diff --git a/dev/computing/scaling_strategies.html b/dev/computing/scaling_strategies.html
index 4e3edb7af2a81..d071afae48c87 100644
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+++ b/dev/computing/scaling_strategies.html
@@ -7,7 +7,7 @@
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     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="viewport" content="width=device-width, initial-scale=1" />
-<meta property="og:title" content="8.1. Strategies to scale computationally: bigger data" />
+<meta property="og:title" content="9.1. Strategies to scale computationally: bigger data" />
 <meta property="og:type" content="website" />
 <meta property="og:url" content="https://scikit-learn/stable/computing/scaling_strategies.html" />
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@@ -16,7 +16,7 @@
 <meta property="og:image:alt" content="accuracy_over_time" />
 <meta name="description" content="For some applications the amount of examples, features (or both) and/or the speed at which they need to be processed are challenging for traditional approaches. In these cases scikit-learn has a nu..." />
 
-    <title>8.1. Strategies to scale computationally: bigger data &#8212; scikit-learn 1.7.dev0 documentation</title>
+    <title>9.1. Strategies to scale computationally: bigger data &#8212; scikit-learn 1.7.dev0 documentation</title>
   
   
   
@@ -42,7 +42,7 @@
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-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
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     <link rel="author" title="About these documents" href="http://github.com/about.html" />
     <link rel="search" title="Search" href="http://github.com/search.html" />
-    <link rel="next" title="8.2. Computational Performance" href="http://github.com/computational_performance.html" />
-    <link rel="prev" title="8. Computing with scikit-learn" href="http://github.com/computing.html" />
+    <link rel="next" title="9.2. Computational Performance" href="http://github.com/computational_performance.html" />
+    <link rel="prev" title="9. Computing with scikit-learn" href="http://github.com/computing.html" />
   <meta name="viewport" content="width=device-width, initial-scale=1"/>
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@@ -593,45 +593,46 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/learning_curve.html">3.5. Validation curves: plotting scores to evaluate models</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/inspection.html">4. Inspection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/partial_dependence.html">4.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/permutation_importance.html">4.2. Permutation feature importance</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/metadata_routing.html">4. Metadata Routing</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/inspection.html">5. Inspection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/partial_dependence.html">5.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/permutation_importance.html">5.2. Permutation feature importance</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/visualizations.html">5. Visualizations</a></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/data_transforms.html">6. Dataset transformations</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/compose.html">6.1. Pipelines and composite estimators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/feature_extraction.html">6.2. Feature extraction</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/preprocessing.html">6.3. Preprocessing data</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/impute.html">6.4. Imputation of missing values</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/unsupervised_reduction.html">6.5. Unsupervised dimensionality reduction</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/random_projection.html">6.6. Random Projection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/kernel_approximation.html">6.7. Kernel Approximation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/metrics.html">6.8. Pairwise metrics, Affinities and Kernels</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/preprocessing_targets.html">6.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/visualizations.html">6. Visualizations</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/data_transforms.html">7. Dataset transformations</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/compose.html">7.1. Pipelines and composite estimators</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/feature_extraction.html">7.2. Feature extraction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/preprocessing.html">7.3. Preprocessing data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/impute.html">7.4. Imputation of missing values</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/unsupervised_reduction.html">7.5. Unsupervised dimensionality reduction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/random_projection.html">7.6. Random Projection</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/kernel_approximation.html">7.7. Kernel Approximation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/metrics.html">7.8. Pairwise metrics, Affinities and Kernels</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/preprocessing_targets.html">7.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/datasets.html">7. Dataset loading utilities</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html">7.1. Toy datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html">7.2. Real world datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html">7.3. Generated datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html">7.4. Loading other datasets</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/datasets.html">8. Dataset loading utilities</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html">8.1. Toy datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html">8.2. Real world datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html">8.3. Generated datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html">8.4. Loading other datasets</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 current active has-children"><a class="reference internal" href="http://github.com/computing.html">8. Computing with scikit-learn</a><details open="open"><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul class="current">
-<li class="toctree-l2 current active"><a class="current reference internal" href="#">8.1. Strategies to scale computationally: bigger data</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computational_performance.html">8.2. Computational Performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/parallelism.html">8.3. Parallelism, resource management, and configuration</a></li>
+<li class="toctree-l1 current active has-children"><a class="reference internal" href="http://github.com/computing.html">9. Computing with scikit-learn</a><details open="open"><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul class="current">
+<li class="toctree-l2 current active"><a class="current reference internal" href="#">9.1. Strategies to scale computationally: bigger data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computational_performance.html">9.2. Computational Performance</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/parallelism.html">9.3. Parallelism, resource management, and configuration</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/model_persistence.html">9. Model persistence</a></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/common_pitfalls.html">10. Common pitfalls and recommended practices</a></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/dispatching.html">11. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/array_api.html">11.1. Array API support (experimental)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/model_persistence.html">10. Model persistence</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/common_pitfalls.html">11. Common pitfalls and recommended practices</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/dispatching.html">12. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/array_api.html">12.1. Array API support (experimental)</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/machine_learning_map.html">12. Choosing the right estimator</a></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/presentations.html">13. External Resources, Videos and Talks</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/machine_learning_map.html">13. Choosing the right estimator</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/presentations.html">14. External Resources, Videos and Talks</a></li>
 </ul>
 </div>
 </nav></div>
@@ -669,9 +670,9 @@
     <li class="breadcrumb-item"><a href="http://github.com/user_guide.html" class="nav-link">User Guide</a></li>
     
     
-    <li class="breadcrumb-item"><a href="http://github.com/computing.html" class="nav-link"><span class="section-number">8. </span>Computing with scikit-learn</a></li>
+    <li class="breadcrumb-item"><a href="http://github.com/computing.html" class="nav-link"><span class="section-number">9. </span>Computing with scikit-learn</a></li>
     
-    <li class="breadcrumb-item active" aria-current="page"><span class="ellipsis"><span class="section-number">8.1. </span>Strategies to scale computationally: bigger data</span></li>
+    <li class="breadcrumb-item active" aria-current="page"><span class="ellipsis"><span class="section-number">9.1. </span>Strategies to scale computationally: bigger data</span></li>
   </ul>
 </nav>
 </div>
@@ -689,13 +690,13 @@
                 <article class="bd-article">
                   
   <section id="strategies-to-scale-computationally-bigger-data">
-<span id="scaling-strategies"></span><h1><span class="section-number">8.1. </span>Strategies to scale computationally: bigger data<a class="headerlink" href="#strategies-to-scale-computationally-bigger-data" title="Link to this heading">#</a></h1>
+<span id="scaling-strategies"></span><h1><span class="section-number">9.1. </span>Strategies to scale computationally: bigger data<a class="headerlink" href="#strategies-to-scale-computationally-bigger-data" title="Link to this heading">#</a></h1>
 <p>For some applications the amount of examples, features (or both) and/or the
 speed at which they need to be processed are challenging for traditional
 approaches. In these cases scikit-learn has a number of options you can
 consider to make your system scale.</p>
 <section id="scaling-with-instances-using-out-of-core-learning">
-<h2><span class="section-number">8.1.1. </span>Scaling with instances using out-of-core learning<a class="headerlink" href="#scaling-with-instances-using-out-of-core-learning" title="Link to this heading">#</a></h2>
+<h2><span class="section-number">9.1.1. </span>Scaling with instances using out-of-core learning<a class="headerlink" href="#scaling-with-instances-using-out-of-core-learning" title="Link to this heading">#</a></h2>
 <p>Out-of-core (or “external memory”) learning is a technique used to learn from
 data that cannot fit in a computer’s main memory (RAM).</p>
 <p>Here is a sketch of a system designed to achieve this goal:</p>
@@ -705,13 +706,13 @@ <h2><span class="section-number">8.1.1. </span>Scaling with instances using out-
 <li><p>an incremental algorithm</p></li>
 </ol>
 <section id="streaming-instances">
-<h3><span class="section-number">8.1.1.1. </span>Streaming instances<a class="headerlink" href="#streaming-instances" title="Link to this heading">#</a></h3>
+<h3><span class="section-number">9.1.1.1. </span>Streaming instances<a class="headerlink" href="#streaming-instances" title="Link to this heading">#</a></h3>
 <p>Basically, 1. may be a reader that yields instances from files on a
 hard drive, a database, from a network stream etc. However,
 details on how to achieve this are beyond the scope of this documentation.</p>
 </section>
 <section id="extracting-features">
-<h3><span class="section-number">8.1.1.2. </span>Extracting features<a class="headerlink" href="#extracting-features" title="Link to this heading">#</a></h3>
+<h3><span class="section-number">9.1.1.2. </span>Extracting features<a class="headerlink" href="#extracting-features" title="Link to this heading">#</a></h3>
 <p>2. could be any relevant way to extract features among the
 different <a class="reference internal" href="http://github.com/modules/feature_extraction.html#feature-extraction"><span class="std std-ref">feature extraction</span></a> methods supported by
 scikit-learn. However, when working with data that needs vectorization and
@@ -727,7 +728,7 @@ <h3><span class="section-number">8.1.1.2. </span>Extracting features<a class="he
 <a class="reference internal" href="http://github.com/modules/generated/sklearn.feature_extraction.text.HashingVectorizer.html#sklearn.feature_extraction.text.HashingVectorizer" title="sklearn.feature_extraction.text.HashingVectorizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.feature_extraction.text.HashingVectorizer</span></code></a> for text documents.</p>
 </section>
 <section id="incremental-learning">
-<h3><span class="section-number">8.1.1.3. </span>Incremental learning<a class="headerlink" href="#incremental-learning" title="Link to this heading">#</a></h3>
+<h3><span class="section-number">9.1.1.3. </span>Incremental learning<a class="headerlink" href="#incremental-learning" title="Link to this heading">#</a></h3>
 <p>Finally, for 3. we have a number of options inside scikit-learn. Although not
 all algorithms can learn incrementally (i.e. without seeing all the instances
 at once), all estimators implementing the <code class="docutils literal notranslate"><span class="pre">partial_fit</span></code> API are candidates.
@@ -801,7 +802,7 @@ <h3><span class="section-number">8.1.1.3. </span>Incremental learning<a class="h
 come late in the stream as their learning rate decreases over time.</p>
 </section>
 <section id="examples">
-<h3><span class="section-number">8.1.1.4. </span>Examples<a class="headerlink" href="#examples" title="Link to this heading">#</a></h3>
+<h3><span class="section-number">9.1.1.4. </span>Examples<a class="headerlink" href="#examples" title="Link to this heading">#</a></h3>
 <p>Finally, we have a full-fledged example of
 <a class="reference internal" href="http://github.com/auto_examples/applications/plot_out_of_core_classification.html#sphx-glr-auto-examples-applications-plot-out-of-core-classification-py"><span class="std std-ref">Out-of-core classification of text documents</span></a>. It is aimed at
 providing a starting point for people wanting to build out-of-core learning
@@ -817,7 +818,7 @@ <h3><span class="section-number">8.1.1.4. </span>Examples<a class="headerlink" h
 <p class="centered">
 <strong><a class="reference external" href="http://github.com/auto_examples/applications/plot_out_of_core_classification.html"><img alt="computation_time" src="http://github.com/_images/sphx_glr_plot_out_of_core_classification_003.png" style="width: 512.0px; height: 384.0px;" /></a></strong></p></section>
 <section id="notes">
-<h3><span class="section-number">8.1.1.5. </span>Notes<a class="headerlink" href="#notes" title="Link to this heading">#</a></h3>
+<h3><span class="section-number">9.1.1.5. </span>Notes<a class="headerlink" href="#notes" title="Link to this heading">#</a></h3>
 <aside class="footnote-list brackets">
 <aside class="footnote brackets" id="id2" role="doc-footnote">
 <span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
@@ -848,7 +849,7 @@ <h3><span class="section-number">8.1.1.5. </span>Notes<a class="headerlink" href
       <i class="fa-solid fa-angle-left"></i>
       <div class="prev-next-info">
         <p class="prev-next-subtitle">previous</p>
-        <p class="prev-next-title"><span class="section-number">8. </span>Computing with scikit-learn</p>
+        <p class="prev-next-title"><span class="section-number">9. </span>Computing with scikit-learn</p>
       </div>
     </a>
     <a class="right-next"
@@ -856,7 +857,7 @@ <h3><span class="section-number">8.1.1.5. </span>Notes<a class="headerlink" href
        title="next page">
       <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">8.2. </span>Computational Performance</p>
+        <p class="prev-next-title"><span class="section-number">9.2. </span>Computational Performance</p>
       </div>
       <i class="fa-solid fa-angle-right"></i>
     </a>
@@ -883,12 +884,12 @@ <h3><span class="section-number">8.1.1.5. </span>Notes<a class="headerlink" href
   </div>
   <nav class="bd-toc-nav page-toc" aria-labelledby="pst-page-navigation-heading-2">
     <ul class="visible nav section-nav flex-column">
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#scaling-with-instances-using-out-of-core-learning">8.1.1. Scaling with instances using out-of-core learning</a><ul class="nav section-nav flex-column">
-<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#streaming-instances">8.1.1.1. Streaming instances</a></li>
-<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#extracting-features">8.1.1.2. Extracting features</a></li>
-<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#incremental-learning">8.1.1.3. Incremental learning</a></li>
-<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#examples">8.1.1.4. Examples</a></li>
-<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#notes">8.1.1.5. Notes</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#scaling-with-instances-using-out-of-core-learning">9.1.1. Scaling with instances using out-of-core learning</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#streaming-instances">9.1.1.1. Streaming instances</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#extracting-features">9.1.1.2. Extracting features</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#incremental-learning">9.1.1.3. Incremental learning</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#examples">9.1.1.4. Examples</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#notes">9.1.1.5. Notes</a></li>
 </ul>
 </li>
 </ul>
diff --git a/dev/contributor_experience_team.html b/dev/contributor_experience_team.html
index bce2797fbe7c9..e89bac9a3184f 100644
--- a/dev/contributor_experience_team.html
+++ b/dev/contributor_experience_team.html
@@ -40,7 +40,7 @@
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     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
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diff --git a/dev/contributor_experience_team_emeritus.html b/dev/contributor_experience_team_emeritus.html
index e4ecd2ac9f376..10d1f6dee8dca 100644
--- a/dev/contributor_experience_team_emeritus.html
+++ b/dev/contributor_experience_team_emeritus.html
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-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
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diff --git a/dev/data_transforms.html b/dev/data_transforms.html
index 010a9bd7e3dcf..82b0ebd6c7ae5 100644
--- a/dev/data_transforms.html
+++ b/dev/data_transforms.html
@@ -7,7 +7,7 @@
   <head>
     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="viewport" content="width=device-width, initial-scale=1" />
-<meta property="og:title" content="6. Dataset transformations" />
+<meta property="og:title" content="7. Dataset transformations" />
 <meta property="og:type" content="website" />
 <meta property="og:url" content="https://scikit-learn/stable/data_transforms.html" />
 <meta property="og:site_name" content="scikit-learn" />
@@ -16,7 +16,7 @@
 <meta property="og:image:alt" content="scikit-learn" />
 <meta name="description" content="scikit-learn provides a library of transformers, which may clean (see Preprocessing data), reduce (see Unsupervised dimensionality reduction), expand (see Kernel Approximation) or generate (see Fea..." />
 
-    <title>6. Dataset transformations &#8212; scikit-learn 1.7.dev0 documentation</title>
+    <title>7. Dataset transformations &#8212; scikit-learn 1.7.dev0 documentation</title>
   
   
   
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
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     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
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@@ -81,8 +81,8 @@
     <link rel="icon" href="http://github.com/_static/favicon.ico"/>
     <link rel="author" title="About these documents" href="http://github.com/about.html" />
     <link rel="search" title="Search" href="http://github.com/search.html" />
-    <link rel="next" title="6.1. Pipelines and composite estimators" href="http://github.com/modules/compose.html" />
-    <link rel="prev" title="5. Visualizations" href="http://github.com/visualizations.html" />
+    <link rel="next" title="7.1. Pipelines and composite estimators" href="http://github.com/modules/compose.html" />
+    <link rel="prev" title="6. Visualizations" href="http://github.com/visualizations.html" />
   <meta name="viewport" content="width=device-width, initial-scale=1"/>
   <meta name="docsearch:language" content="en"/>
   <meta name="docsearch:version" content="1.7" />
@@ -594,45 +594,46 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/learning_curve.html">3.5. Validation curves: plotting scores to evaluate models</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/inspection.html">4. Inspection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/partial_dependence.html">4.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/permutation_importance.html">4.2. Permutation feature importance</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/metadata_routing.html">4. Metadata Routing</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/inspection.html">5. Inspection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/partial_dependence.html">5.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/permutation_importance.html">5.2. Permutation feature importance</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/visualizations.html">5. Visualizations</a></li>
-<li class="toctree-l1 current active has-children"><a class="current reference internal" href="#">6. Dataset transformations</a><details open="open"><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/compose.html">6.1. Pipelines and composite estimators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/feature_extraction.html">6.2. Feature extraction</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/preprocessing.html">6.3. Preprocessing data</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/impute.html">6.4. Imputation of missing values</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/unsupervised_reduction.html">6.5. Unsupervised dimensionality reduction</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/random_projection.html">6.6. Random Projection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/kernel_approximation.html">6.7. Kernel Approximation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/metrics.html">6.8. Pairwise metrics, Affinities and Kernels</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/preprocessing_targets.html">6.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/visualizations.html">6. Visualizations</a></li>
+<li class="toctree-l1 current active has-children"><a class="current reference internal" href="#">7. Dataset transformations</a><details open="open"><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/compose.html">7.1. Pipelines and composite estimators</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/feature_extraction.html">7.2. Feature extraction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/preprocessing.html">7.3. Preprocessing data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/impute.html">7.4. Imputation of missing values</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/unsupervised_reduction.html">7.5. Unsupervised dimensionality reduction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/random_projection.html">7.6. Random Projection</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/kernel_approximation.html">7.7. Kernel Approximation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/metrics.html">7.8. Pairwise metrics, Affinities and Kernels</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/preprocessing_targets.html">7.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/datasets.html">7. Dataset loading utilities</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html">7.1. Toy datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html">7.2. Real world datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html">7.3. Generated datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html">7.4. Loading other datasets</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/datasets.html">8. Dataset loading utilities</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html">8.1. Toy datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html">8.2. Real world datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html">8.3. Generated datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html">8.4. Loading other datasets</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/computing.html">8. Computing with scikit-learn</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/scaling_strategies.html">8.1. Strategies to scale computationally: bigger data</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/computational_performance.html">8.2. Computational Performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/parallelism.html">8.3. Parallelism, resource management, and configuration</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/computing.html">9. Computing with scikit-learn</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/scaling_strategies.html">9.1. Strategies to scale computationally: bigger data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/computational_performance.html">9.2. Computational Performance</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/parallelism.html">9.3. Parallelism, resource management, and configuration</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/model_persistence.html">9. Model persistence</a></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/common_pitfalls.html">10. Common pitfalls and recommended practices</a></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/dispatching.html">11. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/array_api.html">11.1. Array API support (experimental)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/model_persistence.html">10. Model persistence</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/common_pitfalls.html">11. Common pitfalls and recommended practices</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/dispatching.html">12. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/array_api.html">12.1. Array API support (experimental)</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/machine_learning_map.html">12. Choosing the right estimator</a></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/presentations.html">13. External Resources, Videos and Talks</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/machine_learning_map.html">13. Choosing the right estimator</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/presentations.html">14. External Resources, Videos and Talks</a></li>
 </ul>
 </div>
 </nav></div>
@@ -669,7 +670,7 @@
     
     <li class="breadcrumb-item"><a href="http://github.com/user_guide.html" class="nav-link">User Guide</a></li>
     
-    <li class="breadcrumb-item active" aria-current="page"><span class="ellipsis"><span class="section-number">6. </span>Dataset transformations</span></li>
+    <li class="breadcrumb-item active" aria-current="page"><span class="ellipsis"><span class="section-number">7. </span>Dataset transformations</span></li>
   </ul>
 </nav>
 </div>
@@ -687,7 +688,7 @@
                 <article class="bd-article">
                   
   <section id="dataset-transformations">
-<span id="data-transforms"></span><h1><span class="section-number">6. </span>Dataset transformations<a class="headerlink" href="#dataset-transformations" title="Link to this heading">#</a></h1>
+<span id="data-transforms"></span><h1><span class="section-number">7. </span>Dataset transformations<a class="headerlink" href="#dataset-transformations" title="Link to this heading">#</a></h1>
 <p>scikit-learn provides a library of transformers, which may clean (see
 <a class="reference internal" href="http://github.com/modules/preprocessing.html#preprocessing"><span class="std std-ref">Preprocessing data</span></a>), reduce (see <a class="reference internal" href="http://github.com/modules/unsupervised_reduction.html#data-reduction"><span class="std std-ref">Unsupervised dimensionality reduction</span></a>), expand (see
 <a class="reference internal" href="http://github.com/modules/kernel_approximation.html#kernel-approximation"><span class="std std-ref">Kernel Approximation</span></a>) or generate (see <a class="reference internal" href="http://github.com/modules/feature_extraction.html#feature-extraction"><span class="std std-ref">Feature extraction</span></a>)
@@ -705,77 +706,77 @@
 scikit-learn.</p>
 <div class="toctree-wrapper compound">
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/modules/compose.html">6.1. Pipelines and composite estimators</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/compose.html#pipeline-chaining-estimators">6.1.1. Pipeline: chaining estimators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/compose.html#transforming-target-in-regression">6.1.2. Transforming target in regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/compose.html#featureunion-composite-feature-spaces">6.1.3. FeatureUnion: composite feature spaces</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/compose.html#columntransformer-for-heterogeneous-data">6.1.4. ColumnTransformer for heterogeneous data</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/compose.html#visualizing-composite-estimators">6.1.5. Visualizing Composite Estimators</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/modules/compose.html">7.1. Pipelines and composite estimators</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/compose.html#pipeline-chaining-estimators">7.1.1. Pipeline: chaining estimators</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/compose.html#transforming-target-in-regression">7.1.2. Transforming target in regression</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/compose.html#featureunion-composite-feature-spaces">7.1.3. FeatureUnion: composite feature spaces</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/compose.html#columntransformer-for-heterogeneous-data">7.1.4. ColumnTransformer for heterogeneous data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/compose.html#visualizing-composite-estimators">7.1.5. Visualizing Composite Estimators</a></li>
 </ul>
 </li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/modules/feature_extraction.html">6.2. Feature extraction</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/feature_extraction.html#loading-features-from-dicts">6.2.1. Loading features from dicts</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/feature_extraction.html#feature-hashing">6.2.2. Feature hashing</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/feature_extraction.html#text-feature-extraction">6.2.3. Text feature extraction</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/feature_extraction.html#image-feature-extraction">6.2.4. Image feature extraction</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/modules/feature_extraction.html">7.2. Feature extraction</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/feature_extraction.html#loading-features-from-dicts">7.2.1. Loading features from dicts</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/feature_extraction.html#feature-hashing">7.2.2. Feature hashing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/feature_extraction.html#text-feature-extraction">7.2.3. Text feature extraction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/feature_extraction.html#image-feature-extraction">7.2.4. Image feature extraction</a></li>
 </ul>
 </li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/modules/preprocessing.html">6.3. Preprocessing data</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/preprocessing.html#standardization-or-mean-removal-and-variance-scaling">6.3.1. Standardization, or mean removal and variance scaling</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/preprocessing.html#non-linear-transformation">6.3.2. Non-linear transformation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/preprocessing.html#normalization">6.3.3. Normalization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/preprocessing.html#encoding-categorical-features">6.3.4. Encoding categorical features</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/preprocessing.html#discretization">6.3.5. Discretization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/preprocessing.html#imputation-of-missing-values">6.3.6. Imputation of missing values</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/preprocessing.html#generating-polynomial-features">6.3.7. Generating polynomial features</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/preprocessing.html#custom-transformers">6.3.8. Custom transformers</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/modules/preprocessing.html">7.3. Preprocessing data</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/preprocessing.html#standardization-or-mean-removal-and-variance-scaling">7.3.1. Standardization, or mean removal and variance scaling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/preprocessing.html#non-linear-transformation">7.3.2. Non-linear transformation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/preprocessing.html#normalization">7.3.3. Normalization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/preprocessing.html#encoding-categorical-features">7.3.4. Encoding categorical features</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/preprocessing.html#discretization">7.3.5. Discretization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/preprocessing.html#imputation-of-missing-values">7.3.6. Imputation of missing values</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/preprocessing.html#generating-polynomial-features">7.3.7. Generating polynomial features</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/preprocessing.html#custom-transformers">7.3.8. Custom transformers</a></li>
 </ul>
 </li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/modules/impute.html">6.4. Imputation of missing values</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/impute.html#univariate-vs-multivariate-imputation">6.4.1. Univariate vs. Multivariate Imputation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/impute.html#univariate-feature-imputation">6.4.2. Univariate feature imputation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/impute.html#multivariate-feature-imputation">6.4.3. Multivariate feature imputation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/impute.html#nearest-neighbors-imputation">6.4.4. Nearest neighbors imputation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/impute.html#keeping-the-number-of-features-constant">6.4.5. Keeping the number of features constant</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/impute.html#marking-imputed-values">6.4.6. Marking imputed values</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/impute.html#estimators-that-handle-nan-values">6.4.7. Estimators that handle NaN values</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/modules/impute.html">7.4. Imputation of missing values</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/impute.html#univariate-vs-multivariate-imputation">7.4.1. Univariate vs. Multivariate Imputation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/impute.html#univariate-feature-imputation">7.4.2. Univariate feature imputation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/impute.html#multivariate-feature-imputation">7.4.3. Multivariate feature imputation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/impute.html#nearest-neighbors-imputation">7.4.4. Nearest neighbors imputation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/impute.html#keeping-the-number-of-features-constant">7.4.5. Keeping the number of features constant</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/impute.html#marking-imputed-values">7.4.6. Marking imputed values</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/impute.html#estimators-that-handle-nan-values">7.4.7. Estimators that handle NaN values</a></li>
 </ul>
 </li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/modules/unsupervised_reduction.html">6.5. Unsupervised dimensionality reduction</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/unsupervised_reduction.html#pca-principal-component-analysis">6.5.1. PCA: principal component analysis</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/unsupervised_reduction.html#random-projections">6.5.2. Random projections</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/unsupervised_reduction.html#feature-agglomeration">6.5.3. Feature agglomeration</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/modules/unsupervised_reduction.html">7.5. Unsupervised dimensionality reduction</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/unsupervised_reduction.html#pca-principal-component-analysis">7.5.1. PCA: principal component analysis</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/unsupervised_reduction.html#random-projections">7.5.2. Random projections</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/unsupervised_reduction.html#feature-agglomeration">7.5.3. Feature agglomeration</a></li>
 </ul>
 </li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/modules/random_projection.html">6.6. Random Projection</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/random_projection.html#the-johnson-lindenstrauss-lemma">6.6.1. The Johnson-Lindenstrauss lemma</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/random_projection.html#gaussian-random-projection">6.6.2. Gaussian random projection</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/random_projection.html#inverse-transform">6.6.4. Inverse Transform</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/modules/random_projection.html">7.6. Random Projection</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/random_projection.html#the-johnson-lindenstrauss-lemma">7.6.1. The Johnson-Lindenstrauss lemma</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/random_projection.html#gaussian-random-projection">7.6.2. Gaussian random projection</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/random_projection.html#sparse-random-projection">7.6.3. Sparse random projection</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/random_projection.html#inverse-transform">7.6.4. Inverse Transform</a></li>
 </ul>
 </li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/modules/kernel_approximation.html">6.7. Kernel Approximation</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/kernel_approximation.html#nystroem-method-for-kernel-approximation">6.7.1. Nystroem Method for Kernel Approximation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/kernel_approximation.html#radial-basis-function-kernel">6.7.2. Radial Basis Function Kernel</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/kernel_approximation.html#additive-chi-squared-kernel">6.7.3. Additive Chi Squared Kernel</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/kernel_approximation.html#skewed-chi-squared-kernel">6.7.4. Skewed Chi Squared Kernel</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/kernel_approximation.html#polynomial-kernel-approximation-via-tensor-sketch">6.7.5. Polynomial Kernel Approximation via Tensor Sketch</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/kernel_approximation.html#mathematical-details">6.7.6. Mathematical Details</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/modules/kernel_approximation.html">7.7. Kernel Approximation</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/kernel_approximation.html#nystroem-method-for-kernel-approximation">7.7.1. Nystroem Method for Kernel Approximation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/kernel_approximation.html#radial-basis-function-kernel">7.7.2. Radial Basis Function Kernel</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/kernel_approximation.html#additive-chi-squared-kernel">7.7.3. Additive Chi Squared Kernel</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/kernel_approximation.html#skewed-chi-squared-kernel">7.7.4. Skewed Chi Squared Kernel</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/kernel_approximation.html#polynomial-kernel-approximation-via-tensor-sketch">7.7.5. Polynomial Kernel Approximation via Tensor Sketch</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/kernel_approximation.html#mathematical-details">7.7.6. Mathematical Details</a></li>
 </ul>
 </li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/modules/metrics.html">6.8. Pairwise metrics, Affinities and Kernels</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/metrics.html#cosine-similarity">6.8.1. Cosine similarity</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/metrics.html#linear-kernel">6.8.2. Linear kernel</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/metrics.html#polynomial-kernel">6.8.3. Polynomial kernel</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/metrics.html#sigmoid-kernel">6.8.4. Sigmoid kernel</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/metrics.html#rbf-kernel">6.8.5. RBF kernel</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/metrics.html#laplacian-kernel">6.8.6. Laplacian kernel</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/metrics.html#chi-squared-kernel">6.8.7. Chi-squared kernel</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/modules/metrics.html">7.8. Pairwise metrics, Affinities and Kernels</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/metrics.html#cosine-similarity">7.8.1. Cosine similarity</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/metrics.html#linear-kernel">7.8.2. Linear kernel</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/metrics.html#polynomial-kernel">7.8.3. Polynomial kernel</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/metrics.html#sigmoid-kernel">7.8.4. Sigmoid kernel</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/metrics.html#rbf-kernel">7.8.5. RBF kernel</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/metrics.html#laplacian-kernel">7.8.6. Laplacian kernel</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/metrics.html#chi-squared-kernel">7.8.7. Chi-squared kernel</a></li>
 </ul>
 </li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/modules/preprocessing_targets.html">6.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/preprocessing_targets.html#label-binarization">6.9.1. Label binarization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/preprocessing_targets.html#label-encoding">6.9.2. Label encoding</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/modules/preprocessing_targets.html">7.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/preprocessing_targets.html#label-binarization">7.9.1. Label binarization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/preprocessing_targets.html#label-encoding">7.9.2. Label encoding</a></li>
 </ul>
 </li>
 </ul>
@@ -798,7 +799,7 @@
       <i class="fa-solid fa-angle-left"></i>
       <div class="prev-next-info">
         <p class="prev-next-subtitle">previous</p>
-        <p class="prev-next-title"><span class="section-number">5. </span>Visualizations</p>
+        <p class="prev-next-title"><span class="section-number">6. </span>Visualizations</p>
       </div>
     </a>
     <a class="right-next"
@@ -806,7 +807,7 @@
        title="next page">
       <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">6.1. </span>Pipelines and composite estimators</p>
+        <p class="prev-next-title"><span class="section-number">7.1. </span>Pipelines and composite estimators</p>
       </div>
       <i class="fa-solid fa-angle-right"></i>
     </a>
diff --git a/dev/datasets.html b/dev/datasets.html
index 7c2a306ab53f0..1c24387780056 100644
--- a/dev/datasets.html
+++ b/dev/datasets.html
@@ -7,7 +7,7 @@
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     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="viewport" content="width=device-width, initial-scale=1" />
-<meta property="og:title" content="7. Dataset loading utilities" />
+<meta property="og:title" content="8. Dataset loading utilities" />
 <meta property="og:type" content="website" />
 <meta property="og:url" content="https://scikit-learn/stable/datasets.html" />
 <meta property="og:site_name" content="scikit-learn" />
@@ -16,7 +16,7 @@
 <meta property="og:image:alt" content="scikit-learn" />
 <meta name="description" content="The sklearn.datasets package embeds some small toy datasets and provides helpers to fetch larger datasets commonly used by the machine learning community to benchmark algorithms on data that comes ..." />
 
-    <title>7. Dataset loading utilities &#8212; scikit-learn 1.7.dev0 documentation</title>
+    <title>8. Dataset loading utilities &#8212; scikit-learn 1.7.dev0 documentation</title>
   
   
   
@@ -42,7 +42,7 @@
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-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
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@@ -80,8 +80,8 @@
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     <link rel="author" title="About these documents" href="http://github.com/about.html" />
     <link rel="search" title="Search" href="http://github.com/search.html" />
-    <link rel="next" title="7.1. Toy datasets" href="http://github.com/datasets/toy_dataset.html" />
-    <link rel="prev" title="6.9. Transforming the prediction target (y)" href="http://github.com/modules/preprocessing_targets.html" />
+    <link rel="next" title="8.1. Toy datasets" href="http://github.com/datasets/toy_dataset.html" />
+    <link rel="prev" title="7.9. Transforming the prediction target (y)" href="http://github.com/modules/preprocessing_targets.html" />
   <meta name="viewport" content="width=device-width, initial-scale=1"/>
   <meta name="docsearch:language" content="en"/>
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@@ -593,45 +593,46 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/learning_curve.html">3.5. Validation curves: plotting scores to evaluate models</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/inspection.html">4. Inspection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/partial_dependence.html">4.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/permutation_importance.html">4.2. Permutation feature importance</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/metadata_routing.html">4. Metadata Routing</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/inspection.html">5. Inspection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/partial_dependence.html">5.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/permutation_importance.html">5.2. Permutation feature importance</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/visualizations.html">5. Visualizations</a></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/data_transforms.html">6. Dataset transformations</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/compose.html">6.1. Pipelines and composite estimators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/feature_extraction.html">6.2. Feature extraction</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/preprocessing.html">6.3. Preprocessing data</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/impute.html">6.4. Imputation of missing values</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/unsupervised_reduction.html">6.5. Unsupervised dimensionality reduction</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/random_projection.html">6.6. Random Projection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/kernel_approximation.html">6.7. Kernel Approximation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/metrics.html">6.8. Pairwise metrics, Affinities and Kernels</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/preprocessing_targets.html">6.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/visualizations.html">6. Visualizations</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/data_transforms.html">7. Dataset transformations</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/compose.html">7.1. Pipelines and composite estimators</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/feature_extraction.html">7.2. Feature extraction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/preprocessing.html">7.3. Preprocessing data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/impute.html">7.4. Imputation of missing values</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/unsupervised_reduction.html">7.5. Unsupervised dimensionality reduction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/random_projection.html">7.6. Random Projection</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/kernel_approximation.html">7.7. Kernel Approximation</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/preprocessing_targets.html">7.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html">7.4. Loading other datasets</a></li>
+<li class="toctree-l1 current active has-children"><a class="current reference internal" href="#">8. Dataset loading utilities</a><details open="open"><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html">8.1. Toy datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html">8.2. Real world datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html">8.3. Generated datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html">8.4. Loading other datasets</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/scaling_strategies.html">8.1. Strategies to scale computationally: bigger data</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/parallelism.html">8.3. Parallelism, resource management, and configuration</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/computing.html">9. Computing with scikit-learn</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/scaling_strategies.html">9.1. Strategies to scale computationally: bigger data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/computational_performance.html">9.2. Computational Performance</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/parallelism.html">9.3. Parallelism, resource management, and configuration</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="http://github.com/common_pitfalls.html">10. Common pitfalls and recommended practices</a></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/dispatching.html">11. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/array_api.html">11.1. Array API support (experimental)</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="http://github.com/common_pitfalls.html">11. Common pitfalls and recommended practices</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="http://github.com/machine_learning_map.html">12. Choosing the right estimator</a></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/presentations.html">13. External Resources, Videos and Talks</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/machine_learning_map.html">13. Choosing the right estimator</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/presentations.html">14. External Resources, Videos and Talks</a></li>
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@@ -668,7 +669,7 @@
     
     <li class="breadcrumb-item"><a href="http://github.com/user_guide.html" class="nav-link">User Guide</a></li>
     
-    <li class="breadcrumb-item active" aria-current="page"><span class="ellipsis"><span class="section-number">7. </span>Dataset loading utilities</span></li>
+    <li class="breadcrumb-item active" aria-current="page"><span class="ellipsis"><span class="section-number">8. </span>Dataset loading utilities</span></li>
   </ul>
 </nav>
 </div>
@@ -686,7 +687,7 @@
                 <article class="bd-article">
                   
   <section id="dataset-loading-utilities">
-<span id="datasets"></span><h1><span class="section-number">7. </span>Dataset loading utilities<a class="headerlink" href="#dataset-loading-utilities" title="Link to this heading">#</a></h1>
+<span id="datasets"></span><h1><span class="section-number">8. </span>Dataset loading utilities<a class="headerlink" href="#dataset-loading-utilities" title="Link to this heading">#</a></h1>
 <p>The <code class="docutils literal notranslate"><span class="pre">sklearn.datasets</span></code> package embeds some small toy datasets and provides helpers
 to fetch larger datasets commonly used by the machine learning community to benchmark
 algorithms on data that comes from the ‘real world’.</p>
@@ -723,38 +724,38 @@
 section.</p>
 <div class="toctree-wrapper compound">
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html">7.1. Toy datasets</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html#iris-plants-dataset">7.1.1. Iris plants dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html#diabetes-dataset">7.1.2. Diabetes dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html#optical-recognition-of-handwritten-digits-dataset">7.1.3. Optical recognition of handwritten digits dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html#linnerrud-dataset">7.1.4. Linnerrud dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html#wine-recognition-dataset">7.1.5. Wine recognition dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html#breast-cancer-wisconsin-diagnostic-dataset">7.1.6. Breast cancer Wisconsin (diagnostic) dataset</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html">8.1. Toy datasets</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html#iris-plants-dataset">8.1.1. Iris plants dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html#diabetes-dataset">8.1.2. Diabetes dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html#optical-recognition-of-handwritten-digits-dataset">8.1.3. Optical recognition of handwritten digits dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html#linnerrud-dataset">8.1.4. Linnerrud dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html#wine-recognition-dataset">8.1.5. Wine recognition dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html#breast-cancer-wisconsin-diagnostic-dataset">8.1.6. Breast cancer Wisconsin (diagnostic) dataset</a></li>
 </ul>
 </li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/datasets/real_world.html">7.2. Real world datasets</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html#the-olivetti-faces-dataset">7.2.1. The Olivetti faces dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html#the-20-newsgroups-text-dataset">7.2.2. The 20 newsgroups text dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html#the-labeled-faces-in-the-wild-face-recognition-dataset">7.2.3. The Labeled Faces in the Wild face recognition dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html#forest-covertypes">7.2.4. Forest covertypes</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html#rcv1-dataset">7.2.5. RCV1 dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html#kddcup-99-dataset">7.2.6. Kddcup 99 dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html#california-housing-dataset">7.2.7. California Housing dataset</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html#species-distribution-dataset">7.2.8. Species distribution dataset</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/datasets/real_world.html">8.2. Real world datasets</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html#the-olivetti-faces-dataset">8.2.1. The Olivetti faces dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html#the-20-newsgroups-text-dataset">8.2.2. The 20 newsgroups text dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html#the-labeled-faces-in-the-wild-face-recognition-dataset">8.2.3. The Labeled Faces in the Wild face recognition dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html#forest-covertypes">8.2.4. Forest covertypes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html#rcv1-dataset">8.2.5. RCV1 dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html#kddcup-99-dataset">8.2.6. Kddcup 99 dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html#california-housing-dataset">8.2.7. California Housing dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html#species-distribution-dataset">8.2.8. Species distribution dataset</a></li>
 </ul>
 </li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/datasets/sample_generators.html">7.3. Generated datasets</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html#generators-for-classification-and-clustering">7.3.1. Generators for classification and clustering</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html#generators-for-regression">7.3.2. Generators for regression</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html#generators-for-manifold-learning">7.3.3. Generators for manifold learning</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html#generators-for-decomposition">7.3.4. Generators for decomposition</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/datasets/sample_generators.html">8.3. Generated datasets</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html#generators-for-classification-and-clustering">8.3.1. Generators for classification and clustering</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html#generators-for-regression">8.3.2. Generators for regression</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html#generators-for-manifold-learning">8.3.3. Generators for manifold learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html#generators-for-decomposition">8.3.4. Generators for decomposition</a></li>
 </ul>
 </li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html">7.4. Loading other datasets</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html#sample-images">7.4.1. Sample images</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html#datasets-in-svmlight-libsvm-format">7.4.2. Datasets in svmlight / libsvm format</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html#downloading-datasets-from-the-openml-org-repository">7.4.3. Downloading datasets from the openml.org repository</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html#loading-from-external-datasets">7.4.4. Loading from external datasets</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html">8.4. Loading other datasets</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html#sample-images">8.4.1. Sample images</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html#datasets-in-svmlight-libsvm-format">8.4.2. Datasets in svmlight / libsvm format</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html#downloading-datasets-from-the-openml-org-repository">8.4.3. Downloading datasets from the openml.org repository</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html#loading-from-external-datasets">8.4.4. Loading from external datasets</a></li>
 </ul>
 </li>
 </ul>
@@ -777,7 +778,7 @@
       <i class="fa-solid fa-angle-left"></i>
       <div class="prev-next-info">
         <p class="prev-next-subtitle">previous</p>
-        <p class="prev-next-title"><span class="section-number">6.9. </span>Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</p>
+        <p class="prev-next-title"><span class="section-number">7.9. </span>Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</p>
       </div>
     </a>
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@@ -785,7 +786,7 @@
        title="next page">
       <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">7.1. </span>Toy datasets</p>
+        <p class="prev-next-title"><span class="section-number">8.1. </span>Toy datasets</p>
       </div>
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     </a>
diff --git a/dev/datasets/loading_other_datasets.html b/dev/datasets/loading_other_datasets.html
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@@ -7,7 +7,7 @@
   <head>
     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="viewport" content="width=device-width, initial-scale=1" />
-<meta property="og:title" content="7.4. Loading other datasets" />
+<meta property="og:title" content="8.4. Loading other datasets" />
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@@ -16,7 +16,7 @@
 <meta property="og:image:alt" content="scikit-learn" />
 <meta name="description" content="Sample images: Scikit-learn also embeds a couple of sample JPEG images published under Creative Commons license by their authors. Those images can be useful to test algorithms and pipelines on 2D d..." />
 
-    <title>7.4. Loading other datasets &#8212; scikit-learn 1.7.dev0 documentation</title>
+    <title>8.4. Loading other datasets &#8212; scikit-learn 1.7.dev0 documentation</title>
   
   
   
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
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@@ -80,8 +80,8 @@
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     <link rel="author" title="About these documents" href="http://github.com/about.html" />
     <link rel="search" title="Search" href="http://github.com/search.html" />
-    <link rel="next" title="8. Computing with scikit-learn" href="http://github.com/computing.html" />
-    <link rel="prev" title="7.3. Generated datasets" href="http://github.com/sample_generators.html" />
+    <link rel="next" title="9. Computing with scikit-learn" href="http://github.com/computing.html" />
+    <link rel="prev" title="8.3. Generated datasets" href="http://github.com/sample_generators.html" />
   <meta name="viewport" content="width=device-width, initial-scale=1"/>
   <meta name="docsearch:language" content="en"/>
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@@ -593,45 +593,46 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/learning_curve.html">3.5. Validation curves: plotting scores to evaluate models</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/inspection.html">4. Inspection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/partial_dependence.html">4.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/permutation_importance.html">4.2. Permutation feature importance</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/metadata_routing.html">4. Metadata Routing</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/inspection.html">5. Inspection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/partial_dependence.html">5.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/permutation_importance.html">5.2. Permutation feature importance</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/visualizations.html">5. Visualizations</a></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/data_transforms.html">6. Dataset transformations</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/compose.html">6.1. Pipelines and composite estimators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/feature_extraction.html">6.2. Feature extraction</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/preprocessing.html">6.3. Preprocessing data</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/impute.html">6.4. Imputation of missing values</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/unsupervised_reduction.html">6.5. Unsupervised dimensionality reduction</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/random_projection.html">6.6. Random Projection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/kernel_approximation.html">6.7. Kernel Approximation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/metrics.html">6.8. Pairwise metrics, Affinities and Kernels</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/preprocessing_targets.html">6.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/visualizations.html">6. Visualizations</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/data_transforms.html">7. Dataset transformations</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/compose.html">7.1. Pipelines and composite estimators</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/feature_extraction.html">7.2. Feature extraction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/preprocessing.html">7.3. Preprocessing data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/impute.html">7.4. Imputation of missing values</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/unsupervised_reduction.html">7.5. Unsupervised dimensionality reduction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/random_projection.html">7.6. Random Projection</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/kernel_approximation.html">7.7. Kernel Approximation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/metrics.html">7.8. Pairwise metrics, Affinities and Kernels</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/preprocessing_targets.html">7.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
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 </details></li>
-<li class="toctree-l1 current active has-children"><a class="reference internal" href="http://github.com/datasets.html">7. Dataset loading utilities</a><details open="open"><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul class="current">
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/toy_dataset.html">7.1. Toy datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/real_world.html">7.2. Real world datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/sample_generators.html">7.3. Generated datasets</a></li>
-<li class="toctree-l2 current active"><a class="current reference internal" href="#">7.4. Loading other datasets</a></li>
+<li class="toctree-l1 current active has-children"><a class="reference internal" href="http://github.com/datasets.html">8. Dataset loading utilities</a><details open="open"><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/toy_dataset.html">8.1. Toy datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/real_world.html">8.2. Real world datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/sample_generators.html">8.3. Generated datasets</a></li>
+<li class="toctree-l2 current active"><a class="current reference internal" href="#">8.4. Loading other datasets</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/computing.html">8. Computing with scikit-learn</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/scaling_strategies.html">8.1. Strategies to scale computationally: bigger data</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/computational_performance.html">8.2. Computational Performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/parallelism.html">8.3. Parallelism, resource management, and configuration</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/computing.html">9. Computing with scikit-learn</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/scaling_strategies.html">9.1. Strategies to scale computationally: bigger data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/computational_performance.html">9.2. Computational Performance</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/parallelism.html">9.3. Parallelism, resource management, and configuration</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/model_persistence.html">9. Model persistence</a></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/common_pitfalls.html">10. Common pitfalls and recommended practices</a></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/dispatching.html">11. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/array_api.html">11.1. Array API support (experimental)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/model_persistence.html">10. Model persistence</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/common_pitfalls.html">11. Common pitfalls and recommended practices</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/dispatching.html">12. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/array_api.html">12.1. Array API support (experimental)</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/machine_learning_map.html">12. Choosing the right estimator</a></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/presentations.html">13. External Resources, Videos and Talks</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/machine_learning_map.html">13. Choosing the right estimator</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/presentations.html">14. External Resources, Videos and Talks</a></li>
 </ul>
 </div>
 </nav></div>
@@ -669,9 +670,9 @@
     <li class="breadcrumb-item"><a href="http://github.com/user_guide.html" class="nav-link">User Guide</a></li>
     
     
-    <li class="breadcrumb-item"><a href="http://github.com/datasets.html" class="nav-link"><span class="section-number">7. </span>Dataset loading utilities</a></li>
+    <li class="breadcrumb-item"><a href="http://github.com/datasets.html" class="nav-link"><span class="section-number">8. </span>Dataset loading utilities</a></li>
     
-    <li class="breadcrumb-item active" aria-current="page"><span class="ellipsis"><span class="section-number">7.4. </span>Loading other datasets</span></li>
+    <li class="breadcrumb-item active" aria-current="page"><span class="ellipsis"><span class="section-number">8.4. </span>Loading other datasets</span></li>
   </ul>
 </nav>
 </div>
@@ -689,9 +690,9 @@
                 <article class="bd-article">
                   
   <section id="loading-other-datasets">
-<span id="id1"></span><h1><span class="section-number">7.4. </span>Loading other datasets<a class="headerlink" href="#loading-other-datasets" title="Link to this heading">#</a></h1>
+<span id="id1"></span><h1><span class="section-number">8.4. </span>Loading other datasets<a class="headerlink" href="#loading-other-datasets" title="Link to this heading">#</a></h1>
 <section id="sample-images">
-<span id="id2"></span><h2><span class="section-number">7.4.1. </span>Sample images<a class="headerlink" href="#sample-images" title="Link to this heading">#</a></h2>
+<span id="id2"></span><h2><span class="section-number">8.4.1. </span>Sample images<a class="headerlink" href="#sample-images" title="Link to this heading">#</a></h2>
 <p>Scikit-learn also embeds a couple of sample JPEG images published under Creative
 Commons license by their authors. Those images can be useful to test algorithms
 and pipelines on 2D data.</p>
@@ -720,7 +721,7 @@
 </div>
 </section>
 <section id="datasets-in-svmlight-libsvm-format">
-<span id="libsvm-loader"></span><h2><span class="section-number">7.4.2. </span>Datasets in svmlight / libsvm format<a class="headerlink" href="#datasets-in-svmlight-libsvm-format" title="Link to this heading">#</a></h2>
+<span id="libsvm-loader"></span><h2><span class="section-number">8.4.2. </span>Datasets in svmlight / libsvm format<a class="headerlink" href="#datasets-in-svmlight-libsvm-format" title="Link to this heading">#</a></h2>
 <p>scikit-learn includes utility functions for loading
 datasets in the svmlight / libsvm format. In this format, each line
 takes the form <code class="docutils literal notranslate"><span class="pre">&lt;label&gt;</span> <span class="pre">&lt;feature-id&gt;:&lt;feature-value&gt;</span>
@@ -753,7 +754,7 @@
 </ul>
 </section>
 <section id="downloading-datasets-from-the-openml-org-repository">
-<span id="openml"></span><h2><span class="section-number">7.4.3. </span>Downloading datasets from the openml.org repository<a class="headerlink" href="#downloading-datasets-from-the-openml-org-repository" title="Link to this heading">#</a></h2>
+<span id="openml"></span><h2><span class="section-number">8.4.3. </span>Downloading datasets from the openml.org repository<a class="headerlink" href="#downloading-datasets-from-the-openml-org-repository" title="Link to this heading">#</a></h2>
 <p><a class="reference external" href="https://openml.org/">openml.org</a> is a public repository for machine learning
 data and experiments, that allows everybody to upload open datasets.</p>
 <p>The <code class="docutils literal notranslate"><span class="pre">sklearn.datasets</span></code> package is able to download datasets
@@ -822,7 +823,7 @@
 </pre></div>
 </div>
 <section id="dataset-versions">
-<span id="openml-versions"></span><h3><span class="section-number">7.4.3.1. </span>Dataset Versions<a class="headerlink" href="#dataset-versions" title="Link to this heading">#</a></h3>
+<span id="openml-versions"></span><h3><span class="section-number">8.4.3.1. </span>Dataset Versions<a class="headerlink" href="#dataset-versions" title="Link to this heading">#</a></h3>
 <p>A dataset is uniquely specified by its <code class="docutils literal notranslate"><span class="pre">data_id</span></code>, but not necessarily by its
 name. Several different “versions” of a dataset with the same name can exist
 which can contain entirely different datasets.
@@ -883,7 +884,7 @@
 </ul>
 </section>
 <section id="arff-parser">
-<span id="openml-parser"></span><h3><span class="section-number">7.4.3.2. </span>ARFF parser<a class="headerlink" href="#arff-parser" title="Link to this heading">#</a></h3>
+<span id="openml-parser"></span><h3><span class="section-number">8.4.3.2. </span>ARFF parser<a class="headerlink" href="#arff-parser" title="Link to this heading">#</a></h3>
 <p>From version 1.2, scikit-learn provides a new keyword argument <code class="docutils literal notranslate"><span class="pre">parser</span></code> that
 provides several options to parse the ARFF files provided by OpenML. The legacy
 parser (i.e. <code class="docutils literal notranslate"><span class="pre">parser=&quot;liac-arff&quot;</span></code>) is based on the project
@@ -923,7 +924,7 @@
 </section>
 </section>
 <section id="loading-from-external-datasets">
-<span id="external-datasets"></span><h2><span class="section-number">7.4.4. </span>Loading from external datasets<a class="headerlink" href="#loading-from-external-datasets" title="Link to this heading">#</a></h2>
+<span id="external-datasets"></span><h2><span class="section-number">8.4.4. </span>Loading from external datasets<a class="headerlink" href="#loading-from-external-datasets" title="Link to this heading">#</a></h2>
 <p>scikit-learn works on any numeric data stored as numpy arrays or scipy sparse
 matrices. Other types that are convertible to numeric arrays such as pandas
 DataFrame are also acceptable.</p>
@@ -982,7 +983,7 @@
       <i class="fa-solid fa-angle-left"></i>
       <div class="prev-next-info">
         <p class="prev-next-subtitle">previous</p>
-        <p class="prev-next-title"><span class="section-number">7.3. </span>Generated datasets</p>
+        <p class="prev-next-title"><span class="section-number">8.3. </span>Generated datasets</p>
       </div>
     </a>
     <a class="right-next"
@@ -990,7 +991,7 @@
        title="next page">
       <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">8. </span>Computing with scikit-learn</p>
+        <p class="prev-next-title"><span class="section-number">9. </span>Computing with scikit-learn</p>
       </div>
       <i class="fa-solid fa-angle-right"></i>
     </a>
@@ -1017,14 +1018,14 @@
   </div>
   <nav class="bd-toc-nav page-toc" aria-labelledby="pst-page-navigation-heading-2">
     <ul class="visible nav section-nav flex-column">
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#sample-images">7.4.1. Sample images</a></li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#datasets-in-svmlight-libsvm-format">7.4.2. Datasets in svmlight / libsvm format</a></li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#downloading-datasets-from-the-openml-org-repository">7.4.3. Downloading datasets from the openml.org repository</a><ul class="nav section-nav flex-column">
-<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#dataset-versions">7.4.3.1. Dataset Versions</a></li>
-<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#arff-parser">7.4.3.2. ARFF parser</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#sample-images">8.4.1. Sample images</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#datasets-in-svmlight-libsvm-format">8.4.2. Datasets in svmlight / libsvm format</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#downloading-datasets-from-the-openml-org-repository">8.4.3. Downloading datasets from the openml.org repository</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#dataset-versions">8.4.3.1. Dataset Versions</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#arff-parser">8.4.3.2. ARFF parser</a></li>
 </ul>
 </li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#loading-from-external-datasets">7.4.4. Loading from external datasets</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#loading-from-external-datasets">8.4.4. Loading from external datasets</a></li>
 </ul>
   </nav></div>
 
diff --git a/dev/datasets/real_world.html b/dev/datasets/real_world.html
index 1a71bfe253324..c67f008c6dd2b 100644
--- a/dev/datasets/real_world.html
+++ b/dev/datasets/real_world.html
@@ -7,7 +7,7 @@
   <head>
     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="viewport" content="width=device-width, initial-scale=1" />
-<meta property="og:title" content="7.2. Real world datasets" />
+<meta property="og:title" content="8.2. Real world datasets" />
 <meta property="og:type" content="website" />
 <meta property="og:url" content="https://scikit-learn/stable/datasets/real_world.html" />
 <meta property="og:site_name" content="scikit-learn" />
@@ -16,7 +16,7 @@
 <meta property="og:image:alt" content="scikit-learn" />
 <meta name="description" content="scikit-learn provides tools to load larger datasets, downloading them if necessary. They can be loaded using the following functions: The Olivetti faces dataset: This dataset contains a set of face..." />
 
-    <title>7.2. Real world datasets &#8212; scikit-learn 1.7.dev0 documentation</title>
+    <title>8.2. Real world datasets &#8212; scikit-learn 1.7.dev0 documentation</title>
   
   
   
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -80,8 +80,8 @@
     <link rel="icon" href="http://github.com/_static/favicon.ico"/>
     <link rel="author" title="About these documents" href="http://github.com/about.html" />
     <link rel="search" title="Search" href="http://github.com/search.html" />
-    <link rel="next" title="7.3. Generated datasets" href="http://github.com/sample_generators.html" />
-    <link rel="prev" title="7.1. Toy datasets" href="http://github.com/toy_dataset.html" />
+    <link rel="next" title="8.3. Generated datasets" href="http://github.com/sample_generators.html" />
+    <link rel="prev" title="8.1. Toy datasets" href="http://github.com/toy_dataset.html" />
   <meta name="viewport" content="width=device-width, initial-scale=1"/>
   <meta name="docsearch:language" content="en"/>
   <meta name="docsearch:version" content="1.7" />
@@ -593,45 +593,46 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/learning_curve.html">3.5. Validation curves: plotting scores to evaluate models</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/inspection.html">4. Inspection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/partial_dependence.html">4.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/permutation_importance.html">4.2. Permutation feature importance</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/metadata_routing.html">4. Metadata Routing</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/inspection.html">5. Inspection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/partial_dependence.html">5.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/permutation_importance.html">5.2. Permutation feature importance</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/visualizations.html">5. Visualizations</a></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/data_transforms.html">6. Dataset transformations</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/compose.html">6.1. Pipelines and composite estimators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/feature_extraction.html">6.2. Feature extraction</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/preprocessing.html">6.3. Preprocessing data</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/impute.html">6.4. Imputation of missing values</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/unsupervised_reduction.html">6.5. Unsupervised dimensionality reduction</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/random_projection.html">6.6. Random Projection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/kernel_approximation.html">6.7. Kernel Approximation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/metrics.html">6.8. Pairwise metrics, Affinities and Kernels</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/preprocessing_targets.html">6.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/visualizations.html">6. Visualizations</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/data_transforms.html">7. Dataset transformations</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/compose.html">7.1. Pipelines and composite estimators</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/feature_extraction.html">7.2. Feature extraction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/preprocessing.html">7.3. Preprocessing data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/impute.html">7.4. Imputation of missing values</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/unsupervised_reduction.html">7.5. Unsupervised dimensionality reduction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/random_projection.html">7.6. Random Projection</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/kernel_approximation.html">7.7. Kernel Approximation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/metrics.html">7.8. Pairwise metrics, Affinities and Kernels</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/preprocessing_targets.html">7.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 current active has-children"><a class="reference internal" href="http://github.com/datasets.html">7. Dataset loading utilities</a><details open="open"><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul class="current">
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/toy_dataset.html">7.1. Toy datasets</a></li>
-<li class="toctree-l2 current active"><a class="current reference internal" href="#">7.2. Real world datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/sample_generators.html">7.3. Generated datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/loading_other_datasets.html">7.4. Loading other datasets</a></li>
+<li class="toctree-l1 current active has-children"><a class="reference internal" href="http://github.com/datasets.html">8. Dataset loading utilities</a><details open="open"><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/toy_dataset.html">8.1. Toy datasets</a></li>
+<li class="toctree-l2 current active"><a class="current reference internal" href="#">8.2. Real world datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/sample_generators.html">8.3. Generated datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/loading_other_datasets.html">8.4. Loading other datasets</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/computing.html">8. Computing with scikit-learn</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/scaling_strategies.html">8.1. Strategies to scale computationally: bigger data</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/computational_performance.html">8.2. Computational Performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/parallelism.html">8.3. Parallelism, resource management, and configuration</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/computing.html">9. Computing with scikit-learn</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/scaling_strategies.html">9.1. Strategies to scale computationally: bigger data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/computational_performance.html">9.2. Computational Performance</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/parallelism.html">9.3. Parallelism, resource management, and configuration</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/model_persistence.html">9. Model persistence</a></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/common_pitfalls.html">10. Common pitfalls and recommended practices</a></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/dispatching.html">11. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/array_api.html">11.1. Array API support (experimental)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/model_persistence.html">10. Model persistence</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/common_pitfalls.html">11. Common pitfalls and recommended practices</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/dispatching.html">12. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/array_api.html">12.1. Array API support (experimental)</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/machine_learning_map.html">12. Choosing the right estimator</a></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/presentations.html">13. External Resources, Videos and Talks</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/machine_learning_map.html">13. Choosing the right estimator</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/presentations.html">14. External Resources, Videos and Talks</a></li>
 </ul>
 </div>
 </nav></div>
@@ -669,9 +670,9 @@
     <li class="breadcrumb-item"><a href="http://github.com/user_guide.html" class="nav-link">User Guide</a></li>
     
     
-    <li class="breadcrumb-item"><a href="http://github.com/datasets.html" class="nav-link"><span class="section-number">7. </span>Dataset loading utilities</a></li>
+    <li class="breadcrumb-item"><a href="http://github.com/datasets.html" class="nav-link"><span class="section-number">8. </span>Dataset loading utilities</a></li>
     
-    <li class="breadcrumb-item active" aria-current="page"><span class="ellipsis"><span class="section-number">7.2. </span>Real world datasets</span></li>
+    <li class="breadcrumb-item active" aria-current="page"><span class="ellipsis"><span class="section-number">8.2. </span>Real world datasets</span></li>
   </ul>
 </nav>
 </div>
@@ -689,7 +690,7 @@
                 <article class="bd-article">
                   
   <section id="real-world-datasets">
-<span id="id1"></span><h1><span class="section-number">7.2. </span>Real world datasets<a class="headerlink" href="#real-world-datasets" title="Link to this heading">#</a></h1>
+<span id="id1"></span><h1><span class="section-number">8.2. </span>Real world datasets<a class="headerlink" href="#real-world-datasets" title="Link to this heading">#</a></h1>
 <p>scikit-learn provides tools to load larger datasets, downloading them if
 necessary.</p>
 <p>They can be loaded using the following functions:</p>
@@ -729,7 +730,7 @@
 </table>
 </div>
 <section id="the-olivetti-faces-dataset">
-<span id="olivetti-faces-dataset"></span><h2><span class="section-number">7.2.1. </span>The Olivetti faces dataset<a class="headerlink" href="#the-olivetti-faces-dataset" title="Link to this heading">#</a></h2>
+<span id="olivetti-faces-dataset"></span><h2><span class="section-number">8.2.1. </span>The Olivetti faces dataset<a class="headerlink" href="#the-olivetti-faces-dataset" title="Link to this heading">#</a></h2>
 <p><a class="reference external" href="https://cam-orl.co.uk/facedatabase.html">This dataset contains a set of face images</a> taken between April 1992 and
 April 1994 at AT&amp;T Laboratories Cambridge. The
 <a class="reference internal" href="http://github.com/modules/generated/sklearn.datasets.fetch_olivetti_faces.html#sklearn.datasets.fetch_olivetti_faces" title="sklearn.datasets.fetch_olivetti_faces"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.datasets.fetch_olivetti_faces</span></code></a> function is the data
@@ -774,7 +775,7 @@
 <p>When using these images, please give credit to AT&amp;T Laboratories Cambridge.</p>
 </section>
 <section id="the-20-newsgroups-text-dataset">
-<span id="newsgroups-dataset"></span><h2><span class="section-number">7.2.2. </span>The 20 newsgroups text dataset<a class="headerlink" href="#the-20-newsgroups-text-dataset" title="Link to this heading">#</a></h2>
+<span id="newsgroups-dataset"></span><h2><span class="section-number">8.2.2. </span>The 20 newsgroups text dataset<a class="headerlink" href="#the-20-newsgroups-text-dataset" title="Link to this heading">#</a></h2>
 <p>The 20 newsgroups dataset comprises around 18000 newsgroups posts on
 20 topics split in two subsets: one for training (or development)
 and the other one for testing (or for performance evaluation). The split
@@ -1026,7 +1027,7 @@
 </ul>
 </section>
 <section id="the-labeled-faces-in-the-wild-face-recognition-dataset">
-<span id="labeled-faces-in-the-wild-dataset"></span><h2><span class="section-number">7.2.3. </span>The Labeled Faces in the Wild face recognition dataset<a class="headerlink" href="#the-labeled-faces-in-the-wild-face-recognition-dataset" title="Link to this heading">#</a></h2>
+<span id="labeled-faces-in-the-wild-dataset"></span><h2><span class="section-number">8.2.3. </span>The Labeled Faces in the Wild face recognition dataset<a class="headerlink" href="#the-labeled-faces-in-the-wild-face-recognition-dataset" title="Link to this heading">#</a></h2>
 <p>This dataset is a collection of JPEG pictures of famous people collected
 over the internet, and the details are available on the Kaggle website:</p>
 <p><a class="reference external" href="https://www.kaggle.com/datasets/jessicali9530/lfw-dataset">https://www.kaggle.com/datasets/jessicali9530/lfw-dataset</a></p>
@@ -1150,7 +1151,7 @@
 </ul>
 </section>
 <section id="forest-covertypes">
-<span id="covtype-dataset"></span><h2><span class="section-number">7.2.4. </span>Forest covertypes<a class="headerlink" href="#forest-covertypes" title="Link to this heading">#</a></h2>
+<span id="covtype-dataset"></span><h2><span class="section-number">8.2.4. </span>Forest covertypes<a class="headerlink" href="#forest-covertypes" title="Link to this heading">#</a></h2>
 <p>The samples in this dataset correspond to 30×30m patches of forest in the US,
 collected for the task of predicting each patch’s cover type,
 i.e. the dominant species of tree.
@@ -1186,7 +1187,7 @@
 The dataset will be downloaded from the web if necessary.</p>
 </section>
 <section id="rcv1-dataset">
-<span id="id2"></span><h2><span class="section-number">7.2.5. </span>RCV1 dataset<a class="headerlink" href="#rcv1-dataset" title="Link to this heading">#</a></h2>
+<span id="id2"></span><h2><span class="section-number">8.2.5. </span>RCV1 dataset<a class="headerlink" href="#rcv1-dataset" title="Link to this heading">#</a></h2>
 <p>Reuters Corpus Volume I (RCV1) is an archive of over 800,000 manually
 categorized newswire stories made available by Reuters, Ltd. for research
 purposes. The dataset is extensively described in <a class="footnote-reference brackets" href="#id5" id="id3" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a>.</p>
@@ -1264,7 +1265,7 @@
 </aside>
 </section>
 <section id="kddcup-99-dataset">
-<span id="kddcup99-dataset"></span><h2><span class="section-number">7.2.6. </span>Kddcup 99 dataset<a class="headerlink" href="#kddcup-99-dataset" title="Link to this heading">#</a></h2>
+<span id="kddcup99-dataset"></span><h2><span class="section-number">8.2.6. </span>Kddcup 99 dataset<a class="headerlink" href="#kddcup-99-dataset" title="Link to this heading">#</a></h2>
 <p>The KDD Cup ‘99 dataset was created by processing the tcpdump portions
 of the 1998 DARPA Intrusion Detection System (IDS) Evaluation dataset,
 created by MIT Lincoln Lab <a class="footnote-reference brackets" href="#id8" id="id6" role="doc-noteref"><span class="fn-bracket">[</span>2<span class="fn-bracket">]</span></a>. The artificial data (described on the <a class="reference external" href="https://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html">dataset’s
@@ -1404,7 +1405,7 @@
 </aside>
 </section>
 <section id="california-housing-dataset">
-<span id="id10"></span><h2><span class="section-number">7.2.7. </span>California Housing dataset<a class="headerlink" href="#california-housing-dataset" title="Link to this heading">#</a></h2>
+<span id="id10"></span><h2><span class="section-number">8.2.7. </span>California Housing dataset<a class="headerlink" href="#california-housing-dataset" title="Link to this heading">#</a></h2>
 <p><strong>Data Set Characteristics:</strong></p>
 <dl class="field-list simple">
 <dt class="field-odd">Number of Instances<span class="colon">:</span></dt>
@@ -1450,7 +1451,7 @@
 </ul>
 </section>
 <section id="species-distribution-dataset">
-<span id="id11"></span><h2><span class="section-number">7.2.8. </span>Species distribution dataset<a class="headerlink" href="#species-distribution-dataset" title="Link to this heading">#</a></h2>
+<span id="id11"></span><h2><span class="section-number">8.2.8. </span>Species distribution dataset<a class="headerlink" href="#species-distribution-dataset" title="Link to this heading">#</a></h2>
 <p>This dataset represents the geographic distribution of two species in Central and
 South America. The two species are:</p>
 <ul class="simple">
@@ -1499,7 +1500,7 @@
       <i class="fa-solid fa-angle-left"></i>
       <div class="prev-next-info">
         <p class="prev-next-subtitle">previous</p>
-        <p class="prev-next-title"><span class="section-number">7.1. </span>Toy datasets</p>
+        <p class="prev-next-title"><span class="section-number">8.1. </span>Toy datasets</p>
       </div>
     </a>
     <a class="right-next"
@@ -1507,7 +1508,7 @@
        title="next page">
       <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">7.3. </span>Generated datasets</p>
+        <p class="prev-next-title"><span class="section-number">8.3. </span>Generated datasets</p>
       </div>
       <i class="fa-solid fa-angle-right"></i>
     </a>
@@ -1534,14 +1535,14 @@
   </div>
   <nav class="bd-toc-nav page-toc" aria-labelledby="pst-page-navigation-heading-2">
     <ul class="visible nav section-nav flex-column">
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#the-olivetti-faces-dataset">7.2.1. The Olivetti faces dataset</a></li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#the-20-newsgroups-text-dataset">7.2.2. The 20 newsgroups text dataset</a></li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#the-labeled-faces-in-the-wild-face-recognition-dataset">7.2.3. The Labeled Faces in the Wild face recognition dataset</a></li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#forest-covertypes">7.2.4. Forest covertypes</a></li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#rcv1-dataset">7.2.5. RCV1 dataset</a></li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#kddcup-99-dataset">7.2.6. Kddcup 99 dataset</a></li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#california-housing-dataset">7.2.7. California Housing dataset</a></li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#species-distribution-dataset">7.2.8. Species distribution dataset</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#the-olivetti-faces-dataset">8.2.1. The Olivetti faces dataset</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#the-20-newsgroups-text-dataset">8.2.2. The 20 newsgroups text dataset</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#the-labeled-faces-in-the-wild-face-recognition-dataset">8.2.3. The Labeled Faces in the Wild face recognition dataset</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#forest-covertypes">8.2.4. Forest covertypes</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#rcv1-dataset">8.2.5. RCV1 dataset</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#kddcup-99-dataset">8.2.6. Kddcup 99 dataset</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#california-housing-dataset">8.2.7. California Housing dataset</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#species-distribution-dataset">8.2.8. Species distribution dataset</a></li>
 </ul>
   </nav></div>
 
diff --git a/dev/datasets/sample_generators.html b/dev/datasets/sample_generators.html
index dbe08053e424d..4ae02869a45cc 100644
--- a/dev/datasets/sample_generators.html
+++ b/dev/datasets/sample_generators.html
@@ -7,7 +7,7 @@
   <head>
     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="viewport" content="width=device-width, initial-scale=1" />
-<meta property="og:title" content="7.3. Generated datasets" />
+<meta property="og:title" content="8.3. Generated datasets" />
 <meta property="og:type" content="website" />
 <meta property="og:url" content="https://scikit-learn/stable/datasets/sample_generators.html" />
 <meta property="og:site_name" content="scikit-learn" />
@@ -16,7 +16,7 @@
 <meta property="og:image:alt" content="scikit-learn" />
 <meta name="description" content="In addition, scikit-learn includes various random sample generators that can be used to build artificial datasets of controlled size and complexity. Generators for classification and clustering: Th..." />
 
-    <title>7.3. Generated datasets &#8212; scikit-learn 1.7.dev0 documentation</title>
+    <title>8.3. Generated datasets &#8212; scikit-learn 1.7.dev0 documentation</title>
   
   
   
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
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     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -80,8 +80,8 @@
     <link rel="icon" href="http://github.com/_static/favicon.ico"/>
     <link rel="author" title="About these documents" href="http://github.com/about.html" />
     <link rel="search" title="Search" href="http://github.com/search.html" />
-    <link rel="next" title="7.4. Loading other datasets" href="http://github.com/loading_other_datasets.html" />
-    <link rel="prev" title="7.2. Real world datasets" href="http://github.com/real_world.html" />
+    <link rel="next" title="8.4. Loading other datasets" href="http://github.com/loading_other_datasets.html" />
+    <link rel="prev" title="8.2. Real world datasets" href="http://github.com/real_world.html" />
   <meta name="viewport" content="width=device-width, initial-scale=1"/>
   <meta name="docsearch:language" content="en"/>
   <meta name="docsearch:version" content="1.7" />
@@ -593,45 +593,46 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/learning_curve.html">3.5. Validation curves: plotting scores to evaluate models</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/inspection.html">4. Inspection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/partial_dependence.html">4.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/permutation_importance.html">4.2. Permutation feature importance</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/metadata_routing.html">4. Metadata Routing</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/inspection.html">5. Inspection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/partial_dependence.html">5.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/permutation_importance.html">5.2. Permutation feature importance</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/visualizations.html">5. Visualizations</a></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/data_transforms.html">6. Dataset transformations</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/compose.html">6.1. Pipelines and composite estimators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/feature_extraction.html">6.2. Feature extraction</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/preprocessing.html">6.3. Preprocessing data</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/impute.html">6.4. Imputation of missing values</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/unsupervised_reduction.html">6.5. Unsupervised dimensionality reduction</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/random_projection.html">6.6. Random Projection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/kernel_approximation.html">6.7. Kernel Approximation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/metrics.html">6.8. Pairwise metrics, Affinities and Kernels</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/preprocessing_targets.html">6.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/visualizations.html">6. Visualizations</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/data_transforms.html">7. Dataset transformations</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/compose.html">7.1. Pipelines and composite estimators</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/feature_extraction.html">7.2. Feature extraction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/preprocessing.html">7.3. Preprocessing data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/impute.html">7.4. Imputation of missing values</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/unsupervised_reduction.html">7.5. Unsupervised dimensionality reduction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/random_projection.html">7.6. Random Projection</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/kernel_approximation.html">7.7. Kernel Approximation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/metrics.html">7.8. Pairwise metrics, Affinities and Kernels</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/preprocessing_targets.html">7.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 current active has-children"><a class="reference internal" href="http://github.com/datasets.html">7. Dataset loading utilities</a><details open="open"><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul class="current">
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/toy_dataset.html">7.1. Toy datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/real_world.html">7.2. Real world datasets</a></li>
-<li class="toctree-l2 current active"><a class="current reference internal" href="#">7.3. Generated datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/loading_other_datasets.html">7.4. Loading other datasets</a></li>
+<li class="toctree-l1 current active has-children"><a class="reference internal" href="http://github.com/datasets.html">8. Dataset loading utilities</a><details open="open"><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/toy_dataset.html">8.1. Toy datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/real_world.html">8.2. Real world datasets</a></li>
+<li class="toctree-l2 current active"><a class="current reference internal" href="#">8.3. Generated datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/loading_other_datasets.html">8.4. Loading other datasets</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/computing.html">8. Computing with scikit-learn</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/scaling_strategies.html">8.1. Strategies to scale computationally: bigger data</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/computational_performance.html">8.2. Computational Performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/parallelism.html">8.3. Parallelism, resource management, and configuration</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/computing.html">9. Computing with scikit-learn</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/scaling_strategies.html">9.1. Strategies to scale computationally: bigger data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/computational_performance.html">9.2. Computational Performance</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/parallelism.html">9.3. Parallelism, resource management, and configuration</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/model_persistence.html">9. Model persistence</a></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/common_pitfalls.html">10. Common pitfalls and recommended practices</a></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/dispatching.html">11. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/array_api.html">11.1. Array API support (experimental)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/model_persistence.html">10. Model persistence</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/common_pitfalls.html">11. Common pitfalls and recommended practices</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/dispatching.html">12. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/array_api.html">12.1. Array API support (experimental)</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/machine_learning_map.html">12. Choosing the right estimator</a></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/presentations.html">13. External Resources, Videos and Talks</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/machine_learning_map.html">13. Choosing the right estimator</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/presentations.html">14. External Resources, Videos and Talks</a></li>
 </ul>
 </div>
 </nav></div>
@@ -669,9 +670,9 @@
     <li class="breadcrumb-item"><a href="http://github.com/user_guide.html" class="nav-link">User Guide</a></li>
     
     
-    <li class="breadcrumb-item"><a href="http://github.com/datasets.html" class="nav-link"><span class="section-number">7. </span>Dataset loading utilities</a></li>
+    <li class="breadcrumb-item"><a href="http://github.com/datasets.html" class="nav-link"><span class="section-number">8. </span>Dataset loading utilities</a></li>
     
-    <li class="breadcrumb-item active" aria-current="page"><span class="ellipsis"><span class="section-number">7.3. </span>Generated datasets</span></li>
+    <li class="breadcrumb-item active" aria-current="page"><span class="ellipsis"><span class="section-number">8.3. </span>Generated datasets</span></li>
   </ul>
 </nav>
 </div>
@@ -689,15 +690,15 @@
                 <article class="bd-article">
                   
   <section id="generated-datasets">
-<span id="sample-generators"></span><h1><span class="section-number">7.3. </span>Generated datasets<a class="headerlink" href="#generated-datasets" title="Link to this heading">#</a></h1>
+<span id="sample-generators"></span><h1><span class="section-number">8.3. </span>Generated datasets<a class="headerlink" href="#generated-datasets" title="Link to this heading">#</a></h1>
 <p>In addition, scikit-learn includes various random sample generators that
 can be used to build artificial datasets of controlled size and complexity.</p>
 <section id="generators-for-classification-and-clustering">
-<h2><span class="section-number">7.3.1. </span>Generators for classification and clustering<a class="headerlink" href="#generators-for-classification-and-clustering" title="Link to this heading">#</a></h2>
+<h2><span class="section-number">8.3.1. </span>Generators for classification and clustering<a class="headerlink" href="#generators-for-classification-and-clustering" title="Link to this heading">#</a></h2>
 <p>These generators produce a matrix of features and corresponding discrete
 targets.</p>
 <section id="single-label">
-<h3><span class="section-number">7.3.1.1. </span>Single label<a class="headerlink" href="#single-label" title="Link to this heading">#</a></h3>
+<h3><span class="section-number">8.3.1.1. </span>Single label<a class="headerlink" href="#single-label" title="Link to this heading">#</a></h3>
 <p><a class="reference internal" href="http://github.com/modules/generated/sklearn.datasets.make_blobs.html#sklearn.datasets.make_blobs" title="sklearn.datasets.make_blobs"><code class="xref py py-func docutils literal notranslate"><span class="pre">make_blobs</span></code></a> creates a multiclass dataset by allocating each class to one
 normally-distributed cluster of points. It provides control over the centers and
 standard deviations of each cluster. This dataset is used to demonstrate clustering.</p>
@@ -789,7 +790,7 @@ <h3><span class="section-number">7.3.1.1. </span>Single label<a class="headerlin
 </figure>
 </section>
 <section id="multilabel">
-<h3><span class="section-number">7.3.1.2. </span>Multilabel<a class="headerlink" href="#multilabel" title="Link to this heading">#</a></h3>
+<h3><span class="section-number">8.3.1.2. </span>Multilabel<a class="headerlink" href="#multilabel" title="Link to this heading">#</a></h3>
 <p><a class="reference internal" href="http://github.com/modules/generated/sklearn.datasets.make_multilabel_classification.html#sklearn.datasets.make_multilabel_classification" title="sklearn.datasets.make_multilabel_classification"><code class="xref py py-func docutils literal notranslate"><span class="pre">make_multilabel_classification</span></code></a> generates random samples with multiple
 labels, reflecting a bag of words drawn from a mixture of topics. The number of
 topics for each document is drawn from a Poisson distribution, and the topics
@@ -809,7 +810,7 @@ <h3><span class="section-number">7.3.1.2. </span>Multilabel<a class="headerlink"
 </a>
 </section>
 <section id="biclustering">
-<h3><span class="section-number">7.3.1.3. </span>Biclustering<a class="headerlink" href="#biclustering" title="Link to this heading">#</a></h3>
+<h3><span class="section-number">8.3.1.3. </span>Biclustering<a class="headerlink" href="#biclustering" title="Link to this heading">#</a></h3>
 <div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
 <tbody>
 <tr class="row-odd"><td><p><a class="reference internal" href="http://github.com/modules/generated/sklearn.datasets.make_biclusters.html#sklearn.datasets.make_biclusters" title="sklearn.datasets.make_biclusters"><code class="xref py py-obj docutils literal notranslate"><span class="pre">make_biclusters</span></code></a>(shape, n_clusters, *[, ...])</p></td>
@@ -824,7 +825,7 @@ <h3><span class="section-number">7.3.1.3. </span>Biclustering<a class="headerlin
 </section>
 </section>
 <section id="generators-for-regression">
-<h2><span class="section-number">7.3.2. </span>Generators for regression<a class="headerlink" href="#generators-for-regression" title="Link to this heading">#</a></h2>
+<h2><span class="section-number">8.3.2. </span>Generators for regression<a class="headerlink" href="#generators-for-regression" title="Link to this heading">#</a></h2>
 <p><a class="reference internal" href="http://github.com/modules/generated/sklearn.datasets.make_regression.html#sklearn.datasets.make_regression" title="sklearn.datasets.make_regression"><code class="xref py py-func docutils literal notranslate"><span class="pre">make_regression</span></code></a> produces regression targets as an optionally-sparse
 random linear combination of random features, with noise. Its informative
 features may be uncorrelated, or low rank (few features account for most of the
@@ -838,7 +839,7 @@ <h2><span class="section-number">7.3.2. </span>Generators for regression<a class
 <a class="reference internal" href="http://github.com/modules/generated/sklearn.datasets.make_friedman3.html#sklearn.datasets.make_friedman3" title="sklearn.datasets.make_friedman3"><code class="xref py py-func docutils literal notranslate"><span class="pre">make_friedman3</span></code></a> is similar with an arctan transformation on the target.</p>
 </section>
 <section id="generators-for-manifold-learning">
-<h2><span class="section-number">7.3.3. </span>Generators for manifold learning<a class="headerlink" href="#generators-for-manifold-learning" title="Link to this heading">#</a></h2>
+<h2><span class="section-number">8.3.3. </span>Generators for manifold learning<a class="headerlink" href="#generators-for-manifold-learning" title="Link to this heading">#</a></h2>
 <div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
 <tbody>
 <tr class="row-odd"><td><p><a class="reference internal" href="http://github.com/modules/generated/sklearn.datasets.make_s_curve.html#sklearn.datasets.make_s_curve" title="sklearn.datasets.make_s_curve"><code class="xref py py-obj docutils literal notranslate"><span class="pre">make_s_curve</span></code></a>([n_samples, noise, random_state])</p></td>
@@ -852,7 +853,7 @@ <h2><span class="section-number">7.3.3. </span>Generators for manifold learning<
 </div>
 </section>
 <section id="generators-for-decomposition">
-<h2><span class="section-number">7.3.4. </span>Generators for decomposition<a class="headerlink" href="#generators-for-decomposition" title="Link to this heading">#</a></h2>
+<h2><span class="section-number">8.3.4. </span>Generators for decomposition<a class="headerlink" href="#generators-for-decomposition" title="Link to this heading">#</a></h2>
 <div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
 <tbody>
 <tr class="row-odd"><td><p><a class="reference internal" href="http://github.com/modules/generated/sklearn.datasets.make_low_rank_matrix.html#sklearn.datasets.make_low_rank_matrix" title="sklearn.datasets.make_low_rank_matrix"><code class="xref py py-obj docutils literal notranslate"><span class="pre">make_low_rank_matrix</span></code></a>([n_samples, ...])</p></td>
@@ -889,7 +890,7 @@ <h2><span class="section-number">7.3.4. </span>Generators for decomposition<a cl
       <i class="fa-solid fa-angle-left"></i>
       <div class="prev-next-info">
         <p class="prev-next-subtitle">previous</p>
-        <p class="prev-next-title"><span class="section-number">7.2. </span>Real world datasets</p>
+        <p class="prev-next-title"><span class="section-number">8.2. </span>Real world datasets</p>
       </div>
     </a>
     <a class="right-next"
@@ -897,7 +898,7 @@ <h2><span class="section-number">7.3.4. </span>Generators for decomposition<a cl
        title="next page">
       <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">7.4. </span>Loading other datasets</p>
+        <p class="prev-next-title"><span class="section-number">8.4. </span>Loading other datasets</p>
       </div>
       <i class="fa-solid fa-angle-right"></i>
     </a>
@@ -924,15 +925,15 @@ <h2><span class="section-number">7.3.4. </span>Generators for decomposition<a cl
   </div>
   <nav class="bd-toc-nav page-toc" aria-labelledby="pst-page-navigation-heading-2">
     <ul class="visible nav section-nav flex-column">
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#generators-for-classification-and-clustering">7.3.1. Generators for classification and clustering</a><ul class="nav section-nav flex-column">
-<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#single-label">7.3.1.1. Single label</a></li>
-<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#multilabel">7.3.1.2. Multilabel</a></li>
-<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#biclustering">7.3.1.3. Biclustering</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#generators-for-classification-and-clustering">8.3.1. Generators for classification and clustering</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#single-label">8.3.1.1. Single label</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#multilabel">8.3.1.2. Multilabel</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#biclustering">8.3.1.3. Biclustering</a></li>
 </ul>
 </li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#generators-for-regression">7.3.2. Generators for regression</a></li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#generators-for-manifold-learning">7.3.3. Generators for manifold learning</a></li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#generators-for-decomposition">7.3.4. Generators for decomposition</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#generators-for-regression">8.3.2. Generators for regression</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#generators-for-manifold-learning">8.3.3. Generators for manifold learning</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#generators-for-decomposition">8.3.4. Generators for decomposition</a></li>
 </ul>
   </nav></div>
 
diff --git a/dev/datasets/toy_dataset.html b/dev/datasets/toy_dataset.html
index ed3efeb7907ad..32ee8a3eaed7b 100644
--- a/dev/datasets/toy_dataset.html
+++ b/dev/datasets/toy_dataset.html
@@ -7,7 +7,7 @@
   <head>
     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="viewport" content="width=device-width, initial-scale=1" />
-<meta property="og:title" content="7.1. Toy datasets" />
+<meta property="og:title" content="8.1. Toy datasets" />
 <meta property="og:type" content="website" />
 <meta property="og:url" content="https://scikit-learn/stable/datasets/toy_dataset.html" />
 <meta property="og:site_name" content="scikit-learn" />
@@ -16,7 +16,7 @@
 <meta property="og:image:alt" content="scikit-learn" />
 <meta name="description" content="scikit-learn comes with a few small standard datasets that do not require to download any file from some external website. They can be loaded using the following functions: These datasets are usefu..." />
 
-    <title>7.1. Toy datasets &#8212; scikit-learn 1.7.dev0 documentation</title>
+    <title>8.1. Toy datasets &#8212; scikit-learn 1.7.dev0 documentation</title>
   
   
   
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -80,8 +80,8 @@
     <link rel="icon" href="http://github.com/_static/favicon.ico"/>
     <link rel="author" title="About these documents" href="http://github.com/about.html" />
     <link rel="search" title="Search" href="http://github.com/search.html" />
-    <link rel="next" title="7.2. Real world datasets" href="http://github.com/real_world.html" />
-    <link rel="prev" title="7. Dataset loading utilities" href="http://github.com/datasets.html" />
+    <link rel="next" title="8.2. Real world datasets" href="http://github.com/real_world.html" />
+    <link rel="prev" title="8. Dataset loading utilities" href="http://github.com/datasets.html" />
   <meta name="viewport" content="width=device-width, initial-scale=1"/>
   <meta name="docsearch:language" content="en"/>
   <meta name="docsearch:version" content="1.7" />
@@ -593,45 +593,46 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/learning_curve.html">3.5. Validation curves: plotting scores to evaluate models</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/inspection.html">4. Inspection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/partial_dependence.html">4.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/permutation_importance.html">4.2. Permutation feature importance</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/metadata_routing.html">4. Metadata Routing</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/inspection.html">5. Inspection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/partial_dependence.html">5.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/permutation_importance.html">5.2. Permutation feature importance</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/visualizations.html">5. Visualizations</a></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/data_transforms.html">6. Dataset transformations</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/compose.html">6.1. Pipelines and composite estimators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/feature_extraction.html">6.2. Feature extraction</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/preprocessing.html">6.3. Preprocessing data</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/impute.html">6.4. Imputation of missing values</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/unsupervised_reduction.html">6.5. Unsupervised dimensionality reduction</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/random_projection.html">6.6. Random Projection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/kernel_approximation.html">6.7. Kernel Approximation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/metrics.html">6.8. Pairwise metrics, Affinities and Kernels</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/preprocessing_targets.html">6.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/visualizations.html">6. Visualizations</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/data_transforms.html">7. Dataset transformations</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/compose.html">7.1. Pipelines and composite estimators</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/feature_extraction.html">7.2. Feature extraction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/preprocessing.html">7.3. Preprocessing data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/impute.html">7.4. Imputation of missing values</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/unsupervised_reduction.html">7.5. Unsupervised dimensionality reduction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/random_projection.html">7.6. Random Projection</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/kernel_approximation.html">7.7. Kernel Approximation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/metrics.html">7.8. Pairwise metrics, Affinities and Kernels</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/preprocessing_targets.html">7.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 current active has-children"><a class="reference internal" href="http://github.com/datasets.html">7. Dataset loading utilities</a><details open="open"><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul class="current">
-<li class="toctree-l2 current active"><a class="current reference internal" href="#">7.1. Toy datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/real_world.html">7.2. Real world datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/sample_generators.html">7.3. Generated datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/loading_other_datasets.html">7.4. Loading other datasets</a></li>
+<li class="toctree-l1 current active has-children"><a class="reference internal" href="http://github.com/datasets.html">8. Dataset loading utilities</a><details open="open"><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul class="current">
+<li class="toctree-l2 current active"><a class="current reference internal" href="#">8.1. Toy datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/real_world.html">8.2. Real world datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/sample_generators.html">8.3. Generated datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/loading_other_datasets.html">8.4. Loading other datasets</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/computing.html">8. Computing with scikit-learn</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/scaling_strategies.html">8.1. Strategies to scale computationally: bigger data</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/computational_performance.html">8.2. Computational Performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/parallelism.html">8.3. Parallelism, resource management, and configuration</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/computing.html">9. Computing with scikit-learn</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/scaling_strategies.html">9.1. Strategies to scale computationally: bigger data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/computational_performance.html">9.2. Computational Performance</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/parallelism.html">9.3. Parallelism, resource management, and configuration</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/model_persistence.html">9. Model persistence</a></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/common_pitfalls.html">10. Common pitfalls and recommended practices</a></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/dispatching.html">11. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/array_api.html">11.1. Array API support (experimental)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/model_persistence.html">10. Model persistence</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/common_pitfalls.html">11. Common pitfalls and recommended practices</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/dispatching.html">12. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/array_api.html">12.1. Array API support (experimental)</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/machine_learning_map.html">12. Choosing the right estimator</a></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/presentations.html">13. External Resources, Videos and Talks</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/machine_learning_map.html">13. Choosing the right estimator</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/presentations.html">14. External Resources, Videos and Talks</a></li>
 </ul>
 </div>
 </nav></div>
@@ -669,9 +670,9 @@
     <li class="breadcrumb-item"><a href="http://github.com/user_guide.html" class="nav-link">User Guide</a></li>
     
     
-    <li class="breadcrumb-item"><a href="http://github.com/datasets.html" class="nav-link"><span class="section-number">7. </span>Dataset loading utilities</a></li>
+    <li class="breadcrumb-item"><a href="http://github.com/datasets.html" class="nav-link"><span class="section-number">8. </span>Dataset loading utilities</a></li>
     
-    <li class="breadcrumb-item active" aria-current="page"><span class="ellipsis"><span class="section-number">7.1. </span>Toy datasets</span></li>
+    <li class="breadcrumb-item active" aria-current="page"><span class="ellipsis"><span class="section-number">8.1. </span>Toy datasets</span></li>
   </ul>
 </nav>
 </div>
@@ -689,7 +690,7 @@
                 <article class="bd-article">
                   
   <section id="toy-datasets">
-<span id="id1"></span><h1><span class="section-number">7.1. </span>Toy datasets<a class="headerlink" href="#toy-datasets" title="Link to this heading">#</a></h1>
+<span id="id1"></span><h1><span class="section-number">8.1. </span>Toy datasets<a class="headerlink" href="#toy-datasets" title="Link to this heading">#</a></h1>
 <p>scikit-learn comes with a few small standard datasets that do not require to
 download any file from some external website.</p>
 <p>They can be loaded using the following functions:</p>
@@ -720,7 +721,7 @@
 various algorithms implemented in scikit-learn. They are however often too
 small to be representative of real world machine learning tasks.</p>
 <section id="iris-plants-dataset">
-<span id="iris-dataset"></span><h2><span class="section-number">7.1.1. </span>Iris plants dataset<a class="headerlink" href="#iris-plants-dataset" title="Link to this heading">#</a></h2>
+<span id="iris-dataset"></span><h2><span class="section-number">8.1.1. </span>Iris plants dataset<a class="headerlink" href="#iris-plants-dataset" title="Link to this heading">#</a></h2>
 <p><strong>Data Set Characteristics:</strong></p>
 <dl class="field-list simple">
 <dt class="field-odd">Number of Instances<span class="colon">:</span></dt>
@@ -839,7 +840,7 @@
 </div>
 </details></section>
 <section id="diabetes-dataset">
-<span id="id2"></span><h2><span class="section-number">7.1.2. </span>Diabetes dataset<a class="headerlink" href="#diabetes-dataset" title="Link to this heading">#</a></h2>
+<span id="id2"></span><h2><span class="section-number">8.1.2. </span>Diabetes dataset<a class="headerlink" href="#diabetes-dataset" title="Link to this heading">#</a></h2>
 <p>Ten baseline variables, age, sex, body mass index, average blood
 pressure, and six blood serum measurements were obtained for each of n =
 442 diabetes patients, as well as the response of interest, a
@@ -878,7 +879,7 @@
 (<a class="reference external" href="https://web.stanford.edu/~hastie/Papers/LARS/LeastAngle_2002.pdf">https://web.stanford.edu/~hastie/Papers/LARS/LeastAngle_2002.pdf</a>)</p>
 </section>
 <section id="optical-recognition-of-handwritten-digits-dataset">
-<span id="digits-dataset"></span><h2><span class="section-number">7.1.3. </span>Optical recognition of handwritten digits dataset<a class="headerlink" href="#optical-recognition-of-handwritten-digits-dataset" title="Link to this heading">#</a></h2>
+<span id="digits-dataset"></span><h2><span class="section-number">8.1.3. </span>Optical recognition of handwritten digits dataset<a class="headerlink" href="#optical-recognition-of-handwritten-digits-dataset" title="Link to this heading">#</a></h2>
 <p><strong>Data Set Characteristics:</strong></p>
 <dl class="field-list simple">
 <dt class="field-odd">Number of Instances<span class="colon">:</span></dt>
@@ -939,7 +940,7 @@
 </div>
 </details></section>
 <section id="linnerrud-dataset">
-<span id="id3"></span><h2><span class="section-number">7.1.4. </span>Linnerrud dataset<a class="headerlink" href="#linnerrud-dataset" title="Link to this heading">#</a></h2>
+<span id="id3"></span><h2><span class="section-number">8.1.4. </span>Linnerrud dataset<a class="headerlink" href="#linnerrud-dataset" title="Link to this heading">#</a></h2>
 <p><strong>Data Set Characteristics:</strong></p>
 <dl class="field-list simple">
 <dt class="field-odd">Number of Instances<span class="colon">:</span></dt>
@@ -977,7 +978,7 @@
 </div>
 </details></section>
 <section id="wine-recognition-dataset">
-<span id="wine-dataset"></span><h2><span class="section-number">7.1.5. </span>Wine recognition dataset<a class="headerlink" href="#wine-recognition-dataset" title="Link to this heading">#</a></h2>
+<span id="wine-dataset"></span><h2><span class="section-number">8.1.5. </span>Wine recognition dataset<a class="headerlink" href="#wine-recognition-dataset" title="Link to this heading">#</a></h2>
 <p><strong>Data Set Characteristics:</strong></p>
 <dl class="field-list simple">
 <dt class="field-odd">Number of Instances<span class="colon">:</span></dt>
@@ -1159,7 +1160,7 @@
 </div>
 </details></section>
 <section id="breast-cancer-wisconsin-diagnostic-dataset">
-<span id="breast-cancer-dataset"></span><h2><span class="section-number">7.1.6. </span>Breast cancer Wisconsin (diagnostic) dataset<a class="headerlink" href="#breast-cancer-wisconsin-diagnostic-dataset" title="Link to this heading">#</a></h2>
+<span id="breast-cancer-dataset"></span><h2><span class="section-number">8.1.6. </span>Breast cancer Wisconsin (diagnostic) dataset<a class="headerlink" href="#breast-cancer-wisconsin-diagnostic-dataset" title="Link to this heading">#</a></h2>
 <p><strong>Data Set Characteristics:</strong></p>
 <dl class="field-list">
 <dt class="field-odd">Number of Instances<span class="colon">:</span></dt>
@@ -1403,7 +1404,7 @@
       <i class="fa-solid fa-angle-left"></i>
       <div class="prev-next-info">
         <p class="prev-next-subtitle">previous</p>
-        <p class="prev-next-title"><span class="section-number">7. </span>Dataset loading utilities</p>
+        <p class="prev-next-title"><span class="section-number">8. </span>Dataset loading utilities</p>
       </div>
     </a>
     <a class="right-next"
@@ -1411,7 +1412,7 @@
        title="next page">
       <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">7.2. </span>Real world datasets</p>
+        <p class="prev-next-title"><span class="section-number">8.2. </span>Real world datasets</p>
       </div>
       <i class="fa-solid fa-angle-right"></i>
     </a>
@@ -1438,12 +1439,12 @@
   </div>
   <nav class="bd-toc-nav page-toc" aria-labelledby="pst-page-navigation-heading-2">
     <ul class="visible nav section-nav flex-column">
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#iris-plants-dataset">7.1.1. Iris plants dataset</a></li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#diabetes-dataset">7.1.2. Diabetes dataset</a></li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#optical-recognition-of-handwritten-digits-dataset">7.1.3. Optical recognition of handwritten digits dataset</a></li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#linnerrud-dataset">7.1.4. Linnerrud dataset</a></li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#wine-recognition-dataset">7.1.5. Wine recognition dataset</a></li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#breast-cancer-wisconsin-diagnostic-dataset">7.1.6. Breast cancer Wisconsin (diagnostic) dataset</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#iris-plants-dataset">8.1.1. Iris plants dataset</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#diabetes-dataset">8.1.2. Diabetes dataset</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#optical-recognition-of-handwritten-digits-dataset">8.1.3. Optical recognition of handwritten digits dataset</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#linnerrud-dataset">8.1.4. Linnerrud dataset</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#wine-recognition-dataset">8.1.5. Wine recognition dataset</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#breast-cancer-wisconsin-diagnostic-dataset">8.1.6. Breast cancer Wisconsin (diagnostic) dataset</a></li>
 </ul>
   </nav></div>
 
diff --git a/dev/developers/advanced_installation.html b/dev/developers/advanced_installation.html
index 07695e083595b..cbc6572e47706 100644
--- a/dev/developers/advanced_installation.html
+++ b/dev/developers/advanced_installation.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -726,32 +726,14 @@ <h3>Building a specific version from a tag<a class="headerlink" href="#building-
 to build scikit-learn Cython extensions for each supported platform.</p>
 <section id="windows">
 <span id="compiler-windows"></span><h3>Windows<a class="headerlink" href="#windows" title="Link to this heading">#</a></h3>
-<p>First, download the <a class="reference external" href="https://aka.ms/vs/17/release/vs_buildtools.exe">Build Tools for Visual Studio 2019 installer</a>.</p>
+<p>First, download the <a class="reference external" href="https://aka.ms/vs/17/release/vs_buildtools.exe">Build Tools for Visual Studio installer</a>.</p>
 <p>Run the downloaded <code class="docutils literal notranslate"><span class="pre">vs_buildtools.exe</span></code> file, during the installation you will
 need to make sure you select “Desktop development with C++”, similarly to this
 screenshot:</p>
 <img alt="../_images/visual-studio-build-tools-selection.png" src="http://github.com/_images/visual-studio-build-tools-selection.png" />
-<p>Secondly, find out if you are running 64-bit or 32-bit Python. The building
-command depends on the architecture of the Python interpreter. You can check
-the architecture by running the following in <code class="docutils literal notranslate"><span class="pre">cmd</span></code> or <code class="docutils literal notranslate"><span class="pre">powershell</span></code>
-console:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span class="prompt1">python<span class="w"> </span>-c<span class="w"> </span><span class="s2">&quot;import struct; print(struct.calcsize(&#39;P&#39;) * 8)&quot;</span></span>
-</pre></div></div><p>For 64-bit Python, configure the build environment by running the following
-commands in <code class="docutils literal notranslate"><span class="pre">cmd</span></code> or an Anaconda Prompt (if you use Anaconda):</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><style type="text/css">
-span.prompt2:before {
-  content: "C:\> ";
-}
-</style><span class="prompt2">SET<span class="w"> </span><span class="nv">DISTUTILS_USE_SDK</span><span class="o">=</span><span class="m">1</span></span>
-<span class="prompt2"><span class="s2">&quot;C:\Program Files (x86)\Microsoft Visual Studio\2019\BuildTools\VC\Auxiliary\Build\vcvarsall.bat&quot;</span><span class="w"> </span>x64</span>
-</pre></div></div><p>Replace <code class="docutils literal notranslate"><span class="pre">x64</span></code> by <code class="docutils literal notranslate"><span class="pre">x86</span></code> to build for 32-bit Python.</p>
-<p>Please be aware that the path above might be different from user to user. The
-aim is to point to the “vcvarsall.bat” file that will set the necessary
-environment variables in the current command prompt.</p>
-<p>Finally, build scikit-learn with this command prompt:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span class="prompt1">pip<span class="w"> </span>install<span class="w"> </span>--editable<span class="w"> </span>.<span class="w"> </span><span class="se">\</span>
-<span class="w">    </span>--verbose<span class="w"> </span>--no-build-isolation<span class="w"> </span><span class="se">\</span>
-<span class="w">    </span>--config-settings<span class="w"> </span>editable-verbose<span class="o">=</span><span class="nb">true</span></span>
+<p>Build scikit-learn by running the following command in your <code class="docutils literal notranslate"><span class="pre">sklearn-env</span></code> conda environment
+or virtualenv:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span class="prompt1">pip<span class="w"> </span>install<span class="w"> </span>--editable<span class="w"> </span>.<span class="w"> </span>--verbose<span class="w"> </span>--no-build-isolation<span class="w"> </span>--config-settings<span class="w"> </span>editable-verbose<span class="o">=</span><span class="nb">true</span></span>
 </pre></div></div></section>
 <section id="macos">
 <span id="compiler-macos"></span><h3>macOS<a class="headerlink" href="#macos" title="Link to this heading">#</a></h3>
diff --git a/dev/developers/bug_triaging.html b/dev/developers/bug_triaging.html
index 7c250210a621a..238c0b9232456 100644
--- a/dev/developers/bug_triaging.html
+++ b/dev/developers/bug_triaging.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
diff --git a/dev/developers/contributing.html b/dev/developers/contributing.html
index f614798c9920d..90ec1237fe649 100644
--- a/dev/developers/contributing.html
+++ b/dev/developers/contributing.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -831,7 +831,7 @@ <h3>How to contribute<a class="headerlink" href="#how-to-contribute" title="Link
 <li><p>Follow steps 2-6 in <a class="reference internal" href="http://github.com/advanced_installation.html#install-bleeding-edge"><span class="std std-ref">Building from source</span></a> to build scikit-learn in
 development mode and return to this document.</p></li>
 <li><p>Install the development dependencies:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span class="prompt1">pip<span class="w"> </span>install<span class="w"> </span>pytest<span class="w"> </span>pytest-cov<span class="w"> </span>ruff<span class="w"> </span>mypy<span class="w"> </span>numpydoc<span class="w"> </span><span class="nv">black</span><span class="o">==</span><span class="m">24</span>.3.0</span>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span class="prompt1">pip<span class="w"> </span>install<span class="w"> </span>pytest<span class="w"> </span>pytest-cov<span class="w"> </span><span class="nv">ruff</span><span class="o">==</span><span class="m">0</span>.11.2<span class="w"> </span>mypy<span class="w"> </span>numpydoc</span>
 </pre></div></div></li>
 </ol>
 <ol class="arabic" id="upstream" start="6">
@@ -1231,6 +1231,14 @@ <h3>Stalled and Unclaimed Issues<a class="headerlink" href="#stalled-and-unclaim
 <summary class="sd-summary-title sd-card-header">
 <span class="sd-summary-text">Guidelines for writing docstrings<a class="headerlink" href="#guidelines-for-writing-docstrings" title="Link to this dropdown">#</a></span><span class="sd-summary-state-marker sd-summary-chevron-right"><svg version="1.1" width="1.5em" height="1.5em" class="sd-octicon sd-octicon-chevron-right" viewBox="0 0 24 24" aria-hidden="true"><path d="M8.72 18.78a.75.75 0 0 1 0-1.06L14.44 12 8.72 6.28a.751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018l6.25 6.25a.75.75 0 0 1 0 1.06l-6.25 6.25a.75.75 0 0 1-1.06 0Z"></path></svg></span></summary><div class="sd-summary-content sd-card-body docutils">
 <ul>
+<li><p class="sd-card-text">You can use <code class="docutils literal notranslate"><span class="pre">pytest</span></code> to test docstrings, e.g. assuming the
+<code class="docutils literal notranslate"><span class="pre">RandomForestClassifier</span></code> docstring has been modified, the following command
+would test its docstring compliance:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span class="prompt1">pytest<span class="w"> </span>--doctest-modules<span class="w"> </span>sklearn/ensemble/_forest.py<span class="w"> </span>-k<span class="w"> </span>RandomForestClassifier</span>
+</pre></div></div></li>
+<li><p class="sd-card-text">The correct order of sections is: Parameters, Returns, See Also, Notes, Examples.
+See the <a class="reference external" href="https://numpydoc.readthedocs.io/en/latest/format.html#sections">numpydoc documentation</a> for
+information on other possible sections.</p></li>
 <li><p class="sd-card-text">When documenting the parameters and attributes, here is a list of some
 well-formatted examples</p>
 <div class="highlight-text notranslate"><div class="highlight"><pre><span></span>n_clusters : int, default=3
@@ -1293,7 +1301,13 @@ <h3>Stalled and Unclaimed Issues<a class="headerlink" href="#stalled-and-unclaim
 </pre></div>
 </div>
 </li>
-<li><p class="sd-card-text">Add one or two snippets of code in “Example” section to show how it can be used.</p></li>
+<li><p class="sd-card-text">The “Notes” section is optional. It is meant to provide information on
+specific behavior of a function/class/classmethod/method.</p></li>
+<li><p class="sd-card-text">A <code class="docutils literal notranslate"><span class="pre">Note</span></code> can also be added to an attribute, but in that case it requires
+using the <code class="docutils literal notranslate"><span class="pre">..</span> <span class="pre">rubric::</span> <span class="pre">Note</span></code> directive.</p></li>
+<li><p class="sd-card-text">Add one or two <strong>snippets</strong> of code in “Example” section to show how it can
+be used. The code should be runable as is, i.e. it should include all
+required imports. Keep this section as brief as possible.</p></li>
 </ul>
 </div>
 </details><details class="sd-sphinx-override sd-dropdown sd-card sd-mb-3" id="guidelines-for-writing-the-user-guide-and-other-restructuredtext-documents">
@@ -1911,7 +1925,7 @@ <h2>Reading the existing code base<a class="headerlink" href="#reading-the-exist
 </ul>
 </li>
 <li><p>Configure <code class="docutils literal notranslate"><span class="pre">git</span> <span class="pre">blame</span></code> to ignore the commit that migrated the code style to
-<code class="docutils literal notranslate"><span class="pre">black</span></code>.</p>
+<code class="docutils literal notranslate"><span class="pre">black</span></code> and then <code class="docutils literal notranslate"><span class="pre">ruff</span></code>.</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span class="prompt1">git<span class="w"> </span>config<span class="w"> </span>blame.ignoreRevsFile<span class="w"> </span>.git-blame-ignore-revs</span>
 </pre></div></div><p>Find out more information in black’s
 <a class="reference external" href="https://black.readthedocs.io/en/stable/guides/introducing_black_to_your_project.html#avoiding-ruining-git-blame">documentation for avoiding ruining git blame</a>.</p>
diff --git a/dev/developers/cython.html b/dev/developers/cython.html
index 43030b52ab935..470942cac9d19 100644
--- a/dev/developers/cython.html
+++ b/dev/developers/cython.html
@@ -42,7 +42,7 @@
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     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
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diff --git a/dev/developers/develop.html b/dev/developers/develop.html
index fb845c52ac04f..177e41ed24375 100644
--- a/dev/developers/develop.html
+++ b/dev/developers/develop.html
@@ -42,7 +42,7 @@
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     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
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     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
diff --git a/dev/developers/index.html b/dev/developers/index.html
index 91097c4d8f66c..8b275980562c3 100644
--- a/dev/developers/index.html
+++ b/dev/developers/index.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
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     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
diff --git a/dev/developers/maintainer.html b/dev/developers/maintainer.html
index b3da7799b14e2..1173539108c8b 100644
--- a/dev/developers/maintainer.html
+++ b/dev/developers/maintainer.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
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diff --git a/dev/developers/minimal_reproducer.html b/dev/developers/minimal_reproducer.html
index 375936b1fcb4f..f161e8464e3a7 100644
--- a/dev/developers/minimal_reproducer.html
+++ b/dev/developers/minimal_reproducer.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
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diff --git a/dev/developers/performance.html b/dev/developers/performance.html
index 62f1d1a396a6a..fb35005b1011b 100644
--- a/dev/developers/performance.html
+++ b/dev/developers/performance.html
@@ -42,7 +42,7 @@
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     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
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     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
diff --git a/dev/developers/plotting.html b/dev/developers/plotting.html
index 72ab6a05bd242..8afcf9f949aa9 100644
--- a/dev/developers/plotting.html
+++ b/dev/developers/plotting.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
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diff --git a/dev/developers/tips.html b/dev/developers/tips.html
index 4b5bb21f2fd88..d8c107dd8cd48 100644
--- a/dev/developers/tips.html
+++ b/dev/developers/tips.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
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diff --git a/dev/developers/utilities.html b/dev/developers/utilities.html
index ecf0c957087b2..b03598b84ea0e 100644
--- a/dev/developers/utilities.html
+++ b/dev/developers/utilities.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
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diff --git a/dev/dispatching.html b/dev/dispatching.html
index 5a8ed52c5719b..d518554c98e32 100644
--- a/dev/dispatching.html
+++ b/dev/dispatching.html
@@ -7,7 +7,7 @@
   <head>
     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="viewport" content="width=device-width, initial-scale=1" />
-<meta property="og:title" content="11. Dispatching" />
+<meta property="og:title" content="12. Dispatching" />
 <meta property="og:type" content="website" />
 <meta property="og:url" content="https://scikit-learn/stable/dispatching.html" />
 <meta property="og:site_name" content="scikit-learn" />
@@ -16,7 +16,7 @@
 <meta property="og:image:alt" content="scikit-learn" />
 <meta name="description" content="Array API support (experimental)- Example usage, Support for Array API-compatible inputs, Common estimator checks.." />
 
-    <title>11. Dispatching &#8212; scikit-learn 1.7.dev0 documentation</title>
+    <title>12. Dispatching &#8212; scikit-learn 1.7.dev0 documentation</title>
   
   
   
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
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     <link rel="author" title="About these documents" href="http://github.com/about.html" />
     <link rel="search" title="Search" href="http://github.com/search.html" />
-    <link rel="next" title="11.1. Array API support (experimental)" href="http://github.com/modules/array_api.html" />
-    <link rel="prev" title="10. Common pitfalls and recommended practices" href="http://github.com/common_pitfalls.html" />
+    <link rel="next" title="12.1. Array API support (experimental)" href="http://github.com/modules/array_api.html" />
+    <link rel="prev" title="11. Common pitfalls and recommended practices" href="http://github.com/common_pitfalls.html" />
   <meta name="viewport" content="width=device-width, initial-scale=1"/>
   <meta name="docsearch:language" content="en"/>
   <meta name="docsearch:version" content="1.7" />
@@ -593,45 +593,46 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/learning_curve.html">3.5. Validation curves: plotting scores to evaluate models</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/inspection.html">4. Inspection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/partial_dependence.html">4.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/permutation_importance.html">4.2. Permutation feature importance</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/metadata_routing.html">4. Metadata Routing</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/inspection.html">5. Inspection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/partial_dependence.html">5.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/permutation_importance.html">5.2. Permutation feature importance</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/visualizations.html">5. Visualizations</a></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/data_transforms.html">6. Dataset transformations</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/compose.html">6.1. Pipelines and composite estimators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/feature_extraction.html">6.2. Feature extraction</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/preprocessing.html">6.3. Preprocessing data</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/impute.html">6.4. Imputation of missing values</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/unsupervised_reduction.html">6.5. Unsupervised dimensionality reduction</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/random_projection.html">6.6. Random Projection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/kernel_approximation.html">6.7. Kernel Approximation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/metrics.html">6.8. Pairwise metrics, Affinities and Kernels</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/preprocessing_targets.html">6.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/visualizations.html">6. Visualizations</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/data_transforms.html">7. Dataset transformations</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/compose.html">7.1. Pipelines and composite estimators</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/feature_extraction.html">7.2. Feature extraction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/preprocessing.html">7.3. Preprocessing data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/impute.html">7.4. Imputation of missing values</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/unsupervised_reduction.html">7.5. Unsupervised dimensionality reduction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/random_projection.html">7.6. Random Projection</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/kernel_approximation.html">7.7. Kernel Approximation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/metrics.html">7.8. Pairwise metrics, Affinities and Kernels</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/preprocessing_targets.html">7.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/datasets.html">7. Dataset loading utilities</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html">7.1. Toy datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html">7.2. Real world datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html">7.3. Generated datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html">7.4. Loading other datasets</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/datasets.html">8. Dataset loading utilities</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html">8.1. Toy datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html">8.2. Real world datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html">8.3. Generated datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html">8.4. Loading other datasets</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/computing.html">8. Computing with scikit-learn</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/scaling_strategies.html">8.1. Strategies to scale computationally: bigger data</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/computational_performance.html">8.2. Computational Performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/parallelism.html">8.3. Parallelism, resource management, and configuration</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/computing.html">9. Computing with scikit-learn</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/scaling_strategies.html">9.1. Strategies to scale computationally: bigger data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/computational_performance.html">9.2. Computational Performance</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/parallelism.html">9.3. Parallelism, resource management, and configuration</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/model_persistence.html">9. Model persistence</a></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/common_pitfalls.html">10. Common pitfalls and recommended practices</a></li>
-<li class="toctree-l1 current active has-children"><a class="current reference internal" href="#">11. Dispatching</a><details open="open"><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/array_api.html">11.1. Array API support (experimental)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/model_persistence.html">10. Model persistence</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/common_pitfalls.html">11. Common pitfalls and recommended practices</a></li>
+<li class="toctree-l1 current active has-children"><a class="current reference internal" href="#">12. Dispatching</a><details open="open"><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/array_api.html">12.1. Array API support (experimental)</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/machine_learning_map.html">12. Choosing the right estimator</a></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/presentations.html">13. External Resources, Videos and Talks</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/machine_learning_map.html">13. Choosing the right estimator</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/presentations.html">14. External Resources, Videos and Talks</a></li>
 </ul>
 </div>
 </nav></div>
@@ -668,7 +669,7 @@
     
     <li class="breadcrumb-item"><a href="http://github.com/user_guide.html" class="nav-link">User Guide</a></li>
     
-    <li class="breadcrumb-item active" aria-current="page"><span class="ellipsis"><span class="section-number">11. </span>Dispatching</span></li>
+    <li class="breadcrumb-item active" aria-current="page"><span class="ellipsis"><span class="section-number">12. </span>Dispatching</span></li>
   </ul>
 </nav>
 </div>
@@ -686,13 +687,13 @@
                 <article class="bd-article">
                   
   <section id="dispatching">
-<h1><span class="section-number">11. </span>Dispatching<a class="headerlink" href="#dispatching" title="Link to this heading">#</a></h1>
+<h1><span class="section-number">12. </span>Dispatching<a class="headerlink" href="#dispatching" title="Link to this heading">#</a></h1>
 <div class="toctree-wrapper compound">
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/modules/array_api.html">11.1. Array API support (experimental)</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/array_api.html#example-usage">11.1.1. Example usage</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/array_api.html#support-for-array-api-compatible-inputs">11.1.2. Support for <code class="docutils literal notranslate"><span class="pre">Array</span> <span class="pre">API</span></code>-compatible inputs</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/array_api.html#common-estimator-checks">11.1.3. Common estimator checks</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/modules/array_api.html">12.1. Array API support (experimental)</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/array_api.html#example-usage">12.1.1. Example usage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/array_api.html#support-for-array-api-compatible-inputs">12.1.2. Support for <code class="docutils literal notranslate"><span class="pre">Array</span> <span class="pre">API</span></code>-compatible inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/array_api.html#common-estimator-checks">12.1.3. Common estimator checks</a></li>
 </ul>
 </li>
 </ul>
@@ -715,7 +716,7 @@ <h1><span class="section-number">11. </span>Dispatching<a class="headerlink" hre
       <i class="fa-solid fa-angle-left"></i>
       <div class="prev-next-info">
         <p class="prev-next-subtitle">previous</p>
-        <p class="prev-next-title"><span class="section-number">10. </span>Common pitfalls and recommended practices</p>
+        <p class="prev-next-title"><span class="section-number">11. </span>Common pitfalls and recommended practices</p>
       </div>
     </a>
     <a class="right-next"
@@ -723,7 +724,7 @@ <h1><span class="section-number">11. </span>Dispatching<a class="headerlink" hre
        title="next page">
       <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">11.1. </span>Array API support (experimental)</p>
+        <p class="prev-next-title"><span class="section-number">12.1. </span>Array API support (experimental)</p>
       </div>
       <i class="fa-solid fa-angle-right"></i>
     </a>
diff --git a/dev/documentation_team.html b/dev/documentation_team.html
index 653bd0f23450a..1b88698d2e7bc 100644
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diff --git a/dev/faq.html b/dev/faq.html
index 996d713877870..6de41c122a185 100644
--- a/dev/faq.html
+++ b/dev/faq.html
@@ -42,7 +42,7 @@
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@@ -1069,10 +1069,10 @@ <h3><a class="toc-backref" href="#id29" role="doc-backlink">Why do I sometimes g
 <code class="docutils literal notranslate"><span class="pre">&quot;fork&quot;</span></code>) to manage the process pools. To work around this issue when
 using scikit-learn, you can set the <code class="docutils literal notranslate"><span class="pre">JOBLIB_START_METHOD</span></code> environment
 variable to <code class="docutils literal notranslate"><span class="pre">&quot;forkserver&quot;</span></code>. However the user should be aware that using
-the <code class="docutils literal notranslate"><span class="pre">&quot;forkserver&quot;</span></code> method prevents <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.Parallel.html#joblib.Parallel" title="(in joblib v1.5.dev0)"><code class="xref py py-class docutils literal notranslate"><span class="pre">joblib.Parallel</span></code></a> to call function
+the <code class="docutils literal notranslate"><span class="pre">&quot;forkserver&quot;</span></code> method prevents <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.Parallel.html#joblib.Parallel" title="(in joblib v1.6.dev0)"><code class="xref py py-class docutils literal notranslate"><span class="pre">joblib.Parallel</span></code></a> to call function
 interactively defined in a shell session.</p>
 <p>If you have custom code that uses <a class="reference external" href="https://docs.python.org/3/library/multiprocessing.html#module-multiprocessing" title="(in Python v3.13)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">multiprocessing</span></code></a> directly instead of using
-it via <a class="reference external" href="https://joblib.readthedocs.io/en/latest/index.html#module-joblib" title="(in joblib v1.5.dev0)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">joblib</span></code></a> you can enable the <code class="docutils literal notranslate"><span class="pre">&quot;forkserver&quot;</span></code> mode globally for your
+it via <a class="reference external" href="https://joblib.readthedocs.io/en/latest/index.html#module-joblib" title="(in joblib v1.6.dev0)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">joblib</span></code></a> you can enable the <code class="docutils literal notranslate"><span class="pre">&quot;forkserver&quot;</span></code> mode globally for your
 program. Insert the following instructions in your main script:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">multiprocessing</span>
 
@@ -1090,7 +1090,7 @@ <h3><a class="toc-backref" href="#id29" role="doc-backlink">Why do I sometimes g
 <section id="why-does-my-job-use-more-cores-than-specified-with-n-jobs">
 <span id="faq-mkl-threading"></span><h3><a class="toc-backref" href="#id30" role="doc-backlink">Why does my job use more cores than specified with <code class="docutils literal notranslate"><span class="pre">n_jobs</span></code>?</a><a class="headerlink" href="#why-does-my-job-use-more-cores-than-specified-with-n-jobs" title="Link to this heading">#</a></h3>
 <p>This is because <code class="docutils literal notranslate"><span class="pre">n_jobs</span></code> only controls the number of jobs for
-routines that are parallelized with <a class="reference external" href="https://joblib.readthedocs.io/en/latest/index.html#module-joblib" title="(in joblib v1.5.dev0)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">joblib</span></code></a>, but parallel code can come
+routines that are parallelized with <a class="reference external" href="https://joblib.readthedocs.io/en/latest/index.html#module-joblib" title="(in joblib v1.6.dev0)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">joblib</span></code></a>, but parallel code can come
 from other sources:</p>
 <ul class="simple">
 <li><p>some routines may be parallelized with OpenMP (for code written in C or
diff --git a/dev/getting_started.html b/dev/getting_started.html
index e75a69978b368..85142f9d00df4 100644
--- a/dev/getting_started.html
+++ b/dev/getting_started.html
@@ -42,7 +42,7 @@
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diff --git a/dev/glossary.html b/dev/glossary.html
index d1982d787c453..0acb643b15ade 100644
--- a/dev/glossary.html
+++ b/dev/glossary.html
@@ -42,7 +42,7 @@
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@@ -1900,11 +1900,11 @@ <h2>General Concepts<a class="headerlink" href="#general-concepts" title="Link t
 <p>FIXME perhaps we should have some common tests about the relationship
 between ConvergenceWarning and max_iter.</p>
 </dd>
-<dt id="term-memory"><code class="docutils literal notranslate"><span class="pre">memory</span></code><a class="headerlink" href="#term-memory" title="Link to this term">#</a></dt><dd><p>Some estimators make use of <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.Memory.html#joblib.Memory" title="(in joblib v1.5.dev0)"><code class="xref py py-class docutils literal notranslate"><span class="pre">joblib.Memory</span></code></a> to
+<dt id="term-memory"><code class="docutils literal notranslate"><span class="pre">memory</span></code><a class="headerlink" href="#term-memory" title="Link to this term">#</a></dt><dd><p>Some estimators make use of <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.Memory.html#joblib.Memory" title="(in joblib v1.6.dev0)"><code class="xref py py-class docutils literal notranslate"><span class="pre">joblib.Memory</span></code></a> to
 store partial solutions during fitting. Thus when <code class="docutils literal notranslate"><span class="pre">fit</span></code> is called
 again, those partial solutions have been memoized and can be reused.</p>
 <p>A <code class="docutils literal notranslate"><span class="pre">memory</span></code> parameter can be specified as a string with a path to a
-directory, or a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.Memory.html#joblib.Memory" title="(in joblib v1.5.dev0)"><code class="xref py py-class docutils literal notranslate"><span class="pre">joblib.Memory</span></code></a> instance (or an object with a
+directory, or a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.Memory.html#joblib.Memory" title="(in joblib v1.6.dev0)"><code class="xref py py-class docutils literal notranslate"><span class="pre">joblib.Memory</span></code></a> instance (or an object with a
 similar interface, i.e. a <code class="docutils literal notranslate"><span class="pre">cache</span></code> method) can be used.</p>
 <p><code class="docutils literal notranslate"><span class="pre">memory</span></code> values are validated and interpreted with
 <a class="reference internal" href="http://github.com/modules/generated/sklearn.utils.validation.check_memory.html#sklearn.utils.validation.check_memory" title="sklearn.utils.validation.check_memory"><code class="xref py py-func docutils literal notranslate"><span class="pre">utils.validation.check_memory</span></code></a>.</p>
@@ -1936,7 +1936,7 @@ <h2>General Concepts<a class="headerlink" href="#general-concepts" title="Link t
 <code class="docutils literal notranslate"><span class="pre">n_jobs=-2</span></code>, all CPUs but one are used.</p>
 <p><code class="docutils literal notranslate"><span class="pre">n_jobs</span></code> is <code class="docutils literal notranslate"><span class="pre">None</span></code> by default, which means <em>unset</em>; it will
 generally be interpreted as <code class="docutils literal notranslate"><span class="pre">n_jobs=1</span></code>, unless the current
-<a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.Parallel.html#joblib.Parallel" title="(in joblib v1.5.dev0)"><code class="xref py py-class docutils literal notranslate"><span class="pre">joblib.Parallel</span></code></a> backend context specifies otherwise.</p>
+<a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.Parallel.html#joblib.Parallel" title="(in joblib v1.6.dev0)"><code class="xref py py-class docutils literal notranslate"><span class="pre">joblib.Parallel</span></code></a> backend context specifies otherwise.</p>
 <p>Note that even if <code class="docutils literal notranslate"><span class="pre">n_jobs=1</span></code>, low-level parallelism (via Numpy and OpenMP)
 might be used in some configuration.</p>
 <p>For more details on the use of <code class="docutils literal notranslate"><span class="pre">joblib</span></code> and its interactions with
diff --git a/dev/governance.html b/dev/governance.html
index 02054d6c1f688..52a3e0225e620 100644
--- a/dev/governance.html
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diff --git a/dev/index.html b/dev/index.html
index 9e59142d3798d..178031a384f13 100644
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diff --git a/dev/inspection.html b/dev/inspection.html
index 7de8f765d470c..7ee6072516dfb 100644
--- a/dev/inspection.html
+++ b/dev/inspection.html
@@ -7,7 +7,7 @@
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     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="viewport" content="width=device-width, initial-scale=1" />
-<meta property="og:title" content="4. Inspection" />
+<meta property="og:title" content="5. Inspection" />
 <meta property="og:type" content="website" />
 <meta property="og:url" content="https://scikit-learn/stable/inspection.html" />
 <meta property="og:site_name" content="scikit-learn" />
@@ -16,7 +16,7 @@
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 <meta name="description" content="Predictive performance is often the main goal of developing machine learning models. Yet summarizing performance with an evaluation metric is often insufficient: it assumes that the evaluation metr..." />
 
-    <title>4. Inspection &#8212; scikit-learn 1.7.dev0 documentation</title>
+    <title>5. Inspection &#8212; scikit-learn 1.7.dev0 documentation</title>
   
   
   
@@ -42,7 +42,7 @@
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-    <link rel="next" title="4.1. Partial Dependence and Individual Conditional Expectation plots" href="http://github.com/modules/partial_dependence.html" />
-    <link rel="prev" title="3.5. Validation curves: plotting scores to evaluate models" href="http://github.com/modules/learning_curve.html" />
+    <link rel="next" title="5.1. Partial Dependence and Individual Conditional Expectation plots" href="http://github.com/modules/partial_dependence.html" />
+    <link rel="prev" title="4. Metadata Routing" href="http://github.com/metadata_routing.html" />
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@@ -594,45 +594,46 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/learning_curve.html">3.5. Validation curves: plotting scores to evaluate models</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 current active has-children"><a class="current reference internal" href="#">4. Inspection</a><details open="open"><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/partial_dependence.html">4.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/permutation_importance.html">4.2. Permutation feature importance</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/metadata_routing.html">4. Metadata Routing</a></li>
+<li class="toctree-l1 current active has-children"><a class="current reference internal" href="#">5. Inspection</a><details open="open"><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/partial_dependence.html">5.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/permutation_importance.html">5.2. Permutation feature importance</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/visualizations.html">5. Visualizations</a></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/data_transforms.html">6. Dataset transformations</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/compose.html">6.1. Pipelines and composite estimators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/feature_extraction.html">6.2. Feature extraction</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/preprocessing.html">6.3. Preprocessing data</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/impute.html">6.4. Imputation of missing values</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/unsupervised_reduction.html">6.5. Unsupervised dimensionality reduction</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/random_projection.html">6.6. Random Projection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/kernel_approximation.html">6.7. Kernel Approximation</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/preprocessing_targets.html">6.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/visualizations.html">6. Visualizations</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/data_transforms.html">7. Dataset transformations</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/compose.html">7.1. Pipelines and composite estimators</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/feature_extraction.html">7.2. Feature extraction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/preprocessing.html">7.3. Preprocessing data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/impute.html">7.4. Imputation of missing values</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/unsupervised_reduction.html">7.5. Unsupervised dimensionality reduction</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/kernel_approximation.html">7.7. Kernel Approximation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/metrics.html">7.8. Pairwise metrics, Affinities and Kernels</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/preprocessing_targets.html">7.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html">7.3. Generated datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html">7.4. Loading other datasets</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/datasets.html">8. Dataset loading utilities</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html">8.1. Toy datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html">8.2. Real world datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html">8.3. Generated datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html">8.4. Loading other datasets</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/scaling_strategies.html">8.1. Strategies to scale computationally: bigger data</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/computational_performance.html">8.2. Computational Performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/parallelism.html">8.3. Parallelism, resource management, and configuration</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/scaling_strategies.html">9.1. Strategies to scale computationally: bigger data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/computational_performance.html">9.2. Computational Performance</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="http://github.com/common_pitfalls.html">10. Common pitfalls and recommended practices</a></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/dispatching.html">11. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/array_api.html">11.1. Array API support (experimental)</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="http://github.com/common_pitfalls.html">11. Common pitfalls and recommended practices</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/dispatching.html">12. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/array_api.html">12.1. Array API support (experimental)</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="http://github.com/machine_learning_map.html">12. Choosing the right estimator</a></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/presentations.html">13. External Resources, Videos and Talks</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/machine_learning_map.html">13. Choosing the right estimator</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/presentations.html">14. External Resources, Videos and Talks</a></li>
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     <li class="breadcrumb-item"><a href="http://github.com/user_guide.html" class="nav-link">User Guide</a></li>
     
-    <li class="breadcrumb-item active" aria-current="page"><span class="ellipsis"><span class="section-number">4. </span>Inspection</span></li>
+    <li class="breadcrumb-item active" aria-current="page"><span class="ellipsis"><span class="section-number">5. </span>Inspection</span></li>
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 </nav>
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-<span id="id1"></span><h1><span class="section-number">4. </span>Inspection<a class="headerlink" href="#inspection" title="Link to this heading">#</a></h1>
+<span id="id1"></span><h1><span class="section-number">5. </span>Inspection<a class="headerlink" href="#inspection" title="Link to this heading">#</a></h1>
 <p>Predictive performance is often the main goal of developing machine learning
 models. Yet summarizing performance with an evaluation metric is often
 insufficient: it assumes that the evaluation metric and test dataset
@@ -705,17 +706,17 @@
 </ul>
 <div class="toctree-wrapper compound">
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/modules/partial_dependence.html">4.1. Partial Dependence and Individual Conditional Expectation plots</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/partial_dependence.html#partial-dependence-plots">4.1.1. Partial dependence plots</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/partial_dependence.html#individual-conditional-expectation-ice-plot">4.1.2. Individual conditional expectation (ICE) plot</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/partial_dependence.html#mathematical-definition">4.1.3. Mathematical Definition</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/partial_dependence.html#computation-methods">4.1.4. Computation methods</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/modules/partial_dependence.html">5.1. Partial Dependence and Individual Conditional Expectation plots</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/partial_dependence.html#partial-dependence-plots">5.1.1. Partial dependence plots</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/partial_dependence.html#individual-conditional-expectation-ice-plot">5.1.2. Individual conditional expectation (ICE) plot</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/partial_dependence.html#mathematical-definition">5.1.3. Mathematical Definition</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/partial_dependence.html#computation-methods">5.1.4. Computation methods</a></li>
 </ul>
 </li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/modules/permutation_importance.html">4.2. Permutation feature importance</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/permutation_importance.html#outline-of-the-permutation-importance-algorithm">4.2.1. Outline of the permutation importance algorithm</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/permutation_importance.html#relation-to-impurity-based-importance-in-trees">4.2.2. Relation to impurity-based importance in trees</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/permutation_importance.html#misleading-values-on-strongly-correlated-features">4.2.3. Misleading values on strongly correlated features</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/modules/permutation_importance.html">5.2. Permutation feature importance</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/permutation_importance.html#outline-of-the-permutation-importance-algorithm">5.2.1. Outline of the permutation importance algorithm</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/permutation_importance.html#relation-to-impurity-based-importance-in-trees">5.2.2. Relation to impurity-based importance in trees</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/permutation_importance.html#misleading-values-on-strongly-correlated-features">5.2.3. Misleading values on strongly correlated features</a></li>
 </ul>
 </li>
 </ul>
@@ -733,12 +734,12 @@
     <div class="footer-article-item">
 <div class="prev-next-area">
     <a class="left-prev"
-       href="http://github.com/modules/learning_curve.html"
+       href="http://github.com/metadata_routing.html"
        title="previous page">
       <i class="fa-solid fa-angle-left"></i>
       <div class="prev-next-info">
         <p class="prev-next-subtitle">previous</p>
-        <p class="prev-next-title"><span class="section-number">3.5. </span>Validation curves: plotting scores to evaluate models</p>
+        <p class="prev-next-title"><span class="section-number">4. </span>Metadata Routing</p>
       </div>
     </a>
     <a class="right-next"
@@ -746,7 +747,7 @@
        title="next page">
       <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">4.1. </span>Partial Dependence and Individual Conditional Expectation plots</p>
+        <p class="prev-next-title"><span class="section-number">5.1. </span>Partial Dependence and Individual Conditional Expectation plots</p>
       </div>
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 </tr>
-<tr class="row-odd"><td><p>sphinx-remove-toctrees</p></td>
+<tr class="row-even"><td><p>sphinx-remove-toctrees</p></td>
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 <td><p>docs</p></td>
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-<tr class="row-odd"><td><p>conda-lock</p></td>
+<tr class="row-even"><td><p>conda-lock</p></td>
 <td><p>2.5.7</p></td>
 <td><p>maintenance</p></td>
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diff --git a/dev/lite/api/contents/auto_examples/compose/all.json b/dev/lite/api/contents/auto_examples/compose/all.json
index b2f5549919f0b..35052ef9ff2ed 100644
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index 75b95b7dd7d5f..c63aae01ffba0 100644
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index c2073798b8b8f..a97c36fcb31c0 100644
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index da04857bedb0b..0ddf4ec8e4a07 100644
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index b4f396715bc54..f2cae7aa3d3f4 100644
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index f423051359c95..8bcbc2bc9518c 100644
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index 1bb9212d27aa8..c4ea6cc5ff36e 100644
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index e8fa3bdf6ad94..e890d28eeb593 100644
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index dc2e48040cbff..e73d6f1579963 100644
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diff --git a/dev/lite/api/contents/auto_examples/text/all.json b/dev/lite/api/contents/auto_examples/text/all.json
index 51d3e7e081351..e4e437130ad03 100644
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index 198af802c3790..6245e75d7ed28 100644
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@@ -2,11 +2,11 @@
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index c202f772b139f..fe7751da31076 100644
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@@ -64,7 +64,7 @@
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+      "load": "static/remoteEntry.2142187e379506459ae0.js",
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rename from dev/lite/extensions/jupyterlab-jupytext/static/509.b481eda44d6559ccfa16.js
rename to dev/lite/extensions/jupyterlab-jupytext/static/509.b5ac33d255ac2a25ffc1.js
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diff --git a/dev/lite/extensions/jupyterlab-jupytext/static/720.316ba58bc18a32d258ae.js b/dev/lite/extensions/jupyterlab-jupytext/static/720.316ba58bc18a32d258ae.js
new file mode 100644
index 0000000000000..55b73ff5808db
--- /dev/null
+++ b/dev/lite/extensions/jupyterlab-jupytext/static/720.316ba58bc18a32d258ae.js
@@ -0,0 +1 @@
+"use strict";(self.webpackChunkjupyterlab_jupytext=self.webpackChunkjupyterlab_jupytext||[]).push([[720],{720:(e,t,n)=>{n.r(t),n.d(t,{IRisePreviewFactory:()=>R,IRisePreviewTracker:()=>b,default:()=>x});var o=n(611),i=n(594),s=n(970),a=n(612),r=n(828),l=n(53),d=n(926);const c=new d.LabIcon({name:"RISE",svgstr:'<svg xmlns="http://www.w3.org/2000/svg" height="16" viewBox="0 0 24 24" width="16" fill="none" stroke="#4f4f4f">\n    <path d="M15 14.25l3 6m-9-6l-3 6m-4-16h20-2v10H4v-10z" class="jp-icon3" stroke-width="2" />\n    <path d="M9.6 11.6v-1.5m-1.5 1.5V6.1m-1.5 5.5V8.1m5.5 3.5h3m-3-1.5h6m-6-1.5h6m-6-2h5" class="jp-icon3"\n        stroke-width="1.2" />\n</svg>'}),u=new d.LabIcon({name:"RISE:fullScreen",svgstr:'<svg xmlns="http://www.w3.org/2000/svg" height="16" viewBox="0 0 24 24" width="16" fill="#4f4f4f">\n    <path class="jp-icon3" d="M22 22v-6h-2v4h-4v2zM2 22h6v-2H4v-4H2zM22 2h-6v2h4v4h2zM2 2v6h2V4h4V2z" />\n</svg>'});var 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Slideshow"),caption:g.__("Open the current notebook in a new browser tab as an RevealJS slideshow."),execute:async()=>{const e=n.currentWidget;e&&(await e.context.save(),window.open(f.getRiseUrl(e.context.path,e.content.activeCellIndex)))},isEnabled:w}),v.addCommand(S.risePreview,{label:e=>e.toolbar?"":g.__("Render as Reveal Slideshow"),caption:g.__("Render the current notebook as Reveal Slideshow"),icon:c,execute:async e=>{const t=function(e){var t;const o=null!==(t=null==n?void 0:n.currentWidget)&&void 0!==t?t:null;return!1!==e.activate&&o&&p.activateById(o.id),o}(e);let o;if(t){o=t.context,await o.save();const n=await v.execute("docmanager:open",{path:o.path,factory:f.FACTORY_NAME,options:{mode:"split-right"}}),s=e=>{n.setActiveCellIndex(e.activeCellIndex,!1)};n.setActiveCellIndex(t.content.activeCellIndex),t.content.activeCellChanged.connect(s),n.disposed.connect((()=>{t.content.activeCellChanged.disconnect(s)})),!0===e.fullscreen&&n.ready.then((()=>{(0,i.showDialog)({title:g.__("Switch to full screen"),body:g.__("The slideshow is set to automatically open in full screen. Your web browser requires your confirmation to do so.")}).then((e=>{e.button.accept&&v.execute(S.riseFullScreen,{id:n.id})}))})).catch((e=>{console.log(e)}))}},isEnabled:w}),v.addCommand(S.riseFullScreen,{label:g.__("Full screen slideshow"),caption:g.__("Toggle full screen the current active slideshow"),icon:u,execute:async t=>{var n;t.id&&e.shell.activateById(t.id);const o=t.id?(0,l.toArray)(e.shell.widgets("main")).find((e=>e.id===t.id)):e.shell.currentWidget;if(o&&h.has(o)){const e=o.iframe;e&&(document.fullscreenElement||(null===(n=e.contentDocument)||void 0===n?void 0:n.fullscreenElement)?document.exitFullscreen&&await document.exitFullscreen():o.ready.then((()=>{var t,n;null===(n=null===(t=null==e?void 0:e.contentWindow)||void 0===t?void 0:t.document.querySelector("div.reveal"))||void 0===n||n.requestFullscreen()})),e.focus())}},isEnabled:()=>!!e.shell.currentWidget&&h.has(e.shell.currentWidget)}),v.addCommand(S.riseSetSlideType,{label:e=>g.__("Toggle slideshow %1 type",e.type),caption:e=>g.__("(Un)set active cell as a %1 cell",e.type),execute:t=>{var o,i;const s=t.type,a=e.shell.currentWidget;if(a&&n.has(a)){const e=null===(o=a.content.activeCell)||void 0===o?void 0:o.model;if(e){const t=null!==(i=e.getMetadata("slideshow"))&&void 0!==i?i:{};if(s!==t.slide_type){const n={...t};s?n.slide_type=s:delete n.slide_type,Object.keys(n).length>0?e.setMetadata("slideshow",n):e.deleteMetadata("slideshow")}}}},isToggled:t=>{var o,i;const s=t.type,a=e.shell.currentWidget;if(a&&n.has(a)){const e=null===(o=a.content.activeCell)||void 0===o?void 0:o.model;if(e)return(null!==(i=e.getMetadata("slideshow"))&&void 0!==i?i:{}).slide_type===s&&!!s}return!1},isEnabled:e=>{var t;return["slide","subslide","fragment","skip","notes"].includes(null!==(t=e.type)&&void 0!==t?t:"")}}),n.widgetAdded.connect((async(t,n)=>{var o,s,a;if(n.toolbar.insertBefore("kernelName","RISE-button",new i.CommandToolbarButton({commands:v,id:S.risePreview,args:{toolbar:!0}})),"Rise"!==e.name){await n.context.ready;let e=null!==(a=(null!==(s=null===(o=n.content.model)||void 0===o?void 0:o.getMetadata("rise"))&&void 0!==s?s:{}).autolaunch)&&void 0!==a&&a;m&&(e|=m.get("autolaunch").composite),e&&await v.execute(S.risePreview,{fullscreen:!0})}})),o){const e="Notebook Operations";[S.openRise,S.risePreview].forEach((t=>{o.addItem({command:t,category:e})}))}return h}},x=[C,I]}}]);
\ No newline at end of file
diff --git a/dev/lite/extensions/jupyterlab-jupytext/static/720.c1f5f98d47c5fd451c47.js b/dev/lite/extensions/jupyterlab-jupytext/static/720.c1f5f98d47c5fd451c47.js
deleted file mode 100644
index 0acdf19b71c7f..0000000000000
--- a/dev/lite/extensions/jupyterlab-jupytext/static/720.c1f5f98d47c5fd451c47.js
+++ /dev/null
@@ -1 +0,0 @@
-"use strict";(self.webpackChunkjupyterlab_jupytext=self.webpackChunkjupyterlab_jupytext||[]).push([[720],{720:(e,t,n)=>{n.r(t),n.d(t,{IRisePreviewFactory:()=>R,IRisePreviewTracker:()=>b,default:()=>x});var o=n(908),i=n(569),s=n(369),a=n(507),r=n(579),l=n(53),d=n(989);const c=new d.LabIcon({name:"RISE",svgstr:'<svg xmlns="http://www.w3.org/2000/svg" height="16" viewBox="0 0 24 24" width="16" fill="none" stroke="#4f4f4f">\n    <path d="M15 14.25l3 6m-9-6l-3 6m-4-16h20-2v10H4v-10z" class="jp-icon3" stroke-width="2" />\n    <path d="M9.6 11.6v-1.5m-1.5 1.5V6.1m-1.5 5.5V8.1m5.5 3.5h3m-3-1.5h6m-6-1.5h6m-6-2h5" class="jp-icon3"\n        stroke-width="1.2" />\n</svg>'}),u=new d.LabIcon({name:"RISE:fullScreen",svgstr:'<svg xmlns="http://www.w3.org/2000/svg" height="16" viewBox="0 0 24 24" width="16" fill="#4f4f4f">\n    <path class="jp-icon3" d="M22 22v-6h-2v4h-4v2zM2 22h6v-2H4v-4H2zM22 2h-6v2h4v4h2zM2 2v6h2V4h4V2z" />\n</svg>'});var h,v=n(808),p=n(596),g=n(262),m=n(12),w=n(602),y=n(256);class f extends p.DocumentWidget{constructor(e){super({...e,content:new i.IFrame({sandbox:["allow-same-origin","allow-scripts","allow-downloads","allow-modals","allow-popups"]})}),this._ready=new g.PromiseDelegate;const{getRiseUrl:t,context:n,renderOnSave:o,translator:s}=e;this.getRiseUrl=t,this._path=n.path;const a=(null!=s?s:r.nullTranslator).load("rise");this.content.title.icon=c,this._renderOnSave=null!=o&&o,n.pathChanged.connect((()=>{this.path=n.path}));const l=new i.ToolbarButton({icon:d.refreshIcon,tooltip:a.__("Reload Preview"),onClick:()=>{this.reload()}}),v=new h.CheckBox({checked:this._renderOnSave,onChange:e=>{var t,n;this._renderOnSave=null!==(n=null===(t=e.target)||void 0===t?void 0:t.checked)&&void 0!==n&&n},translator:s});this.toolbar.addItem("fullscreen",new i.ToolbarButton({icon:u,tooltip:a.__("Open the slideshow in full screen"),onClick:()=>{e.commands.execute("RISE:fullscreen-plugin")}})),n&&(this.toolbar.addItem("renderOnSave",v),n.ready.then((()=>{this.setActiveCellIndex(0).then((()=>this._ready.resolve())).catch((e=>this._ready.reject(e))),n.fileChanged.connect((()=>{this.renderOnSave&&this.reload()}))}))),this.toolbar.addItem("spacer",i.Toolbar.createSpacerItem()),this.toolbar.addItem("reload",l)}get ready(){return this._ready.promise}get iframe(){return this.content.node.querySelector("iframe")}dispose(){this.isDisposed||(super.dispose(),w.Signal.clearData(this))}reload(){const e=this.content.node.querySelector("iframe");e.contentWindow&&e.contentWindow.location.reload()}get renderOnSave(){return this._renderOnSave}async setActiveCellIndex(e,t=!0){const n=this.iframe;if(!t)return n.contentWindow?(n.contentWindow.history.pushState(null,"",this.getRiseUrl(this.path,e)),Promise.resolve()):Promise.reject("No content.");this.content.url=this.getRiseUrl(this.path,e),await this._waitForIFrame(n)}get path(){return this._path}set path(e){e!==this._path&&(this._path=e,this.setActiveCellIndex(0))}async _waitForIFrame(e){var t,n;const o=new g.PromiseDelegate,i=()=>{var t;null===(t=e.contentWindow)||void 0===t||t.removeEventListener("load",i);const n=setInterval((()=>{var t;(null===(t=e.contentDocument)||void 0===t?void 0:t.querySelector(".reveal"))&&(clearInterval(n),o.resolve(!0))}),500)};return"complete"===(null===(t=e.contentDocument)||void 0===t?void 0:t.readyState)?i():null===(n=e.contentWindow)||void 0===n||n.addEventListener("load",i),o.promise}}!function(e){function t(e,t){let n=`${v.PageConfig.getBaseUrl()}rise/${e}`;return"number"==typeof t&&(n+=v.URLExt.objectToQueryString({activeCellIndex:t})),n}e.FACTORY_NAME="rise",e.getRiseUrl=t,e.FactoryToken=class{constructor({commands:e,docRegistry:t,fileTypes:n,translator:o}){this._disposeFactory=null,this._widgetCreated=new w.Signal(this),this._commands=e,this._docRegistry=t,this._fileTypes=null!=n?n:["notebook"],this._translator=null!=o?o:r.nullTranslator,this._updateFactory()}addFileType(e){this._fileTypes.includes(e)||(this._fileTypes.push(e),this._updateFactory())}get widgetCreated(){return this._widgetCreated}_updateFactory(){this._disposeFactory&&(this._disposeFactory.dispose(),this._disposeFactory=null);const n=this._translator.load("rise"),o=new _(t,this._commands,{name:e.FACTORY_NAME,label:n.__("Rise Slides"),fileTypes:this._fileTypes,modelName:"notebook"});o.widgetCreated.connect(((e,t)=>{this._widgetCreated.emit(t)}),this),this._disposeFactory=m.DisposableSet.from([this._docRegistry.addWidgetFactory(o),o])}}}(f||(f={}));class _ extends p.ABCWidgetFactory{constructor(e,t,n){super(n),this.getRiseUrl=e,this.commands=t,this.defaultRenderOnSave=!1}createNewWidget(e){return new f({context:e,commands:this.commands,getRiseUrl:this.getRiseUrl,renderOnSave:this.defaultRenderOnSave})}}!function(e){class t extends y.Widget{constructor(e={}){var t,n,o;const i=(null!==(t=e.translator)&&void 0!==t?t:r.nullTranslator).load("rise"),s=document.createElement("label");s.insertAdjacentHTML("afterbegin",`<input name="renderOnSave" type="checkbox"></input>${i.__("Render on Save")}`),super({node:s}),this.input=s.childNodes.item(0),this.checked=null!==(n=e.checked)&&void 0!==n&&n,this.onChange=null!==(o=e.onChange)&&void 0!==o?o:()=>{}}get checked(){return this.input.checked}set checked(e){this.input.checked=e}onAfterAttach(e){super.onAfterAttach(e),this.input.addEventListener("change",this.onChange)}onBeforeDetach(e){this.input.removeEventListener("change",this.onChange),super.onBeforeDetach(e)}}e.CheckBox=t,e.setupLog=function(){window.onmessage=e=>{var t,n,o,i,s,a,r,l,d;switch(null===(t=e.data)||void 0===t?void 0:t.level){case"debug":console.debug(...null!==(o=null===(n=e.data)||void 0===n?void 0:n.msg)&&void 0!==o?o:[]);break;case"info":console.info(...null!==(s=null===(i=e.data)||void 0===i?void 0:i.msg)&&void 0!==s?s:[]);break;case"warn":console.warn(...null!==(r=null===(a=e.data)||void 0===a?void 0:a.msg)&&void 0!==r?r:[]);break;case"error":console.error(...null!==(d=null===(l=e.data)||void 0===l?void 0:l.msg)&&void 0!==d?d:[]);break;default:console.log(e)}}}}(h||(h={}));const b=new g.Token("jupyterlab-rise:IRisePreviewTracker","Adds a tracker for RISE slides preview widgets."),R=new g.Token("jupyterlab-rise:IRisePreviewFactory","Customize the RISE slides preview factory.");var S;!function(e){e.openRise="RISE:slideshow",e.riseFullScreen="RISE:fullscreen-plugin",e.risePreview="RISE:preview",e.riseSetSlideType="RISE:set-slide-type"}(S||(S={}));const C={id:"jupyterlab-rise:factory",provides:R,optional:[r.ITranslator],activate:(e,t)=>{const{commands:n,docRegistry:o}=e;return new f.FactoryToken({commands:n,docRegistry:o,translator:null!=t?t:void 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v.execute("docmanager:open",{path:o.path,factory:f.FACTORY_NAME,options:{mode:"split-right"}}),s=e=>{n.setActiveCellIndex(e.activeCellIndex,!1)};n.setActiveCellIndex(t.content.activeCellIndex),t.content.activeCellChanged.connect(s),n.disposed.connect((()=>{t.content.activeCellChanged.disconnect(s)})),!0===e.fullscreen&&n.ready.then((()=>{(0,i.showDialog)({title:g.__("Switch to full screen"),body:g.__("The slideshow is set to automatically open in full screen. Your web browser requires your confirmation to do so.")}).then((e=>{e.button.accept&&v.execute(S.riseFullScreen,{id:n.id})}))})).catch((e=>{console.log(e)}))}},isEnabled:w}),v.addCommand(S.riseFullScreen,{label:g.__("Full screen slideshow"),caption:g.__("Toggle full screen the current active slideshow"),icon:u,execute:async t=>{var n;t.id&&e.shell.activateById(t.id);const o=t.id?(0,l.toArray)(e.shell.widgets("main")).find((e=>e.id===t.id)):e.shell.currentWidget;if(o&&h.has(o)){const e=o.iframe;e&&(document.fullscreenElement||(null===(n=e.contentDocument)||void 0===n?void 0:n.fullscreenElement)?document.exitFullscreen&&await document.exitFullscreen():o.ready.then((()=>{var t,n;null===(n=null===(t=null==e?void 0:e.contentWindow)||void 0===t?void 0:t.document.querySelector("div.reveal"))||void 0===n||n.requestFullscreen()})),e.focus())}},isEnabled:()=>!!e.shell.currentWidget&&h.has(e.shell.currentWidget)}),v.addCommand(S.riseSetSlideType,{label:e=>g.__("Toggle slideshow %1 type",e.type),caption:e=>g.__("(Un)set active cell as a %1 cell",e.type),execute:t=>{var o,i;const s=t.type,a=e.shell.currentWidget;if(a&&n.has(a)){const e=null===(o=a.content.activeCell)||void 0===o?void 0:o.model;if(e){const t=null!==(i=e.getMetadata("slideshow"))&&void 0!==i?i:{};if(s!==t.slide_type){const n={...t};s?n.slide_type=s:delete n.slide_type,Object.keys(n).length>0?e.setMetadata("slideshow",n):e.deleteMetadata("slideshow")}}}},isToggled:t=>{var o,i;const s=t.type,a=e.shell.currentWidget;if(a&&n.has(a)){const e=null===(o=a.content.activeCell)||void 0===o?void 0:o.model;if(e)return(null!==(i=e.getMetadata("slideshow"))&&void 0!==i?i:{}).slide_type===s&&!!s}return!1},isEnabled:e=>{var t;return["slide","subslide","fragment","skip","notes"].includes(null!==(t=e.type)&&void 0!==t?t:"")}}),n.widgetAdded.connect((async(t,n)=>{var o,s,a;if(n.toolbar.insertBefore("kernelName","RISE-button",new i.CommandToolbarButton({commands:v,id:S.risePreview,args:{toolbar:!0}})),"Rise"!==e.name){await n.context.ready;let e=null!==(a=(null!==(s=null===(o=n.content.model)||void 0===o?void 0:o.getMetadata("rise"))&&void 0!==s?s:{}).autolaunch)&&void 0!==a&&a;m&&(e|=m.get("autolaunch").composite),e&&await v.execute(S.risePreview,{fullscreen:!0})}})),o){const e="Notebook Operations";[S.openRise,S.risePreview].forEach((t=>{o.addItem({command:t,category:e})}))}return h}},x=[C,I]}}]);
\ No newline at end of file
diff --git a/dev/lite/extensions/jupyterlab-jupytext/static/remoteEntry.2142187e379506459ae0.js b/dev/lite/extensions/jupyterlab-jupytext/static/remoteEntry.2142187e379506459ae0.js
new file mode 100644
index 0000000000000..576c0599b0896
--- /dev/null
+++ b/dev/lite/extensions/jupyterlab-jupytext/static/remoteEntry.2142187e379506459ae0.js
@@ -0,0 +1 @@
+var _JUPYTERLAB;(()=>{"use strict";var e,r,t,a,n,o,u,i,l,s,f,p,d,c,h,b,v,y,m,g,j,w,S,P,x,k={139:(e,r,t)=>{var a={"./index":()=>Promise.all([t.e(245),t.e(509)]).then((()=>()=>t(509))),"./extension":()=>Promise.all([t.e(245),t.e(509)]).then((()=>()=>t(509)))},n=(e,r)=>(t.R=r,r=t.o(a,e)?a[e]():Promise.resolve().then((()=>{throw new Error('Module "'+e+'" does not exist in container.')})),t.R=void 0,r),o=(e,r)=>{if(t.S){var a="default",n=t.S[a];if(n&&n!==e)throw new Error("Container initialization failed as it has already been initialized with a different share scope");return t.S[a]=e,t.I(a,r)}};t.d(r,{get:()=>n,init:()=>o})}},E={};function T(e){var r=E[e];if(void 0!==r)return r.exports;var t=E[e]={exports:{}};return k[e].call(t.exports,t,t.exports,T),t.exports}T.m=k,T.c=E,T.d=(e,r)=>{for(var t in 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diff --git a/dev/lite/files/auto_examples/applications/plot_cyclical_feature_engineering.ipynb b/dev/lite/files/auto_examples/applications/plot_cyclical_feature_engineering.ipynb
index cbc282318e871..0bfa8af09fd1d 100644
--- a/dev/lite/files/auto_examples/applications/plot_cyclical_feature_engineering.ipynb
+++ b/dev/lite/files/auto_examples/applications/plot_cyclical_feature_engineering.ipynb
@@ -749,7 +749,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/applications/plot_digits_denoising.ipynb b/dev/lite/files/auto_examples/applications/plot_digits_denoising.ipynb
index ab22c95529b1a..441e9fd4d238e 100644
--- a/dev/lite/files/auto_examples/applications/plot_digits_denoising.ipynb
+++ b/dev/lite/files/auto_examples/applications/plot_digits_denoising.ipynb
@@ -179,7 +179,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/applications/plot_face_recognition.ipynb b/dev/lite/files/auto_examples/applications/plot_face_recognition.ipynb
index cc54fe914f683..eb46c455826cb 100644
--- a/dev/lite/files/auto_examples/applications/plot_face_recognition.ipynb
+++ b/dev/lite/files/auto_examples/applications/plot_face_recognition.ipynb
@@ -215,7 +215,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/applications/plot_model_complexity_influence.ipynb b/dev/lite/files/auto_examples/applications/plot_model_complexity_influence.ipynb
index eb645d868e7e1..ed288c2245c99 100644
--- a/dev/lite/files/auto_examples/applications/plot_model_complexity_influence.ipynb
+++ b/dev/lite/files/auto_examples/applications/plot_model_complexity_influence.ipynb
@@ -132,7 +132,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/applications/plot_out_of_core_classification.ipynb b/dev/lite/files/auto_examples/applications/plot_out_of_core_classification.ipynb
index 77223dbae39ce..5a51c4ff0eda4 100644
--- a/dev/lite/files/auto_examples/applications/plot_out_of_core_classification.ipynb
+++ b/dev/lite/files/auto_examples/applications/plot_out_of_core_classification.ipynb
@@ -51,7 +51,7 @@
       },
       "outputs": [],
       "source": [
-        "class ReutersParser(HTMLParser):\n    \"\"\"Utility class to parse a SGML file and yield documents one at a time.\"\"\"\n\n    def __init__(self, encoding=\"latin-1\"):\n        HTMLParser.__init__(self)\n        self._reset()\n        self.encoding = encoding\n\n    def handle_starttag(self, tag, attrs):\n        method = \"start_\" + tag\n        getattr(self, method, lambda x: None)(attrs)\n\n    def handle_endtag(self, tag):\n        method = \"end_\" + tag\n        getattr(self, method, lambda: None)()\n\n    def _reset(self):\n        self.in_title = 0\n        self.in_body = 0\n        self.in_topics = 0\n        self.in_topic_d = 0\n        self.title = \"\"\n        self.body = \"\"\n        self.topics = []\n        self.topic_d = \"\"\n\n    def parse(self, fd):\n        self.docs = []\n        for chunk in fd:\n            self.feed(chunk.decode(self.encoding))\n            for doc in self.docs:\n                yield doc\n            self.docs = []\n        self.close()\n\n    def handle_data(self, data):\n        if self.in_body:\n            self.body += data\n        elif self.in_title:\n            self.title += data\n        elif self.in_topic_d:\n            self.topic_d += data\n\n    def start_reuters(self, attributes):\n        pass\n\n    def end_reuters(self):\n        self.body = re.sub(r\"\\s+\", r\" \", self.body)\n        self.docs.append(\n            {\"title\": self.title, \"body\": self.body, \"topics\": self.topics}\n        )\n        self._reset()\n\n    def start_title(self, attributes):\n        self.in_title = 1\n\n    def end_title(self):\n        self.in_title = 0\n\n    def start_body(self, attributes):\n        self.in_body = 1\n\n    def end_body(self):\n        self.in_body = 0\n\n    def start_topics(self, attributes):\n        self.in_topics = 1\n\n    def end_topics(self):\n        self.in_topics = 0\n\n    def start_d(self, attributes):\n        self.in_topic_d = 1\n\n    def end_d(self):\n        self.in_topic_d = 0\n        self.topics.append(self.topic_d)\n        self.topic_d = \"\"\n\n\ndef stream_reuters_documents(data_path=None):\n    \"\"\"Iterate over documents of the Reuters dataset.\n\n    The Reuters archive will automatically be downloaded and uncompressed if\n    the `data_path` directory does not exist.\n\n    Documents are represented as dictionaries with 'body' (str),\n    'title' (str), 'topics' (list(str)) keys.\n\n    \"\"\"\n\n    DOWNLOAD_URL = (\n        \"http://archive.ics.uci.edu/ml/machine-learning-databases//"\n        \"reuters21578-mld/reuters21578.tar.gz\"\n    )\n    ARCHIVE_SHA256 = \"3bae43c9b14e387f76a61b6d82bf98a4fb5d3ef99ef7e7075ff2ccbcf59f9d30\"\n    ARCHIVE_FILENAME = \"reuters21578.tar.gz\"\n\n    if data_path is None:\n        data_path = Path(get_data_home()) / \"reuters\"\n    else:\n        data_path = Path(data_path)\n    if not data_path.exists():\n        \"\"\"Download the dataset.\"\"\"\n        print(\"downloading dataset (once and for all) into %s\" % data_path)\n        data_path.mkdir(parents=True, exist_ok=True)\n\n        def progress(blocknum, bs, size):\n            total_sz_mb = \"%.2f MB\" % (size / 1e6)\n            current_sz_mb = \"%.2f MB\" % ((blocknum * bs) / 1e6)\n            if _not_in_sphinx():\n                sys.stdout.write(\"\\rdownloaded %s / %s\" % (current_sz_mb, total_sz_mb))\n\n        archive_path = data_path / ARCHIVE_FILENAME\n\n        urlretrieve(DOWNLOAD_URL, filename=archive_path, reporthook=progress)\n        if _not_in_sphinx():\n            sys.stdout.write(\"\\r\")\n\n        # Check that the archive was not tampered:\n        assert sha256(archive_path.read_bytes()).hexdigest() == ARCHIVE_SHA256\n\n        print(\"untarring Reuters dataset...\")\n        with tarfile.open(archive_path, \"r:gz\") as fp:\n            fp.extractall(data_path, filter=\"data\")\n        print(\"done.\")\n\n    parser = ReutersParser()\n    for filename in data_path.glob(\"*.sgm\"):\n        for doc in parser.parse(open(filename, \"rb\")):\n            yield doc"
+        "class ReutersParser(HTMLParser):\n    \"\"\"Utility class to parse a SGML file and yield documents one at a time.\"\"\"\n\n    def __init__(self, encoding=\"latin-1\"):\n        HTMLParser.__init__(self)\n        self._reset()\n        self.encoding = encoding\n\n    def handle_starttag(self, tag, attrs):\n        method = \"start_\" + tag\n        getattr(self, method, lambda x: None)(attrs)\n\n    def handle_endtag(self, tag):\n        method = \"end_\" + tag\n        getattr(self, method, lambda: None)()\n\n    def _reset(self):\n        self.in_title = 0\n        self.in_body = 0\n        self.in_topics = 0\n        self.in_topic_d = 0\n        self.title = \"\"\n        self.body = \"\"\n        self.topics = []\n        self.topic_d = \"\"\n\n    def parse(self, fd):\n        self.docs = []\n        for chunk in fd:\n            self.feed(chunk.decode(self.encoding))\n            for doc in self.docs:\n                yield doc\n            self.docs = []\n        self.close()\n\n    def handle_data(self, data):\n        if self.in_body:\n            self.body += data\n        elif self.in_title:\n            self.title += data\n        elif self.in_topic_d:\n            self.topic_d += data\n\n    def start_reuters(self, attributes):\n        pass\n\n    def end_reuters(self):\n        self.body = re.sub(r\"\\s+\", r\" \", self.body)\n        self.docs.append(\n            {\"title\": self.title, \"body\": self.body, \"topics\": self.topics}\n        )\n        self._reset()\n\n    def start_title(self, attributes):\n        self.in_title = 1\n\n    def end_title(self):\n        self.in_title = 0\n\n    def start_body(self, attributes):\n        self.in_body = 1\n\n    def end_body(self):\n        self.in_body = 0\n\n    def start_topics(self, attributes):\n        self.in_topics = 1\n\n    def end_topics(self):\n        self.in_topics = 0\n\n    def start_d(self, attributes):\n        self.in_topic_d = 1\n\n    def end_d(self):\n        self.in_topic_d = 0\n        self.topics.append(self.topic_d)\n        self.topic_d = \"\"\n\n\ndef stream_reuters_documents(data_path=None):\n    \"\"\"Iterate over documents of the Reuters dataset.\n\n    The Reuters archive will automatically be downloaded and uncompressed if\n    the `data_path` directory does not exist.\n\n    Documents are represented as dictionaries with 'body' (str),\n    'title' (str), 'topics' (list(str)) keys.\n\n    \"\"\"\n\n    DOWNLOAD_URL = \"https://kdd.ics.uci.edu/databases/reuters21578/reuters21578.tar.gz/"\n    ARCHIVE_SHA256 = \"3bae43c9b14e387f76a61b6d82bf98a4fb5d3ef99ef7e7075ff2ccbcf59f9d30\"\n    ARCHIVE_FILENAME = \"reuters21578.tar.gz\"\n\n    if data_path is None:\n        data_path = Path(get_data_home()) / \"reuters\"\n    else:\n        data_path = Path(data_path)\n    if not data_path.exists():\n        \"\"\"Download the dataset.\"\"\"\n        print(\"downloading dataset (once and for all) into %s\" % data_path)\n        data_path.mkdir(parents=True, exist_ok=True)\n\n        def progress(blocknum, bs, size):\n            total_sz_mb = \"%.2f MB\" % (size / 1e6)\n            current_sz_mb = \"%.2f MB\" % ((blocknum * bs) / 1e6)\n            if _not_in_sphinx():\n                sys.stdout.write(\"\\rdownloaded %s / %s\" % (current_sz_mb, total_sz_mb))\n\n        archive_path = data_path / ARCHIVE_FILENAME\n\n        urlretrieve(DOWNLOAD_URL, filename=archive_path, reporthook=progress)\n        if _not_in_sphinx():\n            sys.stdout.write(\"\\r\")\n\n        # Check that the archive was not tampered:\n        assert sha256(archive_path.read_bytes()).hexdigest() == ARCHIVE_SHA256\n\n        print(\"untarring Reuters dataset...\")\n        with tarfile.open(archive_path, \"r:gz\") as fp:\n            fp.extractall(data_path, filter=\"data\")\n        print(\"done.\")\n\n    parser = ReutersParser()\n    for filename in data_path.glob(\"*.sgm\"):\n        for doc in parser.parse(open(filename, \"rb\")):\n            yield doc"
       ]
     },
     {
@@ -107,7 +107,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/applications/plot_outlier_detection_wine.ipynb b/dev/lite/files/auto_examples/applications/plot_outlier_detection_wine.ipynb
index c1753388b0ab4..8ecba5a4f5e28 100644
--- a/dev/lite/files/auto_examples/applications/plot_outlier_detection_wine.ipynb
+++ b/dev/lite/files/auto_examples/applications/plot_outlier_detection_wine.ipynb
@@ -100,7 +100,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/applications/plot_prediction_latency.ipynb b/dev/lite/files/auto_examples/applications/plot_prediction_latency.ipynb
index fd5c90f5988c9..74e066535bb5e 100644
--- a/dev/lite/files/auto_examples/applications/plot_prediction_latency.ipynb
+++ b/dev/lite/files/auto_examples/applications/plot_prediction_latency.ipynb
@@ -125,7 +125,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/applications/plot_species_distribution_modeling.ipynb b/dev/lite/files/auto_examples/applications/plot_species_distribution_modeling.ipynb
index 7cd9ceeaced7b..b604ab17be4d3 100644
--- a/dev/lite/files/auto_examples/applications/plot_species_distribution_modeling.ipynb
+++ b/dev/lite/files/auto_examples/applications/plot_species_distribution_modeling.ipynb
@@ -33,7 +33,7 @@
       },
       "outputs": [],
       "source": [
-        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\n\nfrom time import time\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nfrom sklearn import metrics, svm\nfrom sklearn.datasets import fetch_species_distributions\nfrom sklearn.utils import Bunch\n\n# if basemap is available, we'll use it.\n# otherwise, we'll improvise later...\ntry:\n    from mpl_toolkits.basemap import Basemap\n\n    basemap = True\nexcept ImportError:\n    basemap = False\n\n\ndef construct_grids(batch):\n    \"\"\"Construct the map grid from the batch object\n\n    Parameters\n    ----------\n    batch : Batch object\n        The object returned by :func:`fetch_species_distributions`\n\n    Returns\n    -------\n    (xgrid, ygrid) : 1-D arrays\n        The grid corresponding to the values in batch.coverages\n    \"\"\"\n    # x,y coordinates for corner cells\n    xmin = batch.x_left_lower_corner + batch.grid_size\n    xmax = xmin + (batch.Nx * batch.grid_size)\n    ymin = batch.y_left_lower_corner + batch.grid_size\n    ymax = ymin + (batch.Ny * batch.grid_size)\n\n    # x coordinates of the grid cells\n    xgrid = np.arange(xmin, xmax, batch.grid_size)\n    # y coordinates of the grid cells\n    ygrid = np.arange(ymin, ymax, batch.grid_size)\n\n    return (xgrid, ygrid)\n\n\ndef create_species_bunch(species_name, train, test, coverages, xgrid, ygrid):\n    \"\"\"Create a bunch with information about a particular organism\n\n    This will use the test/train record arrays to extract the\n    data specific to the given species name.\n    \"\"\"\n    bunch = Bunch(name=\" \".join(species_name.split(\"_\")[:2]))\n    species_name = species_name.encode(\"ascii\")\n    points = dict(test=test, train=train)\n\n    for label, pts in points.items():\n        # choose points associated with the desired species\n        pts = pts[pts[\"species\"] == species_name]\n        bunch[\"pts_%s\" % label] = pts\n\n        # determine coverage values for each of the training & testing points\n        ix = np.searchsorted(xgrid, pts[\"dd long\"])\n        iy = np.searchsorted(ygrid, pts[\"dd lat\"])\n        bunch[\"cov_%s\" % label] = coverages[:, -iy, ix].T\n\n    return bunch\n\n\ndef plot_species_distribution(\n    species=(\"bradypus_variegatus_0\", \"microryzomys_minutus_0\")\n):\n    \"\"\"\n    Plot the species distribution.\n    \"\"\"\n    if len(species) > 2:\n        print(\n            \"Note: when more than two species are provided,\"\n            \" only the first two will be used\"\n        )\n\n    t0 = time()\n\n    # Load the compressed data\n    data = fetch_species_distributions()\n\n    # Set up the data grid\n    xgrid, ygrid = construct_grids(data)\n\n    # The grid in x,y coordinates\n    X, Y = np.meshgrid(xgrid, ygrid[::-1])\n\n    # create a bunch for each species\n    BV_bunch = create_species_bunch(\n        species[0], data.train, data.test, data.coverages, xgrid, ygrid\n    )\n    MM_bunch = create_species_bunch(\n        species[1], data.train, data.test, data.coverages, xgrid, ygrid\n    )\n\n    # background points (grid coordinates) for evaluation\n    np.random.seed(13)\n    background_points = np.c_[\n        np.random.randint(low=0, high=data.Ny, size=10000),\n        np.random.randint(low=0, high=data.Nx, size=10000),\n    ].T\n\n    # We'll make use of the fact that coverages[6] has measurements at all\n    # land points.  This will help us decide between land and water.\n    land_reference = data.coverages[6]\n\n    # Fit, predict, and plot for each species.\n    for i, species in enumerate([BV_bunch, MM_bunch]):\n        print(\"_\" * 80)\n        print(\"Modeling distribution of species '%s'\" % species.name)\n\n        # Standardize features\n        mean = species.cov_train.mean(axis=0)\n        std = species.cov_train.std(axis=0)\n        train_cover_std = (species.cov_train - mean) / std\n\n        # Fit OneClassSVM\n        print(\" - fit OneClassSVM ... \", end=\"\")\n        clf = svm.OneClassSVM(nu=0.1, kernel=\"rbf\", gamma=0.5)\n        clf.fit(train_cover_std)\n        print(\"done.\")\n\n        # Plot map of South America\n        plt.subplot(1, 2, i + 1)\n        if basemap:\n            print(\" - plot coastlines using basemap\")\n            m = Basemap(\n                projection=\"cyl\",\n                llcrnrlat=Y.min(),\n                urcrnrlat=Y.max(),\n                llcrnrlon=X.min(),\n                urcrnrlon=X.max(),\n                resolution=\"c\",\n            )\n            m.drawcoastlines()\n            m.drawcountries()\n        else:\n            print(\" - plot coastlines from coverage\")\n            plt.contour(\n                X, Y, land_reference, levels=[-9998], colors=\"k\", linestyles=\"solid\"\n            )\n            plt.xticks([])\n            plt.yticks([])\n\n        print(\" - predict species distribution\")\n\n        # Predict species distribution using the training data\n        Z = np.ones((data.Ny, data.Nx), dtype=np.float64)\n\n        # We'll predict only for the land points.\n        idx = np.where(land_reference > -9999)\n        coverages_land = data.coverages[:, idx[0], idx[1]].T\n\n        pred = clf.decision_function((coverages_land - mean) / std)\n        Z *= pred.min()\n        Z[idx[0], idx[1]] = pred\n\n        levels = np.linspace(Z.min(), Z.max(), 25)\n        Z[land_reference == -9999] = -9999\n\n        # plot contours of the prediction\n        plt.contourf(X, Y, Z, levels=levels, cmap=plt.cm.Reds)\n        plt.colorbar(format=\"%.2f\")\n\n        # scatter training/testing points\n        plt.scatter(\n            species.pts_train[\"dd long\"],\n            species.pts_train[\"dd lat\"],\n            s=2**2,\n            c=\"black\",\n            marker=\"^\",\n            label=\"train\",\n        )\n        plt.scatter(\n            species.pts_test[\"dd long\"],\n            species.pts_test[\"dd lat\"],\n            s=2**2,\n            c=\"black\",\n            marker=\"x\",\n            label=\"test\",\n        )\n        plt.legend()\n        plt.title(species.name)\n        plt.axis(\"equal\")\n\n        # Compute AUC with regards to background points\n        pred_background = Z[background_points[0], background_points[1]]\n        pred_test = clf.decision_function((species.cov_test - mean) / std)\n        scores = np.r_[pred_test, pred_background]\n        y = np.r_[np.ones(pred_test.shape), np.zeros(pred_background.shape)]\n        fpr, tpr, thresholds = metrics.roc_curve(y, scores)\n        roc_auc = metrics.auc(fpr, tpr)\n        plt.text(-35, -70, \"AUC: %.3f\" % roc_auc, ha=\"right\")\n        print(\"\\n Area under the ROC curve : %f\" % roc_auc)\n\n    print(\"\\ntime elapsed: %.2fs\" % (time() - t0))\n\n\nplot_species_distribution()\nplt.show()"
+        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\n\nfrom time import time\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nfrom sklearn import metrics, svm\nfrom sklearn.datasets import fetch_species_distributions\nfrom sklearn.utils import Bunch\n\n# if basemap is available, we'll use it.\n# otherwise, we'll improvise later...\ntry:\n    from mpl_toolkits.basemap import Basemap\n\n    basemap = True\nexcept ImportError:\n    basemap = False\n\n\ndef construct_grids(batch):\n    \"\"\"Construct the map grid from the batch object\n\n    Parameters\n    ----------\n    batch : Batch object\n        The object returned by :func:`fetch_species_distributions`\n\n    Returns\n    -------\n    (xgrid, ygrid) : 1-D arrays\n        The grid corresponding to the values in batch.coverages\n    \"\"\"\n    # x,y coordinates for corner cells\n    xmin = batch.x_left_lower_corner + batch.grid_size\n    xmax = xmin + (batch.Nx * batch.grid_size)\n    ymin = batch.y_left_lower_corner + batch.grid_size\n    ymax = ymin + (batch.Ny * batch.grid_size)\n\n    # x coordinates of the grid cells\n    xgrid = np.arange(xmin, xmax, batch.grid_size)\n    # y coordinates of the grid cells\n    ygrid = np.arange(ymin, ymax, batch.grid_size)\n\n    return (xgrid, ygrid)\n\n\ndef create_species_bunch(species_name, train, test, coverages, xgrid, ygrid):\n    \"\"\"Create a bunch with information about a particular organism\n\n    This will use the test/train record arrays to extract the\n    data specific to the given species name.\n    \"\"\"\n    bunch = Bunch(name=\" \".join(species_name.split(\"_\")[:2]))\n    species_name = species_name.encode(\"ascii\")\n    points = dict(test=test, train=train)\n\n    for label, pts in points.items():\n        # choose points associated with the desired species\n        pts = pts[pts[\"species\"] == species_name]\n        bunch[\"pts_%s\" % label] = pts\n\n        # determine coverage values for each of the training & testing points\n        ix = np.searchsorted(xgrid, pts[\"dd long\"])\n        iy = np.searchsorted(ygrid, pts[\"dd lat\"])\n        bunch[\"cov_%s\" % label] = coverages[:, -iy, ix].T\n\n    return bunch\n\n\ndef plot_species_distribution(\n    species=(\"bradypus_variegatus_0\", \"microryzomys_minutus_0\"),\n):\n    \"\"\"\n    Plot the species distribution.\n    \"\"\"\n    if len(species) > 2:\n        print(\n            \"Note: when more than two species are provided,\"\n            \" only the first two will be used\"\n        )\n\n    t0 = time()\n\n    # Load the compressed data\n    data = fetch_species_distributions()\n\n    # Set up the data grid\n    xgrid, ygrid = construct_grids(data)\n\n    # The grid in x,y coordinates\n    X, Y = np.meshgrid(xgrid, ygrid[::-1])\n\n    # create a bunch for each species\n    BV_bunch = create_species_bunch(\n        species[0], data.train, data.test, data.coverages, xgrid, ygrid\n    )\n    MM_bunch = create_species_bunch(\n        species[1], data.train, data.test, data.coverages, xgrid, ygrid\n    )\n\n    # background points (grid coordinates) for evaluation\n    np.random.seed(13)\n    background_points = np.c_[\n        np.random.randint(low=0, high=data.Ny, size=10000),\n        np.random.randint(low=0, high=data.Nx, size=10000),\n    ].T\n\n    # We'll make use of the fact that coverages[6] has measurements at all\n    # land points.  This will help us decide between land and water.\n    land_reference = data.coverages[6]\n\n    # Fit, predict, and plot for each species.\n    for i, species in enumerate([BV_bunch, MM_bunch]):\n        print(\"_\" * 80)\n        print(\"Modeling distribution of species '%s'\" % species.name)\n\n        # Standardize features\n        mean = species.cov_train.mean(axis=0)\n        std = species.cov_train.std(axis=0)\n        train_cover_std = (species.cov_train - mean) / std\n\n        # Fit OneClassSVM\n        print(\" - fit OneClassSVM ... \", end=\"\")\n        clf = svm.OneClassSVM(nu=0.1, kernel=\"rbf\", gamma=0.5)\n        clf.fit(train_cover_std)\n        print(\"done.\")\n\n        # Plot map of South America\n        plt.subplot(1, 2, i + 1)\n        if basemap:\n            print(\" - plot coastlines using basemap\")\n            m = Basemap(\n                projection=\"cyl\",\n                llcrnrlat=Y.min(),\n                urcrnrlat=Y.max(),\n                llcrnrlon=X.min(),\n                urcrnrlon=X.max(),\n                resolution=\"c\",\n            )\n            m.drawcoastlines()\n            m.drawcountries()\n        else:\n            print(\" - plot coastlines from coverage\")\n            plt.contour(\n                X, Y, land_reference, levels=[-9998], colors=\"k\", linestyles=\"solid\"\n            )\n            plt.xticks([])\n            plt.yticks([])\n\n        print(\" - predict species distribution\")\n\n        # Predict species distribution using the training data\n        Z = np.ones((data.Ny, data.Nx), dtype=np.float64)\n\n        # We'll predict only for the land points.\n        idx = (land_reference > -9999).nonzero()\n        coverages_land = data.coverages[:, idx[0], idx[1]].T\n\n        pred = clf.decision_function((coverages_land - mean) / std)\n        Z *= pred.min()\n        Z[idx[0], idx[1]] = pred\n\n        levels = np.linspace(Z.min(), Z.max(), 25)\n        Z[land_reference == -9999] = -9999\n\n        # plot contours of the prediction\n        plt.contourf(X, Y, Z, levels=levels, cmap=plt.cm.Reds)\n        plt.colorbar(format=\"%.2f\")\n\n        # scatter training/testing points\n        plt.scatter(\n            species.pts_train[\"dd long\"],\n            species.pts_train[\"dd lat\"],\n            s=2**2,\n            c=\"black\",\n            marker=\"^\",\n            label=\"train\",\n        )\n        plt.scatter(\n            species.pts_test[\"dd long\"],\n            species.pts_test[\"dd lat\"],\n            s=2**2,\n            c=\"black\",\n            marker=\"x\",\n            label=\"test\",\n        )\n        plt.legend()\n        plt.title(species.name)\n        plt.axis(\"equal\")\n\n        # Compute AUC with regards to background points\n        pred_background = Z[background_points[0], background_points[1]]\n        pred_test = clf.decision_function((species.cov_test - mean) / std)\n        scores = np.r_[pred_test, pred_background]\n        y = np.r_[np.ones(pred_test.shape), np.zeros(pred_background.shape)]\n        fpr, tpr, thresholds = metrics.roc_curve(y, scores)\n        roc_auc = metrics.auc(fpr, tpr)\n        plt.text(-35, -70, \"AUC: %.3f\" % roc_auc, ha=\"right\")\n        print(\"\\n Area under the ROC curve : %f\" % roc_auc)\n\n    print(\"\\ntime elapsed: %.2fs\" % (time() - t0))\n\n\nplot_species_distribution()\nplt.show()"
       ]
     }
   ],
@@ -53,7 +53,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/applications/plot_stock_market.ipynb b/dev/lite/files/auto_examples/applications/plot_stock_market.ipynb
index ebcedca826e98..a35318680a3a0 100644
--- a/dev/lite/files/auto_examples/applications/plot_stock_market.ipynb
+++ b/dev/lite/files/auto_examples/applications/plot_stock_market.ipynb
@@ -123,7 +123,7 @@
       },
       "outputs": [],
       "source": [
-        "import matplotlib.pyplot as plt\nfrom matplotlib.collections import LineCollection\n\nplt.figure(1, facecolor=\"w\", figsize=(10, 8))\nplt.clf()\nax = plt.axes([0.0, 0.0, 1.0, 1.0])\nplt.axis(\"off\")\n\n# Plot the graph of partial correlations\npartial_correlations = edge_model.precision_.copy()\nd = 1 / np.sqrt(np.diag(partial_correlations))\npartial_correlations *= d\npartial_correlations *= d[:, np.newaxis]\nnon_zero = np.abs(np.triu(partial_correlations, k=1)) > 0.02\n\n# Plot the nodes using the coordinates of our embedding\nplt.scatter(\n    embedding[0], embedding[1], s=100 * d**2, c=labels, cmap=plt.cm.nipy_spectral\n)\n\n# Plot the edges\nstart_idx, end_idx = np.where(non_zero)\n# a sequence of (*line0*, *line1*, *line2*), where::\n#            linen = (x0, y0), (x1, y1), ... (xm, ym)\nsegments = [\n    [embedding[:, start], embedding[:, stop]] for start, stop in zip(start_idx, end_idx)\n]\nvalues = np.abs(partial_correlations[non_zero])\nlc = LineCollection(\n    segments, zorder=0, cmap=plt.cm.hot_r, norm=plt.Normalize(0, 0.7 * values.max())\n)\nlc.set_array(values)\nlc.set_linewidths(15 * values)\nax.add_collection(lc)\n\n# Add a label to each node. The challenge here is that we want to\n# position the labels to avoid overlap with other labels\nfor index, (name, label, (x, y)) in enumerate(zip(names, labels, embedding.T)):\n    dx = x - embedding[0]\n    dx[index] = 1\n    dy = y - embedding[1]\n    dy[index] = 1\n    this_dx = dx[np.argmin(np.abs(dy))]\n    this_dy = dy[np.argmin(np.abs(dx))]\n    if this_dx > 0:\n        horizontalalignment = \"left\"\n        x = x + 0.002\n    else:\n        horizontalalignment = \"right\"\n        x = x - 0.002\n    if this_dy > 0:\n        verticalalignment = \"bottom\"\n        y = y + 0.002\n    else:\n        verticalalignment = \"top\"\n        y = y - 0.002\n    plt.text(\n        x,\n        y,\n        name,\n        size=10,\n        horizontalalignment=horizontalalignment,\n        verticalalignment=verticalalignment,\n        bbox=dict(\n            facecolor=\"w\",\n            edgecolor=plt.cm.nipy_spectral(label / float(n_labels)),\n            alpha=0.6,\n        ),\n    )\n\nplt.xlim(\n    embedding[0].min() - 0.15 * np.ptp(embedding[0]),\n    embedding[0].max() + 0.10 * np.ptp(embedding[0]),\n)\nplt.ylim(\n    embedding[1].min() - 0.03 * np.ptp(embedding[1]),\n    embedding[1].max() + 0.03 * np.ptp(embedding[1]),\n)\n\nplt.show()"
+        "import matplotlib.pyplot as plt\nfrom matplotlib.collections import LineCollection\n\nplt.figure(1, facecolor=\"w\", figsize=(10, 8))\nplt.clf()\nax = plt.axes([0.0, 0.0, 1.0, 1.0])\nplt.axis(\"off\")\n\n# Plot the graph of partial correlations\npartial_correlations = edge_model.precision_.copy()\nd = 1 / np.sqrt(np.diag(partial_correlations))\npartial_correlations *= d\npartial_correlations *= d[:, np.newaxis]\nnon_zero = np.abs(np.triu(partial_correlations, k=1)) > 0.02\n\n# Plot the nodes using the coordinates of our embedding\nplt.scatter(\n    embedding[0], embedding[1], s=100 * d**2, c=labels, cmap=plt.cm.nipy_spectral\n)\n\n# Plot the edges\nstart_idx, end_idx = non_zero.nonzero()\n# a sequence of (*line0*, *line1*, *line2*), where::\n#            linen = (x0, y0), (x1, y1), ... (xm, ym)\nsegments = [\n    [embedding[:, start], embedding[:, stop]] for start, stop in zip(start_idx, end_idx)\n]\nvalues = np.abs(partial_correlations[non_zero])\nlc = LineCollection(\n    segments, zorder=0, cmap=plt.cm.hot_r, norm=plt.Normalize(0, 0.7 * values.max())\n)\nlc.set_array(values)\nlc.set_linewidths(15 * values)\nax.add_collection(lc)\n\n# Add a label to each node. The challenge here is that we want to\n# position the labels to avoid overlap with other labels\nfor index, (name, label, (x, y)) in enumerate(zip(names, labels, embedding.T)):\n    dx = x - embedding[0]\n    dx[index] = 1\n    dy = y - embedding[1]\n    dy[index] = 1\n    this_dx = dx[np.argmin(np.abs(dy))]\n    this_dy = dy[np.argmin(np.abs(dx))]\n    if this_dx > 0:\n        horizontalalignment = \"left\"\n        x = x + 0.002\n    else:\n        horizontalalignment = \"right\"\n        x = x - 0.002\n    if this_dy > 0:\n        verticalalignment = \"bottom\"\n        y = y + 0.002\n    else:\n        verticalalignment = \"top\"\n        y = y - 0.002\n    plt.text(\n        x,\n        y,\n        name,\n        size=10,\n        horizontalalignment=horizontalalignment,\n        verticalalignment=verticalalignment,\n        bbox=dict(\n            facecolor=\"w\",\n            edgecolor=plt.cm.nipy_spectral(label / float(n_labels)),\n            alpha=0.6,\n        ),\n    )\n\nplt.xlim(\n    embedding[0].min() - 0.15 * np.ptp(embedding[0]),\n    embedding[0].max() + 0.10 * np.ptp(embedding[0]),\n)\nplt.ylim(\n    embedding[1].min() - 0.03 * np.ptp(embedding[1]),\n    embedding[1].max() + 0.03 * np.ptp(embedding[1]),\n)\n\nplt.show()"
       ]
     }
   ],
@@ -143,7 +143,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/applications/plot_time_series_lagged_features.ipynb b/dev/lite/files/auto_examples/applications/plot_time_series_lagged_features.ipynb
index eb41b924f7bad..cc5ae0885ec93 100644
--- a/dev/lite/files/auto_examples/applications/plot_time_series_lagged_features.ipynb
+++ b/dev/lite/files/auto_examples/applications/plot_time_series_lagged_features.ipynb
@@ -303,7 +303,7 @@
       },
       "outputs": [],
       "source": [
-        "quantile_list = [0.05, 0.5, 0.95]\n\nfor quantile in quantile_list:\n    model = HistGradientBoostingRegressor(loss=\"quantile\", quantile=quantile)\n    cv_results = cross_validate(\n        model,\n        X,\n        y,\n        cv=ts_cv,\n        scoring=scoring,\n        n_jobs=2,\n    )\n    time = cv_results[\"fit_time\"]\n    scores[\"fit_time\"].append(f\"{time.mean():.2f} \u00b1 {time.std():.2f} s\")\n\n    scores[\"loss\"].append(f\"quantile {int(quantile*100)}\")\n    for key, value in cv_results.items():\n        if key.startswith(\"test_\"):\n            metric = key.split(\"test_\")[1]\n            scores = consolidate_scores(cv_results, scores, metric)\n\nscores_df = pl.DataFrame(scores)\nscores_df"
+        "quantile_list = [0.05, 0.5, 0.95]\n\nfor quantile in quantile_list:\n    model = HistGradientBoostingRegressor(loss=\"quantile\", quantile=quantile)\n    cv_results = cross_validate(\n        model,\n        X,\n        y,\n        cv=ts_cv,\n        scoring=scoring,\n        n_jobs=2,\n    )\n    time = cv_results[\"fit_time\"]\n    scores[\"fit_time\"].append(f\"{time.mean():.2f} \u00b1 {time.std():.2f} s\")\n\n    scores[\"loss\"].append(f\"quantile {int(quantile * 100)}\")\n    for key, value in cv_results.items():\n        if key.startswith(\"test_\"):\n            metric = key.split(\"test_\")[1]\n            scores = consolidate_scores(cv_results, scores, metric)\n\nscores_df = pl.DataFrame(scores)\nscores_df"
       ]
     },
     {
@@ -402,7 +402,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/applications/plot_tomography_l1_reconstruction.ipynb b/dev/lite/files/auto_examples/applications/plot_tomography_l1_reconstruction.ipynb
index 4d0f3b6f5735c..88385bc50a2bb 100644
--- a/dev/lite/files/auto_examples/applications/plot_tomography_l1_reconstruction.ipynb
+++ b/dev/lite/files/auto_examples/applications/plot_tomography_l1_reconstruction.ipynb
@@ -53,7 +53,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/applications/plot_topics_extraction_with_nmf_lda.ipynb b/dev/lite/files/auto_examples/applications/plot_topics_extraction_with_nmf_lda.ipynb
index 35eaf805c5756..90aa71a9b238b 100644
--- a/dev/lite/files/auto_examples/applications/plot_topics_extraction_with_nmf_lda.ipynb
+++ b/dev/lite/files/auto_examples/applications/plot_topics_extraction_with_nmf_lda.ipynb
@@ -33,7 +33,7 @@
       },
       "outputs": [],
       "source": [
-        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\n\nfrom time import time\n\nimport matplotlib.pyplot as plt\n\nfrom sklearn.datasets import fetch_20newsgroups\nfrom sklearn.decomposition import NMF, LatentDirichletAllocation, MiniBatchNMF\nfrom sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\n\nn_samples = 2000\nn_features = 1000\nn_components = 10\nn_top_words = 20\nbatch_size = 128\ninit = \"nndsvda\"\n\n\ndef plot_top_words(model, feature_names, n_top_words, title):\n    fig, axes = plt.subplots(2, 5, figsize=(30, 15), sharex=True)\n    axes = axes.flatten()\n    for topic_idx, topic in enumerate(model.components_):\n        top_features_ind = topic.argsort()[-n_top_words:]\n        top_features = feature_names[top_features_ind]\n        weights = topic[top_features_ind]\n\n        ax = axes[topic_idx]\n        ax.barh(top_features, weights, height=0.7)\n        ax.set_title(f\"Topic {topic_idx +1}\", fontdict={\"fontsize\": 30})\n        ax.tick_params(axis=\"both\", which=\"major\", labelsize=20)\n        for i in \"top right left\".split():\n            ax.spines[i].set_visible(False)\n        fig.suptitle(title, fontsize=40)\n\n    plt.subplots_adjust(top=0.90, bottom=0.05, wspace=0.90, hspace=0.3)\n    plt.show()\n\n\n# Load the 20 newsgroups dataset and vectorize it. We use a few heuristics\n# to filter out useless terms early on: the posts are stripped of headers,\n# footers and quoted replies, and common English words, words occurring in\n# only one document or in at least 95% of the documents are removed.\n\nprint(\"Loading dataset...\")\nt0 = time()\ndata, _ = fetch_20newsgroups(\n    shuffle=True,\n    random_state=1,\n    remove=(\"headers\", \"footers\", \"quotes\"),\n    return_X_y=True,\n)\ndata_samples = data[:n_samples]\nprint(\"done in %0.3fs.\" % (time() - t0))\n\n# Use tf-idf features for NMF.\nprint(\"Extracting tf-idf features for NMF...\")\ntfidf_vectorizer = TfidfVectorizer(\n    max_df=0.95, min_df=2, max_features=n_features, stop_words=\"english\"\n)\nt0 = time()\ntfidf = tfidf_vectorizer.fit_transform(data_samples)\nprint(\"done in %0.3fs.\" % (time() - t0))\n\n# Use tf (raw term count) features for LDA.\nprint(\"Extracting tf features for LDA...\")\ntf_vectorizer = CountVectorizer(\n    max_df=0.95, min_df=2, max_features=n_features, stop_words=\"english\"\n)\nt0 = time()\ntf = tf_vectorizer.fit_transform(data_samples)\nprint(\"done in %0.3fs.\" % (time() - t0))\nprint()\n\n# Fit the NMF model\nprint(\n    \"Fitting the NMF model (Frobenius norm) with tf-idf features, \"\n    \"n_samples=%d and n_features=%d...\" % (n_samples, n_features)\n)\nt0 = time()\nnmf = NMF(\n    n_components=n_components,\n    random_state=1,\n    init=init,\n    beta_loss=\"frobenius\",\n    alpha_W=0.00005,\n    alpha_H=0.00005,\n    l1_ratio=1,\n).fit(tfidf)\nprint(\"done in %0.3fs.\" % (time() - t0))\n\n\ntfidf_feature_names = tfidf_vectorizer.get_feature_names_out()\nplot_top_words(\n    nmf, tfidf_feature_names, n_top_words, \"Topics in NMF model (Frobenius norm)\"\n)\n\n# Fit the NMF model\nprint(\n    \"\\n\" * 2,\n    \"Fitting the NMF model (generalized Kullback-Leibler \"\n    \"divergence) with tf-idf features, n_samples=%d and n_features=%d...\"\n    % (n_samples, n_features),\n)\nt0 = time()\nnmf = NMF(\n    n_components=n_components,\n    random_state=1,\n    init=init,\n    beta_loss=\"kullback-leibler\",\n    solver=\"mu\",\n    max_iter=1000,\n    alpha_W=0.00005,\n    alpha_H=0.00005,\n    l1_ratio=0.5,\n).fit(tfidf)\nprint(\"done in %0.3fs.\" % (time() - t0))\n\ntfidf_feature_names = tfidf_vectorizer.get_feature_names_out()\nplot_top_words(\n    nmf,\n    tfidf_feature_names,\n    n_top_words,\n    \"Topics in NMF model (generalized Kullback-Leibler divergence)\",\n)\n\n# Fit the MiniBatchNMF model\nprint(\n    \"\\n\" * 2,\n    \"Fitting the MiniBatchNMF model (Frobenius norm) with tf-idf \"\n    \"features, n_samples=%d and n_features=%d, batch_size=%d...\"\n    % (n_samples, n_features, batch_size),\n)\nt0 = time()\nmbnmf = MiniBatchNMF(\n    n_components=n_components,\n    random_state=1,\n    batch_size=batch_size,\n    init=init,\n    beta_loss=\"frobenius\",\n    alpha_W=0.00005,\n    alpha_H=0.00005,\n    l1_ratio=0.5,\n).fit(tfidf)\nprint(\"done in %0.3fs.\" % (time() - t0))\n\n\ntfidf_feature_names = tfidf_vectorizer.get_feature_names_out()\nplot_top_words(\n    mbnmf,\n    tfidf_feature_names,\n    n_top_words,\n    \"Topics in MiniBatchNMF model (Frobenius norm)\",\n)\n\n# Fit the MiniBatchNMF model\nprint(\n    \"\\n\" * 2,\n    \"Fitting the MiniBatchNMF model (generalized Kullback-Leibler \"\n    \"divergence) with tf-idf features, n_samples=%d and n_features=%d, \"\n    \"batch_size=%d...\" % (n_samples, n_features, batch_size),\n)\nt0 = time()\nmbnmf = MiniBatchNMF(\n    n_components=n_components,\n    random_state=1,\n    batch_size=batch_size,\n    init=init,\n    beta_loss=\"kullback-leibler\",\n    alpha_W=0.00005,\n    alpha_H=0.00005,\n    l1_ratio=0.5,\n).fit(tfidf)\nprint(\"done in %0.3fs.\" % (time() - t0))\n\ntfidf_feature_names = tfidf_vectorizer.get_feature_names_out()\nplot_top_words(\n    mbnmf,\n    tfidf_feature_names,\n    n_top_words,\n    \"Topics in MiniBatchNMF model (generalized Kullback-Leibler divergence)\",\n)\n\nprint(\n    \"\\n\" * 2,\n    \"Fitting LDA models with tf features, n_samples=%d and n_features=%d...\"\n    % (n_samples, n_features),\n)\nlda = LatentDirichletAllocation(\n    n_components=n_components,\n    max_iter=5,\n    learning_method=\"online\",\n    learning_offset=50.0,\n    random_state=0,\n)\nt0 = time()\nlda.fit(tf)\nprint(\"done in %0.3fs.\" % (time() - t0))\n\ntf_feature_names = tf_vectorizer.get_feature_names_out()\nplot_top_words(lda, tf_feature_names, n_top_words, \"Topics in LDA model\")"
+        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\n\nfrom time import time\n\nimport matplotlib.pyplot as plt\n\nfrom sklearn.datasets import fetch_20newsgroups\nfrom sklearn.decomposition import NMF, LatentDirichletAllocation, MiniBatchNMF\nfrom sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\n\nn_samples = 2000\nn_features = 1000\nn_components = 10\nn_top_words = 20\nbatch_size = 128\ninit = \"nndsvda\"\n\n\ndef plot_top_words(model, feature_names, n_top_words, title):\n    fig, axes = plt.subplots(2, 5, figsize=(30, 15), sharex=True)\n    axes = axes.flatten()\n    for topic_idx, topic in enumerate(model.components_):\n        top_features_ind = topic.argsort()[-n_top_words:]\n        top_features = feature_names[top_features_ind]\n        weights = topic[top_features_ind]\n\n        ax = axes[topic_idx]\n        ax.barh(top_features, weights, height=0.7)\n        ax.set_title(f\"Topic {topic_idx + 1}\", fontdict={\"fontsize\": 30})\n        ax.tick_params(axis=\"both\", which=\"major\", labelsize=20)\n        for i in \"top right left\".split():\n            ax.spines[i].set_visible(False)\n        fig.suptitle(title, fontsize=40)\n\n    plt.subplots_adjust(top=0.90, bottom=0.05, wspace=0.90, hspace=0.3)\n    plt.show()\n\n\n# Load the 20 newsgroups dataset and vectorize it. We use a few heuristics\n# to filter out useless terms early on: the posts are stripped of headers,\n# footers and quoted replies, and common English words, words occurring in\n# only one document or in at least 95% of the documents are removed.\n\nprint(\"Loading dataset...\")\nt0 = time()\ndata, _ = fetch_20newsgroups(\n    shuffle=True,\n    random_state=1,\n    remove=(\"headers\", \"footers\", \"quotes\"),\n    return_X_y=True,\n)\ndata_samples = data[:n_samples]\nprint(\"done in %0.3fs.\" % (time() - t0))\n\n# Use tf-idf features for NMF.\nprint(\"Extracting tf-idf features for NMF...\")\ntfidf_vectorizer = TfidfVectorizer(\n    max_df=0.95, min_df=2, max_features=n_features, stop_words=\"english\"\n)\nt0 = time()\ntfidf = tfidf_vectorizer.fit_transform(data_samples)\nprint(\"done in %0.3fs.\" % (time() - t0))\n\n# Use tf (raw term count) features for LDA.\nprint(\"Extracting tf features for LDA...\")\ntf_vectorizer = CountVectorizer(\n    max_df=0.95, min_df=2, max_features=n_features, stop_words=\"english\"\n)\nt0 = time()\ntf = tf_vectorizer.fit_transform(data_samples)\nprint(\"done in %0.3fs.\" % (time() - t0))\nprint()\n\n# Fit the NMF model\nprint(\n    \"Fitting the NMF model (Frobenius norm) with tf-idf features, \"\n    \"n_samples=%d and n_features=%d...\" % (n_samples, n_features)\n)\nt0 = time()\nnmf = NMF(\n    n_components=n_components,\n    random_state=1,\n    init=init,\n    beta_loss=\"frobenius\",\n    alpha_W=0.00005,\n    alpha_H=0.00005,\n    l1_ratio=1,\n).fit(tfidf)\nprint(\"done in %0.3fs.\" % (time() - t0))\n\n\ntfidf_feature_names = tfidf_vectorizer.get_feature_names_out()\nplot_top_words(\n    nmf, tfidf_feature_names, n_top_words, \"Topics in NMF model (Frobenius norm)\"\n)\n\n# Fit the NMF model\nprint(\n    \"\\n\" * 2,\n    \"Fitting the NMF model (generalized Kullback-Leibler \"\n    \"divergence) with tf-idf features, n_samples=%d and n_features=%d...\"\n    % (n_samples, n_features),\n)\nt0 = time()\nnmf = NMF(\n    n_components=n_components,\n    random_state=1,\n    init=init,\n    beta_loss=\"kullback-leibler\",\n    solver=\"mu\",\n    max_iter=1000,\n    alpha_W=0.00005,\n    alpha_H=0.00005,\n    l1_ratio=0.5,\n).fit(tfidf)\nprint(\"done in %0.3fs.\" % (time() - t0))\n\ntfidf_feature_names = tfidf_vectorizer.get_feature_names_out()\nplot_top_words(\n    nmf,\n    tfidf_feature_names,\n    n_top_words,\n    \"Topics in NMF model (generalized Kullback-Leibler divergence)\",\n)\n\n# Fit the MiniBatchNMF model\nprint(\n    \"\\n\" * 2,\n    \"Fitting the MiniBatchNMF model (Frobenius norm) with tf-idf \"\n    \"features, n_samples=%d and n_features=%d, batch_size=%d...\"\n    % (n_samples, n_features, batch_size),\n)\nt0 = time()\nmbnmf = MiniBatchNMF(\n    n_components=n_components,\n    random_state=1,\n    batch_size=batch_size,\n    init=init,\n    beta_loss=\"frobenius\",\n    alpha_W=0.00005,\n    alpha_H=0.00005,\n    l1_ratio=0.5,\n).fit(tfidf)\nprint(\"done in %0.3fs.\" % (time() - t0))\n\n\ntfidf_feature_names = tfidf_vectorizer.get_feature_names_out()\nplot_top_words(\n    mbnmf,\n    tfidf_feature_names,\n    n_top_words,\n    \"Topics in MiniBatchNMF model (Frobenius norm)\",\n)\n\n# Fit the MiniBatchNMF model\nprint(\n    \"\\n\" * 2,\n    \"Fitting the MiniBatchNMF model (generalized Kullback-Leibler \"\n    \"divergence) with tf-idf features, n_samples=%d and n_features=%d, \"\n    \"batch_size=%d...\" % (n_samples, n_features, batch_size),\n)\nt0 = time()\nmbnmf = MiniBatchNMF(\n    n_components=n_components,\n    random_state=1,\n    batch_size=batch_size,\n    init=init,\n    beta_loss=\"kullback-leibler\",\n    alpha_W=0.00005,\n    alpha_H=0.00005,\n    l1_ratio=0.5,\n).fit(tfidf)\nprint(\"done in %0.3fs.\" % (time() - t0))\n\ntfidf_feature_names = tfidf_vectorizer.get_feature_names_out()\nplot_top_words(\n    mbnmf,\n    tfidf_feature_names,\n    n_top_words,\n    \"Topics in MiniBatchNMF model (generalized Kullback-Leibler divergence)\",\n)\n\nprint(\n    \"\\n\" * 2,\n    \"Fitting LDA models with tf features, n_samples=%d and n_features=%d...\"\n    % (n_samples, n_features),\n)\nlda = LatentDirichletAllocation(\n    n_components=n_components,\n    max_iter=5,\n    learning_method=\"online\",\n    learning_offset=50.0,\n    random_state=0,\n)\nt0 = time()\nlda.fit(tf)\nprint(\"done in %0.3fs.\" % (time() - t0))\n\ntf_feature_names = tf_vectorizer.get_feature_names_out()\nplot_top_words(lda, tf_feature_names, n_top_words, \"Topics in LDA model\")"
       ]
     }
   ],
@@ -53,7 +53,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/applications/wikipedia_principal_eigenvector.ipynb b/dev/lite/files/auto_examples/applications/wikipedia_principal_eigenvector.ipynb
index cfbe8f8b31550..fc684b0fd25de 100644
--- a/dev/lite/files/auto_examples/applications/wikipedia_principal_eigenvector.ipynb
+++ b/dev/lite/files/auto_examples/applications/wikipedia_principal_eigenvector.ipynb
@@ -143,7 +143,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/bicluster/plot_bicluster_newsgroups.ipynb b/dev/lite/files/auto_examples/bicluster/plot_bicluster_newsgroups.ipynb
index 90701e7b5784a..ef0eb99f6f8d3 100644
--- a/dev/lite/files/auto_examples/bicluster/plot_bicluster_newsgroups.ipynb
+++ b/dev/lite/files/auto_examples/bicluster/plot_bicluster_newsgroups.ipynb
@@ -33,7 +33,7 @@
       },
       "outputs": [],
       "source": [
-        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\nfrom collections import Counter\nfrom time import time\n\nimport numpy as np\n\nfrom sklearn.cluster import MiniBatchKMeans, SpectralCoclustering\nfrom sklearn.datasets import fetch_20newsgroups\nfrom sklearn.feature_extraction.text import TfidfVectorizer\nfrom sklearn.metrics.cluster import v_measure_score\n\n\ndef number_normalizer(tokens):\n    \"\"\"Map all numeric tokens to a placeholder.\n\n    For many applications, tokens that begin with a number are not directly\n    useful, but the fact that such a token exists can be relevant.  By applying\n    this form of dimensionality reduction, some methods may perform better.\n    \"\"\"\n    return (\"#NUMBER\" if token[0].isdigit() else token for token in tokens)\n\n\nclass NumberNormalizingVectorizer(TfidfVectorizer):\n    def build_tokenizer(self):\n        tokenize = super().build_tokenizer()\n        return lambda doc: list(number_normalizer(tokenize(doc)))\n\n\n# exclude 'comp.os.ms-windows.misc'\ncategories = [\n    \"alt.atheism\",\n    \"comp.graphics\",\n    \"comp.sys.ibm.pc.hardware\",\n    \"comp.sys.mac.hardware\",\n    \"comp.windows.x\",\n    \"misc.forsale\",\n    \"rec.autos\",\n    \"rec.motorcycles\",\n    \"rec.sport.baseball\",\n    \"rec.sport.hockey\",\n    \"sci.crypt\",\n    \"sci.electronics\",\n    \"sci.med\",\n    \"sci.space\",\n    \"soc.religion.christian\",\n    \"talk.politics.guns\",\n    \"talk.politics.mideast\",\n    \"talk.politics.misc\",\n    \"talk.religion.misc\",\n]\nnewsgroups = fetch_20newsgroups(categories=categories)\ny_true = newsgroups.target\n\nvectorizer = NumberNormalizingVectorizer(stop_words=\"english\", min_df=5)\ncocluster = SpectralCoclustering(\n    n_clusters=len(categories), svd_method=\"arpack\", random_state=0\n)\nkmeans = MiniBatchKMeans(\n    n_clusters=len(categories), batch_size=20000, random_state=0, n_init=3\n)\n\nprint(\"Vectorizing...\")\nX = vectorizer.fit_transform(newsgroups.data)\n\nprint(\"Coclustering...\")\nstart_time = time()\ncocluster.fit(X)\ny_cocluster = cocluster.row_labels_\nprint(\n    f\"Done in {time() - start_time:.2f}s. V-measure: \\\n{v_measure_score(y_cocluster, y_true):.4f}\"\n)\n\n\nprint(\"MiniBatchKMeans...\")\nstart_time = time()\ny_kmeans = kmeans.fit_predict(X)\nprint(\n    f\"Done in {time() - start_time:.2f}s. V-measure: \\\n{v_measure_score(y_kmeans, y_true):.4f}\"\n)\n\n\nfeature_names = vectorizer.get_feature_names_out()\ndocument_names = list(newsgroups.target_names[i] for i in newsgroups.target)\n\n\ndef bicluster_ncut(i):\n    rows, cols = cocluster.get_indices(i)\n    if not (np.any(rows) and np.any(cols)):\n        import sys\n\n        return sys.float_info.max\n    row_complement = np.nonzero(np.logical_not(cocluster.rows_[i]))[0]\n    col_complement = np.nonzero(np.logical_not(cocluster.columns_[i]))[0]\n    # Note: the following is identical to X[rows[:, np.newaxis],\n    # cols].sum() but much faster in scipy <= 0.16\n    weight = X[rows][:, cols].sum()\n    cut = X[row_complement][:, cols].sum() + X[rows][:, col_complement].sum()\n    return cut / weight\n\n\nbicluster_ncuts = list(bicluster_ncut(i) for i in range(len(newsgroups.target_names)))\nbest_idx = np.argsort(bicluster_ncuts)[:5]\n\nprint()\nprint(\"Best biclusters:\")\nprint(\"----------------\")\nfor idx, cluster in enumerate(best_idx):\n    n_rows, n_cols = cocluster.get_shape(cluster)\n    cluster_docs, cluster_words = cocluster.get_indices(cluster)\n    if not len(cluster_docs) or not len(cluster_words):\n        continue\n\n    # categories\n    counter = Counter(document_names[doc] for doc in cluster_docs)\n\n    cat_string = \", \".join(\n        f\"{(c / n_rows * 100):.0f}% {name}\" for name, c in counter.most_common(3)\n    )\n\n    # words\n    out_of_cluster_docs = cocluster.row_labels_ != cluster\n    out_of_cluster_docs = np.where(out_of_cluster_docs)[0]\n    word_col = X[:, cluster_words]\n    word_scores = np.array(\n        word_col[cluster_docs, :].sum(axis=0)\n        - word_col[out_of_cluster_docs, :].sum(axis=0)\n    )\n    word_scores = word_scores.ravel()\n    important_words = list(\n        feature_names[cluster_words[i]] for i in word_scores.argsort()[:-11:-1]\n    )\n\n    print(f\"bicluster {idx} : {n_rows} documents, {n_cols} words\")\n    print(f\"categories   : {cat_string}\")\n    print(f\"words        : {', '.join(important_words)}\\n\")"
+        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\nfrom collections import Counter\nfrom time import time\n\nimport numpy as np\n\nfrom sklearn.cluster import MiniBatchKMeans, SpectralCoclustering\nfrom sklearn.datasets import fetch_20newsgroups\nfrom sklearn.feature_extraction.text import TfidfVectorizer\nfrom sklearn.metrics.cluster import v_measure_score\n\n\ndef number_normalizer(tokens):\n    \"\"\"Map all numeric tokens to a placeholder.\n\n    For many applications, tokens that begin with a number are not directly\n    useful, but the fact that such a token exists can be relevant.  By applying\n    this form of dimensionality reduction, some methods may perform better.\n    \"\"\"\n    return (\"#NUMBER\" if token[0].isdigit() else token for token in tokens)\n\n\nclass NumberNormalizingVectorizer(TfidfVectorizer):\n    def build_tokenizer(self):\n        tokenize = super().build_tokenizer()\n        return lambda doc: list(number_normalizer(tokenize(doc)))\n\n\n# exclude 'comp.os.ms-windows.misc'\ncategories = [\n    \"alt.atheism\",\n    \"comp.graphics\",\n    \"comp.sys.ibm.pc.hardware\",\n    \"comp.sys.mac.hardware\",\n    \"comp.windows.x\",\n    \"misc.forsale\",\n    \"rec.autos\",\n    \"rec.motorcycles\",\n    \"rec.sport.baseball\",\n    \"rec.sport.hockey\",\n    \"sci.crypt\",\n    \"sci.electronics\",\n    \"sci.med\",\n    \"sci.space\",\n    \"soc.religion.christian\",\n    \"talk.politics.guns\",\n    \"talk.politics.mideast\",\n    \"talk.politics.misc\",\n    \"talk.religion.misc\",\n]\nnewsgroups = fetch_20newsgroups(categories=categories)\ny_true = newsgroups.target\n\nvectorizer = NumberNormalizingVectorizer(stop_words=\"english\", min_df=5)\ncocluster = SpectralCoclustering(\n    n_clusters=len(categories), svd_method=\"arpack\", random_state=0\n)\nkmeans = MiniBatchKMeans(\n    n_clusters=len(categories), batch_size=20000, random_state=0, n_init=3\n)\n\nprint(\"Vectorizing...\")\nX = vectorizer.fit_transform(newsgroups.data)\n\nprint(\"Coclustering...\")\nstart_time = time()\ncocluster.fit(X)\ny_cocluster = cocluster.row_labels_\nprint(\n    f\"Done in {time() - start_time:.2f}s. V-measure: \\\n{v_measure_score(y_cocluster, y_true):.4f}\"\n)\n\n\nprint(\"MiniBatchKMeans...\")\nstart_time = time()\ny_kmeans = kmeans.fit_predict(X)\nprint(\n    f\"Done in {time() - start_time:.2f}s. V-measure: \\\n{v_measure_score(y_kmeans, y_true):.4f}\"\n)\n\n\nfeature_names = vectorizer.get_feature_names_out()\ndocument_names = list(newsgroups.target_names[i] for i in newsgroups.target)\n\n\ndef bicluster_ncut(i):\n    rows, cols = cocluster.get_indices(i)\n    if not (np.any(rows) and np.any(cols)):\n        import sys\n\n        return sys.float_info.max\n    row_complement = np.nonzero(np.logical_not(cocluster.rows_[i]))[0]\n    col_complement = np.nonzero(np.logical_not(cocluster.columns_[i]))[0]\n    # Note: the following is identical to X[rows[:, np.newaxis],\n    # cols].sum() but much faster in scipy <= 0.16\n    weight = X[rows][:, cols].sum()\n    cut = X[row_complement][:, cols].sum() + X[rows][:, col_complement].sum()\n    return cut / weight\n\n\nbicluster_ncuts = list(bicluster_ncut(i) for i in range(len(newsgroups.target_names)))\nbest_idx = np.argsort(bicluster_ncuts)[:5]\n\nprint()\nprint(\"Best biclusters:\")\nprint(\"----------------\")\nfor idx, cluster in enumerate(best_idx):\n    n_rows, n_cols = cocluster.get_shape(cluster)\n    cluster_docs, cluster_words = cocluster.get_indices(cluster)\n    if not len(cluster_docs) or not len(cluster_words):\n        continue\n\n    # categories\n    counter = Counter(document_names[doc] for doc in cluster_docs)\n\n    cat_string = \", \".join(\n        f\"{(c / n_rows * 100):.0f}% {name}\" for name, c in counter.most_common(3)\n    )\n\n    # words\n    out_of_cluster_docs = cocluster.row_labels_ != cluster\n    out_of_cluster_docs = out_of_cluster_docs.nonzero()[0]\n    word_col = X[:, cluster_words]\n    word_scores = np.array(\n        word_col[cluster_docs, :].sum(axis=0)\n        - word_col[out_of_cluster_docs, :].sum(axis=0)\n    )\n    word_scores = word_scores.ravel()\n    important_words = list(\n        feature_names[cluster_words[i]] for i in word_scores.argsort()[:-11:-1]\n    )\n\n    print(f\"bicluster {idx} : {n_rows} documents, {n_cols} words\")\n    print(f\"categories   : {cat_string}\")\n    print(f\"words        : {', '.join(important_words)}\\n\")"
       ]
     }
   ],
@@ -53,7 +53,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/bicluster/plot_spectral_biclustering.ipynb b/dev/lite/files/auto_examples/bicluster/plot_spectral_biclustering.ipynb
index 1d2e23b0d5bc2..699d63d6e55e6 100644
--- a/dev/lite/files/auto_examples/bicluster/plot_spectral_biclustering.ipynb
+++ b/dev/lite/files/auto_examples/bicluster/plot_spectral_biclustering.ipynb
@@ -175,7 +175,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/bicluster/plot_spectral_coclustering.ipynb b/dev/lite/files/auto_examples/bicluster/plot_spectral_coclustering.ipynb
index 1a28b2faab433..bc76cbae8a019 100644
--- a/dev/lite/files/auto_examples/bicluster/plot_spectral_coclustering.ipynb
+++ b/dev/lite/files/auto_examples/bicluster/plot_spectral_coclustering.ipynb
@@ -53,7 +53,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/calibration/plot_calibration.ipynb b/dev/lite/files/auto_examples/calibration/plot_calibration.ipynb
index f758f090054d7..6d394d8a36669 100644
--- a/dev/lite/files/auto_examples/calibration/plot_calibration.ipynb
+++ b/dev/lite/files/auto_examples/calibration/plot_calibration.ipynb
@@ -107,7 +107,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/calibration/plot_calibration_curve.ipynb b/dev/lite/files/auto_examples/calibration/plot_calibration_curve.ipynb
index 1c6cdda069664..fc09acd37ee19 100644
--- a/dev/lite/files/auto_examples/calibration/plot_calibration_curve.ipynb
+++ b/dev/lite/files/auto_examples/calibration/plot_calibration_curve.ipynb
@@ -183,7 +183,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/calibration/plot_calibration_multiclass.ipynb b/dev/lite/files/auto_examples/calibration/plot_calibration_multiclass.ipynb
index 81aaf2d1d2f41..03b9d9a21c9a8 100644
--- a/dev/lite/files/auto_examples/calibration/plot_calibration_multiclass.ipynb
+++ b/dev/lite/files/auto_examples/calibration/plot_calibration_multiclass.ipynb
@@ -175,7 +175,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/calibration/plot_compare_calibration.ipynb b/dev/lite/files/auto_examples/calibration/plot_compare_calibration.ipynb
index a6de73f024bb0..26a5e90968bb3 100644
--- a/dev/lite/files/auto_examples/calibration/plot_compare_calibration.ipynb
+++ b/dev/lite/files/auto_examples/calibration/plot_compare_calibration.ipynb
@@ -107,7 +107,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/classification/plot_classification_probability.ipynb b/dev/lite/files/auto_examples/classification/plot_classification_probability.ipynb
index 4bfc55122afb2..2b2135725465a 100644
--- a/dev/lite/files/auto_examples/classification/plot_classification_probability.ipynb
+++ b/dev/lite/files/auto_examples/classification/plot_classification_probability.ipynb
@@ -139,7 +139,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/classification/plot_classifier_comparison.ipynb b/dev/lite/files/auto_examples/classification/plot_classifier_comparison.ipynb
index a450b896ab208..5fd44a5af67e0 100644
--- a/dev/lite/files/auto_examples/classification/plot_classifier_comparison.ipynb
+++ b/dev/lite/files/auto_examples/classification/plot_classifier_comparison.ipynb
@@ -53,7 +53,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/classification/plot_digits_classification.ipynb b/dev/lite/files/auto_examples/classification/plot_digits_classification.ipynb
index c4fc7bd1cdb8c..3931a1c77a24a 100644
--- a/dev/lite/files/auto_examples/classification/plot_digits_classification.ipynb
+++ b/dev/lite/files/auto_examples/classification/plot_digits_classification.ipynb
@@ -161,7 +161,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/classification/plot_lda.ipynb b/dev/lite/files/auto_examples/classification/plot_lda.ipynb
index 486a468d34928..1a13ec0156dbf 100644
--- a/dev/lite/files/auto_examples/classification/plot_lda.ipynb
+++ b/dev/lite/files/auto_examples/classification/plot_lda.ipynb
@@ -53,7 +53,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/classification/plot_lda_qda.ipynb b/dev/lite/files/auto_examples/classification/plot_lda_qda.ipynb
index 846df93b92d4c..d3d6a2c60e632 100644
--- a/dev/lite/files/auto_examples/classification/plot_lda_qda.ipynb
+++ b/dev/lite/files/auto_examples/classification/plot_lda_qda.ipynb
@@ -132,7 +132,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/cluster/plot_adjusted_for_chance_measures.ipynb b/dev/lite/files/auto_examples/cluster/plot_adjusted_for_chance_measures.ipynb
index 3e55ba5466c25..8c683332a734c 100644
--- a/dev/lite/files/auto_examples/cluster/plot_adjusted_for_chance_measures.ipynb
+++ b/dev/lite/files/auto_examples/cluster/plot_adjusted_for_chance_measures.ipynb
@@ -168,7 +168,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/cluster/plot_affinity_propagation.ipynb b/dev/lite/files/auto_examples/cluster/plot_affinity_propagation.ipynb
index a18a5220463aa..c8b6bf9a6ebcf 100644
--- a/dev/lite/files/auto_examples/cluster/plot_affinity_propagation.ipynb
+++ b/dev/lite/files/auto_examples/cluster/plot_affinity_propagation.ipynb
@@ -107,7 +107,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/cluster/plot_agglomerative_clustering.ipynb b/dev/lite/files/auto_examples/cluster/plot_agglomerative_clustering.ipynb
index 942bedfb1b16e..8da7fa3672147 100644
--- a/dev/lite/files/auto_examples/cluster/plot_agglomerative_clustering.ipynb
+++ b/dev/lite/files/auto_examples/cluster/plot_agglomerative_clustering.ipynb
@@ -53,7 +53,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/cluster/plot_agglomerative_clustering_metrics.ipynb b/dev/lite/files/auto_examples/cluster/plot_agglomerative_clustering_metrics.ipynb
index 3df5b56d09bdc..4bcb617046f2b 100644
--- a/dev/lite/files/auto_examples/cluster/plot_agglomerative_clustering_metrics.ipynb
+++ b/dev/lite/files/auto_examples/cluster/plot_agglomerative_clustering_metrics.ipynb
@@ -53,7 +53,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/cluster/plot_agglomerative_dendrogram.ipynb b/dev/lite/files/auto_examples/cluster/plot_agglomerative_dendrogram.ipynb
index e27235b6f1e52..ec31e94d6ed22 100644
--- a/dev/lite/files/auto_examples/cluster/plot_agglomerative_dendrogram.ipynb
+++ b/dev/lite/files/auto_examples/cluster/plot_agglomerative_dendrogram.ipynb
@@ -53,7 +53,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/cluster/plot_birch_vs_minibatchkmeans.ipynb b/dev/lite/files/auto_examples/cluster/plot_birch_vs_minibatchkmeans.ipynb
index 4ab09c7cc55fa..c4f647c328503 100644
--- a/dev/lite/files/auto_examples/cluster/plot_birch_vs_minibatchkmeans.ipynb
+++ b/dev/lite/files/auto_examples/cluster/plot_birch_vs_minibatchkmeans.ipynb
@@ -53,7 +53,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/cluster/plot_bisect_kmeans.ipynb b/dev/lite/files/auto_examples/cluster/plot_bisect_kmeans.ipynb
index 723633247a7df..277350334dab4 100644
--- a/dev/lite/files/auto_examples/cluster/plot_bisect_kmeans.ipynb
+++ b/dev/lite/files/auto_examples/cluster/plot_bisect_kmeans.ipynb
@@ -53,7 +53,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/cluster/plot_cluster_comparison.ipynb b/dev/lite/files/auto_examples/cluster/plot_cluster_comparison.ipynb
index b4e532a150e03..e118c98bcbd5c 100644
--- a/dev/lite/files/auto_examples/cluster/plot_cluster_comparison.ipynb
+++ b/dev/lite/files/auto_examples/cluster/plot_cluster_comparison.ipynb
@@ -53,7 +53,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/cluster/plot_coin_segmentation.ipynb b/dev/lite/files/auto_examples/cluster/plot_coin_segmentation.ipynb
index 508dadb6ad738..55d361dee9737 100644
--- a/dev/lite/files/auto_examples/cluster/plot_coin_segmentation.ipynb
+++ b/dev/lite/files/auto_examples/cluster/plot_coin_segmentation.ipynb
@@ -71,7 +71,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/cluster/plot_coin_ward_segmentation.ipynb b/dev/lite/files/auto_examples/cluster/plot_coin_ward_segmentation.ipynb
index ae3e5fe4177b5..9c3d4e8b5108c 100644
--- a/dev/lite/files/auto_examples/cluster/plot_coin_ward_segmentation.ipynb
+++ b/dev/lite/files/auto_examples/cluster/plot_coin_ward_segmentation.ipynb
@@ -143,7 +143,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
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diff --git a/dev/lite/files/auto_examples/cluster/plot_dbscan.ipynb b/dev/lite/files/auto_examples/cluster/plot_dbscan.ipynb
index b8ed3b1102fea..ce2737e13e49e 100644
--- a/dev/lite/files/auto_examples/cluster/plot_dbscan.ipynb
+++ b/dev/lite/files/auto_examples/cluster/plot_dbscan.ipynb
@@ -143,7 +143,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/cluster/plot_dict_face_patches.ipynb b/dev/lite/files/auto_examples/cluster/plot_dict_face_patches.ipynb
index 3a6a966b00d03..b12684d44df92 100644
--- a/dev/lite/files/auto_examples/cluster/plot_dict_face_patches.ipynb
+++ b/dev/lite/files/auto_examples/cluster/plot_dict_face_patches.ipynb
@@ -107,7 +107,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
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   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/cluster/plot_digits_agglomeration.ipynb b/dev/lite/files/auto_examples/cluster/plot_digits_agglomeration.ipynb
index 680a5f13a6521..3e9cf6e5d78ce 100644
--- a/dev/lite/files/auto_examples/cluster/plot_digits_agglomeration.ipynb
+++ b/dev/lite/files/auto_examples/cluster/plot_digits_agglomeration.ipynb
@@ -53,7 +53,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/cluster/plot_digits_linkage.ipynb b/dev/lite/files/auto_examples/cluster/plot_digits_linkage.ipynb
index d2e0edc7b1a3a..cca9c079019d0 100644
--- a/dev/lite/files/auto_examples/cluster/plot_digits_linkage.ipynb
+++ b/dev/lite/files/auto_examples/cluster/plot_digits_linkage.ipynb
@@ -53,7 +53,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
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   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/cluster/plot_face_compress.ipynb b/dev/lite/files/auto_examples/cluster/plot_face_compress.ipynb
index 56c8099d5cdd0..f23d8a9b87bc3 100644
--- a/dev/lite/files/auto_examples/cluster/plot_face_compress.ipynb
+++ b/dev/lite/files/auto_examples/cluster/plot_face_compress.ipynb
@@ -219,7 +219,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
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   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/cluster/plot_feature_agglomeration_vs_univariate_selection.ipynb b/dev/lite/files/auto_examples/cluster/plot_feature_agglomeration_vs_univariate_selection.ipynb
index 83deaf4280769..9f372f3afa5df 100644
--- a/dev/lite/files/auto_examples/cluster/plot_feature_agglomeration_vs_univariate_selection.ipynb
+++ b/dev/lite/files/auto_examples/cluster/plot_feature_agglomeration_vs_univariate_selection.ipynb
@@ -208,7 +208,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
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diff --git a/dev/lite/files/auto_examples/cluster/plot_hdbscan.ipynb b/dev/lite/files/auto_examples/cluster/plot_hdbscan.ipynb
index 893bd4a4854ce..b971eb2301d1a 100644
--- a/dev/lite/files/auto_examples/cluster/plot_hdbscan.ipynb
+++ b/dev/lite/files/auto_examples/cluster/plot_hdbscan.ipynb
@@ -44,7 +44,7 @@
       },
       "outputs": [],
       "source": [
-        "import matplotlib.pyplot as plt\nimport numpy as np\n\nfrom sklearn.cluster import DBSCAN, HDBSCAN\nfrom sklearn.datasets import make_blobs\n\n\ndef plot(X, labels, probabilities=None, parameters=None, ground_truth=False, ax=None):\n    if ax is None:\n        _, ax = plt.subplots(figsize=(10, 4))\n    labels = labels if labels is not None else np.ones(X.shape[0])\n    probabilities = probabilities if probabilities is not None else np.ones(X.shape[0])\n    # Black removed and is used for noise instead.\n    unique_labels = set(labels)\n    colors = [plt.cm.Spectral(each) for each in np.linspace(0, 1, len(unique_labels))]\n    # The probability of a point belonging to its labeled cluster determines\n    # the size of its marker\n    proba_map = {idx: probabilities[idx] for idx in range(len(labels))}\n    for k, col in zip(unique_labels, colors):\n        if k == -1:\n            # Black used for noise.\n            col = [0, 0, 0, 1]\n\n        class_index = np.where(labels == k)[0]\n        for ci in class_index:\n            ax.plot(\n                X[ci, 0],\n                X[ci, 1],\n                \"x\" if k == -1 else \"o\",\n                markerfacecolor=tuple(col),\n                markeredgecolor=\"k\",\n                markersize=4 if k == -1 else 1 + 5 * proba_map[ci],\n            )\n    n_clusters_ = len(set(labels)) - (1 if -1 in labels else 0)\n    preamble = \"True\" if ground_truth else \"Estimated\"\n    title = f\"{preamble} number of clusters: {n_clusters_}\"\n    if parameters is not None:\n        parameters_str = \", \".join(f\"{k}={v}\" for k, v in parameters.items())\n        title += f\" | {parameters_str}\"\n    ax.set_title(title)\n    plt.tight_layout()"
+        "import matplotlib.pyplot as plt\nimport numpy as np\n\nfrom sklearn.cluster import DBSCAN, HDBSCAN\nfrom sklearn.datasets import make_blobs\n\n\ndef plot(X, labels, probabilities=None, parameters=None, ground_truth=False, ax=None):\n    if ax is None:\n        _, ax = plt.subplots(figsize=(10, 4))\n    labels = labels if labels is not None else np.ones(X.shape[0])\n    probabilities = probabilities if probabilities is not None else np.ones(X.shape[0])\n    # Black removed and is used for noise instead.\n    unique_labels = set(labels)\n    colors = [plt.cm.Spectral(each) for each in np.linspace(0, 1, len(unique_labels))]\n    # The probability of a point belonging to its labeled cluster determines\n    # the size of its marker\n    proba_map = {idx: probabilities[idx] for idx in range(len(labels))}\n    for k, col in zip(unique_labels, colors):\n        if k == -1:\n            # Black used for noise.\n            col = [0, 0, 0, 1]\n\n        class_index = (labels == k).nonzero()[0]\n        for ci in class_index:\n            ax.plot(\n                X[ci, 0],\n                X[ci, 1],\n                \"x\" if k == -1 else \"o\",\n                markerfacecolor=tuple(col),\n                markeredgecolor=\"k\",\n                markersize=4 if k == -1 else 1 + 5 * proba_map[ci],\n            )\n    n_clusters_ = len(set(labels)) - (1 if -1 in labels else 0)\n    preamble = \"True\" if ground_truth else \"Estimated\"\n    title = f\"{preamble} number of clusters: {n_clusters_}\"\n    if parameters is not None:\n        parameters_str = \", \".join(f\"{k}={v}\" for k, v in parameters.items())\n        title += f\" | {parameters_str}\"\n    ax.set_title(title)\n    plt.tight_layout()"
       ]
     },
     {
@@ -251,7 +251,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/cluster/plot_inductive_clustering.ipynb b/dev/lite/files/auto_examples/cluster/plot_inductive_clustering.ipynb
index c9fbd39fc9b93..256771405e9cc 100644
--- a/dev/lite/files/auto_examples/cluster/plot_inductive_clustering.ipynb
+++ b/dev/lite/files/auto_examples/cluster/plot_inductive_clustering.ipynb
@@ -53,7 +53,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/cluster/plot_kmeans_assumptions.ipynb b/dev/lite/files/auto_examples/cluster/plot_kmeans_assumptions.ipynb
index 06e593418345f..5ec98dc0521b4 100644
--- a/dev/lite/files/auto_examples/cluster/plot_kmeans_assumptions.ipynb
+++ b/dev/lite/files/auto_examples/cluster/plot_kmeans_assumptions.ipynb
@@ -168,7 +168,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/cluster/plot_kmeans_digits.ipynb b/dev/lite/files/auto_examples/cluster/plot_kmeans_digits.ipynb
index fd30dd7e5bf81..707455c8db67e 100644
--- a/dev/lite/files/auto_examples/cluster/plot_kmeans_digits.ipynb
+++ b/dev/lite/files/auto_examples/cluster/plot_kmeans_digits.ipynb
@@ -125,7 +125,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/cluster/plot_kmeans_plusplus.ipynb b/dev/lite/files/auto_examples/cluster/plot_kmeans_plusplus.ipynb
index bd12a397aea4e..0f35c685d4c7d 100644
--- a/dev/lite/files/auto_examples/cluster/plot_kmeans_plusplus.ipynb
+++ b/dev/lite/files/auto_examples/cluster/plot_kmeans_plusplus.ipynb
@@ -53,7 +53,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/cluster/plot_kmeans_silhouette_analysis.ipynb b/dev/lite/files/auto_examples/cluster/plot_kmeans_silhouette_analysis.ipynb
index 528b0702eede3..05bc2ff20f67b 100644
--- a/dev/lite/files/auto_examples/cluster/plot_kmeans_silhouette_analysis.ipynb
+++ b/dev/lite/files/auto_examples/cluster/plot_kmeans_silhouette_analysis.ipynb
@@ -53,7 +53,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/cluster/plot_kmeans_stability_low_dim_dense.ipynb b/dev/lite/files/auto_examples/cluster/plot_kmeans_stability_low_dim_dense.ipynb
index 3763f2ce2755f..c30be466998d0 100644
--- a/dev/lite/files/auto_examples/cluster/plot_kmeans_stability_low_dim_dense.ipynb
+++ b/dev/lite/files/auto_examples/cluster/plot_kmeans_stability_low_dim_dense.ipynb
@@ -53,7 +53,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/cluster/plot_linkage_comparison.ipynb b/dev/lite/files/auto_examples/cluster/plot_linkage_comparison.ipynb
index 94a5e3c2ed3ac..4adef9a6a2a58 100644
--- a/dev/lite/files/auto_examples/cluster/plot_linkage_comparison.ipynb
+++ b/dev/lite/files/auto_examples/cluster/plot_linkage_comparison.ipynb
@@ -89,7 +89,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/cluster/plot_mean_shift.ipynb b/dev/lite/files/auto_examples/cluster/plot_mean_shift.ipynb
index 38e7ce7f46664..61d0143ab2a45 100644
--- a/dev/lite/files/auto_examples/cluster/plot_mean_shift.ipynb
+++ b/dev/lite/files/auto_examples/cluster/plot_mean_shift.ipynb
@@ -107,7 +107,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/cluster/plot_mini_batch_kmeans.ipynb b/dev/lite/files/auto_examples/cluster/plot_mini_batch_kmeans.ipynb
index 13175e40d0b15..021082f732b97 100644
--- a/dev/lite/files/auto_examples/cluster/plot_mini_batch_kmeans.ipynb
+++ b/dev/lite/files/auto_examples/cluster/plot_mini_batch_kmeans.ipynb
@@ -143,7 +143,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/cluster/plot_optics.ipynb b/dev/lite/files/auto_examples/cluster/plot_optics.ipynb
index 8f835b3307a7d..d243c50083d30 100644
--- a/dev/lite/files/auto_examples/cluster/plot_optics.ipynb
+++ b/dev/lite/files/auto_examples/cluster/plot_optics.ipynb
@@ -53,7 +53,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/cluster/plot_segmentation_toy.ipynb b/dev/lite/files/auto_examples/cluster/plot_segmentation_toy.ipynb
index 885560a465a8a..0bef94fb5ac26 100644
--- a/dev/lite/files/auto_examples/cluster/plot_segmentation_toy.ipynb
+++ b/dev/lite/files/auto_examples/cluster/plot_segmentation_toy.ipynb
@@ -161,7 +161,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/cluster/plot_ward_structured_vs_unstructured.ipynb b/dev/lite/files/auto_examples/cluster/plot_ward_structured_vs_unstructured.ipynb
index c4703ecb8520f..53fc047e0a5b6 100644
--- a/dev/lite/files/auto_examples/cluster/plot_ward_structured_vs_unstructured.ipynb
+++ b/dev/lite/files/auto_examples/cluster/plot_ward_structured_vs_unstructured.ipynb
@@ -161,7 +161,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/compose/plot_column_transformer.ipynb b/dev/lite/files/auto_examples/compose/plot_column_transformer.ipynb
index f2069ece193db..8e0b9824dcf4d 100644
--- a/dev/lite/files/auto_examples/compose/plot_column_transformer.ipynb
+++ b/dev/lite/files/auto_examples/compose/plot_column_transformer.ipynb
@@ -161,7 +161,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/compose/plot_column_transformer_mixed_types.ipynb b/dev/lite/files/auto_examples/compose/plot_column_transformer_mixed_types.ipynb
index 03b2ad8bda335..53b8a6ead250b 100644
--- a/dev/lite/files/auto_examples/compose/plot_column_transformer_mixed_types.ipynb
+++ b/dev/lite/files/auto_examples/compose/plot_column_transformer_mixed_types.ipynb
@@ -316,7 +316,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/compose/plot_compare_reduction.ipynb b/dev/lite/files/auto_examples/compose/plot_compare_reduction.ipynb
index a19117093a83d..f983b2d9112d9 100644
--- a/dev/lite/files/auto_examples/compose/plot_compare_reduction.ipynb
+++ b/dev/lite/files/auto_examples/compose/plot_compare_reduction.ipynb
@@ -107,7 +107,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/compose/plot_digits_pipe.ipynb b/dev/lite/files/auto_examples/compose/plot_digits_pipe.ipynb
index 12baf2463e2e0..18d5e054baa35 100644
--- a/dev/lite/files/auto_examples/compose/plot_digits_pipe.ipynb
+++ b/dev/lite/files/auto_examples/compose/plot_digits_pipe.ipynb
@@ -53,7 +53,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/compose/plot_feature_union.ipynb b/dev/lite/files/auto_examples/compose/plot_feature_union.ipynb
index a88a09050fa43..1ae4079cde556 100644
--- a/dev/lite/files/auto_examples/compose/plot_feature_union.ipynb
+++ b/dev/lite/files/auto_examples/compose/plot_feature_union.ipynb
@@ -53,7 +53,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/compose/plot_transformed_target.ipynb b/dev/lite/files/auto_examples/compose/plot_transformed_target.ipynb
index 5ed791d542e45..0efb80b558381 100644
--- a/dev/lite/files/auto_examples/compose/plot_transformed_target.ipynb
+++ b/dev/lite/files/auto_examples/compose/plot_transformed_target.ipynb
@@ -183,7 +183,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
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   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/covariance/plot_covariance_estimation.ipynb b/dev/lite/files/auto_examples/covariance/plot_covariance_estimation.ipynb
index 188517af83118..bb013b0f7179b 100644
--- a/dev/lite/files/auto_examples/covariance/plot_covariance_estimation.ipynb
+++ b/dev/lite/files/auto_examples/covariance/plot_covariance_estimation.ipynb
@@ -132,7 +132,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/covariance/plot_lw_vs_oas.ipynb b/dev/lite/files/auto_examples/covariance/plot_lw_vs_oas.ipynb
index fe5ce6ee9e2b4..7e039e217bf06 100644
--- a/dev/lite/files/auto_examples/covariance/plot_lw_vs_oas.ipynb
+++ b/dev/lite/files/auto_examples/covariance/plot_lw_vs_oas.ipynb
@@ -64,7 +64,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/covariance/plot_mahalanobis_distances.ipynb b/dev/lite/files/auto_examples/covariance/plot_mahalanobis_distances.ipynb
index 76192e0a46c17..67820d9929b15 100644
--- a/dev/lite/files/auto_examples/covariance/plot_mahalanobis_distances.ipynb
+++ b/dev/lite/files/auto_examples/covariance/plot_mahalanobis_distances.ipynb
@@ -125,7 +125,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/covariance/plot_robust_vs_empirical_covariance.ipynb b/dev/lite/files/auto_examples/covariance/plot_robust_vs_empirical_covariance.ipynb
index 0dc2ce09bebd7..66913f40bf890 100644
--- a/dev/lite/files/auto_examples/covariance/plot_robust_vs_empirical_covariance.ipynb
+++ b/dev/lite/files/auto_examples/covariance/plot_robust_vs_empirical_covariance.ipynb
@@ -53,7 +53,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/covariance/plot_sparse_cov.ipynb b/dev/lite/files/auto_examples/covariance/plot_sparse_cov.ipynb
index dacd396f81e24..06fd76f57b2f4 100644
--- a/dev/lite/files/auto_examples/covariance/plot_sparse_cov.ipynb
+++ b/dev/lite/files/auto_examples/covariance/plot_sparse_cov.ipynb
@@ -118,7 +118,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
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   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/cross_decomposition/plot_compare_cross_decomposition.ipynb b/dev/lite/files/auto_examples/cross_decomposition/plot_compare_cross_decomposition.ipynb
index e40da33431627..f64b9a1e51149 100644
--- a/dev/lite/files/auto_examples/cross_decomposition/plot_compare_cross_decomposition.ipynb
+++ b/dev/lite/files/auto_examples/cross_decomposition/plot_compare_cross_decomposition.ipynb
@@ -161,7 +161,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/cross_decomposition/plot_pcr_vs_pls.ipynb b/dev/lite/files/auto_examples/cross_decomposition/plot_pcr_vs_pls.ipynb
index 6ee7593cb6073..8209e535a8e42 100644
--- a/dev/lite/files/auto_examples/cross_decomposition/plot_pcr_vs_pls.ipynb
+++ b/dev/lite/files/auto_examples/cross_decomposition/plot_pcr_vs_pls.ipynb
@@ -143,7 +143,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/datasets/plot_random_multilabel_dataset.ipynb b/dev/lite/files/auto_examples/datasets/plot_random_multilabel_dataset.ipynb
index fbc07a6128046..6bf39dabe9744 100644
--- a/dev/lite/files/auto_examples/datasets/plot_random_multilabel_dataset.ipynb
+++ b/dev/lite/files/auto_examples/datasets/plot_random_multilabel_dataset.ipynb
@@ -53,7 +53,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/decomposition/plot_faces_decomposition.ipynb b/dev/lite/files/auto_examples/decomposition/plot_faces_decomposition.ipynb
index ab916aed23848..f6598b1dd2da4 100644
--- a/dev/lite/files/auto_examples/decomposition/plot_faces_decomposition.ipynb
+++ b/dev/lite/files/auto_examples/decomposition/plot_faces_decomposition.ipynb
@@ -326,7 +326,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/decomposition/plot_ica_blind_source_separation.ipynb b/dev/lite/files/auto_examples/decomposition/plot_ica_blind_source_separation.ipynb
index d6621088b33b9..55464c916e0d0 100644
--- a/dev/lite/files/auto_examples/decomposition/plot_ica_blind_source_separation.ipynb
+++ b/dev/lite/files/auto_examples/decomposition/plot_ica_blind_source_separation.ipynb
@@ -107,7 +107,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/decomposition/plot_ica_vs_pca.ipynb b/dev/lite/files/auto_examples/decomposition/plot_ica_vs_pca.ipynb
index 1cfcb70bafc7b..865d88a9fd799 100644
--- a/dev/lite/files/auto_examples/decomposition/plot_ica_vs_pca.ipynb
+++ b/dev/lite/files/auto_examples/decomposition/plot_ica_vs_pca.ipynb
@@ -89,7 +89,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/decomposition/plot_image_denoising.ipynb b/dev/lite/files/auto_examples/decomposition/plot_image_denoising.ipynb
index 50fa2ab486523..00aea9473c1c9 100644
--- a/dev/lite/files/auto_examples/decomposition/plot_image_denoising.ipynb
+++ b/dev/lite/files/auto_examples/decomposition/plot_image_denoising.ipynb
@@ -143,7 +143,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/decomposition/plot_incremental_pca.ipynb b/dev/lite/files/auto_examples/decomposition/plot_incremental_pca.ipynb
index 3aef940f715fe..815b16092821c 100644
--- a/dev/lite/files/auto_examples/decomposition/plot_incremental_pca.ipynb
+++ b/dev/lite/files/auto_examples/decomposition/plot_incremental_pca.ipynb
@@ -53,7 +53,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/decomposition/plot_kernel_pca.ipynb b/dev/lite/files/auto_examples/decomposition/plot_kernel_pca.ipynb
index 3b65115fb14b7..f2f5c72966448 100644
--- a/dev/lite/files/auto_examples/decomposition/plot_kernel_pca.ipynb
+++ b/dev/lite/files/auto_examples/decomposition/plot_kernel_pca.ipynb
@@ -154,7 +154,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/decomposition/plot_pca_iris.ipynb b/dev/lite/files/auto_examples/decomposition/plot_pca_iris.ipynb
index 5b2f1cae39cb2..44cdb9c56834e 100644
--- a/dev/lite/files/auto_examples/decomposition/plot_pca_iris.ipynb
+++ b/dev/lite/files/auto_examples/decomposition/plot_pca_iris.ipynb
@@ -114,7 +114,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/decomposition/plot_pca_vs_fa_model_selection.ipynb b/dev/lite/files/auto_examples/decomposition/plot_pca_vs_fa_model_selection.ipynb
index 397a16a34fed5..e5972b4175cff 100644
--- a/dev/lite/files/auto_examples/decomposition/plot_pca_vs_fa_model_selection.ipynb
+++ b/dev/lite/files/auto_examples/decomposition/plot_pca_vs_fa_model_selection.ipynb
@@ -89,7 +89,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/decomposition/plot_pca_vs_lda.ipynb b/dev/lite/files/auto_examples/decomposition/plot_pca_vs_lda.ipynb
index c54494d42eceb..77da829055ba3 100644
--- a/dev/lite/files/auto_examples/decomposition/plot_pca_vs_lda.ipynb
+++ b/dev/lite/files/auto_examples/decomposition/plot_pca_vs_lda.ipynb
@@ -53,7 +53,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/decomposition/plot_sparse_coding.ipynb b/dev/lite/files/auto_examples/decomposition/plot_sparse_coding.ipynb
index 67192b7ce794b..55b4f54b7f33a 100644
--- a/dev/lite/files/auto_examples/decomposition/plot_sparse_coding.ipynb
+++ b/dev/lite/files/auto_examples/decomposition/plot_sparse_coding.ipynb
@@ -33,7 +33,7 @@
       },
       "outputs": [],
       "source": [
-        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nfrom sklearn.decomposition import SparseCoder\n\n\ndef ricker_function(resolution, center, width):\n    \"\"\"Discrete sub-sampled Ricker (Mexican hat) wavelet\"\"\"\n    x = np.linspace(0, resolution - 1, resolution)\n    x = (\n        (2 / (np.sqrt(3 * width) * np.pi**0.25))\n        * (1 - (x - center) ** 2 / width**2)\n        * np.exp(-((x - center) ** 2) / (2 * width**2))\n    )\n    return x\n\n\ndef ricker_matrix(width, resolution, n_components):\n    \"\"\"Dictionary of Ricker (Mexican hat) wavelets\"\"\"\n    centers = np.linspace(0, resolution - 1, n_components)\n    D = np.empty((n_components, resolution))\n    for i, center in enumerate(centers):\n        D[i] = ricker_function(resolution, center, width)\n    D /= np.sqrt(np.sum(D**2, axis=1))[:, np.newaxis]\n    return D\n\n\nresolution = 1024\nsubsampling = 3  # subsampling factor\nwidth = 100\nn_components = resolution // subsampling\n\n# Compute a wavelet dictionary\nD_fixed = ricker_matrix(width=width, resolution=resolution, n_components=n_components)\nD_multi = np.r_[\n    tuple(\n        ricker_matrix(width=w, resolution=resolution, n_components=n_components // 5)\n        for w in (10, 50, 100, 500, 1000)\n    )\n]\n\n# Generate a signal\ny = np.linspace(0, resolution - 1, resolution)\nfirst_quarter = y < resolution / 4\ny[first_quarter] = 3.0\ny[np.logical_not(first_quarter)] = -1.0\n\n# List the different sparse coding methods in the following format:\n# (title, transform_algorithm, transform_alpha,\n#  transform_n_nozero_coefs, color)\nestimators = [\n    (\"OMP\", \"omp\", None, 15, \"navy\"),\n    (\"Lasso\", \"lasso_lars\", 2, None, \"turquoise\"),\n]\nlw = 2\n\nplt.figure(figsize=(13, 6))\nfor subplot, (D, title) in enumerate(\n    zip((D_fixed, D_multi), (\"fixed width\", \"multiple widths\"))\n):\n    plt.subplot(1, 2, subplot + 1)\n    plt.title(\"Sparse coding against %s dictionary\" % title)\n    plt.plot(y, lw=lw, linestyle=\"--\", label=\"Original signal\")\n    # Do a wavelet approximation\n    for title, algo, alpha, n_nonzero, color in estimators:\n        coder = SparseCoder(\n            dictionary=D,\n            transform_n_nonzero_coefs=n_nonzero,\n            transform_alpha=alpha,\n            transform_algorithm=algo,\n        )\n        x = coder.transform(y.reshape(1, -1))\n        density = len(np.flatnonzero(x))\n        x = np.ravel(np.dot(x, D))\n        squared_error = np.sum((y - x) ** 2)\n        plt.plot(\n            x,\n            color=color,\n            lw=lw,\n            label=\"%s: %s nonzero coefs,\\n%.2f error\" % (title, density, squared_error),\n        )\n\n    # Soft thresholding debiasing\n    coder = SparseCoder(\n        dictionary=D, transform_algorithm=\"threshold\", transform_alpha=20\n    )\n    x = coder.transform(y.reshape(1, -1))\n    _, idx = np.where(x != 0)\n    x[0, idx], _, _, _ = np.linalg.lstsq(D[idx, :].T, y, rcond=None)\n    x = np.ravel(np.dot(x, D))\n    squared_error = np.sum((y - x) ** 2)\n    plt.plot(\n        x,\n        color=\"darkorange\",\n        lw=lw,\n        label=\"Thresholding w/ debiasing:\\n%d nonzero coefs, %.2f error\"\n        % (len(idx), squared_error),\n    )\n    plt.axis(\"tight\")\n    plt.legend(shadow=False, loc=\"best\")\nplt.subplots_adjust(0.04, 0.07, 0.97, 0.90, 0.09, 0.2)\nplt.show()"
+        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nfrom sklearn.decomposition import SparseCoder\n\n\ndef ricker_function(resolution, center, width):\n    \"\"\"Discrete sub-sampled Ricker (Mexican hat) wavelet\"\"\"\n    x = np.linspace(0, resolution - 1, resolution)\n    x = (\n        (2 / (np.sqrt(3 * width) * np.pi**0.25))\n        * (1 - (x - center) ** 2 / width**2)\n        * np.exp(-((x - center) ** 2) / (2 * width**2))\n    )\n    return x\n\n\ndef ricker_matrix(width, resolution, n_components):\n    \"\"\"Dictionary of Ricker (Mexican hat) wavelets\"\"\"\n    centers = np.linspace(0, resolution - 1, n_components)\n    D = np.empty((n_components, resolution))\n    for i, center in enumerate(centers):\n        D[i] = ricker_function(resolution, center, width)\n    D /= np.sqrt(np.sum(D**2, axis=1))[:, np.newaxis]\n    return D\n\n\nresolution = 1024\nsubsampling = 3  # subsampling factor\nwidth = 100\nn_components = resolution // subsampling\n\n# Compute a wavelet dictionary\nD_fixed = ricker_matrix(width=width, resolution=resolution, n_components=n_components)\nD_multi = np.r_[\n    tuple(\n        ricker_matrix(width=w, resolution=resolution, n_components=n_components // 5)\n        for w in (10, 50, 100, 500, 1000)\n    )\n]\n\n# Generate a signal\ny = np.linspace(0, resolution - 1, resolution)\nfirst_quarter = y < resolution / 4\ny[first_quarter] = 3.0\ny[np.logical_not(first_quarter)] = -1.0\n\n# List the different sparse coding methods in the following format:\n# (title, transform_algorithm, transform_alpha,\n#  transform_n_nozero_coefs, color)\nestimators = [\n    (\"OMP\", \"omp\", None, 15, \"navy\"),\n    (\"Lasso\", \"lasso_lars\", 2, None, \"turquoise\"),\n]\nlw = 2\n\nplt.figure(figsize=(13, 6))\nfor subplot, (D, title) in enumerate(\n    zip((D_fixed, D_multi), (\"fixed width\", \"multiple widths\"))\n):\n    plt.subplot(1, 2, subplot + 1)\n    plt.title(\"Sparse coding against %s dictionary\" % title)\n    plt.plot(y, lw=lw, linestyle=\"--\", label=\"Original signal\")\n    # Do a wavelet approximation\n    for title, algo, alpha, n_nonzero, color in estimators:\n        coder = SparseCoder(\n            dictionary=D,\n            transform_n_nonzero_coefs=n_nonzero,\n            transform_alpha=alpha,\n            transform_algorithm=algo,\n        )\n        x = coder.transform(y.reshape(1, -1))\n        density = len(np.flatnonzero(x))\n        x = np.ravel(np.dot(x, D))\n        squared_error = np.sum((y - x) ** 2)\n        plt.plot(\n            x,\n            color=color,\n            lw=lw,\n            label=\"%s: %s nonzero coefs,\\n%.2f error\" % (title, density, squared_error),\n        )\n\n    # Soft thresholding debiasing\n    coder = SparseCoder(\n        dictionary=D, transform_algorithm=\"threshold\", transform_alpha=20\n    )\n    x = coder.transform(y.reshape(1, -1))\n    _, idx = (x != 0).nonzero()\n    x[0, idx], _, _, _ = np.linalg.lstsq(D[idx, :].T, y, rcond=None)\n    x = np.ravel(np.dot(x, D))\n    squared_error = np.sum((y - x) ** 2)\n    plt.plot(\n        x,\n        color=\"darkorange\",\n        lw=lw,\n        label=\"Thresholding w/ debiasing:\\n%d nonzero coefs, %.2f error\"\n        % (len(idx), squared_error),\n    )\n    plt.axis(\"tight\")\n    plt.legend(shadow=False, loc=\"best\")\nplt.subplots_adjust(0.04, 0.07, 0.97, 0.90, 0.09, 0.2)\nplt.show()"
       ]
     }
   ],
@@ -53,7 +53,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/decomposition/plot_varimax_fa.ipynb b/dev/lite/files/auto_examples/decomposition/plot_varimax_fa.ipynb
index 88685576cc513..28e05ff4401fb 100644
--- a/dev/lite/files/auto_examples/decomposition/plot_varimax_fa.ipynb
+++ b/dev/lite/files/auto_examples/decomposition/plot_varimax_fa.ipynb
@@ -107,7 +107,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/developing_estimators/sklearn_is_fitted.ipynb b/dev/lite/files/auto_examples/developing_estimators/sklearn_is_fitted.ipynb
index 53db021149455..9fc66a17a32d9 100644
--- a/dev/lite/files/auto_examples/developing_estimators/sklearn_is_fitted.ipynb
+++ b/dev/lite/files/auto_examples/developing_estimators/sklearn_is_fitted.ipynb
@@ -60,7 +60,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/ensemble/plot_adaboost_multiclass.ipynb b/dev/lite/files/auto_examples/ensemble/plot_adaboost_multiclass.ipynb
index 271493e5213d2..f878ca661d0d7 100644
--- a/dev/lite/files/auto_examples/ensemble/plot_adaboost_multiclass.ipynb
+++ b/dev/lite/files/auto_examples/ensemble/plot_adaboost_multiclass.ipynb
@@ -175,7 +175,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/ensemble/plot_adaboost_regression.ipynb b/dev/lite/files/auto_examples/ensemble/plot_adaboost_regression.ipynb
index 72aa95a0d71f5..70c74dd5ed1f7 100644
--- a/dev/lite/files/auto_examples/ensemble/plot_adaboost_regression.ipynb
+++ b/dev/lite/files/auto_examples/ensemble/plot_adaboost_regression.ipynb
@@ -96,7 +96,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/ensemble/plot_adaboost_twoclass.ipynb b/dev/lite/files/auto_examples/ensemble/plot_adaboost_twoclass.ipynb
index 0da08a144c963..2719014d09829 100644
--- a/dev/lite/files/auto_examples/ensemble/plot_adaboost_twoclass.ipynb
+++ b/dev/lite/files/auto_examples/ensemble/plot_adaboost_twoclass.ipynb
@@ -33,7 +33,7 @@
       },
       "outputs": [],
       "source": [
-        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nfrom sklearn.datasets import make_gaussian_quantiles\nfrom sklearn.ensemble import AdaBoostClassifier\nfrom sklearn.inspection import DecisionBoundaryDisplay\nfrom sklearn.tree import DecisionTreeClassifier\n\n# Construct dataset\nX1, y1 = make_gaussian_quantiles(\n    cov=2.0, n_samples=200, n_features=2, n_classes=2, random_state=1\n)\nX2, y2 = make_gaussian_quantiles(\n    mean=(3, 3), cov=1.5, n_samples=300, n_features=2, n_classes=2, random_state=1\n)\nX = np.concatenate((X1, X2))\ny = np.concatenate((y1, -y2 + 1))\n\n# Create and fit an AdaBoosted decision tree\nbdt = AdaBoostClassifier(DecisionTreeClassifier(max_depth=1), n_estimators=200)\nbdt.fit(X, y)\n\nplot_colors = \"br\"\nplot_step = 0.02\nclass_names = \"AB\"\n\nplt.figure(figsize=(10, 5))\n\n# Plot the decision boundaries\nax = plt.subplot(121)\ndisp = DecisionBoundaryDisplay.from_estimator(\n    bdt,\n    X,\n    cmap=plt.cm.Paired,\n    response_method=\"predict\",\n    ax=ax,\n    xlabel=\"x\",\n    ylabel=\"y\",\n)\nx_min, x_max = disp.xx0.min(), disp.xx0.max()\ny_min, y_max = disp.xx1.min(), disp.xx1.max()\nplt.axis(\"tight\")\n\n# Plot the training points\nfor i, n, c in zip(range(2), class_names, plot_colors):\n    idx = np.where(y == i)\n    plt.scatter(\n        X[idx, 0],\n        X[idx, 1],\n        c=c,\n        s=20,\n        edgecolor=\"k\",\n        label=\"Class %s\" % n,\n    )\nplt.xlim(x_min, x_max)\nplt.ylim(y_min, y_max)\nplt.legend(loc=\"upper right\")\n\nplt.title(\"Decision Boundary\")\n\n# Plot the two-class decision scores\ntwoclass_output = bdt.decision_function(X)\nplot_range = (twoclass_output.min(), twoclass_output.max())\nplt.subplot(122)\nfor i, n, c in zip(range(2), class_names, plot_colors):\n    plt.hist(\n        twoclass_output[y == i],\n        bins=10,\n        range=plot_range,\n        facecolor=c,\n        label=\"Class %s\" % n,\n        alpha=0.5,\n        edgecolor=\"k\",\n    )\nx1, x2, y1, y2 = plt.axis()\nplt.axis((x1, x2, y1, y2 * 1.2))\nplt.legend(loc=\"upper right\")\nplt.ylabel(\"Samples\")\nplt.xlabel(\"Score\")\nplt.title(\"Decision Scores\")\n\nplt.tight_layout()\nplt.subplots_adjust(wspace=0.35)\nplt.show()"
+        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nfrom sklearn.datasets import make_gaussian_quantiles\nfrom sklearn.ensemble import AdaBoostClassifier\nfrom sklearn.inspection import DecisionBoundaryDisplay\nfrom sklearn.tree import DecisionTreeClassifier\n\n# Construct dataset\nX1, y1 = make_gaussian_quantiles(\n    cov=2.0, n_samples=200, n_features=2, n_classes=2, random_state=1\n)\nX2, y2 = make_gaussian_quantiles(\n    mean=(3, 3), cov=1.5, n_samples=300, n_features=2, n_classes=2, random_state=1\n)\nX = np.concatenate((X1, X2))\ny = np.concatenate((y1, -y2 + 1))\n\n# Create and fit an AdaBoosted decision tree\nbdt = AdaBoostClassifier(DecisionTreeClassifier(max_depth=1), n_estimators=200)\nbdt.fit(X, y)\n\nplot_colors = \"br\"\nplot_step = 0.02\nclass_names = \"AB\"\n\nplt.figure(figsize=(10, 5))\n\n# Plot the decision boundaries\nax = plt.subplot(121)\ndisp = DecisionBoundaryDisplay.from_estimator(\n    bdt,\n    X,\n    cmap=plt.cm.Paired,\n    response_method=\"predict\",\n    ax=ax,\n    xlabel=\"x\",\n    ylabel=\"y\",\n)\nx_min, x_max = disp.xx0.min(), disp.xx0.max()\ny_min, y_max = disp.xx1.min(), disp.xx1.max()\nplt.axis(\"tight\")\n\n# Plot the training points\nfor i, n, c in zip(range(2), class_names, plot_colors):\n    idx = (y == i).nonzero()\n    plt.scatter(\n        X[idx, 0],\n        X[idx, 1],\n        c=c,\n        s=20,\n        edgecolor=\"k\",\n        label=\"Class %s\" % n,\n    )\nplt.xlim(x_min, x_max)\nplt.ylim(y_min, y_max)\nplt.legend(loc=\"upper right\")\n\nplt.title(\"Decision Boundary\")\n\n# Plot the two-class decision scores\ntwoclass_output = bdt.decision_function(X)\nplot_range = (twoclass_output.min(), twoclass_output.max())\nplt.subplot(122)\nfor i, n, c in zip(range(2), class_names, plot_colors):\n    plt.hist(\n        twoclass_output[y == i],\n        bins=10,\n        range=plot_range,\n        facecolor=c,\n        label=\"Class %s\" % n,\n        alpha=0.5,\n        edgecolor=\"k\",\n    )\nx1, x2, y1, y2 = plt.axis()\nplt.axis((x1, x2, y1, y2 * 1.2))\nplt.legend(loc=\"upper right\")\nplt.ylabel(\"Samples\")\nplt.xlabel(\"Score\")\nplt.title(\"Decision Scores\")\n\nplt.tight_layout()\nplt.subplots_adjust(wspace=0.35)\nplt.show()"
       ]
     }
   ],
@@ -53,7 +53,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/ensemble/plot_bias_variance.ipynb b/dev/lite/files/auto_examples/ensemble/plot_bias_variance.ipynb
index 8b8f80b176009..43fedb051ed3b 100644
--- a/dev/lite/files/auto_examples/ensemble/plot_bias_variance.ipynb
+++ b/dev/lite/files/auto_examples/ensemble/plot_bias_variance.ipynb
@@ -33,7 +33,7 @@
       },
       "outputs": [],
       "source": [
-        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nfrom sklearn.ensemble import BaggingRegressor\nfrom sklearn.tree import DecisionTreeRegressor\n\n# Settings\nn_repeat = 50  # Number of iterations for computing expectations\nn_train = 50  # Size of the training set\nn_test = 1000  # Size of the test set\nnoise = 0.1  # Standard deviation of the noise\nnp.random.seed(0)\n\n# Change this for exploring the bias-variance decomposition of other\n# estimators. This should work well for estimators with high variance (e.g.,\n# decision trees or KNN), but poorly for estimators with low variance (e.g.,\n# linear models).\nestimators = [\n    (\"Tree\", DecisionTreeRegressor()),\n    (\"Bagging(Tree)\", BaggingRegressor(DecisionTreeRegressor())),\n]\n\nn_estimators = len(estimators)\n\n\n# Generate data\ndef f(x):\n    x = x.ravel()\n\n    return np.exp(-(x**2)) + 1.5 * np.exp(-((x - 2) ** 2))\n\n\ndef generate(n_samples, noise, n_repeat=1):\n    X = np.random.rand(n_samples) * 10 - 5\n    X = np.sort(X)\n\n    if n_repeat == 1:\n        y = f(X) + np.random.normal(0.0, noise, n_samples)\n    else:\n        y = np.zeros((n_samples, n_repeat))\n\n        for i in range(n_repeat):\n            y[:, i] = f(X) + np.random.normal(0.0, noise, n_samples)\n\n    X = X.reshape((n_samples, 1))\n\n    return X, y\n\n\nX_train = []\ny_train = []\n\nfor i in range(n_repeat):\n    X, y = generate(n_samples=n_train, noise=noise)\n    X_train.append(X)\n    y_train.append(y)\n\nX_test, y_test = generate(n_samples=n_test, noise=noise, n_repeat=n_repeat)\n\nplt.figure(figsize=(10, 8))\n\n# Loop over estimators to compare\nfor n, (name, estimator) in enumerate(estimators):\n    # Compute predictions\n    y_predict = np.zeros((n_test, n_repeat))\n\n    for i in range(n_repeat):\n        estimator.fit(X_train[i], y_train[i])\n        y_predict[:, i] = estimator.predict(X_test)\n\n    # Bias^2 + Variance + Noise decomposition of the mean squared error\n    y_error = np.zeros(n_test)\n\n    for i in range(n_repeat):\n        for j in range(n_repeat):\n            y_error += (y_test[:, j] - y_predict[:, i]) ** 2\n\n    y_error /= n_repeat * n_repeat\n\n    y_noise = np.var(y_test, axis=1)\n    y_bias = (f(X_test) - np.mean(y_predict, axis=1)) ** 2\n    y_var = np.var(y_predict, axis=1)\n\n    print(\n        \"{0}: {1:.4f} (error) = {2:.4f} (bias^2) \"\n        \" + {3:.4f} (var) + {4:.4f} (noise)\".format(\n            name, np.mean(y_error), np.mean(y_bias), np.mean(y_var), np.mean(y_noise)\n        )\n    )\n\n    # Plot figures\n    plt.subplot(2, n_estimators, n + 1)\n    plt.plot(X_test, f(X_test), \"b\", label=\"$f(x)$\")\n    plt.plot(X_train[0], y_train[0], \".b\", label=\"LS ~ $y = f(x)+noise$\")\n\n    for i in range(n_repeat):\n        if i == 0:\n            plt.plot(X_test, y_predict[:, i], \"r\", label=r\"$\\^y(x)$\")\n        else:\n            plt.plot(X_test, y_predict[:, i], \"r\", alpha=0.05)\n\n    plt.plot(X_test, np.mean(y_predict, axis=1), \"c\", label=r\"$\\mathbb{E}_{LS} \\^y(x)$\")\n\n    plt.xlim([-5, 5])\n    plt.title(name)\n\n    if n == n_estimators - 1:\n        plt.legend(loc=(1.1, 0.5))\n\n    plt.subplot(2, n_estimators, n_estimators + n + 1)\n    plt.plot(X_test, y_error, \"r\", label=\"$error(x)$\")\n    plt.plot(X_test, y_bias, \"b\", label=\"$bias^2(x)$\"),\n    plt.plot(X_test, y_var, \"g\", label=\"$variance(x)$\"),\n    plt.plot(X_test, y_noise, \"c\", label=\"$noise(x)$\")\n\n    plt.xlim([-5, 5])\n    plt.ylim([0, 0.1])\n\n    if n == n_estimators - 1:\n        plt.legend(loc=(1.1, 0.5))\n\nplt.subplots_adjust(right=0.75)\nplt.show()"
+        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nfrom sklearn.ensemble import BaggingRegressor\nfrom sklearn.tree import DecisionTreeRegressor\n\n# Settings\nn_repeat = 50  # Number of iterations for computing expectations\nn_train = 50  # Size of the training set\nn_test = 1000  # Size of the test set\nnoise = 0.1  # Standard deviation of the noise\nnp.random.seed(0)\n\n# Change this for exploring the bias-variance decomposition of other\n# estimators. This should work well for estimators with high variance (e.g.,\n# decision trees or KNN), but poorly for estimators with low variance (e.g.,\n# linear models).\nestimators = [\n    (\"Tree\", DecisionTreeRegressor()),\n    (\"Bagging(Tree)\", BaggingRegressor(DecisionTreeRegressor())),\n]\n\nn_estimators = len(estimators)\n\n\n# Generate data\ndef f(x):\n    x = x.ravel()\n\n    return np.exp(-(x**2)) + 1.5 * np.exp(-((x - 2) ** 2))\n\n\ndef generate(n_samples, noise, n_repeat=1):\n    X = np.random.rand(n_samples) * 10 - 5\n    X = np.sort(X)\n\n    if n_repeat == 1:\n        y = f(X) + np.random.normal(0.0, noise, n_samples)\n    else:\n        y = np.zeros((n_samples, n_repeat))\n\n        for i in range(n_repeat):\n            y[:, i] = f(X) + np.random.normal(0.0, noise, n_samples)\n\n    X = X.reshape((n_samples, 1))\n\n    return X, y\n\n\nX_train = []\ny_train = []\n\nfor i in range(n_repeat):\n    X, y = generate(n_samples=n_train, noise=noise)\n    X_train.append(X)\n    y_train.append(y)\n\nX_test, y_test = generate(n_samples=n_test, noise=noise, n_repeat=n_repeat)\n\nplt.figure(figsize=(10, 8))\n\n# Loop over estimators to compare\nfor n, (name, estimator) in enumerate(estimators):\n    # Compute predictions\n    y_predict = np.zeros((n_test, n_repeat))\n\n    for i in range(n_repeat):\n        estimator.fit(X_train[i], y_train[i])\n        y_predict[:, i] = estimator.predict(X_test)\n\n    # Bias^2 + Variance + Noise decomposition of the mean squared error\n    y_error = np.zeros(n_test)\n\n    for i in range(n_repeat):\n        for j in range(n_repeat):\n            y_error += (y_test[:, j] - y_predict[:, i]) ** 2\n\n    y_error /= n_repeat * n_repeat\n\n    y_noise = np.var(y_test, axis=1)\n    y_bias = (f(X_test) - np.mean(y_predict, axis=1)) ** 2\n    y_var = np.var(y_predict, axis=1)\n\n    print(\n        \"{0}: {1:.4f} (error) = {2:.4f} (bias^2) \"\n        \" + {3:.4f} (var) + {4:.4f} (noise)\".format(\n            name, np.mean(y_error), np.mean(y_bias), np.mean(y_var), np.mean(y_noise)\n        )\n    )\n\n    # Plot figures\n    plt.subplot(2, n_estimators, n + 1)\n    plt.plot(X_test, f(X_test), \"b\", label=\"$f(x)$\")\n    plt.plot(X_train[0], y_train[0], \".b\", label=\"LS ~ $y = f(x)+noise$\")\n\n    for i in range(n_repeat):\n        if i == 0:\n            plt.plot(X_test, y_predict[:, i], \"r\", label=r\"$\\^y(x)$\")\n        else:\n            plt.plot(X_test, y_predict[:, i], \"r\", alpha=0.05)\n\n    plt.plot(X_test, np.mean(y_predict, axis=1), \"c\", label=r\"$\\mathbb{E}_{LS} \\^y(x)$\")\n\n    plt.xlim([-5, 5])\n    plt.title(name)\n\n    if n == n_estimators - 1:\n        plt.legend(loc=(1.1, 0.5))\n\n    plt.subplot(2, n_estimators, n_estimators + n + 1)\n    plt.plot(X_test, y_error, \"r\", label=\"$error(x)$\")\n    plt.plot(X_test, y_bias, \"b\", label=\"$bias^2(x)$\")\n    plt.plot(X_test, y_var, \"g\", label=\"$variance(x)$\")\n    plt.plot(X_test, y_noise, \"c\", label=\"$noise(x)$\")\n\n    plt.xlim([-5, 5])\n    plt.ylim([0, 0.1])\n\n    if n == n_estimators - 1:\n        plt.legend(loc=(1.1, 0.5))\n\nplt.subplots_adjust(right=0.75)\nplt.show()"
       ]
     }
   ],
@@ -53,7 +53,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/ensemble/plot_ensemble_oob.ipynb b/dev/lite/files/auto_examples/ensemble/plot_ensemble_oob.ipynb
index 178e863e44c42..ccd81be0618da 100644
--- a/dev/lite/files/auto_examples/ensemble/plot_ensemble_oob.ipynb
+++ b/dev/lite/files/auto_examples/ensemble/plot_ensemble_oob.ipynb
@@ -53,7 +53,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/ensemble/plot_feature_transformation.ipynb b/dev/lite/files/auto_examples/ensemble/plot_feature_transformation.ipynb
index f95212ef7200b..1462fb692a143 100644
--- a/dev/lite/files/auto_examples/ensemble/plot_feature_transformation.ipynb
+++ b/dev/lite/files/auto_examples/ensemble/plot_feature_transformation.ipynb
@@ -201,7 +201,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/ensemble/plot_forest_hist_grad_boosting_comparison.ipynb b/dev/lite/files/auto_examples/ensemble/plot_forest_hist_grad_boosting_comparison.ipynb
index e43d48e5a6d14..40e37ddada98c 100644
--- a/dev/lite/files/auto_examples/ensemble/plot_forest_hist_grad_boosting_comparison.ipynb
+++ b/dev/lite/files/auto_examples/ensemble/plot_forest_hist_grad_boosting_comparison.ipynb
@@ -150,7 +150,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/ensemble/plot_forest_importances.ipynb b/dev/lite/files/auto_examples/ensemble/plot_forest_importances.ipynb
index cee416be15b50..58afa70d61567 100644
--- a/dev/lite/files/auto_examples/ensemble/plot_forest_importances.ipynb
+++ b/dev/lite/files/auto_examples/ensemble/plot_forest_importances.ipynb
@@ -168,7 +168,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/ensemble/plot_forest_iris.ipynb b/dev/lite/files/auto_examples/ensemble/plot_forest_iris.ipynb
index ff990813c4d61..8ab0d62002f2b 100644
--- a/dev/lite/files/auto_examples/ensemble/plot_forest_iris.ipynb
+++ b/dev/lite/files/auto_examples/ensemble/plot_forest_iris.ipynb
@@ -53,7 +53,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/ensemble/plot_gradient_boosting_categorical.ipynb b/dev/lite/files/auto_examples/ensemble/plot_gradient_boosting_categorical.ipynb
index db2f470372d3b..7e3fd581ad459 100644
--- a/dev/lite/files/auto_examples/ensemble/plot_gradient_boosting_categorical.ipynb
+++ b/dev/lite/files/auto_examples/ensemble/plot_gradient_boosting_categorical.ipynb
@@ -193,7 +193,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/ensemble/plot_gradient_boosting_early_stopping.ipynb b/dev/lite/files/auto_examples/ensemble/plot_gradient_boosting_early_stopping.ipynb
index 8bbd4bbf53960..172f15000277e 100644
--- a/dev/lite/files/auto_examples/ensemble/plot_gradient_boosting_early_stopping.ipynb
+++ b/dev/lite/files/auto_examples/ensemble/plot_gradient_boosting_early_stopping.ipynb
@@ -139,7 +139,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/ensemble/plot_gradient_boosting_oob.ipynb b/dev/lite/files/auto_examples/ensemble/plot_gradient_boosting_oob.ipynb
index 7b2592e9e1b20..7038c2e0f6736 100644
--- a/dev/lite/files/auto_examples/ensemble/plot_gradient_boosting_oob.ipynb
+++ b/dev/lite/files/auto_examples/ensemble/plot_gradient_boosting_oob.ipynb
@@ -53,7 +53,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/ensemble/plot_gradient_boosting_quantile.ipynb b/dev/lite/files/auto_examples/ensemble/plot_gradient_boosting_quantile.ipynb
index 3e4a950105bb2..532aebd160f95 100644
--- a/dev/lite/files/auto_examples/ensemble/plot_gradient_boosting_quantile.ipynb
+++ b/dev/lite/files/auto_examples/ensemble/plot_gradient_boosting_quantile.ipynb
@@ -249,7 +249,7 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.experimental import enable_halving_search_cv  # noqa\nfrom sklearn.model_selection import HalvingRandomSearchCV\nfrom sklearn.metrics import make_scorer\nfrom pprint import pprint\n\nparam_grid = dict(\n    learning_rate=[0.05, 0.1, 0.2],\n    max_depth=[2, 5, 10],\n    min_samples_leaf=[1, 5, 10, 20],\n    min_samples_split=[5, 10, 20, 30, 50],\n)\nalpha = 0.05\nneg_mean_pinball_loss_05p_scorer = make_scorer(\n    mean_pinball_loss,\n    alpha=alpha,\n    greater_is_better=False,  # maximize the negative loss\n)\ngbr = GradientBoostingRegressor(loss=\"quantile\", alpha=alpha, random_state=0)\nsearch_05p = HalvingRandomSearchCV(\n    gbr,\n    param_grid,\n    resource=\"n_estimators\",\n    max_resources=250,\n    min_resources=50,\n    scoring=neg_mean_pinball_loss_05p_scorer,\n    n_jobs=2,\n    random_state=0,\n).fit(X_train, y_train)\npprint(search_05p.best_params_)"
+        "from pprint import pprint\n\nfrom sklearn.experimental import enable_halving_search_cv  # noqa: F401\nfrom sklearn.metrics import make_scorer\nfrom sklearn.model_selection import HalvingRandomSearchCV\n\nparam_grid = dict(\n    learning_rate=[0.05, 0.1, 0.2],\n    max_depth=[2, 5, 10],\n    min_samples_leaf=[1, 5, 10, 20],\n    min_samples_split=[5, 10, 20, 30, 50],\n)\nalpha = 0.05\nneg_mean_pinball_loss_05p_scorer = make_scorer(\n    mean_pinball_loss,\n    alpha=alpha,\n    greater_is_better=False,  # maximize the negative loss\n)\ngbr = GradientBoostingRegressor(loss=\"quantile\", alpha=alpha, random_state=0)\nsearch_05p = HalvingRandomSearchCV(\n    gbr,\n    param_grid,\n    resource=\"n_estimators\",\n    max_resources=250,\n    min_resources=50,\n    scoring=neg_mean_pinball_loss_05p_scorer,\n    n_jobs=2,\n    random_state=0,\n).fit(X_train, y_train)\npprint(search_05p.best_params_)"
       ]
     },
     {
@@ -341,7 +341,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/ensemble/plot_gradient_boosting_regression.ipynb b/dev/lite/files/auto_examples/ensemble/plot_gradient_boosting_regression.ipynb
index 0a735d116e667..1821b2c489436 100644
--- a/dev/lite/files/auto_examples/ensemble/plot_gradient_boosting_regression.ipynb
+++ b/dev/lite/files/auto_examples/ensemble/plot_gradient_boosting_regression.ipynb
@@ -143,7 +143,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/ensemble/plot_gradient_boosting_regularization.ipynb b/dev/lite/files/auto_examples/ensemble/plot_gradient_boosting_regularization.ipynb
index 6033d5cfaa99e..6f7796a505cd0 100644
--- a/dev/lite/files/auto_examples/ensemble/plot_gradient_boosting_regularization.ipynb
+++ b/dev/lite/files/auto_examples/ensemble/plot_gradient_boosting_regularization.ipynb
@@ -53,7 +53,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/ensemble/plot_hgbt_regression.ipynb b/dev/lite/files/auto_examples/ensemble/plot_hgbt_regression.ipynb
index 0857e15975e4b..f9569d9f95d28 100644
--- a/dev/lite/files/auto_examples/ensemble/plot_hgbt_regression.ipynb
+++ b/dev/lite/files/auto_examples/ensemble/plot_hgbt_regression.ipynb
@@ -251,7 +251,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/ensemble/plot_isolation_forest.ipynb b/dev/lite/files/auto_examples/ensemble/plot_isolation_forest.ipynb
index 8299c98064fd5..f09691667dac0 100644
--- a/dev/lite/files/auto_examples/ensemble/plot_isolation_forest.ipynb
+++ b/dev/lite/files/auto_examples/ensemble/plot_isolation_forest.ipynb
@@ -143,7 +143,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/ensemble/plot_monotonic_constraints.ipynb b/dev/lite/files/auto_examples/ensemble/plot_monotonic_constraints.ipynb
index 29ab03642dbec..34d1bacf91dd7 100644
--- a/dev/lite/files/auto_examples/ensemble/plot_monotonic_constraints.ipynb
+++ b/dev/lite/files/auto_examples/ensemble/plot_monotonic_constraints.ipynb
@@ -143,7 +143,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/ensemble/plot_random_forest_embedding.ipynb b/dev/lite/files/auto_examples/ensemble/plot_random_forest_embedding.ipynb
index ddaa7084d78ea..a63d09f69c8a4 100644
--- a/dev/lite/files/auto_examples/ensemble/plot_random_forest_embedding.ipynb
+++ b/dev/lite/files/auto_examples/ensemble/plot_random_forest_embedding.ipynb
@@ -53,7 +53,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/ensemble/plot_random_forest_regression_multioutput.ipynb b/dev/lite/files/auto_examples/ensemble/plot_random_forest_regression_multioutput.ipynb
index af8b742a6c3cb..6e47f3f8d8578 100644
--- a/dev/lite/files/auto_examples/ensemble/plot_random_forest_regression_multioutput.ipynb
+++ b/dev/lite/files/auto_examples/ensemble/plot_random_forest_regression_multioutput.ipynb
@@ -53,7 +53,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/ensemble/plot_stack_predictors.ipynb b/dev/lite/files/auto_examples/ensemble/plot_stack_predictors.ipynb
index 1c64718d10ce6..96edc8b4d9863 100644
--- a/dev/lite/files/auto_examples/ensemble/plot_stack_predictors.ipynb
+++ b/dev/lite/files/auto_examples/ensemble/plot_stack_predictors.ipynb
@@ -212,7 +212,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/ensemble/plot_voting_decision_regions.ipynb b/dev/lite/files/auto_examples/ensemble/plot_voting_decision_regions.ipynb
index e85ac06a088ae..5a11507e56b40 100644
--- a/dev/lite/files/auto_examples/ensemble/plot_voting_decision_regions.ipynb
+++ b/dev/lite/files/auto_examples/ensemble/plot_voting_decision_regions.ipynb
@@ -22,7 +22,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# Plot the decision boundaries of a VotingClassifier\n\n.. currentmodule:: sklearn\n\nPlot the decision boundaries of a :class:`~ensemble.VotingClassifier` for two\nfeatures of the Iris dataset.\n\nPlot the class probabilities of the first sample in a toy dataset predicted by\nthree different classifiers and averaged by the\n:class:`~ensemble.VotingClassifier`.\n\nFirst, three exemplary classifiers are initialized\n(:class:`~tree.DecisionTreeClassifier`,\n:class:`~neighbors.KNeighborsClassifier`, and :class:`~svm.SVC`) and used to\ninitialize a soft-voting :class:`~ensemble.VotingClassifier` with weights `[2,\n1, 2]`, which means that the predicted probabilities of the\n:class:`~tree.DecisionTreeClassifier` and :class:`~svm.SVC` each count 2 times\nas much as the weights of the :class:`~neighbors.KNeighborsClassifier`\nclassifier when the averaged probability is calculated.\n"
+        "\n# Visualizing the probabilistic predictions of a VotingClassifier\n\n.. currentmodule:: sklearn\n\nPlot the predicted class probabilities in a toy dataset predicted by three\ndifferent classifiers and averaged by the :class:`~ensemble.VotingClassifier`.\n\nFirst, three linear classifiers are initialized. Two are spline models with\ninteraction terms, one using constant extrapolation and the other using periodic\nextrapolation. The third classifier is a :class:`~kernel_approximation.Nystroem`\nwith the default \"rbf\" kernel.\n\nIn the first part of this example, these three classifiers are used to\ndemonstrate soft-voting using :class:`~ensemble.VotingClassifier` with weighted\naverage. We set `weights=[2, 1, 3]`, meaning the constant extrapolation spline\nmodel's predictions are weighted twice as much as the periodic spline model's,\nand the Nystroem model's predictions are weighted three times as much as the\nperiodic spline.\n\nThe second part demonstrates how soft predictions can be converted into hard\npredictions.\n"
       ]
     },
     {
@@ -33,7 +33,162 @@
       },
       "outputs": [],
       "source": [
-        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\n\nfrom itertools import product\n\nimport matplotlib.pyplot as plt\n\nfrom sklearn import datasets\nfrom sklearn.ensemble import VotingClassifier\nfrom sklearn.inspection import DecisionBoundaryDisplay\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.svm import SVC\nfrom sklearn.tree import DecisionTreeClassifier\n\n# Loading some example data\niris = datasets.load_iris()\nX = iris.data[:, [0, 2]]\ny = iris.target\n\n# Training classifiers\nclf1 = DecisionTreeClassifier(max_depth=4)\nclf2 = KNeighborsClassifier(n_neighbors=7)\nclf3 = SVC(gamma=0.1, kernel=\"rbf\", probability=True)\neclf = VotingClassifier(\n    estimators=[(\"dt\", clf1), (\"knn\", clf2), (\"svc\", clf3)],\n    voting=\"soft\",\n    weights=[2, 1, 2],\n)\n\nclf1.fit(X, y)\nclf2.fit(X, y)\nclf3.fit(X, y)\neclf.fit(X, y)\n\n# Plotting decision regions\nf, axarr = plt.subplots(2, 2, sharex=\"col\", sharey=\"row\", figsize=(10, 8))\nfor idx, clf, tt in zip(\n    product([0, 1], [0, 1]),\n    [clf1, clf2, clf3, eclf],\n    [\"Decision Tree (depth=4)\", \"KNN (k=7)\", \"Kernel SVM\", \"Soft Voting\"],\n):\n    DecisionBoundaryDisplay.from_estimator(\n        clf, X, alpha=0.4, ax=axarr[idx[0], idx[1]], response_method=\"predict\"\n    )\n    axarr[idx[0], idx[1]].scatter(X[:, 0], X[:, 1], c=y, s=20, edgecolor=\"k\")\n    axarr[idx[0], idx[1]].set_title(tt)\n\nplt.show()"
+        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We first generate a noisy XOR dataset, which is a binary classification task.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nfrom matplotlib.colors import ListedColormap\n\nn_samples = 500\nrng = np.random.default_rng(0)\nfeature_names = [\"Feature #0\", \"Feature #1\"]\ncommon_scatter_plot_params = dict(\n    cmap=ListedColormap([\"tab:red\", \"tab:blue\"]),\n    edgecolor=\"white\",\n    linewidth=1,\n)\n\nxor = pd.DataFrame(\n    np.random.RandomState(0).uniform(low=-1, high=1, size=(n_samples, 2)),\n    columns=feature_names,\n)\nnoise = rng.normal(loc=0, scale=0.1, size=(n_samples, 2))\ntarget_xor = np.logical_xor(\n    xor[\"Feature #0\"] + noise[:, 0] > 0, xor[\"Feature #1\"] + noise[:, 1] > 0\n)\n\nX = xor[feature_names]\ny = target_xor.astype(np.int32)\n\nfig, ax = plt.subplots()\nax.scatter(X[\"Feature #0\"], X[\"Feature #1\"], c=y, **common_scatter_plot_params)\nax.set_title(\"The XOR dataset\")\nplt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Due to the inherent non-linear separability of the XOR dataset, tree-based\nmodels would often be preferred. However, appropriate feature engineering\ncombined with a linear model can yield effective results, with the added\nbenefit of producing better-calibrated probabilities for samples located in\nthe transition regions affected by noise.\n\nWe define and fit the models on the whole dataset.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn.ensemble import VotingClassifier\nfrom sklearn.kernel_approximation import Nystroem\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.pipeline import make_pipeline\nfrom sklearn.preprocessing import PolynomialFeatures, SplineTransformer, StandardScaler\n\nclf1 = make_pipeline(\n    SplineTransformer(degree=2, n_knots=2),\n    PolynomialFeatures(interaction_only=True),\n    LogisticRegression(C=10),\n)\nclf2 = make_pipeline(\n    SplineTransformer(\n        degree=2,\n        n_knots=4,\n        extrapolation=\"periodic\",\n        include_bias=True,\n    ),\n    PolynomialFeatures(interaction_only=True),\n    LogisticRegression(C=10),\n)\nclf3 = make_pipeline(\n    StandardScaler(),\n    Nystroem(gamma=2, random_state=0),\n    LogisticRegression(C=10),\n)\nweights = [2, 1, 3]\neclf = VotingClassifier(\n    estimators=[\n        (\"constant splines model\", clf1),\n        (\"periodic splines model\", clf2),\n        (\"nystroem model\", clf3),\n    ],\n    voting=\"soft\",\n    weights=weights,\n)\n\nclf1.fit(X, y)\nclf2.fit(X, y)\nclf3.fit(X, y)\neclf.fit(X, y)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Finally we use :class:`~inspection.DecisionBoundaryDisplay` to plot the\npredicted probabilities. By using a diverging colormap (such as `\"RdBu\"`), we\ncan ensure that darker colors correspond to `predict_proba` close to either 0\nor 1, and white corresponds to `predict_proba` of 0.5.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from itertools import product\n\nfrom sklearn.inspection import DecisionBoundaryDisplay\n\nfig, axarr = plt.subplots(2, 2, sharex=\"col\", sharey=\"row\", figsize=(10, 8))\nfor idx, clf, title in zip(\n    product([0, 1], [0, 1]),\n    [clf1, clf2, clf3, eclf],\n    [\n        \"Splines with\\nconstant extrapolation\",\n        \"Splines with\\nperiodic extrapolation\",\n        \"RBF Nystroem\",\n        \"Soft Voting\",\n    ],\n):\n    disp = DecisionBoundaryDisplay.from_estimator(\n        clf,\n        X,\n        response_method=\"predict_proba\",\n        plot_method=\"pcolormesh\",\n        cmap=\"RdBu\",\n        alpha=0.8,\n        ax=axarr[idx[0], idx[1]],\n    )\n    axarr[idx[0], idx[1]].scatter(\n        X[\"Feature #0\"],\n        X[\"Feature #1\"],\n        c=y,\n        **common_scatter_plot_params,\n    )\n    axarr[idx[0], idx[1]].set_title(title)\n    fig.colorbar(disp.surface_, ax=axarr[idx[0], idx[1]], label=\"Probability estimate\")\n\nplt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "As a sanity check, we can verify for a given sample that the probability\npredicted by the :class:`~ensemble.VotingClassifier` is indeed the weighted\naverage of the individual classifiers' soft-predictions.\n\nIn the case of binary classification such as in the present example, the\n:term:`predict_proba` arrays contain the probability of belonging to class 0\n(here in red) as the first entry, and the probability of belonging to class 1\n(here in blue) as the second entry.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "test_sample = pd.DataFrame({\"Feature #0\": [-0.5], \"Feature #1\": [1.5]})\npredict_probas = [est.predict_proba(test_sample).ravel() for est in eclf.estimators_]\nfor (est_name, _), est_probas in zip(eclf.estimators, predict_probas):\n    print(f\"{est_name}'s predicted probabilities: {est_probas}\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "print(\n    \"Weighted average of soft-predictions: \"\n    f\"{np.dot(weights, predict_probas) / np.sum(weights)}\"\n)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We can see that manual calculation of predicted probabilities above is\nequivalent to that produced by the `VotingClassifier`:\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "print(\n    \"Predicted probability of VotingClassifier: \"\n    f\"{eclf.predict_proba(test_sample).ravel()}\"\n)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "To convert soft predictions into hard predictions when weights are provided,\nthe weighted average predicted probabilities are computed for each class.\nThen, the final class label is then derived from the class label with the\nhighest average probability, which corresponds to the default threshold at\n`predict_proba=0.5` in the case of binary classification.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "print(\n    \"Class with the highest weighted average of soft-predictions: \"\n    f\"{np.argmax(np.dot(weights, predict_probas) / np.sum(weights))}\"\n)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "This is equivalent to the output of `VotingClassifier`'s `predict` method:\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "print(f\"Predicted class of VotingClassifier: {eclf.predict(test_sample).ravel()}\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Soft votes can be thresholded as for any other probabilistic classifier. This\nallows you to set a threshold probability at which the positive class will be\npredicted, instead of simply selecting the class with the highest predicted\nprobability.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn.model_selection import FixedThresholdClassifier\n\neclf_other_threshold = FixedThresholdClassifier(\n    eclf, threshold=0.7, response_method=\"predict_proba\"\n).fit(X, y)\nprint(\n    \"Predicted class of thresholded VotingClassifier: \"\n    f\"{eclf_other_threshold.predict(test_sample)}\"\n)"
       ]
     }
   ],
@@ -53,7 +208,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/ensemble/plot_voting_probas.ipynb b/dev/lite/files/auto_examples/ensemble/plot_voting_probas.ipynb
deleted file mode 100644
index f554f89b2f699..0000000000000
--- a/dev/lite/files/auto_examples/ensemble/plot_voting_probas.ipynb
+++ /dev/null
@@ -1,61 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<div class='alert alert-warning'>\n\n# JupyterLite warning\n\nRunning the scikit-learn examples in JupyterLite is experimental and you may encounter some unexpected behavior.\n\nThe main difference is that imports will take a lot longer than usual, for example the first `import sklearn` can take roughly 10-20s.\n\nIf you notice problems, feel free to open an [issue](https://github.com/scikit-learn/scikit-learn/issues/new/choose) about it.\n</div>"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "# JupyterLite-specific code\nimport matplotlib\nimport pandas\nimport piplite\nimport joblib\nimport threadpoolctl\nimport scipy\nawait piplite.install(\n  'scikit-learn==1.7.dev0',\n   index_urls='https://pypi.anaconda.org/scientific-python-nightly-wheels/simple',\n)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "\n# Plot class probabilities calculated by the VotingClassifier\n\n.. currentmodule:: sklearn\n\nPlot the class probabilities of the first sample in a toy dataset predicted by\nthree different classifiers and averaged by the\n:class:`~ensemble.VotingClassifier`.\n\nFirst, three exemplary classifiers are initialized\n(:class:`~linear_model.LogisticRegression`, :class:`~naive_bayes.GaussianNB`,\nand :class:`~ensemble.RandomForestClassifier`) and used to initialize a\nsoft-voting :class:`~ensemble.VotingClassifier` with weights `[1, 1, 5]`, which\nmeans that the predicted probabilities of the\n:class:`~ensemble.RandomForestClassifier` count 5 times as much as the weights\nof the other classifiers when the averaged probability is calculated.\n\nTo visualize the probability weighting, we fit each classifier on the training\nset and plot the predicted class probabilities for the first sample in this\nexample dataset.\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nfrom sklearn.ensemble import RandomForestClassifier, VotingClassifier\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.naive_bayes import GaussianNB\n\nclf1 = LogisticRegression(max_iter=1000, random_state=123)\nclf2 = RandomForestClassifier(n_estimators=100, random_state=123)\nclf3 = GaussianNB()\nX = np.array([[-1.0, -1.0], [-1.2, -1.4], [-3.4, -2.2], [1.1, 1.2]])\ny = np.array([1, 1, 2, 2])\n\neclf = VotingClassifier(\n    estimators=[(\"lr\", clf1), (\"rf\", clf2), (\"gnb\", clf3)],\n    voting=\"soft\",\n    weights=[1, 1, 5],\n)\n\n# predict class probabilities for all classifiers\nprobas = [c.fit(X, y).predict_proba(X) for c in (clf1, clf2, clf3, eclf)]\n\n# get class probabilities for the first sample in the dataset\nclass1_1 = [pr[0, 0] for pr in probas]\nclass2_1 = [pr[0, 1] for pr in probas]\n\n\n# plotting\n\nN = 4  # number of groups\nind = np.arange(N)  # group positions\nwidth = 0.35  # bar width\n\nfig, ax = plt.subplots()\n\n# bars for classifier 1-3\np1 = ax.bar(ind, np.hstack(([class1_1[:-1], [0]])), width, color=\"green\", edgecolor=\"k\")\np2 = ax.bar(\n    ind + width,\n    np.hstack(([class2_1[:-1], [0]])),\n    width,\n    color=\"lightgreen\",\n    edgecolor=\"k\",\n)\n\n# bars for VotingClassifier\np3 = ax.bar(ind, [0, 0, 0, class1_1[-1]], width, color=\"blue\", edgecolor=\"k\")\np4 = ax.bar(\n    ind + width, [0, 0, 0, class2_1[-1]], width, color=\"steelblue\", edgecolor=\"k\"\n)\n\n# plot annotations\nplt.axvline(2.8, color=\"k\", linestyle=\"dashed\")\nax.set_xticks(ind + width)\nax.set_xticklabels(\n    [\n        \"LogisticRegression\\nweight 1\",\n        \"GaussianNB\\nweight 1\",\n        \"RandomForestClassifier\\nweight 5\",\n        \"VotingClassifier\\n(average probabilities)\",\n    ],\n    rotation=40,\n    ha=\"right\",\n)\nplt.ylim([0, 1])\nplt.title(\"Class probabilities for sample 1 by different classifiers\")\nplt.legend([p1[0], p2[0]], [\"class 1\", \"class 2\"], loc=\"upper left\")\nplt.tight_layout()\nplt.show()"
-      ]
-    }
-  ],
-  "metadata": {
-    "kernelspec": {
-      "display_name": "Python 3",
-      "language": "python",
-      "name": "python3"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.10.16"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 0
-}
\ No newline at end of file
diff --git a/dev/lite/files/auto_examples/ensemble/plot_voting_regressor.ipynb b/dev/lite/files/auto_examples/ensemble/plot_voting_regressor.ipynb
index df14e5a65655f..9794ee5bdc2c9 100644
--- a/dev/lite/files/auto_examples/ensemble/plot_voting_regressor.ipynb
+++ b/dev/lite/files/auto_examples/ensemble/plot_voting_regressor.ipynb
@@ -107,7 +107,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/feature_selection/plot_f_test_vs_mi.ipynb b/dev/lite/files/auto_examples/feature_selection/plot_f_test_vs_mi.ipynb
index 532707e2ca5af..23e0827a036b5 100644
--- a/dev/lite/files/auto_examples/feature_selection/plot_f_test_vs_mi.ipynb
+++ b/dev/lite/files/auto_examples/feature_selection/plot_f_test_vs_mi.ipynb
@@ -53,7 +53,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/feature_selection/plot_feature_selection.ipynb b/dev/lite/files/auto_examples/feature_selection/plot_feature_selection.ipynb
index 6e7222e11f8d8..7f3490f560509 100644
--- a/dev/lite/files/auto_examples/feature_selection/plot_feature_selection.ipynb
+++ b/dev/lite/files/auto_examples/feature_selection/plot_feature_selection.ipynb
@@ -161,7 +161,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/feature_selection/plot_feature_selection_pipeline.ipynb b/dev/lite/files/auto_examples/feature_selection/plot_feature_selection_pipeline.ipynb
index 0f9e5072ec4c6..26315dc25fbd4 100644
--- a/dev/lite/files/auto_examples/feature_selection/plot_feature_selection_pipeline.ipynb
+++ b/dev/lite/files/auto_examples/feature_selection/plot_feature_selection_pipeline.ipynb
@@ -150,7 +150,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/feature_selection/plot_rfe_digits.ipynb b/dev/lite/files/auto_examples/feature_selection/plot_rfe_digits.ipynb
index aefaecad765e1..d58881260c1aa 100644
--- a/dev/lite/files/auto_examples/feature_selection/plot_rfe_digits.ipynb
+++ b/dev/lite/files/auto_examples/feature_selection/plot_rfe_digits.ipynb
@@ -53,7 +53,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/feature_selection/plot_rfe_with_cross_validation.ipynb b/dev/lite/files/auto_examples/feature_selection/plot_rfe_with_cross_validation.ipynb
index 635af89ef8801..d6f3c61935777 100644
--- a/dev/lite/files/auto_examples/feature_selection/plot_rfe_with_cross_validation.ipynb
+++ b/dev/lite/files/auto_examples/feature_selection/plot_rfe_with_cross_validation.ipynb
@@ -51,7 +51,7 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.datasets import make_classification\n\nX, y = make_classification(\n    n_samples=500,\n    n_features=15,\n    n_informative=3,\n    n_redundant=2,\n    n_repeated=0,\n    n_classes=8,\n    n_clusters_per_class=1,\n    class_sep=0.8,\n    random_state=0,\n)"
+        "from sklearn.datasets import make_classification\n\nn_features = 15\nfeat_names = [f\"feature_{i}\" for i in range(15)]\n\nX, y = make_classification(\n    n_samples=500,\n    n_features=n_features,\n    n_informative=3,\n    n_redundant=2,\n    n_repeated=0,\n    n_classes=8,\n    n_clusters_per_class=1,\n    class_sep=0.8,\n    random_state=0,\n)"
       ]
     },
     {
@@ -87,7 +87,7 @@
       },
       "outputs": [],
       "source": [
-        "import matplotlib.pyplot as plt\nimport pandas as pd\n\ncv_results = pd.DataFrame(rfecv.cv_results_)\nplt.figure()\nplt.xlabel(\"Number of features selected\")\nplt.ylabel(\"Mean test accuracy\")\nplt.errorbar(\n    x=cv_results[\"n_features\"],\n    y=cv_results[\"mean_test_score\"],\n    yerr=cv_results[\"std_test_score\"],\n)\nplt.title(\"Recursive Feature Elimination \\nwith correlated features\")\nplt.show()"
+        "import matplotlib.pyplot as plt\nimport pandas as pd\n\ndata = {\n    key: value\n    for key, value in rfecv.cv_results_.items()\n    if key in [\"n_features\", \"mean_test_score\", \"std_test_score\"]\n}\ncv_results = pd.DataFrame(data)\nplt.figure()\nplt.xlabel(\"Number of features selected\")\nplt.ylabel(\"Mean test accuracy\")\nplt.errorbar(\n    x=cv_results[\"n_features\"],\n    y=cv_results[\"mean_test_score\"],\n    yerr=cv_results[\"std_test_score\"],\n)\nplt.title(\"Recursive Feature Elimination \\nwith correlated features\")\nplt.show()"
       ]
     },
     {
@@ -96,6 +96,24 @@
       "source": [
         "From the plot above one can further notice a plateau of equivalent scores\n(similar mean value and overlapping errorbars) for 3 to 5 selected features.\nThis is the result of introducing correlated features. Indeed, the optimal\nmodel selected by the RFE can lie within this range, depending on the\ncross-validation technique. The test accuracy decreases above 5 selected\nfeatures, this is, keeping non-informative features leads to over-fitting and\nis therefore detrimental for the statistical performance of the models.\n\n"
       ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n\nfor i in range(cv.n_splits):\n    mask = rfecv.cv_results_[f\"split{i}_support\"][\n        rfecv.n_features_\n    ]  # mask of features selected by the RFE\n    features_selected = np.ma.compressed(np.ma.masked_array(feat_names, mask=1 - mask))\n    print(f\"Features selected in fold {i}: {features_selected}\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "In the five folds, the selected features are consistent. This is good news,\nit means that the selection is stable across folds, and it confirms that\nthese features are the most informative ones.\n\n"
+      ]
     }
   ],
   "metadata": {
@@ -114,7 +132,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/feature_selection/plot_select_from_model_diabetes.ipynb b/dev/lite/files/auto_examples/feature_selection/plot_select_from_model_diabetes.ipynb
index 3d0d02954b6e2..711d37180e417 100644
--- a/dev/lite/files/auto_examples/feature_selection/plot_select_from_model_diabetes.ipynb
+++ b/dev/lite/files/auto_examples/feature_selection/plot_select_from_model_diabetes.ipynb
@@ -168,7 +168,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/frozen/plot_frozen_examples.ipynb b/dev/lite/files/auto_examples/frozen/plot_frozen_examples.ipynb
index 1c5d72c8359b8..e35fa3e7c54ed 100644
--- a/dev/lite/files/auto_examples/frozen/plot_frozen_examples.ipynb
+++ b/dev/lite/files/auto_examples/frozen/plot_frozen_examples.ipynb
@@ -132,7 +132,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/gaussian_process/plot_compare_gpr_krr.ipynb b/dev/lite/files/auto_examples/gaussian_process/plot_compare_gpr_krr.ipynb
index cc6401849603d..c102faef76ebf 100644
--- a/dev/lite/files/auto_examples/gaussian_process/plot_compare_gpr_krr.ipynb
+++ b/dev/lite/files/auto_examples/gaussian_process/plot_compare_gpr_krr.ipynb
@@ -338,7 +338,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/gaussian_process/plot_gpc.ipynb b/dev/lite/files/auto_examples/gaussian_process/plot_gpc.ipynb
index d46dd5ee80578..f51cf2b023d1b 100644
--- a/dev/lite/files/auto_examples/gaussian_process/plot_gpc.ipynb
+++ b/dev/lite/files/auto_examples/gaussian_process/plot_gpc.ipynb
@@ -53,7 +53,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/gaussian_process/plot_gpc_iris.ipynb b/dev/lite/files/auto_examples/gaussian_process/plot_gpc_iris.ipynb
index f023e41b6a5a1..fa1556d99f6f6 100644
--- a/dev/lite/files/auto_examples/gaussian_process/plot_gpc_iris.ipynb
+++ b/dev/lite/files/auto_examples/gaussian_process/plot_gpc_iris.ipynb
@@ -53,7 +53,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/gaussian_process/plot_gpc_isoprobability.ipynb b/dev/lite/files/auto_examples/gaussian_process/plot_gpc_isoprobability.ipynb
index 5538f9484675b..5a9e3277f571f 100644
--- a/dev/lite/files/auto_examples/gaussian_process/plot_gpc_isoprobability.ipynb
+++ b/dev/lite/files/auto_examples/gaussian_process/plot_gpc_isoprobability.ipynb
@@ -53,7 +53,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/gaussian_process/plot_gpc_xor.ipynb b/dev/lite/files/auto_examples/gaussian_process/plot_gpc_xor.ipynb
index 9e16833ebc352..bf54cefdd4b6c 100644
--- a/dev/lite/files/auto_examples/gaussian_process/plot_gpc_xor.ipynb
+++ b/dev/lite/files/auto_examples/gaussian_process/plot_gpc_xor.ipynb
@@ -53,7 +53,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/gaussian_process/plot_gpr_co2.ipynb b/dev/lite/files/auto_examples/gaussian_process/plot_gpr_co2.ipynb
index f3eaed9a3a185..6b9faeae7c536 100644
--- a/dev/lite/files/auto_examples/gaussian_process/plot_gpr_co2.ipynb
+++ b/dev/lite/files/auto_examples/gaussian_process/plot_gpr_co2.ipynb
@@ -316,7 +316,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/gaussian_process/plot_gpr_noisy.ipynb b/dev/lite/files/auto_examples/gaussian_process/plot_gpr_noisy.ipynb
index 3d5a79e484167..7c83c076e27a7 100644
--- a/dev/lite/files/auto_examples/gaussian_process/plot_gpr_noisy.ipynb
+++ b/dev/lite/files/auto_examples/gaussian_process/plot_gpr_noisy.ipynb
@@ -252,7 +252,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/gaussian_process/plot_gpr_noisy_targets.ipynb b/dev/lite/files/auto_examples/gaussian_process/plot_gpr_noisy_targets.ipynb
index 928eeb5128ed7..ac3edeb0efb1a 100644
--- a/dev/lite/files/auto_examples/gaussian_process/plot_gpr_noisy_targets.ipynb
+++ b/dev/lite/files/auto_examples/gaussian_process/plot_gpr_noisy_targets.ipynb
@@ -197,7 +197,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/gaussian_process/plot_gpr_on_structured_data.ipynb b/dev/lite/files/auto_examples/gaussian_process/plot_gpr_on_structured_data.ipynb
index 83fd0a5cbfc9d..609ff62dc87b4 100644
--- a/dev/lite/files/auto_examples/gaussian_process/plot_gpr_on_structured_data.ipynb
+++ b/dev/lite/files/auto_examples/gaussian_process/plot_gpr_on_structured_data.ipynb
@@ -118,7 +118,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/gaussian_process/plot_gpr_prior_posterior.ipynb b/dev/lite/files/auto_examples/gaussian_process/plot_gpr_prior_posterior.ipynb
index 430b70cb7aefb..e2cb0bd30a8b9 100644
--- a/dev/lite/files/auto_examples/gaussian_process/plot_gpr_prior_posterior.ipynb
+++ b/dev/lite/files/auto_examples/gaussian_process/plot_gpr_prior_posterior.ipynb
@@ -234,7 +234,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/impute/plot_iterative_imputer_variants_comparison.ipynb b/dev/lite/files/auto_examples/impute/plot_iterative_imputer_variants_comparison.ipynb
index d7fc232f2b9f9..03c6403ed06ba 100644
--- a/dev/lite/files/auto_examples/impute/plot_iterative_imputer_variants_comparison.ipynb
+++ b/dev/lite/files/auto_examples/impute/plot_iterative_imputer_variants_comparison.ipynb
@@ -33,7 +33,7 @@
       },
       "outputs": [],
       "source": [
-        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\n\nfrom sklearn.datasets import fetch_california_housing\nfrom sklearn.ensemble import RandomForestRegressor\n\n# To use this experimental feature, we need to explicitly ask for it:\nfrom sklearn.experimental import enable_iterative_imputer  # noqa\nfrom sklearn.impute import IterativeImputer, SimpleImputer\nfrom sklearn.kernel_approximation import Nystroem\nfrom sklearn.linear_model import BayesianRidge, Ridge\nfrom sklearn.model_selection import cross_val_score\nfrom sklearn.neighbors import KNeighborsRegressor\nfrom sklearn.pipeline import make_pipeline\n\nN_SPLITS = 5\n\nrng = np.random.RandomState(0)\n\nX_full, y_full = fetch_california_housing(return_X_y=True)\n# ~2k samples is enough for the purpose of the example.\n# Remove the following two lines for a slower run with different error bars.\nX_full = X_full[::10]\ny_full = y_full[::10]\nn_samples, n_features = X_full.shape\n\n# Estimate the score on the entire dataset, with no missing values\nbr_estimator = BayesianRidge()\nscore_full_data = pd.DataFrame(\n    cross_val_score(\n        br_estimator, X_full, y_full, scoring=\"neg_mean_squared_error\", cv=N_SPLITS\n    ),\n    columns=[\"Full Data\"],\n)\n\n# Add a single missing value to each row\nX_missing = X_full.copy()\ny_missing = y_full\nmissing_samples = np.arange(n_samples)\nmissing_features = rng.choice(n_features, n_samples, replace=True)\nX_missing[missing_samples, missing_features] = np.nan\n\n# Estimate the score after imputation (mean and median strategies)\nscore_simple_imputer = pd.DataFrame()\nfor strategy in (\"mean\", \"median\"):\n    estimator = make_pipeline(\n        SimpleImputer(missing_values=np.nan, strategy=strategy), br_estimator\n    )\n    score_simple_imputer[strategy] = cross_val_score(\n        estimator, X_missing, y_missing, scoring=\"neg_mean_squared_error\", cv=N_SPLITS\n    )\n\n# Estimate the score after iterative imputation of the missing values\n# with different estimators\nestimators = [\n    BayesianRidge(),\n    RandomForestRegressor(\n        # We tuned the hyperparameters of the RandomForestRegressor to get a good\n        # enough predictive performance for a restricted execution time.\n        n_estimators=4,\n        max_depth=10,\n        bootstrap=True,\n        max_samples=0.5,\n        n_jobs=2,\n        random_state=0,\n    ),\n    make_pipeline(\n        Nystroem(kernel=\"polynomial\", degree=2, random_state=0), Ridge(alpha=1e3)\n    ),\n    KNeighborsRegressor(n_neighbors=15),\n]\nscore_iterative_imputer = pd.DataFrame()\n# iterative imputer is sensible to the tolerance and\n# dependent on the estimator used internally.\n# we tuned the tolerance to keep this example run with limited computational\n# resources while not changing the results too much compared to keeping the\n# stricter default value for the tolerance parameter.\ntolerances = (1e-3, 1e-1, 1e-1, 1e-2)\nfor impute_estimator, tol in zip(estimators, tolerances):\n    estimator = make_pipeline(\n        IterativeImputer(\n            random_state=0, estimator=impute_estimator, max_iter=25, tol=tol\n        ),\n        br_estimator,\n    )\n    score_iterative_imputer[impute_estimator.__class__.__name__] = cross_val_score(\n        estimator, X_missing, y_missing, scoring=\"neg_mean_squared_error\", cv=N_SPLITS\n    )\n\nscores = pd.concat(\n    [score_full_data, score_simple_imputer, score_iterative_imputer],\n    keys=[\"Original\", \"SimpleImputer\", \"IterativeImputer\"],\n    axis=1,\n)\n\n# plot california housing results\nfig, ax = plt.subplots(figsize=(13, 6))\nmeans = -scores.mean()\nerrors = scores.std()\nmeans.plot.barh(xerr=errors, ax=ax)\nax.set_title(\"California Housing Regression with Different Imputation Methods\")\nax.set_xlabel(\"MSE (smaller is better)\")\nax.set_yticks(np.arange(means.shape[0]))\nax.set_yticklabels([\" w/ \".join(label) for label in means.index.tolist()])\nplt.tight_layout(pad=1)\nplt.show()"
+        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\n\nfrom sklearn.datasets import fetch_california_housing\nfrom sklearn.ensemble import RandomForestRegressor\n\n# To use this experimental feature, we need to explicitly ask for it:\nfrom sklearn.experimental import enable_iterative_imputer  # noqa: F401\nfrom sklearn.impute import IterativeImputer, SimpleImputer\nfrom sklearn.kernel_approximation import Nystroem\nfrom sklearn.linear_model import BayesianRidge, Ridge\nfrom sklearn.model_selection import cross_val_score\nfrom sklearn.neighbors import KNeighborsRegressor\nfrom sklearn.pipeline import make_pipeline\n\nN_SPLITS = 5\n\nrng = np.random.RandomState(0)\n\nX_full, y_full = fetch_california_housing(return_X_y=True)\n# ~2k samples is enough for the purpose of the example.\n# Remove the following two lines for a slower run with different error bars.\nX_full = X_full[::10]\ny_full = y_full[::10]\nn_samples, n_features = X_full.shape\n\n# Estimate the score on the entire dataset, with no missing values\nbr_estimator = BayesianRidge()\nscore_full_data = pd.DataFrame(\n    cross_val_score(\n        br_estimator, X_full, y_full, scoring=\"neg_mean_squared_error\", cv=N_SPLITS\n    ),\n    columns=[\"Full Data\"],\n)\n\n# Add a single missing value to each row\nX_missing = X_full.copy()\ny_missing = y_full\nmissing_samples = np.arange(n_samples)\nmissing_features = rng.choice(n_features, n_samples, replace=True)\nX_missing[missing_samples, missing_features] = np.nan\n\n# Estimate the score after imputation (mean and median strategies)\nscore_simple_imputer = pd.DataFrame()\nfor strategy in (\"mean\", \"median\"):\n    estimator = make_pipeline(\n        SimpleImputer(missing_values=np.nan, strategy=strategy), br_estimator\n    )\n    score_simple_imputer[strategy] = cross_val_score(\n        estimator, X_missing, y_missing, scoring=\"neg_mean_squared_error\", cv=N_SPLITS\n    )\n\n# Estimate the score after iterative imputation of the missing values\n# with different estimators\nestimators = [\n    BayesianRidge(),\n    RandomForestRegressor(\n        # We tuned the hyperparameters of the RandomForestRegressor to get a good\n        # enough predictive performance for a restricted execution time.\n        n_estimators=4,\n        max_depth=10,\n        bootstrap=True,\n        max_samples=0.5,\n        n_jobs=2,\n        random_state=0,\n    ),\n    make_pipeline(\n        Nystroem(kernel=\"polynomial\", degree=2, random_state=0), Ridge(alpha=1e3)\n    ),\n    KNeighborsRegressor(n_neighbors=15),\n]\nscore_iterative_imputer = pd.DataFrame()\n# iterative imputer is sensible to the tolerance and\n# dependent on the estimator used internally.\n# we tuned the tolerance to keep this example run with limited computational\n# resources while not changing the results too much compared to keeping the\n# stricter default value for the tolerance parameter.\ntolerances = (1e-3, 1e-1, 1e-1, 1e-2)\nfor impute_estimator, tol in zip(estimators, tolerances):\n    estimator = make_pipeline(\n        IterativeImputer(\n            random_state=0, estimator=impute_estimator, max_iter=25, tol=tol\n        ),\n        br_estimator,\n    )\n    score_iterative_imputer[impute_estimator.__class__.__name__] = cross_val_score(\n        estimator, X_missing, y_missing, scoring=\"neg_mean_squared_error\", cv=N_SPLITS\n    )\n\nscores = pd.concat(\n    [score_full_data, score_simple_imputer, score_iterative_imputer],\n    keys=[\"Original\", \"SimpleImputer\", \"IterativeImputer\"],\n    axis=1,\n)\n\n# plot california housing results\nfig, ax = plt.subplots(figsize=(13, 6))\nmeans = -scores.mean()\nerrors = scores.std()\nmeans.plot.barh(xerr=errors, ax=ax)\nax.set_title(\"California Housing Regression with Different Imputation Methods\")\nax.set_xlabel(\"MSE (smaller is better)\")\nax.set_yticks(np.arange(means.shape[0]))\nax.set_yticklabels([\" w/ \".join(label) for label in means.index.tolist()])\nplt.tight_layout(pad=1)\nplt.show()"
       ]
     }
   ],
@@ -53,7 +53,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/impute/plot_missing_values.ipynb b/dev/lite/files/auto_examples/impute/plot_missing_values.ipynb
index afd98b4691fdd..85e4b0557020c 100644
--- a/dev/lite/files/auto_examples/impute/plot_missing_values.ipynb
+++ b/dev/lite/files/auto_examples/impute/plot_missing_values.ipynb
@@ -69,7 +69,7 @@
       },
       "outputs": [],
       "source": [
-        "rng = np.random.RandomState(0)\n\nfrom sklearn.ensemble import RandomForestRegressor\n\n# To use the experimental IterativeImputer, we need to explicitly ask for it:\nfrom sklearn.experimental import enable_iterative_imputer  # noqa\nfrom sklearn.impute import IterativeImputer, KNNImputer, SimpleImputer\nfrom sklearn.model_selection import cross_val_score\nfrom sklearn.pipeline import make_pipeline\n\nN_SPLITS = 4\nregressor = RandomForestRegressor(random_state=0)"
+        "rng = np.random.RandomState(0)\n\nfrom sklearn.ensemble import RandomForestRegressor\n\n# To use the experimental IterativeImputer, we need to explicitly ask for it:\nfrom sklearn.experimental import enable_iterative_imputer  # noqa: F401\nfrom sklearn.impute import IterativeImputer, KNNImputer, SimpleImputer\nfrom sklearn.model_selection import cross_val_score\nfrom sklearn.pipeline import make_pipeline\n\nN_SPLITS = 4\nregressor = RandomForestRegressor(random_state=0)"
       ]
     },
     {
@@ -222,7 +222,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/inspection/plot_causal_interpretation.ipynb b/dev/lite/files/auto_examples/inspection/plot_causal_interpretation.ipynb
index 1971a037bcba4..84410a19093db 100644
--- a/dev/lite/files/auto_examples/inspection/plot_causal_interpretation.ipynb
+++ b/dev/lite/files/auto_examples/inspection/plot_causal_interpretation.ipynb
@@ -186,7 +186,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/inspection/plot_linear_model_coefficient_interpretation.ipynb b/dev/lite/files/auto_examples/inspection/plot_linear_model_coefficient_interpretation.ipynb
index c75d57a9dd02f..6856d4d53e7cd 100644
--- a/dev/lite/files/auto_examples/inspection/plot_linear_model_coefficient_interpretation.ipynb
+++ b/dev/lite/files/auto_examples/inspection/plot_linear_model_coefficient_interpretation.ipynb
@@ -731,7 +731,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/inspection/plot_partial_dependence.ipynb b/dev/lite/files/auto_examples/inspection/plot_partial_dependence.ipynb
index 497547d39aafa..c569b52028533 100644
--- a/dev/lite/files/auto_examples/inspection/plot_partial_dependence.ipynb
+++ b/dev/lite/files/auto_examples/inspection/plot_partial_dependence.ipynb
@@ -442,7 +442,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/inspection/plot_permutation_importance.ipynb b/dev/lite/files/auto_examples/inspection/plot_permutation_importance.ipynb
index 7dc200237935d..3e5889846dc63 100644
--- a/dev/lite/files/auto_examples/inspection/plot_permutation_importance.ipynb
+++ b/dev/lite/files/auto_examples/inspection/plot_permutation_importance.ipynb
@@ -255,7 +255,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/inspection/plot_permutation_importance_multicollinear.ipynb b/dev/lite/files/auto_examples/inspection/plot_permutation_importance_multicollinear.ipynb
index 5faf2d1951232..e97218d57b0f9 100644
--- a/dev/lite/files/auto_examples/inspection/plot_permutation_importance_multicollinear.ipynb
+++ b/dev/lite/files/auto_examples/inspection/plot_permutation_importance_multicollinear.ipynb
@@ -179,7 +179,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/kernel_approximation/plot_scalable_poly_kernels.ipynb b/dev/lite/files/auto_examples/kernel_approximation/plot_scalable_poly_kernels.ipynb
index f3dfa8932e3ad..9b934d9be8a10 100644
--- a/dev/lite/files/auto_examples/kernel_approximation/plot_scalable_poly_kernels.ipynb
+++ b/dev/lite/files/auto_examples/kernel_approximation/plot_scalable_poly_kernels.ipynb
@@ -186,7 +186,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/linear_model/plot_ard.ipynb b/dev/lite/files/auto_examples/linear_model/plot_ard.ipynb
index 414eed8265f95..f2d0dbfe7ff05 100644
--- a/dev/lite/files/auto_examples/linear_model/plot_ard.ipynb
+++ b/dev/lite/files/auto_examples/linear_model/plot_ard.ipynb
@@ -193,7 +193,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/linear_model/plot_bayesian_ridge_curvefit.ipynb b/dev/lite/files/auto_examples/linear_model/plot_bayesian_ridge_curvefit.ipynb
index 3331fa81200cf..c5c266c592da5 100644
--- a/dev/lite/files/auto_examples/linear_model/plot_bayesian_ridge_curvefit.ipynb
+++ b/dev/lite/files/auto_examples/linear_model/plot_bayesian_ridge_curvefit.ipynb
@@ -107,7 +107,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/linear_model/plot_elastic_net_precomputed_gram_matrix_with_weighted_samples.ipynb b/dev/lite/files/auto_examples/linear_model/plot_elastic_net_precomputed_gram_matrix_with_weighted_samples.ipynb
index 630f75a1381bf..8d67fe17abd90 100644
--- a/dev/lite/files/auto_examples/linear_model/plot_elastic_net_precomputed_gram_matrix_with_weighted_samples.ipynb
+++ b/dev/lite/files/auto_examples/linear_model/plot_elastic_net_precomputed_gram_matrix_with_weighted_samples.ipynb
@@ -107,7 +107,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/linear_model/plot_huber_vs_ridge.ipynb b/dev/lite/files/auto_examples/linear_model/plot_huber_vs_ridge.ipynb
index ce8d45ebf66a9..b1af83d3e0d83 100644
--- a/dev/lite/files/auto_examples/linear_model/plot_huber_vs_ridge.ipynb
+++ b/dev/lite/files/auto_examples/linear_model/plot_huber_vs_ridge.ipynb
@@ -53,7 +53,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/linear_model/plot_lasso_and_elasticnet.ipynb b/dev/lite/files/auto_examples/linear_model/plot_lasso_and_elasticnet.ipynb
index 35275b8a75592..2d0f671d26e1c 100644
--- a/dev/lite/files/auto_examples/linear_model/plot_lasso_and_elasticnet.ipynb
+++ b/dev/lite/files/auto_examples/linear_model/plot_lasso_and_elasticnet.ipynb
@@ -222,7 +222,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/linear_model/plot_lasso_dense_vs_sparse_data.ipynb b/dev/lite/files/auto_examples/linear_model/plot_lasso_dense_vs_sparse_data.ipynb
index 351298b04663b..d6bd96c97a444 100644
--- a/dev/lite/files/auto_examples/linear_model/plot_lasso_dense_vs_sparse_data.ipynb
+++ b/dev/lite/files/auto_examples/linear_model/plot_lasso_dense_vs_sparse_data.ipynb
@@ -89,7 +89,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/linear_model/plot_lasso_lars_ic.ipynb b/dev/lite/files/auto_examples/linear_model/plot_lasso_lars_ic.ipynb
index a7faeb52c6743..e1710b4c8c404 100644
--- a/dev/lite/files/auto_examples/linear_model/plot_lasso_lars_ic.ipynb
+++ b/dev/lite/files/auto_examples/linear_model/plot_lasso_lars_ic.ipynb
@@ -165,7 +165,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/linear_model/plot_lasso_lasso_lars_elasticnet_path.ipynb b/dev/lite/files/auto_examples/linear_model/plot_lasso_lasso_lars_elasticnet_path.ipynb
index 5520885847f90..a30592f5ea6f5 100644
--- a/dev/lite/files/auto_examples/linear_model/plot_lasso_lasso_lars_elasticnet_path.ipynb
+++ b/dev/lite/files/auto_examples/linear_model/plot_lasso_lasso_lars_elasticnet_path.ipynb
@@ -53,7 +53,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/linear_model/plot_lasso_model_selection.ipynb b/dev/lite/files/auto_examples/linear_model/plot_lasso_model_selection.ipynb
index cf1f7d2165436..3f1f52cf11925 100644
--- a/dev/lite/files/auto_examples/linear_model/plot_lasso_model_selection.ipynb
+++ b/dev/lite/files/auto_examples/linear_model/plot_lasso_model_selection.ipynb
@@ -244,7 +244,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/linear_model/plot_logistic.ipynb b/dev/lite/files/auto_examples/linear_model/plot_logistic.ipynb
index 55c6acaa0268e..744cd48c1b62f 100644
--- a/dev/lite/files/auto_examples/linear_model/plot_logistic.ipynb
+++ b/dev/lite/files/auto_examples/linear_model/plot_logistic.ipynb
@@ -53,7 +53,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/linear_model/plot_logistic_l1_l2_sparsity.ipynb b/dev/lite/files/auto_examples/linear_model/plot_logistic_l1_l2_sparsity.ipynb
index e1673bdad07f5..64f46b79c1bae 100644
--- a/dev/lite/files/auto_examples/linear_model/plot_logistic_l1_l2_sparsity.ipynb
+++ b/dev/lite/files/auto_examples/linear_model/plot_logistic_l1_l2_sparsity.ipynb
@@ -53,7 +53,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/linear_model/plot_logistic_multinomial.ipynb b/dev/lite/files/auto_examples/linear_model/plot_logistic_multinomial.ipynb
index 55b9c6d546519..3cdd0f4c0cf72 100644
--- a/dev/lite/files/auto_examples/linear_model/plot_logistic_multinomial.ipynb
+++ b/dev/lite/files/auto_examples/linear_model/plot_logistic_multinomial.ipynb
@@ -143,7 +143,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/linear_model/plot_logistic_path.ipynb b/dev/lite/files/auto_examples/linear_model/plot_logistic_path.ipynb
index 00b766719e61c..ec2ba9bee2c09 100644
--- a/dev/lite/files/auto_examples/linear_model/plot_logistic_path.ipynb
+++ b/dev/lite/files/auto_examples/linear_model/plot_logistic_path.ipynb
@@ -143,7 +143,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/linear_model/plot_multi_task_lasso_support.ipynb b/dev/lite/files/auto_examples/linear_model/plot_multi_task_lasso_support.ipynb
index 6302e3372e1cc..a110f5e4742d8 100644
--- a/dev/lite/files/auto_examples/linear_model/plot_multi_task_lasso_support.ipynb
+++ b/dev/lite/files/auto_examples/linear_model/plot_multi_task_lasso_support.ipynb
@@ -107,7 +107,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/linear_model/plot_nnls.ipynb b/dev/lite/files/auto_examples/linear_model/plot_nnls.ipynb
index bdbf62fe73310..a6a14d236ba9e 100644
--- a/dev/lite/files/auto_examples/linear_model/plot_nnls.ipynb
+++ b/dev/lite/files/auto_examples/linear_model/plot_nnls.ipynb
@@ -143,7 +143,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/linear_model/plot_ols_ridge.ipynb b/dev/lite/files/auto_examples/linear_model/plot_ols_ridge.ipynb
index 979c102924dca..db822a729f749 100644
--- a/dev/lite/files/auto_examples/linear_model/plot_ols_ridge.ipynb
+++ b/dev/lite/files/auto_examples/linear_model/plot_ols_ridge.ipynb
@@ -157,7 +157,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/linear_model/plot_omp.ipynb b/dev/lite/files/auto_examples/linear_model/plot_omp.ipynb
index ed3cc2b5d7d1e..d3a9570070a6e 100644
--- a/dev/lite/files/auto_examples/linear_model/plot_omp.ipynb
+++ b/dev/lite/files/auto_examples/linear_model/plot_omp.ipynb
@@ -53,7 +53,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/linear_model/plot_poisson_regression_non_normal_loss.ipynb b/dev/lite/files/auto_examples/linear_model/plot_poisson_regression_non_normal_loss.ipynb
index fafc0b0074d89..78a28b3dc9def 100644
--- a/dev/lite/files/auto_examples/linear_model/plot_poisson_regression_non_normal_loss.ipynb
+++ b/dev/lite/files/auto_examples/linear_model/plot_poisson_regression_non_normal_loss.ipynb
@@ -287,7 +287,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/linear_model/plot_polynomial_interpolation.ipynb b/dev/lite/files/auto_examples/linear_model/plot_polynomial_interpolation.ipynb
index 72b4d6c8bd903..9d1e8b00cf81b 100644
--- a/dev/lite/files/auto_examples/linear_model/plot_polynomial_interpolation.ipynb
+++ b/dev/lite/files/auto_examples/linear_model/plot_polynomial_interpolation.ipynb
@@ -161,7 +161,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/linear_model/plot_quantile_regression.ipynb b/dev/lite/files/auto_examples/linear_model/plot_quantile_regression.ipynb
index 346392732d443..aaf5781f70fbe 100644
--- a/dev/lite/files/auto_examples/linear_model/plot_quantile_regression.ipynb
+++ b/dev/lite/files/auto_examples/linear_model/plot_quantile_regression.ipynb
@@ -215,7 +215,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/linear_model/plot_ransac.ipynb b/dev/lite/files/auto_examples/linear_model/plot_ransac.ipynb
index d6b0c34bb2b49..7756e07aedec1 100644
--- a/dev/lite/files/auto_examples/linear_model/plot_ransac.ipynb
+++ b/dev/lite/files/auto_examples/linear_model/plot_ransac.ipynb
@@ -53,7 +53,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/linear_model/plot_ridge_coeffs.ipynb b/dev/lite/files/auto_examples/linear_model/plot_ridge_coeffs.ipynb
index 5e136c09a8078..cb82bec85f786 100644
--- a/dev/lite/files/auto_examples/linear_model/plot_ridge_coeffs.ipynb
+++ b/dev/lite/files/auto_examples/linear_model/plot_ridge_coeffs.ipynb
@@ -114,7 +114,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/linear_model/plot_ridge_path.ipynb b/dev/lite/files/auto_examples/linear_model/plot_ridge_path.ipynb
index 075c59815c46a..0eb17aeadd9cf 100644
--- a/dev/lite/files/auto_examples/linear_model/plot_ridge_path.ipynb
+++ b/dev/lite/files/auto_examples/linear_model/plot_ridge_path.ipynb
@@ -89,7 +89,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/linear_model/plot_robust_fit.ipynb b/dev/lite/files/auto_examples/linear_model/plot_robust_fit.ipynb
index 947c00509b4fa..29964da16274f 100644
--- a/dev/lite/files/auto_examples/linear_model/plot_robust_fit.ipynb
+++ b/dev/lite/files/auto_examples/linear_model/plot_robust_fit.ipynb
@@ -53,7 +53,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/linear_model/plot_sgd_early_stopping.ipynb b/dev/lite/files/auto_examples/linear_model/plot_sgd_early_stopping.ipynb
index 1bba37c1c3210..46f74b6b6b08f 100644
--- a/dev/lite/files/auto_examples/linear_model/plot_sgd_early_stopping.ipynb
+++ b/dev/lite/files/auto_examples/linear_model/plot_sgd_early_stopping.ipynb
@@ -53,7 +53,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/linear_model/plot_sgd_iris.ipynb b/dev/lite/files/auto_examples/linear_model/plot_sgd_iris.ipynb
index a9eedeb6584b2..aa59f745236c5 100644
--- a/dev/lite/files/auto_examples/linear_model/plot_sgd_iris.ipynb
+++ b/dev/lite/files/auto_examples/linear_model/plot_sgd_iris.ipynb
@@ -33,7 +33,7 @@
       },
       "outputs": [],
       "source": [
-        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nfrom sklearn import datasets\nfrom sklearn.inspection import DecisionBoundaryDisplay\nfrom sklearn.linear_model import SGDClassifier\n\n# import some data to play with\niris = datasets.load_iris()\n\n# we only take the first two features. We could\n# avoid this ugly slicing by using a two-dim dataset\nX = iris.data[:, :2]\ny = iris.target\ncolors = \"bry\"\n\n# shuffle\nidx = np.arange(X.shape[0])\nnp.random.seed(13)\nnp.random.shuffle(idx)\nX = X[idx]\ny = y[idx]\n\n# standardize\nmean = X.mean(axis=0)\nstd = X.std(axis=0)\nX = (X - mean) / std\n\nclf = SGDClassifier(alpha=0.001, max_iter=100).fit(X, y)\nax = plt.gca()\nDecisionBoundaryDisplay.from_estimator(\n    clf,\n    X,\n    cmap=plt.cm.Paired,\n    ax=ax,\n    response_method=\"predict\",\n    xlabel=iris.feature_names[0],\n    ylabel=iris.feature_names[1],\n)\nplt.axis(\"tight\")\n\n# Plot also the training points\nfor i, color in zip(clf.classes_, colors):\n    idx = np.where(y == i)\n    plt.scatter(\n        X[idx, 0],\n        X[idx, 1],\n        c=color,\n        label=iris.target_names[i],\n        edgecolor=\"black\",\n        s=20,\n    )\nplt.title(\"Decision surface of multi-class SGD\")\nplt.axis(\"tight\")\n\n# Plot the three one-against-all classifiers\nxmin, xmax = plt.xlim()\nymin, ymax = plt.ylim()\ncoef = clf.coef_\nintercept = clf.intercept_\n\n\ndef plot_hyperplane(c, color):\n    def line(x0):\n        return (-(x0 * coef[c, 0]) - intercept[c]) / coef[c, 1]\n\n    plt.plot([xmin, xmax], [line(xmin), line(xmax)], ls=\"--\", color=color)\n\n\nfor i, color in zip(clf.classes_, colors):\n    plot_hyperplane(i, color)\nplt.legend()\nplt.show()"
+        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nfrom sklearn import datasets\nfrom sklearn.inspection import DecisionBoundaryDisplay\nfrom sklearn.linear_model import SGDClassifier\n\n# import some data to play with\niris = datasets.load_iris()\n\n# we only take the first two features. We could\n# avoid this ugly slicing by using a two-dim dataset\nX = iris.data[:, :2]\ny = iris.target\ncolors = \"bry\"\n\n# shuffle\nidx = np.arange(X.shape[0])\nnp.random.seed(13)\nnp.random.shuffle(idx)\nX = X[idx]\ny = y[idx]\n\n# standardize\nmean = X.mean(axis=0)\nstd = X.std(axis=0)\nX = (X - mean) / std\n\nclf = SGDClassifier(alpha=0.001, max_iter=100).fit(X, y)\nax = plt.gca()\nDecisionBoundaryDisplay.from_estimator(\n    clf,\n    X,\n    cmap=plt.cm.Paired,\n    ax=ax,\n    response_method=\"predict\",\n    xlabel=iris.feature_names[0],\n    ylabel=iris.feature_names[1],\n)\nplt.axis(\"tight\")\n\n# Plot also the training points\nfor i, color in zip(clf.classes_, colors):\n    idx = (y == i).nonzero()\n    plt.scatter(\n        X[idx, 0],\n        X[idx, 1],\n        c=color,\n        label=iris.target_names[i],\n        edgecolor=\"black\",\n        s=20,\n    )\nplt.title(\"Decision surface of multi-class SGD\")\nplt.axis(\"tight\")\n\n# Plot the three one-against-all classifiers\nxmin, xmax = plt.xlim()\nymin, ymax = plt.ylim()\ncoef = clf.coef_\nintercept = clf.intercept_\n\n\ndef plot_hyperplane(c, color):\n    def line(x0):\n        return (-(x0 * coef[c, 0]) - intercept[c]) / coef[c, 1]\n\n    plt.plot([xmin, xmax], [line(xmin), line(xmax)], ls=\"--\", color=color)\n\n\nfor i, color in zip(clf.classes_, colors):\n    plot_hyperplane(i, color)\nplt.legend()\nplt.show()"
       ]
     }
   ],
@@ -53,7 +53,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/linear_model/plot_sgd_loss_functions.ipynb b/dev/lite/files/auto_examples/linear_model/plot_sgd_loss_functions.ipynb
index 4f6a9371ca4a8..5c8588c1f0348 100644
--- a/dev/lite/files/auto_examples/linear_model/plot_sgd_loss_functions.ipynb
+++ b/dev/lite/files/auto_examples/linear_model/plot_sgd_loss_functions.ipynb
@@ -53,7 +53,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/linear_model/plot_sgd_penalties.ipynb b/dev/lite/files/auto_examples/linear_model/plot_sgd_penalties.ipynb
index 9923a4d0ba2b1..68648d039a0d9 100644
--- a/dev/lite/files/auto_examples/linear_model/plot_sgd_penalties.ipynb
+++ b/dev/lite/files/auto_examples/linear_model/plot_sgd_penalties.ipynb
@@ -53,7 +53,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/linear_model/plot_sgd_separating_hyperplane.ipynb b/dev/lite/files/auto_examples/linear_model/plot_sgd_separating_hyperplane.ipynb
index c8bcf0ccf724e..b0adcfdb58da8 100644
--- a/dev/lite/files/auto_examples/linear_model/plot_sgd_separating_hyperplane.ipynb
+++ b/dev/lite/files/auto_examples/linear_model/plot_sgd_separating_hyperplane.ipynb
@@ -53,7 +53,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/linear_model/plot_sgd_weighted_samples.ipynb b/dev/lite/files/auto_examples/linear_model/plot_sgd_weighted_samples.ipynb
index b3b597e79e8ec..de12482bf24c2 100644
--- a/dev/lite/files/auto_examples/linear_model/plot_sgd_weighted_samples.ipynb
+++ b/dev/lite/files/auto_examples/linear_model/plot_sgd_weighted_samples.ipynb
@@ -53,7 +53,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/linear_model/plot_sgdocsvm_vs_ocsvm.ipynb b/dev/lite/files/auto_examples/linear_model/plot_sgdocsvm_vs_ocsvm.ipynb
index d2981f9cd24fe..cdfd8e8bc6dc5 100644
--- a/dev/lite/files/auto_examples/linear_model/plot_sgdocsvm_vs_ocsvm.ipynb
+++ b/dev/lite/files/auto_examples/linear_model/plot_sgdocsvm_vs_ocsvm.ipynb
@@ -86,7 +86,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/linear_model/plot_sparse_logistic_regression_20newsgroups.ipynb b/dev/lite/files/auto_examples/linear_model/plot_sparse_logistic_regression_20newsgroups.ipynb
index 07b3e1dc679d4..1154ba03a9193 100644
--- a/dev/lite/files/auto_examples/linear_model/plot_sparse_logistic_regression_20newsgroups.ipynb
+++ b/dev/lite/files/auto_examples/linear_model/plot_sparse_logistic_regression_20newsgroups.ipynb
@@ -53,7 +53,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/linear_model/plot_sparse_logistic_regression_mnist.ipynb b/dev/lite/files/auto_examples/linear_model/plot_sparse_logistic_regression_mnist.ipynb
index 95cfe876781ab..7d21107dffe54 100644
--- a/dev/lite/files/auto_examples/linear_model/plot_sparse_logistic_regression_mnist.ipynb
+++ b/dev/lite/files/auto_examples/linear_model/plot_sparse_logistic_regression_mnist.ipynb
@@ -33,7 +33,7 @@
       },
       "outputs": [],
       "source": [
-        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\n\nimport time\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nfrom sklearn.datasets import fetch_openml\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.utils import check_random_state\n\n# Turn down for faster convergence\nt0 = time.time()\ntrain_samples = 5000\n\n# Load data from https://www.openml.org/d/554\nX, y = fetch_openml(\"mnist_784\", version=1, return_X_y=True, as_frame=False)\n\nrandom_state = check_random_state(0)\npermutation = random_state.permutation(X.shape[0])\nX = X[permutation]\ny = y[permutation]\nX = X.reshape((X.shape[0], -1))\n\nX_train, X_test, y_train, y_test = train_test_split(\n    X, y, train_size=train_samples, test_size=10000\n)\n\nscaler = StandardScaler()\nX_train = scaler.fit_transform(X_train)\nX_test = scaler.transform(X_test)\n\n# Turn up tolerance for faster convergence\nclf = LogisticRegression(C=50.0 / train_samples, penalty=\"l1\", solver=\"saga\", tol=0.1)\nclf.fit(X_train, y_train)\nsparsity = np.mean(clf.coef_ == 0) * 100\nscore = clf.score(X_test, y_test)\n# print('Best C % .4f' % clf.C_)\nprint(\"Sparsity with L1 penalty: %.2f%%\" % sparsity)\nprint(\"Test score with L1 penalty: %.4f\" % score)\n\ncoef = clf.coef_.copy()\nplt.figure(figsize=(10, 5))\nscale = np.abs(coef).max()\nfor i in range(10):\n    l1_plot = plt.subplot(2, 5, i + 1)\n    l1_plot.imshow(\n        coef[i].reshape(28, 28),\n        interpolation=\"nearest\",\n        cmap=plt.cm.RdBu,\n        vmin=-scale,\n        vmax=scale,\n    )\n    l1_plot.set_xticks(())\n    l1_plot.set_yticks(())\n    l1_plot.set_xlabel(\"Class %i\" % i)\nplt.suptitle(\"Classification vector for...\")\n\nrun_time = time.time() - t0\nprint(\"Example run in %.3f s\" % run_time)\nplt.show()"
+        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\n\nimport time\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nfrom sklearn.datasets import fetch_openml\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.utils import check_random_state\n\n# Turn down for faster convergence\nt0 = time.time()\ntrain_samples = 5000\n\n# Load data from https://www.openml.org/d/554\nX, y = fetch_openml(\"mnist_784\", version=1, return_X_y=True, as_frame=False)\n\nrandom_state = check_random_state(0)\npermutation = random_state.permutation(X.shape[0])\nX = X[permutation]\ny = y[permutation]\nX = X.reshape((X.shape[0], -1))\n\nX_train, X_test, y_train, y_test = train_test_split(\n    X, y, train_size=train_samples, test_size=10000\n)\n\nscaler = StandardScaler()\nX_train = scaler.fit_transform(X_train)\nX_test = scaler.transform(X_test)\n\n# Turn up tolerance for faster convergence\nclf = LogisticRegression(C=50.0 / train_samples, penalty=\"l1\", solver=\"saga\", tol=0.1)\nclf.fit(X_train, y_train)\nsparsity = np.mean(clf.coef_ == 0) * 100\nscore = clf.score(X_test, y_test)\n# print('Best C % .4f' % clf.C_)\nprint(\"Sparsity with L1 penalty: %.2f%%\" % sparsity)\nprint(\"Test score with L1 penalty: %.4f\" % score)\n\ncoef = clf.coef_.copy()\nplt.figure(figsize=(10, 5))\nscale = np.abs(coef).max()\nfor i in range(10):\n    l1_plot = plt.subplot(2, 5, i + 1)\n    l1_plot.imshow(\n        coef[i].reshape(28, 28),\n        interpolation=\"nearest\",\n        cmap=plt.cm.RdBu,\n        vmin=-scale,\n        vmax=scale,\n    )\n    l1_plot.set_xticks(())\n    l1_plot.set_yticks(())\n    l1_plot.set_xlabel(f\"Class {i}\")\nplt.suptitle(\"Classification vector for...\")\n\nrun_time = time.time() - t0\nprint(\"Example run in %.3f s\" % run_time)\nplt.show()"
       ]
     }
   ],
@@ -53,7 +53,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/linear_model/plot_theilsen.ipynb b/dev/lite/files/auto_examples/linear_model/plot_theilsen.ipynb
index 10156f80eb51f..3b9d2390f7a53 100644
--- a/dev/lite/files/auto_examples/linear_model/plot_theilsen.ipynb
+++ b/dev/lite/files/auto_examples/linear_model/plot_theilsen.ipynb
@@ -89,7 +89,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/linear_model/plot_tweedie_regression_insurance_claims.ipynb b/dev/lite/files/auto_examples/linear_model/plot_tweedie_regression_insurance_claims.ipynb
index 1f8c46d8cfccd..38566730c3963 100644
--- a/dev/lite/files/auto_examples/linear_model/plot_tweedie_regression_insurance_claims.ipynb
+++ b/dev/lite/files/auto_examples/linear_model/plot_tweedie_regression_insurance_claims.ipynb
@@ -242,7 +242,7 @@
       },
       "outputs": [],
       "source": [
-        "res = []\nfor subset_label, X, df in [\n    (\"train\", X_train, df_train),\n    (\"test\", X_test, df_test),\n]:\n    exposure = df[\"Exposure\"].values\n    res.append(\n        {\n            \"subset\": subset_label,\n            \"observed\": df[\"ClaimAmount\"].values.sum(),\n            \"predicted, frequency*severity model\": np.sum(\n                exposure * glm_freq.predict(X) * glm_sev.predict(X)\n            ),\n            \"predicted, tweedie, power=%.2f\"\n            % glm_pure_premium.power: np.sum(exposure * glm_pure_premium.predict(X)),\n        }\n    )\n\nprint(pd.DataFrame(res).set_index(\"subset\").T)"
+        "res = []\nfor subset_label, X, df in [\n    (\"train\", X_train, df_train),\n    (\"test\", X_test, df_test),\n]:\n    exposure = df[\"Exposure\"].values\n    res.append(\n        {\n            \"subset\": subset_label,\n            \"observed\": df[\"ClaimAmount\"].values.sum(),\n            \"predicted, frequency*severity model\": np.sum(\n                exposure * glm_freq.predict(X) * glm_sev.predict(X)\n            ),\n            \"predicted, tweedie, power=%.2f\" % glm_pure_premium.power: np.sum(\n                exposure * glm_pure_premium.predict(X)\n            ),\n        }\n    )\n\nprint(pd.DataFrame(res).set_index(\"subset\").T)"
       ]
     },
     {
@@ -280,7 +280,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/manifold/plot_compare_methods.ipynb b/dev/lite/files/auto_examples/manifold/plot_compare_methods.ipynb
index 596c160d263d2..b0f639e6bfe2b 100644
--- a/dev/lite/files/auto_examples/manifold/plot_compare_methods.ipynb
+++ b/dev/lite/files/auto_examples/manifold/plot_compare_methods.ipynb
@@ -152,7 +152,7 @@
       },
       "outputs": [],
       "source": [
-        "md_scaling = manifold.MDS(\n    n_components=n_components,\n    max_iter=50,\n    n_init=4,\n    random_state=0,\n    normalized_stress=False,\n)\nS_scaling = md_scaling.fit_transform(S_points)\n\nplot_2d(S_scaling, S_color, \"Multidimensional scaling\")"
+        "md_scaling = manifold.MDS(\n    n_components=n_components,\n    max_iter=50,\n    n_init=1,\n    random_state=0,\n    normalized_stress=False,\n)\nS_scaling = md_scaling.fit_transform(S_points)\n\nplot_2d(S_scaling, S_color, \"Multidimensional scaling\")"
       ]
     },
     {
@@ -208,7 +208,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/manifold/plot_lle_digits.ipynb b/dev/lite/files/auto_examples/manifold/plot_lle_digits.ipynb
index c214354ad662a..6a7bc5b9c1d71 100644
--- a/dev/lite/files/auto_examples/manifold/plot_lle_digits.ipynb
+++ b/dev/lite/files/auto_examples/manifold/plot_lle_digits.ipynb
@@ -105,7 +105,7 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.decomposition import TruncatedSVD\nfrom sklearn.discriminant_analysis import LinearDiscriminantAnalysis\nfrom sklearn.ensemble import RandomTreesEmbedding\nfrom sklearn.manifold import (\n    MDS,\n    TSNE,\n    Isomap,\n    LocallyLinearEmbedding,\n    SpectralEmbedding,\n)\nfrom sklearn.neighbors import NeighborhoodComponentsAnalysis\nfrom sklearn.pipeline import make_pipeline\nfrom sklearn.random_projection import SparseRandomProjection\n\nembeddings = {\n    \"Random projection embedding\": SparseRandomProjection(\n        n_components=2, random_state=42\n    ),\n    \"Truncated SVD embedding\": TruncatedSVD(n_components=2),\n    \"Linear Discriminant Analysis embedding\": LinearDiscriminantAnalysis(\n        n_components=2\n    ),\n    \"Isomap embedding\": Isomap(n_neighbors=n_neighbors, n_components=2),\n    \"Standard LLE embedding\": LocallyLinearEmbedding(\n        n_neighbors=n_neighbors, n_components=2, method=\"standard\"\n    ),\n    \"Modified LLE embedding\": LocallyLinearEmbedding(\n        n_neighbors=n_neighbors, n_components=2, method=\"modified\"\n    ),\n    \"Hessian LLE embedding\": LocallyLinearEmbedding(\n        n_neighbors=n_neighbors, n_components=2, method=\"hessian\"\n    ),\n    \"LTSA LLE embedding\": LocallyLinearEmbedding(\n        n_neighbors=n_neighbors, n_components=2, method=\"ltsa\"\n    ),\n    \"MDS embedding\": MDS(n_components=2, n_init=1, max_iter=120, n_jobs=2),\n    \"Random Trees embedding\": make_pipeline(\n        RandomTreesEmbedding(n_estimators=200, max_depth=5, random_state=0),\n        TruncatedSVD(n_components=2),\n    ),\n    \"Spectral embedding\": SpectralEmbedding(\n        n_components=2, random_state=0, eigen_solver=\"arpack\"\n    ),\n    \"t-SNE embedding\": TSNE(\n        n_components=2,\n        max_iter=500,\n        n_iter_without_progress=150,\n        n_jobs=2,\n        random_state=0,\n    ),\n    \"NCA embedding\": NeighborhoodComponentsAnalysis(\n        n_components=2, init=\"pca\", random_state=0\n    ),\n}"
+        "from sklearn.decomposition import TruncatedSVD\nfrom sklearn.discriminant_analysis import LinearDiscriminantAnalysis\nfrom sklearn.ensemble import RandomTreesEmbedding\nfrom sklearn.manifold import (\n    MDS,\n    TSNE,\n    Isomap,\n    LocallyLinearEmbedding,\n    SpectralEmbedding,\n)\nfrom sklearn.neighbors import NeighborhoodComponentsAnalysis\nfrom sklearn.pipeline import make_pipeline\nfrom sklearn.random_projection import SparseRandomProjection\n\nembeddings = {\n    \"Random projection embedding\": SparseRandomProjection(\n        n_components=2, random_state=42\n    ),\n    \"Truncated SVD embedding\": TruncatedSVD(n_components=2),\n    \"Linear Discriminant Analysis embedding\": LinearDiscriminantAnalysis(\n        n_components=2\n    ),\n    \"Isomap embedding\": Isomap(n_neighbors=n_neighbors, n_components=2),\n    \"Standard LLE embedding\": LocallyLinearEmbedding(\n        n_neighbors=n_neighbors, n_components=2, method=\"standard\"\n    ),\n    \"Modified LLE embedding\": LocallyLinearEmbedding(\n        n_neighbors=n_neighbors, n_components=2, method=\"modified\"\n    ),\n    \"Hessian LLE embedding\": LocallyLinearEmbedding(\n        n_neighbors=n_neighbors, n_components=2, method=\"hessian\"\n    ),\n    \"LTSA LLE embedding\": LocallyLinearEmbedding(\n        n_neighbors=n_neighbors, n_components=2, method=\"ltsa\"\n    ),\n    \"MDS embedding\": MDS(n_components=2, n_init=1, max_iter=120, eps=1e-6),\n    \"Random Trees embedding\": make_pipeline(\n        RandomTreesEmbedding(n_estimators=200, max_depth=5, random_state=0),\n        TruncatedSVD(n_components=2),\n    ),\n    \"Spectral embedding\": SpectralEmbedding(\n        n_components=2, random_state=0, eigen_solver=\"arpack\"\n    ),\n    \"t-SNE embedding\": TSNE(\n        n_components=2,\n        max_iter=500,\n        n_iter_without_progress=150,\n        n_jobs=2,\n        random_state=0,\n    ),\n    \"NCA embedding\": NeighborhoodComponentsAnalysis(\n        n_components=2, init=\"pca\", random_state=0\n    ),\n}"
       ]
     },
     {
@@ -161,7 +161,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/manifold/plot_manifold_sphere.ipynb b/dev/lite/files/auto_examples/manifold/plot_manifold_sphere.ipynb
index c17d74aea012f..84d5f0924cc3f 100644
--- a/dev/lite/files/auto_examples/manifold/plot_manifold_sphere.ipynb
+++ b/dev/lite/files/auto_examples/manifold/plot_manifold_sphere.ipynb
@@ -33,7 +33,7 @@
       },
       "outputs": [],
       "source": [
-        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\n\nfrom time import time\n\nimport matplotlib.pyplot as plt\n\n# Unused but required import for doing 3d projections with matplotlib < 3.2\nimport mpl_toolkits.mplot3d  # noqa: F401\nimport numpy as np\nfrom matplotlib.ticker import NullFormatter\n\nfrom sklearn import manifold\nfrom sklearn.utils import check_random_state\n\n# Variables for manifold learning.\nn_neighbors = 10\nn_samples = 1000\n\n# Create our sphere.\nrandom_state = check_random_state(0)\np = random_state.rand(n_samples) * (2 * np.pi - 0.55)\nt = random_state.rand(n_samples) * np.pi\n\n# Sever the poles from the sphere.\nindices = (t < (np.pi - (np.pi / 8))) & (t > ((np.pi / 8)))\ncolors = p[indices]\nx, y, z = (\n    np.sin(t[indices]) * np.cos(p[indices]),\n    np.sin(t[indices]) * np.sin(p[indices]),\n    np.cos(t[indices]),\n)\n\n# Plot our dataset.\nfig = plt.figure(figsize=(15, 8))\nplt.suptitle(\n    \"Manifold Learning with %i points, %i neighbors\" % (1000, n_neighbors), fontsize=14\n)\n\nax = fig.add_subplot(251, projection=\"3d\")\nax.scatter(x, y, z, c=p[indices], cmap=plt.cm.rainbow)\nax.view_init(40, -10)\n\nsphere_data = np.array([x, y, z]).T\n\n# Perform Locally Linear Embedding Manifold learning\nmethods = [\"standard\", \"ltsa\", \"hessian\", \"modified\"]\nlabels = [\"LLE\", \"LTSA\", \"Hessian LLE\", \"Modified LLE\"]\n\nfor i, method in enumerate(methods):\n    t0 = time()\n    trans_data = (\n        manifold.LocallyLinearEmbedding(\n            n_neighbors=n_neighbors, n_components=2, method=method, random_state=42\n        )\n        .fit_transform(sphere_data)\n        .T\n    )\n    t1 = time()\n    print(\"%s: %.2g sec\" % (methods[i], t1 - t0))\n\n    ax = fig.add_subplot(252 + i)\n    plt.scatter(trans_data[0], trans_data[1], c=colors, cmap=plt.cm.rainbow)\n    plt.title(\"%s (%.2g sec)\" % (labels[i], t1 - t0))\n    ax.xaxis.set_major_formatter(NullFormatter())\n    ax.yaxis.set_major_formatter(NullFormatter())\n    plt.axis(\"tight\")\n\n# Perform Isomap Manifold learning.\nt0 = time()\ntrans_data = (\n    manifold.Isomap(n_neighbors=n_neighbors, n_components=2)\n    .fit_transform(sphere_data)\n    .T\n)\nt1 = time()\nprint(\"%s: %.2g sec\" % (\"ISO\", t1 - t0))\n\nax = fig.add_subplot(257)\nplt.scatter(trans_data[0], trans_data[1], c=colors, cmap=plt.cm.rainbow)\nplt.title(\"%s (%.2g sec)\" % (\"Isomap\", t1 - t0))\nax.xaxis.set_major_formatter(NullFormatter())\nax.yaxis.set_major_formatter(NullFormatter())\nplt.axis(\"tight\")\n\n# Perform Multi-dimensional scaling.\nt0 = time()\nmds = manifold.MDS(2, max_iter=100, n_init=1, random_state=42)\ntrans_data = mds.fit_transform(sphere_data).T\nt1 = time()\nprint(\"MDS: %.2g sec\" % (t1 - t0))\n\nax = fig.add_subplot(258)\nplt.scatter(trans_data[0], trans_data[1], c=colors, cmap=plt.cm.rainbow)\nplt.title(\"MDS (%.2g sec)\" % (t1 - t0))\nax.xaxis.set_major_formatter(NullFormatter())\nax.yaxis.set_major_formatter(NullFormatter())\nplt.axis(\"tight\")\n\n# Perform Spectral Embedding.\nt0 = time()\nse = manifold.SpectralEmbedding(\n    n_components=2, n_neighbors=n_neighbors, random_state=42\n)\ntrans_data = se.fit_transform(sphere_data).T\nt1 = time()\nprint(\"Spectral Embedding: %.2g sec\" % (t1 - t0))\n\nax = fig.add_subplot(259)\nplt.scatter(trans_data[0], trans_data[1], c=colors, cmap=plt.cm.rainbow)\nplt.title(\"Spectral Embedding (%.2g sec)\" % (t1 - t0))\nax.xaxis.set_major_formatter(NullFormatter())\nax.yaxis.set_major_formatter(NullFormatter())\nplt.axis(\"tight\")\n\n# Perform t-distributed stochastic neighbor embedding.\nt0 = time()\ntsne = manifold.TSNE(n_components=2, random_state=0)\ntrans_data = tsne.fit_transform(sphere_data).T\nt1 = time()\nprint(\"t-SNE: %.2g sec\" % (t1 - t0))\n\nax = fig.add_subplot(2, 5, 10)\nplt.scatter(trans_data[0], trans_data[1], c=colors, cmap=plt.cm.rainbow)\nplt.title(\"t-SNE (%.2g sec)\" % (t1 - t0))\nax.xaxis.set_major_formatter(NullFormatter())\nax.yaxis.set_major_formatter(NullFormatter())\nplt.axis(\"tight\")\n\nplt.show()"
+        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\n\nfrom time import time\n\nimport matplotlib.pyplot as plt\n\n# Unused but required import for doing 3d projections with matplotlib < 3.2\nimport mpl_toolkits.mplot3d  # noqa: F401\nimport numpy as np\nfrom matplotlib.ticker import NullFormatter\n\nfrom sklearn import manifold\nfrom sklearn.utils import check_random_state\n\n# Variables for manifold learning.\nn_neighbors = 10\nn_samples = 1000\n\n# Create our sphere.\nrandom_state = check_random_state(0)\np = random_state.rand(n_samples) * (2 * np.pi - 0.55)\nt = random_state.rand(n_samples) * np.pi\n\n# Sever the poles from the sphere.\nindices = (t < (np.pi - (np.pi / 8))) & (t > (np.pi / 8))\ncolors = p[indices]\nx, y, z = (\n    np.sin(t[indices]) * np.cos(p[indices]),\n    np.sin(t[indices]) * np.sin(p[indices]),\n    np.cos(t[indices]),\n)\n\n# Plot our dataset.\nfig = plt.figure(figsize=(15, 8))\nplt.suptitle(\n    \"Manifold Learning with %i points, %i neighbors\" % (1000, n_neighbors), fontsize=14\n)\n\nax = fig.add_subplot(251, projection=\"3d\")\nax.scatter(x, y, z, c=p[indices], cmap=plt.cm.rainbow)\nax.view_init(40, -10)\n\nsphere_data = np.array([x, y, z]).T\n\n# Perform Locally Linear Embedding Manifold learning\nmethods = [\"standard\", \"ltsa\", \"hessian\", \"modified\"]\nlabels = [\"LLE\", \"LTSA\", \"Hessian LLE\", \"Modified LLE\"]\n\nfor i, method in enumerate(methods):\n    t0 = time()\n    trans_data = (\n        manifold.LocallyLinearEmbedding(\n            n_neighbors=n_neighbors, n_components=2, method=method, random_state=42\n        )\n        .fit_transform(sphere_data)\n        .T\n    )\n    t1 = time()\n    print(\"%s: %.2g sec\" % (methods[i], t1 - t0))\n\n    ax = fig.add_subplot(252 + i)\n    plt.scatter(trans_data[0], trans_data[1], c=colors, cmap=plt.cm.rainbow)\n    plt.title(\"%s (%.2g sec)\" % (labels[i], t1 - t0))\n    ax.xaxis.set_major_formatter(NullFormatter())\n    ax.yaxis.set_major_formatter(NullFormatter())\n    plt.axis(\"tight\")\n\n# Perform Isomap Manifold learning.\nt0 = time()\ntrans_data = (\n    manifold.Isomap(n_neighbors=n_neighbors, n_components=2)\n    .fit_transform(sphere_data)\n    .T\n)\nt1 = time()\nprint(\"%s: %.2g sec\" % (\"ISO\", t1 - t0))\n\nax = fig.add_subplot(257)\nplt.scatter(trans_data[0], trans_data[1], c=colors, cmap=plt.cm.rainbow)\nplt.title(\"%s (%.2g sec)\" % (\"Isomap\", t1 - t0))\nax.xaxis.set_major_formatter(NullFormatter())\nax.yaxis.set_major_formatter(NullFormatter())\nplt.axis(\"tight\")\n\n# Perform Multi-dimensional scaling.\nt0 = time()\nmds = manifold.MDS(2, max_iter=100, n_init=1, random_state=42)\ntrans_data = mds.fit_transform(sphere_data).T\nt1 = time()\nprint(\"MDS: %.2g sec\" % (t1 - t0))\n\nax = fig.add_subplot(258)\nplt.scatter(trans_data[0], trans_data[1], c=colors, cmap=plt.cm.rainbow)\nplt.title(\"MDS (%.2g sec)\" % (t1 - t0))\nax.xaxis.set_major_formatter(NullFormatter())\nax.yaxis.set_major_formatter(NullFormatter())\nplt.axis(\"tight\")\n\n# Perform Spectral Embedding.\nt0 = time()\nse = manifold.SpectralEmbedding(\n    n_components=2, n_neighbors=n_neighbors, random_state=42\n)\ntrans_data = se.fit_transform(sphere_data).T\nt1 = time()\nprint(\"Spectral Embedding: %.2g sec\" % (t1 - t0))\n\nax = fig.add_subplot(259)\nplt.scatter(trans_data[0], trans_data[1], c=colors, cmap=plt.cm.rainbow)\nplt.title(\"Spectral Embedding (%.2g sec)\" % (t1 - t0))\nax.xaxis.set_major_formatter(NullFormatter())\nax.yaxis.set_major_formatter(NullFormatter())\nplt.axis(\"tight\")\n\n# Perform t-distributed stochastic neighbor embedding.\nt0 = time()\ntsne = manifold.TSNE(n_components=2, random_state=0)\ntrans_data = tsne.fit_transform(sphere_data).T\nt1 = time()\nprint(\"t-SNE: %.2g sec\" % (t1 - t0))\n\nax = fig.add_subplot(2, 5, 10)\nplt.scatter(trans_data[0], trans_data[1], c=colors, cmap=plt.cm.rainbow)\nplt.title(\"t-SNE (%.2g sec)\" % (t1 - t0))\nax.xaxis.set_major_formatter(NullFormatter())\nax.yaxis.set_major_formatter(NullFormatter())\nplt.axis(\"tight\")\n\nplt.show()"
       ]
     }
   ],
@@ -53,7 +53,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/manifold/plot_mds.ipynb b/dev/lite/files/auto_examples/manifold/plot_mds.ipynb
index 438b9f697ec26..eace85a20b5fd 100644
--- a/dev/lite/files/auto_examples/manifold/plot_mds.ipynb
+++ b/dev/lite/files/auto_examples/manifold/plot_mds.ipynb
@@ -22,7 +22,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# Multi-dimensional scaling\n\nAn illustration of the metric and non-metric MDS on generated noisy data.\n\nThe reconstructed points using the metric MDS and non metric MDS are slightly\nshifted to avoid overlapping.\n"
+        "\n# Multi-dimensional scaling\n\nAn illustration of the metric and non-metric MDS on generated noisy data.\n"
       ]
     },
     {
@@ -33,7 +33,115 @@
       },
       "outputs": [],
       "source": [
-        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\n\nimport numpy as np\nfrom matplotlib import pyplot as plt\nfrom matplotlib.collections import LineCollection\n\nfrom sklearn import manifold\nfrom sklearn.decomposition import PCA\nfrom sklearn.metrics import euclidean_distances\n\n# Generate the data\nEPSILON = np.finfo(np.float32).eps\nn_samples = 20\nrng = np.random.RandomState(seed=3)\nX_true = rng.randint(0, 20, 2 * n_samples).astype(float)\nX_true = X_true.reshape((n_samples, 2))\n\n# Center the data\nX_true -= X_true.mean()\n\n# Compute pairwise Euclidean distances\ndistances = euclidean_distances(X_true)\n\n# Add noise to the distances\nnoise = rng.rand(n_samples, n_samples)\nnoise = noise + noise.T\nnp.fill_diagonal(noise, 0)\ndistances += noise\n\nmds = manifold.MDS(\n    n_components=2,\n    max_iter=3000,\n    eps=1e-9,\n    random_state=42,\n    dissimilarity=\"precomputed\",\n    n_jobs=1,\n)\nX_mds = mds.fit(distances).embedding_\n\nnmds = manifold.MDS(\n    n_components=2,\n    metric=False,\n    max_iter=3000,\n    eps=1e-12,\n    dissimilarity=\"precomputed\",\n    random_state=42,\n    n_jobs=1,\n    n_init=1,\n)\nX_nmds = nmds.fit_transform(distances)\n\n# Rescale the data\nX_mds *= np.sqrt((X_true**2).sum()) / np.sqrt((X_mds**2).sum())\nX_nmds *= np.sqrt((X_true**2).sum()) / np.sqrt((X_nmds**2).sum())\n\n# Rotate the data\npca = PCA(n_components=2)\nX_true = pca.fit_transform(X_true)\nX_mds = pca.fit_transform(X_mds)\nX_nmds = pca.fit_transform(X_nmds)\n\n# Align the sign of PCs\nfor i in [0, 1]:\n    if np.corrcoef(X_mds[:, i], X_true[:, i])[0, 1] < 0:\n        X_mds[:, i] *= -1\n    if np.corrcoef(X_nmds[:, i], X_true[:, i])[0, 1] < 0:\n        X_nmds[:, i] *= -1\n\nfig = plt.figure(1)\nax = plt.axes([0.0, 0.0, 1.0, 1.0])\n\ns = 100\nplt.scatter(X_true[:, 0], X_true[:, 1], color=\"navy\", s=s, lw=0, label=\"True Position\")\nplt.scatter(X_mds[:, 0], X_mds[:, 1], color=\"turquoise\", s=s, lw=0, label=\"MDS\")\nplt.scatter(X_nmds[:, 0], X_nmds[:, 1], color=\"darkorange\", s=s, lw=0, label=\"NMDS\")\nplt.legend(scatterpoints=1, loc=\"best\", shadow=False)\n\n# Plot the edges\nstart_idx, end_idx = np.where(X_mds)\n# a sequence of (*line0*, *line1*, *line2*), where::\n#            linen = (x0, y0), (x1, y1), ... (xm, ym)\nsegments = [\n    [X_true[i, :], X_true[j, :]] for i in range(len(X_true)) for j in range(len(X_true))\n]\nedges = distances.max() / (distances + EPSILON) * 100\nnp.fill_diagonal(edges, 0)\nedges = np.abs(edges)\nlc = LineCollection(\n    segments, zorder=0, cmap=plt.cm.Blues, norm=plt.Normalize(0, edges.max())\n)\nlc.set_array(edges.flatten())\nlc.set_linewidths(np.full(len(segments), 0.5))\nax.add_collection(lc)\n\nplt.show()"
+        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Dataset preparation\n\nWe start by uniformly generating 20 points in a 2D space.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\nfrom matplotlib import pyplot as plt\nfrom matplotlib.collections import LineCollection\n\nfrom sklearn import manifold\nfrom sklearn.decomposition import PCA\nfrom sklearn.metrics import euclidean_distances\n\n# Generate the data\nEPSILON = np.finfo(np.float32).eps\nn_samples = 20\nrng = np.random.RandomState(seed=3)\nX_true = rng.randint(0, 20, 2 * n_samples).astype(float)\nX_true = X_true.reshape((n_samples, 2))\n\n# Center the data\nX_true -= X_true.mean()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Now we compute pairwise distances between all points and add\na small amount of noise to the distance matrix. We make sure\nto keep the noisy distance matrix symmetric.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# Compute pairwise Euclidean distances\ndistances = euclidean_distances(X_true)\n\n# Add noise to the distances\nnoise = rng.rand(n_samples, n_samples)\nnoise = noise + noise.T\nnp.fill_diagonal(noise, 0)\ndistances += noise"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Here we compute metric and non-metric MDS of the noisy distance matrix.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "mds = manifold.MDS(\n    n_components=2,\n    max_iter=3000,\n    eps=1e-9,\n    n_init=1,\n    random_state=42,\n    dissimilarity=\"precomputed\",\n    n_jobs=1,\n)\nX_mds = mds.fit(distances).embedding_\n\nnmds = manifold.MDS(\n    n_components=2,\n    metric=False,\n    max_iter=3000,\n    eps=1e-12,\n    dissimilarity=\"precomputed\",\n    random_state=42,\n    n_jobs=1,\n    n_init=1,\n)\nX_nmds = nmds.fit_transform(distances)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Rescaling the non-metric MDS solution to match the spread of the original data.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "X_nmds *= np.sqrt((X_true**2).sum()) / np.sqrt((X_nmds**2).sum())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "To make the visual comparisons easier, we rotate the original data and both MDS\nsolutions to their PCA axes. And flip horizontal and vertical MDS axes, if needed,\nto match the original data orientation.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# Rotate the data\npca = PCA(n_components=2)\nX_true = pca.fit_transform(X_true)\nX_mds = pca.fit_transform(X_mds)\nX_nmds = pca.fit_transform(X_nmds)\n\n# Align the sign of PCs\nfor i in [0, 1]:\n    if np.corrcoef(X_mds[:, i], X_true[:, i])[0, 1] < 0:\n        X_mds[:, i] *= -1\n    if np.corrcoef(X_nmds[:, i], X_true[:, i])[0, 1] < 0:\n        X_nmds[:, i] *= -1"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Finally, we plot the original data and both MDS reconstructions.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "fig = plt.figure(1)\nax = plt.axes([0.0, 0.0, 1.0, 1.0])\n\ns = 100\nplt.scatter(X_true[:, 0], X_true[:, 1], color=\"navy\", s=s, lw=0, label=\"True Position\")\nplt.scatter(X_mds[:, 0], X_mds[:, 1], color=\"turquoise\", s=s, lw=0, label=\"MDS\")\nplt.scatter(X_nmds[:, 0], X_nmds[:, 1], color=\"darkorange\", s=s, lw=0, label=\"NMDS\")\nplt.legend(scatterpoints=1, loc=\"best\", shadow=False)\n\n# Plot the edges\nstart_idx, end_idx = X_mds.nonzero()\n# a sequence of (*line0*, *line1*, *line2*), where::\n#            linen = (x0, y0), (x1, y1), ... (xm, ym)\nsegments = [\n    [X_true[i, :], X_true[j, :]] for i in range(len(X_true)) for j in range(len(X_true))\n]\nedges = distances.max() / (distances + EPSILON) * 100\nnp.fill_diagonal(edges, 0)\nedges = np.abs(edges)\nlc = LineCollection(\n    segments, zorder=0, cmap=plt.cm.Blues, norm=plt.Normalize(0, edges.max())\n)\nlc.set_array(edges.flatten())\nlc.set_linewidths(np.full(len(segments), 0.5))\nax.add_collection(lc)\n\nplt.show()"
       ]
     }
   ],
@@ -53,7 +161,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/manifold/plot_swissroll.ipynb b/dev/lite/files/auto_examples/manifold/plot_swissroll.ipynb
index 97a457ade49c8..377845c153538 100644
--- a/dev/lite/files/auto_examples/manifold/plot_swissroll.ipynb
+++ b/dev/lite/files/auto_examples/manifold/plot_swissroll.ipynb
@@ -157,7 +157,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/manifold/plot_t_sne_perplexity.ipynb b/dev/lite/files/auto_examples/manifold/plot_t_sne_perplexity.ipynb
index 363ca3e0cc55e..ac5ac8412e409 100644
--- a/dev/lite/files/auto_examples/manifold/plot_t_sne_perplexity.ipynb
+++ b/dev/lite/files/auto_examples/manifold/plot_t_sne_perplexity.ipynb
@@ -53,7 +53,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/miscellaneous/plot_anomaly_comparison.ipynb b/dev/lite/files/auto_examples/miscellaneous/plot_anomaly_comparison.ipynb
index 64df09a3815f9..61a1bd2bead74 100644
--- a/dev/lite/files/auto_examples/miscellaneous/plot_anomaly_comparison.ipynb
+++ b/dev/lite/files/auto_examples/miscellaneous/plot_anomaly_comparison.ipynb
@@ -53,7 +53,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/miscellaneous/plot_display_object_visualization.ipynb b/dev/lite/files/auto_examples/miscellaneous/plot_display_object_visualization.ipynb
index ee8103a65edb4..137b2f0f443a6 100644
--- a/dev/lite/files/auto_examples/miscellaneous/plot_display_object_visualization.ipynb
+++ b/dev/lite/files/auto_examples/miscellaneous/plot_display_object_visualization.ipynb
@@ -143,7 +143,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/miscellaneous/plot_estimator_representation.ipynb b/dev/lite/files/auto_examples/miscellaneous/plot_estimator_representation.ipynb
index 732137d96bdf9..8e9d0edc64cd0 100644
--- a/dev/lite/files/auto_examples/miscellaneous/plot_estimator_representation.ipynb
+++ b/dev/lite/files/auto_examples/miscellaneous/plot_estimator_representation.ipynb
@@ -89,7 +89,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/miscellaneous/plot_isotonic_regression.ipynb b/dev/lite/files/auto_examples/miscellaneous/plot_isotonic_regression.ipynb
index 57f0782b92f5a..a0b198bf09eb1 100644
--- a/dev/lite/files/auto_examples/miscellaneous/plot_isotonic_regression.ipynb
+++ b/dev/lite/files/auto_examples/miscellaneous/plot_isotonic_regression.ipynb
@@ -96,7 +96,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/miscellaneous/plot_johnson_lindenstrauss_bound.ipynb b/dev/lite/files/auto_examples/miscellaneous/plot_johnson_lindenstrauss_bound.ipynb
index 784932ddb8458..fbeb0a8084f1f 100644
--- a/dev/lite/files/auto_examples/miscellaneous/plot_johnson_lindenstrauss_bound.ipynb
+++ b/dev/lite/files/auto_examples/miscellaneous/plot_johnson_lindenstrauss_bound.ipynb
@@ -132,7 +132,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/miscellaneous/plot_kernel_approximation.ipynb b/dev/lite/files/auto_examples/miscellaneous/plot_kernel_approximation.ipynb
index d8c3bd403ff9c..2c1a10d3adee1 100644
--- a/dev/lite/files/auto_examples/miscellaneous/plot_kernel_approximation.ipynb
+++ b/dev/lite/files/auto_examples/miscellaneous/plot_kernel_approximation.ipynb
@@ -96,7 +96,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/miscellaneous/plot_kernel_ridge_regression.ipynb b/dev/lite/files/auto_examples/miscellaneous/plot_kernel_ridge_regression.ipynb
index c9c176370d6f7..05b5684bae2c0 100644
--- a/dev/lite/files/auto_examples/miscellaneous/plot_kernel_ridge_regression.ipynb
+++ b/dev/lite/files/auto_examples/miscellaneous/plot_kernel_ridge_regression.ipynb
@@ -171,7 +171,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/miscellaneous/plot_metadata_routing.ipynb b/dev/lite/files/auto_examples/miscellaneous/plot_metadata_routing.ipynb
index 65b50f81273c1..6987f8de75a11 100644
--- a/dev/lite/files/auto_examples/miscellaneous/plot_metadata_routing.ipynb
+++ b/dev/lite/files/auto_examples/miscellaneous/plot_metadata_routing.ipynb
@@ -686,7 +686,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/miscellaneous/plot_multilabel.ipynb b/dev/lite/files/auto_examples/miscellaneous/plot_multilabel.ipynb
index ef2d31cd67bb8..69cead501a9dd 100644
--- a/dev/lite/files/auto_examples/miscellaneous/plot_multilabel.ipynb
+++ b/dev/lite/files/auto_examples/miscellaneous/plot_multilabel.ipynb
@@ -33,7 +33,7 @@
       },
       "outputs": [],
       "source": [
-        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nfrom sklearn.cross_decomposition import CCA\nfrom sklearn.datasets import make_multilabel_classification\nfrom sklearn.decomposition import PCA\nfrom sklearn.multiclass import OneVsRestClassifier\nfrom sklearn.svm import SVC\n\n\ndef plot_hyperplane(clf, min_x, max_x, linestyle, label):\n    # get the separating hyperplane\n    w = clf.coef_[0]\n    a = -w[0] / w[1]\n    xx = np.linspace(min_x - 5, max_x + 5)  # make sure the line is long enough\n    yy = a * xx - (clf.intercept_[0]) / w[1]\n    plt.plot(xx, yy, linestyle, label=label)\n\n\ndef plot_subfigure(X, Y, subplot, title, transform):\n    if transform == \"pca\":\n        X = PCA(n_components=2).fit_transform(X)\n    elif transform == \"cca\":\n        X = CCA(n_components=2).fit(X, Y).transform(X)\n    else:\n        raise ValueError\n\n    min_x = np.min(X[:, 0])\n    max_x = np.max(X[:, 0])\n\n    min_y = np.min(X[:, 1])\n    max_y = np.max(X[:, 1])\n\n    classif = OneVsRestClassifier(SVC(kernel=\"linear\"))\n    classif.fit(X, Y)\n\n    plt.subplot(2, 2, subplot)\n    plt.title(title)\n\n    zero_class = np.where(Y[:, 0])\n    one_class = np.where(Y[:, 1])\n    plt.scatter(X[:, 0], X[:, 1], s=40, c=\"gray\", edgecolors=(0, 0, 0))\n    plt.scatter(\n        X[zero_class, 0],\n        X[zero_class, 1],\n        s=160,\n        edgecolors=\"b\",\n        facecolors=\"none\",\n        linewidths=2,\n        label=\"Class 1\",\n    )\n    plt.scatter(\n        X[one_class, 0],\n        X[one_class, 1],\n        s=80,\n        edgecolors=\"orange\",\n        facecolors=\"none\",\n        linewidths=2,\n        label=\"Class 2\",\n    )\n\n    plot_hyperplane(\n        classif.estimators_[0], min_x, max_x, \"k--\", \"Boundary\\nfor class 1\"\n    )\n    plot_hyperplane(\n        classif.estimators_[1], min_x, max_x, \"k-.\", \"Boundary\\nfor class 2\"\n    )\n    plt.xticks(())\n    plt.yticks(())\n\n    plt.xlim(min_x - 0.5 * max_x, max_x + 0.5 * max_x)\n    plt.ylim(min_y - 0.5 * max_y, max_y + 0.5 * max_y)\n    if subplot == 2:\n        plt.xlabel(\"First principal component\")\n        plt.ylabel(\"Second principal component\")\n        plt.legend(loc=\"upper left\")\n\n\nplt.figure(figsize=(8, 6))\n\nX, Y = make_multilabel_classification(\n    n_classes=2, n_labels=1, allow_unlabeled=True, random_state=1\n)\n\nplot_subfigure(X, Y, 1, \"With unlabeled samples + CCA\", \"cca\")\nplot_subfigure(X, Y, 2, \"With unlabeled samples + PCA\", \"pca\")\n\nX, Y = make_multilabel_classification(\n    n_classes=2, n_labels=1, allow_unlabeled=False, random_state=1\n)\n\nplot_subfigure(X, Y, 3, \"Without unlabeled samples + CCA\", \"cca\")\nplot_subfigure(X, Y, 4, \"Without unlabeled samples + PCA\", \"pca\")\n\nplt.subplots_adjust(0.04, 0.02, 0.97, 0.94, 0.09, 0.2)\nplt.show()"
+        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nfrom sklearn.cross_decomposition import CCA\nfrom sklearn.datasets import make_multilabel_classification\nfrom sklearn.decomposition import PCA\nfrom sklearn.multiclass import OneVsRestClassifier\nfrom sklearn.svm import SVC\n\n\ndef plot_hyperplane(clf, min_x, max_x, linestyle, label):\n    # get the separating hyperplane\n    w = clf.coef_[0]\n    a = -w[0] / w[1]\n    xx = np.linspace(min_x - 5, max_x + 5)  # make sure the line is long enough\n    yy = a * xx - (clf.intercept_[0]) / w[1]\n    plt.plot(xx, yy, linestyle, label=label)\n\n\ndef plot_subfigure(X, Y, subplot, title, transform):\n    if transform == \"pca\":\n        X = PCA(n_components=2).fit_transform(X)\n    elif transform == \"cca\":\n        X = CCA(n_components=2).fit(X, Y).transform(X)\n    else:\n        raise ValueError\n\n    min_x = np.min(X[:, 0])\n    max_x = np.max(X[:, 0])\n\n    min_y = np.min(X[:, 1])\n    max_y = np.max(X[:, 1])\n\n    classif = OneVsRestClassifier(SVC(kernel=\"linear\"))\n    classif.fit(X, Y)\n\n    plt.subplot(2, 2, subplot)\n    plt.title(title)\n\n    zero_class = (Y[:, 0]).nonzero()\n    one_class = (Y[:, 1]).nonzero()\n    plt.scatter(X[:, 0], X[:, 1], s=40, c=\"gray\", edgecolors=(0, 0, 0))\n    plt.scatter(\n        X[zero_class, 0],\n        X[zero_class, 1],\n        s=160,\n        edgecolors=\"b\",\n        facecolors=\"none\",\n        linewidths=2,\n        label=\"Class 1\",\n    )\n    plt.scatter(\n        X[one_class, 0],\n        X[one_class, 1],\n        s=80,\n        edgecolors=\"orange\",\n        facecolors=\"none\",\n        linewidths=2,\n        label=\"Class 2\",\n    )\n\n    plot_hyperplane(\n        classif.estimators_[0], min_x, max_x, \"k--\", \"Boundary\\nfor class 1\"\n    )\n    plot_hyperplane(\n        classif.estimators_[1], min_x, max_x, \"k-.\", \"Boundary\\nfor class 2\"\n    )\n    plt.xticks(())\n    plt.yticks(())\n\n    plt.xlim(min_x - 0.5 * max_x, max_x + 0.5 * max_x)\n    plt.ylim(min_y - 0.5 * max_y, max_y + 0.5 * max_y)\n    if subplot == 2:\n        plt.xlabel(\"First principal component\")\n        plt.ylabel(\"Second principal component\")\n        plt.legend(loc=\"upper left\")\n\n\nplt.figure(figsize=(8, 6))\n\nX, Y = make_multilabel_classification(\n    n_classes=2, n_labels=1, allow_unlabeled=True, random_state=1\n)\n\nplot_subfigure(X, Y, 1, \"With unlabeled samples + CCA\", \"cca\")\nplot_subfigure(X, Y, 2, \"With unlabeled samples + PCA\", \"pca\")\n\nX, Y = make_multilabel_classification(\n    n_classes=2, n_labels=1, allow_unlabeled=False, random_state=1\n)\n\nplot_subfigure(X, Y, 3, \"Without unlabeled samples + CCA\", \"cca\")\nplot_subfigure(X, Y, 4, \"Without unlabeled samples + PCA\", \"pca\")\n\nplt.subplots_adjust(0.04, 0.02, 0.97, 0.94, 0.09, 0.2)\nplt.show()"
       ]
     }
   ],
@@ -53,7 +53,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/miscellaneous/plot_multioutput_face_completion.ipynb b/dev/lite/files/auto_examples/miscellaneous/plot_multioutput_face_completion.ipynb
index efe3e54d671c5..ef86636ffedde 100644
--- a/dev/lite/files/auto_examples/miscellaneous/plot_multioutput_face_completion.ipynb
+++ b/dev/lite/files/auto_examples/miscellaneous/plot_multioutput_face_completion.ipynb
@@ -53,7 +53,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/miscellaneous/plot_outlier_detection_bench.ipynb b/dev/lite/files/auto_examples/miscellaneous/plot_outlier_detection_bench.ipynb
index 19c084faba222..35dccf9accf74 100644
--- a/dev/lite/files/auto_examples/miscellaneous/plot_outlier_detection_bench.ipynb
+++ b/dev/lite/files/auto_examples/miscellaneous/plot_outlier_detection_bench.ipynb
@@ -69,7 +69,7 @@
       },
       "outputs": [],
       "source": [
-        "from time import perf_counter\n\n\ndef fit_predict(estimator, X):\n    tic = perf_counter()\n    if estimator[-1].__class__.__name__ == \"LocalOutlierFactor\":\n        estimator.fit(X)\n        y_pred = estimator[-1].negative_outlier_factor_\n    else:  # \"IsolationForest\"\n        y_pred = estimator.fit(X).decision_function(X)\n    toc = perf_counter()\n    print(f\"Duration for {model_name}: {toc - tic:.2f} s\")\n    return y_pred"
+        "from time import perf_counter\n\n\ndef fit_predict(estimator, X):\n    tic = perf_counter()\n    if estimator[-1].__class__.__name__ == \"LocalOutlierFactor\":\n        estimator.fit(X)\n        y_score = estimator[-1].negative_outlier_factor_\n    else:  # \"IsolationForest\"\n        y_score = estimator.fit(X).decision_function(X)\n    toc = perf_counter()\n    print(f\"Duration for {model_name}: {toc - tic:.2f} s\")\n    return y_score"
       ]
     },
     {
@@ -105,7 +105,7 @@
       },
       "outputs": [],
       "source": [
-        "y_true = {}\ny_pred = {\"LOF\": {}, \"IForest\": {}}\nmodel_names = [\"LOF\", \"IForest\"]\ncat_columns = [\"protocol_type\", \"service\", \"flag\"]\n\ny_true[\"KDDCup99 - SA\"] = y\nfor model_name in model_names:\n    model = make_estimator(\n        name=model_name,\n        categorical_columns=cat_columns,\n        lof_kw={\"n_neighbors\": int(n_samples * anomaly_frac)},\n        iforest_kw={\"random_state\": 42},\n    )\n    y_pred[model_name][\"KDDCup99 - SA\"] = fit_predict(model, X)"
+        "y_true = {}\ny_score = {\"LOF\": {}, \"IForest\": {}}\nmodel_names = [\"LOF\", \"IForest\"]\ncat_columns = [\"protocol_type\", \"service\", \"flag\"]\n\ny_true[\"KDDCup99 - SA\"] = y\nfor model_name in model_names:\n    model = make_estimator(\n        name=model_name,\n        categorical_columns=cat_columns,\n        lof_kw={\"n_neighbors\": int(n_samples * anomaly_frac)},\n        iforest_kw={\"random_state\": 42},\n    )\n    y_score[model_name][\"KDDCup99 - SA\"] = fit_predict(model, X)"
       ]
     },
     {
@@ -134,7 +134,7 @@
       },
       "outputs": [],
       "source": [
-        "y_true[\"forestcover\"] = y\nfor model_name in model_names:\n    model = make_estimator(\n        name=model_name,\n        lof_kw={\"n_neighbors\": int(n_samples * anomaly_frac)},\n        iforest_kw={\"random_state\": 42},\n    )\n    y_pred[model_name][\"forestcover\"] = fit_predict(model, X)"
+        "y_true[\"forestcover\"] = y\nfor model_name in model_names:\n    model = make_estimator(\n        name=model_name,\n        lof_kw={\"n_neighbors\": int(n_samples * anomaly_frac)},\n        iforest_kw={\"random_state\": 42},\n    )\n    y_score[model_name][\"forestcover\"] = fit_predict(model, X)"
       ]
     },
     {
@@ -181,7 +181,7 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.compose import make_column_selector as selector\n\ncategorical_columns_selector = selector(dtype_include=\"category\")\ncat_columns = categorical_columns_selector(X)\n\ny_true[\"ames_housing\"] = y\nfor model_name in model_names:\n    model = make_estimator(\n        name=model_name,\n        categorical_columns=cat_columns,\n        lof_kw={\"n_neighbors\": int(n_samples * anomaly_frac)},\n        iforest_kw={\"random_state\": 42},\n    )\n    y_pred[model_name][\"ames_housing\"] = fit_predict(model, X)"
+        "from sklearn.compose import make_column_selector as selector\n\ncategorical_columns_selector = selector(dtype_include=\"category\")\ncat_columns = categorical_columns_selector(X)\n\ny_true[\"ames_housing\"] = y\nfor model_name in model_names:\n    model = make_estimator(\n        name=model_name,\n        categorical_columns=cat_columns,\n        lof_kw={\"n_neighbors\": int(n_samples * anomaly_frac)},\n        iforest_kw={\"random_state\": 42},\n    )\n    y_score[model_name][\"ames_housing\"] = fit_predict(model, X)"
       ]
     },
     {
@@ -210,7 +210,7 @@
       },
       "outputs": [],
       "source": [
-        "y_true[\"cardiotocography\"] = y\nfor model_name in model_names:\n    model = make_estimator(\n        name=model_name,\n        lof_kw={\"n_neighbors\": int(n_samples * anomaly_frac)},\n        iforest_kw={\"random_state\": 42},\n    )\n    y_pred[model_name][\"cardiotocography\"] = fit_predict(model, X)"
+        "y_true[\"cardiotocography\"] = y\nfor model_name in model_names:\n    model = make_estimator(\n        name=model_name,\n        lof_kw={\"n_neighbors\": int(n_samples * anomaly_frac)},\n        iforest_kw={\"random_state\": 42},\n    )\n    y_score[model_name][\"cardiotocography\"] = fit_predict(model, X)"
       ]
     },
     {
@@ -228,7 +228,7 @@
       },
       "outputs": [],
       "source": [
-        "import math\n\nfrom sklearn.metrics import RocCurveDisplay\n\ncols = 2\npos_label = 0  # mean 0 belongs to positive class\ndatasets_names = y_true.keys()\nrows = math.ceil(len(datasets_names) / cols)\n\nfig, axs = plt.subplots(nrows=rows, ncols=cols, squeeze=False, figsize=(10, rows * 4))\n\nfor ax, dataset_name in zip(axs.ravel(), datasets_names):\n    for model_idx, model_name in enumerate(model_names):\n        display = RocCurveDisplay.from_predictions(\n            y_true[dataset_name],\n            y_pred[model_name][dataset_name],\n            pos_label=pos_label,\n            name=model_name,\n            ax=ax,\n            plot_chance_level=(model_idx == len(model_names) - 1),\n            chance_level_kw={\"linestyle\": \":\"},\n        )\n    ax.set_title(dataset_name)\n_ = plt.tight_layout(pad=2.0)  # spacing between subplots"
+        "import math\n\nfrom sklearn.metrics import RocCurveDisplay\n\ncols = 2\npos_label = 0  # mean 0 belongs to positive class\ndatasets_names = y_true.keys()\nrows = math.ceil(len(datasets_names) / cols)\n\nfig, axs = plt.subplots(nrows=rows, ncols=cols, squeeze=False, figsize=(10, rows * 4))\n\nfor ax, dataset_name in zip(axs.ravel(), datasets_names):\n    for model_idx, model_name in enumerate(model_names):\n        display = RocCurveDisplay.from_predictions(\n            y_true[dataset_name],\n            y_score[model_name][dataset_name],\n            pos_label=pos_label,\n            name=model_name,\n            ax=ax,\n            plot_chance_level=(model_idx == len(model_names) - 1),\n            chance_level_kw={\"linestyle\": \":\"},\n        )\n    ax.set_title(dataset_name)\n_ = plt.tight_layout(pad=2.0)  # spacing between subplots"
       ]
     },
     {
@@ -246,7 +246,7 @@
       },
       "outputs": [],
       "source": [
-        "X = X_forestcover\ny = y_true[\"forestcover\"]\n\nn_samples = X.shape[0]\nn_neighbors_list = (n_samples * np.array([0.2, 0.02, 0.01, 0.001])).astype(np.int32)\nmodel = make_pipeline(RobustScaler(), LocalOutlierFactor())\n\nlinestyles = [\"solid\", \"dashed\", \"dashdot\", \":\", (5, (10, 3))]\n\nfig, ax = plt.subplots()\nfor model_idx, (linestyle, n_neighbors) in enumerate(zip(linestyles, n_neighbors_list)):\n    model.set_params(localoutlierfactor__n_neighbors=n_neighbors)\n    model.fit(X)\n    y_pred = model[-1].negative_outlier_factor_\n    display = RocCurveDisplay.from_predictions(\n        y,\n        y_pred,\n        pos_label=pos_label,\n        name=f\"n_neighbors = {n_neighbors}\",\n        ax=ax,\n        plot_chance_level=(model_idx == len(n_neighbors_list) - 1),\n        chance_level_kw={\"linestyle\": (0, (1, 10))},\n        linestyle=linestyle,\n        linewidth=2,\n    )\n_ = ax.set_title(\"RobustScaler with varying n_neighbors\\non forestcover dataset\")"
+        "X = X_forestcover\ny = y_true[\"forestcover\"]\n\nn_samples = X.shape[0]\nn_neighbors_list = (n_samples * np.array([0.2, 0.02, 0.01, 0.001])).astype(np.int32)\nmodel = make_pipeline(RobustScaler(), LocalOutlierFactor())\n\nlinestyles = [\"solid\", \"dashed\", \"dashdot\", \":\", (5, (10, 3))]\n\nfig, ax = plt.subplots()\nfor model_idx, (linestyle, n_neighbors) in enumerate(zip(linestyles, n_neighbors_list)):\n    model.set_params(localoutlierfactor__n_neighbors=n_neighbors)\n    model.fit(X)\n    y_score = model[-1].negative_outlier_factor_\n    display = RocCurveDisplay.from_predictions(\n        y,\n        y_score,\n        pos_label=pos_label,\n        name=f\"n_neighbors = {n_neighbors}\",\n        ax=ax,\n        plot_chance_level=(model_idx == len(n_neighbors_list) - 1),\n        chance_level_kw={\"linestyle\": (0, (1, 10))},\n        linestyle=linestyle,\n        linewidth=2,\n    )\n_ = ax.set_title(\"RobustScaler with varying n_neighbors\\non forestcover dataset\")"
       ]
     },
     {
@@ -264,7 +264,7 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.preprocessing import MinMaxScaler, SplineTransformer, StandardScaler\n\npreprocessor_list = [\n    None,\n    RobustScaler(),\n    StandardScaler(),\n    MinMaxScaler(),\n    SplineTransformer(),\n]\nexpected_anomaly_fraction = 0.02\nlof = LocalOutlierFactor(n_neighbors=int(n_samples * expected_anomaly_fraction))\n\nfig, ax = plt.subplots()\nfor model_idx, (linestyle, preprocessor) in enumerate(\n    zip(linestyles, preprocessor_list)\n):\n    model = make_pipeline(preprocessor, lof)\n    model.fit(X)\n    y_pred = model[-1].negative_outlier_factor_\n    display = RocCurveDisplay.from_predictions(\n        y,\n        y_pred,\n        pos_label=pos_label,\n        name=str(preprocessor).split(\"(\")[0],\n        ax=ax,\n        plot_chance_level=(model_idx == len(preprocessor_list) - 1),\n        chance_level_kw={\"linestyle\": (0, (1, 10))},\n        linestyle=linestyle,\n        linewidth=2,\n    )\n_ = ax.set_title(\"Fixed n_neighbors with varying preprocessing\\non forestcover dataset\")"
+        "from sklearn.preprocessing import MinMaxScaler, SplineTransformer, StandardScaler\n\npreprocessor_list = [\n    None,\n    RobustScaler(),\n    StandardScaler(),\n    MinMaxScaler(),\n    SplineTransformer(),\n]\nexpected_anomaly_fraction = 0.02\nlof = LocalOutlierFactor(n_neighbors=int(n_samples * expected_anomaly_fraction))\n\nfig, ax = plt.subplots()\nfor model_idx, (linestyle, preprocessor) in enumerate(\n    zip(linestyles, preprocessor_list)\n):\n    model = make_pipeline(preprocessor, lof)\n    model.fit(X)\n    y_score = model[-1].negative_outlier_factor_\n    display = RocCurveDisplay.from_predictions(\n        y,\n        y_score,\n        pos_label=pos_label,\n        name=str(preprocessor).split(\"(\")[0],\n        ax=ax,\n        plot_chance_level=(model_idx == len(preprocessor_list) - 1),\n        chance_level_kw={\"linestyle\": (0, (1, 10))},\n        linestyle=linestyle,\n        linewidth=2,\n    )\n_ = ax.set_title(\"Fixed n_neighbors with varying preprocessing\\non forestcover dataset\")"
       ]
     },
     {
@@ -282,7 +282,7 @@
       },
       "outputs": [],
       "source": [
-        "X = X_cardiotocography\ny = y_true[\"cardiotocography\"]\n\nn_samples, expected_anomaly_fraction = X.shape[0], 0.025\nlof = LocalOutlierFactor(n_neighbors=int(n_samples * expected_anomaly_fraction))\n\nfig, ax = plt.subplots()\nfor model_idx, (linestyle, preprocessor) in enumerate(\n    zip(linestyles, preprocessor_list)\n):\n    model = make_pipeline(preprocessor, lof)\n    model.fit(X)\n    y_pred = model[-1].negative_outlier_factor_\n    display = RocCurveDisplay.from_predictions(\n        y,\n        y_pred,\n        pos_label=pos_label,\n        name=str(preprocessor).split(\"(\")[0],\n        ax=ax,\n        plot_chance_level=(model_idx == len(preprocessor_list) - 1),\n        chance_level_kw={\"linestyle\": (0, (1, 10))},\n        linestyle=linestyle,\n        linewidth=2,\n    )\nax.set_title(\n    \"Fixed n_neighbors with varying preprocessing\\non cardiotocography dataset\"\n)\nplt.show()"
+        "X = X_cardiotocography\ny = y_true[\"cardiotocography\"]\n\nn_samples, expected_anomaly_fraction = X.shape[0], 0.025\nlof = LocalOutlierFactor(n_neighbors=int(n_samples * expected_anomaly_fraction))\n\nfig, ax = plt.subplots()\nfor model_idx, (linestyle, preprocessor) in enumerate(\n    zip(linestyles, preprocessor_list)\n):\n    model = make_pipeline(preprocessor, lof)\n    model.fit(X)\n    y_score = model[-1].negative_outlier_factor_\n    display = RocCurveDisplay.from_predictions(\n        y,\n        y_score,\n        pos_label=pos_label,\n        name=str(preprocessor).split(\"(\")[0],\n        ax=ax,\n        plot_chance_level=(model_idx == len(preprocessor_list) - 1),\n        chance_level_kw={\"linestyle\": (0, (1, 10))},\n        linestyle=linestyle,\n        linewidth=2,\n    )\nax.set_title(\n    \"Fixed n_neighbors with varying preprocessing\\non cardiotocography dataset\"\n)\nplt.show()"
       ]
     }
   ],
@@ -302,7 +302,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/miscellaneous/plot_partial_dependence_visualization_api.ipynb b/dev/lite/files/auto_examples/miscellaneous/plot_partial_dependence_visualization_api.ipynb
index c785a41c56b0a..3b5bdc2eacc0d 100644
--- a/dev/lite/files/auto_examples/miscellaneous/plot_partial_dependence_visualization_api.ipynb
+++ b/dev/lite/files/auto_examples/miscellaneous/plot_partial_dependence_visualization_api.ipynb
@@ -179,7 +179,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/miscellaneous/plot_pipeline_display.ipynb b/dev/lite/files/auto_examples/miscellaneous/plot_pipeline_display.ipynb
index 8caad6790d382..6ac82e8456e69 100644
--- a/dev/lite/files/auto_examples/miscellaneous/plot_pipeline_display.ipynb
+++ b/dev/lite/files/auto_examples/miscellaneous/plot_pipeline_display.ipynb
@@ -197,7 +197,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/miscellaneous/plot_roc_curve_visualization_api.ipynb b/dev/lite/files/auto_examples/miscellaneous/plot_roc_curve_visualization_api.ipynb
index 85cd637177134..78c12fd308be7 100644
--- a/dev/lite/files/auto_examples/miscellaneous/plot_roc_curve_visualization_api.ipynb
+++ b/dev/lite/files/auto_examples/miscellaneous/plot_roc_curve_visualization_api.ipynb
@@ -107,7 +107,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/miscellaneous/plot_set_output.ipynb b/dev/lite/files/auto_examples/miscellaneous/plot_set_output.ipynb
index 7c6b29bc82040..244d6c09112cd 100644
--- a/dev/lite/files/auto_examples/miscellaneous/plot_set_output.ipynb
+++ b/dev/lite/files/auto_examples/miscellaneous/plot_set_output.ipynb
@@ -287,7 +287,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/mixture/plot_concentration_prior.ipynb b/dev/lite/files/auto_examples/mixture/plot_concentration_prior.ipynb
index 327c4c6225627..2a6fb8c2fb49e 100644
--- a/dev/lite/files/auto_examples/mixture/plot_concentration_prior.ipynb
+++ b/dev/lite/files/auto_examples/mixture/plot_concentration_prior.ipynb
@@ -53,7 +53,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/mixture/plot_gmm.ipynb b/dev/lite/files/auto_examples/mixture/plot_gmm.ipynb
index 0ab0ef0eb2470..268c39d0e7164 100644
--- a/dev/lite/files/auto_examples/mixture/plot_gmm.ipynb
+++ b/dev/lite/files/auto_examples/mixture/plot_gmm.ipynb
@@ -53,7 +53,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/mixture/plot_gmm_covariances.ipynb b/dev/lite/files/auto_examples/mixture/plot_gmm_covariances.ipynb
index 6226029c15241..f285b55a5ba3b 100644
--- a/dev/lite/files/auto_examples/mixture/plot_gmm_covariances.ipynb
+++ b/dev/lite/files/auto_examples/mixture/plot_gmm_covariances.ipynb
@@ -53,7 +53,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/mixture/plot_gmm_init.ipynb b/dev/lite/files/auto_examples/mixture/plot_gmm_init.ipynb
index 8e81b64cd725f..5dd43443f6b00 100644
--- a/dev/lite/files/auto_examples/mixture/plot_gmm_init.ipynb
+++ b/dev/lite/files/auto_examples/mixture/plot_gmm_init.ipynb
@@ -53,7 +53,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/mixture/plot_gmm_pdf.ipynb b/dev/lite/files/auto_examples/mixture/plot_gmm_pdf.ipynb
index d01325437f5dc..346bd3417a3b9 100644
--- a/dev/lite/files/auto_examples/mixture/plot_gmm_pdf.ipynb
+++ b/dev/lite/files/auto_examples/mixture/plot_gmm_pdf.ipynb
@@ -53,7 +53,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/mixture/plot_gmm_selection.ipynb b/dev/lite/files/auto_examples/mixture/plot_gmm_selection.ipynb
index 46803504469db..4dde5eba0c99e 100644
--- a/dev/lite/files/auto_examples/mixture/plot_gmm_selection.ipynb
+++ b/dev/lite/files/auto_examples/mixture/plot_gmm_selection.ipynb
@@ -154,7 +154,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/mixture/plot_gmm_sin.ipynb b/dev/lite/files/auto_examples/mixture/plot_gmm_sin.ipynb
index 78eafe543d0bb..a39f044e86b82 100644
--- a/dev/lite/files/auto_examples/mixture/plot_gmm_sin.ipynb
+++ b/dev/lite/files/auto_examples/mixture/plot_gmm_sin.ipynb
@@ -53,7 +53,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/model_selection/plot_confusion_matrix.ipynb b/dev/lite/files/auto_examples/model_selection/plot_confusion_matrix.ipynb
index 7a540d2375f9f..b14e3e47be742 100644
--- a/dev/lite/files/auto_examples/model_selection/plot_confusion_matrix.ipynb
+++ b/dev/lite/files/auto_examples/model_selection/plot_confusion_matrix.ipynb
@@ -53,7 +53,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/model_selection/plot_cost_sensitive_learning.ipynb b/dev/lite/files/auto_examples/model_selection/plot_cost_sensitive_learning.ipynb
index 7b77e8768366e..aad92448eb846 100644
--- a/dev/lite/files/auto_examples/model_selection/plot_cost_sensitive_learning.ipynb
+++ b/dev/lite/files/auto_examples/model_selection/plot_cost_sensitive_learning.ipynb
@@ -713,7 +713,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/model_selection/plot_cv_indices.ipynb b/dev/lite/files/auto_examples/model_selection/plot_cv_indices.ipynb
index f769ffa95b997..c954017f24527 100644
--- a/dev/lite/files/auto_examples/model_selection/plot_cv_indices.ipynb
+++ b/dev/lite/files/auto_examples/model_selection/plot_cv_indices.ipynb
@@ -143,7 +143,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/model_selection/plot_cv_predict.ipynb b/dev/lite/files/auto_examples/model_selection/plot_cv_predict.ipynb
index b06f26cb42514..d5097793ee6a9 100644
--- a/dev/lite/files/auto_examples/model_selection/plot_cv_predict.ipynb
+++ b/dev/lite/files/auto_examples/model_selection/plot_cv_predict.ipynb
@@ -114,7 +114,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/model_selection/plot_det.ipynb b/dev/lite/files/auto_examples/model_selection/plot_det.ipynb
index cffe2ab5295e8..6e4dc01f68567 100644
--- a/dev/lite/files/auto_examples/model_selection/plot_det.ipynb
+++ b/dev/lite/files/auto_examples/model_selection/plot_det.ipynb
@@ -58,7 +58,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Define the classifiers\n\nHere we define two different classifiers. The goal is to visually compare their\nstatistical performance across thresholds using the ROC and DET curves. There\nis no particular reason why these classifiers are chosen other classifiers\navailable in scikit-learn.\n\n"
+        "## Define the classifiers\n\nHere we define two different classifiers. The goal is to visually compare their\nstatistical performance across thresholds using the ROC and DET curves.\n\n"
       ]
     },
     {
@@ -69,14 +69,14 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.ensemble import RandomForestClassifier\nfrom sklearn.pipeline import make_pipeline\nfrom sklearn.svm import LinearSVC\n\nclassifiers = {\n    \"Linear SVM\": make_pipeline(StandardScaler(), LinearSVC(C=0.025)),\n    \"Random Forest\": RandomForestClassifier(\n        max_depth=5, n_estimators=10, max_features=1\n    ),\n}"
+        "from sklearn.dummy import DummyClassifier\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.pipeline import make_pipeline\nfrom sklearn.svm import LinearSVC\n\nclassifiers = {\n    \"Linear SVM\": make_pipeline(StandardScaler(), LinearSVC(C=0.025)),\n    \"Random Forest\": RandomForestClassifier(\n        max_depth=5, n_estimators=10, max_features=1, random_state=0\n    ),\n    \"Non-informative baseline\": DummyClassifier(),\n}"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Plot ROC and DET curves\n\nDET curves are commonly plotted in normal deviate scale. To achieve this the\nDET display transforms the error rates as returned by the\n:func:`~sklearn.metrics.det_curve` and the axis scale using\n`scipy.stats.norm`.\n\n"
+        "## Compare ROC and DET curves\n\nDET curves are commonly plotted in normal deviate scale. To achieve this the\nDET display transforms the error rates as returned by the\n:func:`~sklearn.metrics.det_curve` and the axis scale using\n`scipy.stats.norm`.\n\n"
       ]
     },
     {
@@ -87,14 +87,14 @@
       },
       "outputs": [],
       "source": [
-        "import matplotlib.pyplot as plt\n\nfrom sklearn.metrics import DetCurveDisplay, RocCurveDisplay\n\nfig, [ax_roc, ax_det] = plt.subplots(1, 2, figsize=(11, 5))\n\nfor name, clf in classifiers.items():\n    clf.fit(X_train, y_train)\n\n    RocCurveDisplay.from_estimator(clf, X_test, y_test, ax=ax_roc, name=name)\n    DetCurveDisplay.from_estimator(clf, X_test, y_test, ax=ax_det, name=name)\n\nax_roc.set_title(\"Receiver Operating Characteristic (ROC) curves\")\nax_det.set_title(\"Detection Error Tradeoff (DET) curves\")\n\nax_roc.grid(linestyle=\"--\")\nax_det.grid(linestyle=\"--\")\n\nplt.legend()\nplt.show()"
+        "import matplotlib.pyplot as plt\n\nfrom sklearn.dummy import DummyClassifier\nfrom sklearn.metrics import DetCurveDisplay, RocCurveDisplay\n\nfig, [ax_roc, ax_det] = plt.subplots(1, 2, figsize=(11, 5))\n\nax_roc.set_title(\"Receiver Operating Characteristic (ROC) curves\")\nax_det.set_title(\"Detection Error Tradeoff (DET) curves\")\n\nax_roc.grid(linestyle=\"--\")\nax_det.grid(linestyle=\"--\")\n\nfor name, clf in classifiers.items():\n    (color, linestyle) = (\n        (\"black\", \"--\") if name == \"Non-informative baseline\" else (None, None)\n    )\n    clf.fit(X_train, y_train)\n    RocCurveDisplay.from_estimator(\n        clf, X_test, y_test, ax=ax_roc, name=name, color=color, linestyle=linestyle\n    )\n    DetCurveDisplay.from_estimator(\n        clf, X_test, y_test, ax=ax_det, name=name, color=color, linestyle=linestyle\n    )\n\nplt.legend()\nplt.show()"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Notice that it is easier to visually assess the overall performance of\ndifferent classification algorithms using DET curves than using ROC curves. As\nROC curves are plot in a linear scale, different classifiers usually appear\nsimilar for a large part of the plot and differ the most in the top left\ncorner of the graph. On the other hand, because DET curves represent straight\nlines in normal deviate scale, they tend to be distinguishable as a whole and\nthe area of interest spans a large part of the plot.\n\nDET curves give direct feedback of the detection error tradeoff to aid in\noperating point analysis. The user can then decide the FNR they are willing to\naccept at the expense of the FPR (or vice-versa).\n\n"
+        "Notice that it is easier to visually assess the overall performance of\ndifferent classification algorithms using DET curves than using ROC curves. As\nROC curves are plot in a linear scale, different classifiers usually appear\nsimilar for a large part of the plot and differ the most in the top left\ncorner of the graph. On the other hand, because DET curves represent straight\nlines in normal deviate scale, they tend to be distinguishable as a whole and\nthe area of interest spans a large part of the plot.\n\nDET curves give direct feedback of the detection error tradeoff to aid in\noperating point analysis. The user can then decide the FNR they are willing to\naccept at the expense of the FPR (or vice-versa).\n\n## Non-informative classifier baseline for the ROC and DET curves\n\nThe diagonal black-dotted lines in the plots above correspond to a\n:class:`~sklearn.dummy.DummyClassifier` using the default \"prior\" strategy, to\nserve as baseline for comparison with other classifiers. This classifier makes\nconstant predictions, independent of the input features in `X`, making it a\nnon-informative classifier.\n\nTo further understand the non-informative baseline of the ROC and DET curves,\nwe recall the following mathematical definitions:\n\n$\\text{FPR} = \\frac{\\text{FP}}{\\text{FP} + \\text{TN}}$\n\n$\\text{FNR} = \\frac{\\text{FN}}{\\text{TP} + \\text{FN}}$\n\n$\\text{TPR} = \\frac{\\text{TP}}{\\text{TP} + \\text{FN}}$\n\nA classifier that always predict the positive class would have no true\nnegatives nor false negatives, giving $\\text{FPR} = \\text{TPR} = 1$ and\n$\\text{FNR} = 0$, i.e.:\n\n- a single point in the upper right corner of the ROC plane,\n- a single point in the lower right corner of the DET plane.\n\nSimilarly, a classifier that always predict the negative class would have no\ntrue positives nor false positives, thus $\\text{FPR} = \\text{TPR} = 0$\nand $\\text{FNR} = 1$, i.e.:\n\n- a single point in the lower left corner of the ROC plane,\n- a single point in the upper left corner of the DET plane.\n\n"
       ]
     }
   ],
@@ -114,7 +114,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/model_selection/plot_grid_search_digits.ipynb b/dev/lite/files/auto_examples/model_selection/plot_grid_search_digits.ipynb
index 3804c178e1bd7..a879fcf02bcb7 100644
--- a/dev/lite/files/auto_examples/model_selection/plot_grid_search_digits.ipynb
+++ b/dev/lite/files/auto_examples/model_selection/plot_grid_search_digits.ipynb
@@ -204,7 +204,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/model_selection/plot_grid_search_refit_callable.ipynb b/dev/lite/files/auto_examples/model_selection/plot_grid_search_refit_callable.ipynb
index f758dbd51327f..9a307dc4ff856 100644
--- a/dev/lite/files/auto_examples/model_selection/plot_grid_search_refit_callable.ipynb
+++ b/dev/lite/files/auto_examples/model_selection/plot_grid_search_refit_callable.ipynb
@@ -53,7 +53,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/model_selection/plot_grid_search_stats.ipynb b/dev/lite/files/auto_examples/model_selection/plot_grid_search_stats.ipynb
index 6f6aa44cd1685..c0f64e1902407 100644
--- a/dev/lite/files/auto_examples/model_selection/plot_grid_search_stats.ipynb
+++ b/dev/lite/files/auto_examples/model_selection/plot_grid_search_stats.ipynb
@@ -379,7 +379,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/model_selection/plot_grid_search_text_feature_extraction.ipynb b/dev/lite/files/auto_examples/model_selection/plot_grid_search_text_feature_extraction.ipynb
index 16422c2c16ceb..8fa48f3ca0f3c 100644
--- a/dev/lite/files/auto_examples/model_selection/plot_grid_search_text_feature_extraction.ipynb
+++ b/dev/lite/files/auto_examples/model_selection/plot_grid_search_text_feature_extraction.ipynb
@@ -219,7 +219,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/model_selection/plot_learning_curve.ipynb b/dev/lite/files/auto_examples/model_selection/plot_learning_curve.ipynb
index e7f921b32cd0f..577f92e46c9b1 100644
--- a/dev/lite/files/auto_examples/model_selection/plot_learning_curve.ipynb
+++ b/dev/lite/files/auto_examples/model_selection/plot_learning_curve.ipynb
@@ -143,7 +143,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/model_selection/plot_likelihood_ratios.ipynb b/dev/lite/files/auto_examples/model_selection/plot_likelihood_ratios.ipynb
index f6f34661180dc..903b56688af5e 100644
--- a/dev/lite/files/auto_examples/model_selection/plot_likelihood_ratios.ipynb
+++ b/dev/lite/files/auto_examples/model_selection/plot_likelihood_ratios.ipynb
@@ -51,7 +51,7 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.datasets import make_classification\n\nX, y = make_classification(n_samples=10_000, weights=[0.9, 0.1], random_state=0)\nprint(f\"Percentage of people carrying the disease: {100*y.mean():.2f}%\")"
+        "from sklearn.datasets import make_classification\n\nX, y = make_classification(n_samples=10_000, weights=[0.9, 0.1], random_state=0)\nprint(f\"Percentage of people carrying the disease: {100 * y.mean():.2f}%\")"
       ]
     },
     {
@@ -287,7 +287,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/model_selection/plot_multi_metric_evaluation.ipynb b/dev/lite/files/auto_examples/model_selection/plot_multi_metric_evaluation.ipynb
index 31ab2b7c4a88b..bec74583b3f5e 100644
--- a/dev/lite/files/auto_examples/model_selection/plot_multi_metric_evaluation.ipynb
+++ b/dev/lite/files/auto_examples/model_selection/plot_multi_metric_evaluation.ipynb
@@ -89,7 +89,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/model_selection/plot_nested_cross_validation_iris.ipynb b/dev/lite/files/auto_examples/model_selection/plot_nested_cross_validation_iris.ipynb
index e16cbe82dee88..e5db4d3a85f83 100644
--- a/dev/lite/files/auto_examples/model_selection/plot_nested_cross_validation_iris.ipynb
+++ b/dev/lite/files/auto_examples/model_selection/plot_nested_cross_validation_iris.ipynb
@@ -53,7 +53,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/model_selection/plot_permutation_tests_for_classification.ipynb b/dev/lite/files/auto_examples/model_selection/plot_permutation_tests_for_classification.ipynb
index 3b42ca60bfdf4..dfe6cf6d15860 100644
--- a/dev/lite/files/auto_examples/model_selection/plot_permutation_tests_for_classification.ipynb
+++ b/dev/lite/files/auto_examples/model_selection/plot_permutation_tests_for_classification.ipynb
@@ -150,7 +150,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/model_selection/plot_precision_recall.ipynb b/dev/lite/files/auto_examples/model_selection/plot_precision_recall.ipynb
index 7d51bf3ea2784..28ba54692bc58 100644
--- a/dev/lite/files/auto_examples/model_selection/plot_precision_recall.ipynb
+++ b/dev/lite/files/auto_examples/model_selection/plot_precision_recall.ipynb
@@ -215,7 +215,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/model_selection/plot_randomized_search.ipynb b/dev/lite/files/auto_examples/model_selection/plot_randomized_search.ipynb
index 89aac625884bd..feb527b69a6d6 100644
--- a/dev/lite/files/auto_examples/model_selection/plot_randomized_search.ipynb
+++ b/dev/lite/files/auto_examples/model_selection/plot_randomized_search.ipynb
@@ -53,7 +53,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/model_selection/plot_roc.ipynb b/dev/lite/files/auto_examples/model_selection/plot_roc.ipynb
index 7bd31199801ef..4b086b619e654 100644
--- a/dev/lite/files/auto_examples/model_selection/plot_roc.ipynb
+++ b/dev/lite/files/auto_examples/model_selection/plot_roc.ipynb
@@ -152,7 +152,7 @@
       },
       "outputs": [],
       "source": [
-        "print(f\"y_score:\\n{y_score[0:2,:]}\")\nprint()\nprint(f\"y_score.ravel():\\n{y_score[0:2,:].ravel()}\")"
+        "print(f\"y_score:\\n{y_score[0:2, :]}\")\nprint()\nprint(f\"y_score.ravel():\\n{y_score[0:2, :].ravel()}\")"
       ]
     },
     {
@@ -289,7 +289,7 @@
       },
       "outputs": [],
       "source": [
-        "pair_scores = []\nmean_tpr = dict()\n\nfor ix, (label_a, label_b) in enumerate(pair_list):\n    a_mask = y_test == label_a\n    b_mask = y_test == label_b\n    ab_mask = np.logical_or(a_mask, b_mask)\n\n    a_true = a_mask[ab_mask]\n    b_true = b_mask[ab_mask]\n\n    idx_a = np.flatnonzero(label_binarizer.classes_ == label_a)[0]\n    idx_b = np.flatnonzero(label_binarizer.classes_ == label_b)[0]\n\n    fpr_a, tpr_a, _ = roc_curve(a_true, y_score[ab_mask, idx_a])\n    fpr_b, tpr_b, _ = roc_curve(b_true, y_score[ab_mask, idx_b])\n\n    mean_tpr[ix] = np.zeros_like(fpr_grid)\n    mean_tpr[ix] += np.interp(fpr_grid, fpr_a, tpr_a)\n    mean_tpr[ix] += np.interp(fpr_grid, fpr_b, tpr_b)\n    mean_tpr[ix] /= 2\n    mean_score = auc(fpr_grid, mean_tpr[ix])\n    pair_scores.append(mean_score)\n\n    fig, ax = plt.subplots(figsize=(6, 6))\n    plt.plot(\n        fpr_grid,\n        mean_tpr[ix],\n        label=f\"Mean {label_a} vs {label_b} (AUC = {mean_score :.2f})\",\n        linestyle=\":\",\n        linewidth=4,\n    )\n    RocCurveDisplay.from_predictions(\n        a_true,\n        y_score[ab_mask, idx_a],\n        ax=ax,\n        name=f\"{label_a} as positive class\",\n    )\n    RocCurveDisplay.from_predictions(\n        b_true,\n        y_score[ab_mask, idx_b],\n        ax=ax,\n        name=f\"{label_b} as positive class\",\n        plot_chance_level=True,\n        despine=True,\n    )\n    ax.set(\n        xlabel=\"False Positive Rate\",\n        ylabel=\"True Positive Rate\",\n        title=f\"{target_names[idx_a]} vs {label_b} ROC curves\",\n    )\n\nprint(f\"Macro-averaged One-vs-One ROC AUC score:\\n{np.average(pair_scores):.2f}\")"
+        "pair_scores = []\nmean_tpr = dict()\n\nfor ix, (label_a, label_b) in enumerate(pair_list):\n    a_mask = y_test == label_a\n    b_mask = y_test == label_b\n    ab_mask = np.logical_or(a_mask, b_mask)\n\n    a_true = a_mask[ab_mask]\n    b_true = b_mask[ab_mask]\n\n    idx_a = np.flatnonzero(label_binarizer.classes_ == label_a)[0]\n    idx_b = np.flatnonzero(label_binarizer.classes_ == label_b)[0]\n\n    fpr_a, tpr_a, _ = roc_curve(a_true, y_score[ab_mask, idx_a])\n    fpr_b, tpr_b, _ = roc_curve(b_true, y_score[ab_mask, idx_b])\n\n    mean_tpr[ix] = np.zeros_like(fpr_grid)\n    mean_tpr[ix] += np.interp(fpr_grid, fpr_a, tpr_a)\n    mean_tpr[ix] += np.interp(fpr_grid, fpr_b, tpr_b)\n    mean_tpr[ix] /= 2\n    mean_score = auc(fpr_grid, mean_tpr[ix])\n    pair_scores.append(mean_score)\n\n    fig, ax = plt.subplots(figsize=(6, 6))\n    plt.plot(\n        fpr_grid,\n        mean_tpr[ix],\n        label=f\"Mean {label_a} vs {label_b} (AUC = {mean_score:.2f})\",\n        linestyle=\":\",\n        linewidth=4,\n    )\n    RocCurveDisplay.from_predictions(\n        a_true,\n        y_score[ab_mask, idx_a],\n        ax=ax,\n        name=f\"{label_a} as positive class\",\n    )\n    RocCurveDisplay.from_predictions(\n        b_true,\n        y_score[ab_mask, idx_b],\n        ax=ax,\n        name=f\"{label_b} as positive class\",\n        plot_chance_level=True,\n        despine=True,\n    )\n    ax.set(\n        xlabel=\"False Positive Rate\",\n        ylabel=\"True Positive Rate\",\n        title=f\"{target_names[idx_a]} vs {label_b} ROC curves\",\n    )\n\nprint(f\"Macro-averaged One-vs-One ROC AUC score:\\n{np.average(pair_scores):.2f}\")"
       ]
     },
     {
@@ -352,7 +352,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/model_selection/plot_roc_crossval.ipynb b/dev/lite/files/auto_examples/model_selection/plot_roc_crossval.ipynb
index 44d955f2c1bdf..8812e0f199e90 100644
--- a/dev/lite/files/auto_examples/model_selection/plot_roc_crossval.ipynb
+++ b/dev/lite/files/auto_examples/model_selection/plot_roc_crossval.ipynb
@@ -107,7 +107,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/model_selection/plot_successive_halving_heatmap.ipynb b/dev/lite/files/auto_examples/model_selection/plot_successive_halving_heatmap.ipynb
index 14892bbb5ff7a..0bc25d793baf2 100644
--- a/dev/lite/files/auto_examples/model_selection/plot_successive_halving_heatmap.ipynb
+++ b/dev/lite/files/auto_examples/model_selection/plot_successive_halving_heatmap.ipynb
@@ -33,7 +33,7 @@
       },
       "outputs": [],
       "source": [
-        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\n\nfrom time import time\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\n\nfrom sklearn import datasets\nfrom sklearn.experimental import enable_halving_search_cv  # noqa\nfrom sklearn.model_selection import GridSearchCV, HalvingGridSearchCV\nfrom sklearn.svm import SVC"
+        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\n\nfrom time import time\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\n\nfrom sklearn import datasets\nfrom sklearn.experimental import enable_halving_search_cv  # noqa: F401\nfrom sklearn.model_selection import GridSearchCV, HalvingGridSearchCV\nfrom sklearn.svm import SVC"
       ]
     },
     {
@@ -96,7 +96,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/model_selection/plot_successive_halving_iterations.ipynb b/dev/lite/files/auto_examples/model_selection/plot_successive_halving_iterations.ipynb
index 7d539bdcada5a..a2082e3d506b1 100644
--- a/dev/lite/files/auto_examples/model_selection/plot_successive_halving_iterations.ipynb
+++ b/dev/lite/files/auto_examples/model_selection/plot_successive_halving_iterations.ipynb
@@ -33,7 +33,7 @@
       },
       "outputs": [],
       "source": [
-        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nfrom scipy.stats import randint\n\nfrom sklearn import datasets\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.experimental import enable_halving_search_cv  # noqa\nfrom sklearn.model_selection import HalvingRandomSearchCV"
+        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nfrom scipy.stats import randint\n\nfrom sklearn import datasets\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.experimental import enable_halving_search_cv  # noqa: F401\nfrom sklearn.model_selection import HalvingRandomSearchCV"
       ]
     },
     {
@@ -96,7 +96,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/model_selection/plot_train_error_vs_test_error.ipynb b/dev/lite/files/auto_examples/model_selection/plot_train_error_vs_test_error.ipynb
index 84482d9d4c5f1..fe2fc61865f25 100644
--- a/dev/lite/files/auto_examples/model_selection/plot_train_error_vs_test_error.ipynb
+++ b/dev/lite/files/auto_examples/model_selection/plot_train_error_vs_test_error.ipynb
@@ -150,7 +150,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/model_selection/plot_tuned_decision_threshold.ipynb b/dev/lite/files/auto_examples/model_selection/plot_tuned_decision_threshold.ipynb
index 30d34ade31a9f..42a494972f736 100644
--- a/dev/lite/files/auto_examples/model_selection/plot_tuned_decision_threshold.ipynb
+++ b/dev/lite/files/auto_examples/model_selection/plot_tuned_decision_threshold.ipynb
@@ -204,7 +204,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/model_selection/plot_underfitting_overfitting.ipynb b/dev/lite/files/auto_examples/model_selection/plot_underfitting_overfitting.ipynb
index db8c94baa8527..af210ae215daa 100644
--- a/dev/lite/files/auto_examples/model_selection/plot_underfitting_overfitting.ipynb
+++ b/dev/lite/files/auto_examples/model_selection/plot_underfitting_overfitting.ipynb
@@ -53,7 +53,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/multiclass/plot_multiclass_overview.ipynb b/dev/lite/files/auto_examples/multiclass/plot_multiclass_overview.ipynb
index 0f6f70bcb5638..88ac5f7137784 100644
--- a/dev/lite/files/auto_examples/multiclass/plot_multiclass_overview.ipynb
+++ b/dev/lite/files/auto_examples/multiclass/plot_multiclass_overview.ipynb
@@ -150,7 +150,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/multioutput/plot_classifier_chain_yeast.ipynb b/dev/lite/files/auto_examples/multioutput/plot_classifier_chain_yeast.ipynb
index 360eb5369148b..0dd6fe010e1c1 100644
--- a/dev/lite/files/auto_examples/multioutput/plot_classifier_chain_yeast.ipynb
+++ b/dev/lite/files/auto_examples/multioutput/plot_classifier_chain_yeast.ipynb
@@ -132,7 +132,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/neighbors/approximate_nearest_neighbors.ipynb b/dev/lite/files/auto_examples/neighbors/approximate_nearest_neighbors.ipynb
index dbb8d744667d9..2cb843dd10e1c 100644
--- a/dev/lite/files/auto_examples/neighbors/approximate_nearest_neighbors.ipynb
+++ b/dev/lite/files/auto_examples/neighbors/approximate_nearest_neighbors.ipynb
@@ -132,7 +132,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/neighbors/plot_caching_nearest_neighbors.ipynb b/dev/lite/files/auto_examples/neighbors/plot_caching_nearest_neighbors.ipynb
index 1e5a806de32d1..b3355ed89e1a2 100644
--- a/dev/lite/files/auto_examples/neighbors/plot_caching_nearest_neighbors.ipynb
+++ b/dev/lite/files/auto_examples/neighbors/plot_caching_nearest_neighbors.ipynb
@@ -53,7 +53,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/neighbors/plot_classification.ipynb b/dev/lite/files/auto_examples/neighbors/plot_classification.ipynb
index ffa6c983880d1..732a434e35018 100644
--- a/dev/lite/files/auto_examples/neighbors/plot_classification.ipynb
+++ b/dev/lite/files/auto_examples/neighbors/plot_classification.ipynb
@@ -114,7 +114,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/neighbors/plot_digits_kde_sampling.ipynb b/dev/lite/files/auto_examples/neighbors/plot_digits_kde_sampling.ipynb
index ee5e528303d88..e210fc209659d 100644
--- a/dev/lite/files/auto_examples/neighbors/plot_digits_kde_sampling.ipynb
+++ b/dev/lite/files/auto_examples/neighbors/plot_digits_kde_sampling.ipynb
@@ -53,7 +53,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/neighbors/plot_kde_1d.ipynb b/dev/lite/files/auto_examples/neighbors/plot_kde_1d.ipynb
index b492548af75a9..9c703530f5d00 100644
--- a/dev/lite/files/auto_examples/neighbors/plot_kde_1d.ipynb
+++ b/dev/lite/files/auto_examples/neighbors/plot_kde_1d.ipynb
@@ -53,7 +53,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/neighbors/plot_lof_novelty_detection.ipynb b/dev/lite/files/auto_examples/neighbors/plot_lof_novelty_detection.ipynb
index 42f5b467fd6a9..5da95e2b62824 100644
--- a/dev/lite/files/auto_examples/neighbors/plot_lof_novelty_detection.ipynb
+++ b/dev/lite/files/auto_examples/neighbors/plot_lof_novelty_detection.ipynb
@@ -53,7 +53,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/neighbors/plot_lof_outlier_detection.ipynb b/dev/lite/files/auto_examples/neighbors/plot_lof_outlier_detection.ipynb
index 915bfaa0b0603..c06c693d59dcc 100644
--- a/dev/lite/files/auto_examples/neighbors/plot_lof_outlier_detection.ipynb
+++ b/dev/lite/files/auto_examples/neighbors/plot_lof_outlier_detection.ipynb
@@ -107,7 +107,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/neighbors/plot_nca_classification.ipynb b/dev/lite/files/auto_examples/neighbors/plot_nca_classification.ipynb
index 5a1e99409d75d..5fe23fa04ddf0 100644
--- a/dev/lite/files/auto_examples/neighbors/plot_nca_classification.ipynb
+++ b/dev/lite/files/auto_examples/neighbors/plot_nca_classification.ipynb
@@ -53,7 +53,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/neighbors/plot_nca_dim_reduction.ipynb b/dev/lite/files/auto_examples/neighbors/plot_nca_dim_reduction.ipynb
index 474ba6eb3a904..41a37a3d56007 100644
--- a/dev/lite/files/auto_examples/neighbors/plot_nca_dim_reduction.ipynb
+++ b/dev/lite/files/auto_examples/neighbors/plot_nca_dim_reduction.ipynb
@@ -53,7 +53,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/neighbors/plot_nca_illustration.ipynb b/dev/lite/files/auto_examples/neighbors/plot_nca_illustration.ipynb
index e6a31f1032552..32e4857e4d66d 100644
--- a/dev/lite/files/auto_examples/neighbors/plot_nca_illustration.ipynb
+++ b/dev/lite/files/auto_examples/neighbors/plot_nca_illustration.ipynb
@@ -89,7 +89,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/neighbors/plot_nearest_centroid.ipynb b/dev/lite/files/auto_examples/neighbors/plot_nearest_centroid.ipynb
index b41ad5935ab2f..72a7cdcc3d71c 100644
--- a/dev/lite/files/auto_examples/neighbors/plot_nearest_centroid.ipynb
+++ b/dev/lite/files/auto_examples/neighbors/plot_nearest_centroid.ipynb
@@ -53,7 +53,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/neighbors/plot_regression.ipynb b/dev/lite/files/auto_examples/neighbors/plot_regression.ipynb
index ba47bed7d3bd4..cf1f942dbbf4a 100644
--- a/dev/lite/files/auto_examples/neighbors/plot_regression.ipynb
+++ b/dev/lite/files/auto_examples/neighbors/plot_regression.ipynb
@@ -89,7 +89,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/neighbors/plot_species_kde.ipynb b/dev/lite/files/auto_examples/neighbors/plot_species_kde.ipynb
index 0faf1c065f9c8..149ad42018537 100644
--- a/dev/lite/files/auto_examples/neighbors/plot_species_kde.ipynb
+++ b/dev/lite/files/auto_examples/neighbors/plot_species_kde.ipynb
@@ -53,7 +53,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/neural_networks/plot_mlp_alpha.ipynb b/dev/lite/files/auto_examples/neural_networks/plot_mlp_alpha.ipynb
index b078d8cc1f635..3ba6677d62239 100644
--- a/dev/lite/files/auto_examples/neural_networks/plot_mlp_alpha.ipynb
+++ b/dev/lite/files/auto_examples/neural_networks/plot_mlp_alpha.ipynb
@@ -53,7 +53,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/neural_networks/plot_mlp_training_curves.ipynb b/dev/lite/files/auto_examples/neural_networks/plot_mlp_training_curves.ipynb
index 4e0136526cbd2..9c7e46c916498 100644
--- a/dev/lite/files/auto_examples/neural_networks/plot_mlp_training_curves.ipynb
+++ b/dev/lite/files/auto_examples/neural_networks/plot_mlp_training_curves.ipynb
@@ -53,7 +53,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/neural_networks/plot_mnist_filters.ipynb b/dev/lite/files/auto_examples/neural_networks/plot_mnist_filters.ipynb
index 2a392b8d93084..81ea627b088bf 100644
--- a/dev/lite/files/auto_examples/neural_networks/plot_mnist_filters.ipynb
+++ b/dev/lite/files/auto_examples/neural_networks/plot_mnist_filters.ipynb
@@ -53,7 +53,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/neural_networks/plot_rbm_logistic_classification.ipynb b/dev/lite/files/auto_examples/neural_networks/plot_rbm_logistic_classification.ipynb
index bbb293227b06a..2bffa656e53e3 100644
--- a/dev/lite/files/auto_examples/neural_networks/plot_rbm_logistic_classification.ipynb
+++ b/dev/lite/files/auto_examples/neural_networks/plot_rbm_logistic_classification.ipynb
@@ -161,7 +161,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/preprocessing/plot_all_scaling.ipynb b/dev/lite/files/auto_examples/preprocessing/plot_all_scaling.ipynb
index 9acd79fac95b4..8043706632974 100644
--- a/dev/lite/files/auto_examples/preprocessing/plot_all_scaling.ipynb
+++ b/dev/lite/files/auto_examples/preprocessing/plot_all_scaling.ipynb
@@ -233,7 +233,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/preprocessing/plot_discretization.ipynb b/dev/lite/files/auto_examples/preprocessing/plot_discretization.ipynb
index af041a9011dce..44e178e5511e7 100644
--- a/dev/lite/files/auto_examples/preprocessing/plot_discretization.ipynb
+++ b/dev/lite/files/auto_examples/preprocessing/plot_discretization.ipynb
@@ -53,7 +53,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/preprocessing/plot_discretization_classification.ipynb b/dev/lite/files/auto_examples/preprocessing/plot_discretization_classification.ipynb
index 375486bcb6dc5..71116c4d0c6c6 100644
--- a/dev/lite/files/auto_examples/preprocessing/plot_discretization_classification.ipynb
+++ b/dev/lite/files/auto_examples/preprocessing/plot_discretization_classification.ipynb
@@ -53,7 +53,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/preprocessing/plot_discretization_strategies.ipynb b/dev/lite/files/auto_examples/preprocessing/plot_discretization_strategies.ipynb
index 1110fbdbe970f..1d9dae5abbc86 100644
--- a/dev/lite/files/auto_examples/preprocessing/plot_discretization_strategies.ipynb
+++ b/dev/lite/files/auto_examples/preprocessing/plot_discretization_strategies.ipynb
@@ -53,7 +53,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/preprocessing/plot_map_data_to_normal.ipynb b/dev/lite/files/auto_examples/preprocessing/plot_map_data_to_normal.ipynb
index da9b247994917..03b9944e0fb71 100644
--- a/dev/lite/files/auto_examples/preprocessing/plot_map_data_to_normal.ipynb
+++ b/dev/lite/files/auto_examples/preprocessing/plot_map_data_to_normal.ipynb
@@ -53,7 +53,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/preprocessing/plot_scaling_importance.ipynb b/dev/lite/files/auto_examples/preprocessing/plot_scaling_importance.ipynb
index 0993e595360ac..88b72b600cf82 100644
--- a/dev/lite/files/auto_examples/preprocessing/plot_scaling_importance.ipynb
+++ b/dev/lite/files/auto_examples/preprocessing/plot_scaling_importance.ipynb
@@ -168,7 +168,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/preprocessing/plot_target_encoder.ipynb b/dev/lite/files/auto_examples/preprocessing/plot_target_encoder.ipynb
index 797d77aa1452b..01c57db0ffe43 100644
--- a/dev/lite/files/auto_examples/preprocessing/plot_target_encoder.ipynb
+++ b/dev/lite/files/auto_examples/preprocessing/plot_target_encoder.ipynb
@@ -204,7 +204,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/preprocessing/plot_target_encoder_cross_val.ipynb b/dev/lite/files/auto_examples/preprocessing/plot_target_encoder_cross_val.ipynb
index 824fde489cb11..392afe3be4bf7 100644
--- a/dev/lite/files/auto_examples/preprocessing/plot_target_encoder_cross_val.ipynb
+++ b/dev/lite/files/auto_examples/preprocessing/plot_target_encoder_cross_val.ipynb
@@ -240,7 +240,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/release_highlights/plot_release_highlights_0_22_0.ipynb b/dev/lite/files/auto_examples/release_highlights/plot_release_highlights_0_22_0.ipynb
index 9b9bb49a2b65d..67b32952aec12 100644
--- a/dev/lite/files/auto_examples/release_highlights/plot_release_highlights_0_22_0.ipynb
+++ b/dev/lite/files/auto_examples/release_highlights/plot_release_highlights_0_22_0.ipynb
@@ -233,7 +233,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/release_highlights/plot_release_highlights_0_23_0.ipynb b/dev/lite/files/auto_examples/release_highlights/plot_release_highlights_0_23_0.ipynb
index 4df0db87b63e6..b044f8eb396fc 100644
--- a/dev/lite/files/auto_examples/release_highlights/plot_release_highlights_0_23_0.ipynb
+++ b/dev/lite/files/auto_examples/release_highlights/plot_release_highlights_0_23_0.ipynb
@@ -40,7 +40,7 @@
       },
       "outputs": [],
       "source": [
-        "import numpy as np\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.linear_model import PoissonRegressor\nfrom sklearn.ensemble import HistGradientBoostingRegressor\n\nn_samples, n_features = 1000, 20\nrng = np.random.RandomState(0)\nX = rng.randn(n_samples, n_features)\n# positive integer target correlated with X[:, 5] with many zeros:\ny = rng.poisson(lam=np.exp(X[:, 5]) / 2)\nX_train, X_test, y_train, y_test = train_test_split(X, y, random_state=rng)\nglm = PoissonRegressor()\ngbdt = HistGradientBoostingRegressor(loss=\"poisson\", learning_rate=0.01)\nglm.fit(X_train, y_train)\ngbdt.fit(X_train, y_train)\nprint(glm.score(X_test, y_test))\nprint(gbdt.score(X_test, y_test))"
+        "import numpy as np\n\nfrom sklearn.ensemble import HistGradientBoostingRegressor\nfrom sklearn.linear_model import PoissonRegressor\nfrom sklearn.model_selection import train_test_split\n\nn_samples, n_features = 1000, 20\nrng = np.random.RandomState(0)\nX = rng.randn(n_samples, n_features)\n# positive integer target correlated with X[:, 5] with many zeros:\ny = rng.poisson(lam=np.exp(X[:, 5]) / 2)\nX_train, X_test, y_train, y_test = train_test_split(X, y, random_state=rng)\nglm = PoissonRegressor()\ngbdt = HistGradientBoostingRegressor(loss=\"poisson\", learning_rate=0.01)\nglm.fit(X_train, y_train)\ngbdt.fit(X_train, y_train)\nprint(glm.score(X_test, y_test))\nprint(gbdt.score(X_test, y_test))"
       ]
     },
     {
@@ -58,7 +58,7 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn import set_config\nfrom sklearn.pipeline import make_pipeline\nfrom sklearn.preprocessing import OneHotEncoder, StandardScaler\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.compose import make_column_transformer\nfrom sklearn.linear_model import LogisticRegression\n\nset_config(display=\"diagram\")\n\nnum_proc = make_pipeline(SimpleImputer(strategy=\"median\"), StandardScaler())\n\ncat_proc = make_pipeline(\n    SimpleImputer(strategy=\"constant\", fill_value=\"missing\"),\n    OneHotEncoder(handle_unknown=\"ignore\"),\n)\n\npreprocessor = make_column_transformer(\n    (num_proc, (\"feat1\", \"feat3\")), (cat_proc, (\"feat0\", \"feat2\"))\n)\n\nclf = make_pipeline(preprocessor, LogisticRegression())\nclf"
+        "from sklearn import set_config\nfrom sklearn.compose import make_column_transformer\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.pipeline import make_pipeline\nfrom sklearn.preprocessing import OneHotEncoder, StandardScaler\n\nset_config(display=\"diagram\")\n\nnum_proc = make_pipeline(SimpleImputer(strategy=\"median\"), StandardScaler())\n\ncat_proc = make_pipeline(\n    SimpleImputer(strategy=\"constant\", fill_value=\"missing\"),\n    OneHotEncoder(handle_unknown=\"ignore\"),\n)\n\npreprocessor = make_column_transformer(\n    (num_proc, (\"feat1\", \"feat3\")), (cat_proc, (\"feat0\", \"feat2\"))\n)\n\nclf = make_pipeline(preprocessor, LogisticRegression())\nclf"
       ]
     },
     {
@@ -76,7 +76,7 @@
       },
       "outputs": [],
       "source": [
-        "import scipy\nimport numpy as np\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.cluster import KMeans\nfrom sklearn.datasets import make_blobs\nfrom sklearn.metrics import completeness_score\n\nrng = np.random.RandomState(0)\nX, y = make_blobs(random_state=rng)\nX = scipy.sparse.csr_matrix(X)\nX_train, X_test, _, y_test = train_test_split(X, y, random_state=rng)\nkmeans = KMeans(n_init=\"auto\").fit(X_train)\nprint(completeness_score(kmeans.predict(X_test), y_test))"
+        "import numpy as np\nimport scipy\n\nfrom sklearn.cluster import KMeans\nfrom sklearn.datasets import make_blobs\nfrom sklearn.metrics import completeness_score\nfrom sklearn.model_selection import train_test_split\n\nrng = np.random.RandomState(0)\nX, y = make_blobs(random_state=rng)\nX = scipy.sparse.csr_matrix(X)\nX_train, X_test, _, y_test = train_test_split(X, y, random_state=rng)\nkmeans = KMeans(n_init=\"auto\").fit(X_train)\nprint(completeness_score(kmeans.predict(X_test), y_test))"
       ]
     },
     {
@@ -94,7 +94,7 @@
       },
       "outputs": [],
       "source": [
-        "import numpy as np\nfrom matplotlib import pyplot as plt\nfrom sklearn.model_selection import train_test_split\n\n# from sklearn.inspection import plot_partial_dependence\nfrom sklearn.inspection import PartialDependenceDisplay\nfrom sklearn.ensemble import HistGradientBoostingRegressor\n\nn_samples = 500\nrng = np.random.RandomState(0)\nX = rng.randn(n_samples, 2)\nnoise = rng.normal(loc=0.0, scale=0.01, size=n_samples)\ny = 5 * X[:, 0] + np.sin(10 * np.pi * X[:, 0]) - noise\n\ngbdt_no_cst = HistGradientBoostingRegressor().fit(X, y)\ngbdt_cst = HistGradientBoostingRegressor(monotonic_cst=[1, 0]).fit(X, y)\n\n# plot_partial_dependence has been removed in version 1.2. From 1.2, use\n# PartialDependenceDisplay instead.\n# disp = plot_partial_dependence(\ndisp = PartialDependenceDisplay.from_estimator(\n    gbdt_no_cst,\n    X,\n    features=[0],\n    feature_names=[\"feature 0\"],\n    line_kw={\"linewidth\": 4, \"label\": \"unconstrained\", \"color\": \"tab:blue\"},\n)\n# plot_partial_dependence(\nPartialDependenceDisplay.from_estimator(\n    gbdt_cst,\n    X,\n    features=[0],\n    line_kw={\"linewidth\": 4, \"label\": \"constrained\", \"color\": \"tab:orange\"},\n    ax=disp.axes_,\n)\ndisp.axes_[0, 0].plot(\n    X[:, 0], y, \"o\", alpha=0.5, zorder=-1, label=\"samples\", color=\"tab:green\"\n)\ndisp.axes_[0, 0].set_ylim(-3, 3)\ndisp.axes_[0, 0].set_xlim(-1, 1)\nplt.legend()\nplt.show()"
+        "import numpy as np\nfrom matplotlib import pyplot as plt\n\nfrom sklearn.ensemble import HistGradientBoostingRegressor\n\n# from sklearn.inspection import plot_partial_dependence\nfrom sklearn.inspection import PartialDependenceDisplay\nfrom sklearn.model_selection import train_test_split\n\nn_samples = 500\nrng = np.random.RandomState(0)\nX = rng.randn(n_samples, 2)\nnoise = rng.normal(loc=0.0, scale=0.01, size=n_samples)\ny = 5 * X[:, 0] + np.sin(10 * np.pi * X[:, 0]) - noise\n\ngbdt_no_cst = HistGradientBoostingRegressor().fit(X, y)\ngbdt_cst = HistGradientBoostingRegressor(monotonic_cst=[1, 0]).fit(X, y)\n\n# plot_partial_dependence has been removed in version 1.2. From 1.2, use\n# PartialDependenceDisplay instead.\n# disp = plot_partial_dependence(\ndisp = PartialDependenceDisplay.from_estimator(\n    gbdt_no_cst,\n    X,\n    features=[0],\n    feature_names=[\"feature 0\"],\n    line_kw={\"linewidth\": 4, \"label\": \"unconstrained\", \"color\": \"tab:blue\"},\n)\n# plot_partial_dependence(\nPartialDependenceDisplay.from_estimator(\n    gbdt_cst,\n    X,\n    features=[0],\n    line_kw={\"linewidth\": 4, \"label\": \"constrained\", \"color\": \"tab:orange\"},\n    ax=disp.axes_,\n)\ndisp.axes_[0, 0].plot(\n    X[:, 0], y, \"o\", alpha=0.5, zorder=-1, label=\"samples\", color=\"tab:green\"\n)\ndisp.axes_[0, 0].set_ylim(-3, 3)\ndisp.axes_[0, 0].set_xlim(-1, 1)\nplt.legend()\nplt.show()"
       ]
     },
     {
@@ -112,7 +112,7 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.model_selection import train_test_split\nfrom sklearn.datasets import make_regression\nfrom sklearn.linear_model import Lasso\nimport numpy as np\n\nn_samples, n_features = 1000, 20\nrng = np.random.RandomState(0)\nX, y = make_regression(n_samples, n_features, random_state=rng)\nsample_weight = rng.rand(n_samples)\nX_train, X_test, y_train, y_test, sw_train, sw_test = train_test_split(\n    X, y, sample_weight, random_state=rng\n)\nreg = Lasso()\nreg.fit(X_train, y_train, sample_weight=sw_train)\nprint(reg.score(X_test, y_test, sw_test))"
+        "import numpy as np\n\nfrom sklearn.datasets import make_regression\nfrom sklearn.linear_model import Lasso\nfrom sklearn.model_selection import train_test_split\n\nn_samples, n_features = 1000, 20\nrng = np.random.RandomState(0)\nX, y = make_regression(n_samples, n_features, random_state=rng)\nsample_weight = rng.rand(n_samples)\nX_train, X_test, y_train, y_test, sw_train, sw_test = train_test_split(\n    X, y, sample_weight, random_state=rng\n)\nreg = Lasso()\nreg.fit(X_train, y_train, sample_weight=sw_train)\nprint(reg.score(X_test, y_test, sw_test))"
       ]
     }
   ],
@@ -132,7 +132,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/release_highlights/plot_release_highlights_0_24_0.ipynb b/dev/lite/files/auto_examples/release_highlights/plot_release_highlights_0_24_0.ipynb
index eaadfcd340a08..4a7623531b8d6 100644
--- a/dev/lite/files/auto_examples/release_highlights/plot_release_highlights_0_24_0.ipynb
+++ b/dev/lite/files/auto_examples/release_highlights/plot_release_highlights_0_24_0.ipynb
@@ -40,7 +40,7 @@
       },
       "outputs": [],
       "source": [
-        "import numpy as np\nfrom scipy.stats import randint\nfrom sklearn.experimental import enable_halving_search_cv  # noqa\nfrom sklearn.model_selection import HalvingRandomSearchCV\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.datasets import make_classification\n\nrng = np.random.RandomState(0)\n\nX, y = make_classification(n_samples=700, random_state=rng)\n\nclf = RandomForestClassifier(n_estimators=10, random_state=rng)\n\nparam_dist = {\n    \"max_depth\": [3, None],\n    \"max_features\": randint(1, 11),\n    \"min_samples_split\": randint(2, 11),\n    \"bootstrap\": [True, False],\n    \"criterion\": [\"gini\", \"entropy\"],\n}\n\nrsh = HalvingRandomSearchCV(\n    estimator=clf, param_distributions=param_dist, factor=2, random_state=rng\n)\nrsh.fit(X, y)\nrsh.best_params_"
+        "import numpy as np\nfrom scipy.stats import randint\n\nfrom sklearn.datasets import make_classification\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.experimental import enable_halving_search_cv  # noqa: F401\nfrom sklearn.model_selection import HalvingRandomSearchCV\n\nrng = np.random.RandomState(0)\n\nX, y = make_classification(n_samples=700, random_state=rng)\n\nclf = RandomForestClassifier(n_estimators=10, random_state=rng)\n\nparam_dist = {\n    \"max_depth\": [3, None],\n    \"max_features\": randint(1, 11),\n    \"min_samples_split\": randint(2, 11),\n    \"bootstrap\": [True, False],\n    \"criterion\": [\"gini\", \"entropy\"],\n}\n\nrsh = HalvingRandomSearchCV(\n    estimator=clf, param_distributions=param_dist, factor=2, random_state=rng\n)\nrsh.fit(X, y)\nrsh.best_params_"
       ]
     },
     {
@@ -72,7 +72,7 @@
       },
       "outputs": [],
       "source": [
-        "import numpy as np\nfrom sklearn import datasets\nfrom sklearn.semi_supervised import SelfTrainingClassifier\nfrom sklearn.svm import SVC\n\nrng = np.random.RandomState(42)\niris = datasets.load_iris()\nrandom_unlabeled_points = rng.rand(iris.target.shape[0]) < 0.3\niris.target[random_unlabeled_points] = -1\nsvc = SVC(probability=True, gamma=\"auto\")\nself_training_model = SelfTrainingClassifier(svc)\nself_training_model.fit(iris.data, iris.target)"
+        "import numpy as np\n\nfrom sklearn import datasets\nfrom sklearn.semi_supervised import SelfTrainingClassifier\nfrom sklearn.svm import SVC\n\nrng = np.random.RandomState(42)\niris = datasets.load_iris()\nrandom_unlabeled_points = rng.rand(iris.target.shape[0]) < 0.3\niris.target[random_unlabeled_points] = -1\nsvc = SVC(probability=True, gamma=\"auto\")\nself_training_model = SelfTrainingClassifier(svc)\nself_training_model.fit(iris.data, iris.target)"
       ]
     },
     {
@@ -90,7 +90,7 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.feature_selection import SequentialFeatureSelector\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.datasets import load_iris\n\nX, y = load_iris(return_X_y=True, as_frame=True)\nfeature_names = X.columns\nknn = KNeighborsClassifier(n_neighbors=3)\nsfs = SequentialFeatureSelector(knn, n_features_to_select=2)\nsfs.fit(X, y)\nprint(\n    \"Features selected by forward sequential selection: \"\n    f\"{feature_names[sfs.get_support()].tolist()}\"\n)"
+        "from sklearn.datasets import load_iris\nfrom sklearn.feature_selection import SequentialFeatureSelector\nfrom sklearn.neighbors import KNeighborsClassifier\n\nX, y = load_iris(return_X_y=True, as_frame=True)\nfeature_names = X.columns\nknn = KNeighborsClassifier(n_neighbors=3)\nsfs = SequentialFeatureSelector(knn, n_features_to_select=2)\nsfs.fit(X, y)\nprint(\n    \"Features selected by forward sequential selection: \"\n    f\"{feature_names[sfs.get_support()].tolist()}\"\n)"
       ]
     },
     {
@@ -108,7 +108,7 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.datasets import fetch_covtype\nfrom sklearn.pipeline import make_pipeline\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import MinMaxScaler\nfrom sklearn.kernel_approximation import PolynomialCountSketch\nfrom sklearn.linear_model import LogisticRegression\n\nX, y = fetch_covtype(return_X_y=True)\npipe = make_pipeline(\n    MinMaxScaler(),\n    PolynomialCountSketch(degree=2, n_components=300),\n    LogisticRegression(max_iter=1000),\n)\nX_train, X_test, y_train, y_test = train_test_split(\n    X, y, train_size=5000, test_size=10000, random_state=42\n)\npipe.fit(X_train, y_train).score(X_test, y_test)"
+        "from sklearn.datasets import fetch_covtype\nfrom sklearn.kernel_approximation import PolynomialCountSketch\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.pipeline import make_pipeline\nfrom sklearn.preprocessing import MinMaxScaler\n\nX, y = fetch_covtype(return_X_y=True)\npipe = make_pipeline(\n    MinMaxScaler(),\n    PolynomialCountSketch(degree=2, n_components=300),\n    LogisticRegression(max_iter=1000),\n)\nX_train, X_test, y_train, y_test = train_test_split(\n    X, y, train_size=5000, test_size=10000, random_state=42\n)\npipe.fit(X_train, y_train).score(X_test, y_test)"
       ]
     },
     {
@@ -144,7 +144,7 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.ensemble import RandomForestRegressor\nfrom sklearn.datasets import fetch_california_housing\n\n# from sklearn.inspection import plot_partial_dependence\nfrom sklearn.inspection import PartialDependenceDisplay\n\nX, y = fetch_california_housing(return_X_y=True, as_frame=True)\nfeatures = [\"MedInc\", \"AveOccup\", \"HouseAge\", \"AveRooms\"]\nest = RandomForestRegressor(n_estimators=10)\nest.fit(X, y)\n\n# plot_partial_dependence has been removed in version 1.2. From 1.2, use\n# PartialDependenceDisplay instead.\n# display = plot_partial_dependence(\ndisplay = PartialDependenceDisplay.from_estimator(\n    est,\n    X,\n    features,\n    kind=\"individual\",\n    subsample=50,\n    n_jobs=3,\n    grid_resolution=20,\n    random_state=0,\n)\ndisplay.figure_.suptitle(\n    \"Partial dependence of house value on non-location features\\n\"\n    \"for the California housing dataset, with BayesianRidge\"\n)\ndisplay.figure_.subplots_adjust(hspace=0.3)"
+        "from sklearn.datasets import fetch_california_housing\nfrom sklearn.ensemble import RandomForestRegressor\n\n# from sklearn.inspection import plot_partial_dependence\nfrom sklearn.inspection import PartialDependenceDisplay\n\nX, y = fetch_california_housing(return_X_y=True, as_frame=True)\nfeatures = [\"MedInc\", \"AveOccup\", \"HouseAge\", \"AveRooms\"]\nest = RandomForestRegressor(n_estimators=10)\nest.fit(X, y)\n\n# plot_partial_dependence has been removed in version 1.2. From 1.2, use\n# PartialDependenceDisplay instead.\n# display = plot_partial_dependence(\ndisplay = PartialDependenceDisplay.from_estimator(\n    est,\n    X,\n    features,\n    kind=\"individual\",\n    subsample=50,\n    n_jobs=3,\n    grid_resolution=20,\n    random_state=0,\n)\ndisplay.figure_.suptitle(\n    \"Partial dependence of house value on non-location features\\n\"\n    \"for the California housing dataset, with BayesianRidge\"\n)\ndisplay.figure_.subplots_adjust(hspace=0.3)"
       ]
     },
     {
@@ -162,7 +162,7 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.tree import DecisionTreeRegressor\nfrom sklearn.model_selection import train_test_split\nimport numpy as np\n\nn_samples, n_features = 1000, 20\nrng = np.random.RandomState(0)\nX = rng.randn(n_samples, n_features)\n# positive integer target correlated with X[:, 5] with many zeros:\ny = rng.poisson(lam=np.exp(X[:, 5]) / 2)\nX_train, X_test, y_train, y_test = train_test_split(X, y, random_state=rng)\nregressor = DecisionTreeRegressor(criterion=\"poisson\", random_state=0)\nregressor.fit(X_train, y_train)"
+        "import numpy as np\n\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.tree import DecisionTreeRegressor\n\nn_samples, n_features = 1000, 20\nrng = np.random.RandomState(0)\nX = rng.randn(n_samples, n_features)\n# positive integer target correlated with X[:, 5] with many zeros:\ny = rng.poisson(lam=np.exp(X[:, 5]) / 2)\nX_train, X_test, y_train, y_test = train_test_split(X, y, random_state=rng)\nregressor = DecisionTreeRegressor(criterion=\"poisson\", random_state=0)\nregressor.fit(X_train, y_train)"
       ]
     },
     {
@@ -189,7 +189,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/release_highlights/plot_release_highlights_1_0_0.ipynb b/dev/lite/files/auto_examples/release_highlights/plot_release_highlights_1_0_0.ipynb
index 5a849533e9664..6c1404d021851 100644
--- a/dev/lite/files/auto_examples/release_highlights/plot_release_highlights_1_0_0.ipynb
+++ b/dev/lite/files/auto_examples/release_highlights/plot_release_highlights_1_0_0.ipynb
@@ -47,7 +47,7 @@
       },
       "outputs": [],
       "source": [
-        "import numpy as np\nfrom sklearn.preprocessing import SplineTransformer\n\nX = np.arange(5).reshape(5, 1)\nspline = SplineTransformer(degree=2, n_knots=3)\nspline.fit_transform(X)"
+        "import numpy as np\n\nfrom sklearn.preprocessing import SplineTransformer\n\nX = np.arange(5).reshape(5, 1)\nspline = SplineTransformer(degree=2, n_knots=3)\nspline.fit_transform(X)"
       ]
     },
     {
@@ -72,7 +72,7 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.preprocessing import StandardScaler\nimport pandas as pd\n\nX = pd.DataFrame([[1, 2, 3], [4, 5, 6]], columns=[\"a\", \"b\", \"c\"])\nscalar = StandardScaler().fit(X)\nscalar.feature_names_in_"
+        "import pandas as pd\n\nfrom sklearn.preprocessing import StandardScaler\n\nX = pd.DataFrame([[1, 2, 3], [4, 5, 6]], columns=[\"a\", \"b\", \"c\"])\nscalar = StandardScaler().fit(X)\nscalar.feature_names_in_"
       ]
     },
     {
@@ -90,7 +90,7 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.compose import ColumnTransformer\nfrom sklearn.preprocessing import OneHotEncoder\nimport pandas as pd\n\nX = pd.DataFrame({\"pet\": [\"dog\", \"cat\", \"fish\"], \"age\": [3, 7, 1]})\npreprocessor = ColumnTransformer(\n    [\n        (\"numerical\", StandardScaler(), [\"age\"]),\n        (\"categorical\", OneHotEncoder(), [\"pet\"]),\n    ],\n    verbose_feature_names_out=False,\n).fit(X)\n\npreprocessor.get_feature_names_out()"
+        "import pandas as pd\n\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.preprocessing import OneHotEncoder\n\nX = pd.DataFrame({\"pet\": [\"dog\", \"cat\", \"fish\"], \"age\": [3, 7, 1]})\npreprocessor = ColumnTransformer(\n    [\n        (\"numerical\", StandardScaler(), [\"age\"]),\n        (\"categorical\", OneHotEncoder(), [\"pet\"]),\n    ],\n    verbose_feature_names_out=False,\n).fit(X)\n\npreprocessor.get_feature_names_out()"
       ]
     },
     {
@@ -156,7 +156,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/release_highlights/plot_release_highlights_1_1_0.ipynb b/dev/lite/files/auto_examples/release_highlights/plot_release_highlights_1_1_0.ipynb
index e7f66c94cb4d1..c3818e4f83044 100644
--- a/dev/lite/files/auto_examples/release_highlights/plot_release_highlights_1_1_0.ipynb
+++ b/dev/lite/files/auto_examples/release_highlights/plot_release_highlights_1_1_0.ipynb
@@ -40,7 +40,7 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.ensemble import HistGradientBoostingRegressor\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n# Simple regression function for X * cos(X)\nrng = np.random.RandomState(42)\nX_1d = np.linspace(0, 10, num=2000)\nX = X_1d.reshape(-1, 1)\ny = X_1d * np.cos(X_1d) + rng.normal(scale=X_1d / 3)\n\nquantiles = [0.95, 0.5, 0.05]\nparameters = dict(loss=\"quantile\", max_bins=32, max_iter=50)\nhist_quantiles = {\n    f\"quantile={quantile:.2f}\": HistGradientBoostingRegressor(\n        **parameters, quantile=quantile\n    ).fit(X, y)\n    for quantile in quantiles\n}\n\nfig, ax = plt.subplots()\nax.plot(X_1d, y, \"o\", alpha=0.5, markersize=1)\nfor quantile, hist in hist_quantiles.items():\n    ax.plot(X_1d, hist.predict(X), label=quantile)\n_ = ax.legend(loc=\"lower left\")"
+        "import matplotlib.pyplot as plt\nimport numpy as np\n\nfrom sklearn.ensemble import HistGradientBoostingRegressor\n\n# Simple regression function for X * cos(X)\nrng = np.random.RandomState(42)\nX_1d = np.linspace(0, 10, num=2000)\nX = X_1d.reshape(-1, 1)\ny = X_1d * np.cos(X_1d) + rng.normal(scale=X_1d / 3)\n\nquantiles = [0.95, 0.5, 0.05]\nparameters = dict(loss=\"quantile\", max_bins=32, max_iter=50)\nhist_quantiles = {\n    f\"quantile={quantile:.2f}\": HistGradientBoostingRegressor(\n        **parameters, quantile=quantile\n    ).fit(X, y)\n    for quantile in quantiles\n}\n\nfig, ax = plt.subplots()\nax.plot(X_1d, y, \"o\", alpha=0.5, markersize=1)\nfor quantile, hist in hist_quantiles.items():\n    ax.plot(X_1d, hist.predict(X), label=quantile)\n_ = ax.legend(loc=\"lower left\")"
       ]
     },
     {
@@ -65,7 +65,7 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.compose import ColumnTransformer\nfrom sklearn.preprocessing import OneHotEncoder, StandardScaler\nfrom sklearn.pipeline import make_pipeline\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.feature_selection import SelectKBest\nfrom sklearn.datasets import fetch_openml\nfrom sklearn.linear_model import LogisticRegression\n\nX, y = fetch_openml(\n    \"titanic\", version=1, as_frame=True, return_X_y=True, parser=\"pandas\"\n)\nnumeric_features = [\"age\", \"fare\"]\nnumeric_transformer = make_pipeline(SimpleImputer(strategy=\"median\"), StandardScaler())\ncategorical_features = [\"embarked\", \"pclass\"]\n\npreprocessor = ColumnTransformer(\n    [\n        (\"num\", numeric_transformer, numeric_features),\n        (\n            \"cat\",\n            OneHotEncoder(handle_unknown=\"ignore\", sparse_output=False),\n            categorical_features,\n        ),\n    ],\n    verbose_feature_names_out=False,\n)\nlog_reg = make_pipeline(preprocessor, SelectKBest(k=7), LogisticRegression())\nlog_reg.fit(X, y)"
+        "from sklearn.compose import ColumnTransformer\nfrom sklearn.datasets import fetch_openml\nfrom sklearn.feature_selection import SelectKBest\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.pipeline import make_pipeline\nfrom sklearn.preprocessing import OneHotEncoder, StandardScaler\n\nX, y = fetch_openml(\n    \"titanic\", version=1, as_frame=True, return_X_y=True, parser=\"pandas\"\n)\nnumeric_features = [\"age\", \"fare\"]\nnumeric_transformer = make_pipeline(SimpleImputer(strategy=\"median\"), StandardScaler())\ncategorical_features = [\"embarked\", \"pclass\"]\n\npreprocessor = ColumnTransformer(\n    [\n        (\"num\", numeric_transformer, numeric_features),\n        (\n            \"cat\",\n            OneHotEncoder(handle_unknown=\"ignore\", sparse_output=False),\n            categorical_features,\n        ),\n    ],\n    verbose_feature_names_out=False,\n)\nlog_reg = make_pipeline(preprocessor, SelectKBest(k=7), LogisticRegression())\nlog_reg.fit(X, y)"
       ]
     },
     {
@@ -101,7 +101,7 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.preprocessing import OneHotEncoder\nimport numpy as np\n\nX = np.array(\n    [[\"dog\"] * 5 + [\"cat\"] * 20 + [\"rabbit\"] * 10 + [\"snake\"] * 3], dtype=object\n).T\nenc = OneHotEncoder(min_frequency=6, sparse_output=False).fit(X)\nenc.infrequent_categories_"
+        "import numpy as np\n\nfrom sklearn.preprocessing import OneHotEncoder\n\nX = np.array(\n    [[\"dog\"] * 5 + [\"cat\"] * 20 + [\"rabbit\"] * 10 + [\"snake\"] * 3], dtype=object\n).T\nenc = OneHotEncoder(min_frequency=6, sparse_output=False).fit(X)\nenc.infrequent_categories_"
       ]
     },
     {
@@ -144,7 +144,7 @@
       },
       "outputs": [],
       "source": [
-        "import numpy as np\nfrom sklearn.decomposition import MiniBatchNMF\n\nrng = np.random.RandomState(0)\nn_samples, n_features, n_components = 10, 10, 5\ntrue_W = rng.uniform(size=(n_samples, n_components))\ntrue_H = rng.uniform(size=(n_components, n_features))\nX = true_W @ true_H\n\nnmf = MiniBatchNMF(n_components=n_components, random_state=0)\n\nfor _ in range(10):\n    nmf.partial_fit(X)\n\nW = nmf.transform(X)\nH = nmf.components_\nX_reconstructed = W @ H\n\nprint(\n    f\"relative reconstruction error: \",\n    f\"{np.sum((X - X_reconstructed) ** 2) / np.sum(X**2):.5f}\",\n)"
+        "import numpy as np\n\nfrom sklearn.decomposition import MiniBatchNMF\n\nrng = np.random.RandomState(0)\nn_samples, n_features, n_components = 10, 10, 5\ntrue_W = rng.uniform(size=(n_samples, n_components))\ntrue_H = rng.uniform(size=(n_components, n_features))\nX = true_W @ true_H\n\nnmf = MiniBatchNMF(n_components=n_components, random_state=0)\n\nfor _ in range(10):\n    nmf.partial_fit(X)\n\nW = nmf.transform(X)\nH = nmf.components_\nX_reconstructed = W @ H\n\nprint(\n    \"relative reconstruction error: \",\n    f\"{np.sum((X - X_reconstructed) ** 2) / np.sum(X**2):.5f}\",\n)"
       ]
     },
     {
@@ -162,7 +162,7 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.datasets import make_blobs\nfrom sklearn.cluster import KMeans, BisectingKMeans\nimport matplotlib.pyplot as plt\n\nX, _ = make_blobs(n_samples=1000, centers=2, random_state=0)\n\nkm = KMeans(n_clusters=5, random_state=0, n_init=\"auto\").fit(X)\nbisect_km = BisectingKMeans(n_clusters=5, random_state=0).fit(X)\n\nfig, ax = plt.subplots(1, 2, figsize=(10, 5))\nax[0].scatter(X[:, 0], X[:, 1], s=10, c=km.labels_)\nax[0].scatter(km.cluster_centers_[:, 0], km.cluster_centers_[:, 1], s=20, c=\"r\")\nax[0].set_title(\"KMeans\")\n\nax[1].scatter(X[:, 0], X[:, 1], s=10, c=bisect_km.labels_)\nax[1].scatter(\n    bisect_km.cluster_centers_[:, 0], bisect_km.cluster_centers_[:, 1], s=20, c=\"r\"\n)\n_ = ax[1].set_title(\"BisectingKMeans\")"
+        "import matplotlib.pyplot as plt\n\nfrom sklearn.cluster import BisectingKMeans, KMeans\nfrom sklearn.datasets import make_blobs\n\nX, _ = make_blobs(n_samples=1000, centers=2, random_state=0)\n\nkm = KMeans(n_clusters=5, random_state=0, n_init=\"auto\").fit(X)\nbisect_km = BisectingKMeans(n_clusters=5, random_state=0).fit(X)\n\nfig, ax = plt.subplots(1, 2, figsize=(10, 5))\nax[0].scatter(X[:, 0], X[:, 1], s=10, c=km.labels_)\nax[0].scatter(km.cluster_centers_[:, 0], km.cluster_centers_[:, 1], s=20, c=\"r\")\nax[0].set_title(\"KMeans\")\n\nax[1].scatter(X[:, 0], X[:, 1], s=10, c=bisect_km.labels_)\nax[1].scatter(\n    bisect_km.cluster_centers_[:, 0], bisect_km.cluster_centers_[:, 1], s=20, c=\"r\"\n)\n_ = ax[1].set_title(\"BisectingKMeans\")"
       ]
     }
   ],
@@ -182,7 +182,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/release_highlights/plot_release_highlights_1_2_0.ipynb b/dev/lite/files/auto_examples/release_highlights/plot_release_highlights_1_2_0.ipynb
index 09d8878e08ef7..ca76956c11c62 100644
--- a/dev/lite/files/auto_examples/release_highlights/plot_release_highlights_1_2_0.ipynb
+++ b/dev/lite/files/auto_examples/release_highlights/plot_release_highlights_1_2_0.ipynb
@@ -40,7 +40,7 @@
       },
       "outputs": [],
       "source": [
-        "import numpy as np\nfrom sklearn.datasets import load_iris\nfrom sklearn.preprocessing import StandardScaler, KBinsDiscretizer\nfrom sklearn.compose import ColumnTransformer\n\nX, y = load_iris(as_frame=True, return_X_y=True)\nsepal_cols = [\"sepal length (cm)\", \"sepal width (cm)\"]\npetal_cols = [\"petal length (cm)\", \"petal width (cm)\"]\n\npreprocessor = ColumnTransformer(\n    [\n        (\"scaler\", StandardScaler(), sepal_cols),\n        (\n            \"kbin\",\n            KBinsDiscretizer(encode=\"ordinal\", quantile_method=\"averaged_inverted_cdf\"),\n            petal_cols,\n        ),\n    ],\n    verbose_feature_names_out=False,\n).set_output(transform=\"pandas\")\n\nX_out = preprocessor.fit_transform(X)\nX_out.sample(n=5, random_state=0)"
+        "import numpy as np\n\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.datasets import load_iris\nfrom sklearn.preprocessing import KBinsDiscretizer, StandardScaler\n\nX, y = load_iris(as_frame=True, return_X_y=True)\nsepal_cols = [\"sepal length (cm)\", \"sepal width (cm)\"]\npetal_cols = [\"petal length (cm)\", \"petal width (cm)\"]\n\npreprocessor = ColumnTransformer(\n    [\n        (\"scaler\", StandardScaler(), sepal_cols),\n        (\n            \"kbin\",\n            KBinsDiscretizer(encode=\"ordinal\", quantile_method=\"averaged_inverted_cdf\"),\n            petal_cols,\n        ),\n    ],\n    verbose_feature_names_out=False,\n).set_output(transform=\"pandas\")\n\nX_out = preprocessor.fit_transform(X)\nX_out.sample(n=5, random_state=0)"
       ]
     },
     {
@@ -76,7 +76,7 @@
       },
       "outputs": [],
       "source": [
-        "import matplotlib.pyplot as plt\nfrom sklearn.metrics import PredictionErrorDisplay\n\nfig, axs = plt.subplots(nrows=1, ncols=2, figsize=(12, 5))\n_ = PredictionErrorDisplay.from_estimator(\n    hist_no_interact, X, y, kind=\"actual_vs_predicted\", ax=axs[0]\n)\n_ = PredictionErrorDisplay.from_estimator(\n    hist_no_interact, X, y, kind=\"residual_vs_predicted\", ax=axs[1]\n)"
+        "import matplotlib.pyplot as plt\n\nfrom sklearn.metrics import PredictionErrorDisplay\n\nfig, axs = plt.subplots(nrows=1, ncols=2, figsize=(12, 5))\n_ = PredictionErrorDisplay.from_estimator(\n    hist_no_interact, X, y, kind=\"actual_vs_predicted\", ax=axs[0]\n)\n_ = PredictionErrorDisplay.from_estimator(\n    hist_no_interact, X, y, kind=\"residual_vs_predicted\", ax=axs[1]\n)"
       ]
     },
     {
@@ -123,7 +123,7 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.preprocessing import OrdinalEncoder\nfrom sklearn.pipeline import make_pipeline\n\ncategorical_features = [\"pclass\", \"sex\", \"embarked\"]\nmodel = make_pipeline(\n    ColumnTransformer(\n        transformers=[(\"cat\", OrdinalEncoder(), categorical_features)],\n        remainder=\"passthrough\",\n    ),\n    HistGradientBoostingRegressor(random_state=0),\n).fit(X, y)"
+        "from sklearn.pipeline import make_pipeline\nfrom sklearn.preprocessing import OrdinalEncoder\n\ncategorical_features = [\"pclass\", \"sex\", \"embarked\"]\nmodel = make_pipeline(\n    ColumnTransformer(\n        transformers=[(\"cat\", OrdinalEncoder(), categorical_features)],\n        remainder=\"passthrough\",\n    ),\n    HistGradientBoostingRegressor(random_state=0),\n).fit(X, y)"
       ]
     },
     {
@@ -186,7 +186,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/release_highlights/plot_release_highlights_1_3_0.ipynb b/dev/lite/files/auto_examples/release_highlights/plot_release_highlights_1_3_0.ipynb
index 31aed454dc6ad..13707174aa6ca 100644
--- a/dev/lite/files/auto_examples/release_highlights/plot_release_highlights_1_3_0.ipynb
+++ b/dev/lite/files/auto_examples/release_highlights/plot_release_highlights_1_3_0.ipynb
@@ -47,7 +47,7 @@
       },
       "outputs": [],
       "source": [
-        "import numpy as np\nfrom sklearn.cluster import HDBSCAN\nfrom sklearn.datasets import load_digits\nfrom sklearn.metrics import v_measure_score\n\nX, true_labels = load_digits(return_X_y=True)\nprint(f\"number of digits: {len(np.unique(true_labels))}\")\n\nhdbscan = HDBSCAN(min_cluster_size=15).fit(X)\nnon_noisy_labels = hdbscan.labels_[hdbscan.labels_ != -1]\nprint(f\"number of clusters found: {len(np.unique(non_noisy_labels))}\")\n\nprint(v_measure_score(true_labels[hdbscan.labels_ != -1], non_noisy_labels))"
+        "import numpy as np\n\nfrom sklearn.cluster import HDBSCAN\nfrom sklearn.datasets import load_digits\nfrom sklearn.metrics import v_measure_score\n\nX, true_labels = load_digits(return_X_y=True)\nprint(f\"number of digits: {len(np.unique(true_labels))}\")\n\nhdbscan = HDBSCAN(min_cluster_size=15).fit(X)\nnon_noisy_labels = hdbscan.labels_[hdbscan.labels_ != -1]\nprint(f\"number of clusters found: {len(np.unique(non_noisy_labels))}\")\n\nprint(v_measure_score(true_labels[hdbscan.labels_ != -1], non_noisy_labels))"
       ]
     },
     {
@@ -65,7 +65,7 @@
       },
       "outputs": [],
       "source": [
-        "import numpy as np\nfrom sklearn.preprocessing import TargetEncoder\n\nX = np.array([[\"cat\"] * 30 + [\"dog\"] * 20 + [\"snake\"] * 38], dtype=object).T\ny = [90.3] * 30 + [20.4] * 20 + [21.2] * 38\n\nenc = TargetEncoder(random_state=0)\nX_trans = enc.fit_transform(X, y)\n\nenc.encodings_"
+        "import numpy as np\n\nfrom sklearn.preprocessing import TargetEncoder\n\nX = np.array([[\"cat\"] * 30 + [\"dog\"] * 20 + [\"snake\"] * 38], dtype=object).T\ny = [90.3] * 30 + [20.4] * 20 + [21.2] * 38\n\nenc = TargetEncoder(random_state=0)\nX_trans = enc.fit_transform(X, y)\n\nenc.encodings_"
       ]
     },
     {
@@ -83,7 +83,7 @@
       },
       "outputs": [],
       "source": [
-        "import numpy as np\nfrom sklearn.tree import DecisionTreeClassifier\n\nX = np.array([0, 1, 6, np.nan]).reshape(-1, 1)\ny = [0, 0, 1, 1]\n\ntree = DecisionTreeClassifier(random_state=0).fit(X, y)\ntree.predict(X)"
+        "import numpy as np\n\nfrom sklearn.tree import DecisionTreeClassifier\n\nX = np.array([0, 1, 6, np.nan]).reshape(-1, 1)\ny = [0, 0, 1, 1]\n\ntree = DecisionTreeClassifier(random_state=0).fit(X, y)\ntree.predict(X)"
       ]
     },
     {
@@ -119,7 +119,7 @@
       },
       "outputs": [],
       "source": [
-        "import numpy as np\nfrom sklearn.model_selection import cross_val_score\nfrom sklearn.datasets import make_low_rank_matrix\nfrom sklearn.ensemble import HistGradientBoostingRegressor\n\nn_samples, n_features = 500, 10\nrng = np.random.RandomState(0)\nX = make_low_rank_matrix(n_samples, n_features, random_state=rng)\ncoef = rng.uniform(low=-10, high=20, size=n_features)\ny = rng.gamma(shape=2, scale=np.exp(X @ coef) / 2)\ngbdt = HistGradientBoostingRegressor(loss=\"gamma\")\ncross_val_score(gbdt, X, y).mean()"
+        "import numpy as np\n\nfrom sklearn.datasets import make_low_rank_matrix\nfrom sklearn.ensemble import HistGradientBoostingRegressor\nfrom sklearn.model_selection import cross_val_score\n\nn_samples, n_features = 500, 10\nrng = np.random.RandomState(0)\nX = make_low_rank_matrix(n_samples, n_features, random_state=rng)\ncoef = rng.uniform(low=-10, high=20, size=n_features)\ny = rng.gamma(shape=2, scale=np.exp(X @ coef) / 2)\ngbdt = HistGradientBoostingRegressor(loss=\"gamma\")\ncross_val_score(gbdt, X, y).mean()"
       ]
     },
     {
@@ -137,7 +137,7 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.preprocessing import OrdinalEncoder\nimport numpy as np\n\nX = np.array(\n    [[\"dog\"] * 5 + [\"cat\"] * 20 + [\"rabbit\"] * 10 + [\"snake\"] * 3], dtype=object\n).T\nenc = OrdinalEncoder(min_frequency=6).fit(X)\nenc.infrequent_categories_"
+        "import numpy as np\n\nfrom sklearn.preprocessing import OrdinalEncoder\n\nX = np.array(\n    [[\"dog\"] * 5 + [\"cat\"] * 20 + [\"rabbit\"] * 10 + [\"snake\"] * 3], dtype=object\n).T\nenc = OrdinalEncoder(min_frequency=6).fit(X)\nenc.infrequent_categories_"
       ]
     }
   ],
@@ -157,7 +157,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/release_highlights/plot_release_highlights_1_4_0.ipynb b/dev/lite/files/auto_examples/release_highlights/plot_release_highlights_1_4_0.ipynb
index ae21c90cf9c63..ba4af4862df1a 100644
--- a/dev/lite/files/auto_examples/release_highlights/plot_release_highlights_1_4_0.ipynb
+++ b/dev/lite/files/auto_examples/release_highlights/plot_release_highlights_1_4_0.ipynb
@@ -58,7 +58,7 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.ensemble import HistGradientBoostingClassifier\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import roc_auc_score\n\nX_train, X_test, y_train, y_test = train_test_split(X_adult, y_adult, random_state=0)\nhist = HistGradientBoostingClassifier(categorical_features=\"from_dtype\")\n\nhist.fit(X_train, y_train)\ny_decision = hist.decision_function(X_test)\nprint(f\"ROC AUC score is {roc_auc_score(y_test, y_decision)}\")"
+        "from sklearn.ensemble import HistGradientBoostingClassifier\nfrom sklearn.metrics import roc_auc_score\nfrom sklearn.model_selection import train_test_split\n\nX_train, X_test, y_train, y_test = train_test_split(X_adult, y_adult, random_state=0)\nhist = HistGradientBoostingClassifier(categorical_features=\"from_dtype\")\n\nhist.fit(X_train, y_train)\ny_decision = hist.decision_function(X_test)\nprint(f\"ROC AUC score is {roc_auc_score(y_test, y_decision)}\")"
       ]
     },
     {
@@ -76,7 +76,7 @@
       },
       "outputs": [],
       "source": [
-        "import polars as pl\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.preprocessing import OneHotEncoder\nfrom sklearn.compose import ColumnTransformer\n\ndf = pl.DataFrame(\n    {\"height\": [120, 140, 150, 110, 100], \"pet\": [\"dog\", \"cat\", \"dog\", \"cat\", \"cat\"]}\n)\npreprocessor = ColumnTransformer(\n    [\n        (\"numerical\", StandardScaler(), [\"height\"]),\n        (\"categorical\", OneHotEncoder(sparse_output=False), [\"pet\"]),\n    ],\n    verbose_feature_names_out=False,\n)\npreprocessor.set_output(transform=\"polars\")\n\ndf_out = preprocessor.fit_transform(df)\ndf_out"
+        "import polars as pl\n\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.preprocessing import OneHotEncoder, StandardScaler\n\ndf = pl.DataFrame(\n    {\"height\": [120, 140, 150, 110, 100], \"pet\": [\"dog\", \"cat\", \"dog\", \"cat\", \"cat\"]}\n)\npreprocessor = ColumnTransformer(\n    [\n        (\"numerical\", StandardScaler(), [\"height\"]),\n        (\"categorical\", OneHotEncoder(sparse_output=False), [\"pet\"]),\n    ],\n    verbose_feature_names_out=False,\n)\npreprocessor.set_output(transform=\"polars\")\n\ndf_out = preprocessor.fit_transform(df)\ndf_out"
       ]
     },
     {
@@ -105,7 +105,7 @@
       },
       "outputs": [],
       "source": [
-        "import numpy as np\nfrom sklearn.ensemble import RandomForestClassifier\n\nX = np.array([0, 1, 6, np.nan]).reshape(-1, 1)\ny = [0, 0, 1, 1]\n\nforest = RandomForestClassifier(random_state=0).fit(X, y)\nforest.predict(X)"
+        "import numpy as np\n\nfrom sklearn.ensemble import RandomForestClassifier\n\nX = np.array([0, 1, 6, np.nan]).reshape(-1, 1)\ny = [0, 0, 1, 1]\n\nforest = RandomForestClassifier(random_state=0).fit(X, y)\nforest.predict(X)"
       ]
     },
     {
@@ -123,7 +123,7 @@
       },
       "outputs": [],
       "source": [
-        "import matplotlib.pyplot as plt\nfrom sklearn.inspection import PartialDependenceDisplay\nfrom sklearn.ensemble import RandomForestRegressor\n\nn_samples = 500\nrng = np.random.RandomState(0)\nX = rng.randn(n_samples, 2)\nnoise = rng.normal(loc=0.0, scale=0.01, size=n_samples)\ny = 5 * X[:, 0] + np.sin(10 * np.pi * X[:, 0]) - noise\n\nrf_no_cst = RandomForestRegressor().fit(X, y)\nrf_cst = RandomForestRegressor(monotonic_cst=[1, 0]).fit(X, y)\n\ndisp = PartialDependenceDisplay.from_estimator(\n    rf_no_cst,\n    X,\n    features=[0],\n    feature_names=[\"feature 0\"],\n    line_kw={\"linewidth\": 4, \"label\": \"unconstrained\", \"color\": \"tab:blue\"},\n)\nPartialDependenceDisplay.from_estimator(\n    rf_cst,\n    X,\n    features=[0],\n    line_kw={\"linewidth\": 4, \"label\": \"constrained\", \"color\": \"tab:orange\"},\n    ax=disp.axes_,\n)\ndisp.axes_[0, 0].plot(\n    X[:, 0], y, \"o\", alpha=0.5, zorder=-1, label=\"samples\", color=\"tab:green\"\n)\ndisp.axes_[0, 0].set_ylim(-3, 3)\ndisp.axes_[0, 0].set_xlim(-1, 1)\ndisp.axes_[0, 0].legend()\nplt.show()"
+        "import matplotlib.pyplot as plt\n\nfrom sklearn.ensemble import RandomForestRegressor\nfrom sklearn.inspection import PartialDependenceDisplay\n\nn_samples = 500\nrng = np.random.RandomState(0)\nX = rng.randn(n_samples, 2)\nnoise = rng.normal(loc=0.0, scale=0.01, size=n_samples)\ny = 5 * X[:, 0] + np.sin(10 * np.pi * X[:, 0]) - noise\n\nrf_no_cst = RandomForestRegressor().fit(X, y)\nrf_cst = RandomForestRegressor(monotonic_cst=[1, 0]).fit(X, y)\n\ndisp = PartialDependenceDisplay.from_estimator(\n    rf_no_cst,\n    X,\n    features=[0],\n    feature_names=[\"feature 0\"],\n    line_kw={\"linewidth\": 4, \"label\": \"unconstrained\", \"color\": \"tab:blue\"},\n)\nPartialDependenceDisplay.from_estimator(\n    rf_cst,\n    X,\n    features=[0],\n    line_kw={\"linewidth\": 4, \"label\": \"constrained\", \"color\": \"tab:orange\"},\n    ax=disp.axes_,\n)\ndisp.axes_[0, 0].plot(\n    X[:, 0], y, \"o\", alpha=0.5, zorder=-1, label=\"samples\", color=\"tab:green\"\n)\ndisp.axes_[0, 0].set_ylim(-3, 3)\ndisp.axes_[0, 0].set_xlim(-1, 1)\ndisp.axes_[0, 0].legend()\nplt.show()"
       ]
     },
     {
@@ -177,7 +177,7 @@
       },
       "outputs": [],
       "source": [
-        "import sklearn\nfrom sklearn.metrics import get_scorer\nfrom sklearn.datasets import make_regression\nfrom sklearn.linear_model import Lasso\nfrom sklearn.model_selection import GridSearchCV, cross_validate, GroupKFold\n\n# For now by default metadata routing is disabled, and need to be explicitly\n# enabled.\nsklearn.set_config(enable_metadata_routing=True)\n\nn_samples = 100\nX, y = make_regression(n_samples=n_samples, n_features=5, noise=0.5)\nrng = np.random.RandomState(7)\ngroups = rng.randint(0, 10, size=n_samples)\nsample_weights = rng.rand(n_samples)\nestimator = Lasso().set_fit_request(sample_weight=True)\nhyperparameter_grid = {\"alpha\": [0.1, 0.5, 1.0, 2.0]}\nscoring_inner_cv = get_scorer(\"neg_mean_squared_error\").set_score_request(\n    sample_weight=True\n)\ninner_cv = GroupKFold(n_splits=5)\n\ngrid_search = GridSearchCV(\n    estimator=estimator,\n    param_grid=hyperparameter_grid,\n    cv=inner_cv,\n    scoring=scoring_inner_cv,\n)\n\nouter_cv = GroupKFold(n_splits=5)\nscorers = {\n    \"mse\": get_scorer(\"neg_mean_squared_error\").set_score_request(sample_weight=True)\n}\nresults = cross_validate(\n    grid_search,\n    X,\n    y,\n    cv=outer_cv,\n    scoring=scorers,\n    return_estimator=True,\n    params={\"sample_weight\": sample_weights, \"groups\": groups},\n)\nprint(\"cv error on test sets:\", results[\"test_mse\"])\n\n# Setting the flag to the default `False` to avoid interference with other\n# scripts.\nsklearn.set_config(enable_metadata_routing=False)"
+        "import sklearn\nfrom sklearn.datasets import make_regression\nfrom sklearn.linear_model import Lasso\nfrom sklearn.metrics import get_scorer\nfrom sklearn.model_selection import GridSearchCV, GroupKFold, cross_validate\n\n# For now by default metadata routing is disabled, and need to be explicitly\n# enabled.\nsklearn.set_config(enable_metadata_routing=True)\n\nn_samples = 100\nX, y = make_regression(n_samples=n_samples, n_features=5, noise=0.5)\nrng = np.random.RandomState(7)\ngroups = rng.randint(0, 10, size=n_samples)\nsample_weights = rng.rand(n_samples)\nestimator = Lasso().set_fit_request(sample_weight=True)\nhyperparameter_grid = {\"alpha\": [0.1, 0.5, 1.0, 2.0]}\nscoring_inner_cv = get_scorer(\"neg_mean_squared_error\").set_score_request(\n    sample_weight=True\n)\ninner_cv = GroupKFold(n_splits=5)\n\ngrid_search = GridSearchCV(\n    estimator=estimator,\n    param_grid=hyperparameter_grid,\n    cv=inner_cv,\n    scoring=scoring_inner_cv,\n)\n\nouter_cv = GroupKFold(n_splits=5)\nscorers = {\n    \"mse\": get_scorer(\"neg_mean_squared_error\").set_score_request(sample_weight=True)\n}\nresults = cross_validate(\n    grid_search,\n    X,\n    y,\n    cv=outer_cv,\n    scoring=scorers,\n    return_estimator=True,\n    params={\"sample_weight\": sample_weights, \"groups\": groups},\n)\nprint(\"cv error on test sets:\", results[\"test_mse\"])\n\n# Setting the flag to the default `False` to avoid interference with other\n# scripts.\nsklearn.set_config(enable_metadata_routing=False)"
       ]
     },
     {
@@ -195,7 +195,7 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.decomposition import PCA\nimport scipy.sparse as sp\nfrom time import time\n\nX_sparse = sp.random(m=1000, n=1000, random_state=0)\nX_dense = X_sparse.toarray()\n\nt0 = time()\nPCA(n_components=10, svd_solver=\"arpack\").fit(X_sparse)\ntime_sparse = time() - t0\n\nt0 = time()\nPCA(n_components=10, svd_solver=\"arpack\").fit(X_dense)\ntime_dense = time() - t0\n\nprint(f\"Speedup: {time_dense / time_sparse:.1f}x\")"
+        "from time import time\n\nimport scipy.sparse as sp\n\nfrom sklearn.decomposition import PCA\n\nX_sparse = sp.random(m=1000, n=1000, random_state=0)\nX_dense = X_sparse.toarray()\n\nt0 = time()\nPCA(n_components=10, svd_solver=\"arpack\").fit(X_sparse)\ntime_sparse = time() - t0\n\nt0 = time()\nPCA(n_components=10, svd_solver=\"arpack\").fit(X_dense)\ntime_dense = time() - t0\n\nprint(f\"Speedup: {time_dense / time_sparse:.1f}x\")"
       ]
     }
   ],
@@ -215,7 +215,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/release_highlights/plot_release_highlights_1_5_0.ipynb b/dev/lite/files/auto_examples/release_highlights/plot_release_highlights_1_5_0.ipynb
index c0c9b20db9eb1..c1d14cb68b0d0 100644
--- a/dev/lite/files/auto_examples/release_highlights/plot_release_highlights_1_5_0.ipynb
+++ b/dev/lite/files/auto_examples/release_highlights/plot_release_highlights_1_5_0.ipynb
@@ -40,7 +40,7 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.datasets import make_classification\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.metrics import ConfusionMatrixDisplay\n\n\nX, y = make_classification(n_samples=10_000, weights=[0.9, 0.1], random_state=0)\nX_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)\n\nclassifier_05 = LogisticRegression(C=1e6, random_state=0).fit(X_train, y_train)\n_ = ConfusionMatrixDisplay.from_estimator(classifier_05, X_test, y_test)"
+        "from sklearn.datasets import make_classification\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.metrics import ConfusionMatrixDisplay\nfrom sklearn.model_selection import train_test_split\n\nX, y = make_classification(n_samples=10_000, weights=[0.9, 0.1], random_state=0)\nX_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)\n\nclassifier_05 = LogisticRegression(C=1e6, random_state=0).fit(X_train, y_train)\n_ = ConfusionMatrixDisplay.from_estimator(classifier_05, X_test, y_test)"
       ]
     },
     {
@@ -94,7 +94,7 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.model_selection import TunedThresholdClassifierCV\nfrom sklearn.metrics import make_scorer\n\ncustom_scorer = make_scorer(\n    custom_score, response_method=\"predict\", greater_is_better=True\n)\ntuned_classifier = TunedThresholdClassifierCV(\n    classifier_05, cv=5, scoring=custom_scorer\n).fit(X, y)\n\nprint(f\"Tuned decision threshold: {tuned_classifier.best_threshold_:.3f}\")\nprint(f\"Custom score: {custom_score(y_test, tuned_classifier.predict(X_test)):.2f}\")"
+        "from sklearn.metrics import make_scorer\nfrom sklearn.model_selection import TunedThresholdClassifierCV\n\ncustom_scorer = make_scorer(\n    custom_score, response_method=\"predict\", greater_is_better=True\n)\ntuned_classifier = TunedThresholdClassifierCV(\n    classifier_05, cv=5, scoring=custom_scorer\n).fit(X, y)\n\nprint(f\"Tuned decision threshold: {tuned_classifier.best_threshold_:.3f}\")\nprint(f\"Custom score: {custom_score(y_test, tuned_classifier.predict(X_test)):.2f}\")"
       ]
     },
     {
@@ -162,7 +162,7 @@
       },
       "outputs": [],
       "source": [
-        "import numpy as np\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\n\nX = np.array([[0, 1, 2], [3, 4, 5]])\ncolumn_transformer = ColumnTransformer(\n    [(\"std_scaler\", StandardScaler(), [0]), (\"one_hot\", OneHotEncoder(), [1, 2])]\n)\n\ncolumn_transformer.fit(X)\n\nprint(column_transformer[\"std_scaler\"])\nprint(column_transformer[\"one_hot\"])"
+        "import numpy as np\n\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.preprocessing import OneHotEncoder, StandardScaler\n\nX = np.array([[0, 1, 2], [3, 4, 5]])\ncolumn_transformer = ColumnTransformer(\n    [(\"std_scaler\", StandardScaler(), [0]), (\"one_hot\", OneHotEncoder(), [1, 2])]\n)\n\ncolumn_transformer.fit(X)\n\nprint(column_transformer[\"std_scaler\"])\nprint(column_transformer[\"one_hot\"])"
       ]
     },
     {
@@ -218,7 +218,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/release_highlights/plot_release_highlights_1_6_0.ipynb b/dev/lite/files/auto_examples/release_highlights/plot_release_highlights_1_6_0.ipynb
index 838b4658f4257..d7c8e74995c10 100644
--- a/dev/lite/files/auto_examples/release_highlights/plot_release_highlights_1_6_0.ipynb
+++ b/dev/lite/files/auto_examples/release_highlights/plot_release_highlights_1_6_0.ipynb
@@ -40,7 +40,7 @@
       },
       "outputs": [],
       "source": [
-        "import time\nfrom sklearn.datasets import make_classification\nfrom sklearn.frozen import FrozenEstimator\nfrom sklearn.linear_model import SGDClassifier\nfrom sklearn.model_selection import FixedThresholdClassifier\n\nX, y = make_classification(n_samples=1000, random_state=0)\n\nstart = time.time()\nclassifier = SGDClassifier().fit(X, y)\nprint(f\"Fitting the classifier took {(time.time() - start) * 1_000:.2f} milliseconds\")\n\nstart = time.time()\nthreshold_classifier = FixedThresholdClassifier(\n    estimator=FrozenEstimator(classifier), threshold=0.9\n).fit(X, y)\nprint(\n    f\"Fitting the threshold classifier took {(time.time() - start) * 1_000:.2f} \"\n    \"milliseconds\"\n)"
+        "import time\n\nfrom sklearn.datasets import make_classification\nfrom sklearn.frozen import FrozenEstimator\nfrom sklearn.linear_model import SGDClassifier\nfrom sklearn.model_selection import FixedThresholdClassifier\n\nX, y = make_classification(n_samples=1000, random_state=0)\n\nstart = time.time()\nclassifier = SGDClassifier().fit(X, y)\nprint(f\"Fitting the classifier took {(time.time() - start) * 1_000:.2f} milliseconds\")\n\nstart = time.time()\nthreshold_classifier = FixedThresholdClassifier(\n    estimator=FrozenEstimator(classifier), threshold=0.9\n).fit(X, y)\nprint(\n    f\"Fitting the threshold classifier took {(time.time() - start) * 1_000:.2f} \"\n    \"milliseconds\"\n)"
       ]
     },
     {
@@ -79,7 +79,7 @@
       },
       "outputs": [],
       "source": [
-        "import numpy as np\nfrom sklearn.ensemble import ExtraTreesClassifier\n\nX = np.array([0, 1, 6, np.nan]).reshape(-1, 1)\ny = [0, 0, 1, 1]\n\nforest = ExtraTreesClassifier(random_state=0).fit(X, y)\nforest.predict(X)"
+        "import numpy as np\n\nfrom sklearn.ensemble import ExtraTreesClassifier\n\nX = np.array([0, 1, 6, np.nan]).reshape(-1, 1)\ny = [0, 0, 1, 1]\n\nforest = ExtraTreesClassifier(random_state=0).fit(X, y)\nforest.predict(X)"
       ]
     },
     {
@@ -134,7 +134,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/semi_supervised/plot_label_propagation_digits.ipynb b/dev/lite/files/auto_examples/semi_supervised/plot_label_propagation_digits.ipynb
index e25a4175eb343..7786083a2d253 100644
--- a/dev/lite/files/auto_examples/semi_supervised/plot_label_propagation_digits.ipynb
+++ b/dev/lite/files/auto_examples/semi_supervised/plot_label_propagation_digits.ipynb
@@ -215,7 +215,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/semi_supervised/plot_label_propagation_digits_active_learning.ipynb b/dev/lite/files/auto_examples/semi_supervised/plot_label_propagation_digits_active_learning.ipynb
index a680bb0f39b4f..2bb6b00a3ee1f 100644
--- a/dev/lite/files/auto_examples/semi_supervised/plot_label_propagation_digits_active_learning.ipynb
+++ b/dev/lite/files/auto_examples/semi_supervised/plot_label_propagation_digits_active_learning.ipynb
@@ -22,7 +22,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# Label Propagation digits active learning\n\nDemonstrates an active learning technique to learn handwritten digits\nusing label propagation.\n\nWe start by training a label propagation model with only 10 labeled points,\nthen we select the top five most uncertain points to label. Next, we train\nwith 15 labeled points (original 10 + 5 new ones). We repeat this process\nfour times to have a model trained with 30 labeled examples. Note you can\nincrease this to label more than 30 by changing `max_iterations`. Labeling\nmore than 30 can be useful to get a sense for the speed of convergence of\nthis active learning technique.\n\nA plot will appear showing the top 5 most uncertain digits for each iteration\nof training. These may or may not contain mistakes, but we will train the next\nmodel with their true labels.\n"
+        "\n# Label Propagation digits: Active learning\n\nDemonstrates an active learning technique to learn handwritten digits\nusing label propagation.\n\nWe start by training a label propagation model with only 10 labeled points,\nthen we select the top five most uncertain points to label. Next, we train\nwith 15 labeled points (original 10 + 5 new ones). We repeat this process\nfour times to have a model trained with 30 labeled examples. Note you can\nincrease this to label more than 30 by changing `max_iterations`. Labeling\nmore than 30 can be useful to get a sense for the speed of convergence of\nthis active learning technique.\n\nA plot will appear showing the top 5 most uncertain digits for each iteration\nof training. These may or may not contain mistakes, but we will train the next\nmodel with their true labels.\n"
       ]
     },
     {
@@ -33,7 +33,7 @@
       },
       "outputs": [],
       "source": [
-        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom scipy import stats\n\nfrom sklearn import datasets\nfrom sklearn.metrics import classification_report, confusion_matrix\nfrom sklearn.semi_supervised import LabelSpreading\n\ndigits = datasets.load_digits()\nrng = np.random.RandomState(0)\nindices = np.arange(len(digits.data))\nrng.shuffle(indices)\n\nX = digits.data[indices[:330]]\ny = digits.target[indices[:330]]\nimages = digits.images[indices[:330]]\n\nn_total_samples = len(y)\nn_labeled_points = 40\nmax_iterations = 5\n\nunlabeled_indices = np.arange(n_total_samples)[n_labeled_points:]\nf = plt.figure()\n\nfor i in range(max_iterations):\n    if len(unlabeled_indices) == 0:\n        print(\"No unlabeled items left to label.\")\n        break\n    y_train = np.copy(y)\n    y_train[unlabeled_indices] = -1\n\n    lp_model = LabelSpreading(gamma=0.25, max_iter=20)\n    lp_model.fit(X, y_train)\n\n    predicted_labels = lp_model.transduction_[unlabeled_indices]\n    true_labels = y[unlabeled_indices]\n\n    cm = confusion_matrix(true_labels, predicted_labels, labels=lp_model.classes_)\n\n    print(\"Iteration %i %s\" % (i, 70 * \"_\"))\n    print(\n        \"Label Spreading model: %d labeled & %d unlabeled (%d total)\"\n        % (n_labeled_points, n_total_samples - n_labeled_points, n_total_samples)\n    )\n\n    print(classification_report(true_labels, predicted_labels))\n\n    print(\"Confusion matrix\")\n    print(cm)\n\n    # compute the entropies of transduced label distributions\n    pred_entropies = stats.distributions.entropy(lp_model.label_distributions_.T)\n\n    # select up to 5 digit examples that the classifier is most uncertain about\n    uncertainty_index = np.argsort(pred_entropies)[::-1]\n    uncertainty_index = uncertainty_index[\n        np.isin(uncertainty_index, unlabeled_indices)\n    ][:5]\n\n    # keep track of indices that we get labels for\n    delete_indices = np.array([], dtype=int)\n\n    # for more than 5 iterations, visualize the gain only on the first 5\n    if i < 5:\n        f.text(\n            0.05,\n            (1 - (i + 1) * 0.183),\n            \"model %d\\n\\nfit with\\n%d labels\" % ((i + 1), i * 5 + 10),\n            size=10,\n        )\n    for index, image_index in enumerate(uncertainty_index):\n        image = images[image_index]\n\n        # for more than 5 iterations, visualize the gain only on the first 5\n        if i < 5:\n            sub = f.add_subplot(5, 5, index + 1 + (5 * i))\n            sub.imshow(image, cmap=plt.cm.gray_r, interpolation=\"none\")\n            sub.set_title(\n                \"predict: %i\\ntrue: %i\"\n                % (lp_model.transduction_[image_index], y[image_index]),\n                size=10,\n            )\n            sub.axis(\"off\")\n\n        # labeling 5 points, remote from labeled set\n        (delete_index,) = np.where(unlabeled_indices == image_index)\n        delete_indices = np.concatenate((delete_indices, delete_index))\n\n    unlabeled_indices = np.delete(unlabeled_indices, delete_indices)\n    n_labeled_points += len(uncertainty_index)\n\nf.suptitle(\n    (\n        \"Active learning with Label Propagation.\\nRows show 5 most \"\n        \"uncertain labels to learn with the next model.\"\n    ),\n    y=1.15,\n)\nplt.subplots_adjust(left=0.2, bottom=0.03, right=0.9, top=0.9, wspace=0.2, hspace=0.85)\nplt.show()"
+        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom scipy import stats\n\nfrom sklearn import datasets\nfrom sklearn.metrics import classification_report, confusion_matrix\nfrom sklearn.semi_supervised import LabelSpreading\n\ndigits = datasets.load_digits()\nrng = np.random.RandomState(0)\nindices = np.arange(len(digits.data))\nrng.shuffle(indices)\n\nX = digits.data[indices[:330]]\ny = digits.target[indices[:330]]\nimages = digits.images[indices[:330]]\n\nn_total_samples = len(y)\nn_labeled_points = 40\nmax_iterations = 5\n\nunlabeled_indices = np.arange(n_total_samples)[n_labeled_points:]\nf = plt.figure()\n\nfor i in range(max_iterations):\n    if len(unlabeled_indices) == 0:\n        print(\"No unlabeled items left to label.\")\n        break\n    y_train = np.copy(y)\n    y_train[unlabeled_indices] = -1\n\n    lp_model = LabelSpreading(gamma=0.25, max_iter=20)\n    lp_model.fit(X, y_train)\n\n    predicted_labels = lp_model.transduction_[unlabeled_indices]\n    true_labels = y[unlabeled_indices]\n\n    cm = confusion_matrix(true_labels, predicted_labels, labels=lp_model.classes_)\n\n    print(\"Iteration %i %s\" % (i, 70 * \"_\"))\n    print(\n        \"Label Spreading model: %d labeled & %d unlabeled (%d total)\"\n        % (n_labeled_points, n_total_samples - n_labeled_points, n_total_samples)\n    )\n\n    print(classification_report(true_labels, predicted_labels))\n\n    print(\"Confusion matrix\")\n    print(cm)\n\n    # compute the entropies of transduced label distributions\n    pred_entropies = stats.distributions.entropy(lp_model.label_distributions_.T)\n\n    # select up to 5 digit examples that the classifier is most uncertain about\n    uncertainty_index = np.argsort(pred_entropies)[::-1]\n    uncertainty_index = uncertainty_index[\n        np.isin(uncertainty_index, unlabeled_indices)\n    ][:5]\n\n    # keep track of indices that we get labels for\n    delete_indices = np.array([], dtype=int)\n\n    # for more than 5 iterations, visualize the gain only on the first 5\n    if i < 5:\n        f.text(\n            0.05,\n            (1 - (i + 1) * 0.183),\n            \"model %d\\n\\nfit with\\n%d labels\" % ((i + 1), i * 5 + 10),\n            size=10,\n        )\n    for index, image_index in enumerate(uncertainty_index):\n        image = images[image_index]\n\n        # for more than 5 iterations, visualize the gain only on the first 5\n        if i < 5:\n            sub = f.add_subplot(5, 5, index + 1 + (5 * i))\n            sub.imshow(image, cmap=plt.cm.gray_r, interpolation=\"none\")\n            sub.set_title(\n                \"predict: %i\\ntrue: %i\"\n                % (lp_model.transduction_[image_index], y[image_index]),\n                size=10,\n            )\n            sub.axis(\"off\")\n\n        # labeling 5 points, remote from labeled set\n        (delete_index,) = (unlabeled_indices == image_index).nonzero()\n        delete_indices = np.concatenate((delete_indices, delete_index))\n\n    unlabeled_indices = np.delete(unlabeled_indices, delete_indices)\n    n_labeled_points += len(uncertainty_index)\n\nf.suptitle(\n    (\n        \"Active learning with Label Propagation.\\nRows show 5 most \"\n        \"uncertain labels to learn with the next model.\"\n    ),\n    y=1.15,\n)\nplt.subplots_adjust(left=0.2, bottom=0.03, right=0.9, top=0.9, wspace=0.2, hspace=0.85)\nplt.show()"
       ]
     }
   ],
@@ -53,7 +53,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/semi_supervised/plot_label_propagation_structure.ipynb b/dev/lite/files/auto_examples/semi_supervised/plot_label_propagation_structure.ipynb
index 96c1b924ccb37..49758422fc7dd 100644
--- a/dev/lite/files/auto_examples/semi_supervised/plot_label_propagation_structure.ipynb
+++ b/dev/lite/files/auto_examples/semi_supervised/plot_label_propagation_structure.ipynb
@@ -22,7 +22,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# Label Propagation learning a complex structure\n\nExample of LabelPropagation learning a complex internal structure\nto demonstrate \"manifold learning\". The outer circle should be\nlabeled \"red\" and the inner circle \"blue\". Because both label groups\nlie inside their own distinct shape, we can see that the labels\npropagate correctly around the circle.\n"
+        "\n# Label Propagation circles: Learning a complex structure\n\nExample of LabelPropagation learning a complex internal structure\nto demonstrate \"manifold learning\". The outer circle should be\nlabeled \"red\" and the inner circle \"blue\". Because both label groups\nlie inside their own distinct shape, we can see that the labels\npropagate correctly around the circle.\n"
       ]
     },
     {
@@ -105,7 +105,7 @@
       },
       "outputs": [],
       "source": [
-        "output_labels = label_spread.transduction_\noutput_label_array = np.asarray(output_labels)\nouter_numbers = np.where(output_label_array == outer)[0]\ninner_numbers = np.where(output_label_array == inner)[0]\n\nplt.figure(figsize=(4, 4))\nplt.scatter(\n    X[outer_numbers, 0],\n    X[outer_numbers, 1],\n    color=\"navy\",\n    marker=\"s\",\n    lw=0,\n    s=10,\n    label=\"outer learned\",\n)\nplt.scatter(\n    X[inner_numbers, 0],\n    X[inner_numbers, 1],\n    color=\"c\",\n    marker=\"s\",\n    lw=0,\n    s=10,\n    label=\"inner learned\",\n)\nplt.legend(scatterpoints=1, shadow=False, loc=\"center\")\nplt.title(\"Labels learned with Label Spreading (KNN)\")\nplt.show()"
+        "output_labels = label_spread.transduction_\noutput_label_array = np.asarray(output_labels)\nouter_numbers = (output_label_array == outer).nonzero()[0]\ninner_numbers = (output_label_array == inner).nonzero()[0]\n\nplt.figure(figsize=(4, 4))\nplt.scatter(\n    X[outer_numbers, 0],\n    X[outer_numbers, 1],\n    color=\"navy\",\n    marker=\"s\",\n    lw=0,\n    s=10,\n    label=\"outer learned\",\n)\nplt.scatter(\n    X[inner_numbers, 0],\n    X[inner_numbers, 1],\n    color=\"c\",\n    marker=\"s\",\n    lw=0,\n    s=10,\n    label=\"inner learned\",\n)\nplt.legend(scatterpoints=1, shadow=False, loc=\"center\")\nplt.title(\"Labels learned with Label Spreading (KNN)\")\nplt.show()"
       ]
     }
   ],
@@ -125,7 +125,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/semi_supervised/plot_self_training_varying_threshold.ipynb b/dev/lite/files/auto_examples/semi_supervised/plot_self_training_varying_threshold.ipynb
index 200a812d8aee0..96902730103ee 100644
--- a/dev/lite/files/auto_examples/semi_supervised/plot_self_training_varying_threshold.ipynb
+++ b/dev/lite/files/auto_examples/semi_supervised/plot_self_training_varying_threshold.ipynb
@@ -53,7 +53,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/semi_supervised/plot_semi_supervised_newsgroups.ipynb b/dev/lite/files/auto_examples/semi_supervised/plot_semi_supervised_newsgroups.ipynb
index fa4aab223e240..1ec56bbe97843 100644
--- a/dev/lite/files/auto_examples/semi_supervised/plot_semi_supervised_newsgroups.ipynb
+++ b/dev/lite/files/auto_examples/semi_supervised/plot_semi_supervised_newsgroups.ipynb
@@ -53,7 +53,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/semi_supervised/plot_semi_supervised_versus_svm_iris.ipynb b/dev/lite/files/auto_examples/semi_supervised/plot_semi_supervised_versus_svm_iris.ipynb
index 0f079b1f63e6f..d63247d464f32 100644
--- a/dev/lite/files/auto_examples/semi_supervised/plot_semi_supervised_versus_svm_iris.ipynb
+++ b/dev/lite/files/auto_examples/semi_supervised/plot_semi_supervised_versus_svm_iris.ipynb
@@ -53,7 +53,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/lite/files/auto_examples/svm/plot_custom_kernel.ipynb b/dev/lite/files/auto_examples/svm/plot_custom_kernel.ipynb
index 550d3dc2fdd16..732bb320124a3 100644
--- a/dev/lite/files/auto_examples/svm/plot_custom_kernel.ipynb
+++ b/dev/lite/files/auto_examples/svm/plot_custom_kernel.ipynb
@@ -53,7 +53,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
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diff --git a/dev/lite/files/auto_examples/svm/plot_iris_svc.ipynb b/dev/lite/files/auto_examples/svm/plot_iris_svc.ipynb
index 9b674757b5733..ce7453739667f 100644
--- a/dev/lite/files/auto_examples/svm/plot_iris_svc.ipynb
+++ b/dev/lite/files/auto_examples/svm/plot_iris_svc.ipynb
@@ -53,7 +53,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
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diff --git a/dev/lite/files/auto_examples/svm/plot_linearsvc_support_vectors.ipynb b/dev/lite/files/auto_examples/svm/plot_linearsvc_support_vectors.ipynb
index 7d05f91467edd..92980c661ed8e 100644
--- a/dev/lite/files/auto_examples/svm/plot_linearsvc_support_vectors.ipynb
+++ b/dev/lite/files/auto_examples/svm/plot_linearsvc_support_vectors.ipynb
@@ -33,7 +33,7 @@
       },
       "outputs": [],
       "source": [
-        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nfrom sklearn.datasets import make_blobs\nfrom sklearn.inspection import DecisionBoundaryDisplay\nfrom sklearn.svm import LinearSVC\n\nX, y = make_blobs(n_samples=40, centers=2, random_state=0)\n\nplt.figure(figsize=(10, 5))\nfor i, C in enumerate([1, 100]):\n    # \"hinge\" is the standard SVM loss\n    clf = LinearSVC(C=C, loss=\"hinge\", random_state=42).fit(X, y)\n    # obtain the support vectors through the decision function\n    decision_function = clf.decision_function(X)\n    # we can also calculate the decision function manually\n    # decision_function = np.dot(X, clf.coef_[0]) + clf.intercept_[0]\n    # The support vectors are the samples that lie within the margin\n    # boundaries, whose size is conventionally constrained to 1\n    support_vector_indices = np.where(np.abs(decision_function) <= 1 + 1e-15)[0]\n    support_vectors = X[support_vector_indices]\n\n    plt.subplot(1, 2, i + 1)\n    plt.scatter(X[:, 0], X[:, 1], c=y, s=30, cmap=plt.cm.Paired)\n    ax = plt.gca()\n    DecisionBoundaryDisplay.from_estimator(\n        clf,\n        X,\n        ax=ax,\n        grid_resolution=50,\n        plot_method=\"contour\",\n        colors=\"k\",\n        levels=[-1, 0, 1],\n        alpha=0.5,\n        linestyles=[\"--\", \"-\", \"--\"],\n    )\n    plt.scatter(\n        support_vectors[:, 0],\n        support_vectors[:, 1],\n        s=100,\n        linewidth=1,\n        facecolors=\"none\",\n        edgecolors=\"k\",\n    )\n    plt.title(\"C=\" + str(C))\nplt.tight_layout()\nplt.show()"
+        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nfrom sklearn.datasets import make_blobs\nfrom sklearn.inspection import DecisionBoundaryDisplay\nfrom sklearn.svm import LinearSVC\n\nX, y = make_blobs(n_samples=40, centers=2, random_state=0)\n\nplt.figure(figsize=(10, 5))\nfor i, C in enumerate([1, 100]):\n    # \"hinge\" is the standard SVM loss\n    clf = LinearSVC(C=C, loss=\"hinge\", random_state=42).fit(X, y)\n    # obtain the support vectors through the decision function\n    decision_function = clf.decision_function(X)\n    # we can also calculate the decision function manually\n    # decision_function = np.dot(X, clf.coef_[0]) + clf.intercept_[0]\n    # The support vectors are the samples that lie within the margin\n    # boundaries, whose size is conventionally constrained to 1\n    support_vector_indices = (np.abs(decision_function) <= 1 + 1e-15).nonzero()[0]\n    support_vectors = X[support_vector_indices]\n\n    plt.subplot(1, 2, i + 1)\n    plt.scatter(X[:, 0], X[:, 1], c=y, s=30, cmap=plt.cm.Paired)\n    ax = plt.gca()\n    DecisionBoundaryDisplay.from_estimator(\n        clf,\n        X,\n        ax=ax,\n        grid_resolution=50,\n        plot_method=\"contour\",\n        colors=\"k\",\n        levels=[-1, 0, 1],\n        alpha=0.5,\n        linestyles=[\"--\", \"-\", \"--\"],\n    )\n    plt.scatter(\n        support_vectors[:, 0],\n        support_vectors[:, 1],\n        s=100,\n        linewidth=1,\n        facecolors=\"none\",\n        edgecolors=\"k\",\n    )\n    plt.title(\"C=\" + str(C))\nplt.tight_layout()\nplt.show()"
       ]
     }
   ],
@@ -53,7 +53,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
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diff --git a/dev/lite/files/auto_examples/svm/plot_oneclass.ipynb b/dev/lite/files/auto_examples/svm/plot_oneclass.ipynb
index 9d10acec67e51..bfb2ba931366e 100644
--- a/dev/lite/files/auto_examples/svm/plot_oneclass.ipynb
+++ b/dev/lite/files/auto_examples/svm/plot_oneclass.ipynb
@@ -75,7 +75,7 @@
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diff --git a/dev/lite/files/auto_examples/svm/plot_rbf_parameters.ipynb b/dev/lite/files/auto_examples/svm/plot_rbf_parameters.ipynb
index 65c4e3a533a8d..721018e3721fd 100644
--- a/dev/lite/files/auto_examples/svm/plot_rbf_parameters.ipynb
+++ b/dev/lite/files/auto_examples/svm/plot_rbf_parameters.ipynb
@@ -197,7 +197,7 @@
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diff --git a/dev/lite/files/auto_examples/svm/plot_separating_hyperplane.ipynb b/dev/lite/files/auto_examples/svm/plot_separating_hyperplane.ipynb
index 211e365c1342f..a8285b9964298 100644
--- a/dev/lite/files/auto_examples/svm/plot_separating_hyperplane.ipynb
+++ b/dev/lite/files/auto_examples/svm/plot_separating_hyperplane.ipynb
@@ -53,7 +53,7 @@
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diff --git a/dev/lite/files/auto_examples/svm/plot_separating_hyperplane_unbalanced.ipynb b/dev/lite/files/auto_examples/svm/plot_separating_hyperplane_unbalanced.ipynb
index 2830fb860463a..336e25bc34dd0 100644
--- a/dev/lite/files/auto_examples/svm/plot_separating_hyperplane_unbalanced.ipynb
+++ b/dev/lite/files/auto_examples/svm/plot_separating_hyperplane_unbalanced.ipynb
@@ -53,7 +53,7 @@
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diff --git a/dev/lite/files/auto_examples/svm/plot_svm_anova.ipynb b/dev/lite/files/auto_examples/svm/plot_svm_anova.ipynb
index 5a3a78ad1c2a8..f674ae036e74b 100644
--- a/dev/lite/files/auto_examples/svm/plot_svm_anova.ipynb
+++ b/dev/lite/files/auto_examples/svm/plot_svm_anova.ipynb
@@ -107,7 +107,7 @@
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diff --git a/dev/lite/files/auto_examples/svm/plot_svm_kernels.ipynb b/dev/lite/files/auto_examples/svm/plot_svm_kernels.ipynb
index dc00f0b46c012..8eb220d750905 100644
--- a/dev/lite/files/auto_examples/svm/plot_svm_kernels.ipynb
+++ b/dev/lite/files/auto_examples/svm/plot_svm_kernels.ipynb
@@ -193,7 +193,7 @@
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diff --git a/dev/lite/files/auto_examples/svm/plot_svm_margin.ipynb b/dev/lite/files/auto_examples/svm/plot_svm_margin.ipynb
index ada1b79af9ae2..8fdc3ba92436e 100644
--- a/dev/lite/files/auto_examples/svm/plot_svm_margin.ipynb
+++ b/dev/lite/files/auto_examples/svm/plot_svm_margin.ipynb
@@ -53,7 +53,7 @@
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diff --git a/dev/lite/files/auto_examples/svm/plot_svm_regression.ipynb b/dev/lite/files/auto_examples/svm/plot_svm_regression.ipynb
index 6f12cc65f0855..6193cc4bda433 100644
--- a/dev/lite/files/auto_examples/svm/plot_svm_regression.ipynb
+++ b/dev/lite/files/auto_examples/svm/plot_svm_regression.ipynb
@@ -107,7 +107,7 @@
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diff --git a/dev/lite/files/auto_examples/svm/plot_svm_scale_c.ipynb b/dev/lite/files/auto_examples/svm/plot_svm_scale_c.ipynb
index d471fc34a3f76..26cd14a49109f 100644
--- a/dev/lite/files/auto_examples/svm/plot_svm_scale_c.ipynb
+++ b/dev/lite/files/auto_examples/svm/plot_svm_scale_c.ipynb
@@ -154,7 +154,7 @@
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diff --git a/dev/lite/files/auto_examples/svm/plot_svm_tie_breaking.ipynb b/dev/lite/files/auto_examples/svm/plot_svm_tie_breaking.ipynb
index 16a48f47f51a1..c20d4a0c78c38 100644
--- a/dev/lite/files/auto_examples/svm/plot_svm_tie_breaking.ipynb
+++ b/dev/lite/files/auto_examples/svm/plot_svm_tie_breaking.ipynb
@@ -53,7 +53,7 @@
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diff --git a/dev/lite/files/auto_examples/svm/plot_weighted_samples.ipynb b/dev/lite/files/auto_examples/svm/plot_weighted_samples.ipynb
index b92a3dc44f3e3..734ccda9e473a 100644
--- a/dev/lite/files/auto_examples/svm/plot_weighted_samples.ipynb
+++ b/dev/lite/files/auto_examples/svm/plot_weighted_samples.ipynb
@@ -53,7 +53,7 @@
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diff --git a/dev/lite/files/auto_examples/text/plot_document_classification_20newsgroups.ipynb b/dev/lite/files/auto_examples/text/plot_document_classification_20newsgroups.ipynb
index 829e4793df87b..c43fd5a844089 100644
--- a/dev/lite/files/auto_examples/text/plot_document_classification_20newsgroups.ipynb
+++ b/dev/lite/files/auto_examples/text/plot_document_classification_20newsgroups.ipynb
@@ -265,7 +265,7 @@
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       "pygments_lexer": "ipython3",
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diff --git a/dev/lite/files/auto_examples/text/plot_document_clustering.ipynb b/dev/lite/files/auto_examples/text/plot_document_clustering.ipynb
index 1db643a709298..b8e443d18d8cc 100644
--- a/dev/lite/files/auto_examples/text/plot_document_clustering.ipynb
+++ b/dev/lite/files/auto_examples/text/plot_document_clustering.ipynb
@@ -305,7 +305,7 @@
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-      "version": "3.10.16"
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diff --git a/dev/lite/files/auto_examples/text/plot_hashing_vs_dict_vectorizer.ipynb b/dev/lite/files/auto_examples/text/plot_hashing_vs_dict_vectorizer.ipynb
index 831b829d68e44..221824416259e 100644
--- a/dev/lite/files/auto_examples/text/plot_hashing_vs_dict_vectorizer.ipynb
+++ b/dev/lite/files/auto_examples/text/plot_hashing_vs_dict_vectorizer.ipynb
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diff --git a/dev/lite/files/auto_examples/tree/plot_cost_complexity_pruning.ipynb b/dev/lite/files/auto_examples/tree/plot_cost_complexity_pruning.ipynb
index 78620169f42c5..79e53f1847e34 100644
--- a/dev/lite/files/auto_examples/tree/plot_cost_complexity_pruning.ipynb
+++ b/dev/lite/files/auto_examples/tree/plot_cost_complexity_pruning.ipynb
@@ -143,7 +143,7 @@
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       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
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diff --git a/dev/lite/files/auto_examples/tree/plot_iris_dtc.ipynb b/dev/lite/files/auto_examples/tree/plot_iris_dtc.ipynb
index 5cdd2a2b17746..221e95a82e2f6 100644
--- a/dev/lite/files/auto_examples/tree/plot_iris_dtc.ipynb
+++ b/dev/lite/files/auto_examples/tree/plot_iris_dtc.ipynb
@@ -69,7 +69,7 @@
       },
       "outputs": [],
       "source": [
-        "import matplotlib.pyplot as plt\nimport numpy as np\n\nfrom sklearn.datasets import load_iris\nfrom sklearn.inspection import DecisionBoundaryDisplay\nfrom sklearn.tree import DecisionTreeClassifier\n\n# Parameters\nn_classes = 3\nplot_colors = \"ryb\"\nplot_step = 0.02\n\n\nfor pairidx, pair in enumerate([[0, 1], [0, 2], [0, 3], [1, 2], [1, 3], [2, 3]]):\n    # We only take the two corresponding features\n    X = iris.data[:, pair]\n    y = iris.target\n\n    # Train\n    clf = DecisionTreeClassifier().fit(X, y)\n\n    # Plot the decision boundary\n    ax = plt.subplot(2, 3, pairidx + 1)\n    plt.tight_layout(h_pad=0.5, w_pad=0.5, pad=2.5)\n    DecisionBoundaryDisplay.from_estimator(\n        clf,\n        X,\n        cmap=plt.cm.RdYlBu,\n        response_method=\"predict\",\n        ax=ax,\n        xlabel=iris.feature_names[pair[0]],\n        ylabel=iris.feature_names[pair[1]],\n    )\n\n    # Plot the training points\n    for i, color in zip(range(n_classes), plot_colors):\n        idx = np.where(y == i)\n        plt.scatter(\n            X[idx, 0],\n            X[idx, 1],\n            c=color,\n            label=iris.target_names[i],\n            edgecolor=\"black\",\n            s=15,\n        )\n\nplt.suptitle(\"Decision surface of decision trees trained on pairs of features\")\nplt.legend(loc=\"lower right\", borderpad=0, handletextpad=0)\n_ = plt.axis(\"tight\")"
+        "import matplotlib.pyplot as plt\nimport numpy as np\n\nfrom sklearn.datasets import load_iris\nfrom sklearn.inspection import DecisionBoundaryDisplay\nfrom sklearn.tree import DecisionTreeClassifier\n\n# Parameters\nn_classes = 3\nplot_colors = \"ryb\"\nplot_step = 0.02\n\n\nfor pairidx, pair in enumerate([[0, 1], [0, 2], [0, 3], [1, 2], [1, 3], [2, 3]]):\n    # We only take the two corresponding features\n    X = iris.data[:, pair]\n    y = iris.target\n\n    # Train\n    clf = DecisionTreeClassifier().fit(X, y)\n\n    # Plot the decision boundary\n    ax = plt.subplot(2, 3, pairidx + 1)\n    plt.tight_layout(h_pad=0.5, w_pad=0.5, pad=2.5)\n    DecisionBoundaryDisplay.from_estimator(\n        clf,\n        X,\n        cmap=plt.cm.RdYlBu,\n        response_method=\"predict\",\n        ax=ax,\n        xlabel=iris.feature_names[pair[0]],\n        ylabel=iris.feature_names[pair[1]],\n    )\n\n    # Plot the training points\n    for i, color in zip(range(n_classes), plot_colors):\n        idx = np.asarray(y == i).nonzero()\n        plt.scatter(\n            X[idx, 0],\n            X[idx, 1],\n            c=color,\n            label=iris.target_names[i],\n            edgecolor=\"black\",\n            s=15,\n        )\n\nplt.suptitle(\"Decision surface of decision trees trained on pairs of features\")\nplt.legend(loc=\"lower right\", borderpad=0, handletextpad=0)\n_ = plt.axis(\"tight\")"
       ]
     },
     {
@@ -107,7 +107,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
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diff --git a/dev/lite/files/auto_examples/tree/plot_tree_regression.ipynb b/dev/lite/files/auto_examples/tree/plot_tree_regression.ipynb
index b8561b573e4f1..4dd9b339f3fdd 100644
--- a/dev/lite/files/auto_examples/tree/plot_tree_regression.ipynb
+++ b/dev/lite/files/auto_examples/tree/plot_tree_regression.ipynb
@@ -218,7 +218,7 @@
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diff --git a/dev/lite/files/auto_examples/tree/plot_unveil_tree_structure.ipynb b/dev/lite/files/auto_examples/tree/plot_unveil_tree_structure.ipynb
index c70178ee199b3..f838c3ee8c7f5 100644
--- a/dev/lite/files/auto_examples/tree/plot_unveil_tree_structure.ipynb
+++ b/dev/lite/files/auto_examples/tree/plot_unveil_tree_structure.ipynb
@@ -150,7 +150,7 @@
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diff --git a/dev/lite/jupyter-lite.json b/dev/lite/jupyter-lite.json
index 250ad93f9e9f5..b334ca70f6dd3 100644
--- a/dev/lite/jupyter-lite.json
+++ b/dev/lite/jupyter-lite.json
@@ -16,7 +16,7 @@
       {
         "extension": "./extension",
         "liteExtension": false,
-        "load": "static/remoteEntry.f0019c9fe59bc6f02df6.js",
+        "load": "static/remoteEntry.2142187e379506459ae0.js",
         "name": "jupyterlab-jupytext"
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       {
diff --git a/dev/machine_learning_map.html b/dev/machine_learning_map.html
index 293c6a4ce4928..cba98b0795eb7 100644
--- a/dev/machine_learning_map.html
+++ b/dev/machine_learning_map.html
@@ -7,7 +7,7 @@
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     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="viewport" content="width=device-width, initial-scale=1" />
-<meta property="og:title" content="12. Choosing the right estimator" />
+<meta property="og:title" content="13. Choosing the right estimator" />
 <meta property="og:type" content="website" />
 <meta property="og:url" content="https://scikit-learn/stable/machine_learning_map.html" />
 <meta property="og:site_name" content="scikit-learn" />
@@ -16,7 +16,7 @@
 <meta property="og:image:alt" content="scikit-learn" />
 <meta name="description" content="Often the hardest part of solving a machine learning problem can be finding the right estimator for the job. Different estimators are better suited for different types of data and different problem..." />
 
-    <title>12. Choosing the right estimator &#8212; scikit-learn 1.7.dev0 documentation</title>
+    <title>13. Choosing the right estimator &#8212; scikit-learn 1.7.dev0 documentation</title>
   
   
   
@@ -42,7 +42,7 @@
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-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
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     <link rel="author" title="About these documents" href="http://github.com/about.html" />
     <link rel="search" title="Search" href="http://github.com/search.html" />
-    <link rel="next" title="13. External Resources, Videos and Talks" href="http://github.com/presentations.html" />
-    <link rel="prev" title="11.1. Array API support (experimental)" href="http://github.com/modules/array_api.html" />
+    <link rel="next" title="14. External Resources, Videos and Talks" href="http://github.com/presentations.html" />
+    <link rel="prev" title="12.1. Array API support (experimental)" href="http://github.com/modules/array_api.html" />
   <meta name="viewport" content="width=device-width, initial-scale=1"/>
   <meta name="docsearch:language" content="en"/>
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@@ -589,45 +589,46 @@
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 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/inspection.html">4. Inspection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/partial_dependence.html">4.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/permutation_importance.html">4.2. Permutation feature importance</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/metadata_routing.html">4. Metadata Routing</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/inspection.html">5. Inspection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/partial_dependence.html">5.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/permutation_importance.html">5.2. Permutation feature importance</a></li>
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-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/data_transforms.html">6. Dataset transformations</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/compose.html">6.1. Pipelines and composite estimators</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/preprocessing_targets.html">6.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/visualizations.html">6. Visualizations</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/data_transforms.html">7. Dataset transformations</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/compose.html">7.1. Pipelines and composite estimators</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/metrics.html">7.8. Pairwise metrics, Affinities and Kernels</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/preprocessing_targets.html">7.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
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 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/datasets.html">7. Dataset loading utilities</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html">7.1. Toy datasets</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html">7.3. Generated datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html">7.4. Loading other datasets</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html">8.1. Toy datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html">8.2. Real world datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html">8.3. Generated datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html">8.4. Loading other datasets</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/scaling_strategies.html">9.1. Strategies to scale computationally: bigger data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/computational_performance.html">9.2. Computational Performance</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/parallelism.html">9.3. Parallelism, resource management, and configuration</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/array_api.html">11.1. Array API support (experimental)</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="http://github.com/common_pitfalls.html">11. Common pitfalls and recommended practices</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/dispatching.html">12. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/array_api.html">12.1. Array API support (experimental)</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 current active"><a class="current reference internal" href="#">12. Choosing the right estimator</a></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/presentations.html">13. External Resources, Videos and Talks</a></li>
+<li class="toctree-l1 current active"><a class="current reference internal" href="#">13. Choosing the right estimator</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/presentations.html">14. External Resources, Videos and Talks</a></li>
 </ul>
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@@ -664,7 +665,7 @@
     
     <li class="breadcrumb-item"><a href="http://github.com/user_guide.html" class="nav-link">User Guide</a></li>
     
-    <li class="breadcrumb-item active" aria-current="page"><span class="ellipsis"><span class="section-number">12. </span>Choosing the right estimator</span></li>
+    <li class="breadcrumb-item active" aria-current="page"><span class="ellipsis"><span class="section-number">13. </span>Choosing the right estimator</span></li>
   </ul>
 </nav>
 </div>
@@ -682,7 +683,7 @@
                 <article class="bd-article">
                   
   <section id="choosing-the-right-estimator">
-<span id="ml-map"></span><h1><span class="section-number">12. </span>Choosing the right estimator<a class="headerlink" href="#choosing-the-right-estimator" title="Link to this heading">#</a></h1>
+<span id="ml-map"></span><h1><span class="section-number">13. </span>Choosing the right estimator<a class="headerlink" href="#choosing-the-right-estimator" title="Link to this heading">#</a></h1>
 <p>Often the hardest part of solving a machine learning problem can be finding the right
 estimator for the job. Different estimators are better suited for different types of
 data and different problems.</p>
@@ -762,7 +763,7 @@
       <i class="fa-solid fa-angle-left"></i>
       <div class="prev-next-info">
         <p class="prev-next-subtitle">previous</p>
-        <p class="prev-next-title"><span class="section-number">11.1. </span>Array API support (experimental)</p>
+        <p class="prev-next-title"><span class="section-number">12.1. </span>Array API support (experimental)</p>
       </div>
     </a>
     <a class="right-next"
@@ -770,7 +771,7 @@
        title="next page">
       <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">13. </span>External Resources, Videos and Talks</p>
+        <p class="prev-next-title"><span class="section-number">14. </span>External Resources, Videos and Talks</p>
       </div>
       <i class="fa-solid fa-angle-right"></i>
     </a>
diff --git a/dev/maintainers.html b/dev/maintainers.html
index 43123c82e257b..fbf303a9003f1 100644
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-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
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diff --git a/dev/maintainers_emeritus.html b/dev/maintainers_emeritus.html
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-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
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diff --git a/dev/metadata_routing.html b/dev/metadata_routing.html
index 92ec7c0e782fb..a7e1ce9a1b3bc 100644
--- a/dev/metadata_routing.html
+++ b/dev/metadata_routing.html
@@ -7,7 +7,7 @@
   <head>
     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="viewport" content="width=device-width, initial-scale=1" />
-<meta property="og:title" content="1. Metadata Routing" />
+<meta property="og:title" content="4. Metadata Routing" />
 <meta property="og:type" content="website" />
 <meta property="og:url" content="https://scikit-learn/stable/metadata_routing.html" />
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@@ -16,7 +16,7 @@
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 <meta name="description" content="This guide demonstrates how metadata can be routed and passed between objects in scikit-learn. If you are developing a scikit-learn compatible estimator or meta-estimator, you can check our related..." />
 
-    <title>1. Metadata Routing &#8212; scikit-learn 1.7.dev0 documentation</title>
+    <title>4. Metadata Routing &#8212; scikit-learn 1.7.dev0 documentation</title>
   
   
   
@@ -42,7 +42,7 @@
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-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
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-    <link rel="next" title="API Reference" href="http://github.com/api/index.html" />
-    <link rel="prev" title="13. External Resources, Videos and Talks" href="http://github.com/presentations.html" />
+    <link rel="next" title="5. Inspection" href="http://github.com/inspection.html" />
+    <link rel="prev" title="3.5. Validation curves: plotting scores to evaluate models" href="http://github.com/modules/learning_curve.html" />
   <meta name="viewport" content="width=device-width, initial-scale=1"/>
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@@ -552,7 +552,7 @@
 <nav class="bd-docs-nav bd-links"
      aria-label="Section Navigation">
   <p class="bd-links__title" role="heading" aria-level="1">Section Navigation</p>
-  <div class="bd-toc-item navbar-nav"><ul class="nav bd-sidenav">
+  <div class="bd-toc-item navbar-nav"><ul class="current nav bd-sidenav">
 <li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/supervised_learning.html">1. Supervised learning</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/linear_model.html">1.1. Linear Models</a></li>
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/lda_qda.html">1.2. Linear and Quadratic Discriminant Analysis</a></li>
@@ -593,45 +593,46 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/learning_curve.html">3.5. Validation curves: plotting scores to evaluate models</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/inspection.html">4. Inspection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/partial_dependence.html">4.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/permutation_importance.html">4.2. Permutation feature importance</a></li>
+<li class="toctree-l1 current active"><a class="current reference internal" href="#">4. Metadata Routing</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/inspection.html">5. Inspection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/partial_dependence.html">5.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/permutation_importance.html">5.2. Permutation feature importance</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/visualizations.html">5. Visualizations</a></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/data_transforms.html">6. Dataset transformations</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/compose.html">6.1. Pipelines and composite estimators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/feature_extraction.html">6.2. Feature extraction</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/preprocessing.html">6.3. Preprocessing data</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/impute.html">6.4. Imputation of missing values</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/unsupervised_reduction.html">6.5. Unsupervised dimensionality reduction</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/kernel_approximation.html">6.7. Kernel Approximation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/metrics.html">6.8. Pairwise metrics, Affinities and Kernels</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/preprocessing_targets.html">6.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/visualizations.html">6. Visualizations</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/data_transforms.html">7. Dataset transformations</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/compose.html">7.1. Pipelines and composite estimators</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/feature_extraction.html">7.2. Feature extraction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/preprocessing.html">7.3. Preprocessing data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/impute.html">7.4. Imputation of missing values</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/unsupervised_reduction.html">7.5. Unsupervised dimensionality reduction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/random_projection.html">7.6. Random Projection</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/kernel_approximation.html">7.7. Kernel Approximation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/metrics.html">7.8. Pairwise metrics, Affinities and Kernels</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/preprocessing_targets.html">7.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/datasets.html">7. Dataset loading utilities</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html">7.1. Toy datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html">7.2. Real world datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html">7.3. Generated datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html">7.4. Loading other datasets</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/datasets.html">8. Dataset loading utilities</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html">8.1. Toy datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html">8.2. Real world datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html">8.3. Generated datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html">8.4. Loading other datasets</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/computing.html">8. Computing with scikit-learn</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/scaling_strategies.html">8.1. Strategies to scale computationally: bigger data</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/computational_performance.html">8.2. Computational Performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/parallelism.html">8.3. Parallelism, resource management, and configuration</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/computing.html">9. Computing with scikit-learn</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/scaling_strategies.html">9.1. Strategies to scale computationally: bigger data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/computational_performance.html">9.2. Computational Performance</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/parallelism.html">9.3. Parallelism, resource management, and configuration</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/model_persistence.html">9. Model persistence</a></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/common_pitfalls.html">10. Common pitfalls and recommended practices</a></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/dispatching.html">11. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/array_api.html">11.1. Array API support (experimental)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/model_persistence.html">10. Model persistence</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/common_pitfalls.html">11. Common pitfalls and recommended practices</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/dispatching.html">12. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/array_api.html">12.1. Array API support (experimental)</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/machine_learning_map.html">12. Choosing the right estimator</a></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/presentations.html">13. External Resources, Videos and Talks</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/machine_learning_map.html">13. Choosing the right estimator</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/presentations.html">14. External Resources, Videos and Talks</a></li>
 </ul>
 </div>
 </nav></div>
@@ -668,7 +669,7 @@
     
     <li class="breadcrumb-item"><a href="http://github.com/user_guide.html" class="nav-link">User Guide</a></li>
     
-    <li class="breadcrumb-item active" aria-current="page"><span class="ellipsis"><span class="section-number">1. </span>Metadata Routing</span></li>
+    <li class="breadcrumb-item active" aria-current="page"><span class="ellipsis"><span class="section-number">4. </span>Metadata Routing</span></li>
   </ul>
 </nav>
 </div>
@@ -686,7 +687,7 @@
                 <article class="bd-article">
                   
   <section id="metadata-routing">
-<span id="id1"></span><h1><span class="section-number">1. </span>Metadata Routing<a class="headerlink" href="#metadata-routing" title="Link to this heading">#</a></h1>
+<span id="id1"></span><h1><span class="section-number">4. </span>Metadata Routing<a class="headerlink" href="#metadata-routing" title="Link to this heading">#</a></h1>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
 <p>The Metadata Routing API is experimental, and is not yet implemented for all
@@ -731,7 +732,7 @@
 <code class="docutils literal notranslate"><span class="pre">groups</span></code> parameter is requested by default. This is best demonstrated by the
 following examples.</p>
 <section id="usage-examples">
-<h2><span class="section-number">1.1. </span>Usage Examples<a class="headerlink" href="#usage-examples" title="Link to this heading">#</a></h2>
+<h2><span class="section-number">4.1. </span>Usage Examples<a class="headerlink" href="#usage-examples" title="Link to this heading">#</a></h2>
 <p>Here we present a few examples to show some common use-cases. Our goal is to pass
 <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> and <code class="docutils literal notranslate"><span class="pre">groups</span></code> through <a class="reference internal" href="http://github.com/modules/generated/sklearn.model_selection.cross_validate.html#sklearn.model_selection.cross_validate" title="sklearn.model_selection.cross_validate"><code class="xref py py-func docutils literal notranslate"><span class="pre">cross_validate</span></code></a>, which
 routes the metadata to <a class="reference internal" href="http://github.com/modules/generated/sklearn.linear_model.LogisticRegressionCV.html#sklearn.linear_model.LogisticRegressionCV" title="sklearn.linear_model.LogisticRegressionCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">LogisticRegressionCV</span></code></a> and to a custom scorer
@@ -755,7 +756,7 @@ <h2><span class="section-number">1.1. </span>Usage Examples<a class="headerlink"
 </pre></div>
 </div>
 <section id="weighted-scoring-and-fitting">
-<h3><span class="section-number">1.1.1. </span>Weighted scoring and fitting<a class="headerlink" href="#weighted-scoring-and-fitting" title="Link to this heading">#</a></h3>
+<h3><span class="section-number">4.1.1. </span>Weighted scoring and fitting<a class="headerlink" href="#weighted-scoring-and-fitting" title="Link to this heading">#</a></h3>
 <p>The splitter used internally in <a class="reference internal" href="http://github.com/modules/generated/sklearn.linear_model.LogisticRegressionCV.html#sklearn.linear_model.LogisticRegressionCV" title="sklearn.linear_model.LogisticRegressionCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">LogisticRegressionCV</span></code></a>,
 <a class="reference internal" href="http://github.com/modules/generated/sklearn.model_selection.GroupKFold.html#sklearn.model_selection.GroupKFold" title="sklearn.model_selection.GroupKFold"><code class="xref py py-class docutils literal notranslate"><span class="pre">GroupKFold</span></code></a>, requests <code class="docutils literal notranslate"><span class="pre">groups</span></code> by default. However, we need
 to explicitly request <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> for it and for our custom scorer by specifying
@@ -789,7 +790,7 @@ <h3><span class="section-number">1.1.1. </span>Weighted scoring and fitting<a cl
 <code class="docutils literal notranslate"><span class="pre">sample_weights</span></code> was not requested by any of its underlying objects.</p>
 </section>
 <section id="weighted-scoring-and-unweighted-fitting">
-<h3><span class="section-number">1.1.2. </span>Weighted scoring and unweighted fitting<a class="headerlink" href="#weighted-scoring-and-unweighted-fitting" title="Link to this heading">#</a></h3>
+<h3><span class="section-number">4.1.2. </span>Weighted scoring and unweighted fitting<a class="headerlink" href="#weighted-scoring-and-unweighted-fitting" title="Link to this heading">#</a></h3>
 <p>When passing metadata such as <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> into a <a class="reference internal" href="http://github.com/glossary.html#term-router"><span class="xref std std-term">router</span></a>
 (<a class="reference internal" href="http://github.com/glossary.html#term-meta-estimators"><span class="xref std std-term">meta-estimators</span></a> or routing function), all <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> <a class="reference internal" href="http://github.com/glossary.html#term-consumer"><span class="xref std std-term">consumers</span></a> require weights to be either explicitly requested or explicitly not
 requested (i.e. <code class="docutils literal notranslate"><span class="pre">True</span></code> or <code class="docutils literal notranslate"><span class="pre">False</span></code>). Thus, to perform an unweighted fit, we need to
@@ -815,7 +816,7 @@ <h3><span class="section-number">1.1.2. </span>Weighted scoring and unweighted f
 configured to recognize the weights.</p>
 </section>
 <section id="unweighted-feature-selection">
-<h3><span class="section-number">1.1.3. </span>Unweighted feature selection<a class="headerlink" href="#unweighted-feature-selection" title="Link to this heading">#</a></h3>
+<h3><span class="section-number">4.1.3. </span>Unweighted feature selection<a class="headerlink" href="#unweighted-feature-selection" title="Link to this heading">#</a></h3>
 <p>Routing metadata is only possible if the object’s method knows how to use the metadata,
 which in most cases means they have it as an explicit parameter. Only then we can set
 request values for metadata using <code class="docutils literal notranslate"><span class="pre">set_fit_request(sample_weight=True)</span></code>, for instance.
@@ -842,7 +843,7 @@ <h3><span class="section-number">1.1.3. </span>Unweighted feature selection<a cl
 </div>
 </section>
 <section id="different-scoring-and-fitting-weights">
-<h3><span class="section-number">1.1.4. </span>Different scoring and fitting weights<a class="headerlink" href="#different-scoring-and-fitting-weights" title="Link to this heading">#</a></h3>
+<h3><span class="section-number">4.1.4. </span>Different scoring and fitting weights<a class="headerlink" href="#different-scoring-and-fitting-weights" title="Link to this heading">#</a></h3>
 <p>Despite <a class="reference internal" href="http://github.com/modules/generated/sklearn.metrics.make_scorer.html#sklearn.metrics.make_scorer" title="sklearn.metrics.make_scorer"><code class="xref py py-func docutils literal notranslate"><span class="pre">make_scorer</span></code></a> and
 <a class="reference internal" href="http://github.com/modules/generated/sklearn.linear_model.LogisticRegressionCV.html#sklearn.linear_model.LogisticRegressionCV" title="sklearn.linear_model.LogisticRegressionCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">LogisticRegressionCV</span></code></a> both expecting the key
 <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code>, we can use aliases to pass different weights to different
@@ -871,7 +872,7 @@ <h3><span class="section-number">1.1.4. </span>Different scoring and fitting wei
 </section>
 </section>
 <section id="api-interface">
-<h2><span class="section-number">1.2. </span>API Interface<a class="headerlink" href="#api-interface" title="Link to this heading">#</a></h2>
+<h2><span class="section-number">4.2. </span>API Interface<a class="headerlink" href="#api-interface" title="Link to this heading">#</a></h2>
 <p>A <a class="reference internal" href="http://github.com/glossary.html#term-consumer"><span class="xref std std-term">consumer</span></a> is an object (estimator, meta-estimator, scorer, splitter) which
 accepts and uses some <a class="reference internal" href="http://github.com/glossary.html#term-metadata"><span class="xref std std-term">metadata</span></a> in at least one of its methods (for instance
 <code class="docutils literal notranslate"><span class="pre">fit</span></code>, <code class="docutils literal notranslate"><span class="pre">predict</span></code>, <code class="docutils literal notranslate"><span class="pre">inverse_transform</span></code>, <code class="docutils literal notranslate"><span class="pre">transform</span></code>, <code class="docutils literal notranslate"><span class="pre">score</span></code>, <code class="docutils literal notranslate"><span class="pre">split</span></code>).
@@ -934,7 +935,7 @@ <h2><span class="section-number">1.2. </span>API Interface<a class="headerlink"
 </div>
 </section>
 <section id="metadata-routing-support-status">
-<span id="metadata-routing-models"></span><h2><span class="section-number">1.3. </span>Metadata Routing Support Status<a class="headerlink" href="#metadata-routing-support-status" title="Link to this heading">#</a></h2>
+<span id="metadata-routing-models"></span><h2><span class="section-number">4.3. </span>Metadata Routing Support Status<a class="headerlink" href="#metadata-routing-support-status" title="Link to this heading">#</a></h2>
 <p>All consumers (i.e. simple estimators which only consume metadata and don’t
 route them) support metadata routing, meaning they can be used inside
 meta-estimators which support metadata routing. However, development of support
@@ -1008,20 +1009,20 @@ <h2><span class="section-number">1.2. </span>API Interface<a class="headerlink"
     <div class="footer-article-item">
 <div class="prev-next-area">
     <a class="left-prev"
-       href="http://github.com/presentations.html"
+       href="http://github.com/modules/learning_curve.html"
        title="previous page">
       <i class="fa-solid fa-angle-left"></i>
       <div class="prev-next-info">
         <p class="prev-next-subtitle">previous</p>
-        <p class="prev-next-title"><span class="section-number">13. </span>External Resources, Videos and Talks</p>
+        <p class="prev-next-title"><span class="section-number">3.5. </span>Validation curves: plotting scores to evaluate models</p>
       </div>
     </a>
     <a class="right-next"
-       href="http://github.com/api/index.html"
+       href="http://github.com/inspection.html"
        title="next page">
       <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title">API Reference</p>
+        <p class="prev-next-title"><span class="section-number">5. </span>Inspection</p>
       </div>
       <i class="fa-solid fa-angle-right"></i>
     </a>
@@ -1048,15 +1049,15 @@ <h2><span class="section-number">1.2. </span>API Interface<a class="headerlink"
   </div>
   <nav class="bd-toc-nav page-toc" aria-labelledby="pst-page-navigation-heading-2">
     <ul class="visible nav section-nav flex-column">
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#usage-examples">1.1. Usage Examples</a><ul class="nav section-nav flex-column">
-<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#weighted-scoring-and-fitting">1.1.1. Weighted scoring and fitting</a></li>
-<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#weighted-scoring-and-unweighted-fitting">1.1.2. Weighted scoring and unweighted fitting</a></li>
-<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#unweighted-feature-selection">1.1.3. Unweighted feature selection</a></li>
-<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#different-scoring-and-fitting-weights">1.1.4. Different scoring and fitting weights</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#usage-examples">4.1. Usage Examples</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#weighted-scoring-and-fitting">4.1.1. Weighted scoring and fitting</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#weighted-scoring-and-unweighted-fitting">4.1.2. Weighted scoring and unweighted fitting</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#unweighted-feature-selection">4.1.3. Unweighted feature selection</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#different-scoring-and-fitting-weights">4.1.4. Different scoring and fitting weights</a></li>
 </ul>
 </li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#api-interface">1.2. API Interface</a></li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#metadata-routing-support-status">1.3. Metadata Routing Support Status</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#api-interface">4.2. API Interface</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#metadata-routing-support-status">4.3. Metadata Routing Support Status</a></li>
 </ul>
   </nav></div>
 
diff --git a/dev/min_dependency_substitutions.html b/dev/min_dependency_substitutions.html
index b44a9ab3c7323..ab1cd45adaa27 100644
--- a/dev/min_dependency_substitutions.html
+++ b/dev/min_dependency_substitutions.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
diff --git a/dev/min_dependency_table.html b/dev/min_dependency_table.html
index b4e13d67f3b79..d20aa524db33c 100644
--- a/dev/min_dependency_table.html
+++ b/dev/min_dependency_table.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -653,86 +653,82 @@
 <td><p>tests</p></td>
 </tr>
 <tr class="row-odd"><td><p>ruff</p></td>
-<td><p>0.11.0</p></td>
+<td><p>0.11.7</p></td>
 <td><p>tests</p></td>
 </tr>
-<tr class="row-even"><td><p>black</p></td>
-<td><p>24.3.0</p></td>
-<td><p>tests</p></td>
-</tr>
-<tr class="row-odd"><td><p>mypy</p></td>
+<tr class="row-even"><td><p>mypy</p></td>
 <td><p>1.15</p></td>
 <td><p>tests</p></td>
 </tr>
-<tr class="row-even"><td><p>pyamg</p></td>
+<tr class="row-odd"><td><p>pyamg</p></td>
 <td><p>4.2.1</p></td>
 <td><p>tests</p></td>
 </tr>
-<tr class="row-odd"><td><p>polars</p></td>
+<tr class="row-even"><td><p>polars</p></td>
 <td><p>0.20.30</p></td>
 <td><p>docs, tests</p></td>
 </tr>
-<tr class="row-even"><td><p>pyarrow</p></td>
+<tr class="row-odd"><td><p>pyarrow</p></td>
 <td><p>12.0.0</p></td>
 <td><p>tests</p></td>
 </tr>
-<tr class="row-odd"><td><p>sphinx</p></td>
+<tr class="row-even"><td><p>sphinx</p></td>
 <td><p>7.3.7</p></td>
 <td><p>docs</p></td>
 </tr>
-<tr class="row-even"><td><p>sphinx-copybutton</p></td>
+<tr class="row-odd"><td><p>sphinx-copybutton</p></td>
 <td><p>0.5.2</p></td>
 <td><p>docs</p></td>
 </tr>
-<tr class="row-odd"><td><p>sphinx-gallery</p></td>
+<tr class="row-even"><td><p>sphinx-gallery</p></td>
 <td><p>0.17.1</p></td>
 <td><p>docs</p></td>
 </tr>
-<tr class="row-even"><td><p>numpydoc</p></td>
+<tr class="row-odd"><td><p>numpydoc</p></td>
 <td><p>1.2.0</p></td>
 <td><p>docs, tests</p></td>
 </tr>
-<tr class="row-odd"><td><p>Pillow</p></td>
+<tr class="row-even"><td><p>Pillow</p></td>
 <td><p>8.4.0</p></td>
 <td><p>docs</p></td>
 </tr>
-<tr class="row-even"><td><p>pooch</p></td>
+<tr class="row-odd"><td><p>pooch</p></td>
 <td><p>1.6.0</p></td>
 <td><p>docs, examples, tests</p></td>
 </tr>
-<tr class="row-odd"><td><p>sphinx-prompt</p></td>
+<tr class="row-even"><td><p>sphinx-prompt</p></td>
 <td><p>1.4.0</p></td>
 <td><p>docs</p></td>
 </tr>
-<tr class="row-even"><td><p>sphinxext-opengraph</p></td>
+<tr class="row-odd"><td><p>sphinxext-opengraph</p></td>
 <td><p>0.9.1</p></td>
 <td><p>docs</p></td>
 </tr>
-<tr class="row-odd"><td><p>plotly</p></td>
+<tr class="row-even"><td><p>plotly</p></td>
 <td><p>5.14.0</p></td>
 <td><p>docs, examples</p></td>
 </tr>
-<tr class="row-even"><td><p>sphinxcontrib-sass</p></td>
+<tr class="row-odd"><td><p>sphinxcontrib-sass</p></td>
 <td><p>0.3.4</p></td>
 <td><p>docs</p></td>
 </tr>
-<tr class="row-odd"><td><p>sphinx-remove-toctrees</p></td>
+<tr class="row-even"><td><p>sphinx-remove-toctrees</p></td>
 <td><p>1.0.0.post1</p></td>
 <td><p>docs</p></td>
 </tr>
-<tr class="row-even"><td><p>sphinx-design</p></td>
+<tr class="row-odd"><td><p>sphinx-design</p></td>
 <td><p>0.6.0</p></td>
 <td><p>docs</p></td>
 </tr>
-<tr class="row-odd"><td><p>pydata-sphinx-theme</p></td>
+<tr class="row-even"><td><p>pydata-sphinx-theme</p></td>
 <td><p>0.15.3</p></td>
 <td><p>docs</p></td>
 </tr>
-<tr class="row-even"><td><p>towncrier</p></td>
+<tr class="row-odd"><td><p>towncrier</p></td>
 <td><p>24.8.0</p></td>
 <td><p>docs</p></td>
 </tr>
-<tr class="row-odd"><td><p>conda-lock</p></td>
+<tr class="row-even"><td><p>conda-lock</p></td>
 <td><p>2.5.7</p></td>
 <td><p>maintenance</p></td>
 </tr>
diff --git a/dev/model_persistence.html b/dev/model_persistence.html
index 1b2461ff392bb..8d049fcee27b2 100644
--- a/dev/model_persistence.html
+++ b/dev/model_persistence.html
@@ -7,7 +7,7 @@
   <head>
     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="viewport" content="width=device-width, initial-scale=1" />
-<meta property="og:title" content="9. Model persistence" />
+<meta property="og:title" content="10. Model persistence" />
 <meta property="og:type" content="website" />
 <meta property="og:url" content="https://scikit-learn/stable/model_persistence.html" />
 <meta property="og:site_name" content="scikit-learn" />
@@ -16,7 +16,7 @@
 <meta property="og:image:alt" content="scikit-learn" />
 <meta name="description" content="Summary of model persistence methods:,,, Persistence method, Pros, Risks / Cons,,, ONNX, Serve models without a Python environment, Serving and training environments independent of one another, Mos..." />
 
-    <title>9. Model persistence &#8212; scikit-learn 1.7.dev0 documentation</title>
+    <title>10. Model persistence &#8212; scikit-learn 1.7.dev0 documentation</title>
   
   
   
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -80,8 +80,8 @@
     <link rel="icon" href="http://github.com/_static/favicon.ico"/>
     <link rel="author" title="About these documents" href="http://github.com/about.html" />
     <link rel="search" title="Search" href="http://github.com/search.html" />
-    <link rel="next" title="10. Common pitfalls and recommended practices" href="http://github.com/common_pitfalls.html" />
-    <link rel="prev" title="8.3. Parallelism, resource management, and configuration" href="http://github.com/computing/parallelism.html" />
+    <link rel="next" title="11. Common pitfalls and recommended practices" href="http://github.com/common_pitfalls.html" />
+    <link rel="prev" title="9.3. Parallelism, resource management, and configuration" href="http://github.com/computing/parallelism.html" />
   <meta name="viewport" content="width=device-width, initial-scale=1"/>
   <meta name="docsearch:language" content="en"/>
   <meta name="docsearch:version" content="1.7" />
@@ -593,45 +593,46 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/learning_curve.html">3.5. Validation curves: plotting scores to evaluate models</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/inspection.html">4. Inspection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/partial_dependence.html">4.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/permutation_importance.html">4.2. Permutation feature importance</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/metadata_routing.html">4. Metadata Routing</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/inspection.html">5. Inspection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/partial_dependence.html">5.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/permutation_importance.html">5.2. Permutation feature importance</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/visualizations.html">5. Visualizations</a></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/data_transforms.html">6. Dataset transformations</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/compose.html">6.1. Pipelines and composite estimators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/feature_extraction.html">6.2. Feature extraction</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/preprocessing.html">6.3. Preprocessing data</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/impute.html">6.4. Imputation of missing values</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/unsupervised_reduction.html">6.5. Unsupervised dimensionality reduction</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/random_projection.html">6.6. Random Projection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/kernel_approximation.html">6.7. Kernel Approximation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/metrics.html">6.8. Pairwise metrics, Affinities and Kernels</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/preprocessing_targets.html">6.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/visualizations.html">6. Visualizations</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/data_transforms.html">7. Dataset transformations</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/compose.html">7.1. Pipelines and composite estimators</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/feature_extraction.html">7.2. Feature extraction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/preprocessing.html">7.3. Preprocessing data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/impute.html">7.4. Imputation of missing values</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/unsupervised_reduction.html">7.5. Unsupervised dimensionality reduction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/random_projection.html">7.6. Random Projection</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/kernel_approximation.html">7.7. Kernel Approximation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/metrics.html">7.8. Pairwise metrics, Affinities and Kernels</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/preprocessing_targets.html">7.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/datasets.html">7. Dataset loading utilities</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html">7.1. Toy datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html">7.2. Real world datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html">7.3. Generated datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html">7.4. Loading other datasets</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/datasets.html">8. Dataset loading utilities</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html">8.1. Toy datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html">8.2. Real world datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html">8.3. Generated datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html">8.4. Loading other datasets</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/computing.html">8. Computing with scikit-learn</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/scaling_strategies.html">8.1. Strategies to scale computationally: bigger data</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/computational_performance.html">8.2. Computational Performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/parallelism.html">8.3. Parallelism, resource management, and configuration</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/computing.html">9. Computing with scikit-learn</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/scaling_strategies.html">9.1. Strategies to scale computationally: bigger data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/computational_performance.html">9.2. Computational Performance</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/parallelism.html">9.3. Parallelism, resource management, and configuration</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 current active"><a class="current reference internal" href="#">9. Model persistence</a></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/common_pitfalls.html">10. Common pitfalls and recommended practices</a></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/dispatching.html">11. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/array_api.html">11.1. Array API support (experimental)</a></li>
+<li class="toctree-l1 current active"><a class="current reference internal" href="#">10. Model persistence</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/common_pitfalls.html">11. Common pitfalls and recommended practices</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/dispatching.html">12. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/array_api.html">12.1. Array API support (experimental)</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/machine_learning_map.html">12. Choosing the right estimator</a></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/presentations.html">13. External Resources, Videos and Talks</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/machine_learning_map.html">13. Choosing the right estimator</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/presentations.html">14. External Resources, Videos and Talks</a></li>
 </ul>
 </div>
 </nav></div>
@@ -668,7 +669,7 @@
     
     <li class="breadcrumb-item"><a href="http://github.com/user_guide.html" class="nav-link">User Guide</a></li>
     
-    <li class="breadcrumb-item active" aria-current="page"><span class="ellipsis"><span class="section-number">9. </span>Model persistence</span></li>
+    <li class="breadcrumb-item active" aria-current="page"><span class="ellipsis"><span class="section-number">10. </span>Model persistence</span></li>
   </ul>
 </nav>
 </div>
@@ -686,7 +687,7 @@
                 <article class="bd-article">
                   
   <section id="model-persistence">
-<span id="id1"></span><h1><span class="section-number">9. </span>Model persistence<a class="headerlink" href="#model-persistence" title="Link to this heading">#</a></h1>
+<span id="id1"></span><h1><span class="section-number">10. </span>Model persistence<a class="headerlink" href="#model-persistence" title="Link to this heading">#</a></h1>
 <div class="pst-scrollable-table-container"><table class="table" id="id2">
 <caption><span class="caption-text">Summary of model persistence methods</span><a class="headerlink" href="#id2" title="Link to this table">#</a></caption>
 <colgroup>
@@ -741,7 +742,7 @@
 </ul>
 </td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference external" href="https://joblib.readthedocs.io/en/latest/index.html#module-joblib" title="(in joblib v1.5.dev0)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">joblib</span></code></a></p></td>
+<tr class="row-odd"><td><p><a class="reference external" href="https://joblib.readthedocs.io/en/latest/index.html#module-joblib" title="(in joblib v1.6.dev0)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">joblib</span></code></a></p></td>
 <td><ul class="simple">
 <li><p>Efficient memory usage</p></li>
 <li><p>Supports memory mapping</p></li>
@@ -804,19 +805,19 @@
 is not a major concern for you, then you can use the built-in <a class="reference external" href="https://docs.python.org/3/library/pickle.html#module-pickle" title="(in Python v3.13)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">pickle</span></code></a>
 module.</p>
 <ol class="arabic simple" start="4">
-<li><p>Did you try <a class="reference external" href="https://docs.python.org/3/library/pickle.html#module-pickle" title="(in Python v3.13)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">pickle</span></code></a> or <a class="reference external" href="https://joblib.readthedocs.io/en/latest/index.html#module-joblib" title="(in joblib v1.5.dev0)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">joblib</span></code></a> and found that the model cannot
+<li><p>Did you try <a class="reference external" href="https://docs.python.org/3/library/pickle.html#module-pickle" title="(in Python v3.13)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">pickle</span></code></a> or <a class="reference external" href="https://joblib.readthedocs.io/en/latest/index.html#module-joblib" title="(in joblib v1.6.dev0)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">joblib</span></code></a> and found that the model cannot
 be persisted? It can happen for instance when you have user defined
 functions in your model.</p></li>
 </ol>
 <p>If yes, then you can use <a class="reference external" href="https://github.com/cloudpipe/cloudpickle">cloudpickle</a> which can serialize certain objects
-which cannot be serialized by <a class="reference external" href="https://docs.python.org/3/library/pickle.html#module-pickle" title="(in Python v3.13)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">pickle</span></code></a> or <a class="reference external" href="https://joblib.readthedocs.io/en/latest/index.html#module-joblib" title="(in joblib v1.5.dev0)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">joblib</span></code></a>.</p>
+which cannot be serialized by <a class="reference external" href="https://docs.python.org/3/library/pickle.html#module-pickle" title="(in Python v3.13)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">pickle</span></code></a> or <a class="reference external" href="https://joblib.readthedocs.io/en/latest/index.html#module-joblib" title="(in joblib v1.6.dev0)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">joblib</span></code></a>.</p>
 <section id="workflow-overview">
-<h2><span class="section-number">9.1. </span>Workflow Overview<a class="headerlink" href="#workflow-overview" title="Link to this heading">#</a></h2>
+<h2><span class="section-number">10.1. </span>Workflow Overview<a class="headerlink" href="#workflow-overview" title="Link to this heading">#</a></h2>
 <p>In a typical workflow, the first step is to train the model using scikit-learn
 and scikit-learn compatible libraries. Note that support for scikit-learn and
 third party estimators varies across the different persistence methods.</p>
 <section id="train-and-persist-the-model">
-<h3><span class="section-number">9.1.1. </span>Train and Persist the Model<a class="headerlink" href="#train-and-persist-the-model" title="Link to this heading">#</a></h3>
+<h3><span class="section-number">10.1.1. </span>Train and Persist the Model<a class="headerlink" href="#train-and-persist-the-model" title="Link to this heading">#</a></h3>
 <p>Creating an appropriate model depends on your use-case. As an example, here we
 train a <a class="reference internal" href="http://github.com/modules/generated/sklearn.ensemble.HistGradientBoostingClassifier.html#sklearn.ensemble.HistGradientBoostingClassifier" title="sklearn.ensemble.HistGradientBoostingClassifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.ensemble.HistGradientBoostingClassifier</span></code></a> on the iris
 dataset:</p>
@@ -839,7 +840,7 @@ <h3><span class="section-number">9.1.1. </span>Train and Persist the Model<a cla
 even require Python to be installed to load the model and compute
 predictions. Also note that <code class="docutils literal notranslate"><span class="pre">onnxruntime</span></code> typically requires much less RAM
 than Python to compute predictions from small models.</p></li>
-<li><p><a class="reference external" href="https://skops.readthedocs.io/en/stable/modules/classes.html#module-skops.io" title="(in skops)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">skops.io</span></code></a>, <a class="reference external" href="https://docs.python.org/3/library/pickle.html#module-pickle" title="(in Python v3.13)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">pickle</span></code></a>, <a class="reference external" href="https://joblib.readthedocs.io/en/latest/index.html#module-joblib" title="(in joblib v1.5.dev0)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">joblib</span></code></a>, <a class="reference external" href="https://github.com/cloudpipe/cloudpickle">cloudpickle</a>: You need a
+<li><p><a class="reference external" href="https://skops.readthedocs.io/en/stable/modules/classes.html#module-skops.io" title="(in skops)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">skops.io</span></code></a>, <a class="reference external" href="https://docs.python.org/3/library/pickle.html#module-pickle" title="(in Python v3.13)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">pickle</span></code></a>, <a class="reference external" href="https://joblib.readthedocs.io/en/latest/index.html#module-joblib" title="(in joblib v1.6.dev0)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">joblib</span></code></a>, <a class="reference external" href="https://github.com/cloudpipe/cloudpickle">cloudpickle</a>: You need a
 Python environment with the appropriate dependencies installed to load the
 model and get predictions from it. This environment should have the same
 <strong>packages</strong> and the same <strong>versions</strong> as the environment where the model was
@@ -852,7 +853,7 @@ <h3><span class="section-number">9.1.1. </span>Train and Persist the Model<a cla
 </section>
 </section>
 <section id="onnx">
-<span id="onnx-persistence"></span><h2><span class="section-number">9.2. </span>ONNX<a class="headerlink" href="#onnx" title="Link to this heading">#</a></h2>
+<span id="onnx-persistence"></span><h2><span class="section-number">10.2. </span>ONNX<a class="headerlink" href="#onnx" title="Link to this heading">#</a></h2>
 <p><code class="docutils literal notranslate"><span class="pre">ONNX</span></code>, or <a class="reference external" href="https://onnx.ai/">Open Neural Network Exchange</a> format is best
 suitable in use-cases where one needs to persist the model and then use the
 persisted artifact to get predictions without the need to load the Python
@@ -891,10 +892,10 @@ <h3><span class="section-number">9.1.1. </span>Train and Persist the Model<a cla
 </div>
 </details></section>
 <section id="skops-io">
-<span id="skops-persistence"></span><h2><span class="section-number">9.3. </span><code class="docutils literal notranslate"><span class="pre">skops.io</span></code><a class="headerlink" href="#skops-io" title="Link to this heading">#</a></h2>
+<span id="skops-persistence"></span><h2><span class="section-number">10.3. </span><code class="docutils literal notranslate"><span class="pre">skops.io</span></code><a class="headerlink" href="#skops-io" title="Link to this heading">#</a></h2>
 <p><a class="reference external" href="https://skops.readthedocs.io/en/stable/modules/classes.html#module-skops.io" title="(in skops)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">skops.io</span></code></a> avoids using <a class="reference external" href="https://docs.python.org/3/library/pickle.html#module-pickle" title="(in Python v3.13)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">pickle</span></code></a> and only loads files which have types
 and references to functions which are trusted either by default or by the user.
-Therefore it provides a more secure format than <a class="reference external" href="https://docs.python.org/3/library/pickle.html#module-pickle" title="(in Python v3.13)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">pickle</span></code></a>, <a class="reference external" href="https://joblib.readthedocs.io/en/latest/index.html#module-joblib" title="(in joblib v1.5.dev0)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">joblib</span></code></a>,
+Therefore it provides a more secure format than <a class="reference external" href="https://docs.python.org/3/library/pickle.html#module-pickle" title="(in Python v3.13)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">pickle</span></code></a>, <a class="reference external" href="https://joblib.readthedocs.io/en/latest/index.html#module-joblib" title="(in joblib v1.6.dev0)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">joblib</span></code></a>,
 and <a class="reference external" href="https://github.com/cloudpipe/cloudpickle">cloudpickle</a>.</p>
 <details class="sd-sphinx-override sd-dropdown sd-card sd-mb-3" id="using-skops">
 <summary class="sd-summary-title sd-card-header">
@@ -922,17 +923,17 @@ <h3><span class="section-number">9.1.1. </span>Train and Persist the Model<a cla
 </div>
 </details></section>
 <section id="pickle-joblib-and-cloudpickle">
-<span id="pickle-persistence"></span><h2><span class="section-number">9.4. </span><code class="docutils literal notranslate"><span class="pre">pickle</span></code>, <code class="docutils literal notranslate"><span class="pre">joblib</span></code>, and <code class="docutils literal notranslate"><span class="pre">cloudpickle</span></code><a class="headerlink" href="#pickle-joblib-and-cloudpickle" title="Link to this heading">#</a></h2>
+<span id="pickle-persistence"></span><h2><span class="section-number">10.4. </span><code class="docutils literal notranslate"><span class="pre">pickle</span></code>, <code class="docutils literal notranslate"><span class="pre">joblib</span></code>, and <code class="docutils literal notranslate"><span class="pre">cloudpickle</span></code><a class="headerlink" href="#pickle-joblib-and-cloudpickle" title="Link to this heading">#</a></h2>
 <p>These three modules / packages, use the <code class="docutils literal notranslate"><span class="pre">pickle</span></code> protocol under the hood, but
 come with slight variations:</p>
 <ul class="simple">
 <li><p><a class="reference external" href="https://docs.python.org/3/library/pickle.html#module-pickle" title="(in Python v3.13)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">pickle</span></code></a> is a module from the Python Standard Library. It can serialize
 and  deserialize any Python object, including custom Python classes and
 objects.</p></li>
-<li><p><a class="reference external" href="https://joblib.readthedocs.io/en/latest/index.html#module-joblib" title="(in joblib v1.5.dev0)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">joblib</span></code></a> is more efficient than <code class="docutils literal notranslate"><span class="pre">pickle</span></code> when working with large machine
+<li><p><a class="reference external" href="https://joblib.readthedocs.io/en/latest/index.html#module-joblib" title="(in joblib v1.6.dev0)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">joblib</span></code></a> is more efficient than <code class="docutils literal notranslate"><span class="pre">pickle</span></code> when working with large machine
 learning models or large numpy arrays.</p></li>
 <li><p><a class="reference external" href="https://github.com/cloudpipe/cloudpickle">cloudpickle</a> can serialize certain objects which cannot be serialized by
-<a class="reference external" href="https://docs.python.org/3/library/pickle.html#module-pickle" title="(in Python v3.13)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">pickle</span></code></a> or <a class="reference external" href="https://joblib.readthedocs.io/en/latest/index.html#module-joblib" title="(in joblib v1.5.dev0)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">joblib</span></code></a>, such as user defined functions and lambda
+<a class="reference external" href="https://docs.python.org/3/library/pickle.html#module-pickle" title="(in Python v3.13)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">pickle</span></code></a> or <a class="reference external" href="https://joblib.readthedocs.io/en/latest/index.html#module-joblib" title="(in joblib v1.6.dev0)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">joblib</span></code></a>, such as user defined functions and lambda
 functions. This can happen for instance, when using a
 <a class="reference internal" href="http://github.com/modules/generated/sklearn.preprocessing.FunctionTransformer.html#sklearn.preprocessing.FunctionTransformer" title="sklearn.preprocessing.FunctionTransformer"><code class="xref py py-class docutils literal notranslate"><span class="pre">FunctionTransformer</span></code></a> and using a custom
 function to transform the data.</p></li>
@@ -962,8 +963,8 @@ <h3><span class="section-number">9.1.1. </span>Train and Persist the Model<a cla
 </div>
 </details></section>
 <section id="security-maintainability-limitations">
-<span id="persistence-limitations"></span><h2><span class="section-number">9.5. </span>Security &amp; Maintainability Limitations<a class="headerlink" href="#security-maintainability-limitations" title="Link to this heading">#</a></h2>
-<p><a class="reference external" href="https://docs.python.org/3/library/pickle.html#module-pickle" title="(in Python v3.13)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">pickle</span></code></a> (and <a class="reference external" href="https://joblib.readthedocs.io/en/latest/index.html#module-joblib" title="(in joblib v1.5.dev0)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">joblib</span></code></a> and <code class="xref py py-mod docutils literal notranslate"><span class="pre">cloudpickle</span></code> by extension), has
+<span id="persistence-limitations"></span><h2><span class="section-number">10.5. </span>Security &amp; Maintainability Limitations<a class="headerlink" href="#security-maintainability-limitations" title="Link to this heading">#</a></h2>
+<p><a class="reference external" href="https://docs.python.org/3/library/pickle.html#module-pickle" title="(in Python v3.13)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">pickle</span></code></a> (and <a class="reference external" href="https://joblib.readthedocs.io/en/latest/index.html#module-joblib" title="(in joblib v1.6.dev0)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">joblib</span></code></a> and <code class="xref py py-mod docutils literal notranslate"><span class="pre">cloudpickle</span></code> by extension), has
 many documented security vulnerabilities by design and should only be used if
 the artifact, i.e. the pickle-file, is coming from a trusted and verified
 source. You should never load a pickle file from an untrusted source, similarly
@@ -972,7 +973,7 @@ <h3><span class="section-number">9.1.1. </span>Train and Persist the Model<a cla
 format, and it is therefore recommended to serve models using <code class="docutils literal notranslate"><span class="pre">ONNX</span></code> in a
 sandboxed environment to safeguard against computational and memory exploits.</p>
 <p>Also note that there are no supported ways to load a model trained with a
-different version of scikit-learn. While using <a class="reference external" href="https://skops.readthedocs.io/en/stable/modules/classes.html#module-skops.io" title="(in skops)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">skops.io</span></code></a>, <a class="reference external" href="https://joblib.readthedocs.io/en/latest/index.html#module-joblib" title="(in joblib v1.5.dev0)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">joblib</span></code></a>,
+different version of scikit-learn. While using <a class="reference external" href="https://skops.readthedocs.io/en/stable/modules/classes.html#module-skops.io" title="(in skops)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">skops.io</span></code></a>, <a class="reference external" href="https://joblib.readthedocs.io/en/latest/index.html#module-joblib" title="(in joblib v1.6.dev0)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">joblib</span></code></a>,
 <a class="reference external" href="https://docs.python.org/3/library/pickle.html#module-pickle" title="(in Python v3.13)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">pickle</span></code></a>, or <a class="reference external" href="https://github.com/cloudpipe/cloudpickle">cloudpickle</a>, models saved using one version of
 scikit-learn might load in other versions, however, this is entirely
 unsupported and inadvisable. It should also be kept in mind that operations
@@ -1001,7 +1002,7 @@ <h3><span class="section-number">9.1.1. </span>Train and Persist the Model<a cla
 <li><p><a class="reference external" href="https://pyvideo.org/video/2566/pickles-are-for-delis-not-software">Alex Gaynor: Pickles are for Delis, not Software - PyCon 2014</a>.</p></li>
 </ul>
 <section id="replicating-the-training-environment-in-production">
-<span id="serving-environment"></span><h3><span class="section-number">9.5.1. </span>Replicating the training environment in production<a class="headerlink" href="#replicating-the-training-environment-in-production" title="Link to this heading">#</a></h3>
+<span id="serving-environment"></span><h3><span class="section-number">10.5.1. </span>Replicating the training environment in production<a class="headerlink" href="#replicating-the-training-environment-in-production" title="Link to this heading">#</a></h3>
 <p>If the versions of the dependencies used may differ from training to
 production, it may result in unexpected behaviour and errors while using the
 trained model. To prevent such situations it is recommended to use the same
@@ -1035,7 +1036,7 @@ <h3><span class="section-number">9.1.1. </span>Train and Persist the Model<a cla
 </div>
 </details></section>
 <section id="serving-the-model-artifact">
-<h3><span class="section-number">9.5.2. </span>Serving the model artifact<a class="headerlink" href="#serving-the-model-artifact" title="Link to this heading">#</a></h3>
+<h3><span class="section-number">10.5.2. </span>Serving the model artifact<a class="headerlink" href="#serving-the-model-artifact" title="Link to this heading">#</a></h3>
 <p>The last step after training a scikit-learn model is serving the model.
 Once the trained model is successfully loaded, it can be served to manage
 different prediction requests. This can involve deploying the model as a
@@ -1044,7 +1045,7 @@ <h3><span class="section-number">9.5.2. </span>Serving the model artifact<a clas
 </section>
 </section>
 <section id="summarizing-the-key-points">
-<h2><span class="section-number">9.6. </span>Summarizing the key points<a class="headerlink" href="#summarizing-the-key-points" title="Link to this heading">#</a></h2>
+<h2><span class="section-number">10.6. </span>Summarizing the key points<a class="headerlink" href="#summarizing-the-key-points" title="Link to this heading">#</a></h2>
 <p>Based on the different approaches for model persistence, the key points for
 each approach can be summarized as follows:</p>
 <ul class="simple">
@@ -1057,7 +1058,7 @@ <h2><span class="section-number">9.6. </span>Summarizing the key points<a class=
 alternate approaches based on <a class="reference external" href="https://docs.python.org/3/library/pickle.html#module-pickle" title="(in Python v3.13)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">pickle</span></code></a> because it does not load
 arbitrary code unless explicitly asked for by the user. Such code needs to be
 packaged and importable in the target Python environment.</p></li>
-<li><p><a class="reference external" href="https://joblib.readthedocs.io/en/latest/index.html#module-joblib" title="(in joblib v1.5.dev0)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">joblib</span></code></a>: Efficient memory mapping techniques make it faster when using
+<li><p><a class="reference external" href="https://joblib.readthedocs.io/en/latest/index.html#module-joblib" title="(in joblib v1.6.dev0)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">joblib</span></code></a>: Efficient memory mapping techniques make it faster when using
 the same persisted model in multiple Python processes when using
 <code class="docutils literal notranslate"><span class="pre">mmap_mode=&quot;r&quot;</span></code>. It also gives easy shortcuts to compress and decompress the
 persisted object without the need for extra code. However, it may trigger the
@@ -1072,7 +1073,7 @@ <h2><span class="section-number">9.6. </span>Summarizing the key points<a class=
 be very efficient memorywise if the model was persisted with <code class="docutils literal notranslate"><span class="pre">protocol=5</span></code> but
 it does not support memory mapping.</p></li>
 <li><p><a class="reference external" href="https://github.com/cloudpipe/cloudpickle">cloudpickle</a>: It has comparable loading efficiency as <a class="reference external" href="https://docs.python.org/3/library/pickle.html#module-pickle" title="(in Python v3.13)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">pickle</span></code></a> and
-<a class="reference external" href="https://joblib.readthedocs.io/en/latest/index.html#module-joblib" title="(in joblib v1.5.dev0)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">joblib</span></code></a> (without memory mapping), but offers additional flexibility to
+<a class="reference external" href="https://joblib.readthedocs.io/en/latest/index.html#module-joblib" title="(in joblib v1.6.dev0)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">joblib</span></code></a> (without memory mapping), but offers additional flexibility to
 serialize custom Python code such as lambda expressions and interactively
 defined functions and classes. It might be a last resort to persist pipelines
 with custom Python components such as a
@@ -1104,7 +1105,7 @@ <h2><span class="section-number">9.6. </span>Summarizing the key points<a class=
       <i class="fa-solid fa-angle-left"></i>
       <div class="prev-next-info">
         <p class="prev-next-subtitle">previous</p>
-        <p class="prev-next-title"><span class="section-number">8.3. </span>Parallelism, resource management, and configuration</p>
+        <p class="prev-next-title"><span class="section-number">9.3. </span>Parallelism, resource management, and configuration</p>
       </div>
     </a>
     <a class="right-next"
@@ -1112,7 +1113,7 @@ <h2><span class="section-number">9.6. </span>Summarizing the key points<a class=
        title="next page">
       <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">10. </span>Common pitfalls and recommended practices</p>
+        <p class="prev-next-title"><span class="section-number">11. </span>Common pitfalls and recommended practices</p>
       </div>
       <i class="fa-solid fa-angle-right"></i>
     </a>
@@ -1139,19 +1140,19 @@ <h2><span class="section-number">9.6. </span>Summarizing the key points<a class=
   </div>
   <nav class="bd-toc-nav page-toc" aria-labelledby="pst-page-navigation-heading-2">
     <ul class="visible nav section-nav flex-column">
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#workflow-overview">9.1. Workflow Overview</a><ul class="nav section-nav flex-column">
-<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#train-and-persist-the-model">9.1.1. Train and Persist the Model</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#workflow-overview">10.1. Workflow Overview</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#train-and-persist-the-model">10.1.1. Train and Persist the Model</a></li>
 </ul>
 </li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#onnx">9.2. ONNX</a></li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#skops-io">9.3. <code class="docutils literal notranslate"><span class="pre">skops.io</span></code></a></li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pickle-joblib-and-cloudpickle">9.4. <code class="docutils literal notranslate"><span class="pre">pickle</span></code>, <code class="docutils literal notranslate"><span class="pre">joblib</span></code>, and <code class="docutils literal notranslate"><span class="pre">cloudpickle</span></code></a></li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#security-maintainability-limitations">9.5. Security &amp; Maintainability Limitations</a><ul class="nav section-nav flex-column">
-<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#replicating-the-training-environment-in-production">9.5.1. Replicating the training environment in production</a></li>
-<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#serving-the-model-artifact">9.5.2. Serving the model artifact</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#onnx">10.2. ONNX</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#skops-io">10.3. <code class="docutils literal notranslate"><span class="pre">skops.io</span></code></a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pickle-joblib-and-cloudpickle">10.4. <code class="docutils literal notranslate"><span class="pre">pickle</span></code>, <code class="docutils literal notranslate"><span class="pre">joblib</span></code>, and <code class="docutils literal notranslate"><span class="pre">cloudpickle</span></code></a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#security-maintainability-limitations">10.5. Security &amp; Maintainability Limitations</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#replicating-the-training-environment-in-production">10.5.1. Replicating the training environment in production</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#serving-the-model-artifact">10.5.2. Serving the model artifact</a></li>
 </ul>
 </li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#summarizing-the-key-points">9.6. Summarizing the key points</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#summarizing-the-key-points">10.6. Summarizing the key points</a></li>
 </ul>
   </nav></div>
 
diff --git a/dev/model_selection.html b/dev/model_selection.html
index 0853f9bb5ea99..afc1ee1a07094 100644
--- a/dev/model_selection.html
+++ b/dev/model_selection.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -594,45 +594,46 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/learning_curve.html">3.5. Validation curves: plotting scores to evaluate models</a></li>
 </ul>
 </details></li>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/permutation_importance.html">4.2. Permutation feature importance</a></li>
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+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/inspection.html">5. Inspection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/partial_dependence.html">5.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="http://github.com/visualizations.html">6. Visualizations</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/compose.html">7.1. Pipelines and composite estimators</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/preprocessing_targets.html">7.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html">7.1. Toy datasets</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html">7.4. Loading other datasets</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/datasets.html">8. Dataset loading utilities</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
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+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/scaling_strategies.html">9.1. Strategies to scale computationally: bigger data</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/array_api.html">11.1. Array API support (experimental)</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/array_api.html">12.1. Array API support (experimental)</a></li>
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index 605d6c461406d..4947e74c23fe9 100644
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-    <title>11.1. Array API support (experimental) &#8212; scikit-learn 1.7.dev0 documentation</title>
+    <title>12.1. Array API support (experimental) &#8212; scikit-learn 1.7.dev0 documentation</title>
   
   
   
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-    <link rel="prev" title="11. Dispatching" href="http://github.com/dispatching.html" />
+    <link rel="next" title="13. Choosing the right estimator" href="http://github.com/machine_learning_map.html" />
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/permutation_importance.html">4.2. Permutation feature importance</a></li>
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@@ -669,9 +670,9 @@
     <li class="breadcrumb-item"><a href="http://github.com/user_guide.html" class="nav-link">User Guide</a></li>
     
     
-    <li class="breadcrumb-item"><a href="http://github.com/dispatching.html" class="nav-link"><span class="section-number">11. </span>Dispatching</a></li>
+    <li class="breadcrumb-item"><a href="http://github.com/dispatching.html" class="nav-link"><span class="section-number">12. </span>Dispatching</a></li>
     
-    <li class="breadcrumb-item active" aria-current="page"><span class="ellipsis"><span class="section-number">11.1. </span>Array API support (experimental)</span></li>
+    <li class="breadcrumb-item active" aria-current="page"><span class="ellipsis"><span class="section-number">12.1. </span>Array API support (experimental)</span></li>
   </ul>
 </nav>
 </div>
@@ -689,7 +690,7 @@
                 <article class="bd-article">
                   
   <section id="array-api-support-experimental">
-<span id="array-api"></span><h1><span class="section-number">11.1. </span>Array API support (experimental)<a class="headerlink" href="#array-api-support-experimental" title="Link to this heading">#</a></h1>
+<span id="array-api"></span><h1><span class="section-number">12.1. </span>Array API support (experimental)<a class="headerlink" href="#array-api-support-experimental" title="Link to this heading">#</a></h1>
 <p>The <a class="reference external" href="https://data-apis.org/array-api/latest/">Array API</a> specification defines
 a standard API for all array manipulation libraries with a NumPy-like API.
 Scikit-learn vendors pinned copies of
@@ -723,7 +724,7 @@
 by <a class="reference external" href="https://github.com/thomasjpfan">Thomas Fan</a> at PyData NYC 2023.</p></li>
 </ul>
 <section id="example-usage">
-<h2><span class="section-number">11.1.1. </span>Example usage<a class="headerlink" href="#example-usage" title="Link to this heading">#</a></h2>
+<h2><span class="section-number">12.1.1. </span>Example usage<a class="headerlink" href="#example-usage" title="Link to this heading">#</a></h2>
 <p>Here is an example code snippet to demonstrate how to use <a class="reference external" href="https://cupy.dev/">CuPy</a> to run
 <a class="reference internal" href="http://github.com/generated/sklearn.discriminant_analysis.LinearDiscriminantAnalysis.html#sklearn.discriminant_analysis.LinearDiscriminantAnalysis" title="sklearn.discriminant_analysis.LinearDiscriminantAnalysis"><code class="xref py py-class docutils literal notranslate"><span class="pre">LinearDiscriminantAnalysis</span></code></a> on a GPU:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.datasets</span><span class="w"> </span><span class="kn">import</span> <span class="n">make_classification</span>
@@ -758,7 +759,7 @@ <h2><span class="section-number">11.1.1. </span>Example usage<a class="headerlin
 </pre></div>
 </div>
 <section id="pytorch-support">
-<h3><span class="section-number">11.1.1.1. </span>PyTorch Support<a class="headerlink" href="#pytorch-support" title="Link to this heading">#</a></h3>
+<h3><span class="section-number">12.1.1.1. </span>PyTorch Support<a class="headerlink" href="#pytorch-support" title="Link to this heading">#</a></h3>
 <p>PyTorch Tensors are supported by setting <code class="docutils literal notranslate"><span class="pre">array_api_dispatch=True</span></code> and passing in
 the tensors directly:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span><span class="w"> </span><span class="nn">torch</span>
@@ -777,15 +778,16 @@ <h3><span class="section-number">11.1.1.1. </span>PyTorch Support<a class="heade
 </section>
 </section>
 <section id="support-for-array-api-compatible-inputs">
-<span id="array-api-supported"></span><h2><span class="section-number">11.1.2. </span>Support for <code class="docutils literal notranslate"><span class="pre">Array</span> <span class="pre">API</span></code>-compatible inputs<a class="headerlink" href="#support-for-array-api-compatible-inputs" title="Link to this heading">#</a></h2>
+<span id="array-api-supported"></span><h2><span class="section-number">12.1.2. </span>Support for <code class="docutils literal notranslate"><span class="pre">Array</span> <span class="pre">API</span></code>-compatible inputs<a class="headerlink" href="#support-for-array-api-compatible-inputs" title="Link to this heading">#</a></h2>
 <p>Estimators and other tools in scikit-learn that support Array API compatible inputs.</p>
 <section id="estimators">
-<h3><span class="section-number">11.1.2.1. </span>Estimators<a class="headerlink" href="#estimators" title="Link to this heading">#</a></h3>
+<h3><span class="section-number">12.1.2.1. </span>Estimators<a class="headerlink" href="#estimators" title="Link to this heading">#</a></h3>
 <ul class="simple">
 <li><p><a class="reference internal" href="http://github.com/generated/sklearn.decomposition.PCA.html#sklearn.decomposition.PCA" title="sklearn.decomposition.PCA"><code class="xref py py-class docutils literal notranslate"><span class="pre">decomposition.PCA</span></code></a> (with <code class="docutils literal notranslate"><span class="pre">svd_solver=&quot;full&quot;</span></code>,
 <code class="docutils literal notranslate"><span class="pre">svd_solver=&quot;randomized&quot;</span></code> and <code class="docutils literal notranslate"><span class="pre">power_iteration_normalizer=&quot;QR&quot;</span></code>)</p></li>
 <li><p><a class="reference internal" href="http://github.com/generated/sklearn.linear_model.Ridge.html#sklearn.linear_model.Ridge" title="sklearn.linear_model.Ridge"><code class="xref py py-class docutils literal notranslate"><span class="pre">linear_model.Ridge</span></code></a> (with <code class="docutils literal notranslate"><span class="pre">solver=&quot;svd&quot;</span></code>)</p></li>
 <li><p><a class="reference internal" href="http://github.com/generated/sklearn.discriminant_analysis.LinearDiscriminantAnalysis.html#sklearn.discriminant_analysis.LinearDiscriminantAnalysis" title="sklearn.discriminant_analysis.LinearDiscriminantAnalysis"><code class="xref py py-class docutils literal notranslate"><span class="pre">discriminant_analysis.LinearDiscriminantAnalysis</span></code></a> (with <code class="docutils literal notranslate"><span class="pre">solver=&quot;svd&quot;</span></code>)</p></li>
+<li><p><a class="reference internal" href="http://github.com/generated/sklearn.preprocessing.Binarizer.html#sklearn.preprocessing.Binarizer" title="sklearn.preprocessing.Binarizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">preprocessing.Binarizer</span></code></a></p></li>
 <li><p><a class="reference internal" href="http://github.com/generated/sklearn.preprocessing.KernelCenterer.html#sklearn.preprocessing.KernelCenterer" title="sklearn.preprocessing.KernelCenterer"><code class="xref py py-class docutils literal notranslate"><span class="pre">preprocessing.KernelCenterer</span></code></a></p></li>
 <li><p><a class="reference internal" href="http://github.com/generated/sklearn.preprocessing.LabelEncoder.html#sklearn.preprocessing.LabelEncoder" title="sklearn.preprocessing.LabelEncoder"><code class="xref py py-class docutils literal notranslate"><span class="pre">preprocessing.LabelEncoder</span></code></a></p></li>
 <li><p><a class="reference internal" href="http://github.com/generated/sklearn.preprocessing.MaxAbsScaler.html#sklearn.preprocessing.MaxAbsScaler" title="sklearn.preprocessing.MaxAbsScaler"><code class="xref py py-class docutils literal notranslate"><span class="pre">preprocessing.MaxAbsScaler</span></code></a></p></li>
@@ -794,7 +796,7 @@ <h3><span class="section-number">11.1.2.1. </span>Estimators<a class="headerlink
 </ul>
 </section>
 <section id="meta-estimators">
-<h3><span class="section-number">11.1.2.2. </span>Meta-estimators<a class="headerlink" href="#meta-estimators" title="Link to this heading">#</a></h3>
+<h3><span class="section-number">12.1.2.2. </span>Meta-estimators<a class="headerlink" href="#meta-estimators" title="Link to this heading">#</a></h3>
 <p>Meta-estimators that accept Array API inputs conditioned on the fact that the
 base estimator also does:</p>
 <ul class="simple">
@@ -805,7 +807,7 @@ <h3><span class="section-number">11.1.2.2. </span>Meta-estimators<a class="heade
 </ul>
 </section>
 <section id="metrics">
-<h3><span class="section-number">11.1.2.3. </span>Metrics<a class="headerlink" href="#metrics" title="Link to this heading">#</a></h3>
+<h3><span class="section-number">12.1.2.3. </span>Metrics<a class="headerlink" href="#metrics" title="Link to this heading">#</a></h3>
 <ul class="simple">
 <li><p><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.metrics.cluster.entropy</span></code></p></li>
 <li><p><a class="reference internal" href="http://github.com/generated/sklearn.metrics.accuracy_score.html#sklearn.metrics.accuracy_score" title="sklearn.metrics.accuracy_score"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.metrics.accuracy_score</span></code></a></p></li>
@@ -845,7 +847,7 @@ <h3><span class="section-number">11.1.2.3. </span>Metrics<a class="headerlink" h
 </ul>
 </section>
 <section id="tools">
-<h3><span class="section-number">11.1.2.4. </span>Tools<a class="headerlink" href="#tools" title="Link to this heading">#</a></h3>
+<h3><span class="section-number">12.1.2.4. </span>Tools<a class="headerlink" href="#tools" title="Link to this heading">#</a></h3>
 <ul class="simple">
 <li><p><a class="reference internal" href="http://github.com/generated/sklearn.model_selection.train_test_split.html#sklearn.model_selection.train_test_split" title="sklearn.model_selection.train_test_split"><code class="xref py py-func docutils literal notranslate"><span class="pre">model_selection.train_test_split</span></code></a></p></li>
 <li><p><a class="reference internal" href="http://github.com/generated/sklearn.utils.check_consistent_length.html#sklearn.utils.check_consistent_length" title="sklearn.utils.check_consistent_length"><code class="xref py py-func docutils literal notranslate"><span class="pre">utils.check_consistent_length</span></code></a></p></li>
@@ -853,7 +855,7 @@ <h3><span class="section-number">11.1.2.4. </span>Tools<a class="headerlink" hre
 <p>Coverage is expected to grow over time. Please follow the dedicated <a class="reference external" href="https://github.com/scikit-learn/scikit-learn/issues/22352">meta-issue on GitHub</a> to track progress.</p>
 </section>
 <section id="type-of-return-values-and-fitted-attributes">
-<h3><span class="section-number">11.1.2.5. </span>Type of return values and fitted attributes<a class="headerlink" href="#type-of-return-values-and-fitted-attributes" title="Link to this heading">#</a></h3>
+<h3><span class="section-number">12.1.2.5. </span>Type of return values and fitted attributes<a class="headerlink" href="#type-of-return-values-and-fitted-attributes" title="Link to this heading">#</a></h3>
 <p>When calling functions or methods with Array API compatible inputs, the
 convention is to return array values of the same array container type and
 device as the input data.</p>
@@ -867,7 +869,7 @@ <h3><span class="section-number">11.1.2.5. </span>Type of return values and fitt
 </section>
 </section>
 <section id="common-estimator-checks">
-<h2><span class="section-number">11.1.3. </span>Common estimator checks<a class="headerlink" href="#common-estimator-checks" title="Link to this heading">#</a></h2>
+<h2><span class="section-number">12.1.3. </span>Common estimator checks<a class="headerlink" href="#common-estimator-checks" title="Link to this heading">#</a></h2>
 <p>Add the <code class="docutils literal notranslate"><span class="pre">array_api_support</span></code> tag to an estimator’s set of tags to indicate that
 it supports the Array API. This will enable dedicated checks as part of the
 common tests to verify that the estimators’ results are the same when using
@@ -880,7 +882,7 @@ <h2><span class="section-number">11.1.3. </span>Common estimator checks<a class=
 <div class="highlight-default notranslate"><div class="highlight"><pre><span class="prompt1">pip<span class="w"> </span>install<span class="w"> </span>...<span class="w"> </span><span class="c1"># selected libraries as needed</span></span>
 <span class="prompt1">pytest<span class="w"> </span>-k<span class="w"> </span><span class="s2">&quot;array_api&quot;</span><span class="w"> </span>-v</span>
 </pre></div></div><section id="note-on-mps-device-support">
-<span id="mps-support"></span><h3><span class="section-number">11.1.3.1. </span>Note on MPS device support<a class="headerlink" href="#note-on-mps-device-support" title="Link to this heading">#</a></h3>
+<span id="mps-support"></span><h3><span class="section-number">12.1.3.1. </span>Note on MPS device support<a class="headerlink" href="#note-on-mps-device-support" title="Link to this heading">#</a></h3>
 <p>On macOS, PyTorch can use the Metal Performance Shaders (MPS) to access
 hardware accelerators (e.g. the internal GPU component of the M1 or M2 chips).
 However, the MPS device support for PyTorch is incomplete at the time of
@@ -895,7 +897,7 @@ <h2><span class="section-number">11.1.3. </span>Common estimator checks<a class=
 computational speed is not necessarily better than with the CPU device.</p>
 </section>
 <section id="note-on-device-support-for-float64">
-<span id="device-support-for-float64"></span><h3><span class="section-number">11.1.3.2. </span>Note on device support for <code class="docutils literal notranslate"><span class="pre">float64</span></code><a class="headerlink" href="#note-on-device-support-for-float64" title="Link to this heading">#</a></h3>
+<span id="device-support-for-float64"></span><h3><span class="section-number">12.1.3.2. </span>Note on device support for <code class="docutils literal notranslate"><span class="pre">float64</span></code><a class="headerlink" href="#note-on-device-support-for-float64" title="Link to this heading">#</a></h3>
 <p>Certain operations within scikit-learn will automatically perform operations
 on floating-point values with <code class="docutils literal notranslate"><span class="pre">float64</span></code> precision to prevent overflows and ensure
 correctness (e.g., <a class="reference internal" href="http://github.com/generated/sklearn.metrics.pairwise.euclidean_distances.html#sklearn.metrics.pairwise.euclidean_distances" title="sklearn.metrics.pairwise.euclidean_distances"><code class="xref py py-func docutils literal notranslate"><span class="pre">metrics.pairwise.euclidean_distances</span></code></a>). However,
@@ -924,7 +926,7 @@ <h2><span class="section-number">11.1.3. </span>Common estimator checks<a class=
       <i class="fa-solid fa-angle-left"></i>
       <div class="prev-next-info">
         <p class="prev-next-subtitle">previous</p>
-        <p class="prev-next-title"><span class="section-number">11. </span>Dispatching</p>
+        <p class="prev-next-title"><span class="section-number">12. </span>Dispatching</p>
       </div>
     </a>
     <a class="right-next"
@@ -932,7 +934,7 @@ <h2><span class="section-number">11.1.3. </span>Common estimator checks<a class=
        title="next page">
       <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">12. </span>Choosing the right estimator</p>
+        <p class="prev-next-title"><span class="section-number">13. </span>Choosing the right estimator</p>
       </div>
       <i class="fa-solid fa-angle-right"></i>
     </a>
@@ -959,21 +961,21 @@ <h2><span class="section-number">11.1.3. </span>Common estimator checks<a class=
   </div>
   <nav class="bd-toc-nav page-toc" aria-labelledby="pst-page-navigation-heading-2">
     <ul class="visible nav section-nav flex-column">
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#example-usage">11.1.1. Example usage</a><ul class="nav section-nav flex-column">
-<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pytorch-support">11.1.1.1. PyTorch Support</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#example-usage">12.1.1. Example usage</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pytorch-support">12.1.1.1. PyTorch Support</a></li>
 </ul>
 </li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#support-for-array-api-compatible-inputs">11.1.2. Support for <code class="docutils literal notranslate"><span class="pre">Array</span> <span class="pre">API</span></code>-compatible inputs</a><ul class="nav section-nav flex-column">
-<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#estimators">11.1.2.1. Estimators</a></li>
-<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#meta-estimators">11.1.2.2. Meta-estimators</a></li>
-<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#metrics">11.1.2.3. Metrics</a></li>
-<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#tools">11.1.2.4. Tools</a></li>
-<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#type-of-return-values-and-fitted-attributes">11.1.2.5. Type of return values and fitted attributes</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#support-for-array-api-compatible-inputs">12.1.2. Support for <code class="docutils literal notranslate"><span class="pre">Array</span> <span class="pre">API</span></code>-compatible inputs</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#estimators">12.1.2.1. Estimators</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#meta-estimators">12.1.2.2. Meta-estimators</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#metrics">12.1.2.3. Metrics</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#tools">12.1.2.4. Tools</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#type-of-return-values-and-fitted-attributes">12.1.2.5. Type of return values and fitted attributes</a></li>
 </ul>
 </li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#common-estimator-checks">11.1.3. Common estimator checks</a><ul class="nav section-nav flex-column">
-<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#note-on-mps-device-support">11.1.3.1. Note on MPS device support</a></li>
-<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#note-on-device-support-for-float64">11.1.3.2. Note on device support for <code class="docutils literal notranslate"><span class="pre">float64</span></code></a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#common-estimator-checks">12.1.3. Common estimator checks</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#note-on-mps-device-support">12.1.3.1. Note on MPS device support</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#note-on-device-support-for-float64">12.1.3.2. Note on device support for <code class="docutils literal notranslate"><span class="pre">float64</span></code></a></li>
 </ul>
 </li>
 </ul>
diff --git a/dev/modules/biclustering.html b/dev/modules/biclustering.html
index df354f4e946e2..839890ed8a9f1 100644
--- a/dev/modules/biclustering.html
+++ b/dev/modules/biclustering.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -594,45 +594,46 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/learning_curve.html">3.5. Validation curves: plotting scores to evaluate models</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/inspection.html">4. Inspection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/partial_dependence.html">4.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/permutation_importance.html">4.2. Permutation feature importance</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/metadata_routing.html">4. Metadata Routing</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/inspection.html">5. Inspection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/partial_dependence.html">5.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/permutation_importance.html">5.2. Permutation feature importance</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/visualizations.html">5. Visualizations</a></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/data_transforms.html">6. Dataset transformations</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/compose.html">6.1. Pipelines and composite estimators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/feature_extraction.html">6.2. Feature extraction</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing.html">6.3. Preprocessing data</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/impute.html">6.4. Imputation of missing values</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/unsupervised_reduction.html">6.5. Unsupervised dimensionality reduction</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/random_projection.html">6.6. Random Projection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/kernel_approximation.html">6.7. Kernel Approximation</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="http://github.com/visualizations.html">6. Visualizations</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/data_transforms.html">7. Dataset transformations</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/compose.html">7.1. Pipelines and composite estimators</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="http://github.com/kernel_approximation.html">7.7. Kernel Approximation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/metrics.html">7.8. Pairwise metrics, Affinities and Kernels</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing_targets.html">7.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html">7.1. Toy datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html">7.2. Real world datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html">7.3. Generated datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html">7.4. Loading other datasets</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/datasets.html">8. Dataset loading utilities</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html">8.1. Toy datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html">8.2. Real world datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html">8.3. Generated datasets</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/parallelism.html">8.3. Parallelism, resource management, and configuration</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/computing.html">9. Computing with scikit-learn</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/scaling_strategies.html">9.1. Strategies to scale computationally: bigger data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/computational_performance.html">9.2. Computational Performance</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/parallelism.html">9.3. Parallelism, resource management, and configuration</a></li>
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 </details></li>
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-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/dispatching.html">11. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/array_api.html">11.1. Array API support (experimental)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/model_persistence.html">10. Model persistence</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/common_pitfalls.html">11. Common pitfalls and recommended practices</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/dispatching.html">12. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/array_api.html">12.1. Array API support (experimental)</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="http://github.com/machine_learning_map.html">12. Choosing the right estimator</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="http://github.com/machine_learning_map.html">13. Choosing the right estimator</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/presentations.html">14. External Resources, Videos and Talks</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/permutation_importance.html">4.2. Permutation feature importance</a></li>
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+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/inspection.html">5. Inspection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/partial_dependence.html">5.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/scaling_strategies.html">9.1. Strategies to scale computationally: bigger data</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/array_api.html">11.1. Array API support (experimental)</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="http://github.com/common_pitfalls.html">11. Common pitfalls and recommended practices</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing_targets.html">6.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/visualizations.html">6. Visualizations</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/data_transforms.html">7. Dataset transformations</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/compose.html">7.1. Pipelines and composite estimators</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/feature_extraction.html">7.2. Feature extraction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing.html">7.3. Preprocessing data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/impute.html">7.4. Imputation of missing values</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/unsupervised_reduction.html">7.5. Unsupervised dimensionality reduction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/random_projection.html">7.6. Random Projection</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/kernel_approximation.html">7.7. Kernel Approximation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/metrics.html">7.8. Pairwise metrics, Affinities and Kernels</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing_targets.html">7.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/datasets.html">7. Dataset loading utilities</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html">7.1. Toy datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html">7.2. Real world datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html">7.3. Generated datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html">7.4. Loading other datasets</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/datasets.html">8. Dataset loading utilities</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html">8.1. Toy datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html">8.2. Real world datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html">8.3. Generated datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html">8.4. Loading other datasets</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/computing.html">8. Computing with scikit-learn</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/scaling_strategies.html">8.1. Strategies to scale computationally: bigger data</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/computational_performance.html">8.2. Computational Performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/parallelism.html">8.3. Parallelism, resource management, and configuration</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/computing.html">9. Computing with scikit-learn</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/scaling_strategies.html">9.1. Strategies to scale computationally: bigger data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/computational_performance.html">9.2. Computational Performance</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/parallelism.html">9.3. Parallelism, resource management, and configuration</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/model_persistence.html">9. Model persistence</a></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/common_pitfalls.html">10. Common pitfalls and recommended practices</a></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/dispatching.html">11. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/array_api.html">11.1. Array API support (experimental)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/model_persistence.html">10. Model persistence</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/common_pitfalls.html">11. Common pitfalls and recommended practices</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/dispatching.html">12. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/array_api.html">12.1. Array API support (experimental)</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/machine_learning_map.html">12. Choosing the right estimator</a></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/presentations.html">13. External Resources, Videos and Talks</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/machine_learning_map.html">13. Choosing the right estimator</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/presentations.html">14. External Resources, Videos and Talks</a></li>
 </ul>
 </div>
 </nav></div>
@@ -719,8 +720,8 @@
 <span class="gp">&gt;&gt;&gt; </span><span class="n">classifier</span><span class="o">.</span><span class="n">predict_proba</span><span class="p">(</span><span class="n">X</span><span class="p">[:</span><span class="mi">4</span><span class="p">])</span>
 <span class="go">array([[0.94     , 0.06     ],</span>
 <span class="go">       [0.94     , 0.06     ],</span>
-<span class="go">       [0.0416..., 0.9583...],</span>
-<span class="go">       [0.0416..., 0.9583...]])</span>
+<span class="go">       [0.0416, 0.9583],</span>
+<span class="go">       [0.0416, 0.9583]])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">classifier</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X</span><span class="p">[:</span><span class="mi">4</span><span class="p">])</span>
 <span class="go">array([0, 0, 1, 1])</span>
 </pre></div>
@@ -785,10 +786,10 @@ <h3><span class="section-number">3.3.1.1. </span>Options to tune the decision th
 <span class="gp">&gt;&gt;&gt; </span><span class="n">base_model</span> <span class="o">=</span> <span class="n">LogisticRegression</span><span class="p">()</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">model</span> <span class="o">=</span> <span class="n">TunedThresholdClassifierCV</span><span class="p">(</span><span class="n">base_model</span><span class="p">,</span> <span class="n">scoring</span><span class="o">=</span><span class="n">scorer</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">scorer</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">),</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
-<span class="go">0.88...</span>
+<span class="go">0.88</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="c1"># compare it with the internal score found by cross-validation</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">model</span><span class="o">.</span><span class="n">best_score_</span>
-<span class="go">np.float64(0.86...)</span>
+<span class="go">np.float64(0.86)</span>
 </pre></div>
 </div>
 </div>
diff --git a/dev/modules/clustering.html b/dev/modules/clustering.html
index f1e6cce6ef0e5..1d592dc53ecf7 100644
--- a/dev/modules/clustering.html
+++ b/dev/modules/clustering.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
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-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
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@@ -594,45 +594,46 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/learning_curve.html">3.5. Validation curves: plotting scores to evaluate models</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/inspection.html">4. Inspection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/partial_dependence.html">4.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/permutation_importance.html">4.2. Permutation feature importance</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/metadata_routing.html">4. Metadata Routing</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/inspection.html">5. Inspection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/partial_dependence.html">5.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/permutation_importance.html">5.2. Permutation feature importance</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/visualizations.html">5. Visualizations</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/compose.html">6.1. Pipelines and composite estimators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/feature_extraction.html">6.2. Feature extraction</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/unsupervised_reduction.html">6.5. Unsupervised dimensionality reduction</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/metrics.html">6.8. Pairwise metrics, Affinities and Kernels</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing_targets.html">6.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/visualizations.html">6. Visualizations</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/data_transforms.html">7. Dataset transformations</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/compose.html">7.1. Pipelines and composite estimators</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/feature_extraction.html">7.2. Feature extraction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing.html">7.3. Preprocessing data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/impute.html">7.4. Imputation of missing values</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/unsupervised_reduction.html">7.5. Unsupervised dimensionality reduction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/random_projection.html">7.6. Random Projection</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/kernel_approximation.html">7.7. Kernel Approximation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/metrics.html">7.8. Pairwise metrics, Affinities and Kernels</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing_targets.html">7.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/datasets.html">7. Dataset loading utilities</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html">7.1. Toy datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html">7.2. Real world datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html">7.3. Generated datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html">7.4. Loading other datasets</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/datasets.html">8. Dataset loading utilities</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html">8.1. Toy datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html">8.2. Real world datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html">8.3. Generated datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html">8.4. Loading other datasets</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/computing.html">8. Computing with scikit-learn</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/scaling_strategies.html">8.1. Strategies to scale computationally: bigger data</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/computational_performance.html">8.2. Computational Performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/parallelism.html">8.3. Parallelism, resource management, and configuration</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/computing.html">9. Computing with scikit-learn</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/scaling_strategies.html">9.1. Strategies to scale computationally: bigger data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/computational_performance.html">9.2. Computational Performance</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/parallelism.html">9.3. Parallelism, resource management, and configuration</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/model_persistence.html">9. Model persistence</a></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/common_pitfalls.html">10. Common pitfalls and recommended practices</a></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/dispatching.html">11. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/array_api.html">11.1. Array API support (experimental)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/model_persistence.html">10. Model persistence</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/common_pitfalls.html">11. Common pitfalls and recommended practices</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/dispatching.html">12. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/array_api.html">12.1. Array API support (experimental)</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/machine_learning_map.html">12. Choosing the right estimator</a></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/presentations.html">13. External Resources, Videos and Talks</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/machine_learning_map.html">13. Choosing the right estimator</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/presentations.html">14. External Resources, Videos and Talks</a></li>
 </ul>
 </div>
 </nav></div>
@@ -1819,32 +1820,32 @@ <h3><span class="section-number">2.3.8.2. </span>Hierarchical Clustering<a class
 <span class="gp">&gt;&gt;&gt; </span><span class="n">labels_true</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">labels_pred</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">metrics</span><span class="o">.</span><span class="n">rand_score</span><span class="p">(</span><span class="n">labels_true</span><span class="p">,</span> <span class="n">labels_pred</span><span class="p">)</span>
-<span class="go">0.66...</span>
+<span class="go">0.66</span>
 </pre></div>
 </div>
 <p>The Rand index does not ensure to obtain a value close to 0.0 for a
 random labelling. The adjusted Rand index <strong>corrects for chance</strong> and
 will give such a baseline.</p>
 <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">metrics</span><span class="o">.</span><span class="n">adjusted_rand_score</span><span class="p">(</span><span class="n">labels_true</span><span class="p">,</span> <span class="n">labels_pred</span><span class="p">)</span>
-<span class="go">0.24...</span>
+<span class="go">0.24</span>
 </pre></div>
 </div>
 <p>As with all clustering metrics, one can permute 0 and 1 in the predicted
 labels, rename 2 to 3, and get the same score:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">labels_pred</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">]</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">metrics</span><span class="o">.</span><span class="n">rand_score</span><span class="p">(</span><span class="n">labels_true</span><span class="p">,</span> <span class="n">labels_pred</span><span class="p">)</span>
-<span class="go">0.66...</span>
+<span class="go">0.66</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">metrics</span><span class="o">.</span><span class="n">adjusted_rand_score</span><span class="p">(</span><span class="n">labels_true</span><span class="p">,</span> <span class="n">labels_pred</span><span class="p">)</span>
-<span class="go">0.24...</span>
+<span class="go">0.24</span>
 </pre></div>
 </div>
 <p>Furthermore, both <a class="reference internal" href="http://github.com/generated/sklearn.metrics.rand_score.html#sklearn.metrics.rand_score" title="sklearn.metrics.rand_score"><code class="xref py py-func docutils literal notranslate"><span class="pre">rand_score</span></code></a> and <a class="reference internal" href="http://github.com/generated/sklearn.metrics.adjusted_rand_score.html#sklearn.metrics.adjusted_rand_score" title="sklearn.metrics.adjusted_rand_score"><code class="xref py py-func docutils literal notranslate"><span class="pre">adjusted_rand_score</span></code></a> are
 <strong>symmetric</strong>: swapping the argument does not change the scores. They can
 thus be used as <strong>consensus measures</strong>:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">metrics</span><span class="o">.</span><span class="n">rand_score</span><span class="p">(</span><span class="n">labels_pred</span><span class="p">,</span> <span class="n">labels_true</span><span class="p">)</span>
-<span class="go">0.66...</span>
+<span class="go">0.66</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">metrics</span><span class="o">.</span><span class="n">adjusted_rand_score</span><span class="p">(</span><span class="n">labels_pred</span><span class="p">,</span> <span class="n">labels_true</span><span class="p">)</span>
-<span class="go">0.24...</span>
+<span class="go">0.24</span>
 </pre></div>
 </div>
 <p>Perfect labeling is scored 1.0:</p>
@@ -1862,9 +1863,9 @@ <h3><span class="section-number">2.3.8.2. </span>Hierarchical Clustering<a class
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">labels_true</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">labels_pred</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">]</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">metrics</span><span class="o">.</span><span class="n">rand_score</span><span class="p">(</span><span class="n">labels_true</span><span class="p">,</span> <span class="n">labels_pred</span><span class="p">)</span>
-<span class="go">0.39...</span>
+<span class="go">0.39</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">metrics</span><span class="o">.</span><span class="n">adjusted_rand_score</span><span class="p">(</span><span class="n">labels_true</span><span class="p">,</span> <span class="n">labels_pred</span><span class="p">)</span>
-<span class="go">-0.07...</span>
+<span class="go">-0.072</span>
 </pre></div>
 </div>
 <aside class="topic">
@@ -1965,21 +1966,21 @@ <h3><span class="section-number">2.3.8.2. </span>Hierarchical Clustering<a class
 <span class="gp">&gt;&gt;&gt; </span><span class="n">labels_pred</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span>
 
 <span class="gp">&gt;&gt;&gt; </span><span class="n">metrics</span><span class="o">.</span><span class="n">adjusted_mutual_info_score</span><span class="p">(</span><span class="n">labels_true</span><span class="p">,</span> <span class="n">labels_pred</span><span class="p">)</span>  
-<span class="go">0.22504...</span>
+<span class="go">0.22504</span>
 </pre></div>
 </div>
 <p>One can permute 0 and 1 in the predicted labels, rename 2 to 3 and get
 the same score:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">labels_pred</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">]</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">metrics</span><span class="o">.</span><span class="n">adjusted_mutual_info_score</span><span class="p">(</span><span class="n">labels_true</span><span class="p">,</span> <span class="n">labels_pred</span><span class="p">)</span>  
-<span class="go">0.22504...</span>
+<span class="go">0.22504</span>
 </pre></div>
 </div>
 <p>All, <a class="reference internal" href="http://github.com/generated/sklearn.metrics.mutual_info_score.html#sklearn.metrics.mutual_info_score" title="sklearn.metrics.mutual_info_score"><code class="xref py py-func docutils literal notranslate"><span class="pre">mutual_info_score</span></code></a>, <a class="reference internal" href="http://github.com/generated/sklearn.metrics.adjusted_mutual_info_score.html#sklearn.metrics.adjusted_mutual_info_score" title="sklearn.metrics.adjusted_mutual_info_score"><code class="xref py py-func docutils literal notranslate"><span class="pre">adjusted_mutual_info_score</span></code></a> and
 <a class="reference internal" href="http://github.com/generated/sklearn.metrics.normalized_mutual_info_score.html#sklearn.metrics.normalized_mutual_info_score" title="sklearn.metrics.normalized_mutual_info_score"><code class="xref py py-func docutils literal notranslate"><span class="pre">normalized_mutual_info_score</span></code></a> are symmetric: swapping the argument does
 not change the score. Thus they can be used as a <strong>consensus measure</strong>:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">metrics</span><span class="o">.</span><span class="n">adjusted_mutual_info_score</span><span class="p">(</span><span class="n">labels_pred</span><span class="p">,</span> <span class="n">labels_true</span><span class="p">)</span>  
-<span class="go">0.22504...</span>
+<span class="go">0.22504</span>
 </pre></div>
 </div>
 <p>Perfect labeling is scored 1.0:</p>
@@ -1993,14 +1994,14 @@ <h3><span class="section-number">2.3.8.2. </span>Hierarchical Clustering<a class
 </div>
 <p>This is not true for <code class="docutils literal notranslate"><span class="pre">mutual_info_score</span></code>, which is therefore harder to judge:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">metrics</span><span class="o">.</span><span class="n">mutual_info_score</span><span class="p">(</span><span class="n">labels_true</span><span class="p">,</span> <span class="n">labels_pred</span><span class="p">)</span>  
-<span class="go">0.69...</span>
+<span class="go">0.69</span>
 </pre></div>
 </div>
 <p>Bad (e.g. independent labelings) have non-positive scores:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">labels_true</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">labels_pred</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">metrics</span><span class="o">.</span><span class="n">adjusted_mutual_info_score</span><span class="p">(</span><span class="n">labels_true</span><span class="p">,</span> <span class="n">labels_pred</span><span class="p">)</span>  
-<span class="go">-0.10526...</span>
+<span class="go">-0.10526</span>
 </pre></div>
 </div>
 <aside class="topic">
@@ -2136,16 +2137,16 @@ <h3><span class="section-number">2.3.8.2. </span>Hierarchical Clustering<a class
 <span class="gp">&gt;&gt;&gt; </span><span class="n">labels_pred</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span>
 
 <span class="gp">&gt;&gt;&gt; </span><span class="n">metrics</span><span class="o">.</span><span class="n">homogeneity_score</span><span class="p">(</span><span class="n">labels_true</span><span class="p">,</span> <span class="n">labels_pred</span><span class="p">)</span>
-<span class="go">0.66...</span>
+<span class="go">0.66</span>
 
 <span class="gp">&gt;&gt;&gt; </span><span class="n">metrics</span><span class="o">.</span><span class="n">completeness_score</span><span class="p">(</span><span class="n">labels_true</span><span class="p">,</span> <span class="n">labels_pred</span><span class="p">)</span>
-<span class="go">0.42...</span>
+<span class="go">0.42</span>
 </pre></div>
 </div>
 <p>Their harmonic mean called <strong>V-measure</strong> is computed by
 <a class="reference internal" href="http://github.com/generated/sklearn.metrics.v_measure_score.html#sklearn.metrics.v_measure_score" title="sklearn.metrics.v_measure_score"><code class="xref py py-func docutils literal notranslate"><span class="pre">v_measure_score</span></code></a>:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">metrics</span><span class="o">.</span><span class="n">v_measure_score</span><span class="p">(</span><span class="n">labels_true</span><span class="p">,</span> <span class="n">labels_pred</span><span class="p">)</span>
-<span class="go">0.51...</span>
+<span class="go">0.516</span>
 </pre></div>
 </div>
 <p>This function’s formula is as follows:</p>
@@ -2153,12 +2154,12 @@ <h3><span class="section-number">2.3.8.2. </span>Hierarchical Clustering<a class
 \[v = \frac{(1 + \beta) \times \text{homogeneity} \times \text{completeness}}{(\beta \times \text{homogeneity} + \text{completeness})}\]</div>
 <p><code class="docutils literal notranslate"><span class="pre">beta</span></code> defaults to a value of 1.0, but for using a value less than 1 for beta:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">metrics</span><span class="o">.</span><span class="n">v_measure_score</span><span class="p">(</span><span class="n">labels_true</span><span class="p">,</span> <span class="n">labels_pred</span><span class="p">,</span> <span class="n">beta</span><span class="o">=</span><span class="mf">0.6</span><span class="p">)</span>
-<span class="go">0.54...</span>
+<span class="go">0.547</span>
 </pre></div>
 </div>
 <p>more weight will be attributed to homogeneity, and using a value greater than 1:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">metrics</span><span class="o">.</span><span class="n">v_measure_score</span><span class="p">(</span><span class="n">labels_true</span><span class="p">,</span> <span class="n">labels_pred</span><span class="p">,</span> <span class="n">beta</span><span class="o">=</span><span class="mf">1.8</span><span class="p">)</span>
-<span class="go">0.48...</span>
+<span class="go">0.48</span>
 </pre></div>
 </div>
 <p>more weight will be attributed to completeness.</p>
@@ -2167,14 +2168,14 @@ <h3><span class="section-number">2.3.8.2. </span>Hierarchical Clustering<a class
 <p>Homogeneity, completeness and V-measure can be computed at once using
 <a class="reference internal" href="http://github.com/generated/sklearn.metrics.homogeneity_completeness_v_measure.html#sklearn.metrics.homogeneity_completeness_v_measure" title="sklearn.metrics.homogeneity_completeness_v_measure"><code class="xref py py-func docutils literal notranslate"><span class="pre">homogeneity_completeness_v_measure</span></code></a> as follows:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">metrics</span><span class="o">.</span><span class="n">homogeneity_completeness_v_measure</span><span class="p">(</span><span class="n">labels_true</span><span class="p">,</span> <span class="n">labels_pred</span><span class="p">)</span>
-<span class="go">(0.66..., 0.42..., 0.51...)</span>
+<span class="go">(0.67, 0.42, 0.52)</span>
 </pre></div>
 </div>
 <p>The following clustering assignment is slightly better, since it is
 homogeneous but not complete:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">labels_pred</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">metrics</span><span class="o">.</span><span class="n">homogeneity_completeness_v_measure</span><span class="p">(</span><span class="n">labels_true</span><span class="p">,</span> <span class="n">labels_pred</span><span class="p">)</span>
-<span class="go">(1.0, 0.68..., 0.81...)</span>
+<span class="go">(1.0, 0.68, 0.81)</span>
 </pre></div>
 </div>
 <div class="admonition note">
@@ -2298,7 +2299,7 @@ <h3><span class="section-number">2.3.8.2. </span>Hierarchical Clustering<a class
 </pre></div>
 </div>
 <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">metrics</span><span class="o">.</span><span class="n">fowlkes_mallows_score</span><span class="p">(</span><span class="n">labels_true</span><span class="p">,</span> <span class="n">labels_pred</span><span class="p">)</span>
-<span class="go">0.47140...</span>
+<span class="go">0.47140</span>
 </pre></div>
 </div>
 <p>One can permute 0 and 1 in the predicted labels, rename 2 to 3 and get
@@ -2306,7 +2307,7 @@ <h3><span class="section-number">2.3.8.2. </span>Hierarchical Clustering<a class
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">labels_pred</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">]</span>
 
 <span class="gp">&gt;&gt;&gt; </span><span class="n">metrics</span><span class="o">.</span><span class="n">fowlkes_mallows_score</span><span class="p">(</span><span class="n">labels_true</span><span class="p">,</span> <span class="n">labels_pred</span><span class="p">)</span>
-<span class="go">0.47140...</span>
+<span class="go">0.47140</span>
 </pre></div>
 </div>
 <p>Perfect labeling is scored 1.0:</p>
@@ -2392,7 +2393,7 @@ <h3><span class="section-number">2.3.8.2. </span>Hierarchical Clustering<a class
 <span class="gp">&gt;&gt;&gt; </span><span class="n">kmeans_model</span> <span class="o">=</span> <span class="n">KMeans</span><span class="p">(</span><span class="n">n_clusters</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">labels</span> <span class="o">=</span> <span class="n">kmeans_model</span><span class="o">.</span><span class="n">labels_</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">metrics</span><span class="o">.</span><span class="n">silhouette_score</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">labels</span><span class="p">,</span> <span class="n">metric</span><span class="o">=</span><span class="s1">&#39;euclidean&#39;</span><span class="p">)</span>
-<span class="go">0.55...</span>
+<span class="go">0.55</span>
 </pre></div>
 </div>
 <aside class="topic">
@@ -2450,7 +2451,7 @@ <h3><span class="section-number">2.3.8.2. </span>Hierarchical Clustering<a class
 <span class="gp">&gt;&gt;&gt; </span><span class="n">kmeans_model</span> <span class="o">=</span> <span class="n">KMeans</span><span class="p">(</span><span class="n">n_clusters</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">labels</span> <span class="o">=</span> <span class="n">kmeans_model</span><span class="o">.</span><span class="n">labels_</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">metrics</span><span class="o">.</span><span class="n">calinski_harabasz_score</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">labels</span><span class="p">)</span>
-<span class="go">561.59...</span>
+<span class="go">561.59</span>
 </pre></div>
 </div>
 <aside class="topic">
@@ -2519,7 +2520,7 @@ <h3><span class="section-number">2.3.8.2. </span>Hierarchical Clustering<a class
 <span class="gp">&gt;&gt;&gt; </span><span class="n">kmeans</span> <span class="o">=</span> <span class="n">KMeans</span><span class="p">(</span><span class="n">n_clusters</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">labels</span> <span class="o">=</span> <span class="n">kmeans</span><span class="o">.</span><span class="n">labels_</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">davies_bouldin_score</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">labels</span><span class="p">)</span>
-<span class="go">0.666...</span>
+<span class="go">0.666</span>
 </pre></div>
 </div>
 <aside class="topic">
diff --git a/dev/modules/compose.html b/dev/modules/compose.html
index 3ce5eb7799846..50b4c85373f72 100644
--- a/dev/modules/compose.html
+++ b/dev/modules/compose.html
@@ -7,7 +7,7 @@
   <head>
     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="viewport" content="width=device-width, initial-scale=1" />
-<meta property="og:title" content="6.1. Pipelines and composite estimators" />
+<meta property="og:title" content="7.1. Pipelines and composite estimators" />
 <meta property="og:type" content="website" />
 <meta property="og:url" content="https://scikit-learn/stable/modules/compose.html" />
 <meta property="og:site_name" content="scikit-learn" />
@@ -16,7 +16,7 @@
 <meta property="og:image:alt" content="scikit-learn" />
 <meta name="description" content="To build a composite estimator, transformers are usually combined with other transformers or with predictors(such as classifiers or regressors). The most common tool used for composing estimators i..." />
 
-    <title>6.1. Pipelines and composite estimators &#8212; scikit-learn 1.7.dev0 documentation</title>
+    <title>7.1. Pipelines and composite estimators &#8212; scikit-learn 1.7.dev0 documentation</title>
   
   
   
@@ -42,7 +42,7 @@
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+    <link rel="next" title="7.2. Feature extraction" href="http://github.com/feature_extraction.html" />
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@@ -593,45 +593,46 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/learning_curve.html">3.5. Validation curves: plotting scores to evaluate models</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/inspection.html">4. Inspection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/partial_dependence.html">4.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/permutation_importance.html">4.2. Permutation feature importance</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/metadata_routing.html">4. Metadata Routing</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/inspection.html">5. Inspection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/partial_dependence.html">5.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/permutation_importance.html">5.2. Permutation feature importance</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/visualizations.html">5. Visualizations</a></li>
-<li class="toctree-l1 current active has-children"><a class="reference internal" href="http://github.com/data_transforms.html">6. Dataset transformations</a><details open="open"><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul class="current">
-<li class="toctree-l2 current active"><a class="current reference internal" href="#">6.1. Pipelines and composite estimators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/feature_extraction.html">6.2. Feature extraction</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing.html">6.3. Preprocessing data</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/impute.html">6.4. Imputation of missing values</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/unsupervised_reduction.html">6.5. Unsupervised dimensionality reduction</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/random_projection.html">6.6. Random Projection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/kernel_approximation.html">6.7. Kernel Approximation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/metrics.html">6.8. Pairwise metrics, Affinities and Kernels</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing_targets.html">6.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/visualizations.html">6. Visualizations</a></li>
+<li class="toctree-l1 current active has-children"><a class="reference internal" href="http://github.com/data_transforms.html">7. Dataset transformations</a><details open="open"><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul class="current">
+<li class="toctree-l2 current active"><a class="current reference internal" href="#">7.1. Pipelines and composite estimators</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/feature_extraction.html">7.2. Feature extraction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing.html">7.3. Preprocessing data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/impute.html">7.4. Imputation of missing values</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/unsupervised_reduction.html">7.5. Unsupervised dimensionality reduction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/random_projection.html">7.6. Random Projection</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/kernel_approximation.html">7.7. Kernel Approximation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/metrics.html">7.8. Pairwise metrics, Affinities and Kernels</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing_targets.html">7.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/datasets.html">7. Dataset loading utilities</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html">7.1. Toy datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html">7.2. Real world datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html">7.3. Generated datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html">7.4. Loading other datasets</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/datasets.html">8. Dataset loading utilities</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html">8.1. Toy datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html">8.2. Real world datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html">8.3. Generated datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html">8.4. Loading other datasets</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/computing.html">8. Computing with scikit-learn</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/scaling_strategies.html">8.1. Strategies to scale computationally: bigger data</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/computational_performance.html">8.2. Computational Performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/parallelism.html">8.3. Parallelism, resource management, and configuration</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/computing.html">9. Computing with scikit-learn</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/scaling_strategies.html">9.1. Strategies to scale computationally: bigger data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/computational_performance.html">9.2. Computational Performance</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/parallelism.html">9.3. Parallelism, resource management, and configuration</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/model_persistence.html">9. Model persistence</a></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/common_pitfalls.html">10. Common pitfalls and recommended practices</a></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/dispatching.html">11. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/array_api.html">11.1. Array API support (experimental)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/model_persistence.html">10. Model persistence</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/common_pitfalls.html">11. Common pitfalls and recommended practices</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/dispatching.html">12. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/array_api.html">12.1. Array API support (experimental)</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/machine_learning_map.html">12. Choosing the right estimator</a></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/presentations.html">13. External Resources, Videos and Talks</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/machine_learning_map.html">13. Choosing the right estimator</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/presentations.html">14. External Resources, Videos and Talks</a></li>
 </ul>
 </div>
 </nav></div>
@@ -669,9 +670,9 @@
     <li class="breadcrumb-item"><a href="http://github.com/user_guide.html" class="nav-link">User Guide</a></li>
     
     
-    <li class="breadcrumb-item"><a href="http://github.com/data_transforms.html" class="nav-link"><span class="section-number">6. </span>Dataset transformations</a></li>
+    <li class="breadcrumb-item"><a href="http://github.com/data_transforms.html" class="nav-link"><span class="section-number">7. </span>Dataset transformations</a></li>
     
-    <li class="breadcrumb-item active" aria-current="page"><span class="ellipsis"><span class="section-number">6.1. </span>Pipelines and composite estimators</span></li>
+    <li class="breadcrumb-item active" aria-current="page"><span class="ellipsis"><span class="section-number">7.1. </span>Pipelines and composite estimators</span></li>
   </ul>
 </nav>
 </div>
@@ -689,7 +690,7 @@
                 <article class="bd-article">
                   
   <section id="pipelines-and-composite-estimators">
-<span id="combining-estimators"></span><h1><span class="section-number">6.1. </span>Pipelines and composite estimators<a class="headerlink" href="#pipelines-and-composite-estimators" title="Link to this heading">#</a></h1>
+<span id="combining-estimators"></span><h1><span class="section-number">7.1. </span>Pipelines and composite estimators<a class="headerlink" href="#pipelines-and-composite-estimators" title="Link to this heading">#</a></h1>
 <p>To build a composite estimator, transformers are usually combined with other
 transformers or with <a class="reference internal" href="http://github.com/glossary.html#term-predictors"><span class="xref std std-term">predictors</span></a> (such as classifiers or regressors).
 The most common tool used for composing estimators is a <a class="reference internal" href="#pipeline"><span class="std std-ref">Pipeline</span></a>. Pipelines require all steps except the last to be a
@@ -708,7 +709,7 @@
 <a class="reference internal" href="#transformed-target-regressor"><span class="std std-ref">TransformedTargetRegressor</span></a>
 deals with transforming the <a class="reference internal" href="http://github.com/glossary.html#term-target"><span class="xref std std-term">target</span></a> (i.e. log-transform <a class="reference internal" href="http://github.com/glossary.html#term-y"><span class="xref std std-term">y</span></a>).</p>
 <section id="pipeline-chaining-estimators">
-<span id="pipeline"></span><h2><span class="section-number">6.1.1. </span>Pipeline: chaining estimators<a class="headerlink" href="#pipeline-chaining-estimators" title="Link to this heading">#</a></h2>
+<span id="pipeline"></span><h2><span class="section-number">7.1.1. </span>Pipeline: chaining estimators<a class="headerlink" href="#pipeline-chaining-estimators" title="Link to this heading">#</a></h2>
 <p><a class="reference internal" href="http://github.com/generated/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a> can be used to chain multiple estimators
 into one. This is useful as there is often a fixed sequence
 of steps in processing the data, for example feature selection, normalization
@@ -738,9 +739,9 @@
 pipeline.</p>
 </div>
 <section id="usage">
-<h3><span class="section-number">6.1.1.1. </span>Usage<a class="headerlink" href="#usage" title="Link to this heading">#</a></h3>
+<h3><span class="section-number">7.1.1.1. </span>Usage<a class="headerlink" href="#usage" title="Link to this heading">#</a></h3>
 <section id="build-a-pipeline">
-<h4><span class="section-number">6.1.1.1.1. </span>Build a pipeline<a class="headerlink" href="#build-a-pipeline" title="Link to this heading">#</a></h4>
+<h4><span class="section-number">7.1.1.1.1. </span>Build a pipeline<a class="headerlink" href="#build-a-pipeline" title="Link to this heading">#</a></h4>
 <p>The <a class="reference internal" href="http://github.com/generated/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a> is built using a list of <code class="docutils literal notranslate"><span class="pre">(key,</span> <span class="pre">value)</span></code> pairs, where
 the <code class="docutils literal notranslate"><span class="pre">key</span></code> is a string containing the name you want to give this step and <code class="docutils literal notranslate"><span class="pre">value</span></code>
 is an estimator object:</p>
@@ -768,7 +769,7 @@ <h4><span class="section-number">6.1.1.1.1. </span>Build a pipeline<a class="hea
 </div>
 </details></section>
 <section id="access-pipeline-steps">
-<h4><span class="section-number">6.1.1.1.2. </span>Access pipeline steps<a class="headerlink" href="#access-pipeline-steps" title="Link to this heading">#</a></h4>
+<h4><span class="section-number">7.1.1.1.2. </span>Access pipeline steps<a class="headerlink" href="#access-pipeline-steps" title="Link to this heading">#</a></h4>
 <p>The estimators of a pipeline are stored as a list in the <code class="docutils literal notranslate"><span class="pre">steps</span></code> attribute.
 A sub-pipeline can be extracted using the slicing notation commonly used
 for Python Sequences such as lists or strings (although only a step of 1 is
@@ -802,7 +803,7 @@ <h4><span class="section-number">6.1.1.1.2. </span>Access pipeline steps<a class
 </div>
 </details></section>
 <section id="tracking-feature-names-in-a-pipeline">
-<h4><span class="section-number">6.1.1.1.3. </span>Tracking feature names in a pipeline<a class="headerlink" href="#tracking-feature-names-in-a-pipeline" title="Link to this heading">#</a></h4>
+<h4><span class="section-number">7.1.1.1.3. </span>Tracking feature names in a pipeline<a class="headerlink" href="#tracking-feature-names-in-a-pipeline" title="Link to this heading">#</a></h4>
 <p>To enable model inspection, <a class="reference internal" href="http://github.com/generated/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a> has a
 <code class="docutils literal notranslate"><span class="pre">get_feature_names_out()</span></code> method, just like all transformers. You can use
 pipeline slicing to get the feature names going into each step:</p>
@@ -831,7 +832,7 @@ <h4><span class="section-number">6.1.1.1.3. </span>Tracking feature names in a p
 </div>
 </details></section>
 <section id="access-to-nested-parameters">
-<span id="pipeline-nested-parameters"></span><h4><span class="section-number">6.1.1.1.4. </span>Access to nested parameters<a class="headerlink" href="#access-to-nested-parameters" title="Link to this heading">#</a></h4>
+<span id="pipeline-nested-parameters"></span><h4><span class="section-number">7.1.1.1.4. </span>Access to nested parameters<a class="headerlink" href="#access-to-nested-parameters" title="Link to this heading">#</a></h4>
 <p>It is common to adjust the parameters of an estimator within a pipeline. This parameter
 is therefore nested because it belongs to a particular sub-step. Parameters of the
 estimators in the pipeline are accessible using the <code class="docutils literal notranslate"><span class="pre">&lt;estimator&gt;__&lt;parameter&gt;</span></code>
@@ -879,7 +880,7 @@ <h4><span class="section-number">6.1.1.1.3. </span>Tracking feature names in a p
 </section>
 </section>
 <section id="caching-transformers-avoid-repeated-computation">
-<span id="pipeline-cache"></span><h3><span class="section-number">6.1.1.2. </span>Caching transformers: avoid repeated computation<a class="headerlink" href="#caching-transformers-avoid-repeated-computation" title="Link to this heading">#</a></h3>
+<span id="pipeline-cache"></span><h3><span class="section-number">7.1.1.2. </span>Caching transformers: avoid repeated computation<a class="headerlink" href="#caching-transformers-avoid-repeated-computation" title="Link to this heading">#</a></h3>
 <p>Fitting transformers may be computationally expensive. With its
 <code class="docutils literal notranslate"><span class="pre">memory</span></code> parameter set, <a class="reference internal" href="http://github.com/generated/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a> will cache each transformer
 after calling <code class="docutils literal notranslate"><span class="pre">fit</span></code>.
@@ -953,7 +954,7 @@ <h4><span class="section-number">6.1.1.1.3. </span>Tracking feature names in a p
 </section>
 </section>
 <section id="transforming-target-in-regression">
-<span id="transformed-target-regressor"></span><h2><span class="section-number">6.1.2. </span>Transforming target in regression<a class="headerlink" href="#transforming-target-in-regression" title="Link to this heading">#</a></h2>
+<span id="transformed-target-regressor"></span><h2><span class="section-number">7.1.2. </span>Transforming target in regression<a class="headerlink" href="#transforming-target-in-regression" title="Link to this heading">#</a></h2>
 <p><a class="reference internal" href="http://github.com/generated/sklearn.compose.TransformedTargetRegressor.html#sklearn.compose.TransformedTargetRegressor" title="sklearn.compose.TransformedTargetRegressor"><code class="xref py py-class docutils literal notranslate"><span class="pre">TransformedTargetRegressor</span></code></a> transforms the
 targets <code class="docutils literal notranslate"><span class="pre">y</span></code> before fitting a regression model. The predictions are mapped
 back to the original space via an inverse transform. It takes as an argument
@@ -1026,7 +1027,7 @@ <h4><span class="section-number">6.1.1.1.3. </span>Tracking feature names in a p
 </ul>
 </section>
 <section id="featureunion-composite-feature-spaces">
-<span id="feature-union"></span><h2><span class="section-number">6.1.3. </span>FeatureUnion: composite feature spaces<a class="headerlink" href="#featureunion-composite-feature-spaces" title="Link to this heading">#</a></h2>
+<span id="feature-union"></span><h2><span class="section-number">7.1.3. </span>FeatureUnion: composite feature spaces<a class="headerlink" href="#featureunion-composite-feature-spaces" title="Link to this heading">#</a></h2>
 <p><a class="reference internal" href="http://github.com/generated/sklearn.pipeline.FeatureUnion.html#sklearn.pipeline.FeatureUnion" title="sklearn.pipeline.FeatureUnion"><code class="xref py py-class docutils literal notranslate"><span class="pre">FeatureUnion</span></code></a> combines several transformer objects into a new
 transformer that combines their output. A <a class="reference internal" href="http://github.com/generated/sklearn.pipeline.FeatureUnion.html#sklearn.pipeline.FeatureUnion" title="sklearn.pipeline.FeatureUnion"><code class="xref py py-class docutils literal notranslate"><span class="pre">FeatureUnion</span></code></a> takes
 a list of transformer objects. During fitting, each of these
@@ -1045,7 +1046,7 @@ <h4><span class="section-number">6.1.1.1.3. </span>Tracking feature names in a p
 feature sets are disjoint, and making sure they are is the caller’s
 responsibility.)</p>
 <section id="id1">
-<h3><span class="section-number">6.1.3.1. </span>Usage<a class="headerlink" href="#id1" title="Link to this heading">#</a></h3>
+<h3><span class="section-number">7.1.3.1. </span>Usage<a class="headerlink" href="#id1" title="Link to this heading">#</a></h3>
 <p>A <a class="reference internal" href="http://github.com/generated/sklearn.pipeline.FeatureUnion.html#sklearn.pipeline.FeatureUnion" title="sklearn.pipeline.FeatureUnion"><code class="xref py py-class docutils literal notranslate"><span class="pre">FeatureUnion</span></code></a> is built using a list of <code class="docutils literal notranslate"><span class="pre">(key,</span> <span class="pre">value)</span></code> pairs,
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@@ -1076,7 +1077,7 @@ <h3><span class="section-number">6.1.3.1. </span>Usage<a class="headerlink" href
 </section>
 </section>
 <section id="columntransformer-for-heterogeneous-data">
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+<span id="column-transformer"></span><h2><span class="section-number">7.1.4. </span>ColumnTransformer for heterogeneous data<a class="headerlink" href="#columntransformer-for-heterogeneous-data" title="Link to this heading">#</a></h2>
 <p>Many datasets contain features of different types, say text, floats, and dates,
 where each type of feature requires separate preprocessing or feature
 extraction steps.  Often it is easiest to preprocess data before applying
@@ -1159,10 +1160,10 @@ <h3><span class="section-number">6.1.3.1. </span>Usage<a class="headerlink" href
 <span class="gp">... </span>      <span class="n">OneHotEncoder</span><span class="p">(),</span>
 <span class="gp">... </span>      <span class="n">make_column_selector</span><span class="p">(</span><span class="n">pattern</span><span class="o">=</span><span class="s1">&#39;city&#39;</span><span class="p">,</span> <span class="n">dtype_include</span><span class="o">=</span><span class="nb">object</span><span class="p">))])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">ct</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
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+<span class="go">       [ 0.904, -1.414,  0. ,  0. ,  1. ]])</span>
 </pre></div>
 </div>
 <p>Strings can reference columns if the input is a DataFrame, integers are always
@@ -1222,14 +1223,14 @@ <h3><span class="section-number">6.1.3.1. </span>Usage<a class="headerlink" href
 <span class="gp">&gt;&gt;&gt; </span><span class="n">X_new</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s2">&quot;expert_rating&quot;</span><span class="p">:</span> <span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span>
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 <span class="gp">&gt;&gt;&gt; </span><span class="n">ct</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">X_new</span><span class="p">)</span>
-<span class="go">array([[ 0.9...],</span>
-<span class="go">       [ 2.1...],</span>
-<span class="go">       [-3.9...]])</span>
+<span class="go">array([[ 0.9],</span>
+<span class="go">       [ 2.1],</span>
+<span class="go">       [-3.9]])</span>
 </pre></div>
 </div>
 </section>
 <section id="visualizing-composite-estimators">
-<span id="id2"></span><h2><span class="section-number">6.1.5. </span>Visualizing Composite Estimators<a class="headerlink" href="#visualizing-composite-estimators" title="Link to this heading">#</a></h2>
+<span id="id2"></span><h2><span class="section-number">7.1.5. </span>Visualizing Composite Estimators<a class="headerlink" href="#visualizing-composite-estimators" title="Link to this heading">#</a></h2>
 <p>Estimators are displayed with an HTML representation when shown in a
 jupyter notebook. This is useful to diagnose or visualize a Pipeline with
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@@ -1278,7 +1279,7 @@ <h3><span class="section-number">6.1.3.1. </span>Usage<a class="headerlink" href
       <i class="fa-solid fa-angle-left"></i>
       <div class="prev-next-info">
         <p class="prev-next-subtitle">previous</p>
-        <p class="prev-next-title"><span class="section-number">6. </span>Dataset transformations</p>
+        <p class="prev-next-title"><span class="section-number">7. </span>Dataset transformations</p>
       </div>
     </a>
     <a class="right-next"
@@ -1286,7 +1287,7 @@ <h3><span class="section-number">6.1.3.1. </span>Usage<a class="headerlink" href
        title="next page">
       <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">6.2. </span>Feature extraction</p>
+        <p class="prev-next-title"><span class="section-number">7.2. </span>Feature extraction</p>
       </div>
       <i class="fa-solid fa-angle-right"></i>
     </a>
@@ -1313,24 +1314,24 @@ <h3><span class="section-number">6.1.3.1. </span>Usage<a class="headerlink" href
   </div>
   <nav class="bd-toc-nav page-toc" aria-labelledby="pst-page-navigation-heading-2">
     <ul class="visible nav section-nav flex-column">
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pipeline-chaining-estimators">6.1.1. Pipeline: chaining estimators</a><ul class="nav section-nav flex-column">
-<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#usage">6.1.1.1. Usage</a><ul class="nav section-nav flex-column">
-<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#build-a-pipeline">6.1.1.1.1. Build a pipeline</a></li>
-<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#access-pipeline-steps">6.1.1.1.2. Access pipeline steps</a></li>
-<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#tracking-feature-names-in-a-pipeline">6.1.1.1.3. Tracking feature names in a pipeline</a></li>
-<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#access-to-nested-parameters">6.1.1.1.4. Access to nested parameters</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pipeline-chaining-estimators">7.1.1. Pipeline: chaining estimators</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#usage">7.1.1.1. Usage</a><ul class="nav section-nav flex-column">
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#build-a-pipeline">7.1.1.1.1. Build a pipeline</a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#access-pipeline-steps">7.1.1.1.2. Access pipeline steps</a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#tracking-feature-names-in-a-pipeline">7.1.1.1.3. Tracking feature names in a pipeline</a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#access-to-nested-parameters">7.1.1.1.4. Access to nested parameters</a></li>
 </ul>
 </li>
-<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#caching-transformers-avoid-repeated-computation">6.1.1.2. Caching transformers: avoid repeated computation</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#caching-transformers-avoid-repeated-computation">7.1.1.2. Caching transformers: avoid repeated computation</a></li>
 </ul>
 </li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#transforming-target-in-regression">6.1.2. Transforming target in regression</a></li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#featureunion-composite-feature-spaces">6.1.3. FeatureUnion: composite feature spaces</a><ul class="nav section-nav flex-column">
-<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#id1">6.1.3.1. Usage</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#transforming-target-in-regression">7.1.2. Transforming target in regression</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#featureunion-composite-feature-spaces">7.1.3. FeatureUnion: composite feature spaces</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#id1">7.1.3.1. Usage</a></li>
 </ul>
 </li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#columntransformer-for-heterogeneous-data">6.1.4. ColumnTransformer for heterogeneous data</a></li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#visualizing-composite-estimators">6.1.5. Visualizing Composite Estimators</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#columntransformer-for-heterogeneous-data">7.1.4. ColumnTransformer for heterogeneous data</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#visualizing-composite-estimators">7.1.5. Visualizing Composite Estimators</a></li>
 </ul>
   </nav></div>
 
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/permutation_importance.html">4.2. Permutation feature importance</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/metadata_routing.html">4. Metadata Routing</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/inspection.html">5. Inspection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/partial_dependence.html">5.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/permutation_importance.html">5.2. Permutation feature importance</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="http://github.com/compose.html">7.1. Pipelines and composite estimators</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html">7.4. Loading other datasets</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html">8.1. Toy datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html">8.2. Real world datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html">8.3. Generated datasets</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/parallelism.html">8.3. Parallelism, resource management, and configuration</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/computing.html">9. Computing with scikit-learn</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/scaling_strategies.html">9.1. Strategies to scale computationally: bigger data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/computational_performance.html">9.2. Computational Performance</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/parallelism.html">9.3. Parallelism, resource management, and configuration</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/array_api.html">11.1. Array API support (experimental)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/model_persistence.html">10. Model persistence</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/common_pitfalls.html">11. Common pitfalls and recommended practices</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/dispatching.html">12. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/array_api.html">12.1. Array API support (experimental)</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="http://github.com/machine_learning_map.html">12. Choosing the right estimator</a></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/presentations.html">13. External Resources, Videos and Talks</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/machine_learning_map.html">13. Choosing the right estimator</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/presentations.html">14. External Resources, Videos and Talks</a></li>
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@@ -594,45 +594,46 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/learning_curve.html">3.5. Validation curves: plotting scores to evaluate models</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/inspection.html">4. Inspection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/partial_dependence.html">4.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/permutation_importance.html">4.2. Permutation feature importance</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/metadata_routing.html">4. Metadata Routing</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/inspection.html">5. Inspection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/partial_dependence.html">5.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/permutation_importance.html">5.2. Permutation feature importance</a></li>
 </ul>
 </details></li>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/compose.html">6.1. Pipelines and composite estimators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/feature_extraction.html">6.2. Feature extraction</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="http://github.com/visualizations.html">6. Visualizations</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/data_transforms.html">7. Dataset transformations</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/compose.html">7.1. Pipelines and composite estimators</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="http://github.com/unsupervised_reduction.html">7.5. Unsupervised dimensionality reduction</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="http://github.com/kernel_approximation.html">7.7. Kernel Approximation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/metrics.html">7.8. Pairwise metrics, Affinities and Kernels</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing_targets.html">7.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
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-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/datasets.html">7. Dataset loading utilities</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html">7.3. Generated datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html">7.4. Loading other datasets</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/datasets.html">8. Dataset loading utilities</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html">8.1. Toy datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html">8.2. Real world datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html">8.3. Generated datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html">8.4. Loading other datasets</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/parallelism.html">8.3. Parallelism, resource management, and configuration</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/computing.html">9. Computing with scikit-learn</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/scaling_strategies.html">9.1. Strategies to scale computationally: bigger data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/computational_performance.html">9.2. Computational Performance</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/parallelism.html">9.3. Parallelism, resource management, and configuration</a></li>
 </ul>
 </details></li>
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-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/dispatching.html">11. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/array_api.html">11.1. Array API support (experimental)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/model_persistence.html">10. Model persistence</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/common_pitfalls.html">11. Common pitfalls and recommended practices</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/dispatching.html">12. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/array_api.html">12.1. Array API support (experimental)</a></li>
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 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/machine_learning_map.html">12. Choosing the right estimator</a></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/presentations.html">13. External Resources, Videos and Talks</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/machine_learning_map.html">13. Choosing the right estimator</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/presentations.html">14. External Resources, Videos and Talks</a></li>
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diff --git a/dev/modules/cross_validation.html b/dev/modules/cross_validation.html
index bac35c2593543..5607e1be246b5 100644
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/permutation_importance.html">4.2. Permutation feature importance</a></li>
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+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/inspection.html">5. Inspection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/partial_dependence.html">5.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
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 <span class="gp">&gt;&gt;&gt; </span><span class="n">clf</span> <span class="o">=</span> <span class="n">svm</span><span class="o">.</span><span class="n">SVC</span><span class="p">(</span><span class="n">kernel</span><span class="o">=</span><span class="s1">&#39;linear&#39;</span><span class="p">,</span> <span class="n">C</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
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+<span class="go">0.96</span>
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 <p>When evaluating different settings (“hyperparameters”) for estimators,
@@ -789,7 +790,7 @@ <h2><span class="section-number">3.1.1. </span>Computing cross-validated metrics
 <span class="gp">&gt;&gt;&gt; </span><span class="n">clf</span> <span class="o">=</span> <span class="n">svm</span><span class="o">.</span><span class="n">SVC</span><span class="p">(</span><span class="n">kernel</span><span class="o">=</span><span class="s1">&#39;linear&#39;</span><span class="p">,</span> <span class="n">C</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">scores</span> <span class="o">=</span> <span class="n">cross_val_score</span><span class="p">(</span><span class="n">clf</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">cv</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">scores</span>
-<span class="go">array([0.96..., 1. , 0.96..., 0.96..., 1. ])</span>
+<span class="go">array([0.96, 1. , 0.96, 0.96, 1. ])</span>
 </pre></div>
 </div>
 <p>The mean score and the standard deviation are hence given by:</p>
@@ -804,7 +805,7 @@ <h2><span class="section-number">3.1.1. </span>Computing cross-validated metrics
 <span class="gp">&gt;&gt;&gt; </span><span class="n">scores</span> <span class="o">=</span> <span class="n">cross_val_score</span><span class="p">(</span>
 <span class="gp">... </span>    <span class="n">clf</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">cv</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">scoring</span><span class="o">=</span><span class="s1">&#39;f1_macro&#39;</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">scores</span>
-<span class="go">array([0.96..., 1.  ..., 0.96..., 0.96..., 1.        ])</span>
+<span class="go">array([0.96, 1., 0.96, 0.96, 1.])</span>
 </pre></div>
 </div>
 <p>See <a class="reference internal" href="http://github.com/model_evaluation.html#scoring-parameter"><span class="std std-ref">The scoring parameter: defining model evaluation rules</span></a> for details.
@@ -819,7 +820,7 @@ <h2><span class="section-number">3.1.1. </span>Computing cross-validated metrics
 <span class="gp">&gt;&gt;&gt; </span><span class="n">n_samples</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">cv</span> <span class="o">=</span> <span class="n">ShuffleSplit</span><span class="p">(</span><span class="n">n_splits</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">cross_val_score</span><span class="p">(</span><span class="n">clf</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">cv</span><span class="o">=</span><span class="n">cv</span><span class="p">)</span>
-<span class="go">array([0.977..., 0.977..., 1.  ..., 0.955..., 1.        ])</span>
+<span class="go">array([0.977, 0.977, 1., 0.955, 1.])</span>
 </pre></div>
 </div>
 <p>Another option is to use an iterable yielding (train, test) splits as arrays of
@@ -834,7 +835,7 @@ <h2><span class="section-number">3.1.1. </span>Computing cross-validated metrics
 <span class="gp">...</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">custom_cv</span> <span class="o">=</span> <span class="n">custom_cv_2folds</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">cross_val_score</span><span class="p">(</span><span class="n">clf</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">cv</span><span class="o">=</span><span class="n">custom_cv</span><span class="p">)</span>
-<span class="go">array([1.        , 0.973...])</span>
+<span class="go">array([1.        , 0.973])</span>
 </pre></div>
 </div>
 <details class="sd-sphinx-override sd-dropdown sd-card sd-mb-3" id="data-transformation-with-held-out-data">
@@ -852,7 +853,7 @@ <h2><span class="section-number">3.1.1. </span>Computing cross-validated metrics
 <span class="gp">&gt;&gt;&gt; </span><span class="n">clf</span> <span class="o">=</span> <span class="n">svm</span><span class="o">.</span><span class="n">SVC</span><span class="p">(</span><span class="n">C</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train_transformed</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">X_test_transformed</span> <span class="o">=</span> <span class="n">scaler</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">clf</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_test_transformed</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span>
-<span class="go">0.9333...</span>
+<span class="go">0.9333</span>
 </pre></div>
 </div>
 <p class="sd-card-text">A <a class="reference internal" href="http://github.com/generated/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a> makes it easier to compose
@@ -860,7 +861,7 @@ <h2><span class="section-number">3.1.1. </span>Computing cross-validated metrics
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.pipeline</span><span class="w"> </span><span class="kn">import</span> <span class="n">make_pipeline</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">clf</span> <span class="o">=</span> <span class="n">make_pipeline</span><span class="p">(</span><span class="n">preprocessing</span><span class="o">.</span><span class="n">StandardScaler</span><span class="p">(),</span> <span class="n">svm</span><span class="o">.</span><span class="n">SVC</span><span class="p">(</span><span class="n">C</span><span class="o">=</span><span class="mi">1</span><span class="p">))</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">cross_val_score</span><span class="p">(</span><span class="n">clf</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">cv</span><span class="o">=</span><span class="n">cv</span><span class="p">)</span>
-<span class="go">array([0.977..., 0.933..., 0.955..., 0.933..., 0.977...])</span>
+<span class="go">array([0.977, 0.933, 0.955, 0.933, 0.977])</span>
 </pre></div>
 </div>
 <p class="sd-card-text">See <a class="reference internal" href="http://github.com/compose.html#combining-estimators"><span class="std std-ref">Pipelines and composite estimators</span></a>.</p>
@@ -896,7 +897,7 @@ <h2><span class="section-number">3.1.1. </span>Computing cross-validated metrics
 <span class="gp">&gt;&gt;&gt; </span><span class="nb">sorted</span><span class="p">(</span><span class="n">scores</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
 <span class="go">[&#39;fit_time&#39;, &#39;score_time&#39;, &#39;test_precision_macro&#39;, &#39;test_recall_macro&#39;]</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">scores</span><span class="p">[</span><span class="s1">&#39;test_recall_macro&#39;</span><span class="p">]</span>
-<span class="go">array([0.96..., 1.  ..., 0.96..., 0.96..., 1.        ])</span>
+<span class="go">array([0.96, 1., 0.96, 0.96, 1.])</span>
 </pre></div>
 </div>
 <p>Or as a dict mapping scorer name to a predefined or custom scoring function:</p>
@@ -909,7 +910,7 @@ <h2><span class="section-number">3.1.1. </span>Computing cross-validated metrics
 <span class="go">[&#39;fit_time&#39;, &#39;score_time&#39;, &#39;test_prec_macro&#39;, &#39;test_rec_macro&#39;,</span>
 <span class="go"> &#39;train_prec_macro&#39;, &#39;train_rec_macro&#39;]</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">scores</span><span class="p">[</span><span class="s1">&#39;train_rec_macro&#39;</span><span class="p">]</span>
-<span class="go">array([0.97..., 0.97..., 0.99..., 0.98..., 0.98...])</span>
+<span class="go">array([0.97, 0.97, 0.99, 0.98, 0.98])</span>
 </pre></div>
 </div>
 <p>Here is an example of <code class="docutils literal notranslate"><span class="pre">cross_validate</span></code> using a single metric:</p>
diff --git a/dev/modules/decomposition.html b/dev/modules/decomposition.html
index 4c11a6633889d..95562e886df6a 100644
--- a/dev/modules/decomposition.html
+++ b/dev/modules/decomposition.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -594,45 +594,46 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/learning_curve.html">3.5. Validation curves: plotting scores to evaluate models</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/inspection.html">4. Inspection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/partial_dependence.html">4.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/permutation_importance.html">4.2. Permutation feature importance</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/metadata_routing.html">4. Metadata Routing</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/inspection.html">5. Inspection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/partial_dependence.html">5.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/permutation_importance.html">5.2. Permutation feature importance</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/visualizations.html">5. Visualizations</a></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/data_transforms.html">6. Dataset transformations</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/compose.html">6.1. Pipelines and composite estimators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/feature_extraction.html">6.2. Feature extraction</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing.html">6.3. Preprocessing data</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/impute.html">6.4. Imputation of missing values</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/unsupervised_reduction.html">6.5. Unsupervised dimensionality reduction</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/random_projection.html">6.6. Random Projection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/kernel_approximation.html">6.7. Kernel Approximation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/metrics.html">6.8. Pairwise metrics, Affinities and Kernels</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing_targets.html">6.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/visualizations.html">6. Visualizations</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/data_transforms.html">7. Dataset transformations</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/compose.html">7.1. Pipelines and composite estimators</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/feature_extraction.html">7.2. Feature extraction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing.html">7.3. Preprocessing data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/impute.html">7.4. Imputation of missing values</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/unsupervised_reduction.html">7.5. Unsupervised dimensionality reduction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/random_projection.html">7.6. Random Projection</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/kernel_approximation.html">7.7. Kernel Approximation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/metrics.html">7.8. Pairwise metrics, Affinities and Kernels</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing_targets.html">7.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/datasets.html">7. Dataset loading utilities</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html">7.1. Toy datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html">7.2. Real world datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html">7.3. Generated datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html">7.4. Loading other datasets</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/datasets.html">8. Dataset loading utilities</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html">8.1. Toy datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html">8.2. Real world datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html">8.3. Generated datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html">8.4. Loading other datasets</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/computing.html">8. Computing with scikit-learn</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/scaling_strategies.html">8.1. Strategies to scale computationally: bigger data</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/computational_performance.html">8.2. Computational Performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/parallelism.html">8.3. Parallelism, resource management, and configuration</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/computing.html">9. Computing with scikit-learn</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/scaling_strategies.html">9.1. Strategies to scale computationally: bigger data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/computational_performance.html">9.2. Computational Performance</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/parallelism.html">9.3. Parallelism, resource management, and configuration</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/model_persistence.html">9. Model persistence</a></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/common_pitfalls.html">10. Common pitfalls and recommended practices</a></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/dispatching.html">11. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/array_api.html">11.1. Array API support (experimental)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/model_persistence.html">10. Model persistence</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/common_pitfalls.html">11. Common pitfalls and recommended practices</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/dispatching.html">12. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/array_api.html">12.1. Array API support (experimental)</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/machine_learning_map.html">12. Choosing the right estimator</a></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/presentations.html">13. External Resources, Videos and Talks</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/machine_learning_map.html">13. Choosing the right estimator</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/presentations.html">14. External Resources, Videos and Talks</a></li>
 </ul>
 </div>
 </nav></div>
@@ -1250,7 +1251,7 @@ <h3><span class="section-number">2.5.4.2. </span>Generic dictionary learning<a c
 algorithm. Typically, ICA is not used for reducing dimensionality but
 for separating superimposed signals. Since the ICA model does not include
 a noise term, for the model to be correct, whitening must be applied.
-This can be done internally using the whiten argument or manually using one
+This can be done internally using the <code class="docutils literal notranslate"><span class="pre">whiten</span></code> argument or manually using one
 of the PCA variants.</p>
 <p>It is classically used to separate mixed signals (a problem known as
 <em>blind source separation</em>), as in the example below:</p>
diff --git a/dev/modules/density.html b/dev/modules/density.html
index 792f20e60faab..2d259a97dd593 100644
--- a/dev/modules/density.html
+++ b/dev/modules/density.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
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@@ -594,45 +594,46 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/learning_curve.html">3.5. Validation curves: plotting scores to evaluate models</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/inspection.html">4. Inspection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/partial_dependence.html">4.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/permutation_importance.html">4.2. Permutation feature importance</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/metadata_routing.html">4. Metadata Routing</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/inspection.html">5. Inspection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/partial_dependence.html">5.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/permutation_importance.html">5.2. Permutation feature importance</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/visualizations.html">5. Visualizations</a></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/data_transforms.html">6. Dataset transformations</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/compose.html">6.1. Pipelines and composite estimators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/feature_extraction.html">6.2. Feature extraction</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing.html">6.3. Preprocessing data</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/impute.html">6.4. Imputation of missing values</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/unsupervised_reduction.html">6.5. Unsupervised dimensionality reduction</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/kernel_approximation.html">6.7. Kernel Approximation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/metrics.html">6.8. Pairwise metrics, Affinities and Kernels</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing_targets.html">6.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/visualizations.html">6. Visualizations</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/data_transforms.html">7. Dataset transformations</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/compose.html">7.1. Pipelines and composite estimators</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/feature_extraction.html">7.2. Feature extraction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing.html">7.3. Preprocessing data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/impute.html">7.4. Imputation of missing values</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/unsupervised_reduction.html">7.5. Unsupervised dimensionality reduction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/random_projection.html">7.6. Random Projection</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/kernel_approximation.html">7.7. Kernel Approximation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/metrics.html">7.8. Pairwise metrics, Affinities and Kernels</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing_targets.html">7.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/datasets.html">7. Dataset loading utilities</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html">7.1. Toy datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html">7.2. Real world datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html">7.3. Generated datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html">7.4. Loading other datasets</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/datasets.html">8. Dataset loading utilities</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html">8.1. Toy datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html">8.2. Real world datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html">8.3. Generated datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html">8.4. Loading other datasets</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/computing.html">8. Computing with scikit-learn</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/scaling_strategies.html">8.1. Strategies to scale computationally: bigger data</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/parallelism.html">8.3. Parallelism, resource management, and configuration</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/computing.html">9. Computing with scikit-learn</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/scaling_strategies.html">9.1. Strategies to scale computationally: bigger data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/computational_performance.html">9.2. Computational Performance</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/parallelism.html">9.3. Parallelism, resource management, and configuration</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/model_persistence.html">9. Model persistence</a></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/common_pitfalls.html">10. Common pitfalls and recommended practices</a></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/dispatching.html">11. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/array_api.html">11.1. Array API support (experimental)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/model_persistence.html">10. Model persistence</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/common_pitfalls.html">11. Common pitfalls and recommended practices</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/dispatching.html">12. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/array_api.html">12.1. Array API support (experimental)</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/machine_learning_map.html">12. Choosing the right estimator</a></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/presentations.html">13. External Resources, Videos and Talks</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/machine_learning_map.html">13. Choosing the right estimator</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/presentations.html">14. External Resources, Videos and Talks</a></li>
 </ul>
 </div>
 </nav></div>
diff --git a/dev/modules/ensemble.html b/dev/modules/ensemble.html
index 07854f583f9b1..59e5becf11b3b 100644
--- a/dev/modules/ensemble.html
+++ b/dev/modules/ensemble.html
@@ -42,7 +42,7 @@
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-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
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@@ -594,45 +594,46 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/learning_curve.html">3.5. Validation curves: plotting scores to evaluate models</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/inspection.html">4. Inspection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/partial_dependence.html">4.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/permutation_importance.html">4.2. Permutation feature importance</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/metadata_routing.html">4. Metadata Routing</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/inspection.html">5. Inspection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/partial_dependence.html">5.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/permutation_importance.html">5.2. Permutation feature importance</a></li>
 </ul>
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-<li class="toctree-l1"><a class="reference internal" href="http://github.com/visualizations.html">5. Visualizations</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/compose.html">6.1. Pipelines and composite estimators</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="http://github.com/visualizations.html">6. Visualizations</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/data_transforms.html">7. Dataset transformations</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/compose.html">7.1. Pipelines and composite estimators</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="http://github.com/impute.html">7.4. Imputation of missing values</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/unsupervised_reduction.html">7.5. Unsupervised dimensionality reduction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/random_projection.html">7.6. Random Projection</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/kernel_approximation.html">7.7. Kernel Approximation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/metrics.html">7.8. Pairwise metrics, Affinities and Kernels</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing_targets.html">7.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/datasets.html">7. Dataset loading utilities</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html">7.4. Loading other datasets</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/datasets.html">8. Dataset loading utilities</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html">8.1. Toy datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html">8.2. Real world datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html">8.3. Generated datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html">8.4. Loading other datasets</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/computing.html">8. Computing with scikit-learn</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/computational_performance.html">8.2. Computational Performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/parallelism.html">8.3. Parallelism, resource management, and configuration</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/computing.html">9. Computing with scikit-learn</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/scaling_strategies.html">9.1. Strategies to scale computationally: bigger data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/computational_performance.html">9.2. Computational Performance</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/parallelism.html">9.3. Parallelism, resource management, and configuration</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/model_persistence.html">9. Model persistence</a></li>
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-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/dispatching.html">11. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/array_api.html">11.1. Array API support (experimental)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/model_persistence.html">10. Model persistence</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/common_pitfalls.html">11. Common pitfalls and recommended practices</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/dispatching.html">12. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/array_api.html">12.1. Array API support (experimental)</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/machine_learning_map.html">12. Choosing the right estimator</a></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/presentations.html">13. External Resources, Videos and Talks</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/machine_learning_map.html">13. Choosing the right estimator</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/presentations.html">14. External Resources, Videos and Talks</a></li>
 </ul>
 </div>
 </nav></div>
@@ -889,7 +890,7 @@ <h4><span class="section-number">1.11.1.1.1. </span>Usage<a class="headerlink" h
 <span class="gp">&gt;&gt;&gt; </span><span class="n">gb</span><span class="o">.</span><span class="n">predict</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]])</span>
 <span class="go">array([1])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">gb</span><span class="o">.</span><span class="n">predict_proba</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]])[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span>
-<span class="go">np.float64(0.999...)</span>
+<span class="go">np.float64(0.999)</span>
 </pre></div>
 </div>
 <p>As you can see, the <code class="docutils literal notranslate"><span class="pre">[1,</span> <span class="pre">0]</span></code> is comfortably classified as <code class="docutils literal notranslate"><span class="pre">1</span></code> since the first
@@ -1139,7 +1140,7 @@ <h3><span class="section-number">1.11.1.2. </span><a class="reference internal"
 <span class="gp">&gt;&gt;&gt; </span><span class="n">clf</span> <span class="o">=</span> <span class="n">GradientBoostingClassifier</span><span class="p">(</span><span class="n">n_estimators</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">learning_rate</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span>
 <span class="gp">... </span>    <span class="n">max_depth</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">clf</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span>
-<span class="go">0.913...</span>
+<span class="go">0.913</span>
 </pre></div>
 </div>
 <p class="sd-card-text">The number of weak learners (i.e. regression trees) is controlled by the
@@ -1179,7 +1180,7 @@ <h3><span class="section-number">1.11.1.2. </span><a class="reference internal"
 <span class="gp">... </span>    <span class="n">loss</span><span class="o">=</span><span class="s1">&#39;squared_error&#39;</span>
 <span class="gp">... </span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">mean_squared_error</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">est</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">))</span>
-<span class="go">5.00...</span>
+<span class="go">5.00</span>
 </pre></div>
 </div>
 <p class="sd-card-text">The figure below shows the results of applying <a class="reference internal" href="http://github.com/generated/sklearn.ensemble.GradientBoostingRegressor.html#sklearn.ensemble.GradientBoostingRegressor" title="sklearn.ensemble.GradientBoostingRegressor"><code class="xref py py-class docutils literal notranslate"><span class="pre">GradientBoostingRegressor</span></code></a>
@@ -1221,11 +1222,11 @@ <h3><span class="section-number">1.11.1.2. </span><a class="reference internal"
 <span class="gp">... </span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">est</span> <span class="o">=</span> <span class="n">est</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>  <span class="c1"># fit with 100 trees</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">mean_squared_error</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">est</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">))</span>
-<span class="go">5.00...</span>
+<span class="go">5.00</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">_</span> <span class="o">=</span> <span class="n">est</span><span class="o">.</span><span class="n">set_params</span><span class="p">(</span><span class="n">n_estimators</span><span class="o">=</span><span class="mi">200</span><span class="p">,</span> <span class="n">warm_start</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>  <span class="c1"># set warm_start and increase num of trees</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">_</span> <span class="o">=</span> <span class="n">est</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span> <span class="c1"># fit additional 100 trees to est</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">mean_squared_error</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">est</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">))</span>
-<span class="go">3.84...</span>
+<span class="go">3.84</span>
 </pre></div>
 </div>
 </section>
@@ -1477,7 +1478,8 @@ <h4><span class="section-number">1.11.1.2.7. </span>Interpretation with feature
 <span class="gp">&gt;&gt;&gt; </span><span class="n">clf</span> <span class="o">=</span> <span class="n">GradientBoostingClassifier</span><span class="p">(</span><span class="n">n_estimators</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">learning_rate</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span>
 <span class="gp">... </span>    <span class="n">max_depth</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">clf</span><span class="o">.</span><span class="n">feature_importances_</span>
-<span class="go">array([0.10..., 0.10..., 0.11..., ...</span>
+<span class="go">array([0.107, 0.105, 0.113, 0.0987, 0.0947,</span>
+<span class="go">       0.107, 0.0916, 0.0972, 0.0958, 0.0906])</span>
 </pre></div>
 </div>
 <p>Note that this computation of feature importance is based on entropy, and it
@@ -1608,13 +1610,13 @@ <h3><span class="section-number">1.11.2.2. </span>Extremely Randomized Trees<a c
 <span class="gp">... </span>    <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">scores</span> <span class="o">=</span> <span class="n">cross_val_score</span><span class="p">(</span><span class="n">clf</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">cv</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">scores</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
-<span class="go">np.float64(0.98...)</span>
+<span class="go">np.float64(0.98)</span>
 
 <span class="gp">&gt;&gt;&gt; </span><span class="n">clf</span> <span class="o">=</span> <span class="n">RandomForestClassifier</span><span class="p">(</span><span class="n">n_estimators</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">max_depth</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
 <span class="gp">... </span>    <span class="n">min_samples_split</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">scores</span> <span class="o">=</span> <span class="n">cross_val_score</span><span class="p">(</span><span class="n">clf</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">cv</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">scores</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
-<span class="go">np.float64(0.999...)</span>
+<span class="go">np.float64(0.999)</span>
 
 <span class="gp">&gt;&gt;&gt; </span><span class="n">clf</span> <span class="o">=</span> <span class="n">ExtraTreesClassifier</span><span class="p">(</span><span class="n">n_estimators</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">max_depth</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
 <span class="gp">... </span>    <span class="n">min_samples_split</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
@@ -1986,39 +1988,16 @@ <h3><span class="section-number">1.11.4.3. </span>Weighted Average Probabilities
 </tbody>
 </table>
 </div>
-<p>Here, the predicted class label is 2, since it has the highest average probability. See
-this example on <a class="reference internal" href="http://github.com/auto_examples/ensemble/plot_voting_probas.html#sphx-glr-auto-examples-ensemble-plot-voting-probas-py"><span class="std std-ref">Visualising class probabilities in a Voting Classifier</span></a> for a detailed illustration of
-class probabilities averaged by soft voting.</p>
-<p>Also, the following example illustrates how the decision regions may change
-when a soft <a class="reference internal" href="http://github.com/generated/sklearn.ensemble.VotingClassifier.html#sklearn.ensemble.VotingClassifier" title="sklearn.ensemble.VotingClassifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">VotingClassifier</span></code></a> is used based on a linear Support
-Vector Machine, a Decision Tree, and a K-nearest neighbor classifier:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn</span><span class="w"> </span><span class="kn">import</span> <span class="n">datasets</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.tree</span><span class="w"> </span><span class="kn">import</span> <span class="n">DecisionTreeClassifier</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.neighbors</span><span class="w"> </span><span class="kn">import</span> <span class="n">KNeighborsClassifier</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.svm</span><span class="w"> </span><span class="kn">import</span> <span class="n">SVC</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span><span class="w"> </span><span class="nn">itertools</span><span class="w"> </span><span class="kn">import</span> <span class="n">product</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.ensemble</span><span class="w"> </span><span class="kn">import</span> <span class="n">VotingClassifier</span>
-
-<span class="gp">&gt;&gt;&gt; </span><span class="c1"># Loading some example data</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">iris</span> <span class="o">=</span> <span class="n">datasets</span><span class="o">.</span><span class="n">load_iris</span><span class="p">()</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">X</span> <span class="o">=</span> <span class="n">iris</span><span class="o">.</span><span class="n">data</span><span class="p">[:,</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">]]</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">y</span> <span class="o">=</span> <span class="n">iris</span><span class="o">.</span><span class="n">target</span>
-
-<span class="gp">&gt;&gt;&gt; </span><span class="c1"># Training classifiers</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">clf1</span> <span class="o">=</span> <span class="n">DecisionTreeClassifier</span><span class="p">(</span><span class="n">max_depth</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">clf2</span> <span class="o">=</span> <span class="n">KNeighborsClassifier</span><span class="p">(</span><span class="n">n_neighbors</span><span class="o">=</span><span class="mi">7</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">clf3</span> <span class="o">=</span> <span class="n">SVC</span><span class="p">(</span><span class="n">kernel</span><span class="o">=</span><span class="s1">&#39;rbf&#39;</span><span class="p">,</span> <span class="n">probability</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">eclf</span> <span class="o">=</span> <span class="n">VotingClassifier</span><span class="p">(</span><span class="n">estimators</span><span class="o">=</span><span class="p">[(</span><span class="s1">&#39;dt&#39;</span><span class="p">,</span> <span class="n">clf1</span><span class="p">),</span> <span class="p">(</span><span class="s1">&#39;knn&#39;</span><span class="p">,</span> <span class="n">clf2</span><span class="p">),</span> <span class="p">(</span><span class="s1">&#39;svc&#39;</span><span class="p">,</span> <span class="n">clf3</span><span class="p">)],</span>
-<span class="gp">... </span>                        <span class="n">voting</span><span class="o">=</span><span class="s1">&#39;soft&#39;</span><span class="p">,</span> <span class="n">weights</span><span class="o">=</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">])</span>
-
-<span class="gp">&gt;&gt;&gt; </span><span class="n">clf1</span> <span class="o">=</span> <span class="n">clf1</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">clf2</span> <span class="o">=</span> <span class="n">clf2</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">clf3</span> <span class="o">=</span> <span class="n">clf3</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">eclf</span> <span class="o">=</span> <span class="n">eclf</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
-</pre></div>
-</div>
+<p>Here, the predicted class label is 2, since it has the highest average
+predicted probability. See the example on
+<a class="reference internal" href="http://github.com/auto_examples/ensemble/plot_voting_decision_regions.html#sphx-glr-auto-examples-ensemble-plot-voting-decision-regions-py"><span class="std std-ref">Visualizing the probabilistic predictions of a VotingClassifier</span></a> for a
+demonstration of how the predicted class label can be obtained from the weighted
+average of predicted probabilities.</p>
+<p>The following figure illustrates how the decision regions may change when
+a soft <a class="reference internal" href="http://github.com/generated/sklearn.ensemble.VotingClassifier.html#sklearn.ensemble.VotingClassifier" title="sklearn.ensemble.VotingClassifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">VotingClassifier</span></code></a> is trained with weights on three linear
+models:</p>
 <figure class="align-center">
-<a class="reference external image-reference" href="http://github.com/auto_examples/ensemble/plot_voting_decision_regions.html"><img alt="../_images/sphx_glr_plot_voting_decision_regions_001.png" src="http://github.com/_images/sphx_glr_plot_voting_decision_regions_001.png" style="width: 750.0px; height: 600.0px;" />
+<a class="reference external image-reference" href="http://github.com/auto_examples/ensemble/plot_voting_decision_regions.html"><img alt="../_images/sphx_glr_plot_voting_decision_regions_002.png" src="http://github.com/_images/sphx_glr_plot_voting_decision_regions_002.png" style="width: 750.0px; height: 600.0px;" />
 </a>
 </figure>
 </section>
@@ -2166,11 +2145,11 @@ <h3><span class="section-number">1.11.5.1. </span>Usage<a class="headerlink" hre
 <p>Note that it is also possible to get the output of the stacked
 <code class="docutils literal notranslate"><span class="pre">estimators</span></code> using the <code class="docutils literal notranslate"><span class="pre">transform</span></code> method:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">reg</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">X_test</span><span class="p">[:</span><span class="mi">5</span><span class="p">])</span>
-<span class="go">array([[142..., 138..., 146...],</span>
-<span class="go">       [179..., 182..., 151...],</span>
-<span class="go">       [139..., 132..., 158...],</span>
-<span class="go">       [286..., 292..., 225...],</span>
-<span class="go">       [126..., 124..., 164...]])</span>
+<span class="go">array([[142, 138, 146],</span>
+<span class="go">       [179, 182, 151],</span>
+<span class="go">       [139, 132, 158],</span>
+<span class="go">       [286, 292, 225],</span>
+<span class="go">       [126, 124, 164]])</span>
 </pre></div>
 </div>
 <p>In practice, a stacking predictor predicts as good as the best predictor of the
@@ -2268,7 +2247,7 @@ <h3><span class="section-number">1.11.7.1. </span>Usage<a class="headerlink" hre
 <span class="gp">&gt;&gt;&gt; </span><span class="n">clf</span> <span class="o">=</span> <span class="n">AdaBoostClassifier</span><span class="p">(</span><span class="n">n_estimators</span><span class="o">=</span><span class="mi">100</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">scores</span> <span class="o">=</span> <span class="n">cross_val_score</span><span class="p">(</span><span class="n">clf</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">cv</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">scores</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
-<span class="go">np.float64(0.9...)</span>
+<span class="go">np.float64(0.95)</span>
 </pre></div>
 </div>
 <p>The number of weak learners is controlled by the parameter <code class="docutils literal notranslate"><span class="pre">n_estimators</span></code>. The
diff --git a/dev/modules/feature_extraction.html b/dev/modules/feature_extraction.html
index 37eaf46813838..2ee7e0e5c42d3 100644
--- a/dev/modules/feature_extraction.html
+++ b/dev/modules/feature_extraction.html
@@ -7,7 +7,7 @@
   <head>
     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="viewport" content="width=device-width, initial-scale=1" />
-<meta property="og:title" content="6.2. Feature extraction" />
+<meta property="og:title" content="7.2. Feature extraction" />
 <meta property="og:type" content="website" />
 <meta property="og:url" content="https://scikit-learn/stable/modules/feature_extraction.html" />
 <meta property="og:site_name" content="scikit-learn" />
@@ -16,7 +16,7 @@
 <meta property="og:image:alt" content="scikit-learn" />
 <meta name="description" content="The sklearn.feature_extraction module can be used to extract features in a format supported by machine learning algorithms from datasets consisting of formats such as text and image. Loading featur..." />
 
-    <title>6.2. Feature extraction &#8212; scikit-learn 1.7.dev0 documentation</title>
+    <title>7.2. Feature extraction &#8212; scikit-learn 1.7.dev0 documentation</title>
   
   
   
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -81,8 +81,8 @@
     <link rel="icon" href="http://github.com/_static/favicon.ico"/>
     <link rel="author" title="About these documents" href="http://github.com/about.html" />
     <link rel="search" title="Search" href="http://github.com/search.html" />
-    <link rel="next" title="6.3. Preprocessing data" href="http://github.com/preprocessing.html" />
-    <link rel="prev" title="6.1. Pipelines and composite estimators" href="http://github.com/compose.html" />
+    <link rel="next" title="7.3. Preprocessing data" href="http://github.com/preprocessing.html" />
+    <link rel="prev" title="7.1. Pipelines and composite estimators" href="http://github.com/compose.html" />
   <meta name="viewport" content="width=device-width, initial-scale=1"/>
   <meta name="docsearch:language" content="en"/>
   <meta name="docsearch:version" content="1.7" />
@@ -594,45 +594,46 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/learning_curve.html">3.5. Validation curves: plotting scores to evaluate models</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/inspection.html">4. Inspection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/partial_dependence.html">4.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/permutation_importance.html">4.2. Permutation feature importance</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/metadata_routing.html">4. Metadata Routing</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/inspection.html">5. Inspection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/partial_dependence.html">5.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/permutation_importance.html">5.2. Permutation feature importance</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/visualizations.html">5. Visualizations</a></li>
-<li class="toctree-l1 current active has-children"><a class="reference internal" href="http://github.com/data_transforms.html">6. Dataset transformations</a><details open="open"><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul class="current">
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/compose.html">6.1. Pipelines and composite estimators</a></li>
-<li class="toctree-l2 current active"><a class="current reference internal" href="#">6.2. Feature extraction</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing.html">6.3. Preprocessing data</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/impute.html">6.4. Imputation of missing values</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/unsupervised_reduction.html">6.5. Unsupervised dimensionality reduction</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/random_projection.html">6.6. Random Projection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/kernel_approximation.html">6.7. Kernel Approximation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/metrics.html">6.8. Pairwise metrics, Affinities and Kernels</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing_targets.html">6.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/visualizations.html">6. Visualizations</a></li>
+<li class="toctree-l1 current active has-children"><a class="reference internal" href="http://github.com/data_transforms.html">7. Dataset transformations</a><details open="open"><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/compose.html">7.1. Pipelines and composite estimators</a></li>
+<li class="toctree-l2 current active"><a class="current reference internal" href="#">7.2. Feature extraction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing.html">7.3. Preprocessing data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/impute.html">7.4. Imputation of missing values</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/unsupervised_reduction.html">7.5. Unsupervised dimensionality reduction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/random_projection.html">7.6. Random Projection</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/kernel_approximation.html">7.7. Kernel Approximation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/metrics.html">7.8. Pairwise metrics, Affinities and Kernels</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing_targets.html">7.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/datasets.html">7. Dataset loading utilities</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html">7.1. Toy datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html">7.2. Real world datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html">7.3. Generated datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html">7.4. Loading other datasets</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/datasets.html">8. Dataset loading utilities</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html">8.1. Toy datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html">8.2. Real world datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html">8.3. Generated datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html">8.4. Loading other datasets</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/computing.html">8. Computing with scikit-learn</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/scaling_strategies.html">8.1. Strategies to scale computationally: bigger data</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/computational_performance.html">8.2. Computational Performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/parallelism.html">8.3. Parallelism, resource management, and configuration</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/computing.html">9. Computing with scikit-learn</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/scaling_strategies.html">9.1. Strategies to scale computationally: bigger data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/computational_performance.html">9.2. Computational Performance</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/parallelism.html">9.3. Parallelism, resource management, and configuration</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/model_persistence.html">9. Model persistence</a></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/common_pitfalls.html">10. Common pitfalls and recommended practices</a></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/dispatching.html">11. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/array_api.html">11.1. Array API support (experimental)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/model_persistence.html">10. Model persistence</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/common_pitfalls.html">11. Common pitfalls and recommended practices</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/dispatching.html">12. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/array_api.html">12.1. Array API support (experimental)</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/machine_learning_map.html">12. Choosing the right estimator</a></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/presentations.html">13. External Resources, Videos and Talks</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/machine_learning_map.html">13. Choosing the right estimator</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/presentations.html">14. External Resources, Videos and Talks</a></li>
 </ul>
 </div>
 </nav></div>
@@ -670,9 +671,9 @@
     <li class="breadcrumb-item"><a href="http://github.com/user_guide.html" class="nav-link">User Guide</a></li>
     
     
-    <li class="breadcrumb-item"><a href="http://github.com/data_transforms.html" class="nav-link"><span class="section-number">6. </span>Dataset transformations</a></li>
+    <li class="breadcrumb-item"><a href="http://github.com/data_transforms.html" class="nav-link"><span class="section-number">7. </span>Dataset transformations</a></li>
     
-    <li class="breadcrumb-item active" aria-current="page"><span class="ellipsis"><span class="section-number">6.2. </span>Feature extraction</span></li>
+    <li class="breadcrumb-item active" aria-current="page"><span class="ellipsis"><span class="section-number">7.2. </span>Feature extraction</span></li>
   </ul>
 </nav>
 </div>
@@ -690,7 +691,7 @@
                 <article class="bd-article">
                   
   <section id="feature-extraction">
-<span id="id1"></span><h1><span class="section-number">6.2. </span>Feature extraction<a class="headerlink" href="#feature-extraction" title="Link to this heading">#</a></h1>
+<span id="id1"></span><h1><span class="section-number">7.2. </span>Feature extraction<a class="headerlink" href="#feature-extraction" title="Link to this heading">#</a></h1>
 <p>The <a class="reference internal" href="http://github.com/api/sklearn.feature_extraction.html#module-sklearn.feature_extraction" title="sklearn.feature_extraction"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.feature_extraction</span></code></a> module can be used to extract
 features in a format supported by machine learning algorithms from datasets
 consisting of formats such as text and image.</p>
@@ -702,7 +703,7 @@
 is a machine learning technique applied to these features.</p>
 </div>
 <section id="loading-features-from-dicts">
-<span id="dict-feature-extraction"></span><h2><span class="section-number">6.2.1. </span>Loading features from dicts<a class="headerlink" href="#loading-features-from-dicts" title="Link to this heading">#</a></h2>
+<span id="dict-feature-extraction"></span><h2><span class="section-number">7.2.1. </span>Loading features from dicts<a class="headerlink" href="#loading-features-from-dicts" title="Link to this heading">#</a></h2>
 <p>The class <a class="reference internal" href="http://github.com/generated/sklearn.feature_extraction.DictVectorizer.html#sklearn.feature_extraction.DictVectorizer" title="sklearn.feature_extraction.DictVectorizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">DictVectorizer</span></code></a> can be used to convert feature
 arrays represented as lists of standard Python <code class="docutils literal notranslate"><span class="pre">dict</span></code> objects to the
 NumPy/SciPy representation used by scikit-learn estimators.</p>
@@ -798,7 +799,7 @@
 default instead of a <code class="docutils literal notranslate"><span class="pre">numpy.ndarray</span></code>.</p>
 </section>
 <section id="feature-hashing">
-<span id="id2"></span><h2><span class="section-number">6.2.2. </span>Feature hashing<a class="headerlink" href="#feature-hashing" title="Link to this heading">#</a></h2>
+<span id="id2"></span><h2><span class="section-number">7.2.2. </span>Feature hashing<a class="headerlink" href="#feature-hashing" title="Link to this heading">#</a></h2>
 <p>The class <a class="reference internal" href="http://github.com/generated/sklearn.feature_extraction.FeatureHasher.html#sklearn.feature_extraction.FeatureHasher" title="sklearn.feature_extraction.FeatureHasher"><code class="xref py py-class docutils literal notranslate"><span class="pre">FeatureHasher</span></code></a> is a high-speed, low-memory vectorizer that
 uses a technique known as
 <a class="reference external" href="https://en.wikipedia.org/wiki/Feature_hashing">feature hashing</a>,
@@ -896,11 +897,11 @@
 </ul>
 </section>
 <section id="text-feature-extraction">
-<span id="id4"></span><h2><span class="section-number">6.2.3. </span>Text feature extraction<a class="headerlink" href="#text-feature-extraction" title="Link to this heading">#</a></h2>
+<span id="id4"></span><h2><span class="section-number">7.2.3. </span>Text feature extraction<a class="headerlink" href="#text-feature-extraction" title="Link to this heading">#</a></h2>
 <section id="the-bag-of-words-representation">
-<h3><span class="section-number">6.2.3.1. </span>The Bag of Words representation<a class="headerlink" href="#the-bag-of-words-representation" title="Link to this heading">#</a></h3>
+<h3><span class="section-number">7.2.3.1. </span>The Bag of Words representation<a class="headerlink" href="#the-bag-of-words-representation" title="Link to this heading">#</a></h3>
 <p>Text Analysis is a major application field for machine learning
-algorithms. However the raw data, a sequence of symbols cannot be fed
+algorithms. However the raw data, a sequence of symbols, cannot be fed
 directly to the algorithms themselves as most of them expect numerical
 feature vectors with a fixed size rather than the raw text documents
 with variable length.</p>
@@ -930,7 +931,7 @@ <h3><span class="section-number">6.2.3.1. </span>The Bag of Words representation
 of the words in the document.</p>
 </section>
 <section id="sparsity">
-<h3><span class="section-number">6.2.3.2. </span>Sparsity<a class="headerlink" href="#sparsity" title="Link to this heading">#</a></h3>
+<h3><span class="section-number">7.2.3.2. </span>Sparsity<a class="headerlink" href="#sparsity" title="Link to this heading">#</a></h3>
 <p>As most documents will typically use a very small subset of the words used in
 the corpus, the resulting matrix will have many feature values that are
 zeros (typically more than 99% of them).</p>
@@ -943,7 +944,7 @@ <h3><span class="section-number">6.2.3.2. </span>Sparsity<a class="headerlink" h
 <code class="docutils literal notranslate"><span class="pre">scipy.sparse</span></code> package.</p>
 </section>
 <section id="common-vectorizer-usage">
-<h3><span class="section-number">6.2.3.3. </span>Common Vectorizer usage<a class="headerlink" href="#common-vectorizer-usage" title="Link to this heading">#</a></h3>
+<h3><span class="section-number">7.2.3.3. </span>Common Vectorizer usage<a class="headerlink" href="#common-vectorizer-usage" title="Link to this heading">#</a></h3>
 <p><a class="reference internal" href="http://github.com/generated/sklearn.feature_extraction.text.CountVectorizer.html#sklearn.feature_extraction.text.CountVectorizer" title="sklearn.feature_extraction.text.CountVectorizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">CountVectorizer</span></code></a> implements both tokenization and occurrence
 counting in a single class:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.feature_extraction.text</span><span class="w"> </span><span class="kn">import</span> <span class="n">CountVectorizer</span>
@@ -1037,7 +1038,7 @@ <h3><span class="section-number">6.2.3.3. </span>Common Vectorizer usage<a class
 </div>
 </section>
 <section id="using-stop-words">
-<span id="stop-words"></span><h3><span class="section-number">6.2.3.4. </span>Using stop words<a class="headerlink" href="#using-stop-words" title="Link to this heading">#</a></h3>
+<span id="stop-words"></span><h3><span class="section-number">7.2.3.4. </span>Using stop words<a class="headerlink" href="#using-stop-words" title="Link to this heading">#</a></h3>
 <p>Stop words are words like “and”, “the”, “him”, which are presumed to be
 uninformative in representing the content of a text, and which may be
 removed to avoid them being construed as informative for prediction. Sometimes,
@@ -1066,7 +1067,7 @@ <h3><span class="section-number">6.2.3.3. </span>Common Vectorizer usage<a class
 </div>
 </section>
 <section id="tfidf-term-weighting">
-<span id="tfidf"></span><h3><span class="section-number">6.2.3.5. </span>Tf–idf term weighting<a class="headerlink" href="#tfidf-term-weighting" title="Link to this heading">#</a></h3>
+<span id="tfidf"></span><h3><span class="section-number">7.2.3.5. </span>Tf–idf term weighting<a class="headerlink" href="#tfidf-term-weighting" title="Link to this heading">#</a></h3>
 <p>In a large text corpus, some words will be very present (e.g. “the”, “a”,
 “is” in English) hence carrying very little meaningful information about
 the actual contents of the document. If we were to feed the direct count
@@ -1183,7 +1184,7 @@ <h3><span class="section-number">6.2.3.3. </span>Common Vectorizer usage<a class
 feature computed by the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method call are stored in a model
 attribute:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">transformer</span><span class="o">.</span><span class="n">idf_</span>
-<span class="go">array([1. ..., 2.25..., 1.84...])</span>
+<span class="go">array([1., 2.25, 1.84])</span>
 </pre></div>
 </div>
 <p class="sd-card-text">As tf-idf is very often used for text features, there is also another
@@ -1212,7 +1213,7 @@ <h3><span class="section-number">6.2.3.3. </span>Common Vectorizer usage<a class
 </div>
 </details></section>
 <section id="decoding-text-files">
-<h3><span class="section-number">6.2.3.6. </span>Decoding text files<a class="headerlink" href="#decoding-text-files" title="Link to this heading">#</a></h3>
+<h3><span class="section-number">7.2.3.6. </span>Decoding text files<a class="headerlink" href="#decoding-text-files" title="Link to this heading">#</a></h3>
 <p>Text is made of characters, but files are made of bytes. These bytes represent
 characters according to some <em>encoding</em>. To work with text files in Python,
 their bytes must be <em>decoded</em> to a character set called Unicode.
@@ -1288,7 +1289,7 @@ <h3><span class="section-number">6.2.3.6. </span>Decoding text files<a class="he
 </div>
 </details></section>
 <section id="applications-and-examples">
-<h3><span class="section-number">6.2.3.7. </span>Applications and examples<a class="headerlink" href="#applications-and-examples" title="Link to this heading">#</a></h3>
+<h3><span class="section-number">7.2.3.7. </span>Applications and examples<a class="headerlink" href="#applications-and-examples" title="Link to this heading">#</a></h3>
 <p>The bag of words representation is quite simplistic but surprisingly
 useful in practice.</p>
 <p>In particular in a <strong>supervised setting</strong> it can be successfully combined
@@ -1310,7 +1311,7 @@ <h3><span class="section-number">6.2.3.7. </span>Applications and examples<a cla
 </ul>
 </section>
 <section id="limitations-of-the-bag-of-words-representation">
-<h3><span class="section-number">6.2.3.8. </span>Limitations of the Bag of Words representation<a class="headerlink" href="#limitations-of-the-bag-of-words-representation" title="Link to this heading">#</a></h3>
+<h3><span class="section-number">7.2.3.8. </span>Limitations of the Bag of Words representation<a class="headerlink" href="#limitations-of-the-bag-of-words-representation" title="Link to this heading">#</a></h3>
 <p>A collection of unigrams (what bag of words is) cannot capture phrases
 and multi-word expressions, effectively disregarding any word order
 dependence. Additionally, the bag of words model doesn’t account for potential
@@ -1374,7 +1375,7 @@ <h3><span class="section-number">6.2.3.8. </span>Limitations of the Bag of Words
 problems which are currently outside of the scope of scikit-learn.</p>
 </section>
 <section id="vectorizing-a-large-text-corpus-with-the-hashing-trick">
-<span id="hashing-vectorizer"></span><h3><span class="section-number">6.2.3.9. </span>Vectorizing a large text corpus with the hashing trick<a class="headerlink" href="#vectorizing-a-large-text-corpus-with-the-hashing-trick" title="Link to this heading">#</a></h3>
+<span id="hashing-vectorizer"></span><h3><span class="section-number">7.2.3.9. </span>Vectorizing a large text corpus with the hashing trick<a class="headerlink" href="#vectorizing-a-large-text-corpus-with-the-hashing-trick" title="Link to this heading">#</a></h3>
 <p>The above vectorization scheme is simple but the fact that it holds an
 <strong>in-memory mapping from the string tokens to the integer feature indices</strong>
 (the <code class="docutils literal notranslate"><span class="pre">vocabulary_</span></code> attribute) causes several <strong>problems when dealing with large
@@ -1463,7 +1464,7 @@ <h3><span class="section-number">6.2.3.8. </span>Limitations of the Bag of Words
 </div>
 </details></section>
 <section id="customizing-the-vectorizer-classes">
-<h3><span class="section-number">6.2.3.10. </span>Customizing the vectorizer classes<a class="headerlink" href="#customizing-the-vectorizer-classes" title="Link to this heading">#</a></h3>
+<h3><span class="section-number">7.2.3.10. </span>Customizing the vectorizer classes<a class="headerlink" href="#customizing-the-vectorizer-classes" title="Link to this heading">#</a></h3>
 <p>It is possible to customize the behavior by passing a callable
 to the vectorizer constructor:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="k">def</span><span class="w"> </span><span class="nf">my_tokenizer</span><span class="p">(</span><span class="n">s</span><span class="p">):</span>
@@ -1553,9 +1554,9 @@ <h3><span class="section-number">6.2.3.10. </span>Customizing the vectorizer cla
 </details></section>
 </section>
 <section id="image-feature-extraction">
-<span id="id6"></span><h2><span class="section-number">6.2.4. </span>Image feature extraction<a class="headerlink" href="#image-feature-extraction" title="Link to this heading">#</a></h2>
+<span id="id6"></span><h2><span class="section-number">7.2.4. </span>Image feature extraction<a class="headerlink" href="#image-feature-extraction" title="Link to this heading">#</a></h2>
 <section id="patch-extraction">
-<h3><span class="section-number">6.2.4.1. </span>Patch extraction<a class="headerlink" href="#patch-extraction" title="Link to this heading">#</a></h3>
+<h3><span class="section-number">7.2.4.1. </span>Patch extraction<a class="headerlink" href="#patch-extraction" title="Link to this heading">#</a></h3>
 <p>The <a class="reference internal" href="http://github.com/generated/sklearn.feature_extraction.image.extract_patches_2d.html#sklearn.feature_extraction.image.extract_patches_2d" title="sklearn.feature_extraction.image.extract_patches_2d"><code class="xref py py-func docutils literal notranslate"><span class="pre">extract_patches_2d</span></code></a> function extracts patches from an image stored
 as a two-dimensional array, or three-dimensional with color information along
 the third axis. For rebuilding an image from all its patches, use
@@ -1606,7 +1607,7 @@ <h3><span class="section-number">6.2.4.1. </span>Patch extraction<a class="heade
 </div>
 </section>
 <section id="connectivity-graph-of-an-image">
-<h3><span class="section-number">6.2.4.2. </span>Connectivity graph of an image<a class="headerlink" href="#connectivity-graph-of-an-image" title="Link to this heading">#</a></h3>
+<span id="connectivity-graph-image"></span><h3><span class="section-number">7.2.4.2. </span>Connectivity graph of an image<a class="headerlink" href="#connectivity-graph-of-an-image" title="Link to this heading">#</a></h3>
 <p>Several estimators in scikit-learn can use connectivity information between
 features or samples. For instance Ward clustering
 (<a class="reference internal" href="http://github.com/clustering.html#hierarchical-clustering"><span class="std std-ref">Hierarchical clustering</span></a>) can cluster together only neighboring pixels
@@ -1653,7 +1654,7 @@ <h3><span class="section-number">6.2.4.2. </span>Connectivity graph of an image<
       <i class="fa-solid fa-angle-left"></i>
       <div class="prev-next-info">
         <p class="prev-next-subtitle">previous</p>
-        <p class="prev-next-title"><span class="section-number">6.1. </span>Pipelines and composite estimators</p>
+        <p class="prev-next-title"><span class="section-number">7.1. </span>Pipelines and composite estimators</p>
       </div>
     </a>
     <a class="right-next"
@@ -1661,7 +1662,7 @@ <h3><span class="section-number">6.2.4.2. </span>Connectivity graph of an image<
        title="next page">
       <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">6.3. </span>Preprocessing data</p>
+        <p class="prev-next-title"><span class="section-number">7.3. </span>Preprocessing data</p>
       </div>
       <i class="fa-solid fa-angle-right"></i>
     </a>
@@ -1688,24 +1689,24 @@ <h3><span class="section-number">6.2.4.2. </span>Connectivity graph of an image<
   </div>
   <nav class="bd-toc-nav page-toc" aria-labelledby="pst-page-navigation-heading-2">
     <ul class="visible nav section-nav flex-column">
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#loading-features-from-dicts">6.2.1. Loading features from dicts</a></li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#feature-hashing">6.2.2. Feature hashing</a></li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#text-feature-extraction">6.2.3. Text feature extraction</a><ul class="nav section-nav flex-column">
-<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#the-bag-of-words-representation">6.2.3.1. The Bag of Words representation</a></li>
-<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sparsity">6.2.3.2. Sparsity</a></li>
-<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#common-vectorizer-usage">6.2.3.3. Common Vectorizer usage</a></li>
-<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#using-stop-words">6.2.3.4. Using stop words</a></li>
-<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#tfidf-term-weighting">6.2.3.5. Tf–idf term weighting</a></li>
-<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#decoding-text-files">6.2.3.6. Decoding text files</a></li>
-<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#applications-and-examples">6.2.3.7. Applications and examples</a></li>
-<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#limitations-of-the-bag-of-words-representation">6.2.3.8. Limitations of the Bag of Words representation</a></li>
-<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#vectorizing-a-large-text-corpus-with-the-hashing-trick">6.2.3.9. Vectorizing a large text corpus with the hashing trick</a></li>
-<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#customizing-the-vectorizer-classes">6.2.3.10. Customizing the vectorizer classes</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#loading-features-from-dicts">7.2.1. Loading features from dicts</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#feature-hashing">7.2.2. Feature hashing</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#text-feature-extraction">7.2.3. Text feature extraction</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#the-bag-of-words-representation">7.2.3.1. The Bag of Words representation</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sparsity">7.2.3.2. Sparsity</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#common-vectorizer-usage">7.2.3.3. Common Vectorizer usage</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#using-stop-words">7.2.3.4. Using stop words</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#tfidf-term-weighting">7.2.3.5. Tf–idf term weighting</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#decoding-text-files">7.2.3.6. Decoding text files</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#applications-and-examples">7.2.3.7. Applications and examples</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#limitations-of-the-bag-of-words-representation">7.2.3.8. Limitations of the Bag of Words representation</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#vectorizing-a-large-text-corpus-with-the-hashing-trick">7.2.3.9. Vectorizing a large text corpus with the hashing trick</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#customizing-the-vectorizer-classes">7.2.3.10. Customizing the vectorizer classes</a></li>
 </ul>
 </li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#image-feature-extraction">6.2.4. Image feature extraction</a><ul class="nav section-nav flex-column">
-<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#patch-extraction">6.2.4.1. Patch extraction</a></li>
-<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#connectivity-graph-of-an-image">6.2.4.2. Connectivity graph of an image</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#image-feature-extraction">7.2.4. Image feature extraction</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#patch-extraction">7.2.4.1. Patch extraction</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#connectivity-graph-of-an-image">7.2.4.2. Connectivity graph of an image</a></li>
 </ul>
 </li>
 </ul>
diff --git a/dev/modules/feature_selection.html b/dev/modules/feature_selection.html
index 1eb2a838aa11c..a34dd98ab5f99 100644
--- a/dev/modules/feature_selection.html
+++ b/dev/modules/feature_selection.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -594,45 +594,46 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/learning_curve.html">3.5. Validation curves: plotting scores to evaluate models</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/inspection.html">4. Inspection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/partial_dependence.html">4.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/permutation_importance.html">4.2. Permutation feature importance</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/metadata_routing.html">4. Metadata Routing</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/inspection.html">5. Inspection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/partial_dependence.html">5.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/permutation_importance.html">5.2. Permutation feature importance</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/visualizations.html">5. Visualizations</a></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/data_transforms.html">6. Dataset transformations</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/compose.html">6.1. Pipelines and composite estimators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/feature_extraction.html">6.2. Feature extraction</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing.html">6.3. Preprocessing data</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/impute.html">6.4. Imputation of missing values</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/unsupervised_reduction.html">6.5. Unsupervised dimensionality reduction</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/random_projection.html">6.6. Random Projection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/kernel_approximation.html">6.7. Kernel Approximation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/metrics.html">6.8. Pairwise metrics, Affinities and Kernels</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing_targets.html">6.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/visualizations.html">6. Visualizations</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/data_transforms.html">7. Dataset transformations</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/compose.html">7.1. Pipelines and composite estimators</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/feature_extraction.html">7.2. Feature extraction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing.html">7.3. Preprocessing data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/impute.html">7.4. Imputation of missing values</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/unsupervised_reduction.html">7.5. Unsupervised dimensionality reduction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/random_projection.html">7.6. Random Projection</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/kernel_approximation.html">7.7. Kernel Approximation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/metrics.html">7.8. Pairwise metrics, Affinities and Kernels</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing_targets.html">7.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/datasets.html">7. Dataset loading utilities</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html">7.1. Toy datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html">7.2. Real world datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html">7.3. Generated datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html">7.4. Loading other datasets</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/datasets.html">8. Dataset loading utilities</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html">8.1. Toy datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html">8.2. Real world datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html">8.3. Generated datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html">8.4. Loading other datasets</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/computing.html">8. Computing with scikit-learn</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/scaling_strategies.html">8.1. Strategies to scale computationally: bigger data</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/computational_performance.html">8.2. Computational Performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/parallelism.html">8.3. Parallelism, resource management, and configuration</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/computing.html">9. Computing with scikit-learn</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/scaling_strategies.html">9.1. Strategies to scale computationally: bigger data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/computational_performance.html">9.2. Computational Performance</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/parallelism.html">9.3. Parallelism, resource management, and configuration</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/model_persistence.html">9. Model persistence</a></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/common_pitfalls.html">10. Common pitfalls and recommended practices</a></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/dispatching.html">11. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/array_api.html">11.1. Array API support (experimental)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/model_persistence.html">10. Model persistence</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/common_pitfalls.html">11. Common pitfalls and recommended practices</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/dispatching.html">12. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/array_api.html">12.1. Array API support (experimental)</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/machine_learning_map.html">12. Choosing the right estimator</a></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/presentations.html">13. External Resources, Videos and Talks</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/machine_learning_map.html">13. Choosing the right estimator</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/presentations.html">14. External Resources, Videos and Talks</a></li>
 </ul>
 </div>
 </nav></div>
@@ -909,7 +910,7 @@ <h3><span class="section-number">1.13.4.2. </span>Tree-based feature selection<a
 <span class="gp">&gt;&gt;&gt; </span><span class="n">clf</span> <span class="o">=</span> <span class="n">ExtraTreesClassifier</span><span class="p">(</span><span class="n">n_estimators</span><span class="o">=</span><span class="mi">50</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">clf</span> <span class="o">=</span> <span class="n">clf</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">clf</span><span class="o">.</span><span class="n">feature_importances_</span>  
-<span class="go">array([ 0.04...,  0.05...,  0.4...,  0.4...])</span>
+<span class="go">array([ 0.04,  0.05,  0.4,  0.4])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">model</span> <span class="o">=</span> <span class="n">SelectFromModel</span><span class="p">(</span><span class="n">clf</span><span class="p">,</span> <span class="n">prefit</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">X_new</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">X_new</span><span class="o">.</span><span class="n">shape</span>               
diff --git a/dev/modules/gaussian_process.html b/dev/modules/gaussian_process.html
index d7525ad1ceafa..98644de221ce0 100644
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+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -594,45 +594,46 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/learning_curve.html">3.5. Validation curves: plotting scores to evaluate models</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/inspection.html">4. Inspection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/partial_dependence.html">4.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/permutation_importance.html">4.2. Permutation feature importance</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/metadata_routing.html">4. Metadata Routing</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/inspection.html">5. Inspection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/partial_dependence.html">5.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/permutation_importance.html">5.2. Permutation feature importance</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/visualizations.html">5. Visualizations</a></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/data_transforms.html">6. Dataset transformations</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/compose.html">6.1. Pipelines and composite estimators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/feature_extraction.html">6.2. Feature extraction</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing.html">6.3. Preprocessing data</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/impute.html">6.4. Imputation of missing values</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/unsupervised_reduction.html">6.5. Unsupervised dimensionality reduction</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/random_projection.html">6.6. Random Projection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/kernel_approximation.html">6.7. Kernel Approximation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/metrics.html">6.8. Pairwise metrics, Affinities and Kernels</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing_targets.html">6.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/visualizations.html">6. Visualizations</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/data_transforms.html">7. Dataset transformations</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/compose.html">7.1. Pipelines and composite estimators</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/feature_extraction.html">7.2. Feature extraction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing.html">7.3. Preprocessing data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/impute.html">7.4. Imputation of missing values</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/unsupervised_reduction.html">7.5. Unsupervised dimensionality reduction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/random_projection.html">7.6. Random Projection</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/kernel_approximation.html">7.7. Kernel Approximation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/metrics.html">7.8. Pairwise metrics, Affinities and Kernels</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing_targets.html">7.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/datasets.html">7. Dataset loading utilities</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html">7.1. Toy datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html">7.2. Real world datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html">7.3. Generated datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html">7.4. Loading other datasets</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/datasets.html">8. Dataset loading utilities</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html">8.1. Toy datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html">8.2. Real world datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html">8.3. Generated datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html">8.4. Loading other datasets</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/computing.html">8. Computing with scikit-learn</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/scaling_strategies.html">8.1. Strategies to scale computationally: bigger data</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/computational_performance.html">8.2. Computational Performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/parallelism.html">8.3. Parallelism, resource management, and configuration</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/computing.html">9. Computing with scikit-learn</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/scaling_strategies.html">9.1. Strategies to scale computationally: bigger data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/computational_performance.html">9.2. Computational Performance</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/parallelism.html">9.3. Parallelism, resource management, and configuration</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/model_persistence.html">9. Model persistence</a></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/common_pitfalls.html">10. Common pitfalls and recommended practices</a></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/dispatching.html">11. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/array_api.html">11.1. Array API support (experimental)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/model_persistence.html">10. Model persistence</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/common_pitfalls.html">11. Common pitfalls and recommended practices</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/dispatching.html">12. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/array_api.html">12.1. Array API support (experimental)</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/machine_learning_map.html">12. Choosing the right estimator</a></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/presentations.html">13. External Resources, Videos and Talks</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/machine_learning_map.html">13. Choosing the right estimator</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/presentations.html">14. External Resources, Videos and Talks</a></li>
 </ul>
 </div>
 </nav></div>
@@ -771,11 +772,11 @@
 classification purposes, more specifically for probabilistic classification,
 where test predictions take the form of class probabilities.
 GaussianProcessClassifier places a GP prior on a latent function <span class="math notranslate nohighlight">\(f\)</span>,
-which is then squashed through a link function to obtain the probabilistic
+which is then squashed through a link function <span class="math notranslate nohighlight">\(\pi\)</span> to obtain the probabilistic
 classification. The latent function <span class="math notranslate nohighlight">\(f\)</span> is a so-called nuisance function,
 whose values are not observed and are not relevant by themselves.
 Its purpose is to allow a convenient formulation of the model, and <span class="math notranslate nohighlight">\(f\)</span>
-is removed (integrated out) during prediction. GaussianProcessClassifier
+is removed (integrated out) during prediction. <a class="reference internal" href="http://github.com/generated/sklearn.gaussian_process.GaussianProcessClassifier.html#sklearn.gaussian_process.GaussianProcessClassifier" title="sklearn.gaussian_process.GaussianProcessClassifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">GaussianProcessClassifier</span></code></a>
 implements the logistic link function, for which the integral cannot be
 computed analytically but is easily approximated in the binary case.</p>
 <p>In contrast to the regression setting, the posterior of the latent function
@@ -796,6 +797,10 @@
 that have been chosen randomly from the range of allowed values.
 If the initial hyperparameters should be kept fixed, <code class="docutils literal notranslate"><span class="pre">None</span></code> can be passed as
 optimizer.</p>
+<p>In some scenarios, information about the latent function <span class="math notranslate nohighlight">\(f\)</span> is desired
+(i.e. the mean <span class="math notranslate nohighlight">\(\bar{f_*}\)</span> and the variance <span class="math notranslate nohighlight">\(\text{Var}[f_*]\)</span> described
+in Eqs. (3.21) and (3.24) of <a class="reference internal" href="#rw2006" id="id3"><span>[RW2006]</span></a>). The <a class="reference internal" href="http://github.com/generated/sklearn.gaussian_process.GaussianProcessClassifier.html#sklearn.gaussian_process.GaussianProcessClassifier" title="sklearn.gaussian_process.GaussianProcessClassifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">GaussianProcessClassifier</span></code></a>
+provides access to these quantities via the <code class="docutils literal notranslate"><span class="pre">latent_mean_and_variance</span></code> method.</p>
 <p><a class="reference internal" href="http://github.com/generated/sklearn.gaussian_process.GaussianProcessClassifier.html#sklearn.gaussian_process.GaussianProcessClassifier" title="sklearn.gaussian_process.GaussianProcessClassifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">GaussianProcessClassifier</span></code></a> supports multi-class classification
 by performing either one-versus-rest or one-versus-one based training and
 prediction.  In one-versus-rest, one binary Gaussian process classifier is
@@ -881,8 +886,9 @@ <h3><span class="section-number">1.7.3.3. </span>Gaussian process classification
 depend also on the specific values of the datapoints. Stationary kernels can further
 be subdivided into isotropic and anisotropic kernels, where isotropic kernels are
 also invariant to rotations in the input space. For more details, we refer to
-Chapter 4 of <a class="reference internal" href="#rw2006" id="id3"><span>[RW2006]</span></a>. For guidance on how to best combine different kernels,
-we refer to <a class="reference internal" href="#duv2014" id="id4"><span>[Duv2014]</span></a>.</p>
+Chapter 4 of <a class="reference internal" href="#rw2006" id="id4"><span>[RW2006]</span></a>. <a class="reference internal" href="http://github.com/auto_examples/gaussian_process/plot_gpr_on_structured_data.html#sphx-glr-auto-examples-gaussian-process-plot-gpr-on-structured-data-py"><span class="std std-ref">This example</span></a>
+shows how to define a custom kernel over discrete data. For guidance on how to best
+combine different kernels, we refer to <a class="reference internal" href="#duv2014" id="id5"><span>[Duv2014]</span></a>.</p>
 <details class="sd-sphinx-override sd-dropdown sd-card sd-mb-3" id="gaussian-process-kernel-api">
 <summary class="sd-summary-title sd-card-header">
 <span class="sd-summary-text">Gaussian Process Kernel API<a class="headerlink" href="#gaussian-process-kernel-api" title="Link to this dropdown">#</a></span><span class="sd-summary-state-marker sd-summary-chevron-right"><svg version="1.1" width="1.5em" height="1.5em" class="sd-octicon sd-octicon-chevron-right" viewBox="0 0 24 24" aria-hidden="true"><path d="M8.72 18.78a.75.75 0 0 1 0-1.06L14.44 12 8.72 6.28a.751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018l6.25 6.25a.75.75 0 0 1 0 1.06l-6.25 6.25a.75.75 0 0 1-1.06 0Z"></path></svg></span></summary><div class="sd-summary-content sd-card-body docutils">
@@ -1047,7 +1053,7 @@ <h3><span class="section-number">1.7.4.4. </span>Matérn kernel<a class="headerl
 <a class="reference external image-reference" href="http://github.com/auto_examples/gaussian_process/plot_gpr_prior_posterior.html"><img alt="../_images/sphx_glr_plot_gpr_prior_posterior_005.png" src="http://github.com/_images/sphx_glr_plot_gpr_prior_posterior_005.png" />
 </a>
 </figure>
-<p>See <a class="reference internal" href="#rw2006" id="id5"><span>[RW2006]</span></a>, pp84 for further details regarding the
+<p>See <a class="reference internal" href="#rw2006" id="id6"><span>[RW2006]</span></a>, pp84 for further details regarding the
 different variants of the Matérn kernel.</p>
 </section>
 <section id="rational-quadratic-kernel">
@@ -1103,13 +1109,13 @@ <h3><span class="section-number">1.7.4.8. </span>References<a class="headerlink"
 <div role="list" class="citation-list">
 <div class="citation" id="rw2006" role="doc-biblioentry">
 <span class="label"><span class="fn-bracket">[</span>RW2006<span class="fn-bracket">]</span></span>
-<span class="backrefs">(<a role="doc-backlink" href="#id1">1</a>,<a role="doc-backlink" href="#id2">2</a>,<a role="doc-backlink" href="#id3">3</a>,<a role="doc-backlink" href="#id5">4</a>)</span>
+<span class="backrefs">(<a role="doc-backlink" href="#id1">1</a>,<a role="doc-backlink" href="#id2">2</a>,<a role="doc-backlink" href="#id3">3</a>,<a role="doc-backlink" href="#id4">4</a>,<a role="doc-backlink" href="#id6">5</a>)</span>
 <p><a class="reference external" href="https://www.gaussianprocess.org/gpml/chapters/RW.pdf">Carl E. Rasmussen and Christopher K.I. Williams,
 “Gaussian Processes for Machine Learning”,
 MIT Press 2006</a></p>
 </div>
 <div class="citation" id="duv2014" role="doc-biblioentry">
-<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id4">Duv2014</a><span class="fn-bracket">]</span></span>
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id5">Duv2014</a><span class="fn-bracket">]</span></span>
 <p><a class="reference external" href="https://www.cs.toronto.edu/~duvenaud/cookbook/">David Duvenaud, “The Kernel Cookbook: Advice on Covariance functions”, 2014</a></p>
 </div>
 </div>
diff --git a/dev/modules/generated/dbscan-function.html b/dev/modules/generated/dbscan-function.html
index fd63adb51de46..a98f82e00a156 100644
--- a/dev/modules/generated/dbscan-function.html
+++ b/dev/modules/generated/dbscan-function.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>dbscan<a class="headerlink" href="#dbscan" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.cluster.dbscan">
-<span class="sig-prename descclassname"><span class="pre">sklearn.cluster.</span></span><span class="sig-name descname"><span class="pre">dbscan</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'minkowski'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric_params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">algorithm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">leaf_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">30</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/cluster/_dbscan.py#L22"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.dbscan" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.cluster.</span></span><span class="sig-name descname"><span class="pre">dbscan</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'minkowski'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric_params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">algorithm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">leaf_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">30</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/cluster/_dbscan.py#L22"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.dbscan" title="Link to this definition">#</a></dt>
 <dd><p>Perform DBSCAN clustering from vector array or distance matrix.</p>
 <p>Read more in the <a class="reference internal" href="http://github.com/clustering.html#dbscan"><span class="std std-ref">User Guide</span></a>.</p>
 <dl class="field-list">
@@ -1355,7 +1355,7 @@ <h1>dbscan<a class="headerlink" href="#dbscan" title="Link to this heading">#</a
 Note that weights are absolute, and default to 1.</p>
 </dd>
 <dt><strong>n_jobs</strong><span class="classifier">int, default=None</span></dt><dd><p>The number of parallel jobs to run for neighbors search. <code class="docutils literal notranslate"><span class="pre">None</span></code> means
-1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.5.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context. <code class="docutils literal notranslate"><span class="pre">-1</span></code> means
+1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.6.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context. <code class="docutils literal notranslate"><span class="pre">-1</span></code> means
 using all processors. See <a class="reference internal" href="http://github.com/glossary.html#term-n_jobs"><span class="xref std std-term">Glossary</span></a> for more details.
 If precomputed distance are used, parallel execution is not available
 and thus n_jobs will have no effect.</p>
diff --git a/dev/modules/generated/fastica-function.html b/dev/modules/generated/fastica-function.html
index d8c6f159a998a..2b00345a776bb 100644
--- a/dev/modules/generated/fastica-function.html
+++ b/dev/modules/generated/fastica-function.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>fastica<a class="headerlink" href="#fastica" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.decomposition.fastica">
-<span class="sig-prename descclassname"><span class="pre">sklearn.decomposition.</span></span><span class="sig-name descname"><span class="pre">fastica</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_components</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">algorithm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'parallel'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">whiten</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'unit-variance'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fun</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'logcosh'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fun_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">200</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">w_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">whiten_solver</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'svd'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_X_mean</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">compute_sources</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_n_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/decomposition/_fastica.py#L163"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.fastica" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.decomposition.</span></span><span class="sig-name descname"><span class="pre">fastica</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_components</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">algorithm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'parallel'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">whiten</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'unit-variance'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fun</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'logcosh'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fun_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">200</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">w_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">whiten_solver</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'svd'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_X_mean</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">compute_sources</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_n_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/decomposition/_fastica.py#L163"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.fastica" title="Link to this definition">#</a></dt>
 <dd><p>Perform Fast Independent Component Analysis.</p>
 <p>The implementation is based on <a class="reference internal" href="#r4ef46ec4ecf2-1" id="id1">[1]</a>.</p>
 <p>Read more in the <a class="reference internal" href="http://github.com/decomposition.html#ica"><span class="std std-ref">User Guide</span></a>.</p>
diff --git a/dev/modules/generated/oas-function.html b/dev/modules/generated/oas-function.html
index cb7f43d3ec834..cc4400f492f2b 100644
--- a/dev/modules/generated/oas-function.html
+++ b/dev/modules/generated/oas-function.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>oas<a class="headerlink" href="#oas" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.covariance.oas">
-<span class="sig-prename descclassname"><span class="pre">sklearn.covariance.</span></span><span class="sig-name descname"><span class="pre">oas</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">assume_centered</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/covariance/_shrunk_covariance.py#L617"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.oas" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.covariance.</span></span><span class="sig-name descname"><span class="pre">oas</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">assume_centered</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/covariance/_shrunk_covariance.py#L617"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.oas" title="Link to this definition">#</a></dt>
 <dd><p>Estimate covariance with the Oracle Approximating Shrinkage.</p>
 <p>Read more in the <a class="reference internal" href="http://github.com/covariance.html#shrunk-covariance"><span class="std std-ref">User Guide</span></a>.</p>
 <dl class="field-list simple">
@@ -1358,10 +1358,10 @@ <h1>oas<a class="headerlink" href="#oas" title="Link to this heading">#</a></h1>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">X</span> <span class="o">=</span> <span class="n">rng</span><span class="o">.</span><span class="n">multivariate_normal</span><span class="p">(</span><span class="n">mean</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">cov</span><span class="o">=</span><span class="n">real_cov</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">500</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">shrunk_cov</span><span class="p">,</span> <span class="n">shrinkage</span> <span class="o">=</span> <span class="n">oas</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">shrunk_cov</span>
-<span class="go">array([[0.7533..., 0.2763...],</span>
-<span class="go">       [0.2763..., 0.3964...]])</span>
+<span class="go">array([[0.7533, 0.2763],</span>
+<span class="go">       [0.2763, 0.3964]])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">shrinkage</span>
-<span class="go">np.float64(0.0195...)</span>
+<span class="go">np.float64(0.0195)</span>
 </pre></div>
 </div>
 </dd></dl>
diff --git a/dev/modules/generated/sklearn.base.BaseEstimator.html b/dev/modules/generated/sklearn.base.BaseEstimator.html
index a72435242280a..daed01162acf7 100644
--- a/dev/modules/generated/sklearn.base.BaseEstimator.html
+++ b/dev/modules/generated/sklearn.base.BaseEstimator.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>BaseEstimator<a class="headerlink" href="#baseestimator" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.base.BaseEstimator">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.base.</span></span><span class="sig-name descname"><span class="pre">BaseEstimator</span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L156"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.base.BaseEstimator" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.base.</span></span><span class="sig-name descname"><span class="pre">BaseEstimator</span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L153"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.base.BaseEstimator" title="Link to this definition">#</a></dt>
 <dd><p>Base class for all estimators in scikit-learn.</p>
 <p>Inheriting from this class provides default implementations of:</p>
 <ul class="simple">
@@ -1347,7 +1347,7 @@ <h1>BaseEstimator<a class="headerlink" href="#baseestimator" title="Link to this
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.base.BaseEstimator.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.base.BaseEstimator.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.base.BaseEstimator.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1364,7 +1364,7 @@ <h1>BaseEstimator<a class="headerlink" href="#baseestimator" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.base.BaseEstimator.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.base.BaseEstimator.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.base.BaseEstimator.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1385,7 +1385,7 @@ <h1>BaseEstimator<a class="headerlink" href="#baseestimator" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.base.BaseEstimator.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.base.BaseEstimator.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.base.BaseEstimator.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
diff --git a/dev/modules/generated/sklearn.base.BiclusterMixin.html b/dev/modules/generated/sklearn.base.BiclusterMixin.html
index 65262ce7cfcf8..2f1626d2c9ebe 100644
--- a/dev/modules/generated/sklearn.base.BiclusterMixin.html
+++ b/dev/modules/generated/sklearn.base.BiclusterMixin.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>BiclusterMixin<a class="headerlink" href="#biclustermixin" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.base.BiclusterMixin">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.base.</span></span><span class="sig-name descname"><span class="pre">BiclusterMixin</span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L723"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.base.BiclusterMixin" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.base.</span></span><span class="sig-name descname"><span class="pre">BiclusterMixin</span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L664"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.base.BiclusterMixin" title="Link to this definition">#</a></dt>
 <dd><p>Mixin class for all bicluster estimators in scikit-learn.</p>
 <p>This mixin defines the following functionality:</p>
 <ul class="simple">
@@ -1333,7 +1333,7 @@ <h1>BiclusterMixin<a class="headerlink" href="#biclustermixin" title="Link to th
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.base.BiclusterMixin.get_indices">
-<span class="sig-name descname"><span class="pre">get_indices</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">i</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L759"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.base.BiclusterMixin.get_indices" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_indices</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">i</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L700"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.base.BiclusterMixin.get_indices" title="Link to this definition">#</a></dt>
 <dd><p>Row and column indices of the <code class="docutils literal notranslate"><span class="pre">i</span></code>’th bicluster.</p>
 <p>Only works if <code class="docutils literal notranslate"><span class="pre">rows_</span></code> and <code class="docutils literal notranslate"><span class="pre">columns_</span></code> attributes exist.</p>
 <dl class="field-list simple">
@@ -1356,7 +1356,7 @@ <h1>BiclusterMixin<a class="headerlink" href="#biclustermixin" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.base.BiclusterMixin.get_shape">
-<span class="sig-name descname"><span class="pre">get_shape</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">i</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L780"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.base.BiclusterMixin.get_shape" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_shape</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">i</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L721"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.base.BiclusterMixin.get_shape" title="Link to this definition">#</a></dt>
 <dd><p>Shape of the <code class="docutils literal notranslate"><span class="pre">i</span></code>’th bicluster.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1378,7 +1378,7 @@ <h1>BiclusterMixin<a class="headerlink" href="#biclustermixin" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.base.BiclusterMixin.get_submatrix">
-<span class="sig-name descname"><span class="pre">get_submatrix</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">i</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L799"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.base.BiclusterMixin.get_submatrix" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_submatrix</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">i</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L740"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.base.BiclusterMixin.get_submatrix" title="Link to this definition">#</a></dt>
 <dd><p>Return the submatrix corresponding to bicluster <code class="docutils literal notranslate"><span class="pre">i</span></code>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.base.ClassNamePrefixFeaturesOutMixin.html b/dev/modules/generated/sklearn.base.ClassNamePrefixFeaturesOutMixin.html
index 58b79a6096b6e..a1fd8a1f8220e 100644
--- a/dev/modules/generated/sklearn.base.ClassNamePrefixFeaturesOutMixin.html
+++ b/dev/modules/generated/sklearn.base.ClassNamePrefixFeaturesOutMixin.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>ClassNamePrefixFeaturesOutMixin<a class="headerlink" href="#classnameprefixfeaturesoutmixin" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.base.ClassNamePrefixFeaturesOutMixin">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.base.</span></span><span class="sig-name descname"><span class="pre">ClassNamePrefixFeaturesOutMixin</span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L970"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.base.ClassNamePrefixFeaturesOutMixin" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.base.</span></span><span class="sig-name descname"><span class="pre">ClassNamePrefixFeaturesOutMixin</span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L911"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.base.ClassNamePrefixFeaturesOutMixin" title="Link to this definition">#</a></dt>
 <dd><p>Mixin class for transformers that generate their own names by prefixing.</p>
 <p>This mixin is useful when the transformer needs to generate its own feature
 names out, such as <a class="reference internal" href="http://github.com/sklearn.decomposition.PCA.html#sklearn.decomposition.PCA" title="sklearn.decomposition.PCA"><code class="xref py py-class docutils literal notranslate"><span class="pre">PCA</span></code></a>. For example, if
@@ -1328,7 +1328,7 @@ <h1>ClassNamePrefixFeaturesOutMixin<a class="headerlink" href="#classnameprefixf
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.base.ClassNamePrefixFeaturesOutMixin.get_feature_names_out">
-<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L995"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.base.ClassNamePrefixFeaturesOutMixin.get_feature_names_out" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L936"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.base.ClassNamePrefixFeaturesOutMixin.get_feature_names_out" title="Link to this definition">#</a></dt>
 <dd><p>Get output feature names for transformation.</p>
 <p>The feature names out will prefixed by the lowercased class name. For
 example, if the transformer outputs 3 features, then the feature names
diff --git a/dev/modules/generated/sklearn.base.ClassifierMixin.html b/dev/modules/generated/sklearn.base.ClassifierMixin.html
index cac58a201616a..cf39db8f7fdba 100644
--- a/dev/modules/generated/sklearn.base.ClassifierMixin.html
+++ b/dev/modules/generated/sklearn.base.ClassifierMixin.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>ClassifierMixin<a class="headerlink" href="#classifiermixin" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.base.ClassifierMixin">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.base.</span></span><span class="sig-name descname"><span class="pre">ClassifierMixin</span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L502"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.base.ClassifierMixin" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.base.</span></span><span class="sig-name descname"><span class="pre">ClassifierMixin</span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L443"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.base.ClassifierMixin" title="Link to this definition">#</a></dt>
 <dd><p>Mixin class for all classifiers in scikit-learn.</p>
 <p>This mixin defines the following functionality:</p>
 <ul class="simple">
@@ -1340,7 +1340,7 @@ <h1>ClassifierMixin<a class="headerlink" href="#classifiermixin" title="Link to
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.base.ClassifierMixin.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L546"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.base.ClassifierMixin.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L487"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.base.ClassifierMixin.score" title="Link to this definition">#</a></dt>
 <dd><p>Return <a class="reference internal" href="http://github.com/model_evaluation.html#accuracy-score"><span class="std std-ref">accuracy</span></a> on provided data and labels.</p>
 <p>In multi-label classification, this is the subset accuracy
 which is a harsh metric since you require for each sample that
diff --git a/dev/modules/generated/sklearn.base.ClusterMixin.html b/dev/modules/generated/sklearn.base.ClusterMixin.html
index c5c0839bd25bd..a37d8c965c531 100644
--- a/dev/modules/generated/sklearn.base.ClusterMixin.html
+++ b/dev/modules/generated/sklearn.base.ClusterMixin.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>ClusterMixin<a class="headerlink" href="#clustermixin" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.base.ClusterMixin">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.base.</span></span><span class="sig-name descname"><span class="pre">ClusterMixin</span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L666"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.base.ClusterMixin" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.base.</span></span><span class="sig-name descname"><span class="pre">ClusterMixin</span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L607"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.base.ClusterMixin" title="Link to this definition">#</a></dt>
 <dd><p>Mixin class for all cluster estimators in scikit-learn.</p>
 <ul class="simple">
 <li><p>set estimator type to <code class="docutils literal notranslate"><span class="pre">&quot;clusterer&quot;</span></code> through the <code class="docutils literal notranslate"><span class="pre">estimator_type</span></code> tag;</p></li>
@@ -1325,7 +1325,7 @@ <h1>ClusterMixin<a class="headerlink" href="#clustermixin" title="Link to this h
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.base.ClusterMixin.fit_predict">
-<span class="sig-name descname"><span class="pre">fit_predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L695"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.base.ClusterMixin.fit_predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L636"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.base.ClusterMixin.fit_predict" title="Link to this definition">#</a></dt>
 <dd><p>Perform clustering on <code class="docutils literal notranslate"><span class="pre">X</span></code> and returns cluster labels.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.base.DensityMixin.html b/dev/modules/generated/sklearn.base.DensityMixin.html
index fdf97efda5534..5479d49bec860 100644
--- a/dev/modules/generated/sklearn.base.DensityMixin.html
+++ b/dev/modules/generated/sklearn.base.DensityMixin.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>DensityMixin<a class="headerlink" href="#densitymixin" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.base.DensityMixin">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.base.</span></span><span class="sig-name descname"><span class="pre">DensityMixin</span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L1018"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.base.DensityMixin" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.base.</span></span><span class="sig-name descname"><span class="pre">DensityMixin</span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L959"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.base.DensityMixin" title="Link to this definition">#</a></dt>
 <dd><p>Mixin class for all density estimators in scikit-learn.</p>
 <p>This mixin defines the following functionality:</p>
 <ul class="simple">
@@ -1325,7 +1325,7 @@ <h1>DensityMixin<a class="headerlink" href="#densitymixin" title="Link to this h
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.base.DensityMixin.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L1046"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.base.DensityMixin.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L987"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.base.DensityMixin.score" title="Link to this definition">#</a></dt>
 <dd><p>Return the score of the model on the data <code class="docutils literal notranslate"><span class="pre">X</span></code>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.base.MetaEstimatorMixin.html b/dev/modules/generated/sklearn.base.MetaEstimatorMixin.html
index 6772e7418e14a..07786ca720d5d 100644
--- a/dev/modules/generated/sklearn.base.MetaEstimatorMixin.html
+++ b/dev/modules/generated/sklearn.base.MetaEstimatorMixin.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>MetaEstimatorMixin<a class="headerlink" href="#metaestimatormixin" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.base.MetaEstimatorMixin">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.base.</span></span><span class="sig-name descname"><span class="pre">MetaEstimatorMixin</span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L1149"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.base.MetaEstimatorMixin" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.base.</span></span><span class="sig-name descname"><span class="pre">MetaEstimatorMixin</span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L1090"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.base.MetaEstimatorMixin" title="Link to this definition">#</a></dt>
 <dd><p>Mixin class for all meta estimators in scikit-learn.</p>
 <p>This mixin is empty, and only exists to indicate that the estimator is a
 meta-estimator.</p>
diff --git a/dev/modules/generated/sklearn.base.OneToOneFeatureMixin.html b/dev/modules/generated/sklearn.base.OneToOneFeatureMixin.html
index b4e56874016bd..d6208cc8c477c 100644
--- a/dev/modules/generated/sklearn.base.OneToOneFeatureMixin.html
+++ b/dev/modules/generated/sklearn.base.OneToOneFeatureMixin.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>OneToOneFeatureMixin<a class="headerlink" href="#onetoonefeaturemixin" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.base.OneToOneFeatureMixin">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.base.</span></span><span class="sig-name descname"><span class="pre">OneToOneFeatureMixin</span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L924"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.base.OneToOneFeatureMixin" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.base.</span></span><span class="sig-name descname"><span class="pre">OneToOneFeatureMixin</span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L865"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.base.OneToOneFeatureMixin" title="Link to this definition">#</a></dt>
 <dd><p>Provides <code class="docutils literal notranslate"><span class="pre">get_feature_names_out</span></code> for simple transformers.</p>
 <p>This mixin assumes there’s a 1-to-1 correspondence between input features
 and output features, such as <a class="reference internal" href="http://github.com/sklearn.preprocessing.StandardScaler.html#sklearn.preprocessing.StandardScaler" title="sklearn.preprocessing.StandardScaler"><code class="xref py py-class docutils literal notranslate"><span class="pre">StandardScaler</span></code></a>.</p>
@@ -1323,7 +1323,7 @@ <h1>OneToOneFeatureMixin<a class="headerlink" href="#onetoonefeaturemixin" title
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.base.OneToOneFeatureMixin.get_feature_names_out">
-<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L943"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.base.OneToOneFeatureMixin.get_feature_names_out" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L884"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.base.OneToOneFeatureMixin.get_feature_names_out" title="Link to this definition">#</a></dt>
 <dd><p>Get output feature names for transformation.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.base.OutlierMixin.html b/dev/modules/generated/sklearn.base.OutlierMixin.html
index f219a17ddb34a..7b391d5590a1c 100644
--- a/dev/modules/generated/sklearn.base.OutlierMixin.html
+++ b/dev/modules/generated/sklearn.base.OutlierMixin.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>OutlierMixin<a class="headerlink" href="#outliermixin" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.base.OutlierMixin">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.base.</span></span><span class="sig-name descname"><span class="pre">OutlierMixin</span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L1064"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.base.OutlierMixin" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.base.</span></span><span class="sig-name descname"><span class="pre">OutlierMixin</span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L1005"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.base.OutlierMixin" title="Link to this definition">#</a></dt>
 <dd><p>Mixin class for all outlier detection estimators in scikit-learn.</p>
 <p>This mixin defines the following functionality:</p>
 <ul class="simple">
@@ -1329,7 +1329,7 @@ <h1>OutlierMixin<a class="headerlink" href="#outliermixin" title="Link to this h
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.base.OutlierMixin.fit_predict">
-<span class="sig-name descname"><span class="pre">fit_predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L1096"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.base.OutlierMixin.fit_predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L1037"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.base.OutlierMixin.fit_predict" title="Link to this definition">#</a></dt>
 <dd><p>Perform fit on X and returns labels for X.</p>
 <p>Returns -1 for outliers and 1 for inliers.</p>
 <dl class="field-list">
diff --git a/dev/modules/generated/sklearn.base.RegressorMixin.html b/dev/modules/generated/sklearn.base.RegressorMixin.html
index 3d485fc04b6d2..36fb44b227081 100644
--- a/dev/modules/generated/sklearn.base.RegressorMixin.html
+++ b/dev/modules/generated/sklearn.base.RegressorMixin.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1308,7 +1308,7 @@
 <h1>RegressorMixin<a class="headerlink" href="#regressormixin" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.base.RegressorMixin">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.base.</span></span><span class="sig-name descname"><span class="pre">RegressorMixin</span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L575"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.base.RegressorMixin" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.base.</span></span><span class="sig-name descname"><span class="pre">RegressorMixin</span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L516"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.base.RegressorMixin" title="Link to this definition">#</a></dt>
 <dd><p>Mixin class for all regression estimators in scikit-learn.</p>
 <p>This mixin defines the following functionality:</p>
 <ul class="simple">
@@ -1341,7 +1341,7 @@ <h1>RegressorMixin<a class="headerlink" href="#regressormixin" title="Link to th
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.base.RegressorMixin.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L619"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.base.RegressorMixin.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L560"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.base.RegressorMixin.score" title="Link to this definition">#</a></dt>
 <dd><p>Return <a class="reference internal" href="http://github.com/model_evaluation.html#r2-score"><span class="std std-ref">coefficient of determination</span></a> on test data.</p>
 <p>The coefficient of determination, <span class="math notranslate nohighlight">\(R^2\)</span>, is defined as
 <span class="math notranslate nohighlight">\((1 - \frac{u}{v})\)</span>, where <span class="math notranslate nohighlight">\(u\)</span> is the residual
diff --git a/dev/modules/generated/sklearn.base.TransformerMixin.html b/dev/modules/generated/sklearn.base.TransformerMixin.html
index 2e9e49bd34c47..92323914a2e01 100644
--- a/dev/modules/generated/sklearn.base.TransformerMixin.html
+++ b/dev/modules/generated/sklearn.base.TransformerMixin.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>TransformerMixin<a class="headerlink" href="#transformermixin" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.base.TransformerMixin">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.base.</span></span><span class="sig-name descname"><span class="pre">TransformerMixin</span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L825"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.base.TransformerMixin" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.base.</span></span><span class="sig-name descname"><span class="pre">TransformerMixin</span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L766"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.base.TransformerMixin" title="Link to this definition">#</a></dt>
 <dd><p>Mixin class for all transformers in scikit-learn.</p>
 <p>This mixin defines the following functionality:</p>
 <ul class="simple">
@@ -1338,7 +1338,7 @@ <h1>TransformerMixin<a class="headerlink" href="#transformermixin" title="Link t
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.base.TransformerMixin.fit_transform">
-<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L863"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.base.TransformerMixin.fit_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L804"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.base.TransformerMixin.fit_transform" title="Link to this definition">#</a></dt>
 <dd><p>Fit to data, then transform it.</p>
 <p>Fits transformer to <code class="docutils literal notranslate"><span class="pre">X</span></code> and <code class="docutils literal notranslate"><span class="pre">y</span></code> with optional parameters <code class="docutils literal notranslate"><span class="pre">fit_params</span></code>
 and returns a transformed version of <code class="docutils literal notranslate"><span class="pre">X</span></code>.</p>
@@ -1364,7 +1364,7 @@ <h1>TransformerMixin<a class="headerlink" href="#transformermixin" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.base.TransformerMixin.set_output">
-<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.base.TransformerMixin.set_output" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.base.TransformerMixin.set_output" title="Link to this definition">#</a></dt>
 <dd><p>Set output container.</p>
 <p>See <a class="reference internal" href="http://github.com/auto_examples/miscellaneous/plot_set_output.html#sphx-glr-auto-examples-miscellaneous-plot-set-output-py"><span class="std std-ref">Introducing the set_output API</span></a>
 for an example on how to use the API.</p>
diff --git a/dev/modules/generated/sklearn.base.clone.html b/dev/modules/generated/sklearn.base.clone.html
index 0fc87779fd05a..163b08df63cee 100644
--- a/dev/modules/generated/sklearn.base.clone.html
+++ b/dev/modules/generated/sklearn.base.clone.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>clone<a class="headerlink" href="#clone" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.base.clone">
-<span class="sig-prename descclassname"><span class="pre">sklearn.base.</span></span><span class="sig-name descname"><span class="pre">clone</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">safe</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L44"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.base.clone" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.base.</span></span><span class="sig-name descname"><span class="pre">clone</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">safe</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L41"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.base.clone" title="Link to this definition">#</a></dt>
 <dd><p>Construct a new unfitted estimator with the same parameters.</p>
 <p>Clone does a deep copy of the model in an estimator
 without actually copying attached data. It returns a new estimator
diff --git a/dev/modules/generated/sklearn.base.is_classifier.html b/dev/modules/generated/sklearn.base.is_classifier.html
index c7ca5b7f34c24..212b7d2aeb325 100644
--- a/dev/modules/generated/sklearn.base.is_classifier.html
+++ b/dev/modules/generated/sklearn.base.is_classifier.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>is_classifier<a class="headerlink" href="#is-classifier" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.base.is_classifier">
-<span class="sig-prename descclassname"><span class="pre">sklearn.base.</span></span><span class="sig-name descname"><span class="pre">is_classifier</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L1200"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.base.is_classifier" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.base.</span></span><span class="sig-name descname"><span class="pre">is_classifier</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L1141"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.base.is_classifier" title="Link to this definition">#</a></dt>
 <dd><p>Return True if the given estimator is (probably) a classifier.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.base.is_clusterer.html b/dev/modules/generated/sklearn.base.is_clusterer.html
index fae25f07f88c7..f607b1adef21a 100644
--- a/dev/modules/generated/sklearn.base.is_clusterer.html
+++ b/dev/modules/generated/sklearn.base.is_clusterer.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>is_clusterer<a class="headerlink" href="#is-clusterer" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.base.is_clusterer">
-<span class="sig-prename descclassname"><span class="pre">sklearn.base.</span></span><span class="sig-name descname"><span class="pre">is_clusterer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L1280"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.base.is_clusterer" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.base.</span></span><span class="sig-name descname"><span class="pre">is_clusterer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L1221"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.base.is_clusterer" title="Link to this definition">#</a></dt>
 <dd><p>Return True if the given estimator is (probably) a clusterer.</p>
 <div class="versionadded">
 <p><span class="versionmodified added">Added in version 1.6.</span></p>
diff --git a/dev/modules/generated/sklearn.base.is_outlier_detector.html b/dev/modules/generated/sklearn.base.is_outlier_detector.html
index 0d0334cd220b0..9ffb832bc709e 100644
--- a/dev/modules/generated/sklearn.base.is_outlier_detector.html
+++ b/dev/modules/generated/sklearn.base.is_outlier_detector.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>is_outlier_detector<a class="headerlink" href="#is-outlier-detector" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.base.is_outlier_detector">
-<span class="sig-prename descclassname"><span class="pre">sklearn.base.</span></span><span class="sig-name descname"><span class="pre">is_outlier_detector</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L1322"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.base.is_outlier_detector" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.base.</span></span><span class="sig-name descname"><span class="pre">is_outlier_detector</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L1263"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.base.is_outlier_detector" title="Link to this definition">#</a></dt>
 <dd><p>Return True if the given estimator is (probably) an outlier detector.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.base.is_regressor.html b/dev/modules/generated/sklearn.base.is_regressor.html
index 6744aa8530ec2..8aa12114e0bfe 100644
--- a/dev/modules/generated/sklearn.base.is_regressor.html
+++ b/dev/modules/generated/sklearn.base.is_regressor.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>is_regressor<a class="headerlink" href="#is-regressor" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.base.is_regressor">
-<span class="sig-prename descclassname"><span class="pre">sklearn.base.</span></span><span class="sig-name descname"><span class="pre">is_regressor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L1240"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.base.is_regressor" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.base.</span></span><span class="sig-name descname"><span class="pre">is_regressor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L1181"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.base.is_regressor" title="Link to this definition">#</a></dt>
 <dd><p>Return True if the given estimator is (probably) a regressor.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.calibration.CalibratedClassifierCV.html b/dev/modules/generated/sklearn.calibration.CalibratedClassifierCV.html
index 877e72157d7d4..8118fe62fb964 100644
--- a/dev/modules/generated/sklearn.calibration.CalibratedClassifierCV.html
+++ b/dev/modules/generated/sklearn.calibration.CalibratedClassifierCV.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,10 +1307,10 @@
 <h1>CalibratedClassifierCV<a class="headerlink" href="#calibratedclassifiercv" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.calibration.CalibratedClassifierCV">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.calibration.</span></span><span class="sig-name descname"><span class="pre">CalibratedClassifierCV</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'sigmoid'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cv</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ensemble</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/calibration.py#L60"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.calibration.CalibratedClassifierCV" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.calibration.</span></span><span class="sig-name descname"><span class="pre">CalibratedClassifierCV</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'sigmoid'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cv</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ensemble</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/calibration.py#L60"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.calibration.CalibratedClassifierCV" title="Link to this definition">#</a></dt>
 <dd><p>Probability calibration with isotonic regression or logistic regression.</p>
 <p>This class uses cross-validation to both estimate the parameters of a
-classifier and subsequently calibrate a classifier. With default
+classifier and subsequently calibrate a classifier. With
 <code class="docutils literal notranslate"><span class="pre">ensemble=True</span></code>, for each cv split it
 fits a copy of the base estimator to the training subset, and calibrates it
 using the testing subset. For prediction, predicted probabilities are
@@ -1372,7 +1372,7 @@ <h1>CalibratedClassifierCV<a class="headerlink" href="#calibratedclassifiercv" t
 </div>
 </dd>
 <dt><strong>n_jobs</strong><span class="classifier">int, default=None</span></dt><dd><p>Number of jobs to run in parallel.
-<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.5.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
+<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.6.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
 <code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all processors.</p>
 <p>Base estimator clones are fitted in parallel across cross-validation
 iterations. Therefore parallelism happens only when <code class="docutils literal notranslate"><span class="pre">cv</span> <span class="pre">!=</span> <span class="pre">&quot;prefit&quot;</span></code>.</p>
@@ -1477,11 +1477,11 @@ <h1>CalibratedClassifierCV<a class="headerlink" href="#calibratedclassifiercv" t
 <span class="gp">&gt;&gt;&gt; </span><span class="nb">len</span><span class="p">(</span><span class="n">calibrated_clf</span><span class="o">.</span><span class="n">calibrated_classifiers_</span><span class="p">)</span>
 <span class="go">3</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">calibrated_clf</span><span class="o">.</span><span class="n">predict_proba</span><span class="p">(</span><span class="n">X</span><span class="p">)[:</span><span class="mi">5</span><span class="p">,</span> <span class="p">:]</span>
-<span class="go">array([[0.110..., 0.889...],</span>
-<span class="go">       [0.072..., 0.927...],</span>
-<span class="go">       [0.928..., 0.071...],</span>
-<span class="go">       [0.928..., 0.071...],</span>
-<span class="go">       [0.071..., 0.928...]])</span>
+<span class="go">array([[0.110, 0.889],</span>
+<span class="go">       [0.072, 0.927],</span>
+<span class="go">       [0.928, 0.072],</span>
+<span class="go">       [0.928, 0.072],</span>
+<span class="go">       [0.072, 0.928]])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.model_selection</span><span class="w"> </span><span class="kn">import</span> <span class="n">train_test_split</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">make_classification</span><span class="p">(</span><span class="n">n_samples</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">n_features</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
 <span class="gp">... </span>                           <span class="n">n_redundant</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">)</span>
@@ -1498,12 +1498,12 @@ <h1>CalibratedClassifierCV<a class="headerlink" href="#calibratedclassifiercv" t
 <span class="gp">&gt;&gt;&gt; </span><span class="nb">len</span><span class="p">(</span><span class="n">calibrated_clf</span><span class="o">.</span><span class="n">calibrated_classifiers_</span><span class="p">)</span>
 <span class="go">1</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">calibrated_clf</span><span class="o">.</span><span class="n">predict_proba</span><span class="p">([[</span><span class="o">-</span><span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">]])</span>
-<span class="go">array([[0.936..., 0.063...]])</span>
+<span class="go">array([[0.936, 0.063]])</span>
 </pre></div>
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.calibration.CalibratedClassifierCV.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/calibration.py#L292"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.calibration.CalibratedClassifierCV.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/calibration.py#L292"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.calibration.CalibratedClassifierCV.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit the calibrated model.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1530,7 +1530,7 @@ <h1>CalibratedClassifierCV<a class="headerlink" href="#calibratedclassifiercv" t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.calibration.CalibratedClassifierCV.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/calibration.py#L539"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.calibration.CalibratedClassifierCV.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/calibration.py#L539"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.calibration.CalibratedClassifierCV.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1547,7 +1547,7 @@ <h1>CalibratedClassifierCV<a class="headerlink" href="#calibratedclassifiercv" t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.calibration.CalibratedClassifierCV.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.calibration.CalibratedClassifierCV.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.calibration.CalibratedClassifierCV.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1568,7 +1568,7 @@ <h1>CalibratedClassifierCV<a class="headerlink" href="#calibratedclassifiercv" t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.calibration.CalibratedClassifierCV.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/calibration.py#L520"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.calibration.CalibratedClassifierCV.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/calibration.py#L520"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.calibration.CalibratedClassifierCV.predict" title="Link to this definition">#</a></dt>
 <dd><p>Predict the target of new samples.</p>
 <p>The predicted class is the class that has the highest probability,
 and can thus be different from the prediction of the uncalibrated classifier.</p>
@@ -1590,7 +1590,7 @@ <h1>CalibratedClassifierCV<a class="headerlink" href="#calibratedclassifiercv" t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.calibration.CalibratedClassifierCV.predict_proba">
-<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/calibration.py#L492"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.calibration.CalibratedClassifierCV.predict_proba" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/calibration.py#L492"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.calibration.CalibratedClassifierCV.predict_proba" title="Link to this definition">#</a></dt>
 <dd><p>Calibrated probabilities of classification.</p>
 <p>This function returns calibrated probabilities of classification
 according to each class on an array of test vectors X.</p>
@@ -1612,7 +1612,7 @@ <h1>CalibratedClassifierCV<a class="headerlink" href="#calibratedclassifiercv" t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.calibration.CalibratedClassifierCV.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L546"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.calibration.CalibratedClassifierCV.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L487"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.calibration.CalibratedClassifierCV.score" title="Link to this definition">#</a></dt>
 <dd><p>Return <a class="reference internal" href="http://github.com/model_evaluation.html#accuracy-score"><span class="std std-ref">accuracy</span></a> on provided data and labels.</p>
 <p>In multi-label classification, this is the subset accuracy
 which is a harsh metric since you require for each sample that
@@ -1639,7 +1639,7 @@ <h1>CalibratedClassifierCV<a class="headerlink" href="#calibratedclassifiercv" t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.calibration.CalibratedClassifierCV.set_fit_request">
-<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.calibration.CalibratedClassifierCV" title="sklearn.calibration.CalibratedClassifierCV"><span class="pre">CalibratedClassifierCV</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.calibration.CalibratedClassifierCV.set_fit_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.calibration.CalibratedClassifierCV" title="sklearn.calibration.CalibratedClassifierCV"><span class="pre">CalibratedClassifierCV</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.calibration.CalibratedClassifierCV.set_fit_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1682,7 +1682,7 @@ <h1>CalibratedClassifierCV<a class="headerlink" href="#calibratedclassifiercv" t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.calibration.CalibratedClassifierCV.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.calibration.CalibratedClassifierCV.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.calibration.CalibratedClassifierCV.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1706,7 +1706,7 @@ <h1>CalibratedClassifierCV<a class="headerlink" href="#calibratedclassifiercv" t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.calibration.CalibratedClassifierCV.set_score_request">
-<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.calibration.CalibratedClassifierCV" title="sklearn.calibration.CalibratedClassifierCV"><span class="pre">CalibratedClassifierCV</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.calibration.CalibratedClassifierCV.set_score_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.calibration.CalibratedClassifierCV" title="sklearn.calibration.CalibratedClassifierCV"><span class="pre">CalibratedClassifierCV</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.calibration.CalibratedClassifierCV.set_score_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
diff --git a/dev/modules/generated/sklearn.calibration.CalibrationDisplay.html b/dev/modules/generated/sklearn.calibration.CalibrationDisplay.html
index bae5728d83e1f..149d782dbe5a0 100644
--- a/dev/modules/generated/sklearn.calibration.CalibrationDisplay.html
+++ b/dev/modules/generated/sklearn.calibration.CalibrationDisplay.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>CalibrationDisplay<a class="headerlink" href="#calibrationdisplay" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.calibration.CalibrationDisplay">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.calibration.</span></span><span class="sig-name descname"><span class="pre">CalibrationDisplay</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">prob_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prob_pred</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_prob</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">estimator_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pos_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/calibration.py#L1066"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.calibration.CalibrationDisplay" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.calibration.</span></span><span class="sig-name descname"><span class="pre">CalibrationDisplay</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">prob_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prob_pred</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_prob</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">estimator_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pos_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/calibration.py#L1073"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.calibration.CalibrationDisplay" title="Link to this definition">#</a></dt>
 <dd><p>Calibration curve (also known as reliability diagram) visualization.</p>
 <p>It is recommended to use
 <a class="reference internal" href="#sklearn.calibration.CalibrationDisplay.from_estimator" title="sklearn.calibration.CalibrationDisplay.from_estimator"><code class="xref py py-func docutils literal notranslate"><span class="pre">from_estimator</span></code></a> or
@@ -1383,7 +1383,7 @@ <h1>CalibrationDisplay<a class="headerlink" href="#calibrationdisplay" title="Li
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.calibration.CalibrationDisplay.from_estimator">
-<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_estimator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_bins</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">strategy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'uniform'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pos_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ref_line</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/calibration.py#L1206"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.calibration.CalibrationDisplay.from_estimator" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_estimator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_bins</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">strategy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'uniform'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pos_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ref_line</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/calibration.py#L1213"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.calibration.CalibrationDisplay.from_estimator" title="Link to this definition">#</a></dt>
 <dd><p>Plot calibration curve using a binary classifier and data.</p>
 <p>A calibration curve, also known as a reliability diagram, uses inputs
 from a binary classifier and plots the average predicted probability
@@ -1475,7 +1475,7 @@ <h1>CalibrationDisplay<a class="headerlink" href="#calibrationdisplay" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.calibration.CalibrationDisplay.from_predictions">
-<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_predictions</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_prob</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_bins</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">strategy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'uniform'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pos_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ref_line</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/calibration.py#L1330"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.calibration.CalibrationDisplay.from_predictions" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_predictions</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_prob</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_bins</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">strategy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'uniform'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pos_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ref_line</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/calibration.py#L1337"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.calibration.CalibrationDisplay.from_predictions" title="Link to this definition">#</a></dt>
 <dd><p>Plot calibration curve using true labels and predicted probabilities.</p>
 <p>Calibration curve, also known as reliability diagram, uses inputs
 from a binary classifier and plots the average predicted probability
@@ -1562,7 +1562,7 @@ <h1>CalibrationDisplay<a class="headerlink" href="#calibrationdisplay" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.calibration.CalibrationDisplay.plot">
-<span class="sig-name descname"><span class="pre">plot</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ref_line</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/calibration.py#L1152"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.calibration.CalibrationDisplay.plot" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">plot</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ref_line</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/calibration.py#L1159"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.calibration.CalibrationDisplay.plot" title="Link to this definition">#</a></dt>
 <dd><p>Plot visualization.</p>
 <p>Extra keyword arguments will be passed to
 <a class="reference external" href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.plot.html#matplotlib.pyplot.plot" title="(in Matplotlib v3.10.1)"><code class="xref py py-func docutils literal notranslate"><span class="pre">matplotlib.pyplot.plot</span></code></a>.</p>
diff --git a/dev/modules/generated/sklearn.calibration.calibration_curve.html b/dev/modules/generated/sklearn.calibration.calibration_curve.html
index 2cef96acedcc9..48b996d36965e 100644
--- a/dev/modules/generated/sklearn.calibration.calibration_curve.html
+++ b/dev/modules/generated/sklearn.calibration.calibration_curve.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>calibration_curve<a class="headerlink" href="#calibration-curve" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.calibration.calibration_curve">
-<span class="sig-prename descclassname"><span class="pre">sklearn.calibration.</span></span><span class="sig-name descname"><span class="pre">calibration_curve</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_prob</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pos_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_bins</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">strategy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'uniform'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/calibration.py#L945"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.calibration.calibration_curve" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.calibration.</span></span><span class="sig-name descname"><span class="pre">calibration_curve</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_prob</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pos_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_bins</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">strategy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'uniform'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/calibration.py#L945"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.calibration.calibration_curve" title="Link to this definition">#</a></dt>
 <dd><p>Compute true and predicted probabilities for a calibration curve.</p>
 <p>The method assumes the inputs come from a binary classifier, and
 discretize the [0, 1] interval into bins.</p>
@@ -1348,6 +1348,15 @@ <h1>calibration_curve<a class="headerlink" href="#calibration-curve" title="Link
 </dl>
 </dd>
 </dl>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<dl class="simple">
+<dt><a class="reference internal" href="http://github.com/sklearn.calibration.CalibrationDisplay.html#sklearn.calibration.CalibrationDisplay.from_predictions" title="sklearn.calibration.CalibrationDisplay.from_predictions"><code class="xref py py-obj docutils literal notranslate"><span class="pre">CalibrationDisplay.from_predictions</span></code></a></dt><dd><p>Plot calibration curve using true and predicted labels.</p>
+</dd>
+<dt><a class="reference internal" href="http://github.com/sklearn.calibration.CalibrationDisplay.html#sklearn.calibration.CalibrationDisplay.from_estimator" title="sklearn.calibration.CalibrationDisplay.from_estimator"><code class="xref py py-obj docutils literal notranslate"><span class="pre">CalibrationDisplay.from_estimator</span></code></a></dt><dd><p>Plot calibration curve using an estimator and data.</p>
+</dd>
+</dl>
+</div>
 <p class="rubric">References</p>
 <p>Alexandru Niculescu-Mizil and Rich Caruana (2005) Predicting Good
 Probabilities With Supervised Learning, in Proceedings of the 22nd
diff --git a/dev/modules/generated/sklearn.cluster.AffinityPropagation.html b/dev/modules/generated/sklearn.cluster.AffinityPropagation.html
index 0a7ca5d35f83b..bfe08eef04938 100644
--- a/dev/modules/generated/sklearn.cluster.AffinityPropagation.html
+++ b/dev/modules/generated/sklearn.cluster.AffinityPropagation.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>AffinityPropagation<a class="headerlink" href="#affinitypropagation" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.cluster.AffinityPropagation">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.cluster.</span></span><span class="sig-name descname"><span class="pre">AffinityPropagation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">damping</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">200</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">convergence_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">15</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">preference</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">affinity</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'euclidean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/cluster/_affinity_propagation.py#L312"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.AffinityPropagation" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.cluster.</span></span><span class="sig-name descname"><span class="pre">AffinityPropagation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">damping</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">200</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">convergence_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">15</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">preference</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">affinity</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'euclidean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/cluster/_affinity_propagation.py#L312"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.AffinityPropagation" title="Link to this definition">#</a></dt>
 <dd><p>Perform Affinity Propagation Clustering of data.</p>
 <p>Read more in the <a class="reference internal" href="http://github.com/clustering.html#affinity-propagation"><span class="std std-ref">User Guide</span></a>.</p>
 <dl class="field-list">
@@ -1432,7 +1432,7 @@ <h1>AffinityPropagation<a class="headerlink" href="#affinitypropagation" title="
 <a class="reference internal" href="http://github.com/auto_examples/cluster/plot_cluster_comparison.html#sphx-glr-auto-examples-cluster-plot-cluster-comparison-py"><span class="std std-ref">Comparing different clustering algorithms on toy datasets</span></a></p>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.AffinityPropagation.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/cluster/_affinity_propagation.py#L492"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.AffinityPropagation.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/cluster/_affinity_propagation.py#L492"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.AffinityPropagation.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit the clustering from features, or affinity matrix.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1456,7 +1456,7 @@ <h1>AffinityPropagation<a class="headerlink" href="#affinitypropagation" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.AffinityPropagation.fit_predict">
-<span class="sig-name descname"><span class="pre">fit_predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/cluster/_affinity_propagation.py#L588"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.AffinityPropagation.fit_predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/cluster/_affinity_propagation.py#L588"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.AffinityPropagation.fit_predict" title="Link to this definition">#</a></dt>
 <dd><p>Fit clustering from features/affinity matrix; return cluster labels.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1480,7 +1480,7 @@ <h1>AffinityPropagation<a class="headerlink" href="#affinitypropagation" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.AffinityPropagation.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.AffinityPropagation.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.AffinityPropagation.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1497,7 +1497,7 @@ <h1>AffinityPropagation<a class="headerlink" href="#affinitypropagation" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.AffinityPropagation.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.AffinityPropagation.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.AffinityPropagation.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1518,7 +1518,7 @@ <h1>AffinityPropagation<a class="headerlink" href="#affinitypropagation" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.AffinityPropagation.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/cluster/_affinity_propagation.py#L553"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.AffinityPropagation.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/cluster/_affinity_propagation.py#L553"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.AffinityPropagation.predict" title="Link to this definition">#</a></dt>
 <dd><p>Predict the closest cluster each sample in X belongs to.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1539,7 +1539,7 @@ <h1>AffinityPropagation<a class="headerlink" href="#affinitypropagation" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.AffinityPropagation.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.AffinityPropagation.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.AffinityPropagation.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
diff --git a/dev/modules/generated/sklearn.cluster.AgglomerativeClustering.html b/dev/modules/generated/sklearn.cluster.AgglomerativeClustering.html
index 67f9ca057116c..add2cc256e925 100644
--- a/dev/modules/generated/sklearn.cluster.AgglomerativeClustering.html
+++ b/dev/modules/generated/sklearn.cluster.AgglomerativeClustering.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>AgglomerativeClustering<a class="headerlink" href="#agglomerativeclustering" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.cluster.AgglomerativeClustering">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.cluster.</span></span><span class="sig-name descname"><span class="pre">AgglomerativeClustering</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_clusters</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'euclidean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">memory</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">connectivity</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">compute_full_tree</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">linkage</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'ward'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">distance_threshold</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">compute_distances</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/cluster/_agglomerative.py#L779"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.AgglomerativeClustering" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.cluster.</span></span><span class="sig-name descname"><span class="pre">AgglomerativeClustering</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_clusters</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'euclidean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">memory</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">connectivity</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">compute_full_tree</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">linkage</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'ward'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">distance_threshold</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">compute_distances</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/cluster/_agglomerative.py#L779"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.AgglomerativeClustering" title="Link to this definition">#</a></dt>
 <dd><p>Agglomerative Clustering.</p>
 <p>Recursively merges pair of clusters of sample data; uses linkage distance.</p>
 <p>Read more in the <a class="reference internal" href="http://github.com/clustering.html#hierarchical-clustering"><span class="std std-ref">User Guide</span></a>.</p>
@@ -1455,7 +1455,7 @@ <h1>AgglomerativeClustering<a class="headerlink" href="#agglomerativeclustering"
 <a class="reference internal" href="http://github.com/auto_examples/cluster/plot_cluster_comparison.html#sphx-glr-auto-examples-cluster-plot-cluster-comparison-py"><span class="std std-ref">Comparing different clustering algorithms on toy datasets</span></a></p>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.AgglomerativeClustering.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/cluster/_agglomerative.py#L971"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.AgglomerativeClustering.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/cluster/_agglomerative.py#L971"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.AgglomerativeClustering.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit the hierarchical clustering from features, or distance matrix.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1478,7 +1478,7 @@ <h1>AgglomerativeClustering<a class="headerlink" href="#agglomerativeclustering"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.AgglomerativeClustering.fit_predict">
-<span class="sig-name descname"><span class="pre">fit_predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/cluster/_agglomerative.py#L1095"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.AgglomerativeClustering.fit_predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/cluster/_agglomerative.py#L1095"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.AgglomerativeClustering.fit_predict" title="Link to this definition">#</a></dt>
 <dd><p>Fit and return the result of each sample’s clustering assignment.</p>
 <p>In addition to fitting, this method also return the result of the
 clustering assignment for each sample in the training set.</p>
@@ -1503,7 +1503,7 @@ <h1>AgglomerativeClustering<a class="headerlink" href="#agglomerativeclustering"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.AgglomerativeClustering.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.AgglomerativeClustering.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.AgglomerativeClustering.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1520,7 +1520,7 @@ <h1>AgglomerativeClustering<a class="headerlink" href="#agglomerativeclustering"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.AgglomerativeClustering.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.AgglomerativeClustering.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.AgglomerativeClustering.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1541,7 +1541,7 @@ <h1>AgglomerativeClustering<a class="headerlink" href="#agglomerativeclustering"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.AgglomerativeClustering.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.AgglomerativeClustering.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.AgglomerativeClustering.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
diff --git a/dev/modules/generated/sklearn.cluster.Birch.html b/dev/modules/generated/sklearn.cluster.Birch.html
index ed424184a8b04..4c060c81f3ce7 100644
--- a/dev/modules/generated/sklearn.cluster.Birch.html
+++ b/dev/modules/generated/sklearn.cluster.Birch.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>Birch<a class="headerlink" href="#birch" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.cluster.Birch">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.cluster.</span></span><span class="sig-name descname"><span class="pre">Birch</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">threshold</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">branching_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">50</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_clusters</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">compute_labels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'deprecated'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/cluster/_birch.py#L359"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.Birch" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.cluster.</span></span><span class="sig-name descname"><span class="pre">Birch</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">threshold</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">branching_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">50</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_clusters</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">compute_labels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'deprecated'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/cluster/_birch.py#L359"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.Birch" title="Link to this definition">#</a></dt>
 <dd><p>Implements the BIRCH clustering algorithm.</p>
 <p>It is a memory-efficient, online-learning algorithm provided as an
 alternative to <a class="reference internal" href="http://github.com/sklearn.cluster.MiniBatchKMeans.html#sklearn.cluster.MiniBatchKMeans" title="sklearn.cluster.MiniBatchKMeans"><code class="xref py py-class docutils literal notranslate"><span class="pre">MiniBatchKMeans</span></code></a>. It constructs a tree
@@ -1427,7 +1427,7 @@ <h1>Birch<a class="headerlink" href="#birch" title="Link to this heading">#</a><
 see <a class="reference internal" href="http://github.com/auto_examples/cluster/plot_cluster_comparison.html#sphx-glr-auto-examples-cluster-plot-cluster-comparison-py"><span class="std std-ref">Comparing different clustering algorithms on toy datasets</span></a></p>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.Birch.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/cluster/_birch.py#L514"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.Birch.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/cluster/_birch.py#L514"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.Birch.fit" title="Link to this definition">#</a></dt>
 <dd><p>Build a CF Tree for the input data.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1449,7 +1449,7 @@ <h1>Birch<a class="headerlink" href="#birch" title="Link to this heading">#</a><
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.Birch.fit_predict">
-<span class="sig-name descname"><span class="pre">fit_predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L695"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.Birch.fit_predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L636"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.Birch.fit_predict" title="Link to this definition">#</a></dt>
 <dd><p>Perform clustering on <code class="docutils literal notranslate"><span class="pre">X</span></code> and returns cluster labels.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1476,7 +1476,7 @@ <h1>Birch<a class="headerlink" href="#birch" title="Link to this heading">#</a><
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.Birch.fit_transform">
-<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L863"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.Birch.fit_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L804"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.Birch.fit_transform" title="Link to this definition">#</a></dt>
 <dd><p>Fit to data, then transform it.</p>
 <p>Fits transformer to <code class="docutils literal notranslate"><span class="pre">X</span></code> and <code class="docutils literal notranslate"><span class="pre">y</span></code> with optional parameters <code class="docutils literal notranslate"><span class="pre">fit_params</span></code>
 and returns a transformed version of <code class="docutils literal notranslate"><span class="pre">X</span></code>.</p>
@@ -1502,7 +1502,7 @@ <h1>Birch<a class="headerlink" href="#birch" title="Link to this heading">#</a><
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.Birch.get_feature_names_out">
-<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L995"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.Birch.get_feature_names_out" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L936"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.Birch.get_feature_names_out" title="Link to this definition">#</a></dt>
 <dd><p>Get output feature names for transformation.</p>
 <p>The feature names out will prefixed by the lowercased class name. For
 example, if the transformer outputs 3 features, then the feature names
@@ -1525,7 +1525,7 @@ <h1>Birch<a class="headerlink" href="#birch" title="Link to this heading">#</a><
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.Birch.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.Birch.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.Birch.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1542,7 +1542,7 @@ <h1>Birch<a class="headerlink" href="#birch" title="Link to this heading">#</a><
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.Birch.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.Birch.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.Birch.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1563,7 +1563,7 @@ <h1>Birch<a class="headerlink" href="#birch" title="Link to this heading">#</a><
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.Birch.partial_fit">
-<span class="sig-name descname"><span class="pre">partial_fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/cluster/_birch.py#L629"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.Birch.partial_fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">partial_fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/cluster/_birch.py#L629"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.Birch.partial_fit" title="Link to this definition">#</a></dt>
 <dd><p>Online learning. Prevents rebuilding of CFTree from scratch.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1586,7 +1586,7 @@ <h1>Birch<a class="headerlink" href="#birch" title="Link to this heading">#</a><
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.Birch.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/cluster/_birch.py#L656"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.Birch.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/cluster/_birch.py#L656"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.Birch.predict" title="Link to this definition">#</a></dt>
 <dd><p>Predict data using the <code class="docutils literal notranslate"><span class="pre">centroids_</span></code> of subclusters.</p>
 <p>Avoid computation of the row norms of X.</p>
 <dl class="field-list simple">
@@ -1607,7 +1607,7 @@ <h1>Birch<a class="headerlink" href="#birch" title="Link to this heading">#</a><
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.Birch.set_output">
-<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.Birch.set_output" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.Birch.set_output" title="Link to this definition">#</a></dt>
 <dd><p>Set output container.</p>
 <p>See <a class="reference internal" href="http://github.com/auto_examples/miscellaneous/plot_set_output.html#sphx-glr-auto-examples-miscellaneous-plot-set-output-py"><span class="std std-ref">Introducing the set_output API</span></a>
 for an example on how to use the API.</p>
@@ -1638,7 +1638,7 @@ <h1>Birch<a class="headerlink" href="#birch" title="Link to this heading">#</a><
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.Birch.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.Birch.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.Birch.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1662,7 +1662,7 @@ <h1>Birch<a class="headerlink" href="#birch" title="Link to this heading">#</a><
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.Birch.transform">
-<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/cluster/_birch.py#L686"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.Birch.transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/cluster/_birch.py#L686"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.Birch.transform" title="Link to this definition">#</a></dt>
 <dd><p>Transform X into subcluster centroids dimension.</p>
 <p>Each dimension represents the distance from the sample point to each
 cluster centroid.</p>
diff --git a/dev/modules/generated/sklearn.cluster.BisectingKMeans.html b/dev/modules/generated/sklearn.cluster.BisectingKMeans.html
index f03d6925dd0a3..2f24e839e728d 100644
--- a/dev/modules/generated/sklearn.cluster.BisectingKMeans.html
+++ b/dev/modules/generated/sklearn.cluster.BisectingKMeans.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>BisectingKMeans<a class="headerlink" href="#bisectingkmeans" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.cluster.BisectingKMeans">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.cluster.</span></span><span class="sig-name descname"><span class="pre">BisectingKMeans</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_clusters</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">8</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'random'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">300</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy_x</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">algorithm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'lloyd'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bisecting_strategy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'biggest_inertia'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/cluster/_bisect_k_means.py#L83"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.BisectingKMeans" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.cluster.</span></span><span class="sig-name descname"><span class="pre">BisectingKMeans</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_clusters</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">8</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'random'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">300</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy_x</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">algorithm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'lloyd'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bisecting_strategy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'biggest_inertia'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/cluster/_bisect_k_means.py#L83"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.BisectingKMeans" title="Link to this definition">#</a></dt>
 <dd><p>Bisecting K-Means clustering.</p>
 <p>Read more in the <a class="reference internal" href="http://github.com/clustering.html#bisect-k-means"><span class="std std-ref">User Guide</span></a>.</p>
 <div class="versionadded">
@@ -1428,7 +1428,7 @@ <h1>BisectingKMeans<a class="headerlink" href="#bisectingkmeans" title="Link to
 <a class="reference internal" href="http://github.com/auto_examples/cluster/plot_bisect_kmeans.html#sphx-glr-auto-examples-cluster-plot-bisect-kmeans-py"><span class="std std-ref">Bisecting K-Means and Regular K-Means Performance Comparison</span></a>.</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.BisectingKMeans.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/cluster/_bisect_k_means.py#L362"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.BisectingKMeans.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/cluster/_bisect_k_means.py#L362"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.BisectingKMeans.fit" title="Link to this definition">#</a></dt>
 <dd><p>Compute bisecting k-means clustering.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1460,7 +1460,7 @@ <h1>BisectingKMeans<a class="headerlink" href="#bisectingkmeans" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.BisectingKMeans.fit_predict">
-<span class="sig-name descname"><span class="pre">fit_predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/cluster/_kmeans.py#L1041"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.BisectingKMeans.fit_predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/cluster/_kmeans.py#L1041"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.BisectingKMeans.fit_predict" title="Link to this definition">#</a></dt>
 <dd><p>Compute cluster centers and predict cluster index for each sample.</p>
 <p>Convenience method; equivalent to calling fit(X) followed by
 predict(X).</p>
@@ -1487,7 +1487,7 @@ <h1>BisectingKMeans<a class="headerlink" href="#bisectingkmeans" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.BisectingKMeans.fit_transform">
-<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/cluster/_kmeans.py#L1100"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.BisectingKMeans.fit_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/cluster/_kmeans.py#L1100"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.BisectingKMeans.fit_transform" title="Link to this definition">#</a></dt>
 <dd><p>Compute clustering and transform X to cluster-distance space.</p>
 <p>Equivalent to fit(X).transform(X), but more efficiently implemented.</p>
 <dl class="field-list simple">
@@ -1513,7 +1513,7 @@ <h1>BisectingKMeans<a class="headerlink" href="#bisectingkmeans" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.BisectingKMeans.get_feature_names_out">
-<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L995"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.BisectingKMeans.get_feature_names_out" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L936"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.BisectingKMeans.get_feature_names_out" title="Link to this definition">#</a></dt>
 <dd><p>Get output feature names for transformation.</p>
 <p>The feature names out will prefixed by the lowercased class name. For
 example, if the transformer outputs 3 features, then the feature names
@@ -1536,7 +1536,7 @@ <h1>BisectingKMeans<a class="headerlink" href="#bisectingkmeans" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.BisectingKMeans.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.BisectingKMeans.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.BisectingKMeans.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1553,7 +1553,7 @@ <h1>BisectingKMeans<a class="headerlink" href="#bisectingkmeans" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.BisectingKMeans.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.BisectingKMeans.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.BisectingKMeans.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1574,7 +1574,7 @@ <h1>BisectingKMeans<a class="headerlink" href="#bisectingkmeans" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.BisectingKMeans.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/cluster/_bisect_k_means.py#L456"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.BisectingKMeans.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/cluster/_bisect_k_means.py#L456"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.BisectingKMeans.predict" title="Link to this definition">#</a></dt>
 <dd><p>Predict which cluster each sample in X belongs to.</p>
 <p>Prediction is made by going down the hierarchical tree
 in searching of closest leaf cluster.</p>
@@ -1599,7 +1599,7 @@ <h1>BisectingKMeans<a class="headerlink" href="#bisectingkmeans" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.BisectingKMeans.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/cluster/_kmeans.py#L1150"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.BisectingKMeans.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/cluster/_kmeans.py#L1150"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.BisectingKMeans.score" title="Link to this definition">#</a></dt>
 <dd><p>Opposite of the value of X on the K-means objective.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1624,7 +1624,7 @@ <h1>BisectingKMeans<a class="headerlink" href="#bisectingkmeans" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.BisectingKMeans.set_fit_request">
-<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.cluster.BisectingKMeans" title="sklearn.cluster._bisect_k_means.BisectingKMeans"><span class="pre">BisectingKMeans</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.BisectingKMeans.set_fit_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.cluster.BisectingKMeans" title="sklearn.cluster._bisect_k_means.BisectingKMeans"><span class="pre">BisectingKMeans</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.BisectingKMeans.set_fit_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1667,7 +1667,7 @@ <h1>BisectingKMeans<a class="headerlink" href="#bisectingkmeans" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.BisectingKMeans.set_output">
-<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.BisectingKMeans.set_output" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.BisectingKMeans.set_output" title="Link to this definition">#</a></dt>
 <dd><p>Set output container.</p>
 <p>See <a class="reference internal" href="http://github.com/auto_examples/miscellaneous/plot_set_output.html#sphx-glr-auto-examples-miscellaneous-plot-set-output-py"><span class="std std-ref">Introducing the set_output API</span></a>
 for an example on how to use the API.</p>
@@ -1698,7 +1698,7 @@ <h1>BisectingKMeans<a class="headerlink" href="#bisectingkmeans" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.BisectingKMeans.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.BisectingKMeans.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.BisectingKMeans.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1722,7 +1722,7 @@ <h1>BisectingKMeans<a class="headerlink" href="#bisectingkmeans" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.BisectingKMeans.set_score_request">
-<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.cluster.BisectingKMeans" title="sklearn.cluster._bisect_k_means.BisectingKMeans"><span class="pre">BisectingKMeans</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.BisectingKMeans.set_score_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.cluster.BisectingKMeans" title="sklearn.cluster._bisect_k_means.BisectingKMeans"><span class="pre">BisectingKMeans</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.BisectingKMeans.set_score_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1765,7 +1765,7 @@ <h1>BisectingKMeans<a class="headerlink" href="#bisectingkmeans" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.BisectingKMeans.transform">
-<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/cluster/_kmeans.py#L1124"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.BisectingKMeans.transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/cluster/_kmeans.py#L1124"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.BisectingKMeans.transform" title="Link to this definition">#</a></dt>
 <dd><p>Transform X to a cluster-distance space.</p>
 <p>In the new space, each dimension is the distance to the cluster
 centers. Note that even if X is sparse, the array returned by
diff --git a/dev/modules/generated/sklearn.cluster.DBSCAN.html b/dev/modules/generated/sklearn.cluster.DBSCAN.html
index ad0bac18f55c7..9080ade4c550b 100644
--- a/dev/modules/generated/sklearn.cluster.DBSCAN.html
+++ b/dev/modules/generated/sklearn.cluster.DBSCAN.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1308,7 +1308,7 @@
 <h1>DBSCAN<a class="headerlink" href="#dbscan" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.cluster.DBSCAN">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.cluster.</span></span><span class="sig-name descname"><span class="pre">DBSCAN</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'euclidean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric_params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">algorithm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">leaf_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">30</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/cluster/_dbscan.py#L181"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.DBSCAN" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.cluster.</span></span><span class="sig-name descname"><span class="pre">DBSCAN</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'euclidean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric_params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">algorithm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">leaf_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">30</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/cluster/_dbscan.py#L181"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.DBSCAN" title="Link to this definition">#</a></dt>
 <dd><p>Perform DBSCAN clustering from vector array or distance matrix.</p>
 <p>DBSCAN - Density-Based Spatial Clustering of Applications with Noise.
 Finds core samples of high density and expands clusters from them.
@@ -1363,7 +1363,7 @@ <h1>DBSCAN<a class="headerlink" href="#dbscan" title="Link to this heading">#</a
 distance).</p>
 </dd>
 <dt><strong>n_jobs</strong><span class="classifier">int, default=None</span></dt><dd><p>The number of parallel jobs to run.
-<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.5.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
+<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.6.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
 <code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all processors. See <a class="reference internal" href="http://github.com/glossary.html#term-n_jobs"><span class="xref std std-term">Glossary</span></a>
 for more details.</p>
 </dd>
@@ -1439,7 +1439,7 @@ <h1>DBSCAN<a class="headerlink" href="#dbscan" title="Link to this heading">#</a
 <a class="reference internal" href="http://github.com/auto_examples/cluster/plot_cluster_comparison.html#sphx-glr-auto-examples-cluster-plot-cluster-comparison-py"><span class="std std-ref">Comparing different clustering algorithms on toy datasets</span></a></p>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.DBSCAN.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/cluster/_dbscan.py#L365"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.DBSCAN.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/cluster/_dbscan.py#L365"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.DBSCAN.fit" title="Link to this definition">#</a></dt>
 <dd><p>Perform DBSCAN clustering from features, or distance matrix.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1468,7 +1468,7 @@ <h1>DBSCAN<a class="headerlink" href="#dbscan" title="Link to this heading">#</a
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.DBSCAN.fit_predict">
-<span class="sig-name descname"><span class="pre">fit_predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/cluster/_dbscan.py#L448"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.DBSCAN.fit_predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/cluster/_dbscan.py#L448"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.DBSCAN.fit_predict" title="Link to this definition">#</a></dt>
 <dd><p>Compute clusters from a data or distance matrix and predict labels.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1497,7 +1497,7 @@ <h1>DBSCAN<a class="headerlink" href="#dbscan" title="Link to this heading">#</a
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.DBSCAN.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.DBSCAN.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.DBSCAN.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1514,7 +1514,7 @@ <h1>DBSCAN<a class="headerlink" href="#dbscan" title="Link to this heading">#</a
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.DBSCAN.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.DBSCAN.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.DBSCAN.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1535,7 +1535,7 @@ <h1>DBSCAN<a class="headerlink" href="#dbscan" title="Link to this heading">#</a
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.DBSCAN.set_fit_request">
-<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.cluster.DBSCAN" title="sklearn.cluster._dbscan.DBSCAN"><span class="pre">DBSCAN</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.DBSCAN.set_fit_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.cluster.DBSCAN" title="sklearn.cluster._dbscan.DBSCAN"><span class="pre">DBSCAN</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.DBSCAN.set_fit_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1578,7 +1578,7 @@ <h1>DBSCAN<a class="headerlink" href="#dbscan" title="Link to this heading">#</a
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.DBSCAN.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.DBSCAN.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.DBSCAN.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
diff --git a/dev/modules/generated/sklearn.cluster.FeatureAgglomeration.html b/dev/modules/generated/sklearn.cluster.FeatureAgglomeration.html
index c86d586c341b2..1ac647c55c429 100644
--- a/dev/modules/generated/sklearn.cluster.FeatureAgglomeration.html
+++ b/dev/modules/generated/sklearn.cluster.FeatureAgglomeration.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>FeatureAgglomeration<a class="headerlink" href="#featureagglomeration" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.cluster.FeatureAgglomeration">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.cluster.</span></span><span class="sig-name descname"><span class="pre">FeatureAgglomeration</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_clusters=2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric='euclidean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">memory=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">connectivity=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">compute_full_tree='auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">linkage='ward'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pooling_func=&lt;function</span> <span class="pre">mean&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">distance_threshold=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">compute_distances=False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/cluster/_agglomerative.py#L1119"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.FeatureAgglomeration" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.cluster.</span></span><span class="sig-name descname"><span class="pre">FeatureAgglomeration</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_clusters=2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric='euclidean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">memory=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">connectivity=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">compute_full_tree='auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">linkage='ward'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pooling_func=&lt;function</span> <span class="pre">mean&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">distance_threshold=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">compute_distances=False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/cluster/_agglomerative.py#L1119"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.FeatureAgglomeration" title="Link to this definition">#</a></dt>
 <dd><p>Agglomerate features.</p>
 <p>Recursively merges pair of clusters of features.</p>
 <p>Refer to
@@ -1449,7 +1449,7 @@ <h1>FeatureAgglomeration<a class="headerlink" href="#featureagglomeration" title
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.FeatureAgglomeration.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/cluster/_agglomerative.py#L1308"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.FeatureAgglomeration.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/cluster/_agglomerative.py#L1308"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.FeatureAgglomeration.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit the hierarchical clustering on the data.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1477,7 +1477,7 @@ <h1>FeatureAgglomeration<a class="headerlink" href="#featureagglomeration" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.FeatureAgglomeration.fit_transform">
-<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L863"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.FeatureAgglomeration.fit_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L804"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.FeatureAgglomeration.fit_transform" title="Link to this definition">#</a></dt>
 <dd><p>Fit to data, then transform it.</p>
 <p>Fits transformer to <code class="docutils literal notranslate"><span class="pre">X</span></code> and <code class="docutils literal notranslate"><span class="pre">y</span></code> with optional parameters <code class="docutils literal notranslate"><span class="pre">fit_params</span></code>
 and returns a transformed version of <code class="docutils literal notranslate"><span class="pre">X</span></code>.</p>
@@ -1503,7 +1503,7 @@ <h1>FeatureAgglomeration<a class="headerlink" href="#featureagglomeration" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.FeatureAgglomeration.get_feature_names_out">
-<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L995"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.FeatureAgglomeration.get_feature_names_out" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L936"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.FeatureAgglomeration.get_feature_names_out" title="Link to this definition">#</a></dt>
 <dd><p>Get output feature names for transformation.</p>
 <p>The feature names out will prefixed by the lowercased class name. For
 example, if the transformer outputs 3 features, then the feature names
@@ -1526,7 +1526,7 @@ <h1>FeatureAgglomeration<a class="headerlink" href="#featureagglomeration" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.FeatureAgglomeration.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.FeatureAgglomeration.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.FeatureAgglomeration.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1543,7 +1543,7 @@ <h1>FeatureAgglomeration<a class="headerlink" href="#featureagglomeration" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.FeatureAgglomeration.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.FeatureAgglomeration.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.FeatureAgglomeration.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1564,7 +1564,7 @@ <h1>FeatureAgglomeration<a class="headerlink" href="#featureagglomeration" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.FeatureAgglomeration.inverse_transform">
-<span class="sig-name descname"><span class="pre">inverse_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/cluster/_feature_agglomeration.py#L59"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.FeatureAgglomeration.inverse_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">inverse_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/cluster/_feature_agglomeration.py#L58"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.FeatureAgglomeration.inverse_transform" title="Link to this definition">#</a></dt>
 <dd><p>Inverse the transformation and return a vector of size <code class="docutils literal notranslate"><span class="pre">n_features</span></code>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1575,7 +1575,7 @@ <h1>FeatureAgglomeration<a class="headerlink" href="#featureagglomeration" title
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><dl class="simple">
-<dt><strong>X</strong><span class="classifier">ndarray of shape (n_samples, n_features) or (n_features,)</span></dt><dd><p>A vector of size <code class="docutils literal notranslate"><span class="pre">n_samples</span></code> with the values of <code class="docutils literal notranslate"><span class="pre">Xred</span></code> assigned to
+<dt><strong>X_original</strong><span class="classifier">ndarray of shape (n_samples, n_features) or (n_features,)</span></dt><dd><p>A vector of size <code class="docutils literal notranslate"><span class="pre">n_samples</span></code> with the values of <code class="docutils literal notranslate"><span class="pre">X</span></code> assigned to
 each of the cluster of samples.</p>
 </dd>
 </dl>
@@ -1585,7 +1585,7 @@ <h1>FeatureAgglomeration<a class="headerlink" href="#featureagglomeration" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.FeatureAgglomeration.set_output">
-<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.FeatureAgglomeration.set_output" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.FeatureAgglomeration.set_output" title="Link to this definition">#</a></dt>
 <dd><p>Set output container.</p>
 <p>See <a class="reference internal" href="http://github.com/auto_examples/miscellaneous/plot_set_output.html#sphx-glr-auto-examples-miscellaneous-plot-set-output-py"><span class="std std-ref">Introducing the set_output API</span></a>
 for an example on how to use the API.</p>
@@ -1616,7 +1616,7 @@ <h1>FeatureAgglomeration<a class="headerlink" href="#featureagglomeration" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.FeatureAgglomeration.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.FeatureAgglomeration.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.FeatureAgglomeration.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1640,7 +1640,7 @@ <h1>FeatureAgglomeration<a class="headerlink" href="#featureagglomeration" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.FeatureAgglomeration.transform">
-<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/cluster/_feature_agglomeration.py#L25"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.FeatureAgglomeration.transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/cluster/_feature_agglomeration.py#L24"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.FeatureAgglomeration.transform" title="Link to this definition">#</a></dt>
 <dd><p>Transform a new matrix using the built clustering.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.cluster.HDBSCAN.html b/dev/modules/generated/sklearn.cluster.HDBSCAN.html
index 25d788d13b27a..9c76df62bc542 100644
--- a/dev/modules/generated/sklearn.cluster.HDBSCAN.html
+++ b/dev/modules/generated/sklearn.cluster.HDBSCAN.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>HDBSCAN<a class="headerlink" href="#hdbscan" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.cluster.HDBSCAN">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.cluster.</span></span><span class="sig-name descname"><span class="pre">HDBSCAN</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">min_cluster_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cluster_selection_epsilon</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_cluster_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'euclidean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric_params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">algorithm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">leaf_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">40</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cluster_selection_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'eom'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">allow_single_cluster</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">store_centers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/cluster/_hdbscan/hdbscan.py#L419"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.HDBSCAN" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.cluster.</span></span><span class="sig-name descname"><span class="pre">HDBSCAN</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">min_cluster_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cluster_selection_epsilon</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_cluster_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'euclidean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric_params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">algorithm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">leaf_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">40</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cluster_selection_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'eom'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">allow_single_cluster</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">store_centers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/cluster/_hdbscan/hdbscan.py#L419"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.HDBSCAN" title="Link to this definition">#</a></dt>
 <dd><p>Cluster data using hierarchical density-based clustering.</p>
 <p>HDBSCAN - Hierarchical Density-Based Spatial Clustering of Applications
 with Noise. Performs <a class="reference internal" href="http://github.com/sklearn.cluster.DBSCAN.html#sklearn.cluster.DBSCAN" title="sklearn.cluster.DBSCAN"><code class="xref py py-class docutils literal notranslate"><span class="pre">DBSCAN</span></code></a> over varying epsilon
@@ -1370,7 +1370,7 @@ <h1>HDBSCAN<a class="headerlink" href="#hdbscan" title="Link to this heading">#<
 parameter. Ignored for <code class="docutils literal notranslate"><span class="pre">algorithm=&quot;brute&quot;</span></code>.</p>
 </dd>
 <dt><strong>n_jobs</strong><span class="classifier">int, default=None</span></dt><dd><p>Number of jobs to run in parallel to calculate distances.
-<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.5.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
+<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.6.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
 <code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all processors. See <a class="reference internal" href="http://github.com/glossary.html#term-n_jobs"><span class="xref std std-term">Glossary</span></a>
 for more details.</p>
 </dd>
@@ -1514,7 +1514,7 @@ <h1>HDBSCAN<a class="headerlink" href="#hdbscan" title="Link to this heading">#<
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.HDBSCAN.dbscan_clustering">
-<span class="sig-name descname"><span class="pre">dbscan_clustering</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">cut_distance</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_cluster_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/cluster/_hdbscan/hdbscan.py#L947"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.HDBSCAN.dbscan_clustering" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">dbscan_clustering</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">cut_distance</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_cluster_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/cluster/_hdbscan/hdbscan.py#L947"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.HDBSCAN.dbscan_clustering" title="Link to this definition">#</a></dt>
 <dd><p>Return clustering given by DBSCAN without border points.</p>
 <p>Return clustering that would be equivalent to running DBSCAN* for a
 particular cut_distance (or epsilon) DBSCAN* can be thought of as
@@ -1555,7 +1555,7 @@ <h1>HDBSCAN<a class="headerlink" href="#hdbscan" title="Link to this heading">#<
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.HDBSCAN.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/cluster/_hdbscan/hdbscan.py#L689"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.HDBSCAN.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/cluster/_hdbscan/hdbscan.py#L689"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.HDBSCAN.fit" title="Link to this definition">#</a></dt>
 <dd><p>Find clusters based on hierarchical density-based clustering.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1578,7 +1578,7 @@ <h1>HDBSCAN<a class="headerlink" href="#hdbscan" title="Link to this heading">#<
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.HDBSCAN.fit_predict">
-<span class="sig-name descname"><span class="pre">fit_predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/cluster/_hdbscan/hdbscan.py#L882"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.HDBSCAN.fit_predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/cluster/_hdbscan/hdbscan.py#L882"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.HDBSCAN.fit_predict" title="Link to this definition">#</a></dt>
 <dd><p>Cluster X and return the associated cluster labels.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1601,7 +1601,7 @@ <h1>HDBSCAN<a class="headerlink" href="#hdbscan" title="Link to this heading">#<
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.HDBSCAN.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.HDBSCAN.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.HDBSCAN.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1618,7 +1618,7 @@ <h1>HDBSCAN<a class="headerlink" href="#hdbscan" title="Link to this heading">#<
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.HDBSCAN.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.HDBSCAN.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.HDBSCAN.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1639,7 +1639,7 @@ <h1>HDBSCAN<a class="headerlink" href="#hdbscan" title="Link to this heading">#<
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.HDBSCAN.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.HDBSCAN.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.HDBSCAN.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
diff --git a/dev/modules/generated/sklearn.cluster.KMeans.html b/dev/modules/generated/sklearn.cluster.KMeans.html
index e838e519320cf..fb5db6d1598e0 100644
--- a/dev/modules/generated/sklearn.cluster.KMeans.html
+++ b/dev/modules/generated/sklearn.cluster.KMeans.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
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     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>KMeans<a class="headerlink" href="#kmeans" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.cluster.KMeans">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.cluster.</span></span><span class="sig-name descname"><span class="pre">KMeans</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_clusters</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">8</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'k-means++'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">300</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy_x</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">algorithm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'lloyd'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/cluster/_kmeans.py#L1186"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.KMeans" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.cluster.</span></span><span class="sig-name descname"><span class="pre">KMeans</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_clusters</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">8</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'k-means++'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">300</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy_x</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">algorithm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'lloyd'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/cluster/_kmeans.py#L1186"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.KMeans" title="Link to this definition">#</a></dt>
 <dd><p>K-Means clustering.</p>
 <p>Read more in the <a class="reference internal" href="http://github.com/clustering.html#k-means"><span class="std std-ref">User Guide</span></a>.</p>
 <dl class="field-list">
@@ -1457,7 +1457,7 @@ <h1>KMeans<a class="headerlink" href="#kmeans" title="Link to this heading">#</a
 <a class="reference internal" href="http://github.com/auto_examples/cluster/plot_bisect_kmeans.html#sphx-glr-auto-examples-cluster-plot-bisect-kmeans-py"><span class="std std-ref">Bisecting K-Means and Regular K-Means Performance Comparison</span></a>.</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.KMeans.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/cluster/_kmeans.py#L1426"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.KMeans.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/cluster/_kmeans.py#L1426"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.KMeans.fit" title="Link to this definition">#</a></dt>
 <dd><p>Compute k-means clustering.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1490,7 +1490,7 @@ <h1>KMeans<a class="headerlink" href="#kmeans" title="Link to this heading">#</a
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.KMeans.fit_predict">
-<span class="sig-name descname"><span class="pre">fit_predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/cluster/_kmeans.py#L1041"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.KMeans.fit_predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/cluster/_kmeans.py#L1041"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.KMeans.fit_predict" title="Link to this definition">#</a></dt>
 <dd><p>Compute cluster centers and predict cluster index for each sample.</p>
 <p>Convenience method; equivalent to calling fit(X) followed by
 predict(X).</p>
@@ -1517,7 +1517,7 @@ <h1>KMeans<a class="headerlink" href="#kmeans" title="Link to this heading">#</a
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.KMeans.fit_transform">
-<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/cluster/_kmeans.py#L1100"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.KMeans.fit_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/cluster/_kmeans.py#L1100"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.KMeans.fit_transform" title="Link to this definition">#</a></dt>
 <dd><p>Compute clustering and transform X to cluster-distance space.</p>
 <p>Equivalent to fit(X).transform(X), but more efficiently implemented.</p>
 <dl class="field-list simple">
@@ -1543,7 +1543,7 @@ <h1>KMeans<a class="headerlink" href="#kmeans" title="Link to this heading">#</a
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.KMeans.get_feature_names_out">
-<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L995"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.KMeans.get_feature_names_out" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L936"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.KMeans.get_feature_names_out" title="Link to this definition">#</a></dt>
 <dd><p>Get output feature names for transformation.</p>
 <p>The feature names out will prefixed by the lowercased class name. For
 example, if the transformer outputs 3 features, then the feature names
@@ -1566,7 +1566,7 @@ <h1>KMeans<a class="headerlink" href="#kmeans" title="Link to this heading">#</a
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.KMeans.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.KMeans.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.KMeans.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1583,7 +1583,7 @@ <h1>KMeans<a class="headerlink" href="#kmeans" title="Link to this heading">#</a
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.KMeans.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.KMeans.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.KMeans.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1604,7 +1604,7 @@ <h1>KMeans<a class="headerlink" href="#kmeans" title="Link to this heading">#</a
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.KMeans.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/cluster/_kmeans.py#L1066"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.KMeans.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/cluster/_kmeans.py#L1066"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.KMeans.predict" title="Link to this definition">#</a></dt>
 <dd><p>Predict the closest cluster each sample in X belongs to.</p>
 <p>In the vector quantization literature, <code class="docutils literal notranslate"><span class="pre">cluster_centers_</span></code> is called
 the code book and each value returned by <code class="docutils literal notranslate"><span class="pre">predict</span></code> is the index of
@@ -1627,7 +1627,7 @@ <h1>KMeans<a class="headerlink" href="#kmeans" title="Link to this heading">#</a
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.KMeans.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/cluster/_kmeans.py#L1150"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.KMeans.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/cluster/_kmeans.py#L1150"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.KMeans.score" title="Link to this definition">#</a></dt>
 <dd><p>Opposite of the value of X on the K-means objective.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1652,7 +1652,7 @@ <h1>KMeans<a class="headerlink" href="#kmeans" title="Link to this heading">#</a
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.KMeans.set_fit_request">
-<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.cluster.KMeans" title="sklearn.cluster._kmeans.KMeans"><span class="pre">KMeans</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.KMeans.set_fit_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.cluster.KMeans" title="sklearn.cluster._kmeans.KMeans"><span class="pre">KMeans</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.KMeans.set_fit_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1695,7 +1695,7 @@ <h1>KMeans<a class="headerlink" href="#kmeans" title="Link to this heading">#</a
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.KMeans.set_output">
-<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.KMeans.set_output" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.KMeans.set_output" title="Link to this definition">#</a></dt>
 <dd><p>Set output container.</p>
 <p>See <a class="reference internal" href="http://github.com/auto_examples/miscellaneous/plot_set_output.html#sphx-glr-auto-examples-miscellaneous-plot-set-output-py"><span class="std std-ref">Introducing the set_output API</span></a>
 for an example on how to use the API.</p>
@@ -1726,7 +1726,7 @@ <h1>KMeans<a class="headerlink" href="#kmeans" title="Link to this heading">#</a
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.KMeans.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.KMeans.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.KMeans.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1750,7 +1750,7 @@ <h1>KMeans<a class="headerlink" href="#kmeans" title="Link to this heading">#</a
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.KMeans.set_score_request">
-<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.cluster.KMeans" title="sklearn.cluster._kmeans.KMeans"><span class="pre">KMeans</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.KMeans.set_score_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.cluster.KMeans" title="sklearn.cluster._kmeans.KMeans"><span class="pre">KMeans</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.KMeans.set_score_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1793,7 +1793,7 @@ <h1>KMeans<a class="headerlink" href="#kmeans" title="Link to this heading">#</a
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.KMeans.transform">
-<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/cluster/_kmeans.py#L1124"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.KMeans.transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/cluster/_kmeans.py#L1124"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.KMeans.transform" title="Link to this definition">#</a></dt>
 <dd><p>Transform X to a cluster-distance space.</p>
 <p>In the new space, each dimension is the distance to the cluster
 centers. Note that even if X is sparse, the array returned by
diff --git a/dev/modules/generated/sklearn.cluster.MeanShift.html b/dev/modules/generated/sklearn.cluster.MeanShift.html
index 1121e723642f9..9c8a25db5937f 100644
--- a/dev/modules/generated/sklearn.cluster.MeanShift.html
+++ b/dev/modules/generated/sklearn.cluster.MeanShift.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>MeanShift<a class="headerlink" href="#meanshift" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.cluster.MeanShift">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.cluster.</span></span><span class="sig-name descname"><span class="pre">MeanShift</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bandwidth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seeds</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bin_seeding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_bin_freq</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cluster_all</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">300</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/cluster/_mean_shift.py#L300"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.MeanShift" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.cluster.</span></span><span class="sig-name descname"><span class="pre">MeanShift</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bandwidth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seeds</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bin_seeding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_bin_freq</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cluster_all</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">300</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/cluster/_mean_shift.py#L300"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.MeanShift" title="Link to this definition">#</a></dt>
 <dd><p>Mean shift clustering using a flat kernel.</p>
 <p>Mean shift clustering aims to discover “blobs” in a smooth density of
 samples. It is a centroid-based algorithm, which works by updating
@@ -1355,7 +1355,7 @@ <h1>MeanShift<a class="headerlink" href="#meanshift" title="Link to this heading
 <li><p>Hill-climbing optimization for all seeds.</p></li>
 </ul>
 <p>See <a class="reference internal" href="http://github.com/glossary.html#term-n_jobs"><span class="xref std std-term">Glossary</span></a> for more details.</p>
-<p><code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.5.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
+<p><code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.6.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
 <code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all processors. See <a class="reference internal" href="http://github.com/glossary.html#term-n_jobs"><span class="xref std std-term">Glossary</span></a>
 for more details.</p>
 </dd>
@@ -1432,7 +1432,7 @@ <h1>MeanShift<a class="headerlink" href="#meanshift" title="Link to this heading
 <a class="reference internal" href="http://github.com/auto_examples/cluster/plot_cluster_comparison.html#sphx-glr-auto-examples-cluster-plot-cluster-comparison-py"><span class="std std-ref">Comparing different clustering algorithms on toy datasets</span></a></p>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.MeanShift.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/cluster/_mean_shift.py#L469"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.MeanShift.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/cluster/_mean_shift.py#L469"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.MeanShift.fit" title="Link to this definition">#</a></dt>
 <dd><p>Perform clustering.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1454,7 +1454,7 @@ <h1>MeanShift<a class="headerlink" href="#meanshift" title="Link to this heading
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.MeanShift.fit_predict">
-<span class="sig-name descname"><span class="pre">fit_predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L695"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.MeanShift.fit_predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L636"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.MeanShift.fit_predict" title="Link to this definition">#</a></dt>
 <dd><p>Perform clustering on <code class="docutils literal notranslate"><span class="pre">X</span></code> and returns cluster labels.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1481,7 +1481,7 @@ <h1>MeanShift<a class="headerlink" href="#meanshift" title="Link to this heading
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.MeanShift.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.MeanShift.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.MeanShift.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1498,7 +1498,7 @@ <h1>MeanShift<a class="headerlink" href="#meanshift" title="Link to this heading
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.MeanShift.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.MeanShift.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.MeanShift.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1519,7 +1519,7 @@ <h1>MeanShift<a class="headerlink" href="#meanshift" title="Link to this heading
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.MeanShift.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/cluster/_mean_shift.py#L563"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.MeanShift.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/cluster/_mean_shift.py#L563"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.MeanShift.predict" title="Link to this definition">#</a></dt>
 <dd><p>Predict the closest cluster each sample in X belongs to.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1539,7 +1539,7 @@ <h1>MeanShift<a class="headerlink" href="#meanshift" title="Link to this heading
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.MeanShift.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.MeanShift.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.MeanShift.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
diff --git a/dev/modules/generated/sklearn.cluster.MiniBatchKMeans.html b/dev/modules/generated/sklearn.cluster.MiniBatchKMeans.html
index cec4eefdc89ea..df0feaa73ce57 100644
--- a/dev/modules/generated/sklearn.cluster.MiniBatchKMeans.html
+++ b/dev/modules/generated/sklearn.cluster.MiniBatchKMeans.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>MiniBatchKMeans<a class="headerlink" href="#minibatchkmeans" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.cluster.MiniBatchKMeans">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.cluster.</span></span><span class="sig-name descname"><span class="pre">MiniBatchKMeans</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_clusters</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">8</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'k-means++'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1024</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">compute_labels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_no_improvement</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">init_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reassignment_ratio</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/cluster/_kmeans.py#L1678"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.MiniBatchKMeans" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.cluster.</span></span><span class="sig-name descname"><span class="pre">MiniBatchKMeans</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_clusters</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">8</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'k-means++'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1024</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">compute_labels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_no_improvement</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">init_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reassignment_ratio</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/cluster/_kmeans.py#L1678"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.MiniBatchKMeans" title="Link to this definition">#</a></dt>
 <dd><p>Mini-Batch K-Means clustering.</p>
 <p>Read more in the <a class="reference internal" href="http://github.com/clustering.html#mini-batch-kmeans"><span class="std std-ref">User Guide</span></a>.</p>
 <dl class="field-list">
@@ -1482,7 +1482,7 @@ <h1>MiniBatchKMeans<a class="headerlink" href="#minibatchkmeans" title="Link to
 see <a class="reference internal" href="http://github.com/auto_examples/cluster/plot_cluster_comparison.html#sphx-glr-auto-examples-cluster-plot-cluster-comparison-py"><span class="std std-ref">Comparing different clustering algorithms on toy datasets</span></a></p>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.MiniBatchKMeans.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/cluster/_kmeans.py#L2045"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.MiniBatchKMeans.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/cluster/_kmeans.py#L2045"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.MiniBatchKMeans.fit" title="Link to this definition">#</a></dt>
 <dd><p>Compute the centroids on X by chunking it into mini-batches.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1515,7 +1515,7 @@ <h1>MiniBatchKMeans<a class="headerlink" href="#minibatchkmeans" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.MiniBatchKMeans.fit_predict">
-<span class="sig-name descname"><span class="pre">fit_predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/cluster/_kmeans.py#L1041"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.MiniBatchKMeans.fit_predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/cluster/_kmeans.py#L1041"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.MiniBatchKMeans.fit_predict" title="Link to this definition">#</a></dt>
 <dd><p>Compute cluster centers and predict cluster index for each sample.</p>
 <p>Convenience method; equivalent to calling fit(X) followed by
 predict(X).</p>
@@ -1542,7 +1542,7 @@ <h1>MiniBatchKMeans<a class="headerlink" href="#minibatchkmeans" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.MiniBatchKMeans.fit_transform">
-<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/cluster/_kmeans.py#L1100"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.MiniBatchKMeans.fit_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/cluster/_kmeans.py#L1100"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.MiniBatchKMeans.fit_transform" title="Link to this definition">#</a></dt>
 <dd><p>Compute clustering and transform X to cluster-distance space.</p>
 <p>Equivalent to fit(X).transform(X), but more efficiently implemented.</p>
 <dl class="field-list simple">
@@ -1568,7 +1568,7 @@ <h1>MiniBatchKMeans<a class="headerlink" href="#minibatchkmeans" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.MiniBatchKMeans.get_feature_names_out">
-<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L995"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.MiniBatchKMeans.get_feature_names_out" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L936"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.MiniBatchKMeans.get_feature_names_out" title="Link to this definition">#</a></dt>
 <dd><p>Get output feature names for transformation.</p>
 <p>The feature names out will prefixed by the lowercased class name. For
 example, if the transformer outputs 3 features, then the feature names
@@ -1591,7 +1591,7 @@ <h1>MiniBatchKMeans<a class="headerlink" href="#minibatchkmeans" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.MiniBatchKMeans.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.MiniBatchKMeans.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.MiniBatchKMeans.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1608,7 +1608,7 @@ <h1>MiniBatchKMeans<a class="headerlink" href="#minibatchkmeans" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.MiniBatchKMeans.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.MiniBatchKMeans.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.MiniBatchKMeans.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1629,7 +1629,7 @@ <h1>MiniBatchKMeans<a class="headerlink" href="#minibatchkmeans" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.MiniBatchKMeans.partial_fit">
-<span class="sig-name descname"><span class="pre">partial_fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/cluster/_kmeans.py#L2202"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.MiniBatchKMeans.partial_fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">partial_fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/cluster/_kmeans.py#L2202"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.MiniBatchKMeans.partial_fit" title="Link to this definition">#</a></dt>
 <dd><p>Update k means estimate on a single mini-batch X.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1659,7 +1659,7 @@ <h1>MiniBatchKMeans<a class="headerlink" href="#minibatchkmeans" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.MiniBatchKMeans.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/cluster/_kmeans.py#L1066"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.MiniBatchKMeans.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/cluster/_kmeans.py#L1066"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.MiniBatchKMeans.predict" title="Link to this definition">#</a></dt>
 <dd><p>Predict the closest cluster each sample in X belongs to.</p>
 <p>In the vector quantization literature, <code class="docutils literal notranslate"><span class="pre">cluster_centers_</span></code> is called
 the code book and each value returned by <code class="docutils literal notranslate"><span class="pre">predict</span></code> is the index of
@@ -1682,7 +1682,7 @@ <h1>MiniBatchKMeans<a class="headerlink" href="#minibatchkmeans" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.MiniBatchKMeans.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/cluster/_kmeans.py#L1150"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.MiniBatchKMeans.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/cluster/_kmeans.py#L1150"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.MiniBatchKMeans.score" title="Link to this definition">#</a></dt>
 <dd><p>Opposite of the value of X on the K-means objective.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1707,7 +1707,7 @@ <h1>MiniBatchKMeans<a class="headerlink" href="#minibatchkmeans" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.MiniBatchKMeans.set_fit_request">
-<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.cluster.MiniBatchKMeans" title="sklearn.cluster._kmeans.MiniBatchKMeans"><span class="pre">MiniBatchKMeans</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.MiniBatchKMeans.set_fit_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.cluster.MiniBatchKMeans" title="sklearn.cluster._kmeans.MiniBatchKMeans"><span class="pre">MiniBatchKMeans</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.MiniBatchKMeans.set_fit_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1750,7 +1750,7 @@ <h1>MiniBatchKMeans<a class="headerlink" href="#minibatchkmeans" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.MiniBatchKMeans.set_output">
-<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.MiniBatchKMeans.set_output" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.MiniBatchKMeans.set_output" title="Link to this definition">#</a></dt>
 <dd><p>Set output container.</p>
 <p>See <a class="reference internal" href="http://github.com/auto_examples/miscellaneous/plot_set_output.html#sphx-glr-auto-examples-miscellaneous-plot-set-output-py"><span class="std std-ref">Introducing the set_output API</span></a>
 for an example on how to use the API.</p>
@@ -1781,7 +1781,7 @@ <h1>MiniBatchKMeans<a class="headerlink" href="#minibatchkmeans" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.MiniBatchKMeans.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.MiniBatchKMeans.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.MiniBatchKMeans.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1805,7 +1805,7 @@ <h1>MiniBatchKMeans<a class="headerlink" href="#minibatchkmeans" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.MiniBatchKMeans.set_partial_fit_request">
-<span class="sig-name descname"><span class="pre">set_partial_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.cluster.MiniBatchKMeans" title="sklearn.cluster._kmeans.MiniBatchKMeans"><span class="pre">MiniBatchKMeans</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.MiniBatchKMeans.set_partial_fit_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_partial_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.cluster.MiniBatchKMeans" title="sklearn.cluster._kmeans.MiniBatchKMeans"><span class="pre">MiniBatchKMeans</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.MiniBatchKMeans.set_partial_fit_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">partial_fit</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1848,7 +1848,7 @@ <h1>MiniBatchKMeans<a class="headerlink" href="#minibatchkmeans" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.MiniBatchKMeans.set_score_request">
-<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.cluster.MiniBatchKMeans" title="sklearn.cluster._kmeans.MiniBatchKMeans"><span class="pre">MiniBatchKMeans</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.MiniBatchKMeans.set_score_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.cluster.MiniBatchKMeans" title="sklearn.cluster._kmeans.MiniBatchKMeans"><span class="pre">MiniBatchKMeans</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.MiniBatchKMeans.set_score_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1891,7 +1891,7 @@ <h1>MiniBatchKMeans<a class="headerlink" href="#minibatchkmeans" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.MiniBatchKMeans.transform">
-<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/cluster/_kmeans.py#L1124"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.MiniBatchKMeans.transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/cluster/_kmeans.py#L1124"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.MiniBatchKMeans.transform" title="Link to this definition">#</a></dt>
 <dd><p>Transform X to a cluster-distance space.</p>
 <p>In the new space, each dimension is the distance to the cluster
 centers. Note that even if X is sparse, the array returned by
diff --git a/dev/modules/generated/sklearn.cluster.OPTICS.html b/dev/modules/generated/sklearn.cluster.OPTICS.html
index b8cd4f8da87b5..7f767e730f0b6 100644
--- a/dev/modules/generated/sklearn.cluster.OPTICS.html
+++ b/dev/modules/generated/sklearn.cluster.OPTICS.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>OPTICS<a class="headerlink" href="#optics" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.cluster.OPTICS">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.cluster.</span></span><span class="sig-name descname"><span class="pre">OPTICS</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">inf</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'minkowski'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric_params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cluster_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'xi'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xi</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">predecessor_correction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_cluster_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">algorithm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">leaf_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">30</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">memory</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/cluster/_optics.py#L33"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.OPTICS" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.cluster.</span></span><span class="sig-name descname"><span class="pre">OPTICS</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">inf</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'minkowski'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric_params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cluster_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'xi'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xi</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">predecessor_correction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_cluster_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">algorithm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">leaf_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">30</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">memory</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/cluster/_optics.py#L33"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.OPTICS" title="Link to this definition">#</a></dt>
 <dd><p>Estimate clustering structure from vector array.</p>
 <p>OPTICS (Ordering Points To Identify the Clustering Structure), closely
 related to DBSCAN, finds core sample of high density and expands clusters
@@ -1415,7 +1415,7 @@ <h1>OPTICS<a class="headerlink" href="#optics" title="Link to this heading">#</a
 path to the caching directory.</p>
 </dd>
 <dt><strong>n_jobs</strong><span class="classifier">int, default=None</span></dt><dd><p>The number of parallel jobs to run for neighbors search.
-<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.5.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
+<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.6.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
 <code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all processors. See <a class="reference internal" href="http://github.com/glossary.html#term-n_jobs"><span class="xref std std-term">Glossary</span></a>
 for more details.</p>
 </dd>
@@ -1502,7 +1502,7 @@ <h1>OPTICS<a class="headerlink" href="#optics" title="Link to this heading">#</a
 <a class="reference internal" href="http://github.com/auto_examples/cluster/plot_cluster_comparison.html#sphx-glr-auto-examples-cluster-plot-cluster-comparison-py"><span class="std std-ref">Comparing different clustering algorithms on toy datasets</span></a></p>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.OPTICS.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/cluster/_optics.py#L299"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.OPTICS.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/cluster/_optics.py#L299"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.OPTICS.fit" title="Link to this definition">#</a></dt>
 <dd><p>Perform OPTICS clustering.</p>
 <p>Extracts an ordered list of points and reachability distances, and
 performs initial clustering using <code class="docutils literal notranslate"><span class="pre">max_eps</span></code> distance specified at
@@ -1529,7 +1529,7 @@ <h1>OPTICS<a class="headerlink" href="#optics" title="Link to this heading">#</a
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.OPTICS.fit_predict">
-<span class="sig-name descname"><span class="pre">fit_predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L695"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.OPTICS.fit_predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L636"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.OPTICS.fit_predict" title="Link to this definition">#</a></dt>
 <dd><p>Perform clustering on <code class="docutils literal notranslate"><span class="pre">X</span></code> and returns cluster labels.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1556,7 +1556,7 @@ <h1>OPTICS<a class="headerlink" href="#optics" title="Link to this heading">#</a
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.OPTICS.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.OPTICS.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.OPTICS.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1573,7 +1573,7 @@ <h1>OPTICS<a class="headerlink" href="#optics" title="Link to this heading">#</a
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.OPTICS.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.OPTICS.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.OPTICS.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1594,7 +1594,7 @@ <h1>OPTICS<a class="headerlink" href="#optics" title="Link to this heading">#</a
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.OPTICS.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.OPTICS.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.OPTICS.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
diff --git a/dev/modules/generated/sklearn.cluster.SpectralBiclustering.html b/dev/modules/generated/sklearn.cluster.SpectralBiclustering.html
index 1e5953af4cdf2..f81e4d4988086 100644
--- a/dev/modules/generated/sklearn.cluster.SpectralBiclustering.html
+++ b/dev/modules/generated/sklearn.cluster.SpectralBiclustering.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>SpectralBiclustering<a class="headerlink" href="#spectralbiclustering" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.cluster.SpectralBiclustering">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.cluster.</span></span><span class="sig-name descname"><span class="pre">SpectralBiclustering</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_clusters</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'bistochastic'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_components</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">6</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_best</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">svd_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'randomized'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_svd_vecs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mini_batch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'k-means++'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/cluster/_bicluster.py#L357"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.SpectralBiclustering" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.cluster.</span></span><span class="sig-name descname"><span class="pre">SpectralBiclustering</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_clusters</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'bistochastic'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_components</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">6</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_best</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">svd_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'randomized'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_svd_vecs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mini_batch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'k-means++'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/cluster/_bicluster.py#L357"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.SpectralBiclustering" title="Link to this definition">#</a></dt>
 <dd><p>Spectral biclustering (Kluger, 2003).</p>
 <p>Partitions rows and columns under the assumption that the data has
 an underlying checkerboard structure. For instance, if there are
@@ -1423,7 +1423,7 @@ <h1>SpectralBiclustering<a class="headerlink" href="#spectralbiclustering" title
 <a class="reference internal" href="http://github.com/auto_examples/bicluster/plot_spectral_biclustering.html#sphx-glr-auto-examples-bicluster-plot-spectral-biclustering-py"><span class="std std-ref">A demo of the Spectral Biclustering algorithm</span></a></p>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.SpectralBiclustering.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/cluster/_bicluster.py#L117"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.SpectralBiclustering.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/cluster/_bicluster.py#L117"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.SpectralBiclustering.fit" title="Link to this definition">#</a></dt>
 <dd><p>Create a biclustering for X.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1445,7 +1445,7 @@ <h1>SpectralBiclustering<a class="headerlink" href="#spectralbiclustering" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.SpectralBiclustering.get_indices">
-<span class="sig-name descname"><span class="pre">get_indices</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">i</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L759"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.SpectralBiclustering.get_indices" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_indices</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">i</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L700"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.SpectralBiclustering.get_indices" title="Link to this definition">#</a></dt>
 <dd><p>Row and column indices of the <code class="docutils literal notranslate"><span class="pre">i</span></code>’th bicluster.</p>
 <p>Only works if <code class="docutils literal notranslate"><span class="pre">rows_</span></code> and <code class="docutils literal notranslate"><span class="pre">columns_</span></code> attributes exist.</p>
 <dl class="field-list simple">
@@ -1468,7 +1468,7 @@ <h1>SpectralBiclustering<a class="headerlink" href="#spectralbiclustering" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.SpectralBiclustering.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.SpectralBiclustering.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.SpectralBiclustering.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1485,7 +1485,7 @@ <h1>SpectralBiclustering<a class="headerlink" href="#spectralbiclustering" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.SpectralBiclustering.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.SpectralBiclustering.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.SpectralBiclustering.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1506,7 +1506,7 @@ <h1>SpectralBiclustering<a class="headerlink" href="#spectralbiclustering" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.SpectralBiclustering.get_shape">
-<span class="sig-name descname"><span class="pre">get_shape</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">i</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L780"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.SpectralBiclustering.get_shape" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_shape</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">i</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L721"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.SpectralBiclustering.get_shape" title="Link to this definition">#</a></dt>
 <dd><p>Shape of the <code class="docutils literal notranslate"><span class="pre">i</span></code>’th bicluster.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1528,7 +1528,7 @@ <h1>SpectralBiclustering<a class="headerlink" href="#spectralbiclustering" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.SpectralBiclustering.get_submatrix">
-<span class="sig-name descname"><span class="pre">get_submatrix</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">i</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L799"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.SpectralBiclustering.get_submatrix" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_submatrix</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">i</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L740"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.SpectralBiclustering.get_submatrix" title="Link to this definition">#</a></dt>
 <dd><p>Return the submatrix corresponding to bicluster <code class="docutils literal notranslate"><span class="pre">i</span></code>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1553,7 +1553,7 @@ <h1>SpectralBiclustering<a class="headerlink" href="#spectralbiclustering" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.SpectralBiclustering.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.SpectralBiclustering.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.SpectralBiclustering.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
diff --git a/dev/modules/generated/sklearn.cluster.SpectralClustering.html b/dev/modules/generated/sklearn.cluster.SpectralClustering.html
index 4721c0d7c8145..2efbabfdf020f 100644
--- a/dev/modules/generated/sklearn.cluster.SpectralClustering.html
+++ b/dev/modules/generated/sklearn.cluster.SpectralClustering.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>SpectralClustering<a class="headerlink" href="#spectralclustering" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.cluster.SpectralClustering">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.cluster.</span></span><span class="sig-name descname"><span class="pre">SpectralClustering</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_clusters</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">8</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eigen_solver</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_components</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gamma</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">affinity</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'rbf'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_neighbors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eigen_tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">assign_labels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'kmeans'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">degree</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coef0</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/cluster/_spectral.py#L379"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.SpectralClustering" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.cluster.</span></span><span class="sig-name descname"><span class="pre">SpectralClustering</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_clusters</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">8</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eigen_solver</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_components</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gamma</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">affinity</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'rbf'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_neighbors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eigen_tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">assign_labels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'kmeans'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">degree</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coef0</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/cluster/_spectral.py#L379"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.SpectralClustering" title="Link to this definition">#</a></dt>
 <dd><p>Apply clustering to a projection of the normalized Laplacian.</p>
 <p>In practice Spectral Clustering is very useful when the structure of
 the individual clusters is highly non-convex, or more generally when
@@ -1426,7 +1426,7 @@ <h1>SpectralClustering<a class="headerlink" href="#spectralclustering" title="Li
 <dt><strong>n_jobs</strong><span class="classifier">int, default=None</span></dt><dd><p>The number of parallel jobs to run when <code class="docutils literal notranslate"><span class="pre">affinity='nearest_neighbors'</span></code>
 or <code class="docutils literal notranslate"><span class="pre">affinity='precomputed_nearest_neighbors'</span></code>. The neighbors search
 will be done in parallel.
-<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.5.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
+<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.6.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
 <code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all processors. See <a class="reference internal" href="http://github.com/glossary.html#term-n_jobs"><span class="xref std std-term">Glossary</span></a>
 for more details.</p>
 </dd>
@@ -1530,7 +1530,7 @@ <h1>SpectralClustering<a class="headerlink" href="#spectralclustering" title="Li
 <a class="reference internal" href="http://github.com/auto_examples/cluster/plot_cluster_comparison.html#sphx-glr-auto-examples-cluster-plot-cluster-comparison-py"><span class="std std-ref">Comparing different clustering algorithms on toy datasets</span></a></p>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.SpectralClustering.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/cluster/_spectral.py#L671"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.SpectralClustering.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/cluster/_spectral.py#L671"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.SpectralClustering.fit" title="Link to this definition">#</a></dt>
 <dd><p>Perform spectral clustering from features, or affinity matrix.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1557,7 +1557,7 @@ <h1>SpectralClustering<a class="headerlink" href="#spectralclustering" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.SpectralClustering.fit_predict">
-<span class="sig-name descname"><span class="pre">fit_predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/cluster/_spectral.py#L774"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.SpectralClustering.fit_predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/cluster/_spectral.py#L774"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.SpectralClustering.fit_predict" title="Link to this definition">#</a></dt>
 <dd><p>Perform spectral clustering on <code class="docutils literal notranslate"><span class="pre">X</span></code> and return cluster labels.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1584,7 +1584,7 @@ <h1>SpectralClustering<a class="headerlink" href="#spectralclustering" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.SpectralClustering.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.SpectralClustering.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.SpectralClustering.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1601,7 +1601,7 @@ <h1>SpectralClustering<a class="headerlink" href="#spectralclustering" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.SpectralClustering.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.SpectralClustering.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.SpectralClustering.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1622,7 +1622,7 @@ <h1>SpectralClustering<a class="headerlink" href="#spectralclustering" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.SpectralClustering.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.SpectralClustering.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.SpectralClustering.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
diff --git a/dev/modules/generated/sklearn.cluster.SpectralCoclustering.html b/dev/modules/generated/sklearn.cluster.SpectralCoclustering.html
index a900e09d8c589..2b7fc824da7db 100644
--- a/dev/modules/generated/sklearn.cluster.SpectralCoclustering.html
+++ b/dev/modules/generated/sklearn.cluster.SpectralCoclustering.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>SpectralCoclustering<a class="headerlink" href="#spectralcoclustering" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.cluster.SpectralCoclustering">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.cluster.</span></span><span class="sig-name descname"><span class="pre">SpectralCoclustering</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_clusters</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">svd_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'randomized'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_svd_vecs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mini_batch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'k-means++'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/cluster/_bicluster.py#L202"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.SpectralCoclustering" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.cluster.</span></span><span class="sig-name descname"><span class="pre">SpectralCoclustering</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_clusters</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">svd_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'randomized'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_svd_vecs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mini_batch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'k-means++'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/cluster/_bicluster.py#L202"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.SpectralCoclustering" title="Link to this definition">#</a></dt>
 <dd><p>Spectral Co-Clustering algorithm (Dhillon, 2001).</p>
 <p>Clusters rows and columns of an array <code class="docutils literal notranslate"><span class="pre">X</span></code> to solve the relaxed
 normalized cut of the bipartite graph created from <code class="docutils literal notranslate"><span class="pre">X</span></code> as follows:
@@ -1406,7 +1406,7 @@ <h1>SpectralCoclustering<a class="headerlink" href="#spectralcoclustering" title
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.SpectralCoclustering.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/cluster/_bicluster.py#L117"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.SpectralCoclustering.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/cluster/_bicluster.py#L117"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.SpectralCoclustering.fit" title="Link to this definition">#</a></dt>
 <dd><p>Create a biclustering for X.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1428,7 +1428,7 @@ <h1>SpectralCoclustering<a class="headerlink" href="#spectralcoclustering" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.SpectralCoclustering.get_indices">
-<span class="sig-name descname"><span class="pre">get_indices</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">i</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L759"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.SpectralCoclustering.get_indices" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_indices</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">i</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L700"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.SpectralCoclustering.get_indices" title="Link to this definition">#</a></dt>
 <dd><p>Row and column indices of the <code class="docutils literal notranslate"><span class="pre">i</span></code>’th bicluster.</p>
 <p>Only works if <code class="docutils literal notranslate"><span class="pre">rows_</span></code> and <code class="docutils literal notranslate"><span class="pre">columns_</span></code> attributes exist.</p>
 <dl class="field-list simple">
@@ -1451,7 +1451,7 @@ <h1>SpectralCoclustering<a class="headerlink" href="#spectralcoclustering" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.SpectralCoclustering.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.SpectralCoclustering.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.SpectralCoclustering.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1468,7 +1468,7 @@ <h1>SpectralCoclustering<a class="headerlink" href="#spectralcoclustering" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.SpectralCoclustering.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.SpectralCoclustering.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.SpectralCoclustering.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1489,7 +1489,7 @@ <h1>SpectralCoclustering<a class="headerlink" href="#spectralcoclustering" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.SpectralCoclustering.get_shape">
-<span class="sig-name descname"><span class="pre">get_shape</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">i</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L780"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.SpectralCoclustering.get_shape" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_shape</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">i</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L721"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.SpectralCoclustering.get_shape" title="Link to this definition">#</a></dt>
 <dd><p>Shape of the <code class="docutils literal notranslate"><span class="pre">i</span></code>’th bicluster.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1511,7 +1511,7 @@ <h1>SpectralCoclustering<a class="headerlink" href="#spectralcoclustering" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.SpectralCoclustering.get_submatrix">
-<span class="sig-name descname"><span class="pre">get_submatrix</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">i</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L799"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.SpectralCoclustering.get_submatrix" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_submatrix</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">i</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L740"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.SpectralCoclustering.get_submatrix" title="Link to this definition">#</a></dt>
 <dd><p>Return the submatrix corresponding to bicluster <code class="docutils literal notranslate"><span class="pre">i</span></code>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1536,7 +1536,7 @@ <h1>SpectralCoclustering<a class="headerlink" href="#spectralcoclustering" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cluster.SpectralCoclustering.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.SpectralCoclustering.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.SpectralCoclustering.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
diff --git a/dev/modules/generated/sklearn.cluster.affinity_propagation.html b/dev/modules/generated/sklearn.cluster.affinity_propagation.html
index 3c3f4373afcdf..e048fe9a2a32f 100644
--- a/dev/modules/generated/sklearn.cluster.affinity_propagation.html
+++ b/dev/modules/generated/sklearn.cluster.affinity_propagation.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>affinity_propagation<a class="headerlink" href="#affinity-propagation" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.cluster.affinity_propagation">
-<span class="sig-prename descclassname"><span class="pre">sklearn.cluster.</span></span><span class="sig-name descname"><span class="pre">affinity_propagation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">S</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">preference</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">convergence_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">15</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">200</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">damping</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_n_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/cluster/_affinity_propagation.py#L182"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.affinity_propagation" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.cluster.</span></span><span class="sig-name descname"><span class="pre">affinity_propagation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">S</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">preference</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">convergence_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">15</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">200</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">damping</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_n_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/cluster/_affinity_propagation.py#L182"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.affinity_propagation" title="Link to this definition">#</a></dt>
 <dd><p>Perform Affinity Propagation Clustering of data.</p>
 <p>Read more in the <a class="reference internal" href="http://github.com/clustering.html#affinity-propagation"><span class="std std-ref">User Guide</span></a>.</p>
 <dl class="field-list">
diff --git a/dev/modules/generated/sklearn.cluster.cluster_optics_dbscan.html b/dev/modules/generated/sklearn.cluster.cluster_optics_dbscan.html
index 92fe14e3d96d8..fd6f68f79506c 100644
--- a/dev/modules/generated/sklearn.cluster.cluster_optics_dbscan.html
+++ b/dev/modules/generated/sklearn.cluster.cluster_optics_dbscan.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
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     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>cluster_optics_dbscan<a class="headerlink" href="#cluster-optics-dbscan" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.cluster.cluster_optics_dbscan">
-<span class="sig-prename descclassname"><span class="pre">sklearn.cluster.</span></span><span class="sig-name descname"><span class="pre">cluster_optics_dbscan</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reachability</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">core_distances</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ordering</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/cluster/_optics.py#L717"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.cluster_optics_dbscan" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.cluster.</span></span><span class="sig-name descname"><span class="pre">cluster_optics_dbscan</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reachability</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">core_distances</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ordering</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/cluster/_optics.py#L717"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.cluster_optics_dbscan" title="Link to this definition">#</a></dt>
 <dd><p>Perform DBSCAN extraction for an arbitrary epsilon.</p>
 <p>Extracting the clusters runs in linear time. Note that this results in
 <code class="docutils literal notranslate"><span class="pre">labels_</span></code> which are close to a <a class="reference internal" href="http://github.com/sklearn.cluster.DBSCAN.html#sklearn.cluster.DBSCAN" title="sklearn.cluster.DBSCAN"><code class="xref py py-class docutils literal notranslate"><span class="pre">DBSCAN</span></code></a> with
diff --git a/dev/modules/generated/sklearn.cluster.cluster_optics_xi.html b/dev/modules/generated/sklearn.cluster.cluster_optics_xi.html
index 4fd161202c04d..ebc0b1a81eea6 100644
--- a/dev/modules/generated/sklearn.cluster.cluster_optics_xi.html
+++ b/dev/modules/generated/sklearn.cluster.cluster_optics_xi.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
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     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>cluster_optics_xi<a class="headerlink" href="#cluster-optics-xi" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.cluster.cluster_optics_xi">
-<span class="sig-prename descclassname"><span class="pre">sklearn.cluster.</span></span><span class="sig-name descname"><span class="pre">cluster_optics_xi</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reachability</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">predecessor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ordering</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_samples</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_cluster_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xi</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">predecessor_correction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/cluster/_optics.py#L791"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.cluster_optics_xi" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.cluster.</span></span><span class="sig-name descname"><span class="pre">cluster_optics_xi</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reachability</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">predecessor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ordering</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_samples</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_cluster_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xi</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">predecessor_correction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/cluster/_optics.py#L791"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.cluster_optics_xi" title="Link to this definition">#</a></dt>
 <dd><p>Automatically extract clusters according to the Xi-steep method.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.cluster.compute_optics_graph.html b/dev/modules/generated/sklearn.cluster.compute_optics_graph.html
index 163af9ecc8a5d..12f6cc685a186 100644
--- a/dev/modules/generated/sklearn.cluster.compute_optics_graph.html
+++ b/dev/modules/generated/sklearn.cluster.compute_optics_graph.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>compute_optics_graph<a class="headerlink" href="#compute-optics-graph" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.cluster.compute_optics_graph">
-<span class="sig-prename descclassname"><span class="pre">sklearn.cluster.</span></span><span class="sig-name descname"><span class="pre">compute_optics_graph</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_samples</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_eps</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric_params</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">algorithm</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">leaf_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/cluster/_optics.py#L441"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.compute_optics_graph" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.cluster.</span></span><span class="sig-name descname"><span class="pre">compute_optics_graph</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_samples</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_eps</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric_params</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">algorithm</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">leaf_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/cluster/_optics.py#L441"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.compute_optics_graph" title="Link to this definition">#</a></dt>
 <dd><p>Compute the OPTICS reachability graph.</p>
 <p>Read more in the <a class="reference internal" href="http://github.com/clustering.html#optics"><span class="std std-ref">User Guide</span></a>.</p>
 <dl class="field-list">
@@ -1372,7 +1372,7 @@ <h1>compute_optics_graph<a class="headerlink" href="#compute-optics-graph" title
 tree. The optimal value depends on the nature of the problem.</p>
 </dd>
 <dt><strong>n_jobs</strong><span class="classifier">int, default=None</span></dt><dd><p>The number of parallel jobs to run for neighbors search.
-<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.5.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
+<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.6.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
 <code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all processors. See <a class="reference internal" href="http://github.com/glossary.html#term-n_jobs"><span class="xref std std-term">Glossary</span></a>
 for more details.</p>
 </dd>
@@ -1423,10 +1423,10 @@ <h1>compute_optics_graph<a class="headerlink" href="#compute-optics-graph" title
 <span class="gp">&gt;&gt;&gt; </span><span class="n">ordering</span>
 <span class="go">array([0, 1, 2, 5, 3, 4])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">core_distances</span>
-<span class="go">array([3.16..., 1.41..., 1.41..., 1.        , 1.        ,</span>
-<span class="go">       4.12...])</span>
+<span class="go">array([3.16, 1.41, 1.41, 1.        , 1.        ,</span>
+<span class="go">       4.12])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">reachability</span>
-<span class="go">array([       inf, 3.16..., 1.41..., 4.12..., 1.        ,</span>
+<span class="go">array([       inf, 3.16, 1.41, 4.12, 1.        ,</span>
 <span class="go">       5.        ])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">predecessor</span>
 <span class="go">array([-1,  0,  1,  5,  3,  2])</span>
diff --git a/dev/modules/generated/sklearn.cluster.estimate_bandwidth.html b/dev/modules/generated/sklearn.cluster.estimate_bandwidth.html
index 44f22bca19934..05536f2c0fc27 100644
--- a/dev/modules/generated/sklearn.cluster.estimate_bandwidth.html
+++ b/dev/modules/generated/sklearn.cluster.estimate_bandwidth.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>estimate_bandwidth<a class="headerlink" href="#estimate-bandwidth" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.cluster.estimate_bandwidth">
-<span class="sig-prename descclassname"><span class="pre">sklearn.cluster.</span></span><span class="sig-name descname"><span class="pre">estimate_bandwidth</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quantile</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/cluster/_mean_shift.py#L31"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.estimate_bandwidth" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.cluster.</span></span><span class="sig-name descname"><span class="pre">estimate_bandwidth</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quantile</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/cluster/_mean_shift.py#L31"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.estimate_bandwidth" title="Link to this definition">#</a></dt>
 <dd><p>Estimate the bandwidth to use with the mean-shift algorithm.</p>
 <p>This function takes time at least quadratic in <code class="docutils literal notranslate"><span class="pre">n_samples</span></code>. For large
 datasets, it is wise to subsample by setting <code class="docutils literal notranslate"><span class="pre">n_samples</span></code>. Alternatively,
@@ -1329,7 +1329,7 @@ <h1>estimate_bandwidth<a class="headerlink" href="#estimate-bandwidth" title="Li
 See <a class="reference internal" href="http://github.com/glossary.html#term-random_state"><span class="xref std std-term">Glossary</span></a>.</p>
 </dd>
 <dt><strong>n_jobs</strong><span class="classifier">int, default=None</span></dt><dd><p>The number of parallel jobs to run for neighbors search.
-<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.5.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
+<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.6.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
 <code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all processors. See <a class="reference internal" href="http://github.com/glossary.html#term-n_jobs"><span class="xref std std-term">Glossary</span></a>
 for more details.</p>
 </dd>
@@ -1348,7 +1348,7 @@ <h1>estimate_bandwidth<a class="headerlink" href="#estimate-bandwidth" title="Li
 <span class="gp">&gt;&gt;&gt; </span><span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span>
 <span class="gp">... </span>              <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">7</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">5</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">6</span><span class="p">]])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">estimate_bandwidth</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">quantile</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
-<span class="go">np.float64(1.61...)</span>
+<span class="go">np.float64(1.61)</span>
 </pre></div>
 </div>
 </dd></dl>
diff --git a/dev/modules/generated/sklearn.cluster.k_means.html b/dev/modules/generated/sklearn.cluster.k_means.html
index 2264631aba246..fdababeb6c6bd 100644
--- a/dev/modules/generated/sklearn.cluster.k_means.html
+++ b/dev/modules/generated/sklearn.cluster.k_means.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>k_means<a class="headerlink" href="#k-means" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.cluster.k_means">
-<span class="sig-prename descclassname"><span class="pre">sklearn.cluster.</span></span><span class="sig-name descname"><span class="pre">k_means</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_clusters</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'k-means++'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">300</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy_x</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">algorithm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'lloyd'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_n_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/cluster/_kmeans.py#L290"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.k_means" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.cluster.</span></span><span class="sig-name descname"><span class="pre">k_means</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_clusters</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'k-means++'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">300</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy_x</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">algorithm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'lloyd'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_n_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/cluster/_kmeans.py#L290"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.k_means" title="Link to this definition">#</a></dt>
 <dd><p>Perform K-means clustering algorithm.</p>
 <p>Read more in the <a class="reference internal" href="http://github.com/clustering.html#k-means"><span class="std std-ref">User Guide</span></a>.</p>
 <dl class="field-list">
diff --git a/dev/modules/generated/sklearn.cluster.kmeans_plusplus.html b/dev/modules/generated/sklearn.cluster.kmeans_plusplus.html
index cfc7508ab386d..f12a1c0b3e436 100644
--- a/dev/modules/generated/sklearn.cluster.kmeans_plusplus.html
+++ b/dev/modules/generated/sklearn.cluster.kmeans_plusplus.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>kmeans_plusplus<a class="headerlink" href="#kmeans-plusplus" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.cluster.kmeans_plusplus">
-<span class="sig-prename descclassname"><span class="pre">sklearn.cluster.</span></span><span class="sig-name descname"><span class="pre">kmeans_plusplus</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_clusters</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">x_squared_norms</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_local_trials</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/cluster/_kmeans.py#L57"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.kmeans_plusplus" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.cluster.</span></span><span class="sig-name descname"><span class="pre">kmeans_plusplus</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_clusters</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">x_squared_norms</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_local_trials</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/cluster/_kmeans.py#L57"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.kmeans_plusplus" title="Link to this definition">#</a></dt>
 <dd><p>Init n_clusters seeds according to k-means++.</p>
 <div class="versionadded">
 <p><span class="versionmodified added">Added in version 0.24.</span></p>
diff --git a/dev/modules/generated/sklearn.cluster.mean_shift.html b/dev/modules/generated/sklearn.cluster.mean_shift.html
index 476ba970f6b58..1d49a7a4456fa 100644
--- a/dev/modules/generated/sklearn.cluster.mean_shift.html
+++ b/dev/modules/generated/sklearn.cluster.mean_shift.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>mean_shift<a class="headerlink" href="#mean-shift" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.cluster.mean_shift">
-<span class="sig-prename descclassname"><span class="pre">sklearn.cluster.</span></span><span class="sig-name descname"><span class="pre">mean_shift</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bandwidth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seeds</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bin_seeding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_bin_freq</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cluster_all</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">300</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/cluster/_mean_shift.py#L131"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.mean_shift" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.cluster.</span></span><span class="sig-name descname"><span class="pre">mean_shift</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bandwidth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seeds</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bin_seeding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_bin_freq</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cluster_all</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">300</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/cluster/_mean_shift.py#L131"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.mean_shift" title="Link to this definition">#</a></dt>
 <dd><p>Perform mean shift clustering of data using a flat kernel.</p>
 <p>Read more in the <a class="reference internal" href="http://github.com/clustering.html#mean-shift"><span class="std std-ref">User Guide</span></a>.</p>
 <dl class="field-list">
@@ -1349,7 +1349,7 @@ <h1>mean_shift<a class="headerlink" href="#mean-shift" title="Link to this headi
 <li><p>Hill-climbing optimization for all seeds.</p></li>
 </ul>
 <p>See <a class="reference internal" href="http://github.com/glossary.html#term-n_jobs"><span class="xref std std-term">Glossary</span></a> for more details.</p>
-<p><code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.5.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
+<p><code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.6.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
 <code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all processors. See <a class="reference internal" href="http://github.com/glossary.html#term-n_jobs"><span class="xref std std-term">Glossary</span></a>
 for more details.</p>
 <div class="versionadded">
@@ -1377,8 +1377,8 @@ <h1>mean_shift<a class="headerlink" href="#mean-shift" title="Link to this headi
 <span class="gp">... </span>              <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">7</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">5</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">6</span><span class="p">]])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">cluster_centers</span><span class="p">,</span> <span class="n">labels</span> <span class="o">=</span> <span class="n">mean_shift</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">bandwidth</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">cluster_centers</span>
-<span class="go">array([[3.33..., 6.     ],</span>
-<span class="go">       [1.33..., 0.66...]])</span>
+<span class="go">array([[3.33, 6.     ],</span>
+<span class="go">       [1.33, 0.66]])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">labels</span>
 <span class="go">array([1, 1, 1, 0, 0, 0])</span>
 </pre></div>
diff --git a/dev/modules/generated/sklearn.cluster.spectral_clustering.html b/dev/modules/generated/sklearn.cluster.spectral_clustering.html
index ffbb94e6fe7a1..2fe0308e55839 100644
--- a/dev/modules/generated/sklearn.cluster.spectral_clustering.html
+++ b/dev/modules/generated/sklearn.cluster.spectral_clustering.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>spectral_clustering<a class="headerlink" href="#spectral-clustering" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.cluster.spectral_clustering">
-<span class="sig-prename descclassname"><span class="pre">sklearn.cluster.</span></span><span class="sig-name descname"><span class="pre">spectral_clustering</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">affinity</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_clusters</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">8</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_components</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eigen_solver</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eigen_tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">assign_labels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'kmeans'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/cluster/_spectral.py#L190"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.spectral_clustering" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.cluster.</span></span><span class="sig-name descname"><span class="pre">spectral_clustering</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">affinity</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_clusters</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">8</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_components</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eigen_solver</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eigen_tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">assign_labels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'kmeans'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/cluster/_spectral.py#L190"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.spectral_clustering" title="Link to this definition">#</a></dt>
 <dd><p>Apply clustering to a projection of the normalized Laplacian.</p>
 <p>In practice Spectral Clustering is very useful when the structure of
 the individual clusters is highly non-convex or more generally when
diff --git a/dev/modules/generated/sklearn.cluster.ward_tree.html b/dev/modules/generated/sklearn.cluster.ward_tree.html
index e91704d1ee2b7..79cb702471163 100644
--- a/dev/modules/generated/sklearn.cluster.ward_tree.html
+++ b/dev/modules/generated/sklearn.cluster.ward_tree.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1306,7 +1306,7 @@
 <h1>ward_tree<a class="headerlink" href="#ward-tree" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.cluster.ward_tree">
-<span class="sig-prename descclassname"><span class="pre">sklearn.cluster.</span></span><span class="sig-name descname"><span class="pre">ward_tree</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">connectivity</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_clusters</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_distance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/cluster/_agglomerative.py#L182"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.ward_tree" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.cluster.</span></span><span class="sig-name descname"><span class="pre">ward_tree</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">connectivity</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_clusters</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_distance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/cluster/_agglomerative.py#L182"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.ward_tree" title="Link to this definition">#</a></dt>
 <dd><p>Ward clustering based on a Feature matrix.</p>
 <p>Recursively merges the pair of clusters that minimally increases
 within-cluster variance.</p>
diff --git a/dev/modules/generated/sklearn.compose.ColumnTransformer.html b/dev/modules/generated/sklearn.compose.ColumnTransformer.html
index af4fb09583eed..ca92cd6ab393b 100644
--- a/dev/modules/generated/sklearn.compose.ColumnTransformer.html
+++ b/dev/modules/generated/sklearn.compose.ColumnTransformer.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>ColumnTransformer<a class="headerlink" href="#columntransformer" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.compose.ColumnTransformer">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.compose.</span></span><span class="sig-name descname"><span class="pre">ColumnTransformer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">transformers</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">remainder</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'drop'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sparse_threshold</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transformer_weights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose_feature_names_out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">force_int_remainder_cols</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/compose/_column_transformer.py#L63"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.compose.ColumnTransformer" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.compose.</span></span><span class="sig-name descname"><span class="pre">ColumnTransformer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">transformers</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">remainder</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'drop'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sparse_threshold</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transformer_weights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose_feature_names_out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">force_int_remainder_cols</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'deprecated'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/compose/_column_transformer.py#L63"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.compose.ColumnTransformer" title="Link to this definition">#</a></dt>
 <dd><p>Applies transformers to columns of an array or pandas DataFrame.</p>
 <p>This estimator allows different columns or column subsets of the input
 to be transformed separately and the features generated by each transformer
@@ -1366,7 +1366,7 @@ <h1>ColumnTransformer<a class="headerlink" href="#columntransformer" title="Link
 stacked result will be dense, and this keyword will be ignored.</p>
 </dd>
 <dt><strong>n_jobs</strong><span class="classifier">int, default=None</span></dt><dd><p>Number of jobs to run in parallel.
-<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.5.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
+<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.6.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
 <code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all processors. See <a class="reference internal" href="http://github.com/glossary.html#term-n_jobs"><span class="xref std std-term">Glossary</span></a>
 for more details.</p>
 </dd>
@@ -1402,10 +1402,7 @@ <h1>ColumnTransformer<a class="headerlink" href="#columntransformer" title="Link
 <p><span class="versionmodified changed">Changed in version 1.6: </span><code class="docutils literal notranslate"><span class="pre">verbose_feature_names_out</span></code> can be a callable or a string to be formatted.</p>
 </div>
 </dd>
-<dt><strong>force_int_remainder_cols</strong><span class="classifier">bool, default=True</span></dt><dd><p>Force the columns of the last entry of <code class="docutils literal notranslate"><span class="pre">transformers_</span></code>, which
-corresponds to the “remainder” transformer, to always be stored as
-indices (int) rather than column names (str). See description of the
-<code class="docutils literal notranslate"><span class="pre">transformers_</span></code> attribute for details.</p>
+<dt><strong>force_int_remainder_cols</strong><span class="classifier">bool, default=False</span></dt><dd><p>This parameter has no effect.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
 <p>If you do not access the list of columns for the remainder columns
@@ -1419,6 +1416,9 @@ <h1>ColumnTransformer<a class="headerlink" href="#columntransformer" title="Link
 <p><span class="versionmodified changed">Changed in version 1.7: </span>The default value for <code class="docutils literal notranslate"><span class="pre">force_int_remainder_cols</span></code> will change from
 <code class="docutils literal notranslate"><span class="pre">True</span></code> to <code class="docutils literal notranslate"><span class="pre">False</span></code> in version 1.7.</p>
 </div>
+<div class="deprecated">
+<p><span class="versionmodified deprecated">Deprecated since version 1.7: </span><code class="docutils literal notranslate"><span class="pre">force_int_remainder_cols</span></code> is deprecated and will be removed in 1.9.</p>
+</div>
 </dd>
 </dl>
 </dd>
@@ -1434,16 +1434,12 @@ <h1>ColumnTransformer<a class="headerlink" href="#columntransformer" title="Link
 <code class="docutils literal notranslate"><span class="pre">remainder</span></code> parameter. If there are remaining columns, then
 <code class="docutils literal notranslate"><span class="pre">len(transformers_)==len(transformers)+1</span></code>, otherwise
 <code class="docutils literal notranslate"><span class="pre">len(transformers_)==len(transformers)</span></code>.</p>
-<div class="versionchanged">
-<p><span class="versionmodified changed">Changed in version 1.5: </span>If there are remaining columns and <code class="docutils literal notranslate"><span class="pre">force_int_remainder_cols</span></code> is
-True, the remaining columns are always represented by their
-positional indices in the input <code class="docutils literal notranslate"><span class="pre">X</span></code> (as in older versions). If
-<code class="docutils literal notranslate"><span class="pre">force_int_remainder_cols</span></code> is False, the format attempts to match
-that of the other transformers: if all columns were provided as
-column names (<code class="docutils literal notranslate"><span class="pre">str</span></code>), the remaining columns are stored as column
-names; if all columns were provided as mask arrays (<code class="docutils literal notranslate"><span class="pre">bool</span></code>), so are
-the remaining columns; in all other cases the remaining columns are
-stored as indices (<code class="docutils literal notranslate"><span class="pre">int</span></code>).</p>
+<div class="versionadded">
+<p><span class="versionmodified added">Added in version 1.7: </span>The format of the remaining columns now attempts to match that of the other
+transformers: if all columns were provided as column names (<code class="docutils literal notranslate"><span class="pre">str</span></code>), the
+remaining columns are stored as column names; if all columns were provided
+as mask arrays (<code class="docutils literal notranslate"><span class="pre">bool</span></code>), so are the remaining columns; in all other cases
+the remaining columns are stored as indices (<code class="docutils literal notranslate"><span class="pre">int</span></code>).</p>
 </div>
 </dd>
 <dt><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_transformers_</span></code><span class="classifier"><a class="reference internal" href="http://github.com/sklearn.utils.Bunch.html#sklearn.utils.Bunch" title="sklearn.utils.Bunch"><code class="xref py py-class docutils literal notranslate"><span class="pre">Bunch</span></code></a></span></dt><dd><p>Access the fitted transformer by name.</p>
@@ -1529,7 +1525,7 @@ <h1>ColumnTransformer<a class="headerlink" href="#columntransformer" title="Link
 <a class="reference internal" href="http://github.com/auto_examples/compose/plot_column_transformer_mixed_types.html#sphx-glr-auto-examples-compose-plot-column-transformer-mixed-types-py"><span class="std std-ref">Column Transformer with Mixed Types</span></a>.</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.compose.ColumnTransformer.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/compose/_column_transformer.py#L918"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.compose.ColumnTransformer.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/compose/_column_transformer.py#L905"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.compose.ColumnTransformer.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit all transformers using X.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1560,7 +1556,7 @@ <h1>ColumnTransformer<a class="headerlink" href="#columntransformer" title="Link
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.compose.ColumnTransformer.fit_transform">
-<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/compose/_column_transformer.py#L950"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.compose.ColumnTransformer.fit_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/compose/_column_transformer.py#L937"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.compose.ColumnTransformer.fit_transform" title="Link to this definition">#</a></dt>
 <dd><p>Fit all transformers, transform the data and concatenate results.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1594,7 +1590,7 @@ <h1>ColumnTransformer<a class="headerlink" href="#columntransformer" title="Link
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.compose.ColumnTransformer.get_feature_names_out">
-<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/compose/_column_transformer.py#L615"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.compose.ColumnTransformer.get_feature_names_out" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/compose/_column_transformer.py#L602"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.compose.ColumnTransformer.get_feature_names_out" title="Link to this definition">#</a></dt>
 <dd><p>Get output feature names for transformation.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1622,7 +1618,7 @@ <h1>ColumnTransformer<a class="headerlink" href="#columntransformer" title="Link
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.compose.ColumnTransformer.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/compose/_column_transformer.py#L1279"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.compose.ColumnTransformer.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/compose/_column_transformer.py#L1274"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.compose.ColumnTransformer.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1642,7 +1638,7 @@ <h1>ColumnTransformer<a class="headerlink" href="#columntransformer" title="Link
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.compose.ColumnTransformer.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/compose/_column_transformer.py#L397"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.compose.ColumnTransformer.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/compose/_column_transformer.py#L393"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.compose.ColumnTransformer.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <p>Returns the parameters given in the constructor as well as the
 estimators contained within the <code class="docutils literal notranslate"><span class="pre">transformers</span></code> of the
@@ -1666,7 +1662,7 @@ <h1>ColumnTransformer<a class="headerlink" href="#columntransformer" title="Link
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.compose.ColumnTransformer.set_output">
-<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/compose/_column_transformer.py#L356"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.compose.ColumnTransformer.set_output" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/compose/_column_transformer.py#L352"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.compose.ColumnTransformer.set_output" title="Link to this definition">#</a></dt>
 <dd><p>Set the output container when <code class="docutils literal notranslate"><span class="pre">&quot;transform&quot;</span></code> and <code class="docutils literal notranslate"><span class="pre">&quot;fit_transform&quot;</span></code> are called.</p>
 <p>Calling <code class="docutils literal notranslate"><span class="pre">set_output</span></code> will set the output of all estimators in <code class="docutils literal notranslate"><span class="pre">transformers</span></code>
 and <code class="docutils literal notranslate"><span class="pre">transformers_</span></code>.</p>
@@ -1697,7 +1693,7 @@ <h1>ColumnTransformer<a class="headerlink" href="#columntransformer" title="Link
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.compose.ColumnTransformer.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/compose/_column_transformer.py#L417"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.compose.ColumnTransformer.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/compose/_column_transformer.py#L413"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.compose.ColumnTransformer.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>Valid parameter keys can be listed with <code class="docutils literal notranslate"><span class="pre">get_params()</span></code>. Note that you
 can directly set the parameters of the estimators contained in
@@ -1720,7 +1716,7 @@ <h1>ColumnTransformer<a class="headerlink" href="#columntransformer" title="Link
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.compose.ColumnTransformer.transform">
-<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/compose/_column_transformer.py#L1033"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.compose.ColumnTransformer.transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/compose/_column_transformer.py#L1028"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.compose.ColumnTransformer.transform" title="Link to this definition">#</a></dt>
 <dd><p>Transform X separately by each transformer, concatenate results.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.compose.TransformedTargetRegressor.html b/dev/modules/generated/sklearn.compose.TransformedTargetRegressor.html
index d335c6ab9608d..51f09386fc6f5 100644
--- a/dev/modules/generated/sklearn.compose.TransformedTargetRegressor.html
+++ b/dev/modules/generated/sklearn.compose.TransformedTargetRegressor.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1308,7 +1308,7 @@
 <h1>TransformedTargetRegressor<a class="headerlink" href="#transformedtargetregressor" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.compose.TransformedTargetRegressor">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.compose.</span></span><span class="sig-name descname"><span class="pre">TransformedTargetRegressor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">regressor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transformer</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inverse_func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_inverse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/compose/_target.py#L26"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.compose.TransformedTargetRegressor" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.compose.</span></span><span class="sig-name descname"><span class="pre">TransformedTargetRegressor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">regressor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transformer</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inverse_func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_inverse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/compose/_target.py#L26"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.compose.TransformedTargetRegressor" title="Link to this definition">#</a></dt>
 <dd><p>Meta-estimator to regress on a transformed target.</p>
 <p>Useful for applying a non-linear transformation to the target <code class="docutils literal notranslate"><span class="pre">y</span></code> in
 regression problems. This transformation can be given as a Transformer
@@ -1413,7 +1413,7 @@ <h1>TransformedTargetRegressor<a class="headerlink" href="#transformedtargetregr
 <a class="reference internal" href="http://github.com/auto_examples/compose/plot_transformed_target.html#sphx-glr-auto-examples-compose-plot-transformed-target-py"><span class="std std-ref">Effect of transforming the targets in regression model</span></a>.</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.compose.TransformedTargetRegressor.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/compose/_target.py#L220"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.compose.TransformedTargetRegressor.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/compose/_target.py#L220"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.compose.TransformedTargetRegressor.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit the model according to the given training data.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1447,7 +1447,7 @@ <h1>TransformedTargetRegressor<a class="headerlink" href="#transformedtargetregr
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.compose.TransformedTargetRegressor.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/compose/_target.py#L371"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.compose.TransformedTargetRegressor.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/compose/_target.py#L371"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.compose.TransformedTargetRegressor.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1467,7 +1467,7 @@ <h1>TransformedTargetRegressor<a class="headerlink" href="#transformedtargetregr
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.compose.TransformedTargetRegressor.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.compose.TransformedTargetRegressor.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.compose.TransformedTargetRegressor.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1488,7 +1488,7 @@ <h1>TransformedTargetRegressor<a class="headerlink" href="#transformedtargetregr
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.compose.TransformedTargetRegressor.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">predict_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/compose/_target.py#L300"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.compose.TransformedTargetRegressor.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">predict_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/compose/_target.py#L300"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.compose.TransformedTargetRegressor.predict" title="Link to this definition">#</a></dt>
 <dd><p>Predict using the base regressor, applying inverse.</p>
 <p>The regressor is used to predict and the <code class="docutils literal notranslate"><span class="pre">inverse_func</span></code> or
 <code class="docutils literal notranslate"><span class="pre">inverse_transform</span></code> is applied before returning the prediction.</p>
@@ -1521,7 +1521,7 @@ <h1>TransformedTargetRegressor<a class="headerlink" href="#transformedtargetregr
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.compose.TransformedTargetRegressor.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L619"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.compose.TransformedTargetRegressor.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L560"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.compose.TransformedTargetRegressor.score" title="Link to this definition">#</a></dt>
 <dd><p>Return <a class="reference internal" href="http://github.com/model_evaluation.html#r2-score"><span class="std std-ref">coefficient of determination</span></a> on test data.</p>
 <p>The coefficient of determination, <span class="math notranslate nohighlight">\(R^2\)</span>, is defined as
 <span class="math notranslate nohighlight">\((1 - \frac{u}{v})\)</span>, where <span class="math notranslate nohighlight">\(u\)</span> is the residual
@@ -1563,7 +1563,7 @@ <h1>TransformedTargetRegressor<a class="headerlink" href="#transformedtargetregr
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.compose.TransformedTargetRegressor.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.compose.TransformedTargetRegressor.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.compose.TransformedTargetRegressor.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1587,7 +1587,7 @@ <h1>TransformedTargetRegressor<a class="headerlink" href="#transformedtargetregr
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.compose.TransformedTargetRegressor.set_score_request">
-<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.compose.TransformedTargetRegressor" title="sklearn.compose._target.TransformedTargetRegressor"><span class="pre">TransformedTargetRegressor</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.compose.TransformedTargetRegressor.set_score_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.compose.TransformedTargetRegressor" title="sklearn.compose._target.TransformedTargetRegressor"><span class="pre">TransformedTargetRegressor</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.compose.TransformedTargetRegressor.set_score_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
diff --git a/dev/modules/generated/sklearn.compose.make_column_selector.html b/dev/modules/generated/sklearn.compose.make_column_selector.html
index 2830cd55b722a..74f0679597973 100644
--- a/dev/modules/generated/sklearn.compose.make_column_selector.html
+++ b/dev/modules/generated/sklearn.compose.make_column_selector.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>make_column_selector<a class="headerlink" href="#make-column-selector" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.compose.make_column_selector">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.compose.</span></span><span class="sig-name descname"><span class="pre">make_column_selector</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pattern</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype_include</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype_exclude</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/compose/_column_transformer.py#L1510"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.compose.make_column_selector" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.compose.</span></span><span class="sig-name descname"><span class="pre">make_column_selector</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pattern</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype_include</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype_exclude</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/compose/_column_transformer.py#L1505"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.compose.make_column_selector" title="Link to this definition">#</a></dt>
 <dd><p>Create a callable to select columns to be used with
 <a class="reference internal" href="http://github.com/sklearn.compose.ColumnTransformer.html#sklearn.compose.ColumnTransformer" title="sklearn.compose.ColumnTransformer"><code class="xref py py-class docutils literal notranslate"><span class="pre">ColumnTransformer</span></code></a>.</p>
 <p><a class="reference internal" href="#sklearn.compose.make_column_selector" title="sklearn.compose.make_column_selector"><code class="xref py py-func docutils literal notranslate"><span class="pre">make_column_selector</span></code></a> can select columns based on datatype or the
@@ -1368,7 +1368,7 @@ <h1>make_column_selector<a class="headerlink" href="#make-column-selector" title
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.compose.make_column_selector.__call__">
-<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">df</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/compose/_column_transformer.py#L1575"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.compose.make_column_selector.__call__" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">df</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/compose/_column_transformer.py#L1570"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.compose.make_column_selector.__call__" title="Link to this definition">#</a></dt>
 <dd><p>Callable for column selection to be used by a
 <a class="reference internal" href="http://github.com/sklearn.compose.ColumnTransformer.html#sklearn.compose.ColumnTransformer" title="sklearn.compose.ColumnTransformer"><code class="xref py py-class docutils literal notranslate"><span class="pre">ColumnTransformer</span></code></a>.</p>
 <dl class="field-list simple">
diff --git a/dev/modules/generated/sklearn.compose.make_column_transformer.html b/dev/modules/generated/sklearn.compose.make_column_transformer.html
index bf97c2c558583..51528615b68db 100644
--- a/dev/modules/generated/sklearn.compose.make_column_transformer.html
+++ b/dev/modules/generated/sklearn.compose.make_column_transformer.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>make_column_transformer<a class="headerlink" href="#make-column-transformer" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.compose.make_column_transformer">
-<span class="sig-prename descclassname"><span class="pre">sklearn.compose.</span></span><span class="sig-name descname"><span class="pre">make_column_transformer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">transformers</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">remainder</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'drop'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sparse_threshold</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose_feature_names_out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">force_int_remainder_cols</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/compose/_column_transformer.py#L1376"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.compose.make_column_transformer" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.compose.</span></span><span class="sig-name descname"><span class="pre">make_column_transformer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">transformers</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">remainder</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'drop'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sparse_threshold</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose_feature_names_out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">force_int_remainder_cols</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'deprecated'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/compose/_column_transformer.py#L1371"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.compose.make_column_transformer" title="Link to this definition">#</a></dt>
 <dd><p>Construct a ColumnTransformer from the given transformers.</p>
 <p>This is a shorthand for the ColumnTransformer constructor; it does not
 require, and does not permit, naming the transformers. Instead, they will
@@ -1355,7 +1355,7 @@ <h1>make_column_transformer<a class="headerlink" href="#make-column-transformer"
 keyword will be ignored.</p>
 </dd>
 <dt><strong>n_jobs</strong><span class="classifier">int, default=None</span></dt><dd><p>Number of jobs to run in parallel.
-<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.5.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
+<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.6.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
 <code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all processors. See <a class="reference internal" href="http://github.com/glossary.html#term-n_jobs"><span class="xref std std-term">Glossary</span></a>
 for more details.</p>
 </dd>
@@ -1372,10 +1372,7 @@ <h1>make_column_transformer<a class="headerlink" href="#make-column-transformer"
 <p><span class="versionmodified added">Added in version 1.0.</span></p>
 </div>
 </dd>
-<dt><strong>force_int_remainder_cols</strong><span class="classifier">bool, default=True</span></dt><dd><p>Force the columns of the last entry of <code class="docutils literal notranslate"><span class="pre">transformers_</span></code>, which
-corresponds to the “remainder” transformer, to always be stored as
-indices (int) rather than column names (str). See description of the
-<code class="xref py py-attr docutils literal notranslate"><span class="pre">ColumnTransformer.transformers_</span></code> attribute for details.</p>
+<dt><strong>force_int_remainder_cols</strong><span class="classifier">bool, default=True</span></dt><dd><p>This parameter has no effect.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
 <p>If you do not access the list of columns for the remainder columns
@@ -1389,6 +1386,9 @@ <h1>make_column_transformer<a class="headerlink" href="#make-column-transformer"
 <p><span class="versionmodified changed">Changed in version 1.7: </span>The default value for <code class="docutils literal notranslate"><span class="pre">force_int_remainder_cols</span></code> will change from
 <code class="docutils literal notranslate"><span class="pre">True</span></code> to <code class="docutils literal notranslate"><span class="pre">False</span></code> in version 1.7.</p>
 </div>
+<div class="deprecated">
+<p><span class="versionmodified deprecated">Deprecated since version 1.7: </span><code class="docutils literal notranslate"><span class="pre">force_int_remainder_cols</span></code> is deprecated and will be removed in version 1.9.</p>
+</div>
 </dd>
 </dl>
 </dd>
diff --git a/dev/modules/generated/sklearn.config_context.html b/dev/modules/generated/sklearn.config_context.html
index 06b30e1aca1d7..e68e19c57c892 100644
--- a/dev/modules/generated/sklearn.config_context.html
+++ b/dev/modules/generated/sklearn.config_context.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>config_context<a class="headerlink" href="#config-context" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.config_context">
-<span class="sig-prename descclassname"><span class="pre">sklearn.</span></span><span class="sig-name descname"><span class="pre">config_context</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">assume_finite</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">working_memory</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">print_changed_only</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">display</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pairwise_dist_chunk_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enable_cython_pairwise_dist</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">array_api_dispatch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform_output</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enable_metadata_routing</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">skip_parameter_validation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/_config.py#L214"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.config_context" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.</span></span><span class="sig-name descname"><span class="pre">config_context</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">assume_finite</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">working_memory</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">print_changed_only</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">display</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pairwise_dist_chunk_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enable_cython_pairwise_dist</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">array_api_dispatch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform_output</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enable_metadata_routing</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">skip_parameter_validation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/_config.py#L214"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.config_context" title="Link to this definition">#</a></dt>
 <dd><p>Context manager for global scikit-learn configuration.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.covariance.EllipticEnvelope.html b/dev/modules/generated/sklearn.covariance.EllipticEnvelope.html
index e38bb0b7a772e..ebc2f6e7c4fb6 100644
--- a/dev/modules/generated/sklearn.covariance.EllipticEnvelope.html
+++ b/dev/modules/generated/sklearn.covariance.EllipticEnvelope.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>EllipticEnvelope<a class="headerlink" href="#ellipticenvelope" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.covariance.EllipticEnvelope">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.covariance.</span></span><span class="sig-name descname"><span class="pre">EllipticEnvelope</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">store_precision</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">assume_centered</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">support_fraction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">contamination</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/covariance/_elliptic_envelope.py#L15"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.EllipticEnvelope" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.covariance.</span></span><span class="sig-name descname"><span class="pre">EllipticEnvelope</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">store_precision</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">assume_centered</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">support_fraction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">contamination</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/covariance/_elliptic_envelope.py#L15"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.EllipticEnvelope" title="Link to this definition">#</a></dt>
 <dd><p>An object for detecting outliers in a Gaussian distributed dataset.</p>
 <p>Read more in the <a class="reference internal" href="http://github.com/outlier_detection.html#outlier-detection"><span class="std std-ref">User Guide</span></a>.</p>
 <dl class="field-list">
@@ -1427,15 +1427,15 @@ <h1>EllipticEnvelope<a class="headerlink" href="#ellipticenvelope" title="Link t
 <span class="gp">... </span>             <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">]])</span>
 <span class="go">array([ 1, -1])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">cov</span><span class="o">.</span><span class="n">covariance_</span>
-<span class="go">array([[0.7411..., 0.2535...],</span>
-<span class="go">       [0.2535..., 0.3053...]])</span>
+<span class="go">array([[0.7411, 0.2535],</span>
+<span class="go">       [0.2535, 0.3053]])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">cov</span><span class="o">.</span><span class="n">location_</span>
-<span class="go">array([0.0813... , 0.0427...])</span>
+<span class="go">array([0.0813 , 0.0427])</span>
 </pre></div>
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.covariance.EllipticEnvelope.correct_covariance">
-<span class="sig-name descname"><span class="pre">correct_covariance</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/covariance/_robust_covariance.py#L780"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.EllipticEnvelope.correct_covariance" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">correct_covariance</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/covariance/_robust_covariance.py#L780"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.EllipticEnvelope.correct_covariance" title="Link to this definition">#</a></dt>
 <dd><p>Apply a correction to raw Minimum Covariance Determinant estimates.</p>
 <p>Correction using the empirical correction factor suggested
 by Rousseeuw and Van Driessen in <a class="reference internal" href="#rbb2ba44703ed-rvd" id="id2">[RVD]</a>.</p>
@@ -1468,7 +1468,7 @@ <h1>EllipticEnvelope<a class="headerlink" href="#ellipticenvelope" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.covariance.EllipticEnvelope.decision_function">
-<span class="sig-name descname"><span class="pre">decision_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/covariance/_elliptic_envelope.py#L187"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.EllipticEnvelope.decision_function" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">decision_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/covariance/_elliptic_envelope.py#L187"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.EllipticEnvelope.decision_function" title="Link to this definition">#</a></dt>
 <dd><p>Compute the decision function of the given observations.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1491,7 +1491,7 @@ <h1>EllipticEnvelope<a class="headerlink" href="#ellipticenvelope" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.covariance.EllipticEnvelope.error_norm">
-<span class="sig-name descname"><span class="pre">error_norm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">comp_cov</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'frobenius'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scaling</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">squared</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/covariance/_empirical_covariance.py#L292"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.EllipticEnvelope.error_norm" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">error_norm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">comp_cov</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'frobenius'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scaling</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">squared</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/covariance/_empirical_covariance.py#L292"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.EllipticEnvelope.error_norm" title="Link to this definition">#</a></dt>
 <dd><p>Compute the Mean Squared Error between two covariance estimators.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1524,7 +1524,7 @@ <h1>EllipticEnvelope<a class="headerlink" href="#ellipticenvelope" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.covariance.EllipticEnvelope.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/covariance/_elliptic_envelope.py#L166"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.EllipticEnvelope.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/covariance/_elliptic_envelope.py#L166"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.EllipticEnvelope.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit the EllipticEnvelope model.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1546,7 +1546,7 @@ <h1>EllipticEnvelope<a class="headerlink" href="#ellipticenvelope" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.covariance.EllipticEnvelope.fit_predict">
-<span class="sig-name descname"><span class="pre">fit_predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L1096"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.EllipticEnvelope.fit_predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L1037"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.EllipticEnvelope.fit_predict" title="Link to this definition">#</a></dt>
 <dd><p>Perform fit on X and returns labels for X.</p>
 <p>Returns -1 for outliers and 1 for inliers.</p>
 <dl class="field-list">
@@ -1574,7 +1574,7 @@ <h1>EllipticEnvelope<a class="headerlink" href="#ellipticenvelope" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.covariance.EllipticEnvelope.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.EllipticEnvelope.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.EllipticEnvelope.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1591,7 +1591,7 @@ <h1>EllipticEnvelope<a class="headerlink" href="#ellipticenvelope" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.covariance.EllipticEnvelope.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.EllipticEnvelope.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.EllipticEnvelope.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1612,7 +1612,7 @@ <h1>EllipticEnvelope<a class="headerlink" href="#ellipticenvelope" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.covariance.EllipticEnvelope.get_precision">
-<span class="sig-name descname"><span class="pre">get_precision</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/covariance/_empirical_covariance.py#L219"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.EllipticEnvelope.get_precision" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_precision</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/covariance/_empirical_covariance.py#L219"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.EllipticEnvelope.get_precision" title="Link to this definition">#</a></dt>
 <dd><p>Getter for the precision matrix.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Returns<span class="colon">:</span></dt>
@@ -1626,7 +1626,7 @@ <h1>EllipticEnvelope<a class="headerlink" href="#ellipticenvelope" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.covariance.EllipticEnvelope.mahalanobis">
-<span class="sig-name descname"><span class="pre">mahalanobis</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/covariance/_empirical_covariance.py#L343"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.EllipticEnvelope.mahalanobis" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">mahalanobis</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/covariance/_empirical_covariance.py#L343"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.EllipticEnvelope.mahalanobis" title="Link to this definition">#</a></dt>
 <dd><p>Compute the squared Mahalanobis distances of given observations.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1648,7 +1648,7 @@ <h1>EllipticEnvelope<a class="headerlink" href="#ellipticenvelope" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.covariance.EllipticEnvelope.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/covariance/_elliptic_envelope.py#L223"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.EllipticEnvelope.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/covariance/_elliptic_envelope.py#L223"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.EllipticEnvelope.predict" title="Link to this definition">#</a></dt>
 <dd><p>Predict labels (1 inlier, -1 outlier) of X according to fitted model.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1668,7 +1668,7 @@ <h1>EllipticEnvelope<a class="headerlink" href="#ellipticenvelope" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.covariance.EllipticEnvelope.reweight_covariance">
-<span class="sig-name descname"><span class="pre">reweight_covariance</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/covariance/_robust_covariance.py#L820"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.EllipticEnvelope.reweight_covariance" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">reweight_covariance</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/covariance/_robust_covariance.py#L820"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.EllipticEnvelope.reweight_covariance" title="Link to this definition">#</a></dt>
 <dd><p>Re-weight raw Minimum Covariance Determinant estimates.</p>
 <p>Re-weight observations using Rousseeuw’s method (equivalent to
 deleting outlying observations from the data set before
@@ -1708,7 +1708,7 @@ <h1>EllipticEnvelope<a class="headerlink" href="#ellipticenvelope" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.covariance.EllipticEnvelope.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/covariance/_elliptic_envelope.py#L243"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.EllipticEnvelope.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/covariance/_elliptic_envelope.py#L243"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.EllipticEnvelope.score" title="Link to this definition">#</a></dt>
 <dd><p>Return the mean accuracy on the given test data and labels.</p>
 <p>In multi-label classification, this is the subset accuracy
 which is a harsh metric since you require for each sample that
@@ -1735,7 +1735,7 @@ <h1>EllipticEnvelope<a class="headerlink" href="#ellipticenvelope" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.covariance.EllipticEnvelope.score_samples">
-<span class="sig-name descname"><span class="pre">score_samples</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/covariance/_elliptic_envelope.py#L207"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.EllipticEnvelope.score_samples" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score_samples</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/covariance/_elliptic_envelope.py#L207"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.EllipticEnvelope.score_samples" title="Link to this definition">#</a></dt>
 <dd><p>Compute the negative Mahalanobis distances.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1755,7 +1755,7 @@ <h1>EllipticEnvelope<a class="headerlink" href="#ellipticenvelope" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.covariance.EllipticEnvelope.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.EllipticEnvelope.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.EllipticEnvelope.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
diff --git a/dev/modules/generated/sklearn.covariance.EmpiricalCovariance.html b/dev/modules/generated/sklearn.covariance.EmpiricalCovariance.html
index 095f52fe3bd47..2cd8ecca324fc 100644
--- a/dev/modules/generated/sklearn.covariance.EmpiricalCovariance.html
+++ b/dev/modules/generated/sklearn.covariance.EmpiricalCovariance.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>EmpiricalCovariance<a class="headerlink" href="#empiricalcovariance" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.covariance.EmpiricalCovariance">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.covariance.</span></span><span class="sig-name descname"><span class="pre">EmpiricalCovariance</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">store_precision</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">assume_centered</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/covariance/_empirical_covariance.py#L116"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.EmpiricalCovariance" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.covariance.</span></span><span class="sig-name descname"><span class="pre">EmpiricalCovariance</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">store_precision</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">assume_centered</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/covariance/_empirical_covariance.py#L116"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.EmpiricalCovariance" title="Link to this definition">#</a></dt>
 <dd><p>Maximum likelihood covariance estimator.</p>
 <p>Read more in the <a class="reference internal" href="http://github.com/covariance.html#covariance"><span class="std std-ref">User Guide</span></a>.</p>
 <dl class="field-list">
@@ -1374,15 +1374,15 @@ <h1>EmpiricalCovariance<a class="headerlink" href="#empiricalcovariance" title="
 <span class="gp">... </span>                            <span class="n">size</span><span class="o">=</span><span class="mi">500</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">cov</span> <span class="o">=</span> <span class="n">EmpiricalCovariance</span><span class="p">()</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">cov</span><span class="o">.</span><span class="n">covariance_</span>
-<span class="go">array([[0.7569..., 0.2818...],</span>
-<span class="go">       [0.2818..., 0.3928...]])</span>
+<span class="go">array([[0.7569, 0.2818],</span>
+<span class="go">       [0.2818, 0.3928]])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">cov</span><span class="o">.</span><span class="n">location_</span>
-<span class="go">array([0.0622..., 0.0193...])</span>
+<span class="go">array([0.0622, 0.0193])</span>
 </pre></div>
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.covariance.EmpiricalCovariance.error_norm">
-<span class="sig-name descname"><span class="pre">error_norm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">comp_cov</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'frobenius'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scaling</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">squared</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/covariance/_empirical_covariance.py#L292"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.EmpiricalCovariance.error_norm" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">error_norm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">comp_cov</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'frobenius'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scaling</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">squared</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/covariance/_empirical_covariance.py#L292"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.EmpiricalCovariance.error_norm" title="Link to this definition">#</a></dt>
 <dd><p>Compute the Mean Squared Error between two covariance estimators.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1415,7 +1415,7 @@ <h1>EmpiricalCovariance<a class="headerlink" href="#empiricalcovariance" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.covariance.EmpiricalCovariance.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/covariance/_empirical_covariance.py#L233"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.EmpiricalCovariance.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/covariance/_empirical_covariance.py#L233"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.EmpiricalCovariance.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit the maximum likelihood covariance estimator to X.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1438,7 +1438,7 @@ <h1>EmpiricalCovariance<a class="headerlink" href="#empiricalcovariance" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.covariance.EmpiricalCovariance.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.EmpiricalCovariance.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.EmpiricalCovariance.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1455,7 +1455,7 @@ <h1>EmpiricalCovariance<a class="headerlink" href="#empiricalcovariance" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.covariance.EmpiricalCovariance.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.EmpiricalCovariance.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.EmpiricalCovariance.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1476,7 +1476,7 @@ <h1>EmpiricalCovariance<a class="headerlink" href="#empiricalcovariance" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.covariance.EmpiricalCovariance.get_precision">
-<span class="sig-name descname"><span class="pre">get_precision</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/covariance/_empirical_covariance.py#L219"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.EmpiricalCovariance.get_precision" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_precision</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/covariance/_empirical_covariance.py#L219"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.EmpiricalCovariance.get_precision" title="Link to this definition">#</a></dt>
 <dd><p>Getter for the precision matrix.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Returns<span class="colon">:</span></dt>
@@ -1490,7 +1490,7 @@ <h1>EmpiricalCovariance<a class="headerlink" href="#empiricalcovariance" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.covariance.EmpiricalCovariance.mahalanobis">
-<span class="sig-name descname"><span class="pre">mahalanobis</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/covariance/_empirical_covariance.py#L343"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.EmpiricalCovariance.mahalanobis" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">mahalanobis</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/covariance/_empirical_covariance.py#L343"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.EmpiricalCovariance.mahalanobis" title="Link to this definition">#</a></dt>
 <dd><p>Compute the squared Mahalanobis distances of given observations.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1512,7 +1512,7 @@ <h1>EmpiricalCovariance<a class="headerlink" href="#empiricalcovariance" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.covariance.EmpiricalCovariance.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X_test</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/covariance/_empirical_covariance.py#L261"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.EmpiricalCovariance.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X_test</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/covariance/_empirical_covariance.py#L261"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.EmpiricalCovariance.score" title="Link to this definition">#</a></dt>
 <dd><p>Compute the log-likelihood of <code class="docutils literal notranslate"><span class="pre">X_test</span></code> under the estimated Gaussian model.</p>
 <p>The Gaussian model is defined by its mean and covariance matrix which are
 represented respectively by <code class="docutils literal notranslate"><span class="pre">self.location_</span></code> and <code class="docutils literal notranslate"><span class="pre">self.covariance_</span></code>.</p>
@@ -1540,7 +1540,7 @@ <h1>EmpiricalCovariance<a class="headerlink" href="#empiricalcovariance" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.covariance.EmpiricalCovariance.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.EmpiricalCovariance.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.EmpiricalCovariance.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
diff --git a/dev/modules/generated/sklearn.covariance.GraphicalLasso.html b/dev/modules/generated/sklearn.covariance.GraphicalLasso.html
index 10fc886a0337f..6d2873953456f 100644
--- a/dev/modules/generated/sklearn.covariance.GraphicalLasso.html
+++ b/dev/modules/generated/sklearn.covariance.GraphicalLasso.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>GraphicalLasso<a class="headerlink" href="#graphicallasso" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.covariance.GraphicalLasso">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.covariance.</span></span><span class="sig-name descname"><span class="pre">GraphicalLasso</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'cd'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">covariance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enet_tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">np.float64(2.220446049250313e-16)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">assume_centered</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/covariance/_graph_lasso.py#L392"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.GraphicalLasso" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.covariance.</span></span><span class="sig-name descname"><span class="pre">GraphicalLasso</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'cd'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">covariance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enet_tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">np.float64(2.220446049250313e-16)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">assume_centered</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/covariance/_graph_lasso.py#L392"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.GraphicalLasso" title="Link to this definition">#</a></dt>
 <dd><p>Sparse inverse covariance estimation with an l1-penalized estimator.</p>
 <p>For a usage example see
 <a class="reference internal" href="http://github.com/auto_examples/applications/plot_stock_market.html#sphx-glr-auto-examples-applications-plot-stock-market-py"><span class="std std-ref">Visualizing the stock market structure</span></a>.</p>
@@ -1420,7 +1420,7 @@ <h1>GraphicalLasso<a class="headerlink" href="#graphicallasso" title="Link to th
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.covariance.GraphicalLasso.error_norm">
-<span class="sig-name descname"><span class="pre">error_norm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">comp_cov</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'frobenius'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scaling</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">squared</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/covariance/_empirical_covariance.py#L292"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.GraphicalLasso.error_norm" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">error_norm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">comp_cov</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'frobenius'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scaling</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">squared</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/covariance/_empirical_covariance.py#L292"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.GraphicalLasso.error_norm" title="Link to this definition">#</a></dt>
 <dd><p>Compute the Mean Squared Error between two covariance estimators.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1453,7 +1453,7 @@ <h1>GraphicalLasso<a class="headerlink" href="#graphicallasso" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.covariance.GraphicalLasso.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/covariance/_graph_lasso.py#L542"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.GraphicalLasso.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/covariance/_graph_lasso.py#L542"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.GraphicalLasso.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit the GraphicalLasso model to X.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1475,7 +1475,7 @@ <h1>GraphicalLasso<a class="headerlink" href="#graphicallasso" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.covariance.GraphicalLasso.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.GraphicalLasso.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.GraphicalLasso.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1492,7 +1492,7 @@ <h1>GraphicalLasso<a class="headerlink" href="#graphicallasso" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.covariance.GraphicalLasso.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.GraphicalLasso.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.GraphicalLasso.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1513,7 +1513,7 @@ <h1>GraphicalLasso<a class="headerlink" href="#graphicallasso" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.covariance.GraphicalLasso.get_precision">
-<span class="sig-name descname"><span class="pre">get_precision</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/covariance/_empirical_covariance.py#L219"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.GraphicalLasso.get_precision" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_precision</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/covariance/_empirical_covariance.py#L219"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.GraphicalLasso.get_precision" title="Link to this definition">#</a></dt>
 <dd><p>Getter for the precision matrix.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Returns<span class="colon">:</span></dt>
@@ -1527,7 +1527,7 @@ <h1>GraphicalLasso<a class="headerlink" href="#graphicallasso" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.covariance.GraphicalLasso.mahalanobis">
-<span class="sig-name descname"><span class="pre">mahalanobis</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/covariance/_empirical_covariance.py#L343"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.GraphicalLasso.mahalanobis" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">mahalanobis</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/covariance/_empirical_covariance.py#L343"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.GraphicalLasso.mahalanobis" title="Link to this definition">#</a></dt>
 <dd><p>Compute the squared Mahalanobis distances of given observations.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1549,7 +1549,7 @@ <h1>GraphicalLasso<a class="headerlink" href="#graphicallasso" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.covariance.GraphicalLasso.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X_test</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/covariance/_empirical_covariance.py#L261"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.GraphicalLasso.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X_test</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/covariance/_empirical_covariance.py#L261"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.GraphicalLasso.score" title="Link to this definition">#</a></dt>
 <dd><p>Compute the log-likelihood of <code class="docutils literal notranslate"><span class="pre">X_test</span></code> under the estimated Gaussian model.</p>
 <p>The Gaussian model is defined by its mean and covariance matrix which are
 represented respectively by <code class="docutils literal notranslate"><span class="pre">self.location_</span></code> and <code class="docutils literal notranslate"><span class="pre">self.covariance_</span></code>.</p>
@@ -1577,7 +1577,7 @@ <h1>GraphicalLasso<a class="headerlink" href="#graphicallasso" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.covariance.GraphicalLasso.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.GraphicalLasso.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.GraphicalLasso.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
diff --git a/dev/modules/generated/sklearn.covariance.GraphicalLassoCV.html b/dev/modules/generated/sklearn.covariance.GraphicalLassoCV.html
index 91c2ab8ac7047..13865b9ae6917 100644
--- a/dev/modules/generated/sklearn.covariance.GraphicalLassoCV.html
+++ b/dev/modules/generated/sklearn.covariance.GraphicalLassoCV.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>GraphicalLassoCV<a class="headerlink" href="#graphicallassocv" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.covariance.GraphicalLassoCV">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.covariance.</span></span><span class="sig-name descname"><span class="pre">GraphicalLassoCV</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alphas</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">4</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_refinements</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">4</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cv</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enet_tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'cd'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">np.float64(2.220446049250313e-16)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">assume_centered</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/covariance/_graph_lasso.py#L715"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.GraphicalLassoCV" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.covariance.</span></span><span class="sig-name descname"><span class="pre">GraphicalLassoCV</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alphas</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">4</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_refinements</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">4</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cv</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enet_tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'cd'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">np.float64(2.220446049250313e-16)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">assume_centered</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/covariance/_graph_lasso.py#L715"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.GraphicalLassoCV" title="Link to this definition">#</a></dt>
 <dd><p>Sparse inverse covariance w/ cross-validated choice of the l1 penalty.</p>
 <p>See glossary entry for <a class="reference internal" href="http://github.com/glossary.html#term-cross-validation-estimator"><span class="xref std std-term">cross-validation estimator</span></a>.</p>
 <p>Read more in the <a class="reference internal" href="http://github.com/covariance.html#sparse-inverse-covariance"><span class="std std-ref">User Guide</span></a>.</p>
@@ -1357,7 +1357,7 @@ <h1>GraphicalLassoCV<a class="headerlink" href="#graphicallassocv" title="Link t
 stable.</p>
 </dd>
 <dt><strong>n_jobs</strong><span class="classifier">int, default=None</span></dt><dd><p>Number of jobs to run in parallel.
-<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.5.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
+<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.6.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
 <code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all processors. See <a class="reference internal" href="http://github.com/glossary.html#term-n_jobs"><span class="xref std std-term">Glossary</span></a>
 for more details.</p>
 <div class="versionchanged">
@@ -1475,9 +1475,13 @@ <h1>GraphicalLassoCV<a class="headerlink" href="#graphicallassocv" title="Link t
 <span class="go">array([0.073, 0.04 , 0.038, 0.143])</span>
 </pre></div>
 </div>
+<p>For an example comparing <a class="reference internal" href="#sklearn.covariance.GraphicalLassoCV" title="sklearn.covariance.GraphicalLassoCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.covariance.GraphicalLassoCV</span></code></a>,
+<a class="reference internal" href="http://github.com/sklearn.covariance.ledoit_wolf.html#sklearn.covariance.ledoit_wolf" title="sklearn.covariance.ledoit_wolf"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.covariance.ledoit_wolf</span></code></a> shrinkage and the empirical covariance
+on high-dimensional gaussian data, see
+<a class="reference internal" href="http://github.com/auto_examples/covariance/plot_sparse_cov.html#sphx-glr-auto-examples-covariance-plot-sparse-cov-py"><span class="std std-ref">Sparse inverse covariance estimation</span></a>.</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.covariance.GraphicalLassoCV.error_norm">
-<span class="sig-name descname"><span class="pre">error_norm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">comp_cov</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'frobenius'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scaling</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">squared</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/covariance/_empirical_covariance.py#L292"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.GraphicalLassoCV.error_norm" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">error_norm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">comp_cov</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'frobenius'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scaling</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">squared</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/covariance/_empirical_covariance.py#L292"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.GraphicalLassoCV.error_norm" title="Link to this definition">#</a></dt>
 <dd><p>Compute the Mean Squared Error between two covariance estimators.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1510,7 +1514,7 @@ <h1>GraphicalLassoCV<a class="headerlink" href="#graphicallassocv" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.covariance.GraphicalLassoCV.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/covariance/_graph_lasso.py#L935"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.GraphicalLassoCV.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/covariance/_graph_lasso.py#L940"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.GraphicalLassoCV.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit the GraphicalLasso covariance model to X.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1542,7 +1546,7 @@ <h1>GraphicalLassoCV<a class="headerlink" href="#graphicallassocv" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.covariance.GraphicalLassoCV.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/covariance/_graph_lasso.py#L1122"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.GraphicalLassoCV.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/covariance/_graph_lasso.py#L1127"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.GraphicalLassoCV.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1562,7 +1566,7 @@ <h1>GraphicalLassoCV<a class="headerlink" href="#graphicallassocv" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.covariance.GraphicalLassoCV.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.GraphicalLassoCV.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.GraphicalLassoCV.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1583,7 +1587,7 @@ <h1>GraphicalLassoCV<a class="headerlink" href="#graphicallassocv" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.covariance.GraphicalLassoCV.get_precision">
-<span class="sig-name descname"><span class="pre">get_precision</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/covariance/_empirical_covariance.py#L219"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.GraphicalLassoCV.get_precision" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_precision</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/covariance/_empirical_covariance.py#L219"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.GraphicalLassoCV.get_precision" title="Link to this definition">#</a></dt>
 <dd><p>Getter for the precision matrix.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Returns<span class="colon">:</span></dt>
@@ -1597,7 +1601,7 @@ <h1>GraphicalLassoCV<a class="headerlink" href="#graphicallassocv" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.covariance.GraphicalLassoCV.mahalanobis">
-<span class="sig-name descname"><span class="pre">mahalanobis</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/covariance/_empirical_covariance.py#L343"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.GraphicalLassoCV.mahalanobis" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">mahalanobis</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/covariance/_empirical_covariance.py#L343"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.GraphicalLassoCV.mahalanobis" title="Link to this definition">#</a></dt>
 <dd><p>Compute the squared Mahalanobis distances of given observations.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1619,7 +1623,7 @@ <h1>GraphicalLassoCV<a class="headerlink" href="#graphicallassocv" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.covariance.GraphicalLassoCV.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X_test</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/covariance/_empirical_covariance.py#L261"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.GraphicalLassoCV.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X_test</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/covariance/_empirical_covariance.py#L261"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.GraphicalLassoCV.score" title="Link to this definition">#</a></dt>
 <dd><p>Compute the log-likelihood of <code class="docutils literal notranslate"><span class="pre">X_test</span></code> under the estimated Gaussian model.</p>
 <p>The Gaussian model is defined by its mean and covariance matrix which are
 represented respectively by <code class="docutils literal notranslate"><span class="pre">self.location_</span></code> and <code class="docutils literal notranslate"><span class="pre">self.covariance_</span></code>.</p>
@@ -1647,7 +1651,7 @@ <h1>GraphicalLassoCV<a class="headerlink" href="#graphicallassocv" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.covariance.GraphicalLassoCV.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.GraphicalLassoCV.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.GraphicalLassoCV.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
diff --git a/dev/modules/generated/sklearn.covariance.LedoitWolf.html b/dev/modules/generated/sklearn.covariance.LedoitWolf.html
index 7de94b3bd6db2..d2e6c944aedf5 100644
--- a/dev/modules/generated/sklearn.covariance.LedoitWolf.html
+++ b/dev/modules/generated/sklearn.covariance.LedoitWolf.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>LedoitWolf<a class="headerlink" href="#ledoitwolf" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.covariance.LedoitWolf">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.covariance.</span></span><span class="sig-name descname"><span class="pre">LedoitWolf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">store_precision</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">assume_centered</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">block_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/covariance/_shrunk_covariance.py#L467"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.LedoitWolf" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.covariance.</span></span><span class="sig-name descname"><span class="pre">LedoitWolf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">store_precision</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">assume_centered</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">block_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/covariance/_shrunk_covariance.py#L467"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.LedoitWolf" title="Link to this definition">#</a></dt>
 <dd><p>LedoitWolf Estimator.</p>
 <p>Ledoit-Wolf is a particular form of shrinkage, where the shrinkage
 coefficient is computed using O. Ledoit and M. Wolf’s formula as
@@ -1396,10 +1396,10 @@ <h1>LedoitWolf<a class="headerlink" href="#ledoitwolf" title="Link to this headi
 <span class="gp">... </span>                                  <span class="n">size</span><span class="o">=</span><span class="mi">50</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">cov</span> <span class="o">=</span> <span class="n">LedoitWolf</span><span class="p">()</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">cov</span><span class="o">.</span><span class="n">covariance_</span>
-<span class="go">array([[0.4406..., 0.1616...],</span>
-<span class="go">       [0.1616..., 0.8022...]])</span>
+<span class="go">array([[0.4406, 0.1616],</span>
+<span class="go">       [0.1616, 0.8022]])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">cov</span><span class="o">.</span><span class="n">location_</span>
-<span class="go">array([ 0.0595... , -0.0075...])</span>
+<span class="go">array([ 0.0595 , -0.0075])</span>
 </pre></div>
 </div>
 <p>See also <a class="reference internal" href="http://github.com/auto_examples/covariance/plot_covariance_estimation.html#sphx-glr-auto-examples-covariance-plot-covariance-estimation-py"><span class="std std-ref">Shrinkage covariance estimation: LedoitWolf vs OAS and max-likelihood</span></a>
@@ -1407,7 +1407,7 @@ <h1>LedoitWolf<a class="headerlink" href="#ledoitwolf" title="Link to this headi
 for more detailed examples.</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.covariance.LedoitWolf.error_norm">
-<span class="sig-name descname"><span class="pre">error_norm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">comp_cov</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'frobenius'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scaling</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">squared</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/covariance/_empirical_covariance.py#L292"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.LedoitWolf.error_norm" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">error_norm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">comp_cov</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'frobenius'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scaling</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">squared</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/covariance/_empirical_covariance.py#L292"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.LedoitWolf.error_norm" title="Link to this definition">#</a></dt>
 <dd><p>Compute the Mean Squared Error between two covariance estimators.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1440,7 +1440,7 @@ <h1>LedoitWolf<a class="headerlink" href="#ledoitwolf" title="Link to this headi
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.covariance.LedoitWolf.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/covariance/_shrunk_covariance.py#L583"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.LedoitWolf.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/covariance/_shrunk_covariance.py#L583"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.LedoitWolf.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit the Ledoit-Wolf shrunk covariance model to X.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1463,7 +1463,7 @@ <h1>LedoitWolf<a class="headerlink" href="#ledoitwolf" title="Link to this headi
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.covariance.LedoitWolf.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.LedoitWolf.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.LedoitWolf.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1480,7 +1480,7 @@ <h1>LedoitWolf<a class="headerlink" href="#ledoitwolf" title="Link to this headi
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.covariance.LedoitWolf.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.LedoitWolf.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.LedoitWolf.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1501,7 +1501,7 @@ <h1>LedoitWolf<a class="headerlink" href="#ledoitwolf" title="Link to this headi
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.covariance.LedoitWolf.get_precision">
-<span class="sig-name descname"><span class="pre">get_precision</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/covariance/_empirical_covariance.py#L219"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.LedoitWolf.get_precision" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_precision</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/covariance/_empirical_covariance.py#L219"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.LedoitWolf.get_precision" title="Link to this definition">#</a></dt>
 <dd><p>Getter for the precision matrix.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Returns<span class="colon">:</span></dt>
@@ -1515,7 +1515,7 @@ <h1>LedoitWolf<a class="headerlink" href="#ledoitwolf" title="Link to this headi
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.covariance.LedoitWolf.mahalanobis">
-<span class="sig-name descname"><span class="pre">mahalanobis</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/covariance/_empirical_covariance.py#L343"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.LedoitWolf.mahalanobis" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">mahalanobis</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/covariance/_empirical_covariance.py#L343"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.LedoitWolf.mahalanobis" title="Link to this definition">#</a></dt>
 <dd><p>Compute the squared Mahalanobis distances of given observations.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1537,7 +1537,7 @@ <h1>LedoitWolf<a class="headerlink" href="#ledoitwolf" title="Link to this headi
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.covariance.LedoitWolf.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X_test</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/covariance/_empirical_covariance.py#L261"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.LedoitWolf.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X_test</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/covariance/_empirical_covariance.py#L261"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.LedoitWolf.score" title="Link to this definition">#</a></dt>
 <dd><p>Compute the log-likelihood of <code class="docutils literal notranslate"><span class="pre">X_test</span></code> under the estimated Gaussian model.</p>
 <p>The Gaussian model is defined by its mean and covariance matrix which are
 represented respectively by <code class="docutils literal notranslate"><span class="pre">self.location_</span></code> and <code class="docutils literal notranslate"><span class="pre">self.covariance_</span></code>.</p>
@@ -1565,7 +1565,7 @@ <h1>LedoitWolf<a class="headerlink" href="#ledoitwolf" title="Link to this headi
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.covariance.LedoitWolf.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.LedoitWolf.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.LedoitWolf.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
diff --git a/dev/modules/generated/sklearn.covariance.MinCovDet.html b/dev/modules/generated/sklearn.covariance.MinCovDet.html
index e390d85ed5b62..7d2fa624c5556 100644
--- a/dev/modules/generated/sklearn.covariance.MinCovDet.html
+++ b/dev/modules/generated/sklearn.covariance.MinCovDet.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
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     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>MinCovDet<a class="headerlink" href="#mincovdet" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.covariance.MinCovDet">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.covariance.</span></span><span class="sig-name descname"><span class="pre">MinCovDet</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">store_precision</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">assume_centered</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">support_fraction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/covariance/_robust_covariance.py#L581"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.MinCovDet" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.covariance.</span></span><span class="sig-name descname"><span class="pre">MinCovDet</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">store_precision</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">assume_centered</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">support_fraction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/covariance/_robust_covariance.py#L581"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.MinCovDet" title="Link to this definition">#</a></dt>
 <dd><p>Minimum Covariance Determinant (MCD): robust estimator of covariance.</p>
 <p>The Minimum Covariance Determinant covariance estimator is to be applied
 on Gaussian-distributed data, but could still be relevant on data
@@ -1430,15 +1430,15 @@ <h1>MinCovDet<a class="headerlink" href="#mincovdet" title="Link to this heading
 <span class="gp">... </span>                                  <span class="n">size</span><span class="o">=</span><span class="mi">500</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">cov</span> <span class="o">=</span> <span class="n">MinCovDet</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">cov</span><span class="o">.</span><span class="n">covariance_</span>
-<span class="go">array([[0.7411..., 0.2535...],</span>
-<span class="go">       [0.2535..., 0.3053...]])</span>
+<span class="go">array([[0.7411, 0.2535],</span>
+<span class="go">       [0.2535, 0.3053]])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">cov</span><span class="o">.</span><span class="n">location_</span>
-<span class="go">array([0.0813... , 0.0427...])</span>
+<span class="go">array([0.0813 , 0.0427])</span>
 </pre></div>
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.covariance.MinCovDet.correct_covariance">
-<span class="sig-name descname"><span class="pre">correct_covariance</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/covariance/_robust_covariance.py#L780"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.MinCovDet.correct_covariance" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">correct_covariance</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/covariance/_robust_covariance.py#L780"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.MinCovDet.correct_covariance" title="Link to this definition">#</a></dt>
 <dd><p>Apply a correction to raw Minimum Covariance Determinant estimates.</p>
 <p>Correction using the empirical correction factor suggested
 by Rousseeuw and Van Driessen in <a class="reference internal" href="#r491365aeaa84-rvd" id="id4">[RVD]</a>.</p>
@@ -1471,7 +1471,7 @@ <h1>MinCovDet<a class="headerlink" href="#mincovdet" title="Link to this heading
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.covariance.MinCovDet.error_norm">
-<span class="sig-name descname"><span class="pre">error_norm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">comp_cov</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'frobenius'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scaling</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">squared</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/covariance/_empirical_covariance.py#L292"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.MinCovDet.error_norm" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">error_norm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">comp_cov</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'frobenius'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scaling</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">squared</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/covariance/_empirical_covariance.py#L292"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.MinCovDet.error_norm" title="Link to this definition">#</a></dt>
 <dd><p>Compute the Mean Squared Error between two covariance estimators.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1504,7 +1504,7 @@ <h1>MinCovDet<a class="headerlink" href="#mincovdet" title="Link to this heading
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.covariance.MinCovDet.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/covariance/_robust_covariance.py#L726"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.MinCovDet.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/covariance/_robust_covariance.py#L726"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.MinCovDet.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit a Minimum Covariance Determinant with the FastMCD algorithm.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1527,7 +1527,7 @@ <h1>MinCovDet<a class="headerlink" href="#mincovdet" title="Link to this heading
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.covariance.MinCovDet.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.MinCovDet.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.MinCovDet.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1544,7 +1544,7 @@ <h1>MinCovDet<a class="headerlink" href="#mincovdet" title="Link to this heading
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.covariance.MinCovDet.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.MinCovDet.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.MinCovDet.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1565,7 +1565,7 @@ <h1>MinCovDet<a class="headerlink" href="#mincovdet" title="Link to this heading
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.covariance.MinCovDet.get_precision">
-<span class="sig-name descname"><span class="pre">get_precision</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/covariance/_empirical_covariance.py#L219"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.MinCovDet.get_precision" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_precision</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/covariance/_empirical_covariance.py#L219"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.MinCovDet.get_precision" title="Link to this definition">#</a></dt>
 <dd><p>Getter for the precision matrix.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Returns<span class="colon">:</span></dt>
@@ -1579,7 +1579,7 @@ <h1>MinCovDet<a class="headerlink" href="#mincovdet" title="Link to this heading
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.covariance.MinCovDet.mahalanobis">
-<span class="sig-name descname"><span class="pre">mahalanobis</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/covariance/_empirical_covariance.py#L343"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.MinCovDet.mahalanobis" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">mahalanobis</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/covariance/_empirical_covariance.py#L343"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.MinCovDet.mahalanobis" title="Link to this definition">#</a></dt>
 <dd><p>Compute the squared Mahalanobis distances of given observations.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1601,7 +1601,7 @@ <h1>MinCovDet<a class="headerlink" href="#mincovdet" title="Link to this heading
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.covariance.MinCovDet.reweight_covariance">
-<span class="sig-name descname"><span class="pre">reweight_covariance</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/covariance/_robust_covariance.py#L820"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.MinCovDet.reweight_covariance" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">reweight_covariance</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/covariance/_robust_covariance.py#L820"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.MinCovDet.reweight_covariance" title="Link to this definition">#</a></dt>
 <dd><p>Re-weight raw Minimum Covariance Determinant estimates.</p>
 <p>Re-weight observations using Rousseeuw’s method (equivalent to
 deleting outlying observations from the data set before
@@ -1641,7 +1641,7 @@ <h1>MinCovDet<a class="headerlink" href="#mincovdet" title="Link to this heading
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.covariance.MinCovDet.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X_test</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/covariance/_empirical_covariance.py#L261"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.MinCovDet.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X_test</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/covariance/_empirical_covariance.py#L261"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.MinCovDet.score" title="Link to this definition">#</a></dt>
 <dd><p>Compute the log-likelihood of <code class="docutils literal notranslate"><span class="pre">X_test</span></code> under the estimated Gaussian model.</p>
 <p>The Gaussian model is defined by its mean and covariance matrix which are
 represented respectively by <code class="docutils literal notranslate"><span class="pre">self.location_</span></code> and <code class="docutils literal notranslate"><span class="pre">self.covariance_</span></code>.</p>
@@ -1669,7 +1669,7 @@ <h1>MinCovDet<a class="headerlink" href="#mincovdet" title="Link to this heading
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.covariance.MinCovDet.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.MinCovDet.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.MinCovDet.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
diff --git a/dev/modules/generated/sklearn.covariance.OAS.html b/dev/modules/generated/sklearn.covariance.OAS.html
index a4f77678a9598..6da2349dcb7e0 100644
--- a/dev/modules/generated/sklearn.covariance.OAS.html
+++ b/dev/modules/generated/sklearn.covariance.OAS.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>OAS<a class="headerlink" href="#oas" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.covariance.OAS">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.covariance.</span></span><span class="sig-name descname"><span class="pre">OAS</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">store_precision</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">assume_centered</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/covariance/_shrunk_covariance.py#L688"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.OAS" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.covariance.</span></span><span class="sig-name descname"><span class="pre">OAS</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">store_precision</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">assume_centered</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/covariance/_shrunk_covariance.py#L688"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.OAS" title="Link to this definition">#</a></dt>
 <dd><p>Oracle Approximating Shrinkage Estimator.</p>
 <p>Read more in the <a class="reference internal" href="http://github.com/covariance.html#shrunk-covariance"><span class="std std-ref">User Guide</span></a>.</p>
 <dl class="field-list">
@@ -1399,13 +1399,13 @@ <h1>OAS<a class="headerlink" href="#oas" title="Link to this heading">#</a></h1>
 <span class="gp">... </span>                            <span class="n">size</span><span class="o">=</span><span class="mi">500</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">oas</span> <span class="o">=</span> <span class="n">OAS</span><span class="p">()</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">oas</span><span class="o">.</span><span class="n">covariance_</span>
-<span class="go">array([[0.7533..., 0.2763...],</span>
-<span class="go">       [0.2763..., 0.3964...]])</span>
+<span class="go">array([[0.7533, 0.2763],</span>
+<span class="go">       [0.2763, 0.3964]])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">oas</span><span class="o">.</span><span class="n">precision_</span>
-<span class="go">array([[ 1.7833..., -1.2431... ],</span>
-<span class="go">       [-1.2431...,  3.3889...]])</span>
+<span class="go">array([[ 1.7833, -1.2431 ],</span>
+<span class="go">       [-1.2431,  3.3889]])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">oas</span><span class="o">.</span><span class="n">shrinkage_</span>
-<span class="go">np.float64(0.0195...)</span>
+<span class="go">np.float64(0.0195)</span>
 </pre></div>
 </div>
 <p>See also <a class="reference internal" href="http://github.com/auto_examples/covariance/plot_covariance_estimation.html#sphx-glr-auto-examples-covariance-plot-covariance-estimation-py"><span class="std std-ref">Shrinkage covariance estimation: LedoitWolf vs OAS and max-likelihood</span></a>
@@ -1413,7 +1413,7 @@ <h1>OAS<a class="headerlink" href="#oas" title="Link to this heading">#</a></h1>
 for more detailed examples.</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.covariance.OAS.error_norm">
-<span class="sig-name descname"><span class="pre">error_norm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">comp_cov</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'frobenius'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scaling</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">squared</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/covariance/_empirical_covariance.py#L292"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.OAS.error_norm" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">error_norm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">comp_cov</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'frobenius'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scaling</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">squared</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/covariance/_empirical_covariance.py#L292"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.OAS.error_norm" title="Link to this definition">#</a></dt>
 <dd><p>Compute the Mean Squared Error between two covariance estimators.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1446,7 +1446,7 @@ <h1>OAS<a class="headerlink" href="#oas" title="Link to this heading">#</a></h1>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.covariance.OAS.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/covariance/_shrunk_covariance.py#L793"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.OAS.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/covariance/_shrunk_covariance.py#L793"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.OAS.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit the Oracle Approximating Shrinkage covariance model to X.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1469,7 +1469,7 @@ <h1>OAS<a class="headerlink" href="#oas" title="Link to this heading">#</a></h1>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.covariance.OAS.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.OAS.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.OAS.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1486,7 +1486,7 @@ <h1>OAS<a class="headerlink" href="#oas" title="Link to this heading">#</a></h1>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.covariance.OAS.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.OAS.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.OAS.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1507,7 +1507,7 @@ <h1>OAS<a class="headerlink" href="#oas" title="Link to this heading">#</a></h1>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.covariance.OAS.get_precision">
-<span class="sig-name descname"><span class="pre">get_precision</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/covariance/_empirical_covariance.py#L219"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.OAS.get_precision" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_precision</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/covariance/_empirical_covariance.py#L219"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.OAS.get_precision" title="Link to this definition">#</a></dt>
 <dd><p>Getter for the precision matrix.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Returns<span class="colon">:</span></dt>
@@ -1521,7 +1521,7 @@ <h1>OAS<a class="headerlink" href="#oas" title="Link to this heading">#</a></h1>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.covariance.OAS.mahalanobis">
-<span class="sig-name descname"><span class="pre">mahalanobis</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/covariance/_empirical_covariance.py#L343"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.OAS.mahalanobis" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">mahalanobis</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/covariance/_empirical_covariance.py#L343"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.OAS.mahalanobis" title="Link to this definition">#</a></dt>
 <dd><p>Compute the squared Mahalanobis distances of given observations.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1543,7 +1543,7 @@ <h1>OAS<a class="headerlink" href="#oas" title="Link to this heading">#</a></h1>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.covariance.OAS.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X_test</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/covariance/_empirical_covariance.py#L261"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.OAS.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X_test</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/covariance/_empirical_covariance.py#L261"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.OAS.score" title="Link to this definition">#</a></dt>
 <dd><p>Compute the log-likelihood of <code class="docutils literal notranslate"><span class="pre">X_test</span></code> under the estimated Gaussian model.</p>
 <p>The Gaussian model is defined by its mean and covariance matrix which are
 represented respectively by <code class="docutils literal notranslate"><span class="pre">self.location_</span></code> and <code class="docutils literal notranslate"><span class="pre">self.covariance_</span></code>.</p>
@@ -1571,7 +1571,7 @@ <h1>OAS<a class="headerlink" href="#oas" title="Link to this heading">#</a></h1>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.covariance.OAS.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.OAS.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.OAS.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
diff --git a/dev/modules/generated/sklearn.covariance.ShrunkCovariance.html b/dev/modules/generated/sklearn.covariance.ShrunkCovariance.html
index 57b2e7cff8e09..5ab5117794979 100644
--- a/dev/modules/generated/sklearn.covariance.ShrunkCovariance.html
+++ b/dev/modules/generated/sklearn.covariance.ShrunkCovariance.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>ShrunkCovariance<a class="headerlink" href="#shrunkcovariance" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.covariance.ShrunkCovariance">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.covariance.</span></span><span class="sig-name descname"><span class="pre">ShrunkCovariance</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">store_precision</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">assume_centered</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shrinkage</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/covariance/_shrunk_covariance.py#L159"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.ShrunkCovariance" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.covariance.</span></span><span class="sig-name descname"><span class="pre">ShrunkCovariance</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">store_precision</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">assume_centered</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shrinkage</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/covariance/_shrunk_covariance.py#L159"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.ShrunkCovariance" title="Link to this definition">#</a></dt>
 <dd><p>Covariance estimator with shrinkage.</p>
 <p>Read more in the <a class="reference internal" href="http://github.com/covariance.html#shrunk-covariance"><span class="std std-ref">User Guide</span></a>.</p>
 <dl class="field-list">
@@ -1383,15 +1383,15 @@ <h1>ShrunkCovariance<a class="headerlink" href="#shrunkcovariance" title="Link t
 <span class="gp">... </span>                                  <span class="n">size</span><span class="o">=</span><span class="mi">500</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">cov</span> <span class="o">=</span> <span class="n">ShrunkCovariance</span><span class="p">()</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">cov</span><span class="o">.</span><span class="n">covariance_</span>
-<span class="go">array([[0.7387..., 0.2536...],</span>
-<span class="go">       [0.2536..., 0.4110...]])</span>
+<span class="go">array([[0.7387, 0.2536],</span>
+<span class="go">       [0.2536, 0.4110]])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">cov</span><span class="o">.</span><span class="n">location_</span>
-<span class="go">array([0.0622..., 0.0193...])</span>
+<span class="go">array([0.0622, 0.0193])</span>
 </pre></div>
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.covariance.ShrunkCovariance.error_norm">
-<span class="sig-name descname"><span class="pre">error_norm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">comp_cov</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'frobenius'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scaling</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">squared</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/covariance/_empirical_covariance.py#L292"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.ShrunkCovariance.error_norm" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">error_norm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">comp_cov</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'frobenius'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scaling</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">squared</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/covariance/_empirical_covariance.py#L292"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.ShrunkCovariance.error_norm" title="Link to this definition">#</a></dt>
 <dd><p>Compute the Mean Squared Error between two covariance estimators.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1424,7 +1424,7 @@ <h1>ShrunkCovariance<a class="headerlink" href="#shrunkcovariance" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.covariance.ShrunkCovariance.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/covariance/_shrunk_covariance.py#L254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.ShrunkCovariance.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/covariance/_shrunk_covariance.py#L254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.ShrunkCovariance.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit the shrunk covariance model to X.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1447,7 +1447,7 @@ <h1>ShrunkCovariance<a class="headerlink" href="#shrunkcovariance" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.covariance.ShrunkCovariance.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.ShrunkCovariance.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.ShrunkCovariance.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1464,7 +1464,7 @@ <h1>ShrunkCovariance<a class="headerlink" href="#shrunkcovariance" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.covariance.ShrunkCovariance.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.ShrunkCovariance.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.ShrunkCovariance.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1485,7 +1485,7 @@ <h1>ShrunkCovariance<a class="headerlink" href="#shrunkcovariance" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.covariance.ShrunkCovariance.get_precision">
-<span class="sig-name descname"><span class="pre">get_precision</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/covariance/_empirical_covariance.py#L219"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.ShrunkCovariance.get_precision" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_precision</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/covariance/_empirical_covariance.py#L219"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.ShrunkCovariance.get_precision" title="Link to this definition">#</a></dt>
 <dd><p>Getter for the precision matrix.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Returns<span class="colon">:</span></dt>
@@ -1499,7 +1499,7 @@ <h1>ShrunkCovariance<a class="headerlink" href="#shrunkcovariance" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.covariance.ShrunkCovariance.mahalanobis">
-<span class="sig-name descname"><span class="pre">mahalanobis</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/covariance/_empirical_covariance.py#L343"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.ShrunkCovariance.mahalanobis" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">mahalanobis</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/covariance/_empirical_covariance.py#L343"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.ShrunkCovariance.mahalanobis" title="Link to this definition">#</a></dt>
 <dd><p>Compute the squared Mahalanobis distances of given observations.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1521,7 +1521,7 @@ <h1>ShrunkCovariance<a class="headerlink" href="#shrunkcovariance" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.covariance.ShrunkCovariance.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X_test</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/covariance/_empirical_covariance.py#L261"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.ShrunkCovariance.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X_test</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/covariance/_empirical_covariance.py#L261"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.ShrunkCovariance.score" title="Link to this definition">#</a></dt>
 <dd><p>Compute the log-likelihood of <code class="docutils literal notranslate"><span class="pre">X_test</span></code> under the estimated Gaussian model.</p>
 <p>The Gaussian model is defined by its mean and covariance matrix which are
 represented respectively by <code class="docutils literal notranslate"><span class="pre">self.location_</span></code> and <code class="docutils literal notranslate"><span class="pre">self.covariance_</span></code>.</p>
@@ -1549,7 +1549,7 @@ <h1>ShrunkCovariance<a class="headerlink" href="#shrunkcovariance" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.covariance.ShrunkCovariance.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.ShrunkCovariance.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.ShrunkCovariance.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
diff --git a/dev/modules/generated/sklearn.covariance.empirical_covariance.html b/dev/modules/generated/sklearn.covariance.empirical_covariance.html
index 632fa2d240bf0..40fb052d9e284 100644
--- a/dev/modules/generated/sklearn.covariance.empirical_covariance.html
+++ b/dev/modules/generated/sklearn.covariance.empirical_covariance.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>empirical_covariance<a class="headerlink" href="#empirical-covariance" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.covariance.empirical_covariance">
-<span class="sig-prename descclassname"><span class="pre">sklearn.covariance.</span></span><span class="sig-name descname"><span class="pre">empirical_covariance</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">assume_centered</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/covariance/_empirical_covariance.py#L60"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.empirical_covariance" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.covariance.</span></span><span class="sig-name descname"><span class="pre">empirical_covariance</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">assume_centered</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/covariance/_empirical_covariance.py#L60"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.empirical_covariance" title="Link to this definition">#</a></dt>
 <dd><p>Compute the Maximum likelihood covariance estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.covariance.graphical_lasso.html b/dev/modules/generated/sklearn.covariance.graphical_lasso.html
index 9dff96f783e3a..40691fafa4efd 100644
--- a/dev/modules/generated/sklearn.covariance.graphical_lasso.html
+++ b/dev/modules/generated/sklearn.covariance.graphical_lasso.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>graphical_lasso<a class="headerlink" href="#graphical-lasso" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.covariance.graphical_lasso">
-<span class="sig-prename descclassname"><span class="pre">sklearn.covariance.</span></span><span class="sig-name descname"><span class="pre">graphical_lasso</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">emp_cov</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'cd'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enet_tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_costs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">np.float64(2.220446049250313e-16)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_n_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/covariance/_graph_lasso.py#L223"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.graphical_lasso" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.covariance.</span></span><span class="sig-name descname"><span class="pre">graphical_lasso</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">emp_cov</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'cd'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enet_tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_costs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">np.float64(2.220446049250313e-16)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_n_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/covariance/_graph_lasso.py#L223"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.graphical_lasso" title="Link to this definition">#</a></dt>
 <dd><p>L1-penalized covariance estimator.</p>
 <p>Read more in the <a class="reference internal" href="http://github.com/covariance.html#sparse-inverse-covariance"><span class="std std-ref">User Guide</span></a>.</p>
 <div class="versionchanged">
@@ -1387,9 +1387,9 @@ <h1>graphical_lasso<a class="headerlink" href="#graphical-lasso" title="Link to
 <span class="gp">&gt;&gt;&gt; </span><span class="n">emp_cov</span> <span class="o">=</span> <span class="n">empirical_covariance</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">assume_centered</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">emp_cov</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">graphical_lasso</span><span class="p">(</span><span class="n">emp_cov</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.05</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">emp_cov</span>
-<span class="go">array([[ 1.68...,  0.21..., -0.20...],</span>
-<span class="go">       [ 0.21...,  0.22..., -0.08...],</span>
-<span class="go">       [-0.20..., -0.08...,  0.23...]])</span>
+<span class="go">array([[ 1.687,  0.212, -0.209],</span>
+<span class="go">       [ 0.212,  0.221, -0.0817],</span>
+<span class="go">       [-0.209, -0.0817, 0.232]])</span>
 </pre></div>
 </div>
 </dd></dl>
diff --git a/dev/modules/generated/sklearn.covariance.ledoit_wolf.html b/dev/modules/generated/sklearn.covariance.ledoit_wolf.html
index 66aed10dd9b75..3854ca3a77e0c 100644
--- a/dev/modules/generated/sklearn.covariance.ledoit_wolf.html
+++ b/dev/modules/generated/sklearn.covariance.ledoit_wolf.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>ledoit_wolf<a class="headerlink" href="#ledoit-wolf" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.covariance.ledoit_wolf">
-<span class="sig-prename descclassname"><span class="pre">sklearn.covariance.</span></span><span class="sig-name descname"><span class="pre">ledoit_wolf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">assume_centered</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">block_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/covariance/_shrunk_covariance.py#L403"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.ledoit_wolf" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.covariance.</span></span><span class="sig-name descname"><span class="pre">ledoit_wolf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">assume_centered</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">block_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/covariance/_shrunk_covariance.py#L403"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.ledoit_wolf" title="Link to this definition">#</a></dt>
 <dd><p>Estimate the shrunk Ledoit-Wolf covariance matrix.</p>
 <p>Read more in the <a class="reference internal" href="http://github.com/covariance.html#shrunk-covariance"><span class="std std-ref">User Guide</span></a>.</p>
 <dl class="field-list simple">
@@ -1347,10 +1347,10 @@ <h1>ledoit_wolf<a class="headerlink" href="#ledoit-wolf" title="Link to this hea
 <span class="gp">&gt;&gt;&gt; </span><span class="n">X</span> <span class="o">=</span> <span class="n">rng</span><span class="o">.</span><span class="n">multivariate_normal</span><span class="p">(</span><span class="n">mean</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">cov</span><span class="o">=</span><span class="n">real_cov</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">50</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">covariance</span><span class="p">,</span> <span class="n">shrinkage</span> <span class="o">=</span> <span class="n">ledoit_wolf</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">covariance</span>
-<span class="go">array([[0.44..., 0.16...],</span>
-<span class="go">       [0.16..., 0.80...]])</span>
+<span class="go">array([[0.44, 0.16],</span>
+<span class="go">       [0.16, 0.80]])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">shrinkage</span>
-<span class="go">np.float64(0.23...)</span>
+<span class="go">np.float64(0.23)</span>
 </pre></div>
 </div>
 </dd></dl>
diff --git a/dev/modules/generated/sklearn.covariance.ledoit_wolf_shrinkage.html b/dev/modules/generated/sklearn.covariance.ledoit_wolf_shrinkage.html
index 0bb8308af47b2..e635f932215ab 100644
--- a/dev/modules/generated/sklearn.covariance.ledoit_wolf_shrinkage.html
+++ b/dev/modules/generated/sklearn.covariance.ledoit_wolf_shrinkage.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>ledoit_wolf_shrinkage<a class="headerlink" href="#ledoit-wolf-shrinkage" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.covariance.ledoit_wolf_shrinkage">
-<span class="sig-prename descclassname"><span class="pre">sklearn.covariance.</span></span><span class="sig-name descname"><span class="pre">ledoit_wolf_shrinkage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">assume_centered</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">block_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/covariance/_shrunk_covariance.py#L289"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.ledoit_wolf_shrinkage" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.covariance.</span></span><span class="sig-name descname"><span class="pre">ledoit_wolf_shrinkage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">assume_centered</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">block_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/covariance/_shrunk_covariance.py#L289"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.ledoit_wolf_shrinkage" title="Link to this definition">#</a></dt>
 <dd><p>Estimate the shrunk Ledoit-Wolf covariance matrix.</p>
 <p>Read more in the <a class="reference internal" href="http://github.com/covariance.html#shrunk-covariance"><span class="std std-ref">User Guide</span></a>.</p>
 <dl class="field-list simple">
@@ -1342,7 +1342,7 @@ <h1>ledoit_wolf_shrinkage<a class="headerlink" href="#ledoit-wolf-shrinkage" tit
 <span class="gp">&gt;&gt;&gt; </span><span class="n">X</span> <span class="o">=</span> <span class="n">rng</span><span class="o">.</span><span class="n">multivariate_normal</span><span class="p">(</span><span class="n">mean</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">cov</span><span class="o">=</span><span class="n">real_cov</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">50</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">shrinkage_coefficient</span> <span class="o">=</span> <span class="n">ledoit_wolf_shrinkage</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">shrinkage_coefficient</span>
-<span class="go">np.float64(0.23...)</span>
+<span class="go">np.float64(0.23)</span>
 </pre></div>
 </div>
 </dd></dl>
diff --git a/dev/modules/generated/sklearn.covariance.shrunk_covariance.html b/dev/modules/generated/sklearn.covariance.shrunk_covariance.html
index 17ef3bce4ffa6..2cb8a5d57748b 100644
--- a/dev/modules/generated/sklearn.covariance.shrunk_covariance.html
+++ b/dev/modules/generated/sklearn.covariance.shrunk_covariance.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>shrunk_covariance<a class="headerlink" href="#shrunk-covariance" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.covariance.shrunk_covariance">
-<span class="sig-prename descclassname"><span class="pre">sklearn.covariance.</span></span><span class="sig-name descname"><span class="pre">shrunk_covariance</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">emp_cov</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shrinkage</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/covariance/_shrunk_covariance.py#L102"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.shrunk_covariance" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.covariance.</span></span><span class="sig-name descname"><span class="pre">shrunk_covariance</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">emp_cov</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shrinkage</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/covariance/_shrunk_covariance.py#L102"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.shrunk_covariance" title="Link to this definition">#</a></dt>
 <dd><p>Calculate covariance matrices shrunk on the diagonal.</p>
 <p>Read more in the <a class="reference internal" href="http://github.com/covariance.html#shrunk-covariance"><span class="std std-ref">User Guide</span></a>.</p>
 <dl class="field-list simple">
@@ -1339,8 +1339,8 @@ <h1>shrunk_covariance<a class="headerlink" href="#shrunk-covariance" title="Link
 <span class="gp">&gt;&gt;&gt; </span><span class="n">rng</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">RandomState</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">X</span> <span class="o">=</span> <span class="n">rng</span><span class="o">.</span><span class="n">multivariate_normal</span><span class="p">(</span><span class="n">mean</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">cov</span><span class="o">=</span><span class="n">real_cov</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">500</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">shrunk_covariance</span><span class="p">(</span><span class="n">empirical_covariance</span><span class="p">(</span><span class="n">X</span><span class="p">))</span>
-<span class="go">array([[0.73..., 0.25...],</span>
-<span class="go">       [0.25..., 0.41...]])</span>
+<span class="go">array([[0.739, 0.254],</span>
+<span class="go">       [0.254, 0.411]])</span>
 </pre></div>
 </div>
 </dd></dl>
diff --git a/dev/modules/generated/sklearn.cross_decomposition.CCA.html b/dev/modules/generated/sklearn.cross_decomposition.CCA.html
index 77dc6cb9b471d..287bad17dd9f1 100644
--- a/dev/modules/generated/sklearn.cross_decomposition.CCA.html
+++ b/dev/modules/generated/sklearn.cross_decomposition.CCA.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1308,7 +1308,7 @@
 <h1>CCA<a class="headerlink" href="#cca" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.cross_decomposition.CCA">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.cross_decomposition.</span></span><span class="sig-name descname"><span class="pre">CCA</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_components</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">500</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-06</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/cross_decomposition/_pls.py#L793"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.CCA" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.cross_decomposition.</span></span><span class="sig-name descname"><span class="pre">CCA</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_components</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">500</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-06</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/cross_decomposition/_pls.py#L793"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.CCA" title="Link to this definition">#</a></dt>
 <dd><p>Canonical Correlation Analysis, also known as “Mode B” PLS.</p>
 <p>For a comparison between other cross decomposition algorithms, see
 <a class="reference internal" href="http://github.com/auto_examples/cross_decomposition/plot_compare_cross_decomposition.html#sphx-glr-auto-examples-cross-decomposition-plot-compare-cross-decomposition-py"><span class="std std-ref">Compare cross decomposition methods</span></a>.</p>
@@ -1393,7 +1393,7 @@ <h1>CCA<a class="headerlink" href="#cca" title="Link to this heading">#</a></h1>
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cross_decomposition.CCA.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/cross_decomposition/_pls.py#L205"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.CCA.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/cross_decomposition/_pls.py#L205"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.CCA.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit model to data.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1417,7 +1417,7 @@ <h1>CCA<a class="headerlink" href="#cca" title="Link to this heading">#</a></h1>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cross_decomposition.CCA.fit_transform">
-<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/cross_decomposition/_pls.py#L480"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.CCA.fit_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/cross_decomposition/_pls.py#L480"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.CCA.fit_transform" title="Link to this definition">#</a></dt>
 <dd><p>Learn and apply the dimension reduction on the train data.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1441,7 +1441,7 @@ <h1>CCA<a class="headerlink" href="#cca" title="Link to this heading">#</a></h1>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cross_decomposition.CCA.get_feature_names_out">
-<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L995"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.CCA.get_feature_names_out" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L936"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.CCA.get_feature_names_out" title="Link to this definition">#</a></dt>
 <dd><p>Get output feature names for transformation.</p>
 <p>The feature names out will prefixed by the lowercased class name. For
 example, if the transformer outputs 3 features, then the feature names
@@ -1464,7 +1464,7 @@ <h1>CCA<a class="headerlink" href="#cca" title="Link to this heading">#</a></h1>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cross_decomposition.CCA.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.CCA.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.CCA.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1481,7 +1481,7 @@ <h1>CCA<a class="headerlink" href="#cca" title="Link to this heading">#</a></h1>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cross_decomposition.CCA.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.CCA.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.CCA.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1502,7 +1502,7 @@ <h1>CCA<a class="headerlink" href="#cca" title="Link to this heading">#</a></h1>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cross_decomposition.CCA.inverse_transform">
-<span class="sig-name descname"><span class="pre">inverse_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/cross_decomposition/_pls.py#L407"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.CCA.inverse_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">inverse_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/cross_decomposition/_pls.py#L407"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.CCA.inverse_transform" title="Link to this definition">#</a></dt>
 <dd><p>Transform data back to its original space.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1517,9 +1517,9 @@ <h1>CCA<a class="headerlink" href="#cca" title="Link to this heading">#</a></h1>
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><dl class="simple">
-<dt><strong>X_reconstructed</strong><span class="classifier">ndarray of shape (n_samples, n_features)</span></dt><dd><p>Return the reconstructed <code class="docutils literal notranslate"><span class="pre">X</span></code> data.</p>
+<dt><strong>X_original</strong><span class="classifier">ndarray of shape (n_samples, n_features)</span></dt><dd><p>Return the reconstructed <code class="docutils literal notranslate"><span class="pre">X</span></code> data.</p>
 </dd>
-<dt><strong>y_reconstructed</strong><span class="classifier">ndarray of shape (n_samples, n_targets)</span></dt><dd><p>Return the reconstructed <code class="docutils literal notranslate"><span class="pre">X</span></code> target. Only returned when <code class="docutils literal notranslate"><span class="pre">y</span></code> is given.</p>
+<dt><strong>y_original</strong><span class="classifier">ndarray of shape (n_samples, n_targets)</span></dt><dd><p>Return the reconstructed <code class="docutils literal notranslate"><span class="pre">X</span></code> target. Only returned when <code class="docutils literal notranslate"><span class="pre">y</span></code> is given.</p>
 </dd>
 </dl>
 </dd>
@@ -1530,7 +1530,7 @@ <h1>CCA<a class="headerlink" href="#cca" title="Link to this heading">#</a></h1>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cross_decomposition.CCA.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/cross_decomposition/_pls.py#L451"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.CCA.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/cross_decomposition/_pls.py#L451"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.CCA.predict" title="Link to this definition">#</a></dt>
 <dd><p>Predict targets of given samples.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1556,7 +1556,7 @@ <h1>CCA<a class="headerlink" href="#cca" title="Link to this heading">#</a></h1>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cross_decomposition.CCA.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L619"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.CCA.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L560"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.CCA.score" title="Link to this definition">#</a></dt>
 <dd><p>Return <a class="reference internal" href="http://github.com/model_evaluation.html#r2-score"><span class="std std-ref">coefficient of determination</span></a> on test data.</p>
 <p>The coefficient of determination, <span class="math notranslate nohighlight">\(R^2\)</span>, is defined as
 <span class="math notranslate nohighlight">\((1 - \frac{u}{v})\)</span>, where <span class="math notranslate nohighlight">\(u\)</span> is the residual
@@ -1598,7 +1598,7 @@ <h1>CCA<a class="headerlink" href="#cca" title="Link to this heading">#</a></h1>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cross_decomposition.CCA.set_output">
-<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.CCA.set_output" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.CCA.set_output" title="Link to this definition">#</a></dt>
 <dd><p>Set output container.</p>
 <p>See <a class="reference internal" href="http://github.com/auto_examples/miscellaneous/plot_set_output.html#sphx-glr-auto-examples-miscellaneous-plot-set-output-py"><span class="std std-ref">Introducing the set_output API</span></a>
 for an example on how to use the API.</p>
@@ -1629,7 +1629,7 @@ <h1>CCA<a class="headerlink" href="#cca" title="Link to this heading">#</a></h1>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cross_decomposition.CCA.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.CCA.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.CCA.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1653,7 +1653,7 @@ <h1>CCA<a class="headerlink" href="#cca" title="Link to this heading">#</a></h1>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cross_decomposition.CCA.set_predict_request">
-<span class="sig-name descname"><span class="pre">set_predict_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.cross_decomposition.CCA" title="sklearn.cross_decomposition._pls.CCA"><span class="pre">CCA</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.CCA.set_predict_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_predict_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.cross_decomposition.CCA" title="sklearn.cross_decomposition._pls.CCA"><span class="pre">CCA</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.CCA.set_predict_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">predict</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1696,7 +1696,7 @@ <h1>CCA<a class="headerlink" href="#cca" title="Link to this heading">#</a></h1>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cross_decomposition.CCA.set_score_request">
-<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.cross_decomposition.CCA" title="sklearn.cross_decomposition._pls.CCA"><span class="pre">CCA</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.CCA.set_score_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.cross_decomposition.CCA" title="sklearn.cross_decomposition._pls.CCA"><span class="pre">CCA</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.CCA.set_score_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1739,7 +1739,7 @@ <h1>CCA<a class="headerlink" href="#cca" title="Link to this heading">#</a></h1>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cross_decomposition.CCA.set_transform_request">
-<span class="sig-name descname"><span class="pre">set_transform_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.cross_decomposition.CCA" title="sklearn.cross_decomposition._pls.CCA"><span class="pre">CCA</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.CCA.set_transform_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_transform_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.cross_decomposition.CCA" title="sklearn.cross_decomposition._pls.CCA"><span class="pre">CCA</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.CCA.set_transform_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">transform</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1782,7 +1782,7 @@ <h1>CCA<a class="headerlink" href="#cca" title="Link to this heading">#</a></h1>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cross_decomposition.CCA.transform">
-<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/cross_decomposition/_pls.py#L368"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.CCA.transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/cross_decomposition/_pls.py#L368"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.CCA.transform" title="Link to this definition">#</a></dt>
 <dd><p>Apply the dimension reduction.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.cross_decomposition.PLSCanonical.html b/dev/modules/generated/sklearn.cross_decomposition.PLSCanonical.html
index 64d6708da1827..d8f2c6680dd64 100644
--- a/dev/modules/generated/sklearn.cross_decomposition.PLSCanonical.html
+++ b/dev/modules/generated/sklearn.cross_decomposition.PLSCanonical.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1308,7 +1308,7 @@
 <h1>PLSCanonical<a class="headerlink" href="#plscanonical" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.cross_decomposition.PLSCanonical">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.cross_decomposition.</span></span><span class="sig-name descname"><span class="pre">PLSCanonical</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_components</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">algorithm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'nipals'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">500</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-06</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/cross_decomposition/_pls.py#L660"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.PLSCanonical" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.cross_decomposition.</span></span><span class="sig-name descname"><span class="pre">PLSCanonical</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_components</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">algorithm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'nipals'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">500</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-06</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/cross_decomposition/_pls.py#L660"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.PLSCanonical" title="Link to this definition">#</a></dt>
 <dd><p>Partial Least Squares transformer and regressor.</p>
 <p>For a comparison between other cross decomposition algorithms, see
 <a class="reference internal" href="http://github.com/auto_examples/cross_decomposition/plot_compare_cross_decomposition.html#sphx-glr-auto-examples-cross-decomposition-plot-compare-cross-decomposition-py"><span class="std std-ref">Compare cross decomposition methods</span></a>.</p>
@@ -1401,7 +1401,7 @@ <h1>PLSCanonical<a class="headerlink" href="#plscanonical" title="Link to this h
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cross_decomposition.PLSCanonical.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/cross_decomposition/_pls.py#L205"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.PLSCanonical.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/cross_decomposition/_pls.py#L205"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.PLSCanonical.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit model to data.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1425,7 +1425,7 @@ <h1>PLSCanonical<a class="headerlink" href="#plscanonical" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cross_decomposition.PLSCanonical.fit_transform">
-<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/cross_decomposition/_pls.py#L480"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.PLSCanonical.fit_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/cross_decomposition/_pls.py#L480"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.PLSCanonical.fit_transform" title="Link to this definition">#</a></dt>
 <dd><p>Learn and apply the dimension reduction on the train data.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1449,7 +1449,7 @@ <h1>PLSCanonical<a class="headerlink" href="#plscanonical" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cross_decomposition.PLSCanonical.get_feature_names_out">
-<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L995"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.PLSCanonical.get_feature_names_out" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L936"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.PLSCanonical.get_feature_names_out" title="Link to this definition">#</a></dt>
 <dd><p>Get output feature names for transformation.</p>
 <p>The feature names out will prefixed by the lowercased class name. For
 example, if the transformer outputs 3 features, then the feature names
@@ -1472,7 +1472,7 @@ <h1>PLSCanonical<a class="headerlink" href="#plscanonical" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cross_decomposition.PLSCanonical.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.PLSCanonical.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.PLSCanonical.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1489,7 +1489,7 @@ <h1>PLSCanonical<a class="headerlink" href="#plscanonical" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cross_decomposition.PLSCanonical.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.PLSCanonical.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.PLSCanonical.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1510,7 +1510,7 @@ <h1>PLSCanonical<a class="headerlink" href="#plscanonical" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cross_decomposition.PLSCanonical.inverse_transform">
-<span class="sig-name descname"><span class="pre">inverse_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/cross_decomposition/_pls.py#L407"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.PLSCanonical.inverse_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">inverse_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/cross_decomposition/_pls.py#L407"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.PLSCanonical.inverse_transform" title="Link to this definition">#</a></dt>
 <dd><p>Transform data back to its original space.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1525,9 +1525,9 @@ <h1>PLSCanonical<a class="headerlink" href="#plscanonical" title="Link to this h
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><dl class="simple">
-<dt><strong>X_reconstructed</strong><span class="classifier">ndarray of shape (n_samples, n_features)</span></dt><dd><p>Return the reconstructed <code class="docutils literal notranslate"><span class="pre">X</span></code> data.</p>
+<dt><strong>X_original</strong><span class="classifier">ndarray of shape (n_samples, n_features)</span></dt><dd><p>Return the reconstructed <code class="docutils literal notranslate"><span class="pre">X</span></code> data.</p>
 </dd>
-<dt><strong>y_reconstructed</strong><span class="classifier">ndarray of shape (n_samples, n_targets)</span></dt><dd><p>Return the reconstructed <code class="docutils literal notranslate"><span class="pre">X</span></code> target. Only returned when <code class="docutils literal notranslate"><span class="pre">y</span></code> is given.</p>
+<dt><strong>y_original</strong><span class="classifier">ndarray of shape (n_samples, n_targets)</span></dt><dd><p>Return the reconstructed <code class="docutils literal notranslate"><span class="pre">X</span></code> target. Only returned when <code class="docutils literal notranslate"><span class="pre">y</span></code> is given.</p>
 </dd>
 </dl>
 </dd>
@@ -1538,7 +1538,7 @@ <h1>PLSCanonical<a class="headerlink" href="#plscanonical" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cross_decomposition.PLSCanonical.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/cross_decomposition/_pls.py#L451"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.PLSCanonical.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/cross_decomposition/_pls.py#L451"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.PLSCanonical.predict" title="Link to this definition">#</a></dt>
 <dd><p>Predict targets of given samples.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1564,7 +1564,7 @@ <h1>PLSCanonical<a class="headerlink" href="#plscanonical" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cross_decomposition.PLSCanonical.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L619"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.PLSCanonical.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L560"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.PLSCanonical.score" title="Link to this definition">#</a></dt>
 <dd><p>Return <a class="reference internal" href="http://github.com/model_evaluation.html#r2-score"><span class="std std-ref">coefficient of determination</span></a> on test data.</p>
 <p>The coefficient of determination, <span class="math notranslate nohighlight">\(R^2\)</span>, is defined as
 <span class="math notranslate nohighlight">\((1 - \frac{u}{v})\)</span>, where <span class="math notranslate nohighlight">\(u\)</span> is the residual
@@ -1606,7 +1606,7 @@ <h1>PLSCanonical<a class="headerlink" href="#plscanonical" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cross_decomposition.PLSCanonical.set_output">
-<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.PLSCanonical.set_output" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.PLSCanonical.set_output" title="Link to this definition">#</a></dt>
 <dd><p>Set output container.</p>
 <p>See <a class="reference internal" href="http://github.com/auto_examples/miscellaneous/plot_set_output.html#sphx-glr-auto-examples-miscellaneous-plot-set-output-py"><span class="std std-ref">Introducing the set_output API</span></a>
 for an example on how to use the API.</p>
@@ -1637,7 +1637,7 @@ <h1>PLSCanonical<a class="headerlink" href="#plscanonical" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cross_decomposition.PLSCanonical.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.PLSCanonical.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.PLSCanonical.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1661,7 +1661,7 @@ <h1>PLSCanonical<a class="headerlink" href="#plscanonical" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cross_decomposition.PLSCanonical.set_predict_request">
-<span class="sig-name descname"><span class="pre">set_predict_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.cross_decomposition.PLSCanonical" title="sklearn.cross_decomposition._pls.PLSCanonical"><span class="pre">PLSCanonical</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.PLSCanonical.set_predict_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_predict_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.cross_decomposition.PLSCanonical" title="sklearn.cross_decomposition._pls.PLSCanonical"><span class="pre">PLSCanonical</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.PLSCanonical.set_predict_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">predict</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1704,7 +1704,7 @@ <h1>PLSCanonical<a class="headerlink" href="#plscanonical" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cross_decomposition.PLSCanonical.set_score_request">
-<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.cross_decomposition.PLSCanonical" title="sklearn.cross_decomposition._pls.PLSCanonical"><span class="pre">PLSCanonical</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.PLSCanonical.set_score_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.cross_decomposition.PLSCanonical" title="sklearn.cross_decomposition._pls.PLSCanonical"><span class="pre">PLSCanonical</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.PLSCanonical.set_score_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1747,7 +1747,7 @@ <h1>PLSCanonical<a class="headerlink" href="#plscanonical" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cross_decomposition.PLSCanonical.set_transform_request">
-<span class="sig-name descname"><span class="pre">set_transform_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.cross_decomposition.PLSCanonical" title="sklearn.cross_decomposition._pls.PLSCanonical"><span class="pre">PLSCanonical</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.PLSCanonical.set_transform_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_transform_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.cross_decomposition.PLSCanonical" title="sklearn.cross_decomposition._pls.PLSCanonical"><span class="pre">PLSCanonical</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.PLSCanonical.set_transform_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">transform</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1790,7 +1790,7 @@ <h1>PLSCanonical<a class="headerlink" href="#plscanonical" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cross_decomposition.PLSCanonical.transform">
-<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/cross_decomposition/_pls.py#L368"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.PLSCanonical.transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/cross_decomposition/_pls.py#L368"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.PLSCanonical.transform" title="Link to this definition">#</a></dt>
 <dd><p>Apply the dimension reduction.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.cross_decomposition.PLSRegression.html b/dev/modules/generated/sklearn.cross_decomposition.PLSRegression.html
index de05d777de549..992c8ed597c8e 100644
--- a/dev/modules/generated/sklearn.cross_decomposition.PLSRegression.html
+++ b/dev/modules/generated/sklearn.cross_decomposition.PLSRegression.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1308,7 +1308,7 @@
 <h1>PLSRegression<a class="headerlink" href="#plsregression" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.cross_decomposition.PLSRegression">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.cross_decomposition.</span></span><span class="sig-name descname"><span class="pre">PLSRegression</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_components</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">500</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-06</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/cross_decomposition/_pls.py#L507"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.PLSRegression" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.cross_decomposition.</span></span><span class="sig-name descname"><span class="pre">PLSRegression</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_components</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">500</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-06</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/cross_decomposition/_pls.py#L507"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.PLSRegression" title="Link to this definition">#</a></dt>
 <dd><p>PLS regression.</p>
 <p>PLSRegression is also known as PLS2 or PLS1, depending on the number of
 targets.</p>
@@ -1402,7 +1402,7 @@ <h1>PLSRegression<a class="headerlink" href="#plsregression" title="Link to this
 <a class="reference internal" href="http://github.com/auto_examples/cross_decomposition/plot_pcr_vs_pls.html#sphx-glr-auto-examples-cross-decomposition-plot-pcr-vs-pls-py"><span class="std std-ref">Principal Component Regression vs Partial Least Squares Regression</span></a>.</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cross_decomposition.PLSRegression.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/cross_decomposition/_pls.py#L635"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.PLSRegression.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/cross_decomposition/_pls.py#L635"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.PLSRegression.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit model to data.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1426,7 +1426,7 @@ <h1>PLSRegression<a class="headerlink" href="#plsregression" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cross_decomposition.PLSRegression.fit_transform">
-<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/cross_decomposition/_pls.py#L480"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.PLSRegression.fit_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/cross_decomposition/_pls.py#L480"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.PLSRegression.fit_transform" title="Link to this definition">#</a></dt>
 <dd><p>Learn and apply the dimension reduction on the train data.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1450,7 +1450,7 @@ <h1>PLSRegression<a class="headerlink" href="#plsregression" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cross_decomposition.PLSRegression.get_feature_names_out">
-<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L995"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.PLSRegression.get_feature_names_out" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L936"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.PLSRegression.get_feature_names_out" title="Link to this definition">#</a></dt>
 <dd><p>Get output feature names for transformation.</p>
 <p>The feature names out will prefixed by the lowercased class name. For
 example, if the transformer outputs 3 features, then the feature names
@@ -1473,7 +1473,7 @@ <h1>PLSRegression<a class="headerlink" href="#plsregression" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cross_decomposition.PLSRegression.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.PLSRegression.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.PLSRegression.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1490,7 +1490,7 @@ <h1>PLSRegression<a class="headerlink" href="#plsregression" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cross_decomposition.PLSRegression.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.PLSRegression.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.PLSRegression.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1511,7 +1511,7 @@ <h1>PLSRegression<a class="headerlink" href="#plsregression" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cross_decomposition.PLSRegression.inverse_transform">
-<span class="sig-name descname"><span class="pre">inverse_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/cross_decomposition/_pls.py#L407"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.PLSRegression.inverse_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">inverse_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/cross_decomposition/_pls.py#L407"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.PLSRegression.inverse_transform" title="Link to this definition">#</a></dt>
 <dd><p>Transform data back to its original space.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1526,9 +1526,9 @@ <h1>PLSRegression<a class="headerlink" href="#plsregression" title="Link to this
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><dl class="simple">
-<dt><strong>X_reconstructed</strong><span class="classifier">ndarray of shape (n_samples, n_features)</span></dt><dd><p>Return the reconstructed <code class="docutils literal notranslate"><span class="pre">X</span></code> data.</p>
+<dt><strong>X_original</strong><span class="classifier">ndarray of shape (n_samples, n_features)</span></dt><dd><p>Return the reconstructed <code class="docutils literal notranslate"><span class="pre">X</span></code> data.</p>
 </dd>
-<dt><strong>y_reconstructed</strong><span class="classifier">ndarray of shape (n_samples, n_targets)</span></dt><dd><p>Return the reconstructed <code class="docutils literal notranslate"><span class="pre">X</span></code> target. Only returned when <code class="docutils literal notranslate"><span class="pre">y</span></code> is given.</p>
+<dt><strong>y_original</strong><span class="classifier">ndarray of shape (n_samples, n_targets)</span></dt><dd><p>Return the reconstructed <code class="docutils literal notranslate"><span class="pre">X</span></code> target. Only returned when <code class="docutils literal notranslate"><span class="pre">y</span></code> is given.</p>
 </dd>
 </dl>
 </dd>
@@ -1539,7 +1539,7 @@ <h1>PLSRegression<a class="headerlink" href="#plsregression" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cross_decomposition.PLSRegression.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/cross_decomposition/_pls.py#L451"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.PLSRegression.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/cross_decomposition/_pls.py#L451"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.PLSRegression.predict" title="Link to this definition">#</a></dt>
 <dd><p>Predict targets of given samples.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1565,7 +1565,7 @@ <h1>PLSRegression<a class="headerlink" href="#plsregression" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cross_decomposition.PLSRegression.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L619"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.PLSRegression.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L560"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.PLSRegression.score" title="Link to this definition">#</a></dt>
 <dd><p>Return <a class="reference internal" href="http://github.com/model_evaluation.html#r2-score"><span class="std std-ref">coefficient of determination</span></a> on test data.</p>
 <p>The coefficient of determination, <span class="math notranslate nohighlight">\(R^2\)</span>, is defined as
 <span class="math notranslate nohighlight">\((1 - \frac{u}{v})\)</span>, where <span class="math notranslate nohighlight">\(u\)</span> is the residual
@@ -1607,7 +1607,7 @@ <h1>PLSRegression<a class="headerlink" href="#plsregression" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cross_decomposition.PLSRegression.set_output">
-<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.PLSRegression.set_output" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.PLSRegression.set_output" title="Link to this definition">#</a></dt>
 <dd><p>Set output container.</p>
 <p>See <a class="reference internal" href="http://github.com/auto_examples/miscellaneous/plot_set_output.html#sphx-glr-auto-examples-miscellaneous-plot-set-output-py"><span class="std std-ref">Introducing the set_output API</span></a>
 for an example on how to use the API.</p>
@@ -1638,7 +1638,7 @@ <h1>PLSRegression<a class="headerlink" href="#plsregression" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cross_decomposition.PLSRegression.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.PLSRegression.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.PLSRegression.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1662,7 +1662,7 @@ <h1>PLSRegression<a class="headerlink" href="#plsregression" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cross_decomposition.PLSRegression.set_predict_request">
-<span class="sig-name descname"><span class="pre">set_predict_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.cross_decomposition.PLSRegression" title="sklearn.cross_decomposition._pls.PLSRegression"><span class="pre">PLSRegression</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.PLSRegression.set_predict_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_predict_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.cross_decomposition.PLSRegression" title="sklearn.cross_decomposition._pls.PLSRegression"><span class="pre">PLSRegression</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.PLSRegression.set_predict_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">predict</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1705,7 +1705,7 @@ <h1>PLSRegression<a class="headerlink" href="#plsregression" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cross_decomposition.PLSRegression.set_score_request">
-<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.cross_decomposition.PLSRegression" title="sklearn.cross_decomposition._pls.PLSRegression"><span class="pre">PLSRegression</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.PLSRegression.set_score_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.cross_decomposition.PLSRegression" title="sklearn.cross_decomposition._pls.PLSRegression"><span class="pre">PLSRegression</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.PLSRegression.set_score_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1748,7 +1748,7 @@ <h1>PLSRegression<a class="headerlink" href="#plsregression" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cross_decomposition.PLSRegression.set_transform_request">
-<span class="sig-name descname"><span class="pre">set_transform_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.cross_decomposition.PLSRegression" title="sklearn.cross_decomposition._pls.PLSRegression"><span class="pre">PLSRegression</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.PLSRegression.set_transform_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_transform_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.cross_decomposition.PLSRegression" title="sklearn.cross_decomposition._pls.PLSRegression"><span class="pre">PLSRegression</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.PLSRegression.set_transform_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">transform</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1791,7 +1791,7 @@ <h1>PLSRegression<a class="headerlink" href="#plsregression" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cross_decomposition.PLSRegression.transform">
-<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/cross_decomposition/_pls.py#L368"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.PLSRegression.transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/cross_decomposition/_pls.py#L368"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.PLSRegression.transform" title="Link to this definition">#</a></dt>
 <dd><p>Apply the dimension reduction.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.cross_decomposition.PLSSVD.html b/dev/modules/generated/sklearn.cross_decomposition.PLSSVD.html
index 271f5b3d3ce03..f91064dd9f2cd 100644
--- a/dev/modules/generated/sklearn.cross_decomposition.PLSSVD.html
+++ b/dev/modules/generated/sklearn.cross_decomposition.PLSSVD.html
@@ -40,7 +40,7 @@
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-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>PLSSVD<a class="headerlink" href="#plssvd" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.cross_decomposition.PLSSVD">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.cross_decomposition.</span></span><span class="sig-name descname"><span class="pre">PLSSVD</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_components</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/cross_decomposition/_pls.py#L903"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.PLSSVD" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.cross_decomposition.</span></span><span class="sig-name descname"><span class="pre">PLSSVD</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_components</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/cross_decomposition/_pls.py#L903"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.PLSSVD" title="Link to this definition">#</a></dt>
 <dd><p>Partial Least Square SVD.</p>
 <p>This transformer simply performs a SVD on the cross-covariance matrix
 <code class="docutils literal notranslate"><span class="pre">X'y</span></code>. It is able to project both the training data <code class="docutils literal notranslate"><span class="pre">X</span></code> and the targets
@@ -1376,7 +1376,7 @@ <h1>PLSSVD<a class="headerlink" href="#plssvd" title="Link to this heading">#</a
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cross_decomposition.PLSSVD.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/cross_decomposition/_pls.py#L982"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.PLSSVD.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/cross_decomposition/_pls.py#L982"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.PLSSVD.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit model to data.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1398,7 +1398,7 @@ <h1>PLSSVD<a class="headerlink" href="#plssvd" title="Link to this heading">#</a
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cross_decomposition.PLSSVD.fit_transform">
-<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/cross_decomposition/_pls.py#L1079"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.PLSSVD.fit_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/cross_decomposition/_pls.py#L1079"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.PLSSVD.fit_transform" title="Link to this definition">#</a></dt>
 <dd><p>Learn and apply the dimensionality reduction.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1421,7 +1421,7 @@ <h1>PLSSVD<a class="headerlink" href="#plssvd" title="Link to this heading">#</a
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cross_decomposition.PLSSVD.get_feature_names_out">
-<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L995"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.PLSSVD.get_feature_names_out" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L936"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.PLSSVD.get_feature_names_out" title="Link to this definition">#</a></dt>
 <dd><p>Get output feature names for transformation.</p>
 <p>The feature names out will prefixed by the lowercased class name. For
 example, if the transformer outputs 3 features, then the feature names
@@ -1444,7 +1444,7 @@ <h1>PLSSVD<a class="headerlink" href="#plssvd" title="Link to this heading">#</a
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cross_decomposition.PLSSVD.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.PLSSVD.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.PLSSVD.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1461,7 +1461,7 @@ <h1>PLSSVD<a class="headerlink" href="#plssvd" title="Link to this heading">#</a
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cross_decomposition.PLSSVD.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.PLSSVD.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.PLSSVD.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1482,7 +1482,7 @@ <h1>PLSSVD<a class="headerlink" href="#plssvd" title="Link to this heading">#</a
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cross_decomposition.PLSSVD.set_output">
-<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.PLSSVD.set_output" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.PLSSVD.set_output" title="Link to this definition">#</a></dt>
 <dd><p>Set output container.</p>
 <p>See <a class="reference internal" href="http://github.com/auto_examples/miscellaneous/plot_set_output.html#sphx-glr-auto-examples-miscellaneous-plot-set-output-py"><span class="std std-ref">Introducing the set_output API</span></a>
 for an example on how to use the API.</p>
@@ -1513,7 +1513,7 @@ <h1>PLSSVD<a class="headerlink" href="#plssvd" title="Link to this heading">#</a
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cross_decomposition.PLSSVD.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.PLSSVD.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.PLSSVD.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1537,7 +1537,7 @@ <h1>PLSSVD<a class="headerlink" href="#plssvd" title="Link to this heading">#</a
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.cross_decomposition.PLSSVD.transform">
-<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/cross_decomposition/_pls.py#L1047"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.PLSSVD.transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/cross_decomposition/_pls.py#L1047"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.PLSSVD.transform" title="Link to this definition">#</a></dt>
 <dd><p>Apply the dimensionality reduction.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.datasets.clear_data_home.html b/dev/modules/generated/sklearn.datasets.clear_data_home.html
index b24defd27f939..dc326b3c05924 100644
--- a/dev/modules/generated/sklearn.datasets.clear_data_home.html
+++ b/dev/modules/generated/sklearn.datasets.clear_data_home.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>clear_data_home<a class="headerlink" href="#clear-data-home" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.datasets.clear_data_home">
-<span class="sig-prename descclassname"><span class="pre">sklearn.datasets.</span></span><span class="sig-name descname"><span class="pre">clear_data_home</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data_home</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/datasets/_base.py#L89"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.datasets.clear_data_home" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.datasets.</span></span><span class="sig-name descname"><span class="pre">clear_data_home</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data_home</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/datasets/_base.py#L89"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.datasets.clear_data_home" title="Link to this definition">#</a></dt>
 <dd><p>Delete all the content of the data home cache.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.datasets.dump_svmlight_file.html b/dev/modules/generated/sklearn.datasets.dump_svmlight_file.html
index 048016a7afcb6..57d2e9a7dbf55 100644
--- a/dev/modules/generated/sklearn.datasets.dump_svmlight_file.html
+++ b/dev/modules/generated/sklearn.datasets.dump_svmlight_file.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>dump_svmlight_file<a class="headerlink" href="#dump-svmlight-file" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.datasets.dump_svmlight_file">
-<span class="sig-prename descclassname"><span class="pre">sklearn.datasets.</span></span><span class="sig-name descname"><span class="pre">dump_svmlight_file</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zero_based</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">comment</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">query_id</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">multilabel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/datasets/_svmlight_format_io.py#L445"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.datasets.dump_svmlight_file" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.datasets.</span></span><span class="sig-name descname"><span class="pre">dump_svmlight_file</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zero_based</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">comment</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">query_id</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">multilabel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/datasets/_svmlight_format_io.py#L445"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.datasets.dump_svmlight_file" title="Link to this definition">#</a></dt>
 <dd><p>Dump the dataset in svmlight / libsvm file format.</p>
 <p>This format is a text-based format, with one sample per line. It does
 not store zero valued features hence is suitable for sparse dataset.</p>
diff --git a/dev/modules/generated/sklearn.datasets.fetch_20newsgroups.html b/dev/modules/generated/sklearn.datasets.fetch_20newsgroups.html
index f35d336bdd9d7..0246f10ad56ea 100644
--- a/dev/modules/generated/sklearn.datasets.fetch_20newsgroups.html
+++ b/dev/modules/generated/sklearn.datasets.fetch_20newsgroups.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>fetch_20newsgroups<a class="headerlink" href="#fetch-20newsgroups" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.datasets.fetch_20newsgroups">
-<span class="sig-prename descclassname"><span class="pre">sklearn.datasets.</span></span><span class="sig-name descname"><span class="pre">fetch_20newsgroups</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_home</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">subset</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'train'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">categories</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">42</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">remove</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">()</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">download_if_missing</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_X_y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_retries</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">delay</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/datasets/_twenty_newsgroups.py#L164"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.datasets.fetch_20newsgroups" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.datasets.</span></span><span class="sig-name descname"><span class="pre">fetch_20newsgroups</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_home</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">subset</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'train'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">categories</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">42</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">remove</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">()</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">download_if_missing</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_X_y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_retries</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">delay</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/datasets/_twenty_newsgroups.py#L164"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.datasets.fetch_20newsgroups" title="Link to this definition">#</a></dt>
 <dd><p>Load the filenames and data from the 20 newsgroups dataset (classification).</p>
 <p>Download it if necessary.</p>
 <div class="pst-scrollable-table-container"><table class="table">
diff --git a/dev/modules/generated/sklearn.datasets.fetch_20newsgroups_vectorized.html b/dev/modules/generated/sklearn.datasets.fetch_20newsgroups_vectorized.html
index bb6cebf0440c3..1fccfe1d97670 100644
--- a/dev/modules/generated/sklearn.datasets.fetch_20newsgroups_vectorized.html
+++ b/dev/modules/generated/sklearn.datasets.fetch_20newsgroups_vectorized.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>fetch_20newsgroups_vectorized<a class="headerlink" href="#fetch-20newsgroups-vectorized" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.datasets.fetch_20newsgroups_vectorized">
-<span class="sig-prename descclassname"><span class="pre">sklearn.datasets.</span></span><span class="sig-name descname"><span class="pre">fetch_20newsgroups_vectorized</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">subset</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'train'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">remove</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">()</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_home</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">download_if_missing</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_X_y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">normalize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">as_frame</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_retries</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">delay</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/datasets/_twenty_newsgroups.py#L391"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.datasets.fetch_20newsgroups_vectorized" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.datasets.</span></span><span class="sig-name descname"><span class="pre">fetch_20newsgroups_vectorized</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">subset</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'train'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">remove</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">()</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_home</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">download_if_missing</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_X_y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">normalize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">as_frame</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_retries</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">delay</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/datasets/_twenty_newsgroups.py#L391"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.datasets.fetch_20newsgroups_vectorized" title="Link to this definition">#</a></dt>
 <dd><p>Load and vectorize the 20 newsgroups dataset (classification).</p>
 <p>Download it if necessary.</p>
 <p>This is a convenience function; the transformation is done using the
diff --git a/dev/modules/generated/sklearn.datasets.fetch_california_housing.html b/dev/modules/generated/sklearn.datasets.fetch_california_housing.html
index c030d99dcc096..3a36dbaf10277 100644
--- a/dev/modules/generated/sklearn.datasets.fetch_california_housing.html
+++ b/dev/modules/generated/sklearn.datasets.fetch_california_housing.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>fetch_california_housing<a class="headerlink" href="#fetch-california-housing" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.datasets.fetch_california_housing">
-<span class="sig-prename descclassname"><span class="pre">sklearn.datasets.</span></span><span class="sig-name descname"><span class="pre">fetch_california_housing</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_home</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">download_if_missing</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_X_y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">as_frame</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_retries</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">delay</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/datasets/_california_housing.py#L56"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.datasets.fetch_california_housing" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.datasets.</span></span><span class="sig-name descname"><span class="pre">fetch_california_housing</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_home</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">download_if_missing</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_X_y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">as_frame</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_retries</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">delay</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/datasets/_california_housing.py#L56"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.datasets.fetch_california_housing" title="Link to this definition">#</a></dt>
 <dd><p>Load the California housing dataset (regression).</p>
 <div class="pst-scrollable-table-container"><table class="table">
 <tbody>
diff --git a/dev/modules/generated/sklearn.datasets.fetch_covtype.html b/dev/modules/generated/sklearn.datasets.fetch_covtype.html
index a53ee75c9b81a..c3df2930bc451 100644
--- a/dev/modules/generated/sklearn.datasets.fetch_covtype.html
+++ b/dev/modules/generated/sklearn.datasets.fetch_covtype.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>fetch_covtype<a class="headerlink" href="#fetch-covtype" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.datasets.fetch_covtype">
-<span class="sig-prename descclassname"><span class="pre">sklearn.datasets.</span></span><span class="sig-name descname"><span class="pre">fetch_covtype</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_home</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">download_if_missing</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_X_y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">as_frame</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_retries</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">delay</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/datasets/_covtype.py#L66"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.datasets.fetch_covtype" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.datasets.</span></span><span class="sig-name descname"><span class="pre">fetch_covtype</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_home</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">download_if_missing</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_X_y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">as_frame</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_retries</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">delay</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/datasets/_covtype.py#L66"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.datasets.fetch_covtype" title="Link to this definition">#</a></dt>
 <dd><p>Load the covertype dataset (classification).</p>
 <p>Download it if necessary.</p>
 <div class="pst-scrollable-table-container"><table class="table">
diff --git a/dev/modules/generated/sklearn.datasets.fetch_file.html b/dev/modules/generated/sklearn.datasets.fetch_file.html
index 07df11a35de5a..4f6a6e700f998 100644
--- a/dev/modules/generated/sklearn.datasets.fetch_file.html
+++ b/dev/modules/generated/sklearn.datasets.fetch_file.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>fetch_file<a class="headerlink" href="#fetch-file" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.datasets.fetch_file">
-<span class="sig-prename descclassname"><span class="pre">sklearn.datasets.</span></span><span class="sig-name descname"><span class="pre">fetch_file</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">url</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">folder</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">local_filename</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sha256</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_retries</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">delay</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/datasets/_base.py#L1584"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.datasets.fetch_file" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.datasets.</span></span><span class="sig-name descname"><span class="pre">fetch_file</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">url</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">folder</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">local_filename</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sha256</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_retries</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">delay</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/datasets/_base.py#L1584"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.datasets.fetch_file" title="Link to this definition">#</a></dt>
 <dd><p>Fetch a file from the web if not already present in the local folder.</p>
 <p>If the file already exists locally (and the SHA256 checksums match when
 provided), the path to the local file is returned without re-downloading.</p>
diff --git a/dev/modules/generated/sklearn.datasets.fetch_kddcup99.html b/dev/modules/generated/sklearn.datasets.fetch_kddcup99.html
index 395cddc738ba5..dc3b6f4863b20 100644
--- a/dev/modules/generated/sklearn.datasets.fetch_kddcup99.html
+++ b/dev/modules/generated/sklearn.datasets.fetch_kddcup99.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>fetch_kddcup99<a class="headerlink" href="#fetch-kddcup99" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.datasets.fetch_kddcup99">
-<span class="sig-prename descclassname"><span class="pre">sklearn.datasets.</span></span><span class="sig-name descname"><span class="pre">fetch_kddcup99</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">subset</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_home</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">percent10</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">download_if_missing</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_X_y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">as_frame</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_retries</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">delay</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/datasets/_kddcup99.py#L54"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.datasets.fetch_kddcup99" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.datasets.</span></span><span class="sig-name descname"><span class="pre">fetch_kddcup99</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">subset</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_home</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">percent10</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">download_if_missing</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_X_y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">as_frame</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_retries</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">delay</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/datasets/_kddcup99.py#L54"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.datasets.fetch_kddcup99" title="Link to this definition">#</a></dt>
 <dd><p>Load the kddcup99 dataset (classification).</p>
 <p>Download it if necessary.</p>
 <div class="pst-scrollable-table-container"><table class="table">
diff --git a/dev/modules/generated/sklearn.datasets.fetch_lfw_pairs.html b/dev/modules/generated/sklearn.datasets.fetch_lfw_pairs.html
index d11a391672be0..a4a321a50ec58 100644
--- a/dev/modules/generated/sklearn.datasets.fetch_lfw_pairs.html
+++ b/dev/modules/generated/sklearn.datasets.fetch_lfw_pairs.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>fetch_lfw_pairs<a class="headerlink" href="#fetch-lfw-pairs" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.datasets.fetch_lfw_pairs">
-<span class="sig-prename descclassname"><span class="pre">sklearn.datasets.</span></span><span class="sig-name descname"><span class="pre">fetch_lfw_pairs</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">subset</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'train'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_home</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">funneled</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">resize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">color</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">slice_</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(slice(70,</span> <span class="pre">195,</span> <span class="pre">None),</span> <span class="pre">slice(78,</span> <span class="pre">172,</span> <span class="pre">None))</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">download_if_missing</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_retries</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">delay</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/datasets/_lfw.py#L480"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.datasets.fetch_lfw_pairs" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.datasets.</span></span><span class="sig-name descname"><span class="pre">fetch_lfw_pairs</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">subset</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'train'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_home</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">funneled</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">resize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">color</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">slice_</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(slice(70,</span> <span class="pre">195,</span> <span class="pre">None),</span> <span class="pre">slice(78,</span> <span class="pre">172,</span> <span class="pre">None))</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">download_if_missing</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_retries</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">delay</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/datasets/_lfw.py#L480"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.datasets.fetch_lfw_pairs" title="Link to this definition">#</a></dt>
 <dd><p>Load the Labeled Faces in the Wild (LFW) pairs dataset (classification).</p>
 <p>Download it if necessary.</p>
 <div class="pst-scrollable-table-container"><table class="table">
diff --git a/dev/modules/generated/sklearn.datasets.fetch_lfw_people.html b/dev/modules/generated/sklearn.datasets.fetch_lfw_people.html
index 4f46859ad4d8e..02829aff6743c 100644
--- a/dev/modules/generated/sklearn.datasets.fetch_lfw_people.html
+++ b/dev/modules/generated/sklearn.datasets.fetch_lfw_people.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>fetch_lfw_people<a class="headerlink" href="#fetch-lfw-people" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.datasets.fetch_lfw_people">
-<span class="sig-prename descclassname"><span class="pre">sklearn.datasets.</span></span><span class="sig-name descname"><span class="pre">fetch_lfw_people</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_home</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">funneled</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">resize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_faces_per_person</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">color</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">slice_</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(slice(70,</span> <span class="pre">195,</span> <span class="pre">None),</span> <span class="pre">slice(78,</span> <span class="pre">172,</span> <span class="pre">None))</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">download_if_missing</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_X_y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_retries</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">delay</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/datasets/_lfw.py#L247"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.datasets.fetch_lfw_people" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.datasets.</span></span><span class="sig-name descname"><span class="pre">fetch_lfw_people</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_home</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">funneled</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">resize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_faces_per_person</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">color</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">slice_</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(slice(70,</span> <span class="pre">195,</span> <span class="pre">None),</span> <span class="pre">slice(78,</span> <span class="pre">172,</span> <span class="pre">None))</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">download_if_missing</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_X_y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_retries</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">delay</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/datasets/_lfw.py#L247"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.datasets.fetch_lfw_people" title="Link to this definition">#</a></dt>
 <dd><p>Load the Labeled Faces in the Wild (LFW) people dataset (classification).</p>
 <p>Download it if necessary.</p>
 <div class="pst-scrollable-table-container"><table class="table">
diff --git a/dev/modules/generated/sklearn.datasets.fetch_olivetti_faces.html b/dev/modules/generated/sklearn.datasets.fetch_olivetti_faces.html
index 3a12bd7c61db0..e19b1a163fc10 100644
--- a/dev/modules/generated/sklearn.datasets.fetch_olivetti_faces.html
+++ b/dev/modules/generated/sklearn.datasets.fetch_olivetti_faces.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>fetch_olivetti_faces<a class="headerlink" href="#fetch-olivetti-faces" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.datasets.fetch_olivetti_faces">
-<span class="sig-prename descclassname"><span class="pre">sklearn.datasets.</span></span><span class="sig-name descname"><span class="pre">fetch_olivetti_faces</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_home</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">download_if_missing</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_X_y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_retries</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">delay</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/datasets/_olivetti_faces.py#L38"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.datasets.fetch_olivetti_faces" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.datasets.</span></span><span class="sig-name descname"><span class="pre">fetch_olivetti_faces</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_home</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">download_if_missing</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_X_y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_retries</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">delay</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/datasets/_olivetti_faces.py#L38"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.datasets.fetch_olivetti_faces" title="Link to this definition">#</a></dt>
 <dd><p>Load the Olivetti faces data-set from AT&amp;T (classification).</p>
 <p>Download it if necessary.</p>
 <div class="pst-scrollable-table-container"><table class="table">
diff --git a/dev/modules/generated/sklearn.datasets.fetch_openml.html b/dev/modules/generated/sklearn.datasets.fetch_openml.html
index 9e71d76b00110..464c617d7ce25 100644
--- a/dev/modules/generated/sklearn.datasets.fetch_openml.html
+++ b/dev/modules/generated/sklearn.datasets.fetch_openml.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>fetch_openml<a class="headerlink" href="#fetch-openml" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.datasets.fetch_openml">
-<span class="sig-prename descclassname"><span class="pre">sklearn.datasets.</span></span><span class="sig-name descname"><span class="pre">fetch_openml</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">version</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'active'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_id</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_home</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/os.html#os.PathLike" title="(in Python v3.13)"><span class="pre">PathLike</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target_column</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.List" title="(in Python v3.13)"><span class="pre">List</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'default-target'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cache</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_X_y</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">as_frame</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_retries</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">delay</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">parser</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">read_csv_kwargs</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Dict" title="(in Python v3.13)"><span class="pre">Dict</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/datasets/_openml.py#L755"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.datasets.fetch_openml" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.datasets.</span></span><span class="sig-name descname"><span class="pre">fetch_openml</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">version</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'active'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_id</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_home</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/os.html#os.PathLike" title="(in Python v3.13)"><span class="pre">PathLike</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target_column</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.List" title="(in Python v3.13)"><span class="pre">List</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'default-target'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cache</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_X_y</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">as_frame</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_retries</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">delay</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">parser</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">read_csv_kwargs</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Dict" title="(in Python v3.13)"><span class="pre">Dict</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/datasets/_openml.py#L755"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.datasets.fetch_openml" title="Link to this definition">#</a></dt>
 <dd><p>Fetch dataset from openml by name or dataset id.</p>
 <p>Datasets are uniquely identified by either an integer ID or by a
 combination of name and version (i.e. there might be multiple
diff --git a/dev/modules/generated/sklearn.datasets.fetch_rcv1.html b/dev/modules/generated/sklearn.datasets.fetch_rcv1.html
index eeff5b4951d3f..25d86b6d5ddc1 100644
--- a/dev/modules/generated/sklearn.datasets.fetch_rcv1.html
+++ b/dev/modules/generated/sklearn.datasets.fetch_rcv1.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>fetch_rcv1<a class="headerlink" href="#fetch-rcv1" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.datasets.fetch_rcv1">
-<span class="sig-prename descclassname"><span class="pre">sklearn.datasets.</span></span><span class="sig-name descname"><span class="pre">fetch_rcv1</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_home</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">subset</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'all'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">download_if_missing</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_X_y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_retries</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">delay</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/datasets/_rcv1.py#L76"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.datasets.fetch_rcv1" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.datasets.</span></span><span class="sig-name descname"><span class="pre">fetch_rcv1</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_home</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">subset</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'all'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">download_if_missing</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_X_y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_retries</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">delay</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/datasets/_rcv1.py#L76"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.datasets.fetch_rcv1" title="Link to this definition">#</a></dt>
 <dd><p>Load the RCV1 multilabel dataset (classification).</p>
 <p>Download it if necessary.</p>
 <p>Version: RCV1-v2, vectors, full sets, topics multilabels.</p>
diff --git a/dev/modules/generated/sklearn.datasets.fetch_species_distributions.html b/dev/modules/generated/sklearn.datasets.fetch_species_distributions.html
index 2ad340ae445fc..db6d91232c2af 100644
--- a/dev/modules/generated/sklearn.datasets.fetch_species_distributions.html
+++ b/dev/modules/generated/sklearn.datasets.fetch_species_distributions.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>fetch_species_distributions<a class="headerlink" href="#fetch-species-distributions" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.datasets.fetch_species_distributions">
-<span class="sig-prename descclassname"><span class="pre">sklearn.datasets.</span></span><span class="sig-name descname"><span class="pre">fetch_species_distributions</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_home</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">download_if_missing</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_retries</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">delay</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/datasets/_species_distributions.py#L130"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.datasets.fetch_species_distributions" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.datasets.</span></span><span class="sig-name descname"><span class="pre">fetch_species_distributions</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_home</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">download_if_missing</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_retries</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">delay</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/datasets/_species_distributions.py#L130"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.datasets.fetch_species_distributions" title="Link to this definition">#</a></dt>
 <dd><p>Loader for species distribution dataset from Phillips et. al. (2006).</p>
 <p>Read more in the <a class="reference internal" href="http://github.com/datasets/real_world.html#species-distribution-dataset"><span class="std std-ref">User Guide</span></a>.</p>
 <dl class="field-list">
diff --git a/dev/modules/generated/sklearn.datasets.get_data_home.html b/dev/modules/generated/sklearn.datasets.get_data_home.html
index 0bc7edeeb26ab..78bf9db6d68d0 100644
--- a/dev/modules/generated/sklearn.datasets.get_data_home.html
+++ b/dev/modules/generated/sklearn.datasets.get_data_home.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>get_data_home<a class="headerlink" href="#get-data-home" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.datasets.get_data_home">
-<span class="sig-prename descclassname"><span class="pre">sklearn.datasets.</span></span><span class="sig-name descname"><span class="pre">get_data_home</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data_home</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/datasets/_base.py#L42"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.datasets.get_data_home" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.datasets.</span></span><span class="sig-name descname"><span class="pre">get_data_home</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data_home</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/datasets/_base.py#L42"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.datasets.get_data_home" title="Link to this definition">#</a></dt>
 <dd><p>Return the path of the scikit-learn data directory.</p>
 <p>This folder is used by some large dataset loaders to avoid downloading the
 data several times.</p>
diff --git a/dev/modules/generated/sklearn.datasets.load_breast_cancer.html b/dev/modules/generated/sklearn.datasets.load_breast_cancer.html
index a9719dec0d9f6..66310c091f6ac 100644
--- a/dev/modules/generated/sklearn.datasets.load_breast_cancer.html
+++ b/dev/modules/generated/sklearn.datasets.load_breast_cancer.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>load_breast_cancer<a class="headerlink" href="#load-breast-cancer" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.datasets.load_breast_cancer">
-<span class="sig-prename descclassname"><span class="pre">sklearn.datasets.</span></span><span class="sig-name descname"><span class="pre">load_breast_cancer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_X_y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">as_frame</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/datasets/_base.py#L749"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.datasets.load_breast_cancer" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.datasets.</span></span><span class="sig-name descname"><span class="pre">load_breast_cancer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_X_y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">as_frame</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/datasets/_base.py#L749"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.datasets.load_breast_cancer" title="Link to this definition">#</a></dt>
 <dd><p>Load and return the breast cancer Wisconsin dataset (classification).</p>
 <p>The breast cancer dataset is a classic and very easy binary classification
 dataset.</p>
diff --git a/dev/modules/generated/sklearn.datasets.load_diabetes.html b/dev/modules/generated/sklearn.datasets.load_diabetes.html
index 57f901593f21c..8f137f4d91435 100644
--- a/dev/modules/generated/sklearn.datasets.load_diabetes.html
+++ b/dev/modules/generated/sklearn.datasets.load_diabetes.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>load_diabetes<a class="headerlink" href="#load-diabetes" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.datasets.load_diabetes">
-<span class="sig-prename descclassname"><span class="pre">sklearn.datasets.</span></span><span class="sig-name descname"><span class="pre">load_diabetes</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_X_y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">as_frame</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scaled</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/datasets/_base.py#L1042"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.datasets.load_diabetes" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.datasets.</span></span><span class="sig-name descname"><span class="pre">load_diabetes</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_X_y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">as_frame</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scaled</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/datasets/_base.py#L1042"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.datasets.load_diabetes" title="Link to this definition">#</a></dt>
 <dd><p>Load and return the diabetes dataset (regression).</p>
 <div class="pst-scrollable-table-container"><table class="table">
 <tbody>
diff --git a/dev/modules/generated/sklearn.datasets.load_digits.html b/dev/modules/generated/sklearn.datasets.load_digits.html
index 0bdff40a7c8b1..c37de2f10d658 100644
--- a/dev/modules/generated/sklearn.datasets.load_digits.html
+++ b/dev/modules/generated/sklearn.datasets.load_digits.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>load_digits<a class="headerlink" href="#load-digits" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.datasets.load_digits">
-<span class="sig-prename descclassname"><span class="pre">sklearn.datasets.</span></span><span class="sig-name descname"><span class="pre">load_digits</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_class</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_X_y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">as_frame</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/datasets/_base.py#L902"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.datasets.load_digits" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.datasets.</span></span><span class="sig-name descname"><span class="pre">load_digits</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_class</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_X_y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">as_frame</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/datasets/_base.py#L902"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.datasets.load_digits" title="Link to this definition">#</a></dt>
 <dd><p>Load and return the digits dataset (classification).</p>
 <p>Each datapoint is a 8x8 image of a digit.</p>
 <div class="pst-scrollable-table-container"><table class="table">
@@ -1482,8 +1482,8 @@ <h2>Gallery examples<a class="headerlink" href="#gallery-examples" title="Link t
 <p><a class="reference internal" href="http://github.com/auto_examples/semi_supervised/plot_label_propagation_digits.html#sphx-glr-auto-examples-semi-supervised-plot-label-propagation-digits-py"><span class="std std-ref">Label Propagation digits: Demonstrating performance</span></a></p>
   <div class="sphx-glr-thumbnail-title">Label Propagation digits: Demonstrating performance</div>
 </div><div class="sphx-glr-thumbcontainer" tooltip="Demonstrates an active learning technique to learn handwritten digits using label propagation."><img alt="" src="http://github.com/_images/sphx_glr_plot_label_propagation_digits_active_learning_thumb.png" />
-<p><a class="reference internal" href="http://github.com/auto_examples/semi_supervised/plot_label_propagation_digits_active_learning.html#sphx-glr-auto-examples-semi-supervised-plot-label-propagation-digits-active-learning-py"><span class="std std-ref">Label Propagation digits active learning</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Label Propagation digits active learning</div>
+<p><a class="reference internal" href="http://github.com/auto_examples/semi_supervised/plot_label_propagation_digits_active_learning.html#sphx-glr-auto-examples-semi-supervised-plot-label-propagation-digits-active-learning-py"><span class="std std-ref">Label Propagation digits: Active learning</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Label Propagation digits: Active learning</div>
 </div></div></section>
 </section>
 
diff --git a/dev/modules/generated/sklearn.datasets.load_files.html b/dev/modules/generated/sklearn.datasets.load_files.html
index 1dc463450d635..099d0e0746486 100644
--- a/dev/modules/generated/sklearn.datasets.load_files.html
+++ b/dev/modules/generated/sklearn.datasets.load_files.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>load_files<a class="headerlink" href="#load-files" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.datasets.load_files">
-<span class="sig-prename descclassname"><span class="pre">sklearn.datasets.</span></span><span class="sig-name descname"><span class="pre">load_files</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">container_path</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">description</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">categories</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">load_content</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">encoding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">decode_error</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'strict'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">allowed_extensions</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/datasets/_base.py#L131"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.datasets.load_files" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.datasets.</span></span><span class="sig-name descname"><span class="pre">load_files</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">container_path</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">description</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">categories</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">load_content</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">encoding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">decode_error</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'strict'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">allowed_extensions</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/datasets/_base.py#L131"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.datasets.load_files" title="Link to this definition">#</a></dt>
 <dd><p>Load text files with categories as subfolder names.</p>
 <p>Individual samples are assumed to be files stored a two levels folder
 structure such as the following:</p>
diff --git a/dev/modules/generated/sklearn.datasets.load_iris.html b/dev/modules/generated/sklearn.datasets.load_iris.html
index 85c8f0d15da2d..5f6486c38a193 100644
--- a/dev/modules/generated/sklearn.datasets.load_iris.html
+++ b/dev/modules/generated/sklearn.datasets.load_iris.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>load_iris<a class="headerlink" href="#load-iris" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.datasets.load_iris">
-<span class="sig-prename descclassname"><span class="pre">sklearn.datasets.</span></span><span class="sig-name descname"><span class="pre">load_iris</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_X_y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">as_frame</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/datasets/_base.py#L621"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.datasets.load_iris" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.datasets.</span></span><span class="sig-name descname"><span class="pre">load_iris</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_X_y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">as_frame</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/datasets/_base.py#L621"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.datasets.load_iris" title="Link to this definition">#</a></dt>
 <dd><p>Load and return the iris dataset (classification).</p>
 <p>The iris dataset is a classic and very easy multi-class classification
 dataset.</p>
@@ -1438,9 +1438,6 @@ <h2>Gallery examples<a class="headerlink" href="#gallery-examples" title="Link t
 </div><div class="sphx-glr-thumbcontainer" tooltip="Plot the decision surfaces of forests of randomized trees trained on pairs of features of the iris dataset."><img alt="" src="http://github.com/_images/sphx_glr_plot_forest_iris_thumb.png" />
 <p><a class="reference internal" href="http://github.com/auto_examples/ensemble/plot_forest_iris.html#sphx-glr-auto-examples-ensemble-plot-forest-iris-py"><span class="std std-ref">Plot the decision surfaces of ensembles of trees on the iris dataset</span></a></p>
   <div class="sphx-glr-thumbnail-title">Plot the decision surfaces of ensembles of trees on the iris dataset</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="Plot the decision boundaries of a VotingClassifier for two features of the Iris dataset."><img alt="" src="http://github.com/_images/sphx_glr_plot_voting_decision_regions_thumb.png" />
-<p><a class="reference internal" href="http://github.com/auto_examples/ensemble/plot_voting_decision_regions.html#sphx-glr-auto-examples-ensemble-plot-voting-decision-regions-py"><span class="std std-ref">Plot the decision boundaries of a VotingClassifier</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Plot the decision boundaries of a VotingClassifier</div>
 </div><div class="sphx-glr-thumbcontainer" tooltip="This notebook is an example of using univariate feature selection to improve classification accuracy on a noisy dataset."><img alt="" src="http://github.com/_images/sphx_glr_plot_feature_selection_thumb.png" />
 <p><a class="reference internal" href="http://github.com/auto_examples/feature_selection/plot_feature_selection.html#sphx-glr-auto-examples-feature-selection-plot-feature-selection-py"><span class="std std-ref">Univariate Feature Selection</span></a></p>
   <div class="sphx-glr-thumbnail-title">Univariate Feature Selection</div>
diff --git a/dev/modules/generated/sklearn.datasets.load_linnerud.html b/dev/modules/generated/sklearn.datasets.load_linnerud.html
index e21aefe812383..574fa1e0e4dd9 100644
--- a/dev/modules/generated/sklearn.datasets.load_linnerud.html
+++ b/dev/modules/generated/sklearn.datasets.load_linnerud.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>load_linnerud<a class="headerlink" href="#load-linnerud" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.datasets.load_linnerud">
-<span class="sig-prename descclassname"><span class="pre">sklearn.datasets.</span></span><span class="sig-name descname"><span class="pre">load_linnerud</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_X_y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">as_frame</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/datasets/_base.py#L1166"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.datasets.load_linnerud" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.datasets.</span></span><span class="sig-name descname"><span class="pre">load_linnerud</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_X_y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">as_frame</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/datasets/_base.py#L1166"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.datasets.load_linnerud" title="Link to this definition">#</a></dt>
 <dd><p>Load and return the physical exercise Linnerud dataset.</p>
 <p>This dataset is suitable for multi-output regression tasks.</p>
 <div class="pst-scrollable-table-container"><table class="table">
diff --git a/dev/modules/generated/sklearn.datasets.load_sample_image.html b/dev/modules/generated/sklearn.datasets.load_sample_image.html
index 388fd9ebdce2c..c0c03a3bc124c 100644
--- a/dev/modules/generated/sklearn.datasets.load_sample_image.html
+++ b/dev/modules/generated/sklearn.datasets.load_sample_image.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>load_sample_image<a class="headerlink" href="#load-sample-image" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.datasets.load_sample_image">
-<span class="sig-prename descclassname"><span class="pre">sklearn.datasets.</span></span><span class="sig-name descname"><span class="pre">load_sample_image</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">image_name</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/datasets/_base.py#L1356"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.datasets.load_sample_image" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.datasets.</span></span><span class="sig-name descname"><span class="pre">load_sample_image</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">image_name</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/datasets/_base.py#L1356"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.datasets.load_sample_image" title="Link to this definition">#</a></dt>
 <dd><p>Load the numpy array of a single sample image.</p>
 <p>Read more in the <a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html#sample-images"><span class="std std-ref">User Guide</span></a>.</p>
 <dl class="field-list">
diff --git a/dev/modules/generated/sklearn.datasets.load_sample_images.html b/dev/modules/generated/sklearn.datasets.load_sample_images.html
index ce87f78057aca..492d04d311817 100644
--- a/dev/modules/generated/sklearn.datasets.load_sample_images.html
+++ b/dev/modules/generated/sklearn.datasets.load_sample_images.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>load_sample_images<a class="headerlink" href="#load-sample-images" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.datasets.load_sample_images">
-<span class="sig-prename descclassname"><span class="pre">sklearn.datasets.</span></span><span class="sig-name descname"><span class="pre">load_sample_images</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/datasets/_base.py#L1293"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.datasets.load_sample_images" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.datasets.</span></span><span class="sig-name descname"><span class="pre">load_sample_images</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/datasets/_base.py#L1293"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.datasets.load_sample_images" title="Link to this definition">#</a></dt>
 <dd><p>Load sample images for image manipulation.</p>
 <p>Loads both, <code class="docutils literal notranslate"><span class="pre">china</span></code> and <code class="docutils literal notranslate"><span class="pre">flower</span></code>.</p>
 <p>Read more in the <a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html#sample-images"><span class="std std-ref">User Guide</span></a>.</p>
diff --git a/dev/modules/generated/sklearn.datasets.load_svmlight_file.html b/dev/modules/generated/sklearn.datasets.load_svmlight_file.html
index e96d2971b0fbf..2a8aad6a5d236 100644
--- a/dev/modules/generated/sklearn.datasets.load_svmlight_file.html
+++ b/dev/modules/generated/sklearn.datasets.load_svmlight_file.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>load_svmlight_file<a class="headerlink" href="#load-svmlight-file" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.datasets.load_svmlight_file">
-<span class="sig-prename descclassname"><span class="pre">sklearn.datasets.</span></span><span class="sig-name descname"><span class="pre">load_svmlight_file</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">f</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_features=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype=&lt;class</span> <span class="pre">'numpy.float64'&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">multilabel=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zero_based='auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">query_id=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">offset=0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">length=-1</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/datasets/_svmlight_format_io.py#L32"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.datasets.load_svmlight_file" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.datasets.</span></span><span class="sig-name descname"><span class="pre">load_svmlight_file</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">f</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_features=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype=&lt;class</span> <span class="pre">'numpy.float64'&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">multilabel=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zero_based='auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">query_id=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">offset=0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">length=-1</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/datasets/_svmlight_format_io.py#L32"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.datasets.load_svmlight_file" title="Link to this definition">#</a></dt>
 <dd><p>Load datasets in the svmlight / libsvm format into sparse CSR matrix.</p>
 <p>This format is a text-based format, with one sample per line. It does
 not store zero valued features hence is suitable for sparse dataset.</p>
diff --git a/dev/modules/generated/sklearn.datasets.load_svmlight_files.html b/dev/modules/generated/sklearn.datasets.load_svmlight_files.html
index a2d6c1c03b7ea..118ee4c1f41fe 100644
--- a/dev/modules/generated/sklearn.datasets.load_svmlight_files.html
+++ b/dev/modules/generated/sklearn.datasets.load_svmlight_files.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>load_svmlight_files<a class="headerlink" href="#load-svmlight-files" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.datasets.load_svmlight_files">
-<span class="sig-prename descclassname"><span class="pre">sklearn.datasets.</span></span><span class="sig-name descname"><span class="pre">load_svmlight_files</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">files</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_features=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype=&lt;class</span> <span class="pre">'numpy.float64'&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">multilabel=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zero_based='auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">query_id=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">offset=0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">length=-1</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/datasets/_svmlight_format_io.py#L233"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.datasets.load_svmlight_files" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.datasets.</span></span><span class="sig-name descname"><span class="pre">load_svmlight_files</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">files</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_features=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype=&lt;class</span> <span class="pre">'numpy.float64'&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">multilabel=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zero_based='auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">query_id=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">offset=0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">length=-1</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/datasets/_svmlight_format_io.py#L233"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.datasets.load_svmlight_files" title="Link to this definition">#</a></dt>
 <dd><p>Load dataset from multiple files in SVMlight format.</p>
 <p>This function is equivalent to mapping load_svmlight_file over a list of
 files, except that the results are concatenated into a single, flat list
diff --git a/dev/modules/generated/sklearn.datasets.load_wine.html b/dev/modules/generated/sklearn.datasets.load_wine.html
index 86ba8bddf9823..5d971a410e55b 100644
--- a/dev/modules/generated/sklearn.datasets.load_wine.html
+++ b/dev/modules/generated/sklearn.datasets.load_wine.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>load_wine<a class="headerlink" href="#load-wine" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.datasets.load_wine">
-<span class="sig-prename descclassname"><span class="pre">sklearn.datasets.</span></span><span class="sig-name descname"><span class="pre">load_wine</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_X_y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">as_frame</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/datasets/_base.py#L494"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.datasets.load_wine" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.datasets.</span></span><span class="sig-name descname"><span class="pre">load_wine</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_X_y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">as_frame</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/datasets/_base.py#L494"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.datasets.load_wine" title="Link to this definition">#</a></dt>
 <dd><p>Load and return the wine dataset (classification).</p>
 <div class="versionadded">
 <p><span class="versionmodified added">Added in version 0.18.</span></p>
diff --git a/dev/modules/generated/sklearn.datasets.make_biclusters.html b/dev/modules/generated/sklearn.datasets.make_biclusters.html
index 86e8a7bb37626..657b469c56fd1 100644
--- a/dev/modules/generated/sklearn.datasets.make_biclusters.html
+++ b/dev/modules/generated/sklearn.datasets.make_biclusters.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>make_biclusters<a class="headerlink" href="#make-biclusters" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.datasets.make_biclusters">
-<span class="sig-prename descclassname"><span class="pre">sklearn.datasets.</span></span><span class="sig-name descname"><span class="pre">make_biclusters</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_clusters</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">noise</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">minval</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">maxval</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/datasets/_samples_generator.py#L2120"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.datasets.make_biclusters" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.datasets.</span></span><span class="sig-name descname"><span class="pre">make_biclusters</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_clusters</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">noise</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">minval</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">maxval</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/datasets/_samples_generator.py#L2120"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.datasets.make_biclusters" title="Link to this definition">#</a></dt>
 <dd><p>Generate a constant block diagonal structure array for biclustering.</p>
 <p>Read more in the <a class="reference internal" href="http://github.com/datasets/sample_generators.html#sample-generators"><span class="std std-ref">User Guide</span></a>.</p>
 <dl class="field-list">
diff --git a/dev/modules/generated/sklearn.datasets.make_blobs.html b/dev/modules/generated/sklearn.datasets.make_blobs.html
index 9ce3518868719..169a0093d2781 100644
--- a/dev/modules/generated/sklearn.datasets.make_blobs.html
+++ b/dev/modules/generated/sklearn.datasets.make_blobs.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>make_blobs<a class="headerlink" href="#make-blobs" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.datasets.make_blobs">
-<span class="sig-prename descclassname"><span class="pre">sklearn.datasets.</span></span><span class="sig-name descname"><span class="pre">make_blobs</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">centers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cluster_std</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">center_box</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(-10.0,</span> <span class="pre">10.0)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_centers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/datasets/_samples_generator.py#L983"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.datasets.make_blobs" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.datasets.</span></span><span class="sig-name descname"><span class="pre">make_blobs</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">centers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cluster_std</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">center_box</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(-10.0,</span> <span class="pre">10.0)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_centers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/datasets/_samples_generator.py#L983"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.datasets.make_blobs" title="Link to this definition">#</a></dt>
 <dd><p>Generate isotropic Gaussian blobs for clustering.</p>
 <p>Read more in the <a class="reference internal" href="http://github.com/datasets/sample_generators.html#sample-generators"><span class="std std-ref">User Guide</span></a>.</p>
 <dl class="field-list">
diff --git a/dev/modules/generated/sklearn.datasets.make_checkerboard.html b/dev/modules/generated/sklearn.datasets.make_checkerboard.html
index 791d4039ac844..99b44cba0597f 100644
--- a/dev/modules/generated/sklearn.datasets.make_checkerboard.html
+++ b/dev/modules/generated/sklearn.datasets.make_checkerboard.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>make_checkerboard<a class="headerlink" href="#make-checkerboard" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.datasets.make_checkerboard">
-<span class="sig-prename descclassname"><span class="pre">sklearn.datasets.</span></span><span class="sig-name descname"><span class="pre">make_checkerboard</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_clusters</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">noise</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">minval</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">maxval</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/datasets/_samples_generator.py#L2242"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.datasets.make_checkerboard" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.datasets.</span></span><span class="sig-name descname"><span class="pre">make_checkerboard</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_clusters</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">noise</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">minval</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">maxval</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/datasets/_samples_generator.py#L2242"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.datasets.make_checkerboard" title="Link to this definition">#</a></dt>
 <dd><p>Generate an array with block checkerboard structure for biclustering.</p>
 <p>Read more in the <a class="reference internal" href="http://github.com/datasets/sample_generators.html#sample-generators"><span class="std std-ref">User Guide</span></a>.</p>
 <dl class="field-list">
diff --git a/dev/modules/generated/sklearn.datasets.make_circles.html b/dev/modules/generated/sklearn.datasets.make_circles.html
index a4cda03395be7..308743bc9b6e4 100644
--- a/dev/modules/generated/sklearn.datasets.make_circles.html
+++ b/dev/modules/generated/sklearn.datasets.make_circles.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>make_circles<a class="headerlink" href="#make-circles" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.datasets.make_circles">
-<span class="sig-prename descclassname"><span class="pre">sklearn.datasets.</span></span><span class="sig-name descname"><span class="pre">make_circles</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">noise</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.8</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/datasets/_samples_generator.py#L799"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.datasets.make_circles" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.datasets.</span></span><span class="sig-name descname"><span class="pre">make_circles</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">noise</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.8</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/datasets/_samples_generator.py#L799"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.datasets.make_circles" title="Link to this definition">#</a></dt>
 <dd><p>Make a large circle containing a smaller circle in 2d.</p>
 <p>A simple toy dataset to visualize clustering and classification
 algorithms.</p>
@@ -1388,8 +1388,8 @@ <h2>Gallery examples<a class="headerlink" href="#gallery-examples" title="Link t
 <p><a class="reference internal" href="http://github.com/auto_examples/preprocessing/plot_discretization_classification.html#sphx-glr-auto-examples-preprocessing-plot-discretization-classification-py"><span class="std std-ref">Feature discretization</span></a></p>
   <div class="sphx-glr-thumbnail-title">Feature discretization</div>
 </div><div class="sphx-glr-thumbcontainer" tooltip="Example of LabelPropagation learning a complex internal structure to demonstrate &quot;manifold learning&quot;. The outer circle should be labeled &quot;red&quot; and the inner circle &quot;blue&quot;. Because both label groups lie inside their own distinct shape, we can see that the labels propagate correctly around the circle."><img alt="" src="http://github.com/_images/sphx_glr_plot_label_propagation_structure_thumb.png" />
-<p><a class="reference internal" href="http://github.com/auto_examples/semi_supervised/plot_label_propagation_structure.html#sphx-glr-auto-examples-semi-supervised-plot-label-propagation-structure-py"><span class="std std-ref">Label Propagation learning a complex structure</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Label Propagation learning a complex structure</div>
+<p><a class="reference internal" href="http://github.com/auto_examples/semi_supervised/plot_label_propagation_structure.html#sphx-glr-auto-examples-semi-supervised-plot-label-propagation-structure-py"><span class="std std-ref">Label Propagation circles: Learning a complex structure</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Label Propagation circles: Learning a complex structure</div>
 </div></div></section>
 </section>
 
diff --git a/dev/modules/generated/sklearn.datasets.make_classification.html b/dev/modules/generated/sklearn.datasets.make_classification.html
index faa268bce03ca..b51bf582febd1 100644
--- a/dev/modules/generated/sklearn.datasets.make_classification.html
+++ b/dev/modules/generated/sklearn.datasets.make_classification.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>make_classification<a class="headerlink" href="#make-classification" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.datasets.make_classification">
-<span class="sig-prename descclassname"><span class="pre">sklearn.datasets.</span></span><span class="sig-name descname"><span class="pre">make_classification</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">20</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_informative</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_redundant</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_repeated</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_classes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_clusters_per_class</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">flip_y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">class_sep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hypercube</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shift</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_X_y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/datasets/_samples_generator.py#L42"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.datasets.make_classification" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.datasets.</span></span><span class="sig-name descname"><span class="pre">make_classification</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">20</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_informative</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_redundant</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_repeated</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_classes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_clusters_per_class</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">flip_y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">class_sep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hypercube</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shift</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_X_y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/datasets/_samples_generator.py#L42"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.datasets.make_classification" title="Link to this definition">#</a></dt>
 <dd><p>Generate a random n-class classification problem.</p>
 <p>This initially creates clusters of points normally distributed (std=1)
 about vertices of an <code class="docutils literal notranslate"><span class="pre">n_informative</span></code>-dimensional hypercube with sides of
diff --git a/dev/modules/generated/sklearn.datasets.make_friedman1.html b/dev/modules/generated/sklearn.datasets.make_friedman1.html
index 79c860d0f9799..35a40a271d689 100644
--- a/dev/modules/generated/sklearn.datasets.make_friedman1.html
+++ b/dev/modules/generated/sklearn.datasets.make_friedman1.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>make_friedman1<a class="headerlink" href="#make-friedman1" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.datasets.make_friedman1">
-<span class="sig-prename descclassname"><span class="pre">sklearn.datasets.</span></span><span class="sig-name descname"><span class="pre">make_friedman1</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">noise</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/datasets/_samples_generator.py#L1163"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.datasets.make_friedman1" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.datasets.</span></span><span class="sig-name descname"><span class="pre">make_friedman1</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">noise</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/datasets/_samples_generator.py#L1163"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.datasets.make_friedman1" title="Link to this definition">#</a></dt>
 <dd><p>Generate the “Friedman #1” regression problem.</p>
 <p>This dataset is described in Friedman [1] and Breiman [2].</p>
 <p>Inputs <code class="docutils literal notranslate"><span class="pre">X</span></code> are independent features uniformly distributed on the interval
@@ -1362,7 +1362,7 @@ <h1>make_friedman1<a class="headerlink" href="#make-friedman1" title="Link to th
 <span class="gp">&gt;&gt;&gt; </span><span class="n">y</span><span class="o">.</span><span class="n">shape</span>
 <span class="go">(100,)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="nb">list</span><span class="p">(</span><span class="n">y</span><span class="p">[:</span><span class="mi">3</span><span class="p">])</span>
-<span class="go">[np.float64(16.8...), np.float64(5.8...), np.float64(9.4...)]</span>
+<span class="go">[np.float64(16.8), np.float64(5.87), np.float64(9.46)]</span>
 </pre></div>
 </div>
 </dd></dl>
diff --git a/dev/modules/generated/sklearn.datasets.make_friedman2.html b/dev/modules/generated/sklearn.datasets.make_friedman2.html
index 5e2df2a9e4fa0..b6d77b848be20 100644
--- a/dev/modules/generated/sklearn.datasets.make_friedman2.html
+++ b/dev/modules/generated/sklearn.datasets.make_friedman2.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>make_friedman2<a class="headerlink" href="#make-friedman2" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.datasets.make_friedman2">
-<span class="sig-prename descclassname"><span class="pre">sklearn.datasets.</span></span><span class="sig-name descname"><span class="pre">make_friedman2</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">noise</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/datasets/_samples_generator.py#L1247"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.datasets.make_friedman2" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.datasets.</span></span><span class="sig-name descname"><span class="pre">make_friedman2</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">noise</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/datasets/_samples_generator.py#L1247"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.datasets.make_friedman2" title="Link to this definition">#</a></dt>
 <dd><p>Generate the “Friedman #2” regression problem.</p>
 <p>This dataset is described in Friedman [1] and Breiman [2].</p>
 <p>Inputs <code class="docutils literal notranslate"><span class="pre">X</span></code> are 4 independent features uniformly distributed on the
@@ -1364,7 +1364,7 @@ <h1>make_friedman2<a class="headerlink" href="#make-friedman2" title="Link to th
 <span class="gp">&gt;&gt;&gt; </span><span class="n">y</span><span class="o">.</span><span class="n">shape</span>
 <span class="go">(100,)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="nb">list</span><span class="p">(</span><span class="n">y</span><span class="p">[:</span><span class="mi">3</span><span class="p">])</span>
-<span class="go">[np.float64(1229.4...), np.float64(27.0...), np.float64(65.6...)]</span>
+<span class="go">[np.float64(1229.4), np.float64(27.0), np.float64(65.6)]</span>
 </pre></div>
 </div>
 </dd></dl>
diff --git a/dev/modules/generated/sklearn.datasets.make_friedman3.html b/dev/modules/generated/sklearn.datasets.make_friedman3.html
index 08971dcb5f1d1..0ea6558033c3f 100644
--- a/dev/modules/generated/sklearn.datasets.make_friedman3.html
+++ b/dev/modules/generated/sklearn.datasets.make_friedman3.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>make_friedman3<a class="headerlink" href="#make-friedman3" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.datasets.make_friedman3">
-<span class="sig-prename descclassname"><span class="pre">sklearn.datasets.</span></span><span class="sig-name descname"><span class="pre">make_friedman3</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">noise</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/datasets/_samples_generator.py#L1331"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.datasets.make_friedman3" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.datasets.</span></span><span class="sig-name descname"><span class="pre">make_friedman3</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">noise</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/datasets/_samples_generator.py#L1331"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.datasets.make_friedman3" title="Link to this definition">#</a></dt>
 <dd><p>Generate the “Friedman #3” regression problem.</p>
 <p>This dataset is described in Friedman [1] and Breiman [2].</p>
 <p>Inputs <code class="docutils literal notranslate"><span class="pre">X</span></code> are 4 independent features uniformly distributed on the
@@ -1364,7 +1364,7 @@ <h1>make_friedman3<a class="headerlink" href="#make-friedman3" title="Link to th
 <span class="gp">&gt;&gt;&gt; </span><span class="n">y</span><span class="o">.</span><span class="n">shape</span>
 <span class="go">(100,)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="nb">list</span><span class="p">(</span><span class="n">y</span><span class="p">[:</span><span class="mi">3</span><span class="p">])</span>
-<span class="go">[np.float64(1.5...), np.float64(0.9...), np.float64(0.4...)]</span>
+<span class="go">[np.float64(1.54), np.float64(0.956), np.float64(0.414)]</span>
 </pre></div>
 </div>
 </dd></dl>
diff --git a/dev/modules/generated/sklearn.datasets.make_gaussian_quantiles.html b/dev/modules/generated/sklearn.datasets.make_gaussian_quantiles.html
index 9b58cb13cc7f5..0f76aa8798699 100644
--- a/dev/modules/generated/sklearn.datasets.make_gaussian_quantiles.html
+++ b/dev/modules/generated/sklearn.datasets.make_gaussian_quantiles.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1308,7 +1308,7 @@
 <h1>make_gaussian_quantiles<a class="headerlink" href="#make-gaussian-quantiles" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.datasets.make_gaussian_quantiles">
-<span class="sig-prename descclassname"><span class="pre">sklearn.datasets.</span></span><span class="sig-name descname"><span class="pre">make_gaussian_quantiles</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mean</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cov</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_classes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/datasets/_samples_generator.py#L1993"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.datasets.make_gaussian_quantiles" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.datasets.</span></span><span class="sig-name descname"><span class="pre">make_gaussian_quantiles</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mean</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cov</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_classes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/datasets/_samples_generator.py#L1993"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.datasets.make_gaussian_quantiles" title="Link to this definition">#</a></dt>
 <dd><p>Generate isotropic Gaussian and label samples by quantile.</p>
 <p>This classification dataset is constructed by taking a multi-dimensional
 standard normal distribution and defining classes separated by nested
diff --git a/dev/modules/generated/sklearn.datasets.make_hastie_10_2.html b/dev/modules/generated/sklearn.datasets.make_hastie_10_2.html
index c9c94c0fcb165..c2571c49b9f10 100644
--- a/dev/modules/generated/sklearn.datasets.make_hastie_10_2.html
+++ b/dev/modules/generated/sklearn.datasets.make_hastie_10_2.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>make_hastie_10_2<a class="headerlink" href="#make-hastie-10-2" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.datasets.make_hastie_10_2">
-<span class="sig-prename descclassname"><span class="pre">sklearn.datasets.</span></span><span class="sig-name descname"><span class="pre">make_hastie_10_2</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">12000</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/datasets/_samples_generator.py#L568"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.datasets.make_hastie_10_2" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.datasets.</span></span><span class="sig-name descname"><span class="pre">make_hastie_10_2</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">12000</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/datasets/_samples_generator.py#L568"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.datasets.make_hastie_10_2" title="Link to this definition">#</a></dt>
 <dd><p>Generate data for binary classification used in Hastie et al. 2009, Example 10.2.</p>
 <p>The ten features are standard independent Gaussian and
 the target <code class="docutils literal notranslate"><span class="pre">y</span></code> is defined by:</p>
diff --git a/dev/modules/generated/sklearn.datasets.make_low_rank_matrix.html b/dev/modules/generated/sklearn.datasets.make_low_rank_matrix.html
index 994b7e900c29c..65dcae49ab602 100644
--- a/dev/modules/generated/sklearn.datasets.make_low_rank_matrix.html
+++ b/dev/modules/generated/sklearn.datasets.make_low_rank_matrix.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>make_low_rank_matrix<a class="headerlink" href="#make-low-rank-matrix" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.datasets.make_low_rank_matrix">
-<span class="sig-prename descclassname"><span class="pre">sklearn.datasets.</span></span><span class="sig-name descname"><span class="pre">make_low_rank_matrix</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">effective_rank</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tail_strength</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/datasets/_samples_generator.py#L1415"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.datasets.make_low_rank_matrix" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.datasets.</span></span><span class="sig-name descname"><span class="pre">make_low_rank_matrix</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">effective_rank</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tail_strength</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/datasets/_samples_generator.py#L1415"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.datasets.make_low_rank_matrix" title="Link to this definition">#</a></dt>
 <dd><p>Generate a mostly low rank matrix with bell-shaped singular values.</p>
 <p>Most of the variance can be explained by a bell-shaped curve of width
 effective_rank: the low rank part of the singular values profile is:</p>
diff --git a/dev/modules/generated/sklearn.datasets.make_moons.html b/dev/modules/generated/sklearn.datasets.make_moons.html
index ad83d8026f773..0de524f9cf9e7 100644
--- a/dev/modules/generated/sklearn.datasets.make_moons.html
+++ b/dev/modules/generated/sklearn.datasets.make_moons.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>make_moons<a class="headerlink" href="#make-moons" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.datasets.make_moons">
-<span class="sig-prename descclassname"><span class="pre">sklearn.datasets.</span></span><span class="sig-name descname"><span class="pre">make_moons</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">noise</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/datasets/_samples_generator.py#L896"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.datasets.make_moons" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.datasets.</span></span><span class="sig-name descname"><span class="pre">make_moons</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">noise</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/datasets/_samples_generator.py#L896"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.datasets.make_moons" title="Link to this definition">#</a></dt>
 <dd><p>Make two interleaving half circles.</p>
 <p>A simple toy dataset to visualize clustering and classification
 algorithms. Read more in the <a class="reference internal" href="http://github.com/datasets/sample_generators.html#sample-generators"><span class="std std-ref">User Guide</span></a>.</p>
diff --git a/dev/modules/generated/sklearn.datasets.make_multilabel_classification.html b/dev/modules/generated/sklearn.datasets.make_multilabel_classification.html
index a8ef7787ad1ec..263a392210178 100644
--- a/dev/modules/generated/sklearn.datasets.make_multilabel_classification.html
+++ b/dev/modules/generated/sklearn.datasets.make_multilabel_classification.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>make_multilabel_classification<a class="headerlink" href="#make-multilabel-classification" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.datasets.make_multilabel_classification">
-<span class="sig-prename descclassname"><span class="pre">sklearn.datasets.</span></span><span class="sig-name descname"><span class="pre">make_multilabel_classification</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">20</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_classes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_labels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">length</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">50</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">allow_unlabeled</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sparse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_indicator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'dense'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_distributions</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/datasets/_samples_generator.py#L386"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.datasets.make_multilabel_classification" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.datasets.</span></span><span class="sig-name descname"><span class="pre">make_multilabel_classification</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">20</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_classes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_labels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">length</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">50</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">allow_unlabeled</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sparse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_indicator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'dense'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_distributions</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/datasets/_samples_generator.py#L386"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.datasets.make_multilabel_classification" title="Link to this definition">#</a></dt>
 <dd><p>Generate a random multilabel classification problem.</p>
 <dl class="simple">
 <dt>For each sample, the generative process is:</dt><dd><ul class="simple">
diff --git a/dev/modules/generated/sklearn.datasets.make_regression.html b/dev/modules/generated/sklearn.datasets.make_regression.html
index ec87acf1e94c9..6641e9b6de806 100644
--- a/dev/modules/generated/sklearn.datasets.make_regression.html
+++ b/dev/modules/generated/sklearn.datasets.make_regression.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>make_regression<a class="headerlink" href="#make-regression" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.datasets.make_regression">
-<span class="sig-prename descclassname"><span class="pre">sklearn.datasets.</span></span><span class="sig-name descname"><span class="pre">make_regression</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_informative</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_targets</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">effective_rank</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tail_strength</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">noise</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coef</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/datasets/_samples_generator.py#L634"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.datasets.make_regression" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.datasets.</span></span><span class="sig-name descname"><span class="pre">make_regression</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_informative</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_targets</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">effective_rank</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tail_strength</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">noise</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coef</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/datasets/_samples_generator.py#L634"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.datasets.make_regression" title="Link to this definition">#</a></dt>
 <dd><p>Generate a random regression problem.</p>
 <p>The input set can either be well conditioned (by default) or have a low
 rank-fat tail singular profile. See <a class="reference internal" href="http://github.com/sklearn.datasets.make_low_rank_matrix.html#sklearn.datasets.make_low_rank_matrix" title="sklearn.datasets.make_low_rank_matrix"><code class="xref py py-func docutils literal notranslate"><span class="pre">make_low_rank_matrix</span></code></a> for
@@ -1375,13 +1375,13 @@ <h1>make_regression<a class="headerlink" href="#make-regression" title="Link to
 <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.datasets</span><span class="w"> </span><span class="kn">import</span> <span class="n">make_regression</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">make_regression</span><span class="p">(</span><span class="n">n_samples</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">n_features</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">noise</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">X</span>
-<span class="go">array([[ 0.4967..., -0.1382... ],</span>
-<span class="go">    [ 0.6476...,  1.523...],</span>
-<span class="go">    [-0.2341..., -0.2341...],</span>
-<span class="go">    [-0.4694...,  0.5425...],</span>
-<span class="go">    [ 1.579...,  0.7674...]])</span>
+<span class="go">array([[ 0.4967, -0.1382 ],</span>
+<span class="go">    [ 0.6476,  1.523],</span>
+<span class="go">    [-0.2341, -0.2341],</span>
+<span class="go">    [-0.4694,  0.5425],</span>
+<span class="go">    [ 1.579,  0.7674]])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">y</span>
-<span class="go">array([  6.737...,  37.79..., -10.27...,   0.4017...,   42.22...])</span>
+<span class="go">array([  6.737,  37.79, -10.27,   0.4017,   42.22])</span>
 </pre></div>
 </div>
 </dd></dl>
diff --git a/dev/modules/generated/sklearn.datasets.make_s_curve.html b/dev/modules/generated/sklearn.datasets.make_s_curve.html
index 41d6184664fe0..5185f887e8552 100644
--- a/dev/modules/generated/sklearn.datasets.make_s_curve.html
+++ b/dev/modules/generated/sklearn.datasets.make_s_curve.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>make_s_curve<a class="headerlink" href="#make-s-curve" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.datasets.make_s_curve">
-<span class="sig-prename descclassname"><span class="pre">sklearn.datasets.</span></span><span class="sig-name descname"><span class="pre">make_s_curve</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">noise</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/datasets/_samples_generator.py#L1936"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.datasets.make_s_curve" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.datasets.</span></span><span class="sig-name descname"><span class="pre">make_s_curve</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">noise</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/datasets/_samples_generator.py#L1936"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.datasets.make_s_curve" title="Link to this definition">#</a></dt>
 <dd><p>Generate an S curve dataset.</p>
 <p>Read more in the <a class="reference internal" href="http://github.com/datasets/sample_generators.html#sample-generators"><span class="std std-ref">User Guide</span></a>.</p>
 <dl class="field-list simple">
diff --git a/dev/modules/generated/sklearn.datasets.make_sparse_coded_signal.html b/dev/modules/generated/sklearn.datasets.make_sparse_coded_signal.html
index 10cd897a4aa0a..fd904924f50cb 100644
--- a/dev/modules/generated/sklearn.datasets.make_sparse_coded_signal.html
+++ b/dev/modules/generated/sklearn.datasets.make_sparse_coded_signal.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>make_sparse_coded_signal<a class="headerlink" href="#make-sparse-coded-signal" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.datasets.make_sparse_coded_signal">
-<span class="sig-prename descclassname"><span class="pre">sklearn.datasets.</span></span><span class="sig-name descname"><span class="pre">make_sparse_coded_signal</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_samples</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_components</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_nonzero_coefs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/datasets/_samples_generator.py#L1521"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.datasets.make_sparse_coded_signal" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.datasets.</span></span><span class="sig-name descname"><span class="pre">make_sparse_coded_signal</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_samples</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_components</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_nonzero_coefs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/datasets/_samples_generator.py#L1521"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.datasets.make_sparse_coded_signal" title="Link to this definition">#</a></dt>
 <dd><p>Generate a signal as a sparse combination of dictionary elements.</p>
 <p>Returns matrices <code class="docutils literal notranslate"><span class="pre">Y</span></code>, <code class="docutils literal notranslate"><span class="pre">D</span></code> and <code class="docutils literal notranslate"><span class="pre">X</span></code> such that <code class="docutils literal notranslate"><span class="pre">Y</span> <span class="pre">=</span> <span class="pre">XD</span></code> where <code class="docutils literal notranslate"><span class="pre">X</span></code> is of shape
 <code class="docutils literal notranslate"><span class="pre">(n_samples,</span> <span class="pre">n_components)</span></code>, <code class="docutils literal notranslate"><span class="pre">D</span></code> is of shape <code class="docutils literal notranslate"><span class="pre">(n_components,</span> <span class="pre">n_features)</span></code>, and
diff --git a/dev/modules/generated/sklearn.datasets.make_sparse_spd_matrix.html b/dev/modules/generated/sklearn.datasets.make_sparse_spd_matrix.html
index 659f53d3a56cd..b31a306172f8c 100644
--- a/dev/modules/generated/sklearn.datasets.make_sparse_spd_matrix.html
+++ b/dev/modules/generated/sklearn.datasets.make_sparse_spd_matrix.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>make_sparse_spd_matrix<a class="headerlink" href="#make-sparse-spd-matrix" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.datasets.make_sparse_spd_matrix">
-<span class="sig-prename descclassname"><span class="pre">sklearn.datasets.</span></span><span class="sig-name descname"><span class="pre">make_sparse_spd_matrix</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.95</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm_diag</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">smallest_coef</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">largest_coef</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.9</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sparse_format</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/datasets/_samples_generator.py#L1733"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.datasets.make_sparse_spd_matrix" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.datasets.</span></span><span class="sig-name descname"><span class="pre">make_sparse_spd_matrix</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.95</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm_diag</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">smallest_coef</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">largest_coef</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.9</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sparse_format</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/datasets/_samples_generator.py#L1733"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.datasets.make_sparse_spd_matrix" title="Link to this definition">#</a></dt>
 <dd><p>Generate a sparse symmetric definite positive matrix.</p>
 <p>Read more in the <a class="reference internal" href="http://github.com/datasets/sample_generators.html#sample-generators"><span class="std std-ref">User Guide</span></a>.</p>
 <dl class="field-list">
diff --git a/dev/modules/generated/sklearn.datasets.make_sparse_uncorrelated.html b/dev/modules/generated/sklearn.datasets.make_sparse_uncorrelated.html
index 6969b05070468..67b5ed75d8ac5 100644
--- a/dev/modules/generated/sklearn.datasets.make_sparse_uncorrelated.html
+++ b/dev/modules/generated/sklearn.datasets.make_sparse_uncorrelated.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>make_sparse_uncorrelated<a class="headerlink" href="#make-sparse-uncorrelated" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.datasets.make_sparse_uncorrelated">
-<span class="sig-prename descclassname"><span class="pre">sklearn.datasets.</span></span><span class="sig-name descname"><span class="pre">make_sparse_uncorrelated</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/datasets/_samples_generator.py#L1618"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.datasets.make_sparse_uncorrelated" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.datasets.</span></span><span class="sig-name descname"><span class="pre">make_sparse_uncorrelated</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/datasets/_samples_generator.py#L1618"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.datasets.make_sparse_uncorrelated" title="Link to this definition">#</a></dt>
 <dd><p>Generate a random regression problem with sparse uncorrelated design.</p>
 <p>This dataset is described in Celeux et al [1]. as:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">X</span> <span class="o">~</span> <span class="n">N</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
diff --git a/dev/modules/generated/sklearn.datasets.make_spd_matrix.html b/dev/modules/generated/sklearn.datasets.make_spd_matrix.html
index a2c19dfea63ff..23392399683d5 100644
--- a/dev/modules/generated/sklearn.datasets.make_spd_matrix.html
+++ b/dev/modules/generated/sklearn.datasets.make_spd_matrix.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>make_spd_matrix<a class="headerlink" href="#make-spd-matrix" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.datasets.make_spd_matrix">
-<span class="sig-prename descclassname"><span class="pre">sklearn.datasets.</span></span><span class="sig-name descname"><span class="pre">make_spd_matrix</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_dim</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/datasets/_samples_generator.py#L1686"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.datasets.make_spd_matrix" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.datasets.</span></span><span class="sig-name descname"><span class="pre">make_spd_matrix</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_dim</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/datasets/_samples_generator.py#L1686"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.datasets.make_spd_matrix" title="Link to this definition">#</a></dt>
 <dd><p>Generate a random symmetric, positive-definite matrix.</p>
 <p>Read more in the <a class="reference internal" href="http://github.com/datasets/sample_generators.html#sample-generators"><span class="std std-ref">User Guide</span></a>.</p>
 <dl class="field-list simple">
@@ -1336,8 +1336,8 @@ <h1>make_spd_matrix<a class="headerlink" href="#make-spd-matrix" title="Link to
 <p class="rubric">Examples</p>
 <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.datasets</span><span class="w"> </span><span class="kn">import</span> <span class="n">make_spd_matrix</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">make_spd_matrix</span><span class="p">(</span><span class="n">n_dim</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">)</span>
-<span class="go">array([[2.09..., 0.34...],</span>
-<span class="go">       [0.34..., 0.21...]])</span>
+<span class="go">array([[2.093, 0.346],</span>
+<span class="go">       [0.346, 0.218]])</span>
 </pre></div>
 </div>
 </dd></dl>
diff --git a/dev/modules/generated/sklearn.datasets.make_swiss_roll.html b/dev/modules/generated/sklearn.datasets.make_swiss_roll.html
index abea3dbd4ef70..6c4f2e236f2c0 100644
--- a/dev/modules/generated/sklearn.datasets.make_swiss_roll.html
+++ b/dev/modules/generated/sklearn.datasets.make_swiss_roll.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>make_swiss_roll<a class="headerlink" href="#make-swiss-roll" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.datasets.make_swiss_roll">
-<span class="sig-prename descclassname"><span class="pre">sklearn.datasets.</span></span><span class="sig-name descname"><span class="pre">make_swiss_roll</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">noise</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hole</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/datasets/_samples_generator.py#L1853"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.datasets.make_swiss_roll" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.datasets.</span></span><span class="sig-name descname"><span class="pre">make_swiss_roll</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">noise</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hole</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/datasets/_samples_generator.py#L1853"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.datasets.make_swiss_roll" title="Link to this definition">#</a></dt>
 <dd><p>Generate a swiss roll dataset.</p>
 <p>Read more in the <a class="reference internal" href="http://github.com/datasets/sample_generators.html#sample-generators"><span class="std std-ref">User Guide</span></a>.</p>
 <dl class="field-list simple">
diff --git a/dev/modules/generated/sklearn.decomposition.DictionaryLearning.html b/dev/modules/generated/sklearn.decomposition.DictionaryLearning.html
index 6ae733abe7269..49c6a42669c4e 100644
--- a/dev/modules/generated/sklearn.decomposition.DictionaryLearning.html
+++ b/dev/modules/generated/sklearn.decomposition.DictionaryLearning.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>DictionaryLearning<a class="headerlink" href="#dictionarylearning" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.decomposition.DictionaryLearning">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.decomposition.</span></span><span class="sig-name descname"><span class="pre">DictionaryLearning</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_components</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-08</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_algorithm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'lars'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform_algorithm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'omp'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform_n_nonzero_coefs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform_alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">code_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dict_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">callback</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">split_sign</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">positive_code</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">positive_dict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform_max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/decomposition/_dict_learning.py#L1408"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.DictionaryLearning" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.decomposition.</span></span><span class="sig-name descname"><span class="pre">DictionaryLearning</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_components</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-08</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_algorithm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'lars'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform_algorithm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'omp'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform_n_nonzero_coefs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform_alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">code_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dict_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">callback</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">split_sign</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">positive_code</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">positive_dict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform_max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/decomposition/_dict_learning.py#L1408"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.DictionaryLearning" title="Link to this definition">#</a></dt>
 <dd><p>Dictionary learning.</p>
 <p>Finds a dictionary (a set of atoms) that performs well at sparsely
 encoding the fitted data.</p>
@@ -1374,7 +1374,7 @@ <h1>DictionaryLearning<a class="headerlink" href="#dictionarylearning" title="Li
 </div>
 </dd>
 <dt><strong>n_jobs</strong><span class="classifier">int or None, default=None</span></dt><dd><p>Number of parallel jobs to run.
-<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.5.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
+<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.6.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
 <code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all processors. See <a class="reference internal" href="http://github.com/glossary.html#term-n_jobs"><span class="xref std std-term">Glossary</span></a>
 for more details.</p>
 </dd>
@@ -1474,7 +1474,7 @@ <h1>DictionaryLearning<a class="headerlink" href="#dictionarylearning" title="Li
 </div>
 <p>We can check the level of sparsity of <code class="docutils literal notranslate"><span class="pre">X_transformed</span></code>:</p>
 <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">X_transformed</span> <span class="o">==</span> <span class="mi">0</span><span class="p">)</span>
-<span class="go">np.float64(0.52...)</span>
+<span class="go">np.float64(0.527)</span>
 </pre></div>
 </div>
 <p>We can compare the average squared euclidean norm of the reconstruction
@@ -1482,12 +1482,12 @@ <h1>DictionaryLearning<a class="headerlink" href="#dictionarylearning" title="Li
 the original signal:</p>
 <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">X_hat</span> <span class="o">=</span> <span class="n">X_transformed</span> <span class="o">@</span> <span class="n">dict_learner</span><span class="o">.</span><span class="n">components_</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">((</span><span class="n">X_hat</span> <span class="o">-</span> <span class="n">X</span><span class="p">)</span> <span class="o">**</span> <span class="mi">2</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">X</span> <span class="o">**</span> <span class="mi">2</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">))</span>
-<span class="go">np.float64(0.05...)</span>
+<span class="go">np.float64(0.056)</span>
 </pre></div>
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.DictionaryLearning.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/decomposition/_dict_learning.py#L1667"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.DictionaryLearning.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/decomposition/_dict_learning.py#L1667"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.DictionaryLearning.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit the model from data in X.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1510,7 +1510,7 @@ <h1>DictionaryLearning<a class="headerlink" href="#dictionarylearning" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.DictionaryLearning.fit_transform">
-<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/decomposition/_dict_learning.py#L1687"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.DictionaryLearning.fit_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/decomposition/_dict_learning.py#L1687"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.DictionaryLearning.fit_transform" title="Link to this definition">#</a></dt>
 <dd><p>Fit the model from data in X and return the transformed data.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1533,7 +1533,7 @@ <h1>DictionaryLearning<a class="headerlink" href="#dictionarylearning" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.DictionaryLearning.get_feature_names_out">
-<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L995"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.DictionaryLearning.get_feature_names_out" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L936"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.DictionaryLearning.get_feature_names_out" title="Link to this definition">#</a></dt>
 <dd><p>Get output feature names for transformation.</p>
 <p>The feature names out will prefixed by the lowercased class name. For
 example, if the transformer outputs 3 features, then the feature names
@@ -1556,7 +1556,7 @@ <h1>DictionaryLearning<a class="headerlink" href="#dictionarylearning" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.DictionaryLearning.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.DictionaryLearning.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.DictionaryLearning.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1573,7 +1573,7 @@ <h1>DictionaryLearning<a class="headerlink" href="#dictionarylearning" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.DictionaryLearning.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.DictionaryLearning.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.DictionaryLearning.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1594,7 +1594,7 @@ <h1>DictionaryLearning<a class="headerlink" href="#dictionarylearning" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.DictionaryLearning.inverse_transform">
-<span class="sig-name descname"><span class="pre">inverse_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/decomposition/_dict_learning.py#L1166"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.DictionaryLearning.inverse_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">inverse_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/decomposition/_dict_learning.py#L1166"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.DictionaryLearning.inverse_transform" title="Link to this definition">#</a></dt>
 <dd><p>Transform data back to its original space.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1606,7 +1606,7 @@ <h1>DictionaryLearning<a class="headerlink" href="#dictionarylearning" title="Li
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><dl class="simple">
-<dt><strong>X_new</strong><span class="classifier">ndarray of shape (n_samples, n_features)</span></dt><dd><p>Transformed data.</p>
+<dt><strong>X_original</strong><span class="classifier">ndarray of shape (n_samples, n_features)</span></dt><dd><p>Transformed data.</p>
 </dd>
 </dl>
 </dd>
@@ -1615,7 +1615,7 @@ <h1>DictionaryLearning<a class="headerlink" href="#dictionarylearning" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.DictionaryLearning.set_output">
-<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.DictionaryLearning.set_output" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.DictionaryLearning.set_output" title="Link to this definition">#</a></dt>
 <dd><p>Set output container.</p>
 <p>See <a class="reference internal" href="http://github.com/auto_examples/miscellaneous/plot_set_output.html#sphx-glr-auto-examples-miscellaneous-plot-set-output-py"><span class="std std-ref">Introducing the set_output API</span></a>
 for an example on how to use the API.</p>
@@ -1646,7 +1646,7 @@ <h1>DictionaryLearning<a class="headerlink" href="#dictionarylearning" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.DictionaryLearning.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.DictionaryLearning.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.DictionaryLearning.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1670,7 +1670,7 @@ <h1>DictionaryLearning<a class="headerlink" href="#dictionarylearning" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.DictionaryLearning.transform">
-<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/decomposition/_dict_learning.py#L1125"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.DictionaryLearning.transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/decomposition/_dict_learning.py#L1125"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.DictionaryLearning.transform" title="Link to this definition">#</a></dt>
 <dd><p>Encode the data as a sparse combination of the dictionary atoms.</p>
 <p>Coding method is determined by the object parameter
 <code class="docutils literal notranslate"><span class="pre">transform_algorithm</span></code>.</p>
diff --git a/dev/modules/generated/sklearn.decomposition.FactorAnalysis.html b/dev/modules/generated/sklearn.decomposition.FactorAnalysis.html
index 8f2d2300368dc..adf0e0887952a 100644
--- a/dev/modules/generated/sklearn.decomposition.FactorAnalysis.html
+++ b/dev/modules/generated/sklearn.decomposition.FactorAnalysis.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>FactorAnalysis<a class="headerlink" href="#factoranalysis" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.decomposition.FactorAnalysis">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.decomposition.</span></span><span class="sig-name descname"><span class="pre">FactorAnalysis</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_components</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">noise_variance_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">svd_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'randomized'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">iterated_power</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rotation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/decomposition/_factor_analysis.py#L39"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.FactorAnalysis" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.decomposition.</span></span><span class="sig-name descname"><span class="pre">FactorAnalysis</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_components</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">noise_variance_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">svd_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'randomized'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">iterated_power</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rotation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/decomposition/_factor_analysis.py#L39"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.FactorAnalysis" title="Link to this definition">#</a></dt>
 <dd><p>Factor Analysis (FA).</p>
 <p>A simple linear generative model with Gaussian latent variables.</p>
 <p>The observations are assumed to be caused by a linear transformation of
@@ -1421,7 +1421,7 @@ <h1>FactorAnalysis<a class="headerlink" href="#factoranalysis" title="Link to th
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.FactorAnalysis.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/decomposition/_factor_analysis.py#L202"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.FactorAnalysis.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/decomposition/_factor_analysis.py#L202"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.FactorAnalysis.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit the FactorAnalysis model to X using SVD based approach.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1443,7 +1443,7 @@ <h1>FactorAnalysis<a class="headerlink" href="#factoranalysis" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.FactorAnalysis.fit_transform">
-<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L863"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.FactorAnalysis.fit_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L804"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.FactorAnalysis.fit_transform" title="Link to this definition">#</a></dt>
 <dd><p>Fit to data, then transform it.</p>
 <p>Fits transformer to <code class="docutils literal notranslate"><span class="pre">X</span></code> and <code class="docutils literal notranslate"><span class="pre">y</span></code> with optional parameters <code class="docutils literal notranslate"><span class="pre">fit_params</span></code>
 and returns a transformed version of <code class="docutils literal notranslate"><span class="pre">X</span></code>.</p>
@@ -1469,7 +1469,7 @@ <h1>FactorAnalysis<a class="headerlink" href="#factoranalysis" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.FactorAnalysis.get_covariance">
-<span class="sig-name descname"><span class="pre">get_covariance</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/decomposition/_factor_analysis.py#L341"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.FactorAnalysis.get_covariance" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_covariance</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/decomposition/_factor_analysis.py#L341"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.FactorAnalysis.get_covariance" title="Link to this definition">#</a></dt>
 <dd><p>Compute data covariance with the FactorAnalysis model.</p>
 <p><code class="docutils literal notranslate"><span class="pre">cov</span> <span class="pre">=</span> <span class="pre">components_.T</span> <span class="pre">*</span> <span class="pre">components_</span> <span class="pre">+</span> <span class="pre">diag(noise_variance)</span></code></p>
 <dl class="field-list simple">
@@ -1484,7 +1484,7 @@ <h1>FactorAnalysis<a class="headerlink" href="#factoranalysis" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.FactorAnalysis.get_feature_names_out">
-<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L995"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.FactorAnalysis.get_feature_names_out" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L936"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.FactorAnalysis.get_feature_names_out" title="Link to this definition">#</a></dt>
 <dd><p>Get output feature names for transformation.</p>
 <p>The feature names out will prefixed by the lowercased class name. For
 example, if the transformer outputs 3 features, then the feature names
@@ -1507,7 +1507,7 @@ <h1>FactorAnalysis<a class="headerlink" href="#factoranalysis" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.FactorAnalysis.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.FactorAnalysis.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.FactorAnalysis.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1524,7 +1524,7 @@ <h1>FactorAnalysis<a class="headerlink" href="#factoranalysis" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.FactorAnalysis.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.FactorAnalysis.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.FactorAnalysis.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1545,7 +1545,7 @@ <h1>FactorAnalysis<a class="headerlink" href="#factoranalysis" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.FactorAnalysis.get_precision">
-<span class="sig-name descname"><span class="pre">get_precision</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/decomposition/_factor_analysis.py#L357"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.FactorAnalysis.get_precision" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_precision</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/decomposition/_factor_analysis.py#L357"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.FactorAnalysis.get_precision" title="Link to this definition">#</a></dt>
 <dd><p>Compute data precision matrix with the FactorAnalysis model.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Returns<span class="colon">:</span></dt>
@@ -1559,7 +1559,7 @@ <h1>FactorAnalysis<a class="headerlink" href="#factoranalysis" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.FactorAnalysis.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/decomposition/_factor_analysis.py#L407"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.FactorAnalysis.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/decomposition/_factor_analysis.py#L407"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.FactorAnalysis.score" title="Link to this definition">#</a></dt>
 <dd><p>Compute the average log-likelihood of the samples.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1581,7 +1581,7 @@ <h1>FactorAnalysis<a class="headerlink" href="#factoranalysis" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.FactorAnalysis.score_samples">
-<span class="sig-name descname"><span class="pre">score_samples</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/decomposition/_factor_analysis.py#L385"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.FactorAnalysis.score_samples" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score_samples</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/decomposition/_factor_analysis.py#L385"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.FactorAnalysis.score_samples" title="Link to this definition">#</a></dt>
 <dd><p>Compute the log-likelihood of each sample.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1601,7 +1601,7 @@ <h1>FactorAnalysis<a class="headerlink" href="#factoranalysis" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.FactorAnalysis.set_output">
-<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.FactorAnalysis.set_output" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.FactorAnalysis.set_output" title="Link to this definition">#</a></dt>
 <dd><p>Set output container.</p>
 <p>See <a class="reference internal" href="http://github.com/auto_examples/miscellaneous/plot_set_output.html#sphx-glr-auto-examples-miscellaneous-plot-set-output-py"><span class="std std-ref">Introducing the set_output API</span></a>
 for an example on how to use the API.</p>
@@ -1632,7 +1632,7 @@ <h1>FactorAnalysis<a class="headerlink" href="#factoranalysis" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.FactorAnalysis.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.FactorAnalysis.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.FactorAnalysis.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1656,7 +1656,7 @@ <h1>FactorAnalysis<a class="headerlink" href="#factoranalysis" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.FactorAnalysis.transform">
-<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/decomposition/_factor_analysis.py#L311"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.FactorAnalysis.transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/decomposition/_factor_analysis.py#L311"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.FactorAnalysis.transform" title="Link to this definition">#</a></dt>
 <dd><p>Apply dimensionality reduction to X using the model.</p>
 <p>Compute the expected mean of the latent variables.
 See Barber, 21.2.33 (or Bishop, 12.66).</p>
diff --git a/dev/modules/generated/sklearn.decomposition.FastICA.html b/dev/modules/generated/sklearn.decomposition.FastICA.html
index 52b7c4ccfaff7..40c741d32daac 100644
--- a/dev/modules/generated/sklearn.decomposition.FastICA.html
+++ b/dev/modules/generated/sklearn.decomposition.FastICA.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>FastICA<a class="headerlink" href="#fastica" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.decomposition.FastICA">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.decomposition.</span></span><span class="sig-name descname"><span class="pre">FastICA</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_components</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">algorithm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'parallel'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">whiten</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'unit-variance'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fun</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'logcosh'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fun_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">200</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">w_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">whiten_solver</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'svd'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/decomposition/_fastica.py#L367"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.FastICA" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.decomposition.</span></span><span class="sig-name descname"><span class="pre">FastICA</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_components</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">algorithm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'parallel'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">whiten</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'unit-variance'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fun</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'logcosh'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fun_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">200</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">w_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">whiten_solver</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'svd'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/decomposition/_fastica.py#L367"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.FastICA" title="Link to this definition">#</a></dt>
 <dd><p>FastICA: a fast algorithm for Independent Component Analysis.</p>
 <p>The implementation is based on <a class="reference internal" href="#r44c805292efc-1" id="id1">[1]</a>.</p>
 <p>Read more in the <a class="reference internal" href="http://github.com/decomposition.html#ica"><span class="std std-ref">User Guide</span></a>.</p>
@@ -1445,7 +1445,7 @@ <h1>FastICA<a class="headerlink" href="#fastica" title="Link to this heading">#<
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.FastICA.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/decomposition/_fastica.py#L718"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.FastICA.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/decomposition/_fastica.py#L718"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.FastICA.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit the model to X.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1468,7 +1468,7 @@ <h1>FastICA<a class="headerlink" href="#fastica" title="Link to this heading">#<
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.FastICA.fit_transform">
-<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/decomposition/_fastica.py#L697"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.FastICA.fit_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/decomposition/_fastica.py#L697"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.FastICA.fit_transform" title="Link to this definition">#</a></dt>
 <dd><p>Fit the model and recover the sources from X.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1492,7 +1492,7 @@ <h1>FastICA<a class="headerlink" href="#fastica" title="Link to this heading">#<
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.FastICA.get_feature_names_out">
-<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L995"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.FastICA.get_feature_names_out" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L936"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.FastICA.get_feature_names_out" title="Link to this definition">#</a></dt>
 <dd><p>Get output feature names for transformation.</p>
 <p>The feature names out will prefixed by the lowercased class name. For
 example, if the transformer outputs 3 features, then the feature names
@@ -1515,7 +1515,7 @@ <h1>FastICA<a class="headerlink" href="#fastica" title="Link to this heading">#<
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.FastICA.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.FastICA.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.FastICA.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1532,7 +1532,7 @@ <h1>FastICA<a class="headerlink" href="#fastica" title="Link to this heading">#<
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.FastICA.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.FastICA.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.FastICA.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1553,7 +1553,7 @@ <h1>FastICA<a class="headerlink" href="#fastica" title="Link to this heading">#<
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.FastICA.inverse_transform">
-<span class="sig-name descname"><span class="pre">inverse_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/decomposition/_fastica.py#L771"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.FastICA.inverse_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">inverse_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/decomposition/_fastica.py#L771"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.FastICA.inverse_transform" title="Link to this definition">#</a></dt>
 <dd><p>Transform the sources back to the mixed data (apply mixing matrix).</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1567,7 +1567,7 @@ <h1>FastICA<a class="headerlink" href="#fastica" title="Link to this heading">#<
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><dl class="simple">
-<dt><strong>X_new</strong><span class="classifier">ndarray of shape (n_samples, n_features)</span></dt><dd><p>Reconstructed data obtained with the mixing matrix.</p>
+<dt><strong>X_original</strong><span class="classifier">ndarray of shape (n_samples, n_features)</span></dt><dd><p>Reconstructed data obtained with the mixing matrix.</p>
 </dd>
 </dl>
 </dd>
@@ -1576,7 +1576,7 @@ <h1>FastICA<a class="headerlink" href="#fastica" title="Link to this heading">#<
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.FastICA.set_inverse_transform_request">
-<span class="sig-name descname"><span class="pre">set_inverse_transform_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.decomposition.FastICA" title="sklearn.decomposition._fastica.FastICA"><span class="pre">FastICA</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.FastICA.set_inverse_transform_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_inverse_transform_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.decomposition.FastICA" title="sklearn.decomposition._fastica.FastICA"><span class="pre">FastICA</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.FastICA.set_inverse_transform_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">inverse_transform</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1619,7 +1619,7 @@ <h1>FastICA<a class="headerlink" href="#fastica" title="Link to this heading">#<
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.FastICA.set_output">
-<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.FastICA.set_output" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.FastICA.set_output" title="Link to this definition">#</a></dt>
 <dd><p>Set output container.</p>
 <p>See <a class="reference internal" href="http://github.com/auto_examples/miscellaneous/plot_set_output.html#sphx-glr-auto-examples-miscellaneous-plot-set-output-py"><span class="std std-ref">Introducing the set_output API</span></a>
 for an example on how to use the API.</p>
@@ -1650,7 +1650,7 @@ <h1>FastICA<a class="headerlink" href="#fastica" title="Link to this heading">#<
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.FastICA.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.FastICA.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.FastICA.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1674,7 +1674,7 @@ <h1>FastICA<a class="headerlink" href="#fastica" title="Link to this heading">#<
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.FastICA.set_transform_request">
-<span class="sig-name descname"><span class="pre">set_transform_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.decomposition.FastICA" title="sklearn.decomposition._fastica.FastICA"><span class="pre">FastICA</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.FastICA.set_transform_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_transform_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.decomposition.FastICA" title="sklearn.decomposition._fastica.FastICA"><span class="pre">FastICA</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.FastICA.set_transform_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">transform</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1717,7 +1717,7 @@ <h1>FastICA<a class="headerlink" href="#fastica" title="Link to this heading">#<
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.FastICA.transform">
-<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/decomposition/_fastica.py#L739"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.FastICA.transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/decomposition/_fastica.py#L739"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.FastICA.transform" title="Link to this definition">#</a></dt>
 <dd><p>Recover the sources from X (apply the unmixing matrix).</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.decomposition.IncrementalPCA.html b/dev/modules/generated/sklearn.decomposition.IncrementalPCA.html
index 5b0f03c36449b..d1268dc979a07 100644
--- a/dev/modules/generated/sklearn.decomposition.IncrementalPCA.html
+++ b/dev/modules/generated/sklearn.decomposition.IncrementalPCA.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>IncrementalPCA<a class="headerlink" href="#incrementalpca" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.decomposition.IncrementalPCA">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.decomposition.</span></span><span class="sig-name descname"><span class="pre">IncrementalPCA</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_components</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">whiten</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/decomposition/_incremental_pca.py#L21"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.IncrementalPCA" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.decomposition.</span></span><span class="sig-name descname"><span class="pre">IncrementalPCA</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_components</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">whiten</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/decomposition/_incremental_pca.py#L21"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.IncrementalPCA" title="Link to this definition">#</a></dt>
 <dd><p>Incremental principal components analysis (IPCA).</p>
 <p>Linear dimensionality reduction using Singular Value Decomposition of
 the data, keeping only the most significant singular vectors to
@@ -1460,7 +1460,7 @@ <h1>IncrementalPCA<a class="headerlink" href="#incrementalpca" title="Link to th
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.IncrementalPCA.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/decomposition/_incremental_pca.py#L204"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.IncrementalPCA.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/decomposition/_incremental_pca.py#L204"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.IncrementalPCA.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit the model with X, using minibatches of size batch_size.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1483,7 +1483,7 @@ <h1>IncrementalPCA<a class="headerlink" href="#incrementalpca" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.IncrementalPCA.fit_transform">
-<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L863"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.IncrementalPCA.fit_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L804"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.IncrementalPCA.fit_transform" title="Link to this definition">#</a></dt>
 <dd><p>Fit to data, then transform it.</p>
 <p>Fits transformer to <code class="docutils literal notranslate"><span class="pre">X</span></code> and <code class="docutils literal notranslate"><span class="pre">y</span></code> with optional parameters <code class="docutils literal notranslate"><span class="pre">fit_params</span></code>
 and returns a transformed version of <code class="docutils literal notranslate"><span class="pre">X</span></code>.</p>
@@ -1509,7 +1509,7 @@ <h1>IncrementalPCA<a class="headerlink" href="#incrementalpca" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.IncrementalPCA.get_covariance">
-<span class="sig-name descname"><span class="pre">get_covariance</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/decomposition/_base.py#L25"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.IncrementalPCA.get_covariance" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_covariance</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/decomposition/_base.py#L25"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.IncrementalPCA.get_covariance" title="Link to this definition">#</a></dt>
 <dd><p>Compute data covariance with the generative model.</p>
 <p><code class="docutils literal notranslate"><span class="pre">cov</span> <span class="pre">=</span> <span class="pre">components_.T</span> <span class="pre">*</span> <span class="pre">S**2</span> <span class="pre">*</span> <span class="pre">components_</span> <span class="pre">+</span> <span class="pre">sigma2</span> <span class="pre">*</span> <span class="pre">eye(n_features)</span></code>
 where S**2 contains the explained variances, and sigma2 contains the
@@ -1526,7 +1526,7 @@ <h1>IncrementalPCA<a class="headerlink" href="#incrementalpca" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.IncrementalPCA.get_feature_names_out">
-<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L995"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.IncrementalPCA.get_feature_names_out" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L936"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.IncrementalPCA.get_feature_names_out" title="Link to this definition">#</a></dt>
 <dd><p>Get output feature names for transformation.</p>
 <p>The feature names out will prefixed by the lowercased class name. For
 example, if the transformer outputs 3 features, then the feature names
@@ -1549,7 +1549,7 @@ <h1>IncrementalPCA<a class="headerlink" href="#incrementalpca" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.IncrementalPCA.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.IncrementalPCA.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.IncrementalPCA.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1566,7 +1566,7 @@ <h1>IncrementalPCA<a class="headerlink" href="#incrementalpca" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.IncrementalPCA.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.IncrementalPCA.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.IncrementalPCA.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1587,7 +1587,7 @@ <h1>IncrementalPCA<a class="headerlink" href="#incrementalpca" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.IncrementalPCA.get_precision">
-<span class="sig-name descname"><span class="pre">get_precision</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/decomposition/_base.py#L53"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.IncrementalPCA.get_precision" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_precision</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/decomposition/_base.py#L53"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.IncrementalPCA.get_precision" title="Link to this definition">#</a></dt>
 <dd><p>Compute data precision matrix with the generative model.</p>
 <p>Equals the inverse of the covariance but computed with
 the matrix inversion lemma for efficiency.</p>
@@ -1603,7 +1603,7 @@ <h1>IncrementalPCA<a class="headerlink" href="#incrementalpca" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.IncrementalPCA.inverse_transform">
-<span class="sig-name descname"><span class="pre">inverse_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/decomposition/_base.py#L167"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.IncrementalPCA.inverse_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">inverse_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/decomposition/_base.py#L167"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.IncrementalPCA.inverse_transform" title="Link to this definition">#</a></dt>
 <dd><p>Transform data back to its original space.</p>
 <p>In other words, return an input <code class="docutils literal notranslate"><span class="pre">X_original</span></code> whose transform would be X.</p>
 <dl class="field-list simple">
@@ -1616,7 +1616,7 @@ <h1>IncrementalPCA<a class="headerlink" href="#incrementalpca" title="Link to th
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><dl class="simple">
-<dt>X_original array-like of shape (n_samples, n_features)</dt><dd><p>Original data, where <code class="docutils literal notranslate"><span class="pre">n_samples</span></code> is the number of samples
+<dt><strong>X_original</strong><span class="classifier">array-like of shape (n_samples, n_features)</span></dt><dd><p>Original data, where <code class="docutils literal notranslate"><span class="pre">n_samples</span></code> is the number of samples
 and <code class="docutils literal notranslate"><span class="pre">n_features</span></code> is the number of features.</p>
 </dd>
 </dl>
@@ -1629,7 +1629,7 @@ <h1>IncrementalPCA<a class="headerlink" href="#incrementalpca" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.IncrementalPCA.partial_fit">
-<span class="sig-name descname"><span class="pre">partial_fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_input</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/decomposition/_incremental_pca.py#L256"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.IncrementalPCA.partial_fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">partial_fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_input</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/decomposition/_incremental_pca.py#L256"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.IncrementalPCA.partial_fit" title="Link to this definition">#</a></dt>
 <dd><p>Incremental fit with X. All of X is processed as a single batch.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1654,7 +1654,7 @@ <h1>IncrementalPCA<a class="headerlink" href="#incrementalpca" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.IncrementalPCA.set_output">
-<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.IncrementalPCA.set_output" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.IncrementalPCA.set_output" title="Link to this definition">#</a></dt>
 <dd><p>Set output container.</p>
 <p>See <a class="reference internal" href="http://github.com/auto_examples/miscellaneous/plot_set_output.html#sphx-glr-auto-examples-miscellaneous-plot-set-output-py"><span class="std std-ref">Introducing the set_output API</span></a>
 for an example on how to use the API.</p>
@@ -1685,7 +1685,7 @@ <h1>IncrementalPCA<a class="headerlink" href="#incrementalpca" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.IncrementalPCA.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.IncrementalPCA.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.IncrementalPCA.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1709,7 +1709,7 @@ <h1>IncrementalPCA<a class="headerlink" href="#incrementalpca" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.IncrementalPCA.transform">
-<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/decomposition/_incremental_pca.py#L381"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.IncrementalPCA.transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/decomposition/_incremental_pca.py#L381"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.IncrementalPCA.transform" title="Link to this definition">#</a></dt>
 <dd><p>Apply dimensionality reduction to X.</p>
 <p>X is projected on the first principal components previously extracted
 from a training set, using minibatches of size batch_size if X is
diff --git a/dev/modules/generated/sklearn.decomposition.KernelPCA.html b/dev/modules/generated/sklearn.decomposition.KernelPCA.html
index 6d8706a333979..d4159ed3292f6 100644
--- a/dev/modules/generated/sklearn.decomposition.KernelPCA.html
+++ b/dev/modules/generated/sklearn.decomposition.KernelPCA.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>KernelPCA<a class="headerlink" href="#kernelpca" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.decomposition.KernelPCA">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.decomposition.</span></span><span class="sig-name descname"><span class="pre">KernelPCA</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_components</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'linear'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gamma</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">degree</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coef0</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_inverse_transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eigen_solver</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">iterated_power</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">remove_zero_eig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy_X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/decomposition/_kernel_pca.py#L32"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.KernelPCA" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.decomposition.</span></span><span class="sig-name descname"><span class="pre">KernelPCA</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_components</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'linear'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gamma</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">degree</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coef0</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_inverse_transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eigen_solver</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">iterated_power</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">remove_zero_eig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy_X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/decomposition/_kernel_pca.py#L32"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.KernelPCA" title="Link to this definition">#</a></dt>
 <dd><p>Kernel Principal component analysis (KPCA).</p>
 <p>Non-linear dimensionality reduction through the use of kernels <a class="reference internal" href="#r396fc7d924b8-1" id="id1">[1]</a>, see also
 <a class="reference internal" href="http://github.com/metrics.html#metrics"><span class="std std-ref">Pairwise metrics, Affinities and Kernels</span></a>.</p>
@@ -1414,7 +1414,7 @@ <h1>KernelPCA<a class="headerlink" href="#kernelpca" title="Link to this heading
 </div>
 </dd>
 <dt><strong>n_jobs</strong><span class="classifier">int, default=None</span></dt><dd><p>The number of parallel jobs to run.
-<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.5.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
+<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.6.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
 <code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all processors. See <a class="reference internal" href="http://github.com/glossary.html#term-n_jobs"><span class="xref std std-term">Glossary</span></a>
 for more details.</p>
 <div class="versionadded">
@@ -1521,7 +1521,7 @@ <h1>KernelPCA<a class="headerlink" href="#kernelpca" title="Link to this heading
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.KernelPCA.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/decomposition/_kernel_pca.py#L418"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.KernelPCA.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/decomposition/_kernel_pca.py#L418"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.KernelPCA.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit the model from data in X.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1544,7 +1544,7 @@ <h1>KernelPCA<a class="headerlink" href="#kernelpca" title="Link to this heading
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.KernelPCA.fit_transform">
-<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/decomposition/_kernel_pca.py#L455"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.KernelPCA.fit_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/decomposition/_kernel_pca.py#L455"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.KernelPCA.fit_transform" title="Link to this definition">#</a></dt>
 <dd><p>Fit the model from data in X and transform X.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1570,7 +1570,7 @@ <h1>KernelPCA<a class="headerlink" href="#kernelpca" title="Link to this heading
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.KernelPCA.get_feature_names_out">
-<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L995"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.KernelPCA.get_feature_names_out" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L936"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.KernelPCA.get_feature_names_out" title="Link to this definition">#</a></dt>
 <dd><p>Get output feature names for transformation.</p>
 <p>The feature names out will prefixed by the lowercased class name. For
 example, if the transformer outputs 3 features, then the feature names
@@ -1593,7 +1593,7 @@ <h1>KernelPCA<a class="headerlink" href="#kernelpca" title="Link to this heading
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.KernelPCA.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.KernelPCA.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.KernelPCA.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1610,7 +1610,7 @@ <h1>KernelPCA<a class="headerlink" href="#kernelpca" title="Link to this heading
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.KernelPCA.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.KernelPCA.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.KernelPCA.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1631,7 +1631,7 @@ <h1>KernelPCA<a class="headerlink" href="#kernelpca" title="Link to this heading
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.KernelPCA.inverse_transform">
-<span class="sig-name descname"><span class="pre">inverse_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/decomposition/_kernel_pca.py#L516"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.KernelPCA.inverse_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">inverse_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/decomposition/_kernel_pca.py#L516"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.KernelPCA.inverse_transform" title="Link to this definition">#</a></dt>
 <dd><p>Transform X back to original space.</p>
 <p><code class="docutils literal notranslate"><span class="pre">inverse_transform</span></code> approximates the inverse transformation using
 a learned pre-image. The pre-image is learned by kernel ridge
@@ -1657,7 +1657,7 @@ <h1>KernelPCA<a class="headerlink" href="#kernelpca" title="Link to this heading
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><dl class="simple">
-<dt><strong>X_new</strong><span class="classifier">ndarray of shape (n_samples, n_features)</span></dt><dd><p>Returns the instance itself.</p>
+<dt><strong>X_original</strong><span class="classifier">ndarray of shape (n_samples, n_features)</span></dt><dd><p>Returns the instance itself.</p>
 </dd>
 </dl>
 </dd>
@@ -1670,7 +1670,7 @@ <h1>KernelPCA<a class="headerlink" href="#kernelpca" title="Link to this heading
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.KernelPCA.set_output">
-<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.KernelPCA.set_output" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.KernelPCA.set_output" title="Link to this definition">#</a></dt>
 <dd><p>Set output container.</p>
 <p>See <a class="reference internal" href="http://github.com/auto_examples/miscellaneous/plot_set_output.html#sphx-glr-auto-examples-miscellaneous-plot-set-output-py"><span class="std std-ref">Introducing the set_output API</span></a>
 for an example on how to use the API.</p>
@@ -1701,7 +1701,7 @@ <h1>KernelPCA<a class="headerlink" href="#kernelpca" title="Link to this heading
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.KernelPCA.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.KernelPCA.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.KernelPCA.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1725,7 +1725,7 @@ <h1>KernelPCA<a class="headerlink" href="#kernelpca" title="Link to this heading
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.KernelPCA.transform">
-<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/decomposition/_kernel_pca.py#L486"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.KernelPCA.transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/decomposition/_kernel_pca.py#L486"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.KernelPCA.transform" title="Link to this definition">#</a></dt>
 <dd><p>Transform X.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.decomposition.LatentDirichletAllocation.html b/dev/modules/generated/sklearn.decomposition.LatentDirichletAllocation.html
index d5fdf34070206..344b1f885cda8 100644
--- a/dev/modules/generated/sklearn.decomposition.LatentDirichletAllocation.html
+++ b/dev/modules/generated/sklearn.decomposition.LatentDirichletAllocation.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>LatentDirichletAllocation<a class="headerlink" href="#latentdirichletallocation" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.decomposition.LatentDirichletAllocation">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.decomposition.</span></span><span class="sig-name descname"><span class="pre">LatentDirichletAllocation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_components</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">doc_topic_prior</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">topic_word_prior</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">learning_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'batch'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">learning_decay</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.7</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">learning_offset</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">128</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">evaluate_every</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">total_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000000.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">perp_tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mean_change_tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_doc_update_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/decomposition/_lda.py#L164"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.LatentDirichletAllocation" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.decomposition.</span></span><span class="sig-name descname"><span class="pre">LatentDirichletAllocation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_components</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">doc_topic_prior</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">topic_word_prior</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">learning_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'batch'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">learning_decay</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.7</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">learning_offset</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">128</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">evaluate_every</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">total_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000000.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">perp_tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mean_change_tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_doc_update_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/decomposition/_lda.py#L164"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.LatentDirichletAllocation" title="Link to this definition">#</a></dt>
 <dd><p>Latent Dirichlet Allocation with online variational Bayes algorithm.</p>
 <p>The implementation is based on <a class="reference internal" href="#re25e5648fc37-1" id="id1">[1]</a> and <a class="reference internal" href="#re25e5648fc37-2" id="id2">[2]</a>.</p>
 <div class="versionadded">
@@ -1380,7 +1380,7 @@ <h1>LatentDirichletAllocation<a class="headerlink" href="#latentdirichletallocat
 the E-step.</p>
 </dd>
 <dt><strong>n_jobs</strong><span class="classifier">int, default=None</span></dt><dd><p>The number of jobs to use in the E-step.
-<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.5.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
+<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.6.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
 <code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all processors. See <a class="reference internal" href="http://github.com/glossary.html#term-n_jobs"><span class="xref std std-term">Glossary</span></a>
 for more details.</p>
 </dd>
@@ -1473,7 +1473,7 @@ <h1>LatentDirichletAllocation<a class="headerlink" href="#latentdirichletallocat
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.LatentDirichletAllocation.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/decomposition/_lda.py#L627"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.LatentDirichletAllocation.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/decomposition/_lda.py#L627"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.LatentDirichletAllocation.fit" title="Link to this definition">#</a></dt>
 <dd><p>Learn model for the data X with variational Bayes method.</p>
 <p>When <code class="docutils literal notranslate"><span class="pre">learning_method</span></code> is ‘online’, use mini-batch update.
 Otherwise, use batch update.</p>
@@ -1497,7 +1497,7 @@ <h1>LatentDirichletAllocation<a class="headerlink" href="#latentdirichletallocat
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.LatentDirichletAllocation.fit_transform">
-<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">normalize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/decomposition/_lda.py#L755"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.LatentDirichletAllocation.fit_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">normalize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/decomposition/_lda.py#L755"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.LatentDirichletAllocation.fit_transform" title="Link to this definition">#</a></dt>
 <dd><p>Fit to data, then transform it.</p>
 <p>Fits transformer to <code class="docutils literal notranslate"><span class="pre">X</span></code> and <code class="docutils literal notranslate"><span class="pre">y</span></code> and returns a transformed version of <code class="docutils literal notranslate"><span class="pre">X</span></code>.</p>
 <dl class="field-list simple">
@@ -1513,7 +1513,7 @@ <h1>LatentDirichletAllocation<a class="headerlink" href="#latentdirichletallocat
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><dl class="simple">
-<dt><strong>X_new</strong><span class="classifier">ndarray array of shape (n_samples, n_features_new)</span></dt><dd><p>Transformed array.</p>
+<dt><strong>X_new</strong><span class="classifier">ndarray array of shape (n_samples, n_components)</span></dt><dd><p>Transformed array.</p>
 </dd>
 </dl>
 </dd>
@@ -1522,7 +1522,7 @@ <h1>LatentDirichletAllocation<a class="headerlink" href="#latentdirichletallocat
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.LatentDirichletAllocation.get_feature_names_out">
-<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L995"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.LatentDirichletAllocation.get_feature_names_out" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L936"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.LatentDirichletAllocation.get_feature_names_out" title="Link to this definition">#</a></dt>
 <dd><p>Get output feature names for transformation.</p>
 <p>The feature names out will prefixed by the lowercased class name. For
 example, if the transformer outputs 3 features, then the feature names
@@ -1545,7 +1545,7 @@ <h1>LatentDirichletAllocation<a class="headerlink" href="#latentdirichletallocat
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.LatentDirichletAllocation.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.LatentDirichletAllocation.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.LatentDirichletAllocation.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1562,7 +1562,7 @@ <h1>LatentDirichletAllocation<a class="headerlink" href="#latentdirichletallocat
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.LatentDirichletAllocation.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.LatentDirichletAllocation.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.LatentDirichletAllocation.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1583,7 +1583,7 @@ <h1>LatentDirichletAllocation<a class="headerlink" href="#latentdirichletallocat
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.LatentDirichletAllocation.partial_fit">
-<span class="sig-name descname"><span class="pre">partial_fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/decomposition/_lda.py#L579"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.LatentDirichletAllocation.partial_fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">partial_fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/decomposition/_lda.py#L579"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.LatentDirichletAllocation.partial_fit" title="Link to this definition">#</a></dt>
 <dd><p>Online VB with Mini-Batch update.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1605,7 +1605,7 @@ <h1>LatentDirichletAllocation<a class="headerlink" href="#latentdirichletallocat
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.LatentDirichletAllocation.perplexity">
-<span class="sig-name descname"><span class="pre">perplexity</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sub_sampling</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/decomposition/_lda.py#L928"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.LatentDirichletAllocation.perplexity" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">perplexity</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sub_sampling</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/decomposition/_lda.py#L928"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.LatentDirichletAllocation.perplexity" title="Link to this definition">#</a></dt>
 <dd><p>Calculate approximate perplexity for data X.</p>
 <p>Perplexity is defined as exp(-1. * log-likelihood per word)</p>
 <div class="versionchanged">
@@ -1632,7 +1632,7 @@ <h1>LatentDirichletAllocation<a class="headerlink" href="#latentdirichletallocat
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.LatentDirichletAllocation.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/decomposition/_lda.py#L859"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.LatentDirichletAllocation.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/decomposition/_lda.py#L859"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.LatentDirichletAllocation.score" title="Link to this definition">#</a></dt>
 <dd><p>Calculate approximate log-likelihood as score.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1654,7 +1654,7 @@ <h1>LatentDirichletAllocation<a class="headerlink" href="#latentdirichletallocat
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.LatentDirichletAllocation.set_output">
-<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.LatentDirichletAllocation.set_output" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.LatentDirichletAllocation.set_output" title="Link to this definition">#</a></dt>
 <dd><p>Set output container.</p>
 <p>See <a class="reference internal" href="http://github.com/auto_examples/miscellaneous/plot_set_output.html#sphx-glr-auto-examples-miscellaneous-plot-set-output-py"><span class="std std-ref">Introducing the set_output API</span></a>
 for an example on how to use the API.</p>
@@ -1685,7 +1685,7 @@ <h1>LatentDirichletAllocation<a class="headerlink" href="#latentdirichletallocat
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.LatentDirichletAllocation.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.LatentDirichletAllocation.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.LatentDirichletAllocation.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1709,7 +1709,7 @@ <h1>LatentDirichletAllocation<a class="headerlink" href="#latentdirichletallocat
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.LatentDirichletAllocation.set_transform_request">
-<span class="sig-name descname"><span class="pre">set_transform_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">normalize</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.decomposition.LatentDirichletAllocation" title="sklearn.decomposition._lda.LatentDirichletAllocation"><span class="pre">LatentDirichletAllocation</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.LatentDirichletAllocation.set_transform_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_transform_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">normalize</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.decomposition.LatentDirichletAllocation" title="sklearn.decomposition._lda.LatentDirichletAllocation"><span class="pre">LatentDirichletAllocation</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.LatentDirichletAllocation.set_transform_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">transform</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1752,7 +1752,7 @@ <h1>LatentDirichletAllocation<a class="headerlink" href="#latentdirichletallocat
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.LatentDirichletAllocation.transform">
-<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">normalize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/decomposition/_lda.py#L727"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.LatentDirichletAllocation.transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">normalize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/decomposition/_lda.py#L727"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.LatentDirichletAllocation.transform" title="Link to this definition">#</a></dt>
 <dd><p>Transform data X according to the fitted model.</p>
 <div class="versionchanged">
 <p><span class="versionmodified changed">Changed in version 0.18: </span><code class="docutils literal notranslate"><span class="pre">doc_topic_distr</span></code> is now normalized.</p>
diff --git a/dev/modules/generated/sklearn.decomposition.MiniBatchDictionaryLearning.html b/dev/modules/generated/sklearn.decomposition.MiniBatchDictionaryLearning.html
index f5590683cf19f..e4e19c52ab29a 100644
--- a/dev/modules/generated/sklearn.decomposition.MiniBatchDictionaryLearning.html
+++ b/dev/modules/generated/sklearn.decomposition.MiniBatchDictionaryLearning.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>MiniBatchDictionaryLearning<a class="headerlink" href="#minibatchdictionarylearning" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.decomposition.MiniBatchDictionaryLearning">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.decomposition.</span></span><span class="sig-name descname"><span class="pre">MiniBatchDictionaryLearning</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_components</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_algorithm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'lars'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">256</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dict_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform_algorithm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'omp'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform_n_nonzero_coefs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform_alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">split_sign</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">positive_code</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">positive_dict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform_max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">callback</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_no_improvement</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/decomposition/_dict_learning.py#L1751"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.MiniBatchDictionaryLearning" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.decomposition.</span></span><span class="sig-name descname"><span class="pre">MiniBatchDictionaryLearning</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_components</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_algorithm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'lars'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">256</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dict_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform_algorithm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'omp'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform_n_nonzero_coefs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform_alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">split_sign</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">positive_code</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">positive_dict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform_max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">callback</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_no_improvement</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/decomposition/_dict_learning.py#L1751"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.MiniBatchDictionaryLearning" title="Link to this definition">#</a></dt>
 <dd><p>Mini-batch dictionary learning.</p>
 <p>Finds a dictionary (a set of atoms) that performs well at sparsely
 encoding the fitted data.</p>
@@ -1344,7 +1344,7 @@ <h1>MiniBatchDictionaryLearning<a class="headerlink" href="#minibatchdictionaryl
 </ul>
 </dd>
 <dt><strong>n_jobs</strong><span class="classifier">int, default=None</span></dt><dd><p>Number of parallel jobs to run.
-<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.5.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
+<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.6.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
 <code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all processors. See <a class="reference internal" href="http://github.com/glossary.html#term-n_jobs"><span class="xref std std-term">Glossary</span></a>
 for more details.</p>
 </dd>
@@ -1500,12 +1500,12 @@ <h1>MiniBatchDictionaryLearning<a class="headerlink" href="#minibatchdictionaryl
 the original signal:</p>
 <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">X_hat</span> <span class="o">=</span> <span class="n">X_transformed</span> <span class="o">@</span> <span class="n">dict_learner</span><span class="o">.</span><span class="n">components_</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">((</span><span class="n">X_hat</span> <span class="o">-</span> <span class="n">X</span><span class="p">)</span> <span class="o">**</span> <span class="mi">2</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">X</span> <span class="o">**</span> <span class="mi">2</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">))</span>
-<span class="go">np.float64(0.052...)</span>
+<span class="go">np.float64(0.052)</span>
 </pre></div>
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.MiniBatchDictionaryLearning.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/decomposition/_dict_learning.py#L2194"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.MiniBatchDictionaryLearning.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/decomposition/_dict_learning.py#L2194"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.MiniBatchDictionaryLearning.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit the model from data in X.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1528,7 +1528,7 @@ <h1>MiniBatchDictionaryLearning<a class="headerlink" href="#minibatchdictionaryl
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.MiniBatchDictionaryLearning.fit_transform">
-<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L863"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.MiniBatchDictionaryLearning.fit_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L804"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.MiniBatchDictionaryLearning.fit_transform" title="Link to this definition">#</a></dt>
 <dd><p>Fit to data, then transform it.</p>
 <p>Fits transformer to <code class="docutils literal notranslate"><span class="pre">X</span></code> and <code class="docutils literal notranslate"><span class="pre">y</span></code> with optional parameters <code class="docutils literal notranslate"><span class="pre">fit_params</span></code>
 and returns a transformed version of <code class="docutils literal notranslate"><span class="pre">X</span></code>.</p>
@@ -1554,7 +1554,7 @@ <h1>MiniBatchDictionaryLearning<a class="headerlink" href="#minibatchdictionaryl
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.MiniBatchDictionaryLearning.get_feature_names_out">
-<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L995"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.MiniBatchDictionaryLearning.get_feature_names_out" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L936"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.MiniBatchDictionaryLearning.get_feature_names_out" title="Link to this definition">#</a></dt>
 <dd><p>Get output feature names for transformation.</p>
 <p>The feature names out will prefixed by the lowercased class name. For
 example, if the transformer outputs 3 features, then the feature names
@@ -1577,7 +1577,7 @@ <h1>MiniBatchDictionaryLearning<a class="headerlink" href="#minibatchdictionaryl
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.MiniBatchDictionaryLearning.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.MiniBatchDictionaryLearning.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.MiniBatchDictionaryLearning.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1594,7 +1594,7 @@ <h1>MiniBatchDictionaryLearning<a class="headerlink" href="#minibatchdictionaryl
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.MiniBatchDictionaryLearning.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.MiniBatchDictionaryLearning.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.MiniBatchDictionaryLearning.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1615,7 +1615,7 @@ <h1>MiniBatchDictionaryLearning<a class="headerlink" href="#minibatchdictionaryl
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.MiniBatchDictionaryLearning.inverse_transform">
-<span class="sig-name descname"><span class="pre">inverse_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/decomposition/_dict_learning.py#L1166"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.MiniBatchDictionaryLearning.inverse_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">inverse_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/decomposition/_dict_learning.py#L1166"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.MiniBatchDictionaryLearning.inverse_transform" title="Link to this definition">#</a></dt>
 <dd><p>Transform data back to its original space.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1627,7 +1627,7 @@ <h1>MiniBatchDictionaryLearning<a class="headerlink" href="#minibatchdictionaryl
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><dl class="simple">
-<dt><strong>X_new</strong><span class="classifier">ndarray of shape (n_samples, n_features)</span></dt><dd><p>Transformed data.</p>
+<dt><strong>X_original</strong><span class="classifier">ndarray of shape (n_samples, n_features)</span></dt><dd><p>Transformed data.</p>
 </dd>
 </dl>
 </dd>
@@ -1636,7 +1636,7 @@ <h1>MiniBatchDictionaryLearning<a class="headerlink" href="#minibatchdictionaryl
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.MiniBatchDictionaryLearning.partial_fit">
-<span class="sig-name descname"><span class="pre">partial_fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/decomposition/_dict_learning.py#L2276"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.MiniBatchDictionaryLearning.partial_fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">partial_fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/decomposition/_dict_learning.py#L2276"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.MiniBatchDictionaryLearning.partial_fit" title="Link to this definition">#</a></dt>
 <dd><p>Update the model using the data in X as a mini-batch.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1659,7 +1659,7 @@ <h1>MiniBatchDictionaryLearning<a class="headerlink" href="#minibatchdictionaryl
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.MiniBatchDictionaryLearning.set_output">
-<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.MiniBatchDictionaryLearning.set_output" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.MiniBatchDictionaryLearning.set_output" title="Link to this definition">#</a></dt>
 <dd><p>Set output container.</p>
 <p>See <a class="reference internal" href="http://github.com/auto_examples/miscellaneous/plot_set_output.html#sphx-glr-auto-examples-miscellaneous-plot-set-output-py"><span class="std std-ref">Introducing the set_output API</span></a>
 for an example on how to use the API.</p>
@@ -1690,7 +1690,7 @@ <h1>MiniBatchDictionaryLearning<a class="headerlink" href="#minibatchdictionaryl
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.MiniBatchDictionaryLearning.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.MiniBatchDictionaryLearning.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.MiniBatchDictionaryLearning.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1714,7 +1714,7 @@ <h1>MiniBatchDictionaryLearning<a class="headerlink" href="#minibatchdictionaryl
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.MiniBatchDictionaryLearning.transform">
-<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/decomposition/_dict_learning.py#L1125"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.MiniBatchDictionaryLearning.transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/decomposition/_dict_learning.py#L1125"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.MiniBatchDictionaryLearning.transform" title="Link to this definition">#</a></dt>
 <dd><p>Encode the data as a sparse combination of the dictionary atoms.</p>
 <p>Coding method is determined by the object parameter
 <code class="docutils literal notranslate"><span class="pre">transform_algorithm</span></code>.</p>
diff --git a/dev/modules/generated/sklearn.decomposition.MiniBatchNMF.html b/dev/modules/generated/sklearn.decomposition.MiniBatchNMF.html
index 6c3f910c9e795..b28c5de4537cf 100644
--- a/dev/modules/generated/sklearn.decomposition.MiniBatchNMF.html
+++ b/dev/modules/generated/sklearn.decomposition.MiniBatchNMF.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1308,7 +1308,7 @@
 <h1>MiniBatchNMF<a class="headerlink" href="#minibatchnmf" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.decomposition.MiniBatchNMF">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.decomposition.</span></span><span class="sig-name descname"><span class="pre">MiniBatchNMF</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_components</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1024</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">beta_loss</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'frobenius'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_no_improvement</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">200</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha_W</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha_H</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'same'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">l1_ratio</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">forget_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.7</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fresh_restarts</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fresh_restarts_max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">30</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform_max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/decomposition/_nmf.py#L1766"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.MiniBatchNMF" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.decomposition.</span></span><span class="sig-name descname"><span class="pre">MiniBatchNMF</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_components</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1024</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">beta_loss</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'frobenius'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_no_improvement</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">200</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha_W</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha_H</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'same'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">l1_ratio</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">forget_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.7</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fresh_restarts</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fresh_restarts_max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">30</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform_max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/decomposition/_nmf.py#L1766"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.MiniBatchNMF" title="Link to this definition">#</a></dt>
 <dd><p>Mini-Batch Non-Negative Matrix Factorization (NMF).</p>
 <div class="versionadded">
 <p><span class="versionmodified added">Added in version 1.1.</span></p>
@@ -1484,7 +1484,7 @@ <h1>MiniBatchNMF<a class="headerlink" href="#minibatchnmf" title="Link to this h
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.MiniBatchNMF.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/decomposition/_nmf.py#L1267"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.MiniBatchNMF.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/decomposition/_nmf.py#L1267"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.MiniBatchNMF.fit" title="Link to this definition">#</a></dt>
 <dd><p>Learn a NMF model for the data X.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1510,7 +1510,7 @@ <h1>MiniBatchNMF<a class="headerlink" href="#minibatchnmf" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.MiniBatchNMF.fit_transform">
-<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">W</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">H</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/decomposition/_nmf.py#L2182"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.MiniBatchNMF.fit_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">W</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">H</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/decomposition/_nmf.py#L2182"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.MiniBatchNMF.fit_transform" title="Link to this definition">#</a></dt>
 <dd><p>Learn a NMF model for the data X and returns the transformed data.</p>
 <p>This is more efficient than calling fit followed by transform.</p>
 <dl class="field-list simple">
@@ -1539,7 +1539,7 @@ <h1>MiniBatchNMF<a class="headerlink" href="#minibatchnmf" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.MiniBatchNMF.get_feature_names_out">
-<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L995"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.MiniBatchNMF.get_feature_names_out" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L936"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.MiniBatchNMF.get_feature_names_out" title="Link to this definition">#</a></dt>
 <dd><p>Get output feature names for transformation.</p>
 <p>The feature names out will prefixed by the lowercased class name. For
 example, if the transformer outputs 3 features, then the feature names
@@ -1562,7 +1562,7 @@ <h1>MiniBatchNMF<a class="headerlink" href="#minibatchnmf" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.MiniBatchNMF.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.MiniBatchNMF.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.MiniBatchNMF.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1579,7 +1579,7 @@ <h1>MiniBatchNMF<a class="headerlink" href="#minibatchnmf" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.MiniBatchNMF.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.MiniBatchNMF.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.MiniBatchNMF.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1600,7 +1600,7 @@ <h1>MiniBatchNMF<a class="headerlink" href="#minibatchnmf" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.MiniBatchNMF.inverse_transform">
-<span class="sig-name descname"><span class="pre">inverse_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/decomposition/_nmf.py#L1293"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.MiniBatchNMF.inverse_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">inverse_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/decomposition/_nmf.py#L1293"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.MiniBatchNMF.inverse_transform" title="Link to this definition">#</a></dt>
 <dd><p>Transform data back to its original space.</p>
 <div class="versionadded">
 <p><span class="versionmodified added">Added in version 0.18.</span></p>
@@ -1614,7 +1614,7 @@ <h1>MiniBatchNMF<a class="headerlink" href="#minibatchnmf" title="Link to this h
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><dl class="simple">
-<dt><strong>X</strong><span class="classifier">ndarray of shape (n_samples, n_features)</span></dt><dd><p>Returns a data matrix of the original shape.</p>
+<dt><strong>X_original</strong><span class="classifier">ndarray of shape (n_samples, n_features)</span></dt><dd><p>Returns a data matrix of the original shape.</p>
 </dd>
 </dl>
 </dd>
@@ -1623,7 +1623,7 @@ <h1>MiniBatchNMF<a class="headerlink" href="#minibatchnmf" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.MiniBatchNMF.partial_fit">
-<span class="sig-name descname"><span class="pre">partial_fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">W</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">H</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/decomposition/_nmf.py#L2350"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.MiniBatchNMF.partial_fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">partial_fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">W</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">H</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/decomposition/_nmf.py#L2350"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.MiniBatchNMF.partial_fit" title="Link to this definition">#</a></dt>
 <dd><p>Update the model using the data in <code class="docutils literal notranslate"><span class="pre">X</span></code> as a mini-batch.</p>
 <p>This method is expected to be called several times consecutively
 on different chunks of a dataset so as to implement out-of-core
@@ -1656,7 +1656,7 @@ <h1>MiniBatchNMF<a class="headerlink" href="#minibatchnmf" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.MiniBatchNMF.set_output">
-<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.MiniBatchNMF.set_output" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.MiniBatchNMF.set_output" title="Link to this definition">#</a></dt>
 <dd><p>Set output container.</p>
 <p>See <a class="reference internal" href="http://github.com/auto_examples/miscellaneous/plot_set_output.html#sphx-glr-auto-examples-miscellaneous-plot-set-output-py"><span class="std std-ref">Introducing the set_output API</span></a>
 for an example on how to use the API.</p>
@@ -1687,7 +1687,7 @@ <h1>MiniBatchNMF<a class="headerlink" href="#minibatchnmf" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.MiniBatchNMF.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.MiniBatchNMF.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.MiniBatchNMF.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1711,7 +1711,7 @@ <h1>MiniBatchNMF<a class="headerlink" href="#minibatchnmf" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.MiniBatchNMF.set_partial_fit_request">
-<span class="sig-name descname"><span class="pre">set_partial_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">H</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">W</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.decomposition.MiniBatchNMF" title="sklearn.decomposition._nmf.MiniBatchNMF"><span class="pre">MiniBatchNMF</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.MiniBatchNMF.set_partial_fit_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_partial_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">H</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">W</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.decomposition.MiniBatchNMF" title="sklearn.decomposition._nmf.MiniBatchNMF"><span class="pre">MiniBatchNMF</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.MiniBatchNMF.set_partial_fit_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">partial_fit</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1756,7 +1756,7 @@ <h1>MiniBatchNMF<a class="headerlink" href="#minibatchnmf" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.MiniBatchNMF.transform">
-<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/decomposition/_nmf.py#L2324"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.MiniBatchNMF.transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/decomposition/_nmf.py#L2324"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.MiniBatchNMF.transform" title="Link to this definition">#</a></dt>
 <dd><p>Transform the data X according to the fitted MiniBatchNMF model.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.decomposition.MiniBatchSparsePCA.html b/dev/modules/generated/sklearn.decomposition.MiniBatchSparsePCA.html
index 63983dad7be9a..5b88e39faef49 100644
--- a/dev/modules/generated/sklearn.decomposition.MiniBatchSparsePCA.html
+++ b/dev/modules/generated/sklearn.decomposition.MiniBatchSparsePCA.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>MiniBatchSparsePCA<a class="headerlink" href="#minibatchsparsepca" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.decomposition.MiniBatchSparsePCA">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.decomposition.</span></span><span class="sig-name descname"><span class="pre">MiniBatchSparsePCA</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_components</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ridge_alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">callback</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'lars'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_no_improvement</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/decomposition/_sparse_pca.py#L339"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.MiniBatchSparsePCA" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.decomposition.</span></span><span class="sig-name descname"><span class="pre">MiniBatchSparsePCA</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_components</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ridge_alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">callback</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'lars'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_no_improvement</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/decomposition/_sparse_pca.py#L339"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.MiniBatchSparsePCA" title="Link to this definition">#</a></dt>
 <dd><p>Mini-batch Sparse Principal Components Analysis.</p>
 <p>Finds the set of sparse components that can optimally reconstruct
 the data.  The amount of sparseness is controllable by the coefficient
@@ -1342,7 +1342,7 @@ <h1>MiniBatchSparsePCA<a class="headerlink" href="#minibatchsparsepca" title="Li
 <dt><strong>shuffle</strong><span class="classifier">bool, default=True</span></dt><dd><p>Whether to shuffle the data before splitting it in batches.</p>
 </dd>
 <dt><strong>n_jobs</strong><span class="classifier">int, default=None</span></dt><dd><p>Number of parallel jobs to run.
-<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.5.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
+<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.6.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
 <code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all processors. See <a class="reference internal" href="http://github.com/glossary.html#term-n_jobs"><span class="xref std std-term">Glossary</span></a>
 for more details.</p>
 </dd>
@@ -1433,12 +1433,12 @@ <h1>MiniBatchSparsePCA<a class="headerlink" href="#minibatchsparsepca" title="Li
 <span class="go">(200, 5)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="c1"># most values in the components_ are zero (sparsity)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">transformer</span><span class="o">.</span><span class="n">components_</span> <span class="o">==</span> <span class="mi">0</span><span class="p">)</span>
-<span class="go">np.float64(0.9...)</span>
+<span class="go">np.float64(0.9)</span>
 </pre></div>
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.MiniBatchSparsePCA.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/decomposition/_sparse_pca.py#L62"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.MiniBatchSparsePCA.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/decomposition/_sparse_pca.py#L62"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.MiniBatchSparsePCA.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit the model from data in X.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1461,7 +1461,7 @@ <h1>MiniBatchSparsePCA<a class="headerlink" href="#minibatchsparsepca" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.MiniBatchSparsePCA.fit_transform">
-<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L863"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.MiniBatchSparsePCA.fit_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L804"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.MiniBatchSparsePCA.fit_transform" title="Link to this definition">#</a></dt>
 <dd><p>Fit to data, then transform it.</p>
 <p>Fits transformer to <code class="docutils literal notranslate"><span class="pre">X</span></code> and <code class="docutils literal notranslate"><span class="pre">y</span></code> with optional parameters <code class="docutils literal notranslate"><span class="pre">fit_params</span></code>
 and returns a transformed version of <code class="docutils literal notranslate"><span class="pre">X</span></code>.</p>
@@ -1487,7 +1487,7 @@ <h1>MiniBatchSparsePCA<a class="headerlink" href="#minibatchsparsepca" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.MiniBatchSparsePCA.get_feature_names_out">
-<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L995"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.MiniBatchSparsePCA.get_feature_names_out" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L936"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.MiniBatchSparsePCA.get_feature_names_out" title="Link to this definition">#</a></dt>
 <dd><p>Get output feature names for transformation.</p>
 <p>The feature names out will prefixed by the lowercased class name. For
 example, if the transformer outputs 3 features, then the feature names
@@ -1510,7 +1510,7 @@ <h1>MiniBatchSparsePCA<a class="headerlink" href="#minibatchsparsepca" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.MiniBatchSparsePCA.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.MiniBatchSparsePCA.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.MiniBatchSparsePCA.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1527,7 +1527,7 @@ <h1>MiniBatchSparsePCA<a class="headerlink" href="#minibatchsparsepca" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.MiniBatchSparsePCA.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.MiniBatchSparsePCA.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.MiniBatchSparsePCA.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1548,7 +1548,7 @@ <h1>MiniBatchSparsePCA<a class="headerlink" href="#minibatchsparsepca" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.MiniBatchSparsePCA.inverse_transform">
-<span class="sig-name descname"><span class="pre">inverse_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/decomposition/_sparse_pca.py#L125"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.MiniBatchSparsePCA.inverse_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">inverse_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/decomposition/_sparse_pca.py#L125"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.MiniBatchSparsePCA.inverse_transform" title="Link to this definition">#</a></dt>
 <dd><p>Transform data from the latent space to the original space.</p>
 <p>This inversion is an approximation due to the loss of information
 induced by the forward decomposition.</p>
@@ -1573,7 +1573,7 @@ <h1>MiniBatchSparsePCA<a class="headerlink" href="#minibatchsparsepca" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.MiniBatchSparsePCA.set_output">
-<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.MiniBatchSparsePCA.set_output" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.MiniBatchSparsePCA.set_output" title="Link to this definition">#</a></dt>
 <dd><p>Set output container.</p>
 <p>See <a class="reference internal" href="http://github.com/auto_examples/miscellaneous/plot_set_output.html#sphx-glr-auto-examples-miscellaneous-plot-set-output-py"><span class="std std-ref">Introducing the set_output API</span></a>
 for an example on how to use the API.</p>
@@ -1604,7 +1604,7 @@ <h1>MiniBatchSparsePCA<a class="headerlink" href="#minibatchsparsepca" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.MiniBatchSparsePCA.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.MiniBatchSparsePCA.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.MiniBatchSparsePCA.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1628,7 +1628,7 @@ <h1>MiniBatchSparsePCA<a class="headerlink" href="#minibatchsparsepca" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.MiniBatchSparsePCA.transform">
-<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/decomposition/_sparse_pca.py#L93"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.MiniBatchSparsePCA.transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/decomposition/_sparse_pca.py#L93"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.MiniBatchSparsePCA.transform" title="Link to this definition">#</a></dt>
 <dd><p>Least Squares projection of the data onto the sparse components.</p>
 <p>To avoid instability issues in case the system is under-determined,
 regularization can be applied (Ridge regression) via the
diff --git a/dev/modules/generated/sklearn.decomposition.NMF.html b/dev/modules/generated/sklearn.decomposition.NMF.html
index ff6c9ec5db1bf..99b45d657e896 100644
--- a/dev/modules/generated/sklearn.decomposition.NMF.html
+++ b/dev/modules/generated/sklearn.decomposition.NMF.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1308,7 +1308,7 @@
 <h1>NMF<a class="headerlink" href="#nmf" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.decomposition.NMF">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.decomposition.</span></span><span class="sig-name descname"><span class="pre">NMF</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_components</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">solver</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'cd'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">beta_loss</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'frobenius'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">200</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha_W</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha_H</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'same'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">l1_ratio</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/decomposition/_nmf.py#L1325"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.NMF" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.decomposition.</span></span><span class="sig-name descname"><span class="pre">NMF</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_components</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">solver</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'cd'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">beta_loss</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'frobenius'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">200</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha_W</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha_H</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'same'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">l1_ratio</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/decomposition/_nmf.py#L1325"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.NMF" title="Link to this definition">#</a></dt>
 <dd><p>Non-Negative Matrix Factorization (NMF).</p>
 <p>Find two non-negative matrices, i.e. matrices with all non-negative elements, (W, H)
 whose product approximates the non-negative matrix X. This factorization can be used
@@ -1498,7 +1498,7 @@ <h1>NMF<a class="headerlink" href="#nmf" title="Link to this heading">#</a></h1>
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.NMF.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/decomposition/_nmf.py#L1267"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.NMF.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/decomposition/_nmf.py#L1267"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.NMF.fit" title="Link to this definition">#</a></dt>
 <dd><p>Learn a NMF model for the data X.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1524,7 +1524,7 @@ <h1>NMF<a class="headerlink" href="#nmf" title="Link to this heading">#</a></h1>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.NMF.fit_transform">
-<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">W</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">H</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/decomposition/_nmf.py#L1593"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.NMF.fit_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">W</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">H</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/decomposition/_nmf.py#L1593"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.NMF.fit_transform" title="Link to this definition">#</a></dt>
 <dd><p>Learn a NMF model for the data X and returns the transformed data.</p>
 <p>This is more efficient than calling fit followed by transform.</p>
 <dl class="field-list simple">
@@ -1554,7 +1554,7 @@ <h1>NMF<a class="headerlink" href="#nmf" title="Link to this heading">#</a></h1>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.NMF.get_feature_names_out">
-<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L995"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.NMF.get_feature_names_out" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L936"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.NMF.get_feature_names_out" title="Link to this definition">#</a></dt>
 <dd><p>Get output feature names for transformation.</p>
 <p>The feature names out will prefixed by the lowercased class name. For
 example, if the transformer outputs 3 features, then the feature names
@@ -1577,7 +1577,7 @@ <h1>NMF<a class="headerlink" href="#nmf" title="Link to this heading">#</a></h1>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.NMF.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.NMF.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.NMF.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1594,7 +1594,7 @@ <h1>NMF<a class="headerlink" href="#nmf" title="Link to this heading">#</a></h1>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.NMF.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.NMF.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.NMF.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1615,7 +1615,7 @@ <h1>NMF<a class="headerlink" href="#nmf" title="Link to this heading">#</a></h1>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.NMF.inverse_transform">
-<span class="sig-name descname"><span class="pre">inverse_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/decomposition/_nmf.py#L1293"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.NMF.inverse_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">inverse_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/decomposition/_nmf.py#L1293"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.NMF.inverse_transform" title="Link to this definition">#</a></dt>
 <dd><p>Transform data back to its original space.</p>
 <div class="versionadded">
 <p><span class="versionmodified added">Added in version 0.18.</span></p>
@@ -1629,7 +1629,7 @@ <h1>NMF<a class="headerlink" href="#nmf" title="Link to this heading">#</a></h1>
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><dl class="simple">
-<dt><strong>X</strong><span class="classifier">ndarray of shape (n_samples, n_features)</span></dt><dd><p>Returns a data matrix of the original shape.</p>
+<dt><strong>X_original</strong><span class="classifier">ndarray of shape (n_samples, n_features)</span></dt><dd><p>Returns a data matrix of the original shape.</p>
 </dd>
 </dl>
 </dd>
@@ -1638,7 +1638,7 @@ <h1>NMF<a class="headerlink" href="#nmf" title="Link to this heading">#</a></h1>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.NMF.set_output">
-<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.NMF.set_output" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.NMF.set_output" title="Link to this definition">#</a></dt>
 <dd><p>Set output container.</p>
 <p>See <a class="reference internal" href="http://github.com/auto_examples/miscellaneous/plot_set_output.html#sphx-glr-auto-examples-miscellaneous-plot-set-output-py"><span class="std std-ref">Introducing the set_output API</span></a>
 for an example on how to use the API.</p>
@@ -1669,7 +1669,7 @@ <h1>NMF<a class="headerlink" href="#nmf" title="Link to this heading">#</a></h1>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.NMF.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.NMF.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.NMF.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1693,7 +1693,7 @@ <h1>NMF<a class="headerlink" href="#nmf" title="Link to this heading">#</a></h1>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.NMF.transform">
-<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/decomposition/_nmf.py#L1736"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.NMF.transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/decomposition/_nmf.py#L1736"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.NMF.transform" title="Link to this definition">#</a></dt>
 <dd><p>Transform the data X according to the fitted NMF model.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.decomposition.PCA.html b/dev/modules/generated/sklearn.decomposition.PCA.html
index 4e79ae2381369..d8add5541f11f 100644
--- a/dev/modules/generated/sklearn.decomposition.PCA.html
+++ b/dev/modules/generated/sklearn.decomposition.PCA.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>PCA<a class="headerlink" href="#pca" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.decomposition.PCA">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.decomposition.</span></span><span class="sig-name descname"><span class="pre">PCA</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_components</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">whiten</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">svd_solver</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">iterated_power</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_oversamples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">power_iteration_normalizer</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/decomposition/_pca.py#L113"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.PCA" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.decomposition.</span></span><span class="sig-name descname"><span class="pre">PCA</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_components</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">whiten</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">svd_solver</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">iterated_power</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_oversamples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">power_iteration_normalizer</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/decomposition/_pca.py#L113"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.PCA" title="Link to this definition">#</a></dt>
 <dd><p>Principal component analysis (PCA).</p>
 <p>Linear dimensionality reduction using Singular Value Decomposition of the
 data to project it to a lower dimensional space. The input data is centered
@@ -1531,32 +1531,32 @@ <h1>PCA<a class="headerlink" href="#pca" title="Link to this heading">#</a></h1>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">pca</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
 <span class="go">PCA(n_components=2)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">pca</span><span class="o">.</span><span class="n">explained_variance_ratio_</span><span class="p">)</span>
-<span class="go">[0.9924... 0.0075...]</span>
+<span class="go">[0.9924 0.0075]</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">pca</span><span class="o">.</span><span class="n">singular_values_</span><span class="p">)</span>
-<span class="go">[6.30061... 0.54980...]</span>
+<span class="go">[6.30061 0.54980]</span>
 </pre></div>
 </div>
 <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">pca</span> <span class="o">=</span> <span class="n">PCA</span><span class="p">(</span><span class="n">n_components</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">svd_solver</span><span class="o">=</span><span class="s1">&#39;full&#39;</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">pca</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
 <span class="go">PCA(n_components=2, svd_solver=&#39;full&#39;)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">pca</span><span class="o">.</span><span class="n">explained_variance_ratio_</span><span class="p">)</span>
-<span class="go">[0.9924... 0.00755...]</span>
+<span class="go">[0.9924 0.00755]</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">pca</span><span class="o">.</span><span class="n">singular_values_</span><span class="p">)</span>
-<span class="go">[6.30061... 0.54980...]</span>
+<span class="go">[6.30061 0.54980]</span>
 </pre></div>
 </div>
 <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">pca</span> <span class="o">=</span> <span class="n">PCA</span><span class="p">(</span><span class="n">n_components</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">svd_solver</span><span class="o">=</span><span class="s1">&#39;arpack&#39;</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">pca</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
 <span class="go">PCA(n_components=1, svd_solver=&#39;arpack&#39;)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">pca</span><span class="o">.</span><span class="n">explained_variance_ratio_</span><span class="p">)</span>
-<span class="go">[0.99244...]</span>
+<span class="go">[0.99244]</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">pca</span><span class="o">.</span><span class="n">singular_values_</span><span class="p">)</span>
-<span class="go">[6.30061...]</span>
+<span class="go">[6.30061]</span>
 </pre></div>
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.PCA.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/decomposition/_pca.py#L422"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.PCA.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/decomposition/_pca.py#L422"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.PCA.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit the model with X.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1579,7 +1579,7 @@ <h1>PCA<a class="headerlink" href="#pca" title="Link to this heading">#</a></h1>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.PCA.fit_transform">
-<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/decomposition/_pca.py#L443"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.PCA.fit_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/decomposition/_pca.py#L443"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.PCA.fit_transform" title="Link to this definition">#</a></dt>
 <dd><p>Fit the model with X and apply the dimensionality reduction on X.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1605,7 +1605,7 @@ <h1>PCA<a class="headerlink" href="#pca" title="Link to this heading">#</a></h1>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.PCA.get_covariance">
-<span class="sig-name descname"><span class="pre">get_covariance</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/decomposition/_base.py#L25"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.PCA.get_covariance" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_covariance</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/decomposition/_base.py#L25"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.PCA.get_covariance" title="Link to this definition">#</a></dt>
 <dd><p>Compute data covariance with the generative model.</p>
 <p><code class="docutils literal notranslate"><span class="pre">cov</span> <span class="pre">=</span> <span class="pre">components_.T</span> <span class="pre">*</span> <span class="pre">S**2</span> <span class="pre">*</span> <span class="pre">components_</span> <span class="pre">+</span> <span class="pre">sigma2</span> <span class="pre">*</span> <span class="pre">eye(n_features)</span></code>
 where S**2 contains the explained variances, and sigma2 contains the
@@ -1622,7 +1622,7 @@ <h1>PCA<a class="headerlink" href="#pca" title="Link to this heading">#</a></h1>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.PCA.get_feature_names_out">
-<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L995"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.PCA.get_feature_names_out" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L936"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.PCA.get_feature_names_out" title="Link to this definition">#</a></dt>
 <dd><p>Get output feature names for transformation.</p>
 <p>The feature names out will prefixed by the lowercased class name. For
 example, if the transformer outputs 3 features, then the feature names
@@ -1645,7 +1645,7 @@ <h1>PCA<a class="headerlink" href="#pca" title="Link to this heading">#</a></h1>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.PCA.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.PCA.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.PCA.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1662,7 +1662,7 @@ <h1>PCA<a class="headerlink" href="#pca" title="Link to this heading">#</a></h1>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.PCA.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.PCA.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.PCA.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1683,7 +1683,7 @@ <h1>PCA<a class="headerlink" href="#pca" title="Link to this heading">#</a></h1>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.PCA.get_precision">
-<span class="sig-name descname"><span class="pre">get_precision</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/decomposition/_base.py#L53"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.PCA.get_precision" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_precision</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/decomposition/_base.py#L53"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.PCA.get_precision" title="Link to this definition">#</a></dt>
 <dd><p>Compute data precision matrix with the generative model.</p>
 <p>Equals the inverse of the covariance but computed with
 the matrix inversion lemma for efficiency.</p>
@@ -1699,7 +1699,7 @@ <h1>PCA<a class="headerlink" href="#pca" title="Link to this heading">#</a></h1>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.PCA.inverse_transform">
-<span class="sig-name descname"><span class="pre">inverse_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/decomposition/_base.py#L167"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.PCA.inverse_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">inverse_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/decomposition/_base.py#L167"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.PCA.inverse_transform" title="Link to this definition">#</a></dt>
 <dd><p>Transform data back to its original space.</p>
 <p>In other words, return an input <code class="docutils literal notranslate"><span class="pre">X_original</span></code> whose transform would be X.</p>
 <dl class="field-list simple">
@@ -1712,7 +1712,7 @@ <h1>PCA<a class="headerlink" href="#pca" title="Link to this heading">#</a></h1>
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><dl class="simple">
-<dt>X_original array-like of shape (n_samples, n_features)</dt><dd><p>Original data, where <code class="docutils literal notranslate"><span class="pre">n_samples</span></code> is the number of samples
+<dt><strong>X_original</strong><span class="classifier">array-like of shape (n_samples, n_features)</span></dt><dd><p>Original data, where <code class="docutils literal notranslate"><span class="pre">n_samples</span></code> is the number of samples
 and <code class="docutils literal notranslate"><span class="pre">n_features</span></code> is the number of features.</p>
 </dd>
 </dl>
@@ -1725,7 +1725,7 @@ <h1>PCA<a class="headerlink" href="#pca" title="Link to this heading">#</a></h1>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.PCA.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/decomposition/_pca.py#L825"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.PCA.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/decomposition/_pca.py#L825"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.PCA.score" title="Link to this definition">#</a></dt>
 <dd><p>Return the average log-likelihood of all samples.</p>
 <p>See. “Pattern Recognition and Machine Learning”
 by C. Bishop, 12.2.1 p. 574
@@ -1750,7 +1750,7 @@ <h1>PCA<a class="headerlink" href="#pca" title="Link to this heading">#</a></h1>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.PCA.score_samples">
-<span class="sig-name descname"><span class="pre">score_samples</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/decomposition/_pca.py#L798"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.PCA.score_samples" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score_samples</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/decomposition/_pca.py#L798"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.PCA.score_samples" title="Link to this definition">#</a></dt>
 <dd><p>Return the log-likelihood of each sample.</p>
 <p>See. “Pattern Recognition and Machine Learning”
 by C. Bishop, 12.2.1 p. 574
@@ -1773,7 +1773,7 @@ <h1>PCA<a class="headerlink" href="#pca" title="Link to this heading">#</a></h1>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.PCA.set_output">
-<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.PCA.set_output" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.PCA.set_output" title="Link to this definition">#</a></dt>
 <dd><p>Set output container.</p>
 <p>See <a class="reference internal" href="http://github.com/auto_examples/miscellaneous/plot_set_output.html#sphx-glr-auto-examples-miscellaneous-plot-set-output-py"><span class="std std-ref">Introducing the set_output API</span></a>
 for an example on how to use the API.</p>
@@ -1804,7 +1804,7 @@ <h1>PCA<a class="headerlink" href="#pca" title="Link to this heading">#</a></h1>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.PCA.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.PCA.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.PCA.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1828,7 +1828,7 @@ <h1>PCA<a class="headerlink" href="#pca" title="Link to this heading">#</a></h1>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.PCA.transform">
-<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/decomposition/_base.py#L116"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.PCA.transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/decomposition/_base.py#L116"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.PCA.transform" title="Link to this definition">#</a></dt>
 <dd><p>Apply dimensionality reduction to X.</p>
 <p>X is projected on the first principal components previously extracted
 from a training set.</p>
diff --git a/dev/modules/generated/sklearn.decomposition.SparseCoder.html b/dev/modules/generated/sklearn.decomposition.SparseCoder.html
index 0d0c72f453a96..35dafd599c9c1 100644
--- a/dev/modules/generated/sklearn.decomposition.SparseCoder.html
+++ b/dev/modules/generated/sklearn.decomposition.SparseCoder.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>SparseCoder<a class="headerlink" href="#sparsecoder" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.decomposition.SparseCoder">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.decomposition.</span></span><span class="sig-name descname"><span class="pre">SparseCoder</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dictionary</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform_algorithm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'omp'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform_n_nonzero_coefs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform_alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">split_sign</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">positive_code</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform_max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/decomposition/_dict_learning.py#L1184"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.SparseCoder" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.decomposition.</span></span><span class="sig-name descname"><span class="pre">SparseCoder</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dictionary</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform_algorithm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'omp'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform_n_nonzero_coefs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform_alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">split_sign</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">positive_code</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform_max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/decomposition/_dict_learning.py#L1184"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.SparseCoder" title="Link to this definition">#</a></dt>
 <dd><p>Sparse coding.</p>
 <p>Finds a sparse representation of data against a fixed, precomputed
 dictionary.</p>
@@ -1356,7 +1356,7 @@ <h1>SparseCoder<a class="headerlink" href="#sparsecoder" title="Link to this hea
 performance of downstream classifiers.</p>
 </dd>
 <dt><strong>n_jobs</strong><span class="classifier">int, default=None</span></dt><dd><p>Number of parallel jobs to run.
-<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.5.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
+<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.6.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
 <code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all processors. See <a class="reference internal" href="http://github.com/glossary.html#term-n_jobs"><span class="xref std std-term">Glossary</span></a>
 for more details.</p>
 </dd>
@@ -1426,7 +1426,7 @@ <h1>SparseCoder<a class="headerlink" href="#sparsecoder" title="Link to this hea
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.SparseCoder.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/decomposition/_dict_learning.py#L1327"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.SparseCoder.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/decomposition/_dict_learning.py#L1327"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.SparseCoder.fit" title="Link to this definition">#</a></dt>
 <dd><p>Do nothing and return the estimator unchanged.</p>
 <p>This method is just there to implement the usual API and hence
 work in pipelines.</p>
@@ -1450,7 +1450,7 @@ <h1>SparseCoder<a class="headerlink" href="#sparsecoder" title="Link to this hea
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.SparseCoder.fit_transform">
-<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L863"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.SparseCoder.fit_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L804"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.SparseCoder.fit_transform" title="Link to this definition">#</a></dt>
 <dd><p>Fit to data, then transform it.</p>
 <p>Fits transformer to <code class="docutils literal notranslate"><span class="pre">X</span></code> and <code class="docutils literal notranslate"><span class="pre">y</span></code> with optional parameters <code class="docutils literal notranslate"><span class="pre">fit_params</span></code>
 and returns a transformed version of <code class="docutils literal notranslate"><span class="pre">X</span></code>.</p>
@@ -1476,7 +1476,7 @@ <h1>SparseCoder<a class="headerlink" href="#sparsecoder" title="Link to this hea
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.SparseCoder.get_feature_names_out">
-<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L995"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.SparseCoder.get_feature_names_out" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L936"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.SparseCoder.get_feature_names_out" title="Link to this definition">#</a></dt>
 <dd><p>Get output feature names for transformation.</p>
 <p>The feature names out will prefixed by the lowercased class name. For
 example, if the transformer outputs 3 features, then the feature names
@@ -1499,7 +1499,7 @@ <h1>SparseCoder<a class="headerlink" href="#sparsecoder" title="Link to this hea
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.SparseCoder.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.SparseCoder.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.SparseCoder.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1516,7 +1516,7 @@ <h1>SparseCoder<a class="headerlink" href="#sparsecoder" title="Link to this hea
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.SparseCoder.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.SparseCoder.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.SparseCoder.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1537,7 +1537,7 @@ <h1>SparseCoder<a class="headerlink" href="#sparsecoder" title="Link to this hea
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.SparseCoder.inverse_transform">
-<span class="sig-name descname"><span class="pre">inverse_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/decomposition/_dict_learning.py#L1370"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.SparseCoder.inverse_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">inverse_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/decomposition/_dict_learning.py#L1370"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.SparseCoder.inverse_transform" title="Link to this definition">#</a></dt>
 <dd><p>Transform data back to its original space.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1549,7 +1549,7 @@ <h1>SparseCoder<a class="headerlink" href="#sparsecoder" title="Link to this hea
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><dl class="simple">
-<dt><strong>X_new</strong><span class="classifier">ndarray of shape (n_samples, n_features)</span></dt><dd><p>Transformed data.</p>
+<dt><strong>X_original</strong><span class="classifier">ndarray of shape (n_samples, n_features)</span></dt><dd><p>Transformed data.</p>
 </dd>
 </dl>
 </dd>
@@ -1558,7 +1558,7 @@ <h1>SparseCoder<a class="headerlink" href="#sparsecoder" title="Link to this hea
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.SparseCoder.set_output">
-<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.SparseCoder.set_output" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.SparseCoder.set_output" title="Link to this definition">#</a></dt>
 <dd><p>Set output container.</p>
 <p>See <a class="reference internal" href="http://github.com/auto_examples/miscellaneous/plot_set_output.html#sphx-glr-auto-examples-miscellaneous-plot-set-output-py"><span class="std std-ref">Introducing the set_output API</span></a>
 for an example on how to use the API.</p>
@@ -1589,7 +1589,7 @@ <h1>SparseCoder<a class="headerlink" href="#sparsecoder" title="Link to this hea
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.SparseCoder.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.SparseCoder.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.SparseCoder.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1613,7 +1613,7 @@ <h1>SparseCoder<a class="headerlink" href="#sparsecoder" title="Link to this hea
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.SparseCoder.transform">
-<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/decomposition/_dict_learning.py#L1348"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.SparseCoder.transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/decomposition/_dict_learning.py#L1348"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.SparseCoder.transform" title="Link to this definition">#</a></dt>
 <dd><p>Encode the data as a sparse combination of the dictionary atoms.</p>
 <p>Coding method is determined by the object parameter
 <code class="docutils literal notranslate"><span class="pre">transform_algorithm</span></code>.</p>
diff --git a/dev/modules/generated/sklearn.decomposition.SparsePCA.html b/dev/modules/generated/sklearn.decomposition.SparsePCA.html
index a3279efb2c3a6..c70e59b469d95 100644
--- a/dev/modules/generated/sklearn.decomposition.SparsePCA.html
+++ b/dev/modules/generated/sklearn.decomposition.SparsePCA.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>SparsePCA<a class="headerlink" href="#sparsepca" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.decomposition.SparsePCA">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.decomposition.</span></span><span class="sig-name descname"><span class="pre">SparsePCA</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_components</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ridge_alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-08</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'lars'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">U_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">V_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/decomposition/_sparse_pca.py#L159"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.SparsePCA" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.decomposition.</span></span><span class="sig-name descname"><span class="pre">SparsePCA</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_components</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ridge_alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-08</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'lars'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">U_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">V_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/decomposition/_sparse_pca.py#L159"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.SparsePCA" title="Link to this definition">#</a></dt>
 <dd><p>Sparse Principal Components Analysis (SparsePCA).</p>
 <p>Finds the set of sparse components that can optimally reconstruct
 the data.  The amount of sparseness is controllable by the coefficient
@@ -1337,7 +1337,7 @@ <h1>SparsePCA<a class="headerlink" href="#sparsepca" title="Link to this heading
 the estimated components are sparse.</p>
 </dd>
 <dt><strong>n_jobs</strong><span class="classifier">int, default=None</span></dt><dd><p>Number of parallel jobs to run.
-<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.5.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
+<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.6.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
 <code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all processors. See <a class="reference internal" href="http://github.com/glossary.html#term-n_jobs"><span class="xref std std-term">Glossary</span></a>
 for more details.</p>
 </dd>
@@ -1409,12 +1409,12 @@ <h1>SparsePCA<a class="headerlink" href="#sparsepca" title="Link to this heading
 <span class="go">(200, 5)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="c1"># most values in the components_ are zero (sparsity)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">transformer</span><span class="o">.</span><span class="n">components_</span> <span class="o">==</span> <span class="mi">0</span><span class="p">)</span>
-<span class="go">np.float64(0.9666...)</span>
+<span class="go">np.float64(0.9666)</span>
 </pre></div>
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.SparsePCA.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/decomposition/_sparse_pca.py#L62"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.SparsePCA.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/decomposition/_sparse_pca.py#L62"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.SparsePCA.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit the model from data in X.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1437,7 +1437,7 @@ <h1>SparsePCA<a class="headerlink" href="#sparsepca" title="Link to this heading
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.SparsePCA.fit_transform">
-<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L863"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.SparsePCA.fit_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L804"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.SparsePCA.fit_transform" title="Link to this definition">#</a></dt>
 <dd><p>Fit to data, then transform it.</p>
 <p>Fits transformer to <code class="docutils literal notranslate"><span class="pre">X</span></code> and <code class="docutils literal notranslate"><span class="pre">y</span></code> with optional parameters <code class="docutils literal notranslate"><span class="pre">fit_params</span></code>
 and returns a transformed version of <code class="docutils literal notranslate"><span class="pre">X</span></code>.</p>
@@ -1463,7 +1463,7 @@ <h1>SparsePCA<a class="headerlink" href="#sparsepca" title="Link to this heading
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.SparsePCA.get_feature_names_out">
-<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L995"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.SparsePCA.get_feature_names_out" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L936"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.SparsePCA.get_feature_names_out" title="Link to this definition">#</a></dt>
 <dd><p>Get output feature names for transformation.</p>
 <p>The feature names out will prefixed by the lowercased class name. For
 example, if the transformer outputs 3 features, then the feature names
@@ -1486,7 +1486,7 @@ <h1>SparsePCA<a class="headerlink" href="#sparsepca" title="Link to this heading
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.SparsePCA.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.SparsePCA.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.SparsePCA.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1503,7 +1503,7 @@ <h1>SparsePCA<a class="headerlink" href="#sparsepca" title="Link to this heading
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.SparsePCA.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.SparsePCA.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.SparsePCA.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1524,7 +1524,7 @@ <h1>SparsePCA<a class="headerlink" href="#sparsepca" title="Link to this heading
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.SparsePCA.inverse_transform">
-<span class="sig-name descname"><span class="pre">inverse_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/decomposition/_sparse_pca.py#L125"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.SparsePCA.inverse_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">inverse_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/decomposition/_sparse_pca.py#L125"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.SparsePCA.inverse_transform" title="Link to this definition">#</a></dt>
 <dd><p>Transform data from the latent space to the original space.</p>
 <p>This inversion is an approximation due to the loss of information
 induced by the forward decomposition.</p>
@@ -1549,7 +1549,7 @@ <h1>SparsePCA<a class="headerlink" href="#sparsepca" title="Link to this heading
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.SparsePCA.set_output">
-<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.SparsePCA.set_output" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.SparsePCA.set_output" title="Link to this definition">#</a></dt>
 <dd><p>Set output container.</p>
 <p>See <a class="reference internal" href="http://github.com/auto_examples/miscellaneous/plot_set_output.html#sphx-glr-auto-examples-miscellaneous-plot-set-output-py"><span class="std std-ref">Introducing the set_output API</span></a>
 for an example on how to use the API.</p>
@@ -1580,7 +1580,7 @@ <h1>SparsePCA<a class="headerlink" href="#sparsepca" title="Link to this heading
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.SparsePCA.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.SparsePCA.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.SparsePCA.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1604,7 +1604,7 @@ <h1>SparsePCA<a class="headerlink" href="#sparsepca" title="Link to this heading
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.SparsePCA.transform">
-<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/decomposition/_sparse_pca.py#L93"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.SparsePCA.transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/decomposition/_sparse_pca.py#L93"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.SparsePCA.transform" title="Link to this definition">#</a></dt>
 <dd><p>Least Squares projection of the data onto the sparse components.</p>
 <p>To avoid instability issues in case the system is under-determined,
 regularization can be applied (Ridge regression) via the
diff --git a/dev/modules/generated/sklearn.decomposition.TruncatedSVD.html b/dev/modules/generated/sklearn.decomposition.TruncatedSVD.html
index 273ac91e060f1..ed371d31c736e 100644
--- a/dev/modules/generated/sklearn.decomposition.TruncatedSVD.html
+++ b/dev/modules/generated/sklearn.decomposition.TruncatedSVD.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>TruncatedSVD<a class="headerlink" href="#truncatedsvd" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.decomposition.TruncatedSVD">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.decomposition.</span></span><span class="sig-name descname"><span class="pre">TruncatedSVD</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_components</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">algorithm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'randomized'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_oversamples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">power_iteration_normalizer</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/decomposition/_truncated_svd.py#L28"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.TruncatedSVD" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.decomposition.</span></span><span class="sig-name descname"><span class="pre">TruncatedSVD</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_components</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">algorithm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'randomized'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_oversamples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">power_iteration_normalizer</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/decomposition/_truncated_svd.py#L28"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.TruncatedSVD" title="Link to this definition">#</a></dt>
 <dd><p>Dimensionality reduction using truncated SVD (aka LSA).</p>
 <p>This transformer performs linear dimensionality reduction by means of
 truncated singular value decomposition (SVD). Contrary to PCA, this
@@ -1426,16 +1426,16 @@ <h1>TruncatedSVD<a class="headerlink" href="#truncatedsvd" title="Link to this h
 <span class="gp">&gt;&gt;&gt; </span><span class="n">svd</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
 <span class="go">TruncatedSVD(n_components=5, n_iter=7, random_state=42)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">svd</span><span class="o">.</span><span class="n">explained_variance_ratio_</span><span class="p">)</span>
-<span class="go">[0.0157... 0.0512... 0.0499... 0.0479... 0.0453...]</span>
+<span class="go">[0.0157 0.0512 0.0499 0.0479 0.0453]</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">svd</span><span class="o">.</span><span class="n">explained_variance_ratio_</span><span class="o">.</span><span class="n">sum</span><span class="p">())</span>
-<span class="go">0.2102...</span>
+<span class="go">0.2102</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">svd</span><span class="o">.</span><span class="n">singular_values_</span><span class="p">)</span>
-<span class="go">[35.2410...  4.5981...   4.5420...  4.4486...  4.3288...]</span>
+<span class="go">[35.2410  4.5981   4.5420  4.4486  4.3288]</span>
 </pre></div>
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.TruncatedSVD.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/decomposition/_truncated_svd.py#L190"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.TruncatedSVD.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/decomposition/_truncated_svd.py#L190"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.TruncatedSVD.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit model on training data X.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1457,7 +1457,7 @@ <h1>TruncatedSVD<a class="headerlink" href="#truncatedsvd" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.TruncatedSVD.fit_transform">
-<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/decomposition/_truncated_svd.py#L209"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.TruncatedSVD.fit_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/decomposition/_truncated_svd.py#L209"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.TruncatedSVD.fit_transform" title="Link to this definition">#</a></dt>
 <dd><p>Fit model to X and perform dimensionality reduction on X.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1479,7 +1479,7 @@ <h1>TruncatedSVD<a class="headerlink" href="#truncatedsvd" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.TruncatedSVD.get_feature_names_out">
-<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L995"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.TruncatedSVD.get_feature_names_out" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L936"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.TruncatedSVD.get_feature_names_out" title="Link to this definition">#</a></dt>
 <dd><p>Get output feature names for transformation.</p>
 <p>The feature names out will prefixed by the lowercased class name. For
 example, if the transformer outputs 3 features, then the feature names
@@ -1502,7 +1502,7 @@ <h1>TruncatedSVD<a class="headerlink" href="#truncatedsvd" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.TruncatedSVD.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.TruncatedSVD.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.TruncatedSVD.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1519,7 +1519,7 @@ <h1>TruncatedSVD<a class="headerlink" href="#truncatedsvd" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.TruncatedSVD.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.TruncatedSVD.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.TruncatedSVD.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1540,7 +1540,7 @@ <h1>TruncatedSVD<a class="headerlink" href="#truncatedsvd" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.TruncatedSVD.inverse_transform">
-<span class="sig-name descname"><span class="pre">inverse_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/decomposition/_truncated_svd.py#L295"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.TruncatedSVD.inverse_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">inverse_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/decomposition/_truncated_svd.py#L295"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.TruncatedSVD.inverse_transform" title="Link to this definition">#</a></dt>
 <dd><p>Transform X back to its original space.</p>
 <p>Returns an array X_original whose transform would be X.</p>
 <dl class="field-list simple">
@@ -1561,7 +1561,7 @@ <h1>TruncatedSVD<a class="headerlink" href="#truncatedsvd" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.TruncatedSVD.set_output">
-<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.TruncatedSVD.set_output" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.TruncatedSVD.set_output" title="Link to this definition">#</a></dt>
 <dd><p>Set output container.</p>
 <p>See <a class="reference internal" href="http://github.com/auto_examples/miscellaneous/plot_set_output.html#sphx-glr-auto-examples-miscellaneous-plot-set-output-py"><span class="std std-ref">Introducing the set_output API</span></a>
 for an example on how to use the API.</p>
@@ -1592,7 +1592,7 @@ <h1>TruncatedSVD<a class="headerlink" href="#truncatedsvd" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.TruncatedSVD.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.TruncatedSVD.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.TruncatedSVD.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1616,7 +1616,7 @@ <h1>TruncatedSVD<a class="headerlink" href="#truncatedsvd" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.decomposition.TruncatedSVD.transform">
-<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/decomposition/_truncated_svd.py#L278"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.TruncatedSVD.transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/decomposition/_truncated_svd.py#L278"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.TruncatedSVD.transform" title="Link to this definition">#</a></dt>
 <dd><p>Perform dimensionality reduction on X.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.decomposition.dict_learning.html b/dev/modules/generated/sklearn.decomposition.dict_learning.html
index 380d1130d2d5c..c0686d22beb4e 100644
--- a/dev/modules/generated/sklearn.decomposition.dict_learning.html
+++ b/dev/modules/generated/sklearn.decomposition.dict_learning.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>dict_learning<a class="headerlink" href="#dict-learning" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.decomposition.dict_learning">
-<span class="sig-prename descclassname"><span class="pre">sklearn.decomposition.</span></span><span class="sig-name descname"><span class="pre">dict_learning</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_components</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-08</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'lars'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dict_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">code_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">callback</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_n_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">positive_dict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">positive_code</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method_max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/decomposition/_dict_learning.py#L885"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.dict_learning" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.decomposition.</span></span><span class="sig-name descname"><span class="pre">dict_learning</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_components</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-08</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'lars'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dict_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">code_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">callback</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_n_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">positive_dict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">positive_code</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method_max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/decomposition/_dict_learning.py#L885"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.dict_learning" title="Link to this definition">#</a></dt>
 <dd><p>Solve a dictionary learning matrix factorization problem.</p>
 <p>Finds the best dictionary and the corresponding sparse code for
 approximating the data matrix X by solving:</p>
@@ -1344,7 +1344,7 @@ <h1>dict_learning<a class="headerlink" href="#dict-learning" title="Link to this
 </ul>
 </dd>
 <dt><strong>n_jobs</strong><span class="classifier">int, default=None</span></dt><dd><p>Number of parallel jobs to run.
-<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.5.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
+<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.6.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
 <code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all processors. See <a class="reference internal" href="http://github.com/glossary.html#term-n_jobs"><span class="xref std std-term">Glossary</span></a>
 for more details.</p>
 </dd>
@@ -1423,7 +1423,7 @@ <h1>dict_learning<a class="headerlink" href="#dict-learning" title="Link to this
 </div>
 <p>We can check the level of sparsity of <code class="docutils literal notranslate"><span class="pre">U</span></code>:</p>
 <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">U</span> <span class="o">==</span> <span class="mi">0</span><span class="p">)</span>
-<span class="go">np.float64(0.6...)</span>
+<span class="go">np.float64(0.62)</span>
 </pre></div>
 </div>
 <p>We can compare the average squared euclidean norm of the reconstruction
@@ -1431,7 +1431,7 @@ <h1>dict_learning<a class="headerlink" href="#dict-learning" title="Link to this
 the original signal:</p>
 <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">X_hat</span> <span class="o">=</span> <span class="n">U</span> <span class="o">@</span> <span class="n">V</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">((</span><span class="n">X_hat</span> <span class="o">-</span> <span class="n">X</span><span class="p">)</span> <span class="o">**</span> <span class="mi">2</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">X</span> <span class="o">**</span> <span class="mi">2</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">))</span>
-<span class="go">np.float64(0.01...)</span>
+<span class="go">np.float64(0.0192)</span>
 </pre></div>
 </div>
 </dd></dl>
diff --git a/dev/modules/generated/sklearn.decomposition.dict_learning_online.html b/dev/modules/generated/sklearn.decomposition.dict_learning_online.html
index ed36cddc7be36..8f47874c910c4 100644
--- a/dev/modules/generated/sklearn.decomposition.dict_learning_online.html
+++ b/dev/modules/generated/sklearn.decomposition.dict_learning_online.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>dict_learning_online<a class="headerlink" href="#dict-learning-online" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.decomposition.dict_learning_online">
-<span class="sig-prename descclassname"><span class="pre">sklearn.decomposition.</span></span><span class="sig-name descname"><span class="pre">dict_learning_online</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_components</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_code</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dict_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">callback</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">256</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'lars'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">positive_dict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">positive_code</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method_max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_no_improvement</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/decomposition/_dict_learning.py#L666"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.dict_learning_online" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.decomposition.</span></span><span class="sig-name descname"><span class="pre">dict_learning_online</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_components</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_code</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dict_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">callback</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">256</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'lars'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">positive_dict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">positive_code</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method_max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_no_improvement</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/decomposition/_dict_learning.py#L666"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.dict_learning_online" title="Link to this definition">#</a></dt>
 <dd><p>Solve a dictionary learning matrix factorization problem online.</p>
 <p>Finds the best dictionary and the corresponding sparse code for
 approximating the data matrix X by solving:</p>
@@ -1355,7 +1355,7 @@ <h1>dict_learning_online<a class="headerlink" href="#dict-learning-online" title
 <dt><strong>shuffle</strong><span class="classifier">bool, default=True</span></dt><dd><p>Whether to shuffle the data before splitting it in batches.</p>
 </dd>
 <dt><strong>n_jobs</strong><span class="classifier">int, default=None</span></dt><dd><p>Number of parallel jobs to run.
-<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.5.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
+<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.6.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
 <code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all processors. See <a class="reference internal" href="http://github.com/glossary.html#term-n_jobs"><span class="xref std std-term">Glossary</span></a>
 for more details.</p>
 </dd>
@@ -1448,7 +1448,7 @@ <h1>dict_learning_online<a class="headerlink" href="#dict-learning-online" title
 </div>
 <p>We can check the level of sparsity of <code class="docutils literal notranslate"><span class="pre">U</span></code>:</p>
 <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">U</span> <span class="o">==</span> <span class="mi">0</span><span class="p">)</span>
-<span class="go">np.float64(0.53...)</span>
+<span class="go">np.float64(0.53)</span>
 </pre></div>
 </div>
 <p>We can compare the average squared euclidean norm of the reconstruction
@@ -1456,7 +1456,7 @@ <h1>dict_learning_online<a class="headerlink" href="#dict-learning-online" title
 the original signal:</p>
 <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">X_hat</span> <span class="o">=</span> <span class="n">U</span> <span class="o">@</span> <span class="n">V</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">((</span><span class="n">X_hat</span> <span class="o">-</span> <span class="n">X</span><span class="p">)</span> <span class="o">**</span> <span class="mi">2</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">X</span> <span class="o">**</span> <span class="mi">2</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">))</span>
-<span class="go">np.float64(0.05...)</span>
+<span class="go">np.float64(0.053)</span>
 </pre></div>
 </div>
 </dd></dl>
diff --git a/dev/modules/generated/sklearn.decomposition.non_negative_factorization.html b/dev/modules/generated/sklearn.decomposition.non_negative_factorization.html
index 0eed98de79eeb..5b8834dcb3794 100644
--- a/dev/modules/generated/sklearn.decomposition.non_negative_factorization.html
+++ b/dev/modules/generated/sklearn.decomposition.non_negative_factorization.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1306,7 +1306,7 @@
 <h1>non_negative_factorization<a class="headerlink" href="#non-negative-factorization" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.decomposition.non_negative_factorization">
-<span class="sig-prename descclassname"><span class="pre">sklearn.decomposition.</span></span><span class="sig-name descname"><span class="pre">non_negative_factorization</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">W</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">H</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_components</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">update_H</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">solver</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'cd'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">beta_loss</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'frobenius'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">200</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha_W</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha_H</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'same'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">l1_ratio</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/decomposition/_nmf.py#L896"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.non_negative_factorization" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.decomposition.</span></span><span class="sig-name descname"><span class="pre">non_negative_factorization</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">W</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">H</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_components</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">update_H</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">solver</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'cd'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">beta_loss</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'frobenius'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">200</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha_W</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha_H</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'same'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">l1_ratio</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/decomposition/_nmf.py#L896"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.non_negative_factorization" title="Link to this definition">#</a></dt>
 <dd><p>Compute Non-negative Matrix Factorization (NMF).</p>
 <p>Find two non-negative matrices (W, H) whose product approximates the non-
 negative matrix X. This factorization can be used for example for
diff --git a/dev/modules/generated/sklearn.decomposition.sparse_encode.html b/dev/modules/generated/sklearn.decomposition.sparse_encode.html
index b0838103c715c..422a550bd326a 100644
--- a/dev/modules/generated/sklearn.decomposition.sparse_encode.html
+++ b/dev/modules/generated/sklearn.decomposition.sparse_encode.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>sparse_encode<a class="headerlink" href="#sparse-encode" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.decomposition.sparse_encode">
-<span class="sig-prename descclassname"><span class="pre">sklearn.decomposition.</span></span><span class="sig-name descname"><span class="pre">sparse_encode</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dictionary</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gram</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cov</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">algorithm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'lasso_lars'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_nonzero_coefs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy_cov</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_input</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">positive</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/decomposition/_dict_learning.py#L203"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.sparse_encode" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.decomposition.</span></span><span class="sig-name descname"><span class="pre">sparse_encode</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dictionary</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gram</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cov</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">algorithm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'lasso_lars'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_nonzero_coefs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy_cov</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_input</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">positive</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/decomposition/_dict_learning.py#L203"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.decomposition.sparse_encode" title="Link to this definition">#</a></dt>
 <dd><p>Sparse coding.</p>
 <p>Each row of the result is the solution to a sparse coding problem.
 The goal is to find a sparse array <code class="docutils literal notranslate"><span class="pre">code</span></code> such that:</p>
@@ -1364,7 +1364,7 @@ <h1>sparse_encode<a class="headerlink" href="#sparse-encode" title="Link to this
 <code class="docutils literal notranslate"><span class="pre">'lasso_lars'</span></code>.</p>
 </dd>
 <dt><strong>n_jobs</strong><span class="classifier">int, default=None</span></dt><dd><p>Number of parallel jobs to run.
-<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.5.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
+<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.6.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
 <code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all processors. See <a class="reference internal" href="http://github.com/glossary.html#term-n_jobs"><span class="xref std std-term">Glossary</span></a>
 for more details.</p>
 </dd>
diff --git a/dev/modules/generated/sklearn.discriminant_analysis.LinearDiscriminantAnalysis.html b/dev/modules/generated/sklearn.discriminant_analysis.LinearDiscriminantAnalysis.html
index a04f1e78ca4a1..357c19d8c4d17 100644
--- a/dev/modules/generated/sklearn.discriminant_analysis.LinearDiscriminantAnalysis.html
+++ b/dev/modules/generated/sklearn.discriminant_analysis.LinearDiscriminantAnalysis.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>LinearDiscriminantAnalysis<a class="headerlink" href="#lineardiscriminantanalysis" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.discriminant_analysis.LinearDiscriminantAnalysis">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.discriminant_analysis.</span></span><span class="sig-name descname"><span class="pre">LinearDiscriminantAnalysis</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">solver</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'svd'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shrinkage</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">priors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_components</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">store_covariance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">covariance_estimator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/discriminant_analysis.py#L249"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.discriminant_analysis.LinearDiscriminantAnalysis" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.discriminant_analysis.</span></span><span class="sig-name descname"><span class="pre">LinearDiscriminantAnalysis</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">solver</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'svd'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shrinkage</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">priors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_components</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">store_covariance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">covariance_estimator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/discriminant_analysis.py#L249"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.discriminant_analysis.LinearDiscriminantAnalysis" title="Link to this definition">#</a></dt>
 <dd><p>Linear Discriminant Analysis.</p>
 <p>A classifier with a linear decision boundary, generated by fitting class
 conditional densities to the data and using Bayes’ rule.</p>
@@ -1460,7 +1460,7 @@ <h1>LinearDiscriminantAnalysis<a class="headerlink" href="#lineardiscriminantana
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.discriminant_analysis.LinearDiscriminantAnalysis.decision_function">
-<span class="sig-name descname"><span class="pre">decision_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/discriminant_analysis.py#L810"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.discriminant_analysis.LinearDiscriminantAnalysis.decision_function" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">decision_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/discriminant_analysis.py#L810"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.discriminant_analysis.LinearDiscriminantAnalysis.decision_function" title="Link to this definition">#</a></dt>
 <dd><p>Apply decision function to an array of samples.</p>
 <p>The decision function is equal (up to a constant factor) to the
 log-posterior of the model, i.e. <code class="docutils literal notranslate"><span class="pre">log</span> <span class="pre">p(y</span> <span class="pre">=</span> <span class="pre">k</span> <span class="pre">|</span> <span class="pre">x)</span></code>. In a binary
@@ -1486,7 +1486,7 @@ <h1>LinearDiscriminantAnalysis<a class="headerlink" href="#lineardiscriminantana
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.discriminant_analysis.LinearDiscriminantAnalysis.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/discriminant_analysis.py#L635"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.discriminant_analysis.LinearDiscriminantAnalysis.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/discriminant_analysis.py#L635"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.discriminant_analysis.LinearDiscriminantAnalysis.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit the Linear Discriminant Analysis model.</p>
 <div class="versionchanged">
 <p><span class="versionmodified changed">Changed in version 0.19: </span><code class="docutils literal notranslate"><span class="pre">store_covariance</span></code> and <code class="docutils literal notranslate"><span class="pre">tol</span></code> has been moved to main constructor.</p>
@@ -1511,7 +1511,7 @@ <h1>LinearDiscriminantAnalysis<a class="headerlink" href="#lineardiscriminantana
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.discriminant_analysis.LinearDiscriminantAnalysis.fit_transform">
-<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L863"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.discriminant_analysis.LinearDiscriminantAnalysis.fit_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L804"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.discriminant_analysis.LinearDiscriminantAnalysis.fit_transform" title="Link to this definition">#</a></dt>
 <dd><p>Fit to data, then transform it.</p>
 <p>Fits transformer to <code class="docutils literal notranslate"><span class="pre">X</span></code> and <code class="docutils literal notranslate"><span class="pre">y</span></code> with optional parameters <code class="docutils literal notranslate"><span class="pre">fit_params</span></code>
 and returns a transformed version of <code class="docutils literal notranslate"><span class="pre">X</span></code>.</p>
@@ -1537,7 +1537,7 @@ <h1>LinearDiscriminantAnalysis<a class="headerlink" href="#lineardiscriminantana
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.discriminant_analysis.LinearDiscriminantAnalysis.get_feature_names_out">
-<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L995"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.discriminant_analysis.LinearDiscriminantAnalysis.get_feature_names_out" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L936"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.discriminant_analysis.LinearDiscriminantAnalysis.get_feature_names_out" title="Link to this definition">#</a></dt>
 <dd><p>Get output feature names for transformation.</p>
 <p>The feature names out will prefixed by the lowercased class name. For
 example, if the transformer outputs 3 features, then the feature names
@@ -1560,7 +1560,7 @@ <h1>LinearDiscriminantAnalysis<a class="headerlink" href="#lineardiscriminantana
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.discriminant_analysis.LinearDiscriminantAnalysis.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.discriminant_analysis.LinearDiscriminantAnalysis.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.discriminant_analysis.LinearDiscriminantAnalysis.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1577,7 +1577,7 @@ <h1>LinearDiscriminantAnalysis<a class="headerlink" href="#lineardiscriminantana
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.discriminant_analysis.LinearDiscriminantAnalysis.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.discriminant_analysis.LinearDiscriminantAnalysis.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.discriminant_analysis.LinearDiscriminantAnalysis.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1598,7 +1598,7 @@ <h1>LinearDiscriminantAnalysis<a class="headerlink" href="#lineardiscriminantana
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.discriminant_analysis.LinearDiscriminantAnalysis.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_base.py#L360"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.discriminant_analysis.LinearDiscriminantAnalysis.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_base.py#L360"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.discriminant_analysis.LinearDiscriminantAnalysis.predict" title="Link to this definition">#</a></dt>
 <dd><p>Predict class labels for samples in X.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1618,7 +1618,7 @@ <h1>LinearDiscriminantAnalysis<a class="headerlink" href="#lineardiscriminantana
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.discriminant_analysis.LinearDiscriminantAnalysis.predict_log_proba">
-<span class="sig-name descname"><span class="pre">predict_log_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/discriminant_analysis.py#L784"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.discriminant_analysis.LinearDiscriminantAnalysis.predict_log_proba" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict_log_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/discriminant_analysis.py#L784"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.discriminant_analysis.LinearDiscriminantAnalysis.predict_log_proba" title="Link to this definition">#</a></dt>
 <dd><p>Estimate log probability.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1638,7 +1638,7 @@ <h1>LinearDiscriminantAnalysis<a class="headerlink" href="#lineardiscriminantana
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.discriminant_analysis.LinearDiscriminantAnalysis.predict_proba">
-<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/discriminant_analysis.py#L762"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.discriminant_analysis.LinearDiscriminantAnalysis.predict_proba" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/discriminant_analysis.py#L762"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.discriminant_analysis.LinearDiscriminantAnalysis.predict_proba" title="Link to this definition">#</a></dt>
 <dd><p>Estimate probability.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1658,7 +1658,7 @@ <h1>LinearDiscriminantAnalysis<a class="headerlink" href="#lineardiscriminantana
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.discriminant_analysis.LinearDiscriminantAnalysis.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L546"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.discriminant_analysis.LinearDiscriminantAnalysis.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L487"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.discriminant_analysis.LinearDiscriminantAnalysis.score" title="Link to this definition">#</a></dt>
 <dd><p>Return <a class="reference internal" href="http://github.com/model_evaluation.html#accuracy-score"><span class="std std-ref">accuracy</span></a> on provided data and labels.</p>
 <p>In multi-label classification, this is the subset accuracy
 which is a harsh metric since you require for each sample that
@@ -1685,7 +1685,7 @@ <h1>LinearDiscriminantAnalysis<a class="headerlink" href="#lineardiscriminantana
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.discriminant_analysis.LinearDiscriminantAnalysis.set_output">
-<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.discriminant_analysis.LinearDiscriminantAnalysis.set_output" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.discriminant_analysis.LinearDiscriminantAnalysis.set_output" title="Link to this definition">#</a></dt>
 <dd><p>Set output container.</p>
 <p>See <a class="reference internal" href="http://github.com/auto_examples/miscellaneous/plot_set_output.html#sphx-glr-auto-examples-miscellaneous-plot-set-output-py"><span class="std std-ref">Introducing the set_output API</span></a>
 for an example on how to use the API.</p>
@@ -1716,7 +1716,7 @@ <h1>LinearDiscriminantAnalysis<a class="headerlink" href="#lineardiscriminantana
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.discriminant_analysis.LinearDiscriminantAnalysis.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.discriminant_analysis.LinearDiscriminantAnalysis.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.discriminant_analysis.LinearDiscriminantAnalysis.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1740,7 +1740,7 @@ <h1>LinearDiscriminantAnalysis<a class="headerlink" href="#lineardiscriminantana
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.discriminant_analysis.LinearDiscriminantAnalysis.set_score_request">
-<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.discriminant_analysis.LinearDiscriminantAnalysis" title="sklearn.discriminant_analysis.LinearDiscriminantAnalysis"><span class="pre">LinearDiscriminantAnalysis</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.discriminant_analysis.LinearDiscriminantAnalysis.set_score_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.discriminant_analysis.LinearDiscriminantAnalysis" title="sklearn.discriminant_analysis.LinearDiscriminantAnalysis"><span class="pre">LinearDiscriminantAnalysis</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.discriminant_analysis.LinearDiscriminantAnalysis.set_score_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1783,7 +1783,7 @@ <h1>LinearDiscriminantAnalysis<a class="headerlink" href="#lineardiscriminantana
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.discriminant_analysis.LinearDiscriminantAnalysis.transform">
-<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/discriminant_analysis.py#L732"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.discriminant_analysis.LinearDiscriminantAnalysis.transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/discriminant_analysis.py#L732"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.discriminant_analysis.LinearDiscriminantAnalysis.transform" title="Link to this definition">#</a></dt>
 <dd><p>Project data to maximize class separation.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.html b/dev/modules/generated/sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.html
index d5082659b0a62..bb02ff00c300c 100644
--- a/dev/modules/generated/sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.html
+++ b/dev/modules/generated/sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
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-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
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@@ -1307,7 +1307,7 @@
 <h1>QuadraticDiscriminantAnalysis<a class="headerlink" href="#quadraticdiscriminantanalysis" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.discriminant_analysis.</span></span><span class="sig-name descname"><span class="pre">QuadraticDiscriminantAnalysis</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">priors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reg_param</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">store_covariance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/discriminant_analysis.py#L839"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.discriminant_analysis.</span></span><span class="sig-name descname"><span class="pre">QuadraticDiscriminantAnalysis</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">priors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reg_param</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">store_covariance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/discriminant_analysis.py#L839"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis" title="Link to this definition">#</a></dt>
 <dd><p>Quadratic Discriminant Analysis.</p>
 <p>A classifier with a quadratic decision boundary, generated
 by fitting class conditional densities to the data
@@ -1409,7 +1409,7 @@ <h1>QuadraticDiscriminantAnalysis<a class="headerlink" href="#quadraticdiscrimin
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.decision_function">
-<span class="sig-name descname"><span class="pre">decision_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/discriminant_analysis.py#L1058"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.decision_function" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">decision_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/discriminant_analysis.py#L1058"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.decision_function" title="Link to this definition">#</a></dt>
 <dd><p>Apply decision function to an array of samples.</p>
 <p>The decision function is equal (up to a constant factor) to the
 log-posterior of the model, i.e. <code class="docutils literal notranslate"><span class="pre">log</span> <span class="pre">p(y</span> <span class="pre">=</span> <span class="pre">k</span> <span class="pre">|</span> <span class="pre">x)</span></code>. In a binary
@@ -1435,7 +1435,7 @@ <h1>QuadraticDiscriminantAnalysis<a class="headerlink" href="#quadraticdiscrimin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/discriminant_analysis.py#L961"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/discriminant_analysis.py#L961"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit the model according to the given training data and parameters.</p>
 <div class="versionchanged">
 <p><span class="versionmodified changed">Changed in version 0.19: </span><code class="docutils literal notranslate"><span class="pre">store_covariances</span></code> has been moved to main constructor as
@@ -1465,7 +1465,7 @@ <h1>QuadraticDiscriminantAnalysis<a class="headerlink" href="#quadraticdiscrimin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1482,7 +1482,7 @@ <h1>QuadraticDiscriminantAnalysis<a class="headerlink" href="#quadraticdiscrimin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1503,7 +1503,7 @@ <h1>QuadraticDiscriminantAnalysis<a class="headerlink" href="#quadraticdiscrimin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/discriminant_analysis.py#L1080"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/discriminant_analysis.py#L1080"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.predict" title="Link to this definition">#</a></dt>
 <dd><p>Perform classification on an array of test vectors X.</p>
 <p>The predicted class C for each sample in X is returned.</p>
 <dl class="field-list simple">
@@ -1525,7 +1525,7 @@ <h1>QuadraticDiscriminantAnalysis<a class="headerlink" href="#quadraticdiscrimin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.predict_log_proba">
-<span class="sig-name descname"><span class="pre">predict_log_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/discriminant_analysis.py#L1115"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.predict_log_proba" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict_log_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/discriminant_analysis.py#L1115"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.predict_log_proba" title="Link to this definition">#</a></dt>
 <dd><p>Return log of posterior probabilities of classification.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1545,7 +1545,7 @@ <h1>QuadraticDiscriminantAnalysis<a class="headerlink" href="#quadraticdiscrimin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.predict_proba">
-<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/discriminant_analysis.py#L1098"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.predict_proba" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/discriminant_analysis.py#L1098"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.predict_proba" title="Link to this definition">#</a></dt>
 <dd><p>Return posterior probabilities of classification.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1565,7 +1565,7 @@ <h1>QuadraticDiscriminantAnalysis<a class="headerlink" href="#quadraticdiscrimin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L546"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L487"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.score" title="Link to this definition">#</a></dt>
 <dd><p>Return <a class="reference internal" href="http://github.com/model_evaluation.html#accuracy-score"><span class="std std-ref">accuracy</span></a> on provided data and labels.</p>
 <p>In multi-label classification, this is the subset accuracy
 which is a harsh metric since you require for each sample that
@@ -1592,7 +1592,7 @@ <h1>QuadraticDiscriminantAnalysis<a class="headerlink" href="#quadraticdiscrimin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1616,7 +1616,7 @@ <h1>QuadraticDiscriminantAnalysis<a class="headerlink" href="#quadraticdiscrimin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.set_score_request">
-<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis" title="sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis"><span class="pre">QuadraticDiscriminantAnalysis</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.set_score_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis" title="sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis"><span class="pre">QuadraticDiscriminantAnalysis</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.set_score_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
diff --git a/dev/modules/generated/sklearn.dummy.DummyClassifier.html b/dev/modules/generated/sklearn.dummy.DummyClassifier.html
index b113493f0b8bf..db594b5c89c16 100644
--- a/dev/modules/generated/sklearn.dummy.DummyClassifier.html
+++ b/dev/modules/generated/sklearn.dummy.DummyClassifier.html
@@ -11,10 +11,10 @@
 <meta property="og:type" content="website" />
 <meta property="og:url" content="https://scikit-learn/stable/modules/generated/sklearn.dummy.DummyClassifier.html" />
 <meta property="og:site_name" content="scikit-learn" />
-<meta property="og:description" content="Gallery examples: Multi-class AdaBoosted Decision Trees Post-tuning the decision threshold for cost-sensitive learning Class Likelihood Ratios to measure classification performance" />
+<meta property="og:description" content="Gallery examples: Multi-class AdaBoosted Decision Trees Post-tuning the decision threshold for cost-sensitive learning Detection error tradeoff (DET) curve Class Likelihood Ratios to measure classi..." />
 <meta property="og:image" content="https://scikit-learn/stable/_images/sphx_glr_plot_adaboost_multiclass_thumb.png" />
 <meta property="og:image:alt" content="" />
-<meta name="description" content="Gallery examples: Multi-class AdaBoosted Decision Trees Post-tuning the decision threshold for cost-sensitive learning Class Likelihood Ratios to measure classification performance" />
+<meta name="description" content="Gallery examples: Multi-class AdaBoosted Decision Trees Post-tuning the decision threshold for cost-sensitive learning Detection error tradeoff (DET) curve Class Likelihood Ratios to measure classi..." />
 
     <title>DummyClassifier &#8212; scikit-learn 1.7.dev0 documentation</title>
   
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>DummyClassifier<a class="headerlink" href="#dummyclassifier" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.dummy.DummyClassifier">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.dummy.</span></span><span class="sig-name descname"><span class="pre">DummyClassifier</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">strategy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'prior'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">constant</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/dummy.py#L34"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.dummy.DummyClassifier" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.dummy.</span></span><span class="sig-name descname"><span class="pre">DummyClassifier</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">strategy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'prior'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">constant</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/dummy.py#L34"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.dummy.DummyClassifier" title="Link to this definition">#</a></dt>
 <dd><p>DummyClassifier makes predictions that ignore the input features.</p>
 <p>This classifier serves as a simple baseline to compare against other more
 complex classifiers.</p>
@@ -1417,7 +1417,7 @@ <h1>DummyClassifier<a class="headerlink" href="#dummyclassifier" title="Link to
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.dummy.DummyClassifier.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/dummy.py#L161"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.dummy.DummyClassifier.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/dummy.py#L161"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.dummy.DummyClassifier.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit the baseline classifier.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1441,7 +1441,7 @@ <h1>DummyClassifier<a class="headerlink" href="#dummyclassifier" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.dummy.DummyClassifier.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.dummy.DummyClassifier.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.dummy.DummyClassifier.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1458,7 +1458,7 @@ <h1>DummyClassifier<a class="headerlink" href="#dummyclassifier" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.dummy.DummyClassifier.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.dummy.DummyClassifier.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.dummy.DummyClassifier.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1479,7 +1479,7 @@ <h1>DummyClassifier<a class="headerlink" href="#dummyclassifier" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.dummy.DummyClassifier.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/dummy.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.dummy.DummyClassifier.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/dummy.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.dummy.DummyClassifier.predict" title="Link to this definition">#</a></dt>
 <dd><p>Perform classification on test vectors X.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1499,7 +1499,7 @@ <h1>DummyClassifier<a class="headerlink" href="#dummyclassifier" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.dummy.DummyClassifier.predict_log_proba">
-<span class="sig-name descname"><span class="pre">predict_log_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/dummy.py#L402"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.dummy.DummyClassifier.predict_log_proba" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict_log_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/dummy.py#L402"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.dummy.DummyClassifier.predict_log_proba" title="Link to this definition">#</a></dt>
 <dd><p>Return log probability estimates for the test vectors X.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1521,7 +1521,7 @@ <h1>DummyClassifier<a class="headerlink" href="#dummyclassifier" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.dummy.DummyClassifier.predict_proba">
-<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/dummy.py#L339"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.dummy.DummyClassifier.predict_proba" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/dummy.py#L339"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.dummy.DummyClassifier.predict_proba" title="Link to this definition">#</a></dt>
 <dd><p>Return probability estimates for the test vectors X.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1543,7 +1543,7 @@ <h1>DummyClassifier<a class="headerlink" href="#dummyclassifier" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.dummy.DummyClassifier.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/dummy.py#L431"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.dummy.DummyClassifier.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/dummy.py#L431"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.dummy.DummyClassifier.score" title="Link to this definition">#</a></dt>
 <dd><p>Return the mean accuracy on the given test data and labels.</p>
 <p>In multi-label classification, this is the subset accuracy
 which is a harsh metric since you require for each sample that
@@ -1572,7 +1572,7 @@ <h1>DummyClassifier<a class="headerlink" href="#dummyclassifier" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.dummy.DummyClassifier.set_fit_request">
-<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.dummy.DummyClassifier" title="sklearn.dummy.DummyClassifier"><span class="pre">DummyClassifier</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.dummy.DummyClassifier.set_fit_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.dummy.DummyClassifier" title="sklearn.dummy.DummyClassifier"><span class="pre">DummyClassifier</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.dummy.DummyClassifier.set_fit_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1615,7 +1615,7 @@ <h1>DummyClassifier<a class="headerlink" href="#dummyclassifier" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.dummy.DummyClassifier.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.dummy.DummyClassifier.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.dummy.DummyClassifier.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1639,7 +1639,7 @@ <h1>DummyClassifier<a class="headerlink" href="#dummyclassifier" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.dummy.DummyClassifier.set_score_request">
-<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.dummy.DummyClassifier" title="sklearn.dummy.DummyClassifier"><span class="pre">DummyClassifier</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.dummy.DummyClassifier.set_score_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.dummy.DummyClassifier" title="sklearn.dummy.DummyClassifier"><span class="pre">DummyClassifier</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.dummy.DummyClassifier.set_score_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1690,6 +1690,9 @@ <h2>Gallery examples<a class="headerlink" href="#gallery-examples" title="Link t
 </div><div class="sphx-glr-thumbcontainer" tooltip="Once a classifier is trained, the output of the predict method outputs class label predictions corresponding to a thresholding of either the decision_function or the predict_proba output. For a binary classifier, the default threshold is defined as a posterior probability estimate of 0.5 or a decision score of 0.0."><img alt="" src="http://github.com/_images/sphx_glr_plot_cost_sensitive_learning_thumb.png" />
 <p><a class="reference internal" href="http://github.com/auto_examples/model_selection/plot_cost_sensitive_learning.html#sphx-glr-auto-examples-model-selection-plot-cost-sensitive-learning-py"><span class="std std-ref">Post-tuning the decision threshold for cost-sensitive learning</span></a></p>
   <div class="sphx-glr-thumbnail-title">Post-tuning the decision threshold for cost-sensitive learning</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we compare two binary classification multi-threshold metrics: the Receiver Operating Characteristic (ROC) and the Detection Error Tradeoff (DET). For such purpose, we evaluate two different classifiers for the same classification task."><img alt="" src="http://github.com/_images/sphx_glr_plot_det_thumb.png" />
+<p><a class="reference internal" href="http://github.com/auto_examples/model_selection/plot_det.html#sphx-glr-auto-examples-model-selection-plot-det-py"><span class="std std-ref">Detection error tradeoff (DET) curve</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Detection error tradeoff (DET) curve</div>
 </div><div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates the class_likelihood_ratios function, which computes the positive and negative likelihood ratios (`LR+`, LR-) to assess the predictive power of a binary classifier. As we will see, these metrics are independent of the proportion between classes in the test set, which makes them very useful when the available data for a study has a different class proportion than the target application."><img alt="" src="http://github.com/_images/sphx_glr_plot_likelihood_ratios_thumb.png" />
 <p><a class="reference internal" href="http://github.com/auto_examples/model_selection/plot_likelihood_ratios.html#sphx-glr-auto-examples-model-selection-plot-likelihood-ratios-py"><span class="std std-ref">Class Likelihood Ratios to measure classification performance</span></a></p>
   <div class="sphx-glr-thumbnail-title">Class Likelihood Ratios to measure classification performance</div>
diff --git a/dev/modules/generated/sklearn.dummy.DummyRegressor.html b/dev/modules/generated/sklearn.dummy.DummyRegressor.html
index bf55fb03644ba..93e6f9ad57dba 100644
--- a/dev/modules/generated/sklearn.dummy.DummyRegressor.html
+++ b/dev/modules/generated/sklearn.dummy.DummyRegressor.html
@@ -42,7 +42,7 @@
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-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
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@@ -1307,7 +1307,7 @@
 <h1>DummyRegressor<a class="headerlink" href="#dummyregressor" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.dummy.DummyRegressor">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.dummy.</span></span><span class="sig-name descname"><span class="pre">DummyRegressor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">strategy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">constant</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quantile</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/dummy.py#L461"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.dummy.DummyRegressor" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.dummy.</span></span><span class="sig-name descname"><span class="pre">DummyRegressor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">strategy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">constant</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quantile</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/dummy.py#L461"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.dummy.DummyRegressor" title="Link to this definition">#</a></dt>
 <dd><p>Regressor that makes predictions using simple rules.</p>
 <p>This regressor is useful as a simple baseline to compare with other
 (real) regressors. Do not use it for real problems.</p>
@@ -1375,7 +1375,7 @@ <h1>DummyRegressor<a class="headerlink" href="#dummyregressor" title="Link to th
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.dummy.DummyRegressor.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/dummy.py#L542"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.dummy.DummyRegressor.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/dummy.py#L542"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.dummy.DummyRegressor.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit the baseline regressor.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1399,7 +1399,7 @@ <h1>DummyRegressor<a class="headerlink" href="#dummyregressor" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.dummy.DummyRegressor.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.dummy.DummyRegressor.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.dummy.DummyRegressor.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1416,7 +1416,7 @@ <h1>DummyRegressor<a class="headerlink" href="#dummyregressor" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.dummy.DummyRegressor.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.dummy.DummyRegressor.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.dummy.DummyRegressor.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1437,7 +1437,7 @@ <h1>DummyRegressor<a class="headerlink" href="#dummyregressor" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.dummy.DummyRegressor.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_std</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/dummy.py#L628"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.dummy.DummyRegressor.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_std</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/dummy.py#L628"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.dummy.DummyRegressor.predict" title="Link to this definition">#</a></dt>
 <dd><p>Perform classification on test vectors X.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1465,7 +1465,7 @@ <h1>DummyRegressor<a class="headerlink" href="#dummyregressor" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.dummy.DummyRegressor.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/dummy.py#L673"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.dummy.DummyRegressor.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/dummy.py#L673"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.dummy.DummyRegressor.score" title="Link to this definition">#</a></dt>
 <dd><p>Return the coefficient of determination R^2 of the prediction.</p>
 <p>The coefficient R^2 is defined as <code class="docutils literal notranslate"><span class="pre">(1</span> <span class="pre">-</span> <span class="pre">u/v)</span></code>, where <code class="docutils literal notranslate"><span class="pre">u</span></code> is the
 residual sum of squares <code class="docutils literal notranslate"><span class="pre">((y_true</span> <span class="pre">-</span> <span class="pre">y_pred)</span> <span class="pre">**</span> <span class="pre">2).sum()</span></code> and <code class="docutils literal notranslate"><span class="pre">v</span></code> is the
@@ -1498,7 +1498,7 @@ <h1>DummyRegressor<a class="headerlink" href="#dummyregressor" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.dummy.DummyRegressor.set_fit_request">
-<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.dummy.DummyRegressor" title="sklearn.dummy.DummyRegressor"><span class="pre">DummyRegressor</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.dummy.DummyRegressor.set_fit_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.dummy.DummyRegressor" title="sklearn.dummy.DummyRegressor"><span class="pre">DummyRegressor</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.dummy.DummyRegressor.set_fit_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1541,7 +1541,7 @@ <h1>DummyRegressor<a class="headerlink" href="#dummyregressor" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.dummy.DummyRegressor.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.dummy.DummyRegressor.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.dummy.DummyRegressor.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1565,7 +1565,7 @@ <h1>DummyRegressor<a class="headerlink" href="#dummyregressor" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.dummy.DummyRegressor.set_predict_request">
-<span class="sig-name descname"><span class="pre">set_predict_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_std</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.dummy.DummyRegressor" title="sklearn.dummy.DummyRegressor"><span class="pre">DummyRegressor</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.dummy.DummyRegressor.set_predict_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_predict_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_std</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.dummy.DummyRegressor" title="sklearn.dummy.DummyRegressor"><span class="pre">DummyRegressor</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.dummy.DummyRegressor.set_predict_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">predict</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1608,7 +1608,7 @@ <h1>DummyRegressor<a class="headerlink" href="#dummyregressor" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.dummy.DummyRegressor.set_score_request">
-<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.dummy.DummyRegressor" title="sklearn.dummy.DummyRegressor"><span class="pre">DummyRegressor</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.dummy.DummyRegressor.set_score_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.dummy.DummyRegressor" title="sklearn.dummy.DummyRegressor"><span class="pre">DummyRegressor</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.dummy.DummyRegressor.set_score_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
diff --git a/dev/modules/generated/sklearn.ensemble.AdaBoostClassifier.html b/dev/modules/generated/sklearn.ensemble.AdaBoostClassifier.html
index 3d7d96f162905..86c0c7abf5f6d 100644
--- a/dev/modules/generated/sklearn.ensemble.AdaBoostClassifier.html
+++ b/dev/modules/generated/sklearn.ensemble.AdaBoostClassifier.html
@@ -42,7 +42,7 @@
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-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
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@@ -1307,7 +1307,7 @@
 <h1>AdaBoostClassifier<a class="headerlink" href="#adaboostclassifier" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.ensemble.AdaBoostClassifier">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.ensemble.</span></span><span class="sig-name descname"><span class="pre">AdaBoostClassifier</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_estimators</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">50</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">learning_rate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">algorithm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'deprecated'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_weight_boosting.py#L342"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.AdaBoostClassifier" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.ensemble.</span></span><span class="sig-name descname"><span class="pre">AdaBoostClassifier</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_estimators</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">50</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">learning_rate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">algorithm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'deprecated'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_weight_boosting.py#L342"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.AdaBoostClassifier" title="Link to this definition">#</a></dt>
 <dd><p>An AdaBoost classifier.</p>
 <p>An AdaBoost <a class="reference internal" href="#r33e4ec8c4ad5-1" id="id1">[1]</a> classifier is a meta-estimator that begins by fitting a
 classifier on the original dataset and then fits additional copies of the
@@ -1424,7 +1424,7 @@ <h1>AdaBoostClassifier<a class="headerlink" href="#adaboostclassifier" title="Li
 <span class="gp">&gt;&gt;&gt; </span><span class="n">clf</span><span class="o">.</span><span class="n">predict</span><span class="p">([[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]])</span>
 <span class="go">array([1])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">clf</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
-<span class="go">0.96...</span>
+<span class="go">0.96</span>
 </pre></div>
 </div>
 <p>For a detailed example of using AdaBoost to fit a sequence of DecisionTrees
@@ -1435,7 +1435,7 @@ <h1>AdaBoostClassifier<a class="headerlink" href="#adaboostclassifier" title="Li
 refer to <a class="reference internal" href="http://github.com/auto_examples/ensemble/plot_adaboost_twoclass.html#sphx-glr-auto-examples-ensemble-plot-adaboost-twoclass-py"><span class="std std-ref">Two-class AdaBoost</span></a>.</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.AdaBoostClassifier.decision_function">
-<span class="sig-name descname"><span class="pre">decision_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_weight_boosting.py#L674"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.AdaBoostClassifier.decision_function" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">decision_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_weight_boosting.py#L674"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.AdaBoostClassifier.decision_function" title="Link to this definition">#</a></dt>
 <dd><p>Compute the decision function of <code class="docutils literal notranslate"><span class="pre">X</span></code>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1461,7 +1461,7 @@ <h1>AdaBoostClassifier<a class="headerlink" href="#adaboostclassifier" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.AdaBoostClassifier.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_weight_boosting.py#L104"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.AdaBoostClassifier.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_weight_boosting.py#L104"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.AdaBoostClassifier.fit" title="Link to this definition">#</a></dt>
 <dd><p>Build a boosted classifier/regressor from the training set (X, y).</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1487,14 +1487,14 @@ <h1>AdaBoostClassifier<a class="headerlink" href="#adaboostclassifier" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.AdaBoostClassifier.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L206"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.AdaBoostClassifier.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L213"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.AdaBoostClassifier.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Raise <code class="docutils literal notranslate"><span class="pre">NotImplementedError</span></code>.</p>
 <p>This estimator does not support metadata routing yet.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.AdaBoostClassifier.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.AdaBoostClassifier.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.AdaBoostClassifier.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1515,7 +1515,7 @@ <h1>AdaBoostClassifier<a class="headerlink" href="#adaboostclassifier" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.AdaBoostClassifier.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_weight_boosting.py#L616"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.AdaBoostClassifier.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_weight_boosting.py#L616"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.AdaBoostClassifier.predict" title="Link to this definition">#</a></dt>
 <dd><p>Predict classes for X.</p>
 <p>The predicted class of an input sample is computed as the weighted mean
 prediction of the classifiers in the ensemble.</p>
@@ -1538,7 +1538,7 @@ <h1>AdaBoostClassifier<a class="headerlink" href="#adaboostclassifier" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.AdaBoostClassifier.predict_log_proba">
-<span class="sig-name descname"><span class="pre">predict_log_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_weight_boosting.py#L845"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.AdaBoostClassifier.predict_log_proba" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict_log_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_weight_boosting.py#L845"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.AdaBoostClassifier.predict_log_proba" title="Link to this definition">#</a></dt>
 <dd><p>Predict class log-probabilities for X.</p>
 <p>The predicted class log-probabilities of an input sample is computed as
 the weighted mean predicted class log-probabilities of the classifiers
@@ -1563,7 +1563,7 @@ <h1>AdaBoostClassifier<a class="headerlink" href="#adaboostclassifier" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.AdaBoostClassifier.predict_proba">
-<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_weight_boosting.py#L787"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.AdaBoostClassifier.predict_proba" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_weight_boosting.py#L787"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.AdaBoostClassifier.predict_proba" title="Link to this definition">#</a></dt>
 <dd><p>Predict class probabilities for X.</p>
 <p>The predicted class probabilities of an input sample is computed as
 the weighted mean predicted class probabilities of the classifiers
@@ -1588,7 +1588,7 @@ <h1>AdaBoostClassifier<a class="headerlink" href="#adaboostclassifier" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.AdaBoostClassifier.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L546"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.AdaBoostClassifier.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L487"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.AdaBoostClassifier.score" title="Link to this definition">#</a></dt>
 <dd><p>Return <a class="reference internal" href="http://github.com/model_evaluation.html#accuracy-score"><span class="std std-ref">accuracy</span></a> on provided data and labels.</p>
 <p>In multi-label classification, this is the subset accuracy
 which is a harsh metric since you require for each sample that
@@ -1615,7 +1615,7 @@ <h1>AdaBoostClassifier<a class="headerlink" href="#adaboostclassifier" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.AdaBoostClassifier.set_fit_request">
-<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.ensemble.AdaBoostClassifier" title="sklearn.ensemble._weight_boosting.AdaBoostClassifier"><span class="pre">AdaBoostClassifier</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.AdaBoostClassifier.set_fit_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.ensemble.AdaBoostClassifier" title="sklearn.ensemble._weight_boosting.AdaBoostClassifier"><span class="pre">AdaBoostClassifier</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.AdaBoostClassifier.set_fit_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1658,7 +1658,7 @@ <h1>AdaBoostClassifier<a class="headerlink" href="#adaboostclassifier" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.AdaBoostClassifier.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.AdaBoostClassifier.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.AdaBoostClassifier.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1682,7 +1682,7 @@ <h1>AdaBoostClassifier<a class="headerlink" href="#adaboostclassifier" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.AdaBoostClassifier.set_score_request">
-<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.ensemble.AdaBoostClassifier" title="sklearn.ensemble._weight_boosting.AdaBoostClassifier"><span class="pre">AdaBoostClassifier</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.AdaBoostClassifier.set_score_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.ensemble.AdaBoostClassifier" title="sklearn.ensemble._weight_boosting.AdaBoostClassifier"><span class="pre">AdaBoostClassifier</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.AdaBoostClassifier.set_score_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1725,7 +1725,7 @@ <h1>AdaBoostClassifier<a class="headerlink" href="#adaboostclassifier" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.AdaBoostClassifier.staged_decision_function">
-<span class="sig-name descname"><span class="pre">staged_decision_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_weight_boosting.py#L717"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.AdaBoostClassifier.staged_decision_function" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">staged_decision_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_weight_boosting.py#L717"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.AdaBoostClassifier.staged_decision_function" title="Link to this definition">#</a></dt>
 <dd><p>Compute decision function of <code class="docutils literal notranslate"><span class="pre">X</span></code> for each boosting iteration.</p>
 <p>This method allows monitoring (i.e. determine error on testing set)
 after each boosting iteration.</p>
@@ -1753,7 +1753,7 @@ <h1>AdaBoostClassifier<a class="headerlink" href="#adaboostclassifier" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.AdaBoostClassifier.staged_predict">
-<span class="sig-name descname"><span class="pre">staged_predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_weight_boosting.py#L640"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.AdaBoostClassifier.staged_predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">staged_predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_weight_boosting.py#L640"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.AdaBoostClassifier.staged_predict" title="Link to this definition">#</a></dt>
 <dd><p>Return staged predictions for X.</p>
 <p>The predicted class of an input sample is computed as the weighted mean
 prediction of the classifiers in the ensemble.</p>
@@ -1779,7 +1779,7 @@ <h1>AdaBoostClassifier<a class="headerlink" href="#adaboostclassifier" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.AdaBoostClassifier.staged_predict_proba">
-<span class="sig-name descname"><span class="pre">staged_predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_weight_boosting.py#L815"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.AdaBoostClassifier.staged_predict_proba" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">staged_predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_weight_boosting.py#L815"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.AdaBoostClassifier.staged_predict_proba" title="Link to this definition">#</a></dt>
 <dd><p>Predict class probabilities for X.</p>
 <p>The predicted class probabilities of an input sample is computed as
 the weighted mean predicted class probabilities of the classifiers
@@ -1808,7 +1808,7 @@ <h1>AdaBoostClassifier<a class="headerlink" href="#adaboostclassifier" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.AdaBoostClassifier.staged_score">
-<span class="sig-name descname"><span class="pre">staged_score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_weight_boosting.py#L244"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.AdaBoostClassifier.staged_score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">staged_score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_weight_boosting.py#L244"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.AdaBoostClassifier.staged_score" title="Link to this definition">#</a></dt>
 <dd><p>Return staged scores for X, y.</p>
 <p>This generator method yields the ensemble score after each iteration of
 boosting and therefore allows monitoring, such as to determine the
diff --git a/dev/modules/generated/sklearn.ensemble.AdaBoostRegressor.html b/dev/modules/generated/sklearn.ensemble.AdaBoostRegressor.html
index 7d208da916be1..078a54cfe2d15 100644
--- a/dev/modules/generated/sklearn.ensemble.AdaBoostRegressor.html
+++ b/dev/modules/generated/sklearn.ensemble.AdaBoostRegressor.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1308,7 +1308,7 @@
 <h1>AdaBoostRegressor<a class="headerlink" href="#adaboostregressor" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.ensemble.AdaBoostRegressor">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.ensemble.</span></span><span class="sig-name descname"><span class="pre">AdaBoostRegressor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_estimators</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">50</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">learning_rate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">loss</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'linear'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_weight_boosting.py#L867"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.AdaBoostRegressor" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.ensemble.</span></span><span class="sig-name descname"><span class="pre">AdaBoostRegressor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_estimators</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">50</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">learning_rate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">loss</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'linear'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_weight_boosting.py#L867"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.AdaBoostRegressor" title="Link to this definition">#</a></dt>
 <dd><p>An AdaBoost regressor.</p>
 <p>An AdaBoost [1] regressor is a meta-estimator that begins by fitting a
 regressor on the original dataset and then fits additional copies of the
@@ -1416,9 +1416,9 @@ <h1>AdaBoostRegressor<a class="headerlink" href="#adaboostregressor" title="Link
 <span class="gp">&gt;&gt;&gt; </span><span class="n">regr</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
 <span class="go">AdaBoostRegressor(n_estimators=100, random_state=0)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">regr</span><span class="o">.</span><span class="n">predict</span><span class="p">([[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]])</span>
-<span class="go">array([4.7972...])</span>
+<span class="go">array([4.7972])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">regr</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
-<span class="go">0.9771...</span>
+<span class="go">0.9771</span>
 </pre></div>
 </div>
 <p>For a detailed example of utilizing <a class="reference internal" href="#sklearn.ensemble.AdaBoostRegressor" title="sklearn.ensemble.AdaBoostRegressor"><code class="xref py py-class docutils literal notranslate"><span class="pre">AdaBoostRegressor</span></code></a>
@@ -1426,7 +1426,7 @@ <h1>AdaBoostRegressor<a class="headerlink" href="#adaboostregressor" title="Link
 <a class="reference internal" href="http://github.com/auto_examples/ensemble/plot_adaboost_regression.html#sphx-glr-auto-examples-ensemble-plot-adaboost-regression-py"><span class="std std-ref">Decision Tree Regression with AdaBoost</span></a>.</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.AdaBoostRegressor.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_weight_boosting.py#L104"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.AdaBoostRegressor.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_weight_boosting.py#L104"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.AdaBoostRegressor.fit" title="Link to this definition">#</a></dt>
 <dd><p>Build a boosted classifier/regressor from the training set (X, y).</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1452,14 +1452,14 @@ <h1>AdaBoostRegressor<a class="headerlink" href="#adaboostregressor" title="Link
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.AdaBoostRegressor.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L206"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.AdaBoostRegressor.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L213"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.AdaBoostRegressor.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Raise <code class="docutils literal notranslate"><span class="pre">NotImplementedError</span></code>.</p>
 <p>This estimator does not support metadata routing yet.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.AdaBoostRegressor.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.AdaBoostRegressor.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.AdaBoostRegressor.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1480,7 +1480,7 @@ <h1>AdaBoostRegressor<a class="headerlink" href="#adaboostregressor" title="Link
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.AdaBoostRegressor.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_weight_boosting.py#L1127"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.AdaBoostRegressor.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_weight_boosting.py#L1127"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.AdaBoostRegressor.predict" title="Link to this definition">#</a></dt>
 <dd><p>Predict regression value for X.</p>
 <p>The predicted regression value of an input sample is computed
 as the weighted median prediction of the regressors in the ensemble.</p>
@@ -1503,7 +1503,7 @@ <h1>AdaBoostRegressor<a class="headerlink" href="#adaboostregressor" title="Link
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.AdaBoostRegressor.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L619"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.AdaBoostRegressor.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L560"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.AdaBoostRegressor.score" title="Link to this definition">#</a></dt>
 <dd><p>Return <a class="reference internal" href="http://github.com/model_evaluation.html#r2-score"><span class="std std-ref">coefficient of determination</span></a> on test data.</p>
 <p>The coefficient of determination, <span class="math notranslate nohighlight">\(R^2\)</span>, is defined as
 <span class="math notranslate nohighlight">\((1 - \frac{u}{v})\)</span>, where <span class="math notranslate nohighlight">\(u\)</span> is the residual
@@ -1545,7 +1545,7 @@ <h1>AdaBoostRegressor<a class="headerlink" href="#adaboostregressor" title="Link
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.AdaBoostRegressor.set_fit_request">
-<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.ensemble.AdaBoostRegressor" title="sklearn.ensemble._weight_boosting.AdaBoostRegressor"><span class="pre">AdaBoostRegressor</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.AdaBoostRegressor.set_fit_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.ensemble.AdaBoostRegressor" title="sklearn.ensemble._weight_boosting.AdaBoostRegressor"><span class="pre">AdaBoostRegressor</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.AdaBoostRegressor.set_fit_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1588,7 +1588,7 @@ <h1>AdaBoostRegressor<a class="headerlink" href="#adaboostregressor" title="Link
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.AdaBoostRegressor.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.AdaBoostRegressor.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.AdaBoostRegressor.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1612,7 +1612,7 @@ <h1>AdaBoostRegressor<a class="headerlink" href="#adaboostregressor" title="Link
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.AdaBoostRegressor.set_score_request">
-<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.ensemble.AdaBoostRegressor" title="sklearn.ensemble._weight_boosting.AdaBoostRegressor"><span class="pre">AdaBoostRegressor</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.AdaBoostRegressor.set_score_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.ensemble.AdaBoostRegressor" title="sklearn.ensemble._weight_boosting.AdaBoostRegressor"><span class="pre">AdaBoostRegressor</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.AdaBoostRegressor.set_score_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1655,7 +1655,7 @@ <h1>AdaBoostRegressor<a class="headerlink" href="#adaboostregressor" title="Link
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.AdaBoostRegressor.staged_predict">
-<span class="sig-name descname"><span class="pre">staged_predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_weight_boosting.py#L1149"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.AdaBoostRegressor.staged_predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">staged_predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_weight_boosting.py#L1149"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.AdaBoostRegressor.staged_predict" title="Link to this definition">#</a></dt>
 <dd><p>Return staged predictions for X.</p>
 <p>The predicted regression value of an input sample is computed
 as the weighted median prediction of the regressors in the ensemble.</p>
@@ -1680,7 +1680,7 @@ <h1>AdaBoostRegressor<a class="headerlink" href="#adaboostregressor" title="Link
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.AdaBoostRegressor.staged_score">
-<span class="sig-name descname"><span class="pre">staged_score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_weight_boosting.py#L244"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.AdaBoostRegressor.staged_score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">staged_score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_weight_boosting.py#L244"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.AdaBoostRegressor.staged_score" title="Link to this definition">#</a></dt>
 <dd><p>Return staged scores for X, y.</p>
 <p>This generator method yields the ensemble score after each iteration of
 boosting and therefore allows monitoring, such as to determine the
diff --git a/dev/modules/generated/sklearn.ensemble.BaggingClassifier.html b/dev/modules/generated/sklearn.ensemble.BaggingClassifier.html
index 7e334c4e57105..2a9854a173282 100644
--- a/dev/modules/generated/sklearn.ensemble.BaggingClassifier.html
+++ b/dev/modules/generated/sklearn.ensemble.BaggingClassifier.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>BaggingClassifier<a class="headerlink" href="#baggingclassifier" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.ensemble.BaggingClassifier">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.ensemble.</span></span><span class="sig-name descname"><span class="pre">BaggingClassifier</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_estimators</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bootstrap</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bootstrap_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">oob_score</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">warm_start</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_bagging.py#L683"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.BaggingClassifier" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.ensemble.</span></span><span class="sig-name descname"><span class="pre">BaggingClassifier</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_estimators</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bootstrap</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bootstrap_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">oob_score</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">warm_start</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_bagging.py#L687"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.BaggingClassifier" title="Link to this definition">#</a></dt>
 <dd><p>A Bagging classifier.</p>
 <p>A Bagging classifier is an ensemble meta-estimator that fits base
 classifiers each on random subsets of the original dataset and then
@@ -1370,7 +1370,7 @@ <h1>BaggingClassifier<a class="headerlink" href="#baggingclassifier" title="Link
 </dd>
 <dt><strong>n_jobs</strong><span class="classifier">int, default=None</span></dt><dd><p>The number of jobs to run in parallel for both <a class="reference internal" href="#sklearn.ensemble.BaggingClassifier.fit" title="sklearn.ensemble.BaggingClassifier.fit"><code class="xref py py-meth docutils literal notranslate"><span class="pre">fit</span></code></a> and
 <a class="reference internal" href="#sklearn.ensemble.BaggingClassifier.predict" title="sklearn.ensemble.BaggingClassifier.predict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">predict</span></code></a>. <code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a
-<a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.5.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context. <code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all
+<a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.6.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context. <code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all
 processors. See <a class="reference internal" href="http://github.com/glossary.html#term-n_jobs"><span class="xref std std-term">Glossary</span></a> for more details.</p>
 </dd>
 <dt><strong>random_state</strong><span class="classifier">int, RandomState instance or None, default=None</span></dt><dd><p>Controls the random resampling of the original dataset
@@ -1470,7 +1470,7 @@ <h1>BaggingClassifier<a class="headerlink" href="#baggingclassifier" title="Link
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.BaggingClassifier.decision_function">
-<span class="sig-name descname"><span class="pre">decision_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_bagging.py#L1121"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.BaggingClassifier.decision_function" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">decision_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_bagging.py#L1125"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.BaggingClassifier.decision_function" title="Link to this definition">#</a></dt>
 <dd><p>Average of the decision functions of the base classifiers.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1503,7 +1503,7 @@ <h1>BaggingClassifier<a class="headerlink" href="#baggingclassifier" title="Link
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.BaggingClassifier.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_bagging.py#L344"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.BaggingClassifier.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_bagging.py#L339"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.BaggingClassifier.fit" title="Link to this definition">#</a></dt>
 <dd><p>Build a Bagging ensemble of estimators from the training set (X, y).</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1540,7 +1540,7 @@ <h1>BaggingClassifier<a class="headerlink" href="#baggingclassifier" title="Link
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.BaggingClassifier.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_bagging.py#L614"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.BaggingClassifier.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_bagging.py#L618"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.BaggingClassifier.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1560,7 +1560,7 @@ <h1>BaggingClassifier<a class="headerlink" href="#baggingclassifier" title="Link
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.BaggingClassifier.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.BaggingClassifier.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.BaggingClassifier.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1581,7 +1581,7 @@ <h1>BaggingClassifier<a class="headerlink" href="#baggingclassifier" title="Link
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.BaggingClassifier.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_bagging.py#L933"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.BaggingClassifier.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_bagging.py#L937"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.BaggingClassifier.predict" title="Link to this definition">#</a></dt>
 <dd><p>Predict class for X.</p>
 <p>The predicted class of an input sample is computed as the class with
 the highest mean predicted probability. If base estimators do not
@@ -1614,7 +1614,7 @@ <h1>BaggingClassifier<a class="headerlink" href="#baggingclassifier" title="Link
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.BaggingClassifier.predict_log_proba">
-<span class="sig-name descname"><span class="pre">predict_log_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_bagging.py#L1041"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.BaggingClassifier.predict_log_proba" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict_log_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_bagging.py#L1045"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.BaggingClassifier.predict_log_proba" title="Link to this definition">#</a></dt>
 <dd><p>Predict class log-probabilities for X.</p>
 <p>The predicted class log-probabilities of an input sample is computed as
 the log of the mean predicted class probabilities of the base
@@ -1650,7 +1650,7 @@ <h1>BaggingClassifier<a class="headerlink" href="#baggingclassifier" title="Link
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.BaggingClassifier.predict_proba">
-<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_bagging.py#L967"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.BaggingClassifier.predict_proba" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_bagging.py#L971"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.BaggingClassifier.predict_proba" title="Link to this definition">#</a></dt>
 <dd><p>Predict class probabilities for X.</p>
 <p>The predicted class probabilities of an input sample is computed as
 the mean predicted class probabilities of the base estimators in the
@@ -1687,7 +1687,7 @@ <h1>BaggingClassifier<a class="headerlink" href="#baggingclassifier" title="Link
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.BaggingClassifier.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L546"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.BaggingClassifier.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L487"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.BaggingClassifier.score" title="Link to this definition">#</a></dt>
 <dd><p>Return <a class="reference internal" href="http://github.com/model_evaluation.html#accuracy-score"><span class="std std-ref">accuracy</span></a> on provided data and labels.</p>
 <p>In multi-label classification, this is the subset accuracy
 which is a harsh metric since you require for each sample that
@@ -1714,7 +1714,7 @@ <h1>BaggingClassifier<a class="headerlink" href="#baggingclassifier" title="Link
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.BaggingClassifier.set_fit_request">
-<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.ensemble.BaggingClassifier" title="sklearn.ensemble._bagging.BaggingClassifier"><span class="pre">BaggingClassifier</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.BaggingClassifier.set_fit_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.ensemble.BaggingClassifier" title="sklearn.ensemble._bagging.BaggingClassifier"><span class="pre">BaggingClassifier</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.BaggingClassifier.set_fit_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1757,7 +1757,7 @@ <h1>BaggingClassifier<a class="headerlink" href="#baggingclassifier" title="Link
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.BaggingClassifier.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.BaggingClassifier.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.BaggingClassifier.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1781,7 +1781,7 @@ <h1>BaggingClassifier<a class="headerlink" href="#baggingclassifier" title="Link
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.BaggingClassifier.set_score_request">
-<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.ensemble.BaggingClassifier" title="sklearn.ensemble._bagging.BaggingClassifier"><span class="pre">BaggingClassifier</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.BaggingClassifier.set_score_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.ensemble.BaggingClassifier" title="sklearn.ensemble._bagging.BaggingClassifier"><span class="pre">BaggingClassifier</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.BaggingClassifier.set_score_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
diff --git a/dev/modules/generated/sklearn.ensemble.BaggingRegressor.html b/dev/modules/generated/sklearn.ensemble.BaggingRegressor.html
index d4605af3083d2..f8983ab94c9cd 100644
--- a/dev/modules/generated/sklearn.ensemble.BaggingRegressor.html
+++ b/dev/modules/generated/sklearn.ensemble.BaggingRegressor.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1308,7 +1308,7 @@
 <h1>BaggingRegressor<a class="headerlink" href="#baggingregressor" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.ensemble.BaggingRegressor">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.ensemble.</span></span><span class="sig-name descname"><span class="pre">BaggingRegressor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_estimators</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bootstrap</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bootstrap_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">oob_score</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">warm_start</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_bagging.py#L1191"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.BaggingRegressor" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.ensemble.</span></span><span class="sig-name descname"><span class="pre">BaggingRegressor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_estimators</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bootstrap</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bootstrap_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">oob_score</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">warm_start</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_bagging.py#L1195"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.BaggingRegressor" title="Link to this definition">#</a></dt>
 <dd><p>A Bagging regressor.</p>
 <p>A Bagging regressor is an ensemble meta-estimator that fits base
 regressors each on random subsets of the original dataset and then
@@ -1370,7 +1370,7 @@ <h1>BaggingRegressor<a class="headerlink" href="#baggingregressor" title="Link t
 </dd>
 <dt><strong>n_jobs</strong><span class="classifier">int, default=None</span></dt><dd><p>The number of jobs to run in parallel for both <a class="reference internal" href="#sklearn.ensemble.BaggingRegressor.fit" title="sklearn.ensemble.BaggingRegressor.fit"><code class="xref py py-meth docutils literal notranslate"><span class="pre">fit</span></code></a> and
 <a class="reference internal" href="#sklearn.ensemble.BaggingRegressor.predict" title="sklearn.ensemble.BaggingRegressor.predict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">predict</span></code></a>. <code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a
-<a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.5.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context. <code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all
+<a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.6.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context. <code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all
 processors. See <a class="reference internal" href="http://github.com/glossary.html#term-n_jobs"><span class="xref std std-term">Glossary</span></a> for more details.</p>
 </dd>
 <dt><strong>random_state</strong><span class="classifier">int, RandomState instance or None, default=None</span></dt><dd><p>Controls the random resampling of the original dataset
@@ -1461,12 +1461,12 @@ <h1>BaggingRegressor<a class="headerlink" href="#baggingregressor" title="Link t
 <span class="gp">&gt;&gt;&gt; </span><span class="n">regr</span> <span class="o">=</span> <span class="n">BaggingRegressor</span><span class="p">(</span><span class="n">estimator</span><span class="o">=</span><span class="n">SVR</span><span class="p">(),</span>
 <span class="gp">... </span>                        <span class="n">n_estimators</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">regr</span><span class="o">.</span><span class="n">predict</span><span class="p">([[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]])</span>
-<span class="go">array([-2.8720...])</span>
+<span class="go">array([-2.8720])</span>
 </pre></div>
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.BaggingRegressor.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_bagging.py#L344"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.BaggingRegressor.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_bagging.py#L339"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.BaggingRegressor.fit" title="Link to this definition">#</a></dt>
 <dd><p>Build a Bagging ensemble of estimators from the training set (X, y).</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1503,7 +1503,7 @@ <h1>BaggingRegressor<a class="headerlink" href="#baggingregressor" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.BaggingRegressor.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_bagging.py#L614"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.BaggingRegressor.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_bagging.py#L618"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.BaggingRegressor.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1523,7 +1523,7 @@ <h1>BaggingRegressor<a class="headerlink" href="#baggingregressor" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.BaggingRegressor.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.BaggingRegressor.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.BaggingRegressor.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1544,7 +1544,7 @@ <h1>BaggingRegressor<a class="headerlink" href="#baggingregressor" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.BaggingRegressor.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_bagging.py#L1379"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.BaggingRegressor.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_bagging.py#L1383"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.BaggingRegressor.predict" title="Link to this definition">#</a></dt>
 <dd><p>Predict regression target for X.</p>
 <p>The predicted regression target of an input sample is computed as the
 mean predicted regression targets of the estimators in the ensemble.</p>
@@ -1576,7 +1576,7 @@ <h1>BaggingRegressor<a class="headerlink" href="#baggingregressor" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.BaggingRegressor.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L619"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.BaggingRegressor.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L560"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.BaggingRegressor.score" title="Link to this definition">#</a></dt>
 <dd><p>Return <a class="reference internal" href="http://github.com/model_evaluation.html#r2-score"><span class="std std-ref">coefficient of determination</span></a> on test data.</p>
 <p>The coefficient of determination, <span class="math notranslate nohighlight">\(R^2\)</span>, is defined as
 <span class="math notranslate nohighlight">\((1 - \frac{u}{v})\)</span>, where <span class="math notranslate nohighlight">\(u\)</span> is the residual
@@ -1618,7 +1618,7 @@ <h1>BaggingRegressor<a class="headerlink" href="#baggingregressor" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.BaggingRegressor.set_fit_request">
-<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.ensemble.BaggingRegressor" title="sklearn.ensemble._bagging.BaggingRegressor"><span class="pre">BaggingRegressor</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.BaggingRegressor.set_fit_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.ensemble.BaggingRegressor" title="sklearn.ensemble._bagging.BaggingRegressor"><span class="pre">BaggingRegressor</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.BaggingRegressor.set_fit_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1661,7 +1661,7 @@ <h1>BaggingRegressor<a class="headerlink" href="#baggingregressor" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.BaggingRegressor.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.BaggingRegressor.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.BaggingRegressor.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1685,7 +1685,7 @@ <h1>BaggingRegressor<a class="headerlink" href="#baggingregressor" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.BaggingRegressor.set_score_request">
-<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.ensemble.BaggingRegressor" title="sklearn.ensemble._bagging.BaggingRegressor"><span class="pre">BaggingRegressor</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.BaggingRegressor.set_score_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.ensemble.BaggingRegressor" title="sklearn.ensemble._bagging.BaggingRegressor"><span class="pre">BaggingRegressor</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.BaggingRegressor.set_score_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
diff --git a/dev/modules/generated/sklearn.ensemble.ExtraTreesClassifier.html b/dev/modules/generated/sklearn.ensemble.ExtraTreesClassifier.html
index 0a6242f472163..7d1fcf85474c5 100644
--- a/dev/modules/generated/sklearn.ensemble.ExtraTreesClassifier.html
+++ b/dev/modules/generated/sklearn.ensemble.ExtraTreesClassifier.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>ExtraTreesClassifier<a class="headerlink" href="#extratreesclassifier" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.ensemble.ExtraTreesClassifier">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.ensemble.</span></span><span class="sig-name descname"><span class="pre">ExtraTreesClassifier</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_estimators</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">criterion</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'gini'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_depth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_samples_split</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_samples_leaf</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_weight_fraction_leaf</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'sqrt'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_leaf_nodes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_impurity_decrease</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bootstrap</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">oob_score</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">warm_start</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">class_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ccp_alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">monotonic_cst</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_forest.py#L1937"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.ExtraTreesClassifier" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.ensemble.</span></span><span class="sig-name descname"><span class="pre">ExtraTreesClassifier</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_estimators</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">criterion</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'gini'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_depth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_samples_split</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_samples_leaf</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_weight_fraction_leaf</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'sqrt'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_leaf_nodes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_impurity_decrease</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bootstrap</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">oob_score</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">warm_start</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">class_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ccp_alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">monotonic_cst</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_forest.py#L1936"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.ExtraTreesClassifier" title="Link to this definition">#</a></dt>
 <dd><p>An extra-trees classifier.</p>
 <p>This class implements a meta estimator that fits a number of
 randomized decision trees (a.k.a. extra-trees) on various sub-samples
@@ -1416,7 +1416,7 @@ <h1>ExtraTreesClassifier<a class="headerlink" href="#extratreesclassifier" title
 </dd>
 <dt><strong>n_jobs</strong><span class="classifier">int, default=None</span></dt><dd><p>The number of jobs to run in parallel. <a class="reference internal" href="#sklearn.ensemble.ExtraTreesClassifier.fit" title="sklearn.ensemble.ExtraTreesClassifier.fit"><code class="xref py py-meth docutils literal notranslate"><span class="pre">fit</span></code></a>, <a class="reference internal" href="#sklearn.ensemble.ExtraTreesClassifier.predict" title="sklearn.ensemble.ExtraTreesClassifier.predict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">predict</span></code></a>,
 <a class="reference internal" href="#sklearn.ensemble.ExtraTreesClassifier.decision_path" title="sklearn.ensemble.ExtraTreesClassifier.decision_path"><code class="xref py py-meth docutils literal notranslate"><span class="pre">decision_path</span></code></a> and <a class="reference internal" href="#sklearn.ensemble.ExtraTreesClassifier.apply" title="sklearn.ensemble.ExtraTreesClassifier.apply"><code class="xref py py-meth docutils literal notranslate"><span class="pre">apply</span></code></a> are all parallelized over the
-trees. <code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.5.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a>
+trees. <code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.6.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a>
 context. <code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all processors. See <a class="reference internal" href="http://github.com/glossary.html#term-n_jobs"><span class="xref std std-term">Glossary</span></a> for more details.</p>
 </dd>
 <dt><strong>random_state</strong><span class="classifier">int, RandomState instance or None, default=None</span></dt><dd><p>Controls 3 sources of randomness:</p>
@@ -1585,7 +1585,7 @@ <h1>ExtraTreesClassifier<a class="headerlink" href="#extratreesclassifier" title
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.ExtraTreesClassifier.apply">
-<span class="sig-name descname"><span class="pre">apply</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_forest.py#L262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.ExtraTreesClassifier.apply" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">apply</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_forest.py#L261"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.ExtraTreesClassifier.apply" title="Link to this definition">#</a></dt>
 <dd><p>Apply trees in the forest to X, return leaf indices.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1608,7 +1608,7 @@ <h1>ExtraTreesClassifier<a class="headerlink" href="#extratreesclassifier" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.ExtraTreesClassifier.decision_path">
-<span class="sig-name descname"><span class="pre">decision_path</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_forest.py#L288"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.ExtraTreesClassifier.decision_path" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">decision_path</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_forest.py#L287"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.ExtraTreesClassifier.decision_path" title="Link to this definition">#</a></dt>
 <dd><p>Return the decision path in the forest.</p>
 <div class="versionadded">
 <p><span class="versionmodified added">Added in version 0.18.</span></p>
@@ -1638,7 +1638,7 @@ <h1>ExtraTreesClassifier<a class="headerlink" href="#extratreesclassifier" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.ExtraTreesClassifier.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_forest.py#L328"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.ExtraTreesClassifier.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_forest.py#L327"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.ExtraTreesClassifier.fit" title="Link to this definition">#</a></dt>
 <dd><p>Build a forest of trees from the training set (X, y).</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1669,7 +1669,7 @@ <h1>ExtraTreesClassifier<a class="headerlink" href="#extratreesclassifier" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.ExtraTreesClassifier.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.ExtraTreesClassifier.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.ExtraTreesClassifier.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1686,7 +1686,7 @@ <h1>ExtraTreesClassifier<a class="headerlink" href="#extratreesclassifier" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.ExtraTreesClassifier.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.ExtraTreesClassifier.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.ExtraTreesClassifier.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1707,7 +1707,7 @@ <h1>ExtraTreesClassifier<a class="headerlink" href="#extratreesclassifier" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.ExtraTreesClassifier.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_forest.py#L883"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.ExtraTreesClassifier.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_forest.py#L882"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.ExtraTreesClassifier.predict" title="Link to this definition">#</a></dt>
 <dd><p>Predict class for X.</p>
 <p>The predicted class of an input sample is a vote by the trees in
 the forest, weighted by their probability estimates. That is,
@@ -1733,7 +1733,7 @@ <h1>ExtraTreesClassifier<a class="headerlink" href="#extratreesclassifier" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.ExtraTreesClassifier.predict_log_proba">
-<span class="sig-name descname"><span class="pre">predict_log_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_forest.py#L970"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.ExtraTreesClassifier.predict_log_proba" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict_log_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_forest.py#L969"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.ExtraTreesClassifier.predict_log_proba" title="Link to this definition">#</a></dt>
 <dd><p>Predict class log-probabilities for X.</p>
 <p>The predicted class log-probabilities of an input sample is computed as
 the log of the mean predicted class probabilities of the trees in the
@@ -1759,7 +1759,7 @@ <h1>ExtraTreesClassifier<a class="headerlink" href="#extratreesclassifier" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.ExtraTreesClassifier.predict_proba">
-<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_forest.py#L922"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.ExtraTreesClassifier.predict_proba" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_forest.py#L921"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.ExtraTreesClassifier.predict_proba" title="Link to this definition">#</a></dt>
 <dd><p>Predict class probabilities for X.</p>
 <p>The predicted class probabilities of an input sample are computed as
 the mean predicted class probabilities of the trees in the forest.
@@ -1786,7 +1786,7 @@ <h1>ExtraTreesClassifier<a class="headerlink" href="#extratreesclassifier" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.ExtraTreesClassifier.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L546"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.ExtraTreesClassifier.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L487"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.ExtraTreesClassifier.score" title="Link to this definition">#</a></dt>
 <dd><p>Return <a class="reference internal" href="http://github.com/model_evaluation.html#accuracy-score"><span class="std std-ref">accuracy</span></a> on provided data and labels.</p>
 <p>In multi-label classification, this is the subset accuracy
 which is a harsh metric since you require for each sample that
@@ -1813,7 +1813,7 @@ <h1>ExtraTreesClassifier<a class="headerlink" href="#extratreesclassifier" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.ExtraTreesClassifier.set_fit_request">
-<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.ensemble.ExtraTreesClassifier" title="sklearn.ensemble._forest.ExtraTreesClassifier"><span class="pre">ExtraTreesClassifier</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.ExtraTreesClassifier.set_fit_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.ensemble.ExtraTreesClassifier" title="sklearn.ensemble._forest.ExtraTreesClassifier"><span class="pre">ExtraTreesClassifier</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.ExtraTreesClassifier.set_fit_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1856,7 +1856,7 @@ <h1>ExtraTreesClassifier<a class="headerlink" href="#extratreesclassifier" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.ExtraTreesClassifier.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.ExtraTreesClassifier.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.ExtraTreesClassifier.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1880,7 +1880,7 @@ <h1>ExtraTreesClassifier<a class="headerlink" href="#extratreesclassifier" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.ExtraTreesClassifier.set_score_request">
-<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.ensemble.ExtraTreesClassifier" title="sklearn.ensemble._forest.ExtraTreesClassifier"><span class="pre">ExtraTreesClassifier</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.ExtraTreesClassifier.set_score_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.ensemble.ExtraTreesClassifier" title="sklearn.ensemble._forest.ExtraTreesClassifier"><span class="pre">ExtraTreesClassifier</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.ExtraTreesClassifier.set_score_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
diff --git a/dev/modules/generated/sklearn.ensemble.ExtraTreesRegressor.html b/dev/modules/generated/sklearn.ensemble.ExtraTreesRegressor.html
index 3b143d0dc0962..4186b537793fe 100644
--- a/dev/modules/generated/sklearn.ensemble.ExtraTreesRegressor.html
+++ b/dev/modules/generated/sklearn.ensemble.ExtraTreesRegressor.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1308,7 +1308,7 @@
 <h1>ExtraTreesRegressor<a class="headerlink" href="#extratreesregressor" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.ensemble.ExtraTreesRegressor">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.ensemble.</span></span><span class="sig-name descname"><span class="pre">ExtraTreesRegressor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_estimators</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">criterion</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'squared_error'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_depth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_samples_split</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_samples_leaf</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_weight_fraction_leaf</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_leaf_nodes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_impurity_decrease</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bootstrap</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">oob_score</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">warm_start</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ccp_alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">monotonic_cst</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_forest.py#L2318"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.ExtraTreesRegressor" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.ensemble.</span></span><span class="sig-name descname"><span class="pre">ExtraTreesRegressor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_estimators</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">criterion</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'squared_error'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_depth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_samples_split</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_samples_leaf</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_weight_fraction_leaf</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_leaf_nodes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_impurity_decrease</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bootstrap</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">oob_score</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">warm_start</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ccp_alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">monotonic_cst</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_forest.py#L2317"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.ExtraTreesRegressor" title="Link to this definition">#</a></dt>
 <dd><p>An extra-trees regressor.</p>
 <p>This class implements a meta estimator that fits a number of
 randomized decision trees (a.k.a. extra-trees) on various sub-samples
@@ -1431,7 +1431,7 @@ <h1>ExtraTreesRegressor<a class="headerlink" href="#extratreesregressor" title="
 </dd>
 <dt><strong>n_jobs</strong><span class="classifier">int, default=None</span></dt><dd><p>The number of jobs to run in parallel. <a class="reference internal" href="#sklearn.ensemble.ExtraTreesRegressor.fit" title="sklearn.ensemble.ExtraTreesRegressor.fit"><code class="xref py py-meth docutils literal notranslate"><span class="pre">fit</span></code></a>, <a class="reference internal" href="#sklearn.ensemble.ExtraTreesRegressor.predict" title="sklearn.ensemble.ExtraTreesRegressor.predict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">predict</span></code></a>,
 <a class="reference internal" href="#sklearn.ensemble.ExtraTreesRegressor.decision_path" title="sklearn.ensemble.ExtraTreesRegressor.decision_path"><code class="xref py py-meth docutils literal notranslate"><span class="pre">decision_path</span></code></a> and <a class="reference internal" href="#sklearn.ensemble.ExtraTreesRegressor.apply" title="sklearn.ensemble.ExtraTreesRegressor.apply"><code class="xref py py-meth docutils literal notranslate"><span class="pre">apply</span></code></a> are all parallelized over the
-trees. <code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.5.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a>
+trees. <code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.6.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a>
 context. <code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all processors. See <a class="reference internal" href="http://github.com/glossary.html#term-n_jobs"><span class="xref std std-term">Glossary</span></a> for more details.</p>
 </dd>
 <dt><strong>random_state</strong><span class="classifier">int, RandomState instance or None, default=None</span></dt><dd><p>Controls 3 sources of randomness:</p>
@@ -1572,7 +1572,7 @@ <h1>ExtraTreesRegressor<a class="headerlink" href="#extratreesregressor" title="
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.ExtraTreesRegressor.apply">
-<span class="sig-name descname"><span class="pre">apply</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_forest.py#L262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.ExtraTreesRegressor.apply" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">apply</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_forest.py#L261"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.ExtraTreesRegressor.apply" title="Link to this definition">#</a></dt>
 <dd><p>Apply trees in the forest to X, return leaf indices.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1595,7 +1595,7 @@ <h1>ExtraTreesRegressor<a class="headerlink" href="#extratreesregressor" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.ExtraTreesRegressor.decision_path">
-<span class="sig-name descname"><span class="pre">decision_path</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_forest.py#L288"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.ExtraTreesRegressor.decision_path" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">decision_path</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_forest.py#L287"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.ExtraTreesRegressor.decision_path" title="Link to this definition">#</a></dt>
 <dd><p>Return the decision path in the forest.</p>
 <div class="versionadded">
 <p><span class="versionmodified added">Added in version 0.18.</span></p>
@@ -1625,7 +1625,7 @@ <h1>ExtraTreesRegressor<a class="headerlink" href="#extratreesregressor" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.ExtraTreesRegressor.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_forest.py#L328"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.ExtraTreesRegressor.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_forest.py#L327"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.ExtraTreesRegressor.fit" title="Link to this definition">#</a></dt>
 <dd><p>Build a forest of trees from the training set (X, y).</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1656,7 +1656,7 @@ <h1>ExtraTreesRegressor<a class="headerlink" href="#extratreesregressor" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.ExtraTreesRegressor.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.ExtraTreesRegressor.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.ExtraTreesRegressor.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1673,7 +1673,7 @@ <h1>ExtraTreesRegressor<a class="headerlink" href="#extratreesregressor" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.ExtraTreesRegressor.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.ExtraTreesRegressor.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.ExtraTreesRegressor.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1694,7 +1694,7 @@ <h1>ExtraTreesRegressor<a class="headerlink" href="#extratreesregressor" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.ExtraTreesRegressor.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_forest.py#L1045"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.ExtraTreesRegressor.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_forest.py#L1044"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.ExtraTreesRegressor.predict" title="Link to this definition">#</a></dt>
 <dd><p>Predict regression target for X.</p>
 <p>The predicted regression target of an input sample is computed as the
 mean predicted regression targets of the trees in the forest.</p>
@@ -1718,7 +1718,7 @@ <h1>ExtraTreesRegressor<a class="headerlink" href="#extratreesregressor" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.ExtraTreesRegressor.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L619"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.ExtraTreesRegressor.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L560"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.ExtraTreesRegressor.score" title="Link to this definition">#</a></dt>
 <dd><p>Return <a class="reference internal" href="http://github.com/model_evaluation.html#r2-score"><span class="std std-ref">coefficient of determination</span></a> on test data.</p>
 <p>The coefficient of determination, <span class="math notranslate nohighlight">\(R^2\)</span>, is defined as
 <span class="math notranslate nohighlight">\((1 - \frac{u}{v})\)</span>, where <span class="math notranslate nohighlight">\(u\)</span> is the residual
@@ -1760,7 +1760,7 @@ <h1>ExtraTreesRegressor<a class="headerlink" href="#extratreesregressor" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.ExtraTreesRegressor.set_fit_request">
-<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.ensemble.ExtraTreesRegressor" title="sklearn.ensemble._forest.ExtraTreesRegressor"><span class="pre">ExtraTreesRegressor</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.ExtraTreesRegressor.set_fit_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.ensemble.ExtraTreesRegressor" title="sklearn.ensemble._forest.ExtraTreesRegressor"><span class="pre">ExtraTreesRegressor</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.ExtraTreesRegressor.set_fit_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1803,7 +1803,7 @@ <h1>ExtraTreesRegressor<a class="headerlink" href="#extratreesregressor" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.ExtraTreesRegressor.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.ExtraTreesRegressor.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.ExtraTreesRegressor.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1827,7 +1827,7 @@ <h1>ExtraTreesRegressor<a class="headerlink" href="#extratreesregressor" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.ExtraTreesRegressor.set_score_request">
-<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.ensemble.ExtraTreesRegressor" title="sklearn.ensemble._forest.ExtraTreesRegressor"><span class="pre">ExtraTreesRegressor</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.ExtraTreesRegressor.set_score_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.ensemble.ExtraTreesRegressor" title="sklearn.ensemble._forest.ExtraTreesRegressor"><span class="pre">ExtraTreesRegressor</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.ExtraTreesRegressor.set_score_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
diff --git a/dev/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html b/dev/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html
index 2dfc7edf52713..499d7ecba131f 100644
--- a/dev/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html
+++ b/dev/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>GradientBoostingClassifier<a class="headerlink" href="#gradientboostingclassifier" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.ensemble.GradientBoostingClassifier">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.ensemble.</span></span><span class="sig-name descname"><span class="pre">GradientBoostingClassifier</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">loss</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'log_loss'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">learning_rate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_estimators</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">subsample</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">criterion</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'friedman_mse'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_samples_split</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_samples_leaf</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_weight_fraction_leaf</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_depth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_impurity_decrease</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_leaf_nodes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">warm_start</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validation_fraction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_iter_no_change</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ccp_alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_gb.py#L1126"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.GradientBoostingClassifier" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.ensemble.</span></span><span class="sig-name descname"><span class="pre">GradientBoostingClassifier</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">loss</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'log_loss'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">learning_rate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_estimators</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">subsample</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">criterion</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'friedman_mse'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_samples_split</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_samples_leaf</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_weight_fraction_leaf</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_depth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_impurity_decrease</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_leaf_nodes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">warm_start</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validation_fraction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_iter_no_change</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ccp_alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_gb.py#L1126"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.GradientBoostingClassifier" title="Link to this definition">#</a></dt>
 <dd><p>Gradient Boosting for classification.</p>
 <p>This algorithm builds an additive model in a forward stage-wise fashion; it
 allows for the optimization of arbitrary differentiable loss functions. In
@@ -1602,12 +1602,12 @@ <h1>GradientBoostingClassifier<a class="headerlink" href="#gradientboostingclass
 <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">clf</span> <span class="o">=</span> <span class="n">GradientBoostingClassifier</span><span class="p">(</span><span class="n">n_estimators</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">learning_rate</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span>
 <span class="gp">... </span>    <span class="n">max_depth</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">clf</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span>
-<span class="go">0.913...</span>
+<span class="go">0.913</span>
 </pre></div>
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.GradientBoostingClassifier.apply">
-<span class="sig-name descname"><span class="pre">apply</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_gb.py#L1085"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.GradientBoostingClassifier.apply" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">apply</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_gb.py#L1085"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.GradientBoostingClassifier.apply" title="Link to this definition">#</a></dt>
 <dd><p>Apply trees in the ensemble to X, return leaf indices.</p>
 <div class="versionadded">
 <p><span class="versionmodified added">Added in version 0.17.</span></p>
@@ -1634,7 +1634,7 @@ <h1>GradientBoostingClassifier<a class="headerlink" href="#gradientboostingclass
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.GradientBoostingClassifier.decision_function">
-<span class="sig-name descname"><span class="pre">decision_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_gb.py#L1561"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.GradientBoostingClassifier.decision_function" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">decision_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_gb.py#L1561"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.GradientBoostingClassifier.decision_function" title="Link to this definition">#</a></dt>
 <dd><p>Compute the decision function of <code class="docutils literal notranslate"><span class="pre">X</span></code>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1660,7 +1660,7 @@ <h1>GradientBoostingClassifier<a class="headerlink" href="#gradientboostingclass
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.GradientBoostingClassifier.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">monitor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_gb.py#L611"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.GradientBoostingClassifier.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">monitor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_gb.py#L611"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.GradientBoostingClassifier.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit the gradient boosting model.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1700,7 +1700,7 @@ <h1>GradientBoostingClassifier<a class="headerlink" href="#gradientboostingclass
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.GradientBoostingClassifier.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.GradientBoostingClassifier.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.GradientBoostingClassifier.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1717,7 +1717,7 @@ <h1>GradientBoostingClassifier<a class="headerlink" href="#gradientboostingclass
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.GradientBoostingClassifier.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.GradientBoostingClassifier.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.GradientBoostingClassifier.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1738,7 +1738,7 @@ <h1>GradientBoostingClassifier<a class="headerlink" href="#gradientboostingclass
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.GradientBoostingClassifier.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_gb.py#L1612"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.GradientBoostingClassifier.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_gb.py#L1612"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.GradientBoostingClassifier.predict" title="Link to this definition">#</a></dt>
 <dd><p>Predict class for X.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1760,7 +1760,7 @@ <h1>GradientBoostingClassifier<a class="headerlink" href="#gradientboostingclass
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.GradientBoostingClassifier.predict_log_proba">
-<span class="sig-name descname"><span class="pre">predict_log_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_gb.py#L1685"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.GradientBoostingClassifier.predict_log_proba" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict_log_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_gb.py#L1685"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.GradientBoostingClassifier.predict_log_proba" title="Link to this definition">#</a></dt>
 <dd><p>Predict class log-probabilities for X.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1789,7 +1789,7 @@ <h1>GradientBoostingClassifier<a class="headerlink" href="#gradientboostingclass
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.GradientBoostingClassifier.predict_proba">
-<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_gb.py#L1661"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.GradientBoostingClassifier.predict_proba" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_gb.py#L1661"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.GradientBoostingClassifier.predict_proba" title="Link to this definition">#</a></dt>
 <dd><p>Predict class probabilities for X.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1818,7 +1818,7 @@ <h1>GradientBoostingClassifier<a class="headerlink" href="#gradientboostingclass
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.GradientBoostingClassifier.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L546"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.GradientBoostingClassifier.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L487"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.GradientBoostingClassifier.score" title="Link to this definition">#</a></dt>
 <dd><p>Return <a class="reference internal" href="http://github.com/model_evaluation.html#accuracy-score"><span class="std std-ref">accuracy</span></a> on provided data and labels.</p>
 <p>In multi-label classification, this is the subset accuracy
 which is a harsh metric since you require for each sample that
@@ -1845,7 +1845,7 @@ <h1>GradientBoostingClassifier<a class="headerlink" href="#gradientboostingclass
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.GradientBoostingClassifier.set_fit_request">
-<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">monitor</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.ensemble.GradientBoostingClassifier" title="sklearn.ensemble._gb.GradientBoostingClassifier"><span class="pre">GradientBoostingClassifier</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.GradientBoostingClassifier.set_fit_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">monitor</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.ensemble.GradientBoostingClassifier" title="sklearn.ensemble._gb.GradientBoostingClassifier"><span class="pre">GradientBoostingClassifier</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.GradientBoostingClassifier.set_fit_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1890,7 +1890,7 @@ <h1>GradientBoostingClassifier<a class="headerlink" href="#gradientboostingclass
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.GradientBoostingClassifier.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.GradientBoostingClassifier.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.GradientBoostingClassifier.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1914,7 +1914,7 @@ <h1>GradientBoostingClassifier<a class="headerlink" href="#gradientboostingclass
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.GradientBoostingClassifier.set_score_request">
-<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.ensemble.GradientBoostingClassifier" title="sklearn.ensemble._gb.GradientBoostingClassifier"><span class="pre">GradientBoostingClassifier</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.GradientBoostingClassifier.set_score_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.ensemble.GradientBoostingClassifier" title="sklearn.ensemble._gb.GradientBoostingClassifier"><span class="pre">GradientBoostingClassifier</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.GradientBoostingClassifier.set_score_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1957,7 +1957,7 @@ <h1>GradientBoostingClassifier<a class="headerlink" href="#gradientboostingclass
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.GradientBoostingClassifier.staged_decision_function">
-<span class="sig-name descname"><span class="pre">staged_decision_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_gb.py#L1588"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.GradientBoostingClassifier.staged_decision_function" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">staged_decision_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_gb.py#L1588"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.GradientBoostingClassifier.staged_decision_function" title="Link to this definition">#</a></dt>
 <dd><p>Compute decision function of <code class="docutils literal notranslate"><span class="pre">X</span></code> for each iteration.</p>
 <p>This method allows monitoring (i.e. determine error on testing set)
 after each stage.</p>
@@ -1985,7 +1985,7 @@ <h1>GradientBoostingClassifier<a class="headerlink" href="#gradientboostingclass
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.GradientBoostingClassifier.staged_predict">
-<span class="sig-name descname"><span class="pre">staged_predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_gb.py#L1634"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.GradientBoostingClassifier.staged_predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">staged_predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_gb.py#L1634"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.GradientBoostingClassifier.staged_predict" title="Link to this definition">#</a></dt>
 <dd><p>Predict class at each stage for X.</p>
 <p>This method allows monitoring (i.e. determine error on testing set)
 after each stage.</p>
@@ -2009,7 +2009,7 @@ <h1>GradientBoostingClassifier<a class="headerlink" href="#gradientboostingclass
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.GradientBoostingClassifier.staged_predict_proba">
-<span class="sig-name descname"><span class="pre">staged_predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_gb.py#L1709"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.GradientBoostingClassifier.staged_predict_proba" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">staged_predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_gb.py#L1709"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.GradientBoostingClassifier.staged_predict_proba" title="Link to this definition">#</a></dt>
 <dd><p>Predict class probabilities at each stage for X.</p>
 <p>This method allows monitoring (i.e. determine error on testing set)
 after each stage.</p>
diff --git a/dev/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html b/dev/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html
index ddddf82bb468e..46982317d0d82 100644
--- a/dev/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html
+++ b/dev/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1308,7 +1308,7 @@
 <h1>GradientBoostingRegressor<a class="headerlink" href="#gradientboostingregressor" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.ensemble.GradientBoostingRegressor">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.ensemble.</span></span><span class="sig-name descname"><span class="pre">GradientBoostingRegressor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">loss</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'squared_error'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">learning_rate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_estimators</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">subsample</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">criterion</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'friedman_mse'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_samples_split</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_samples_leaf</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_weight_fraction_leaf</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_depth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_impurity_decrease</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.9</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_leaf_nodes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">warm_start</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validation_fraction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_iter_no_change</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ccp_alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_gb.py#L1738"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.GradientBoostingRegressor" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.ensemble.</span></span><span class="sig-name descname"><span class="pre">GradientBoostingRegressor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">loss</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'squared_error'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">learning_rate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_estimators</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">subsample</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">criterion</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'friedman_mse'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_samples_split</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_samples_leaf</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_weight_fraction_leaf</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_depth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_impurity_decrease</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.9</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_leaf_nodes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">warm_start</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validation_fraction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_iter_no_change</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ccp_alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_gb.py#L1738"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.GradientBoostingRegressor" title="Link to this definition">#</a></dt>
 <dd><p>Gradient Boosting for regression.</p>
 <p>This estimator builds an additive model in a forward stage-wise fashion; it
 allows for the optimization of arbitrary differentiable loss functions. In
@@ -1594,7 +1594,7 @@ <h1>GradientBoostingRegressor<a class="headerlink" href="#gradientboostingregres
 <span class="gp">&gt;&gt;&gt; </span><span class="n">reg</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
 <span class="go">GradientBoostingRegressor(random_state=0)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">reg</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">[</span><span class="mi">1</span><span class="p">:</span><span class="mi">2</span><span class="p">])</span>
-<span class="go">array([-61...])</span>
+<span class="go">array([-61.1])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">reg</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span>
 <span class="go">0.4...</span>
 </pre></div>
@@ -1605,7 +1605,7 @@ <h1>GradientBoostingRegressor<a class="headerlink" href="#gradientboostingregres
 <a class="reference internal" href="http://github.com/auto_examples/ensemble/plot_gradient_boosting_regression.html#sphx-glr-auto-examples-ensemble-plot-gradient-boosting-regression-py"><span class="std std-ref">Gradient Boosting regression</span></a>.</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.GradientBoostingRegressor.apply">
-<span class="sig-name descname"><span class="pre">apply</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_gb.py#L2175"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.GradientBoostingRegressor.apply" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">apply</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_gb.py#L2175"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.GradientBoostingRegressor.apply" title="Link to this definition">#</a></dt>
 <dd><p>Apply trees in the ensemble to X, return leaf indices.</p>
 <div class="versionadded">
 <p><span class="versionmodified added">Added in version 0.17.</span></p>
@@ -1631,7 +1631,7 @@ <h1>GradientBoostingRegressor<a class="headerlink" href="#gradientboostingregres
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.GradientBoostingRegressor.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">monitor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_gb.py#L611"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.GradientBoostingRegressor.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">monitor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_gb.py#L611"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.GradientBoostingRegressor.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit the gradient boosting model.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1671,7 +1671,7 @@ <h1>GradientBoostingRegressor<a class="headerlink" href="#gradientboostingregres
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.GradientBoostingRegressor.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.GradientBoostingRegressor.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.GradientBoostingRegressor.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1688,7 +1688,7 @@ <h1>GradientBoostingRegressor<a class="headerlink" href="#gradientboostingregres
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.GradientBoostingRegressor.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.GradientBoostingRegressor.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.GradientBoostingRegressor.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1709,7 +1709,7 @@ <h1>GradientBoostingRegressor<a class="headerlink" href="#gradientboostingregres
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.GradientBoostingRegressor.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_gb.py#L2133"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.GradientBoostingRegressor.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_gb.py#L2133"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.GradientBoostingRegressor.predict" title="Link to this definition">#</a></dt>
 <dd><p>Predict regression target for X.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1731,7 +1731,7 @@ <h1>GradientBoostingRegressor<a class="headerlink" href="#gradientboostingregres
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.GradientBoostingRegressor.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L619"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.GradientBoostingRegressor.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L560"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.GradientBoostingRegressor.score" title="Link to this definition">#</a></dt>
 <dd><p>Return <a class="reference internal" href="http://github.com/model_evaluation.html#r2-score"><span class="std std-ref">coefficient of determination</span></a> on test data.</p>
 <p>The coefficient of determination, <span class="math notranslate nohighlight">\(R^2\)</span>, is defined as
 <span class="math notranslate nohighlight">\((1 - \frac{u}{v})\)</span>, where <span class="math notranslate nohighlight">\(u\)</span> is the residual
@@ -1773,7 +1773,7 @@ <h1>GradientBoostingRegressor<a class="headerlink" href="#gradientboostingregres
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.GradientBoostingRegressor.set_fit_request">
-<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">monitor</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.ensemble.GradientBoostingRegressor" title="sklearn.ensemble._gb.GradientBoostingRegressor"><span class="pre">GradientBoostingRegressor</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.GradientBoostingRegressor.set_fit_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">monitor</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.ensemble.GradientBoostingRegressor" title="sklearn.ensemble._gb.GradientBoostingRegressor"><span class="pre">GradientBoostingRegressor</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.GradientBoostingRegressor.set_fit_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1818,7 +1818,7 @@ <h1>GradientBoostingRegressor<a class="headerlink" href="#gradientboostingregres
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.GradientBoostingRegressor.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.GradientBoostingRegressor.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.GradientBoostingRegressor.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1842,7 +1842,7 @@ <h1>GradientBoostingRegressor<a class="headerlink" href="#gradientboostingregres
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.GradientBoostingRegressor.set_score_request">
-<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.ensemble.GradientBoostingRegressor" title="sklearn.ensemble._gb.GradientBoostingRegressor"><span class="pre">GradientBoostingRegressor</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.GradientBoostingRegressor.set_score_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.ensemble.GradientBoostingRegressor" title="sklearn.ensemble._gb.GradientBoostingRegressor"><span class="pre">GradientBoostingRegressor</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.GradientBoostingRegressor.set_score_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1885,7 +1885,7 @@ <h1>GradientBoostingRegressor<a class="headerlink" href="#gradientboostingregres
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.GradientBoostingRegressor.staged_predict">
-<span class="sig-name descname"><span class="pre">staged_predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_gb.py#L2154"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.GradientBoostingRegressor.staged_predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">staged_predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_gb.py#L2154"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.GradientBoostingRegressor.staged_predict" title="Link to this definition">#</a></dt>
 <dd><p>Predict regression target at each stage for X.</p>
 <p>This method allows monitoring (i.e. determine error on testing set)
 after each stage.</p>
diff --git a/dev/modules/generated/sklearn.ensemble.HistGradientBoostingClassifier.html b/dev/modules/generated/sklearn.ensemble.HistGradientBoostingClassifier.html
index b6110a5adf5ff..13fd8c162f94b 100644
--- a/dev/modules/generated/sklearn.ensemble.HistGradientBoostingClassifier.html
+++ b/dev/modules/generated/sklearn.ensemble.HistGradientBoostingClassifier.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>HistGradientBoostingClassifier<a class="headerlink" href="#histgradientboostingclassifier" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.ensemble.HistGradientBoostingClassifier">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.ensemble.</span></span><span class="sig-name descname"><span class="pre">HistGradientBoostingClassifier</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">loss</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'log_loss'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">learning_rate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_leaf_nodes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">31</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_depth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_samples_leaf</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">20</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">l2_regularization</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_bins</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">255</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">categorical_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'from_dtype'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">monotonic_cst</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">interaction_cst</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">warm_start</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">early_stopping</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scoring</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'loss'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validation_fraction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_iter_no_change</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-07</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">class_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py#L1807"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.HistGradientBoostingClassifier" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.ensemble.</span></span><span class="sig-name descname"><span class="pre">HistGradientBoostingClassifier</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">loss</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'log_loss'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">learning_rate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_leaf_nodes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">31</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_depth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_samples_leaf</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">20</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">l2_regularization</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_bins</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">255</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">categorical_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'from_dtype'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">monotonic_cst</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">interaction_cst</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">warm_start</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">early_stopping</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scoring</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'loss'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validation_fraction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_iter_no_change</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-07</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">class_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py#L1873"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.HistGradientBoostingClassifier" title="Link to this definition">#</a></dt>
 <dd><p>Histogram-based Gradient Boosting Classification Tree.</p>
 <p>This estimator is much faster than
 <a class="reference internal" href="http://github.com/sklearn.ensemble.GradientBoostingClassifier.html#sklearn.ensemble.GradientBoostingClassifier" title="sklearn.ensemble.GradientBoostingClassifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">GradientBoostingClassifier</span></code></a>
@@ -1451,8 +1451,8 @@ <h1>HistGradientBoostingClassifier<a class="headerlink" href="#histgradientboost
 See <a class="reference internal" href="http://github.com/glossary.html#term-warm_start"><span class="xref std std-term">the Glossary</span></a>.</p>
 </dd>
 <dt><strong>early_stopping</strong><span class="classifier">‘auto’ or bool, default=’auto’</span></dt><dd><p>If ‘auto’, early stopping is enabled if the sample size is larger than
-10000. If True, early stopping is enabled, otherwise early stopping is
-disabled.</p>
+10000 or if <code class="docutils literal notranslate"><span class="pre">X_val</span></code> and <code class="docutils literal notranslate"><span class="pre">y_val</span></code> are passed to <code class="docutils literal notranslate"><span class="pre">fit</span></code>. If True, early stopping
+is enabled, otherwise early stopping is disabled.</p>
 <div class="versionadded">
 <p><span class="versionmodified added">Added in version 0.23.</span></p>
 </div>
@@ -1469,7 +1469,9 @@ <h1>HistGradientBoostingClassifier<a class="headerlink" href="#histgradientboost
 </dd>
 <dt><strong>validation_fraction</strong><span class="classifier">int or float or None, default=0.1</span></dt><dd><p>Proportion (or absolute size) of training data to set aside as
 validation data for early stopping. If None, early stopping is done on
-the training data. Only used if early stopping is performed.</p>
+the training data.
+The value is ignored if either early stopping is not performed, e.g.
+<code class="docutils literal notranslate"><span class="pre">early_stopping=False</span></code>, or if <code class="docutils literal notranslate"><span class="pre">X_val</span></code> and <code class="docutils literal notranslate"><span class="pre">y_val</span></code> are passed to fit.</p>
 </dd>
 <dt><strong>n_iter_no_change</strong><span class="classifier">int, default=10</span></dt><dd><p>Used to determine when to “early stop”. The fitting process is
 stopped when none of the last <code class="docutils literal notranslate"><span class="pre">n_iter_no_change</span></code> scores are better
@@ -1568,7 +1570,7 @@ <h1>HistGradientBoostingClassifier<a class="headerlink" href="#histgradientboost
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.HistGradientBoostingClassifier.decision_function">
-<span class="sig-name descname"><span class="pre">decision_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py#L2229"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.HistGradientBoostingClassifier.decision_function" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">decision_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py#L2297"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.HistGradientBoostingClassifier.decision_function" title="Link to this definition">#</a></dt>
 <dd><p>Compute the decision function of <code class="docutils literal notranslate"><span class="pre">X</span></code>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1590,7 +1592,7 @@ <h1>HistGradientBoostingClassifier<a class="headerlink" href="#histgradientboost
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.HistGradientBoostingClassifier.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py#L510"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.HistGradientBoostingClassifier.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X_val</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_val</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight_val</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py#L510"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.HistGradientBoostingClassifier.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit the gradient boosting model.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1604,6 +1606,23 @@ <h1>HistGradientBoostingClassifier<a class="headerlink" href="#histgradientboost
 <p><span class="versionmodified added">Added in version 0.23.</span></p>
 </div>
 </dd>
+<dt><strong>X_val</strong><span class="classifier">array-like of shape (n_val, n_features)</span></dt><dd><p>Additional sample of features for validation used in early stopping.
+In a <code class="docutils literal notranslate"><span class="pre">Pipeline</span></code>, <code class="docutils literal notranslate"><span class="pre">X_val</span></code> can be transformed the same way as <code class="docutils literal notranslate"><span class="pre">X</span></code> with
+<code class="docutils literal notranslate"><span class="pre">Pipeline(...,</span> <span class="pre">transform_input=[&quot;X_val&quot;])</span></code>.</p>
+<div class="versionadded">
+<p><span class="versionmodified added">Added in version 1.7.</span></p>
+</div>
+</dd>
+<dt><strong>y_val</strong><span class="classifier">array-like of shape (n_samples,)</span></dt><dd><p>Additional sample of target values for validation used in early stopping.</p>
+<div class="versionadded">
+<p><span class="versionmodified added">Added in version 1.7.</span></p>
+</div>
+</dd>
+<dt><strong>sample_weight_val</strong><span class="classifier">array-like of shape (n_samples,) default=None</span></dt><dd><p>Additional weights for validation used in early stopping.</p>
+<div class="versionadded">
+<p><span class="versionmodified added">Added in version 1.7.</span></p>
+</div>
+</dd>
 </dl>
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
@@ -1617,7 +1636,7 @@ <h1>HistGradientBoostingClassifier<a class="headerlink" href="#histgradientboost
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.HistGradientBoostingClassifier.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.HistGradientBoostingClassifier.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.HistGradientBoostingClassifier.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1634,7 +1653,7 @@ <h1>HistGradientBoostingClassifier<a class="headerlink" href="#histgradientboost
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.HistGradientBoostingClassifier.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.HistGradientBoostingClassifier.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.HistGradientBoostingClassifier.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1655,7 +1674,7 @@ <h1>HistGradientBoostingClassifier<a class="headerlink" href="#histgradientboost
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.HistGradientBoostingClassifier.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py#L2144"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.HistGradientBoostingClassifier.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py#L2212"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.HistGradientBoostingClassifier.predict" title="Link to this definition">#</a></dt>
 <dd><p>Predict classes for X.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1675,7 +1694,7 @@ <h1>HistGradientBoostingClassifier<a class="headerlink" href="#histgradientboost
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.HistGradientBoostingClassifier.predict_proba">
-<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py#L2193"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.HistGradientBoostingClassifier.predict_proba" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py#L2261"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.HistGradientBoostingClassifier.predict_proba" title="Link to this definition">#</a></dt>
 <dd><p>Predict class probabilities for X.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1695,7 +1714,7 @@ <h1>HistGradientBoostingClassifier<a class="headerlink" href="#histgradientboost
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.HistGradientBoostingClassifier.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L546"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.HistGradientBoostingClassifier.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L487"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.HistGradientBoostingClassifier.score" title="Link to this definition">#</a></dt>
 <dd><p>Return <a class="reference internal" href="http://github.com/model_evaluation.html#accuracy-score"><span class="std std-ref">accuracy</span></a> on provided data and labels.</p>
 <p>In multi-label classification, this is the subset accuracy
 which is a harsh metric since you require for each sample that
@@ -1722,7 +1741,7 @@ <h1>HistGradientBoostingClassifier<a class="headerlink" href="#histgradientboost
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.HistGradientBoostingClassifier.set_fit_request">
-<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.ensemble.HistGradientBoostingClassifier" title="sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier"><span class="pre">HistGradientBoostingClassifier</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.HistGradientBoostingClassifier.set_fit_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X_val</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight_val</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_val</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.ensemble.HistGradientBoostingClassifier" title="sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier"><span class="pre">HistGradientBoostingClassifier</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.HistGradientBoostingClassifier.set_fit_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1750,8 +1769,14 @@ <h1>HistGradientBoostingClassifier<a class="headerlink" href="#histgradientboost
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><dl class="simple">
+<dt><strong>X_val</strong><span class="classifier">str, True, False, or None,                     default=sklearn.utils.metadata_routing.UNCHANGED</span></dt><dd><p>Metadata routing for <code class="docutils literal notranslate"><span class="pre">X_val</span></code> parameter in <code class="docutils literal notranslate"><span class="pre">fit</span></code>.</p>
+</dd>
 <dt><strong>sample_weight</strong><span class="classifier">str, True, False, or None,                     default=sklearn.utils.metadata_routing.UNCHANGED</span></dt><dd><p>Metadata routing for <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> parameter in <code class="docutils literal notranslate"><span class="pre">fit</span></code>.</p>
 </dd>
+<dt><strong>sample_weight_val</strong><span class="classifier">str, True, False, or None,                     default=sklearn.utils.metadata_routing.UNCHANGED</span></dt><dd><p>Metadata routing for <code class="docutils literal notranslate"><span class="pre">sample_weight_val</span></code> parameter in <code class="docutils literal notranslate"><span class="pre">fit</span></code>.</p>
+</dd>
+<dt><strong>y_val</strong><span class="classifier">str, True, False, or None,                     default=sklearn.utils.metadata_routing.UNCHANGED</span></dt><dd><p>Metadata routing for <code class="docutils literal notranslate"><span class="pre">y_val</span></code> parameter in <code class="docutils literal notranslate"><span class="pre">fit</span></code>.</p>
+</dd>
 </dl>
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
@@ -1765,7 +1790,7 @@ <h1>HistGradientBoostingClassifier<a class="headerlink" href="#histgradientboost
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.HistGradientBoostingClassifier.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.HistGradientBoostingClassifier.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.HistGradientBoostingClassifier.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1789,7 +1814,7 @@ <h1>HistGradientBoostingClassifier<a class="headerlink" href="#histgradientboost
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.HistGradientBoostingClassifier.set_score_request">
-<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.ensemble.HistGradientBoostingClassifier" title="sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier"><span class="pre">HistGradientBoostingClassifier</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.HistGradientBoostingClassifier.set_score_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.ensemble.HistGradientBoostingClassifier" title="sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier"><span class="pre">HistGradientBoostingClassifier</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.HistGradientBoostingClassifier.set_score_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1832,7 +1857,7 @@ <h1>HistGradientBoostingClassifier<a class="headerlink" href="#histgradientboost
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.HistGradientBoostingClassifier.staged_decision_function">
-<span class="sig-name descname"><span class="pre">staged_decision_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py#L2250"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.HistGradientBoostingClassifier.staged_decision_function" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">staged_decision_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py#L2318"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.HistGradientBoostingClassifier.staged_decision_function" title="Link to this definition">#</a></dt>
 <dd><p>Compute decision function of <code class="docutils literal notranslate"><span class="pre">X</span></code> for each iteration.</p>
 <p>This method allows monitoring (i.e. determine error on testing set)
 after each stage.</p>
@@ -1856,7 +1881,7 @@ <h1>HistGradientBoostingClassifier<a class="headerlink" href="#histgradientboost
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.HistGradientBoostingClassifier.staged_predict">
-<span class="sig-name descname"><span class="pre">staged_predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py#L2167"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.HistGradientBoostingClassifier.staged_predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">staged_predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py#L2235"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.HistGradientBoostingClassifier.staged_predict" title="Link to this definition">#</a></dt>
 <dd><p>Predict classes at each iteration.</p>
 <p>This method allows monitoring (i.e. determine error on testing set)
 after each stage.</p>
@@ -1881,7 +1906,7 @@ <h1>HistGradientBoostingClassifier<a class="headerlink" href="#histgradientboost
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.HistGradientBoostingClassifier.staged_predict_proba">
-<span class="sig-name descname"><span class="pre">staged_predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py#L2209"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.HistGradientBoostingClassifier.staged_predict_proba" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">staged_predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py#L2277"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.HistGradientBoostingClassifier.staged_predict_proba" title="Link to this definition">#</a></dt>
 <dd><p>Predict class probabilities at each iteration.</p>
 <p>This method allows monitoring (i.e. determine error on testing set)
 after each stage.</p>
diff --git a/dev/modules/generated/sklearn.ensemble.HistGradientBoostingRegressor.html b/dev/modules/generated/sklearn.ensemble.HistGradientBoostingRegressor.html
index 4970816b8043a..ecf5d99c5c993 100644
--- a/dev/modules/generated/sklearn.ensemble.HistGradientBoostingRegressor.html
+++ b/dev/modules/generated/sklearn.ensemble.HistGradientBoostingRegressor.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1308,7 +1308,7 @@
 <h1>HistGradientBoostingRegressor<a class="headerlink" href="#histgradientboostingregressor" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.ensemble.HistGradientBoostingRegressor">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.ensemble.</span></span><span class="sig-name descname"><span class="pre">HistGradientBoostingRegressor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">loss</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'squared_error'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quantile</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">learning_rate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_leaf_nodes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">31</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_depth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_samples_leaf</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">20</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">l2_regularization</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_bins</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">255</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">categorical_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'from_dtype'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">monotonic_cst</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">interaction_cst</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">warm_start</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">early_stopping</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scoring</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'loss'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validation_fraction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_iter_no_change</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-07</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py#L1409"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.HistGradientBoostingRegressor" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.ensemble.</span></span><span class="sig-name descname"><span class="pre">HistGradientBoostingRegressor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">loss</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'squared_error'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quantile</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">learning_rate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_leaf_nodes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">31</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_depth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_samples_leaf</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">20</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">l2_regularization</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_bins</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">255</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">categorical_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'from_dtype'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">monotonic_cst</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">interaction_cst</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">warm_start</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">early_stopping</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scoring</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'loss'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validation_fraction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_iter_no_change</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-07</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py#L1470"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.HistGradientBoostingRegressor" title="Link to this definition">#</a></dt>
 <dd><p>Histogram-based Gradient Boosting Regression Tree.</p>
 <p>This estimator is much faster than
 <a class="reference internal" href="http://github.com/sklearn.ensemble.GradientBoostingRegressor.html#sklearn.ensemble.GradientBoostingRegressor" title="sklearn.ensemble.GradientBoostingRegressor"><code class="xref py py-class docutils literal notranslate"><span class="pre">GradientBoostingRegressor</span></code></a>
@@ -1462,8 +1462,8 @@ <h1>HistGradientBoostingRegressor<a class="headerlink" href="#histgradientboosti
 See <a class="reference internal" href="http://github.com/glossary.html#term-warm_start"><span class="xref std std-term">the Glossary</span></a>.</p>
 </dd>
 <dt><strong>early_stopping</strong><span class="classifier">‘auto’ or bool, default=’auto’</span></dt><dd><p>If ‘auto’, early stopping is enabled if the sample size is larger than
-10000. If True, early stopping is enabled, otherwise early stopping is
-disabled.</p>
+10000 or if <code class="docutils literal notranslate"><span class="pre">X_val</span></code> and <code class="docutils literal notranslate"><span class="pre">y_val</span></code> are passed to <code class="docutils literal notranslate"><span class="pre">fit</span></code>. If True, early stopping
+is enabled, otherwise early stopping is disabled.</p>
 <div class="versionadded">
 <p><span class="versionmodified added">Added in version 0.23.</span></p>
 </div>
@@ -1481,7 +1481,9 @@ <h1>HistGradientBoostingRegressor<a class="headerlink" href="#histgradientboosti
 </dd>
 <dt><strong>validation_fraction</strong><span class="classifier">int or float or None, default=0.1</span></dt><dd><p>Proportion (or absolute size) of training data to set aside as
 validation data for early stopping. If None, early stopping is done on
-the training data. Only used if early stopping is performed.</p>
+the training data.
+The value is ignored if either early stopping is not performed, e.g.
+<code class="docutils literal notranslate"><span class="pre">early_stopping=False</span></code>, or if <code class="docutils literal notranslate"><span class="pre">X_val</span></code> and <code class="docutils literal notranslate"><span class="pre">y_val</span></code> are passed to fit.</p>
 </dd>
 <dt><strong>n_iter_no_change</strong><span class="classifier">int, default=10</span></dt><dd><p>Used to determine when to “early stop”. The fitting process is
 stopped when none of the last <code class="docutils literal notranslate"><span class="pre">n_iter_no_change</span></code> scores are better
@@ -1566,7 +1568,7 @@ <h1>HistGradientBoostingRegressor<a class="headerlink" href="#histgradientboosti
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.HistGradientBoostingRegressor.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py#L510"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.HistGradientBoostingRegressor.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X_val</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_val</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight_val</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py#L510"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.HistGradientBoostingRegressor.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit the gradient boosting model.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1580,6 +1582,23 @@ <h1>HistGradientBoostingRegressor<a class="headerlink" href="#histgradientboosti
 <p><span class="versionmodified added">Added in version 0.23.</span></p>
 </div>
 </dd>
+<dt><strong>X_val</strong><span class="classifier">array-like of shape (n_val, n_features)</span></dt><dd><p>Additional sample of features for validation used in early stopping.
+In a <code class="docutils literal notranslate"><span class="pre">Pipeline</span></code>, <code class="docutils literal notranslate"><span class="pre">X_val</span></code> can be transformed the same way as <code class="docutils literal notranslate"><span class="pre">X</span></code> with
+<code class="docutils literal notranslate"><span class="pre">Pipeline(...,</span> <span class="pre">transform_input=[&quot;X_val&quot;])</span></code>.</p>
+<div class="versionadded">
+<p><span class="versionmodified added">Added in version 1.7.</span></p>
+</div>
+</dd>
+<dt><strong>y_val</strong><span class="classifier">array-like of shape (n_samples,)</span></dt><dd><p>Additional sample of target values for validation used in early stopping.</p>
+<div class="versionadded">
+<p><span class="versionmodified added">Added in version 1.7.</span></p>
+</div>
+</dd>
+<dt><strong>sample_weight_val</strong><span class="classifier">array-like of shape (n_samples,) default=None</span></dt><dd><p>Additional weights for validation used in early stopping.</p>
+<div class="versionadded">
+<p><span class="versionmodified added">Added in version 1.7.</span></p>
+</div>
+</dd>
 </dl>
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
@@ -1593,7 +1612,7 @@ <h1>HistGradientBoostingRegressor<a class="headerlink" href="#histgradientboosti
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.HistGradientBoostingRegressor.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.HistGradientBoostingRegressor.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.HistGradientBoostingRegressor.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1610,7 +1629,7 @@ <h1>HistGradientBoostingRegressor<a class="headerlink" href="#histgradientboosti
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.HistGradientBoostingRegressor.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.HistGradientBoostingRegressor.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.HistGradientBoostingRegressor.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1631,7 +1650,7 @@ <h1>HistGradientBoostingRegressor<a class="headerlink" href="#histgradientboosti
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.HistGradientBoostingRegressor.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py#L1743"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.HistGradientBoostingRegressor.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py#L1806"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.HistGradientBoostingRegressor.predict" title="Link to this definition">#</a></dt>
 <dd><p>Predict values for X.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1651,7 +1670,7 @@ <h1>HistGradientBoostingRegressor<a class="headerlink" href="#histgradientboosti
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.HistGradientBoostingRegressor.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L619"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.HistGradientBoostingRegressor.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L560"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.HistGradientBoostingRegressor.score" title="Link to this definition">#</a></dt>
 <dd><p>Return <a class="reference internal" href="http://github.com/model_evaluation.html#r2-score"><span class="std std-ref">coefficient of determination</span></a> on test data.</p>
 <p>The coefficient of determination, <span class="math notranslate nohighlight">\(R^2\)</span>, is defined as
 <span class="math notranslate nohighlight">\((1 - \frac{u}{v})\)</span>, where <span class="math notranslate nohighlight">\(u\)</span> is the residual
@@ -1693,7 +1712,7 @@ <h1>HistGradientBoostingRegressor<a class="headerlink" href="#histgradientboosti
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.HistGradientBoostingRegressor.set_fit_request">
-<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.ensemble.HistGradientBoostingRegressor" title="sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingRegressor"><span class="pre">HistGradientBoostingRegressor</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.HistGradientBoostingRegressor.set_fit_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X_val</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight_val</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_val</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.ensemble.HistGradientBoostingRegressor" title="sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingRegressor"><span class="pre">HistGradientBoostingRegressor</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.HistGradientBoostingRegressor.set_fit_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1721,8 +1740,14 @@ <h1>HistGradientBoostingRegressor<a class="headerlink" href="#histgradientboosti
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><dl class="simple">
+<dt><strong>X_val</strong><span class="classifier">str, True, False, or None,                     default=sklearn.utils.metadata_routing.UNCHANGED</span></dt><dd><p>Metadata routing for <code class="docutils literal notranslate"><span class="pre">X_val</span></code> parameter in <code class="docutils literal notranslate"><span class="pre">fit</span></code>.</p>
+</dd>
 <dt><strong>sample_weight</strong><span class="classifier">str, True, False, or None,                     default=sklearn.utils.metadata_routing.UNCHANGED</span></dt><dd><p>Metadata routing for <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> parameter in <code class="docutils literal notranslate"><span class="pre">fit</span></code>.</p>
 </dd>
+<dt><strong>sample_weight_val</strong><span class="classifier">str, True, False, or None,                     default=sklearn.utils.metadata_routing.UNCHANGED</span></dt><dd><p>Metadata routing for <code class="docutils literal notranslate"><span class="pre">sample_weight_val</span></code> parameter in <code class="docutils literal notranslate"><span class="pre">fit</span></code>.</p>
+</dd>
+<dt><strong>y_val</strong><span class="classifier">str, True, False, or None,                     default=sklearn.utils.metadata_routing.UNCHANGED</span></dt><dd><p>Metadata routing for <code class="docutils literal notranslate"><span class="pre">y_val</span></code> parameter in <code class="docutils literal notranslate"><span class="pre">fit</span></code>.</p>
+</dd>
 </dl>
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
@@ -1736,7 +1761,7 @@ <h1>HistGradientBoostingRegressor<a class="headerlink" href="#histgradientboosti
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.HistGradientBoostingRegressor.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.HistGradientBoostingRegressor.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.HistGradientBoostingRegressor.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1760,7 +1785,7 @@ <h1>HistGradientBoostingRegressor<a class="headerlink" href="#histgradientboosti
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.HistGradientBoostingRegressor.set_score_request">
-<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.ensemble.HistGradientBoostingRegressor" title="sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingRegressor"><span class="pre">HistGradientBoostingRegressor</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.HistGradientBoostingRegressor.set_score_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.ensemble.HistGradientBoostingRegressor" title="sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingRegressor"><span class="pre">HistGradientBoostingRegressor</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.HistGradientBoostingRegressor.set_score_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1803,7 +1828,7 @@ <h1>HistGradientBoostingRegressor<a class="headerlink" href="#histgradientboosti
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.HistGradientBoostingRegressor.staged_predict">
-<span class="sig-name descname"><span class="pre">staged_predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py#L1761"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.HistGradientBoostingRegressor.staged_predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">staged_predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py#L1824"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.HistGradientBoostingRegressor.staged_predict" title="Link to this definition">#</a></dt>
 <dd><p>Predict regression target for each iteration.</p>
 <p>This method allows monitoring (i.e. determine error on testing set)
 after each stage.</p>
diff --git a/dev/modules/generated/sklearn.ensemble.IsolationForest.html b/dev/modules/generated/sklearn.ensemble.IsolationForest.html
index 6b5a8b51b810d..5195a283b0b11 100644
--- a/dev/modules/generated/sklearn.ensemble.IsolationForest.html
+++ b/dev/modules/generated/sklearn.ensemble.IsolationForest.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1308,7 +1308,7 @@
 <h1>IsolationForest<a class="headerlink" href="#isolationforest" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.ensemble.IsolationForest">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.ensemble.</span></span><span class="sig-name descname"><span class="pre">IsolationForest</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_estimators</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">contamination</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bootstrap</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">warm_start</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_iforest.py#L54"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.IsolationForest" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.ensemble.</span></span><span class="sig-name descname"><span class="pre">IsolationForest</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_estimators</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">contamination</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bootstrap</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">warm_start</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_iforest.py#L54"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.IsolationForest" title="Link to this definition">#</a></dt>
 <dd><p>Isolation Forest Algorithm.</p>
 <p>Return the anomaly score of each sample using the IsolationForest algorithm</p>
 <p>The IsolationForest ‘isolates’ observations by randomly selecting a feature
@@ -1366,7 +1366,7 @@ <h1>IsolationForest<a class="headerlink" href="#isolationforest" title="Link to
 is performed.</p>
 </dd>
 <dt><strong>n_jobs</strong><span class="classifier">int, default=None</span></dt><dd><p>The number of jobs to run in parallel for <a class="reference internal" href="#sklearn.ensemble.IsolationForest.fit" title="sklearn.ensemble.IsolationForest.fit"><code class="xref py py-meth docutils literal notranslate"><span class="pre">fit</span></code></a>. <code class="docutils literal notranslate"><span class="pre">None</span></code> means 1
-unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.5.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context. <code class="docutils literal notranslate"><span class="pre">-1</span></code> means using
+unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.6.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context. <code class="docutils literal notranslate"><span class="pre">-1</span></code> means using
 all processors. See <a class="reference internal" href="http://github.com/glossary.html#term-n_jobs"><span class="xref std std-term">Glossary</span></a> for more details.</p>
 </dd>
 <dt><strong>random_state</strong><span class="classifier">int, RandomState instance or None, default=None</span></dt><dd><p>Controls the pseudo-randomness of the selection of the feature
@@ -1469,7 +1469,7 @@ <h1>IsolationForest<a class="headerlink" href="#isolationforest" title="Link to
 <a class="reference internal" href="http://github.com/auto_examples/ensemble/plot_isolation_forest.html#sphx-glr-auto-examples-ensemble-plot-isolation-forest-py"><span class="std std-ref">IsolationForest example</span></a>.</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.IsolationForest.decision_function">
-<span class="sig-name descname"><span class="pre">decision_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_iforest.py#L425"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.IsolationForest.decision_function" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">decision_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_iforest.py#L425"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.IsolationForest.decision_function" title="Link to this definition">#</a></dt>
 <dd><p>Average anomaly score of X of the base classifiers.</p>
 <p>The anomaly score of an input sample is computed as
 the mean anomaly score of the trees in the forest.</p>
@@ -1516,7 +1516,7 @@ <h1>IsolationForest<a class="headerlink" href="#isolationforest" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.IsolationForest.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_iforest.py#L294"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.IsolationForest.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_iforest.py#L294"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.IsolationForest.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1542,7 +1542,7 @@ <h1>IsolationForest<a class="headerlink" href="#isolationforest" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.IsolationForest.fit_predict">
-<span class="sig-name descname"><span class="pre">fit_predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L1096"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.IsolationForest.fit_predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L1037"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.IsolationForest.fit_predict" title="Link to this definition">#</a></dt>
 <dd><p>Perform fit on X and returns labels for X.</p>
 <p>Returns -1 for outliers and 1 for inliers.</p>
 <dl class="field-list">
@@ -1570,7 +1570,7 @@ <h1>IsolationForest<a class="headerlink" href="#isolationforest" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.IsolationForest.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_bagging.py#L614"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.IsolationForest.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_bagging.py#L618"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.IsolationForest.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1590,7 +1590,7 @@ <h1>IsolationForest<a class="headerlink" href="#isolationforest" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.IsolationForest.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.IsolationForest.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.IsolationForest.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1611,7 +1611,7 @@ <h1>IsolationForest<a class="headerlink" href="#isolationforest" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.IsolationForest.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_iforest.py#L385"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.IsolationForest.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_iforest.py#L385"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.IsolationForest.predict" title="Link to this definition">#</a></dt>
 <dd><p>Predict if a particular sample is an outlier or not.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1648,7 +1648,7 @@ <h1>IsolationForest<a class="headerlink" href="#isolationforest" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.IsolationForest.score_samples">
-<span class="sig-name descname"><span class="pre">score_samples</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_iforest.py#L476"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.IsolationForest.score_samples" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score_samples</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_iforest.py#L476"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.IsolationForest.score_samples" title="Link to this definition">#</a></dt>
 <dd><p>Opposite of the anomaly score defined in the original paper.</p>
 <p>The anomaly score of an input sample is computed as
 the mean anomaly score of the trees in the forest.</p>
@@ -1691,7 +1691,7 @@ <h1>IsolationForest<a class="headerlink" href="#isolationforest" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.IsolationForest.set_fit_request">
-<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.ensemble.IsolationForest" title="sklearn.ensemble._iforest.IsolationForest"><span class="pre">IsolationForest</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.IsolationForest.set_fit_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.ensemble.IsolationForest" title="sklearn.ensemble._iforest.IsolationForest"><span class="pre">IsolationForest</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.IsolationForest.set_fit_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1734,7 +1734,7 @@ <h1>IsolationForest<a class="headerlink" href="#isolationforest" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.IsolationForest.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.IsolationForest.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.IsolationForest.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
diff --git a/dev/modules/generated/sklearn.ensemble.RandomForestClassifier.html b/dev/modules/generated/sklearn.ensemble.RandomForestClassifier.html
index 4ed0a386d2dd6..6e42f21677183 100644
--- a/dev/modules/generated/sklearn.ensemble.RandomForestClassifier.html
+++ b/dev/modules/generated/sklearn.ensemble.RandomForestClassifier.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>RandomForestClassifier<a class="headerlink" href="#randomforestclassifier" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.ensemble.RandomForestClassifier">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.ensemble.</span></span><span class="sig-name descname"><span class="pre">RandomForestClassifier</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_estimators</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">criterion</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'gini'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_depth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_samples_split</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_samples_leaf</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_weight_fraction_leaf</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'sqrt'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_leaf_nodes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_impurity_decrease</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bootstrap</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">oob_score</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">warm_start</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">class_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ccp_alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">monotonic_cst</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_forest.py#L1175"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.RandomForestClassifier" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.ensemble.</span></span><span class="sig-name descname"><span class="pre">RandomForestClassifier</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_estimators</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">criterion</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'gini'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_depth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_samples_split</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_samples_leaf</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_weight_fraction_leaf</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'sqrt'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_leaf_nodes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_impurity_decrease</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bootstrap</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">oob_score</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">warm_start</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">class_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ccp_alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">monotonic_cst</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_forest.py#L1174"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.RandomForestClassifier" title="Link to this definition">#</a></dt>
 <dd><p>A random forest classifier.</p>
 <p>A random forest is a meta estimator that fits a number of decision tree
 classifiers on various sub-samples of the dataset and uses averaging to
@@ -1421,7 +1421,7 @@ <h1>RandomForestClassifier<a class="headerlink" href="#randomforestclassifier" t
 </dd>
 <dt><strong>n_jobs</strong><span class="classifier">int, default=None</span></dt><dd><p>The number of jobs to run in parallel. <a class="reference internal" href="#sklearn.ensemble.RandomForestClassifier.fit" title="sklearn.ensemble.RandomForestClassifier.fit"><code class="xref py py-meth docutils literal notranslate"><span class="pre">fit</span></code></a>, <a class="reference internal" href="#sklearn.ensemble.RandomForestClassifier.predict" title="sklearn.ensemble.RandomForestClassifier.predict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">predict</span></code></a>,
 <a class="reference internal" href="#sklearn.ensemble.RandomForestClassifier.decision_path" title="sklearn.ensemble.RandomForestClassifier.decision_path"><code class="xref py py-meth docutils literal notranslate"><span class="pre">decision_path</span></code></a> and <a class="reference internal" href="#sklearn.ensemble.RandomForestClassifier.apply" title="sklearn.ensemble.RandomForestClassifier.apply"><code class="xref py py-meth docutils literal notranslate"><span class="pre">apply</span></code></a> are all parallelized over the
-trees. <code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.5.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a>
+trees. <code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.6.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a>
 context. <code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all processors. See <a class="reference internal" href="http://github.com/glossary.html#term-n_jobs"><span class="xref std std-term">Glossary</span></a> for more details.</p>
 </dd>
 <dt><strong>random_state</strong><span class="classifier">int, RandomState instance or None, default=None</span></dt><dd><p>Controls both the randomness of the bootstrapping of the samples used
@@ -1595,7 +1595,7 @@ <h1>RandomForestClassifier<a class="headerlink" href="#randomforestclassifier" t
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.RandomForestClassifier.apply">
-<span class="sig-name descname"><span class="pre">apply</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_forest.py#L262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.RandomForestClassifier.apply" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">apply</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_forest.py#L261"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.RandomForestClassifier.apply" title="Link to this definition">#</a></dt>
 <dd><p>Apply trees in the forest to X, return leaf indices.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1618,7 +1618,7 @@ <h1>RandomForestClassifier<a class="headerlink" href="#randomforestclassifier" t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.RandomForestClassifier.decision_path">
-<span class="sig-name descname"><span class="pre">decision_path</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_forest.py#L288"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.RandomForestClassifier.decision_path" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">decision_path</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_forest.py#L287"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.RandomForestClassifier.decision_path" title="Link to this definition">#</a></dt>
 <dd><p>Return the decision path in the forest.</p>
 <div class="versionadded">
 <p><span class="versionmodified added">Added in version 0.18.</span></p>
@@ -1648,7 +1648,7 @@ <h1>RandomForestClassifier<a class="headerlink" href="#randomforestclassifier" t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.RandomForestClassifier.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_forest.py#L328"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.RandomForestClassifier.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_forest.py#L327"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.RandomForestClassifier.fit" title="Link to this definition">#</a></dt>
 <dd><p>Build a forest of trees from the training set (X, y).</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1679,7 +1679,7 @@ <h1>RandomForestClassifier<a class="headerlink" href="#randomforestclassifier" t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.RandomForestClassifier.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.RandomForestClassifier.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.RandomForestClassifier.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1696,7 +1696,7 @@ <h1>RandomForestClassifier<a class="headerlink" href="#randomforestclassifier" t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.RandomForestClassifier.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.RandomForestClassifier.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.RandomForestClassifier.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1717,7 +1717,7 @@ <h1>RandomForestClassifier<a class="headerlink" href="#randomforestclassifier" t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.RandomForestClassifier.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_forest.py#L883"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.RandomForestClassifier.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_forest.py#L882"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.RandomForestClassifier.predict" title="Link to this definition">#</a></dt>
 <dd><p>Predict class for X.</p>
 <p>The predicted class of an input sample is a vote by the trees in
 the forest, weighted by their probability estimates. That is,
@@ -1743,7 +1743,7 @@ <h1>RandomForestClassifier<a class="headerlink" href="#randomforestclassifier" t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.RandomForestClassifier.predict_log_proba">
-<span class="sig-name descname"><span class="pre">predict_log_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_forest.py#L970"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.RandomForestClassifier.predict_log_proba" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict_log_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_forest.py#L969"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.RandomForestClassifier.predict_log_proba" title="Link to this definition">#</a></dt>
 <dd><p>Predict class log-probabilities for X.</p>
 <p>The predicted class log-probabilities of an input sample is computed as
 the log of the mean predicted class probabilities of the trees in the
@@ -1769,7 +1769,7 @@ <h1>RandomForestClassifier<a class="headerlink" href="#randomforestclassifier" t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.RandomForestClassifier.predict_proba">
-<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_forest.py#L922"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.RandomForestClassifier.predict_proba" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_forest.py#L921"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.RandomForestClassifier.predict_proba" title="Link to this definition">#</a></dt>
 <dd><p>Predict class probabilities for X.</p>
 <p>The predicted class probabilities of an input sample are computed as
 the mean predicted class probabilities of the trees in the forest.
@@ -1796,7 +1796,7 @@ <h1>RandomForestClassifier<a class="headerlink" href="#randomforestclassifier" t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.RandomForestClassifier.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L546"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.RandomForestClassifier.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L487"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.RandomForestClassifier.score" title="Link to this definition">#</a></dt>
 <dd><p>Return <a class="reference internal" href="http://github.com/model_evaluation.html#accuracy-score"><span class="std std-ref">accuracy</span></a> on provided data and labels.</p>
 <p>In multi-label classification, this is the subset accuracy
 which is a harsh metric since you require for each sample that
@@ -1823,7 +1823,7 @@ <h1>RandomForestClassifier<a class="headerlink" href="#randomforestclassifier" t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.RandomForestClassifier.set_fit_request">
-<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.ensemble.RandomForestClassifier" title="sklearn.ensemble._forest.RandomForestClassifier"><span class="pre">RandomForestClassifier</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.RandomForestClassifier.set_fit_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.ensemble.RandomForestClassifier" title="sklearn.ensemble._forest.RandomForestClassifier"><span class="pre">RandomForestClassifier</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.RandomForestClassifier.set_fit_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1866,7 +1866,7 @@ <h1>RandomForestClassifier<a class="headerlink" href="#randomforestclassifier" t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.RandomForestClassifier.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.RandomForestClassifier.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.RandomForestClassifier.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1890,7 +1890,7 @@ <h1>RandomForestClassifier<a class="headerlink" href="#randomforestclassifier" t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.RandomForestClassifier.set_score_request">
-<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.ensemble.RandomForestClassifier" title="sklearn.ensemble._forest.RandomForestClassifier"><span class="pre">RandomForestClassifier</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.RandomForestClassifier.set_score_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.ensemble.RandomForestClassifier" title="sklearn.ensemble._forest.RandomForestClassifier"><span class="pre">RandomForestClassifier</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.RandomForestClassifier.set_score_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1962,9 +1962,6 @@ <h2>Gallery examples<a class="headerlink" href="#gallery-examples" title="Link t
 </div><div class="sphx-glr-thumbcontainer" tooltip="Plot the decision surfaces of forests of randomized trees trained on pairs of features of the iris dataset."><img alt="" src="http://github.com/_images/sphx_glr_plot_forest_iris_thumb.png" />
 <p><a class="reference internal" href="http://github.com/auto_examples/ensemble/plot_forest_iris.html#sphx-glr-auto-examples-ensemble-plot-forest-iris-py"><span class="std std-ref">Plot the decision surfaces of ensembles of trees on the iris dataset</span></a></p>
   <div class="sphx-glr-thumbnail-title">Plot the decision surfaces of ensembles of trees on the iris dataset</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="Plot the class probabilities of the first sample in a toy dataset predicted by three different classifiers and averaged by the VotingClassifier."><img alt="" src="http://github.com/_images/sphx_glr_plot_voting_probas_thumb.png" />
-<p><a class="reference internal" href="http://github.com/auto_examples/ensemble/plot_voting_probas.html#sphx-glr-auto-examples-ensemble-plot-voting-probas-py"><span class="std std-ref">Plot class probabilities calculated by the VotingClassifier</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Plot class probabilities calculated by the VotingClassifier</div>
 </div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we will compare the impurity-based feature importance of RandomForestClassifier with the permutation importance on the titanic dataset using permutation_importance. We will show that the impurity-based feature importance can inflate the importance of numerical features."><img alt="" src="http://github.com/_images/sphx_glr_plot_permutation_importance_thumb.png" />
 <p><a class="reference internal" href="http://github.com/auto_examples/inspection/plot_permutation_importance.html#sphx-glr-auto-examples-inspection-plot-permutation-importance-py"><span class="std std-ref">Permutation Importance vs Random Forest Feature Importance (MDI)</span></a></p>
   <div class="sphx-glr-thumbnail-title">Permutation Importance vs Random Forest Feature Importance (MDI)</div>
diff --git a/dev/modules/generated/sklearn.ensemble.RandomForestRegressor.html b/dev/modules/generated/sklearn.ensemble.RandomForestRegressor.html
index 3b720ce098545..2748516023251 100644
--- a/dev/modules/generated/sklearn.ensemble.RandomForestRegressor.html
+++ b/dev/modules/generated/sklearn.ensemble.RandomForestRegressor.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1308,7 +1308,7 @@
 <h1>RandomForestRegressor<a class="headerlink" href="#randomforestregressor" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.ensemble.RandomForestRegressor">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.ensemble.</span></span><span class="sig-name descname"><span class="pre">RandomForestRegressor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_estimators</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">criterion</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'squared_error'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_depth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_samples_split</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_samples_leaf</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_weight_fraction_leaf</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_leaf_nodes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_impurity_decrease</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bootstrap</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">oob_score</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">warm_start</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ccp_alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">monotonic_cst</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_forest.py#L1569"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.RandomForestRegressor" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.ensemble.</span></span><span class="sig-name descname"><span class="pre">RandomForestRegressor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_estimators</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">criterion</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'squared_error'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_depth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_samples_split</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_samples_leaf</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_weight_fraction_leaf</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_leaf_nodes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_impurity_decrease</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bootstrap</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">oob_score</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">warm_start</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ccp_alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">monotonic_cst</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_forest.py#L1568"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.RandomForestRegressor" title="Link to this definition">#</a></dt>
 <dd><p>A random forest regressor.</p>
 <p>A random forest is a meta estimator that fits a number of decision tree
 regressors on various sub-samples of the dataset and uses averaging to
@@ -1439,7 +1439,7 @@ <h1>RandomForestRegressor<a class="headerlink" href="#randomforestregressor" tit
 </dd>
 <dt><strong>n_jobs</strong><span class="classifier">int, default=None</span></dt><dd><p>The number of jobs to run in parallel. <a class="reference internal" href="#sklearn.ensemble.RandomForestRegressor.fit" title="sklearn.ensemble.RandomForestRegressor.fit"><code class="xref py py-meth docutils literal notranslate"><span class="pre">fit</span></code></a>, <a class="reference internal" href="#sklearn.ensemble.RandomForestRegressor.predict" title="sklearn.ensemble.RandomForestRegressor.predict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">predict</span></code></a>,
 <a class="reference internal" href="#sklearn.ensemble.RandomForestRegressor.decision_path" title="sklearn.ensemble.RandomForestRegressor.decision_path"><code class="xref py py-meth docutils literal notranslate"><span class="pre">decision_path</span></code></a> and <a class="reference internal" href="#sklearn.ensemble.RandomForestRegressor.apply" title="sklearn.ensemble.RandomForestRegressor.apply"><code class="xref py py-meth docutils literal notranslate"><span class="pre">apply</span></code></a> are all parallelized over the
-trees. <code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.5.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a>
+trees. <code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.6.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a>
 context. <code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all processors. See <a class="reference internal" href="http://github.com/glossary.html#term-n_jobs"><span class="xref std std-term">Glossary</span></a> for more details.</p>
 </dd>
 <dt><strong>random_state</strong><span class="classifier">int, RandomState instance or None, default=None</span></dt><dd><p>Controls both the randomness of the bootstrapping of the samples used
@@ -1590,7 +1590,7 @@ <h1>RandomForestRegressor<a class="headerlink" href="#randomforestregressor" tit
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.RandomForestRegressor.apply">
-<span class="sig-name descname"><span class="pre">apply</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_forest.py#L262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.RandomForestRegressor.apply" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">apply</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_forest.py#L261"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.RandomForestRegressor.apply" title="Link to this definition">#</a></dt>
 <dd><p>Apply trees in the forest to X, return leaf indices.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1613,7 +1613,7 @@ <h1>RandomForestRegressor<a class="headerlink" href="#randomforestregressor" tit
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.RandomForestRegressor.decision_path">
-<span class="sig-name descname"><span class="pre">decision_path</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_forest.py#L288"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.RandomForestRegressor.decision_path" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">decision_path</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_forest.py#L287"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.RandomForestRegressor.decision_path" title="Link to this definition">#</a></dt>
 <dd><p>Return the decision path in the forest.</p>
 <div class="versionadded">
 <p><span class="versionmodified added">Added in version 0.18.</span></p>
@@ -1643,7 +1643,7 @@ <h1>RandomForestRegressor<a class="headerlink" href="#randomforestregressor" tit
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.RandomForestRegressor.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_forest.py#L328"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.RandomForestRegressor.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_forest.py#L327"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.RandomForestRegressor.fit" title="Link to this definition">#</a></dt>
 <dd><p>Build a forest of trees from the training set (X, y).</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1674,7 +1674,7 @@ <h1>RandomForestRegressor<a class="headerlink" href="#randomforestregressor" tit
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.RandomForestRegressor.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.RandomForestRegressor.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.RandomForestRegressor.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1691,7 +1691,7 @@ <h1>RandomForestRegressor<a class="headerlink" href="#randomforestregressor" tit
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.RandomForestRegressor.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.RandomForestRegressor.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.RandomForestRegressor.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1712,7 +1712,7 @@ <h1>RandomForestRegressor<a class="headerlink" href="#randomforestregressor" tit
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.RandomForestRegressor.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_forest.py#L1045"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.RandomForestRegressor.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_forest.py#L1044"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.RandomForestRegressor.predict" title="Link to this definition">#</a></dt>
 <dd><p>Predict regression target for X.</p>
 <p>The predicted regression target of an input sample is computed as the
 mean predicted regression targets of the trees in the forest.</p>
@@ -1736,7 +1736,7 @@ <h1>RandomForestRegressor<a class="headerlink" href="#randomforestregressor" tit
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.RandomForestRegressor.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L619"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.RandomForestRegressor.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L560"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.RandomForestRegressor.score" title="Link to this definition">#</a></dt>
 <dd><p>Return <a class="reference internal" href="http://github.com/model_evaluation.html#r2-score"><span class="std std-ref">coefficient of determination</span></a> on test data.</p>
 <p>The coefficient of determination, <span class="math notranslate nohighlight">\(R^2\)</span>, is defined as
 <span class="math notranslate nohighlight">\((1 - \frac{u}{v})\)</span>, where <span class="math notranslate nohighlight">\(u\)</span> is the residual
@@ -1778,7 +1778,7 @@ <h1>RandomForestRegressor<a class="headerlink" href="#randomforestregressor" tit
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.RandomForestRegressor.set_fit_request">
-<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.ensemble.RandomForestRegressor" title="sklearn.ensemble._forest.RandomForestRegressor"><span class="pre">RandomForestRegressor</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.RandomForestRegressor.set_fit_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.ensemble.RandomForestRegressor" title="sklearn.ensemble._forest.RandomForestRegressor"><span class="pre">RandomForestRegressor</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.RandomForestRegressor.set_fit_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1821,7 +1821,7 @@ <h1>RandomForestRegressor<a class="headerlink" href="#randomforestregressor" tit
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.RandomForestRegressor.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.RandomForestRegressor.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.RandomForestRegressor.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1845,7 +1845,7 @@ <h1>RandomForestRegressor<a class="headerlink" href="#randomforestregressor" tit
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.RandomForestRegressor.set_score_request">
-<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.ensemble.RandomForestRegressor" title="sklearn.ensemble._forest.RandomForestRegressor"><span class="pre">RandomForestRegressor</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.RandomForestRegressor.set_score_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.ensemble.RandomForestRegressor" title="sklearn.ensemble._forest.RandomForestRegressor"><span class="pre">RandomForestRegressor</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.RandomForestRegressor.set_score_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
diff --git a/dev/modules/generated/sklearn.ensemble.RandomTreesEmbedding.html b/dev/modules/generated/sklearn.ensemble.RandomTreesEmbedding.html
index 8ea658a0a484b..0074c1de5f5d5 100644
--- a/dev/modules/generated/sklearn.ensemble.RandomTreesEmbedding.html
+++ b/dev/modules/generated/sklearn.ensemble.RandomTreesEmbedding.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>RandomTreesEmbedding<a class="headerlink" href="#randomtreesembedding" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.ensemble.RandomTreesEmbedding">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.ensemble.</span></span><span class="sig-name descname"><span class="pre">RandomTreesEmbedding</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_estimators</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_depth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_samples_split</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_samples_leaf</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_weight_fraction_leaf</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_leaf_nodes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_impurity_decrease</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sparse_output</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">warm_start</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_forest.py#L2666"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.RandomTreesEmbedding" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.ensemble.</span></span><span class="sig-name descname"><span class="pre">RandomTreesEmbedding</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_estimators</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_depth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_samples_split</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_samples_leaf</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_weight_fraction_leaf</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_leaf_nodes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_impurity_decrease</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sparse_output</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">warm_start</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_forest.py#L2665"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.RandomTreesEmbedding" title="Link to this definition">#</a></dt>
 <dd><p>An ensemble of totally random trees.</p>
 <p>An unsupervised transformation of a dataset to a high-dimensional
 sparse representation. A datapoint is coded according to which leaf of
@@ -1389,7 +1389,7 @@ <h1>RandomTreesEmbedding<a class="headerlink" href="#randomtreesembedding" title
 </dd>
 <dt><strong>n_jobs</strong><span class="classifier">int, default=None</span></dt><dd><p>The number of jobs to run in parallel. <a class="reference internal" href="#sklearn.ensemble.RandomTreesEmbedding.fit" title="sklearn.ensemble.RandomTreesEmbedding.fit"><code class="xref py py-meth docutils literal notranslate"><span class="pre">fit</span></code></a>, <a class="reference internal" href="#sklearn.ensemble.RandomTreesEmbedding.transform" title="sklearn.ensemble.RandomTreesEmbedding.transform"><code class="xref py py-meth docutils literal notranslate"><span class="pre">transform</span></code></a>,
 <a class="reference internal" href="#sklearn.ensemble.RandomTreesEmbedding.decision_path" title="sklearn.ensemble.RandomTreesEmbedding.decision_path"><code class="xref py py-meth docutils literal notranslate"><span class="pre">decision_path</span></code></a> and <a class="reference internal" href="#sklearn.ensemble.RandomTreesEmbedding.apply" title="sklearn.ensemble.RandomTreesEmbedding.apply"><code class="xref py py-meth docutils literal notranslate"><span class="pre">apply</span></code></a> are all parallelized over the
-trees. <code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.5.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a>
+trees. <code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.6.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a>
 context. <code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all processors. See <a class="reference internal" href="http://github.com/glossary.html#term-n_jobs"><span class="xref std std-term">Glossary</span></a> for more details.</p>
 </dd>
 <dt><strong>random_state</strong><span class="classifier">int, RandomState instance or None, default=None</span></dt><dd><p>Controls the generation of the random <code class="docutils literal notranslate"><span class="pre">y</span></code> used to fit the trees
@@ -1484,7 +1484,7 @@ <h1>RandomTreesEmbedding<a class="headerlink" href="#randomtreesembedding" title
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.RandomTreesEmbedding.apply">
-<span class="sig-name descname"><span class="pre">apply</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_forest.py#L262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.RandomTreesEmbedding.apply" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">apply</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_forest.py#L261"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.RandomTreesEmbedding.apply" title="Link to this definition">#</a></dt>
 <dd><p>Apply trees in the forest to X, return leaf indices.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1507,7 +1507,7 @@ <h1>RandomTreesEmbedding<a class="headerlink" href="#randomtreesembedding" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.RandomTreesEmbedding.decision_path">
-<span class="sig-name descname"><span class="pre">decision_path</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_forest.py#L288"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.RandomTreesEmbedding.decision_path" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">decision_path</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_forest.py#L287"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.RandomTreesEmbedding.decision_path" title="Link to this definition">#</a></dt>
 <dd><p>Return the decision path in the forest.</p>
 <div class="versionadded">
 <p><span class="versionmodified added">Added in version 0.18.</span></p>
@@ -1537,7 +1537,7 @@ <h1>RandomTreesEmbedding<a class="headerlink" href="#randomtreesembedding" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.RandomTreesEmbedding.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_forest.py#L2915"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.RandomTreesEmbedding.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_forest.py#L2914"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.RandomTreesEmbedding.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1567,7 +1567,7 @@ <h1>RandomTreesEmbedding<a class="headerlink" href="#randomtreesembedding" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.RandomTreesEmbedding.fit_transform">
-<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_forest.py#L2945"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.RandomTreesEmbedding.fit_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_forest.py#L2944"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.RandomTreesEmbedding.fit_transform" title="Link to this definition">#</a></dt>
 <dd><p>Fit estimator and transform dataset.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1596,7 +1596,7 @@ <h1>RandomTreesEmbedding<a class="headerlink" href="#randomtreesembedding" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.RandomTreesEmbedding.get_feature_names_out">
-<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_forest.py#L2980"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.RandomTreesEmbedding.get_feature_names_out" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_forest.py#L2979"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.RandomTreesEmbedding.get_feature_names_out" title="Link to this definition">#</a></dt>
 <dd><p>Get output feature names for transformation.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1623,7 +1623,7 @@ <h1>RandomTreesEmbedding<a class="headerlink" href="#randomtreesembedding" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.RandomTreesEmbedding.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.RandomTreesEmbedding.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.RandomTreesEmbedding.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1640,7 +1640,7 @@ <h1>RandomTreesEmbedding<a class="headerlink" href="#randomtreesembedding" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.RandomTreesEmbedding.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.RandomTreesEmbedding.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.RandomTreesEmbedding.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1661,7 +1661,7 @@ <h1>RandomTreesEmbedding<a class="headerlink" href="#randomtreesembedding" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.RandomTreesEmbedding.set_fit_request">
-<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.ensemble.RandomTreesEmbedding" title="sklearn.ensemble._forest.RandomTreesEmbedding"><span class="pre">RandomTreesEmbedding</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.RandomTreesEmbedding.set_fit_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.ensemble.RandomTreesEmbedding" title="sklearn.ensemble._forest.RandomTreesEmbedding"><span class="pre">RandomTreesEmbedding</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.RandomTreesEmbedding.set_fit_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1704,7 +1704,7 @@ <h1>RandomTreesEmbedding<a class="headerlink" href="#randomtreesembedding" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.RandomTreesEmbedding.set_output">
-<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.RandomTreesEmbedding.set_output" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.RandomTreesEmbedding.set_output" title="Link to this definition">#</a></dt>
 <dd><p>Set output container.</p>
 <p>See <a class="reference internal" href="http://github.com/auto_examples/miscellaneous/plot_set_output.html#sphx-glr-auto-examples-miscellaneous-plot-set-output-py"><span class="std std-ref">Introducing the set_output API</span></a>
 for an example on how to use the API.</p>
@@ -1735,7 +1735,7 @@ <h1>RandomTreesEmbedding<a class="headerlink" href="#randomtreesembedding" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.RandomTreesEmbedding.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.RandomTreesEmbedding.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.RandomTreesEmbedding.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1759,7 +1759,7 @@ <h1>RandomTreesEmbedding<a class="headerlink" href="#randomtreesembedding" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.RandomTreesEmbedding.transform">
-<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_forest.py#L3012"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.RandomTreesEmbedding.transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_forest.py#L3011"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.RandomTreesEmbedding.transform" title="Link to this definition">#</a></dt>
 <dd><p>Transform dataset.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.ensemble.StackingClassifier.html b/dev/modules/generated/sklearn.ensemble.StackingClassifier.html
index 7a2f895037b49..885168f97f558 100644
--- a/dev/modules/generated/sklearn.ensemble.StackingClassifier.html
+++ b/dev/modules/generated/sklearn.ensemble.StackingClassifier.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>StackingClassifier<a class="headerlink" href="#stackingclassifier" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.ensemble.StackingClassifier">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.ensemble.</span></span><span class="sig-name descname"><span class="pre">StackingClassifier</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimators</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">final_estimator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cv</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stack_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">passthrough</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_stacking.py#L423"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.StackingClassifier" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.ensemble.</span></span><span class="sig-name descname"><span class="pre">StackingClassifier</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimators</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">final_estimator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cv</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stack_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">passthrough</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_stacking.py#L422"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.StackingClassifier" title="Link to this definition">#</a></dt>
 <dd><p>Stack of estimators with a final classifier.</p>
 <p>Stacked generalization consists in stacking the output of individual
 estimator and use a classifier to compute the final prediction. Stacking
@@ -1472,7 +1472,7 @@ <h1>StackingClassifier<a class="headerlink" href="#stackingclassifier" title="Li
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.StackingClassifier.decision_function">
-<span class="sig-name descname"><span class="pre">decision_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_stacking.py#L802"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.StackingClassifier.decision_function" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">decision_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_stacking.py#L791"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.StackingClassifier.decision_function" title="Link to this definition">#</a></dt>
 <dd><p>Decision function for samples in <code class="docutils literal notranslate"><span class="pre">X</span></code> using the final estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1493,7 +1493,7 @@ <h1>StackingClassifier<a class="headerlink" href="#stackingclassifier" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.StackingClassifier.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_stacking.py#L663"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.StackingClassifier.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_stacking.py#L659"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.StackingClassifier.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit the estimators.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1506,10 +1506,6 @@ <h1>StackingClassifier<a class="headerlink" href="#stackingclassifier" title="Li
 matter (e.g. for ordinal regression), one should numerically encode
 the target <code class="docutils literal notranslate"><span class="pre">y</span></code> before calling <a class="reference internal" href="http://github.com/glossary.html#term-fit"><span class="xref std std-term">fit</span></a>.</p>
 </dd>
-<dt><strong>sample_weight</strong><span class="classifier">array-like of shape (n_samples,), default=None</span></dt><dd><p>Sample weights. If None, then samples are equally weighted.
-Note that this is supported only if all underlying estimators
-support sample weights.</p>
-</dd>
 <dt><strong>**fit_params</strong><span class="classifier">dict</span></dt><dd><p>Parameters to pass to the underlying estimators.</p>
 <div class="versionadded">
 <p><span class="versionmodified added">Added in version 1.6: </span>Only available if <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code>, which can be
@@ -1531,7 +1527,7 @@ <h1>StackingClassifier<a class="headerlink" href="#stackingclassifier" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.StackingClassifier.fit_transform">
-<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L863"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.StackingClassifier.fit_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L804"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.StackingClassifier.fit_transform" title="Link to this definition">#</a></dt>
 <dd><p>Fit to data, then transform it.</p>
 <p>Fits transformer to <code class="docutils literal notranslate"><span class="pre">X</span></code> and <code class="docutils literal notranslate"><span class="pre">y</span></code> with optional parameters <code class="docutils literal notranslate"><span class="pre">fit_params</span></code>
 and returns a transformed version of <code class="docutils literal notranslate"><span class="pre">X</span></code>.</p>
@@ -1557,7 +1553,7 @@ <h1>StackingClassifier<a class="headerlink" href="#stackingclassifier" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.StackingClassifier.get_feature_names_out">
-<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_stacking.py#L303"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.StackingClassifier.get_feature_names_out" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_stacking.py#L302"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.StackingClassifier.get_feature_names_out" title="Link to this definition">#</a></dt>
 <dd><p>Get output feature names for transformation.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1587,7 +1583,7 @@ <h1>StackingClassifier<a class="headerlink" href="#stackingclassifier" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.StackingClassifier.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_stacking.py#L387"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.StackingClassifier.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_stacking.py#L386"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.StackingClassifier.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1607,7 +1603,7 @@ <h1>StackingClassifier<a class="headerlink" href="#stackingclassifier" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.StackingClassifier.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_base.py#L270"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.StackingClassifier.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_base.py#L270"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.StackingClassifier.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get the parameters of an estimator from the ensemble.</p>
 <p>Returns the parameters given in the constructor as well as the
 estimators contained within the <code class="docutils literal notranslate"><span class="pre">estimators</span></code> parameter.</p>
@@ -1644,7 +1640,7 @@ <h1>StackingClassifier<a class="headerlink" href="#stackingclassifier" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.StackingClassifier.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">predict_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_stacking.py#L719"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.StackingClassifier.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">predict_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_stacking.py#L708"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.StackingClassifier.predict" title="Link to this definition">#</a></dt>
 <dd><p>Predict target for X.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1681,7 +1677,7 @@ <h1>StackingClassifier<a class="headerlink" href="#stackingclassifier" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.StackingClassifier.predict_proba">
-<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_stacking.py#L774"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.StackingClassifier.predict_proba" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_stacking.py#L763"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.StackingClassifier.predict_proba" title="Link to this definition">#</a></dt>
 <dd><p>Predict class probabilities for <code class="docutils literal notranslate"><span class="pre">X</span></code> using the final estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1702,7 +1698,7 @@ <h1>StackingClassifier<a class="headerlink" href="#stackingclassifier" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.StackingClassifier.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L546"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.StackingClassifier.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L487"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.StackingClassifier.score" title="Link to this definition">#</a></dt>
 <dd><p>Return <a class="reference internal" href="http://github.com/model_evaluation.html#accuracy-score"><span class="std std-ref">accuracy</span></a> on provided data and labels.</p>
 <p>In multi-label classification, this is the subset accuracy
 which is a harsh metric since you require for each sample that
@@ -1727,52 +1723,9 @@ <h1>StackingClassifier<a class="headerlink" href="#stackingclassifier" title="Li
 </dl>
 </dd></dl>
 
-<dl class="py method">
-<dt class="sig sig-object py" id="sklearn.ensemble.StackingClassifier.set_fit_request">
-<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.ensemble.StackingClassifier" title="sklearn.ensemble._stacking.StackingClassifier"><span class="pre">StackingClassifier</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.StackingClassifier.set_fit_request" title="Link to this definition">#</a></dt>
-<dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method.</p>
-<p>Note that this method is only relevant if
-<code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
-Please see <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
-mechanism works.</p>
-<p>The options for each parameter are:</p>
-<ul class="simple">
-<li><p><code class="docutils literal notranslate"><span class="pre">True</span></code>: metadata is requested, and passed to <code class="docutils literal notranslate"><span class="pre">fit</span></code> if provided. The request is ignored if metadata is not provided.</p></li>
-<li><p><code class="docutils literal notranslate"><span class="pre">False</span></code>: metadata is not requested and the meta-estimator will not pass it to <code class="docutils literal notranslate"><span class="pre">fit</span></code>.</p></li>
-<li><p><code class="docutils literal notranslate"><span class="pre">None</span></code>: metadata is not requested, and the meta-estimator will raise an error if the user provides it.</p></li>
-<li><p><code class="docutils literal notranslate"><span class="pre">str</span></code>: metadata should be passed to the meta-estimator with this given alias instead of the original name.</p></li>
-</ul>
-<p>The default (<code class="docutils literal notranslate"><span class="pre">sklearn.utils.metadata_routing.UNCHANGED</span></code>) retains the
-existing request. This allows you to change the request for some
-parameters and not others.</p>
-<div class="versionadded">
-<p><span class="versionmodified added">Added in version 1.3.</span></p>
-</div>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>This method is only relevant if this estimator is used as a
-sub-estimator of a meta-estimator, e.g. used inside a
-<a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>. Otherwise it has no effect.</p>
-</div>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><dl class="simple">
-<dt><strong>sample_weight</strong><span class="classifier">str, True, False, or None,                     default=sklearn.utils.metadata_routing.UNCHANGED</span></dt><dd><p>Metadata routing for <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> parameter in <code class="docutils literal notranslate"><span class="pre">fit</span></code>.</p>
-</dd>
-</dl>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><dl class="simple">
-<dt><strong>self</strong><span class="classifier">object</span></dt><dd><p>The updated object.</p>
-</dd>
-</dl>
-</dd>
-</dl>
-</dd></dl>
-
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.StackingClassifier.set_output">
-<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.StackingClassifier.set_output" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.StackingClassifier.set_output" title="Link to this definition">#</a></dt>
 <dd><p>Set output container.</p>
 <p>See <a class="reference internal" href="http://github.com/auto_examples/miscellaneous/plot_set_output.html#sphx-glr-auto-examples-miscellaneous-plot-set-output-py"><span class="std std-ref">Introducing the set_output API</span></a>
 for an example on how to use the API.</p>
@@ -1803,7 +1756,7 @@ <h1>StackingClassifier<a class="headerlink" href="#stackingclassifier" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.StackingClassifier.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_base.py#L245"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.StackingClassifier.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_base.py#L245"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.StackingClassifier.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of an estimator from the ensemble.</p>
 <p>Valid parameter keys can be listed with <code class="docutils literal notranslate"><span class="pre">get_params()</span></code>. Note that you
 can directly set the parameters of the estimators contained in
@@ -1830,7 +1783,7 @@ <h1>StackingClassifier<a class="headerlink" href="#stackingclassifier" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.StackingClassifier.set_score_request">
-<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.ensemble.StackingClassifier" title="sklearn.ensemble._stacking.StackingClassifier"><span class="pre">StackingClassifier</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.StackingClassifier.set_score_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.ensemble.StackingClassifier" title="sklearn.ensemble._stacking.StackingClassifier"><span class="pre">StackingClassifier</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.StackingClassifier.set_score_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1873,7 +1826,7 @@ <h1>StackingClassifier<a class="headerlink" href="#stackingclassifier" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.StackingClassifier.transform">
-<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_stacking.py#L825"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.StackingClassifier.transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_stacking.py#L814"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.StackingClassifier.transform" title="Link to this definition">#</a></dt>
 <dd><p>Return class labels or probabilities for X for each estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1965,7 +1918,6 @@ <h2>Gallery examples<a class="headerlink" href="#gallery-examples" title="Link t
 <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.ensemble.StackingClassifier.predict"><code class="docutils literal notranslate"><span class="pre">predict</span></code></a></li>
 <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.ensemble.StackingClassifier.predict_proba"><code class="docutils literal notranslate"><span class="pre">predict_proba</span></code></a></li>
 <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.ensemble.StackingClassifier.score"><code class="docutils literal notranslate"><span class="pre">score</span></code></a></li>
-<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.ensemble.StackingClassifier.set_fit_request"><code class="docutils literal notranslate"><span class="pre">set_fit_request</span></code></a></li>
 <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.ensemble.StackingClassifier.set_output"><code class="docutils literal notranslate"><span class="pre">set_output</span></code></a></li>
 <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.ensemble.StackingClassifier.set_params"><code class="docutils literal notranslate"><span class="pre">set_params</span></code></a></li>
 <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.ensemble.StackingClassifier.set_score_request"><code class="docutils literal notranslate"><span class="pre">set_score_request</span></code></a></li>
diff --git a/dev/modules/generated/sklearn.ensemble.StackingRegressor.html b/dev/modules/generated/sklearn.ensemble.StackingRegressor.html
index 96166c9779025..97e6350dbf255 100644
--- a/dev/modules/generated/sklearn.ensemble.StackingRegressor.html
+++ b/dev/modules/generated/sklearn.ensemble.StackingRegressor.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1308,7 +1308,7 @@
 <h1>StackingRegressor<a class="headerlink" href="#stackingregressor" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.ensemble.StackingRegressor">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.ensemble.</span></span><span class="sig-name descname"><span class="pre">StackingRegressor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimators</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">final_estimator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cv</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">passthrough</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_stacking.py#L852"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.StackingRegressor" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.ensemble.</span></span><span class="sig-name descname"><span class="pre">StackingRegressor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimators</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">final_estimator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cv</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">passthrough</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_stacking.py#L841"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.StackingRegressor" title="Link to this definition">#</a></dt>
 <dd><p>Stack of estimators with a final regressor.</p>
 <p>Stacked generalization consists in stacking the output of individual
 estimator and use a regressor to compute the final prediction. Stacking
@@ -1445,7 +1445,7 @@ <h1>StackingRegressor<a class="headerlink" href="#stackingregressor" title="Link
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.StackingRegressor.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_stacking.py#L1026"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.StackingRegressor.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_stacking.py#L1012"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.StackingRegressor.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit the estimators.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1455,10 +1455,6 @@ <h1>StackingRegressor<a class="headerlink" href="#stackingregressor" title="Link
 </dd>
 <dt><strong>y</strong><span class="classifier">array-like of shape (n_samples,)</span></dt><dd><p>Target values.</p>
 </dd>
-<dt><strong>sample_weight</strong><span class="classifier">array-like of shape (n_samples,), default=None</span></dt><dd><p>Sample weights. If None, then samples are equally weighted.
-Note that this is supported only if all underlying estimators
-support sample weights.</p>
-</dd>
 <dt><strong>**fit_params</strong><span class="classifier">dict</span></dt><dd><p>Parameters to pass to the underlying estimators.</p>
 <div class="versionadded">
 <p><span class="versionmodified added">Added in version 1.6: </span>Only available if <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code>, which can be
@@ -1480,7 +1476,7 @@ <h1>StackingRegressor<a class="headerlink" href="#stackingregressor" title="Link
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.StackingRegressor.fit_transform">
-<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_stacking.py#L1084"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.StackingRegressor.fit_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_stacking.py#L1061"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.StackingRegressor.fit_transform" title="Link to this definition">#</a></dt>
 <dd><p>Fit the estimators and return the predictions for X for each estimator.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1490,10 +1486,6 @@ <h1>StackingRegressor<a class="headerlink" href="#stackingregressor" title="Link
 </dd>
 <dt><strong>y</strong><span class="classifier">array-like of shape (n_samples,)</span></dt><dd><p>Target values.</p>
 </dd>
-<dt><strong>sample_weight</strong><span class="classifier">array-like of shape (n_samples,), default=None</span></dt><dd><p>Sample weights. If None, then samples are equally weighted.
-Note that this is supported only if all underlying estimators
-support sample weights.</p>
-</dd>
 <dt><strong>**fit_params</strong><span class="classifier">dict</span></dt><dd><p>Parameters to pass to the underlying estimators.</p>
 <div class="versionadded">
 <p><span class="versionmodified added">Added in version 1.6: </span>Only available if <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code>, which can be
@@ -1515,7 +1507,7 @@ <h1>StackingRegressor<a class="headerlink" href="#stackingregressor" title="Link
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.StackingRegressor.get_feature_names_out">
-<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_stacking.py#L303"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.StackingRegressor.get_feature_names_out" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_stacking.py#L302"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.StackingRegressor.get_feature_names_out" title="Link to this definition">#</a></dt>
 <dd><p>Get output feature names for transformation.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1545,7 +1537,7 @@ <h1>StackingRegressor<a class="headerlink" href="#stackingregressor" title="Link
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.StackingRegressor.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_stacking.py#L387"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.StackingRegressor.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_stacking.py#L386"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.StackingRegressor.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1565,7 +1557,7 @@ <h1>StackingRegressor<a class="headerlink" href="#stackingregressor" title="Link
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.StackingRegressor.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_base.py#L270"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.StackingRegressor.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_base.py#L270"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.StackingRegressor.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get the parameters of an estimator from the ensemble.</p>
 <p>Returns the parameters given in the constructor as well as the
 estimators contained within the <code class="docutils literal notranslate"><span class="pre">estimators</span></code> parameter.</p>
@@ -1602,7 +1594,7 @@ <h1>StackingRegressor<a class="headerlink" href="#stackingregressor" title="Link
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.StackingRegressor.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">predict_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_stacking.py#L1122"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.StackingRegressor.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">predict_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_stacking.py#L1092"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.StackingRegressor.predict" title="Link to this definition">#</a></dt>
 <dd><p>Predict target for X.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1639,7 +1631,7 @@ <h1>StackingRegressor<a class="headerlink" href="#stackingregressor" title="Link
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.StackingRegressor.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L619"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.StackingRegressor.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L560"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.StackingRegressor.score" title="Link to this definition">#</a></dt>
 <dd><p>Return <a class="reference internal" href="http://github.com/model_evaluation.html#r2-score"><span class="std std-ref">coefficient of determination</span></a> on test data.</p>
 <p>The coefficient of determination, <span class="math notranslate nohighlight">\(R^2\)</span>, is defined as
 <span class="math notranslate nohighlight">\((1 - \frac{u}{v})\)</span>, where <span class="math notranslate nohighlight">\(u\)</span> is the residual
@@ -1679,52 +1671,9 @@ <h1>StackingRegressor<a class="headerlink" href="#stackingregressor" title="Link
 <a class="reference internal" href="http://github.com/sklearn.multioutput.MultiOutputRegressor.html#sklearn.multioutput.MultiOutputRegressor" title="sklearn.multioutput.MultiOutputRegressor"><code class="xref py py-class docutils literal notranslate"><span class="pre">MultiOutputRegressor</span></code></a>).</p>
 </dd></dl>
 
-<dl class="py method">
-<dt class="sig sig-object py" id="sklearn.ensemble.StackingRegressor.set_fit_request">
-<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.ensemble.StackingRegressor" title="sklearn.ensemble._stacking.StackingRegressor"><span class="pre">StackingRegressor</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.StackingRegressor.set_fit_request" title="Link to this definition">#</a></dt>
-<dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method.</p>
-<p>Note that this method is only relevant if
-<code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
-Please see <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
-mechanism works.</p>
-<p>The options for each parameter are:</p>
-<ul class="simple">
-<li><p><code class="docutils literal notranslate"><span class="pre">True</span></code>: metadata is requested, and passed to <code class="docutils literal notranslate"><span class="pre">fit</span></code> if provided. The request is ignored if metadata is not provided.</p></li>
-<li><p><code class="docutils literal notranslate"><span class="pre">False</span></code>: metadata is not requested and the meta-estimator will not pass it to <code class="docutils literal notranslate"><span class="pre">fit</span></code>.</p></li>
-<li><p><code class="docutils literal notranslate"><span class="pre">None</span></code>: metadata is not requested, and the meta-estimator will raise an error if the user provides it.</p></li>
-<li><p><code class="docutils literal notranslate"><span class="pre">str</span></code>: metadata should be passed to the meta-estimator with this given alias instead of the original name.</p></li>
-</ul>
-<p>The default (<code class="docutils literal notranslate"><span class="pre">sklearn.utils.metadata_routing.UNCHANGED</span></code>) retains the
-existing request. This allows you to change the request for some
-parameters and not others.</p>
-<div class="versionadded">
-<p><span class="versionmodified added">Added in version 1.3.</span></p>
-</div>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>This method is only relevant if this estimator is used as a
-sub-estimator of a meta-estimator, e.g. used inside a
-<a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>. Otherwise it has no effect.</p>
-</div>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><dl class="simple">
-<dt><strong>sample_weight</strong><span class="classifier">str, True, False, or None,                     default=sklearn.utils.metadata_routing.UNCHANGED</span></dt><dd><p>Metadata routing for <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> parameter in <code class="docutils literal notranslate"><span class="pre">fit</span></code>.</p>
-</dd>
-</dl>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><dl class="simple">
-<dt><strong>self</strong><span class="classifier">object</span></dt><dd><p>The updated object.</p>
-</dd>
-</dl>
-</dd>
-</dl>
-</dd></dl>
-
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.StackingRegressor.set_output">
-<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.StackingRegressor.set_output" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.StackingRegressor.set_output" title="Link to this definition">#</a></dt>
 <dd><p>Set output container.</p>
 <p>See <a class="reference internal" href="http://github.com/auto_examples/miscellaneous/plot_set_output.html#sphx-glr-auto-examples-miscellaneous-plot-set-output-py"><span class="std std-ref">Introducing the set_output API</span></a>
 for an example on how to use the API.</p>
@@ -1755,7 +1704,7 @@ <h1>StackingRegressor<a class="headerlink" href="#stackingregressor" title="Link
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.StackingRegressor.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_base.py#L245"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.StackingRegressor.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_base.py#L245"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.StackingRegressor.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of an estimator from the ensemble.</p>
 <p>Valid parameter keys can be listed with <code class="docutils literal notranslate"><span class="pre">get_params()</span></code>. Note that you
 can directly set the parameters of the estimators contained in
@@ -1782,7 +1731,7 @@ <h1>StackingRegressor<a class="headerlink" href="#stackingregressor" title="Link
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.StackingRegressor.set_score_request">
-<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.ensemble.StackingRegressor" title="sklearn.ensemble._stacking.StackingRegressor"><span class="pre">StackingRegressor</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.StackingRegressor.set_score_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.ensemble.StackingRegressor" title="sklearn.ensemble._stacking.StackingRegressor"><span class="pre">StackingRegressor</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.StackingRegressor.set_score_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1825,7 +1774,7 @@ <h1>StackingRegressor<a class="headerlink" href="#stackingregressor" title="Link
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.StackingRegressor.transform">
-<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_stacking.py#L1065"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.StackingRegressor.transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_stacking.py#L1045"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.StackingRegressor.transform" title="Link to this definition">#</a></dt>
 <dd><p>Return the predictions for X for each estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1915,7 +1864,6 @@ <h2>Gallery examples<a class="headerlink" href="#gallery-examples" title="Link t
 <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.ensemble.StackingRegressor.named_estimators"><code class="docutils literal notranslate"><span class="pre">named_estimators</span></code></a></li>
 <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.ensemble.StackingRegressor.predict"><code class="docutils literal notranslate"><span class="pre">predict</span></code></a></li>
 <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.ensemble.StackingRegressor.score"><code class="docutils literal notranslate"><span class="pre">score</span></code></a></li>
-<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.ensemble.StackingRegressor.set_fit_request"><code class="docutils literal notranslate"><span class="pre">set_fit_request</span></code></a></li>
 <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.ensemble.StackingRegressor.set_output"><code class="docutils literal notranslate"><span class="pre">set_output</span></code></a></li>
 <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.ensemble.StackingRegressor.set_params"><code class="docutils literal notranslate"><span class="pre">set_params</span></code></a></li>
 <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.ensemble.StackingRegressor.set_score_request"><code class="docutils literal notranslate"><span class="pre">set_score_request</span></code></a></li>
diff --git a/dev/modules/generated/sklearn.ensemble.VotingClassifier.html b/dev/modules/generated/sklearn.ensemble.VotingClassifier.html
index a63f9bf8a508b..f73c2368d54ba 100644
--- a/dev/modules/generated/sklearn.ensemble.VotingClassifier.html
+++ b/dev/modules/generated/sklearn.ensemble.VotingClassifier.html
@@ -11,10 +11,10 @@
 <meta property="og:type" content="website" />
 <meta property="og:url" content="https://scikit-learn/stable/modules/generated/sklearn.ensemble.VotingClassifier.html" />
 <meta property="og:site_name" content="scikit-learn" />
-<meta property="og:description" content="Gallery examples: Plot the decision boundaries of a VotingClassifier Plot class probabilities calculated by the VotingClassifier" />
+<meta property="og:description" content="Gallery examples: Visualizing the probabilistic predictions of a VotingClassifier" />
 <meta property="og:image" content="https://scikit-learn/stable/_images/sphx_glr_plot_voting_decision_regions_thumb.png" />
 <meta property="og:image:alt" content="" />
-<meta name="description" content="Gallery examples: Plot the decision boundaries of a VotingClassifier Plot class probabilities calculated by the VotingClassifier" />
+<meta name="description" content="Gallery examples: Visualizing the probabilistic predictions of a VotingClassifier" />
 
     <title>VotingClassifier &#8212; scikit-learn 1.7.dev0 documentation</title>
   
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>VotingClassifier<a class="headerlink" href="#votingclassifier" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.ensemble.VotingClassifier">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.ensemble.</span></span><span class="sig-name descname"><span class="pre">VotingClassifier</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimators</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">voting</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'hard'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">flatten_transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_voting.py#L195"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.VotingClassifier" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.ensemble.</span></span><span class="sig-name descname"><span class="pre">VotingClassifier</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimators</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">voting</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'hard'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">flatten_transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_voting.py#L194"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.VotingClassifier" title="Link to this definition">#</a></dt>
 <dd><p>Soft Voting/Majority Rule classifier for unfitted estimators.</p>
 <p>Read more in the <a class="reference internal" href="http://github.com/ensemble.html#voting-classifier"><span class="std std-ref">User Guide</span></a>.</p>
 <div class="versionadded">
@@ -1335,7 +1335,7 @@ <h1>VotingClassifier<a class="headerlink" href="#votingclassifier" title="Link t
 before averaging (<code class="docutils literal notranslate"><span class="pre">soft</span></code> voting). Uses uniform weights if <code class="docutils literal notranslate"><span class="pre">None</span></code>.</p>
 </dd>
 <dt><strong>n_jobs</strong><span class="classifier">int, default=None</span></dt><dd><p>The number of jobs to run in parallel for <code class="docutils literal notranslate"><span class="pre">fit</span></code>.
-<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.5.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
+<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.6.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
 <code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all processors. See <a class="reference internal" href="http://github.com/glossary.html#term-n_jobs"><span class="xref std std-term">Glossary</span></a>
 for more details.</p>
 <div class="versionadded">
@@ -1438,7 +1438,7 @@ <h1>VotingClassifier<a class="headerlink" href="#votingclassifier" title="Link t
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.VotingClassifier.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_voting.py#L351"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.VotingClassifier.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_voting.py#L350"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.VotingClassifier.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit the estimators.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1448,13 +1448,6 @@ <h1>VotingClassifier<a class="headerlink" href="#votingclassifier" title="Link t
 </dd>
 <dt><strong>y</strong><span class="classifier">array-like of shape (n_samples,)</span></dt><dd><p>Target values.</p>
 </dd>
-<dt><strong>sample_weight</strong><span class="classifier">array-like of shape (n_samples,), default=None</span></dt><dd><p>Sample weights. If None, then samples are equally weighted.
-Note that this is supported only if all underlying estimators
-support sample weights.</p>
-<div class="versionadded">
-<p><span class="versionmodified added">Added in version 0.18.</span></p>
-</div>
-</dd>
 <dt><strong>**fit_params</strong><span class="classifier">dict</span></dt><dd><p>Parameters to pass to the underlying estimators.</p>
 <div class="versionadded">
 <p><span class="versionmodified added">Added in version 1.5: </span>Only available if <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code>,
@@ -1477,7 +1470,7 @@ <h1>VotingClassifier<a class="headerlink" href="#votingclassifier" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.VotingClassifier.fit_transform">
-<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_voting.py#L126"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.VotingClassifier.fit_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_voting.py#L125"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.VotingClassifier.fit_transform" title="Link to this definition">#</a></dt>
 <dd><p>Return class labels or probabilities for each estimator.</p>
 <p>Return predictions for X for each estimator.</p>
 <dl class="field-list simple">
@@ -1502,7 +1495,7 @@ <h1>VotingClassifier<a class="headerlink" href="#votingclassifier" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.VotingClassifier.get_feature_names_out">
-<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_voting.py#L513"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.VotingClassifier.get_feature_names_out" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_voting.py#L499"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.VotingClassifier.get_feature_names_out" title="Link to this definition">#</a></dt>
 <dd><p>Get output feature names for transformation.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1522,7 +1515,7 @@ <h1>VotingClassifier<a class="headerlink" href="#votingclassifier" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.VotingClassifier.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_voting.py#L170"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.VotingClassifier.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_voting.py#L169"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.VotingClassifier.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1542,7 +1535,7 @@ <h1>VotingClassifier<a class="headerlink" href="#votingclassifier" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.VotingClassifier.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_base.py#L270"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.VotingClassifier.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_base.py#L270"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.VotingClassifier.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get the parameters of an estimator from the ensemble.</p>
 <p>Returns the parameters given in the constructor as well as the
 estimators contained within the <code class="docutils literal notranslate"><span class="pre">estimators</span></code> parameter.</p>
@@ -1579,7 +1572,7 @@ <h1>VotingClassifier<a class="headerlink" href="#votingclassifier" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.VotingClassifier.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_voting.py#L421"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.VotingClassifier.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_voting.py#L407"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.VotingClassifier.predict" title="Link to this definition">#</a></dt>
 <dd><p>Predict class labels for X.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1599,7 +1592,7 @@ <h1>VotingClassifier<a class="headerlink" href="#votingclassifier" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.VotingClassifier.predict_proba">
-<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_voting.py#L461"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.VotingClassifier.predict_proba" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_voting.py#L447"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.VotingClassifier.predict_proba" title="Link to this definition">#</a></dt>
 <dd><p>Compute probabilities of possible outcomes for samples in X.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1619,7 +1612,7 @@ <h1>VotingClassifier<a class="headerlink" href="#votingclassifier" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.VotingClassifier.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L546"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.VotingClassifier.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L487"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.VotingClassifier.score" title="Link to this definition">#</a></dt>
 <dd><p>Return <a class="reference internal" href="http://github.com/model_evaluation.html#accuracy-score"><span class="std std-ref">accuracy</span></a> on provided data and labels.</p>
 <p>In multi-label classification, this is the subset accuracy
 which is a harsh metric since you require for each sample that
@@ -1644,52 +1637,9 @@ <h1>VotingClassifier<a class="headerlink" href="#votingclassifier" title="Link t
 </dl>
 </dd></dl>
 
-<dl class="py method">
-<dt class="sig sig-object py" id="sklearn.ensemble.VotingClassifier.set_fit_request">
-<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.ensemble.VotingClassifier" title="sklearn.ensemble._voting.VotingClassifier"><span class="pre">VotingClassifier</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.VotingClassifier.set_fit_request" title="Link to this definition">#</a></dt>
-<dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method.</p>
-<p>Note that this method is only relevant if
-<code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
-Please see <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
-mechanism works.</p>
-<p>The options for each parameter are:</p>
-<ul class="simple">
-<li><p><code class="docutils literal notranslate"><span class="pre">True</span></code>: metadata is requested, and passed to <code class="docutils literal notranslate"><span class="pre">fit</span></code> if provided. The request is ignored if metadata is not provided.</p></li>
-<li><p><code class="docutils literal notranslate"><span class="pre">False</span></code>: metadata is not requested and the meta-estimator will not pass it to <code class="docutils literal notranslate"><span class="pre">fit</span></code>.</p></li>
-<li><p><code class="docutils literal notranslate"><span class="pre">None</span></code>: metadata is not requested, and the meta-estimator will raise an error if the user provides it.</p></li>
-<li><p><code class="docutils literal notranslate"><span class="pre">str</span></code>: metadata should be passed to the meta-estimator with this given alias instead of the original name.</p></li>
-</ul>
-<p>The default (<code class="docutils literal notranslate"><span class="pre">sklearn.utils.metadata_routing.UNCHANGED</span></code>) retains the
-existing request. This allows you to change the request for some
-parameters and not others.</p>
-<div class="versionadded">
-<p><span class="versionmodified added">Added in version 1.3.</span></p>
-</div>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>This method is only relevant if this estimator is used as a
-sub-estimator of a meta-estimator, e.g. used inside a
-<a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>. Otherwise it has no effect.</p>
-</div>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><dl class="simple">
-<dt><strong>sample_weight</strong><span class="classifier">str, True, False, or None,                     default=sklearn.utils.metadata_routing.UNCHANGED</span></dt><dd><p>Metadata routing for <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> parameter in <code class="docutils literal notranslate"><span class="pre">fit</span></code>.</p>
-</dd>
-</dl>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><dl class="simple">
-<dt><strong>self</strong><span class="classifier">object</span></dt><dd><p>The updated object.</p>
-</dd>
-</dl>
-</dd>
-</dl>
-</dd></dl>
-
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.VotingClassifier.set_output">
-<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.VotingClassifier.set_output" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.VotingClassifier.set_output" title="Link to this definition">#</a></dt>
 <dd><p>Set output container.</p>
 <p>See <a class="reference internal" href="http://github.com/auto_examples/miscellaneous/plot_set_output.html#sphx-glr-auto-examples-miscellaneous-plot-set-output-py"><span class="std std-ref">Introducing the set_output API</span></a>
 for an example on how to use the API.</p>
@@ -1720,7 +1670,7 @@ <h1>VotingClassifier<a class="headerlink" href="#votingclassifier" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.VotingClassifier.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_base.py#L245"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.VotingClassifier.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_base.py#L245"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.VotingClassifier.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of an estimator from the ensemble.</p>
 <p>Valid parameter keys can be listed with <code class="docutils literal notranslate"><span class="pre">get_params()</span></code>. Note that you
 can directly set the parameters of the estimators contained in
@@ -1747,7 +1697,7 @@ <h1>VotingClassifier<a class="headerlink" href="#votingclassifier" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.VotingClassifier.set_score_request">
-<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.ensemble.VotingClassifier" title="sklearn.ensemble._voting.VotingClassifier"><span class="pre">VotingClassifier</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.VotingClassifier.set_score_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.ensemble.VotingClassifier" title="sklearn.ensemble._voting.VotingClassifier"><span class="pre">VotingClassifier</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.VotingClassifier.set_score_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1790,7 +1740,7 @@ <h1>VotingClassifier<a class="headerlink" href="#votingclassifier" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.VotingClassifier.transform">
-<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_voting.py#L481"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.VotingClassifier.transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_voting.py#L467"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.VotingClassifier.transform" title="Link to this definition">#</a></dt>
 <dd><p>Return class labels or probabilities for X for each estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1822,12 +1772,9 @@ <h1>VotingClassifier<a class="headerlink" href="#votingclassifier" title="Link t
 
 <section id="gallery-examples">
 <h2>Gallery examples<a class="headerlink" href="#gallery-examples" title="Link to this heading">#</a></h2>
-<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="Plot the decision boundaries of a VotingClassifier for two features of the Iris dataset."><img alt="" src="http://github.com/_images/sphx_glr_plot_voting_decision_regions_thumb.png" />
-<p><a class="reference internal" href="http://github.com/auto_examples/ensemble/plot_voting_decision_regions.html#sphx-glr-auto-examples-ensemble-plot-voting-decision-regions-py"><span class="std std-ref">Plot the decision boundaries of a VotingClassifier</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Plot the decision boundaries of a VotingClassifier</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="Plot the class probabilities of the first sample in a toy dataset predicted by three different classifiers and averaged by the VotingClassifier."><img alt="" src="http://github.com/_images/sphx_glr_plot_voting_probas_thumb.png" />
-<p><a class="reference internal" href="http://github.com/auto_examples/ensemble/plot_voting_probas.html#sphx-glr-auto-examples-ensemble-plot-voting-probas-py"><span class="std std-ref">Plot class probabilities calculated by the VotingClassifier</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Plot class probabilities calculated by the VotingClassifier</div>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="Plot the predicted class probabilities in a toy dataset predicted by three different classifiers and averaged by the VotingClassifier."><img alt="" src="http://github.com/_images/sphx_glr_plot_voting_decision_regions_thumb.png" />
+<p><a class="reference internal" href="http://github.com/auto_examples/ensemble/plot_voting_decision_regions.html#sphx-glr-auto-examples-ensemble-plot-voting-decision-regions-py"><span class="std std-ref">Visualizing the probabilistic predictions of a VotingClassifier</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Visualizing the probabilistic predictions of a VotingClassifier</div>
 </div></div></section>
 </section>
 
@@ -1893,7 +1840,6 @@ <h2>Gallery examples<a class="headerlink" href="#gallery-examples" title="Link t
 <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.ensemble.VotingClassifier.predict"><code class="docutils literal notranslate"><span class="pre">predict</span></code></a></li>
 <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.ensemble.VotingClassifier.predict_proba"><code class="docutils literal notranslate"><span class="pre">predict_proba</span></code></a></li>
 <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.ensemble.VotingClassifier.score"><code class="docutils literal notranslate"><span class="pre">score</span></code></a></li>
-<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.ensemble.VotingClassifier.set_fit_request"><code class="docutils literal notranslate"><span class="pre">set_fit_request</span></code></a></li>
 <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.ensemble.VotingClassifier.set_output"><code class="docutils literal notranslate"><span class="pre">set_output</span></code></a></li>
 <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.ensemble.VotingClassifier.set_params"><code class="docutils literal notranslate"><span class="pre">set_params</span></code></a></li>
 <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.ensemble.VotingClassifier.set_score_request"><code class="docutils literal notranslate"><span class="pre">set_score_request</span></code></a></li>
diff --git a/dev/modules/generated/sklearn.ensemble.VotingRegressor.html b/dev/modules/generated/sklearn.ensemble.VotingRegressor.html
index c1d26cb5f872c..0b499ccc2a4d4 100644
--- a/dev/modules/generated/sklearn.ensemble.VotingRegressor.html
+++ b/dev/modules/generated/sklearn.ensemble.VotingRegressor.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1308,7 +1308,7 @@
 <h1>VotingRegressor<a class="headerlink" href="#votingregressor" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.ensemble.VotingRegressor">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.ensemble.</span></span><span class="sig-name descname"><span class="pre">VotingRegressor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimators</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_voting.py#L556"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.VotingRegressor" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.ensemble.</span></span><span class="sig-name descname"><span class="pre">VotingRegressor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimators</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_voting.py#L542"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.VotingRegressor" title="Link to this definition">#</a></dt>
 <dd><p>Prediction voting regressor for unfitted estimators.</p>
 <p>A voting regressor is an ensemble meta-estimator that fits several base
 regressors, each on the whole dataset. Then it averages the individual
@@ -1335,7 +1335,7 @@ <h1>VotingRegressor<a class="headerlink" href="#votingregressor" title="Link to
 predicted values before averaging. Uses uniform weights if <code class="docutils literal notranslate"><span class="pre">None</span></code>.</p>
 </dd>
 <dt><strong>n_jobs</strong><span class="classifier">int, default=None</span></dt><dd><p>The number of jobs to run in parallel for <code class="docutils literal notranslate"><span class="pre">fit</span></code>.
-<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.5.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
+<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.6.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
 <code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all processors. See <a class="reference internal" href="http://github.com/glossary.html#term-n_jobs"><span class="xref std std-term">Glossary</span></a>
 for more details.</p>
 </dd>
@@ -1388,7 +1388,7 @@ <h1>VotingRegressor<a class="headerlink" href="#votingregressor" title="Link to
 <span class="gp">&gt;&gt;&gt; </span><span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">2</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">12</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">30</span><span class="p">,</span> <span class="mi">42</span><span class="p">])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">er</span> <span class="o">=</span> <span class="n">VotingRegressor</span><span class="p">([(</span><span class="s1">&#39;lr&#39;</span><span class="p">,</span> <span class="n">r1</span><span class="p">),</span> <span class="p">(</span><span class="s1">&#39;rf&#39;</span><span class="p">,</span> <span class="n">r2</span><span class="p">),</span> <span class="p">(</span><span class="s1">&#39;r3&#39;</span><span class="p">,</span> <span class="n">r3</span><span class="p">)])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">er</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X</span><span class="p">))</span>
-<span class="go">[ 6.8...  8.4... 12.5... 17.8... 26...  34...]</span>
+<span class="go">[ 6.8  8.4 12.5 17.8 26  34]</span>
 </pre></div>
 </div>
 <p>In the following example, we drop the <code class="docutils literal notranslate"><span class="pre">'lr'</span></code> estimator with
@@ -1401,7 +1401,7 @@ <h1>VotingRegressor<a class="headerlink" href="#votingregressor" title="Link to
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.VotingRegressor.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_voting.py#L656"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.VotingRegressor.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_voting.py#L642"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.VotingRegressor.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit the estimators.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1411,10 +1411,6 @@ <h1>VotingRegressor<a class="headerlink" href="#votingregressor" title="Link to
 </dd>
 <dt><strong>y</strong><span class="classifier">array-like of shape (n_samples,)</span></dt><dd><p>Target values.</p>
 </dd>
-<dt><strong>sample_weight</strong><span class="classifier">array-like of shape (n_samples,), default=None</span></dt><dd><p>Sample weights. If None, then samples are equally weighted.
-Note that this is supported only if all underlying estimators
-support sample weights.</p>
-</dd>
 <dt><strong>**fit_params</strong><span class="classifier">dict</span></dt><dd><p>Parameters to pass to the underlying estimators.</p>
 <div class="versionadded">
 <p><span class="versionmodified added">Added in version 1.5: </span>Only available if <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code>,
@@ -1437,7 +1433,7 @@ <h1>VotingRegressor<a class="headerlink" href="#votingregressor" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.VotingRegressor.fit_transform">
-<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_voting.py#L126"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.VotingRegressor.fit_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_voting.py#L125"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.VotingRegressor.fit_transform" title="Link to this definition">#</a></dt>
 <dd><p>Return class labels or probabilities for each estimator.</p>
 <p>Return predictions for X for each estimator.</p>
 <dl class="field-list simple">
@@ -1462,7 +1458,7 @@ <h1>VotingRegressor<a class="headerlink" href="#votingregressor" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.VotingRegressor.get_feature_names_out">
-<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_voting.py#L738"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.VotingRegressor.get_feature_names_out" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_voting.py#L715"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.VotingRegressor.get_feature_names_out" title="Link to this definition">#</a></dt>
 <dd><p>Get output feature names for transformation.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1482,7 +1478,7 @@ <h1>VotingRegressor<a class="headerlink" href="#votingregressor" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.VotingRegressor.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_voting.py#L170"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.VotingRegressor.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_voting.py#L169"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.VotingRegressor.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1502,7 +1498,7 @@ <h1>VotingRegressor<a class="headerlink" href="#votingregressor" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.VotingRegressor.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_base.py#L270"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.VotingRegressor.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_base.py#L270"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.VotingRegressor.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get the parameters of an estimator from the ensemble.</p>
 <p>Returns the parameters given in the constructor as well as the
 estimators contained within the <code class="docutils literal notranslate"><span class="pre">estimators</span></code> parameter.</p>
@@ -1539,7 +1535,7 @@ <h1>VotingRegressor<a class="headerlink" href="#votingregressor" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.VotingRegressor.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_voting.py#L703"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.VotingRegressor.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_voting.py#L680"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.VotingRegressor.predict" title="Link to this definition">#</a></dt>
 <dd><p>Predict regression target for X.</p>
 <p>The predicted regression target of an input sample is computed as the
 mean predicted regression targets of the estimators in the ensemble.</p>
@@ -1561,7 +1557,7 @@ <h1>VotingRegressor<a class="headerlink" href="#votingregressor" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.VotingRegressor.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L619"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.VotingRegressor.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L560"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.VotingRegressor.score" title="Link to this definition">#</a></dt>
 <dd><p>Return <a class="reference internal" href="http://github.com/model_evaluation.html#r2-score"><span class="std std-ref">coefficient of determination</span></a> on test data.</p>
 <p>The coefficient of determination, <span class="math notranslate nohighlight">\(R^2\)</span>, is defined as
 <span class="math notranslate nohighlight">\((1 - \frac{u}{v})\)</span>, where <span class="math notranslate nohighlight">\(u\)</span> is the residual
@@ -1601,52 +1597,9 @@ <h1>VotingRegressor<a class="headerlink" href="#votingregressor" title="Link to
 <a class="reference internal" href="http://github.com/sklearn.multioutput.MultiOutputRegressor.html#sklearn.multioutput.MultiOutputRegressor" title="sklearn.multioutput.MultiOutputRegressor"><code class="xref py py-class docutils literal notranslate"><span class="pre">MultiOutputRegressor</span></code></a>).</p>
 </dd></dl>
 
-<dl class="py method">
-<dt class="sig sig-object py" id="sklearn.ensemble.VotingRegressor.set_fit_request">
-<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.ensemble.VotingRegressor" title="sklearn.ensemble._voting.VotingRegressor"><span class="pre">VotingRegressor</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.VotingRegressor.set_fit_request" title="Link to this definition">#</a></dt>
-<dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method.</p>
-<p>Note that this method is only relevant if
-<code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
-Please see <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
-mechanism works.</p>
-<p>The options for each parameter are:</p>
-<ul class="simple">
-<li><p><code class="docutils literal notranslate"><span class="pre">True</span></code>: metadata is requested, and passed to <code class="docutils literal notranslate"><span class="pre">fit</span></code> if provided. The request is ignored if metadata is not provided.</p></li>
-<li><p><code class="docutils literal notranslate"><span class="pre">False</span></code>: metadata is not requested and the meta-estimator will not pass it to <code class="docutils literal notranslate"><span class="pre">fit</span></code>.</p></li>
-<li><p><code class="docutils literal notranslate"><span class="pre">None</span></code>: metadata is not requested, and the meta-estimator will raise an error if the user provides it.</p></li>
-<li><p><code class="docutils literal notranslate"><span class="pre">str</span></code>: metadata should be passed to the meta-estimator with this given alias instead of the original name.</p></li>
-</ul>
-<p>The default (<code class="docutils literal notranslate"><span class="pre">sklearn.utils.metadata_routing.UNCHANGED</span></code>) retains the
-existing request. This allows you to change the request for some
-parameters and not others.</p>
-<div class="versionadded">
-<p><span class="versionmodified added">Added in version 1.3.</span></p>
-</div>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>This method is only relevant if this estimator is used as a
-sub-estimator of a meta-estimator, e.g. used inside a
-<a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>. Otherwise it has no effect.</p>
-</div>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><dl class="simple">
-<dt><strong>sample_weight</strong><span class="classifier">str, True, False, or None,                     default=sklearn.utils.metadata_routing.UNCHANGED</span></dt><dd><p>Metadata routing for <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> parameter in <code class="docutils literal notranslate"><span class="pre">fit</span></code>.</p>
-</dd>
-</dl>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><dl class="simple">
-<dt><strong>self</strong><span class="classifier">object</span></dt><dd><p>The updated object.</p>
-</dd>
-</dl>
-</dd>
-</dl>
-</dd></dl>
-
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.VotingRegressor.set_output">
-<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.VotingRegressor.set_output" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.VotingRegressor.set_output" title="Link to this definition">#</a></dt>
 <dd><p>Set output container.</p>
 <p>See <a class="reference internal" href="http://github.com/auto_examples/miscellaneous/plot_set_output.html#sphx-glr-auto-examples-miscellaneous-plot-set-output-py"><span class="std std-ref">Introducing the set_output API</span></a>
 for an example on how to use the API.</p>
@@ -1677,7 +1630,7 @@ <h1>VotingRegressor<a class="headerlink" href="#votingregressor" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.VotingRegressor.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_base.py#L245"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.VotingRegressor.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_base.py#L245"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.VotingRegressor.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of an estimator from the ensemble.</p>
 <p>Valid parameter keys can be listed with <code class="docutils literal notranslate"><span class="pre">get_params()</span></code>. Note that you
 can directly set the parameters of the estimators contained in
@@ -1704,7 +1657,7 @@ <h1>VotingRegressor<a class="headerlink" href="#votingregressor" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.VotingRegressor.set_score_request">
-<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.ensemble.VotingRegressor" title="sklearn.ensemble._voting.VotingRegressor"><span class="pre">VotingRegressor</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.VotingRegressor.set_score_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.ensemble.VotingRegressor" title="sklearn.ensemble._voting.VotingRegressor"><span class="pre">VotingRegressor</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.VotingRegressor.set_score_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1747,7 +1700,7 @@ <h1>VotingRegressor<a class="headerlink" href="#votingregressor" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.ensemble.VotingRegressor.transform">
-<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/ensemble/_voting.py#L722"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.VotingRegressor.transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/ensemble/_voting.py#L699"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.VotingRegressor.transform" title="Link to this definition">#</a></dt>
 <dd><p>Return predictions for X for each estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1836,7 +1789,6 @@ <h2>Gallery examples<a class="headerlink" href="#gallery-examples" title="Link t
 <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.ensemble.VotingRegressor.named_estimators"><code class="docutils literal notranslate"><span class="pre">named_estimators</span></code></a></li>
 <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.ensemble.VotingRegressor.predict"><code class="docutils literal notranslate"><span class="pre">predict</span></code></a></li>
 <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.ensemble.VotingRegressor.score"><code class="docutils literal notranslate"><span class="pre">score</span></code></a></li>
-<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.ensemble.VotingRegressor.set_fit_request"><code class="docutils literal notranslate"><span class="pre">set_fit_request</span></code></a></li>
 <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.ensemble.VotingRegressor.set_output"><code class="docutils literal notranslate"><span class="pre">set_output</span></code></a></li>
 <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.ensemble.VotingRegressor.set_params"><code class="docutils literal notranslate"><span class="pre">set_params</span></code></a></li>
 <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.ensemble.VotingRegressor.set_score_request"><code class="docutils literal notranslate"><span class="pre">set_score_request</span></code></a></li>
diff --git a/dev/modules/generated/sklearn.exceptions.ConvergenceWarning.html b/dev/modules/generated/sklearn.exceptions.ConvergenceWarning.html
index 0329a87e8752f..592d297202499 100644
--- a/dev/modules/generated/sklearn.exceptions.ConvergenceWarning.html
+++ b/dev/modules/generated/sklearn.exceptions.ConvergenceWarning.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>ConvergenceWarning<a class="headerlink" href="#convergencewarning" title="Link to this heading">#</a></h1>
 <dl class="py exception">
 <dt class="sig sig-object py" id="sklearn.exceptions.ConvergenceWarning">
-<em class="property"><span class="pre">exception</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.exceptions.</span></span><span class="sig-name descname"><span class="pre">ConvergenceWarning</span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/exceptions.py#L68"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.exceptions.ConvergenceWarning" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">exception</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.exceptions.</span></span><span class="sig-name descname"><span class="pre">ConvergenceWarning</span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/exceptions.py#L68"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.exceptions.ConvergenceWarning" title="Link to this definition">#</a></dt>
 <dd><p>Custom warning to capture convergence problems</p>
 <div class="versionchanged">
 <p><span class="versionmodified changed">Changed in version 0.18: </span>Moved from sklearn.utils.</p>
diff --git a/dev/modules/generated/sklearn.exceptions.DataConversionWarning.html b/dev/modules/generated/sklearn.exceptions.DataConversionWarning.html
index 87fe9a03bb043..d8fb99f7ace8d 100644
--- a/dev/modules/generated/sklearn.exceptions.DataConversionWarning.html
+++ b/dev/modules/generated/sklearn.exceptions.DataConversionWarning.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>DataConversionWarning<a class="headerlink" href="#dataconversionwarning" title="Link to this heading">#</a></h1>
 <dl class="py exception">
 <dt class="sig sig-object py" id="sklearn.exceptions.DataConversionWarning">
-<em class="property"><span class="pre">exception</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.exceptions.</span></span><span class="sig-name descname"><span class="pre">DataConversionWarning</span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/exceptions.py#L76"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.exceptions.DataConversionWarning" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">exception</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.exceptions.</span></span><span class="sig-name descname"><span class="pre">DataConversionWarning</span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/exceptions.py#L76"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.exceptions.DataConversionWarning" title="Link to this definition">#</a></dt>
 <dd><p>Warning used to notify implicit data conversions happening in the code.</p>
 <p>This warning occurs when some input data needs to be converted or
 interpreted in a way that may not match the user’s expectations.</p>
diff --git a/dev/modules/generated/sklearn.exceptions.DataDimensionalityWarning.html b/dev/modules/generated/sklearn.exceptions.DataDimensionalityWarning.html
index ec3b8b9906912..2abbae8e95b99 100644
--- a/dev/modules/generated/sklearn.exceptions.DataDimensionalityWarning.html
+++ b/dev/modules/generated/sklearn.exceptions.DataDimensionalityWarning.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>DataDimensionalityWarning<a class="headerlink" href="#datadimensionalitywarning" title="Link to this heading">#</a></h1>
 <dl class="py exception">
 <dt class="sig sig-object py" id="sklearn.exceptions.DataDimensionalityWarning">
-<em class="property"><span class="pre">exception</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.exceptions.</span></span><span class="sig-name descname"><span class="pre">DataDimensionalityWarning</span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/exceptions.py#L94"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.exceptions.DataDimensionalityWarning" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">exception</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.exceptions.</span></span><span class="sig-name descname"><span class="pre">DataDimensionalityWarning</span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/exceptions.py#L94"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.exceptions.DataDimensionalityWarning" title="Link to this definition">#</a></dt>
 <dd><p>Custom warning to notify potential issues with data dimensionality.</p>
 <p>For example, in random projection, this warning is raised when the
 number of components, which quantifies the dimensionality of the target
diff --git a/dev/modules/generated/sklearn.exceptions.EfficiencyWarning.html b/dev/modules/generated/sklearn.exceptions.EfficiencyWarning.html
index 639aad1f52bf3..151c5afbae206 100644
--- a/dev/modules/generated/sklearn.exceptions.EfficiencyWarning.html
+++ b/dev/modules/generated/sklearn.exceptions.EfficiencyWarning.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>EfficiencyWarning<a class="headerlink" href="#efficiencywarning" title="Link to this heading">#</a></h1>
 <dl class="py exception">
 <dt class="sig sig-object py" id="sklearn.exceptions.EfficiencyWarning">
-<em class="property"><span class="pre">exception</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.exceptions.</span></span><span class="sig-name descname"><span class="pre">EfficiencyWarning</span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/exceptions.py#L108"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.exceptions.EfficiencyWarning" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">exception</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.exceptions.</span></span><span class="sig-name descname"><span class="pre">EfficiencyWarning</span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/exceptions.py#L108"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.exceptions.EfficiencyWarning" title="Link to this definition">#</a></dt>
 <dd><p>Warning used to notify the user of inefficient computation.</p>
 <p>This warning notifies the user that the efficiency may not be optimal due
 to some reason which may be included as a part of the warning message.
diff --git a/dev/modules/generated/sklearn.exceptions.EstimatorCheckFailedWarning.html b/dev/modules/generated/sklearn.exceptions.EstimatorCheckFailedWarning.html
index f49fcb035f8e9..8622f7a5ba4db 100644
--- a/dev/modules/generated/sklearn.exceptions.EstimatorCheckFailedWarning.html
+++ b/dev/modules/generated/sklearn.exceptions.EstimatorCheckFailedWarning.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
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     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>EstimatorCheckFailedWarning<a class="headerlink" href="#estimatorcheckfailedwarning" title="Link to this heading">#</a></h1>
 <dl class="py exception">
 <dt class="sig sig-object py" id="sklearn.exceptions.EstimatorCheckFailedWarning">
-<em class="property"><span class="pre">exception</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.exceptions.</span></span><span class="sig-name descname"><span class="pre">EstimatorCheckFailedWarning</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">estimator</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_name</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">exception</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#Exception" title="(in Python v3.13)"><span class="pre">Exception</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">status</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">expected_to_fail</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">expected_to_fail_reason</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/exceptions.py#L195"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.exceptions.EstimatorCheckFailedWarning" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">exception</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.exceptions.</span></span><span class="sig-name descname"><span class="pre">EstimatorCheckFailedWarning</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">estimator</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_name</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">exception</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#Exception" title="(in Python v3.13)"><span class="pre">Exception</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">status</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">expected_to_fail</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">expected_to_fail_reason</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/exceptions.py#L195"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.exceptions.EstimatorCheckFailedWarning" title="Link to this definition">#</a></dt>
 <dd><p>Warning raised when an estimator check from the common tests fails.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.exceptions.FitFailedWarning.html b/dev/modules/generated/sklearn.exceptions.FitFailedWarning.html
index f981f16a96b93..aa487fe00d201 100644
--- a/dev/modules/generated/sklearn.exceptions.FitFailedWarning.html
+++ b/dev/modules/generated/sklearn.exceptions.FitFailedWarning.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>FitFailedWarning<a class="headerlink" href="#fitfailedwarning" title="Link to this heading">#</a></h1>
 <dl class="py exception">
 <dt class="sig sig-object py" id="sklearn.exceptions.FitFailedWarning">
-<em class="property"><span class="pre">exception</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.exceptions.</span></span><span class="sig-name descname"><span class="pre">FitFailedWarning</span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/exceptions.py#L119"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.exceptions.FitFailedWarning" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">exception</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.exceptions.</span></span><span class="sig-name descname"><span class="pre">FitFailedWarning</span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/exceptions.py#L119"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.exceptions.FitFailedWarning" title="Link to this definition">#</a></dt>
 <dd><p>Warning class used if there is an error while fitting the estimator.</p>
 <p>This Warning is used in meta estimators GridSearchCV and RandomizedSearchCV
 and the cross-validation helper function cross_val_score to warn when there
diff --git a/dev/modules/generated/sklearn.exceptions.InconsistentVersionWarning.html b/dev/modules/generated/sklearn.exceptions.InconsistentVersionWarning.html
index dac528b7eaedf..4acab169bf79f 100644
--- a/dev/modules/generated/sklearn.exceptions.InconsistentVersionWarning.html
+++ b/dev/modules/generated/sklearn.exceptions.InconsistentVersionWarning.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
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     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>InconsistentVersionWarning<a class="headerlink" href="#inconsistentversionwarning" title="Link to this heading">#</a></h1>
 <dl class="py exception">
 <dt class="sig sig-object py" id="sklearn.exceptions.InconsistentVersionWarning">
-<em class="property"><span class="pre">exception</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.exceptions.</span></span><span class="sig-name descname"><span class="pre">InconsistentVersionWarning</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">estimator_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">current_sklearn_version</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">original_sklearn_version</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/exceptions.py#L160"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.exceptions.InconsistentVersionWarning" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">exception</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.exceptions.</span></span><span class="sig-name descname"><span class="pre">InconsistentVersionWarning</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">estimator_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">current_sklearn_version</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">original_sklearn_version</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/exceptions.py#L160"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.exceptions.InconsistentVersionWarning" title="Link to this definition">#</a></dt>
 <dd><p>Warning raised when an estimator is unpickled with an inconsistent version.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.exceptions.NotFittedError.html b/dev/modules/generated/sklearn.exceptions.NotFittedError.html
index 0d22926f31874..5e3577ed27687 100644
--- a/dev/modules/generated/sklearn.exceptions.NotFittedError.html
+++ b/dev/modules/generated/sklearn.exceptions.NotFittedError.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>NotFittedError<a class="headerlink" href="#notfittederror" title="Link to this heading">#</a></h1>
 <dl class="py exception">
 <dt class="sig sig-object py" id="sklearn.exceptions.NotFittedError">
-<em class="property"><span class="pre">exception</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.exceptions.</span></span><span class="sig-name descname"><span class="pre">NotFittedError</span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/exceptions.py#L46"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.exceptions.NotFittedError" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">exception</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.exceptions.</span></span><span class="sig-name descname"><span class="pre">NotFittedError</span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/exceptions.py#L46"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.exceptions.NotFittedError" title="Link to this definition">#</a></dt>
 <dd><p>Exception class to raise if estimator is used before fitting.</p>
 <p>This class inherits from both ValueError and AttributeError to help with
 exception handling and backward compatibility.</p>
diff --git a/dev/modules/generated/sklearn.exceptions.UndefinedMetricWarning.html b/dev/modules/generated/sklearn.exceptions.UndefinedMetricWarning.html
index fbb441fbf6c07..8d3078c5b9780 100644
--- a/dev/modules/generated/sklearn.exceptions.UndefinedMetricWarning.html
+++ b/dev/modules/generated/sklearn.exceptions.UndefinedMetricWarning.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>UndefinedMetricWarning<a class="headerlink" href="#undefinedmetricwarning" title="Link to this heading">#</a></h1>
 <dl class="py exception">
 <dt class="sig sig-object py" id="sklearn.exceptions.UndefinedMetricWarning">
-<em class="property"><span class="pre">exception</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.exceptions.</span></span><span class="sig-name descname"><span class="pre">UndefinedMetricWarning</span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/exceptions.py#L140"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.exceptions.UndefinedMetricWarning" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">exception</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.exceptions.</span></span><span class="sig-name descname"><span class="pre">UndefinedMetricWarning</span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/exceptions.py#L140"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.exceptions.UndefinedMetricWarning" title="Link to this definition">#</a></dt>
 <dd><p>Warning used when the metric is invalid</p>
 <div class="versionchanged">
 <p><span class="versionmodified changed">Changed in version 0.18: </span>Moved from sklearn.base.</p>
diff --git a/dev/modules/generated/sklearn.experimental.enable_halving_search_cv.html b/dev/modules/generated/sklearn.experimental.enable_halving_search_cv.html
index 14576070ad0c1..e7bb581bc1e6a 100644
--- a/dev/modules/generated/sklearn.experimental.enable_halving_search_cv.html
+++ b/dev/modules/generated/sklearn.experimental.enable_halving_search_cv.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
diff --git a/dev/modules/generated/sklearn.experimental.enable_iterative_imputer.html b/dev/modules/generated/sklearn.experimental.enable_iterative_imputer.html
index 41842fb2176e6..9ca21227f28cc 100644
--- a/dev/modules/generated/sklearn.experimental.enable_iterative_imputer.html
+++ b/dev/modules/generated/sklearn.experimental.enable_iterative_imputer.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
diff --git a/dev/modules/generated/sklearn.feature_extraction.DictVectorizer.html b/dev/modules/generated/sklearn.feature_extraction.DictVectorizer.html
index 60ee2f443838f..f151c7fda431e 100644
--- a/dev/modules/generated/sklearn.feature_extraction.DictVectorizer.html
+++ b/dev/modules/generated/sklearn.feature_extraction.DictVectorizer.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
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     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>DictVectorizer<a class="headerlink" href="#dictvectorizer" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.feature_extraction.DictVectorizer">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.feature_extraction.</span></span><span class="sig-name descname"><span class="pre">DictVectorizer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype=&lt;class</span> <span class="pre">'numpy.float64'&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">separator='='</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sparse=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sort=True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_extraction/_dict_vectorizer.py#L19"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.DictVectorizer" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.feature_extraction.</span></span><span class="sig-name descname"><span class="pre">DictVectorizer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype=&lt;class</span> <span class="pre">'numpy.float64'&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">separator='='</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sparse=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sort=True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_extraction/_dict_vectorizer.py#L19"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.DictVectorizer" title="Link to this definition">#</a></dt>
 <dd><p>Transforms lists of feature-value mappings to vectors.</p>
 <p>This transformer turns lists of mappings (dict-like objects) of feature
 names to feature values into Numpy arrays or scipy.sparse matrices for use
@@ -1383,7 +1383,7 @@ <h1>DictVectorizer<a class="headerlink" href="#dictvectorizer" title="Link to th
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_extraction.DictVectorizer.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_extraction/_dict_vectorizer.py#L143"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.DictVectorizer.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_extraction/_dict_vectorizer.py#L143"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.DictVectorizer.fit" title="Link to this definition">#</a></dt>
 <dd><p>Learn a list of feature name -&gt; indices mappings.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1409,7 +1409,7 @@ <h1>DictVectorizer<a class="headerlink" href="#dictvectorizer" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_extraction.DictVectorizer.fit_transform">
-<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_extraction/_dict_vectorizer.py#L296"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.DictVectorizer.fit_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_extraction/_dict_vectorizer.py#L296"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.DictVectorizer.fit_transform" title="Link to this definition">#</a></dt>
 <dd><p>Learn a list of feature name -&gt; indices mappings and transform X.</p>
 <p>Like fit(X) followed by transform(X), but does not require
 materializing X in memory.</p>
@@ -1437,7 +1437,7 @@ <h1>DictVectorizer<a class="headerlink" href="#dictvectorizer" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_extraction.DictVectorizer.get_feature_names_out">
-<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_extraction/_dict_vectorizer.py#L385"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.DictVectorizer.get_feature_names_out" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_extraction/_dict_vectorizer.py#L385"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.DictVectorizer.get_feature_names_out" title="Link to this definition">#</a></dt>
 <dd><p>Get output feature names for transformation.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1457,7 +1457,7 @@ <h1>DictVectorizer<a class="headerlink" href="#dictvectorizer" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_extraction.DictVectorizer.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.DictVectorizer.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.DictVectorizer.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1474,7 +1474,7 @@ <h1>DictVectorizer<a class="headerlink" href="#dictvectorizer" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_extraction.DictVectorizer.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.DictVectorizer.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.DictVectorizer.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1495,7 +1495,7 @@ <h1>DictVectorizer<a class="headerlink" href="#dictvectorizer" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_extraction.DictVectorizer.inverse_transform">
-<span class="sig-name descname"><span class="pre">inverse_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dict_type=&lt;class</span> <span class="pre">'dict'&gt;</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_extraction/_dict_vectorizer.py#L322"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.DictVectorizer.inverse_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">inverse_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dict_type=&lt;class</span> <span class="pre">'dict'&gt;</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_extraction/_dict_vectorizer.py#L322"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.DictVectorizer.inverse_transform" title="Link to this definition">#</a></dt>
 <dd><p>Transform array or sparse matrix X back to feature mappings.</p>
 <p>X must have been produced by this DictVectorizer’s transform or
 fit_transform method; it may only have passed through transformers
@@ -1514,7 +1514,7 @@ <h1>DictVectorizer<a class="headerlink" href="#dictvectorizer" title="Link to th
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><dl class="simple">
-<dt><strong>D</strong><span class="classifier">list of dict_type objects of shape (n_samples,)</span></dt><dd><p>Feature mappings for the samples in X.</p>
+<dt><strong>X_original</strong><span class="classifier">list of dict_type objects of shape (n_samples,)</span></dt><dd><p>Feature mappings for the samples in X.</p>
 </dd>
 </dl>
 </dd>
@@ -1523,7 +1523,7 @@ <h1>DictVectorizer<a class="headerlink" href="#dictvectorizer" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_extraction.DictVectorizer.restrict">
-<span class="sig-name descname"><span class="pre">restrict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">support</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_extraction/_dict_vectorizer.py#L405"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.DictVectorizer.restrict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">restrict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">support</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_extraction/_dict_vectorizer.py#L405"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.DictVectorizer.restrict" title="Link to this definition">#</a></dt>
 <dd><p>Restrict the features to those in support using feature selection.</p>
 <p>This function modifies the estimator in-place.</p>
 <dl class="field-list simple">
@@ -1562,7 +1562,7 @@ <h1>DictVectorizer<a class="headerlink" href="#dictvectorizer" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_extraction.DictVectorizer.set_output">
-<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.DictVectorizer.set_output" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.DictVectorizer.set_output" title="Link to this definition">#</a></dt>
 <dd><p>Set output container.</p>
 <p>See <a class="reference internal" href="http://github.com/auto_examples/miscellaneous/plot_set_output.html#sphx-glr-auto-examples-miscellaneous-plot-set-output-py"><span class="std std-ref">Introducing the set_output API</span></a>
 for an example on how to use the API.</p>
@@ -1593,7 +1593,7 @@ <h1>DictVectorizer<a class="headerlink" href="#dictvectorizer" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_extraction.DictVectorizer.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.DictVectorizer.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.DictVectorizer.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1617,7 +1617,7 @@ <h1>DictVectorizer<a class="headerlink" href="#dictvectorizer" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_extraction.DictVectorizer.transform">
-<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_extraction/_dict_vectorizer.py#L365"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.DictVectorizer.transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_extraction/_dict_vectorizer.py#L365"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.DictVectorizer.transform" title="Link to this definition">#</a></dt>
 <dd><p>Transform feature-&gt;value dicts to array or sparse matrix.</p>
 <p>Named features not encountered during fit or fit_transform will be
 silently ignored.</p>
diff --git a/dev/modules/generated/sklearn.feature_extraction.FeatureHasher.html b/dev/modules/generated/sklearn.feature_extraction.FeatureHasher.html
index 543ac1f0d1f83..7e44e7c86adb9 100644
--- a/dev/modules/generated/sklearn.feature_extraction.FeatureHasher.html
+++ b/dev/modules/generated/sklearn.feature_extraction.FeatureHasher.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>FeatureHasher<a class="headerlink" href="#featurehasher" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.feature_extraction.FeatureHasher">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.feature_extraction.</span></span><span class="sig-name descname"><span class="pre">FeatureHasher</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_features=1048576</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_type='dict'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype=&lt;class</span> <span class="pre">'numpy.float64'&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alternate_sign=True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_extraction/_hash.py#L22"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.FeatureHasher" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.feature_extraction.</span></span><span class="sig-name descname"><span class="pre">FeatureHasher</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_features=1048576</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_type='dict'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype=&lt;class</span> <span class="pre">'numpy.float64'&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alternate_sign=True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_extraction/_hash.py#L22"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.FeatureHasher" title="Link to this definition">#</a></dt>
 <dd><p>Implements feature hashing, aka the hashing trick.</p>
 <p>This class turns sequences of symbolic feature names (strings) into
 scipy.sparse matrices, using a hash function to compute the matrix column
@@ -1395,7 +1395,7 @@ <h1>FeatureHasher<a class="headerlink" href="#featurehasher" title="Link to this
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_extraction.FeatureHasher.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_extraction/_hash.py#L132"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.FeatureHasher.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_extraction/_hash.py#L132"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.FeatureHasher.fit" title="Link to this definition">#</a></dt>
 <dd><p>Only validates estimator’s parameters.</p>
 <p>This method allows to: (i) validate the estimator’s parameters and
 (ii) be consistent with the scikit-learn transformer API.</p>
@@ -1419,7 +1419,7 @@ <h1>FeatureHasher<a class="headerlink" href="#featurehasher" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_extraction.FeatureHasher.fit_transform">
-<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L863"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.FeatureHasher.fit_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L804"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.FeatureHasher.fit_transform" title="Link to this definition">#</a></dt>
 <dd><p>Fit to data, then transform it.</p>
 <p>Fits transformer to <code class="docutils literal notranslate"><span class="pre">X</span></code> and <code class="docutils literal notranslate"><span class="pre">y</span></code> with optional parameters <code class="docutils literal notranslate"><span class="pre">fit_params</span></code>
 and returns a transformed version of <code class="docutils literal notranslate"><span class="pre">X</span></code>.</p>
@@ -1445,7 +1445,7 @@ <h1>FeatureHasher<a class="headerlink" href="#featurehasher" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_extraction.FeatureHasher.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.FeatureHasher.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.FeatureHasher.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1462,7 +1462,7 @@ <h1>FeatureHasher<a class="headerlink" href="#featurehasher" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_extraction.FeatureHasher.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.FeatureHasher.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.FeatureHasher.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1483,7 +1483,7 @@ <h1>FeatureHasher<a class="headerlink" href="#featurehasher" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_extraction.FeatureHasher.set_output">
-<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.FeatureHasher.set_output" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.FeatureHasher.set_output" title="Link to this definition">#</a></dt>
 <dd><p>Set output container.</p>
 <p>See <a class="reference internal" href="http://github.com/auto_examples/miscellaneous/plot_set_output.html#sphx-glr-auto-examples-miscellaneous-plot-set-output-py"><span class="std std-ref">Introducing the set_output API</span></a>
 for an example on how to use the API.</p>
@@ -1514,7 +1514,7 @@ <h1>FeatureHasher<a class="headerlink" href="#featurehasher" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_extraction.FeatureHasher.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.FeatureHasher.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.FeatureHasher.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1538,7 +1538,7 @@ <h1>FeatureHasher<a class="headerlink" href="#featurehasher" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_extraction.FeatureHasher.transform">
-<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">raw_X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_extraction/_hash.py#L154"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.FeatureHasher.transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">raw_X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_extraction/_hash.py#L154"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.FeatureHasher.transform" title="Link to this definition">#</a></dt>
 <dd><p>Transform a sequence of instances to a scipy.sparse matrix.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.feature_extraction.image.PatchExtractor.html b/dev/modules/generated/sklearn.feature_extraction.image.PatchExtractor.html
index e8f3cc1d57e84..f3b3180c795e6 100644
--- a/dev/modules/generated/sklearn.feature_extraction.image.PatchExtractor.html
+++ b/dev/modules/generated/sklearn.feature_extraction.image.PatchExtractor.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>PatchExtractor<a class="headerlink" href="#patchextractor" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.feature_extraction.image.PatchExtractor">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.feature_extraction.image.</span></span><span class="sig-name descname"><span class="pre">PatchExtractor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">patch_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_patches</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_extraction/image.py#L523"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.image.PatchExtractor" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.feature_extraction.image.</span></span><span class="sig-name descname"><span class="pre">PatchExtractor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">patch_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_patches</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_extraction/image.py#L525"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.image.PatchExtractor" title="Link to this definition">#</a></dt>
 <dd><p>Extracts patches from a collection of images.</p>
 <p>Read more in the <a class="reference internal" href="http://github.com/feature_extraction.html#image-feature-extraction"><span class="std std-ref">User Guide</span></a>.</p>
 <div class="versionadded">
@@ -1360,7 +1360,7 @@ <h1>PatchExtractor<a class="headerlink" href="#patchextractor" title="Link to th
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_extraction.image.PatchExtractor.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_extraction/image.py#L591"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.image.PatchExtractor.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_extraction/image.py#L593"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.image.PatchExtractor.fit" title="Link to this definition">#</a></dt>
 <dd><p>Only validate the parameters of the estimator.</p>
 <p>This method allows to: (i) validate the parameters of the estimator  and
 (ii) be consistent with the scikit-learn transformer API.</p>
@@ -1386,7 +1386,7 @@ <h1>PatchExtractor<a class="headerlink" href="#patchextractor" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_extraction.image.PatchExtractor.fit_transform">
-<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L863"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.image.PatchExtractor.fit_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L804"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.image.PatchExtractor.fit_transform" title="Link to this definition">#</a></dt>
 <dd><p>Fit to data, then transform it.</p>
 <p>Fits transformer to <code class="docutils literal notranslate"><span class="pre">X</span></code> and <code class="docutils literal notranslate"><span class="pre">y</span></code> with optional parameters <code class="docutils literal notranslate"><span class="pre">fit_params</span></code>
 and returns a transformed version of <code class="docutils literal notranslate"><span class="pre">X</span></code>.</p>
@@ -1412,7 +1412,7 @@ <h1>PatchExtractor<a class="headerlink" href="#patchextractor" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_extraction.image.PatchExtractor.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.image.PatchExtractor.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.image.PatchExtractor.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1429,7 +1429,7 @@ <h1>PatchExtractor<a class="headerlink" href="#patchextractor" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_extraction.image.PatchExtractor.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.image.PatchExtractor.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.image.PatchExtractor.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1450,7 +1450,7 @@ <h1>PatchExtractor<a class="headerlink" href="#patchextractor" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_extraction.image.PatchExtractor.set_output">
-<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.image.PatchExtractor.set_output" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.image.PatchExtractor.set_output" title="Link to this definition">#</a></dt>
 <dd><p>Set output container.</p>
 <p>See <a class="reference internal" href="http://github.com/auto_examples/miscellaneous/plot_set_output.html#sphx-glr-auto-examples-miscellaneous-plot-set-output-py"><span class="std std-ref">Introducing the set_output API</span></a>
 for an example on how to use the API.</p>
@@ -1481,7 +1481,7 @@ <h1>PatchExtractor<a class="headerlink" href="#patchextractor" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_extraction.image.PatchExtractor.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.image.PatchExtractor.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.image.PatchExtractor.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1505,7 +1505,7 @@ <h1>PatchExtractor<a class="headerlink" href="#patchextractor" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_extraction.image.PatchExtractor.transform">
-<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_extraction/image.py#L616"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.image.PatchExtractor.transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_extraction/image.py#L618"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.image.PatchExtractor.transform" title="Link to this definition">#</a></dt>
 <dd><p>Transform the image samples in <code class="docutils literal notranslate"><span class="pre">X</span></code> into a matrix of patch data.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.feature_extraction.image.extract_patches_2d.html b/dev/modules/generated/sklearn.feature_extraction.image.extract_patches_2d.html
index e100cf0100821..5145a4e2a0645 100644
--- a/dev/modules/generated/sklearn.feature_extraction.image.extract_patches_2d.html
+++ b/dev/modules/generated/sklearn.feature_extraction.image.extract_patches_2d.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>extract_patches_2d<a class="headerlink" href="#extract-patches-2d" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.feature_extraction.image.extract_patches_2d">
-<span class="sig-prename descclassname"><span class="pre">sklearn.feature_extraction.image.</span></span><span class="sig-name descname"><span class="pre">extract_patches_2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">image</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">patch_size</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_patches</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_extraction/image.py#L348"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.image.extract_patches_2d" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.feature_extraction.image.</span></span><span class="sig-name descname"><span class="pre">extract_patches_2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">image</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">patch_size</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_patches</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_extraction/image.py#L350"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.image.extract_patches_2d" title="Link to this definition">#</a></dt>
 <dd><p>Reshape a 2D image into a collection of patches.</p>
 <p>The resulting patches are allocated in a dedicated array.</p>
 <p>Read more in the <a class="reference internal" href="http://github.com/feature_extraction.html#image-feature-extraction"><span class="std std-ref">User Guide</span></a>.</p>
diff --git a/dev/modules/generated/sklearn.feature_extraction.image.grid_to_graph.html b/dev/modules/generated/sklearn.feature_extraction.image.grid_to_graph.html
index 3e9aa5072d7f3..2aba4ed5c5c8f 100644
--- a/dev/modules/generated/sklearn.feature_extraction.image.grid_to_graph.html
+++ b/dev/modules/generated/sklearn.feature_extraction.image.grid_to_graph.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,9 +1305,10 @@
 <h1>grid_to_graph<a class="headerlink" href="#grid-to-graph" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.feature_extraction.image.grid_to_graph">
-<span class="sig-prename descclassname"><span class="pre">sklearn.feature_extraction.image.</span></span><span class="sig-name descname"><span class="pre">grid_to_graph</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_z=1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mask=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_as=&lt;class</span> <span class="pre">'scipy.sparse._coo.coo_matrix'&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype=&lt;class</span> <span class="pre">'int'&gt;</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_extraction/image.py#L190"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.image.grid_to_graph" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.feature_extraction.image.</span></span><span class="sig-name descname"><span class="pre">grid_to_graph</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_z=1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mask=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_as=&lt;class</span> <span class="pre">'scipy.sparse._coo.coo_matrix'&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype=&lt;class</span> <span class="pre">'int'&gt;</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_extraction/image.py#L190"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.image.grid_to_graph" title="Link to this definition">#</a></dt>
 <dd><p>Graph of the pixel-to-pixel connections.</p>
 <p>Edges exist if 2 voxels are connected.</p>
+<p>Read more in the <a class="reference internal" href="http://github.com/feature_extraction.html#connectivity-graph-image"><span class="std std-ref">User Guide</span></a>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><dl class="simple">
diff --git a/dev/modules/generated/sklearn.feature_extraction.image.img_to_graph.html b/dev/modules/generated/sklearn.feature_extraction.image.img_to_graph.html
index f64d00d841db3..903136b37b2be 100644
--- a/dev/modules/generated/sklearn.feature_extraction.image.img_to_graph.html
+++ b/dev/modules/generated/sklearn.feature_extraction.image.img_to_graph.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>img_to_graph<a class="headerlink" href="#img-to-graph" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.feature_extraction.image.img_to_graph">
-<span class="sig-prename descclassname"><span class="pre">sklearn.feature_extraction.image.</span></span><span class="sig-name descname"><span class="pre">img_to_graph</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">img</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mask=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_as=&lt;class</span> <span class="pre">'scipy.sparse._coo.coo_matrix'&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype=None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_extraction/image.py#L138"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.image.img_to_graph" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.feature_extraction.image.</span></span><span class="sig-name descname"><span class="pre">img_to_graph</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">img</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mask=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_as=&lt;class</span> <span class="pre">'scipy.sparse._coo.coo_matrix'&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype=None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_extraction/image.py#L138"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.image.img_to_graph" title="Link to this definition">#</a></dt>
 <dd><p>Graph of the pixel-to-pixel gradient connections.</p>
 <p>Edges are weighted with the gradient values.</p>
 <p>Read more in the <a class="reference internal" href="http://github.com/feature_extraction.html#image-feature-extraction"><span class="std std-ref">User Guide</span></a>.</p>
diff --git a/dev/modules/generated/sklearn.feature_extraction.image.reconstruct_from_patches_2d.html b/dev/modules/generated/sklearn.feature_extraction.image.reconstruct_from_patches_2d.html
index 81353fce08055..5055a533a0eea 100644
--- a/dev/modules/generated/sklearn.feature_extraction.image.reconstruct_from_patches_2d.html
+++ b/dev/modules/generated/sklearn.feature_extraction.image.reconstruct_from_patches_2d.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>reconstruct_from_patches_2d<a class="headerlink" href="#reconstruct-from-patches-2d" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.feature_extraction.image.reconstruct_from_patches_2d">
-<span class="sig-prename descclassname"><span class="pre">sklearn.feature_extraction.image.</span></span><span class="sig-name descname"><span class="pre">reconstruct_from_patches_2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">patches</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">image_size</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_extraction/image.py#L459"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.image.reconstruct_from_patches_2d" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.feature_extraction.image.</span></span><span class="sig-name descname"><span class="pre">reconstruct_from_patches_2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">patches</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">image_size</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_extraction/image.py#L461"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.image.reconstruct_from_patches_2d" title="Link to this definition">#</a></dt>
 <dd><p>Reconstruct the image from all of its patches.</p>
 <p>Patches are assumed to overlap and the image is constructed by filling in
 the patches from left to right, top to bottom, averaging the overlapping
diff --git a/dev/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html b/dev/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html
index e30e2644789ec..0ab66e29ed698 100644
--- a/dev/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html
+++ b/dev/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>CountVectorizer<a class="headerlink" href="#countvectorizer" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.feature_extraction.text.CountVectorizer">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.feature_extraction.text.</span></span><span class="sig-name descname"><span class="pre">CountVectorizer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input='content'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">encoding='utf-8'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">decode_error='strict'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">strip_accents=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lowercase=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">preprocessor=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tokenizer=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stop_words=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">token_pattern='(?u)\\b\\w\\w+\\b'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ngram_range=(1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">1)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">analyzer='word'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_df=1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_df=1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_features=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vocabulary=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">binary=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype=&lt;class</span> <span class="pre">'numpy.int64'&gt;</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_extraction/text.py#L928"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.text.CountVectorizer" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.feature_extraction.text.</span></span><span class="sig-name descname"><span class="pre">CountVectorizer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input='content'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">encoding='utf-8'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">decode_error='strict'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">strip_accents=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lowercase=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">preprocessor=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tokenizer=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stop_words=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">token_pattern='(?u)\\b\\w\\w+\\b'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ngram_range=(1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">1)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">analyzer='word'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_df=1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_df=1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_features=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vocabulary=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">binary=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype=&lt;class</span> <span class="pre">'numpy.int64'&gt;</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_extraction/text.py#L928"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.text.CountVectorizer" title="Link to this definition">#</a></dt>
 <dd><p>Convert a collection of text documents to a matrix of token counts.</p>
 <p>This implementation produces a sparse representation of the counts using
 scipy.sparse.csr_matrix.</p>
@@ -1480,7 +1480,7 @@ <h1>CountVectorizer<a class="headerlink" href="#countvectorizer" title="Link to
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_extraction.text.CountVectorizer.build_analyzer">
-<span class="sig-name descname"><span class="pre">build_analyzer</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_extraction/text.py#L415"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.text.CountVectorizer.build_analyzer" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">build_analyzer</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_extraction/text.py#L415"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.text.CountVectorizer.build_analyzer" title="Link to this definition">#</a></dt>
 <dd><p>Return a callable to process input data.</p>
 <p>The callable handles preprocessing, tokenization, and n-grams generation.</p>
 <dl class="field-list simple">
@@ -1496,7 +1496,7 @@ <h1>CountVectorizer<a class="headerlink" href="#countvectorizer" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_extraction.text.CountVectorizer.build_preprocessor">
-<span class="sig-name descname"><span class="pre">build_preprocessor</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_extraction/text.py#L319"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.text.CountVectorizer.build_preprocessor" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">build_preprocessor</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_extraction/text.py#L319"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.text.CountVectorizer.build_preprocessor" title="Link to this definition">#</a></dt>
 <dd><p>Return a function to preprocess the text before tokenization.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Returns<span class="colon">:</span></dt>
@@ -1510,7 +1510,7 @@ <h1>CountVectorizer<a class="headerlink" href="#countvectorizer" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_extraction.text.CountVectorizer.build_tokenizer">
-<span class="sig-name descname"><span class="pre">build_tokenizer</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_extraction/text.py#L346"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.text.CountVectorizer.build_tokenizer" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">build_tokenizer</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_extraction/text.py#L346"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.text.CountVectorizer.build_tokenizer" title="Link to this definition">#</a></dt>
 <dd><p>Return a function that splits a string into a sequence of tokens.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Returns<span class="colon">:</span></dt>
@@ -1524,7 +1524,7 @@ <h1>CountVectorizer<a class="headerlink" href="#countvectorizer" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_extraction.text.CountVectorizer.decode">
-<span class="sig-name descname"><span class="pre">decode</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">doc</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_extraction/text.py#L206"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.text.CountVectorizer.decode" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">decode</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">doc</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_extraction/text.py#L206"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.text.CountVectorizer.decode" title="Link to this definition">#</a></dt>
 <dd><p>Decode the input into a string of unicode symbols.</p>
 <p>The decoding strategy depends on the vectorizer parameters.</p>
 <dl class="field-list simple">
@@ -1545,7 +1545,7 @@ <h1>CountVectorizer<a class="headerlink" href="#countvectorizer" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_extraction.text.CountVectorizer.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">raw_documents</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_extraction/text.py#L1311"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.text.CountVectorizer.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">raw_documents</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_extraction/text.py#L1311"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.text.CountVectorizer.fit" title="Link to this definition">#</a></dt>
 <dd><p>Learn a vocabulary dictionary of all tokens in the raw documents.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1567,7 +1567,7 @@ <h1>CountVectorizer<a class="headerlink" href="#countvectorizer" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_extraction.text.CountVectorizer.fit_transform">
-<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">raw_documents</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_extraction/text.py#L1330"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.text.CountVectorizer.fit_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">raw_documents</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_extraction/text.py#L1330"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.text.CountVectorizer.fit_transform" title="Link to this definition">#</a></dt>
 <dd><p>Learn the vocabulary dictionary and return document-term matrix.</p>
 <p>This is equivalent to fit followed by transform, but more efficiently
 implemented.</p>
@@ -1591,7 +1591,7 @@ <h1>CountVectorizer<a class="headerlink" href="#countvectorizer" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_extraction.text.CountVectorizer.get_feature_names_out">
-<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_extraction/text.py#L1459"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.text.CountVectorizer.get_feature_names_out" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_extraction/text.py#L1459"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.text.CountVectorizer.get_feature_names_out" title="Link to this definition">#</a></dt>
 <dd><p>Get output feature names for transformation.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1611,7 +1611,7 @@ <h1>CountVectorizer<a class="headerlink" href="#countvectorizer" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_extraction.text.CountVectorizer.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.text.CountVectorizer.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.text.CountVectorizer.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1628,7 +1628,7 @@ <h1>CountVectorizer<a class="headerlink" href="#countvectorizer" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_extraction.text.CountVectorizer.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.text.CountVectorizer.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.text.CountVectorizer.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1649,7 +1649,7 @@ <h1>CountVectorizer<a class="headerlink" href="#countvectorizer" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_extraction.text.CountVectorizer.get_stop_words">
-<span class="sig-name descname"><span class="pre">get_stop_words</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_extraction/text.py#L366"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.text.CountVectorizer.get_stop_words" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_stop_words</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_extraction/text.py#L366"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.text.CountVectorizer.get_stop_words" title="Link to this definition">#</a></dt>
 <dd><p>Build or fetch the effective stop words list.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Returns<span class="colon">:</span></dt>
@@ -1663,7 +1663,7 @@ <h1>CountVectorizer<a class="headerlink" href="#countvectorizer" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_extraction.text.CountVectorizer.inverse_transform">
-<span class="sig-name descname"><span class="pre">inverse_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_extraction/text.py#L1426"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.text.CountVectorizer.inverse_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">inverse_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_extraction/text.py#L1426"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.text.CountVectorizer.inverse_transform" title="Link to this definition">#</a></dt>
 <dd><p>Return terms per document with nonzero entries in X.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1674,7 +1674,7 @@ <h1>CountVectorizer<a class="headerlink" href="#countvectorizer" title="Link to
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><dl class="simple">
-<dt><strong>X_inv</strong><span class="classifier">list of arrays of shape (n_samples,)</span></dt><dd><p>List of arrays of terms.</p>
+<dt><strong>X_original</strong><span class="classifier">list of arrays of shape (n_samples,)</span></dt><dd><p>List of arrays of terms.</p>
 </dd>
 </dl>
 </dd>
@@ -1683,7 +1683,7 @@ <h1>CountVectorizer<a class="headerlink" href="#countvectorizer" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_extraction.text.CountVectorizer.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.text.CountVectorizer.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.text.CountVectorizer.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1707,7 +1707,7 @@ <h1>CountVectorizer<a class="headerlink" href="#countvectorizer" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_extraction.text.CountVectorizer.transform">
-<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">raw_documents</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_extraction/text.py#L1398"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.text.CountVectorizer.transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">raw_documents</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_extraction/text.py#L1398"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.text.CountVectorizer.transform" title="Link to this definition">#</a></dt>
 <dd><p>Transform documents to document-term matrix.</p>
 <p>Extract token counts out of raw text documents using the vocabulary
 fitted with fit or the one provided to the constructor.</p>
diff --git a/dev/modules/generated/sklearn.feature_extraction.text.HashingVectorizer.html b/dev/modules/generated/sklearn.feature_extraction.text.HashingVectorizer.html
index 8db3869b39b34..7c7f88d962563 100644
--- a/dev/modules/generated/sklearn.feature_extraction.text.HashingVectorizer.html
+++ b/dev/modules/generated/sklearn.feature_extraction.text.HashingVectorizer.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>HashingVectorizer<a class="headerlink" href="#hashingvectorizer" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.feature_extraction.text.HashingVectorizer">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.feature_extraction.text.</span></span><span class="sig-name descname"><span class="pre">HashingVectorizer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input='content'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">encoding='utf-8'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">decode_error='strict'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">strip_accents=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lowercase=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">preprocessor=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tokenizer=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stop_words=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">token_pattern='(?u)\\b\\w\\w+\\b'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ngram_range=(1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">1)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">analyzer='word'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_features=1048576</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">binary=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm='l2'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alternate_sign=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype=&lt;class</span> <span class="pre">'numpy.float64'&gt;</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_extraction/text.py#L558"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.text.HashingVectorizer" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.feature_extraction.text.</span></span><span class="sig-name descname"><span class="pre">HashingVectorizer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input='content'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">encoding='utf-8'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">decode_error='strict'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">strip_accents=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lowercase=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">preprocessor=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tokenizer=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stop_words=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">token_pattern='(?u)\\b\\w\\w+\\b'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ngram_range=(1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">1)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">analyzer='word'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_features=1048576</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">binary=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm='l2'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alternate_sign=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype=&lt;class</span> <span class="pre">'numpy.float64'&gt;</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_extraction/text.py#L558"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.text.HashingVectorizer" title="Link to this definition">#</a></dt>
 <dd><p>Convert a collection of text documents to a matrix of token occurrences.</p>
 <p>It turns a collection of text documents into a scipy.sparse matrix holding
 token occurrence counts (or binary occurrence information), possibly
@@ -1467,7 +1467,7 @@ <h1>HashingVectorizer<a class="headerlink" href="#hashingvectorizer" title="Link
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_extraction.text.HashingVectorizer.build_analyzer">
-<span class="sig-name descname"><span class="pre">build_analyzer</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_extraction/text.py#L415"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.text.HashingVectorizer.build_analyzer" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">build_analyzer</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_extraction/text.py#L415"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.text.HashingVectorizer.build_analyzer" title="Link to this definition">#</a></dt>
 <dd><p>Return a callable to process input data.</p>
 <p>The callable handles preprocessing, tokenization, and n-grams generation.</p>
 <dl class="field-list simple">
@@ -1483,7 +1483,7 @@ <h1>HashingVectorizer<a class="headerlink" href="#hashingvectorizer" title="Link
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_extraction.text.HashingVectorizer.build_preprocessor">
-<span class="sig-name descname"><span class="pre">build_preprocessor</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_extraction/text.py#L319"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.text.HashingVectorizer.build_preprocessor" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">build_preprocessor</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_extraction/text.py#L319"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.text.HashingVectorizer.build_preprocessor" title="Link to this definition">#</a></dt>
 <dd><p>Return a function to preprocess the text before tokenization.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Returns<span class="colon">:</span></dt>
@@ -1497,7 +1497,7 @@ <h1>HashingVectorizer<a class="headerlink" href="#hashingvectorizer" title="Link
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_extraction.text.HashingVectorizer.build_tokenizer">
-<span class="sig-name descname"><span class="pre">build_tokenizer</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_extraction/text.py#L346"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.text.HashingVectorizer.build_tokenizer" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">build_tokenizer</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_extraction/text.py#L346"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.text.HashingVectorizer.build_tokenizer" title="Link to this definition">#</a></dt>
 <dd><p>Return a function that splits a string into a sequence of tokens.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Returns<span class="colon">:</span></dt>
@@ -1511,7 +1511,7 @@ <h1>HashingVectorizer<a class="headerlink" href="#hashingvectorizer" title="Link
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_extraction.text.HashingVectorizer.decode">
-<span class="sig-name descname"><span class="pre">decode</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">doc</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_extraction/text.py#L206"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.text.HashingVectorizer.decode" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">decode</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">doc</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_extraction/text.py#L206"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.text.HashingVectorizer.decode" title="Link to this definition">#</a></dt>
 <dd><p>Decode the input into a string of unicode symbols.</p>
 <p>The decoding strategy depends on the vectorizer parameters.</p>
 <dl class="field-list simple">
@@ -1532,7 +1532,7 @@ <h1>HashingVectorizer<a class="headerlink" href="#hashingvectorizer" title="Link
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_extraction.text.HashingVectorizer.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_extraction/text.py#L823"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.text.HashingVectorizer.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_extraction/text.py#L823"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.text.HashingVectorizer.fit" title="Link to this definition">#</a></dt>
 <dd><p>Only validates estimator’s parameters.</p>
 <p>This method allows to: (i) validate the estimator’s parameters and
 (ii) be consistent with the scikit-learn transformer API.</p>
@@ -1556,7 +1556,7 @@ <h1>HashingVectorizer<a class="headerlink" href="#hashingvectorizer" title="Link
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_extraction.text.HashingVectorizer.fit_transform">
-<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_extraction/text.py#L885"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.text.HashingVectorizer.fit_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_extraction/text.py#L885"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.text.HashingVectorizer.fit_transform" title="Link to this definition">#</a></dt>
 <dd><p>Transform a sequence of documents to a document-term matrix.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1581,7 +1581,7 @@ <h1>HashingVectorizer<a class="headerlink" href="#hashingvectorizer" title="Link
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_extraction.text.HashingVectorizer.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.text.HashingVectorizer.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.text.HashingVectorizer.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1598,7 +1598,7 @@ <h1>HashingVectorizer<a class="headerlink" href="#hashingvectorizer" title="Link
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_extraction.text.HashingVectorizer.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.text.HashingVectorizer.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.text.HashingVectorizer.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1619,7 +1619,7 @@ <h1>HashingVectorizer<a class="headerlink" href="#hashingvectorizer" title="Link
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_extraction.text.HashingVectorizer.get_stop_words">
-<span class="sig-name descname"><span class="pre">get_stop_words</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_extraction/text.py#L366"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.text.HashingVectorizer.get_stop_words" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_stop_words</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_extraction/text.py#L366"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.text.HashingVectorizer.get_stop_words" title="Link to this definition">#</a></dt>
 <dd><p>Build or fetch the effective stop words list.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Returns<span class="colon">:</span></dt>
@@ -1633,7 +1633,7 @@ <h1>HashingVectorizer<a class="headerlink" href="#hashingvectorizer" title="Link
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_extraction.text.HashingVectorizer.partial_fit">
-<span class="sig-name descname"><span class="pre">partial_fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_extraction/text.py#L801"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.text.HashingVectorizer.partial_fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">partial_fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_extraction/text.py#L801"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.text.HashingVectorizer.partial_fit" title="Link to this definition">#</a></dt>
 <dd><p>Only validates estimator’s parameters.</p>
 <p>This method allows to: (i) validate the estimator’s parameters and
 (ii) be consistent with the scikit-learn transformer API.</p>
@@ -1657,7 +1657,7 @@ <h1>HashingVectorizer<a class="headerlink" href="#hashingvectorizer" title="Link
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_extraction.text.HashingVectorizer.set_output">
-<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.text.HashingVectorizer.set_output" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.text.HashingVectorizer.set_output" title="Link to this definition">#</a></dt>
 <dd><p>Set output container.</p>
 <p>See <a class="reference internal" href="http://github.com/auto_examples/miscellaneous/plot_set_output.html#sphx-glr-auto-examples-miscellaneous-plot-set-output-py"><span class="std std-ref">Introducing the set_output API</span></a>
 for an example on how to use the API.</p>
@@ -1688,7 +1688,7 @@ <h1>HashingVectorizer<a class="headerlink" href="#hashingvectorizer" title="Link
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_extraction.text.HashingVectorizer.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.text.HashingVectorizer.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.text.HashingVectorizer.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1712,7 +1712,7 @@ <h1>HashingVectorizer<a class="headerlink" href="#hashingvectorizer" title="Link
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_extraction.text.HashingVectorizer.transform">
-<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_extraction/text.py#L855"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.text.HashingVectorizer.transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_extraction/text.py#L855"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.text.HashingVectorizer.transform" title="Link to this definition">#</a></dt>
 <dd><p>Transform a sequence of documents to a document-term matrix.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.feature_extraction.text.TfidfTransformer.html b/dev/modules/generated/sklearn.feature_extraction.text.TfidfTransformer.html
index 31ff6d9946e44..f7c463df3bbf9 100644
--- a/dev/modules/generated/sklearn.feature_extraction.text.TfidfTransformer.html
+++ b/dev/modules/generated/sklearn.feature_extraction.text.TfidfTransformer.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>TfidfTransformer<a class="headerlink" href="#tfidftransformer" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.feature_extraction.text.TfidfTransformer">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.feature_extraction.text.</span></span><span class="sig-name descname"><span class="pre">TfidfTransformer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'l2'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_idf</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">smooth_idf</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sublinear_tf</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_extraction/text.py#L1490"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.text.TfidfTransformer" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.feature_extraction.text.</span></span><span class="sig-name descname"><span class="pre">TfidfTransformer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'l2'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_idf</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">smooth_idf</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sublinear_tf</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_extraction/text.py#L1490"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.text.TfidfTransformer" title="Link to this definition">#</a></dt>
 <dd><p>Transform a count matrix to a normalized tf or tf-idf representation.</p>
 <p>Tf means term-frequency while tf-idf means term-frequency times inverse
 document-frequency. This is a common term weighting scheme in information
@@ -1438,7 +1438,7 @@ <h1>TfidfTransformer<a class="headerlink" href="#tfidftransformer" title="Link t
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_extraction.text.TfidfTransformer.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_extraction/text.py#L1634"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.text.TfidfTransformer.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_extraction/text.py#L1634"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.text.TfidfTransformer.fit" title="Link to this definition">#</a></dt>
 <dd><p>Learn the idf vector (global term weights).</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1460,7 +1460,7 @@ <h1>TfidfTransformer<a class="headerlink" href="#tfidftransformer" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_extraction.text.TfidfTransformer.fit_transform">
-<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L863"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.text.TfidfTransformer.fit_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L804"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.text.TfidfTransformer.fit_transform" title="Link to this definition">#</a></dt>
 <dd><p>Fit to data, then transform it.</p>
 <p>Fits transformer to <code class="docutils literal notranslate"><span class="pre">X</span></code> and <code class="docutils literal notranslate"><span class="pre">y</span></code> with optional parameters <code class="docutils literal notranslate"><span class="pre">fit_params</span></code>
 and returns a transformed version of <code class="docutils literal notranslate"><span class="pre">X</span></code>.</p>
@@ -1486,7 +1486,7 @@ <h1>TfidfTransformer<a class="headerlink" href="#tfidftransformer" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_extraction.text.TfidfTransformer.get_feature_names_out">
-<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L943"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.text.TfidfTransformer.get_feature_names_out" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L884"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.text.TfidfTransformer.get_feature_names_out" title="Link to this definition">#</a></dt>
 <dd><p>Get output feature names for transformation.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1514,7 +1514,7 @@ <h1>TfidfTransformer<a class="headerlink" href="#tfidftransformer" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_extraction.text.TfidfTransformer.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.text.TfidfTransformer.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.text.TfidfTransformer.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1531,7 +1531,7 @@ <h1>TfidfTransformer<a class="headerlink" href="#tfidftransformer" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_extraction.text.TfidfTransformer.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.text.TfidfTransformer.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.text.TfidfTransformer.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1552,7 +1552,7 @@ <h1>TfidfTransformer<a class="headerlink" href="#tfidftransformer" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_extraction.text.TfidfTransformer.set_output">
-<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.text.TfidfTransformer.set_output" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.text.TfidfTransformer.set_output" title="Link to this definition">#</a></dt>
 <dd><p>Set output container.</p>
 <p>See <a class="reference internal" href="http://github.com/auto_examples/miscellaneous/plot_set_output.html#sphx-glr-auto-examples-miscellaneous-plot-set-output-py"><span class="std std-ref">Introducing the set_output API</span></a>
 for an example on how to use the API.</p>
@@ -1583,7 +1583,7 @@ <h1>TfidfTransformer<a class="headerlink" href="#tfidftransformer" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_extraction.text.TfidfTransformer.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.text.TfidfTransformer.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.text.TfidfTransformer.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1607,7 +1607,7 @@ <h1>TfidfTransformer<a class="headerlink" href="#tfidftransformer" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_extraction.text.TfidfTransformer.set_transform_request">
-<span class="sig-name descname"><span class="pre">set_transform_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.feature_extraction.text.TfidfTransformer" title="sklearn.feature_extraction.text.TfidfTransformer"><span class="pre">TfidfTransformer</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.text.TfidfTransformer.set_transform_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_transform_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.feature_extraction.text.TfidfTransformer" title="sklearn.feature_extraction.text.TfidfTransformer"><span class="pre">TfidfTransformer</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.text.TfidfTransformer.set_transform_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">transform</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1650,7 +1650,7 @@ <h1>TfidfTransformer<a class="headerlink" href="#tfidftransformer" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_extraction.text.TfidfTransformer.transform">
-<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_extraction/text.py#L1682"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.text.TfidfTransformer.transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_extraction/text.py#L1682"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.text.TfidfTransformer.transform" title="Link to this definition">#</a></dt>
 <dd><p>Transform a count matrix to a tf or tf-idf representation.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html b/dev/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html
index 42f6ce36d0cbc..d3a2b0a393350 100644
--- a/dev/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html
+++ b/dev/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>TfidfVectorizer<a class="headerlink" href="#tfidfvectorizer" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.feature_extraction.text.TfidfVectorizer">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.feature_extraction.text.</span></span><span class="sig-name descname"><span class="pre">TfidfVectorizer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input='content'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">encoding='utf-8'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">decode_error='strict'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">strip_accents=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lowercase=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">preprocessor=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tokenizer=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">analyzer='word'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stop_words=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">token_pattern='(?u)\\b\\w\\w+\\b'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ngram_range=(1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">1)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_df=1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_df=1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_features=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vocabulary=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">binary=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype=&lt;class</span> <span class="pre">'numpy.float64'&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm='l2'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_idf=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">smooth_idf=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sublinear_tf=False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_extraction/text.py#L1734"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.text.TfidfVectorizer" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.feature_extraction.text.</span></span><span class="sig-name descname"><span class="pre">TfidfVectorizer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input='content'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">encoding='utf-8'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">decode_error='strict'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">strip_accents=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lowercase=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">preprocessor=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tokenizer=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">analyzer='word'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stop_words=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">token_pattern='(?u)\\b\\w\\w+\\b'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ngram_range=(1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">1)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_df=1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_df=1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_features=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vocabulary=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">binary=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype=&lt;class</span> <span class="pre">'numpy.float64'&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm='l2'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_idf=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">smooth_idf=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sublinear_tf=False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_extraction/text.py#L1734"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.text.TfidfVectorizer" title="Link to this definition">#</a></dt>
 <dd><p>Convert a collection of raw documents to a matrix of TF-IDF features.</p>
 <p>Equivalent to <a class="reference internal" href="http://github.com/sklearn.feature_extraction.text.CountVectorizer.html#sklearn.feature_extraction.text.CountVectorizer" title="sklearn.feature_extraction.text.CountVectorizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">CountVectorizer</span></code></a> followed by
 <a class="reference internal" href="http://github.com/sklearn.feature_extraction.text.TfidfTransformer.html#sklearn.feature_extraction.text.TfidfTransformer" title="sklearn.feature_extraction.text.TfidfTransformer"><code class="xref py py-class docutils literal notranslate"><span class="pre">TfidfTransformer</span></code></a>.</p>
@@ -1487,7 +1487,7 @@ <h1>TfidfVectorizer<a class="headerlink" href="#tfidfvectorizer" title="Link to
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_extraction.text.TfidfVectorizer.build_analyzer">
-<span class="sig-name descname"><span class="pre">build_analyzer</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_extraction/text.py#L415"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.text.TfidfVectorizer.build_analyzer" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">build_analyzer</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_extraction/text.py#L415"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.text.TfidfVectorizer.build_analyzer" title="Link to this definition">#</a></dt>
 <dd><p>Return a callable to process input data.</p>
 <p>The callable handles preprocessing, tokenization, and n-grams generation.</p>
 <dl class="field-list simple">
@@ -1503,7 +1503,7 @@ <h1>TfidfVectorizer<a class="headerlink" href="#tfidfvectorizer" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_extraction.text.TfidfVectorizer.build_preprocessor">
-<span class="sig-name descname"><span class="pre">build_preprocessor</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_extraction/text.py#L319"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.text.TfidfVectorizer.build_preprocessor" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">build_preprocessor</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_extraction/text.py#L319"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.text.TfidfVectorizer.build_preprocessor" title="Link to this definition">#</a></dt>
 <dd><p>Return a function to preprocess the text before tokenization.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Returns<span class="colon">:</span></dt>
@@ -1517,7 +1517,7 @@ <h1>TfidfVectorizer<a class="headerlink" href="#tfidfvectorizer" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_extraction.text.TfidfVectorizer.build_tokenizer">
-<span class="sig-name descname"><span class="pre">build_tokenizer</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_extraction/text.py#L346"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.text.TfidfVectorizer.build_tokenizer" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">build_tokenizer</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_extraction/text.py#L346"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.text.TfidfVectorizer.build_tokenizer" title="Link to this definition">#</a></dt>
 <dd><p>Return a function that splits a string into a sequence of tokens.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Returns<span class="colon">:</span></dt>
@@ -1531,7 +1531,7 @@ <h1>TfidfVectorizer<a class="headerlink" href="#tfidfvectorizer" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_extraction.text.TfidfVectorizer.decode">
-<span class="sig-name descname"><span class="pre">decode</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">doc</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_extraction/text.py#L206"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.text.TfidfVectorizer.decode" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">decode</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">doc</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_extraction/text.py#L206"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.text.TfidfVectorizer.decode" title="Link to this definition">#</a></dt>
 <dd><p>Decode the input into a string of unicode symbols.</p>
 <p>The decoding strategy depends on the vectorizer parameters.</p>
 <dl class="field-list simple">
@@ -1552,7 +1552,7 @@ <h1>TfidfVectorizer<a class="headerlink" href="#tfidfvectorizer" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_extraction.text.TfidfVectorizer.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">raw_documents</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_extraction/text.py#L2049"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.text.TfidfVectorizer.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">raw_documents</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_extraction/text.py#L2049"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.text.TfidfVectorizer.fit" title="Link to this definition">#</a></dt>
 <dd><p>Learn vocabulary and idf from training set.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1574,7 +1574,7 @@ <h1>TfidfVectorizer<a class="headerlink" href="#tfidfvectorizer" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_extraction.text.TfidfVectorizer.fit_transform">
-<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">raw_documents</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_extraction/text.py#L2078"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.text.TfidfVectorizer.fit_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">raw_documents</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_extraction/text.py#L2078"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.text.TfidfVectorizer.fit_transform" title="Link to this definition">#</a></dt>
 <dd><p>Learn vocabulary and idf, return document-term matrix.</p>
 <p>This is equivalent to fit followed by transform, but more efficiently
 implemented.</p>
@@ -1598,7 +1598,7 @@ <h1>TfidfVectorizer<a class="headerlink" href="#tfidfvectorizer" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_extraction.text.TfidfVectorizer.get_feature_names_out">
-<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_extraction/text.py#L1459"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.text.TfidfVectorizer.get_feature_names_out" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_extraction/text.py#L1459"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.text.TfidfVectorizer.get_feature_names_out" title="Link to this definition">#</a></dt>
 <dd><p>Get output feature names for transformation.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1618,7 +1618,7 @@ <h1>TfidfVectorizer<a class="headerlink" href="#tfidfvectorizer" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_extraction.text.TfidfVectorizer.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.text.TfidfVectorizer.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.text.TfidfVectorizer.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1635,7 +1635,7 @@ <h1>TfidfVectorizer<a class="headerlink" href="#tfidfvectorizer" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_extraction.text.TfidfVectorizer.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.text.TfidfVectorizer.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.text.TfidfVectorizer.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1656,7 +1656,7 @@ <h1>TfidfVectorizer<a class="headerlink" href="#tfidfvectorizer" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_extraction.text.TfidfVectorizer.get_stop_words">
-<span class="sig-name descname"><span class="pre">get_stop_words</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_extraction/text.py#L366"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.text.TfidfVectorizer.get_stop_words" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_stop_words</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_extraction/text.py#L366"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.text.TfidfVectorizer.get_stop_words" title="Link to this definition">#</a></dt>
 <dd><p>Build or fetch the effective stop words list.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Returns<span class="colon">:</span></dt>
@@ -1670,7 +1670,7 @@ <h1>TfidfVectorizer<a class="headerlink" href="#tfidfvectorizer" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_extraction.text.TfidfVectorizer.inverse_transform">
-<span class="sig-name descname"><span class="pre">inverse_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_extraction/text.py#L1426"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.text.TfidfVectorizer.inverse_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">inverse_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_extraction/text.py#L1426"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.text.TfidfVectorizer.inverse_transform" title="Link to this definition">#</a></dt>
 <dd><p>Return terms per document with nonzero entries in X.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1681,7 +1681,7 @@ <h1>TfidfVectorizer<a class="headerlink" href="#tfidfvectorizer" title="Link to
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><dl class="simple">
-<dt><strong>X_inv</strong><span class="classifier">list of arrays of shape (n_samples,)</span></dt><dd><p>List of arrays of terms.</p>
+<dt><strong>X_original</strong><span class="classifier">list of arrays of shape (n_samples,)</span></dt><dd><p>List of arrays of terms.</p>
 </dd>
 </dl>
 </dd>
@@ -1690,7 +1690,7 @@ <h1>TfidfVectorizer<a class="headerlink" href="#tfidfvectorizer" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_extraction.text.TfidfVectorizer.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.text.TfidfVectorizer.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.text.TfidfVectorizer.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1714,7 +1714,7 @@ <h1>TfidfVectorizer<a class="headerlink" href="#tfidfvectorizer" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_extraction.text.TfidfVectorizer.transform">
-<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">raw_documents</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_extraction/text.py#L2110"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.text.TfidfVectorizer.transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">raw_documents</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_extraction/text.py#L2110"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_extraction.text.TfidfVectorizer.transform" title="Link to this definition">#</a></dt>
 <dd><p>Transform documents to document-term matrix.</p>
 <p>Uses the vocabulary and document frequencies (df) learned by fit (or
 fit_transform).</p>
diff --git a/dev/modules/generated/sklearn.feature_selection.GenericUnivariateSelect.html b/dev/modules/generated/sklearn.feature_selection.GenericUnivariateSelect.html
index 04efe5916a88d..bc0ca18ff03ec 100644
--- a/dev/modules/generated/sklearn.feature_selection.GenericUnivariateSelect.html
+++ b/dev/modules/generated/sklearn.feature_selection.GenericUnivariateSelect.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>GenericUnivariateSelect<a class="headerlink" href="#genericunivariateselect" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.feature_selection.GenericUnivariateSelect">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.feature_selection.</span></span><span class="sig-name descname"><span class="pre">GenericUnivariateSelect</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">score_func=&lt;function</span> <span class="pre">f_classif&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode='percentile'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">param=1e-05</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_selection/_univariate_selection.py#L1063"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.GenericUnivariateSelect" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.feature_selection.</span></span><span class="sig-name descname"><span class="pre">GenericUnivariateSelect</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">score_func=&lt;function</span> <span class="pre">f_classif&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode='percentile'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">param=1e-05</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_selection/_univariate_selection.py#L1062"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.GenericUnivariateSelect" title="Link to this definition">#</a></dt>
 <dd><p>Univariate feature selector with configurable strategy.</p>
 <p>Read more in the <a class="reference internal" href="http://github.com/feature_selection.html#univariate-feature-selection"><span class="std std-ref">User Guide</span></a>.</p>
 <dl class="field-list">
@@ -1381,7 +1381,7 @@ <h1>GenericUnivariateSelect<a class="headerlink" href="#genericunivariateselect"
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.GenericUnivariateSelect.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_selection/_univariate_selection.py#L543"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.GenericUnivariateSelect.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_selection/_univariate_selection.py#L542"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.GenericUnivariateSelect.fit" title="Link to this definition">#</a></dt>
 <dd><p>Run score function on (X, y) and get the appropriate features.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1404,7 +1404,7 @@ <h1>GenericUnivariateSelect<a class="headerlink" href="#genericunivariateselect"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.GenericUnivariateSelect.fit_transform">
-<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L863"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.GenericUnivariateSelect.fit_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L804"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.GenericUnivariateSelect.fit_transform" title="Link to this definition">#</a></dt>
 <dd><p>Fit to data, then transform it.</p>
 <p>Fits transformer to <code class="docutils literal notranslate"><span class="pre">X</span></code> and <code class="docutils literal notranslate"><span class="pre">y</span></code> with optional parameters <code class="docutils literal notranslate"><span class="pre">fit_params</span></code>
 and returns a transformed version of <code class="docutils literal notranslate"><span class="pre">X</span></code>.</p>
@@ -1430,7 +1430,7 @@ <h1>GenericUnivariateSelect<a class="headerlink" href="#genericunivariateselect"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.GenericUnivariateSelect.get_feature_names_out">
-<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_selection/_base.py#L174"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.GenericUnivariateSelect.get_feature_names_out" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_selection/_base.py#L174"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.GenericUnivariateSelect.get_feature_names_out" title="Link to this definition">#</a></dt>
 <dd><p>Mask feature names according to selected features.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1458,7 +1458,7 @@ <h1>GenericUnivariateSelect<a class="headerlink" href="#genericunivariateselect"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.GenericUnivariateSelect.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.GenericUnivariateSelect.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.GenericUnivariateSelect.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1475,7 +1475,7 @@ <h1>GenericUnivariateSelect<a class="headerlink" href="#genericunivariateselect"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.GenericUnivariateSelect.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.GenericUnivariateSelect.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.GenericUnivariateSelect.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1496,7 +1496,7 @@ <h1>GenericUnivariateSelect<a class="headerlink" href="#genericunivariateselect"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.GenericUnivariateSelect.get_support">
-<span class="sig-name descname"><span class="pre">get_support</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_selection/_base.py#L52"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.GenericUnivariateSelect.get_support" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_support</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_selection/_base.py#L52"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.GenericUnivariateSelect.get_support" title="Link to this definition">#</a></dt>
 <dd><p>Get a mask, or integer index, of the features selected.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1522,7 +1522,7 @@ <h1>GenericUnivariateSelect<a class="headerlink" href="#genericunivariateselect"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.GenericUnivariateSelect.inverse_transform">
-<span class="sig-name descname"><span class="pre">inverse_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_selection/_base.py#L134"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.GenericUnivariateSelect.inverse_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">inverse_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_selection/_base.py#L134"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.GenericUnivariateSelect.inverse_transform" title="Link to this definition">#</a></dt>
 <dd><p>Reverse the transformation operation.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1533,7 +1533,7 @@ <h1>GenericUnivariateSelect<a class="headerlink" href="#genericunivariateselect"
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><dl class="simple">
-<dt><strong>X_r</strong><span class="classifier">array of shape [n_samples, n_original_features]</span></dt><dd><p><code class="docutils literal notranslate"><span class="pre">X</span></code> with columns of zeros inserted where features would have
+<dt><strong>X_original</strong><span class="classifier">array of shape [n_samples, n_original_features]</span></dt><dd><p><code class="docutils literal notranslate"><span class="pre">X</span></code> with columns of zeros inserted where features would have
 been removed by <a class="reference internal" href="#sklearn.feature_selection.GenericUnivariateSelect.transform" title="sklearn.feature_selection.GenericUnivariateSelect.transform"><code class="xref py py-meth docutils literal notranslate"><span class="pre">transform</span></code></a>.</p>
 </dd>
 </dl>
@@ -1543,7 +1543,7 @@ <h1>GenericUnivariateSelect<a class="headerlink" href="#genericunivariateselect"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.GenericUnivariateSelect.set_output">
-<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.GenericUnivariateSelect.set_output" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.GenericUnivariateSelect.set_output" title="Link to this definition">#</a></dt>
 <dd><p>Set output container.</p>
 <p>See <a class="reference internal" href="http://github.com/auto_examples/miscellaneous/plot_set_output.html#sphx-glr-auto-examples-miscellaneous-plot-set-output-py"><span class="std std-ref">Introducing the set_output API</span></a>
 for an example on how to use the API.</p>
@@ -1574,7 +1574,7 @@ <h1>GenericUnivariateSelect<a class="headerlink" href="#genericunivariateselect"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.GenericUnivariateSelect.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.GenericUnivariateSelect.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.GenericUnivariateSelect.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1598,7 +1598,7 @@ <h1>GenericUnivariateSelect<a class="headerlink" href="#genericunivariateselect"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.GenericUnivariateSelect.transform">
-<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_selection/_base.py#L87"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.GenericUnivariateSelect.transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_selection/_base.py#L87"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.GenericUnivariateSelect.transform" title="Link to this definition">#</a></dt>
 <dd><p>Reduce X to the selected features.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.feature_selection.RFE.html b/dev/modules/generated/sklearn.feature_selection.RFE.html
index 3cffcf26c5178..d12bc6104bcb5 100644
--- a/dev/modules/generated/sklearn.feature_selection.RFE.html
+++ b/dev/modules/generated/sklearn.feature_selection.RFE.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>RFE<a class="headerlink" href="#rfe" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.feature_selection.RFE">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.feature_selection.</span></span><span class="sig-name descname"><span class="pre">RFE</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_features_to_select</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">step</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">importance_getter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_selection/_rfe.py#L68"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.RFE" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.feature_selection.</span></span><span class="sig-name descname"><span class="pre">RFE</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_features_to_select</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">step</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">importance_getter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_selection/_rfe.py#L68"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.RFE" title="Link to this definition">#</a></dt>
 <dd><p>Feature ranking with recursive feature elimination.</p>
 <p>Given an external estimator that assigns weights to features (e.g., the
 coefficients of a linear model), the goal of recursive feature elimination
@@ -1428,7 +1428,7 @@ <h1>RFE<a class="headerlink" href="#rfe" title="Link to this heading">#</a></h1>
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.RFE.decision_function">
-<span class="sig-name descname"><span class="pre">decision_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_selection/_rfe.py#L453"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.RFE.decision_function" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">decision_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_selection/_rfe.py#L459"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.RFE.decision_function" title="Link to this definition">#</a></dt>
 <dd><p>Compute the decision function of <code class="docutils literal notranslate"><span class="pre">X</span></code>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1453,7 +1453,7 @@ <h1>RFE<a class="headerlink" href="#rfe" title="Link to this heading">#</a></h1>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.RFE.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_selection/_rfe.py#L240"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.RFE.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_selection/_rfe.py#L240"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.RFE.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit the RFE model and then the underlying estimator on the selected features.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1486,7 +1486,7 @@ <h1>RFE<a class="headerlink" href="#rfe" title="Link to this heading">#</a></h1>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.RFE.fit_transform">
-<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L863"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.RFE.fit_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L804"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.RFE.fit_transform" title="Link to this definition">#</a></dt>
 <dd><p>Fit to data, then transform it.</p>
 <p>Fits transformer to <code class="docutils literal notranslate"><span class="pre">X</span></code> and <code class="docutils literal notranslate"><span class="pre">y</span></code> with optional parameters <code class="docutils literal notranslate"><span class="pre">fit_params</span></code>
 and returns a transformed version of <code class="docutils literal notranslate"><span class="pre">X</span></code>.</p>
@@ -1512,7 +1512,7 @@ <h1>RFE<a class="headerlink" href="#rfe" title="Link to this heading">#</a></h1>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.RFE.get_feature_names_out">
-<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_selection/_base.py#L174"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.RFE.get_feature_names_out" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_selection/_base.py#L174"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.RFE.get_feature_names_out" title="Link to this definition">#</a></dt>
 <dd><p>Mask feature names according to selected features.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1540,7 +1540,7 @@ <h1>RFE<a class="headerlink" href="#rfe" title="Link to this heading">#</a></h1>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.RFE.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_selection/_rfe.py#L528"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.RFE.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_selection/_rfe.py#L534"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.RFE.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1560,7 +1560,7 @@ <h1>RFE<a class="headerlink" href="#rfe" title="Link to this heading">#</a></h1>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.RFE.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.RFE.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.RFE.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1581,7 +1581,7 @@ <h1>RFE<a class="headerlink" href="#rfe" title="Link to this heading">#</a></h1>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.RFE.get_support">
-<span class="sig-name descname"><span class="pre">get_support</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_selection/_base.py#L52"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.RFE.get_support" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_support</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_selection/_base.py#L52"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.RFE.get_support" title="Link to this definition">#</a></dt>
 <dd><p>Get a mask, or integer index, of the features selected.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1607,7 +1607,7 @@ <h1>RFE<a class="headerlink" href="#rfe" title="Link to this heading">#</a></h1>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.RFE.inverse_transform">
-<span class="sig-name descname"><span class="pre">inverse_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_selection/_base.py#L134"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.RFE.inverse_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">inverse_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_selection/_base.py#L134"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.RFE.inverse_transform" title="Link to this definition">#</a></dt>
 <dd><p>Reverse the transformation operation.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1618,7 +1618,7 @@ <h1>RFE<a class="headerlink" href="#rfe" title="Link to this heading">#</a></h1>
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><dl class="simple">
-<dt><strong>X_r</strong><span class="classifier">array of shape [n_samples, n_original_features]</span></dt><dd><p><code class="docutils literal notranslate"><span class="pre">X</span></code> with columns of zeros inserted where features would have
+<dt><strong>X_original</strong><span class="classifier">array of shape [n_samples, n_original_features]</span></dt><dd><p><code class="docutils literal notranslate"><span class="pre">X</span></code> with columns of zeros inserted where features would have
 been removed by <a class="reference internal" href="#sklearn.feature_selection.RFE.transform" title="sklearn.feature_selection.RFE.transform"><code class="xref py py-meth docutils literal notranslate"><span class="pre">transform</span></code></a>.</p>
 </dd>
 </dl>
@@ -1628,7 +1628,7 @@ <h1>RFE<a class="headerlink" href="#rfe" title="Link to this heading">#</a></h1>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.RFE.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">predict_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_selection/_rfe.py#L372"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.RFE.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">predict_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_selection/_rfe.py#L378"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.RFE.predict" title="Link to this definition">#</a></dt>
 <dd><p>Reduce X to the selected features and predict using the estimator.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1658,7 +1658,7 @@ <h1>RFE<a class="headerlink" href="#rfe" title="Link to this heading">#</a></h1>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.RFE.predict_log_proba">
-<span class="sig-name descname"><span class="pre">predict_log_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_selection/_rfe.py#L495"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.RFE.predict_log_proba" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict_log_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_selection/_rfe.py#L501"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.RFE.predict_log_proba" title="Link to this definition">#</a></dt>
 <dd><p>Predict class log-probabilities for X.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1679,7 +1679,7 @@ <h1>RFE<a class="headerlink" href="#rfe" title="Link to this heading">#</a></h1>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.RFE.predict_proba">
-<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_selection/_rfe.py#L475"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.RFE.predict_proba" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_selection/_rfe.py#L481"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.RFE.predict_proba" title="Link to this definition">#</a></dt>
 <dd><p>Predict class probabilities for X.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1702,7 +1702,7 @@ <h1>RFE<a class="headerlink" href="#rfe" title="Link to this heading">#</a></h1>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.RFE.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">score_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_selection/_rfe.py#L408"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.RFE.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">score_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_selection/_rfe.py#L414"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.RFE.score" title="Link to this definition">#</a></dt>
 <dd><p>Reduce X to the selected features and return the score of the estimator.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1739,7 +1739,7 @@ <h1>RFE<a class="headerlink" href="#rfe" title="Link to this heading">#</a></h1>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.RFE.set_output">
-<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.RFE.set_output" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.RFE.set_output" title="Link to this definition">#</a></dt>
 <dd><p>Set output container.</p>
 <p>See <a class="reference internal" href="http://github.com/auto_examples/miscellaneous/plot_set_output.html#sphx-glr-auto-examples-miscellaneous-plot-set-output-py"><span class="std std-ref">Introducing the set_output API</span></a>
 for an example on how to use the API.</p>
@@ -1770,7 +1770,7 @@ <h1>RFE<a class="headerlink" href="#rfe" title="Link to this heading">#</a></h1>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.RFE.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.RFE.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.RFE.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1794,7 +1794,7 @@ <h1>RFE<a class="headerlink" href="#rfe" title="Link to this heading">#</a></h1>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.RFE.transform">
-<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_selection/_base.py#L87"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.RFE.transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_selection/_base.py#L87"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.RFE.transform" title="Link to this definition">#</a></dt>
 <dd><p>Reduce X to the selected features.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.feature_selection.RFECV.html b/dev/modules/generated/sklearn.feature_selection.RFECV.html
index 237aa99d56296..c94226c538619 100644
--- a/dev/modules/generated/sklearn.feature_selection.RFECV.html
+++ b/dev/modules/generated/sklearn.feature_selection.RFECV.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>RFECV<a class="headerlink" href="#rfecv" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.feature_selection.RFECV">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.feature_selection.</span></span><span class="sig-name descname"><span class="pre">RFECV</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">step</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_features_to_select</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cv</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scoring</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">importance_getter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_selection/_rfe.py#L552"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.RFECV" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.feature_selection.</span></span><span class="sig-name descname"><span class="pre">RFECV</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">step</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_features_to_select</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cv</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scoring</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">importance_getter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_selection/_rfe.py#L558"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.RFECV" title="Link to this definition">#</a></dt>
 <dd><p>Recursive feature elimination with cross-validation to select features.</p>
 <p>The number of features selected is tuned automatically by fitting an <a class="reference internal" href="http://github.com/sklearn.feature_selection.RFE.html#sklearn.feature_selection.RFE" title="sklearn.feature_selection.RFE"><code class="xref py py-class docutils literal notranslate"><span class="pre">RFE</span></code></a>
 selector on the different cross-validation splits (provided by the <code class="docutils literal notranslate"><span class="pre">cv</span></code> parameter).
@@ -1369,7 +1369,7 @@ <h1>RFECV<a class="headerlink" href="#rfecv" title="Link to this heading">#</a><
 <dt><strong>verbose</strong><span class="classifier">int, default=0</span></dt><dd><p>Controls verbosity of output.</p>
 </dd>
 <dt><strong>n_jobs</strong><span class="classifier">int or None, default=None</span></dt><dd><p>Number of cores to run in parallel while fitting across folds.
-<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.5.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
+<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.6.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
 <code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all processors. See <a class="reference internal" href="http://github.com/glossary.html#term-n_jobs"><span class="xref std std-term">Glossary</span></a>
 for more details.</p>
 <div class="versionadded">
@@ -1419,6 +1419,20 @@ <h1>RFECV<a class="headerlink" href="#rfecv" title="Link to this heading">#</a><
 <p><span class="versionmodified added">Added in version 1.5.</span></p>
 </div>
 </dd>
+<dt>split(k)_ranking<span class="classifier">ndarray of shape (n_subsets_of_features,)</span></dt><dd><p>The cross-validation rankings across (k)th fold.
+Selected (i.e., estimated best) features are assigned rank 1.
+Illustration in
+<a class="reference internal" href="http://github.com/auto_examples/feature_selection/plot_rfe_with_cross_validation.html#sphx-glr-auto-examples-feature-selection-plot-rfe-with-cross-validation-py"><span class="std std-ref">Recursive feature elimination with cross-validation</span></a></p>
+<div class="versionadded">
+<p><span class="versionmodified added">Added in version 1.7.</span></p>
+</div>
+</dd>
+<dt>split(k)_support<span class="classifier">ndarray of shape (n_subsets_of_features,)</span></dt><dd><p>The cross-validation supports across (k)th fold. The support
+is the mask of selected features.</p>
+<div class="versionadded">
+<p><span class="versionmodified added">Added in version 1.7.</span></p>
+</div>
+</dd>
 </dl>
 </dd>
 <dt><strong>n_features_</strong><span class="classifier">int</span></dt><dd><p>The number of selected features with cross-validation.</p>
@@ -1486,7 +1500,7 @@ <h1>RFECV<a class="headerlink" href="#rfecv" title="Link to this heading">#</a><
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.RFECV.decision_function">
-<span class="sig-name descname"><span class="pre">decision_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_selection/_rfe.py#L453"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.RFECV.decision_function" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">decision_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_selection/_rfe.py#L459"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.RFECV.decision_function" title="Link to this definition">#</a></dt>
 <dd><p>Compute the decision function of <code class="docutils literal notranslate"><span class="pre">X</span></code>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1511,7 +1525,7 @@ <h1>RFECV<a class="headerlink" href="#rfecv" title="Link to this heading">#</a><
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.RFECV.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_selection/_rfe.py#L772"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.RFECV.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_selection/_rfe.py#L792"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.RFECV.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit the RFE model and automatically tune the number of selected features.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1552,7 +1566,7 @@ <h1>RFECV<a class="headerlink" href="#rfecv" title="Link to this heading">#</a><
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.RFECV.fit_transform">
-<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L863"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.RFECV.fit_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L804"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.RFECV.fit_transform" title="Link to this definition">#</a></dt>
 <dd><p>Fit to data, then transform it.</p>
 <p>Fits transformer to <code class="docutils literal notranslate"><span class="pre">X</span></code> and <code class="docutils literal notranslate"><span class="pre">y</span></code> with optional parameters <code class="docutils literal notranslate"><span class="pre">fit_params</span></code>
 and returns a transformed version of <code class="docutils literal notranslate"><span class="pre">X</span></code>.</p>
@@ -1578,7 +1592,7 @@ <h1>RFECV<a class="headerlink" href="#rfecv" title="Link to this heading">#</a><
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.RFECV.get_feature_names_out">
-<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_selection/_base.py#L174"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.RFECV.get_feature_names_out" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_selection/_base.py#L174"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.RFECV.get_feature_names_out" title="Link to this definition">#</a></dt>
 <dd><p>Mask feature names according to selected features.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1606,7 +1620,7 @@ <h1>RFECV<a class="headerlink" href="#rfecv" title="Link to this heading">#</a><
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.RFECV.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_selection/_rfe.py#L954"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.RFECV.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_selection/_rfe.py#L980"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.RFECV.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1626,7 +1640,7 @@ <h1>RFECV<a class="headerlink" href="#rfecv" title="Link to this heading">#</a><
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.RFECV.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.RFECV.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.RFECV.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1647,7 +1661,7 @@ <h1>RFECV<a class="headerlink" href="#rfecv" title="Link to this heading">#</a><
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.RFECV.get_support">
-<span class="sig-name descname"><span class="pre">get_support</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_selection/_base.py#L52"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.RFECV.get_support" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_support</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_selection/_base.py#L52"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.RFECV.get_support" title="Link to this definition">#</a></dt>
 <dd><p>Get a mask, or integer index, of the features selected.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1673,7 +1687,7 @@ <h1>RFECV<a class="headerlink" href="#rfecv" title="Link to this heading">#</a><
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.RFECV.inverse_transform">
-<span class="sig-name descname"><span class="pre">inverse_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_selection/_base.py#L134"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.RFECV.inverse_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">inverse_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_selection/_base.py#L134"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.RFECV.inverse_transform" title="Link to this definition">#</a></dt>
 <dd><p>Reverse the transformation operation.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1684,7 +1698,7 @@ <h1>RFECV<a class="headerlink" href="#rfecv" title="Link to this heading">#</a><
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><dl class="simple">
-<dt><strong>X_r</strong><span class="classifier">array of shape [n_samples, n_original_features]</span></dt><dd><p><code class="docutils literal notranslate"><span class="pre">X</span></code> with columns of zeros inserted where features would have
+<dt><strong>X_original</strong><span class="classifier">array of shape [n_samples, n_original_features]</span></dt><dd><p><code class="docutils literal notranslate"><span class="pre">X</span></code> with columns of zeros inserted where features would have
 been removed by <a class="reference internal" href="#sklearn.feature_selection.RFECV.transform" title="sklearn.feature_selection.RFECV.transform"><code class="xref py py-meth docutils literal notranslate"><span class="pre">transform</span></code></a>.</p>
 </dd>
 </dl>
@@ -1694,7 +1708,7 @@ <h1>RFECV<a class="headerlink" href="#rfecv" title="Link to this heading">#</a><
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.RFECV.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">predict_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_selection/_rfe.py#L372"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.RFECV.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">predict_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_selection/_rfe.py#L378"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.RFECV.predict" title="Link to this definition">#</a></dt>
 <dd><p>Reduce X to the selected features and predict using the estimator.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1724,7 +1738,7 @@ <h1>RFECV<a class="headerlink" href="#rfecv" title="Link to this heading">#</a><
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.RFECV.predict_log_proba">
-<span class="sig-name descname"><span class="pre">predict_log_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_selection/_rfe.py#L495"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.RFECV.predict_log_proba" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict_log_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_selection/_rfe.py#L501"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.RFECV.predict_log_proba" title="Link to this definition">#</a></dt>
 <dd><p>Predict class log-probabilities for X.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1745,7 +1759,7 @@ <h1>RFECV<a class="headerlink" href="#rfecv" title="Link to this heading">#</a><
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.RFECV.predict_proba">
-<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_selection/_rfe.py#L475"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.RFECV.predict_proba" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_selection/_rfe.py#L481"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.RFECV.predict_proba" title="Link to this definition">#</a></dt>
 <dd><p>Predict class probabilities for X.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1768,7 +1782,7 @@ <h1>RFECV<a class="headerlink" href="#rfecv" title="Link to this heading">#</a><
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.RFECV.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">score_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_selection/_rfe.py#L918"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.RFECV.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">score_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_selection/_rfe.py#L944"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.RFECV.score" title="Link to this definition">#</a></dt>
 <dd><p>Score using the <code class="docutils literal notranslate"><span class="pre">scoring</span></code> option on the given test data and labels.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1799,7 +1813,7 @@ <h1>RFECV<a class="headerlink" href="#rfecv" title="Link to this heading">#</a><
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.RFECV.set_output">
-<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.RFECV.set_output" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.RFECV.set_output" title="Link to this definition">#</a></dt>
 <dd><p>Set output container.</p>
 <p>See <a class="reference internal" href="http://github.com/auto_examples/miscellaneous/plot_set_output.html#sphx-glr-auto-examples-miscellaneous-plot-set-output-py"><span class="std std-ref">Introducing the set_output API</span></a>
 for an example on how to use the API.</p>
@@ -1830,7 +1844,7 @@ <h1>RFECV<a class="headerlink" href="#rfecv" title="Link to this heading">#</a><
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.RFECV.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.RFECV.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.RFECV.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1854,7 +1868,7 @@ <h1>RFECV<a class="headerlink" href="#rfecv" title="Link to this heading">#</a><
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.RFECV.transform">
-<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_selection/_base.py#L87"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.RFECV.transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_selection/_base.py#L87"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.RFECV.transform" title="Link to this definition">#</a></dt>
 <dd><p>Reduce X to the selected features.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.feature_selection.SelectFdr.html b/dev/modules/generated/sklearn.feature_selection.SelectFdr.html
index 32d814ed5cf8d..a6b457c48c40d 100644
--- a/dev/modules/generated/sklearn.feature_selection.SelectFdr.html
+++ b/dev/modules/generated/sklearn.feature_selection.SelectFdr.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>SelectFdr<a class="headerlink" href="#selectfdr" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.feature_selection.SelectFdr">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.feature_selection.</span></span><span class="sig-name descname"><span class="pre">SelectFdr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">score_func=&lt;function</span> <span class="pre">f_classif&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha=0.05</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_selection/_univariate_selection.py#L890"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectFdr" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.feature_selection.</span></span><span class="sig-name descname"><span class="pre">SelectFdr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">score_func=&lt;function</span> <span class="pre">f_classif&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha=0.05</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_selection/_univariate_selection.py#L889"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectFdr" title="Link to this definition">#</a></dt>
 <dd><p>Filter: Select the p-values for an estimated false discovery rate.</p>
 <p>This uses the Benjamini-Hochberg procedure. <code class="docutils literal notranslate"><span class="pre">alpha</span></code> is an upper bound
 on the expected false discovery rate.</p>
@@ -1382,7 +1382,7 @@ <h1>SelectFdr<a class="headerlink" href="#selectfdr" title="Link to this heading
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.SelectFdr.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_selection/_univariate_selection.py#L543"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectFdr.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_selection/_univariate_selection.py#L542"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectFdr.fit" title="Link to this definition">#</a></dt>
 <dd><p>Run score function on (X, y) and get the appropriate features.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1405,7 +1405,7 @@ <h1>SelectFdr<a class="headerlink" href="#selectfdr" title="Link to this heading
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.SelectFdr.fit_transform">
-<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L863"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectFdr.fit_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L804"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectFdr.fit_transform" title="Link to this definition">#</a></dt>
 <dd><p>Fit to data, then transform it.</p>
 <p>Fits transformer to <code class="docutils literal notranslate"><span class="pre">X</span></code> and <code class="docutils literal notranslate"><span class="pre">y</span></code> with optional parameters <code class="docutils literal notranslate"><span class="pre">fit_params</span></code>
 and returns a transformed version of <code class="docutils literal notranslate"><span class="pre">X</span></code>.</p>
@@ -1431,7 +1431,7 @@ <h1>SelectFdr<a class="headerlink" href="#selectfdr" title="Link to this heading
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.SelectFdr.get_feature_names_out">
-<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_selection/_base.py#L174"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectFdr.get_feature_names_out" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_selection/_base.py#L174"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectFdr.get_feature_names_out" title="Link to this definition">#</a></dt>
 <dd><p>Mask feature names according to selected features.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1459,7 +1459,7 @@ <h1>SelectFdr<a class="headerlink" href="#selectfdr" title="Link to this heading
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.SelectFdr.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectFdr.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectFdr.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1476,7 +1476,7 @@ <h1>SelectFdr<a class="headerlink" href="#selectfdr" title="Link to this heading
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.SelectFdr.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectFdr.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectFdr.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1497,7 +1497,7 @@ <h1>SelectFdr<a class="headerlink" href="#selectfdr" title="Link to this heading
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.SelectFdr.get_support">
-<span class="sig-name descname"><span class="pre">get_support</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_selection/_base.py#L52"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectFdr.get_support" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_support</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_selection/_base.py#L52"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectFdr.get_support" title="Link to this definition">#</a></dt>
 <dd><p>Get a mask, or integer index, of the features selected.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1523,7 +1523,7 @@ <h1>SelectFdr<a class="headerlink" href="#selectfdr" title="Link to this heading
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.SelectFdr.inverse_transform">
-<span class="sig-name descname"><span class="pre">inverse_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_selection/_base.py#L134"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectFdr.inverse_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">inverse_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_selection/_base.py#L134"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectFdr.inverse_transform" title="Link to this definition">#</a></dt>
 <dd><p>Reverse the transformation operation.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1534,7 +1534,7 @@ <h1>SelectFdr<a class="headerlink" href="#selectfdr" title="Link to this heading
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><dl class="simple">
-<dt><strong>X_r</strong><span class="classifier">array of shape [n_samples, n_original_features]</span></dt><dd><p><code class="docutils literal notranslate"><span class="pre">X</span></code> with columns of zeros inserted where features would have
+<dt><strong>X_original</strong><span class="classifier">array of shape [n_samples, n_original_features]</span></dt><dd><p><code class="docutils literal notranslate"><span class="pre">X</span></code> with columns of zeros inserted where features would have
 been removed by <a class="reference internal" href="#sklearn.feature_selection.SelectFdr.transform" title="sklearn.feature_selection.SelectFdr.transform"><code class="xref py py-meth docutils literal notranslate"><span class="pre">transform</span></code></a>.</p>
 </dd>
 </dl>
@@ -1544,7 +1544,7 @@ <h1>SelectFdr<a class="headerlink" href="#selectfdr" title="Link to this heading
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.SelectFdr.set_output">
-<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectFdr.set_output" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectFdr.set_output" title="Link to this definition">#</a></dt>
 <dd><p>Set output container.</p>
 <p>See <a class="reference internal" href="http://github.com/auto_examples/miscellaneous/plot_set_output.html#sphx-glr-auto-examples-miscellaneous-plot-set-output-py"><span class="std std-ref">Introducing the set_output API</span></a>
 for an example on how to use the API.</p>
@@ -1575,7 +1575,7 @@ <h1>SelectFdr<a class="headerlink" href="#selectfdr" title="Link to this heading
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.SelectFdr.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectFdr.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectFdr.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1599,7 +1599,7 @@ <h1>SelectFdr<a class="headerlink" href="#selectfdr" title="Link to this heading
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.SelectFdr.transform">
-<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_selection/_base.py#L87"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectFdr.transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_selection/_base.py#L87"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectFdr.transform" title="Link to this definition">#</a></dt>
 <dd><p>Reduce X to the selected features.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.feature_selection.SelectFpr.html b/dev/modules/generated/sklearn.feature_selection.SelectFpr.html
index d154be6b059de..46a44bd22eeb3 100644
--- a/dev/modules/generated/sklearn.feature_selection.SelectFpr.html
+++ b/dev/modules/generated/sklearn.feature_selection.SelectFpr.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>SelectFpr<a class="headerlink" href="#selectfpr" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.feature_selection.SelectFpr">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.feature_selection.</span></span><span class="sig-name descname"><span class="pre">SelectFpr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">score_func=&lt;function</span> <span class="pre">f_classif&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha=0.05</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_selection/_univariate_selection.py#L810"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectFpr" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.feature_selection.</span></span><span class="sig-name descname"><span class="pre">SelectFpr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">score_func=&lt;function</span> <span class="pre">f_classif&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha=0.05</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_selection/_univariate_selection.py#L809"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectFpr" title="Link to this definition">#</a></dt>
 <dd><p>Filter: Select the pvalues below alpha based on a FPR test.</p>
 <p>FPR test stands for False Positive Rate test. It controls the total
 amount of false detections.</p>
@@ -1380,7 +1380,7 @@ <h1>SelectFpr<a class="headerlink" href="#selectfpr" title="Link to this heading
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.SelectFpr.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_selection/_univariate_selection.py#L543"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectFpr.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_selection/_univariate_selection.py#L542"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectFpr.fit" title="Link to this definition">#</a></dt>
 <dd><p>Run score function on (X, y) and get the appropriate features.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1403,7 +1403,7 @@ <h1>SelectFpr<a class="headerlink" href="#selectfpr" title="Link to this heading
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.SelectFpr.fit_transform">
-<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L863"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectFpr.fit_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L804"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectFpr.fit_transform" title="Link to this definition">#</a></dt>
 <dd><p>Fit to data, then transform it.</p>
 <p>Fits transformer to <code class="docutils literal notranslate"><span class="pre">X</span></code> and <code class="docutils literal notranslate"><span class="pre">y</span></code> with optional parameters <code class="docutils literal notranslate"><span class="pre">fit_params</span></code>
 and returns a transformed version of <code class="docutils literal notranslate"><span class="pre">X</span></code>.</p>
@@ -1429,7 +1429,7 @@ <h1>SelectFpr<a class="headerlink" href="#selectfpr" title="Link to this heading
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.SelectFpr.get_feature_names_out">
-<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_selection/_base.py#L174"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectFpr.get_feature_names_out" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_selection/_base.py#L174"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectFpr.get_feature_names_out" title="Link to this definition">#</a></dt>
 <dd><p>Mask feature names according to selected features.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1457,7 +1457,7 @@ <h1>SelectFpr<a class="headerlink" href="#selectfpr" title="Link to this heading
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.SelectFpr.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectFpr.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectFpr.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1474,7 +1474,7 @@ <h1>SelectFpr<a class="headerlink" href="#selectfpr" title="Link to this heading
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.SelectFpr.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectFpr.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectFpr.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1495,7 +1495,7 @@ <h1>SelectFpr<a class="headerlink" href="#selectfpr" title="Link to this heading
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.SelectFpr.get_support">
-<span class="sig-name descname"><span class="pre">get_support</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_selection/_base.py#L52"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectFpr.get_support" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_support</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_selection/_base.py#L52"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectFpr.get_support" title="Link to this definition">#</a></dt>
 <dd><p>Get a mask, or integer index, of the features selected.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1521,7 +1521,7 @@ <h1>SelectFpr<a class="headerlink" href="#selectfpr" title="Link to this heading
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.SelectFpr.inverse_transform">
-<span class="sig-name descname"><span class="pre">inverse_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_selection/_base.py#L134"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectFpr.inverse_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">inverse_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_selection/_base.py#L134"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectFpr.inverse_transform" title="Link to this definition">#</a></dt>
 <dd><p>Reverse the transformation operation.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1532,7 +1532,7 @@ <h1>SelectFpr<a class="headerlink" href="#selectfpr" title="Link to this heading
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><dl class="simple">
-<dt><strong>X_r</strong><span class="classifier">array of shape [n_samples, n_original_features]</span></dt><dd><p><code class="docutils literal notranslate"><span class="pre">X</span></code> with columns of zeros inserted where features would have
+<dt><strong>X_original</strong><span class="classifier">array of shape [n_samples, n_original_features]</span></dt><dd><p><code class="docutils literal notranslate"><span class="pre">X</span></code> with columns of zeros inserted where features would have
 been removed by <a class="reference internal" href="#sklearn.feature_selection.SelectFpr.transform" title="sklearn.feature_selection.SelectFpr.transform"><code class="xref py py-meth docutils literal notranslate"><span class="pre">transform</span></code></a>.</p>
 </dd>
 </dl>
@@ -1542,7 +1542,7 @@ <h1>SelectFpr<a class="headerlink" href="#selectfpr" title="Link to this heading
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.SelectFpr.set_output">
-<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectFpr.set_output" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectFpr.set_output" title="Link to this definition">#</a></dt>
 <dd><p>Set output container.</p>
 <p>See <a class="reference internal" href="http://github.com/auto_examples/miscellaneous/plot_set_output.html#sphx-glr-auto-examples-miscellaneous-plot-set-output-py"><span class="std std-ref">Introducing the set_output API</span></a>
 for an example on how to use the API.</p>
@@ -1573,7 +1573,7 @@ <h1>SelectFpr<a class="headerlink" href="#selectfpr" title="Link to this heading
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.SelectFpr.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectFpr.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectFpr.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1597,7 +1597,7 @@ <h1>SelectFpr<a class="headerlink" href="#selectfpr" title="Link to this heading
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.SelectFpr.transform">
-<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_selection/_base.py#L87"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectFpr.transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_selection/_base.py#L87"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectFpr.transform" title="Link to this definition">#</a></dt>
 <dd><p>Reduce X to the selected features.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.feature_selection.SelectFromModel.html b/dev/modules/generated/sklearn.feature_selection.SelectFromModel.html
index 2d1731263f716..b4de8b0bc3d24 100644
--- a/dev/modules/generated/sklearn.feature_selection.SelectFromModel.html
+++ b/dev/modules/generated/sklearn.feature_selection.SelectFromModel.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>SelectFromModel<a class="headerlink" href="#selectfrommodel" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.feature_selection.SelectFromModel">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.feature_selection.</span></span><span class="sig-name descname"><span class="pre">SelectFromModel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">threshold</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prefit</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm_order</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">importance_getter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_selection/_from_model.py#L80"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectFromModel" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.feature_selection.</span></span><span class="sig-name descname"><span class="pre">SelectFromModel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">threshold</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prefit</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm_order</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">importance_getter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_selection/_from_model.py#L87"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectFromModel" title="Link to this definition">#</a></dt>
 <dd><p>Meta-transformer for selecting features based on importance weights.</p>
 <div class="versionadded">
 <p><span class="versionmodified added">Added in version 0.17.</span></p>
@@ -1430,9 +1430,9 @@ <h1>SelectFromModel<a class="headerlink" href="#selectfrommodel" title="Link to
 <span class="gp">&gt;&gt;&gt; </span><span class="n">y</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">selector</span> <span class="o">=</span> <span class="n">SelectFromModel</span><span class="p">(</span><span class="n">estimator</span><span class="o">=</span><span class="n">LogisticRegression</span><span class="p">())</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">selector</span><span class="o">.</span><span class="n">estimator_</span><span class="o">.</span><span class="n">coef_</span>
-<span class="go">array([[-0.3252...,  0.8345...,  0.4976...]])</span>
+<span class="go">array([[-0.3252,  0.8345,  0.4976]])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">selector</span><span class="o">.</span><span class="n">threshold_</span>
-<span class="go">np.float64(0.55249...)</span>
+<span class="go">np.float64(0.55249)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">selector</span><span class="o">.</span><span class="n">get_support</span><span class="p">()</span>
 <span class="go">array([False,  True, False])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">selector</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
@@ -1455,7 +1455,7 @@ <h1>SelectFromModel<a class="headerlink" href="#selectfrommodel" title="Link to
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.SelectFromModel.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_selection/_from_model.py#L320"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectFromModel.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_selection/_from_model.py#L327"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectFromModel.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit the SelectFromModel meta-transformer.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1490,7 +1490,7 @@ <h1>SelectFromModel<a class="headerlink" href="#selectfrommodel" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.SelectFromModel.fit_transform">
-<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L863"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectFromModel.fit_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L804"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectFromModel.fit_transform" title="Link to this definition">#</a></dt>
 <dd><p>Fit to data, then transform it.</p>
 <p>Fits transformer to <code class="docutils literal notranslate"><span class="pre">X</span></code> and <code class="docutils literal notranslate"><span class="pre">y</span></code> with optional parameters <code class="docutils literal notranslate"><span class="pre">fit_params</span></code>
 and returns a transformed version of <code class="docutils literal notranslate"><span class="pre">X</span></code>.</p>
@@ -1516,7 +1516,7 @@ <h1>SelectFromModel<a class="headerlink" href="#selectfrommodel" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.SelectFromModel.get_feature_names_out">
-<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_selection/_base.py#L174"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectFromModel.get_feature_names_out" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_selection/_base.py#L174"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectFromModel.get_feature_names_out" title="Link to this definition">#</a></dt>
 <dd><p>Mask feature names according to selected features.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1544,7 +1544,7 @@ <h1>SelectFromModel<a class="headerlink" href="#selectfrommodel" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.SelectFromModel.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_selection/_from_model.py#L480"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectFromModel.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_selection/_from_model.py#L487"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectFromModel.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1564,7 +1564,7 @@ <h1>SelectFromModel<a class="headerlink" href="#selectfrommodel" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.SelectFromModel.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectFromModel.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectFromModel.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1585,7 +1585,7 @@ <h1>SelectFromModel<a class="headerlink" href="#selectfrommodel" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.SelectFromModel.get_support">
-<span class="sig-name descname"><span class="pre">get_support</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_selection/_base.py#L52"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectFromModel.get_support" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_support</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_selection/_base.py#L52"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectFromModel.get_support" title="Link to this definition">#</a></dt>
 <dd><p>Get a mask, or integer index, of the features selected.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1611,7 +1611,7 @@ <h1>SelectFromModel<a class="headerlink" href="#selectfrommodel" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.SelectFromModel.inverse_transform">
-<span class="sig-name descname"><span class="pre">inverse_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_selection/_base.py#L134"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectFromModel.inverse_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">inverse_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_selection/_base.py#L134"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectFromModel.inverse_transform" title="Link to this definition">#</a></dt>
 <dd><p>Reverse the transformation operation.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1622,7 +1622,7 @@ <h1>SelectFromModel<a class="headerlink" href="#selectfrommodel" title="Link to
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><dl class="simple">
-<dt><strong>X_r</strong><span class="classifier">array of shape [n_samples, n_original_features]</span></dt><dd><p><code class="docutils literal notranslate"><span class="pre">X</span></code> with columns of zeros inserted where features would have
+<dt><strong>X_original</strong><span class="classifier">array of shape [n_samples, n_original_features]</span></dt><dd><p><code class="docutils literal notranslate"><span class="pre">X</span></code> with columns of zeros inserted where features would have
 been removed by <a class="reference internal" href="#sklearn.feature_selection.SelectFromModel.transform" title="sklearn.feature_selection.SelectFromModel.transform"><code class="xref py py-meth docutils literal notranslate"><span class="pre">transform</span></code></a>.</p>
 </dd>
 </dl>
@@ -1632,7 +1632,7 @@ <h1>SelectFromModel<a class="headerlink" href="#selectfrommodel" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.SelectFromModel.partial_fit">
-<span class="sig-name descname"><span class="pre">partial_fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">partial_fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_selection/_from_model.py#L392"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectFromModel.partial_fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">partial_fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">partial_fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_selection/_from_model.py#L399"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectFromModel.partial_fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit the SelectFromModel meta-transformer only once.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1669,7 +1669,7 @@ <h1>SelectFromModel<a class="headerlink" href="#selectfrommodel" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.SelectFromModel.set_output">
-<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectFromModel.set_output" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectFromModel.set_output" title="Link to this definition">#</a></dt>
 <dd><p>Set output container.</p>
 <p>See <a class="reference internal" href="http://github.com/auto_examples/miscellaneous/plot_set_output.html#sphx-glr-auto-examples-miscellaneous-plot-set-output-py"><span class="std std-ref">Introducing the set_output API</span></a>
 for an example on how to use the API.</p>
@@ -1700,7 +1700,7 @@ <h1>SelectFromModel<a class="headerlink" href="#selectfrommodel" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.SelectFromModel.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectFromModel.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectFromModel.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1724,7 +1724,7 @@ <h1>SelectFromModel<a class="headerlink" href="#selectfrommodel" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.SelectFromModel.transform">
-<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_selection/_base.py#L87"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectFromModel.transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_selection/_base.py#L87"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectFromModel.transform" title="Link to this definition">#</a></dt>
 <dd><p>Reduce X to the selected features.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.feature_selection.SelectFwe.html b/dev/modules/generated/sklearn.feature_selection.SelectFwe.html
index 2ff4cdc8ee6a6..0b67b5211ba53 100644
--- a/dev/modules/generated/sklearn.feature_selection.SelectFwe.html
+++ b/dev/modules/generated/sklearn.feature_selection.SelectFwe.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>SelectFwe<a class="headerlink" href="#selectfwe" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.feature_selection.SelectFwe">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.feature_selection.</span></span><span class="sig-name descname"><span class="pre">SelectFwe</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">score_func=&lt;function</span> <span class="pre">f_classif&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha=0.05</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_selection/_univariate_selection.py#L981"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectFwe" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.feature_selection.</span></span><span class="sig-name descname"><span class="pre">SelectFwe</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">score_func=&lt;function</span> <span class="pre">f_classif&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha=0.05</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_selection/_univariate_selection.py#L980"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectFwe" title="Link to this definition">#</a></dt>
 <dd><p>Filter: Select the p-values corresponding to Family-wise error rate.</p>
 <p>Read more in the <a class="reference internal" href="http://github.com/feature_selection.html#univariate-feature-selection"><span class="std std-ref">User Guide</span></a>.</p>
 <dl class="field-list">
@@ -1374,7 +1374,7 @@ <h1>SelectFwe<a class="headerlink" href="#selectfwe" title="Link to this heading
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.SelectFwe.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_selection/_univariate_selection.py#L543"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectFwe.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_selection/_univariate_selection.py#L542"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectFwe.fit" title="Link to this definition">#</a></dt>
 <dd><p>Run score function on (X, y) and get the appropriate features.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1397,7 +1397,7 @@ <h1>SelectFwe<a class="headerlink" href="#selectfwe" title="Link to this heading
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.SelectFwe.fit_transform">
-<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L863"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectFwe.fit_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L804"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectFwe.fit_transform" title="Link to this definition">#</a></dt>
 <dd><p>Fit to data, then transform it.</p>
 <p>Fits transformer to <code class="docutils literal notranslate"><span class="pre">X</span></code> and <code class="docutils literal notranslate"><span class="pre">y</span></code> with optional parameters <code class="docutils literal notranslate"><span class="pre">fit_params</span></code>
 and returns a transformed version of <code class="docutils literal notranslate"><span class="pre">X</span></code>.</p>
@@ -1423,7 +1423,7 @@ <h1>SelectFwe<a class="headerlink" href="#selectfwe" title="Link to this heading
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.SelectFwe.get_feature_names_out">
-<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_selection/_base.py#L174"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectFwe.get_feature_names_out" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_selection/_base.py#L174"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectFwe.get_feature_names_out" title="Link to this definition">#</a></dt>
 <dd><p>Mask feature names according to selected features.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1451,7 +1451,7 @@ <h1>SelectFwe<a class="headerlink" href="#selectfwe" title="Link to this heading
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.SelectFwe.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectFwe.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectFwe.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1468,7 +1468,7 @@ <h1>SelectFwe<a class="headerlink" href="#selectfwe" title="Link to this heading
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.SelectFwe.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectFwe.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectFwe.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1489,7 +1489,7 @@ <h1>SelectFwe<a class="headerlink" href="#selectfwe" title="Link to this heading
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.SelectFwe.get_support">
-<span class="sig-name descname"><span class="pre">get_support</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_selection/_base.py#L52"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectFwe.get_support" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_support</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_selection/_base.py#L52"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectFwe.get_support" title="Link to this definition">#</a></dt>
 <dd><p>Get a mask, or integer index, of the features selected.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1515,7 +1515,7 @@ <h1>SelectFwe<a class="headerlink" href="#selectfwe" title="Link to this heading
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.SelectFwe.inverse_transform">
-<span class="sig-name descname"><span class="pre">inverse_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_selection/_base.py#L134"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectFwe.inverse_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">inverse_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_selection/_base.py#L134"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectFwe.inverse_transform" title="Link to this definition">#</a></dt>
 <dd><p>Reverse the transformation operation.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1526,7 +1526,7 @@ <h1>SelectFwe<a class="headerlink" href="#selectfwe" title="Link to this heading
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><dl class="simple">
-<dt><strong>X_r</strong><span class="classifier">array of shape [n_samples, n_original_features]</span></dt><dd><p><code class="docutils literal notranslate"><span class="pre">X</span></code> with columns of zeros inserted where features would have
+<dt><strong>X_original</strong><span class="classifier">array of shape [n_samples, n_original_features]</span></dt><dd><p><code class="docutils literal notranslate"><span class="pre">X</span></code> with columns of zeros inserted where features would have
 been removed by <a class="reference internal" href="#sklearn.feature_selection.SelectFwe.transform" title="sklearn.feature_selection.SelectFwe.transform"><code class="xref py py-meth docutils literal notranslate"><span class="pre">transform</span></code></a>.</p>
 </dd>
 </dl>
@@ -1536,7 +1536,7 @@ <h1>SelectFwe<a class="headerlink" href="#selectfwe" title="Link to this heading
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.SelectFwe.set_output">
-<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectFwe.set_output" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectFwe.set_output" title="Link to this definition">#</a></dt>
 <dd><p>Set output container.</p>
 <p>See <a class="reference internal" href="http://github.com/auto_examples/miscellaneous/plot_set_output.html#sphx-glr-auto-examples-miscellaneous-plot-set-output-py"><span class="std std-ref">Introducing the set_output API</span></a>
 for an example on how to use the API.</p>
@@ -1567,7 +1567,7 @@ <h1>SelectFwe<a class="headerlink" href="#selectfwe" title="Link to this heading
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.SelectFwe.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectFwe.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectFwe.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1591,7 +1591,7 @@ <h1>SelectFwe<a class="headerlink" href="#selectfwe" title="Link to this heading
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.SelectFwe.transform">
-<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_selection/_base.py#L87"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectFwe.transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_selection/_base.py#L87"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectFwe.transform" title="Link to this definition">#</a></dt>
 <dd><p>Reduce X to the selected features.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.feature_selection.SelectKBest.html b/dev/modules/generated/sklearn.feature_selection.SelectKBest.html
index ff26efbb31244..7715800f58d6a 100644
--- a/dev/modules/generated/sklearn.feature_selection.SelectKBest.html
+++ b/dev/modules/generated/sklearn.feature_selection.SelectKBest.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>SelectKBest<a class="headerlink" href="#selectkbest" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.feature_selection.SelectKBest">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.feature_selection.</span></span><span class="sig-name descname"><span class="pre">SelectKBest</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">score_func=&lt;function</span> <span class="pre">f_classif&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">k=10</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_selection/_univariate_selection.py#L699"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectKBest" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.feature_selection.</span></span><span class="sig-name descname"><span class="pre">SelectKBest</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">score_func=&lt;function</span> <span class="pre">f_classif&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">k=10</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_selection/_univariate_selection.py#L698"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectKBest" title="Link to this definition">#</a></dt>
 <dd><p>Select features according to the k highest scores.</p>
 <p>Read more in the <a class="reference internal" href="http://github.com/feature_selection.html#univariate-feature-selection"><span class="std std-ref">User Guide</span></a>.</p>
 <dl class="field-list">
@@ -1389,7 +1389,7 @@ <h1>SelectKBest<a class="headerlink" href="#selectkbest" title="Link to this hea
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.SelectKBest.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_selection/_univariate_selection.py#L543"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectKBest.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_selection/_univariate_selection.py#L542"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectKBest.fit" title="Link to this definition">#</a></dt>
 <dd><p>Run score function on (X, y) and get the appropriate features.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1412,7 +1412,7 @@ <h1>SelectKBest<a class="headerlink" href="#selectkbest" title="Link to this hea
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.SelectKBest.fit_transform">
-<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L863"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectKBest.fit_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L804"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectKBest.fit_transform" title="Link to this definition">#</a></dt>
 <dd><p>Fit to data, then transform it.</p>
 <p>Fits transformer to <code class="docutils literal notranslate"><span class="pre">X</span></code> and <code class="docutils literal notranslate"><span class="pre">y</span></code> with optional parameters <code class="docutils literal notranslate"><span class="pre">fit_params</span></code>
 and returns a transformed version of <code class="docutils literal notranslate"><span class="pre">X</span></code>.</p>
@@ -1438,7 +1438,7 @@ <h1>SelectKBest<a class="headerlink" href="#selectkbest" title="Link to this hea
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.SelectKBest.get_feature_names_out">
-<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_selection/_base.py#L174"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectKBest.get_feature_names_out" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_selection/_base.py#L174"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectKBest.get_feature_names_out" title="Link to this definition">#</a></dt>
 <dd><p>Mask feature names according to selected features.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1466,7 +1466,7 @@ <h1>SelectKBest<a class="headerlink" href="#selectkbest" title="Link to this hea
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.SelectKBest.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectKBest.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectKBest.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1483,7 +1483,7 @@ <h1>SelectKBest<a class="headerlink" href="#selectkbest" title="Link to this hea
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.SelectKBest.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectKBest.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectKBest.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1504,7 +1504,7 @@ <h1>SelectKBest<a class="headerlink" href="#selectkbest" title="Link to this hea
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.SelectKBest.get_support">
-<span class="sig-name descname"><span class="pre">get_support</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_selection/_base.py#L52"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectKBest.get_support" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_support</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_selection/_base.py#L52"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectKBest.get_support" title="Link to this definition">#</a></dt>
 <dd><p>Get a mask, or integer index, of the features selected.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1530,7 +1530,7 @@ <h1>SelectKBest<a class="headerlink" href="#selectkbest" title="Link to this hea
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.SelectKBest.inverse_transform">
-<span class="sig-name descname"><span class="pre">inverse_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_selection/_base.py#L134"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectKBest.inverse_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">inverse_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_selection/_base.py#L134"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectKBest.inverse_transform" title="Link to this definition">#</a></dt>
 <dd><p>Reverse the transformation operation.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1541,7 +1541,7 @@ <h1>SelectKBest<a class="headerlink" href="#selectkbest" title="Link to this hea
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><dl class="simple">
-<dt><strong>X_r</strong><span class="classifier">array of shape [n_samples, n_original_features]</span></dt><dd><p><code class="docutils literal notranslate"><span class="pre">X</span></code> with columns of zeros inserted where features would have
+<dt><strong>X_original</strong><span class="classifier">array of shape [n_samples, n_original_features]</span></dt><dd><p><code class="docutils literal notranslate"><span class="pre">X</span></code> with columns of zeros inserted where features would have
 been removed by <a class="reference internal" href="#sklearn.feature_selection.SelectKBest.transform" title="sklearn.feature_selection.SelectKBest.transform"><code class="xref py py-meth docutils literal notranslate"><span class="pre">transform</span></code></a>.</p>
 </dd>
 </dl>
@@ -1551,7 +1551,7 @@ <h1>SelectKBest<a class="headerlink" href="#selectkbest" title="Link to this hea
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.SelectKBest.set_output">
-<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectKBest.set_output" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectKBest.set_output" title="Link to this definition">#</a></dt>
 <dd><p>Set output container.</p>
 <p>See <a class="reference internal" href="http://github.com/auto_examples/miscellaneous/plot_set_output.html#sphx-glr-auto-examples-miscellaneous-plot-set-output-py"><span class="std std-ref">Introducing the set_output API</span></a>
 for an example on how to use the API.</p>
@@ -1582,7 +1582,7 @@ <h1>SelectKBest<a class="headerlink" href="#selectkbest" title="Link to this hea
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.SelectKBest.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectKBest.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectKBest.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1606,7 +1606,7 @@ <h1>SelectKBest<a class="headerlink" href="#selectkbest" title="Link to this hea
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.SelectKBest.transform">
-<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_selection/_base.py#L87"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectKBest.transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_selection/_base.py#L87"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectKBest.transform" title="Link to this definition">#</a></dt>
 <dd><p>Reduce X to the selected features.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.feature_selection.SelectPercentile.html b/dev/modules/generated/sklearn.feature_selection.SelectPercentile.html
index d68bf2b2cd327..d39f2f7b9281c 100644
--- a/dev/modules/generated/sklearn.feature_selection.SelectPercentile.html
+++ b/dev/modules/generated/sklearn.feature_selection.SelectPercentile.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>SelectPercentile<a class="headerlink" href="#selectpercentile" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.feature_selection.SelectPercentile">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.feature_selection.</span></span><span class="sig-name descname"><span class="pre">SelectPercentile</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">score_func=&lt;function</span> <span class="pre">f_classif&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">percentile=10</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_selection/_univariate_selection.py#L594"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectPercentile" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.feature_selection.</span></span><span class="sig-name descname"><span class="pre">SelectPercentile</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">score_func=&lt;function</span> <span class="pre">f_classif&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">percentile=10</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_selection/_univariate_selection.py#L593"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectPercentile" title="Link to this definition">#</a></dt>
 <dd><p>Select features according to a percentile of the highest scores.</p>
 <p>Read more in the <a class="reference internal" href="http://github.com/feature_selection.html#univariate-feature-selection"><span class="std std-ref">User Guide</span></a>.</p>
 <dl class="field-list">
@@ -1388,7 +1388,7 @@ <h1>SelectPercentile<a class="headerlink" href="#selectpercentile" title="Link t
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.SelectPercentile.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_selection/_univariate_selection.py#L543"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectPercentile.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_selection/_univariate_selection.py#L542"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectPercentile.fit" title="Link to this definition">#</a></dt>
 <dd><p>Run score function on (X, y) and get the appropriate features.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1411,7 +1411,7 @@ <h1>SelectPercentile<a class="headerlink" href="#selectpercentile" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.SelectPercentile.fit_transform">
-<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L863"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectPercentile.fit_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L804"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectPercentile.fit_transform" title="Link to this definition">#</a></dt>
 <dd><p>Fit to data, then transform it.</p>
 <p>Fits transformer to <code class="docutils literal notranslate"><span class="pre">X</span></code> and <code class="docutils literal notranslate"><span class="pre">y</span></code> with optional parameters <code class="docutils literal notranslate"><span class="pre">fit_params</span></code>
 and returns a transformed version of <code class="docutils literal notranslate"><span class="pre">X</span></code>.</p>
@@ -1437,7 +1437,7 @@ <h1>SelectPercentile<a class="headerlink" href="#selectpercentile" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.SelectPercentile.get_feature_names_out">
-<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_selection/_base.py#L174"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectPercentile.get_feature_names_out" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_selection/_base.py#L174"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectPercentile.get_feature_names_out" title="Link to this definition">#</a></dt>
 <dd><p>Mask feature names according to selected features.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1465,7 +1465,7 @@ <h1>SelectPercentile<a class="headerlink" href="#selectpercentile" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.SelectPercentile.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectPercentile.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectPercentile.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1482,7 +1482,7 @@ <h1>SelectPercentile<a class="headerlink" href="#selectpercentile" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.SelectPercentile.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectPercentile.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectPercentile.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1503,7 +1503,7 @@ <h1>SelectPercentile<a class="headerlink" href="#selectpercentile" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.SelectPercentile.get_support">
-<span class="sig-name descname"><span class="pre">get_support</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_selection/_base.py#L52"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectPercentile.get_support" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_support</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_selection/_base.py#L52"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectPercentile.get_support" title="Link to this definition">#</a></dt>
 <dd><p>Get a mask, or integer index, of the features selected.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1529,7 +1529,7 @@ <h1>SelectPercentile<a class="headerlink" href="#selectpercentile" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.SelectPercentile.inverse_transform">
-<span class="sig-name descname"><span class="pre">inverse_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_selection/_base.py#L134"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectPercentile.inverse_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">inverse_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_selection/_base.py#L134"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectPercentile.inverse_transform" title="Link to this definition">#</a></dt>
 <dd><p>Reverse the transformation operation.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1540,7 +1540,7 @@ <h1>SelectPercentile<a class="headerlink" href="#selectpercentile" title="Link t
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><dl class="simple">
-<dt><strong>X_r</strong><span class="classifier">array of shape [n_samples, n_original_features]</span></dt><dd><p><code class="docutils literal notranslate"><span class="pre">X</span></code> with columns of zeros inserted where features would have
+<dt><strong>X_original</strong><span class="classifier">array of shape [n_samples, n_original_features]</span></dt><dd><p><code class="docutils literal notranslate"><span class="pre">X</span></code> with columns of zeros inserted where features would have
 been removed by <a class="reference internal" href="#sklearn.feature_selection.SelectPercentile.transform" title="sklearn.feature_selection.SelectPercentile.transform"><code class="xref py py-meth docutils literal notranslate"><span class="pre">transform</span></code></a>.</p>
 </dd>
 </dl>
@@ -1550,7 +1550,7 @@ <h1>SelectPercentile<a class="headerlink" href="#selectpercentile" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.SelectPercentile.set_output">
-<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectPercentile.set_output" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectPercentile.set_output" title="Link to this definition">#</a></dt>
 <dd><p>Set output container.</p>
 <p>See <a class="reference internal" href="http://github.com/auto_examples/miscellaneous/plot_set_output.html#sphx-glr-auto-examples-miscellaneous-plot-set-output-py"><span class="std std-ref">Introducing the set_output API</span></a>
 for an example on how to use the API.</p>
@@ -1581,7 +1581,7 @@ <h1>SelectPercentile<a class="headerlink" href="#selectpercentile" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.SelectPercentile.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectPercentile.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectPercentile.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1605,7 +1605,7 @@ <h1>SelectPercentile<a class="headerlink" href="#selectpercentile" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.SelectPercentile.transform">
-<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_selection/_base.py#L87"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectPercentile.transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_selection/_base.py#L87"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectPercentile.transform" title="Link to this definition">#</a></dt>
 <dd><p>Reduce X to the selected features.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.feature_selection.SelectorMixin.html b/dev/modules/generated/sklearn.feature_selection.SelectorMixin.html
index 1a12b3b202a2d..df608f4b4cc28 100644
--- a/dev/modules/generated/sklearn.feature_selection.SelectorMixin.html
+++ b/dev/modules/generated/sklearn.feature_selection.SelectorMixin.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>SelectorMixin<a class="headerlink" href="#selectormixin" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.feature_selection.SelectorMixin">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.feature_selection.</span></span><span class="sig-name descname"><span class="pre">SelectorMixin</span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_selection/_base.py#L25"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectorMixin" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.feature_selection.</span></span><span class="sig-name descname"><span class="pre">SelectorMixin</span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_selection/_base.py#L25"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectorMixin" title="Link to this definition">#</a></dt>
 <dd><p>Transformer mixin that performs feature selection given a support mask</p>
 <p>This mixin provides a feature selector implementation with <code class="docutils literal notranslate"><span class="pre">transform</span></code> and
 <code class="docutils literal notranslate"><span class="pre">inverse_transform</span></code> functionality given an implementation of
@@ -1330,7 +1330,7 @@ <h1>SelectorMixin<a class="headerlink" href="#selectormixin" title="Link to this
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.SelectorMixin.fit_transform">
-<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L863"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectorMixin.fit_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L804"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectorMixin.fit_transform" title="Link to this definition">#</a></dt>
 <dd><p>Fit to data, then transform it.</p>
 <p>Fits transformer to <code class="docutils literal notranslate"><span class="pre">X</span></code> and <code class="docutils literal notranslate"><span class="pre">y</span></code> with optional parameters <code class="docutils literal notranslate"><span class="pre">fit_params</span></code>
 and returns a transformed version of <code class="docutils literal notranslate"><span class="pre">X</span></code>.</p>
@@ -1356,7 +1356,7 @@ <h1>SelectorMixin<a class="headerlink" href="#selectormixin" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.SelectorMixin.get_feature_names_out">
-<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_selection/_base.py#L174"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectorMixin.get_feature_names_out" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_selection/_base.py#L174"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectorMixin.get_feature_names_out" title="Link to this definition">#</a></dt>
 <dd><p>Mask feature names according to selected features.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1384,7 +1384,7 @@ <h1>SelectorMixin<a class="headerlink" href="#selectormixin" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.SelectorMixin.get_support">
-<span class="sig-name descname"><span class="pre">get_support</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_selection/_base.py#L52"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectorMixin.get_support" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_support</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_selection/_base.py#L52"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectorMixin.get_support" title="Link to this definition">#</a></dt>
 <dd><p>Get a mask, or integer index, of the features selected.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1410,7 +1410,7 @@ <h1>SelectorMixin<a class="headerlink" href="#selectormixin" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.SelectorMixin.inverse_transform">
-<span class="sig-name descname"><span class="pre">inverse_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_selection/_base.py#L134"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectorMixin.inverse_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">inverse_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_selection/_base.py#L134"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectorMixin.inverse_transform" title="Link to this definition">#</a></dt>
 <dd><p>Reverse the transformation operation.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1421,7 +1421,7 @@ <h1>SelectorMixin<a class="headerlink" href="#selectormixin" title="Link to this
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><dl class="simple">
-<dt><strong>X_r</strong><span class="classifier">array of shape [n_samples, n_original_features]</span></dt><dd><p><code class="docutils literal notranslate"><span class="pre">X</span></code> with columns of zeros inserted where features would have
+<dt><strong>X_original</strong><span class="classifier">array of shape [n_samples, n_original_features]</span></dt><dd><p><code class="docutils literal notranslate"><span class="pre">X</span></code> with columns of zeros inserted where features would have
 been removed by <a class="reference internal" href="#sklearn.feature_selection.SelectorMixin.transform" title="sklearn.feature_selection.SelectorMixin.transform"><code class="xref py py-meth docutils literal notranslate"><span class="pre">transform</span></code></a>.</p>
 </dd>
 </dl>
@@ -1431,7 +1431,7 @@ <h1>SelectorMixin<a class="headerlink" href="#selectormixin" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.SelectorMixin.set_output">
-<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectorMixin.set_output" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectorMixin.set_output" title="Link to this definition">#</a></dt>
 <dd><p>Set output container.</p>
 <p>See <a class="reference internal" href="http://github.com/auto_examples/miscellaneous/plot_set_output.html#sphx-glr-auto-examples-miscellaneous-plot-set-output-py"><span class="std std-ref">Introducing the set_output API</span></a>
 for an example on how to use the API.</p>
@@ -1462,7 +1462,7 @@ <h1>SelectorMixin<a class="headerlink" href="#selectormixin" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.SelectorMixin.transform">
-<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_selection/_base.py#L87"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectorMixin.transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_selection/_base.py#L87"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SelectorMixin.transform" title="Link to this definition">#</a></dt>
 <dd><p>Reduce X to the selected features.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.feature_selection.SequentialFeatureSelector.html b/dev/modules/generated/sklearn.feature_selection.SequentialFeatureSelector.html
index 15b2d197ea1c5..9f347de49fff9 100644
--- a/dev/modules/generated/sklearn.feature_selection.SequentialFeatureSelector.html
+++ b/dev/modules/generated/sklearn.feature_selection.SequentialFeatureSelector.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>SequentialFeatureSelector<a class="headerlink" href="#sequentialfeatureselector" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.feature_selection.SequentialFeatureSelector">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.feature_selection.</span></span><span class="sig-name descname"><span class="pre">SequentialFeatureSelector</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_features_to_select</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">direction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'forward'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scoring</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cv</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_selection/_sequential.py#L28"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SequentialFeatureSelector" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.feature_selection.</span></span><span class="sig-name descname"><span class="pre">SequentialFeatureSelector</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_features_to_select</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">direction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'forward'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scoring</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cv</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_selection/_sequential.py#L28"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SequentialFeatureSelector" title="Link to this definition">#</a></dt>
 <dd><p>Transformer that performs Sequential Feature Selection.</p>
 <p>This Sequential Feature Selector adds (forward selection) or
 removes (backward selection) features to form a feature subset in a
@@ -1382,7 +1382,7 @@ <h1>SequentialFeatureSelector<a class="headerlink" href="#sequentialfeatureselec
 <dt><strong>n_jobs</strong><span class="classifier">int, default=None</span></dt><dd><p>Number of jobs to run in parallel. When evaluating a new feature to
 add or remove, the cross-validation procedure is parallel over the
 folds.
-<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.5.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
+<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.6.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
 <code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all processors. See <a class="reference internal" href="http://github.com/glossary.html#term-n_jobs"><span class="xref std std-term">Glossary</span></a>
 for more details.</p>
 </dd>
@@ -1440,7 +1440,7 @@ <h1>SequentialFeatureSelector<a class="headerlink" href="#sequentialfeatureselec
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.SequentialFeatureSelector.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_selection/_sequential.py#L196"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SequentialFeatureSelector.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_selection/_sequential.py#L196"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SequentialFeatureSelector.fit" title="Link to this definition">#</a></dt>
 <dd><p>Learn the features to select from X.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1474,7 +1474,7 @@ <h1>SequentialFeatureSelector<a class="headerlink" href="#sequentialfeatureselec
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.SequentialFeatureSelector.fit_transform">
-<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L863"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SequentialFeatureSelector.fit_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L804"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SequentialFeatureSelector.fit_transform" title="Link to this definition">#</a></dt>
 <dd><p>Fit to data, then transform it.</p>
 <p>Fits transformer to <code class="docutils literal notranslate"><span class="pre">X</span></code> and <code class="docutils literal notranslate"><span class="pre">y</span></code> with optional parameters <code class="docutils literal notranslate"><span class="pre">fit_params</span></code>
 and returns a transformed version of <code class="docutils literal notranslate"><span class="pre">X</span></code>.</p>
@@ -1500,7 +1500,7 @@ <h1>SequentialFeatureSelector<a class="headerlink" href="#sequentialfeatureselec
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.SequentialFeatureSelector.get_feature_names_out">
-<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_selection/_base.py#L174"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SequentialFeatureSelector.get_feature_names_out" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_selection/_base.py#L174"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SequentialFeatureSelector.get_feature_names_out" title="Link to this definition">#</a></dt>
 <dd><p>Mask feature names according to selected features.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1528,7 +1528,7 @@ <h1>SequentialFeatureSelector<a class="headerlink" href="#sequentialfeatureselec
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.SequentialFeatureSelector.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_selection/_sequential.py#L336"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SequentialFeatureSelector.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_selection/_sequential.py#L336"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SequentialFeatureSelector.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1548,7 +1548,7 @@ <h1>SequentialFeatureSelector<a class="headerlink" href="#sequentialfeatureselec
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.SequentialFeatureSelector.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SequentialFeatureSelector.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SequentialFeatureSelector.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1569,7 +1569,7 @@ <h1>SequentialFeatureSelector<a class="headerlink" href="#sequentialfeatureselec
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.SequentialFeatureSelector.get_support">
-<span class="sig-name descname"><span class="pre">get_support</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_selection/_base.py#L52"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SequentialFeatureSelector.get_support" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_support</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_selection/_base.py#L52"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SequentialFeatureSelector.get_support" title="Link to this definition">#</a></dt>
 <dd><p>Get a mask, or integer index, of the features selected.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1595,7 +1595,7 @@ <h1>SequentialFeatureSelector<a class="headerlink" href="#sequentialfeatureselec
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.SequentialFeatureSelector.inverse_transform">
-<span class="sig-name descname"><span class="pre">inverse_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_selection/_base.py#L134"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SequentialFeatureSelector.inverse_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">inverse_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_selection/_base.py#L134"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SequentialFeatureSelector.inverse_transform" title="Link to this definition">#</a></dt>
 <dd><p>Reverse the transformation operation.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1606,7 +1606,7 @@ <h1>SequentialFeatureSelector<a class="headerlink" href="#sequentialfeatureselec
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><dl class="simple">
-<dt><strong>X_r</strong><span class="classifier">array of shape [n_samples, n_original_features]</span></dt><dd><p><code class="docutils literal notranslate"><span class="pre">X</span></code> with columns of zeros inserted where features would have
+<dt><strong>X_original</strong><span class="classifier">array of shape [n_samples, n_original_features]</span></dt><dd><p><code class="docutils literal notranslate"><span class="pre">X</span></code> with columns of zeros inserted where features would have
 been removed by <a class="reference internal" href="#sklearn.feature_selection.SequentialFeatureSelector.transform" title="sklearn.feature_selection.SequentialFeatureSelector.transform"><code class="xref py py-meth docutils literal notranslate"><span class="pre">transform</span></code></a>.</p>
 </dd>
 </dl>
@@ -1616,7 +1616,7 @@ <h1>SequentialFeatureSelector<a class="headerlink" href="#sequentialfeatureselec
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.SequentialFeatureSelector.set_output">
-<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SequentialFeatureSelector.set_output" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SequentialFeatureSelector.set_output" title="Link to this definition">#</a></dt>
 <dd><p>Set output container.</p>
 <p>See <a class="reference internal" href="http://github.com/auto_examples/miscellaneous/plot_set_output.html#sphx-glr-auto-examples-miscellaneous-plot-set-output-py"><span class="std std-ref">Introducing the set_output API</span></a>
 for an example on how to use the API.</p>
@@ -1647,7 +1647,7 @@ <h1>SequentialFeatureSelector<a class="headerlink" href="#sequentialfeatureselec
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.SequentialFeatureSelector.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SequentialFeatureSelector.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SequentialFeatureSelector.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1671,7 +1671,7 @@ <h1>SequentialFeatureSelector<a class="headerlink" href="#sequentialfeatureselec
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.SequentialFeatureSelector.transform">
-<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_selection/_base.py#L87"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SequentialFeatureSelector.transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_selection/_base.py#L87"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.SequentialFeatureSelector.transform" title="Link to this definition">#</a></dt>
 <dd><p>Reduce X to the selected features.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.feature_selection.VarianceThreshold.html b/dev/modules/generated/sklearn.feature_selection.VarianceThreshold.html
index d19005fbbcf34..82f96b067cc6e 100644
--- a/dev/modules/generated/sklearn.feature_selection.VarianceThreshold.html
+++ b/dev/modules/generated/sklearn.feature_selection.VarianceThreshold.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>VarianceThreshold<a class="headerlink" href="#variancethreshold" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.feature_selection.VarianceThreshold">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.feature_selection.</span></span><span class="sig-name descname"><span class="pre">VarianceThreshold</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">threshold</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_selection/_variance_threshold.py#L15"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.VarianceThreshold" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.feature_selection.</span></span><span class="sig-name descname"><span class="pre">VarianceThreshold</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">threshold</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_selection/_variance_threshold.py#L15"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.VarianceThreshold" title="Link to this definition">#</a></dt>
 <dd><p>Feature selector that removes all low-variance features.</p>
 <p>This feature selection algorithm looks only at the features (X), not the
 desired outputs (y), and can thus be used for unsupervised learning.</p>
@@ -1365,7 +1365,7 @@ <h1>VarianceThreshold<a class="headerlink" href="#variancethreshold" title="Link
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.VarianceThreshold.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_selection/_variance_threshold.py#L81"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.VarianceThreshold.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_selection/_variance_threshold.py#L81"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.VarianceThreshold.fit" title="Link to this definition">#</a></dt>
 <dd><p>Learn empirical variances from X.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1389,7 +1389,7 @@ <h1>VarianceThreshold<a class="headerlink" href="#variancethreshold" title="Link
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.VarianceThreshold.fit_transform">
-<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L863"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.VarianceThreshold.fit_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L804"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.VarianceThreshold.fit_transform" title="Link to this definition">#</a></dt>
 <dd><p>Fit to data, then transform it.</p>
 <p>Fits transformer to <code class="docutils literal notranslate"><span class="pre">X</span></code> and <code class="docutils literal notranslate"><span class="pre">y</span></code> with optional parameters <code class="docutils literal notranslate"><span class="pre">fit_params</span></code>
 and returns a transformed version of <code class="docutils literal notranslate"><span class="pre">X</span></code>.</p>
@@ -1415,7 +1415,7 @@ <h1>VarianceThreshold<a class="headerlink" href="#variancethreshold" title="Link
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.VarianceThreshold.get_feature_names_out">
-<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_selection/_base.py#L174"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.VarianceThreshold.get_feature_names_out" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_selection/_base.py#L174"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.VarianceThreshold.get_feature_names_out" title="Link to this definition">#</a></dt>
 <dd><p>Mask feature names according to selected features.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1443,7 +1443,7 @@ <h1>VarianceThreshold<a class="headerlink" href="#variancethreshold" title="Link
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.VarianceThreshold.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.VarianceThreshold.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.VarianceThreshold.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1460,7 +1460,7 @@ <h1>VarianceThreshold<a class="headerlink" href="#variancethreshold" title="Link
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.VarianceThreshold.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.VarianceThreshold.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.VarianceThreshold.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1481,7 +1481,7 @@ <h1>VarianceThreshold<a class="headerlink" href="#variancethreshold" title="Link
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.VarianceThreshold.get_support">
-<span class="sig-name descname"><span class="pre">get_support</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_selection/_base.py#L52"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.VarianceThreshold.get_support" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_support</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_selection/_base.py#L52"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.VarianceThreshold.get_support" title="Link to this definition">#</a></dt>
 <dd><p>Get a mask, or integer index, of the features selected.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1507,7 +1507,7 @@ <h1>VarianceThreshold<a class="headerlink" href="#variancethreshold" title="Link
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.VarianceThreshold.inverse_transform">
-<span class="sig-name descname"><span class="pre">inverse_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_selection/_base.py#L134"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.VarianceThreshold.inverse_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">inverse_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_selection/_base.py#L134"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.VarianceThreshold.inverse_transform" title="Link to this definition">#</a></dt>
 <dd><p>Reverse the transformation operation.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1518,7 +1518,7 @@ <h1>VarianceThreshold<a class="headerlink" href="#variancethreshold" title="Link
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><dl class="simple">
-<dt><strong>X_r</strong><span class="classifier">array of shape [n_samples, n_original_features]</span></dt><dd><p><code class="docutils literal notranslate"><span class="pre">X</span></code> with columns of zeros inserted where features would have
+<dt><strong>X_original</strong><span class="classifier">array of shape [n_samples, n_original_features]</span></dt><dd><p><code class="docutils literal notranslate"><span class="pre">X</span></code> with columns of zeros inserted where features would have
 been removed by <a class="reference internal" href="#sklearn.feature_selection.VarianceThreshold.transform" title="sklearn.feature_selection.VarianceThreshold.transform"><code class="xref py py-meth docutils literal notranslate"><span class="pre">transform</span></code></a>.</p>
 </dd>
 </dl>
@@ -1528,7 +1528,7 @@ <h1>VarianceThreshold<a class="headerlink" href="#variancethreshold" title="Link
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.VarianceThreshold.set_output">
-<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.VarianceThreshold.set_output" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.VarianceThreshold.set_output" title="Link to this definition">#</a></dt>
 <dd><p>Set output container.</p>
 <p>See <a class="reference internal" href="http://github.com/auto_examples/miscellaneous/plot_set_output.html#sphx-glr-auto-examples-miscellaneous-plot-set-output-py"><span class="std std-ref">Introducing the set_output API</span></a>
 for an example on how to use the API.</p>
@@ -1559,7 +1559,7 @@ <h1>VarianceThreshold<a class="headerlink" href="#variancethreshold" title="Link
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.VarianceThreshold.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.VarianceThreshold.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.VarianceThreshold.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1583,7 +1583,7 @@ <h1>VarianceThreshold<a class="headerlink" href="#variancethreshold" title="Link
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.feature_selection.VarianceThreshold.transform">
-<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_selection/_base.py#L87"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.VarianceThreshold.transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_selection/_base.py#L87"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.VarianceThreshold.transform" title="Link to this definition">#</a></dt>
 <dd><p>Reduce X to the selected features.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.feature_selection.chi2.html b/dev/modules/generated/sklearn.feature_selection.chi2.html
index 19382762bfa6b..893307cc4fbf9 100644
--- a/dev/modules/generated/sklearn.feature_selection.chi2.html
+++ b/dev/modules/generated/sklearn.feature_selection.chi2.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>chi2<a class="headerlink" href="#chi2" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.feature_selection.chi2">
-<span class="sig-prename descclassname"><span class="pre">sklearn.feature_selection.</span></span><span class="sig-name descname"><span class="pre">chi2</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_selection/_univariate_selection.py#L194"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.chi2" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.feature_selection.</span></span><span class="sig-name descname"><span class="pre">chi2</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_selection/_univariate_selection.py#L193"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.chi2" title="Link to this definition">#</a></dt>
 <dd><p>Compute chi-squared stats between each non-negative feature and class.</p>
 <p>This score can be used to select the <code class="docutils literal notranslate"><span class="pre">n_features</span></code> features with the
 highest values for the test chi-squared statistic from X, which must
@@ -1361,9 +1361,9 @@ <h1>chi2<a class="headerlink" href="#chi2" title="Link to this heading">#</a></h
 <span class="gp">&gt;&gt;&gt; </span><span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">chi2_stats</span><span class="p">,</span> <span class="n">p_values</span> <span class="o">=</span> <span class="n">chi2</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">chi2_stats</span>
-<span class="go">array([15.3...,  6.5       ,  8.9...])</span>
+<span class="go">array([15.3,  6.5       ,  8.9])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">p_values</span>
-<span class="go">array([0.0004..., 0.0387..., 0.0116... ])</span>
+<span class="go">array([0.000456, 0.0387, 0.0116 ])</span>
 </pre></div>
 </div>
 </dd></dl>
diff --git a/dev/modules/generated/sklearn.feature_selection.f_classif.html b/dev/modules/generated/sklearn.feature_selection.f_classif.html
index 98f5c19e0e76f..aef7c66079d3a 100644
--- a/dev/modules/generated/sklearn.feature_selection.f_classif.html
+++ b/dev/modules/generated/sklearn.feature_selection.f_classif.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>f_classif<a class="headerlink" href="#f-classif" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.feature_selection.f_classif">
-<span class="sig-prename descclassname"><span class="pre">sklearn.feature_selection.</span></span><span class="sig-name descname"><span class="pre">f_classif</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_selection/_univariate_selection.py#L119"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.f_classif" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.feature_selection.</span></span><span class="sig-name descname"><span class="pre">f_classif</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_selection/_univariate_selection.py#L118"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.f_classif" title="Link to this definition">#</a></dt>
 <dd><p>Compute the ANOVA F-value for the provided sample.</p>
 <p>Read more in the <a class="reference internal" href="http://github.com/feature_selection.html#univariate-feature-selection"><span class="std std-ref">User Guide</span></a>.</p>
 <dl class="field-list simple">
@@ -1346,13 +1346,13 @@ <h1>f_classif<a class="headerlink" href="#f-classif" title="Link to this heading
 <span class="gp">... </span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">f_statistic</span><span class="p">,</span> <span class="n">p_values</span> <span class="o">=</span> <span class="n">f_classif</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">f_statistic</span>
-<span class="go">array([2.2...e+02, 7.0...e-01, 1.6...e+00, 9.3...e-01,</span>
-<span class="go">       5.4...e+00, 3.2...e-01, 4.7...e-02, 5.7...e-01,</span>
-<span class="go">       7.5...e-01, 8.9...e-02])</span>
+<span class="go">array([2.21e+02, 7.02e-01, 1.70e+00, 9.31e-01,</span>
+<span class="go">       5.41e+00, 3.25e-01, 4.71e-02, 5.72e-01,</span>
+<span class="go">       7.54e-01, 8.90e-02])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">p_values</span>
-<span class="go">array([7.1...e-27, 4.0...e-01, 1.9...e-01, 3.3...e-01,</span>
-<span class="go">       2.2...e-02, 5.7...e-01, 8.2...e-01, 4.5...e-01,</span>
-<span class="go">       3.8...e-01, 7.6...e-01])</span>
+<span class="go">array([7.14e-27, 4.04e-01, 1.96e-01, 3.37e-01,</span>
+<span class="go">       2.21e-02, 5.70e-01, 8.29e-01, 4.51e-01,</span>
+<span class="go">       3.87e-01, 7.66e-01])</span>
 </pre></div>
 </div>
 </dd></dl>
diff --git a/dev/modules/generated/sklearn.feature_selection.f_regression.html b/dev/modules/generated/sklearn.feature_selection.f_regression.html
index 7a60983655422..e52dcd3ebe8b6 100644
--- a/dev/modules/generated/sklearn.feature_selection.f_regression.html
+++ b/dev/modules/generated/sklearn.feature_selection.f_regression.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>f_regression<a class="headerlink" href="#f-regression" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.feature_selection.f_regression">
-<span class="sig-prename descclassname"><span class="pre">sklearn.feature_selection.</span></span><span class="sig-name descname"><span class="pre">f_regression</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">center</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">force_finite</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_selection/_univariate_selection.py#L398"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.f_regression" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.feature_selection.</span></span><span class="sig-name descname"><span class="pre">f_regression</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">center</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">force_finite</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_selection/_univariate_selection.py#L397"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.f_regression" title="Link to this definition">#</a></dt>
 <dd><p>Univariate linear regression tests returning F-statistic and p-values.</p>
 <p>Quick linear model for testing the effect of a single regressor,
 sequentially for many regressors.</p>
@@ -1401,9 +1401,9 @@ <h1>f_regression<a class="headerlink" href="#f-regression" title="Link to this h
 <span class="gp">... </span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">f_statistic</span><span class="p">,</span> <span class="n">p_values</span> <span class="o">=</span> <span class="n">f_regression</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">f_statistic</span>
-<span class="go">array([1.2...+00, 2.6...+13, 2.6...+00])</span>
+<span class="go">array([1.21, 2.67e13, 2.66])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">p_values</span>
-<span class="go">array([2.7..., 1.5..., 1.0...])</span>
+<span class="go">array([0.276, 1.54e-283, 0.11])</span>
 </pre></div>
 </div>
 </dd></dl>
diff --git a/dev/modules/generated/sklearn.feature_selection.mutual_info_classif.html b/dev/modules/generated/sklearn.feature_selection.mutual_info_classif.html
index e2cbb9ec0dc36..a692610a45df4 100644
--- a/dev/modules/generated/sklearn.feature_selection.mutual_info_classif.html
+++ b/dev/modules/generated/sklearn.feature_selection.mutual_info_classif.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>mutual_info_classif<a class="headerlink" href="#mutual-info-classif" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.feature_selection.mutual_info_classif">
-<span class="sig-prename descclassname"><span class="pre">sklearn.feature_selection.</span></span><span class="sig-name descname"><span class="pre">mutual_info_classif</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">discrete_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_neighbors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_selection/_mutual_info.py#L453"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.mutual_info_classif" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.feature_selection.</span></span><span class="sig-name descname"><span class="pre">mutual_info_classif</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">discrete_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_neighbors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_selection/_mutual_info.py#L453"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.mutual_info_classif" title="Link to this definition">#</a></dt>
 <dd><p>Estimate mutual information for a discrete target variable.</p>
 <p>Mutual information (MI) <a class="reference internal" href="#r50b872b699c4-1" id="id1">[1]</a> between two random variables is a non-negative
 value, which measures the dependency between the variables. It is equal
@@ -1345,7 +1345,7 @@ <h1>mutual_info_classif<a class="headerlink" href="#mutual-info-classif" title="
 </dd>
 <dt><strong>n_jobs</strong><span class="classifier">int, default=None</span></dt><dd><p>The number of jobs to use for computing the mutual information.
 The parallelization is done on the columns of <code class="docutils literal notranslate"><span class="pre">X</span></code>.
-<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.5.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
+<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.6.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
 <code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all processors. See <a class="reference internal" href="http://github.com/glossary.html#term-n_jobs"><span class="xref std std-term">Glossary</span></a>
 for more details.</p>
 <div class="versionadded">
@@ -1407,8 +1407,8 @@ <h1>mutual_info_classif<a class="headerlink" href="#mutual-info-classif" title="
 <span class="gp">... </span>    <span class="n">shuffle</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span>
 <span class="gp">... </span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">mutual_info_classif</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
-<span class="go">array([0.58..., 0.10..., 0.19..., 0.09... , 0.        ,</span>
-<span class="go">       0.     , 0.     , 0.     , 0.      , 0.        ])</span>
+<span class="go">array([0.589, 0.107, 0.196, 0.0968 , 0.,</span>
+<span class="go">       0.   , 0.   , 0.   , 0.     , 0.])</span>
 </pre></div>
 </div>
 </dd></dl>
diff --git a/dev/modules/generated/sklearn.feature_selection.mutual_info_regression.html b/dev/modules/generated/sklearn.feature_selection.mutual_info_regression.html
index 471bd1cd4ff3c..6905720802fc8 100644
--- a/dev/modules/generated/sklearn.feature_selection.mutual_info_regression.html
+++ b/dev/modules/generated/sklearn.feature_selection.mutual_info_regression.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
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     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>mutual_info_regression<a class="headerlink" href="#mutual-info-regression" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.feature_selection.mutual_info_regression">
-<span class="sig-prename descclassname"><span class="pre">sklearn.feature_selection.</span></span><span class="sig-name descname"><span class="pre">mutual_info_regression</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">discrete_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_neighbors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_selection/_mutual_info.py#L325"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.mutual_info_regression" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.feature_selection.</span></span><span class="sig-name descname"><span class="pre">mutual_info_regression</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">discrete_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_neighbors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_selection/_mutual_info.py#L325"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.mutual_info_regression" title="Link to this definition">#</a></dt>
 <dd><p>Estimate mutual information for a continuous target variable.</p>
 <p>Mutual information (MI) <a class="reference internal" href="#r37d39d7589e2-1" id="id1">[1]</a> between two random variables is a non-negative
 value, which measures the dependency between the variables. It is equal
@@ -1345,7 +1345,7 @@ <h1>mutual_info_regression<a class="headerlink" href="#mutual-info-regression" t
 </dd>
 <dt><strong>n_jobs</strong><span class="classifier">int, default=None</span></dt><dd><p>The number of jobs to use for computing the mutual information.
 The parallelization is done on the columns of <code class="docutils literal notranslate"><span class="pre">X</span></code>.</p>
-<p><code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.5.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
+<p><code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.6.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
 <code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all processors. See <a class="reference internal" href="http://github.com/glossary.html#term-n_jobs"><span class="xref std std-term">Glossary</span></a>
 for more details.</p>
 <div class="versionadded">
@@ -1406,7 +1406,7 @@ <h1>mutual_info_regression<a class="headerlink" href="#mutual-info-regression" t
 <span class="gp">... </span>    <span class="n">n_samples</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span> <span class="n">n_features</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">n_informative</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">noise</span><span class="o">=</span><span class="mf">1e-4</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span>
 <span class="gp">... </span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">mutual_info_regression</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
-<span class="go">array([0.1..., 2.6...  , 0.0...])</span>
+<span class="go">array([0.117, 2.645, 0.0287])</span>
 </pre></div>
 </div>
 </dd></dl>
diff --git a/dev/modules/generated/sklearn.feature_selection.r_regression.html b/dev/modules/generated/sklearn.feature_selection.r_regression.html
index ecb57d2634dd3..dfe9c1782570e 100644
--- a/dev/modules/generated/sklearn.feature_selection.r_regression.html
+++ b/dev/modules/generated/sklearn.feature_selection.r_regression.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>r_regression<a class="headerlink" href="#r-regression" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.feature_selection.r_regression">
-<span class="sig-prename descclassname"><span class="pre">sklearn.feature_selection.</span></span><span class="sig-name descname"><span class="pre">r_regression</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">center</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">force_finite</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/feature_selection/_univariate_selection.py#L293"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.r_regression" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.feature_selection.</span></span><span class="sig-name descname"><span class="pre">r_regression</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">center</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">force_finite</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/feature_selection/_univariate_selection.py#L292"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.feature_selection.r_regression" title="Link to this definition">#</a></dt>
 <dd><p>Compute Pearson’s r for each features and the target.</p>
 <p>Pearson’s r is also known as the Pearson correlation coefficient.</p>
 <p>Linear model for testing the individual effect of each of many regressors.
@@ -1369,7 +1369,7 @@ <h1>r_regression<a class="headerlink" href="#r-regression" title="Link to this h
 <span class="gp">... </span>    <span class="n">n_samples</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span> <span class="n">n_features</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">n_informative</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">noise</span><span class="o">=</span><span class="mf">1e-4</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span>
 <span class="gp">... </span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">r_regression</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
-<span class="go">array([-0.15...,  1.        , -0.22...])</span>
+<span class="go">array([-0.157,  1.        , -0.229])</span>
 </pre></div>
 </div>
 </dd></dl>
diff --git a/dev/modules/generated/sklearn.frozen.FrozenEstimator.html b/dev/modules/generated/sklearn.frozen.FrozenEstimator.html
index 5402bcd4c5325..d539de697a437 100644
--- a/dev/modules/generated/sklearn.frozen.FrozenEstimator.html
+++ b/dev/modules/generated/sklearn.frozen.FrozenEstimator.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>FrozenEstimator<a class="headerlink" href="#frozenestimator" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.frozen.FrozenEstimator">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.frozen.</span></span><span class="sig-name descname"><span class="pre">FrozenEstimator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/frozen/_frozen.py#L27"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.frozen.FrozenEstimator" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.frozen.</span></span><span class="sig-name descname"><span class="pre">FrozenEstimator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/frozen/_frozen.py#L27"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.frozen.FrozenEstimator" title="Link to this definition">#</a></dt>
 <dd><p>Estimator that wraps a fitted estimator to prevent re-fitting.</p>
 <p>This meta-estimator takes an estimator and freezes it, in the sense that calling
 <code class="docutils literal notranslate"><span class="pre">fit</span></code> on it has no effect. <code class="docutils literal notranslate"><span class="pre">fit_predict</span></code> and <code class="docutils literal notranslate"><span class="pre">fit_transform</span></code> are also disabled.
@@ -1346,7 +1346,7 @@ <h1>FrozenEstimator<a class="headerlink" href="#frozenestimator" title="Link to
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.frozen.FrozenEstimator.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/frozen/_frozen.py#L89"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.frozen.FrozenEstimator.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/frozen/_frozen.py#L89"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.frozen.FrozenEstimator.fit" title="Link to this definition">#</a></dt>
 <dd><p>No-op.</p>
 <p>As a frozen estimator, calling <code class="docutils literal notranslate"><span class="pre">fit</span></code> has no effect.</p>
 <dl class="field-list simple">
@@ -1375,7 +1375,7 @@ <h1>FrozenEstimator<a class="headerlink" href="#frozenestimator" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.frozen.FrozenEstimator.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.frozen.FrozenEstimator.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.frozen.FrozenEstimator.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1392,7 +1392,7 @@ <h1>FrozenEstimator<a class="headerlink" href="#frozenestimator" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.frozen.FrozenEstimator.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/frozen/_frozen.py#L145"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.frozen.FrozenEstimator.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/frozen/_frozen.py#L145"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.frozen.FrozenEstimator.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <p>Returns a <code class="docutils literal notranslate"><span class="pre">{&quot;estimator&quot;:</span> <span class="pre">estimator}</span></code> dict. The parameters of the inner
 estimator are not included.</p>
@@ -1414,7 +1414,7 @@ <h1>FrozenEstimator<a class="headerlink" href="#frozenestimator" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.frozen.FrozenEstimator.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/frozen/_frozen.py#L118"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.frozen.FrozenEstimator.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/frozen/_frozen.py#L118"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.frozen.FrozenEstimator.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The only valid key here is <code class="docutils literal notranslate"><span class="pre">estimator</span></code>. You cannot set the parameters of the
 inner estimator.</p>
diff --git a/dev/modules/generated/sklearn.gaussian_process.GaussianProcessClassifier.html b/dev/modules/generated/sklearn.gaussian_process.GaussianProcessClassifier.html
index 5c5b196f88085..e05b7fd1fafa5 100644
--- a/dev/modules/generated/sklearn.gaussian_process.GaussianProcessClassifier.html
+++ b/dev/modules/generated/sklearn.gaussian_process.GaussianProcessClassifier.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>GaussianProcessClassifier<a class="headerlink" href="#gaussianprocessclassifier" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.GaussianProcessClassifier">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.gaussian_process.</span></span><span class="sig-name descname"><span class="pre">GaussianProcessClassifier</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optimizer</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'fmin_l_bfgs_b'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_restarts_optimizer</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter_predict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">warm_start</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy_X_train</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">multi_class</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'one_vs_rest'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/gaussian_process/_gpc.py#L486"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.GaussianProcessClassifier" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.gaussian_process.</span></span><span class="sig-name descname"><span class="pre">GaussianProcessClassifier</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optimizer</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'fmin_l_bfgs_b'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_restarts_optimizer</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter_predict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">warm_start</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy_X_train</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">multi_class</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'one_vs_rest'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/gaussian_process/_gpc.py#L516"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.GaussianProcessClassifier" title="Link to this definition">#</a></dt>
 <dd><p>Gaussian process classification (GPC) based on Laplace approximation.</p>
 <p>The implementation is based on Algorithm 3.1, 3.2, and 5.1 from <a class="reference internal" href="#r2da648a61a73-rw2006" id="id1">[RW2006]</a>.</p>
 <p>Internally, the Laplace approximation is used for approximating the
@@ -1392,7 +1392,7 @@ <h1>GaussianProcessClassifier<a class="headerlink" href="#gaussianprocessclassif
 </dd>
 <dt><strong>n_jobs</strong><span class="classifier">int, default=None</span></dt><dd><p>The number of jobs to use for the computation: the specified
 multiclass problems are computed in parallel.
-<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.5.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
+<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.6.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
 <code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all processors. See <a class="reference internal" href="http://github.com/glossary.html#term-n_jobs"><span class="xref std std-term">Glossary</span></a>
 for more details.</p>
 </dd>
@@ -1460,7 +1460,7 @@ <h1>GaussianProcessClassifier<a class="headerlink" href="#gaussianprocessclassif
 <a class="reference internal" href="http://github.com/auto_examples/classification/plot_classification_probability.html#sphx-glr-auto-examples-classification-plot-classification-probability-py"><span class="std std-ref">Plot classification probability</span></a>.</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.GaussianProcessClassifier.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/gaussian_process/_gpc.py#L684"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.GaussianProcessClassifier.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/gaussian_process/_gpc.py#L714"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.GaussianProcessClassifier.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit Gaussian process classification model.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1482,7 +1482,7 @@ <h1>GaussianProcessClassifier<a class="headerlink" href="#gaussianprocessclassif
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.GaussianProcessClassifier.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.GaussianProcessClassifier.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.GaussianProcessClassifier.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1499,7 +1499,7 @@ <h1>GaussianProcessClassifier<a class="headerlink" href="#gaussianprocessclassif
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.GaussianProcessClassifier.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.GaussianProcessClassifier.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.GaussianProcessClassifier.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1518,9 +1518,38 @@ <h1>GaussianProcessClassifier<a class="headerlink" href="#gaussianprocessclassif
 </dl>
 </dd></dl>
 
+<dl class="py method">
+<dt class="sig sig-object py" id="sklearn.gaussian_process.GaussianProcessClassifier.latent_mean_and_variance">
+<span class="sig-name descname"><span class="pre">latent_mean_and_variance</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/gaussian_process/_gpc.py#L936"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.GaussianProcessClassifier.latent_mean_and_variance" title="Link to this definition">#</a></dt>
+<dd><p>Compute the mean and variance of the latent function.</p>
+<p>Based on algorithm 3.2 of <a class="reference internal" href="http://github.com/gaussian_process.html#rw2006" id="id3"><span>[RW2006]</span></a>, this function returns the latent
+mean (Line 4) and variance (Line 6) of the Gaussian process
+classification model.</p>
+<p>Note that this function is only supported for binary classification.</p>
+<div class="versionadded">
+<p><span class="versionmodified added">Added in version 1.7.</span></p>
+</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl class="simple">
+<dt><strong>X</strong><span class="classifier">array-like of shape (n_samples, n_features) or list of object</span></dt><dd><p>Query points where the GP is evaluated for classification.</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl class="simple">
+<dt><strong>latent_mean</strong><span class="classifier">array-like of shape (n_samples,)</span></dt><dd><p>Mean of the latent function values at the query points.</p>
+</dd>
+<dt><strong>latent_var</strong><span class="classifier">array-like of shape (n_samples,)</span></dt><dd><p>Variance of the latent function values at the query points.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+</dd></dl>
+
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.GaussianProcessClassifier.log_marginal_likelihood">
-<span class="sig-name descname"><span class="pre">log_marginal_likelihood</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">theta</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eval_gradient</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">clone_kernel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/gaussian_process/_gpc.py#L821"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.GaussianProcessClassifier.log_marginal_likelihood" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">log_marginal_likelihood</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">theta</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eval_gradient</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">clone_kernel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/gaussian_process/_gpc.py#L851"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.GaussianProcessClassifier.log_marginal_likelihood" title="Link to this definition">#</a></dt>
 <dd><p>Return log-marginal likelihood of theta for training data.</p>
 <p>In the case of multi-class classification, the mean log-marginal
 likelihood of the one-versus-rest classifiers are returned.</p>
@@ -1559,7 +1588,7 @@ <h1>GaussianProcessClassifier<a class="headerlink" href="#gaussianprocessclassif
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.GaussianProcessClassifier.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/gaussian_process/_gpc.py#L759"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.GaussianProcessClassifier.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/gaussian_process/_gpc.py#L789"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.GaussianProcessClassifier.predict" title="Link to this definition">#</a></dt>
 <dd><p>Perform classification on an array of test vectors X.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1579,7 +1608,7 @@ <h1>GaussianProcessClassifier<a class="headerlink" href="#gaussianprocessclassif
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.GaussianProcessClassifier.predict_proba">
-<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/gaussian_process/_gpc.py#L781"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.GaussianProcessClassifier.predict_proba" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/gaussian_process/_gpc.py#L811"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.GaussianProcessClassifier.predict_proba" title="Link to this definition">#</a></dt>
 <dd><p>Return probability estimates for the test vector X.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1601,7 +1630,7 @@ <h1>GaussianProcessClassifier<a class="headerlink" href="#gaussianprocessclassif
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.GaussianProcessClassifier.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L546"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.GaussianProcessClassifier.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L487"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.GaussianProcessClassifier.score" title="Link to this definition">#</a></dt>
 <dd><p>Return <a class="reference internal" href="http://github.com/model_evaluation.html#accuracy-score"><span class="std std-ref">accuracy</span></a> on provided data and labels.</p>
 <p>In multi-label classification, this is the subset accuracy
 which is a harsh metric since you require for each sample that
@@ -1628,7 +1657,7 @@ <h1>GaussianProcessClassifier<a class="headerlink" href="#gaussianprocessclassif
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.GaussianProcessClassifier.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.GaussianProcessClassifier.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.GaussianProcessClassifier.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1652,7 +1681,7 @@ <h1>GaussianProcessClassifier<a class="headerlink" href="#gaussianprocessclassif
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.GaussianProcessClassifier.set_score_request">
-<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.gaussian_process.GaussianProcessClassifier" title="sklearn.gaussian_process._gpc.GaussianProcessClassifier"><span class="pre">GaussianProcessClassifier</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.GaussianProcessClassifier.set_score_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.gaussian_process.GaussianProcessClassifier" title="sklearn.gaussian_process._gpc.GaussianProcessClassifier"><span class="pre">GaussianProcessClassifier</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.GaussianProcessClassifier.set_score_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1777,6 +1806,7 @@ <h2>Gallery examples<a class="headerlink" href="#gallery-examples" title="Link t
 <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.gaussian_process.GaussianProcessClassifier.fit"><code class="docutils literal notranslate"><span class="pre">fit</span></code></a></li>
 <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.gaussian_process.GaussianProcessClassifier.get_metadata_routing"><code class="docutils literal notranslate"><span class="pre">get_metadata_routing</span></code></a></li>
 <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.gaussian_process.GaussianProcessClassifier.get_params"><code class="docutils literal notranslate"><span class="pre">get_params</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.gaussian_process.GaussianProcessClassifier.latent_mean_and_variance"><code class="docutils literal notranslate"><span class="pre">latent_mean_and_variance</span></code></a></li>
 <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.gaussian_process.GaussianProcessClassifier.log_marginal_likelihood"><code class="docutils literal notranslate"><span class="pre">log_marginal_likelihood</span></code></a></li>
 <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.gaussian_process.GaussianProcessClassifier.predict"><code class="docutils literal notranslate"><span class="pre">predict</span></code></a></li>
 <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.gaussian_process.GaussianProcessClassifier.predict_proba"><code class="docutils literal notranslate"><span class="pre">predict_proba</span></code></a></li>
diff --git a/dev/modules/generated/sklearn.gaussian_process.GaussianProcessRegressor.html b/dev/modules/generated/sklearn.gaussian_process.GaussianProcessRegressor.html
index 6458aab1421bd..9aa733a118430 100644
--- a/dev/modules/generated/sklearn.gaussian_process.GaussianProcessRegressor.html
+++ b/dev/modules/generated/sklearn.gaussian_process.GaussianProcessRegressor.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1308,7 +1308,7 @@
 <h1>GaussianProcessRegressor<a class="headerlink" href="#gaussianprocessregressor" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.GaussianProcessRegressor">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.gaussian_process.</span></span><span class="sig-name descname"><span class="pre">GaussianProcessRegressor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optimizer</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'fmin_l_bfgs_b'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_restarts_optimizer</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">normalize_y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy_X_train</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_targets</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/gaussian_process/_gpr.py#L26"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.GaussianProcessRegressor" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.gaussian_process.</span></span><span class="sig-name descname"><span class="pre">GaussianProcessRegressor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optimizer</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'fmin_l_bfgs_b'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_restarts_optimizer</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">normalize_y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy_X_train</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_targets</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/gaussian_process/_gpr.py#L26"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.GaussianProcessRegressor" title="Link to this definition">#</a></dt>
 <dd><p>Gaussian process regression (GPR).</p>
 <p>The implementation is based on Algorithm 2.1 of <a class="reference internal" href="#rf75674b0f418-rw2006" id="id1">[RW2006]</a>.</p>
 <p>In addition to standard scikit-learn estimator API,
@@ -1466,12 +1466,12 @@ <h1>GaussianProcessRegressor<a class="headerlink" href="#gaussianprocessregresso
 <span class="gp">&gt;&gt;&gt; </span><span class="n">gpr</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
 <span class="go">0.3680...</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">gpr</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X</span><span class="p">[:</span><span class="mi">2</span><span class="p">,:],</span> <span class="n">return_std</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-<span class="go">(array([653.0..., 592.1...]), array([316.6..., 316.6...]))</span>
+<span class="go">(array([653.0, 592.1]), array([316.6, 316.6]))</span>
 </pre></div>
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.GaussianProcessRegressor.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/gaussian_process/_gpr.py#L224"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.GaussianProcessRegressor.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/gaussian_process/_gpr.py#L224"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.GaussianProcessRegressor.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit Gaussian process regression model.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1493,7 +1493,7 @@ <h1>GaussianProcessRegressor<a class="headerlink" href="#gaussianprocessregresso
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.GaussianProcessRegressor.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.GaussianProcessRegressor.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.GaussianProcessRegressor.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1510,7 +1510,7 @@ <h1>GaussianProcessRegressor<a class="headerlink" href="#gaussianprocessregresso
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.GaussianProcessRegressor.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.GaussianProcessRegressor.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.GaussianProcessRegressor.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1531,7 +1531,7 @@ <h1>GaussianProcessRegressor<a class="headerlink" href="#gaussianprocessregresso
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.GaussianProcessRegressor.log_marginal_likelihood">
-<span class="sig-name descname"><span class="pre">log_marginal_likelihood</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">theta</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eval_gradient</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">clone_kernel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/gaussian_process/_gpr.py#L537"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.GaussianProcessRegressor.log_marginal_likelihood" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">log_marginal_likelihood</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">theta</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eval_gradient</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">clone_kernel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/gaussian_process/_gpr.py#L537"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.GaussianProcessRegressor.log_marginal_likelihood" title="Link to this definition">#</a></dt>
 <dd><p>Return log-marginal likelihood of theta for training data.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1564,7 +1564,7 @@ <h1>GaussianProcessRegressor<a class="headerlink" href="#gaussianprocessregresso
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.GaussianProcessRegressor.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_std</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_cov</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/gaussian_process/_gpr.py#L367"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.GaussianProcessRegressor.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_std</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_cov</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/gaussian_process/_gpr.py#L367"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.GaussianProcessRegressor.predict" title="Link to this definition">#</a></dt>
 <dd><p>Predict using the Gaussian process regression model.</p>
 <p>We can also predict based on an unfitted model by using the GP prior.
 In addition to the mean of the predictive distribution, optionally also
@@ -1600,7 +1600,7 @@ <h1>GaussianProcessRegressor<a class="headerlink" href="#gaussianprocessregresso
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.GaussianProcessRegressor.sample_y">
-<span class="sig-name descname"><span class="pre">sample_y</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/gaussian_process/_gpr.py#L498"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.GaussianProcessRegressor.sample_y" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">sample_y</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/gaussian_process/_gpr.py#L498"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.GaussianProcessRegressor.sample_y" title="Link to this definition">#</a></dt>
 <dd><p>Draw samples from Gaussian process and evaluate at X.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1628,7 +1628,7 @@ <h1>GaussianProcessRegressor<a class="headerlink" href="#gaussianprocessregresso
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.GaussianProcessRegressor.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L619"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.GaussianProcessRegressor.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L560"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.GaussianProcessRegressor.score" title="Link to this definition">#</a></dt>
 <dd><p>Return <a class="reference internal" href="http://github.com/model_evaluation.html#r2-score"><span class="std std-ref">coefficient of determination</span></a> on test data.</p>
 <p>The coefficient of determination, <span class="math notranslate nohighlight">\(R^2\)</span>, is defined as
 <span class="math notranslate nohighlight">\((1 - \frac{u}{v})\)</span>, where <span class="math notranslate nohighlight">\(u\)</span> is the residual
@@ -1670,7 +1670,7 @@ <h1>GaussianProcessRegressor<a class="headerlink" href="#gaussianprocessregresso
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.GaussianProcessRegressor.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.GaussianProcessRegressor.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.GaussianProcessRegressor.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1694,7 +1694,7 @@ <h1>GaussianProcessRegressor<a class="headerlink" href="#gaussianprocessregresso
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.GaussianProcessRegressor.set_predict_request">
-<span class="sig-name descname"><span class="pre">set_predict_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_cov</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_std</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.gaussian_process.GaussianProcessRegressor" title="sklearn.gaussian_process._gpr.GaussianProcessRegressor"><span class="pre">GaussianProcessRegressor</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.GaussianProcessRegressor.set_predict_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_predict_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_cov</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_std</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.gaussian_process.GaussianProcessRegressor" title="sklearn.gaussian_process._gpr.GaussianProcessRegressor"><span class="pre">GaussianProcessRegressor</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.GaussianProcessRegressor.set_predict_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">predict</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1739,7 +1739,7 @@ <h1>GaussianProcessRegressor<a class="headerlink" href="#gaussianprocessregresso
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.GaussianProcessRegressor.set_score_request">
-<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.gaussian_process.GaussianProcessRegressor" title="sklearn.gaussian_process._gpr.GaussianProcessRegressor"><span class="pre">GaussianProcessRegressor</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.GaussianProcessRegressor.set_score_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.gaussian_process.GaussianProcessRegressor" title="sklearn.gaussian_process._gpr.GaussianProcessRegressor"><span class="pre">GaussianProcessRegressor</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.GaussianProcessRegressor.set_score_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
diff --git a/dev/modules/generated/sklearn.gaussian_process.kernels.CompoundKernel.html b/dev/modules/generated/sklearn.gaussian_process.kernels.CompoundKernel.html
index 7776cd2d2d9b9..4887fc44d4779 100644
--- a/dev/modules/generated/sklearn.gaussian_process.kernels.CompoundKernel.html
+++ b/dev/modules/generated/sklearn.gaussian_process.kernels.CompoundKernel.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>CompoundKernel<a class="headerlink" href="#compoundkernel" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.kernels.CompoundKernel">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.gaussian_process.kernels.</span></span><span class="sig-name descname"><span class="pre">CompoundKernel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernels</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/gaussian_process/kernels.py#L512"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.CompoundKernel" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.gaussian_process.kernels.</span></span><span class="sig-name descname"><span class="pre">CompoundKernel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernels</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/gaussian_process/kernels.py#L512"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.CompoundKernel" title="Link to this definition">#</a></dt>
 <dd><p>Kernel which is composed of a set of other kernels.</p>
 <div class="versionadded">
 <p><span class="versionmodified added">Added in version 0.18.</span></p>
@@ -1335,7 +1335,7 @@ <h1>CompoundKernel<a class="headerlink" href="#compoundkernel" title="Link to th
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.kernels.CompoundKernel.__call__">
-<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eval_gradient</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/gaussian_process/kernels.py#L597"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.CompoundKernel.__call__" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eval_gradient</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/gaussian_process/kernels.py#L597"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.CompoundKernel.__call__" title="Link to this definition">#</a></dt>
 <dd><p>Return the kernel k(X, Y) and optionally its gradient.</p>
 <p>Note that this compound kernel returns the results of all simple kernel
 stacked along an additional axis.</p>
@@ -1381,7 +1381,7 @@ <h1>CompoundKernel<a class="headerlink" href="#compoundkernel" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.kernels.CompoundKernel.clone_with_theta">
-<span class="sig-name descname"><span class="pre">clone_with_theta</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">theta</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/gaussian_process/kernels.py#L258"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.CompoundKernel.clone_with_theta" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">clone_with_theta</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">theta</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/gaussian_process/kernels.py#L258"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.CompoundKernel.clone_with_theta" title="Link to this definition">#</a></dt>
 <dd><p>Returns a clone of self with given hyperparameters theta.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1395,7 +1395,7 @@ <h1>CompoundKernel<a class="headerlink" href="#compoundkernel" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.kernels.CompoundKernel.diag">
-<span class="sig-name descname"><span class="pre">diag</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/gaussian_process/kernels.py#L656"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.CompoundKernel.diag" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">diag</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/gaussian_process/kernels.py#L656"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.CompoundKernel.diag" title="Link to this definition">#</a></dt>
 <dd><p>Returns the diagonal of the kernel k(X, X).</p>
 <p>The result of this method is identical to <code class="docutils literal notranslate"><span class="pre">np.diag(self(X))</span></code>; however,
 it can be evaluated more efficiently since only the diagonal is
@@ -1418,7 +1418,7 @@ <h1>CompoundKernel<a class="headerlink" href="#compoundkernel" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.kernels.CompoundKernel.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/gaussian_process/kernels.py#L541"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.CompoundKernel.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/gaussian_process/kernels.py#L541"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.CompoundKernel.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters of this kernel.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1445,7 +1445,7 @@ <h1>CompoundKernel<a class="headerlink" href="#compoundkernel" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.kernels.CompoundKernel.is_stationary">
-<span class="sig-name descname"><span class="pre">is_stationary</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/gaussian_process/kernels.py#L647"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.CompoundKernel.is_stationary" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">is_stationary</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/gaussian_process/kernels.py#L647"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.CompoundKernel.is_stationary" title="Link to this definition">#</a></dt>
 <dd><p>Returns whether the kernel is stationary.</p>
 </dd></dl>
 
@@ -1463,7 +1463,7 @@ <h1>CompoundKernel<a class="headerlink" href="#compoundkernel" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.kernels.CompoundKernel.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/gaussian_process/kernels.py#L218"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.CompoundKernel.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/gaussian_process/kernels.py#L218"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.CompoundKernel.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this kernel.</p>
 <p>The method works on simple kernels as well as on nested kernels.
 The latter have parameters of the form <code class="docutils literal notranslate"><span class="pre">&lt;component&gt;__&lt;parameter&gt;</span></code>
diff --git a/dev/modules/generated/sklearn.gaussian_process.kernels.ConstantKernel.html b/dev/modules/generated/sklearn.gaussian_process.kernels.ConstantKernel.html
index 515ff77ccf00f..0bad8e50bea09 100644
--- a/dev/modules/generated/sklearn.gaussian_process.kernels.ConstantKernel.html
+++ b/dev/modules/generated/sklearn.gaussian_process.kernels.ConstantKernel.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1308,7 +1308,7 @@
 <h1>ConstantKernel<a class="headerlink" href="#constantkernel" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.kernels.ConstantKernel">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.gaussian_process.kernels.</span></span><span class="sig-name descname"><span class="pre">ConstantKernel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">constant_value</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">constant_value_bounds</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(1e-05,</span> <span class="pre">100000.0)</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/gaussian_process/kernels.py#L1182"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.ConstantKernel" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.gaussian_process.kernels.</span></span><span class="sig-name descname"><span class="pre">ConstantKernel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">constant_value</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">constant_value_bounds</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(1e-05,</span> <span class="pre">100000.0)</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/gaussian_process/kernels.py#L1182"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.ConstantKernel" title="Link to this definition">#</a></dt>
 <dd><p>Constant kernel.</p>
 <p>Can be used as part of a product-kernel where it scales the magnitude of
 the other factor (kernel) or as part of a sum-kernel, where it modifies
@@ -1349,14 +1349,14 @@ <h1>ConstantKernel<a class="headerlink" href="#constantkernel" title="Link to th
 <span class="gp">&gt;&gt;&gt; </span><span class="n">gpr</span> <span class="o">=</span> <span class="n">GaussianProcessRegressor</span><span class="p">(</span><span class="n">kernel</span><span class="o">=</span><span class="n">kernel</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span>
 <span class="gp">... </span>        <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">gpr</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
-<span class="go">0.3696...</span>
+<span class="go">0.3696</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">gpr</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X</span><span class="p">[:</span><span class="mi">1</span><span class="p">,:],</span> <span class="n">return_std</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-<span class="go">(array([606.1...]), array([0.24...]))</span>
+<span class="go">(array([606.1]), array([0.248]))</span>
 </pre></div>
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.kernels.ConstantKernel.__call__">
-<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eval_gradient</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/gaussian_process/kernels.py#L1239"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.ConstantKernel.__call__" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eval_gradient</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/gaussian_process/kernels.py#L1239"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.ConstantKernel.__call__" title="Link to this definition">#</a></dt>
 <dd><p>Return the kernel k(X, Y) and optionally its gradient.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1401,7 +1401,7 @@ <h1>ConstantKernel<a class="headerlink" href="#constantkernel" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.kernels.ConstantKernel.clone_with_theta">
-<span class="sig-name descname"><span class="pre">clone_with_theta</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">theta</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/gaussian_process/kernels.py#L258"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.ConstantKernel.clone_with_theta" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">clone_with_theta</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">theta</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/gaussian_process/kernels.py#L258"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.ConstantKernel.clone_with_theta" title="Link to this definition">#</a></dt>
 <dd><p>Returns a clone of self with given hyperparameters theta.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1415,7 +1415,7 @@ <h1>ConstantKernel<a class="headerlink" href="#constantkernel" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.kernels.ConstantKernel.diag">
-<span class="sig-name descname"><span class="pre">diag</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/gaussian_process/kernels.py#L1293"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.ConstantKernel.diag" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">diag</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/gaussian_process/kernels.py#L1293"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.ConstantKernel.diag" title="Link to this definition">#</a></dt>
 <dd><p>Returns the diagonal of the kernel k(X, X).</p>
 <p>The result of this method is identical to np.diag(self(X)); however,
 it can be evaluated more efficiently since only the diagonal is
@@ -1438,7 +1438,7 @@ <h1>ConstantKernel<a class="headerlink" href="#constantkernel" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.kernels.ConstantKernel.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/gaussian_process/kernels.py#L178"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.ConstantKernel.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/gaussian_process/kernels.py#L178"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.ConstantKernel.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters of this kernel.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1465,7 +1465,7 @@ <h1>ConstantKernel<a class="headerlink" href="#constantkernel" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.kernels.ConstantKernel.is_stationary">
-<span class="sig-name descname"><span class="pre">is_stationary</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/gaussian_process/kernels.py#L494"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.ConstantKernel.is_stationary" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">is_stationary</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/gaussian_process/kernels.py#L494"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.ConstantKernel.is_stationary" title="Link to this definition">#</a></dt>
 <dd><p>Returns whether the kernel is stationary.</p>
 </dd></dl>
 
@@ -1483,7 +1483,7 @@ <h1>ConstantKernel<a class="headerlink" href="#constantkernel" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.kernels.ConstantKernel.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/gaussian_process/kernels.py#L218"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.ConstantKernel.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/gaussian_process/kernels.py#L218"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.ConstantKernel.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this kernel.</p>
 <p>The method works on simple kernels as well as on nested kernels.
 The latter have parameters of the form <code class="docutils literal notranslate"><span class="pre">&lt;component&gt;__&lt;parameter&gt;</span></code>
diff --git a/dev/modules/generated/sklearn.gaussian_process.kernels.DotProduct.html b/dev/modules/generated/sklearn.gaussian_process.kernels.DotProduct.html
index cc7859dddefca..8e4b7bc4903ca 100644
--- a/dev/modules/generated/sklearn.gaussian_process.kernels.DotProduct.html
+++ b/dev/modules/generated/sklearn.gaussian_process.kernels.DotProduct.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1308,7 +1308,7 @@
 <h1>DotProduct<a class="headerlink" href="#dotproduct" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.kernels.DotProduct">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.gaussian_process.kernels.</span></span><span class="sig-name descname"><span class="pre">DotProduct</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sigma_0</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sigma_0_bounds</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(1e-05,</span> <span class="pre">100000.0)</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/gaussian_process/kernels.py#L2097"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.DotProduct" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.gaussian_process.kernels.</span></span><span class="sig-name descname"><span class="pre">DotProduct</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sigma_0</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sigma_0_bounds</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(1e-05,</span> <span class="pre">100000.0)</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/gaussian_process/kernels.py#L2097"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.DotProduct" title="Link to this definition">#</a></dt>
 <dd><p>Dot-Product kernel.</p>
 <p>The DotProduct kernel is non-stationary and can be obtained from linear
 regression by putting <span class="math notranslate nohighlight">\(N(0, 1)\)</span> priors on the coefficients
@@ -1358,14 +1358,14 @@ <h1>DotProduct<a class="headerlink" href="#dotproduct" title="Link to this headi
 <span class="gp">&gt;&gt;&gt; </span><span class="n">gpr</span> <span class="o">=</span> <span class="n">GaussianProcessRegressor</span><span class="p">(</span><span class="n">kernel</span><span class="o">=</span><span class="n">kernel</span><span class="p">,</span>
 <span class="gp">... </span>        <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">gpr</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
-<span class="go">0.3680...</span>
+<span class="go">0.3680</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">gpr</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X</span><span class="p">[:</span><span class="mi">2</span><span class="p">,:],</span> <span class="n">return_std</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-<span class="go">(array([653.0..., 592.1...]), array([316.6..., 316.6...]))</span>
+<span class="go">(array([653.0, 592.1]), array([316.6, 316.6]))</span>
 </pre></div>
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.kernels.DotProduct.__call__">
-<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eval_gradient</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/gaussian_process/kernels.py#L2162"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.DotProduct.__call__" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eval_gradient</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/gaussian_process/kernels.py#L2162"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.DotProduct.__call__" title="Link to this definition">#</a></dt>
 <dd><p>Return the kernel k(X, Y) and optionally its gradient.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1410,7 +1410,7 @@ <h1>DotProduct<a class="headerlink" href="#dotproduct" title="Link to this headi
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.kernels.DotProduct.clone_with_theta">
-<span class="sig-name descname"><span class="pre">clone_with_theta</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">theta</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/gaussian_process/kernels.py#L258"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.DotProduct.clone_with_theta" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">clone_with_theta</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">theta</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/gaussian_process/kernels.py#L258"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.DotProduct.clone_with_theta" title="Link to this definition">#</a></dt>
 <dd><p>Returns a clone of self with given hyperparameters theta.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1424,7 +1424,7 @@ <h1>DotProduct<a class="headerlink" href="#dotproduct" title="Link to this headi
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.kernels.DotProduct.diag">
-<span class="sig-name descname"><span class="pre">diag</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/gaussian_process/kernels.py#L2208"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.DotProduct.diag" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">diag</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/gaussian_process/kernels.py#L2208"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.DotProduct.diag" title="Link to this definition">#</a></dt>
 <dd><p>Returns the diagonal of the kernel k(X, X).</p>
 <p>The result of this method is identical to np.diag(self(X)); however,
 it can be evaluated more efficiently since only the diagonal is
@@ -1447,7 +1447,7 @@ <h1>DotProduct<a class="headerlink" href="#dotproduct" title="Link to this headi
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.kernels.DotProduct.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/gaussian_process/kernels.py#L178"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.DotProduct.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/gaussian_process/kernels.py#L178"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.DotProduct.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters of this kernel.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1474,7 +1474,7 @@ <h1>DotProduct<a class="headerlink" href="#dotproduct" title="Link to this headi
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.kernels.DotProduct.is_stationary">
-<span class="sig-name descname"><span class="pre">is_stationary</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/gaussian_process/kernels.py#L2227"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.DotProduct.is_stationary" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">is_stationary</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/gaussian_process/kernels.py#L2227"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.DotProduct.is_stationary" title="Link to this definition">#</a></dt>
 <dd><p>Returns whether the kernel is stationary.</p>
 </dd></dl>
 
@@ -1494,7 +1494,7 @@ <h1>DotProduct<a class="headerlink" href="#dotproduct" title="Link to this headi
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.kernels.DotProduct.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/gaussian_process/kernels.py#L218"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.DotProduct.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/gaussian_process/kernels.py#L218"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.DotProduct.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this kernel.</p>
 <p>The method works on simple kernels as well as on nested kernels.
 The latter have parameters of the form <code class="docutils literal notranslate"><span class="pre">&lt;component&gt;__&lt;parameter&gt;</span></code>
diff --git a/dev/modules/generated/sklearn.gaussian_process.kernels.ExpSineSquared.html b/dev/modules/generated/sklearn.gaussian_process.kernels.ExpSineSquared.html
index 8c8e9b6c07b08..4dbcab2cb4652 100644
--- a/dev/modules/generated/sklearn.gaussian_process.kernels.ExpSineSquared.html
+++ b/dev/modules/generated/sklearn.gaussian_process.kernels.ExpSineSquared.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1308,7 +1308,7 @@
 <h1>ExpSineSquared<a class="headerlink" href="#expsinesquared" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.kernels.ExpSineSquared">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.gaussian_process.kernels.</span></span><span class="sig-name descname"><span class="pre">ExpSineSquared</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">length_scale</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">periodicity</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">length_scale_bounds</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(1e-05,</span> <span class="pre">100000.0)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">periodicity_bounds</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(1e-05,</span> <span class="pre">100000.0)</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/gaussian_process/kernels.py#L1952"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.ExpSineSquared" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.gaussian_process.kernels.</span></span><span class="sig-name descname"><span class="pre">ExpSineSquared</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">length_scale</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">periodicity</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">length_scale_bounds</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(1e-05,</span> <span class="pre">100000.0)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">periodicity_bounds</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(1e-05,</span> <span class="pre">100000.0)</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/gaussian_process/kernels.py#L1952"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.ExpSineSquared" title="Link to this definition">#</a></dt>
 <dd><p>Exp-Sine-Squared kernel (aka periodic kernel).</p>
 <p>The ExpSineSquared kernel allows one to model functions which repeat
 themselves exactly. It is parameterized by a length scale
@@ -1352,14 +1352,14 @@ <h1>ExpSineSquared<a class="headerlink" href="#expsinesquared" title="Link to th
 <span class="gp">&gt;&gt;&gt; </span><span class="n">gpr</span> <span class="o">=</span> <span class="n">GaussianProcessRegressor</span><span class="p">(</span><span class="n">kernel</span><span class="o">=</span><span class="n">kernel</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span>
 <span class="gp">... </span>        <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">gpr</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
-<span class="go">0.0144...</span>
+<span class="go">0.0144</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">gpr</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X</span><span class="p">[:</span><span class="mi">2</span><span class="p">,:],</span> <span class="n">return_std</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-<span class="go">(array([425.6..., 457.5...]), array([0.3894..., 0.3467...]))</span>
+<span class="go">(array([425.6, 457.5]), array([0.3894, 0.3467]))</span>
 </pre></div>
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.kernels.ExpSineSquared.__call__">
-<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eval_gradient</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/gaussian_process/kernels.py#L2028"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.ExpSineSquared.__call__" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eval_gradient</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/gaussian_process/kernels.py#L2028"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.ExpSineSquared.__call__" title="Link to this definition">#</a></dt>
 <dd><p>Return the kernel k(X, Y) and optionally its gradient.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1404,7 +1404,7 @@ <h1>ExpSineSquared<a class="headerlink" href="#expsinesquared" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.kernels.ExpSineSquared.clone_with_theta">
-<span class="sig-name descname"><span class="pre">clone_with_theta</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">theta</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/gaussian_process/kernels.py#L258"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.ExpSineSquared.clone_with_theta" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">clone_with_theta</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">theta</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/gaussian_process/kernels.py#L258"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.ExpSineSquared.clone_with_theta" title="Link to this definition">#</a></dt>
 <dd><p>Returns a clone of self with given hyperparameters theta.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1418,7 +1418,7 @@ <h1>ExpSineSquared<a class="headerlink" href="#expsinesquared" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.kernels.ExpSineSquared.diag">
-<span class="sig-name descname"><span class="pre">diag</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/gaussian_process/kernels.py#L468"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.ExpSineSquared.diag" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">diag</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/gaussian_process/kernels.py#L468"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.ExpSineSquared.diag" title="Link to this definition">#</a></dt>
 <dd><p>Returns the diagonal of the kernel k(X, X).</p>
 <p>The result of this method is identical to np.diag(self(X)); however,
 it can be evaluated more efficiently since only the diagonal is
@@ -1441,7 +1441,7 @@ <h1>ExpSineSquared<a class="headerlink" href="#expsinesquared" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.kernels.ExpSineSquared.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/gaussian_process/kernels.py#L178"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.ExpSineSquared.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/gaussian_process/kernels.py#L178"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.ExpSineSquared.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters of this kernel.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1474,7 +1474,7 @@ <h1>ExpSineSquared<a class="headerlink" href="#expsinesquared" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.kernels.ExpSineSquared.is_stationary">
-<span class="sig-name descname"><span class="pre">is_stationary</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/gaussian_process/kernels.py#L494"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.ExpSineSquared.is_stationary" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">is_stationary</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/gaussian_process/kernels.py#L494"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.ExpSineSquared.is_stationary" title="Link to this definition">#</a></dt>
 <dd><p>Returns whether the kernel is stationary.</p>
 </dd></dl>
 
@@ -1494,7 +1494,7 @@ <h1>ExpSineSquared<a class="headerlink" href="#expsinesquared" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.kernels.ExpSineSquared.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/gaussian_process/kernels.py#L218"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.ExpSineSquared.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/gaussian_process/kernels.py#L218"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.ExpSineSquared.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this kernel.</p>
 <p>The method works on simple kernels as well as on nested kernels.
 The latter have parameters of the form <code class="docutils literal notranslate"><span class="pre">&lt;component&gt;__&lt;parameter&gt;</span></code>
diff --git a/dev/modules/generated/sklearn.gaussian_process.kernels.Exponentiation.html b/dev/modules/generated/sklearn.gaussian_process.kernels.Exponentiation.html
index 23483ecbbd766..f92e4961d01a0 100644
--- a/dev/modules/generated/sklearn.gaussian_process.kernels.Exponentiation.html
+++ b/dev/modules/generated/sklearn.gaussian_process.kernels.Exponentiation.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1306,7 +1306,7 @@
 <h1>Exponentiation<a class="headerlink" href="#exponentiation" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.kernels.Exponentiation">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.gaussian_process.kernels.</span></span><span class="sig-name descname"><span class="pre">Exponentiation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">exponent</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/gaussian_process/kernels.py#L991"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.Exponentiation" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.gaussian_process.kernels.</span></span><span class="sig-name descname"><span class="pre">Exponentiation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">exponent</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/gaussian_process/kernels.py#L991"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.Exponentiation" title="Link to this definition">#</a></dt>
 <dd><p>The Exponentiation kernel takes one base kernel and a scalar parameter
 <span class="math notranslate nohighlight">\(p\)</span> and combines them via</p>
 <div class="math notranslate nohighlight">
@@ -1338,14 +1338,14 @@ <h1>Exponentiation<a class="headerlink" href="#exponentiation" title="Link to th
 <span class="gp">&gt;&gt;&gt; </span><span class="n">gpr</span> <span class="o">=</span> <span class="n">GaussianProcessRegressor</span><span class="p">(</span><span class="n">kernel</span><span class="o">=</span><span class="n">kernel</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span>
 <span class="gp">... </span>        <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">gpr</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
-<span class="go">0.419...</span>
+<span class="go">0.419</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">gpr</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X</span><span class="p">[:</span><span class="mi">1</span><span class="p">,:],</span> <span class="n">return_std</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-<span class="go">(array([635.5...]), array([0.559...]))</span>
+<span class="go">(array([635.5]), array([0.559]))</span>
 </pre></div>
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.kernels.Exponentiation.__call__">
-<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eval_gradient</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/gaussian_process/kernels.py#L1114"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.Exponentiation.__call__" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eval_gradient</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/gaussian_process/kernels.py#L1114"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.Exponentiation.__call__" title="Link to this definition">#</a></dt>
 <dd><p>Return the kernel k(X, Y) and optionally its gradient.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1389,7 +1389,7 @@ <h1>Exponentiation<a class="headerlink" href="#exponentiation" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.kernels.Exponentiation.clone_with_theta">
-<span class="sig-name descname"><span class="pre">clone_with_theta</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">theta</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/gaussian_process/kernels.py#L258"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.Exponentiation.clone_with_theta" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">clone_with_theta</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">theta</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/gaussian_process/kernels.py#L258"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.Exponentiation.clone_with_theta" title="Link to this definition">#</a></dt>
 <dd><p>Returns a clone of self with given hyperparameters theta.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1403,7 +1403,7 @@ <h1>Exponentiation<a class="headerlink" href="#exponentiation" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.kernels.Exponentiation.diag">
-<span class="sig-name descname"><span class="pre">diag</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/gaussian_process/kernels.py#L1150"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.Exponentiation.diag" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">diag</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/gaussian_process/kernels.py#L1150"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.Exponentiation.diag" title="Link to this definition">#</a></dt>
 <dd><p>Returns the diagonal of the kernel k(X, X).</p>
 <p>The result of this method is identical to np.diag(self(X)); however,
 it can be evaluated more efficiently since only the diagonal is
@@ -1426,7 +1426,7 @@ <h1>Exponentiation<a class="headerlink" href="#exponentiation" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.kernels.Exponentiation.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/gaussian_process/kernels.py#L1036"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.Exponentiation.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/gaussian_process/kernels.py#L1036"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.Exponentiation.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters of this kernel.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1453,7 +1453,7 @@ <h1>Exponentiation<a class="headerlink" href="#exponentiation" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.kernels.Exponentiation.is_stationary">
-<span class="sig-name descname"><span class="pre">is_stationary</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/gaussian_process/kernels.py#L1172"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.Exponentiation.is_stationary" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">is_stationary</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/gaussian_process/kernels.py#L1172"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.Exponentiation.is_stationary" title="Link to this definition">#</a></dt>
 <dd><p>Returns whether the kernel is stationary.</p>
 </dd></dl>
 
@@ -1471,7 +1471,7 @@ <h1>Exponentiation<a class="headerlink" href="#exponentiation" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.kernels.Exponentiation.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/gaussian_process/kernels.py#L218"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.Exponentiation.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/gaussian_process/kernels.py#L218"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.Exponentiation.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this kernel.</p>
 <p>The method works on simple kernels as well as on nested kernels.
 The latter have parameters of the form <code class="docutils literal notranslate"><span class="pre">&lt;component&gt;__&lt;parameter&gt;</span></code>
diff --git a/dev/modules/generated/sklearn.gaussian_process.kernels.Hyperparameter.html b/dev/modules/generated/sklearn.gaussian_process.kernels.Hyperparameter.html
index d0f5b5c4bd3ca..3ead337a79f0a 100644
--- a/dev/modules/generated/sklearn.gaussian_process.kernels.Hyperparameter.html
+++ b/dev/modules/generated/sklearn.gaussian_process.kernels.Hyperparameter.html
@@ -42,7 +42,7 @@
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     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>Hyperparameter<a class="headerlink" href="#hyperparameter" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.kernels.Hyperparameter">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.gaussian_process.kernels.</span></span><span class="sig-name descname"><span class="pre">Hyperparameter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">value_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bounds</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_elements</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fixed</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/gaussian_process/kernels.py#L52"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.Hyperparameter" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.gaussian_process.kernels.</span></span><span class="sig-name descname"><span class="pre">Hyperparameter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">value_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bounds</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_elements</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fixed</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/gaussian_process/kernels.py#L52"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.Hyperparameter" title="Link to this definition">#</a></dt>
 <dd><p>A kernel hyperparameter’s specification in form of a namedtuple.</p>
 <div class="versionadded">
 <p><span class="versionmodified added">Added in version 0.18.</span></p>
diff --git a/dev/modules/generated/sklearn.gaussian_process.kernels.Kernel.html b/dev/modules/generated/sklearn.gaussian_process.kernels.Kernel.html
index 6a035f100a7ae..31cde8277d229 100644
--- a/dev/modules/generated/sklearn.gaussian_process.kernels.Kernel.html
+++ b/dev/modules/generated/sklearn.gaussian_process.kernels.Kernel.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>Kernel<a class="headerlink" href="#kernel" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.kernels.Kernel">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.gaussian_process.kernels.</span></span><span class="sig-name descname"><span class="pre">Kernel</span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/gaussian_process/kernels.py#L151"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.Kernel" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.gaussian_process.kernels.</span></span><span class="sig-name descname"><span class="pre">Kernel</span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/gaussian_process/kernels.py#L151"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.Kernel" title="Link to this definition">#</a></dt>
 <dd><p>Base class for all kernels.</p>
 <div class="versionadded">
 <p><span class="versionmodified added">Added in version 0.18.</span></p>
@@ -1335,7 +1335,7 @@ <h1>Kernel<a class="headerlink" href="#kernel" title="Link to this heading">#</a
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.kernels.Kernel.__call__">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eval_gradient</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/gaussian_process/kernels.py#L397"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.Kernel.__call__" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eval_gradient</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/gaussian_process/kernels.py#L397"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.Kernel.__call__" title="Link to this definition">#</a></dt>
 <dd><p>Evaluate the kernel.</p>
 </dd></dl>
 
@@ -1355,7 +1355,7 @@ <h1>Kernel<a class="headerlink" href="#kernel" title="Link to this heading">#</a
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.kernels.Kernel.clone_with_theta">
-<span class="sig-name descname"><span class="pre">clone_with_theta</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">theta</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/gaussian_process/kernels.py#L258"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.Kernel.clone_with_theta" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">clone_with_theta</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">theta</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/gaussian_process/kernels.py#L258"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.Kernel.clone_with_theta" title="Link to this definition">#</a></dt>
 <dd><p>Returns a clone of self with given hyperparameters theta.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1369,7 +1369,7 @@ <h1>Kernel<a class="headerlink" href="#kernel" title="Link to this heading">#</a
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.kernels.Kernel.diag">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">diag</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/gaussian_process/kernels.py#L401"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.Kernel.diag" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">diag</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/gaussian_process/kernels.py#L401"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.Kernel.diag" title="Link to this definition">#</a></dt>
 <dd><p>Returns the diagonal of the kernel k(X, X).</p>
 <p>The result of this method is identical to np.diag(self(X)); however,
 it can be evaluated more efficiently since only the diagonal is
@@ -1392,7 +1392,7 @@ <h1>Kernel<a class="headerlink" href="#kernel" title="Link to this heading">#</a
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.kernels.Kernel.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/gaussian_process/kernels.py#L178"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.Kernel.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/gaussian_process/kernels.py#L178"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.Kernel.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters of this kernel.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1419,7 +1419,7 @@ <h1>Kernel<a class="headerlink" href="#kernel" title="Link to this heading">#</a
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.kernels.Kernel.is_stationary">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">is_stationary</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/gaussian_process/kernels.py#L420"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.Kernel.is_stationary" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">is_stationary</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/gaussian_process/kernels.py#L420"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.Kernel.is_stationary" title="Link to this definition">#</a></dt>
 <dd><p>Returns whether the kernel is stationary.</p>
 </dd></dl>
 
@@ -1439,7 +1439,7 @@ <h1>Kernel<a class="headerlink" href="#kernel" title="Link to this heading">#</a
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.kernels.Kernel.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/gaussian_process/kernels.py#L218"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.Kernel.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/gaussian_process/kernels.py#L218"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.Kernel.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this kernel.</p>
 <p>The method works on simple kernels as well as on nested kernels.
 The latter have parameters of the form <code class="docutils literal notranslate"><span class="pre">&lt;component&gt;__&lt;parameter&gt;</span></code>
diff --git a/dev/modules/generated/sklearn.gaussian_process.kernels.Matern.html b/dev/modules/generated/sklearn.gaussian_process.kernels.Matern.html
index 7740a88149967..eb9fac51c80c0 100644
--- a/dev/modules/generated/sklearn.gaussian_process.kernels.Matern.html
+++ b/dev/modules/generated/sklearn.gaussian_process.kernels.Matern.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1308,7 +1308,7 @@
 <h1>Matern<a class="headerlink" href="#matern" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.kernels.Matern">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.gaussian_process.kernels.</span></span><span class="sig-name descname"><span class="pre">Matern</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">length_scale</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">length_scale_bounds</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(1e-05,</span> <span class="pre">100000.0)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nu</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.5</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/gaussian_process/kernels.py#L1596"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.Matern" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.gaussian_process.kernels.</span></span><span class="sig-name descname"><span class="pre">Matern</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">length_scale</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">length_scale_bounds</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(1e-05,</span> <span class="pre">100000.0)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nu</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.5</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/gaussian_process/kernels.py#L1596"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.Matern" title="Link to this definition">#</a></dt>
 <dd><p>Matern kernel.</p>
 <p>The class of Matern kernels is a generalization of the <a class="reference internal" href="http://github.com/sklearn.gaussian_process.kernels.RBF.html#sklearn.gaussian_process.kernels.RBF" title="sklearn.gaussian_process.kernels.RBF"><code class="xref py py-class docutils literal notranslate"><span class="pre">RBF</span></code></a>.
 It has an additional parameter <span class="math notranslate nohighlight">\(\nu\)</span> which controls the
@@ -1377,15 +1377,15 @@ <h1>Matern<a class="headerlink" href="#matern" title="Link to this heading">#</a
 <span class="gp">&gt;&gt;&gt; </span><span class="n">gpc</span> <span class="o">=</span> <span class="n">GaussianProcessClassifier</span><span class="p">(</span><span class="n">kernel</span><span class="o">=</span><span class="n">kernel</span><span class="p">,</span>
 <span class="gp">... </span>        <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">gpc</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
-<span class="go">0.9866...</span>
+<span class="go">0.9866</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">gpc</span><span class="o">.</span><span class="n">predict_proba</span><span class="p">(</span><span class="n">X</span><span class="p">[:</span><span class="mi">2</span><span class="p">,:])</span>
-<span class="go">array([[0.8513..., 0.0368..., 0.1117...],</span>
-<span class="go">        [0.8086..., 0.0693..., 0.1220...]])</span>
+<span class="go">array([[0.8513, 0.0368, 0.1117],</span>
+<span class="go">        [0.8086, 0.0693, 0.1220]])</span>
 </pre></div>
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.kernels.Matern.__call__">
-<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eval_gradient</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/gaussian_process/kernels.py#L1680"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.Matern.__call__" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eval_gradient</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/gaussian_process/kernels.py#L1680"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.Matern.__call__" title="Link to this definition">#</a></dt>
 <dd><p>Return the kernel k(X, Y) and optionally its gradient.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1430,7 +1430,7 @@ <h1>Matern<a class="headerlink" href="#matern" title="Link to this heading">#</a
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.kernels.Matern.clone_with_theta">
-<span class="sig-name descname"><span class="pre">clone_with_theta</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">theta</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/gaussian_process/kernels.py#L258"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.Matern.clone_with_theta" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">clone_with_theta</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">theta</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/gaussian_process/kernels.py#L258"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.Matern.clone_with_theta" title="Link to this definition">#</a></dt>
 <dd><p>Returns a clone of self with given hyperparameters theta.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1444,7 +1444,7 @@ <h1>Matern<a class="headerlink" href="#matern" title="Link to this heading">#</a
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.kernels.Matern.diag">
-<span class="sig-name descname"><span class="pre">diag</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/gaussian_process/kernels.py#L468"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.Matern.diag" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">diag</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/gaussian_process/kernels.py#L468"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.Matern.diag" title="Link to this definition">#</a></dt>
 <dd><p>Returns the diagonal of the kernel k(X, X).</p>
 <p>The result of this method is identical to np.diag(self(X)); however,
 it can be evaluated more efficiently since only the diagonal is
@@ -1467,7 +1467,7 @@ <h1>Matern<a class="headerlink" href="#matern" title="Link to this heading">#</a
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.kernels.Matern.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/gaussian_process/kernels.py#L178"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.Matern.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/gaussian_process/kernels.py#L178"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.Matern.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters of this kernel.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1494,7 +1494,7 @@ <h1>Matern<a class="headerlink" href="#matern" title="Link to this heading">#</a
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.kernels.Matern.is_stationary">
-<span class="sig-name descname"><span class="pre">is_stationary</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/gaussian_process/kernels.py#L494"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.Matern.is_stationary" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">is_stationary</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/gaussian_process/kernels.py#L494"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.Matern.is_stationary" title="Link to this definition">#</a></dt>
 <dd><p>Returns whether the kernel is stationary.</p>
 </dd></dl>
 
@@ -1514,7 +1514,7 @@ <h1>Matern<a class="headerlink" href="#matern" title="Link to this heading">#</a
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.kernels.Matern.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/gaussian_process/kernels.py#L218"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.Matern.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/gaussian_process/kernels.py#L218"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.Matern.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this kernel.</p>
 <p>The method works on simple kernels as well as on nested kernels.
 The latter have parameters of the form <code class="docutils literal notranslate"><span class="pre">&lt;component&gt;__&lt;parameter&gt;</span></code>
diff --git a/dev/modules/generated/sklearn.gaussian_process.kernels.PairwiseKernel.html b/dev/modules/generated/sklearn.gaussian_process.kernels.PairwiseKernel.html
index 3d45daffbf275..21ef85ddb3bb0 100644
--- a/dev/modules/generated/sklearn.gaussian_process.kernels.PairwiseKernel.html
+++ b/dev/modules/generated/sklearn.gaussian_process.kernels.PairwiseKernel.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>PairwiseKernel<a class="headerlink" href="#pairwisekernel" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.kernels.PairwiseKernel">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.gaussian_process.kernels.</span></span><span class="sig-name descname"><span class="pre">PairwiseKernel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">gamma</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gamma_bounds</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(1e-05,</span> <span class="pre">100000.0)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'linear'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pairwise_kernels_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/gaussian_process/kernels.py#L2248"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.PairwiseKernel" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.gaussian_process.kernels.</span></span><span class="sig-name descname"><span class="pre">PairwiseKernel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">gamma</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gamma_bounds</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(1e-05,</span> <span class="pre">100000.0)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'linear'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pairwise_kernels_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/gaussian_process/kernels.py#L2248"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.PairwiseKernel" title="Link to this definition">#</a></dt>
 <dd><p>Wrapper for kernels in sklearn.metrics.pairwise.</p>
 <p>A thin wrapper around the functionality of the kernels in
 sklearn.metrics.pairwise.</p>
@@ -1353,15 +1353,15 @@ <h1>PairwiseKernel<a class="headerlink" href="#pairwisekernel" title="Link to th
 <span class="gp">&gt;&gt;&gt; </span><span class="n">gpc</span> <span class="o">=</span> <span class="n">GaussianProcessClassifier</span><span class="p">(</span><span class="n">kernel</span><span class="o">=</span><span class="n">kernel</span><span class="p">,</span>
 <span class="gp">... </span>        <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">gpc</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
-<span class="go">0.9733...</span>
+<span class="go">0.9733</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">gpc</span><span class="o">.</span><span class="n">predict_proba</span><span class="p">(</span><span class="n">X</span><span class="p">[:</span><span class="mi">2</span><span class="p">,:])</span>
-<span class="go">array([[0.8880..., 0.05663..., 0.05532...],</span>
-<span class="go">       [0.8676..., 0.07073..., 0.06165...]])</span>
+<span class="go">array([[0.8880, 0.05663, 0.05532],</span>
+<span class="go">       [0.8676, 0.07073, 0.06165]])</span>
 </pre></div>
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.kernels.PairwiseKernel.__call__">
-<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eval_gradient</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/gaussian_process/kernels.py#L2321"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.PairwiseKernel.__call__" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eval_gradient</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/gaussian_process/kernels.py#L2321"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.PairwiseKernel.__call__" title="Link to this definition">#</a></dt>
 <dd><p>Return the kernel k(X, Y) and optionally its gradient.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1406,7 +1406,7 @@ <h1>PairwiseKernel<a class="headerlink" href="#pairwisekernel" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.kernels.PairwiseKernel.clone_with_theta">
-<span class="sig-name descname"><span class="pre">clone_with_theta</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">theta</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/gaussian_process/kernels.py#L258"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.PairwiseKernel.clone_with_theta" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">clone_with_theta</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">theta</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/gaussian_process/kernels.py#L258"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.PairwiseKernel.clone_with_theta" title="Link to this definition">#</a></dt>
 <dd><p>Returns a clone of self with given hyperparameters theta.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1420,7 +1420,7 @@ <h1>PairwiseKernel<a class="headerlink" href="#pairwisekernel" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.kernels.PairwiseKernel.diag">
-<span class="sig-name descname"><span class="pre">diag</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/gaussian_process/kernels.py#L2381"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.PairwiseKernel.diag" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">diag</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/gaussian_process/kernels.py#L2381"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.PairwiseKernel.diag" title="Link to this definition">#</a></dt>
 <dd><p>Returns the diagonal of the kernel k(X, X).</p>
 <p>The result of this method is identical to np.diag(self(X)); however,
 it can be evaluated more efficiently since only the diagonal is
@@ -1443,7 +1443,7 @@ <h1>PairwiseKernel<a class="headerlink" href="#pairwisekernel" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.kernels.PairwiseKernel.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/gaussian_process/kernels.py#L178"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.PairwiseKernel.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/gaussian_process/kernels.py#L178"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.PairwiseKernel.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters of this kernel.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1470,7 +1470,7 @@ <h1>PairwiseKernel<a class="headerlink" href="#pairwisekernel" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.kernels.PairwiseKernel.is_stationary">
-<span class="sig-name descname"><span class="pre">is_stationary</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/gaussian_process/kernels.py#L2401"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.PairwiseKernel.is_stationary" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">is_stationary</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/gaussian_process/kernels.py#L2401"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.PairwiseKernel.is_stationary" title="Link to this definition">#</a></dt>
 <dd><p>Returns whether the kernel is stationary.</p>
 </dd></dl>
 
@@ -1490,7 +1490,7 @@ <h1>PairwiseKernel<a class="headerlink" href="#pairwisekernel" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.kernels.PairwiseKernel.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/gaussian_process/kernels.py#L218"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.PairwiseKernel.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/gaussian_process/kernels.py#L218"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.PairwiseKernel.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this kernel.</p>
 <p>The method works on simple kernels as well as on nested kernels.
 The latter have parameters of the form <code class="docutils literal notranslate"><span class="pre">&lt;component&gt;__&lt;parameter&gt;</span></code>
diff --git a/dev/modules/generated/sklearn.gaussian_process.kernels.Product.html b/dev/modules/generated/sklearn.gaussian_process.kernels.Product.html
index ee91311a9c0b7..efa38b3b7d498 100644
--- a/dev/modules/generated/sklearn.gaussian_process.kernels.Product.html
+++ b/dev/modules/generated/sklearn.gaussian_process.kernels.Product.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1306,7 +1306,7 @@
 <h1>Product<a class="headerlink" href="#product" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.kernels.Product">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.gaussian_process.kernels.</span></span><span class="sig-name descname"><span class="pre">Product</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">k1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">k2</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/gaussian_process/kernels.py#L891"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.Product" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.gaussian_process.kernels.</span></span><span class="sig-name descname"><span class="pre">Product</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">k1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">k2</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/gaussian_process/kernels.py#L891"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.Product" title="Link to this definition">#</a></dt>
 <dd><p>The <code class="docutils literal notranslate"><span class="pre">Product</span></code> kernel takes two kernels <span class="math notranslate nohighlight">\(k_1\)</span> and <span class="math notranslate nohighlight">\(k_2\)</span>
 and combines them via</p>
 <div class="math notranslate nohighlight">
@@ -1345,7 +1345,7 @@ <h1>Product<a class="headerlink" href="#product" title="Link to this heading">#<
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.kernels.Product.__call__">
-<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eval_gradient</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/gaussian_process/kernels.py#L931"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.Product.__call__" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eval_gradient</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/gaussian_process/kernels.py#L931"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.Product.__call__" title="Link to this definition">#</a></dt>
 <dd><p>Return the kernel k(X, Y) and optionally its gradient.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1389,7 +1389,7 @@ <h1>Product<a class="headerlink" href="#product" title="Link to this heading">#<
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.kernels.Product.clone_with_theta">
-<span class="sig-name descname"><span class="pre">clone_with_theta</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">theta</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/gaussian_process/kernels.py#L258"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.Product.clone_with_theta" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">clone_with_theta</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">theta</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/gaussian_process/kernels.py#L258"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.Product.clone_with_theta" title="Link to this definition">#</a></dt>
 <dd><p>Returns a clone of self with given hyperparameters theta.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1403,7 +1403,7 @@ <h1>Product<a class="headerlink" href="#product" title="Link to this heading">#<
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.kernels.Product.diag">
-<span class="sig-name descname"><span class="pre">diag</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/gaussian_process/kernels.py#L968"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.Product.diag" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">diag</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/gaussian_process/kernels.py#L968"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.Product.diag" title="Link to this definition">#</a></dt>
 <dd><p>Returns the diagonal of the kernel k(X, X).</p>
 <p>The result of this method is identical to np.diag(self(X)); however,
 it can be evaluated more efficiently since only the diagonal is
@@ -1426,7 +1426,7 @@ <h1>Product<a class="headerlink" href="#product" title="Link to this heading">#<
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.kernels.Product.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/gaussian_process/kernels.py#L686"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.Product.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/gaussian_process/kernels.py#L686"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.Product.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters of this kernel.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1453,7 +1453,7 @@ <h1>Product<a class="headerlink" href="#product" title="Link to this heading">#<
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.kernels.Product.is_stationary">
-<span class="sig-name descname"><span class="pre">is_stationary</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/gaussian_process/kernels.py#L784"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.Product.is_stationary" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">is_stationary</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/gaussian_process/kernels.py#L784"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.Product.is_stationary" title="Link to this definition">#</a></dt>
 <dd><p>Returns whether the kernel is stationary.</p>
 </dd></dl>
 
@@ -1471,7 +1471,7 @@ <h1>Product<a class="headerlink" href="#product" title="Link to this heading">#<
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.kernels.Product.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/gaussian_process/kernels.py#L218"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.Product.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/gaussian_process/kernels.py#L218"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.Product.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this kernel.</p>
 <p>The method works on simple kernels as well as on nested kernels.
 The latter have parameters of the form <code class="docutils literal notranslate"><span class="pre">&lt;component&gt;__&lt;parameter&gt;</span></code>
diff --git a/dev/modules/generated/sklearn.gaussian_process.kernels.RBF.html b/dev/modules/generated/sklearn.gaussian_process.kernels.RBF.html
index 4e487d4b7fd3f..b8c76818db444 100644
--- a/dev/modules/generated/sklearn.gaussian_process.kernels.RBF.html
+++ b/dev/modules/generated/sklearn.gaussian_process.kernels.RBF.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1308,7 +1308,7 @@
 <h1>RBF<a class="headerlink" href="#rbf" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.kernels.RBF">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.gaussian_process.kernels.</span></span><span class="sig-name descname"><span class="pre">RBF</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">length_scale</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">length_scale_bounds</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(1e-05,</span> <span class="pre">100000.0)</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/gaussian_process/kernels.py#L1443"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.RBF" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.gaussian_process.kernels.</span></span><span class="sig-name descname"><span class="pre">RBF</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">length_scale</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">length_scale_bounds</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(1e-05,</span> <span class="pre">100000.0)</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/gaussian_process/kernels.py#L1443"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.RBF" title="Link to this definition">#</a></dt>
 <dd><p>Radial basis function kernel (aka squared-exponential kernel).</p>
 <p>The RBF kernel is a stationary kernel. It is also known as the
 “squared exponential” kernel. It is parameterized by a length scale
@@ -1364,15 +1364,15 @@ <h1>RBF<a class="headerlink" href="#rbf" title="Link to this heading">#</a></h1>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">gpc</span> <span class="o">=</span> <span class="n">GaussianProcessClassifier</span><span class="p">(</span><span class="n">kernel</span><span class="o">=</span><span class="n">kernel</span><span class="p">,</span>
 <span class="gp">... </span>        <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">gpc</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
-<span class="go">0.9866...</span>
+<span class="go">0.9866</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">gpc</span><span class="o">.</span><span class="n">predict_proba</span><span class="p">(</span><span class="n">X</span><span class="p">[:</span><span class="mi">2</span><span class="p">,:])</span>
-<span class="go">array([[0.8354..., 0.03228..., 0.1322...],</span>
-<span class="go">       [0.7906..., 0.0652..., 0.1441...]])</span>
+<span class="go">array([[0.8354, 0.03228, 0.1322],</span>
+<span class="go">       [0.7906, 0.0652, 0.1441]])</span>
 </pre></div>
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.kernels.RBF.__call__">
-<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eval_gradient</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/gaussian_process/kernels.py#L1525"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.RBF.__call__" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eval_gradient</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/gaussian_process/kernels.py#L1525"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.RBF.__call__" title="Link to this definition">#</a></dt>
 <dd><p>Return the kernel k(X, Y) and optionally its gradient.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1417,7 +1417,7 @@ <h1>RBF<a class="headerlink" href="#rbf" title="Link to this heading">#</a></h1>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.kernels.RBF.clone_with_theta">
-<span class="sig-name descname"><span class="pre">clone_with_theta</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">theta</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/gaussian_process/kernels.py#L258"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.RBF.clone_with_theta" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">clone_with_theta</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">theta</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/gaussian_process/kernels.py#L258"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.RBF.clone_with_theta" title="Link to this definition">#</a></dt>
 <dd><p>Returns a clone of self with given hyperparameters theta.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1431,7 +1431,7 @@ <h1>RBF<a class="headerlink" href="#rbf" title="Link to this heading">#</a></h1>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.kernels.RBF.diag">
-<span class="sig-name descname"><span class="pre">diag</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/gaussian_process/kernels.py#L468"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.RBF.diag" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">diag</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/gaussian_process/kernels.py#L468"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.RBF.diag" title="Link to this definition">#</a></dt>
 <dd><p>Returns the diagonal of the kernel k(X, X).</p>
 <p>The result of this method is identical to np.diag(self(X)); however,
 it can be evaluated more efficiently since only the diagonal is
@@ -1454,7 +1454,7 @@ <h1>RBF<a class="headerlink" href="#rbf" title="Link to this heading">#</a></h1>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.kernels.RBF.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/gaussian_process/kernels.py#L178"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.RBF.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/gaussian_process/kernels.py#L178"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.RBF.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters of this kernel.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1481,7 +1481,7 @@ <h1>RBF<a class="headerlink" href="#rbf" title="Link to this heading">#</a></h1>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.kernels.RBF.is_stationary">
-<span class="sig-name descname"><span class="pre">is_stationary</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/gaussian_process/kernels.py#L494"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.RBF.is_stationary" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">is_stationary</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/gaussian_process/kernels.py#L494"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.RBF.is_stationary" title="Link to this definition">#</a></dt>
 <dd><p>Returns whether the kernel is stationary.</p>
 </dd></dl>
 
@@ -1501,7 +1501,7 @@ <h1>RBF<a class="headerlink" href="#rbf" title="Link to this heading">#</a></h1>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.kernels.RBF.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/gaussian_process/kernels.py#L218"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.RBF.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/gaussian_process/kernels.py#L218"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.RBF.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this kernel.</p>
 <p>The method works on simple kernels as well as on nested kernels.
 The latter have parameters of the form <code class="docutils literal notranslate"><span class="pre">&lt;component&gt;__&lt;parameter&gt;</span></code>
diff --git a/dev/modules/generated/sklearn.gaussian_process.kernels.RationalQuadratic.html b/dev/modules/generated/sklearn.gaussian_process.kernels.RationalQuadratic.html
index 657acb797d642..02a08c49437f8 100644
--- a/dev/modules/generated/sklearn.gaussian_process.kernels.RationalQuadratic.html
+++ b/dev/modules/generated/sklearn.gaussian_process.kernels.RationalQuadratic.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1308,7 +1308,7 @@
 <h1>RationalQuadratic<a class="headerlink" href="#rationalquadratic" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.kernels.RationalQuadratic">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.gaussian_process.kernels.</span></span><span class="sig-name descname"><span class="pre">RationalQuadratic</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">length_scale</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">length_scale_bounds</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(1e-05,</span> <span class="pre">100000.0)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha_bounds</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(1e-05,</span> <span class="pre">100000.0)</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/gaussian_process/kernels.py#L1796"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.RationalQuadratic" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.gaussian_process.kernels.</span></span><span class="sig-name descname"><span class="pre">RationalQuadratic</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">length_scale</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">length_scale_bounds</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(1e-05,</span> <span class="pre">100000.0)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha_bounds</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(1e-05,</span> <span class="pre">100000.0)</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/gaussian_process/kernels.py#L1796"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.RationalQuadratic" title="Link to this definition">#</a></dt>
 <dd><p>Rational Quadratic kernel.</p>
 <p>The RationalQuadratic kernel can be seen as a scale mixture (an infinite
 sum) of RBF kernels with different characteristic length scales. It is
@@ -1362,15 +1362,15 @@ <h1>RationalQuadratic<a class="headerlink" href="#rationalquadratic" title="Link
 <span class="gp">&gt;&gt;&gt; </span><span class="n">gpc</span> <span class="o">=</span> <span class="n">GaussianProcessClassifier</span><span class="p">(</span><span class="n">kernel</span><span class="o">=</span><span class="n">kernel</span><span class="p">,</span>
 <span class="gp">... </span>        <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">gpc</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
-<span class="go">0.9733...</span>
+<span class="go">0.9733</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">gpc</span><span class="o">.</span><span class="n">predict_proba</span><span class="p">(</span><span class="n">X</span><span class="p">[:</span><span class="mi">2</span><span class="p">,:])</span>
-<span class="go">array([[0.8881..., 0.0566..., 0.05518...],</span>
-<span class="go">        [0.8678..., 0.0707... , 0.0614...]])</span>
+<span class="go">array([[0.8881, 0.0566, 0.05518],</span>
+<span class="go">        [0.8678, 0.0707 , 0.0614]])</span>
 </pre></div>
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.kernels.RationalQuadratic.__call__">
-<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eval_gradient</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/gaussian_process/kernels.py#L1879"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.RationalQuadratic.__call__" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eval_gradient</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/gaussian_process/kernels.py#L1879"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.RationalQuadratic.__call__" title="Link to this definition">#</a></dt>
 <dd><p>Return the kernel k(X, Y) and optionally its gradient.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1415,7 +1415,7 @@ <h1>RationalQuadratic<a class="headerlink" href="#rationalquadratic" title="Link
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.kernels.RationalQuadratic.clone_with_theta">
-<span class="sig-name descname"><span class="pre">clone_with_theta</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">theta</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/gaussian_process/kernels.py#L258"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.RationalQuadratic.clone_with_theta" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">clone_with_theta</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">theta</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/gaussian_process/kernels.py#L258"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.RationalQuadratic.clone_with_theta" title="Link to this definition">#</a></dt>
 <dd><p>Returns a clone of self with given hyperparameters theta.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1429,7 +1429,7 @@ <h1>RationalQuadratic<a class="headerlink" href="#rationalquadratic" title="Link
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.kernels.RationalQuadratic.diag">
-<span class="sig-name descname"><span class="pre">diag</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/gaussian_process/kernels.py#L468"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.RationalQuadratic.diag" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">diag</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/gaussian_process/kernels.py#L468"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.RationalQuadratic.diag" title="Link to this definition">#</a></dt>
 <dd><p>Returns the diagonal of the kernel k(X, X).</p>
 <p>The result of this method is identical to np.diag(self(X)); however,
 it can be evaluated more efficiently since only the diagonal is
@@ -1452,7 +1452,7 @@ <h1>RationalQuadratic<a class="headerlink" href="#rationalquadratic" title="Link
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.kernels.RationalQuadratic.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/gaussian_process/kernels.py#L178"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.RationalQuadratic.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/gaussian_process/kernels.py#L178"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.RationalQuadratic.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters of this kernel.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1479,7 +1479,7 @@ <h1>RationalQuadratic<a class="headerlink" href="#rationalquadratic" title="Link
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.kernels.RationalQuadratic.is_stationary">
-<span class="sig-name descname"><span class="pre">is_stationary</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/gaussian_process/kernels.py#L494"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.RationalQuadratic.is_stationary" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">is_stationary</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/gaussian_process/kernels.py#L494"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.RationalQuadratic.is_stationary" title="Link to this definition">#</a></dt>
 <dd><p>Returns whether the kernel is stationary.</p>
 </dd></dl>
 
@@ -1499,7 +1499,7 @@ <h1>RationalQuadratic<a class="headerlink" href="#rationalquadratic" title="Link
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.kernels.RationalQuadratic.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/gaussian_process/kernels.py#L218"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.RationalQuadratic.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/gaussian_process/kernels.py#L218"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.RationalQuadratic.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this kernel.</p>
 <p>The method works on simple kernels as well as on nested kernels.
 The latter have parameters of the form <code class="docutils literal notranslate"><span class="pre">&lt;component&gt;__&lt;parameter&gt;</span></code>
diff --git a/dev/modules/generated/sklearn.gaussian_process.kernels.Sum.html b/dev/modules/generated/sklearn.gaussian_process.kernels.Sum.html
index 13e2673813de9..f0037eb1faeea 100644
--- a/dev/modules/generated/sklearn.gaussian_process.kernels.Sum.html
+++ b/dev/modules/generated/sklearn.gaussian_process.kernels.Sum.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1306,7 +1306,7 @@
 <h1>Sum<a class="headerlink" href="#sum" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.kernels.Sum">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.gaussian_process.kernels.</span></span><span class="sig-name descname"><span class="pre">Sum</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">k1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">k2</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/gaussian_process/kernels.py#L794"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.Sum" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.gaussian_process.kernels.</span></span><span class="sig-name descname"><span class="pre">Sum</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">k1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">k2</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/gaussian_process/kernels.py#L794"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.Sum" title="Link to this definition">#</a></dt>
 <dd><p>The <code class="docutils literal notranslate"><span class="pre">Sum</span></code> kernel takes two kernels <span class="math notranslate nohighlight">\(k_1\)</span> and <span class="math notranslate nohighlight">\(k_2\)</span>
 and combines them via</p>
 <div class="math notranslate nohighlight">
@@ -1344,7 +1344,7 @@ <h1>Sum<a class="headerlink" href="#sum" title="Link to this heading">#</a></h1>
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.kernels.Sum.__call__">
-<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eval_gradient</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/gaussian_process/kernels.py#L833"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.Sum.__call__" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eval_gradient</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/gaussian_process/kernels.py#L833"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.Sum.__call__" title="Link to this definition">#</a></dt>
 <dd><p>Return the kernel k(X, Y) and optionally its gradient.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1388,7 +1388,7 @@ <h1>Sum<a class="headerlink" href="#sum" title="Link to this heading">#</a></h1>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.kernels.Sum.clone_with_theta">
-<span class="sig-name descname"><span class="pre">clone_with_theta</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">theta</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/gaussian_process/kernels.py#L258"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.Sum.clone_with_theta" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">clone_with_theta</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">theta</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/gaussian_process/kernels.py#L258"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.Sum.clone_with_theta" title="Link to this definition">#</a></dt>
 <dd><p>Returns a clone of self with given hyperparameters theta.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1402,7 +1402,7 @@ <h1>Sum<a class="headerlink" href="#sum" title="Link to this heading">#</a></h1>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.kernels.Sum.diag">
-<span class="sig-name descname"><span class="pre">diag</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/gaussian_process/kernels.py#L868"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.Sum.diag" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">diag</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/gaussian_process/kernels.py#L868"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.Sum.diag" title="Link to this definition">#</a></dt>
 <dd><p>Returns the diagonal of the kernel k(X, X).</p>
 <p>The result of this method is identical to <code class="docutils literal notranslate"><span class="pre">np.diag(self(X))</span></code>; however,
 it can be evaluated more efficiently since only the diagonal is
@@ -1425,7 +1425,7 @@ <h1>Sum<a class="headerlink" href="#sum" title="Link to this heading">#</a></h1>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.kernels.Sum.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/gaussian_process/kernels.py#L686"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.Sum.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/gaussian_process/kernels.py#L686"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.Sum.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters of this kernel.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1452,7 +1452,7 @@ <h1>Sum<a class="headerlink" href="#sum" title="Link to this heading">#</a></h1>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.kernels.Sum.is_stationary">
-<span class="sig-name descname"><span class="pre">is_stationary</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/gaussian_process/kernels.py#L784"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.Sum.is_stationary" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">is_stationary</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/gaussian_process/kernels.py#L784"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.Sum.is_stationary" title="Link to this definition">#</a></dt>
 <dd><p>Returns whether the kernel is stationary.</p>
 </dd></dl>
 
@@ -1470,7 +1470,7 @@ <h1>Sum<a class="headerlink" href="#sum" title="Link to this heading">#</a></h1>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.kernels.Sum.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/gaussian_process/kernels.py#L218"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.Sum.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/gaussian_process/kernels.py#L218"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.Sum.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this kernel.</p>
 <p>The method works on simple kernels as well as on nested kernels.
 The latter have parameters of the form <code class="docutils literal notranslate"><span class="pre">&lt;component&gt;__&lt;parameter&gt;</span></code>
diff --git a/dev/modules/generated/sklearn.gaussian_process.kernels.WhiteKernel.html b/dev/modules/generated/sklearn.gaussian_process.kernels.WhiteKernel.html
index 28964cdedbe51..36f4ac7b24dae 100644
--- a/dev/modules/generated/sklearn.gaussian_process.kernels.WhiteKernel.html
+++ b/dev/modules/generated/sklearn.gaussian_process.kernels.WhiteKernel.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1308,7 +1308,7 @@
 <h1>WhiteKernel<a class="headerlink" href="#whitekernel" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.kernels.WhiteKernel">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.gaussian_process.kernels.</span></span><span class="sig-name descname"><span class="pre">WhiteKernel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">noise_level</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">noise_level_bounds</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(1e-05,</span> <span class="pre">100000.0)</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/gaussian_process/kernels.py#L1320"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.WhiteKernel" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.gaussian_process.kernels.</span></span><span class="sig-name descname"><span class="pre">WhiteKernel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">noise_level</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">noise_level_bounds</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(1e-05,</span> <span class="pre">100000.0)</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/gaussian_process/kernels.py#L1320"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.WhiteKernel" title="Link to this definition">#</a></dt>
 <dd><p>White kernel.</p>
 <p>The main use-case of this kernel is as part of a sum-kernel where it
 explains the noise of the signal as independently and identically
@@ -1341,14 +1341,14 @@ <h1>WhiteKernel<a class="headerlink" href="#whitekernel" title="Link to this hea
 <span class="gp">&gt;&gt;&gt; </span><span class="n">gpr</span> <span class="o">=</span> <span class="n">GaussianProcessRegressor</span><span class="p">(</span><span class="n">kernel</span><span class="o">=</span><span class="n">kernel</span><span class="p">,</span>
 <span class="gp">... </span>        <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">gpr</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
-<span class="go">0.3680...</span>
+<span class="go">0.3680</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">gpr</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X</span><span class="p">[:</span><span class="mi">2</span><span class="p">,:],</span> <span class="n">return_std</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-<span class="go">(array([653.0..., 592.1... ]), array([316.6..., 316.6...]))</span>
+<span class="go">(array([653.0, 592.1 ]), array([316.6, 316.6]))</span>
 </pre></div>
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.kernels.WhiteKernel.__call__">
-<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eval_gradient</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/gaussian_process/kernels.py#L1369"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.WhiteKernel.__call__" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eval_gradient</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/gaussian_process/kernels.py#L1369"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.WhiteKernel.__call__" title="Link to this definition">#</a></dt>
 <dd><p>Return the kernel k(X, Y) and optionally its gradient.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1393,7 +1393,7 @@ <h1>WhiteKernel<a class="headerlink" href="#whitekernel" title="Link to this hea
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.kernels.WhiteKernel.clone_with_theta">
-<span class="sig-name descname"><span class="pre">clone_with_theta</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">theta</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/gaussian_process/kernels.py#L258"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.WhiteKernel.clone_with_theta" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">clone_with_theta</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">theta</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/gaussian_process/kernels.py#L258"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.WhiteKernel.clone_with_theta" title="Link to this definition">#</a></dt>
 <dd><p>Returns a clone of self with given hyperparameters theta.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1407,7 +1407,7 @@ <h1>WhiteKernel<a class="headerlink" href="#whitekernel" title="Link to this hea
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.kernels.WhiteKernel.diag">
-<span class="sig-name descname"><span class="pre">diag</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/gaussian_process/kernels.py#L1416"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.WhiteKernel.diag" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">diag</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/gaussian_process/kernels.py#L1416"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.WhiteKernel.diag" title="Link to this definition">#</a></dt>
 <dd><p>Returns the diagonal of the kernel k(X, X).</p>
 <p>The result of this method is identical to np.diag(self(X)); however,
 it can be evaluated more efficiently since only the diagonal is
@@ -1430,7 +1430,7 @@ <h1>WhiteKernel<a class="headerlink" href="#whitekernel" title="Link to this hea
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.kernels.WhiteKernel.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/gaussian_process/kernels.py#L178"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.WhiteKernel.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/gaussian_process/kernels.py#L178"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.WhiteKernel.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters of this kernel.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1457,7 +1457,7 @@ <h1>WhiteKernel<a class="headerlink" href="#whitekernel" title="Link to this hea
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.kernels.WhiteKernel.is_stationary">
-<span class="sig-name descname"><span class="pre">is_stationary</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/gaussian_process/kernels.py#L494"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.WhiteKernel.is_stationary" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">is_stationary</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/gaussian_process/kernels.py#L494"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.WhiteKernel.is_stationary" title="Link to this definition">#</a></dt>
 <dd><p>Returns whether the kernel is stationary.</p>
 </dd></dl>
 
@@ -1475,7 +1475,7 @@ <h1>WhiteKernel<a class="headerlink" href="#whitekernel" title="Link to this hea
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.gaussian_process.kernels.WhiteKernel.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/gaussian_process/kernels.py#L218"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.WhiteKernel.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/gaussian_process/kernels.py#L218"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.gaussian_process.kernels.WhiteKernel.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this kernel.</p>
 <p>The method works on simple kernels as well as on nested kernels.
 The latter have parameters of the form <code class="docutils literal notranslate"><span class="pre">&lt;component&gt;__&lt;parameter&gt;</span></code>
diff --git a/dev/modules/generated/sklearn.get_config.html b/dev/modules/generated/sklearn.get_config.html
index bfa2c2e4c7ab3..19229de2558cf 100644
--- a/dev/modules/generated/sklearn.get_config.html
+++ b/dev/modules/generated/sklearn.get_config.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>get_config<a class="headerlink" href="#get-config" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.get_config">
-<span class="sig-prename descclassname"><span class="pre">sklearn.</span></span><span class="sig-name descname"><span class="pre">get_config</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/_config.py#L35"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.get_config" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.</span></span><span class="sig-name descname"><span class="pre">get_config</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/_config.py#L35"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.get_config" title="Link to this definition">#</a></dt>
 <dd><p>Retrieve current values for configuration set by <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">set_config</span></code></a>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Returns<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.impute.IterativeImputer.html b/dev/modules/generated/sklearn.impute.IterativeImputer.html
index d2b9ac9247222..a63e446286fd7 100644
--- a/dev/modules/generated/sklearn.impute.IterativeImputer.html
+++ b/dev/modules/generated/sklearn.impute.IterativeImputer.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1308,7 +1308,7 @@
 <h1>IterativeImputer<a class="headerlink" href="#iterativeimputer" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.impute.IterativeImputer">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.impute.</span></span><span class="sig-name descname"><span class="pre">IterativeImputer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">missing_values</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">nan</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_posterior</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_nearest_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">initial_strategy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fill_value</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">imputation_order</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'ascending'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">skip_complete</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_value</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-inf</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_value</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">inf</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_indicator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">keep_empty_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/impute/_iterative.py#L60"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.impute.IterativeImputer" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.impute.</span></span><span class="sig-name descname"><span class="pre">IterativeImputer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">missing_values</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">nan</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_posterior</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_nearest_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">initial_strategy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fill_value</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">imputation_order</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'ascending'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">skip_complete</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_value</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-inf</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_value</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">inf</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_indicator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">keep_empty_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/impute/_iterative.py#L60"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.impute.IterativeImputer" title="Link to this definition">#</a></dt>
 <dd><p>Multivariate imputer that estimates each feature from all the others.</p>
 <p>A strategy for imputing missing values by modeling each feature with
 missing values as a function of other features in a round-robin fashion.</p>
@@ -1515,9 +1515,9 @@ <h1>IterativeImputer<a class="headerlink" href="#iterativeimputer" title="Link t
 <span class="go">IterativeImputer(random_state=0)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">X</span> <span class="o">=</span> <span class="p">[[</span><span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">,</span> <span class="mi">6</span><span class="p">],</span> <span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">,</span> <span class="mi">9</span><span class="p">]]</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">imp_mean</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
-<span class="go">array([[ 6.9584...,  2.       ,  3.        ],</span>
-<span class="go">       [ 4.       ,  2.6000...,  6.        ],</span>
-<span class="go">       [10.       ,  4.9999...,  9.        ]])</span>
+<span class="go">array([[ 6.9584,  2.       ,  3.        ],</span>
+<span class="go">       [ 4.       ,  2.6000,  6.        ],</span>
+<span class="go">       [10.       ,  4.9999,  9.        ]])</span>
 </pre></div>
 </div>
 <p>For a more detailed example see
@@ -1525,7 +1525,7 @@ <h1>IterativeImputer<a class="headerlink" href="#iterativeimputer" title="Link t
 <a class="reference internal" href="http://github.com/auto_examples/impute/plot_iterative_imputer_variants_comparison.html#sphx-glr-auto-examples-impute-plot-iterative-imputer-variants-comparison-py"><span class="std std-ref">Imputing missing values with variants of IterativeImputer</span></a>.</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.impute.IterativeImputer.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/impute/_iterative.py#L957"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.impute.IterativeImputer.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/impute/_iterative.py#L957"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.impute.IterativeImputer.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit the imputer on <code class="docutils literal notranslate"><span class="pre">X</span></code> and return self.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1557,7 +1557,7 @@ <h1>IterativeImputer<a class="headerlink" href="#iterativeimputer" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.impute.IterativeImputer.fit_transform">
-<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/impute/_iterative.py#L747"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.impute.IterativeImputer.fit_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/impute/_iterative.py#L747"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.impute.IterativeImputer.fit_transform" title="Link to this definition">#</a></dt>
 <dd><p>Fit the imputer on <code class="docutils literal notranslate"><span class="pre">X</span></code> and return the transformed <code class="docutils literal notranslate"><span class="pre">X</span></code>.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1589,7 +1589,7 @@ <h1>IterativeImputer<a class="headerlink" href="#iterativeimputer" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.impute.IterativeImputer.get_feature_names_out">
-<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/impute/_iterative.py#L987"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.impute.IterativeImputer.get_feature_names_out" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/impute/_iterative.py#L987"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.impute.IterativeImputer.get_feature_names_out" title="Link to this definition">#</a></dt>
 <dd><p>Get output feature names for transformation.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1617,7 +1617,7 @@ <h1>IterativeImputer<a class="headerlink" href="#iterativeimputer" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.impute.IterativeImputer.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/impute/_iterative.py#L1012"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.impute.IterativeImputer.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/impute/_iterative.py#L1012"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.impute.IterativeImputer.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1637,7 +1637,7 @@ <h1>IterativeImputer<a class="headerlink" href="#iterativeimputer" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.impute.IterativeImputer.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.impute.IterativeImputer.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.impute.IterativeImputer.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1658,7 +1658,7 @@ <h1>IterativeImputer<a class="headerlink" href="#iterativeimputer" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.impute.IterativeImputer.set_output">
-<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.impute.IterativeImputer.set_output" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.impute.IterativeImputer.set_output" title="Link to this definition">#</a></dt>
 <dd><p>Set output container.</p>
 <p>See <a class="reference internal" href="http://github.com/auto_examples/miscellaneous/plot_set_output.html#sphx-glr-auto-examples-miscellaneous-plot-set-output-py"><span class="std std-ref">Introducing the set_output API</span></a>
 for an example on how to use the API.</p>
@@ -1689,7 +1689,7 @@ <h1>IterativeImputer<a class="headerlink" href="#iterativeimputer" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.impute.IterativeImputer.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.impute.IterativeImputer.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.impute.IterativeImputer.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1713,7 +1713,7 @@ <h1>IterativeImputer<a class="headerlink" href="#iterativeimputer" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.impute.IterativeImputer.transform">
-<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/impute/_iterative.py#L903"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.impute.IterativeImputer.transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/impute/_iterative.py#L903"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.impute.IterativeImputer.transform" title="Link to this definition">#</a></dt>
 <dd><p>Impute all missing values in <code class="docutils literal notranslate"><span class="pre">X</span></code>.</p>
 <p>Note that this is stochastic, and that if <code class="docutils literal notranslate"><span class="pre">random_state</span></code> is not fixed,
 repeated calls, or permuted input, results will differ.</p>
diff --git a/dev/modules/generated/sklearn.impute.KNNImputer.html b/dev/modules/generated/sklearn.impute.KNNImputer.html
index 7be6d3e1f4910..87fba7ac2c047 100644
--- a/dev/modules/generated/sklearn.impute.KNNImputer.html
+++ b/dev/modules/generated/sklearn.impute.KNNImputer.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>KNNImputer<a class="headerlink" href="#knnimputer" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.impute.KNNImputer">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.impute.</span></span><span class="sig-name descname"><span class="pre">KNNImputer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">missing_values</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">nan</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_neighbors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'uniform'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'nan_euclidean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_indicator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">keep_empty_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/impute/_knn.py#L24"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.impute.KNNImputer" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.impute.</span></span><span class="sig-name descname"><span class="pre">KNNImputer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">missing_values</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">nan</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_neighbors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'uniform'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'nan_euclidean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_indicator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">keep_empty_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/impute/_knn.py#L24"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.impute.KNNImputer" title="Link to this definition">#</a></dt>
 <dd><p>Imputation for completing missing values using k-Nearest Neighbors.</p>
 <p>Each sample’s missing values are imputed using the mean value from
 <code class="docutils literal notranslate"><span class="pre">n_neighbors</span></code> nearest neighbors found in the training set. Two samples are
@@ -1417,7 +1417,7 @@ <h1>KNNImputer<a class="headerlink" href="#knnimputer" title="Link to this headi
 <a class="reference internal" href="http://github.com/auto_examples/impute/plot_missing_values.html#sphx-glr-auto-examples-impute-plot-missing-values-py"><span class="std std-ref">Imputing missing values before building an estimator</span></a>.</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.impute.KNNImputer.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/impute/_knn.py#L213"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.impute.KNNImputer.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/impute/_knn.py#L213"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.impute.KNNImputer.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit the imputer on X.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1440,7 +1440,7 @@ <h1>KNNImputer<a class="headerlink" href="#knnimputer" title="Link to this headi
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.impute.KNNImputer.fit_transform">
-<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L863"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.impute.KNNImputer.fit_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L804"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.impute.KNNImputer.fit_transform" title="Link to this definition">#</a></dt>
 <dd><p>Fit to data, then transform it.</p>
 <p>Fits transformer to <code class="docutils literal notranslate"><span class="pre">X</span></code> and <code class="docutils literal notranslate"><span class="pre">y</span></code> with optional parameters <code class="docutils literal notranslate"><span class="pre">fit_params</span></code>
 and returns a transformed version of <code class="docutils literal notranslate"><span class="pre">X</span></code>.</p>
@@ -1466,7 +1466,7 @@ <h1>KNNImputer<a class="headerlink" href="#knnimputer" title="Link to this headi
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.impute.KNNImputer.get_feature_names_out">
-<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/impute/_knn.py#L388"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.impute.KNNImputer.get_feature_names_out" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/impute/_knn.py#L388"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.impute.KNNImputer.get_feature_names_out" title="Link to this definition">#</a></dt>
 <dd><p>Get output feature names for transformation.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1494,7 +1494,7 @@ <h1>KNNImputer<a class="headerlink" href="#knnimputer" title="Link to this headi
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.impute.KNNImputer.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.impute.KNNImputer.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.impute.KNNImputer.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1511,7 +1511,7 @@ <h1>KNNImputer<a class="headerlink" href="#knnimputer" title="Link to this headi
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.impute.KNNImputer.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.impute.KNNImputer.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.impute.KNNImputer.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1532,7 +1532,7 @@ <h1>KNNImputer<a class="headerlink" href="#knnimputer" title="Link to this headi
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.impute.KNNImputer.set_output">
-<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.impute.KNNImputer.set_output" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.impute.KNNImputer.set_output" title="Link to this definition">#</a></dt>
 <dd><p>Set output container.</p>
 <p>See <a class="reference internal" href="http://github.com/auto_examples/miscellaneous/plot_set_output.html#sphx-glr-auto-examples-miscellaneous-plot-set-output-py"><span class="std std-ref">Introducing the set_output API</span></a>
 for an example on how to use the API.</p>
@@ -1563,7 +1563,7 @@ <h1>KNNImputer<a class="headerlink" href="#knnimputer" title="Link to this headi
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.impute.KNNImputer.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.impute.KNNImputer.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.impute.KNNImputer.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1587,7 +1587,7 @@ <h1>KNNImputer<a class="headerlink" href="#knnimputer" title="Link to this headi
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.impute.KNNImputer.transform">
-<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/impute/_knn.py#L254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.impute.KNNImputer.transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/impute/_knn.py#L254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.impute.KNNImputer.transform" title="Link to this definition">#</a></dt>
 <dd><p>Impute all missing values in X.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.impute.MissingIndicator.html b/dev/modules/generated/sklearn.impute.MissingIndicator.html
index cdb9193e98a37..aaab3d2f58cf5 100644
--- a/dev/modules/generated/sklearn.impute.MissingIndicator.html
+++ b/dev/modules/generated/sklearn.impute.MissingIndicator.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>MissingIndicator<a class="headerlink" href="#missingindicator" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.impute.MissingIndicator">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.impute.</span></span><span class="sig-name descname"><span class="pre">MissingIndicator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">missing_values</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">nan</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'missing-only'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sparse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_on_new</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/impute/_base.py#L775"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.impute.MissingIndicator" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.impute.</span></span><span class="sig-name descname"><span class="pre">MissingIndicator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">missing_values</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">nan</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'missing-only'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sparse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_on_new</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/impute/_base.py#L777"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.impute.MissingIndicator" title="Link to this definition">#</a></dt>
 <dd><p>Binary indicators for missing values.</p>
 <p>Note that this component typically should not be used in a vanilla
 <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a> consisting of transformers and a
@@ -1396,7 +1396,7 @@ <h1>MissingIndicator<a class="headerlink" href="#missingindicator" title="Link t
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.impute.MissingIndicator.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/impute/_base.py#L1011"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.impute.MissingIndicator.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/impute/_base.py#L1013"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.impute.MissingIndicator.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit the transformer on <code class="docutils literal notranslate"><span class="pre">X</span></code>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1419,7 +1419,7 @@ <h1>MissingIndicator<a class="headerlink" href="#missingindicator" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.impute.MissingIndicator.fit_transform">
-<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/impute/_base.py#L1074"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.impute.MissingIndicator.fit_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/impute/_base.py#L1076"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.impute.MissingIndicator.fit_transform" title="Link to this definition">#</a></dt>
 <dd><p>Generate missing values indicator for <code class="docutils literal notranslate"><span class="pre">X</span></code>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1442,7 +1442,7 @@ <h1>MissingIndicator<a class="headerlink" href="#missingindicator" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.impute.MissingIndicator.get_feature_names_out">
-<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/impute/_base.py#L1100"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.impute.MissingIndicator.get_feature_names_out" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/impute/_base.py#L1102"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.impute.MissingIndicator.get_feature_names_out" title="Link to this definition">#</a></dt>
 <dd><p>Get output feature names for transformation.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1470,7 +1470,7 @@ <h1>MissingIndicator<a class="headerlink" href="#missingindicator" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.impute.MissingIndicator.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.impute.MissingIndicator.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.impute.MissingIndicator.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1487,7 +1487,7 @@ <h1>MissingIndicator<a class="headerlink" href="#missingindicator" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.impute.MissingIndicator.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.impute.MissingIndicator.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.impute.MissingIndicator.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1508,7 +1508,7 @@ <h1>MissingIndicator<a class="headerlink" href="#missingindicator" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.impute.MissingIndicator.set_output">
-<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.impute.MissingIndicator.set_output" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.impute.MissingIndicator.set_output" title="Link to this definition">#</a></dt>
 <dd><p>Set output container.</p>
 <p>See <a class="reference internal" href="http://github.com/auto_examples/miscellaneous/plot_set_output.html#sphx-glr-auto-examples-miscellaneous-plot-set-output-py"><span class="std std-ref">Introducing the set_output API</span></a>
 for an example on how to use the API.</p>
@@ -1539,7 +1539,7 @@ <h1>MissingIndicator<a class="headerlink" href="#missingindicator" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.impute.MissingIndicator.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.impute.MissingIndicator.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.impute.MissingIndicator.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1563,7 +1563,7 @@ <h1>MissingIndicator<a class="headerlink" href="#missingindicator" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.impute.MissingIndicator.transform">
-<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/impute/_base.py#L1033"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.impute.MissingIndicator.transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/impute/_base.py#L1035"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.impute.MissingIndicator.transform" title="Link to this definition">#</a></dt>
 <dd><p>Generate missing values indicator for <code class="docutils literal notranslate"><span class="pre">X</span></code>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.impute.SimpleImputer.html b/dev/modules/generated/sklearn.impute.SimpleImputer.html
index 66a9c3d09cb05..5f4d044013c00 100644
--- a/dev/modules/generated/sklearn.impute.SimpleImputer.html
+++ b/dev/modules/generated/sklearn.impute.SimpleImputer.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>SimpleImputer<a class="headerlink" href="#simpleimputer" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.impute.SimpleImputer">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.impute.</span></span><span class="sig-name descname"><span class="pre">SimpleImputer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">missing_values</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">nan</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">strategy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fill_value</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_indicator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">keep_empty_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/impute/_base.py#L154"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.impute.SimpleImputer" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.impute.</span></span><span class="sig-name descname"><span class="pre">SimpleImputer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">missing_values</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">nan</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">strategy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fill_value</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_indicator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">keep_empty_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/impute/_base.py#L154"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.impute.SimpleImputer" title="Link to this definition">#</a></dt>
 <dd><p>Univariate imputer for completing missing values with simple strategies.</p>
 <p>Replace missing values using a descriptive statistic (e.g. mean, median, or
 most frequent) along each column, or using a constant value.</p>
@@ -1440,7 +1440,7 @@ <h1>SimpleImputer<a class="headerlink" href="#simpleimputer" title="Link to this
 <a class="reference internal" href="http://github.com/auto_examples/impute/plot_missing_values.html#sphx-glr-auto-examples-impute-plot-missing-values-py"><span class="std std-ref">Imputing missing values before building an estimator</span></a>.</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.impute.SimpleImputer.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/impute/_base.py#L416"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.impute.SimpleImputer.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/impute/_base.py#L418"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.impute.SimpleImputer.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit the imputer on <code class="docutils literal notranslate"><span class="pre">X</span></code>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1463,7 +1463,7 @@ <h1>SimpleImputer<a class="headerlink" href="#simpleimputer" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.impute.SimpleImputer.fit_transform">
-<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L863"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.impute.SimpleImputer.fit_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L804"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.impute.SimpleImputer.fit_transform" title="Link to this definition">#</a></dt>
 <dd><p>Fit to data, then transform it.</p>
 <p>Fits transformer to <code class="docutils literal notranslate"><span class="pre">X</span></code> and <code class="docutils literal notranslate"><span class="pre">y</span></code> with optional parameters <code class="docutils literal notranslate"><span class="pre">fit_params</span></code>
 and returns a transformed version of <code class="docutils literal notranslate"><span class="pre">X</span></code>.</p>
@@ -1489,7 +1489,7 @@ <h1>SimpleImputer<a class="headerlink" href="#simpleimputer" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.impute.SimpleImputer.get_feature_names_out">
-<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/impute/_base.py#L748"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.impute.SimpleImputer.get_feature_names_out" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/impute/_base.py#L750"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.impute.SimpleImputer.get_feature_names_out" title="Link to this definition">#</a></dt>
 <dd><p>Get output feature names for transformation.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1517,7 +1517,7 @@ <h1>SimpleImputer<a class="headerlink" href="#simpleimputer" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.impute.SimpleImputer.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.impute.SimpleImputer.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.impute.SimpleImputer.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1534,7 +1534,7 @@ <h1>SimpleImputer<a class="headerlink" href="#simpleimputer" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.impute.SimpleImputer.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.impute.SimpleImputer.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.impute.SimpleImputer.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1555,7 +1555,7 @@ <h1>SimpleImputer<a class="headerlink" href="#simpleimputer" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.impute.SimpleImputer.inverse_transform">
-<span class="sig-name descname"><span class="pre">inverse_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/impute/_base.py#L678"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.impute.SimpleImputer.inverse_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">inverse_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/impute/_base.py#L680"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.impute.SimpleImputer.inverse_transform" title="Link to this definition">#</a></dt>
 <dd><p>Convert the data back to the original representation.</p>
 <p>Inverts the <code class="docutils literal notranslate"><span class="pre">transform</span></code> operation performed on an array.
 This operation can only be performed after <a class="reference internal" href="#sklearn.impute.SimpleImputer" title="sklearn.impute.SimpleImputer"><code class="xref py py-class docutils literal notranslate"><span class="pre">SimpleImputer</span></code></a> is
@@ -1588,7 +1588,7 @@ <h1>SimpleImputer<a class="headerlink" href="#simpleimputer" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.impute.SimpleImputer.set_output">
-<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.impute.SimpleImputer.set_output" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.impute.SimpleImputer.set_output" title="Link to this definition">#</a></dt>
 <dd><p>Set output container.</p>
 <p>See <a class="reference internal" href="http://github.com/auto_examples/miscellaneous/plot_set_output.html#sphx-glr-auto-examples-miscellaneous-plot-set-output-py"><span class="std std-ref">Introducing the set_output API</span></a>
 for an example on how to use the API.</p>
@@ -1619,7 +1619,7 @@ <h1>SimpleImputer<a class="headerlink" href="#simpleimputer" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.impute.SimpleImputer.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.impute.SimpleImputer.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.impute.SimpleImputer.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1643,7 +1643,7 @@ <h1>SimpleImputer<a class="headerlink" href="#simpleimputer" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.impute.SimpleImputer.transform">
-<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/impute/_base.py#L591"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.impute.SimpleImputer.transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/impute/_base.py#L593"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.impute.SimpleImputer.transform" title="Link to this definition">#</a></dt>
 <dd><p>Impute all missing values in <code class="docutils literal notranslate"><span class="pre">X</span></code>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.inspection.DecisionBoundaryDisplay.html b/dev/modules/generated/sklearn.inspection.DecisionBoundaryDisplay.html
index a0e5292f4eb30..ae0983e98f0b6 100644
--- a/dev/modules/generated/sklearn.inspection.DecisionBoundaryDisplay.html
+++ b/dev/modules/generated/sklearn.inspection.DecisionBoundaryDisplay.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>DecisionBoundaryDisplay<a class="headerlink" href="#decisionboundarydisplay" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.inspection.DecisionBoundaryDisplay">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.inspection.</span></span><span class="sig-name descname"><span class="pre">DecisionBoundaryDisplay</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xx0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xx1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">response</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">multiclass_colors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xlabel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ylabel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/inspection/_plot/decision_boundary.py#L63"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.inspection.DecisionBoundaryDisplay" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.inspection.</span></span><span class="sig-name descname"><span class="pre">DecisionBoundaryDisplay</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xx0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xx1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">response</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">multiclass_colors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xlabel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ylabel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/inspection/_plot/decision_boundary.py#L63"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.inspection.DecisionBoundaryDisplay" title="Link to this definition">#</a></dt>
 <dd><p>Decisions boundary visualization.</p>
 <p>It is recommended to use
 <a class="reference internal" href="#sklearn.inspection.DecisionBoundaryDisplay.from_estimator" title="sklearn.inspection.DecisionBoundaryDisplay.from_estimator"><code class="xref py py-func docutils literal notranslate"><span class="pre">from_estimator</span></code></a>
@@ -1411,7 +1411,7 @@ <h1>DecisionBoundaryDisplay<a class="headerlink" href="#decisionboundarydisplay"
 </figure>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.inspection.DecisionBoundaryDisplay.from_estimator">
-<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_estimator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">grid_resolution</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">plot_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'contourf'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">response_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">class_of_interest</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">multiclass_colors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xlabel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ylabel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/inspection/_plot/decision_boundary.py#L290"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.inspection.DecisionBoundaryDisplay.from_estimator" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_estimator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">grid_resolution</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">plot_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'contourf'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">response_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">class_of_interest</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">multiclass_colors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xlabel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ylabel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/inspection/_plot/decision_boundary.py#L290"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.inspection.DecisionBoundaryDisplay.from_estimator" title="Link to this definition">#</a></dt>
 <dd><p>Plot decision boundary given an estimator.</p>
 <p>Read more in the <a class="reference internal" href="http://github.com/visualizations.html#visualizations"><span class="std std-ref">User Guide</span></a>.</p>
 <dl class="field-list">
@@ -1535,7 +1535,7 @@ <h1>DecisionBoundaryDisplay<a class="headerlink" href="#decisionboundarydisplay"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.inspection.DecisionBoundaryDisplay.plot">
-<span class="sig-name descname"><span class="pre">plot</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">plot_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'contourf'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xlabel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ylabel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/inspection/_plot/decision_boundary.py#L176"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.inspection.DecisionBoundaryDisplay.plot" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">plot</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">plot_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'contourf'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xlabel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ylabel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/inspection/_plot/decision_boundary.py#L176"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.inspection.DecisionBoundaryDisplay.plot" title="Link to this definition">#</a></dt>
 <dd><p>Plot visualization.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1591,9 +1591,9 @@ <h2>Gallery examples<a class="headerlink" href="#gallery-examples" title="Link t
 </div><div class="sphx-glr-thumbcontainer" tooltip="An example using IsolationForest for anomaly detection."><img alt="" src="http://github.com/_images/sphx_glr_plot_isolation_forest_thumb.png" />
 <p><a class="reference internal" href="http://github.com/auto_examples/ensemble/plot_isolation_forest.html#sphx-glr-auto-examples-ensemble-plot-isolation-forest-py"><span class="std std-ref">IsolationForest example</span></a></p>
   <div class="sphx-glr-thumbnail-title">IsolationForest example</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="Plot the decision boundaries of a VotingClassifier for two features of the Iris dataset."><img alt="" src="http://github.com/_images/sphx_glr_plot_voting_decision_regions_thumb.png" />
-<p><a class="reference internal" href="http://github.com/auto_examples/ensemble/plot_voting_decision_regions.html#sphx-glr-auto-examples-ensemble-plot-voting-decision-regions-py"><span class="std std-ref">Plot the decision boundaries of a VotingClassifier</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Plot the decision boundaries of a VotingClassifier</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="Plot the predicted class probabilities in a toy dataset predicted by three different classifiers and averaged by the VotingClassifier."><img alt="" src="http://github.com/_images/sphx_glr_plot_voting_decision_regions_thumb.png" />
+<p><a class="reference internal" href="http://github.com/auto_examples/ensemble/plot_voting_decision_regions.html#sphx-glr-auto-examples-ensemble-plot-voting-decision-regions-py"><span class="std std-ref">Visualizing the probabilistic predictions of a VotingClassifier</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Visualizing the probabilistic predictions of a VotingClassifier</div>
 </div><div class="sphx-glr-thumbcontainer" tooltip="This example compares decision boundaries of multinomial and one-vs-rest logistic regression on a 2D dataset with three classes."><img alt="" src="http://github.com/_images/sphx_glr_plot_logistic_multinomial_thumb.png" />
 <p><a class="reference internal" href="http://github.com/auto_examples/linear_model/plot_logistic_multinomial.html#sphx-glr-auto-examples-linear-model-plot-logistic-multinomial-py"><span class="std std-ref">Decision Boundaries of Multinomial and One-vs-Rest Logistic Regression</span></a></p>
   <div class="sphx-glr-thumbnail-title">Decision Boundaries of Multinomial and One-vs-Rest Logistic Regression</div>
diff --git a/dev/modules/generated/sklearn.inspection.PartialDependenceDisplay.html b/dev/modules/generated/sklearn.inspection.PartialDependenceDisplay.html
index a08d35e65b280..c6bb09da4b7fd 100644
--- a/dev/modules/generated/sklearn.inspection.PartialDependenceDisplay.html
+++ b/dev/modules/generated/sklearn.inspection.PartialDependenceDisplay.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>PartialDependenceDisplay<a class="headerlink" href="#partialdependencedisplay" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.inspection.PartialDependenceDisplay">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.inspection.</span></span><span class="sig-name descname"><span class="pre">PartialDependenceDisplay</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pd_results</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">feature_names</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target_idx</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">deciles</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kind</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'average'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">subsample</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_categorical</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/inspection/_plot/partial_dependence.py#L27"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.inspection.PartialDependenceDisplay" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.inspection.</span></span><span class="sig-name descname"><span class="pre">PartialDependenceDisplay</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pd_results</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">feature_names</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target_idx</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">deciles</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kind</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'average'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">subsample</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_categorical</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/inspection/_plot/partial_dependence.py#L27"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.inspection.PartialDependenceDisplay" title="Link to this definition">#</a></dt>
 <dd><p>Partial Dependence Plot (PDP).</p>
 <p>This can also display individual partial dependencies which are often
 referred to as: Individual Condition Expectation (ICE).</p>
@@ -1315,9 +1315,12 @@ <h1>PartialDependenceDisplay<a class="headerlink" href="#partialdependencedispla
 <a class="reference internal" href="#sklearn.inspection.PartialDependenceDisplay.from_estimator" title="sklearn.inspection.PartialDependenceDisplay.from_estimator"><code class="xref py py-func docutils literal notranslate"><span class="pre">from_estimator</span></code></a> to create a
 <a class="reference internal" href="#sklearn.inspection.PartialDependenceDisplay" title="sklearn.inspection.PartialDependenceDisplay"><code class="xref py py-class docutils literal notranslate"><span class="pre">PartialDependenceDisplay</span></code></a>. All parameters are
 stored as attributes.</p>
-<p>Read more in
-<a class="reference internal" href="http://github.com/auto_examples/miscellaneous/plot_partial_dependence_visualization_api.html#sphx-glr-auto-examples-miscellaneous-plot-partial-dependence-visualization-api-py"><span class="std std-ref">Advanced Plotting With Partial Dependence</span></a>
-and the <a class="reference internal" href="http://github.com/partial_dependence.html#partial-dependence"><span class="std std-ref">User Guide</span></a>.</p>
+<p>For general information regarding <code class="docutils literal notranslate"><span class="pre">scikit-learn</span></code> visualization tools, see
+the <a class="reference internal" href="http://github.com/visualizations.html#visualizations"><span class="std std-ref">Visualization Guide</span></a>.
+For guidance on interpreting these plots, refer to the
+<a class="reference internal" href="http://github.com/partial_dependence.html#partial-dependence"><span class="std std-ref">Partial Dependence and ICE plots</span></a>.</p>
+<p>For an example on how to use this class, see the following example:
+<a class="reference internal" href="http://github.com/auto_examples/miscellaneous/plot_partial_dependence_visualization_api.html#sphx-glr-auto-examples-miscellaneous-plot-partial-dependence-visualization-api-py"><span class="std std-ref">Advanced Plotting With Partial Dependence</span></a>.</p>
 <div class="versionadded">
 <p><span class="versionmodified added">Added in version 0.22.</span></p>
 </div>
@@ -1505,7 +1508,7 @@ <h1>PartialDependenceDisplay<a class="headerlink" href="#partialdependencedispla
 </figure>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.inspection.PartialDependenceDisplay.from_estimator">
-<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_estimator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">features</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">categorical_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">feature_names</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">response_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_cols</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">grid_resolution</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">percentiles</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(0.05,</span> <span class="pre">0.95)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">custom_values</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">line_kw</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ice_lines_kw</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pd_line_kw</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">contour_kw</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kind</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'average'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">centered</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">subsample</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/inspection/_plot/partial_dependence.py#L248"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.inspection.PartialDependenceDisplay.from_estimator" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_estimator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">features</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">categorical_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">feature_names</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">response_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_cols</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">grid_resolution</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">percentiles</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(0.05,</span> <span class="pre">0.95)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">custom_values</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">line_kw</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ice_lines_kw</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pd_line_kw</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">contour_kw</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kind</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'average'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">centered</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">subsample</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/inspection/_plot/partial_dependence.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.inspection.PartialDependenceDisplay.from_estimator" title="Link to this definition">#</a></dt>
 <dd><p>Partial dependence (PD) and individual conditional expectation (ICE) plots.</p>
 <p>Partial dependence plots, individual conditional expectation plots or an
 overlay of both of them can be plotted by setting the <code class="docutils literal notranslate"><span class="pre">kind</span></code>
@@ -1665,7 +1668,7 @@ <h1>PartialDependenceDisplay<a class="headerlink" href="#partialdependencedispla
 <dt><strong>n_jobs</strong><span class="classifier">int, default=None</span></dt><dd><p>The number of CPUs to use to compute the partial dependences.
 Computation is parallelized over features specified by the <code class="docutils literal notranslate"><span class="pre">features</span></code>
 parameter.</p>
-<p><code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.5.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
+<p><code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.6.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
 <code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all processors. See <a class="reference internal" href="http://github.com/glossary.html#term-n_jobs"><span class="xref std std-term">Glossary</span></a>
 for more details.</p>
 </dd>
@@ -1761,7 +1764,7 @@ <h1>PartialDependenceDisplay<a class="headerlink" href="#partialdependencedispla
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.inspection.PartialDependenceDisplay.plot">
-<span class="sig-name descname"><span class="pre">plot</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_cols</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">line_kw</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ice_lines_kw</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pd_line_kw</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">contour_kw</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bar_kw</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">heatmap_kw</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pdp_lim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">centered</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/inspection/_plot/partial_dependence.py#L1133"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.inspection.PartialDependenceDisplay.plot" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">plot</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_cols</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">line_kw</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ice_lines_kw</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pd_line_kw</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">contour_kw</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bar_kw</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">heatmap_kw</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pdp_lim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">centered</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/inspection/_plot/partial_dependence.py#L1137"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.inspection.PartialDependenceDisplay.plot" title="Link to this definition">#</a></dt>
 <dd><p>Plot partial dependence plots.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.inspection.partial_dependence.html b/dev/modules/generated/sklearn.inspection.partial_dependence.html
index 21d61701e0f57..333a3d810f609 100644
--- a/dev/modules/generated/sklearn.inspection.partial_dependence.html
+++ b/dev/modules/generated/sklearn.inspection.partial_dependence.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>partial_dependence<a class="headerlink" href="#partial-dependence" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.inspection.partial_dependence">
-<span class="sig-prename descclassname"><span class="pre">sklearn.inspection.</span></span><span class="sig-name descname"><span class="pre">partial_dependence</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">features</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">categorical_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">feature_names</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">response_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">percentiles</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(0.05,</span> <span class="pre">0.95)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">grid_resolution</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">custom_values</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kind</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'average'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/inspection/_partial_dependence.py#L346"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.inspection.partial_dependence" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.inspection.</span></span><span class="sig-name descname"><span class="pre">partial_dependence</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">features</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">categorical_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">feature_names</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">response_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">percentiles</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(0.05,</span> <span class="pre">0.95)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">grid_resolution</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">custom_values</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kind</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'average'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/inspection/_partial_dependence.py#L346"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.inspection.partial_dependence" title="Link to this definition">#</a></dt>
 <dd><p>Partial dependence of <code class="docutils literal notranslate"><span class="pre">features</span></code>.</p>
 <p>Partial dependence of a feature (or a set of features) corresponds to
 the average response of an estimator for each possible value of the
@@ -1496,7 +1496,7 @@ <h1>partial_dependence<a class="headerlink" href="#partial-dependence" title="Li
 <span class="gp">&gt;&gt;&gt; </span><span class="n">gb</span> <span class="o">=</span> <span class="n">GradientBoostingClassifier</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">partial_dependence</span><span class="p">(</span><span class="n">gb</span><span class="p">,</span> <span class="n">features</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">X</span><span class="o">=</span><span class="n">X</span><span class="p">,</span> <span class="n">percentiles</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
 <span class="gp">... </span>                   <span class="n">grid_resolution</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span> 
-<span class="go">(array([[-4.52...,  4.52...]]), [array([ 0.,  1.])])</span>
+<span class="go">(array([[-4.52,  4.52]]), [array([ 0.,  1.])])</span>
 </pre></div>
 </div>
 </dd></dl>
diff --git a/dev/modules/generated/sklearn.inspection.permutation_importance.html b/dev/modules/generated/sklearn.inspection.permutation_importance.html
index 22a517bc755bf..861e99ae46386 100644
--- a/dev/modules/generated/sklearn.inspection.permutation_importance.html
+++ b/dev/modules/generated/sklearn.inspection.permutation_importance.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>permutation_importance<a class="headerlink" href="#permutation-importance" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.inspection.permutation_importance">
-<span class="sig-prename descclassname"><span class="pre">sklearn.inspection.</span></span><span class="sig-name descname"><span class="pre">permutation_importance</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scoring</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_repeats</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/inspection/_permutation_importance.py#L114"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.inspection.permutation_importance" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.inspection.</span></span><span class="sig-name descname"><span class="pre">permutation_importance</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scoring</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_repeats</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/inspection/_permutation_importance.py#L114"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.inspection.permutation_importance" title="Link to this definition">#</a></dt>
 <dd><p>Permutation importance for feature evaluation <a class="reference internal" href="#rd9e56ef97513-bre" id="id1">[BRE]</a>.</p>
 <p>The <a class="reference internal" href="http://github.com/glossary.html#term-estimator"><span class="xref std std-term">estimator</span></a> is required to be a fitted estimator. <code class="docutils literal notranslate"><span class="pre">X</span></code> can be the
 data set used to train the estimator or a hold-out set. The permutation
@@ -1352,7 +1352,7 @@ <h1>permutation_importance<a class="headerlink" href="#permutation-importance" t
 </dd>
 <dt><strong>n_jobs</strong><span class="classifier">int or None, default=None</span></dt><dd><p>Number of jobs to run in parallel. The computation is done by computing
 permutation score for each columns and parallelized over the columns.
-<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.5.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
+<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.6.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
 <code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all processors. See <a class="reference internal" href="http://github.com/glossary.html#term-n_jobs"><span class="xref std std-term">Glossary</span></a>
 for more details.</p>
 </dd>
@@ -1420,9 +1420,9 @@ <h1>permutation_importance<a class="headerlink" href="#permutation-importance" t
 <span class="gp">&gt;&gt;&gt; </span><span class="n">result</span> <span class="o">=</span> <span class="n">permutation_importance</span><span class="p">(</span><span class="n">clf</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">n_repeats</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
 <span class="gp">... </span>                                <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">result</span><span class="o">.</span><span class="n">importances_mean</span>
-<span class="go">array([0.4666..., 0.       , 0.       ])</span>
+<span class="go">array([0.4666, 0.       , 0.       ])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">result</span><span class="o">.</span><span class="n">importances_std</span>
-<span class="go">array([0.2211..., 0.       , 0.       ])</span>
+<span class="go">array([0.2211, 0.       , 0.       ])</span>
 </pre></div>
 </div>
 </dd></dl>
diff --git a/dev/modules/generated/sklearn.isotonic.IsotonicRegression.html b/dev/modules/generated/sklearn.isotonic.IsotonicRegression.html
index f60ba2f86ed6b..78ec8ff3f309c 100644
--- a/dev/modules/generated/sklearn.isotonic.IsotonicRegression.html
+++ b/dev/modules/generated/sklearn.isotonic.IsotonicRegression.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1308,7 +1308,7 @@
 <h1>IsotonicRegression<a class="headerlink" href="#isotonicregression" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.isotonic.IsotonicRegression">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.isotonic.</span></span><span class="sig-name descname"><span class="pre">IsotonicRegression</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_min</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">increasing</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_of_bounds</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'nan'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/isotonic.py#L183"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.isotonic.IsotonicRegression" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.isotonic.</span></span><span class="sig-name descname"><span class="pre">IsotonicRegression</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_min</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">increasing</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_of_bounds</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'nan'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/isotonic.py#L183"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.isotonic.IsotonicRegression" title="Link to this definition">#</a></dt>
 <dd><p>Isotonic regression model.</p>
 <p>Read more in the <a class="reference internal" href="http://github.com/isotonic.html#isotonic"><span class="std std-ref">User Guide</span></a>.</p>
 <div class="versionadded">
@@ -1393,12 +1393,12 @@ <h1>IsotonicRegression<a class="headerlink" href="#isotonicregression" title="Li
 <span class="gp">&gt;&gt;&gt; </span><span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">make_regression</span><span class="p">(</span><span class="n">n_samples</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">n_features</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">41</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">iso_reg</span> <span class="o">=</span> <span class="n">IsotonicRegression</span><span class="p">()</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">iso_reg</span><span class="o">.</span><span class="n">predict</span><span class="p">([</span><span class="mf">.1</span><span class="p">,</span> <span class="mf">.2</span><span class="p">])</span>
-<span class="go">array([1.8628..., 3.7256...])</span>
+<span class="go">array([1.8628, 3.7256])</span>
 </pre></div>
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.isotonic.IsotonicRegression.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/isotonic.py#L363"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.isotonic.IsotonicRegression.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/isotonic.py#L363"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.isotonic.IsotonicRegression.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit the model using X, y as training data.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1429,7 +1429,7 @@ <h1>IsotonicRegression<a class="headerlink" href="#isotonicregression" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.isotonic.IsotonicRegression.fit_transform">
-<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L863"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.isotonic.IsotonicRegression.fit_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L804"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.isotonic.IsotonicRegression.fit_transform" title="Link to this definition">#</a></dt>
 <dd><p>Fit to data, then transform it.</p>
 <p>Fits transformer to <code class="docutils literal notranslate"><span class="pre">X</span></code> and <code class="docutils literal notranslate"><span class="pre">y</span></code> with optional parameters <code class="docutils literal notranslate"><span class="pre">fit_params</span></code>
 and returns a transformed version of <code class="docutils literal notranslate"><span class="pre">X</span></code>.</p>
@@ -1455,7 +1455,7 @@ <h1>IsotonicRegression<a class="headerlink" href="#isotonicregression" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.isotonic.IsotonicRegression.get_feature_names_out">
-<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/isotonic.py#L480"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.isotonic.IsotonicRegression.get_feature_names_out" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/isotonic.py#L480"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.isotonic.IsotonicRegression.get_feature_names_out" title="Link to this definition">#</a></dt>
 <dd><p>Get output feature names for transformation.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1475,7 +1475,7 @@ <h1>IsotonicRegression<a class="headerlink" href="#isotonicregression" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.isotonic.IsotonicRegression.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.isotonic.IsotonicRegression.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.isotonic.IsotonicRegression.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1492,7 +1492,7 @@ <h1>IsotonicRegression<a class="headerlink" href="#isotonicregression" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.isotonic.IsotonicRegression.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.isotonic.IsotonicRegression.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.isotonic.IsotonicRegression.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1513,7 +1513,7 @@ <h1>IsotonicRegression<a class="headerlink" href="#isotonicregression" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.isotonic.IsotonicRegression.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">T</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/isotonic.py#L461"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.isotonic.IsotonicRegression.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">T</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/isotonic.py#L461"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.isotonic.IsotonicRegression.predict" title="Link to this definition">#</a></dt>
 <dd><p>Predict new data by linear interpolation.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1533,7 +1533,7 @@ <h1>IsotonicRegression<a class="headerlink" href="#isotonicregression" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.isotonic.IsotonicRegression.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L619"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.isotonic.IsotonicRegression.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L560"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.isotonic.IsotonicRegression.score" title="Link to this definition">#</a></dt>
 <dd><p>Return <a class="reference internal" href="http://github.com/model_evaluation.html#r2-score"><span class="std std-ref">coefficient of determination</span></a> on test data.</p>
 <p>The coefficient of determination, <span class="math notranslate nohighlight">\(R^2\)</span>, is defined as
 <span class="math notranslate nohighlight">\((1 - \frac{u}{v})\)</span>, where <span class="math notranslate nohighlight">\(u\)</span> is the residual
@@ -1575,7 +1575,7 @@ <h1>IsotonicRegression<a class="headerlink" href="#isotonicregression" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.isotonic.IsotonicRegression.set_fit_request">
-<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.isotonic.IsotonicRegression" title="sklearn.isotonic.IsotonicRegression"><span class="pre">IsotonicRegression</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.isotonic.IsotonicRegression.set_fit_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.isotonic.IsotonicRegression" title="sklearn.isotonic.IsotonicRegression"><span class="pre">IsotonicRegression</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.isotonic.IsotonicRegression.set_fit_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1618,7 +1618,7 @@ <h1>IsotonicRegression<a class="headerlink" href="#isotonicregression" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.isotonic.IsotonicRegression.set_output">
-<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.isotonic.IsotonicRegression.set_output" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.isotonic.IsotonicRegression.set_output" title="Link to this definition">#</a></dt>
 <dd><p>Set output container.</p>
 <p>See <a class="reference internal" href="http://github.com/auto_examples/miscellaneous/plot_set_output.html#sphx-glr-auto-examples-miscellaneous-plot-set-output-py"><span class="std std-ref">Introducing the set_output API</span></a>
 for an example on how to use the API.</p>
@@ -1649,7 +1649,7 @@ <h1>IsotonicRegression<a class="headerlink" href="#isotonicregression" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.isotonic.IsotonicRegression.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.isotonic.IsotonicRegression.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.isotonic.IsotonicRegression.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1673,7 +1673,7 @@ <h1>IsotonicRegression<a class="headerlink" href="#isotonicregression" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.isotonic.IsotonicRegression.set_score_request">
-<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.isotonic.IsotonicRegression" title="sklearn.isotonic.IsotonicRegression"><span class="pre">IsotonicRegression</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.isotonic.IsotonicRegression.set_score_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.isotonic.IsotonicRegression" title="sklearn.isotonic.IsotonicRegression"><span class="pre">IsotonicRegression</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.isotonic.IsotonicRegression.set_score_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1716,7 +1716,7 @@ <h1>IsotonicRegression<a class="headerlink" href="#isotonicregression" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.isotonic.IsotonicRegression.transform">
-<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">T</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/isotonic.py#L443"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.isotonic.IsotonicRegression.transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">T</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/isotonic.py#L443"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.isotonic.IsotonicRegression.transform" title="Link to this definition">#</a></dt>
 <dd><p>Transform new data by linear interpolation.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.isotonic.check_increasing.html b/dev/modules/generated/sklearn.isotonic.check_increasing.html
index e038061b9675c..e82810bf4daa5 100644
--- a/dev/modules/generated/sklearn.isotonic.check_increasing.html
+++ b/dev/modules/generated/sklearn.isotonic.check_increasing.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>check_increasing<a class="headerlink" href="#check-increasing" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.isotonic.check_increasing">
-<span class="sig-prename descclassname"><span class="pre">sklearn.isotonic.</span></span><span class="sig-name descname"><span class="pre">check_increasing</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/isotonic.py#L26"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.isotonic.check_increasing" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.isotonic.</span></span><span class="sig-name descname"><span class="pre">check_increasing</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/isotonic.py#L26"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.isotonic.check_increasing" title="Link to this definition">#</a></dt>
 <dd><p>Determine whether y is monotonically correlated with x.</p>
 <p>y is found increasing or decreasing with respect to x based on a Spearman
 correlation test.</p>
diff --git a/dev/modules/generated/sklearn.isotonic.isotonic_regression.html b/dev/modules/generated/sklearn.isotonic.isotonic_regression.html
index 2609560ded3fd..ae4f46eaf5ab0 100644
--- a/dev/modules/generated/sklearn.isotonic.isotonic_regression.html
+++ b/dev/modules/generated/sklearn.isotonic.isotonic_regression.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>isotonic_regression<a class="headerlink" href="#isotonic-regression" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.isotonic.isotonic_regression">
-<span class="sig-prename descclassname"><span class="pre">sklearn.isotonic.</span></span><span class="sig-name descname"><span class="pre">isotonic_regression</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_min</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">increasing</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/isotonic.py#L102"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.isotonic.isotonic_regression" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.isotonic.</span></span><span class="sig-name descname"><span class="pre">isotonic_regression</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_min</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">increasing</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/isotonic.py#L102"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.isotonic.isotonic_regression" title="Link to this definition">#</a></dt>
 <dd><p>Solve the isotonic regression model.</p>
 <p>Read more in the <a class="reference internal" href="http://github.com/isotonic.html#isotonic"><span class="std std-ref">User Guide</span></a>.</p>
 <dl class="field-list simple">
@@ -1340,8 +1340,8 @@ <h1>isotonic_regression<a class="headerlink" href="#isotonic-regression" title="
 <p class="rubric">Examples</p>
 <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.isotonic</span><span class="w"> </span><span class="kn">import</span> <span class="n">isotonic_regression</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">isotonic_regression</span><span class="p">([</span><span class="mi">5</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">7</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">4</span><span class="p">])</span>
-<span class="go">array([2.75   , 2.75   , 2.75   , 2.75   , 7.33...,</span>
-<span class="go">       7.33..., 7.33..., 7.33..., 7.33..., 7.33...])</span>
+<span class="go">array([2.75   , 2.75   , 2.75   , 2.75   , 7.33,</span>
+<span class="go">       7.33, 7.33, 7.33, 7.33, 7.33])</span>
 </pre></div>
 </div>
 </dd></dl>
diff --git a/dev/modules/generated/sklearn.kernel_approximation.AdditiveChi2Sampler.html b/dev/modules/generated/sklearn.kernel_approximation.AdditiveChi2Sampler.html
index f56fa1c652f36..f2716199d3ba3 100644
--- a/dev/modules/generated/sklearn.kernel_approximation.AdditiveChi2Sampler.html
+++ b/dev/modules/generated/sklearn.kernel_approximation.AdditiveChi2Sampler.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>AdditiveChi2Sampler<a class="headerlink" href="#additivechi2sampler" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.kernel_approximation.AdditiveChi2Sampler">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.kernel_approximation.</span></span><span class="sig-name descname"><span class="pre">AdditiveChi2Sampler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_steps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_interval</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/kernel_approximation.py#L577"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_approximation.AdditiveChi2Sampler" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.kernel_approximation.</span></span><span class="sig-name descname"><span class="pre">AdditiveChi2Sampler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_steps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_interval</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/kernel_approximation.py#L577"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_approximation.AdditiveChi2Sampler" title="Link to this definition">#</a></dt>
 <dd><p>Approximate feature map for additive chi2 kernel.</p>
 <p>Uses sampling the fourier transform of the kernel characteristic
 at regular intervals.</p>
@@ -1379,7 +1379,7 @@ <h1>AdditiveChi2Sampler<a class="headerlink" href="#additivechi2sampler" title="
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.kernel_approximation.AdditiveChi2Sampler.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/kernel_approximation.py#L665"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_approximation.AdditiveChi2Sampler.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/kernel_approximation.py#L665"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_approximation.AdditiveChi2Sampler.fit" title="Link to this definition">#</a></dt>
 <dd><p>Only validates estimator’s parameters.</p>
 <p>This method allows to: (i) validate the estimator’s parameters and
 (ii) be consistent with the scikit-learn transformer API.</p>
@@ -1404,7 +1404,7 @@ <h1>AdditiveChi2Sampler<a class="headerlink" href="#additivechi2sampler" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.kernel_approximation.AdditiveChi2Sampler.fit_transform">
-<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L863"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_approximation.AdditiveChi2Sampler.fit_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L804"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_approximation.AdditiveChi2Sampler.fit_transform" title="Link to this definition">#</a></dt>
 <dd><p>Fit to data, then transform it.</p>
 <p>Fits transformer to <code class="docutils literal notranslate"><span class="pre">X</span></code> and <code class="docutils literal notranslate"><span class="pre">y</span></code> with optional parameters <code class="docutils literal notranslate"><span class="pre">fit_params</span></code>
 and returns a transformed version of <code class="docutils literal notranslate"><span class="pre">X</span></code>.</p>
@@ -1430,7 +1430,7 @@ <h1>AdditiveChi2Sampler<a class="headerlink" href="#additivechi2sampler" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.kernel_approximation.AdditiveChi2Sampler.get_feature_names_out">
-<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/kernel_approximation.py#L743"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_approximation.AdditiveChi2Sampler.get_feature_names_out" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/kernel_approximation.py#L743"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_approximation.AdditiveChi2Sampler.get_feature_names_out" title="Link to this definition">#</a></dt>
 <dd><p>Get output feature names for transformation.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1450,7 +1450,7 @@ <h1>AdditiveChi2Sampler<a class="headerlink" href="#additivechi2sampler" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.kernel_approximation.AdditiveChi2Sampler.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_approximation.AdditiveChi2Sampler.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_approximation.AdditiveChi2Sampler.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1467,7 +1467,7 @@ <h1>AdditiveChi2Sampler<a class="headerlink" href="#additivechi2sampler" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.kernel_approximation.AdditiveChi2Sampler.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_approximation.AdditiveChi2Sampler.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_approximation.AdditiveChi2Sampler.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1488,7 +1488,7 @@ <h1>AdditiveChi2Sampler<a class="headerlink" href="#additivechi2sampler" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.kernel_approximation.AdditiveChi2Sampler.set_output">
-<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_approximation.AdditiveChi2Sampler.set_output" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_approximation.AdditiveChi2Sampler.set_output" title="Link to this definition">#</a></dt>
 <dd><p>Set output container.</p>
 <p>See <a class="reference internal" href="http://github.com/auto_examples/miscellaneous/plot_set_output.html#sphx-glr-auto-examples-miscellaneous-plot-set-output-py"><span class="std std-ref">Introducing the set_output API</span></a>
 for an example on how to use the API.</p>
@@ -1519,7 +1519,7 @@ <h1>AdditiveChi2Sampler<a class="headerlink" href="#additivechi2sampler" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.kernel_approximation.AdditiveChi2Sampler.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_approximation.AdditiveChi2Sampler.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_approximation.AdditiveChi2Sampler.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1543,7 +1543,7 @@ <h1>AdditiveChi2Sampler<a class="headerlink" href="#additivechi2sampler" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.kernel_approximation.AdditiveChi2Sampler.transform">
-<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/kernel_approximation.py#L697"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_approximation.AdditiveChi2Sampler.transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/kernel_approximation.py#L697"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_approximation.AdditiveChi2Sampler.transform" title="Link to this definition">#</a></dt>
 <dd><p>Apply approximate feature map to X.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.kernel_approximation.Nystroem.html b/dev/modules/generated/sklearn.kernel_approximation.Nystroem.html
index 6e2700034fb7e..a07225541d890 100644
--- a/dev/modules/generated/sklearn.kernel_approximation.Nystroem.html
+++ b/dev/modules/generated/sklearn.kernel_approximation.Nystroem.html
@@ -11,10 +11,10 @@
 <meta property="og:type" content="website" />
 <meta property="og:url" content="https://scikit-learn/stable/modules/generated/sklearn.kernel_approximation.Nystroem.html" />
 <meta property="og:site_name" content="scikit-learn" />
-<meta property="og:description" content="Gallery examples: Time-related feature engineering Plot classification probability Imputing missing values with variants of IterativeImputer One-Class SVM versus One-Class SVM using Stochastic Grad..." />
+<meta property="og:description" content="Gallery examples: Time-related feature engineering Plot classification probability Visualizing the probabilistic predictions of a VotingClassifier Imputing missing values with variants of Iterative..." />
 <meta property="og:image" content="https://scikit-learn/stable/_images/sphx_glr_plot_cyclical_feature_engineering_thumb.png" />
 <meta property="og:image:alt" content="" />
-<meta name="description" content="Gallery examples: Time-related feature engineering Plot classification probability Imputing missing values with variants of IterativeImputer One-Class SVM versus One-Class SVM using Stochastic Grad..." />
+<meta name="description" content="Gallery examples: Time-related feature engineering Plot classification probability Visualizing the probabilistic predictions of a VotingClassifier Imputing missing values with variants of Iterative..." />
 
     <title>Nystroem &#8212; scikit-learn 1.7.dev0 documentation</title>
   
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>Nystroem<a class="headerlink" href="#nystroem" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.kernel_approximation.Nystroem">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.kernel_approximation.</span></span><span class="sig-name descname"><span class="pre">Nystroem</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'rbf'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gamma</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coef0</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">degree</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_components</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/kernel_approximation.py#L839"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_approximation.Nystroem" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.kernel_approximation.</span></span><span class="sig-name descname"><span class="pre">Nystroem</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'rbf'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gamma</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coef0</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">degree</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_components</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/kernel_approximation.py#L839"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_approximation.Nystroem" title="Link to this definition">#</a></dt>
 <dd><p>Approximate a kernel map using a subset of the training data.</p>
 <p>Constructs an approximate feature map for an arbitrary kernel
 using a subset of the data as basis.</p>
@@ -1347,7 +1347,7 @@ <h1>Nystroem<a class="headerlink" href="#nystroem" title="Link to this heading">
 <dt><strong>n_jobs</strong><span class="classifier">int, default=None</span></dt><dd><p>The number of jobs to use for the computation. This works by breaking
 down the kernel matrix into <code class="docutils literal notranslate"><span class="pre">n_jobs</span></code> even slices and computing them in
 parallel.</p>
-<p><code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.5.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
+<p><code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.6.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
 <code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all processors. See <a class="reference internal" href="http://github.com/glossary.html#term-n_jobs"><span class="xref std std-term">Glossary</span></a>
 for more details.</p>
 <div class="versionadded">
@@ -1422,7 +1422,7 @@ <h1>Nystroem<a class="headerlink" href="#nystroem" title="Link to this heading">
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.kernel_approximation.Nystroem.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/kernel_approximation.py#L990"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_approximation.Nystroem.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/kernel_approximation.py#L990"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_approximation.Nystroem.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit estimator to data.</p>
 <p>Samples a subset of training points, computes kernel
 on these and computes normalization matrix.</p>
@@ -1447,7 +1447,7 @@ <h1>Nystroem<a class="headerlink" href="#nystroem" title="Link to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.kernel_approximation.Nystroem.fit_transform">
-<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L863"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_approximation.Nystroem.fit_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L804"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_approximation.Nystroem.fit_transform" title="Link to this definition">#</a></dt>
 <dd><p>Fit to data, then transform it.</p>
 <p>Fits transformer to <code class="docutils literal notranslate"><span class="pre">X</span></code> and <code class="docutils literal notranslate"><span class="pre">y</span></code> with optional parameters <code class="docutils literal notranslate"><span class="pre">fit_params</span></code>
 and returns a transformed version of <code class="docutils literal notranslate"><span class="pre">X</span></code>.</p>
@@ -1473,7 +1473,7 @@ <h1>Nystroem<a class="headerlink" href="#nystroem" title="Link to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.kernel_approximation.Nystroem.get_feature_names_out">
-<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L995"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_approximation.Nystroem.get_feature_names_out" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L936"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_approximation.Nystroem.get_feature_names_out" title="Link to this definition">#</a></dt>
 <dd><p>Get output feature names for transformation.</p>
 <p>The feature names out will prefixed by the lowercased class name. For
 example, if the transformer outputs 3 features, then the feature names
@@ -1496,7 +1496,7 @@ <h1>Nystroem<a class="headerlink" href="#nystroem" title="Link to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.kernel_approximation.Nystroem.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_approximation.Nystroem.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_approximation.Nystroem.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1513,7 +1513,7 @@ <h1>Nystroem<a class="headerlink" href="#nystroem" title="Link to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.kernel_approximation.Nystroem.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_approximation.Nystroem.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_approximation.Nystroem.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1534,7 +1534,7 @@ <h1>Nystroem<a class="headerlink" href="#nystroem" title="Link to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.kernel_approximation.Nystroem.set_output">
-<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_approximation.Nystroem.set_output" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_approximation.Nystroem.set_output" title="Link to this definition">#</a></dt>
 <dd><p>Set output container.</p>
 <p>See <a class="reference internal" href="http://github.com/auto_examples/miscellaneous/plot_set_output.html#sphx-glr-auto-examples-miscellaneous-plot-set-output-py"><span class="std std-ref">Introducing the set_output API</span></a>
 for an example on how to use the API.</p>
@@ -1565,7 +1565,7 @@ <h1>Nystroem<a class="headerlink" href="#nystroem" title="Link to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.kernel_approximation.Nystroem.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_approximation.Nystroem.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_approximation.Nystroem.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1589,7 +1589,7 @@ <h1>Nystroem<a class="headerlink" href="#nystroem" title="Link to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.kernel_approximation.Nystroem.transform">
-<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/kernel_approximation.py#L1050"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_approximation.Nystroem.transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/kernel_approximation.py#L1050"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_approximation.Nystroem.transform" title="Link to this definition">#</a></dt>
 <dd><p>Apply feature map to X.</p>
 <p>Computes an approximate feature map using the kernel
 between some training points and X.</p>
@@ -1619,6 +1619,9 @@ <h2>Gallery examples<a class="headerlink" href="#gallery-examples" title="Link t
 </div><div class="sphx-glr-thumbcontainer" tooltip="This example illustrates the use of sklearn.inspection.DecisionBoundaryDisplay to plot the predicted class probabilities of various classifiers in a 2D feature space, mostly for didactic purposes."><img alt="" src="http://github.com/_images/sphx_glr_plot_classification_probability_thumb.png" />
 <p><a class="reference internal" href="http://github.com/auto_examples/classification/plot_classification_probability.html#sphx-glr-auto-examples-classification-plot-classification-probability-py"><span class="std std-ref">Plot classification probability</span></a></p>
   <div class="sphx-glr-thumbnail-title">Plot classification probability</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="Plot the predicted class probabilities in a toy dataset predicted by three different classifiers and averaged by the VotingClassifier."><img alt="" src="http://github.com/_images/sphx_glr_plot_voting_decision_regions_thumb.png" />
+<p><a class="reference internal" href="http://github.com/auto_examples/ensemble/plot_voting_decision_regions.html#sphx-glr-auto-examples-ensemble-plot-voting-decision-regions-py"><span class="std std-ref">Visualizing the probabilistic predictions of a VotingClassifier</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Visualizing the probabilistic predictions of a VotingClassifier</div>
 </div><div class="sphx-glr-thumbcontainer" tooltip="The IterativeImputer class is very flexible - it can be used with a variety of estimators to do round-robin regression, treating every variable as an output in turn."><img alt="" src="http://github.com/_images/sphx_glr_plot_iterative_imputer_variants_comparison_thumb.png" />
 <p><a class="reference internal" href="http://github.com/auto_examples/impute/plot_iterative_imputer_variants_comparison.html#sphx-glr-auto-examples-impute-plot-iterative-imputer-variants-comparison-py"><span class="std std-ref">Imputing missing values with variants of IterativeImputer</span></a></p>
   <div class="sphx-glr-thumbnail-title">Imputing missing values with variants of IterativeImputer</div>
diff --git a/dev/modules/generated/sklearn.kernel_approximation.PolynomialCountSketch.html b/dev/modules/generated/sklearn.kernel_approximation.PolynomialCountSketch.html
index b5cc4f905c666..76179c317d464 100644
--- a/dev/modules/generated/sklearn.kernel_approximation.PolynomialCountSketch.html
+++ b/dev/modules/generated/sklearn.kernel_approximation.PolynomialCountSketch.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>PolynomialCountSketch<a class="headerlink" href="#polynomialcountsketch" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.kernel_approximation.PolynomialCountSketch">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.kernel_approximation.</span></span><span class="sig-name descname"><span class="pre">PolynomialCountSketch</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gamma</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">degree</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coef0</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_components</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/kernel_approximation.py#L31"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_approximation.PolynomialCountSketch" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.kernel_approximation.</span></span><span class="sig-name descname"><span class="pre">PolynomialCountSketch</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gamma</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">degree</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coef0</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_components</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/kernel_approximation.py#L31"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_approximation.PolynomialCountSketch" title="Link to this definition">#</a></dt>
 <dd><p>Polynomial kernel approximation via Tensor Sketch.</p>
 <p>Implements Tensor Sketch, which approximates the feature map
 of the polynomial kernel:</p>
@@ -1399,7 +1399,7 @@ <h1>PolynomialCountSketch<a class="headerlink" href="#polynomialcountsketch" tit
 <a class="reference internal" href="http://github.com/auto_examples/kernel_approximation/plot_scalable_poly_kernels.html#sphx-glr-auto-examples-kernel-approximation-plot-scalable-poly-kernels-py"><span class="std std-ref">Scalable learning with polynomial kernel approximation</span></a></p>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.kernel_approximation.PolynomialCountSketch.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/kernel_approximation.py#L138"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_approximation.PolynomialCountSketch.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/kernel_approximation.py#L138"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_approximation.PolynomialCountSketch.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit the model with X.</p>
 <p>Initializes the internal variables. The method needs no information
 about the distribution of data, so we only care about n_features in X.</p>
@@ -1424,7 +1424,7 @@ <h1>PolynomialCountSketch<a class="headerlink" href="#polynomialcountsketch" tit
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.kernel_approximation.PolynomialCountSketch.fit_transform">
-<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L863"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_approximation.PolynomialCountSketch.fit_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L804"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_approximation.PolynomialCountSketch.fit_transform" title="Link to this definition">#</a></dt>
 <dd><p>Fit to data, then transform it.</p>
 <p>Fits transformer to <code class="docutils literal notranslate"><span class="pre">X</span></code> and <code class="docutils literal notranslate"><span class="pre">y</span></code> with optional parameters <code class="docutils literal notranslate"><span class="pre">fit_params</span></code>
 and returns a transformed version of <code class="docutils literal notranslate"><span class="pre">X</span></code>.</p>
@@ -1450,7 +1450,7 @@ <h1>PolynomialCountSketch<a class="headerlink" href="#polynomialcountsketch" tit
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.kernel_approximation.PolynomialCountSketch.get_feature_names_out">
-<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L995"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_approximation.PolynomialCountSketch.get_feature_names_out" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L936"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_approximation.PolynomialCountSketch.get_feature_names_out" title="Link to this definition">#</a></dt>
 <dd><p>Get output feature names for transformation.</p>
 <p>The feature names out will prefixed by the lowercased class name. For
 example, if the transformer outputs 3 features, then the feature names
@@ -1473,7 +1473,7 @@ <h1>PolynomialCountSketch<a class="headerlink" href="#polynomialcountsketch" tit
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.kernel_approximation.PolynomialCountSketch.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_approximation.PolynomialCountSketch.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_approximation.PolynomialCountSketch.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1490,7 +1490,7 @@ <h1>PolynomialCountSketch<a class="headerlink" href="#polynomialcountsketch" tit
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.kernel_approximation.PolynomialCountSketch.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_approximation.PolynomialCountSketch.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_approximation.PolynomialCountSketch.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1511,7 +1511,7 @@ <h1>PolynomialCountSketch<a class="headerlink" href="#polynomialcountsketch" tit
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.kernel_approximation.PolynomialCountSketch.set_output">
-<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_approximation.PolynomialCountSketch.set_output" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_approximation.PolynomialCountSketch.set_output" title="Link to this definition">#</a></dt>
 <dd><p>Set output container.</p>
 <p>See <a class="reference internal" href="http://github.com/auto_examples/miscellaneous/plot_set_output.html#sphx-glr-auto-examples-miscellaneous-plot-set-output-py"><span class="std std-ref">Introducing the set_output API</span></a>
 for an example on how to use the API.</p>
@@ -1542,7 +1542,7 @@ <h1>PolynomialCountSketch<a class="headerlink" href="#polynomialcountsketch" tit
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.kernel_approximation.PolynomialCountSketch.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_approximation.PolynomialCountSketch.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_approximation.PolynomialCountSketch.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1566,7 +1566,7 @@ <h1>PolynomialCountSketch<a class="headerlink" href="#polynomialcountsketch" tit
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.kernel_approximation.PolynomialCountSketch.transform">
-<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/kernel_approximation.py#L175"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_approximation.PolynomialCountSketch.transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/kernel_approximation.py#L175"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_approximation.PolynomialCountSketch.transform" title="Link to this definition">#</a></dt>
 <dd><p>Generate the feature map approximation for X.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.kernel_approximation.RBFSampler.html b/dev/modules/generated/sklearn.kernel_approximation.RBFSampler.html
index 6fef8c46bae5b..3c06cfba37622 100644
--- a/dev/modules/generated/sklearn.kernel_approximation.RBFSampler.html
+++ b/dev/modules/generated/sklearn.kernel_approximation.RBFSampler.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>RBFSampler<a class="headerlink" href="#rbfsampler" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.kernel_approximation.RBFSampler">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.kernel_approximation.</span></span><span class="sig-name descname"><span class="pre">RBFSampler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gamma</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_components</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/kernel_approximation.py#L244"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_approximation.RBFSampler" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.kernel_approximation.</span></span><span class="sig-name descname"><span class="pre">RBFSampler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gamma</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_components</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/kernel_approximation.py#L244"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_approximation.RBFSampler" title="Link to this definition">#</a></dt>
 <dd><p>Approximate a RBF kernel feature map using random Fourier features.</p>
 <p>It implements a variant of Random Kitchen Sinks.[1]</p>
 <p>Read more in the <a class="reference internal" href="http://github.com/kernel_approximation.html#rbf-kernel-approx"><span class="std std-ref">User Guide</span></a>.</p>
@@ -1391,7 +1391,7 @@ <h1>RBFSampler<a class="headerlink" href="#rbfsampler" title="Link to this headi
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.kernel_approximation.RBFSampler.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/kernel_approximation.py#L341"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_approximation.RBFSampler.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/kernel_approximation.py#L341"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_approximation.RBFSampler.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit the model with X.</p>
 <p>Samples random projection according to n_features.</p>
 <dl class="field-list simple">
@@ -1415,7 +1415,7 @@ <h1>RBFSampler<a class="headerlink" href="#rbfsampler" title="Link to this headi
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.kernel_approximation.RBFSampler.fit_transform">
-<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L863"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_approximation.RBFSampler.fit_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L804"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_approximation.RBFSampler.fit_transform" title="Link to this definition">#</a></dt>
 <dd><p>Fit to data, then transform it.</p>
 <p>Fits transformer to <code class="docutils literal notranslate"><span class="pre">X</span></code> and <code class="docutils literal notranslate"><span class="pre">y</span></code> with optional parameters <code class="docutils literal notranslate"><span class="pre">fit_params</span></code>
 and returns a transformed version of <code class="docutils literal notranslate"><span class="pre">X</span></code>.</p>
@@ -1441,7 +1441,7 @@ <h1>RBFSampler<a class="headerlink" href="#rbfsampler" title="Link to this headi
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.kernel_approximation.RBFSampler.get_feature_names_out">
-<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L995"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_approximation.RBFSampler.get_feature_names_out" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L936"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_approximation.RBFSampler.get_feature_names_out" title="Link to this definition">#</a></dt>
 <dd><p>Get output feature names for transformation.</p>
 <p>The feature names out will prefixed by the lowercased class name. For
 example, if the transformer outputs 3 features, then the feature names
@@ -1464,7 +1464,7 @@ <h1>RBFSampler<a class="headerlink" href="#rbfsampler" title="Link to this headi
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.kernel_approximation.RBFSampler.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_approximation.RBFSampler.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_approximation.RBFSampler.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1481,7 +1481,7 @@ <h1>RBFSampler<a class="headerlink" href="#rbfsampler" title="Link to this headi
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.kernel_approximation.RBFSampler.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_approximation.RBFSampler.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_approximation.RBFSampler.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1502,7 +1502,7 @@ <h1>RBFSampler<a class="headerlink" href="#rbfsampler" title="Link to this headi
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.kernel_approximation.RBFSampler.set_output">
-<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_approximation.RBFSampler.set_output" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_approximation.RBFSampler.set_output" title="Link to this definition">#</a></dt>
 <dd><p>Set output container.</p>
 <p>See <a class="reference internal" href="http://github.com/auto_examples/miscellaneous/plot_set_output.html#sphx-glr-auto-examples-miscellaneous-plot-set-output-py"><span class="std std-ref">Introducing the set_output API</span></a>
 for an example on how to use the API.</p>
@@ -1533,7 +1533,7 @@ <h1>RBFSampler<a class="headerlink" href="#rbfsampler" title="Link to this headi
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.kernel_approximation.RBFSampler.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_approximation.RBFSampler.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_approximation.RBFSampler.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1557,7 +1557,7 @@ <h1>RBFSampler<a class="headerlink" href="#rbfsampler" title="Link to this headi
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.kernel_approximation.RBFSampler.transform">
-<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/kernel_approximation.py#L387"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_approximation.RBFSampler.transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/kernel_approximation.py#L387"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_approximation.RBFSampler.transform" title="Link to this definition">#</a></dt>
 <dd><p>Apply the approximate feature map to X.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.kernel_approximation.SkewedChi2Sampler.html b/dev/modules/generated/sklearn.kernel_approximation.SkewedChi2Sampler.html
index bdacd1c87411c..43a7552899af9 100644
--- a/dev/modules/generated/sklearn.kernel_approximation.SkewedChi2Sampler.html
+++ b/dev/modules/generated/sklearn.kernel_approximation.SkewedChi2Sampler.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>SkewedChi2Sampler<a class="headerlink" href="#skewedchi2sampler" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.kernel_approximation.SkewedChi2Sampler">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.kernel_approximation.</span></span><span class="sig-name descname"><span class="pre">SkewedChi2Sampler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">skewedness</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_components</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/kernel_approximation.py#L417"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_approximation.SkewedChi2Sampler" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.kernel_approximation.</span></span><span class="sig-name descname"><span class="pre">SkewedChi2Sampler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">skewedness</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_components</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/kernel_approximation.py#L417"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_approximation.SkewedChi2Sampler" title="Link to this definition">#</a></dt>
 <dd><p>Approximate feature map for “skewed chi-squared” kernel.</p>
 <p>Read more in the <a class="reference internal" href="http://github.com/kernel_approximation.html#skewed-chi-kernel-approx"><span class="std std-ref">User Guide</span></a>.</p>
 <dl class="field-list">
@@ -1383,7 +1383,7 @@ <h1>SkewedChi2Sampler<a class="headerlink" href="#skewedchi2sampler" title="Link
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.kernel_approximation.SkewedChi2Sampler.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/kernel_approximation.py#L503"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_approximation.SkewedChi2Sampler.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/kernel_approximation.py#L503"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_approximation.SkewedChi2Sampler.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit the model with X.</p>
 <p>Samples random projection according to n_features.</p>
 <dl class="field-list simple">
@@ -1407,7 +1407,7 @@ <h1>SkewedChi2Sampler<a class="headerlink" href="#skewedchi2sampler" title="Link
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.kernel_approximation.SkewedChi2Sampler.fit_transform">
-<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L863"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_approximation.SkewedChi2Sampler.fit_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L804"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_approximation.SkewedChi2Sampler.fit_transform" title="Link to this definition">#</a></dt>
 <dd><p>Fit to data, then transform it.</p>
 <p>Fits transformer to <code class="docutils literal notranslate"><span class="pre">X</span></code> and <code class="docutils literal notranslate"><span class="pre">y</span></code> with optional parameters <code class="docutils literal notranslate"><span class="pre">fit_params</span></code>
 and returns a transformed version of <code class="docutils literal notranslate"><span class="pre">X</span></code>.</p>
@@ -1433,7 +1433,7 @@ <h1>SkewedChi2Sampler<a class="headerlink" href="#skewedchi2sampler" title="Link
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.kernel_approximation.SkewedChi2Sampler.get_feature_names_out">
-<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L995"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_approximation.SkewedChi2Sampler.get_feature_names_out" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L936"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_approximation.SkewedChi2Sampler.get_feature_names_out" title="Link to this definition">#</a></dt>
 <dd><p>Get output feature names for transformation.</p>
 <p>The feature names out will prefixed by the lowercased class name. For
 example, if the transformer outputs 3 features, then the feature names
@@ -1456,7 +1456,7 @@ <h1>SkewedChi2Sampler<a class="headerlink" href="#skewedchi2sampler" title="Link
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.kernel_approximation.SkewedChi2Sampler.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_approximation.SkewedChi2Sampler.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_approximation.SkewedChi2Sampler.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1473,7 +1473,7 @@ <h1>SkewedChi2Sampler<a class="headerlink" href="#skewedchi2sampler" title="Link
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.kernel_approximation.SkewedChi2Sampler.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_approximation.SkewedChi2Sampler.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_approximation.SkewedChi2Sampler.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1494,7 +1494,7 @@ <h1>SkewedChi2Sampler<a class="headerlink" href="#skewedchi2sampler" title="Link
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.kernel_approximation.SkewedChi2Sampler.set_output">
-<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_approximation.SkewedChi2Sampler.set_output" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_approximation.SkewedChi2Sampler.set_output" title="Link to this definition">#</a></dt>
 <dd><p>Set output container.</p>
 <p>See <a class="reference internal" href="http://github.com/auto_examples/miscellaneous/plot_set_output.html#sphx-glr-auto-examples-miscellaneous-plot-set-output-py"><span class="std std-ref">Introducing the set_output API</span></a>
 for an example on how to use the API.</p>
@@ -1525,7 +1525,7 @@ <h1>SkewedChi2Sampler<a class="headerlink" href="#skewedchi2sampler" title="Link
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.kernel_approximation.SkewedChi2Sampler.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_approximation.SkewedChi2Sampler.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_approximation.SkewedChi2Sampler.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1549,7 +1549,7 @@ <h1>SkewedChi2Sampler<a class="headerlink" href="#skewedchi2sampler" title="Link
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.kernel_approximation.SkewedChi2Sampler.transform">
-<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/kernel_approximation.py#L541"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_approximation.SkewedChi2Sampler.transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/kernel_approximation.py#L541"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_approximation.SkewedChi2Sampler.transform" title="Link to this definition">#</a></dt>
 <dd><p>Apply the approximate feature map to X.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.kernel_ridge.KernelRidge.html b/dev/modules/generated/sklearn.kernel_ridge.KernelRidge.html
index fac8d910bda2b..b8e5c77995e49 100644
--- a/dev/modules/generated/sklearn.kernel_ridge.KernelRidge.html
+++ b/dev/modules/generated/sklearn.kernel_ridge.KernelRidge.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1308,7 +1308,7 @@
 <h1>KernelRidge<a class="headerlink" href="#kernelridge" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.kernel_ridge.KernelRidge">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.kernel_ridge.</span></span><span class="sig-name descname"><span class="pre">KernelRidge</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'linear'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gamma</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">degree</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coef0</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/kernel_ridge.py#L17"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_ridge.KernelRidge" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.kernel_ridge.</span></span><span class="sig-name descname"><span class="pre">KernelRidge</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'linear'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gamma</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">degree</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coef0</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/kernel_ridge.py#L17"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_ridge.KernelRidge" title="Link to this definition">#</a></dt>
 <dd><p>Kernel ridge regression.</p>
 <p>Kernel ridge regression (KRR) combines ridge regression (linear least
 squares with l2-norm regularization) with the kernel trick. It thus
@@ -1421,7 +1421,7 @@ <h1>KernelRidge<a class="headerlink" href="#kernelridge" title="Link to this hea
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.kernel_ridge.KernelRidge.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/kernel_ridge.py#L176"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_ridge.KernelRidge.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/kernel_ridge.py#L176"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_ridge.KernelRidge.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit Kernel Ridge regression model.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1446,7 +1446,7 @@ <h1>KernelRidge<a class="headerlink" href="#kernelridge" title="Link to this hea
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.kernel_ridge.KernelRidge.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_ridge.KernelRidge.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_ridge.KernelRidge.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1463,7 +1463,7 @@ <h1>KernelRidge<a class="headerlink" href="#kernelridge" title="Link to this hea
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.kernel_ridge.KernelRidge.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_ridge.KernelRidge.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_ridge.KernelRidge.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1484,7 +1484,7 @@ <h1>KernelRidge<a class="headerlink" href="#kernelridge" title="Link to this hea
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.kernel_ridge.KernelRidge.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/kernel_ridge.py#L221"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_ridge.KernelRidge.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/kernel_ridge.py#L221"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_ridge.KernelRidge.predict" title="Link to this definition">#</a></dt>
 <dd><p>Predict using the kernel ridge model.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1507,7 +1507,7 @@ <h1>KernelRidge<a class="headerlink" href="#kernelridge" title="Link to this hea
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.kernel_ridge.KernelRidge.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L619"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_ridge.KernelRidge.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L560"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_ridge.KernelRidge.score" title="Link to this definition">#</a></dt>
 <dd><p>Return <a class="reference internal" href="http://github.com/model_evaluation.html#r2-score"><span class="std std-ref">coefficient of determination</span></a> on test data.</p>
 <p>The coefficient of determination, <span class="math notranslate nohighlight">\(R^2\)</span>, is defined as
 <span class="math notranslate nohighlight">\((1 - \frac{u}{v})\)</span>, where <span class="math notranslate nohighlight">\(u\)</span> is the residual
@@ -1549,7 +1549,7 @@ <h1>KernelRidge<a class="headerlink" href="#kernelridge" title="Link to this hea
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.kernel_ridge.KernelRidge.set_fit_request">
-<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.kernel_ridge.KernelRidge" title="sklearn.kernel_ridge.KernelRidge"><span class="pre">KernelRidge</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_ridge.KernelRidge.set_fit_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.kernel_ridge.KernelRidge" title="sklearn.kernel_ridge.KernelRidge"><span class="pre">KernelRidge</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_ridge.KernelRidge.set_fit_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1592,7 +1592,7 @@ <h1>KernelRidge<a class="headerlink" href="#kernelridge" title="Link to this hea
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.kernel_ridge.KernelRidge.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_ridge.KernelRidge.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_ridge.KernelRidge.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1616,7 +1616,7 @@ <h1>KernelRidge<a class="headerlink" href="#kernelridge" title="Link to this hea
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.kernel_ridge.KernelRidge.set_score_request">
-<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.kernel_ridge.KernelRidge" title="sklearn.kernel_ridge.KernelRidge"><span class="pre">KernelRidge</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_ridge.KernelRidge.set_score_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.kernel_ridge.KernelRidge" title="sklearn.kernel_ridge.KernelRidge"><span class="pre">KernelRidge</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_ridge.KernelRidge.set_score_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
diff --git a/dev/modules/generated/sklearn.linear_model.ARDRegression.html b/dev/modules/generated/sklearn.linear_model.ARDRegression.html
index f983e0a802b70..ab95af241252f 100644
--- a/dev/modules/generated/sklearn.linear_model.ARDRegression.html
+++ b/dev/modules/generated/sklearn.linear_model.ARDRegression.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1308,7 +1308,7 @@
 <h1>ARDRegression<a class="headerlink" href="#ardregression" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.linear_model.ARDRegression">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.linear_model.</span></span><span class="sig-name descname"><span class="pre">ARDRegression</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">300</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha_1</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-06</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha_2</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-06</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lambda_1</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-06</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lambda_2</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-06</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">compute_score</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">threshold_lambda</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10000.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_intercept</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy_X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_bayes.py#L458"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.ARDRegression" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.linear_model.</span></span><span class="sig-name descname"><span class="pre">ARDRegression</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">300</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha_1</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-06</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha_2</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-06</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lambda_1</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-06</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lambda_2</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-06</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">compute_score</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">threshold_lambda</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10000.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_intercept</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy_X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_bayes.py#L468"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.ARDRegression" title="Link to this definition">#</a></dt>
 <dd><p>Bayesian ARD regression.</p>
 <p>Fit the weights of a regression model, using an ARD prior. The weights of
 the regression model are assumed to be in Gaussian distributions.
@@ -1422,7 +1422,7 @@ <h1>ARDRegression<a class="headerlink" href="#ardregression" title="Link to this
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.ARDRegression.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_bayes.py#L630"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.ARDRegression.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_bayes.py#L640"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.ARDRegression.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit the model according to the given training data and parameters.</p>
 <p>Iterative procedure to maximize the evidence</p>
 <dl class="field-list simple">
@@ -1446,7 +1446,7 @@ <h1>ARDRegression<a class="headerlink" href="#ardregression" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.ARDRegression.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.ARDRegression.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.ARDRegression.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1463,7 +1463,7 @@ <h1>ARDRegression<a class="headerlink" href="#ardregression" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.ARDRegression.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.ARDRegression.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.ARDRegression.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1484,7 +1484,7 @@ <h1>ARDRegression<a class="headerlink" href="#ardregression" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.ARDRegression.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_std</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_bayes.py#L785"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.ARDRegression.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_std</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_bayes.py#L795"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.ARDRegression.predict" title="Link to this definition">#</a></dt>
 <dd><p>Predict using the linear model.</p>
 <p>In addition to the mean of the predictive distribution, also its
 standard deviation can be returned.</p>
@@ -1510,7 +1510,7 @@ <h1>ARDRegression<a class="headerlink" href="#ardregression" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.ARDRegression.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L619"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.ARDRegression.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L560"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.ARDRegression.score" title="Link to this definition">#</a></dt>
 <dd><p>Return <a class="reference internal" href="http://github.com/model_evaluation.html#r2-score"><span class="std std-ref">coefficient of determination</span></a> on test data.</p>
 <p>The coefficient of determination, <span class="math notranslate nohighlight">\(R^2\)</span>, is defined as
 <span class="math notranslate nohighlight">\((1 - \frac{u}{v})\)</span>, where <span class="math notranslate nohighlight">\(u\)</span> is the residual
@@ -1552,7 +1552,7 @@ <h1>ARDRegression<a class="headerlink" href="#ardregression" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.ARDRegression.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.ARDRegression.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.ARDRegression.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1576,7 +1576,7 @@ <h1>ARDRegression<a class="headerlink" href="#ardregression" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.ARDRegression.set_predict_request">
-<span class="sig-name descname"><span class="pre">set_predict_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_std</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.ARDRegression" title="sklearn.linear_model._bayes.ARDRegression"><span class="pre">ARDRegression</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.ARDRegression.set_predict_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_predict_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_std</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.ARDRegression" title="sklearn.linear_model._bayes.ARDRegression"><span class="pre">ARDRegression</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.ARDRegression.set_predict_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">predict</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1619,7 +1619,7 @@ <h1>ARDRegression<a class="headerlink" href="#ardregression" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.ARDRegression.set_score_request">
-<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.ARDRegression" title="sklearn.linear_model._bayes.ARDRegression"><span class="pre">ARDRegression</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.ARDRegression.set_score_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.ARDRegression" title="sklearn.linear_model._bayes.ARDRegression"><span class="pre">ARDRegression</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.ARDRegression.set_score_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
diff --git a/dev/modules/generated/sklearn.linear_model.BayesianRidge.html b/dev/modules/generated/sklearn.linear_model.BayesianRidge.html
index d223970c66b5e..2607c60b459b5 100644
--- a/dev/modules/generated/sklearn.linear_model.BayesianRidge.html
+++ b/dev/modules/generated/sklearn.linear_model.BayesianRidge.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1308,7 +1308,7 @@
 <h1>BayesianRidge<a class="headerlink" href="#bayesianridge" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.linear_model.BayesianRidge">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.linear_model.</span></span><span class="sig-name descname"><span class="pre">BayesianRidge</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">300</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha_1</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-06</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha_2</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-06</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lambda_1</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-06</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lambda_2</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-06</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lambda_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">compute_score</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_intercept</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy_X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_bayes.py#L26"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.BayesianRidge" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.linear_model.</span></span><span class="sig-name descname"><span class="pre">BayesianRidge</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">300</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha_1</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-06</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha_2</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-06</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lambda_1</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-06</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lambda_2</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-06</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lambda_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">compute_score</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_intercept</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy_X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_bayes.py#L26"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.BayesianRidge" title="Link to this definition">#</a></dt>
 <dd><p>Bayesian ridge regression.</p>
 <p>Fit a Bayesian ridge model. See the Notes section for details on this
 implementation and the optimization of the regularization parameters
@@ -1438,7 +1438,7 @@ <h1>BayesianRidge<a class="headerlink" href="#bayesianridge" title="Link to this
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.BayesianRidge.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_bayes.py#L216"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.BayesianRidge.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_bayes.py#L216"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.BayesianRidge.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit the model.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1465,7 +1465,7 @@ <h1>BayesianRidge<a class="headerlink" href="#bayesianridge" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.BayesianRidge.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.BayesianRidge.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.BayesianRidge.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1482,7 +1482,7 @@ <h1>BayesianRidge<a class="headerlink" href="#bayesianridge" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.BayesianRidge.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.BayesianRidge.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.BayesianRidge.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1503,7 +1503,7 @@ <h1>BayesianRidge<a class="headerlink" href="#bayesianridge" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.BayesianRidge.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_std</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_bayes.py#L366"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.BayesianRidge.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_std</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_bayes.py#L376"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.BayesianRidge.predict" title="Link to this definition">#</a></dt>
 <dd><p>Predict using the linear model.</p>
 <p>In addition to the mean of the predictive distribution, also its
 standard deviation can be returned.</p>
@@ -1529,7 +1529,7 @@ <h1>BayesianRidge<a class="headerlink" href="#bayesianridge" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.BayesianRidge.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L619"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.BayesianRidge.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L560"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.BayesianRidge.score" title="Link to this definition">#</a></dt>
 <dd><p>Return <a class="reference internal" href="http://github.com/model_evaluation.html#r2-score"><span class="std std-ref">coefficient of determination</span></a> on test data.</p>
 <p>The coefficient of determination, <span class="math notranslate nohighlight">\(R^2\)</span>, is defined as
 <span class="math notranslate nohighlight">\((1 - \frac{u}{v})\)</span>, where <span class="math notranslate nohighlight">\(u\)</span> is the residual
@@ -1571,7 +1571,7 @@ <h1>BayesianRidge<a class="headerlink" href="#bayesianridge" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.BayesianRidge.set_fit_request">
-<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.BayesianRidge" title="sklearn.linear_model._bayes.BayesianRidge"><span class="pre">BayesianRidge</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.BayesianRidge.set_fit_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.BayesianRidge" title="sklearn.linear_model._bayes.BayesianRidge"><span class="pre">BayesianRidge</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.BayesianRidge.set_fit_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1614,7 +1614,7 @@ <h1>BayesianRidge<a class="headerlink" href="#bayesianridge" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.BayesianRidge.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.BayesianRidge.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.BayesianRidge.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1638,7 +1638,7 @@ <h1>BayesianRidge<a class="headerlink" href="#bayesianridge" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.BayesianRidge.set_predict_request">
-<span class="sig-name descname"><span class="pre">set_predict_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_std</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.BayesianRidge" title="sklearn.linear_model._bayes.BayesianRidge"><span class="pre">BayesianRidge</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.BayesianRidge.set_predict_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_predict_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_std</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.BayesianRidge" title="sklearn.linear_model._bayes.BayesianRidge"><span class="pre">BayesianRidge</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.BayesianRidge.set_predict_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">predict</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1681,7 +1681,7 @@ <h1>BayesianRidge<a class="headerlink" href="#bayesianridge" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.BayesianRidge.set_score_request">
-<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.BayesianRidge" title="sklearn.linear_model._bayes.BayesianRidge"><span class="pre">BayesianRidge</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.BayesianRidge.set_score_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.BayesianRidge" title="sklearn.linear_model._bayes.BayesianRidge"><span class="pre">BayesianRidge</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.BayesianRidge.set_score_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
diff --git a/dev/modules/generated/sklearn.linear_model.ElasticNet.html b/dev/modules/generated/sklearn.linear_model.ElasticNet.html
index fa8bc7d8ebb8c..b85a740b333fe 100644
--- a/dev/modules/generated/sklearn.linear_model.ElasticNet.html
+++ b/dev/modules/generated/sklearn.linear_model.ElasticNet.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1308,7 +1308,7 @@
 <h1>ElasticNet<a class="headerlink" href="#elasticnet" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.linear_model.ElasticNet">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.linear_model.</span></span><span class="sig-name descname"><span class="pre">ElasticNet</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">l1_ratio</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_intercept</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">precompute</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy_X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">warm_start</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">positive</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">selection</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'cyclic'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_coordinate_descent.py#L727"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.ElasticNet" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.linear_model.</span></span><span class="sig-name descname"><span class="pre">ElasticNet</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">l1_ratio</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_intercept</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">precompute</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy_X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">warm_start</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">positive</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">selection</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'cyclic'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_coordinate_descent.py#L727"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.ElasticNet" title="Link to this definition">#</a></dt>
 <dd><p>Linear regression with combined L1 and L2 priors as regularizer.</p>
 <p>Minimizes the objective function:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="mi">1</span> <span class="o">/</span> <span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="n">n_samples</span><span class="p">)</span> <span class="o">*</span> <span class="o">||</span><span class="n">y</span> <span class="o">-</span> <span class="n">Xw</span><span class="o">||^</span><span class="mi">2_2</span>
@@ -1440,14 +1440,14 @@ <h1>ElasticNet<a class="headerlink" href="#elasticnet" title="Link to this headi
 <span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">regr</span><span class="o">.</span><span class="n">coef_</span><span class="p">)</span>
 <span class="go">[18.83816048 64.55968825]</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">regr</span><span class="o">.</span><span class="n">intercept_</span><span class="p">)</span>
-<span class="go">1.451...</span>
+<span class="go">1.451</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">regr</span><span class="o">.</span><span class="n">predict</span><span class="p">([[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]]))</span>
-<span class="go">[1.451...]</span>
+<span class="go">[1.451]</span>
 </pre></div>
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.ElasticNet.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_input</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_coordinate_descent.py#L927"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.ElasticNet.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_input</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_coordinate_descent.py#L927"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.ElasticNet.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit model with coordinate descent.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1486,7 +1486,7 @@ <h1>ElasticNet<a class="headerlink" href="#elasticnet" title="Link to this headi
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.ElasticNet.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.ElasticNet.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.ElasticNet.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1503,7 +1503,7 @@ <h1>ElasticNet<a class="headerlink" href="#elasticnet" title="Link to this headi
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.ElasticNet.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.ElasticNet.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.ElasticNet.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1524,7 +1524,7 @@ <h1>ElasticNet<a class="headerlink" href="#elasticnet" title="Link to this headi
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.ElasticNet.path">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">path</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">l1_ratio</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_alphas</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alphas</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">precompute</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Xy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy_X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coef_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_n_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">positive</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_input</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_coordinate_descent.py#L377"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.ElasticNet.path" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">path</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">l1_ratio</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_alphas</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alphas</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">precompute</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Xy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy_X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coef_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_n_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">positive</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_input</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_coordinate_descent.py#L377"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.ElasticNet.path" title="Link to this definition">#</a></dt>
 <dd><p>Compute elastic net path with coordinate descent.</p>
 <p>The elastic net optimization function varies for mono and multi-outputs.</p>
 <p>For mono-output tasks it is:</p>
@@ -1629,23 +1629,23 @@ <h1>ElasticNet<a class="headerlink" href="#elasticnet" title="Link to this headi
 <span class="gp">... </span>   <span class="n">n_samples</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">n_features</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">n_informative</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">coef</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span>
 <span class="gp">... </span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">true_coef</span>
-<span class="go">array([ 0.        ,  0.        ,  0.        , 97.9..., 45.7...])</span>
+<span class="go">array([ 0.        ,  0.        ,  0.        , 97.9, 45.7])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">alphas</span><span class="p">,</span> <span class="n">estimated_coef</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">enet_path</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">n_alphas</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">alphas</span><span class="o">.</span><span class="n">shape</span>
 <span class="go">(3,)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">estimated_coef</span>
-<span class="go"> array([[ 0.        ,  0.78...,  0.56...],</span>
-<span class="go">        [ 0.        ,  1.12...,  0.61...],</span>
-<span class="go">        [-0.        , -2.12..., -1.12...],</span>
-<span class="go">        [ 0.        , 23.04..., 88.93...],</span>
-<span class="go">        [ 0.        , 10.63..., 41.56...]])</span>
+<span class="go"> array([[ 0.,  0.787,  0.568],</span>
+<span class="go">        [ 0.,  1.120,  0.620],</span>
+<span class="go">        [-0., -2.129, -1.128],</span>
+<span class="go">        [ 0., 23.046, 88.939],</span>
+<span class="go">        [ 0., 10.637, 41.566]])</span>
 </pre></div>
 </div>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.ElasticNet.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_base.py#L284"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.ElasticNet.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_base.py#L284"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.ElasticNet.predict" title="Link to this definition">#</a></dt>
 <dd><p>Predict using the linear model.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1665,7 +1665,7 @@ <h1>ElasticNet<a class="headerlink" href="#elasticnet" title="Link to this headi
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.ElasticNet.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L619"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.ElasticNet.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L560"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.ElasticNet.score" title="Link to this definition">#</a></dt>
 <dd><p>Return <a class="reference internal" href="http://github.com/model_evaluation.html#r2-score"><span class="std std-ref">coefficient of determination</span></a> on test data.</p>
 <p>The coefficient of determination, <span class="math notranslate nohighlight">\(R^2\)</span>, is defined as
 <span class="math notranslate nohighlight">\((1 - \frac{u}{v})\)</span>, where <span class="math notranslate nohighlight">\(u\)</span> is the residual
@@ -1707,7 +1707,7 @@ <h1>ElasticNet<a class="headerlink" href="#elasticnet" title="Link to this headi
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.ElasticNet.set_fit_request">
-<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.ElasticNet" title="sklearn.linear_model._coordinate_descent.ElasticNet"><span class="pre">ElasticNet</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.ElasticNet.set_fit_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.ElasticNet" title="sklearn.linear_model._coordinate_descent.ElasticNet"><span class="pre">ElasticNet</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.ElasticNet.set_fit_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1750,7 +1750,7 @@ <h1>ElasticNet<a class="headerlink" href="#elasticnet" title="Link to this headi
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.ElasticNet.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.ElasticNet.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.ElasticNet.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1774,7 +1774,7 @@ <h1>ElasticNet<a class="headerlink" href="#elasticnet" title="Link to this headi
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.ElasticNet.set_score_request">
-<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.ElasticNet" title="sklearn.linear_model._coordinate_descent.ElasticNet"><span class="pre">ElasticNet</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.ElasticNet.set_score_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.ElasticNet" title="sklearn.linear_model._coordinate_descent.ElasticNet"><span class="pre">ElasticNet</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.ElasticNet.set_score_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
diff --git a/dev/modules/generated/sklearn.linear_model.ElasticNetCV.html b/dev/modules/generated/sklearn.linear_model.ElasticNetCV.html
index 735a7bcb1c62e..8ad98d909936b 100644
--- a/dev/modules/generated/sklearn.linear_model.ElasticNetCV.html
+++ b/dev/modules/generated/sklearn.linear_model.ElasticNetCV.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1308,7 +1308,7 @@
 <h1>ElasticNetCV<a class="headerlink" href="#elasticnetcv" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.linear_model.ElasticNetCV">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.linear_model.</span></span><span class="sig-name descname"><span class="pre">ElasticNetCV</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">l1_ratio</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_alphas</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alphas</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_intercept</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">precompute</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cv</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy_X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">positive</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">selection</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'cyclic'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_coordinate_descent.py#L2130"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.ElasticNetCV" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.linear_model.</span></span><span class="sig-name descname"><span class="pre">ElasticNetCV</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">l1_ratio</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_alphas</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'deprecated'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alphas</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'warn'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_intercept</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">precompute</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cv</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy_X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">positive</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">selection</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'cyclic'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_coordinate_descent.py#L2185"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.ElasticNetCV" title="Link to this definition">#</a></dt>
 <dd><p>Elastic Net model with iterative fitting along a regularization path.</p>
 <p>See glossary entry for <a class="reference internal" href="http://github.com/glossary.html#term-cross-validation-estimator"><span class="xref std std-term">cross-validation estimator</span></a>.</p>
 <p>Read more in the <a class="reference internal" href="http://github.com/linear_model.html#elastic-net"><span class="std std-ref">User Guide</span></a>.</p>
@@ -1330,9 +1330,22 @@ <h1>ElasticNetCV<a class="headerlink" href="#elasticnetcv" title="Link to this h
 <code class="docutils literal notranslate"><span class="pre">alpha_min</span> <span class="pre">/</span> <span class="pre">alpha_max</span> <span class="pre">=</span> <span class="pre">1e-3</span></code>.</p>
 </dd>
 <dt><strong>n_alphas</strong><span class="classifier">int, default=100</span></dt><dd><p>Number of alphas along the regularization path, used for each l1_ratio.</p>
+<div class="deprecated">
+<p><span class="versionmodified deprecated">Deprecated since version 1.7: </span><code class="docutils literal notranslate"><span class="pre">n_alphas</span></code> was deprecated in 1.7 and will be removed in 1.9. Use <code class="docutils literal notranslate"><span class="pre">alphas</span></code>
+instead.</p>
+</div>
 </dd>
-<dt><strong>alphas</strong><span class="classifier">array-like, default=None</span></dt><dd><p>List of alphas where to compute the models.
-If None alphas are set automatically.</p>
+<dt><strong>alphas</strong><span class="classifier">array-like or int, default=None</span></dt><dd><p>Values of alphas to test along the regularization path, used for each l1_ratio.
+If int, <code class="docutils literal notranslate"><span class="pre">alphas</span></code> values are generated automatically.
+If array-like, list of alpha values to use.</p>
+<div class="versionchanged">
+<p><span class="versionmodified changed">Changed in version 1.7: </span><code class="docutils literal notranslate"><span class="pre">alphas</span></code> accepts an integer value which removes the need to pass
+<code class="docutils literal notranslate"><span class="pre">n_alphas</span></code>.</p>
+</div>
+<div class="deprecated">
+<p><span class="versionmodified deprecated">Deprecated since version 1.7: </span><code class="docutils literal notranslate"><span class="pre">alphas=None</span></code> was deprecated in 1.7 and will be removed in 1.9, at which
+point the default value will be set to 100.</p>
+</div>
 </dd>
 <dt><strong>fit_intercept</strong><span class="classifier">bool, default=True</span></dt><dd><p>Whether to calculate the intercept for this model. If set
 to false, no intercept will be used in calculations
@@ -1369,7 +1382,7 @@ <h1>ElasticNetCV<a class="headerlink" href="#elasticnetcv" title="Link to this h
 <dt><strong>verbose</strong><span class="classifier">bool or int, default=0</span></dt><dd><p>Amount of verbosity.</p>
 </dd>
 <dt><strong>n_jobs</strong><span class="classifier">int, default=None</span></dt><dd><p>Number of CPUs to use during the cross validation.
-<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.5.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
+<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.6.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
 <code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all processors. See <a class="reference internal" href="http://github.com/glossary.html#term-n_jobs"><span class="xref std std-term">Glossary</span></a>
 for more details.</p>
 </dd>
@@ -1465,16 +1478,16 @@ <h1>ElasticNetCV<a class="headerlink" href="#elasticnetcv" title="Link to this h
 <span class="gp">&gt;&gt;&gt; </span><span class="n">regr</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
 <span class="go">ElasticNetCV(cv=5, random_state=0)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">regr</span><span class="o">.</span><span class="n">alpha_</span><span class="p">)</span>
-<span class="go">0.199...</span>
+<span class="go">0.199</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">regr</span><span class="o">.</span><span class="n">intercept_</span><span class="p">)</span>
-<span class="go">0.398...</span>
+<span class="go">0.398</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">regr</span><span class="o">.</span><span class="n">predict</span><span class="p">([[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]]))</span>
-<span class="go">[0.398...]</span>
+<span class="go">[0.398]</span>
 </pre></div>
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.ElasticNetCV.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_coordinate_descent.py#L2365"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.ElasticNetCV.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_coordinate_descent.py#L2433"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.ElasticNetCV.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit ElasticNet model with coordinate descent.</p>
 <p>Fit is on grid of alphas and best alpha estimated by cross-validation.</p>
 <dl class="field-list">
@@ -1514,7 +1527,7 @@ <h1>ElasticNetCV<a class="headerlink" href="#elasticnetcv" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.ElasticNetCV.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_coordinate_descent.py#L1846"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.ElasticNetCV.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_coordinate_descent.py#L1888"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.ElasticNetCV.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1534,7 +1547,7 @@ <h1>ElasticNetCV<a class="headerlink" href="#elasticnetcv" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.ElasticNetCV.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.ElasticNetCV.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.ElasticNetCV.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1555,7 +1568,7 @@ <h1>ElasticNetCV<a class="headerlink" href="#elasticnetcv" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.ElasticNetCV.path">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">path</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">l1_ratio</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_alphas</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alphas</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">precompute</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Xy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy_X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coef_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_n_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">positive</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_input</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_coordinate_descent.py#L377"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.ElasticNetCV.path" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">path</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">l1_ratio</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_alphas</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alphas</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">precompute</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Xy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy_X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coef_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_n_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">positive</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_input</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_coordinate_descent.py#L377"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.ElasticNetCV.path" title="Link to this definition">#</a></dt>
 <dd><p>Compute elastic net path with coordinate descent.</p>
 <p>The elastic net optimization function varies for mono and multi-outputs.</p>
 <p>For mono-output tasks it is:</p>
@@ -1660,23 +1673,23 @@ <h1>ElasticNetCV<a class="headerlink" href="#elasticnetcv" title="Link to this h
 <span class="gp">... </span>   <span class="n">n_samples</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">n_features</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">n_informative</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">coef</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span>
 <span class="gp">... </span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">true_coef</span>
-<span class="go">array([ 0.        ,  0.        ,  0.        , 97.9..., 45.7...])</span>
+<span class="go">array([ 0.        ,  0.        ,  0.        , 97.9, 45.7])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">alphas</span><span class="p">,</span> <span class="n">estimated_coef</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">enet_path</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">n_alphas</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">alphas</span><span class="o">.</span><span class="n">shape</span>
 <span class="go">(3,)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">estimated_coef</span>
-<span class="go"> array([[ 0.        ,  0.78...,  0.56...],</span>
-<span class="go">        [ 0.        ,  1.12...,  0.61...],</span>
-<span class="go">        [-0.        , -2.12..., -1.12...],</span>
-<span class="go">        [ 0.        , 23.04..., 88.93...],</span>
-<span class="go">        [ 0.        , 10.63..., 41.56...]])</span>
+<span class="go"> array([[ 0.,  0.787,  0.568],</span>
+<span class="go">        [ 0.,  1.120,  0.620],</span>
+<span class="go">        [-0., -2.129, -1.128],</span>
+<span class="go">        [ 0., 23.046, 88.939],</span>
+<span class="go">        [ 0., 10.637, 41.566]])</span>
 </pre></div>
 </div>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.ElasticNetCV.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_base.py#L284"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.ElasticNetCV.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_base.py#L284"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.ElasticNetCV.predict" title="Link to this definition">#</a></dt>
 <dd><p>Predict using the linear model.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1696,7 +1709,7 @@ <h1>ElasticNetCV<a class="headerlink" href="#elasticnetcv" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.ElasticNetCV.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L619"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.ElasticNetCV.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L560"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.ElasticNetCV.score" title="Link to this definition">#</a></dt>
 <dd><p>Return <a class="reference internal" href="http://github.com/model_evaluation.html#r2-score"><span class="std std-ref">coefficient of determination</span></a> on test data.</p>
 <p>The coefficient of determination, <span class="math notranslate nohighlight">\(R^2\)</span>, is defined as
 <span class="math notranslate nohighlight">\((1 - \frac{u}{v})\)</span>, where <span class="math notranslate nohighlight">\(u\)</span> is the residual
@@ -1738,7 +1751,7 @@ <h1>ElasticNetCV<a class="headerlink" href="#elasticnetcv" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.ElasticNetCV.set_fit_request">
-<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.ElasticNetCV" title="sklearn.linear_model._coordinate_descent.ElasticNetCV"><span class="pre">ElasticNetCV</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.ElasticNetCV.set_fit_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.ElasticNetCV" title="sklearn.linear_model._coordinate_descent.ElasticNetCV"><span class="pre">ElasticNetCV</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.ElasticNetCV.set_fit_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1781,7 +1794,7 @@ <h1>ElasticNetCV<a class="headerlink" href="#elasticnetcv" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.ElasticNetCV.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.ElasticNetCV.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.ElasticNetCV.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1805,7 +1818,7 @@ <h1>ElasticNetCV<a class="headerlink" href="#elasticnetcv" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.ElasticNetCV.set_score_request">
-<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.ElasticNetCV" title="sklearn.linear_model._coordinate_descent.ElasticNetCV"><span class="pre">ElasticNetCV</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.ElasticNetCV.set_score_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.ElasticNetCV" title="sklearn.linear_model._coordinate_descent.ElasticNetCV"><span class="pre">ElasticNetCV</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.ElasticNetCV.set_score_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
diff --git a/dev/modules/generated/sklearn.linear_model.GammaRegressor.html b/dev/modules/generated/sklearn.linear_model.GammaRegressor.html
index e187aecece87d..a3483048b2f5a 100644
--- a/dev/modules/generated/sklearn.linear_model.GammaRegressor.html
+++ b/dev/modules/generated/sklearn.linear_model.GammaRegressor.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1308,7 +1308,7 @@
 <h1>GammaRegressor<a class="headerlink" href="#gammaregressor" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.linear_model.GammaRegressor">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.linear_model.</span></span><span class="sig-name descname"><span class="pre">GammaRegressor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_intercept</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">solver</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'lbfgs'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">warm_start</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_glm/glm.py#L599"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.GammaRegressor" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.linear_model.</span></span><span class="sig-name descname"><span class="pre">GammaRegressor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_intercept</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">solver</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'lbfgs'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">warm_start</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_glm/glm.py#L599"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.GammaRegressor" title="Link to this definition">#</a></dt>
 <dd><p>Generalized Linear Model with a Gamma distribution.</p>
 <p>This regressor uses the ‘log’ link function.</p>
 <p>Read more in the <a class="reference internal" href="http://github.com/linear_model.html#generalized-linear-models"><span class="std std-ref">User Guide</span></a>.</p>
@@ -1400,18 +1400,18 @@ <h1>GammaRegressor<a class="headerlink" href="#gammaregressor" title="Link to th
 <span class="gp">&gt;&gt;&gt; </span><span class="n">clf</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
 <span class="go">GammaRegressor()</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">clf</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
-<span class="go">np.float64(0.773...)</span>
+<span class="go">np.float64(0.773)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">clf</span><span class="o">.</span><span class="n">coef_</span>
-<span class="go">array([0.072..., 0.066...])</span>
+<span class="go">array([0.073, 0.067])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">clf</span><span class="o">.</span><span class="n">intercept_</span>
-<span class="go">np.float64(2.896...)</span>
+<span class="go">np.float64(2.896)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">clf</span><span class="o">.</span><span class="n">predict</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">8</span><span class="p">]])</span>
-<span class="go">array([19.483..., 35.795...])</span>
+<span class="go">array([19.483, 35.795])</span>
 </pre></div>
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.GammaRegressor.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_glm/glm.py#L170"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.GammaRegressor.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_glm/glm.py#L170"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.GammaRegressor.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit a Generalized Linear Model.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1435,7 +1435,7 @@ <h1>GammaRegressor<a class="headerlink" href="#gammaregressor" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.GammaRegressor.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.GammaRegressor.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.GammaRegressor.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1452,7 +1452,7 @@ <h1>GammaRegressor<a class="headerlink" href="#gammaregressor" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.GammaRegressor.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.GammaRegressor.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.GammaRegressor.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1473,7 +1473,7 @@ <h1>GammaRegressor<a class="headerlink" href="#gammaregressor" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.GammaRegressor.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_glm/glm.py#L350"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.GammaRegressor.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_glm/glm.py#L350"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.GammaRegressor.predict" title="Link to this definition">#</a></dt>
 <dd><p>Predict using GLM with feature matrix X.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1493,7 +1493,7 @@ <h1>GammaRegressor<a class="headerlink" href="#gammaregressor" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.GammaRegressor.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_glm/glm.py#L368"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.GammaRegressor.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_glm/glm.py#L368"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.GammaRegressor.score" title="Link to this definition">#</a></dt>
 <dd><p>Compute D^2, the percentage of deviance explained.</p>
 <p>D^2 is a generalization of the coefficient of determination R^2.
 R^2 uses squared error and D^2 uses the deviance of this GLM, see the
@@ -1527,7 +1527,7 @@ <h1>GammaRegressor<a class="headerlink" href="#gammaregressor" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.GammaRegressor.set_fit_request">
-<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.GammaRegressor" title="sklearn.linear_model._glm.glm.GammaRegressor"><span class="pre">GammaRegressor</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.GammaRegressor.set_fit_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.GammaRegressor" title="sklearn.linear_model._glm.glm.GammaRegressor"><span class="pre">GammaRegressor</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.GammaRegressor.set_fit_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1570,7 +1570,7 @@ <h1>GammaRegressor<a class="headerlink" href="#gammaregressor" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.GammaRegressor.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.GammaRegressor.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.GammaRegressor.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1594,7 +1594,7 @@ <h1>GammaRegressor<a class="headerlink" href="#gammaregressor" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.GammaRegressor.set_score_request">
-<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.GammaRegressor" title="sklearn.linear_model._glm.glm.GammaRegressor"><span class="pre">GammaRegressor</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.GammaRegressor.set_score_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.GammaRegressor" title="sklearn.linear_model._glm.glm.GammaRegressor"><span class="pre">GammaRegressor</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.GammaRegressor.set_score_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
diff --git a/dev/modules/generated/sklearn.linear_model.HuberRegressor.html b/dev/modules/generated/sklearn.linear_model.HuberRegressor.html
index 2c3c667d941cc..bc5dca8f2acae 100644
--- a/dev/modules/generated/sklearn.linear_model.HuberRegressor.html
+++ b/dev/modules/generated/sklearn.linear_model.HuberRegressor.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1308,7 +1308,7 @@
 <h1>HuberRegressor<a class="headerlink" href="#huberregressor" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.linear_model.HuberRegressor">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.linear_model.</span></span><span class="sig-name descname"><span class="pre">HuberRegressor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epsilon</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.35</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">warm_start</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_intercept</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_huber.py#L128"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.HuberRegressor" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.linear_model.</span></span><span class="sig-name descname"><span class="pre">HuberRegressor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epsilon</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.35</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">warm_start</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_intercept</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_huber.py#L128"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.HuberRegressor" title="Link to this definition">#</a></dt>
 <dd><p>L2-regularized linear regression model that is robust to outliers.</p>
 <p>The Huber Regressor optimizes the squared loss for the samples where
 <code class="docutils literal notranslate"><span class="pre">|(y</span> <span class="pre">-</span> <span class="pre">Xw</span> <span class="pre">-</span> <span class="pre">c)</span> <span class="pre">/</span> <span class="pre">sigma|</span> <span class="pre">&lt;</span> <span class="pre">epsilon</span></code> and the absolute loss for the samples
@@ -1418,9 +1418,9 @@ <h1>HuberRegressor<a class="headerlink" href="#huberregressor" title="Link to th
 <span class="gp">&gt;&gt;&gt; </span><span class="n">y</span><span class="p">[:</span><span class="mi">4</span><span class="p">]</span> <span class="o">=</span> <span class="n">rng</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">huber</span> <span class="o">=</span> <span class="n">HuberRegressor</span><span class="p">()</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">huber</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
-<span class="go">-7.284...</span>
+<span class="go">-7.284</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">huber</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X</span><span class="p">[:</span><span class="mi">1</span><span class="p">,])</span>
-<span class="go">array([806.7200...])</span>
+<span class="go">array([806.7200])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">linear</span> <span class="o">=</span> <span class="n">LinearRegression</span><span class="p">()</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="s2">&quot;True coefficients:&quot;</span><span class="p">,</span> <span class="n">coef</span><span class="p">)</span>
 <span class="go">True coefficients: [20.4923...  34.1698...]</span>
@@ -1432,7 +1432,7 @@ <h1>HuberRegressor<a class="headerlink" href="#huberregressor" title="Link to th
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.HuberRegressor.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_huber.py#L276"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.HuberRegressor.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_huber.py#L276"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.HuberRegressor.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit the model according to the given training data.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1457,7 +1457,7 @@ <h1>HuberRegressor<a class="headerlink" href="#huberregressor" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.HuberRegressor.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.HuberRegressor.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.HuberRegressor.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1474,7 +1474,7 @@ <h1>HuberRegressor<a class="headerlink" href="#huberregressor" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.HuberRegressor.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.HuberRegressor.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.HuberRegressor.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1495,7 +1495,7 @@ <h1>HuberRegressor<a class="headerlink" href="#huberregressor" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.HuberRegressor.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_base.py#L284"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.HuberRegressor.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_base.py#L284"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.HuberRegressor.predict" title="Link to this definition">#</a></dt>
 <dd><p>Predict using the linear model.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1515,7 +1515,7 @@ <h1>HuberRegressor<a class="headerlink" href="#huberregressor" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.HuberRegressor.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L619"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.HuberRegressor.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L560"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.HuberRegressor.score" title="Link to this definition">#</a></dt>
 <dd><p>Return <a class="reference internal" href="http://github.com/model_evaluation.html#r2-score"><span class="std std-ref">coefficient of determination</span></a> on test data.</p>
 <p>The coefficient of determination, <span class="math notranslate nohighlight">\(R^2\)</span>, is defined as
 <span class="math notranslate nohighlight">\((1 - \frac{u}{v})\)</span>, where <span class="math notranslate nohighlight">\(u\)</span> is the residual
@@ -1557,7 +1557,7 @@ <h1>HuberRegressor<a class="headerlink" href="#huberregressor" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.HuberRegressor.set_fit_request">
-<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.HuberRegressor" title="sklearn.linear_model._huber.HuberRegressor"><span class="pre">HuberRegressor</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.HuberRegressor.set_fit_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.HuberRegressor" title="sklearn.linear_model._huber.HuberRegressor"><span class="pre">HuberRegressor</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.HuberRegressor.set_fit_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1600,7 +1600,7 @@ <h1>HuberRegressor<a class="headerlink" href="#huberregressor" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.HuberRegressor.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.HuberRegressor.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.HuberRegressor.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1624,7 +1624,7 @@ <h1>HuberRegressor<a class="headerlink" href="#huberregressor" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.HuberRegressor.set_score_request">
-<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.HuberRegressor" title="sklearn.linear_model._huber.HuberRegressor"><span class="pre">HuberRegressor</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.HuberRegressor.set_score_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.HuberRegressor" title="sklearn.linear_model._huber.HuberRegressor"><span class="pre">HuberRegressor</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.HuberRegressor.set_score_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
diff --git a/dev/modules/generated/sklearn.linear_model.Lars.html b/dev/modules/generated/sklearn.linear_model.Lars.html
index 69de21eaef36a..e83383e30aadb 100644
--- a/dev/modules/generated/sklearn.linear_model.Lars.html
+++ b/dev/modules/generated/sklearn.linear_model.Lars.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1306,7 +1306,7 @@
 <h1>Lars<a class="headerlink" href="#lars" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.linear_model.Lars">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.linear_model.</span></span><span class="sig-name descname"><span class="pre">Lars</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_intercept</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">precompute</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_nonzero_coefs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">500</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">np.float64(2.220446049250313e-16)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy_X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">jitter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_least_angle.py#L920"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.Lars" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.linear_model.</span></span><span class="sig-name descname"><span class="pre">Lars</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_intercept</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">precompute</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_nonzero_coefs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">500</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">np.float64(2.220446049250313e-16)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy_X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">jitter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_least_angle.py#L920"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.Lars" title="Link to this definition">#</a></dt>
 <dd><p>Least Angle Regression model a.k.a. LAR.</p>
 <p>Read more in the <a class="reference internal" href="http://github.com/linear_model.html#least-angle-regression"><span class="std std-ref">User Guide</span></a>.</p>
 <dl class="field-list">
@@ -1406,12 +1406,12 @@ <h1>Lars<a class="headerlink" href="#lars" title="Link to this heading">#</a></h
 <span class="gp">&gt;&gt;&gt; </span><span class="n">reg</span><span class="o">.</span><span class="n">fit</span><span class="p">([[</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]],</span> <span class="p">[</span><span class="o">-</span><span class="mf">1.1111</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mf">1.1111</span><span class="p">])</span>
 <span class="go">Lars(n_nonzero_coefs=1)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">reg</span><span class="o">.</span><span class="n">coef_</span><span class="p">)</span>
-<span class="go">[ 0. -1.11...]</span>
+<span class="go">[ 0. -1.11]</span>
 </pre></div>
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.Lars.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Xy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_least_angle.py#L1159"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.Lars.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Xy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_least_angle.py#L1159"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.Lars.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit the model using X, y as training data.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1436,7 +1436,7 @@ <h1>Lars<a class="headerlink" href="#lars" title="Link to this heading">#</a></h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.Lars.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.Lars.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.Lars.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1453,7 +1453,7 @@ <h1>Lars<a class="headerlink" href="#lars" title="Link to this heading">#</a></h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.Lars.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.Lars.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.Lars.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1474,7 +1474,7 @@ <h1>Lars<a class="headerlink" href="#lars" title="Link to this heading">#</a></h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.Lars.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_base.py#L284"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.Lars.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_base.py#L284"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.Lars.predict" title="Link to this definition">#</a></dt>
 <dd><p>Predict using the linear model.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1494,7 +1494,7 @@ <h1>Lars<a class="headerlink" href="#lars" title="Link to this heading">#</a></h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.Lars.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L619"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.Lars.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L560"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.Lars.score" title="Link to this definition">#</a></dt>
 <dd><p>Return <a class="reference internal" href="http://github.com/model_evaluation.html#r2-score"><span class="std std-ref">coefficient of determination</span></a> on test data.</p>
 <p>The coefficient of determination, <span class="math notranslate nohighlight">\(R^2\)</span>, is defined as
 <span class="math notranslate nohighlight">\((1 - \frac{u}{v})\)</span>, where <span class="math notranslate nohighlight">\(u\)</span> is the residual
@@ -1536,7 +1536,7 @@ <h1>Lars<a class="headerlink" href="#lars" title="Link to this heading">#</a></h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.Lars.set_fit_request">
-<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Xy</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.Lars" title="sklearn.linear_model._least_angle.Lars"><span class="pre">Lars</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.Lars.set_fit_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Xy</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.Lars" title="sklearn.linear_model._least_angle.Lars"><span class="pre">Lars</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.Lars.set_fit_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1579,7 +1579,7 @@ <h1>Lars<a class="headerlink" href="#lars" title="Link to this heading">#</a></h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.Lars.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.Lars.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.Lars.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1603,7 +1603,7 @@ <h1>Lars<a class="headerlink" href="#lars" title="Link to this heading">#</a></h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.Lars.set_score_request">
-<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.Lars" title="sklearn.linear_model._least_angle.Lars"><span class="pre">Lars</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.Lars.set_score_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.Lars" title="sklearn.linear_model._least_angle.Lars"><span class="pre">Lars</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.Lars.set_score_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
diff --git a/dev/modules/generated/sklearn.linear_model.LarsCV.html b/dev/modules/generated/sklearn.linear_model.LarsCV.html
index 3ef8a632f353c..ea4e8a856b33c 100644
--- a/dev/modules/generated/sklearn.linear_model.LarsCV.html
+++ b/dev/modules/generated/sklearn.linear_model.LarsCV.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1306,7 +1306,7 @@
 <h1>LarsCV<a class="headerlink" href="#larscv" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.linear_model.LarsCV">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.linear_model.</span></span><span class="sig-name descname"><span class="pre">LarsCV</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_intercept</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">500</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">precompute</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cv</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_n_alphas</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">np.float64(2.220446049250313e-16)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy_X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_least_angle.py#L1515"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LarsCV" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.linear_model.</span></span><span class="sig-name descname"><span class="pre">LarsCV</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_intercept</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">500</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">precompute</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cv</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_n_alphas</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">np.float64(2.220446049250313e-16)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy_X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_least_angle.py#L1515"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LarsCV" title="Link to this definition">#</a></dt>
 <dd><p>Cross-validated Least Angle Regression model.</p>
 <p>See glossary entry for <a class="reference internal" href="http://github.com/glossary.html#term-cross-validation-estimator"><span class="xref std std-term">cross-validation estimator</span></a>.</p>
 <p>Read more in the <a class="reference internal" href="http://github.com/linear_model.html#least-angle-regression"><span class="std std-ref">User Guide</span></a>.</p>
@@ -1344,7 +1344,7 @@ <h1>LarsCV<a class="headerlink" href="#larscv" title="Link to this heading">#</a
 residuals in the cross-validation.</p>
 </dd>
 <dt><strong>n_jobs</strong><span class="classifier">int or None, default=None</span></dt><dd><p>Number of CPUs to use during the cross validation.
-<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.5.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
+<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.6.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
 <code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all processors. See <a class="reference internal" href="http://github.com/glossary.html#term-n_jobs"><span class="xref std std-term">Glossary</span></a>
 for more details.</p>
 </dd>
@@ -1422,16 +1422,16 @@ <h1>LarsCV<a class="headerlink" href="#larscv" title="Link to this heading">#</a
 <span class="gp">&gt;&gt;&gt; </span><span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">make_regression</span><span class="p">(</span><span class="n">n_samples</span><span class="o">=</span><span class="mi">200</span><span class="p">,</span> <span class="n">noise</span><span class="o">=</span><span class="mf">4.0</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">reg</span> <span class="o">=</span> <span class="n">LarsCV</span><span class="p">(</span><span class="n">cv</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">reg</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
-<span class="go">0.9996...</span>
+<span class="go">0.9996</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">reg</span><span class="o">.</span><span class="n">alpha_</span>
-<span class="go">np.float64(0.2961...)</span>
+<span class="go">np.float64(0.2961)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">reg</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X</span><span class="p">[:</span><span class="mi">1</span><span class="p">,])</span>
-<span class="go">array([154.3996...])</span>
+<span class="go">array([154.3996])</span>
 </pre></div>
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.LarsCV.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_least_angle.py#L1697"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LarsCV.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_least_angle.py#L1697"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LarsCV.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit the model using X, y as training data.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1462,7 +1462,7 @@ <h1>LarsCV<a class="headerlink" href="#larscv" title="Link to this heading">#</a
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.LarsCV.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_least_angle.py#L1810"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LarsCV.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_least_angle.py#L1810"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LarsCV.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1482,7 +1482,7 @@ <h1>LarsCV<a class="headerlink" href="#larscv" title="Link to this heading">#</a
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.LarsCV.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LarsCV.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LarsCV.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1503,7 +1503,7 @@ <h1>LarsCV<a class="headerlink" href="#larscv" title="Link to this heading">#</a
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.LarsCV.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_base.py#L284"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LarsCV.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_base.py#L284"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LarsCV.predict" title="Link to this definition">#</a></dt>
 <dd><p>Predict using the linear model.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1523,7 +1523,7 @@ <h1>LarsCV<a class="headerlink" href="#larscv" title="Link to this heading">#</a
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.LarsCV.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L619"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LarsCV.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L560"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LarsCV.score" title="Link to this definition">#</a></dt>
 <dd><p>Return <a class="reference internal" href="http://github.com/model_evaluation.html#r2-score"><span class="std std-ref">coefficient of determination</span></a> on test data.</p>
 <p>The coefficient of determination, <span class="math notranslate nohighlight">\(R^2\)</span>, is defined as
 <span class="math notranslate nohighlight">\((1 - \frac{u}{v})\)</span>, where <span class="math notranslate nohighlight">\(u\)</span> is the residual
@@ -1565,7 +1565,7 @@ <h1>LarsCV<a class="headerlink" href="#larscv" title="Link to this heading">#</a
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.LarsCV.set_fit_request">
-<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Xy</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.LarsCV" title="sklearn.linear_model._least_angle.LarsCV"><span class="pre">LarsCV</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LarsCV.set_fit_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Xy</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.LarsCV" title="sklearn.linear_model._least_angle.LarsCV"><span class="pre">LarsCV</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LarsCV.set_fit_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1608,7 +1608,7 @@ <h1>LarsCV<a class="headerlink" href="#larscv" title="Link to this heading">#</a
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.LarsCV.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LarsCV.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LarsCV.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1632,7 +1632,7 @@ <h1>LarsCV<a class="headerlink" href="#larscv" title="Link to this heading">#</a
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.LarsCV.set_score_request">
-<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.LarsCV" title="sklearn.linear_model._least_angle.LarsCV"><span class="pre">LarsCV</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LarsCV.set_score_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.LarsCV" title="sklearn.linear_model._least_angle.LarsCV"><span class="pre">LarsCV</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LarsCV.set_score_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
diff --git a/dev/modules/generated/sklearn.linear_model.Lasso.html b/dev/modules/generated/sklearn.linear_model.Lasso.html
index aa82bda741bd1..2981d2c7034ae 100644
--- a/dev/modules/generated/sklearn.linear_model.Lasso.html
+++ b/dev/modules/generated/sklearn.linear_model.Lasso.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1308,7 +1308,7 @@
 <h1>Lasso<a class="headerlink" href="#lasso" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.linear_model.Lasso">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.linear_model.</span></span><span class="sig-name descname"><span class="pre">Lasso</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_intercept</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">precompute</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy_X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">warm_start</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">positive</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">selection</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'cyclic'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_coordinate_descent.py#L1162"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.Lasso" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.linear_model.</span></span><span class="sig-name descname"><span class="pre">Lasso</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_intercept</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">precompute</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy_X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">warm_start</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">positive</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">selection</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'cyclic'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_coordinate_descent.py#L1162"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.Lasso" title="Link to this definition">#</a></dt>
 <dd><p>Linear Model trained with L1 prior as regularizer (aka the Lasso).</p>
 <p>The optimization objective for Lasso is:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">(</span><span class="mi">1</span> <span class="o">/</span> <span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="n">n_samples</span><span class="p">))</span> <span class="o">*</span> <span class="o">||</span><span class="n">y</span> <span class="o">-</span> <span class="n">Xw</span><span class="o">||^</span><span class="mi">2_2</span> <span class="o">+</span> <span class="n">alpha</span> <span class="o">*</span> <span class="o">||</span><span class="n">w</span><span class="o">||</span><span class="n">_1</span>
@@ -1438,12 +1438,12 @@ <h1>Lasso<a class="headerlink" href="#lasso" title="Link to this heading">#</a><
 <span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">clf</span><span class="o">.</span><span class="n">coef_</span><span class="p">)</span>
 <span class="go">[0.85 0.  ]</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">clf</span><span class="o">.</span><span class="n">intercept_</span><span class="p">)</span>
-<span class="go">0.15...</span>
+<span class="go">0.15</span>
 </pre></div>
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.Lasso.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_input</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_coordinate_descent.py#L927"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.Lasso.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_input</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_coordinate_descent.py#L927"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.Lasso.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit model with coordinate descent.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1482,7 +1482,7 @@ <h1>Lasso<a class="headerlink" href="#lasso" title="Link to this heading">#</a><
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.Lasso.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.Lasso.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.Lasso.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1499,7 +1499,7 @@ <h1>Lasso<a class="headerlink" href="#lasso" title="Link to this heading">#</a><
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.Lasso.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.Lasso.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.Lasso.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1520,7 +1520,7 @@ <h1>Lasso<a class="headerlink" href="#lasso" title="Link to this heading">#</a><
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.Lasso.path">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">path</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">l1_ratio</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_alphas</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alphas</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">precompute</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Xy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy_X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coef_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_n_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">positive</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_input</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_coordinate_descent.py#L377"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.Lasso.path" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">path</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">l1_ratio</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_alphas</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alphas</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">precompute</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Xy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy_X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coef_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_n_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">positive</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_input</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_coordinate_descent.py#L377"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.Lasso.path" title="Link to this definition">#</a></dt>
 <dd><p>Compute elastic net path with coordinate descent.</p>
 <p>The elastic net optimization function varies for mono and multi-outputs.</p>
 <p>For mono-output tasks it is:</p>
@@ -1625,23 +1625,23 @@ <h1>Lasso<a class="headerlink" href="#lasso" title="Link to this heading">#</a><
 <span class="gp">... </span>   <span class="n">n_samples</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">n_features</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">n_informative</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">coef</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span>
 <span class="gp">... </span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">true_coef</span>
-<span class="go">array([ 0.        ,  0.        ,  0.        , 97.9..., 45.7...])</span>
+<span class="go">array([ 0.        ,  0.        ,  0.        , 97.9, 45.7])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">alphas</span><span class="p">,</span> <span class="n">estimated_coef</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">enet_path</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">n_alphas</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">alphas</span><span class="o">.</span><span class="n">shape</span>
 <span class="go">(3,)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">estimated_coef</span>
-<span class="go"> array([[ 0.        ,  0.78...,  0.56...],</span>
-<span class="go">        [ 0.        ,  1.12...,  0.61...],</span>
-<span class="go">        [-0.        , -2.12..., -1.12...],</span>
-<span class="go">        [ 0.        , 23.04..., 88.93...],</span>
-<span class="go">        [ 0.        , 10.63..., 41.56...]])</span>
+<span class="go"> array([[ 0.,  0.787,  0.568],</span>
+<span class="go">        [ 0.,  1.120,  0.620],</span>
+<span class="go">        [-0., -2.129, -1.128],</span>
+<span class="go">        [ 0., 23.046, 88.939],</span>
+<span class="go">        [ 0., 10.637, 41.566]])</span>
 </pre></div>
 </div>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.Lasso.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_base.py#L284"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.Lasso.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_base.py#L284"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.Lasso.predict" title="Link to this definition">#</a></dt>
 <dd><p>Predict using the linear model.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1661,7 +1661,7 @@ <h1>Lasso<a class="headerlink" href="#lasso" title="Link to this heading">#</a><
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.Lasso.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L619"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.Lasso.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L560"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.Lasso.score" title="Link to this definition">#</a></dt>
 <dd><p>Return <a class="reference internal" href="http://github.com/model_evaluation.html#r2-score"><span class="std std-ref">coefficient of determination</span></a> on test data.</p>
 <p>The coefficient of determination, <span class="math notranslate nohighlight">\(R^2\)</span>, is defined as
 <span class="math notranslate nohighlight">\((1 - \frac{u}{v})\)</span>, where <span class="math notranslate nohighlight">\(u\)</span> is the residual
@@ -1703,7 +1703,7 @@ <h1>Lasso<a class="headerlink" href="#lasso" title="Link to this heading">#</a><
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.Lasso.set_fit_request">
-<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.Lasso" title="sklearn.linear_model._coordinate_descent.Lasso"><span class="pre">Lasso</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.Lasso.set_fit_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.Lasso" title="sklearn.linear_model._coordinate_descent.Lasso"><span class="pre">Lasso</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.Lasso.set_fit_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1746,7 +1746,7 @@ <h1>Lasso<a class="headerlink" href="#lasso" title="Link to this heading">#</a><
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.Lasso.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.Lasso.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.Lasso.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1770,7 +1770,7 @@ <h1>Lasso<a class="headerlink" href="#lasso" title="Link to this heading">#</a><
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.Lasso.set_score_request">
-<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.Lasso" title="sklearn.linear_model._coordinate_descent.Lasso"><span class="pre">Lasso</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.Lasso.set_score_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.Lasso" title="sklearn.linear_model._coordinate_descent.Lasso"><span class="pre">Lasso</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.Lasso.set_score_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
diff --git a/dev/modules/generated/sklearn.linear_model.LassoCV.html b/dev/modules/generated/sklearn.linear_model.LassoCV.html
index 145074209f402..d00bc22657058 100644
--- a/dev/modules/generated/sklearn.linear_model.LassoCV.html
+++ b/dev/modules/generated/sklearn.linear_model.LassoCV.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1308,7 +1308,7 @@
 <h1>LassoCV<a class="headerlink" href="#lassocv" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.linear_model.LassoCV">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.linear_model.</span></span><span class="sig-name descname"><span class="pre">LassoCV</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_alphas</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alphas</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_intercept</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">precompute</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy_X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cv</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">positive</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">selection</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'cyclic'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_coordinate_descent.py#L1878"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LassoCV" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.linear_model.</span></span><span class="sig-name descname"><span class="pre">LassoCV</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_alphas</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'deprecated'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alphas</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'warn'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_intercept</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">precompute</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy_X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cv</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">positive</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">selection</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'cyclic'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_coordinate_descent.py#L1920"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LassoCV" title="Link to this definition">#</a></dt>
 <dd><p>Lasso linear model with iterative fitting along a regularization path.</p>
 <p>See glossary entry for <a class="reference internal" href="http://github.com/glossary.html#term-cross-validation-estimator"><span class="xref std std-term">cross-validation estimator</span></a>.</p>
 <p>The best model is selected by cross-validation.</p>
@@ -1324,9 +1324,22 @@ <h1>LassoCV<a class="headerlink" href="#lassocv" title="Link to this heading">#<
 <code class="docutils literal notranslate"><span class="pre">alpha_min</span> <span class="pre">/</span> <span class="pre">alpha_max</span> <span class="pre">=</span> <span class="pre">1e-3</span></code>.</p>
 </dd>
 <dt><strong>n_alphas</strong><span class="classifier">int, default=100</span></dt><dd><p>Number of alphas along the regularization path.</p>
+<div class="deprecated">
+<p><span class="versionmodified deprecated">Deprecated since version 1.7: </span><code class="docutils literal notranslate"><span class="pre">n_alphas</span></code> was deprecated in 1.7 and will be removed in 1.9. Use <code class="docutils literal notranslate"><span class="pre">alphas</span></code>
+instead.</p>
+</div>
 </dd>
-<dt><strong>alphas</strong><span class="classifier">array-like, default=None</span></dt><dd><p>List of alphas where to compute the models.
-If <code class="docutils literal notranslate"><span class="pre">None</span></code> alphas are set automatically.</p>
+<dt><strong>alphas</strong><span class="classifier">array-like or int, default=None</span></dt><dd><p>Values of alphas to test along the regularization path.
+If int, <code class="docutils literal notranslate"><span class="pre">alphas</span></code> values are generated automatically.
+If array-like, list of alpha values to use.</p>
+<div class="versionchanged">
+<p><span class="versionmodified changed">Changed in version 1.7: </span><code class="docutils literal notranslate"><span class="pre">alphas</span></code> accepts an integer value which removes the need to pass
+<code class="docutils literal notranslate"><span class="pre">n_alphas</span></code>.</p>
+</div>
+<div class="deprecated">
+<p><span class="versionmodified deprecated">Deprecated since version 1.7: </span><code class="docutils literal notranslate"><span class="pre">alphas=None</span></code> was deprecated in 1.7 and will be removed in 1.9, at which
+point the default value will be set to 100.</p>
+</div>
 </dd>
 <dt><strong>fit_intercept</strong><span class="classifier">bool, default=True</span></dt><dd><p>Whether to calculate the intercept for this model. If set
 to false, no intercept will be used in calculations
@@ -1363,7 +1376,7 @@ <h1>LassoCV<a class="headerlink" href="#lassocv" title="Link to this heading">#<
 <dt><strong>verbose</strong><span class="classifier">bool or int, default=False</span></dt><dd><p>Amount of verbosity.</p>
 </dd>
 <dt><strong>n_jobs</strong><span class="classifier">int, default=None</span></dt><dd><p>Number of CPUs to use during the cross validation.
-<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.5.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
+<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.6.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
 <code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all processors. See <a class="reference internal" href="http://github.com/glossary.html#term-n_jobs"><span class="xref std std-term">Glossary</span></a>
 for more details.</p>
 </dd>
@@ -1449,14 +1462,14 @@ <h1>LassoCV<a class="headerlink" href="#lassocv" title="Link to this heading">#<
 <span class="gp">&gt;&gt;&gt; </span><span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">make_regression</span><span class="p">(</span><span class="n">noise</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">reg</span> <span class="o">=</span> <span class="n">LassoCV</span><span class="p">(</span><span class="n">cv</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">reg</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
-<span class="go">0.9993...</span>
+<span class="go">0.9993</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">reg</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X</span><span class="p">[:</span><span class="mi">1</span><span class="p">,])</span>
-<span class="go">array([-78.4951...])</span>
+<span class="go">array([-78.4951])</span>
 </pre></div>
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.LassoCV.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_coordinate_descent.py#L2089"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LassoCV.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_coordinate_descent.py#L2144"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LassoCV.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit Lasso model with coordinate descent.</p>
 <p>Fit is on grid of alphas and best alpha estimated by cross-validation.</p>
 <dl class="field-list">
@@ -1496,7 +1509,7 @@ <h1>LassoCV<a class="headerlink" href="#lassocv" title="Link to this heading">#<
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.LassoCV.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_coordinate_descent.py#L1846"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LassoCV.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_coordinate_descent.py#L1888"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LassoCV.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1516,7 +1529,7 @@ <h1>LassoCV<a class="headerlink" href="#lassocv" title="Link to this heading">#<
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.LassoCV.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LassoCV.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LassoCV.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1537,7 +1550,7 @@ <h1>LassoCV<a class="headerlink" href="#lassocv" title="Link to this heading">#<
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.LassoCV.path">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">path</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_alphas</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alphas</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">precompute</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Xy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy_X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coef_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_n_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">positive</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_coordinate_descent.py#L186"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LassoCV.path" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">path</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_alphas</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alphas</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">precompute</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Xy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy_X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coef_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_n_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">positive</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_coordinate_descent.py#L186"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LassoCV.path" title="Link to this definition">#</a></dt>
 <dd><p>Compute Lasso path with coordinate descent.</p>
 <p>The Lasso optimization function varies for mono and multi-outputs.</p>
 <p>For mono-output tasks it is:</p>
@@ -1662,7 +1675,7 @@ <h1>LassoCV<a class="headerlink" href="#lassocv" title="Link to this heading">#<
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.LassoCV.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_base.py#L284"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LassoCV.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_base.py#L284"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LassoCV.predict" title="Link to this definition">#</a></dt>
 <dd><p>Predict using the linear model.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1682,7 +1695,7 @@ <h1>LassoCV<a class="headerlink" href="#lassocv" title="Link to this heading">#<
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.LassoCV.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L619"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LassoCV.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L560"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LassoCV.score" title="Link to this definition">#</a></dt>
 <dd><p>Return <a class="reference internal" href="http://github.com/model_evaluation.html#r2-score"><span class="std std-ref">coefficient of determination</span></a> on test data.</p>
 <p>The coefficient of determination, <span class="math notranslate nohighlight">\(R^2\)</span>, is defined as
 <span class="math notranslate nohighlight">\((1 - \frac{u}{v})\)</span>, where <span class="math notranslate nohighlight">\(u\)</span> is the residual
@@ -1724,7 +1737,7 @@ <h1>LassoCV<a class="headerlink" href="#lassocv" title="Link to this heading">#<
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.LassoCV.set_fit_request">
-<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.LassoCV" title="sklearn.linear_model._coordinate_descent.LassoCV"><span class="pre">LassoCV</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LassoCV.set_fit_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.LassoCV" title="sklearn.linear_model._coordinate_descent.LassoCV"><span class="pre">LassoCV</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LassoCV.set_fit_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1767,7 +1780,7 @@ <h1>LassoCV<a class="headerlink" href="#lassocv" title="Link to this heading">#<
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.LassoCV.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LassoCV.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LassoCV.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1791,7 +1804,7 @@ <h1>LassoCV<a class="headerlink" href="#lassocv" title="Link to this heading">#<
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.LassoCV.set_score_request">
-<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.LassoCV" title="sklearn.linear_model._coordinate_descent.LassoCV"><span class="pre">LassoCV</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LassoCV.set_score_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.LassoCV" title="sklearn.linear_model._coordinate_descent.LassoCV"><span class="pre">LassoCV</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LassoCV.set_score_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
diff --git a/dev/modules/generated/sklearn.linear_model.LassoLars.html b/dev/modules/generated/sklearn.linear_model.LassoLars.html
index e0d858fbbea97..c265240dd6b9b 100644
--- a/dev/modules/generated/sklearn.linear_model.LassoLars.html
+++ b/dev/modules/generated/sklearn.linear_model.LassoLars.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1306,7 +1306,7 @@
 <h1>LassoLars<a class="headerlink" href="#lassolars" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.linear_model.LassoLars">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.linear_model.</span></span><span class="sig-name descname"><span class="pre">LassoLars</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_intercept</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">precompute</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">500</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">np.float64(2.220446049250313e-16)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy_X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">positive</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">jitter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_least_angle.py#L1210"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LassoLars" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.linear_model.</span></span><span class="sig-name descname"><span class="pre">LassoLars</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_intercept</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">precompute</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">500</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">np.float64(2.220446049250313e-16)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy_X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">positive</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">jitter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_least_angle.py#L1210"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LassoLars" title="Link to this definition">#</a></dt>
 <dd><p>Lasso model fit with Least Angle Regression a.k.a. Lars.</p>
 <p>It is a Linear Model trained with an L1 prior as regularizer.</p>
 <p>The optimization objective for Lasso is:</p>
@@ -1435,12 +1435,12 @@ <h1>LassoLars<a class="headerlink" href="#lassolars" title="Link to this heading
 <span class="gp">&gt;&gt;&gt; </span><span class="n">reg</span><span class="o">.</span><span class="n">fit</span><span class="p">([[</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]],</span> <span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">])</span>
 <span class="go">LassoLars(alpha=0.01)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">reg</span><span class="o">.</span><span class="n">coef_</span><span class="p">)</span>
-<span class="go">[ 0.         -0.955...]</span>
+<span class="go">[ 0.         -0.955]</span>
 </pre></div>
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.LassoLars.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Xy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_least_angle.py#L1159"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LassoLars.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Xy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_least_angle.py#L1159"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LassoLars.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit the model using X, y as training data.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1465,7 +1465,7 @@ <h1>LassoLars<a class="headerlink" href="#lassolars" title="Link to this heading
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.LassoLars.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LassoLars.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LassoLars.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1482,7 +1482,7 @@ <h1>LassoLars<a class="headerlink" href="#lassolars" title="Link to this heading
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.LassoLars.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LassoLars.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LassoLars.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1503,7 +1503,7 @@ <h1>LassoLars<a class="headerlink" href="#lassolars" title="Link to this heading
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.LassoLars.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_base.py#L284"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LassoLars.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_base.py#L284"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LassoLars.predict" title="Link to this definition">#</a></dt>
 <dd><p>Predict using the linear model.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1523,7 +1523,7 @@ <h1>LassoLars<a class="headerlink" href="#lassolars" title="Link to this heading
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.LassoLars.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L619"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LassoLars.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L560"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LassoLars.score" title="Link to this definition">#</a></dt>
 <dd><p>Return <a class="reference internal" href="http://github.com/model_evaluation.html#r2-score"><span class="std std-ref">coefficient of determination</span></a> on test data.</p>
 <p>The coefficient of determination, <span class="math notranslate nohighlight">\(R^2\)</span>, is defined as
 <span class="math notranslate nohighlight">\((1 - \frac{u}{v})\)</span>, where <span class="math notranslate nohighlight">\(u\)</span> is the residual
@@ -1565,7 +1565,7 @@ <h1>LassoLars<a class="headerlink" href="#lassolars" title="Link to this heading
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.LassoLars.set_fit_request">
-<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Xy</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.LassoLars" title="sklearn.linear_model._least_angle.LassoLars"><span class="pre">LassoLars</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LassoLars.set_fit_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Xy</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.LassoLars" title="sklearn.linear_model._least_angle.LassoLars"><span class="pre">LassoLars</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LassoLars.set_fit_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1608,7 +1608,7 @@ <h1>LassoLars<a class="headerlink" href="#lassolars" title="Link to this heading
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.LassoLars.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LassoLars.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LassoLars.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1632,7 +1632,7 @@ <h1>LassoLars<a class="headerlink" href="#lassolars" title="Link to this heading
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.LassoLars.set_score_request">
-<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.LassoLars" title="sklearn.linear_model._least_angle.LassoLars"><span class="pre">LassoLars</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LassoLars.set_score_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.LassoLars" title="sklearn.linear_model._least_angle.LassoLars"><span class="pre">LassoLars</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LassoLars.set_score_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
diff --git a/dev/modules/generated/sklearn.linear_model.LassoLarsCV.html b/dev/modules/generated/sklearn.linear_model.LassoLarsCV.html
index 94366b71b68f2..e20310af138e5 100644
--- a/dev/modules/generated/sklearn.linear_model.LassoLarsCV.html
+++ b/dev/modules/generated/sklearn.linear_model.LassoLarsCV.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1308,7 +1308,7 @@
 <h1>LassoLarsCV<a class="headerlink" href="#lassolarscv" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.linear_model.LassoLarsCV">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.linear_model.</span></span><span class="sig-name descname"><span class="pre">LassoLarsCV</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_intercept</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">500</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">precompute</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cv</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_n_alphas</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">np.float64(2.220446049250313e-16)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy_X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">positive</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_least_angle.py#L1831"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LassoLarsCV" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.linear_model.</span></span><span class="sig-name descname"><span class="pre">LassoLarsCV</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_intercept</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">500</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">precompute</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cv</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_n_alphas</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">np.float64(2.220446049250313e-16)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy_X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">positive</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_least_angle.py#L1831"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LassoLarsCV" title="Link to this definition">#</a></dt>
 <dd><p>Cross-validated Lasso, using the LARS algorithm.</p>
 <p>See glossary entry for <a class="reference internal" href="http://github.com/glossary.html#term-cross-validation-estimator"><span class="xref std std-term">cross-validation estimator</span></a>.</p>
 <p>The optimization objective for Lasso is:</p>
@@ -1350,7 +1350,7 @@ <h1>LassoLarsCV<a class="headerlink" href="#lassolarscv" title="Link to this hea
 residuals in the cross-validation.</p>
 </dd>
 <dt><strong>n_jobs</strong><span class="classifier">int or None, default=None</span></dt><dd><p>Number of CPUs to use during the cross validation.
-<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.5.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
+<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.6.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
 <code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all processors. See <a class="reference internal" href="http://github.com/glossary.html#term-n_jobs"><span class="xref std std-term">Glossary</span></a>
 for more details.</p>
 </dd>
@@ -1447,16 +1447,16 @@ <h1>LassoLarsCV<a class="headerlink" href="#lassolarscv" title="Link to this hea
 <span class="gp">&gt;&gt;&gt; </span><span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">make_regression</span><span class="p">(</span><span class="n">noise</span><span class="o">=</span><span class="mf">4.0</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">reg</span> <span class="o">=</span> <span class="n">LassoLarsCV</span><span class="p">(</span><span class="n">cv</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">reg</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
-<span class="go">0.9993...</span>
+<span class="go">0.9993</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">reg</span><span class="o">.</span><span class="n">alpha_</span>
-<span class="go">np.float64(0.3972...)</span>
+<span class="go">np.float64(0.3972)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">reg</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X</span><span class="p">[:</span><span class="mi">1</span><span class="p">,])</span>
-<span class="go">array([-78.4831...])</span>
+<span class="go">array([-78.4831])</span>
 </pre></div>
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.LassoLarsCV.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_least_angle.py#L1697"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LassoLarsCV.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_least_angle.py#L1697"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LassoLarsCV.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit the model using X, y as training data.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1487,7 +1487,7 @@ <h1>LassoLarsCV<a class="headerlink" href="#lassolarscv" title="Link to this hea
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.LassoLarsCV.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_least_angle.py#L1810"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LassoLarsCV.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_least_angle.py#L1810"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LassoLarsCV.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1507,7 +1507,7 @@ <h1>LassoLarsCV<a class="headerlink" href="#lassolarscv" title="Link to this hea
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.LassoLarsCV.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LassoLarsCV.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LassoLarsCV.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1528,7 +1528,7 @@ <h1>LassoLarsCV<a class="headerlink" href="#lassolarscv" title="Link to this hea
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.LassoLarsCV.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_base.py#L284"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LassoLarsCV.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_base.py#L284"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LassoLarsCV.predict" title="Link to this definition">#</a></dt>
 <dd><p>Predict using the linear model.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1548,7 +1548,7 @@ <h1>LassoLarsCV<a class="headerlink" href="#lassolarscv" title="Link to this hea
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.LassoLarsCV.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L619"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LassoLarsCV.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L560"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LassoLarsCV.score" title="Link to this definition">#</a></dt>
 <dd><p>Return <a class="reference internal" href="http://github.com/model_evaluation.html#r2-score"><span class="std std-ref">coefficient of determination</span></a> on test data.</p>
 <p>The coefficient of determination, <span class="math notranslate nohighlight">\(R^2\)</span>, is defined as
 <span class="math notranslate nohighlight">\((1 - \frac{u}{v})\)</span>, where <span class="math notranslate nohighlight">\(u\)</span> is the residual
@@ -1590,7 +1590,7 @@ <h1>LassoLarsCV<a class="headerlink" href="#lassolarscv" title="Link to this hea
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.LassoLarsCV.set_fit_request">
-<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Xy</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.LassoLarsCV" title="sklearn.linear_model._least_angle.LassoLarsCV"><span class="pre">LassoLarsCV</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LassoLarsCV.set_fit_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Xy</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.LassoLarsCV" title="sklearn.linear_model._least_angle.LassoLarsCV"><span class="pre">LassoLarsCV</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LassoLarsCV.set_fit_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1633,7 +1633,7 @@ <h1>LassoLarsCV<a class="headerlink" href="#lassolarscv" title="Link to this hea
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.LassoLarsCV.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LassoLarsCV.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LassoLarsCV.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1657,7 +1657,7 @@ <h1>LassoLarsCV<a class="headerlink" href="#lassolarscv" title="Link to this hea
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.LassoLarsCV.set_score_request">
-<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.LassoLarsCV" title="sklearn.linear_model._least_angle.LassoLarsCV"><span class="pre">LassoLarsCV</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LassoLarsCV.set_score_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.LassoLarsCV" title="sklearn.linear_model._least_angle.LassoLarsCV"><span class="pre">LassoLarsCV</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LassoLarsCV.set_score_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
diff --git a/dev/modules/generated/sklearn.linear_model.LassoLarsIC.html b/dev/modules/generated/sklearn.linear_model.LassoLarsIC.html
index 197c87aee911a..62abaa99da64a 100644
--- a/dev/modules/generated/sklearn.linear_model.LassoLarsIC.html
+++ b/dev/modules/generated/sklearn.linear_model.LassoLarsIC.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
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-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
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@@ -1308,7 +1308,7 @@
 <h1>LassoLarsIC<a class="headerlink" href="#lassolarsic" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.linear_model.LassoLarsIC">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.linear_model.</span></span><span class="sig-name descname"><span class="pre">LassoLarsIC</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">criterion</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'aic'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_intercept</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">precompute</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">500</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">np.float64(2.220446049250313e-16)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy_X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">positive</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">noise_variance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_least_angle.py#L2029"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LassoLarsIC" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.linear_model.</span></span><span class="sig-name descname"><span class="pre">LassoLarsIC</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">criterion</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'aic'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_intercept</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">precompute</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">500</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">np.float64(2.220446049250313e-16)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy_X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">positive</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">noise_variance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_least_angle.py#L2029"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LassoLarsIC" title="Link to this definition">#</a></dt>
 <dd><p>Lasso model fit with Lars using BIC or AIC for model selection.</p>
 <p>The optimization objective for Lasso is:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">(</span><span class="mi">1</span> <span class="o">/</span> <span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="n">n_samples</span><span class="p">))</span> <span class="o">*</span> <span class="o">||</span><span class="n">y</span> <span class="o">-</span> <span class="n">Xw</span><span class="o">||^</span><span class="mi">2_2</span> <span class="o">+</span> <span class="n">alpha</span> <span class="o">*</span> <span class="o">||</span><span class="n">w</span><span class="o">||</span><span class="n">_1</span>
@@ -1455,12 +1455,12 @@ <h1>LassoLarsIC<a class="headerlink" href="#lassolarsic" title="Link to this hea
 <span class="gp">&gt;&gt;&gt; </span><span class="n">reg</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
 <span class="go">LassoLarsIC(criterion=&#39;bic&#39;)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">reg</span><span class="o">.</span><span class="n">coef_</span><span class="p">)</span>
-<span class="go">[ 0.  -1.11...]</span>
+<span class="go">[ 0.  -1.11]</span>
 </pre></div>
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.LassoLarsIC.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy_X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_least_angle.py#L2221"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LassoLarsIC.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy_X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_least_angle.py#L2221"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LassoLarsIC.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit the model using X, y as training data.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1486,7 +1486,7 @@ <h1>LassoLarsIC<a class="headerlink" href="#lassolarsic" title="Link to this hea
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.LassoLarsIC.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LassoLarsIC.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LassoLarsIC.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1503,7 +1503,7 @@ <h1>LassoLarsIC<a class="headerlink" href="#lassolarsic" title="Link to this hea
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.LassoLarsIC.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LassoLarsIC.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LassoLarsIC.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1524,7 +1524,7 @@ <h1>LassoLarsIC<a class="headerlink" href="#lassolarsic" title="Link to this hea
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.LassoLarsIC.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_base.py#L284"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LassoLarsIC.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_base.py#L284"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LassoLarsIC.predict" title="Link to this definition">#</a></dt>
 <dd><p>Predict using the linear model.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1544,7 +1544,7 @@ <h1>LassoLarsIC<a class="headerlink" href="#lassolarsic" title="Link to this hea
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.LassoLarsIC.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L619"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LassoLarsIC.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L560"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LassoLarsIC.score" title="Link to this definition">#</a></dt>
 <dd><p>Return <a class="reference internal" href="http://github.com/model_evaluation.html#r2-score"><span class="std std-ref">coefficient of determination</span></a> on test data.</p>
 <p>The coefficient of determination, <span class="math notranslate nohighlight">\(R^2\)</span>, is defined as
 <span class="math notranslate nohighlight">\((1 - \frac{u}{v})\)</span>, where <span class="math notranslate nohighlight">\(u\)</span> is the residual
@@ -1586,7 +1586,7 @@ <h1>LassoLarsIC<a class="headerlink" href="#lassolarsic" title="Link to this hea
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.LassoLarsIC.set_fit_request">
-<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy_X</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.LassoLarsIC" title="sklearn.linear_model._least_angle.LassoLarsIC"><span class="pre">LassoLarsIC</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LassoLarsIC.set_fit_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy_X</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.LassoLarsIC" title="sklearn.linear_model._least_angle.LassoLarsIC"><span class="pre">LassoLarsIC</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LassoLarsIC.set_fit_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1629,7 +1629,7 @@ <h1>LassoLarsIC<a class="headerlink" href="#lassolarsic" title="Link to this hea
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.LassoLarsIC.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LassoLarsIC.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LassoLarsIC.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1653,7 +1653,7 @@ <h1>LassoLarsIC<a class="headerlink" href="#lassolarsic" title="Link to this hea
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.LassoLarsIC.set_score_request">
-<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.LassoLarsIC" title="sklearn.linear_model._least_angle.LassoLarsIC"><span class="pre">LassoLarsIC</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LassoLarsIC.set_score_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.LassoLarsIC" title="sklearn.linear_model._least_angle.LassoLarsIC"><span class="pre">LassoLarsIC</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LassoLarsIC.set_score_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
diff --git a/dev/modules/generated/sklearn.linear_model.LinearRegression.html b/dev/modules/generated/sklearn.linear_model.LinearRegression.html
index c882cc7d07b19..85cbc93071e18 100644
--- a/dev/modules/generated/sklearn.linear_model.LinearRegression.html
+++ b/dev/modules/generated/sklearn.linear_model.LinearRegression.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1308,7 +1308,7 @@
 <h1>LinearRegression<a class="headerlink" href="#linearregression" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.linear_model.LinearRegression">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.linear_model.</span></span><span class="sig-name descname"><span class="pre">LinearRegression</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_intercept</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy_X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-06</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">positive</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_base.py#L458"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LinearRegression" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.linear_model.</span></span><span class="sig-name descname"><span class="pre">LinearRegression</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_intercept</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy_X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-06</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">positive</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_base.py#L458"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LinearRegression" title="Link to this definition">#</a></dt>
 <dd><p>Ordinary least squares Linear Regression.</p>
 <p>LinearRegression fits a linear model with coefficients w = (w1, …, wp)
 to minimize the residual sum of squares between the observed targets in
@@ -1335,11 +1335,14 @@ <h1>LinearRegression<a class="headerlink" href="#linearregression" title="Link t
 speedup in case of sufficiently large problems, that is if firstly
 <code class="docutils literal notranslate"><span class="pre">n_targets</span> <span class="pre">&gt;</span> <span class="pre">1</span></code> and secondly <code class="docutils literal notranslate"><span class="pre">X</span></code> is sparse or if <code class="docutils literal notranslate"><span class="pre">positive</span></code> is set
 to <code class="docutils literal notranslate"><span class="pre">True</span></code>. <code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a
-<a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.5.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context. <code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all
+<a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.6.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context. <code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all
 processors. See <a class="reference internal" href="http://github.com/glossary.html#term-n_jobs"><span class="xref std std-term">Glossary</span></a> for more details.</p>
 </dd>
 <dt><strong>positive</strong><span class="classifier">bool, default=False</span></dt><dd><p>When set to <code class="docutils literal notranslate"><span class="pre">True</span></code>, forces the coefficients to be positive. This
 option is only supported for dense arrays.</p>
+<p>For a comparison between a linear regression model with positive constraints
+on the regression coefficients and a linear regression without such constraints,
+see <a class="reference internal" href="http://github.com/auto_examples/linear_model/plot_nnls.html#sphx-glr-auto-examples-linear-model-plot-nnls-py"><span class="std std-ref">Non-negative least squares</span></a>.</p>
 <div class="versionadded">
 <p><span class="versionmodified added">Added in version 0.24.</span></p>
 </div>
@@ -1401,14 +1404,14 @@ <h1>LinearRegression<a class="headerlink" href="#linearregression" title="Link t
 <span class="gp">&gt;&gt;&gt; </span><span class="n">reg</span><span class="o">.</span><span class="n">coef_</span>
 <span class="go">array([1., 2.])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">reg</span><span class="o">.</span><span class="n">intercept_</span>
-<span class="go">np.float64(3.0...)</span>
+<span class="go">np.float64(3.0)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">reg</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">5</span><span class="p">]]))</span>
 <span class="go">array([16.])</span>
 </pre></div>
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.LinearRegression.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_base.py#L586"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LinearRegression.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_base.py#L590"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LinearRegression.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit linear model.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1435,7 +1438,7 @@ <h1>LinearRegression<a class="headerlink" href="#linearregression" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.LinearRegression.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LinearRegression.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LinearRegression.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1452,7 +1455,7 @@ <h1>LinearRegression<a class="headerlink" href="#linearregression" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.LinearRegression.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LinearRegression.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LinearRegression.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1473,7 +1476,7 @@ <h1>LinearRegression<a class="headerlink" href="#linearregression" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.LinearRegression.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_base.py#L284"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LinearRegression.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_base.py#L284"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LinearRegression.predict" title="Link to this definition">#</a></dt>
 <dd><p>Predict using the linear model.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1493,7 +1496,7 @@ <h1>LinearRegression<a class="headerlink" href="#linearregression" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.LinearRegression.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L619"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LinearRegression.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L560"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LinearRegression.score" title="Link to this definition">#</a></dt>
 <dd><p>Return <a class="reference internal" href="http://github.com/model_evaluation.html#r2-score"><span class="std std-ref">coefficient of determination</span></a> on test data.</p>
 <p>The coefficient of determination, <span class="math notranslate nohighlight">\(R^2\)</span>, is defined as
 <span class="math notranslate nohighlight">\((1 - \frac{u}{v})\)</span>, where <span class="math notranslate nohighlight">\(u\)</span> is the residual
@@ -1535,7 +1538,7 @@ <h1>LinearRegression<a class="headerlink" href="#linearregression" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.LinearRegression.set_fit_request">
-<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.LinearRegression" title="sklearn.linear_model._base.LinearRegression"><span class="pre">LinearRegression</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LinearRegression.set_fit_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.LinearRegression" title="sklearn.linear_model._base.LinearRegression"><span class="pre">LinearRegression</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LinearRegression.set_fit_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1578,7 +1581,7 @@ <h1>LinearRegression<a class="headerlink" href="#linearregression" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.LinearRegression.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LinearRegression.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LinearRegression.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1602,7 +1605,7 @@ <h1>LinearRegression<a class="headerlink" href="#linearregression" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.LinearRegression.set_score_request">
-<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.LinearRegression" title="sklearn.linear_model._base.LinearRegression"><span class="pre">LinearRegression</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LinearRegression.set_score_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.LinearRegression" title="sklearn.linear_model._base.LinearRegression"><span class="pre">LinearRegression</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LinearRegression.set_score_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
diff --git a/dev/modules/generated/sklearn.linear_model.LogisticRegression.html b/dev/modules/generated/sklearn.linear_model.LogisticRegression.html
index 3496d92bf557c..82427c82402b8 100644
--- a/dev/modules/generated/sklearn.linear_model.LogisticRegression.html
+++ b/dev/modules/generated/sklearn.linear_model.LogisticRegression.html
@@ -42,7 +42,7 @@
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-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
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@@ -1307,7 +1307,7 @@
 <h1>LogisticRegression<a class="headerlink" href="#logisticregression" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.linear_model.LogisticRegression">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.linear_model.</span></span><span class="sig-name descname"><span class="pre">LogisticRegression</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">penalty</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'l2'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dual</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">C</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_intercept</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">intercept_scaling</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">class_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">solver</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'lbfgs'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">multi_class</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'deprecated'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">warm_start</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">l1_ratio</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_logistic.py#L814"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LogisticRegression" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.linear_model.</span></span><span class="sig-name descname"><span class="pre">LogisticRegression</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">penalty</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'l2'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dual</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">C</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_intercept</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">intercept_scaling</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">class_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">solver</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'lbfgs'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">multi_class</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'deprecated'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">warm_start</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">l1_ratio</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_logistic.py#L823"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LogisticRegression" title="Link to this definition">#</a></dt>
 <dd><p>Logistic Regression (aka logit, MaxEnt) classifier.</p>
 <p>This class implements regularized logistic regression using the
 ‘liblinear’ library, ‘newton-cg’, ‘sag’, ‘saga’ and ‘lbfgs’ solvers. <strong>Note
@@ -1426,7 +1426,7 @@ <h1>LogisticRegression<a class="headerlink" href="#logisticregression" title="Li
 </tr>
 <tr class="row-odd"><td><p>‘newton-cholesky’</p></td>
 <td><p>‘l2’, None</p></td>
-<td><p>no</p></td>
+<td><p>yes</p></td>
 </tr>
 <tr class="row-even"><td><p>‘sag’</p></td>
 <td><p>‘l2’, None</p></td>
@@ -1502,7 +1502,7 @@ <h1>LogisticRegression<a class="headerlink" href="#logisticregression" title="Li
 <dt><strong>n_jobs</strong><span class="classifier">int, default=None</span></dt><dd><p>Number of CPU cores used when parallelizing over classes if
 multi_class=’ovr’”. This parameter is ignored when the <code class="docutils literal notranslate"><span class="pre">solver</span></code> is
 set to ‘liblinear’ regardless of whether ‘multi_class’ is specified or
-not. <code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.5.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a>
+not. <code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.6.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a>
 context. <code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all processors.
 See <a class="reference internal" href="http://github.com/glossary.html#term-n_jobs"><span class="xref std std-term">Glossary</span></a> for more details.</p>
 </dd>
@@ -1595,17 +1595,17 @@ <h1>LogisticRegression<a class="headerlink" href="#logisticregression" title="Li
 <span class="gp">&gt;&gt;&gt; </span><span class="n">clf</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X</span><span class="p">[:</span><span class="mi">2</span><span class="p">,</span> <span class="p">:])</span>
 <span class="go">array([0, 0])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">clf</span><span class="o">.</span><span class="n">predict_proba</span><span class="p">(</span><span class="n">X</span><span class="p">[:</span><span class="mi">2</span><span class="p">,</span> <span class="p">:])</span>
-<span class="go">array([[9.8...e-01, 1.8...e-02, 1.4...e-08],</span>
-<span class="go">       [9.7...e-01, 2.8...e-02, ...e-08]])</span>
+<span class="go">array([[9.82e-01, 1.82e-02, 1.44e-08],</span>
+<span class="go">       [9.72e-01, 2.82e-02, 3.02e-08]])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">clf</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
-<span class="go">0.97...</span>
+<span class="go">0.97</span>
 </pre></div>
 </div>
 <p>For a comparison of the LogisticRegression with other classifiers see:
 <a class="reference internal" href="http://github.com/auto_examples/classification/plot_classification_probability.html#sphx-glr-auto-examples-classification-plot-classification-probability-py"><span class="std std-ref">Plot classification probability</span></a>.</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.LogisticRegression.decision_function">
-<span class="sig-name descname"><span class="pre">decision_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_base.py#L330"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LogisticRegression.decision_function" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">decision_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_base.py#L330"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LogisticRegression.decision_function" title="Link to this definition">#</a></dt>
 <dd><p>Predict confidence scores for samples.</p>
 <p>The confidence score for a sample is proportional to the signed
 distance of that sample to the hyperplane.</p>
@@ -1629,7 +1629,7 @@ <h1>LogisticRegression<a class="headerlink" href="#logisticregression" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.LogisticRegression.densify">
-<span class="sig-name descname"><span class="pre">densify</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_base.py#L406"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LogisticRegression.densify" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">densify</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_base.py#L406"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LogisticRegression.densify" title="Link to this definition">#</a></dt>
 <dd><p>Convert coefficient matrix to dense array format.</p>
 <p>Converts the <code class="docutils literal notranslate"><span class="pre">coef_</span></code> member (back) to a numpy.ndarray. This is the
 default format of <code class="docutils literal notranslate"><span class="pre">coef_</span></code> and is required for fitting, so calling
@@ -1647,7 +1647,7 @@ <h1>LogisticRegression<a class="headerlink" href="#logisticregression" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.LogisticRegression.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_logistic.py#L1170"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LogisticRegression.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_logistic.py#L1179"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LogisticRegression.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit the model according to the given training data.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1678,7 +1678,7 @@ <h1>LogisticRegression<a class="headerlink" href="#logisticregression" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.LogisticRegression.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LogisticRegression.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LogisticRegression.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1695,7 +1695,7 @@ <h1>LogisticRegression<a class="headerlink" href="#logisticregression" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.LogisticRegression.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LogisticRegression.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LogisticRegression.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1716,7 +1716,7 @@ <h1>LogisticRegression<a class="headerlink" href="#logisticregression" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.LogisticRegression.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_base.py#L360"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LogisticRegression.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_base.py#L360"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LogisticRegression.predict" title="Link to this definition">#</a></dt>
 <dd><p>Predict class labels for samples in X.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1736,7 +1736,7 @@ <h1>LogisticRegression<a class="headerlink" href="#logisticregression" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.LogisticRegression.predict_log_proba">
-<span class="sig-name descname"><span class="pre">predict_log_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_logistic.py#L1446"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LogisticRegression.predict_log_proba" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict_log_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_logistic.py#L1464"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LogisticRegression.predict_log_proba" title="Link to this definition">#</a></dt>
 <dd><p>Predict logarithm of probability estimates.</p>
 <p>The returned estimates for all classes are ordered by the
 label of classes.</p>
@@ -1760,7 +1760,7 @@ <h1>LogisticRegression<a class="headerlink" href="#logisticregression" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.LogisticRegression.predict_proba">
-<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_logistic.py#L1402"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LogisticRegression.predict_proba" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_logistic.py#L1420"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LogisticRegression.predict_proba" title="Link to this definition">#</a></dt>
 <dd><p>Probability estimates.</p>
 <p>The returned estimates for all classes are ordered by the
 label of classes.</p>
@@ -1790,7 +1790,7 @@ <h1>LogisticRegression<a class="headerlink" href="#logisticregression" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.LogisticRegression.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L546"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LogisticRegression.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L487"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LogisticRegression.score" title="Link to this definition">#</a></dt>
 <dd><p>Return <a class="reference internal" href="http://github.com/model_evaluation.html#accuracy-score"><span class="std std-ref">accuracy</span></a> on provided data and labels.</p>
 <p>In multi-label classification, this is the subset accuracy
 which is a harsh metric since you require for each sample that
@@ -1817,7 +1817,7 @@ <h1>LogisticRegression<a class="headerlink" href="#logisticregression" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.LogisticRegression.set_fit_request">
-<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.LogisticRegression" title="sklearn.linear_model._logistic.LogisticRegression"><span class="pre">LogisticRegression</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LogisticRegression.set_fit_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.LogisticRegression" title="sklearn.linear_model._logistic.LogisticRegression"><span class="pre">LogisticRegression</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LogisticRegression.set_fit_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1860,7 +1860,7 @@ <h1>LogisticRegression<a class="headerlink" href="#logisticregression" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.LogisticRegression.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LogisticRegression.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LogisticRegression.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1884,7 +1884,7 @@ <h1>LogisticRegression<a class="headerlink" href="#logisticregression" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.LogisticRegression.set_score_request">
-<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.LogisticRegression" title="sklearn.linear_model._logistic.LogisticRegression"><span class="pre">LogisticRegression</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LogisticRegression.set_score_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.LogisticRegression" title="sklearn.linear_model._logistic.LogisticRegression"><span class="pre">LogisticRegression</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LogisticRegression.set_score_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1927,7 +1927,7 @@ <h1>LogisticRegression<a class="headerlink" href="#logisticregression" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.LogisticRegression.sparsify">
-<span class="sig-name descname"><span class="pre">sparsify</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_base.py#L426"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LogisticRegression.sparsify" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">sparsify</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_base.py#L426"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LogisticRegression.sparsify" title="Link to this definition">#</a></dt>
 <dd><p>Convert coefficient matrix to sparse format.</p>
 <p>Converts the <code class="docutils literal notranslate"><span class="pre">coef_</span></code> member to a scipy.sparse matrix, which for
 L1-regularized models can be much more memory- and storage-efficient
@@ -1970,9 +1970,9 @@ <h2>Gallery examples<a class="headerlink" href="#gallery-examples" title="Link t
 </div><div class="sphx-glr-thumbcontainer" tooltip="Transform your features into a higher dimensional, sparse space. Then train a linear model on these features."><img alt="" src="http://github.com/_images/sphx_glr_plot_feature_transformation_thumb.png" />
 <p><a class="reference internal" href="http://github.com/auto_examples/ensemble/plot_feature_transformation.html#sphx-glr-auto-examples-ensemble-plot-feature-transformation-py"><span class="std std-ref">Feature transformations with ensembles of trees</span></a></p>
   <div class="sphx-glr-thumbnail-title">Feature transformations with ensembles of trees</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="Plot the class probabilities of the first sample in a toy dataset predicted by three different classifiers and averaged by the VotingClassifier."><img alt="" src="http://github.com/_images/sphx_glr_plot_voting_probas_thumb.png" />
-<p><a class="reference internal" href="http://github.com/auto_examples/ensemble/plot_voting_probas.html#sphx-glr-auto-examples-ensemble-plot-voting-probas-py"><span class="std std-ref">Plot class probabilities calculated by the VotingClassifier</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Plot class probabilities calculated by the VotingClassifier</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="Plot the predicted class probabilities in a toy dataset predicted by three different classifiers and averaged by the VotingClassifier."><img alt="" src="http://github.com/_images/sphx_glr_plot_voting_decision_regions_thumb.png" />
+<p><a class="reference internal" href="http://github.com/auto_examples/ensemble/plot_voting_decision_regions.html#sphx-glr-auto-examples-ensemble-plot-voting-decision-regions-py"><span class="std std-ref">Visualizing the probabilistic predictions of a VotingClassifier</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Visualizing the probabilistic predictions of a VotingClassifier</div>
 </div><div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how Recursive Feature Elimination (~sklearn.feature_selection.RFE) can be used to determine the importance of individual pixels for classifying handwritten digits. RFE recursively removes the least significant features, assigning ranks based on their importance, where higher ranking_ values denote lower importance. The ranking is visualized using both shades of blue and pixel annotations for clarity. As expected, pixels positioned at the center of the image tend to be more predictive than those near the edges."><img alt="" src="http://github.com/_images/sphx_glr_plot_rfe_digits_thumb.png" />
 <p><a class="reference internal" href="http://github.com/auto_examples/feature_selection/plot_rfe_digits.html#sphx-glr-auto-examples-feature-selection-plot-rfe-digits-py"><span class="std std-ref">Recursive feature elimination</span></a></p>
   <div class="sphx-glr-thumbnail-title">Recursive feature elimination</div>
diff --git a/dev/modules/generated/sklearn.linear_model.LogisticRegressionCV.html b/dev/modules/generated/sklearn.linear_model.LogisticRegressionCV.html
index 938f3064708c2..f7c2e68cb6866 100644
--- a/dev/modules/generated/sklearn.linear_model.LogisticRegressionCV.html
+++ b/dev/modules/generated/sklearn.linear_model.LogisticRegressionCV.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>LogisticRegressionCV<a class="headerlink" href="#logisticregressioncv" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.linear_model.LogisticRegressionCV">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.linear_model.</span></span><span class="sig-name descname"><span class="pre">LogisticRegressionCV</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Cs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_intercept</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cv</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dual</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">penalty</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'l2'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scoring</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">solver</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'lbfgs'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">class_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">refit</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">intercept_scaling</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">multi_class</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'deprecated'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">l1_ratios</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_logistic.py#L1473"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LogisticRegressionCV" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.linear_model.</span></span><span class="sig-name descname"><span class="pre">LogisticRegressionCV</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Cs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_intercept</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cv</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dual</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">penalty</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'l2'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scoring</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">solver</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'lbfgs'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">class_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">refit</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">intercept_scaling</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">multi_class</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'deprecated'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">l1_ratios</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_logistic.py#L1491"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LogisticRegressionCV" title="Link to this definition">#</a></dt>
 <dd><p>Logistic Regression CV (aka logit, MaxEnt) classifier.</p>
 <p>See glossary entry for <a class="reference internal" href="http://github.com/glossary.html#term-cross-validation-estimator"><span class="xref std std-term">cross-validation estimator</span></a>.</p>
 <p>This class implements logistic regression using liblinear, newton-cg, sag
@@ -1411,7 +1411,7 @@ <h1>LogisticRegressionCV<a class="headerlink" href="#logisticregressioncv" title
 </tr>
 <tr class="row-odd"><td><p>‘newton-cholesky’</p></td>
 <td><p>‘l2’,</p></td>
-<td><p>no</p></td>
+<td><p>yes</p></td>
 </tr>
 <tr class="row-even"><td><p>‘sag’</p></td>
 <td><p>‘l2’,</p></td>
@@ -1457,7 +1457,7 @@ <h1>LogisticRegressionCV<a class="headerlink" href="#logisticregressioncv" title
 </div>
 </dd>
 <dt><strong>n_jobs</strong><span class="classifier">int, default=None</span></dt><dd><p>Number of CPU cores used during the cross-validation loop.
-<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.5.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
+<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.6.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
 <code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all processors. See <a class="reference internal" href="http://github.com/glossary.html#term-n_jobs"><span class="xref std std-term">Glossary</span></a>
 for more details.</p>
 </dd>
@@ -1602,7 +1602,7 @@ <h1>LogisticRegressionCV<a class="headerlink" href="#logisticregressioncv" title
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.LogisticRegressionCV.decision_function">
-<span class="sig-name descname"><span class="pre">decision_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_base.py#L330"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LogisticRegressionCV.decision_function" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">decision_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_base.py#L330"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LogisticRegressionCV.decision_function" title="Link to this definition">#</a></dt>
 <dd><p>Predict confidence scores for samples.</p>
 <p>The confidence score for a sample is proportional to the signed
 distance of that sample to the hyperplane.</p>
@@ -1626,7 +1626,7 @@ <h1>LogisticRegressionCV<a class="headerlink" href="#logisticregressioncv" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.LogisticRegressionCV.densify">
-<span class="sig-name descname"><span class="pre">densify</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_base.py#L406"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LogisticRegressionCV.densify" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">densify</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_base.py#L406"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LogisticRegressionCV.densify" title="Link to this definition">#</a></dt>
 <dd><p>Convert coefficient matrix to dense array format.</p>
 <p>Converts the <code class="docutils literal notranslate"><span class="pre">coef_</span></code> member (back) to a numpy.ndarray. This is the
 default format of <code class="docutils literal notranslate"><span class="pre">coef_</span></code> and is required for fitting, so calling
@@ -1644,7 +1644,7 @@ <h1>LogisticRegressionCV<a class="headerlink" href="#logisticregressioncv" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.LogisticRegressionCV.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_logistic.py#L1820"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LogisticRegressionCV.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_logistic.py#L1838"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LogisticRegressionCV.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit the model according to the given training data.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1675,7 +1675,7 @@ <h1>LogisticRegressionCV<a class="headerlink" href="#logisticregressioncv" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.LogisticRegressionCV.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_logistic.py#L2256"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LogisticRegressionCV.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_logistic.py#L2274"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LogisticRegressionCV.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1695,7 +1695,7 @@ <h1>LogisticRegressionCV<a class="headerlink" href="#logisticregressioncv" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.LogisticRegressionCV.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LogisticRegressionCV.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LogisticRegressionCV.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1716,7 +1716,7 @@ <h1>LogisticRegressionCV<a class="headerlink" href="#logisticregressioncv" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.LogisticRegressionCV.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_base.py#L360"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LogisticRegressionCV.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_base.py#L360"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LogisticRegressionCV.predict" title="Link to this definition">#</a></dt>
 <dd><p>Predict class labels for samples in X.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1736,7 +1736,7 @@ <h1>LogisticRegressionCV<a class="headerlink" href="#logisticregressioncv" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.LogisticRegressionCV.predict_log_proba">
-<span class="sig-name descname"><span class="pre">predict_log_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_logistic.py#L1446"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LogisticRegressionCV.predict_log_proba" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict_log_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_logistic.py#L1464"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LogisticRegressionCV.predict_log_proba" title="Link to this definition">#</a></dt>
 <dd><p>Predict logarithm of probability estimates.</p>
 <p>The returned estimates for all classes are ordered by the
 label of classes.</p>
@@ -1760,7 +1760,7 @@ <h1>LogisticRegressionCV<a class="headerlink" href="#logisticregressioncv" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.LogisticRegressionCV.predict_proba">
-<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_logistic.py#L1402"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LogisticRegressionCV.predict_proba" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_logistic.py#L1420"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LogisticRegressionCV.predict_proba" title="Link to this definition">#</a></dt>
 <dd><p>Probability estimates.</p>
 <p>The returned estimates for all classes are ordered by the
 label of classes.</p>
@@ -1790,7 +1790,7 @@ <h1>LogisticRegressionCV<a class="headerlink" href="#logisticregressioncv" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.LogisticRegressionCV.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">score_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_logistic.py#L2209"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LogisticRegressionCV.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">score_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_logistic.py#L2227"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LogisticRegressionCV.score" title="Link to this definition">#</a></dt>
 <dd><p>Score using the <code class="docutils literal notranslate"><span class="pre">scoring</span></code> option on the given test data and labels.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1819,7 +1819,7 @@ <h1>LogisticRegressionCV<a class="headerlink" href="#logisticregressioncv" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.LogisticRegressionCV.set_fit_request">
-<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.LogisticRegressionCV" title="sklearn.linear_model._logistic.LogisticRegressionCV"><span class="pre">LogisticRegressionCV</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LogisticRegressionCV.set_fit_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.LogisticRegressionCV" title="sklearn.linear_model._logistic.LogisticRegressionCV"><span class="pre">LogisticRegressionCV</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LogisticRegressionCV.set_fit_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1862,7 +1862,7 @@ <h1>LogisticRegressionCV<a class="headerlink" href="#logisticregressioncv" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.LogisticRegressionCV.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LogisticRegressionCV.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LogisticRegressionCV.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1886,7 +1886,7 @@ <h1>LogisticRegressionCV<a class="headerlink" href="#logisticregressioncv" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.LogisticRegressionCV.set_score_request">
-<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.LogisticRegressionCV" title="sklearn.linear_model._logistic.LogisticRegressionCV"><span class="pre">LogisticRegressionCV</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LogisticRegressionCV.set_score_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.LogisticRegressionCV" title="sklearn.linear_model._logistic.LogisticRegressionCV"><span class="pre">LogisticRegressionCV</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LogisticRegressionCV.set_score_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1929,7 +1929,7 @@ <h1>LogisticRegressionCV<a class="headerlink" href="#logisticregressioncv" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.LogisticRegressionCV.sparsify">
-<span class="sig-name descname"><span class="pre">sparsify</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_base.py#L426"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LogisticRegressionCV.sparsify" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">sparsify</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_base.py#L426"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LogisticRegressionCV.sparsify" title="Link to this definition">#</a></dt>
 <dd><p>Convert coefficient matrix to sparse format.</p>
 <p>Converts the <code class="docutils literal notranslate"><span class="pre">coef_</span></code> member to a scipy.sparse matrix, which for
 L1-regularized models can be much more memory- and storage-efficient
diff --git a/dev/modules/generated/sklearn.linear_model.MultiTaskElasticNet.html b/dev/modules/generated/sklearn.linear_model.MultiTaskElasticNet.html
index 47bad8361d2ce..1f90bb5bff151 100644
--- a/dev/modules/generated/sklearn.linear_model.MultiTaskElasticNet.html
+++ b/dev/modules/generated/sklearn.linear_model.MultiTaskElasticNet.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1306,7 +1306,7 @@
 <h1>MultiTaskElasticNet<a class="headerlink" href="#multitaskelasticnet" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.linear_model.MultiTaskElasticNet">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.linear_model.</span></span><span class="sig-name descname"><span class="pre">MultiTaskElasticNet</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">l1_ratio</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_intercept</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy_X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">warm_start</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">selection</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'cyclic'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_coordinate_descent.py#L2410"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.MultiTaskElasticNet" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.linear_model.</span></span><span class="sig-name descname"><span class="pre">MultiTaskElasticNet</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">l1_ratio</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_intercept</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy_X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">warm_start</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">selection</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'cyclic'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_coordinate_descent.py#L2478"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.MultiTaskElasticNet" title="Link to this definition">#</a></dt>
 <dd><p>Multi-task ElasticNet model trained with L1/L2 mixed-norm as regularizer.</p>
 <p>The optimization objective for MultiTaskElasticNet is:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">(</span><span class="mi">1</span> <span class="o">/</span> <span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="n">n_samples</span><span class="p">))</span> <span class="o">*</span> <span class="o">||</span><span class="n">Y</span> <span class="o">-</span> <span class="n">XW</span><span class="o">||</span><span class="n">_Fro</span><span class="o">^</span><span class="mi">2</span>
@@ -1419,7 +1419,7 @@ <h1>MultiTaskElasticNet<a class="headerlink" href="#multitaskelasticnet" title="
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.MultiTaskElasticNet.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_coordinate_descent.py#L2564"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.MultiTaskElasticNet.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_coordinate_descent.py#L2632"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.MultiTaskElasticNet.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit MultiTaskElasticNet model with coordinate descent.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1447,7 +1447,7 @@ <h1>MultiTaskElasticNet<a class="headerlink" href="#multitaskelasticnet" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.MultiTaskElasticNet.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.MultiTaskElasticNet.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.MultiTaskElasticNet.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1464,7 +1464,7 @@ <h1>MultiTaskElasticNet<a class="headerlink" href="#multitaskelasticnet" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.MultiTaskElasticNet.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.MultiTaskElasticNet.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.MultiTaskElasticNet.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1485,7 +1485,7 @@ <h1>MultiTaskElasticNet<a class="headerlink" href="#multitaskelasticnet" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.MultiTaskElasticNet.path">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">path</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">l1_ratio</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_alphas</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alphas</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">precompute</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Xy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy_X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coef_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_n_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">positive</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_input</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_coordinate_descent.py#L377"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.MultiTaskElasticNet.path" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">path</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">l1_ratio</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_alphas</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alphas</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">precompute</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Xy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy_X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coef_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_n_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">positive</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_input</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_coordinate_descent.py#L377"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.MultiTaskElasticNet.path" title="Link to this definition">#</a></dt>
 <dd><p>Compute elastic net path with coordinate descent.</p>
 <p>The elastic net optimization function varies for mono and multi-outputs.</p>
 <p>For mono-output tasks it is:</p>
@@ -1590,23 +1590,23 @@ <h1>MultiTaskElasticNet<a class="headerlink" href="#multitaskelasticnet" title="
 <span class="gp">... </span>   <span class="n">n_samples</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">n_features</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">n_informative</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">coef</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span>
 <span class="gp">... </span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">true_coef</span>
-<span class="go">array([ 0.        ,  0.        ,  0.        , 97.9..., 45.7...])</span>
+<span class="go">array([ 0.        ,  0.        ,  0.        , 97.9, 45.7])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">alphas</span><span class="p">,</span> <span class="n">estimated_coef</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">enet_path</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">n_alphas</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">alphas</span><span class="o">.</span><span class="n">shape</span>
 <span class="go">(3,)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">estimated_coef</span>
-<span class="go"> array([[ 0.        ,  0.78...,  0.56...],</span>
-<span class="go">        [ 0.        ,  1.12...,  0.61...],</span>
-<span class="go">        [-0.        , -2.12..., -1.12...],</span>
-<span class="go">        [ 0.        , 23.04..., 88.93...],</span>
-<span class="go">        [ 0.        , 10.63..., 41.56...]])</span>
+<span class="go"> array([[ 0.,  0.787,  0.568],</span>
+<span class="go">        [ 0.,  1.120,  0.620],</span>
+<span class="go">        [-0., -2.129, -1.128],</span>
+<span class="go">        [ 0., 23.046, 88.939],</span>
+<span class="go">        [ 0., 10.637, 41.566]])</span>
 </pre></div>
 </div>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.MultiTaskElasticNet.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_base.py#L284"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.MultiTaskElasticNet.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_base.py#L284"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.MultiTaskElasticNet.predict" title="Link to this definition">#</a></dt>
 <dd><p>Predict using the linear model.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1626,7 +1626,7 @@ <h1>MultiTaskElasticNet<a class="headerlink" href="#multitaskelasticnet" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.MultiTaskElasticNet.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L619"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.MultiTaskElasticNet.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L560"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.MultiTaskElasticNet.score" title="Link to this definition">#</a></dt>
 <dd><p>Return <a class="reference internal" href="http://github.com/model_evaluation.html#r2-score"><span class="std std-ref">coefficient of determination</span></a> on test data.</p>
 <p>The coefficient of determination, <span class="math notranslate nohighlight">\(R^2\)</span>, is defined as
 <span class="math notranslate nohighlight">\((1 - \frac{u}{v})\)</span>, where <span class="math notranslate nohighlight">\(u\)</span> is the residual
@@ -1668,7 +1668,7 @@ <h1>MultiTaskElasticNet<a class="headerlink" href="#multitaskelasticnet" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.MultiTaskElasticNet.set_fit_request">
-<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.MultiTaskElasticNet" title="sklearn.linear_model._coordinate_descent.MultiTaskElasticNet"><span class="pre">MultiTaskElasticNet</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.MultiTaskElasticNet.set_fit_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.MultiTaskElasticNet" title="sklearn.linear_model._coordinate_descent.MultiTaskElasticNet"><span class="pre">MultiTaskElasticNet</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.MultiTaskElasticNet.set_fit_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1711,7 +1711,7 @@ <h1>MultiTaskElasticNet<a class="headerlink" href="#multitaskelasticnet" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.MultiTaskElasticNet.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.MultiTaskElasticNet.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.MultiTaskElasticNet.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1735,7 +1735,7 @@ <h1>MultiTaskElasticNet<a class="headerlink" href="#multitaskelasticnet" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.MultiTaskElasticNet.set_score_request">
-<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.MultiTaskElasticNet" title="sklearn.linear_model._coordinate_descent.MultiTaskElasticNet"><span class="pre">MultiTaskElasticNet</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.MultiTaskElasticNet.set_score_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.MultiTaskElasticNet" title="sklearn.linear_model._coordinate_descent.MultiTaskElasticNet"><span class="pre">MultiTaskElasticNet</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.MultiTaskElasticNet.set_score_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
diff --git a/dev/modules/generated/sklearn.linear_model.MultiTaskElasticNetCV.html b/dev/modules/generated/sklearn.linear_model.MultiTaskElasticNetCV.html
index 4f3ed62e4ea38..2dcbe57c2028c 100644
--- a/dev/modules/generated/sklearn.linear_model.MultiTaskElasticNetCV.html
+++ b/dev/modules/generated/sklearn.linear_model.MultiTaskElasticNetCV.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1306,7 +1306,7 @@
 <h1>MultiTaskElasticNetCV<a class="headerlink" href="#multitaskelasticnetcv" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.linear_model.MultiTaskElasticNetCV">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.linear_model.</span></span><span class="sig-name descname"><span class="pre">MultiTaskElasticNetCV</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">l1_ratio</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_alphas</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alphas</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_intercept</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cv</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy_X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">selection</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'cyclic'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_coordinate_descent.py#L2806"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.MultiTaskElasticNetCV" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.linear_model.</span></span><span class="sig-name descname"><span class="pre">MultiTaskElasticNetCV</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">l1_ratio</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_alphas</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'deprecated'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alphas</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'warn'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_intercept</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cv</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy_X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">selection</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'cyclic'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_coordinate_descent.py#L2874"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.MultiTaskElasticNetCV" title="Link to this definition">#</a></dt>
 <dd><p>Multi-task L1/L2 ElasticNet with built-in cross-validation.</p>
 <p>See glossary entry for <a class="reference internal" href="http://github.com/glossary.html#term-cross-validation-estimator"><span class="xref std std-term">cross-validation estimator</span></a>.</p>
 <p>The optimization objective for MultiTaskElasticNet is:</p>
@@ -1342,9 +1342,22 @@ <h1>MultiTaskElasticNetCV<a class="headerlink" href="#multitaskelasticnetcv" tit
 <code class="docutils literal notranslate"><span class="pre">alpha_min</span> <span class="pre">/</span> <span class="pre">alpha_max</span> <span class="pre">=</span> <span class="pre">1e-3</span></code>.</p>
 </dd>
 <dt><strong>n_alphas</strong><span class="classifier">int, default=100</span></dt><dd><p>Number of alphas along the regularization path.</p>
+<div class="deprecated">
+<p><span class="versionmodified deprecated">Deprecated since version 1.7: </span><code class="docutils literal notranslate"><span class="pre">n_alphas</span></code> was deprecated in 1.7 and will be removed in 1.9. Use <code class="docutils literal notranslate"><span class="pre">alphas</span></code>
+instead.</p>
+</div>
 </dd>
-<dt><strong>alphas</strong><span class="classifier">array-like, default=None</span></dt><dd><p>List of alphas where to compute the models.
-If not provided, set automatically.</p>
+<dt><strong>alphas</strong><span class="classifier">array-like or int, default=None</span></dt><dd><p>Values of alphas to test along the regularization path, used for each l1_ratio.
+If int, <code class="docutils literal notranslate"><span class="pre">alphas</span></code> values are generated automatically.
+If array-like, list of alpha values to use.</p>
+<div class="versionchanged">
+<p><span class="versionmodified changed">Changed in version 1.7: </span><code class="docutils literal notranslate"><span class="pre">alphas</span></code> accepts an integer value which removes the need to pass
+<code class="docutils literal notranslate"><span class="pre">n_alphas</span></code>.</p>
+</div>
+<div class="deprecated">
+<p><span class="versionmodified deprecated">Deprecated since version 1.7: </span><code class="docutils literal notranslate"><span class="pre">alphas=None</span></code> was deprecated in 1.7 and will be removed in 1.9, at which
+point the default value will be set to 100.</p>
+</div>
 </dd>
 <dt><strong>fit_intercept</strong><span class="classifier">bool, default=True</span></dt><dd><p>Whether to calculate the intercept for this model. If set
 to false, no intercept will be used in calculations
@@ -1378,7 +1391,7 @@ <h1>MultiTaskElasticNetCV<a class="headerlink" href="#multitaskelasticnetcv" tit
 </dd>
 <dt><strong>n_jobs</strong><span class="classifier">int, default=None</span></dt><dd><p>Number of CPUs to use during the cross validation. Note that this is
 used only if multiple values for l1_ratio are given.
-<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.5.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
+<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.6.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
 <code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all processors. See <a class="reference internal" href="http://github.com/glossary.html#term-n_jobs"><span class="xref std std-term">Glossary</span></a>
 for more details.</p>
 </dd>
@@ -1460,7 +1473,7 @@ <h1>MultiTaskElasticNetCV<a class="headerlink" href="#multitaskelasticnetcv" tit
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.MultiTaskElasticNetCV.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_coordinate_descent.py#L3033"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.MultiTaskElasticNetCV.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_coordinate_descent.py#L3114"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.MultiTaskElasticNetCV.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit MultiTaskElasticNet model with coordinate descent.</p>
 <p>Fit is on grid of alphas and best alpha estimated by cross-validation.</p>
 <dl class="field-list">
@@ -1492,7 +1505,7 @@ <h1>MultiTaskElasticNetCV<a class="headerlink" href="#multitaskelasticnetcv" tit
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.MultiTaskElasticNetCV.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_coordinate_descent.py#L1846"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.MultiTaskElasticNetCV.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_coordinate_descent.py#L1888"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.MultiTaskElasticNetCV.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1512,7 +1525,7 @@ <h1>MultiTaskElasticNetCV<a class="headerlink" href="#multitaskelasticnetcv" tit
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.MultiTaskElasticNetCV.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.MultiTaskElasticNetCV.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.MultiTaskElasticNetCV.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1533,7 +1546,7 @@ <h1>MultiTaskElasticNetCV<a class="headerlink" href="#multitaskelasticnetcv" tit
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.MultiTaskElasticNetCV.path">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">path</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">l1_ratio</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_alphas</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alphas</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">precompute</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Xy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy_X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coef_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_n_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">positive</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_input</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_coordinate_descent.py#L377"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.MultiTaskElasticNetCV.path" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">path</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">l1_ratio</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_alphas</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alphas</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">precompute</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Xy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy_X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coef_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_n_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">positive</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_input</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_coordinate_descent.py#L377"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.MultiTaskElasticNetCV.path" title="Link to this definition">#</a></dt>
 <dd><p>Compute elastic net path with coordinate descent.</p>
 <p>The elastic net optimization function varies for mono and multi-outputs.</p>
 <p>For mono-output tasks it is:</p>
@@ -1638,23 +1651,23 @@ <h1>MultiTaskElasticNetCV<a class="headerlink" href="#multitaskelasticnetcv" tit
 <span class="gp">... </span>   <span class="n">n_samples</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">n_features</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">n_informative</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">coef</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span>
 <span class="gp">... </span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">true_coef</span>
-<span class="go">array([ 0.        ,  0.        ,  0.        , 97.9..., 45.7...])</span>
+<span class="go">array([ 0.        ,  0.        ,  0.        , 97.9, 45.7])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">alphas</span><span class="p">,</span> <span class="n">estimated_coef</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">enet_path</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">n_alphas</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">alphas</span><span class="o">.</span><span class="n">shape</span>
 <span class="go">(3,)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">estimated_coef</span>
-<span class="go"> array([[ 0.        ,  0.78...,  0.56...],</span>
-<span class="go">        [ 0.        ,  1.12...,  0.61...],</span>
-<span class="go">        [-0.        , -2.12..., -1.12...],</span>
-<span class="go">        [ 0.        , 23.04..., 88.93...],</span>
-<span class="go">        [ 0.        , 10.63..., 41.56...]])</span>
+<span class="go"> array([[ 0.,  0.787,  0.568],</span>
+<span class="go">        [ 0.,  1.120,  0.620],</span>
+<span class="go">        [-0., -2.129, -1.128],</span>
+<span class="go">        [ 0., 23.046, 88.939],</span>
+<span class="go">        [ 0., 10.637, 41.566]])</span>
 </pre></div>
 </div>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.MultiTaskElasticNetCV.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_base.py#L284"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.MultiTaskElasticNetCV.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_base.py#L284"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.MultiTaskElasticNetCV.predict" title="Link to this definition">#</a></dt>
 <dd><p>Predict using the linear model.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1674,7 +1687,7 @@ <h1>MultiTaskElasticNetCV<a class="headerlink" href="#multitaskelasticnetcv" tit
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.MultiTaskElasticNetCV.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L619"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.MultiTaskElasticNetCV.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L560"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.MultiTaskElasticNetCV.score" title="Link to this definition">#</a></dt>
 <dd><p>Return <a class="reference internal" href="http://github.com/model_evaluation.html#r2-score"><span class="std std-ref">coefficient of determination</span></a> on test data.</p>
 <p>The coefficient of determination, <span class="math notranslate nohighlight">\(R^2\)</span>, is defined as
 <span class="math notranslate nohighlight">\((1 - \frac{u}{v})\)</span>, where <span class="math notranslate nohighlight">\(u\)</span> is the residual
@@ -1716,7 +1729,7 @@ <h1>MultiTaskElasticNetCV<a class="headerlink" href="#multitaskelasticnetcv" tit
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.MultiTaskElasticNetCV.set_fit_request">
-<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.MultiTaskElasticNetCV" title="sklearn.linear_model._coordinate_descent.MultiTaskElasticNetCV"><span class="pre">MultiTaskElasticNetCV</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.MultiTaskElasticNetCV.set_fit_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.MultiTaskElasticNetCV" title="sklearn.linear_model._coordinate_descent.MultiTaskElasticNetCV"><span class="pre">MultiTaskElasticNetCV</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.MultiTaskElasticNetCV.set_fit_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1759,7 +1772,7 @@ <h1>MultiTaskElasticNetCV<a class="headerlink" href="#multitaskelasticnetcv" tit
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.MultiTaskElasticNetCV.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.MultiTaskElasticNetCV.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.MultiTaskElasticNetCV.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1783,7 +1796,7 @@ <h1>MultiTaskElasticNetCV<a class="headerlink" href="#multitaskelasticnetcv" tit
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.MultiTaskElasticNetCV.set_score_request">
-<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.MultiTaskElasticNetCV" title="sklearn.linear_model._coordinate_descent.MultiTaskElasticNetCV"><span class="pre">MultiTaskElasticNetCV</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.MultiTaskElasticNetCV.set_score_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.MultiTaskElasticNetCV" title="sklearn.linear_model._coordinate_descent.MultiTaskElasticNetCV"><span class="pre">MultiTaskElasticNetCV</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.MultiTaskElasticNetCV.set_score_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
diff --git a/dev/modules/generated/sklearn.linear_model.MultiTaskLasso.html b/dev/modules/generated/sklearn.linear_model.MultiTaskLasso.html
index 6cbd2898d9fdf..43b9694aa3a23 100644
--- a/dev/modules/generated/sklearn.linear_model.MultiTaskLasso.html
+++ b/dev/modules/generated/sklearn.linear_model.MultiTaskLasso.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
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@@ -1308,7 +1308,7 @@
 <h1>MultiTaskLasso<a class="headerlink" href="#multitasklasso" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.linear_model.MultiTaskLasso">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.linear_model.</span></span><span class="sig-name descname"><span class="pre">MultiTaskLasso</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_intercept</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy_X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">warm_start</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">selection</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'cyclic'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_coordinate_descent.py#L2663"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.MultiTaskLasso" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.linear_model.</span></span><span class="sig-name descname"><span class="pre">MultiTaskLasso</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_intercept</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy_X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">warm_start</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">selection</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'cyclic'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_coordinate_descent.py#L2731"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.MultiTaskLasso" title="Link to this definition">#</a></dt>
 <dd><p>Multi-task Lasso model trained with L1/L2 mixed-norm as regularizer.</p>
 <p>The optimization objective for Lasso is:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">(</span><span class="mi">1</span> <span class="o">/</span> <span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="n">n_samples</span><span class="p">))</span> <span class="o">*</span> <span class="o">||</span><span class="n">Y</span> <span class="o">-</span> <span class="n">XW</span><span class="o">||^</span><span class="mi">2</span><span class="n">_Fro</span> <span class="o">+</span> <span class="n">alpha</span> <span class="o">*</span> <span class="o">||</span><span class="n">W</span><span class="o">||</span><span class="n">_21</span>
@@ -1413,7 +1413,7 @@ <h1>MultiTaskLasso<a class="headerlink" href="#multitasklasso" title="Link to th
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.MultiTaskLasso.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_coordinate_descent.py#L2564"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.MultiTaskLasso.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_coordinate_descent.py#L2632"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.MultiTaskLasso.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit MultiTaskElasticNet model with coordinate descent.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1441,7 +1441,7 @@ <h1>MultiTaskLasso<a class="headerlink" href="#multitasklasso" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.MultiTaskLasso.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.MultiTaskLasso.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.MultiTaskLasso.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1458,7 +1458,7 @@ <h1>MultiTaskLasso<a class="headerlink" href="#multitasklasso" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.MultiTaskLasso.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.MultiTaskLasso.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.MultiTaskLasso.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1479,7 +1479,7 @@ <h1>MultiTaskLasso<a class="headerlink" href="#multitasklasso" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.MultiTaskLasso.path">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">path</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">l1_ratio</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_alphas</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alphas</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">precompute</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Xy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy_X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coef_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_n_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">positive</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_input</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_coordinate_descent.py#L377"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.MultiTaskLasso.path" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">path</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">l1_ratio</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_alphas</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alphas</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">precompute</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Xy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy_X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coef_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_n_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">positive</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_input</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_coordinate_descent.py#L377"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.MultiTaskLasso.path" title="Link to this definition">#</a></dt>
 <dd><p>Compute elastic net path with coordinate descent.</p>
 <p>The elastic net optimization function varies for mono and multi-outputs.</p>
 <p>For mono-output tasks it is:</p>
@@ -1584,23 +1584,23 @@ <h1>MultiTaskLasso<a class="headerlink" href="#multitasklasso" title="Link to th
 <span class="gp">... </span>   <span class="n">n_samples</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">n_features</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">n_informative</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">coef</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span>
 <span class="gp">... </span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">true_coef</span>
-<span class="go">array([ 0.        ,  0.        ,  0.        , 97.9..., 45.7...])</span>
+<span class="go">array([ 0.        ,  0.        ,  0.        , 97.9, 45.7])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">alphas</span><span class="p">,</span> <span class="n">estimated_coef</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">enet_path</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">n_alphas</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">alphas</span><span class="o">.</span><span class="n">shape</span>
 <span class="go">(3,)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">estimated_coef</span>
-<span class="go"> array([[ 0.        ,  0.78...,  0.56...],</span>
-<span class="go">        [ 0.        ,  1.12...,  0.61...],</span>
-<span class="go">        [-0.        , -2.12..., -1.12...],</span>
-<span class="go">        [ 0.        , 23.04..., 88.93...],</span>
-<span class="go">        [ 0.        , 10.63..., 41.56...]])</span>
+<span class="go"> array([[ 0.,  0.787,  0.568],</span>
+<span class="go">        [ 0.,  1.120,  0.620],</span>
+<span class="go">        [-0., -2.129, -1.128],</span>
+<span class="go">        [ 0., 23.046, 88.939],</span>
+<span class="go">        [ 0., 10.637, 41.566]])</span>
 </pre></div>
 </div>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.MultiTaskLasso.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_base.py#L284"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.MultiTaskLasso.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_base.py#L284"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.MultiTaskLasso.predict" title="Link to this definition">#</a></dt>
 <dd><p>Predict using the linear model.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1620,7 +1620,7 @@ <h1>MultiTaskLasso<a class="headerlink" href="#multitasklasso" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.MultiTaskLasso.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L619"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.MultiTaskLasso.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L560"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.MultiTaskLasso.score" title="Link to this definition">#</a></dt>
 <dd><p>Return <a class="reference internal" href="http://github.com/model_evaluation.html#r2-score"><span class="std std-ref">coefficient of determination</span></a> on test data.</p>
 <p>The coefficient of determination, <span class="math notranslate nohighlight">\(R^2\)</span>, is defined as
 <span class="math notranslate nohighlight">\((1 - \frac{u}{v})\)</span>, where <span class="math notranslate nohighlight">\(u\)</span> is the residual
@@ -1662,7 +1662,7 @@ <h1>MultiTaskLasso<a class="headerlink" href="#multitasklasso" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.MultiTaskLasso.set_fit_request">
-<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.MultiTaskLasso" title="sklearn.linear_model._coordinate_descent.MultiTaskLasso"><span class="pre">MultiTaskLasso</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.MultiTaskLasso.set_fit_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.MultiTaskLasso" title="sklearn.linear_model._coordinate_descent.MultiTaskLasso"><span class="pre">MultiTaskLasso</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.MultiTaskLasso.set_fit_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1705,7 +1705,7 @@ <h1>MultiTaskLasso<a class="headerlink" href="#multitasklasso" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.MultiTaskLasso.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.MultiTaskLasso.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.MultiTaskLasso.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1729,7 +1729,7 @@ <h1>MultiTaskLasso<a class="headerlink" href="#multitasklasso" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.MultiTaskLasso.set_score_request">
-<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.MultiTaskLasso" title="sklearn.linear_model._coordinate_descent.MultiTaskLasso"><span class="pre">MultiTaskLasso</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.MultiTaskLasso.set_score_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.MultiTaskLasso" title="sklearn.linear_model._coordinate_descent.MultiTaskLasso"><span class="pre">MultiTaskLasso</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.MultiTaskLasso.set_score_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
diff --git a/dev/modules/generated/sklearn.linear_model.MultiTaskLassoCV.html b/dev/modules/generated/sklearn.linear_model.MultiTaskLassoCV.html
index 49c4387e86f0e..638a8946540aa 100644
--- a/dev/modules/generated/sklearn.linear_model.MultiTaskLassoCV.html
+++ b/dev/modules/generated/sklearn.linear_model.MultiTaskLassoCV.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1306,7 +1306,7 @@
 <h1>MultiTaskLassoCV<a class="headerlink" href="#multitasklassocv" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.linear_model.MultiTaskLassoCV">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.linear_model.</span></span><span class="sig-name descname"><span class="pre">MultiTaskLassoCV</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_alphas</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alphas</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_intercept</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy_X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cv</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">selection</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'cyclic'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_coordinate_descent.py#L3063"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.MultiTaskLassoCV" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.linear_model.</span></span><span class="sig-name descname"><span class="pre">MultiTaskLassoCV</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_alphas</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'deprecated'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alphas</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'warn'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_intercept</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy_X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cv</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">selection</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'cyclic'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_coordinate_descent.py#L3144"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.MultiTaskLassoCV" title="Link to this definition">#</a></dt>
 <dd><p>Multi-task Lasso model trained with L1/L2 mixed-norm as regularizer.</p>
 <p>See glossary entry for <a class="reference internal" href="http://github.com/glossary.html#term-cross-validation-estimator"><span class="xref std std-term">cross-validation estimator</span></a>.</p>
 <p>The optimization objective for MultiTaskLasso is:</p>
@@ -1329,9 +1329,22 @@ <h1>MultiTaskLassoCV<a class="headerlink" href="#multitasklassocv" title="Link t
 <code class="docutils literal notranslate"><span class="pre">alpha_min</span> <span class="pre">/</span> <span class="pre">alpha_max</span> <span class="pre">=</span> <span class="pre">1e-3</span></code>.</p>
 </dd>
 <dt><strong>n_alphas</strong><span class="classifier">int, default=100</span></dt><dd><p>Number of alphas along the regularization path.</p>
+<div class="deprecated">
+<p><span class="versionmodified deprecated">Deprecated since version 1.7: </span><code class="docutils literal notranslate"><span class="pre">n_alphas</span></code> was deprecated in 1.7 and will be removed in 1.9. Use <code class="docutils literal notranslate"><span class="pre">alphas</span></code>
+instead.</p>
+</div>
 </dd>
-<dt><strong>alphas</strong><span class="classifier">array-like, default=None</span></dt><dd><p>List of alphas where to compute the models.
-If not provided, set automatically.</p>
+<dt><strong>alphas</strong><span class="classifier">array-like or int, default=None</span></dt><dd><p>Values of alphas to test along the regularization path.
+If int, <code class="docutils literal notranslate"><span class="pre">alphas</span></code> values are generated automatically.
+If array-like, list of alpha values to use.</p>
+<div class="versionchanged">
+<p><span class="versionmodified changed">Changed in version 1.7: </span><code class="docutils literal notranslate"><span class="pre">alphas</span></code> accepts an integer value which removes the need to pass
+<code class="docutils literal notranslate"><span class="pre">n_alphas</span></code>.</p>
+</div>
+<div class="deprecated">
+<p><span class="versionmodified deprecated">Deprecated since version 1.7: </span><code class="docutils literal notranslate"><span class="pre">alphas=None</span></code> was deprecated in 1.7 and will be removed in 1.9, at which
+point the default value will be set to 100.</p>
+</div>
 </dd>
 <dt><strong>fit_intercept</strong><span class="classifier">bool, default=True</span></dt><dd><p>Whether to calculate the intercept for this model. If set
 to false, no intercept will be used in calculations
@@ -1365,7 +1378,7 @@ <h1>MultiTaskLassoCV<a class="headerlink" href="#multitasklassocv" title="Link t
 </dd>
 <dt><strong>n_jobs</strong><span class="classifier">int, default=None</span></dt><dd><p>Number of CPUs to use during the cross validation. Note that this is
 used only if multiple values for l1_ratio are given.
-<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.5.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
+<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.6.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
 <code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all processors. See <a class="reference internal" href="http://github.com/glossary.html#term-n_jobs"><span class="xref std std-term">Glossary</span></a>
 for more details.</p>
 </dd>
@@ -1437,16 +1450,16 @@ <h1>MultiTaskLassoCV<a class="headerlink" href="#multitasklassocv" title="Link t
 <span class="gp">&gt;&gt;&gt; </span><span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">make_regression</span><span class="p">(</span><span class="n">n_targets</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">noise</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">reg</span> <span class="o">=</span> <span class="n">MultiTaskLassoCV</span><span class="p">(</span><span class="n">cv</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">r2_score</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">reg</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X</span><span class="p">))</span>
-<span class="go">0.9994...</span>
+<span class="go">0.9994</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">reg</span><span class="o">.</span><span class="n">alpha_</span>
-<span class="go">np.float64(0.5713...)</span>
+<span class="go">np.float64(0.5713)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">reg</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X</span><span class="p">[:</span><span class="mi">1</span><span class="p">,])</span>
-<span class="go">array([[153.7971...,  94.9015...]])</span>
+<span class="go">array([[153.7971,  94.9015]])</span>
 </pre></div>
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.MultiTaskLassoCV.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_coordinate_descent.py#L3273"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.MultiTaskLassoCV.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_coordinate_descent.py#L3367"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.MultiTaskLassoCV.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit MultiTaskLasso model with coordinate descent.</p>
 <p>Fit is on grid of alphas and best alpha estimated by cross-validation.</p>
 <dl class="field-list">
@@ -1478,7 +1491,7 @@ <h1>MultiTaskLassoCV<a class="headerlink" href="#multitasklassocv" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.MultiTaskLassoCV.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_coordinate_descent.py#L1846"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.MultiTaskLassoCV.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_coordinate_descent.py#L1888"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.MultiTaskLassoCV.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1498,7 +1511,7 @@ <h1>MultiTaskLassoCV<a class="headerlink" href="#multitasklassocv" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.MultiTaskLassoCV.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.MultiTaskLassoCV.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.MultiTaskLassoCV.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1519,7 +1532,7 @@ <h1>MultiTaskLassoCV<a class="headerlink" href="#multitasklassocv" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.MultiTaskLassoCV.path">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">path</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_alphas</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alphas</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">precompute</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Xy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy_X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coef_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_n_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">positive</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_coordinate_descent.py#L186"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.MultiTaskLassoCV.path" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">path</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_alphas</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alphas</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">precompute</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Xy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy_X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coef_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_n_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">positive</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_coordinate_descent.py#L186"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.MultiTaskLassoCV.path" title="Link to this definition">#</a></dt>
 <dd><p>Compute Lasso path with coordinate descent.</p>
 <p>The Lasso optimization function varies for mono and multi-outputs.</p>
 <p>For mono-output tasks it is:</p>
@@ -1644,7 +1657,7 @@ <h1>MultiTaskLassoCV<a class="headerlink" href="#multitasklassocv" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.MultiTaskLassoCV.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_base.py#L284"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.MultiTaskLassoCV.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_base.py#L284"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.MultiTaskLassoCV.predict" title="Link to this definition">#</a></dt>
 <dd><p>Predict using the linear model.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1664,7 +1677,7 @@ <h1>MultiTaskLassoCV<a class="headerlink" href="#multitasklassocv" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.MultiTaskLassoCV.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L619"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.MultiTaskLassoCV.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L560"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.MultiTaskLassoCV.score" title="Link to this definition">#</a></dt>
 <dd><p>Return <a class="reference internal" href="http://github.com/model_evaluation.html#r2-score"><span class="std std-ref">coefficient of determination</span></a> on test data.</p>
 <p>The coefficient of determination, <span class="math notranslate nohighlight">\(R^2\)</span>, is defined as
 <span class="math notranslate nohighlight">\((1 - \frac{u}{v})\)</span>, where <span class="math notranslate nohighlight">\(u\)</span> is the residual
@@ -1706,7 +1719,7 @@ <h1>MultiTaskLassoCV<a class="headerlink" href="#multitasklassocv" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.MultiTaskLassoCV.set_fit_request">
-<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.MultiTaskLassoCV" title="sklearn.linear_model._coordinate_descent.MultiTaskLassoCV"><span class="pre">MultiTaskLassoCV</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.MultiTaskLassoCV.set_fit_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.MultiTaskLassoCV" title="sklearn.linear_model._coordinate_descent.MultiTaskLassoCV"><span class="pre">MultiTaskLassoCV</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.MultiTaskLassoCV.set_fit_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1749,7 +1762,7 @@ <h1>MultiTaskLassoCV<a class="headerlink" href="#multitasklassocv" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.MultiTaskLassoCV.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.MultiTaskLassoCV.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.MultiTaskLassoCV.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1773,7 +1786,7 @@ <h1>MultiTaskLassoCV<a class="headerlink" href="#multitasklassocv" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.MultiTaskLassoCV.set_score_request">
-<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.MultiTaskLassoCV" title="sklearn.linear_model._coordinate_descent.MultiTaskLassoCV"><span class="pre">MultiTaskLassoCV</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.MultiTaskLassoCV.set_score_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.MultiTaskLassoCV" title="sklearn.linear_model._coordinate_descent.MultiTaskLassoCV"><span class="pre">MultiTaskLassoCV</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.MultiTaskLassoCV.set_score_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
diff --git a/dev/modules/generated/sklearn.linear_model.OrthogonalMatchingPursuit.html b/dev/modules/generated/sklearn.linear_model.OrthogonalMatchingPursuit.html
index cc7b0932123b3..3bf0aee5ea2ce 100644
--- a/dev/modules/generated/sklearn.linear_model.OrthogonalMatchingPursuit.html
+++ b/dev/modules/generated/sklearn.linear_model.OrthogonalMatchingPursuit.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1308,7 +1308,7 @@
 <h1>OrthogonalMatchingPursuit<a class="headerlink" href="#orthogonalmatchingpursuit" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.linear_model.OrthogonalMatchingPursuit">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.linear_model.</span></span><span class="sig-name descname"><span class="pre">OrthogonalMatchingPursuit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_nonzero_coefs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_intercept</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">precompute</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_omp.py#L645"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.OrthogonalMatchingPursuit" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.linear_model.</span></span><span class="sig-name descname"><span class="pre">OrthogonalMatchingPursuit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_nonzero_coefs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_intercept</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">precompute</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_omp.py#L645"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.OrthogonalMatchingPursuit" title="Link to this definition">#</a></dt>
 <dd><p>Orthogonal Matching Pursuit model (OMP).</p>
 <p>Read more in the <a class="reference internal" href="http://github.com/linear_model.html#omp"><span class="std std-ref">User Guide</span></a>.</p>
 <dl class="field-list">
@@ -1391,14 +1391,14 @@ <h1>OrthogonalMatchingPursuit<a class="headerlink" href="#orthogonalmatchingpurs
 <span class="gp">&gt;&gt;&gt; </span><span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">make_regression</span><span class="p">(</span><span class="n">noise</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">reg</span> <span class="o">=</span> <span class="n">OrthogonalMatchingPursuit</span><span class="p">()</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">reg</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
-<span class="go">0.9991...</span>
+<span class="go">0.9991</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">reg</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X</span><span class="p">[:</span><span class="mi">1</span><span class="p">,])</span>
-<span class="go">array([-78.3854...])</span>
+<span class="go">array([-78.3854])</span>
 </pre></div>
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.OrthogonalMatchingPursuit.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_omp.py#L755"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.OrthogonalMatchingPursuit.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_omp.py#L755"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.OrthogonalMatchingPursuit.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit the model using X, y as training data.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1420,7 +1420,7 @@ <h1>OrthogonalMatchingPursuit<a class="headerlink" href="#orthogonalmatchingpurs
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.OrthogonalMatchingPursuit.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.OrthogonalMatchingPursuit.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.OrthogonalMatchingPursuit.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1437,7 +1437,7 @@ <h1>OrthogonalMatchingPursuit<a class="headerlink" href="#orthogonalmatchingpurs
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.OrthogonalMatchingPursuit.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.OrthogonalMatchingPursuit.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.OrthogonalMatchingPursuit.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1458,7 +1458,7 @@ <h1>OrthogonalMatchingPursuit<a class="headerlink" href="#orthogonalmatchingpurs
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.OrthogonalMatchingPursuit.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_base.py#L284"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.OrthogonalMatchingPursuit.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_base.py#L284"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.OrthogonalMatchingPursuit.predict" title="Link to this definition">#</a></dt>
 <dd><p>Predict using the linear model.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1478,7 +1478,7 @@ <h1>OrthogonalMatchingPursuit<a class="headerlink" href="#orthogonalmatchingpurs
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.OrthogonalMatchingPursuit.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L619"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.OrthogonalMatchingPursuit.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L560"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.OrthogonalMatchingPursuit.score" title="Link to this definition">#</a></dt>
 <dd><p>Return <a class="reference internal" href="http://github.com/model_evaluation.html#r2-score"><span class="std std-ref">coefficient of determination</span></a> on test data.</p>
 <p>The coefficient of determination, <span class="math notranslate nohighlight">\(R^2\)</span>, is defined as
 <span class="math notranslate nohighlight">\((1 - \frac{u}{v})\)</span>, where <span class="math notranslate nohighlight">\(u\)</span> is the residual
@@ -1520,7 +1520,7 @@ <h1>OrthogonalMatchingPursuit<a class="headerlink" href="#orthogonalmatchingpurs
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.OrthogonalMatchingPursuit.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.OrthogonalMatchingPursuit.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.OrthogonalMatchingPursuit.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1544,7 +1544,7 @@ <h1>OrthogonalMatchingPursuit<a class="headerlink" href="#orthogonalmatchingpurs
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.OrthogonalMatchingPursuit.set_score_request">
-<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.OrthogonalMatchingPursuit" title="sklearn.linear_model._omp.OrthogonalMatchingPursuit"><span class="pre">OrthogonalMatchingPursuit</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.OrthogonalMatchingPursuit.set_score_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.OrthogonalMatchingPursuit" title="sklearn.linear_model._omp.OrthogonalMatchingPursuit"><span class="pre">OrthogonalMatchingPursuit</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.OrthogonalMatchingPursuit.set_score_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
diff --git a/dev/modules/generated/sklearn.linear_model.OrthogonalMatchingPursuitCV.html b/dev/modules/generated/sklearn.linear_model.OrthogonalMatchingPursuitCV.html
index c929da015e824..cf3e0a56f39b0 100644
--- a/dev/modules/generated/sklearn.linear_model.OrthogonalMatchingPursuitCV.html
+++ b/dev/modules/generated/sklearn.linear_model.OrthogonalMatchingPursuitCV.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1308,7 +1308,7 @@
 <h1>OrthogonalMatchingPursuitCV<a class="headerlink" href="#orthogonalmatchingpursuitcv" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.linear_model.OrthogonalMatchingPursuitCV">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.linear_model.</span></span><span class="sig-name descname"><span class="pre">OrthogonalMatchingPursuitCV</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_intercept</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cv</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_omp.py#L894"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.OrthogonalMatchingPursuitCV" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.linear_model.</span></span><span class="sig-name descname"><span class="pre">OrthogonalMatchingPursuitCV</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_intercept</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cv</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_omp.py#L894"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.OrthogonalMatchingPursuitCV" title="Link to this definition">#</a></dt>
 <dd><p>Cross-validated Orthogonal Matching Pursuit model (OMP).</p>
 <p>See glossary entry for <a class="reference internal" href="http://github.com/glossary.html#term-cross-validation-estimator"><span class="xref std std-term">cross-validation estimator</span></a>.</p>
 <p>Read more in the <a class="reference internal" href="http://github.com/linear_model.html#omp"><span class="std std-ref">User Guide</span></a>.</p>
@@ -1342,7 +1342,7 @@ <h1>OrthogonalMatchingPursuitCV<a class="headerlink" href="#orthogonalmatchingpu
 </div>
 </dd>
 <dt><strong>n_jobs</strong><span class="classifier">int, default=None</span></dt><dd><p>Number of CPUs to use during the cross validation.
-<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.5.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
+<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.6.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
 <code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all processors. See <a class="reference internal" href="http://github.com/glossary.html#term-n_jobs"><span class="xref std std-term">Glossary</span></a>
 for more details.</p>
 </dd>
@@ -1409,16 +1409,16 @@ <h1>OrthogonalMatchingPursuitCV<a class="headerlink" href="#orthogonalmatchingpu
 <span class="gp">... </span>                       <span class="n">noise</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">reg</span> <span class="o">=</span> <span class="n">OrthogonalMatchingPursuitCV</span><span class="p">(</span><span class="n">cv</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">reg</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
-<span class="go">0.9991...</span>
+<span class="go">0.9991</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">reg</span><span class="o">.</span><span class="n">n_nonzero_coefs_</span>
 <span class="go">np.int64(10)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">reg</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X</span><span class="p">[:</span><span class="mi">1</span><span class="p">,])</span>
-<span class="go">array([-78.3854...])</span>
+<span class="go">array([-78.3854])</span>
 </pre></div>
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.OrthogonalMatchingPursuitCV.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_omp.py#L1030"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.OrthogonalMatchingPursuitCV.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_omp.py#L1030"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.OrthogonalMatchingPursuitCV.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit the model using X, y as training data.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1449,7 +1449,7 @@ <h1>OrthogonalMatchingPursuitCV<a class="headerlink" href="#orthogonalmatchingpu
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.OrthogonalMatchingPursuitCV.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_omp.py#L1102"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.OrthogonalMatchingPursuitCV.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_omp.py#L1102"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.OrthogonalMatchingPursuitCV.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1469,7 +1469,7 @@ <h1>OrthogonalMatchingPursuitCV<a class="headerlink" href="#orthogonalmatchingpu
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.OrthogonalMatchingPursuitCV.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.OrthogonalMatchingPursuitCV.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.OrthogonalMatchingPursuitCV.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1490,7 +1490,7 @@ <h1>OrthogonalMatchingPursuitCV<a class="headerlink" href="#orthogonalmatchingpu
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.OrthogonalMatchingPursuitCV.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_base.py#L284"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.OrthogonalMatchingPursuitCV.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_base.py#L284"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.OrthogonalMatchingPursuitCV.predict" title="Link to this definition">#</a></dt>
 <dd><p>Predict using the linear model.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1510,7 +1510,7 @@ <h1>OrthogonalMatchingPursuitCV<a class="headerlink" href="#orthogonalmatchingpu
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.OrthogonalMatchingPursuitCV.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L619"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.OrthogonalMatchingPursuitCV.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L560"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.OrthogonalMatchingPursuitCV.score" title="Link to this definition">#</a></dt>
 <dd><p>Return <a class="reference internal" href="http://github.com/model_evaluation.html#r2-score"><span class="std std-ref">coefficient of determination</span></a> on test data.</p>
 <p>The coefficient of determination, <span class="math notranslate nohighlight">\(R^2\)</span>, is defined as
 <span class="math notranslate nohighlight">\((1 - \frac{u}{v})\)</span>, where <span class="math notranslate nohighlight">\(u\)</span> is the residual
@@ -1552,7 +1552,7 @@ <h1>OrthogonalMatchingPursuitCV<a class="headerlink" href="#orthogonalmatchingpu
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.OrthogonalMatchingPursuitCV.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.OrthogonalMatchingPursuitCV.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.OrthogonalMatchingPursuitCV.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1576,7 +1576,7 @@ <h1>OrthogonalMatchingPursuitCV<a class="headerlink" href="#orthogonalmatchingpu
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.OrthogonalMatchingPursuitCV.set_score_request">
-<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.OrthogonalMatchingPursuitCV" title="sklearn.linear_model._omp.OrthogonalMatchingPursuitCV"><span class="pre">OrthogonalMatchingPursuitCV</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.OrthogonalMatchingPursuitCV.set_score_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.OrthogonalMatchingPursuitCV" title="sklearn.linear_model._omp.OrthogonalMatchingPursuitCV"><span class="pre">OrthogonalMatchingPursuitCV</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.OrthogonalMatchingPursuitCV.set_score_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
diff --git a/dev/modules/generated/sklearn.linear_model.PassiveAggressiveClassifier.html b/dev/modules/generated/sklearn.linear_model.PassiveAggressiveClassifier.html
index 4dffd8282322d..663f1730e4ea6 100644
--- a/dev/modules/generated/sklearn.linear_model.PassiveAggressiveClassifier.html
+++ b/dev/modules/generated/sklearn.linear_model.PassiveAggressiveClassifier.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>PassiveAggressiveClassifier<a class="headerlink" href="#passiveaggressiveclassifier" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.linear_model.PassiveAggressiveClassifier">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.linear_model.</span></span><span class="sig-name descname"><span class="pre">PassiveAggressiveClassifier</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">C</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_intercept</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">early_stopping</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validation_fraction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_iter_no_change</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">loss</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'hinge'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">warm_start</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">class_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_passive_aggressive.py#L11"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.PassiveAggressiveClassifier" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.linear_model.</span></span><span class="sig-name descname"><span class="pre">PassiveAggressiveClassifier</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">C</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_intercept</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">early_stopping</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validation_fraction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_iter_no_change</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">loss</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'hinge'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">warm_start</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">class_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_passive_aggressive.py#L11"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.PassiveAggressiveClassifier" title="Link to this definition">#</a></dt>
 <dd><p>Passive Aggressive Classifier.</p>
 <p>Read more in the <a class="reference internal" href="http://github.com/linear_model.html#passive-aggressive"><span class="std std-ref">User Guide</span></a>.</p>
 <dl class="field-list">
@@ -1362,7 +1362,7 @@ <h1>PassiveAggressiveClassifier<a class="headerlink" href="#passiveaggressivecla
 </dd>
 <dt><strong>n_jobs</strong><span class="classifier">int or None, default=None</span></dt><dd><p>The number of CPUs to use to do the OVA (One Versus All, for
 multi-class problems) computation.
-<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.5.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
+<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.6.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
 <code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all processors. See <a class="reference internal" href="http://github.com/glossary.html#term-n_jobs"><span class="xref std std-term">Glossary</span></a>
 for more details.</p>
 </dd>
@@ -1457,7 +1457,7 @@ <h1>PassiveAggressiveClassifier<a class="headerlink" href="#passiveaggressivecla
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.PassiveAggressiveClassifier.decision_function">
-<span class="sig-name descname"><span class="pre">decision_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_base.py#L330"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.PassiveAggressiveClassifier.decision_function" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">decision_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_base.py#L330"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.PassiveAggressiveClassifier.decision_function" title="Link to this definition">#</a></dt>
 <dd><p>Predict confidence scores for samples.</p>
 <p>The confidence score for a sample is proportional to the signed
 distance of that sample to the hyperplane.</p>
@@ -1481,7 +1481,7 @@ <h1>PassiveAggressiveClassifier<a class="headerlink" href="#passiveaggressivecla
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.PassiveAggressiveClassifier.densify">
-<span class="sig-name descname"><span class="pre">densify</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_base.py#L406"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.PassiveAggressiveClassifier.densify" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">densify</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_base.py#L406"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.PassiveAggressiveClassifier.densify" title="Link to this definition">#</a></dt>
 <dd><p>Convert coefficient matrix to dense array format.</p>
 <p>Converts the <code class="docutils literal notranslate"><span class="pre">coef_</span></code> member (back) to a numpy.ndarray. This is the
 default format of <code class="docutils literal notranslate"><span class="pre">coef_</span></code> and is required for fitting, so calling
@@ -1499,7 +1499,7 @@ <h1>PassiveAggressiveClassifier<a class="headerlink" href="#passiveaggressivecla
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.PassiveAggressiveClassifier.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coef_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">intercept_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_passive_aggressive.py#L276"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.PassiveAggressiveClassifier.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coef_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">intercept_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_passive_aggressive.py#L276"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.PassiveAggressiveClassifier.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit linear model with Passive Aggressive algorithm.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1525,7 +1525,7 @@ <h1>PassiveAggressiveClassifier<a class="headerlink" href="#passiveaggressivecla
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.PassiveAggressiveClassifier.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.PassiveAggressiveClassifier.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.PassiveAggressiveClassifier.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1542,7 +1542,7 @@ <h1>PassiveAggressiveClassifier<a class="headerlink" href="#passiveaggressivecla
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.PassiveAggressiveClassifier.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.PassiveAggressiveClassifier.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.PassiveAggressiveClassifier.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1563,7 +1563,7 @@ <h1>PassiveAggressiveClassifier<a class="headerlink" href="#passiveaggressivecla
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.PassiveAggressiveClassifier.partial_fit">
-<span class="sig-name descname"><span class="pre">partial_fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">classes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_passive_aggressive.py#L220"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.PassiveAggressiveClassifier.partial_fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">partial_fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">classes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_passive_aggressive.py#L220"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.PassiveAggressiveClassifier.partial_fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit linear model with Passive Aggressive algorithm.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1592,7 +1592,7 @@ <h1>PassiveAggressiveClassifier<a class="headerlink" href="#passiveaggressivecla
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.PassiveAggressiveClassifier.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_base.py#L360"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.PassiveAggressiveClassifier.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_base.py#L360"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.PassiveAggressiveClassifier.predict" title="Link to this definition">#</a></dt>
 <dd><p>Predict class labels for samples in X.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1612,7 +1612,7 @@ <h1>PassiveAggressiveClassifier<a class="headerlink" href="#passiveaggressivecla
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.PassiveAggressiveClassifier.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L546"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.PassiveAggressiveClassifier.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L487"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.PassiveAggressiveClassifier.score" title="Link to this definition">#</a></dt>
 <dd><p>Return <a class="reference internal" href="http://github.com/model_evaluation.html#accuracy-score"><span class="std std-ref">accuracy</span></a> on provided data and labels.</p>
 <p>In multi-label classification, this is the subset accuracy
 which is a harsh metric since you require for each sample that
@@ -1639,7 +1639,7 @@ <h1>PassiveAggressiveClassifier<a class="headerlink" href="#passiveaggressivecla
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.PassiveAggressiveClassifier.set_fit_request">
-<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coef_init</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">intercept_init</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.PassiveAggressiveClassifier" title="sklearn.linear_model._passive_aggressive.PassiveAggressiveClassifier"><span class="pre">PassiveAggressiveClassifier</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.PassiveAggressiveClassifier.set_fit_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coef_init</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">intercept_init</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.PassiveAggressiveClassifier" title="sklearn.linear_model._passive_aggressive.PassiveAggressiveClassifier"><span class="pre">PassiveAggressiveClassifier</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.PassiveAggressiveClassifier.set_fit_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1684,7 +1684,7 @@ <h1>PassiveAggressiveClassifier<a class="headerlink" href="#passiveaggressivecla
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.PassiveAggressiveClassifier.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.PassiveAggressiveClassifier.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.PassiveAggressiveClassifier.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1708,7 +1708,7 @@ <h1>PassiveAggressiveClassifier<a class="headerlink" href="#passiveaggressivecla
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.PassiveAggressiveClassifier.set_partial_fit_request">
-<span class="sig-name descname"><span class="pre">set_partial_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">classes</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.PassiveAggressiveClassifier" title="sklearn.linear_model._passive_aggressive.PassiveAggressiveClassifier"><span class="pre">PassiveAggressiveClassifier</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.PassiveAggressiveClassifier.set_partial_fit_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_partial_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">classes</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.PassiveAggressiveClassifier" title="sklearn.linear_model._passive_aggressive.PassiveAggressiveClassifier"><span class="pre">PassiveAggressiveClassifier</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.PassiveAggressiveClassifier.set_partial_fit_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">partial_fit</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1751,7 +1751,7 @@ <h1>PassiveAggressiveClassifier<a class="headerlink" href="#passiveaggressivecla
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.PassiveAggressiveClassifier.set_score_request">
-<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.PassiveAggressiveClassifier" title="sklearn.linear_model._passive_aggressive.PassiveAggressiveClassifier"><span class="pre">PassiveAggressiveClassifier</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.PassiveAggressiveClassifier.set_score_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.PassiveAggressiveClassifier" title="sklearn.linear_model._passive_aggressive.PassiveAggressiveClassifier"><span class="pre">PassiveAggressiveClassifier</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.PassiveAggressiveClassifier.set_score_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1794,7 +1794,7 @@ <h1>PassiveAggressiveClassifier<a class="headerlink" href="#passiveaggressivecla
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.PassiveAggressiveClassifier.sparsify">
-<span class="sig-name descname"><span class="pre">sparsify</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_base.py#L426"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.PassiveAggressiveClassifier.sparsify" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">sparsify</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_base.py#L426"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.PassiveAggressiveClassifier.sparsify" title="Link to this definition">#</a></dt>
 <dd><p>Convert coefficient matrix to sparse format.</p>
 <p>Converts the <code class="docutils literal notranslate"><span class="pre">coef_</span></code> member to a scipy.sparse matrix, which for
 L1-regularized models can be much more memory- and storage-efficient
diff --git a/dev/modules/generated/sklearn.linear_model.PassiveAggressiveRegressor.html b/dev/modules/generated/sklearn.linear_model.PassiveAggressiveRegressor.html
index c0d2583214de3..8dfbbd572ad1d 100644
--- a/dev/modules/generated/sklearn.linear_model.PassiveAggressiveRegressor.html
+++ b/dev/modules/generated/sklearn.linear_model.PassiveAggressiveRegressor.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
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@@ -1306,7 +1306,7 @@
 <h1>PassiveAggressiveRegressor<a class="headerlink" href="#passiveaggressiveregressor" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.linear_model.PassiveAggressiveRegressor">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.linear_model.</span></span><span class="sig-name descname"><span class="pre">PassiveAggressiveRegressor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">C</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_intercept</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">early_stopping</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validation_fraction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_iter_no_change</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">loss</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'epsilon_insensitive'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epsilon</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">warm_start</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_passive_aggressive.py#L314"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.PassiveAggressiveRegressor" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.linear_model.</span></span><span class="sig-name descname"><span class="pre">PassiveAggressiveRegressor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">C</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_intercept</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">early_stopping</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validation_fraction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_iter_no_change</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">loss</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'epsilon_insensitive'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epsilon</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">warm_start</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_passive_aggressive.py#L314"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.PassiveAggressiveRegressor" title="Link to this definition">#</a></dt>
 <dd><p>Passive Aggressive Regressor.</p>
 <p>Read more in the <a class="reference internal" href="http://github.com/linear_model.html#passive-aggressive"><span class="std std-ref">User Guide</span></a>.</p>
 <dl class="field-list">
@@ -1441,7 +1441,7 @@ <h1>PassiveAggressiveRegressor<a class="headerlink" href="#passiveaggressiveregr
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.PassiveAggressiveRegressor.densify">
-<span class="sig-name descname"><span class="pre">densify</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_base.py#L406"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.PassiveAggressiveRegressor.densify" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">densify</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_base.py#L406"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.PassiveAggressiveRegressor.densify" title="Link to this definition">#</a></dt>
 <dd><p>Convert coefficient matrix to dense array format.</p>
 <p>Converts the <code class="docutils literal notranslate"><span class="pre">coef_</span></code> member (back) to a numpy.ndarray. This is the
 default format of <code class="docutils literal notranslate"><span class="pre">coef_</span></code> and is required for fitting, so calling
@@ -1459,7 +1459,7 @@ <h1>PassiveAggressiveRegressor<a class="headerlink" href="#passiveaggressiveregr
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.PassiveAggressiveRegressor.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coef_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">intercept_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_passive_aggressive.py#L538"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.PassiveAggressiveRegressor.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coef_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">intercept_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_passive_aggressive.py#L538"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.PassiveAggressiveRegressor.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit linear model with Passive Aggressive algorithm.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1485,7 +1485,7 @@ <h1>PassiveAggressiveRegressor<a class="headerlink" href="#passiveaggressiveregr
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.PassiveAggressiveRegressor.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.PassiveAggressiveRegressor.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.PassiveAggressiveRegressor.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1502,7 +1502,7 @@ <h1>PassiveAggressiveRegressor<a class="headerlink" href="#passiveaggressiveregr
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.PassiveAggressiveRegressor.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.PassiveAggressiveRegressor.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.PassiveAggressiveRegressor.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1523,7 +1523,7 @@ <h1>PassiveAggressiveRegressor<a class="headerlink" href="#passiveaggressiveregr
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.PassiveAggressiveRegressor.partial_fit">
-<span class="sig-name descname"><span class="pre">partial_fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_passive_aggressive.py#L504"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.PassiveAggressiveRegressor.partial_fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">partial_fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_passive_aggressive.py#L504"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.PassiveAggressiveRegressor.partial_fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit linear model with Passive Aggressive algorithm.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1545,7 +1545,7 @@ <h1>PassiveAggressiveRegressor<a class="headerlink" href="#passiveaggressiveregr
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.PassiveAggressiveRegressor.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_stochastic_gradient.py#L1636"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.PassiveAggressiveRegressor.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_stochastic_gradient.py#L1649"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.PassiveAggressiveRegressor.predict" title="Link to this definition">#</a></dt>
 <dd><p>Predict using the linear model.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1565,7 +1565,7 @@ <h1>PassiveAggressiveRegressor<a class="headerlink" href="#passiveaggressiveregr
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.PassiveAggressiveRegressor.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L619"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.PassiveAggressiveRegressor.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L560"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.PassiveAggressiveRegressor.score" title="Link to this definition">#</a></dt>
 <dd><p>Return <a class="reference internal" href="http://github.com/model_evaluation.html#r2-score"><span class="std std-ref">coefficient of determination</span></a> on test data.</p>
 <p>The coefficient of determination, <span class="math notranslate nohighlight">\(R^2\)</span>, is defined as
 <span class="math notranslate nohighlight">\((1 - \frac{u}{v})\)</span>, where <span class="math notranslate nohighlight">\(u\)</span> is the residual
@@ -1607,7 +1607,7 @@ <h1>PassiveAggressiveRegressor<a class="headerlink" href="#passiveaggressiveregr
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.PassiveAggressiveRegressor.set_fit_request">
-<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coef_init</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">intercept_init</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.PassiveAggressiveRegressor" title="sklearn.linear_model._passive_aggressive.PassiveAggressiveRegressor"><span class="pre">PassiveAggressiveRegressor</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.PassiveAggressiveRegressor.set_fit_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coef_init</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">intercept_init</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.PassiveAggressiveRegressor" title="sklearn.linear_model._passive_aggressive.PassiveAggressiveRegressor"><span class="pre">PassiveAggressiveRegressor</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.PassiveAggressiveRegressor.set_fit_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1652,7 +1652,7 @@ <h1>PassiveAggressiveRegressor<a class="headerlink" href="#passiveaggressiveregr
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.PassiveAggressiveRegressor.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.PassiveAggressiveRegressor.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.PassiveAggressiveRegressor.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1676,7 +1676,7 @@ <h1>PassiveAggressiveRegressor<a class="headerlink" href="#passiveaggressiveregr
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.PassiveAggressiveRegressor.set_partial_fit_request">
-<span class="sig-name descname"><span class="pre">set_partial_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.PassiveAggressiveRegressor" title="sklearn.linear_model._passive_aggressive.PassiveAggressiveRegressor"><span class="pre">PassiveAggressiveRegressor</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.PassiveAggressiveRegressor.set_partial_fit_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_partial_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.PassiveAggressiveRegressor" title="sklearn.linear_model._passive_aggressive.PassiveAggressiveRegressor"><span class="pre">PassiveAggressiveRegressor</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.PassiveAggressiveRegressor.set_partial_fit_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">partial_fit</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1719,7 +1719,7 @@ <h1>PassiveAggressiveRegressor<a class="headerlink" href="#passiveaggressiveregr
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.PassiveAggressiveRegressor.set_score_request">
-<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.PassiveAggressiveRegressor" title="sklearn.linear_model._passive_aggressive.PassiveAggressiveRegressor"><span class="pre">PassiveAggressiveRegressor</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.PassiveAggressiveRegressor.set_score_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.PassiveAggressiveRegressor" title="sklearn.linear_model._passive_aggressive.PassiveAggressiveRegressor"><span class="pre">PassiveAggressiveRegressor</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.PassiveAggressiveRegressor.set_score_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1762,7 +1762,7 @@ <h1>PassiveAggressiveRegressor<a class="headerlink" href="#passiveaggressiveregr
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.PassiveAggressiveRegressor.sparsify">
-<span class="sig-name descname"><span class="pre">sparsify</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_base.py#L426"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.PassiveAggressiveRegressor.sparsify" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">sparsify</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_base.py#L426"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.PassiveAggressiveRegressor.sparsify" title="Link to this definition">#</a></dt>
 <dd><p>Convert coefficient matrix to sparse format.</p>
 <p>Converts the <code class="docutils literal notranslate"><span class="pre">coef_</span></code> member to a scipy.sparse matrix, which for
 L1-regularized models can be much more memory- and storage-efficient
diff --git a/dev/modules/generated/sklearn.linear_model.Perceptron.html b/dev/modules/generated/sklearn.linear_model.Perceptron.html
index 5527391cdbdd6..b76c6de5a0034 100644
--- a/dev/modules/generated/sklearn.linear_model.Perceptron.html
+++ b/dev/modules/generated/sklearn.linear_model.Perceptron.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>Perceptron<a class="headerlink" href="#perceptron" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.linear_model.Perceptron">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.linear_model.</span></span><span class="sig-name descname"><span class="pre">Perceptron</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">penalty</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">l1_ratio</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.15</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_intercept</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eta0</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">early_stopping</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validation_fraction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_iter_no_change</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">class_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">warm_start</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_perceptron.py#L10"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.Perceptron" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.linear_model.</span></span><span class="sig-name descname"><span class="pre">Perceptron</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">penalty</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">l1_ratio</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.15</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_intercept</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eta0</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">early_stopping</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validation_fraction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_iter_no_change</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">class_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">warm_start</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_perceptron.py#L10"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.Perceptron" title="Link to this definition">#</a></dt>
 <dd><p>Linear perceptron classifier.</p>
 <p>The implementation is a wrapper around <a class="reference internal" href="http://github.com/sklearn.linear_model.SGDClassifier.html#sklearn.linear_model.SGDClassifier" title="sklearn.linear_model.SGDClassifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">SGDClassifier</span></code></a>
 by fixing the <code class="docutils literal notranslate"><span class="pre">loss</span></code> and <code class="docutils literal notranslate"><span class="pre">learning_rate</span></code> parameters as:</p>
@@ -1356,7 +1356,7 @@ <h1>Perceptron<a class="headerlink" href="#perceptron" title="Link to this headi
 </dd>
 <dt><strong>n_jobs</strong><span class="classifier">int, default=None</span></dt><dd><p>The number of CPUs to use to do the OVA (One Versus All, for
 multi-class problems) computation.
-<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.5.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
+<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.6.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
 <code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all processors. See <a class="reference internal" href="http://github.com/glossary.html#term-n_jobs"><span class="xref std std-term">Glossary</span></a>
 for more details.</p>
 </dd>
@@ -1454,7 +1454,7 @@ <h1>Perceptron<a class="headerlink" href="#perceptron" title="Link to this headi
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.Perceptron.decision_function">
-<span class="sig-name descname"><span class="pre">decision_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_base.py#L330"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.Perceptron.decision_function" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">decision_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_base.py#L330"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.Perceptron.decision_function" title="Link to this definition">#</a></dt>
 <dd><p>Predict confidence scores for samples.</p>
 <p>The confidence score for a sample is proportional to the signed
 distance of that sample to the hyperplane.</p>
@@ -1478,7 +1478,7 @@ <h1>Perceptron<a class="headerlink" href="#perceptron" title="Link to this headi
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.Perceptron.densify">
-<span class="sig-name descname"><span class="pre">densify</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_base.py#L406"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.Perceptron.densify" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">densify</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_base.py#L406"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.Perceptron.densify" title="Link to this definition">#</a></dt>
 <dd><p>Convert coefficient matrix to dense array format.</p>
 <p>Converts the <code class="docutils literal notranslate"><span class="pre">coef_</span></code> member (back) to a numpy.ndarray. This is the
 default format of <code class="docutils literal notranslate"><span class="pre">coef_</span></code> and is required for fitting, so calling
@@ -1496,7 +1496,7 @@ <h1>Perceptron<a class="headerlink" href="#perceptron" title="Link to this headi
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.Perceptron.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coef_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">intercept_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_stochastic_gradient.py#L880"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.Perceptron.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coef_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">intercept_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_stochastic_gradient.py#L889"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.Perceptron.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit linear model with Stochastic Gradient Descent.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1527,7 +1527,7 @@ <h1>Perceptron<a class="headerlink" href="#perceptron" title="Link to this headi
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.Perceptron.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.Perceptron.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.Perceptron.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1544,7 +1544,7 @@ <h1>Perceptron<a class="headerlink" href="#perceptron" title="Link to this headi
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.Perceptron.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.Perceptron.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.Perceptron.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1565,7 +1565,7 @@ <h1>Perceptron<a class="headerlink" href="#perceptron" title="Link to this headi
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.Perceptron.partial_fit">
-<span class="sig-name descname"><span class="pre">partial_fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">classes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_stochastic_gradient.py#L816"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.Perceptron.partial_fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">partial_fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">classes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_stochastic_gradient.py#L825"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.Perceptron.partial_fit" title="Link to this definition">#</a></dt>
 <dd><p>Perform one epoch of stochastic gradient descent on given samples.</p>
 <p>Internally, this method uses <code class="docutils literal notranslate"><span class="pre">max_iter</span> <span class="pre">=</span> <span class="pre">1</span></code>. Therefore, it is not
 guaranteed that a minimum of the cost function is reached after calling
@@ -1601,7 +1601,7 @@ <h1>Perceptron<a class="headerlink" href="#perceptron" title="Link to this headi
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.Perceptron.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_base.py#L360"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.Perceptron.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_base.py#L360"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.Perceptron.predict" title="Link to this definition">#</a></dt>
 <dd><p>Predict class labels for samples in X.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1621,7 +1621,7 @@ <h1>Perceptron<a class="headerlink" href="#perceptron" title="Link to this headi
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.Perceptron.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L546"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.Perceptron.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L487"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.Perceptron.score" title="Link to this definition">#</a></dt>
 <dd><p>Return <a class="reference internal" href="http://github.com/model_evaluation.html#accuracy-score"><span class="std std-ref">accuracy</span></a> on provided data and labels.</p>
 <p>In multi-label classification, this is the subset accuracy
 which is a harsh metric since you require for each sample that
@@ -1648,7 +1648,7 @@ <h1>Perceptron<a class="headerlink" href="#perceptron" title="Link to this headi
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.Perceptron.set_fit_request">
-<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coef_init</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">intercept_init</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.Perceptron" title="sklearn.linear_model._perceptron.Perceptron"><span class="pre">Perceptron</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.Perceptron.set_fit_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coef_init</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">intercept_init</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.Perceptron" title="sklearn.linear_model._perceptron.Perceptron"><span class="pre">Perceptron</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.Perceptron.set_fit_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1695,7 +1695,7 @@ <h1>Perceptron<a class="headerlink" href="#perceptron" title="Link to this headi
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.Perceptron.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.Perceptron.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.Perceptron.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1719,7 +1719,7 @@ <h1>Perceptron<a class="headerlink" href="#perceptron" title="Link to this headi
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.Perceptron.set_partial_fit_request">
-<span class="sig-name descname"><span class="pre">set_partial_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">classes</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.Perceptron" title="sklearn.linear_model._perceptron.Perceptron"><span class="pre">Perceptron</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.Perceptron.set_partial_fit_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_partial_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">classes</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.Perceptron" title="sklearn.linear_model._perceptron.Perceptron"><span class="pre">Perceptron</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.Perceptron.set_partial_fit_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">partial_fit</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1764,7 +1764,7 @@ <h1>Perceptron<a class="headerlink" href="#perceptron" title="Link to this headi
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.Perceptron.set_score_request">
-<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.Perceptron" title="sklearn.linear_model._perceptron.Perceptron"><span class="pre">Perceptron</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.Perceptron.set_score_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.Perceptron" title="sklearn.linear_model._perceptron.Perceptron"><span class="pre">Perceptron</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.Perceptron.set_score_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1807,7 +1807,7 @@ <h1>Perceptron<a class="headerlink" href="#perceptron" title="Link to this headi
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.Perceptron.sparsify">
-<span class="sig-name descname"><span class="pre">sparsify</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_base.py#L426"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.Perceptron.sparsify" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">sparsify</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_base.py#L426"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.Perceptron.sparsify" title="Link to this definition">#</a></dt>
 <dd><p>Convert coefficient matrix to sparse format.</p>
 <p>Converts the <code class="docutils literal notranslate"><span class="pre">coef_</span></code> member to a scipy.sparse matrix, which for
 L1-regularized models can be much more memory- and storage-efficient
diff --git a/dev/modules/generated/sklearn.linear_model.PoissonRegressor.html b/dev/modules/generated/sklearn.linear_model.PoissonRegressor.html
index 132b3fc6b59b7..ca911031b4196 100644
--- a/dev/modules/generated/sklearn.linear_model.PoissonRegressor.html
+++ b/dev/modules/generated/sklearn.linear_model.PoissonRegressor.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1308,7 +1308,7 @@
 <h1>PoissonRegressor<a class="headerlink" href="#poissonregressor" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.linear_model.PoissonRegressor">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.linear_model.</span></span><span class="sig-name descname"><span class="pre">PoissonRegressor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_intercept</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">solver</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'lbfgs'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">warm_start</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_glm/glm.py#L468"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.PoissonRegressor" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.linear_model.</span></span><span class="sig-name descname"><span class="pre">PoissonRegressor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_intercept</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">solver</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'lbfgs'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">warm_start</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_glm/glm.py#L468"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.PoissonRegressor" title="Link to this definition">#</a></dt>
 <dd><p>Generalized Linear Model with a Poisson distribution.</p>
 <p>This regressor uses the ‘log’ link function.</p>
 <p>Read more in the <a class="reference internal" href="http://github.com/linear_model.html#generalized-linear-models"><span class="std std-ref">User Guide</span></a>.</p>
@@ -1398,18 +1398,18 @@ <h1>PoissonRegressor<a class="headerlink" href="#poissonregressor" title="Link t
 <span class="gp">&gt;&gt;&gt; </span><span class="n">clf</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
 <span class="go">PoissonRegressor()</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">clf</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
-<span class="go">np.float64(0.990...)</span>
+<span class="go">np.float64(0.990)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">clf</span><span class="o">.</span><span class="n">coef_</span>
-<span class="go">array([0.121..., 0.158...])</span>
+<span class="go">array([0.121, 0.158])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">clf</span><span class="o">.</span><span class="n">intercept_</span>
-<span class="go">np.float64(2.088...)</span>
+<span class="go">np.float64(2.088)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">clf</span><span class="o">.</span><span class="n">predict</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">]])</span>
-<span class="go">array([10.676..., 21.875...])</span>
+<span class="go">array([10.676, 21.875])</span>
 </pre></div>
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.PoissonRegressor.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_glm/glm.py#L170"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.PoissonRegressor.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_glm/glm.py#L170"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.PoissonRegressor.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit a Generalized Linear Model.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1433,7 +1433,7 @@ <h1>PoissonRegressor<a class="headerlink" href="#poissonregressor" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.PoissonRegressor.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.PoissonRegressor.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.PoissonRegressor.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1450,7 +1450,7 @@ <h1>PoissonRegressor<a class="headerlink" href="#poissonregressor" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.PoissonRegressor.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.PoissonRegressor.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.PoissonRegressor.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1471,7 +1471,7 @@ <h1>PoissonRegressor<a class="headerlink" href="#poissonregressor" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.PoissonRegressor.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_glm/glm.py#L350"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.PoissonRegressor.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_glm/glm.py#L350"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.PoissonRegressor.predict" title="Link to this definition">#</a></dt>
 <dd><p>Predict using GLM with feature matrix X.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1491,7 +1491,7 @@ <h1>PoissonRegressor<a class="headerlink" href="#poissonregressor" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.PoissonRegressor.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_glm/glm.py#L368"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.PoissonRegressor.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_glm/glm.py#L368"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.PoissonRegressor.score" title="Link to this definition">#</a></dt>
 <dd><p>Compute D^2, the percentage of deviance explained.</p>
 <p>D^2 is a generalization of the coefficient of determination R^2.
 R^2 uses squared error and D^2 uses the deviance of this GLM, see the
@@ -1525,7 +1525,7 @@ <h1>PoissonRegressor<a class="headerlink" href="#poissonregressor" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.PoissonRegressor.set_fit_request">
-<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.PoissonRegressor" title="sklearn.linear_model._glm.glm.PoissonRegressor"><span class="pre">PoissonRegressor</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.PoissonRegressor.set_fit_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.PoissonRegressor" title="sklearn.linear_model._glm.glm.PoissonRegressor"><span class="pre">PoissonRegressor</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.PoissonRegressor.set_fit_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1568,7 +1568,7 @@ <h1>PoissonRegressor<a class="headerlink" href="#poissonregressor" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.PoissonRegressor.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.PoissonRegressor.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.PoissonRegressor.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1592,7 +1592,7 @@ <h1>PoissonRegressor<a class="headerlink" href="#poissonregressor" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.PoissonRegressor.set_score_request">
-<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.PoissonRegressor" title="sklearn.linear_model._glm.glm.PoissonRegressor"><span class="pre">PoissonRegressor</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.PoissonRegressor.set_score_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.PoissonRegressor" title="sklearn.linear_model._glm.glm.PoissonRegressor"><span class="pre">PoissonRegressor</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.PoissonRegressor.set_score_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
diff --git a/dev/modules/generated/sklearn.linear_model.QuantileRegressor.html b/dev/modules/generated/sklearn.linear_model.QuantileRegressor.html
index c488e01b5ffe0..1fe24cf73ef96 100644
--- a/dev/modules/generated/sklearn.linear_model.QuantileRegressor.html
+++ b/dev/modules/generated/sklearn.linear_model.QuantileRegressor.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1308,7 +1308,7 @@
 <h1>QuantileRegressor<a class="headerlink" href="#quantileregressor" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.linear_model.QuantileRegressor">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.linear_model.</span></span><span class="sig-name descname"><span class="pre">QuantileRegressor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quantile</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_intercept</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">solver</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'highs'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">solver_options</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_quantile.py#L20"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.QuantileRegressor" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.linear_model.</span></span><span class="sig-name descname"><span class="pre">QuantileRegressor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quantile</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_intercept</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">solver</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'highs'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">solver_options</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_quantile.py#L20"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.QuantileRegressor" title="Link to this definition">#</a></dt>
 <dd><p>Linear regression model that predicts conditional quantiles.</p>
 <p>The linear <a class="reference internal" href="#sklearn.linear_model.QuantileRegressor" title="sklearn.linear_model.QuantileRegressor"><code class="xref py py-class docutils literal notranslate"><span class="pre">QuantileRegressor</span></code></a> optimizes the pinball loss for a
 desired <code class="docutils literal notranslate"><span class="pre">quantile</span></code> and is robust to outliers.</p>
@@ -1393,7 +1393,7 @@ <h1>QuantileRegressor<a class="headerlink" href="#quantileregressor" title="Link
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.QuantileRegressor.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_quantile.py#L142"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.QuantileRegressor.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_quantile.py#L142"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.QuantileRegressor.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit the model according to the given training data.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1417,7 +1417,7 @@ <h1>QuantileRegressor<a class="headerlink" href="#quantileregressor" title="Link
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.QuantileRegressor.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.QuantileRegressor.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.QuantileRegressor.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1434,7 +1434,7 @@ <h1>QuantileRegressor<a class="headerlink" href="#quantileregressor" title="Link
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.QuantileRegressor.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.QuantileRegressor.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.QuantileRegressor.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1455,7 +1455,7 @@ <h1>QuantileRegressor<a class="headerlink" href="#quantileregressor" title="Link
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.QuantileRegressor.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_base.py#L284"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.QuantileRegressor.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_base.py#L284"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.QuantileRegressor.predict" title="Link to this definition">#</a></dt>
 <dd><p>Predict using the linear model.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1475,7 +1475,7 @@ <h1>QuantileRegressor<a class="headerlink" href="#quantileregressor" title="Link
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.QuantileRegressor.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L619"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.QuantileRegressor.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L560"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.QuantileRegressor.score" title="Link to this definition">#</a></dt>
 <dd><p>Return <a class="reference internal" href="http://github.com/model_evaluation.html#r2-score"><span class="std std-ref">coefficient of determination</span></a> on test data.</p>
 <p>The coefficient of determination, <span class="math notranslate nohighlight">\(R^2\)</span>, is defined as
 <span class="math notranslate nohighlight">\((1 - \frac{u}{v})\)</span>, where <span class="math notranslate nohighlight">\(u\)</span> is the residual
@@ -1517,7 +1517,7 @@ <h1>QuantileRegressor<a class="headerlink" href="#quantileregressor" title="Link
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.QuantileRegressor.set_fit_request">
-<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.QuantileRegressor" title="sklearn.linear_model._quantile.QuantileRegressor"><span class="pre">QuantileRegressor</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.QuantileRegressor.set_fit_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.QuantileRegressor" title="sklearn.linear_model._quantile.QuantileRegressor"><span class="pre">QuantileRegressor</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.QuantileRegressor.set_fit_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1560,7 +1560,7 @@ <h1>QuantileRegressor<a class="headerlink" href="#quantileregressor" title="Link
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.QuantileRegressor.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.QuantileRegressor.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.QuantileRegressor.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1584,7 +1584,7 @@ <h1>QuantileRegressor<a class="headerlink" href="#quantileregressor" title="Link
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.QuantileRegressor.set_score_request">
-<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.QuantileRegressor" title="sklearn.linear_model._quantile.QuantileRegressor"><span class="pre">QuantileRegressor</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.QuantileRegressor.set_score_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.QuantileRegressor" title="sklearn.linear_model._quantile.QuantileRegressor"><span class="pre">QuantileRegressor</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.QuantileRegressor.set_score_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
diff --git a/dev/modules/generated/sklearn.linear_model.RANSACRegressor.html b/dev/modules/generated/sklearn.linear_model.RANSACRegressor.html
index 61bee0571d0d0..fc13601a76633 100644
--- a/dev/modules/generated/sklearn.linear_model.RANSACRegressor.html
+++ b/dev/modules/generated/sklearn.linear_model.RANSACRegressor.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>RANSACRegressor<a class="headerlink" href="#ransacregressor" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.linear_model.RANSACRegressor">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.linear_model.</span></span><span class="sig-name descname"><span class="pre">RANSACRegressor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">residual_threshold</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_data_valid</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_model_valid</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_trials</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_skips</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">inf</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stop_n_inliers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">inf</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stop_score</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">inf</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stop_probability</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.99</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">loss</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'absolute_error'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_ransac.py#L82"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.RANSACRegressor" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.linear_model.</span></span><span class="sig-name descname"><span class="pre">RANSACRegressor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">residual_threshold</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_data_valid</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_model_valid</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_trials</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_skips</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">inf</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stop_n_inliers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">inf</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stop_score</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">inf</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stop_probability</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.99</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">loss</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'absolute_error'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_ransac.py#L81"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.RANSACRegressor" title="Link to this definition">#</a></dt>
 <dd><p>RANSAC (RANdom SAmple Consensus) algorithm.</p>
 <p>RANSAC is an iterative algorithm for the robust estimation of parameters
 from a subset of inliers from the complete data set.</p>
@@ -1473,16 +1473,16 @@ <h1>RANSACRegressor<a class="headerlink" href="#ransacregressor" title="Link to
 <span class="gp">... </span>    <span class="n">n_samples</span><span class="o">=</span><span class="mi">200</span><span class="p">,</span> <span class="n">n_features</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">noise</span><span class="o">=</span><span class="mf">4.0</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">reg</span> <span class="o">=</span> <span class="n">RANSACRegressor</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">reg</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
-<span class="go">0.9885...</span>
+<span class="go">0.9885</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">reg</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X</span><span class="p">[:</span><span class="mi">1</span><span class="p">,])</span>
-<span class="go">array([-31.9417...])</span>
+<span class="go">array([-31.9417])</span>
 </pre></div>
 </div>
 <p>For a more detailed example, see
 <a class="reference internal" href="http://github.com/auto_examples/linear_model/plot_ransac.html#sphx-glr-auto-examples-linear-model-plot-ransac-py"><span class="std std-ref">Robust linear model estimation using RANSAC</span></a></p>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.RANSACRegressor.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_ransac.py#L318"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.RANSACRegressor.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_ransac.py#L317"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.RANSACRegressor.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit estimator using RANSAC algorithm.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1528,7 +1528,7 @@ <h1>RANSACRegressor<a class="headerlink" href="#ransacregressor" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.RANSACRegressor.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_ransac.py#L701"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.RANSACRegressor.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_ransac.py#L696"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.RANSACRegressor.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1548,7 +1548,7 @@ <h1>RANSACRegressor<a class="headerlink" href="#ransacregressor" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.RANSACRegressor.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.RANSACRegressor.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.RANSACRegressor.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1569,7 +1569,7 @@ <h1>RANSACRegressor<a class="headerlink" href="#ransacregressor" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.RANSACRegressor.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_ransac.py#L609"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.RANSACRegressor.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_ransac.py#L604"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.RANSACRegressor.predict" title="Link to this definition">#</a></dt>
 <dd><p>Predict using the estimated model.</p>
 <p>This is a wrapper for <code class="docutils literal notranslate"><span class="pre">estimator_.predict(X)</span></code>.</p>
 <dl class="field-list">
@@ -1599,7 +1599,7 @@ <h1>RANSACRegressor<a class="headerlink" href="#ransacregressor" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.RANSACRegressor.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_ransac.py#L655"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.RANSACRegressor.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_ransac.py#L650"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.RANSACRegressor.score" title="Link to this definition">#</a></dt>
 <dd><p>Return the score of the prediction.</p>
 <p>This is a wrapper for <code class="docutils literal notranslate"><span class="pre">estimator_.score(X,</span> <span class="pre">y)</span></code>.</p>
 <dl class="field-list">
@@ -1631,7 +1631,7 @@ <h1>RANSACRegressor<a class="headerlink" href="#ransacregressor" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.RANSACRegressor.set_fit_request">
-<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.RANSACRegressor" title="sklearn.linear_model._ransac.RANSACRegressor"><span class="pre">RANSACRegressor</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.RANSACRegressor.set_fit_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.RANSACRegressor" title="sklearn.linear_model._ransac.RANSACRegressor"><span class="pre">RANSACRegressor</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.RANSACRegressor.set_fit_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1674,7 +1674,7 @@ <h1>RANSACRegressor<a class="headerlink" href="#ransacregressor" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.RANSACRegressor.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.RANSACRegressor.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.RANSACRegressor.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
diff --git a/dev/modules/generated/sklearn.linear_model.Ridge.html b/dev/modules/generated/sklearn.linear_model.Ridge.html
index 90b73b696b6f7..5578076237a0a 100644
--- a/dev/modules/generated/sklearn.linear_model.Ridge.html
+++ b/dev/modules/generated/sklearn.linear_model.Ridge.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1308,7 +1308,7 @@
 <h1>Ridge<a class="headerlink" href="#ridge" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.linear_model.Ridge">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.linear_model.</span></span><span class="sig-name descname"><span class="pre">Ridge</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_intercept</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy_X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">solver</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">positive</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_ridge.py#L1012"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.Ridge" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.linear_model.</span></span><span class="sig-name descname"><span class="pre">Ridge</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_intercept</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy_X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">solver</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">positive</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_ridge.py#L1012"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.Ridge" title="Link to this definition">#</a></dt>
 <dd><p>Linear least squares with l2 regularization.</p>
 <p>Minimizes the objective function:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="o">||</span><span class="n">y</span> <span class="o">-</span> <span class="n">Xw</span><span class="o">||^</span><span class="mi">2_2</span> <span class="o">+</span> <span class="n">alpha</span> <span class="o">*</span> <span class="o">||</span><span class="n">w</span><span class="o">||^</span><span class="mi">2_2</span>
@@ -1472,7 +1472,7 @@ <h1>Ridge<a class="headerlink" href="#ridge" title="Link to this heading">#</a><
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.Ridge.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_ridge.py#L1215"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.Ridge.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_ridge.py#L1215"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.Ridge.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit Ridge regression model.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1497,7 +1497,7 @@ <h1>Ridge<a class="headerlink" href="#ridge" title="Link to this heading">#</a><
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.Ridge.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.Ridge.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.Ridge.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1514,7 +1514,7 @@ <h1>Ridge<a class="headerlink" href="#ridge" title="Link to this heading">#</a><
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.Ridge.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.Ridge.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.Ridge.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1535,7 +1535,7 @@ <h1>Ridge<a class="headerlink" href="#ridge" title="Link to this heading">#</a><
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.Ridge.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_base.py#L284"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.Ridge.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_base.py#L284"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.Ridge.predict" title="Link to this definition">#</a></dt>
 <dd><p>Predict using the linear model.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1555,7 +1555,7 @@ <h1>Ridge<a class="headerlink" href="#ridge" title="Link to this heading">#</a><
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.Ridge.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L619"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.Ridge.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L560"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.Ridge.score" title="Link to this definition">#</a></dt>
 <dd><p>Return <a class="reference internal" href="http://github.com/model_evaluation.html#r2-score"><span class="std std-ref">coefficient of determination</span></a> on test data.</p>
 <p>The coefficient of determination, <span class="math notranslate nohighlight">\(R^2\)</span>, is defined as
 <span class="math notranslate nohighlight">\((1 - \frac{u}{v})\)</span>, where <span class="math notranslate nohighlight">\(u\)</span> is the residual
@@ -1597,7 +1597,7 @@ <h1>Ridge<a class="headerlink" href="#ridge" title="Link to this heading">#</a><
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.Ridge.set_fit_request">
-<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.Ridge" title="sklearn.linear_model._ridge.Ridge"><span class="pre">Ridge</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.Ridge.set_fit_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.Ridge" title="sklearn.linear_model._ridge.Ridge"><span class="pre">Ridge</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.Ridge.set_fit_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1640,7 +1640,7 @@ <h1>Ridge<a class="headerlink" href="#ridge" title="Link to this heading">#</a><
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.Ridge.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.Ridge.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.Ridge.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1664,7 +1664,7 @@ <h1>Ridge<a class="headerlink" href="#ridge" title="Link to this heading">#</a><
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.Ridge.set_score_request">
-<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.Ridge" title="sklearn.linear_model._ridge.Ridge"><span class="pre">Ridge</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.Ridge.set_score_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.Ridge" title="sklearn.linear_model._ridge.Ridge"><span class="pre">Ridge</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.Ridge.set_score_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
diff --git a/dev/modules/generated/sklearn.linear_model.RidgeCV.html b/dev/modules/generated/sklearn.linear_model.RidgeCV.html
index afe066a732db6..4ae7fbae09bcb 100644
--- a/dev/modules/generated/sklearn.linear_model.RidgeCV.html
+++ b/dev/modules/generated/sklearn.linear_model.RidgeCV.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1308,7 +1308,7 @@
 <h1>RidgeCV<a class="headerlink" href="#ridgecv" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.linear_model.RidgeCV">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.linear_model.</span></span><span class="sig-name descname"><span class="pre">RidgeCV</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">alphas</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(0.1,</span> <span class="pre">1.0,</span> <span class="pre">10.0)</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_intercept</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scoring</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cv</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gcv_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">store_cv_results</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha_per_target</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_ridge.py#L2515"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.RidgeCV" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.linear_model.</span></span><span class="sig-name descname"><span class="pre">RidgeCV</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">alphas</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(0.1,</span> <span class="pre">1.0,</span> <span class="pre">10.0)</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_intercept</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scoring</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cv</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gcv_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">store_cv_results</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha_per_target</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_ridge.py#L2515"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.RidgeCV" title="Link to this definition">#</a></dt>
 <dd><p>Ridge regression with built-in cross-validation.</p>
 <p>See glossary entry for <a class="reference internal" href="http://github.com/glossary.html#term-cross-validation-estimator"><span class="xref std std-term">cross-validation estimator</span></a>.</p>
 <p>By default, it performs efficient Leave-One-Out Cross-Validation.</p>
@@ -1444,7 +1444,7 @@ <h1>RidgeCV<a class="headerlink" href="#ridgecv" title="Link to this heading">#<
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.RidgeCV.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_ridge.py#L2654"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.RidgeCV.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_ridge.py#L2654"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.RidgeCV.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit Ridge regression model with cv.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1485,7 +1485,7 @@ <h1>RidgeCV<a class="headerlink" href="#ridgecv" title="Link to this heading">#<
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.RidgeCV.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_ridge.py#L2471"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.RidgeCV.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_ridge.py#L2471"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.RidgeCV.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1505,7 +1505,7 @@ <h1>RidgeCV<a class="headerlink" href="#ridgecv" title="Link to this heading">#<
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.RidgeCV.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.RidgeCV.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.RidgeCV.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1526,7 +1526,7 @@ <h1>RidgeCV<a class="headerlink" href="#ridgecv" title="Link to this heading">#<
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.RidgeCV.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_base.py#L284"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.RidgeCV.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_base.py#L284"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.RidgeCV.predict" title="Link to this definition">#</a></dt>
 <dd><p>Predict using the linear model.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1546,7 +1546,7 @@ <h1>RidgeCV<a class="headerlink" href="#ridgecv" title="Link to this heading">#<
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.RidgeCV.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L619"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.RidgeCV.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L560"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.RidgeCV.score" title="Link to this definition">#</a></dt>
 <dd><p>Return <a class="reference internal" href="http://github.com/model_evaluation.html#r2-score"><span class="std std-ref">coefficient of determination</span></a> on test data.</p>
 <p>The coefficient of determination, <span class="math notranslate nohighlight">\(R^2\)</span>, is defined as
 <span class="math notranslate nohighlight">\((1 - \frac{u}{v})\)</span>, where <span class="math notranslate nohighlight">\(u\)</span> is the residual
@@ -1588,7 +1588,7 @@ <h1>RidgeCV<a class="headerlink" href="#ridgecv" title="Link to this heading">#<
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.RidgeCV.set_fit_request">
-<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.RidgeCV" title="sklearn.linear_model._ridge.RidgeCV"><span class="pre">RidgeCV</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.RidgeCV.set_fit_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.RidgeCV" title="sklearn.linear_model._ridge.RidgeCV"><span class="pre">RidgeCV</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.RidgeCV.set_fit_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1631,7 +1631,7 @@ <h1>RidgeCV<a class="headerlink" href="#ridgecv" title="Link to this heading">#<
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.RidgeCV.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.RidgeCV.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.RidgeCV.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1655,7 +1655,7 @@ <h1>RidgeCV<a class="headerlink" href="#ridgecv" title="Link to this heading">#<
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.RidgeCV.set_score_request">
-<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.RidgeCV" title="sklearn.linear_model._ridge.RidgeCV"><span class="pre">RidgeCV</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.RidgeCV.set_score_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.RidgeCV" title="sklearn.linear_model._ridge.RidgeCV"><span class="pre">RidgeCV</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.RidgeCV.set_score_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
diff --git a/dev/modules/generated/sklearn.linear_model.RidgeClassifier.html b/dev/modules/generated/sklearn.linear_model.RidgeClassifier.html
index 252b2f8a93497..1ed856061a31a 100644
--- a/dev/modules/generated/sklearn.linear_model.RidgeClassifier.html
+++ b/dev/modules/generated/sklearn.linear_model.RidgeClassifier.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>RidgeClassifier<a class="headerlink" href="#ridgeclassifier" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.linear_model.RidgeClassifier">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.linear_model.</span></span><span class="sig-name descname"><span class="pre">RidgeClassifier</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_intercept</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy_X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">class_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">solver</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">positive</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_ridge.py#L1349"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.RidgeClassifier" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.linear_model.</span></span><span class="sig-name descname"><span class="pre">RidgeClassifier</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_intercept</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy_X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">class_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">solver</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">positive</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_ridge.py#L1349"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.RidgeClassifier" title="Link to this definition">#</a></dt>
 <dd><p>Classifier using Ridge regression.</p>
 <p>This classifier first converts the target values into <code class="docutils literal notranslate"><span class="pre">{-1,</span> <span class="pre">1}</span></code> and
 then treats the problem as a regression task (multi-output regression in
@@ -1455,7 +1455,7 @@ <h1>RidgeClassifier<a class="headerlink" href="#ridgeclassifier" title="Link to
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.RidgeClassifier.decision_function">
-<span class="sig-name descname"><span class="pre">decision_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_base.py#L330"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.RidgeClassifier.decision_function" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">decision_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_base.py#L330"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.RidgeClassifier.decision_function" title="Link to this definition">#</a></dt>
 <dd><p>Predict confidence scores for samples.</p>
 <p>The confidence score for a sample is proportional to the signed
 distance of that sample to the hyperplane.</p>
@@ -1479,7 +1479,7 @@ <h1>RidgeClassifier<a class="headerlink" href="#ridgeclassifier" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.RidgeClassifier.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_ridge.py#L1544"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.RidgeClassifier.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_ridge.py#L1544"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.RidgeClassifier.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit Ridge classifier model.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1507,7 +1507,7 @@ <h1>RidgeClassifier<a class="headerlink" href="#ridgeclassifier" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.RidgeClassifier.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.RidgeClassifier.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.RidgeClassifier.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1524,7 +1524,7 @@ <h1>RidgeClassifier<a class="headerlink" href="#ridgeclassifier" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.RidgeClassifier.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.RidgeClassifier.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.RidgeClassifier.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1545,7 +1545,7 @@ <h1>RidgeClassifier<a class="headerlink" href="#ridgeclassifier" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.RidgeClassifier.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_ridge.py#L1313"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.RidgeClassifier.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_ridge.py#L1313"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.RidgeClassifier.predict" title="Link to this definition">#</a></dt>
 <dd><p>Predict class labels for samples in <code class="docutils literal notranslate"><span class="pre">X</span></code>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1568,7 +1568,7 @@ <h1>RidgeClassifier<a class="headerlink" href="#ridgeclassifier" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.RidgeClassifier.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L546"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.RidgeClassifier.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L487"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.RidgeClassifier.score" title="Link to this definition">#</a></dt>
 <dd><p>Return <a class="reference internal" href="http://github.com/model_evaluation.html#accuracy-score"><span class="std std-ref">accuracy</span></a> on provided data and labels.</p>
 <p>In multi-label classification, this is the subset accuracy
 which is a harsh metric since you require for each sample that
@@ -1595,7 +1595,7 @@ <h1>RidgeClassifier<a class="headerlink" href="#ridgeclassifier" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.RidgeClassifier.set_fit_request">
-<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.RidgeClassifier" title="sklearn.linear_model._ridge.RidgeClassifier"><span class="pre">RidgeClassifier</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.RidgeClassifier.set_fit_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.RidgeClassifier" title="sklearn.linear_model._ridge.RidgeClassifier"><span class="pre">RidgeClassifier</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.RidgeClassifier.set_fit_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1638,7 +1638,7 @@ <h1>RidgeClassifier<a class="headerlink" href="#ridgeclassifier" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.RidgeClassifier.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.RidgeClassifier.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.RidgeClassifier.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1662,7 +1662,7 @@ <h1>RidgeClassifier<a class="headerlink" href="#ridgeclassifier" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.RidgeClassifier.set_score_request">
-<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.RidgeClassifier" title="sklearn.linear_model._ridge.RidgeClassifier"><span class="pre">RidgeClassifier</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.RidgeClassifier.set_score_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.RidgeClassifier" title="sklearn.linear_model._ridge.RidgeClassifier"><span class="pre">RidgeClassifier</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.RidgeClassifier.set_score_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
diff --git a/dev/modules/generated/sklearn.linear_model.RidgeClassifierCV.html b/dev/modules/generated/sklearn.linear_model.RidgeClassifierCV.html
index c700e0c953482..f7a029eae7314 100644
--- a/dev/modules/generated/sklearn.linear_model.RidgeClassifierCV.html
+++ b/dev/modules/generated/sklearn.linear_model.RidgeClassifierCV.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
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-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
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@@ -1305,7 +1305,7 @@
 <h1>RidgeClassifierCV<a class="headerlink" href="#ridgeclassifiercv" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.linear_model.RidgeClassifierCV">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.linear_model.</span></span><span class="sig-name descname"><span class="pre">RidgeClassifierCV</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">alphas</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(0.1,</span> <span class="pre">1.0,</span> <span class="pre">10.0)</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_intercept</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scoring</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cv</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">class_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">store_cv_results</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_ridge.py#L2698"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.RidgeClassifierCV" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.linear_model.</span></span><span class="sig-name descname"><span class="pre">RidgeClassifierCV</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">alphas</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(0.1,</span> <span class="pre">1.0,</span> <span class="pre">10.0)</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_intercept</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scoring</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cv</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">class_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">store_cv_results</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_ridge.py#L2698"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.RidgeClassifierCV" title="Link to this definition">#</a></dt>
 <dd><p>Ridge classifier with built-in cross-validation.</p>
 <p>See glossary entry for <a class="reference internal" href="http://github.com/glossary.html#term-cross-validation-estimator"><span class="xref std std-term">cross-validation estimator</span></a>.</p>
 <p>By default, it performs Leave-One-Out Cross-Validation. Currently,
@@ -1429,7 +1429,7 @@ <h1>RidgeClassifierCV<a class="headerlink" href="#ridgeclassifiercv" title="Link
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.RidgeClassifierCV.decision_function">
-<span class="sig-name descname"><span class="pre">decision_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_base.py#L330"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.RidgeClassifierCV.decision_function" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">decision_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_base.py#L330"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.RidgeClassifierCV.decision_function" title="Link to this definition">#</a></dt>
 <dd><p>Predict confidence scores for samples.</p>
 <p>The confidence score for a sample is proportional to the signed
 distance of that sample to the hyperplane.</p>
@@ -1453,7 +1453,7 @@ <h1>RidgeClassifierCV<a class="headerlink" href="#ridgeclassifiercv" title="Link
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.RidgeClassifierCV.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_ridge.py#L2854"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.RidgeClassifierCV.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_ridge.py#L2854"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.RidgeClassifierCV.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit Ridge classifier with cv.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1489,7 +1489,7 @@ <h1>RidgeClassifierCV<a class="headerlink" href="#ridgeclassifiercv" title="Link
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.RidgeClassifierCV.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_ridge.py#L2471"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.RidgeClassifierCV.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_ridge.py#L2471"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.RidgeClassifierCV.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1509,7 +1509,7 @@ <h1>RidgeClassifierCV<a class="headerlink" href="#ridgeclassifiercv" title="Link
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.RidgeClassifierCV.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.RidgeClassifierCV.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.RidgeClassifierCV.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1530,7 +1530,7 @@ <h1>RidgeClassifierCV<a class="headerlink" href="#ridgeclassifiercv" title="Link
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.RidgeClassifierCV.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_ridge.py#L1313"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.RidgeClassifierCV.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_ridge.py#L1313"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.RidgeClassifierCV.predict" title="Link to this definition">#</a></dt>
 <dd><p>Predict class labels for samples in <code class="docutils literal notranslate"><span class="pre">X</span></code>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1553,7 +1553,7 @@ <h1>RidgeClassifierCV<a class="headerlink" href="#ridgeclassifiercv" title="Link
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.RidgeClassifierCV.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L546"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.RidgeClassifierCV.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L487"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.RidgeClassifierCV.score" title="Link to this definition">#</a></dt>
 <dd><p>Return <a class="reference internal" href="http://github.com/model_evaluation.html#accuracy-score"><span class="std std-ref">accuracy</span></a> on provided data and labels.</p>
 <p>In multi-label classification, this is the subset accuracy
 which is a harsh metric since you require for each sample that
@@ -1580,7 +1580,7 @@ <h1>RidgeClassifierCV<a class="headerlink" href="#ridgeclassifiercv" title="Link
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.RidgeClassifierCV.set_fit_request">
-<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.RidgeClassifierCV" title="sklearn.linear_model._ridge.RidgeClassifierCV"><span class="pre">RidgeClassifierCV</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.RidgeClassifierCV.set_fit_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.RidgeClassifierCV" title="sklearn.linear_model._ridge.RidgeClassifierCV"><span class="pre">RidgeClassifierCV</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.RidgeClassifierCV.set_fit_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1623,7 +1623,7 @@ <h1>RidgeClassifierCV<a class="headerlink" href="#ridgeclassifiercv" title="Link
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.RidgeClassifierCV.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.RidgeClassifierCV.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.RidgeClassifierCV.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1647,7 +1647,7 @@ <h1>RidgeClassifierCV<a class="headerlink" href="#ridgeclassifiercv" title="Link
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.RidgeClassifierCV.set_score_request">
-<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.RidgeClassifierCV" title="sklearn.linear_model._ridge.RidgeClassifierCV"><span class="pre">RidgeClassifierCV</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.RidgeClassifierCV.set_score_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.RidgeClassifierCV" title="sklearn.linear_model._ridge.RidgeClassifierCV"><span class="pre">RidgeClassifierCV</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.RidgeClassifierCV.set_score_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
diff --git a/dev/modules/generated/sklearn.linear_model.SGDClassifier.html b/dev/modules/generated/sklearn.linear_model.SGDClassifier.html
index cd26d73e533ac..948747eeccb74 100644
--- a/dev/modules/generated/sklearn.linear_model.SGDClassifier.html
+++ b/dev/modules/generated/sklearn.linear_model.SGDClassifier.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
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-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
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@@ -1307,7 +1307,7 @@
 <h1>SGDClassifier<a class="headerlink" href="#sgdclassifier" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.linear_model.SGDClassifier">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.linear_model.</span></span><span class="sig-name descname"><span class="pre">SGDClassifier</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">loss</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'hinge'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">penalty</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'l2'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">l1_ratio</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.15</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_intercept</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epsilon</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">learning_rate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'optimal'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eta0</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">power_t</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">early_stopping</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validation_fraction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_iter_no_change</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">class_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">warm_start</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_stochastic_gradient.py#L929"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.SGDClassifier" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.linear_model.</span></span><span class="sig-name descname"><span class="pre">SGDClassifier</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">loss</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'hinge'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">penalty</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'l2'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">l1_ratio</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.15</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_intercept</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epsilon</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">learning_rate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'optimal'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eta0</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">power_t</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">early_stopping</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validation_fraction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_iter_no_change</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">class_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">warm_start</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_stochastic_gradient.py#L938"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.SGDClassifier" title="Link to this definition">#</a></dt>
 <dd><p>Linear classifiers (SVM, logistic regression, etc.) with SGD training.</p>
 <p>This estimator implements regularized linear models with stochastic
 gradient descent (SGD) learning: the gradient of the loss is estimated
@@ -1362,7 +1362,11 @@ <h1>SGDClassifier<a class="headerlink" href="#sgdclassifier" title="Link to this
 <dt><strong>l1_ratio</strong><span class="classifier">float, default=0.15</span></dt><dd><p>The Elastic Net mixing parameter, with 0 &lt;= l1_ratio &lt;= 1.
 l1_ratio=0 corresponds to L2 penalty, l1_ratio=1 to L1.
 Only used if <code class="docutils literal notranslate"><span class="pre">penalty</span></code> is ‘elasticnet’.
-Values must be in the range <code class="docutils literal notranslate"><span class="pre">[0.0,</span> <span class="pre">1.0]</span></code>.</p>
+Values must be in the range <code class="docutils literal notranslate"><span class="pre">[0.0,</span> <span class="pre">1.0]</span></code> or can be <code class="docutils literal notranslate"><span class="pre">None</span></code> if
+<code class="docutils literal notranslate"><span class="pre">penalty</span></code> is not <code class="docutils literal notranslate"><span class="pre">elasticnet</span></code>.</p>
+<div class="versionchanged">
+<p><span class="versionmodified changed">Changed in version 1.7: </span><code class="docutils literal notranslate"><span class="pre">l1_ratio</span></code> can be <code class="docutils literal notranslate"><span class="pre">None</span></code> when <code class="docutils literal notranslate"><span class="pre">penalty</span></code> is not “elasticnet”.</p>
+</div>
 </dd>
 <dt><strong>fit_intercept</strong><span class="classifier">bool, default=True</span></dt><dd><p>Whether the intercept should be estimated or not. If False, the
 data is assumed to be already centered.</p>
@@ -1400,7 +1404,7 @@ <h1>SGDClassifier<a class="headerlink" href="#sgdclassifier" title="Link to this
 </dd>
 <dt><strong>n_jobs</strong><span class="classifier">int, default=None</span></dt><dd><p>The number of CPUs to use to do the OVA (One Versus All, for
 multi-class problems) computation.
-<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.5.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
+<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.6.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
 <code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all processors. See <a class="reference internal" href="http://github.com/glossary.html#term-n_jobs"><span class="xref std std-term">Glossary</span></a>
 for more details.</p>
 </dd>
@@ -1544,7 +1548,7 @@ <h1>SGDClassifier<a class="headerlink" href="#sgdclassifier" title="Link to this
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.SGDClassifier.decision_function">
-<span class="sig-name descname"><span class="pre">decision_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_base.py#L330"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.SGDClassifier.decision_function" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">decision_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_base.py#L330"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.SGDClassifier.decision_function" title="Link to this definition">#</a></dt>
 <dd><p>Predict confidence scores for samples.</p>
 <p>The confidence score for a sample is proportional to the signed
 distance of that sample to the hyperplane.</p>
@@ -1568,7 +1572,7 @@ <h1>SGDClassifier<a class="headerlink" href="#sgdclassifier" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.SGDClassifier.densify">
-<span class="sig-name descname"><span class="pre">densify</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_base.py#L406"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.SGDClassifier.densify" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">densify</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_base.py#L406"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.SGDClassifier.densify" title="Link to this definition">#</a></dt>
 <dd><p>Convert coefficient matrix to dense array format.</p>
 <p>Converts the <code class="docutils literal notranslate"><span class="pre">coef_</span></code> member (back) to a numpy.ndarray. This is the
 default format of <code class="docutils literal notranslate"><span class="pre">coef_</span></code> and is required for fitting, so calling
@@ -1586,7 +1590,7 @@ <h1>SGDClassifier<a class="headerlink" href="#sgdclassifier" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.SGDClassifier.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coef_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">intercept_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_stochastic_gradient.py#L880"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.SGDClassifier.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coef_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">intercept_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_stochastic_gradient.py#L889"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.SGDClassifier.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit linear model with Stochastic Gradient Descent.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1617,7 +1621,7 @@ <h1>SGDClassifier<a class="headerlink" href="#sgdclassifier" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.SGDClassifier.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.SGDClassifier.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.SGDClassifier.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1634,7 +1638,7 @@ <h1>SGDClassifier<a class="headerlink" href="#sgdclassifier" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.SGDClassifier.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.SGDClassifier.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.SGDClassifier.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1655,7 +1659,7 @@ <h1>SGDClassifier<a class="headerlink" href="#sgdclassifier" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.SGDClassifier.partial_fit">
-<span class="sig-name descname"><span class="pre">partial_fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">classes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_stochastic_gradient.py#L816"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.SGDClassifier.partial_fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">partial_fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">classes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_stochastic_gradient.py#L825"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.SGDClassifier.partial_fit" title="Link to this definition">#</a></dt>
 <dd><p>Perform one epoch of stochastic gradient descent on given samples.</p>
 <p>Internally, this method uses <code class="docutils literal notranslate"><span class="pre">max_iter</span> <span class="pre">=</span> <span class="pre">1</span></code>. Therefore, it is not
 guaranteed that a minimum of the cost function is reached after calling
@@ -1691,7 +1695,7 @@ <h1>SGDClassifier<a class="headerlink" href="#sgdclassifier" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.SGDClassifier.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_base.py#L360"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.SGDClassifier.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_base.py#L360"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.SGDClassifier.predict" title="Link to this definition">#</a></dt>
 <dd><p>Predict class labels for samples in X.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1711,7 +1715,7 @@ <h1>SGDClassifier<a class="headerlink" href="#sgdclassifier" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.SGDClassifier.predict_log_proba">
-<span class="sig-name descname"><span class="pre">predict_log_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_stochastic_gradient.py#L1344"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.SGDClassifier.predict_log_proba" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict_log_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_stochastic_gradient.py#L1357"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.SGDClassifier.predict_log_proba" title="Link to this definition">#</a></dt>
 <dd><p>Log of probability estimates.</p>
 <p>This method is only available for log loss and modified Huber loss.</p>
 <p>When loss=”modified_huber”, probability estimates may be hard zeros
@@ -1737,7 +1741,7 @@ <h1>SGDClassifier<a class="headerlink" href="#sgdclassifier" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.SGDClassifier.predict_proba">
-<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_stochastic_gradient.py#L1263"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.SGDClassifier.predict_proba" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_stochastic_gradient.py#L1276"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.SGDClassifier.predict_proba" title="Link to this definition">#</a></dt>
 <dd><p>Probability estimates.</p>
 <p>This method is only available for log loss and modified Huber loss.</p>
 <p>Multiclass probability estimates are derived from binary (one-vs.-rest)
@@ -1774,7 +1778,7 @@ <h1>SGDClassifier<a class="headerlink" href="#sgdclassifier" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.SGDClassifier.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L546"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.SGDClassifier.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L487"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.SGDClassifier.score" title="Link to this definition">#</a></dt>
 <dd><p>Return <a class="reference internal" href="http://github.com/model_evaluation.html#accuracy-score"><span class="std std-ref">accuracy</span></a> on provided data and labels.</p>
 <p>In multi-label classification, this is the subset accuracy
 which is a harsh metric since you require for each sample that
@@ -1801,7 +1805,7 @@ <h1>SGDClassifier<a class="headerlink" href="#sgdclassifier" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.SGDClassifier.set_fit_request">
-<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coef_init</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">intercept_init</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.SGDClassifier" title="sklearn.linear_model._stochastic_gradient.SGDClassifier"><span class="pre">SGDClassifier</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.SGDClassifier.set_fit_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coef_init</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">intercept_init</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.SGDClassifier" title="sklearn.linear_model._stochastic_gradient.SGDClassifier"><span class="pre">SGDClassifier</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.SGDClassifier.set_fit_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1848,7 +1852,7 @@ <h1>SGDClassifier<a class="headerlink" href="#sgdclassifier" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.SGDClassifier.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.SGDClassifier.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.SGDClassifier.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1872,7 +1876,7 @@ <h1>SGDClassifier<a class="headerlink" href="#sgdclassifier" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.SGDClassifier.set_partial_fit_request">
-<span class="sig-name descname"><span class="pre">set_partial_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">classes</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.SGDClassifier" title="sklearn.linear_model._stochastic_gradient.SGDClassifier"><span class="pre">SGDClassifier</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.SGDClassifier.set_partial_fit_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_partial_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">classes</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.SGDClassifier" title="sklearn.linear_model._stochastic_gradient.SGDClassifier"><span class="pre">SGDClassifier</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.SGDClassifier.set_partial_fit_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">partial_fit</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1917,7 +1921,7 @@ <h1>SGDClassifier<a class="headerlink" href="#sgdclassifier" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.SGDClassifier.set_score_request">
-<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.SGDClassifier" title="sklearn.linear_model._stochastic_gradient.SGDClassifier"><span class="pre">SGDClassifier</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.SGDClassifier.set_score_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.SGDClassifier" title="sklearn.linear_model._stochastic_gradient.SGDClassifier"><span class="pre">SGDClassifier</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.SGDClassifier.set_score_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1960,7 +1964,7 @@ <h1>SGDClassifier<a class="headerlink" href="#sgdclassifier" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.SGDClassifier.sparsify">
-<span class="sig-name descname"><span class="pre">sparsify</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_base.py#L426"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.SGDClassifier.sparsify" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">sparsify</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_base.py#L426"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.SGDClassifier.sparsify" title="Link to this definition">#</a></dt>
 <dd><p>Convert coefficient matrix to sparse format.</p>
 <p>Converts the <code class="docutils literal notranslate"><span class="pre">coef_</span></code> member to a scipy.sparse matrix, which for
 L1-regularized models can be much more memory- and storage-efficient
diff --git a/dev/modules/generated/sklearn.linear_model.SGDOneClassSVM.html b/dev/modules/generated/sklearn.linear_model.SGDOneClassSVM.html
index d7dc2291eafa5..c5706609f257a 100644
--- a/dev/modules/generated/sklearn.linear_model.SGDOneClassSVM.html
+++ b/dev/modules/generated/sklearn.linear_model.SGDOneClassSVM.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>SGDOneClassSVM<a class="headerlink" href="#sgdoneclasssvm" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.linear_model.SGDOneClassSVM">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.linear_model.</span></span><span class="sig-name descname"><span class="pre">SGDOneClassSVM</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">nu</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_intercept</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">learning_rate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'optimal'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eta0</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">power_t</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">warm_start</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_stochastic_gradient.py#L2035"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.SGDOneClassSVM" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.linear_model.</span></span><span class="sig-name descname"><span class="pre">SGDOneClassSVM</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">nu</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_intercept</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">learning_rate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'optimal'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eta0</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">power_t</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">warm_start</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_stochastic_gradient.py#L2052"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.SGDOneClassSVM" title="Link to this definition">#</a></dt>
 <dd><p>Solves linear One-Class SVM using Stochastic Gradient Descent.</p>
 <p>This implementation is meant to be used with a kernel approximation
 technique (e.g. <code class="docutils literal notranslate"><span class="pre">sklearn.kernel_approximation.Nystroem</span></code>) to obtain results
@@ -1440,7 +1440,7 @@ <h1>SGDOneClassSVM<a class="headerlink" href="#sgdoneclasssvm" title="Link to th
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.SGDOneClassSVM.decision_function">
-<span class="sig-name descname"><span class="pre">decision_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_stochastic_gradient.py#L2527"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.SGDOneClassSVM.decision_function" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">decision_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_stochastic_gradient.py#L2544"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.SGDOneClassSVM.decision_function" title="Link to this definition">#</a></dt>
 <dd><p>Signed distance to the separating hyperplane.</p>
 <p>Signed distance is positive for an inlier and negative for an
 outlier.</p>
@@ -1462,7 +1462,7 @@ <h1>SGDOneClassSVM<a class="headerlink" href="#sgdoneclasssvm" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.SGDOneClassSVM.densify">
-<span class="sig-name descname"><span class="pre">densify</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_base.py#L406"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.SGDOneClassSVM.densify" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">densify</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_base.py#L406"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.SGDOneClassSVM.densify" title="Link to this definition">#</a></dt>
 <dd><p>Convert coefficient matrix to dense array format.</p>
 <p>Converts the <code class="docutils literal notranslate"><span class="pre">coef_</span></code> member (back) to a numpy.ndarray. This is the
 default format of <code class="docutils literal notranslate"><span class="pre">coef_</span></code> and is required for fitting, so calling
@@ -1480,7 +1480,7 @@ <h1>SGDOneClassSVM<a class="headerlink" href="#sgdoneclasssvm" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.SGDOneClassSVM.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coef_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">offset_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_stochastic_gradient.py#L2478"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.SGDOneClassSVM.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coef_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">offset_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_stochastic_gradient.py#L2495"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.SGDOneClassSVM.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit linear One-Class SVM with Stochastic Gradient Descent.</p>
 <p>This solves an equivalent optimization problem of the
 One-Class SVM primal optimization problem and returns a weight vector
@@ -1515,7 +1515,7 @@ <h1>SGDOneClassSVM<a class="headerlink" href="#sgdoneclasssvm" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.SGDOneClassSVM.fit_predict">
-<span class="sig-name descname"><span class="pre">fit_predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L1096"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.SGDOneClassSVM.fit_predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L1037"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.SGDOneClassSVM.fit_predict" title="Link to this definition">#</a></dt>
 <dd><p>Perform fit on X and returns labels for X.</p>
 <p>Returns -1 for outliers and 1 for inliers.</p>
 <dl class="field-list">
@@ -1543,7 +1543,7 @@ <h1>SGDOneClassSVM<a class="headerlink" href="#sgdoneclasssvm" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.SGDOneClassSVM.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.SGDOneClassSVM.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.SGDOneClassSVM.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1560,7 +1560,7 @@ <h1>SGDOneClassSVM<a class="headerlink" href="#sgdoneclasssvm" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.SGDOneClassSVM.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.SGDOneClassSVM.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.SGDOneClassSVM.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1581,7 +1581,7 @@ <h1>SGDOneClassSVM<a class="headerlink" href="#sgdoneclasssvm" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.SGDOneClassSVM.partial_fit">
-<span class="sig-name descname"><span class="pre">partial_fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_stochastic_gradient.py#L2391"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.SGDOneClassSVM.partial_fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">partial_fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_stochastic_gradient.py#L2408"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.SGDOneClassSVM.partial_fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit linear One-Class SVM with Stochastic Gradient Descent.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1606,7 +1606,7 @@ <h1>SGDOneClassSVM<a class="headerlink" href="#sgdoneclasssvm" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.SGDOneClassSVM.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_stochastic_gradient.py#L2567"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.SGDOneClassSVM.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_stochastic_gradient.py#L2584"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.SGDOneClassSVM.predict" title="Link to this definition">#</a></dt>
 <dd><p>Return labels (1 inlier, -1 outlier) of the samples.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1626,7 +1626,7 @@ <h1>SGDOneClassSVM<a class="headerlink" href="#sgdoneclasssvm" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.SGDOneClassSVM.score_samples">
-<span class="sig-name descname"><span class="pre">score_samples</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_stochastic_gradient.py#L2551"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.SGDOneClassSVM.score_samples" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score_samples</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_stochastic_gradient.py#L2568"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.SGDOneClassSVM.score_samples" title="Link to this definition">#</a></dt>
 <dd><p>Raw scoring function of the samples.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1646,7 +1646,7 @@ <h1>SGDOneClassSVM<a class="headerlink" href="#sgdoneclasssvm" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.SGDOneClassSVM.set_fit_request">
-<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coef_init</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">offset_init</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.SGDOneClassSVM" title="sklearn.linear_model._stochastic_gradient.SGDOneClassSVM"><span class="pre">SGDOneClassSVM</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.SGDOneClassSVM.set_fit_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coef_init</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">offset_init</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.SGDOneClassSVM" title="sklearn.linear_model._stochastic_gradient.SGDOneClassSVM"><span class="pre">SGDOneClassSVM</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.SGDOneClassSVM.set_fit_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1693,7 +1693,7 @@ <h1>SGDOneClassSVM<a class="headerlink" href="#sgdoneclasssvm" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.SGDOneClassSVM.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.SGDOneClassSVM.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.SGDOneClassSVM.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1717,7 +1717,7 @@ <h1>SGDOneClassSVM<a class="headerlink" href="#sgdoneclasssvm" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.SGDOneClassSVM.set_partial_fit_request">
-<span class="sig-name descname"><span class="pre">set_partial_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.SGDOneClassSVM" title="sklearn.linear_model._stochastic_gradient.SGDOneClassSVM"><span class="pre">SGDOneClassSVM</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.SGDOneClassSVM.set_partial_fit_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_partial_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.SGDOneClassSVM" title="sklearn.linear_model._stochastic_gradient.SGDOneClassSVM"><span class="pre">SGDOneClassSVM</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.SGDOneClassSVM.set_partial_fit_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">partial_fit</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1760,7 +1760,7 @@ <h1>SGDOneClassSVM<a class="headerlink" href="#sgdoneclasssvm" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.SGDOneClassSVM.sparsify">
-<span class="sig-name descname"><span class="pre">sparsify</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_base.py#L426"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.SGDOneClassSVM.sparsify" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">sparsify</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_base.py#L426"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.SGDOneClassSVM.sparsify" title="Link to this definition">#</a></dt>
 <dd><p>Convert coefficient matrix to sparse format.</p>
 <p>Converts the <code class="docutils literal notranslate"><span class="pre">coef_</span></code> member to a scipy.sparse matrix, which for
 L1-regularized models can be much more memory- and storage-efficient
diff --git a/dev/modules/generated/sklearn.linear_model.SGDRegressor.html b/dev/modules/generated/sklearn.linear_model.SGDRegressor.html
index 5bd8018939874..e7f45472e14dd 100644
--- a/dev/modules/generated/sklearn.linear_model.SGDRegressor.html
+++ b/dev/modules/generated/sklearn.linear_model.SGDRegressor.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
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     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1308,7 +1308,7 @@
 <h1>SGDRegressor<a class="headerlink" href="#sgdregressor" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.linear_model.SGDRegressor">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.linear_model.</span></span><span class="sig-name descname"><span class="pre">SGDRegressor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">loss</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'squared_error'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">penalty</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'l2'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">l1_ratio</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.15</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_intercept</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epsilon</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">learning_rate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'invscaling'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eta0</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">power_t</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.25</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">early_stopping</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validation_fraction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_iter_no_change</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">warm_start</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_stochastic_gradient.py#L1744"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.SGDRegressor" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.linear_model.</span></span><span class="sig-name descname"><span class="pre">SGDRegressor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">loss</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'squared_error'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">penalty</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'l2'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">l1_ratio</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.15</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_intercept</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epsilon</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">learning_rate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'invscaling'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eta0</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">power_t</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.25</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">early_stopping</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validation_fraction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_iter_no_change</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">warm_start</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_stochastic_gradient.py#L1757"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.SGDRegressor" title="Link to this definition">#</a></dt>
 <dd><p>Linear model fitted by minimizing a regularized empirical loss with SGD.</p>
 <p>SGD stands for Stochastic Gradient Descent: the gradient of the loss is
 estimated each sample at a time and the model is updated along the way with
@@ -1352,7 +1352,11 @@ <h1>SGDRegressor<a class="headerlink" href="#sgdregressor" title="Link to this h
 <dt><strong>l1_ratio</strong><span class="classifier">float, default=0.15</span></dt><dd><p>The Elastic Net mixing parameter, with 0 &lt;= l1_ratio &lt;= 1.
 l1_ratio=0 corresponds to L2 penalty, l1_ratio=1 to L1.
 Only used if <code class="docutils literal notranslate"><span class="pre">penalty</span></code> is ‘elasticnet’.
-Values must be in the range <code class="docutils literal notranslate"><span class="pre">[0.0,</span> <span class="pre">1.0]</span></code>.</p>
+Values must be in the range <code class="docutils literal notranslate"><span class="pre">[0.0,</span> <span class="pre">1.0]</span></code> or can be <code class="docutils literal notranslate"><span class="pre">None</span></code> if
+<code class="docutils literal notranslate"><span class="pre">penalty</span></code> is not <code class="docutils literal notranslate"><span class="pre">elasticnet</span></code>.</p>
+<div class="versionchanged">
+<p><span class="versionmodified changed">Changed in version 1.7: </span><code class="docutils literal notranslate"><span class="pre">l1_ratio</span></code> can be <code class="docutils literal notranslate"><span class="pre">None</span></code> when <code class="docutils literal notranslate"><span class="pre">penalty</span></code> is not “elasticnet”.</p>
+</div>
 </dd>
 <dt><strong>fit_intercept</strong><span class="classifier">bool, default=True</span></dt><dd><p>Whether the intercept should be estimated or not. If False, the
 data is assumed to be already centered.</p>
@@ -1525,7 +1529,7 @@ <h1>SGDRegressor<a class="headerlink" href="#sgdregressor" title="Link to this h
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.SGDRegressor.densify">
-<span class="sig-name descname"><span class="pre">densify</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_base.py#L406"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.SGDRegressor.densify" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">densify</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_base.py#L406"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.SGDRegressor.densify" title="Link to this definition">#</a></dt>
 <dd><p>Convert coefficient matrix to dense array format.</p>
 <p>Converts the <code class="docutils literal notranslate"><span class="pre">coef_</span></code> member (back) to a numpy.ndarray. This is the
 default format of <code class="docutils literal notranslate"><span class="pre">coef_</span></code> and is required for fitting, so calling
@@ -1543,7 +1547,7 @@ <h1>SGDRegressor<a class="headerlink" href="#sgdregressor" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.SGDRegressor.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coef_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">intercept_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_stochastic_gradient.py#L1577"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.SGDRegressor.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coef_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">intercept_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_stochastic_gradient.py#L1590"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.SGDRegressor.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit linear model with Stochastic Gradient Descent.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1571,7 +1575,7 @@ <h1>SGDRegressor<a class="headerlink" href="#sgdregressor" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.SGDRegressor.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.SGDRegressor.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.SGDRegressor.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1588,7 +1592,7 @@ <h1>SGDRegressor<a class="headerlink" href="#sgdregressor" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.SGDRegressor.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.SGDRegressor.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.SGDRegressor.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1609,7 +1613,7 @@ <h1>SGDRegressor<a class="headerlink" href="#sgdregressor" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.SGDRegressor.partial_fit">
-<span class="sig-name descname"><span class="pre">partial_fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_stochastic_gradient.py#L1482"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.SGDRegressor.partial_fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">partial_fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_stochastic_gradient.py#L1495"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.SGDRegressor.partial_fit" title="Link to this definition">#</a></dt>
 <dd><p>Perform one epoch of stochastic gradient descent on given samples.</p>
 <p>Internally, this method uses <code class="docutils literal notranslate"><span class="pre">max_iter</span> <span class="pre">=</span> <span class="pre">1</span></code>. Therefore, it is not
 guaranteed that a minimum of the cost function is reached after calling
@@ -1638,7 +1642,7 @@ <h1>SGDRegressor<a class="headerlink" href="#sgdregressor" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.SGDRegressor.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_stochastic_gradient.py#L1636"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.SGDRegressor.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_stochastic_gradient.py#L1649"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.SGDRegressor.predict" title="Link to this definition">#</a></dt>
 <dd><p>Predict using the linear model.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1658,7 +1662,7 @@ <h1>SGDRegressor<a class="headerlink" href="#sgdregressor" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.SGDRegressor.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L619"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.SGDRegressor.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L560"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.SGDRegressor.score" title="Link to this definition">#</a></dt>
 <dd><p>Return <a class="reference internal" href="http://github.com/model_evaluation.html#r2-score"><span class="std std-ref">coefficient of determination</span></a> on test data.</p>
 <p>The coefficient of determination, <span class="math notranslate nohighlight">\(R^2\)</span>, is defined as
 <span class="math notranslate nohighlight">\((1 - \frac{u}{v})\)</span>, where <span class="math notranslate nohighlight">\(u\)</span> is the residual
@@ -1700,7 +1704,7 @@ <h1>SGDRegressor<a class="headerlink" href="#sgdregressor" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.SGDRegressor.set_fit_request">
-<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coef_init</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">intercept_init</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.SGDRegressor" title="sklearn.linear_model._stochastic_gradient.SGDRegressor"><span class="pre">SGDRegressor</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.SGDRegressor.set_fit_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coef_init</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">intercept_init</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.SGDRegressor" title="sklearn.linear_model._stochastic_gradient.SGDRegressor"><span class="pre">SGDRegressor</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.SGDRegressor.set_fit_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1747,7 +1751,7 @@ <h1>SGDRegressor<a class="headerlink" href="#sgdregressor" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.SGDRegressor.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.SGDRegressor.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.SGDRegressor.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1771,7 +1775,7 @@ <h1>SGDRegressor<a class="headerlink" href="#sgdregressor" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.SGDRegressor.set_partial_fit_request">
-<span class="sig-name descname"><span class="pre">set_partial_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.SGDRegressor" title="sklearn.linear_model._stochastic_gradient.SGDRegressor"><span class="pre">SGDRegressor</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.SGDRegressor.set_partial_fit_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_partial_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.SGDRegressor" title="sklearn.linear_model._stochastic_gradient.SGDRegressor"><span class="pre">SGDRegressor</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.SGDRegressor.set_partial_fit_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">partial_fit</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1814,7 +1818,7 @@ <h1>SGDRegressor<a class="headerlink" href="#sgdregressor" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.SGDRegressor.set_score_request">
-<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.SGDRegressor" title="sklearn.linear_model._stochastic_gradient.SGDRegressor"><span class="pre">SGDRegressor</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.SGDRegressor.set_score_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.SGDRegressor" title="sklearn.linear_model._stochastic_gradient.SGDRegressor"><span class="pre">SGDRegressor</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.SGDRegressor.set_score_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1857,7 +1861,7 @@ <h1>SGDRegressor<a class="headerlink" href="#sgdregressor" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.SGDRegressor.sparsify">
-<span class="sig-name descname"><span class="pre">sparsify</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_base.py#L426"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.SGDRegressor.sparsify" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">sparsify</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_base.py#L426"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.SGDRegressor.sparsify" title="Link to this definition">#</a></dt>
 <dd><p>Convert coefficient matrix to sparse format.</p>
 <p>Converts the <code class="docutils literal notranslate"><span class="pre">coef_</span></code> member to a scipy.sparse matrix, which for
 L1-regularized models can be much more memory- and storage-efficient
diff --git a/dev/modules/generated/sklearn.linear_model.TheilSenRegressor.html b/dev/modules/generated/sklearn.linear_model.TheilSenRegressor.html
index 2284edf9ff948..b4501ad03ea4e 100644
--- a/dev/modules/generated/sklearn.linear_model.TheilSenRegressor.html
+++ b/dev/modules/generated/sklearn.linear_model.TheilSenRegressor.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1308,7 +1308,7 @@
 <h1>TheilSenRegressor<a class="headerlink" href="#theilsenregressor" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.linear_model.TheilSenRegressor">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.linear_model.</span></span><span class="sig-name descname"><span class="pre">TheilSenRegressor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_intercept</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy_X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'deprecated'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_subpopulation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10000.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_subsamples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">300</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_theil_sen.py#L208"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.TheilSenRegressor" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.linear_model.</span></span><span class="sig-name descname"><span class="pre">TheilSenRegressor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_intercept</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy_X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'deprecated'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_subpopulation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10000.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_subsamples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">300</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_theil_sen.py#L207"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.TheilSenRegressor" title="Link to this definition">#</a></dt>
 <dd><p>Theil-Sen Estimator: robust multivariate regression model.</p>
 <p>The algorithm calculates least square solutions on subsets with size
 n_subsamples of the samples in X. Any value of n_subsamples between the
@@ -1358,7 +1358,7 @@ <h1>TheilSenRegressor<a class="headerlink" href="#theilsenregressor" title="Link
 See <a class="reference internal" href="http://github.com/glossary.html#term-random_state"><span class="xref std std-term">Glossary</span></a>.</p>
 </dd>
 <dt><strong>n_jobs</strong><span class="classifier">int, default=None</span></dt><dd><p>Number of CPUs to use during the cross validation.
-<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.5.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
+<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.6.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
 <code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all processors. See <a class="reference internal" href="http://github.com/glossary.html#term-n_jobs"><span class="xref std std-term">Glossary</span></a>
 for more details.</p>
 </dd>
@@ -1417,14 +1417,14 @@ <h1>TheilSenRegressor<a class="headerlink" href="#theilsenregressor" title="Link
 <span class="gp">... </span>    <span class="n">n_samples</span><span class="o">=</span><span class="mi">200</span><span class="p">,</span> <span class="n">n_features</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">noise</span><span class="o">=</span><span class="mf">4.0</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">reg</span> <span class="o">=</span> <span class="n">TheilSenRegressor</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">reg</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
-<span class="go">0.9884...</span>
+<span class="go">0.9884</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">reg</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X</span><span class="p">[:</span><span class="mi">1</span><span class="p">,])</span>
-<span class="go">array([-31.5871...])</span>
+<span class="go">array([-31.5871])</span>
 </pre></div>
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.TheilSenRegressor.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_theil_sen.py#L402"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.TheilSenRegressor.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_theil_sen.py#L401"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.TheilSenRegressor.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit linear model.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1446,7 +1446,7 @@ <h1>TheilSenRegressor<a class="headerlink" href="#theilsenregressor" title="Link
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.TheilSenRegressor.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.TheilSenRegressor.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.TheilSenRegressor.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1463,7 +1463,7 @@ <h1>TheilSenRegressor<a class="headerlink" href="#theilsenregressor" title="Link
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.TheilSenRegressor.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.TheilSenRegressor.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.TheilSenRegressor.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1484,7 +1484,7 @@ <h1>TheilSenRegressor<a class="headerlink" href="#theilsenregressor" title="Link
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.TheilSenRegressor.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_base.py#L284"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.TheilSenRegressor.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_base.py#L284"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.TheilSenRegressor.predict" title="Link to this definition">#</a></dt>
 <dd><p>Predict using the linear model.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1504,7 +1504,7 @@ <h1>TheilSenRegressor<a class="headerlink" href="#theilsenregressor" title="Link
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.TheilSenRegressor.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L619"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.TheilSenRegressor.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L560"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.TheilSenRegressor.score" title="Link to this definition">#</a></dt>
 <dd><p>Return <a class="reference internal" href="http://github.com/model_evaluation.html#r2-score"><span class="std std-ref">coefficient of determination</span></a> on test data.</p>
 <p>The coefficient of determination, <span class="math notranslate nohighlight">\(R^2\)</span>, is defined as
 <span class="math notranslate nohighlight">\((1 - \frac{u}{v})\)</span>, where <span class="math notranslate nohighlight">\(u\)</span> is the residual
@@ -1546,7 +1546,7 @@ <h1>TheilSenRegressor<a class="headerlink" href="#theilsenregressor" title="Link
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.TheilSenRegressor.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.TheilSenRegressor.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.TheilSenRegressor.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1570,7 +1570,7 @@ <h1>TheilSenRegressor<a class="headerlink" href="#theilsenregressor" title="Link
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.TheilSenRegressor.set_score_request">
-<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.TheilSenRegressor" title="sklearn.linear_model._theil_sen.TheilSenRegressor"><span class="pre">TheilSenRegressor</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.TheilSenRegressor.set_score_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.TheilSenRegressor" title="sklearn.linear_model._theil_sen.TheilSenRegressor"><span class="pre">TheilSenRegressor</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.TheilSenRegressor.set_score_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
diff --git a/dev/modules/generated/sklearn.linear_model.TweedieRegressor.html b/dev/modules/generated/sklearn.linear_model.TweedieRegressor.html
index c0037c7a2e0bb..c8b704b26c639 100644
--- a/dev/modules/generated/sklearn.linear_model.TweedieRegressor.html
+++ b/dev/modules/generated/sklearn.linear_model.TweedieRegressor.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1308,7 +1308,7 @@
 <h1>TweedieRegressor<a class="headerlink" href="#tweedieregressor" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.linear_model.TweedieRegressor">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.linear_model.</span></span><span class="sig-name descname"><span class="pre">TweedieRegressor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">power</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_intercept</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">link</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">solver</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'lbfgs'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">warm_start</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_glm/glm.py#L731"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.TweedieRegressor" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.linear_model.</span></span><span class="sig-name descname"><span class="pre">TweedieRegressor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">power</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_intercept</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">link</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">solver</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'lbfgs'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">warm_start</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_glm/glm.py#L731"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.TweedieRegressor" title="Link to this definition">#</a></dt>
 <dd><p>Generalized Linear Model with a Tweedie distribution.</p>
 <p>This estimator can be used to model different GLMs depending on the
 <code class="docutils literal notranslate"><span class="pre">power</span></code> parameter, which determines the underlying distribution.</p>
@@ -1439,18 +1439,18 @@ <h1>TweedieRegressor<a class="headerlink" href="#tweedieregressor" title="Link t
 <span class="gp">&gt;&gt;&gt; </span><span class="n">clf</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
 <span class="go">TweedieRegressor()</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">clf</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
-<span class="go">np.float64(0.839...)</span>
+<span class="go">np.float64(0.839)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">clf</span><span class="o">.</span><span class="n">coef_</span>
-<span class="go">array([0.599..., 0.299...])</span>
+<span class="go">array([0.599, 0.299])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">clf</span><span class="o">.</span><span class="n">intercept_</span>
-<span class="go">np.float64(1.600...)</span>
+<span class="go">np.float64(1.600)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">clf</span><span class="o">.</span><span class="n">predict</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">]])</span>
-<span class="go">array([2.500..., 4.599...])</span>
+<span class="go">array([2.500, 4.599])</span>
 </pre></div>
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.TweedieRegressor.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_glm/glm.py#L170"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.TweedieRegressor.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_glm/glm.py#L170"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.TweedieRegressor.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit a Generalized Linear Model.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1474,7 +1474,7 @@ <h1>TweedieRegressor<a class="headerlink" href="#tweedieregressor" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.TweedieRegressor.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.TweedieRegressor.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.TweedieRegressor.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1491,7 +1491,7 @@ <h1>TweedieRegressor<a class="headerlink" href="#tweedieregressor" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.TweedieRegressor.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.TweedieRegressor.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.TweedieRegressor.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1512,7 +1512,7 @@ <h1>TweedieRegressor<a class="headerlink" href="#tweedieregressor" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.TweedieRegressor.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_glm/glm.py#L350"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.TweedieRegressor.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_glm/glm.py#L350"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.TweedieRegressor.predict" title="Link to this definition">#</a></dt>
 <dd><p>Predict using GLM with feature matrix X.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1532,7 +1532,7 @@ <h1>TweedieRegressor<a class="headerlink" href="#tweedieregressor" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.TweedieRegressor.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_glm/glm.py#L368"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.TweedieRegressor.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_glm/glm.py#L368"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.TweedieRegressor.score" title="Link to this definition">#</a></dt>
 <dd><p>Compute D^2, the percentage of deviance explained.</p>
 <p>D^2 is a generalization of the coefficient of determination R^2.
 R^2 uses squared error and D^2 uses the deviance of this GLM, see the
@@ -1566,7 +1566,7 @@ <h1>TweedieRegressor<a class="headerlink" href="#tweedieregressor" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.TweedieRegressor.set_fit_request">
-<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.TweedieRegressor" title="sklearn.linear_model._glm.glm.TweedieRegressor"><span class="pre">TweedieRegressor</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.TweedieRegressor.set_fit_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.TweedieRegressor" title="sklearn.linear_model._glm.glm.TweedieRegressor"><span class="pre">TweedieRegressor</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.TweedieRegressor.set_fit_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1609,7 +1609,7 @@ <h1>TweedieRegressor<a class="headerlink" href="#tweedieregressor" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.TweedieRegressor.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.TweedieRegressor.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.TweedieRegressor.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1633,7 +1633,7 @@ <h1>TweedieRegressor<a class="headerlink" href="#tweedieregressor" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.linear_model.TweedieRegressor.set_score_request">
-<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.TweedieRegressor" title="sklearn.linear_model._glm.glm.TweedieRegressor"><span class="pre">TweedieRegressor</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.TweedieRegressor.set_score_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.TweedieRegressor" title="sklearn.linear_model._glm.glm.TweedieRegressor"><span class="pre">TweedieRegressor</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.TweedieRegressor.set_score_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
diff --git a/dev/modules/generated/sklearn.linear_model.enet_path.html b/dev/modules/generated/sklearn.linear_model.enet_path.html
index 303f3e6f25b54..c55585561f603 100644
--- a/dev/modules/generated/sklearn.linear_model.enet_path.html
+++ b/dev/modules/generated/sklearn.linear_model.enet_path.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>enet_path<a class="headerlink" href="#enet-path" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.linear_model.enet_path">
-<span class="sig-prename descclassname"><span class="pre">sklearn.linear_model.</span></span><span class="sig-name descname"><span class="pre">enet_path</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">l1_ratio</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_alphas</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alphas</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">precompute</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Xy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy_X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coef_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_n_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">positive</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_input</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_coordinate_descent.py#L377"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.enet_path" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.linear_model.</span></span><span class="sig-name descname"><span class="pre">enet_path</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">l1_ratio</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_alphas</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alphas</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">precompute</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Xy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy_X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coef_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_n_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">positive</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_input</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_coordinate_descent.py#L377"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.enet_path" title="Link to this definition">#</a></dt>
 <dd><p>Compute elastic net path with coordinate descent.</p>
 <p>The elastic net optimization function varies for mono and multi-outputs.</p>
 <p>For mono-output tasks it is:</p>
@@ -1412,16 +1412,16 @@ <h1>enet_path<a class="headerlink" href="#enet-path" title="Link to this heading
 <span class="gp">... </span>   <span class="n">n_samples</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">n_features</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">n_informative</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">coef</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span>
 <span class="gp">... </span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">true_coef</span>
-<span class="go">array([ 0.        ,  0.        ,  0.        , 97.9..., 45.7...])</span>
+<span class="go">array([ 0.        ,  0.        ,  0.        , 97.9, 45.7])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">alphas</span><span class="p">,</span> <span class="n">estimated_coef</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">enet_path</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">n_alphas</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">alphas</span><span class="o">.</span><span class="n">shape</span>
 <span class="go">(3,)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">estimated_coef</span>
-<span class="go"> array([[ 0.        ,  0.78...,  0.56...],</span>
-<span class="go">        [ 0.        ,  1.12...,  0.61...],</span>
-<span class="go">        [-0.        , -2.12..., -1.12...],</span>
-<span class="go">        [ 0.        , 23.04..., 88.93...],</span>
-<span class="go">        [ 0.        , 10.63..., 41.56...]])</span>
+<span class="go"> array([[ 0.,  0.787,  0.568],</span>
+<span class="go">        [ 0.,  1.120,  0.620],</span>
+<span class="go">        [-0., -2.129, -1.128],</span>
+<span class="go">        [ 0., 23.046, 88.939],</span>
+<span class="go">        [ 0., 10.637, 41.566]])</span>
 </pre></div>
 </div>
 </dd></dl>
diff --git a/dev/modules/generated/sklearn.linear_model.lars_path.html b/dev/modules/generated/sklearn.linear_model.lars_path.html
index e341b39f36a0d..bf62d225f6f28 100644
--- a/dev/modules/generated/sklearn.linear_model.lars_path.html
+++ b/dev/modules/generated/sklearn.linear_model.lars_path.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>lars_path<a class="headerlink" href="#lars-path" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.linear_model.lars_path">
-<span class="sig-prename descclassname"><span class="pre">sklearn.linear_model.</span></span><span class="sig-name descname"><span class="pre">lars_path</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Xy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Gram</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">500</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha_min</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'lar'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy_X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">np.float64(2.220446049250313e-16)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy_Gram</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_n_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">positive</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_least_angle.py#L44"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.lars_path" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.linear_model.</span></span><span class="sig-name descname"><span class="pre">lars_path</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Xy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Gram</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">500</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha_min</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'lar'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy_X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">np.float64(2.220446049250313e-16)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy_Gram</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_n_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">positive</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_least_angle.py#L44"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.lars_path" title="Link to this definition">#</a></dt>
 <dd><p>Compute Least Angle Regression or Lasso path using the LARS algorithm.</p>
 <p>The optimization objective for the case method=’lasso’ is:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">(</span><span class="mi">1</span> <span class="o">/</span> <span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="n">n_samples</span><span class="p">))</span> <span class="o">*</span> <span class="o">||</span><span class="n">y</span> <span class="o">-</span> <span class="n">Xw</span><span class="o">||^</span><span class="mi">2_2</span> <span class="o">+</span> <span class="n">alpha</span> <span class="o">*</span> <span class="o">||</span><span class="n">w</span><span class="o">||</span><span class="n">_1</span>
@@ -1425,7 +1425,7 @@ <h1>lars_path<a class="headerlink" href="#lars-path" title="Link to this heading
 <span class="gp">... </span>   <span class="n">n_samples</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">n_features</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">n_informative</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">coef</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span>
 <span class="gp">... </span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">true_coef</span>
-<span class="go">array([ 0.        ,  0.        ,  0.        , 97.9..., 45.7...])</span>
+<span class="go">array([ 0.        ,  0.        ,  0.        , 97.9, 45.7])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">alphas</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">estimated_coef</span> <span class="o">=</span> <span class="n">lars_path</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">alphas</span><span class="o">.</span><span class="n">shape</span>
 <span class="go">(3,)</span>
@@ -1433,8 +1433,8 @@ <h1>lars_path<a class="headerlink" href="#lars-path" title="Link to this heading
 <span class="go">array([[ 0.     ,  0.     ,  0.     ],</span>
 <span class="go">       [ 0.     ,  0.     ,  0.     ],</span>
 <span class="go">       [ 0.     ,  0.     ,  0.     ],</span>
-<span class="go">       [ 0.     , 46.96..., 97.99...],</span>
-<span class="go">       [ 0.     ,  0.     , 45.70...]])</span>
+<span class="go">       [ 0.     , 46.96, 97.99],</span>
+<span class="go">       [ 0.     ,  0.     , 45.70]])</span>
 </pre></div>
 </div>
 </dd></dl>
diff --git a/dev/modules/generated/sklearn.linear_model.lars_path_gram.html b/dev/modules/generated/sklearn.linear_model.lars_path_gram.html
index 271d59fa011d1..cd7b3b27050b5 100644
--- a/dev/modules/generated/sklearn.linear_model.lars_path_gram.html
+++ b/dev/modules/generated/sklearn.linear_model.lars_path_gram.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>lars_path_gram<a class="headerlink" href="#lars-path-gram" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.linear_model.lars_path_gram">
-<span class="sig-prename descclassname"><span class="pre">sklearn.linear_model.</span></span><span class="sig-name descname"><span class="pre">lars_path_gram</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">Xy</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Gram</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_samples</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">500</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha_min</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'lar'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy_X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">np.float64(2.220446049250313e-16)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy_Gram</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_n_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">positive</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_least_angle.py#L235"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.lars_path_gram" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.linear_model.</span></span><span class="sig-name descname"><span class="pre">lars_path_gram</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">Xy</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Gram</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_samples</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">500</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha_min</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'lar'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy_X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">np.float64(2.220446049250313e-16)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy_Gram</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_n_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">positive</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_least_angle.py#L235"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.lars_path_gram" title="Link to this definition">#</a></dt>
 <dd><p>The lars_path in the sufficient stats mode.</p>
 <p>The optimization objective for the case method=’lasso’ is:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">(</span><span class="mi">1</span> <span class="o">/</span> <span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="n">n_samples</span><span class="p">))</span> <span class="o">*</span> <span class="o">||</span><span class="n">y</span> <span class="o">-</span> <span class="n">Xw</span><span class="o">||^</span><span class="mi">2_2</span> <span class="o">+</span> <span class="n">alpha</span> <span class="o">*</span> <span class="o">||</span><span class="n">w</span><span class="o">||</span><span class="n">_1</span>
@@ -1417,7 +1417,7 @@ <h1>lars_path_gram<a class="headerlink" href="#lars-path-gram" title="Link to th
 <span class="gp">... </span>   <span class="n">n_samples</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">n_features</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">n_informative</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">coef</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span>
 <span class="gp">... </span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">true_coef</span>
-<span class="go">array([ 0.        ,  0.        ,  0.        , 97.9..., 45.7...])</span>
+<span class="go">array([ 0.        ,  0.        ,  0.        , 97.9, 45.7])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">alphas</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">estimated_coef</span> <span class="o">=</span> <span class="n">lars_path_gram</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">T</span> <span class="o">@</span> <span class="n">y</span><span class="p">,</span> <span class="n">X</span><span class="o">.</span><span class="n">T</span> <span class="o">@</span> <span class="n">X</span><span class="p">,</span> <span class="n">n_samples</span><span class="o">=</span><span class="mi">100</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">alphas</span><span class="o">.</span><span class="n">shape</span>
 <span class="go">(3,)</span>
@@ -1425,8 +1425,8 @@ <h1>lars_path_gram<a class="headerlink" href="#lars-path-gram" title="Link to th
 <span class="go">array([[ 0.     ,  0.     ,  0.     ],</span>
 <span class="go">       [ 0.     ,  0.     ,  0.     ],</span>
 <span class="go">       [ 0.     ,  0.     ,  0.     ],</span>
-<span class="go">       [ 0.     , 46.96..., 97.99...],</span>
-<span class="go">       [ 0.     ,  0.     , 45.70...]])</span>
+<span class="go">       [ 0.     , 46.96, 97.99],</span>
+<span class="go">       [ 0.     ,  0.     , 45.70]])</span>
 </pre></div>
 </div>
 </dd></dl>
diff --git a/dev/modules/generated/sklearn.linear_model.lasso_path.html b/dev/modules/generated/sklearn.linear_model.lasso_path.html
index cfba19772eefa..195580fd60ddd 100644
--- a/dev/modules/generated/sklearn.linear_model.lasso_path.html
+++ b/dev/modules/generated/sklearn.linear_model.lasso_path.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>lasso_path<a class="headerlink" href="#lasso-path" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.linear_model.lasso_path">
-<span class="sig-prename descclassname"><span class="pre">sklearn.linear_model.</span></span><span class="sig-name descname"><span class="pre">lasso_path</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_alphas</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alphas</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">precompute</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Xy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy_X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coef_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_n_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">positive</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_coordinate_descent.py#L186"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.lasso_path" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.linear_model.</span></span><span class="sig-name descname"><span class="pre">lasso_path</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_alphas</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alphas</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">precompute</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Xy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy_X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coef_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_n_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">positive</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_coordinate_descent.py#L186"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.lasso_path" title="Link to this definition">#</a></dt>
 <dd><p>Compute Lasso path with coordinate descent.</p>
 <p>The Lasso optimization function varies for mono and multi-outputs.</p>
 <p>For mono-output tasks it is:</p>
diff --git a/dev/modules/generated/sklearn.linear_model.orthogonal_mp.html b/dev/modules/generated/sklearn.linear_model.orthogonal_mp.html
index 41b20814fe34f..4735ed085a5ab 100644
--- a/dev/modules/generated/sklearn.linear_model.orthogonal_mp.html
+++ b/dev/modules/generated/sklearn.linear_model.orthogonal_mp.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>orthogonal_mp<a class="headerlink" href="#orthogonal-mp" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.linear_model.orthogonal_mp">
-<span class="sig-prename descclassname"><span class="pre">sklearn.linear_model.</span></span><span class="sig-name descname"><span class="pre">orthogonal_mp</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_nonzero_coefs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">precompute</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy_X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_n_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_omp.py#L290"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.orthogonal_mp" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.linear_model.</span></span><span class="sig-name descname"><span class="pre">orthogonal_mp</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_nonzero_coefs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">precompute</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy_X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_n_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_omp.py#L290"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.orthogonal_mp" title="Link to this definition">#</a></dt>
 <dd><p>Orthogonal Matching Pursuit (OMP).</p>
 <p>Solves n_targets Orthogonal Matching Pursuit problems.
 An instance of the problem has the form:</p>
@@ -1385,7 +1385,7 @@ <h1>orthogonal_mp<a class="headerlink" href="#orthogonal-mp" title="Link to this
 <span class="gp">&gt;&gt;&gt; </span><span class="n">coef</span><span class="o">.</span><span class="n">shape</span>
 <span class="go">(100,)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">X</span><span class="p">[:</span><span class="mi">1</span><span class="p">,]</span> <span class="o">@</span> <span class="n">coef</span>
-<span class="go">array([-78.68...])</span>
+<span class="go">array([-78.68])</span>
 </pre></div>
 </div>
 </dd></dl>
diff --git a/dev/modules/generated/sklearn.linear_model.orthogonal_mp_gram.html b/dev/modules/generated/sklearn.linear_model.orthogonal_mp_gram.html
index cf982a12a4138..6e045d56a90a6 100644
--- a/dev/modules/generated/sklearn.linear_model.orthogonal_mp_gram.html
+++ b/dev/modules/generated/sklearn.linear_model.orthogonal_mp_gram.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>orthogonal_mp_gram<a class="headerlink" href="#orthogonal-mp-gram" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.linear_model.orthogonal_mp_gram">
-<span class="sig-prename descclassname"><span class="pre">sklearn.linear_model.</span></span><span class="sig-name descname"><span class="pre">orthogonal_mp_gram</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">Gram</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Xy</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_nonzero_coefs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norms_squared</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy_Gram</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy_Xy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_n_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_omp.py#L467"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.orthogonal_mp_gram" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.linear_model.</span></span><span class="sig-name descname"><span class="pre">orthogonal_mp_gram</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">Gram</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Xy</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_nonzero_coefs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norms_squared</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy_Gram</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy_Xy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_n_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_omp.py#L467"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.orthogonal_mp_gram" title="Link to this definition">#</a></dt>
 <dd><p>Gram Orthogonal Matching Pursuit (OMP).</p>
 <p>Solves n_targets Orthogonal Matching Pursuit problems using only
 the Gram matrix X.T * X and the product X.T * y.</p>
@@ -1383,7 +1383,7 @@ <h1>orthogonal_mp_gram<a class="headerlink" href="#orthogonal-mp-gram" title="Li
 <span class="gp">&gt;&gt;&gt; </span><span class="n">coef</span><span class="o">.</span><span class="n">shape</span>
 <span class="go">(100,)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">X</span><span class="p">[:</span><span class="mi">1</span><span class="p">,]</span> <span class="o">@</span> <span class="n">coef</span>
-<span class="go">array([-78.68...])</span>
+<span class="go">array([-78.68])</span>
 </pre></div>
 </div>
 </dd></dl>
diff --git a/dev/modules/generated/sklearn.linear_model.ridge_regression.html b/dev/modules/generated/sklearn.linear_model.ridge_regression.html
index bf75ff5446d03..6a014004fbcb9 100644
--- a/dev/modules/generated/sklearn.linear_model.ridge_regression.html
+++ b/dev/modules/generated/sklearn.linear_model.ridge_regression.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>ridge_regression<a class="headerlink" href="#ridge-regression" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.linear_model.ridge_regression">
-<span class="sig-prename descclassname"><span class="pre">sklearn.linear_model.</span></span><span class="sig-name descname"><span class="pre">ridge_regression</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">solver</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">positive</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_n_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_intercept</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_input</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_ridge.py#L376"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.ridge_regression" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.linear_model.</span></span><span class="sig-name descname"><span class="pre">ridge_regression</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">solver</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">positive</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_n_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_intercept</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_input</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_ridge.py#L375"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.ridge_regression" title="Link to this definition">#</a></dt>
 <dd><p>Solve the ridge equation by the method of normal equations.</p>
 <p>Read more in the <a class="reference internal" href="http://github.com/linear_model.html#ridge-regression"><span class="std std-ref">User Guide</span></a>.</p>
 <dl class="field-list">
@@ -1439,11 +1439,12 @@ <h1>ridge_regression<a class="headerlink" href="#ridge-regression" title="Link t
 <span class="gp">&gt;&gt;&gt; </span><span class="n">rng</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">RandomState</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">X</span> <span class="o">=</span> <span class="n">rng</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">y</span> <span class="o">=</span> <span class="mf">2.0</span> <span class="o">*</span> <span class="n">X</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="mf">1.0</span> <span class="o">*</span> <span class="n">X</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="mf">0.1</span> <span class="o">*</span> <span class="n">rng</span><span class="o">.</span><span class="n">standard_normal</span><span class="p">(</span><span class="mi">100</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">coef</span><span class="p">,</span> <span class="n">intercept</span> <span class="o">=</span> <span class="n">ridge_regression</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">return_intercept</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="nb">list</span><span class="p">(</span><span class="n">coef</span><span class="p">)</span>
-<span class="go">[np.float64(1.9...), np.float64(-1.0...), np.float64(-0.0...), np.float64(-0.0...)]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">coef</span><span class="p">,</span> <span class="n">intercept</span> <span class="o">=</span> <span class="n">ridge_regression</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">return_intercept</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+<span class="gp">... </span>                                   <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">coef</span>
+<span class="go">array([ 1.97, -1., -2.69e-3, -9.27e-4 ])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">intercept</span>
-<span class="go">np.float64(-0.0...)</span>
+<span class="go">np.float64(-.0012)</span>
 </pre></div>
 </div>
 </dd></dl>
diff --git a/dev/modules/generated/sklearn.manifold.Isomap.html b/dev/modules/generated/sklearn.manifold.Isomap.html
index 675dd7d061778..f4d2716ea73cb 100644
--- a/dev/modules/generated/sklearn.manifold.Isomap.html
+++ b/dev/modules/generated/sklearn.manifold.Isomap.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>Isomap<a class="headerlink" href="#isomap" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.manifold.Isomap">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.manifold.</span></span><span class="sig-name descname"><span class="pre">Isomap</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_neighbors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">radius</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_components</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eigen_solver</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">path_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">neighbors_algorithm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'minkowski'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric_params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/manifold/_isomap.py#L28"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.manifold.Isomap" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.manifold.</span></span><span class="sig-name descname"><span class="pre">Isomap</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_neighbors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">radius</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_components</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eigen_solver</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">path_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">neighbors_algorithm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'minkowski'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric_params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/manifold/_isomap.py#L28"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.manifold.Isomap" title="Link to this definition">#</a></dt>
 <dd><p>Isomap Embedding.</p>
 <p>Non-linear dimensionality reduction through Isometric Mapping</p>
 <p>Read more in the <a class="reference internal" href="http://github.com/manifold.html#isomap"><span class="std std-ref">User Guide</span></a>.</p>
@@ -1347,7 +1347,7 @@ <h1>Isomap<a class="headerlink" href="#isomap" title="Link to this heading">#</a
 passed to neighbors.NearestNeighbors instance.</p>
 </dd>
 <dt><strong>n_jobs</strong><span class="classifier">int or None, default=None</span></dt><dd><p>The number of parallel jobs to run.
-<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.5.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
+<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.6.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
 <code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all processors. See <a class="reference internal" href="http://github.com/glossary.html#term-n_jobs"><span class="xref std std-term">Glossary</span></a>
 for more details.</p>
 </dd>
@@ -1441,7 +1441,7 @@ <h1>Isomap<a class="headerlink" href="#isomap" title="Link to this heading">#</a
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.manifold.Isomap.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/manifold/_isomap.py#L338"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.manifold.Isomap.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/manifold/_isomap.py#L338"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.manifold.Isomap.fit" title="Link to this definition">#</a></dt>
 <dd><p>Compute the embedding vectors for data X.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1465,7 +1465,7 @@ <h1>Isomap<a class="headerlink" href="#isomap" title="Link to this heading">#</a
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.manifold.Isomap.fit_transform">
-<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/manifold/_isomap.py#L363"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.manifold.Isomap.fit_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/manifold/_isomap.py#L363"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.manifold.Isomap.fit_transform" title="Link to this definition">#</a></dt>
 <dd><p>Fit the model from data in X and transform X.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1488,7 +1488,7 @@ <h1>Isomap<a class="headerlink" href="#isomap" title="Link to this heading">#</a
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.manifold.Isomap.get_feature_names_out">
-<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L995"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.manifold.Isomap.get_feature_names_out" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L936"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.manifold.Isomap.get_feature_names_out" title="Link to this definition">#</a></dt>
 <dd><p>Get output feature names for transformation.</p>
 <p>The feature names out will prefixed by the lowercased class name. For
 example, if the transformer outputs 3 features, then the feature names
@@ -1511,7 +1511,7 @@ <h1>Isomap<a class="headerlink" href="#isomap" title="Link to this heading">#</a
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.manifold.Isomap.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.manifold.Isomap.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.manifold.Isomap.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1528,7 +1528,7 @@ <h1>Isomap<a class="headerlink" href="#isomap" title="Link to this heading">#</a
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.manifold.Isomap.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.manifold.Isomap.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.manifold.Isomap.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1549,7 +1549,7 @@ <h1>Isomap<a class="headerlink" href="#isomap" title="Link to this heading">#</a
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.manifold.Isomap.reconstruction_error">
-<span class="sig-name descname"><span class="pre">reconstruction_error</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/manifold/_isomap.py#L313"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.manifold.Isomap.reconstruction_error" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">reconstruction_error</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/manifold/_isomap.py#L313"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.manifold.Isomap.reconstruction_error" title="Link to this definition">#</a></dt>
 <dd><p>Compute the reconstruction error for the embedding.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Returns<span class="colon">:</span></dt>
@@ -1570,7 +1570,7 @@ <h1>Isomap<a class="headerlink" href="#isomap" title="Link to this heading">#</a
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.manifold.Isomap.set_output">
-<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.manifold.Isomap.set_output" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.manifold.Isomap.set_output" title="Link to this definition">#</a></dt>
 <dd><p>Set output container.</p>
 <p>See <a class="reference internal" href="http://github.com/auto_examples/miscellaneous/plot_set_output.html#sphx-glr-auto-examples-miscellaneous-plot-set-output-py"><span class="std std-ref">Introducing the set_output API</span></a>
 for an example on how to use the API.</p>
@@ -1601,7 +1601,7 @@ <h1>Isomap<a class="headerlink" href="#isomap" title="Link to this heading">#</a
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.manifold.Isomap.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.manifold.Isomap.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.manifold.Isomap.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1625,7 +1625,7 @@ <h1>Isomap<a class="headerlink" href="#isomap" title="Link to this heading">#</a
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.manifold.Isomap.transform">
-<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/manifold/_isomap.py#L387"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.manifold.Isomap.transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/manifold/_isomap.py#L387"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.manifold.Isomap.transform" title="Link to this definition">#</a></dt>
 <dd><p>Transform X.</p>
 <p>This is implemented by linking the points X into the graph of geodesic
 distances of the training data. First the <code class="docutils literal notranslate"><span class="pre">n_neighbors</span></code> nearest
diff --git a/dev/modules/generated/sklearn.manifold.LocallyLinearEmbedding.html b/dev/modules/generated/sklearn.manifold.LocallyLinearEmbedding.html
index 08a8e38dd45d7..cfd4c35ed1a59 100644
--- a/dev/modules/generated/sklearn.manifold.LocallyLinearEmbedding.html
+++ b/dev/modules/generated/sklearn.manifold.LocallyLinearEmbedding.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>LocallyLinearEmbedding<a class="headerlink" href="#locallylinearembedding" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.manifold.LocallyLinearEmbedding">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.manifold.</span></span><span class="sig-name descname"><span class="pre">LocallyLinearEmbedding</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_neighbors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_components</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reg</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eigen_solver</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-06</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'standard'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hessian_tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">modified_tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-12</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">neighbors_algorithm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/manifold/_locally_linear.py#L602"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.manifold.LocallyLinearEmbedding" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.manifold.</span></span><span class="sig-name descname"><span class="pre">LocallyLinearEmbedding</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_neighbors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_components</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reg</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eigen_solver</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-06</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'standard'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hessian_tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">modified_tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-12</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">neighbors_algorithm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/manifold/_locally_linear.py#L602"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.manifold.LocallyLinearEmbedding" title="Link to this definition">#</a></dt>
 <dd><p>Locally Linear Embedding.</p>
 <p>Read more in the <a class="reference internal" href="http://github.com/manifold.html#locally-linear-embedding"><span class="std std-ref">User Guide</span></a>.</p>
 <dl class="field-list">
@@ -1369,7 +1369,7 @@ <h1>LocallyLinearEmbedding<a class="headerlink" href="#locallylinearembedding" t
 across multiple function calls. See <a class="reference internal" href="http://github.com/glossary.html#term-random_state"><span class="xref std std-term">Glossary</span></a>.</p>
 </dd>
 <dt><strong>n_jobs</strong><span class="classifier">int or None, default=None</span></dt><dd><p>The number of parallel jobs to run.
-<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.5.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
+<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.6.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
 <code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all processors. See <a class="reference internal" href="http://github.com/glossary.html#term-n_jobs"><span class="xref std std-term">Glossary</span></a>
 for more details.</p>
 </dd>
@@ -1446,7 +1446,7 @@ <h1>LocallyLinearEmbedding<a class="headerlink" href="#locallylinearembedding" t
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.manifold.LocallyLinearEmbedding.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/manifold/_locally_linear.py#L810"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.manifold.LocallyLinearEmbedding.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/manifold/_locally_linear.py#L810"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.manifold.LocallyLinearEmbedding.fit" title="Link to this definition">#</a></dt>
 <dd><p>Compute the embedding vectors for data X.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1468,7 +1468,7 @@ <h1>LocallyLinearEmbedding<a class="headerlink" href="#locallylinearembedding" t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.manifold.LocallyLinearEmbedding.fit_transform">
-<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/manifold/_locally_linear.py#L830"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.manifold.LocallyLinearEmbedding.fit_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/manifold/_locally_linear.py#L830"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.manifold.LocallyLinearEmbedding.fit_transform" title="Link to this definition">#</a></dt>
 <dd><p>Compute the embedding vectors for data X and transform X.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1490,7 +1490,7 @@ <h1>LocallyLinearEmbedding<a class="headerlink" href="#locallylinearembedding" t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.manifold.LocallyLinearEmbedding.get_feature_names_out">
-<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L995"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.manifold.LocallyLinearEmbedding.get_feature_names_out" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L936"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.manifold.LocallyLinearEmbedding.get_feature_names_out" title="Link to this definition">#</a></dt>
 <dd><p>Get output feature names for transformation.</p>
 <p>The feature names out will prefixed by the lowercased class name. For
 example, if the transformer outputs 3 features, then the feature names
@@ -1513,7 +1513,7 @@ <h1>LocallyLinearEmbedding<a class="headerlink" href="#locallylinearembedding" t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.manifold.LocallyLinearEmbedding.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.manifold.LocallyLinearEmbedding.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.manifold.LocallyLinearEmbedding.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1530,7 +1530,7 @@ <h1>LocallyLinearEmbedding<a class="headerlink" href="#locallylinearembedding" t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.manifold.LocallyLinearEmbedding.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.manifold.LocallyLinearEmbedding.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.manifold.LocallyLinearEmbedding.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1551,7 +1551,7 @@ <h1>LocallyLinearEmbedding<a class="headerlink" href="#locallylinearembedding" t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.manifold.LocallyLinearEmbedding.set_output">
-<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.manifold.LocallyLinearEmbedding.set_output" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.manifold.LocallyLinearEmbedding.set_output" title="Link to this definition">#</a></dt>
 <dd><p>Set output container.</p>
 <p>See <a class="reference internal" href="http://github.com/auto_examples/miscellaneous/plot_set_output.html#sphx-glr-auto-examples-miscellaneous-plot-set-output-py"><span class="std std-ref">Introducing the set_output API</span></a>
 for an example on how to use the API.</p>
@@ -1582,7 +1582,7 @@ <h1>LocallyLinearEmbedding<a class="headerlink" href="#locallylinearembedding" t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.manifold.LocallyLinearEmbedding.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.manifold.LocallyLinearEmbedding.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.manifold.LocallyLinearEmbedding.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1606,7 +1606,7 @@ <h1>LocallyLinearEmbedding<a class="headerlink" href="#locallylinearembedding" t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.manifold.LocallyLinearEmbedding.transform">
-<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/manifold/_locally_linear.py#L850"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.manifold.LocallyLinearEmbedding.transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/manifold/_locally_linear.py#L850"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.manifold.LocallyLinearEmbedding.transform" title="Link to this definition">#</a></dt>
 <dd><p>Transform new points into embedding space.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.manifold.MDS.html b/dev/modules/generated/sklearn.manifold.MDS.html
index 82056552fda29..cae6e073a70f0 100644
--- a/dev/modules/generated/sklearn.manifold.MDS.html
+++ b/dev/modules/generated/sklearn.manifold.MDS.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>MDS<a class="headerlink" href="#mds" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.manifold.MDS">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.manifold.</span></span><span class="sig-name descname"><span class="pre">MDS</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_components</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">4</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">300</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dissimilarity</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'euclidean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">normalized_stress</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/manifold/_mds.py#L411"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.manifold.MDS" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.manifold.</span></span><span class="sig-name descname"><span class="pre">MDS</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_components</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'warn'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">300</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-06</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dissimilarity</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'euclidean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">normalized_stress</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/manifold/_mds.py#L431"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.manifold.MDS" title="Link to this definition">#</a></dt>
 <dd><p>Multidimensional scaling.</p>
 <p>Read more in the <a class="reference internal" href="http://github.com/manifold.html#multidimensional-scaling"><span class="std std-ref">User Guide</span></a>.</p>
 <dl class="field-list">
@@ -1322,19 +1322,25 @@ <h1>MDS<a class="headerlink" href="#mds" title="Link to this heading">#</a></h1>
 <dt><strong>n_init</strong><span class="classifier">int, default=4</span></dt><dd><p>Number of times the SMACOF algorithm will be run with different
 initializations. The final results will be the best output of the runs,
 determined by the run with the smallest final stress.</p>
+<div class="versionchanged">
+<p><span class="versionmodified changed">Changed in version 1.9: </span>The default value for <code class="docutils literal notranslate"><span class="pre">n_init</span></code> will change from 4 to 1 in version 1.9.</p>
+</div>
 </dd>
 <dt><strong>max_iter</strong><span class="classifier">int, default=300</span></dt><dd><p>Maximum number of iterations of the SMACOF algorithm for a single run.</p>
 </dd>
 <dt><strong>verbose</strong><span class="classifier">int, default=0</span></dt><dd><p>Level of verbosity.</p>
 </dd>
-<dt><strong>eps</strong><span class="classifier">float, default=1e-3</span></dt><dd><p>Relative tolerance with respect to stress at which to declare
-convergence. The value of <code class="docutils literal notranslate"><span class="pre">eps</span></code> should be tuned separately depending
-on whether or not <code class="docutils literal notranslate"><span class="pre">normalized_stress</span></code> is being used.</p>
+<dt><strong>eps</strong><span class="classifier">float, default=1e-6</span></dt><dd><p>The tolerance with respect to stress (normalized by the sum of squared
+embedding distances) at which to declare convergence.</p>
+<div class="versionchanged">
+<p><span class="versionmodified changed">Changed in version 1.7: </span>The default value for <code class="docutils literal notranslate"><span class="pre">eps</span></code> has changed from 1e-3 to 1e-6, as a result
+of a bugfix in the computation of the convergence criterion.</p>
+</div>
 </dd>
 <dt><strong>n_jobs</strong><span class="classifier">int, default=None</span></dt><dd><p>The number of jobs to use for the computation. If multiple
 initializations are used (<code class="docutils literal notranslate"><span class="pre">n_init</span></code>), each run of the algorithm is
 computed in parallel.</p>
-<p><code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.5.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
+<p><code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.6.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
 <code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all processors. See <a class="reference internal" href="http://github.com/glossary.html#term-n_jobs"><span class="xref std std-term">Glossary</span></a>
 for more details.</p>
 </dd>
@@ -1357,9 +1363,9 @@ <h1>MDS<a class="headerlink" href="#mds" title="Link to this heading">#</a></h1>
 </li>
 </ul>
 </dd>
-<dt><strong>normalized_stress</strong><span class="classifier">bool or “auto” default=”auto”</span></dt><dd><p>Whether use and return normalized stress value (Stress-1) instead of raw
-stress. By default, metric MDS uses raw stress while non-metric MDS uses
-normalized stress.</p>
+<dt><strong>normalized_stress</strong><span class="classifier">bool or “auto” default=”auto”</span></dt><dd><p>Whether to return normalized stress value (Stress-1) instead of raw
+stress. By default, metric MDS returns raw stress while non-metric MDS
+returns normalized stress.</p>
 <div class="versionadded">
 <p><span class="versionmodified added">Added in version 1.2.</span></p>
 </div>
@@ -1447,7 +1453,7 @@ <h1>MDS<a class="headerlink" href="#mds" title="Link to this heading">#</a></h1>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">X</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">load_digits</span><span class="p">(</span><span class="n">return_X_y</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">X</span><span class="o">.</span><span class="n">shape</span>
 <span class="go">(1797, 64)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">embedding</span> <span class="o">=</span> <span class="n">MDS</span><span class="p">(</span><span class="n">n_components</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">normalized_stress</span><span class="o">=</span><span class="s1">&#39;auto&#39;</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">embedding</span> <span class="o">=</span> <span class="n">MDS</span><span class="p">(</span><span class="n">n_components</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">n_init</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">X_transformed</span> <span class="o">=</span> <span class="n">embedding</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">X</span><span class="p">[:</span><span class="mi">100</span><span class="p">])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">X_transformed</span><span class="o">.</span><span class="n">shape</span>
 <span class="go">(100, 2)</span>
@@ -1459,7 +1465,7 @@ <h1>MDS<a class="headerlink" href="#mds" title="Link to this heading">#</a></h1>
 <a class="reference internal" href="http://github.com/auto_examples/manifold/plot_compare_methods.html#sphx-glr-auto-examples-manifold-plot-compare-methods-py"><span class="std std-ref">Comparison of Manifold Learning methods</span></a>.</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.manifold.MDS.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/manifold/_mds.py#L597"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.manifold.MDS.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/manifold/_mds.py#L623"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.manifold.MDS.fit" title="Link to this definition">#</a></dt>
 <dd><p>Compute the position of the points in the embedding space.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1486,7 +1492,7 @@ <h1>MDS<a class="headerlink" href="#mds" title="Link to this heading">#</a></h1>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.manifold.MDS.fit_transform">
-<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/manifold/_mds.py#L624"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.manifold.MDS.fit_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/manifold/_mds.py#L650"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.manifold.MDS.fit_transform" title="Link to this definition">#</a></dt>
 <dd><p>Fit the data from <code class="docutils literal notranslate"><span class="pre">X</span></code>, and returns the embedded coordinates.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1513,7 +1519,7 @@ <h1>MDS<a class="headerlink" href="#mds" title="Link to this heading">#</a></h1>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.manifold.MDS.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.manifold.MDS.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.manifold.MDS.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1530,7 +1536,7 @@ <h1>MDS<a class="headerlink" href="#mds" title="Link to this heading">#</a></h1>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.manifold.MDS.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.manifold.MDS.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.manifold.MDS.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1551,7 +1557,7 @@ <h1>MDS<a class="headerlink" href="#mds" title="Link to this heading">#</a></h1>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.manifold.MDS.set_fit_request">
-<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">init</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.manifold.MDS" title="sklearn.manifold._mds.MDS"><span class="pre">MDS</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.manifold.MDS.set_fit_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">init</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.manifold.MDS" title="sklearn.manifold._mds.MDS"><span class="pre">MDS</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.manifold.MDS.set_fit_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1594,7 +1600,7 @@ <h1>MDS<a class="headerlink" href="#mds" title="Link to this heading">#</a></h1>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.manifold.MDS.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.manifold.MDS.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.manifold.MDS.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
diff --git a/dev/modules/generated/sklearn.manifold.SpectralEmbedding.html b/dev/modules/generated/sklearn.manifold.SpectralEmbedding.html
index 0be5af9c4164d..1a213e120c65a 100644
--- a/dev/modules/generated/sklearn.manifold.SpectralEmbedding.html
+++ b/dev/modules/generated/sklearn.manifold.SpectralEmbedding.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>SpectralEmbedding<a class="headerlink" href="#spectralembedding" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.manifold.SpectralEmbedding">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.manifold.</span></span><span class="sig-name descname"><span class="pre">SpectralEmbedding</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_components</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">affinity</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'nearest_neighbors'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gamma</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eigen_solver</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eigen_tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_neighbors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/manifold/_spectral_embedding.py#L471"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.manifold.SpectralEmbedding" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.manifold.</span></span><span class="sig-name descname"><span class="pre">SpectralEmbedding</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_components</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">affinity</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'nearest_neighbors'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gamma</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eigen_solver</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eigen_tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_neighbors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/manifold/_spectral_embedding.py#L470"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.manifold.SpectralEmbedding" title="Link to this definition">#</a></dt>
 <dd><p>Spectral embedding for non-linear dimensionality reduction.</p>
 <p>Forms an affinity matrix given by the specified function and
 applies spectral decomposition to the corresponding graph laplacian.
@@ -1379,7 +1379,7 @@ <h1>SpectralEmbedding<a class="headerlink" href="#spectralembedding" title="Link
 If None, n_neighbors will be set to max(n_samples/10, 1).</p>
 </dd>
 <dt><strong>n_jobs</strong><span class="classifier">int, default=None</span></dt><dd><p>The number of parallel jobs to run.
-<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.5.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
+<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.6.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
 <code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all processors. See <a class="reference internal" href="http://github.com/glossary.html#term-n_jobs"><span class="xref std std-term">Glossary</span></a>
 for more details.</p>
 </dd>
@@ -1437,7 +1437,7 @@ <h1>SpectralEmbedding<a class="headerlink" href="#spectralembedding" title="Link
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.manifold.SpectralEmbedding.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/manifold/_spectral_embedding.py#L717"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.manifold.SpectralEmbedding.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/manifold/_spectral_embedding.py#L716"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.manifold.SpectralEmbedding.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit the model from data in X.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1464,7 +1464,7 @@ <h1>SpectralEmbedding<a class="headerlink" href="#spectralembedding" title="Link
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.manifold.SpectralEmbedding.fit_transform">
-<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/manifold/_spectral_embedding.py#L754"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.manifold.SpectralEmbedding.fit_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/manifold/_spectral_embedding.py#L753"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.manifold.SpectralEmbedding.fit_transform" title="Link to this definition">#</a></dt>
 <dd><p>Fit the model from data in X and transform X.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1491,7 +1491,7 @@ <h1>SpectralEmbedding<a class="headerlink" href="#spectralembedding" title="Link
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.manifold.SpectralEmbedding.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.manifold.SpectralEmbedding.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.manifold.SpectralEmbedding.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1508,7 +1508,7 @@ <h1>SpectralEmbedding<a class="headerlink" href="#spectralembedding" title="Link
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.manifold.SpectralEmbedding.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.manifold.SpectralEmbedding.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.manifold.SpectralEmbedding.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1529,7 +1529,7 @@ <h1>SpectralEmbedding<a class="headerlink" href="#spectralembedding" title="Link
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.manifold.SpectralEmbedding.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.manifold.SpectralEmbedding.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.manifold.SpectralEmbedding.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
diff --git a/dev/modules/generated/sklearn.manifold.TSNE.html b/dev/modules/generated/sklearn.manifold.TSNE.html
index 45f735cfbb001..a460ee91f0ad1 100644
--- a/dev/modules/generated/sklearn.manifold.TSNE.html
+++ b/dev/modules/generated/sklearn.manifold.TSNE.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>TSNE<a class="headerlink" href="#tsne" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.manifold.TSNE">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.manifold.</span></span><span class="sig-name descname"><span class="pre">TSNE</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_components</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">perplexity</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">30.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">early_exaggeration</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">12.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">learning_rate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_iter_without_progress</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">300</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_grad_norm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-07</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'euclidean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric_params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'pca'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'barnes_hut'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">angle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'deprecated'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/manifold/_t_sne.py#L561"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.manifold.TSNE" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.manifold.</span></span><span class="sig-name descname"><span class="pre">TSNE</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_components</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">perplexity</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">30.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">early_exaggeration</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">12.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">learning_rate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_iter_without_progress</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">300</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_grad_norm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-07</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'euclidean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric_params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'pca'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'barnes_hut'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">angle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/manifold/_t_sne.py#L560"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.manifold.TSNE" title="Link to this definition">#</a></dt>
 <dd><p>T-distributed Stochastic Neighbor Embedding.</p>
 <p>t-SNE [1] is a tool to visualize high-dimensional data. It converts
 similarities between data points to joint probabilities and tries
@@ -1427,20 +1427,13 @@ <h1>TSNE<a class="headerlink" href="#tsne" title="Link to this heading">#</a></h
 <dt><strong>n_jobs</strong><span class="classifier">int, default=None</span></dt><dd><p>The number of parallel jobs to run for neighbors search. This parameter
 has no impact when <code class="docutils literal notranslate"><span class="pre">metric=&quot;precomputed&quot;</span></code> or
 (<code class="docutils literal notranslate"><span class="pre">metric=&quot;euclidean&quot;</span></code> and <code class="docutils literal notranslate"><span class="pre">method=&quot;exact&quot;</span></code>).
-<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.5.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
+<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.6.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
 <code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all processors. See <a class="reference internal" href="http://github.com/glossary.html#term-n_jobs"><span class="xref std std-term">Glossary</span></a>
 for more details.</p>
 <div class="versionadded">
 <p><span class="versionmodified added">Added in version 0.22.</span></p>
 </div>
 </dd>
-<dt><strong>n_iter</strong><span class="classifier">int</span></dt><dd><p>Maximum number of iterations for the optimization. Should be at
-least 250.</p>
-<div class="deprecated">
-<p><span class="versionmodified deprecated">Deprecated since version 1.5: </span><code class="docutils literal notranslate"><span class="pre">n_iter</span></code> was deprecated in version 1.5 and will be removed in 1.7.
-Please use <code class="docutils literal notranslate"><span class="pre">max_iter</span></code> instead.</p>
-</div>
-</dd>
 </dl>
 </dd>
 <dt class="field-even">Attributes<span class="colon">:</span></dt>
@@ -1519,7 +1512,7 @@ <h1>TSNE<a class="headerlink" href="#tsne" title="Link to this heading">#</a></h
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.manifold.TSNE.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/manifold/_t_sne.py#L1185"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.manifold.TSNE.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/manifold/_t_sne.py#L1148"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.manifold.TSNE.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit X into an embedded space.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1545,7 +1538,7 @@ <h1>TSNE<a class="headerlink" href="#tsne" title="Link to this heading">#</a></h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.manifold.TSNE.fit_transform">
-<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/manifold/_t_sne.py#L1133"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.manifold.TSNE.fit_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/manifold/_t_sne.py#L1118"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.manifold.TSNE.fit_transform" title="Link to this definition">#</a></dt>
 <dd><p>Fit X into an embedded space and return that transformed output.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1571,7 +1564,7 @@ <h1>TSNE<a class="headerlink" href="#tsne" title="Link to this heading">#</a></h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.manifold.TSNE.get_feature_names_out">
-<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L995"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.manifold.TSNE.get_feature_names_out" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L936"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.manifold.TSNE.get_feature_names_out" title="Link to this definition">#</a></dt>
 <dd><p>Get output feature names for transformation.</p>
 <p>The feature names out will prefixed by the lowercased class name. For
 example, if the transformer outputs 3 features, then the feature names
@@ -1594,7 +1587,7 @@ <h1>TSNE<a class="headerlink" href="#tsne" title="Link to this heading">#</a></h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.manifold.TSNE.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.manifold.TSNE.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.manifold.TSNE.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1611,7 +1604,7 @@ <h1>TSNE<a class="headerlink" href="#tsne" title="Link to this heading">#</a></h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.manifold.TSNE.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.manifold.TSNE.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.manifold.TSNE.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1632,7 +1625,7 @@ <h1>TSNE<a class="headerlink" href="#tsne" title="Link to this heading">#</a></h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.manifold.TSNE.set_output">
-<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.manifold.TSNE.set_output" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.manifold.TSNE.set_output" title="Link to this definition">#</a></dt>
 <dd><p>Set output container.</p>
 <p>See <a class="reference internal" href="http://github.com/auto_examples/miscellaneous/plot_set_output.html#sphx-glr-auto-examples-miscellaneous-plot-set-output-py"><span class="std std-ref">Introducing the set_output API</span></a>
 for an example on how to use the API.</p>
@@ -1663,7 +1656,7 @@ <h1>TSNE<a class="headerlink" href="#tsne" title="Link to this heading">#</a></h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.manifold.TSNE.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.manifold.TSNE.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.manifold.TSNE.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
diff --git a/dev/modules/generated/sklearn.manifold.locally_linear_embedding.html b/dev/modules/generated/sklearn.manifold.locally_linear_embedding.html
index 8e04fdac7013a..2bd1ff17453d5 100644
--- a/dev/modules/generated/sklearn.manifold.locally_linear_embedding.html
+++ b/dev/modules/generated/sklearn.manifold.locally_linear_embedding.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>locally_linear_embedding<a class="headerlink" href="#locally-linear-embedding" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.manifold.locally_linear_embedding">
-<span class="sig-prename descclassname"><span class="pre">sklearn.manifold.</span></span><span class="sig-name descname"><span class="pre">locally_linear_embedding</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_neighbors</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_components</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reg</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eigen_solver</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-06</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'standard'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hessian_tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">modified_tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-12</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/manifold/_locally_linear.py#L448"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.manifold.locally_linear_embedding" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.manifold.</span></span><span class="sig-name descname"><span class="pre">locally_linear_embedding</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_neighbors</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_components</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reg</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eigen_solver</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-06</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'standard'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hessian_tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">modified_tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-12</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/manifold/_locally_linear.py#L448"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.manifold.locally_linear_embedding" title="Link to this definition">#</a></dt>
 <dd><p>Perform a Locally Linear Embedding analysis on the data.</p>
 <p>Read more in the <a class="reference internal" href="http://github.com/manifold.html#locally-linear-embedding"><span class="std std-ref">User Guide</span></a>.</p>
 <dl class="field-list">
@@ -1364,7 +1364,7 @@ <h1>locally_linear_embedding<a class="headerlink" href="#locally-linear-embeddin
 See <a class="reference internal" href="http://github.com/glossary.html#term-random_state"><span class="xref std std-term">Glossary</span></a>.</p>
 </dd>
 <dt><strong>n_jobs</strong><span class="classifier">int or None, default=None</span></dt><dd><p>The number of parallel jobs to run for neighbors search.
-<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.5.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
+<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.6.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
 <code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all processors. See <a class="reference internal" href="http://github.com/glossary.html#term-n_jobs"><span class="xref std std-term">Glossary</span></a>
 for more details.</p>
 </dd>
diff --git a/dev/modules/generated/sklearn.manifold.smacof.html b/dev/modules/generated/sklearn.manifold.smacof.html
index 55cd85f2b58d2..432e0a0e19425 100644
--- a/dev/modules/generated/sklearn.manifold.smacof.html
+++ b/dev/modules/generated/sklearn.manifold.smacof.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>smacof<a class="headerlink" href="#smacof" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.manifold.smacof">
-<span class="sig-prename descclassname"><span class="pre">sklearn.manifold.</span></span><span class="sig-name descname"><span class="pre">smacof</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dissimilarities</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_components</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">8</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">300</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_n_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">normalized_stress</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/manifold/_mds.py#L187"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.manifold.smacof" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.manifold.</span></span><span class="sig-name descname"><span class="pre">smacof</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dissimilarities</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_components</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'warn'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">300</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-06</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_n_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">normalized_stress</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/manifold/_mds.py#L193"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.manifold.smacof" title="Link to this definition">#</a></dt>
 <dd><p>Compute multidimensional scaling using the SMACOF algorithm.</p>
 <p>The SMACOF (Scaling by MAjorizing a COmplicated Function) algorithm is a
 multidimensional scaling algorithm which minimizes an objective function
@@ -1344,11 +1344,14 @@ <h1>smacof<a class="headerlink" href="#smacof" title="Link to this heading">#</a
 initializations. The final results will be the best output of the runs,
 determined by the run with the smallest final stress. If <code class="docutils literal notranslate"><span class="pre">init</span></code> is
 provided, this option is overridden and a single run is performed.</p>
+<div class="versionchanged">
+<p><span class="versionmodified changed">Changed in version 1.9: </span>The default value for <code class="docutils literal notranslate"><span class="pre">n_iter</span></code> will change from 8 to 1 in version 1.9.</p>
+</div>
 </dd>
 <dt><strong>n_jobs</strong><span class="classifier">int, default=None</span></dt><dd><p>The number of jobs to use for the computation. If multiple
 initializations are used (<code class="docutils literal notranslate"><span class="pre">n_init</span></code>), each run of the algorithm is
 computed in parallel.</p>
-<p><code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.5.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
+<p><code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.6.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
 <code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all processors. See <a class="reference internal" href="http://github.com/glossary.html#term-n_jobs"><span class="xref std std-term">Glossary</span></a>
 for more details.</p>
 </dd>
@@ -1356,9 +1359,12 @@ <h1>smacof<a class="headerlink" href="#smacof" title="Link to this heading">#</a
 </dd>
 <dt><strong>verbose</strong><span class="classifier">int, default=0</span></dt><dd><p>Level of verbosity.</p>
 </dd>
-<dt><strong>eps</strong><span class="classifier">float, default=1e-3</span></dt><dd><p>Relative tolerance with respect to stress at which to declare
-convergence. The value of <code class="docutils literal notranslate"><span class="pre">eps</span></code> should be tuned separately depending
-on whether or not <code class="docutils literal notranslate"><span class="pre">normalized_stress</span></code> is being used.</p>
+<dt><strong>eps</strong><span class="classifier">float, default=1e-6</span></dt><dd><p>The tolerance with respect to stress (normalized by the sum of squared
+embedding distances) at which to declare convergence.</p>
+<div class="versionchanged">
+<p><span class="versionmodified changed">Changed in version 1.7: </span>The default value for <code class="docutils literal notranslate"><span class="pre">eps</span></code> has changed from 1e-3 to 1e-6, as a result
+of a bugfix in the computation of the convergence criterion.</p>
+</div>
 </dd>
 <dt><strong>random_state</strong><span class="classifier">int, RandomState instance or None, default=None</span></dt><dd><p>Determines the random number generator used to initialize the centers.
 Pass an int for reproducible results across multiple function calls.
@@ -1366,7 +1372,7 @@ <h1>smacof<a class="headerlink" href="#smacof" title="Link to this heading">#</a
 </dd>
 <dt><strong>return_n_iter</strong><span class="classifier">bool, default=False</span></dt><dd><p>Whether or not to return the number of iterations.</p>
 </dd>
-<dt><strong>normalized_stress</strong><span class="classifier">bool or “auto” default=”auto”</span></dt><dd><p>Whether to return normalized stress value (Stress-1) instead of raw
+<dt><strong>normalized_stress</strong><span class="classifier">bool or “auto”, default=”auto”</span></dt><dd><p>Whether to return normalized stress value (Stress-1) instead of raw
 stress. By default, metric MDS returns raw stress while non-metric MDS
 returns normalized stress.</p>
 <div class="versionadded">
@@ -1419,15 +1425,15 @@ <h1>smacof<a class="headerlink" href="#smacof" title="Link to this heading">#</a
 <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.manifold</span><span class="w"> </span><span class="kn">import</span> <span class="n">smacof</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.metrics</span><span class="w"> </span><span class="kn">import</span> <span class="n">euclidean_distances</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">3</span><span class="p">],[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">0</span><span class="p">]])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">0</span><span class="p">]])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">dissimilarities</span> <span class="o">=</span> <span class="n">euclidean_distances</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">mds_result</span><span class="p">,</span> <span class="n">stress</span> <span class="o">=</span> <span class="n">smacof</span><span class="p">(</span><span class="n">dissimilarities</span><span class="p">,</span> <span class="n">n_components</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">mds_result</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
-<span class="go">array([[ 0.05352, -1.07253],</span>
-<span class="go">       [ 1.74231, -0.75675],</span>
-<span class="go">       [-1.79583,  1.82928]])</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">stress</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
-<span class="go">0.00128</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">Z</span><span class="p">,</span> <span class="n">stress</span> <span class="o">=</span> <span class="n">smacof</span><span class="p">(</span>
+<span class="gp">... </span>    <span class="n">dissimilarities</span><span class="p">,</span> <span class="n">n_components</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">n_init</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="mf">1e-6</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span>
+<span class="gp">... </span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">Z</span><span class="o">.</span><span class="n">shape</span>
+<span class="go">(3, 2)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">stress</span><span class="p">,</span> <span class="mi">6</span><span class="p">)</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
+<span class="go">3.2e-05</span>
 </pre></div>
 </div>
 </dd></dl>
diff --git a/dev/modules/generated/sklearn.manifold.spectral_embedding.html b/dev/modules/generated/sklearn.manifold.spectral_embedding.html
index 8c4c6c91b4086..943987abc28e2 100644
--- a/dev/modules/generated/sklearn.manifold.spectral_embedding.html
+++ b/dev/modules/generated/sklearn.manifold.spectral_embedding.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>spectral_embedding<a class="headerlink" href="#spectral-embedding" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.manifold.spectral_embedding">
-<span class="sig-prename descclassname"><span class="pre">sklearn.manifold.</span></span><span class="sig-name descname"><span class="pre">spectral_embedding</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">adjacency</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_components</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">8</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eigen_solver</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eigen_tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm_laplacian</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_first</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/manifold/_spectral_embedding.py#L155"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.manifold.spectral_embedding" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.manifold.</span></span><span class="sig-name descname"><span class="pre">spectral_embedding</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">adjacency</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_components</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">8</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eigen_solver</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eigen_tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm_laplacian</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_first</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/manifold/_spectral_embedding.py#L154"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.manifold.spectral_embedding" title="Link to this definition">#</a></dt>
 <dd><p>Project the sample on the first eigenvectors of the graph Laplacian.</p>
 <p>The adjacency matrix is used to compute a normalized graph Laplacian
 whose spectrum (especially the eigenvectors associated to the
diff --git a/dev/modules/generated/sklearn.manifold.trustworthiness.html b/dev/modules/generated/sklearn.manifold.trustworthiness.html
index dc5b1d644430d..031461fcb5d39 100644
--- a/dev/modules/generated/sklearn.manifold.trustworthiness.html
+++ b/dev/modules/generated/sklearn.manifold.trustworthiness.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1306,7 +1306,7 @@
 <h1>trustworthiness<a class="headerlink" href="#trustworthiness" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.manifold.trustworthiness">
-<span class="sig-prename descclassname"><span class="pre">sklearn.manifold.</span></span><span class="sig-name descname"><span class="pre">trustworthiness</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X_embedded</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_neighbors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'euclidean'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/manifold/_t_sne.py#L448"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.manifold.trustworthiness" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.manifold.</span></span><span class="sig-name descname"><span class="pre">trustworthiness</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X_embedded</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_neighbors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'euclidean'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/manifold/_t_sne.py#L447"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.manifold.trustworthiness" title="Link to this definition">#</a></dt>
 <dd><p>Indicate to what extent the local structure is retained.</p>
 <p>The trustworthiness is within [0, 1]. It is defined as</p>
 <div class="math notranslate nohighlight">
diff --git a/dev/modules/generated/sklearn.metrics.ConfusionMatrixDisplay.html b/dev/modules/generated/sklearn.metrics.ConfusionMatrixDisplay.html
index c1f478054ed3a..09a718bfed1ca 100644
--- a/dev/modules/generated/sklearn.metrics.ConfusionMatrixDisplay.html
+++ b/dev/modules/generated/sklearn.metrics.ConfusionMatrixDisplay.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,14 +1307,17 @@
 <h1>ConfusionMatrixDisplay<a class="headerlink" href="#confusionmatrixdisplay" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.metrics.ConfusionMatrixDisplay">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">ConfusionMatrixDisplay</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">confusion_matrix</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">display_labels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/_plot/confusion_matrix.py#L15"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.ConfusionMatrixDisplay" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">ConfusionMatrixDisplay</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">confusion_matrix</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">display_labels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/_plot/confusion_matrix.py#L15"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.ConfusionMatrixDisplay" title="Link to this definition">#</a></dt>
 <dd><p>Confusion Matrix visualization.</p>
 <p>It is recommend to use
 <a class="reference internal" href="#sklearn.metrics.ConfusionMatrixDisplay.from_estimator" title="sklearn.metrics.ConfusionMatrixDisplay.from_estimator"><code class="xref py py-func docutils literal notranslate"><span class="pre">from_estimator</span></code></a> or
 <a class="reference internal" href="#sklearn.metrics.ConfusionMatrixDisplay.from_predictions" title="sklearn.metrics.ConfusionMatrixDisplay.from_predictions"><code class="xref py py-func docutils literal notranslate"><span class="pre">from_predictions</span></code></a> to
 create a <a class="reference internal" href="#sklearn.metrics.ConfusionMatrixDisplay" title="sklearn.metrics.ConfusionMatrixDisplay"><code class="xref py py-class docutils literal notranslate"><span class="pre">ConfusionMatrixDisplay</span></code></a>. All parameters are stored as
 attributes.</p>
-<p>Read more in the <a class="reference internal" href="http://github.com/visualizations.html#visualizations"><span class="std std-ref">User Guide</span></a>.</p>
+<p>For general information regarding <code class="docutils literal notranslate"><span class="pre">scikit-learn</span></code> visualization tools, see
+the <a class="reference internal" href="http://github.com/visualizations.html#visualizations"><span class="std std-ref">Visualization Guide</span></a>.
+For guidance on interpreting these plots, refer to the
+<a class="reference internal" href="http://github.com/model_evaluation.html#confusion-matrix"><span class="std std-ref">Model Evaluation Guide</span></a>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><dl class="simple">
@@ -1375,9 +1378,12 @@ <h1>ConfusionMatrixDisplay<a class="headerlink" href="#confusionmatrixdisplay" t
 </figure>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.metrics.ConfusionMatrixDisplay.from_estimator">
-<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_estimator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">normalize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">display_labels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">include_values</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xticks_rotation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'horizontal'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">values_format</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cmap</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'viridis'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">colorbar</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">im_kw</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">text_kw</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/_plot/confusion_matrix.py#L201"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.ConfusionMatrixDisplay.from_estimator" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_estimator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">normalize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">display_labels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">include_values</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xticks_rotation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'horizontal'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">values_format</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cmap</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'viridis'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">colorbar</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">im_kw</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">text_kw</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/_plot/confusion_matrix.py#L204"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.ConfusionMatrixDisplay.from_estimator" title="Link to this definition">#</a></dt>
 <dd><p>Plot Confusion Matrix given an estimator and some data.</p>
-<p>Read more in the <a class="reference internal" href="http://github.com/model_evaluation.html#confusion-matrix"><span class="std std-ref">User Guide</span></a>.</p>
+<p>For general information regarding <code class="docutils literal notranslate"><span class="pre">scikit-learn</span></code> visualization tools, see
+the <a class="reference internal" href="http://github.com/visualizations.html#visualizations"><span class="std std-ref">Visualization Guide</span></a>.
+For guidance on interpreting these plots, refer to the
+<a class="reference internal" href="http://github.com/model_evaluation.html#confusion-matrix"><span class="std std-ref">Model Evaluation Guide</span></a>.</p>
 <div class="versionadded">
 <p><span class="versionmodified added">Added in version 1.0.</span></p>
 </div>
@@ -1468,15 +1474,21 @@ <h1>ConfusionMatrixDisplay<a class="headerlink" href="#confusionmatrixdisplay" t
 </pre></div>
 </div>
 <figure class="align-default">
-<img alt="../../_images/sklearn-metrics-ConfusionMatrixDisplay-2.png" class="plot-directive" src="http://github.com/_images/sklearn-metrics-ConfusionMatrixDisplay-2.png" />
+<img alt="../../_images/sklearn-metrics-ConfusionMatrixDisplay-2_00_00.png" class="plot-directive" src="http://github.com/_images/sklearn-metrics-ConfusionMatrixDisplay-2_00_00.png" />
 </figure>
+<p>For a detailed example of using a confusion matrix to evaluate a
+Support Vector Classifier, please see
+<a class="reference internal" href="http://github.com/auto_examples/model_selection/plot_confusion_matrix.html#sphx-glr-auto-examples-model-selection-plot-confusion-matrix-py"><span class="std std-ref">Confusion matrix</span></a></p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.metrics.ConfusionMatrixDisplay.from_predictions">
-<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_predictions</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_pred</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">normalize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">display_labels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">include_values</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xticks_rotation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'horizontal'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">values_format</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cmap</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'viridis'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">colorbar</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">im_kw</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">text_kw</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/_plot/confusion_matrix.py#L343"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.ConfusionMatrixDisplay.from_predictions" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_predictions</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_pred</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">normalize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">display_labels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">include_values</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xticks_rotation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'horizontal'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">values_format</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cmap</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'viridis'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">colorbar</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">im_kw</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">text_kw</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/_plot/confusion_matrix.py#L353"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.ConfusionMatrixDisplay.from_predictions" title="Link to this definition">#</a></dt>
 <dd><p>Plot Confusion Matrix given true and predicted labels.</p>
-<p>Read more in the <a class="reference internal" href="http://github.com/model_evaluation.html#confusion-matrix"><span class="std std-ref">User Guide</span></a>.</p>
+<p>For general information regarding <code class="docutils literal notranslate"><span class="pre">scikit-learn</span></code> visualization tools, see
+the <a class="reference internal" href="http://github.com/visualizations.html#visualizations"><span class="std std-ref">Visualization Guide</span></a>.
+For guidance on interpreting these plots, refer to the
+<a class="reference internal" href="http://github.com/model_evaluation.html#confusion-matrix"><span class="std std-ref">Model Evaluation Guide</span></a>.</p>
 <div class="versionadded">
 <p><span class="versionmodified added">Added in version 1.0.</span></p>
 </div>
@@ -1572,7 +1584,7 @@ <h1>ConfusionMatrixDisplay<a class="headerlink" href="#confusionmatrixdisplay" t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.metrics.ConfusionMatrixDisplay.plot">
-<span class="sig-name descname"><span class="pre">plot</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">include_values</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cmap</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'viridis'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xticks_rotation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'horizontal'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">values_format</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">colorbar</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">im_kw</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">text_kw</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/_plot/confusion_matrix.py#L85"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.ConfusionMatrixDisplay.plot" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">plot</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">include_values</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cmap</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'viridis'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xticks_rotation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'horizontal'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">values_format</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">colorbar</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">im_kw</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">text_kw</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/_plot/confusion_matrix.py#L88"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.ConfusionMatrixDisplay.plot" title="Link to this definition">#</a></dt>
 <dd><p>Plot visualization.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.metrics.DetCurveDisplay.html b/dev/modules/generated/sklearn.metrics.DetCurveDisplay.html
index b5f44a03536ec..5269caa0fd4ea 100644
--- a/dev/modules/generated/sklearn.metrics.DetCurveDisplay.html
+++ b/dev/modules/generated/sklearn.metrics.DetCurveDisplay.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,12 +1307,12 @@
 <h1>DetCurveDisplay<a class="headerlink" href="#detcurvedisplay" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.metrics.DetCurveDisplay">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">DetCurveDisplay</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fpr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fnr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">estimator_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pos_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/_plot/det_curve.py#L10"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.DetCurveDisplay" title="Link to this definition">#</a></dt>
-<dd><p>DET curve visualization.</p>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">DetCurveDisplay</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fpr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fnr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">estimator_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pos_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/_plot/det_curve.py#L11"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.DetCurveDisplay" title="Link to this definition">#</a></dt>
+<dd><p>Detection Error Tradeoff (DET) curve visualization.</p>
 <p>It is recommend to use <a class="reference internal" href="#sklearn.metrics.DetCurveDisplay.from_estimator" title="sklearn.metrics.DetCurveDisplay.from_estimator"><code class="xref py py-func docutils literal notranslate"><span class="pre">from_estimator</span></code></a>
 or <a class="reference internal" href="#sklearn.metrics.DetCurveDisplay.from_predictions" title="sklearn.metrics.DetCurveDisplay.from_predictions"><code class="xref py py-func docutils literal notranslate"><span class="pre">from_predictions</span></code></a> to create a
 visualizer. All parameters are stored as attributes.</p>
-<p>Read more in the <a class="reference internal" href="http://github.com/visualizations.html#visualizations"><span class="std std-ref">User Guide</span></a>.</p>
+<p>Read more in the <a class="reference internal" href="http://github.com/model_evaluation.html#det-curve"><span class="std std-ref">User Guide</span></a>.</p>
 <div class="versionadded">
 <p><span class="versionmodified added">Added in version 0.24.</span></p>
 </div>
@@ -1376,15 +1376,15 @@ <h1>DetCurveDisplay<a class="headerlink" href="#detcurvedisplay" title="Link to
 </figure>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.metrics.DetCurveDisplay.from_estimator">
-<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_estimator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">response_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pos_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/_plot/det_curve.py#L81"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.DetCurveDisplay.from_estimator" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_estimator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_intermediate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">response_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pos_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/_plot/det_curve.py#L82"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.DetCurveDisplay.from_estimator" title="Link to this definition">#</a></dt>
 <dd><p>Plot DET curve given an estimator and data.</p>
-<p>Read more in the <a class="reference internal" href="http://github.com/visualizations.html#visualizations"><span class="std std-ref">User Guide</span></a>.</p>
+<p>Read more in the <a class="reference internal" href="http://github.com/model_evaluation.html#det-curve"><span class="std std-ref">User Guide</span></a>.</p>
 <div class="versionadded">
 <p><span class="versionmodified added">Added in version 1.0.</span></p>
 </div>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><dl class="simple">
+<dd class="field-odd"><dl>
 <dt><strong>estimator</strong><span class="classifier">estimator instance</span></dt><dd><p>Fitted classifier or a fitted <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>
 in which the last estimator is a classifier.</p>
 </dd>
@@ -1394,6 +1394,13 @@ <h1>DetCurveDisplay<a class="headerlink" href="#detcurvedisplay" title="Link to
 </dd>
 <dt><strong>sample_weight</strong><span class="classifier">array-like of shape (n_samples,), default=None</span></dt><dd><p>Sample weights.</p>
 </dd>
+<dt><strong>drop_intermediate</strong><span class="classifier">bool, default=True</span></dt><dd><p>Whether to drop thresholds where true positives (tp) do not change
+from the previous or subsequent threshold. All points with the same
+tp value have the same <code class="docutils literal notranslate"><span class="pre">fnr</span></code> and thus same y coordinate.</p>
+<div class="versionadded">
+<p><span class="versionmodified added">Added in version 1.7.</span></p>
+</div>
+</dd>
 <dt><strong>response_method</strong><span class="classifier">{‘predict_proba’, ‘decision_function’, ‘auto’}                 default=’auto’</span></dt><dd><p>Specifies whether to use <a class="reference internal" href="http://github.com/glossary.html#term-predict_proba"><span class="xref std std-term">predict_proba</span></a> or
 <a class="reference internal" href="http://github.com/glossary.html#term-decision_function"><span class="xref std std-term">decision_function</span></a> as the predicted target response. If set
 to ‘auto’, <a class="reference internal" href="http://github.com/glossary.html#term-predict_proba"><span class="xref std std-term">predict_proba</span></a> is tried first and if it does not
@@ -1452,15 +1459,15 @@ <h1>DetCurveDisplay<a class="headerlink" href="#detcurvedisplay" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.metrics.DetCurveDisplay.from_predictions">
-<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_predictions</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_pred</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pos_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/_plot/det_curve.py#L185"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.DetCurveDisplay.from_predictions" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_predictions</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_pred</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_intermediate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pos_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/_plot/det_curve.py#L195"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.DetCurveDisplay.from_predictions" title="Link to this definition">#</a></dt>
 <dd><p>Plot the DET curve given the true and predicted labels.</p>
-<p>Read more in the <a class="reference internal" href="http://github.com/visualizations.html#visualizations"><span class="std std-ref">User Guide</span></a>.</p>
+<p>Read more in the <a class="reference internal" href="http://github.com/model_evaluation.html#det-curve"><span class="std std-ref">User Guide</span></a>.</p>
 <div class="versionadded">
 <p><span class="versionmodified added">Added in version 1.0.</span></p>
 </div>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><dl class="simple">
+<dd class="field-odd"><dl>
 <dt><strong>y_true</strong><span class="classifier">array-like of shape (n_samples,)</span></dt><dd><p>True labels.</p>
 </dd>
 <dt><strong>y_pred</strong><span class="classifier">array-like of shape (n_samples,)</span></dt><dd><p>Target scores, can either be probability estimates of the positive
@@ -1469,6 +1476,13 @@ <h1>DetCurveDisplay<a class="headerlink" href="#detcurvedisplay" title="Link to
 </dd>
 <dt><strong>sample_weight</strong><span class="classifier">array-like of shape (n_samples,), default=None</span></dt><dd><p>Sample weights.</p>
 </dd>
+<dt><strong>drop_intermediate</strong><span class="classifier">bool, default=True</span></dt><dd><p>Whether to drop thresholds where true positives (tp) do not change
+from the previous or subsequent threshold. All points with the same
+tp value have the same <code class="docutils literal notranslate"><span class="pre">fnr</span></code> and thus same y coordinate.</p>
+<div class="versionadded">
+<p><span class="versionmodified added">Added in version 1.7.</span></p>
+</div>
+</dd>
 <dt><strong>pos_label</strong><span class="classifier">int, float, bool or str, default=None</span></dt><dd><p>The label of the positive class. When <code class="docutils literal notranslate"><span class="pre">pos_label=None</span></code>, if <code class="docutils literal notranslate"><span class="pre">y_true</span></code>
 is in {-1, 1} or {0, 1}, <code class="docutils literal notranslate"><span class="pre">pos_label</span></code> is set to 1, otherwise an
 error will be raised.</p>
@@ -1523,7 +1537,7 @@ <h1>DetCurveDisplay<a class="headerlink" href="#detcurvedisplay" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.metrics.DetCurveDisplay.plot">
-<span class="sig-name descname"><span class="pre">plot</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/_plot/det_curve.py#L280"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.DetCurveDisplay.plot" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">plot</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/_plot/det_curve.py#L299"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.DetCurveDisplay.plot" title="Link to this definition">#</a></dt>
 <dd><p>Plot visualization.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.metrics.DistanceMetric.html b/dev/modules/generated/sklearn.metrics.DistanceMetric.html
index 7ff75380b8246..d86ed7954312a 100644
--- a/dev/modules/generated/sklearn.metrics.DistanceMetric.html
+++ b/dev/modules/generated/sklearn.metrics.DistanceMetric.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
diff --git a/dev/modules/generated/sklearn.metrics.PrecisionRecallDisplay.html b/dev/modules/generated/sklearn.metrics.PrecisionRecallDisplay.html
index 8499c3980ae4f..1fee094c4433b 100644
--- a/dev/modules/generated/sklearn.metrics.PrecisionRecallDisplay.html
+++ b/dev/modules/generated/sklearn.metrics.PrecisionRecallDisplay.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,14 +1307,17 @@
 <h1>PrecisionRecallDisplay<a class="headerlink" href="#precisionrecalldisplay" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.metrics.PrecisionRecallDisplay">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">PrecisionRecallDisplay</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">precision</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">recall</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">average_precision</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">estimator_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pos_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prevalence_pos_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/_plot/precision_recall_curve.py#L14"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.PrecisionRecallDisplay" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">PrecisionRecallDisplay</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">precision</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">recall</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">average_precision</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">estimator_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pos_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prevalence_pos_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/_plot/precision_recall_curve.py#L14"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.PrecisionRecallDisplay" title="Link to this definition">#</a></dt>
 <dd><p>Precision Recall visualization.</p>
 <p>It is recommend to use
 <a class="reference internal" href="#sklearn.metrics.PrecisionRecallDisplay.from_estimator" title="sklearn.metrics.PrecisionRecallDisplay.from_estimator"><code class="xref py py-func docutils literal notranslate"><span class="pre">from_estimator</span></code></a> or
 <a class="reference internal" href="#sklearn.metrics.PrecisionRecallDisplay.from_predictions" title="sklearn.metrics.PrecisionRecallDisplay.from_predictions"><code class="xref py py-func docutils literal notranslate"><span class="pre">from_predictions</span></code></a> to create
 a <a class="reference internal" href="#sklearn.metrics.PrecisionRecallDisplay" title="sklearn.metrics.PrecisionRecallDisplay"><code class="xref py py-class docutils literal notranslate"><span class="pre">PrecisionRecallDisplay</span></code></a>. All parameters are
 stored as attributes.</p>
-<p>Read more in the <a class="reference internal" href="http://github.com/visualizations.html#visualizations"><span class="std std-ref">User Guide</span></a>.</p>
+<p>For general information regarding <code class="docutils literal notranslate"><span class="pre">scikit-learn</span></code> visualization tools, see
+the <a class="reference internal" href="http://github.com/visualizations.html#visualizations"><span class="std std-ref">Visualization Guide</span></a>.
+For guidance on interpreting these plots, refer to the <a class="reference internal" href="http://github.com/model_evaluation.html#precision-recall-f-measure-metrics"><span class="std std-ref">Model
+Evaluation Guide</span></a>.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><dl>
@@ -1403,8 +1406,12 @@ <h1>PrecisionRecallDisplay<a class="headerlink" href="#precisionrecalldisplay" t
 </figure>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.metrics.PrecisionRecallDisplay.from_estimator">
-<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_estimator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_intermediate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">response_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pos_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">plot_chance_level</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chance_level_kw</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">despine</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/_plot/precision_recall_curve.py#L259"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.PrecisionRecallDisplay.from_estimator" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_estimator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_intermediate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">response_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pos_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">plot_chance_level</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chance_level_kw</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">despine</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/_plot/precision_recall_curve.py#L262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.PrecisionRecallDisplay.from_estimator" title="Link to this definition">#</a></dt>
 <dd><p>Plot precision-recall curve given an estimator and some data.</p>
+<p>For general information regarding <code class="docutils literal notranslate"><span class="pre">scikit-learn</span></code> visualization tools, see
+the <a class="reference internal" href="http://github.com/visualizations.html#visualizations"><span class="std std-ref">Visualization Guide</span></a>.
+For guidance on interpreting these plots, refer to the <a class="reference internal" href="http://github.com/model_evaluation.html#precision-recall-f-measure-metrics"><span class="std std-ref">Model
+Evaluation Guide</span></a>.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><dl>
@@ -1505,8 +1512,12 @@ <h1>PrecisionRecallDisplay<a class="headerlink" href="#precisionrecalldisplay" t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.metrics.PrecisionRecallDisplay.from_predictions">
-<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_predictions</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_pred</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_intermediate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pos_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">plot_chance_level</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chance_level_kw</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">despine</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/_plot/precision_recall_curve.py#L401"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.PrecisionRecallDisplay.from_predictions" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_predictions</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_pred</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_intermediate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pos_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">plot_chance_level</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chance_level_kw</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">despine</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/_plot/precision_recall_curve.py#L409"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.PrecisionRecallDisplay.from_predictions" title="Link to this definition">#</a></dt>
 <dd><p>Plot precision-recall curve given binary class predictions.</p>
+<p>For general information regarding <code class="docutils literal notranslate"><span class="pre">scikit-learn</span></code> visualization tools, see
+the <a class="reference internal" href="http://github.com/visualizations.html#visualizations"><span class="std std-ref">Visualization Guide</span></a>.
+For guidance on interpreting these plots, refer to the <a class="reference internal" href="http://github.com/model_evaluation.html#precision-recall-f-measure-metrics"><span class="std std-ref">Model
+Evaluation Guide</span></a>.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><dl>
@@ -1600,7 +1611,7 @@ <h1>PrecisionRecallDisplay<a class="headerlink" href="#precisionrecalldisplay" t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.metrics.PrecisionRecallDisplay.plot">
-<span class="sig-name descname"><span class="pre">plot</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">plot_chance_level</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chance_level_kw</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">despine</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/_plot/precision_recall_curve.py#L128"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.PrecisionRecallDisplay.plot" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">plot</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">plot_chance_level</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chance_level_kw</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">despine</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/_plot/precision_recall_curve.py#L131"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.PrecisionRecallDisplay.plot" title="Link to this definition">#</a></dt>
 <dd><p>Plot visualization.</p>
 <p>Extra keyword arguments will be passed to matplotlib’s <code class="docutils literal notranslate"><span class="pre">plot</span></code>.</p>
 <dl class="field-list">
diff --git a/dev/modules/generated/sklearn.metrics.PredictionErrorDisplay.html b/dev/modules/generated/sklearn.metrics.PredictionErrorDisplay.html
index 3e88c4dfe3af8..40f3ea6728f17 100644
--- a/dev/modules/generated/sklearn.metrics.PredictionErrorDisplay.html
+++ b/dev/modules/generated/sklearn.metrics.PredictionErrorDisplay.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>PredictionErrorDisplay<a class="headerlink" href="#predictionerrordisplay" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.metrics.PredictionErrorDisplay">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">PredictionErrorDisplay</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_pred</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/_plot/regression.py#L13"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.PredictionErrorDisplay" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">PredictionErrorDisplay</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_pred</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/_plot/regression.py#L13"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.PredictionErrorDisplay" title="Link to this definition">#</a></dt>
 <dd><p>Visualization of the prediction error of a regression model.</p>
 <p>This tool can display “residuals vs predicted” or “actual vs predicted”
 using scatter plots to qualitatively assess the behavior of a regressor,
@@ -1377,7 +1377,7 @@ <h1>PredictionErrorDisplay<a class="headerlink" href="#predictionerrordisplay" t
 </figure>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.metrics.PredictionErrorDisplay.from_estimator">
-<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_estimator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kind</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'residual_vs_predicted'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">subsample</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scatter_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">line_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/_plot/regression.py#L189"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.PredictionErrorDisplay.from_estimator" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_estimator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kind</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'residual_vs_predicted'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">subsample</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scatter_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">line_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/_plot/regression.py#L189"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.PredictionErrorDisplay.from_estimator" title="Link to this definition">#</a></dt>
 <dd><p>Plot the prediction error given a regressor and some data.</p>
 <p>For general information regarding <code class="docutils literal notranslate"><span class="pre">scikit-learn</span></code> visualization tools,
 read more in the <a class="reference internal" href="http://github.com/visualizations.html#visualizations"><span class="std std-ref">Visualization Guide</span></a>.
@@ -1459,7 +1459,7 @@ <h1>PredictionErrorDisplay<a class="headerlink" href="#predictionerrordisplay" t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.metrics.PredictionErrorDisplay.from_predictions">
-<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_predictions</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_pred</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kind</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'residual_vs_predicted'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">subsample</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scatter_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">line_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/_plot/regression.py#L294"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.PredictionErrorDisplay.from_predictions" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_predictions</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_pred</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kind</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'residual_vs_predicted'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">subsample</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scatter_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">line_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/_plot/regression.py#L294"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.PredictionErrorDisplay.from_predictions" title="Link to this definition">#</a></dt>
 <dd><p>Plot the prediction error given the true and predicted targets.</p>
 <p>For general information regarding <code class="docutils literal notranslate"><span class="pre">scikit-learn</span></code> visualization tools,
 read more in the <a class="reference internal" href="http://github.com/visualizations.html#visualizations"><span class="std std-ref">Visualization Guide</span></a>.
@@ -1539,7 +1539,7 @@ <h1>PredictionErrorDisplay<a class="headerlink" href="#predictionerrordisplay" t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.metrics.PredictionErrorDisplay.plot">
-<span class="sig-name descname"><span class="pre">plot</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kind</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'residual_vs_predicted'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scatter_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">line_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/_plot/regression.py#L85"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.PredictionErrorDisplay.plot" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">plot</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kind</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'residual_vs_predicted'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scatter_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">line_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/_plot/regression.py#L85"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.PredictionErrorDisplay.plot" title="Link to this definition">#</a></dt>
 <dd><p>Plot visualization.</p>
 <p>Extra keyword arguments will be passed to matplotlib’s <code class="docutils literal notranslate"><span class="pre">plot</span></code>.</p>
 <dl class="field-list">
diff --git a/dev/modules/generated/sklearn.metrics.RocCurveDisplay.html b/dev/modules/generated/sklearn.metrics.RocCurveDisplay.html
index f46428df6845b..84254d3b5d217 100644
--- a/dev/modules/generated/sklearn.metrics.RocCurveDisplay.html
+++ b/dev/modules/generated/sklearn.metrics.RocCurveDisplay.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,14 +1307,17 @@
 <h1>RocCurveDisplay<a class="headerlink" href="#roccurvedisplay" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.metrics.RocCurveDisplay">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">RocCurveDisplay</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fpr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tpr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">roc_auc</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">estimator_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pos_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/_plot/roc_curve.py#L12"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.RocCurveDisplay" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">RocCurveDisplay</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fpr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tpr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">roc_auc</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">estimator_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pos_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/_plot/roc_curve.py#L14"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.RocCurveDisplay" title="Link to this definition">#</a></dt>
 <dd><p>ROC Curve visualization.</p>
 <p>It is recommend to use
 <a class="reference internal" href="#sklearn.metrics.RocCurveDisplay.from_estimator" title="sklearn.metrics.RocCurveDisplay.from_estimator"><code class="xref py py-func docutils literal notranslate"><span class="pre">from_estimator</span></code></a> or
 <a class="reference internal" href="#sklearn.metrics.RocCurveDisplay.from_predictions" title="sklearn.metrics.RocCurveDisplay.from_predictions"><code class="xref py py-func docutils literal notranslate"><span class="pre">from_predictions</span></code></a> to create
 a <a class="reference internal" href="#sklearn.metrics.RocCurveDisplay" title="sklearn.metrics.RocCurveDisplay"><code class="xref py py-class docutils literal notranslate"><span class="pre">RocCurveDisplay</span></code></a>. All parameters are
 stored as attributes.</p>
-<p>Read more in the <a class="reference internal" href="http://github.com/visualizations.html#visualizations"><span class="std std-ref">User Guide</span></a>.</p>
+<p>For general information regarding <code class="docutils literal notranslate"><span class="pre">scikit-learn</span></code> visualization tools, see
+the <a class="reference internal" href="http://github.com/visualizations.html#visualizations"><span class="std std-ref">Visualization Guide</span></a>.
+For guidance on interpreting these plots, refer to the <a class="reference internal" href="http://github.com/model_evaluation.html#roc-metrics"><span class="std std-ref">Model
+Evaluation Guide</span></a>.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><dl>
@@ -1368,9 +1371,9 @@ <h1>RocCurveDisplay<a class="headerlink" href="#roccurvedisplay" title="Link to
 <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span><span class="w"> </span><span class="nn">matplotlib.pyplot</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">plt</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn</span><span class="w"> </span><span class="kn">import</span> <span class="n">metrics</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">pred</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.4</span><span class="p">,</span> <span class="mf">0.35</span><span class="p">,</span> <span class="mf">0.8</span><span class="p">])</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">fpr</span><span class="p">,</span> <span class="n">tpr</span><span class="p">,</span> <span class="n">thresholds</span> <span class="o">=</span> <span class="n">metrics</span><span class="o">.</span><span class="n">roc_curve</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">pred</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">y_true</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">y_score</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.4</span><span class="p">,</span> <span class="mf">0.35</span><span class="p">,</span> <span class="mf">0.8</span><span class="p">])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">fpr</span><span class="p">,</span> <span class="n">tpr</span><span class="p">,</span> <span class="n">thresholds</span> <span class="o">=</span> <span class="n">metrics</span><span class="o">.</span><span class="n">roc_curve</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_score</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">roc_auc</span> <span class="o">=</span> <span class="n">metrics</span><span class="o">.</span><span class="n">auc</span><span class="p">(</span><span class="n">fpr</span><span class="p">,</span> <span class="n">tpr</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">display</span> <span class="o">=</span> <span class="n">metrics</span><span class="o">.</span><span class="n">RocCurveDisplay</span><span class="p">(</span><span class="n">fpr</span><span class="o">=</span><span class="n">fpr</span><span class="p">,</span> <span class="n">tpr</span><span class="o">=</span><span class="n">tpr</span><span class="p">,</span> <span class="n">roc_auc</span><span class="o">=</span><span class="n">roc_auc</span><span class="p">,</span>
 <span class="gp">... </span>                                  <span class="n">estimator_name</span><span class="o">=</span><span class="s1">&#39;example estimator&#39;</span><span class="p">)</span>
@@ -1384,8 +1387,12 @@ <h1>RocCurveDisplay<a class="headerlink" href="#roccurvedisplay" title="Link to
 </figure>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.metrics.RocCurveDisplay.from_estimator">
-<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_estimator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_intermediate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">response_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pos_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">plot_chance_level</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chance_level_kw</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">despine</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/_plot/roc_curve.py#L196"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.RocCurveDisplay.from_estimator" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_estimator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_intermediate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">response_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pos_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">plot_chance_level</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chance_level_kw</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">despine</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/_plot/roc_curve.py#L201"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.RocCurveDisplay.from_estimator" title="Link to this definition">#</a></dt>
 <dd><p>Create a ROC Curve display from an estimator.</p>
+<p>For general information regarding <code class="docutils literal notranslate"><span class="pre">scikit-learn</span></code> visualization tools,
+see the <a class="reference internal" href="http://github.com/visualizations.html#visualizations"><span class="std std-ref">Visualization Guide</span></a>.
+For guidance on interpreting these plots, refer to the <a class="reference internal" href="http://github.com/model_evaluation.html#roc-metrics"><span class="std std-ref">Model
+Evaluation Guide</span></a>.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><dl>
@@ -1398,9 +1405,10 @@ <h1>RocCurveDisplay<a class="headerlink" href="#roccurvedisplay" title="Link to
 </dd>
 <dt><strong>sample_weight</strong><span class="classifier">array-like of shape (n_samples,), default=None</span></dt><dd><p>Sample weights.</p>
 </dd>
-<dt><strong>drop_intermediate</strong><span class="classifier">bool, default=True</span></dt><dd><p>Whether to drop some suboptimal thresholds which would not appear
-on a plotted ROC curve. This is useful in order to create lighter
-ROC curves.</p>
+<dt><strong>drop_intermediate</strong><span class="classifier">bool, default=True</span></dt><dd><p>Whether to drop thresholds where the resulting point is collinear
+with its neighbors in ROC space. This has no effect on the ROC AUC
+or visual shape of the curve, but reduces the number of plotted
+points.</p>
 </dd>
 <dt><strong>response_method</strong><span class="classifier">{‘predict_proba’, ‘decision_function’, ‘auto’}                 default=’auto’</span></dt><dd><p>Specifies whether to use <a class="reference internal" href="http://github.com/glossary.html#term-predict_proba"><span class="xref std std-term">predict_proba</span></a> or
 <a class="reference internal" href="http://github.com/glossary.html#term-decision_function"><span class="xref std std-term">decision_function</span></a> as the target response. If set to ‘auto’,
@@ -1477,9 +1485,12 @@ <h1>RocCurveDisplay<a class="headerlink" href="#roccurvedisplay" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.metrics.RocCurveDisplay.from_predictions">
-<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_predictions</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_pred</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_intermediate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pos_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">plot_chance_level</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chance_level_kw</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">despine</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/_plot/roc_curve.py#L325"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.RocCurveDisplay.from_predictions" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_predictions</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_score</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_intermediate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pos_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">plot_chance_level</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chance_level_kw</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">despine</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_pred</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'deprecated'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/_plot/roc_curve.py#L336"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.RocCurveDisplay.from_predictions" title="Link to this definition">#</a></dt>
 <dd><p>Plot ROC curve given the true and predicted values.</p>
-<p>Read more in the <a class="reference internal" href="http://github.com/visualizations.html#visualizations"><span class="std std-ref">User Guide</span></a>.</p>
+<p>For general information regarding <code class="docutils literal notranslate"><span class="pre">scikit-learn</span></code> visualization tools,
+see the <a class="reference internal" href="http://github.com/visualizations.html#visualizations"><span class="std std-ref">Visualization Guide</span></a>.
+For guidance on interpreting these plots, refer to the <a class="reference internal" href="http://github.com/model_evaluation.html#roc-metrics"><span class="std std-ref">Model
+Evaluation Guide</span></a>.</p>
 <div class="versionadded">
 <p><span class="versionmodified added">Added in version 1.0.</span></p>
 </div>
@@ -1488,15 +1499,19 @@ <h1>RocCurveDisplay<a class="headerlink" href="#roccurvedisplay" title="Link to
 <dd class="field-odd"><dl>
 <dt><strong>y_true</strong><span class="classifier">array-like of shape (n_samples,)</span></dt><dd><p>True labels.</p>
 </dd>
-<dt><strong>y_pred</strong><span class="classifier">array-like of shape (n_samples,)</span></dt><dd><p>Target scores, can either be probability estimates of the positive
+<dt><strong>y_score</strong><span class="classifier">array-like of shape (n_samples,)</span></dt><dd><p>Target scores, can either be probability estimates of the positive
 class, confidence values, or non-thresholded measure of decisions
 (as returned by “decision_function” on some classifiers).</p>
+<div class="versionadded">
+<p><span class="versionmodified added">Added in version 1.7: </span><code class="docutils literal notranslate"><span class="pre">y_pred</span></code> has been renamed to <code class="docutils literal notranslate"><span class="pre">y_score</span></code>.</p>
+</div>
 </dd>
 <dt><strong>sample_weight</strong><span class="classifier">array-like of shape (n_samples,), default=None</span></dt><dd><p>Sample weights.</p>
 </dd>
-<dt><strong>drop_intermediate</strong><span class="classifier">bool, default=True</span></dt><dd><p>Whether to drop some suboptimal thresholds which would not appear
-on a plotted ROC curve. This is useful in order to create lighter
-ROC curves.</p>
+<dt><strong>drop_intermediate</strong><span class="classifier">bool, default=True</span></dt><dd><p>Whether to drop thresholds where the resulting point is collinear
+with its neighbors in ROC space. This has no effect on the ROC AUC
+or visual shape of the curve, but reduces the number of plotted
+points.</p>
 </dd>
 <dt><strong>pos_label</strong><span class="classifier">int, float, bool or str, default=None</span></dt><dd><p>The label of the positive class. When <code class="docutils literal notranslate"><span class="pre">pos_label=None</span></code>, if <code class="docutils literal notranslate"><span class="pre">y_true</span></code>
 is in {-1, 1} or {0, 1}, <code class="docutils literal notranslate"><span class="pre">pos_label</span></code> is set to 1, otherwise an
@@ -1524,6 +1539,14 @@ <h1>RocCurveDisplay<a class="headerlink" href="#roccurvedisplay" title="Link to
 <p><span class="versionmodified added">Added in version 1.6.</span></p>
 </div>
 </dd>
+<dt><strong>y_pred</strong><span class="classifier">array-like of shape (n_samples,)</span></dt><dd><p>Target scores, can either be probability estimates of the positive
+class, confidence values, or non-thresholded measure of decisions
+(as returned by “decision_function” on some classifiers).</p>
+<div class="deprecated">
+<p><span class="versionmodified deprecated">Deprecated since version 1.7: </span><code class="docutils literal notranslate"><span class="pre">y_pred</span></code> is deprecated and will be removed in 1.9. Use
+<code class="docutils literal notranslate"><span class="pre">y_score</span></code> instead.</p>
+</div>
+</dd>
 <dt><strong>**kwargs</strong><span class="classifier">dict</span></dt><dd><p>Additional keywords arguments passed to matplotlib <code class="docutils literal notranslate"><span class="pre">plot</span></code> function.</p>
 </dd>
 </dl>
@@ -1556,9 +1579,8 @@ <h1>RocCurveDisplay<a class="headerlink" href="#roccurvedisplay" title="Link to
 <span class="gp">&gt;&gt;&gt; </span><span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span>
 <span class="gp">... </span>    <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">clf</span> <span class="o">=</span> <span class="n">SVC</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">y_pred</span> <span class="o">=</span> <span class="n">clf</span><span class="o">.</span><span class="n">decision_function</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">RocCurveDisplay</span><span class="o">.</span><span class="n">from_predictions</span><span class="p">(</span>
-<span class="gp">... </span>   <span class="n">y_test</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">y_score</span> <span class="o">=</span> <span class="n">clf</span><span class="o">.</span><span class="n">decision_function</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">RocCurveDisplay</span><span class="o">.</span><span class="n">from_predictions</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_score</span><span class="p">)</span>
 <span class="go">&lt;...&gt;</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
 </pre></div>
@@ -1570,7 +1592,7 @@ <h1>RocCurveDisplay<a class="headerlink" href="#roccurvedisplay" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.metrics.RocCurveDisplay.plot">
-<span class="sig-name descname"><span class="pre">plot</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">plot_chance_level</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chance_level_kw</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">despine</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/_plot/roc_curve.py#L92"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.RocCurveDisplay.plot" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">plot</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">plot_chance_level</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chance_level_kw</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">despine</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/_plot/roc_curve.py#L97"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.RocCurveDisplay.plot" title="Link to this definition">#</a></dt>
 <dd><p>Plot visualization.</p>
 <p>Extra keyword arguments will be passed to matplotlib’s <code class="docutils literal notranslate"><span class="pre">plot</span></code>.</p>
 <dl class="field-list">
diff --git a/dev/modules/generated/sklearn.metrics.accuracy_score.html b/dev/modules/generated/sklearn.metrics.accuracy_score.html
index 9f1df26f7ef28..d0fb3922c926a 100644
--- a/dev/modules/generated/sklearn.metrics.accuracy_score.html
+++ b/dev/modules/generated/sklearn.metrics.accuracy_score.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>accuracy_score<a class="headerlink" href="#accuracy-score" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.metrics.accuracy_score">
-<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">accuracy_score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_pred</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">normalize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/_classification.py#L288"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.accuracy_score" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">accuracy_score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_pred</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">normalize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/_classification.py#L287"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.accuracy_score" title="Link to this definition">#</a></dt>
 <dd><p>Accuracy classification score.</p>
 <p>In multilabel classification, this function computes subset accuracy:
 the set of labels predicted for a sample must <em>exactly</em> match the
diff --git a/dev/modules/generated/sklearn.metrics.adjusted_mutual_info_score.html b/dev/modules/generated/sklearn.metrics.adjusted_mutual_info_score.html
index fae7a05de2359..c683101b3af19 100644
--- a/dev/modules/generated/sklearn.metrics.adjusted_mutual_info_score.html
+++ b/dev/modules/generated/sklearn.metrics.adjusted_mutual_info_score.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1308,7 +1308,7 @@
 <h1>adjusted_mutual_info_score<a class="headerlink" href="#adjusted-mutual-info-score" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.metrics.adjusted_mutual_info_score">
-<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">adjusted_mutual_info_score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">labels_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels_pred</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">average_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'arithmetic'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/cluster/_supervised.py#L928"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.adjusted_mutual_info_score" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">adjusted_mutual_info_score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">labels_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels_pred</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">average_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'arithmetic'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/cluster/_supervised.py#L927"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.adjusted_mutual_info_score" title="Link to this definition">#</a></dt>
 <dd><p>Adjusted Mutual Information between two clusterings.</p>
 <p>Adjusted Mutual Information (AMI) is an adjustment of the Mutual
 Information (MI) score to account for chance. It accounts for the fact that
diff --git a/dev/modules/generated/sklearn.metrics.adjusted_rand_score.html b/dev/modules/generated/sklearn.metrics.adjusted_rand_score.html
index 6f4aac53f1864..9c565760d9b9a 100644
--- a/dev/modules/generated/sklearn.metrics.adjusted_rand_score.html
+++ b/dev/modules/generated/sklearn.metrics.adjusted_rand_score.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>adjusted_rand_score<a class="headerlink" href="#adjusted-rand-score" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.metrics.adjusted_rand_score">
-<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">adjusted_rand_score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">labels_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels_pred</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/cluster/_supervised.py#L343"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.adjusted_rand_score" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">adjusted_rand_score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">labels_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels_pred</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/cluster/_supervised.py#L342"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.adjusted_rand_score" title="Link to this definition">#</a></dt>
 <dd><p>Rand index adjusted for chance.</p>
 <p>The Rand Index computes a similarity measure between two clusterings
 by considering all pairs of samples and counting pairs that are
@@ -1387,13 +1387,13 @@ <h1>adjusted_rand_score<a class="headerlink" href="#adjusted-rand-score" title="
 <p>Labelings that assign all classes members to the same clusters
 are complete but may not always be pure, hence penalized:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">adjusted_rand_score</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>
-<span class="go">0.57...</span>
+<span class="go">0.57</span>
 </pre></div>
 </div>
 <p>ARI is symmetric, so labelings that have pure clusters with members
 coming from the same classes but unnecessary splits are penalized:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">adjusted_rand_score</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">])</span>
-<span class="go">0.57...</span>
+<span class="go">0.57</span>
 </pre></div>
 </div>
 <p>If classes members are completely split across different clusters, the
diff --git a/dev/modules/generated/sklearn.metrics.auc.html b/dev/modules/generated/sklearn.metrics.auc.html
index 317b359840b10..f84165ceda636 100644
--- a/dev/modules/generated/sklearn.metrics.auc.html
+++ b/dev/modules/generated/sklearn.metrics.auc.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>auc<a class="headerlink" href="#auc" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.metrics.auc">
-<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">auc</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/_ranking.py#L40"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.auc" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">auc</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/_ranking.py#L39"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.auc" title="Link to this definition">#</a></dt>
 <dd><p>Compute Area Under the Curve (AUC) using the trapezoidal rule.</p>
 <p>This is a general function, given points on a curve.  For computing the
 area under the ROC-curve, see <a class="reference internal" href="http://github.com/sklearn.metrics.roc_auc_score.html#sklearn.metrics.roc_auc_score" title="sklearn.metrics.roc_auc_score"><code class="xref py py-func docutils literal notranslate"><span class="pre">roc_auc_score</span></code></a>.  For an alternative
@@ -1344,9 +1344,9 @@ <h1>auc<a class="headerlink" href="#auc" title="Link to this heading">#</a></h1>
 <p class="rubric">Examples</p>
 <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn</span><span class="w"> </span><span class="kn">import</span> <span class="n">metrics</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">])</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">pred</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.4</span><span class="p">,</span> <span class="mf">0.35</span><span class="p">,</span> <span class="mf">0.8</span><span class="p">])</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">fpr</span><span class="p">,</span> <span class="n">tpr</span><span class="p">,</span> <span class="n">thresholds</span> <span class="o">=</span> <span class="n">metrics</span><span class="o">.</span><span class="n">roc_curve</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">pred</span><span class="p">,</span> <span class="n">pos_label</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">y_true</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">y_score</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.4</span><span class="p">,</span> <span class="mf">0.35</span><span class="p">,</span> <span class="mf">0.8</span><span class="p">])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">fpr</span><span class="p">,</span> <span class="n">tpr</span><span class="p">,</span> <span class="n">thresholds</span> <span class="o">=</span> <span class="n">metrics</span><span class="o">.</span><span class="n">roc_curve</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_score</span><span class="p">,</span> <span class="n">pos_label</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">metrics</span><span class="o">.</span><span class="n">auc</span><span class="p">(</span><span class="n">fpr</span><span class="p">,</span> <span class="n">tpr</span><span class="p">)</span>
 <span class="go">0.75</span>
 </pre></div>
diff --git a/dev/modules/generated/sklearn.metrics.average_precision_score.html b/dev/modules/generated/sklearn.metrics.average_precision_score.html
index 5558d929ed97c..23f4d38f20625 100644
--- a/dev/modules/generated/sklearn.metrics.average_precision_score.html
+++ b/dev/modules/generated/sklearn.metrics.average_precision_score.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1308,7 +1308,7 @@
 <h1>average_precision_score<a class="headerlink" href="#average-precision-score" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.metrics.average_precision_score">
-<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">average_precision_score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_score</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'macro'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pos_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/_ranking.py#L109"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.average_precision_score" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">average_precision_score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_score</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'macro'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pos_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/_ranking.py#L108"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.average_precision_score" title="Link to this definition">#</a></dt>
 <dd><p>Compute average precision (AP) from prediction scores.</p>
 <p>AP summarizes a precision-recall curve as the weighted mean of precisions
 achieved at each threshold, with the increase in recall from the previous
@@ -1390,7 +1390,7 @@ <h1>average_precision_score<a class="headerlink" href="#average-precision-score"
 <span class="gp">&gt;&gt;&gt; </span><span class="n">y_true</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">y_scores</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.4</span><span class="p">,</span> <span class="mf">0.35</span><span class="p">,</span> <span class="mf">0.8</span><span class="p">])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">average_precision_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_scores</span><span class="p">)</span>
-<span class="go">0.83...</span>
+<span class="go">0.83</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">y_true</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">y_scores</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span>
 <span class="gp">... </span>    <span class="p">[</span><span class="mf">0.7</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">],</span>
@@ -1401,7 +1401,7 @@ <h1>average_precision_score<a class="headerlink" href="#average-precision-score"
 <span class="gp">... </span>    <span class="p">[</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">,</span> <span class="mf">0.7</span><span class="p">],</span>
 <span class="gp">... </span><span class="p">])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">average_precision_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_scores</span><span class="p">)</span>
-<span class="go">0.77...</span>
+<span class="go">0.77</span>
 </pre></div>
 </div>
 </dd></dl>
diff --git a/dev/modules/generated/sklearn.metrics.balanced_accuracy_score.html b/dev/modules/generated/sklearn.metrics.balanced_accuracy_score.html
index 9a1a9a31c20c5..93dd3f7213a9a 100644
--- a/dev/modules/generated/sklearn.metrics.balanced_accuracy_score.html
+++ b/dev/modules/generated/sklearn.metrics.balanced_accuracy_score.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>balanced_accuracy_score<a class="headerlink" href="#balanced-accuracy-score" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.metrics.balanced_accuracy_score">
-<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">balanced_accuracy_score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_pred</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">adjusted</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/_classification.py#L2707"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.balanced_accuracy_score" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">balanced_accuracy_score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_pred</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">adjusted</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/_classification.py#L2693"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.balanced_accuracy_score" title="Link to this definition">#</a></dt>
 <dd><p>Compute the balanced accuracy.</p>
 <p>The balanced accuracy in binary and multiclass classification problems to
 deal with imbalanced datasets. It is defined as the average of recall
diff --git a/dev/modules/generated/sklearn.metrics.brier_score_loss.html b/dev/modules/generated/sklearn.metrics.brier_score_loss.html
index c56fbd11cf633..1464e63a86e59 100644
--- a/dev/modules/generated/sklearn.metrics.brier_score_loss.html
+++ b/dev/modules/generated/sklearn.metrics.brier_score_loss.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1308,7 +1308,7 @@
 <h1>brier_score_loss<a class="headerlink" href="#brier-score-loss" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.metrics.brier_score_loss">
-<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">brier_score_loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_proba</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pos_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_by_half</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/_classification.py#L3464"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.brier_score_loss" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">brier_score_loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_proba</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pos_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_by_half</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/_classification.py#L3450"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.brier_score_loss" title="Link to this definition">#</a></dt>
 <dd><p>Compute the Brier score loss.</p>
 <p>The smaller the Brier score loss, the better, hence the naming with “loss”.
 The Brier score measures the mean squared difference between the predicted
@@ -1393,21 +1393,21 @@ <h1>brier_score_loss<a class="headerlink" href="#brier-score-loss" title="Link t
 <span class="gp">&gt;&gt;&gt; </span><span class="n">y_true_categorical</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="s2">&quot;spam&quot;</span><span class="p">,</span> <span class="s2">&quot;ham&quot;</span><span class="p">,</span> <span class="s2">&quot;ham&quot;</span><span class="p">,</span> <span class="s2">&quot;spam&quot;</span><span class="p">])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">y_prob</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.9</span><span class="p">,</span> <span class="mf">0.8</span><span class="p">,</span> <span class="mf">0.3</span><span class="p">])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">brier_score_loss</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_prob</span><span class="p">)</span>
-<span class="go">0.037...</span>
+<span class="go">0.0375</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">brier_score_loss</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="mi">1</span><span class="o">-</span><span class="n">y_prob</span><span class="p">,</span> <span class="n">pos_label</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-<span class="go">0.037...</span>
+<span class="go">0.0375</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">brier_score_loss</span><span class="p">(</span><span class="n">y_true_categorical</span><span class="p">,</span> <span class="n">y_prob</span><span class="p">,</span> <span class="n">pos_label</span><span class="o">=</span><span class="s2">&quot;ham&quot;</span><span class="p">)</span>
-<span class="go">0.037...</span>
+<span class="go">0.0375</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">brier_score_loss</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">y_prob</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mf">0.5</span><span class="p">)</span>
 <span class="go">0.0</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">brier_score_loss</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_prob</span><span class="p">,</span> <span class="n">scale_by_half</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-<span class="go">0.074...</span>
+<span class="go">0.075</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">brier_score_loss</span><span class="p">(</span>
 <span class="gp">... </span>   <span class="p">[</span><span class="s2">&quot;eggs&quot;</span><span class="p">,</span> <span class="s2">&quot;ham&quot;</span><span class="p">,</span> <span class="s2">&quot;spam&quot;</span><span class="p">],</span>
 <span class="gp">... </span>   <span class="p">[[</span><span class="mf">0.8</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.2</span><span class="p">,</span> <span class="mf">0.7</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.2</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">,</span> <span class="mf">0.6</span><span class="p">]],</span>
 <span class="gp">... </span>   <span class="n">labels</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;eggs&quot;</span><span class="p">,</span> <span class="s2">&quot;ham&quot;</span><span class="p">,</span> <span class="s2">&quot;spam&quot;</span><span class="p">]</span>
 <span class="gp">... </span><span class="p">)</span>
-<span class="go">0.146...</span>
+<span class="go">0.146</span>
 </pre></div>
 </div>
 </dd></dl>
diff --git a/dev/modules/generated/sklearn.metrics.calinski_harabasz_score.html b/dev/modules/generated/sklearn.metrics.calinski_harabasz_score.html
index 6fcc6314de001..712d2a7698d4a 100644
--- a/dev/modules/generated/sklearn.metrics.calinski_harabasz_score.html
+++ b/dev/modules/generated/sklearn.metrics.calinski_harabasz_score.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>calinski_harabasz_score<a class="headerlink" href="#calinski-harabasz-score" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.metrics.calinski_harabasz_score">
-<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">calinski_harabasz_score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/cluster/_unsupervised.py#L319"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.calinski_harabasz_score" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">calinski_harabasz_score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/cluster/_unsupervised.py#L318"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.calinski_harabasz_score" title="Link to this definition">#</a></dt>
 <dd><p>Compute the Calinski and Harabasz score.</p>
 <p>It is also known as the Variance Ratio Criterion.</p>
 <p>The score is defined as ratio of the sum of between-cluster dispersion and
diff --git a/dev/modules/generated/sklearn.metrics.check_scoring.html b/dev/modules/generated/sklearn.metrics.check_scoring.html
index c9efa1688de83..1d88d35c9d326 100644
--- a/dev/modules/generated/sklearn.metrics.check_scoring.html
+++ b/dev/modules/generated/sklearn.metrics.check_scoring.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>check_scoring<a class="headerlink" href="#check-scoring" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.metrics.check_scoring">
-<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">check_scoring</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scoring</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">allow_none</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">raise_exc</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/_scorer.py#L928"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.check_scoring" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">check_scoring</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scoring</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">allow_none</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">raise_exc</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/_scorer.py#L928"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.check_scoring" title="Link to this definition">#</a></dt>
 <dd><p>Determine scorer from user options.</p>
 <p>A TypeError will be thrown if the estimator cannot be scored.</p>
 <dl class="field-list">
diff --git a/dev/modules/generated/sklearn.metrics.class_likelihood_ratios.html b/dev/modules/generated/sklearn.metrics.class_likelihood_ratios.html
index ac7fdb51e88e0..59b9d189ef5a3 100644
--- a/dev/modules/generated/sklearn.metrics.class_likelihood_ratios.html
+++ b/dev/modules/generated/sklearn.metrics.class_likelihood_ratios.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>class_likelihood_ratios<a class="headerlink" href="#class-likelihood-ratios" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.metrics.class_likelihood_ratios">
-<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">class_likelihood_ratios</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_pred</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">raise_warning</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'deprecated'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">replace_undefined_by</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'default'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/_classification.py#L2036"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.class_likelihood_ratios" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">class_likelihood_ratios</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_pred</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">raise_warning</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'deprecated'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">replace_undefined_by</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">nan</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/_classification.py#L2047"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.class_likelihood_ratios" title="Link to this definition">#</a></dt>
 <dd><p>Compute binary classification positive and negative likelihood ratios.</p>
 <p>The positive likelihood ratio is <code class="docutils literal notranslate"><span class="pre">LR+</span> <span class="pre">=</span> <span class="pre">sensitivity</span> <span class="pre">/</span> <span class="pre">(1</span> <span class="pre">-</span> <span class="pre">specificity)</span></code>
 where the sensitivity or recall is the ratio <code class="docutils literal notranslate"><span class="pre">tp</span> <span class="pre">/</span> <span class="pre">(tp</span> <span class="pre">+</span> <span class="pre">fn)</span></code> and the
@@ -1414,16 +1414,15 @@ <h1>class_likelihood_ratios<a class="headerlink" href="#class-likelihood-ratios"
 <p class="rubric">Examples</p>
 <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.metrics</span><span class="w"> </span><span class="kn">import</span> <span class="n">class_likelihood_ratios</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">class_likelihood_ratios</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span>
-<span class="gp">... </span>                         <span class="n">replace_undefined_by</span><span class="o">=</span><span class="mf">1.0</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">class_likelihood_ratios</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">])</span>
 <span class="go">(1.5, 0.75)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">y_true</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="s2">&quot;non-cat&quot;</span><span class="p">,</span> <span class="s2">&quot;cat&quot;</span><span class="p">,</span> <span class="s2">&quot;non-cat&quot;</span><span class="p">,</span> <span class="s2">&quot;cat&quot;</span><span class="p">,</span> <span class="s2">&quot;non-cat&quot;</span><span class="p">])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">y_pred</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="s2">&quot;cat&quot;</span><span class="p">,</span> <span class="s2">&quot;cat&quot;</span><span class="p">,</span> <span class="s2">&quot;non-cat&quot;</span><span class="p">,</span> <span class="s2">&quot;non-cat&quot;</span><span class="p">,</span> <span class="s2">&quot;non-cat&quot;</span><span class="p">])</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">class_likelihood_ratios</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">,</span> <span class="n">replace_undefined_by</span><span class="o">=</span><span class="mf">1.0</span><span class="p">)</span>
-<span class="go">(1.33..., 0.66...)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">class_likelihood_ratios</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">)</span>
+<span class="go">(1.33, 0.66)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">y_true</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="s2">&quot;non-zebra&quot;</span><span class="p">,</span> <span class="s2">&quot;zebra&quot;</span><span class="p">,</span> <span class="s2">&quot;non-zebra&quot;</span><span class="p">,</span> <span class="s2">&quot;zebra&quot;</span><span class="p">,</span> <span class="s2">&quot;non-zebra&quot;</span><span class="p">])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">y_pred</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="s2">&quot;zebra&quot;</span><span class="p">,</span> <span class="s2">&quot;zebra&quot;</span><span class="p">,</span> <span class="s2">&quot;non-zebra&quot;</span><span class="p">,</span> <span class="s2">&quot;non-zebra&quot;</span><span class="p">,</span> <span class="s2">&quot;non-zebra&quot;</span><span class="p">])</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">class_likelihood_ratios</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">,</span> <span class="n">replace_undefined_by</span><span class="o">=</span><span class="mf">1.0</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">class_likelihood_ratios</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">)</span>
 <span class="go">(1.5, 0.75)</span>
 </pre></div>
 </div>
@@ -1431,8 +1430,7 @@ <h1>class_likelihood_ratios<a class="headerlink" href="#class-likelihood-ratios"
 <span class="pre">positive_class]</span></code></p>
 <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">y_true</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="s2">&quot;non-cat&quot;</span><span class="p">,</span> <span class="s2">&quot;cat&quot;</span><span class="p">,</span> <span class="s2">&quot;non-cat&quot;</span><span class="p">,</span> <span class="s2">&quot;cat&quot;</span><span class="p">,</span> <span class="s2">&quot;non-cat&quot;</span><span class="p">])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">y_pred</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="s2">&quot;cat&quot;</span><span class="p">,</span> <span class="s2">&quot;cat&quot;</span><span class="p">,</span> <span class="s2">&quot;non-cat&quot;</span><span class="p">,</span> <span class="s2">&quot;non-cat&quot;</span><span class="p">,</span> <span class="s2">&quot;non-cat&quot;</span><span class="p">])</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">class_likelihood_ratios</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;non-cat&quot;</span><span class="p">,</span> <span class="s2">&quot;cat&quot;</span><span class="p">],</span>
-<span class="gp">... </span>                         <span class="n">replace_undefined_by</span><span class="o">=</span><span class="mf">1.0</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">class_likelihood_ratios</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;non-cat&quot;</span><span class="p">,</span> <span class="s2">&quot;cat&quot;</span><span class="p">])</span>
 <span class="go">(1.5, 0.75)</span>
 </pre></div>
 </div>
diff --git a/dev/modules/generated/sklearn.metrics.classification_report.html b/dev/modules/generated/sklearn.metrics.classification_report.html
index c0ae13ab4353e..6cac6da5bba87 100644
--- a/dev/modules/generated/sklearn.metrics.classification_report.html
+++ b/dev/modules/generated/sklearn.metrics.classification_report.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>classification_report<a class="headerlink" href="#classification-report" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.metrics.classification_report">
-<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">classification_report</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_pred</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target_names</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">digits</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_dict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zero_division</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'warn'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/_classification.py#L2800"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.classification_report" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">classification_report</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_pred</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target_names</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">digits</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_dict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zero_division</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'warn'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/_classification.py#L2786"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.classification_report" title="Link to this definition">#</a></dt>
 <dd><p>Build a text report showing the main classification metrics.</p>
 <p>Read more in the <a class="reference internal" href="http://github.com/model_evaluation.html#classification-report"><span class="std std-ref">User Guide</span></a>.</p>
 <dl class="field-list">
@@ -1437,8 +1437,8 @@ <h2>Gallery examples<a class="headerlink" href="#gallery-examples" title="Link t
 <p><a class="reference internal" href="http://github.com/auto_examples/semi_supervised/plot_label_propagation_digits.html#sphx-glr-auto-examples-semi-supervised-plot-label-propagation-digits-py"><span class="std std-ref">Label Propagation digits: Demonstrating performance</span></a></p>
   <div class="sphx-glr-thumbnail-title">Label Propagation digits: Demonstrating performance</div>
 </div><div class="sphx-glr-thumbcontainer" tooltip="Demonstrates an active learning technique to learn handwritten digits using label propagation."><img alt="" src="http://github.com/_images/sphx_glr_plot_label_propagation_digits_active_learning_thumb.png" />
-<p><a class="reference internal" href="http://github.com/auto_examples/semi_supervised/plot_label_propagation_digits_active_learning.html#sphx-glr-auto-examples-semi-supervised-plot-label-propagation-digits-active-learning-py"><span class="std std-ref">Label Propagation digits active learning</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Label Propagation digits active learning</div>
+<p><a class="reference internal" href="http://github.com/auto_examples/semi_supervised/plot_label_propagation_digits_active_learning.html#sphx-glr-auto-examples-semi-supervised-plot-label-propagation-digits-active-learning-py"><span class="std std-ref">Label Propagation digits: Active learning</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Label Propagation digits: Active learning</div>
 </div></div></section>
 </section>
 
diff --git a/dev/modules/generated/sklearn.metrics.cluster.contingency_matrix.html b/dev/modules/generated/sklearn.metrics.cluster.contingency_matrix.html
index 3282f72a6ad41..6489a57a5b556 100644
--- a/dev/modules/generated/sklearn.metrics.cluster.contingency_matrix.html
+++ b/dev/modules/generated/sklearn.metrics.cluster.contingency_matrix.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1306,7 +1306,7 @@
 <h1>contingency_matrix<a class="headerlink" href="#contingency-matrix" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.metrics.cluster.contingency_matrix">
-<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.cluster.</span></span><span class="sig-name descname"><span class="pre">contingency_matrix</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">labels_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels_pred</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sparse=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype=&lt;class</span> <span class="pre">'numpy.int64'&gt;</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/cluster/_supervised.py#L87"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.cluster.contingency_matrix" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.cluster.</span></span><span class="sig-name descname"><span class="pre">contingency_matrix</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">labels_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels_pred</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sparse=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype=&lt;class</span> <span class="pre">'numpy.int64'&gt;</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/cluster/_supervised.py#L86"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.cluster.contingency_matrix" title="Link to this definition">#</a></dt>
 <dd><p>Build a contingency matrix describing the relationship between labels.</p>
 <p>Read more in the <a class="reference internal" href="http://github.com/clustering.html#contingency-matrix"><span class="std std-ref">User Guide</span></a>.</p>
 <dl class="field-list">
diff --git a/dev/modules/generated/sklearn.metrics.cluster.pair_confusion_matrix.html b/dev/modules/generated/sklearn.metrics.cluster.pair_confusion_matrix.html
index c8c0cd9dac8f6..072605b512e95 100644
--- a/dev/modules/generated/sklearn.metrics.cluster.pair_confusion_matrix.html
+++ b/dev/modules/generated/sklearn.metrics.cluster.pair_confusion_matrix.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1306,7 +1306,7 @@
 <h1>pair_confusion_matrix<a class="headerlink" href="#pair-confusion-matrix" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.metrics.cluster.pair_confusion_matrix">
-<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.cluster.</span></span><span class="sig-name descname"><span class="pre">pair_confusion_matrix</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">labels_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels_pred</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/cluster/_supervised.py#L178"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.cluster.pair_confusion_matrix" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.cluster.</span></span><span class="sig-name descname"><span class="pre">pair_confusion_matrix</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">labels_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels_pred</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/cluster/_supervised.py#L177"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.cluster.pair_confusion_matrix" title="Link to this definition">#</a></dt>
 <dd><p>Pair confusion matrix arising from two clusterings.</p>
 <p>The pair confusion matrix <span class="math notranslate nohighlight">\(C\)</span> computes a 2 by 2 similarity matrix
 between two clusterings by considering all pairs of samples and counting
diff --git a/dev/modules/generated/sklearn.metrics.cohen_kappa_score.html b/dev/modules/generated/sklearn.metrics.cohen_kappa_score.html
index fea7bc21319d7..ec9fb6073f358 100644
--- a/dev/modules/generated/sklearn.metrics.cohen_kappa_score.html
+++ b/dev/modules/generated/sklearn.metrics.cohen_kappa_score.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1306,7 +1306,7 @@
 <h1>cohen_kappa_score<a class="headerlink" href="#cohen-kappa-score" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.metrics.cohen_kappa_score">
-<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">cohen_kappa_score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y2</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/_classification.py#L795"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.cohen_kappa_score" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">cohen_kappa_score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y2</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/_classification.py#L793"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.cohen_kappa_score" title="Link to this definition">#</a></dt>
 <dd><p>Compute Cohen’s kappa: a statistic that measures inter-annotator agreement.</p>
 <p>This function computes Cohen’s kappa <a class="reference internal" href="#r219a3b9132e1-1" id="id1">[1]</a>, a score that expresses the level
 of agreement between two annotators on a classification problem. It is
@@ -1319,9 +1319,9 @@ <h1>cohen_kappa_score<a class="headerlink" href="#cohen-kappa-score" title="Link
 <span class="math notranslate nohighlight">\(p_e\)</span> is estimated using a per-annotator empirical prior over the
 class labels <a class="reference internal" href="#r219a3b9132e1-2" id="id2">[2]</a>.</p>
 <p>Read more in the <a class="reference internal" href="http://github.com/model_evaluation.html#cohen-kappa"><span class="std std-ref">User Guide</span></a>.</p>
-<dl class="field-list simple">
+<dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><dl class="simple">
+<dd class="field-odd"><dl>
 <dt><strong>y1</strong><span class="classifier">array-like of shape (n_samples,)</span></dt><dd><p>Labels assigned by the first annotator.</p>
 </dd>
 <dt><strong>y2</strong><span class="classifier">array-like of shape (n_samples,)</span></dt><dd><p>Labels assigned by the second annotator. The kappa statistic is
@@ -1329,7 +1329,11 @@ <h1>cohen_kappa_score<a class="headerlink" href="#cohen-kappa-score" title="Link
 </dd>
 <dt><strong>labels</strong><span class="classifier">array-like of shape (n_classes,), default=None</span></dt><dd><p>List of labels to index the matrix. This may be used to select a
 subset of labels. If <code class="docutils literal notranslate"><span class="pre">None</span></code>, all labels that appear at least once in
-<code class="docutils literal notranslate"><span class="pre">y1</span></code> or <code class="docutils literal notranslate"><span class="pre">y2</span></code> are used.</p>
+<code class="docutils literal notranslate"><span class="pre">y1</span></code> or <code class="docutils literal notranslate"><span class="pre">y2</span></code> are used. Note that at least one label in <code class="docutils literal notranslate"><span class="pre">labels</span></code> must be</p>
+<blockquote>
+<div><p>present in <code class="docutils literal notranslate"><span class="pre">y1</span></code>, even though this function is otherwise agnostic to the order
+of <code class="docutils literal notranslate"><span class="pre">y1</span></code> and <code class="docutils literal notranslate"><span class="pre">y2</span></code>.</p>
+</div></blockquote>
 </dd>
 <dt><strong>weights</strong><span class="classifier">{‘linear’, ‘quadratic’}, default=None</span></dt><dd><p>Weighting type to calculate the score. <code class="docutils literal notranslate"><span class="pre">None</span></code> means not weighted;
 “linear” means linear weighting; “quadratic” means quadratic weighting.</p>
diff --git a/dev/modules/generated/sklearn.metrics.completeness_score.html b/dev/modules/generated/sklearn.metrics.completeness_score.html
index 09935fb04821c..9ce2bd1ff3722 100644
--- a/dev/modules/generated/sklearn.metrics.completeness_score.html
+++ b/dev/modules/generated/sklearn.metrics.completeness_score.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>completeness_score<a class="headerlink" href="#completeness-score" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.metrics.completeness_score">
-<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">completeness_score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">labels_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels_pred</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/cluster/_supervised.py#L632"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.completeness_score" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">completeness_score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">labels_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels_pred</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/cluster/_supervised.py#L631"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.completeness_score" title="Link to this definition">#</a></dt>
 <dd><p>Compute completeness metric of a cluster labeling given a ground truth.</p>
 <p>A clustering result satisfies completeness if all the data points
 that are members of a given class are elements of the same cluster.</p>
@@ -1363,7 +1363,7 @@ <h1>completeness_score<a class="headerlink" href="#completeness-score" title="Li
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">completeness_score</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]))</span>
 <span class="go">1.0</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">completeness_score</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]))</span>
-<span class="go">0.999...</span>
+<span class="go">0.999</span>
 </pre></div>
 </div>
 <p>If classes members are split across different clusters, the
diff --git a/dev/modules/generated/sklearn.metrics.confusion_matrix.html b/dev/modules/generated/sklearn.metrics.confusion_matrix.html
index 5e3ba50df2790..2e079686f3a19 100644
--- a/dev/modules/generated/sklearn.metrics.confusion_matrix.html
+++ b/dev/modules/generated/sklearn.metrics.confusion_matrix.html
@@ -11,10 +11,10 @@
 <meta property="og:type" content="website" />
 <meta property="og:url" content="https://scikit-learn/stable/modules/generated/sklearn.metrics.confusion_matrix.html" />
 <meta property="og:site_name" content="scikit-learn" />
-<meta property="og:description" content="Gallery examples: Visualizations with Display Objects Post-tuning the decision threshold for cost-sensitive learning Release Highlights for scikit-learn 1.5 Label Propagation digits active learning" />
+<meta property="og:description" content="Gallery examples: Visualizations with Display Objects Post-tuning the decision threshold for cost-sensitive learning Release Highlights for scikit-learn 1.5 Label Propagation digits: Active learning" />
 <meta property="og:image" content="https://scikit-learn/stable/_images/sphx_glr_plot_display_object_visualization_thumb.png" />
 <meta property="og:image:alt" content="" />
-<meta name="description" content="Gallery examples: Visualizations with Display Objects Post-tuning the decision threshold for cost-sensitive learning Release Highlights for scikit-learn 1.5 Label Propagation digits active learning" />
+<meta name="description" content="Gallery examples: Visualizations with Display Objects Post-tuning the decision threshold for cost-sensitive learning Release Highlights for scikit-learn 1.5 Label Propagation digits: Active learning" />
 
     <title>confusion_matrix &#8212; scikit-learn 1.7.dev0 documentation</title>
   
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1308,7 +1308,7 @@
 <h1>confusion_matrix<a class="headerlink" href="#confusion-matrix" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.metrics.confusion_matrix">
-<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">confusion_matrix</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_pred</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">normalize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/_classification.py#L372"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.confusion_matrix" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">confusion_matrix</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_pred</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">normalize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/_classification.py#L371"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.confusion_matrix" title="Link to this definition">#</a></dt>
 <dd><p>Compute confusion matrix to evaluate the accuracy of a classification.</p>
 <p>By definition a confusion matrix <span class="math notranslate nohighlight">\(C\)</span> is such that <span class="math notranslate nohighlight">\(C_{i, j}\)</span>
 is equal to the number of observations known to be in group <span class="math notranslate nohighlight">\(i\)</span> and
@@ -1408,8 +1408,8 @@ <h2>Gallery examples<a class="headerlink" href="#gallery-examples" title="Link t
 <p><a class="reference internal" href="http://github.com/auto_examples/release_highlights/plot_release_highlights_1_5_0.html#sphx-glr-auto-examples-release-highlights-plot-release-highlights-1-5-0-py"><span class="std std-ref">Release Highlights for scikit-learn 1.5</span></a></p>
   <div class="sphx-glr-thumbnail-title">Release Highlights for scikit-learn 1.5</div>
 </div><div class="sphx-glr-thumbcontainer" tooltip="Demonstrates an active learning technique to learn handwritten digits using label propagation."><img alt="" src="http://github.com/_images/sphx_glr_plot_label_propagation_digits_active_learning_thumb.png" />
-<p><a class="reference internal" href="http://github.com/auto_examples/semi_supervised/plot_label_propagation_digits_active_learning.html#sphx-glr-auto-examples-semi-supervised-plot-label-propagation-digits-active-learning-py"><span class="std std-ref">Label Propagation digits active learning</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Label Propagation digits active learning</div>
+<p><a class="reference internal" href="http://github.com/auto_examples/semi_supervised/plot_label_propagation_digits_active_learning.html#sphx-glr-auto-examples-semi-supervised-plot-label-propagation-digits-active-learning-py"><span class="std std-ref">Label Propagation digits: Active learning</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Label Propagation digits: Active learning</div>
 </div></div></section>
 </section>
 
diff --git a/dev/modules/generated/sklearn.metrics.consensus_score.html b/dev/modules/generated/sklearn.metrics.consensus_score.html
index 87ba6f90a939c..442ea89537287 100644
--- a/dev/modules/generated/sklearn.metrics.consensus_score.html
+++ b/dev/modules/generated/sklearn.metrics.consensus_score.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>consensus_score<a class="headerlink" href="#consensus-score" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.metrics.consensus_score">
-<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">consensus_score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">a</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">b</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">similarity</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'jaccard'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/cluster/_bicluster.py#L52"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.consensus_score" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">consensus_score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">a</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">b</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">similarity</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'jaccard'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/cluster/_bicluster.py#L52"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.consensus_score" title="Link to this definition">#</a></dt>
 <dd><p>The similarity of two sets of biclusters.</p>
 <p>Similarity between individual biclusters is computed. Then the best
 matching between sets is found by solving a linear sum assignment problem,
diff --git a/dev/modules/generated/sklearn.metrics.coverage_error.html b/dev/modules/generated/sklearn.metrics.coverage_error.html
index 36d35c2a96293..2bb656f7eebbf 100644
--- a/dev/modules/generated/sklearn.metrics.coverage_error.html
+++ b/dev/modules/generated/sklearn.metrics.coverage_error.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>coverage_error<a class="headerlink" href="#coverage-error" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.metrics.coverage_error">
-<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">coverage_error</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_score</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/_ranking.py#L1268"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.coverage_error" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">coverage_error</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_score</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/_ranking.py#L1309"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.coverage_error" title="Link to this definition">#</a></dt>
 <dd><p>Coverage error measure.</p>
 <p>Compute how far we need to go through the ranked scores to cover all
 true labels. The best value is equal to the average number
diff --git a/dev/modules/generated/sklearn.metrics.d2_absolute_error_score.html b/dev/modules/generated/sklearn.metrics.d2_absolute_error_score.html
index e860a3acac960..8d1cb3724b13d 100644
--- a/dev/modules/generated/sklearn.metrics.d2_absolute_error_score.html
+++ b/dev/modules/generated/sklearn.metrics.d2_absolute_error_score.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1306,7 +1306,7 @@
 <h1>d2_absolute_error_score<a class="headerlink" href="#d2-absolute-error-score" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.metrics.d2_absolute_error_score">
-<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">d2_absolute_error_score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_pred</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">multioutput</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'uniform_average'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/_regression.py#L1836"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.d2_absolute_error_score" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">d2_absolute_error_score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_pred</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">multioutput</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'uniform_average'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/_regression.py#L1836"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.d2_absolute_error_score" title="Link to this definition">#</a></dt>
 <dd><p><span class="math notranslate nohighlight">\(D^2\)</span> regression score function, fraction of absolute error explained.</p>
 <p>Best possible score is 1.0 and it can be negative (because the model can be
 arbitrarily worse). A model that always uses the empirical median of <code class="docutils literal notranslate"><span class="pre">y_true</span></code>
diff --git a/dev/modules/generated/sklearn.metrics.d2_log_loss_score.html b/dev/modules/generated/sklearn.metrics.d2_log_loss_score.html
index b5ffac453d9e8..381e11f5809f2 100644
--- a/dev/modules/generated/sklearn.metrics.d2_log_loss_score.html
+++ b/dev/modules/generated/sklearn.metrics.d2_log_loss_score.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1306,7 +1306,7 @@
 <h1>d2_log_loss_score<a class="headerlink" href="#d2-log-loss-score" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.metrics.d2_log_loss_score">
-<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">d2_log_loss_score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_pred</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/_classification.py#L3617"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.d2_log_loss_score" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">d2_log_loss_score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_pred</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/_classification.py#L3603"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.d2_log_loss_score" title="Link to this definition">#</a></dt>
 <dd><p><span class="math notranslate nohighlight">\(D^2\)</span> score function, fraction of log loss explained.</p>
 <p>Best possible score is 1.0 and it can be negative (because the model can be
 arbitrarily worse). A model that always predicts the per-class proportions
diff --git a/dev/modules/generated/sklearn.metrics.d2_pinball_score.html b/dev/modules/generated/sklearn.metrics.d2_pinball_score.html
index 4faf26455851e..84a4d8020dced 100644
--- a/dev/modules/generated/sklearn.metrics.d2_pinball_score.html
+++ b/dev/modules/generated/sklearn.metrics.d2_pinball_score.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1306,7 +1306,7 @@
 <h1>d2_pinball_score<a class="headerlink" href="#d2-pinball-score" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.metrics.d2_pinball_score">
-<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">d2_pinball_score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_pred</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">multioutput</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'uniform_average'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/_regression.py#L1684"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.d2_pinball_score" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">d2_pinball_score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_pred</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">multioutput</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'uniform_average'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/_regression.py#L1684"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.d2_pinball_score" title="Link to this definition">#</a></dt>
 <dd><p><span class="math notranslate nohighlight">\(D^2\)</span> regression score function, fraction of pinball loss explained.</p>
 <p>Best possible score is 1.0 and it can be negative (because the model can be
 arbitrarily worse). A model that always uses the empirical alpha-quantile of
diff --git a/dev/modules/generated/sklearn.metrics.d2_tweedie_score.html b/dev/modules/generated/sklearn.metrics.d2_tweedie_score.html
index d21a84ce68549..8848eed0e0f5f 100644
--- a/dev/modules/generated/sklearn.metrics.d2_tweedie_score.html
+++ b/dev/modules/generated/sklearn.metrics.d2_tweedie_score.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1306,7 +1306,7 @@
 <h1>d2_tweedie_score<a class="headerlink" href="#d2-tweedie-score" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.metrics.d2_tweedie_score">
-<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">d2_tweedie_score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_pred</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">power</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/_regression.py#L1569"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.d2_tweedie_score" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">d2_tweedie_score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_pred</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">power</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/_regression.py#L1569"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.d2_tweedie_score" title="Link to this definition">#</a></dt>
 <dd><p><span class="math notranslate nohighlight">\(D^2\)</span> regression score function, fraction of Tweedie deviance explained.</p>
 <p>Best possible score is 1.0 and it can be negative (because the model can be
 arbitrarily worse). A model that always uses the empirical mean of <code class="docutils literal notranslate"><span class="pre">y_true</span></code> as
@@ -1346,7 +1346,7 @@ <h1>d2_tweedie_score<a class="headerlink" href="#d2-tweedie-score" title="Link t
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><dl class="simple">
-<dt><strong>z</strong><span class="classifier">float or ndarray of floats</span></dt><dd><p>The D^2 score.</p>
+<dt><strong>z</strong><span class="classifier">float</span></dt><dd><p>The D^2 score.</p>
 </dd>
 </dl>
 </dd>
diff --git a/dev/modules/generated/sklearn.metrics.davies_bouldin_score.html b/dev/modules/generated/sklearn.metrics.davies_bouldin_score.html
index 8c77c2748f799..e36a3c88eb48d 100644
--- a/dev/modules/generated/sklearn.metrics.davies_bouldin_score.html
+++ b/dev/modules/generated/sklearn.metrics.davies_bouldin_score.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>davies_bouldin_score<a class="headerlink" href="#davies-bouldin-score" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.metrics.davies_bouldin_score">
-<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">davies_bouldin_score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/cluster/_unsupervised.py#L390"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.davies_bouldin_score" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">davies_bouldin_score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/cluster/_unsupervised.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.davies_bouldin_score" title="Link to this definition">#</a></dt>
 <dd><p>Compute the Davies-Bouldin score.</p>
 <p>The score is defined as the average similarity measure of each cluster with
 its most similar cluster, where similarity is the ratio of within-cluster
diff --git a/dev/modules/generated/sklearn.metrics.dcg_score.html b/dev/modules/generated/sklearn.metrics.dcg_score.html
index 191e46abde3fc..3454d271571d2 100644
--- a/dev/modules/generated/sklearn.metrics.dcg_score.html
+++ b/dev/modules/generated/sklearn.metrics.dcg_score.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>dcg_score<a class="headerlink" href="#dcg-score" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.metrics.dcg_score">
-<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">dcg_score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_score</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">k</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">log_base</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ignore_ties</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/_ranking.py#L1570"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.dcg_score" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">dcg_score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_score</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">k</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">log_base</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ignore_ties</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/_ranking.py#L1611"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.dcg_score" title="Link to this definition">#</a></dt>
 <dd><p>Compute Discounted Cumulative Gain.</p>
 <p>Sum the true scores ranked in the order induced by the predicted scores,
 after applying a logarithmic discount.</p>
@@ -1370,10 +1370,10 @@ <h1>dcg_score<a class="headerlink" href="#dcg-score" title="Link to this heading
 <span class="gp">&gt;&gt;&gt; </span><span class="c1"># we predict scores for the answers</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">scores</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">([[</span><span class="mf">.1</span><span class="p">,</span> <span class="mf">.2</span><span class="p">,</span> <span class="mf">.3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">70</span><span class="p">]])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">dcg_score</span><span class="p">(</span><span class="n">true_relevance</span><span class="p">,</span> <span class="n">scores</span><span class="p">)</span>
-<span class="go">9.49...</span>
+<span class="go">9.49</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="c1"># we can set k to truncate the sum; only top k answers contribute</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">dcg_score</span><span class="p">(</span><span class="n">true_relevance</span><span class="p">,</span> <span class="n">scores</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
-<span class="go">5.63...</span>
+<span class="go">5.63</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="c1"># now we have some ties in our prediction</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">scores</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="c1"># by default ties are averaged, so here we get the average true</span>
diff --git a/dev/modules/generated/sklearn.metrics.det_curve.html b/dev/modules/generated/sklearn.metrics.det_curve.html
index e2aaecd37a586..9f50b83c902e1 100644
--- a/dev/modules/generated/sklearn.metrics.det_curve.html
+++ b/dev/modules/generated/sklearn.metrics.det_curve.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,8 +1307,8 @@
 <h1>det_curve<a class="headerlink" href="#det-curve" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.metrics.det_curve">
-<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">det_curve</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_score</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pos_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/_ranking.py#L268"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.det_curve" title="Link to this definition">#</a></dt>
-<dd><p>Compute error rates for different probability thresholds.</p>
+<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">det_curve</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_score</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pos_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_intermediate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/_ranking.py#L267"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.det_curve" title="Link to this definition">#</a></dt>
+<dd><p>Compute Detection Error Tradeoff (DET) for different probability thresholds.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
 <p>This metric is used for evaluation of ranking and error tradeoffs of
@@ -1318,9 +1318,14 @@ <h1>det_curve<a class="headerlink" href="#det-curve" title="Link to this heading
 <div class="versionadded">
 <p><span class="versionmodified added">Added in version 0.24.</span></p>
 </div>
-<dl class="field-list simple">
+<div class="versionchanged">
+<p><span class="versionmodified changed">Changed in version 1.7: </span>An arbitrary threshold at infinity is added to represent a classifier
+that always predicts the negative class, i.e. <code class="docutils literal notranslate"><span class="pre">fpr=0</span></code> and <code class="docutils literal notranslate"><span class="pre">fnr=1</span></code>, unless
+<code class="docutils literal notranslate"><span class="pre">fpr=0</span></code> is already reached at a finite threshold.</p>
+</div>
+<dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><dl class="simple">
+<dd class="field-odd"><dl>
 <dt><strong>y_true</strong><span class="classifier">ndarray of shape (n_samples,)</span></dt><dd><p>True binary labels. If labels are not either {-1, 1} or {0, 1}, then
 pos_label should be explicitly given.</p>
 </dd>
@@ -1336,10 +1341,17 @@ <h1>det_curve<a class="headerlink" href="#det-curve" title="Link to this heading
 </dd>
 <dt><strong>sample_weight</strong><span class="classifier">array-like of shape (n_samples,), default=None</span></dt><dd><p>Sample weights.</p>
 </dd>
+<dt><strong>drop_intermediate</strong><span class="classifier">bool, default=False</span></dt><dd><p>Whether to drop thresholds where true positives (tp) do not change from
+the previous or subsequent threshold. All points with the same tp value
+have the same <code class="docutils literal notranslate"><span class="pre">fnr</span></code> and thus same y coordinate.</p>
+<div class="versionadded">
+<p><span class="versionmodified added">Added in version 1.7.</span></p>
+</div>
+</dd>
 </dl>
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><dl class="simple">
+<dd class="field-even"><dl>
 <dt><strong>fpr</strong><span class="classifier">ndarray of shape (n_thresholds,)</span></dt><dd><p>False positive rate (FPR) such that element i is the false positive
 rate of predictions with score &gt;= thresholds[i]. This is occasionally
 referred to as false acceptance probability or fall-out.</p>
@@ -1348,7 +1360,12 @@ <h1>det_curve<a class="headerlink" href="#det-curve" title="Link to this heading
 rate of predictions with score &gt;= thresholds[i]. This is occasionally
 referred to as false rejection or miss rate.</p>
 </dd>
-<dt><strong>thresholds</strong><span class="classifier">ndarray of shape (n_thresholds,)</span></dt><dd><p>Decreasing score values.</p>
+<dt><strong>thresholds</strong><span class="classifier">ndarray of shape (n_thresholds,)</span></dt><dd><p>Decreasing thresholds on the decision function (either <code class="docutils literal notranslate"><span class="pre">predict_proba</span></code>
+or <code class="docutils literal notranslate"><span class="pre">decision_function</span></code>) used to compute FPR and FNR.</p>
+<div class="versionchanged">
+<p><span class="versionmodified changed">Changed in version 1.7: </span>An arbitrary threshold at infinity is added for the case <code class="docutils literal notranslate"><span class="pre">fpr=0</span></code>
+and <code class="docutils literal notranslate"><span class="pre">fnr=1</span></code>.</p>
+</div>
 </dd>
 </dl>
 </dd>
diff --git a/dev/modules/generated/sklearn.metrics.explained_variance_score.html b/dev/modules/generated/sklearn.metrics.explained_variance_score.html
index 6dba0b7248f17..effaea8c2f7af 100644
--- a/dev/modules/generated/sklearn.metrics.explained_variance_score.html
+++ b/dev/modules/generated/sklearn.metrics.explained_variance_score.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1306,7 +1306,7 @@
 <h1>explained_variance_score<a class="headerlink" href="#explained-variance-score" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.metrics.explained_variance_score">
-<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">explained_variance_score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_pred</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">multioutput</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'uniform_average'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">force_finite</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/_regression.py#L985"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.explained_variance_score" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">explained_variance_score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_pred</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">multioutput</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'uniform_average'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">force_finite</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/_regression.py#L985"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.explained_variance_score" title="Link to this definition">#</a></dt>
 <dd><p>Explained variance regression score function.</p>
 <p>Best possible score is 1.0, lower values are worse.</p>
 <p>In the particular case when <code class="docutils literal notranslate"><span class="pre">y_true</span></code> is constant, the explained variance
diff --git a/dev/modules/generated/sklearn.metrics.f1_score.html b/dev/modules/generated/sklearn.metrics.f1_score.html
index 4e92e3a151c55..f57e9d5abfd1c 100644
--- a/dev/modules/generated/sklearn.metrics.f1_score.html
+++ b/dev/modules/generated/sklearn.metrics.f1_score.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1308,7 +1308,7 @@
 <h1>f1_score<a class="headerlink" href="#f1-score" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.metrics.f1_score">
-<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">f1_score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_pred</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pos_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'binary'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zero_division</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'warn'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/_classification.py#L1273"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.f1_score" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">f1_score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_pred</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pos_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'binary'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zero_division</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'warn'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/_classification.py#L1284"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.f1_score" title="Link to this definition">#</a></dt>
 <dd><p>Compute the F1 score, also known as balanced F-score or F-measure.</p>
 <p>The F1 score can be interpreted as a harmonic mean of the precision and
 recall, where an F1 score reaches its best value at 1 and worst score at 0.
@@ -1430,11 +1430,11 @@ <h1>f1_score<a class="headerlink" href="#f1-score" title="Link to this heading">
 <span class="gp">&gt;&gt;&gt; </span><span class="n">y_true</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">y_pred</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">f1_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">,</span> <span class="n">average</span><span class="o">=</span><span class="s1">&#39;macro&#39;</span><span class="p">)</span>
-<span class="go">0.26...</span>
+<span class="go">0.267</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">f1_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">,</span> <span class="n">average</span><span class="o">=</span><span class="s1">&#39;micro&#39;</span><span class="p">)</span>
-<span class="go">0.33...</span>
+<span class="go">0.33</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">f1_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">,</span> <span class="n">average</span><span class="o">=</span><span class="s1">&#39;weighted&#39;</span><span class="p">)</span>
-<span class="go">0.26...</span>
+<span class="go">0.267</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">f1_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">,</span> <span class="n">average</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
 <span class="go">array([0.8, 0. , 0. ])</span>
 </pre></div>
diff --git a/dev/modules/generated/sklearn.metrics.fbeta_score.html b/dev/modules/generated/sklearn.metrics.fbeta_score.html
index 9d76b7424bd45..89db8a9b0a8ef 100644
--- a/dev/modules/generated/sklearn.metrics.fbeta_score.html
+++ b/dev/modules/generated/sklearn.metrics.fbeta_score.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1306,7 +1306,7 @@
 <h1>fbeta_score<a class="headerlink" href="#fbeta-score" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.metrics.fbeta_score">
-<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">fbeta_score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_pred</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">beta</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pos_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'binary'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zero_division</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'warn'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/_classification.py#L1465"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.fbeta_score" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">fbeta_score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_pred</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">beta</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pos_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'binary'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zero_division</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'warn'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/_classification.py#L1476"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.fbeta_score" title="Link to this definition">#</a></dt>
 <dd><p>Compute the F-beta score.</p>
 <p>The F-beta score is the weighted harmonic mean of precision and recall,
 reaching its optimal value at 1 and its worst value at 0.</p>
@@ -1434,17 +1434,17 @@ <h1>fbeta_score<a class="headerlink" href="#fbeta-score" title="Link to this hea
 <span class="gp">&gt;&gt;&gt; </span><span class="n">y_true</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">y_pred</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">fbeta_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">,</span> <span class="n">average</span><span class="o">=</span><span class="s1">&#39;macro&#39;</span><span class="p">,</span> <span class="n">beta</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
-<span class="go">0.23...</span>
+<span class="go">0.238</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">fbeta_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">,</span> <span class="n">average</span><span class="o">=</span><span class="s1">&#39;micro&#39;</span><span class="p">,</span> <span class="n">beta</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
-<span class="go">0.33...</span>
+<span class="go">0.33</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">fbeta_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">,</span> <span class="n">average</span><span class="o">=</span><span class="s1">&#39;weighted&#39;</span><span class="p">,</span> <span class="n">beta</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
-<span class="go">0.23...</span>
+<span class="go">0.238</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">fbeta_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">,</span> <span class="n">average</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">beta</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
-<span class="go">array([0.71..., 0.        , 0.        ])</span>
+<span class="go">array([0.71, 0.        , 0.        ])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">y_pred_empty</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">fbeta_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred_empty</span><span class="p">,</span>
 <span class="gp">... </span>            <span class="n">average</span><span class="o">=</span><span class="s2">&quot;macro&quot;</span><span class="p">,</span> <span class="n">zero_division</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">,</span> <span class="n">beta</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
-<span class="go">0.12...</span>
+<span class="go">0.128</span>
 </pre></div>
 </div>
 </dd></dl>
diff --git a/dev/modules/generated/sklearn.metrics.fowlkes_mallows_score.html b/dev/modules/generated/sklearn.metrics.fowlkes_mallows_score.html
index b482db8f94a71..c6ba2b18a4795 100644
--- a/dev/modules/generated/sklearn.metrics.fowlkes_mallows_score.html
+++ b/dev/modules/generated/sklearn.metrics.fowlkes_mallows_score.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>fowlkes_mallows_score<a class="headerlink" href="#fowlkes-mallows-score" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.metrics.fowlkes_mallows_score">
-<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">fowlkes_mallows_score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">labels_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels_pred</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sparse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/cluster/_supervised.py#L1178"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.fowlkes_mallows_score" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">fowlkes_mallows_score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">labels_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels_pred</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sparse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'deprecated'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/cluster/_supervised.py#L1177"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.fowlkes_mallows_score" title="Link to this definition">#</a></dt>
 <dd><p>Measure the similarity of two clusterings of a set of points.</p>
 <div class="versionadded">
 <p><span class="versionmodified added">Added in version 0.18.</span></p>
@@ -1325,14 +1325,18 @@ <h1>fowlkes_mallows_score<a class="headerlink" href="#fowlkes-mallows-score" tit
 <p>The score ranges from 0 to 1. A high value indicates a good similarity
 between two clusters.</p>
 <p>Read more in the <a class="reference internal" href="http://github.com/clustering.html#fowlkes-mallows-scores"><span class="std std-ref">User Guide</span></a>.</p>
-<dl class="field-list simple">
+<dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><dl class="simple">
+<dd class="field-odd"><dl>
 <dt><strong>labels_true</strong><span class="classifier">array-like of shape (n_samples,), dtype=int</span></dt><dd><p>A clustering of the data into disjoint subsets.</p>
 </dd>
 <dt><strong>labels_pred</strong><span class="classifier">array-like of shape (n_samples,), dtype=int</span></dt><dd><p>A clustering of the data into disjoint subsets.</p>
 </dd>
 <dt><strong>sparse</strong><span class="classifier">bool, default=False</span></dt><dd><p>Compute contingency matrix internally with sparse matrix.</p>
+<div class="deprecated">
+<p><span class="versionmodified deprecated">Deprecated since version 1.7: </span>The <code class="docutils literal notranslate"><span class="pre">sparse</span></code> parameter is deprecated and will be removed in 1.9. It has
+no effect.</p>
+</div>
 </dd>
 </dl>
 </dd>
diff --git a/dev/modules/generated/sklearn.metrics.get_scorer.html b/dev/modules/generated/sklearn.metrics.get_scorer.html
index 43b72536b4c80..e093f91685a67 100644
--- a/dev/modules/generated/sklearn.metrics.get_scorer.html
+++ b/dev/modules/generated/sklearn.metrics.get_scorer.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>get_scorer<a class="headerlink" href="#get-scorer" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.metrics.get_scorer">
-<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">get_scorer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">scoring</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/_scorer.py#L411"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.get_scorer" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">get_scorer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">scoring</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/_scorer.py#L411"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.get_scorer" title="Link to this definition">#</a></dt>
 <dd><p>Get a scorer from string.</p>
 <p>Read more in the <a class="reference internal" href="http://github.com/model_evaluation.html#scoring-parameter"><span class="std std-ref">User Guide</span></a>.
 <a class="reference internal" href="http://github.com/sklearn.metrics.get_scorer_names.html#sklearn.metrics.get_scorer_names" title="sklearn.metrics.get_scorer_names"><code class="xref py py-func docutils literal notranslate"><span class="pre">get_scorer_names</span></code></a> can be used to retrieve the names
diff --git a/dev/modules/generated/sklearn.metrics.get_scorer_names.html b/dev/modules/generated/sklearn.metrics.get_scorer_names.html
index a8cb3667b87ce..e3beae7b3dbf7 100644
--- a/dev/modules/generated/sklearn.metrics.get_scorer_names.html
+++ b/dev/modules/generated/sklearn.metrics.get_scorer_names.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>get_scorer_names<a class="headerlink" href="#get-scorer-names" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.metrics.get_scorer_names">
-<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">get_scorer_names</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/_scorer.py#L891"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.get_scorer_names" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">get_scorer_names</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/_scorer.py#L891"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.get_scorer_names" title="Link to this definition">#</a></dt>
 <dd><p>Get the names of all available scorers.</p>
 <p>These names can be passed to <a class="reference internal" href="http://github.com/sklearn.metrics.get_scorer.html#sklearn.metrics.get_scorer" title="sklearn.metrics.get_scorer"><code class="xref py py-func docutils literal notranslate"><span class="pre">get_scorer</span></code></a> to
 retrieve the scorer object.</p>
diff --git a/dev/modules/generated/sklearn.metrics.hamming_loss.html b/dev/modules/generated/sklearn.metrics.hamming_loss.html
index 281d4dda20e88..822db02e2f298 100644
--- a/dev/modules/generated/sklearn.metrics.hamming_loss.html
+++ b/dev/modules/generated/sklearn.metrics.hamming_loss.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>hamming_loss<a class="headerlink" href="#hamming-loss" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.metrics.hamming_loss">
-<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">hamming_loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_pred</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/_classification.py#L3041"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.hamming_loss" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">hamming_loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_pred</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/_classification.py#L3027"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.hamming_loss" title="Link to this definition">#</a></dt>
 <dd><p>Compute the average Hamming loss.</p>
 <p>The Hamming loss is the fraction of labels that are incorrectly predicted.</p>
 <p>Read more in the <a class="reference internal" href="http://github.com/model_evaluation.html#hamming-loss"><span class="std std-ref">User Guide</span></a>.</p>
diff --git a/dev/modules/generated/sklearn.metrics.hinge_loss.html b/dev/modules/generated/sklearn.metrics.hinge_loss.html
index f91a4ea6af951..ef5f63d7ea578 100644
--- a/dev/modules/generated/sklearn.metrics.hinge_loss.html
+++ b/dev/modules/generated/sklearn.metrics.hinge_loss.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>hinge_loss<a class="headerlink" href="#hinge-loss" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.metrics.hinge_loss">
-<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">hinge_loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pred_decision</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/_classification.py#L3241"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.hinge_loss" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">hinge_loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pred_decision</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/_classification.py#L3227"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.hinge_loss" title="Link to this definition">#</a></dt>
 <dd><p>Average hinge loss (non-regularized).</p>
 <p>In binary class case, assuming labels in y_true are encoded with +1 and -1,
 when a prediction mistake is made, <code class="docutils literal notranslate"><span class="pre">margin</span> <span class="pre">=</span> <span class="pre">y_true</span> <span class="pre">*</span> <span class="pre">pred_decision</span></code> is
@@ -1370,9 +1370,9 @@ <h1>hinge_loss<a class="headerlink" href="#hinge-loss" title="Link to this headi
 <span class="go">LinearSVC(random_state=0)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">pred_decision</span> <span class="o">=</span> <span class="n">est</span><span class="o">.</span><span class="n">decision_function</span><span class="p">([[</span><span class="o">-</span><span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.5</span><span class="p">]])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">pred_decision</span>
-<span class="go">array([-2.18...,  2.36...,  0.09...])</span>
+<span class="go">array([-2.18,  2.36,  0.09])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">hinge_loss</span><span class="p">([</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">pred_decision</span><span class="p">)</span>
-<span class="go">0.30...</span>
+<span class="go">0.30</span>
 </pre></div>
 </div>
 <p>In the multiclass case:</p>
@@ -1386,7 +1386,7 @@ <h1>hinge_loss<a class="headerlink" href="#hinge-loss" title="Link to this headi
 <span class="gp">&gt;&gt;&gt; </span><span class="n">pred_decision</span> <span class="o">=</span> <span class="n">est</span><span class="o">.</span><span class="n">decision_function</span><span class="p">([[</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">]])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">y_true</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">]</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">hinge_loss</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">pred_decision</span><span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="n">labels</span><span class="p">)</span>
-<span class="go">0.56...</span>
+<span class="go">0.56</span>
 </pre></div>
 </div>
 </dd></dl>
diff --git a/dev/modules/generated/sklearn.metrics.homogeneity_completeness_v_measure.html b/dev/modules/generated/sklearn.metrics.homogeneity_completeness_v_measure.html
index 250b44e7d7048..66436ce97de5c 100644
--- a/dev/modules/generated/sklearn.metrics.homogeneity_completeness_v_measure.html
+++ b/dev/modules/generated/sklearn.metrics.homogeneity_completeness_v_measure.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>homogeneity_completeness_v_measure<a class="headerlink" href="#homogeneity-completeness-v-measure" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.metrics.homogeneity_completeness_v_measure">
-<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">homogeneity_completeness_v_measure</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">labels_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels_pred</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">beta</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/cluster/_supervised.py#L455"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.homogeneity_completeness_v_measure" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">homogeneity_completeness_v_measure</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">labels_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels_pred</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">beta</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/cluster/_supervised.py#L454"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.homogeneity_completeness_v_measure" title="Link to this definition">#</a></dt>
 <dd><p>Compute the homogeneity and completeness and V-Measure scores at once.</p>
 <p>Those metrics are based on normalized conditional entropy measures of
 the clustering labeling to evaluate given the knowledge of a Ground
@@ -1365,7 +1365,7 @@ <h1>homogeneity_completeness_v_measure<a class="headerlink" href="#homogeneity-c
 <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.metrics</span><span class="w"> </span><span class="kn">import</span> <span class="n">homogeneity_completeness_v_measure</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">homogeneity_completeness_v_measure</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">)</span>
-<span class="go">(0.71..., 0.77..., 0.73...)</span>
+<span class="go">(0.71, 0.771, 0.74)</span>
 </pre></div>
 </div>
 </dd></dl>
diff --git a/dev/modules/generated/sklearn.metrics.homogeneity_score.html b/dev/modules/generated/sklearn.metrics.homogeneity_score.html
index c125abda98a0f..7831b84024ffe 100644
--- a/dev/modules/generated/sklearn.metrics.homogeneity_score.html
+++ b/dev/modules/generated/sklearn.metrics.homogeneity_score.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>homogeneity_score<a class="headerlink" href="#homogeneity-score" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.metrics.homogeneity_score">
-<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">homogeneity_score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">labels_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels_pred</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/cluster/_supervised.py#L556"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.homogeneity_score" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">homogeneity_score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">labels_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels_pred</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/cluster/_supervised.py#L555"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.homogeneity_score" title="Link to this definition">#</a></dt>
 <dd><p>Homogeneity metric of a cluster labeling given a ground truth.</p>
 <p>A clustering result satisfies homogeneity if all of its clusters
 contain only data points which are members of a single class.</p>
diff --git a/dev/modules/generated/sklearn.metrics.jaccard_score.html b/dev/modules/generated/sklearn.metrics.jaccard_score.html
index 47c7a5521deb2..316742360f751 100644
--- a/dev/modules/generated/sklearn.metrics.jaccard_score.html
+++ b/dev/modules/generated/sklearn.metrics.jaccard_score.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>jaccard_score<a class="headerlink" href="#jaccard-score" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.metrics.jaccard_score">
-<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">jaccard_score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_pred</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pos_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'binary'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zero_division</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'warn'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/_classification.py#L890"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.jaccard_score" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">jaccard_score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_pred</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pos_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'binary'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zero_division</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'warn'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/_classification.py#L901"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.jaccard_score" title="Link to this definition">#</a></dt>
 <dd><p>Jaccard similarity coefficient score.</p>
 <p>The Jaccard index [1], or Jaccard similarity coefficient, defined as
 the size of the intersection divided by the size of the union of two label
@@ -1416,7 +1416,7 @@ <h1>jaccard_score<a class="headerlink" href="#jaccard-score" title="Link to this
 </div>
 <p>In the binary case:</p>
 <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">jaccard_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">y_pred</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-<span class="go">0.6666...</span>
+<span class="go">0.6666</span>
 </pre></div>
 </div>
 <p>In the 2D comparison case (e.g. image similarity):</p>
@@ -1426,9 +1426,9 @@ <h1>jaccard_score<a class="headerlink" href="#jaccard-score" title="Link to this
 </div>
 <p>In the multilabel case:</p>
 <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">jaccard_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">,</span> <span class="n">average</span><span class="o">=</span><span class="s1">&#39;samples&#39;</span><span class="p">)</span>
-<span class="go">0.5833...</span>
+<span class="go">0.5833</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">jaccard_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">,</span> <span class="n">average</span><span class="o">=</span><span class="s1">&#39;macro&#39;</span><span class="p">)</span>
-<span class="go">0.6666...</span>
+<span class="go">0.6666</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">jaccard_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">,</span> <span class="n">average</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
 <span class="go">array([0.5, 0.5, 1. ])</span>
 </pre></div>
@@ -1437,7 +1437,7 @@ <h1>jaccard_score<a class="headerlink" href="#jaccard-score" title="Link to this
 <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">y_pred</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">y_true</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">jaccard_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">,</span> <span class="n">average</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
-<span class="go">array([1. , 0. , 0.33...])</span>
+<span class="go">array([1. , 0. , 0.33])</span>
 </pre></div>
 </div>
 </dd></dl>
diff --git a/dev/modules/generated/sklearn.metrics.label_ranking_average_precision_score.html b/dev/modules/generated/sklearn.metrics.label_ranking_average_precision_score.html
index 2a53fa10872d2..d4edee635c20a 100644
--- a/dev/modules/generated/sklearn.metrics.label_ranking_average_precision_score.html
+++ b/dev/modules/generated/sklearn.metrics.label_ranking_average_precision_score.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>label_ranking_average_precision_score<a class="headerlink" href="#label-ranking-average-precision-score" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.metrics.label_ranking_average_precision_score">
-<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">label_ranking_average_precision_score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_score</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/_ranking.py#L1167"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.label_ranking_average_precision_score" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">label_ranking_average_precision_score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_score</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/_ranking.py#L1208"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.label_ranking_average_precision_score" title="Link to this definition">#</a></dt>
 <dd><p>Compute ranking-based average precision.</p>
 <p>Label ranking average precision (LRAP) is the average over each ground
 truth label assigned to each sample, of the ratio of true vs. total
@@ -1346,7 +1346,7 @@ <h1>label_ranking_average_precision_score<a class="headerlink" href="#label-rank
 <span class="gp">&gt;&gt;&gt; </span><span class="n">y_true</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">y_score</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">0.75</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">]])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">label_ranking_average_precision_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_score</span><span class="p">)</span>
-<span class="go">0.416...</span>
+<span class="go">0.416</span>
 </pre></div>
 </div>
 </dd></dl>
diff --git a/dev/modules/generated/sklearn.metrics.label_ranking_loss.html b/dev/modules/generated/sklearn.metrics.label_ranking_loss.html
index 26276c34e38f2..d2cea89b3664f 100644
--- a/dev/modules/generated/sklearn.metrics.label_ranking_loss.html
+++ b/dev/modules/generated/sklearn.metrics.label_ranking_loss.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>label_ranking_loss<a class="headerlink" href="#label-ranking-loss" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.metrics.label_ranking_loss">
-<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">label_ranking_loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_score</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/_ranking.py#L1345"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.label_ranking_loss" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">label_ranking_loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_score</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/_ranking.py#L1386"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.label_ranking_loss" title="Link to this definition">#</a></dt>
 <dd><p>Compute Ranking loss measure.</p>
 <p>Compute the average number of label pairs that are incorrectly ordered
 given y_score weighted by the size of the label set and the number of
@@ -1355,7 +1355,7 @@ <h1>label_ranking_loss<a class="headerlink" href="#label-ranking-loss" title="Li
 <span class="gp">&gt;&gt;&gt; </span><span class="n">y_true</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]]</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">y_score</span> <span class="o">=</span> <span class="p">[[</span><span class="mf">0.75</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">]]</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">label_ranking_loss</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_score</span><span class="p">)</span>
-<span class="go">0.75...</span>
+<span class="go">0.75</span>
 </pre></div>
 </div>
 </dd></dl>
diff --git a/dev/modules/generated/sklearn.metrics.log_loss.html b/dev/modules/generated/sklearn.metrics.log_loss.html
index 171f5219433af..573cc2a9aed7f 100644
--- a/dev/modules/generated/sklearn.metrics.log_loss.html
+++ b/dev/modules/generated/sklearn.metrics.log_loss.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1308,7 +1308,7 @@
 <h1>log_loss<a class="headerlink" href="#log-loss" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.metrics.log_loss">
-<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">log_loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_pred</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">normalize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/_classification.py#L3150"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.log_loss" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">log_loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_pred</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">normalize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/_classification.py#L3136"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.log_loss" title="Link to this definition">#</a></dt>
 <dd><p>Log loss, aka logistic loss or cross-entropy loss.</p>
 <p>This is the loss function used in (multinomial) logistic regression
 and extensions of it such as neural networks, defined as the negative
@@ -1365,7 +1365,7 @@ <h1>log_loss<a class="headerlink" href="#log-loss" title="Link to this heading">
 <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.metrics</span><span class="w"> </span><span class="kn">import</span> <span class="n">log_loss</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">log_loss</span><span class="p">([</span><span class="s2">&quot;spam&quot;</span><span class="p">,</span> <span class="s2">&quot;ham&quot;</span><span class="p">,</span> <span class="s2">&quot;ham&quot;</span><span class="p">,</span> <span class="s2">&quot;spam&quot;</span><span class="p">],</span>
 <span class="gp">... </span>         <span class="p">[[</span><span class="mf">.1</span><span class="p">,</span> <span class="mf">.9</span><span class="p">],</span> <span class="p">[</span><span class="mf">.9</span><span class="p">,</span> <span class="mf">.1</span><span class="p">],</span> <span class="p">[</span><span class="mf">.8</span><span class="p">,</span> <span class="mf">.2</span><span class="p">],</span> <span class="p">[</span><span class="mf">.35</span><span class="p">,</span> <span class="mf">.65</span><span class="p">]])</span>
-<span class="go">0.21616...</span>
+<span class="go">0.21616</span>
 </pre></div>
 </div>
 </dd></dl>
diff --git a/dev/modules/generated/sklearn.metrics.make_scorer.html b/dev/modules/generated/sklearn.metrics.make_scorer.html
index bd74ef9bbb921..0253d4b96b7cf 100644
--- a/dev/modules/generated/sklearn.metrics.make_scorer.html
+++ b/dev/modules/generated/sklearn.metrics.make_scorer.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>make_scorer<a class="headerlink" href="#make-scorer" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.metrics.make_scorer">
-<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">make_scorer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">score_func</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">response_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'default'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">greater_is_better</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/_scorer.py#L639"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.make_scorer" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">make_scorer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">score_func</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">response_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'default'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">greater_is_better</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/_scorer.py#L639"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.make_scorer" title="Link to this definition">#</a></dt>
 <dd><p>Make a scorer from a performance metric or loss function.</p>
 <p>A scorer is a wrapper around an arbitrary metric or loss function that is called
 with the signature <code class="docutils literal notranslate"><span class="pre">scorer(estimator,</span> <span class="pre">X,</span> <span class="pre">y_true,</span> <span class="pre">**kwargs)</span></code>.</p>
diff --git a/dev/modules/generated/sklearn.metrics.matthews_corrcoef.html b/dev/modules/generated/sklearn.metrics.matthews_corrcoef.html
index 6c7178375c590..5ff530afb2cee 100644
--- a/dev/modules/generated/sklearn.metrics.matthews_corrcoef.html
+++ b/dev/modules/generated/sklearn.metrics.matthews_corrcoef.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>matthews_corrcoef<a class="headerlink" href="#matthews-corrcoef" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.metrics.matthews_corrcoef">
-<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">matthews_corrcoef</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_pred</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/_classification.py#L1090"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.matthews_corrcoef" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">matthews_corrcoef</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_pred</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/_classification.py#L1101"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.matthews_corrcoef" title="Link to this definition">#</a></dt>
 <dd><p>Compute the Matthews correlation coefficient (MCC).</p>
 <p>The Matthews correlation coefficient is used in machine learning as a
 measure of the quality of binary and multiclass classifications. It takes
@@ -1369,7 +1369,7 @@ <h1>matthews_corrcoef<a class="headerlink" href="#matthews-corrcoef" title="Link
 <span class="gp">&gt;&gt;&gt; </span><span class="n">y_true</span> <span class="o">=</span> <span class="p">[</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span> <span class="o">+</span><span class="mi">1</span><span class="p">,</span> <span class="o">+</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">]</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">y_pred</span> <span class="o">=</span> <span class="p">[</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">+</span><span class="mi">1</span><span class="p">,</span> <span class="o">+</span><span class="mi">1</span><span class="p">]</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">matthews_corrcoef</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">)</span>
-<span class="go">-0.33...</span>
+<span class="go">-0.33</span>
 </pre></div>
 </div>
 </dd></dl>
diff --git a/dev/modules/generated/sklearn.metrics.max_error.html b/dev/modules/generated/sklearn.metrics.max_error.html
index c1c94d146f0d1..21d3a9381c0cb 100644
--- a/dev/modules/generated/sklearn.metrics.max_error.html
+++ b/dev/modules/generated/sklearn.metrics.max_error.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>max_error<a class="headerlink" href="#max-error" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.metrics.max_error">
-<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">max_error</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_pred</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/_regression.py#L1311"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.max_error" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">max_error</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_pred</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/_regression.py#L1311"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.max_error" title="Link to this definition">#</a></dt>
 <dd><p>The max_error metric calculates the maximum residual error.</p>
 <p>Read more in the <a class="reference internal" href="http://github.com/model_evaluation.html#max-error"><span class="std std-ref">User Guide</span></a>.</p>
 <dl class="field-list simple">
diff --git a/dev/modules/generated/sklearn.metrics.mean_absolute_error.html b/dev/modules/generated/sklearn.metrics.mean_absolute_error.html
index f331db4bfee8c..81ca786694ece 100644
--- a/dev/modules/generated/sklearn.metrics.mean_absolute_error.html
+++ b/dev/modules/generated/sklearn.metrics.mean_absolute_error.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>mean_absolute_error<a class="headerlink" href="#mean-absolute-error" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.metrics.mean_absolute_error">
-<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">mean_absolute_error</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_pred</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">multioutput</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'uniform_average'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/_regression.py#L212"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.mean_absolute_error" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">mean_absolute_error</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_pred</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">multioutput</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'uniform_average'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/_regression.py#L212"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.mean_absolute_error" title="Link to this definition">#</a></dt>
 <dd><p>Mean absolute error regression loss.</p>
 <p>The mean absolute error is a non-negative floating point value, where best value
 is 0.0. Read more in the <a class="reference internal" href="http://github.com/model_evaluation.html#mean-absolute-error"><span class="std std-ref">User Guide</span></a>.</p>
diff --git a/dev/modules/generated/sklearn.metrics.mean_absolute_percentage_error.html b/dev/modules/generated/sklearn.metrics.mean_absolute_percentage_error.html
index 98273af577896..3333e0143c54f 100644
--- a/dev/modules/generated/sklearn.metrics.mean_absolute_percentage_error.html
+++ b/dev/modules/generated/sklearn.metrics.mean_absolute_percentage_error.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>mean_absolute_percentage_error<a class="headerlink" href="#mean-absolute-percentage-error" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.metrics.mean_absolute_percentage_error">
-<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">mean_absolute_percentage_error</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_pred</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">multioutput</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'uniform_average'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/_regression.py#L407"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.mean_absolute_percentage_error" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">mean_absolute_percentage_error</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_pred</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">multioutput</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'uniform_average'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/_regression.py#L407"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.mean_absolute_percentage_error" title="Link to this definition">#</a></dt>
 <dd><p>Mean absolute percentage error (MAPE) regression loss.</p>
 <p>Note that we are not using the common “percentage” definition: the percentage
 in the range [0, 100] is converted to a relative value in the range [0, 1]
diff --git a/dev/modules/generated/sklearn.metrics.mean_gamma_deviance.html b/dev/modules/generated/sklearn.metrics.mean_gamma_deviance.html
index bad820d511d91..32fa65e93e4cf 100644
--- a/dev/modules/generated/sklearn.metrics.mean_gamma_deviance.html
+++ b/dev/modules/generated/sklearn.metrics.mean_gamma_deviance.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>mean_gamma_deviance<a class="headerlink" href="#mean-gamma-deviance" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.metrics.mean_gamma_deviance">
-<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">mean_gamma_deviance</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_pred</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/_regression.py#L1525"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.mean_gamma_deviance" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">mean_gamma_deviance</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_pred</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/_regression.py#L1525"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.mean_gamma_deviance" title="Link to this definition">#</a></dt>
 <dd><p>Mean Gamma deviance regression loss.</p>
 <p>Gamma deviance is equivalent to the Tweedie deviance with
 the power parameter <code class="docutils literal notranslate"><span class="pre">power=2</span></code>. It is invariant to scaling of
diff --git a/dev/modules/generated/sklearn.metrics.mean_pinball_loss.html b/dev/modules/generated/sklearn.metrics.mean_pinball_loss.html
index 1611756977971..52fb3becdebdb 100644
--- a/dev/modules/generated/sklearn.metrics.mean_pinball_loss.html
+++ b/dev/modules/generated/sklearn.metrics.mean_pinball_loss.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>mean_pinball_loss<a class="headerlink" href="#mean-pinball-loss" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.metrics.mean_pinball_loss">
-<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">mean_pinball_loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_pred</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">multioutput</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'uniform_average'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/_regression.py#L307"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.mean_pinball_loss" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">mean_pinball_loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_pred</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">multioutput</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'uniform_average'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/_regression.py#L307"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.mean_pinball_loss" title="Link to this definition">#</a></dt>
 <dd><p>Pinball loss for quantile regression.</p>
 <p>Read more in the <a class="reference internal" href="http://github.com/model_evaluation.html#pinball-loss"><span class="std std-ref">User Guide</span></a>.</p>
 <dl class="field-list">
diff --git a/dev/modules/generated/sklearn.metrics.mean_poisson_deviance.html b/dev/modules/generated/sklearn.metrics.mean_poisson_deviance.html
index 787bc49d47cf8..d1ee8a6a574cd 100644
--- a/dev/modules/generated/sklearn.metrics.mean_poisson_deviance.html
+++ b/dev/modules/generated/sklearn.metrics.mean_poisson_deviance.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>mean_poisson_deviance<a class="headerlink" href="#mean-poisson-deviance" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.metrics.mean_poisson_deviance">
-<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">mean_poisson_deviance</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_pred</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/_regression.py#L1482"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.mean_poisson_deviance" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">mean_poisson_deviance</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_pred</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/_regression.py#L1482"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.mean_poisson_deviance" title="Link to this definition">#</a></dt>
 <dd><p>Mean Poisson deviance regression loss.</p>
 <p>Poisson deviance is equivalent to the Tweedie deviance with
 the power parameter <code class="docutils literal notranslate"><span class="pre">power=1</span></code>.</p>
diff --git a/dev/modules/generated/sklearn.metrics.mean_squared_error.html b/dev/modules/generated/sklearn.metrics.mean_squared_error.html
index c1101790c13ba..d85e8d784772e 100644
--- a/dev/modules/generated/sklearn.metrics.mean_squared_error.html
+++ b/dev/modules/generated/sklearn.metrics.mean_squared_error.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>mean_squared_error<a class="headerlink" href="#mean-squared-error" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.metrics.mean_squared_error">
-<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">mean_squared_error</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_pred</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">multioutput</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'uniform_average'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/_regression.py#L514"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.mean_squared_error" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">mean_squared_error</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_pred</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">multioutput</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'uniform_average'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/_regression.py#L514"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.mean_squared_error" title="Link to this definition">#</a></dt>
 <dd><p>Mean squared error regression loss.</p>
 <p>Read more in the <a class="reference internal" href="http://github.com/model_evaluation.html#mean-squared-error"><span class="std std-ref">User Guide</span></a>.</p>
 <dl class="field-list">
diff --git a/dev/modules/generated/sklearn.metrics.mean_squared_log_error.html b/dev/modules/generated/sklearn.metrics.mean_squared_log_error.html
index fd8bb6e6f63a1..d097d96f891a0 100644
--- a/dev/modules/generated/sklearn.metrics.mean_squared_log_error.html
+++ b/dev/modules/generated/sklearn.metrics.mean_squared_log_error.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>mean_squared_log_error<a class="headerlink" href="#mean-squared-log-error" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.metrics.mean_squared_log_error">
-<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">mean_squared_log_error</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_pred</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">multioutput</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'uniform_average'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/_regression.py#L688"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.mean_squared_log_error" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">mean_squared_log_error</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_pred</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">multioutput</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'uniform_average'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/_regression.py#L688"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.mean_squared_log_error" title="Link to this definition">#</a></dt>
 <dd><p>Mean squared logarithmic error regression loss.</p>
 <p>Read more in the <a class="reference internal" href="http://github.com/model_evaluation.html#mean-squared-log-error"><span class="std std-ref">User Guide</span></a>.</p>
 <dl class="field-list">
diff --git a/dev/modules/generated/sklearn.metrics.mean_tweedie_deviance.html b/dev/modules/generated/sklearn.metrics.mean_tweedie_deviance.html
index ab6387571f016..1ace752f20c8c 100644
--- a/dev/modules/generated/sklearn.metrics.mean_tweedie_deviance.html
+++ b/dev/modules/generated/sklearn.metrics.mean_tweedie_deviance.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>mean_tweedie_deviance<a class="headerlink" href="#mean-tweedie-deviance" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.metrics.mean_tweedie_deviance">
-<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">mean_tweedie_deviance</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_pred</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">power</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/_regression.py#L1385"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.mean_tweedie_deviance" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">mean_tweedie_deviance</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_pred</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">power</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/_regression.py#L1385"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.mean_tweedie_deviance" title="Link to this definition">#</a></dt>
 <dd><p>Mean Tweedie deviance regression loss.</p>
 <p>Read more in the <a class="reference internal" href="http://github.com/model_evaluation.html#mean-tweedie-deviance"><span class="std std-ref">User Guide</span></a>.</p>
 <dl class="field-list">
diff --git a/dev/modules/generated/sklearn.metrics.median_absolute_error.html b/dev/modules/generated/sklearn.metrics.median_absolute_error.html
index 46c5fd1bd8465..3d1ec70ec495d 100644
--- a/dev/modules/generated/sklearn.metrics.median_absolute_error.html
+++ b/dev/modules/generated/sklearn.metrics.median_absolute_error.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>median_absolute_error<a class="headerlink" href="#median-absolute-error" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.metrics.median_absolute_error">
-<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">median_absolute_error</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_pred</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">multioutput</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'uniform_average'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/_regression.py#L850"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.median_absolute_error" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">median_absolute_error</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_pred</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">multioutput</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'uniform_average'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/_regression.py#L850"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.median_absolute_error" title="Link to this definition">#</a></dt>
 <dd><p>Median absolute error regression loss.</p>
 <p>Median absolute error output is non-negative floating point. The best value
 is 0.0. Read more in the <a class="reference internal" href="http://github.com/model_evaluation.html#median-absolute-error"><span class="std std-ref">User Guide</span></a>.</p>
diff --git a/dev/modules/generated/sklearn.metrics.multilabel_confusion_matrix.html b/dev/modules/generated/sklearn.metrics.multilabel_confusion_matrix.html
index d62db4da68bc8..0386e40246e17 100644
--- a/dev/modules/generated/sklearn.metrics.multilabel_confusion_matrix.html
+++ b/dev/modules/generated/sklearn.metrics.multilabel_confusion_matrix.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1306,7 +1306,7 @@
 <h1>multilabel_confusion_matrix<a class="headerlink" href="#multilabel-confusion-matrix" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.metrics.multilabel_confusion_matrix">
-<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">multilabel_confusion_matrix</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_pred</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">samplewise</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/_classification.py#L547"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.multilabel_confusion_matrix" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">multilabel_confusion_matrix</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_pred</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">samplewise</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/_classification.py#L546"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.multilabel_confusion_matrix" title="Link to this definition">#</a></dt>
 <dd><p>Compute a confusion matrix for each class or sample.</p>
 <div class="versionadded">
 <p><span class="versionmodified added">Added in version 0.21.</span></p>
diff --git a/dev/modules/generated/sklearn.metrics.mutual_info_score.html b/dev/modules/generated/sklearn.metrics.mutual_info_score.html
index d034eef59c8c0..a943b162ad435 100644
--- a/dev/modules/generated/sklearn.metrics.mutual_info_score.html
+++ b/dev/modules/generated/sklearn.metrics.mutual_info_score.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1308,7 +1308,7 @@
 <h1>mutual_info_score<a class="headerlink" href="#mutual-info-score" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.metrics.mutual_info_score">
-<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">mutual_info_score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">labels_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels_pred</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">contingency</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/cluster/_supervised.py#L812"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.mutual_info_score" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">mutual_info_score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">labels_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels_pred</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">contingency</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/cluster/_supervised.py#L811"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.mutual_info_score" title="Link to this definition">#</a></dt>
 <dd><p>Mutual Information between two clusterings.</p>
 <p>The Mutual Information is a measure of the similarity between two labels
 of the same data. Where <span class="math notranslate nohighlight">\(|U_i|\)</span> is the number of the samples
@@ -1367,7 +1367,7 @@ <h1>mutual_info_score<a class="headerlink" href="#mutual-info-score" title="Link
 <span class="gp">&gt;&gt;&gt; </span><span class="n">labels_true</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">labels_pred</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">mutual_info_score</span><span class="p">(</span><span class="n">labels_true</span><span class="p">,</span> <span class="n">labels_pred</span><span class="p">)</span>
-<span class="go">0.056...</span>
+<span class="go">0.0566</span>
 </pre></div>
 </div>
 </dd></dl>
diff --git a/dev/modules/generated/sklearn.metrics.ndcg_score.html b/dev/modules/generated/sklearn.metrics.ndcg_score.html
index 7f6fb74341c4c..fe0fdf443e2dd 100644
--- a/dev/modules/generated/sklearn.metrics.ndcg_score.html
+++ b/dev/modules/generated/sklearn.metrics.ndcg_score.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>ndcg_score<a class="headerlink" href="#ndcg-score" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.metrics.ndcg_score">
-<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">ndcg_score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_score</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">k</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ignore_ties</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/_ranking.py#L1741"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.ndcg_score" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">ndcg_score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_score</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">k</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ignore_ties</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/_ranking.py#L1782"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.ndcg_score" title="Link to this definition">#</a></dt>
 <dd><p>Compute Normalized Discounted Cumulative Gain.</p>
 <p>Sum the true scores ranked in the order induced by the predicted scores,
 after applying a logarithmic discount. Then divide by the best possible
@@ -1368,13 +1368,13 @@ <h1>ndcg_score<a class="headerlink" href="#ndcg-score" title="Link to this headi
 <span class="gp">&gt;&gt;&gt; </span><span class="c1"># we predict some scores (relevance) for the answers</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">scores</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">([[</span><span class="mf">.1</span><span class="p">,</span> <span class="mf">.2</span><span class="p">,</span> <span class="mf">.3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">70</span><span class="p">]])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">ndcg_score</span><span class="p">(</span><span class="n">true_relevance</span><span class="p">,</span> <span class="n">scores</span><span class="p">)</span>
-<span class="go">0.69...</span>
+<span class="go">0.69</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">scores</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">([[</span><span class="mf">.05</span><span class="p">,</span> <span class="mf">1.1</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">.5</span><span class="p">,</span> <span class="mf">.0</span><span class="p">]])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">ndcg_score</span><span class="p">(</span><span class="n">true_relevance</span><span class="p">,</span> <span class="n">scores</span><span class="p">)</span>
-<span class="go">0.49...</span>
+<span class="go">0.49</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="c1"># we can set k to truncate the sum; only top k answers contribute.</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">ndcg_score</span><span class="p">(</span><span class="n">true_relevance</span><span class="p">,</span> <span class="n">scores</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span>
-<span class="go">0.35...</span>
+<span class="go">0.35</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="c1"># the normalization takes k into account so a perfect answer</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="c1"># would still get 1.0</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">ndcg_score</span><span class="p">(</span><span class="n">true_relevance</span><span class="p">,</span> <span class="n">true_relevance</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span>
@@ -1384,7 +1384,7 @@ <h1>ndcg_score<a class="headerlink" href="#ndcg-score" title="Link to this headi
 <span class="gp">&gt;&gt;&gt; </span><span class="c1"># by default ties are averaged, so here we get the average (normalized)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="c1"># true relevance of our top predictions: (10 / 10 + 5 / 10) / 2 = .75</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">ndcg_score</span><span class="p">(</span><span class="n">true_relevance</span><span class="p">,</span> <span class="n">scores</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-<span class="go">0.75...</span>
+<span class="go">0.75</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="c1"># we can choose to ignore ties for faster results, but only</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="c1"># if we know there aren&#39;t ties in our scores, otherwise we get</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="c1"># wrong results:</span>
diff --git a/dev/modules/generated/sklearn.metrics.normalized_mutual_info_score.html b/dev/modules/generated/sklearn.metrics.normalized_mutual_info_score.html
index 257e96acf8800..79c98222e2e86 100644
--- a/dev/modules/generated/sklearn.metrics.normalized_mutual_info_score.html
+++ b/dev/modules/generated/sklearn.metrics.normalized_mutual_info_score.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>normalized_mutual_info_score<a class="headerlink" href="#normalized-mutual-info-score" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.metrics.normalized_mutual_info_score">
-<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">normalized_mutual_info_score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">labels_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels_pred</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">average_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'arithmetic'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/cluster/_supervised.py#L1066"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.normalized_mutual_info_score" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">normalized_mutual_info_score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">labels_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels_pred</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">average_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'arithmetic'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/cluster/_supervised.py#L1065"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.normalized_mutual_info_score" title="Link to this definition">#</a></dt>
 <dd><p>Normalized Mutual Information between two clusterings.</p>
 <p>Normalized Mutual Information (NMI) is a normalization of the Mutual
 Information (MI) score to scale the results between 0 (no mutual
diff --git a/dev/modules/generated/sklearn.metrics.pairwise.additive_chi2_kernel.html b/dev/modules/generated/sklearn.metrics.pairwise.additive_chi2_kernel.html
index 5388783dbb789..0dea501a05b28 100644
--- a/dev/modules/generated/sklearn.metrics.pairwise.additive_chi2_kernel.html
+++ b/dev/modules/generated/sklearn.metrics.pairwise.additive_chi2_kernel.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
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     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>additive_chi2_kernel<a class="headerlink" href="#additive-chi2-kernel" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.metrics.pairwise.additive_chi2_kernel">
-<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.pairwise.</span></span><span class="sig-name descname"><span class="pre">additive_chi2_kernel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/pairwise.py#L1751"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.pairwise.additive_chi2_kernel" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.pairwise.</span></span><span class="sig-name descname"><span class="pre">additive_chi2_kernel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/pairwise.py#L1741"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.pairwise.additive_chi2_kernel" title="Link to this definition">#</a></dt>
 <dd><p>Compute the additive chi-squared kernel between observations in X and Y.</p>
 <p>The chi-squared kernel is computed between each pair of rows in X and Y.  X
 and Y have to be non-negative. This kernel is most commonly applied to
diff --git a/dev/modules/generated/sklearn.metrics.pairwise.chi2_kernel.html b/dev/modules/generated/sklearn.metrics.pairwise.chi2_kernel.html
index ce9911a6a2c89..9dac90f24535d 100644
--- a/dev/modules/generated/sklearn.metrics.pairwise.chi2_kernel.html
+++ b/dev/modules/generated/sklearn.metrics.pairwise.chi2_kernel.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>chi2_kernel<a class="headerlink" href="#chi2-kernel" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.metrics.pairwise.chi2_kernel">
-<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.pairwise.</span></span><span class="sig-name descname"><span class="pre">chi2_kernel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gamma</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/pairwise.py#L1836"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.pairwise.chi2_kernel" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.pairwise.</span></span><span class="sig-name descname"><span class="pre">chi2_kernel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gamma</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/pairwise.py#L1826"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.pairwise.chi2_kernel" title="Link to this definition">#</a></dt>
 <dd><p>Compute the exponential chi-squared kernel between X and Y.</p>
 <p>The chi-squared kernel is computed between each pair of rows in X and Y.  X
 and Y have to be non-negative. This kernel is most commonly applied to
@@ -1356,8 +1356,8 @@ <h1>chi2_kernel<a class="headerlink" href="#chi2-kernel" title="Link to this hea
 <span class="gp">&gt;&gt;&gt; </span><span class="n">X</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]]</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">Y</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]]</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">chi2_kernel</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">)</span>
-<span class="go">array([[0.36..., 0.13...],</span>
-<span class="go">       [0.13..., 0.36...]])</span>
+<span class="go">array([[0.368, 0.135],</span>
+<span class="go">       [0.135, 0.368]])</span>
 </pre></div>
 </div>
 </dd></dl>
diff --git a/dev/modules/generated/sklearn.metrics.pairwise.cosine_distances.html b/dev/modules/generated/sklearn.metrics.pairwise.cosine_distances.html
index bf73f8be52e60..4ac00b618af6f 100644
--- a/dev/modules/generated/sklearn.metrics.pairwise.cosine_distances.html
+++ b/dev/modules/generated/sklearn.metrics.pairwise.cosine_distances.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
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     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>cosine_distances<a class="headerlink" href="#cosine-distances" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.metrics.pairwise.cosine_distances">
-<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.pairwise.</span></span><span class="sig-name descname"><span class="pre">cosine_distances</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/pairwise.py#L1123"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.pairwise.cosine_distances" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.pairwise.</span></span><span class="sig-name descname"><span class="pre">cosine_distances</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/pairwise.py#L1122"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.pairwise.cosine_distances" title="Link to this definition">#</a></dt>
 <dd><p>Compute cosine distance between samples in X and Y.</p>
 <p>Cosine distance is defined as 1.0 minus the cosine similarity.</p>
 <p>Read more in the <a class="reference internal" href="http://github.com/metrics.html#metrics"><span class="std std-ref">User Guide</span></a>.</p>
@@ -1339,8 +1339,8 @@ <h1>cosine_distances<a class="headerlink" href="#cosine-distances" title="Link t
 <span class="gp">&gt;&gt;&gt; </span><span class="n">X</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]]</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">Y</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]]</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">cosine_distances</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">)</span>
-<span class="go">array([[1.     , 1.     ],</span>
-<span class="go">       [0.42..., 0.18...]])</span>
+<span class="go">array([[1.   , 1.   ],</span>
+<span class="go">       [0.422, 0.183]])</span>
 </pre></div>
 </div>
 </dd></dl>
diff --git a/dev/modules/generated/sklearn.metrics.pairwise.cosine_similarity.html b/dev/modules/generated/sklearn.metrics.pairwise.cosine_similarity.html
index 4e67a2c9f1d99..a7c0737224056 100644
--- a/dev/modules/generated/sklearn.metrics.pairwise.cosine_similarity.html
+++ b/dev/modules/generated/sklearn.metrics.pairwise.cosine_similarity.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>cosine_similarity<a class="headerlink" href="#cosine-similarity" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.metrics.pairwise.cosine_similarity">
-<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.pairwise.</span></span><span class="sig-name descname"><span class="pre">cosine_similarity</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dense_output</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/pairwise.py#L1683"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.pairwise.cosine_similarity" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.pairwise.</span></span><span class="sig-name descname"><span class="pre">cosine_similarity</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dense_output</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/pairwise.py#L1675"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.pairwise.cosine_similarity" title="Link to this definition">#</a></dt>
 <dd><p>Compute cosine similarity between samples in X and Y.</p>
 <p>Cosine similarity, or the cosine kernel, computes similarity as the
 normalized dot product of X and Y:</p>
@@ -1344,8 +1344,8 @@ <h1>cosine_similarity<a class="headerlink" href="#cosine-similarity" title="Link
 <span class="gp">&gt;&gt;&gt; </span><span class="n">X</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]]</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">Y</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]]</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">cosine_similarity</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">)</span>
-<span class="go">array([[0.     , 0.     ],</span>
-<span class="go">       [0.57..., 0.81...]])</span>
+<span class="go">array([[0.   , 0.   ],</span>
+<span class="go">       [0.577, 0.816]])</span>
 </pre></div>
 </div>
 </dd></dl>
diff --git a/dev/modules/generated/sklearn.metrics.pairwise.distance_metrics.html b/dev/modules/generated/sklearn.metrics.pairwise.distance_metrics.html
index cec32d2b6f06c..d9e9cd2b61c1a 100644
--- a/dev/modules/generated/sklearn.metrics.pairwise.distance_metrics.html
+++ b/dev/modules/generated/sklearn.metrics.pairwise.distance_metrics.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>distance_metrics<a class="headerlink" href="#distance-metrics" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.metrics.pairwise.distance_metrics">
-<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.pairwise.</span></span><span class="sig-name descname"><span class="pre">distance_metrics</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/pairwise.py#L1924"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.pairwise.distance_metrics" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.pairwise.</span></span><span class="sig-name descname"><span class="pre">distance_metrics</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/pairwise.py#L1914"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.pairwise.distance_metrics" title="Link to this definition">#</a></dt>
 <dd><p>Valid metrics for pairwise_distances.</p>
 <p>This function simply returns the valid pairwise distance metrics.
 It exists to allow for a description of the mapping for
diff --git a/dev/modules/generated/sklearn.metrics.pairwise.euclidean_distances.html b/dev/modules/generated/sklearn.metrics.pairwise.euclidean_distances.html
index bd60b92f235b6..bf7569f6c5214 100644
--- a/dev/modules/generated/sklearn.metrics.pairwise.euclidean_distances.html
+++ b/dev/modules/generated/sklearn.metrics.pairwise.euclidean_distances.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,8 +1305,8 @@
 <h1>euclidean_distances<a class="headerlink" href="#euclidean-distances" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.metrics.pairwise.euclidean_distances">
-<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.pairwise.</span></span><span class="sig-name descname"><span class="pre">euclidean_distances</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y_norm_squared</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">squared</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X_norm_squared</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/pairwise.py#L274"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.pairwise.euclidean_distances" title="Link to this definition">#</a></dt>
-<dd><p>Compute the distance matrix between each pair from a vector array X and Y.</p>
+<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.pairwise.</span></span><span class="sig-name descname"><span class="pre">euclidean_distances</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y_norm_squared</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">squared</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X_norm_squared</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/pairwise.py#L273"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.pairwise.euclidean_distances" title="Link to this definition">#</a></dt>
+<dd><p>Compute the distance matrix between each pair from a feature array X and Y.</p>
 <p>For efficiency reasons, the euclidean distance between a pair of row
 vector x and y is computed as:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">dist</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span> <span class="o">=</span> <span class="n">sqrt</span><span class="p">(</span><span class="n">dot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">x</span><span class="p">)</span> <span class="o">-</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">dot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span> <span class="o">+</span> <span class="n">dot</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">y</span><span class="p">))</span>
diff --git a/dev/modules/generated/sklearn.metrics.pairwise.haversine_distances.html b/dev/modules/generated/sklearn.metrics.pairwise.haversine_distances.html
index c826589116b48..3e4c7068773fb 100644
--- a/dev/modules/generated/sklearn.metrics.pairwise.haversine_distances.html
+++ b/dev/modules/generated/sklearn.metrics.pairwise.haversine_distances.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1306,7 +1306,7 @@
 <h1>haversine_distances<a class="headerlink" href="#haversine-distances" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.metrics.pairwise.haversine_distances">
-<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.pairwise.</span></span><span class="sig-name descname"><span class="pre">haversine_distances</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/pairwise.py#L1005"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.pairwise.haversine_distances" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.pairwise.</span></span><span class="sig-name descname"><span class="pre">haversine_distances</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/pairwise.py#L1004"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.pairwise.haversine_distances" title="Link to this definition">#</a></dt>
 <dd><p>Compute the Haversine distance between samples in X and Y.</p>
 <p>The Haversine (or great circle) distance is the angular distance between
 two points on the surface of a sphere. The first coordinate of each point
diff --git a/dev/modules/generated/sklearn.metrics.pairwise.kernel_metrics.html b/dev/modules/generated/sklearn.metrics.pairwise.kernel_metrics.html
index b4754ae51a43d..2b33adcb1d2b4 100644
--- a/dev/modules/generated/sklearn.metrics.pairwise.kernel_metrics.html
+++ b/dev/modules/generated/sklearn.metrics.pairwise.kernel_metrics.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>kernel_metrics<a class="headerlink" href="#kernel-metrics" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.metrics.pairwise.kernel_metrics">
-<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.pairwise.</span></span><span class="sig-name descname"><span class="pre">kernel_metrics</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/pairwise.py#L2515"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.pairwise.kernel_metrics" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.pairwise.</span></span><span class="sig-name descname"><span class="pre">kernel_metrics</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/pairwise.py#L2515"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.pairwise.kernel_metrics" title="Link to this definition">#</a></dt>
 <dd><p>Valid metrics for pairwise_kernels.</p>
 <p>This function simply returns the valid pairwise distance metrics.
 It exists, however, to allow for a verbose description of the mapping for
diff --git a/dev/modules/generated/sklearn.metrics.pairwise.laplacian_kernel.html b/dev/modules/generated/sklearn.metrics.pairwise.laplacian_kernel.html
index ec2f2127d7533..9241d0422f241 100644
--- a/dev/modules/generated/sklearn.metrics.pairwise.laplacian_kernel.html
+++ b/dev/modules/generated/sklearn.metrics.pairwise.laplacian_kernel.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>laplacian_kernel<a class="headerlink" href="#laplacian-kernel" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.metrics.pairwise.laplacian_kernel">
-<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.pairwise.</span></span><span class="sig-name descname"><span class="pre">laplacian_kernel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gamma</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/pairwise.py#L1623"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.pairwise.laplacian_kernel" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.pairwise.</span></span><span class="sig-name descname"><span class="pre">laplacian_kernel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gamma</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/pairwise.py#L1615"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.pairwise.laplacian_kernel" title="Link to this definition">#</a></dt>
 <dd><p>Compute the laplacian kernel between X and Y.</p>
 <p>The laplacian kernel is defined as:</p>
 <div class="highlight-text notranslate"><div class="highlight"><pre><span></span>K(x, y) = exp(-gamma ||x-y||_1)
@@ -1339,8 +1339,8 @@ <h1>laplacian_kernel<a class="headerlink" href="#laplacian-kernel" title="Link t
 <span class="gp">&gt;&gt;&gt; </span><span class="n">X</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]]</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">Y</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]]</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">laplacian_kernel</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">)</span>
-<span class="go">array([[0.71..., 0.51...],</span>
-<span class="go">       [0.51..., 0.71...]])</span>
+<span class="go">array([[0.71, 0.51],</span>
+<span class="go">       [0.51, 0.71]])</span>
 </pre></div>
 </div>
 </dd></dl>
diff --git a/dev/modules/generated/sklearn.metrics.pairwise.linear_kernel.html b/dev/modules/generated/sklearn.metrics.pairwise.linear_kernel.html
index 06d923243fe61..94a927cc2e173 100644
--- a/dev/modules/generated/sklearn.metrics.pairwise.linear_kernel.html
+++ b/dev/modules/generated/sklearn.metrics.pairwise.linear_kernel.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
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     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>linear_kernel<a class="headerlink" href="#linear-kernel" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.metrics.pairwise.linear_kernel">
-<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.pairwise.</span></span><span class="sig-name descname"><span class="pre">linear_kernel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dense_output</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/pairwise.py#L1388"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.pairwise.linear_kernel" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.pairwise.</span></span><span class="sig-name descname"><span class="pre">linear_kernel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dense_output</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/pairwise.py#L1380"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.pairwise.linear_kernel" title="Link to this definition">#</a></dt>
 <dd><p>Compute the linear kernel between X and Y.</p>
 <p>Read more in the <a class="reference internal" href="http://github.com/metrics.html#linear-kernel"><span class="std std-ref">User Guide</span></a>.</p>
 <dl class="field-list">
diff --git a/dev/modules/generated/sklearn.metrics.pairwise.manhattan_distances.html b/dev/modules/generated/sklearn.metrics.pairwise.manhattan_distances.html
index fb1d88fbfd5fb..8134f9dba7251 100644
--- a/dev/modules/generated/sklearn.metrics.pairwise.manhattan_distances.html
+++ b/dev/modules/generated/sklearn.metrics.pairwise.manhattan_distances.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>manhattan_distances<a class="headerlink" href="#manhattan-distances" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.metrics.pairwise.manhattan_distances">
-<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.pairwise.</span></span><span class="sig-name descname"><span class="pre">manhattan_distances</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/pairwise.py#L1063"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.pairwise.manhattan_distances" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.pairwise.</span></span><span class="sig-name descname"><span class="pre">manhattan_distances</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/pairwise.py#L1062"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.pairwise.manhattan_distances" title="Link to this definition">#</a></dt>
 <dd><p>Compute the L1 distances between the vectors in X and Y.</p>
 <p>Read more in the <a class="reference internal" href="http://github.com/metrics.html#metrics"><span class="std std-ref">User Guide</span></a>.</p>
 <dl class="field-list simple">
diff --git a/dev/modules/generated/sklearn.metrics.pairwise.nan_euclidean_distances.html b/dev/modules/generated/sklearn.metrics.pairwise.nan_euclidean_distances.html
index ad8700ac20e16..ae456e566ec55 100644
--- a/dev/modules/generated/sklearn.metrics.pairwise.nan_euclidean_distances.html
+++ b/dev/modules/generated/sklearn.metrics.pairwise.nan_euclidean_distances.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1306,7 +1306,7 @@
 <h1>nan_euclidean_distances<a class="headerlink" href="#nan-euclidean-distances" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.metrics.pairwise.nan_euclidean_distances">
-<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.pairwise.</span></span><span class="sig-name descname"><span class="pre">nan_euclidean_distances</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">squared</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">missing_values</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">nan</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/pairwise.py#L446"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.pairwise.nan_euclidean_distances" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.pairwise.</span></span><span class="sig-name descname"><span class="pre">nan_euclidean_distances</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">squared</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">missing_values</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">nan</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/pairwise.py#L445"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.pairwise.nan_euclidean_distances" title="Link to this definition">#</a></dt>
 <dd><p>Calculate the euclidean distances in the presence of missing values.</p>
 <p>Compute the euclidean distance between each pair of samples in X and Y,
 where Y=X is assumed if Y=None. When calculating the distance between a
diff --git a/dev/modules/generated/sklearn.metrics.pairwise.paired_cosine_distances.html b/dev/modules/generated/sklearn.metrics.pairwise.paired_cosine_distances.html
index dc4bda82cd31d..448af182f08d6 100644
--- a/dev/modules/generated/sklearn.metrics.pairwise.paired_cosine_distances.html
+++ b/dev/modules/generated/sklearn.metrics.pairwise.paired_cosine_distances.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>paired_cosine_distances<a class="headerlink" href="#paired-cosine-distances" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.metrics.pairwise.paired_cosine_distances">
-<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.pairwise.</span></span><span class="sig-name descname"><span class="pre">paired_cosine_distances</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/pairwise.py#L1266"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.pairwise.paired_cosine_distances" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.pairwise.</span></span><span class="sig-name descname"><span class="pre">paired_cosine_distances</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/pairwise.py#L1258"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.pairwise.paired_cosine_distances" title="Link to this definition">#</a></dt>
 <dd><p>Compute the paired cosine distances between X and Y.</p>
 <p>Read more in the <a class="reference internal" href="http://github.com/metrics.html#metrics"><span class="std std-ref">User Guide</span></a>.</p>
 <dl class="field-list simple">
@@ -1334,7 +1334,7 @@ <h1>paired_cosine_distances<a class="headerlink" href="#paired-cosine-distances"
 <span class="gp">&gt;&gt;&gt; </span><span class="n">X</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]]</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">Y</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]]</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">paired_cosine_distances</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">)</span>
-<span class="go">array([0.5       , 0.18...])</span>
+<span class="go">array([0.5       , 0.184])</span>
 </pre></div>
 </div>
 </dd></dl>
diff --git a/dev/modules/generated/sklearn.metrics.pairwise.paired_distances.html b/dev/modules/generated/sklearn.metrics.pairwise.paired_distances.html
index a24c03ccacd94..17a629200e0ef 100644
--- a/dev/modules/generated/sklearn.metrics.pairwise.paired_distances.html
+++ b/dev/modules/generated/sklearn.metrics.pairwise.paired_distances.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>paired_distances<a class="headerlink" href="#paired-distances" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.metrics.pairwise.paired_distances">
-<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.pairwise.</span></span><span class="sig-name descname"><span class="pre">paired_distances</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'euclidean'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwds</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/pairwise.py#L1318"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.pairwise.paired_distances" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.pairwise.</span></span><span class="sig-name descname"><span class="pre">paired_distances</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'euclidean'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwds</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/pairwise.py#L1310"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.pairwise.paired_distances" title="Link to this definition">#</a></dt>
 <dd><p>Compute the paired distances between X and Y.</p>
 <p>Compute the distances between (X[0], Y[0]), (X[1], Y[1]), etc…</p>
 <p>Read more in the <a class="reference internal" href="http://github.com/metrics.html#metrics"><span class="std std-ref">User Guide</span></a>.</p>
diff --git a/dev/modules/generated/sklearn.metrics.pairwise.paired_euclidean_distances.html b/dev/modules/generated/sklearn.metrics.pairwise.paired_euclidean_distances.html
index 2b07acba86a5f..c8f3edba017b6 100644
--- a/dev/modules/generated/sklearn.metrics.pairwise.paired_euclidean_distances.html
+++ b/dev/modules/generated/sklearn.metrics.pairwise.paired_euclidean_distances.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>paired_euclidean_distances<a class="headerlink" href="#paired-euclidean-distances" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.metrics.pairwise.paired_euclidean_distances">
-<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.pairwise.</span></span><span class="sig-name descname"><span class="pre">paired_euclidean_distances</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/pairwise.py#L1187"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.pairwise.paired_euclidean_distances" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.pairwise.</span></span><span class="sig-name descname"><span class="pre">paired_euclidean_distances</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/pairwise.py#L1179"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.pairwise.paired_euclidean_distances" title="Link to this definition">#</a></dt>
 <dd><p>Compute the paired euclidean distances between X and Y.</p>
 <p>Read more in the <a class="reference internal" href="http://github.com/metrics.html#metrics"><span class="std std-ref">User Guide</span></a>.</p>
 <dl class="field-list simple">
diff --git a/dev/modules/generated/sklearn.metrics.pairwise.paired_manhattan_distances.html b/dev/modules/generated/sklearn.metrics.pairwise.paired_manhattan_distances.html
index 91269d83acd1d..74fdbd5b4f999 100644
--- a/dev/modules/generated/sklearn.metrics.pairwise.paired_manhattan_distances.html
+++ b/dev/modules/generated/sklearn.metrics.pairwise.paired_manhattan_distances.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>paired_manhattan_distances<a class="headerlink" href="#paired-manhattan-distances" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.metrics.pairwise.paired_manhattan_distances">
-<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.pairwise.</span></span><span class="sig-name descname"><span class="pre">paired_manhattan_distances</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/pairwise.py#L1222"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.pairwise.paired_manhattan_distances" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.pairwise.</span></span><span class="sig-name descname"><span class="pre">paired_manhattan_distances</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/pairwise.py#L1214"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.pairwise.paired_manhattan_distances" title="Link to this definition">#</a></dt>
 <dd><p>Compute the paired L1 distances between X and Y.</p>
 <p>Distances are calculated between (X[0], Y[0]), (X[1], Y[1]), …,
 (X[n_samples], Y[n_samples]).</p>
diff --git a/dev/modules/generated/sklearn.metrics.pairwise.pairwise_kernels.html b/dev/modules/generated/sklearn.metrics.pairwise.pairwise_kernels.html
index 075dd39d099d3..3b62e1c7ee808 100644
--- a/dev/modules/generated/sklearn.metrics.pairwise.pairwise_kernels.html
+++ b/dev/modules/generated/sklearn.metrics.pairwise.pairwise_kernels.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,17 +1305,17 @@
 <h1>pairwise_kernels<a class="headerlink" href="#pairwise-kernels" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.metrics.pairwise.pairwise_kernels">
-<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.pairwise.</span></span><span class="sig-name descname"><span class="pre">pairwise_kernels</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'linear'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">filter_params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwds</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/pairwise.py#L2560"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.pairwise.pairwise_kernels" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.pairwise.</span></span><span class="sig-name descname"><span class="pre">pairwise_kernels</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'linear'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">filter_params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwds</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/pairwise.py#L2560"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.pairwise.pairwise_kernels" title="Link to this definition">#</a></dt>
 <dd><p>Compute the kernel between arrays X and optional array Y.</p>
-<p>This method takes one or two vector arrays or a kernel matrix, and returns
+<p>This function takes one or two feature arrays or a kernel matrix, and returns
 a kernel matrix.</p>
 <ul class="simple">
-<li><p>If <code class="docutils literal notranslate"><span class="pre">X</span></code> is a vector array, of shape (n_samples_X, n_features), and:</p>
+<li><p>If <code class="docutils literal notranslate"><span class="pre">X</span></code> is a feature array, of shape (n_samples_X, n_features), and:</p>
 <ul>
 <li><p><code class="docutils literal notranslate"><span class="pre">Y</span></code> is <code class="docutils literal notranslate"><span class="pre">None</span></code> and <code class="docutils literal notranslate"><span class="pre">metric</span></code> is not ‘precomputed’, the pairwise kernels
-between <code class="docutils literal notranslate"><span class="pre">X</span></code> and itself are computed.</p></li>
-<li><p><code class="docutils literal notranslate"><span class="pre">Y</span></code> is a vector array of shape (n_samples_Y, n_features), the pairwise
-kernels between arrays <code class="docutils literal notranslate"><span class="pre">X</span></code> and <code class="docutils literal notranslate"><span class="pre">Y</span></code> is returned.</p></li>
+between <code class="docutils literal notranslate"><span class="pre">X</span></code> and itself are returned.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">Y</span></code> is a feature array of shape (n_samples_Y, n_features), the pairwise
+kernels between <code class="docutils literal notranslate"><span class="pre">X</span></code> and <code class="docutils literal notranslate"><span class="pre">Y</span></code> is returned.</p></li>
 </ul>
 </li>
 <li><p>If <code class="docutils literal notranslate"><span class="pre">X</span></code> is a kernel matrix, of shape (n_samples_X, n_samples_X), <code class="docutils literal notranslate"><span class="pre">metric</span></code>
@@ -1356,7 +1356,7 @@ <h1>pairwise_kernels<a class="headerlink" href="#pairwise-kernels" title="Link t
 <dt><strong>n_jobs</strong><span class="classifier">int, default=None</span></dt><dd><p>The number of jobs to use for the computation. This works by breaking
 down the pairwise matrix into n_jobs even slices and computing them
 using multithreading.</p>
-<p><code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.5.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
+<p><code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.6.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
 <code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all processors. See <a class="reference internal" href="http://github.com/glossary.html#term-n_jobs"><span class="xref std std-term">Glossary</span></a>
 for more details.</p>
 </dd>
@@ -1375,7 +1375,7 @@ <h1>pairwise_kernels<a class="headerlink" href="#pairwise-kernels" title="Link t
 </dd>
 </dl>
 <p class="rubric">Notes</p>
-<p>If metric is ‘precomputed’, Y is ignored and X is returned.</p>
+<p>If metric is a callable, no restrictions are placed on <code class="docutils literal notranslate"><span class="pre">X</span></code> and <code class="docutils literal notranslate"><span class="pre">Y</span></code> dimensions.</p>
 <p class="rubric">Examples</p>
 <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.metrics.pairwise</span><span class="w"> </span><span class="kn">import</span> <span class="n">pairwise_kernels</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">X</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]]</span>
diff --git a/dev/modules/generated/sklearn.metrics.pairwise.polynomial_kernel.html b/dev/modules/generated/sklearn.metrics.pairwise.polynomial_kernel.html
index 741434638c563..be12fae88ec44 100644
--- a/dev/modules/generated/sklearn.metrics.pairwise.polynomial_kernel.html
+++ b/dev/modules/generated/sklearn.metrics.pairwise.polynomial_kernel.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>polynomial_kernel<a class="headerlink" href="#polynomial-kernel" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.metrics.pairwise.polynomial_kernel">
-<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.pairwise.</span></span><span class="sig-name descname"><span class="pre">polynomial_kernel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">degree</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gamma</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coef0</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/pairwise.py#L1434"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.pairwise.polynomial_kernel" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.pairwise.</span></span><span class="sig-name descname"><span class="pre">polynomial_kernel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">degree</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gamma</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coef0</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/pairwise.py#L1426"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.pairwise.polynomial_kernel" title="Link to this definition">#</a></dt>
 <dd><p>Compute the polynomial kernel between X and Y.</p>
 <div class="highlight-text notranslate"><div class="highlight"><pre><span></span>K(X, Y) = (gamma &lt;X, Y&gt; + coef0) ^ degree
 </pre></div>
@@ -1339,7 +1339,7 @@ <h1>polynomial_kernel<a class="headerlink" href="#polynomial-kernel" title="Link
 <span class="gp">&gt;&gt;&gt; </span><span class="n">Y</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]]</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">polynomial_kernel</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">degree</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
 <span class="go">array([[1.     , 1.     ],</span>
-<span class="go">       [1.77..., 2.77...]])</span>
+<span class="go">       [1.77, 2.77]])</span>
 </pre></div>
 </div>
 </dd></dl>
diff --git a/dev/modules/generated/sklearn.metrics.pairwise.rbf_kernel.html b/dev/modules/generated/sklearn.metrics.pairwise.rbf_kernel.html
index 533c8b40ec20e..cb71de739d673 100644
--- a/dev/modules/generated/sklearn.metrics.pairwise.rbf_kernel.html
+++ b/dev/modules/generated/sklearn.metrics.pairwise.rbf_kernel.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>rbf_kernel<a class="headerlink" href="#rbf-kernel" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.metrics.pairwise.rbf_kernel">
-<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.pairwise.</span></span><span class="sig-name descname"><span class="pre">rbf_kernel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gamma</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/pairwise.py#L1563"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.pairwise.rbf_kernel" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.pairwise.</span></span><span class="sig-name descname"><span class="pre">rbf_kernel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gamma</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/pairwise.py#L1555"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.pairwise.rbf_kernel" title="Link to this definition">#</a></dt>
 <dd><p>Compute the rbf (gaussian) kernel between X and Y.</p>
 <div class="highlight-text notranslate"><div class="highlight"><pre><span></span>K(x, y) = exp(-gamma ||x-y||^2)
 </pre></div>
@@ -1335,8 +1335,8 @@ <h1>rbf_kernel<a class="headerlink" href="#rbf-kernel" title="Link to this headi
 <span class="gp">&gt;&gt;&gt; </span><span class="n">X</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]]</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">Y</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]]</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">rbf_kernel</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">)</span>
-<span class="go">array([[0.71..., 0.51...],</span>
-<span class="go">       [0.51..., 0.71...]])</span>
+<span class="go">array([[0.71, 0.51],</span>
+<span class="go">       [0.51, 0.71]])</span>
 </pre></div>
 </div>
 </dd></dl>
diff --git a/dev/modules/generated/sklearn.metrics.pairwise.sigmoid_kernel.html b/dev/modules/generated/sklearn.metrics.pairwise.sigmoid_kernel.html
index cdeb4c0240cf8..1930fe82573fd 100644
--- a/dev/modules/generated/sklearn.metrics.pairwise.sigmoid_kernel.html
+++ b/dev/modules/generated/sklearn.metrics.pairwise.sigmoid_kernel.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>sigmoid_kernel<a class="headerlink" href="#sigmoid-kernel" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.metrics.pairwise.sigmoid_kernel">
-<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.pairwise.</span></span><span class="sig-name descname"><span class="pre">sigmoid_kernel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gamma</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coef0</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/pairwise.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.pairwise.sigmoid_kernel" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.pairwise.</span></span><span class="sig-name descname"><span class="pre">sigmoid_kernel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gamma</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coef0</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/pairwise.py#L1492"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.pairwise.sigmoid_kernel" title="Link to this definition">#</a></dt>
 <dd><p>Compute the sigmoid kernel between X and Y.</p>
 <div class="highlight-text notranslate"><div class="highlight"><pre><span></span>K(X, Y) = tanh(gamma &lt;X, Y&gt; + coef0)
 </pre></div>
@@ -1336,8 +1336,8 @@ <h1>sigmoid_kernel<a class="headerlink" href="#sigmoid-kernel" title="Link to th
 <span class="gp">&gt;&gt;&gt; </span><span class="n">X</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]]</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">Y</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]]</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">sigmoid_kernel</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">)</span>
-<span class="go">array([[0.76..., 0.76...],</span>
-<span class="go">       [0.87..., 0.93...]])</span>
+<span class="go">array([[0.76, 0.76],</span>
+<span class="go">       [0.87, 0.93]])</span>
 </pre></div>
 </div>
 </dd></dl>
diff --git a/dev/modules/generated/sklearn.metrics.pairwise_distances.html b/dev/modules/generated/sklearn.metrics.pairwise_distances.html
index a8d1acd3c6ec8..0d461945d51e1 100644
--- a/dev/modules/generated/sklearn.metrics.pairwise_distances.html
+++ b/dev/modules/generated/sklearn.metrics.pairwise_distances.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,25 +1307,32 @@
 <h1>pairwise_distances<a class="headerlink" href="#pairwise-distances" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.metrics.pairwise_distances">
-<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">pairwise_distances</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'euclidean'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">force_all_finite</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'deprecated'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ensure_all_finite</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwds</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/pairwise.py#L2263"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.pairwise_distances" title="Link to this definition">#</a></dt>
-<dd><p>Compute the distance matrix from a vector array X and optional Y.</p>
-<p>This method takes either a vector array or a distance matrix, and returns
-a distance matrix.
-If the input is a vector array, the distances are computed.
-If the input is a distances matrix, it is returned instead.
-If the input is a collection of non-numeric data (e.g. a list of strings or a
+<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">pairwise_distances</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'euclidean'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">force_all_finite</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'deprecated'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ensure_all_finite</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwds</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/pairwise.py#L2254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.pairwise_distances" title="Link to this definition">#</a></dt>
+<dd><p>Compute the distance matrix from a feature array X and optional Y.</p>
+<p>This function takes one or two feature arrays or a distance matrix, and returns
+a distance matrix.</p>
+<ul class="simple">
+<li><p>If <code class="docutils literal notranslate"><span class="pre">X</span></code> is a feature array, of shape (n_samples_X, n_features), and:</p>
+<ul>
+<li><p><code class="docutils literal notranslate"><span class="pre">Y</span></code> is <code class="docutils literal notranslate"><span class="pre">None</span></code> and <code class="docutils literal notranslate"><span class="pre">metric</span></code> is not ‘precomputed’, the pairwise distances
+between <code class="docutils literal notranslate"><span class="pre">X</span></code> and itself are returned.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">Y</span></code> is a feature array of shape (n_samples_Y, n_features), the pairwise
+distances between <code class="docutils literal notranslate"><span class="pre">X</span></code> and <code class="docutils literal notranslate"><span class="pre">Y</span></code> is returned.</p></li>
+</ul>
+</li>
+<li><p>If <code class="docutils literal notranslate"><span class="pre">X</span></code> is a distance matrix, of shape (n_samples_X, n_samples_X), <code class="docutils literal notranslate"><span class="pre">metric</span></code>
+should be ‘precomputed’. <code class="docutils literal notranslate"><span class="pre">Y</span></code> is thus ignored and <code class="docutils literal notranslate"><span class="pre">X</span></code> is returned as is.</p></li>
+</ul>
+<p>If the input is a collection of non-numeric data (e.g. a list of strings or a
 boolean array), a custom metric must be passed.</p>
 <p>This method provides a safe way to take a distance matrix as input, while
 preserving compatibility with many other algorithms that take a vector
 array.</p>
-<p>If Y is given (default is None), then the returned matrix is the pairwise
-distance between the arrays from both X and Y.</p>
 <p>Valid values for metric are:</p>
 <ul class="simple">
 <li><p>From scikit-learn: [‘cityblock’, ‘cosine’, ‘euclidean’, ‘l1’, ‘l2’,
-‘manhattan’]. These metrics support sparse matrix
-inputs.
-[‘nan_euclidean’] but it does not yet support sparse matrices.</p></li>
+‘manhattan’, ‘nan_euclidean’]. All metrics support sparse matrix
+inputs except ‘nan_euclidean’.</p></li>
 <li><p>From scipy.spatial.distance: [‘braycurtis’, ‘canberra’, ‘chebyshev’,
 ‘correlation’, ‘dice’, ‘hamming’, ‘jaccard’, ‘kulsinski’, ‘mahalanobis’,
 ‘minkowski’, ‘rogerstanimoto’, ‘russellrao’, ‘seuclidean’,
@@ -1371,7 +1378,7 @@ <h1>pairwise_distances<a class="headerlink" href="#pairwise-distances" title="Li
 <dt><strong>n_jobs</strong><span class="classifier">int, default=None</span></dt><dd><p>The number of jobs to use for the computation. This works by breaking
 down the pairwise matrix into n_jobs even slices and computing them
 using multithreading.</p>
-<p><code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.5.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
+<p><code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.6.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
 <code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all processors. See <a class="reference internal" href="http://github.com/glossary.html#term-n_jobs"><span class="xref std std-term">Glossary</span></a>
 for more details.</p>
 <p>The “euclidean” and “cosine” metrics rely heavily on BLAS which is already
@@ -1436,6 +1443,8 @@ <h1>pairwise_distances<a class="headerlink" href="#pairwise-distances" title="Li
 </dd>
 </dl>
 </div>
+<p class="rubric">Notes</p>
+<p>If metric is a callable, no restrictions are placed on <code class="docutils literal notranslate"><span class="pre">X</span></code> and <code class="docutils literal notranslate"><span class="pre">Y</span></code> dimensions.</p>
 <p class="rubric">Examples</p>
 <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.metrics.pairwise</span><span class="w"> </span><span class="kn">import</span> <span class="n">pairwise_distances</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">X</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]]</span>
diff --git a/dev/modules/generated/sklearn.metrics.pairwise_distances_argmin.html b/dev/modules/generated/sklearn.metrics.pairwise_distances_argmin.html
index 65968efe185ac..38a0e30f662c8 100644
--- a/dev/modules/generated/sklearn.metrics.pairwise_distances_argmin.html
+++ b/dev/modules/generated/sklearn.metrics.pairwise_distances_argmin.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>pairwise_distances_argmin<a class="headerlink" href="#pairwise-distances-argmin" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.metrics.pairwise_distances_argmin">
-<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">pairwise_distances_argmin</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">axis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'euclidean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/pairwise.py#L863"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.pairwise_distances_argmin" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">pairwise_distances_argmin</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">axis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'euclidean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/pairwise.py#L862"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.pairwise_distances_argmin" title="Link to this definition">#</a></dt>
 <dd><p>Compute minimum distances between one point and a set of points.</p>
 <p>This function computes for each row in X, the index of the row of Y which
 is closest (according to the specified distance).</p>
diff --git a/dev/modules/generated/sklearn.metrics.pairwise_distances_argmin_min.html b/dev/modules/generated/sklearn.metrics.pairwise_distances_argmin_min.html
index 99d9e7a6f72e9..ddc2cdcf32107 100644
--- a/dev/modules/generated/sklearn.metrics.pairwise_distances_argmin_min.html
+++ b/dev/modules/generated/sklearn.metrics.pairwise_distances_argmin_min.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>pairwise_distances_argmin_min<a class="headerlink" href="#pairwise-distances-argmin-min" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.metrics.pairwise_distances_argmin_min">
-<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">pairwise_distances_argmin_min</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">axis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'euclidean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/pairwise.py#L713"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.pairwise_distances_argmin_min" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">pairwise_distances_argmin_min</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">axis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'euclidean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/pairwise.py#L712"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.pairwise_distances_argmin_min" title="Link to this definition">#</a></dt>
 <dd><p>Compute minimum distances between one point and a set of points.</p>
 <p>This function computes for each row in X, the index of the row of Y which
 is closest (according to the specified distance). The minimal distances are
diff --git a/dev/modules/generated/sklearn.metrics.pairwise_distances_chunked.html b/dev/modules/generated/sklearn.metrics.pairwise_distances_chunked.html
index 9e82693c6de2c..8a93c1b3bcafb 100644
--- a/dev/modules/generated/sklearn.metrics.pairwise_distances_chunked.html
+++ b/dev/modules/generated/sklearn.metrics.pairwise_distances_chunked.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>pairwise_distances_chunked<a class="headerlink" href="#pairwise-distances-chunked" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.metrics.pairwise_distances_chunked">
-<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">pairwise_distances_chunked</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce_func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'euclidean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">working_memory</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwds</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/pairwise.py#L2079"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.pairwise_distances_chunked" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">pairwise_distances_chunked</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce_func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'euclidean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">working_memory</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwds</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/pairwise.py#L2070"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.pairwise_distances_chunked" title="Link to this definition">#</a></dt>
 <dd><p>Generate a distance matrix chunk by chunk with optional reduction.</p>
 <p>In cases where not all of a pairwise distance matrix needs to be
 stored at once, this is used to calculate pairwise distances in
@@ -1346,7 +1346,7 @@ <h1>pairwise_distances_chunked<a class="headerlink" href="#pairwise-distances-ch
 <dt><strong>n_jobs</strong><span class="classifier">int, default=None</span></dt><dd><p>The number of jobs to use for the computation. This works by
 breaking down the pairwise matrix into n_jobs even slices and
 computing them in parallel.</p>
-<p><code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.5.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
+<p><code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.6.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
 <code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all processors. See <a class="reference internal" href="http://github.com/glossary.html#term-n_jobs"><span class="xref std std-term">Glossary</span></a>
 for more details.</p>
 </dd>
@@ -1375,11 +1375,11 @@ <h1>pairwise_distances_chunked<a class="headerlink" href="#pairwise-distances-ch
 <span class="gp">&gt;&gt;&gt; </span><span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">RandomState</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">D_chunk</span> <span class="o">=</span> <span class="nb">next</span><span class="p">(</span><span class="n">pairwise_distances_chunked</span><span class="p">(</span><span class="n">X</span><span class="p">))</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">D_chunk</span>
-<span class="go">array([[0.  ..., 0.29..., 0.41..., 0.19..., 0.57...],</span>
-<span class="go">       [0.29..., 0.  ..., 0.57..., 0.41..., 0.76...],</span>
-<span class="go">       [0.41..., 0.57..., 0.  ..., 0.44..., 0.90...],</span>
-<span class="go">       [0.19..., 0.41..., 0.44..., 0.  ..., 0.51...],</span>
-<span class="go">       [0.57..., 0.76..., 0.90..., 0.51..., 0.  ...]])</span>
+<span class="go">array([[0.   , 0.295, 0.417, 0.197, 0.572],</span>
+<span class="go">       [0.295, 0.   , 0.576, 0.419, 0.764],</span>
+<span class="go">       [0.417, 0.576, 0.   , 0.449, 0.903],</span>
+<span class="go">       [0.197, 0.419, 0.449, 0.   , 0.512],</span>
+<span class="go">       [0.572, 0.764, 0.903, 0.512, 0.   ]])</span>
 </pre></div>
 </div>
 <p>Retrieve all neighbors and average distance within radius r:</p>
@@ -1393,7 +1393,7 @@ <h1>pairwise_distances_chunked<a class="headerlink" href="#pairwise-distances-ch
 <span class="gp">&gt;&gt;&gt; </span><span class="n">neigh</span>
 <span class="go">[array([0, 3]), array([1]), array([2]), array([0, 3]), array([4])]</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">avg_dist</span>
-<span class="go">array([0.039..., 0.        , 0.        , 0.039..., 0.        ])</span>
+<span class="go">array([0.039, 0.        , 0.        , 0.039, 0.        ])</span>
 </pre></div>
 </div>
 <p>Where r is defined per sample, we need to make use of <code class="docutils literal notranslate"><span class="pre">start</span></code>:</p>
diff --git a/dev/modules/generated/sklearn.metrics.precision_recall_curve.html b/dev/modules/generated/sklearn.metrics.precision_recall_curve.html
index d7a256c7581be..09a65a0a16ff9 100644
--- a/dev/modules/generated/sklearn.metrics.precision_recall_curve.html
+++ b/dev/modules/generated/sklearn.metrics.precision_recall_curve.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>precision_recall_curve<a class="headerlink" href="#precision-recall-curve" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.metrics.precision_recall_curve">
-<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">precision_recall_curve</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_score</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pos_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_intermediate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/_ranking.py#L866"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.precision_recall_curve" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">precision_recall_curve</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_score</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pos_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_intermediate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/_ranking.py#L907"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.precision_recall_curve" title="Link to this definition">#</a></dt>
 <dd><p>Compute precision-recall pairs for different probability thresholds.</p>
 <p>Note: this implementation is restricted to the binary classification task.</p>
 <p>The precision is the ratio <code class="docutils literal notranslate"><span class="pre">tp</span> <span class="pre">/</span> <span class="pre">(tp</span> <span class="pre">+</span> <span class="pre">fp)</span></code> where <code class="docutils literal notranslate"><span class="pre">tp</span></code> is the number of
diff --git a/dev/modules/generated/sklearn.metrics.precision_recall_fscore_support.html b/dev/modules/generated/sklearn.metrics.precision_recall_fscore_support.html
index 65a3f62fd7518..c20d6ddcdc3b6 100644
--- a/dev/modules/generated/sklearn.metrics.precision_recall_fscore_support.html
+++ b/dev/modules/generated/sklearn.metrics.precision_recall_fscore_support.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>precision_recall_fscore_support<a class="headerlink" href="#precision-recall-fscore-support" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.metrics.precision_recall_fscore_support">
-<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">precision_recall_fscore_support</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_pred</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">beta</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pos_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">warn_for</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">('precision',</span> <span class="pre">'recall',</span> <span class="pre">'f-score')</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zero_division</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'warn'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/_classification.py#L1757"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.precision_recall_fscore_support" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">precision_recall_fscore_support</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_pred</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">beta</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pos_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">warn_for</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">('precision',</span> <span class="pre">'recall',</span> <span class="pre">'f-score')</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zero_division</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'warn'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/_classification.py#L1768"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.precision_recall_fscore_support" title="Link to this definition">#</a></dt>
 <dd><p>Compute precision, recall, F-measure and support for each class.</p>
 <p>The precision is the ratio <code class="docutils literal notranslate"><span class="pre">tp</span> <span class="pre">/</span> <span class="pre">(tp</span> <span class="pre">+</span> <span class="pre">fp)</span></code> where <code class="docutils literal notranslate"><span class="pre">tp</span></code> is the number of
 true positives and <code class="docutils literal notranslate"><span class="pre">fp</span></code> the number of false positives. The precision is
@@ -1442,18 +1442,18 @@ <h1>precision_recall_fscore_support<a class="headerlink" href="#precision-recall
 <span class="gp">&gt;&gt;&gt; </span><span class="n">y_true</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="s1">&#39;cat&#39;</span><span class="p">,</span> <span class="s1">&#39;dog&#39;</span><span class="p">,</span> <span class="s1">&#39;pig&#39;</span><span class="p">,</span> <span class="s1">&#39;cat&#39;</span><span class="p">,</span> <span class="s1">&#39;dog&#39;</span><span class="p">,</span> <span class="s1">&#39;pig&#39;</span><span class="p">])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">y_pred</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="s1">&#39;cat&#39;</span><span class="p">,</span> <span class="s1">&#39;pig&#39;</span><span class="p">,</span> <span class="s1">&#39;dog&#39;</span><span class="p">,</span> <span class="s1">&#39;cat&#39;</span><span class="p">,</span> <span class="s1">&#39;cat&#39;</span><span class="p">,</span> <span class="s1">&#39;dog&#39;</span><span class="p">])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">precision_recall_fscore_support</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">,</span> <span class="n">average</span><span class="o">=</span><span class="s1">&#39;macro&#39;</span><span class="p">)</span>
-<span class="go">(0.22..., 0.33..., 0.26..., None)</span>
+<span class="go">(0.222, 0.333, 0.267, None)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">precision_recall_fscore_support</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">,</span> <span class="n">average</span><span class="o">=</span><span class="s1">&#39;micro&#39;</span><span class="p">)</span>
-<span class="go">(0.33..., 0.33..., 0.33..., None)</span>
+<span class="go">(0.33, 0.33, 0.33, None)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">precision_recall_fscore_support</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">,</span> <span class="n">average</span><span class="o">=</span><span class="s1">&#39;weighted&#39;</span><span class="p">)</span>
-<span class="go">(0.22..., 0.33..., 0.26..., None)</span>
+<span class="go">(0.222, 0.333, 0.267, None)</span>
 </pre></div>
 </div>
 <p>It is possible to compute per-label precisions, recalls, F1-scores and
 supports instead of averaging:</p>
 <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">precision_recall_fscore_support</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">,</span> <span class="n">average</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
 <span class="gp">... </span><span class="n">labels</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;pig&#39;</span><span class="p">,</span> <span class="s1">&#39;dog&#39;</span><span class="p">,</span> <span class="s1">&#39;cat&#39;</span><span class="p">])</span>
-<span class="go">(array([0.        , 0.        , 0.66...]),</span>
+<span class="go">(array([0.        , 0.        , 0.66]),</span>
 <span class="go"> array([0., 0., 1.]), array([0. , 0. , 0.8]),</span>
 <span class="go"> array([2, 2, 2]))</span>
 </pre></div>
diff --git a/dev/modules/generated/sklearn.metrics.precision_score.html b/dev/modules/generated/sklearn.metrics.precision_score.html
index d709d90a6cbfd..7dbc698f1f758 100644
--- a/dev/modules/generated/sklearn.metrics.precision_score.html
+++ b/dev/modules/generated/sklearn.metrics.precision_score.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>precision_score<a class="headerlink" href="#precision-score" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.metrics.precision_score">
-<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">precision_score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_pred</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pos_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'binary'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zero_division</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'warn'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/_classification.py#L2344"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.precision_score" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">precision_score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_pred</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pos_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'binary'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zero_division</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'warn'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/_classification.py#L2330"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.precision_score" title="Link to this definition">#</a></dt>
 <dd><p>Compute the precision.</p>
 <p>The precision is the ratio <code class="docutils literal notranslate"><span class="pre">tp</span> <span class="pre">/</span> <span class="pre">(tp</span> <span class="pre">+</span> <span class="pre">fp)</span></code> where <code class="docutils literal notranslate"><span class="pre">tp</span></code> is the number of
 true positives and <code class="docutils literal notranslate"><span class="pre">fp</span></code> the number of false positives. The precision is
@@ -1417,20 +1417,20 @@ <h1>precision_score<a class="headerlink" href="#precision-score" title="Link to
 <span class="gp">&gt;&gt;&gt; </span><span class="n">y_true</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">y_pred</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">precision_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">,</span> <span class="n">average</span><span class="o">=</span><span class="s1">&#39;macro&#39;</span><span class="p">)</span>
-<span class="go">0.22...</span>
+<span class="go">0.22</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">precision_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">,</span> <span class="n">average</span><span class="o">=</span><span class="s1">&#39;micro&#39;</span><span class="p">)</span>
-<span class="go">0.33...</span>
+<span class="go">0.33</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">precision_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">,</span> <span class="n">average</span><span class="o">=</span><span class="s1">&#39;weighted&#39;</span><span class="p">)</span>
-<span class="go">0.22...</span>
+<span class="go">0.22</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">precision_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">,</span> <span class="n">average</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
-<span class="go">array([0.66..., 0.        , 0.        ])</span>
+<span class="go">array([0.66, 0.        , 0.        ])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">y_pred</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">precision_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">,</span> <span class="n">average</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
-<span class="go">array([0.33..., 0.        , 0.        ])</span>
+<span class="go">array([0.33, 0.        , 0.        ])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">precision_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">,</span> <span class="n">average</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">zero_division</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-<span class="go">array([0.33..., 1.        , 1.        ])</span>
+<span class="go">array([0.33, 1.        , 1.        ])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">precision_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">,</span> <span class="n">average</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">zero_division</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">)</span>
-<span class="go">array([0.33...,        nan,        nan])</span>
+<span class="go">array([0.33,        nan,        nan])</span>
 </pre></div>
 </div>
 <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="c1"># multilabel classification</span>
diff --git a/dev/modules/generated/sklearn.metrics.r2_score.html b/dev/modules/generated/sklearn.metrics.r2_score.html
index 7437c87817c5b..4b105cdd204df 100644
--- a/dev/modules/generated/sklearn.metrics.r2_score.html
+++ b/dev/modules/generated/sklearn.metrics.r2_score.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1308,7 +1308,7 @@
 <h1>r2_score<a class="headerlink" href="#r2-score" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.metrics.r2_score">
-<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">r2_score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_pred</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">multioutput</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'uniform_average'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">force_finite</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/_regression.py#L1130"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.r2_score" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">r2_score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_pred</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">multioutput</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'uniform_average'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">force_finite</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/_regression.py#L1130"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.r2_score" title="Link to this definition">#</a></dt>
 <dd><p><span class="math notranslate nohighlight">\(R^2\)</span> (coefficient of determination) regression score function.</p>
 <p>Best possible score is 1.0 and it can be negative (because the
 model can be arbitrarily worse). In the general case when the true y is
diff --git a/dev/modules/generated/sklearn.metrics.rand_score.html b/dev/modules/generated/sklearn.metrics.rand_score.html
index c9d5d62af940b..25bf22b39ab01 100644
--- a/dev/modules/generated/sklearn.metrics.rand_score.html
+++ b/dev/modules/generated/sklearn.metrics.rand_score.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>rand_score<a class="headerlink" href="#rand-score" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.metrics.rand_score">
-<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">rand_score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">labels_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels_pred</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/cluster/_supervised.py#L263"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.rand_score" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">rand_score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">labels_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels_pred</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/cluster/_supervised.py#L262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.rand_score" title="Link to this definition">#</a></dt>
 <dd><p>Rand index.</p>
 <p>The Rand Index computes a similarity measure between two clusterings
 by considering all pairs of samples and counting pairs that are
@@ -1370,7 +1370,7 @@ <h1>rand_score<a class="headerlink" href="#rand-score" title="Link to this headi
 <p>Labelings that assign all classes members to the same clusters
 are complete but may not always be pure, hence penalized:</p>
 <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">rand_score</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>
-<span class="go">0.83...</span>
+<span class="go">0.83</span>
 </pre></div>
 </div>
 </dd></dl>
diff --git a/dev/modules/generated/sklearn.metrics.recall_score.html b/dev/modules/generated/sklearn.metrics.recall_score.html
index 991e1bdc1b413..1134db4d37166 100644
--- a/dev/modules/generated/sklearn.metrics.recall_score.html
+++ b/dev/modules/generated/sklearn.metrics.recall_score.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>recall_score<a class="headerlink" href="#recall-score" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.metrics.recall_score">
-<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">recall_score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_pred</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pos_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'binary'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zero_division</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'warn'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/_classification.py#L2525"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.recall_score" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">recall_score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_pred</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pos_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'binary'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zero_division</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'warn'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/_classification.py#L2511"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.recall_score" title="Link to this definition">#</a></dt>
 <dd><p>Compute the recall.</p>
 <p>The recall is the ratio <code class="docutils literal notranslate"><span class="pre">tp</span> <span class="pre">/</span> <span class="pre">(tp</span> <span class="pre">+</span> <span class="pre">fn)</span></code> where <code class="docutils literal notranslate"><span class="pre">tp</span></code> is the number of
 true positives and <code class="docutils literal notranslate"><span class="pre">fn</span></code> the number of false negatives. The recall is
@@ -1418,11 +1418,11 @@ <h1>recall_score<a class="headerlink" href="#recall-score" title="Link to this h
 <span class="gp">&gt;&gt;&gt; </span><span class="n">y_true</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">y_pred</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">recall_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">,</span> <span class="n">average</span><span class="o">=</span><span class="s1">&#39;macro&#39;</span><span class="p">)</span>
-<span class="go">0.33...</span>
+<span class="go">0.33</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">recall_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">,</span> <span class="n">average</span><span class="o">=</span><span class="s1">&#39;micro&#39;</span><span class="p">)</span>
-<span class="go">0.33...</span>
+<span class="go">0.33</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">recall_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">,</span> <span class="n">average</span><span class="o">=</span><span class="s1">&#39;weighted&#39;</span><span class="p">)</span>
-<span class="go">0.33...</span>
+<span class="go">0.33</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">recall_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">,</span> <span class="n">average</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
 <span class="go">array([1., 0., 0.])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">y_true</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span>
diff --git a/dev/modules/generated/sklearn.metrics.roc_auc_score.html b/dev/modules/generated/sklearn.metrics.roc_auc_score.html
index c3bd91f78cf88..6209b7dad2919 100644
--- a/dev/modules/generated/sklearn.metrics.roc_auc_score.html
+++ b/dev/modules/generated/sklearn.metrics.roc_auc_score.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>roc_auc_score<a class="headerlink" href="#roc-auc-score" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.metrics.roc_auc_score">
-<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">roc_auc_score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_score</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'macro'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_fpr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">multi_class</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'raise'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/_ranking.py#L409"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.roc_auc_score" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">roc_auc_score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_score</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'macro'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_fpr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">multi_class</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'raise'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/_ranking.py#L450"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.roc_auc_score" title="Link to this definition">#</a></dt>
 <dd><p>Compute Area Under the Receiver Operating Characteristic Curve (ROC AUC)     from prediction scores.</p>
 <p>Note: this implementation can be used with binary, multiclass and
 multilabel classification, but some restrictions apply (see Parameters).</p>
@@ -1463,19 +1463,19 @@ <h1>roc_auc_score<a class="headerlink" href="#roc-auc-score" title="Link to this
 <span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.linear_model</span><span class="w"> </span><span class="kn">import</span> <span class="n">LogisticRegression</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.metrics</span><span class="w"> </span><span class="kn">import</span> <span class="n">roc_auc_score</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">load_breast_cancer</span><span class="p">(</span><span class="n">return_X_y</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">clf</span> <span class="o">=</span> <span class="n">LogisticRegression</span><span class="p">(</span><span class="n">solver</span><span class="o">=</span><span class="s2">&quot;liblinear&quot;</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">clf</span> <span class="o">=</span> <span class="n">LogisticRegression</span><span class="p">(</span><span class="n">solver</span><span class="o">=</span><span class="s2">&quot;newton-cholesky&quot;</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">roc_auc_score</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">clf</span><span class="o">.</span><span class="n">predict_proba</span><span class="p">(</span><span class="n">X</span><span class="p">)[:,</span> <span class="mi">1</span><span class="p">])</span>
-<span class="go">0.99...</span>
+<span class="go">0.99</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">roc_auc_score</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">clf</span><span class="o">.</span><span class="n">decision_function</span><span class="p">(</span><span class="n">X</span><span class="p">))</span>
-<span class="go">0.99...</span>
+<span class="go">0.99</span>
 </pre></div>
 </div>
 <p>Multiclass case:</p>
 <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.datasets</span><span class="w"> </span><span class="kn">import</span> <span class="n">load_iris</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">load_iris</span><span class="p">(</span><span class="n">return_X_y</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">clf</span> <span class="o">=</span> <span class="n">LogisticRegression</span><span class="p">(</span><span class="n">solver</span><span class="o">=</span><span class="s2">&quot;liblinear&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">clf</span> <span class="o">=</span> <span class="n">LogisticRegression</span><span class="p">(</span><span class="n">solver</span><span class="o">=</span><span class="s2">&quot;newton-cholesky&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">roc_auc_score</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">clf</span><span class="o">.</span><span class="n">predict_proba</span><span class="p">(</span><span class="n">X</span><span class="p">),</span> <span class="n">multi_class</span><span class="o">=</span><span class="s1">&#39;ovr&#39;</span><span class="p">)</span>
-<span class="go">0.99...</span>
+<span class="go">0.99</span>
 </pre></div>
 </div>
 <p>Multilabel case:</p>
@@ -1486,15 +1486,15 @@ <h1>roc_auc_score<a class="headerlink" href="#roc-auc-score" title="Link to this
 <span class="gp">&gt;&gt;&gt; </span><span class="n">clf</span> <span class="o">=</span> <span class="n">MultiOutputClassifier</span><span class="p">(</span><span class="n">clf</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="c1"># get a list of n_output containing probability arrays of shape</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="c1"># (n_samples, n_classes)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">y_pred</span> <span class="o">=</span> <span class="n">clf</span><span class="o">.</span><span class="n">predict_proba</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">y_score</span> <span class="o">=</span> <span class="n">clf</span><span class="o">.</span><span class="n">predict_proba</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="c1"># extract the positive columns for each output</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">y_pred</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">transpose</span><span class="p">([</span><span class="n">pred</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">pred</span> <span class="ow">in</span> <span class="n">y_pred</span><span class="p">])</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">roc_auc_score</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">,</span> <span class="n">average</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
-<span class="go">array([0.82..., 0.86..., 0.94..., 0.85... , 0.94...])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">y_score</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">transpose</span><span class="p">([</span><span class="n">score</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">score</span> <span class="ow">in</span> <span class="n">y_score</span><span class="p">])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">roc_auc_score</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">y_score</span><span class="p">,</span> <span class="n">average</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
+<span class="go">array([0.828, 0.852, 0.94, 0.869, 0.95])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.linear_model</span><span class="w"> </span><span class="kn">import</span> <span class="n">RidgeClassifierCV</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">clf</span> <span class="o">=</span> <span class="n">RidgeClassifierCV</span><span class="p">()</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">roc_auc_score</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">clf</span><span class="o">.</span><span class="n">decision_function</span><span class="p">(</span><span class="n">X</span><span class="p">),</span> <span class="n">average</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
-<span class="go">array([0.81..., 0.84... , 0.93..., 0.87..., 0.94...])</span>
+<span class="go">array([0.82, 0.847, 0.93, 0.872, 0.944])</span>
 </pre></div>
 </div>
 </dd></dl>
diff --git a/dev/modules/generated/sklearn.metrics.roc_curve.html b/dev/modules/generated/sklearn.metrics.roc_curve.html
index 677f0e96558c5..4294189016047 100644
--- a/dev/modules/generated/sklearn.metrics.roc_curve.html
+++ b/dev/modules/generated/sklearn.metrics.roc_curve.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>roc_curve<a class="headerlink" href="#roc-curve" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.metrics.roc_curve">
-<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">roc_curve</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_score</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pos_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_intermediate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/_ranking.py#L1014"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.roc_curve" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">roc_curve</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_score</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pos_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_intermediate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/_ranking.py#L1055"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.roc_curve" title="Link to this definition">#</a></dt>
 <dd><p>Compute Receiver operating characteristic (ROC).</p>
 <p>Note: this implementation is restricted to the binary classification task.</p>
 <p>Read more in the <a class="reference internal" href="http://github.com/model_evaluation.html#roc-metrics"><span class="std std-ref">User Guide</span></a>.</p>
@@ -1329,9 +1329,9 @@ <h1>roc_curve<a class="headerlink" href="#roc-curve" title="Link to this heading
 </dd>
 <dt><strong>sample_weight</strong><span class="classifier">array-like of shape (n_samples,), default=None</span></dt><dd><p>Sample weights.</p>
 </dd>
-<dt><strong>drop_intermediate</strong><span class="classifier">bool, default=True</span></dt><dd><p>Whether to drop some suboptimal thresholds which would not appear
-on a plotted ROC curve. This is useful in order to create lighter
-ROC curves.</p>
+<dt><strong>drop_intermediate</strong><span class="classifier">bool, default=True</span></dt><dd><p>Whether to drop thresholds where the resulting point is collinear with
+its neighbors in ROC space. This has no effect on the ROC AUC or visual
+shape of the curve, but reduces the number of plotted points.</p>
 <div class="versionadded">
 <p><span class="versionmodified added">Added in version 0.17: </span>parameter <em>drop_intermediate</em>.</p>
 </div>
@@ -1339,7 +1339,7 @@ <h1>roc_curve<a class="headerlink" href="#roc-curve" title="Link to this heading
 </dl>
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><dl class="simple">
+<dd class="field-even"><dl>
 <dt><strong>fpr</strong><span class="classifier">ndarray of shape (&gt;2,)</span></dt><dd><p>Increasing false positive rates such that element i is the false
 positive rate of predictions with score &gt;= <code class="docutils literal notranslate"><span class="pre">thresholds[i]</span></code>.</p>
 </dd>
@@ -1347,8 +1347,12 @@ <h1>roc_curve<a class="headerlink" href="#roc-curve" title="Link to this heading
 positive rate of predictions with score &gt;= <code class="docutils literal notranslate"><span class="pre">thresholds[i]</span></code>.</p>
 </dd>
 <dt><strong>thresholds</strong><span class="classifier">ndarray of shape (n_thresholds,)</span></dt><dd><p>Decreasing thresholds on the decision function used to compute
-fpr and tpr. <code class="docutils literal notranslate"><span class="pre">thresholds[0]</span></code> represents no instances being predicted
-and is arbitrarily set to <code class="docutils literal notranslate"><span class="pre">np.inf</span></code>.</p>
+fpr and tpr. The first threshold is set to <code class="docutils literal notranslate"><span class="pre">np.inf</span></code>.</p>
+<div class="versionchanged">
+<p><span class="versionmodified changed">Changed in version 1.3: </span>An arbitrary threshold at infinity (stored in <code class="docutils literal notranslate"><span class="pre">thresholds[0]</span></code>) is
+added to represent a classifier that always predicts the negative
+class, i.e. <code class="docutils literal notranslate"><span class="pre">fpr=0</span></code> and <code class="docutils literal notranslate"><span class="pre">tpr=0</span></code>.</p>
+</div>
 </dd>
 </dl>
 </dd>
@@ -1370,9 +1374,6 @@ <h1>roc_curve<a class="headerlink" href="#roc-curve" title="Link to this heading
 <p>Since the thresholds are sorted from low to high values, they
 are reversed upon returning them to ensure they correspond to both <code class="docutils literal notranslate"><span class="pre">fpr</span></code>
 and <code class="docutils literal notranslate"><span class="pre">tpr</span></code>, which are sorted in reversed order during their calculation.</p>
-<p>An arbitrary threshold is added for the case <code class="docutils literal notranslate"><span class="pre">tpr=0</span></code> and <code class="docutils literal notranslate"><span class="pre">fpr=0</span></code> to
-ensure that the curve starts at <code class="docutils literal notranslate"><span class="pre">(0,</span> <span class="pre">0)</span></code>. This threshold corresponds to the
-<code class="docutils literal notranslate"><span class="pre">np.inf</span></code>.</p>
 <p class="rubric">References</p>
 <div role="list" class="citation-list">
 <div class="citation" id="rfc6782941ac9-1" role="doc-biblioentry">
diff --git a/dev/modules/generated/sklearn.metrics.root_mean_squared_error.html b/dev/modules/generated/sklearn.metrics.root_mean_squared_error.html
index 1b87abf9ed076..76d29fba7ba8a 100644
--- a/dev/modules/generated/sklearn.metrics.root_mean_squared_error.html
+++ b/dev/modules/generated/sklearn.metrics.root_mean_squared_error.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>root_mean_squared_error<a class="headerlink" href="#root-mean-squared-error" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.metrics.root_mean_squared_error">
-<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">root_mean_squared_error</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_pred</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">multioutput</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'uniform_average'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/_regression.py#L604"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.root_mean_squared_error" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">root_mean_squared_error</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_pred</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">multioutput</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'uniform_average'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/_regression.py#L604"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.root_mean_squared_error" title="Link to this definition">#</a></dt>
 <dd><p>Root mean squared error regression loss.</p>
 <p>Read more in the <a class="reference internal" href="http://github.com/model_evaluation.html#mean-squared-error"><span class="std std-ref">User Guide</span></a>.</p>
 <div class="versionadded">
diff --git a/dev/modules/generated/sklearn.metrics.root_mean_squared_log_error.html b/dev/modules/generated/sklearn.metrics.root_mean_squared_log_error.html
index a00c9ce7379b4..3964fb24fe544 100644
--- a/dev/modules/generated/sklearn.metrics.root_mean_squared_log_error.html
+++ b/dev/modules/generated/sklearn.metrics.root_mean_squared_log_error.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>root_mean_squared_log_error<a class="headerlink" href="#root-mean-squared-log-error" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.metrics.root_mean_squared_log_error">
-<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">root_mean_squared_log_error</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_pred</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">multioutput</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'uniform_average'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/_regression.py#L774"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.root_mean_squared_log_error" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">root_mean_squared_log_error</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_pred</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">multioutput</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'uniform_average'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/_regression.py#L774"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.root_mean_squared_log_error" title="Link to this definition">#</a></dt>
 <dd><p>Root mean squared logarithmic error regression loss.</p>
 <p>Read more in the <a class="reference internal" href="http://github.com/model_evaluation.html#mean-squared-log-error"><span class="std std-ref">User Guide</span></a>.</p>
 <div class="versionadded">
diff --git a/dev/modules/generated/sklearn.metrics.silhouette_samples.html b/dev/modules/generated/sklearn.metrics.silhouette_samples.html
index b466711ea7076..d6bec5674c90b 100644
--- a/dev/modules/generated/sklearn.metrics.silhouette_samples.html
+++ b/dev/modules/generated/sklearn.metrics.silhouette_samples.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>silhouette_samples<a class="headerlink" href="#silhouette-samples" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.metrics.silhouette_samples">
-<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">silhouette_samples</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'euclidean'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwds</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/cluster/_unsupervised.py#L196"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.silhouette_samples" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">silhouette_samples</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'euclidean'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwds</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/cluster/_unsupervised.py#L195"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.silhouette_samples" title="Link to this definition">#</a></dt>
 <dd><p>Compute the Silhouette Coefficient for each sample.</p>
 <p>The Silhouette Coefficient is a measure of how well samples are clustered
 with samples that are similar to themselves. Clustering models with a high
diff --git a/dev/modules/generated/sklearn.metrics.silhouette_score.html b/dev/modules/generated/sklearn.metrics.silhouette_score.html
index dcaffe4ef7a74..5ad540666d1be 100644
--- a/dev/modules/generated/sklearn.metrics.silhouette_score.html
+++ b/dev/modules/generated/sklearn.metrics.silhouette_score.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>silhouette_score<a class="headerlink" href="#silhouette-score" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.metrics.silhouette_score">
-<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">silhouette_score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'euclidean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwds</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/cluster/_unsupervised.py#L42"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.silhouette_score" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">silhouette_score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'euclidean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwds</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/cluster/_unsupervised.py#L41"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.silhouette_score" title="Link to this definition">#</a></dt>
 <dd><p>Compute the mean Silhouette Coefficient of all samples.</p>
 <p>The Silhouette Coefficient is calculated using the mean intra-cluster
 distance (<code class="docutils literal notranslate"><span class="pre">a</span></code>) and the mean nearest-cluster distance (<code class="docutils literal notranslate"><span class="pre">b</span></code>) for each
diff --git a/dev/modules/generated/sklearn.metrics.top_k_accuracy_score.html b/dev/modules/generated/sklearn.metrics.top_k_accuracy_score.html
index 340af19fd76b8..be8ddf050bc3c 100644
--- a/dev/modules/generated/sklearn.metrics.top_k_accuracy_score.html
+++ b/dev/modules/generated/sklearn.metrics.top_k_accuracy_score.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>top_k_accuracy_score<a class="headerlink" href="#top-k-accuracy-score" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.metrics.top_k_accuracy_score">
-<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">top_k_accuracy_score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_score</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">k</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">normalize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/_ranking.py#L1861"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.top_k_accuracy_score" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">top_k_accuracy_score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_score</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">k</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">normalize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/_ranking.py#L1902"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.top_k_accuracy_score" title="Link to this definition">#</a></dt>
 <dd><p>Top-k Accuracy classification score.</p>
 <p>This metric computes the number of times where the correct label is among
 the top <code class="docutils literal notranslate"><span class="pre">k</span></code> labels predicted (ranked by predicted scores). Note that the
diff --git a/dev/modules/generated/sklearn.metrics.v_measure_score.html b/dev/modules/generated/sklearn.metrics.v_measure_score.html
index a3fff079b2de8..76eab8d3c869d 100644
--- a/dev/modules/generated/sklearn.metrics.v_measure_score.html
+++ b/dev/modules/generated/sklearn.metrics.v_measure_score.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>v_measure_score<a class="headerlink" href="#v-measure-score" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.metrics.v_measure_score">
-<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">v_measure_score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">labels_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels_pred</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">beta</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/cluster/_supervised.py#L708"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.v_measure_score" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">v_measure_score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">labels_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels_pred</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">beta</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/cluster/_supervised.py#L707"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.v_measure_score" title="Link to this definition">#</a></dt>
 <dd><p>V-measure cluster labeling given a ground truth.</p>
 <p>This score is identical to <a class="reference internal" href="http://github.com/sklearn.metrics.normalized_mutual_info_score.html#sklearn.metrics.normalized_mutual_info_score" title="sklearn.metrics.normalized_mutual_info_score"><code class="xref py py-func docutils literal notranslate"><span class="pre">normalized_mutual_info_score</span></code></a> with
 the <code class="docutils literal notranslate"><span class="pre">'arithmetic'</span></code> option for averaging.</p>
@@ -1376,30 +1376,30 @@ <h1>v_measure_score<a class="headerlink" href="#v-measure-score" title="Link to
 <p>Labelings that assign all classes members to the same clusters
 are complete but not homogeneous, hence penalized:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">%.6f</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="n">v_measure_score</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]))</span>
-<span class="go">0.8...</span>
+<span class="go">0.8</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">%.6f</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="n">v_measure_score</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]))</span>
-<span class="go">0.66...</span>
+<span class="go">0.67</span>
 </pre></div>
 </div>
 <p>Labelings that have pure clusters with members coming from the same
 classes are homogeneous but un-necessary splits harm completeness
 and thus penalize V-measure as well:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">%.6f</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="n">v_measure_score</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]))</span>
-<span class="go">0.8...</span>
+<span class="go">0.8</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">%.6f</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="n">v_measure_score</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">]))</span>
-<span class="go">0.66...</span>
+<span class="go">0.67</span>
 </pre></div>
 </div>
 <p>If classes members are completely split across different clusters,
 the assignment is totally incomplete, hence the V-Measure is null:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">%.6f</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="n">v_measure_score</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">]))</span>
-<span class="go">0.0...</span>
+<span class="go">0.0</span>
 </pre></div>
 </div>
 <p>Clusters that include samples from totally different classes totally
 destroy the homogeneity of the labeling, hence:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">%.6f</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="n">v_measure_score</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]))</span>
-<span class="go">0.0...</span>
+<span class="go">0.0</span>
 </pre></div>
 </div>
 </dd></dl>
diff --git a/dev/modules/generated/sklearn.metrics.zero_one_loss.html b/dev/modules/generated/sklearn.metrics.zero_one_loss.html
index a4fa16ad53991..6e40ff02d408a 100644
--- a/dev/modules/generated/sklearn.metrics.zero_one_loss.html
+++ b/dev/modules/generated/sklearn.metrics.zero_one_loss.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>zero_one_loss<a class="headerlink" href="#zero-one-loss" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.metrics.zero_one_loss">
-<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">zero_one_loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_pred</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">normalize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/metrics/_classification.py#L1188"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.zero_one_loss" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">zero_one_loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_pred</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">normalize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/metrics/_classification.py#L1199"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.zero_one_loss" title="Link to this definition">#</a></dt>
 <dd><p>Zero-one classification loss.</p>
 <p>If normalize is <code class="docutils literal notranslate"><span class="pre">True</span></code>, return the fraction of misclassifications
 (float), else it returns the number of misclassifications (int). The best
diff --git a/dev/modules/generated/sklearn.mixture.BayesianGaussianMixture.html b/dev/modules/generated/sklearn.mixture.BayesianGaussianMixture.html
index bbab3440ba1e2..ddaebfe730c3c 100644
--- a/dev/modules/generated/sklearn.mixture.BayesianGaussianMixture.html
+++ b/dev/modules/generated/sklearn.mixture.BayesianGaussianMixture.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>BayesianGaussianMixture<a class="headerlink" href="#bayesiangaussianmixture" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.mixture.BayesianGaussianMixture">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.mixture.</span></span><span class="sig-name descname"><span class="pre">BayesianGaussianMixture</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_components</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">covariance_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'full'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reg_covar</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-06</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">init_params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'kmeans'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight_concentration_prior_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'dirichlet_process'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight_concentration_prior</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mean_precision_prior</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mean_prior</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">degrees_of_freedom_prior</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">covariance_prior</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">warm_start</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose_interval</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/mixture/_bayesian_mixture.py#L74"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.mixture.BayesianGaussianMixture" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.mixture.</span></span><span class="sig-name descname"><span class="pre">BayesianGaussianMixture</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_components</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">covariance_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'full'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reg_covar</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-06</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">init_params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'kmeans'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight_concentration_prior_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'dirichlet_process'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight_concentration_prior</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mean_precision_prior</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mean_prior</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">degrees_of_freedom_prior</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">covariance_prior</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">warm_start</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose_interval</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/mixture/_bayesian_mixture.py#L74"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.mixture.BayesianGaussianMixture" title="Link to this definition">#</a></dt>
 <dd><p>Variational Bayesian estimation of a Gaussian mixture.</p>
 <p>This class allows to infer an approximate posterior distribution over the
 parameters of a Gaussian mixture distribution. The effective number of
@@ -1557,15 +1557,15 @@ <h1>BayesianGaussianMixture<a class="headerlink" href="#bayesiangaussianmixture"
 <span class="gp">&gt;&gt;&gt; </span><span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">12</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">7</span><span class="p">]])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">bgm</span> <span class="o">=</span> <span class="n">BayesianGaussianMixture</span><span class="p">(</span><span class="n">n_components</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">bgm</span><span class="o">.</span><span class="n">means_</span>
-<span class="go">array([[2.49... , 2.29...],</span>
-<span class="go">       [8.45..., 4.52... ]])</span>
+<span class="go">array([[2.49 , 2.29],</span>
+<span class="go">       [8.45, 4.52 ]])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">bgm</span><span class="o">.</span><span class="n">predict</span><span class="p">([[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">9</span><span class="p">,</span> <span class="mi">3</span><span class="p">]])</span>
 <span class="go">array([0, 1])</span>
 </pre></div>
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.mixture.BayesianGaussianMixture.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/mixture/_base.py#L155"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.mixture.BayesianGaussianMixture.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/mixture/_base.py#L155"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.mixture.BayesianGaussianMixture.fit" title="Link to this definition">#</a></dt>
 <dd><p>Estimate model parameters with the EM algorithm.</p>
 <p>The method fits the model <code class="docutils literal notranslate"><span class="pre">n_init</span></code> times and sets the parameters with
 which the model has the largest likelihood or lower bound. Within each
@@ -1596,7 +1596,7 @@ <h1>BayesianGaussianMixture<a class="headerlink" href="#bayesiangaussianmixture"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.mixture.BayesianGaussianMixture.fit_predict">
-<span class="sig-name descname"><span class="pre">fit_predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/mixture/_base.py#L185"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.mixture.BayesianGaussianMixture.fit_predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/mixture/_base.py#L185"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.mixture.BayesianGaussianMixture.fit_predict" title="Link to this definition">#</a></dt>
 <dd><p>Estimate model parameters using X and predict the labels for X.</p>
 <p>The method fits the model n_init times and sets the parameters with
 which the model has the largest likelihood or lower bound. Within each
@@ -1629,7 +1629,7 @@ <h1>BayesianGaussianMixture<a class="headerlink" href="#bayesiangaussianmixture"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.mixture.BayesianGaussianMixture.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.mixture.BayesianGaussianMixture.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.mixture.BayesianGaussianMixture.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1646,7 +1646,7 @@ <h1>BayesianGaussianMixture<a class="headerlink" href="#bayesiangaussianmixture"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.mixture.BayesianGaussianMixture.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.mixture.BayesianGaussianMixture.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.mixture.BayesianGaussianMixture.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1667,7 +1667,7 @@ <h1>BayesianGaussianMixture<a class="headerlink" href="#bayesiangaussianmixture"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.mixture.BayesianGaussianMixture.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/mixture/_base.py#L375"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.mixture.BayesianGaussianMixture.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/mixture/_base.py#L375"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.mixture.BayesianGaussianMixture.predict" title="Link to this definition">#</a></dt>
 <dd><p>Predict the labels for the data samples in X using trained model.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1688,7 +1688,7 @@ <h1>BayesianGaussianMixture<a class="headerlink" href="#bayesiangaussianmixture"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.mixture.BayesianGaussianMixture.predict_proba">
-<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/mixture/_base.py#L393"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.mixture.BayesianGaussianMixture.predict_proba" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/mixture/_base.py#L393"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.mixture.BayesianGaussianMixture.predict_proba" title="Link to this definition">#</a></dt>
 <dd><p>Evaluate the components’ density for each sample.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1709,7 +1709,7 @@ <h1>BayesianGaussianMixture<a class="headerlink" href="#bayesiangaussianmixture"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.mixture.BayesianGaussianMixture.sample">
-<span class="sig-name descname"><span class="pre">sample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/mixture/_base.py#L412"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.mixture.BayesianGaussianMixture.sample" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">sample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/mixture/_base.py#L412"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.mixture.BayesianGaussianMixture.sample" title="Link to this definition">#</a></dt>
 <dd><p>Generate random samples from the fitted Gaussian distribution.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1731,7 +1731,7 @@ <h1>BayesianGaussianMixture<a class="headerlink" href="#bayesiangaussianmixture"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.mixture.BayesianGaussianMixture.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/mixture/_base.py#L356"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.mixture.BayesianGaussianMixture.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/mixture/_base.py#L356"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.mixture.BayesianGaussianMixture.score" title="Link to this definition">#</a></dt>
 <dd><p>Compute the per-sample average log-likelihood of the given data X.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1754,7 +1754,7 @@ <h1>BayesianGaussianMixture<a class="headerlink" href="#bayesiangaussianmixture"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.mixture.BayesianGaussianMixture.score_samples">
-<span class="sig-name descname"><span class="pre">score_samples</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/mixture/_base.py#L337"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.mixture.BayesianGaussianMixture.score_samples" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score_samples</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/mixture/_base.py#L337"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.mixture.BayesianGaussianMixture.score_samples" title="Link to this definition">#</a></dt>
 <dd><p>Compute the log-likelihood of each sample.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1775,7 +1775,7 @@ <h1>BayesianGaussianMixture<a class="headerlink" href="#bayesiangaussianmixture"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.mixture.BayesianGaussianMixture.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.mixture.BayesianGaussianMixture.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.mixture.BayesianGaussianMixture.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
diff --git a/dev/modules/generated/sklearn.mixture.GaussianMixture.html b/dev/modules/generated/sklearn.mixture.GaussianMixture.html
index e3c682bfd6857..f614360ce51a4 100644
--- a/dev/modules/generated/sklearn.mixture.GaussianMixture.html
+++ b/dev/modules/generated/sklearn.mixture.GaussianMixture.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>GaussianMixture<a class="headerlink" href="#gaussianmixture" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.mixture.GaussianMixture">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.mixture.</span></span><span class="sig-name descname"><span class="pre">GaussianMixture</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_components</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">covariance_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'full'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reg_covar</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-06</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">init_params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'kmeans'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weights_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">means_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">precisions_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">warm_start</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose_interval</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/mixture/_gaussian_mixture.py#L515"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.mixture.GaussianMixture" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.mixture.</span></span><span class="sig-name descname"><span class="pre">GaussianMixture</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_components</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">covariance_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'full'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reg_covar</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-06</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">init_params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'kmeans'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weights_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">means_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">precisions_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">warm_start</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose_interval</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/mixture/_gaussian_mixture.py#L515"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.mixture.GaussianMixture" title="Link to this definition">#</a></dt>
 <dd><p>Gaussian Mixture.</p>
 <p>Representation of a Gaussian mixture model probability distribution.
 This class allows to estimate the parameters of a Gaussian mixture
@@ -1411,6 +1411,8 @@ <h1>GaussianMixture<a class="headerlink" href="#gaussianmixture" title="Link to
 <span class="p">(</span><span class="n">n_components</span><span class="p">,</span> <span class="n">n_features</span><span class="p">,</span> <span class="n">n_features</span><span class="p">)</span> <span class="k">if</span> <span class="s1">&#39;full&#39;</span>
 </pre></div>
 </div>
+<p>For an example of using covariances, refer to
+<a class="reference internal" href="http://github.com/auto_examples/mixture/plot_gmm_covariances.html#sphx-glr-auto-examples-mixture-plot-gmm-covariances-py"><span class="std std-ref">GMM covariances</span></a>.</p>
 </dd>
 <dt><strong>precisions_</strong><span class="classifier">array-like</span></dt><dd><p>The precision matrices for each component in the mixture. A precision
 matrix is the inverse of a covariance matrix. A covariance matrix is
@@ -1487,7 +1489,7 @@ <h1>GaussianMixture<a class="headerlink" href="#gaussianmixture" title="Link to
 <a class="reference internal" href="http://github.com/auto_examples/cluster/plot_cluster_comparison.html#sphx-glr-auto-examples-cluster-plot-cluster-comparison-py"><span class="std std-ref">Comparing different clustering algorithms on toy datasets</span></a></p>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.mixture.GaussianMixture.aic">
-<span class="sig-name descname"><span class="pre">aic</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/mixture/_gaussian_mixture.py#L915"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.mixture.GaussianMixture.aic" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">aic</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/mixture/_gaussian_mixture.py#L918"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.mixture.GaussianMixture.aic" title="Link to this definition">#</a></dt>
 <dd><p>Akaike information criterion for the current model on the input X.</p>
 <p>You can refer to this <a class="reference internal" href="http://github.com/linear_model.html#aic-bic"><span class="std std-ref">mathematical section</span></a> for more
 details regarding the formulation of the AIC used.</p>
@@ -1509,7 +1511,7 @@ <h1>GaussianMixture<a class="headerlink" href="#gaussianmixture" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.mixture.GaussianMixture.bic">
-<span class="sig-name descname"><span class="pre">bic</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/mixture/_gaussian_mixture.py#L892"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.mixture.GaussianMixture.bic" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">bic</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/mixture/_gaussian_mixture.py#L895"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.mixture.GaussianMixture.bic" title="Link to this definition">#</a></dt>
 <dd><p>Bayesian information criterion for the current model on the input X.</p>
 <p>You can refer to this <a class="reference internal" href="http://github.com/linear_model.html#aic-bic"><span class="std std-ref">mathematical section</span></a> for more
 details regarding the formulation of the BIC used.</p>
@@ -1533,7 +1535,7 @@ <h1>GaussianMixture<a class="headerlink" href="#gaussianmixture" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.mixture.GaussianMixture.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/mixture/_base.py#L155"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.mixture.GaussianMixture.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/mixture/_base.py#L155"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.mixture.GaussianMixture.fit" title="Link to this definition">#</a></dt>
 <dd><p>Estimate model parameters with the EM algorithm.</p>
 <p>The method fits the model <code class="docutils literal notranslate"><span class="pre">n_init</span></code> times and sets the parameters with
 which the model has the largest likelihood or lower bound. Within each
@@ -1564,7 +1566,7 @@ <h1>GaussianMixture<a class="headerlink" href="#gaussianmixture" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.mixture.GaussianMixture.fit_predict">
-<span class="sig-name descname"><span class="pre">fit_predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/mixture/_base.py#L185"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.mixture.GaussianMixture.fit_predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/mixture/_base.py#L185"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.mixture.GaussianMixture.fit_predict" title="Link to this definition">#</a></dt>
 <dd><p>Estimate model parameters using X and predict the labels for X.</p>
 <p>The method fits the model n_init times and sets the parameters with
 which the model has the largest likelihood or lower bound. Within each
@@ -1597,7 +1599,7 @@ <h1>GaussianMixture<a class="headerlink" href="#gaussianmixture" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.mixture.GaussianMixture.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.mixture.GaussianMixture.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.mixture.GaussianMixture.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1614,7 +1616,7 @@ <h1>GaussianMixture<a class="headerlink" href="#gaussianmixture" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.mixture.GaussianMixture.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.mixture.GaussianMixture.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.mixture.GaussianMixture.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1635,7 +1637,7 @@ <h1>GaussianMixture<a class="headerlink" href="#gaussianmixture" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.mixture.GaussianMixture.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/mixture/_base.py#L375"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.mixture.GaussianMixture.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/mixture/_base.py#L375"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.mixture.GaussianMixture.predict" title="Link to this definition">#</a></dt>
 <dd><p>Predict the labels for the data samples in X using trained model.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1656,7 +1658,7 @@ <h1>GaussianMixture<a class="headerlink" href="#gaussianmixture" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.mixture.GaussianMixture.predict_proba">
-<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/mixture/_base.py#L393"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.mixture.GaussianMixture.predict_proba" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/mixture/_base.py#L393"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.mixture.GaussianMixture.predict_proba" title="Link to this definition">#</a></dt>
 <dd><p>Evaluate the components’ density for each sample.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1677,7 +1679,7 @@ <h1>GaussianMixture<a class="headerlink" href="#gaussianmixture" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.mixture.GaussianMixture.sample">
-<span class="sig-name descname"><span class="pre">sample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/mixture/_base.py#L412"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.mixture.GaussianMixture.sample" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">sample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/mixture/_base.py#L412"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.mixture.GaussianMixture.sample" title="Link to this definition">#</a></dt>
 <dd><p>Generate random samples from the fitted Gaussian distribution.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1699,7 +1701,7 @@ <h1>GaussianMixture<a class="headerlink" href="#gaussianmixture" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.mixture.GaussianMixture.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/mixture/_base.py#L356"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.mixture.GaussianMixture.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/mixture/_base.py#L356"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.mixture.GaussianMixture.score" title="Link to this definition">#</a></dt>
 <dd><p>Compute the per-sample average log-likelihood of the given data X.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1722,7 +1724,7 @@ <h1>GaussianMixture<a class="headerlink" href="#gaussianmixture" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.mixture.GaussianMixture.score_samples">
-<span class="sig-name descname"><span class="pre">score_samples</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/mixture/_base.py#L337"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.mixture.GaussianMixture.score_samples" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score_samples</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/mixture/_base.py#L337"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.mixture.GaussianMixture.score_samples" title="Link to this definition">#</a></dt>
 <dd><p>Compute the log-likelihood of each sample.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1743,7 +1745,7 @@ <h1>GaussianMixture<a class="headerlink" href="#gaussianmixture" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.mixture.GaussianMixture.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.mixture.GaussianMixture.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.mixture.GaussianMixture.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
diff --git a/dev/modules/generated/sklearn.model_selection.FixedThresholdClassifier.html b/dev/modules/generated/sklearn.model_selection.FixedThresholdClassifier.html
index 45a8a586ccfcf..e4013ab50f014 100644
--- a/dev/modules/generated/sklearn.model_selection.FixedThresholdClassifier.html
+++ b/dev/modules/generated/sklearn.model_selection.FixedThresholdClassifier.html
@@ -11,10 +11,10 @@
 <meta property="og:type" content="website" />
 <meta property="og:url" content="https://scikit-learn/stable/modules/generated/sklearn.model_selection.FixedThresholdClassifier.html" />
 <meta property="og:site_name" content="scikit-learn" />
-<meta property="og:description" content="Gallery examples: Examples of Using FrozenEstimator Post-tuning the decision threshold for cost-sensitive learning Release Highlights for scikit-learn 1.5 Release Highlights for scikit-learn 1.6" />
-<meta property="og:image" content="https://scikit-learn/stable/_images/sphx_glr_plot_frozen_examples_thumb.png" />
+<meta property="og:description" content="Gallery examples: Visualizing the probabilistic predictions of a VotingClassifier Examples of Using FrozenEstimator Post-tuning the decision threshold for cost-sensitive learning Release Highlights..." />
+<meta property="og:image" content="https://scikit-learn/stable/_images/sphx_glr_plot_voting_decision_regions_thumb.png" />
 <meta property="og:image:alt" content="" />
-<meta name="description" content="Gallery examples: Examples of Using FrozenEstimator Post-tuning the decision threshold for cost-sensitive learning Release Highlights for scikit-learn 1.5 Release Highlights for scikit-learn 1.6" />
+<meta name="description" content="Gallery examples: Visualizing the probabilistic predictions of a VotingClassifier Examples of Using FrozenEstimator Post-tuning the decision threshold for cost-sensitive learning Release Highlights..." />
 
     <title>FixedThresholdClassifier &#8212; scikit-learn 1.7.dev0 documentation</title>
   
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
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     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>FixedThresholdClassifier<a class="headerlink" href="#fixedthresholdclassifier" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.model_selection.FixedThresholdClassifier">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.model_selection.</span></span><span class="sig-name descname"><span class="pre">FixedThresholdClassifier</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">threshold</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pos_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">response_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_classification_threshold.py#L213"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.FixedThresholdClassifier" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.model_selection.</span></span><span class="sig-name descname"><span class="pre">FixedThresholdClassifier</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">threshold</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pos_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">response_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_classification_threshold.py#L213"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.FixedThresholdClassifier" title="Link to this definition">#</a></dt>
 <dd><p>Binary classifier that manually sets the decision threshold.</p>
 <p>This classifier allows to change the default decision threshold used for
 converting posterior probability estimates (i.e. output of <code class="docutils literal notranslate"><span class="pre">predict_proba</span></code>) or
@@ -1394,7 +1394,7 @@ <h1>FixedThresholdClassifier<a class="headerlink" href="#fixedthresholdclassifie
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.FixedThresholdClassifier.decision_function">
-<span class="sig-name descname"><span class="pre">decision_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_classification_threshold.py#L187"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.FixedThresholdClassifier.decision_function" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">decision_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_classification_threshold.py#L187"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.FixedThresholdClassifier.decision_function" title="Link to this definition">#</a></dt>
 <dd><p>Decision function for samples in <code class="docutils literal notranslate"><span class="pre">X</span></code> using the fitted estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1415,7 +1415,7 @@ <h1>FixedThresholdClassifier<a class="headerlink" href="#fixedthresholdclassifie
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.FixedThresholdClassifier.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_classification_threshold.py#L101"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.FixedThresholdClassifier.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_classification_threshold.py#L101"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.FixedThresholdClassifier.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit the classifier.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1440,7 +1440,7 @@ <h1>FixedThresholdClassifier<a class="headerlink" href="#fixedthresholdclassifie
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.FixedThresholdClassifier.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_classification_threshold.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.FixedThresholdClassifier.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_classification_threshold.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.FixedThresholdClassifier.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1457,7 +1457,7 @@ <h1>FixedThresholdClassifier<a class="headerlink" href="#fixedthresholdclassifie
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.FixedThresholdClassifier.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.FixedThresholdClassifier.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.FixedThresholdClassifier.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1478,7 +1478,7 @@ <h1>FixedThresholdClassifier<a class="headerlink" href="#fixedthresholdclassifie
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.FixedThresholdClassifier.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_classification_threshold.py#L355"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.FixedThresholdClassifier.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_classification_threshold.py#L355"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.FixedThresholdClassifier.predict" title="Link to this definition">#</a></dt>
 <dd><p>Predict the target of new samples.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1498,7 +1498,7 @@ <h1>FixedThresholdClassifier<a class="headerlink" href="#fixedthresholdclassifie
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.FixedThresholdClassifier.predict_log_proba">
-<span class="sig-name descname"><span class="pre">predict_log_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_classification_threshold.py#L168"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.FixedThresholdClassifier.predict_log_proba" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict_log_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_classification_threshold.py#L168"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.FixedThresholdClassifier.predict_log_proba" title="Link to this definition">#</a></dt>
 <dd><p>Predict logarithm class probabilities for <code class="docutils literal notranslate"><span class="pre">X</span></code> using the fitted estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1519,7 +1519,7 @@ <h1>FixedThresholdClassifier<a class="headerlink" href="#fixedthresholdclassifie
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.FixedThresholdClassifier.predict_proba">
-<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_classification_threshold.py#L149"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.FixedThresholdClassifier.predict_proba" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_classification_threshold.py#L149"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.FixedThresholdClassifier.predict_proba" title="Link to this definition">#</a></dt>
 <dd><p>Predict class probabilities for <code class="docutils literal notranslate"><span class="pre">X</span></code> using the fitted estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1540,7 +1540,7 @@ <h1>FixedThresholdClassifier<a class="headerlink" href="#fixedthresholdclassifie
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.FixedThresholdClassifier.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L546"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.FixedThresholdClassifier.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L487"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.FixedThresholdClassifier.score" title="Link to this definition">#</a></dt>
 <dd><p>Return <a class="reference internal" href="http://github.com/model_evaluation.html#accuracy-score"><span class="std std-ref">accuracy</span></a> on provided data and labels.</p>
 <p>In multi-label classification, this is the subset accuracy
 which is a harsh metric since you require for each sample that
@@ -1567,7 +1567,7 @@ <h1>FixedThresholdClassifier<a class="headerlink" href="#fixedthresholdclassifie
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.FixedThresholdClassifier.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.FixedThresholdClassifier.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.FixedThresholdClassifier.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1591,7 +1591,7 @@ <h1>FixedThresholdClassifier<a class="headerlink" href="#fixedthresholdclassifie
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.FixedThresholdClassifier.set_score_request">
-<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.model_selection.FixedThresholdClassifier" title="sklearn.model_selection._classification_threshold.FixedThresholdClassifier"><span class="pre">FixedThresholdClassifier</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.FixedThresholdClassifier.set_score_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.model_selection.FixedThresholdClassifier" title="sklearn.model_selection._classification_threshold.FixedThresholdClassifier"><span class="pre">FixedThresholdClassifier</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.FixedThresholdClassifier.set_score_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1636,7 +1636,10 @@ <h1>FixedThresholdClassifier<a class="headerlink" href="#fixedthresholdclassifie
 
 <section id="gallery-examples">
 <h2>Gallery examples<a class="headerlink" href="#gallery-examples" title="Link to this heading">#</a></h2>
-<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This examples showcases some use cases of FrozenEstimator."><img alt="" src="http://github.com/_images/sphx_glr_plot_frozen_examples_thumb.png" />
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="Plot the predicted class probabilities in a toy dataset predicted by three different classifiers and averaged by the VotingClassifier."><img alt="" src="http://github.com/_images/sphx_glr_plot_voting_decision_regions_thumb.png" />
+<p><a class="reference internal" href="http://github.com/auto_examples/ensemble/plot_voting_decision_regions.html#sphx-glr-auto-examples-ensemble-plot-voting-decision-regions-py"><span class="std std-ref">Visualizing the probabilistic predictions of a VotingClassifier</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Visualizing the probabilistic predictions of a VotingClassifier</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This examples showcases some use cases of FrozenEstimator."><img alt="" src="http://github.com/_images/sphx_glr_plot_frozen_examples_thumb.png" />
 <p><a class="reference internal" href="http://github.com/auto_examples/frozen/plot_frozen_examples.html#sphx-glr-auto-examples-frozen-plot-frozen-examples-py"><span class="std std-ref">Examples of Using FrozenEstimator</span></a></p>
   <div class="sphx-glr-thumbnail-title">Examples of Using FrozenEstimator</div>
 </div><div class="sphx-glr-thumbcontainer" tooltip="Once a classifier is trained, the output of the predict method outputs class label predictions corresponding to a thresholding of either the decision_function or the predict_proba output. For a binary classifier, the default threshold is defined as a posterior probability estimate of 0.5 or a decision score of 0.0."><img alt="" src="http://github.com/_images/sphx_glr_plot_cost_sensitive_learning_thumb.png" />
diff --git a/dev/modules/generated/sklearn.model_selection.GridSearchCV.html b/dev/modules/generated/sklearn.model_selection.GridSearchCV.html
index 2b3e9bc1553b8..c7f0d9669fcf8 100644
--- a/dev/modules/generated/sklearn.model_selection.GridSearchCV.html
+++ b/dev/modules/generated/sklearn.model_selection.GridSearchCV.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>GridSearchCV<a class="headerlink" href="#gridsearchcv" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.model_selection.GridSearchCV">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.model_selection.</span></span><span class="sig-name descname"><span class="pre">GridSearchCV</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">param_grid</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scoring</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">refit</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cv</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pre_dispatch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'2*n_jobs'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_score</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">nan</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_train_score</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_search.py#L1242"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.GridSearchCV" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.model_selection.</span></span><span class="sig-name descname"><span class="pre">GridSearchCV</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">param_grid</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scoring</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">refit</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cv</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pre_dispatch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'2*n_jobs'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_score</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">nan</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_train_score</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_search.py#L1242"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.GridSearchCV" title="Link to this definition">#</a></dt>
 <dd><p>Exhaustive search over specified parameter values for an estimator.</p>
 <p>Important members are fit, predict.</p>
 <p>GridSearchCV implements a “fit” and a “score” method.
@@ -1349,7 +1349,7 @@ <h1>GridSearchCV<a class="headerlink" href="#gridsearchcv" title="Link to this h
 <p>See <a class="reference internal" href="http://github.com/grid_search.html#multimetric-grid-search"><span class="std std-ref">Specifying multiple metrics for evaluation</span></a> for an example.</p>
 </dd>
 <dt><strong>n_jobs</strong><span class="classifier">int, default=None</span></dt><dd><p>Number of jobs to run in parallel.
-<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.5.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
+<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.6.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
 <code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all processors. See <a class="reference internal" href="http://github.com/glossary.html#term-n_jobs"><span class="xref std std-term">Glossary</span></a>
 for more details.</p>
 <div class="versionchanged">
@@ -1618,7 +1618,7 @@ <h1>GridSearchCV<a class="headerlink" href="#gridsearchcv" title="Link to this h
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.GridSearchCV.decision_function">
-<span class="sig-name descname"><span class="pre">decision_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_search.py#L646"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.GridSearchCV.decision_function" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">decision_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_search.py#L646"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.GridSearchCV.decision_function" title="Link to this definition">#</a></dt>
 <dd><p>Call decision_function on the estimator with the best found parameters.</p>
 <p>Only available if <code class="docutils literal notranslate"><span class="pre">refit=True</span></code> and the underlying estimator supports
 <code class="docutils literal notranslate"><span class="pre">decision_function</span></code>.</p>
@@ -1642,7 +1642,7 @@ <h1>GridSearchCV<a class="headerlink" href="#gridsearchcv" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.GridSearchCV.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_search.py#L914"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.GridSearchCV.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_search.py#L914"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.GridSearchCV.fit" title="Link to this definition">#</a></dt>
 <dd><p>Run fit with all sets of parameters.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1678,7 +1678,7 @@ <h1>GridSearchCV<a class="headerlink" href="#gridsearchcv" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.GridSearchCV.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_search.py#L1195"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.GridSearchCV.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_search.py#L1195"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.GridSearchCV.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1698,7 +1698,7 @@ <h1>GridSearchCV<a class="headerlink" href="#gridsearchcv" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.GridSearchCV.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.GridSearchCV.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.GridSearchCV.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1719,7 +1719,7 @@ <h1>GridSearchCV<a class="headerlink" href="#gridsearchcv" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.GridSearchCV.inverse_transform">
-<span class="sig-name descname"><span class="pre">inverse_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_search.py#L691"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.GridSearchCV.inverse_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">inverse_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_search.py#L691"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.GridSearchCV.inverse_transform" title="Link to this definition">#</a></dt>
 <dd><p>Call inverse_transform on the estimator with the best found params.</p>
 <p>Only available if the underlying estimator implements
 <code class="docutils literal notranslate"><span class="pre">inverse_transform</span></code> and <code class="docutils literal notranslate"><span class="pre">refit=True</span></code>.</p>
@@ -1733,7 +1733,7 @@ <h1>GridSearchCV<a class="headerlink" href="#gridsearchcv" title="Link to this h
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><dl class="simple">
-<dt><strong>X</strong><span class="classifier">{ndarray, sparse matrix} of shape (n_samples, n_features)</span></dt><dd><p>Result of the <code class="docutils literal notranslate"><span class="pre">inverse_transform</span></code> function for <code class="docutils literal notranslate"><span class="pre">Xt</span></code> based on the
+<dt><strong>X_original</strong><span class="classifier">{ndarray, sparse matrix} of shape (n_samples, n_features)</span></dt><dd><p>Result of the <code class="docutils literal notranslate"><span class="pre">inverse_transform</span></code> function for <code class="docutils literal notranslate"><span class="pre">X</span></code> based on the
 estimator with the best found parameters.</p>
 </dd>
 </dl>
@@ -1743,7 +1743,7 @@ <h1>GridSearchCV<a class="headerlink" href="#gridsearchcv" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.GridSearchCV.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_search.py#L578"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.GridSearchCV.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_search.py#L578"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.GridSearchCV.predict" title="Link to this definition">#</a></dt>
 <dd><p>Call predict on the estimator with the best found parameters.</p>
 <p>Only available if <code class="docutils literal notranslate"><span class="pre">refit=True</span></code> and the underlying estimator supports
 <code class="docutils literal notranslate"><span class="pre">predict</span></code>.</p>
@@ -1767,7 +1767,7 @@ <h1>GridSearchCV<a class="headerlink" href="#gridsearchcv" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.GridSearchCV.predict_log_proba">
-<span class="sig-name descname"><span class="pre">predict_log_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_search.py#L623"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.GridSearchCV.predict_log_proba" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict_log_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_search.py#L623"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.GridSearchCV.predict_log_proba" title="Link to this definition">#</a></dt>
 <dd><p>Call predict_log_proba on the estimator with the best found parameters.</p>
 <p>Only available if <code class="docutils literal notranslate"><span class="pre">refit=True</span></code> and the underlying estimator supports
 <code class="docutils literal notranslate"><span class="pre">predict_log_proba</span></code>.</p>
@@ -1792,7 +1792,7 @@ <h1>GridSearchCV<a class="headerlink" href="#gridsearchcv" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.GridSearchCV.predict_proba">
-<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_search.py#L600"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.GridSearchCV.predict_proba" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_search.py#L600"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.GridSearchCV.predict_proba" title="Link to this definition">#</a></dt>
 <dd><p>Call predict_proba on the estimator with the best found parameters.</p>
 <p>Only available if <code class="docutils literal notranslate"><span class="pre">refit=True</span></code> and the underlying estimator supports
 <code class="docutils literal notranslate"><span class="pre">predict_proba</span></code>.</p>
@@ -1817,7 +1817,7 @@ <h1>GridSearchCV<a class="headerlink" href="#gridsearchcv" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.GridSearchCV.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_search.py#L496"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.GridSearchCV.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_search.py#L496"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.GridSearchCV.score" title="Link to this definition">#</a></dt>
 <dd><p>Return the score on the given data, if the estimator has been refit.</p>
 <p>This uses the score defined by <code class="docutils literal notranslate"><span class="pre">scoring</span></code> where provided, and the
 <code class="docutils literal notranslate"><span class="pre">best_estimator_.score</span></code> method otherwise.</p>
@@ -1851,7 +1851,7 @@ <h1>GridSearchCV<a class="headerlink" href="#gridsearchcv" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.GridSearchCV.score_samples">
-<span class="sig-name descname"><span class="pre">score_samples</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_search.py#L555"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.GridSearchCV.score_samples" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score_samples</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_search.py#L555"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.GridSearchCV.score_samples" title="Link to this definition">#</a></dt>
 <dd><p>Call score_samples on the estimator with the best found parameters.</p>
 <p>Only available if <code class="docutils literal notranslate"><span class="pre">refit=True</span></code> and the underlying estimator supports
 <code class="docutils literal notranslate"><span class="pre">score_samples</span></code>.</p>
@@ -1877,7 +1877,7 @@ <h1>GridSearchCV<a class="headerlink" href="#gridsearchcv" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.GridSearchCV.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.GridSearchCV.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.GridSearchCV.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1901,7 +1901,7 @@ <h1>GridSearchCV<a class="headerlink" href="#gridsearchcv" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.GridSearchCV.transform">
-<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_search.py#L669"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.GridSearchCV.transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_search.py#L669"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.GridSearchCV.transform" title="Link to this definition">#</a></dt>
 <dd><p>Call transform on the estimator with the best found parameters.</p>
 <p>Only available if the underlying estimator supports <code class="docutils literal notranslate"><span class="pre">transform</span></code> and
 <code class="docutils literal notranslate"><span class="pre">refit=True</span></code>.</p>
diff --git a/dev/modules/generated/sklearn.model_selection.GroupKFold.html b/dev/modules/generated/sklearn.model_selection.GroupKFold.html
index 8def9d9a24020..4ce312e7947c5 100644
--- a/dev/modules/generated/sklearn.model_selection.GroupKFold.html
+++ b/dev/modules/generated/sklearn.model_selection.GroupKFold.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>GroupKFold<a class="headerlink" href="#groupkfold" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.model_selection.GroupKFold">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.model_selection.</span></span><span class="sig-name descname"><span class="pre">GroupKFold</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_splits</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_split.py#L532"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.GroupKFold" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.model_selection.</span></span><span class="sig-name descname"><span class="pre">GroupKFold</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_splits</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_split.py#L532"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.GroupKFold" title="Link to this definition">#</a></dt>
 <dd><p>K-fold iterator variant with non-overlapping groups.</p>
 <p>Each group will appear exactly once in the test set across all folds (the
 number of distinct groups has to be at least equal to the number of folds).</p>
@@ -1379,7 +1379,7 @@ <h1>GroupKFold<a class="headerlink" href="#groupkfold" title="Link to this headi
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.GroupKFold.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.GroupKFold.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.GroupKFold.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1396,7 +1396,7 @@ <h1>GroupKFold<a class="headerlink" href="#groupkfold" title="Link to this headi
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.GroupKFold.get_n_splits">
-<span class="sig-name descname"><span class="pre">get_n_splits</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_split.py#L414"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.GroupKFold.get_n_splits" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_n_splits</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_split.py#L414"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.GroupKFold.get_n_splits" title="Link to this definition">#</a></dt>
 <dd><p>Returns the number of splitting iterations in the cross-validator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1420,7 +1420,7 @@ <h1>GroupKFold<a class="headerlink" href="#groupkfold" title="Link to this headi
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.GroupKFold.set_split_request">
-<span class="sig-name descname"><span class="pre">set_split_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.model_selection.GroupKFold" title="sklearn.model_selection._split.GroupKFold"><span class="pre">GroupKFold</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.GroupKFold.set_split_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_split_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.model_selection.GroupKFold" title="sklearn.model_selection._split.GroupKFold"><span class="pre">GroupKFold</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.GroupKFold.set_split_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">split</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1463,7 +1463,7 @@ <h1>GroupKFold<a class="headerlink" href="#groupkfold" title="Link to this headi
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.GroupKFold.split">
-<span class="sig-name descname"><span class="pre">split</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_split.py#L659"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.GroupKFold.split" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">split</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_split.py#L659"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.GroupKFold.split" title="Link to this definition">#</a></dt>
 <dd><p>Generate indices to split data into training and test set.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.model_selection.GroupShuffleSplit.html b/dev/modules/generated/sklearn.model_selection.GroupShuffleSplit.html
index 6b91555da8663..0013db0d81922 100644
--- a/dev/modules/generated/sklearn.model_selection.GroupShuffleSplit.html
+++ b/dev/modules/generated/sklearn.model_selection.GroupShuffleSplit.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>GroupShuffleSplit<a class="headerlink" href="#groupshufflesplit" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.model_selection.GroupShuffleSplit">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.model_selection.</span></span><span class="sig-name descname"><span class="pre">GroupShuffleSplit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_splits</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">test_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">train_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_split.py#L2078"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.GroupShuffleSplit" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.model_selection.</span></span><span class="sig-name descname"><span class="pre">GroupShuffleSplit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_splits</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">test_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">train_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_split.py#L2078"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.GroupShuffleSplit" title="Link to this definition">#</a></dt>
 <dd><p>Shuffle-Group(s)-Out cross-validation iterator.</p>
 <p>Provides randomized train/test indices to split data according to a
 third-party provided group. This group information can be used to encode
@@ -1390,7 +1390,7 @@ <h1>GroupShuffleSplit<a class="headerlink" href="#groupshufflesplit" title="Link
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.GroupShuffleSplit.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.GroupShuffleSplit.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.GroupShuffleSplit.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1407,7 +1407,7 @@ <h1>GroupShuffleSplit<a class="headerlink" href="#groupshufflesplit" title="Link
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.GroupShuffleSplit.get_n_splits">
-<span class="sig-name descname"><span class="pre">get_n_splits</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_split.py#L1948"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.GroupShuffleSplit.get_n_splits" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_n_splits</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_split.py#L1948"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.GroupShuffleSplit.get_n_splits" title="Link to this definition">#</a></dt>
 <dd><p>Returns the number of splitting iterations in the cross-validator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1431,7 +1431,7 @@ <h1>GroupShuffleSplit<a class="headerlink" href="#groupshufflesplit" title="Link
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.GroupShuffleSplit.set_split_request">
-<span class="sig-name descname"><span class="pre">set_split_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.model_selection.GroupShuffleSplit" title="sklearn.model_selection._split.GroupShuffleSplit"><span class="pre">GroupShuffleSplit</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.GroupShuffleSplit.set_split_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_split_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.model_selection.GroupShuffleSplit" title="sklearn.model_selection._split.GroupShuffleSplit"><span class="pre">GroupShuffleSplit</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.GroupShuffleSplit.set_split_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">split</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1474,7 +1474,7 @@ <h1>GroupShuffleSplit<a class="headerlink" href="#groupshufflesplit" title="Link
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.GroupShuffleSplit.split">
-<span class="sig-name descname"><span class="pre">split</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_split.py#L2191"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.GroupShuffleSplit.split" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">split</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_split.py#L2191"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.GroupShuffleSplit.split" title="Link to this definition">#</a></dt>
 <dd><p>Generate indices to split data into training and test set.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.model_selection.HalvingGridSearchCV.html b/dev/modules/generated/sklearn.model_selection.HalvingGridSearchCV.html
index c6cd2953a9ad8..3d6b680548ce4 100644
--- a/dev/modules/generated/sklearn.model_selection.HalvingGridSearchCV.html
+++ b/dev/modules/generated/sklearn.model_selection.HalvingGridSearchCV.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
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@@ -1307,7 +1307,7 @@
 <h1>HalvingGridSearchCV<a class="headerlink" href="#halvinggridsearchcv" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.model_selection.HalvingGridSearchCV">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.model_selection.</span></span><span class="sig-name descname"><span class="pre">HalvingGridSearchCV</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">param_grid</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">resource</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'n_samples'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_resources</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_resources</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'exhaust'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">aggressive_elimination</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cv</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scoring</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">refit</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_score</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">nan</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_train_score</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_search_successive_halving.py#L374"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.HalvingGridSearchCV" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.model_selection.</span></span><span class="sig-name descname"><span class="pre">HalvingGridSearchCV</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">param_grid</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">resource</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'n_samples'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_resources</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_resources</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'exhaust'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">aggressive_elimination</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cv</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scoring</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">refit</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_score</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">nan</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_train_score</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_search_successive_halving.py#L374"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.HalvingGridSearchCV" title="Link to this definition">#</a></dt>
 <dd><p>Search over specified parameter values with successive halving.</p>
 <p>The search strategy starts evaluating all the candidates with a small
 amount of resources and iteratively selects the best candidates, using
@@ -1440,7 +1440,7 @@ <h1>HalvingGridSearchCV<a class="headerlink" href="#halvinggridsearchcv" title="
 See <a class="reference internal" href="http://github.com/glossary.html#term-random_state"><span class="xref std std-term">Glossary</span></a>.</p>
 </dd>
 <dt><strong>n_jobs</strong><span class="classifier">int or None, default=None</span></dt><dd><p>Number of jobs to run in parallel.
-<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.5.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
+<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.6.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
 <code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all processors. See <a class="reference internal" href="http://github.com/glossary.html#term-n_jobs"><span class="xref std std-term">Glossary</span></a>
 for more details.</p>
 </dd>
@@ -1557,7 +1557,7 @@ <h1>HalvingGridSearchCV<a class="headerlink" href="#halvinggridsearchcv" title="
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.HalvingGridSearchCV.decision_function">
-<span class="sig-name descname"><span class="pre">decision_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_search.py#L646"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.HalvingGridSearchCV.decision_function" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">decision_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_search.py#L646"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.HalvingGridSearchCV.decision_function" title="Link to this definition">#</a></dt>
 <dd><p>Call decision_function on the estimator with the best found parameters.</p>
 <p>Only available if <code class="docutils literal notranslate"><span class="pre">refit=True</span></code> and the underlying estimator supports
 <code class="docutils literal notranslate"><span class="pre">decision_function</span></code>.</p>
@@ -1581,7 +1581,7 @@ <h1>HalvingGridSearchCV<a class="headerlink" href="#halvinggridsearchcv" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.HalvingGridSearchCV.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_search_successive_halving.py#L216"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.HalvingGridSearchCV.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_search_successive_halving.py#L216"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.HalvingGridSearchCV.fit" title="Link to this definition">#</a></dt>
 <dd><p>Run fit with all sets of parameters.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1607,7 +1607,7 @@ <h1>HalvingGridSearchCV<a class="headerlink" href="#halvinggridsearchcv" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.HalvingGridSearchCV.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_search.py#L1195"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.HalvingGridSearchCV.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_search.py#L1195"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.HalvingGridSearchCV.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1627,7 +1627,7 @@ <h1>HalvingGridSearchCV<a class="headerlink" href="#halvinggridsearchcv" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.HalvingGridSearchCV.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.HalvingGridSearchCV.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.HalvingGridSearchCV.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1648,7 +1648,7 @@ <h1>HalvingGridSearchCV<a class="headerlink" href="#halvinggridsearchcv" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.HalvingGridSearchCV.inverse_transform">
-<span class="sig-name descname"><span class="pre">inverse_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_search.py#L691"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.HalvingGridSearchCV.inverse_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">inverse_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_search.py#L691"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.HalvingGridSearchCV.inverse_transform" title="Link to this definition">#</a></dt>
 <dd><p>Call inverse_transform on the estimator with the best found params.</p>
 <p>Only available if the underlying estimator implements
 <code class="docutils literal notranslate"><span class="pre">inverse_transform</span></code> and <code class="docutils literal notranslate"><span class="pre">refit=True</span></code>.</p>
@@ -1662,7 +1662,7 @@ <h1>HalvingGridSearchCV<a class="headerlink" href="#halvinggridsearchcv" title="
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><dl class="simple">
-<dt><strong>X</strong><span class="classifier">{ndarray, sparse matrix} of shape (n_samples, n_features)</span></dt><dd><p>Result of the <code class="docutils literal notranslate"><span class="pre">inverse_transform</span></code> function for <code class="docutils literal notranslate"><span class="pre">Xt</span></code> based on the
+<dt><strong>X_original</strong><span class="classifier">{ndarray, sparse matrix} of shape (n_samples, n_features)</span></dt><dd><p>Result of the <code class="docutils literal notranslate"><span class="pre">inverse_transform</span></code> function for <code class="docutils literal notranslate"><span class="pre">X</span></code> based on the
 estimator with the best found parameters.</p>
 </dd>
 </dl>
@@ -1672,7 +1672,7 @@ <h1>HalvingGridSearchCV<a class="headerlink" href="#halvinggridsearchcv" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.HalvingGridSearchCV.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_search.py#L578"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.HalvingGridSearchCV.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_search.py#L578"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.HalvingGridSearchCV.predict" title="Link to this definition">#</a></dt>
 <dd><p>Call predict on the estimator with the best found parameters.</p>
 <p>Only available if <code class="docutils literal notranslate"><span class="pre">refit=True</span></code> and the underlying estimator supports
 <code class="docutils literal notranslate"><span class="pre">predict</span></code>.</p>
@@ -1696,7 +1696,7 @@ <h1>HalvingGridSearchCV<a class="headerlink" href="#halvinggridsearchcv" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.HalvingGridSearchCV.predict_log_proba">
-<span class="sig-name descname"><span class="pre">predict_log_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_search.py#L623"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.HalvingGridSearchCV.predict_log_proba" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict_log_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_search.py#L623"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.HalvingGridSearchCV.predict_log_proba" title="Link to this definition">#</a></dt>
 <dd><p>Call predict_log_proba on the estimator with the best found parameters.</p>
 <p>Only available if <code class="docutils literal notranslate"><span class="pre">refit=True</span></code> and the underlying estimator supports
 <code class="docutils literal notranslate"><span class="pre">predict_log_proba</span></code>.</p>
@@ -1721,7 +1721,7 @@ <h1>HalvingGridSearchCV<a class="headerlink" href="#halvinggridsearchcv" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.HalvingGridSearchCV.predict_proba">
-<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_search.py#L600"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.HalvingGridSearchCV.predict_proba" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_search.py#L600"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.HalvingGridSearchCV.predict_proba" title="Link to this definition">#</a></dt>
 <dd><p>Call predict_proba on the estimator with the best found parameters.</p>
 <p>Only available if <code class="docutils literal notranslate"><span class="pre">refit=True</span></code> and the underlying estimator supports
 <code class="docutils literal notranslate"><span class="pre">predict_proba</span></code>.</p>
@@ -1746,7 +1746,7 @@ <h1>HalvingGridSearchCV<a class="headerlink" href="#halvinggridsearchcv" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.HalvingGridSearchCV.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_search.py#L496"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.HalvingGridSearchCV.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_search.py#L496"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.HalvingGridSearchCV.score" title="Link to this definition">#</a></dt>
 <dd><p>Return the score on the given data, if the estimator has been refit.</p>
 <p>This uses the score defined by <code class="docutils literal notranslate"><span class="pre">scoring</span></code> where provided, and the
 <code class="docutils literal notranslate"><span class="pre">best_estimator_.score</span></code> method otherwise.</p>
@@ -1780,7 +1780,7 @@ <h1>HalvingGridSearchCV<a class="headerlink" href="#halvinggridsearchcv" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.HalvingGridSearchCV.score_samples">
-<span class="sig-name descname"><span class="pre">score_samples</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_search.py#L555"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.HalvingGridSearchCV.score_samples" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score_samples</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_search.py#L555"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.HalvingGridSearchCV.score_samples" title="Link to this definition">#</a></dt>
 <dd><p>Call score_samples on the estimator with the best found parameters.</p>
 <p>Only available if <code class="docutils literal notranslate"><span class="pre">refit=True</span></code> and the underlying estimator supports
 <code class="docutils literal notranslate"><span class="pre">score_samples</span></code>.</p>
@@ -1806,7 +1806,7 @@ <h1>HalvingGridSearchCV<a class="headerlink" href="#halvinggridsearchcv" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.HalvingGridSearchCV.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.HalvingGridSearchCV.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.HalvingGridSearchCV.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1830,7 +1830,7 @@ <h1>HalvingGridSearchCV<a class="headerlink" href="#halvinggridsearchcv" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.HalvingGridSearchCV.transform">
-<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_search.py#L669"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.HalvingGridSearchCV.transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_search.py#L669"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.HalvingGridSearchCV.transform" title="Link to this definition">#</a></dt>
 <dd><p>Call transform on the estimator with the best found parameters.</p>
 <p>Only available if the underlying estimator supports <code class="docutils literal notranslate"><span class="pre">transform</span></code> and
 <code class="docutils literal notranslate"><span class="pre">refit=True</span></code>.</p>
diff --git a/dev/modules/generated/sklearn.model_selection.HalvingRandomSearchCV.html b/dev/modules/generated/sklearn.model_selection.HalvingRandomSearchCV.html
index f50e9378d8a41..f944764592503 100644
--- a/dev/modules/generated/sklearn.model_selection.HalvingRandomSearchCV.html
+++ b/dev/modules/generated/sklearn.model_selection.HalvingRandomSearchCV.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>HalvingRandomSearchCV<a class="headerlink" href="#halvingrandomsearchcv" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.model_selection.HalvingRandomSearchCV">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.model_selection.</span></span><span class="sig-name descname"><span class="pre">HalvingRandomSearchCV</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">param_distributions</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_candidates</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'exhaust'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">resource</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'n_samples'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_resources</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_resources</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'smallest'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">aggressive_elimination</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cv</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scoring</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">refit</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_score</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">nan</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_train_score</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_search_successive_halving.py#L708"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.HalvingRandomSearchCV" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.model_selection.</span></span><span class="sig-name descname"><span class="pre">HalvingRandomSearchCV</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">param_distributions</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_candidates</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'exhaust'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">resource</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'n_samples'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_resources</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_resources</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'smallest'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">aggressive_elimination</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cv</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scoring</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">refit</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_score</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">nan</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_train_score</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_search_successive_halving.py#L708"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.HalvingRandomSearchCV" title="Link to this definition">#</a></dt>
 <dd><p>Randomized search on hyper parameters.</p>
 <p>The search strategy starts evaluating all the candidates with a small
 amount of resources and iteratively selects the best candidates, using more
@@ -1451,7 +1451,7 @@ <h1>HalvingRandomSearchCV<a class="headerlink" href="#halvingrandomsearchcv" tit
 See <a class="reference internal" href="http://github.com/glossary.html#term-random_state"><span class="xref std std-term">Glossary</span></a>.</p>
 </dd>
 <dt><strong>n_jobs</strong><span class="classifier">int or None, default=None</span></dt><dd><p>Number of jobs to run in parallel.
-<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.5.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
+<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.6.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
 <code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all processors. See <a class="reference internal" href="http://github.com/glossary.html#term-n_jobs"><span class="xref std std-term">Glossary</span></a>
 for more details.</p>
 </dd>
@@ -1572,7 +1572,7 @@ <h1>HalvingRandomSearchCV<a class="headerlink" href="#halvingrandomsearchcv" tit
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.HalvingRandomSearchCV.decision_function">
-<span class="sig-name descname"><span class="pre">decision_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_search.py#L646"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.HalvingRandomSearchCV.decision_function" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">decision_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_search.py#L646"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.HalvingRandomSearchCV.decision_function" title="Link to this definition">#</a></dt>
 <dd><p>Call decision_function on the estimator with the best found parameters.</p>
 <p>Only available if <code class="docutils literal notranslate"><span class="pre">refit=True</span></code> and the underlying estimator supports
 <code class="docutils literal notranslate"><span class="pre">decision_function</span></code>.</p>
@@ -1596,7 +1596,7 @@ <h1>HalvingRandomSearchCV<a class="headerlink" href="#halvingrandomsearchcv" tit
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.HalvingRandomSearchCV.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_search_successive_halving.py#L216"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.HalvingRandomSearchCV.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_search_successive_halving.py#L216"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.HalvingRandomSearchCV.fit" title="Link to this definition">#</a></dt>
 <dd><p>Run fit with all sets of parameters.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1622,7 +1622,7 @@ <h1>HalvingRandomSearchCV<a class="headerlink" href="#halvingrandomsearchcv" tit
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.HalvingRandomSearchCV.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_search.py#L1195"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.HalvingRandomSearchCV.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_search.py#L1195"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.HalvingRandomSearchCV.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1642,7 +1642,7 @@ <h1>HalvingRandomSearchCV<a class="headerlink" href="#halvingrandomsearchcv" tit
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.HalvingRandomSearchCV.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.HalvingRandomSearchCV.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.HalvingRandomSearchCV.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1663,7 +1663,7 @@ <h1>HalvingRandomSearchCV<a class="headerlink" href="#halvingrandomsearchcv" tit
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.HalvingRandomSearchCV.inverse_transform">
-<span class="sig-name descname"><span class="pre">inverse_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_search.py#L691"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.HalvingRandomSearchCV.inverse_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">inverse_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_search.py#L691"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.HalvingRandomSearchCV.inverse_transform" title="Link to this definition">#</a></dt>
 <dd><p>Call inverse_transform on the estimator with the best found params.</p>
 <p>Only available if the underlying estimator implements
 <code class="docutils literal notranslate"><span class="pre">inverse_transform</span></code> and <code class="docutils literal notranslate"><span class="pre">refit=True</span></code>.</p>
@@ -1677,7 +1677,7 @@ <h1>HalvingRandomSearchCV<a class="headerlink" href="#halvingrandomsearchcv" tit
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><dl class="simple">
-<dt><strong>X</strong><span class="classifier">{ndarray, sparse matrix} of shape (n_samples, n_features)</span></dt><dd><p>Result of the <code class="docutils literal notranslate"><span class="pre">inverse_transform</span></code> function for <code class="docutils literal notranslate"><span class="pre">Xt</span></code> based on the
+<dt><strong>X_original</strong><span class="classifier">{ndarray, sparse matrix} of shape (n_samples, n_features)</span></dt><dd><p>Result of the <code class="docutils literal notranslate"><span class="pre">inverse_transform</span></code> function for <code class="docutils literal notranslate"><span class="pre">X</span></code> based on the
 estimator with the best found parameters.</p>
 </dd>
 </dl>
@@ -1687,7 +1687,7 @@ <h1>HalvingRandomSearchCV<a class="headerlink" href="#halvingrandomsearchcv" tit
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.HalvingRandomSearchCV.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_search.py#L578"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.HalvingRandomSearchCV.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_search.py#L578"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.HalvingRandomSearchCV.predict" title="Link to this definition">#</a></dt>
 <dd><p>Call predict on the estimator with the best found parameters.</p>
 <p>Only available if <code class="docutils literal notranslate"><span class="pre">refit=True</span></code> and the underlying estimator supports
 <code class="docutils literal notranslate"><span class="pre">predict</span></code>.</p>
@@ -1711,7 +1711,7 @@ <h1>HalvingRandomSearchCV<a class="headerlink" href="#halvingrandomsearchcv" tit
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.HalvingRandomSearchCV.predict_log_proba">
-<span class="sig-name descname"><span class="pre">predict_log_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_search.py#L623"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.HalvingRandomSearchCV.predict_log_proba" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict_log_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_search.py#L623"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.HalvingRandomSearchCV.predict_log_proba" title="Link to this definition">#</a></dt>
 <dd><p>Call predict_log_proba on the estimator with the best found parameters.</p>
 <p>Only available if <code class="docutils literal notranslate"><span class="pre">refit=True</span></code> and the underlying estimator supports
 <code class="docutils literal notranslate"><span class="pre">predict_log_proba</span></code>.</p>
@@ -1736,7 +1736,7 @@ <h1>HalvingRandomSearchCV<a class="headerlink" href="#halvingrandomsearchcv" tit
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.HalvingRandomSearchCV.predict_proba">
-<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_search.py#L600"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.HalvingRandomSearchCV.predict_proba" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_search.py#L600"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.HalvingRandomSearchCV.predict_proba" title="Link to this definition">#</a></dt>
 <dd><p>Call predict_proba on the estimator with the best found parameters.</p>
 <p>Only available if <code class="docutils literal notranslate"><span class="pre">refit=True</span></code> and the underlying estimator supports
 <code class="docutils literal notranslate"><span class="pre">predict_proba</span></code>.</p>
@@ -1761,7 +1761,7 @@ <h1>HalvingRandomSearchCV<a class="headerlink" href="#halvingrandomsearchcv" tit
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.HalvingRandomSearchCV.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_search.py#L496"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.HalvingRandomSearchCV.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_search.py#L496"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.HalvingRandomSearchCV.score" title="Link to this definition">#</a></dt>
 <dd><p>Return the score on the given data, if the estimator has been refit.</p>
 <p>This uses the score defined by <code class="docutils literal notranslate"><span class="pre">scoring</span></code> where provided, and the
 <code class="docutils literal notranslate"><span class="pre">best_estimator_.score</span></code> method otherwise.</p>
@@ -1795,7 +1795,7 @@ <h1>HalvingRandomSearchCV<a class="headerlink" href="#halvingrandomsearchcv" tit
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.HalvingRandomSearchCV.score_samples">
-<span class="sig-name descname"><span class="pre">score_samples</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_search.py#L555"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.HalvingRandomSearchCV.score_samples" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score_samples</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_search.py#L555"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.HalvingRandomSearchCV.score_samples" title="Link to this definition">#</a></dt>
 <dd><p>Call score_samples on the estimator with the best found parameters.</p>
 <p>Only available if <code class="docutils literal notranslate"><span class="pre">refit=True</span></code> and the underlying estimator supports
 <code class="docutils literal notranslate"><span class="pre">score_samples</span></code>.</p>
@@ -1821,7 +1821,7 @@ <h1>HalvingRandomSearchCV<a class="headerlink" href="#halvingrandomsearchcv" tit
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.HalvingRandomSearchCV.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.HalvingRandomSearchCV.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.HalvingRandomSearchCV.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1845,7 +1845,7 @@ <h1>HalvingRandomSearchCV<a class="headerlink" href="#halvingrandomsearchcv" tit
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.HalvingRandomSearchCV.transform">
-<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_search.py#L669"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.HalvingRandomSearchCV.transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_search.py#L669"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.HalvingRandomSearchCV.transform" title="Link to this definition">#</a></dt>
 <dd><p>Call transform on the estimator with the best found parameters.</p>
 <p>Only available if the underlying estimator supports <code class="docutils literal notranslate"><span class="pre">transform</span></code> and
 <code class="docutils literal notranslate"><span class="pre">refit=True</span></code>.</p>
diff --git a/dev/modules/generated/sklearn.model_selection.KFold.html b/dev/modules/generated/sklearn.model_selection.KFold.html
index 24497476669df..72623021abd67 100644
--- a/dev/modules/generated/sklearn.model_selection.KFold.html
+++ b/dev/modules/generated/sklearn.model_selection.KFold.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>KFold<a class="headerlink" href="#kfold" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.model_selection.KFold">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.model_selection.</span></span><span class="sig-name descname"><span class="pre">KFold</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_splits</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_split.py#L436"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.KFold" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.model_selection.</span></span><span class="sig-name descname"><span class="pre">KFold</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_splits</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_split.py#L436"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.KFold" title="Link to this definition">#</a></dt>
 <dd><p>K-Fold cross-validator.</p>
 <p>Provides train/test indices to split data in train/test sets. Split
 dataset into k consecutive folds (without shuffling by default).</p>
@@ -1379,7 +1379,7 @@ <h1>KFold<a class="headerlink" href="#kfold" title="Link to this heading">#</a><
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.KFold.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.KFold.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.KFold.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1396,7 +1396,7 @@ <h1>KFold<a class="headerlink" href="#kfold" title="Link to this heading">#</a><
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.KFold.get_n_splits">
-<span class="sig-name descname"><span class="pre">get_n_splits</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_split.py#L414"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.KFold.get_n_splits" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_n_splits</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_split.py#L414"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.KFold.get_n_splits" title="Link to this definition">#</a></dt>
 <dd><p>Returns the number of splitting iterations in the cross-validator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1420,7 +1420,7 @@ <h1>KFold<a class="headerlink" href="#kfold" title="Link to this heading">#</a><
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.KFold.split">
-<span class="sig-name descname"><span class="pre">split</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_split.py#L62"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.KFold.split" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">split</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_split.py#L62"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.KFold.split" title="Link to this definition">#</a></dt>
 <dd><p>Generate indices to split data into training and test set.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.model_selection.LearningCurveDisplay.html b/dev/modules/generated/sklearn.model_selection.LearningCurveDisplay.html
index 1b66e5ebfed37..512eb792fc09f 100644
--- a/dev/modules/generated/sklearn.model_selection.LearningCurveDisplay.html
+++ b/dev/modules/generated/sklearn.model_selection.LearningCurveDisplay.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>LearningCurveDisplay<a class="headerlink" href="#learningcurvedisplay" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.model_selection.LearningCurveDisplay">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.model_selection.</span></span><span class="sig-name descname"><span class="pre">LearningCurveDisplay</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">train_sizes</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">train_scores</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">test_scores</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">score_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_plot.py#L126"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.LearningCurveDisplay" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.model_selection.</span></span><span class="sig-name descname"><span class="pre">LearningCurveDisplay</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">train_sizes</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">train_scores</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">test_scores</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">score_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_plot.py#L126"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.LearningCurveDisplay" title="Link to this definition">#</a></dt>
 <dd><p>Learning Curve visualization.</p>
 <p>It is recommended to use
 <a class="reference internal" href="#sklearn.model_selection.LearningCurveDisplay.from_estimator" title="sklearn.model_selection.LearningCurveDisplay.from_estimator"><code class="xref py py-meth docutils literal notranslate"><span class="pre">from_estimator</span></code></a> to
@@ -1388,7 +1388,7 @@ <h1>LearningCurveDisplay<a class="headerlink" href="#learningcurvedisplay" title
 </figure>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.LearningCurveDisplay.from_estimator">
-<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_estimator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">train_sizes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">array([0.1,</span> <span class="pre">0.33,</span> <span class="pre">0.55,</span> <span class="pre">0.78,</span> <span class="pre">1.])</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cv</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scoring</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">exploit_incremental_learning</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pre_dispatch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'all'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_score</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">nan</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">negate_score</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">score_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">score_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'both'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">std_display_style</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'fill_between'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">line_kw</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fill_between_kw</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">errorbar_kw</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_plot.py#L286"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.LearningCurveDisplay.from_estimator" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_estimator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">train_sizes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">array([0.1,</span> <span class="pre">0.33,</span> <span class="pre">0.55,</span> <span class="pre">0.78,</span> <span class="pre">1.])</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cv</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scoring</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">exploit_incremental_learning</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pre_dispatch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'all'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_score</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">nan</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">negate_score</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">score_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">score_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'both'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">std_display_style</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'fill_between'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">line_kw</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fill_between_kw</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">errorbar_kw</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_plot.py#L286"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.LearningCurveDisplay.from_estimator" title="Link to this definition">#</a></dt>
 <dd><p>Create a learning curve display from an estimator.</p>
 <p>Read more in the <a class="reference internal" href="http://github.com/visualizations.html#visualizations"><span class="std std-ref">User Guide</span></a> for general
 information about the visualization API and <a class="reference internal" href="http://github.com/learning_curve.html#learning-curve"><span class="std std-ref">detailed
@@ -1450,7 +1450,7 @@ <h1>LearningCurveDisplay<a class="headerlink" href="#learningcurvedisplay" title
 <dt><strong>n_jobs</strong><span class="classifier">int, default=None</span></dt><dd><p>Number of jobs to run in parallel. Training the estimator and
 computing the score are parallelized over the different training
 and test sets. <code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a
-<a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.5.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context. <code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all
+<a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.6.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context. <code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all
 processors. See <a class="reference internal" href="http://github.com/glossary.html#term-n_jobs"><span class="xref std std-term">Glossary</span></a> for more details.</p>
 </dd>
 <dt><strong>pre_dispatch</strong><span class="classifier">int or str, default=’all’</span></dt><dd><p>Number of predispatched jobs for parallel execution (default is
@@ -1532,7 +1532,7 @@ <h1>LearningCurveDisplay<a class="headerlink" href="#learningcurvedisplay" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.LearningCurveDisplay.plot">
-<span class="sig-name descname"><span class="pre">plot</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">negate_score</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">score_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">score_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'both'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">std_display_style</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'fill_between'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">line_kw</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fill_between_kw</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">errorbar_kw</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_plot.py#L211"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.LearningCurveDisplay.plot" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">plot</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">negate_score</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">score_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">score_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'both'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">std_display_style</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'fill_between'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">line_kw</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fill_between_kw</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">errorbar_kw</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_plot.py#L211"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.LearningCurveDisplay.plot" title="Link to this definition">#</a></dt>
 <dd><p>Plot visualization.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.model_selection.LeaveOneGroupOut.html b/dev/modules/generated/sklearn.model_selection.LeaveOneGroupOut.html
index 7666e141237e8..7f5ec8ea3ccc7 100644
--- a/dev/modules/generated/sklearn.model_selection.LeaveOneGroupOut.html
+++ b/dev/modules/generated/sklearn.model_selection.LeaveOneGroupOut.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>LeaveOneGroupOut<a class="headerlink" href="#leaveonegroupout" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.model_selection.LeaveOneGroupOut">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.model_selection.</span></span><span class="sig-name descname"><span class="pre">LeaveOneGroupOut</span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_split.py#L1316"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.LeaveOneGroupOut" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.model_selection.</span></span><span class="sig-name descname"><span class="pre">LeaveOneGroupOut</span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_split.py#L1316"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.LeaveOneGroupOut" title="Link to this definition">#</a></dt>
 <dd><p>Leave One Group Out cross-validator.</p>
 <p>Provides train/test indices to split data such that each training set is
 comprised of all samples except ones belonging to one specific group.
@@ -1352,7 +1352,7 @@ <h1>LeaveOneGroupOut<a class="headerlink" href="#leaveonegroupout" title="Link t
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.LeaveOneGroupOut.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.LeaveOneGroupOut.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.LeaveOneGroupOut.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1369,7 +1369,7 @@ <h1>LeaveOneGroupOut<a class="headerlink" href="#leaveonegroupout" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.LeaveOneGroupOut.get_n_splits">
-<span class="sig-name descname"><span class="pre">get_n_splits</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_split.py#L1381"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.LeaveOneGroupOut.get_n_splits" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_n_splits</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_split.py#L1381"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.LeaveOneGroupOut.get_n_splits" title="Link to this definition">#</a></dt>
 <dd><p>Returns the number of splitting iterations in the cross-validator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1396,7 +1396,7 @@ <h1>LeaveOneGroupOut<a class="headerlink" href="#leaveonegroupout" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.LeaveOneGroupOut.set_split_request">
-<span class="sig-name descname"><span class="pre">set_split_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.model_selection.LeaveOneGroupOut" title="sklearn.model_selection._split.LeaveOneGroupOut"><span class="pre">LeaveOneGroupOut</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.LeaveOneGroupOut.set_split_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_split_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.model_selection.LeaveOneGroupOut" title="sklearn.model_selection._split.LeaveOneGroupOut"><span class="pre">LeaveOneGroupOut</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.LeaveOneGroupOut.set_split_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">split</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1439,7 +1439,7 @@ <h1>LeaveOneGroupOut<a class="headerlink" href="#leaveonegroupout" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.LeaveOneGroupOut.split">
-<span class="sig-name descname"><span class="pre">split</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_split.py#L1408"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.LeaveOneGroupOut.split" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">split</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_split.py#L1408"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.LeaveOneGroupOut.split" title="Link to this definition">#</a></dt>
 <dd><p>Generate indices to split data into training and test set.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.model_selection.LeaveOneOut.html b/dev/modules/generated/sklearn.model_selection.LeaveOneOut.html
index f6c287af6ecb3..6ef0b8d941b65 100644
--- a/dev/modules/generated/sklearn.model_selection.LeaveOneOut.html
+++ b/dev/modules/generated/sklearn.model_selection.LeaveOneOut.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>LeaveOneOut<a class="headerlink" href="#leaveoneout" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.model_selection.LeaveOneOut">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.model_selection.</span></span><span class="sig-name descname"><span class="pre">LeaveOneOut</span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_split.py#L171"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.LeaveOneOut" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.model_selection.</span></span><span class="sig-name descname"><span class="pre">LeaveOneOut</span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_split.py#L171"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.LeaveOneOut" title="Link to this definition">#</a></dt>
 <dd><p>Leave-One-Out cross-validator.</p>
 <p>Provides train/test indices to split data in train/test sets. Each
 sample is used once as a test set (singleton) while the remaining
@@ -1350,7 +1350,7 @@ <h1>LeaveOneOut<a class="headerlink" href="#leaveoneout" title="Link to this hea
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.LeaveOneOut.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.LeaveOneOut.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.LeaveOneOut.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1367,7 +1367,7 @@ <h1>LeaveOneOut<a class="headerlink" href="#leaveoneout" title="Link to this hea
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.LeaveOneOut.get_n_splits">
-<span class="sig-name descname"><span class="pre">get_n_splits</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_split.py#L225"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.LeaveOneOut.get_n_splits" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_n_splits</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_split.py#L225"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.LeaveOneOut.get_n_splits" title="Link to this definition">#</a></dt>
 <dd><p>Returns the number of splitting iterations in the cross-validator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1392,7 +1392,7 @@ <h1>LeaveOneOut<a class="headerlink" href="#leaveoneout" title="Link to this hea
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.LeaveOneOut.split">
-<span class="sig-name descname"><span class="pre">split</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_split.py#L62"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.LeaveOneOut.split" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">split</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_split.py#L62"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.LeaveOneOut.split" title="Link to this definition">#</a></dt>
 <dd><p>Generate indices to split data into training and test set.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.model_selection.LeavePGroupsOut.html b/dev/modules/generated/sklearn.model_selection.LeavePGroupsOut.html
index ec1568aa93e05..8196a7842f40a 100644
--- a/dev/modules/generated/sklearn.model_selection.LeavePGroupsOut.html
+++ b/dev/modules/generated/sklearn.model_selection.LeavePGroupsOut.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>LeavePGroupsOut<a class="headerlink" href="#leavepgroupsout" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.model_selection.LeavePGroupsOut">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.model_selection.</span></span><span class="sig-name descname"><span class="pre">LeavePGroupsOut</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_groups</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_split.py#L1435"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.LeavePGroupsOut" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.model_selection.</span></span><span class="sig-name descname"><span class="pre">LeavePGroupsOut</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_groups</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_split.py#L1435"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.LeavePGroupsOut" title="Link to this definition">#</a></dt>
 <dd><p>Leave P Group(s) Out cross-validator.</p>
 <p>Provides train/test indices to split data according to a third-party
 provided group. This group information can be used to encode arbitrary
@@ -1362,7 +1362,7 @@ <h1>LeavePGroupsOut<a class="headerlink" href="#leavepgroupsout" title="Link to
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.LeavePGroupsOut.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.LeavePGroupsOut.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.LeavePGroupsOut.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1379,7 +1379,7 @@ <h1>LeavePGroupsOut<a class="headerlink" href="#leavepgroupsout" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.LeavePGroupsOut.get_n_splits">
-<span class="sig-name descname"><span class="pre">get_n_splits</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_split.py#L1514"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.LeavePGroupsOut.get_n_splits" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_n_splits</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_split.py#L1514"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.LeavePGroupsOut.get_n_splits" title="Link to this definition">#</a></dt>
 <dd><p>Returns the number of splitting iterations in the cross-validator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1406,7 +1406,7 @@ <h1>LeavePGroupsOut<a class="headerlink" href="#leavepgroupsout" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.LeavePGroupsOut.set_split_request">
-<span class="sig-name descname"><span class="pre">set_split_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.model_selection.LeavePGroupsOut" title="sklearn.model_selection._split.LeavePGroupsOut"><span class="pre">LeavePGroupsOut</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.LeavePGroupsOut.set_split_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_split_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.model_selection.LeavePGroupsOut" title="sklearn.model_selection._split.LeavePGroupsOut"><span class="pre">LeavePGroupsOut</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.LeavePGroupsOut.set_split_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">split</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1449,7 +1449,7 @@ <h1>LeavePGroupsOut<a class="headerlink" href="#leavepgroupsout" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.LeavePGroupsOut.split">
-<span class="sig-name descname"><span class="pre">split</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_split.py#L1541"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.LeavePGroupsOut.split" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">split</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_split.py#L1541"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.LeavePGroupsOut.split" title="Link to this definition">#</a></dt>
 <dd><p>Generate indices to split data into training and test set.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.model_selection.LeavePOut.html b/dev/modules/generated/sklearn.model_selection.LeavePOut.html
index 88a4307812b92..023e0bfc271a3 100644
--- a/dev/modules/generated/sklearn.model_selection.LeavePOut.html
+++ b/dev/modules/generated/sklearn.model_selection.LeavePOut.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>LeavePOut<a class="headerlink" href="#leavepout" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.model_selection.LeavePOut">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.model_selection.</span></span><span class="sig-name descname"><span class="pre">LeavePOut</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">p</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_split.py#L250"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.LeavePOut" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.model_selection.</span></span><span class="sig-name descname"><span class="pre">LeavePOut</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">p</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_split.py#L250"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.LeavePOut" title="Link to this definition">#</a></dt>
 <dd><p>Leave-P-Out cross-validator.</p>
 <p>Provides train/test indices to split data in train/test sets. This results
 in testing on all distinct samples of size p, while the remaining n - p
@@ -1362,7 +1362,7 @@ <h1>LeavePOut<a class="headerlink" href="#leavepout" title="Link to this heading
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.LeavePOut.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.LeavePOut.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.LeavePOut.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1379,7 +1379,7 @@ <h1>LeavePOut<a class="headerlink" href="#leavepout" title="Link to this heading
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.LeavePOut.get_n_splits">
-<span class="sig-name descname"><span class="pre">get_n_splits</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_split.py#L322"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.LeavePOut.get_n_splits" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_n_splits</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_split.py#L322"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.LeavePOut.get_n_splits" title="Link to this definition">#</a></dt>
 <dd><p>Returns the number of splitting iterations in the cross-validator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1398,7 +1398,7 @@ <h1>LeavePOut<a class="headerlink" href="#leavepout" title="Link to this heading
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.LeavePOut.split">
-<span class="sig-name descname"><span class="pre">split</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_split.py#L62"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.LeavePOut.split" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">split</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_split.py#L62"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.LeavePOut.split" title="Link to this definition">#</a></dt>
 <dd><p>Generate indices to split data into training and test set.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.model_selection.ParameterGrid.html b/dev/modules/generated/sklearn.model_selection.ParameterGrid.html
index df93a2b9a20b3..39b6ff60aef1b 100644
--- a/dev/modules/generated/sklearn.model_selection.ParameterGrid.html
+++ b/dev/modules/generated/sklearn.model_selection.ParameterGrid.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>ParameterGrid<a class="headerlink" href="#parametergrid" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.model_selection.ParameterGrid">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.model_selection.</span></span><span class="sig-name descname"><span class="pre">ParameterGrid</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">param_grid</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_search.py#L60"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.ParameterGrid" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.model_selection.</span></span><span class="sig-name descname"><span class="pre">ParameterGrid</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">param_grid</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_search.py#L60"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.ParameterGrid" title="Link to this definition">#</a></dt>
 <dd><p>Grid of parameters with a discrete number of values for each.</p>
 <p>Can be used to iterate over parameter value combinations with the
 Python built-in function iter.
diff --git a/dev/modules/generated/sklearn.model_selection.ParameterSampler.html b/dev/modules/generated/sklearn.model_selection.ParameterSampler.html
index 0b93b4563087d..877fe40f27d5f 100644
--- a/dev/modules/generated/sklearn.model_selection.ParameterSampler.html
+++ b/dev/modules/generated/sklearn.model_selection.ParameterSampler.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>ParameterSampler<a class="headerlink" href="#parametersampler" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.model_selection.ParameterSampler">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.model_selection.</span></span><span class="sig-name descname"><span class="pre">ParameterSampler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">param_distributions</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_iter</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_search.py#L213"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.ParameterSampler" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.model_selection.</span></span><span class="sig-name descname"><span class="pre">ParameterSampler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">param_distributions</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_iter</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_search.py#L213"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.ParameterSampler" title="Link to this definition">#</a></dt>
 <dd><p>Generator on parameters sampled from given distributions.</p>
 <p>Non-deterministic iterable over random candidate combinations for hyper-
 parameter search. If all parameters are presented as a list,
diff --git a/dev/modules/generated/sklearn.model_selection.PredefinedSplit.html b/dev/modules/generated/sklearn.model_selection.PredefinedSplit.html
index 28c810f1c7fec..519a053e630da 100644
--- a/dev/modules/generated/sklearn.model_selection.PredefinedSplit.html
+++ b/dev/modules/generated/sklearn.model_selection.PredefinedSplit.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>PredefinedSplit<a class="headerlink" href="#predefinedsplit" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.model_selection.PredefinedSplit">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.model_selection.</span></span><span class="sig-name descname"><span class="pre">PredefinedSplit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">test_fold</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_split.py#L2508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.PredefinedSplit" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.model_selection.</span></span><span class="sig-name descname"><span class="pre">PredefinedSplit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">test_fold</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_split.py#L2508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.PredefinedSplit" title="Link to this definition">#</a></dt>
 <dd><p>Predefined split cross-validator.</p>
 <p>Provides train/test indices to split data into train/test sets using a
 predefined scheme specified by the user with the <code class="docutils literal notranslate"><span class="pre">test_fold</span></code> parameter.</p>
@@ -1349,7 +1349,7 @@ <h1>PredefinedSplit<a class="headerlink" href="#predefinedsplit" title="Link to
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.PredefinedSplit.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.PredefinedSplit.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.PredefinedSplit.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1366,7 +1366,7 @@ <h1>PredefinedSplit<a class="headerlink" href="#predefinedsplit" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.PredefinedSplit.get_n_splits">
-<span class="sig-name descname"><span class="pre">get_n_splits</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_split.py#L2610"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.PredefinedSplit.get_n_splits" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_n_splits</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_split.py#L2610"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.PredefinedSplit.get_n_splits" title="Link to this definition">#</a></dt>
 <dd><p>Returns the number of splitting iterations in the cross-validator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1390,7 +1390,7 @@ <h1>PredefinedSplit<a class="headerlink" href="#predefinedsplit" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.PredefinedSplit.split">
-<span class="sig-name descname"><span class="pre">split</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_split.py#L2556"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.PredefinedSplit.split" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">split</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_split.py#L2556"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.PredefinedSplit.split" title="Link to this definition">#</a></dt>
 <dd><p>Generate indices to split data into training and test set.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.model_selection.RandomizedSearchCV.html b/dev/modules/generated/sklearn.model_selection.RandomizedSearchCV.html
index d7a5025d0359d..290b4a710adbb 100644
--- a/dev/modules/generated/sklearn.model_selection.RandomizedSearchCV.html
+++ b/dev/modules/generated/sklearn.model_selection.RandomizedSearchCV.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>RandomizedSearchCV<a class="headerlink" href="#randomizedsearchcv" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.model_selection.RandomizedSearchCV">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.model_selection.</span></span><span class="sig-name descname"><span class="pre">RandomizedSearchCV</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">param_distributions</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scoring</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">refit</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cv</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pre_dispatch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'2*n_jobs'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_score</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">nan</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_train_score</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_search.py#L1603"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.RandomizedSearchCV" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.model_selection.</span></span><span class="sig-name descname"><span class="pre">RandomizedSearchCV</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">param_distributions</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scoring</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">refit</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cv</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pre_dispatch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'2*n_jobs'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_score</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">nan</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_train_score</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_search.py#L1603"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.RandomizedSearchCV" title="Link to this definition">#</a></dt>
 <dd><p>Randomized search on hyper parameters.</p>
 <p>RandomizedSearchCV implements a “fit” and a “score” method.
 It also implements “score_samples”, “predict”, “predict_proba”,
@@ -1366,7 +1366,7 @@ <h1>RandomizedSearchCV<a class="headerlink" href="#randomizedsearchcv" title="Li
 <p>If None, the estimator’s score method is used.</p>
 </dd>
 <dt><strong>n_jobs</strong><span class="classifier">int, default=None</span></dt><dd><p>Number of jobs to run in parallel.
-<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.5.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
+<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.6.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
 <code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all processors. See <a class="reference internal" href="http://github.com/glossary.html#term-n_jobs"><span class="xref std std-term">Glossary</span></a>
 for more details.</p>
 <div class="versionchanged">
@@ -1617,12 +1617,12 @@ <h1>RandomizedSearchCV<a class="headerlink" href="#randomizedsearchcv" title="Li
 <span class="gp">&gt;&gt;&gt; </span><span class="n">clf</span> <span class="o">=</span> <span class="n">RandomizedSearchCV</span><span class="p">(</span><span class="n">logistic</span><span class="p">,</span> <span class="n">distributions</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">search</span> <span class="o">=</span> <span class="n">clf</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">iris</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="n">iris</span><span class="o">.</span><span class="n">target</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">search</span><span class="o">.</span><span class="n">best_params_</span>
-<span class="go">{&#39;C&#39;: np.float64(2...), &#39;penalty&#39;: &#39;l1&#39;}</span>
+<span class="go">{&#39;C&#39;: np.float64(2.2), &#39;penalty&#39;: &#39;l1&#39;}</span>
 </pre></div>
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.RandomizedSearchCV.decision_function">
-<span class="sig-name descname"><span class="pre">decision_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_search.py#L646"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.RandomizedSearchCV.decision_function" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">decision_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_search.py#L646"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.RandomizedSearchCV.decision_function" title="Link to this definition">#</a></dt>
 <dd><p>Call decision_function on the estimator with the best found parameters.</p>
 <p>Only available if <code class="docutils literal notranslate"><span class="pre">refit=True</span></code> and the underlying estimator supports
 <code class="docutils literal notranslate"><span class="pre">decision_function</span></code>.</p>
@@ -1646,7 +1646,7 @@ <h1>RandomizedSearchCV<a class="headerlink" href="#randomizedsearchcv" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.RandomizedSearchCV.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_search.py#L914"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.RandomizedSearchCV.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_search.py#L914"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.RandomizedSearchCV.fit" title="Link to this definition">#</a></dt>
 <dd><p>Run fit with all sets of parameters.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1682,7 +1682,7 @@ <h1>RandomizedSearchCV<a class="headerlink" href="#randomizedsearchcv" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.RandomizedSearchCV.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_search.py#L1195"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.RandomizedSearchCV.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_search.py#L1195"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.RandomizedSearchCV.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1702,7 +1702,7 @@ <h1>RandomizedSearchCV<a class="headerlink" href="#randomizedsearchcv" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.RandomizedSearchCV.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.RandomizedSearchCV.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.RandomizedSearchCV.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1723,7 +1723,7 @@ <h1>RandomizedSearchCV<a class="headerlink" href="#randomizedsearchcv" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.RandomizedSearchCV.inverse_transform">
-<span class="sig-name descname"><span class="pre">inverse_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_search.py#L691"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.RandomizedSearchCV.inverse_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">inverse_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_search.py#L691"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.RandomizedSearchCV.inverse_transform" title="Link to this definition">#</a></dt>
 <dd><p>Call inverse_transform on the estimator with the best found params.</p>
 <p>Only available if the underlying estimator implements
 <code class="docutils literal notranslate"><span class="pre">inverse_transform</span></code> and <code class="docutils literal notranslate"><span class="pre">refit=True</span></code>.</p>
@@ -1737,7 +1737,7 @@ <h1>RandomizedSearchCV<a class="headerlink" href="#randomizedsearchcv" title="Li
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><dl class="simple">
-<dt><strong>X</strong><span class="classifier">{ndarray, sparse matrix} of shape (n_samples, n_features)</span></dt><dd><p>Result of the <code class="docutils literal notranslate"><span class="pre">inverse_transform</span></code> function for <code class="docutils literal notranslate"><span class="pre">Xt</span></code> based on the
+<dt><strong>X_original</strong><span class="classifier">{ndarray, sparse matrix} of shape (n_samples, n_features)</span></dt><dd><p>Result of the <code class="docutils literal notranslate"><span class="pre">inverse_transform</span></code> function for <code class="docutils literal notranslate"><span class="pre">X</span></code> based on the
 estimator with the best found parameters.</p>
 </dd>
 </dl>
@@ -1747,7 +1747,7 @@ <h1>RandomizedSearchCV<a class="headerlink" href="#randomizedsearchcv" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.RandomizedSearchCV.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_search.py#L578"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.RandomizedSearchCV.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_search.py#L578"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.RandomizedSearchCV.predict" title="Link to this definition">#</a></dt>
 <dd><p>Call predict on the estimator with the best found parameters.</p>
 <p>Only available if <code class="docutils literal notranslate"><span class="pre">refit=True</span></code> and the underlying estimator supports
 <code class="docutils literal notranslate"><span class="pre">predict</span></code>.</p>
@@ -1771,7 +1771,7 @@ <h1>RandomizedSearchCV<a class="headerlink" href="#randomizedsearchcv" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.RandomizedSearchCV.predict_log_proba">
-<span class="sig-name descname"><span class="pre">predict_log_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_search.py#L623"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.RandomizedSearchCV.predict_log_proba" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict_log_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_search.py#L623"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.RandomizedSearchCV.predict_log_proba" title="Link to this definition">#</a></dt>
 <dd><p>Call predict_log_proba on the estimator with the best found parameters.</p>
 <p>Only available if <code class="docutils literal notranslate"><span class="pre">refit=True</span></code> and the underlying estimator supports
 <code class="docutils literal notranslate"><span class="pre">predict_log_proba</span></code>.</p>
@@ -1796,7 +1796,7 @@ <h1>RandomizedSearchCV<a class="headerlink" href="#randomizedsearchcv" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.RandomizedSearchCV.predict_proba">
-<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_search.py#L600"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.RandomizedSearchCV.predict_proba" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_search.py#L600"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.RandomizedSearchCV.predict_proba" title="Link to this definition">#</a></dt>
 <dd><p>Call predict_proba on the estimator with the best found parameters.</p>
 <p>Only available if <code class="docutils literal notranslate"><span class="pre">refit=True</span></code> and the underlying estimator supports
 <code class="docutils literal notranslate"><span class="pre">predict_proba</span></code>.</p>
@@ -1821,7 +1821,7 @@ <h1>RandomizedSearchCV<a class="headerlink" href="#randomizedsearchcv" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.RandomizedSearchCV.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_search.py#L496"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.RandomizedSearchCV.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_search.py#L496"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.RandomizedSearchCV.score" title="Link to this definition">#</a></dt>
 <dd><p>Return the score on the given data, if the estimator has been refit.</p>
 <p>This uses the score defined by <code class="docutils literal notranslate"><span class="pre">scoring</span></code> where provided, and the
 <code class="docutils literal notranslate"><span class="pre">best_estimator_.score</span></code> method otherwise.</p>
@@ -1855,7 +1855,7 @@ <h1>RandomizedSearchCV<a class="headerlink" href="#randomizedsearchcv" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.RandomizedSearchCV.score_samples">
-<span class="sig-name descname"><span class="pre">score_samples</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_search.py#L555"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.RandomizedSearchCV.score_samples" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score_samples</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_search.py#L555"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.RandomizedSearchCV.score_samples" title="Link to this definition">#</a></dt>
 <dd><p>Call score_samples on the estimator with the best found parameters.</p>
 <p>Only available if <code class="docutils literal notranslate"><span class="pre">refit=True</span></code> and the underlying estimator supports
 <code class="docutils literal notranslate"><span class="pre">score_samples</span></code>.</p>
@@ -1881,7 +1881,7 @@ <h1>RandomizedSearchCV<a class="headerlink" href="#randomizedsearchcv" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.RandomizedSearchCV.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.RandomizedSearchCV.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.RandomizedSearchCV.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1905,7 +1905,7 @@ <h1>RandomizedSearchCV<a class="headerlink" href="#randomizedsearchcv" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.RandomizedSearchCV.transform">
-<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_search.py#L669"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.RandomizedSearchCV.transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_search.py#L669"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.RandomizedSearchCV.transform" title="Link to this definition">#</a></dt>
 <dd><p>Call transform on the estimator with the best found parameters.</p>
 <p>Only available if the underlying estimator supports <code class="docutils literal notranslate"><span class="pre">transform</span></code> and
 <code class="docutils literal notranslate"><span class="pre">refit=True</span></code>.</p>
diff --git a/dev/modules/generated/sklearn.model_selection.RepeatedKFold.html b/dev/modules/generated/sklearn.model_selection.RepeatedKFold.html
index a1a6a589f103c..201f7a436aeb9 100644
--- a/dev/modules/generated/sklearn.model_selection.RepeatedKFold.html
+++ b/dev/modules/generated/sklearn.model_selection.RepeatedKFold.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>RepeatedKFold<a class="headerlink" href="#repeatedkfold" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.model_selection.RepeatedKFold">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.model_selection.</span></span><span class="sig-name descname"><span class="pre">RepeatedKFold</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_splits</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_repeats</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_split.py#L1675"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.RepeatedKFold" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.model_selection.</span></span><span class="sig-name descname"><span class="pre">RepeatedKFold</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_splits</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_repeats</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_split.py#L1675"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.RepeatedKFold" title="Link to this definition">#</a></dt>
 <dd><p>Repeated K-Fold cross validator.</p>
 <p>Repeats K-Fold <code class="docutils literal notranslate"><span class="pre">n_repeats</span></code> times with different randomization in each repetition.</p>
 <p>Read more in the <a class="reference internal" href="http://github.com/cross_validation.html#repeated-k-fold"><span class="std std-ref">User Guide</span></a>.</p>
@@ -1367,7 +1367,7 @@ <h1>RepeatedKFold<a class="headerlink" href="#repeatedkfold" title="Link to this
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.RepeatedKFold.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.RepeatedKFold.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.RepeatedKFold.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1384,7 +1384,7 @@ <h1>RepeatedKFold<a class="headerlink" href="#repeatedkfold" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.RepeatedKFold.get_n_splits">
-<span class="sig-name descname"><span class="pre">get_n_splits</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_split.py#L1645"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.RepeatedKFold.get_n_splits" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_n_splits</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_split.py#L1645"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.RepeatedKFold.get_n_splits" title="Link to this definition">#</a></dt>
 <dd><p>Returns the number of splitting iterations in the cross-validator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1411,7 +1411,7 @@ <h1>RepeatedKFold<a class="headerlink" href="#repeatedkfold" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.RepeatedKFold.split">
-<span class="sig-name descname"><span class="pre">split</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_split.py#L62"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.RepeatedKFold.split" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">split</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_split.py#L62"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.RepeatedKFold.split" title="Link to this definition">#</a></dt>
 <dd><p>Generate indices to split data into training and test set.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.model_selection.RepeatedStratifiedKFold.html b/dev/modules/generated/sklearn.model_selection.RepeatedStratifiedKFold.html
index ba7b626fcd9d5..70fee02e6af74 100644
--- a/dev/modules/generated/sklearn.model_selection.RepeatedStratifiedKFold.html
+++ b/dev/modules/generated/sklearn.model_selection.RepeatedStratifiedKFold.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>RepeatedStratifiedKFold<a class="headerlink" href="#repeatedstratifiedkfold" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.model_selection.RepeatedStratifiedKFold">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.model_selection.</span></span><span class="sig-name descname"><span class="pre">RepeatedStratifiedKFold</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_splits</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_repeats</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_split.py#L1741"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.RepeatedStratifiedKFold" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.model_selection.</span></span><span class="sig-name descname"><span class="pre">RepeatedStratifiedKFold</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_splits</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_repeats</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_split.py#L1741"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.RepeatedStratifiedKFold" title="Link to this definition">#</a></dt>
 <dd><p>Repeated class-wise stratified K-Fold cross validator.</p>
 <p>Repeats Stratified K-Fold n times with different randomization in each
 repetition.</p>
@@ -1374,7 +1374,7 @@ <h1>RepeatedStratifiedKFold<a class="headerlink" href="#repeatedstratifiedkfold"
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.RepeatedStratifiedKFold.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.RepeatedStratifiedKFold.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.RepeatedStratifiedKFold.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1391,7 +1391,7 @@ <h1>RepeatedStratifiedKFold<a class="headerlink" href="#repeatedstratifiedkfold"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.RepeatedStratifiedKFold.get_n_splits">
-<span class="sig-name descname"><span class="pre">get_n_splits</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_split.py#L1645"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.RepeatedStratifiedKFold.get_n_splits" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_n_splits</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_split.py#L1645"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.RepeatedStratifiedKFold.get_n_splits" title="Link to this definition">#</a></dt>
 <dd><p>Returns the number of splitting iterations in the cross-validator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1418,7 +1418,7 @@ <h1>RepeatedStratifiedKFold<a class="headerlink" href="#repeatedstratifiedkfold"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.RepeatedStratifiedKFold.split">
-<span class="sig-name descname"><span class="pre">split</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_split.py#L1816"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.RepeatedStratifiedKFold.split" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">split</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_split.py#L1816"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.RepeatedStratifiedKFold.split" title="Link to this definition">#</a></dt>
 <dd><p>Generate indices to split data into training and test set.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.model_selection.ShuffleSplit.html b/dev/modules/generated/sklearn.model_selection.ShuffleSplit.html
index 4b514c10c3179..577dfd5da84a3 100644
--- a/dev/modules/generated/sklearn.model_selection.ShuffleSplit.html
+++ b/dev/modules/generated/sklearn.model_selection.ShuffleSplit.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>ShuffleSplit<a class="headerlink" href="#shufflesplit" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.model_selection.ShuffleSplit">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.model_selection.</span></span><span class="sig-name descname"><span class="pre">ShuffleSplit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_splits</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">test_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">train_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_split.py#L1973"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.ShuffleSplit" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.model_selection.</span></span><span class="sig-name descname"><span class="pre">ShuffleSplit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_splits</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">test_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">train_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_split.py#L1973"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.ShuffleSplit" title="Link to this definition">#</a></dt>
 <dd><p>Random permutation cross-validator.</p>
 <p>Yields indices to split data into training and test sets.</p>
 <p>Note: contrary to other cross-validation strategies, random splits
@@ -1396,7 +1396,7 @@ <h1>ShuffleSplit<a class="headerlink" href="#shufflesplit" title="Link to this h
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.ShuffleSplit.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.ShuffleSplit.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.ShuffleSplit.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1413,7 +1413,7 @@ <h1>ShuffleSplit<a class="headerlink" href="#shufflesplit" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.ShuffleSplit.get_n_splits">
-<span class="sig-name descname"><span class="pre">get_n_splits</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_split.py#L1948"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.ShuffleSplit.get_n_splits" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_n_splits</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_split.py#L1948"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.ShuffleSplit.get_n_splits" title="Link to this definition">#</a></dt>
 <dd><p>Returns the number of splitting iterations in the cross-validator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1437,7 +1437,7 @@ <h1>ShuffleSplit<a class="headerlink" href="#shufflesplit" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.ShuffleSplit.split">
-<span class="sig-name descname"><span class="pre">split</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_split.py#L62"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.ShuffleSplit.split" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">split</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_split.py#L62"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.ShuffleSplit.split" title="Link to this definition">#</a></dt>
 <dd><p>Generate indices to split data into training and test set.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.model_selection.StratifiedGroupKFold.html b/dev/modules/generated/sklearn.model_selection.StratifiedGroupKFold.html
index 6783bfa2aee5d..c24254aad4e03 100644
--- a/dev/modules/generated/sklearn.model_selection.StratifiedGroupKFold.html
+++ b/dev/modules/generated/sklearn.model_selection.StratifiedGroupKFold.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>StratifiedGroupKFold<a class="headerlink" href="#stratifiedgroupkfold" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.model_selection.StratifiedGroupKFold">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.model_selection.</span></span><span class="sig-name descname"><span class="pre">StratifiedGroupKFold</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_splits</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_split.py#L891"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.StratifiedGroupKFold" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.model_selection.</span></span><span class="sig-name descname"><span class="pre">StratifiedGroupKFold</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_splits</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_split.py#L891"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.StratifiedGroupKFold" title="Link to this definition">#</a></dt>
 <dd><p>Class-wise stratified K-Fold iterator variant with non-overlapping groups.</p>
 <p>This cross-validation object is a variation of StratifiedKFold attempts to
 return stratified folds with non-overlapping groups. The folds are made by
@@ -1408,7 +1408,7 @@ <h1>StratifiedGroupKFold<a class="headerlink" href="#stratifiedgroupkfold" title
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.StratifiedGroupKFold.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.StratifiedGroupKFold.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.StratifiedGroupKFold.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1425,7 +1425,7 @@ <h1>StratifiedGroupKFold<a class="headerlink" href="#stratifiedgroupkfold" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.StratifiedGroupKFold.get_n_splits">
-<span class="sig-name descname"><span class="pre">get_n_splits</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_split.py#L414"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.StratifiedGroupKFold.get_n_splits" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_n_splits</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_split.py#L414"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.StratifiedGroupKFold.get_n_splits" title="Link to this definition">#</a></dt>
 <dd><p>Returns the number of splitting iterations in the cross-validator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1449,7 +1449,7 @@ <h1>StratifiedGroupKFold<a class="headerlink" href="#stratifiedgroupkfold" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.StratifiedGroupKFold.set_split_request">
-<span class="sig-name descname"><span class="pre">set_split_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.model_selection.StratifiedGroupKFold" title="sklearn.model_selection._split.StratifiedGroupKFold"><span class="pre">StratifiedGroupKFold</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.StratifiedGroupKFold.set_split_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_split_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.model_selection.StratifiedGroupKFold" title="sklearn.model_selection._split.StratifiedGroupKFold"><span class="pre">StratifiedGroupKFold</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.StratifiedGroupKFold.set_split_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">split</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1492,7 +1492,7 @@ <h1>StratifiedGroupKFold<a class="headerlink" href="#stratifiedgroupkfold" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.StratifiedGroupKFold.split">
-<span class="sig-name descname"><span class="pre">split</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_split.py#L377"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.StratifiedGroupKFold.split" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">split</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_split.py#L377"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.StratifiedGroupKFold.split" title="Link to this definition">#</a></dt>
 <dd><p>Generate indices to split data into training and test set.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.model_selection.StratifiedKFold.html b/dev/modules/generated/sklearn.model_selection.StratifiedKFold.html
index 55d7e9767911a..a99fee5815c5d 100644
--- a/dev/modules/generated/sklearn.model_selection.StratifiedKFold.html
+++ b/dev/modules/generated/sklearn.model_selection.StratifiedKFold.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>StratifiedKFold<a class="headerlink" href="#stratifiedkfold" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.model_selection.StratifiedKFold">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.model_selection.</span></span><span class="sig-name descname"><span class="pre">StratifiedKFold</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_splits</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_split.py#L686"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.StratifiedKFold" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.model_selection.</span></span><span class="sig-name descname"><span class="pre">StratifiedKFold</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_splits</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_split.py#L686"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.StratifiedKFold" title="Link to this definition">#</a></dt>
 <dd><p>Class-wise stratified K-Fold cross-validator.</p>
 <p>Provides train/test indices to split data in train/test sets.</p>
 <p>This cross-validation object is a variation of KFold that returns
@@ -1390,7 +1390,7 @@ <h1>StratifiedKFold<a class="headerlink" href="#stratifiedkfold" title="Link to
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.StratifiedKFold.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.StratifiedKFold.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.StratifiedKFold.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1407,7 +1407,7 @@ <h1>StratifiedKFold<a class="headerlink" href="#stratifiedkfold" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.StratifiedKFold.get_n_splits">
-<span class="sig-name descname"><span class="pre">get_n_splits</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_split.py#L414"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.StratifiedKFold.get_n_splits" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_n_splits</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_split.py#L414"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.StratifiedKFold.get_n_splits" title="Link to this definition">#</a></dt>
 <dd><p>Returns the number of splitting iterations in the cross-validator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1431,7 +1431,7 @@ <h1>StratifiedKFold<a class="headerlink" href="#stratifiedkfold" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.StratifiedKFold.split">
-<span class="sig-name descname"><span class="pre">split</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_split.py#L848"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.StratifiedKFold.split" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">split</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_split.py#L848"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.StratifiedKFold.split" title="Link to this definition">#</a></dt>
 <dd><p>Generate indices to split data into training and test set.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.model_selection.StratifiedShuffleSplit.html b/dev/modules/generated/sklearn.model_selection.StratifiedShuffleSplit.html
index 3e21e8bdb5773..7f9c16a8ef42d 100644
--- a/dev/modules/generated/sklearn.model_selection.StratifiedShuffleSplit.html
+++ b/dev/modules/generated/sklearn.model_selection.StratifiedShuffleSplit.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>StratifiedShuffleSplit<a class="headerlink" href="#stratifiedshufflesplit" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.model_selection.StratifiedShuffleSplit">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.model_selection.</span></span><span class="sig-name descname"><span class="pre">StratifiedShuffleSplit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_splits</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">test_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">train_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_split.py#L2224"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.StratifiedShuffleSplit" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.model_selection.</span></span><span class="sig-name descname"><span class="pre">StratifiedShuffleSplit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_splits</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">test_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">train_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_split.py#L2224"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.StratifiedShuffleSplit" title="Link to this definition">#</a></dt>
 <dd><p>Class-wise stratified ShuffleSplit cross-validator.</p>
 <p>Provides train/test indices to split data in train/test sets.</p>
 <p>This cross-validation object is a merge of <a class="reference internal" href="http://github.com/sklearn.model_selection.StratifiedKFold.html#sklearn.model_selection.StratifiedKFold" title="sklearn.model_selection.StratifiedKFold"><code class="xref py py-class docutils literal notranslate"><span class="pre">StratifiedKFold</span></code></a> and
@@ -1383,7 +1383,7 @@ <h1>StratifiedShuffleSplit<a class="headerlink" href="#stratifiedshufflesplit" t
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.StratifiedShuffleSplit.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.StratifiedShuffleSplit.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.StratifiedShuffleSplit.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1400,7 +1400,7 @@ <h1>StratifiedShuffleSplit<a class="headerlink" href="#stratifiedshufflesplit" t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.StratifiedShuffleSplit.get_n_splits">
-<span class="sig-name descname"><span class="pre">get_n_splits</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_split.py#L1948"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.StratifiedShuffleSplit.get_n_splits" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_n_splits</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_split.py#L1948"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.StratifiedShuffleSplit.get_n_splits" title="Link to this definition">#</a></dt>
 <dd><p>Returns the number of splitting iterations in the cross-validator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1424,7 +1424,7 @@ <h1>StratifiedShuffleSplit<a class="headerlink" href="#stratifiedshufflesplit" t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.StratifiedShuffleSplit.split">
-<span class="sig-name descname"><span class="pre">split</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_split.py#L2390"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.StratifiedShuffleSplit.split" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">split</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_split.py#L2390"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.StratifiedShuffleSplit.split" title="Link to this definition">#</a></dt>
 <dd><p>Generate indices to split data into training and test set.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.model_selection.TimeSeriesSplit.html b/dev/modules/generated/sklearn.model_selection.TimeSeriesSplit.html
index 2120090e548ae..bf4cd4ad384ff 100644
--- a/dev/modules/generated/sklearn.model_selection.TimeSeriesSplit.html
+++ b/dev/modules/generated/sklearn.model_selection.TimeSeriesSplit.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>TimeSeriesSplit<a class="headerlink" href="#timeseriessplit" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.model_selection.TimeSeriesSplit">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.model_selection.</span></span><span class="sig-name descname"><span class="pre">TimeSeriesSplit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_splits</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_train_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">test_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gap</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_split.py#L1102"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.TimeSeriesSplit" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.model_selection.</span></span><span class="sig-name descname"><span class="pre">TimeSeriesSplit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_splits</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_train_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">test_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gap</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_split.py#L1102"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.TimeSeriesSplit" title="Link to this definition">#</a></dt>
 <dd><p>Time Series cross-validator.</p>
 <p>Provides train/test indices to split time-ordered data, where other
 cross-validation methods are inappropriate, as they would lead to training
@@ -1425,7 +1425,7 @@ <h1>TimeSeriesSplit<a class="headerlink" href="#timeseriessplit" title="Link to
 <a class="reference internal" href="http://github.com/auto_examples/applications/plot_cyclical_feature_engineering.html#sphx-glr-auto-examples-applications-plot-cyclical-feature-engineering-py"><span class="std std-ref">Time-related feature engineering</span></a>.</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.TimeSeriesSplit.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.TimeSeriesSplit.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.TimeSeriesSplit.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1442,7 +1442,7 @@ <h1>TimeSeriesSplit<a class="headerlink" href="#timeseriessplit" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.TimeSeriesSplit.get_n_splits">
-<span class="sig-name descname"><span class="pre">get_n_splits</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_split.py#L414"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.TimeSeriesSplit.get_n_splits" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_n_splits</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_split.py#L414"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.TimeSeriesSplit.get_n_splits" title="Link to this definition">#</a></dt>
 <dd><p>Returns the number of splitting iterations in the cross-validator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1466,7 +1466,7 @@ <h1>TimeSeriesSplit<a class="headerlink" href="#timeseriessplit" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.TimeSeriesSplit.split">
-<span class="sig-name descname"><span class="pre">split</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_split.py#L1231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.TimeSeriesSplit.split" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">split</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_split.py#L1231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.TimeSeriesSplit.split" title="Link to this definition">#</a></dt>
 <dd><p>Generate indices to split data into training and test set.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.model_selection.TunedThresholdClassifierCV.html b/dev/modules/generated/sklearn.model_selection.TunedThresholdClassifierCV.html
index a9784319289fb..09bb9f6f3be57 100644
--- a/dev/modules/generated/sklearn.model_selection.TunedThresholdClassifierCV.html
+++ b/dev/modules/generated/sklearn.model_selection.TunedThresholdClassifierCV.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>TunedThresholdClassifierCV<a class="headerlink" href="#tunedthresholdclassifiercv" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.model_selection.TunedThresholdClassifierCV">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.model_selection.</span></span><span class="sig-name descname"><span class="pre">TunedThresholdClassifierCV</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scoring</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'balanced_accuracy'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">response_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">thresholds</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cv</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">refit</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">store_cv_results</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_classification_threshold.py#L509"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.TunedThresholdClassifierCV" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.model_selection.</span></span><span class="sig-name descname"><span class="pre">TunedThresholdClassifierCV</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scoring</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'balanced_accuracy'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">response_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">thresholds</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cv</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">refit</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">store_cv_results</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_classification_threshold.py#L509"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.TunedThresholdClassifierCV" title="Link to this definition">#</a></dt>
 <dd><p>Classifier that post-tunes the decision threshold using cross-validation.</p>
 <p>This estimator post-tunes the decision threshold (cut-off point) that is
 used for converting posterior probability estimates (i.e. output of
@@ -1378,7 +1378,7 @@ <h1>TunedThresholdClassifierCV<a class="headerlink" href="#tunedthresholdclassif
 <dt><strong>n_jobs</strong><span class="classifier">int, default=None</span></dt><dd><p>The number of jobs to run in parallel. When <code class="docutils literal notranslate"><span class="pre">cv</span></code> represents a
 cross-validation strategy, the fitting and scoring on each data split
 is done in parallel. <code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a
-<a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.5.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context. <code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all
+<a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.6.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context. <code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all
 processors. See <a class="reference internal" href="http://github.com/glossary.html#term-n_jobs"><span class="xref std std-term">Glossary</span></a> for more details.</p>
 </dd>
 <dt><strong>random_state</strong><span class="classifier">int, RandomState instance or None, default=None</span></dt><dd><p>Controls the randomness of cross-validation when <code class="docutils literal notranslate"><span class="pre">cv</span></code> is a float.
@@ -1463,7 +1463,7 @@ <h1>TunedThresholdClassifierCV<a class="headerlink" href="#tunedthresholdclassif
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.TunedThresholdClassifierCV.decision_function">
-<span class="sig-name descname"><span class="pre">decision_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_classification_threshold.py#L187"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.TunedThresholdClassifierCV.decision_function" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">decision_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_classification_threshold.py#L187"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.TunedThresholdClassifierCV.decision_function" title="Link to this definition">#</a></dt>
 <dd><p>Decision function for samples in <code class="docutils literal notranslate"><span class="pre">X</span></code> using the fitted estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1484,7 +1484,7 @@ <h1>TunedThresholdClassifierCV<a class="headerlink" href="#tunedthresholdclassif
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.TunedThresholdClassifierCV.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_classification_threshold.py#L101"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.TunedThresholdClassifierCV.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_classification_threshold.py#L101"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.TunedThresholdClassifierCV.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit the classifier.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1509,7 +1509,7 @@ <h1>TunedThresholdClassifierCV<a class="headerlink" href="#tunedthresholdclassif
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.TunedThresholdClassifierCV.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_classification_threshold.py#L859"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.TunedThresholdClassifierCV.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_classification_threshold.py#L859"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.TunedThresholdClassifierCV.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1526,7 +1526,7 @@ <h1>TunedThresholdClassifierCV<a class="headerlink" href="#tunedthresholdclassif
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.TunedThresholdClassifierCV.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.TunedThresholdClassifierCV.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.TunedThresholdClassifierCV.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1547,7 +1547,7 @@ <h1>TunedThresholdClassifierCV<a class="headerlink" href="#tunedthresholdclassif
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.TunedThresholdClassifierCV.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_classification_threshold.py#L833"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.TunedThresholdClassifierCV.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_classification_threshold.py#L833"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.TunedThresholdClassifierCV.predict" title="Link to this definition">#</a></dt>
 <dd><p>Predict the target of new samples.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1567,7 +1567,7 @@ <h1>TunedThresholdClassifierCV<a class="headerlink" href="#tunedthresholdclassif
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.TunedThresholdClassifierCV.predict_log_proba">
-<span class="sig-name descname"><span class="pre">predict_log_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_classification_threshold.py#L168"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.TunedThresholdClassifierCV.predict_log_proba" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict_log_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_classification_threshold.py#L168"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.TunedThresholdClassifierCV.predict_log_proba" title="Link to this definition">#</a></dt>
 <dd><p>Predict logarithm class probabilities for <code class="docutils literal notranslate"><span class="pre">X</span></code> using the fitted estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1588,7 +1588,7 @@ <h1>TunedThresholdClassifierCV<a class="headerlink" href="#tunedthresholdclassif
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.TunedThresholdClassifierCV.predict_proba">
-<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_classification_threshold.py#L149"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.TunedThresholdClassifierCV.predict_proba" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_classification_threshold.py#L149"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.TunedThresholdClassifierCV.predict_proba" title="Link to this definition">#</a></dt>
 <dd><p>Predict class probabilities for <code class="docutils literal notranslate"><span class="pre">X</span></code> using the fitted estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1609,7 +1609,7 @@ <h1>TunedThresholdClassifierCV<a class="headerlink" href="#tunedthresholdclassif
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.TunedThresholdClassifierCV.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L546"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.TunedThresholdClassifierCV.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L487"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.TunedThresholdClassifierCV.score" title="Link to this definition">#</a></dt>
 <dd><p>Return <a class="reference internal" href="http://github.com/model_evaluation.html#accuracy-score"><span class="std std-ref">accuracy</span></a> on provided data and labels.</p>
 <p>In multi-label classification, this is the subset accuracy
 which is a harsh metric since you require for each sample that
@@ -1636,7 +1636,7 @@ <h1>TunedThresholdClassifierCV<a class="headerlink" href="#tunedthresholdclassif
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.TunedThresholdClassifierCV.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.TunedThresholdClassifierCV.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.TunedThresholdClassifierCV.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1660,7 +1660,7 @@ <h1>TunedThresholdClassifierCV<a class="headerlink" href="#tunedthresholdclassif
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.TunedThresholdClassifierCV.set_score_request">
-<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.model_selection.TunedThresholdClassifierCV" title="sklearn.model_selection._classification_threshold.TunedThresholdClassifierCV"><span class="pre">TunedThresholdClassifierCV</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.TunedThresholdClassifierCV.set_score_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.model_selection.TunedThresholdClassifierCV" title="sklearn.model_selection._classification_threshold.TunedThresholdClassifierCV"><span class="pre">TunedThresholdClassifierCV</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.TunedThresholdClassifierCV.set_score_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
diff --git a/dev/modules/generated/sklearn.model_selection.ValidationCurveDisplay.html b/dev/modules/generated/sklearn.model_selection.ValidationCurveDisplay.html
index f5a1be7af9a21..e5dd7e009a9be 100644
--- a/dev/modules/generated/sklearn.model_selection.ValidationCurveDisplay.html
+++ b/dev/modules/generated/sklearn.model_selection.ValidationCurveDisplay.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>ValidationCurveDisplay<a class="headerlink" href="#validationcurvedisplay" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.model_selection.ValidationCurveDisplay">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.model_selection.</span></span><span class="sig-name descname"><span class="pre">ValidationCurveDisplay</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">param_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">param_range</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">train_scores</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">test_scores</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">score_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_plot.py#L511"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.ValidationCurveDisplay" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.model_selection.</span></span><span class="sig-name descname"><span class="pre">ValidationCurveDisplay</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">param_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">param_range</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">train_scores</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">test_scores</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">score_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_plot.py#L511"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.ValidationCurveDisplay" title="Link to this definition">#</a></dt>
 <dd><p>Validation Curve visualization.</p>
 <p>It is recommended to use
 <a class="reference internal" href="#sklearn.model_selection.ValidationCurveDisplay.from_estimator" title="sklearn.model_selection.ValidationCurveDisplay.from_estimator"><code class="xref py py-meth docutils literal notranslate"><span class="pre">from_estimator</span></code></a> to
@@ -1392,7 +1392,7 @@ <h1>ValidationCurveDisplay<a class="headerlink" href="#validationcurvedisplay" t
 </figure>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.ValidationCurveDisplay.from_estimator">
-<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_estimator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">param_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">param_range</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cv</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scoring</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pre_dispatch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'all'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_score</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">nan</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">negate_score</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">score_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">score_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'both'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">std_display_style</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'fill_between'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">line_kw</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fill_between_kw</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">errorbar_kw</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_plot.py#L680"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.ValidationCurveDisplay.from_estimator" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_estimator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">param_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">param_range</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cv</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scoring</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pre_dispatch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'all'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_score</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">nan</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">negate_score</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">score_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">score_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'both'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">std_display_style</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'fill_between'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">line_kw</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fill_between_kw</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">errorbar_kw</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_plot.py#L680"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.ValidationCurveDisplay.from_estimator" title="Link to this definition">#</a></dt>
 <dd><p>Create a validation curve display from an estimator.</p>
 <p>Read more in the <a class="reference internal" href="http://github.com/visualizations.html#visualizations"><span class="std std-ref">User Guide</span></a> for general
 information about the visualization API and <a class="reference internal" href="http://github.com/learning_curve.html#validation-curve"><span class="std std-ref">detailed
@@ -1446,7 +1446,7 @@ <h1>ValidationCurveDisplay<a class="headerlink" href="#validationcurvedisplay" t
 <dt><strong>n_jobs</strong><span class="classifier">int, default=None</span></dt><dd><p>Number of jobs to run in parallel. Training the estimator and
 computing the score are parallelized over the different training
 and test sets. <code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a
-<a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.5.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context. <code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all
+<a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.6.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context. <code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all
 processors. See <a class="reference internal" href="http://github.com/glossary.html#term-n_jobs"><span class="xref std std-term">Glossary</span></a> for more details.</p>
 </dd>
 <dt><strong>pre_dispatch</strong><span class="classifier">int or str, default=’all’</span></dt><dd><p>Number of predispatched jobs for parallel execution (default is
@@ -1526,7 +1526,7 @@ <h1>ValidationCurveDisplay<a class="headerlink" href="#validationcurvedisplay" t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.model_selection.ValidationCurveDisplay.plot">
-<span class="sig-name descname"><span class="pre">plot</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">negate_score</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">score_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">score_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'both'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">std_display_style</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'fill_between'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">line_kw</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fill_between_kw</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">errorbar_kw</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_plot.py#L605"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.ValidationCurveDisplay.plot" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">plot</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">negate_score</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">score_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">score_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'both'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">std_display_style</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'fill_between'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">line_kw</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fill_between_kw</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">errorbar_kw</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_plot.py#L605"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.ValidationCurveDisplay.plot" title="Link to this definition">#</a></dt>
 <dd><p>Plot visualization.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.model_selection.check_cv.html b/dev/modules/generated/sklearn.model_selection.check_cv.html
index 892b4f65d6797..aae0350f0c94a 100644
--- a/dev/modules/generated/sklearn.model_selection.check_cv.html
+++ b/dev/modules/generated/sklearn.model_selection.check_cv.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>check_cv<a class="headerlink" href="#check-cv" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.model_selection.check_cv">
-<span class="sig-prename descclassname"><span class="pre">sklearn.model_selection.</span></span><span class="sig-name descname"><span class="pre">check_cv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">cv</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">classifier</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_split.py#L2685"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.check_cv" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.model_selection.</span></span><span class="sig-name descname"><span class="pre">check_cv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">cv</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">classifier</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_split.py#L2685"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.check_cv" title="Link to this definition">#</a></dt>
 <dd><p>Input checker utility for building a cross-validator.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.model_selection.cross_val_predict.html b/dev/modules/generated/sklearn.model_selection.cross_val_predict.html
index 0d4d3ba8fa57e..4f2e74f3c3939 100644
--- a/dev/modules/generated/sklearn.model_selection.cross_val_predict.html
+++ b/dev/modules/generated/sklearn.model_selection.cross_val_predict.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>cross_val_predict<a class="headerlink" href="#cross-val-predict" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.model_selection.cross_val_predict">
-<span class="sig-prename descclassname"><span class="pre">sklearn.model_selection.</span></span><span class="sig-name descname"><span class="pre">cross_val_predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cv</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pre_dispatch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'2*n_jobs'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'predict'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_validation.py#L1001"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.cross_val_predict" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.model_selection.</span></span><span class="sig-name descname"><span class="pre">cross_val_predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cv</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pre_dispatch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'2*n_jobs'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'predict'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_validation.py#L1000"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.cross_val_predict" title="Link to this definition">#</a></dt>
 <dd><p>Generate cross-validated estimates for each input data point.</p>
 <p>The data is split according to the cv parameter. Each sample belongs
 to exactly one test set, and its prediction is computed with an
@@ -1359,7 +1359,7 @@ <h1>cross_val_predict<a class="headerlink" href="#cross-val-predict" title="Link
 </dd>
 <dt><strong>n_jobs</strong><span class="classifier">int, default=None</span></dt><dd><p>Number of jobs to run in parallel. Training the estimator and
 predicting are parallelized over the cross-validation splits.
-<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.5.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
+<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.6.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
 <code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all processors. See <a class="reference internal" href="http://github.com/glossary.html#term-n_jobs"><span class="xref std std-term">Glossary</span></a>
 for more details.</p>
 </dd>
diff --git a/dev/modules/generated/sklearn.model_selection.cross_val_score.html b/dev/modules/generated/sklearn.model_selection.cross_val_score.html
index d678ff6c66d0f..afbb1338d4f3b 100644
--- a/dev/modules/generated/sklearn.model_selection.cross_val_score.html
+++ b/dev/modules/generated/sklearn.model_selection.cross_val_score.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>cross_val_score<a class="headerlink" href="#cross-val-score" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.model_selection.cross_val_score">
-<span class="sig-prename descclassname"><span class="pre">sklearn.model_selection.</span></span><span class="sig-name descname"><span class="pre">cross_val_score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scoring</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cv</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pre_dispatch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'2*n_jobs'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_score</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">nan</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_validation.py#L520"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.cross_val_score" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.model_selection.</span></span><span class="sig-name descname"><span class="pre">cross_val_score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scoring</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cv</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pre_dispatch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'2*n_jobs'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_score</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">nan</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_validation.py#L519"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.cross_val_score" title="Link to this definition">#</a></dt>
 <dd><p>Evaluate a score by cross-validation.</p>
 <p>Read more in the <a class="reference internal" href="http://github.com/cross_validation.html#cross-validation"><span class="std std-ref">User Guide</span></a>.</p>
 <dl class="field-list">
@@ -1364,7 +1364,7 @@ <h1>cross_val_score<a class="headerlink" href="#cross-val-score" title="Link to
 </dd>
 <dt><strong>n_jobs</strong><span class="classifier">int, default=None</span></dt><dd><p>Number of jobs to run in parallel. Training the estimator and computing
 the score are parallelized over the cross-validation splits.
-<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.5.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
+<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.6.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
 <code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all processors. See <a class="reference internal" href="http://github.com/glossary.html#term-n_jobs"><span class="xref std std-term">Glossary</span></a>
 for more details.</p>
 </dd>
diff --git a/dev/modules/generated/sklearn.model_selection.cross_validate.html b/dev/modules/generated/sklearn.model_selection.cross_validate.html
index 384c691ca5723..6a6297197666a 100644
--- a/dev/modules/generated/sklearn.model_selection.cross_validate.html
+++ b/dev/modules/generated/sklearn.model_selection.cross_validate.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>cross_validate<a class="headerlink" href="#cross-validate" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.model_selection.cross_validate">
-<span class="sig-prename descclassname"><span class="pre">sklearn.model_selection.</span></span><span class="sig-name descname"><span class="pre">cross_validate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scoring</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cv</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pre_dispatch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'2*n_jobs'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_train_score</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_estimator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_score</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">nan</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_validation.py#L99"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.cross_validate" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.model_selection.</span></span><span class="sig-name descname"><span class="pre">cross_validate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scoring</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cv</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pre_dispatch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'2*n_jobs'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_train_score</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_estimator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_score</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">nan</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_validation.py#L98"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.cross_validate" title="Link to this definition">#</a></dt>
 <dd><p>Evaluate metric(s) by cross-validation and also record fit/score times.</p>
 <p>Read more in the <a class="reference internal" href="http://github.com/cross_validation.html#multimetric-cross-validation"><span class="std std-ref">User Guide</span></a>.</p>
 <dl class="field-list">
@@ -1369,7 +1369,7 @@ <h1>cross_validate<a class="headerlink" href="#cross-validate" title="Link to th
 </dd>
 <dt><strong>n_jobs</strong><span class="classifier">int, default=None</span></dt><dd><p>Number of jobs to run in parallel. Training the estimator and computing
 the score are parallelized over the cross-validation splits.
-<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.5.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
+<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.6.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
 <code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all processors. See <a class="reference internal" href="http://github.com/glossary.html#term-n_jobs"><span class="xref std std-term">Glossary</span></a>
 for more details.</p>
 </dd>
@@ -1498,7 +1498,7 @@ <h1>cross_validate<a class="headerlink" href="#cross-validate" title="Link to th
 <span class="gp">... </span>                        <span class="n">scoring</span><span class="o">=</span><span class="p">(</span><span class="s1">&#39;r2&#39;</span><span class="p">,</span> <span class="s1">&#39;neg_mean_squared_error&#39;</span><span class="p">),</span>
 <span class="gp">... </span>                        <span class="n">return_train_score</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">scores</span><span class="p">[</span><span class="s1">&#39;test_neg_mean_squared_error&#39;</span><span class="p">])</span>
-<span class="go">[-3635.5... -3573.3... -6114.7...]</span>
+<span class="go">[-3635.5 -3573.3 -6114.7]</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">scores</span><span class="p">[</span><span class="s1">&#39;train_r2&#39;</span><span class="p">])</span>
 <span class="go">[0.28009951 0.3908844  0.22784907]</span>
 </pre></div>
diff --git a/dev/modules/generated/sklearn.model_selection.learning_curve.html b/dev/modules/generated/sklearn.model_selection.learning_curve.html
index 4f7252f7a928e..7c54a125d893c 100644
--- a/dev/modules/generated/sklearn.model_selection.learning_curve.html
+++ b/dev/modules/generated/sklearn.model_selection.learning_curve.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>learning_curve<a class="headerlink" href="#learning-curve" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.model_selection.learning_curve">
-<span class="sig-prename descclassname"><span class="pre">sklearn.model_selection.</span></span><span class="sig-name descname"><span class="pre">learning_curve</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">train_sizes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">array([0.1,</span> <span class="pre">0.33,</span> <span class="pre">0.55,</span> <span class="pre">0.78,</span> <span class="pre">1.])</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cv</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scoring</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">exploit_incremental_learning</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pre_dispatch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'all'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_score</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">nan</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_times</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_validation.py#L1734"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.learning_curve" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.model_selection.</span></span><span class="sig-name descname"><span class="pre">learning_curve</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">train_sizes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">array([0.1,</span> <span class="pre">0.33,</span> <span class="pre">0.55,</span> <span class="pre">0.78,</span> <span class="pre">1.])</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cv</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scoring</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">exploit_incremental_learning</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pre_dispatch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'all'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_score</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">nan</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_times</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_validation.py#L1733"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.learning_curve" title="Link to this definition">#</a></dt>
 <dd><p>Learning curve.</p>
 <p>Determines cross-validated training and test scores for different training
 set sizes.</p>
@@ -1381,7 +1381,7 @@ <h1>learning_curve<a class="headerlink" href="#learning-curve" title="Link to th
 </dd>
 <dt><strong>n_jobs</strong><span class="classifier">int, default=None</span></dt><dd><p>Number of jobs to run in parallel. Training the estimator and computing
 the score are parallelized over the different training and test sets.
-<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.5.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
+<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.6.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
 <code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all processors. See <a class="reference internal" href="http://github.com/glossary.html#term-n_jobs"><span class="xref std std-term">Glossary</span></a>
 for more details.</p>
 </dd>
diff --git a/dev/modules/generated/sklearn.model_selection.permutation_test_score.html b/dev/modules/generated/sklearn.model_selection.permutation_test_score.html
index 9008ae0185fca..01f64afb06ba2 100644
--- a/dev/modules/generated/sklearn.model_selection.permutation_test_score.html
+++ b/dev/modules/generated/sklearn.model_selection.permutation_test_score.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>permutation_test_score<a class="headerlink" href="#permutation-test-score" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.model_selection.permutation_test_score">
-<span class="sig-prename descclassname"><span class="pre">sklearn.model_selection.</span></span><span class="sig-name descname"><span class="pre">permutation_test_score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cv</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_permutations</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scoring</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_validation.py#L1435"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.permutation_test_score" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.model_selection.</span></span><span class="sig-name descname"><span class="pre">permutation_test_score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cv</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_permutations</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scoring</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_validation.py#L1434"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.permutation_test_score" title="Link to this definition">#</a></dt>
 <dd><p>Evaluate the significance of a cross-validated score with permutations.</p>
 <p>Permutes targets to generate ‘randomized data’ and compute the empirical
 p-value against the null hypothesis that features and targets are
@@ -1367,7 +1367,7 @@ <h1>permutation_test_score<a class="headerlink" href="#permutation-test-score" t
 </dd>
 <dt><strong>n_jobs</strong><span class="classifier">int, default=None</span></dt><dd><p>Number of jobs to run in parallel. Training the estimator and computing
 the cross-validated score are parallelized over the permutations.
-<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.5.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
+<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.6.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
 <code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all processors. See <a class="reference internal" href="http://github.com/glossary.html#term-n_jobs"><span class="xref std std-term">Glossary</span></a>
 for more details.</p>
 </dd>
diff --git a/dev/modules/generated/sklearn.model_selection.train_test_split.html b/dev/modules/generated/sklearn.model_selection.train_test_split.html
index 1387940d85a40..d98179745c6e7 100644
--- a/dev/modules/generated/sklearn.model_selection.train_test_split.html
+++ b/dev/modules/generated/sklearn.model_selection.train_test_split.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>train_test_split<a class="headerlink" href="#train-test-split" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.model_selection.train_test_split">
-<span class="sig-prename descclassname"><span class="pre">sklearn.model_selection.</span></span><span class="sig-name descname"><span class="pre">train_test_split</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">arrays</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">test_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">train_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stratify</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_split.py#L2752"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.train_test_split" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.model_selection.</span></span><span class="sig-name descname"><span class="pre">train_test_split</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">arrays</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">test_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">train_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stratify</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_split.py#L2752"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.train_test_split" title="Link to this definition">#</a></dt>
 <dd><p>Split arrays or matrices into random train and test subsets.</p>
 <p>Quick utility that wraps input validation,
 <code class="docutils literal notranslate"><span class="pre">next(ShuffleSplit().split(X,</span> <span class="pre">y))</span></code>, and application to input data
diff --git a/dev/modules/generated/sklearn.model_selection.validation_curve.html b/dev/modules/generated/sklearn.model_selection.validation_curve.html
index f70d5a4d73ea7..b465252c6fd16 100644
--- a/dev/modules/generated/sklearn.model_selection.validation_curve.html
+++ b/dev/modules/generated/sklearn.model_selection.validation_curve.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>validation_curve<a class="headerlink" href="#validation-curve" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.model_selection.validation_curve">
-<span class="sig-prename descclassname"><span class="pre">sklearn.model_selection.</span></span><span class="sig-name descname"><span class="pre">validation_curve</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">param_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">param_range</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cv</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scoring</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pre_dispatch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'all'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_score</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">nan</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/model_selection/_validation.py#L2236"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.validation_curve" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.model_selection.</span></span><span class="sig-name descname"><span class="pre">validation_curve</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">param_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">param_range</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cv</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scoring</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pre_dispatch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'all'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_score</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">nan</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/model_selection/_validation.py#L2235"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.model_selection.validation_curve" title="Link to this definition">#</a></dt>
 <dd><p>Validation curve.</p>
 <p>Determine training and test scores for varying parameter values.</p>
 <p>Compute scores for an estimator with different values of a specified
@@ -1373,7 +1373,7 @@ <h1>validation_curve<a class="headerlink" href="#validation-curve" title="Link t
 <dt><strong>n_jobs</strong><span class="classifier">int, default=None</span></dt><dd><p>Number of jobs to run in parallel. Training the estimator and computing
 the score are parallelized over the combinations of each parameter
 value and each cross-validation split.
-<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.5.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
+<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.6.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
 <code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all processors. See <a class="reference internal" href="http://github.com/glossary.html#term-n_jobs"><span class="xref std std-term">Glossary</span></a>
 for more details.</p>
 </dd>
diff --git a/dev/modules/generated/sklearn.multiclass.OneVsOneClassifier.html b/dev/modules/generated/sklearn.multiclass.OneVsOneClassifier.html
index 2f6111c03442f..c67c9a6c04d60 100644
--- a/dev/modules/generated/sklearn.multiclass.OneVsOneClassifier.html
+++ b/dev/modules/generated/sklearn.multiclass.OneVsOneClassifier.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>OneVsOneClassifier<a class="headerlink" href="#onevsoneclassifier" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.multiclass.OneVsOneClassifier">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.multiclass.</span></span><span class="sig-name descname"><span class="pre">OneVsOneClassifier</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/multiclass.py#L674"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multiclass.OneVsOneClassifier" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.multiclass.</span></span><span class="sig-name descname"><span class="pre">OneVsOneClassifier</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/multiclass.py#L676"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multiclass.OneVsOneClassifier" title="Link to this definition">#</a></dt>
 <dd><p>One-vs-one multiclass strategy.</p>
 <p>This strategy consists in fitting one classifier per class pair.
 At prediction time, the class which received the most votes is selected.
@@ -1330,7 +1330,7 @@ <h1>OneVsOneClassifier<a class="headerlink" href="#onevsoneclassifier" title="Li
 </dd>
 <dt><strong>n_jobs</strong><span class="classifier">int, default=None</span></dt><dd><p>The number of jobs to use for the computation: the <code class="docutils literal notranslate"><span class="pre">n_classes</span> <span class="pre">*</span> <span class="pre">(</span>
 <span class="pre">n_classes</span> <span class="pre">-</span> <span class="pre">1)</span> <span class="pre">/</span> <span class="pre">2</span></code> OVO problems are computed in parallel.</p>
-<p><code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.5.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
+<p><code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.6.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
 <code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all processors. See <a class="reference internal" href="http://github.com/glossary.html#term-n_jobs"><span class="xref std std-term">Glossary</span></a>
 for more details.</p>
 </dd>
@@ -1386,7 +1386,7 @@ <h1>OneVsOneClassifier<a class="headerlink" href="#onevsoneclassifier" title="Li
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.multiclass.OneVsOneClassifier.decision_function">
-<span class="sig-name descname"><span class="pre">decision_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/multiclass.py#L951"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multiclass.OneVsOneClassifier.decision_function" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">decision_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/multiclass.py#L953"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multiclass.OneVsOneClassifier.decision_function" title="Link to this definition">#</a></dt>
 <dd><p>Decision function for the OneVsOneClassifier.</p>
 <p>The decision values for the samples are computed by adding the
 normalized sum of pair-wise classification confidence levels to the
@@ -1414,7 +1414,7 @@ <h1>OneVsOneClassifier<a class="headerlink" href="#onevsoneclassifier" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.multiclass.OneVsOneClassifier.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/multiclass.py#L761"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multiclass.OneVsOneClassifier.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/multiclass.py#L763"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multiclass.OneVsOneClassifier.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit underlying estimators.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1444,7 +1444,7 @@ <h1>OneVsOneClassifier<a class="headerlink" href="#onevsoneclassifier" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.multiclass.OneVsOneClassifier.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/multiclass.py#L1011"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multiclass.OneVsOneClassifier.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/multiclass.py#L1013"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multiclass.OneVsOneClassifier.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1464,7 +1464,7 @@ <h1>OneVsOneClassifier<a class="headerlink" href="#onevsoneclassifier" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.multiclass.OneVsOneClassifier.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multiclass.OneVsOneClassifier.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multiclass.OneVsOneClassifier.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1485,7 +1485,7 @@ <h1>OneVsOneClassifier<a class="headerlink" href="#onevsoneclassifier" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.multiclass.OneVsOneClassifier.partial_fit">
-<span class="sig-name descname"><span class="pre">partial_fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">classes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">partial_fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/multiclass.py#L836"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multiclass.OneVsOneClassifier.partial_fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">partial_fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">classes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">partial_fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/multiclass.py#L838"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multiclass.OneVsOneClassifier.partial_fit" title="Link to this definition">#</a></dt>
 <dd><p>Partially fit underlying estimators.</p>
 <p>Should be used when memory is inefficient to train all data. Chunks
 of data can be passed in several iteration, where the first call
@@ -1524,7 +1524,7 @@ <h1>OneVsOneClassifier<a class="headerlink" href="#onevsoneclassifier" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.multiclass.OneVsOneClassifier.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/multiclass.py#L928"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multiclass.OneVsOneClassifier.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/multiclass.py#L930"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multiclass.OneVsOneClassifier.predict" title="Link to this definition">#</a></dt>
 <dd><p>Estimate the best class label for each sample in X.</p>
 <p>This is implemented as <code class="docutils literal notranslate"><span class="pre">argmax(decision_function(X),</span> <span class="pre">axis=1)</span></code> which
 will return the label of the class with most votes by estimators
@@ -1547,7 +1547,7 @@ <h1>OneVsOneClassifier<a class="headerlink" href="#onevsoneclassifier" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.multiclass.OneVsOneClassifier.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L546"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multiclass.OneVsOneClassifier.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L487"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multiclass.OneVsOneClassifier.score" title="Link to this definition">#</a></dt>
 <dd><p>Return <a class="reference internal" href="http://github.com/model_evaluation.html#accuracy-score"><span class="std std-ref">accuracy</span></a> on provided data and labels.</p>
 <p>In multi-label classification, this is the subset accuracy
 which is a harsh metric since you require for each sample that
@@ -1574,7 +1574,7 @@ <h1>OneVsOneClassifier<a class="headerlink" href="#onevsoneclassifier" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.multiclass.OneVsOneClassifier.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multiclass.OneVsOneClassifier.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multiclass.OneVsOneClassifier.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1598,7 +1598,7 @@ <h1>OneVsOneClassifier<a class="headerlink" href="#onevsoneclassifier" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.multiclass.OneVsOneClassifier.set_partial_fit_request">
-<span class="sig-name descname"><span class="pre">set_partial_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">classes</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.multiclass.OneVsOneClassifier" title="sklearn.multiclass.OneVsOneClassifier"><span class="pre">OneVsOneClassifier</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multiclass.OneVsOneClassifier.set_partial_fit_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_partial_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">classes</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.multiclass.OneVsOneClassifier" title="sklearn.multiclass.OneVsOneClassifier"><span class="pre">OneVsOneClassifier</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multiclass.OneVsOneClassifier.set_partial_fit_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">partial_fit</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1641,7 +1641,7 @@ <h1>OneVsOneClassifier<a class="headerlink" href="#onevsoneclassifier" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.multiclass.OneVsOneClassifier.set_score_request">
-<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.multiclass.OneVsOneClassifier" title="sklearn.multiclass.OneVsOneClassifier"><span class="pre">OneVsOneClassifier</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multiclass.OneVsOneClassifier.set_score_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.multiclass.OneVsOneClassifier" title="sklearn.multiclass.OneVsOneClassifier"><span class="pre">OneVsOneClassifier</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multiclass.OneVsOneClassifier.set_score_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
diff --git a/dev/modules/generated/sklearn.multiclass.OneVsRestClassifier.html b/dev/modules/generated/sklearn.multiclass.OneVsRestClassifier.html
index d0d2709ecb0ac..a18e99b194608 100644
--- a/dev/modules/generated/sklearn.multiclass.OneVsRestClassifier.html
+++ b/dev/modules/generated/sklearn.multiclass.OneVsRestClassifier.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>OneVsRestClassifier<a class="headerlink" href="#onevsrestclassifier" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.multiclass.OneVsRestClassifier">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.multiclass.</span></span><span class="sig-name descname"><span class="pre">OneVsRestClassifier</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/multiclass.py#L202"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multiclass.OneVsRestClassifier" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.multiclass.</span></span><span class="sig-name descname"><span class="pre">OneVsRestClassifier</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/multiclass.py#L202"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multiclass.OneVsRestClassifier" title="Link to this definition">#</a></dt>
 <dd><p>One-vs-the-rest (OvR) multiclass strategy.</p>
 <p>Also known as one-vs-all, this strategy consists in fitting one classifier
 per class. For each classifier, the class is fitted against all the other
@@ -1336,7 +1336,7 @@ <h1>OneVsRestClassifier<a class="headerlink" href="#onevsrestclassifier" title="
 </dd>
 <dt><strong>n_jobs</strong><span class="classifier">int, default=None</span></dt><dd><p>The number of jobs to use for the computation: the <code class="docutils literal notranslate"><span class="pre">n_classes</span></code>
 one-vs-rest problems are computed in parallel.</p>
-<p><code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.5.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
+<p><code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.6.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
 <code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all processors. See <a class="reference internal" href="http://github.com/glossary.html#term-n_jobs"><span class="xref std std-term">Glossary</span></a>
 for more details.</p>
 <div class="versionchanged">
@@ -1346,7 +1346,7 @@ <h1>OneVsRestClassifier<a class="headerlink" href="#onevsrestclassifier" title="
 <dt><strong>verbose</strong><span class="classifier">int, default=0</span></dt><dd><p>The verbosity level, if non zero, progress messages are printed.
 Below 50, the output is sent to stderr. Otherwise, the output is sent
 to stdout. The frequency of the messages increases with the verbosity
-level, reporting all iterations at 10. See <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.Parallel.html#joblib.Parallel" title="(in joblib v1.5.dev0)"><code class="xref py py-class docutils literal notranslate"><span class="pre">joblib.Parallel</span></code></a> for
+level, reporting all iterations at 10. See <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.Parallel.html#joblib.Parallel" title="(in joblib v1.6.dev0)"><code class="xref py py-class docutils literal notranslate"><span class="pre">joblib.Parallel</span></code></a> for
 more details.</p>
 <div class="versionadded">
 <p><span class="versionmodified added">Added in version 1.1.</span></p>
@@ -1415,7 +1415,7 @@ <h1>OneVsRestClassifier<a class="headerlink" href="#onevsrestclassifier" title="
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.multiclass.OneVsRestClassifier.decision_function">
-<span class="sig-name descname"><span class="pre">decision_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/multiclass.py#L560"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multiclass.OneVsRestClassifier.decision_function" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">decision_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/multiclass.py#L562"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multiclass.OneVsRestClassifier.decision_function" title="Link to this definition">#</a></dt>
 <dd><p>Decision function for the OneVsRestClassifier.</p>
 <p>Return the distance of each sample from the decision boundary for each
 class. This can only be used with estimators which implement the
@@ -1442,7 +1442,7 @@ <h1>OneVsRestClassifier<a class="headerlink" href="#onevsrestclassifier" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.multiclass.OneVsRestClassifier.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/multiclass.py#L327"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multiclass.OneVsRestClassifier.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/multiclass.py#L327"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multiclass.OneVsRestClassifier.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit underlying estimators.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1473,7 +1473,7 @@ <h1>OneVsRestClassifier<a class="headerlink" href="#onevsrestclassifier" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.multiclass.OneVsRestClassifier.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/multiclass.py#L607"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multiclass.OneVsRestClassifier.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/multiclass.py#L609"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multiclass.OneVsRestClassifier.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1493,7 +1493,7 @@ <h1>OneVsRestClassifier<a class="headerlink" href="#onevsrestclassifier" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.multiclass.OneVsRestClassifier.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multiclass.OneVsRestClassifier.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multiclass.OneVsRestClassifier.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1514,7 +1514,7 @@ <h1>OneVsRestClassifier<a class="headerlink" href="#onevsrestclassifier" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.multiclass.OneVsRestClassifier.partial_fit">
-<span class="sig-name descname"><span class="pre">partial_fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">classes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">partial_fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/multiclass.py#L397"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multiclass.OneVsRestClassifier.partial_fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">partial_fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">classes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">partial_fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/multiclass.py#L397"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multiclass.OneVsRestClassifier.partial_fit" title="Link to this definition">#</a></dt>
 <dd><p>Partially fit underlying estimators.</p>
 <p>Should be used when memory is inefficient to train all data.
 Chunks of data can be passed in several iterations.</p>
@@ -1553,7 +1553,7 @@ <h1>OneVsRestClassifier<a class="headerlink" href="#onevsrestclassifier" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.multiclass.OneVsRestClassifier.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/multiclass.py#L482"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multiclass.OneVsRestClassifier.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/multiclass.py#L482"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multiclass.OneVsRestClassifier.predict" title="Link to this definition">#</a></dt>
 <dd><p>Predict multi-class targets using underlying estimators.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1573,7 +1573,7 @@ <h1>OneVsRestClassifier<a class="headerlink" href="#onevsrestclassifier" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.multiclass.OneVsRestClassifier.predict_proba">
-<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/multiclass.py#L520"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multiclass.OneVsRestClassifier.predict_proba" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/multiclass.py#L520"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multiclass.OneVsRestClassifier.predict_proba" title="Link to this definition">#</a></dt>
 <dd><p>Probability estimates.</p>
 <p>The returned estimates for all classes are ordered by label of classes.</p>
 <p>Note that in the multilabel case, each sample can have any number of
@@ -1601,7 +1601,7 @@ <h1>OneVsRestClassifier<a class="headerlink" href="#onevsrestclassifier" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.multiclass.OneVsRestClassifier.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L546"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multiclass.OneVsRestClassifier.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L487"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multiclass.OneVsRestClassifier.score" title="Link to this definition">#</a></dt>
 <dd><p>Return <a class="reference internal" href="http://github.com/model_evaluation.html#accuracy-score"><span class="std std-ref">accuracy</span></a> on provided data and labels.</p>
 <p>In multi-label classification, this is the subset accuracy
 which is a harsh metric since you require for each sample that
@@ -1628,7 +1628,7 @@ <h1>OneVsRestClassifier<a class="headerlink" href="#onevsrestclassifier" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.multiclass.OneVsRestClassifier.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multiclass.OneVsRestClassifier.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multiclass.OneVsRestClassifier.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1652,7 +1652,7 @@ <h1>OneVsRestClassifier<a class="headerlink" href="#onevsrestclassifier" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.multiclass.OneVsRestClassifier.set_partial_fit_request">
-<span class="sig-name descname"><span class="pre">set_partial_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">classes</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.multiclass.OneVsRestClassifier" title="sklearn.multiclass.OneVsRestClassifier"><span class="pre">OneVsRestClassifier</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multiclass.OneVsRestClassifier.set_partial_fit_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_partial_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">classes</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.multiclass.OneVsRestClassifier" title="sklearn.multiclass.OneVsRestClassifier"><span class="pre">OneVsRestClassifier</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multiclass.OneVsRestClassifier.set_partial_fit_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">partial_fit</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1695,7 +1695,7 @@ <h1>OneVsRestClassifier<a class="headerlink" href="#onevsrestclassifier" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.multiclass.OneVsRestClassifier.set_score_request">
-<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.multiclass.OneVsRestClassifier" title="sklearn.multiclass.OneVsRestClassifier"><span class="pre">OneVsRestClassifier</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multiclass.OneVsRestClassifier.set_score_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.multiclass.OneVsRestClassifier" title="sklearn.multiclass.OneVsRestClassifier"><span class="pre">OneVsRestClassifier</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multiclass.OneVsRestClassifier.set_score_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
diff --git a/dev/modules/generated/sklearn.multiclass.OutputCodeClassifier.html b/dev/modules/generated/sklearn.multiclass.OutputCodeClassifier.html
index 2f410ae8caeb9..5b945a72564fb 100644
--- a/dev/modules/generated/sklearn.multiclass.OutputCodeClassifier.html
+++ b/dev/modules/generated/sklearn.multiclass.OutputCodeClassifier.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>OutputCodeClassifier<a class="headerlink" href="#outputcodeclassifier" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.multiclass.OutputCodeClassifier">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.multiclass.</span></span><span class="sig-name descname"><span class="pre">OutputCodeClassifier</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">code_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/multiclass.py#L1039"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multiclass.OutputCodeClassifier" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.multiclass.</span></span><span class="sig-name descname"><span class="pre">OutputCodeClassifier</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">code_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/multiclass.py#L1041"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multiclass.OutputCodeClassifier" title="Link to this definition">#</a></dt>
 <dd><p>(Error-Correcting) Output-Code multiclass strategy.</p>
 <p>Output-code based strategies consist in representing each class with a
 binary code (an array of 0s and 1s). At fitting time, one binary
@@ -1335,7 +1335,7 @@ <h1>OutputCodeClassifier<a class="headerlink" href="#outputcodeclassifier" title
 </dd>
 <dt><strong>n_jobs</strong><span class="classifier">int, default=None</span></dt><dd><p>The number of jobs to use for the computation: the multiclass problems
 are computed in parallel.</p>
-<p><code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.5.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
+<p><code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.6.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
 <code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all processors. See <a class="reference internal" href="http://github.com/glossary.html#term-n_jobs"><span class="xref std std-term">Glossary</span></a>
 for more details.</p>
 </dd>
@@ -1413,7 +1413,7 @@ <h1>OutputCodeClassifier<a class="headerlink" href="#outputcodeclassifier" title
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.multiclass.OutputCodeClassifier.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/multiclass.py#L1155"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multiclass.OutputCodeClassifier.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/multiclass.py#L1157"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multiclass.OutputCodeClassifier.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit underlying estimators.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1443,7 +1443,7 @@ <h1>OutputCodeClassifier<a class="headerlink" href="#outputcodeclassifier" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.multiclass.OutputCodeClassifier.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/multiclass.py#L1261"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multiclass.OutputCodeClassifier.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/multiclass.py#L1263"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multiclass.OutputCodeClassifier.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1463,7 +1463,7 @@ <h1>OutputCodeClassifier<a class="headerlink" href="#outputcodeclassifier" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.multiclass.OutputCodeClassifier.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multiclass.OutputCodeClassifier.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multiclass.OutputCodeClassifier.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1484,7 +1484,7 @@ <h1>OutputCodeClassifier<a class="headerlink" href="#outputcodeclassifier" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.multiclass.OutputCodeClassifier.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/multiclass.py#L1236"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multiclass.OutputCodeClassifier.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/multiclass.py#L1238"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multiclass.OutputCodeClassifier.predict" title="Link to this definition">#</a></dt>
 <dd><p>Predict multi-class targets using underlying estimators.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1504,7 +1504,7 @@ <h1>OutputCodeClassifier<a class="headerlink" href="#outputcodeclassifier" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.multiclass.OutputCodeClassifier.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L546"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multiclass.OutputCodeClassifier.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L487"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multiclass.OutputCodeClassifier.score" title="Link to this definition">#</a></dt>
 <dd><p>Return <a class="reference internal" href="http://github.com/model_evaluation.html#accuracy-score"><span class="std std-ref">accuracy</span></a> on provided data and labels.</p>
 <p>In multi-label classification, this is the subset accuracy
 which is a harsh metric since you require for each sample that
@@ -1531,7 +1531,7 @@ <h1>OutputCodeClassifier<a class="headerlink" href="#outputcodeclassifier" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.multiclass.OutputCodeClassifier.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multiclass.OutputCodeClassifier.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multiclass.OutputCodeClassifier.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1555,7 +1555,7 @@ <h1>OutputCodeClassifier<a class="headerlink" href="#outputcodeclassifier" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.multiclass.OutputCodeClassifier.set_score_request">
-<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.multiclass.OutputCodeClassifier" title="sklearn.multiclass.OutputCodeClassifier"><span class="pre">OutputCodeClassifier</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multiclass.OutputCodeClassifier.set_score_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.multiclass.OutputCodeClassifier" title="sklearn.multiclass.OutputCodeClassifier"><span class="pre">OutputCodeClassifier</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multiclass.OutputCodeClassifier.set_score_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
diff --git a/dev/modules/generated/sklearn.multioutput.ClassifierChain.html b/dev/modules/generated/sklearn.multioutput.ClassifierChain.html
index 6088a32806fc5..1ff7f7444afcb 100644
--- a/dev/modules/generated/sklearn.multioutput.ClassifierChain.html
+++ b/dev/modules/generated/sklearn.multioutput.ClassifierChain.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>ClassifierChain<a class="headerlink" href="#classifierchain" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.multioutput.ClassifierChain">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.multioutput.</span></span><span class="sig-name descname"><span class="pre">ClassifierChain</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">order</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cv</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chain_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'predict'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">base_estimator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'deprecated'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/multioutput.py#L883"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multioutput.ClassifierChain" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.multioutput.</span></span><span class="sig-name descname"><span class="pre">ClassifierChain</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">order</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cv</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chain_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'predict'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">base_estimator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'deprecated'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/multioutput.py#L881"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multioutput.ClassifierChain" title="Link to this definition">#</a></dt>
 <dd><p>A multi-label model that arranges binary classifiers into a chain.</p>
 <p>Each model makes a prediction in the order specified by the chain using
 all of the available features provided to the model plus the predictions
@@ -1439,14 +1439,14 @@ <h1>ClassifierChain<a class="headerlink" href="#classifierchain" title="Link to
 <span class="go">       [1., 0., 0.],</span>
 <span class="go">       [0., 1., 0.]])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">chain</span><span class="o">.</span><span class="n">predict_proba</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
-<span class="go">array([[0.8387..., 0.9431..., 0.4576...],</span>
-<span class="go">       [0.8878..., 0.3684..., 0.2640...],</span>
-<span class="go">       [0.0321..., 0.9935..., 0.0626...]])</span>
+<span class="go">array([[0.8387, 0.9431, 0.4576],</span>
+<span class="go">       [0.8878, 0.3684, 0.2640],</span>
+<span class="go">       [0.0321, 0.9935, 0.0626]])</span>
 </pre></div>
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.multioutput.ClassifierChain.decision_function">
-<span class="sig-name descname"><span class="pre">decision_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/multioutput.py#L1126"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multioutput.ClassifierChain.decision_function" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">decision_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/multioutput.py#L1124"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multioutput.ClassifierChain.decision_function" title="Link to this definition">#</a></dt>
 <dd><p>Evaluate the decision_function of the models in the chain.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1467,7 +1467,7 @@ <h1>ClassifierChain<a class="headerlink" href="#classifierchain" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.multioutput.ClassifierChain.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/multioutput.py#L1061"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multioutput.ClassifierChain.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/multioutput.py#L1059"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multioutput.ClassifierChain.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit the model to data matrix X and targets Y.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1496,7 +1496,7 @@ <h1>ClassifierChain<a class="headerlink" href="#classifierchain" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.multioutput.ClassifierChain.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/multioutput.py#L1143"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multioutput.ClassifierChain.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/multioutput.py#L1141"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multioutput.ClassifierChain.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1516,7 +1516,7 @@ <h1>ClassifierChain<a class="headerlink" href="#classifierchain" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.multioutput.ClassifierChain.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multioutput.ClassifierChain.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multioutput.ClassifierChain.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1537,7 +1537,7 @@ <h1>ClassifierChain<a class="headerlink" href="#classifierchain" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.multioutput.ClassifierChain.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/multioutput.py#L862"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multioutput.ClassifierChain.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/multioutput.py#L860"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multioutput.ClassifierChain.predict" title="Link to this definition">#</a></dt>
 <dd><p>Predict on the data matrix X using the ClassifierChain model.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1557,7 +1557,7 @@ <h1>ClassifierChain<a class="headerlink" href="#classifierchain" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.multioutput.ClassifierChain.predict_log_proba">
-<span class="sig-name descname"><span class="pre">predict_log_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/multioutput.py#L1111"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multioutput.ClassifierChain.predict_log_proba" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict_log_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/multioutput.py#L1109"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multioutput.ClassifierChain.predict_log_proba" title="Link to this definition">#</a></dt>
 <dd><p>Predict logarithm of probability estimates.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1577,7 +1577,7 @@ <h1>ClassifierChain<a class="headerlink" href="#classifierchain" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.multioutput.ClassifierChain.predict_proba">
-<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/multioutput.py#L1095"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multioutput.ClassifierChain.predict_proba" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/multioutput.py#L1093"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multioutput.ClassifierChain.predict_proba" title="Link to this definition">#</a></dt>
 <dd><p>Predict probability estimates.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1597,7 +1597,7 @@ <h1>ClassifierChain<a class="headerlink" href="#classifierchain" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.multioutput.ClassifierChain.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L546"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multioutput.ClassifierChain.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L487"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multioutput.ClassifierChain.score" title="Link to this definition">#</a></dt>
 <dd><p>Return <a class="reference internal" href="http://github.com/model_evaluation.html#accuracy-score"><span class="std std-ref">accuracy</span></a> on provided data and labels.</p>
 <p>In multi-label classification, this is the subset accuracy
 which is a harsh metric since you require for each sample that
@@ -1624,7 +1624,7 @@ <h1>ClassifierChain<a class="headerlink" href="#classifierchain" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.multioutput.ClassifierChain.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multioutput.ClassifierChain.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multioutput.ClassifierChain.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1648,7 +1648,7 @@ <h1>ClassifierChain<a class="headerlink" href="#classifierchain" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.multioutput.ClassifierChain.set_score_request">
-<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.multioutput.ClassifierChain" title="sklearn.multioutput.ClassifierChain"><span class="pre">ClassifierChain</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multioutput.ClassifierChain.set_score_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.multioutput.ClassifierChain" title="sklearn.multioutput.ClassifierChain"><span class="pre">ClassifierChain</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multioutput.ClassifierChain.set_score_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
diff --git a/dev/modules/generated/sklearn.multioutput.MultiOutputClassifier.html b/dev/modules/generated/sklearn.multioutput.MultiOutputClassifier.html
index 79415c56cdf08..bdab546609088 100644
--- a/dev/modules/generated/sklearn.multioutput.MultiOutputClassifier.html
+++ b/dev/modules/generated/sklearn.multioutput.MultiOutputClassifier.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>MultiOutputClassifier<a class="headerlink" href="#multioutputclassifier" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.multioutput.MultiOutputClassifier">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.multioutput.</span></span><span class="sig-name descname"><span class="pre">MultiOutputClassifier</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/multioutput.py#L450"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multioutput.MultiOutputClassifier" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.multioutput.</span></span><span class="sig-name descname"><span class="pre">MultiOutputClassifier</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/multioutput.py#L449"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multioutput.MultiOutputClassifier" title="Link to this definition">#</a></dt>
 <dd><p>Multi target classification.</p>
 <p>This strategy consists of fitting one classifier per target. This is a
 simple strategy for extending classifiers that do not natively support
@@ -1323,7 +1323,7 @@ <h1>MultiOutputClassifier<a class="headerlink" href="#multioutputclassifier" tit
 <p>When individual estimators are fast to train or predict,
 using <code class="docutils literal notranslate"><span class="pre">n_jobs</span> <span class="pre">&gt;</span> <span class="pre">1</span></code> can result in slower performance due
 to the parallelism overhead.</p>
-<p><code class="docutils literal notranslate"><span class="pre">None</span></code> means <code class="docutils literal notranslate"><span class="pre">1</span></code> unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.5.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
+<p><code class="docutils literal notranslate"><span class="pre">None</span></code> means <code class="docutils literal notranslate"><span class="pre">1</span></code> unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.6.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
 <code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all available processes / threads.
 See <a class="reference internal" href="http://github.com/glossary.html#term-n_jobs"><span class="xref std std-term">Glossary</span></a> for more details.</p>
 <div class="versionchanged">
@@ -1376,7 +1376,7 @@ <h1>MultiOutputClassifier<a class="headerlink" href="#multioutputclassifier" tit
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.multioutput.MultiOutputClassifier.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/multioutput.py#L522"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multioutput.MultiOutputClassifier.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/multioutput.py#L521"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multioutput.MultiOutputClassifier.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit the model to data matrix X and targets Y.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1407,7 +1407,7 @@ <h1>MultiOutputClassifier<a class="headerlink" href="#multioutputclassifier" tit
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.multioutput.MultiOutputClassifier.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/multioutput.py#L324"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multioutput.MultiOutputClassifier.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/multioutput.py#L323"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multioutput.MultiOutputClassifier.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1427,7 +1427,7 @@ <h1>MultiOutputClassifier<a class="headerlink" href="#multioutputclassifier" tit
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.multioutput.MultiOutputClassifier.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multioutput.MultiOutputClassifier.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multioutput.MultiOutputClassifier.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1448,7 +1448,7 @@ <h1>MultiOutputClassifier<a class="headerlink" href="#multioutputclassifier" tit
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.multioutput.MultiOutputClassifier.partial_fit">
-<span class="sig-name descname"><span class="pre">partial_fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">classes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">partial_fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/multioutput.py#L115"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multioutput.MultiOutputClassifier.partial_fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">partial_fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">classes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">partial_fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/multioutput.py#L114"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multioutput.MultiOutputClassifier.partial_fit" title="Link to this definition">#</a></dt>
 <dd><p>Incrementally fit a separate model for each class output.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1490,7 +1490,7 @@ <h1>MultiOutputClassifier<a class="headerlink" href="#multioutputclassifier" tit
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.multioutput.MultiOutputClassifier.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/multioutput.py#L293"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multioutput.MultiOutputClassifier.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/multioutput.py#L292"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multioutput.MultiOutputClassifier.predict" title="Link to this definition">#</a></dt>
 <dd><p>Predict multi-output variable using model for each target variable.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1511,7 +1511,7 @@ <h1>MultiOutputClassifier<a class="headerlink" href="#multioutputclassifier" tit
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.multioutput.MultiOutputClassifier.predict_proba">
-<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/multioutput.py#L563"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multioutput.MultiOutputClassifier.predict_proba" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/multioutput.py#L562"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multioutput.MultiOutputClassifier.predict_proba" title="Link to this definition">#</a></dt>
 <dd><p>Return prediction probabilities for each class of each output.</p>
 <p>This method will raise a <code class="docutils literal notranslate"><span class="pre">ValueError</span></code> if any of the
 estimators do not have <code class="docutils literal notranslate"><span class="pre">predict_proba</span></code>.</p>
@@ -1539,7 +1539,7 @@ <h1>MultiOutputClassifier<a class="headerlink" href="#multioutputclassifier" tit
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.multioutput.MultiOutputClassifier.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/multioutput.py#L591"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multioutput.MultiOutputClassifier.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/multioutput.py#L590"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multioutput.MultiOutputClassifier.score" title="Link to this definition">#</a></dt>
 <dd><p>Return the mean accuracy on the given test data and labels.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1561,7 +1561,7 @@ <h1>MultiOutputClassifier<a class="headerlink" href="#multioutputclassifier" tit
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.multioutput.MultiOutputClassifier.set_fit_request">
-<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.multioutput.MultiOutputClassifier" title="sklearn.multioutput.MultiOutputClassifier"><span class="pre">MultiOutputClassifier</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multioutput.MultiOutputClassifier.set_fit_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.multioutput.MultiOutputClassifier" title="sklearn.multioutput.MultiOutputClassifier"><span class="pre">MultiOutputClassifier</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multioutput.MultiOutputClassifier.set_fit_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1604,7 +1604,7 @@ <h1>MultiOutputClassifier<a class="headerlink" href="#multioutputclassifier" tit
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.multioutput.MultiOutputClassifier.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multioutput.MultiOutputClassifier.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multioutput.MultiOutputClassifier.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1628,7 +1628,7 @@ <h1>MultiOutputClassifier<a class="headerlink" href="#multioutputclassifier" tit
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.multioutput.MultiOutputClassifier.set_partial_fit_request">
-<span class="sig-name descname"><span class="pre">set_partial_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">classes</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.multioutput.MultiOutputClassifier" title="sklearn.multioutput.MultiOutputClassifier"><span class="pre">MultiOutputClassifier</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multioutput.MultiOutputClassifier.set_partial_fit_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_partial_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">classes</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.multioutput.MultiOutputClassifier" title="sklearn.multioutput.MultiOutputClassifier"><span class="pre">MultiOutputClassifier</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multioutput.MultiOutputClassifier.set_partial_fit_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">partial_fit</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
diff --git a/dev/modules/generated/sklearn.multioutput.MultiOutputRegressor.html b/dev/modules/generated/sklearn.multioutput.MultiOutputRegressor.html
index 3c50892c6ee4f..1770b2eca43d8 100644
--- a/dev/modules/generated/sklearn.multioutput.MultiOutputRegressor.html
+++ b/dev/modules/generated/sklearn.multioutput.MultiOutputRegressor.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1308,7 +1308,7 @@
 <h1>MultiOutputRegressor<a class="headerlink" href="#multioutputregressor" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.multioutput.MultiOutputRegressor">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.multioutput.</span></span><span class="sig-name descname"><span class="pre">MultiOutputRegressor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/multioutput.py#L347"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multioutput.MultiOutputRegressor" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.multioutput.</span></span><span class="sig-name descname"><span class="pre">MultiOutputRegressor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/multioutput.py#L346"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multioutput.MultiOutputRegressor" title="Link to this definition">#</a></dt>
 <dd><p>Multi target regression.</p>
 <p>This strategy consists of fitting one regressor per target. This is a
 simple strategy for extending regressors that do not natively support
@@ -1327,7 +1327,7 @@ <h1>MultiOutputRegressor<a class="headerlink" href="#multioutputregressor" title
 <p>When individual estimators are fast to train or predict,
 using <code class="docutils literal notranslate"><span class="pre">n_jobs</span> <span class="pre">&gt;</span> <span class="pre">1</span></code> can result in slower performance due
 to the parallelism overhead.</p>
-<p><code class="docutils literal notranslate"><span class="pre">None</span></code> means <code class="docutils literal notranslate"><span class="pre">1</span></code> unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.5.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
+<p><code class="docutils literal notranslate"><span class="pre">None</span></code> means <code class="docutils literal notranslate"><span class="pre">1</span></code> unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.6.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
 <code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all available processes / threads.
 See <a class="reference internal" href="http://github.com/glossary.html#term-n_jobs"><span class="xref std std-term">Glossary</span></a> for more details.</p>
 <div class="versionchanged">
@@ -1372,12 +1372,12 @@ <h1>MultiOutputRegressor<a class="headerlink" href="#multioutputregressor" title
 <span class="gp">&gt;&gt;&gt; </span><span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">load_linnerud</span><span class="p">(</span><span class="n">return_X_y</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">regr</span> <span class="o">=</span> <span class="n">MultiOutputRegressor</span><span class="p">(</span><span class="n">Ridge</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="mi">123</span><span class="p">))</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">regr</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X</span><span class="p">[[</span><span class="mi">0</span><span class="p">]])</span>
-<span class="go">array([[176..., 35..., 57...]])</span>
+<span class="go">array([[176, 35.1, 57.1]])</span>
 </pre></div>
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.multioutput.MultiOutputRegressor.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/multioutput.py#L213"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multioutput.MultiOutputRegressor.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/multioutput.py#L212"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multioutput.MultiOutputRegressor.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit the model to data, separately for each output variable.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1409,7 +1409,7 @@ <h1>MultiOutputRegressor<a class="headerlink" href="#multioutputregressor" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.multioutput.MultiOutputRegressor.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/multioutput.py#L324"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multioutput.MultiOutputRegressor.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/multioutput.py#L323"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multioutput.MultiOutputRegressor.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1429,7 +1429,7 @@ <h1>MultiOutputRegressor<a class="headerlink" href="#multioutputregressor" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.multioutput.MultiOutputRegressor.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multioutput.MultiOutputRegressor.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multioutput.MultiOutputRegressor.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1450,7 +1450,7 @@ <h1>MultiOutputRegressor<a class="headerlink" href="#multioutputregressor" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.multioutput.MultiOutputRegressor.partial_fit">
-<span class="sig-name descname"><span class="pre">partial_fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">partial_fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/multioutput.py#L416"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multioutput.MultiOutputRegressor.partial_fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">partial_fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">partial_fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/multioutput.py#L415"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multioutput.MultiOutputRegressor.partial_fit" title="Link to this definition">#</a></dt>
 <dd><p>Incrementally fit the model to data, for each output variable.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1484,7 +1484,7 @@ <h1>MultiOutputRegressor<a class="headerlink" href="#multioutputregressor" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.multioutput.MultiOutputRegressor.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/multioutput.py#L293"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multioutput.MultiOutputRegressor.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/multioutput.py#L292"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multioutput.MultiOutputRegressor.predict" title="Link to this definition">#</a></dt>
 <dd><p>Predict multi-output variable using model for each target variable.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1505,7 +1505,7 @@ <h1>MultiOutputRegressor<a class="headerlink" href="#multioutputregressor" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.multioutput.MultiOutputRegressor.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L619"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multioutput.MultiOutputRegressor.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L560"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multioutput.MultiOutputRegressor.score" title="Link to this definition">#</a></dt>
 <dd><p>Return <a class="reference internal" href="http://github.com/model_evaluation.html#r2-score"><span class="std std-ref">coefficient of determination</span></a> on test data.</p>
 <p>The coefficient of determination, <span class="math notranslate nohighlight">\(R^2\)</span>, is defined as
 <span class="math notranslate nohighlight">\((1 - \frac{u}{v})\)</span>, where <span class="math notranslate nohighlight">\(u\)</span> is the residual
@@ -1547,7 +1547,7 @@ <h1>MultiOutputRegressor<a class="headerlink" href="#multioutputregressor" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.multioutput.MultiOutputRegressor.set_fit_request">
-<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.multioutput.MultiOutputRegressor" title="sklearn.multioutput.MultiOutputRegressor"><span class="pre">MultiOutputRegressor</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multioutput.MultiOutputRegressor.set_fit_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.multioutput.MultiOutputRegressor" title="sklearn.multioutput.MultiOutputRegressor"><span class="pre">MultiOutputRegressor</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multioutput.MultiOutputRegressor.set_fit_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1590,7 +1590,7 @@ <h1>MultiOutputRegressor<a class="headerlink" href="#multioutputregressor" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.multioutput.MultiOutputRegressor.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multioutput.MultiOutputRegressor.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multioutput.MultiOutputRegressor.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1614,7 +1614,7 @@ <h1>MultiOutputRegressor<a class="headerlink" href="#multioutputregressor" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.multioutput.MultiOutputRegressor.set_partial_fit_request">
-<span class="sig-name descname"><span class="pre">set_partial_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.multioutput.MultiOutputRegressor" title="sklearn.multioutput.MultiOutputRegressor"><span class="pre">MultiOutputRegressor</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multioutput.MultiOutputRegressor.set_partial_fit_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_partial_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.multioutput.MultiOutputRegressor" title="sklearn.multioutput.MultiOutputRegressor"><span class="pre">MultiOutputRegressor</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multioutput.MultiOutputRegressor.set_partial_fit_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">partial_fit</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1657,7 +1657,7 @@ <h1>MultiOutputRegressor<a class="headerlink" href="#multioutputregressor" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.multioutput.MultiOutputRegressor.set_score_request">
-<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.multioutput.MultiOutputRegressor" title="sklearn.multioutput.MultiOutputRegressor"><span class="pre">MultiOutputRegressor</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multioutput.MultiOutputRegressor.set_score_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.multioutput.MultiOutputRegressor" title="sklearn.multioutput.MultiOutputRegressor"><span class="pre">MultiOutputRegressor</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multioutput.MultiOutputRegressor.set_score_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
diff --git a/dev/modules/generated/sklearn.multioutput.RegressorChain.html b/dev/modules/generated/sklearn.multioutput.RegressorChain.html
index 2aa146f639561..5482fc0a5ca3c 100644
--- a/dev/modules/generated/sklearn.multioutput.RegressorChain.html
+++ b/dev/modules/generated/sklearn.multioutput.RegressorChain.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1306,7 +1306,7 @@
 <h1>RegressorChain<a class="headerlink" href="#regressorchain" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.multioutput.RegressorChain">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.multioutput.</span></span><span class="sig-name descname"><span class="pre">RegressorChain</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">order</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cv</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">base_estimator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'deprecated'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/multioutput.py#L1173"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multioutput.RegressorChain" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.multioutput.</span></span><span class="sig-name descname"><span class="pre">RegressorChain</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">order</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cv</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">base_estimator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'deprecated'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/multioutput.py#L1171"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multioutput.RegressorChain" title="Link to this definition">#</a></dt>
 <dd><p>A multi-label model that arranges regressions into a chain.</p>
 <p>Each model makes a prediction in the order specified by the chain using
 all of the available features provided to the model plus the predictions
@@ -1410,7 +1410,7 @@ <h1>RegressorChain<a class="headerlink" href="#regressorchain" title="Link to th
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.multioutput.RegressorChain.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/multioutput.py#L1276"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multioutput.RegressorChain.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/multioutput.py#L1274"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multioutput.RegressorChain.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit the model to data matrix X and targets Y.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1438,7 +1438,7 @@ <h1>RegressorChain<a class="headerlink" href="#regressorchain" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.multioutput.RegressorChain.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/multioutput.py#L1305"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multioutput.RegressorChain.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/multioutput.py#L1303"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multioutput.RegressorChain.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1458,7 +1458,7 @@ <h1>RegressorChain<a class="headerlink" href="#regressorchain" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.multioutput.RegressorChain.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multioutput.RegressorChain.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multioutput.RegressorChain.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1479,7 +1479,7 @@ <h1>RegressorChain<a class="headerlink" href="#regressorchain" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.multioutput.RegressorChain.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/multioutput.py#L862"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multioutput.RegressorChain.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/multioutput.py#L860"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multioutput.RegressorChain.predict" title="Link to this definition">#</a></dt>
 <dd><p>Predict on the data matrix X using the ClassifierChain model.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1499,7 +1499,7 @@ <h1>RegressorChain<a class="headerlink" href="#regressorchain" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.multioutput.RegressorChain.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L619"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multioutput.RegressorChain.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L560"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multioutput.RegressorChain.score" title="Link to this definition">#</a></dt>
 <dd><p>Return <a class="reference internal" href="http://github.com/model_evaluation.html#r2-score"><span class="std std-ref">coefficient of determination</span></a> on test data.</p>
 <p>The coefficient of determination, <span class="math notranslate nohighlight">\(R^2\)</span>, is defined as
 <span class="math notranslate nohighlight">\((1 - \frac{u}{v})\)</span>, where <span class="math notranslate nohighlight">\(u\)</span> is the residual
@@ -1541,7 +1541,7 @@ <h1>RegressorChain<a class="headerlink" href="#regressorchain" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.multioutput.RegressorChain.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multioutput.RegressorChain.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multioutput.RegressorChain.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1565,7 +1565,7 @@ <h1>RegressorChain<a class="headerlink" href="#regressorchain" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.multioutput.RegressorChain.set_score_request">
-<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.multioutput.RegressorChain" title="sklearn.multioutput.RegressorChain"><span class="pre">RegressorChain</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multioutput.RegressorChain.set_score_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.multioutput.RegressorChain" title="sklearn.multioutput.RegressorChain"><span class="pre">RegressorChain</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multioutput.RegressorChain.set_score_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
diff --git a/dev/modules/generated/sklearn.naive_bayes.BernoulliNB.html b/dev/modules/generated/sklearn.naive_bayes.BernoulliNB.html
index 5114ae2e6959f..47429c3262c5b 100644
--- a/dev/modules/generated/sklearn.naive_bayes.BernoulliNB.html
+++ b/dev/modules/generated/sklearn.naive_bayes.BernoulliNB.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>BernoulliNB<a class="headerlink" href="#bernoullinb" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.naive_bayes.BernoulliNB">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.naive_bayes.</span></span><span class="sig-name descname"><span class="pre">BernoulliNB</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">force_alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">binarize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_prior</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">class_prior</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/naive_bayes.py#L1062"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.BernoulliNB" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.naive_bayes.</span></span><span class="sig-name descname"><span class="pre">BernoulliNB</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">force_alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">binarize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_prior</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">class_prior</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/naive_bayes.py#L1062"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.BernoulliNB" title="Link to this definition">#</a></dt>
 <dd><p>Naive Bayes classifier for multivariate Bernoulli models.</p>
 <p>Like MultinomialNB, this classifier is suitable for discrete data. The
 difference is that while MultinomialNB works with occurrence counts,
@@ -1406,7 +1406,7 @@ <h1>BernoulliNB<a class="headerlink" href="#bernoullinb" title="Link to this hea
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.naive_bayes.BernoulliNB.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/naive_bayes.py#L714"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.BernoulliNB.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/naive_bayes.py#L714"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.BernoulliNB.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit Naive Bayes classifier according to X, y.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1431,7 +1431,7 @@ <h1>BernoulliNB<a class="headerlink" href="#bernoullinb" title="Link to this hea
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.naive_bayes.BernoulliNB.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.BernoulliNB.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.BernoulliNB.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1448,7 +1448,7 @@ <h1>BernoulliNB<a class="headerlink" href="#bernoullinb" title="Link to this hea
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.naive_bayes.BernoulliNB.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.BernoulliNB.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.BernoulliNB.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1469,7 +1469,7 @@ <h1>BernoulliNB<a class="headerlink" href="#bernoullinb" title="Link to this hea
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.naive_bayes.BernoulliNB.partial_fit">
-<span class="sig-name descname"><span class="pre">partial_fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">classes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/naive_bayes.py#L631"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.BernoulliNB.partial_fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">partial_fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">classes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/naive_bayes.py#L631"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.BernoulliNB.partial_fit" title="Link to this definition">#</a></dt>
 <dd><p>Incremental fit on a batch of samples.</p>
 <p>This method is expected to be called several times consecutively
 on different chunks of a dataset so as to implement out-of-core
@@ -1506,7 +1506,7 @@ <h1>BernoulliNB<a class="headerlink" href="#bernoullinb" title="Link to this hea
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.naive_bayes.BernoulliNB.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/naive_bayes.py#L90"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.BernoulliNB.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/naive_bayes.py#L90"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.BernoulliNB.predict" title="Link to this definition">#</a></dt>
 <dd><p>Perform classification on an array of test vectors X.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1526,7 +1526,7 @@ <h1>BernoulliNB<a class="headerlink" href="#bernoullinb" title="Link to this hea
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.naive_bayes.BernoulliNB.predict_joint_log_proba">
-<span class="sig-name descname"><span class="pre">predict_joint_log_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/naive_bayes.py#L66"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.BernoulliNB.predict_joint_log_proba" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict_joint_log_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/naive_bayes.py#L66"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.BernoulliNB.predict_joint_log_proba" title="Link to this definition">#</a></dt>
 <dd><p>Return joint log probability estimates for the test vector X.</p>
 <p>For each row x of X and class y, the joint log probability is given by
 <code class="docutils literal notranslate"><span class="pre">log</span> <span class="pre">P(x,</span> <span class="pre">y)</span> <span class="pre">=</span> <span class="pre">log</span> <span class="pre">P(y)</span> <span class="pre">+</span> <span class="pre">log</span> <span class="pre">P(x|y),</span></code>
@@ -1552,7 +1552,7 @@ <h1>BernoulliNB<a class="headerlink" href="#bernoullinb" title="Link to this hea
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.naive_bayes.BernoulliNB.predict_log_proba">
-<span class="sig-name descname"><span class="pre">predict_log_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/naive_bayes.py#L109"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.BernoulliNB.predict_log_proba" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict_log_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/naive_bayes.py#L109"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.BernoulliNB.predict_log_proba" title="Link to this definition">#</a></dt>
 <dd><p>Return log-probability estimates for the test vector X.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1574,7 +1574,7 @@ <h1>BernoulliNB<a class="headerlink" href="#bernoullinb" title="Link to this hea
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.naive_bayes.BernoulliNB.predict_proba">
-<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/naive_bayes.py#L132"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.BernoulliNB.predict_proba" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/naive_bayes.py#L132"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.BernoulliNB.predict_proba" title="Link to this definition">#</a></dt>
 <dd><p>Return probability estimates for the test vector X.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1596,7 +1596,7 @@ <h1>BernoulliNB<a class="headerlink" href="#bernoullinb" title="Link to this hea
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.naive_bayes.BernoulliNB.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L546"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.BernoulliNB.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L487"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.BernoulliNB.score" title="Link to this definition">#</a></dt>
 <dd><p>Return <a class="reference internal" href="http://github.com/model_evaluation.html#accuracy-score"><span class="std std-ref">accuracy</span></a> on provided data and labels.</p>
 <p>In multi-label classification, this is the subset accuracy
 which is a harsh metric since you require for each sample that
@@ -1623,7 +1623,7 @@ <h1>BernoulliNB<a class="headerlink" href="#bernoullinb" title="Link to this hea
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.naive_bayes.BernoulliNB.set_fit_request">
-<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.naive_bayes.BernoulliNB" title="sklearn.naive_bayes.BernoulliNB"><span class="pre">BernoulliNB</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.BernoulliNB.set_fit_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.naive_bayes.BernoulliNB" title="sklearn.naive_bayes.BernoulliNB"><span class="pre">BernoulliNB</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.BernoulliNB.set_fit_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1666,7 +1666,7 @@ <h1>BernoulliNB<a class="headerlink" href="#bernoullinb" title="Link to this hea
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.naive_bayes.BernoulliNB.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.BernoulliNB.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.BernoulliNB.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1690,7 +1690,7 @@ <h1>BernoulliNB<a class="headerlink" href="#bernoullinb" title="Link to this hea
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.naive_bayes.BernoulliNB.set_partial_fit_request">
-<span class="sig-name descname"><span class="pre">set_partial_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">classes</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.naive_bayes.BernoulliNB" title="sklearn.naive_bayes.BernoulliNB"><span class="pre">BernoulliNB</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.BernoulliNB.set_partial_fit_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_partial_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">classes</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.naive_bayes.BernoulliNB" title="sklearn.naive_bayes.BernoulliNB"><span class="pre">BernoulliNB</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.BernoulliNB.set_partial_fit_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">partial_fit</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1735,7 +1735,7 @@ <h1>BernoulliNB<a class="headerlink" href="#bernoullinb" title="Link to this hea
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.naive_bayes.BernoulliNB.set_score_request">
-<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.naive_bayes.BernoulliNB" title="sklearn.naive_bayes.BernoulliNB"><span class="pre">BernoulliNB</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.BernoulliNB.set_score_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.naive_bayes.BernoulliNB" title="sklearn.naive_bayes.BernoulliNB"><span class="pre">BernoulliNB</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.BernoulliNB.set_score_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
diff --git a/dev/modules/generated/sklearn.naive_bayes.CategoricalNB.html b/dev/modules/generated/sklearn.naive_bayes.CategoricalNB.html
index ff2a1ea29fa5b..42a9f5046d6d7 100644
--- a/dev/modules/generated/sklearn.naive_bayes.CategoricalNB.html
+++ b/dev/modules/generated/sklearn.naive_bayes.CategoricalNB.html
@@ -40,7 +40,7 @@
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-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>CategoricalNB<a class="headerlink" href="#categoricalnb" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.naive_bayes.CategoricalNB">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.naive_bayes.</span></span><span class="sig-name descname"><span class="pre">CategoricalNB</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">force_alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_prior</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">class_prior</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_categories</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/naive_bayes.py#L1232"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.CategoricalNB" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.naive_bayes.</span></span><span class="sig-name descname"><span class="pre">CategoricalNB</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">force_alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_prior</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">class_prior</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_categories</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/naive_bayes.py#L1232"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.CategoricalNB" title="Link to this definition">#</a></dt>
 <dd><p>Naive Bayes classifier for categorical features.</p>
 <p>The categorical Naive Bayes classifier is suitable for classification with
 discrete features that are categorically distributed. The categories of
@@ -1413,7 +1413,7 @@ <h1>CategoricalNB<a class="headerlink" href="#categoricalnb" title="Link to this
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.naive_bayes.CategoricalNB.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/naive_bayes.py#L1363"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.CategoricalNB.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/naive_bayes.py#L1363"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.CategoricalNB.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit Naive Bayes classifier according to X, y.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1443,7 +1443,7 @@ <h1>CategoricalNB<a class="headerlink" href="#categoricalnb" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.naive_bayes.CategoricalNB.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.CategoricalNB.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.CategoricalNB.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1460,7 +1460,7 @@ <h1>CategoricalNB<a class="headerlink" href="#categoricalnb" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.naive_bayes.CategoricalNB.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.CategoricalNB.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.CategoricalNB.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1481,7 +1481,7 @@ <h1>CategoricalNB<a class="headerlink" href="#categoricalnb" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.naive_bayes.CategoricalNB.partial_fit">
-<span class="sig-name descname"><span class="pre">partial_fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">classes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/naive_bayes.py#L1390"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.CategoricalNB.partial_fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">partial_fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">classes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/naive_bayes.py#L1390"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.CategoricalNB.partial_fit" title="Link to this definition">#</a></dt>
 <dd><p>Incremental fit on a batch of samples.</p>
 <p>This method is expected to be called several times consecutively
 on different chunks of a dataset so as to implement out-of-core
@@ -1523,7 +1523,7 @@ <h1>CategoricalNB<a class="headerlink" href="#categoricalnb" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.naive_bayes.CategoricalNB.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/naive_bayes.py#L90"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.CategoricalNB.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/naive_bayes.py#L90"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.CategoricalNB.predict" title="Link to this definition">#</a></dt>
 <dd><p>Perform classification on an array of test vectors X.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1543,7 +1543,7 @@ <h1>CategoricalNB<a class="headerlink" href="#categoricalnb" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.naive_bayes.CategoricalNB.predict_joint_log_proba">
-<span class="sig-name descname"><span class="pre">predict_joint_log_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/naive_bayes.py#L66"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.CategoricalNB.predict_joint_log_proba" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict_joint_log_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/naive_bayes.py#L66"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.CategoricalNB.predict_joint_log_proba" title="Link to this definition">#</a></dt>
 <dd><p>Return joint log probability estimates for the test vector X.</p>
 <p>For each row x of X and class y, the joint log probability is given by
 <code class="docutils literal notranslate"><span class="pre">log</span> <span class="pre">P(x,</span> <span class="pre">y)</span> <span class="pre">=</span> <span class="pre">log</span> <span class="pre">P(y)</span> <span class="pre">+</span> <span class="pre">log</span> <span class="pre">P(x|y),</span></code>
@@ -1569,7 +1569,7 @@ <h1>CategoricalNB<a class="headerlink" href="#categoricalnb" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.naive_bayes.CategoricalNB.predict_log_proba">
-<span class="sig-name descname"><span class="pre">predict_log_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/naive_bayes.py#L109"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.CategoricalNB.predict_log_proba" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict_log_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/naive_bayes.py#L109"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.CategoricalNB.predict_log_proba" title="Link to this definition">#</a></dt>
 <dd><p>Return log-probability estimates for the test vector X.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1591,7 +1591,7 @@ <h1>CategoricalNB<a class="headerlink" href="#categoricalnb" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.naive_bayes.CategoricalNB.predict_proba">
-<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/naive_bayes.py#L132"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.CategoricalNB.predict_proba" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/naive_bayes.py#L132"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.CategoricalNB.predict_proba" title="Link to this definition">#</a></dt>
 <dd><p>Return probability estimates for the test vector X.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1613,7 +1613,7 @@ <h1>CategoricalNB<a class="headerlink" href="#categoricalnb" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.naive_bayes.CategoricalNB.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L546"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.CategoricalNB.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L487"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.CategoricalNB.score" title="Link to this definition">#</a></dt>
 <dd><p>Return <a class="reference internal" href="http://github.com/model_evaluation.html#accuracy-score"><span class="std std-ref">accuracy</span></a> on provided data and labels.</p>
 <p>In multi-label classification, this is the subset accuracy
 which is a harsh metric since you require for each sample that
@@ -1640,7 +1640,7 @@ <h1>CategoricalNB<a class="headerlink" href="#categoricalnb" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.naive_bayes.CategoricalNB.set_fit_request">
-<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.naive_bayes.CategoricalNB" title="sklearn.naive_bayes.CategoricalNB"><span class="pre">CategoricalNB</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.CategoricalNB.set_fit_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.naive_bayes.CategoricalNB" title="sklearn.naive_bayes.CategoricalNB"><span class="pre">CategoricalNB</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.CategoricalNB.set_fit_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1683,7 +1683,7 @@ <h1>CategoricalNB<a class="headerlink" href="#categoricalnb" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.naive_bayes.CategoricalNB.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.CategoricalNB.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.CategoricalNB.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1707,7 +1707,7 @@ <h1>CategoricalNB<a class="headerlink" href="#categoricalnb" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.naive_bayes.CategoricalNB.set_partial_fit_request">
-<span class="sig-name descname"><span class="pre">set_partial_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">classes</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.naive_bayes.CategoricalNB" title="sklearn.naive_bayes.CategoricalNB"><span class="pre">CategoricalNB</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.CategoricalNB.set_partial_fit_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_partial_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">classes</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.naive_bayes.CategoricalNB" title="sklearn.naive_bayes.CategoricalNB"><span class="pre">CategoricalNB</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.CategoricalNB.set_partial_fit_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">partial_fit</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1752,7 +1752,7 @@ <h1>CategoricalNB<a class="headerlink" href="#categoricalnb" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.naive_bayes.CategoricalNB.set_score_request">
-<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.naive_bayes.CategoricalNB" title="sklearn.naive_bayes.CategoricalNB"><span class="pre">CategoricalNB</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.CategoricalNB.set_score_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.naive_bayes.CategoricalNB" title="sklearn.naive_bayes.CategoricalNB"><span class="pre">CategoricalNB</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.CategoricalNB.set_score_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
diff --git a/dev/modules/generated/sklearn.naive_bayes.ComplementNB.html b/dev/modules/generated/sklearn.naive_bayes.ComplementNB.html
index 8c6281afab8ca..9cf594c56eda1 100644
--- a/dev/modules/generated/sklearn.naive_bayes.ComplementNB.html
+++ b/dev/modules/generated/sklearn.naive_bayes.ComplementNB.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>ComplementNB<a class="headerlink" href="#complementnb" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.naive_bayes.ComplementNB">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.naive_bayes.</span></span><span class="sig-name descname"><span class="pre">ComplementNB</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">force_alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_prior</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">class_prior</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/naive_bayes.py#L907"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.ComplementNB" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.naive_bayes.</span></span><span class="sig-name descname"><span class="pre">ComplementNB</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">force_alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_prior</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">class_prior</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/naive_bayes.py#L907"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.ComplementNB" title="Link to this definition">#</a></dt>
 <dd><p>The Complement Naive Bayes classifier described in Rennie et al. (2003).</p>
 <p>The Complement Naive Bayes classifier was designed to correct the “severe
 assumptions” made by the standard Multinomial Naive Bayes classifier. It is
@@ -1408,7 +1408,7 @@ <h1>ComplementNB<a class="headerlink" href="#complementnb" title="Link to this h
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.naive_bayes.ComplementNB.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/naive_bayes.py#L714"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.ComplementNB.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/naive_bayes.py#L714"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.ComplementNB.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit Naive Bayes classifier according to X, y.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1433,7 +1433,7 @@ <h1>ComplementNB<a class="headerlink" href="#complementnb" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.naive_bayes.ComplementNB.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.ComplementNB.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.ComplementNB.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1450,7 +1450,7 @@ <h1>ComplementNB<a class="headerlink" href="#complementnb" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.naive_bayes.ComplementNB.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.ComplementNB.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.ComplementNB.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1471,7 +1471,7 @@ <h1>ComplementNB<a class="headerlink" href="#complementnb" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.naive_bayes.ComplementNB.partial_fit">
-<span class="sig-name descname"><span class="pre">partial_fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">classes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/naive_bayes.py#L631"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.ComplementNB.partial_fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">partial_fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">classes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/naive_bayes.py#L631"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.ComplementNB.partial_fit" title="Link to this definition">#</a></dt>
 <dd><p>Incremental fit on a batch of samples.</p>
 <p>This method is expected to be called several times consecutively
 on different chunks of a dataset so as to implement out-of-core
@@ -1508,7 +1508,7 @@ <h1>ComplementNB<a class="headerlink" href="#complementnb" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.naive_bayes.ComplementNB.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/naive_bayes.py#L90"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.ComplementNB.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/naive_bayes.py#L90"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.ComplementNB.predict" title="Link to this definition">#</a></dt>
 <dd><p>Perform classification on an array of test vectors X.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1528,7 +1528,7 @@ <h1>ComplementNB<a class="headerlink" href="#complementnb" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.naive_bayes.ComplementNB.predict_joint_log_proba">
-<span class="sig-name descname"><span class="pre">predict_joint_log_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/naive_bayes.py#L66"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.ComplementNB.predict_joint_log_proba" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict_joint_log_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/naive_bayes.py#L66"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.ComplementNB.predict_joint_log_proba" title="Link to this definition">#</a></dt>
 <dd><p>Return joint log probability estimates for the test vector X.</p>
 <p>For each row x of X and class y, the joint log probability is given by
 <code class="docutils literal notranslate"><span class="pre">log</span> <span class="pre">P(x,</span> <span class="pre">y)</span> <span class="pre">=</span> <span class="pre">log</span> <span class="pre">P(y)</span> <span class="pre">+</span> <span class="pre">log</span> <span class="pre">P(x|y),</span></code>
@@ -1554,7 +1554,7 @@ <h1>ComplementNB<a class="headerlink" href="#complementnb" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.naive_bayes.ComplementNB.predict_log_proba">
-<span class="sig-name descname"><span class="pre">predict_log_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/naive_bayes.py#L109"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.ComplementNB.predict_log_proba" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict_log_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/naive_bayes.py#L109"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.ComplementNB.predict_log_proba" title="Link to this definition">#</a></dt>
 <dd><p>Return log-probability estimates for the test vector X.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1576,7 +1576,7 @@ <h1>ComplementNB<a class="headerlink" href="#complementnb" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.naive_bayes.ComplementNB.predict_proba">
-<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/naive_bayes.py#L132"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.ComplementNB.predict_proba" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/naive_bayes.py#L132"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.ComplementNB.predict_proba" title="Link to this definition">#</a></dt>
 <dd><p>Return probability estimates for the test vector X.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1598,7 +1598,7 @@ <h1>ComplementNB<a class="headerlink" href="#complementnb" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.naive_bayes.ComplementNB.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L546"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.ComplementNB.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L487"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.ComplementNB.score" title="Link to this definition">#</a></dt>
 <dd><p>Return <a class="reference internal" href="http://github.com/model_evaluation.html#accuracy-score"><span class="std std-ref">accuracy</span></a> on provided data and labels.</p>
 <p>In multi-label classification, this is the subset accuracy
 which is a harsh metric since you require for each sample that
@@ -1625,7 +1625,7 @@ <h1>ComplementNB<a class="headerlink" href="#complementnb" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.naive_bayes.ComplementNB.set_fit_request">
-<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.naive_bayes.ComplementNB" title="sklearn.naive_bayes.ComplementNB"><span class="pre">ComplementNB</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.ComplementNB.set_fit_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.naive_bayes.ComplementNB" title="sklearn.naive_bayes.ComplementNB"><span class="pre">ComplementNB</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.ComplementNB.set_fit_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1668,7 +1668,7 @@ <h1>ComplementNB<a class="headerlink" href="#complementnb" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.naive_bayes.ComplementNB.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.ComplementNB.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.ComplementNB.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1692,7 +1692,7 @@ <h1>ComplementNB<a class="headerlink" href="#complementnb" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.naive_bayes.ComplementNB.set_partial_fit_request">
-<span class="sig-name descname"><span class="pre">set_partial_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">classes</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.naive_bayes.ComplementNB" title="sklearn.naive_bayes.ComplementNB"><span class="pre">ComplementNB</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.ComplementNB.set_partial_fit_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_partial_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">classes</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.naive_bayes.ComplementNB" title="sklearn.naive_bayes.ComplementNB"><span class="pre">ComplementNB</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.ComplementNB.set_partial_fit_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">partial_fit</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1737,7 +1737,7 @@ <h1>ComplementNB<a class="headerlink" href="#complementnb" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.naive_bayes.ComplementNB.set_score_request">
-<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.naive_bayes.ComplementNB" title="sklearn.naive_bayes.ComplementNB"><span class="pre">ComplementNB</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.ComplementNB.set_score_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.naive_bayes.ComplementNB" title="sklearn.naive_bayes.ComplementNB"><span class="pre">ComplementNB</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.ComplementNB.set_score_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
diff --git a/dev/modules/generated/sklearn.naive_bayes.GaussianNB.html b/dev/modules/generated/sklearn.naive_bayes.GaussianNB.html
index 9054a08091d41..d848ad631cc73 100644
--- a/dev/modules/generated/sklearn.naive_bayes.GaussianNB.html
+++ b/dev/modules/generated/sklearn.naive_bayes.GaussianNB.html
@@ -11,10 +11,10 @@
 <meta property="og:type" content="website" />
 <meta property="og:url" content="https://scikit-learn/stable/modules/generated/sklearn.naive_bayes.GaussianNB.html" />
 <meta property="og:site_name" content="scikit-learn" />
-<meta property="og:description" content="Gallery examples: Probability calibration of classifiers Probability Calibration curves Comparison of Calibration of Classifiers Classifier comparison Plot class probabilities calculated by the Vot..." />
+<meta property="og:description" content="Gallery examples: Probability calibration of classifiers Probability Calibration curves Comparison of Calibration of Classifiers Classifier comparison Plotting Learning Curves and Checking Models’ ..." />
 <meta property="og:image" content="https://scikit-learn/stable/_images/sphx_glr_plot_calibration_thumb.png" />
 <meta property="og:image:alt" content="" />
-<meta name="description" content="Gallery examples: Probability calibration of classifiers Probability Calibration curves Comparison of Calibration of Classifiers Classifier comparison Plot class probabilities calculated by the Vot..." />
+<meta name="description" content="Gallery examples: Probability calibration of classifiers Probability Calibration curves Comparison of Calibration of Classifiers Classifier comparison Plotting Learning Curves and Checking Models’ ..." />
 
     <title>GaussianNB &#8212; scikit-learn 1.7.dev0 documentation</title>
   
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>GaussianNB<a class="headerlink" href="#gaussiannb" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.naive_bayes.GaussianNB">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.naive_bayes.</span></span><span class="sig-name descname"><span class="pre">GaussianNB</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">priors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">var_smoothing</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-09</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/naive_bayes.py#L151"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.GaussianNB" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.naive_bayes.</span></span><span class="sig-name descname"><span class="pre">GaussianNB</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">priors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">var_smoothing</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-09</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/naive_bayes.py#L151"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.GaussianNB" title="Link to this definition">#</a></dt>
 <dd><p>Gaussian Naive Bayes (GaussianNB).</p>
 <p>Can perform online updates to model parameters via <a class="reference internal" href="#sklearn.naive_bayes.GaussianNB.partial_fit" title="sklearn.naive_bayes.GaussianNB.partial_fit"><code class="xref py py-meth docutils literal notranslate"><span class="pre">partial_fit</span></code></a>.
 For details on algorithm used to update feature means and variance online,
@@ -1390,7 +1390,7 @@ <h1>GaussianNB<a class="headerlink" href="#gaussiannb" title="Link to this headi
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.naive_bayes.GaussianNB.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/naive_bayes.py#L241"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.GaussianNB.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/naive_bayes.py#L241"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.GaussianNB.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit Gaussian Naive Bayes according to X, y.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1418,7 +1418,7 @@ <h1>GaussianNB<a class="headerlink" href="#gaussiannb" title="Link to this headi
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.naive_bayes.GaussianNB.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.GaussianNB.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.GaussianNB.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1435,7 +1435,7 @@ <h1>GaussianNB<a class="headerlink" href="#gaussiannb" title="Link to this headi
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.naive_bayes.GaussianNB.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.GaussianNB.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.GaussianNB.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1456,7 +1456,7 @@ <h1>GaussianNB<a class="headerlink" href="#gaussiannb" title="Link to this headi
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.naive_bayes.GaussianNB.partial_fit">
-<span class="sig-name descname"><span class="pre">partial_fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">classes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/naive_bayes.py#L348"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.GaussianNB.partial_fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">partial_fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">classes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/naive_bayes.py#L348"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.GaussianNB.partial_fit" title="Link to this definition">#</a></dt>
 <dd><p>Incremental fit on a batch of samples.</p>
 <p>This method is expected to be called several times consecutively
 on different chunks of a dataset so as to implement out-of-core
@@ -1497,7 +1497,7 @@ <h1>GaussianNB<a class="headerlink" href="#gaussiannb" title="Link to this headi
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.naive_bayes.GaussianNB.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/naive_bayes.py#L90"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.GaussianNB.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/naive_bayes.py#L90"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.GaussianNB.predict" title="Link to this definition">#</a></dt>
 <dd><p>Perform classification on an array of test vectors X.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1517,7 +1517,7 @@ <h1>GaussianNB<a class="headerlink" href="#gaussiannb" title="Link to this headi
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.naive_bayes.GaussianNB.predict_joint_log_proba">
-<span class="sig-name descname"><span class="pre">predict_joint_log_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/naive_bayes.py#L66"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.GaussianNB.predict_joint_log_proba" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict_joint_log_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/naive_bayes.py#L66"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.GaussianNB.predict_joint_log_proba" title="Link to this definition">#</a></dt>
 <dd><p>Return joint log probability estimates for the test vector X.</p>
 <p>For each row x of X and class y, the joint log probability is given by
 <code class="docutils literal notranslate"><span class="pre">log</span> <span class="pre">P(x,</span> <span class="pre">y)</span> <span class="pre">=</span> <span class="pre">log</span> <span class="pre">P(y)</span> <span class="pre">+</span> <span class="pre">log</span> <span class="pre">P(x|y),</span></code>
@@ -1543,7 +1543,7 @@ <h1>GaussianNB<a class="headerlink" href="#gaussiannb" title="Link to this headi
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.naive_bayes.GaussianNB.predict_log_proba">
-<span class="sig-name descname"><span class="pre">predict_log_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/naive_bayes.py#L109"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.GaussianNB.predict_log_proba" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict_log_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/naive_bayes.py#L109"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.GaussianNB.predict_log_proba" title="Link to this definition">#</a></dt>
 <dd><p>Return log-probability estimates for the test vector X.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1565,7 +1565,7 @@ <h1>GaussianNB<a class="headerlink" href="#gaussiannb" title="Link to this headi
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.naive_bayes.GaussianNB.predict_proba">
-<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/naive_bayes.py#L132"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.GaussianNB.predict_proba" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/naive_bayes.py#L132"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.GaussianNB.predict_proba" title="Link to this definition">#</a></dt>
 <dd><p>Return probability estimates for the test vector X.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1587,7 +1587,7 @@ <h1>GaussianNB<a class="headerlink" href="#gaussiannb" title="Link to this headi
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.naive_bayes.GaussianNB.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L546"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.GaussianNB.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L487"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.GaussianNB.score" title="Link to this definition">#</a></dt>
 <dd><p>Return <a class="reference internal" href="http://github.com/model_evaluation.html#accuracy-score"><span class="std std-ref">accuracy</span></a> on provided data and labels.</p>
 <p>In multi-label classification, this is the subset accuracy
 which is a harsh metric since you require for each sample that
@@ -1614,7 +1614,7 @@ <h1>GaussianNB<a class="headerlink" href="#gaussiannb" title="Link to this headi
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.naive_bayes.GaussianNB.set_fit_request">
-<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.naive_bayes.GaussianNB" title="sklearn.naive_bayes.GaussianNB"><span class="pre">GaussianNB</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.GaussianNB.set_fit_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.naive_bayes.GaussianNB" title="sklearn.naive_bayes.GaussianNB"><span class="pre">GaussianNB</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.GaussianNB.set_fit_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1657,7 +1657,7 @@ <h1>GaussianNB<a class="headerlink" href="#gaussiannb" title="Link to this headi
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.naive_bayes.GaussianNB.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.GaussianNB.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.GaussianNB.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1681,7 +1681,7 @@ <h1>GaussianNB<a class="headerlink" href="#gaussiannb" title="Link to this headi
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.naive_bayes.GaussianNB.set_partial_fit_request">
-<span class="sig-name descname"><span class="pre">set_partial_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">classes</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.naive_bayes.GaussianNB" title="sklearn.naive_bayes.GaussianNB"><span class="pre">GaussianNB</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.GaussianNB.set_partial_fit_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_partial_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">classes</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.naive_bayes.GaussianNB" title="sklearn.naive_bayes.GaussianNB"><span class="pre">GaussianNB</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.GaussianNB.set_partial_fit_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">partial_fit</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1726,7 +1726,7 @@ <h1>GaussianNB<a class="headerlink" href="#gaussiannb" title="Link to this headi
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.naive_bayes.GaussianNB.set_score_request">
-<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.naive_bayes.GaussianNB" title="sklearn.naive_bayes.GaussianNB"><span class="pre">GaussianNB</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.GaussianNB.set_score_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.naive_bayes.GaussianNB" title="sklearn.naive_bayes.GaussianNB"><span class="pre">GaussianNB</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.GaussianNB.set_score_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1783,9 +1783,6 @@ <h2>Gallery examples<a class="headerlink" href="#gallery-examples" title="Link t
 </div><div class="sphx-glr-thumbcontainer" tooltip="A comparison of several classifiers in scikit-learn on synthetic datasets. The point of this example is to illustrate the nature of decision boundaries of different classifiers. This should be taken with a grain of salt, as the intuition conveyed by these examples does not necessarily carry over to real datasets."><img alt="" src="http://github.com/_images/sphx_glr_plot_classifier_comparison_thumb.png" />
 <p><a class="reference internal" href="http://github.com/auto_examples/classification/plot_classifier_comparison.html#sphx-glr-auto-examples-classification-plot-classifier-comparison-py"><span class="std std-ref">Classifier comparison</span></a></p>
   <div class="sphx-glr-thumbnail-title">Classifier comparison</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="Plot the class probabilities of the first sample in a toy dataset predicted by three different classifiers and averaged by the VotingClassifier."><img alt="" src="http://github.com/_images/sphx_glr_plot_voting_probas_thumb.png" />
-<p><a class="reference internal" href="http://github.com/auto_examples/ensemble/plot_voting_probas.html#sphx-glr-auto-examples-ensemble-plot-voting-probas-py"><span class="std std-ref">Plot class probabilities calculated by the VotingClassifier</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Plot class probabilities calculated by the VotingClassifier</div>
 </div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we show how to use the class LearningCurveDisplay to easily plot learning curves. In addition, we give an interpretation to the learning curves obtained for a naive Bayes and SVM classifiers."><img alt="" src="http://github.com/_images/sphx_glr_plot_learning_curve_thumb.png" />
 <p><a class="reference internal" href="http://github.com/auto_examples/model_selection/plot_learning_curve.html#sphx-glr-auto-examples-model-selection-plot-learning-curve-py"><span class="std std-ref">Plotting Learning Curves and Checking Models’ Scalability</span></a></p>
   <div class="sphx-glr-thumbnail-title">Plotting Learning Curves and Checking Models' Scalability</div>
diff --git a/dev/modules/generated/sklearn.naive_bayes.MultinomialNB.html b/dev/modules/generated/sklearn.naive_bayes.MultinomialNB.html
index 2a8f2a8352280..2cc2264080f35 100644
--- a/dev/modules/generated/sklearn.naive_bayes.MultinomialNB.html
+++ b/dev/modules/generated/sklearn.naive_bayes.MultinomialNB.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>MultinomialNB<a class="headerlink" href="#multinomialnb" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.naive_bayes.MultinomialNB">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.naive_bayes.</span></span><span class="sig-name descname"><span class="pre">MultinomialNB</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">force_alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_prior</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">class_prior</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/naive_bayes.py#L779"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.MultinomialNB" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.naive_bayes.</span></span><span class="sig-name descname"><span class="pre">MultinomialNB</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">force_alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_prior</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">class_prior</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/naive_bayes.py#L779"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.MultinomialNB" title="Link to this definition">#</a></dt>
 <dd><p>Naive Bayes classifier for multinomial models.</p>
 <p>The multinomial Naive Bayes classifier is suitable for classification with
 discrete features (e.g., word counts for text classification). The
@@ -1400,7 +1400,7 @@ <h1>MultinomialNB<a class="headerlink" href="#multinomialnb" title="Link to this
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.naive_bayes.MultinomialNB.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/naive_bayes.py#L714"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.MultinomialNB.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/naive_bayes.py#L714"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.MultinomialNB.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit Naive Bayes classifier according to X, y.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1425,7 +1425,7 @@ <h1>MultinomialNB<a class="headerlink" href="#multinomialnb" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.naive_bayes.MultinomialNB.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.MultinomialNB.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.MultinomialNB.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1442,7 +1442,7 @@ <h1>MultinomialNB<a class="headerlink" href="#multinomialnb" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.naive_bayes.MultinomialNB.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.MultinomialNB.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.MultinomialNB.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1463,7 +1463,7 @@ <h1>MultinomialNB<a class="headerlink" href="#multinomialnb" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.naive_bayes.MultinomialNB.partial_fit">
-<span class="sig-name descname"><span class="pre">partial_fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">classes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/naive_bayes.py#L631"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.MultinomialNB.partial_fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">partial_fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">classes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/naive_bayes.py#L631"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.MultinomialNB.partial_fit" title="Link to this definition">#</a></dt>
 <dd><p>Incremental fit on a batch of samples.</p>
 <p>This method is expected to be called several times consecutively
 on different chunks of a dataset so as to implement out-of-core
@@ -1500,7 +1500,7 @@ <h1>MultinomialNB<a class="headerlink" href="#multinomialnb" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.naive_bayes.MultinomialNB.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/naive_bayes.py#L90"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.MultinomialNB.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/naive_bayes.py#L90"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.MultinomialNB.predict" title="Link to this definition">#</a></dt>
 <dd><p>Perform classification on an array of test vectors X.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1520,7 +1520,7 @@ <h1>MultinomialNB<a class="headerlink" href="#multinomialnb" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.naive_bayes.MultinomialNB.predict_joint_log_proba">
-<span class="sig-name descname"><span class="pre">predict_joint_log_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/naive_bayes.py#L66"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.MultinomialNB.predict_joint_log_proba" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict_joint_log_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/naive_bayes.py#L66"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.MultinomialNB.predict_joint_log_proba" title="Link to this definition">#</a></dt>
 <dd><p>Return joint log probability estimates for the test vector X.</p>
 <p>For each row x of X and class y, the joint log probability is given by
 <code class="docutils literal notranslate"><span class="pre">log</span> <span class="pre">P(x,</span> <span class="pre">y)</span> <span class="pre">=</span> <span class="pre">log</span> <span class="pre">P(y)</span> <span class="pre">+</span> <span class="pre">log</span> <span class="pre">P(x|y),</span></code>
@@ -1546,7 +1546,7 @@ <h1>MultinomialNB<a class="headerlink" href="#multinomialnb" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.naive_bayes.MultinomialNB.predict_log_proba">
-<span class="sig-name descname"><span class="pre">predict_log_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/naive_bayes.py#L109"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.MultinomialNB.predict_log_proba" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict_log_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/naive_bayes.py#L109"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.MultinomialNB.predict_log_proba" title="Link to this definition">#</a></dt>
 <dd><p>Return log-probability estimates for the test vector X.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1568,7 +1568,7 @@ <h1>MultinomialNB<a class="headerlink" href="#multinomialnb" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.naive_bayes.MultinomialNB.predict_proba">
-<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/naive_bayes.py#L132"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.MultinomialNB.predict_proba" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/naive_bayes.py#L132"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.MultinomialNB.predict_proba" title="Link to this definition">#</a></dt>
 <dd><p>Return probability estimates for the test vector X.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1590,7 +1590,7 @@ <h1>MultinomialNB<a class="headerlink" href="#multinomialnb" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.naive_bayes.MultinomialNB.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L546"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.MultinomialNB.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L487"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.MultinomialNB.score" title="Link to this definition">#</a></dt>
 <dd><p>Return <a class="reference internal" href="http://github.com/model_evaluation.html#accuracy-score"><span class="std std-ref">accuracy</span></a> on provided data and labels.</p>
 <p>In multi-label classification, this is the subset accuracy
 which is a harsh metric since you require for each sample that
@@ -1617,7 +1617,7 @@ <h1>MultinomialNB<a class="headerlink" href="#multinomialnb" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.naive_bayes.MultinomialNB.set_fit_request">
-<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.naive_bayes.MultinomialNB" title="sklearn.naive_bayes.MultinomialNB"><span class="pre">MultinomialNB</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.MultinomialNB.set_fit_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.naive_bayes.MultinomialNB" title="sklearn.naive_bayes.MultinomialNB"><span class="pre">MultinomialNB</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.MultinomialNB.set_fit_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1660,7 +1660,7 @@ <h1>MultinomialNB<a class="headerlink" href="#multinomialnb" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.naive_bayes.MultinomialNB.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.MultinomialNB.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.MultinomialNB.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1684,7 +1684,7 @@ <h1>MultinomialNB<a class="headerlink" href="#multinomialnb" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.naive_bayes.MultinomialNB.set_partial_fit_request">
-<span class="sig-name descname"><span class="pre">set_partial_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">classes</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.naive_bayes.MultinomialNB" title="sklearn.naive_bayes.MultinomialNB"><span class="pre">MultinomialNB</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.MultinomialNB.set_partial_fit_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_partial_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">classes</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.naive_bayes.MultinomialNB" title="sklearn.naive_bayes.MultinomialNB"><span class="pre">MultinomialNB</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.MultinomialNB.set_partial_fit_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">partial_fit</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1729,7 +1729,7 @@ <h1>MultinomialNB<a class="headerlink" href="#multinomialnb" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.naive_bayes.MultinomialNB.set_score_request">
-<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.naive_bayes.MultinomialNB" title="sklearn.naive_bayes.MultinomialNB"><span class="pre">MultinomialNB</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.MultinomialNB.set_score_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.naive_bayes.MultinomialNB" title="sklearn.naive_bayes.MultinomialNB"><span class="pre">MultinomialNB</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.naive_bayes.MultinomialNB.set_score_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
diff --git a/dev/modules/generated/sklearn.neighbors.BallTree.html b/dev/modules/generated/sklearn.neighbors.BallTree.html
index 4c3d69679672e..4cf6e0289a6c0 100644
--- a/dev/modules/generated/sklearn.neighbors.BallTree.html
+++ b/dev/modules/generated/sklearn.neighbors.BallTree.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
diff --git a/dev/modules/generated/sklearn.neighbors.KDTree.html b/dev/modules/generated/sklearn.neighbors.KDTree.html
index c11bfbbfe8f66..f2c1792235fc5 100644
--- a/dev/modules/generated/sklearn.neighbors.KDTree.html
+++ b/dev/modules/generated/sklearn.neighbors.KDTree.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
diff --git a/dev/modules/generated/sklearn.neighbors.KNeighborsClassifier.html b/dev/modules/generated/sklearn.neighbors.KNeighborsClassifier.html
index ae28b0f11e9c1..b204de100d551 100644
--- a/dev/modules/generated/sklearn.neighbors.KNeighborsClassifier.html
+++ b/dev/modules/generated/sklearn.neighbors.KNeighborsClassifier.html
@@ -11,10 +11,10 @@
 <meta property="og:type" content="website" />
 <meta property="og:url" content="https://scikit-learn/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html" />
 <meta property="og:site_name" content="scikit-learn" />
-<meta property="og:description" content="Gallery examples: Classifier comparison Plot the decision boundaries of a VotingClassifier Caching nearest neighbors Nearest Neighbors Classification Comparing Nearest Neighbors with and without Ne..." />
+<meta property="og:description" content="Gallery examples: Classifier comparison Caching nearest neighbors Nearest Neighbors Classification Comparing Nearest Neighbors with and without Neighborhood Components Analysis Dimensionality Reduc..." />
 <meta property="og:image" content="https://scikit-learn/stable/_images/sphx_glr_plot_classifier_comparison_thumb.png" />
 <meta property="og:image:alt" content="" />
-<meta name="description" content="Gallery examples: Classifier comparison Plot the decision boundaries of a VotingClassifier Caching nearest neighbors Nearest Neighbors Classification Comparing Nearest Neighbors with and without Ne..." />
+<meta name="description" content="Gallery examples: Classifier comparison Caching nearest neighbors Nearest Neighbors Classification Comparing Nearest Neighbors with and without Neighborhood Components Analysis Dimensionality Reduc..." />
 
     <title>KNeighborsClassifier &#8212; scikit-learn 1.7.dev0 documentation</title>
   
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>KNeighborsClassifier<a class="headerlink" href="#kneighborsclassifier" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.neighbors.KNeighborsClassifier">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.neighbors.</span></span><span class="sig-name descname"><span class="pre">KNeighborsClassifier</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_neighbors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'uniform'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">algorithm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">leaf_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">30</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'minkowski'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric_params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/neighbors/_classification.py#L40"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.KNeighborsClassifier" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.neighbors.</span></span><span class="sig-name descname"><span class="pre">KNeighborsClassifier</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_neighbors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'uniform'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">algorithm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">leaf_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">30</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'minkowski'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric_params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/neighbors/_classification.py#L40"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.KNeighborsClassifier" title="Link to this definition">#</a></dt>
 <dd><p>Classifier implementing the k-nearest neighbors vote.</p>
 <p>Read more in the <a class="reference internal" href="http://github.com/neighbors.html#classification"><span class="std std-ref">User Guide</span></a>.</p>
 <dl class="field-list">
@@ -1369,7 +1369,7 @@ <h1>KNeighborsClassifier<a class="headerlink" href="#kneighborsclassifier" title
 <dt><strong>metric_params</strong><span class="classifier">dict, default=None</span></dt><dd><p>Additional keyword arguments for the metric function.</p>
 </dd>
 <dt><strong>n_jobs</strong><span class="classifier">int, default=None</span></dt><dd><p>The number of parallel jobs to run for neighbors search.
-<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.5.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
+<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.6.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
 <code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all processors. See <a class="reference internal" href="http://github.com/glossary.html#term-n_jobs"><span class="xref std std-term">Glossary</span></a>
 for more details.
 Doesn’t affect <a class="reference internal" href="#sklearn.neighbors.KNeighborsClassifier.fit" title="sklearn.neighbors.KNeighborsClassifier.fit"><code class="xref py py-meth docutils literal notranslate"><span class="pre">fit</span></code></a> method.</p>
@@ -1442,12 +1442,12 @@ <h1>KNeighborsClassifier<a class="headerlink" href="#kneighborsclassifier" title
 <span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">neigh</span><span class="o">.</span><span class="n">predict</span><span class="p">([[</span><span class="mf">1.1</span><span class="p">]]))</span>
 <span class="go">[0]</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">neigh</span><span class="o">.</span><span class="n">predict_proba</span><span class="p">([[</span><span class="mf">0.9</span><span class="p">]]))</span>
-<span class="go">[[0.666... 0.333...]]</span>
+<span class="go">[[0.666 0.333]]</span>
 </pre></div>
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neighbors.KNeighborsClassifier.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/neighbors/_classification.py#L217"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.KNeighborsClassifier.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/neighbors/_classification.py#L217"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.KNeighborsClassifier.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit the k-nearest neighbors classifier from the training dataset.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1469,7 +1469,7 @@ <h1>KNeighborsClassifier<a class="headerlink" href="#kneighborsclassifier" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neighbors.KNeighborsClassifier.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.KNeighborsClassifier.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.KNeighborsClassifier.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1486,7 +1486,7 @@ <h1>KNeighborsClassifier<a class="headerlink" href="#kneighborsclassifier" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neighbors.KNeighborsClassifier.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.KNeighborsClassifier.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.KNeighborsClassifier.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1507,7 +1507,7 @@ <h1>KNeighborsClassifier<a class="headerlink" href="#kneighborsclassifier" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neighbors.KNeighborsClassifier.kneighbors">
-<span class="sig-name descname"><span class="pre">kneighbors</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_neighbors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_distance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/neighbors/_base.py#L763"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.KNeighborsClassifier.kneighbors" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">kneighbors</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_neighbors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_distance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/neighbors/_base.py#L763"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.KNeighborsClassifier.kneighbors" title="Link to this definition">#</a></dt>
 <dd><p>Find the K-neighbors of a point.</p>
 <p>Returns indices of and distances to the neighbors of each point.</p>
 <dl class="field-list simple">
@@ -1560,7 +1560,7 @@ <h1>KNeighborsClassifier<a class="headerlink" href="#kneighborsclassifier" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neighbors.KNeighborsClassifier.kneighbors_graph">
-<span class="sig-name descname"><span class="pre">kneighbors_graph</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_neighbors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'connectivity'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/neighbors/_base.py#L967"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.KNeighborsClassifier.kneighbors_graph" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">kneighbors_graph</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_neighbors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'connectivity'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/neighbors/_base.py#L967"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.KNeighborsClassifier.kneighbors_graph" title="Link to this definition">#</a></dt>
 <dd><p>Compute the (weighted) graph of k-Neighbors for points in X.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1616,7 +1616,7 @@ <h1>KNeighborsClassifier<a class="headerlink" href="#kneighborsclassifier" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neighbors.KNeighborsClassifier.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/neighbors/_classification.py#L241"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.KNeighborsClassifier.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/neighbors/_classification.py#L241"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.KNeighborsClassifier.predict" title="Link to this definition">#</a></dt>
 <dd><p>Predict the class labels for the provided data.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1638,7 +1638,7 @@ <h1>KNeighborsClassifier<a class="headerlink" href="#kneighborsclassifier" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neighbors.KNeighborsClassifier.predict_proba">
-<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/neighbors/_classification.py#L310"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.KNeighborsClassifier.predict_proba" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/neighbors/_classification.py#L310"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.KNeighborsClassifier.predict_proba" title="Link to this definition">#</a></dt>
 <dd><p>Return probability estimates for the test data X.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1661,7 +1661,7 @@ <h1>KNeighborsClassifier<a class="headerlink" href="#kneighborsclassifier" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neighbors.KNeighborsClassifier.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/neighbors/_classification.py#L417"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.KNeighborsClassifier.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/neighbors/_classification.py#L417"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.KNeighborsClassifier.score" title="Link to this definition">#</a></dt>
 <dd><p>Return the mean accuracy on the given test data and labels.</p>
 <p>In multi-label classification, this is the subset accuracy
 which is a harsh metric since you require for each sample that
@@ -1693,7 +1693,7 @@ <h1>KNeighborsClassifier<a class="headerlink" href="#kneighborsclassifier" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neighbors.KNeighborsClassifier.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.KNeighborsClassifier.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.KNeighborsClassifier.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1717,7 +1717,7 @@ <h1>KNeighborsClassifier<a class="headerlink" href="#kneighborsclassifier" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neighbors.KNeighborsClassifier.set_score_request">
-<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.neighbors.KNeighborsClassifier" title="sklearn.neighbors._classification.KNeighborsClassifier"><span class="pre">KNeighborsClassifier</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.KNeighborsClassifier.set_score_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.neighbors.KNeighborsClassifier" title="sklearn.neighbors._classification.KNeighborsClassifier"><span class="pre">KNeighborsClassifier</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.KNeighborsClassifier.set_score_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1765,9 +1765,6 @@ <h2>Gallery examples<a class="headerlink" href="#gallery-examples" title="Link t
 <div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="A comparison of several classifiers in scikit-learn on synthetic datasets. The point of this example is to illustrate the nature of decision boundaries of different classifiers. This should be taken with a grain of salt, as the intuition conveyed by these examples does not necessarily carry over to real datasets."><img alt="" src="http://github.com/_images/sphx_glr_plot_classifier_comparison_thumb.png" />
 <p><a class="reference internal" href="http://github.com/auto_examples/classification/plot_classifier_comparison.html#sphx-glr-auto-examples-classification-plot-classifier-comparison-py"><span class="std std-ref">Classifier comparison</span></a></p>
   <div class="sphx-glr-thumbnail-title">Classifier comparison</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="Plot the decision boundaries of a VotingClassifier for two features of the Iris dataset."><img alt="" src="http://github.com/_images/sphx_glr_plot_voting_decision_regions_thumb.png" />
-<p><a class="reference internal" href="http://github.com/auto_examples/ensemble/plot_voting_decision_regions.html#sphx-glr-auto-examples-ensemble-plot-voting-decision-regions-py"><span class="std std-ref">Plot the decision boundaries of a VotingClassifier</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Plot the decision boundaries of a VotingClassifier</div>
 </div><div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how to precompute the k nearest neighbors before using them in KNeighborsClassifier. KNeighborsClassifier can compute the nearest neighbors internally, but precomputing them can have several benefits, such as finer parameter control, caching for multiple use, or custom implementations."><img alt="" src="http://github.com/_images/sphx_glr_plot_caching_nearest_neighbors_thumb.png" />
 <p><a class="reference internal" href="http://github.com/auto_examples/neighbors/plot_caching_nearest_neighbors.html#sphx-glr-auto-examples-neighbors-plot-caching-nearest-neighbors-py"><span class="std std-ref">Caching nearest neighbors</span></a></p>
   <div class="sphx-glr-thumbnail-title">Caching nearest neighbors</div>
diff --git a/dev/modules/generated/sklearn.neighbors.KNeighborsRegressor.html b/dev/modules/generated/sklearn.neighbors.KNeighborsRegressor.html
index 242a07b023b99..f37d2295466da 100644
--- a/dev/modules/generated/sklearn.neighbors.KNeighborsRegressor.html
+++ b/dev/modules/generated/sklearn.neighbors.KNeighborsRegressor.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1308,7 +1308,7 @@
 <h1>KNeighborsRegressor<a class="headerlink" href="#kneighborsregressor" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.neighbors.KNeighborsRegressor">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.neighbors.</span></span><span class="sig-name descname"><span class="pre">KNeighborsRegressor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_neighbors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'uniform'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">algorithm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">leaf_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">30</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'minkowski'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric_params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/neighbors/_regression.py#L16"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.KNeighborsRegressor" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.neighbors.</span></span><span class="sig-name descname"><span class="pre">KNeighborsRegressor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_neighbors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'uniform'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">algorithm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">leaf_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">30</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'minkowski'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric_params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/neighbors/_regression.py#L16"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.KNeighborsRegressor" title="Link to this definition">#</a></dt>
 <dd><p>Regression based on k-nearest neighbors.</p>
 <p>The target is predicted by local interpolation of the targets
 associated of the nearest neighbors in the training set.</p>
@@ -1376,7 +1376,7 @@ <h1>KNeighborsRegressor<a class="headerlink" href="#kneighborsregressor" title="
 <dt><strong>metric_params</strong><span class="classifier">dict, default=None</span></dt><dd><p>Additional keyword arguments for the metric function.</p>
 </dd>
 <dt><strong>n_jobs</strong><span class="classifier">int, default=None</span></dt><dd><p>The number of parallel jobs to run for neighbors search.
-<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.5.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
+<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.6.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
 <code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all processors. See <a class="reference internal" href="http://github.com/glossary.html#term-n_jobs"><span class="xref std std-term">Glossary</span></a>
 for more details.
 Doesn’t affect <a class="reference internal" href="#sklearn.neighbors.KNeighborsRegressor.fit" title="sklearn.neighbors.KNeighborsRegressor.fit"><code class="xref py py-meth docutils literal notranslate"><span class="pre">fit</span></code></a> method.</p>
@@ -1447,7 +1447,7 @@ <h1>KNeighborsRegressor<a class="headerlink" href="#kneighborsregressor" title="
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neighbors.KNeighborsRegressor.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/neighbors/_regression.py#L200"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.KNeighborsRegressor.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/neighbors/_regression.py#L200"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.KNeighborsRegressor.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit the k-nearest neighbors regressor from the training dataset.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1469,7 +1469,7 @@ <h1>KNeighborsRegressor<a class="headerlink" href="#kneighborsregressor" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neighbors.KNeighborsRegressor.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.KNeighborsRegressor.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.KNeighborsRegressor.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1486,7 +1486,7 @@ <h1>KNeighborsRegressor<a class="headerlink" href="#kneighborsregressor" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neighbors.KNeighborsRegressor.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.KNeighborsRegressor.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.KNeighborsRegressor.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1507,7 +1507,7 @@ <h1>KNeighborsRegressor<a class="headerlink" href="#kneighborsregressor" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neighbors.KNeighborsRegressor.kneighbors">
-<span class="sig-name descname"><span class="pre">kneighbors</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_neighbors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_distance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/neighbors/_base.py#L763"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.KNeighborsRegressor.kneighbors" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">kneighbors</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_neighbors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_distance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/neighbors/_base.py#L763"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.KNeighborsRegressor.kneighbors" title="Link to this definition">#</a></dt>
 <dd><p>Find the K-neighbors of a point.</p>
 <p>Returns indices of and distances to the neighbors of each point.</p>
 <dl class="field-list simple">
@@ -1560,7 +1560,7 @@ <h1>KNeighborsRegressor<a class="headerlink" href="#kneighborsregressor" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neighbors.KNeighborsRegressor.kneighbors_graph">
-<span class="sig-name descname"><span class="pre">kneighbors_graph</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_neighbors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'connectivity'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/neighbors/_base.py#L967"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.KNeighborsRegressor.kneighbors_graph" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">kneighbors_graph</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_neighbors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'connectivity'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/neighbors/_base.py#L967"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.KNeighborsRegressor.kneighbors_graph" title="Link to this definition">#</a></dt>
 <dd><p>Compute the (weighted) graph of k-Neighbors for points in X.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1616,7 +1616,7 @@ <h1>KNeighborsRegressor<a class="headerlink" href="#kneighborsregressor" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neighbors.KNeighborsRegressor.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/neighbors/_regression.py#L224"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.KNeighborsRegressor.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/neighbors/_regression.py#L224"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.KNeighborsRegressor.predict" title="Link to this definition">#</a></dt>
 <dd><p>Predict the target for the provided data.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1638,7 +1638,7 @@ <h1>KNeighborsRegressor<a class="headerlink" href="#kneighborsregressor" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neighbors.KNeighborsRegressor.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L619"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.KNeighborsRegressor.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L560"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.KNeighborsRegressor.score" title="Link to this definition">#</a></dt>
 <dd><p>Return <a class="reference internal" href="http://github.com/model_evaluation.html#r2-score"><span class="std std-ref">coefficient of determination</span></a> on test data.</p>
 <p>The coefficient of determination, <span class="math notranslate nohighlight">\(R^2\)</span>, is defined as
 <span class="math notranslate nohighlight">\((1 - \frac{u}{v})\)</span>, where <span class="math notranslate nohighlight">\(u\)</span> is the residual
@@ -1680,7 +1680,7 @@ <h1>KNeighborsRegressor<a class="headerlink" href="#kneighborsregressor" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neighbors.KNeighborsRegressor.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.KNeighborsRegressor.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.KNeighborsRegressor.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1704,7 +1704,7 @@ <h1>KNeighborsRegressor<a class="headerlink" href="#kneighborsregressor" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neighbors.KNeighborsRegressor.set_score_request">
-<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.neighbors.KNeighborsRegressor" title="sklearn.neighbors._regression.KNeighborsRegressor"><span class="pre">KNeighborsRegressor</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.KNeighborsRegressor.set_score_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.neighbors.KNeighborsRegressor" title="sklearn.neighbors._regression.KNeighborsRegressor"><span class="pre">KNeighborsRegressor</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.KNeighborsRegressor.set_score_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
diff --git a/dev/modules/generated/sklearn.neighbors.KNeighborsTransformer.html b/dev/modules/generated/sklearn.neighbors.KNeighborsTransformer.html
index ad2dbf3e1bdfb..aaf2652ae62a9 100644
--- a/dev/modules/generated/sklearn.neighbors.KNeighborsTransformer.html
+++ b/dev/modules/generated/sklearn.neighbors.KNeighborsTransformer.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>KNeighborsTransformer<a class="headerlink" href="#kneighborstransformer" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.neighbors.KNeighborsTransformer">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.neighbors.</span></span><span class="sig-name descname"><span class="pre">KNeighborsTransformer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'distance'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_neighbors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">algorithm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">leaf_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">30</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'minkowski'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric_params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/neighbors/_graph.py#L258"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.KNeighborsTransformer" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.neighbors.</span></span><span class="sig-name descname"><span class="pre">KNeighborsTransformer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'distance'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_neighbors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">algorithm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">leaf_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">30</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'minkowski'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric_params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/neighbors/_graph.py#L258"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.KNeighborsTransformer" title="Link to this definition">#</a></dt>
 <dd><p>Transform X into a (weighted) graph of k nearest neighbors.</p>
 <p>The transformed data is a sparse graph as returned by kneighbors_graph.</p>
 <p>Read more in the <a class="reference internal" href="http://github.com/neighbors.html#neighbors-transformer"><span class="std std-ref">User Guide</span></a>.</p>
@@ -1421,7 +1421,7 @@ <h1>KNeighborsTransformer<a class="headerlink" href="#kneighborstransformer" tit
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neighbors.KNeighborsTransformer.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/neighbors/_graph.py#L413"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.KNeighborsTransformer.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/neighbors/_graph.py#L413"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.KNeighborsTransformer.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit the k-nearest neighbors transformer from the training dataset.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1443,7 +1443,7 @@ <h1>KNeighborsTransformer<a class="headerlink" href="#kneighborstransformer" tit
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neighbors.KNeighborsTransformer.fit_transform">
-<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/neighbors/_graph.py#L459"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.KNeighborsTransformer.fit_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/neighbors/_graph.py#L459"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.KNeighborsTransformer.fit_transform" title="Link to this definition">#</a></dt>
 <dd><p>Fit to data, then transform it.</p>
 <p>Fits transformer to X and y with optional parameters fit_params
 and returns a transformed version of X.</p>
@@ -1470,7 +1470,7 @@ <h1>KNeighborsTransformer<a class="headerlink" href="#kneighborstransformer" tit
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neighbors.KNeighborsTransformer.get_feature_names_out">
-<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L995"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.KNeighborsTransformer.get_feature_names_out" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L936"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.KNeighborsTransformer.get_feature_names_out" title="Link to this definition">#</a></dt>
 <dd><p>Get output feature names for transformation.</p>
 <p>The feature names out will prefixed by the lowercased class name. For
 example, if the transformer outputs 3 features, then the feature names
@@ -1493,7 +1493,7 @@ <h1>KNeighborsTransformer<a class="headerlink" href="#kneighborstransformer" tit
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neighbors.KNeighborsTransformer.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.KNeighborsTransformer.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.KNeighborsTransformer.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1510,7 +1510,7 @@ <h1>KNeighborsTransformer<a class="headerlink" href="#kneighborstransformer" tit
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neighbors.KNeighborsTransformer.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.KNeighborsTransformer.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.KNeighborsTransformer.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1531,7 +1531,7 @@ <h1>KNeighborsTransformer<a class="headerlink" href="#kneighborstransformer" tit
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neighbors.KNeighborsTransformer.kneighbors">
-<span class="sig-name descname"><span class="pre">kneighbors</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_neighbors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_distance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/neighbors/_base.py#L763"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.KNeighborsTransformer.kneighbors" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">kneighbors</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_neighbors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_distance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/neighbors/_base.py#L763"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.KNeighborsTransformer.kneighbors" title="Link to this definition">#</a></dt>
 <dd><p>Find the K-neighbors of a point.</p>
 <p>Returns indices of and distances to the neighbors of each point.</p>
 <dl class="field-list simple">
@@ -1584,7 +1584,7 @@ <h1>KNeighborsTransformer<a class="headerlink" href="#kneighborstransformer" tit
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neighbors.KNeighborsTransformer.kneighbors_graph">
-<span class="sig-name descname"><span class="pre">kneighbors_graph</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_neighbors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'connectivity'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/neighbors/_base.py#L967"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.KNeighborsTransformer.kneighbors_graph" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">kneighbors_graph</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_neighbors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'connectivity'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/neighbors/_base.py#L967"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.KNeighborsTransformer.kneighbors_graph" title="Link to this definition">#</a></dt>
 <dd><p>Compute the (weighted) graph of k-Neighbors for points in X.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1640,7 +1640,7 @@ <h1>KNeighborsTransformer<a class="headerlink" href="#kneighborstransformer" tit
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neighbors.KNeighborsTransformer.set_output">
-<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.KNeighborsTransformer.set_output" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.KNeighborsTransformer.set_output" title="Link to this definition">#</a></dt>
 <dd><p>Set output container.</p>
 <p>See <a class="reference internal" href="http://github.com/auto_examples/miscellaneous/plot_set_output.html#sphx-glr-auto-examples-miscellaneous-plot-set-output-py"><span class="std std-ref">Introducing the set_output API</span></a>
 for an example on how to use the API.</p>
@@ -1671,7 +1671,7 @@ <h1>KNeighborsTransformer<a class="headerlink" href="#kneighborstransformer" tit
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neighbors.KNeighborsTransformer.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.KNeighborsTransformer.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.KNeighborsTransformer.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1695,7 +1695,7 @@ <h1>KNeighborsTransformer<a class="headerlink" href="#kneighborstransformer" tit
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neighbors.KNeighborsTransformer.transform">
-<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/neighbors/_graph.py#L437"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.KNeighborsTransformer.transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/neighbors/_graph.py#L437"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.KNeighborsTransformer.transform" title="Link to this definition">#</a></dt>
 <dd><p>Compute the (weighted) graph of Neighbors for points in X.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.neighbors.KernelDensity.html b/dev/modules/generated/sklearn.neighbors.KernelDensity.html
index d62d838ee7557..e62cf49f5e633 100644
--- a/dev/modules/generated/sklearn.neighbors.KernelDensity.html
+++ b/dev/modules/generated/sklearn.neighbors.KernelDensity.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>KernelDensity<a class="headerlink" href="#kerneldensity" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.neighbors.KernelDensity">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.neighbors.</span></span><span class="sig-name descname"><span class="pre">KernelDensity</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bandwidth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">algorithm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'gaussian'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'euclidean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">atol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rtol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">breadth_first</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">leaf_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">40</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric_params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/neighbors/_kde.py#L38"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.KernelDensity" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.neighbors.</span></span><span class="sig-name descname"><span class="pre">KernelDensity</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bandwidth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">algorithm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'gaussian'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'euclidean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">atol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rtol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">breadth_first</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">leaf_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">40</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric_params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/neighbors/_kde.py#L38"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.KernelDensity" title="Link to this definition">#</a></dt>
 <dd><p>Kernel Density Estimation.</p>
 <p>Read more in the <a class="reference internal" href="http://github.com/density.html#kernel-density"><span class="std std-ref">User Guide</span></a>.</p>
 <dl class="field-list">
@@ -1393,7 +1393,7 @@ <h1>KernelDensity<a class="headerlink" href="#kerneldensity" title="Link to this
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neighbors.KernelDensity.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/neighbors/_kde.py#L190"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.KernelDensity.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/neighbors/_kde.py#L190"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.KernelDensity.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit the Kernel Density model on the data.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1422,7 +1422,7 @@ <h1>KernelDensity<a class="headerlink" href="#kerneldensity" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neighbors.KernelDensity.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.KernelDensity.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.KernelDensity.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1439,7 +1439,7 @@ <h1>KernelDensity<a class="headerlink" href="#kerneldensity" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neighbors.KernelDensity.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.KernelDensity.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.KernelDensity.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1460,7 +1460,7 @@ <h1>KernelDensity<a class="headerlink" href="#kerneldensity" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neighbors.KernelDensity.sample">
-<span class="sig-name descname"><span class="pre">sample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/neighbors/_kde.py#L308"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.KernelDensity.sample" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">sample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/neighbors/_kde.py#L308"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.KernelDensity.sample" title="Link to this definition">#</a></dt>
 <dd><p>Generate random samples from the model.</p>
 <p>Currently, this is implemented only for gaussian and tophat kernels.</p>
 <dl class="field-list simple">
@@ -1486,7 +1486,7 @@ <h1>KernelDensity<a class="headerlink" href="#kerneldensity" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neighbors.KernelDensity.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/neighbors/_kde.py#L286"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.KernelDensity.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/neighbors/_kde.py#L286"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.KernelDensity.score" title="Link to this definition">#</a></dt>
 <dd><p>Compute the total log-likelihood under the model.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1512,7 +1512,7 @@ <h1>KernelDensity<a class="headerlink" href="#kerneldensity" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neighbors.KernelDensity.score_samples">
-<span class="sig-name descname"><span class="pre">score_samples</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/neighbors/_kde.py#L248"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.KernelDensity.score_samples" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score_samples</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/neighbors/_kde.py#L248"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.KernelDensity.score_samples" title="Link to this definition">#</a></dt>
 <dd><p>Compute the log-likelihood of each sample under the model.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1535,7 +1535,7 @@ <h1>KernelDensity<a class="headerlink" href="#kerneldensity" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neighbors.KernelDensity.set_fit_request">
-<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.neighbors.KernelDensity" title="sklearn.neighbors._kde.KernelDensity"><span class="pre">KernelDensity</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.KernelDensity.set_fit_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.neighbors.KernelDensity" title="sklearn.neighbors._kde.KernelDensity"><span class="pre">KernelDensity</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.KernelDensity.set_fit_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1578,7 +1578,7 @@ <h1>KernelDensity<a class="headerlink" href="#kerneldensity" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neighbors.KernelDensity.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.KernelDensity.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.KernelDensity.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
diff --git a/dev/modules/generated/sklearn.neighbors.LocalOutlierFactor.html b/dev/modules/generated/sklearn.neighbors.LocalOutlierFactor.html
index 382cb8a86740f..9eed7e30cae78 100644
--- a/dev/modules/generated/sklearn.neighbors.LocalOutlierFactor.html
+++ b/dev/modules/generated/sklearn.neighbors.LocalOutlierFactor.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>LocalOutlierFactor<a class="headerlink" href="#localoutlierfactor" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.neighbors.LocalOutlierFactor">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.neighbors.</span></span><span class="sig-name descname"><span class="pre">LocalOutlierFactor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_neighbors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">20</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">algorithm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">leaf_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">30</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'minkowski'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric_params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">contamination</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">novelty</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/neighbors/_lof.py#L19"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.LocalOutlierFactor" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.neighbors.</span></span><span class="sig-name descname"><span class="pre">LocalOutlierFactor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_neighbors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">20</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">algorithm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">leaf_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">30</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'minkowski'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric_params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">contamination</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">novelty</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/neighbors/_lof.py#L19"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.LocalOutlierFactor" title="Link to this definition">#</a></dt>
 <dd><p>Unsupervised Outlier Detection using the Local Outlier Factor (LOF).</p>
 <p>The anomaly score of each sample is called the Local Outlier Factor.
 It measures the local deviation of the density of a given sample with respect
@@ -1390,7 +1390,7 @@ <h1>LocalOutlierFactor<a class="headerlink" href="#localoutlierfactor" title="Li
 </div>
 </dd>
 <dt><strong>n_jobs</strong><span class="classifier">int, default=None</span></dt><dd><p>The number of parallel jobs to run for neighbors search.
-<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.5.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
+<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.6.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
 <code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all processors. See <a class="reference internal" href="http://github.com/glossary.html#term-n_jobs"><span class="xref std std-term">Glossary</span></a>
 for more details.</p>
 </dd>
@@ -1463,12 +1463,12 @@ <h1>LocalOutlierFactor<a class="headerlink" href="#localoutlierfactor" title="Li
 <span class="gp">&gt;&gt;&gt; </span><span class="n">clf</span><span class="o">.</span><span class="n">fit_predict</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
 <span class="go">array([ 1,  1, -1,  1])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">clf</span><span class="o">.</span><span class="n">negative_outlier_factor_</span>
-<span class="go">array([ -0.9821...,  -1.0370..., -73.3697...,  -0.9821...])</span>
+<span class="go">array([ -0.9821,  -1.0370, -73.3697,  -0.9821])</span>
 </pre></div>
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neighbors.LocalOutlierFactor.decision_function">
-<span class="sig-name descname"><span class="pre">decision_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/neighbors/_lof.py#L405"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.LocalOutlierFactor.decision_function" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">decision_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/neighbors/_lof.py#L405"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.LocalOutlierFactor.decision_function" title="Link to this definition">#</a></dt>
 <dd><p>Shifted opposite of the Local Outlier Factor of X.</p>
 <p>Bigger is better, i.e. large values correspond to inliers.</p>
 <p><strong>Only available for novelty detection (when novelty is set to True).</strong>
@@ -1498,7 +1498,7 @@ <h1>LocalOutlierFactor<a class="headerlink" href="#localoutlierfactor" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neighbors.LocalOutlierFactor.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/neighbors/_lof.py#L257"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.LocalOutlierFactor.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/neighbors/_lof.py#L257"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.LocalOutlierFactor.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit the local outlier factor detector from the training dataset.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1520,7 +1520,7 @@ <h1>LocalOutlierFactor<a class="headerlink" href="#localoutlierfactor" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neighbors.LocalOutlierFactor.fit_predict">
-<span class="sig-name descname"><span class="pre">fit_predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/neighbors/_lof.py#L229"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.LocalOutlierFactor.fit_predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/neighbors/_lof.py#L229"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.LocalOutlierFactor.fit_predict" title="Link to this definition">#</a></dt>
 <dd><p>Fit the model to the training set X and return the labels.</p>
 <p><strong>Not available for novelty detection (when novelty is set to True).</strong>
 Label is 1 for an inlier and -1 for an outlier according to the LOF
@@ -1546,7 +1546,7 @@ <h1>LocalOutlierFactor<a class="headerlink" href="#localoutlierfactor" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neighbors.LocalOutlierFactor.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.LocalOutlierFactor.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.LocalOutlierFactor.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1563,7 +1563,7 @@ <h1>LocalOutlierFactor<a class="headerlink" href="#localoutlierfactor" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neighbors.LocalOutlierFactor.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.LocalOutlierFactor.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.LocalOutlierFactor.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1584,7 +1584,7 @@ <h1>LocalOutlierFactor<a class="headerlink" href="#localoutlierfactor" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neighbors.LocalOutlierFactor.kneighbors">
-<span class="sig-name descname"><span class="pre">kneighbors</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_neighbors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_distance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/neighbors/_base.py#L763"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.LocalOutlierFactor.kneighbors" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">kneighbors</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_neighbors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_distance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/neighbors/_base.py#L763"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.LocalOutlierFactor.kneighbors" title="Link to this definition">#</a></dt>
 <dd><p>Find the K-neighbors of a point.</p>
 <p>Returns indices of and distances to the neighbors of each point.</p>
 <dl class="field-list simple">
@@ -1637,7 +1637,7 @@ <h1>LocalOutlierFactor<a class="headerlink" href="#localoutlierfactor" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neighbors.LocalOutlierFactor.kneighbors_graph">
-<span class="sig-name descname"><span class="pre">kneighbors_graph</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_neighbors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'connectivity'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/neighbors/_base.py#L967"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.LocalOutlierFactor.kneighbors_graph" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">kneighbors_graph</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_neighbors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'connectivity'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/neighbors/_base.py#L967"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.LocalOutlierFactor.kneighbors_graph" title="Link to this definition">#</a></dt>
 <dd><p>Compute the (weighted) graph of k-Neighbors for points in X.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1693,7 +1693,7 @@ <h1>LocalOutlierFactor<a class="headerlink" href="#localoutlierfactor" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neighbors.LocalOutlierFactor.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/neighbors/_lof.py#L340"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.LocalOutlierFactor.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/neighbors/_lof.py#L340"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.LocalOutlierFactor.predict" title="Link to this definition">#</a></dt>
 <dd><p>Predict the labels (1 inlier, -1 outlier) of X according to LOF.</p>
 <p><strong>Only available for novelty detection (when novelty is set to True).</strong>
 This method allows to generalize prediction to <em>new observations</em> (not
@@ -1719,7 +1719,7 @@ <h1>LocalOutlierFactor<a class="headerlink" href="#localoutlierfactor" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neighbors.LocalOutlierFactor.score_samples">
-<span class="sig-name descname"><span class="pre">score_samples</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/neighbors/_lof.py#L445"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.LocalOutlierFactor.score_samples" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score_samples</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/neighbors/_lof.py#L445"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.LocalOutlierFactor.score_samples" title="Link to this definition">#</a></dt>
 <dd><p>Opposite of the Local Outlier Factor of X.</p>
 <p>It is the opposite as bigger is better, i.e. large values correspond
 to inliers.</p>
@@ -1751,7 +1751,7 @@ <h1>LocalOutlierFactor<a class="headerlink" href="#localoutlierfactor" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neighbors.LocalOutlierFactor.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.LocalOutlierFactor.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.LocalOutlierFactor.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
diff --git a/dev/modules/generated/sklearn.neighbors.NearestCentroid.html b/dev/modules/generated/sklearn.neighbors.NearestCentroid.html
index 4f7aa77382edc..772784d8fdf17 100644
--- a/dev/modules/generated/sklearn.neighbors.NearestCentroid.html
+++ b/dev/modules/generated/sklearn.neighbors.NearestCentroid.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>NearestCentroid<a class="headerlink" href="#nearestcentroid" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.neighbors.NearestCentroid">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.neighbors.</span></span><span class="sig-name descname"><span class="pre">NearestCentroid</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">metric</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'euclidean'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shrink_threshold</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">priors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'uniform'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/neighbors/_nearest_centroid.py#L29"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.NearestCentroid" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.neighbors.</span></span><span class="sig-name descname"><span class="pre">NearestCentroid</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">metric</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'euclidean'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shrink_threshold</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">priors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'uniform'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/neighbors/_nearest_centroid.py#L29"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.NearestCentroid" title="Link to this definition">#</a></dt>
 <dd><p>Nearest centroid classifier.</p>
 <p>Each class is represented by its centroid, with test samples classified to
 the class with the nearest centroid.</p>
@@ -1407,7 +1407,7 @@ <h1>NearestCentroid<a class="headerlink" href="#nearestcentroid" title="Link to
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neighbors.NearestCentroid.decision_function">
-<span class="sig-name descname"><span class="pre">decision_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/discriminant_analysis.py#L174"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.NearestCentroid.decision_function" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">decision_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/discriminant_analysis.py#L174"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.NearestCentroid.decision_function" title="Link to this definition">#</a></dt>
 <dd><p>Apply decision function to an array of samples.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1429,7 +1429,7 @@ <h1>NearestCentroid<a class="headerlink" href="#nearestcentroid" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neighbors.NearestCentroid.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/neighbors/_nearest_centroid.py#L152"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.NearestCentroid.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/neighbors/_nearest_centroid.py#L152"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.NearestCentroid.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit the NearestCentroid model according to the given training data.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1453,7 +1453,7 @@ <h1>NearestCentroid<a class="headerlink" href="#nearestcentroid" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neighbors.NearestCentroid.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.NearestCentroid.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.NearestCentroid.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1470,7 +1470,7 @@ <h1>NearestCentroid<a class="headerlink" href="#nearestcentroid" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neighbors.NearestCentroid.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.NearestCentroid.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.NearestCentroid.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1491,7 +1491,7 @@ <h1>NearestCentroid<a class="headerlink" href="#nearestcentroid" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neighbors.NearestCentroid.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/neighbors/_nearest_centroid.py#L278"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.NearestCentroid.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/neighbors/_nearest_centroid.py#L278"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.NearestCentroid.predict" title="Link to this definition">#</a></dt>
 <dd><p>Perform classification on an array of test vectors <code class="docutils literal notranslate"><span class="pre">X</span></code>.</p>
 <p>The predicted class <code class="docutils literal notranslate"><span class="pre">C</span></code> for each sample in <code class="docutils literal notranslate"><span class="pre">X</span></code> is returned.</p>
 <dl class="field-list simple">
@@ -1512,7 +1512,7 @@ <h1>NearestCentroid<a class="headerlink" href="#nearestcentroid" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neighbors.NearestCentroid.predict_log_proba">
-<span class="sig-name descname"><span class="pre">predict_log_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/discriminant_analysis.py#L229"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.NearestCentroid.predict_log_proba" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict_log_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/discriminant_analysis.py#L229"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.NearestCentroid.predict_log_proba" title="Link to this definition">#</a></dt>
 <dd><p>Estimate log class probabilities.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1532,7 +1532,7 @@ <h1>NearestCentroid<a class="headerlink" href="#nearestcentroid" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neighbors.NearestCentroid.predict_proba">
-<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/discriminant_analysis.py#L213"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.NearestCentroid.predict_proba" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/discriminant_analysis.py#L213"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.NearestCentroid.predict_proba" title="Link to this definition">#</a></dt>
 <dd><p>Estimate class probabilities.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1553,7 +1553,7 @@ <h1>NearestCentroid<a class="headerlink" href="#nearestcentroid" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neighbors.NearestCentroid.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L546"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.NearestCentroid.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L487"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.NearestCentroid.score" title="Link to this definition">#</a></dt>
 <dd><p>Return <a class="reference internal" href="http://github.com/model_evaluation.html#accuracy-score"><span class="std std-ref">accuracy</span></a> on provided data and labels.</p>
 <p>In multi-label classification, this is the subset accuracy
 which is a harsh metric since you require for each sample that
@@ -1580,7 +1580,7 @@ <h1>NearestCentroid<a class="headerlink" href="#nearestcentroid" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neighbors.NearestCentroid.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.NearestCentroid.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.NearestCentroid.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1604,7 +1604,7 @@ <h1>NearestCentroid<a class="headerlink" href="#nearestcentroid" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neighbors.NearestCentroid.set_score_request">
-<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.neighbors.NearestCentroid" title="sklearn.neighbors._nearest_centroid.NearestCentroid"><span class="pre">NearestCentroid</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.NearestCentroid.set_score_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.neighbors.NearestCentroid" title="sklearn.neighbors._nearest_centroid.NearestCentroid"><span class="pre">NearestCentroid</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.NearestCentroid.set_score_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
diff --git a/dev/modules/generated/sklearn.neighbors.NearestNeighbors.html b/dev/modules/generated/sklearn.neighbors.NearestNeighbors.html
index 9464c87fb3ec2..9f00aba86043a 100644
--- a/dev/modules/generated/sklearn.neighbors.NearestNeighbors.html
+++ b/dev/modules/generated/sklearn.neighbors.NearestNeighbors.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>NearestNeighbors<a class="headerlink" href="#nearestneighbors" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.neighbors.NearestNeighbors">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.neighbors.</span></span><span class="sig-name descname"><span class="pre">NearestNeighbors</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_neighbors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">radius</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">algorithm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">leaf_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">30</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'minkowski'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric_params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/neighbors/_unsupervised.py#L10"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.NearestNeighbors" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.neighbors.</span></span><span class="sig-name descname"><span class="pre">NearestNeighbors</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_neighbors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">radius</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">algorithm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">leaf_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">30</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'minkowski'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric_params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/neighbors/_unsupervised.py#L10"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.NearestNeighbors" title="Link to this definition">#</a></dt>
 <dd><p>Unsupervised learner for implementing neighbor searches.</p>
 <p>Read more in the <a class="reference internal" href="http://github.com/neighbors.html#unsupervised-neighbors"><span class="std std-ref">User Guide</span></a>.</p>
 <div class="versionadded">
@@ -1359,7 +1359,7 @@ <h1>NearestNeighbors<a class="headerlink" href="#nearestneighbors" title="Link t
 <dt><strong>metric_params</strong><span class="classifier">dict, default=None</span></dt><dd><p>Additional keyword arguments for the metric function.</p>
 </dd>
 <dt><strong>n_jobs</strong><span class="classifier">int, default=None</span></dt><dd><p>The number of parallel jobs to run for neighbors search.
-<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.5.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
+<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.6.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
 <code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all processors. See <a class="reference internal" href="http://github.com/glossary.html#term-n_jobs"><span class="xref std std-term">Glossary</span></a>
 for more details.</p>
 </dd>
@@ -1424,7 +1424,7 @@ <h1>NearestNeighbors<a class="headerlink" href="#nearestneighbors" title="Link t
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neighbors.NearestNeighbors.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/neighbors/_unsupervised.py#L158"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.NearestNeighbors.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/neighbors/_unsupervised.py#L158"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.NearestNeighbors.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit the nearest neighbors estimator from the training dataset.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1446,7 +1446,7 @@ <h1>NearestNeighbors<a class="headerlink" href="#nearestneighbors" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neighbors.NearestNeighbors.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.NearestNeighbors.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.NearestNeighbors.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1463,7 +1463,7 @@ <h1>NearestNeighbors<a class="headerlink" href="#nearestneighbors" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neighbors.NearestNeighbors.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.NearestNeighbors.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.NearestNeighbors.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1484,7 +1484,7 @@ <h1>NearestNeighbors<a class="headerlink" href="#nearestneighbors" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neighbors.NearestNeighbors.kneighbors">
-<span class="sig-name descname"><span class="pre">kneighbors</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_neighbors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_distance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/neighbors/_base.py#L763"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.NearestNeighbors.kneighbors" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">kneighbors</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_neighbors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_distance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/neighbors/_base.py#L763"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.NearestNeighbors.kneighbors" title="Link to this definition">#</a></dt>
 <dd><p>Find the K-neighbors of a point.</p>
 <p>Returns indices of and distances to the neighbors of each point.</p>
 <dl class="field-list simple">
@@ -1537,7 +1537,7 @@ <h1>NearestNeighbors<a class="headerlink" href="#nearestneighbors" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neighbors.NearestNeighbors.kneighbors_graph">
-<span class="sig-name descname"><span class="pre">kneighbors_graph</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_neighbors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'connectivity'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/neighbors/_base.py#L967"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.NearestNeighbors.kneighbors_graph" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">kneighbors_graph</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_neighbors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'connectivity'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/neighbors/_base.py#L967"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.NearestNeighbors.kneighbors_graph" title="Link to this definition">#</a></dt>
 <dd><p>Compute the (weighted) graph of k-Neighbors for points in X.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1593,7 +1593,7 @@ <h1>NearestNeighbors<a class="headerlink" href="#nearestneighbors" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neighbors.NearestNeighbors.radius_neighbors">
-<span class="sig-name descname"><span class="pre">radius_neighbors</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">radius</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_distance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sort_results</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/neighbors/_base.py#L1093"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.NearestNeighbors.radius_neighbors" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">radius_neighbors</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">radius</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_distance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sort_results</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/neighbors/_base.py#L1093"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.NearestNeighbors.radius_neighbors" title="Link to this definition">#</a></dt>
 <dd><p>Find the neighbors within a given radius of a point or points.</p>
 <p>Return the indices and distances of each point from the dataset
 lying in a ball with size <code class="docutils literal notranslate"><span class="pre">radius</span></code> around the points of the query
@@ -1665,7 +1665,7 @@ <h1>NearestNeighbors<a class="headerlink" href="#nearestneighbors" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neighbors.NearestNeighbors.radius_neighbors_graph">
-<span class="sig-name descname"><span class="pre">radius_neighbors_graph</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">radius</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'connectivity'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sort_results</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/neighbors/_base.py#L1311"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.NearestNeighbors.radius_neighbors_graph" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">radius_neighbors_graph</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">radius</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'connectivity'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sort_results</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/neighbors/_base.py#L1311"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.NearestNeighbors.radius_neighbors_graph" title="Link to this definition">#</a></dt>
 <dd><p>Compute the (weighted) graph of Neighbors for points in X.</p>
 <p>Neighborhoods are restricted the points at a distance lower than
 radius.</p>
@@ -1727,7 +1727,7 @@ <h1>NearestNeighbors<a class="headerlink" href="#nearestneighbors" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neighbors.NearestNeighbors.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.NearestNeighbors.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.NearestNeighbors.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
diff --git a/dev/modules/generated/sklearn.neighbors.NeighborhoodComponentsAnalysis.html b/dev/modules/generated/sklearn.neighbors.NeighborhoodComponentsAnalysis.html
index e39118d249e3f..b9b91a5c0e7ca 100644
--- a/dev/modules/generated/sklearn.neighbors.NeighborhoodComponentsAnalysis.html
+++ b/dev/modules/generated/sklearn.neighbors.NeighborhoodComponentsAnalysis.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>NeighborhoodComponentsAnalysis<a class="headerlink" href="#neighborhoodcomponentsanalysis" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.neighbors.NeighborhoodComponentsAnalysis">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.neighbors.</span></span><span class="sig-name descname"><span class="pre">NeighborhoodComponentsAnalysis</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_components</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">warm_start</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">50</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">callback</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/neighbors/_nca.py#L33"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.NeighborhoodComponentsAnalysis" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.neighbors.</span></span><span class="sig-name descname"><span class="pre">NeighborhoodComponentsAnalysis</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_components</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">warm_start</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">50</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">callback</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/neighbors/_nca.py#L33"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.NeighborhoodComponentsAnalysis" title="Link to this definition">#</a></dt>
 <dd><p>Neighborhood Components Analysis.</p>
 <p>Neighborhood Component Analysis (NCA) is a machine learning algorithm for
 metric learning. It learns a linear transformation in a supervised fashion
@@ -1472,7 +1472,7 @@ <h1>NeighborhoodComponentsAnalysis<a class="headerlink" href="#neighborhoodcompo
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neighbors.NeighborhoodComponentsAnalysis.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/neighbors/_nca.py#L224"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.NeighborhoodComponentsAnalysis.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/neighbors/_nca.py#L224"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.NeighborhoodComponentsAnalysis.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit the model according to the given training data.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1494,7 +1494,7 @@ <h1>NeighborhoodComponentsAnalysis<a class="headerlink" href="#neighborhoodcompo
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neighbors.NeighborhoodComponentsAnalysis.fit_transform">
-<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L863"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.NeighborhoodComponentsAnalysis.fit_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L804"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.NeighborhoodComponentsAnalysis.fit_transform" title="Link to this definition">#</a></dt>
 <dd><p>Fit to data, then transform it.</p>
 <p>Fits transformer to <code class="docutils literal notranslate"><span class="pre">X</span></code> and <code class="docutils literal notranslate"><span class="pre">y</span></code> with optional parameters <code class="docutils literal notranslate"><span class="pre">fit_params</span></code>
 and returns a transformed version of <code class="docutils literal notranslate"><span class="pre">X</span></code>.</p>
@@ -1520,7 +1520,7 @@ <h1>NeighborhoodComponentsAnalysis<a class="headerlink" href="#neighborhoodcompo
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neighbors.NeighborhoodComponentsAnalysis.get_feature_names_out">
-<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L995"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.NeighborhoodComponentsAnalysis.get_feature_names_out" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L936"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.NeighborhoodComponentsAnalysis.get_feature_names_out" title="Link to this definition">#</a></dt>
 <dd><p>Get output feature names for transformation.</p>
 <p>The feature names out will prefixed by the lowercased class name. For
 example, if the transformer outputs 3 features, then the feature names
@@ -1543,7 +1543,7 @@ <h1>NeighborhoodComponentsAnalysis<a class="headerlink" href="#neighborhoodcompo
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neighbors.NeighborhoodComponentsAnalysis.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.NeighborhoodComponentsAnalysis.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.NeighborhoodComponentsAnalysis.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1560,7 +1560,7 @@ <h1>NeighborhoodComponentsAnalysis<a class="headerlink" href="#neighborhoodcompo
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neighbors.NeighborhoodComponentsAnalysis.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.NeighborhoodComponentsAnalysis.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.NeighborhoodComponentsAnalysis.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1581,7 +1581,7 @@ <h1>NeighborhoodComponentsAnalysis<a class="headerlink" href="#neighborhoodcompo
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neighbors.NeighborhoodComponentsAnalysis.set_output">
-<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.NeighborhoodComponentsAnalysis.set_output" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.NeighborhoodComponentsAnalysis.set_output" title="Link to this definition">#</a></dt>
 <dd><p>Set output container.</p>
 <p>See <a class="reference internal" href="http://github.com/auto_examples/miscellaneous/plot_set_output.html#sphx-glr-auto-examples-miscellaneous-plot-set-output-py"><span class="std std-ref">Introducing the set_output API</span></a>
 for an example on how to use the API.</p>
@@ -1612,7 +1612,7 @@ <h1>NeighborhoodComponentsAnalysis<a class="headerlink" href="#neighborhoodcompo
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neighbors.NeighborhoodComponentsAnalysis.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.NeighborhoodComponentsAnalysis.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.NeighborhoodComponentsAnalysis.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1636,7 +1636,7 @@ <h1>NeighborhoodComponentsAnalysis<a class="headerlink" href="#neighborhoodcompo
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neighbors.NeighborhoodComponentsAnalysis.transform">
-<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/neighbors/_nca.py#L344"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.NeighborhoodComponentsAnalysis.transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/neighbors/_nca.py#L344"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.NeighborhoodComponentsAnalysis.transform" title="Link to this definition">#</a></dt>
 <dd><p>Apply the learned transformation to the given data.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.neighbors.RadiusNeighborsClassifier.html b/dev/modules/generated/sklearn.neighbors.RadiusNeighborsClassifier.html
index 247a684f12ae8..d210d4115d567 100644
--- a/dev/modules/generated/sklearn.neighbors.RadiusNeighborsClassifier.html
+++ b/dev/modules/generated/sklearn.neighbors.RadiusNeighborsClassifier.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>RadiusNeighborsClassifier<a class="headerlink" href="#radiusneighborsclassifier" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.neighbors.RadiusNeighborsClassifier">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.neighbors.</span></span><span class="sig-name descname"><span class="pre">RadiusNeighborsClassifier</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">radius</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'uniform'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">algorithm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">leaf_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">30</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'minkowski'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">outlier_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric_params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/neighbors/_classification.py#L455"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.RadiusNeighborsClassifier" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.neighbors.</span></span><span class="sig-name descname"><span class="pre">RadiusNeighborsClassifier</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">radius</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'uniform'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">algorithm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">leaf_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">30</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'minkowski'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">outlier_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric_params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/neighbors/_classification.py#L455"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.RadiusNeighborsClassifier" title="Link to this definition">#</a></dt>
 <dd><p>Classifier implementing a vote among neighbors within a given radius.</p>
 <p>Read more in the <a class="reference internal" href="http://github.com/neighbors.html#classification"><span class="std std-ref">User Guide</span></a>.</p>
 <dl class="field-list">
@@ -1376,7 +1376,7 @@ <h1>RadiusNeighborsClassifier<a class="headerlink" href="#radiusneighborsclassif
 <dt><strong>metric_params</strong><span class="classifier">dict, default=None</span></dt><dd><p>Additional keyword arguments for the metric function.</p>
 </dd>
 <dt><strong>n_jobs</strong><span class="classifier">int, default=None</span></dt><dd><p>The number of parallel jobs to run for neighbors search.
-<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.5.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
+<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.6.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
 <code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all processors. See <a class="reference internal" href="http://github.com/glossary.html#term-n_jobs"><span class="xref std std-term">Glossary</span></a>
 for more details.</p>
 </dd>
@@ -1449,7 +1449,7 @@ <h1>RadiusNeighborsClassifier<a class="headerlink" href="#radiusneighborsclassif
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neighbors.RadiusNeighborsClassifier.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/neighbors/_classification.py#L644"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.RadiusNeighborsClassifier.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/neighbors/_classification.py#L644"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.RadiusNeighborsClassifier.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit the radius neighbors classifier from the training dataset.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1471,7 +1471,7 @@ <h1>RadiusNeighborsClassifier<a class="headerlink" href="#radiusneighborsclassif
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neighbors.RadiusNeighborsClassifier.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.RadiusNeighborsClassifier.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.RadiusNeighborsClassifier.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1488,7 +1488,7 @@ <h1>RadiusNeighborsClassifier<a class="headerlink" href="#radiusneighborsclassif
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neighbors.RadiusNeighborsClassifier.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.RadiusNeighborsClassifier.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.RadiusNeighborsClassifier.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1509,7 +1509,7 @@ <h1>RadiusNeighborsClassifier<a class="headerlink" href="#radiusneighborsclassif
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neighbors.RadiusNeighborsClassifier.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/neighbors/_classification.py#L719"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.RadiusNeighborsClassifier.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/neighbors/_classification.py#L719"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.RadiusNeighborsClassifier.predict" title="Link to this definition">#</a></dt>
 <dd><p>Predict the class labels for the provided data.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1531,7 +1531,7 @@ <h1>RadiusNeighborsClassifier<a class="headerlink" href="#radiusneighborsclassif
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neighbors.RadiusNeighborsClassifier.predict_proba">
-<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/neighbors/_classification.py#L763"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.RadiusNeighborsClassifier.predict_proba" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/neighbors/_classification.py#L763"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.RadiusNeighborsClassifier.predict_proba" title="Link to this definition">#</a></dt>
 <dd><p>Return probability estimates for the test data X.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1554,7 +1554,7 @@ <h1>RadiusNeighborsClassifier<a class="headerlink" href="#radiusneighborsclassif
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neighbors.RadiusNeighborsClassifier.radius_neighbors">
-<span class="sig-name descname"><span class="pre">radius_neighbors</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">radius</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_distance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sort_results</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/neighbors/_base.py#L1093"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.RadiusNeighborsClassifier.radius_neighbors" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">radius_neighbors</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">radius</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_distance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sort_results</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/neighbors/_base.py#L1093"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.RadiusNeighborsClassifier.radius_neighbors" title="Link to this definition">#</a></dt>
 <dd><p>Find the neighbors within a given radius of a point or points.</p>
 <p>Return the indices and distances of each point from the dataset
 lying in a ball with size <code class="docutils literal notranslate"><span class="pre">radius</span></code> around the points of the query
@@ -1626,7 +1626,7 @@ <h1>RadiusNeighborsClassifier<a class="headerlink" href="#radiusneighborsclassif
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neighbors.RadiusNeighborsClassifier.radius_neighbors_graph">
-<span class="sig-name descname"><span class="pre">radius_neighbors_graph</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">radius</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'connectivity'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sort_results</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/neighbors/_base.py#L1311"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.RadiusNeighborsClassifier.radius_neighbors_graph" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">radius_neighbors_graph</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">radius</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'connectivity'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sort_results</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/neighbors/_base.py#L1311"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.RadiusNeighborsClassifier.radius_neighbors_graph" title="Link to this definition">#</a></dt>
 <dd><p>Compute the (weighted) graph of Neighbors for points in X.</p>
 <p>Neighborhoods are restricted the points at a distance lower than
 radius.</p>
@@ -1688,7 +1688,7 @@ <h1>RadiusNeighborsClassifier<a class="headerlink" href="#radiusneighborsclassif
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neighbors.RadiusNeighborsClassifier.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/neighbors/_classification.py#L885"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.RadiusNeighborsClassifier.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/neighbors/_classification.py#L885"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.RadiusNeighborsClassifier.score" title="Link to this definition">#</a></dt>
 <dd><p>Return the mean accuracy on the given test data and labels.</p>
 <p>In multi-label classification, this is the subset accuracy
 which is a harsh metric since you require for each sample that
@@ -1720,7 +1720,7 @@ <h1>RadiusNeighborsClassifier<a class="headerlink" href="#radiusneighborsclassif
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neighbors.RadiusNeighborsClassifier.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.RadiusNeighborsClassifier.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.RadiusNeighborsClassifier.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1744,7 +1744,7 @@ <h1>RadiusNeighborsClassifier<a class="headerlink" href="#radiusneighborsclassif
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neighbors.RadiusNeighborsClassifier.set_score_request">
-<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.neighbors.RadiusNeighborsClassifier" title="sklearn.neighbors._classification.RadiusNeighborsClassifier"><span class="pre">RadiusNeighborsClassifier</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.RadiusNeighborsClassifier.set_score_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.neighbors.RadiusNeighborsClassifier" title="sklearn.neighbors._classification.RadiusNeighborsClassifier"><span class="pre">RadiusNeighborsClassifier</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.RadiusNeighborsClassifier.set_score_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
diff --git a/dev/modules/generated/sklearn.neighbors.RadiusNeighborsRegressor.html b/dev/modules/generated/sklearn.neighbors.RadiusNeighborsRegressor.html
index 286cb9bc6a056..5507b424a3c8c 100644
--- a/dev/modules/generated/sklearn.neighbors.RadiusNeighborsRegressor.html
+++ b/dev/modules/generated/sklearn.neighbors.RadiusNeighborsRegressor.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1306,7 +1306,7 @@
 <h1>RadiusNeighborsRegressor<a class="headerlink" href="#radiusneighborsregressor" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.neighbors.RadiusNeighborsRegressor">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.neighbors.</span></span><span class="sig-name descname"><span class="pre">RadiusNeighborsRegressor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">radius</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'uniform'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">algorithm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">leaf_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">30</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'minkowski'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric_params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/neighbors/_regression.py#L270"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.RadiusNeighborsRegressor" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.neighbors.</span></span><span class="sig-name descname"><span class="pre">RadiusNeighborsRegressor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">radius</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'uniform'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">algorithm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">leaf_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">30</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'minkowski'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric_params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/neighbors/_regression.py#L270"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.RadiusNeighborsRegressor" title="Link to this definition">#</a></dt>
 <dd><p>Regression based on neighbors within a fixed radius.</p>
 <p>The target is predicted by local interpolation of the targets
 associated of the nearest neighbors in the training set.</p>
@@ -1370,7 +1370,7 @@ <h1>RadiusNeighborsRegressor<a class="headerlink" href="#radiusneighborsregresso
 <dt><strong>metric_params</strong><span class="classifier">dict, default=None</span></dt><dd><p>Additional keyword arguments for the metric function.</p>
 </dd>
 <dt><strong>n_jobs</strong><span class="classifier">int, default=None</span></dt><dd><p>The number of parallel jobs to run for neighbors search.
-<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.5.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
+<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.6.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
 <code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all processors. See <a class="reference internal" href="http://github.com/glossary.html#term-n_jobs"><span class="xref std std-term">Glossary</span></a>
 for more details.</p>
 </dd>
@@ -1433,7 +1433,7 @@ <h1>RadiusNeighborsRegressor<a class="headerlink" href="#radiusneighborsregresso
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neighbors.RadiusNeighborsRegressor.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/neighbors/_regression.py#L432"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.RadiusNeighborsRegressor.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/neighbors/_regression.py#L432"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.RadiusNeighborsRegressor.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit the radius neighbors regressor from the training dataset.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1455,7 +1455,7 @@ <h1>RadiusNeighborsRegressor<a class="headerlink" href="#radiusneighborsregresso
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neighbors.RadiusNeighborsRegressor.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.RadiusNeighborsRegressor.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.RadiusNeighborsRegressor.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1472,7 +1472,7 @@ <h1>RadiusNeighborsRegressor<a class="headerlink" href="#radiusneighborsregresso
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neighbors.RadiusNeighborsRegressor.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.RadiusNeighborsRegressor.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.RadiusNeighborsRegressor.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1493,7 +1493,7 @@ <h1>RadiusNeighborsRegressor<a class="headerlink" href="#radiusneighborsregresso
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neighbors.RadiusNeighborsRegressor.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/neighbors/_regression.py#L456"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.RadiusNeighborsRegressor.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/neighbors/_regression.py#L456"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.RadiusNeighborsRegressor.predict" title="Link to this definition">#</a></dt>
 <dd><p>Predict the target for the provided data.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1515,7 +1515,7 @@ <h1>RadiusNeighborsRegressor<a class="headerlink" href="#radiusneighborsregresso
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neighbors.RadiusNeighborsRegressor.radius_neighbors">
-<span class="sig-name descname"><span class="pre">radius_neighbors</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">radius</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_distance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sort_results</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/neighbors/_base.py#L1093"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.RadiusNeighborsRegressor.radius_neighbors" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">radius_neighbors</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">radius</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_distance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sort_results</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/neighbors/_base.py#L1093"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.RadiusNeighborsRegressor.radius_neighbors" title="Link to this definition">#</a></dt>
 <dd><p>Find the neighbors within a given radius of a point or points.</p>
 <p>Return the indices and distances of each point from the dataset
 lying in a ball with size <code class="docutils literal notranslate"><span class="pre">radius</span></code> around the points of the query
@@ -1587,7 +1587,7 @@ <h1>RadiusNeighborsRegressor<a class="headerlink" href="#radiusneighborsregresso
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neighbors.RadiusNeighborsRegressor.radius_neighbors_graph">
-<span class="sig-name descname"><span class="pre">radius_neighbors_graph</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">radius</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'connectivity'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sort_results</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/neighbors/_base.py#L1311"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.RadiusNeighborsRegressor.radius_neighbors_graph" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">radius_neighbors_graph</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">radius</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'connectivity'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sort_results</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/neighbors/_base.py#L1311"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.RadiusNeighborsRegressor.radius_neighbors_graph" title="Link to this definition">#</a></dt>
 <dd><p>Compute the (weighted) graph of Neighbors for points in X.</p>
 <p>Neighborhoods are restricted the points at a distance lower than
 radius.</p>
@@ -1649,7 +1649,7 @@ <h1>RadiusNeighborsRegressor<a class="headerlink" href="#radiusneighborsregresso
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neighbors.RadiusNeighborsRegressor.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L619"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.RadiusNeighborsRegressor.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L560"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.RadiusNeighborsRegressor.score" title="Link to this definition">#</a></dt>
 <dd><p>Return <a class="reference internal" href="http://github.com/model_evaluation.html#r2-score"><span class="std std-ref">coefficient of determination</span></a> on test data.</p>
 <p>The coefficient of determination, <span class="math notranslate nohighlight">\(R^2\)</span>, is defined as
 <span class="math notranslate nohighlight">\((1 - \frac{u}{v})\)</span>, where <span class="math notranslate nohighlight">\(u\)</span> is the residual
@@ -1691,7 +1691,7 @@ <h1>RadiusNeighborsRegressor<a class="headerlink" href="#radiusneighborsregresso
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neighbors.RadiusNeighborsRegressor.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.RadiusNeighborsRegressor.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.RadiusNeighborsRegressor.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1715,7 +1715,7 @@ <h1>RadiusNeighborsRegressor<a class="headerlink" href="#radiusneighborsregresso
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neighbors.RadiusNeighborsRegressor.set_score_request">
-<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.neighbors.RadiusNeighborsRegressor" title="sklearn.neighbors._regression.RadiusNeighborsRegressor"><span class="pre">RadiusNeighborsRegressor</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.RadiusNeighborsRegressor.set_score_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.neighbors.RadiusNeighborsRegressor" title="sklearn.neighbors._regression.RadiusNeighborsRegressor"><span class="pre">RadiusNeighborsRegressor</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.RadiusNeighborsRegressor.set_score_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
diff --git a/dev/modules/generated/sklearn.neighbors.RadiusNeighborsTransformer.html b/dev/modules/generated/sklearn.neighbors.RadiusNeighborsTransformer.html
index dbb27c18d427b..0631b073fa3c6 100644
--- a/dev/modules/generated/sklearn.neighbors.RadiusNeighborsTransformer.html
+++ b/dev/modules/generated/sklearn.neighbors.RadiusNeighborsTransformer.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>RadiusNeighborsTransformer<a class="headerlink" href="#radiusneighborstransformer" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.neighbors.RadiusNeighborsTransformer">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.neighbors.</span></span><span class="sig-name descname"><span class="pre">RadiusNeighborsTransformer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'distance'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">radius</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">algorithm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">leaf_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">30</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'minkowski'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric_params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/neighbors/_graph.py#L484"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.RadiusNeighborsTransformer" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.neighbors.</span></span><span class="sig-name descname"><span class="pre">RadiusNeighborsTransformer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'distance'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">radius</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">algorithm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">leaf_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">30</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'minkowski'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric_params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/neighbors/_graph.py#L484"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.RadiusNeighborsTransformer" title="Link to this definition">#</a></dt>
 <dd><p>Transform X into a (weighted) graph of neighbors nearer than a radius.</p>
 <p>The transformed data is a sparse graph as returned by
 <code class="docutils literal notranslate"><span class="pre">radius_neighbors_graph</span></code>.</p>
@@ -1417,7 +1417,7 @@ <h1>RadiusNeighborsTransformer<a class="headerlink" href="#radiusneighborstransf
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neighbors.RadiusNeighborsTransformer.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/neighbors/_graph.py#L638"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.RadiusNeighborsTransformer.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/neighbors/_graph.py#L638"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.RadiusNeighborsTransformer.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit the radius neighbors transformer from the training dataset.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1439,7 +1439,7 @@ <h1>RadiusNeighborsTransformer<a class="headerlink" href="#radiusneighborstransf
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neighbors.RadiusNeighborsTransformer.fit_transform">
-<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/neighbors/_graph.py#L682"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.RadiusNeighborsTransformer.fit_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/neighbors/_graph.py#L682"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.RadiusNeighborsTransformer.fit_transform" title="Link to this definition">#</a></dt>
 <dd><p>Fit to data, then transform it.</p>
 <p>Fits transformer to X and y with optional parameters fit_params
 and returns a transformed version of X.</p>
@@ -1466,7 +1466,7 @@ <h1>RadiusNeighborsTransformer<a class="headerlink" href="#radiusneighborstransf
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neighbors.RadiusNeighborsTransformer.get_feature_names_out">
-<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L995"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.RadiusNeighborsTransformer.get_feature_names_out" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L936"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.RadiusNeighborsTransformer.get_feature_names_out" title="Link to this definition">#</a></dt>
 <dd><p>Get output feature names for transformation.</p>
 <p>The feature names out will prefixed by the lowercased class name. For
 example, if the transformer outputs 3 features, then the feature names
@@ -1489,7 +1489,7 @@ <h1>RadiusNeighborsTransformer<a class="headerlink" href="#radiusneighborstransf
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neighbors.RadiusNeighborsTransformer.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.RadiusNeighborsTransformer.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.RadiusNeighborsTransformer.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1506,7 +1506,7 @@ <h1>RadiusNeighborsTransformer<a class="headerlink" href="#radiusneighborstransf
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neighbors.RadiusNeighborsTransformer.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.RadiusNeighborsTransformer.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.RadiusNeighborsTransformer.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1527,7 +1527,7 @@ <h1>RadiusNeighborsTransformer<a class="headerlink" href="#radiusneighborstransf
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neighbors.RadiusNeighborsTransformer.radius_neighbors">
-<span class="sig-name descname"><span class="pre">radius_neighbors</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">radius</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_distance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sort_results</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/neighbors/_base.py#L1093"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.RadiusNeighborsTransformer.radius_neighbors" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">radius_neighbors</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">radius</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_distance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sort_results</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/neighbors/_base.py#L1093"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.RadiusNeighborsTransformer.radius_neighbors" title="Link to this definition">#</a></dt>
 <dd><p>Find the neighbors within a given radius of a point or points.</p>
 <p>Return the indices and distances of each point from the dataset
 lying in a ball with size <code class="docutils literal notranslate"><span class="pre">radius</span></code> around the points of the query
@@ -1599,7 +1599,7 @@ <h1>RadiusNeighborsTransformer<a class="headerlink" href="#radiusneighborstransf
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neighbors.RadiusNeighborsTransformer.radius_neighbors_graph">
-<span class="sig-name descname"><span class="pre">radius_neighbors_graph</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">radius</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'connectivity'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sort_results</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/neighbors/_base.py#L1311"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.RadiusNeighborsTransformer.radius_neighbors_graph" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">radius_neighbors_graph</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">radius</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'connectivity'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sort_results</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/neighbors/_base.py#L1311"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.RadiusNeighborsTransformer.radius_neighbors_graph" title="Link to this definition">#</a></dt>
 <dd><p>Compute the (weighted) graph of Neighbors for points in X.</p>
 <p>Neighborhoods are restricted the points at a distance lower than
 radius.</p>
@@ -1661,7 +1661,7 @@ <h1>RadiusNeighborsTransformer<a class="headerlink" href="#radiusneighborstransf
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neighbors.RadiusNeighborsTransformer.set_output">
-<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.RadiusNeighborsTransformer.set_output" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.RadiusNeighborsTransformer.set_output" title="Link to this definition">#</a></dt>
 <dd><p>Set output container.</p>
 <p>See <a class="reference internal" href="http://github.com/auto_examples/miscellaneous/plot_set_output.html#sphx-glr-auto-examples-miscellaneous-plot-set-output-py"><span class="std std-ref">Introducing the set_output API</span></a>
 for an example on how to use the API.</p>
@@ -1692,7 +1692,7 @@ <h1>RadiusNeighborsTransformer<a class="headerlink" href="#radiusneighborstransf
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neighbors.RadiusNeighborsTransformer.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.RadiusNeighborsTransformer.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.RadiusNeighborsTransformer.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1716,7 +1716,7 @@ <h1>RadiusNeighborsTransformer<a class="headerlink" href="#radiusneighborstransf
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neighbors.RadiusNeighborsTransformer.transform">
-<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/neighbors/_graph.py#L663"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.RadiusNeighborsTransformer.transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/neighbors/_graph.py#L663"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.RadiusNeighborsTransformer.transform" title="Link to this definition">#</a></dt>
 <dd><p>Compute the (weighted) graph of Neighbors for points in X.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.neighbors.kneighbors_graph.html b/dev/modules/generated/sklearn.neighbors.kneighbors_graph.html
index c0a39fc8544d8..4c602d5c8edf6 100644
--- a/dev/modules/generated/sklearn.neighbors.kneighbors_graph.html
+++ b/dev/modules/generated/sklearn.neighbors.kneighbors_graph.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>kneighbors_graph<a class="headerlink" href="#kneighbors-graph" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.neighbors.kneighbors_graph">
-<span class="sig-prename descclassname"><span class="pre">sklearn.neighbors.</span></span><span class="sig-name descname"><span class="pre">kneighbors_graph</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_neighbors</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'connectivity'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'minkowski'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric_params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">include_self</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/neighbors/_graph.py#L45"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.kneighbors_graph" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.neighbors.</span></span><span class="sig-name descname"><span class="pre">kneighbors_graph</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_neighbors</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'connectivity'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'minkowski'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric_params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">include_self</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/neighbors/_graph.py#L45"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.kneighbors_graph" title="Link to this definition">#</a></dt>
 <dd><p>Compute the (weighted) graph of k-Neighbors for points in X.</p>
 <p>Read more in the <a class="reference internal" href="http://github.com/neighbors.html#unsupervised-neighbors"><span class="std std-ref">User Guide</span></a>.</p>
 <dl class="field-list simple">
@@ -1340,7 +1340,7 @@ <h1>kneighbors_graph<a class="headerlink" href="#kneighbors-graph" title="Link t
 for mode=’distance’.</p>
 </dd>
 <dt><strong>n_jobs</strong><span class="classifier">int, default=None</span></dt><dd><p>The number of parallel jobs to run for neighbors search.
-<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.5.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
+<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.6.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
 <code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all processors. See <a class="reference internal" href="http://github.com/glossary.html#term-n_jobs"><span class="xref std std-term">Glossary</span></a>
 for more details.</p>
 </dd>
diff --git a/dev/modules/generated/sklearn.neighbors.radius_neighbors_graph.html b/dev/modules/generated/sklearn.neighbors.radius_neighbors_graph.html
index 49361d10f5387..d04727171ac65 100644
--- a/dev/modules/generated/sklearn.neighbors.radius_neighbors_graph.html
+++ b/dev/modules/generated/sklearn.neighbors.radius_neighbors_graph.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>radius_neighbors_graph<a class="headerlink" href="#radius-neighbors-graph" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.neighbors.radius_neighbors_graph">
-<span class="sig-prename descclassname"><span class="pre">sklearn.neighbors.</span></span><span class="sig-name descname"><span class="pre">radius_neighbors_graph</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">radius</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'connectivity'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'minkowski'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric_params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">include_self</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/neighbors/_graph.py#L150"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.radius_neighbors_graph" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.neighbors.</span></span><span class="sig-name descname"><span class="pre">radius_neighbors_graph</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">radius</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'connectivity'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'minkowski'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric_params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">include_self</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/neighbors/_graph.py#L150"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.radius_neighbors_graph" title="Link to this definition">#</a></dt>
 <dd><p>Compute the (weighted) graph of Neighbors for points in X.</p>
 <p>Neighborhoods are restricted the points at a distance lower than
 radius.</p>
@@ -1339,7 +1339,7 @@ <h1>radius_neighbors_graph<a class="headerlink" href="#radius-neighbors-graph" t
 for mode=’distance’.</p>
 </dd>
 <dt><strong>n_jobs</strong><span class="classifier">int, default=None</span></dt><dd><p>The number of parallel jobs to run for neighbors search.
-<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.5.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
+<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.6.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
 <code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all processors. See <a class="reference internal" href="http://github.com/glossary.html#term-n_jobs"><span class="xref std std-term">Glossary</span></a>
 for more details.</p>
 </dd>
diff --git a/dev/modules/generated/sklearn.neighbors.sort_graph_by_row_values.html b/dev/modules/generated/sklearn.neighbors.sort_graph_by_row_values.html
index a7028f379b14d..def55a0fe9c85 100644
--- a/dev/modules/generated/sklearn.neighbors.sort_graph_by_row_values.html
+++ b/dev/modules/generated/sklearn.neighbors.sort_graph_by_row_values.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>sort_graph_by_row_values<a class="headerlink" href="#sort-graph-by-row-values" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.neighbors.sort_graph_by_row_values">
-<span class="sig-prename descclassname"><span class="pre">sklearn.neighbors.</span></span><span class="sig-name descname"><span class="pre">sort_graph_by_row_values</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">graph</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">warn_when_not_sorted</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/neighbors/_base.py#L195"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.sort_graph_by_row_values" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.neighbors.</span></span><span class="sig-name descname"><span class="pre">sort_graph_by_row_values</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">graph</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">warn_when_not_sorted</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/neighbors/_base.py#L195"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neighbors.sort_graph_by_row_values" title="Link to this definition">#</a></dt>
 <dd><p>Sort a sparse graph such that each row is stored with increasing values.</p>
 <div class="versionadded">
 <p><span class="versionmodified added">Added in version 1.2.</span></p>
diff --git a/dev/modules/generated/sklearn.neural_network.BernoulliRBM.html b/dev/modules/generated/sklearn.neural_network.BernoulliRBM.html
index e87b54aa8535d..db9722dc9a7ec 100644
--- a/dev/modules/generated/sklearn.neural_network.BernoulliRBM.html
+++ b/dev/modules/generated/sklearn.neural_network.BernoulliRBM.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>BernoulliRBM<a class="headerlink" href="#bernoullirbm" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.neural_network.BernoulliRBM">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.neural_network.</span></span><span class="sig-name descname"><span class="pre">BernoulliRBM</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_components</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">256</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">learning_rate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/neural_network/_rbm.py#L25"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neural_network.BernoulliRBM" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.neural_network.</span></span><span class="sig-name descname"><span class="pre">BernoulliRBM</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_components</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">256</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">learning_rate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/neural_network/_rbm.py#L25"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neural_network.BernoulliRBM" title="Link to this definition">#</a></dt>
 <dd><p>Bernoulli Restricted Boltzmann Machine (RBM).</p>
 <p>A Restricted Boltzmann Machine with binary visible units and
 binary hidden units. Parameters are estimated using Stochastic Maximum
@@ -1404,7 +1404,7 @@ <h1>BernoulliRBM<a class="headerlink" href="#bernoullirbm" title="Link to this h
 <a class="reference internal" href="http://github.com/auto_examples/neural_networks/plot_rbm_logistic_classification.html#sphx-glr-auto-examples-neural-networks-plot-rbm-logistic-classification-py"><span class="std std-ref">Restricted Boltzmann Machine features for digit classification</span></a>.</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neural_network.BernoulliRBM.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/neural_network/_rbm.py#L385"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neural_network.BernoulliRBM.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/neural_network/_rbm.py#L385"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neural_network.BernoulliRBM.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit the model to the data X.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1426,7 +1426,7 @@ <h1>BernoulliRBM<a class="headerlink" href="#bernoullirbm" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neural_network.BernoulliRBM.fit_transform">
-<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L863"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neural_network.BernoulliRBM.fit_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L804"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neural_network.BernoulliRBM.fit_transform" title="Link to this definition">#</a></dt>
 <dd><p>Fit to data, then transform it.</p>
 <p>Fits transformer to <code class="docutils literal notranslate"><span class="pre">X</span></code> and <code class="docutils literal notranslate"><span class="pre">y</span></code> with optional parameters <code class="docutils literal notranslate"><span class="pre">fit_params</span></code>
 and returns a transformed version of <code class="docutils literal notranslate"><span class="pre">X</span></code>.</p>
@@ -1452,7 +1452,7 @@ <h1>BernoulliRBM<a class="headerlink" href="#bernoullirbm" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neural_network.BernoulliRBM.get_feature_names_out">
-<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L995"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neural_network.BernoulliRBM.get_feature_names_out" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L936"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neural_network.BernoulliRBM.get_feature_names_out" title="Link to this definition">#</a></dt>
 <dd><p>Get output feature names for transformation.</p>
 <p>The feature names out will prefixed by the lowercased class name. For
 example, if the transformer outputs 3 features, then the feature names
@@ -1475,7 +1475,7 @@ <h1>BernoulliRBM<a class="headerlink" href="#bernoullirbm" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neural_network.BernoulliRBM.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neural_network.BernoulliRBM.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neural_network.BernoulliRBM.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1492,7 +1492,7 @@ <h1>BernoulliRBM<a class="headerlink" href="#bernoullirbm" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neural_network.BernoulliRBM.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neural_network.BernoulliRBM.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neural_network.BernoulliRBM.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1513,7 +1513,7 @@ <h1>BernoulliRBM<a class="headerlink" href="#bernoullirbm" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neural_network.BernoulliRBM.gibbs">
-<span class="sig-name descname"><span class="pre">gibbs</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">v</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/neural_network/_rbm.py#L251"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neural_network.BernoulliRBM.gibbs" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">gibbs</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">v</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/neural_network/_rbm.py#L251"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neural_network.BernoulliRBM.gibbs" title="Link to this definition">#</a></dt>
 <dd><p>Perform one Gibbs sampling step.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1533,7 +1533,7 @@ <h1>BernoulliRBM<a class="headerlink" href="#bernoullirbm" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neural_network.BernoulliRBM.partial_fit">
-<span class="sig-name descname"><span class="pre">partial_fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/neural_network/_rbm.py#L272"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neural_network.BernoulliRBM.partial_fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">partial_fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/neural_network/_rbm.py#L272"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neural_network.BernoulliRBM.partial_fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit the model to the partial segment of the data X.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1555,7 +1555,7 @@ <h1>BernoulliRBM<a class="headerlink" href="#bernoullirbm" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neural_network.BernoulliRBM.score_samples">
-<span class="sig-name descname"><span class="pre">score_samples</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/neural_network/_rbm.py#L344"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neural_network.BernoulliRBM.score_samples" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score_samples</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/neural_network/_rbm.py#L344"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neural_network.BernoulliRBM.score_samples" title="Link to this definition">#</a></dt>
 <dd><p>Compute the pseudo-likelihood of X.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1579,7 +1579,7 @@ <h1>BernoulliRBM<a class="headerlink" href="#bernoullirbm" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neural_network.BernoulliRBM.set_output">
-<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neural_network.BernoulliRBM.set_output" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neural_network.BernoulliRBM.set_output" title="Link to this definition">#</a></dt>
 <dd><p>Set output container.</p>
 <p>See <a class="reference internal" href="http://github.com/auto_examples/miscellaneous/plot_set_output.html#sphx-glr-auto-examples-miscellaneous-plot-set-output-py"><span class="std std-ref">Introducing the set_output API</span></a>
 for an example on how to use the API.</p>
@@ -1610,7 +1610,7 @@ <h1>BernoulliRBM<a class="headerlink" href="#bernoullirbm" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neural_network.BernoulliRBM.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neural_network.BernoulliRBM.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neural_network.BernoulliRBM.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1634,7 +1634,7 @@ <h1>BernoulliRBM<a class="headerlink" href="#bernoullirbm" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neural_network.BernoulliRBM.transform">
-<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/neural_network/_rbm.py#L157"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neural_network.BernoulliRBM.transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/neural_network/_rbm.py#L157"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neural_network.BernoulliRBM.transform" title="Link to this definition">#</a></dt>
 <dd><p>Compute the hidden layer activation probabilities, P(h=1|v=X).</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.neural_network.MLPClassifier.html b/dev/modules/generated/sklearn.neural_network.MLPClassifier.html
index 5f957f1c95e62..a9d958db3906f 100644
--- a/dev/modules/generated/sklearn.neural_network.MLPClassifier.html
+++ b/dev/modules/generated/sklearn.neural_network.MLPClassifier.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>MLPClassifier<a class="headerlink" href="#mlpclassifier" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.neural_network.MLPClassifier">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.neural_network.</span></span><span class="sig-name descname"><span class="pre">MLPClassifier</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hidden_layer_sizes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(100,)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">activation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'relu'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">solver</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'adam'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">learning_rate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'constant'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">learning_rate_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">power_t</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">200</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">warm_start</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.9</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nesterovs_momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">early_stopping</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validation_fraction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">beta_1</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.9</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">beta_2</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.999</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epsilon</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-08</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_iter_no_change</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_fun</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">15000</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/neural_network/_multilayer_perceptron.py#L868"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neural_network.MLPClassifier" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.neural_network.</span></span><span class="sig-name descname"><span class="pre">MLPClassifier</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hidden_layer_sizes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(100,)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">activation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'relu'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">solver</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'adam'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">learning_rate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'constant'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">learning_rate_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">power_t</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">200</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">warm_start</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.9</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nesterovs_momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">early_stopping</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validation_fraction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">beta_1</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.9</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">beta_2</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.999</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epsilon</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-08</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_iter_no_change</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_fun</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">15000</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/neural_network/_multilayer_perceptron.py#L874"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neural_network.MLPClassifier" title="Link to this definition">#</a></dt>
 <dd><p>Multi-layer Perceptron classifier.</p>
 <p>This model optimizes the log-loss function using LBFGS or stochastic
 gradient descent.</p>
@@ -1536,7 +1536,7 @@ <h1>MLPClassifier<a class="headerlink" href="#mlpclassifier" title="Link to this
 <span class="gp">... </span>                                                    <span class="n">random_state</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">clf</span> <span class="o">=</span> <span class="n">MLPClassifier</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">max_iter</span><span class="o">=</span><span class="mi">300</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">clf</span><span class="o">.</span><span class="n">predict_proba</span><span class="p">(</span><span class="n">X_test</span><span class="p">[:</span><span class="mi">1</span><span class="p">])</span>
-<span class="go">array([[0.038..., 0.961...]])</span>
+<span class="go">array([[0.0383, 0.961]])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">clf</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">[:</span><span class="mi">5</span><span class="p">,</span> <span class="p">:])</span>
 <span class="go">array([1, 0, 1, 0, 1])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">clf</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span>
@@ -1545,7 +1545,7 @@ <h1>MLPClassifier<a class="headerlink" href="#mlpclassifier" title="Link to this
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neural_network.MLPClassifier.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/neural_network/_multilayer_perceptron.py#L819"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neural_network.MLPClassifier.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/neural_network/_multilayer_perceptron.py#L825"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neural_network.MLPClassifier.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit the model to data matrix X and target(s) y.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1573,7 +1573,7 @@ <h1>MLPClassifier<a class="headerlink" href="#mlpclassifier" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neural_network.MLPClassifier.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neural_network.MLPClassifier.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neural_network.MLPClassifier.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1590,7 +1590,7 @@ <h1>MLPClassifier<a class="headerlink" href="#mlpclassifier" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neural_network.MLPClassifier.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neural_network.MLPClassifier.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neural_network.MLPClassifier.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1611,7 +1611,7 @@ <h1>MLPClassifier<a class="headerlink" href="#mlpclassifier" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neural_network.MLPClassifier.partial_fit">
-<span class="sig-name descname"><span class="pre">partial_fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">classes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/neural_network/_multilayer_perceptron.py#L1286"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neural_network.MLPClassifier.partial_fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">partial_fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">classes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/neural_network/_multilayer_perceptron.py#L1292"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neural_network.MLPClassifier.partial_fit" title="Link to this definition">#</a></dt>
 <dd><p>Update the model with a single iteration over the given data.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1645,7 +1645,7 @@ <h1>MLPClassifier<a class="headerlink" href="#mlpclassifier" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neural_network.MLPClassifier.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/neural_network/_multilayer_perceptron.py#L1256"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neural_network.MLPClassifier.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/neural_network/_multilayer_perceptron.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neural_network.MLPClassifier.predict" title="Link to this definition">#</a></dt>
 <dd><p>Predict using the multi-layer perceptron classifier.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1665,7 +1665,7 @@ <h1>MLPClassifier<a class="headerlink" href="#mlpclassifier" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neural_network.MLPClassifier.predict_log_proba">
-<span class="sig-name descname"><span class="pre">predict_log_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/neural_network/_multilayer_perceptron.py#L1326"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neural_network.MLPClassifier.predict_log_proba" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict_log_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/neural_network/_multilayer_perceptron.py#L1332"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neural_network.MLPClassifier.predict_log_proba" title="Link to this definition">#</a></dt>
 <dd><p>Return the log of probability estimates.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1687,7 +1687,7 @@ <h1>MLPClassifier<a class="headerlink" href="#mlpclassifier" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neural_network.MLPClassifier.predict_proba">
-<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/neural_network/_multilayer_perceptron.py#L1344"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neural_network.MLPClassifier.predict_proba" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/neural_network/_multilayer_perceptron.py#L1350"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neural_network.MLPClassifier.predict_proba" title="Link to this definition">#</a></dt>
 <dd><p>Probability estimates.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1708,7 +1708,7 @@ <h1>MLPClassifier<a class="headerlink" href="#mlpclassifier" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neural_network.MLPClassifier.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L546"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neural_network.MLPClassifier.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L487"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neural_network.MLPClassifier.score" title="Link to this definition">#</a></dt>
 <dd><p>Return <a class="reference internal" href="http://github.com/model_evaluation.html#accuracy-score"><span class="std std-ref">accuracy</span></a> on provided data and labels.</p>
 <p>In multi-label classification, this is the subset accuracy
 which is a harsh metric since you require for each sample that
@@ -1735,7 +1735,7 @@ <h1>MLPClassifier<a class="headerlink" href="#mlpclassifier" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neural_network.MLPClassifier.set_fit_request">
-<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.neural_network.MLPClassifier" title="sklearn.neural_network._multilayer_perceptron.MLPClassifier"><span class="pre">MLPClassifier</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neural_network.MLPClassifier.set_fit_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.neural_network.MLPClassifier" title="sklearn.neural_network._multilayer_perceptron.MLPClassifier"><span class="pre">MLPClassifier</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neural_network.MLPClassifier.set_fit_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1778,7 +1778,7 @@ <h1>MLPClassifier<a class="headerlink" href="#mlpclassifier" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neural_network.MLPClassifier.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neural_network.MLPClassifier.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neural_network.MLPClassifier.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1802,7 +1802,7 @@ <h1>MLPClassifier<a class="headerlink" href="#mlpclassifier" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neural_network.MLPClassifier.set_partial_fit_request">
-<span class="sig-name descname"><span class="pre">set_partial_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">classes</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.neural_network.MLPClassifier" title="sklearn.neural_network._multilayer_perceptron.MLPClassifier"><span class="pre">MLPClassifier</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neural_network.MLPClassifier.set_partial_fit_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_partial_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">classes</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.neural_network.MLPClassifier" title="sklearn.neural_network._multilayer_perceptron.MLPClassifier"><span class="pre">MLPClassifier</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neural_network.MLPClassifier.set_partial_fit_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">partial_fit</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1847,7 +1847,7 @@ <h1>MLPClassifier<a class="headerlink" href="#mlpclassifier" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neural_network.MLPClassifier.set_score_request">
-<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.neural_network.MLPClassifier" title="sklearn.neural_network._multilayer_perceptron.MLPClassifier"><span class="pre">MLPClassifier</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neural_network.MLPClassifier.set_score_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.neural_network.MLPClassifier" title="sklearn.neural_network._multilayer_perceptron.MLPClassifier"><span class="pre">MLPClassifier</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neural_network.MLPClassifier.set_score_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
diff --git a/dev/modules/generated/sklearn.neural_network.MLPRegressor.html b/dev/modules/generated/sklearn.neural_network.MLPRegressor.html
index cb9dc06b48297..df24273ad9101 100644
--- a/dev/modules/generated/sklearn.neural_network.MLPRegressor.html
+++ b/dev/modules/generated/sklearn.neural_network.MLPRegressor.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1308,7 +1308,7 @@
 <h1>MLPRegressor<a class="headerlink" href="#mlpregressor" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.neural_network.MLPRegressor">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.neural_network.</span></span><span class="sig-name descname"><span class="pre">MLPRegressor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">loss</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'squared_error'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hidden_layer_sizes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(100,)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">activation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'relu'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">solver</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'adam'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">learning_rate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'constant'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">learning_rate_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">power_t</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">200</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">warm_start</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.9</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nesterovs_momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">early_stopping</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validation_fraction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">beta_1</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.9</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">beta_2</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.999</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epsilon</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-08</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_iter_no_change</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_fun</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">15000</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/neural_network/_multilayer_perceptron.py#L1375"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neural_network.MLPRegressor" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.neural_network.</span></span><span class="sig-name descname"><span class="pre">MLPRegressor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">loss</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'squared_error'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hidden_layer_sizes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(100,)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">activation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'relu'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">solver</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'adam'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">learning_rate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'constant'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">learning_rate_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">power_t</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">200</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">warm_start</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.9</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nesterovs_momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">early_stopping</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validation_fraction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">beta_1</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.9</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">beta_2</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.999</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epsilon</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-08</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_iter_no_change</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_fun</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">15000</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/neural_network/_multilayer_perceptron.py#L1381"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neural_network.MLPRegressor" title="Link to this definition">#</a></dt>
 <dd><p>Multi-layer Perceptron regressor.</p>
 <p>This model optimizes the squared error using LBFGS or stochastic gradient
 descent.</p>
@@ -1550,14 +1550,14 @@ <h1>MLPRegressor<a class="headerlink" href="#mlpregressor" title="Link to this h
 <span class="gp">&gt;&gt;&gt; </span><span class="n">regr</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
 <span class="go">MLPRegressor(max_iter=2000, random_state=1, tol=0.1)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">regr</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">[:</span><span class="mi">2</span><span class="p">])</span>
-<span class="go">array([  28..., -290...])</span>
+<span class="go">array([  28.98, -291])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">regr</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span>
-<span class="go">0.98...</span>
+<span class="go">0.98</span>
 </pre></div>
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neural_network.MLPRegressor.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/neural_network/_multilayer_perceptron.py#L819"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neural_network.MLPRegressor.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/neural_network/_multilayer_perceptron.py#L825"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neural_network.MLPRegressor.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit the model to data matrix X and target(s) y.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1585,7 +1585,7 @@ <h1>MLPRegressor<a class="headerlink" href="#mlpregressor" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neural_network.MLPRegressor.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neural_network.MLPRegressor.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neural_network.MLPRegressor.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1602,7 +1602,7 @@ <h1>MLPRegressor<a class="headerlink" href="#mlpregressor" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neural_network.MLPRegressor.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neural_network.MLPRegressor.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neural_network.MLPRegressor.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1623,7 +1623,7 @@ <h1>MLPRegressor<a class="headerlink" href="#mlpregressor" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neural_network.MLPRegressor.partial_fit">
-<span class="sig-name descname"><span class="pre">partial_fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/neural_network/_multilayer_perceptron.py#L1767"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neural_network.MLPRegressor.partial_fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">partial_fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/neural_network/_multilayer_perceptron.py#L1773"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neural_network.MLPRegressor.partial_fit" title="Link to this definition">#</a></dt>
 <dd><p>Update the model with a single iteration over the given data.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1650,7 +1650,7 @@ <h1>MLPRegressor<a class="headerlink" href="#mlpregressor" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neural_network.MLPRegressor.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/neural_network/_multilayer_perceptron.py#L1724"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neural_network.MLPRegressor.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/neural_network/_multilayer_perceptron.py#L1730"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neural_network.MLPRegressor.predict" title="Link to this definition">#</a></dt>
 <dd><p>Predict using the multi-layer perceptron model.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1670,7 +1670,7 @@ <h1>MLPRegressor<a class="headerlink" href="#mlpregressor" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neural_network.MLPRegressor.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L619"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neural_network.MLPRegressor.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L560"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neural_network.MLPRegressor.score" title="Link to this definition">#</a></dt>
 <dd><p>Return <a class="reference internal" href="http://github.com/model_evaluation.html#r2-score"><span class="std std-ref">coefficient of determination</span></a> on test data.</p>
 <p>The coefficient of determination, <span class="math notranslate nohighlight">\(R^2\)</span>, is defined as
 <span class="math notranslate nohighlight">\((1 - \frac{u}{v})\)</span>, where <span class="math notranslate nohighlight">\(u\)</span> is the residual
@@ -1712,7 +1712,7 @@ <h1>MLPRegressor<a class="headerlink" href="#mlpregressor" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neural_network.MLPRegressor.set_fit_request">
-<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.neural_network.MLPRegressor" title="sklearn.neural_network._multilayer_perceptron.MLPRegressor"><span class="pre">MLPRegressor</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neural_network.MLPRegressor.set_fit_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.neural_network.MLPRegressor" title="sklearn.neural_network._multilayer_perceptron.MLPRegressor"><span class="pre">MLPRegressor</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neural_network.MLPRegressor.set_fit_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1755,7 +1755,7 @@ <h1>MLPRegressor<a class="headerlink" href="#mlpregressor" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neural_network.MLPRegressor.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neural_network.MLPRegressor.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neural_network.MLPRegressor.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1779,7 +1779,7 @@ <h1>MLPRegressor<a class="headerlink" href="#mlpregressor" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neural_network.MLPRegressor.set_partial_fit_request">
-<span class="sig-name descname"><span class="pre">set_partial_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.neural_network.MLPRegressor" title="sklearn.neural_network._multilayer_perceptron.MLPRegressor"><span class="pre">MLPRegressor</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neural_network.MLPRegressor.set_partial_fit_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_partial_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.neural_network.MLPRegressor" title="sklearn.neural_network._multilayer_perceptron.MLPRegressor"><span class="pre">MLPRegressor</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neural_network.MLPRegressor.set_partial_fit_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">partial_fit</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1822,7 +1822,7 @@ <h1>MLPRegressor<a class="headerlink" href="#mlpregressor" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.neural_network.MLPRegressor.set_score_request">
-<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.neural_network.MLPRegressor" title="sklearn.neural_network._multilayer_perceptron.MLPRegressor"><span class="pre">MLPRegressor</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neural_network.MLPRegressor.set_score_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.neural_network.MLPRegressor" title="sklearn.neural_network._multilayer_perceptron.MLPRegressor"><span class="pre">MLPRegressor</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.neural_network.MLPRegressor.set_score_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
diff --git a/dev/modules/generated/sklearn.pipeline.FeatureUnion.html b/dev/modules/generated/sklearn.pipeline.FeatureUnion.html
index 3cc7fab865dbe..57ef2bc401d85 100644
--- a/dev/modules/generated/sklearn.pipeline.FeatureUnion.html
+++ b/dev/modules/generated/sklearn.pipeline.FeatureUnion.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>FeatureUnion<a class="headerlink" href="#featureunion" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.pipeline.FeatureUnion">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.pipeline.</span></span><span class="sig-name descname"><span class="pre">FeatureUnion</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">transformer_list</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transformer_weights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose_feature_names_out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/pipeline.py#L1558"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.pipeline.FeatureUnion" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.pipeline.</span></span><span class="sig-name descname"><span class="pre">FeatureUnion</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">transformer_list</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transformer_weights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose_feature_names_out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/pipeline.py#L1558"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.pipeline.FeatureUnion" title="Link to this definition">#</a></dt>
 <dd><p>Concatenates results of multiple transformer objects.</p>
 <p>This estimator applies a list of transformer objects in parallel to the
 input data, then concatenates the results. This is useful to combine
@@ -1336,7 +1336,7 @@ <h1>FeatureUnion<a class="headerlink" href="#featureunion" title="Link to this h
 </div>
 </dd>
 <dt><strong>n_jobs</strong><span class="classifier">int, default=None</span></dt><dd><p>Number of jobs to run in parallel.
-<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.5.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
+<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.6.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
 <code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all processors. See <a class="reference internal" href="http://github.com/glossary.html#term-n_jobs"><span class="xref std std-term">Glossary</span></a>
 for more details.</p>
 <div class="versionchanged">
@@ -1391,19 +1391,19 @@ <h1>FeatureUnion<a class="headerlink" href="#featureunion" title="Link to this h
 <span class="gp">... </span>                      <span class="p">(</span><span class="s2">&quot;svd&quot;</span><span class="p">,</span> <span class="n">TruncatedSVD</span><span class="p">(</span><span class="n">n_components</span><span class="o">=</span><span class="mi">2</span><span class="p">))])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">X</span> <span class="o">=</span> <span class="p">[[</span><span class="mf">0.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="p">[</span><span class="mf">2.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">,</span> <span class="mi">5</span><span class="p">]]</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">union</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
-<span class="go">array([[-1.5       ,  3.0..., -0.8...],</span>
-<span class="go">       [ 1.5       ,  5.7...,  0.4...]])</span>
+<span class="go">array([[-1.5       ,  3.04, -0.872],</span>
+<span class="go">       [ 1.5       ,  5.72,  0.463]])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="c1"># An estimator&#39;s parameter can be set using &#39;__&#39; syntax</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">union</span><span class="o">.</span><span class="n">set_params</span><span class="p">(</span><span class="n">svd__n_components</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
-<span class="go">array([[-1.5       ,  3.0...],</span>
-<span class="go">       [ 1.5       ,  5.7...]])</span>
+<span class="go">array([[-1.5       ,  3.04],</span>
+<span class="go">       [ 1.5       ,  5.72]])</span>
 </pre></div>
 </div>
 <p>For a more detailed example of usage, see
 <a class="reference internal" href="http://github.com/auto_examples/compose/plot_feature_union.html#sphx-glr-auto-examples-compose-plot-feature-union-py"><span class="std std-ref">Concatenating multiple feature extraction methods</span></a>.</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.pipeline.FeatureUnion.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/pipeline.py#L1870"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.pipeline.FeatureUnion.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/pipeline.py#L1870"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.pipeline.FeatureUnion.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit all transformers using X.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1437,7 +1437,7 @@ <h1>FeatureUnion<a class="headerlink" href="#featureunion" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.pipeline.FeatureUnion.fit_transform">
-<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/pipeline.py#L1917"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.pipeline.FeatureUnion.fit_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/pipeline.py#L1917"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.pipeline.FeatureUnion.fit_transform" title="Link to this definition">#</a></dt>
 <dd><p>Fit all transformers, transform the data and concatenate results.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1472,7 +1472,7 @@ <h1>FeatureUnion<a class="headerlink" href="#featureunion" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.pipeline.FeatureUnion.get_feature_names_out">
-<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/pipeline.py#L1794"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.pipeline.FeatureUnion.get_feature_names_out" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/pipeline.py#L1794"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.pipeline.FeatureUnion.get_feature_names_out" title="Link to this definition">#</a></dt>
 <dd><p>Get output feature names for transformation.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1492,7 +1492,7 @@ <h1>FeatureUnion<a class="headerlink" href="#featureunion" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.pipeline.FeatureUnion.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/pipeline.py#L2086"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.pipeline.FeatureUnion.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/pipeline.py#L2086"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.pipeline.FeatureUnion.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1512,7 +1512,7 @@ <h1>FeatureUnion<a class="headerlink" href="#featureunion" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.pipeline.FeatureUnion.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/pipeline.py#L1707"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.pipeline.FeatureUnion.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/pipeline.py#L1707"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.pipeline.FeatureUnion.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <p>Returns the parameters given in the constructor as well as the
 estimators contained within the <code class="docutils literal notranslate"><span class="pre">transformer_list</span></code> of the
@@ -1536,7 +1536,7 @@ <h1>FeatureUnion<a class="headerlink" href="#featureunion" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.pipeline.FeatureUnion.set_output">
-<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/pipeline.py#L1677"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.pipeline.FeatureUnion.set_output" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/pipeline.py#L1677"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.pipeline.FeatureUnion.set_output" title="Link to this definition">#</a></dt>
 <dd><p>Set the output container when <code class="docutils literal notranslate"><span class="pre">&quot;transform&quot;</span></code> and <code class="docutils literal notranslate"><span class="pre">&quot;fit_transform&quot;</span></code> are called.</p>
 <p><code class="docutils literal notranslate"><span class="pre">set_output</span></code> will set the output of all estimators in <code class="docutils literal notranslate"><span class="pre">transformer_list</span></code>.</p>
 <dl class="field-list simple">
@@ -1563,7 +1563,7 @@ <h1>FeatureUnion<a class="headerlink" href="#featureunion" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.pipeline.FeatureUnion.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/pipeline.py#L1727"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.pipeline.FeatureUnion.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/pipeline.py#L1727"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.pipeline.FeatureUnion.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>Valid parameter keys can be listed with <code class="docutils literal notranslate"><span class="pre">get_params()</span></code>. Note that
 you can directly set the parameters of the estimators contained in
@@ -1588,7 +1588,7 @@ <h1>FeatureUnion<a class="headerlink" href="#featureunion" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.pipeline.FeatureUnion.transform">
-<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/pipeline.py#L1997"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.pipeline.FeatureUnion.transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/pipeline.py#L1997"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.pipeline.FeatureUnion.transform" title="Link to this definition">#</a></dt>
 <dd><p>Transform X separately by each transformer, concatenate results.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.pipeline.Pipeline.html b/dev/modules/generated/sklearn.pipeline.Pipeline.html
index 2449b0492f3bd..f525d93dc297f 100644
--- a/dev/modules/generated/sklearn.pipeline.Pipeline.html
+++ b/dev/modules/generated/sklearn.pipeline.Pipeline.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>Pipeline<a class="headerlink" href="#pipeline" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.pipeline.Pipeline">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.pipeline.</span></span><span class="sig-name descname"><span class="pre">Pipeline</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">steps</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform_input</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">memory</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/pipeline.py#L123"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.pipeline.Pipeline" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.pipeline.</span></span><span class="sig-name descname"><span class="pre">Pipeline</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">steps</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform_input</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">memory</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/pipeline.py#L123"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.pipeline.Pipeline" title="Link to this definition">#</a></dt>
 <dd><p>A sequence of data transformers with an optional final predictor.</p>
 <p><code class="docutils literal notranslate"><span class="pre">Pipeline</span></code> allows you to sequentially apply a list of transformers to
 preprocess the data and, if desired, conclude the sequence with a final
@@ -1409,7 +1409,7 @@ <h1>Pipeline<a class="headerlink" href="#pipeline" title="Link to this heading">
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.pipeline.Pipeline.decision_function">
-<span class="sig-name descname"><span class="pre">decision_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/pipeline.py#L913"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.pipeline.Pipeline.decision_function" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">decision_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/pipeline.py#L913"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.pipeline.Pipeline.decision_function" title="Link to this definition">#</a></dt>
 <dd><p>Transform the data, and apply <code class="docutils literal notranslate"><span class="pre">decision_function</span></code> with the final estimator.</p>
 <p>Call <code class="docutils literal notranslate"><span class="pre">transform</span></code> of each transformer in the pipeline. The transformed
 data are finally passed to the final estimator that calls
@@ -1443,7 +1443,7 @@ <h1>Pipeline<a class="headerlink" href="#pipeline" title="Link to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.pipeline.Pipeline.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/pipeline.py#L602"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.pipeline.Pipeline.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/pipeline.py#L602"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.pipeline.Pipeline.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit the model.</p>
 <p>Fit all the transformers one after the other and sequentially transform the
 data. Finally, fit the transformed data using the final estimator.</p>
@@ -1486,7 +1486,7 @@ <h1>Pipeline<a class="headerlink" href="#pipeline" title="Link to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.pipeline.Pipeline.fit_predict">
-<span class="sig-name descname"><span class="pre">fit_predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/pipeline.py#L797"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.pipeline.Pipeline.fit_predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/pipeline.py#L797"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.pipeline.Pipeline.fit_predict" title="Link to this definition">#</a></dt>
 <dd><p>Transform the data, and apply <code class="docutils literal notranslate"><span class="pre">fit_predict</span></code> with the final estimator.</p>
 <p>Call <code class="docutils literal notranslate"><span class="pre">fit_transform</span></code> of each transformer in the pipeline. The
 transformed data are finally passed to the final estimator that calls
@@ -1536,7 +1536,7 @@ <h1>Pipeline<a class="headerlink" href="#pipeline" title="Link to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.pipeline.Pipeline.fit_transform">
-<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/pipeline.py#L672"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.pipeline.Pipeline.fit_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/pipeline.py#L672"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.pipeline.Pipeline.fit_transform" title="Link to this definition">#</a></dt>
 <dd><p>Fit the model and transform with the final estimator.</p>
 <p>Fit all the transformers one after the other and sequentially transform
 the data. Only valid if the final estimator either implements
@@ -1579,7 +1579,7 @@ <h1>Pipeline<a class="headerlink" href="#pipeline" title="Link to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.pipeline.Pipeline.get_feature_names_out">
-<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/pipeline.py#L1246"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.pipeline.Pipeline.get_feature_names_out" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/pipeline.py#L1246"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.pipeline.Pipeline.get_feature_names_out" title="Link to this definition">#</a></dt>
 <dd><p>Get output feature names for transformation.</p>
 <p>Transform input features using the pipeline.</p>
 <dl class="field-list simple">
@@ -1600,7 +1600,7 @@ <h1>Pipeline<a class="headerlink" href="#pipeline" title="Link to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.pipeline.Pipeline.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/pipeline.py#L1333"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.pipeline.Pipeline.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/pipeline.py#L1333"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.pipeline.Pipeline.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1617,7 +1617,7 @@ <h1>Pipeline<a class="headerlink" href="#pipeline" title="Link to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.pipeline.Pipeline.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/pipeline.py#L281"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.pipeline.Pipeline.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/pipeline.py#L281"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.pipeline.Pipeline.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <p>Returns the parameters given in the constructor as well as the
 estimators contained within the <code class="docutils literal notranslate"><span class="pre">steps</span></code> of the <code class="docutils literal notranslate"><span class="pre">Pipeline</span></code>.</p>
@@ -1640,7 +1640,7 @@ <h1>Pipeline<a class="headerlink" href="#pipeline" title="Link to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.pipeline.Pipeline.inverse_transform">
-<span class="sig-name descname"><span class="pre">inverse_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/pipeline.py#L1097"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.pipeline.Pipeline.inverse_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">inverse_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/pipeline.py#L1097"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.pipeline.Pipeline.inverse_transform" title="Link to this definition">#</a></dt>
 <dd><p>Apply <code class="docutils literal notranslate"><span class="pre">inverse_transform</span></code> for each step in a reverse order.</p>
 <p>All estimators in the pipeline must support <code class="docutils literal notranslate"><span class="pre">inverse_transform</span></code>.</p>
 <dl class="field-list">
@@ -1664,7 +1664,7 @@ <h1>Pipeline<a class="headerlink" href="#pipeline" title="Link to this heading">
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><dl class="simple">
-<dt><strong>Xt</strong><span class="classifier">ndarray of shape (n_samples, n_features)</span></dt><dd><p>Inverse transformed data, that is, data in the original feature
+<dt><strong>X_original</strong><span class="classifier">ndarray of shape (n_samples, n_features)</span></dt><dd><p>Inverse transformed data, that is, data in the original feature
 space.</p>
 </dd>
 </dl>
@@ -1682,7 +1682,7 @@ <h1>Pipeline<a class="headerlink" href="#pipeline" title="Link to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.pipeline.Pipeline.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/pipeline.py#L737"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.pipeline.Pipeline.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/pipeline.py#L737"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.pipeline.Pipeline.predict" title="Link to this definition">#</a></dt>
 <dd><p>Transform the data, and apply <code class="docutils literal notranslate"><span class="pre">predict</span></code> with the final estimator.</p>
 <p>Call <code class="docutils literal notranslate"><span class="pre">transform</span></code> of each transformer in the pipeline. The transformed
 data are finally passed to the final estimator that calls <code class="docutils literal notranslate"><span class="pre">predict</span></code>
@@ -1729,7 +1729,7 @@ <h1>Pipeline<a class="headerlink" href="#pipeline" title="Link to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.pipeline.Pipeline.predict_log_proba">
-<span class="sig-name descname"><span class="pre">predict_log_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/pipeline.py#L988"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.pipeline.Pipeline.predict_log_proba" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict_log_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/pipeline.py#L988"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.pipeline.Pipeline.predict_log_proba" title="Link to this definition">#</a></dt>
 <dd><p>Transform the data, and apply <code class="docutils literal notranslate"><span class="pre">predict_log_proba</span></code> with the final estimator.</p>
 <p>Call <code class="docutils literal notranslate"><span class="pre">transform</span></code> of each transformer in the pipeline. The transformed
 data are finally passed to the final estimator that calls
@@ -1773,7 +1773,7 @@ <h1>Pipeline<a class="headerlink" href="#pipeline" title="Link to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.pipeline.Pipeline.predict_proba">
-<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/pipeline.py#L858"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.pipeline.Pipeline.predict_proba" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/pipeline.py#L858"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.pipeline.Pipeline.predict_proba" title="Link to this definition">#</a></dt>
 <dd><p>Transform the data, and apply <code class="docutils literal notranslate"><span class="pre">predict_proba</span></code> with the final estimator.</p>
 <p>Call <code class="docutils literal notranslate"><span class="pre">transform</span></code> of each transformer in the pipeline. The transformed
 data are finally passed to the final estimator that calls
@@ -1816,7 +1816,7 @@ <h1>Pipeline<a class="headerlink" href="#pipeline" title="Link to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.pipeline.Pipeline.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/pipeline.py#L1141"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.pipeline.Pipeline.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/pipeline.py#L1141"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.pipeline.Pipeline.score" title="Link to this definition">#</a></dt>
 <dd><p>Transform the data, and apply <code class="docutils literal notranslate"><span class="pre">score</span></code> with the final estimator.</p>
 <p>Call <code class="docutils literal notranslate"><span class="pre">transform</span></code> of each transformer in the pipeline. The transformed
 data are finally passed to the final estimator that calls
@@ -1855,7 +1855,7 @@ <h1>Pipeline<a class="headerlink" href="#pipeline" title="Link to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.pipeline.Pipeline.score_samples">
-<span class="sig-name descname"><span class="pre">score_samples</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/pipeline.py#L961"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.pipeline.Pipeline.score_samples" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score_samples</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/pipeline.py#L961"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.pipeline.Pipeline.score_samples" title="Link to this definition">#</a></dt>
 <dd><p>Transform the data, and apply <code class="docutils literal notranslate"><span class="pre">score_samples</span></code> with the final estimator.</p>
 <p>Call <code class="docutils literal notranslate"><span class="pre">transform</span></code> of each transformer in the pipeline. The transformed
 data are finally passed to the final estimator that calls
@@ -1880,7 +1880,7 @@ <h1>Pipeline<a class="headerlink" href="#pipeline" title="Link to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.pipeline.Pipeline.set_output">
-<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/pipeline.py#L254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.pipeline.Pipeline.set_output" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/pipeline.py#L254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.pipeline.Pipeline.set_output" title="Link to this definition">#</a></dt>
 <dd><p>Set the output container when <code class="docutils literal notranslate"><span class="pre">&quot;transform&quot;</span></code> and <code class="docutils literal notranslate"><span class="pre">&quot;fit_transform&quot;</span></code> are called.</p>
 <p>Calling <code class="docutils literal notranslate"><span class="pre">set_output</span></code> will set the output of all estimators in <code class="docutils literal notranslate"><span class="pre">steps</span></code>.</p>
 <dl class="field-list">
@@ -1910,7 +1910,7 @@ <h1>Pipeline<a class="headerlink" href="#pipeline" title="Link to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.pipeline.Pipeline.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/pipeline.py#L300"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.pipeline.Pipeline.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/pipeline.py#L300"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.pipeline.Pipeline.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>Valid parameter keys can be listed with <code class="docutils literal notranslate"><span class="pre">get_params()</span></code>. Note that
 you can directly set the parameters of the estimators contained in
@@ -1935,7 +1935,7 @@ <h1>Pipeline<a class="headerlink" href="#pipeline" title="Link to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.pipeline.Pipeline.set_score_request">
-<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><span class="pre">Pipeline</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.pipeline.Pipeline.set_score_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><span class="pre">Pipeline</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.pipeline.Pipeline.set_score_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1978,7 +1978,7 @@ <h1>Pipeline<a class="headerlink" href="#pipeline" title="Link to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.pipeline.Pipeline.transform">
-<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/pipeline.py#L1049"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.pipeline.Pipeline.transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/pipeline.py#L1049"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.pipeline.Pipeline.transform" title="Link to this definition">#</a></dt>
 <dd><p>Transform the data, and apply <code class="docutils literal notranslate"><span class="pre">transform</span></code> with the final estimator.</p>
 <p>Call <code class="docutils literal notranslate"><span class="pre">transform</span></code> of each transformer in the pipeline. The transformed
 data are finally passed to the final estimator that calls
diff --git a/dev/modules/generated/sklearn.pipeline.make_pipeline.html b/dev/modules/generated/sklearn.pipeline.make_pipeline.html
index ed58e6cd1a62a..1d4dd5df7f200 100644
--- a/dev/modules/generated/sklearn.pipeline.make_pipeline.html
+++ b/dev/modules/generated/sklearn.pipeline.make_pipeline.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>make_pipeline<a class="headerlink" href="#make-pipeline" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.pipeline.make_pipeline">
-<span class="sig-prename descclassname"><span class="pre">sklearn.pipeline.</span></span><span class="sig-name descname"><span class="pre">make_pipeline</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">steps</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">memory</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform_input</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/pipeline.py#L1433"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.pipeline.make_pipeline" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.pipeline.</span></span><span class="sig-name descname"><span class="pre">make_pipeline</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">steps</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">memory</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform_input</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/pipeline.py#L1433"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.pipeline.make_pipeline" title="Link to this definition">#</a></dt>
 <dd><p>Construct a <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a> from the given estimators.</p>
 <p>This is a shorthand for the <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a> constructor; it does not
 require, and does not permit, naming the estimators. Instead, their names
@@ -1392,6 +1392,9 @@ <h2>Gallery examples<a class="headerlink" href="#gallery-examples" title="Link t
 </div><div class="sphx-glr-thumbcontainer" tooltip="Stacking refers to a method to blend estimators. In this strategy, some estimators are individually fitted on some training data while a final estimator is trained using the stacked predictions of these base estimators."><img alt="" src="http://github.com/_images/sphx_glr_plot_stack_predictors_thumb.png" />
 <p><a class="reference internal" href="http://github.com/auto_examples/ensemble/plot_stack_predictors.html#sphx-glr-auto-examples-ensemble-plot-stack-predictors-py"><span class="std std-ref">Combine predictors using stacking</span></a></p>
   <div class="sphx-glr-thumbnail-title">Combine predictors using stacking</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="Plot the predicted class probabilities in a toy dataset predicted by three different classifiers and averaged by the VotingClassifier."><img alt="" src="http://github.com/_images/sphx_glr_plot_voting_decision_regions_thumb.png" />
+<p><a class="reference internal" href="http://github.com/auto_examples/ensemble/plot_voting_decision_regions.html#sphx-glr-auto-examples-ensemble-plot-voting-decision-regions-py"><span class="std std-ref">Visualizing the probabilistic predictions of a VotingClassifier</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Visualizing the probabilistic predictions of a VotingClassifier</div>
 </div><div class="sphx-glr-thumbcontainer" tooltip="This notebook is an example of using univariate feature selection to improve classification accuracy on a noisy dataset."><img alt="" src="http://github.com/_images/sphx_glr_plot_feature_selection_thumb.png" />
 <p><a class="reference internal" href="http://github.com/auto_examples/feature_selection/plot_feature_selection.html#sphx-glr-auto-examples-feature-selection-plot-feature-selection-py"><span class="std std-ref">Univariate Feature Selection</span></a></p>
   <div class="sphx-glr-thumbnail-title">Univariate Feature Selection</div>
diff --git a/dev/modules/generated/sklearn.pipeline.make_union.html b/dev/modules/generated/sklearn.pipeline.make_union.html
index 1cad74132fe99..c56c6a9c456b8 100644
--- a/dev/modules/generated/sklearn.pipeline.make_union.html
+++ b/dev/modules/generated/sklearn.pipeline.make_union.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>make_union<a class="headerlink" href="#make-union" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.pipeline.make_union">
-<span class="sig-prename descclassname"><span class="pre">sklearn.pipeline.</span></span><span class="sig-name descname"><span class="pre">make_union</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">transformers</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose_feature_names_out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/pipeline.py#L2131"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.pipeline.make_union" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.pipeline.</span></span><span class="sig-name descname"><span class="pre">make_union</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">transformers</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose_feature_names_out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/pipeline.py#L2131"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.pipeline.make_union" title="Link to this definition">#</a></dt>
 <dd><p>Construct a <a class="reference internal" href="http://github.com/sklearn.pipeline.FeatureUnion.html#sklearn.pipeline.FeatureUnion" title="sklearn.pipeline.FeatureUnion"><code class="xref py py-class docutils literal notranslate"><span class="pre">FeatureUnion</span></code></a> from the given transformers.</p>
 <p>This is a shorthand for the <a class="reference internal" href="http://github.com/sklearn.pipeline.FeatureUnion.html#sklearn.pipeline.FeatureUnion" title="sklearn.pipeline.FeatureUnion"><code class="xref py py-class docutils literal notranslate"><span class="pre">FeatureUnion</span></code></a> constructor; it does not
 require, and does not permit, naming the transformers. Instead, they will
@@ -1317,7 +1317,7 @@ <h1>make_union<a class="headerlink" href="#make-union" title="Link to this headi
 <dt><strong>*transformers</strong><span class="classifier">list of estimators</span></dt><dd><p>One or more estimators.</p>
 </dd>
 <dt><strong>n_jobs</strong><span class="classifier">int, default=None</span></dt><dd><p>Number of jobs to run in parallel.
-<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.5.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
+<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.6.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
 <code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all processors. See <a class="reference internal" href="http://github.com/glossary.html#term-n_jobs"><span class="xref std std-term">Glossary</span></a>
 for more details.</p>
 <div class="versionchanged">
diff --git a/dev/modules/generated/sklearn.preprocessing.Binarizer.html b/dev/modules/generated/sklearn.preprocessing.Binarizer.html
index 4860f423163ad..d6460833c0823 100644
--- a/dev/modules/generated/sklearn.preprocessing.Binarizer.html
+++ b/dev/modules/generated/sklearn.preprocessing.Binarizer.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>Binarizer<a class="headerlink" href="#binarizer" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.preprocessing.Binarizer">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.preprocessing.</span></span><span class="sig-name descname"><span class="pre">Binarizer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">threshold</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/preprocessing/_data.py#L2219"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.Binarizer" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.preprocessing.</span></span><span class="sig-name descname"><span class="pre">Binarizer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">threshold</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/preprocessing/_data.py#L2229"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.Binarizer" title="Link to this definition">#</a></dt>
 <dd><p>Binarize data (set feature values to 0 or 1) according to a threshold.</p>
 <p>Values greater than the threshold map to 1, while values less than
 or equal to the threshold map to 0. With the default threshold of 0,
@@ -1378,7 +1378,7 @@ <h1>Binarizer<a class="headerlink" href="#binarizer" title="Link to this heading
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.Binarizer.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/preprocessing/_data.py#L2299"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.Binarizer.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/preprocessing/_data.py#L2309"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.Binarizer.fit" title="Link to this definition">#</a></dt>
 <dd><p>Only validates estimator’s parameters.</p>
 <p>This method allows to: (i) validate the estimator’s parameters and
 (ii) be consistent with the scikit-learn transformer API.</p>
@@ -1402,7 +1402,7 @@ <h1>Binarizer<a class="headerlink" href="#binarizer" title="Link to this heading
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.Binarizer.fit_transform">
-<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L863"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.Binarizer.fit_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L804"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.Binarizer.fit_transform" title="Link to this definition">#</a></dt>
 <dd><p>Fit to data, then transform it.</p>
 <p>Fits transformer to <code class="docutils literal notranslate"><span class="pre">X</span></code> and <code class="docutils literal notranslate"><span class="pre">y</span></code> with optional parameters <code class="docutils literal notranslate"><span class="pre">fit_params</span></code>
 and returns a transformed version of <code class="docutils literal notranslate"><span class="pre">X</span></code>.</p>
@@ -1428,7 +1428,7 @@ <h1>Binarizer<a class="headerlink" href="#binarizer" title="Link to this heading
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.Binarizer.get_feature_names_out">
-<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L943"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.Binarizer.get_feature_names_out" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L884"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.Binarizer.get_feature_names_out" title="Link to this definition">#</a></dt>
 <dd><p>Get output feature names for transformation.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1456,7 +1456,7 @@ <h1>Binarizer<a class="headerlink" href="#binarizer" title="Link to this heading
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.Binarizer.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.Binarizer.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.Binarizer.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1473,7 +1473,7 @@ <h1>Binarizer<a class="headerlink" href="#binarizer" title="Link to this heading
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.Binarizer.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.Binarizer.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.Binarizer.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1494,7 +1494,7 @@ <h1>Binarizer<a class="headerlink" href="#binarizer" title="Link to this heading
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.Binarizer.set_output">
-<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.Binarizer.set_output" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.Binarizer.set_output" title="Link to this definition">#</a></dt>
 <dd><p>Set output container.</p>
 <p>See <a class="reference internal" href="http://github.com/auto_examples/miscellaneous/plot_set_output.html#sphx-glr-auto-examples-miscellaneous-plot-set-output-py"><span class="std std-ref">Introducing the set_output API</span></a>
 for an example on how to use the API.</p>
@@ -1525,7 +1525,7 @@ <h1>Binarizer<a class="headerlink" href="#binarizer" title="Link to this heading
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.Binarizer.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.Binarizer.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.Binarizer.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1549,7 +1549,7 @@ <h1>Binarizer<a class="headerlink" href="#binarizer" title="Link to this heading
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.Binarizer.set_transform_request">
-<span class="sig-name descname"><span class="pre">set_transform_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.preprocessing.Binarizer" title="sklearn.preprocessing._data.Binarizer"><span class="pre">Binarizer</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.Binarizer.set_transform_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_transform_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.preprocessing.Binarizer" title="sklearn.preprocessing._data.Binarizer"><span class="pre">Binarizer</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.Binarizer.set_transform_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">transform</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1592,7 +1592,7 @@ <h1>Binarizer<a class="headerlink" href="#binarizer" title="Link to this heading
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.Binarizer.transform">
-<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/preprocessing/_data.py#L2322"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.Binarizer.transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/preprocessing/_data.py#L2332"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.Binarizer.transform" title="Link to this definition">#</a></dt>
 <dd><p>Binarize each element of X.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.preprocessing.FunctionTransformer.html b/dev/modules/generated/sklearn.preprocessing.FunctionTransformer.html
index f058e0e562d9f..2c7fb3527be36 100644
--- a/dev/modules/generated/sklearn.preprocessing.FunctionTransformer.html
+++ b/dev/modules/generated/sklearn.preprocessing.FunctionTransformer.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>FunctionTransformer<a class="headerlink" href="#functiontransformer" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.preprocessing.FunctionTransformer">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.preprocessing.</span></span><span class="sig-name descname"><span class="pre">FunctionTransformer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inverse_func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">accept_sparse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_inverse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">feature_names_out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kw_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inv_kw_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/preprocessing/_function_transformer.py#L35"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.FunctionTransformer" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.preprocessing.</span></span><span class="sig-name descname"><span class="pre">FunctionTransformer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inverse_func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">accept_sparse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_inverse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">feature_names_out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kw_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inv_kw_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/preprocessing/_function_transformer.py#L35"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.FunctionTransformer" title="Link to this definition">#</a></dt>
 <dd><p>Constructs a transformer from an arbitrary callable.</p>
 <p>A FunctionTransformer forwards its X (and optionally y) arguments to a
 user-defined function or function object and returns the result of this
@@ -1417,13 +1417,13 @@ <h1>FunctionTransformer<a class="headerlink" href="#functiontransformer" title="
 <span class="gp">&gt;&gt;&gt; </span><span class="n">transformer</span> <span class="o">=</span> <span class="n">FunctionTransformer</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">log1p</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">]])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">transformer</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
-<span class="go">array([[0.       , 0.6931...],</span>
-<span class="go">       [1.0986..., 1.3862...]])</span>
+<span class="go">array([[0.       , 0.6931],</span>
+<span class="go">       [1.0986, 1.3862]])</span>
 </pre></div>
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.FunctionTransformer.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/preprocessing/_function_transformer.py#L220"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.FunctionTransformer.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/preprocessing/_function_transformer.py#L220"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.FunctionTransformer.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit transformer by checking X.</p>
 <p>If <code class="docutils literal notranslate"><span class="pre">validate</span></code> is <code class="docutils literal notranslate"><span class="pre">True</span></code>, <code class="docutils literal notranslate"><span class="pre">X</span></code> will be checked.</p>
 <dl class="field-list">
@@ -1446,7 +1446,7 @@ <h1>FunctionTransformer<a class="headerlink" href="#functiontransformer" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.FunctionTransformer.fit_transform">
-<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L863"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.FunctionTransformer.fit_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L804"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.FunctionTransformer.fit_transform" title="Link to this definition">#</a></dt>
 <dd><p>Fit to data, then transform it.</p>
 <p>Fits transformer to <code class="docutils literal notranslate"><span class="pre">X</span></code> and <code class="docutils literal notranslate"><span class="pre">y</span></code> with optional parameters <code class="docutils literal notranslate"><span class="pre">fit_params</span></code>
 and returns a transformed version of <code class="docutils literal notranslate"><span class="pre">X</span></code>.</p>
@@ -1472,7 +1472,7 @@ <h1>FunctionTransformer<a class="headerlink" href="#functiontransformer" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.FunctionTransformer.get_feature_names_out">
-<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/preprocessing/_function_transformer.py#L335"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.FunctionTransformer.get_feature_names_out" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/preprocessing/_function_transformer.py#L335"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.FunctionTransformer.get_feature_names_out" title="Link to this definition">#</a></dt>
 <dd><p>Get output feature names for transformation.</p>
 <p>This method is only defined if <code class="docutils literal notranslate"><span class="pre">feature_names_out</span></code> is not None.</p>
 <dl class="field-list simple">
@@ -1511,7 +1511,7 @@ <h1>FunctionTransformer<a class="headerlink" href="#functiontransformer" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.FunctionTransformer.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.FunctionTransformer.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.FunctionTransformer.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1528,7 +1528,7 @@ <h1>FunctionTransformer<a class="headerlink" href="#functiontransformer" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.FunctionTransformer.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.FunctionTransformer.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.FunctionTransformer.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1549,7 +1549,7 @@ <h1>FunctionTransformer<a class="headerlink" href="#functiontransformer" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.FunctionTransformer.inverse_transform">
-<span class="sig-name descname"><span class="pre">inverse_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/preprocessing/_function_transformer.py#L317"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.FunctionTransformer.inverse_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">inverse_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/preprocessing/_function_transformer.py#L317"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.FunctionTransformer.inverse_transform" title="Link to this definition">#</a></dt>
 <dd><p>Transform X using the inverse function.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1560,7 +1560,7 @@ <h1>FunctionTransformer<a class="headerlink" href="#functiontransformer" title="
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><dl class="simple">
-<dt><strong>X_out</strong><span class="classifier">array-like, shape (n_samples, n_features)</span></dt><dd><p>Transformed input.</p>
+<dt><strong>X_original</strong><span class="classifier">array-like, shape (n_samples, n_features)</span></dt><dd><p>Transformed input.</p>
 </dd>
 </dl>
 </dd>
@@ -1569,7 +1569,7 @@ <h1>FunctionTransformer<a class="headerlink" href="#functiontransformer" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.FunctionTransformer.set_output">
-<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/preprocessing/_function_transformer.py#L400"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.FunctionTransformer.set_output" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/preprocessing/_function_transformer.py#L400"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.FunctionTransformer.set_output" title="Link to this definition">#</a></dt>
 <dd><p>Set output container.</p>
 <p>See <a class="reference internal" href="http://github.com/auto_examples/miscellaneous/plot_set_output.html#sphx-glr-auto-examples-miscellaneous-plot-set-output-py"><span class="std std-ref">Introducing the set_output API</span></a>
 for an example on how to use the API.</p>
@@ -1600,7 +1600,7 @@ <h1>FunctionTransformer<a class="headerlink" href="#functiontransformer" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.FunctionTransformer.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.FunctionTransformer.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.FunctionTransformer.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1624,7 +1624,7 @@ <h1>FunctionTransformer<a class="headerlink" href="#functiontransformer" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.FunctionTransformer.transform">
-<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/preprocessing/_function_transformer.py#L245"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.FunctionTransformer.transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/preprocessing/_function_transformer.py#L245"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.FunctionTransformer.transform" title="Link to this definition">#</a></dt>
 <dd><p>Transform X using the forward function.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.preprocessing.KBinsDiscretizer.html b/dev/modules/generated/sklearn.preprocessing.KBinsDiscretizer.html
index cb72de00e9576..aa3b860e63aa6 100644
--- a/dev/modules/generated/sklearn.preprocessing.KBinsDiscretizer.html
+++ b/dev/modules/generated/sklearn.preprocessing.KBinsDiscretizer.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>KBinsDiscretizer<a class="headerlink" href="#kbinsdiscretizer" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.preprocessing.KBinsDiscretizer">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.preprocessing.</span></span><span class="sig-name descname"><span class="pre">KBinsDiscretizer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_bins</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">encode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'onehot'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">strategy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'quantile'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quantile_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'warn'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">subsample</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">200000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/preprocessing/_discretization.py#L24"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.KBinsDiscretizer" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.preprocessing.</span></span><span class="sig-name descname"><span class="pre">KBinsDiscretizer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_bins</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">encode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'onehot'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">strategy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'quantile'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quantile_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'warn'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">subsample</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">200000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/preprocessing/_discretization.py#L24"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.KBinsDiscretizer" title="Link to this definition">#</a></dt>
 <dd><p>Bin continuous data into intervals.</p>
 <p>Read more in the <a class="reference internal" href="http://github.com/preprocessing.html#preprocessing-discretization"><span class="std std-ref">User Guide</span></a>.</p>
 <div class="versionadded">
@@ -1464,7 +1464,7 @@ <h1>KBinsDiscretizer<a class="headerlink" href="#kbinsdiscretizer" title="Link t
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.KBinsDiscretizer.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/preprocessing/_discretization.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.KBinsDiscretizer.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/preprocessing/_discretization.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.KBinsDiscretizer.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit the estimator.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1495,7 +1495,7 @@ <h1>KBinsDiscretizer<a class="headerlink" href="#kbinsdiscretizer" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.KBinsDiscretizer.fit_transform">
-<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L863"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.KBinsDiscretizer.fit_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L804"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.KBinsDiscretizer.fit_transform" title="Link to this definition">#</a></dt>
 <dd><p>Fit to data, then transform it.</p>
 <p>Fits transformer to <code class="docutils literal notranslate"><span class="pre">X</span></code> and <code class="docutils literal notranslate"><span class="pre">y</span></code> with optional parameters <code class="docutils literal notranslate"><span class="pre">fit_params</span></code>
 and returns a transformed version of <code class="docutils literal notranslate"><span class="pre">X</span></code>.</p>
@@ -1521,7 +1521,7 @@ <h1>KBinsDiscretizer<a class="headerlink" href="#kbinsdiscretizer" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.KBinsDiscretizer.get_feature_names_out">
-<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/preprocessing/_discretization.py#L522"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.KBinsDiscretizer.get_feature_names_out" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/preprocessing/_discretization.py#L522"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.KBinsDiscretizer.get_feature_names_out" title="Link to this definition">#</a></dt>
 <dd><p>Get output feature names.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1549,7 +1549,7 @@ <h1>KBinsDiscretizer<a class="headerlink" href="#kbinsdiscretizer" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.KBinsDiscretizer.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.KBinsDiscretizer.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.KBinsDiscretizer.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1566,7 +1566,7 @@ <h1>KBinsDiscretizer<a class="headerlink" href="#kbinsdiscretizer" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.KBinsDiscretizer.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.KBinsDiscretizer.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.KBinsDiscretizer.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1587,7 +1587,7 @@ <h1>KBinsDiscretizer<a class="headerlink" href="#kbinsdiscretizer" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.KBinsDiscretizer.inverse_transform">
-<span class="sig-name descname"><span class="pre">inverse_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/preprocessing/_discretization.py#L483"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.KBinsDiscretizer.inverse_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">inverse_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/preprocessing/_discretization.py#L483"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.KBinsDiscretizer.inverse_transform" title="Link to this definition">#</a></dt>
 <dd><p>Transform discretized data back to original feature space.</p>
 <p>Note that this function does not regenerate the original data
 due to discretization rounding.</p>
@@ -1600,7 +1600,7 @@ <h1>KBinsDiscretizer<a class="headerlink" href="#kbinsdiscretizer" title="Link t
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><dl class="simple">
-<dt><strong>Xinv</strong><span class="classifier">ndarray, dtype={np.float32, np.float64}</span></dt><dd><p>Data in the original feature space.</p>
+<dt><strong>X_original</strong><span class="classifier">ndarray, dtype={np.float32, np.float64}</span></dt><dd><p>Data in the original feature space.</p>
 </dd>
 </dl>
 </dd>
@@ -1609,7 +1609,7 @@ <h1>KBinsDiscretizer<a class="headerlink" href="#kbinsdiscretizer" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.KBinsDiscretizer.set_fit_request">
-<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.preprocessing.KBinsDiscretizer" title="sklearn.preprocessing._discretization.KBinsDiscretizer"><span class="pre">KBinsDiscretizer</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.KBinsDiscretizer.set_fit_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.preprocessing.KBinsDiscretizer" title="sklearn.preprocessing._discretization.KBinsDiscretizer"><span class="pre">KBinsDiscretizer</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.KBinsDiscretizer.set_fit_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1652,7 +1652,7 @@ <h1>KBinsDiscretizer<a class="headerlink" href="#kbinsdiscretizer" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.KBinsDiscretizer.set_output">
-<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.KBinsDiscretizer.set_output" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.KBinsDiscretizer.set_output" title="Link to this definition">#</a></dt>
 <dd><p>Set output container.</p>
 <p>See <a class="reference internal" href="http://github.com/auto_examples/miscellaneous/plot_set_output.html#sphx-glr-auto-examples-miscellaneous-plot-set-output-py"><span class="std std-ref">Introducing the set_output API</span></a>
 for an example on how to use the API.</p>
@@ -1683,7 +1683,7 @@ <h1>KBinsDiscretizer<a class="headerlink" href="#kbinsdiscretizer" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.KBinsDiscretizer.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.KBinsDiscretizer.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.KBinsDiscretizer.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1707,7 +1707,7 @@ <h1>KBinsDiscretizer<a class="headerlink" href="#kbinsdiscretizer" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.KBinsDiscretizer.transform">
-<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/preprocessing/_discretization.py#L444"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.KBinsDiscretizer.transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/preprocessing/_discretization.py#L444"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.KBinsDiscretizer.transform" title="Link to this definition">#</a></dt>
 <dd><p>Discretize the data.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.preprocessing.KernelCenterer.html b/dev/modules/generated/sklearn.preprocessing.KernelCenterer.html
index 436caae4d0cd8..190f68ee345f2 100644
--- a/dev/modules/generated/sklearn.preprocessing.KernelCenterer.html
+++ b/dev/modules/generated/sklearn.preprocessing.KernelCenterer.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1306,7 +1306,7 @@
 <h1>KernelCenterer<a class="headerlink" href="#kernelcenterer" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.preprocessing.KernelCenterer">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.preprocessing.</span></span><span class="sig-name descname"><span class="pre">KernelCenterer</span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/preprocessing/_data.py#L2360"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.KernelCenterer" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.preprocessing.</span></span><span class="sig-name descname"><span class="pre">KernelCenterer</span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/preprocessing/_data.py#L2371"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.KernelCenterer" title="Link to this definition">#</a></dt>
 <dd><p>Center an arbitrary kernel matrix <span class="math notranslate nohighlight">\(K\)</span>.</p>
 <p>Let define a kernel <span class="math notranslate nohighlight">\(K\)</span> such that:</p>
 <div class="math notranslate nohighlight">
@@ -1382,7 +1382,7 @@ <h1>KernelCenterer<a class="headerlink" href="#kernelcenterer" title="Link to th
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.KernelCenterer.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">K</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/preprocessing/_data.py#L2442"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.KernelCenterer.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">K</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/preprocessing/_data.py#L2453"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.KernelCenterer.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit KernelCenterer.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1404,7 +1404,7 @@ <h1>KernelCenterer<a class="headerlink" href="#kernelcenterer" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.KernelCenterer.fit_transform">
-<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L863"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.KernelCenterer.fit_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L804"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.KernelCenterer.fit_transform" title="Link to this definition">#</a></dt>
 <dd><p>Fit to data, then transform it.</p>
 <p>Fits transformer to <code class="docutils literal notranslate"><span class="pre">X</span></code> and <code class="docutils literal notranslate"><span class="pre">y</span></code> with optional parameters <code class="docutils literal notranslate"><span class="pre">fit_params</span></code>
 and returns a transformed version of <code class="docutils literal notranslate"><span class="pre">X</span></code>.</p>
@@ -1430,7 +1430,7 @@ <h1>KernelCenterer<a class="headerlink" href="#kernelcenterer" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.KernelCenterer.get_feature_names_out">
-<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L995"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.KernelCenterer.get_feature_names_out" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L936"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.KernelCenterer.get_feature_names_out" title="Link to this definition">#</a></dt>
 <dd><p>Get output feature names for transformation.</p>
 <p>The feature names out will prefixed by the lowercased class name. For
 example, if the transformer outputs 3 features, then the feature names
@@ -1453,7 +1453,7 @@ <h1>KernelCenterer<a class="headerlink" href="#kernelcenterer" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.KernelCenterer.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.KernelCenterer.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.KernelCenterer.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1470,7 +1470,7 @@ <h1>KernelCenterer<a class="headerlink" href="#kernelcenterer" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.KernelCenterer.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.KernelCenterer.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.KernelCenterer.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1491,7 +1491,7 @@ <h1>KernelCenterer<a class="headerlink" href="#kernelcenterer" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.KernelCenterer.set_output">
-<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.KernelCenterer.set_output" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.KernelCenterer.set_output" title="Link to this definition">#</a></dt>
 <dd><p>Set output container.</p>
 <p>See <a class="reference internal" href="http://github.com/auto_examples/miscellaneous/plot_set_output.html#sphx-glr-auto-examples-miscellaneous-plot-set-output-py"><span class="std std-ref">Introducing the set_output API</span></a>
 for an example on how to use the API.</p>
@@ -1522,7 +1522,7 @@ <h1>KernelCenterer<a class="headerlink" href="#kernelcenterer" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.KernelCenterer.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.KernelCenterer.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.KernelCenterer.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1546,7 +1546,7 @@ <h1>KernelCenterer<a class="headerlink" href="#kernelcenterer" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.KernelCenterer.set_transform_request">
-<span class="sig-name descname"><span class="pre">set_transform_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.preprocessing.KernelCenterer" title="sklearn.preprocessing._data.KernelCenterer"><span class="pre">KernelCenterer</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.KernelCenterer.set_transform_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_transform_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.preprocessing.KernelCenterer" title="sklearn.preprocessing._data.KernelCenterer"><span class="pre">KernelCenterer</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.KernelCenterer.set_transform_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">transform</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1589,7 +1589,7 @@ <h1>KernelCenterer<a class="headerlink" href="#kernelcenterer" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.KernelCenterer.transform">
-<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">K</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/preprocessing/_data.py#L2473"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.KernelCenterer.transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">K</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/preprocessing/_data.py#L2484"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.KernelCenterer.transform" title="Link to this definition">#</a></dt>
 <dd><p>Center kernel matrix.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.preprocessing.LabelBinarizer.html b/dev/modules/generated/sklearn.preprocessing.LabelBinarizer.html
index 78fe3a77d9a9d..16468c6cf5d3b 100644
--- a/dev/modules/generated/sklearn.preprocessing.LabelBinarizer.html
+++ b/dev/modules/generated/sklearn.preprocessing.LabelBinarizer.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>LabelBinarizer<a class="headerlink" href="#labelbinarizer" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.preprocessing.LabelBinarizer">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.preprocessing.</span></span><span class="sig-name descname"><span class="pre">LabelBinarizer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">neg_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pos_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sparse_output</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/preprocessing/_label.py#L174"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.LabelBinarizer" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.preprocessing.</span></span><span class="sig-name descname"><span class="pre">LabelBinarizer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">neg_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pos_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sparse_output</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/preprocessing/_label.py#L174"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.LabelBinarizer" title="Link to this definition">#</a></dt>
 <dd><p>Binarize labels in a one-vs-all fashion.</p>
 <p>Several regression and binary classification algorithms are
 available in scikit-learn. A simple way to extend these algorithms
@@ -1396,7 +1396,7 @@ <h1>LabelBinarizer<a class="headerlink" href="#labelbinarizer" title="Link to th
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.LabelBinarizer.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/preprocessing/_label.py#L274"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.LabelBinarizer.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/preprocessing/_label.py#L274"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.LabelBinarizer.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit label binarizer.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1417,7 +1417,7 @@ <h1>LabelBinarizer<a class="headerlink" href="#labelbinarizer" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.LabelBinarizer.fit_transform">
-<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/preprocessing/_label.py#L315"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.LabelBinarizer.fit_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/preprocessing/_label.py#L315"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.LabelBinarizer.fit_transform" title="Link to this definition">#</a></dt>
 <dd><p>Fit label binarizer/transform multi-class labels to binary labels.</p>
 <p>The output of transform is sometimes referred to as
 the 1-of-K coding scheme.</p>
@@ -1442,7 +1442,7 @@ <h1>LabelBinarizer<a class="headerlink" href="#labelbinarizer" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.LabelBinarizer.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.LabelBinarizer.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.LabelBinarizer.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1459,7 +1459,7 @@ <h1>LabelBinarizer<a class="headerlink" href="#labelbinarizer" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.LabelBinarizer.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.LabelBinarizer.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.LabelBinarizer.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1480,7 +1480,7 @@ <h1>LabelBinarizer<a class="headerlink" href="#labelbinarizer" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.LabelBinarizer.inverse_transform">
-<span class="sig-name descname"><span class="pre">inverse_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">Y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">threshold</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/preprocessing/_label.py#L371"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.LabelBinarizer.inverse_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">inverse_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">Y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">threshold</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/preprocessing/_label.py#L371"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.LabelBinarizer.inverse_transform" title="Link to this definition">#</a></dt>
 <dd><p>Transform binary labels back to multi-class labels.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1499,7 +1499,7 @@ <h1>LabelBinarizer<a class="headerlink" href="#labelbinarizer" title="Link to th
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><dl class="simple">
-<dt><strong>y</strong><span class="classifier">{ndarray, sparse matrix} of shape (n_samples,)</span></dt><dd><p>Target values. Sparse matrix will be of CSR format.</p>
+<dt><strong>y_original</strong><span class="classifier">{ndarray, sparse matrix} of shape (n_samples,)</span></dt><dd><p>Target values. Sparse matrix will be of CSR format.</p>
 </dd>
 </dl>
 </dd>
@@ -1514,7 +1514,7 @@ <h1>LabelBinarizer<a class="headerlink" href="#labelbinarizer" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.LabelBinarizer.set_inverse_transform_request">
-<span class="sig-name descname"><span class="pre">set_inverse_transform_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">threshold</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.preprocessing.LabelBinarizer" title="sklearn.preprocessing._label.LabelBinarizer"><span class="pre">LabelBinarizer</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.LabelBinarizer.set_inverse_transform_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_inverse_transform_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">threshold</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.preprocessing.LabelBinarizer" title="sklearn.preprocessing._label.LabelBinarizer"><span class="pre">LabelBinarizer</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.LabelBinarizer.set_inverse_transform_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">inverse_transform</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1557,7 +1557,7 @@ <h1>LabelBinarizer<a class="headerlink" href="#labelbinarizer" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.LabelBinarizer.set_output">
-<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.LabelBinarizer.set_output" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.LabelBinarizer.set_output" title="Link to this definition">#</a></dt>
 <dd><p>Set output container.</p>
 <p>See <a class="reference internal" href="http://github.com/auto_examples/miscellaneous/plot_set_output.html#sphx-glr-auto-examples-miscellaneous-plot-set-output-py"><span class="std std-ref">Introducing the set_output API</span></a>
 for an example on how to use the API.</p>
@@ -1588,7 +1588,7 @@ <h1>LabelBinarizer<a class="headerlink" href="#labelbinarizer" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.LabelBinarizer.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.LabelBinarizer.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.LabelBinarizer.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1612,7 +1612,7 @@ <h1>LabelBinarizer<a class="headerlink" href="#labelbinarizer" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.LabelBinarizer.transform">
-<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/preprocessing/_label.py#L337"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.LabelBinarizer.transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/preprocessing/_label.py#L337"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.LabelBinarizer.transform" title="Link to this definition">#</a></dt>
 <dd><p>Transform multi-class labels to binary labels.</p>
 <p>The output of transform is sometimes referred to by some authors as
 the 1-of-K coding scheme.</p>
diff --git a/dev/modules/generated/sklearn.preprocessing.LabelEncoder.html b/dev/modules/generated/sklearn.preprocessing.LabelEncoder.html
index 9e2011d2aa1a5..2ff72a89e4d32 100644
--- a/dev/modules/generated/sklearn.preprocessing.LabelEncoder.html
+++ b/dev/modules/generated/sklearn.preprocessing.LabelEncoder.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>LabelEncoder<a class="headerlink" href="#labelencoder" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.preprocessing.LabelEncoder">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.preprocessing.</span></span><span class="sig-name descname"><span class="pre">LabelEncoder</span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/preprocessing/_label.py#L30"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.LabelEncoder" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.preprocessing.</span></span><span class="sig-name descname"><span class="pre">LabelEncoder</span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/preprocessing/_label.py#L30"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.LabelEncoder" title="Link to this definition">#</a></dt>
 <dd><p>Encode target labels with value between 0 and n_classes-1.</p>
 <p>This transformer should be used to encode target values, <em>i.e.</em> <code class="docutils literal notranslate"><span class="pre">y</span></code>, and
 not the input <code class="docutils literal notranslate"><span class="pre">X</span></code>.</p>
@@ -1359,7 +1359,7 @@ <h1>LabelEncoder<a class="headerlink" href="#labelencoder" title="Link to this h
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.LabelEncoder.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/preprocessing/_label.py#L80"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.LabelEncoder.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/preprocessing/_label.py#L80"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.LabelEncoder.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit label encoder.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1379,7 +1379,7 @@ <h1>LabelEncoder<a class="headerlink" href="#labelencoder" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.LabelEncoder.fit_transform">
-<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/preprocessing/_label.py#L97"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.LabelEncoder.fit_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/preprocessing/_label.py#L97"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.LabelEncoder.fit_transform" title="Link to this definition">#</a></dt>
 <dd><p>Fit label encoder and return encoded labels.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1399,7 +1399,7 @@ <h1>LabelEncoder<a class="headerlink" href="#labelencoder" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.LabelEncoder.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.LabelEncoder.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.LabelEncoder.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1416,7 +1416,7 @@ <h1>LabelEncoder<a class="headerlink" href="#labelencoder" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.LabelEncoder.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.LabelEncoder.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.LabelEncoder.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1437,7 +1437,7 @@ <h1>LabelEncoder<a class="headerlink" href="#labelencoder" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.LabelEncoder.inverse_transform">
-<span class="sig-name descname"><span class="pre">inverse_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/preprocessing/_label.py#L136"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.LabelEncoder.inverse_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">inverse_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/preprocessing/_label.py#L136"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.LabelEncoder.inverse_transform" title="Link to this definition">#</a></dt>
 <dd><p>Transform labels back to original encoding.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1448,7 +1448,7 @@ <h1>LabelEncoder<a class="headerlink" href="#labelencoder" title="Link to this h
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><dl class="simple">
-<dt><strong>y</strong><span class="classifier">ndarray of shape (n_samples,)</span></dt><dd><p>Original encoding.</p>
+<dt><strong>y_original</strong><span class="classifier">ndarray of shape (n_samples,)</span></dt><dd><p>Original encoding.</p>
 </dd>
 </dl>
 </dd>
@@ -1457,7 +1457,7 @@ <h1>LabelEncoder<a class="headerlink" href="#labelencoder" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.LabelEncoder.set_output">
-<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.LabelEncoder.set_output" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.LabelEncoder.set_output" title="Link to this definition">#</a></dt>
 <dd><p>Set output container.</p>
 <p>See <a class="reference internal" href="http://github.com/auto_examples/miscellaneous/plot_set_output.html#sphx-glr-auto-examples-miscellaneous-plot-set-output-py"><span class="std std-ref">Introducing the set_output API</span></a>
 for an example on how to use the API.</p>
@@ -1488,7 +1488,7 @@ <h1>LabelEncoder<a class="headerlink" href="#labelencoder" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.LabelEncoder.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.LabelEncoder.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.LabelEncoder.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1512,7 +1512,7 @@ <h1>LabelEncoder<a class="headerlink" href="#labelencoder" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.LabelEncoder.transform">
-<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/preprocessing/_label.py#L114"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.LabelEncoder.transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/preprocessing/_label.py#L114"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.LabelEncoder.transform" title="Link to this definition">#</a></dt>
 <dd><p>Transform labels to normalized encoding.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.preprocessing.MaxAbsScaler.html b/dev/modules/generated/sklearn.preprocessing.MaxAbsScaler.html
index 210e83918c486..2f7e60f38c131 100644
--- a/dev/modules/generated/sklearn.preprocessing.MaxAbsScaler.html
+++ b/dev/modules/generated/sklearn.preprocessing.MaxAbsScaler.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>MaxAbsScaler<a class="headerlink" href="#maxabsscaler" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.preprocessing.MaxAbsScaler">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.preprocessing.</span></span><span class="sig-name descname"><span class="pre">MaxAbsScaler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/preprocessing/_data.py#L1144"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.MaxAbsScaler" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.preprocessing.</span></span><span class="sig-name descname"><span class="pre">MaxAbsScaler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/preprocessing/_data.py#L1152"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.MaxAbsScaler" title="Link to this definition">#</a></dt>
 <dd><p>Scale each feature by its maximum absolute value.</p>
 <p>This estimator scales and translates each feature individually such
 that the maximal absolute value of each feature in the
@@ -1380,7 +1380,7 @@ <h1>MaxAbsScaler<a class="headerlink" href="#maxabsscaler" title="Link to this h
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.MaxAbsScaler.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/preprocessing/_data.py#L1233"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.MaxAbsScaler.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/preprocessing/_data.py#L1241"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.MaxAbsScaler.fit" title="Link to this definition">#</a></dt>
 <dd><p>Compute the maximum absolute value to be used for later scaling.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1403,7 +1403,7 @@ <h1>MaxAbsScaler<a class="headerlink" href="#maxabsscaler" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.MaxAbsScaler.fit_transform">
-<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L863"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.MaxAbsScaler.fit_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L804"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.MaxAbsScaler.fit_transform" title="Link to this definition">#</a></dt>
 <dd><p>Fit to data, then transform it.</p>
 <p>Fits transformer to <code class="docutils literal notranslate"><span class="pre">X</span></code> and <code class="docutils literal notranslate"><span class="pre">y</span></code> with optional parameters <code class="docutils literal notranslate"><span class="pre">fit_params</span></code>
 and returns a transformed version of <code class="docutils literal notranslate"><span class="pre">X</span></code>.</p>
@@ -1429,7 +1429,7 @@ <h1>MaxAbsScaler<a class="headerlink" href="#maxabsscaler" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.MaxAbsScaler.get_feature_names_out">
-<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L943"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.MaxAbsScaler.get_feature_names_out" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L884"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.MaxAbsScaler.get_feature_names_out" title="Link to this definition">#</a></dt>
 <dd><p>Get output feature names for transformation.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1457,7 +1457,7 @@ <h1>MaxAbsScaler<a class="headerlink" href="#maxabsscaler" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.MaxAbsScaler.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.MaxAbsScaler.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.MaxAbsScaler.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1474,7 +1474,7 @@ <h1>MaxAbsScaler<a class="headerlink" href="#maxabsscaler" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.MaxAbsScaler.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.MaxAbsScaler.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.MaxAbsScaler.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1495,7 +1495,7 @@ <h1>MaxAbsScaler<a class="headerlink" href="#maxabsscaler" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.MaxAbsScaler.inverse_transform">
-<span class="sig-name descname"><span class="pre">inverse_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/preprocessing/_data.py#L1338"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.MaxAbsScaler.inverse_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">inverse_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/preprocessing/_data.py#L1346"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.MaxAbsScaler.inverse_transform" title="Link to this definition">#</a></dt>
 <dd><p>Scale back the data to the original representation.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1506,7 +1506,7 @@ <h1>MaxAbsScaler<a class="headerlink" href="#maxabsscaler" title="Link to this h
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><dl class="simple">
-<dt><strong>X_tr</strong><span class="classifier">{ndarray, sparse matrix} of shape (n_samples, n_features)</span></dt><dd><p>Transformed array.</p>
+<dt><strong>X_original</strong><span class="classifier">{ndarray, sparse matrix} of shape (n_samples, n_features)</span></dt><dd><p>Transformed array.</p>
 </dd>
 </dl>
 </dd>
@@ -1515,7 +1515,7 @@ <h1>MaxAbsScaler<a class="headerlink" href="#maxabsscaler" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.MaxAbsScaler.partial_fit">
-<span class="sig-name descname"><span class="pre">partial_fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/preprocessing/_data.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.MaxAbsScaler.partial_fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">partial_fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/preprocessing/_data.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.MaxAbsScaler.partial_fit" title="Link to this definition">#</a></dt>
 <dd><p>Online computation of max absolute value of X for later scaling.</p>
 <p>All of X is processed as a single batch. This is intended for cases
 when <a class="reference internal" href="#sklearn.preprocessing.MaxAbsScaler.fit" title="sklearn.preprocessing.MaxAbsScaler.fit"><code class="xref py py-meth docutils literal notranslate"><span class="pre">fit</span></code></a> is not feasible due to very large number of
@@ -1541,7 +1541,7 @@ <h1>MaxAbsScaler<a class="headerlink" href="#maxabsscaler" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.MaxAbsScaler.set_output">
-<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.MaxAbsScaler.set_output" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.MaxAbsScaler.set_output" title="Link to this definition">#</a></dt>
 <dd><p>Set output container.</p>
 <p>See <a class="reference internal" href="http://github.com/auto_examples/miscellaneous/plot_set_output.html#sphx-glr-auto-examples-miscellaneous-plot-set-output-py"><span class="std std-ref">Introducing the set_output API</span></a>
 for an example on how to use the API.</p>
@@ -1572,7 +1572,7 @@ <h1>MaxAbsScaler<a class="headerlink" href="#maxabsscaler" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.MaxAbsScaler.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.MaxAbsScaler.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.MaxAbsScaler.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1596,7 +1596,7 @@ <h1>MaxAbsScaler<a class="headerlink" href="#maxabsscaler" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.MaxAbsScaler.transform">
-<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/preprocessing/_data.py#L1304"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.MaxAbsScaler.transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/preprocessing/_data.py#L1312"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.MaxAbsScaler.transform" title="Link to this definition">#</a></dt>
 <dd><p>Scale the data.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.preprocessing.MinMaxScaler.html b/dev/modules/generated/sklearn.preprocessing.MinMaxScaler.html
index d66f33a619c2a..a2006d424825a 100644
--- a/dev/modules/generated/sklearn.preprocessing.MinMaxScaler.html
+++ b/dev/modules/generated/sklearn.preprocessing.MinMaxScaler.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>MinMaxScaler<a class="headerlink" href="#minmaxscaler" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.preprocessing.MinMaxScaler">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.preprocessing.</span></span><span class="sig-name descname"><span class="pre">MinMaxScaler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">feature_range</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(0,</span> <span class="pre">1)</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">clip</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/preprocessing/_data.py#L288"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.MinMaxScaler" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.preprocessing.</span></span><span class="sig-name descname"><span class="pre">MinMaxScaler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">feature_range</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(0,</span> <span class="pre">1)</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">clip</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/preprocessing/_data.py#L295"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.MinMaxScaler" title="Link to this definition">#</a></dt>
 <dd><p>Transform features by scaling each feature to a given range.</p>
 <p>This estimator scales and translates each feature individually such
 that it is in the given range on the training set, e.g. between
@@ -1415,7 +1415,7 @@ <h1>MinMaxScaler<a class="headerlink" href="#minmaxscaler" title="Link to this h
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.MinMaxScaler.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/preprocessing/_data.py#L428"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.MinMaxScaler.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/preprocessing/_data.py#L435"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.MinMaxScaler.fit" title="Link to this definition">#</a></dt>
 <dd><p>Compute the minimum and maximum to be used for later scaling.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1438,7 +1438,7 @@ <h1>MinMaxScaler<a class="headerlink" href="#minmaxscaler" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.MinMaxScaler.fit_transform">
-<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L863"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.MinMaxScaler.fit_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L804"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.MinMaxScaler.fit_transform" title="Link to this definition">#</a></dt>
 <dd><p>Fit to data, then transform it.</p>
 <p>Fits transformer to <code class="docutils literal notranslate"><span class="pre">X</span></code> and <code class="docutils literal notranslate"><span class="pre">y</span></code> with optional parameters <code class="docutils literal notranslate"><span class="pre">fit_params</span></code>
 and returns a transformed version of <code class="docutils literal notranslate"><span class="pre">X</span></code>.</p>
@@ -1464,7 +1464,7 @@ <h1>MinMaxScaler<a class="headerlink" href="#minmaxscaler" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.MinMaxScaler.get_feature_names_out">
-<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L943"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.MinMaxScaler.get_feature_names_out" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L884"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.MinMaxScaler.get_feature_names_out" title="Link to this definition">#</a></dt>
 <dd><p>Get output feature names for transformation.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1492,7 +1492,7 @@ <h1>MinMaxScaler<a class="headerlink" href="#minmaxscaler" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.MinMaxScaler.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.MinMaxScaler.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.MinMaxScaler.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1509,7 +1509,7 @@ <h1>MinMaxScaler<a class="headerlink" href="#minmaxscaler" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.MinMaxScaler.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.MinMaxScaler.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.MinMaxScaler.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1530,7 +1530,7 @@ <h1>MinMaxScaler<a class="headerlink" href="#minmaxscaler" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.MinMaxScaler.inverse_transform">
-<span class="sig-name descname"><span class="pre">inverse_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/preprocessing/_data.py#L562"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.MinMaxScaler.inverse_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">inverse_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/preprocessing/_data.py#L569"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.MinMaxScaler.inverse_transform" title="Link to this definition">#</a></dt>
 <dd><p>Undo the scaling of X according to feature_range.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1541,7 +1541,7 @@ <h1>MinMaxScaler<a class="headerlink" href="#minmaxscaler" title="Link to this h
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><dl class="simple">
-<dt><strong>Xt</strong><span class="classifier">ndarray of shape (n_samples, n_features)</span></dt><dd><p>Transformed data.</p>
+<dt><strong>X_original</strong><span class="classifier">ndarray of shape (n_samples, n_features)</span></dt><dd><p>Transformed data.</p>
 </dd>
 </dl>
 </dd>
@@ -1550,7 +1550,7 @@ <h1>MinMaxScaler<a class="headerlink" href="#minmaxscaler" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.MinMaxScaler.partial_fit">
-<span class="sig-name descname"><span class="pre">partial_fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/preprocessing/_data.py#L449"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.MinMaxScaler.partial_fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">partial_fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/preprocessing/_data.py#L456"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.MinMaxScaler.partial_fit" title="Link to this definition">#</a></dt>
 <dd><p>Online computation of min and max on X for later scaling.</p>
 <p>All of X is processed as a single batch. This is intended for cases
 when <a class="reference internal" href="#sklearn.preprocessing.MinMaxScaler.fit" title="sklearn.preprocessing.MinMaxScaler.fit"><code class="xref py py-meth docutils literal notranslate"><span class="pre">fit</span></code></a> is not feasible due to very large number of
@@ -1576,7 +1576,7 @@ <h1>MinMaxScaler<a class="headerlink" href="#minmaxscaler" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.MinMaxScaler.set_output">
-<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.MinMaxScaler.set_output" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.MinMaxScaler.set_output" title="Link to this definition">#</a></dt>
 <dd><p>Set output container.</p>
 <p>See <a class="reference internal" href="http://github.com/auto_examples/miscellaneous/plot_set_output.html#sphx-glr-auto-examples-miscellaneous-plot-set-output-py"><span class="std std-ref">Introducing the set_output API</span></a>
 for an example on how to use the API.</p>
@@ -1607,7 +1607,7 @@ <h1>MinMaxScaler<a class="headerlink" href="#minmaxscaler" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.MinMaxScaler.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.MinMaxScaler.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.MinMaxScaler.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1631,7 +1631,7 @@ <h1>MinMaxScaler<a class="headerlink" href="#minmaxscaler" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.MinMaxScaler.transform">
-<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/preprocessing/_data.py#L521"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.MinMaxScaler.transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/preprocessing/_data.py#L528"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.MinMaxScaler.transform" title="Link to this definition">#</a></dt>
 <dd><p>Scale features of X according to feature_range.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.preprocessing.MultiLabelBinarizer.html b/dev/modules/generated/sklearn.preprocessing.MultiLabelBinarizer.html
index 88f6b04439a5b..7ade8c33f333a 100644
--- a/dev/modules/generated/sklearn.preprocessing.MultiLabelBinarizer.html
+++ b/dev/modules/generated/sklearn.preprocessing.MultiLabelBinarizer.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>MultiLabelBinarizer<a class="headerlink" href="#multilabelbinarizer" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.preprocessing.MultiLabelBinarizer">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.preprocessing.</span></span><span class="sig-name descname"><span class="pre">MultiLabelBinarizer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">classes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sparse_output</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/preprocessing/_label.py#L697"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.MultiLabelBinarizer" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.preprocessing.</span></span><span class="sig-name descname"><span class="pre">MultiLabelBinarizer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">classes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sparse_output</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/preprocessing/_label.py#L697"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.MultiLabelBinarizer" title="Link to this definition">#</a></dt>
 <dd><p>Transform between iterable of iterables and a multilabel format.</p>
 <p>Although a list of sets or tuples is a very intuitive format for multilabel
 data, it is unwieldy to process. This transformer converts between this
@@ -1373,7 +1373,7 @@ <h1>MultiLabelBinarizer<a class="headerlink" href="#multilabelbinarizer" title="
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.MultiLabelBinarizer.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/preprocessing/_label.py#L769"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.MultiLabelBinarizer.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/preprocessing/_label.py#L769"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.MultiLabelBinarizer.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit the label sets binarizer, storing <a class="reference internal" href="http://github.com/glossary.html#term-classes_"><span class="xref std std-term">classes_</span></a>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1395,7 +1395,7 @@ <h1>MultiLabelBinarizer<a class="headerlink" href="#multilabelbinarizer" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.MultiLabelBinarizer.fit_transform">
-<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/preprocessing/_label.py#L802"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.MultiLabelBinarizer.fit_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/preprocessing/_label.py#L802"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.MultiLabelBinarizer.fit_transform" title="Link to this definition">#</a></dt>
 <dd><p>Fit the label sets binarizer and transform the given label sets.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1419,7 +1419,7 @@ <h1>MultiLabelBinarizer<a class="headerlink" href="#multilabelbinarizer" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.MultiLabelBinarizer.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.MultiLabelBinarizer.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.MultiLabelBinarizer.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1436,7 +1436,7 @@ <h1>MultiLabelBinarizer<a class="headerlink" href="#multilabelbinarizer" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.MultiLabelBinarizer.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.MultiLabelBinarizer.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.MultiLabelBinarizer.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1457,7 +1457,7 @@ <h1>MultiLabelBinarizer<a class="headerlink" href="#multilabelbinarizer" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.MultiLabelBinarizer.inverse_transform">
-<span class="sig-name descname"><span class="pre">inverse_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">yt</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/preprocessing/_label.py#L918"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.MultiLabelBinarizer.inverse_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">inverse_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">yt</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/preprocessing/_label.py#L918"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.MultiLabelBinarizer.inverse_transform" title="Link to this definition">#</a></dt>
 <dd><p>Transform the given indicator matrix into label sets.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1468,7 +1468,7 @@ <h1>MultiLabelBinarizer<a class="headerlink" href="#multilabelbinarizer" title="
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><dl class="simple">
-<dt><strong>y</strong><span class="classifier">list of tuples</span></dt><dd><p>The set of labels for each sample such that <code class="docutils literal notranslate"><span class="pre">y[i]</span></code> consists of
+<dt><strong>y_original</strong><span class="classifier">list of tuples</span></dt><dd><p>The set of labels for each sample such that <code class="docutils literal notranslate"><span class="pre">y[i]</span></code> consists of
 <code class="docutils literal notranslate"><span class="pre">classes_[j]</span></code> for each <code class="docutils literal notranslate"><span class="pre">yt[i,</span> <span class="pre">j]</span> <span class="pre">==</span> <span class="pre">1</span></code>.</p>
 </dd>
 </dl>
@@ -1478,7 +1478,7 @@ <h1>MultiLabelBinarizer<a class="headerlink" href="#multilabelbinarizer" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.MultiLabelBinarizer.set_output">
-<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.MultiLabelBinarizer.set_output" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.MultiLabelBinarizer.set_output" title="Link to this definition">#</a></dt>
 <dd><p>Set output container.</p>
 <p>See <a class="reference internal" href="http://github.com/auto_examples/miscellaneous/plot_set_output.html#sphx-glr-auto-examples-miscellaneous-plot-set-output-py"><span class="std std-ref">Introducing the set_output API</span></a>
 for an example on how to use the API.</p>
@@ -1509,7 +1509,7 @@ <h1>MultiLabelBinarizer<a class="headerlink" href="#multilabelbinarizer" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.MultiLabelBinarizer.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.MultiLabelBinarizer.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.MultiLabelBinarizer.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1533,7 +1533,7 @@ <h1>MultiLabelBinarizer<a class="headerlink" href="#multilabelbinarizer" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.MultiLabelBinarizer.transform">
-<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/preprocessing/_label.py#L846"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.MultiLabelBinarizer.transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/preprocessing/_label.py#L846"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.MultiLabelBinarizer.transform" title="Link to this definition">#</a></dt>
 <dd><p>Transform the given label sets.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.preprocessing.Normalizer.html b/dev/modules/generated/sklearn.preprocessing.Normalizer.html
index 58da6d231696e..5fe3815c26324 100644
--- a/dev/modules/generated/sklearn.preprocessing.Normalizer.html
+++ b/dev/modules/generated/sklearn.preprocessing.Normalizer.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>Normalizer<a class="headerlink" href="#normalizer" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.preprocessing.Normalizer">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.preprocessing.</span></span><span class="sig-name descname"><span class="pre">Normalizer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">norm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'l2'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/preprocessing/_data.py#L2018"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.Normalizer" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.preprocessing.</span></span><span class="sig-name descname"><span class="pre">Normalizer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">norm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'l2'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/preprocessing/_data.py#L2026"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.Normalizer" title="Link to this definition">#</a></dt>
 <dd><p>Normalize samples individually to unit norm.</p>
 <p>Each sample (i.e. each row of the data matrix) with at least one
 non zero component is rescaled independently of other samples so
@@ -1380,7 +1380,7 @@ <h1>Normalizer<a class="headerlink" href="#normalizer" title="Link to this headi
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.Normalizer.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/preprocessing/_data.py#L2100"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.Normalizer.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/preprocessing/_data.py#L2108"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.Normalizer.fit" title="Link to this definition">#</a></dt>
 <dd><p>Only validates estimator’s parameters.</p>
 <p>This method allows to: (i) validate the estimator’s parameters and
 (ii) be consistent with the scikit-learn transformer API.</p>
@@ -1404,7 +1404,7 @@ <h1>Normalizer<a class="headerlink" href="#normalizer" title="Link to this headi
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.Normalizer.fit_transform">
-<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L863"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.Normalizer.fit_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L804"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.Normalizer.fit_transform" title="Link to this definition">#</a></dt>
 <dd><p>Fit to data, then transform it.</p>
 <p>Fits transformer to <code class="docutils literal notranslate"><span class="pre">X</span></code> and <code class="docutils literal notranslate"><span class="pre">y</span></code> with optional parameters <code class="docutils literal notranslate"><span class="pre">fit_params</span></code>
 and returns a transformed version of <code class="docutils literal notranslate"><span class="pre">X</span></code>.</p>
@@ -1430,7 +1430,7 @@ <h1>Normalizer<a class="headerlink" href="#normalizer" title="Link to this headi
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.Normalizer.get_feature_names_out">
-<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L943"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.Normalizer.get_feature_names_out" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L884"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.Normalizer.get_feature_names_out" title="Link to this definition">#</a></dt>
 <dd><p>Get output feature names for transformation.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1458,7 +1458,7 @@ <h1>Normalizer<a class="headerlink" href="#normalizer" title="Link to this headi
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.Normalizer.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.Normalizer.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.Normalizer.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1475,7 +1475,7 @@ <h1>Normalizer<a class="headerlink" href="#normalizer" title="Link to this headi
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.Normalizer.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.Normalizer.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.Normalizer.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1496,7 +1496,7 @@ <h1>Normalizer<a class="headerlink" href="#normalizer" title="Link to this headi
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.Normalizer.set_output">
-<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.Normalizer.set_output" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.Normalizer.set_output" title="Link to this definition">#</a></dt>
 <dd><p>Set output container.</p>
 <p>See <a class="reference internal" href="http://github.com/auto_examples/miscellaneous/plot_set_output.html#sphx-glr-auto-examples-miscellaneous-plot-set-output-py"><span class="std std-ref">Introducing the set_output API</span></a>
 for an example on how to use the API.</p>
@@ -1527,7 +1527,7 @@ <h1>Normalizer<a class="headerlink" href="#normalizer" title="Link to this headi
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.Normalizer.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.Normalizer.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.Normalizer.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1551,7 +1551,7 @@ <h1>Normalizer<a class="headerlink" href="#normalizer" title="Link to this headi
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.Normalizer.set_transform_request">
-<span class="sig-name descname"><span class="pre">set_transform_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.preprocessing.Normalizer" title="sklearn.preprocessing._data.Normalizer"><span class="pre">Normalizer</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.Normalizer.set_transform_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_transform_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.preprocessing.Normalizer" title="sklearn.preprocessing._data.Normalizer"><span class="pre">Normalizer</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.Normalizer.set_transform_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">transform</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1594,7 +1594,7 @@ <h1>Normalizer<a class="headerlink" href="#normalizer" title="Link to this headi
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.Normalizer.transform">
-<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/preprocessing/_data.py#L2123"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.Normalizer.transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/preprocessing/_data.py#L2131"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.Normalizer.transform" title="Link to this definition">#</a></dt>
 <dd><p>Scale each non zero row of X to unit norm.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.preprocessing.OneHotEncoder.html b/dev/modules/generated/sklearn.preprocessing.OneHotEncoder.html
index 54919aa04edf5..a2a26f8ef846d 100644
--- a/dev/modules/generated/sklearn.preprocessing.OneHotEncoder.html
+++ b/dev/modules/generated/sklearn.preprocessing.OneHotEncoder.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>OneHotEncoder<a class="headerlink" href="#onehotencoder" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.preprocessing.OneHotEncoder">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.preprocessing.</span></span><span class="sig-name descname"><span class="pre">OneHotEncoder</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">categories='auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sparse_output=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype=&lt;class</span> <span class="pre">'numpy.float64'&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">handle_unknown='error'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_frequency=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_categories=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">feature_name_combiner='concat'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/preprocessing/_encoders.py#L465"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.OneHotEncoder" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.preprocessing.</span></span><span class="sig-name descname"><span class="pre">OneHotEncoder</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">categories='auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sparse_output=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype=&lt;class</span> <span class="pre">'numpy.float64'&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">handle_unknown='error'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_frequency=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_categories=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">feature_name_combiner='concat'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/preprocessing/_encoders.py#L465"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.OneHotEncoder" title="Link to this definition">#</a></dt>
 <dd><p>Encode categorical features as a one-hot numeric array.</p>
 <p>The input to this transformer should be an array-like of integers or
 strings, denoting the values taken on by categorical (discrete) features.
@@ -1567,7 +1567,7 @@ <h1>OneHotEncoder<a class="headerlink" href="#onehotencoder" title="Link to this
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.OneHotEncoder.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/preprocessing/_encoders.py#L972"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.OneHotEncoder.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/preprocessing/_encoders.py#L972"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.OneHotEncoder.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit OneHotEncoder to X.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1590,7 +1590,7 @@ <h1>OneHotEncoder<a class="headerlink" href="#onehotencoder" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.OneHotEncoder.fit_transform">
-<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L863"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.OneHotEncoder.fit_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L804"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.OneHotEncoder.fit_transform" title="Link to this definition">#</a></dt>
 <dd><p>Fit to data, then transform it.</p>
 <p>Fits transformer to <code class="docutils literal notranslate"><span class="pre">X</span></code> and <code class="docutils literal notranslate"><span class="pre">y</span></code> with optional parameters <code class="docutils literal notranslate"><span class="pre">fit_params</span></code>
 and returns a transformed version of <code class="docutils literal notranslate"><span class="pre">X</span></code>.</p>
@@ -1616,7 +1616,7 @@ <h1>OneHotEncoder<a class="headerlink" href="#onehotencoder" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.OneHotEncoder.get_feature_names_out">
-<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/preprocessing/_encoders.py#L1206"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.OneHotEncoder.get_feature_names_out" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/preprocessing/_encoders.py#L1206"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.OneHotEncoder.get_feature_names_out" title="Link to this definition">#</a></dt>
 <dd><p>Get output feature names for transformation.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1644,7 +1644,7 @@ <h1>OneHotEncoder<a class="headerlink" href="#onehotencoder" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.OneHotEncoder.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.OneHotEncoder.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.OneHotEncoder.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1661,7 +1661,7 @@ <h1>OneHotEncoder<a class="headerlink" href="#onehotencoder" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.OneHotEncoder.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.OneHotEncoder.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.OneHotEncoder.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1682,7 +1682,7 @@ <h1>OneHotEncoder<a class="headerlink" href="#onehotencoder" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.OneHotEncoder.inverse_transform">
-<span class="sig-name descname"><span class="pre">inverse_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/preprocessing/_encoders.py#L1087"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.OneHotEncoder.inverse_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">inverse_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/preprocessing/_encoders.py#L1087"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.OneHotEncoder.inverse_transform" title="Link to this definition">#</a></dt>
 <dd><p>Convert the data back to the original representation.</p>
 <p>When unknown categories are encountered (all zeros in the
 one-hot encoding), <code class="docutils literal notranslate"><span class="pre">None</span></code> is used to represent this category. If the
@@ -1699,7 +1699,7 @@ <h1>OneHotEncoder<a class="headerlink" href="#onehotencoder" title="Link to this
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><dl class="simple">
-<dt><strong>X_tr</strong><span class="classifier">ndarray of shape (n_samples, n_features)</span></dt><dd><p>Inverse transformed array.</p>
+<dt><strong>X_original</strong><span class="classifier">ndarray of shape (n_samples, n_features)</span></dt><dd><p>Inverse transformed array.</p>
 </dd>
 </dl>
 </dd>
@@ -1708,7 +1708,7 @@ <h1>OneHotEncoder<a class="headerlink" href="#onehotencoder" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.OneHotEncoder.set_output">
-<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.OneHotEncoder.set_output" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.OneHotEncoder.set_output" title="Link to this definition">#</a></dt>
 <dd><p>Set output container.</p>
 <p>See <a class="reference internal" href="http://github.com/auto_examples/miscellaneous/plot_set_output.html#sphx-glr-auto-examples-miscellaneous-plot-set-output-py"><span class="std std-ref">Introducing the set_output API</span></a>
 for an example on how to use the API.</p>
@@ -1739,7 +1739,7 @@ <h1>OneHotEncoder<a class="headerlink" href="#onehotencoder" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.OneHotEncoder.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.OneHotEncoder.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.OneHotEncoder.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1763,7 +1763,7 @@ <h1>OneHotEncoder<a class="headerlink" href="#onehotencoder" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.OneHotEncoder.transform">
-<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/preprocessing/_encoders.py#L1000"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.OneHotEncoder.transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/preprocessing/_encoders.py#L1000"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.OneHotEncoder.transform" title="Link to this definition">#</a></dt>
 <dd><p>Transform X using one-hot encoding.</p>
 <p>If <code class="docutils literal notranslate"><span class="pre">sparse_output=True</span></code> (default), it returns an instance of
 <a class="reference external" href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.csr_matrix.html#scipy.sparse.csr_matrix" title="(in SciPy v1.15.2)"><code class="xref py py-class docutils literal notranslate"><span class="pre">scipy.sparse._csr.csr_matrix</span></code></a> (CSR format).</p>
diff --git a/dev/modules/generated/sklearn.preprocessing.OrdinalEncoder.html b/dev/modules/generated/sklearn.preprocessing.OrdinalEncoder.html
index cf65cb5d0dbd9..8409e09de43a5 100644
--- a/dev/modules/generated/sklearn.preprocessing.OrdinalEncoder.html
+++ b/dev/modules/generated/sklearn.preprocessing.OrdinalEncoder.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>OrdinalEncoder<a class="headerlink" href="#ordinalencoder" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.preprocessing.OrdinalEncoder">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.preprocessing.</span></span><span class="sig-name descname"><span class="pre">OrdinalEncoder</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">categories='auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype=&lt;class</span> <span class="pre">'numpy.float64'&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">handle_unknown='error'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">unknown_value=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">encoded_missing_value=nan</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_frequency=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_categories=None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/preprocessing/_encoders.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.OrdinalEncoder" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.preprocessing.</span></span><span class="sig-name descname"><span class="pre">OrdinalEncoder</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">categories='auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype=&lt;class</span> <span class="pre">'numpy.float64'&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">handle_unknown='error'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">unknown_value=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">encoded_missing_value=nan</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_frequency=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_categories=None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/preprocessing/_encoders.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.OrdinalEncoder" title="Link to this definition">#</a></dt>
 <dd><p>Encode categorical features as an integer array.</p>
 <p>The input to this transformer should be an array-like of integers or
 strings, denoting the values taken on by categorical (discrete) features.
@@ -1482,7 +1482,7 @@ <h1>OrdinalEncoder<a class="headerlink" href="#ordinalencoder" title="Link to th
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.OrdinalEncoder.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/preprocessing/_encoders.py#L1473"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.OrdinalEncoder.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/preprocessing/_encoders.py#L1473"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.OrdinalEncoder.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit the OrdinalEncoder to X.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1505,7 +1505,7 @@ <h1>OrdinalEncoder<a class="headerlink" href="#ordinalencoder" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.OrdinalEncoder.fit_transform">
-<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L863"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.OrdinalEncoder.fit_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L804"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.OrdinalEncoder.fit_transform" title="Link to this definition">#</a></dt>
 <dd><p>Fit to data, then transform it.</p>
 <p>Fits transformer to <code class="docutils literal notranslate"><span class="pre">X</span></code> and <code class="docutils literal notranslate"><span class="pre">y</span></code> with optional parameters <code class="docutils literal notranslate"><span class="pre">fit_params</span></code>
 and returns a transformed version of <code class="docutils literal notranslate"><span class="pre">X</span></code>.</p>
@@ -1531,7 +1531,7 @@ <h1>OrdinalEncoder<a class="headerlink" href="#ordinalencoder" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.OrdinalEncoder.get_feature_names_out">
-<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L943"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.OrdinalEncoder.get_feature_names_out" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L884"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.OrdinalEncoder.get_feature_names_out" title="Link to this definition">#</a></dt>
 <dd><p>Get output feature names for transformation.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1559,7 +1559,7 @@ <h1>OrdinalEncoder<a class="headerlink" href="#ordinalencoder" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.OrdinalEncoder.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.OrdinalEncoder.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.OrdinalEncoder.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1576,7 +1576,7 @@ <h1>OrdinalEncoder<a class="headerlink" href="#ordinalencoder" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.OrdinalEncoder.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.OrdinalEncoder.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.OrdinalEncoder.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1597,7 +1597,7 @@ <h1>OrdinalEncoder<a class="headerlink" href="#ordinalencoder" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.OrdinalEncoder.inverse_transform">
-<span class="sig-name descname"><span class="pre">inverse_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/preprocessing/_encoders.py#L1614"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.OrdinalEncoder.inverse_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">inverse_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/preprocessing/_encoders.py#L1614"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.OrdinalEncoder.inverse_transform" title="Link to this definition">#</a></dt>
 <dd><p>Convert the data back to the original representation.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1608,7 +1608,7 @@ <h1>OrdinalEncoder<a class="headerlink" href="#ordinalencoder" title="Link to th
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><dl class="simple">
-<dt><strong>X_tr</strong><span class="classifier">ndarray of shape (n_samples, n_features)</span></dt><dd><p>Inverse transformed array.</p>
+<dt><strong>X_original</strong><span class="classifier">ndarray of shape (n_samples, n_features)</span></dt><dd><p>Inverse transformed array.</p>
 </dd>
 </dl>
 </dd>
@@ -1617,7 +1617,7 @@ <h1>OrdinalEncoder<a class="headerlink" href="#ordinalencoder" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.OrdinalEncoder.set_output">
-<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.OrdinalEncoder.set_output" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.OrdinalEncoder.set_output" title="Link to this definition">#</a></dt>
 <dd><p>Set output container.</p>
 <p>See <a class="reference internal" href="http://github.com/auto_examples/miscellaneous/plot_set_output.html#sphx-glr-auto-examples-miscellaneous-plot-set-output-py"><span class="std std-ref">Introducing the set_output API</span></a>
 for an example on how to use the API.</p>
@@ -1648,7 +1648,7 @@ <h1>OrdinalEncoder<a class="headerlink" href="#ordinalencoder" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.OrdinalEncoder.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.OrdinalEncoder.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.OrdinalEncoder.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1672,7 +1672,7 @@ <h1>OrdinalEncoder<a class="headerlink" href="#ordinalencoder" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.OrdinalEncoder.transform">
-<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/preprocessing/_encoders.py#L1582"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.OrdinalEncoder.transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/preprocessing/_encoders.py#L1582"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.OrdinalEncoder.transform" title="Link to this definition">#</a></dt>
 <dd><p>Transform X to ordinal codes.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.preprocessing.PolynomialFeatures.html b/dev/modules/generated/sklearn.preprocessing.PolynomialFeatures.html
index 9c72294e9b253..8e2c45b2be2bc 100644
--- a/dev/modules/generated/sklearn.preprocessing.PolynomialFeatures.html
+++ b/dev/modules/generated/sklearn.preprocessing.PolynomialFeatures.html
@@ -11,10 +11,10 @@
 <meta property="og:type" content="website" />
 <meta property="og:url" content="https://scikit-learn/stable/modules/generated/sklearn.preprocessing.PolynomialFeatures.html" />
 <meta property="og:site_name" content="scikit-learn" />
-<meta property="og:description" content="Gallery examples: Time-related feature engineering Plot classification probability Comparing Linear Bayesian Regressors Poisson regression and non-normal loss Polynomial and Spline interpolation Ro..." />
+<meta property="og:description" content="Gallery examples: Time-related feature engineering Plot classification probability Visualizing the probabilistic predictions of a VotingClassifier Comparing Linear Bayesian Regressors Poisson regre..." />
 <meta property="og:image" content="https://scikit-learn/stable/_images/sphx_glr_plot_cyclical_feature_engineering_thumb.png" />
 <meta property="og:image:alt" content="" />
-<meta name="description" content="Gallery examples: Time-related feature engineering Plot classification probability Comparing Linear Bayesian Regressors Poisson regression and non-normal loss Polynomial and Spline interpolation Ro..." />
+<meta name="description" content="Gallery examples: Time-related feature engineering Plot classification probability Visualizing the probabilistic predictions of a VotingClassifier Comparing Linear Bayesian Regressors Poisson regre..." />
 
     <title>PolynomialFeatures &#8212; scikit-learn 1.7.dev0 documentation</title>
   
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>PolynomialFeatures<a class="headerlink" href="#polynomialfeatures" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.preprocessing.PolynomialFeatures">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.preprocessing.</span></span><span class="sig-name descname"><span class="pre">PolynomialFeatures</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">degree</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">interaction_only</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">include_bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">order</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'C'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/preprocessing/_polynomial.py#L85"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.PolynomialFeatures" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.preprocessing.</span></span><span class="sig-name descname"><span class="pre">PolynomialFeatures</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">degree</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">interaction_only</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">include_bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">order</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'C'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/preprocessing/_polynomial.py#L85"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.PolynomialFeatures" title="Link to this definition">#</a></dt>
 <dd><p>Generate polynomial and interaction features.</p>
 <p>Generate a new feature matrix consisting of all polynomial combinations
 of the features with degree less than or equal to the specified degree.
@@ -1400,7 +1400,7 @@ <h1>PolynomialFeatures<a class="headerlink" href="#polynomialfeatures" title="Li
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.PolynomialFeatures.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/preprocessing/_polynomial.py#L291"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.PolynomialFeatures.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/preprocessing/_polynomial.py#L291"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.PolynomialFeatures.fit" title="Link to this definition">#</a></dt>
 <dd><p>Compute number of output features.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1422,7 +1422,7 @@ <h1>PolynomialFeatures<a class="headerlink" href="#polynomialfeatures" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.PolynomialFeatures.fit_transform">
-<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L863"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.PolynomialFeatures.fit_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L804"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.PolynomialFeatures.fit_transform" title="Link to this definition">#</a></dt>
 <dd><p>Fit to data, then transform it.</p>
 <p>Fits transformer to <code class="docutils literal notranslate"><span class="pre">X</span></code> and <code class="docutils literal notranslate"><span class="pre">y</span></code> with optional parameters <code class="docutils literal notranslate"><span class="pre">fit_params</span></code>
 and returns a transformed version of <code class="docutils literal notranslate"><span class="pre">X</span></code>.</p>
@@ -1448,7 +1448,7 @@ <h1>PolynomialFeatures<a class="headerlink" href="#polynomialfeatures" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.PolynomialFeatures.get_feature_names_out">
-<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/preprocessing/_polynomial.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.PolynomialFeatures.get_feature_names_out" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/preprocessing/_polynomial.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.PolynomialFeatures.get_feature_names_out" title="Link to this definition">#</a></dt>
 <dd><p>Get output feature names for transformation.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1476,7 +1476,7 @@ <h1>PolynomialFeatures<a class="headerlink" href="#polynomialfeatures" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.PolynomialFeatures.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.PolynomialFeatures.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.PolynomialFeatures.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1493,7 +1493,7 @@ <h1>PolynomialFeatures<a class="headerlink" href="#polynomialfeatures" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.PolynomialFeatures.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.PolynomialFeatures.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.PolynomialFeatures.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1514,7 +1514,7 @@ <h1>PolynomialFeatures<a class="headerlink" href="#polynomialfeatures" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.PolynomialFeatures.set_output">
-<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.PolynomialFeatures.set_output" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.PolynomialFeatures.set_output" title="Link to this definition">#</a></dt>
 <dd><p>Set output container.</p>
 <p>See <a class="reference internal" href="http://github.com/auto_examples/miscellaneous/plot_set_output.html#sphx-glr-auto-examples-miscellaneous-plot-set-output-py"><span class="std std-ref">Introducing the set_output API</span></a>
 for an example on how to use the API.</p>
@@ -1545,7 +1545,7 @@ <h1>PolynomialFeatures<a class="headerlink" href="#polynomialfeatures" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.PolynomialFeatures.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.PolynomialFeatures.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.PolynomialFeatures.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1569,7 +1569,7 @@ <h1>PolynomialFeatures<a class="headerlink" href="#polynomialfeatures" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.PolynomialFeatures.transform">
-<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/preprocessing/_polynomial.py#L388"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.PolynomialFeatures.transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/preprocessing/_polynomial.py#L388"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.PolynomialFeatures.transform" title="Link to this definition">#</a></dt>
 <dd><p>Transform data to polynomial features.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1611,6 +1611,9 @@ <h2>Gallery examples<a class="headerlink" href="#gallery-examples" title="Link t
 </div><div class="sphx-glr-thumbcontainer" tooltip="This example illustrates the use of sklearn.inspection.DecisionBoundaryDisplay to plot the predicted class probabilities of various classifiers in a 2D feature space, mostly for didactic purposes."><img alt="" src="http://github.com/_images/sphx_glr_plot_classification_probability_thumb.png" />
 <p><a class="reference internal" href="http://github.com/auto_examples/classification/plot_classification_probability.html#sphx-glr-auto-examples-classification-plot-classification-probability-py"><span class="std std-ref">Plot classification probability</span></a></p>
   <div class="sphx-glr-thumbnail-title">Plot classification probability</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="Plot the predicted class probabilities in a toy dataset predicted by three different classifiers and averaged by the VotingClassifier."><img alt="" src="http://github.com/_images/sphx_glr_plot_voting_decision_regions_thumb.png" />
+<p><a class="reference internal" href="http://github.com/auto_examples/ensemble/plot_voting_decision_regions.html#sphx-glr-auto-examples-ensemble-plot-voting-decision-regions-py"><span class="std std-ref">Visualizing the probabilistic predictions of a VotingClassifier</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Visualizing the probabilistic predictions of a VotingClassifier</div>
 </div><div class="sphx-glr-thumbcontainer" tooltip="This example compares two different bayesian regressors:"><img alt="" src="http://github.com/_images/sphx_glr_plot_ard_thumb.png" />
 <p><a class="reference internal" href="http://github.com/auto_examples/linear_model/plot_ard.html#sphx-glr-auto-examples-linear-model-plot-ard-py"><span class="std std-ref">Comparing Linear Bayesian Regressors</span></a></p>
   <div class="sphx-glr-thumbnail-title">Comparing Linear Bayesian Regressors</div>
diff --git a/dev/modules/generated/sklearn.preprocessing.PowerTransformer.html b/dev/modules/generated/sklearn.preprocessing.PowerTransformer.html
index 8b2d38fa148dc..87703de2f1f27 100644
--- a/dev/modules/generated/sklearn.preprocessing.PowerTransformer.html
+++ b/dev/modules/generated/sklearn.preprocessing.PowerTransformer.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>PowerTransformer<a class="headerlink" href="#powertransformer" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.preprocessing.PowerTransformer">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.preprocessing.</span></span><span class="sig-name descname"><span class="pre">PowerTransformer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'yeo-johnson'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">standardize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/preprocessing/_data.py#L3176"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.PowerTransformer" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.preprocessing.</span></span><span class="sig-name descname"><span class="pre">PowerTransformer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'yeo-johnson'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">standardize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/preprocessing/_data.py#L3187"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.PowerTransformer" title="Link to this definition">#</a></dt>
 <dd><p>Apply a power transform featurewise to make data more Gaussian-like.</p>
 <p>Power transforms are a family of parametric, monotonic transformations
 that are applied to make data more Gaussian-like. This is useful for
@@ -1397,16 +1397,16 @@ <h1>PowerTransformer<a class="headerlink" href="#powertransformer" title="Link t
 <span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">pt</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">data</span><span class="p">))</span>
 <span class="go">PowerTransformer()</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">pt</span><span class="o">.</span><span class="n">lambdas_</span><span class="p">)</span>
-<span class="go">[ 1.386... -3.100...]</span>
+<span class="go">[ 1.386 -3.100]</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">pt</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">data</span><span class="p">))</span>
-<span class="go">[[-1.316... -0.707...]</span>
-<span class="go"> [ 0.209... -0.707...]</span>
-<span class="go"> [ 1.106...  1.414...]]</span>
+<span class="go">[[-1.316 -0.707]</span>
+<span class="go"> [ 0.209 -0.707]</span>
+<span class="go"> [ 1.106  1.414]]</span>
 </pre></div>
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.PowerTransformer.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/preprocessing/_data.py#L3285"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.PowerTransformer.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/preprocessing/_data.py#L3296"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.PowerTransformer.fit" title="Link to this definition">#</a></dt>
 <dd><p>Estimate the optimal parameter lambda for each feature.</p>
 <p>The optimal lambda parameter for minimizing skewness is estimated on
 each feature independently using maximum likelihood.</p>
@@ -1430,7 +1430,7 @@ <h1>PowerTransformer<a class="headerlink" href="#powertransformer" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.PowerTransformer.fit_transform">
-<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/preprocessing/_data.py#L3308"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.PowerTransformer.fit_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/preprocessing/_data.py#L3319"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.PowerTransformer.fit_transform" title="Link to this definition">#</a></dt>
 <dd><p>Fit <code class="docutils literal notranslate"><span class="pre">PowerTransformer</span></code> to <code class="docutils literal notranslate"><span class="pre">X</span></code>, then transform <code class="docutils literal notranslate"><span class="pre">X</span></code>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1453,7 +1453,7 @@ <h1>PowerTransformer<a class="headerlink" href="#powertransformer" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.PowerTransformer.get_feature_names_out">
-<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L943"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.PowerTransformer.get_feature_names_out" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L884"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.PowerTransformer.get_feature_names_out" title="Link to this definition">#</a></dt>
 <dd><p>Get output feature names for transformation.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1481,7 +1481,7 @@ <h1>PowerTransformer<a class="headerlink" href="#powertransformer" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.PowerTransformer.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.PowerTransformer.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.PowerTransformer.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1498,7 +1498,7 @@ <h1>PowerTransformer<a class="headerlink" href="#powertransformer" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.PowerTransformer.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.PowerTransformer.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.PowerTransformer.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1519,24 +1519,24 @@ <h1>PowerTransformer<a class="headerlink" href="#powertransformer" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.PowerTransformer.inverse_transform">
-<span class="sig-name descname"><span class="pre">inverse_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/preprocessing/_data.py#L3401"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.PowerTransformer.inverse_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">inverse_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/preprocessing/_data.py#L3412"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.PowerTransformer.inverse_transform" title="Link to this definition">#</a></dt>
 <dd><p>Apply the inverse power transformation using the fitted lambdas.</p>
 <p>The inverse of the Box-Cox transformation is given by:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">if</span> <span class="n">lambda_</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-    <span class="n">X</span> <span class="o">=</span> <span class="n">exp</span><span class="p">(</span><span class="n">X_trans</span><span class="p">)</span>
+    <span class="n">X_original</span> <span class="o">=</span> <span class="n">exp</span><span class="p">(</span><span class="n">X_trans</span><span class="p">)</span>
 <span class="k">else</span><span class="p">:</span>
-    <span class="n">X</span> <span class="o">=</span> <span class="p">(</span><span class="n">X_trans</span> <span class="o">*</span> <span class="n">lambda_</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">**</span> <span class="p">(</span><span class="mi">1</span> <span class="o">/</span> <span class="n">lambda_</span><span class="p">)</span>
+    <span class="n">X_original</span> <span class="o">=</span> <span class="p">(</span><span class="n">X</span> <span class="o">*</span> <span class="n">lambda_</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">**</span> <span class="p">(</span><span class="mi">1</span> <span class="o">/</span> <span class="n">lambda_</span><span class="p">)</span>
 </pre></div>
 </div>
 <p>The inverse of the Yeo-Johnson transformation is given by:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">if</span> <span class="n">X</span> <span class="o">&gt;=</span> <span class="mi">0</span> <span class="ow">and</span> <span class="n">lambda_</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-    <span class="n">X</span> <span class="o">=</span> <span class="n">exp</span><span class="p">(</span><span class="n">X_trans</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span>
+    <span class="n">X_original</span> <span class="o">=</span> <span class="n">exp</span><span class="p">(</span><span class="n">X</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span>
 <span class="k">elif</span> <span class="n">X</span> <span class="o">&gt;=</span> <span class="mi">0</span> <span class="ow">and</span> <span class="n">lambda_</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
-    <span class="n">X</span> <span class="o">=</span> <span class="p">(</span><span class="n">X_trans</span> <span class="o">*</span> <span class="n">lambda_</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">**</span> <span class="p">(</span><span class="mi">1</span> <span class="o">/</span> <span class="n">lambda_</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span>
+    <span class="n">X_original</span> <span class="o">=</span> <span class="p">(</span><span class="n">X</span> <span class="o">*</span> <span class="n">lambda_</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">**</span> <span class="p">(</span><span class="mi">1</span> <span class="o">/</span> <span class="n">lambda_</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span>
 <span class="k">elif</span> <span class="n">X</span> <span class="o">&lt;</span> <span class="mi">0</span> <span class="ow">and</span> <span class="n">lambda_</span> <span class="o">!=</span> <span class="mi">2</span><span class="p">:</span>
-    <span class="n">X</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">-</span> <span class="p">(</span><span class="o">-</span><span class="p">(</span><span class="mi">2</span> <span class="o">-</span> <span class="n">lambda_</span><span class="p">)</span> <span class="o">*</span> <span class="n">X_trans</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">**</span> <span class="p">(</span><span class="mi">1</span> <span class="o">/</span> <span class="p">(</span><span class="mi">2</span> <span class="o">-</span> <span class="n">lambda_</span><span class="p">))</span>
+    <span class="n">X_original</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">-</span> <span class="p">(</span><span class="o">-</span><span class="p">(</span><span class="mi">2</span> <span class="o">-</span> <span class="n">lambda_</span><span class="p">)</span> <span class="o">*</span> <span class="n">X</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">**</span> <span class="p">(</span><span class="mi">1</span> <span class="o">/</span> <span class="p">(</span><span class="mi">2</span> <span class="o">-</span> <span class="n">lambda_</span><span class="p">))</span>
 <span class="k">elif</span> <span class="n">X</span> <span class="o">&lt;</span> <span class="mi">0</span> <span class="ow">and</span> <span class="n">lambda_</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
-    <span class="n">X</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">X_trans</span><span class="p">)</span>
+    <span class="n">X_original</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">X</span><span class="p">)</span>
 </pre></div>
 </div>
 <dl class="field-list simple">
@@ -1548,7 +1548,7 @@ <h1>PowerTransformer<a class="headerlink" href="#powertransformer" title="Link t
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><dl class="simple">
-<dt><strong>X</strong><span class="classifier">ndarray of shape (n_samples, n_features)</span></dt><dd><p>The original data.</p>
+<dt><strong>X_original</strong><span class="classifier">ndarray of shape (n_samples, n_features)</span></dt><dd><p>The original data.</p>
 </dd>
 </dl>
 </dd>
@@ -1557,7 +1557,7 @@ <h1>PowerTransformer<a class="headerlink" href="#powertransformer" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.PowerTransformer.set_output">
-<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.PowerTransformer.set_output" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.PowerTransformer.set_output" title="Link to this definition">#</a></dt>
 <dd><p>Set output container.</p>
 <p>See <a class="reference internal" href="http://github.com/auto_examples/miscellaneous/plot_set_output.html#sphx-glr-auto-examples-miscellaneous-plot-set-output-py"><span class="std std-ref">Introducing the set_output API</span></a>
 for an example on how to use the API.</p>
@@ -1588,7 +1588,7 @@ <h1>PowerTransformer<a class="headerlink" href="#powertransformer" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.PowerTransformer.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.PowerTransformer.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.PowerTransformer.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1612,7 +1612,7 @@ <h1>PowerTransformer<a class="headerlink" href="#powertransformer" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.PowerTransformer.transform">
-<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/preprocessing/_data.py#L3372"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.PowerTransformer.transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/preprocessing/_data.py#L3383"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.PowerTransformer.transform" title="Link to this definition">#</a></dt>
 <dd><p>Apply the power transform to each feature using the fitted lambdas.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.preprocessing.QuantileTransformer.html b/dev/modules/generated/sklearn.preprocessing.QuantileTransformer.html
index 3be0850bec375..c4ca5612428e6 100644
--- a/dev/modules/generated/sklearn.preprocessing.QuantileTransformer.html
+++ b/dev/modules/generated/sklearn.preprocessing.QuantileTransformer.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>QuantileTransformer<a class="headerlink" href="#quantiletransformer" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.preprocessing.QuantileTransformer">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.preprocessing.</span></span><span class="sig-name descname"><span class="pre">QuantileTransformer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_quantiles</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_distribution</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'uniform'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ignore_implicit_zeros</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">subsample</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/preprocessing/_data.py#L2590"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.QuantileTransformer" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.preprocessing.</span></span><span class="sig-name descname"><span class="pre">QuantileTransformer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_quantiles</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_distribution</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'uniform'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ignore_implicit_zeros</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">subsample</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/preprocessing/_data.py#L2601"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.QuantileTransformer" title="Link to this definition">#</a></dt>
 <dd><p>Transform features using quantiles information.</p>
 <p>This method transforms the features to follow a uniform or a normal
 distribution. Therefore, for a given feature, this transformation tends
@@ -1415,7 +1415,7 @@ <h1>QuantileTransformer<a class="headerlink" href="#quantiletransformer" title="
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.QuantileTransformer.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/preprocessing/_data.py#L2804"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.QuantileTransformer.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/preprocessing/_data.py#L2815"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.QuantileTransformer.fit" title="Link to this definition">#</a></dt>
 <dd><p>Compute the quantiles used for transforming.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1440,7 +1440,7 @@ <h1>QuantileTransformer<a class="headerlink" href="#quantiletransformer" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.QuantileTransformer.fit_transform">
-<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L863"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.QuantileTransformer.fit_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L804"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.QuantileTransformer.fit_transform" title="Link to this definition">#</a></dt>
 <dd><p>Fit to data, then transform it.</p>
 <p>Fits transformer to <code class="docutils literal notranslate"><span class="pre">X</span></code> and <code class="docutils literal notranslate"><span class="pre">y</span></code> with optional parameters <code class="docutils literal notranslate"><span class="pre">fit_params</span></code>
 and returns a transformed version of <code class="docutils literal notranslate"><span class="pre">X</span></code>.</p>
@@ -1466,7 +1466,7 @@ <h1>QuantileTransformer<a class="headerlink" href="#quantiletransformer" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.QuantileTransformer.get_feature_names_out">
-<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L943"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.QuantileTransformer.get_feature_names_out" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L884"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.QuantileTransformer.get_feature_names_out" title="Link to this definition">#</a></dt>
 <dd><p>Get output feature names for transformation.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1494,7 +1494,7 @@ <h1>QuantileTransformer<a class="headerlink" href="#quantiletransformer" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.QuantileTransformer.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.QuantileTransformer.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.QuantileTransformer.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1511,7 +1511,7 @@ <h1>QuantileTransformer<a class="headerlink" href="#quantiletransformer" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.QuantileTransformer.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.QuantileTransformer.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.QuantileTransformer.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1532,7 +1532,7 @@ <h1>QuantileTransformer<a class="headerlink" href="#quantiletransformer" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.QuantileTransformer.inverse_transform">
-<span class="sig-name descname"><span class="pre">inverse_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/preprocessing/_data.py#L2998"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.QuantileTransformer.inverse_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">inverse_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/preprocessing/_data.py#L3009"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.QuantileTransformer.inverse_transform" title="Link to this definition">#</a></dt>
 <dd><p>Back-projection to the original space.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1546,7 +1546,7 @@ <h1>QuantileTransformer<a class="headerlink" href="#quantiletransformer" title="
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><dl class="simple">
-<dt><strong>Xt</strong><span class="classifier">{ndarray, sparse matrix} of (n_samples, n_features)</span></dt><dd><p>The projected data.</p>
+<dt><strong>X_original</strong><span class="classifier">{ndarray, sparse matrix} of (n_samples, n_features)</span></dt><dd><p>The projected data.</p>
 </dd>
 </dl>
 </dd>
@@ -1555,7 +1555,7 @@ <h1>QuantileTransformer<a class="headerlink" href="#quantiletransformer" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.QuantileTransformer.set_output">
-<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.QuantileTransformer.set_output" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.QuantileTransformer.set_output" title="Link to this definition">#</a></dt>
 <dd><p>Set output container.</p>
 <p>See <a class="reference internal" href="http://github.com/auto_examples/miscellaneous/plot_set_output.html#sphx-glr-auto-examples-miscellaneous-plot-set-output-py"><span class="std std-ref">Introducing the set_output API</span></a>
 for an example on how to use the API.</p>
@@ -1586,7 +1586,7 @@ <h1>QuantileTransformer<a class="headerlink" href="#quantiletransformer" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.QuantileTransformer.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.QuantileTransformer.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.QuantileTransformer.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1610,7 +1610,7 @@ <h1>QuantileTransformer<a class="headerlink" href="#quantiletransformer" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.QuantileTransformer.transform">
-<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/preprocessing/_data.py#L2977"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.QuantileTransformer.transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/preprocessing/_data.py#L2988"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.QuantileTransformer.transform" title="Link to this definition">#</a></dt>
 <dd><p>Feature-wise transformation of the data.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.preprocessing.RobustScaler.html b/dev/modules/generated/sklearn.preprocessing.RobustScaler.html
index a4e64801e90a3..e7d9b395df26f 100644
--- a/dev/modules/generated/sklearn.preprocessing.RobustScaler.html
+++ b/dev/modules/generated/sklearn.preprocessing.RobustScaler.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>RobustScaler<a class="headerlink" href="#robustscaler" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.preprocessing.RobustScaler">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.preprocessing.</span></span><span class="sig-name descname"><span class="pre">RobustScaler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_centering</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_scaling</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quantile_range</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(25.0,</span> <span class="pre">75.0)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">unit_variance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/preprocessing/_data.py#L1478"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.RobustScaler" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.preprocessing.</span></span><span class="sig-name descname"><span class="pre">RobustScaler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_centering</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_scaling</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quantile_range</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(25.0,</span> <span class="pre">75.0)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">unit_variance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/preprocessing/_data.py#L1486"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.RobustScaler" title="Link to this definition">#</a></dt>
 <dd><p>Scale features using statistics that are robust to outliers.</p>
 <p>This Scaler removes the median and scales the data according to
 the quantile range (defaults to IQR: Interquartile Range).
@@ -1413,7 +1413,7 @@ <h1>RobustScaler<a class="headerlink" href="#robustscaler" title="Link to this h
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.RobustScaler.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/preprocessing/_data.py#L1610"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.RobustScaler.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/preprocessing/_data.py#L1618"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.RobustScaler.fit" title="Link to this definition">#</a></dt>
 <dd><p>Compute the median and quantiles to be used for scaling.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1436,7 +1436,7 @@ <h1>RobustScaler<a class="headerlink" href="#robustscaler" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.RobustScaler.fit_transform">
-<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L863"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.RobustScaler.fit_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L804"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.RobustScaler.fit_transform" title="Link to this definition">#</a></dt>
 <dd><p>Fit to data, then transform it.</p>
 <p>Fits transformer to <code class="docutils literal notranslate"><span class="pre">X</span></code> and <code class="docutils literal notranslate"><span class="pre">y</span></code> with optional parameters <code class="docutils literal notranslate"><span class="pre">fit_params</span></code>
 and returns a transformed version of <code class="docutils literal notranslate"><span class="pre">X</span></code>.</p>
@@ -1462,7 +1462,7 @@ <h1>RobustScaler<a class="headerlink" href="#robustscaler" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.RobustScaler.get_feature_names_out">
-<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L943"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.RobustScaler.get_feature_names_out" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L884"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.RobustScaler.get_feature_names_out" title="Link to this definition">#</a></dt>
 <dd><p>Get output feature names for transformation.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1490,7 +1490,7 @@ <h1>RobustScaler<a class="headerlink" href="#robustscaler" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.RobustScaler.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.RobustScaler.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.RobustScaler.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1507,7 +1507,7 @@ <h1>RobustScaler<a class="headerlink" href="#robustscaler" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.RobustScaler.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.RobustScaler.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.RobustScaler.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1528,7 +1528,7 @@ <h1>RobustScaler<a class="headerlink" href="#robustscaler" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.RobustScaler.inverse_transform">
-<span class="sig-name descname"><span class="pre">inverse_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/preprocessing/_data.py#L1713"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.RobustScaler.inverse_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">inverse_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/preprocessing/_data.py#L1721"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.RobustScaler.inverse_transform" title="Link to this definition">#</a></dt>
 <dd><p>Scale back the data to the original representation.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1539,7 +1539,7 @@ <h1>RobustScaler<a class="headerlink" href="#robustscaler" title="Link to this h
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><dl class="simple">
-<dt><strong>X_tr</strong><span class="classifier">{ndarray, sparse matrix} of shape (n_samples, n_features)</span></dt><dd><p>Transformed array.</p>
+<dt><strong>X_original</strong><span class="classifier">{ndarray, sparse matrix} of shape (n_samples, n_features)</span></dt><dd><p>Transformed array.</p>
 </dd>
 </dl>
 </dd>
@@ -1548,7 +1548,7 @@ <h1>RobustScaler<a class="headerlink" href="#robustscaler" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.RobustScaler.set_output">
-<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.RobustScaler.set_output" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.RobustScaler.set_output" title="Link to this definition">#</a></dt>
 <dd><p>Set output container.</p>
 <p>See <a class="reference internal" href="http://github.com/auto_examples/miscellaneous/plot_set_output.html#sphx-glr-auto-examples-miscellaneous-plot-set-output-py"><span class="std std-ref">Introducing the set_output API</span></a>
 for an example on how to use the API.</p>
@@ -1579,7 +1579,7 @@ <h1>RobustScaler<a class="headerlink" href="#robustscaler" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.RobustScaler.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.RobustScaler.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.RobustScaler.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1603,7 +1603,7 @@ <h1>RobustScaler<a class="headerlink" href="#robustscaler" title="Link to this h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.RobustScaler.transform">
-<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/preprocessing/_data.py#L1678"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.RobustScaler.transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/preprocessing/_data.py#L1686"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.RobustScaler.transform" title="Link to this definition">#</a></dt>
 <dd><p>Center and scale the data.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.preprocessing.SplineTransformer.html b/dev/modules/generated/sklearn.preprocessing.SplineTransformer.html
index 23a2c072409dd..8e2af02be89ad 100644
--- a/dev/modules/generated/sklearn.preprocessing.SplineTransformer.html
+++ b/dev/modules/generated/sklearn.preprocessing.SplineTransformer.html
@@ -11,10 +11,10 @@
 <meta property="og:type" content="website" />
 <meta property="og:url" content="https://scikit-learn/stable/modules/generated/sklearn.preprocessing.SplineTransformer.html" />
 <meta property="og:site_name" content="scikit-learn" />
-<meta property="og:description" content="Gallery examples: Time-related feature engineering Plot classification probability Polynomial and Spline interpolation Evaluation of outlier detection estimators Release Highlights for scikit-learn..." />
+<meta property="og:description" content="Gallery examples: Time-related feature engineering Plot classification probability Visualizing the probabilistic predictions of a VotingClassifier Polynomial and Spline interpolation Evaluation of ..." />
 <meta property="og:image" content="https://scikit-learn/stable/_images/sphx_glr_plot_cyclical_feature_engineering_thumb.png" />
 <meta property="og:image:alt" content="" />
-<meta name="description" content="Gallery examples: Time-related feature engineering Plot classification probability Polynomial and Spline interpolation Evaluation of outlier detection estimators Release Highlights for scikit-learn..." />
+<meta name="description" content="Gallery examples: Time-related feature engineering Plot classification probability Visualizing the probabilistic predictions of a VotingClassifier Polynomial and Spline interpolation Evaluation of ..." />
 
     <title>SplineTransformer &#8212; scikit-learn 1.7.dev0 documentation</title>
   
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>SplineTransformer<a class="headerlink" href="#splinetransformer" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.preprocessing.SplineTransformer">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.preprocessing.</span></span><span class="sig-name descname"><span class="pre">SplineTransformer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_knots</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">degree</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">knots</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'uniform'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">extrapolation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'constant'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">include_bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">order</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'C'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sparse_output</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/preprocessing/_polynomial.py#L576"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.SplineTransformer" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.preprocessing.</span></span><span class="sig-name descname"><span class="pre">SplineTransformer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_knots</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">degree</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">knots</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'uniform'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">extrapolation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'constant'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">include_bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">order</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'C'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sparse_output</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/preprocessing/_polynomial.py#L576"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.SplineTransformer" title="Link to this definition">#</a></dt>
 <dd><p>Generate univariate B-spline bases for features.</p>
 <p>Generate a new feature matrix consisting of
 <code class="docutils literal notranslate"><span class="pre">n_splines=n_knots</span> <span class="pre">+</span> <span class="pre">degree</span> <span class="pre">-</span> <span class="pre">1</span></code> (<code class="docutils literal notranslate"><span class="pre">n_knots</span> <span class="pre">-</span> <span class="pre">1</span></code> for
@@ -1421,7 +1421,7 @@ <h1>SplineTransformer<a class="headerlink" href="#splinetransformer" title="Link
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.SplineTransformer.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/preprocessing/_polynomial.py#L806"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.SplineTransformer.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/preprocessing/_polynomial.py#L806"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.SplineTransformer.fit" title="Link to this definition">#</a></dt>
 <dd><p>Compute knot positions of splines.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1447,7 +1447,7 @@ <h1>SplineTransformer<a class="headerlink" href="#splinetransformer" title="Link
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.SplineTransformer.fit_transform">
-<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L863"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.SplineTransformer.fit_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L804"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.SplineTransformer.fit_transform" title="Link to this definition">#</a></dt>
 <dd><p>Fit to data, then transform it.</p>
 <p>Fits transformer to <code class="docutils literal notranslate"><span class="pre">X</span></code> and <code class="docutils literal notranslate"><span class="pre">y</span></code> with optional parameters <code class="docutils literal notranslate"><span class="pre">fit_params</span></code>
 and returns a transformed version of <code class="docutils literal notranslate"><span class="pre">X</span></code>.</p>
@@ -1473,7 +1473,7 @@ <h1>SplineTransformer<a class="headerlink" href="#splinetransformer" title="Link
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.SplineTransformer.get_feature_names_out">
-<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/preprocessing/_polynomial.py#L776"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.SplineTransformer.get_feature_names_out" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/preprocessing/_polynomial.py#L776"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.SplineTransformer.get_feature_names_out" title="Link to this definition">#</a></dt>
 <dd><p>Get output feature names for transformation.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1501,7 +1501,7 @@ <h1>SplineTransformer<a class="headerlink" href="#splinetransformer" title="Link
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.SplineTransformer.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.SplineTransformer.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.SplineTransformer.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1518,7 +1518,7 @@ <h1>SplineTransformer<a class="headerlink" href="#splinetransformer" title="Link
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.SplineTransformer.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.SplineTransformer.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.SplineTransformer.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1539,7 +1539,7 @@ <h1>SplineTransformer<a class="headerlink" href="#splinetransformer" title="Link
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.SplineTransformer.set_fit_request">
-<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.preprocessing.SplineTransformer" title="sklearn.preprocessing._polynomial.SplineTransformer"><span class="pre">SplineTransformer</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.SplineTransformer.set_fit_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.preprocessing.SplineTransformer" title="sklearn.preprocessing._polynomial.SplineTransformer"><span class="pre">SplineTransformer</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.SplineTransformer.set_fit_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1582,7 +1582,7 @@ <h1>SplineTransformer<a class="headerlink" href="#splinetransformer" title="Link
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.SplineTransformer.set_output">
-<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.SplineTransformer.set_output" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.SplineTransformer.set_output" title="Link to this definition">#</a></dt>
 <dd><p>Set output container.</p>
 <p>See <a class="reference internal" href="http://github.com/auto_examples/miscellaneous/plot_set_output.html#sphx-glr-auto-examples-miscellaneous-plot-set-output-py"><span class="std std-ref">Introducing the set_output API</span></a>
 for an example on how to use the API.</p>
@@ -1613,7 +1613,7 @@ <h1>SplineTransformer<a class="headerlink" href="#splinetransformer" title="Link
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.SplineTransformer.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.SplineTransformer.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.SplineTransformer.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1637,7 +1637,7 @@ <h1>SplineTransformer<a class="headerlink" href="#splinetransformer" title="Link
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.SplineTransformer.transform">
-<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/preprocessing/_polynomial.py#L935"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.SplineTransformer.transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/preprocessing/_polynomial.py#L935"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.SplineTransformer.transform" title="Link to this definition">#</a></dt>
 <dd><p>Transform each feature data to B-splines.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1666,6 +1666,9 @@ <h2>Gallery examples<a class="headerlink" href="#gallery-examples" title="Link t
 </div><div class="sphx-glr-thumbcontainer" tooltip="This example illustrates the use of sklearn.inspection.DecisionBoundaryDisplay to plot the predicted class probabilities of various classifiers in a 2D feature space, mostly for didactic purposes."><img alt="" src="http://github.com/_images/sphx_glr_plot_classification_probability_thumb.png" />
 <p><a class="reference internal" href="http://github.com/auto_examples/classification/plot_classification_probability.html#sphx-glr-auto-examples-classification-plot-classification-probability-py"><span class="std std-ref">Plot classification probability</span></a></p>
   <div class="sphx-glr-thumbnail-title">Plot classification probability</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="Plot the predicted class probabilities in a toy dataset predicted by three different classifiers and averaged by the VotingClassifier."><img alt="" src="http://github.com/_images/sphx_glr_plot_voting_decision_regions_thumb.png" />
+<p><a class="reference internal" href="http://github.com/auto_examples/ensemble/plot_voting_decision_regions.html#sphx-glr-auto-examples-ensemble-plot-voting-decision-regions-py"><span class="std std-ref">Visualizing the probabilistic predictions of a VotingClassifier</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Visualizing the probabilistic predictions of a VotingClassifier</div>
 </div><div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how to approximate a function with polynomials up to degree degree by using ridge regression. We show two different ways given n_samples of 1d points x_i:"><img alt="" src="http://github.com/_images/sphx_glr_plot_polynomial_interpolation_thumb.png" />
 <p><a class="reference internal" href="http://github.com/auto_examples/linear_model/plot_polynomial_interpolation.html#sphx-glr-auto-examples-linear-model-plot-polynomial-interpolation-py"><span class="std std-ref">Polynomial and Spline interpolation</span></a></p>
   <div class="sphx-glr-thumbnail-title">Polynomial and Spline interpolation</div>
diff --git a/dev/modules/generated/sklearn.preprocessing.StandardScaler.html b/dev/modules/generated/sklearn.preprocessing.StandardScaler.html
index 74302df417974..1e0a4754328af 100644
--- a/dev/modules/generated/sklearn.preprocessing.StandardScaler.html
+++ b/dev/modules/generated/sklearn.preprocessing.StandardScaler.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>StandardScaler<a class="headerlink" href="#standardscaler" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.preprocessing.StandardScaler">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.preprocessing.</span></span><span class="sig-name descname"><span class="pre">StandardScaler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_mean</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_std</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/preprocessing/_data.py#L716"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.StandardScaler" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.preprocessing.</span></span><span class="sig-name descname"><span class="pre">StandardScaler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_mean</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_std</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/preprocessing/_data.py#L723"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.StandardScaler" title="Link to this definition">#</a></dt>
 <dd><p>Standardize features by removing the mean and scaling to unit variance.</p>
 <p>The standard score of a sample <code class="docutils literal notranslate"><span class="pre">x</span></code> is calculated as:</p>
 <div class="highlight-text notranslate"><div class="highlight"><pre><span></span>z = (x - u) / s
@@ -1430,7 +1430,7 @@ <h1>StandardScaler<a class="headerlink" href="#standardscaler" title="Link to th
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.StandardScaler.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/preprocessing/_data.py#L875"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.StandardScaler.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/preprocessing/_data.py#L882"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.StandardScaler.fit" title="Link to this definition">#</a></dt>
 <dd><p>Compute the mean and std to be used for later scaling.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1458,7 +1458,7 @@ <h1>StandardScaler<a class="headerlink" href="#standardscaler" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.StandardScaler.fit_transform">
-<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L863"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.StandardScaler.fit_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L804"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.StandardScaler.fit_transform" title="Link to this definition">#</a></dt>
 <dd><p>Fit to data, then transform it.</p>
 <p>Fits transformer to <code class="docutils literal notranslate"><span class="pre">X</span></code> and <code class="docutils literal notranslate"><span class="pre">y</span></code> with optional parameters <code class="docutils literal notranslate"><span class="pre">fit_params</span></code>
 and returns a transformed version of <code class="docutils literal notranslate"><span class="pre">X</span></code>.</p>
@@ -1484,7 +1484,7 @@ <h1>StandardScaler<a class="headerlink" href="#standardscaler" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.StandardScaler.get_feature_names_out">
-<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L943"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.StandardScaler.get_feature_names_out" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L884"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.StandardScaler.get_feature_names_out" title="Link to this definition">#</a></dt>
 <dd><p>Get output feature names for transformation.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1512,7 +1512,7 @@ <h1>StandardScaler<a class="headerlink" href="#standardscaler" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.StandardScaler.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.StandardScaler.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.StandardScaler.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1529,7 +1529,7 @@ <h1>StandardScaler<a class="headerlink" href="#standardscaler" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.StandardScaler.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.StandardScaler.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.StandardScaler.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1550,20 +1550,20 @@ <h1>StandardScaler<a class="headerlink" href="#standardscaler" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.StandardScaler.inverse_transform">
-<span class="sig-name descname"><span class="pre">inverse_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/preprocessing/_data.py#L1094"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.StandardScaler.inverse_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">inverse_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/preprocessing/_data.py#L1101"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.StandardScaler.inverse_transform" title="Link to this definition">#</a></dt>
 <dd><p>Scale back the data to the original representation.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><dl class="simple">
 <dt><strong>X</strong><span class="classifier">{array-like, sparse matrix} of shape (n_samples, n_features)</span></dt><dd><p>The data used to scale along the features axis.</p>
 </dd>
-<dt><strong>copy</strong><span class="classifier">bool, default=None</span></dt><dd><p>Copy the input X or not.</p>
+<dt><strong>copy</strong><span class="classifier">bool, default=None</span></dt><dd><p>Copy the input <code class="docutils literal notranslate"><span class="pre">X</span></code> or not.</p>
 </dd>
 </dl>
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><dl class="simple">
-<dt><strong>X_tr</strong><span class="classifier">{ndarray, sparse matrix} of shape (n_samples, n_features)</span></dt><dd><p>Transformed array.</p>
+<dt><strong>X_original</strong><span class="classifier">{ndarray, sparse matrix} of shape (n_samples, n_features)</span></dt><dd><p>Transformed array.</p>
 </dd>
 </dl>
 </dd>
@@ -1572,7 +1572,7 @@ <h1>StandardScaler<a class="headerlink" href="#standardscaler" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.StandardScaler.partial_fit">
-<span class="sig-name descname"><span class="pre">partial_fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/preprocessing/_data.py#L902"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.StandardScaler.partial_fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">partial_fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/preprocessing/_data.py#L909"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.StandardScaler.partial_fit" title="Link to this definition">#</a></dt>
 <dd><p>Online computation of mean and std on X for later scaling.</p>
 <p>All of X is processed as a single batch. This is intended for cases
 when <a class="reference internal" href="#sklearn.preprocessing.StandardScaler.fit" title="sklearn.preprocessing.StandardScaler.fit"><code class="xref py py-meth docutils literal notranslate"><span class="pre">fit</span></code></a> is not feasible due to very large number of
@@ -1607,7 +1607,7 @@ <h1>StandardScaler<a class="headerlink" href="#standardscaler" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.StandardScaler.set_fit_request">
-<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.preprocessing.StandardScaler" title="sklearn.preprocessing._data.StandardScaler"><span class="pre">StandardScaler</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.StandardScaler.set_fit_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.preprocessing.StandardScaler" title="sklearn.preprocessing._data.StandardScaler"><span class="pre">StandardScaler</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.StandardScaler.set_fit_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1650,7 +1650,7 @@ <h1>StandardScaler<a class="headerlink" href="#standardscaler" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.StandardScaler.set_inverse_transform_request">
-<span class="sig-name descname"><span class="pre">set_inverse_transform_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.preprocessing.StandardScaler" title="sklearn.preprocessing._data.StandardScaler"><span class="pre">StandardScaler</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.StandardScaler.set_inverse_transform_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_inverse_transform_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.preprocessing.StandardScaler" title="sklearn.preprocessing._data.StandardScaler"><span class="pre">StandardScaler</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.StandardScaler.set_inverse_transform_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">inverse_transform</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1693,7 +1693,7 @@ <h1>StandardScaler<a class="headerlink" href="#standardscaler" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.StandardScaler.set_output">
-<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.StandardScaler.set_output" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.StandardScaler.set_output" title="Link to this definition">#</a></dt>
 <dd><p>Set output container.</p>
 <p>See <a class="reference internal" href="http://github.com/auto_examples/miscellaneous/plot_set_output.html#sphx-glr-auto-examples-miscellaneous-plot-set-output-py"><span class="std std-ref">Introducing the set_output API</span></a>
 for an example on how to use the API.</p>
@@ -1724,7 +1724,7 @@ <h1>StandardScaler<a class="headerlink" href="#standardscaler" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.StandardScaler.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.StandardScaler.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.StandardScaler.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1748,7 +1748,7 @@ <h1>StandardScaler<a class="headerlink" href="#standardscaler" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.StandardScaler.set_partial_fit_request">
-<span class="sig-name descname"><span class="pre">set_partial_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.preprocessing.StandardScaler" title="sklearn.preprocessing._data.StandardScaler"><span class="pre">StandardScaler</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.StandardScaler.set_partial_fit_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_partial_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.preprocessing.StandardScaler" title="sklearn.preprocessing._data.StandardScaler"><span class="pre">StandardScaler</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.StandardScaler.set_partial_fit_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">partial_fit</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1791,7 +1791,7 @@ <h1>StandardScaler<a class="headerlink" href="#standardscaler" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.StandardScaler.set_transform_request">
-<span class="sig-name descname"><span class="pre">set_transform_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.preprocessing.StandardScaler" title="sklearn.preprocessing._data.StandardScaler"><span class="pre">StandardScaler</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.StandardScaler.set_transform_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_transform_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.preprocessing.StandardScaler" title="sklearn.preprocessing._data.StandardScaler"><span class="pre">StandardScaler</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.StandardScaler.set_transform_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">transform</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1834,7 +1834,7 @@ <h1>StandardScaler<a class="headerlink" href="#standardscaler" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.StandardScaler.transform">
-<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/preprocessing/_data.py#L1050"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.StandardScaler.transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/preprocessing/_data.py#L1057"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.StandardScaler.transform" title="Link to this definition">#</a></dt>
 <dd><p>Perform standardization by centering and scaling.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1897,6 +1897,9 @@ <h2>Gallery examples<a class="headerlink" href="#gallery-examples" title="Link t
 </div><div class="sphx-glr-thumbcontainer" tooltip="Stacking refers to a method to blend estimators. In this strategy, some estimators are individually fitted on some training data while a final estimator is trained using the stacked predictions of these base estimators."><img alt="" src="http://github.com/_images/sphx_glr_plot_stack_predictors_thumb.png" />
 <p><a class="reference internal" href="http://github.com/auto_examples/ensemble/plot_stack_predictors.html#sphx-glr-auto-examples-ensemble-plot-stack-predictors-py"><span class="std std-ref">Combine predictors using stacking</span></a></p>
   <div class="sphx-glr-thumbnail-title">Combine predictors using stacking</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="Plot the predicted class probabilities in a toy dataset predicted by three different classifiers and averaged by the VotingClassifier."><img alt="" src="http://github.com/_images/sphx_glr_plot_voting_decision_regions_thumb.png" />
+<p><a class="reference internal" href="http://github.com/auto_examples/ensemble/plot_voting_decision_regions.html#sphx-glr-auto-examples-ensemble-plot-voting-decision-regions-py"><span class="std std-ref">Visualizing the probabilistic predictions of a VotingClassifier</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Visualizing the probabilistic predictions of a VotingClassifier</div>
 </div><div class="sphx-glr-thumbcontainer" tooltip="This example illustrates and compares two approaches for feature selection: SelectFromModel which is based on feature importance, and SequentialFeatureSelector which relies on a greedy approach."><img alt="" src="http://github.com/_images/sphx_glr_plot_select_from_model_diabetes_thumb.png" />
 <p><a class="reference internal" href="http://github.com/auto_examples/feature_selection/plot_select_from_model_diabetes.html#sphx-glr-auto-examples-feature-selection-plot-select-from-model-diabetes-py"><span class="std std-ref">Model-based and sequential feature selection</span></a></p>
   <div class="sphx-glr-thumbnail-title">Model-based and sequential feature selection</div>
diff --git a/dev/modules/generated/sklearn.preprocessing.TargetEncoder.html b/dev/modules/generated/sklearn.preprocessing.TargetEncoder.html
index b054bea24de94..f219ff1543a2b 100644
--- a/dev/modules/generated/sklearn.preprocessing.TargetEncoder.html
+++ b/dev/modules/generated/sklearn.preprocessing.TargetEncoder.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>TargetEncoder<a class="headerlink" href="#targetencoder" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.preprocessing.TargetEncoder">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.preprocessing.</span></span><span class="sig-name descname"><span class="pre">TargetEncoder</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">categories</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">smooth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cv</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/preprocessing/_target_encoder.py#L21"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.TargetEncoder" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.preprocessing.</span></span><span class="sig-name descname"><span class="pre">TargetEncoder</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">categories</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">smooth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cv</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/preprocessing/_target_encoder.py#L21"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.TargetEncoder" title="Link to this definition">#</a></dt>
 <dd><p>Target Encoder for regression and classification targets.</p>
 <p>Each category is encoded based on a shrunk estimate of the average target
 values for observations belonging to the category. The encoding scheme mixes
@@ -1455,21 +1455,21 @@ <h1>TargetEncoder<a class="headerlink" href="#targetencoder" title="Link to this
 <span class="gp">&gt;&gt;&gt; </span><span class="c1"># encodings:</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">enc_high_smooth</span> <span class="o">=</span> <span class="n">TargetEncoder</span><span class="p">(</span><span class="n">smooth</span><span class="o">=</span><span class="mf">5000.0</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">enc_high_smooth</span><span class="o">.</span><span class="n">target_mean_</span>
-<span class="go">np.float64(44...)</span>
+<span class="go">np.float64(44.3)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">enc_high_smooth</span><span class="o">.</span><span class="n">encodings_</span>
-<span class="go">[array([44..., 44..., 44...])]</span>
+<span class="go">[array([44.1, 44.4, 44.3])]</span>
 </pre></div>
 </div>
 <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="c1"># On the other hand, a low `smooth` parameter puts more weight on target</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="c1"># conditioned on the value of the categorical:</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">enc_low_smooth</span> <span class="o">=</span> <span class="n">TargetEncoder</span><span class="p">(</span><span class="n">smooth</span><span class="o">=</span><span class="mf">1.0</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">enc_low_smooth</span><span class="o">.</span><span class="n">encodings_</span>
-<span class="go">[array([20..., 80..., 43...])]</span>
+<span class="go">[array([21, 80.8, 43.2])]</span>
 </pre></div>
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.TargetEncoder.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/preprocessing/_target_encoder.py#L214"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.TargetEncoder.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/preprocessing/_target_encoder.py#L214"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.TargetEncoder.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit the <a class="reference internal" href="#sklearn.preprocessing.TargetEncoder" title="sklearn.preprocessing.TargetEncoder"><code class="xref py py-class docutils literal notranslate"><span class="pre">TargetEncoder</span></code></a> to X and y.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1491,7 +1491,7 @@ <h1>TargetEncoder<a class="headerlink" href="#targetencoder" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.TargetEncoder.fit_transform">
-<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/preprocessing/_target_encoder.py#L234"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.TargetEncoder.fit_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/preprocessing/_target_encoder.py#L234"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.TargetEncoder.fit_transform" title="Link to this definition">#</a></dt>
 <dd><p>Fit <a class="reference internal" href="#sklearn.preprocessing.TargetEncoder" title="sklearn.preprocessing.TargetEncoder"><code class="xref py py-class docutils literal notranslate"><span class="pre">TargetEncoder</span></code></a> and transform X with the target encoding.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
@@ -1519,7 +1519,7 @@ <h1>TargetEncoder<a class="headerlink" href="#targetencoder" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.TargetEncoder.get_feature_names_out">
-<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/preprocessing/_target_encoder.py#L502"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.TargetEncoder.get_feature_names_out" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/preprocessing/_target_encoder.py#L502"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.TargetEncoder.get_feature_names_out" title="Link to this definition">#</a></dt>
 <dd><p>Get output feature names for transformation.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1543,7 +1543,7 @@ <h1>TargetEncoder<a class="headerlink" href="#targetencoder" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.TargetEncoder.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.TargetEncoder.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.TargetEncoder.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1560,7 +1560,7 @@ <h1>TargetEncoder<a class="headerlink" href="#targetencoder" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.TargetEncoder.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.TargetEncoder.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.TargetEncoder.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1581,7 +1581,7 @@ <h1>TargetEncoder<a class="headerlink" href="#targetencoder" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.TargetEncoder.set_output">
-<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.TargetEncoder.set_output" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.TargetEncoder.set_output" title="Link to this definition">#</a></dt>
 <dd><p>Set output container.</p>
 <p>See <a class="reference internal" href="http://github.com/auto_examples/miscellaneous/plot_set_output.html#sphx-glr-auto-examples-miscellaneous-plot-set-output-py"><span class="std std-ref">Introducing the set_output API</span></a>
 for an example on how to use the API.</p>
@@ -1612,7 +1612,7 @@ <h1>TargetEncoder<a class="headerlink" href="#targetencoder" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.TargetEncoder.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.TargetEncoder.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.TargetEncoder.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1636,7 +1636,7 @@ <h1>TargetEncoder<a class="headerlink" href="#targetencoder" title="Link to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.preprocessing.TargetEncoder.transform">
-<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/preprocessing/_target_encoder.py#L308"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.TargetEncoder.transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/preprocessing/_target_encoder.py#L308"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.TargetEncoder.transform" title="Link to this definition">#</a></dt>
 <dd><p>Transform X with the target encoding.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
diff --git a/dev/modules/generated/sklearn.preprocessing.add_dummy_feature.html b/dev/modules/generated/sklearn.preprocessing.add_dummy_feature.html
index 2e22b3f625e2e..d7acb70f49770 100644
--- a/dev/modules/generated/sklearn.preprocessing.add_dummy_feature.html
+++ b/dev/modules/generated/sklearn.preprocessing.add_dummy_feature.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>add_dummy_feature<a class="headerlink" href="#add-dummy-feature" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.preprocessing.add_dummy_feature">
-<span class="sig-prename descclassname"><span class="pre">sklearn.preprocessing.</span></span><span class="sig-name descname"><span class="pre">add_dummy_feature</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">value</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/preprocessing/_data.py#L2526"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.add_dummy_feature" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.preprocessing.</span></span><span class="sig-name descname"><span class="pre">add_dummy_feature</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">value</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/preprocessing/_data.py#L2537"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.add_dummy_feature" title="Link to this definition">#</a></dt>
 <dd><p>Augment dataset with an additional dummy feature.</p>
 <p>This is useful for fitting an intercept term with implementations which
 cannot otherwise fit it directly.</p>
diff --git a/dev/modules/generated/sklearn.preprocessing.binarize.html b/dev/modules/generated/sklearn.preprocessing.binarize.html
index 0b44eb933e4e1..9ca475a421777 100644
--- a/dev/modules/generated/sklearn.preprocessing.binarize.html
+++ b/dev/modules/generated/sklearn.preprocessing.binarize.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>binarize<a class="headerlink" href="#binarize" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.preprocessing.binarize">
-<span class="sig-prename descclassname"><span class="pre">sklearn.preprocessing.</span></span><span class="sig-name descname"><span class="pre">binarize</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">threshold</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/preprocessing/_data.py#L2154"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.binarize" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.preprocessing.</span></span><span class="sig-name descname"><span class="pre">binarize</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">threshold</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/preprocessing/_data.py#L2162"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.binarize" title="Link to this definition">#</a></dt>
 <dd><p>Boolean thresholding of array-like or scipy.sparse matrix.</p>
 <p>Read more in the <a class="reference internal" href="http://github.com/preprocessing.html#preprocessing-binarization"><span class="std std-ref">User Guide</span></a>.</p>
 <dl class="field-list simple">
diff --git a/dev/modules/generated/sklearn.preprocessing.label_binarize.html b/dev/modules/generated/sklearn.preprocessing.label_binarize.html
index badbfba8a8e91..b2d48a30625a7 100644
--- a/dev/modules/generated/sklearn.preprocessing.label_binarize.html
+++ b/dev/modules/generated/sklearn.preprocessing.label_binarize.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>label_binarize<a class="headerlink" href="#label-binarize" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.preprocessing.label_binarize">
-<span class="sig-prename descclassname"><span class="pre">sklearn.preprocessing.</span></span><span class="sig-name descname"><span class="pre">label_binarize</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">classes</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">neg_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pos_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sparse_output</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/preprocessing/_label.py#L429"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.label_binarize" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.preprocessing.</span></span><span class="sig-name descname"><span class="pre">label_binarize</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">classes</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">neg_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pos_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sparse_output</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/preprocessing/_label.py#L429"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.label_binarize" title="Link to this definition">#</a></dt>
 <dd><p>Binarize labels in a one-vs-all fashion.</p>
 <p>Several regression and binary classification algorithms are
 available in scikit-learn. A simple way to extend these algorithms
diff --git a/dev/modules/generated/sklearn.preprocessing.maxabs_scale.html b/dev/modules/generated/sklearn.preprocessing.maxabs_scale.html
index 25653c1eb923c..8c69528caa277 100644
--- a/dev/modules/generated/sklearn.preprocessing.maxabs_scale.html
+++ b/dev/modules/generated/sklearn.preprocessing.maxabs_scale.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>maxabs_scale<a class="headerlink" href="#maxabs-scale" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.preprocessing.maxabs_scale">
-<span class="sig-prename descclassname"><span class="pre">sklearn.preprocessing.</span></span><span class="sig-name descname"><span class="pre">maxabs_scale</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">axis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/preprocessing/_data.py#L1377"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.maxabs_scale" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.preprocessing.</span></span><span class="sig-name descname"><span class="pre">maxabs_scale</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">axis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/preprocessing/_data.py#L1385"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.maxabs_scale" title="Link to this definition">#</a></dt>
 <dd><p>Scale each feature to the [-1, 1] range without breaking the sparsity.</p>
 <p>This estimator scales each feature individually such
 that the maximal absolute value of each feature in the
diff --git a/dev/modules/generated/sklearn.preprocessing.minmax_scale.html b/dev/modules/generated/sklearn.preprocessing.minmax_scale.html
index d40c313441e30..e43fe434e611c 100644
--- a/dev/modules/generated/sklearn.preprocessing.minmax_scale.html
+++ b/dev/modules/generated/sklearn.preprocessing.minmax_scale.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>minmax_scale<a class="headerlink" href="#minmax-scale" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.preprocessing.minmax_scale">
-<span class="sig-prename descclassname"><span class="pre">sklearn.preprocessing.</span></span><span class="sig-name descname"><span class="pre">minmax_scale</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">feature_range</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(0,</span> <span class="pre">1)</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">axis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/preprocessing/_data.py#L598"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.minmax_scale" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.preprocessing.</span></span><span class="sig-name descname"><span class="pre">minmax_scale</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">feature_range</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(0,</span> <span class="pre">1)</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">axis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/preprocessing/_data.py#L605"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.minmax_scale" title="Link to this definition">#</a></dt>
 <dd><p>Transform features by scaling each feature to a given range.</p>
 <p>This estimator scales and translates each feature individually such
 that it is in the given range on the training set, i.e. between
diff --git a/dev/modules/generated/sklearn.preprocessing.normalize.html b/dev/modules/generated/sklearn.preprocessing.normalize.html
index 221215de8d7b2..f535bc375f90c 100644
--- a/dev/modules/generated/sklearn.preprocessing.normalize.html
+++ b/dev/modules/generated/sklearn.preprocessing.normalize.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>normalize<a class="headerlink" href="#normalize" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.preprocessing.normalize">
-<span class="sig-prename descclassname"><span class="pre">sklearn.preprocessing.</span></span><span class="sig-name descname"><span class="pre">normalize</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'l2'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">axis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_norm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/preprocessing/_data.py#L1894"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.normalize" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.preprocessing.</span></span><span class="sig-name descname"><span class="pre">normalize</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'l2'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">axis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_norm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/preprocessing/_data.py#L1902"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.normalize" title="Link to this definition">#</a></dt>
 <dd><p>Scale input vectors individually to unit norm (vector length).</p>
 <p>Read more in the <a class="reference internal" href="http://github.com/preprocessing.html#preprocessing-normalization"><span class="std std-ref">User Guide</span></a>.</p>
 <dl class="field-list simple">
@@ -1358,8 +1358,8 @@ <h1>normalize<a class="headerlink" href="#normalize" title="Link to this heading
 <span class="go">array([[-0.4,  0.2,  0.4],</span>
 <span class="go">       [-0.5,  0. ,  0.5]])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">normalize</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">norm</span><span class="o">=</span><span class="s2">&quot;l2&quot;</span><span class="p">)</span>  <span class="c1"># L2 normalization each row independently</span>
-<span class="go">array([[-0.66...,  0.33...,  0.66...],</span>
-<span class="go">       [-0.70...,  0.     ,  0.70...]])</span>
+<span class="go">array([[-0.67, 0.33, 0.67],</span>
+<span class="go">       [-0.71, 0.  , 0.71]])</span>
 </pre></div>
 </div>
 </dd></dl>
diff --git a/dev/modules/generated/sklearn.preprocessing.power_transform.html b/dev/modules/generated/sklearn.preprocessing.power_transform.html
index e698e338349ed..6f018a432641b 100644
--- a/dev/modules/generated/sklearn.preprocessing.power_transform.html
+++ b/dev/modules/generated/sklearn.preprocessing.power_transform.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>power_transform<a class="headerlink" href="#power-transform" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.preprocessing.power_transform">
-<span class="sig-prename descclassname"><span class="pre">sklearn.preprocessing.</span></span><span class="sig-name descname"><span class="pre">power_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'yeo-johnson'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">standardize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/preprocessing/_data.py#L3591"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.power_transform" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.preprocessing.</span></span><span class="sig-name descname"><span class="pre">power_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'yeo-johnson'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">standardize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/preprocessing/_data.py#L3602"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.power_transform" title="Link to this definition">#</a></dt>
 <dd><p>Parametric, monotonic transformation to make data more Gaussian-like.</p>
 <p>Power transforms are a family of parametric, monotonic transformations
 that are applied to make data more Gaussian-like. This is useful for
@@ -1384,9 +1384,9 @@ <h1>power_transform<a class="headerlink" href="#power-transform" title="Link to
 <span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.preprocessing</span><span class="w"> </span><span class="kn">import</span> <span class="n">power_transform</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">data</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">]]</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">power_transform</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">&#39;box-cox&#39;</span><span class="p">))</span>
-<span class="go">[[-1.332... -0.707...]</span>
-<span class="go"> [ 0.256... -0.707...]</span>
-<span class="go"> [ 1.076...  1.414...]]</span>
+<span class="go">[[-1.332 -0.707]</span>
+<span class="go"> [ 0.256 -0.707]</span>
+<span class="go"> [ 1.076  1.414]]</span>
 </pre></div>
 </div>
 <div class="admonition warning">
diff --git a/dev/modules/generated/sklearn.preprocessing.quantile_transform.html b/dev/modules/generated/sklearn.preprocessing.quantile_transform.html
index 475797885c991..c32a8c416574f 100644
--- a/dev/modules/generated/sklearn.preprocessing.quantile_transform.html
+++ b/dev/modules/generated/sklearn.preprocessing.quantile_transform.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>quantile_transform<a class="headerlink" href="#quantile-transform" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.preprocessing.quantile_transform">
-<span class="sig-prename descclassname"><span class="pre">sklearn.preprocessing.</span></span><span class="sig-name descname"><span class="pre">quantile_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">axis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_quantiles</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_distribution</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'uniform'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ignore_implicit_zeros</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">subsample</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/preprocessing/_data.py#L3028"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.quantile_transform" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.preprocessing.</span></span><span class="sig-name descname"><span class="pre">quantile_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">axis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_quantiles</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_distribution</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'uniform'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ignore_implicit_zeros</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">subsample</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/preprocessing/_data.py#L3039"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.quantile_transform" title="Link to this definition">#</a></dt>
 <dd><p>Transform features using quantiles information.</p>
 <p>This method transforms the features to follow a uniform or a normal
 distribution. Therefore, for a given feature, this transformation tends
diff --git a/dev/modules/generated/sklearn.preprocessing.robust_scale.html b/dev/modules/generated/sklearn.preprocessing.robust_scale.html
index 77d53fdb04487..9d9b0183bff76 100644
--- a/dev/modules/generated/sklearn.preprocessing.robust_scale.html
+++ b/dev/modules/generated/sklearn.preprocessing.robust_scale.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>robust_scale<a class="headerlink" href="#robust-scale" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.preprocessing.robust_scale">
-<span class="sig-prename descclassname"><span class="pre">sklearn.preprocessing.</span></span><span class="sig-name descname"><span class="pre">robust_scale</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">axis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_centering</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_scaling</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quantile_range</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(25.0,</span> <span class="pre">75.0)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">unit_variance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/preprocessing/_data.py#L1753"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.robust_scale" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.preprocessing.</span></span><span class="sig-name descname"><span class="pre">robust_scale</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">axis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_centering</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_scaling</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quantile_range</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(25.0,</span> <span class="pre">75.0)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">unit_variance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/preprocessing/_data.py#L1761"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.robust_scale" title="Link to this definition">#</a></dt>
 <dd><p>Standardize a dataset along any axis.</p>
 <p>Center to the median and component wise scale
 according to the interquartile range.</p>
diff --git a/dev/modules/generated/sklearn.preprocessing.scale.html b/dev/modules/generated/sklearn.preprocessing.scale.html
index 79e68b67c98f4..1bd28f7d991a7 100644
--- a/dev/modules/generated/sklearn.preprocessing.scale.html
+++ b/dev/modules/generated/sklearn.preprocessing.scale.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>scale<a class="headerlink" href="#scale" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.preprocessing.scale">
-<span class="sig-prename descclassname"><span class="pre">sklearn.preprocessing.</span></span><span class="sig-name descname"><span class="pre">scale</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">axis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_mean</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_std</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/preprocessing/_data.py#L120"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.scale" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.preprocessing.</span></span><span class="sig-name descname"><span class="pre">scale</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">axis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_mean</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_std</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/preprocessing/_data.py#L127"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.scale" title="Link to this definition">#</a></dt>
 <dd><p>Standardize a dataset along any axis.</p>
 <p>Center to the mean and component wise scale to unit variance.</p>
 <p>Read more in the <a class="reference internal" href="http://github.com/preprocessing.html#preprocessing-scaler"><span class="std std-ref">User Guide</span></a>.</p>
@@ -1380,8 +1380,8 @@ <h1>scale<a class="headerlink" href="#scale" title="Link to this heading">#</a><
 <span class="go">array([[-1.,  1.,  1.],</span>
 <span class="go">       [ 1., -1., -1.]])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">scale</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>  <span class="c1"># scaling each row independently</span>
-<span class="go">array([[-1.37...,  0.39...,  0.98...],</span>
-<span class="go">       [-1.22...,  0.     ,  1.22...]])</span>
+<span class="go">array([[-1.37,  0.39,  0.98],</span>
+<span class="go">       [-1.22,  0.     ,  1.22]])</span>
 </pre></div>
 </div>
 </dd></dl>
diff --git a/dev/modules/generated/sklearn.random_projection.GaussianRandomProjection.html b/dev/modules/generated/sklearn.random_projection.GaussianRandomProjection.html
index 365f76ad9f5ec..53893d86a19c3 100644
--- a/dev/modules/generated/sklearn.random_projection.GaussianRandomProjection.html
+++ b/dev/modules/generated/sklearn.random_projection.GaussianRandomProjection.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>GaussianRandomProjection<a class="headerlink" href="#gaussianrandomprojection" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.random_projection.GaussianRandomProjection">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.random_projection.</span></span><span class="sig-name descname"><span class="pre">GaussianRandomProjection</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_components</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">compute_inverse_components</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/random_projection.py#L470"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.random_projection.GaussianRandomProjection" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.random_projection.</span></span><span class="sig-name descname"><span class="pre">GaussianRandomProjection</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_components</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">compute_inverse_components</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/random_projection.py#L470"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.random_projection.GaussianRandomProjection" title="Link to this definition">#</a></dt>
 <dd><p>Reduce dimensionality through Gaussian random projection.</p>
 <p>The components of the random matrix are drawn from N(0, 1 / n_components).</p>
 <p>Read more in the <a class="reference internal" href="http://github.com/random_projection.html#gaussian-random-matrix"><span class="std std-ref">User Guide</span></a>.</p>
@@ -1387,7 +1387,7 @@ <h1>GaussianRandomProjection<a class="headerlink" href="#gaussianrandomprojectio
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.random_projection.GaussianRandomProjection.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/random_projection.py#L366"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.random_projection.GaussianRandomProjection.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/random_projection.py#L366"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.random_projection.GaussianRandomProjection.fit" title="Link to this definition">#</a></dt>
 <dd><p>Generate a sparse random projection matrix.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1411,7 +1411,7 @@ <h1>GaussianRandomProjection<a class="headerlink" href="#gaussianrandomprojectio
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.random_projection.GaussianRandomProjection.fit_transform">
-<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L863"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.random_projection.GaussianRandomProjection.fit_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L804"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.random_projection.GaussianRandomProjection.fit_transform" title="Link to this definition">#</a></dt>
 <dd><p>Fit to data, then transform it.</p>
 <p>Fits transformer to <code class="docutils literal notranslate"><span class="pre">X</span></code> and <code class="docutils literal notranslate"><span class="pre">y</span></code> with optional parameters <code class="docutils literal notranslate"><span class="pre">fit_params</span></code>
 and returns a transformed version of <code class="docutils literal notranslate"><span class="pre">X</span></code>.</p>
@@ -1437,7 +1437,7 @@ <h1>GaussianRandomProjection<a class="headerlink" href="#gaussianrandomprojectio
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.random_projection.GaussianRandomProjection.get_feature_names_out">
-<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L995"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.random_projection.GaussianRandomProjection.get_feature_names_out" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L936"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.random_projection.GaussianRandomProjection.get_feature_names_out" title="Link to this definition">#</a></dt>
 <dd><p>Get output feature names for transformation.</p>
 <p>The feature names out will prefixed by the lowercased class name. For
 example, if the transformer outputs 3 features, then the feature names
@@ -1460,7 +1460,7 @@ <h1>GaussianRandomProjection<a class="headerlink" href="#gaussianrandomprojectio
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.random_projection.GaussianRandomProjection.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.random_projection.GaussianRandomProjection.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.random_projection.GaussianRandomProjection.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1477,7 +1477,7 @@ <h1>GaussianRandomProjection<a class="headerlink" href="#gaussianrandomprojectio
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.random_projection.GaussianRandomProjection.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.random_projection.GaussianRandomProjection.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.random_projection.GaussianRandomProjection.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1498,7 +1498,7 @@ <h1>GaussianRandomProjection<a class="headerlink" href="#gaussianrandomprojectio
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.random_projection.GaussianRandomProjection.inverse_transform">
-<span class="sig-name descname"><span class="pre">inverse_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/random_projection.py#L434"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.random_projection.GaussianRandomProjection.inverse_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">inverse_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/random_projection.py#L434"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.random_projection.GaussianRandomProjection.inverse_transform" title="Link to this definition">#</a></dt>
 <dd><p>Project data back to its original space.</p>
 <p>Returns an array X_original whose transform would be X. Note that even
 if X is sparse, X_original is dense: this may use a lot of RAM.</p>
@@ -1522,7 +1522,7 @@ <h1>GaussianRandomProjection<a class="headerlink" href="#gaussianrandomprojectio
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.random_projection.GaussianRandomProjection.set_output">
-<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.random_projection.GaussianRandomProjection.set_output" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.random_projection.GaussianRandomProjection.set_output" title="Link to this definition">#</a></dt>
 <dd><p>Set output container.</p>
 <p>See <a class="reference internal" href="http://github.com/auto_examples/miscellaneous/plot_set_output.html#sphx-glr-auto-examples-miscellaneous-plot-set-output-py"><span class="std std-ref">Introducing the set_output API</span></a>
 for an example on how to use the API.</p>
@@ -1553,7 +1553,7 @@ <h1>GaussianRandomProjection<a class="headerlink" href="#gaussianrandomprojectio
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.random_projection.GaussianRandomProjection.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.random_projection.GaussianRandomProjection.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.random_projection.GaussianRandomProjection.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1577,7 +1577,7 @@ <h1>GaussianRandomProjection<a class="headerlink" href="#gaussianrandomprojectio
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.random_projection.GaussianRandomProjection.transform">
-<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/random_projection.py#L590"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.random_projection.GaussianRandomProjection.transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/random_projection.py#L590"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.random_projection.GaussianRandomProjection.transform" title="Link to this definition">#</a></dt>
 <dd><p>Project the data by using matrix product with the random matrix.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.random_projection.SparseRandomProjection.html b/dev/modules/generated/sklearn.random_projection.SparseRandomProjection.html
index 71d4e3715ef43..2ae53ae1d9ec6 100644
--- a/dev/modules/generated/sklearn.random_projection.SparseRandomProjection.html
+++ b/dev/modules/generated/sklearn.random_projection.SparseRandomProjection.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>SparseRandomProjection<a class="headerlink" href="#sparserandomprojection" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.random_projection.SparseRandomProjection">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.random_projection.</span></span><span class="sig-name descname"><span class="pre">SparseRandomProjection</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_components</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">density</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dense_output</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">compute_inverse_components</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/random_projection.py#L615"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.random_projection.SparseRandomProjection" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.random_projection.</span></span><span class="sig-name descname"><span class="pre">SparseRandomProjection</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_components</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">density</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dense_output</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">compute_inverse_components</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/random_projection.py#L615"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.random_projection.SparseRandomProjection" title="Link to this definition">#</a></dt>
 <dd><p>Reduce dimensionality through sparse random projection.</p>
 <p>Sparse random matrix is an alternative to dense random
 projection matrix that guarantees similar embedding quality while being
@@ -1433,12 +1433,12 @@ <h1>SparseRandomProjection<a class="headerlink" href="#sparserandomprojection" t
 <span class="go">(25, 2759)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="c1"># very few components are non-zero</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">transformer</span><span class="o">.</span><span class="n">components_</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">)</span>
-<span class="go">np.float64(0.0182...)</span>
+<span class="go">np.float64(0.0182)</span>
 </pre></div>
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.random_projection.SparseRandomProjection.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/random_projection.py#L366"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.random_projection.SparseRandomProjection.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/random_projection.py#L366"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.random_projection.SparseRandomProjection.fit" title="Link to this definition">#</a></dt>
 <dd><p>Generate a sparse random projection matrix.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1462,7 +1462,7 @@ <h1>SparseRandomProjection<a class="headerlink" href="#sparserandomprojection" t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.random_projection.SparseRandomProjection.fit_transform">
-<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L863"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.random_projection.SparseRandomProjection.fit_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L804"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.random_projection.SparseRandomProjection.fit_transform" title="Link to this definition">#</a></dt>
 <dd><p>Fit to data, then transform it.</p>
 <p>Fits transformer to <code class="docutils literal notranslate"><span class="pre">X</span></code> and <code class="docutils literal notranslate"><span class="pre">y</span></code> with optional parameters <code class="docutils literal notranslate"><span class="pre">fit_params</span></code>
 and returns a transformed version of <code class="docutils literal notranslate"><span class="pre">X</span></code>.</p>
@@ -1488,7 +1488,7 @@ <h1>SparseRandomProjection<a class="headerlink" href="#sparserandomprojection" t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.random_projection.SparseRandomProjection.get_feature_names_out">
-<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L995"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.random_projection.SparseRandomProjection.get_feature_names_out" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L936"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.random_projection.SparseRandomProjection.get_feature_names_out" title="Link to this definition">#</a></dt>
 <dd><p>Get output feature names for transformation.</p>
 <p>The feature names out will prefixed by the lowercased class name. For
 example, if the transformer outputs 3 features, then the feature names
@@ -1511,7 +1511,7 @@ <h1>SparseRandomProjection<a class="headerlink" href="#sparserandomprojection" t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.random_projection.SparseRandomProjection.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.random_projection.SparseRandomProjection.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.random_projection.SparseRandomProjection.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1528,7 +1528,7 @@ <h1>SparseRandomProjection<a class="headerlink" href="#sparserandomprojection" t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.random_projection.SparseRandomProjection.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.random_projection.SparseRandomProjection.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.random_projection.SparseRandomProjection.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1549,7 +1549,7 @@ <h1>SparseRandomProjection<a class="headerlink" href="#sparserandomprojection" t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.random_projection.SparseRandomProjection.inverse_transform">
-<span class="sig-name descname"><span class="pre">inverse_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/random_projection.py#L434"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.random_projection.SparseRandomProjection.inverse_transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">inverse_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/random_projection.py#L434"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.random_projection.SparseRandomProjection.inverse_transform" title="Link to this definition">#</a></dt>
 <dd><p>Project data back to its original space.</p>
 <p>Returns an array X_original whose transform would be X. Note that even
 if X is sparse, X_original is dense: this may use a lot of RAM.</p>
@@ -1573,7 +1573,7 @@ <h1>SparseRandomProjection<a class="headerlink" href="#sparserandomprojection" t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.random_projection.SparseRandomProjection.set_output">
-<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.random_projection.SparseRandomProjection.set_output" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.random_projection.SparseRandomProjection.set_output" title="Link to this definition">#</a></dt>
 <dd><p>Set output container.</p>
 <p>See <a class="reference internal" href="http://github.com/auto_examples/miscellaneous/plot_set_output.html#sphx-glr-auto-examples-miscellaneous-plot-set-output-py"><span class="std std-ref">Introducing the set_output API</span></a>
 for an example on how to use the API.</p>
@@ -1604,7 +1604,7 @@ <h1>SparseRandomProjection<a class="headerlink" href="#sparserandomprojection" t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.random_projection.SparseRandomProjection.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.random_projection.SparseRandomProjection.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.random_projection.SparseRandomProjection.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1628,7 +1628,7 @@ <h1>SparseRandomProjection<a class="headerlink" href="#sparserandomprojection" t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.random_projection.SparseRandomProjection.transform">
-<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/random_projection.py#L801"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.random_projection.SparseRandomProjection.transform" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/random_projection.py#L801"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.random_projection.SparseRandomProjection.transform" title="Link to this definition">#</a></dt>
 <dd><p>Project the data by using matrix product with the random matrix.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.random_projection.johnson_lindenstrauss_min_dim.html b/dev/modules/generated/sklearn.random_projection.johnson_lindenstrauss_min_dim.html
index 828f8abc4a333..edb9c9d3d7050 100644
--- a/dev/modules/generated/sklearn.random_projection.johnson_lindenstrauss_min_dim.html
+++ b/dev/modules/generated/sklearn.random_projection.johnson_lindenstrauss_min_dim.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>johnson_lindenstrauss_min_dim<a class="headerlink" href="#johnson-lindenstrauss-min-dim" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.random_projection.johnson_lindenstrauss_min_dim">
-<span class="sig-prename descclassname"><span class="pre">sklearn.random_projection.</span></span><span class="sig-name descname"><span class="pre">johnson_lindenstrauss_min_dim</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_samples</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/random_projection.py#L56"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.random_projection.johnson_lindenstrauss_min_dim" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.random_projection.</span></span><span class="sig-name descname"><span class="pre">johnson_lindenstrauss_min_dim</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_samples</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/random_projection.py#L56"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.random_projection.johnson_lindenstrauss_min_dim" title="Link to this definition">#</a></dt>
 <dd><p>Find a ‘safe’ number of components to randomly project to.</p>
 <p>The distortion introduced by a random projection <code class="docutils literal notranslate"><span class="pre">p</span></code> only changes the
 distance between two points by a factor (1 +- eps) in a euclidean space
diff --git a/dev/modules/generated/sklearn.semi_supervised.LabelPropagation.html b/dev/modules/generated/sklearn.semi_supervised.LabelPropagation.html
index 575a12e28e9d0..637d75f273c67 100644
--- a/dev/modules/generated/sklearn.semi_supervised.LabelPropagation.html
+++ b/dev/modules/generated/sklearn.semi_supervised.LabelPropagation.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>LabelPropagation<a class="headerlink" href="#labelpropagation" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.semi_supervised.LabelPropagation">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.semi_supervised.</span></span><span class="sig-name descname"><span class="pre">LabelPropagation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'rbf'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gamma</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">20</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_neighbors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">7</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/semi_supervised/_label_propagation.py#L345"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.semi_supervised.LabelPropagation" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.semi_supervised.</span></span><span class="sig-name descname"><span class="pre">LabelPropagation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'rbf'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gamma</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">20</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_neighbors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">7</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/semi_supervised/_label_propagation.py#L345"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.semi_supervised.LabelPropagation" title="Link to this definition">#</a></dt>
 <dd><p>Label Propagation classifier.</p>
 <p>Read more in the <a class="reference internal" href="http://github.com/semi_supervised.html#label-propagation"><span class="std std-ref">User Guide</span></a>.</p>
 <dl class="field-list">
@@ -1326,7 +1326,7 @@ <h1>LabelPropagation<a class="headerlink" href="#labelpropagation" title="Link t
 state.</p>
 </dd>
 <dt><strong>n_jobs</strong><span class="classifier">int, default=None</span></dt><dd><p>The number of parallel jobs to run.
-<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.5.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
+<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.6.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
 <code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all processors. See <a class="reference internal" href="http://github.com/glossary.html#term-n_jobs"><span class="xref std std-term">Glossary</span></a>
 for more details.</p>
 </dd>
@@ -1385,7 +1385,7 @@ <h1>LabelPropagation<a class="headerlink" href="#labelpropagation" title="Link t
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.semi_supervised.LabelPropagation.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/semi_supervised/_label_propagation.py#L471"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.semi_supervised.LabelPropagation.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/semi_supervised/_label_propagation.py#L471"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.semi_supervised.LabelPropagation.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit a semi-supervised label propagation model to X.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1410,7 +1410,7 @@ <h1>LabelPropagation<a class="headerlink" href="#labelpropagation" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.semi_supervised.LabelPropagation.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.semi_supervised.LabelPropagation.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.semi_supervised.LabelPropagation.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1427,7 +1427,7 @@ <h1>LabelPropagation<a class="headerlink" href="#labelpropagation" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.semi_supervised.LabelPropagation.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.semi_supervised.LabelPropagation.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.semi_supervised.LabelPropagation.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1448,7 +1448,7 @@ <h1>LabelPropagation<a class="headerlink" href="#labelpropagation" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.semi_supervised.LabelPropagation.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/semi_supervised/_label_propagation.py#L173"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.semi_supervised.LabelPropagation.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/semi_supervised/_label_propagation.py#L173"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.semi_supervised.LabelPropagation.predict" title="Link to this definition">#</a></dt>
 <dd><p>Perform inductive inference across the model.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1468,7 +1468,7 @@ <h1>LabelPropagation<a class="headerlink" href="#labelpropagation" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.semi_supervised.LabelPropagation.predict_proba">
-<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/semi_supervised/_label_propagation.py#L193"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.semi_supervised.LabelPropagation.predict_proba" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/semi_supervised/_label_propagation.py#L193"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.semi_supervised.LabelPropagation.predict_proba" title="Link to this definition">#</a></dt>
 <dd><p>Predict probability for each possible outcome.</p>
 <p>Compute the probability estimates for each single sample in X
 and each possible outcome seen during training (categorical
@@ -1492,7 +1492,7 @@ <h1>LabelPropagation<a class="headerlink" href="#labelpropagation" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.semi_supervised.LabelPropagation.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L546"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.semi_supervised.LabelPropagation.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L487"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.semi_supervised.LabelPropagation.score" title="Link to this definition">#</a></dt>
 <dd><p>Return <a class="reference internal" href="http://github.com/model_evaluation.html#accuracy-score"><span class="std std-ref">accuracy</span></a> on provided data and labels.</p>
 <p>In multi-label classification, this is the subset accuracy
 which is a harsh metric since you require for each sample that
@@ -1519,7 +1519,7 @@ <h1>LabelPropagation<a class="headerlink" href="#labelpropagation" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.semi_supervised.LabelPropagation.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.semi_supervised.LabelPropagation.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.semi_supervised.LabelPropagation.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1543,7 +1543,7 @@ <h1>LabelPropagation<a class="headerlink" href="#labelpropagation" title="Link t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.semi_supervised.LabelPropagation.set_score_request">
-<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.semi_supervised.LabelPropagation" title="sklearn.semi_supervised._label_propagation.LabelPropagation"><span class="pre">LabelPropagation</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.semi_supervised.LabelPropagation.set_score_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.semi_supervised.LabelPropagation" title="sklearn.semi_supervised._label_propagation.LabelPropagation"><span class="pre">LabelPropagation</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.semi_supervised.LabelPropagation.set_score_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
diff --git a/dev/modules/generated/sklearn.semi_supervised.LabelSpreading.html b/dev/modules/generated/sklearn.semi_supervised.LabelSpreading.html
index 1d117b739232e..804c4db9f9b41 100644
--- a/dev/modules/generated/sklearn.semi_supervised.LabelSpreading.html
+++ b/dev/modules/generated/sklearn.semi_supervised.LabelSpreading.html
@@ -11,10 +11,10 @@
 <meta property="og:type" content="website" />
 <meta property="og:url" content="https://scikit-learn/stable/modules/generated/sklearn.semi_supervised.LabelSpreading.html" />
 <meta property="og:site_name" content="scikit-learn" />
-<meta property="og:description" content="Gallery examples: Label Propagation digits: Demonstrating performance Label Propagation digits active learning Label Propagation learning a complex structure Semi-supervised Classification on a Tex..." />
+<meta property="og:description" content="Gallery examples: Label Propagation digits: Demonstrating performance Label Propagation digits: Active learning Label Propagation circles: Learning a complex structure Semi-supervised Classificatio..." />
 <meta property="og:image" content="https://scikit-learn/stable/_images/sphx_glr_plot_label_propagation_digits_thumb.png" />
 <meta property="og:image:alt" content="" />
-<meta name="description" content="Gallery examples: Label Propagation digits: Demonstrating performance Label Propagation digits active learning Label Propagation learning a complex structure Semi-supervised Classification on a Tex..." />
+<meta name="description" content="Gallery examples: Label Propagation digits: Demonstrating performance Label Propagation digits: Active learning Label Propagation circles: Learning a complex structure Semi-supervised Classificatio..." />
 
     <title>LabelSpreading &#8212; scikit-learn 1.7.dev0 documentation</title>
   
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
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@@ -1307,7 +1307,7 @@
 <h1>LabelSpreading<a class="headerlink" href="#labelspreading" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.semi_supervised.LabelSpreading">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.semi_supervised.</span></span><span class="sig-name descname"><span class="pre">LabelSpreading</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'rbf'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gamma</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">20</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_neighbors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">7</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">30</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/semi_supervised/_label_propagation.py#L493"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.semi_supervised.LabelSpreading" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.semi_supervised.</span></span><span class="sig-name descname"><span class="pre">LabelSpreading</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'rbf'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gamma</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">20</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_neighbors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">7</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">30</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/semi_supervised/_label_propagation.py#L493"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.semi_supervised.LabelSpreading" title="Link to this definition">#</a></dt>
 <dd><p>LabelSpreading model for semi-supervised learning.</p>
 <p>This model is similar to the basic Label Propagation algorithm,
 but uses affinity matrix based on the normalized graph Laplacian
@@ -1337,7 +1337,7 @@ <h1>LabelSpreading<a class="headerlink" href="#labelspreading" title="Link to th
 state.</p>
 </dd>
 <dt><strong>n_jobs</strong><span class="classifier">int, default=None</span></dt><dd><p>The number of parallel jobs to run.
-<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.5.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
+<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.6.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
 <code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all processors. See <a class="reference internal" href="http://github.com/glossary.html#term-n_jobs"><span class="xref std std-term">Glossary</span></a>
 for more details.</p>
 </dd>
@@ -1395,7 +1395,7 @@ <h1>LabelSpreading<a class="headerlink" href="#labelspreading" title="Link to th
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.semi_supervised.LabelSpreading.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/semi_supervised/_label_propagation.py#L234"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.semi_supervised.LabelSpreading.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/semi_supervised/_label_propagation.py#L234"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.semi_supervised.LabelSpreading.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit a semi-supervised label propagation model to X.</p>
 <p>The input samples (labeled and unlabeled) are provided by matrix X,
 and target labels are provided by matrix y. We conventionally apply the
@@ -1424,7 +1424,7 @@ <h1>LabelSpreading<a class="headerlink" href="#labelspreading" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.semi_supervised.LabelSpreading.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.semi_supervised.LabelSpreading.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.semi_supervised.LabelSpreading.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1441,7 +1441,7 @@ <h1>LabelSpreading<a class="headerlink" href="#labelspreading" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.semi_supervised.LabelSpreading.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.semi_supervised.LabelSpreading.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.semi_supervised.LabelSpreading.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1462,7 +1462,7 @@ <h1>LabelSpreading<a class="headerlink" href="#labelspreading" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.semi_supervised.LabelSpreading.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/semi_supervised/_label_propagation.py#L173"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.semi_supervised.LabelSpreading.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/semi_supervised/_label_propagation.py#L173"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.semi_supervised.LabelSpreading.predict" title="Link to this definition">#</a></dt>
 <dd><p>Perform inductive inference across the model.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1482,7 +1482,7 @@ <h1>LabelSpreading<a class="headerlink" href="#labelspreading" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.semi_supervised.LabelSpreading.predict_proba">
-<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/semi_supervised/_label_propagation.py#L193"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.semi_supervised.LabelSpreading.predict_proba" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/semi_supervised/_label_propagation.py#L193"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.semi_supervised.LabelSpreading.predict_proba" title="Link to this definition">#</a></dt>
 <dd><p>Predict probability for each possible outcome.</p>
 <p>Compute the probability estimates for each single sample in X
 and each possible outcome seen during training (categorical
@@ -1506,7 +1506,7 @@ <h1>LabelSpreading<a class="headerlink" href="#labelspreading" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.semi_supervised.LabelSpreading.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L546"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.semi_supervised.LabelSpreading.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L487"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.semi_supervised.LabelSpreading.score" title="Link to this definition">#</a></dt>
 <dd><p>Return <a class="reference internal" href="http://github.com/model_evaluation.html#accuracy-score"><span class="std std-ref">accuracy</span></a> on provided data and labels.</p>
 <p>In multi-label classification, this is the subset accuracy
 which is a harsh metric since you require for each sample that
@@ -1533,7 +1533,7 @@ <h1>LabelSpreading<a class="headerlink" href="#labelspreading" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.semi_supervised.LabelSpreading.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.semi_supervised.LabelSpreading.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.semi_supervised.LabelSpreading.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1557,7 +1557,7 @@ <h1>LabelSpreading<a class="headerlink" href="#labelspreading" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.semi_supervised.LabelSpreading.set_score_request">
-<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.semi_supervised.LabelSpreading" title="sklearn.semi_supervised._label_propagation.LabelSpreading"><span class="pre">LabelSpreading</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.semi_supervised.LabelSpreading.set_score_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.semi_supervised.LabelSpreading" title="sklearn.semi_supervised._label_propagation.LabelSpreading"><span class="pre">LabelSpreading</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.semi_supervised.LabelSpreading.set_score_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1606,11 +1606,11 @@ <h2>Gallery examples<a class="headerlink" href="#gallery-examples" title="Link t
 <p><a class="reference internal" href="http://github.com/auto_examples/semi_supervised/plot_label_propagation_digits.html#sphx-glr-auto-examples-semi-supervised-plot-label-propagation-digits-py"><span class="std std-ref">Label Propagation digits: Demonstrating performance</span></a></p>
   <div class="sphx-glr-thumbnail-title">Label Propagation digits: Demonstrating performance</div>
 </div><div class="sphx-glr-thumbcontainer" tooltip="Demonstrates an active learning technique to learn handwritten digits using label propagation."><img alt="" src="http://github.com/_images/sphx_glr_plot_label_propagation_digits_active_learning_thumb.png" />
-<p><a class="reference internal" href="http://github.com/auto_examples/semi_supervised/plot_label_propagation_digits_active_learning.html#sphx-glr-auto-examples-semi-supervised-plot-label-propagation-digits-active-learning-py"><span class="std std-ref">Label Propagation digits active learning</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Label Propagation digits active learning</div>
+<p><a class="reference internal" href="http://github.com/auto_examples/semi_supervised/plot_label_propagation_digits_active_learning.html#sphx-glr-auto-examples-semi-supervised-plot-label-propagation-digits-active-learning-py"><span class="std std-ref">Label Propagation digits: Active learning</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Label Propagation digits: Active learning</div>
 </div><div class="sphx-glr-thumbcontainer" tooltip="Example of LabelPropagation learning a complex internal structure to demonstrate &quot;manifold learning&quot;. The outer circle should be labeled &quot;red&quot; and the inner circle &quot;blue&quot;. Because both label groups lie inside their own distinct shape, we can see that the labels propagate correctly around the circle."><img alt="" src="http://github.com/_images/sphx_glr_plot_label_propagation_structure_thumb.png" />
-<p><a class="reference internal" href="http://github.com/auto_examples/semi_supervised/plot_label_propagation_structure.html#sphx-glr-auto-examples-semi-supervised-plot-label-propagation-structure-py"><span class="std std-ref">Label Propagation learning a complex structure</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Label Propagation learning a complex structure</div>
+<p><a class="reference internal" href="http://github.com/auto_examples/semi_supervised/plot_label_propagation_structure.html#sphx-glr-auto-examples-semi-supervised-plot-label-propagation-structure-py"><span class="std std-ref">Label Propagation circles: Learning a complex structure</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Label Propagation circles: Learning a complex structure</div>
 </div><div class="sphx-glr-thumbcontainer" tooltip="In this example, semi-supervised classifiers are trained on the 20 newsgroups dataset (which will be automatically downloaded)."><img alt="" src="http://github.com/_images/sphx_glr_plot_semi_supervised_newsgroups_thumb.png" />
 <p><a class="reference internal" href="http://github.com/auto_examples/semi_supervised/plot_semi_supervised_newsgroups.html#sphx-glr-auto-examples-semi-supervised-plot-semi-supervised-newsgroups-py"><span class="std std-ref">Semi-supervised Classification on a Text Dataset</span></a></p>
   <div class="sphx-glr-thumbnail-title">Semi-supervised Classification on a Text Dataset</div>
diff --git a/dev/modules/generated/sklearn.semi_supervised.SelfTrainingClassifier.html b/dev/modules/generated/sklearn.semi_supervised.SelfTrainingClassifier.html
index b2ba209f9b5a4..498096ad81fd7 100644
--- a/dev/modules/generated/sklearn.semi_supervised.SelfTrainingClassifier.html
+++ b/dev/modules/generated/sklearn.semi_supervised.SelfTrainingClassifier.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>SelfTrainingClassifier<a class="headerlink" href="#selftrainingclassifier" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.semi_supervised.SelfTrainingClassifier">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.semi_supervised.</span></span><span class="sig-name descname"><span class="pre">SelfTrainingClassifier</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">base_estimator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'deprecated'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">threshold</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.75</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">criterion</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'threshold'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">k_best</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/semi_supervised/_self_training.py#L32"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.semi_supervised.SelfTrainingClassifier" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.semi_supervised.</span></span><span class="sig-name descname"><span class="pre">SelfTrainingClassifier</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">base_estimator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'deprecated'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">threshold</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.75</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">criterion</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'threshold'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">k_best</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/semi_supervised/_self_training.py#L32"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.semi_supervised.SelfTrainingClassifier" title="Link to this definition">#</a></dt>
 <dd><p>Self-training classifier.</p>
 <p>This <a class="reference internal" href="http://github.com/glossary.html#term-metaestimator"><span class="xref std std-term">metaestimator</span></a> allows a given supervised classifier to function as a
 semi-supervised classifier, allowing it to learn from unlabeled data. It
@@ -1428,7 +1428,7 @@ <h1>SelfTrainingClassifier<a class="headerlink" href="#selftrainingclassifier" t
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.semi_supervised.SelfTrainingClassifier.decision_function">
-<span class="sig-name descname"><span class="pre">decision_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/semi_supervised/_self_training.py#L456"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.semi_supervised.SelfTrainingClassifier.decision_function" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">decision_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/semi_supervised/_self_training.py#L455"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.semi_supervised.SelfTrainingClassifier.decision_function" title="Link to this definition">#</a></dt>
 <dd><p>Call decision function of the <code class="docutils literal notranslate"><span class="pre">estimator</span></code>.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1458,7 +1458,7 @@ <h1>SelfTrainingClassifier<a class="headerlink" href="#selftrainingclassifier" t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.semi_supervised.SelfTrainingClassifier.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/semi_supervised/_self_training.py#L232"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.semi_supervised.SelfTrainingClassifier.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/semi_supervised/_self_training.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.semi_supervised.SelfTrainingClassifier.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit self-training classifier using <code class="docutils literal notranslate"><span class="pre">X</span></code>, <code class="docutils literal notranslate"><span class="pre">y</span></code> as training data.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1490,7 +1490,7 @@ <h1>SelfTrainingClassifier<a class="headerlink" href="#selftrainingclassifier" t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.semi_supervised.SelfTrainingClassifier.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/semi_supervised/_self_training.py#L591"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.semi_supervised.SelfTrainingClassifier.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/semi_supervised/_self_training.py#L590"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.semi_supervised.SelfTrainingClassifier.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1510,7 +1510,7 @@ <h1>SelfTrainingClassifier<a class="headerlink" href="#selftrainingclassifier" t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.semi_supervised.SelfTrainingClassifier.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.semi_supervised.SelfTrainingClassifier.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.semi_supervised.SelfTrainingClassifier.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1531,7 +1531,7 @@ <h1>SelfTrainingClassifier<a class="headerlink" href="#selftrainingclassifier" t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.semi_supervised.SelfTrainingClassifier.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/semi_supervised/_self_training.py#L371"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.semi_supervised.SelfTrainingClassifier.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/semi_supervised/_self_training.py#L370"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.semi_supervised.SelfTrainingClassifier.predict" title="Link to this definition">#</a></dt>
 <dd><p>Predict the classes of <code class="docutils literal notranslate"><span class="pre">X</span></code>.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1560,7 +1560,7 @@ <h1>SelfTrainingClassifier<a class="headerlink" href="#selftrainingclassifier" t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.semi_supervised.SelfTrainingClassifier.predict_log_proba">
-<span class="sig-name descname"><span class="pre">predict_log_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/semi_supervised/_self_training.py#L501"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.semi_supervised.SelfTrainingClassifier.predict_log_proba" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict_log_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/semi_supervised/_self_training.py#L500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.semi_supervised.SelfTrainingClassifier.predict_log_proba" title="Link to this definition">#</a></dt>
 <dd><p>Predict log probability for each possible outcome.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1590,7 +1590,7 @@ <h1>SelfTrainingClassifier<a class="headerlink" href="#selftrainingclassifier" t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.semi_supervised.SelfTrainingClassifier.predict_proba">
-<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/semi_supervised/_self_training.py#L413"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.semi_supervised.SelfTrainingClassifier.predict_proba" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/semi_supervised/_self_training.py#L412"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.semi_supervised.SelfTrainingClassifier.predict_proba" title="Link to this definition">#</a></dt>
 <dd><p>Predict probability for each possible outcome.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1620,7 +1620,7 @@ <h1>SelfTrainingClassifier<a class="headerlink" href="#selftrainingclassifier" t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.semi_supervised.SelfTrainingClassifier.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/semi_supervised/_self_training.py#L546"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.semi_supervised.SelfTrainingClassifier.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/semi_supervised/_self_training.py#L545"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.semi_supervised.SelfTrainingClassifier.score" title="Link to this definition">#</a></dt>
 <dd><p>Call score on the <code class="docutils literal notranslate"><span class="pre">estimator</span></code>.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1651,7 +1651,7 @@ <h1>SelfTrainingClassifier<a class="headerlink" href="#selftrainingclassifier" t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.semi_supervised.SelfTrainingClassifier.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.semi_supervised.SelfTrainingClassifier.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.semi_supervised.SelfTrainingClassifier.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
diff --git a/dev/modules/generated/sklearn.set_config.html b/dev/modules/generated/sklearn.set_config.html
index 711414e4d9df1..8fa8313b046f0 100644
--- a/dev/modules/generated/sklearn.set_config.html
+++ b/dev/modules/generated/sklearn.set_config.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>set_config<a class="headerlink" href="#set-config" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.set_config">
-<span class="sig-prename descclassname"><span class="pre">sklearn.</span></span><span class="sig-name descname"><span class="pre">set_config</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">assume_finite</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">working_memory</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">print_changed_only</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">display</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pairwise_dist_chunk_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enable_cython_pairwise_dist</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">array_api_dispatch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform_output</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enable_metadata_routing</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">skip_parameter_validation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/_config.py#L60"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.set_config" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.</span></span><span class="sig-name descname"><span class="pre">set_config</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">assume_finite</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">working_memory</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">print_changed_only</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">display</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pairwise_dist_chunk_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enable_cython_pairwise_dist</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">array_api_dispatch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform_output</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enable_metadata_routing</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">skip_parameter_validation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/_config.py#L60"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.set_config" title="Link to this definition">#</a></dt>
 <dd><p>Set global scikit-learn configuration.</p>
 <div class="versionadded">
 <p><span class="versionmodified added">Added in version 0.19.</span></p>
diff --git a/dev/modules/generated/sklearn.show_versions.html b/dev/modules/generated/sklearn.show_versions.html
index c694727810ca9..632b5c6d5cedf 100644
--- a/dev/modules/generated/sklearn.show_versions.html
+++ b/dev/modules/generated/sklearn.show_versions.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>show_versions<a class="headerlink" href="#show-versions" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.show_versions">
-<span class="sig-prename descclassname"><span class="pre">sklearn.</span></span><span class="sig-name descname"><span class="pre">show_versions</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_show_versions.py#L77"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.show_versions" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.</span></span><span class="sig-name descname"><span class="pre">show_versions</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_show_versions.py#L77"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.show_versions" title="Link to this definition">#</a></dt>
 <dd><p>Print useful debugging information”</p>
 <div class="versionadded">
 <p><span class="versionmodified added">Added in version 0.20.</span></p>
diff --git a/dev/modules/generated/sklearn.svm.LinearSVC.html b/dev/modules/generated/sklearn.svm.LinearSVC.html
index 54c939456bfcf..67e385ac1a175 100644
--- a/dev/modules/generated/sklearn.svm.LinearSVC.html
+++ b/dev/modules/generated/sklearn.svm.LinearSVC.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>LinearSVC<a class="headerlink" href="#linearsvc" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.svm.LinearSVC">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.svm.</span></span><span class="sig-name descname"><span class="pre">LinearSVC</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">penalty</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'l2'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">loss</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'squared_hinge'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dual</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">C</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">multi_class</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'ovr'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_intercept</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">intercept_scaling</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">class_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/svm/_classes.py#L35"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.LinearSVC" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.svm.</span></span><span class="sig-name descname"><span class="pre">LinearSVC</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">penalty</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'l2'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">loss</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'squared_hinge'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dual</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">C</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">multi_class</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'ovr'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_intercept</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">intercept_scaling</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">class_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/svm/_classes.py#L35"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.LinearSVC" title="Link to this definition">#</a></dt>
 <dd><p>Linear Support Vector Classification.</p>
 <p>Similar to SVC with parameter kernel=’linear’, but implemented in terms of
 liblinear rather than libsvm, so it has more flexibility in the choice of
@@ -1465,18 +1465,18 @@ <h1>LinearSVC<a class="headerlink" href="#linearsvc" title="Link to this heading
 </pre></div>
 </div>
 <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">clf</span><span class="o">.</span><span class="n">named_steps</span><span class="p">[</span><span class="s1">&#39;linearsvc&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">coef_</span><span class="p">)</span>
-<span class="go">[[0.141...   0.526... 0.679... 0.493...]]</span>
+<span class="go">[[0.141   0.526 0.679 0.493]]</span>
 </pre></div>
 </div>
 <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">clf</span><span class="o">.</span><span class="n">named_steps</span><span class="p">[</span><span class="s1">&#39;linearsvc&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">intercept_</span><span class="p">)</span>
-<span class="go">[0.1693...]</span>
+<span class="go">[0.1693]</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">clf</span><span class="o">.</span><span class="n">predict</span><span class="p">([[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]]))</span>
 <span class="go">[1]</span>
 </pre></div>
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.svm.LinearSVC.decision_function">
-<span class="sig-name descname"><span class="pre">decision_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_base.py#L330"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.LinearSVC.decision_function" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">decision_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_base.py#L330"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.LinearSVC.decision_function" title="Link to this definition">#</a></dt>
 <dd><p>Predict confidence scores for samples.</p>
 <p>The confidence score for a sample is proportional to the signed
 distance of that sample to the hyperplane.</p>
@@ -1500,7 +1500,7 @@ <h1>LinearSVC<a class="headerlink" href="#linearsvc" title="Link to this heading
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.svm.LinearSVC.densify">
-<span class="sig-name descname"><span class="pre">densify</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_base.py#L406"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.LinearSVC.densify" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">densify</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_base.py#L406"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.LinearSVC.densify" title="Link to this definition">#</a></dt>
 <dd><p>Convert coefficient matrix to dense array format.</p>
 <p>Converts the <code class="docutils literal notranslate"><span class="pre">coef_</span></code> member (back) to a numpy.ndarray. This is the
 default format of <code class="docutils literal notranslate"><span class="pre">coef_</span></code> and is required for fitting, so calling
@@ -1518,7 +1518,7 @@ <h1>LinearSVC<a class="headerlink" href="#linearsvc" title="Link to this heading
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.svm.LinearSVC.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/svm/_classes.py#L280"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.LinearSVC.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/svm/_classes.py#L280"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.LinearSVC.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit the model according to the given training data.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1548,7 +1548,7 @@ <h1>LinearSVC<a class="headerlink" href="#linearsvc" title="Link to this heading
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.svm.LinearSVC.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.LinearSVC.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.LinearSVC.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1565,7 +1565,7 @@ <h1>LinearSVC<a class="headerlink" href="#linearsvc" title="Link to this heading
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.svm.LinearSVC.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.LinearSVC.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.LinearSVC.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1586,7 +1586,7 @@ <h1>LinearSVC<a class="headerlink" href="#linearsvc" title="Link to this heading
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.svm.LinearSVC.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_base.py#L360"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.LinearSVC.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_base.py#L360"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.LinearSVC.predict" title="Link to this definition">#</a></dt>
 <dd><p>Predict class labels for samples in X.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1606,7 +1606,7 @@ <h1>LinearSVC<a class="headerlink" href="#linearsvc" title="Link to this heading
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.svm.LinearSVC.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L546"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.LinearSVC.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L487"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.LinearSVC.score" title="Link to this definition">#</a></dt>
 <dd><p>Return <a class="reference internal" href="http://github.com/model_evaluation.html#accuracy-score"><span class="std std-ref">accuracy</span></a> on provided data and labels.</p>
 <p>In multi-label classification, this is the subset accuracy
 which is a harsh metric since you require for each sample that
@@ -1633,7 +1633,7 @@ <h1>LinearSVC<a class="headerlink" href="#linearsvc" title="Link to this heading
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.svm.LinearSVC.set_fit_request">
-<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.svm.LinearSVC" title="sklearn.svm._classes.LinearSVC"><span class="pre">LinearSVC</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.LinearSVC.set_fit_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.svm.LinearSVC" title="sklearn.svm._classes.LinearSVC"><span class="pre">LinearSVC</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.LinearSVC.set_fit_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1676,7 +1676,7 @@ <h1>LinearSVC<a class="headerlink" href="#linearsvc" title="Link to this heading
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.svm.LinearSVC.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.LinearSVC.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.LinearSVC.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1700,7 +1700,7 @@ <h1>LinearSVC<a class="headerlink" href="#linearsvc" title="Link to this heading
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.svm.LinearSVC.set_score_request">
-<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.svm.LinearSVC" title="sklearn.svm._classes.LinearSVC"><span class="pre">LinearSVC</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.LinearSVC.set_score_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.svm.LinearSVC" title="sklearn.svm._classes.LinearSVC"><span class="pre">LinearSVC</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.LinearSVC.set_score_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1743,7 +1743,7 @@ <h1>LinearSVC<a class="headerlink" href="#linearsvc" title="Link to this heading
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.svm.LinearSVC.sparsify">
-<span class="sig-name descname"><span class="pre">sparsify</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_base.py#L426"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.LinearSVC.sparsify" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">sparsify</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_base.py#L426"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.LinearSVC.sparsify" title="Link to this definition">#</a></dt>
 <dd><p>Convert coefficient matrix to sparse format.</p>
 <p>Converts the <code class="docutils literal notranslate"><span class="pre">coef_</span></code> member to a scipy.sparse matrix, which for
 L1-regularized models can be much more memory- and storage-efficient
diff --git a/dev/modules/generated/sklearn.svm.LinearSVR.html b/dev/modules/generated/sklearn.svm.LinearSVR.html
index 290acb820cf35..2ecf5ad77c29e 100644
--- a/dev/modules/generated/sklearn.svm.LinearSVR.html
+++ b/dev/modules/generated/sklearn.svm.LinearSVR.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1306,7 +1306,7 @@
 <h1>LinearSVR<a class="headerlink" href="#linearsvr" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.svm.LinearSVR">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.svm.</span></span><span class="sig-name descname"><span class="pre">LinearSVR</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epsilon</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">C</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">loss</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'epsilon_insensitive'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_intercept</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">intercept_scaling</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dual</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/svm/_classes.py#L358"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.LinearSVR" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.svm.</span></span><span class="sig-name descname"><span class="pre">LinearSVR</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epsilon</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">C</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">loss</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'epsilon_insensitive'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_intercept</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">intercept_scaling</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dual</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/svm/_classes.py#L358"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.LinearSVR" title="Link to this definition">#</a></dt>
 <dd><p>Linear Support Vector Regression.</p>
 <p>Similar to SVR with parameter kernel=’linear’, but implemented in terms of
 liblinear rather than libsvm, so it has more flexibility in the choice of
@@ -1429,16 +1429,16 @@ <h1>LinearSVR<a class="headerlink" href="#linearsvr" title="Link to this heading
 </pre></div>
 </div>
 <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">regr</span><span class="o">.</span><span class="n">named_steps</span><span class="p">[</span><span class="s1">&#39;linearsvr&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">coef_</span><span class="p">)</span>
-<span class="go">[18.582... 27.023... 44.357... 64.522...]</span>
+<span class="go">[18.582 27.023 44.357 64.522]</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">regr</span><span class="o">.</span><span class="n">named_steps</span><span class="p">[</span><span class="s1">&#39;linearsvr&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">intercept_</span><span class="p">)</span>
-<span class="go">[-4...]</span>
+<span class="go">[-4.]</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">regr</span><span class="o">.</span><span class="n">predict</span><span class="p">([[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]]))</span>
-<span class="go">[-2.384...]</span>
+<span class="go">[-2.384]</span>
 </pre></div>
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.svm.LinearSVR.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/svm/_classes.py#L544"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.LinearSVR.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/svm/_classes.py#L544"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.LinearSVR.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit the model according to the given training data.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1468,7 +1468,7 @@ <h1>LinearSVR<a class="headerlink" href="#linearsvr" title="Link to this heading
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.svm.LinearSVR.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.LinearSVR.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.LinearSVR.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1485,7 +1485,7 @@ <h1>LinearSVR<a class="headerlink" href="#linearsvr" title="Link to this heading
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.svm.LinearSVR.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.LinearSVR.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.LinearSVR.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1506,7 +1506,7 @@ <h1>LinearSVR<a class="headerlink" href="#linearsvr" title="Link to this heading
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.svm.LinearSVR.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/linear_model/_base.py#L284"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.LinearSVR.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/linear_model/_base.py#L284"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.LinearSVR.predict" title="Link to this definition">#</a></dt>
 <dd><p>Predict using the linear model.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1526,7 +1526,7 @@ <h1>LinearSVR<a class="headerlink" href="#linearsvr" title="Link to this heading
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.svm.LinearSVR.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L619"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.LinearSVR.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L560"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.LinearSVR.score" title="Link to this definition">#</a></dt>
 <dd><p>Return <a class="reference internal" href="http://github.com/model_evaluation.html#r2-score"><span class="std std-ref">coefficient of determination</span></a> on test data.</p>
 <p>The coefficient of determination, <span class="math notranslate nohighlight">\(R^2\)</span>, is defined as
 <span class="math notranslate nohighlight">\((1 - \frac{u}{v})\)</span>, where <span class="math notranslate nohighlight">\(u\)</span> is the residual
@@ -1568,7 +1568,7 @@ <h1>LinearSVR<a class="headerlink" href="#linearsvr" title="Link to this heading
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.svm.LinearSVR.set_fit_request">
-<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.svm.LinearSVR" title="sklearn.svm._classes.LinearSVR"><span class="pre">LinearSVR</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.LinearSVR.set_fit_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.svm.LinearSVR" title="sklearn.svm._classes.LinearSVR"><span class="pre">LinearSVR</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.LinearSVR.set_fit_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1611,7 +1611,7 @@ <h1>LinearSVR<a class="headerlink" href="#linearsvr" title="Link to this heading
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.svm.LinearSVR.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.LinearSVR.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.LinearSVR.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1635,7 +1635,7 @@ <h1>LinearSVR<a class="headerlink" href="#linearsvr" title="Link to this heading
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.svm.LinearSVR.set_score_request">
-<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.svm.LinearSVR" title="sklearn.svm._classes.LinearSVR"><span class="pre">LinearSVR</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.LinearSVR.set_score_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.svm.LinearSVR" title="sklearn.svm._classes.LinearSVR"><span class="pre">LinearSVR</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.LinearSVR.set_score_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
diff --git a/dev/modules/generated/sklearn.svm.NuSVC.html b/dev/modules/generated/sklearn.svm.NuSVC.html
index ae087aa55ea05..b8b5749ee1bef 100644
--- a/dev/modules/generated/sklearn.svm.NuSVC.html
+++ b/dev/modules/generated/sklearn.svm.NuSVC.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>NuSVC<a class="headerlink" href="#nusvc" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.svm.NuSVC">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.svm.</span></span><span class="sig-name descname"><span class="pre">NuSVC</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nu</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'rbf'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">degree</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gamma</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'scale'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coef0</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shrinking</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">probability</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cache_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">200</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">class_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">decision_function_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'ovr'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">break_ties</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/svm/_classes.py#L891"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.NuSVC" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.svm.</span></span><span class="sig-name descname"><span class="pre">NuSVC</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nu</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'rbf'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">degree</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gamma</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'scale'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coef0</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shrinking</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">probability</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cache_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">200</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">class_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">decision_function_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'ovr'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">break_ties</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/svm/_classes.py#L891"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.NuSVC" title="Link to this definition">#</a></dt>
 <dd><p>Nu-Support Vector Classification.</p>
 <p>Similar to SVC but uses a parameter to control the number of support
 vectors.</p>
@@ -1490,7 +1490,7 @@ <h1>NuSVC<a class="headerlink" href="#nusvc" title="Link to this heading">#</a><
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.svm.NuSVC.decision_function">
-<span class="sig-name descname"><span class="pre">decision_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/svm/_base.py#L760"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.NuSVC.decision_function" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">decision_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/svm/_base.py#L760"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.NuSVC.decision_function" title="Link to this definition">#</a></dt>
 <dd><p>Evaluate the decision function for the samples in X.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1520,7 +1520,7 @@ <h1>NuSVC<a class="headerlink" href="#nusvc" title="Link to this heading">#</a><
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.svm.NuSVC.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/svm/_base.py#L153"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.NuSVC.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/svm/_base.py#L153"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.NuSVC.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit the SVM model according to the given training data.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1554,7 +1554,7 @@ <h1>NuSVC<a class="headerlink" href="#nusvc" title="Link to this heading">#</a><
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.svm.NuSVC.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.NuSVC.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.NuSVC.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1571,7 +1571,7 @@ <h1>NuSVC<a class="headerlink" href="#nusvc" title="Link to this heading">#</a><
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.svm.NuSVC.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.NuSVC.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.NuSVC.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1592,7 +1592,7 @@ <h1>NuSVC<a class="headerlink" href="#nusvc" title="Link to this heading">#</a><
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.svm.NuSVC.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/svm/_base.py#L792"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.NuSVC.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/svm/_base.py#L792"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.NuSVC.predict" title="Link to this definition">#</a></dt>
 <dd><p>Perform classification on samples in X.</p>
 <p>For an one-class model, +1 or -1 is returned.</p>
 <dl class="field-list simple">
@@ -1614,7 +1614,7 @@ <h1>NuSVC<a class="headerlink" href="#nusvc" title="Link to this heading">#</a><
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.svm.NuSVC.predict_log_proba">
-<span class="sig-name descname"><span class="pre">predict_log_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/svm/_base.py#L875"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.NuSVC.predict_log_proba" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict_log_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/svm/_base.py#L875"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.NuSVC.predict_log_proba" title="Link to this definition">#</a></dt>
 <dd><p>Compute log probabilities of possible outcomes for samples in X.</p>
 <p>The model need to have probability information computed at training
 time: fit with attribute <code class="docutils literal notranslate"><span class="pre">probability</span></code> set to True.</p>
@@ -1644,7 +1644,7 @@ <h1>NuSVC<a class="headerlink" href="#nusvc" title="Link to this heading">#</a><
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.svm.NuSVC.predict_proba">
-<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/svm/_base.py#L838"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.NuSVC.predict_proba" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/svm/_base.py#L838"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.NuSVC.predict_proba" title="Link to this definition">#</a></dt>
 <dd><p>Compute probabilities of possible outcomes for samples in X.</p>
 <p>The model needs to have probability information computed at training
 time: fit with attribute <code class="docutils literal notranslate"><span class="pre">probability</span></code> set to True.</p>
@@ -1674,7 +1674,7 @@ <h1>NuSVC<a class="headerlink" href="#nusvc" title="Link to this heading">#</a><
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.svm.NuSVC.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L546"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.NuSVC.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L487"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.NuSVC.score" title="Link to this definition">#</a></dt>
 <dd><p>Return <a class="reference internal" href="http://github.com/model_evaluation.html#accuracy-score"><span class="std std-ref">accuracy</span></a> on provided data and labels.</p>
 <p>In multi-label classification, this is the subset accuracy
 which is a harsh metric since you require for each sample that
@@ -1701,7 +1701,7 @@ <h1>NuSVC<a class="headerlink" href="#nusvc" title="Link to this heading">#</a><
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.svm.NuSVC.set_fit_request">
-<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.svm.NuSVC" title="sklearn.svm._classes.NuSVC"><span class="pre">NuSVC</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.NuSVC.set_fit_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.svm.NuSVC" title="sklearn.svm._classes.NuSVC"><span class="pre">NuSVC</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.NuSVC.set_fit_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1744,7 +1744,7 @@ <h1>NuSVC<a class="headerlink" href="#nusvc" title="Link to this heading">#</a><
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.svm.NuSVC.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.NuSVC.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.NuSVC.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1768,7 +1768,7 @@ <h1>NuSVC<a class="headerlink" href="#nusvc" title="Link to this heading">#</a><
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.svm.NuSVC.set_score_request">
-<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.svm.NuSVC" title="sklearn.svm._classes.NuSVC"><span class="pre">NuSVC</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.NuSVC.set_score_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.svm.NuSVC" title="sklearn.svm._classes.NuSVC"><span class="pre">NuSVC</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.NuSVC.set_score_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
diff --git a/dev/modules/generated/sklearn.svm.NuSVR.html b/dev/modules/generated/sklearn.svm.NuSVR.html
index d5377a6f44768..c92c6edd506fa 100644
--- a/dev/modules/generated/sklearn.svm.NuSVR.html
+++ b/dev/modules/generated/sklearn.svm.NuSVR.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1308,7 +1308,7 @@
 <h1>NuSVR<a class="headerlink" href="#nusvr" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.svm.NuSVR">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.svm.</span></span><span class="sig-name descname"><span class="pre">NuSVR</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nu</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">C</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'rbf'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">degree</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gamma</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'scale'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coef0</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shrinking</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cache_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">200</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/svm/_classes.py#L1348"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.NuSVR" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.svm.</span></span><span class="sig-name descname"><span class="pre">NuSVR</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nu</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">C</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'rbf'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">degree</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gamma</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'scale'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coef0</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shrinking</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cache_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">200</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/svm/_classes.py#L1348"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.NuSVR" title="Link to this definition">#</a></dt>
 <dd><p>Nu Support Vector Regression.</p>
 <p>Similar to NuSVC, for regression, uses a parameter nu to control
 the number of support vectors. However, unlike NuSVC, where nu
@@ -1439,7 +1439,7 @@ <h1>NuSVR<a class="headerlink" href="#nusvr" title="Link to this heading">#</a><
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.svm.NuSVR.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/svm/_base.py#L153"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.NuSVR.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/svm/_base.py#L153"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.NuSVR.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit the SVM model according to the given training data.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1473,7 +1473,7 @@ <h1>NuSVR<a class="headerlink" href="#nusvr" title="Link to this heading">#</a><
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.svm.NuSVR.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.NuSVR.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.NuSVR.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1490,7 +1490,7 @@ <h1>NuSVR<a class="headerlink" href="#nusvr" title="Link to this heading">#</a><
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.svm.NuSVR.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.NuSVR.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.NuSVR.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1511,7 +1511,7 @@ <h1>NuSVR<a class="headerlink" href="#nusvr" title="Link to this heading">#</a><
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.svm.NuSVR.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/svm/_base.py#L420"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.NuSVR.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/svm/_base.py#L420"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.NuSVR.predict" title="Link to this definition">#</a></dt>
 <dd><p>Perform regression on samples in X.</p>
 <p>For an one-class model, +1 (inlier) or -1 (outlier) is returned.</p>
 <dl class="field-list simple">
@@ -1533,7 +1533,7 @@ <h1>NuSVR<a class="headerlink" href="#nusvr" title="Link to this heading">#</a><
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.svm.NuSVR.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L619"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.NuSVR.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L560"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.NuSVR.score" title="Link to this definition">#</a></dt>
 <dd><p>Return <a class="reference internal" href="http://github.com/model_evaluation.html#r2-score"><span class="std std-ref">coefficient of determination</span></a> on test data.</p>
 <p>The coefficient of determination, <span class="math notranslate nohighlight">\(R^2\)</span>, is defined as
 <span class="math notranslate nohighlight">\((1 - \frac{u}{v})\)</span>, where <span class="math notranslate nohighlight">\(u\)</span> is the residual
@@ -1575,7 +1575,7 @@ <h1>NuSVR<a class="headerlink" href="#nusvr" title="Link to this heading">#</a><
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.svm.NuSVR.set_fit_request">
-<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.svm.NuSVR" title="sklearn.svm._classes.NuSVR"><span class="pre">NuSVR</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.NuSVR.set_fit_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.svm.NuSVR" title="sklearn.svm._classes.NuSVR"><span class="pre">NuSVR</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.NuSVR.set_fit_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1618,7 +1618,7 @@ <h1>NuSVR<a class="headerlink" href="#nusvr" title="Link to this heading">#</a><
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.svm.NuSVR.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.NuSVR.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.NuSVR.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1642,7 +1642,7 @@ <h1>NuSVR<a class="headerlink" href="#nusvr" title="Link to this heading">#</a><
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.svm.NuSVR.set_score_request">
-<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.svm.NuSVR" title="sklearn.svm._classes.NuSVR"><span class="pre">NuSVR</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.NuSVR.set_score_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.svm.NuSVR" title="sklearn.svm._classes.NuSVR"><span class="pre">NuSVR</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.NuSVR.set_score_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
diff --git a/dev/modules/generated/sklearn.svm.OneClassSVM.html b/dev/modules/generated/sklearn.svm.OneClassSVM.html
index 31c6862a11d35..1a38bb1423495 100644
--- a/dev/modules/generated/sklearn.svm.OneClassSVM.html
+++ b/dev/modules/generated/sklearn.svm.OneClassSVM.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>OneClassSVM<a class="headerlink" href="#oneclasssvm" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.svm.OneClassSVM">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.svm.</span></span><span class="sig-name descname"><span class="pre">OneClassSVM</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'rbf'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">degree</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gamma</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'scale'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coef0</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nu</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shrinking</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cache_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">200</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/svm/_classes.py#L1535"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.OneClassSVM" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.svm.</span></span><span class="sig-name descname"><span class="pre">OneClassSVM</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'rbf'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">degree</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gamma</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'scale'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coef0</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nu</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shrinking</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cache_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">200</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/svm/_classes.py#L1535"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.OneClassSVM" title="Link to this definition">#</a></dt>
 <dd><p>Unsupervised Outlier Detection.</p>
 <p>Estimate the support of a high-dimensional distribution.</p>
 <p>The implementation is based on libsvm.</p>
@@ -1419,14 +1419,14 @@ <h1>OneClassSVM<a class="headerlink" href="#oneclasssvm" title="Link to this hea
 <span class="gp">&gt;&gt;&gt; </span><span class="n">clf</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
 <span class="go">array([-1,  1,  1,  1, -1])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">clf</span><span class="o">.</span><span class="n">score_samples</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
-<span class="go">array([1.7798..., 2.0547..., 2.0556..., 2.0561..., 1.7332...])</span>
+<span class="go">array([1.7798, 2.0547, 2.0556, 2.0561, 1.7332])</span>
 </pre></div>
 </div>
 <p>For a more extended example,
 see <a class="reference internal" href="http://github.com/auto_examples/applications/plot_species_distribution_modeling.html#sphx-glr-auto-examples-applications-plot-species-distribution-modeling-py"><span class="std std-ref">Species distribution modeling</span></a></p>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.svm.OneClassSVM.decision_function">
-<span class="sig-name descname"><span class="pre">decision_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/svm/_classes.py#L1738"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.OneClassSVM.decision_function" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">decision_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/svm/_classes.py#L1738"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.OneClassSVM.decision_function" title="Link to this definition">#</a></dt>
 <dd><p>Signed distance to the separating hyperplane.</p>
 <p>Signed distance is positive for an inlier and negative for an outlier.</p>
 <dl class="field-list simple">
@@ -1447,7 +1447,7 @@ <h1>OneClassSVM<a class="headerlink" href="#oneclasssvm" title="Link to this hea
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.svm.OneClassSVM.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/svm/_classes.py#L1709"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.OneClassSVM.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/svm/_classes.py#L1709"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.OneClassSVM.fit" title="Link to this definition">#</a></dt>
 <dd><p>Detect the soft boundary of the set of samples X.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1475,7 +1475,7 @@ <h1>OneClassSVM<a class="headerlink" href="#oneclasssvm" title="Link to this hea
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.svm.OneClassSVM.fit_predict">
-<span class="sig-name descname"><span class="pre">fit_predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L1096"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.OneClassSVM.fit_predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit_predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L1037"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.OneClassSVM.fit_predict" title="Link to this definition">#</a></dt>
 <dd><p>Perform fit on X and returns labels for X.</p>
 <p>Returns -1 for outliers and 1 for inliers.</p>
 <dl class="field-list">
@@ -1503,7 +1503,7 @@ <h1>OneClassSVM<a class="headerlink" href="#oneclasssvm" title="Link to this hea
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.svm.OneClassSVM.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.OneClassSVM.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.OneClassSVM.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1520,7 +1520,7 @@ <h1>OneClassSVM<a class="headerlink" href="#oneclasssvm" title="Link to this hea
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.svm.OneClassSVM.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.OneClassSVM.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.OneClassSVM.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1541,7 +1541,7 @@ <h1>OneClassSVM<a class="headerlink" href="#oneclasssvm" title="Link to this hea
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.svm.OneClassSVM.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/svm/_classes.py#L1771"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.OneClassSVM.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/svm/_classes.py#L1771"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.OneClassSVM.predict" title="Link to this definition">#</a></dt>
 <dd><p>Perform classification on samples in X.</p>
 <p>For a one-class model, +1 or -1 is returned.</p>
 <dl class="field-list simple">
@@ -1563,7 +1563,7 @@ <h1>OneClassSVM<a class="headerlink" href="#oneclasssvm" title="Link to this hea
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.svm.OneClassSVM.score_samples">
-<span class="sig-name descname"><span class="pre">score_samples</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/svm/_classes.py#L1756"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.OneClassSVM.score_samples" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score_samples</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/svm/_classes.py#L1756"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.OneClassSVM.score_samples" title="Link to this definition">#</a></dt>
 <dd><p>Raw scoring function of the samples.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1583,7 +1583,7 @@ <h1>OneClassSVM<a class="headerlink" href="#oneclasssvm" title="Link to this hea
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.svm.OneClassSVM.set_fit_request">
-<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.svm.OneClassSVM" title="sklearn.svm._classes.OneClassSVM"><span class="pre">OneClassSVM</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.OneClassSVM.set_fit_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.svm.OneClassSVM" title="sklearn.svm._classes.OneClassSVM"><span class="pre">OneClassSVM</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.OneClassSVM.set_fit_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1626,7 +1626,7 @@ <h1>OneClassSVM<a class="headerlink" href="#oneclasssvm" title="Link to this hea
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.svm.OneClassSVM.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.OneClassSVM.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.OneClassSVM.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
diff --git a/dev/modules/generated/sklearn.svm.SVC.html b/dev/modules/generated/sklearn.svm.SVC.html
index ee99de323c075..e1c57f3144248 100644
--- a/dev/modules/generated/sklearn.svm.SVC.html
+++ b/dev/modules/generated/sklearn.svm.SVC.html
@@ -11,10 +11,10 @@
 <meta property="og:type" content="website" />
 <meta property="og:url" content="https://scikit-learn/stable/modules/generated/sklearn.svm.SVC.html" />
 <meta property="og:site_name" content="scikit-learn" />
-<meta property="og:description" content="Gallery examples: Faces recognition example using eigenfaces and SVMs Classifier comparison Recognizing hand-written digits Concatenating multiple feature extraction methods Plot the decision bound..." />
+<meta property="og:description" content="Gallery examples: Faces recognition example using eigenfaces and SVMs Classifier comparison Recognizing hand-written digits Concatenating multiple feature extraction methods Scalable learning with ..." />
 <meta property="og:image" content="https://scikit-learn/stable/_images/sphx_glr_plot_face_recognition_thumb.png" />
 <meta property="og:image:alt" content="" />
-<meta name="description" content="Gallery examples: Faces recognition example using eigenfaces and SVMs Classifier comparison Recognizing hand-written digits Concatenating multiple feature extraction methods Plot the decision bound..." />
+<meta name="description" content="Gallery examples: Faces recognition example using eigenfaces and SVMs Classifier comparison Recognizing hand-written digits Concatenating multiple feature extraction methods Scalable learning with ..." />
 
     <title>SVC &#8212; scikit-learn 1.7.dev0 documentation</title>
   
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
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     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>SVC<a class="headerlink" href="#svc" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.svm.SVC">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.svm.</span></span><span class="sig-name descname"><span class="pre">SVC</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">C</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'rbf'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">degree</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gamma</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'scale'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coef0</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shrinking</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">probability</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cache_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">200</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">class_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">decision_function_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'ovr'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">break_ties</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/svm/_classes.py#L614"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.SVC" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.svm.</span></span><span class="sig-name descname"><span class="pre">SVC</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">C</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'rbf'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">degree</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gamma</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'scale'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coef0</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shrinking</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">probability</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cache_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">200</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">class_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">decision_function_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'ovr'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">break_ties</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/svm/_classes.py#L614"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.SVC" title="Link to this definition">#</a></dt>
 <dd><p>C-Support Vector Classification.</p>
 <p>The implementation is based on libsvm. The fit time scales at least
 quadratically with the number of samples and may be impractical
@@ -1512,7 +1512,7 @@ <h1>SVC<a class="headerlink" href="#svc" title="Link to this heading">#</a></h1>
 <a class="reference internal" href="http://github.com/auto_examples/classification/plot_classification_probability.html#sphx-glr-auto-examples-classification-plot-classification-probability-py"><span class="std std-ref">Plot classification probability</span></a>.</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.svm.SVC.decision_function">
-<span class="sig-name descname"><span class="pre">decision_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/svm/_base.py#L760"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.SVC.decision_function" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">decision_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/svm/_base.py#L760"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.SVC.decision_function" title="Link to this definition">#</a></dt>
 <dd><p>Evaluate the decision function for the samples in X.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1542,7 +1542,7 @@ <h1>SVC<a class="headerlink" href="#svc" title="Link to this heading">#</a></h1>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.svm.SVC.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/svm/_base.py#L153"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.SVC.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/svm/_base.py#L153"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.SVC.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit the SVM model according to the given training data.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1576,7 +1576,7 @@ <h1>SVC<a class="headerlink" href="#svc" title="Link to this heading">#</a></h1>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.svm.SVC.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.SVC.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.SVC.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1593,7 +1593,7 @@ <h1>SVC<a class="headerlink" href="#svc" title="Link to this heading">#</a></h1>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.svm.SVC.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.SVC.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.SVC.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1614,7 +1614,7 @@ <h1>SVC<a class="headerlink" href="#svc" title="Link to this heading">#</a></h1>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.svm.SVC.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/svm/_base.py#L792"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.SVC.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/svm/_base.py#L792"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.SVC.predict" title="Link to this definition">#</a></dt>
 <dd><p>Perform classification on samples in X.</p>
 <p>For an one-class model, +1 or -1 is returned.</p>
 <dl class="field-list simple">
@@ -1636,7 +1636,7 @@ <h1>SVC<a class="headerlink" href="#svc" title="Link to this heading">#</a></h1>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.svm.SVC.predict_log_proba">
-<span class="sig-name descname"><span class="pre">predict_log_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/svm/_base.py#L875"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.SVC.predict_log_proba" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict_log_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/svm/_base.py#L875"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.SVC.predict_log_proba" title="Link to this definition">#</a></dt>
 <dd><p>Compute log probabilities of possible outcomes for samples in X.</p>
 <p>The model need to have probability information computed at training
 time: fit with attribute <code class="docutils literal notranslate"><span class="pre">probability</span></code> set to True.</p>
@@ -1666,7 +1666,7 @@ <h1>SVC<a class="headerlink" href="#svc" title="Link to this heading">#</a></h1>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.svm.SVC.predict_proba">
-<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/svm/_base.py#L838"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.SVC.predict_proba" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/svm/_base.py#L838"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.SVC.predict_proba" title="Link to this definition">#</a></dt>
 <dd><p>Compute probabilities of possible outcomes for samples in X.</p>
 <p>The model needs to have probability information computed at training
 time: fit with attribute <code class="docutils literal notranslate"><span class="pre">probability</span></code> set to True.</p>
@@ -1696,7 +1696,7 @@ <h1>SVC<a class="headerlink" href="#svc" title="Link to this heading">#</a></h1>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.svm.SVC.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L546"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.SVC.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L487"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.SVC.score" title="Link to this definition">#</a></dt>
 <dd><p>Return <a class="reference internal" href="http://github.com/model_evaluation.html#accuracy-score"><span class="std std-ref">accuracy</span></a> on provided data and labels.</p>
 <p>In multi-label classification, this is the subset accuracy
 which is a harsh metric since you require for each sample that
@@ -1723,7 +1723,7 @@ <h1>SVC<a class="headerlink" href="#svc" title="Link to this heading">#</a></h1>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.svm.SVC.set_fit_request">
-<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.svm.SVC" title="sklearn.svm._classes.SVC"><span class="pre">SVC</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.SVC.set_fit_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.svm.SVC" title="sklearn.svm._classes.SVC"><span class="pre">SVC</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.SVC.set_fit_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1766,7 +1766,7 @@ <h1>SVC<a class="headerlink" href="#svc" title="Link to this heading">#</a></h1>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.svm.SVC.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.SVC.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.SVC.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1790,7 +1790,7 @@ <h1>SVC<a class="headerlink" href="#svc" title="Link to this heading">#</a></h1>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.svm.SVC.set_score_request">
-<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.svm.SVC" title="sklearn.svm._classes.SVC"><span class="pre">SVC</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.SVC.set_score_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.svm.SVC" title="sklearn.svm._classes.SVC"><span class="pre">SVC</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.SVC.set_score_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1847,9 +1847,6 @@ <h2>Gallery examples<a class="headerlink" href="#gallery-examples" title="Link t
 </div><div class="sphx-glr-thumbcontainer" tooltip="In many real-world examples, there are many ways to extract features from a dataset. Often it is beneficial to combine several methods to obtain good performance. This example shows how to use FeatureUnion to combine features obtained by PCA and univariate selection."><img alt="" src="http://github.com/_images/sphx_glr_plot_feature_union_thumb.png" />
 <p><a class="reference internal" href="http://github.com/auto_examples/compose/plot_feature_union.html#sphx-glr-auto-examples-compose-plot-feature-union-py"><span class="std std-ref">Concatenating multiple feature extraction methods</span></a></p>
   <div class="sphx-glr-thumbnail-title">Concatenating multiple feature extraction methods</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="Plot the decision boundaries of a VotingClassifier for two features of the Iris dataset."><img alt="" src="http://github.com/_images/sphx_glr_plot_voting_decision_regions_thumb.png" />
-<p><a class="reference internal" href="http://github.com/auto_examples/ensemble/plot_voting_decision_regions.html#sphx-glr-auto-examples-ensemble-plot-voting-decision-regions-py"><span class="std std-ref">Plot the decision boundaries of a VotingClassifier</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Plot the decision boundaries of a VotingClassifier</div>
 </div><div class="sphx-glr-thumbcontainer" tooltip="This example illustrates the use of PolynomialCountSketch to efficiently generate polynomial kernel feature-space approximations. This is used to train linear classifiers that approximate the accuracy of kernelized ones."><img alt="" src="http://github.com/_images/sphx_glr_plot_scalable_poly_kernels_thumb.png" />
 <p><a class="reference internal" href="http://github.com/auto_examples/kernel_approximation/plot_scalable_poly_kernels.html#sphx-glr-auto-examples-kernel-approximation-plot-scalable-poly-kernels-py"><span class="std std-ref">Scalable learning with polynomial kernel approximation</span></a></p>
   <div class="sphx-glr-thumbnail-title">Scalable learning with polynomial kernel approximation</div>
diff --git a/dev/modules/generated/sklearn.svm.SVR.html b/dev/modules/generated/sklearn.svm.SVR.html
index b536262299ab1..65c83661b0ba8 100644
--- a/dev/modules/generated/sklearn.svm.SVR.html
+++ b/dev/modules/generated/sklearn.svm.SVR.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1308,7 +1308,7 @@
 <h1>SVR<a class="headerlink" href="#svr" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.svm.SVR">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.svm.</span></span><span class="sig-name descname"><span class="pre">SVR</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'rbf'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">degree</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gamma</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'scale'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coef0</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">C</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epsilon</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shrinking</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cache_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">200</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/svm/_classes.py#L1154"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.SVR" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.svm.</span></span><span class="sig-name descname"><span class="pre">SVR</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'rbf'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">degree</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gamma</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'scale'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coef0</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">C</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epsilon</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shrinking</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cache_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">200</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/svm/_classes.py#L1154"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.SVR" title="Link to this definition">#</a></dt>
 <dd><p>Epsilon-Support Vector Regression.</p>
 <p>The free parameters in the model are C and epsilon.</p>
 <p>The implementation is based on libsvm. The fit time complexity
@@ -1446,7 +1446,7 @@ <h1>SVR<a class="headerlink" href="#svr" title="Link to this heading">#</a></h1>
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.svm.SVR.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/svm/_base.py#L153"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.SVR.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/svm/_base.py#L153"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.SVR.fit" title="Link to this definition">#</a></dt>
 <dd><p>Fit the SVM model according to the given training data.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1480,7 +1480,7 @@ <h1>SVR<a class="headerlink" href="#svr" title="Link to this heading">#</a></h1>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.svm.SVR.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.SVR.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.SVR.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1497,7 +1497,7 @@ <h1>SVR<a class="headerlink" href="#svr" title="Link to this heading">#</a></h1>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.svm.SVR.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.SVR.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.SVR.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1518,7 +1518,7 @@ <h1>SVR<a class="headerlink" href="#svr" title="Link to this heading">#</a></h1>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.svm.SVR.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/svm/_base.py#L420"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.SVR.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/svm/_base.py#L420"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.SVR.predict" title="Link to this definition">#</a></dt>
 <dd><p>Perform regression on samples in X.</p>
 <p>For an one-class model, +1 (inlier) or -1 (outlier) is returned.</p>
 <dl class="field-list simple">
@@ -1540,7 +1540,7 @@ <h1>SVR<a class="headerlink" href="#svr" title="Link to this heading">#</a></h1>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.svm.SVR.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L619"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.SVR.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L560"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.SVR.score" title="Link to this definition">#</a></dt>
 <dd><p>Return <a class="reference internal" href="http://github.com/model_evaluation.html#r2-score"><span class="std std-ref">coefficient of determination</span></a> on test data.</p>
 <p>The coefficient of determination, <span class="math notranslate nohighlight">\(R^2\)</span>, is defined as
 <span class="math notranslate nohighlight">\((1 - \frac{u}{v})\)</span>, where <span class="math notranslate nohighlight">\(u\)</span> is the residual
@@ -1582,7 +1582,7 @@ <h1>SVR<a class="headerlink" href="#svr" title="Link to this heading">#</a></h1>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.svm.SVR.set_fit_request">
-<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.svm.SVR" title="sklearn.svm._classes.SVR"><span class="pre">SVR</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.SVR.set_fit_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.svm.SVR" title="sklearn.svm._classes.SVR"><span class="pre">SVR</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.SVR.set_fit_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1625,7 +1625,7 @@ <h1>SVR<a class="headerlink" href="#svr" title="Link to this heading">#</a></h1>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.svm.SVR.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.SVR.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.SVR.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1649,7 +1649,7 @@ <h1>SVR<a class="headerlink" href="#svr" title="Link to this heading">#</a></h1>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.svm.SVR.set_score_request">
-<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.svm.SVR" title="sklearn.svm._classes.SVR"><span class="pre">SVR</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.SVR.set_score_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.svm.SVR" title="sklearn.svm._classes.SVR"><span class="pre">SVR</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.SVR.set_score_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
diff --git a/dev/modules/generated/sklearn.svm.l1_min_c.html b/dev/modules/generated/sklearn.svm.l1_min_c.html
index 006094c80079b..df7920427342c 100644
--- a/dev/modules/generated/sklearn.svm.l1_min_c.html
+++ b/dev/modules/generated/sklearn.svm.l1_min_c.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>l1_min_c<a class="headerlink" href="#l1-min-c" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.svm.l1_min_c">
-<span class="sig-prename descclassname"><span class="pre">sklearn.svm.</span></span><span class="sig-name descname"><span class="pre">l1_min_c</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">loss</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'squared_hinge'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_intercept</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">intercept_scaling</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/svm/_bounds.py#L16"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.l1_min_c" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.svm.</span></span><span class="sig-name descname"><span class="pre">l1_min_c</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">loss</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'squared_hinge'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_intercept</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">intercept_scaling</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/svm/_bounds.py#L16"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.svm.l1_min_c" title="Link to this definition">#</a></dt>
 <dd><p>Return the lowest bound for <code class="docutils literal notranslate"><span class="pre">C</span></code>.</p>
 <p>The lower bound for <code class="docutils literal notranslate"><span class="pre">C</span></code> is computed such that for <code class="docutils literal notranslate"><span class="pre">C</span></code> in <code class="docutils literal notranslate"><span class="pre">(l1_min_C,</span> <span class="pre">infinity)</span></code>
 the model is guaranteed not to be empty. This applies to l1 penalized
diff --git a/dev/modules/generated/sklearn.tree.DecisionTreeClassifier.html b/dev/modules/generated/sklearn.tree.DecisionTreeClassifier.html
index fd06fcb99aa3a..df81e42b00caf 100644
--- a/dev/modules/generated/sklearn.tree.DecisionTreeClassifier.html
+++ b/dev/modules/generated/sklearn.tree.DecisionTreeClassifier.html
@@ -11,10 +11,10 @@
 <meta property="og:type" content="website" />
 <meta property="og:url" content="https://scikit-learn/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html" />
 <meta property="og:site_name" content="scikit-learn" />
-<meta property="og:description" content="Gallery examples: Classifier comparison Multi-class AdaBoosted Decision Trees Two-class AdaBoost Plot the decision surfaces of ensembles of trees on the iris dataset Plot the decision boundaries of..." />
+<meta property="og:description" content="Gallery examples: Classifier comparison Multi-class AdaBoosted Decision Trees Two-class AdaBoost Plot the decision surfaces of ensembles of trees on the iris dataset Demonstration of multi-metric e..." />
 <meta property="og:image" content="https://scikit-learn/stable/_images/sphx_glr_plot_classifier_comparison_thumb.png" />
 <meta property="og:image:alt" content="" />
-<meta name="description" content="Gallery examples: Classifier comparison Multi-class AdaBoosted Decision Trees Two-class AdaBoost Plot the decision surfaces of ensembles of trees on the iris dataset Plot the decision boundaries of..." />
+<meta name="description" content="Gallery examples: Classifier comparison Multi-class AdaBoosted Decision Trees Two-class AdaBoost Plot the decision surfaces of ensembles of trees on the iris dataset Demonstration of multi-metric e..." />
 
     <title>DecisionTreeClassifier &#8212; scikit-learn 1.7.dev0 documentation</title>
   
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>DecisionTreeClassifier<a class="headerlink" href="#decisiontreeclassifier" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.tree.DecisionTreeClassifier">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.tree.</span></span><span class="sig-name descname"><span class="pre">DecisionTreeClassifier</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">criterion</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'gini'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">splitter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'best'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_depth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_samples_split</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_samples_leaf</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_weight_fraction_leaf</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_leaf_nodes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_impurity_decrease</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">class_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ccp_alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">monotonic_cst</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/tree/_classes.py#L704"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.DecisionTreeClassifier" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.tree.</span></span><span class="sig-name descname"><span class="pre">DecisionTreeClassifier</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">criterion</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'gini'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">splitter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'best'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_depth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_samples_split</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_samples_leaf</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_weight_fraction_leaf</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_leaf_nodes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_impurity_decrease</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">class_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ccp_alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">monotonic_cst</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/tree/_classes.py#L704"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.DecisionTreeClassifier" title="Link to this definition">#</a></dt>
 <dd><p>A decision tree classifier.</p>
 <p>Read more in the <a class="reference internal" href="http://github.com/tree.html#tree"><span class="std std-ref">User Guide</span></a>.</p>
 <dl class="field-list">
@@ -1536,13 +1536,13 @@ <h1>DecisionTreeClassifier<a class="headerlink" href="#decisiontreeclassifier" t
 <span class="gp">&gt;&gt;&gt; </span><span class="n">cross_val_score</span><span class="p">(</span><span class="n">clf</span><span class="p">,</span> <span class="n">iris</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="n">iris</span><span class="o">.</span><span class="n">target</span><span class="p">,</span> <span class="n">cv</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
 <span class="gp">... </span>                            
 <span class="gp">...</span>
-<span class="go">array([ 1.     ,  0.93...,  0.86...,  0.93...,  0.93...,</span>
-<span class="go">        0.93...,  0.93...,  1.     ,  0.93...,  1.      ])</span>
+<span class="go">array([ 1.     ,  0.93,  0.86,  0.93,  0.93,</span>
+<span class="go">        0.93,  0.93,  1.     ,  0.93,  1.      ])</span>
 </pre></div>
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.tree.DecisionTreeClassifier.apply">
-<span class="sig-name descname"><span class="pre">apply</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_input</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/tree/_classes.py#L557"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.DecisionTreeClassifier.apply" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">apply</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_input</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/tree/_classes.py#L557"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.DecisionTreeClassifier.apply" title="Link to this definition">#</a></dt>
 <dd><p>Return the index of the leaf that each sample is predicted as.</p>
 <div class="versionadded">
 <p><span class="versionmodified added">Added in version 0.17.</span></p>
@@ -1573,7 +1573,7 @@ <h1>DecisionTreeClassifier<a class="headerlink" href="#decisiontreeclassifier" t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.tree.DecisionTreeClassifier.cost_complexity_pruning_path">
-<span class="sig-name descname"><span class="pre">cost_complexity_pruning_path</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/tree/_classes.py#L632"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.DecisionTreeClassifier.cost_complexity_pruning_path" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">cost_complexity_pruning_path</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/tree/_classes.py#L632"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.DecisionTreeClassifier.cost_complexity_pruning_path" title="Link to this definition">#</a></dt>
 <dd><p>Compute the pruning path during Minimal Cost-Complexity Pruning.</p>
 <p>See <a class="reference internal" href="http://github.com/tree.html#minimal-cost-complexity-pruning"><span class="std std-ref">Minimal Cost-Complexity Pruning</span></a> for details on the pruning
 process.</p>
@@ -1612,7 +1612,7 @@ <h1>DecisionTreeClassifier<a class="headerlink" href="#decisiontreeclassifier" t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.tree.DecisionTreeClassifier.decision_path">
-<span class="sig-name descname"><span class="pre">decision_path</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_input</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/tree/_classes.py#L585"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.DecisionTreeClassifier.decision_path" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">decision_path</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_input</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/tree/_classes.py#L585"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.DecisionTreeClassifier.decision_path" title="Link to this definition">#</a></dt>
 <dd><p>Return the decision path in the tree.</p>
 <div class="versionadded">
 <p><span class="versionmodified added">Added in version 0.18.</span></p>
@@ -1641,7 +1641,7 @@ <h1>DecisionTreeClassifier<a class="headerlink" href="#decisiontreeclassifier" t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.tree.DecisionTreeClassifier.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_input</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/tree/_classes.py#L993"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.DecisionTreeClassifier.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_input</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/tree/_classes.py#L993"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.DecisionTreeClassifier.fit" title="Link to this definition">#</a></dt>
 <dd><p>Build a decision tree classifier from the training set (X, y).</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1674,7 +1674,7 @@ <h1>DecisionTreeClassifier<a class="headerlink" href="#decisiontreeclassifier" t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.tree.DecisionTreeClassifier.get_depth">
-<span class="sig-name descname"><span class="pre">get_depth</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/tree/_classes.py#L159"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.DecisionTreeClassifier.get_depth" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_depth</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/tree/_classes.py#L159"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.DecisionTreeClassifier.get_depth" title="Link to this definition">#</a></dt>
 <dd><p>Return the depth of the decision tree.</p>
 <p>The depth of a tree is the maximum distance between the root
 and any leaf.</p>
@@ -1690,7 +1690,7 @@ <h1>DecisionTreeClassifier<a class="headerlink" href="#decisiontreeclassifier" t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.tree.DecisionTreeClassifier.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.DecisionTreeClassifier.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.DecisionTreeClassifier.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1707,7 +1707,7 @@ <h1>DecisionTreeClassifier<a class="headerlink" href="#decisiontreeclassifier" t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.tree.DecisionTreeClassifier.get_n_leaves">
-<span class="sig-name descname"><span class="pre">get_n_leaves</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/tree/_classes.py#L173"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.DecisionTreeClassifier.get_n_leaves" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_n_leaves</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/tree/_classes.py#L173"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.DecisionTreeClassifier.get_n_leaves" title="Link to this definition">#</a></dt>
 <dd><p>Return the number of leaves of the decision tree.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Returns<span class="colon">:</span></dt>
@@ -1721,7 +1721,7 @@ <h1>DecisionTreeClassifier<a class="headerlink" href="#decisiontreeclassifier" t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.tree.DecisionTreeClassifier.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.DecisionTreeClassifier.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.DecisionTreeClassifier.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1742,7 +1742,7 @@ <h1>DecisionTreeClassifier<a class="headerlink" href="#decisiontreeclassifier" t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.tree.DecisionTreeClassifier.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_input</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/tree/_classes.py#L506"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.DecisionTreeClassifier.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_input</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/tree/_classes.py#L506"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.DecisionTreeClassifier.predict" title="Link to this definition">#</a></dt>
 <dd><p>Predict class or regression value for X.</p>
 <p>For a classification model, the predicted class for each sample in X is
 returned. For a regression model, the predicted value based on X is
@@ -1770,7 +1770,7 @@ <h1>DecisionTreeClassifier<a class="headerlink" href="#decisiontreeclassifier" t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.tree.DecisionTreeClassifier.predict_log_proba">
-<span class="sig-name descname"><span class="pre">predict_log_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/tree/_classes.py#L1069"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.DecisionTreeClassifier.predict_log_proba" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict_log_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/tree/_classes.py#L1069"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.DecisionTreeClassifier.predict_log_proba" title="Link to this definition">#</a></dt>
 <dd><p>Predict class log-probabilities of the input samples X.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1793,7 +1793,7 @@ <h1>DecisionTreeClassifier<a class="headerlink" href="#decisiontreeclassifier" t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.tree.DecisionTreeClassifier.predict_proba">
-<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_input</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/tree/_classes.py#L1032"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.DecisionTreeClassifier.predict_proba" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_input</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/tree/_classes.py#L1032"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.DecisionTreeClassifier.predict_proba" title="Link to this definition">#</a></dt>
 <dd><p>Predict class probabilities of the input samples X.</p>
 <p>The predicted class probability is the fraction of samples of the same
 class in a leaf.</p>
@@ -1821,7 +1821,7 @@ <h1>DecisionTreeClassifier<a class="headerlink" href="#decisiontreeclassifier" t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.tree.DecisionTreeClassifier.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L546"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.DecisionTreeClassifier.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L487"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.DecisionTreeClassifier.score" title="Link to this definition">#</a></dt>
 <dd><p>Return <a class="reference internal" href="http://github.com/model_evaluation.html#accuracy-score"><span class="std std-ref">accuracy</span></a> on provided data and labels.</p>
 <p>In multi-label classification, this is the subset accuracy
 which is a harsh metric since you require for each sample that
@@ -1848,7 +1848,7 @@ <h1>DecisionTreeClassifier<a class="headerlink" href="#decisiontreeclassifier" t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.tree.DecisionTreeClassifier.set_fit_request">
-<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.tree.DecisionTreeClassifier" title="sklearn.tree._classes.DecisionTreeClassifier"><span class="pre">DecisionTreeClassifier</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.DecisionTreeClassifier.set_fit_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.tree.DecisionTreeClassifier" title="sklearn.tree._classes.DecisionTreeClassifier"><span class="pre">DecisionTreeClassifier</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.DecisionTreeClassifier.set_fit_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1891,7 +1891,7 @@ <h1>DecisionTreeClassifier<a class="headerlink" href="#decisiontreeclassifier" t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.tree.DecisionTreeClassifier.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.DecisionTreeClassifier.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.DecisionTreeClassifier.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1915,7 +1915,7 @@ <h1>DecisionTreeClassifier<a class="headerlink" href="#decisiontreeclassifier" t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.tree.DecisionTreeClassifier.set_score_request">
-<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.tree.DecisionTreeClassifier" title="sklearn.tree._classes.DecisionTreeClassifier"><span class="pre">DecisionTreeClassifier</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.DecisionTreeClassifier.set_score_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.tree.DecisionTreeClassifier" title="sklearn.tree._classes.DecisionTreeClassifier"><span class="pre">DecisionTreeClassifier</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.DecisionTreeClassifier.set_score_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1972,9 +1972,6 @@ <h2>Gallery examples<a class="headerlink" href="#gallery-examples" title="Link t
 </div><div class="sphx-glr-thumbcontainer" tooltip="Plot the decision surfaces of forests of randomized trees trained on pairs of features of the iris dataset."><img alt="" src="http://github.com/_images/sphx_glr_plot_forest_iris_thumb.png" />
 <p><a class="reference internal" href="http://github.com/auto_examples/ensemble/plot_forest_iris.html#sphx-glr-auto-examples-ensemble-plot-forest-iris-py"><span class="std std-ref">Plot the decision surfaces of ensembles of trees on the iris dataset</span></a></p>
   <div class="sphx-glr-thumbnail-title">Plot the decision surfaces of ensembles of trees on the iris dataset</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="Plot the decision boundaries of a VotingClassifier for two features of the Iris dataset."><img alt="" src="http://github.com/_images/sphx_glr_plot_voting_decision_regions_thumb.png" />
-<p><a class="reference internal" href="http://github.com/auto_examples/ensemble/plot_voting_decision_regions.html#sphx-glr-auto-examples-ensemble-plot-voting-decision-regions-py"><span class="std std-ref">Plot the decision boundaries of a VotingClassifier</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Plot the decision boundaries of a VotingClassifier</div>
 </div><div class="sphx-glr-thumbcontainer" tooltip="Multiple metric parameter search can be done by setting the scoring parameter to a list of metric scorer names or a dict mapping the scorer names to the scorer callables."><img alt="" src="http://github.com/_images/sphx_glr_plot_multi_metric_evaluation_thumb.png" />
 <p><a class="reference internal" href="http://github.com/auto_examples/model_selection/plot_multi_metric_evaluation.html#sphx-glr-auto-examples-model-selection-plot-multi-metric-evaluation-py"><span class="std std-ref">Demonstration of multi-metric evaluation on cross_val_score and GridSearchCV</span></a></p>
   <div class="sphx-glr-thumbnail-title">Demonstration of multi-metric evaluation on cross_val_score and GridSearchCV</div>
diff --git a/dev/modules/generated/sklearn.tree.DecisionTreeRegressor.html b/dev/modules/generated/sklearn.tree.DecisionTreeRegressor.html
index 2d0b6598262e1..d26bf6f145b49 100644
--- a/dev/modules/generated/sklearn.tree.DecisionTreeRegressor.html
+++ b/dev/modules/generated/sklearn.tree.DecisionTreeRegressor.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1308,7 +1308,7 @@
 <h1>DecisionTreeRegressor<a class="headerlink" href="#decisiontreeregressor" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.tree.DecisionTreeRegressor">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.tree.</span></span><span class="sig-name descname"><span class="pre">DecisionTreeRegressor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">criterion</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'squared_error'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">splitter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'best'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_depth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_samples_split</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_samples_leaf</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_weight_fraction_leaf</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_leaf_nodes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_impurity_decrease</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ccp_alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">monotonic_cst</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/tree/_classes.py#L1111"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.DecisionTreeRegressor" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.tree.</span></span><span class="sig-name descname"><span class="pre">DecisionTreeRegressor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">criterion</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'squared_error'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">splitter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'best'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_depth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_samples_split</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_samples_leaf</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_weight_fraction_leaf</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_leaf_nodes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_impurity_decrease</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ccp_alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">monotonic_cst</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/tree/_classes.py#L1111"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.DecisionTreeRegressor" title="Link to this definition">#</a></dt>
 <dd><p>A decision tree regressor.</p>
 <p>Read more in the <a class="reference internal" href="http://github.com/tree.html#tree"><span class="std std-ref">User Guide</span></a>.</p>
 <dl class="field-list">
@@ -1518,13 +1518,13 @@ <h1>DecisionTreeRegressor<a class="headerlink" href="#decisiontreeregressor" tit
 <span class="gp">&gt;&gt;&gt; </span><span class="n">cross_val_score</span><span class="p">(</span><span class="n">regressor</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">cv</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
 <span class="gp">... </span>                   
 <span class="gp">...</span>
-<span class="go">array([-0.39..., -0.46...,  0.02...,  0.06..., -0.50...,</span>
-<span class="go">       0.16...,  0.11..., -0.73..., -0.30..., -0.00...])</span>
+<span class="go">array([-0.39, -0.46,  0.02,  0.06, -0.50,</span>
+<span class="go">       0.16,  0.11, -0.73, -0.30, -0.00])</span>
 </pre></div>
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.tree.DecisionTreeRegressor.apply">
-<span class="sig-name descname"><span class="pre">apply</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_input</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/tree/_classes.py#L557"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.DecisionTreeRegressor.apply" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">apply</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_input</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/tree/_classes.py#L557"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.DecisionTreeRegressor.apply" title="Link to this definition">#</a></dt>
 <dd><p>Return the index of the leaf that each sample is predicted as.</p>
 <div class="versionadded">
 <p><span class="versionmodified added">Added in version 0.17.</span></p>
@@ -1555,7 +1555,7 @@ <h1>DecisionTreeRegressor<a class="headerlink" href="#decisiontreeregressor" tit
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.tree.DecisionTreeRegressor.cost_complexity_pruning_path">
-<span class="sig-name descname"><span class="pre">cost_complexity_pruning_path</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/tree/_classes.py#L632"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.DecisionTreeRegressor.cost_complexity_pruning_path" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">cost_complexity_pruning_path</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/tree/_classes.py#L632"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.DecisionTreeRegressor.cost_complexity_pruning_path" title="Link to this definition">#</a></dt>
 <dd><p>Compute the pruning path during Minimal Cost-Complexity Pruning.</p>
 <p>See <a class="reference internal" href="http://github.com/tree.html#minimal-cost-complexity-pruning"><span class="std std-ref">Minimal Cost-Complexity Pruning</span></a> for details on the pruning
 process.</p>
@@ -1594,7 +1594,7 @@ <h1>DecisionTreeRegressor<a class="headerlink" href="#decisiontreeregressor" tit
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.tree.DecisionTreeRegressor.decision_path">
-<span class="sig-name descname"><span class="pre">decision_path</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_input</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/tree/_classes.py#L585"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.DecisionTreeRegressor.decision_path" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">decision_path</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_input</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/tree/_classes.py#L585"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.DecisionTreeRegressor.decision_path" title="Link to this definition">#</a></dt>
 <dd><p>Return the decision path in the tree.</p>
 <div class="versionadded">
 <p><span class="versionmodified added">Added in version 0.18.</span></p>
@@ -1623,7 +1623,7 @@ <h1>DecisionTreeRegressor<a class="headerlink" href="#decisiontreeregressor" tit
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.tree.DecisionTreeRegressor.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_input</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/tree/_classes.py#L1374"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.DecisionTreeRegressor.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_input</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/tree/_classes.py#L1374"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.DecisionTreeRegressor.fit" title="Link to this definition">#</a></dt>
 <dd><p>Build a decision tree regressor from the training set (X, y).</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1655,7 +1655,7 @@ <h1>DecisionTreeRegressor<a class="headerlink" href="#decisiontreeregressor" tit
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.tree.DecisionTreeRegressor.get_depth">
-<span class="sig-name descname"><span class="pre">get_depth</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/tree/_classes.py#L159"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.DecisionTreeRegressor.get_depth" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_depth</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/tree/_classes.py#L159"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.DecisionTreeRegressor.get_depth" title="Link to this definition">#</a></dt>
 <dd><p>Return the depth of the decision tree.</p>
 <p>The depth of a tree is the maximum distance between the root
 and any leaf.</p>
@@ -1671,7 +1671,7 @@ <h1>DecisionTreeRegressor<a class="headerlink" href="#decisiontreeregressor" tit
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.tree.DecisionTreeRegressor.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.DecisionTreeRegressor.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.DecisionTreeRegressor.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1688,7 +1688,7 @@ <h1>DecisionTreeRegressor<a class="headerlink" href="#decisiontreeregressor" tit
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.tree.DecisionTreeRegressor.get_n_leaves">
-<span class="sig-name descname"><span class="pre">get_n_leaves</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/tree/_classes.py#L173"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.DecisionTreeRegressor.get_n_leaves" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_n_leaves</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/tree/_classes.py#L173"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.DecisionTreeRegressor.get_n_leaves" title="Link to this definition">#</a></dt>
 <dd><p>Return the number of leaves of the decision tree.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Returns<span class="colon">:</span></dt>
@@ -1702,7 +1702,7 @@ <h1>DecisionTreeRegressor<a class="headerlink" href="#decisiontreeregressor" tit
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.tree.DecisionTreeRegressor.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.DecisionTreeRegressor.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.DecisionTreeRegressor.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1723,7 +1723,7 @@ <h1>DecisionTreeRegressor<a class="headerlink" href="#decisiontreeregressor" tit
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.tree.DecisionTreeRegressor.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_input</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/tree/_classes.py#L506"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.DecisionTreeRegressor.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_input</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/tree/_classes.py#L506"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.DecisionTreeRegressor.predict" title="Link to this definition">#</a></dt>
 <dd><p>Predict class or regression value for X.</p>
 <p>For a classification model, the predicted class for each sample in X is
 returned. For a regression model, the predicted value based on X is
@@ -1751,7 +1751,7 @@ <h1>DecisionTreeRegressor<a class="headerlink" href="#decisiontreeregressor" tit
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.tree.DecisionTreeRegressor.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L619"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.DecisionTreeRegressor.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L560"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.DecisionTreeRegressor.score" title="Link to this definition">#</a></dt>
 <dd><p>Return <a class="reference internal" href="http://github.com/model_evaluation.html#r2-score"><span class="std std-ref">coefficient of determination</span></a> on test data.</p>
 <p>The coefficient of determination, <span class="math notranslate nohighlight">\(R^2\)</span>, is defined as
 <span class="math notranslate nohighlight">\((1 - \frac{u}{v})\)</span>, where <span class="math notranslate nohighlight">\(u\)</span> is the residual
@@ -1793,7 +1793,7 @@ <h1>DecisionTreeRegressor<a class="headerlink" href="#decisiontreeregressor" tit
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.tree.DecisionTreeRegressor.set_fit_request">
-<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.tree.DecisionTreeRegressor" title="sklearn.tree._classes.DecisionTreeRegressor"><span class="pre">DecisionTreeRegressor</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.DecisionTreeRegressor.set_fit_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.tree.DecisionTreeRegressor" title="sklearn.tree._classes.DecisionTreeRegressor"><span class="pre">DecisionTreeRegressor</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.DecisionTreeRegressor.set_fit_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1836,7 +1836,7 @@ <h1>DecisionTreeRegressor<a class="headerlink" href="#decisiontreeregressor" tit
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.tree.DecisionTreeRegressor.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.DecisionTreeRegressor.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.DecisionTreeRegressor.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1860,7 +1860,7 @@ <h1>DecisionTreeRegressor<a class="headerlink" href="#decisiontreeregressor" tit
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.tree.DecisionTreeRegressor.set_score_request">
-<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.tree.DecisionTreeRegressor" title="sklearn.tree._classes.DecisionTreeRegressor"><span class="pre">DecisionTreeRegressor</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.DecisionTreeRegressor.set_score_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.tree.DecisionTreeRegressor" title="sklearn.tree._classes.DecisionTreeRegressor"><span class="pre">DecisionTreeRegressor</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.DecisionTreeRegressor.set_score_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
diff --git a/dev/modules/generated/sklearn.tree.ExtraTreeClassifier.html b/dev/modules/generated/sklearn.tree.ExtraTreeClassifier.html
index 51657e3bfdf33..28e39ca7839b3 100644
--- a/dev/modules/generated/sklearn.tree.ExtraTreeClassifier.html
+++ b/dev/modules/generated/sklearn.tree.ExtraTreeClassifier.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>ExtraTreeClassifier<a class="headerlink" href="#extratreeclassifier" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.tree.ExtraTreeClassifier">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.tree.</span></span><span class="sig-name descname"><span class="pre">ExtraTreeClassifier</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">criterion</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'gini'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">splitter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'random'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_depth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_samples_split</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_samples_leaf</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_weight_fraction_leaf</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'sqrt'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_leaf_nodes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_impurity_decrease</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">class_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ccp_alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">monotonic_cst</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/tree/_classes.py#L1453"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.ExtraTreeClassifier" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.tree.</span></span><span class="sig-name descname"><span class="pre">ExtraTreeClassifier</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">criterion</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'gini'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">splitter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'random'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_depth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_samples_split</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_samples_leaf</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_weight_fraction_leaf</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'sqrt'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_leaf_nodes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_impurity_decrease</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">class_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ccp_alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">monotonic_cst</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/tree/_classes.py#L1453"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.ExtraTreeClassifier" title="Link to this definition">#</a></dt>
 <dd><p>An extremely randomized tree classifier.</p>
 <p>Extra-trees differ from classic decision trees in the way they are built.
 When looking for the best split to separate the samples of a node into two
@@ -1528,12 +1528,12 @@ <h1>ExtraTreeClassifier<a class="headerlink" href="#extratreeclassifier" title="
 <span class="gp">&gt;&gt;&gt; </span><span class="bp">cls</span> <span class="o">=</span> <span class="n">BaggingClassifier</span><span class="p">(</span><span class="n">extra_tree</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span>
 <span class="gp">... </span>   <span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="bp">cls</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span>
-<span class="go">0.8947...</span>
+<span class="go">0.8947</span>
 </pre></div>
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.tree.ExtraTreeClassifier.apply">
-<span class="sig-name descname"><span class="pre">apply</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_input</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/tree/_classes.py#L557"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.ExtraTreeClassifier.apply" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">apply</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_input</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/tree/_classes.py#L557"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.ExtraTreeClassifier.apply" title="Link to this definition">#</a></dt>
 <dd><p>Return the index of the leaf that each sample is predicted as.</p>
 <div class="versionadded">
 <p><span class="versionmodified added">Added in version 0.17.</span></p>
@@ -1564,7 +1564,7 @@ <h1>ExtraTreeClassifier<a class="headerlink" href="#extratreeclassifier" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.tree.ExtraTreeClassifier.cost_complexity_pruning_path">
-<span class="sig-name descname"><span class="pre">cost_complexity_pruning_path</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/tree/_classes.py#L632"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.ExtraTreeClassifier.cost_complexity_pruning_path" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">cost_complexity_pruning_path</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/tree/_classes.py#L632"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.ExtraTreeClassifier.cost_complexity_pruning_path" title="Link to this definition">#</a></dt>
 <dd><p>Compute the pruning path during Minimal Cost-Complexity Pruning.</p>
 <p>See <a class="reference internal" href="http://github.com/tree.html#minimal-cost-complexity-pruning"><span class="std std-ref">Minimal Cost-Complexity Pruning</span></a> for details on the pruning
 process.</p>
@@ -1603,7 +1603,7 @@ <h1>ExtraTreeClassifier<a class="headerlink" href="#extratreeclassifier" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.tree.ExtraTreeClassifier.decision_path">
-<span class="sig-name descname"><span class="pre">decision_path</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_input</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/tree/_classes.py#L585"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.ExtraTreeClassifier.decision_path" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">decision_path</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_input</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/tree/_classes.py#L585"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.ExtraTreeClassifier.decision_path" title="Link to this definition">#</a></dt>
 <dd><p>Return the decision path in the tree.</p>
 <div class="versionadded">
 <p><span class="versionmodified added">Added in version 0.18.</span></p>
@@ -1632,7 +1632,7 @@ <h1>ExtraTreeClassifier<a class="headerlink" href="#extratreeclassifier" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.tree.ExtraTreeClassifier.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_input</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/tree/_classes.py#L993"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.ExtraTreeClassifier.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_input</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/tree/_classes.py#L993"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.ExtraTreeClassifier.fit" title="Link to this definition">#</a></dt>
 <dd><p>Build a decision tree classifier from the training set (X, y).</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1665,7 +1665,7 @@ <h1>ExtraTreeClassifier<a class="headerlink" href="#extratreeclassifier" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.tree.ExtraTreeClassifier.get_depth">
-<span class="sig-name descname"><span class="pre">get_depth</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/tree/_classes.py#L159"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.ExtraTreeClassifier.get_depth" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_depth</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/tree/_classes.py#L159"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.ExtraTreeClassifier.get_depth" title="Link to this definition">#</a></dt>
 <dd><p>Return the depth of the decision tree.</p>
 <p>The depth of a tree is the maximum distance between the root
 and any leaf.</p>
@@ -1681,7 +1681,7 @@ <h1>ExtraTreeClassifier<a class="headerlink" href="#extratreeclassifier" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.tree.ExtraTreeClassifier.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.ExtraTreeClassifier.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.ExtraTreeClassifier.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1698,7 +1698,7 @@ <h1>ExtraTreeClassifier<a class="headerlink" href="#extratreeclassifier" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.tree.ExtraTreeClassifier.get_n_leaves">
-<span class="sig-name descname"><span class="pre">get_n_leaves</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/tree/_classes.py#L173"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.ExtraTreeClassifier.get_n_leaves" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_n_leaves</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/tree/_classes.py#L173"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.ExtraTreeClassifier.get_n_leaves" title="Link to this definition">#</a></dt>
 <dd><p>Return the number of leaves of the decision tree.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Returns<span class="colon">:</span></dt>
@@ -1712,7 +1712,7 @@ <h1>ExtraTreeClassifier<a class="headerlink" href="#extratreeclassifier" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.tree.ExtraTreeClassifier.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.ExtraTreeClassifier.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.ExtraTreeClassifier.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1733,7 +1733,7 @@ <h1>ExtraTreeClassifier<a class="headerlink" href="#extratreeclassifier" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.tree.ExtraTreeClassifier.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_input</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/tree/_classes.py#L506"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.ExtraTreeClassifier.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_input</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/tree/_classes.py#L506"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.ExtraTreeClassifier.predict" title="Link to this definition">#</a></dt>
 <dd><p>Predict class or regression value for X.</p>
 <p>For a classification model, the predicted class for each sample in X is
 returned. For a regression model, the predicted value based on X is
@@ -1761,7 +1761,7 @@ <h1>ExtraTreeClassifier<a class="headerlink" href="#extratreeclassifier" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.tree.ExtraTreeClassifier.predict_log_proba">
-<span class="sig-name descname"><span class="pre">predict_log_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/tree/_classes.py#L1069"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.ExtraTreeClassifier.predict_log_proba" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict_log_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/tree/_classes.py#L1069"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.ExtraTreeClassifier.predict_log_proba" title="Link to this definition">#</a></dt>
 <dd><p>Predict class log-probabilities of the input samples X.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1784,7 +1784,7 @@ <h1>ExtraTreeClassifier<a class="headerlink" href="#extratreeclassifier" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.tree.ExtraTreeClassifier.predict_proba">
-<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_input</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/tree/_classes.py#L1032"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.ExtraTreeClassifier.predict_proba" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_input</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/tree/_classes.py#L1032"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.ExtraTreeClassifier.predict_proba" title="Link to this definition">#</a></dt>
 <dd><p>Predict class probabilities of the input samples X.</p>
 <p>The predicted class probability is the fraction of samples of the same
 class in a leaf.</p>
@@ -1812,7 +1812,7 @@ <h1>ExtraTreeClassifier<a class="headerlink" href="#extratreeclassifier" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.tree.ExtraTreeClassifier.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L546"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.ExtraTreeClassifier.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L487"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.ExtraTreeClassifier.score" title="Link to this definition">#</a></dt>
 <dd><p>Return <a class="reference internal" href="http://github.com/model_evaluation.html#accuracy-score"><span class="std std-ref">accuracy</span></a> on provided data and labels.</p>
 <p>In multi-label classification, this is the subset accuracy
 which is a harsh metric since you require for each sample that
@@ -1839,7 +1839,7 @@ <h1>ExtraTreeClassifier<a class="headerlink" href="#extratreeclassifier" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.tree.ExtraTreeClassifier.set_fit_request">
-<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.tree.ExtraTreeClassifier" title="sklearn.tree._classes.ExtraTreeClassifier"><span class="pre">ExtraTreeClassifier</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.ExtraTreeClassifier.set_fit_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.tree.ExtraTreeClassifier" title="sklearn.tree._classes.ExtraTreeClassifier"><span class="pre">ExtraTreeClassifier</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.ExtraTreeClassifier.set_fit_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1882,7 +1882,7 @@ <h1>ExtraTreeClassifier<a class="headerlink" href="#extratreeclassifier" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.tree.ExtraTreeClassifier.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.ExtraTreeClassifier.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.ExtraTreeClassifier.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1906,7 +1906,7 @@ <h1>ExtraTreeClassifier<a class="headerlink" href="#extratreeclassifier" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.tree.ExtraTreeClassifier.set_score_request">
-<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.tree.ExtraTreeClassifier" title="sklearn.tree._classes.ExtraTreeClassifier"><span class="pre">ExtraTreeClassifier</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.ExtraTreeClassifier.set_score_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.tree.ExtraTreeClassifier" title="sklearn.tree._classes.ExtraTreeClassifier"><span class="pre">ExtraTreeClassifier</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.ExtraTreeClassifier.set_score_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
diff --git a/dev/modules/generated/sklearn.tree.ExtraTreeRegressor.html b/dev/modules/generated/sklearn.tree.ExtraTreeRegressor.html
index ca8bb60c144b2..8d2d127e303d4 100644
--- a/dev/modules/generated/sklearn.tree.ExtraTreeRegressor.html
+++ b/dev/modules/generated/sklearn.tree.ExtraTreeRegressor.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1306,7 +1306,7 @@
 <h1>ExtraTreeRegressor<a class="headerlink" href="#extratreeregressor" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.tree.ExtraTreeRegressor">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.tree.</span></span><span class="sig-name descname"><span class="pre">ExtraTreeRegressor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">criterion</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'squared_error'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">splitter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'random'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_depth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_samples_split</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_samples_leaf</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_weight_fraction_leaf</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_impurity_decrease</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_leaf_nodes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ccp_alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">monotonic_cst</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/tree/_classes.py#L1742"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.ExtraTreeRegressor" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.tree.</span></span><span class="sig-name descname"><span class="pre">ExtraTreeRegressor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">criterion</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'squared_error'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">splitter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'random'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_depth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_samples_split</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_samples_leaf</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_weight_fraction_leaf</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_impurity_decrease</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_leaf_nodes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ccp_alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">monotonic_cst</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/tree/_classes.py#L1742"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.ExtraTreeRegressor" title="Link to this definition">#</a></dt>
 <dd><p>An extremely randomized tree regressor.</p>
 <p>Extra-trees differ from classic decision trees in the way they are built.
 When looking for the best split to separate the samples of a node into two
@@ -1509,12 +1509,12 @@ <h1>ExtraTreeRegressor<a class="headerlink" href="#extratreeregressor" title="Li
 <span class="gp">&gt;&gt;&gt; </span><span class="n">reg</span> <span class="o">=</span> <span class="n">BaggingRegressor</span><span class="p">(</span><span class="n">extra_tree</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span>
 <span class="gp">... </span>    <span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">reg</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span>
-<span class="go">0.33...</span>
+<span class="go">0.33</span>
 </pre></div>
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.tree.ExtraTreeRegressor.apply">
-<span class="sig-name descname"><span class="pre">apply</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_input</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/tree/_classes.py#L557"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.ExtraTreeRegressor.apply" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">apply</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_input</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/tree/_classes.py#L557"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.ExtraTreeRegressor.apply" title="Link to this definition">#</a></dt>
 <dd><p>Return the index of the leaf that each sample is predicted as.</p>
 <div class="versionadded">
 <p><span class="versionmodified added">Added in version 0.17.</span></p>
@@ -1545,7 +1545,7 @@ <h1>ExtraTreeRegressor<a class="headerlink" href="#extratreeregressor" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.tree.ExtraTreeRegressor.cost_complexity_pruning_path">
-<span class="sig-name descname"><span class="pre">cost_complexity_pruning_path</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/tree/_classes.py#L632"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.ExtraTreeRegressor.cost_complexity_pruning_path" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">cost_complexity_pruning_path</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/tree/_classes.py#L632"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.ExtraTreeRegressor.cost_complexity_pruning_path" title="Link to this definition">#</a></dt>
 <dd><p>Compute the pruning path during Minimal Cost-Complexity Pruning.</p>
 <p>See <a class="reference internal" href="http://github.com/tree.html#minimal-cost-complexity-pruning"><span class="std std-ref">Minimal Cost-Complexity Pruning</span></a> for details on the pruning
 process.</p>
@@ -1584,7 +1584,7 @@ <h1>ExtraTreeRegressor<a class="headerlink" href="#extratreeregressor" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.tree.ExtraTreeRegressor.decision_path">
-<span class="sig-name descname"><span class="pre">decision_path</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_input</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/tree/_classes.py#L585"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.ExtraTreeRegressor.decision_path" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">decision_path</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_input</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/tree/_classes.py#L585"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.ExtraTreeRegressor.decision_path" title="Link to this definition">#</a></dt>
 <dd><p>Return the decision path in the tree.</p>
 <div class="versionadded">
 <p><span class="versionmodified added">Added in version 0.18.</span></p>
@@ -1613,7 +1613,7 @@ <h1>ExtraTreeRegressor<a class="headerlink" href="#extratreeregressor" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.tree.ExtraTreeRegressor.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_input</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/tree/_classes.py#L1374"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.ExtraTreeRegressor.fit" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_input</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/tree/_classes.py#L1374"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.ExtraTreeRegressor.fit" title="Link to this definition">#</a></dt>
 <dd><p>Build a decision tree regressor from the training set (X, y).</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1645,7 +1645,7 @@ <h1>ExtraTreeRegressor<a class="headerlink" href="#extratreeregressor" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.tree.ExtraTreeRegressor.get_depth">
-<span class="sig-name descname"><span class="pre">get_depth</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/tree/_classes.py#L159"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.ExtraTreeRegressor.get_depth" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_depth</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/tree/_classes.py#L159"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.ExtraTreeRegressor.get_depth" title="Link to this definition">#</a></dt>
 <dd><p>Return the depth of the decision tree.</p>
 <p>The depth of a tree is the maximum distance between the root
 and any leaf.</p>
@@ -1661,7 +1661,7 @@ <h1>ExtraTreeRegressor<a class="headerlink" href="#extratreeregressor" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.tree.ExtraTreeRegressor.get_metadata_routing">
-<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.ExtraTreeRegressor.get_metadata_routing" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.ExtraTreeRegressor.get_metadata_routing" title="Link to this definition">#</a></dt>
 <dd><p>Get metadata routing of this object.</p>
 <p>Please check <a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
 mechanism works.</p>
@@ -1678,7 +1678,7 @@ <h1>ExtraTreeRegressor<a class="headerlink" href="#extratreeregressor" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.tree.ExtraTreeRegressor.get_n_leaves">
-<span class="sig-name descname"><span class="pre">get_n_leaves</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/tree/_classes.py#L173"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.ExtraTreeRegressor.get_n_leaves" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_n_leaves</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/tree/_classes.py#L173"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.ExtraTreeRegressor.get_n_leaves" title="Link to this definition">#</a></dt>
 <dd><p>Return the number of leaves of the decision tree.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Returns<span class="colon">:</span></dt>
@@ -1692,7 +1692,7 @@ <h1>ExtraTreeRegressor<a class="headerlink" href="#extratreeregressor" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.tree.ExtraTreeRegressor.get_params">
-<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.ExtraTreeRegressor.get_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.ExtraTreeRegressor.get_params" title="Link to this definition">#</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1713,7 +1713,7 @@ <h1>ExtraTreeRegressor<a class="headerlink" href="#extratreeregressor" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.tree.ExtraTreeRegressor.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_input</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/tree/_classes.py#L506"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.ExtraTreeRegressor.predict" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_input</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/tree/_classes.py#L506"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.ExtraTreeRegressor.predict" title="Link to this definition">#</a></dt>
 <dd><p>Predict class or regression value for X.</p>
 <p>For a classification model, the predicted class for each sample in X is
 returned. For a regression model, the predicted value based on X is
@@ -1741,7 +1741,7 @@ <h1>ExtraTreeRegressor<a class="headerlink" href="#extratreeregressor" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.tree.ExtraTreeRegressor.score">
-<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L619"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.ExtraTreeRegressor.score" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L560"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.ExtraTreeRegressor.score" title="Link to this definition">#</a></dt>
 <dd><p>Return <a class="reference internal" href="http://github.com/model_evaluation.html#r2-score"><span class="std std-ref">coefficient of determination</span></a> on test data.</p>
 <p>The coefficient of determination, <span class="math notranslate nohighlight">\(R^2\)</span>, is defined as
 <span class="math notranslate nohighlight">\((1 - \frac{u}{v})\)</span>, where <span class="math notranslate nohighlight">\(u\)</span> is the residual
@@ -1783,7 +1783,7 @@ <h1>ExtraTreeRegressor<a class="headerlink" href="#extratreeregressor" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.tree.ExtraTreeRegressor.set_fit_request">
-<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.tree.ExtraTreeRegressor" title="sklearn.tree._classes.ExtraTreeRegressor"><span class="pre">ExtraTreeRegressor</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.ExtraTreeRegressor.set_fit_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.tree.ExtraTreeRegressor" title="sklearn.tree._classes.ExtraTreeRegressor"><span class="pre">ExtraTreeRegressor</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.ExtraTreeRegressor.set_fit_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
@@ -1826,7 +1826,7 @@ <h1>ExtraTreeRegressor<a class="headerlink" href="#extratreeregressor" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.tree.ExtraTreeRegressor.set_params">
-<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.ExtraTreeRegressor.set_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.ExtraTreeRegressor.set_params" title="Link to this definition">#</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <a class="reference internal" href="http://github.com/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
@@ -1850,7 +1850,7 @@ <h1>ExtraTreeRegressor<a class="headerlink" href="#extratreeregressor" title="Li
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.tree.ExtraTreeRegressor.set_score_request">
-<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.tree.ExtraTreeRegressor" title="sklearn.tree._classes.ExtraTreeRegressor"><span class="pre">ExtraTreeRegressor</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.ExtraTreeRegressor.set_score_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.tree.ExtraTreeRegressor" title="sklearn.tree._classes.ExtraTreeRegressor"><span class="pre">ExtraTreeRegressor</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.ExtraTreeRegressor.set_score_request" title="Link to this definition">#</a></dt>
 <dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
 <p>Note that this method is only relevant if
 <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="http://github.com/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
diff --git a/dev/modules/generated/sklearn.tree.export_graphviz.html b/dev/modules/generated/sklearn.tree.export_graphviz.html
index 1314c20a9948b..0de8ff9dbf8b2 100644
--- a/dev/modules/generated/sklearn.tree.export_graphviz.html
+++ b/dev/modules/generated/sklearn.tree.export_graphviz.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>export_graphviz<a class="headerlink" href="#export-graphviz" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.tree.export_graphviz">
-<span class="sig-prename descclassname"><span class="pre">sklearn.tree.</span></span><span class="sig-name descname"><span class="pre">export_graphviz</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">decision_tree</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_file</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_depth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">feature_names</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">class_names</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'all'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">filled</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">leaves_parallel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">impurity</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_ids</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">proportion</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rotate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rounded</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">special_characters</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">precision</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fontname</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'helvetica'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/tree/_export.py#L761"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.export_graphviz" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.tree.</span></span><span class="sig-name descname"><span class="pre">export_graphviz</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">decision_tree</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_file</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_depth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">feature_names</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">class_names</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'all'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">filled</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">leaves_parallel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">impurity</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_ids</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">proportion</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rotate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rounded</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">special_characters</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">precision</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fontname</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'helvetica'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/tree/_export.py#L761"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.export_graphviz" title="Link to this definition">#</a></dt>
 <dd><p>Export a decision tree in DOT format.</p>
 <p>This function generates a GraphViz representation of the decision tree,
 which is then written into <code class="docutils literal notranslate"><span class="pre">out_file</span></code>. Once exported, graphical renderings
diff --git a/dev/modules/generated/sklearn.tree.export_text.html b/dev/modules/generated/sklearn.tree.export_text.html
index 8da56d168cc5f..f6c3df85517ed 100644
--- a/dev/modules/generated/sklearn.tree.export_text.html
+++ b/dev/modules/generated/sklearn.tree.export_text.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>export_text<a class="headerlink" href="#export-text" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.tree.export_text">
-<span class="sig-prename descclassname"><span class="pre">sklearn.tree.</span></span><span class="sig-name descname"><span class="pre">export_text</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">decision_tree</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">feature_names</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">class_names</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_depth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">spacing</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">decimals</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">show_weights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/tree/_export.py#L972"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.export_text" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.tree.</span></span><span class="sig-name descname"><span class="pre">export_text</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">decision_tree</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">feature_names</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">class_names</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_depth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">spacing</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">decimals</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">show_weights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/tree/_export.py#L972"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.export_text" title="Link to this definition">#</a></dt>
 <dd><p>Build a text report showing the rules of a decision tree.</p>
 <p>Note that backwards compatibility may not be supported.</p>
 <dl class="field-list">
diff --git a/dev/modules/generated/sklearn.tree.plot_tree.html b/dev/modules/generated/sklearn.tree.plot_tree.html
index 8fa799193a2b1..ecac3c52bc0c0 100644
--- a/dev/modules/generated/sklearn.tree.plot_tree.html
+++ b/dev/modules/generated/sklearn.tree.plot_tree.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>plot_tree<a class="headerlink" href="#plot-tree" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.tree.plot_tree">
-<span class="sig-prename descclassname"><span class="pre">sklearn.tree.</span></span><span class="sig-name descname"><span class="pre">plot_tree</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">decision_tree</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_depth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">feature_names</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">class_names</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'all'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">filled</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">impurity</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_ids</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">proportion</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rounded</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">precision</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fontsize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/tree/_export.py#L71"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.plot_tree" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.tree.</span></span><span class="sig-name descname"><span class="pre">plot_tree</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">decision_tree</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_depth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">feature_names</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">class_names</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'all'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">filled</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">impurity</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_ids</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">proportion</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rounded</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">precision</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fontsize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/tree/_export.py#L71"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.tree.plot_tree" title="Link to this definition">#</a></dt>
 <dd><p>Plot a decision tree.</p>
 <p>The sample counts that are shown are weighted with any sample_weights that
 might be present.</p>
diff --git a/dev/modules/generated/sklearn.utils.Bunch.html b/dev/modules/generated/sklearn.utils.Bunch.html
index 8eedefaf364fa..52ac8b92726c2 100644
--- a/dev/modules/generated/sklearn.utils.Bunch.html
+++ b/dev/modules/generated/sklearn.utils.Bunch.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>Bunch<a class="headerlink" href="#bunch" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.utils.Bunch">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.utils.</span></span><span class="sig-name descname"><span class="pre">Bunch</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_bunch.py#L7"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.Bunch" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.utils.</span></span><span class="sig-name descname"><span class="pre">Bunch</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_bunch.py#L7"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.Bunch" title="Link to this definition">#</a></dt>
 <dd><p>Container object exposing keys as attributes.</p>
 <p>Bunch objects are sometimes used as an output for functions and methods.
 They extend dictionaries by enabling values to be accessed by key,
diff --git a/dev/modules/generated/sklearn.utils.ClassifierTags.html b/dev/modules/generated/sklearn.utils.ClassifierTags.html
index e20633bcbe77b..6bb18e4c103bf 100644
--- a/dev/modules/generated/sklearn.utils.ClassifierTags.html
+++ b/dev/modules/generated/sklearn.utils.ClassifierTags.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>ClassifierTags<a class="headerlink" href="#classifiertags" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.utils.ClassifierTags">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.utils.</span></span><span class="sig-name descname"><span class="pre">ClassifierTags</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">poor_score</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">multi_class</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">multi_label</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_tags.py#L138"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.ClassifierTags" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.utils.</span></span><span class="sig-name descname"><span class="pre">ClassifierTags</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">poor_score</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">multi_class</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">multi_label</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_tags.py#L136"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.ClassifierTags" title="Link to this definition">#</a></dt>
 <dd><p>Tags for the classifier.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.utils.InputTags.html b/dev/modules/generated/sklearn.utils.InputTags.html
index 91f6416fcc96e..c1233d2f3db84 100644
--- a/dev/modules/generated/sklearn.utils.InputTags.html
+++ b/dev/modules/generated/sklearn.utils.InputTags.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>InputTags<a class="headerlink" href="#inputtags" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.utils.InputTags">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.utils.</span></span><span class="sig-name descname"><span class="pre">InputTags</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">one_d_array</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">two_d_array</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">three_d_array</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sparse</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">categorical</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">string</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dict</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">positive_only</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">allow_nan</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pairwise</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_tags.py#L12"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.InputTags" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.utils.</span></span><span class="sig-name descname"><span class="pre">InputTags</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">one_d_array</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">two_d_array</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">three_d_array</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sparse</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">categorical</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">string</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dict</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">positive_only</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">allow_nan</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pairwise</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_tags.py#L10"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.InputTags" title="Link to this definition">#</a></dt>
 <dd><p>Tags for the input data.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.utils.RegressorTags.html b/dev/modules/generated/sklearn.utils.RegressorTags.html
index 45d92112ee680..ebd43ba5a26d3 100644
--- a/dev/modules/generated/sklearn.utils.RegressorTags.html
+++ b/dev/modules/generated/sklearn.utils.RegressorTags.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>RegressorTags<a class="headerlink" href="#regressortags" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.utils.RegressorTags">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.utils.</span></span><span class="sig-name descname"><span class="pre">RegressorTags</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">poor_score</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_tags.py#L171"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.RegressorTags" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.utils.</span></span><span class="sig-name descname"><span class="pre">RegressorTags</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">poor_score</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_tags.py#L169"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.RegressorTags" title="Link to this definition">#</a></dt>
 <dd><p>Tags for the regressor.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.utils.Tags.html b/dev/modules/generated/sklearn.utils.Tags.html
index 33cc552bafded..e3c47fff0b46c 100644
--- a/dev/modules/generated/sklearn.utils.Tags.html
+++ b/dev/modules/generated/sklearn.utils.Tags.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>Tags<a class="headerlink" href="#tags" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.utils.Tags">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.utils.</span></span><span class="sig-name descname"><span class="pre">Tags</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator_type:</span> <span class="pre">str</span> <span class="pre">|</span> <span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target_tags:</span> <span class="pre">~sklearn.utils._tags.TargetTags</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transformer_tags:</span> <span class="pre">~sklearn.utils._tags.TransformerTags</span> <span class="pre">|</span> <span class="pre">None</span> <span class="pre">=</span> <span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">classifier_tags:</span> <span class="pre">~sklearn.utils._tags.ClassifierTags</span> <span class="pre">|</span> <span class="pre">None</span> <span class="pre">=</span> <span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">regressor_tags:</span> <span class="pre">~sklearn.utils._tags.RegressorTags</span> <span class="pre">|</span> <span class="pre">None</span> <span class="pre">=</span> <span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">array_api_support:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">no_validation:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">non_deterministic:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">requires_fit:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_skip_test:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_tags:</span> <span class="pre">~sklearn.utils._tags.InputTags</span> <span class="pre">=</span> <span class="pre">&lt;factory&gt;</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_tags.py#L189"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.Tags" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.utils.</span></span><span class="sig-name descname"><span class="pre">Tags</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator_type:</span> <span class="pre">str</span> <span class="pre">|</span> <span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target_tags:</span> <span class="pre">~sklearn.utils._tags.TargetTags</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transformer_tags:</span> <span class="pre">~sklearn.utils._tags.TransformerTags</span> <span class="pre">|</span> <span class="pre">None</span> <span class="pre">=</span> <span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">classifier_tags:</span> <span class="pre">~sklearn.utils._tags.ClassifierTags</span> <span class="pre">|</span> <span class="pre">None</span> <span class="pre">=</span> <span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">regressor_tags:</span> <span class="pre">~sklearn.utils._tags.RegressorTags</span> <span class="pre">|</span> <span class="pre">None</span> <span class="pre">=</span> <span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">array_api_support:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">no_validation:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">non_deterministic:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">requires_fit:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_skip_test:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_tags:</span> <span class="pre">~sklearn.utils._tags.InputTags</span> <span class="pre">=</span> <span class="pre">&lt;factory&gt;</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_tags.py#L187"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.Tags" title="Link to this definition">#</a></dt>
 <dd><p>Tags for the estimator.</p>
 <p>See <a class="reference internal" href="http://github.com/developers/develop.html#estimator-tags"><span class="std std-ref">Estimator Tags</span></a> for more information.</p>
 <dl class="field-list">
diff --git a/dev/modules/generated/sklearn.utils.TargetTags.html b/dev/modules/generated/sklearn.utils.TargetTags.html
index c8b920c3dd2c9..478ac6061af48 100644
--- a/dev/modules/generated/sklearn.utils.TargetTags.html
+++ b/dev/modules/generated/sklearn.utils.TargetTags.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>TargetTags<a class="headerlink" href="#targettags" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.utils.TargetTags">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.utils.</span></span><span class="sig-name descname"><span class="pre">TargetTags</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">required</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">one_d_labels</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">two_d_labels</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">positive_only</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">multi_output</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">single_output</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_tags.py#L77"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.TargetTags" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.utils.</span></span><span class="sig-name descname"><span class="pre">TargetTags</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">required</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">one_d_labels</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">two_d_labels</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">positive_only</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">multi_output</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">single_output</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_tags.py#L75"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.TargetTags" title="Link to this definition">#</a></dt>
 <dd><p>Tags for the target data.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.utils.TransformerTags.html b/dev/modules/generated/sklearn.utils.TransformerTags.html
index 33cae1e2c4c56..661411a547516 100644
--- a/dev/modules/generated/sklearn.utils.TransformerTags.html
+++ b/dev/modules/generated/sklearn.utils.TransformerTags.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>TransformerTags<a class="headerlink" href="#transformertags" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.utils.TransformerTags">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.utils.</span></span><span class="sig-name descname"><span class="pre">TransformerTags</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">preserves_dtype:</span> <span class="pre">list[str]</span> <span class="pre">=</span> <span class="pre">&lt;factory&gt;</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_tags.py#L118"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.TransformerTags" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.utils.</span></span><span class="sig-name descname"><span class="pre">TransformerTags</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">preserves_dtype:</span> <span class="pre">list[str]</span> <span class="pre">=</span> <span class="pre">&lt;factory&gt;</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_tags.py#L116"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.TransformerTags" title="Link to this definition">#</a></dt>
 <dd><p>Tags for the transformer.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.utils._safe_indexing.html b/dev/modules/generated/sklearn.utils._safe_indexing.html
index 17018049db358..7f4e4a70d78ae 100644
--- a/dev/modules/generated/sklearn.utils._safe_indexing.html
+++ b/dev/modules/generated/sklearn.utils._safe_indexing.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>_safe_indexing<a class="headerlink" href="#safe-indexing" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.utils._safe_indexing">
-<span class="sig-prename descclassname"><span class="pre">sklearn.utils.</span></span><span class="sig-name descname"><span class="pre">_safe_indexing</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">indices</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">axis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_indexing.py#L180"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils._safe_indexing" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.utils.</span></span><span class="sig-name descname"><span class="pre">_safe_indexing</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">indices</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">axis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_indexing.py#L230"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils._safe_indexing" title="Link to this definition">#</a></dt>
 <dd><p>Return rows, items or columns of X using indices.</p>
 <div class="admonition warning">
 <p class="admonition-title">Warning</p>
diff --git a/dev/modules/generated/sklearn.utils.arrayfuncs.min_pos.html b/dev/modules/generated/sklearn.utils.arrayfuncs.min_pos.html
index adad519c56007..45841e261f1ee 100644
--- a/dev/modules/generated/sklearn.utils.arrayfuncs.min_pos.html
+++ b/dev/modules/generated/sklearn.utils.arrayfuncs.min_pos.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
diff --git a/dev/modules/generated/sklearn.utils.as_float_array.html b/dev/modules/generated/sklearn.utils.as_float_array.html
index c5487bf1dd03b..47813e5b3a0a2 100644
--- a/dev/modules/generated/sklearn.utils.as_float_array.html
+++ b/dev/modules/generated/sklearn.utils.as_float_array.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>as_float_array<a class="headerlink" href="#as-float-array" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.utils.as_float_array">
-<span class="sig-prename descclassname"><span class="pre">sklearn.utils.</span></span><span class="sig-name descname"><span class="pre">as_float_array</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">force_all_finite</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'deprecated'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ensure_all_finite</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/validation.py#L218"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.as_float_array" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.utils.</span></span><span class="sig-name descname"><span class="pre">as_float_array</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">force_all_finite</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'deprecated'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ensure_all_finite</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/validation.py#L218"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.as_float_array" title="Link to this definition">#</a></dt>
 <dd><p>Convert an array-like to an array of floats.</p>
 <p>The new dtype will be np.float32 or np.float64, depending on the original
 type. The function can create a copy or modify the argument depending
diff --git a/dev/modules/generated/sklearn.utils.assert_all_finite.html b/dev/modules/generated/sklearn.utils.assert_all_finite.html
index 3a3e33a973eff..50e30af22b7a8 100644
--- a/dev/modules/generated/sklearn.utils.assert_all_finite.html
+++ b/dev/modules/generated/sklearn.utils.assert_all_finite.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>assert_all_finite<a class="headerlink" href="#assert-all-finite" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.utils.assert_all_finite">
-<span class="sig-prename descclassname"><span class="pre">sklearn.utils.</span></span><span class="sig-name descname"><span class="pre">assert_all_finite</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">allow_nan</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">estimator_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/validation.py#L172"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.assert_all_finite" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.utils.</span></span><span class="sig-name descname"><span class="pre">assert_all_finite</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">allow_nan</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">estimator_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/validation.py#L172"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.assert_all_finite" title="Link to this definition">#</a></dt>
 <dd><p>Throw a ValueError if X contains NaN or infinity.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.utils.check_X_y.html b/dev/modules/generated/sklearn.utils.check_X_y.html
index d4ed11edc7b54..bed60ba0941e6 100644
--- a/dev/modules/generated/sklearn.utils.check_X_y.html
+++ b/dev/modules/generated/sklearn.utils.check_X_y.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>check_X_y<a class="headerlink" href="#check-x-y" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.utils.check_X_y">
-<span class="sig-prename descclassname"><span class="pre">sklearn.utils.</span></span><span class="sig-name descname"><span class="pre">check_X_y</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">accept_sparse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">accept_large_sparse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'numeric'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">order</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">force_writeable</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">force_all_finite</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'deprecated'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ensure_all_finite</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ensure_2d</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">allow_nd</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">multi_output</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ensure_min_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ensure_min_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_numeric</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">estimator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/validation.py#L1201"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.check_X_y" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.utils.</span></span><span class="sig-name descname"><span class="pre">check_X_y</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">accept_sparse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">accept_large_sparse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'numeric'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">order</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">force_writeable</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">force_all_finite</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'deprecated'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ensure_all_finite</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ensure_2d</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">allow_nd</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">multi_output</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ensure_min_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ensure_min_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_numeric</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">estimator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/validation.py#L1201"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.check_X_y" title="Link to this definition">#</a></dt>
 <dd><p>Input validation for standard estimators.</p>
 <p>Checks X and y for consistent length, enforces X to be 2D and y 1D. By
 default, X is checked to be non-empty and containing only finite values.
diff --git a/dev/modules/generated/sklearn.utils.check_array.html b/dev/modules/generated/sklearn.utils.check_array.html
index bf8ea5474a497..7d5a875a2c354 100644
--- a/dev/modules/generated/sklearn.utils.check_array.html
+++ b/dev/modules/generated/sklearn.utils.check_array.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>check_array<a class="headerlink" href="#check-array" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.utils.check_array">
-<span class="sig-prename descclassname"><span class="pre">sklearn.utils.</span></span><span class="sig-name descname"><span class="pre">check_array</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">array</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">accept_sparse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">accept_large_sparse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'numeric'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">order</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">force_writeable</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">force_all_finite</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'deprecated'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ensure_all_finite</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ensure_non_negative</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ensure_2d</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">allow_nd</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ensure_min_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ensure_min_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">estimator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/validation.py#L734"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.check_array" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.utils.</span></span><span class="sig-name descname"><span class="pre">check_array</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">array</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">accept_sparse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">accept_large_sparse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'numeric'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">order</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">force_writeable</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">force_all_finite</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'deprecated'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ensure_all_finite</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ensure_non_negative</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ensure_2d</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">allow_nd</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ensure_min_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ensure_min_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">estimator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/validation.py#L734"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.check_array" title="Link to this definition">#</a></dt>
 <dd><p>Input validation on an array, list, sparse matrix or similar.</p>
 <p>By default, the input is checked to be a non-empty 2D array containing
 only finite values. If the dtype of the array is object, attempt
diff --git a/dev/modules/generated/sklearn.utils.check_consistent_length.html b/dev/modules/generated/sklearn.utils.check_consistent_length.html
index 831e36856a891..cb29634c45826 100644
--- a/dev/modules/generated/sklearn.utils.check_consistent_length.html
+++ b/dev/modules/generated/sklearn.utils.check_consistent_length.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>check_consistent_length<a class="headerlink" href="#check-consistent-length" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.utils.check_consistent_length">
-<span class="sig-prename descclassname"><span class="pre">sklearn.utils.</span></span><span class="sig-name descname"><span class="pre">check_consistent_length</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">arrays</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/validation.py#L454"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.check_consistent_length" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.utils.</span></span><span class="sig-name descname"><span class="pre">check_consistent_length</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">arrays</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/validation.py#L454"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.check_consistent_length" title="Link to this definition">#</a></dt>
 <dd><p>Check that all arrays have consistent first dimensions.</p>
 <p>Checks whether all objects in arrays have the same shape or length.</p>
 <dl class="field-list simple">
diff --git a/dev/modules/generated/sklearn.utils.check_random_state.html b/dev/modules/generated/sklearn.utils.check_random_state.html
index cf59fa13cd908..84733d2269534 100644
--- a/dev/modules/generated/sklearn.utils.check_random_state.html
+++ b/dev/modules/generated/sklearn.utils.check_random_state.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>check_random_state<a class="headerlink" href="#check-random-state" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.utils.check_random_state">
-<span class="sig-prename descclassname"><span class="pre">sklearn.utils.</span></span><span class="sig-name descname"><span class="pre">check_random_state</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">seed</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/validation.py#L1488"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.check_random_state" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.utils.</span></span><span class="sig-name descname"><span class="pre">check_random_state</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">seed</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/validation.py#L1488"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.check_random_state" title="Link to this definition">#</a></dt>
 <dd><p>Turn seed into a np.random.RandomState instance.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.utils.check_scalar.html b/dev/modules/generated/sklearn.utils.check_scalar.html
index 6e3598f5c9097..4d99a7eee7e0d 100644
--- a/dev/modules/generated/sklearn.utils.check_scalar.html
+++ b/dev/modules/generated/sklearn.utils.check_scalar.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>check_scalar<a class="headerlink" href="#check-scalar" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.utils.check_scalar">
-<span class="sig-prename descclassname"><span class="pre">sklearn.utils.</span></span><span class="sig-name descname"><span class="pre">check_scalar</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target_type</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_val</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_val</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">include_boundaries</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'both'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/validation.py#L1828"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.check_scalar" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.utils.</span></span><span class="sig-name descname"><span class="pre">check_scalar</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target_type</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_val</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_val</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">include_boundaries</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'both'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/validation.py#L1827"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.check_scalar" title="Link to this definition">#</a></dt>
 <dd><p>Validate scalar parameters type and value.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.utils.class_weight.compute_class_weight.html b/dev/modules/generated/sklearn.utils.class_weight.compute_class_weight.html
index 91b3f69875634..8490c1ec82bbf 100644
--- a/dev/modules/generated/sklearn.utils.class_weight.compute_class_weight.html
+++ b/dev/modules/generated/sklearn.utils.class_weight.compute_class_weight.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>compute_class_weight<a class="headerlink" href="#compute-class-weight" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.utils.class_weight.compute_class_weight">
-<span class="sig-prename descclassname"><span class="pre">sklearn.utils.class_weight.</span></span><span class="sig-name descname"><span class="pre">compute_class_weight</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">class_weight</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">classes</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/class_weight.py#L13"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.class_weight.compute_class_weight" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.utils.class_weight.</span></span><span class="sig-name descname"><span class="pre">compute_class_weight</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">class_weight</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">classes</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/class_weight.py#L13"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.class_weight.compute_class_weight" title="Link to this definition">#</a></dt>
 <dd><p>Estimate class weights for unbalanced datasets.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.utils.class_weight.compute_sample_weight.html b/dev/modules/generated/sklearn.utils.class_weight.compute_sample_weight.html
index b50210a0cd9ff..d3b89e4d9213c 100644
--- a/dev/modules/generated/sklearn.utils.class_weight.compute_sample_weight.html
+++ b/dev/modules/generated/sklearn.utils.class_weight.compute_sample_weight.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>compute_sample_weight<a class="headerlink" href="#compute-sample-weight" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.utils.class_weight.compute_sample_weight">
-<span class="sig-prename descclassname"><span class="pre">sklearn.utils.class_weight.</span></span><span class="sig-name descname"><span class="pre">compute_sample_weight</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">class_weight</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/class_weight.py#L106"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.class_weight.compute_sample_weight" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.utils.class_weight.</span></span><span class="sig-name descname"><span class="pre">compute_sample_weight</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">class_weight</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/class_weight.py#L106"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.class_weight.compute_sample_weight" title="Link to this definition">#</a></dt>
 <dd><p>Estimate sample weights by class for unbalanced datasets.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.utils.deprecated.html b/dev/modules/generated/sklearn.utils.deprecated.html
index 27e96ada987be..0ffc13ef5d527 100644
--- a/dev/modules/generated/sklearn.utils.deprecated.html
+++ b/dev/modules/generated/sklearn.utils.deprecated.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>deprecated<a class="headerlink" href="#deprecated" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.utils.deprecated">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.utils.</span></span><span class="sig-name descname"><span class="pre">deprecated</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">extra</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/deprecation.py#L11"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.deprecated" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.utils.</span></span><span class="sig-name descname"><span class="pre">deprecated</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">extra</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/deprecation.py#L11"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.deprecated" title="Link to this definition">#</a></dt>
 <dd><p>Decorator to mark a function or class as deprecated.</p>
 <p>Issue a warning when the function is called/the class is instantiated and
 adds a warning to the docstring.</p>
@@ -1330,7 +1330,7 @@ <h1>deprecated<a class="headerlink" href="#deprecated" title="Link to this headi
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.utils.deprecated.__call__">
-<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">obj</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/deprecation.py#L41"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.deprecated.__call__" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">obj</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/deprecation.py#L41"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.deprecated.__call__" title="Link to this definition">#</a></dt>
 <dd><p>Call method</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.utils.discovery.all_displays.html b/dev/modules/generated/sklearn.utils.discovery.all_displays.html
index c65b0a80ee99d..ef051e8bd0921 100644
--- a/dev/modules/generated/sklearn.utils.discovery.all_displays.html
+++ b/dev/modules/generated/sklearn.utils.discovery.all_displays.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>all_displays<a class="headerlink" href="#all-displays" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.utils.discovery.all_displays">
-<span class="sig-prename descclassname"><span class="pre">sklearn.utils.discovery.</span></span><span class="sig-name descname"><span class="pre">all_displays</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/discovery.py#L153"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.discovery.all_displays" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.utils.discovery.</span></span><span class="sig-name descname"><span class="pre">all_displays</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/discovery.py#L153"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.discovery.all_displays" title="Link to this definition">#</a></dt>
 <dd><p>Get a list of all displays from <code class="docutils literal notranslate"><span class="pre">sklearn</span></code>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Returns<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.utils.discovery.all_estimators.html b/dev/modules/generated/sklearn.utils.discovery.all_estimators.html
index 36d6379b52419..6c66d99870b09 100644
--- a/dev/modules/generated/sklearn.utils.discovery.all_estimators.html
+++ b/dev/modules/generated/sklearn.utils.discovery.all_estimators.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>all_estimators<a class="headerlink" href="#all-estimators" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.utils.discovery.all_estimators">
-<span class="sig-prename descclassname"><span class="pre">sklearn.utils.discovery.</span></span><span class="sig-name descname"><span class="pre">all_estimators</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">type_filter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/discovery.py#L22"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.discovery.all_estimators" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.utils.discovery.</span></span><span class="sig-name descname"><span class="pre">all_estimators</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">type_filter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/discovery.py#L22"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.discovery.all_estimators" title="Link to this definition">#</a></dt>
 <dd><p>Get a list of all estimators from <code class="docutils literal notranslate"><span class="pre">sklearn</span></code>.</p>
 <p>This function crawls the module and gets all classes that inherit
 from BaseEstimator. Classes that are defined in test-modules are not
diff --git a/dev/modules/generated/sklearn.utils.discovery.all_functions.html b/dev/modules/generated/sklearn.utils.discovery.all_functions.html
index bca80a0aef294..673b54a0e3e25 100644
--- a/dev/modules/generated/sklearn.utils.discovery.all_functions.html
+++ b/dev/modules/generated/sklearn.utils.discovery.all_functions.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>all_functions<a class="headerlink" href="#all-functions" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.utils.discovery.all_functions">
-<span class="sig-prename descclassname"><span class="pre">sklearn.utils.discovery.</span></span><span class="sig-name descname"><span class="pre">all_functions</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/discovery.py#L210"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.discovery.all_functions" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.utils.discovery.</span></span><span class="sig-name descname"><span class="pre">all_functions</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/discovery.py#L210"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.discovery.all_functions" title="Link to this definition">#</a></dt>
 <dd><p>Get a list of all functions from <code class="docutils literal notranslate"><span class="pre">sklearn</span></code>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Returns<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.utils.estimator_checks.check_estimator.html b/dev/modules/generated/sklearn.utils.estimator_checks.check_estimator.html
index 9886b7aa0f393..d36feda44482f 100644
--- a/dev/modules/generated/sklearn.utils.estimator_checks.check_estimator.html
+++ b/dev/modules/generated/sklearn.utils.estimator_checks.check_estimator.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>check_estimator<a class="headerlink" href="#check-estimator" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.utils.estimator_checks.check_estimator">
-<span class="sig-prename descclassname"><span class="pre">sklearn.utils.estimator_checks.</span></span><span class="sig-name descname"><span class="pre">check_estimator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">generate_only</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">legacy</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">expected_failed_checks</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">on_skip</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Literal" title="(in Python v3.13)"><span class="pre">Literal</span></a><span class="p"><span class="pre">[</span></span><span class="s"><span class="pre">'warn'</span></span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'warn'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">on_fail</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Literal" title="(in Python v3.13)"><span class="pre">Literal</span></a><span class="p"><span class="pre">[</span></span><span class="s"><span class="pre">'raise'</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="s"><span class="pre">'warn'</span></span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'raise'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">callback</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Callable" title="(in Python v3.13)"><span class="pre">Callable</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/estimator_checks.py#L650"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.estimator_checks.check_estimator" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.utils.estimator_checks.</span></span><span class="sig-name descname"><span class="pre">check_estimator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">generate_only</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">legacy</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">expected_failed_checks</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">on_skip</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Literal" title="(in Python v3.13)"><span class="pre">Literal</span></a><span class="p"><span class="pre">[</span></span><span class="s"><span class="pre">'warn'</span></span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'warn'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">on_fail</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Literal" title="(in Python v3.13)"><span class="pre">Literal</span></a><span class="p"><span class="pre">[</span></span><span class="s"><span class="pre">'raise'</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="s"><span class="pre">'warn'</span></span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'raise'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">callback</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Callable" title="(in Python v3.13)"><span class="pre">Callable</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/estimator_checks.py#L650"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.estimator_checks.check_estimator" title="Link to this definition">#</a></dt>
 <dd><p>Check if estimator adheres to scikit-learn conventions.</p>
 <p>This function will run an extensive test-suite for input validation,
 shapes, etc, making sure that the estimator complies with <code class="docutils literal notranslate"><span class="pre">scikit-learn</span></code>
diff --git a/dev/modules/generated/sklearn.utils.estimator_checks.estimator_checks_generator.html b/dev/modules/generated/sklearn.utils.estimator_checks.estimator_checks_generator.html
index f927a51e26553..602d686b36ab9 100644
--- a/dev/modules/generated/sklearn.utils.estimator_checks.estimator_checks_generator.html
+++ b/dev/modules/generated/sklearn.utils.estimator_checks.estimator_checks_generator.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>estimator_checks_generator<a class="headerlink" href="#estimator-checks-generator" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.utils.estimator_checks.estimator_checks_generator">
-<span class="sig-prename descclassname"><span class="pre">sklearn.utils.estimator_checks.</span></span><span class="sig-name descname"><span class="pre">estimator_checks_generator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">legacy</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">expected_failed_checks</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mark</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Literal" title="(in Python v3.13)"><span class="pre">Literal</span></a><span class="p"><span class="pre">[</span></span><span class="s"><span class="pre">'xfail'</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="s"><span class="pre">'skip'</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">None</span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/estimator_checks.py#L500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.estimator_checks.estimator_checks_generator" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.utils.estimator_checks.</span></span><span class="sig-name descname"><span class="pre">estimator_checks_generator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">legacy</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">expected_failed_checks</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mark</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Literal" title="(in Python v3.13)"><span class="pre">Literal</span></a><span class="p"><span class="pre">[</span></span><span class="s"><span class="pre">'xfail'</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="s"><span class="pre">'skip'</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">None</span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/estimator_checks.py#L500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.estimator_checks.estimator_checks_generator" title="Link to this definition">#</a></dt>
 <dd><p>Iteratively yield all check callables for an estimator.</p>
 <div class="versionadded">
 <p><span class="versionmodified added">Added in version 1.6.</span></p>
diff --git a/dev/modules/generated/sklearn.utils.estimator_checks.parametrize_with_checks.html b/dev/modules/generated/sklearn.utils.estimator_checks.parametrize_with_checks.html
index 0c239b5f63e44..fccbe52b8ce24 100644
--- a/dev/modules/generated/sklearn.utils.estimator_checks.parametrize_with_checks.html
+++ b/dev/modules/generated/sklearn.utils.estimator_checks.parametrize_with_checks.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>parametrize_with_checks<a class="headerlink" href="#parametrize-with-checks" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.utils.estimator_checks.parametrize_with_checks">
-<span class="sig-prename descclassname"><span class="pre">sklearn.utils.estimator_checks.</span></span><span class="sig-name descname"><span class="pre">parametrize_with_checks</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimators</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">legacy</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">expected_failed_checks</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Callable" title="(in Python v3.13)"><span class="pre">Callable</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/estimator_checks.py#L553"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.estimator_checks.parametrize_with_checks" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.utils.estimator_checks.</span></span><span class="sig-name descname"><span class="pre">parametrize_with_checks</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimators</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">legacy</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">expected_failed_checks</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Callable" title="(in Python v3.13)"><span class="pre">Callable</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/estimator_checks.py#L553"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.estimator_checks.parametrize_with_checks" title="Link to this definition">#</a></dt>
 <dd><p>Pytest specific decorator for parametrizing estimator checks.</p>
 <p>Checks are categorised into the following groups:</p>
 <ul class="simple">
diff --git a/dev/modules/generated/sklearn.utils.estimator_html_repr.html b/dev/modules/generated/sklearn.utils.estimator_html_repr.html
index 7c3dbbbc217c7..0538298f00c1e 100644
--- a/dev/modules/generated/sklearn.utils.estimator_html_repr.html
+++ b/dev/modules/generated/sklearn.utils.estimator_html_repr.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>estimator_html_repr<a class="headerlink" href="#estimator-html-repr" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.utils.estimator_html_repr">
-<span class="sig-prename descclassname"><span class="pre">sklearn.utils.</span></span><span class="sig-name descname"><span class="pre">estimator_html_repr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_estimator_html_repr.py#L357"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.estimator_html_repr" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.utils.</span></span><span class="sig-name descname"><span class="pre">estimator_html_repr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_estimator_html_repr.py#L357"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.estimator_html_repr" title="Link to this definition">#</a></dt>
 <dd><p>Build a HTML representation of an estimator.</p>
 <p>Read more in the <a class="reference internal" href="http://github.com/compose.html#visualizing-composite-estimators"><span class="std std-ref">User Guide</span></a>.</p>
 <dl class="field-list simple">
diff --git a/dev/modules/generated/sklearn.utils.extmath.density.html b/dev/modules/generated/sklearn.utils.extmath.density.html
index b00d14c6439df..de8f9b332c83c 100644
--- a/dev/modules/generated/sklearn.utils.extmath.density.html
+++ b/dev/modules/generated/sklearn.utils.extmath.density.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>density<a class="headerlink" href="#density" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.utils.extmath.density">
-<span class="sig-prename descclassname"><span class="pre">sklearn.utils.extmath.</span></span><span class="sig-name descname"><span class="pre">density</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">w</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/extmath.py#L125"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.extmath.density" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.utils.extmath.</span></span><span class="sig-name descname"><span class="pre">density</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">w</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/extmath.py#L125"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.extmath.density" title="Link to this definition">#</a></dt>
 <dd><p>Compute density of a sparse vector.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.utils.extmath.fast_logdet.html b/dev/modules/generated/sklearn.utils.extmath.fast_logdet.html
index fda1fb4c8426f..8ca0d5ceaa506 100644
--- a/dev/modules/generated/sklearn.utils.extmath.fast_logdet.html
+++ b/dev/modules/generated/sklearn.utils.extmath.fast_logdet.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>fast_logdet<a class="headerlink" href="#fast-logdet" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.utils.extmath.fast_logdet">
-<span class="sig-prename descclassname"><span class="pre">sklearn.utils.extmath.</span></span><span class="sig-name descname"><span class="pre">fast_logdet</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">A</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/extmath.py#L85"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.extmath.fast_logdet" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.utils.extmath.</span></span><span class="sig-name descname"><span class="pre">fast_logdet</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">A</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/extmath.py#L85"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.extmath.fast_logdet" title="Link to this definition">#</a></dt>
 <dd><p>Compute logarithm of determinant of a square matrix.</p>
 <p>The (natural) logarithm of the determinant of a square matrix
 is returned if det(A) is non-negative and well defined.
diff --git a/dev/modules/generated/sklearn.utils.extmath.randomized_range_finder.html b/dev/modules/generated/sklearn.utils.extmath.randomized_range_finder.html
index cb503cf06d18b..d3d9ffa797249 100644
--- a/dev/modules/generated/sklearn.utils.extmath.randomized_range_finder.html
+++ b/dev/modules/generated/sklearn.utils.extmath.randomized_range_finder.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,12 +1305,12 @@
 <h1>randomized_range_finder<a class="headerlink" href="#randomized-range-finder" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.utils.extmath.randomized_range_finder">
-<span class="sig-prename descclassname"><span class="pre">sklearn.utils.extmath.</span></span><span class="sig-name descname"><span class="pre">randomized_range_finder</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">A</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_iter</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">power_iteration_normalizer</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/extmath.py#L215"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.extmath.randomized_range_finder" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.utils.extmath.</span></span><span class="sig-name descname"><span class="pre">randomized_range_finder</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">A</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_iter</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">power_iteration_normalizer</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/extmath.py#L215"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.extmath.randomized_range_finder" title="Link to this definition">#</a></dt>
 <dd><p>Compute an orthonormal matrix whose range approximates the range of A.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><dl>
-<dt><strong>A</strong><span class="classifier">2D array</span></dt><dd><p>The input data matrix.</p>
+<dt><strong>A</strong><span class="classifier">{array-like, sparse matrix} of shape (n_samples, n_features)</span></dt><dd><p>The input data matrix.</p>
 </dd>
 <dt><strong>size</strong><span class="classifier">int</span></dt><dd><p>Size of the return array.</p>
 </dd>
@@ -1335,8 +1335,8 @@ <h1>randomized_range_finder<a class="headerlink" href="#randomized-range-finder"
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><dl class="simple">
-<dt><strong>Q</strong><span class="classifier">ndarray</span></dt><dd><p>A (size x size) projection matrix, the range of which
-approximates well the range of the input matrix A.</p>
+<dt><strong>Q</strong><span class="classifier">ndarray of shape (size, size)</span></dt><dd><p>A projection matrix, the range of which approximates well the range of the
+input matrix A.</p>
 </dd>
 </dl>
 </dd>
@@ -1354,9 +1354,9 @@ <h1>randomized_range_finder<a class="headerlink" href="#randomized-range-finder"
 <span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.utils.extmath</span><span class="w"> </span><span class="kn">import</span> <span class="n">randomized_range_finder</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">A</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">],</span> <span class="p">[</span><span class="mi">7</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">9</span><span class="p">]])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">randomized_range_finder</span><span class="p">(</span><span class="n">A</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">n_iter</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">)</span>
-<span class="go">array([[-0.21...,  0.88...],</span>
-<span class="go">       [-0.52...,  0.24...],</span>
-<span class="go">       [-0.82..., -0.38...]])</span>
+<span class="go">array([[-0.214,  0.887],</span>
+<span class="go">       [-0.521,  0.249],</span>
+<span class="go">       [-0.826, -0.388]])</span>
 </pre></div>
 </div>
 </dd></dl>
diff --git a/dev/modules/generated/sklearn.utils.extmath.randomized_svd.html b/dev/modules/generated/sklearn.utils.extmath.randomized_svd.html
index 5b9335f3f11b4..62eadd7a35f2e 100644
--- a/dev/modules/generated/sklearn.utils.extmath.randomized_svd.html
+++ b/dev/modules/generated/sklearn.utils.extmath.randomized_svd.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>randomized_svd<a class="headerlink" href="#randomized-svd" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.utils.extmath.randomized_svd">
-<span class="sig-prename descclassname"><span class="pre">sklearn.utils.extmath.</span></span><span class="sig-name descname"><span class="pre">randomized_svd</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">M</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_components</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_oversamples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">power_iteration_normalizer</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transpose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">flip_sign</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">svd_lapack_driver</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'gesdd'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/extmath.py#L345"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.extmath.randomized_svd" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.utils.extmath.</span></span><span class="sig-name descname"><span class="pre">randomized_svd</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">M</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_components</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_oversamples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">power_iteration_normalizer</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transpose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">flip_sign</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">svd_lapack_driver</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'gesdd'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/extmath.py#L360"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.extmath.randomized_svd" title="Link to this definition">#</a></dt>
 <dd><p>Compute a truncated randomized SVD.</p>
 <p>This method solves the fixed-rank approximation problem described in <a class="reference internal" href="#rf38c2b656ebc-1" id="id1">[1]</a>
 (problem (1.5), p5).</p>
@@ -1317,7 +1317,7 @@ <h1>randomized_svd<a class="headerlink" href="#randomized-svd" title="Link to th
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><dl>
-<dt><strong>M</strong><span class="classifier">{ndarray, sparse matrix}</span></dt><dd><p>Matrix to decompose.</p>
+<dt><strong>M</strong><span class="classifier">{array-like, sparse matrix} of shape (n_samples, n_features)</span></dt><dd><p>Matrix to decompose.</p>
 </dd>
 <dt><strong>n_components</strong><span class="classifier">int</span></dt><dd><p>Number of singular values and vectors to extract.</p>
 </dd>
diff --git a/dev/modules/generated/sklearn.utils.extmath.safe_sparse_dot.html b/dev/modules/generated/sklearn.utils.extmath.safe_sparse_dot.html
index a45f7d4de6ce0..c5252878ef9bc 100644
--- a/dev/modules/generated/sklearn.utils.extmath.safe_sparse_dot.html
+++ b/dev/modules/generated/sklearn.utils.extmath.safe_sparse_dot.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>safe_sparse_dot<a class="headerlink" href="#safe-sparse-dot" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.utils.extmath.safe_sparse_dot">
-<span class="sig-prename descclassname"><span class="pre">sklearn.utils.extmath.</span></span><span class="sig-name descname"><span class="pre">safe_sparse_dot</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">a</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">b</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dense_output</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/extmath.py#L153"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.extmath.safe_sparse_dot" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.utils.extmath.</span></span><span class="sig-name descname"><span class="pre">safe_sparse_dot</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">a</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">b</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dense_output</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/extmath.py#L153"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.extmath.safe_sparse_dot" title="Link to this definition">#</a></dt>
 <dd><p>Dot product that handle the sparse matrix case correctly.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.utils.extmath.weighted_mode.html b/dev/modules/generated/sklearn.utils.extmath.weighted_mode.html
index 2e4ed30ea8e5a..5cf419d7318f9 100644
--- a/dev/modules/generated/sklearn.utils.extmath.weighted_mode.html
+++ b/dev/modules/generated/sklearn.utils.extmath.weighted_mode.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>weighted_mode<a class="headerlink" href="#weighted-mode" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.utils.extmath.weighted_mode">
-<span class="sig-prename descclassname"><span class="pre">sklearn.utils.extmath.</span></span><span class="sig-name descname"><span class="pre">weighted_mode</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">a</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">w</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">axis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/extmath.py#L719"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.extmath.weighted_mode" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.utils.extmath.</span></span><span class="sig-name descname"><span class="pre">weighted_mode</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">a</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">w</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">axis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/extmath.py#L762"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.extmath.weighted_mode" title="Link to this definition">#</a></dt>
 <dd><p>Return an array of the weighted modal (most common) value in the passed array.</p>
 <p>If there is more than one such value, only the first is returned.
 The bin-count for the modal bins is also returned.</p>
diff --git a/dev/modules/generated/sklearn.utils.gen_batches.html b/dev/modules/generated/sklearn.utils.gen_batches.html
index 069a41e2cd8b9..ed4fe9c7d853e 100644
--- a/dev/modules/generated/sklearn.utils.gen_batches.html
+++ b/dev/modules/generated/sklearn.utils.gen_batches.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>gen_batches<a class="headerlink" href="#gen-batches" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.utils.gen_batches">
-<span class="sig-prename descclassname"><span class="pre">sklearn.utils.</span></span><span class="sig-name descname"><span class="pre">gen_batches</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_size</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_batch_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_chunking.py#L25"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.gen_batches" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.utils.</span></span><span class="sig-name descname"><span class="pre">gen_batches</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_size</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_batch_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_chunking.py#L25"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.gen_batches" title="Link to this definition">#</a></dt>
 <dd><p>Generator to create slices containing <code class="docutils literal notranslate"><span class="pre">batch_size</span></code> elements from 0 to <code class="docutils literal notranslate"><span class="pre">n</span></code>.</p>
 <p>The last slice may contain less than <code class="docutils literal notranslate"><span class="pre">batch_size</span></code> elements, when
 <code class="docutils literal notranslate"><span class="pre">batch_size</span></code> does not divide <code class="docutils literal notranslate"><span class="pre">n</span></code>.</p>
diff --git a/dev/modules/generated/sklearn.utils.gen_even_slices.html b/dev/modules/generated/sklearn.utils.gen_even_slices.html
index 5c643e6fa78df..dc9905750bdf3 100644
--- a/dev/modules/generated/sklearn.utils.gen_even_slices.html
+++ b/dev/modules/generated/sklearn.utils.gen_even_slices.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>gen_even_slices<a class="headerlink" href="#gen-even-slices" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.utils.gen_even_slices">
-<span class="sig-prename descclassname"><span class="pre">sklearn.utils.</span></span><span class="sig-name descname"><span class="pre">gen_even_slices</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_packs</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_chunking.py#L81"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.gen_even_slices" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.utils.</span></span><span class="sig-name descname"><span class="pre">gen_even_slices</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_packs</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_chunking.py#L81"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.gen_even_slices" title="Link to this definition">#</a></dt>
 <dd><p>Generator to create <code class="docutils literal notranslate"><span class="pre">n_packs</span></code> evenly spaced slices going up to <code class="docutils literal notranslate"><span class="pre">n</span></code>.</p>
 <p>If <code class="docutils literal notranslate"><span class="pre">n_packs</span></code> does not divide <code class="docutils literal notranslate"><span class="pre">n</span></code>, except for the first <code class="docutils literal notranslate"><span class="pre">n</span> <span class="pre">%</span> <span class="pre">n_packs</span></code>
 slices, remaining slices may contain fewer elements.</p>
diff --git a/dev/modules/generated/sklearn.utils.get_tags.html b/dev/modules/generated/sklearn.utils.get_tags.html
index 4306825668b3a..e366dc133a388 100644
--- a/dev/modules/generated/sklearn.utils.get_tags.html
+++ b/dev/modules/generated/sklearn.utils.get_tags.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>get_tags<a class="headerlink" href="#get-tags" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.utils.get_tags">
-<span class="sig-prename descclassname"><span class="pre">sklearn.utils.</span></span><span class="sig-name descname"><span class="pre">get_tags</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="http://github.com/sklearn.utils.Tags.html#sklearn.utils.Tags" title="sklearn.utils._tags.Tags"><span class="pre">Tags</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_tags.py#L365"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.get_tags" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.utils.</span></span><span class="sig-name descname"><span class="pre">get_tags</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="http://github.com/sklearn.utils.Tags.html#sklearn.utils.Tags" title="sklearn.utils._tags.Tags"><span class="pre">Tags</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_tags.py#L298"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.get_tags" title="Link to this definition">#</a></dt>
 <dd><p>Get estimator tags.</p>
 <p><code class="xref py py-class docutils literal notranslate"><span class="pre">BaseEstimator</span></code> provides the estimator tags machinery.
 However, if an estimator does not inherit from this base class, we should
diff --git a/dev/modules/generated/sklearn.utils.graph.single_source_shortest_path_length.html b/dev/modules/generated/sklearn.utils.graph.single_source_shortest_path_length.html
index 716508cbede5f..f7bc2f323aadc 100644
--- a/dev/modules/generated/sklearn.utils.graph.single_source_shortest_path_length.html
+++ b/dev/modules/generated/sklearn.utils.graph.single_source_shortest_path_length.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>single_source_shortest_path_length<a class="headerlink" href="#single-source-shortest-path-length" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.utils.graph.single_source_shortest_path_length">
-<span class="sig-prename descclassname"><span class="pre">sklearn.utils.graph.</span></span><span class="sig-name descname"><span class="pre">single_source_shortest_path_length</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">graph</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">source</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cutoff</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/graph.py#L16"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.graph.single_source_shortest_path_length" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.utils.graph.</span></span><span class="sig-name descname"><span class="pre">single_source_shortest_path_length</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">graph</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">source</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cutoff</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/graph.py#L16"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.graph.single_source_shortest_path_length" title="Link to this definition">#</a></dt>
 <dd><p>Return the length of the shortest path from source to all reachable nodes.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.utils.indexable.html b/dev/modules/generated/sklearn.utils.indexable.html
index 3cdaae9568e01..5070445a49b34 100644
--- a/dev/modules/generated/sklearn.utils.indexable.html
+++ b/dev/modules/generated/sklearn.utils.indexable.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>indexable<a class="headerlink" href="#indexable" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.utils.indexable">
-<span class="sig-prename descclassname"><span class="pre">sklearn.utils.</span></span><span class="sig-name descname"><span class="pre">indexable</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">iterables</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/validation.py#L499"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.indexable" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.utils.</span></span><span class="sig-name descname"><span class="pre">indexable</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">iterables</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/validation.py#L499"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.indexable" title="Link to this definition">#</a></dt>
 <dd><p>Make arrays indexable for cross-validation.</p>
 <p>Checks consistent length, passes through None, and ensures that everything
 can be indexed by converting sparse matrices to csr and converting
diff --git a/dev/modules/generated/sklearn.utils.metadata_routing.MetadataRequest.html b/dev/modules/generated/sklearn.utils.metadata_routing.MetadataRequest.html
index 07b8eab2ffb50..a155694bd6a73 100644
--- a/dev/modules/generated/sklearn.utils.metadata_routing.MetadataRequest.html
+++ b/dev/modules/generated/sklearn.utils.metadata_routing.MetadataRequest.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>MetadataRequest<a class="headerlink" href="#metadatarequest" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.utils.metadata_routing.MetadataRequest">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.utils.metadata_routing.</span></span><span class="sig-name descname"><span class="pre">MetadataRequest</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">owner</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L512"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.metadata_routing.MetadataRequest" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.utils.metadata_routing.</span></span><span class="sig-name descname"><span class="pre">MetadataRequest</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">owner</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L519"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.metadata_routing.MetadataRequest" title="Link to this definition">#</a></dt>
 <dd><p>Contains the metadata request info of a consumer.</p>
 <p>Instances of <code class="docutils literal notranslate"><span class="pre">MethodMetadataRequest</span></code> are used in this class for each
 available method under <code class="docutils literal notranslate"><span class="pre">metadatarequest.{method}</span></code>.</p>
@@ -1324,7 +1324,7 @@ <h1>MetadataRequest<a class="headerlink" href="#metadatarequest" title="Link to
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.utils.metadata_routing.MetadataRequest.consumes">
-<span class="sig-name descname"><span class="pre">consumes</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">method</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L543"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.metadata_routing.MetadataRequest.consumes" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">consumes</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">method</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L550"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.metadata_routing.MetadataRequest.consumes" title="Link to this definition">#</a></dt>
 <dd><p>Check whether the given parameters are consumed by the given method.</p>
 <div class="versionadded">
 <p><span class="versionmodified added">Added in version 1.4.</span></p>
diff --git a/dev/modules/generated/sklearn.utils.metadata_routing.MetadataRouter.html b/dev/modules/generated/sklearn.utils.metadata_routing.MetadataRouter.html
index d9741f478d545..c136b43b60b2f 100644
--- a/dev/modules/generated/sklearn.utils.metadata_routing.MetadataRouter.html
+++ b/dev/modules/generated/sklearn.utils.metadata_routing.MetadataRouter.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>MetadataRouter<a class="headerlink" href="#metadatarouter" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.utils.metadata_routing.MetadataRouter">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.utils.metadata_routing.</span></span><span class="sig-name descname"><span class="pre">MetadataRouter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">owner</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L772"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.metadata_routing.MetadataRouter" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.utils.metadata_routing.</span></span><span class="sig-name descname"><span class="pre">MetadataRouter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">owner</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L779"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.metadata_routing.MetadataRouter" title="Link to this definition">#</a></dt>
 <dd><p>Stores and handles metadata routing for a router object.</p>
 <p>This class is used by router objects to store and handle metadata routing.
 Routing information is stored as a dictionary of the form <code class="docutils literal notranslate"><span class="pre">{&quot;object_name&quot;:</span>
@@ -1329,7 +1329,7 @@ <h1>MetadataRouter<a class="headerlink" href="#metadatarouter" title="Link to th
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.utils.metadata_routing.MetadataRouter.add">
-<span class="sig-name descname"><span class="pre">add</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method_mapping</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">objs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L841"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.metadata_routing.MetadataRouter.add" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">add</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method_mapping</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">objs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L848"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.metadata_routing.MetadataRouter.add" title="Link to this definition">#</a></dt>
 <dd><p>Add named objects with their corresponding method mapping.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1352,7 +1352,7 @@ <h1>MetadataRouter<a class="headerlink" href="#metadatarouter" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.utils.metadata_routing.MetadataRouter.add_self_request">
-<span class="sig-name descname"><span class="pre">add_self_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">obj</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L805"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.metadata_routing.MetadataRouter.add_self_request" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">add_self_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">obj</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L812"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.metadata_routing.MetadataRouter.add_self_request" title="Link to this definition">#</a></dt>
 <dd><p>Add <code class="docutils literal notranslate"><span class="pre">self</span></code> (as a consumer) to the routing.</p>
 <p>This method is used if the router is also a consumer, and hence the
 router itself needs to be included in the routing. The passed object
@@ -1381,7 +1381,7 @@ <h1>MetadataRouter<a class="headerlink" href="#metadatarouter" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.utils.metadata_routing.MetadataRouter.consumes">
-<span class="sig-name descname"><span class="pre">consumes</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">method</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L866"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.metadata_routing.MetadataRouter.consumes" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">consumes</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">method</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L873"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.metadata_routing.MetadataRouter.consumes" title="Link to this definition">#</a></dt>
 <dd><p>Check whether the given parameters are consumed by the given method.</p>
 <div class="versionadded">
 <p><span class="versionmodified added">Added in version 1.4.</span></p>
@@ -1406,7 +1406,7 @@ <h1>MetadataRouter<a class="headerlink" href="#metadatarouter" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.utils.metadata_routing.MetadataRouter.route_params">
-<span class="sig-name descname"><span class="pre">route_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">caller</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1003"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.metadata_routing.MetadataRouter.route_params" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">route_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">caller</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1010"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.metadata_routing.MetadataRouter.route_params" title="Link to this definition">#</a></dt>
 <dd><p>Return the input parameters requested by child objects.</p>
 <p>The output of this method is a <a class="reference internal" href="http://github.com/sklearn.utils.Bunch.html#sklearn.utils.Bunch" title="sklearn.utils.Bunch"><code class="xref py py-class docutils literal notranslate"><span class="pre">Bunch</span></code></a>, which includes the
 metadata for all methods of each child object that is used in the router’s
@@ -1438,7 +1438,7 @@ <h1>MetadataRouter<a class="headerlink" href="#metadatarouter" title="Link to th
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.utils.metadata_routing.MetadataRouter.validate_metadata">
-<span class="sig-name descname"><span class="pre">validate_metadata</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1049"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.metadata_routing.MetadataRouter.validate_metadata" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">validate_metadata</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1056"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.metadata_routing.MetadataRouter.validate_metadata" title="Link to this definition">#</a></dt>
 <dd><p>Validate given metadata for a method.</p>
 <p>This raises a <code class="docutils literal notranslate"><span class="pre">TypeError</span></code> if some of the passed metadata are not
 understood by child objects.</p>
diff --git a/dev/modules/generated/sklearn.utils.metadata_routing.MethodMapping.html b/dev/modules/generated/sklearn.utils.metadata_routing.MethodMapping.html
index 9a1d9fb1b41cb..8b59cffe9537a 100644
--- a/dev/modules/generated/sklearn.utils.metadata_routing.MethodMapping.html
+++ b/dev/modules/generated/sklearn.utils.metadata_routing.MethodMapping.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>MethodMapping<a class="headerlink" href="#methodmapping" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.utils.metadata_routing.MethodMapping">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.utils.metadata_routing.</span></span><span class="sig-name descname"><span class="pre">MethodMapping</span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L703"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.metadata_routing.MethodMapping" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.utils.metadata_routing.</span></span><span class="sig-name descname"><span class="pre">MethodMapping</span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L710"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.metadata_routing.MethodMapping" title="Link to this definition">#</a></dt>
 <dd><p>Stores the mapping between caller and callee methods for a router.</p>
 <p>This class is primarily used in a <code class="docutils literal notranslate"><span class="pre">get_metadata_routing()</span></code> of a router
 object when defining the mapping between the router’s methods and a sub-object (a
@@ -1319,7 +1319,7 @@ <h1>MethodMapping<a class="headerlink" href="#methodmapping" title="Link to this
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.utils.metadata_routing.MethodMapping.add">
-<span class="sig-name descname"><span class="pre">add</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">caller</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">callee</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L722"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.metadata_routing.MethodMapping.add" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">add</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">caller</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">callee</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L729"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.metadata_routing.MethodMapping.add" title="Link to this definition">#</a></dt>
 <dd><p>Add a method mapping.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.utils.metadata_routing.get_routing_for_object.html b/dev/modules/generated/sklearn.utils.metadata_routing.get_routing_for_object.html
index 946fc779460ef..7f2bc74140cf1 100644
--- a/dev/modules/generated/sklearn.utils.metadata_routing.get_routing_for_object.html
+++ b/dev/modules/generated/sklearn.utils.metadata_routing.get_routing_for_object.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>get_routing_for_object<a class="headerlink" href="#get-routing-for-object" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.utils.metadata_routing.get_routing_for_object">
-<span class="sig-prename descclassname"><span class="pre">sklearn.utils.metadata_routing.</span></span><span class="sig-name descname"><span class="pre">get_routing_for_object</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">obj</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1117"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.metadata_routing.get_routing_for_object" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.utils.metadata_routing.</span></span><span class="sig-name descname"><span class="pre">get_routing_for_object</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">obj</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1125"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.metadata_routing.get_routing_for_object" title="Link to this definition">#</a></dt>
 <dd><p>Get a <code class="docutils literal notranslate"><span class="pre">Metadata{Router,</span> <span class="pre">Request}</span></code> instance from the given object.</p>
 <p>This function returns a
 <a class="reference internal" href="http://github.com/sklearn.utils.metadata_routing.MetadataRouter.html#sklearn.utils.metadata_routing.MetadataRouter" title="sklearn.utils.metadata_routing.MetadataRouter"><code class="xref py py-class docutils literal notranslate"><span class="pre">MetadataRouter</span></code></a> or a
diff --git a/dev/modules/generated/sklearn.utils.metadata_routing.process_routing.html b/dev/modules/generated/sklearn.utils.metadata_routing.process_routing.html
index a9a0dc4601ce1..f4dba7e17007c 100644
--- a/dev/modules/generated/sklearn.utils.metadata_routing.process_routing.html
+++ b/dev/modules/generated/sklearn.utils.metadata_routing.process_routing.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>process_routing<a class="headerlink" href="#process-routing" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.utils.metadata_routing.process_routing">
-<span class="sig-prename descclassname"><span class="pre">sklearn.utils.metadata_routing.</span></span><span class="sig-name descname"><span class="pre">process_routing</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">_obj</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_method</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_metadata_requests.py#L1525"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.metadata_routing.process_routing" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.utils.metadata_routing.</span></span><span class="sig-name descname"><span class="pre">process_routing</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">_obj</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_method</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_metadata_requests.py#L1533"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.metadata_routing.process_routing" title="Link to this definition">#</a></dt>
 <dd><p>Validate and route input parameters.</p>
 <p>This function is used inside a router’s method, e.g. <a class="reference internal" href="http://github.com/glossary.html#term-fit"><span class="xref std std-term">fit</span></a>,
 to validate the metadata and handle the routing.</p>
diff --git a/dev/modules/generated/sklearn.utils.metaestimators.available_if.html b/dev/modules/generated/sklearn.utils.metaestimators.available_if.html
index 85cd6825f6433..40b5575341f3f 100644
--- a/dev/modules/generated/sklearn.utils.metaestimators.available_if.html
+++ b/dev/modules/generated/sklearn.utils.metaestimators.available_if.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>available_if<a class="headerlink" href="#available-if" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.utils.metaestimators.available_if">
-<span class="sig-prename descclassname"><span class="pre">sklearn.utils.metaestimators.</span></span><span class="sig-name descname"><span class="pre">available_if</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">check</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_available_if.py#L57"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.metaestimators.available_if" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.utils.metaestimators.</span></span><span class="sig-name descname"><span class="pre">available_if</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">check</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_available_if.py#L57"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.metaestimators.available_if" title="Link to this definition">#</a></dt>
 <dd><p>An attribute that is available only if check returns a truthy value.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.utils.multiclass.is_multilabel.html b/dev/modules/generated/sklearn.utils.multiclass.is_multilabel.html
index 70333f02c0873..378f075398184 100644
--- a/dev/modules/generated/sklearn.utils.multiclass.is_multilabel.html
+++ b/dev/modules/generated/sklearn.utils.multiclass.is_multilabel.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>is_multilabel<a class="headerlink" href="#is-multilabel" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.utils.multiclass.is_multilabel">
-<span class="sig-prename descclassname"><span class="pre">sklearn.utils.multiclass.</span></span><span class="sig-name descname"><span class="pre">is_multilabel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/multiclass.py#L129"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.multiclass.is_multilabel" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.utils.multiclass.</span></span><span class="sig-name descname"><span class="pre">is_multilabel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/multiclass.py#L129"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.multiclass.is_multilabel" title="Link to this definition">#</a></dt>
 <dd><p>Check if <code class="docutils literal notranslate"><span class="pre">y</span></code> is in a multilabel format.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.utils.multiclass.type_of_target.html b/dev/modules/generated/sklearn.utils.multiclass.type_of_target.html
index 7423d5e82b5e8..e52ddbde659a8 100644
--- a/dev/modules/generated/sklearn.utils.multiclass.type_of_target.html
+++ b/dev/modules/generated/sklearn.utils.multiclass.type_of_target.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>type_of_target<a class="headerlink" href="#type-of-target" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.utils.multiclass.type_of_target">
-<span class="sig-prename descclassname"><span class="pre">sklearn.utils.multiclass.</span></span><span class="sig-name descname"><span class="pre">type_of_target</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">raise_unknown</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/multiclass.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.multiclass.type_of_target" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.utils.multiclass.</span></span><span class="sig-name descname"><span class="pre">type_of_target</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">raise_unknown</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/multiclass.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.multiclass.type_of_target" title="Link to this definition">#</a></dt>
 <dd><p>Determine the type of data indicated by the target.</p>
 <p>Note that this type is the most specific type that can be inferred.
 For example:</p>
diff --git a/dev/modules/generated/sklearn.utils.multiclass.unique_labels.html b/dev/modules/generated/sklearn.utils.multiclass.unique_labels.html
index 1a8e9de9d39b7..283974a0b9c0d 100644
--- a/dev/modules/generated/sklearn.utils.multiclass.unique_labels.html
+++ b/dev/modules/generated/sklearn.utils.multiclass.unique_labels.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>unique_labels<a class="headerlink" href="#unique-labels" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.utils.multiclass.unique_labels">
-<span class="sig-prename descclassname"><span class="pre">sklearn.utils.multiclass.</span></span><span class="sig-name descname"><span class="pre">unique_labels</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">ys</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/multiclass.py#L41"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.multiclass.unique_labels" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.utils.multiclass.</span></span><span class="sig-name descname"><span class="pre">unique_labels</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">ys</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/multiclass.py#L41"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.multiclass.unique_labels" title="Link to this definition">#</a></dt>
 <dd><p>Extract an ordered array of unique labels.</p>
 <dl class="simple">
 <dt>We don’t allow:</dt><dd><ul class="simple">
diff --git a/dev/modules/generated/sklearn.utils.murmurhash3_32.html b/dev/modules/generated/sklearn.utils.murmurhash3_32.html
index ee68fa2ae5205..2cbe65bc92494 100644
--- a/dev/modules/generated/sklearn.utils.murmurhash3_32.html
+++ b/dev/modules/generated/sklearn.utils.murmurhash3_32.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
diff --git a/dev/modules/generated/sklearn.utils.parallel.Parallel.html b/dev/modules/generated/sklearn.utils.parallel.Parallel.html
index ca67bd7edf473..96da3c09d599f 100644
--- a/dev/modules/generated/sklearn.utils.parallel.Parallel.html
+++ b/dev/modules/generated/sklearn.utils.parallel.Parallel.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,19 +1305,19 @@
 <h1>Parallel<a class="headerlink" href="#parallel" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="sklearn.utils.parallel.Parallel">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.utils.parallel.</span></span><span class="sig-name descname"><span class="pre">Parallel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">default(None)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">backend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">default(None)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_as</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'list'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">default(0)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">timeout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pre_dispatch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'2</span> <span class="pre">*</span> <span class="pre">n_jobs'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">temp_folder</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">default(None)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_nbytes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">default('1M')</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mmap_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">default('r')</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prefer</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">default(None)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">require</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">default(None)</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/parallel.py#L41"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.parallel.Parallel" title="Link to this definition">#</a></dt>
-<dd><p>Tweak of <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.Parallel.html#joblib.Parallel" title="(in joblib v1.5.dev0)"><code class="xref py py-class docutils literal notranslate"><span class="pre">joblib.Parallel</span></code></a> that propagates the scikit-learn configuration.</p>
-<p>This subclass of <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.Parallel.html#joblib.Parallel" title="(in joblib v1.5.dev0)"><code class="xref py py-class docutils literal notranslate"><span class="pre">joblib.Parallel</span></code></a> ensures that the active configuration
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.utils.parallel.</span></span><span class="sig-name descname"><span class="pre">Parallel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">default(None)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">backend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">default(None)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_as</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'list'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">default(0)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">timeout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pre_dispatch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'2</span> <span class="pre">*</span> <span class="pre">n_jobs'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">temp_folder</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">default(None)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_nbytes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">default('1M')</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mmap_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">default('r')</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prefer</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">default(None)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">require</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">default(None)</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">backend_kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/parallel.py#L41"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.parallel.Parallel" title="Link to this definition">#</a></dt>
+<dd><p>Tweak of <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.Parallel.html#joblib.Parallel" title="(in joblib v1.6.dev0)"><code class="xref py py-class docutils literal notranslate"><span class="pre">joblib.Parallel</span></code></a> that propagates the scikit-learn configuration.</p>
+<p>This subclass of <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.Parallel.html#joblib.Parallel" title="(in joblib v1.6.dev0)"><code class="xref py py-class docutils literal notranslate"><span class="pre">joblib.Parallel</span></code></a> ensures that the active configuration
 (thread-local) of scikit-learn is propagated to the parallel workers for the
 duration of the execution of the parallel tasks.</p>
-<p>The API does not change and you can refer to <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.Parallel.html#joblib.Parallel" title="(in joblib v1.5.dev0)"><code class="xref py py-class docutils literal notranslate"><span class="pre">joblib.Parallel</span></code></a>
+<p>The API does not change and you can refer to <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.Parallel.html#joblib.Parallel" title="(in joblib v1.6.dev0)"><code class="xref py py-class docutils literal notranslate"><span class="pre">joblib.Parallel</span></code></a>
 documentation for more details.</p>
 <div class="versionadded">
 <p><span class="versionmodified added">Added in version 1.3.</span></p>
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.utils.parallel.Parallel.__call__">
-<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">iterable</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/parallel.py#L54"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.parallel.Parallel.__call__" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">iterable</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/parallel.py#L54"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.parallel.Parallel.__call__" title="Link to this definition">#</a></dt>
 <dd><p>Dispatch the tasks and return the results.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1338,7 +1338,7 @@ <h1>Parallel<a class="headerlink" href="#parallel" title="Link to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.utils.parallel.Parallel.dispatch_next">
-<span class="sig-name descname"><span class="pre">dispatch_next</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/miniforge3/envs/testenv/lib/python3.10/site-packages/joblib/parallel.py#L1421"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.parallel.Parallel.dispatch_next" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">dispatch_next</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/miniforge3/envs/testenv/lib/python3.10/site-packages/joblib/parallel.py#L1445"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.parallel.Parallel.dispatch_next" title="Link to this definition">#</a></dt>
 <dd><p>Dispatch more data for parallel processing</p>
 <p>This method is meant to be called concurrently by the multiprocessing
 callback. We rely on the thread-safety of dispatch_one_batch to protect
@@ -1347,7 +1347,7 @@ <h1>Parallel<a class="headerlink" href="#parallel" title="Link to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.utils.parallel.Parallel.dispatch_one_batch">
-<span class="sig-name descname"><span class="pre">dispatch_one_batch</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">iterator</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/miniforge3/envs/testenv/lib/python3.10/site-packages/joblib/parallel.py#L1433"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.parallel.Parallel.dispatch_one_batch" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">dispatch_one_batch</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">iterator</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/miniforge3/envs/testenv/lib/python3.10/site-packages/joblib/parallel.py#L1456"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.parallel.Parallel.dispatch_one_batch" title="Link to this definition">#</a></dt>
 <dd><p>Prefetch the tasks for the next batch and dispatch them.</p>
 <p>The effective size of the batch is computed here.
 If there are no more jobs to dispatch, return False, else return True.</p>
@@ -1357,13 +1357,13 @@ <h1>Parallel<a class="headerlink" href="#parallel" title="Link to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.utils.parallel.Parallel.format">
-<span class="sig-name descname"><span class="pre">format</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">obj</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">indent</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/miniforge3/envs/testenv/lib/python3.10/site-packages/joblib/logger.py#L89"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.parallel.Parallel.format" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">format</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">obj</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">indent</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/miniforge3/envs/testenv/lib/python3.10/site-packages/joblib/logger.py#L89"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.parallel.Parallel.format" title="Link to this definition">#</a></dt>
 <dd><p>Return the formatted representation of the object.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sklearn.utils.parallel.Parallel.print_progress">
-<span class="sig-name descname"><span class="pre">print_progress</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/miniforge3/envs/testenv/lib/python3.10/site-packages/joblib/parallel.py#L1546"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.parallel.Parallel.print_progress" title="Link to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">print_progress</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/miniforge3/envs/testenv/lib/python3.10/site-packages/joblib/parallel.py#L1568"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.parallel.Parallel.print_progress" title="Link to this definition">#</a></dt>
 <dd><p>Display the process of the parallel execution only a fraction
 of time, controlled by self.verbose.</p>
 </dd></dl>
diff --git a/dev/modules/generated/sklearn.utils.parallel.delayed.html b/dev/modules/generated/sklearn.utils.parallel.delayed.html
index 245accf671d3f..bc20a12fae02a 100644
--- a/dev/modules/generated/sklearn.utils.parallel.delayed.html
+++ b/dev/modules/generated/sklearn.utils.parallel.delayed.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>delayed<a class="headerlink" href="#delayed" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.utils.parallel.delayed">
-<span class="sig-prename descclassname"><span class="pre">sklearn.utils.parallel.</span></span><span class="sig-name descname"><span class="pre">delayed</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">function</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/parallel.py#L86"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.parallel.delayed" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.utils.parallel.</span></span><span class="sig-name descname"><span class="pre">delayed</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">function</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/parallel.py#L86"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.parallel.delayed" title="Link to this definition">#</a></dt>
 <dd><p>Decorator used to capture the arguments of a function.</p>
 <p>This alternative to <code class="docutils literal notranslate"><span class="pre">joblib.delayed</span></code> is meant to be used in conjunction
 with <code class="docutils literal notranslate"><span class="pre">sklearn.utils.parallel.Parallel</span></code>. The latter captures the scikit-
diff --git a/dev/modules/generated/sklearn.utils.random.sample_without_replacement.html b/dev/modules/generated/sklearn.utils.random.sample_without_replacement.html
index bfd199db8a232..1924e2daf6b16 100644
--- a/dev/modules/generated/sklearn.utils.random.sample_without_replacement.html
+++ b/dev/modules/generated/sklearn.utils.random.sample_without_replacement.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
diff --git a/dev/modules/generated/sklearn.utils.resample.html b/dev/modules/generated/sklearn.utils.resample.html
index d198bc8418d83..d332b55470e67 100644
--- a/dev/modules/generated/sklearn.utils.resample.html
+++ b/dev/modules/generated/sklearn.utils.resample.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>resample<a class="headerlink" href="#resample" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.utils.resample">
-<span class="sig-prename descclassname"><span class="pre">sklearn.utils.</span></span><span class="sig-name descname"><span class="pre">resample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">arrays</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">replace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stratify</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_indexing.py#L412"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.resample" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.utils.</span></span><span class="sig-name descname"><span class="pre">resample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">arrays</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">replace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stratify</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_indexing.py#L478"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.resample" title="Link to this definition">#</a></dt>
 <dd><p>Resample arrays or sparse matrices in a consistent way.</p>
 <p>The default strategy implements one step of the bootstrapping
 procedure.</p>
diff --git a/dev/modules/generated/sklearn.utils.safe_mask.html b/dev/modules/generated/sklearn.utils.safe_mask.html
index 07fec281bd3a2..c01a08d780221 100644
--- a/dev/modules/generated/sklearn.utils.safe_mask.html
+++ b/dev/modules/generated/sklearn.utils.safe_mask.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>safe_mask<a class="headerlink" href="#safe-mask" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.utils.safe_mask">
-<span class="sig-prename descclassname"><span class="pre">sklearn.utils.</span></span><span class="sig-name descname"><span class="pre">safe_mask</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mask</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_mask.py#L70"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.safe_mask" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.utils.</span></span><span class="sig-name descname"><span class="pre">safe_mask</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mask</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_mask.py#L70"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.safe_mask" title="Link to this definition">#</a></dt>
 <dd><p>Return a mask which is safe to use on X.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.utils.safe_sqr.html b/dev/modules/generated/sklearn.utils.safe_sqr.html
index cd8b101abc49c..776d76804c83a 100644
--- a/dev/modules/generated/sklearn.utils.safe_sqr.html
+++ b/dev/modules/generated/sklearn.utils.safe_sqr.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>safe_sqr<a class="headerlink" href="#safe-sqr" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.utils.safe_sqr">
-<span class="sig-prename descclassname"><span class="pre">sklearn.utils.</span></span><span class="sig-name descname"><span class="pre">safe_sqr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/extmath.py#L1251"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.safe_sqr" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.utils.</span></span><span class="sig-name descname"><span class="pre">safe_sqr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/extmath.py#L1294"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.safe_sqr" title="Link to this definition">#</a></dt>
 <dd><p>Element wise squaring of array-likes and sparse matrices.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.utils.shuffle.html b/dev/modules/generated/sklearn.utils.shuffle.html
index 27dbb7d792bee..096e2abe8996f 100644
--- a/dev/modules/generated/sklearn.utils.shuffle.html
+++ b/dev/modules/generated/sklearn.utils.shuffle.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>shuffle<a class="headerlink" href="#shuffle" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.utils.shuffle">
-<span class="sig-prename descclassname"><span class="pre">sklearn.utils.</span></span><span class="sig-name descname"><span class="pre">shuffle</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">arrays</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/_indexing.py#L609"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.shuffle" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.utils.</span></span><span class="sig-name descname"><span class="pre">shuffle</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">arrays</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/_indexing.py#L675"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.shuffle" title="Link to this definition">#</a></dt>
 <dd><p>Shuffle arrays or sparse matrices in a consistent way.</p>
 <p>This is a convenience alias to <code class="docutils literal notranslate"><span class="pre">resample(*arrays,</span> <span class="pre">replace=False)</span></code> to do
 random permutations of the collections.</p>
diff --git a/dev/modules/generated/sklearn.utils.sparsefuncs.incr_mean_variance_axis.html b/dev/modules/generated/sklearn.utils.sparsefuncs.incr_mean_variance_axis.html
index b1e5685a8c32b..12147fa85ef84 100644
--- a/dev/modules/generated/sklearn.utils.sparsefuncs.incr_mean_variance_axis.html
+++ b/dev/modules/generated/sklearn.utils.sparsefuncs.incr_mean_variance_axis.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>incr_mean_variance_axis<a class="headerlink" href="#incr-mean-variance-axis" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.utils.sparsefuncs.incr_mean_variance_axis">
-<span class="sig-prename descclassname"><span class="pre">sklearn.utils.sparsefuncs.</span></span><span class="sig-name descname"><span class="pre">incr_mean_variance_axis</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">axis</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">last_mean</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">last_var</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">last_n</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/sparsefuncs.py#L176"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.sparsefuncs.incr_mean_variance_axis" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.utils.sparsefuncs.</span></span><span class="sig-name descname"><span class="pre">incr_mean_variance_axis</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">axis</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">last_mean</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">last_var</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">last_n</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/sparsefuncs.py#L176"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.sparsefuncs.incr_mean_variance_axis" title="Link to this definition">#</a></dt>
 <dd><p>Compute incremental mean and variance along an axis on a CSR or CSC matrix.</p>
 <p>last_mean, last_var are the statistics computed at the last step by this
 function. Both must be initialized to 0-arrays of the proper size, i.e.
@@ -1374,7 +1374,7 @@ <h1>incr_mean_variance_axis<a class="headerlink" href="#incr-mean-variance-axis"
 <span class="gp">&gt;&gt;&gt; </span><span class="n">sparsefuncs</span><span class="o">.</span><span class="n">incr_mean_variance_axis</span><span class="p">(</span>
 <span class="gp">... </span>    <span class="n">csr</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">last_mean</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="mi">3</span><span class="p">),</span> <span class="n">last_var</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="mi">3</span><span class="p">),</span> <span class="n">last_n</span><span class="o">=</span><span class="mi">2</span>
 <span class="gp">... </span><span class="p">)</span>
-<span class="go">(array([1.3..., 0.1..., 1.1...]), array([8.8..., 0.1..., 3.4...]),</span>
+<span class="go">(array([1.33, 0.167, 1.17]), array([8.88, 0.139, 3.47]),</span>
 <span class="go">array([6., 6., 6.]))</span>
 </pre></div>
 </div>
diff --git a/dev/modules/generated/sklearn.utils.sparsefuncs.inplace_column_scale.html b/dev/modules/generated/sklearn.utils.sparsefuncs.inplace_column_scale.html
index d2fd91869bdb2..291090904968b 100644
--- a/dev/modules/generated/sklearn.utils.sparsefuncs.inplace_column_scale.html
+++ b/dev/modules/generated/sklearn.utils.sparsefuncs.inplace_column_scale.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>inplace_column_scale<a class="headerlink" href="#inplace-column-scale" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.utils.sparsefuncs.inplace_column_scale">
-<span class="sig-prename descclassname"><span class="pre">sklearn.utils.sparsefuncs.</span></span><span class="sig-name descname"><span class="pre">inplace_column_scale</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/sparsefuncs.py#L291"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.sparsefuncs.inplace_column_scale" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.utils.sparsefuncs.</span></span><span class="sig-name descname"><span class="pre">inplace_column_scale</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/sparsefuncs.py#L291"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.sparsefuncs.inplace_column_scale" title="Link to this definition">#</a></dt>
 <dd><p>Inplace column scaling of a CSC/CSR matrix.</p>
 <p>Scale each feature of the data matrix by multiplying with specific scale
 provided by the caller assuming a (n_samples, n_features) shape.</p>
diff --git a/dev/modules/generated/sklearn.utils.sparsefuncs.inplace_csr_column_scale.html b/dev/modules/generated/sklearn.utils.sparsefuncs.inplace_csr_column_scale.html
index 49ef36d2545aa..0357b7c8abf74 100644
--- a/dev/modules/generated/sklearn.utils.sparsefuncs.inplace_csr_column_scale.html
+++ b/dev/modules/generated/sklearn.utils.sparsefuncs.inplace_csr_column_scale.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>inplace_csr_column_scale<a class="headerlink" href="#inplace-csr-column-scale" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.utils.sparsefuncs.inplace_csr_column_scale">
-<span class="sig-prename descclassname"><span class="pre">sklearn.utils.sparsefuncs.</span></span><span class="sig-name descname"><span class="pre">inplace_csr_column_scale</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/sparsefuncs.py#L39"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.sparsefuncs.inplace_csr_column_scale" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.utils.sparsefuncs.</span></span><span class="sig-name descname"><span class="pre">inplace_csr_column_scale</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/sparsefuncs.py#L39"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.sparsefuncs.inplace_csr_column_scale" title="Link to this definition">#</a></dt>
 <dd><p>Inplace column scaling of a CSR matrix.</p>
 <p>Scale each feature of the data matrix by multiplying with specific scale
 provided by the caller assuming a (n_samples, n_features) shape.</p>
diff --git a/dev/modules/generated/sklearn.utils.sparsefuncs.inplace_row_scale.html b/dev/modules/generated/sklearn.utils.sparsefuncs.inplace_row_scale.html
index c1176993dcdf9..2cf81206e8616 100644
--- a/dev/modules/generated/sklearn.utils.sparsefuncs.inplace_row_scale.html
+++ b/dev/modules/generated/sklearn.utils.sparsefuncs.inplace_row_scale.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>inplace_row_scale<a class="headerlink" href="#inplace-row-scale" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.utils.sparsefuncs.inplace_row_scale">
-<span class="sig-prename descclassname"><span class="pre">sklearn.utils.sparsefuncs.</span></span><span class="sig-name descname"><span class="pre">inplace_row_scale</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/sparsefuncs.py#L336"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.sparsefuncs.inplace_row_scale" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.utils.sparsefuncs.</span></span><span class="sig-name descname"><span class="pre">inplace_row_scale</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/sparsefuncs.py#L336"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.sparsefuncs.inplace_row_scale" title="Link to this definition">#</a></dt>
 <dd><p>Inplace row scaling of a CSR or CSC matrix.</p>
 <p>Scale each row of the data matrix by multiplying with specific scale
 provided by the caller assuming a (n_samples, n_features) shape.</p>
diff --git a/dev/modules/generated/sklearn.utils.sparsefuncs.inplace_swap_column.html b/dev/modules/generated/sklearn.utils.sparsefuncs.inplace_swap_column.html
index 6cf015d0d4c6b..3bd715481b960 100644
--- a/dev/modules/generated/sklearn.utils.sparsefuncs.inplace_swap_column.html
+++ b/dev/modules/generated/sklearn.utils.sparsefuncs.inplace_swap_column.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>inplace_swap_column<a class="headerlink" href="#inplace-swap-column" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.utils.sparsefuncs.inplace_swap_column">
-<span class="sig-prename descclassname"><span class="pre">sklearn.utils.sparsefuncs.</span></span><span class="sig-name descname"><span class="pre">inplace_swap_column</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">m</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/sparsefuncs.py#L517"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.sparsefuncs.inplace_swap_column" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.utils.sparsefuncs.</span></span><span class="sig-name descname"><span class="pre">inplace_swap_column</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">m</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/sparsefuncs.py#L517"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.sparsefuncs.inplace_swap_column" title="Link to this definition">#</a></dt>
 <dd><p>Swap two columns of a CSC/CSR matrix in-place.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.utils.sparsefuncs.inplace_swap_row.html b/dev/modules/generated/sklearn.utils.sparsefuncs.inplace_swap_row.html
index 65ea19d0ab4a0..06a8ef7e39018 100644
--- a/dev/modules/generated/sklearn.utils.sparsefuncs.inplace_swap_row.html
+++ b/dev/modules/generated/sklearn.utils.sparsefuncs.inplace_swap_row.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>inplace_swap_row<a class="headerlink" href="#inplace-swap-row" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.utils.sparsefuncs.inplace_swap_row">
-<span class="sig-prename descclassname"><span class="pre">sklearn.utils.sparsefuncs.</span></span><span class="sig-name descname"><span class="pre">inplace_swap_row</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">m</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/sparsefuncs.py#L472"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.sparsefuncs.inplace_swap_row" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.utils.sparsefuncs.</span></span><span class="sig-name descname"><span class="pre">inplace_swap_row</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">m</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/sparsefuncs.py#L472"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.sparsefuncs.inplace_swap_row" title="Link to this definition">#</a></dt>
 <dd><p>Swap two rows of a CSC/CSR matrix in-place.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.utils.sparsefuncs.mean_variance_axis.html b/dev/modules/generated/sklearn.utils.sparsefuncs.mean_variance_axis.html
index 32307e2733b81..11c36d2fdff61 100644
--- a/dev/modules/generated/sklearn.utils.sparsefuncs.mean_variance_axis.html
+++ b/dev/modules/generated/sklearn.utils.sparsefuncs.mean_variance_axis.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>mean_variance_axis<a class="headerlink" href="#mean-variance-axis" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.utils.sparsefuncs.mean_variance_axis">
-<span class="sig-prename descclassname"><span class="pre">sklearn.utils.sparsefuncs.</span></span><span class="sig-name descname"><span class="pre">mean_variance_axis</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">axis</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_sum_weights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/sparsefuncs.py#L98"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.sparsefuncs.mean_variance_axis" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.utils.sparsefuncs.</span></span><span class="sig-name descname"><span class="pre">mean_variance_axis</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">axis</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_sum_weights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/sparsefuncs.py#L98"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.sparsefuncs.mean_variance_axis" title="Link to this definition">#</a></dt>
 <dd><p>Compute mean and variance along an axis on a CSR or CSC matrix.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.utils.sparsefuncs_fast.inplace_csr_row_normalize_l1.html b/dev/modules/generated/sklearn.utils.sparsefuncs_fast.inplace_csr_row_normalize_l1.html
index 3378b1ce0eb5b..476370f198a2b 100644
--- a/dev/modules/generated/sklearn.utils.sparsefuncs_fast.inplace_csr_row_normalize_l1.html
+++ b/dev/modules/generated/sklearn.utils.sparsefuncs_fast.inplace_csr_row_normalize_l1.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
diff --git a/dev/modules/generated/sklearn.utils.sparsefuncs_fast.inplace_csr_row_normalize_l2.html b/dev/modules/generated/sklearn.utils.sparsefuncs_fast.inplace_csr_row_normalize_l2.html
index 60229fe715a72..d1f92c549ea3f 100644
--- a/dev/modules/generated/sklearn.utils.sparsefuncs_fast.inplace_csr_row_normalize_l2.html
+++ b/dev/modules/generated/sklearn.utils.sparsefuncs_fast.inplace_csr_row_normalize_l2.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
diff --git a/dev/modules/generated/sklearn.utils.validation.check_is_fitted.html b/dev/modules/generated/sklearn.utils.validation.check_is_fitted.html
index 6913996cc8c9d..ea12f17272486 100644
--- a/dev/modules/generated/sklearn.utils.validation.check_is_fitted.html
+++ b/dev/modules/generated/sklearn.utils.validation.check_is_fitted.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>check_is_fitted<a class="headerlink" href="#check-is-fitted" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.utils.validation.check_is_fitted">
-<span class="sig-prename descclassname"><span class="pre">sklearn.utils.validation.</span></span><span class="sig-name descname"><span class="pre">check_is_fitted</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">attributes=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">msg=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">all_or_any=&lt;built-in</span> <span class="pre">function</span> <span class="pre">all&gt;</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/validation.py#L1671"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.validation.check_is_fitted" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.utils.validation.</span></span><span class="sig-name descname"><span class="pre">check_is_fitted</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">attributes=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">msg=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">all_or_any=&lt;built-in</span> <span class="pre">function</span> <span class="pre">all&gt;</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/validation.py#L1670"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.validation.check_is_fitted" title="Link to this definition">#</a></dt>
 <dd><p>Perform is_fitted validation for estimator.</p>
 <p>Checks if the estimator is fitted by verifying the presence of
 fitted attributes (ending with a trailing underscore) and otherwise
diff --git a/dev/modules/generated/sklearn.utils.validation.check_memory.html b/dev/modules/generated/sklearn.utils.validation.check_memory.html
index 18bcf7cad495a..7eb911c2c9923 100644
--- a/dev/modules/generated/sklearn.utils.validation.check_memory.html
+++ b/dev/modules/generated/sklearn.utils.validation.check_memory.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>check_memory<a class="headerlink" href="#check-memory" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.utils.validation.check_memory">
-<span class="sig-prename descclassname"><span class="pre">sklearn.utils.validation.</span></span><span class="sig-name descname"><span class="pre">check_memory</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">memory</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/validation.py#L414"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.validation.check_memory" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.utils.validation.</span></span><span class="sig-name descname"><span class="pre">check_memory</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">memory</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/validation.py#L414"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.validation.check_memory" title="Link to this definition">#</a></dt>
 <dd><p>Check that <code class="docutils literal notranslate"><span class="pre">memory</span></code> is joblib.Memory-like.</p>
 <p>joblib.Memory-like means that <code class="docutils literal notranslate"><span class="pre">memory</span></code> can be converted into a
 joblib.Memory instance (typically a str denoting the <code class="docutils literal notranslate"><span class="pre">location</span></code>)
diff --git a/dev/modules/generated/sklearn.utils.validation.check_symmetric.html b/dev/modules/generated/sklearn.utils.validation.check_symmetric.html
index ea03fb83b777b..58dd45c3855a9 100644
--- a/dev/modules/generated/sklearn.utils.validation.check_symmetric.html
+++ b/dev/modules/generated/sklearn.utils.validation.check_symmetric.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>check_symmetric<a class="headerlink" href="#check-symmetric" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.utils.validation.check_symmetric">
-<span class="sig-prename descclassname"><span class="pre">sklearn.utils.validation.</span></span><span class="sig-name descname"><span class="pre">check_symmetric</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">array</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">raise_warning</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">raise_exception</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/validation.py#L1555"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.validation.check_symmetric" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.utils.validation.</span></span><span class="sig-name descname"><span class="pre">check_symmetric</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">array</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">raise_warning</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">raise_exception</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/validation.py#L1554"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.validation.check_symmetric" title="Link to this definition">#</a></dt>
 <dd><p>Make sure that array is 2D, square and symmetric.</p>
 <p>If the array is not symmetric, then a symmetrized version is returned.
 Optionally, a warning or exception is raised if the matrix is not
diff --git a/dev/modules/generated/sklearn.utils.validation.column_or_1d.html b/dev/modules/generated/sklearn.utils.validation.column_or_1d.html
index 7ff4a39e5e6bd..c241014da57b0 100644
--- a/dev/modules/generated/sklearn.utils.validation.column_or_1d.html
+++ b/dev/modules/generated/sklearn.utils.validation.column_or_1d.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>column_or_1d<a class="headerlink" href="#column-or-1d" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.utils.validation.column_or_1d">
-<span class="sig-prename descclassname"><span class="pre">sklearn.utils.validation.</span></span><span class="sig-name descname"><span class="pre">column_or_1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">warn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/validation.py#L1415"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.validation.column_or_1d" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.utils.validation.</span></span><span class="sig-name descname"><span class="pre">column_or_1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">warn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/validation.py#L1415"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.validation.column_or_1d" title="Link to this definition">#</a></dt>
 <dd><p>Ravel column or 1d numpy array, else raises an error.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.utils.validation.has_fit_parameter.html b/dev/modules/generated/sklearn.utils.validation.has_fit_parameter.html
index eaa741945c091..dbd1139305eb3 100644
--- a/dev/modules/generated/sklearn.utils.validation.has_fit_parameter.html
+++ b/dev/modules/generated/sklearn.utils.validation.has_fit_parameter.html
@@ -40,7 +40,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1305,7 +1305,7 @@
 <h1>has_fit_parameter<a class="headerlink" href="#has-fit-parameter" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.utils.validation.has_fit_parameter">
-<span class="sig-prename descclassname"><span class="pre">sklearn.utils.validation.</span></span><span class="sig-name descname"><span class="pre">has_fit_parameter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">parameter</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/validation.py#L1521"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.validation.has_fit_parameter" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.utils.validation.</span></span><span class="sig-name descname"><span class="pre">has_fit_parameter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">parameter</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/validation.py#L1521"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.validation.has_fit_parameter" title="Link to this definition">#</a></dt>
 <dd><p>Check whether the estimator’s fit method supports the given parameter.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
diff --git a/dev/modules/generated/sklearn.utils.validation.validate_data.html b/dev/modules/generated/sklearn.utils.validation.validate_data.html
index 47cf618a21527..3a54ca88a740b 100644
--- a/dev/modules/generated/sklearn.utils.validation.validate_data.html
+++ b/dev/modules/generated/sklearn.utils.validation.validate_data.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -1307,7 +1307,7 @@
 <h1>validate_data<a class="headerlink" href="#validate-data" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sklearn.utils.validation.validate_data">
-<span class="sig-prename descclassname"><span class="pre">sklearn.utils.validation.</span></span><span class="sig-name descname"><span class="pre">validate_data</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">_estimator</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'no_validation'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'no_validation'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reset</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_separately</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">skip_check_array</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">check_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/35c431d51/sklearn/utils/validation.py#L2835"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.validation.validate_data" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">sklearn.utils.validation.</span></span><span class="sig-name descname"><span class="pre">validate_data</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">_estimator</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'no_validation'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'no_validation'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reset</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_separately</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">skip_check_array</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">check_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/aca49c1de/sklearn/utils/validation.py#L2843"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.validation.validate_data" title="Link to this definition">#</a></dt>
 <dd><p>Validate input data and set or check feature names and counts of the input.</p>
 <p>This helper function should be used in an estimator that requires input
 validation. This mutates the estimator and sets the <code class="docutils literal notranslate"><span class="pre">n_features_in_</span></code> and
diff --git a/dev/modules/grid_search.html b/dev/modules/grid_search.html
index f4b81bf16be35..9900dae80a76a 100644
--- a/dev/modules/grid_search.html
+++ b/dev/modules/grid_search.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -593,45 +593,46 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/learning_curve.html">3.5. Validation curves: plotting scores to evaluate models</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/inspection.html">4. Inspection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/partial_dependence.html">4.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/permutation_importance.html">4.2. Permutation feature importance</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/metadata_routing.html">4. Metadata Routing</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/inspection.html">5. Inspection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/partial_dependence.html">5.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/permutation_importance.html">5.2. Permutation feature importance</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/visualizations.html">5. Visualizations</a></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/data_transforms.html">6. Dataset transformations</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/compose.html">6.1. Pipelines and composite estimators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/feature_extraction.html">6.2. Feature extraction</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing.html">6.3. Preprocessing data</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/impute.html">6.4. Imputation of missing values</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/unsupervised_reduction.html">6.5. Unsupervised dimensionality reduction</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/random_projection.html">6.6. Random Projection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/kernel_approximation.html">6.7. Kernel Approximation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/metrics.html">6.8. Pairwise metrics, Affinities and Kernels</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing_targets.html">6.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/visualizations.html">6. Visualizations</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/data_transforms.html">7. Dataset transformations</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/compose.html">7.1. Pipelines and composite estimators</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/feature_extraction.html">7.2. Feature extraction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing.html">7.3. Preprocessing data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/impute.html">7.4. Imputation of missing values</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/unsupervised_reduction.html">7.5. Unsupervised dimensionality reduction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/random_projection.html">7.6. Random Projection</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/kernel_approximation.html">7.7. Kernel Approximation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/metrics.html">7.8. Pairwise metrics, Affinities and Kernels</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing_targets.html">7.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/datasets.html">7. Dataset loading utilities</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html">7.1. Toy datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html">7.2. Real world datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html">7.3. Generated datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html">7.4. Loading other datasets</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/datasets.html">8. Dataset loading utilities</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html">8.1. Toy datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html">8.2. Real world datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html">8.3. Generated datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html">8.4. Loading other datasets</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/computing.html">8. Computing with scikit-learn</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/scaling_strategies.html">8.1. Strategies to scale computationally: bigger data</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/computational_performance.html">8.2. Computational Performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/parallelism.html">8.3. Parallelism, resource management, and configuration</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/computing.html">9. Computing with scikit-learn</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/scaling_strategies.html">9.1. Strategies to scale computationally: bigger data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/computational_performance.html">9.2. Computational Performance</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/parallelism.html">9.3. Parallelism, resource management, and configuration</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/model_persistence.html">9. Model persistence</a></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/common_pitfalls.html">10. Common pitfalls and recommended practices</a></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/dispatching.html">11. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/array_api.html">11.1. Array API support (experimental)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/model_persistence.html">10. Model persistence</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/common_pitfalls.html">11. Common pitfalls and recommended practices</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/dispatching.html">12. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/array_api.html">12.1. Array API support (experimental)</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/machine_learning_map.html">12. Choosing the right estimator</a></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/presentations.html">13. External Resources, Videos and Talks</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/machine_learning_map.html">13. Choosing the right estimator</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/presentations.html">14. External Resources, Videos and Talks</a></li>
 </ul>
 </div>
 </nav></div>
diff --git a/dev/modules/impute.html b/dev/modules/impute.html
index e16bcc7a13614..f3045005bf1fe 100644
--- a/dev/modules/impute.html
+++ b/dev/modules/impute.html
@@ -7,7 +7,7 @@
   <head>
     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="viewport" content="width=device-width, initial-scale=1" />
-<meta property="og:title" content="6.4. Imputation of missing values" />
+<meta property="og:title" content="7.4. Imputation of missing values" />
 <meta property="og:type" content="website" />
 <meta property="og:url" content="https://scikit-learn/stable/modules/impute.html" />
 <meta property="og:site_name" content="scikit-learn" />
@@ -16,7 +16,7 @@
 <meta property="og:image:alt" content="scikit-learn" />
 <meta name="description" content="For various reasons, many real world datasets contain missing values, often encoded as blanks, NaNs or other placeholders. Such datasets however are incompatible with scikit-learn estimators which ..." />
 
-    <title>6.4. Imputation of missing values &#8212; scikit-learn 1.7.dev0 documentation</title>
+    <title>7.4. Imputation of missing values &#8212; scikit-learn 1.7.dev0 documentation</title>
   
   
   
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -80,8 +80,8 @@
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     <link rel="author" title="About these documents" href="http://github.com/about.html" />
     <link rel="search" title="Search" href="http://github.com/search.html" />
-    <link rel="next" title="6.5. Unsupervised dimensionality reduction" href="http://github.com/unsupervised_reduction.html" />
-    <link rel="prev" title="6.3. Preprocessing data" href="http://github.com/preprocessing.html" />
+    <link rel="next" title="7.5. Unsupervised dimensionality reduction" href="http://github.com/unsupervised_reduction.html" />
+    <link rel="prev" title="7.3. Preprocessing data" href="http://github.com/preprocessing.html" />
   <meta name="viewport" content="width=device-width, initial-scale=1"/>
   <meta name="docsearch:language" content="en"/>
   <meta name="docsearch:version" content="1.7" />
@@ -593,45 +593,46 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/learning_curve.html">3.5. Validation curves: plotting scores to evaluate models</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/inspection.html">4. Inspection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/partial_dependence.html">4.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/permutation_importance.html">4.2. Permutation feature importance</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/metadata_routing.html">4. Metadata Routing</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/inspection.html">5. Inspection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/partial_dependence.html">5.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/permutation_importance.html">5.2. Permutation feature importance</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/visualizations.html">5. Visualizations</a></li>
-<li class="toctree-l1 current active has-children"><a class="reference internal" href="http://github.com/data_transforms.html">6. Dataset transformations</a><details open="open"><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul class="current">
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/compose.html">6.1. Pipelines and composite estimators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/feature_extraction.html">6.2. Feature extraction</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing.html">6.3. Preprocessing data</a></li>
-<li class="toctree-l2 current active"><a class="current reference internal" href="#">6.4. Imputation of missing values</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/unsupervised_reduction.html">6.5. Unsupervised dimensionality reduction</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/random_projection.html">6.6. Random Projection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/kernel_approximation.html">6.7. Kernel Approximation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/metrics.html">6.8. Pairwise metrics, Affinities and Kernels</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing_targets.html">6.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/visualizations.html">6. Visualizations</a></li>
+<li class="toctree-l1 current active has-children"><a class="reference internal" href="http://github.com/data_transforms.html">7. Dataset transformations</a><details open="open"><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/compose.html">7.1. Pipelines and composite estimators</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/feature_extraction.html">7.2. Feature extraction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing.html">7.3. Preprocessing data</a></li>
+<li class="toctree-l2 current active"><a class="current reference internal" href="#">7.4. Imputation of missing values</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/unsupervised_reduction.html">7.5. Unsupervised dimensionality reduction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/random_projection.html">7.6. Random Projection</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/kernel_approximation.html">7.7. Kernel Approximation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/metrics.html">7.8. Pairwise metrics, Affinities and Kernels</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing_targets.html">7.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/datasets.html">7. Dataset loading utilities</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html">7.1. Toy datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html">7.2. Real world datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html">7.3. Generated datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html">7.4. Loading other datasets</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/datasets.html">8. Dataset loading utilities</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html">8.1. Toy datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html">8.2. Real world datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html">8.3. Generated datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html">8.4. Loading other datasets</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/computing.html">8. Computing with scikit-learn</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/scaling_strategies.html">8.1. Strategies to scale computationally: bigger data</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/computational_performance.html">8.2. Computational Performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/parallelism.html">8.3. Parallelism, resource management, and configuration</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/computing.html">9. Computing with scikit-learn</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/scaling_strategies.html">9.1. Strategies to scale computationally: bigger data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/computational_performance.html">9.2. Computational Performance</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/parallelism.html">9.3. Parallelism, resource management, and configuration</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/model_persistence.html">9. Model persistence</a></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/common_pitfalls.html">10. Common pitfalls and recommended practices</a></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/dispatching.html">11. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/array_api.html">11.1. Array API support (experimental)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/model_persistence.html">10. Model persistence</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/common_pitfalls.html">11. Common pitfalls and recommended practices</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/dispatching.html">12. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/array_api.html">12.1. Array API support (experimental)</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/machine_learning_map.html">12. Choosing the right estimator</a></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/presentations.html">13. External Resources, Videos and Talks</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/machine_learning_map.html">13. Choosing the right estimator</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/presentations.html">14. External Resources, Videos and Talks</a></li>
 </ul>
 </div>
 </nav></div>
@@ -669,9 +670,9 @@
     <li class="breadcrumb-item"><a href="http://github.com/user_guide.html" class="nav-link">User Guide</a></li>
     
     
-    <li class="breadcrumb-item"><a href="http://github.com/data_transforms.html" class="nav-link"><span class="section-number">6. </span>Dataset transformations</a></li>
+    <li class="breadcrumb-item"><a href="http://github.com/data_transforms.html" class="nav-link"><span class="section-number">7. </span>Dataset transformations</a></li>
     
-    <li class="breadcrumb-item active" aria-current="page"><span class="ellipsis"><span class="section-number">6.4. </span>Imputation of missing values</span></li>
+    <li class="breadcrumb-item active" aria-current="page"><span class="ellipsis"><span class="section-number">7.4. </span>Imputation of missing values</span></li>
   </ul>
 </nav>
 </div>
@@ -689,7 +690,7 @@
                 <article class="bd-article">
                   
   <section id="imputation-of-missing-values">
-<span id="impute"></span><h1><span class="section-number">6.4. </span>Imputation of missing values<a class="headerlink" href="#imputation-of-missing-values" title="Link to this heading">#</a></h1>
+<span id="impute"></span><h1><span class="section-number">7.4. </span>Imputation of missing values<a class="headerlink" href="#imputation-of-missing-values" title="Link to this heading">#</a></h1>
 <p>For various reasons, many real world datasets contain missing values, often
 encoded as blanks, NaNs or other placeholders. Such datasets however are
 incompatible with scikit-learn estimators which assume that all values in an
@@ -700,7 +701,7 @@
 values, i.e., to infer them from the known part of the data. See the
 glossary entry on <a class="reference internal" href="http://github.com/glossary.html#term-imputation"><span class="xref std std-term">imputation</span></a>.</p>
 <section id="univariate-vs-multivariate-imputation">
-<h2><span class="section-number">6.4.1. </span>Univariate vs. Multivariate Imputation<a class="headerlink" href="#univariate-vs-multivariate-imputation" title="Link to this heading">#</a></h2>
+<h2><span class="section-number">7.4.1. </span>Univariate vs. Multivariate Imputation<a class="headerlink" href="#univariate-vs-multivariate-imputation" title="Link to this heading">#</a></h2>
 <p>One type of imputation algorithm is univariate, which imputes values in the
 i-th feature dimension using only non-missing values in that feature dimension
 (e.g. <a class="reference internal" href="http://github.com/generated/sklearn.impute.SimpleImputer.html#sklearn.impute.SimpleImputer" title="sklearn.impute.SimpleImputer"><code class="xref py py-class docutils literal notranslate"><span class="pre">SimpleImputer</span></code></a>). By contrast, multivariate imputation
@@ -708,7 +709,7 @@ <h2><span class="section-number">6.4.1. </span>Univariate vs. Multivariate Imput
 missing values (e.g. <a class="reference internal" href="http://github.com/generated/sklearn.impute.IterativeImputer.html#sklearn.impute.IterativeImputer" title="sklearn.impute.IterativeImputer"><code class="xref py py-class docutils literal notranslate"><span class="pre">IterativeImputer</span></code></a>).</p>
 </section>
 <section id="univariate-feature-imputation">
-<span id="single-imputer"></span><h2><span class="section-number">6.4.2. </span>Univariate feature imputation<a class="headerlink" href="#univariate-feature-imputation" title="Link to this heading">#</a></h2>
+<span id="single-imputer"></span><h2><span class="section-number">7.4.2. </span>Univariate feature imputation<a class="headerlink" href="#univariate-feature-imputation" title="Link to this heading">#</a></h2>
 <p>The <a class="reference internal" href="http://github.com/generated/sklearn.impute.SimpleImputer.html#sklearn.impute.SimpleImputer" title="sklearn.impute.SimpleImputer"><code class="xref py py-class docutils literal notranslate"><span class="pre">SimpleImputer</span></code></a> class provides basic strategies for imputing missing
 values. Missing values can be imputed with a provided constant value, or using
 the statistics (mean, median or most frequent) of each column in which the
@@ -725,7 +726,7 @@ <h2><span class="section-number">6.4.1. </span>Univariate vs. Multivariate Imput
 <span class="gp">&gt;&gt;&gt; </span><span class="n">X</span> <span class="o">=</span> <span class="p">[[</span><span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">6</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">],</span> <span class="p">[</span><span class="mi">7</span><span class="p">,</span> <span class="mi">6</span><span class="p">]]</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">imp</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">X</span><span class="p">))</span>
 <span class="go">[[4.          2.        ]</span>
-<span class="go"> [6.          3.666...]</span>
+<span class="go"> [6.          3.666]</span>
 <span class="go"> [7.          6.        ]]</span>
 </pre></div>
 </div>
@@ -765,7 +766,7 @@ <h2><span class="section-number">6.4.1. </span>Univariate vs. Multivariate Imput
 <p>For another example on usage, see <a class="reference internal" href="http://github.com/auto_examples/impute/plot_missing_values.html#sphx-glr-auto-examples-impute-plot-missing-values-py"><span class="std std-ref">Imputing missing values before building an estimator</span></a>.</p>
 </section>
 <section id="multivariate-feature-imputation">
-<span id="iterative-imputer"></span><h2><span class="section-number">6.4.3. </span>Multivariate feature imputation<a class="headerlink" href="#multivariate-feature-imputation" title="Link to this heading">#</a></h2>
+<span id="iterative-imputer"></span><h2><span class="section-number">7.4.3. </span>Multivariate feature imputation<a class="headerlink" href="#multivariate-feature-imputation" title="Link to this heading">#</a></h2>
 <p>A more sophisticated approach is to use the <a class="reference internal" href="http://github.com/generated/sklearn.impute.IterativeImputer.html#sklearn.impute.IterativeImputer" title="sklearn.impute.IterativeImputer"><code class="xref py py-class docutils literal notranslate"><span class="pre">IterativeImputer</span></code></a> class,
 which models each feature with missing values as a function of other features,
 and uses that estimate for imputation. It does so in an iterated round-robin
@@ -802,7 +803,7 @@ <h2><span class="section-number">6.4.1. </span>Univariate vs. Multivariate Imput
 Pipeline as a way to build a composite estimator that supports imputation.
 See <a class="reference internal" href="http://github.com/auto_examples/impute/plot_missing_values.html#sphx-glr-auto-examples-impute-plot-missing-values-py"><span class="std std-ref">Imputing missing values before building an estimator</span></a>.</p>
 <section id="flexibility-of-iterativeimputer">
-<h3><span class="section-number">6.4.3.1. </span>Flexibility of IterativeImputer<a class="headerlink" href="#flexibility-of-iterativeimputer" title="Link to this heading">#</a></h3>
+<h3><span class="section-number">7.4.3.1. </span>Flexibility of IterativeImputer<a class="headerlink" href="#flexibility-of-iterativeimputer" title="Link to this heading">#</a></h3>
 <p>There are many well-established imputation packages in the R data science
 ecosystem: Amelia, mi, mice, missForest, etc. missForest is popular, and turns
 out to be a particular instance of different sequential imputation algorithms
@@ -812,7 +813,7 @@ <h3><span class="section-number">6.4.3.1. </span>Flexibility of IterativeImputer
 See <a class="reference internal" href="http://github.com/auto_examples/impute/plot_iterative_imputer_variants_comparison.html#sphx-glr-auto-examples-impute-plot-iterative-imputer-variants-comparison-py"><span class="std std-ref">Imputing missing values with variants of IterativeImputer</span></a>.</p>
 </section>
 <section id="multiple-vs-single-imputation">
-<span id="multiple-imputation"></span><h3><span class="section-number">6.4.3.2. </span>Multiple vs. Single Imputation<a class="headerlink" href="#multiple-vs-single-imputation" title="Link to this heading">#</a></h3>
+<span id="multiple-imputation"></span><h3><span class="section-number">7.4.3.2. </span>Multiple vs. Single Imputation<a class="headerlink" href="#multiple-vs-single-imputation" title="Link to this heading">#</a></h3>
 <p>In the statistics community, it is common practice to perform multiple
 imputations, generating, for example, <code class="docutils literal notranslate"><span class="pre">m</span></code> separate imputations for a single
 feature matrix. Each of these <code class="docutils literal notranslate"><span class="pre">m</span></code> imputations is then put through the
@@ -851,7 +852,7 @@ <h3><span class="section-number">6.4.3.1. </span>Flexibility of IterativeImputer
 </section>
 </section>
 <section id="nearest-neighbors-imputation">
-<span id="knnimpute"></span><h2><span class="section-number">6.4.4. </span>Nearest neighbors imputation<a class="headerlink" href="#nearest-neighbors-imputation" title="Link to this heading">#</a></h2>
+<span id="knnimpute"></span><h2><span class="section-number">7.4.4. </span>Nearest neighbors imputation<a class="headerlink" href="#nearest-neighbors-imputation" title="Link to this heading">#</a></h2>
 <p>The <a class="reference internal" href="http://github.com/generated/sklearn.impute.KNNImputer.html#sklearn.impute.KNNImputer" title="sklearn.impute.KNNImputer"><code class="xref py py-class docutils literal notranslate"><span class="pre">KNNImputer</span></code></a> class provides imputation for filling in missing values
 using the k-Nearest Neighbors approach. By default, a euclidean distance metric
 that supports missing values,
@@ -896,7 +897,7 @@ <h3><span class="section-number">6.4.3.1. </span>Flexibility of IterativeImputer
 </div>
 </section>
 <section id="keeping-the-number-of-features-constant">
-<h2><span class="section-number">6.4.5. </span>Keeping the number of features constant<a class="headerlink" href="#keeping-the-number-of-features-constant" title="Link to this heading">#</a></h2>
+<h2><span class="section-number">7.4.5. </span>Keeping the number of features constant<a class="headerlink" href="#keeping-the-number-of-features-constant" title="Link to this heading">#</a></h2>
 <p>By default, the scikit-learn imputers will drop fully empty features, i.e.
 columns containing only missing values. For instance:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">imputer</span> <span class="o">=</span> <span class="n">SimpleImputer</span><span class="p">()</span>
@@ -923,7 +924,7 @@ <h2><span class="section-number">6.4.5. </span>Keeping the number of features co
 </div>
 </section>
 <section id="marking-imputed-values">
-<span id="missing-indicator"></span><h2><span class="section-number">6.4.6. </span>Marking imputed values<a class="headerlink" href="#marking-imputed-values" title="Link to this heading">#</a></h2>
+<span id="missing-indicator"></span><h2><span class="section-number">7.4.6. </span>Marking imputed values<a class="headerlink" href="#marking-imputed-values" title="Link to this heading">#</a></h2>
 <p>The <a class="reference internal" href="http://github.com/generated/sklearn.impute.MissingIndicator.html#sklearn.impute.MissingIndicator" title="sklearn.impute.MissingIndicator"><code class="xref py py-class docutils literal notranslate"><span class="pre">MissingIndicator</span></code></a> transformer is useful to transform a dataset into
 corresponding binary matrix indicating the presence of missing values in the
 dataset. This transformation is useful in conjunction with imputation. When
@@ -1012,7 +1013,7 @@ <h2><span class="section-number">6.4.5. </span>Keeping the number of features co
 </div>
 </section>
 <section id="estimators-that-handle-nan-values">
-<h2><span class="section-number">6.4.7. </span>Estimators that handle NaN values<a class="headerlink" href="#estimators-that-handle-nan-values" title="Link to this heading">#</a></h2>
+<h2><span class="section-number">7.4.7. </span>Estimators that handle NaN values<a class="headerlink" href="#estimators-that-handle-nan-values" title="Link to this heading">#</a></h2>
 <p>Some estimators are designed to handle NaN values without preprocessing.
 Below is the list of these estimators, classified by type
 (cluster, regressor, classifier, transform):</p>
@@ -1089,7 +1090,7 @@ <h2><span class="section-number">6.4.7. </span>Estimators that handle NaN values
       <i class="fa-solid fa-angle-left"></i>
       <div class="prev-next-info">
         <p class="prev-next-subtitle">previous</p>
-        <p class="prev-next-title"><span class="section-number">6.3. </span>Preprocessing data</p>
+        <p class="prev-next-title"><span class="section-number">7.3. </span>Preprocessing data</p>
       </div>
     </a>
     <a class="right-next"
@@ -1097,7 +1098,7 @@ <h2><span class="section-number">6.4.7. </span>Estimators that handle NaN values
        title="next page">
       <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">6.5. </span>Unsupervised dimensionality reduction</p>
+        <p class="prev-next-title"><span class="section-number">7.5. </span>Unsupervised dimensionality reduction</p>
       </div>
       <i class="fa-solid fa-angle-right"></i>
     </a>
@@ -1124,17 +1125,17 @@ <h2><span class="section-number">6.4.7. </span>Estimators that handle NaN values
   </div>
   <nav class="bd-toc-nav page-toc" aria-labelledby="pst-page-navigation-heading-2">
     <ul class="visible nav section-nav flex-column">
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#univariate-vs-multivariate-imputation">6.4.1. Univariate vs. Multivariate Imputation</a></li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#univariate-feature-imputation">6.4.2. Univariate feature imputation</a></li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#multivariate-feature-imputation">6.4.3. Multivariate feature imputation</a><ul class="nav section-nav flex-column">
-<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#flexibility-of-iterativeimputer">6.4.3.1. Flexibility of IterativeImputer</a></li>
-<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#multiple-vs-single-imputation">6.4.3.2. Multiple vs. Single Imputation</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#univariate-vs-multivariate-imputation">7.4.1. Univariate vs. Multivariate Imputation</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#univariate-feature-imputation">7.4.2. Univariate feature imputation</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#multivariate-feature-imputation">7.4.3. Multivariate feature imputation</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#flexibility-of-iterativeimputer">7.4.3.1. Flexibility of IterativeImputer</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#multiple-vs-single-imputation">7.4.3.2. Multiple vs. Single Imputation</a></li>
 </ul>
 </li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#nearest-neighbors-imputation">6.4.4. Nearest neighbors imputation</a></li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#keeping-the-number-of-features-constant">6.4.5. Keeping the number of features constant</a></li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#marking-imputed-values">6.4.6. Marking imputed values</a></li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#estimators-that-handle-nan-values">6.4.7. Estimators that handle NaN values</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#nearest-neighbors-imputation">7.4.4. Nearest neighbors imputation</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#keeping-the-number-of-features-constant">7.4.5. Keeping the number of features constant</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#marking-imputed-values">7.4.6. Marking imputed values</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#estimators-that-handle-nan-values">7.4.7. Estimators that handle NaN values</a></li>
 </ul>
   </nav></div>
 
diff --git a/dev/modules/isotonic.html b/dev/modules/isotonic.html
index fd47ca4002881..09cbf84bf5b45 100644
--- a/dev/modules/isotonic.html
+++ b/dev/modules/isotonic.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -594,45 +594,46 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/learning_curve.html">3.5. Validation curves: plotting scores to evaluate models</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/inspection.html">4. Inspection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/partial_dependence.html">4.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/permutation_importance.html">4.2. Permutation feature importance</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/metadata_routing.html">4. Metadata Routing</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/inspection.html">5. Inspection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/partial_dependence.html">5.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/permutation_importance.html">5.2. Permutation feature importance</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/visualizations.html">5. Visualizations</a></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/data_transforms.html">6. Dataset transformations</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/compose.html">6.1. Pipelines and composite estimators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/feature_extraction.html">6.2. Feature extraction</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing.html">6.3. Preprocessing data</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/impute.html">6.4. Imputation of missing values</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/unsupervised_reduction.html">6.5. Unsupervised dimensionality reduction</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/random_projection.html">6.6. Random Projection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/kernel_approximation.html">6.7. Kernel Approximation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/metrics.html">6.8. Pairwise metrics, Affinities and Kernels</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing_targets.html">6.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/visualizations.html">6. Visualizations</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/data_transforms.html">7. Dataset transformations</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/compose.html">7.1. Pipelines and composite estimators</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/feature_extraction.html">7.2. Feature extraction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing.html">7.3. Preprocessing data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/impute.html">7.4. Imputation of missing values</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/unsupervised_reduction.html">7.5. Unsupervised dimensionality reduction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/random_projection.html">7.6. Random Projection</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/kernel_approximation.html">7.7. Kernel Approximation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/metrics.html">7.8. Pairwise metrics, Affinities and Kernels</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing_targets.html">7.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/datasets.html">7. Dataset loading utilities</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html">7.1. Toy datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html">7.2. Real world datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html">7.3. Generated datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html">7.4. Loading other datasets</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/datasets.html">8. Dataset loading utilities</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html">8.1. Toy datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html">8.2. Real world datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html">8.3. Generated datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html">8.4. Loading other datasets</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/computing.html">8. Computing with scikit-learn</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/scaling_strategies.html">8.1. Strategies to scale computationally: bigger data</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/computational_performance.html">8.2. Computational Performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/parallelism.html">8.3. Parallelism, resource management, and configuration</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/computing.html">9. Computing with scikit-learn</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/scaling_strategies.html">9.1. Strategies to scale computationally: bigger data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/computational_performance.html">9.2. Computational Performance</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/parallelism.html">9.3. Parallelism, resource management, and configuration</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/model_persistence.html">9. Model persistence</a></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/common_pitfalls.html">10. Common pitfalls and recommended practices</a></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/dispatching.html">11. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/array_api.html">11.1. Array API support (experimental)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/model_persistence.html">10. Model persistence</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/common_pitfalls.html">11. Common pitfalls and recommended practices</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/dispatching.html">12. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/array_api.html">12.1. Array API support (experimental)</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/machine_learning_map.html">12. Choosing the right estimator</a></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/presentations.html">13. External Resources, Videos and Talks</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/machine_learning_map.html">13. Choosing the right estimator</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/presentations.html">14. External Resources, Videos and Talks</a></li>
 </ul>
 </div>
 </nav></div>
diff --git a/dev/modules/kernel_approximation.html b/dev/modules/kernel_approximation.html
index c6f605c27c041..5921363f0ad57 100644
--- a/dev/modules/kernel_approximation.html
+++ b/dev/modules/kernel_approximation.html
@@ -7,7 +7,7 @@
   <head>
     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="viewport" content="width=device-width, initial-scale=1" />
-<meta property="og:title" content="6.7. Kernel Approximation" />
+<meta property="og:title" content="7.7. Kernel Approximation" />
 <meta property="og:type" content="website" />
 <meta property="og:url" content="https://scikit-learn/stable/modules/kernel_approximation.html" />
 <meta property="og:site_name" content="scikit-learn" />
@@ -16,7 +16,7 @@
 <meta property="og:image:alt" content="scikit-learn" />
 <meta name="description" content="This submodule contains functions that approximate the feature mappings that correspond to certain kernels, as they are used for example in support vector machines (see Support Vector Machines). Th..." />
 
-    <title>6.7. Kernel Approximation &#8212; scikit-learn 1.7.dev0 documentation</title>
+    <title>7.7. Kernel Approximation &#8212; scikit-learn 1.7.dev0 documentation</title>
   
   
   
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
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@@ -81,8 +81,8 @@
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     <link rel="author" title="About these documents" href="http://github.com/about.html" />
     <link rel="search" title="Search" href="http://github.com/search.html" />
-    <link rel="next" title="6.8. Pairwise metrics, Affinities and Kernels" href="http://github.com/metrics.html" />
-    <link rel="prev" title="6.6. Random Projection" href="http://github.com/random_projection.html" />
+    <link rel="next" title="7.8. Pairwise metrics, Affinities and Kernels" href="http://github.com/metrics.html" />
+    <link rel="prev" title="7.6. Random Projection" href="http://github.com/random_projection.html" />
   <meta name="viewport" content="width=device-width, initial-scale=1"/>
   <meta name="docsearch:language" content="en"/>
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@@ -594,45 +594,46 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/learning_curve.html">3.5. Validation curves: plotting scores to evaluate models</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/inspection.html">4. Inspection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/partial_dependence.html">4.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/permutation_importance.html">4.2. Permutation feature importance</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/metadata_routing.html">4. Metadata Routing</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/inspection.html">5. Inspection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/partial_dependence.html">5.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/permutation_importance.html">5.2. Permutation feature importance</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/visualizations.html">5. Visualizations</a></li>
-<li class="toctree-l1 current active has-children"><a class="reference internal" href="http://github.com/data_transforms.html">6. Dataset transformations</a><details open="open"><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul class="current">
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/compose.html">6.1. Pipelines and composite estimators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/feature_extraction.html">6.2. Feature extraction</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing.html">6.3. Preprocessing data</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/impute.html">6.4. Imputation of missing values</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/unsupervised_reduction.html">6.5. Unsupervised dimensionality reduction</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/random_projection.html">6.6. Random Projection</a></li>
-<li class="toctree-l2 current active"><a class="current reference internal" href="#">6.7. Kernel Approximation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/metrics.html">6.8. Pairwise metrics, Affinities and Kernels</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing_targets.html">6.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/visualizations.html">6. Visualizations</a></li>
+<li class="toctree-l1 current active has-children"><a class="reference internal" href="http://github.com/data_transforms.html">7. Dataset transformations</a><details open="open"><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/compose.html">7.1. Pipelines and composite estimators</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/feature_extraction.html">7.2. Feature extraction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing.html">7.3. Preprocessing data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/impute.html">7.4. Imputation of missing values</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/unsupervised_reduction.html">7.5. Unsupervised dimensionality reduction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/random_projection.html">7.6. Random Projection</a></li>
+<li class="toctree-l2 current active"><a class="current reference internal" href="#">7.7. Kernel Approximation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/metrics.html">7.8. Pairwise metrics, Affinities and Kernels</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing_targets.html">7.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/datasets.html">7. Dataset loading utilities</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html">7.1. Toy datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html">7.2. Real world datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html">7.3. Generated datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html">7.4. Loading other datasets</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/datasets.html">8. Dataset loading utilities</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html">8.1. Toy datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html">8.2. Real world datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html">8.3. Generated datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html">8.4. Loading other datasets</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/computing.html">8. Computing with scikit-learn</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/scaling_strategies.html">8.1. Strategies to scale computationally: bigger data</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/computational_performance.html">8.2. Computational Performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/parallelism.html">8.3. Parallelism, resource management, and configuration</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/computing.html">9. Computing with scikit-learn</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/scaling_strategies.html">9.1. Strategies to scale computationally: bigger data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/computational_performance.html">9.2. Computational Performance</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/parallelism.html">9.3. Parallelism, resource management, and configuration</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/model_persistence.html">9. Model persistence</a></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/common_pitfalls.html">10. Common pitfalls and recommended practices</a></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/dispatching.html">11. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/array_api.html">11.1. Array API support (experimental)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/model_persistence.html">10. Model persistence</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/common_pitfalls.html">11. Common pitfalls and recommended practices</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/dispatching.html">12. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/array_api.html">12.1. Array API support (experimental)</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="http://github.com/machine_learning_map.html">12. Choosing the right estimator</a></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/presentations.html">13. External Resources, Videos and Talks</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/machine_learning_map.html">13. Choosing the right estimator</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/presentations.html">14. External Resources, Videos and Talks</a></li>
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@@ -670,9 +671,9 @@
     <li class="breadcrumb-item"><a href="http://github.com/user_guide.html" class="nav-link">User Guide</a></li>
     
     
-    <li class="breadcrumb-item"><a href="http://github.com/data_transforms.html" class="nav-link"><span class="section-number">6. </span>Dataset transformations</a></li>
+    <li class="breadcrumb-item"><a href="http://github.com/data_transforms.html" class="nav-link"><span class="section-number">7. </span>Dataset transformations</a></li>
     
-    <li class="breadcrumb-item active" aria-current="page"><span class="ellipsis"><span class="section-number">6.7. </span>Kernel Approximation</span></li>
+    <li class="breadcrumb-item active" aria-current="page"><span class="ellipsis"><span class="section-number">7.7. </span>Kernel Approximation</span></li>
   </ul>
 </nav>
 </div>
@@ -690,7 +691,7 @@
                 <article class="bd-article">
                   
   <section id="kernel-approximation">
-<span id="id1"></span><h1><span class="section-number">6.7. </span>Kernel Approximation<a class="headerlink" href="#kernel-approximation" title="Link to this heading">#</a></h1>
+<span id="id1"></span><h1><span class="section-number">7.7. </span>Kernel Approximation<a class="headerlink" href="#kernel-approximation" title="Link to this heading">#</a></h1>
 <p>This submodule contains functions that approximate the feature mappings that
 correspond to certain kernels, as they are used for example in support vector
 machines (see <a class="reference internal" href="http://github.com/svm.html#svm"><span class="std std-ref">Support Vector Machines</span></a>).
@@ -713,7 +714,7 @@
 <p><a class="reference internal" href="http://github.com/linear_model.html#polynomial-regression"><span class="std std-ref">Polynomial regression: extending linear models with basis functions</span></a> for an exact polynomial transformation.</p>
 </div>
 <section id="nystroem-method-for-kernel-approximation">
-<span id="nystroem-kernel-approx"></span><h2><span class="section-number">6.7.1. </span>Nystroem Method for Kernel Approximation<a class="headerlink" href="#nystroem-method-for-kernel-approximation" title="Link to this heading">#</a></h2>
+<span id="nystroem-kernel-approx"></span><h2><span class="section-number">7.7.1. </span>Nystroem Method for Kernel Approximation<a class="headerlink" href="#nystroem-method-for-kernel-approximation" title="Link to this heading">#</a></h2>
 <p>The Nystroem method, as implemented in <a class="reference internal" href="http://github.com/generated/sklearn.kernel_approximation.Nystroem.html#sklearn.kernel_approximation.Nystroem" title="sklearn.kernel_approximation.Nystroem"><code class="xref py py-class docutils literal notranslate"><span class="pre">Nystroem</span></code></a> is a general method for
 reduced rank approximations of kernels. It achieves this by subsampling without
 replacement rows/columns of the data on which the kernel is evaluated. While the
@@ -767,7 +768,7 @@
 </ul>
 </section>
 <section id="radial-basis-function-kernel">
-<span id="rbf-kernel-approx"></span><h2><span class="section-number">6.7.2. </span>Radial Basis Function Kernel<a class="headerlink" href="#radial-basis-function-kernel" title="Link to this heading">#</a></h2>
+<span id="rbf-kernel-approx"></span><h2><span class="section-number">7.7.2. </span>Radial Basis Function Kernel<a class="headerlink" href="#radial-basis-function-kernel" title="Link to this heading">#</a></h2>
 <p>The <a class="reference internal" href="http://github.com/generated/sklearn.kernel_approximation.RBFSampler.html#sklearn.kernel_approximation.RBFSampler" title="sklearn.kernel_approximation.RBFSampler"><code class="xref py py-class docutils literal notranslate"><span class="pre">RBFSampler</span></code></a> constructs an approximate mapping for the radial basis
 function kernel, also known as <em>Random Kitchen Sinks</em> <a class="reference internal" href="#rr2007" id="id3"><span>[RR2007]</span></a>. This
 transformation can be used to explicitly model a kernel map, prior to applying
@@ -814,7 +815,7 @@
 </ul>
 </section>
 <section id="additive-chi-squared-kernel">
-<span id="additive-chi-kernel-approx"></span><h2><span class="section-number">6.7.3. </span>Additive Chi Squared Kernel<a class="headerlink" href="#additive-chi-squared-kernel" title="Link to this heading">#</a></h2>
+<span id="additive-chi-kernel-approx"></span><h2><span class="section-number">7.7.3. </span>Additive Chi Squared Kernel<a class="headerlink" href="#additive-chi-squared-kernel" title="Link to this heading">#</a></h2>
 <p>The additive chi squared kernel is a kernel on histograms, often used in computer vision.</p>
 <p>The additive chi squared kernel as used here is given by</p>
 <div class="math notranslate nohighlight">
@@ -838,7 +839,7 @@
 See the <a class="reference internal" href="#vz2010" id="id6"><span>[VZ2010]</span></a> for details and <a class="reference internal" href="#vvz2010" id="id7"><span>[VVZ2010]</span></a> for combination with the <a class="reference internal" href="http://github.com/generated/sklearn.kernel_approximation.RBFSampler.html#sklearn.kernel_approximation.RBFSampler" title="sklearn.kernel_approximation.RBFSampler"><code class="xref py py-class docutils literal notranslate"><span class="pre">RBFSampler</span></code></a>.</p>
 </section>
 <section id="skewed-chi-squared-kernel">
-<span id="skewed-chi-kernel-approx"></span><h2><span class="section-number">6.7.4. </span>Skewed Chi Squared Kernel<a class="headerlink" href="#skewed-chi-squared-kernel" title="Link to this heading">#</a></h2>
+<span id="skewed-chi-kernel-approx"></span><h2><span class="section-number">7.7.4. </span>Skewed Chi Squared Kernel<a class="headerlink" href="#skewed-chi-squared-kernel" title="Link to this heading">#</a></h2>
 <p>The skewed chi squared kernel is given by:</p>
 <div class="math notranslate nohighlight">
 \[k(x,y) = \prod_i \frac{2\sqrt{x_i+c}\sqrt{y_i+c}}{x_i + y_i + 2c}\]</div>
@@ -851,7 +852,7 @@
 For a motivation for this mapping and the mathematical details see <a class="reference internal" href="#ls2010" id="id8"><span>[LS2010]</span></a>.</p>
 </section>
 <section id="polynomial-kernel-approximation-via-tensor-sketch">
-<span id="polynomial-kernel-approx"></span><h2><span class="section-number">6.7.5. </span>Polynomial Kernel Approximation via Tensor Sketch<a class="headerlink" href="#polynomial-kernel-approximation-via-tensor-sketch" title="Link to this heading">#</a></h2>
+<span id="polynomial-kernel-approx"></span><h2><span class="section-number">7.7.5. </span>Polynomial Kernel Approximation via Tensor Sketch<a class="headerlink" href="#polynomial-kernel-approximation-via-tensor-sketch" title="Link to this heading">#</a></h2>
 <p>The <a class="reference internal" href="http://github.com/metrics.html#polynomial-kernel"><span class="std std-ref">polynomial kernel</span></a> is a popular type of kernel
 function given by:</p>
 <div class="math notranslate nohighlight">
@@ -887,7 +888,7 @@
 </ul>
 </section>
 <section id="mathematical-details">
-<span id="tensor-sketch-kernel-approx"></span><h2><span class="section-number">6.7.6. </span>Mathematical Details<a class="headerlink" href="#mathematical-details" title="Link to this heading">#</a></h2>
+<span id="tensor-sketch-kernel-approx"></span><h2><span class="section-number">7.7.6. </span>Mathematical Details<a class="headerlink" href="#mathematical-details" title="Link to this heading">#</a></h2>
 <p>Kernel methods like support vector machines or kernelized
 PCA rely on a property of reproducing kernel Hilbert spaces.
 For any positive definite kernel function <span class="math notranslate nohighlight">\(k\)</span> (a so called Mercer kernel),
@@ -977,7 +978,7 @@
       <i class="fa-solid fa-angle-left"></i>
       <div class="prev-next-info">
         <p class="prev-next-subtitle">previous</p>
-        <p class="prev-next-title"><span class="section-number">6.6. </span>Random Projection</p>
+        <p class="prev-next-title"><span class="section-number">7.6. </span>Random Projection</p>
       </div>
     </a>
     <a class="right-next"
@@ -985,7 +986,7 @@
        title="next page">
       <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">6.8. </span>Pairwise metrics, Affinities and Kernels</p>
+        <p class="prev-next-title"><span class="section-number">7.8. </span>Pairwise metrics, Affinities and Kernels</p>
       </div>
       <i class="fa-solid fa-angle-right"></i>
     </a>
@@ -1012,12 +1013,12 @@
   </div>
   <nav class="bd-toc-nav page-toc" aria-labelledby="pst-page-navigation-heading-2">
     <ul class="visible nav section-nav flex-column">
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#nystroem-method-for-kernel-approximation">6.7.1. Nystroem Method for Kernel Approximation</a></li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#radial-basis-function-kernel">6.7.2. Radial Basis Function Kernel</a></li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#additive-chi-squared-kernel">6.7.3. Additive Chi Squared Kernel</a></li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#skewed-chi-squared-kernel">6.7.4. Skewed Chi Squared Kernel</a></li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#polynomial-kernel-approximation-via-tensor-sketch">6.7.5. Polynomial Kernel Approximation via Tensor Sketch</a></li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#mathematical-details">6.7.6. Mathematical Details</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#nystroem-method-for-kernel-approximation">7.7.1. Nystroem Method for Kernel Approximation</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#radial-basis-function-kernel">7.7.2. Radial Basis Function Kernel</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#additive-chi-squared-kernel">7.7.3. Additive Chi Squared Kernel</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#skewed-chi-squared-kernel">7.7.4. Skewed Chi Squared Kernel</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#polynomial-kernel-approximation-via-tensor-sketch">7.7.5. Polynomial Kernel Approximation via Tensor Sketch</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#mathematical-details">7.7.6. Mathematical Details</a></li>
 </ul>
   </nav></div>
 
diff --git a/dev/modules/kernel_ridge.html b/dev/modules/kernel_ridge.html
index 6410552cc5ca5..6dda6211c0aa1 100644
--- a/dev/modules/kernel_ridge.html
+++ b/dev/modules/kernel_ridge.html
@@ -42,7 +42,7 @@
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     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
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@@ -594,45 +594,46 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/learning_curve.html">3.5. Validation curves: plotting scores to evaluate models</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/inspection.html">4. Inspection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/partial_dependence.html">4.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/permutation_importance.html">4.2. Permutation feature importance</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/metadata_routing.html">4. Metadata Routing</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/inspection.html">5. Inspection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/partial_dependence.html">5.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/permutation_importance.html">5.2. Permutation feature importance</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/visualizations.html">5. Visualizations</a></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/data_transforms.html">6. Dataset transformations</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/compose.html">6.1. Pipelines and composite estimators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/feature_extraction.html">6.2. Feature extraction</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="http://github.com/visualizations.html">6. Visualizations</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/data_transforms.html">7. Dataset transformations</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/compose.html">7.1. Pipelines and composite estimators</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="http://github.com/impute.html">7.4. Imputation of missing values</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/unsupervised_reduction.html">7.5. Unsupervised dimensionality reduction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/random_projection.html">7.6. Random Projection</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/kernel_approximation.html">7.7. Kernel Approximation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/metrics.html">7.8. Pairwise metrics, Affinities and Kernels</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing_targets.html">7.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
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-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/datasets.html">7. Dataset loading utilities</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html">7.1. Toy datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html">7.2. Real world datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html">7.3. Generated datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html">7.4. Loading other datasets</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/datasets.html">8. Dataset loading utilities</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html">8.1. Toy datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html">8.2. Real world datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html">8.3. Generated datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html">8.4. Loading other datasets</a></li>
 </ul>
 </details></li>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/computational_performance.html">8.2. Computational Performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/parallelism.html">8.3. Parallelism, resource management, and configuration</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/computing.html">9. Computing with scikit-learn</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/scaling_strategies.html">9.1. Strategies to scale computationally: bigger data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/computational_performance.html">9.2. Computational Performance</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/parallelism.html">9.3. Parallelism, resource management, and configuration</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/array_api.html">11.1. Array API support (experimental)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/model_persistence.html">10. Model persistence</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/common_pitfalls.html">11. Common pitfalls and recommended practices</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/dispatching.html">12. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/array_api.html">12.1. Array API support (experimental)</a></li>
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 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/machine_learning_map.html">12. Choosing the right estimator</a></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/presentations.html">13. External Resources, Videos and Talks</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/machine_learning_map.html">13. Choosing the right estimator</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/presentations.html">14. External Resources, Videos and Talks</a></li>
 </ul>
 </div>
 </nav></div>
diff --git a/dev/modules/lda_qda.html b/dev/modules/lda_qda.html
index 22f580615e1a4..863639f67c1b0 100644
--- a/dev/modules/lda_qda.html
+++ b/dev/modules/lda_qda.html
@@ -42,7 +42,7 @@
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-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
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@@ -594,45 +594,46 @@
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 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/inspection.html">4. Inspection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/partial_dependence.html">4.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/permutation_importance.html">4.2. Permutation feature importance</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/metadata_routing.html">4. Metadata Routing</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/inspection.html">5. Inspection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/partial_dependence.html">5.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/permutation_importance.html">5.2. Permutation feature importance</a></li>
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-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/data_transforms.html">6. Dataset transformations</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/compose.html">6.1. Pipelines and composite estimators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/feature_extraction.html">6.2. Feature extraction</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing_targets.html">6.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/visualizations.html">6. Visualizations</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/data_transforms.html">7. Dataset transformations</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/compose.html">7.1. Pipelines and composite estimators</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing.html">7.3. Preprocessing data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/impute.html">7.4. Imputation of missing values</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/unsupervised_reduction.html">7.5. Unsupervised dimensionality reduction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/random_projection.html">7.6. Random Projection</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/kernel_approximation.html">7.7. Kernel Approximation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/metrics.html">7.8. Pairwise metrics, Affinities and Kernels</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing_targets.html">7.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/datasets.html">7. Dataset loading utilities</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html">7.1. Toy datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html">7.2. Real world datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html">7.3. Generated datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html">7.4. Loading other datasets</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/datasets.html">8. Dataset loading utilities</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html">8.1. Toy datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html">8.2. Real world datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html">8.3. Generated datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html">8.4. Loading other datasets</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/computing.html">8. Computing with scikit-learn</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/scaling_strategies.html">8.1. Strategies to scale computationally: bigger data</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/computational_performance.html">8.2. Computational Performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/parallelism.html">8.3. Parallelism, resource management, and configuration</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/computing.html">9. Computing with scikit-learn</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/scaling_strategies.html">9.1. Strategies to scale computationally: bigger data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/computational_performance.html">9.2. Computational Performance</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/parallelism.html">9.3. Parallelism, resource management, and configuration</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/model_persistence.html">9. Model persistence</a></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/common_pitfalls.html">10. Common pitfalls and recommended practices</a></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/dispatching.html">11. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/array_api.html">11.1. Array API support (experimental)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/model_persistence.html">10. Model persistence</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/common_pitfalls.html">11. Common pitfalls and recommended practices</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/dispatching.html">12. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/array_api.html">12.1. Array API support (experimental)</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/machine_learning_map.html">12. Choosing the right estimator</a></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/presentations.html">13. External Resources, Videos and Talks</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/machine_learning_map.html">13. Choosing the right estimator</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/presentations.html">14. External Resources, Videos and Talks</a></li>
 </ul>
 </div>
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diff --git a/dev/modules/learning_curve.html b/dev/modules/learning_curve.html
index 35f86bd70bd48..77982e7293909 100644
--- a/dev/modules/learning_curve.html
+++ b/dev/modules/learning_curve.html
@@ -42,7 +42,7 @@
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     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
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@@ -81,7 +81,7 @@
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     <link rel="author" title="About these documents" href="http://github.com/about.html" />
     <link rel="search" title="Search" href="http://github.com/search.html" />
-    <link rel="next" title="4. Inspection" href="http://github.com/inspection.html" />
+    <link rel="next" title="4. Metadata Routing" href="http://github.com/metadata_routing.html" />
     <link rel="prev" title="3.4. Metrics and scoring: quantifying the quality of predictions" href="http://github.com/model_evaluation.html" />
   <meta name="viewport" content="width=device-width, initial-scale=1"/>
   <meta name="docsearch:language" content="en"/>
@@ -594,45 +594,46 @@
 <li class="toctree-l2 current active"><a class="current reference internal" href="#">3.5. Validation curves: plotting scores to evaluate models</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/inspection.html">4. Inspection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/partial_dependence.html">4.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/permutation_importance.html">4.2. Permutation feature importance</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/metadata_routing.html">4. Metadata Routing</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/inspection.html">5. Inspection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/partial_dependence.html">5.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/permutation_importance.html">5.2. Permutation feature importance</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/visualizations.html">5. Visualizations</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/compose.html">6.1. Pipelines and composite estimators</a></li>
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+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/data_transforms.html">7. Dataset transformations</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/compose.html">7.1. Pipelines and composite estimators</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="http://github.com/unsupervised_reduction.html">7.5. Unsupervised dimensionality reduction</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="http://github.com/kernel_approximation.html">7.7. Kernel Approximation</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing_targets.html">7.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
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 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/datasets.html">7. Dataset loading utilities</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html">7.3. Generated datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html">7.4. Loading other datasets</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/datasets.html">8. Dataset loading utilities</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html">8.1. Toy datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html">8.2. Real world datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html">8.3. Generated datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html">8.4. Loading other datasets</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/parallelism.html">8.3. Parallelism, resource management, and configuration</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/computing.html">9. Computing with scikit-learn</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/scaling_strategies.html">9.1. Strategies to scale computationally: bigger data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/computational_performance.html">9.2. Computational Performance</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/parallelism.html">9.3. Parallelism, resource management, and configuration</a></li>
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-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/dispatching.html">11. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/array_api.html">11.1. Array API support (experimental)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/model_persistence.html">10. Model persistence</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/common_pitfalls.html">11. Common pitfalls and recommended practices</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/dispatching.html">12. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/array_api.html">12.1. Array API support (experimental)</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="http://github.com/presentations.html">13. External Resources, Videos and Talks</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/machine_learning_map.html">13. Choosing the right estimator</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/presentations.html">14. External Resources, Videos and Talks</a></li>
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@@ -755,13 +756,13 @@
 <span class="gp">... </span>    <span class="n">SVC</span><span class="p">(</span><span class="n">kernel</span><span class="o">=</span><span class="s2">&quot;linear&quot;</span><span class="p">),</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">param_name</span><span class="o">=</span><span class="s2">&quot;C&quot;</span><span class="p">,</span> <span class="n">param_range</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">logspace</span><span class="p">(</span><span class="o">-</span><span class="mi">7</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span>
 <span class="gp">... </span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">train_scores</span>
-<span class="go">array([[0.90..., 0.94..., 0.91..., 0.89..., 0.92...],</span>
-<span class="go">       [0.9... , 0.92..., 0.93..., 0.92..., 0.93...],</span>
-<span class="go">       [0.97..., 1...   , 0.98..., 0.97..., 0.99...]])</span>
+<span class="go">array([[0.90, 0.94, 0.91, 0.89, 0.92],</span>
+<span class="go">       [0.9 , 0.92, 0.93, 0.92, 0.93],</span>
+<span class="go">       [0.97, 1   , 0.98, 0.97, 0.99]])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">valid_scores</span>
-<span class="go">array([[0.9..., 0.9... , 0.9... , 0.96..., 0.9... ],</span>
-<span class="go">       [0.9..., 0.83..., 0.96..., 0.96..., 0.93...],</span>
-<span class="go">       [1.... , 0.93..., 1....  , 1....  , 0.9... ]])</span>
+<span class="go">array([[0.9, 0.9 , 0.9 , 0.96, 0.9 ],</span>
+<span class="go">       [0.9, 0.83, 0.96, 0.96, 0.93],</span>
+<span class="go">       [1. , 0.93, 1   , 1   , 0.9 ]])</span>
 </pre></div>
 </div>
 <p>If you intend to plot the validation curves only, the class
@@ -818,13 +819,13 @@
 <span class="gp">&gt;&gt;&gt; </span><span class="n">train_sizes</span>
 <span class="go">array([ 50, 80, 110])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">train_scores</span>
-<span class="go">array([[0.98..., 0.98 , 0.98..., 0.98..., 0.98...],</span>
-<span class="go">       [0.98..., 1.   , 0.98..., 0.98..., 0.98...],</span>
-<span class="go">       [0.98..., 1.   , 0.98..., 0.98..., 0.99...]])</span>
+<span class="go">array([[0.98, 0.98 , 0.98, 0.98, 0.98],</span>
+<span class="go">       [0.98, 1.   , 0.98, 0.98, 0.98],</span>
+<span class="go">       [0.98, 1.   , 0.98, 0.98, 0.99]])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">valid_scores</span>
-<span class="go">array([[1. ,  0.93...,  1. ,  1. ,  0.96...],</span>
-<span class="go">       [1. ,  0.96...,  1. ,  1. ,  0.96...],</span>
-<span class="go">       [1. ,  0.96...,  1. ,  1. ,  0.96...]])</span>
+<span class="go">array([[1. ,  0.93,  1. ,  1. ,  0.96],</span>
+<span class="go">       [1. ,  0.96,  1. ,  1. ,  0.96],</span>
+<span class="go">       [1. ,  0.96,  1. ,  1. ,  0.96]])</span>
 </pre></div>
 </div>
 <p>If you intend to plot the learning curves only, the class
@@ -873,11 +874,11 @@
       </div>
     </a>
     <a class="right-next"
-       href="http://github.com/inspection.html"
+       href="http://github.com/metadata_routing.html"
        title="next page">
       <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">4. </span>Inspection</p>
+        <p class="prev-next-title"><span class="section-number">4. </span>Metadata Routing</p>
       </div>
       <i class="fa-solid fa-angle-right"></i>
     </a>
diff --git a/dev/modules/linear_model.html b/dev/modules/linear_model.html
index 12008716bb642..39a8f5deff5ba 100644
--- a/dev/modules/linear_model.html
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-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
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     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -594,45 +594,46 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/learning_curve.html">3.5. Validation curves: plotting scores to evaluate models</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/inspection.html">4. Inspection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/partial_dependence.html">4.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/permutation_importance.html">4.2. Permutation feature importance</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/metadata_routing.html">4. Metadata Routing</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/inspection.html">5. Inspection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/partial_dependence.html">5.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/permutation_importance.html">5.2. Permutation feature importance</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/visualizations.html">5. Visualizations</a></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/data_transforms.html">6. Dataset transformations</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/compose.html">6.1. Pipelines and composite estimators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/feature_extraction.html">6.2. Feature extraction</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing.html">6.3. Preprocessing data</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/impute.html">6.4. Imputation of missing values</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/unsupervised_reduction.html">6.5. Unsupervised dimensionality reduction</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/random_projection.html">6.6. Random Projection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/kernel_approximation.html">6.7. Kernel Approximation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/metrics.html">6.8. Pairwise metrics, Affinities and Kernels</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing_targets.html">6.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/visualizations.html">6. Visualizations</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/data_transforms.html">7. Dataset transformations</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/compose.html">7.1. Pipelines and composite estimators</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/feature_extraction.html">7.2. Feature extraction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing.html">7.3. Preprocessing data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/impute.html">7.4. Imputation of missing values</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/unsupervised_reduction.html">7.5. Unsupervised dimensionality reduction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/random_projection.html">7.6. Random Projection</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/kernel_approximation.html">7.7. Kernel Approximation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/metrics.html">7.8. Pairwise metrics, Affinities and Kernels</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing_targets.html">7.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/datasets.html">7. Dataset loading utilities</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html">7.1. Toy datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html">7.2. Real world datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html">7.3. Generated datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html">7.4. Loading other datasets</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/datasets.html">8. Dataset loading utilities</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html">8.1. Toy datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html">8.2. Real world datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html">8.3. Generated datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html">8.4. Loading other datasets</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/computing.html">8. Computing with scikit-learn</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/scaling_strategies.html">8.1. Strategies to scale computationally: bigger data</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/computational_performance.html">8.2. Computational Performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/parallelism.html">8.3. Parallelism, resource management, and configuration</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/computing.html">9. Computing with scikit-learn</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/scaling_strategies.html">9.1. Strategies to scale computationally: bigger data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/computational_performance.html">9.2. Computational Performance</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/parallelism.html">9.3. Parallelism, resource management, and configuration</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/model_persistence.html">9. Model persistence</a></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/common_pitfalls.html">10. Common pitfalls and recommended practices</a></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/dispatching.html">11. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/array_api.html">11.1. Array API support (experimental)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/model_persistence.html">10. Model persistence</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/common_pitfalls.html">11. Common pitfalls and recommended practices</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/dispatching.html">12. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/array_api.html">12.1. Array API support (experimental)</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/machine_learning_map.html">12. Choosing the right estimator</a></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/presentations.html">13. External Resources, Videos and Talks</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/machine_learning_map.html">13. Choosing the right estimator</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/presentations.html">14. External Resources, Videos and Talks</a></li>
 </ul>
 </div>
 </nav></div>
@@ -787,7 +788,7 @@ <h3><span class="section-number">1.1.2.1. </span>Regression<a class="headerlink"
 <span class="gp">&gt;&gt;&gt; </span><span class="n">reg</span><span class="o">.</span><span class="n">coef_</span>
 <span class="go">array([0.34545455, 0.34545455])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">reg</span><span class="o">.</span><span class="n">intercept_</span>
-<span class="go">np.float64(0.13636...)</span>
+<span class="go">np.float64(0.13636)</span>
 </pre></div>
 </div>
 <p>Note that the class <a class="reference internal" href="http://github.com/generated/sklearn.linear_model.Ridge.html#sklearn.linear_model.Ridge" title="sklearn.linear_model.Ridge"><code class="xref py py-class docutils literal notranslate"><span class="pre">Ridge</span></code></a> allows for the user to specify that the
@@ -1202,7 +1203,7 @@ <h2><span class="section-number">1.1.8. </span>LARS Lasso<a class="headerlink" h
 <span class="gp">&gt;&gt;&gt; </span><span class="n">reg</span><span class="o">.</span><span class="n">fit</span><span class="p">([[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>
 <span class="go">LassoLars(alpha=0.1)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">reg</span><span class="o">.</span><span class="n">coef_</span>
-<span class="go">array([0.6..., 0.        ])</span>
+<span class="go">array([0.6, 0.        ])</span>
 </pre></div>
 </div>
 <p class="rubric">Examples</p>
@@ -1916,9 +1917,9 @@ <h3><span class="section-number">1.1.12.1. </span>Usage<a class="headerlink" hre
 <span class="gp">&gt;&gt;&gt; </span><span class="n">reg</span><span class="o">.</span><span class="n">fit</span><span class="p">([[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">])</span>
 <span class="go">TweedieRegressor(alpha=0.5, link=&#39;log&#39;, power=1)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">reg</span><span class="o">.</span><span class="n">coef_</span>
-<span class="go">array([0.2463..., 0.4337...])</span>
+<span class="go">array([0.2463, 0.4337])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">reg</span><span class="o">.</span><span class="n">intercept_</span>
-<span class="go">np.float64(-0.7638...)</span>
+<span class="go">np.float64(-0.7638)</span>
 </pre></div>
 </div>
 <p class="rubric">Examples</p>
diff --git a/dev/modules/manifold.html b/dev/modules/manifold.html
index bb2f17945167f..d7c7a1890b0cc 100644
--- a/dev/modules/manifold.html
+++ b/dev/modules/manifold.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -594,45 +594,46 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/learning_curve.html">3.5. Validation curves: plotting scores to evaluate models</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/inspection.html">4. Inspection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/partial_dependence.html">4.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/permutation_importance.html">4.2. Permutation feature importance</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/metadata_routing.html">4. Metadata Routing</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/inspection.html">5. Inspection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/partial_dependence.html">5.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/permutation_importance.html">5.2. Permutation feature importance</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/visualizations.html">5. Visualizations</a></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/data_transforms.html">6. Dataset transformations</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/compose.html">6.1. Pipelines and composite estimators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/feature_extraction.html">6.2. Feature extraction</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing.html">6.3. Preprocessing data</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/impute.html">6.4. Imputation of missing values</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/unsupervised_reduction.html">6.5. Unsupervised dimensionality reduction</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/random_projection.html">6.6. Random Projection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/kernel_approximation.html">6.7. Kernel Approximation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/metrics.html">6.8. Pairwise metrics, Affinities and Kernels</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing_targets.html">6.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/visualizations.html">6. Visualizations</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/data_transforms.html">7. Dataset transformations</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/compose.html">7.1. Pipelines and composite estimators</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/feature_extraction.html">7.2. Feature extraction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing.html">7.3. Preprocessing data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/impute.html">7.4. Imputation of missing values</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/unsupervised_reduction.html">7.5. Unsupervised dimensionality reduction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/random_projection.html">7.6. Random Projection</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/kernel_approximation.html">7.7. Kernel Approximation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/metrics.html">7.8. Pairwise metrics, Affinities and Kernels</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing_targets.html">7.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/datasets.html">7. Dataset loading utilities</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html">7.1. Toy datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html">7.2. Real world datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html">7.3. Generated datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html">7.4. Loading other datasets</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/datasets.html">8. Dataset loading utilities</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html">8.1. Toy datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html">8.2. Real world datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html">8.3. Generated datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html">8.4. Loading other datasets</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/computing.html">8. Computing with scikit-learn</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/scaling_strategies.html">8.1. Strategies to scale computationally: bigger data</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/computational_performance.html">8.2. Computational Performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/parallelism.html">8.3. Parallelism, resource management, and configuration</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/computing.html">9. Computing with scikit-learn</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/scaling_strategies.html">9.1. Strategies to scale computationally: bigger data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/computational_performance.html">9.2. Computational Performance</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/parallelism.html">9.3. Parallelism, resource management, and configuration</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/model_persistence.html">9. Model persistence</a></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/common_pitfalls.html">10. Common pitfalls and recommended practices</a></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/dispatching.html">11. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/array_api.html">11.1. Array API support (experimental)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/model_persistence.html">10. Model persistence</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/common_pitfalls.html">11. Common pitfalls and recommended practices</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/dispatching.html">12. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/array_api.html">12.1. Array API support (experimental)</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/machine_learning_map.html">12. Choosing the right estimator</a></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/presentations.html">13. External Resources, Videos and Talks</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/machine_learning_map.html">13. Choosing the right estimator</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/presentations.html">14. External Resources, Videos and Talks</a></li>
 </ul>
 </div>
 </nav></div>
@@ -746,6 +747,8 @@ <h2><span class="section-number">2.2.1. </span>Introduction<a class="headerlink"
 <li><p>See <a class="reference internal" href="http://github.com/auto_examples/applications/plot_stock_market.html#sphx-glr-auto-examples-applications-plot-stock-market-py"><span class="std std-ref">Visualizing the stock market structure</span></a> for an example of
 using manifold learning to map the stock market structure based on historical stock
 prices.</p></li>
+<li><p>See <a class="reference internal" href="http://github.com/auto_examples/manifold/plot_manifold_sphere.html#sphx-glr-auto-examples-manifold-plot-manifold-sphere-py"><span class="std std-ref">Manifold Learning methods on a severed sphere</span></a> for an example of
+manifold learning techniques applied to a spherical data-set.</p></li>
 </ul>
 <p>The manifold learning implementations available in scikit-learn are
 summarized below</p>
diff --git a/dev/modules/metrics.html b/dev/modules/metrics.html
index 0b00b80ce5644..043a569f78c1d 100644
--- a/dev/modules/metrics.html
+++ b/dev/modules/metrics.html
@@ -7,7 +7,7 @@
   <head>
     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="viewport" content="width=device-width, initial-scale=1" />
-<meta property="og:title" content="6.8. Pairwise metrics, Affinities and Kernels" />
+<meta property="og:title" content="7.8. Pairwise metrics, Affinities and Kernels" />
 <meta property="og:type" content="website" />
 <meta property="og:url" content="https://scikit-learn/stable/modules/metrics.html" />
 <meta property="og:site_name" content="scikit-learn" />
@@ -16,7 +16,7 @@
 <meta property="og:image:alt" content="scikit-learn" />
 <meta name="description" content="The sklearn.metrics.pairwise submodule implements utilities to evaluate pairwise distances or affinity of sets of samples. This module contains both distance metrics and kernels. A brief summary is..." />
 
-    <title>6.8. Pairwise metrics, Affinities and Kernels &#8212; scikit-learn 1.7.dev0 documentation</title>
+    <title>7.8. Pairwise metrics, Affinities and Kernels &#8212; scikit-learn 1.7.dev0 documentation</title>
   
   
   
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -81,8 +81,8 @@
     <link rel="icon" href="http://github.com/_static/favicon.ico"/>
     <link rel="author" title="About these documents" href="http://github.com/about.html" />
     <link rel="search" title="Search" href="http://github.com/search.html" />
-    <link rel="next" title="6.9. Transforming the prediction target (y)" href="http://github.com/preprocessing_targets.html" />
-    <link rel="prev" title="6.7. Kernel Approximation" href="http://github.com/kernel_approximation.html" />
+    <link rel="next" title="7.9. Transforming the prediction target (y)" href="http://github.com/preprocessing_targets.html" />
+    <link rel="prev" title="7.7. Kernel Approximation" href="http://github.com/kernel_approximation.html" />
   <meta name="viewport" content="width=device-width, initial-scale=1"/>
   <meta name="docsearch:language" content="en"/>
   <meta name="docsearch:version" content="1.7" />
@@ -594,45 +594,46 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/learning_curve.html">3.5. Validation curves: plotting scores to evaluate models</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/inspection.html">4. Inspection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/partial_dependence.html">4.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/permutation_importance.html">4.2. Permutation feature importance</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/metadata_routing.html">4. Metadata Routing</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/inspection.html">5. Inspection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/partial_dependence.html">5.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/permutation_importance.html">5.2. Permutation feature importance</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/visualizations.html">5. Visualizations</a></li>
-<li class="toctree-l1 current active has-children"><a class="reference internal" href="http://github.com/data_transforms.html">6. Dataset transformations</a><details open="open"><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul class="current">
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/compose.html">6.1. Pipelines and composite estimators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/feature_extraction.html">6.2. Feature extraction</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing.html">6.3. Preprocessing data</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/impute.html">6.4. Imputation of missing values</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/unsupervised_reduction.html">6.5. Unsupervised dimensionality reduction</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/random_projection.html">6.6. Random Projection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/kernel_approximation.html">6.7. Kernel Approximation</a></li>
-<li class="toctree-l2 current active"><a class="current reference internal" href="#">6.8. Pairwise metrics, Affinities and Kernels</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing_targets.html">6.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/visualizations.html">6. Visualizations</a></li>
+<li class="toctree-l1 current active has-children"><a class="reference internal" href="http://github.com/data_transforms.html">7. Dataset transformations</a><details open="open"><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/compose.html">7.1. Pipelines and composite estimators</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/feature_extraction.html">7.2. Feature extraction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing.html">7.3. Preprocessing data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/impute.html">7.4. Imputation of missing values</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/unsupervised_reduction.html">7.5. Unsupervised dimensionality reduction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/random_projection.html">7.6. Random Projection</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/kernel_approximation.html">7.7. Kernel Approximation</a></li>
+<li class="toctree-l2 current active"><a class="current reference internal" href="#">7.8. Pairwise metrics, Affinities and Kernels</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing_targets.html">7.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/datasets.html">7. Dataset loading utilities</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html">7.1. Toy datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html">7.2. Real world datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html">7.3. Generated datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html">7.4. Loading other datasets</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/datasets.html">8. Dataset loading utilities</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html">8.1. Toy datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html">8.2. Real world datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html">8.3. Generated datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html">8.4. Loading other datasets</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/computing.html">8. Computing with scikit-learn</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/scaling_strategies.html">8.1. Strategies to scale computationally: bigger data</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/computational_performance.html">8.2. Computational Performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/parallelism.html">8.3. Parallelism, resource management, and configuration</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/computing.html">9. Computing with scikit-learn</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/scaling_strategies.html">9.1. Strategies to scale computationally: bigger data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/computational_performance.html">9.2. Computational Performance</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/parallelism.html">9.3. Parallelism, resource management, and configuration</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/model_persistence.html">9. Model persistence</a></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/common_pitfalls.html">10. Common pitfalls and recommended practices</a></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/dispatching.html">11. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/array_api.html">11.1. Array API support (experimental)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/model_persistence.html">10. Model persistence</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/common_pitfalls.html">11. Common pitfalls and recommended practices</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/dispatching.html">12. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/array_api.html">12.1. Array API support (experimental)</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/machine_learning_map.html">12. Choosing the right estimator</a></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/presentations.html">13. External Resources, Videos and Talks</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/machine_learning_map.html">13. Choosing the right estimator</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/presentations.html">14. External Resources, Videos and Talks</a></li>
 </ul>
 </div>
 </nav></div>
@@ -670,9 +671,9 @@
     <li class="breadcrumb-item"><a href="http://github.com/user_guide.html" class="nav-link">User Guide</a></li>
     
     
-    <li class="breadcrumb-item"><a href="http://github.com/data_transforms.html" class="nav-link"><span class="section-number">6. </span>Dataset transformations</a></li>
+    <li class="breadcrumb-item"><a href="http://github.com/data_transforms.html" class="nav-link"><span class="section-number">7. </span>Dataset transformations</a></li>
     
-    <li class="breadcrumb-item active" aria-current="page"><span class="ellipsis"><span class="section-number">6.8. </span>Pairwise metrics, Affinities and Kernels</span></li>
+    <li class="breadcrumb-item active" aria-current="page"><span class="ellipsis"><span class="section-number">7.8. </span>Pairwise metrics, Affinities and Kernels</span></li>
   </ul>
 </nav>
 </div>
@@ -690,7 +691,7 @@
                 <article class="bd-article">
                   
   <section id="pairwise-metrics-affinities-and-kernels">
-<span id="metrics"></span><h1><span class="section-number">6.8. </span>Pairwise metrics, Affinities and Kernels<a class="headerlink" href="#pairwise-metrics-affinities-and-kernels" title="Link to this heading">#</a></h1>
+<span id="metrics"></span><h1><span class="section-number">7.8. </span>Pairwise metrics, Affinities and Kernels<a class="headerlink" href="#pairwise-metrics-affinities-and-kernels" title="Link to this heading">#</a></h1>
 <p>The <a class="reference internal" href="http://github.com/api/sklearn.metrics.html#module-sklearn.metrics.pairwise" title="sklearn.metrics.pairwise"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.metrics.pairwise</span></code></a> submodule implements utilities to evaluate
 pairwise distances or affinity of sets of samples.</p>
 <p>This module contains both distance metrics and kernels. A brief summary is
@@ -746,7 +747,7 @@
 </pre></div>
 </div>
 <section id="cosine-similarity">
-<span id="id1"></span><h2><span class="section-number">6.8.1. </span>Cosine similarity<a class="headerlink" href="#cosine-similarity" title="Link to this heading">#</a></h2>
+<span id="id1"></span><h2><span class="section-number">7.8.1. </span>Cosine similarity<a class="headerlink" href="#cosine-similarity" title="Link to this heading">#</a></h2>
 <p><a class="reference internal" href="http://github.com/generated/sklearn.metrics.pairwise.cosine_similarity.html#sklearn.metrics.pairwise.cosine_similarity" title="sklearn.metrics.pairwise.cosine_similarity"><code class="xref py py-func docutils literal notranslate"><span class="pre">cosine_similarity</span></code></a> computes the L2-normalized dot product of vectors.
 That is, if <span class="math notranslate nohighlight">\(x\)</span> and <span class="math notranslate nohighlight">\(y\)</span> are row vectors,
 their cosine similarity <span class="math notranslate nohighlight">\(k\)</span> is defined as:</p>
@@ -770,7 +771,7 @@
 </ul>
 </section>
 <section id="linear-kernel">
-<span id="id2"></span><h2><span class="section-number">6.8.2. </span>Linear kernel<a class="headerlink" href="#linear-kernel" title="Link to this heading">#</a></h2>
+<span id="id2"></span><h2><span class="section-number">7.8.2. </span>Linear kernel<a class="headerlink" href="#linear-kernel" title="Link to this heading">#</a></h2>
 <p>The function <a class="reference internal" href="http://github.com/generated/sklearn.metrics.pairwise.linear_kernel.html#sklearn.metrics.pairwise.linear_kernel" title="sklearn.metrics.pairwise.linear_kernel"><code class="xref py py-func docutils literal notranslate"><span class="pre">linear_kernel</span></code></a> computes the linear kernel, that is, a
 special case of <a class="reference internal" href="http://github.com/generated/sklearn.metrics.pairwise.polynomial_kernel.html#sklearn.metrics.pairwise.polynomial_kernel" title="sklearn.metrics.pairwise.polynomial_kernel"><code class="xref py py-func docutils literal notranslate"><span class="pre">polynomial_kernel</span></code></a> with <code class="docutils literal notranslate"><span class="pre">degree=1</span></code> and <code class="docutils literal notranslate"><span class="pre">coef0=0</span></code> (homogeneous).
 If <code class="docutils literal notranslate"><span class="pre">x</span></code> and <code class="docutils literal notranslate"><span class="pre">y</span></code> are column vectors, their linear kernel is:</p>
@@ -778,7 +779,7 @@
 \[k(x, y) = x^\top y\]</div>
 </section>
 <section id="polynomial-kernel">
-<span id="id3"></span><h2><span class="section-number">6.8.3. </span>Polynomial kernel<a class="headerlink" href="#polynomial-kernel" title="Link to this heading">#</a></h2>
+<span id="id3"></span><h2><span class="section-number">7.8.3. </span>Polynomial kernel<a class="headerlink" href="#polynomial-kernel" title="Link to this heading">#</a></h2>
 <p>The function <a class="reference internal" href="http://github.com/generated/sklearn.metrics.pairwise.polynomial_kernel.html#sklearn.metrics.pairwise.polynomial_kernel" title="sklearn.metrics.pairwise.polynomial_kernel"><code class="xref py py-func docutils literal notranslate"><span class="pre">polynomial_kernel</span></code></a> computes the degree-d polynomial kernel
 between two vectors. The polynomial kernel represents the similarity between two
 vectors. Conceptually, the polynomial kernel considers not only the similarity
@@ -795,7 +796,7 @@
 <p>If <span class="math notranslate nohighlight">\(c_0 = 0\)</span> the kernel is said to be homogeneous.</p>
 </section>
 <section id="sigmoid-kernel">
-<span id="id4"></span><h2><span class="section-number">6.8.4. </span>Sigmoid kernel<a class="headerlink" href="#sigmoid-kernel" title="Link to this heading">#</a></h2>
+<span id="id4"></span><h2><span class="section-number">7.8.4. </span>Sigmoid kernel<a class="headerlink" href="#sigmoid-kernel" title="Link to this heading">#</a></h2>
 <p>The function <a class="reference internal" href="http://github.com/generated/sklearn.metrics.pairwise.sigmoid_kernel.html#sklearn.metrics.pairwise.sigmoid_kernel" title="sklearn.metrics.pairwise.sigmoid_kernel"><code class="xref py py-func docutils literal notranslate"><span class="pre">sigmoid_kernel</span></code></a> computes the sigmoid kernel between two
 vectors. The sigmoid kernel is also known as hyperbolic tangent, or Multilayer
 Perceptron (because, in the neural network field, it is often used as neuron
@@ -810,7 +811,7 @@
 </ul>
 </section>
 <section id="rbf-kernel">
-<span id="id5"></span><h2><span class="section-number">6.8.5. </span>RBF kernel<a class="headerlink" href="#rbf-kernel" title="Link to this heading">#</a></h2>
+<span id="id5"></span><h2><span class="section-number">7.8.5. </span>RBF kernel<a class="headerlink" href="#rbf-kernel" title="Link to this heading">#</a></h2>
 <p>The function <a class="reference internal" href="http://github.com/generated/sklearn.metrics.pairwise.rbf_kernel.html#sklearn.metrics.pairwise.rbf_kernel" title="sklearn.metrics.pairwise.rbf_kernel"><code class="xref py py-func docutils literal notranslate"><span class="pre">rbf_kernel</span></code></a> computes the radial basis function (RBF) kernel
 between two vectors. This kernel is defined as:</p>
 <div class="math notranslate nohighlight">
@@ -819,7 +820,7 @@
 the kernel is known as the Gaussian kernel of variance <span class="math notranslate nohighlight">\(\sigma^2\)</span>.</p>
 </section>
 <section id="laplacian-kernel">
-<span id="id6"></span><h2><span class="section-number">6.8.6. </span>Laplacian kernel<a class="headerlink" href="#laplacian-kernel" title="Link to this heading">#</a></h2>
+<span id="id6"></span><h2><span class="section-number">7.8.6. </span>Laplacian kernel<a class="headerlink" href="#laplacian-kernel" title="Link to this heading">#</a></h2>
 <p>The function <a class="reference internal" href="http://github.com/generated/sklearn.metrics.pairwise.laplacian_kernel.html#sklearn.metrics.pairwise.laplacian_kernel" title="sklearn.metrics.pairwise.laplacian_kernel"><code class="xref py py-func docutils literal notranslate"><span class="pre">laplacian_kernel</span></code></a> is a variant on the radial basis
 function kernel defined as:</p>
 <div class="math notranslate nohighlight">
@@ -830,7 +831,7 @@
 See e.g. <a class="reference external" href="https://onlinelibrary.wiley.com/doi/10.1002/qua.24954/abstract/">Machine learning for quantum mechanics in a nutshell</a>.</p>
 </section>
 <section id="chi-squared-kernel">
-<span id="chi2-kernel"></span><h2><span class="section-number">6.8.7. </span>Chi-squared kernel<a class="headerlink" href="#chi-squared-kernel" title="Link to this heading">#</a></h2>
+<span id="chi2-kernel"></span><h2><span class="section-number">7.8.7. </span>Chi-squared kernel<a class="headerlink" href="#chi-squared-kernel" title="Link to this heading">#</a></h2>
 <p>The chi-squared kernel is a very popular choice for training non-linear SVMs in
 computer vision applications.
 It can be computed using <a class="reference internal" href="http://github.com/generated/sklearn.metrics.pairwise.chi2_kernel.html#sklearn.metrics.pairwise.chi2_kernel" title="sklearn.metrics.pairwise.chi2_kernel"><code class="xref py py-func docutils literal notranslate"><span class="pre">chi2_kernel</span></code></a> and then passed to an
@@ -891,7 +892,7 @@
       <i class="fa-solid fa-angle-left"></i>
       <div class="prev-next-info">
         <p class="prev-next-subtitle">previous</p>
-        <p class="prev-next-title"><span class="section-number">6.7. </span>Kernel Approximation</p>
+        <p class="prev-next-title"><span class="section-number">7.7. </span>Kernel Approximation</p>
       </div>
     </a>
     <a class="right-next"
@@ -899,7 +900,7 @@
        title="next page">
       <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">6.9. </span>Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</p>
+        <p class="prev-next-title"><span class="section-number">7.9. </span>Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</p>
       </div>
       <i class="fa-solid fa-angle-right"></i>
     </a>
@@ -926,13 +927,13 @@
   </div>
   <nav class="bd-toc-nav page-toc" aria-labelledby="pst-page-navigation-heading-2">
     <ul class="visible nav section-nav flex-column">
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#cosine-similarity">6.8.1. Cosine similarity</a></li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#linear-kernel">6.8.2. Linear kernel</a></li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#polynomial-kernel">6.8.3. Polynomial kernel</a></li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#sigmoid-kernel">6.8.4. Sigmoid kernel</a></li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#rbf-kernel">6.8.5. RBF kernel</a></li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#laplacian-kernel">6.8.6. Laplacian kernel</a></li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#chi-squared-kernel">6.8.7. Chi-squared kernel</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#cosine-similarity">7.8.1. Cosine similarity</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#linear-kernel">7.8.2. Linear kernel</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#polynomial-kernel">7.8.3. Polynomial kernel</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#sigmoid-kernel">7.8.4. Sigmoid kernel</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#rbf-kernel">7.8.5. RBF kernel</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#laplacian-kernel">7.8.6. Laplacian kernel</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#chi-squared-kernel">7.8.7. Chi-squared kernel</a></li>
 </ul>
   </nav></div>
 
diff --git a/dev/modules/mixture.html b/dev/modules/mixture.html
index 278f5e646a329..7defd01e23952 100644
--- a/dev/modules/mixture.html
+++ b/dev/modules/mixture.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -593,45 +593,46 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/learning_curve.html">3.5. Validation curves: plotting scores to evaluate models</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/inspection.html">4. Inspection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/partial_dependence.html">4.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/permutation_importance.html">4.2. Permutation feature importance</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/metadata_routing.html">4. Metadata Routing</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/inspection.html">5. Inspection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/partial_dependence.html">5.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/permutation_importance.html">5.2. Permutation feature importance</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/visualizations.html">5. Visualizations</a></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/data_transforms.html">6. Dataset transformations</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/compose.html">6.1. Pipelines and composite estimators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/feature_extraction.html">6.2. Feature extraction</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing.html">6.3. Preprocessing data</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/impute.html">6.4. Imputation of missing values</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/unsupervised_reduction.html">6.5. Unsupervised dimensionality reduction</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/random_projection.html">6.6. Random Projection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/kernel_approximation.html">6.7. Kernel Approximation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/metrics.html">6.8. Pairwise metrics, Affinities and Kernels</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing_targets.html">6.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/visualizations.html">6. Visualizations</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/data_transforms.html">7. Dataset transformations</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/compose.html">7.1. Pipelines and composite estimators</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/feature_extraction.html">7.2. Feature extraction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing.html">7.3. Preprocessing data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/impute.html">7.4. Imputation of missing values</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/unsupervised_reduction.html">7.5. Unsupervised dimensionality reduction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/random_projection.html">7.6. Random Projection</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/kernel_approximation.html">7.7. Kernel Approximation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/metrics.html">7.8. Pairwise metrics, Affinities and Kernels</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing_targets.html">7.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/datasets.html">7. Dataset loading utilities</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html">7.1. Toy datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html">7.2. Real world datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html">7.3. Generated datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html">7.4. Loading other datasets</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/datasets.html">8. Dataset loading utilities</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html">8.1. Toy datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html">8.2. Real world datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html">8.3. Generated datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html">8.4. Loading other datasets</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/computing.html">8. Computing with scikit-learn</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/scaling_strategies.html">8.1. Strategies to scale computationally: bigger data</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/computational_performance.html">8.2. Computational Performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/parallelism.html">8.3. Parallelism, resource management, and configuration</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/computing.html">9. Computing with scikit-learn</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/scaling_strategies.html">9.1. Strategies to scale computationally: bigger data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/computational_performance.html">9.2. Computational Performance</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/parallelism.html">9.3. Parallelism, resource management, and configuration</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/model_persistence.html">9. Model persistence</a></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/common_pitfalls.html">10. Common pitfalls and recommended practices</a></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/dispatching.html">11. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/array_api.html">11.1. Array API support (experimental)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/model_persistence.html">10. Model persistence</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/common_pitfalls.html">11. Common pitfalls and recommended practices</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/dispatching.html">12. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/array_api.html">12.1. Array API support (experimental)</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/machine_learning_map.html">12. Choosing the right estimator</a></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/presentations.html">13. External Resources, Videos and Talks</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/machine_learning_map.html">13. Choosing the right estimator</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/presentations.html">14. External Resources, Videos and Talks</a></li>
 </ul>
 </div>
 </nav></div>
diff --git a/dev/modules/model_evaluation.html b/dev/modules/model_evaluation.html
index 8515fd35fdd5a..87176806568e2 100644
--- a/dev/modules/model_evaluation.html
+++ b/dev/modules/model_evaluation.html
@@ -42,7 +42,7 @@
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-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
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@@ -594,45 +594,46 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/learning_curve.html">3.5. Validation curves: plotting scores to evaluate models</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/inspection.html">4. Inspection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/partial_dependence.html">4.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/permutation_importance.html">4.2. Permutation feature importance</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/metadata_routing.html">4. Metadata Routing</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/inspection.html">5. Inspection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/partial_dependence.html">5.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/permutation_importance.html">5.2. Permutation feature importance</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/visualizations.html">5. Visualizations</a></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/data_transforms.html">6. Dataset transformations</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/compose.html">6.1. Pipelines and composite estimators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/feature_extraction.html">6.2. Feature extraction</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing.html">6.3. Preprocessing data</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/impute.html">6.4. Imputation of missing values</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/unsupervised_reduction.html">6.5. Unsupervised dimensionality reduction</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/random_projection.html">6.6. Random Projection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/kernel_approximation.html">6.7. Kernel Approximation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/metrics.html">6.8. Pairwise metrics, Affinities and Kernels</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing_targets.html">6.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/visualizations.html">6. Visualizations</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/data_transforms.html">7. Dataset transformations</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/compose.html">7.1. Pipelines and composite estimators</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/feature_extraction.html">7.2. Feature extraction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing.html">7.3. Preprocessing data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/impute.html">7.4. Imputation of missing values</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/unsupervised_reduction.html">7.5. Unsupervised dimensionality reduction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/random_projection.html">7.6. Random Projection</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/kernel_approximation.html">7.7. Kernel Approximation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/metrics.html">7.8. Pairwise metrics, Affinities and Kernels</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing_targets.html">7.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/datasets.html">7. Dataset loading utilities</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html">7.1. Toy datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html">7.2. Real world datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html">7.3. Generated datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html">7.4. Loading other datasets</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/datasets.html">8. Dataset loading utilities</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html">8.1. Toy datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html">8.2. Real world datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html">8.3. Generated datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html">8.4. Loading other datasets</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/computing.html">8. Computing with scikit-learn</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/scaling_strategies.html">8.1. Strategies to scale computationally: bigger data</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/computational_performance.html">8.2. Computational Performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/parallelism.html">8.3. Parallelism, resource management, and configuration</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/computing.html">9. Computing with scikit-learn</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/scaling_strategies.html">9.1. Strategies to scale computationally: bigger data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/computational_performance.html">9.2. Computational Performance</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/parallelism.html">9.3. Parallelism, resource management, and configuration</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/model_persistence.html">9. Model persistence</a></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/common_pitfalls.html">10. Common pitfalls and recommended practices</a></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/dispatching.html">11. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/array_api.html">11.1. Array API support (experimental)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/model_persistence.html">10. Model persistence</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/common_pitfalls.html">11. Common pitfalls and recommended practices</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/dispatching.html">12. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/array_api.html">12.1. Array API support (experimental)</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/machine_learning_map.html">12. Choosing the right estimator</a></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/presentations.html">13. External Resources, Videos and Talks</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/machine_learning_map.html">13. Choosing the right estimator</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/presentations.html">14. External Resources, Videos and Talks</a></li>
 </ul>
 </div>
 </nav></div>
@@ -1128,7 +1129,7 @@
 <span class="gp">&gt;&gt;&gt; </span><span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">datasets</span><span class="o">.</span><span class="n">load_iris</span><span class="p">(</span><span class="n">return_X_y</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">clf</span> <span class="o">=</span> <span class="n">svm</span><span class="o">.</span><span class="n">SVC</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">cross_val_score</span><span class="p">(</span><span class="n">clf</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">cv</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">scoring</span><span class="o">=</span><span class="s1">&#39;recall_macro&#39;</span><span class="p">)</span>
-<span class="go">array([0.96..., 0.96..., 0.96..., 0.93..., 1.        ])</span>
+<span class="go">array([0.96, 0.96, 0.96, 0.93, 1.        ])</span>
 </pre></div>
 </div>
 <div class="admonition note">
@@ -1261,9 +1262,9 @@
 <span class="gp">&gt;&gt;&gt; </span><span class="n">clf</span> <span class="o">=</span> <span class="n">DummyClassifier</span><span class="p">(</span><span class="n">strategy</span><span class="o">=</span><span class="s1">&#39;most_frequent&#39;</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">clf</span> <span class="o">=</span> <span class="n">clf</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">my_custom_loss_func</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">clf</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X</span><span class="p">))</span>
-<span class="go">0.69...</span>
+<span class="go">0.69</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">score</span><span class="p">(</span><span class="n">clf</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
-<span class="go">-0.69...</span>
+<span class="go">-0.69</span>
 </pre></div>
 </div>
 </div>
@@ -1381,7 +1382,7 @@
 <td><p>Compute binary classification positive and negative likelihood ratios.</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="reference internal" href="http://github.com/generated/sklearn.metrics.det_curve.html#sklearn.metrics.det_curve" title="sklearn.metrics.det_curve"><code class="xref py py-obj docutils literal notranslate"><span class="pre">det_curve</span></code></a>(y_true, y_score[, pos_label, ...])</p></td>
-<td><p>Compute error rates for different probability thresholds.</p></td>
+<td><p>Compute Detection Error Tradeoff (DET) for different probability thresholds.</p></td>
 </tr>
 </tbody>
 </table>
@@ -1962,15 +1963,15 @@ <h4><span class="section-number">3.4.4.9.1. </span>Binary classification<a class
 <span class="gp">&gt;&gt;&gt; </span><span class="n">metrics</span><span class="o">.</span><span class="n">recall_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">)</span>
 <span class="go">0.5</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">metrics</span><span class="o">.</span><span class="n">f1_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">)</span>
-<span class="go">0.66...</span>
+<span class="go">0.66</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">metrics</span><span class="o">.</span><span class="n">fbeta_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">,</span> <span class="n">beta</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
-<span class="go">0.83...</span>
+<span class="go">0.83</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">metrics</span><span class="o">.</span><span class="n">fbeta_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">,</span> <span class="n">beta</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-<span class="go">0.66...</span>
+<span class="go">0.66</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">metrics</span><span class="o">.</span><span class="n">fbeta_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">,</span> <span class="n">beta</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
-<span class="go">0.55...</span>
+<span class="go">0.55</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">metrics</span><span class="o">.</span><span class="n">precision_recall_fscore_support</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">,</span> <span class="n">beta</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
-<span class="go">(array([0.66..., 1.        ]), array([1. , 0.5]), array([0.71..., 0.83...]), array([2, 2]))</span>
+<span class="go">(array([0.66, 1.        ]), array([1. , 0.5]), array([0.71, 0.83]), array([2, 2]))</span>
 
 
 <span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
@@ -1980,13 +1981,13 @@ <h4><span class="section-number">3.4.4.9.1. </span>Binary classification<a class
 <span class="gp">&gt;&gt;&gt; </span><span class="n">y_scores</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.4</span><span class="p">,</span> <span class="mf">0.35</span><span class="p">,</span> <span class="mf">0.8</span><span class="p">])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">precision</span><span class="p">,</span> <span class="n">recall</span><span class="p">,</span> <span class="n">threshold</span> <span class="o">=</span> <span class="n">precision_recall_curve</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_scores</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">precision</span>
-<span class="go">array([0.5       , 0.66..., 0.5       , 1.        , 1.        ])</span>
+<span class="go">array([0.5       , 0.66, 0.5       , 1.        , 1.        ])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">recall</span>
 <span class="go">array([1. , 1. , 0.5, 0.5, 0. ])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">threshold</span>
 <span class="go">array([0.1 , 0.35, 0.4 , 0.8 ])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">average_precision_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_scores</span><span class="p">)</span>
-<span class="go">0.83...</span>
+<span class="go">0.83</span>
 </pre></div>
 </div>
 </section>
@@ -2070,15 +2071,15 @@ <h4><span class="section-number">3.4.4.9.2. </span>Multiclass and multilabel cla
 <span class="gp">&gt;&gt;&gt; </span><span class="n">y_true</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">y_pred</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">metrics</span><span class="o">.</span><span class="n">precision_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">,</span> <span class="n">average</span><span class="o">=</span><span class="s1">&#39;macro&#39;</span><span class="p">)</span>
-<span class="go">0.22...</span>
+<span class="go">0.22</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">metrics</span><span class="o">.</span><span class="n">recall_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">,</span> <span class="n">average</span><span class="o">=</span><span class="s1">&#39;micro&#39;</span><span class="p">)</span>
-<span class="go">0.33...</span>
+<span class="go">0.33</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">metrics</span><span class="o">.</span><span class="n">f1_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">,</span> <span class="n">average</span><span class="o">=</span><span class="s1">&#39;weighted&#39;</span><span class="p">)</span>
-<span class="go">0.26...</span>
+<span class="go">0.267</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">metrics</span><span class="o">.</span><span class="n">fbeta_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">,</span> <span class="n">average</span><span class="o">=</span><span class="s1">&#39;macro&#39;</span><span class="p">,</span> <span class="n">beta</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
-<span class="go">0.23...</span>
+<span class="go">0.238</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">metrics</span><span class="o">.</span><span class="n">precision_recall_fscore_support</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">,</span> <span class="n">beta</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">average</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
-<span class="go">(array([0.66..., 0.        , 0.        ]), array([1., 0., 0.]), array([0.71..., 0.        , 0.        ]), array([2, 2, 2]...))</span>
+<span class="go">(array([0.667, 0., 0.]), array([1., 0., 0.]), array([0.714, 0., 0.]), array([2, 2, 2]))</span>
 </pre></div>
 </div>
 <p>For multiclass classification with a “negative class”, it is possible to exclude some labels:</p>
@@ -2089,7 +2090,7 @@ <h4><span class="section-number">3.4.4.9.2. </span>Multiclass and multilabel cla
 </div>
 <p>Similarly, labels not present in the data sample may be accounted for in macro-averaging.</p>
 <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">metrics</span><span class="o">.</span><span class="n">precision_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="n">average</span><span class="o">=</span><span class="s1">&#39;macro&#39;</span><span class="p">)</span>
-<span class="go">0.166...</span>
+<span class="go">0.166</span>
 </pre></div>
 </div>
 <p class="rubric">References</p>
@@ -2122,7 +2123,7 @@ <h4><span class="section-number">3.4.4.9.2. </span>Multiclass and multilabel cla
 <span class="gp">&gt;&gt;&gt; </span><span class="n">y_pred</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span>
 <span class="gp">... </span>                   <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">jaccard_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">y_pred</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-<span class="go">0.6666...</span>
+<span class="go">0.6666</span>
 </pre></div>
 </div>
 <p>In the 2D comparison case (e.g. image similarity):</p>
@@ -2132,9 +2133,9 @@ <h4><span class="section-number">3.4.4.9.2. </span>Multiclass and multilabel cla
 </div>
 <p>In the multilabel case with binary label indicators:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">jaccard_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">,</span> <span class="n">average</span><span class="o">=</span><span class="s1">&#39;samples&#39;</span><span class="p">)</span>
-<span class="go">0.5833...</span>
+<span class="go">0.5833</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">jaccard_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">,</span> <span class="n">average</span><span class="o">=</span><span class="s1">&#39;macro&#39;</span><span class="p">)</span>
-<span class="go">0.6666...</span>
+<span class="go">0.6666</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">jaccard_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">,</span> <span class="n">average</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
 <span class="go">array([0.5, 0.5, 1. ])</span>
 </pre></div>
@@ -2144,11 +2145,11 @@ <h4><span class="section-number">3.4.4.9.2. </span>Multiclass and multilabel cla
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">y_pred</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">y_true</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">jaccard_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">,</span> <span class="n">average</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
-<span class="go">array([1. , 0. , 0.33...])</span>
+<span class="go">array([1. , 0. , 0.33])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">jaccard_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">,</span> <span class="n">average</span><span class="o">=</span><span class="s1">&#39;macro&#39;</span><span class="p">)</span>
-<span class="go">0.44...</span>
+<span class="go">0.44</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">jaccard_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">,</span> <span class="n">average</span><span class="o">=</span><span class="s1">&#39;micro&#39;</span><span class="p">)</span>
-<span class="go">0.33...</span>
+<span class="go">0.33</span>
 </pre></div>
 </div>
 </section>
@@ -2191,9 +2192,9 @@ <h4><span class="section-number">3.4.4.9.2. </span>Multiclass and multilabel cla
 <span class="go">LinearSVC(random_state=0)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">pred_decision</span> <span class="o">=</span> <span class="n">est</span><span class="o">.</span><span class="n">decision_function</span><span class="p">([[</span><span class="o">-</span><span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.5</span><span class="p">]])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">pred_decision</span>
-<span class="go">array([-2.18...,  2.36...,  0.09...])</span>
+<span class="go">array([-2.18,  2.36,  0.09])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">hinge_loss</span><span class="p">([</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">pred_decision</span><span class="p">)</span>
-<span class="go">0.3...</span>
+<span class="go">0.3</span>
 </pre></div>
 </div>
 <p>Here is an example demonstrating the use of the <a class="reference internal" href="http://github.com/generated/sklearn.metrics.hinge_loss.html#sklearn.metrics.hinge_loss" title="sklearn.metrics.hinge_loss"><code class="xref py py-func docutils literal notranslate"><span class="pre">hinge_loss</span></code></a> function
@@ -2207,7 +2208,7 @@ <h4><span class="section-number">3.4.4.9.2. </span>Multiclass and multilabel cla
 <span class="gp">&gt;&gt;&gt; </span><span class="n">pred_decision</span> <span class="o">=</span> <span class="n">est</span><span class="o">.</span><span class="n">decision_function</span><span class="p">([[</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">]])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">y_true</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">]</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">hinge_loss</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">pred_decision</span><span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="n">labels</span><span class="p">)</span>
-<span class="go">0.56...</span>
+<span class="go">0.56</span>
 </pre></div>
 </div>
 </section>
@@ -2247,7 +2248,7 @@ <h4><span class="section-number">3.4.4.9.2. </span>Multiclass and multilabel cla
 <span class="gp">&gt;&gt;&gt; </span><span class="n">y_true</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">y_pred</span> <span class="o">=</span> <span class="p">[[</span><span class="mf">.9</span><span class="p">,</span> <span class="mf">.1</span><span class="p">],</span> <span class="p">[</span><span class="mf">.8</span><span class="p">,</span> <span class="mf">.2</span><span class="p">],</span> <span class="p">[</span><span class="mf">.3</span><span class="p">,</span> <span class="mf">.7</span><span class="p">],</span> <span class="p">[</span><span class="mf">.01</span><span class="p">,</span> <span class="mf">.99</span><span class="p">]]</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">log_loss</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">)</span>
-<span class="go">0.1738...</span>
+<span class="go">0.1738</span>
 </pre></div>
 </div>
 <p>The first <code class="docutils literal notranslate"><span class="pre">[.9,</span> <span class="pre">.1]</span></code> in <code class="docutils literal notranslate"><span class="pre">y_pred</span></code> denotes 90% probability that the first
@@ -2301,7 +2302,7 @@ <h4><span class="section-number">3.4.4.9.2. </span>Multiclass and multilabel cla
 <span class="gp">&gt;&gt;&gt; </span><span class="n">y_true</span> <span class="o">=</span> <span class="p">[</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span> <span class="o">+</span><span class="mi">1</span><span class="p">,</span> <span class="o">+</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">]</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">y_pred</span> <span class="o">=</span> <span class="p">[</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">+</span><span class="mi">1</span><span class="p">,</span> <span class="o">+</span><span class="mi">1</span><span class="p">]</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">matthews_corrcoef</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">)</span>
-<span class="go">-0.33...</span>
+<span class="go">-0.33</span>
 </pre></div>
 </div>
 <p class="rubric">References</p>
@@ -2470,7 +2471,7 @@ <h4><span class="section-number">3.4.4.9.2. </span>Multiclass and multilabel cla
 <span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.linear_model</span><span class="w"> </span><span class="kn">import</span> <span class="n">LogisticRegression</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.metrics</span><span class="w"> </span><span class="kn">import</span> <span class="n">roc_auc_score</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">load_breast_cancer</span><span class="p">(</span><span class="n">return_X_y</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">clf</span> <span class="o">=</span> <span class="n">LogisticRegression</span><span class="p">(</span><span class="n">solver</span><span class="o">=</span><span class="s2">&quot;liblinear&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">clf</span> <span class="o">=</span> <span class="n">LogisticRegression</span><span class="p">()</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">clf</span><span class="o">.</span><span class="n">classes_</span>
 <span class="go">array([0, 1])</span>
 </pre></div>
@@ -2478,12 +2479,12 @@ <h4><span class="section-number">3.4.4.9.2. </span>Multiclass and multilabel cla
 <p>We can use the probability estimates corresponding to <code class="docutils literal notranslate"><span class="pre">clf.classes_[1]</span></code>.</p>
 <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">y_score</span> <span class="o">=</span> <span class="n">clf</span><span class="o">.</span><span class="n">predict_proba</span><span class="p">(</span><span class="n">X</span><span class="p">)[:,</span> <span class="mi">1</span><span class="p">]</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">roc_auc_score</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">y_score</span><span class="p">)</span>
-<span class="go">0.99...</span>
+<span class="go">0.99</span>
 </pre></div>
 </div>
 <p>Otherwise, we can use the non-thresholded decision values</p>
 <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">roc_auc_score</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">clf</span><span class="o">.</span><span class="n">decision_function</span><span class="p">(</span><span class="n">X</span><span class="p">))</span>
-<span class="go">0.99...</span>
+<span class="go">0.99</span>
 </pre></div>
 </div>
 </section>
@@ -2551,11 +2552,11 @@ <h4><span class="section-number">3.4.4.9.2. </span>Multiclass and multilabel cla
 <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.datasets</span><span class="w"> </span><span class="kn">import</span> <span class="n">make_multilabel_classification</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.multioutput</span><span class="w"> </span><span class="kn">import</span> <span class="n">MultiOutputClassifier</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">make_multilabel_classification</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">inner_clf</span> <span class="o">=</span> <span class="n">LogisticRegression</span><span class="p">(</span><span class="n">solver</span><span class="o">=</span><span class="s2">&quot;liblinear&quot;</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">inner_clf</span> <span class="o">=</span> <span class="n">LogisticRegression</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">clf</span> <span class="o">=</span> <span class="n">MultiOutputClassifier</span><span class="p">(</span><span class="n">inner_clf</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">y_score</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">transpose</span><span class="p">([</span><span class="n">y_pred</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">y_pred</span> <span class="ow">in</span> <span class="n">clf</span><span class="o">.</span><span class="n">predict_proba</span><span class="p">(</span><span class="n">X</span><span class="p">)])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">roc_auc_score</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">y_score</span><span class="p">,</span> <span class="n">average</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
-<span class="go">array([0.82..., 0.86..., 0.94..., 0.85... , 0.94...])</span>
+<span class="go">array([0.828, 0.851, 0.94, 0.87, 0.95])</span>
 </pre></div>
 </div>
 <p>And the decision values do not require such processing.</p>
@@ -2563,7 +2564,7 @@ <h4><span class="section-number">3.4.4.9.2. </span>Multiclass and multilabel cla
 <span class="gp">&gt;&gt;&gt; </span><span class="n">clf</span> <span class="o">=</span> <span class="n">RidgeClassifierCV</span><span class="p">()</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">y_score</span> <span class="o">=</span> <span class="n">clf</span><span class="o">.</span><span class="n">decision_function</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">roc_auc_score</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">y_score</span><span class="p">,</span> <span class="n">average</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
-<span class="go">array([0.81..., 0.84... , 0.93..., 0.87..., 0.94...])</span>
+<span class="go">array([0.82, 0.85, 0.93, 0.87, 0.94])</span>
 </pre></div>
 </div>
 <p class="rubric">Examples</p>
@@ -2790,7 +2791,7 @@ <h4><span class="section-number">3.4.4.9.2. </span>Multiclass and multilabel cla
 <span class="gp">... </span>   <span class="p">[[</span><span class="mf">0.8</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.2</span><span class="p">,</span> <span class="mf">0.7</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.2</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">,</span> <span class="mf">0.6</span><span class="p">]],</span>
 <span class="gp">... </span>   <span class="n">labels</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;eggs&quot;</span><span class="p">,</span> <span class="s2">&quot;ham&quot;</span><span class="p">,</span> <span class="s2">&quot;spam&quot;</span><span class="p">],</span>
 <span class="gp">... </span><span class="p">)</span>
-<span class="go">0.146...</span>
+<span class="go">0.146</span>
 </pre></div>
 </div>
 <p>The Brier score can be used to assess how well a classifier is calibrated.
@@ -2975,7 +2976,7 @@ <h4><span class="section-number">3.4.4.9.2. </span>Multiclass and multilabel cla
 <span class="gp">... </span>    <span class="p">[</span><span class="mf">0.01</span><span class="p">,</span> <span class="mf">0.01</span><span class="p">,</span> <span class="mf">0.98</span><span class="p">],</span>
 <span class="gp">... </span><span class="p">]</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">d2_log_loss_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">)</span>
-<span class="go">0.981...</span>
+<span class="go">0.981</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">y_true</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">]</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">y_pred</span> <span class="o">=</span> <span class="p">[</span>
 <span class="gp">... </span>    <span class="p">[</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.6</span><span class="p">,</span> <span class="mf">0.3</span><span class="p">],</span>
@@ -2983,7 +2984,7 @@ <h4><span class="section-number">3.4.4.9.2. </span>Multiclass and multilabel cla
 <span class="gp">... </span>    <span class="p">[</span><span class="mf">0.4</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">],</span>
 <span class="gp">... </span><span class="p">]</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">d2_log_loss_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">)</span>
-<span class="go">-0.552...</span>
+<span class="go">-0.552</span>
 </pre></div>
 </div>
 </div>
@@ -3063,7 +3064,7 @@ <h4><span class="section-number">3.4.4.9.2. </span>Multiclass and multilabel cla
 <span class="gp">&gt;&gt;&gt; </span><span class="n">y_true</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">y_score</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">0.75</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">]])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">label_ranking_average_precision_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_score</span><span class="p">)</span>
-<span class="go">0.416...</span>
+<span class="go">0.416</span>
 </pre></div>
 </div>
 </section>
@@ -3092,7 +3093,7 @@ <h4><span class="section-number">3.4.4.9.2. </span>Multiclass and multilabel cla
 <span class="gp">&gt;&gt;&gt; </span><span class="n">y_true</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">y_score</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">0.75</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">]])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">label_ranking_loss</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_score</span><span class="p">)</span>
-<span class="go">0.75...</span>
+<span class="go">0.75</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="c1"># With the following prediction, we have perfect and minimal loss</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">y_score</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">,</span> <span class="mf">0.9</span><span class="p">]])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">label_ranking_loss</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_score</span><span class="p">)</span>
@@ -3121,7 +3122,7 @@ <h4><span class="section-number">3.4.4.9.2. </span>Multiclass and multilabel cla
 documents in a search-engine result set, DCG measures the usefulness, or gain,
 of a document based on its position in the result list. The gain is accumulated
 from the top of the result list to the bottom, with the gain of each result
-discounted at lower ranks”</p>
+discounted at lower ranks.”</p>
 <p>DCG orders the true targets (e.g. relevance of query answers) in the predicted
 order, then multiplies them by a logarithmic decay and sums the result. The sum
 can be truncated after the first <span class="math notranslate nohighlight">\(K\)</span> results, in which case we call it
@@ -3218,19 +3219,19 @@ <h4><span class="section-number">3.4.4.9.2. </span>Multiclass and multilabel cla
 <span class="gp">&gt;&gt;&gt; </span><span class="n">y_true</span> <span class="o">=</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.5</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">7</span><span class="p">]</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">y_pred</span> <span class="o">=</span> <span class="p">[</span><span class="mf">2.5</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">8</span><span class="p">]</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">r2_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">)</span>
-<span class="go">0.948...</span>
+<span class="go">0.948</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">y_true</span> <span class="o">=</span> <span class="p">[[</span><span class="mf">0.5</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">7</span><span class="p">,</span> <span class="o">-</span><span class="mi">6</span><span class="p">]]</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">y_pred</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">8</span><span class="p">,</span> <span class="o">-</span><span class="mi">5</span><span class="p">]]</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">r2_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">,</span> <span class="n">multioutput</span><span class="o">=</span><span class="s1">&#39;variance_weighted&#39;</span><span class="p">)</span>
-<span class="go">0.938...</span>
+<span class="go">0.938</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">y_true</span> <span class="o">=</span> <span class="p">[[</span><span class="mf">0.5</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">7</span><span class="p">,</span> <span class="o">-</span><span class="mi">6</span><span class="p">]]</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">y_pred</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">8</span><span class="p">,</span> <span class="o">-</span><span class="mi">5</span><span class="p">]]</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">r2_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">,</span> <span class="n">multioutput</span><span class="o">=</span><span class="s1">&#39;uniform_average&#39;</span><span class="p">)</span>
-<span class="go">0.936...</span>
+<span class="go">0.936</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">r2_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">,</span> <span class="n">multioutput</span><span class="o">=</span><span class="s1">&#39;raw_values&#39;</span><span class="p">)</span>
-<span class="go">array([0.965..., 0.908...])</span>
+<span class="go">array([0.965, 0.908])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">r2_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">,</span> <span class="n">multioutput</span><span class="o">=</span><span class="p">[</span><span class="mf">0.3</span><span class="p">,</span> <span class="mf">0.7</span><span class="p">])</span>
-<span class="go">0.925...</span>
+<span class="go">0.925</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">y_true</span> <span class="o">=</span> <span class="p">[</span><span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="o">-</span><span class="mi">2</span><span class="p">]</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">y_pred</span> <span class="o">=</span> <span class="p">[</span><span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="o">-</span><span class="mi">2</span><span class="p">]</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">r2_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">)</span>
@@ -3276,7 +3277,7 @@ <h4><span class="section-number">3.4.4.9.2. </span>Multiclass and multilabel cla
 <span class="gp">&gt;&gt;&gt; </span><span class="n">mean_absolute_error</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">,</span> <span class="n">multioutput</span><span class="o">=</span><span class="s1">&#39;raw_values&#39;</span><span class="p">)</span>
 <span class="go">array([0.5, 1. ])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">mean_absolute_error</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">,</span> <span class="n">multioutput</span><span class="o">=</span><span class="p">[</span><span class="mf">0.3</span><span class="p">,</span> <span class="mf">0.7</span><span class="p">])</span>
-<span class="go">0.85...</span>
+<span class="go">0.85</span>
 </pre></div>
 </div>
 </section>
@@ -3301,7 +3302,7 @@ <h4><span class="section-number">3.4.4.9.2. </span>Multiclass and multilabel cla
 <span class="gp">&gt;&gt;&gt; </span><span class="n">y_true</span> <span class="o">=</span> <span class="p">[[</span><span class="mf">0.5</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">7</span><span class="p">,</span> <span class="o">-</span><span class="mi">6</span><span class="p">]]</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">y_pred</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">8</span><span class="p">,</span> <span class="o">-</span><span class="mi">5</span><span class="p">]]</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">mean_squared_error</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">)</span>
-<span class="go">0.7083...</span>
+<span class="go">0.7083</span>
 </pre></div>
 </div>
 <p class="rubric">Examples</p>
@@ -3335,11 +3336,11 @@ <h4><span class="section-number">3.4.4.9.2. </span>Multiclass and multilabel cla
 <span class="gp">&gt;&gt;&gt; </span><span class="n">y_true</span> <span class="o">=</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mf">2.5</span><span class="p">,</span> <span class="mi">7</span><span class="p">]</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">y_pred</span> <span class="o">=</span> <span class="p">[</span><span class="mf">2.5</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">8</span><span class="p">]</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">mean_squared_log_error</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">)</span>
-<span class="go">0.039...</span>
+<span class="go">0.0397</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">y_true</span> <span class="o">=</span> <span class="p">[[</span><span class="mf">0.5</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">7</span><span class="p">,</span> <span class="mi">6</span><span class="p">]]</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">y_pred</span> <span class="o">=</span> <span class="p">[[</span><span class="mf">0.5</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mf">2.5</span><span class="p">],</span> <span class="p">[</span><span class="mi">8</span><span class="p">,</span> <span class="mi">8</span><span class="p">]]</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">mean_squared_log_error</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">)</span>
-<span class="go">0.044...</span>
+<span class="go">0.044</span>
 </pre></div>
 </div>
 <p>The root mean squared logarithmic error (RMSLE) is available through the
@@ -3365,7 +3366,7 @@ <h4><span class="section-number">3.4.4.9.2. </span>Multiclass and multilabel cla
 <span class="gp">&gt;&gt;&gt; </span><span class="n">y_true</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mf">1e6</span><span class="p">]</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">y_pred</span> <span class="o">=</span> <span class="p">[</span><span class="mf">0.9</span><span class="p">,</span> <span class="mi">15</span><span class="p">,</span> <span class="mf">1.2e6</span><span class="p">]</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">mean_absolute_percentage_error</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">)</span>
-<span class="go">0.2666...</span>
+<span class="go">0.2666</span>
 </pre></div>
 </div>
 <p>In above example, if we had used <code class="docutils literal notranslate"><span class="pre">mean_absolute_error</span></code>, it would have ignored
@@ -3466,13 +3467,13 @@ <h4><span class="section-number">3.4.4.9.2. </span>Multiclass and multilabel cla
 <span class="gp">&gt;&gt;&gt; </span><span class="n">y_true</span> <span class="o">=</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.5</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">7</span><span class="p">]</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">y_pred</span> <span class="o">=</span> <span class="p">[</span><span class="mf">2.5</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">8</span><span class="p">]</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">explained_variance_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">)</span>
-<span class="go">0.957...</span>
+<span class="go">0.957</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">y_true</span> <span class="o">=</span> <span class="p">[[</span><span class="mf">0.5</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">7</span><span class="p">,</span> <span class="o">-</span><span class="mi">6</span><span class="p">]]</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">y_pred</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">8</span><span class="p">,</span> <span class="o">-</span><span class="mi">5</span><span class="p">]]</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">explained_variance_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">,</span> <span class="n">multioutput</span><span class="o">=</span><span class="s1">&#39;raw_values&#39;</span><span class="p">)</span>
-<span class="go">array([0.967..., 1.        ])</span>
+<span class="go">array([0.967, 1.        ])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">explained_variance_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">,</span> <span class="n">multioutput</span><span class="o">=</span><span class="p">[</span><span class="mf">0.3</span><span class="p">,</span> <span class="mf">0.7</span><span class="p">])</span>
-<span class="go">0.990...</span>
+<span class="go">0.990</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">y_true</span> <span class="o">=</span> <span class="p">[</span><span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="o">-</span><span class="mi">2</span><span class="p">]</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">y_pred</span> <span class="o">=</span> <span class="p">[</span><span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="o">-</span><span class="mi">2</span><span class="p">]</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">explained_variance_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">)</span>
@@ -3535,16 +3536,16 @@ <h4><span class="section-number">3.4.4.9.2. </span>Multiclass and multilabel cla
 </div>
 <p>If we increase <code class="docutils literal notranslate"><span class="pre">power</span></code> to 1,:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">mean_tweedie_deviance</span><span class="p">([</span><span class="mf">1.0</span><span class="p">],</span> <span class="p">[</span><span class="mf">1.5</span><span class="p">],</span> <span class="n">power</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-<span class="go">0.18...</span>
+<span class="go">0.189</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">mean_tweedie_deviance</span><span class="p">([</span><span class="mf">100.</span><span class="p">],</span> <span class="p">[</span><span class="mf">150.</span><span class="p">],</span> <span class="n">power</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-<span class="go">18.9...</span>
+<span class="go">18.9</span>
 </pre></div>
 </div>
 <p>the difference in errors decreases. Finally, by setting, <code class="docutils literal notranslate"><span class="pre">power=2</span></code>:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">mean_tweedie_deviance</span><span class="p">([</span><span class="mf">1.0</span><span class="p">],</span> <span class="p">[</span><span class="mf">1.5</span><span class="p">],</span> <span class="n">power</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
-<span class="go">0.14...</span>
+<span class="go">0.144</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">mean_tweedie_deviance</span><span class="p">([</span><span class="mf">100.</span><span class="p">],</span> <span class="p">[</span><span class="mf">150.</span><span class="p">],</span> <span class="n">power</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
-<span class="go">0.14...</span>
+<span class="go">0.144</span>
 </pre></div>
 </div>
 <p>we would get identical errors. The deviance when <code class="docutils literal notranslate"><span class="pre">power=2</span></code> is thus only
@@ -3562,13 +3563,13 @@ <h4><span class="section-number">3.4.4.9.2. </span>Multiclass and multilabel cla
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.metrics</span><span class="w"> </span><span class="kn">import</span> <span class="n">mean_pinball_loss</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">y_true</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">]</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">mean_pinball_loss</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.1</span><span class="p">)</span>
-<span class="go">0.03...</span>
+<span class="go">0.033</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">mean_pinball_loss</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.1</span><span class="p">)</span>
-<span class="go">0.3...</span>
+<span class="go">0.3</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">mean_pinball_loss</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.9</span><span class="p">)</span>
-<span class="go">0.3...</span>
+<span class="go">0.3</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">mean_pinball_loss</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.9</span><span class="p">)</span>
-<span class="go">0.03...</span>
+<span class="go">0.033</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">mean_pinball_loss</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_true</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.1</span><span class="p">)</span>
 <span class="go">0.0</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">mean_pinball_loss</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_true</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.9</span><span class="p">)</span>
@@ -3593,7 +3594,7 @@ <h4><span class="section-number">3.4.4.9.2. </span>Multiclass and multilabel cla
 <span class="gp">... </span>    <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
 <span class="gp">... </span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">cross_val_score</span><span class="p">(</span><span class="n">estimator</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">cv</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">scoring</span><span class="o">=</span><span class="n">mean_pinball_loss_95p</span><span class="p">)</span>
-<span class="go">array([13.6..., 9.7..., 23.3..., 9.5..., 10.4...])</span>
+<span class="go">array([13.6, 9.7, 23.3, 9.5, 10.4])</span>
 </pre></div>
 </div>
 <p>It is also possible to build scorer objects for hyper-parameter tuning. The
@@ -3668,7 +3669,7 @@ <h4><span class="section-number">3.4.4.9.2. </span>Multiclass and multilabel cla
 <span class="gp">&gt;&gt;&gt; </span><span class="n">y_true</span> <span class="o">=</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.5</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">7</span><span class="p">]</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">y_pred</span> <span class="o">=</span> <span class="p">[</span><span class="mf">2.5</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">8</span><span class="p">]</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">d2_absolute_error_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">)</span>
-<span class="go">0.764...</span>
+<span class="go">0.764</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">y_true</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">]</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">y_pred</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">]</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">d2_absolute_error_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">)</span>
@@ -3781,19 +3782,19 @@ <h4><span class="section-number">3.4.4.9.2. </span>Multiclass and multilabel cla
 <span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.svm</span><span class="w"> </span><span class="kn">import</span> <span class="n">SVC</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">clf</span> <span class="o">=</span> <span class="n">SVC</span><span class="p">(</span><span class="n">kernel</span><span class="o">=</span><span class="s1">&#39;linear&#39;</span><span class="p">,</span> <span class="n">C</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">clf</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span>
-<span class="go">0.63...</span>
+<span class="go">0.63</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">clf</span> <span class="o">=</span> <span class="n">DummyClassifier</span><span class="p">(</span><span class="n">strategy</span><span class="o">=</span><span class="s1">&#39;most_frequent&#39;</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">clf</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
 <span class="go">DummyClassifier(random_state=0, strategy=&#39;most_frequent&#39;)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">clf</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span>
-<span class="go">0.57...</span>
+<span class="go">0.579</span>
 </pre></div>
 </div>
 <p>We see that <code class="docutils literal notranslate"><span class="pre">SVC</span></code> doesn’t do much better than a dummy classifier. Now, let’s
 change the kernel:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">clf</span> <span class="o">=</span> <span class="n">SVC</span><span class="p">(</span><span class="n">kernel</span><span class="o">=</span><span class="s1">&#39;rbf&#39;</span><span class="p">,</span> <span class="n">C</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">clf</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span>
-<span class="go">0.94...</span>
+<span class="go">0.94</span>
 </pre></div>
 </div>
 <p>We see that the accuracy was boosted to almost 100%.  A cross validation
diff --git a/dev/modules/multiclass.html b/dev/modules/multiclass.html
index 2f2c926ad6c26..c625de780edaf 100644
--- a/dev/modules/multiclass.html
+++ b/dev/modules/multiclass.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -593,45 +593,46 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/learning_curve.html">3.5. Validation curves: plotting scores to evaluate models</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/inspection.html">4. Inspection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/partial_dependence.html">4.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/permutation_importance.html">4.2. Permutation feature importance</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/metadata_routing.html">4. Metadata Routing</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/inspection.html">5. Inspection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/partial_dependence.html">5.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/permutation_importance.html">5.2. Permutation feature importance</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/visualizations.html">5. Visualizations</a></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/data_transforms.html">6. Dataset transformations</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/compose.html">6.1. Pipelines and composite estimators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/feature_extraction.html">6.2. Feature extraction</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing.html">6.3. Preprocessing data</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/impute.html">6.4. Imputation of missing values</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/unsupervised_reduction.html">6.5. Unsupervised dimensionality reduction</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/random_projection.html">6.6. Random Projection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/kernel_approximation.html">6.7. Kernel Approximation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/metrics.html">6.8. Pairwise metrics, Affinities and Kernels</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing_targets.html">6.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/visualizations.html">6. Visualizations</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/data_transforms.html">7. Dataset transformations</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/compose.html">7.1. Pipelines and composite estimators</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/feature_extraction.html">7.2. Feature extraction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing.html">7.3. Preprocessing data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/impute.html">7.4. Imputation of missing values</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/unsupervised_reduction.html">7.5. Unsupervised dimensionality reduction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/random_projection.html">7.6. Random Projection</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/kernel_approximation.html">7.7. Kernel Approximation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/metrics.html">7.8. Pairwise metrics, Affinities and Kernels</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing_targets.html">7.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/datasets.html">7. Dataset loading utilities</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html">7.1. Toy datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html">7.2. Real world datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html">7.3. Generated datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html">7.4. Loading other datasets</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/datasets.html">8. Dataset loading utilities</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html">8.1. Toy datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html">8.2. Real world datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html">8.3. Generated datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html">8.4. Loading other datasets</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/computing.html">8. Computing with scikit-learn</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/scaling_strategies.html">8.1. Strategies to scale computationally: bigger data</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/computational_performance.html">8.2. Computational Performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/parallelism.html">8.3. Parallelism, resource management, and configuration</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/computing.html">9. Computing with scikit-learn</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/scaling_strategies.html">9.1. Strategies to scale computationally: bigger data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/computational_performance.html">9.2. Computational Performance</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/parallelism.html">9.3. Parallelism, resource management, and configuration</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/model_persistence.html">9. Model persistence</a></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/common_pitfalls.html">10. Common pitfalls and recommended practices</a></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/dispatching.html">11. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/array_api.html">11.1. Array API support (experimental)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/model_persistence.html">10. Model persistence</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/common_pitfalls.html">11. Common pitfalls and recommended practices</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/dispatching.html">12. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/array_api.html">12.1. Array API support (experimental)</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/machine_learning_map.html">12. Choosing the right estimator</a></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/presentations.html">13. External Resources, Videos and Talks</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/machine_learning_map.html">13. Choosing the right estimator</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/presentations.html">14. External Resources, Videos and Talks</a></li>
 </ul>
 </div>
 </nav></div>
diff --git a/dev/modules/naive_bayes.html b/dev/modules/naive_bayes.html
index e1d7342989d70..05e0710116048 100644
--- a/dev/modules/naive_bayes.html
+++ b/dev/modules/naive_bayes.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -594,45 +594,46 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/learning_curve.html">3.5. Validation curves: plotting scores to evaluate models</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/inspection.html">4. Inspection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/partial_dependence.html">4.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/permutation_importance.html">4.2. Permutation feature importance</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/metadata_routing.html">4. Metadata Routing</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/inspection.html">5. Inspection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/partial_dependence.html">5.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/permutation_importance.html">5.2. Permutation feature importance</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/visualizations.html">5. Visualizations</a></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/data_transforms.html">6. Dataset transformations</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/compose.html">6.1. Pipelines and composite estimators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/feature_extraction.html">6.2. Feature extraction</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing.html">6.3. Preprocessing data</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/impute.html">6.4. Imputation of missing values</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/unsupervised_reduction.html">6.5. Unsupervised dimensionality reduction</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/random_projection.html">6.6. Random Projection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/kernel_approximation.html">6.7. Kernel Approximation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/metrics.html">6.8. Pairwise metrics, Affinities and Kernels</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing_targets.html">6.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/visualizations.html">6. Visualizations</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/data_transforms.html">7. Dataset transformations</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/compose.html">7.1. Pipelines and composite estimators</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="http://github.com/kernel_approximation.html">7.7. Kernel Approximation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/metrics.html">7.8. Pairwise metrics, Affinities and Kernels</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing_targets.html">7.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
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 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/datasets.html">7. Dataset loading utilities</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html">7.1. Toy datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html">7.2. Real world datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html">7.3. Generated datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html">7.4. Loading other datasets</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/datasets.html">8. Dataset loading utilities</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html">8.1. Toy datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html">8.2. Real world datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html">8.3. Generated datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html">8.4. Loading other datasets</a></li>
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 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/computing.html">8. Computing with scikit-learn</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/scaling_strategies.html">8.1. Strategies to scale computationally: bigger data</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/parallelism.html">8.3. Parallelism, resource management, and configuration</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/computing.html">9. Computing with scikit-learn</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/scaling_strategies.html">9.1. Strategies to scale computationally: bigger data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/computational_performance.html">9.2. Computational Performance</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/parallelism.html">9.3. Parallelism, resource management, and configuration</a></li>
 </ul>
 </details></li>
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-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/dispatching.html">11. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/array_api.html">11.1. Array API support (experimental)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/model_persistence.html">10. Model persistence</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/common_pitfalls.html">11. Common pitfalls and recommended practices</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/dispatching.html">12. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/array_api.html">12.1. Array API support (experimental)</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/machine_learning_map.html">12. Choosing the right estimator</a></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/presentations.html">13. External Resources, Videos and Talks</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/machine_learning_map.html">13. Choosing the right estimator</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/presentations.html">14. External Resources, Videos and Talks</a></li>
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diff --git a/dev/modules/neighbors.html b/dev/modules/neighbors.html
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/permutation_importance.html">4.2. Permutation feature importance</a></li>
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+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/inspection.html">5. Inspection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/partial_dependence.html">5.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/permutation_importance.html">5.2. Permutation feature importance</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/compose.html">6.1. Pipelines and composite estimators</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="http://github.com/visualizations.html">6. Visualizations</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/data_transforms.html">7. Dataset transformations</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/compose.html">7.1. Pipelines and composite estimators</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html">7.4. Loading other datasets</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/datasets.html">8. Dataset loading utilities</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html">8.1. Toy datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html">8.2. Real world datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html">8.3. Generated datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html">8.4. Loading other datasets</a></li>
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+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/computing.html">9. Computing with scikit-learn</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/scaling_strategies.html">9.1. Strategies to scale computationally: bigger data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/computational_performance.html">9.2. Computational Performance</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/parallelism.html">9.3. Parallelism, resource management, and configuration</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/array_api.html">11.1. Array API support (experimental)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/model_persistence.html">10. Model persistence</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/common_pitfalls.html">11. Common pitfalls and recommended practices</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/dispatching.html">12. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/array_api.html">12.1. Array API support (experimental)</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="http://github.com/machine_learning_map.html">12. Choosing the right estimator</a></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/presentations.html">13. External Resources, Videos and Talks</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/machine_learning_map.html">13. Choosing the right estimator</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/presentations.html">14. External Resources, Videos and Talks</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/permutation_importance.html">4.2. Permutation feature importance</a></li>
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+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/inspection.html">5. Inspection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/partial_dependence.html">5.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/permutation_importance.html">5.2. Permutation feature importance</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing_targets.html">6.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/visualizations.html">6. Visualizations</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/data_transforms.html">7. Dataset transformations</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/compose.html">7.1. Pipelines and composite estimators</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/feature_extraction.html">7.2. Feature extraction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing.html">7.3. Preprocessing data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/impute.html">7.4. Imputation of missing values</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/unsupervised_reduction.html">7.5. Unsupervised dimensionality reduction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/random_projection.html">7.6. Random Projection</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/kernel_approximation.html">7.7. Kernel Approximation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/metrics.html">7.8. Pairwise metrics, Affinities and Kernels</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing_targets.html">7.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/datasets.html">7. Dataset loading utilities</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html">7.1. Toy datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html">7.2. Real world datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html">7.3. Generated datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html">7.4. Loading other datasets</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/datasets.html">8. Dataset loading utilities</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html">8.1. Toy datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html">8.2. Real world datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html">8.3. Generated datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html">8.4. Loading other datasets</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/computing.html">8. Computing with scikit-learn</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/scaling_strategies.html">8.1. Strategies to scale computationally: bigger data</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/computational_performance.html">8.2. Computational Performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/parallelism.html">8.3. Parallelism, resource management, and configuration</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/computing.html">9. Computing with scikit-learn</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/scaling_strategies.html">9.1. Strategies to scale computationally: bigger data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/computational_performance.html">9.2. Computational Performance</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/parallelism.html">9.3. Parallelism, resource management, and configuration</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/model_persistence.html">9. Model persistence</a></li>
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-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/dispatching.html">11. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/array_api.html">11.1. Array API support (experimental)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/model_persistence.html">10. Model persistence</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/common_pitfalls.html">11. Common pitfalls and recommended practices</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/dispatching.html">12. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/array_api.html">12.1. Array API support (experimental)</a></li>
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 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/machine_learning_map.html">12. Choosing the right estimator</a></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/presentations.html">13. External Resources, Videos and Talks</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/machine_learning_map.html">13. Choosing the right estimator</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/presentations.html">14. External Resources, Videos and Talks</a></li>
 </ul>
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 </nav></div>
@@ -788,8 +789,8 @@ <h2><span class="section-number">1.17.2. </span>Classification<a class="headerli
 classification, it minimizes the Cross-Entropy loss function, giving a vector
 of probability estimates <span class="math notranslate nohighlight">\(P(y|x)\)</span> per sample <span class="math notranslate nohighlight">\(x\)</span>:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">clf</span><span class="o">.</span><span class="n">predict_proba</span><span class="p">([[</span><span class="mf">2.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">],</span> <span class="p">[</span><span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">]])</span>
-<span class="go">array([[1.967...e-04, 9.998...-01],</span>
-<span class="go">       [1.967...e-04, 9.998...-01]])</span>
+<span class="go">array([[1.967e-04, 9.998e-01],</span>
+<span class="go">       [1.967e-04, 9.998e-01]])</span>
 </pre></div>
 </div>
 <p><a class="reference internal" href="http://github.com/generated/sklearn.neural_network.MLPClassifier.html#sklearn.neural_network.MLPClassifier" title="sklearn.neural_network.MLPClassifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">MLPClassifier</span></code></a> supports multi-class classification by
diff --git a/dev/modules/neural_networks_unsupervised.html b/dev/modules/neural_networks_unsupervised.html
index b6755e44876c2..8cfd53e9809fc 100644
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@@ -42,7 +42,7 @@
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@@ -594,45 +594,46 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/learning_curve.html">3.5. Validation curves: plotting scores to evaluate models</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/inspection.html">4. Inspection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/partial_dependence.html">4.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/permutation_importance.html">4.2. Permutation feature importance</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/metadata_routing.html">4. Metadata Routing</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/inspection.html">5. Inspection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/partial_dependence.html">5.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/permutation_importance.html">5.2. Permutation feature importance</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/visualizations.html">5. Visualizations</a></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/data_transforms.html">6. Dataset transformations</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/compose.html">6.1. Pipelines and composite estimators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/feature_extraction.html">6.2. Feature extraction</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/unsupervised_reduction.html">6.5. Unsupervised dimensionality reduction</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/random_projection.html">6.6. Random Projection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/kernel_approximation.html">6.7. Kernel Approximation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/metrics.html">6.8. Pairwise metrics, Affinities and Kernels</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing_targets.html">6.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/visualizations.html">6. Visualizations</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/data_transforms.html">7. Dataset transformations</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/compose.html">7.1. Pipelines and composite estimators</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/feature_extraction.html">7.2. Feature extraction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing.html">7.3. Preprocessing data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/impute.html">7.4. Imputation of missing values</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/unsupervised_reduction.html">7.5. Unsupervised dimensionality reduction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/random_projection.html">7.6. Random Projection</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/kernel_approximation.html">7.7. Kernel Approximation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/metrics.html">7.8. Pairwise metrics, Affinities and Kernels</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing_targets.html">7.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/datasets.html">7. Dataset loading utilities</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html">7.1. Toy datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html">7.2. Real world datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html">7.3. Generated datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html">7.4. Loading other datasets</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/datasets.html">8. Dataset loading utilities</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html">8.1. Toy datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html">8.2. Real world datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html">8.3. Generated datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html">8.4. Loading other datasets</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/computing.html">8. Computing with scikit-learn</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/scaling_strategies.html">8.1. Strategies to scale computationally: bigger data</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/computational_performance.html">8.2. Computational Performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/parallelism.html">8.3. Parallelism, resource management, and configuration</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/computing.html">9. Computing with scikit-learn</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/scaling_strategies.html">9.1. Strategies to scale computationally: bigger data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/computational_performance.html">9.2. Computational Performance</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/parallelism.html">9.3. Parallelism, resource management, and configuration</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/model_persistence.html">9. Model persistence</a></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/common_pitfalls.html">10. Common pitfalls and recommended practices</a></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/dispatching.html">11. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/array_api.html">11.1. Array API support (experimental)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/model_persistence.html">10. Model persistence</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/common_pitfalls.html">11. Common pitfalls and recommended practices</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/dispatching.html">12. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/array_api.html">12.1. Array API support (experimental)</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/machine_learning_map.html">12. Choosing the right estimator</a></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/presentations.html">13. External Resources, Videos and Talks</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/machine_learning_map.html">13. Choosing the right estimator</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/presentations.html">14. External Resources, Videos and Talks</a></li>
 </ul>
 </div>
 </nav></div>
diff --git a/dev/modules/outlier_detection.html b/dev/modules/outlier_detection.html
index f6212339fc2d4..129f49edb830f 100644
--- a/dev/modules/outlier_detection.html
+++ b/dev/modules/outlier_detection.html
@@ -42,7 +42,7 @@
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-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
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@@ -594,45 +594,46 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/learning_curve.html">3.5. Validation curves: plotting scores to evaluate models</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/inspection.html">4. Inspection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/partial_dependence.html">4.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/permutation_importance.html">4.2. Permutation feature importance</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/metadata_routing.html">4. Metadata Routing</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/inspection.html">5. Inspection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/partial_dependence.html">5.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/permutation_importance.html">5.2. Permutation feature importance</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/visualizations.html">5. Visualizations</a></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/data_transforms.html">6. Dataset transformations</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/compose.html">6.1. Pipelines and composite estimators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/feature_extraction.html">6.2. Feature extraction</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing.html">6.3. Preprocessing data</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/impute.html">6.4. Imputation of missing values</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/unsupervised_reduction.html">6.5. Unsupervised dimensionality reduction</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/random_projection.html">6.6. Random Projection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/kernel_approximation.html">6.7. Kernel Approximation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/metrics.html">6.8. Pairwise metrics, Affinities and Kernels</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing_targets.html">6.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/visualizations.html">6. Visualizations</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/data_transforms.html">7. Dataset transformations</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/compose.html">7.1. Pipelines and composite estimators</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/feature_extraction.html">7.2. Feature extraction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing.html">7.3. Preprocessing data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/impute.html">7.4. Imputation of missing values</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/unsupervised_reduction.html">7.5. Unsupervised dimensionality reduction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/random_projection.html">7.6. Random Projection</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/kernel_approximation.html">7.7. Kernel Approximation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/metrics.html">7.8. Pairwise metrics, Affinities and Kernels</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing_targets.html">7.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/datasets.html">7. Dataset loading utilities</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html">7.1. Toy datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html">7.2. Real world datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html">7.3. Generated datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html">7.4. Loading other datasets</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/datasets.html">8. Dataset loading utilities</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html">8.1. Toy datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html">8.2. Real world datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html">8.3. Generated datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html">8.4. Loading other datasets</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/computing.html">8. Computing with scikit-learn</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/scaling_strategies.html">8.1. Strategies to scale computationally: bigger data</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/computational_performance.html">8.2. Computational Performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/parallelism.html">8.3. Parallelism, resource management, and configuration</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/computing.html">9. Computing with scikit-learn</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/scaling_strategies.html">9.1. Strategies to scale computationally: bigger data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/computational_performance.html">9.2. Computational Performance</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/parallelism.html">9.3. Parallelism, resource management, and configuration</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/model_persistence.html">9. Model persistence</a></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/common_pitfalls.html">10. Common pitfalls and recommended practices</a></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/dispatching.html">11. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/array_api.html">11.1. Array API support (experimental)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/model_persistence.html">10. Model persistence</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/common_pitfalls.html">11. Common pitfalls and recommended practices</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/dispatching.html">12. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/array_api.html">12.1. Array API support (experimental)</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/machine_learning_map.html">12. Choosing the right estimator</a></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/presentations.html">13. External Resources, Videos and Talks</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/machine_learning_map.html">13. Choosing the right estimator</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/presentations.html">14. External Resources, Videos and Talks</a></li>
 </ul>
 </div>
 </nav></div>
@@ -1003,16 +1004,14 @@ <h3><span class="section-number">2.7.3.1. </span>Fitting an elliptic envelope<a
 average local density of its k-nearest neighbors, and its own local density:
 a normal instance is expected to have a local density similar to that of its
 neighbors, while abnormal data are expected to have much smaller local density.</p>
-<p>The number k of neighbors considered, (alias parameter n_neighbors) is typically
-chosen 1) greater than the minimum number of objects a cluster has to contain,
-so that other objects can be local outliers relative to this cluster, and 2)
-smaller than the maximum number of close by objects that can potentially be
-local outliers.
-In practice, such information is generally not available, and taking
-n_neighbors=20 appears to work well in general.
-When the proportion of outliers is high (i.e. greater than 10 %, as in the
-example below), n_neighbors should be greater (n_neighbors=35 in the example
-below).</p>
+<p>The number k of neighbors considered, (alias parameter <code class="docutils literal notranslate"><span class="pre">n_neighbors</span></code>) is
+typically chosen 1) greater than the minimum number of objects a cluster has to
+contain, so that other objects can be local outliers relative to this cluster,
+and 2) smaller than the maximum number of close by objects that can potentially
+be local outliers. In practice, such information is generally not available, and
+taking <code class="docutils literal notranslate"><span class="pre">n_neighbors=20</span></code> appears to work well in general. When the proportion of
+outliers is high (i.e. greater than 10 %, as in the example below),
+<code class="docutils literal notranslate"><span class="pre">n_neighbors</span></code> should be greater (<code class="docutils literal notranslate"><span class="pre">n_neighbors=35</span></code> in the example below).</p>
 <p>The strength of the LOF algorithm is that it takes both local and global
 properties of datasets into consideration: it can perform well even in datasets
 where abnormal samples have different underlying densities.
diff --git a/dev/modules/partial_dependence.html b/dev/modules/partial_dependence.html
index 328022982fe4f..0a6a19a34a51f 100644
--- a/dev/modules/partial_dependence.html
+++ b/dev/modules/partial_dependence.html
@@ -7,7 +7,7 @@
   <head>
     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="viewport" content="width=device-width, initial-scale=1" />
-<meta property="og:title" content="4.1. Partial Dependence and Individual Conditional Expectation plots" />
+<meta property="og:title" content="5.1. Partial Dependence and Individual Conditional Expectation plots" />
 <meta property="og:type" content="website" />
 <meta property="og:url" content="https://scikit-learn/stable/modules/partial_dependence.html" />
 <meta property="og:site_name" content="scikit-learn" />
@@ -16,7 +16,7 @@
 <meta property="og:image:alt" content="scikit-learn" />
 <meta name="description" content="Partial dependence plots (PDP) and individual conditional expectation (ICE) plots can be used to visualize and analyze interaction between the target response 1 and a set of input features of inter..." />
 
-    <title>4.1. Partial Dependence and Individual Conditional Expectation plots &#8212; scikit-learn 1.7.dev0 documentation</title>
+    <title>5.1. Partial Dependence and Individual Conditional Expectation plots &#8212; scikit-learn 1.7.dev0 documentation</title>
   
   
   
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
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     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -81,8 +81,8 @@
     <link rel="icon" href="http://github.com/_static/favicon.ico"/>
     <link rel="author" title="About these documents" href="http://github.com/about.html" />
     <link rel="search" title="Search" href="http://github.com/search.html" />
-    <link rel="next" title="4.2. Permutation feature importance" href="http://github.com/permutation_importance.html" />
-    <link rel="prev" title="4. Inspection" href="http://github.com/inspection.html" />
+    <link rel="next" title="5.2. Permutation feature importance" href="http://github.com/permutation_importance.html" />
+    <link rel="prev" title="5. Inspection" href="http://github.com/inspection.html" />
   <meta name="viewport" content="width=device-width, initial-scale=1"/>
   <meta name="docsearch:language" content="en"/>
   <meta name="docsearch:version" content="1.7" />
@@ -594,45 +594,46 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/learning_curve.html">3.5. Validation curves: plotting scores to evaluate models</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 current active has-children"><a class="reference internal" href="http://github.com/inspection.html">4. Inspection</a><details open="open"><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul class="current">
-<li class="toctree-l2 current active"><a class="current reference internal" href="#">4.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/permutation_importance.html">4.2. Permutation feature importance</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/metadata_routing.html">4. Metadata Routing</a></li>
+<li class="toctree-l1 current active has-children"><a class="reference internal" href="http://github.com/inspection.html">5. Inspection</a><details open="open"><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul class="current">
+<li class="toctree-l2 current active"><a class="current reference internal" href="#">5.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/permutation_importance.html">5.2. Permutation feature importance</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/visualizations.html">5. Visualizations</a></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/data_transforms.html">6. Dataset transformations</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/compose.html">6.1. Pipelines and composite estimators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/feature_extraction.html">6.2. Feature extraction</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing.html">6.3. Preprocessing data</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/impute.html">6.4. Imputation of missing values</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/unsupervised_reduction.html">6.5. Unsupervised dimensionality reduction</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/random_projection.html">6.6. Random Projection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/kernel_approximation.html">6.7. Kernel Approximation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/metrics.html">6.8. Pairwise metrics, Affinities and Kernels</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing_targets.html">6.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/visualizations.html">6. Visualizations</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/data_transforms.html">7. Dataset transformations</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/compose.html">7.1. Pipelines and composite estimators</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/feature_extraction.html">7.2. Feature extraction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing.html">7.3. Preprocessing data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/impute.html">7.4. Imputation of missing values</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/unsupervised_reduction.html">7.5. Unsupervised dimensionality reduction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/random_projection.html">7.6. Random Projection</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/kernel_approximation.html">7.7. Kernel Approximation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/metrics.html">7.8. Pairwise metrics, Affinities and Kernels</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing_targets.html">7.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/datasets.html">7. Dataset loading utilities</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html">7.1. Toy datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html">7.2. Real world datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html">7.3. Generated datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html">7.4. Loading other datasets</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/datasets.html">8. Dataset loading utilities</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html">8.1. Toy datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html">8.2. Real world datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html">8.3. Generated datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html">8.4. Loading other datasets</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/computing.html">8. Computing with scikit-learn</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/scaling_strategies.html">8.1. Strategies to scale computationally: bigger data</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/computational_performance.html">8.2. Computational Performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/parallelism.html">8.3. Parallelism, resource management, and configuration</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/computing.html">9. Computing with scikit-learn</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/scaling_strategies.html">9.1. Strategies to scale computationally: bigger data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/computational_performance.html">9.2. Computational Performance</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/parallelism.html">9.3. Parallelism, resource management, and configuration</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/model_persistence.html">9. Model persistence</a></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/common_pitfalls.html">10. Common pitfalls and recommended practices</a></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/dispatching.html">11. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/array_api.html">11.1. Array API support (experimental)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/model_persistence.html">10. Model persistence</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/common_pitfalls.html">11. Common pitfalls and recommended practices</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/dispatching.html">12. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/array_api.html">12.1. Array API support (experimental)</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/machine_learning_map.html">12. Choosing the right estimator</a></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/presentations.html">13. External Resources, Videos and Talks</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/machine_learning_map.html">13. Choosing the right estimator</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/presentations.html">14. External Resources, Videos and Talks</a></li>
 </ul>
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@@ -670,9 +671,9 @@
     <li class="breadcrumb-item"><a href="http://github.com/user_guide.html" class="nav-link">User Guide</a></li>
     
     
-    <li class="breadcrumb-item"><a href="http://github.com/inspection.html" class="nav-link"><span class="section-number">4. </span>Inspection</a></li>
+    <li class="breadcrumb-item"><a href="http://github.com/inspection.html" class="nav-link"><span class="section-number">5. </span>Inspection</a></li>
     
-    <li class="breadcrumb-item active" aria-current="page"><span class="ellipsis"><span class="section-number">4.1. </span>Partial Dependence and Individual Conditional Expectation plots</span></li>
+    <li class="breadcrumb-item active" aria-current="page"><span class="ellipsis"><span class="section-number">5.1. </span>Partial Dependence and Individual Conditional Expectation plots</span></li>
   </ul>
 </nav>
 </div>
@@ -690,7 +691,7 @@
                 <article class="bd-article">
                   
   <section id="partial-dependence-and-individual-conditional-expectation-plots">
-<span id="partial-dependence"></span><h1><span class="section-number">4.1. </span>Partial Dependence and Individual Conditional Expectation plots<a class="headerlink" href="#partial-dependence-and-individual-conditional-expectation-plots" title="Link to this heading">#</a></h1>
+<span id="partial-dependence"></span><h1><span class="section-number">5.1. </span>Partial Dependence and Individual Conditional Expectation plots<a class="headerlink" href="#partial-dependence-and-individual-conditional-expectation-plots" title="Link to this heading">#</a></h1>
 <p>Partial dependence plots (PDP) and individual conditional expectation (ICE)
 plots can be used to visualize and analyze interaction between the target
 response <a class="footnote-reference brackets" href="#id6" id="id1" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a> and a set of input features of interest.</p>
@@ -699,7 +700,7 @@
 violated in practice. Thus, in the case of correlated features, we will
 create absurd data points to compute the PDP/ICE <a class="reference internal" href="#m2019" id="id4"><span>[M2019]</span></a>.</p>
 <section id="partial-dependence-plots">
-<h2><span class="section-number">4.1.1. </span>Partial dependence plots<a class="headerlink" href="#partial-dependence-plots" title="Link to this heading">#</a></h2>
+<h2><span class="section-number">5.1.1. </span>Partial dependence plots<a class="headerlink" href="#partial-dependence-plots" title="Link to this heading">#</a></h2>
 <p>Partial dependence plots (PDP) show the dependence between the target response
 and a set of input features of interest, marginalizing over the values
 of all other input features (the ‘complement’ features). Intuitively, we can
@@ -789,7 +790,7 @@ <h2><span class="section-number">4.1.1. </span>Partial dependence plots<a class=
 the axis of the plots.</p>
 </section>
 <section id="individual-conditional-expectation-ice-plot">
-<span id="individual-conditional"></span><h2><span class="section-number">4.1.2. </span>Individual conditional expectation (ICE) plot<a class="headerlink" href="#individual-conditional-expectation-ice-plot" title="Link to this heading">#</a></h2>
+<span id="individual-conditional"></span><h2><span class="section-number">5.1.2. </span>Individual conditional expectation (ICE) plot<a class="headerlink" href="#individual-conditional-expectation-ice-plot" title="Link to this heading">#</a></h2>
 <p>Similar to a PDP, an individual conditional expectation (ICE) plot
 shows the dependence between the target function and an input feature of
 interest. However, unlike a PDP, which shows the average effect of the input
@@ -853,7 +854,7 @@ <h2><span class="section-number">4.1.1. </span>Partial dependence plots<a class=
 </div>
 </section>
 <section id="mathematical-definition">
-<h2><span class="section-number">4.1.3. </span>Mathematical Definition<a class="headerlink" href="#mathematical-definition" title="Link to this heading">#</a></h2>
+<h2><span class="section-number">5.1.3. </span>Mathematical Definition<a class="headerlink" href="#mathematical-definition" title="Link to this heading">#</a></h2>
 <p>Let <span class="math notranslate nohighlight">\(X_S\)</span> be the set of input features of interest (i.e. the <code class="docutils literal notranslate"><span class="pre">features</span></code>
 parameter) and let <span class="math notranslate nohighlight">\(X_C\)</span> be its complement.</p>
 <p>The partial dependence of the response <span class="math notranslate nohighlight">\(f\)</span> at a point <span class="math notranslate nohighlight">\(x_S\)</span> is
@@ -871,7 +872,7 @@ <h2><span class="section-number">4.1.3. </span>Mathematical Definition<a class="
 evaluated at <span class="math notranslate nohighlight">\(x_{S}\)</span>.</p>
 </section>
 <section id="computation-methods">
-<h2><span class="section-number">4.1.4. </span>Computation methods<a class="headerlink" href="#computation-methods" title="Link to this heading">#</a></h2>
+<h2><span class="section-number">5.1.4. </span>Computation methods<a class="headerlink" href="#computation-methods" title="Link to this heading">#</a></h2>
 <p>There are two main methods to approximate the integral above, namely the
 ‘brute’ and ‘recursion’ methods. The <code class="docutils literal notranslate"><span class="pre">method</span></code> parameter controls which method
 to use.</p>
@@ -968,7 +969,7 @@ <h2><span class="section-number">4.1.4. </span>Computation methods<a class="head
       <i class="fa-solid fa-angle-left"></i>
       <div class="prev-next-info">
         <p class="prev-next-subtitle">previous</p>
-        <p class="prev-next-title"><span class="section-number">4. </span>Inspection</p>
+        <p class="prev-next-title"><span class="section-number">5. </span>Inspection</p>
       </div>
     </a>
     <a class="right-next"
@@ -976,7 +977,7 @@ <h2><span class="section-number">4.1.4. </span>Computation methods<a class="head
        title="next page">
       <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">4.2. </span>Permutation feature importance</p>
+        <p class="prev-next-title"><span class="section-number">5.2. </span>Permutation feature importance</p>
       </div>
       <i class="fa-solid fa-angle-right"></i>
     </a>
@@ -1003,10 +1004,10 @@ <h2><span class="section-number">4.1.4. </span>Computation methods<a class="head
   </div>
   <nav class="bd-toc-nav page-toc" aria-labelledby="pst-page-navigation-heading-2">
     <ul class="visible nav section-nav flex-column">
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#partial-dependence-plots">4.1.1. Partial dependence plots</a></li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#individual-conditional-expectation-ice-plot">4.1.2. Individual conditional expectation (ICE) plot</a></li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#mathematical-definition">4.1.3. Mathematical Definition</a></li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#computation-methods">4.1.4. Computation methods</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#partial-dependence-plots">5.1.1. Partial dependence plots</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#individual-conditional-expectation-ice-plot">5.1.2. Individual conditional expectation (ICE) plot</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#mathematical-definition">5.1.3. Mathematical Definition</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#computation-methods">5.1.4. Computation methods</a></li>
 </ul>
   </nav></div>
 
diff --git a/dev/modules/permutation_importance.html b/dev/modules/permutation_importance.html
index f6da6bf061e79..96ac8e4fd4cb1 100644
--- a/dev/modules/permutation_importance.html
+++ b/dev/modules/permutation_importance.html
@@ -7,7 +7,7 @@
   <head>
     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="viewport" content="width=device-width, initial-scale=1" />
-<meta property="og:title" content="4.2. Permutation feature importance" />
+<meta property="og:title" content="5.2. Permutation feature importance" />
 <meta property="og:type" content="website" />
 <meta property="og:url" content="https://scikit-learn/stable/modules/permutation_importance.html" />
 <meta property="og:site_name" content="scikit-learn" />
@@ -16,7 +16,7 @@
 <meta property="og:image:alt" content="scikit-learn" />
 <meta name="description" content="Permutation feature importance is a model inspection technique that measures the contribution of each feature to a fitted model’s statistical performance on a given tabular dataset. This technique ..." />
 
-    <title>4.2. Permutation feature importance &#8212; scikit-learn 1.7.dev0 documentation</title>
+    <title>5.2. Permutation feature importance &#8212; scikit-learn 1.7.dev0 documentation</title>
   
   
   
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -81,8 +81,8 @@
     <link rel="icon" href="http://github.com/_static/favicon.ico"/>
     <link rel="author" title="About these documents" href="http://github.com/about.html" />
     <link rel="search" title="Search" href="http://github.com/search.html" />
-    <link rel="next" title="5. Visualizations" href="http://github.com/visualizations.html" />
-    <link rel="prev" title="4.1. Partial Dependence and Individual Conditional Expectation plots" href="http://github.com/partial_dependence.html" />
+    <link rel="next" title="6. Visualizations" href="http://github.com/visualizations.html" />
+    <link rel="prev" title="5.1. Partial Dependence and Individual Conditional Expectation plots" href="http://github.com/partial_dependence.html" />
   <meta name="viewport" content="width=device-width, initial-scale=1"/>
   <meta name="docsearch:language" content="en"/>
   <meta name="docsearch:version" content="1.7" />
@@ -594,45 +594,46 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/learning_curve.html">3.5. Validation curves: plotting scores to evaluate models</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 current active has-children"><a class="reference internal" href="http://github.com/inspection.html">4. Inspection</a><details open="open"><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul class="current">
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/partial_dependence.html">4.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
-<li class="toctree-l2 current active"><a class="current reference internal" href="#">4.2. Permutation feature importance</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/metadata_routing.html">4. Metadata Routing</a></li>
+<li class="toctree-l1 current active has-children"><a class="reference internal" href="http://github.com/inspection.html">5. Inspection</a><details open="open"><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/partial_dependence.html">5.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
+<li class="toctree-l2 current active"><a class="current reference internal" href="#">5.2. Permutation feature importance</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/visualizations.html">5. Visualizations</a></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/data_transforms.html">6. Dataset transformations</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/compose.html">6.1. Pipelines and composite estimators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/feature_extraction.html">6.2. Feature extraction</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing.html">6.3. Preprocessing data</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/impute.html">6.4. Imputation of missing values</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/unsupervised_reduction.html">6.5. Unsupervised dimensionality reduction</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/random_projection.html">6.6. Random Projection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/kernel_approximation.html">6.7. Kernel Approximation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/metrics.html">6.8. Pairwise metrics, Affinities and Kernels</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing_targets.html">6.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/visualizations.html">6. Visualizations</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/data_transforms.html">7. Dataset transformations</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/compose.html">7.1. Pipelines and composite estimators</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/feature_extraction.html">7.2. Feature extraction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing.html">7.3. Preprocessing data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/impute.html">7.4. Imputation of missing values</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/unsupervised_reduction.html">7.5. Unsupervised dimensionality reduction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/random_projection.html">7.6. Random Projection</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/kernel_approximation.html">7.7. Kernel Approximation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/metrics.html">7.8. Pairwise metrics, Affinities and Kernels</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing_targets.html">7.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/datasets.html">7. Dataset loading utilities</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html">7.1. Toy datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html">7.2. Real world datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html">7.3. Generated datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html">7.4. Loading other datasets</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/datasets.html">8. Dataset loading utilities</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html">8.1. Toy datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html">8.2. Real world datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html">8.3. Generated datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html">8.4. Loading other datasets</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/computing.html">8. Computing with scikit-learn</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/scaling_strategies.html">8.1. Strategies to scale computationally: bigger data</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/computational_performance.html">8.2. Computational Performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/parallelism.html">8.3. Parallelism, resource management, and configuration</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/computing.html">9. Computing with scikit-learn</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/scaling_strategies.html">9.1. Strategies to scale computationally: bigger data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/computational_performance.html">9.2. Computational Performance</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/parallelism.html">9.3. Parallelism, resource management, and configuration</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/model_persistence.html">9. Model persistence</a></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/common_pitfalls.html">10. Common pitfalls and recommended practices</a></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/dispatching.html">11. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/array_api.html">11.1. Array API support (experimental)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/model_persistence.html">10. Model persistence</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/common_pitfalls.html">11. Common pitfalls and recommended practices</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/dispatching.html">12. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/array_api.html">12.1. Array API support (experimental)</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/machine_learning_map.html">12. Choosing the right estimator</a></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/presentations.html">13. External Resources, Videos and Talks</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/machine_learning_map.html">13. Choosing the right estimator</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/presentations.html">14. External Resources, Videos and Talks</a></li>
 </ul>
 </div>
 </nav></div>
@@ -670,9 +671,9 @@
     <li class="breadcrumb-item"><a href="http://github.com/user_guide.html" class="nav-link">User Guide</a></li>
     
     
-    <li class="breadcrumb-item"><a href="http://github.com/inspection.html" class="nav-link"><span class="section-number">4. </span>Inspection</a></li>
+    <li class="breadcrumb-item"><a href="http://github.com/inspection.html" class="nav-link"><span class="section-number">5. </span>Inspection</a></li>
     
-    <li class="breadcrumb-item active" aria-current="page"><span class="ellipsis"><span class="section-number">4.2. </span>Permutation feature importance</span></li>
+    <li class="breadcrumb-item active" aria-current="page"><span class="ellipsis"><span class="section-number">5.2. </span>Permutation feature importance</span></li>
   </ul>
 </nav>
 </div>
@@ -690,7 +691,7 @@
                 <article class="bd-article">
                   
   <section id="permutation-feature-importance">
-<span id="permutation-importance"></span><h1><span class="section-number">4.2. </span>Permutation feature importance<a class="headerlink" href="#permutation-feature-importance" title="Link to this heading">#</a></h1>
+<span id="permutation-importance"></span><h1><span class="section-number">5.2. </span>Permutation feature importance<a class="headerlink" href="#permutation-feature-importance" title="Link to this heading">#</a></h1>
 <p>Permutation feature importance is a model inspection technique that measures the
 contribution of each feature to a <a class="reference internal" href="http://github.com/glossary.html#term-fitted"><span class="xref std std-term">fitted</span></a> model’s statistical performance
 on a given tabular dataset. This technique is particularly useful for non-linear
@@ -823,7 +824,7 @@
 for which <strong>the choice of the classification metric can be critical</strong>.</p>
 </div>
 </details><section id="outline-of-the-permutation-importance-algorithm">
-<h2><span class="section-number">4.2.1. </span>Outline of the permutation importance algorithm<a class="headerlink" href="#outline-of-the-permutation-importance-algorithm" title="Link to this heading">#</a></h2>
+<h2><span class="section-number">5.2.1. </span>Outline of the permutation importance algorithm<a class="headerlink" href="#outline-of-the-permutation-importance-algorithm" title="Link to this heading">#</a></h2>
 <ul>
 <li><p>Inputs: fitted predictive model <span class="math notranslate nohighlight">\(m\)</span>, tabular dataset (training or
 validation) <span class="math notranslate nohighlight">\(D\)</span>.</p></li>
@@ -849,7 +850,7 @@ <h2><span class="section-number">4.2.1. </span>Outline of the permutation import
 </ul>
 </section>
 <section id="relation-to-impurity-based-importance-in-trees">
-<h2><span class="section-number">4.2.2. </span>Relation to impurity-based importance in trees<a class="headerlink" href="#relation-to-impurity-based-importance-in-trees" title="Link to this heading">#</a></h2>
+<h2><span class="section-number">5.2.2. </span>Relation to impurity-based importance in trees<a class="headerlink" href="#relation-to-impurity-based-importance-in-trees" title="Link to this heading">#</a></h2>
 <p>Tree-based models provide an alternative measure of <a class="reference internal" href="http://github.com/ensemble.html#random-forest-feature-importance"><span class="std std-ref">feature importances
 based on the mean decrease in impurity</span></a>
 (MDI). Impurity is quantified by the splitting criterion of the decision trees
@@ -870,7 +871,7 @@ <h2><span class="section-number">4.2.2. </span>Relation to impurity-based import
 <a class="reference internal" href="http://github.com/auto_examples/inspection/plot_permutation_importance.html#sphx-glr-auto-examples-inspection-plot-permutation-importance-py"><span class="std std-ref">Permutation Importance vs Random Forest Feature Importance (MDI)</span></a>.</p>
 </section>
 <section id="misleading-values-on-strongly-correlated-features">
-<h2><span class="section-number">4.2.3. </span>Misleading values on strongly correlated features<a class="headerlink" href="#misleading-values-on-strongly-correlated-features" title="Link to this heading">#</a></h2>
+<h2><span class="section-number">5.2.3. </span>Misleading values on strongly correlated features<a class="headerlink" href="#misleading-values-on-strongly-correlated-features" title="Link to this heading">#</a></h2>
 <p>When two features are correlated and one of the features is permuted, the model
 still has access to the latter through its correlated feature. This results in a
 lower reported importance value for both features, though they might <em>actually</em>
@@ -923,7 +924,7 @@ <h2><span class="section-number">4.2.3. </span>Misleading values on strongly cor
       <i class="fa-solid fa-angle-left"></i>
       <div class="prev-next-info">
         <p class="prev-next-subtitle">previous</p>
-        <p class="prev-next-title"><span class="section-number">4.1. </span>Partial Dependence and Individual Conditional Expectation plots</p>
+        <p class="prev-next-title"><span class="section-number">5.1. </span>Partial Dependence and Individual Conditional Expectation plots</p>
       </div>
     </a>
     <a class="right-next"
@@ -931,7 +932,7 @@ <h2><span class="section-number">4.2.3. </span>Misleading values on strongly cor
        title="next page">
       <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">5. </span>Visualizations</p>
+        <p class="prev-next-title"><span class="section-number">6. </span>Visualizations</p>
       </div>
       <i class="fa-solid fa-angle-right"></i>
     </a>
@@ -958,9 +959,9 @@ <h2><span class="section-number">4.2.3. </span>Misleading values on strongly cor
   </div>
   <nav class="bd-toc-nav page-toc" aria-labelledby="pst-page-navigation-heading-2">
     <ul class="visible nav section-nav flex-column">
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#outline-of-the-permutation-importance-algorithm">4.2.1. Outline of the permutation importance algorithm</a></li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#relation-to-impurity-based-importance-in-trees">4.2.2. Relation to impurity-based importance in trees</a></li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#misleading-values-on-strongly-correlated-features">4.2.3. Misleading values on strongly correlated features</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#outline-of-the-permutation-importance-algorithm">5.2.1. Outline of the permutation importance algorithm</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#relation-to-impurity-based-importance-in-trees">5.2.2. Relation to impurity-based importance in trees</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#misleading-values-on-strongly-correlated-features">5.2.3. Misleading values on strongly correlated features</a></li>
 </ul>
   </nav></div>
 
diff --git a/dev/modules/pipeline.html b/dev/modules/pipeline.html
index 984f249552736..21ae8f5eab97c 100644
--- a/dev/modules/pipeline.html
+++ b/dev/modules/pipeline.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
diff --git a/dev/modules/preprocessing.html b/dev/modules/preprocessing.html
index 0c30692b7d8d2..0eb2ff5e65590 100644
--- a/dev/modules/preprocessing.html
+++ b/dev/modules/preprocessing.html
@@ -7,7 +7,7 @@
   <head>
     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="viewport" content="width=device-width, initial-scale=1" />
-<meta property="og:title" content="6.3. Preprocessing data" />
+<meta property="og:title" content="7.3. Preprocessing data" />
 <meta property="og:type" content="website" />
 <meta property="og:url" content="https://scikit-learn/stable/modules/preprocessing.html" />
 <meta property="og:site_name" content="scikit-learn" />
@@ -16,7 +16,7 @@
 <meta property="og:image:alt" content="scikit-learn" />
 <meta name="description" content="The sklearn.preprocessing package provides several common utility functions and transformer classes to change raw feature vectors into a representation that is more suitable for the downstream esti..." />
 
-    <title>6.3. Preprocessing data &#8212; scikit-learn 1.7.dev0 documentation</title>
+    <title>7.3. Preprocessing data &#8212; scikit-learn 1.7.dev0 documentation</title>
   
   
   
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
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     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -81,8 +81,8 @@
     <link rel="icon" href="http://github.com/_static/favicon.ico"/>
     <link rel="author" title="About these documents" href="http://github.com/about.html" />
     <link rel="search" title="Search" href="http://github.com/search.html" />
-    <link rel="next" title="6.4. Imputation of missing values" href="http://github.com/impute.html" />
-    <link rel="prev" title="6.2. Feature extraction" href="http://github.com/feature_extraction.html" />
+    <link rel="next" title="7.4. Imputation of missing values" href="http://github.com/impute.html" />
+    <link rel="prev" title="7.2. Feature extraction" href="http://github.com/feature_extraction.html" />
   <meta name="viewport" content="width=device-width, initial-scale=1"/>
   <meta name="docsearch:language" content="en"/>
   <meta name="docsearch:version" content="1.7" />
@@ -594,45 +594,46 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/learning_curve.html">3.5. Validation curves: plotting scores to evaluate models</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/inspection.html">4. Inspection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/partial_dependence.html">4.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/permutation_importance.html">4.2. Permutation feature importance</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/metadata_routing.html">4. Metadata Routing</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/inspection.html">5. Inspection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/partial_dependence.html">5.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/permutation_importance.html">5.2. Permutation feature importance</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/visualizations.html">5. Visualizations</a></li>
-<li class="toctree-l1 current active has-children"><a class="reference internal" href="http://github.com/data_transforms.html">6. Dataset transformations</a><details open="open"><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul class="current">
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/compose.html">6.1. Pipelines and composite estimators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/feature_extraction.html">6.2. Feature extraction</a></li>
-<li class="toctree-l2 current active"><a class="current reference internal" href="#">6.3. Preprocessing data</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/impute.html">6.4. Imputation of missing values</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/unsupervised_reduction.html">6.5. Unsupervised dimensionality reduction</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/random_projection.html">6.6. Random Projection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/kernel_approximation.html">6.7. Kernel Approximation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/metrics.html">6.8. Pairwise metrics, Affinities and Kernels</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing_targets.html">6.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/visualizations.html">6. Visualizations</a></li>
+<li class="toctree-l1 current active has-children"><a class="reference internal" href="http://github.com/data_transforms.html">7. Dataset transformations</a><details open="open"><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/compose.html">7.1. Pipelines and composite estimators</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/feature_extraction.html">7.2. Feature extraction</a></li>
+<li class="toctree-l2 current active"><a class="current reference internal" href="#">7.3. Preprocessing data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/impute.html">7.4. Imputation of missing values</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/unsupervised_reduction.html">7.5. Unsupervised dimensionality reduction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/random_projection.html">7.6. Random Projection</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/kernel_approximation.html">7.7. Kernel Approximation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/metrics.html">7.8. Pairwise metrics, Affinities and Kernels</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing_targets.html">7.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/datasets.html">7. Dataset loading utilities</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html">7.1. Toy datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html">7.2. Real world datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html">7.3. Generated datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html">7.4. Loading other datasets</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/datasets.html">8. Dataset loading utilities</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html">8.1. Toy datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html">8.2. Real world datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html">8.3. Generated datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html">8.4. Loading other datasets</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/computing.html">8. Computing with scikit-learn</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/scaling_strategies.html">8.1. Strategies to scale computationally: bigger data</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/computational_performance.html">8.2. Computational Performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/parallelism.html">8.3. Parallelism, resource management, and configuration</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/computing.html">9. Computing with scikit-learn</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/scaling_strategies.html">9.1. Strategies to scale computationally: bigger data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/computational_performance.html">9.2. Computational Performance</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/parallelism.html">9.3. Parallelism, resource management, and configuration</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/model_persistence.html">9. Model persistence</a></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/common_pitfalls.html">10. Common pitfalls and recommended practices</a></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/dispatching.html">11. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/array_api.html">11.1. Array API support (experimental)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/model_persistence.html">10. Model persistence</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/common_pitfalls.html">11. Common pitfalls and recommended practices</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/dispatching.html">12. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/array_api.html">12.1. Array API support (experimental)</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/machine_learning_map.html">12. Choosing the right estimator</a></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/presentations.html">13. External Resources, Videos and Talks</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/machine_learning_map.html">13. Choosing the right estimator</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/presentations.html">14. External Resources, Videos and Talks</a></li>
 </ul>
 </div>
 </nav></div>
@@ -670,9 +671,9 @@
     <li class="breadcrumb-item"><a href="http://github.com/user_guide.html" class="nav-link">User Guide</a></li>
     
     
-    <li class="breadcrumb-item"><a href="http://github.com/data_transforms.html" class="nav-link"><span class="section-number">6. </span>Dataset transformations</a></li>
+    <li class="breadcrumb-item"><a href="http://github.com/data_transforms.html" class="nav-link"><span class="section-number">7. </span>Dataset transformations</a></li>
     
-    <li class="breadcrumb-item active" aria-current="page"><span class="ellipsis"><span class="section-number">6.3. </span>Preprocessing data</span></li>
+    <li class="breadcrumb-item active" aria-current="page"><span class="ellipsis"><span class="section-number">7.3. </span>Preprocessing data</span></li>
   </ul>
 </nav>
 </div>
@@ -690,7 +691,7 @@
                 <article class="bd-article">
                   
   <section id="preprocessing-data">
-<span id="preprocessing"></span><h1><span class="section-number">6.3. </span>Preprocessing data<a class="headerlink" href="#preprocessing-data" title="Link to this heading">#</a></h1>
+<span id="preprocessing"></span><h1><span class="section-number">7.3. </span>Preprocessing data<a class="headerlink" href="#preprocessing-data" title="Link to this heading">#</a></h1>
 <p>The <code class="docutils literal notranslate"><span class="pre">sklearn.preprocessing</span></code> package provides several common
 utility functions and transformer classes to change raw feature vectors
 into a representation that is more suitable for the downstream estimators.</p>
@@ -701,7 +702,7 @@
 normalizers on a dataset containing marginal outliers are highlighted in
 <a class="reference internal" href="http://github.com/auto_examples/preprocessing/plot_all_scaling.html#sphx-glr-auto-examples-preprocessing-plot-all-scaling-py"><span class="std std-ref">Compare the effect of different scalers on data with outliers</span></a>.</p>
 <section id="standardization-or-mean-removal-and-variance-scaling">
-<span id="preprocessing-scaler"></span><h2><span class="section-number">6.3.1. </span>Standardization, or mean removal and variance scaling<a class="headerlink" href="#standardization-or-mean-removal-and-variance-scaling" title="Link to this heading">#</a></h2>
+<span id="preprocessing-scaler"></span><h2><span class="section-number">7.3.1. </span>Standardization, or mean removal and variance scaling<a class="headerlink" href="#standardization-or-mean-removal-and-variance-scaling" title="Link to this heading">#</a></h2>
 <p><strong>Standardization</strong> of datasets is a <strong>common requirement for many
 machine learning estimators</strong> implemented in scikit-learn; they might behave
 badly if the individual features do not more or less look like standard
@@ -731,16 +732,16 @@
 <span class="go">StandardScaler()</span>
 
 <span class="gp">&gt;&gt;&gt; </span><span class="n">scaler</span><span class="o">.</span><span class="n">mean_</span>
-<span class="go">array([1. ..., 0. ..., 0.33...])</span>
+<span class="go">array([1., 0., 0.33])</span>
 
 <span class="gp">&gt;&gt;&gt; </span><span class="n">scaler</span><span class="o">.</span><span class="n">scale_</span>
-<span class="go">array([0.81..., 0.81..., 1.24...])</span>
+<span class="go">array([0.81, 0.81, 1.24])</span>
 
 <span class="gp">&gt;&gt;&gt; </span><span class="n">X_scaled</span> <span class="o">=</span> <span class="n">scaler</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">X_train</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">X_scaled</span>
-<span class="go">array([[ 0.  ..., -1.22...,  1.33...],</span>
-<span class="go">       [ 1.22...,  0.  ..., -0.26...],</span>
-<span class="go">       [-1.22...,  1.22..., -1.06...]])</span>
+<span class="go">array([[ 0.  , -1.22,  1.33 ],</span>
+<span class="go">       [ 1.22,  0.  , -0.267],</span>
+<span class="go">       [-1.22,  1.22, -1.06 ]])</span>
 </pre></div>
 </div>
 <p>Scaled data has zero mean and unit variance:</p>
@@ -776,7 +777,7 @@
 passing <code class="docutils literal notranslate"><span class="pre">with_mean=False</span></code> or <code class="docutils literal notranslate"><span class="pre">with_std=False</span></code> to the constructor
 of <a class="reference internal" href="http://github.com/generated/sklearn.preprocessing.StandardScaler.html#sklearn.preprocessing.StandardScaler" title="sklearn.preprocessing.StandardScaler"><code class="xref py py-class docutils literal notranslate"><span class="pre">StandardScaler</span></code></a>.</p>
 <section id="scaling-features-to-a-range">
-<h3><span class="section-number">6.3.1.1. </span>Scaling features to a range<a class="headerlink" href="#scaling-features-to-a-range" title="Link to this heading">#</a></h3>
+<h3><span class="section-number">7.3.1.1. </span>Scaling features to a range<a class="headerlink" href="#scaling-features-to-a-range" title="Link to this heading">#</a></h3>
 <p>An alternative standardization is scaling features to
 lie between a given minimum and maximum value, often between zero and one,
 or so that the maximum absolute value of each feature is scaled to unit size.
@@ -809,10 +810,10 @@ <h3><span class="section-number">6.3.1.1. </span>Scaling features to a range<a c
 <p>It is possible to introspect the scaler attributes to find about the exact
 nature of the transformation learned on the training data:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">min_max_scaler</span><span class="o">.</span><span class="n">scale_</span>
-<span class="go">array([0.5       , 0.5       , 0.33...])</span>
+<span class="go">array([0.5       , 0.5       , 0.33])</span>
 
 <span class="gp">&gt;&gt;&gt; </span><span class="n">min_max_scaler</span><span class="o">.</span><span class="n">min_</span>
-<span class="go">array([0.        , 0.5       , 0.33...])</span>
+<span class="go">array([0.        , 0.5       , 0.33])</span>
 </pre></div>
 </div>
 <p>If <a class="reference internal" href="http://github.com/generated/sklearn.preprocessing.MinMaxScaler.html#sklearn.preprocessing.MinMaxScaler" title="sklearn.preprocessing.MinMaxScaler"><code class="xref py py-class docutils literal notranslate"><span class="pre">MinMaxScaler</span></code></a> is given an explicit <code class="docutils literal notranslate"><span class="pre">feature_range=(min,</span> <span class="pre">max)</span></code> the
@@ -847,7 +848,7 @@ <h3><span class="section-number">6.3.1.1. </span>Scaling features to a range<a c
 </div>
 </section>
 <section id="scaling-sparse-data">
-<h3><span class="section-number">6.3.1.2. </span>Scaling sparse data<a class="headerlink" href="#scaling-sparse-data" title="Link to this heading">#</a></h3>
+<h3><span class="section-number">7.3.1.2. </span>Scaling sparse data<a class="headerlink" href="#scaling-sparse-data" title="Link to this heading">#</a></h3>
 <p>Centering sparse data would destroy the sparseness structure in the data, and
 thus rarely is a sensible thing to do. However, it can make sense to scale
 sparse inputs, especially if features are on different scales.</p>
@@ -870,7 +871,7 @@ <h3><span class="section-number">6.3.1.2. </span>Scaling sparse data<a class="he
 is another option.</p>
 </section>
 <section id="scaling-data-with-outliers">
-<h3><span class="section-number">6.3.1.3. </span>Scaling data with outliers<a class="headerlink" href="#scaling-data-with-outliers" title="Link to this heading">#</a></h3>
+<h3><span class="section-number">7.3.1.3. </span>Scaling data with outliers<a class="headerlink" href="#scaling-data-with-outliers" title="Link to this heading">#</a></h3>
 <p>If your data contains many outliers, scaling using the mean and variance
 of the data is likely to not work very well. In these cases, you can use
 <a class="reference internal" href="http://github.com/generated/sklearn.preprocessing.RobustScaler.html#sklearn.preprocessing.RobustScaler" title="sklearn.preprocessing.RobustScaler"><code class="xref py py-class docutils literal notranslate"><span class="pre">RobustScaler</span></code></a> as a drop-in replacement instead. It uses
@@ -892,7 +893,7 @@ <h3><span class="section-number">6.3.1.3. </span>Scaling data with outliers<a cl
 </div>
 </details></section>
 <section id="centering-kernel-matrices">
-<span id="kernel-centering"></span><h3><span class="section-number">6.3.1.4. </span>Centering kernel matrices<a class="headerlink" href="#centering-kernel-matrices" title="Link to this heading">#</a></h3>
+<span id="kernel-centering"></span><h3><span class="section-number">7.3.1.4. </span>Centering kernel matrices<a class="headerlink" href="#centering-kernel-matrices" title="Link to this heading">#</a></h3>
 <p>If you have a kernel matrix of a kernel <span class="math notranslate nohighlight">\(K\)</span> that computes a dot product
 in a feature space (possibly implicitly) defined by a function
 <span class="math notranslate nohighlight">\(\phi(\cdot)\)</span>, a <a class="reference internal" href="http://github.com/generated/sklearn.preprocessing.KernelCenterer.html#sklearn.preprocessing.KernelCenterer" title="sklearn.preprocessing.KernelCenterer"><code class="xref py py-class docutils literal notranslate"><span class="pre">KernelCenterer</span></code></a> can transform the kernel matrix
@@ -948,7 +949,7 @@ <h3><span class="section-number">6.3.1.3. </span>Scaling data with outliers<a cl
 </details></section>
 </section>
 <section id="non-linear-transformation">
-<span id="preprocessing-transformer"></span><h2><span class="section-number">6.3.2. </span>Non-linear transformation<a class="headerlink" href="#non-linear-transformation" title="Link to this heading">#</a></h2>
+<span id="preprocessing-transformer"></span><h2><span class="section-number">7.3.2. </span>Non-linear transformation<a class="headerlink" href="#non-linear-transformation" title="Link to this heading">#</a></h2>
 <p>Two types of transformations are available: quantile transforms and power
 transforms. Both quantile and power transforms are based on monotonic
 transformations of the features and thus preserve the rank of the values
@@ -968,7 +969,7 @@ <h3><span class="section-number">6.3.1.3. </span>Scaling data with outliers<a cl
 <p>Power transforms are a family of parametric transformations that aim to map
 data from any distribution to as close to a Gaussian distribution.</p>
 <section id="mapping-to-a-uniform-distribution">
-<h3><span class="section-number">6.3.2.1. </span>Mapping to a Uniform distribution<a class="headerlink" href="#mapping-to-a-uniform-distribution" title="Link to this heading">#</a></h3>
+<h3><span class="section-number">7.3.2.1. </span>Mapping to a Uniform distribution<a class="headerlink" href="#mapping-to-a-uniform-distribution" title="Link to this heading">#</a></h3>
 <p><a class="reference internal" href="http://github.com/generated/sklearn.preprocessing.QuantileTransformer.html#sklearn.preprocessing.QuantileTransformer" title="sklearn.preprocessing.QuantileTransformer"><code class="xref py py-class docutils literal notranslate"><span class="pre">QuantileTransformer</span></code></a> provides a non-parametric
 transformation to map the data to a uniform distribution
 with values between 0 and 1:</p>
@@ -988,7 +989,7 @@ <h3><span class="section-number">6.3.2.1. </span>Mapping to a Uniform distributi
 previously defined:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">np</span><span class="o">.</span><span class="n">percentile</span><span class="p">(</span><span class="n">X_train_trans</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">25</span><span class="p">,</span> <span class="mi">50</span><span class="p">,</span> <span class="mi">75</span><span class="p">,</span> <span class="mi">100</span><span class="p">])</span>
 <span class="gp">... </span>
-<span class="go">array([ 0.00... ,  0.24...,  0.49...,  0.73...,  0.99... ])</span>
+<span class="go">array([ 0.00 ,  0.24,  0.49,  0.73,  0.99 ])</span>
 </pre></div>
 </div>
 <p>This can be confirmed on an independent testing set with similar remarks:</p>
@@ -997,12 +998,12 @@ <h3><span class="section-number">6.3.2.1. </span>Mapping to a Uniform distributi
 <span class="go">array([ 4.4  ,  5.125,  5.75 ,  6.175,  7.3  ])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">np</span><span class="o">.</span><span class="n">percentile</span><span class="p">(</span><span class="n">X_test_trans</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">25</span><span class="p">,</span> <span class="mi">50</span><span class="p">,</span> <span class="mi">75</span><span class="p">,</span> <span class="mi">100</span><span class="p">])</span>
 <span class="gp">... </span>
-<span class="go">array([ 0.01...,  0.25...,  0.46...,  0.60... ,  0.94...])</span>
+<span class="go">array([ 0.01,  0.25,  0.46,  0.60 ,  0.94])</span>
 </pre></div>
 </div>
 </section>
 <section id="mapping-to-a-gaussian-distribution">
-<h3><span class="section-number">6.3.2.2. </span>Mapping to a Gaussian distribution<a class="headerlink" href="#mapping-to-a-gaussian-distribution" title="Link to this heading">#</a></h3>
+<h3><span class="section-number">7.3.2.2. </span>Mapping to a Gaussian distribution<a class="headerlink" href="#mapping-to-a-gaussian-distribution" title="Link to this heading">#</a></h3>
 <p>In many modeling scenarios, normality of the features in a dataset is desirable.
 Power transforms are a family of parametric, monotonic transformations that aim
 to map data from any distribution to as close to a Gaussian distribution as
@@ -1037,13 +1038,13 @@ <h3><span class="section-number">6.3.2.2. </span>Mapping to a Gaussian distribut
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">pt</span> <span class="o">=</span> <span class="n">preprocessing</span><span class="o">.</span><span class="n">PowerTransformer</span><span class="p">(</span><span class="n">method</span><span class="o">=</span><span class="s1">&#39;box-cox&#39;</span><span class="p">,</span> <span class="n">standardize</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">X_lognormal</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">RandomState</span><span class="p">(</span><span class="mi">616</span><span class="p">)</span><span class="o">.</span><span class="n">lognormal</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">X_lognormal</span>
-<span class="go">array([[1.28..., 1.18..., 0.84...],</span>
-<span class="go">      [0.94..., 1.60..., 0.38...],</span>
-<span class="go">      [1.35..., 0.21..., 1.09...]])</span>
+<span class="go">array([[1.28, 1.18 , 0.84 ],</span>
+<span class="go">       [0.94, 1.60 , 0.388],</span>
+<span class="go">       [1.35, 0.217, 1.09 ]])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">pt</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">X_lognormal</span><span class="p">)</span>
-<span class="go">array([[ 0.49...,  0.17..., -0.15...],</span>
-<span class="go">      [-0.05...,  0.58..., -0.57...],</span>
-<span class="go">      [ 0.69..., -0.84...,  0.10...]])</span>
+<span class="go">array([[ 0.49 ,  0.179, -0.156],</span>
+<span class="go">       [-0.051,  0.589, -0.576],</span>
+<span class="go">       [ 0.69 , -0.849,  0.101]])</span>
 </pre></div>
 </div>
 <p class="sd-card-text">While the above example sets the <code class="docutils literal notranslate"><span class="pre">standardize</span></code> option to <code class="docutils literal notranslate"><span class="pre">False</span></code>,
@@ -1082,7 +1083,7 @@ <h3><span class="section-number">6.3.2.2. </span>Mapping to a Gaussian distribut
 </section>
 </section>
 <section id="normalization">
-<span id="preprocessing-normalization"></span><h2><span class="section-number">6.3.3. </span>Normalization<a class="headerlink" href="#normalization" title="Link to this heading">#</a></h2>
+<span id="preprocessing-normalization"></span><h2><span class="section-number">7.3.3. </span>Normalization<a class="headerlink" href="#normalization" title="Link to this heading">#</a></h2>
 <p><strong>Normalization</strong> is the process of <strong>scaling individual samples to have
 unit norm</strong>. This process can be useful if you plan to use a quadratic form
 such as the dot-product or any other kernel to quantify the similarity
@@ -1098,9 +1099,9 @@ <h3><span class="section-number">6.3.2.2. </span>Mapping to a Gaussian distribut
 <span class="gp">&gt;&gt;&gt; </span><span class="n">X_normalized</span> <span class="o">=</span> <span class="n">preprocessing</span><span class="o">.</span><span class="n">normalize</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">norm</span><span class="o">=</span><span class="s1">&#39;l2&#39;</span><span class="p">)</span>
 
 <span class="gp">&gt;&gt;&gt; </span><span class="n">X_normalized</span>
-<span class="go">array([[ 0.40..., -0.40...,  0.81...],</span>
-<span class="go">       [ 1.  ...,  0.  ...,  0.  ...],</span>
-<span class="go">       [ 0.  ...,  0.70..., -0.70...]])</span>
+<span class="go">array([[ 0.408, -0.408,  0.812],</span>
+<span class="go">       [ 1.   ,  0.   ,  0.   ],</span>
+<span class="go">       [ 0.   ,  0.707, -0.707]])</span>
 </pre></div>
 </div>
 <p>The <code class="docutils literal notranslate"><span class="pre">preprocessing</span></code> module further provides a utility class
@@ -1116,12 +1117,12 @@ <h3><span class="section-number">6.3.2.2. </span>Mapping to a Gaussian distribut
 </div>
 <p>The normalizer instance can then be used on sample vectors as any transformer:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">normalizer</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
-<span class="go">array([[ 0.40..., -0.40...,  0.81...],</span>
-<span class="go">       [ 1.  ...,  0.  ...,  0.  ...],</span>
-<span class="go">       [ 0.  ...,  0.70..., -0.70...]])</span>
+<span class="go">array([[ 0.408, -0.408,  0.812],</span>
+<span class="go">       [ 1.   ,  0.   ,  0.   ],</span>
+<span class="go">       [ 0.   ,  0.707, -0.707]])</span>
 
 <span class="gp">&gt;&gt;&gt; </span><span class="n">normalizer</span><span class="o">.</span><span class="n">transform</span><span class="p">([[</span><span class="o">-</span><span class="mf">1.</span><span class="p">,</span>  <span class="mf">1.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">]])</span>
-<span class="go">array([[-0.70...,  0.70...,  0.  ...]])</span>
+<span class="go">array([[-0.707,  0.707,  0.]])</span>
 </pre></div>
 </div>
 <p>Note: L2 normalization is also known as spatial sign preprocessing.</p>
@@ -1137,7 +1138,7 @@ <h3><span class="section-number">6.3.2.2. </span>Mapping to a Gaussian distribut
 </div>
 </details></section>
 <section id="encoding-categorical-features">
-<span id="preprocessing-categorical-features"></span><h2><span class="section-number">6.3.4. </span>Encoding categorical features<a class="headerlink" href="#encoding-categorical-features" title="Link to this heading">#</a></h2>
+<span id="preprocessing-categorical-features"></span><h2><span class="section-number">7.3.4. </span>Encoding categorical features<a class="headerlink" href="#encoding-categorical-features" title="Link to this heading">#</a></h2>
 <p>Often features are not given as continuous values but categorical.
 For example a person could have features <code class="docutils literal notranslate"><span class="pre">[&quot;male&quot;,</span> <span class="pre">&quot;female&quot;]</span></code>,
 <code class="docutils literal notranslate"><span class="pre">[&quot;from</span> <span class="pre">Europe&quot;,</span> <span class="pre">&quot;from</span> <span class="pre">US&quot;,</span> <span class="pre">&quot;from</span> <span class="pre">Asia&quot;]</span></code>,
@@ -1354,7 +1355,7 @@ <h3><span class="section-number">6.3.2.2. </span>Mapping to a Gaussian distribut
 represented as a dict, not as scalars.</p>
 </div>
 </details><section id="infrequent-categories">
-<span id="encoder-infrequent-categories"></span><h3><span class="section-number">6.3.4.1. </span>Infrequent categories<a class="headerlink" href="#infrequent-categories" title="Link to this heading">#</a></h3>
+<span id="encoder-infrequent-categories"></span><h3><span class="section-number">7.3.4.1. </span>Infrequent categories<a class="headerlink" href="#infrequent-categories" title="Link to this heading">#</a></h3>
 <p><a class="reference internal" href="http://github.com/generated/sklearn.preprocessing.OneHotEncoder.html#sklearn.preprocessing.OneHotEncoder" title="sklearn.preprocessing.OneHotEncoder"><code class="xref py py-class docutils literal notranslate"><span class="pre">OneHotEncoder</span></code></a> and <a class="reference internal" href="http://github.com/generated/sklearn.preprocessing.OrdinalEncoder.html#sklearn.preprocessing.OrdinalEncoder" title="sklearn.preprocessing.OrdinalEncoder"><code class="xref py py-class docutils literal notranslate"><span class="pre">OrdinalEncoder</span></code></a> support aggregating
 infrequent categories into a single output for each feature. The parameters to
 enable the gathering of infrequent categories are <code class="docutils literal notranslate"><span class="pre">min_frequency</span></code> and
@@ -1371,8 +1372,8 @@ <h3><span class="section-number">6.3.2.2. </span>Mapping to a Gaussian distribut
 input feature. <code class="docutils literal notranslate"><span class="pre">max_categories</span></code> includes the feature that combines
 infrequent categories.</p></li>
 </ol>
-<p>In the following example with <a class="reference internal" href="http://github.com/generated/sklearn.preprocessing.OrdinalEncoder.html#sklearn.preprocessing.OrdinalEncoder" title="sklearn.preprocessing.OrdinalEncoder"><code class="xref py py-class docutils literal notranslate"><span class="pre">OrdinalEncoder</span></code></a>, the categories <code class="docutils literal notranslate"><span class="pre">'dog'</span></code> and
-<code class="docutils literal notranslate"><span class="pre">'snake'</span></code> are considered infrequent:</p>
+<p>In the following example with <a class="reference internal" href="http://github.com/generated/sklearn.preprocessing.OrdinalEncoder.html#sklearn.preprocessing.OrdinalEncoder" title="sklearn.preprocessing.OrdinalEncoder"><code class="xref py py-class docutils literal notranslate"><span class="pre">OrdinalEncoder</span></code></a>, the categories <code class="docutils literal notranslate"><span class="pre">'dog'</span></code>
+and <code class="docutils literal notranslate"><span class="pre">'snake'</span></code> are considered infrequent:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="s1">&#39;dog&#39;</span><span class="p">]</span> <span class="o">*</span> <span class="mi">5</span> <span class="o">+</span> <span class="p">[</span><span class="s1">&#39;cat&#39;</span><span class="p">]</span> <span class="o">*</span> <span class="mi">20</span> <span class="o">+</span> <span class="p">[</span><span class="s1">&#39;rabbit&#39;</span><span class="p">]</span> <span class="o">*</span> <span class="mi">10</span> <span class="o">+</span>
 <span class="gp">... </span>              <span class="p">[</span><span class="s1">&#39;snake&#39;</span><span class="p">]</span> <span class="o">*</span> <span class="mi">3</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">object</span><span class="p">)</span><span class="o">.</span><span class="n">T</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">enc</span> <span class="o">=</span> <span class="n">preprocessing</span><span class="o">.</span><span class="n">OrdinalEncoder</span><span class="p">(</span><span class="n">min_frequency</span><span class="o">=</span><span class="mi">6</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
@@ -1488,7 +1489,7 @@ <h3><span class="section-number">6.3.2.2. </span>Mapping to a Gaussian distribut
 </div>
 </section>
 <section id="target-encoder">
-<span id="id2"></span><h3><span class="section-number">6.3.4.2. </span>Target Encoder<a class="headerlink" href="#target-encoder" title="Link to this heading">#</a></h3>
+<span id="id2"></span><h3><span class="section-number">7.3.4.2. </span>Target Encoder<a class="headerlink" href="#target-encoder" title="Link to this heading">#</a></h3>
 <p>The <a class="reference internal" href="http://github.com/generated/sklearn.preprocessing.TargetEncoder.html#sklearn.preprocessing.TargetEncoder" title="sklearn.preprocessing.TargetEncoder"><code class="xref py py-class docutils literal notranslate"><span class="pre">TargetEncoder</span></code></a> uses the target mean conditioned on the categorical
 feature for encoding unordered categories, i.e. nominal categories <a class="reference internal" href="#par" id="id3"><span>[PAR]</span></a>
 <a class="reference internal" href="#mic" id="id4"><span>[MIC]</span></a>. This encoding scheme is useful with categorical features with high
@@ -1592,7 +1593,7 @@ <h3><span class="section-number">6.3.2.2. </span>Mapping to a Gaussian distribut
 </section>
 </section>
 <section id="discretization">
-<span id="preprocessing-discretization"></span><h2><span class="section-number">6.3.5. </span>Discretization<a class="headerlink" href="#discretization" title="Link to this heading">#</a></h2>
+<span id="preprocessing-discretization"></span><h2><span class="section-number">7.3.5. </span>Discretization<a class="headerlink" href="#discretization" title="Link to this heading">#</a></h2>
 <p><a class="reference external" href="https://en.wikipedia.org/wiki/Discretization_of_continuous_features">Discretization</a>
 (otherwise known as quantization or binning) provides a way to partition continuous
 features into discrete values. Certain datasets with continuous features
@@ -1604,7 +1605,7 @@ <h3><span class="section-number">6.3.2.2. </span>Mapping to a Gaussian distribut
 in particular smooth ones, see <a class="reference internal" href="#generating-polynomial-features"><span class="std std-ref">Generating polynomial features</span></a> further
 below.</p>
 <section id="k-bins-discretization">
-<h3><span class="section-number">6.3.5.1. </span>K-bins discretization<a class="headerlink" href="#k-bins-discretization" title="Link to this heading">#</a></h3>
+<h3><span class="section-number">7.3.5.1. </span>K-bins discretization<a class="headerlink" href="#k-bins-discretization" title="Link to this heading">#</a></h3>
 <p><a class="reference internal" href="http://github.com/generated/sklearn.preprocessing.KBinsDiscretizer.html#sklearn.preprocessing.KBinsDiscretizer" title="sklearn.preprocessing.KBinsDiscretizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">KBinsDiscretizer</span></code></a> discretizes features into <code class="docutils literal notranslate"><span class="pre">k</span></code> bins:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span> <span class="o">-</span><span class="mf">3.</span><span class="p">,</span> <span class="mf">5.</span><span class="p">,</span> <span class="mi">15</span> <span class="p">],</span>
 <span class="gp">... </span>              <span class="p">[</span>  <span class="mf">0.</span><span class="p">,</span> <span class="mf">6.</span><span class="p">,</span> <span class="mi">14</span> <span class="p">],</span>
@@ -1666,7 +1667,7 @@ <h3><span class="section-number">6.3.5.1. </span>K-bins discretization<a class="
 </ul>
 </section>
 <section id="feature-binarization">
-<span id="preprocessing-binarization"></span><h3><span class="section-number">6.3.5.2. </span>Feature binarization<a class="headerlink" href="#feature-binarization" title="Link to this heading">#</a></h3>
+<span id="preprocessing-binarization"></span><h3><span class="section-number">7.3.5.2. </span>Feature binarization<a class="headerlink" href="#feature-binarization" title="Link to this heading">#</a></h3>
 <p><strong>Feature binarization</strong> is the process of <strong>thresholding numerical
 features to get boolean values</strong>. This can be useful for downstream
 probabilistic estimators that make assumption that the input data
@@ -1719,17 +1720,17 @@ <h3><span class="section-number">6.3.5.1. </span>K-bins discretization<a class="
 </section>
 </section>
 <section id="imputation-of-missing-values">
-<span id="imputation"></span><h2><span class="section-number">6.3.6. </span>Imputation of missing values<a class="headerlink" href="#imputation-of-missing-values" title="Link to this heading">#</a></h2>
+<span id="imputation"></span><h2><span class="section-number">7.3.6. </span>Imputation of missing values<a class="headerlink" href="#imputation-of-missing-values" title="Link to this heading">#</a></h2>
 <p>Tools for imputing missing values are discussed at <a class="reference internal" href="http://github.com/impute.html#impute"><span class="std std-ref">Imputation of missing values</span></a>.</p>
 </section>
 <section id="generating-polynomial-features">
-<span id="id6"></span><h2><span class="section-number">6.3.7. </span>Generating polynomial features<a class="headerlink" href="#generating-polynomial-features" title="Link to this heading">#</a></h2>
+<span id="id6"></span><h2><span class="section-number">7.3.7. </span>Generating polynomial features<a class="headerlink" href="#generating-polynomial-features" title="Link to this heading">#</a></h2>
 <p>Often it’s useful to add complexity to a model by considering nonlinear
 features of the input data. We show two possibilities that are both based on
 polynomials: The first one uses pure polynomials, the second one uses splines,
 i.e. piecewise polynomials.</p>
 <section id="polynomial-features">
-<span id="id7"></span><h3><span class="section-number">6.3.7.1. </span>Polynomial features<a class="headerlink" href="#polynomial-features" title="Link to this heading">#</a></h3>
+<span id="id7"></span><h3><span class="section-number">7.3.7.1. </span>Polynomial features<a class="headerlink" href="#polynomial-features" title="Link to this heading">#</a></h3>
 <p>A simple and common method to use is polynomial features, which can get
 features’ high-order and interaction terms. It is implemented in
 <a class="reference internal" href="http://github.com/generated/sklearn.preprocessing.PolynomialFeatures.html#sklearn.preprocessing.PolynomialFeatures" title="sklearn.preprocessing.PolynomialFeatures"><code class="xref py py-class docutils literal notranslate"><span class="pre">PolynomialFeatures</span></code></a>:</p>
@@ -1771,7 +1772,7 @@ <h3><span class="section-number">6.3.5.1. </span>K-bins discretization<a class="
 for Ridge regression using created polynomial features.</p>
 </section>
 <section id="spline-transformer">
-<span id="id8"></span><h3><span class="section-number">6.3.7.2. </span>Spline transformer<a class="headerlink" href="#spline-transformer" title="Link to this heading">#</a></h3>
+<span id="id8"></span><h3><span class="section-number">7.3.7.2. </span>Spline transformer<a class="headerlink" href="#spline-transformer" title="Link to this heading">#</a></h3>
 <p>Another way to add nonlinear terms instead of pure polynomials of features is
 to generate spline basis functions for each feature with the
 <a class="reference internal" href="http://github.com/generated/sklearn.preprocessing.SplineTransformer.html#sklearn.preprocessing.SplineTransformer" title="sklearn.preprocessing.SplineTransformer"><code class="xref py py-class docutils literal notranslate"><span class="pre">SplineTransformer</span></code></a>. Splines are piecewise polynomials, parametrized by
@@ -1848,7 +1849,7 @@ <h3><span class="section-number">6.3.5.1. </span>K-bins discretization<a class="
 </details></section>
 </section>
 <section id="custom-transformers">
-<span id="function-transformer"></span><h2><span class="section-number">6.3.8. </span>Custom transformers<a class="headerlink" href="#custom-transformers" title="Link to this heading">#</a></h2>
+<span id="function-transformer"></span><h2><span class="section-number">7.3.8. </span>Custom transformers<a class="headerlink" href="#custom-transformers" title="Link to this heading">#</a></h2>
 <p>Often, you will want to convert an existing Python function into a transformer
 to assist in data cleaning or processing. You can implement a transformer from
 an arbitrary function with <a class="reference internal" href="http://github.com/generated/sklearn.preprocessing.FunctionTransformer.html#sklearn.preprocessing.FunctionTransformer" title="sklearn.preprocessing.FunctionTransformer"><code class="xref py py-class docutils literal notranslate"><span class="pre">FunctionTransformer</span></code></a>. For example, to build
@@ -1895,7 +1896,7 @@ <h3><span class="section-number">6.3.5.1. </span>K-bins discretization<a class="
       <i class="fa-solid fa-angle-left"></i>
       <div class="prev-next-info">
         <p class="prev-next-subtitle">previous</p>
-        <p class="prev-next-title"><span class="section-number">6.2. </span>Feature extraction</p>
+        <p class="prev-next-title"><span class="section-number">7.2. </span>Feature extraction</p>
       </div>
     </a>
     <a class="right-next"
@@ -1903,7 +1904,7 @@ <h3><span class="section-number">6.3.5.1. </span>K-bins discretization<a class="
        title="next page">
       <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">6.4. </span>Imputation of missing values</p>
+        <p class="prev-next-title"><span class="section-number">7.4. </span>Imputation of missing values</p>
       </div>
       <i class="fa-solid fa-angle-right"></i>
     </a>
@@ -1930,36 +1931,36 @@ <h3><span class="section-number">6.3.5.1. </span>K-bins discretization<a class="
   </div>
   <nav class="bd-toc-nav page-toc" aria-labelledby="pst-page-navigation-heading-2">
     <ul class="visible nav section-nav flex-column">
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#standardization-or-mean-removal-and-variance-scaling">6.3.1. Standardization, or mean removal and variance scaling</a><ul class="nav section-nav flex-column">
-<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#scaling-features-to-a-range">6.3.1.1. Scaling features to a range</a></li>
-<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#scaling-sparse-data">6.3.1.2. Scaling sparse data</a></li>
-<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#scaling-data-with-outliers">6.3.1.3. Scaling data with outliers</a></li>
-<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#centering-kernel-matrices">6.3.1.4. Centering kernel matrices</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#standardization-or-mean-removal-and-variance-scaling">7.3.1. Standardization, or mean removal and variance scaling</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#scaling-features-to-a-range">7.3.1.1. Scaling features to a range</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#scaling-sparse-data">7.3.1.2. Scaling sparse data</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#scaling-data-with-outliers">7.3.1.3. Scaling data with outliers</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#centering-kernel-matrices">7.3.1.4. Centering kernel matrices</a></li>
 </ul>
 </li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#non-linear-transformation">6.3.2. Non-linear transformation</a><ul class="nav section-nav flex-column">
-<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#mapping-to-a-uniform-distribution">6.3.2.1. Mapping to a Uniform distribution</a></li>
-<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#mapping-to-a-gaussian-distribution">6.3.2.2. Mapping to a Gaussian distribution</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#non-linear-transformation">7.3.2. Non-linear transformation</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#mapping-to-a-uniform-distribution">7.3.2.1. Mapping to a Uniform distribution</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#mapping-to-a-gaussian-distribution">7.3.2.2. Mapping to a Gaussian distribution</a></li>
 </ul>
 </li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#normalization">6.3.3. Normalization</a></li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#encoding-categorical-features">6.3.4. Encoding categorical features</a><ul class="nav section-nav flex-column">
-<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#infrequent-categories">6.3.4.1. Infrequent categories</a></li>
-<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#target-encoder">6.3.4.2. Target Encoder</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#normalization">7.3.3. Normalization</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#encoding-categorical-features">7.3.4. Encoding categorical features</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#infrequent-categories">7.3.4.1. Infrequent categories</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#target-encoder">7.3.4.2. Target Encoder</a></li>
 </ul>
 </li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#discretization">6.3.5. Discretization</a><ul class="nav section-nav flex-column">
-<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#k-bins-discretization">6.3.5.1. K-bins discretization</a></li>
-<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#feature-binarization">6.3.5.2. Feature binarization</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#discretization">7.3.5. Discretization</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#k-bins-discretization">7.3.5.1. K-bins discretization</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#feature-binarization">7.3.5.2. Feature binarization</a></li>
 </ul>
 </li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#imputation-of-missing-values">6.3.6. Imputation of missing values</a></li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#generating-polynomial-features">6.3.7. Generating polynomial features</a><ul class="nav section-nav flex-column">
-<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#polynomial-features">6.3.7.1. Polynomial features</a></li>
-<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#spline-transformer">6.3.7.2. Spline transformer</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#imputation-of-missing-values">7.3.6. Imputation of missing values</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#generating-polynomial-features">7.3.7. Generating polynomial features</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#polynomial-features">7.3.7.1. Polynomial features</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#spline-transformer">7.3.7.2. Spline transformer</a></li>
 </ul>
 </li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#custom-transformers">6.3.8. Custom transformers</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#custom-transformers">7.3.8. Custom transformers</a></li>
 </ul>
   </nav></div>
 
diff --git a/dev/modules/preprocessing_targets.html b/dev/modules/preprocessing_targets.html
index 9a24ec895593c..01b7300412bf7 100644
--- a/dev/modules/preprocessing_targets.html
+++ b/dev/modules/preprocessing_targets.html
@@ -7,7 +7,7 @@
   <head>
     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="viewport" content="width=device-width, initial-scale=1" />
-<meta property="og:title" content="6.9. Transforming the prediction target (y)" />
+<meta property="og:title" content="7.9. Transforming the prediction target (y)" />
 <meta property="og:type" content="website" />
 <meta property="og:url" content="https://scikit-learn/stable/modules/preprocessing_targets.html" />
 <meta property="og:site_name" content="scikit-learn" />
@@ -16,7 +16,7 @@
 <meta property="og:image:alt" content="scikit-learn" />
 <meta name="description" content="Transforming the prediction target ( y): These are transformers that are not intended to be used on features, only on supervised learning targets. See also Transforming target in regression if you ..." />
 
-    <title>6.9. Transforming the prediction target (y) &#8212; scikit-learn 1.7.dev0 documentation</title>
+    <title>7.9. Transforming the prediction target (y) &#8212; scikit-learn 1.7.dev0 documentation</title>
   
   
   
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -80,8 +80,8 @@
     <link rel="icon" href="http://github.com/_static/favicon.ico"/>
     <link rel="author" title="About these documents" href="http://github.com/about.html" />
     <link rel="search" title="Search" href="http://github.com/search.html" />
-    <link rel="next" title="7. Dataset loading utilities" href="http://github.com/datasets.html" />
-    <link rel="prev" title="6.8. Pairwise metrics, Affinities and Kernels" href="http://github.com/metrics.html" />
+    <link rel="next" title="8. Dataset loading utilities" href="http://github.com/datasets.html" />
+    <link rel="prev" title="7.8. Pairwise metrics, Affinities and Kernels" href="http://github.com/metrics.html" />
   <meta name="viewport" content="width=device-width, initial-scale=1"/>
   <meta name="docsearch:language" content="en"/>
   <meta name="docsearch:version" content="1.7" />
@@ -593,45 +593,46 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/learning_curve.html">3.5. Validation curves: plotting scores to evaluate models</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/inspection.html">4. Inspection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/partial_dependence.html">4.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/permutation_importance.html">4.2. Permutation feature importance</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/metadata_routing.html">4. Metadata Routing</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/inspection.html">5. Inspection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/partial_dependence.html">5.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/permutation_importance.html">5.2. Permutation feature importance</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/visualizations.html">5. Visualizations</a></li>
-<li class="toctree-l1 current active has-children"><a class="reference internal" href="http://github.com/data_transforms.html">6. Dataset transformations</a><details open="open"><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul class="current">
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/compose.html">6.1. Pipelines and composite estimators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/feature_extraction.html">6.2. Feature extraction</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing.html">6.3. Preprocessing data</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/impute.html">6.4. Imputation of missing values</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/unsupervised_reduction.html">6.5. Unsupervised dimensionality reduction</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/random_projection.html">6.6. Random Projection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/kernel_approximation.html">6.7. Kernel Approximation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/metrics.html">6.8. Pairwise metrics, Affinities and Kernels</a></li>
-<li class="toctree-l2 current active"><a class="current reference internal" href="#">6.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/visualizations.html">6. Visualizations</a></li>
+<li class="toctree-l1 current active has-children"><a class="reference internal" href="http://github.com/data_transforms.html">7. Dataset transformations</a><details open="open"><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/compose.html">7.1. Pipelines and composite estimators</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/feature_extraction.html">7.2. Feature extraction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing.html">7.3. Preprocessing data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/impute.html">7.4. Imputation of missing values</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/unsupervised_reduction.html">7.5. Unsupervised dimensionality reduction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/random_projection.html">7.6. Random Projection</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/kernel_approximation.html">7.7. Kernel Approximation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/metrics.html">7.8. Pairwise metrics, Affinities and Kernels</a></li>
+<li class="toctree-l2 current active"><a class="current reference internal" href="#">7.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/datasets.html">7. Dataset loading utilities</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html">7.1. Toy datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html">7.2. Real world datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html">7.3. Generated datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html">7.4. Loading other datasets</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/datasets.html">8. Dataset loading utilities</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html">8.1. Toy datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html">8.2. Real world datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html">8.3. Generated datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html">8.4. Loading other datasets</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/computing.html">8. Computing with scikit-learn</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/scaling_strategies.html">8.1. Strategies to scale computationally: bigger data</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/computational_performance.html">8.2. Computational Performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/parallelism.html">8.3. Parallelism, resource management, and configuration</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/computing.html">9. Computing with scikit-learn</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/scaling_strategies.html">9.1. Strategies to scale computationally: bigger data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/computational_performance.html">9.2. Computational Performance</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/parallelism.html">9.3. Parallelism, resource management, and configuration</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/model_persistence.html">9. Model persistence</a></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/common_pitfalls.html">10. Common pitfalls and recommended practices</a></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/dispatching.html">11. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/array_api.html">11.1. Array API support (experimental)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/model_persistence.html">10. Model persistence</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/common_pitfalls.html">11. Common pitfalls and recommended practices</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/dispatching.html">12. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/array_api.html">12.1. Array API support (experimental)</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/machine_learning_map.html">12. Choosing the right estimator</a></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/presentations.html">13. External Resources, Videos and Talks</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/machine_learning_map.html">13. Choosing the right estimator</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/presentations.html">14. External Resources, Videos and Talks</a></li>
 </ul>
 </div>
 </nav></div>
@@ -669,9 +670,9 @@
     <li class="breadcrumb-item"><a href="http://github.com/user_guide.html" class="nav-link">User Guide</a></li>
     
     
-    <li class="breadcrumb-item"><a href="http://github.com/data_transforms.html" class="nav-link"><span class="section-number">6. </span>Dataset transformations</a></li>
+    <li class="breadcrumb-item"><a href="http://github.com/data_transforms.html" class="nav-link"><span class="section-number">7. </span>Dataset transformations</a></li>
     
-    <li class="breadcrumb-item active" aria-current="page"><span class="ellipsis"><span class="section-number">6.9. </span>Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</span></li>
+    <li class="breadcrumb-item active" aria-current="page"><span class="ellipsis"><span class="section-number">7.9. </span>Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</span></li>
   </ul>
 </nav>
 </div>
@@ -689,15 +690,15 @@
                 <article class="bd-article">
                   
   <section id="transforming-the-prediction-target-y">
-<span id="preprocessing-targets"></span><h1><span class="section-number">6.9. </span>Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)<a class="headerlink" href="#transforming-the-prediction-target-y" title="Link to this heading">#</a></h1>
+<span id="preprocessing-targets"></span><h1><span class="section-number">7.9. </span>Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)<a class="headerlink" href="#transforming-the-prediction-target-y" title="Link to this heading">#</a></h1>
 <p>These are transformers that are not intended to be used on features, only on
 supervised learning targets. See also <a class="reference internal" href="http://github.com/compose.html#transformed-target-regressor"><span class="std std-ref">Transforming target in regression</span></a> if
 you want to transform the prediction target for learning, but evaluate the
 model in the original (untransformed) space.</p>
 <section id="label-binarization">
-<h2><span class="section-number">6.9.1. </span>Label binarization<a class="headerlink" href="#label-binarization" title="Link to this heading">#</a></h2>
+<h2><span class="section-number">7.9.1. </span>Label binarization<a class="headerlink" href="#label-binarization" title="Link to this heading">#</a></h2>
 <section id="labelbinarizer">
-<h3><span class="section-number">6.9.1.1. </span>LabelBinarizer<a class="headerlink" href="#labelbinarizer" title="Link to this heading">#</a></h3>
+<h3><span class="section-number">7.9.1.1. </span>LabelBinarizer<a class="headerlink" href="#labelbinarizer" title="Link to this heading">#</a></h3>
 <p><a class="reference internal" href="http://github.com/generated/sklearn.preprocessing.LabelBinarizer.html#sklearn.preprocessing.LabelBinarizer" title="sklearn.preprocessing.LabelBinarizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">LabelBinarizer</span></code></a> is a utility class to help create a <a class="reference internal" href="http://github.com/glossary.html#term-label-indicator-matrix"><span class="xref std std-term">label
 indicator matrix</span></a> from a list of <a class="reference internal" href="http://github.com/glossary.html#term-multiclass"><span class="xref std std-term">multiclass</span></a> labels:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn</span><span class="w"> </span><span class="kn">import</span> <span class="n">preprocessing</span>
@@ -722,7 +723,7 @@ <h3><span class="section-number">6.9.1.1. </span>LabelBinarizer<a class="headerl
 <a class="reference internal" href="http://github.com/multiclass.html#multiclass-classification"><span class="std std-ref">Multiclass classification</span></a>.</p>
 </section>
 <section id="multilabelbinarizer">
-<h3><span class="section-number">6.9.1.2. </span>MultiLabelBinarizer<a class="headerlink" href="#multilabelbinarizer" title="Link to this heading">#</a></h3>
+<h3><span class="section-number">7.9.1.2. </span>MultiLabelBinarizer<a class="headerlink" href="#multilabelbinarizer" title="Link to this heading">#</a></h3>
 <p>In <a class="reference internal" href="http://github.com/glossary.html#term-multilabel"><span class="xref std std-term">multilabel</span></a> learning, the joint set of binary classification tasks is
 expressed with a label binary indicator array: each sample is one row of a 2d
 array of shape (n_samples, n_classes) with binary values where the one, i.e. the
@@ -749,7 +750,7 @@ <h3><span class="section-number">6.9.1.2. </span>MultiLabelBinarizer<a class="he
 </section>
 </section>
 <section id="label-encoding">
-<h2><span class="section-number">6.9.2. </span>Label encoding<a class="headerlink" href="#label-encoding" title="Link to this heading">#</a></h2>
+<h2><span class="section-number">7.9.2. </span>Label encoding<a class="headerlink" href="#label-encoding" title="Link to this heading">#</a></h2>
 <p><a class="reference internal" href="http://github.com/generated/sklearn.preprocessing.LabelEncoder.html#sklearn.preprocessing.LabelEncoder" title="sklearn.preprocessing.LabelEncoder"><code class="xref py py-class docutils literal notranslate"><span class="pre">LabelEncoder</span></code></a> is a utility class to help normalize labels such that
 they contain only values between 0 and n_classes-1. This is sometimes useful
 for writing efficient Cython routines. <a class="reference internal" href="http://github.com/generated/sklearn.preprocessing.LabelEncoder.html#sklearn.preprocessing.LabelEncoder" title="sklearn.preprocessing.LabelEncoder"><code class="xref py py-class docutils literal notranslate"><span class="pre">LabelEncoder</span></code></a> can be used as
@@ -798,7 +799,7 @@ <h2><span class="section-number">6.9.2. </span>Label encoding<a class="headerlin
       <i class="fa-solid fa-angle-left"></i>
       <div class="prev-next-info">
         <p class="prev-next-subtitle">previous</p>
-        <p class="prev-next-title"><span class="section-number">6.8. </span>Pairwise metrics, Affinities and Kernels</p>
+        <p class="prev-next-title"><span class="section-number">7.8. </span>Pairwise metrics, Affinities and Kernels</p>
       </div>
     </a>
     <a class="right-next"
@@ -806,7 +807,7 @@ <h2><span class="section-number">6.9.2. </span>Label encoding<a class="headerlin
        title="next page">
       <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">7. </span>Dataset loading utilities</p>
+        <p class="prev-next-title"><span class="section-number">8. </span>Dataset loading utilities</p>
       </div>
       <i class="fa-solid fa-angle-right"></i>
     </a>
@@ -833,12 +834,12 @@ <h2><span class="section-number">6.9.2. </span>Label encoding<a class="headerlin
   </div>
   <nav class="bd-toc-nav page-toc" aria-labelledby="pst-page-navigation-heading-2">
     <ul class="visible nav section-nav flex-column">
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#label-binarization">6.9.1. Label binarization</a><ul class="nav section-nav flex-column">
-<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#labelbinarizer">6.9.1.1. LabelBinarizer</a></li>
-<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#multilabelbinarizer">6.9.1.2. MultiLabelBinarizer</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#label-binarization">7.9.1. Label binarization</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#labelbinarizer">7.9.1.1. LabelBinarizer</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#multilabelbinarizer">7.9.1.2. MultiLabelBinarizer</a></li>
 </ul>
 </li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#label-encoding">6.9.2. Label encoding</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#label-encoding">7.9.2. Label encoding</a></li>
 </ul>
   </nav></div>
 
diff --git a/dev/modules/random_projection.html b/dev/modules/random_projection.html
index c65dae798c802..6d47ee01d1f85 100644
--- a/dev/modules/random_projection.html
+++ b/dev/modules/random_projection.html
@@ -7,7 +7,7 @@
   <head>
     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="viewport" content="width=device-width, initial-scale=1" />
-<meta property="og:title" content="6.6. Random Projection" />
+<meta property="og:title" content="7.6. Random Projection" />
 <meta property="og:type" content="website" />
 <meta property="og:url" content="https://scikit-learn/stable/modules/random_projection.html" />
 <meta property="og:site_name" content="scikit-learn" />
@@ -16,7 +16,7 @@
 <meta property="og:image:alt" content="scikit-learn" />
 <meta name="description" content="The sklearn.random_projection module implements a simple and computationally efficient way to reduce the dimensionality of the data by trading a controlled amount of accuracy (as additional varianc..." />
 
-    <title>6.6. Random Projection &#8212; scikit-learn 1.7.dev0 documentation</title>
+    <title>7.6. Random Projection &#8212; scikit-learn 1.7.dev0 documentation</title>
   
   
   
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -81,8 +81,8 @@
     <link rel="icon" href="http://github.com/_static/favicon.ico"/>
     <link rel="author" title="About these documents" href="http://github.com/about.html" />
     <link rel="search" title="Search" href="http://github.com/search.html" />
-    <link rel="next" title="6.7. Kernel Approximation" href="http://github.com/kernel_approximation.html" />
-    <link rel="prev" title="6.5. Unsupervised dimensionality reduction" href="http://github.com/unsupervised_reduction.html" />
+    <link rel="next" title="7.7. Kernel Approximation" href="http://github.com/kernel_approximation.html" />
+    <link rel="prev" title="7.5. Unsupervised dimensionality reduction" href="http://github.com/unsupervised_reduction.html" />
   <meta name="viewport" content="width=device-width, initial-scale=1"/>
   <meta name="docsearch:language" content="en"/>
   <meta name="docsearch:version" content="1.7" />
@@ -594,45 +594,46 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/learning_curve.html">3.5. Validation curves: plotting scores to evaluate models</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/inspection.html">4. Inspection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/partial_dependence.html">4.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/permutation_importance.html">4.2. Permutation feature importance</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/metadata_routing.html">4. Metadata Routing</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/inspection.html">5. Inspection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/partial_dependence.html">5.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/permutation_importance.html">5.2. Permutation feature importance</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/visualizations.html">5. Visualizations</a></li>
-<li class="toctree-l1 current active has-children"><a class="reference internal" href="http://github.com/data_transforms.html">6. Dataset transformations</a><details open="open"><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul class="current">
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/compose.html">6.1. Pipelines and composite estimators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/feature_extraction.html">6.2. Feature extraction</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing.html">6.3. Preprocessing data</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/impute.html">6.4. Imputation of missing values</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/unsupervised_reduction.html">6.5. Unsupervised dimensionality reduction</a></li>
-<li class="toctree-l2 current active"><a class="current reference internal" href="#">6.6. Random Projection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/kernel_approximation.html">6.7. Kernel Approximation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/metrics.html">6.8. Pairwise metrics, Affinities and Kernels</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing_targets.html">6.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/visualizations.html">6. Visualizations</a></li>
+<li class="toctree-l1 current active has-children"><a class="reference internal" href="http://github.com/data_transforms.html">7. Dataset transformations</a><details open="open"><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/compose.html">7.1. Pipelines and composite estimators</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/feature_extraction.html">7.2. Feature extraction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing.html">7.3. Preprocessing data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/impute.html">7.4. Imputation of missing values</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/unsupervised_reduction.html">7.5. Unsupervised dimensionality reduction</a></li>
+<li class="toctree-l2 current active"><a class="current reference internal" href="#">7.6. Random Projection</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/kernel_approximation.html">7.7. Kernel Approximation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/metrics.html">7.8. Pairwise metrics, Affinities and Kernels</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing_targets.html">7.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/datasets.html">7. Dataset loading utilities</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html">7.1. Toy datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html">7.2. Real world datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html">7.3. Generated datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html">7.4. Loading other datasets</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/datasets.html">8. Dataset loading utilities</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html">8.1. Toy datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html">8.2. Real world datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html">8.3. Generated datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html">8.4. Loading other datasets</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/computing.html">8. Computing with scikit-learn</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/scaling_strategies.html">8.1. Strategies to scale computationally: bigger data</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/computational_performance.html">8.2. Computational Performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/parallelism.html">8.3. Parallelism, resource management, and configuration</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/computing.html">9. Computing with scikit-learn</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/scaling_strategies.html">9.1. Strategies to scale computationally: bigger data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/computational_performance.html">9.2. Computational Performance</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/parallelism.html">9.3. Parallelism, resource management, and configuration</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/model_persistence.html">9. Model persistence</a></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/common_pitfalls.html">10. Common pitfalls and recommended practices</a></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/dispatching.html">11. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/array_api.html">11.1. Array API support (experimental)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/model_persistence.html">10. Model persistence</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/common_pitfalls.html">11. Common pitfalls and recommended practices</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/dispatching.html">12. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/array_api.html">12.1. Array API support (experimental)</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/machine_learning_map.html">12. Choosing the right estimator</a></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/presentations.html">13. External Resources, Videos and Talks</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/machine_learning_map.html">13. Choosing the right estimator</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/presentations.html">14. External Resources, Videos and Talks</a></li>
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@@ -670,9 +671,9 @@
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-    <li class="breadcrumb-item"><a href="http://github.com/data_transforms.html" class="nav-link"><span class="section-number">6. </span>Dataset transformations</a></li>
+    <li class="breadcrumb-item"><a href="http://github.com/data_transforms.html" class="nav-link"><span class="section-number">7. </span>Dataset transformations</a></li>
     
-    <li class="breadcrumb-item active" aria-current="page"><span class="ellipsis"><span class="section-number">6.6. </span>Random Projection</span></li>
+    <li class="breadcrumb-item active" aria-current="page"><span class="ellipsis"><span class="section-number">7.6. </span>Random Projection</span></li>
   </ul>
 </nav>
 </div>
@@ -690,7 +691,7 @@
                 <article class="bd-article">
                   
   <section id="random-projection">
-<span id="id1"></span><h1><span class="section-number">6.6. </span>Random Projection<a class="headerlink" href="#random-projection" title="Link to this heading">#</a></h1>
+<span id="id1"></span><h1><span class="section-number">7.6. </span>Random Projection<a class="headerlink" href="#random-projection" title="Link to this heading">#</a></h1>
 <p>The <a class="reference internal" href="http://github.com/api/sklearn.random_projection.html#module-sklearn.random_projection" title="sklearn.random_projection"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.random_projection</span></code></a> module implements a simple and
 computationally efficient way to reduce the dimensionality of the data by
 trading a controlled amount of accuracy (as additional variance) for faster
@@ -716,7 +717,7 @@
 245-250.</p></li>
 </ul>
 <section id="the-johnson-lindenstrauss-lemma">
-<span id="johnson-lindenstrauss"></span><h2><span class="section-number">6.6.1. </span>The Johnson-Lindenstrauss lemma<a class="headerlink" href="#the-johnson-lindenstrauss-lemma" title="Link to this heading">#</a></h2>
+<span id="johnson-lindenstrauss"></span><h2><span class="section-number">7.6.1. </span>The Johnson-Lindenstrauss lemma<a class="headerlink" href="#the-johnson-lindenstrauss-lemma" title="Link to this heading">#</a></h2>
 <p>The main theoretical result behind the efficiency of random projection is the
 <a class="reference external" href="https://en.wikipedia.org/wiki/Johnson%E2%80%93Lindenstrauss_lemma">Johnson-Lindenstrauss lemma (quoting Wikipedia)</a>:</p>
 <blockquote>
@@ -762,7 +763,7 @@
 </ul>
 </section>
 <section id="gaussian-random-projection">
-<span id="gaussian-random-matrix"></span><h2><span class="section-number">6.6.2. </span>Gaussian random projection<a class="headerlink" href="#gaussian-random-projection" title="Link to this heading">#</a></h2>
+<span id="gaussian-random-matrix"></span><h2><span class="section-number">7.6.2. </span>Gaussian random projection<a class="headerlink" href="#gaussian-random-projection" title="Link to this heading">#</a></h2>
 <p>The <a class="reference internal" href="http://github.com/generated/sklearn.random_projection.GaussianRandomProjection.html#sklearn.random_projection.GaussianRandomProjection" title="sklearn.random_projection.GaussianRandomProjection"><code class="xref py py-class docutils literal notranslate"><span class="pre">GaussianRandomProjection</span></code></a> reduces the
 dimensionality by projecting the original input space on a randomly generated
 matrix where components are drawn from the following distribution
@@ -780,7 +781,7 @@
 </div>
 </section>
 <section id="sparse-random-projection">
-<span id="sparse-random-matrix"></span><h2><span class="section-number">6.6.3. </span>Sparse random projection<a class="headerlink" href="#sparse-random-projection" title="Link to this heading">#</a></h2>
+<span id="sparse-random-matrix"></span><h2><span class="section-number">7.6.3. </span>Sparse random projection<a class="headerlink" href="#sparse-random-projection" title="Link to this heading">#</a></h2>
 <p>The <a class="reference internal" href="http://github.com/generated/sklearn.random_projection.SparseRandomProjection.html#sklearn.random_projection.SparseRandomProjection" title="sklearn.random_projection.SparseRandomProjection"><code class="xref py py-class docutils literal notranslate"><span class="pre">SparseRandomProjection</span></code></a> reduces the
 dimensionality by projecting the original input space using a sparse
 random matrix.</p>
@@ -824,7 +825,7 @@
 </ul>
 </section>
 <section id="inverse-transform">
-<span id="random-projection-inverse-transform"></span><h2><span class="section-number">6.6.4. </span>Inverse Transform<a class="headerlink" href="#inverse-transform" title="Link to this heading">#</a></h2>
+<span id="random-projection-inverse-transform"></span><h2><span class="section-number">7.6.4. </span>Inverse Transform<a class="headerlink" href="#inverse-transform" title="Link to this heading">#</a></h2>
 <p>The random projection transformers have <code class="docutils literal notranslate"><span class="pre">compute_inverse_components</span></code> parameter. When
 set to True, after creating the random <code class="docutils literal notranslate"><span class="pre">components_</span></code> matrix during fitting,
 the transformer computes the pseudo-inverse of this matrix and stores it as
@@ -876,7 +877,7 @@
       <i class="fa-solid fa-angle-left"></i>
       <div class="prev-next-info">
         <p class="prev-next-subtitle">previous</p>
-        <p class="prev-next-title"><span class="section-number">6.5. </span>Unsupervised dimensionality reduction</p>
+        <p class="prev-next-title"><span class="section-number">7.5. </span>Unsupervised dimensionality reduction</p>
       </div>
     </a>
     <a class="right-next"
@@ -884,7 +885,7 @@
        title="next page">
       <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">6.7. </span>Kernel Approximation</p>
+        <p class="prev-next-title"><span class="section-number">7.7. </span>Kernel Approximation</p>
       </div>
       <i class="fa-solid fa-angle-right"></i>
     </a>
@@ -911,10 +912,10 @@
   </div>
   <nav class="bd-toc-nav page-toc" aria-labelledby="pst-page-navigation-heading-2">
     <ul class="visible nav section-nav flex-column">
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#the-johnson-lindenstrauss-lemma">6.6.1. The Johnson-Lindenstrauss lemma</a></li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#gaussian-random-projection">6.6.2. Gaussian random projection</a></li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#sparse-random-projection">6.6.3. Sparse random projection</a></li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#inverse-transform">6.6.4. Inverse Transform</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#the-johnson-lindenstrauss-lemma">7.6.1. The Johnson-Lindenstrauss lemma</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#gaussian-random-projection">7.6.2. Gaussian random projection</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#sparse-random-projection">7.6.3. Sparse random projection</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#inverse-transform">7.6.4. Inverse Transform</a></li>
 </ul>
   </nav></div>
 
diff --git a/dev/modules/semi_supervised.html b/dev/modules/semi_supervised.html
index 81920a7cdeffa..50bff73bd2fc0 100644
--- a/dev/modules/semi_supervised.html
+++ b/dev/modules/semi_supervised.html
@@ -42,7 +42,7 @@
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-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
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@@ -594,45 +594,46 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/learning_curve.html">3.5. Validation curves: plotting scores to evaluate models</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/inspection.html">4. Inspection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/partial_dependence.html">4.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/permutation_importance.html">4.2. Permutation feature importance</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/metadata_routing.html">4. Metadata Routing</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/inspection.html">5. Inspection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/partial_dependence.html">5.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/permutation_importance.html">5.2. Permutation feature importance</a></li>
 </ul>
 </details></li>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/compose.html">6.1. Pipelines and composite estimators</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/unsupervised_reduction.html">6.5. Unsupervised dimensionality reduction</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/random_projection.html">6.6. Random Projection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/kernel_approximation.html">6.7. Kernel Approximation</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing_targets.html">6.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/visualizations.html">6. Visualizations</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/data_transforms.html">7. Dataset transformations</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/compose.html">7.1. Pipelines and composite estimators</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/feature_extraction.html">7.2. Feature extraction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing.html">7.3. Preprocessing data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/impute.html">7.4. Imputation of missing values</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/unsupervised_reduction.html">7.5. Unsupervised dimensionality reduction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/random_projection.html">7.6. Random Projection</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/kernel_approximation.html">7.7. Kernel Approximation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/metrics.html">7.8. Pairwise metrics, Affinities and Kernels</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing_targets.html">7.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
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-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/datasets.html">7. Dataset loading utilities</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html">7.3. Generated datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html">7.4. Loading other datasets</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/datasets.html">8. Dataset loading utilities</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html">8.1. Toy datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html">8.2. Real world datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html">8.3. Generated datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html">8.4. Loading other datasets</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/parallelism.html">8.3. Parallelism, resource management, and configuration</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/computing.html">9. Computing with scikit-learn</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/scaling_strategies.html">9.1. Strategies to scale computationally: bigger data</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="http://github.com/model_persistence.html">10. Model persistence</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/common_pitfalls.html">11. Common pitfalls and recommended practices</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/dispatching.html">12. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/array_api.html">12.1. Array API support (experimental)</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="http://github.com/machine_learning_map.html">13. Choosing the right estimator</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/presentations.html">14. External Resources, Videos and Talks</a></li>
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@@ -808,9 +809,9 @@
 <p class="rubric">Examples</p>
 <ul class="simple">
 <li><p><a class="reference internal" href="http://github.com/auto_examples/semi_supervised/plot_semi_supervised_versus_svm_iris.html#sphx-glr-auto-examples-semi-supervised-plot-semi-supervised-versus-svm-iris-py"><span class="std std-ref">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</span></a></p></li>
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+<li><p><a class="reference internal" href="http://github.com/auto_examples/semi_supervised/plot_label_propagation_structure.html#sphx-glr-auto-examples-semi-supervised-plot-label-propagation-structure-py"><span class="std std-ref">Label Propagation circles: Learning a complex structure</span></a></p></li>
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+<li><p><a class="reference internal" href="http://github.com/auto_examples/semi_supervised/plot_label_propagation_digits_active_learning.html#sphx-glr-auto-examples-semi-supervised-plot-label-propagation-digits-active-learning-py"><span class="std std-ref">Label Propagation digits: Active learning</span></a></p></li>
 </ul>
 <p class="rubric">References</p>
 <p>[2] Yoshua Bengio, Olivier Delalleau, Nicolas Le Roux. In Semi-Supervised
diff --git a/dev/modules/sgd.html b/dev/modules/sgd.html
index b031721ad417f..32e16d7439560 100644
--- a/dev/modules/sgd.html
+++ b/dev/modules/sgd.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
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     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -594,45 +594,46 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/learning_curve.html">3.5. Validation curves: plotting scores to evaluate models</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/inspection.html">4. Inspection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/partial_dependence.html">4.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/permutation_importance.html">4.2. Permutation feature importance</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/metadata_routing.html">4. Metadata Routing</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/inspection.html">5. Inspection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/partial_dependence.html">5.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/permutation_importance.html">5.2. Permutation feature importance</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/visualizations.html">5. Visualizations</a></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/data_transforms.html">6. Dataset transformations</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/compose.html">6.1. Pipelines and composite estimators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/feature_extraction.html">6.2. Feature extraction</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing.html">6.3. Preprocessing data</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/impute.html">6.4. Imputation of missing values</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/unsupervised_reduction.html">6.5. Unsupervised dimensionality reduction</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/random_projection.html">6.6. Random Projection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/kernel_approximation.html">6.7. Kernel Approximation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/metrics.html">6.8. Pairwise metrics, Affinities and Kernels</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing_targets.html">6.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/visualizations.html">6. Visualizations</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/data_transforms.html">7. Dataset transformations</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/compose.html">7.1. Pipelines and composite estimators</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/feature_extraction.html">7.2. Feature extraction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing.html">7.3. Preprocessing data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/impute.html">7.4. Imputation of missing values</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/unsupervised_reduction.html">7.5. Unsupervised dimensionality reduction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/random_projection.html">7.6. Random Projection</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/kernel_approximation.html">7.7. Kernel Approximation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/metrics.html">7.8. Pairwise metrics, Affinities and Kernels</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing_targets.html">7.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/datasets.html">7. Dataset loading utilities</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html">7.1. Toy datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html">7.2. Real world datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html">7.3. Generated datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html">7.4. Loading other datasets</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/datasets.html">8. Dataset loading utilities</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html">8.1. Toy datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html">8.2. Real world datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html">8.3. Generated datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html">8.4. Loading other datasets</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/computing.html">8. Computing with scikit-learn</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/scaling_strategies.html">8.1. Strategies to scale computationally: bigger data</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/computational_performance.html">8.2. Computational Performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/parallelism.html">8.3. Parallelism, resource management, and configuration</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/computing.html">9. Computing with scikit-learn</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/scaling_strategies.html">9.1. Strategies to scale computationally: bigger data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/computational_performance.html">9.2. Computational Performance</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/parallelism.html">9.3. Parallelism, resource management, and configuration</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/model_persistence.html">9. Model persistence</a></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/common_pitfalls.html">10. Common pitfalls and recommended practices</a></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/dispatching.html">11. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/array_api.html">11.1. Array API support (experimental)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/model_persistence.html">10. Model persistence</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/common_pitfalls.html">11. Common pitfalls and recommended practices</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/dispatching.html">12. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/array_api.html">12.1. Array API support (experimental)</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/machine_learning_map.html">12. Choosing the right estimator</a></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/presentations.html">13. External Resources, Videos and Talks</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/machine_learning_map.html">13. Choosing the right estimator</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/presentations.html">14. External Resources, Videos and Talks</a></li>
 </ul>
 </div>
 </nav></div>
@@ -763,12 +764,12 @@ <h2><span class="section-number">1.5.1. </span>Classification<a class="headerlin
 <p>SGD fits a linear model to the training data. The <code class="docutils literal notranslate"><span class="pre">coef_</span></code> attribute holds
 the model parameters:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">clf</span><span class="o">.</span><span class="n">coef_</span>
-<span class="go">array([[9.9..., 9.9...]])</span>
+<span class="go">array([[9.9, 9.9]])</span>
 </pre></div>
 </div>
 <p>The <code class="docutils literal notranslate"><span class="pre">intercept_</span></code> attribute holds the intercept (aka offset or bias):</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">clf</span><span class="o">.</span><span class="n">intercept_</span>
-<span class="go">array([-9.9...])</span>
+<span class="go">array([-9.9])</span>
 </pre></div>
 </div>
 <p>Whether or not the model should use an intercept, i.e. a biased
@@ -777,7 +778,7 @@ <h2><span class="section-number">1.5.1. </span>Classification<a class="headerlin
 the coefficients and the input sample, plus the intercept) is given by
 <a class="reference internal" href="http://github.com/generated/sklearn.linear_model.SGDClassifier.html#sklearn.linear_model.SGDClassifier.decision_function" title="sklearn.linear_model.SGDClassifier.decision_function"><code class="xref py py-meth docutils literal notranslate"><span class="pre">SGDClassifier.decision_function</span></code></a>:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">clf</span><span class="o">.</span><span class="n">decision_function</span><span class="p">([[</span><span class="mf">2.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">]])</span>
-<span class="go">array([29.6...])</span>
+<span class="go">array([29.6])</span>
 </pre></div>
 </div>
 <p>The concrete loss function can be set via the <code class="docutils literal notranslate"><span class="pre">loss</span></code>
@@ -800,7 +801,7 @@ <h2><span class="section-number">1.5.1. </span>Classification<a class="headerlin
 <span class="math notranslate nohighlight">\(P(y|x)\)</span> per sample <span class="math notranslate nohighlight">\(x\)</span>:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">clf</span> <span class="o">=</span> <span class="n">SGDClassifier</span><span class="p">(</span><span class="n">loss</span><span class="o">=</span><span class="s2">&quot;log_loss&quot;</span><span class="p">,</span> <span class="n">max_iter</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">clf</span><span class="o">.</span><span class="n">predict_proba</span><span class="p">([[</span><span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">]])</span> 
-<span class="go">array([[0.00..., 0.99...]])</span>
+<span class="go">array([[0.00, 0.99]])</span>
 </pre></div>
 </div>
 <p>The concrete penalty can be set via the <code class="docutils literal notranslate"><span class="pre">penalty</span></code> parameter.
diff --git a/dev/modules/svm.html b/dev/modules/svm.html
index 84d2df1e4ad0b..51d3e983ce6f3 100644
--- a/dev/modules/svm.html
+++ b/dev/modules/svm.html
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-binder.css?v=f4aeca0c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -594,45 +594,46 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/learning_curve.html">3.5. Validation curves: plotting scores to evaluate models</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/inspection.html">4. Inspection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/partial_dependence.html">4.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/permutation_importance.html">4.2. Permutation feature importance</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/metadata_routing.html">4. Metadata Routing</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/inspection.html">5. Inspection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/partial_dependence.html">5.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/permutation_importance.html">5.2. Permutation feature importance</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/visualizations.html">5. Visualizations</a></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/data_transforms.html">6. Dataset transformations</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/compose.html">6.1. Pipelines and composite estimators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/feature_extraction.html">6.2. Feature extraction</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing.html">6.3. Preprocessing data</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/impute.html">6.4. Imputation of missing values</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/unsupervised_reduction.html">6.5. Unsupervised dimensionality reduction</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/random_projection.html">6.6. Random Projection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/kernel_approximation.html">6.7. Kernel Approximation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/metrics.html">6.8. Pairwise metrics, Affinities and Kernels</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing_targets.html">6.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/visualizations.html">6. Visualizations</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/data_transforms.html">7. Dataset transformations</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/compose.html">7.1. Pipelines and composite estimators</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/feature_extraction.html">7.2. Feature extraction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing.html">7.3. Preprocessing data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/impute.html">7.4. Imputation of missing values</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/unsupervised_reduction.html">7.5. Unsupervised dimensionality reduction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/random_projection.html">7.6. Random Projection</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/kernel_approximation.html">7.7. Kernel Approximation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/metrics.html">7.8. Pairwise metrics, Affinities and Kernels</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/preprocessing_targets.html">7.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/datasets.html">7. Dataset loading utilities</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html">7.1. Toy datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html">7.2. Real world datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html">7.3. Generated datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html">7.4. Loading other datasets</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/datasets.html">8. Dataset loading utilities</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html">8.1. Toy datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html">8.2. Real world datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html">8.3. Generated datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html">8.4. Loading other datasets</a></li>
 </ul>
 </details></li>
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+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/scaling_strategies.html">9.1. Strategies to scale computationally: bigger data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/computational_performance.html">9.2. Computational Performance</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/parallelism.html">9.3. Parallelism, resource management, and configuration</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/array_api.html">11.1. Array API support (experimental)</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/compose.html">6.1. Pipelines and composite estimators</a></li>
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-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/datasets.html">7. Dataset loading utilities</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html">7.1. Toy datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html">7.2. Real world datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html">7.3. Generated datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html">7.4. Loading other datasets</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/datasets.html">8. Dataset loading utilities</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html">8.1. Toy datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html">8.2. Real world datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html">8.3. Generated datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html">8.4. Loading other datasets</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/computing.html">8. Computing with scikit-learn</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/scaling_strategies.html">8.1. Strategies to scale computationally: bigger data</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/computational_performance.html">8.2. Computational Performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/parallelism.html">8.3. Parallelism, resource management, and configuration</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/computing.html">9. Computing with scikit-learn</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/scaling_strategies.html">9.1. Strategies to scale computationally: bigger data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/computational_performance.html">9.2. Computational Performance</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/parallelism.html">9.3. Parallelism, resource management, and configuration</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/model_persistence.html">9. Model persistence</a></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/common_pitfalls.html">10. Common pitfalls and recommended practices</a></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/dispatching.html">11. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/array_api.html">11.1. Array API support (experimental)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/model_persistence.html">10. Model persistence</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/common_pitfalls.html">11. Common pitfalls and recommended practices</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/dispatching.html">12. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/array_api.html">12.1. Array API support (experimental)</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/machine_learning_map.html">12. Choosing the right estimator</a></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/presentations.html">13. External Resources, Videos and Talks</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/machine_learning_map.html">13. Choosing the right estimator</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/presentations.html">14. External Resources, Videos and Talks</a></li>
 </ul>
 </div>
 </nav></div>
@@ -669,9 +670,9 @@
     <li class="breadcrumb-item"><a href="http://github.com/user_guide.html" class="nav-link">User Guide</a></li>
     
     
-    <li class="breadcrumb-item"><a href="http://github.com/data_transforms.html" class="nav-link"><span class="section-number">6. </span>Dataset transformations</a></li>
+    <li class="breadcrumb-item"><a href="http://github.com/data_transforms.html" class="nav-link"><span class="section-number">7. </span>Dataset transformations</a></li>
     
-    <li class="breadcrumb-item active" aria-current="page"><span class="ellipsis"><span class="section-number">6.5. </span>Unsupervised dimensionality reduction</span></li>
+    <li class="breadcrumb-item active" aria-current="page"><span class="ellipsis"><span class="section-number">7.5. </span>Unsupervised dimensionality reduction</span></li>
   </ul>
 </nav>
 </div>
@@ -689,7 +690,7 @@
                 <article class="bd-article">
                   
   <section id="unsupervised-dimensionality-reduction">
-<span id="data-reduction"></span><h1><span class="section-number">6.5. </span>Unsupervised dimensionality reduction<a class="headerlink" href="#unsupervised-dimensionality-reduction" title="Link to this heading">#</a></h1>
+<span id="data-reduction"></span><h1><span class="section-number">7.5. </span>Unsupervised dimensionality reduction<a class="headerlink" href="#unsupervised-dimensionality-reduction" title="Link to this heading">#</a></h1>
 <p>If your number of features is high, it may be useful to reduce it with an
 unsupervised step prior to supervised steps. Many of the
 <a class="reference internal" href="http://github.com/unsupervised_learning.html#unsupervised-learning"><span class="std std-ref">Unsupervised learning</span></a> methods implement a <code class="docutils literal notranslate"><span class="pre">transform</span></code> method that
@@ -701,7 +702,7 @@
 chained in one step. See <a class="reference internal" href="http://github.com/compose.html#pipeline"><span class="std std-ref">Pipeline: chaining estimators</span></a>.</p>
 </aside>
 <section id="pca-principal-component-analysis">
-<h2><span class="section-number">6.5.1. </span>PCA: principal component analysis<a class="headerlink" href="#pca-principal-component-analysis" title="Link to this heading">#</a></h2>
+<h2><span class="section-number">7.5.1. </span>PCA: principal component analysis<a class="headerlink" href="#pca-principal-component-analysis" title="Link to this heading">#</a></h2>
 <p><a class="reference internal" href="http://github.com/generated/sklearn.decomposition.PCA.html#sklearn.decomposition.PCA" title="sklearn.decomposition.PCA"><code class="xref py py-class docutils literal notranslate"><span class="pre">decomposition.PCA</span></code></a> looks for a combination of features that
 capture well the variance of the original features. See <a class="reference internal" href="http://github.com/decomposition.html#decompositions"><span class="std std-ref">Decomposing signals in components (matrix factorization problems)</span></a>.</p>
 <p class="rubric">Examples</p>
@@ -710,7 +711,7 @@ <h2><span class="section-number">6.5.1. </span>PCA: principal component analysis
 </ul>
 </section>
 <section id="random-projections">
-<h2><span class="section-number">6.5.2. </span>Random projections<a class="headerlink" href="#random-projections" title="Link to this heading">#</a></h2>
+<h2><span class="section-number">7.5.2. </span>Random projections<a class="headerlink" href="#random-projections" title="Link to this heading">#</a></h2>
 <p>The module: <a class="reference internal" href="http://github.com/api/sklearn.random_projection.html#module-sklearn.random_projection" title="sklearn.random_projection"><code class="xref py py-mod docutils literal notranslate"><span class="pre">random_projection</span></code></a> provides several tools for data
 reduction by random projections. See the relevant section of the
 documentation: <a class="reference internal" href="http://github.com/random_projection.html#random-projection"><span class="std std-ref">Random Projection</span></a>.</p>
@@ -720,7 +721,7 @@ <h2><span class="section-number">6.5.2. </span>Random projections<a class="heade
 </ul>
 </section>
 <section id="feature-agglomeration">
-<h2><span class="section-number">6.5.3. </span>Feature agglomeration<a class="headerlink" href="#feature-agglomeration" title="Link to this heading">#</a></h2>
+<h2><span class="section-number">7.5.3. </span>Feature agglomeration<a class="headerlink" href="#feature-agglomeration" title="Link to this heading">#</a></h2>
 <p><a class="reference internal" href="http://github.com/generated/sklearn.cluster.FeatureAgglomeration.html#sklearn.cluster.FeatureAgglomeration" title="sklearn.cluster.FeatureAgglomeration"><code class="xref py py-class docutils literal notranslate"><span class="pre">cluster.FeatureAgglomeration</span></code></a> applies
 <a class="reference internal" href="http://github.com/clustering.html#hierarchical-clustering"><span class="std std-ref">Hierarchical clustering</span></a> to group together features that behave
 similarly.</p>
@@ -755,7 +756,7 @@ <h2><span class="section-number">6.5.3. </span>Feature agglomeration<a class="he
       <i class="fa-solid fa-angle-left"></i>
       <div class="prev-next-info">
         <p class="prev-next-subtitle">previous</p>
-        <p class="prev-next-title"><span class="section-number">6.4. </span>Imputation of missing values</p>
+        <p class="prev-next-title"><span class="section-number">7.4. </span>Imputation of missing values</p>
       </div>
     </a>
     <a class="right-next"
@@ -763,7 +764,7 @@ <h2><span class="section-number">6.5.3. </span>Feature agglomeration<a class="he
        title="next page">
       <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">6.6. </span>Random Projection</p>
+        <p class="prev-next-title"><span class="section-number">7.6. </span>Random Projection</p>
       </div>
       <i class="fa-solid fa-angle-right"></i>
     </a>
@@ -790,9 +791,9 @@ <h2><span class="section-number">6.5.3. </span>Feature agglomeration<a class="he
   </div>
   <nav class="bd-toc-nav page-toc" aria-labelledby="pst-page-navigation-heading-2">
     <ul class="visible nav section-nav flex-column">
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pca-principal-component-analysis">6.5.1. PCA: principal component analysis</a></li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#random-projections">6.5.2. Random projections</a></li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#feature-agglomeration">6.5.3. Feature agglomeration</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pca-principal-component-analysis">7.5.1. PCA: principal component analysis</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#random-projections">7.5.2. Random projections</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#feature-agglomeration">7.5.3. Feature agglomeration</a></li>
 </ul>
   </nav></div>
 
diff --git a/dev/notebooks/auto_examples/applications/plot_cyclical_feature_engineering.ipynb b/dev/notebooks/auto_examples/applications/plot_cyclical_feature_engineering.ipynb
index 678c6f714c126..7e6657375b673 100644
--- a/dev/notebooks/auto_examples/applications/plot_cyclical_feature_engineering.ipynb
+++ b/dev/notebooks/auto_examples/applications/plot_cyclical_feature_engineering.ipynb
@@ -731,7 +731,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/applications/plot_digits_denoising.ipynb b/dev/notebooks/auto_examples/applications/plot_digits_denoising.ipynb
index 780adc5faee88..aecf13b66b43b 100644
--- a/dev/notebooks/auto_examples/applications/plot_digits_denoising.ipynb
+++ b/dev/notebooks/auto_examples/applications/plot_digits_denoising.ipynb
@@ -161,7 +161,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/applications/plot_face_recognition.ipynb b/dev/notebooks/auto_examples/applications/plot_face_recognition.ipynb
index 658488b935f3a..60a9c51d5d5ec 100644
--- a/dev/notebooks/auto_examples/applications/plot_face_recognition.ipynb
+++ b/dev/notebooks/auto_examples/applications/plot_face_recognition.ipynb
@@ -197,7 +197,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/applications/plot_model_complexity_influence.ipynb b/dev/notebooks/auto_examples/applications/plot_model_complexity_influence.ipynb
index ac0dd91feb7fb..02db34e3a787e 100644
--- a/dev/notebooks/auto_examples/applications/plot_model_complexity_influence.ipynb
+++ b/dev/notebooks/auto_examples/applications/plot_model_complexity_influence.ipynb
@@ -114,7 +114,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/applications/plot_out_of_core_classification.ipynb b/dev/notebooks/auto_examples/applications/plot_out_of_core_classification.ipynb
index 05acc6627965e..ed35349db8c79 100644
--- a/dev/notebooks/auto_examples/applications/plot_out_of_core_classification.ipynb
+++ b/dev/notebooks/auto_examples/applications/plot_out_of_core_classification.ipynb
@@ -33,7 +33,7 @@
       },
       "outputs": [],
       "source": [
-        "class ReutersParser(HTMLParser):\n    \"\"\"Utility class to parse a SGML file and yield documents one at a time.\"\"\"\n\n    def __init__(self, encoding=\"latin-1\"):\n        HTMLParser.__init__(self)\n        self._reset()\n        self.encoding = encoding\n\n    def handle_starttag(self, tag, attrs):\n        method = \"start_\" + tag\n        getattr(self, method, lambda x: None)(attrs)\n\n    def handle_endtag(self, tag):\n        method = \"end_\" + tag\n        getattr(self, method, lambda: None)()\n\n    def _reset(self):\n        self.in_title = 0\n        self.in_body = 0\n        self.in_topics = 0\n        self.in_topic_d = 0\n        self.title = \"\"\n        self.body = \"\"\n        self.topics = []\n        self.topic_d = \"\"\n\n    def parse(self, fd):\n        self.docs = []\n        for chunk in fd:\n            self.feed(chunk.decode(self.encoding))\n            for doc in self.docs:\n                yield doc\n            self.docs = []\n        self.close()\n\n    def handle_data(self, data):\n        if self.in_body:\n            self.body += data\n        elif self.in_title:\n            self.title += data\n        elif self.in_topic_d:\n            self.topic_d += data\n\n    def start_reuters(self, attributes):\n        pass\n\n    def end_reuters(self):\n        self.body = re.sub(r\"\\s+\", r\" \", self.body)\n        self.docs.append(\n            {\"title\": self.title, \"body\": self.body, \"topics\": self.topics}\n        )\n        self._reset()\n\n    def start_title(self, attributes):\n        self.in_title = 1\n\n    def end_title(self):\n        self.in_title = 0\n\n    def start_body(self, attributes):\n        self.in_body = 1\n\n    def end_body(self):\n        self.in_body = 0\n\n    def start_topics(self, attributes):\n        self.in_topics = 1\n\n    def end_topics(self):\n        self.in_topics = 0\n\n    def start_d(self, attributes):\n        self.in_topic_d = 1\n\n    def end_d(self):\n        self.in_topic_d = 0\n        self.topics.append(self.topic_d)\n        self.topic_d = \"\"\n\n\ndef stream_reuters_documents(data_path=None):\n    \"\"\"Iterate over documents of the Reuters dataset.\n\n    The Reuters archive will automatically be downloaded and uncompressed if\n    the `data_path` directory does not exist.\n\n    Documents are represented as dictionaries with 'body' (str),\n    'title' (str), 'topics' (list(str)) keys.\n\n    \"\"\"\n\n    DOWNLOAD_URL = (\n        \"http://archive.ics.uci.edu/ml/machine-learning-databases//"\n        \"reuters21578-mld/reuters21578.tar.gz\"\n    )\n    ARCHIVE_SHA256 = \"3bae43c9b14e387f76a61b6d82bf98a4fb5d3ef99ef7e7075ff2ccbcf59f9d30\"\n    ARCHIVE_FILENAME = \"reuters21578.tar.gz\"\n\n    if data_path is None:\n        data_path = Path(get_data_home()) / \"reuters\"\n    else:\n        data_path = Path(data_path)\n    if not data_path.exists():\n        \"\"\"Download the dataset.\"\"\"\n        print(\"downloading dataset (once and for all) into %s\" % data_path)\n        data_path.mkdir(parents=True, exist_ok=True)\n\n        def progress(blocknum, bs, size):\n            total_sz_mb = \"%.2f MB\" % (size / 1e6)\n            current_sz_mb = \"%.2f MB\" % ((blocknum * bs) / 1e6)\n            if _not_in_sphinx():\n                sys.stdout.write(\"\\rdownloaded %s / %s\" % (current_sz_mb, total_sz_mb))\n\n        archive_path = data_path / ARCHIVE_FILENAME\n\n        urlretrieve(DOWNLOAD_URL, filename=archive_path, reporthook=progress)\n        if _not_in_sphinx():\n            sys.stdout.write(\"\\r\")\n\n        # Check that the archive was not tampered:\n        assert sha256(archive_path.read_bytes()).hexdigest() == ARCHIVE_SHA256\n\n        print(\"untarring Reuters dataset...\")\n        with tarfile.open(archive_path, \"r:gz\") as fp:\n            fp.extractall(data_path, filter=\"data\")\n        print(\"done.\")\n\n    parser = ReutersParser()\n    for filename in data_path.glob(\"*.sgm\"):\n        for doc in parser.parse(open(filename, \"rb\")):\n            yield doc"
+        "class ReutersParser(HTMLParser):\n    \"\"\"Utility class to parse a SGML file and yield documents one at a time.\"\"\"\n\n    def __init__(self, encoding=\"latin-1\"):\n        HTMLParser.__init__(self)\n        self._reset()\n        self.encoding = encoding\n\n    def handle_starttag(self, tag, attrs):\n        method = \"start_\" + tag\n        getattr(self, method, lambda x: None)(attrs)\n\n    def handle_endtag(self, tag):\n        method = \"end_\" + tag\n        getattr(self, method, lambda: None)()\n\n    def _reset(self):\n        self.in_title = 0\n        self.in_body = 0\n        self.in_topics = 0\n        self.in_topic_d = 0\n        self.title = \"\"\n        self.body = \"\"\n        self.topics = []\n        self.topic_d = \"\"\n\n    def parse(self, fd):\n        self.docs = []\n        for chunk in fd:\n            self.feed(chunk.decode(self.encoding))\n            for doc in self.docs:\n                yield doc\n            self.docs = []\n        self.close()\n\n    def handle_data(self, data):\n        if self.in_body:\n            self.body += data\n        elif self.in_title:\n            self.title += data\n        elif self.in_topic_d:\n            self.topic_d += data\n\n    def start_reuters(self, attributes):\n        pass\n\n    def end_reuters(self):\n        self.body = re.sub(r\"\\s+\", r\" \", self.body)\n        self.docs.append(\n            {\"title\": self.title, \"body\": self.body, \"topics\": self.topics}\n        )\n        self._reset()\n\n    def start_title(self, attributes):\n        self.in_title = 1\n\n    def end_title(self):\n        self.in_title = 0\n\n    def start_body(self, attributes):\n        self.in_body = 1\n\n    def end_body(self):\n        self.in_body = 0\n\n    def start_topics(self, attributes):\n        self.in_topics = 1\n\n    def end_topics(self):\n        self.in_topics = 0\n\n    def start_d(self, attributes):\n        self.in_topic_d = 1\n\n    def end_d(self):\n        self.in_topic_d = 0\n        self.topics.append(self.topic_d)\n        self.topic_d = \"\"\n\n\ndef stream_reuters_documents(data_path=None):\n    \"\"\"Iterate over documents of the Reuters dataset.\n\n    The Reuters archive will automatically be downloaded and uncompressed if\n    the `data_path` directory does not exist.\n\n    Documents are represented as dictionaries with 'body' (str),\n    'title' (str), 'topics' (list(str)) keys.\n\n    \"\"\"\n\n    DOWNLOAD_URL = \"https://kdd.ics.uci.edu/databases/reuters21578/reuters21578.tar.gz/"\n    ARCHIVE_SHA256 = \"3bae43c9b14e387f76a61b6d82bf98a4fb5d3ef99ef7e7075ff2ccbcf59f9d30\"\n    ARCHIVE_FILENAME = \"reuters21578.tar.gz\"\n\n    if data_path is None:\n        data_path = Path(get_data_home()) / \"reuters\"\n    else:\n        data_path = Path(data_path)\n    if not data_path.exists():\n        \"\"\"Download the dataset.\"\"\"\n        print(\"downloading dataset (once and for all) into %s\" % data_path)\n        data_path.mkdir(parents=True, exist_ok=True)\n\n        def progress(blocknum, bs, size):\n            total_sz_mb = \"%.2f MB\" % (size / 1e6)\n            current_sz_mb = \"%.2f MB\" % ((blocknum * bs) / 1e6)\n            if _not_in_sphinx():\n                sys.stdout.write(\"\\rdownloaded %s / %s\" % (current_sz_mb, total_sz_mb))\n\n        archive_path = data_path / ARCHIVE_FILENAME\n\n        urlretrieve(DOWNLOAD_URL, filename=archive_path, reporthook=progress)\n        if _not_in_sphinx():\n            sys.stdout.write(\"\\r\")\n\n        # Check that the archive was not tampered:\n        assert sha256(archive_path.read_bytes()).hexdigest() == ARCHIVE_SHA256\n\n        print(\"untarring Reuters dataset...\")\n        with tarfile.open(archive_path, \"r:gz\") as fp:\n            fp.extractall(data_path, filter=\"data\")\n        print(\"done.\")\n\n    parser = ReutersParser()\n    for filename in data_path.glob(\"*.sgm\"):\n        for doc in parser.parse(open(filename, \"rb\")):\n            yield doc"
       ]
     },
     {
@@ -89,7 +89,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/applications/plot_outlier_detection_wine.ipynb b/dev/notebooks/auto_examples/applications/plot_outlier_detection_wine.ipynb
index de4cfc4276ab0..7bef91a44d0bb 100644
--- a/dev/notebooks/auto_examples/applications/plot_outlier_detection_wine.ipynb
+++ b/dev/notebooks/auto_examples/applications/plot_outlier_detection_wine.ipynb
@@ -82,7 +82,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/applications/plot_prediction_latency.ipynb b/dev/notebooks/auto_examples/applications/plot_prediction_latency.ipynb
index eead66cf2c131..e32b7c0367498 100644
--- a/dev/notebooks/auto_examples/applications/plot_prediction_latency.ipynb
+++ b/dev/notebooks/auto_examples/applications/plot_prediction_latency.ipynb
@@ -107,7 +107,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/applications/plot_species_distribution_modeling.ipynb b/dev/notebooks/auto_examples/applications/plot_species_distribution_modeling.ipynb
index 84afeec19e578..2e9ffe60d7d7c 100644
--- a/dev/notebooks/auto_examples/applications/plot_species_distribution_modeling.ipynb
+++ b/dev/notebooks/auto_examples/applications/plot_species_distribution_modeling.ipynb
@@ -15,7 +15,7 @@
       },
       "outputs": [],
       "source": [
-        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\n\nfrom time import time\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nfrom sklearn import metrics, svm\nfrom sklearn.datasets import fetch_species_distributions\nfrom sklearn.utils import Bunch\n\n# if basemap is available, we'll use it.\n# otherwise, we'll improvise later...\ntry:\n    from mpl_toolkits.basemap import Basemap\n\n    basemap = True\nexcept ImportError:\n    basemap = False\n\n\ndef construct_grids(batch):\n    \"\"\"Construct the map grid from the batch object\n\n    Parameters\n    ----------\n    batch : Batch object\n        The object returned by :func:`fetch_species_distributions`\n\n    Returns\n    -------\n    (xgrid, ygrid) : 1-D arrays\n        The grid corresponding to the values in batch.coverages\n    \"\"\"\n    # x,y coordinates for corner cells\n    xmin = batch.x_left_lower_corner + batch.grid_size\n    xmax = xmin + (batch.Nx * batch.grid_size)\n    ymin = batch.y_left_lower_corner + batch.grid_size\n    ymax = ymin + (batch.Ny * batch.grid_size)\n\n    # x coordinates of the grid cells\n    xgrid = np.arange(xmin, xmax, batch.grid_size)\n    # y coordinates of the grid cells\n    ygrid = np.arange(ymin, ymax, batch.grid_size)\n\n    return (xgrid, ygrid)\n\n\ndef create_species_bunch(species_name, train, test, coverages, xgrid, ygrid):\n    \"\"\"Create a bunch with information about a particular organism\n\n    This will use the test/train record arrays to extract the\n    data specific to the given species name.\n    \"\"\"\n    bunch = Bunch(name=\" \".join(species_name.split(\"_\")[:2]))\n    species_name = species_name.encode(\"ascii\")\n    points = dict(test=test, train=train)\n\n    for label, pts in points.items():\n        # choose points associated with the desired species\n        pts = pts[pts[\"species\"] == species_name]\n        bunch[\"pts_%s\" % label] = pts\n\n        # determine coverage values for each of the training & testing points\n        ix = np.searchsorted(xgrid, pts[\"dd long\"])\n        iy = np.searchsorted(ygrid, pts[\"dd lat\"])\n        bunch[\"cov_%s\" % label] = coverages[:, -iy, ix].T\n\n    return bunch\n\n\ndef plot_species_distribution(\n    species=(\"bradypus_variegatus_0\", \"microryzomys_minutus_0\")\n):\n    \"\"\"\n    Plot the species distribution.\n    \"\"\"\n    if len(species) > 2:\n        print(\n            \"Note: when more than two species are provided,\"\n            \" only the first two will be used\"\n        )\n\n    t0 = time()\n\n    # Load the compressed data\n    data = fetch_species_distributions()\n\n    # Set up the data grid\n    xgrid, ygrid = construct_grids(data)\n\n    # The grid in x,y coordinates\n    X, Y = np.meshgrid(xgrid, ygrid[::-1])\n\n    # create a bunch for each species\n    BV_bunch = create_species_bunch(\n        species[0], data.train, data.test, data.coverages, xgrid, ygrid\n    )\n    MM_bunch = create_species_bunch(\n        species[1], data.train, data.test, data.coverages, xgrid, ygrid\n    )\n\n    # background points (grid coordinates) for evaluation\n    np.random.seed(13)\n    background_points = np.c_[\n        np.random.randint(low=0, high=data.Ny, size=10000),\n        np.random.randint(low=0, high=data.Nx, size=10000),\n    ].T\n\n    # We'll make use of the fact that coverages[6] has measurements at all\n    # land points.  This will help us decide between land and water.\n    land_reference = data.coverages[6]\n\n    # Fit, predict, and plot for each species.\n    for i, species in enumerate([BV_bunch, MM_bunch]):\n        print(\"_\" * 80)\n        print(\"Modeling distribution of species '%s'\" % species.name)\n\n        # Standardize features\n        mean = species.cov_train.mean(axis=0)\n        std = species.cov_train.std(axis=0)\n        train_cover_std = (species.cov_train - mean) / std\n\n        # Fit OneClassSVM\n        print(\" - fit OneClassSVM ... \", end=\"\")\n        clf = svm.OneClassSVM(nu=0.1, kernel=\"rbf\", gamma=0.5)\n        clf.fit(train_cover_std)\n        print(\"done.\")\n\n        # Plot map of South America\n        plt.subplot(1, 2, i + 1)\n        if basemap:\n            print(\" - plot coastlines using basemap\")\n            m = Basemap(\n                projection=\"cyl\",\n                llcrnrlat=Y.min(),\n                urcrnrlat=Y.max(),\n                llcrnrlon=X.min(),\n                urcrnrlon=X.max(),\n                resolution=\"c\",\n            )\n            m.drawcoastlines()\n            m.drawcountries()\n        else:\n            print(\" - plot coastlines from coverage\")\n            plt.contour(\n                X, Y, land_reference, levels=[-9998], colors=\"k\", linestyles=\"solid\"\n            )\n            plt.xticks([])\n            plt.yticks([])\n\n        print(\" - predict species distribution\")\n\n        # Predict species distribution using the training data\n        Z = np.ones((data.Ny, data.Nx), dtype=np.float64)\n\n        # We'll predict only for the land points.\n        idx = np.where(land_reference > -9999)\n        coverages_land = data.coverages[:, idx[0], idx[1]].T\n\n        pred = clf.decision_function((coverages_land - mean) / std)\n        Z *= pred.min()\n        Z[idx[0], idx[1]] = pred\n\n        levels = np.linspace(Z.min(), Z.max(), 25)\n        Z[land_reference == -9999] = -9999\n\n        # plot contours of the prediction\n        plt.contourf(X, Y, Z, levels=levels, cmap=plt.cm.Reds)\n        plt.colorbar(format=\"%.2f\")\n\n        # scatter training/testing points\n        plt.scatter(\n            species.pts_train[\"dd long\"],\n            species.pts_train[\"dd lat\"],\n            s=2**2,\n            c=\"black\",\n            marker=\"^\",\n            label=\"train\",\n        )\n        plt.scatter(\n            species.pts_test[\"dd long\"],\n            species.pts_test[\"dd lat\"],\n            s=2**2,\n            c=\"black\",\n            marker=\"x\",\n            label=\"test\",\n        )\n        plt.legend()\n        plt.title(species.name)\n        plt.axis(\"equal\")\n\n        # Compute AUC with regards to background points\n        pred_background = Z[background_points[0], background_points[1]]\n        pred_test = clf.decision_function((species.cov_test - mean) / std)\n        scores = np.r_[pred_test, pred_background]\n        y = np.r_[np.ones(pred_test.shape), np.zeros(pred_background.shape)]\n        fpr, tpr, thresholds = metrics.roc_curve(y, scores)\n        roc_auc = metrics.auc(fpr, tpr)\n        plt.text(-35, -70, \"AUC: %.3f\" % roc_auc, ha=\"right\")\n        print(\"\\n Area under the ROC curve : %f\" % roc_auc)\n\n    print(\"\\ntime elapsed: %.2fs\" % (time() - t0))\n\n\nplot_species_distribution()\nplt.show()"
+        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\n\nfrom time import time\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nfrom sklearn import metrics, svm\nfrom sklearn.datasets import fetch_species_distributions\nfrom sklearn.utils import Bunch\n\n# if basemap is available, we'll use it.\n# otherwise, we'll improvise later...\ntry:\n    from mpl_toolkits.basemap import Basemap\n\n    basemap = True\nexcept ImportError:\n    basemap = False\n\n\ndef construct_grids(batch):\n    \"\"\"Construct the map grid from the batch object\n\n    Parameters\n    ----------\n    batch : Batch object\n        The object returned by :func:`fetch_species_distributions`\n\n    Returns\n    -------\n    (xgrid, ygrid) : 1-D arrays\n        The grid corresponding to the values in batch.coverages\n    \"\"\"\n    # x,y coordinates for corner cells\n    xmin = batch.x_left_lower_corner + batch.grid_size\n    xmax = xmin + (batch.Nx * batch.grid_size)\n    ymin = batch.y_left_lower_corner + batch.grid_size\n    ymax = ymin + (batch.Ny * batch.grid_size)\n\n    # x coordinates of the grid cells\n    xgrid = np.arange(xmin, xmax, batch.grid_size)\n    # y coordinates of the grid cells\n    ygrid = np.arange(ymin, ymax, batch.grid_size)\n\n    return (xgrid, ygrid)\n\n\ndef create_species_bunch(species_name, train, test, coverages, xgrid, ygrid):\n    \"\"\"Create a bunch with information about a particular organism\n\n    This will use the test/train record arrays to extract the\n    data specific to the given species name.\n    \"\"\"\n    bunch = Bunch(name=\" \".join(species_name.split(\"_\")[:2]))\n    species_name = species_name.encode(\"ascii\")\n    points = dict(test=test, train=train)\n\n    for label, pts in points.items():\n        # choose points associated with the desired species\n        pts = pts[pts[\"species\"] == species_name]\n        bunch[\"pts_%s\" % label] = pts\n\n        # determine coverage values for each of the training & testing points\n        ix = np.searchsorted(xgrid, pts[\"dd long\"])\n        iy = np.searchsorted(ygrid, pts[\"dd lat\"])\n        bunch[\"cov_%s\" % label] = coverages[:, -iy, ix].T\n\n    return bunch\n\n\ndef plot_species_distribution(\n    species=(\"bradypus_variegatus_0\", \"microryzomys_minutus_0\"),\n):\n    \"\"\"\n    Plot the species distribution.\n    \"\"\"\n    if len(species) > 2:\n        print(\n            \"Note: when more than two species are provided,\"\n            \" only the first two will be used\"\n        )\n\n    t0 = time()\n\n    # Load the compressed data\n    data = fetch_species_distributions()\n\n    # Set up the data grid\n    xgrid, ygrid = construct_grids(data)\n\n    # The grid in x,y coordinates\n    X, Y = np.meshgrid(xgrid, ygrid[::-1])\n\n    # create a bunch for each species\n    BV_bunch = create_species_bunch(\n        species[0], data.train, data.test, data.coverages, xgrid, ygrid\n    )\n    MM_bunch = create_species_bunch(\n        species[1], data.train, data.test, data.coverages, xgrid, ygrid\n    )\n\n    # background points (grid coordinates) for evaluation\n    np.random.seed(13)\n    background_points = np.c_[\n        np.random.randint(low=0, high=data.Ny, size=10000),\n        np.random.randint(low=0, high=data.Nx, size=10000),\n    ].T\n\n    # We'll make use of the fact that coverages[6] has measurements at all\n    # land points.  This will help us decide between land and water.\n    land_reference = data.coverages[6]\n\n    # Fit, predict, and plot for each species.\n    for i, species in enumerate([BV_bunch, MM_bunch]):\n        print(\"_\" * 80)\n        print(\"Modeling distribution of species '%s'\" % species.name)\n\n        # Standardize features\n        mean = species.cov_train.mean(axis=0)\n        std = species.cov_train.std(axis=0)\n        train_cover_std = (species.cov_train - mean) / std\n\n        # Fit OneClassSVM\n        print(\" - fit OneClassSVM ... \", end=\"\")\n        clf = svm.OneClassSVM(nu=0.1, kernel=\"rbf\", gamma=0.5)\n        clf.fit(train_cover_std)\n        print(\"done.\")\n\n        # Plot map of South America\n        plt.subplot(1, 2, i + 1)\n        if basemap:\n            print(\" - plot coastlines using basemap\")\n            m = Basemap(\n                projection=\"cyl\",\n                llcrnrlat=Y.min(),\n                urcrnrlat=Y.max(),\n                llcrnrlon=X.min(),\n                urcrnrlon=X.max(),\n                resolution=\"c\",\n            )\n            m.drawcoastlines()\n            m.drawcountries()\n        else:\n            print(\" - plot coastlines from coverage\")\n            plt.contour(\n                X, Y, land_reference, levels=[-9998], colors=\"k\", linestyles=\"solid\"\n            )\n            plt.xticks([])\n            plt.yticks([])\n\n        print(\" - predict species distribution\")\n\n        # Predict species distribution using the training data\n        Z = np.ones((data.Ny, data.Nx), dtype=np.float64)\n\n        # We'll predict only for the land points.\n        idx = (land_reference > -9999).nonzero()\n        coverages_land = data.coverages[:, idx[0], idx[1]].T\n\n        pred = clf.decision_function((coverages_land - mean) / std)\n        Z *= pred.min()\n        Z[idx[0], idx[1]] = pred\n\n        levels = np.linspace(Z.min(), Z.max(), 25)\n        Z[land_reference == -9999] = -9999\n\n        # plot contours of the prediction\n        plt.contourf(X, Y, Z, levels=levels, cmap=plt.cm.Reds)\n        plt.colorbar(format=\"%.2f\")\n\n        # scatter training/testing points\n        plt.scatter(\n            species.pts_train[\"dd long\"],\n            species.pts_train[\"dd lat\"],\n            s=2**2,\n            c=\"black\",\n            marker=\"^\",\n            label=\"train\",\n        )\n        plt.scatter(\n            species.pts_test[\"dd long\"],\n            species.pts_test[\"dd lat\"],\n            s=2**2,\n            c=\"black\",\n            marker=\"x\",\n            label=\"test\",\n        )\n        plt.legend()\n        plt.title(species.name)\n        plt.axis(\"equal\")\n\n        # Compute AUC with regards to background points\n        pred_background = Z[background_points[0], background_points[1]]\n        pred_test = clf.decision_function((species.cov_test - mean) / std)\n        scores = np.r_[pred_test, pred_background]\n        y = np.r_[np.ones(pred_test.shape), np.zeros(pred_background.shape)]\n        fpr, tpr, thresholds = metrics.roc_curve(y, scores)\n        roc_auc = metrics.auc(fpr, tpr)\n        plt.text(-35, -70, \"AUC: %.3f\" % roc_auc, ha=\"right\")\n        print(\"\\n Area under the ROC curve : %f\" % roc_auc)\n\n    print(\"\\ntime elapsed: %.2fs\" % (time() - t0))\n\n\nplot_species_distribution()\nplt.show()"
       ]
     }
   ],
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/applications/plot_stock_market.ipynb b/dev/notebooks/auto_examples/applications/plot_stock_market.ipynb
index 174c23461eb1b..8a78ea5119758 100644
--- a/dev/notebooks/auto_examples/applications/plot_stock_market.ipynb
+++ b/dev/notebooks/auto_examples/applications/plot_stock_market.ipynb
@@ -105,7 +105,7 @@
       },
       "outputs": [],
       "source": [
-        "import matplotlib.pyplot as plt\nfrom matplotlib.collections import LineCollection\n\nplt.figure(1, facecolor=\"w\", figsize=(10, 8))\nplt.clf()\nax = plt.axes([0.0, 0.0, 1.0, 1.0])\nplt.axis(\"off\")\n\n# Plot the graph of partial correlations\npartial_correlations = edge_model.precision_.copy()\nd = 1 / np.sqrt(np.diag(partial_correlations))\npartial_correlations *= d\npartial_correlations *= d[:, np.newaxis]\nnon_zero = np.abs(np.triu(partial_correlations, k=1)) > 0.02\n\n# Plot the nodes using the coordinates of our embedding\nplt.scatter(\n    embedding[0], embedding[1], s=100 * d**2, c=labels, cmap=plt.cm.nipy_spectral\n)\n\n# Plot the edges\nstart_idx, end_idx = np.where(non_zero)\n# a sequence of (*line0*, *line1*, *line2*), where::\n#            linen = (x0, y0), (x1, y1), ... (xm, ym)\nsegments = [\n    [embedding[:, start], embedding[:, stop]] for start, stop in zip(start_idx, end_idx)\n]\nvalues = np.abs(partial_correlations[non_zero])\nlc = LineCollection(\n    segments, zorder=0, cmap=plt.cm.hot_r, norm=plt.Normalize(0, 0.7 * values.max())\n)\nlc.set_array(values)\nlc.set_linewidths(15 * values)\nax.add_collection(lc)\n\n# Add a label to each node. The challenge here is that we want to\n# position the labels to avoid overlap with other labels\nfor index, (name, label, (x, y)) in enumerate(zip(names, labels, embedding.T)):\n    dx = x - embedding[0]\n    dx[index] = 1\n    dy = y - embedding[1]\n    dy[index] = 1\n    this_dx = dx[np.argmin(np.abs(dy))]\n    this_dy = dy[np.argmin(np.abs(dx))]\n    if this_dx > 0:\n        horizontalalignment = \"left\"\n        x = x + 0.002\n    else:\n        horizontalalignment = \"right\"\n        x = x - 0.002\n    if this_dy > 0:\n        verticalalignment = \"bottom\"\n        y = y + 0.002\n    else:\n        verticalalignment = \"top\"\n        y = y - 0.002\n    plt.text(\n        x,\n        y,\n        name,\n        size=10,\n        horizontalalignment=horizontalalignment,\n        verticalalignment=verticalalignment,\n        bbox=dict(\n            facecolor=\"w\",\n            edgecolor=plt.cm.nipy_spectral(label / float(n_labels)),\n            alpha=0.6,\n        ),\n    )\n\nplt.xlim(\n    embedding[0].min() - 0.15 * np.ptp(embedding[0]),\n    embedding[0].max() + 0.10 * np.ptp(embedding[0]),\n)\nplt.ylim(\n    embedding[1].min() - 0.03 * np.ptp(embedding[1]),\n    embedding[1].max() + 0.03 * np.ptp(embedding[1]),\n)\n\nplt.show()"
+        "import matplotlib.pyplot as plt\nfrom matplotlib.collections import LineCollection\n\nplt.figure(1, facecolor=\"w\", figsize=(10, 8))\nplt.clf()\nax = plt.axes([0.0, 0.0, 1.0, 1.0])\nplt.axis(\"off\")\n\n# Plot the graph of partial correlations\npartial_correlations = edge_model.precision_.copy()\nd = 1 / np.sqrt(np.diag(partial_correlations))\npartial_correlations *= d\npartial_correlations *= d[:, np.newaxis]\nnon_zero = np.abs(np.triu(partial_correlations, k=1)) > 0.02\n\n# Plot the nodes using the coordinates of our embedding\nplt.scatter(\n    embedding[0], embedding[1], s=100 * d**2, c=labels, cmap=plt.cm.nipy_spectral\n)\n\n# Plot the edges\nstart_idx, end_idx = non_zero.nonzero()\n# a sequence of (*line0*, *line1*, *line2*), where::\n#            linen = (x0, y0), (x1, y1), ... (xm, ym)\nsegments = [\n    [embedding[:, start], embedding[:, stop]] for start, stop in zip(start_idx, end_idx)\n]\nvalues = np.abs(partial_correlations[non_zero])\nlc = LineCollection(\n    segments, zorder=0, cmap=plt.cm.hot_r, norm=plt.Normalize(0, 0.7 * values.max())\n)\nlc.set_array(values)\nlc.set_linewidths(15 * values)\nax.add_collection(lc)\n\n# Add a label to each node. The challenge here is that we want to\n# position the labels to avoid overlap with other labels\nfor index, (name, label, (x, y)) in enumerate(zip(names, labels, embedding.T)):\n    dx = x - embedding[0]\n    dx[index] = 1\n    dy = y - embedding[1]\n    dy[index] = 1\n    this_dx = dx[np.argmin(np.abs(dy))]\n    this_dy = dy[np.argmin(np.abs(dx))]\n    if this_dx > 0:\n        horizontalalignment = \"left\"\n        x = x + 0.002\n    else:\n        horizontalalignment = \"right\"\n        x = x - 0.002\n    if this_dy > 0:\n        verticalalignment = \"bottom\"\n        y = y + 0.002\n    else:\n        verticalalignment = \"top\"\n        y = y - 0.002\n    plt.text(\n        x,\n        y,\n        name,\n        size=10,\n        horizontalalignment=horizontalalignment,\n        verticalalignment=verticalalignment,\n        bbox=dict(\n            facecolor=\"w\",\n            edgecolor=plt.cm.nipy_spectral(label / float(n_labels)),\n            alpha=0.6,\n        ),\n    )\n\nplt.xlim(\n    embedding[0].min() - 0.15 * np.ptp(embedding[0]),\n    embedding[0].max() + 0.10 * np.ptp(embedding[0]),\n)\nplt.ylim(\n    embedding[1].min() - 0.03 * np.ptp(embedding[1]),\n    embedding[1].max() + 0.03 * np.ptp(embedding[1]),\n)\n\nplt.show()"
       ]
     }
   ],
@@ -125,7 +125,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/applications/plot_time_series_lagged_features.ipynb b/dev/notebooks/auto_examples/applications/plot_time_series_lagged_features.ipynb
index c42205e5ea5c9..777ea9319b48a 100644
--- a/dev/notebooks/auto_examples/applications/plot_time_series_lagged_features.ipynb
+++ b/dev/notebooks/auto_examples/applications/plot_time_series_lagged_features.ipynb
@@ -285,7 +285,7 @@
       },
       "outputs": [],
       "source": [
-        "quantile_list = [0.05, 0.5, 0.95]\n\nfor quantile in quantile_list:\n    model = HistGradientBoostingRegressor(loss=\"quantile\", quantile=quantile)\n    cv_results = cross_validate(\n        model,\n        X,\n        y,\n        cv=ts_cv,\n        scoring=scoring,\n        n_jobs=2,\n    )\n    time = cv_results[\"fit_time\"]\n    scores[\"fit_time\"].append(f\"{time.mean():.2f} \u00b1 {time.std():.2f} s\")\n\n    scores[\"loss\"].append(f\"quantile {int(quantile*100)}\")\n    for key, value in cv_results.items():\n        if key.startswith(\"test_\"):\n            metric = key.split(\"test_\")[1]\n            scores = consolidate_scores(cv_results, scores, metric)\n\nscores_df = pl.DataFrame(scores)\nscores_df"
+        "quantile_list = [0.05, 0.5, 0.95]\n\nfor quantile in quantile_list:\n    model = HistGradientBoostingRegressor(loss=\"quantile\", quantile=quantile)\n    cv_results = cross_validate(\n        model,\n        X,\n        y,\n        cv=ts_cv,\n        scoring=scoring,\n        n_jobs=2,\n    )\n    time = cv_results[\"fit_time\"]\n    scores[\"fit_time\"].append(f\"{time.mean():.2f} \u00b1 {time.std():.2f} s\")\n\n    scores[\"loss\"].append(f\"quantile {int(quantile * 100)}\")\n    for key, value in cv_results.items():\n        if key.startswith(\"test_\"):\n            metric = key.split(\"test_\")[1]\n            scores = consolidate_scores(cv_results, scores, metric)\n\nscores_df = pl.DataFrame(scores)\nscores_df"
       ]
     },
     {
@@ -384,7 +384,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/applications/plot_tomography_l1_reconstruction.ipynb b/dev/notebooks/auto_examples/applications/plot_tomography_l1_reconstruction.ipynb
index 97adaae9ed657..65bbf8072ab87 100644
--- a/dev/notebooks/auto_examples/applications/plot_tomography_l1_reconstruction.ipynb
+++ b/dev/notebooks/auto_examples/applications/plot_tomography_l1_reconstruction.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/applications/plot_topics_extraction_with_nmf_lda.ipynb b/dev/notebooks/auto_examples/applications/plot_topics_extraction_with_nmf_lda.ipynb
index 8becce9589a29..4c35617abcf2e 100644
--- a/dev/notebooks/auto_examples/applications/plot_topics_extraction_with_nmf_lda.ipynb
+++ b/dev/notebooks/auto_examples/applications/plot_topics_extraction_with_nmf_lda.ipynb
@@ -15,7 +15,7 @@
       },
       "outputs": [],
       "source": [
-        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\n\nfrom time import time\n\nimport matplotlib.pyplot as plt\n\nfrom sklearn.datasets import fetch_20newsgroups\nfrom sklearn.decomposition import NMF, LatentDirichletAllocation, MiniBatchNMF\nfrom sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\n\nn_samples = 2000\nn_features = 1000\nn_components = 10\nn_top_words = 20\nbatch_size = 128\ninit = \"nndsvda\"\n\n\ndef plot_top_words(model, feature_names, n_top_words, title):\n    fig, axes = plt.subplots(2, 5, figsize=(30, 15), sharex=True)\n    axes = axes.flatten()\n    for topic_idx, topic in enumerate(model.components_):\n        top_features_ind = topic.argsort()[-n_top_words:]\n        top_features = feature_names[top_features_ind]\n        weights = topic[top_features_ind]\n\n        ax = axes[topic_idx]\n        ax.barh(top_features, weights, height=0.7)\n        ax.set_title(f\"Topic {topic_idx +1}\", fontdict={\"fontsize\": 30})\n        ax.tick_params(axis=\"both\", which=\"major\", labelsize=20)\n        for i in \"top right left\".split():\n            ax.spines[i].set_visible(False)\n        fig.suptitle(title, fontsize=40)\n\n    plt.subplots_adjust(top=0.90, bottom=0.05, wspace=0.90, hspace=0.3)\n    plt.show()\n\n\n# Load the 20 newsgroups dataset and vectorize it. We use a few heuristics\n# to filter out useless terms early on: the posts are stripped of headers,\n# footers and quoted replies, and common English words, words occurring in\n# only one document or in at least 95% of the documents are removed.\n\nprint(\"Loading dataset...\")\nt0 = time()\ndata, _ = fetch_20newsgroups(\n    shuffle=True,\n    random_state=1,\n    remove=(\"headers\", \"footers\", \"quotes\"),\n    return_X_y=True,\n)\ndata_samples = data[:n_samples]\nprint(\"done in %0.3fs.\" % (time() - t0))\n\n# Use tf-idf features for NMF.\nprint(\"Extracting tf-idf features for NMF...\")\ntfidf_vectorizer = TfidfVectorizer(\n    max_df=0.95, min_df=2, max_features=n_features, stop_words=\"english\"\n)\nt0 = time()\ntfidf = tfidf_vectorizer.fit_transform(data_samples)\nprint(\"done in %0.3fs.\" % (time() - t0))\n\n# Use tf (raw term count) features for LDA.\nprint(\"Extracting tf features for LDA...\")\ntf_vectorizer = CountVectorizer(\n    max_df=0.95, min_df=2, max_features=n_features, stop_words=\"english\"\n)\nt0 = time()\ntf = tf_vectorizer.fit_transform(data_samples)\nprint(\"done in %0.3fs.\" % (time() - t0))\nprint()\n\n# Fit the NMF model\nprint(\n    \"Fitting the NMF model (Frobenius norm) with tf-idf features, \"\n    \"n_samples=%d and n_features=%d...\" % (n_samples, n_features)\n)\nt0 = time()\nnmf = NMF(\n    n_components=n_components,\n    random_state=1,\n    init=init,\n    beta_loss=\"frobenius\",\n    alpha_W=0.00005,\n    alpha_H=0.00005,\n    l1_ratio=1,\n).fit(tfidf)\nprint(\"done in %0.3fs.\" % (time() - t0))\n\n\ntfidf_feature_names = tfidf_vectorizer.get_feature_names_out()\nplot_top_words(\n    nmf, tfidf_feature_names, n_top_words, \"Topics in NMF model (Frobenius norm)\"\n)\n\n# Fit the NMF model\nprint(\n    \"\\n\" * 2,\n    \"Fitting the NMF model (generalized Kullback-Leibler \"\n    \"divergence) with tf-idf features, n_samples=%d and n_features=%d...\"\n    % (n_samples, n_features),\n)\nt0 = time()\nnmf = NMF(\n    n_components=n_components,\n    random_state=1,\n    init=init,\n    beta_loss=\"kullback-leibler\",\n    solver=\"mu\",\n    max_iter=1000,\n    alpha_W=0.00005,\n    alpha_H=0.00005,\n    l1_ratio=0.5,\n).fit(tfidf)\nprint(\"done in %0.3fs.\" % (time() - t0))\n\ntfidf_feature_names = tfidf_vectorizer.get_feature_names_out()\nplot_top_words(\n    nmf,\n    tfidf_feature_names,\n    n_top_words,\n    \"Topics in NMF model (generalized Kullback-Leibler divergence)\",\n)\n\n# Fit the MiniBatchNMF model\nprint(\n    \"\\n\" * 2,\n    \"Fitting the MiniBatchNMF model (Frobenius norm) with tf-idf \"\n    \"features, n_samples=%d and n_features=%d, batch_size=%d...\"\n    % (n_samples, n_features, batch_size),\n)\nt0 = time()\nmbnmf = MiniBatchNMF(\n    n_components=n_components,\n    random_state=1,\n    batch_size=batch_size,\n    init=init,\n    beta_loss=\"frobenius\",\n    alpha_W=0.00005,\n    alpha_H=0.00005,\n    l1_ratio=0.5,\n).fit(tfidf)\nprint(\"done in %0.3fs.\" % (time() - t0))\n\n\ntfidf_feature_names = tfidf_vectorizer.get_feature_names_out()\nplot_top_words(\n    mbnmf,\n    tfidf_feature_names,\n    n_top_words,\n    \"Topics in MiniBatchNMF model (Frobenius norm)\",\n)\n\n# Fit the MiniBatchNMF model\nprint(\n    \"\\n\" * 2,\n    \"Fitting the MiniBatchNMF model (generalized Kullback-Leibler \"\n    \"divergence) with tf-idf features, n_samples=%d and n_features=%d, \"\n    \"batch_size=%d...\" % (n_samples, n_features, batch_size),\n)\nt0 = time()\nmbnmf = MiniBatchNMF(\n    n_components=n_components,\n    random_state=1,\n    batch_size=batch_size,\n    init=init,\n    beta_loss=\"kullback-leibler\",\n    alpha_W=0.00005,\n    alpha_H=0.00005,\n    l1_ratio=0.5,\n).fit(tfidf)\nprint(\"done in %0.3fs.\" % (time() - t0))\n\ntfidf_feature_names = tfidf_vectorizer.get_feature_names_out()\nplot_top_words(\n    mbnmf,\n    tfidf_feature_names,\n    n_top_words,\n    \"Topics in MiniBatchNMF model (generalized Kullback-Leibler divergence)\",\n)\n\nprint(\n    \"\\n\" * 2,\n    \"Fitting LDA models with tf features, n_samples=%d and n_features=%d...\"\n    % (n_samples, n_features),\n)\nlda = LatentDirichletAllocation(\n    n_components=n_components,\n    max_iter=5,\n    learning_method=\"online\",\n    learning_offset=50.0,\n    random_state=0,\n)\nt0 = time()\nlda.fit(tf)\nprint(\"done in %0.3fs.\" % (time() - t0))\n\ntf_feature_names = tf_vectorizer.get_feature_names_out()\nplot_top_words(lda, tf_feature_names, n_top_words, \"Topics in LDA model\")"
+        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\n\nfrom time import time\n\nimport matplotlib.pyplot as plt\n\nfrom sklearn.datasets import fetch_20newsgroups\nfrom sklearn.decomposition import NMF, LatentDirichletAllocation, MiniBatchNMF\nfrom sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\n\nn_samples = 2000\nn_features = 1000\nn_components = 10\nn_top_words = 20\nbatch_size = 128\ninit = \"nndsvda\"\n\n\ndef plot_top_words(model, feature_names, n_top_words, title):\n    fig, axes = plt.subplots(2, 5, figsize=(30, 15), sharex=True)\n    axes = axes.flatten()\n    for topic_idx, topic in enumerate(model.components_):\n        top_features_ind = topic.argsort()[-n_top_words:]\n        top_features = feature_names[top_features_ind]\n        weights = topic[top_features_ind]\n\n        ax = axes[topic_idx]\n        ax.barh(top_features, weights, height=0.7)\n        ax.set_title(f\"Topic {topic_idx + 1}\", fontdict={\"fontsize\": 30})\n        ax.tick_params(axis=\"both\", which=\"major\", labelsize=20)\n        for i in \"top right left\".split():\n            ax.spines[i].set_visible(False)\n        fig.suptitle(title, fontsize=40)\n\n    plt.subplots_adjust(top=0.90, bottom=0.05, wspace=0.90, hspace=0.3)\n    plt.show()\n\n\n# Load the 20 newsgroups dataset and vectorize it. We use a few heuristics\n# to filter out useless terms early on: the posts are stripped of headers,\n# footers and quoted replies, and common English words, words occurring in\n# only one document or in at least 95% of the documents are removed.\n\nprint(\"Loading dataset...\")\nt0 = time()\ndata, _ = fetch_20newsgroups(\n    shuffle=True,\n    random_state=1,\n    remove=(\"headers\", \"footers\", \"quotes\"),\n    return_X_y=True,\n)\ndata_samples = data[:n_samples]\nprint(\"done in %0.3fs.\" % (time() - t0))\n\n# Use tf-idf features for NMF.\nprint(\"Extracting tf-idf features for NMF...\")\ntfidf_vectorizer = TfidfVectorizer(\n    max_df=0.95, min_df=2, max_features=n_features, stop_words=\"english\"\n)\nt0 = time()\ntfidf = tfidf_vectorizer.fit_transform(data_samples)\nprint(\"done in %0.3fs.\" % (time() - t0))\n\n# Use tf (raw term count) features for LDA.\nprint(\"Extracting tf features for LDA...\")\ntf_vectorizer = CountVectorizer(\n    max_df=0.95, min_df=2, max_features=n_features, stop_words=\"english\"\n)\nt0 = time()\ntf = tf_vectorizer.fit_transform(data_samples)\nprint(\"done in %0.3fs.\" % (time() - t0))\nprint()\n\n# Fit the NMF model\nprint(\n    \"Fitting the NMF model (Frobenius norm) with tf-idf features, \"\n    \"n_samples=%d and n_features=%d...\" % (n_samples, n_features)\n)\nt0 = time()\nnmf = NMF(\n    n_components=n_components,\n    random_state=1,\n    init=init,\n    beta_loss=\"frobenius\",\n    alpha_W=0.00005,\n    alpha_H=0.00005,\n    l1_ratio=1,\n).fit(tfidf)\nprint(\"done in %0.3fs.\" % (time() - t0))\n\n\ntfidf_feature_names = tfidf_vectorizer.get_feature_names_out()\nplot_top_words(\n    nmf, tfidf_feature_names, n_top_words, \"Topics in NMF model (Frobenius norm)\"\n)\n\n# Fit the NMF model\nprint(\n    \"\\n\" * 2,\n    \"Fitting the NMF model (generalized Kullback-Leibler \"\n    \"divergence) with tf-idf features, n_samples=%d and n_features=%d...\"\n    % (n_samples, n_features),\n)\nt0 = time()\nnmf = NMF(\n    n_components=n_components,\n    random_state=1,\n    init=init,\n    beta_loss=\"kullback-leibler\",\n    solver=\"mu\",\n    max_iter=1000,\n    alpha_W=0.00005,\n    alpha_H=0.00005,\n    l1_ratio=0.5,\n).fit(tfidf)\nprint(\"done in %0.3fs.\" % (time() - t0))\n\ntfidf_feature_names = tfidf_vectorizer.get_feature_names_out()\nplot_top_words(\n    nmf,\n    tfidf_feature_names,\n    n_top_words,\n    \"Topics in NMF model (generalized Kullback-Leibler divergence)\",\n)\n\n# Fit the MiniBatchNMF model\nprint(\n    \"\\n\" * 2,\n    \"Fitting the MiniBatchNMF model (Frobenius norm) with tf-idf \"\n    \"features, n_samples=%d and n_features=%d, batch_size=%d...\"\n    % (n_samples, n_features, batch_size),\n)\nt0 = time()\nmbnmf = MiniBatchNMF(\n    n_components=n_components,\n    random_state=1,\n    batch_size=batch_size,\n    init=init,\n    beta_loss=\"frobenius\",\n    alpha_W=0.00005,\n    alpha_H=0.00005,\n    l1_ratio=0.5,\n).fit(tfidf)\nprint(\"done in %0.3fs.\" % (time() - t0))\n\n\ntfidf_feature_names = tfidf_vectorizer.get_feature_names_out()\nplot_top_words(\n    mbnmf,\n    tfidf_feature_names,\n    n_top_words,\n    \"Topics in MiniBatchNMF model (Frobenius norm)\",\n)\n\n# Fit the MiniBatchNMF model\nprint(\n    \"\\n\" * 2,\n    \"Fitting the MiniBatchNMF model (generalized Kullback-Leibler \"\n    \"divergence) with tf-idf features, n_samples=%d and n_features=%d, \"\n    \"batch_size=%d...\" % (n_samples, n_features, batch_size),\n)\nt0 = time()\nmbnmf = MiniBatchNMF(\n    n_components=n_components,\n    random_state=1,\n    batch_size=batch_size,\n    init=init,\n    beta_loss=\"kullback-leibler\",\n    alpha_W=0.00005,\n    alpha_H=0.00005,\n    l1_ratio=0.5,\n).fit(tfidf)\nprint(\"done in %0.3fs.\" % (time() - t0))\n\ntfidf_feature_names = tfidf_vectorizer.get_feature_names_out()\nplot_top_words(\n    mbnmf,\n    tfidf_feature_names,\n    n_top_words,\n    \"Topics in MiniBatchNMF model (generalized Kullback-Leibler divergence)\",\n)\n\nprint(\n    \"\\n\" * 2,\n    \"Fitting LDA models with tf features, n_samples=%d and n_features=%d...\"\n    % (n_samples, n_features),\n)\nlda = LatentDirichletAllocation(\n    n_components=n_components,\n    max_iter=5,\n    learning_method=\"online\",\n    learning_offset=50.0,\n    random_state=0,\n)\nt0 = time()\nlda.fit(tf)\nprint(\"done in %0.3fs.\" % (time() - t0))\n\ntf_feature_names = tf_vectorizer.get_feature_names_out()\nplot_top_words(lda, tf_feature_names, n_top_words, \"Topics in LDA model\")"
       ]
     }
   ],
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/applications/wikipedia_principal_eigenvector.ipynb b/dev/notebooks/auto_examples/applications/wikipedia_principal_eigenvector.ipynb
index 8d166b29388eb..2a3fea28775d7 100644
--- a/dev/notebooks/auto_examples/applications/wikipedia_principal_eigenvector.ipynb
+++ b/dev/notebooks/auto_examples/applications/wikipedia_principal_eigenvector.ipynb
@@ -125,7 +125,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/bicluster/plot_bicluster_newsgroups.ipynb b/dev/notebooks/auto_examples/bicluster/plot_bicluster_newsgroups.ipynb
index a44f4b44e292e..ae9db1a5291c1 100644
--- a/dev/notebooks/auto_examples/bicluster/plot_bicluster_newsgroups.ipynb
+++ b/dev/notebooks/auto_examples/bicluster/plot_bicluster_newsgroups.ipynb
@@ -15,7 +15,7 @@
       },
       "outputs": [],
       "source": [
-        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\nfrom collections import Counter\nfrom time import time\n\nimport numpy as np\n\nfrom sklearn.cluster import MiniBatchKMeans, SpectralCoclustering\nfrom sklearn.datasets import fetch_20newsgroups\nfrom sklearn.feature_extraction.text import TfidfVectorizer\nfrom sklearn.metrics.cluster import v_measure_score\n\n\ndef number_normalizer(tokens):\n    \"\"\"Map all numeric tokens to a placeholder.\n\n    For many applications, tokens that begin with a number are not directly\n    useful, but the fact that such a token exists can be relevant.  By applying\n    this form of dimensionality reduction, some methods may perform better.\n    \"\"\"\n    return (\"#NUMBER\" if token[0].isdigit() else token for token in tokens)\n\n\nclass NumberNormalizingVectorizer(TfidfVectorizer):\n    def build_tokenizer(self):\n        tokenize = super().build_tokenizer()\n        return lambda doc: list(number_normalizer(tokenize(doc)))\n\n\n# exclude 'comp.os.ms-windows.misc'\ncategories = [\n    \"alt.atheism\",\n    \"comp.graphics\",\n    \"comp.sys.ibm.pc.hardware\",\n    \"comp.sys.mac.hardware\",\n    \"comp.windows.x\",\n    \"misc.forsale\",\n    \"rec.autos\",\n    \"rec.motorcycles\",\n    \"rec.sport.baseball\",\n    \"rec.sport.hockey\",\n    \"sci.crypt\",\n    \"sci.electronics\",\n    \"sci.med\",\n    \"sci.space\",\n    \"soc.religion.christian\",\n    \"talk.politics.guns\",\n    \"talk.politics.mideast\",\n    \"talk.politics.misc\",\n    \"talk.religion.misc\",\n]\nnewsgroups = fetch_20newsgroups(categories=categories)\ny_true = newsgroups.target\n\nvectorizer = NumberNormalizingVectorizer(stop_words=\"english\", min_df=5)\ncocluster = SpectralCoclustering(\n    n_clusters=len(categories), svd_method=\"arpack\", random_state=0\n)\nkmeans = MiniBatchKMeans(\n    n_clusters=len(categories), batch_size=20000, random_state=0, n_init=3\n)\n\nprint(\"Vectorizing...\")\nX = vectorizer.fit_transform(newsgroups.data)\n\nprint(\"Coclustering...\")\nstart_time = time()\ncocluster.fit(X)\ny_cocluster = cocluster.row_labels_\nprint(\n    f\"Done in {time() - start_time:.2f}s. V-measure: \\\n{v_measure_score(y_cocluster, y_true):.4f}\"\n)\n\n\nprint(\"MiniBatchKMeans...\")\nstart_time = time()\ny_kmeans = kmeans.fit_predict(X)\nprint(\n    f\"Done in {time() - start_time:.2f}s. V-measure: \\\n{v_measure_score(y_kmeans, y_true):.4f}\"\n)\n\n\nfeature_names = vectorizer.get_feature_names_out()\ndocument_names = list(newsgroups.target_names[i] for i in newsgroups.target)\n\n\ndef bicluster_ncut(i):\n    rows, cols = cocluster.get_indices(i)\n    if not (np.any(rows) and np.any(cols)):\n        import sys\n\n        return sys.float_info.max\n    row_complement = np.nonzero(np.logical_not(cocluster.rows_[i]))[0]\n    col_complement = np.nonzero(np.logical_not(cocluster.columns_[i]))[0]\n    # Note: the following is identical to X[rows[:, np.newaxis],\n    # cols].sum() but much faster in scipy <= 0.16\n    weight = X[rows][:, cols].sum()\n    cut = X[row_complement][:, cols].sum() + X[rows][:, col_complement].sum()\n    return cut / weight\n\n\nbicluster_ncuts = list(bicluster_ncut(i) for i in range(len(newsgroups.target_names)))\nbest_idx = np.argsort(bicluster_ncuts)[:5]\n\nprint()\nprint(\"Best biclusters:\")\nprint(\"----------------\")\nfor idx, cluster in enumerate(best_idx):\n    n_rows, n_cols = cocluster.get_shape(cluster)\n    cluster_docs, cluster_words = cocluster.get_indices(cluster)\n    if not len(cluster_docs) or not len(cluster_words):\n        continue\n\n    # categories\n    counter = Counter(document_names[doc] for doc in cluster_docs)\n\n    cat_string = \", \".join(\n        f\"{(c / n_rows * 100):.0f}% {name}\" for name, c in counter.most_common(3)\n    )\n\n    # words\n    out_of_cluster_docs = cocluster.row_labels_ != cluster\n    out_of_cluster_docs = np.where(out_of_cluster_docs)[0]\n    word_col = X[:, cluster_words]\n    word_scores = np.array(\n        word_col[cluster_docs, :].sum(axis=0)\n        - word_col[out_of_cluster_docs, :].sum(axis=0)\n    )\n    word_scores = word_scores.ravel()\n    important_words = list(\n        feature_names[cluster_words[i]] for i in word_scores.argsort()[:-11:-1]\n    )\n\n    print(f\"bicluster {idx} : {n_rows} documents, {n_cols} words\")\n    print(f\"categories   : {cat_string}\")\n    print(f\"words        : {', '.join(important_words)}\\n\")"
+        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\nfrom collections import Counter\nfrom time import time\n\nimport numpy as np\n\nfrom sklearn.cluster import MiniBatchKMeans, SpectralCoclustering\nfrom sklearn.datasets import fetch_20newsgroups\nfrom sklearn.feature_extraction.text import TfidfVectorizer\nfrom sklearn.metrics.cluster import v_measure_score\n\n\ndef number_normalizer(tokens):\n    \"\"\"Map all numeric tokens to a placeholder.\n\n    For many applications, tokens that begin with a number are not directly\n    useful, but the fact that such a token exists can be relevant.  By applying\n    this form of dimensionality reduction, some methods may perform better.\n    \"\"\"\n    return (\"#NUMBER\" if token[0].isdigit() else token for token in tokens)\n\n\nclass NumberNormalizingVectorizer(TfidfVectorizer):\n    def build_tokenizer(self):\n        tokenize = super().build_tokenizer()\n        return lambda doc: list(number_normalizer(tokenize(doc)))\n\n\n# exclude 'comp.os.ms-windows.misc'\ncategories = [\n    \"alt.atheism\",\n    \"comp.graphics\",\n    \"comp.sys.ibm.pc.hardware\",\n    \"comp.sys.mac.hardware\",\n    \"comp.windows.x\",\n    \"misc.forsale\",\n    \"rec.autos\",\n    \"rec.motorcycles\",\n    \"rec.sport.baseball\",\n    \"rec.sport.hockey\",\n    \"sci.crypt\",\n    \"sci.electronics\",\n    \"sci.med\",\n    \"sci.space\",\n    \"soc.religion.christian\",\n    \"talk.politics.guns\",\n    \"talk.politics.mideast\",\n    \"talk.politics.misc\",\n    \"talk.religion.misc\",\n]\nnewsgroups = fetch_20newsgroups(categories=categories)\ny_true = newsgroups.target\n\nvectorizer = NumberNormalizingVectorizer(stop_words=\"english\", min_df=5)\ncocluster = SpectralCoclustering(\n    n_clusters=len(categories), svd_method=\"arpack\", random_state=0\n)\nkmeans = MiniBatchKMeans(\n    n_clusters=len(categories), batch_size=20000, random_state=0, n_init=3\n)\n\nprint(\"Vectorizing...\")\nX = vectorizer.fit_transform(newsgroups.data)\n\nprint(\"Coclustering...\")\nstart_time = time()\ncocluster.fit(X)\ny_cocluster = cocluster.row_labels_\nprint(\n    f\"Done in {time() - start_time:.2f}s. V-measure: \\\n{v_measure_score(y_cocluster, y_true):.4f}\"\n)\n\n\nprint(\"MiniBatchKMeans...\")\nstart_time = time()\ny_kmeans = kmeans.fit_predict(X)\nprint(\n    f\"Done in {time() - start_time:.2f}s. V-measure: \\\n{v_measure_score(y_kmeans, y_true):.4f}\"\n)\n\n\nfeature_names = vectorizer.get_feature_names_out()\ndocument_names = list(newsgroups.target_names[i] for i in newsgroups.target)\n\n\ndef bicluster_ncut(i):\n    rows, cols = cocluster.get_indices(i)\n    if not (np.any(rows) and np.any(cols)):\n        import sys\n\n        return sys.float_info.max\n    row_complement = np.nonzero(np.logical_not(cocluster.rows_[i]))[0]\n    col_complement = np.nonzero(np.logical_not(cocluster.columns_[i]))[0]\n    # Note: the following is identical to X[rows[:, np.newaxis],\n    # cols].sum() but much faster in scipy <= 0.16\n    weight = X[rows][:, cols].sum()\n    cut = X[row_complement][:, cols].sum() + X[rows][:, col_complement].sum()\n    return cut / weight\n\n\nbicluster_ncuts = list(bicluster_ncut(i) for i in range(len(newsgroups.target_names)))\nbest_idx = np.argsort(bicluster_ncuts)[:5]\n\nprint()\nprint(\"Best biclusters:\")\nprint(\"----------------\")\nfor idx, cluster in enumerate(best_idx):\n    n_rows, n_cols = cocluster.get_shape(cluster)\n    cluster_docs, cluster_words = cocluster.get_indices(cluster)\n    if not len(cluster_docs) or not len(cluster_words):\n        continue\n\n    # categories\n    counter = Counter(document_names[doc] for doc in cluster_docs)\n\n    cat_string = \", \".join(\n        f\"{(c / n_rows * 100):.0f}% {name}\" for name, c in counter.most_common(3)\n    )\n\n    # words\n    out_of_cluster_docs = cocluster.row_labels_ != cluster\n    out_of_cluster_docs = out_of_cluster_docs.nonzero()[0]\n    word_col = X[:, cluster_words]\n    word_scores = np.array(\n        word_col[cluster_docs, :].sum(axis=0)\n        - word_col[out_of_cluster_docs, :].sum(axis=0)\n    )\n    word_scores = word_scores.ravel()\n    important_words = list(\n        feature_names[cluster_words[i]] for i in word_scores.argsort()[:-11:-1]\n    )\n\n    print(f\"bicluster {idx} : {n_rows} documents, {n_cols} words\")\n    print(f\"categories   : {cat_string}\")\n    print(f\"words        : {', '.join(important_words)}\\n\")"
       ]
     }
   ],
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/bicluster/plot_spectral_biclustering.ipynb b/dev/notebooks/auto_examples/bicluster/plot_spectral_biclustering.ipynb
index 04b916ca968e8..0e549b0b0b197 100644
--- a/dev/notebooks/auto_examples/bicluster/plot_spectral_biclustering.ipynb
+++ b/dev/notebooks/auto_examples/bicluster/plot_spectral_biclustering.ipynb
@@ -157,7 +157,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/bicluster/plot_spectral_coclustering.ipynb b/dev/notebooks/auto_examples/bicluster/plot_spectral_coclustering.ipynb
index fea47d0c4c150..441c7aa7dd04b 100644
--- a/dev/notebooks/auto_examples/bicluster/plot_spectral_coclustering.ipynb
+++ b/dev/notebooks/auto_examples/bicluster/plot_spectral_coclustering.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/calibration/plot_calibration.ipynb b/dev/notebooks/auto_examples/calibration/plot_calibration.ipynb
index b7e9d9c2eb660..a95223c49a051 100644
--- a/dev/notebooks/auto_examples/calibration/plot_calibration.ipynb
+++ b/dev/notebooks/auto_examples/calibration/plot_calibration.ipynb
@@ -89,7 +89,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/calibration/plot_calibration_curve.ipynb b/dev/notebooks/auto_examples/calibration/plot_calibration_curve.ipynb
index e93a932035d00..2f0a748ed1335 100644
--- a/dev/notebooks/auto_examples/calibration/plot_calibration_curve.ipynb
+++ b/dev/notebooks/auto_examples/calibration/plot_calibration_curve.ipynb
@@ -165,7 +165,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/calibration/plot_calibration_multiclass.ipynb b/dev/notebooks/auto_examples/calibration/plot_calibration_multiclass.ipynb
index 3e44631b41208..5e0e7a42e026e 100644
--- a/dev/notebooks/auto_examples/calibration/plot_calibration_multiclass.ipynb
+++ b/dev/notebooks/auto_examples/calibration/plot_calibration_multiclass.ipynb
@@ -157,7 +157,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/calibration/plot_compare_calibration.ipynb b/dev/notebooks/auto_examples/calibration/plot_compare_calibration.ipynb
index b0b6d0d6e2d1e..5fa0e2103b0de 100644
--- a/dev/notebooks/auto_examples/calibration/plot_compare_calibration.ipynb
+++ b/dev/notebooks/auto_examples/calibration/plot_compare_calibration.ipynb
@@ -89,7 +89,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/classification/plot_classification_probability.ipynb b/dev/notebooks/auto_examples/classification/plot_classification_probability.ipynb
index 92ed174a58bd0..04d8e35a94a17 100644
--- a/dev/notebooks/auto_examples/classification/plot_classification_probability.ipynb
+++ b/dev/notebooks/auto_examples/classification/plot_classification_probability.ipynb
@@ -121,7 +121,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/classification/plot_classifier_comparison.ipynb b/dev/notebooks/auto_examples/classification/plot_classifier_comparison.ipynb
index 0924d6641976d..d57beae484cf1 100644
--- a/dev/notebooks/auto_examples/classification/plot_classifier_comparison.ipynb
+++ b/dev/notebooks/auto_examples/classification/plot_classifier_comparison.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/classification/plot_digits_classification.ipynb b/dev/notebooks/auto_examples/classification/plot_digits_classification.ipynb
index e6e5c26652336..bffe5fdbd4c5b 100644
--- a/dev/notebooks/auto_examples/classification/plot_digits_classification.ipynb
+++ b/dev/notebooks/auto_examples/classification/plot_digits_classification.ipynb
@@ -143,7 +143,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/classification/plot_lda.ipynb b/dev/notebooks/auto_examples/classification/plot_lda.ipynb
index 3a679b792dbcb..00edaead8d6b5 100644
--- a/dev/notebooks/auto_examples/classification/plot_lda.ipynb
+++ b/dev/notebooks/auto_examples/classification/plot_lda.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/classification/plot_lda_qda.ipynb b/dev/notebooks/auto_examples/classification/plot_lda_qda.ipynb
index dce74a932455b..f750392ff0a09 100644
--- a/dev/notebooks/auto_examples/classification/plot_lda_qda.ipynb
+++ b/dev/notebooks/auto_examples/classification/plot_lda_qda.ipynb
@@ -114,7 +114,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/cluster/plot_adjusted_for_chance_measures.ipynb b/dev/notebooks/auto_examples/cluster/plot_adjusted_for_chance_measures.ipynb
index 7424ea859a5bc..11f88d526aceb 100644
--- a/dev/notebooks/auto_examples/cluster/plot_adjusted_for_chance_measures.ipynb
+++ b/dev/notebooks/auto_examples/cluster/plot_adjusted_for_chance_measures.ipynb
@@ -150,7 +150,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/cluster/plot_affinity_propagation.ipynb b/dev/notebooks/auto_examples/cluster/plot_affinity_propagation.ipynb
index f5f23adb30719..8d0774c4d4616 100644
--- a/dev/notebooks/auto_examples/cluster/plot_affinity_propagation.ipynb
+++ b/dev/notebooks/auto_examples/cluster/plot_affinity_propagation.ipynb
@@ -89,7 +89,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/cluster/plot_agglomerative_clustering.ipynb b/dev/notebooks/auto_examples/cluster/plot_agglomerative_clustering.ipynb
index 946c96f37556d..b6993efd3e2f4 100644
--- a/dev/notebooks/auto_examples/cluster/plot_agglomerative_clustering.ipynb
+++ b/dev/notebooks/auto_examples/cluster/plot_agglomerative_clustering.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/cluster/plot_agglomerative_clustering_metrics.ipynb b/dev/notebooks/auto_examples/cluster/plot_agglomerative_clustering_metrics.ipynb
index 96d79e4044eba..94bbae90ba260 100644
--- a/dev/notebooks/auto_examples/cluster/plot_agglomerative_clustering_metrics.ipynb
+++ b/dev/notebooks/auto_examples/cluster/plot_agglomerative_clustering_metrics.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/cluster/plot_agglomerative_dendrogram.ipynb b/dev/notebooks/auto_examples/cluster/plot_agglomerative_dendrogram.ipynb
index 0e2a740e4b951..ed707ef8fcdd5 100644
--- a/dev/notebooks/auto_examples/cluster/plot_agglomerative_dendrogram.ipynb
+++ b/dev/notebooks/auto_examples/cluster/plot_agglomerative_dendrogram.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/cluster/plot_birch_vs_minibatchkmeans.ipynb b/dev/notebooks/auto_examples/cluster/plot_birch_vs_minibatchkmeans.ipynb
index 288cb28db92d1..13353fe55d9fe 100644
--- a/dev/notebooks/auto_examples/cluster/plot_birch_vs_minibatchkmeans.ipynb
+++ b/dev/notebooks/auto_examples/cluster/plot_birch_vs_minibatchkmeans.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/cluster/plot_bisect_kmeans.ipynb b/dev/notebooks/auto_examples/cluster/plot_bisect_kmeans.ipynb
index a4da43b20db18..fa057883e818a 100644
--- a/dev/notebooks/auto_examples/cluster/plot_bisect_kmeans.ipynb
+++ b/dev/notebooks/auto_examples/cluster/plot_bisect_kmeans.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/cluster/plot_cluster_comparison.ipynb b/dev/notebooks/auto_examples/cluster/plot_cluster_comparison.ipynb
index 524f43a62566e..3a5d472d4253b 100644
--- a/dev/notebooks/auto_examples/cluster/plot_cluster_comparison.ipynb
+++ b/dev/notebooks/auto_examples/cluster/plot_cluster_comparison.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/cluster/plot_coin_segmentation.ipynb b/dev/notebooks/auto_examples/cluster/plot_coin_segmentation.ipynb
index 0592e23302ea2..36d7c71ab0df9 100644
--- a/dev/notebooks/auto_examples/cluster/plot_coin_segmentation.ipynb
+++ b/dev/notebooks/auto_examples/cluster/plot_coin_segmentation.ipynb
@@ -53,7 +53,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/cluster/plot_coin_ward_segmentation.ipynb b/dev/notebooks/auto_examples/cluster/plot_coin_ward_segmentation.ipynb
index 76e35207f311c..a5c13153ca5d0 100644
--- a/dev/notebooks/auto_examples/cluster/plot_coin_ward_segmentation.ipynb
+++ b/dev/notebooks/auto_examples/cluster/plot_coin_ward_segmentation.ipynb
@@ -125,7 +125,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/cluster/plot_dbscan.ipynb b/dev/notebooks/auto_examples/cluster/plot_dbscan.ipynb
index 197cb8a230f08..eb21ee8d01bd4 100644
--- a/dev/notebooks/auto_examples/cluster/plot_dbscan.ipynb
+++ b/dev/notebooks/auto_examples/cluster/plot_dbscan.ipynb
@@ -125,7 +125,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/cluster/plot_dict_face_patches.ipynb b/dev/notebooks/auto_examples/cluster/plot_dict_face_patches.ipynb
index cda258c1b1499..d933dc2cf11fc 100644
--- a/dev/notebooks/auto_examples/cluster/plot_dict_face_patches.ipynb
+++ b/dev/notebooks/auto_examples/cluster/plot_dict_face_patches.ipynb
@@ -89,7 +89,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/cluster/plot_digits_agglomeration.ipynb b/dev/notebooks/auto_examples/cluster/plot_digits_agglomeration.ipynb
index 818ad231c3b72..f860e0d9b7706 100644
--- a/dev/notebooks/auto_examples/cluster/plot_digits_agglomeration.ipynb
+++ b/dev/notebooks/auto_examples/cluster/plot_digits_agglomeration.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/cluster/plot_digits_linkage.ipynb b/dev/notebooks/auto_examples/cluster/plot_digits_linkage.ipynb
index 2e20f442ae574..339c8420e4e76 100644
--- a/dev/notebooks/auto_examples/cluster/plot_digits_linkage.ipynb
+++ b/dev/notebooks/auto_examples/cluster/plot_digits_linkage.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/cluster/plot_face_compress.ipynb b/dev/notebooks/auto_examples/cluster/plot_face_compress.ipynb
index 6ffa77fd61f56..acd6a9cea0cc0 100644
--- a/dev/notebooks/auto_examples/cluster/plot_face_compress.ipynb
+++ b/dev/notebooks/auto_examples/cluster/plot_face_compress.ipynb
@@ -201,7 +201,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/cluster/plot_feature_agglomeration_vs_univariate_selection.ipynb b/dev/notebooks/auto_examples/cluster/plot_feature_agglomeration_vs_univariate_selection.ipynb
index 702627d742887..73c352d030ff5 100644
--- a/dev/notebooks/auto_examples/cluster/plot_feature_agglomeration_vs_univariate_selection.ipynb
+++ b/dev/notebooks/auto_examples/cluster/plot_feature_agglomeration_vs_univariate_selection.ipynb
@@ -190,7 +190,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/cluster/plot_hdbscan.ipynb b/dev/notebooks/auto_examples/cluster/plot_hdbscan.ipynb
index d8d243a5be342..960ff4b0e413d 100644
--- a/dev/notebooks/auto_examples/cluster/plot_hdbscan.ipynb
+++ b/dev/notebooks/auto_examples/cluster/plot_hdbscan.ipynb
@@ -26,7 +26,7 @@
       },
       "outputs": [],
       "source": [
-        "import matplotlib.pyplot as plt\nimport numpy as np\n\nfrom sklearn.cluster import DBSCAN, HDBSCAN\nfrom sklearn.datasets import make_blobs\n\n\ndef plot(X, labels, probabilities=None, parameters=None, ground_truth=False, ax=None):\n    if ax is None:\n        _, ax = plt.subplots(figsize=(10, 4))\n    labels = labels if labels is not None else np.ones(X.shape[0])\n    probabilities = probabilities if probabilities is not None else np.ones(X.shape[0])\n    # Black removed and is used for noise instead.\n    unique_labels = set(labels)\n    colors = [plt.cm.Spectral(each) for each in np.linspace(0, 1, len(unique_labels))]\n    # The probability of a point belonging to its labeled cluster determines\n    # the size of its marker\n    proba_map = {idx: probabilities[idx] for idx in range(len(labels))}\n    for k, col in zip(unique_labels, colors):\n        if k == -1:\n            # Black used for noise.\n            col = [0, 0, 0, 1]\n\n        class_index = np.where(labels == k)[0]\n        for ci in class_index:\n            ax.plot(\n                X[ci, 0],\n                X[ci, 1],\n                \"x\" if k == -1 else \"o\",\n                markerfacecolor=tuple(col),\n                markeredgecolor=\"k\",\n                markersize=4 if k == -1 else 1 + 5 * proba_map[ci],\n            )\n    n_clusters_ = len(set(labels)) - (1 if -1 in labels else 0)\n    preamble = \"True\" if ground_truth else \"Estimated\"\n    title = f\"{preamble} number of clusters: {n_clusters_}\"\n    if parameters is not None:\n        parameters_str = \", \".join(f\"{k}={v}\" for k, v in parameters.items())\n        title += f\" | {parameters_str}\"\n    ax.set_title(title)\n    plt.tight_layout()"
+        "import matplotlib.pyplot as plt\nimport numpy as np\n\nfrom sklearn.cluster import DBSCAN, HDBSCAN\nfrom sklearn.datasets import make_blobs\n\n\ndef plot(X, labels, probabilities=None, parameters=None, ground_truth=False, ax=None):\n    if ax is None:\n        _, ax = plt.subplots(figsize=(10, 4))\n    labels = labels if labels is not None else np.ones(X.shape[0])\n    probabilities = probabilities if probabilities is not None else np.ones(X.shape[0])\n    # Black removed and is used for noise instead.\n    unique_labels = set(labels)\n    colors = [plt.cm.Spectral(each) for each in np.linspace(0, 1, len(unique_labels))]\n    # The probability of a point belonging to its labeled cluster determines\n    # the size of its marker\n    proba_map = {idx: probabilities[idx] for idx in range(len(labels))}\n    for k, col in zip(unique_labels, colors):\n        if k == -1:\n            # Black used for noise.\n            col = [0, 0, 0, 1]\n\n        class_index = (labels == k).nonzero()[0]\n        for ci in class_index:\n            ax.plot(\n                X[ci, 0],\n                X[ci, 1],\n                \"x\" if k == -1 else \"o\",\n                markerfacecolor=tuple(col),\n                markeredgecolor=\"k\",\n                markersize=4 if k == -1 else 1 + 5 * proba_map[ci],\n            )\n    n_clusters_ = len(set(labels)) - (1 if -1 in labels else 0)\n    preamble = \"True\" if ground_truth else \"Estimated\"\n    title = f\"{preamble} number of clusters: {n_clusters_}\"\n    if parameters is not None:\n        parameters_str = \", \".join(f\"{k}={v}\" for k, v in parameters.items())\n        title += f\" | {parameters_str}\"\n    ax.set_title(title)\n    plt.tight_layout()"
       ]
     },
     {
@@ -233,7 +233,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/cluster/plot_inductive_clustering.ipynb b/dev/notebooks/auto_examples/cluster/plot_inductive_clustering.ipynb
index 0d5adefba4ad4..4aed30c4076ca 100644
--- a/dev/notebooks/auto_examples/cluster/plot_inductive_clustering.ipynb
+++ b/dev/notebooks/auto_examples/cluster/plot_inductive_clustering.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/cluster/plot_kmeans_assumptions.ipynb b/dev/notebooks/auto_examples/cluster/plot_kmeans_assumptions.ipynb
index 617bd22a63d16..b53d9446b469b 100644
--- a/dev/notebooks/auto_examples/cluster/plot_kmeans_assumptions.ipynb
+++ b/dev/notebooks/auto_examples/cluster/plot_kmeans_assumptions.ipynb
@@ -150,7 +150,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/cluster/plot_kmeans_digits.ipynb b/dev/notebooks/auto_examples/cluster/plot_kmeans_digits.ipynb
index d4151ae164cc4..f784101fa27e3 100644
--- a/dev/notebooks/auto_examples/cluster/plot_kmeans_digits.ipynb
+++ b/dev/notebooks/auto_examples/cluster/plot_kmeans_digits.ipynb
@@ -107,7 +107,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/cluster/plot_kmeans_plusplus.ipynb b/dev/notebooks/auto_examples/cluster/plot_kmeans_plusplus.ipynb
index b435b800e1b3b..7ddb8cdbf8b52 100644
--- a/dev/notebooks/auto_examples/cluster/plot_kmeans_plusplus.ipynb
+++ b/dev/notebooks/auto_examples/cluster/plot_kmeans_plusplus.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/cluster/plot_kmeans_silhouette_analysis.ipynb b/dev/notebooks/auto_examples/cluster/plot_kmeans_silhouette_analysis.ipynb
index 6088d252578db..b320f4ec5a8c9 100644
--- a/dev/notebooks/auto_examples/cluster/plot_kmeans_silhouette_analysis.ipynb
+++ b/dev/notebooks/auto_examples/cluster/plot_kmeans_silhouette_analysis.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/cluster/plot_kmeans_stability_low_dim_dense.ipynb b/dev/notebooks/auto_examples/cluster/plot_kmeans_stability_low_dim_dense.ipynb
index 868e59ce8dcc7..7d048d3113030 100644
--- a/dev/notebooks/auto_examples/cluster/plot_kmeans_stability_low_dim_dense.ipynb
+++ b/dev/notebooks/auto_examples/cluster/plot_kmeans_stability_low_dim_dense.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/cluster/plot_linkage_comparison.ipynb b/dev/notebooks/auto_examples/cluster/plot_linkage_comparison.ipynb
index 3e66d12b335ef..7f6624d7c62cd 100644
--- a/dev/notebooks/auto_examples/cluster/plot_linkage_comparison.ipynb
+++ b/dev/notebooks/auto_examples/cluster/plot_linkage_comparison.ipynb
@@ -71,7 +71,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/cluster/plot_mean_shift.ipynb b/dev/notebooks/auto_examples/cluster/plot_mean_shift.ipynb
index a4604ec820658..2af7a6d8e84ae 100644
--- a/dev/notebooks/auto_examples/cluster/plot_mean_shift.ipynb
+++ b/dev/notebooks/auto_examples/cluster/plot_mean_shift.ipynb
@@ -89,7 +89,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/cluster/plot_mini_batch_kmeans.ipynb b/dev/notebooks/auto_examples/cluster/plot_mini_batch_kmeans.ipynb
index b935b6bc32407..9814ed71e7754 100644
--- a/dev/notebooks/auto_examples/cluster/plot_mini_batch_kmeans.ipynb
+++ b/dev/notebooks/auto_examples/cluster/plot_mini_batch_kmeans.ipynb
@@ -125,7 +125,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/cluster/plot_optics.ipynb b/dev/notebooks/auto_examples/cluster/plot_optics.ipynb
index 6c90fae955dae..1af1e95bea82a 100644
--- a/dev/notebooks/auto_examples/cluster/plot_optics.ipynb
+++ b/dev/notebooks/auto_examples/cluster/plot_optics.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/cluster/plot_segmentation_toy.ipynb b/dev/notebooks/auto_examples/cluster/plot_segmentation_toy.ipynb
index 36d393a97abdb..5ec55cf86837b 100644
--- a/dev/notebooks/auto_examples/cluster/plot_segmentation_toy.ipynb
+++ b/dev/notebooks/auto_examples/cluster/plot_segmentation_toy.ipynb
@@ -143,7 +143,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/cluster/plot_ward_structured_vs_unstructured.ipynb b/dev/notebooks/auto_examples/cluster/plot_ward_structured_vs_unstructured.ipynb
index 8252979054167..76fe2fc20155d 100644
--- a/dev/notebooks/auto_examples/cluster/plot_ward_structured_vs_unstructured.ipynb
+++ b/dev/notebooks/auto_examples/cluster/plot_ward_structured_vs_unstructured.ipynb
@@ -143,7 +143,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/compose/plot_column_transformer.ipynb b/dev/notebooks/auto_examples/compose/plot_column_transformer.ipynb
index a87eb107a8983..a70973a044de2 100644
--- a/dev/notebooks/auto_examples/compose/plot_column_transformer.ipynb
+++ b/dev/notebooks/auto_examples/compose/plot_column_transformer.ipynb
@@ -143,7 +143,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/compose/plot_column_transformer_mixed_types.ipynb b/dev/notebooks/auto_examples/compose/plot_column_transformer_mixed_types.ipynb
index b9622368df884..6611fe42d5109 100644
--- a/dev/notebooks/auto_examples/compose/plot_column_transformer_mixed_types.ipynb
+++ b/dev/notebooks/auto_examples/compose/plot_column_transformer_mixed_types.ipynb
@@ -298,7 +298,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/compose/plot_compare_reduction.ipynb b/dev/notebooks/auto_examples/compose/plot_compare_reduction.ipynb
index 9c4396d4aa509..c14b12173d9c2 100644
--- a/dev/notebooks/auto_examples/compose/plot_compare_reduction.ipynb
+++ b/dev/notebooks/auto_examples/compose/plot_compare_reduction.ipynb
@@ -89,7 +89,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/compose/plot_digits_pipe.ipynb b/dev/notebooks/auto_examples/compose/plot_digits_pipe.ipynb
index 70a730d8b03ee..81a27096f8e30 100644
--- a/dev/notebooks/auto_examples/compose/plot_digits_pipe.ipynb
+++ b/dev/notebooks/auto_examples/compose/plot_digits_pipe.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/compose/plot_feature_union.ipynb b/dev/notebooks/auto_examples/compose/plot_feature_union.ipynb
index 7c5133c80e09f..f554e0a5c4d5a 100644
--- a/dev/notebooks/auto_examples/compose/plot_feature_union.ipynb
+++ b/dev/notebooks/auto_examples/compose/plot_feature_union.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/compose/plot_transformed_target.ipynb b/dev/notebooks/auto_examples/compose/plot_transformed_target.ipynb
index 1e8420adb9c8b..bf0638fe1c964 100644
--- a/dev/notebooks/auto_examples/compose/plot_transformed_target.ipynb
+++ b/dev/notebooks/auto_examples/compose/plot_transformed_target.ipynb
@@ -165,7 +165,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/covariance/plot_covariance_estimation.ipynb b/dev/notebooks/auto_examples/covariance/plot_covariance_estimation.ipynb
index 26e3e9aeb2ffa..c99656443931c 100644
--- a/dev/notebooks/auto_examples/covariance/plot_covariance_estimation.ipynb
+++ b/dev/notebooks/auto_examples/covariance/plot_covariance_estimation.ipynb
@@ -114,7 +114,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/covariance/plot_lw_vs_oas.ipynb b/dev/notebooks/auto_examples/covariance/plot_lw_vs_oas.ipynb
index f6a69f08f7e24..367e498f6166e 100644
--- a/dev/notebooks/auto_examples/covariance/plot_lw_vs_oas.ipynb
+++ b/dev/notebooks/auto_examples/covariance/plot_lw_vs_oas.ipynb
@@ -46,7 +46,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/covariance/plot_mahalanobis_distances.ipynb b/dev/notebooks/auto_examples/covariance/plot_mahalanobis_distances.ipynb
index e24218b127e36..cb3e31ff956a5 100644
--- a/dev/notebooks/auto_examples/covariance/plot_mahalanobis_distances.ipynb
+++ b/dev/notebooks/auto_examples/covariance/plot_mahalanobis_distances.ipynb
@@ -107,7 +107,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/covariance/plot_robust_vs_empirical_covariance.ipynb b/dev/notebooks/auto_examples/covariance/plot_robust_vs_empirical_covariance.ipynb
index 818200c2057da..9cb4461dcbb44 100644
--- a/dev/notebooks/auto_examples/covariance/plot_robust_vs_empirical_covariance.ipynb
+++ b/dev/notebooks/auto_examples/covariance/plot_robust_vs_empirical_covariance.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/covariance/plot_sparse_cov.ipynb b/dev/notebooks/auto_examples/covariance/plot_sparse_cov.ipynb
index 20300468f1af9..8e42dfa219a2c 100644
--- a/dev/notebooks/auto_examples/covariance/plot_sparse_cov.ipynb
+++ b/dev/notebooks/auto_examples/covariance/plot_sparse_cov.ipynb
@@ -100,7 +100,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/cross_decomposition/plot_compare_cross_decomposition.ipynb b/dev/notebooks/auto_examples/cross_decomposition/plot_compare_cross_decomposition.ipynb
index 121b4c5c10064..7eb051b228020 100644
--- a/dev/notebooks/auto_examples/cross_decomposition/plot_compare_cross_decomposition.ipynb
+++ b/dev/notebooks/auto_examples/cross_decomposition/plot_compare_cross_decomposition.ipynb
@@ -143,7 +143,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/cross_decomposition/plot_pcr_vs_pls.ipynb b/dev/notebooks/auto_examples/cross_decomposition/plot_pcr_vs_pls.ipynb
index 678a9240cff12..3a3bb1e4020a5 100644
--- a/dev/notebooks/auto_examples/cross_decomposition/plot_pcr_vs_pls.ipynb
+++ b/dev/notebooks/auto_examples/cross_decomposition/plot_pcr_vs_pls.ipynb
@@ -125,7 +125,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/datasets/plot_random_multilabel_dataset.ipynb b/dev/notebooks/auto_examples/datasets/plot_random_multilabel_dataset.ipynb
index 81eece858b5a3..1d3c233cea4b5 100644
--- a/dev/notebooks/auto_examples/datasets/plot_random_multilabel_dataset.ipynb
+++ b/dev/notebooks/auto_examples/datasets/plot_random_multilabel_dataset.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/decomposition/plot_faces_decomposition.ipynb b/dev/notebooks/auto_examples/decomposition/plot_faces_decomposition.ipynb
index f2bc7774e3456..433102b218cbf 100644
--- a/dev/notebooks/auto_examples/decomposition/plot_faces_decomposition.ipynb
+++ b/dev/notebooks/auto_examples/decomposition/plot_faces_decomposition.ipynb
@@ -308,7 +308,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/decomposition/plot_ica_blind_source_separation.ipynb b/dev/notebooks/auto_examples/decomposition/plot_ica_blind_source_separation.ipynb
index 3df409d956123..a61bc2e9d4405 100644
--- a/dev/notebooks/auto_examples/decomposition/plot_ica_blind_source_separation.ipynb
+++ b/dev/notebooks/auto_examples/decomposition/plot_ica_blind_source_separation.ipynb
@@ -89,7 +89,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/decomposition/plot_ica_vs_pca.ipynb b/dev/notebooks/auto_examples/decomposition/plot_ica_vs_pca.ipynb
index 333f45be77521..5fcce019a141b 100644
--- a/dev/notebooks/auto_examples/decomposition/plot_ica_vs_pca.ipynb
+++ b/dev/notebooks/auto_examples/decomposition/plot_ica_vs_pca.ipynb
@@ -71,7 +71,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/decomposition/plot_image_denoising.ipynb b/dev/notebooks/auto_examples/decomposition/plot_image_denoising.ipynb
index 170e651c0a24e..67789bbfff774 100644
--- a/dev/notebooks/auto_examples/decomposition/plot_image_denoising.ipynb
+++ b/dev/notebooks/auto_examples/decomposition/plot_image_denoising.ipynb
@@ -125,7 +125,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/decomposition/plot_incremental_pca.ipynb b/dev/notebooks/auto_examples/decomposition/plot_incremental_pca.ipynb
index b27d76905036a..6121bd005c18f 100644
--- a/dev/notebooks/auto_examples/decomposition/plot_incremental_pca.ipynb
+++ b/dev/notebooks/auto_examples/decomposition/plot_incremental_pca.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/decomposition/plot_kernel_pca.ipynb b/dev/notebooks/auto_examples/decomposition/plot_kernel_pca.ipynb
index 5e78bcc854846..ec9975e7f0091 100644
--- a/dev/notebooks/auto_examples/decomposition/plot_kernel_pca.ipynb
+++ b/dev/notebooks/auto_examples/decomposition/plot_kernel_pca.ipynb
@@ -136,7 +136,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/decomposition/plot_pca_iris.ipynb b/dev/notebooks/auto_examples/decomposition/plot_pca_iris.ipynb
index b7c2158cfa6aa..540cacfdbc415 100644
--- a/dev/notebooks/auto_examples/decomposition/plot_pca_iris.ipynb
+++ b/dev/notebooks/auto_examples/decomposition/plot_pca_iris.ipynb
@@ -96,7 +96,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/decomposition/plot_pca_vs_fa_model_selection.ipynb b/dev/notebooks/auto_examples/decomposition/plot_pca_vs_fa_model_selection.ipynb
index 8547a976c2278..9dc05afff8d60 100644
--- a/dev/notebooks/auto_examples/decomposition/plot_pca_vs_fa_model_selection.ipynb
+++ b/dev/notebooks/auto_examples/decomposition/plot_pca_vs_fa_model_selection.ipynb
@@ -71,7 +71,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/decomposition/plot_pca_vs_lda.ipynb b/dev/notebooks/auto_examples/decomposition/plot_pca_vs_lda.ipynb
index fa3202ee92d5e..e2682ceb18724 100644
--- a/dev/notebooks/auto_examples/decomposition/plot_pca_vs_lda.ipynb
+++ b/dev/notebooks/auto_examples/decomposition/plot_pca_vs_lda.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/decomposition/plot_sparse_coding.ipynb b/dev/notebooks/auto_examples/decomposition/plot_sparse_coding.ipynb
index 3d51e7bf9139e..5726e7444d806 100644
--- a/dev/notebooks/auto_examples/decomposition/plot_sparse_coding.ipynb
+++ b/dev/notebooks/auto_examples/decomposition/plot_sparse_coding.ipynb
@@ -15,7 +15,7 @@
       },
       "outputs": [],
       "source": [
-        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nfrom sklearn.decomposition import SparseCoder\n\n\ndef ricker_function(resolution, center, width):\n    \"\"\"Discrete sub-sampled Ricker (Mexican hat) wavelet\"\"\"\n    x = np.linspace(0, resolution - 1, resolution)\n    x = (\n        (2 / (np.sqrt(3 * width) * np.pi**0.25))\n        * (1 - (x - center) ** 2 / width**2)\n        * np.exp(-((x - center) ** 2) / (2 * width**2))\n    )\n    return x\n\n\ndef ricker_matrix(width, resolution, n_components):\n    \"\"\"Dictionary of Ricker (Mexican hat) wavelets\"\"\"\n    centers = np.linspace(0, resolution - 1, n_components)\n    D = np.empty((n_components, resolution))\n    for i, center in enumerate(centers):\n        D[i] = ricker_function(resolution, center, width)\n    D /= np.sqrt(np.sum(D**2, axis=1))[:, np.newaxis]\n    return D\n\n\nresolution = 1024\nsubsampling = 3  # subsampling factor\nwidth = 100\nn_components = resolution // subsampling\n\n# Compute a wavelet dictionary\nD_fixed = ricker_matrix(width=width, resolution=resolution, n_components=n_components)\nD_multi = np.r_[\n    tuple(\n        ricker_matrix(width=w, resolution=resolution, n_components=n_components // 5)\n        for w in (10, 50, 100, 500, 1000)\n    )\n]\n\n# Generate a signal\ny = np.linspace(0, resolution - 1, resolution)\nfirst_quarter = y < resolution / 4\ny[first_quarter] = 3.0\ny[np.logical_not(first_quarter)] = -1.0\n\n# List the different sparse coding methods in the following format:\n# (title, transform_algorithm, transform_alpha,\n#  transform_n_nozero_coefs, color)\nestimators = [\n    (\"OMP\", \"omp\", None, 15, \"navy\"),\n    (\"Lasso\", \"lasso_lars\", 2, None, \"turquoise\"),\n]\nlw = 2\n\nplt.figure(figsize=(13, 6))\nfor subplot, (D, title) in enumerate(\n    zip((D_fixed, D_multi), (\"fixed width\", \"multiple widths\"))\n):\n    plt.subplot(1, 2, subplot + 1)\n    plt.title(\"Sparse coding against %s dictionary\" % title)\n    plt.plot(y, lw=lw, linestyle=\"--\", label=\"Original signal\")\n    # Do a wavelet approximation\n    for title, algo, alpha, n_nonzero, color in estimators:\n        coder = SparseCoder(\n            dictionary=D,\n            transform_n_nonzero_coefs=n_nonzero,\n            transform_alpha=alpha,\n            transform_algorithm=algo,\n        )\n        x = coder.transform(y.reshape(1, -1))\n        density = len(np.flatnonzero(x))\n        x = np.ravel(np.dot(x, D))\n        squared_error = np.sum((y - x) ** 2)\n        plt.plot(\n            x,\n            color=color,\n            lw=lw,\n            label=\"%s: %s nonzero coefs,\\n%.2f error\" % (title, density, squared_error),\n        )\n\n    # Soft thresholding debiasing\n    coder = SparseCoder(\n        dictionary=D, transform_algorithm=\"threshold\", transform_alpha=20\n    )\n    x = coder.transform(y.reshape(1, -1))\n    _, idx = np.where(x != 0)\n    x[0, idx], _, _, _ = np.linalg.lstsq(D[idx, :].T, y, rcond=None)\n    x = np.ravel(np.dot(x, D))\n    squared_error = np.sum((y - x) ** 2)\n    plt.plot(\n        x,\n        color=\"darkorange\",\n        lw=lw,\n        label=\"Thresholding w/ debiasing:\\n%d nonzero coefs, %.2f error\"\n        % (len(idx), squared_error),\n    )\n    plt.axis(\"tight\")\n    plt.legend(shadow=False, loc=\"best\")\nplt.subplots_adjust(0.04, 0.07, 0.97, 0.90, 0.09, 0.2)\nplt.show()"
+        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nfrom sklearn.decomposition import SparseCoder\n\n\ndef ricker_function(resolution, center, width):\n    \"\"\"Discrete sub-sampled Ricker (Mexican hat) wavelet\"\"\"\n    x = np.linspace(0, resolution - 1, resolution)\n    x = (\n        (2 / (np.sqrt(3 * width) * np.pi**0.25))\n        * (1 - (x - center) ** 2 / width**2)\n        * np.exp(-((x - center) ** 2) / (2 * width**2))\n    )\n    return x\n\n\ndef ricker_matrix(width, resolution, n_components):\n    \"\"\"Dictionary of Ricker (Mexican hat) wavelets\"\"\"\n    centers = np.linspace(0, resolution - 1, n_components)\n    D = np.empty((n_components, resolution))\n    for i, center in enumerate(centers):\n        D[i] = ricker_function(resolution, center, width)\n    D /= np.sqrt(np.sum(D**2, axis=1))[:, np.newaxis]\n    return D\n\n\nresolution = 1024\nsubsampling = 3  # subsampling factor\nwidth = 100\nn_components = resolution // subsampling\n\n# Compute a wavelet dictionary\nD_fixed = ricker_matrix(width=width, resolution=resolution, n_components=n_components)\nD_multi = np.r_[\n    tuple(\n        ricker_matrix(width=w, resolution=resolution, n_components=n_components // 5)\n        for w in (10, 50, 100, 500, 1000)\n    )\n]\n\n# Generate a signal\ny = np.linspace(0, resolution - 1, resolution)\nfirst_quarter = y < resolution / 4\ny[first_quarter] = 3.0\ny[np.logical_not(first_quarter)] = -1.0\n\n# List the different sparse coding methods in the following format:\n# (title, transform_algorithm, transform_alpha,\n#  transform_n_nozero_coefs, color)\nestimators = [\n    (\"OMP\", \"omp\", None, 15, \"navy\"),\n    (\"Lasso\", \"lasso_lars\", 2, None, \"turquoise\"),\n]\nlw = 2\n\nplt.figure(figsize=(13, 6))\nfor subplot, (D, title) in enumerate(\n    zip((D_fixed, D_multi), (\"fixed width\", \"multiple widths\"))\n):\n    plt.subplot(1, 2, subplot + 1)\n    plt.title(\"Sparse coding against %s dictionary\" % title)\n    plt.plot(y, lw=lw, linestyle=\"--\", label=\"Original signal\")\n    # Do a wavelet approximation\n    for title, algo, alpha, n_nonzero, color in estimators:\n        coder = SparseCoder(\n            dictionary=D,\n            transform_n_nonzero_coefs=n_nonzero,\n            transform_alpha=alpha,\n            transform_algorithm=algo,\n        )\n        x = coder.transform(y.reshape(1, -1))\n        density = len(np.flatnonzero(x))\n        x = np.ravel(np.dot(x, D))\n        squared_error = np.sum((y - x) ** 2)\n        plt.plot(\n            x,\n            color=color,\n            lw=lw,\n            label=\"%s: %s nonzero coefs,\\n%.2f error\" % (title, density, squared_error),\n        )\n\n    # Soft thresholding debiasing\n    coder = SparseCoder(\n        dictionary=D, transform_algorithm=\"threshold\", transform_alpha=20\n    )\n    x = coder.transform(y.reshape(1, -1))\n    _, idx = (x != 0).nonzero()\n    x[0, idx], _, _, _ = np.linalg.lstsq(D[idx, :].T, y, rcond=None)\n    x = np.ravel(np.dot(x, D))\n    squared_error = np.sum((y - x) ** 2)\n    plt.plot(\n        x,\n        color=\"darkorange\",\n        lw=lw,\n        label=\"Thresholding w/ debiasing:\\n%d nonzero coefs, %.2f error\"\n        % (len(idx), squared_error),\n    )\n    plt.axis(\"tight\")\n    plt.legend(shadow=False, loc=\"best\")\nplt.subplots_adjust(0.04, 0.07, 0.97, 0.90, 0.09, 0.2)\nplt.show()"
       ]
     }
   ],
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/decomposition/plot_varimax_fa.ipynb b/dev/notebooks/auto_examples/decomposition/plot_varimax_fa.ipynb
index 87b9d254c1761..8b257d3d2d161 100644
--- a/dev/notebooks/auto_examples/decomposition/plot_varimax_fa.ipynb
+++ b/dev/notebooks/auto_examples/decomposition/plot_varimax_fa.ipynb
@@ -89,7 +89,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/developing_estimators/sklearn_is_fitted.ipynb b/dev/notebooks/auto_examples/developing_estimators/sklearn_is_fitted.ipynb
index e3079434652ce..1d6cf509f6fa1 100644
--- a/dev/notebooks/auto_examples/developing_estimators/sklearn_is_fitted.ipynb
+++ b/dev/notebooks/auto_examples/developing_estimators/sklearn_is_fitted.ipynb
@@ -42,7 +42,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/ensemble/plot_adaboost_multiclass.ipynb b/dev/notebooks/auto_examples/ensemble/plot_adaboost_multiclass.ipynb
index 487c61287ad32..41d29de2ea3da 100644
--- a/dev/notebooks/auto_examples/ensemble/plot_adaboost_multiclass.ipynb
+++ b/dev/notebooks/auto_examples/ensemble/plot_adaboost_multiclass.ipynb
@@ -157,7 +157,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/ensemble/plot_adaboost_regression.ipynb b/dev/notebooks/auto_examples/ensemble/plot_adaboost_regression.ipynb
index 5a1822b9cf330..605b3edc85a04 100644
--- a/dev/notebooks/auto_examples/ensemble/plot_adaboost_regression.ipynb
+++ b/dev/notebooks/auto_examples/ensemble/plot_adaboost_regression.ipynb
@@ -78,7 +78,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/ensemble/plot_adaboost_twoclass.ipynb b/dev/notebooks/auto_examples/ensemble/plot_adaboost_twoclass.ipynb
index 4b8c62c535883..e7d7ebbc2e5f5 100644
--- a/dev/notebooks/auto_examples/ensemble/plot_adaboost_twoclass.ipynb
+++ b/dev/notebooks/auto_examples/ensemble/plot_adaboost_twoclass.ipynb
@@ -15,7 +15,7 @@
       },
       "outputs": [],
       "source": [
-        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nfrom sklearn.datasets import make_gaussian_quantiles\nfrom sklearn.ensemble import AdaBoostClassifier\nfrom sklearn.inspection import DecisionBoundaryDisplay\nfrom sklearn.tree import DecisionTreeClassifier\n\n# Construct dataset\nX1, y1 = make_gaussian_quantiles(\n    cov=2.0, n_samples=200, n_features=2, n_classes=2, random_state=1\n)\nX2, y2 = make_gaussian_quantiles(\n    mean=(3, 3), cov=1.5, n_samples=300, n_features=2, n_classes=2, random_state=1\n)\nX = np.concatenate((X1, X2))\ny = np.concatenate((y1, -y2 + 1))\n\n# Create and fit an AdaBoosted decision tree\nbdt = AdaBoostClassifier(DecisionTreeClassifier(max_depth=1), n_estimators=200)\nbdt.fit(X, y)\n\nplot_colors = \"br\"\nplot_step = 0.02\nclass_names = \"AB\"\n\nplt.figure(figsize=(10, 5))\n\n# Plot the decision boundaries\nax = plt.subplot(121)\ndisp = DecisionBoundaryDisplay.from_estimator(\n    bdt,\n    X,\n    cmap=plt.cm.Paired,\n    response_method=\"predict\",\n    ax=ax,\n    xlabel=\"x\",\n    ylabel=\"y\",\n)\nx_min, x_max = disp.xx0.min(), disp.xx0.max()\ny_min, y_max = disp.xx1.min(), disp.xx1.max()\nplt.axis(\"tight\")\n\n# Plot the training points\nfor i, n, c in zip(range(2), class_names, plot_colors):\n    idx = np.where(y == i)\n    plt.scatter(\n        X[idx, 0],\n        X[idx, 1],\n        c=c,\n        s=20,\n        edgecolor=\"k\",\n        label=\"Class %s\" % n,\n    )\nplt.xlim(x_min, x_max)\nplt.ylim(y_min, y_max)\nplt.legend(loc=\"upper right\")\n\nplt.title(\"Decision Boundary\")\n\n# Plot the two-class decision scores\ntwoclass_output = bdt.decision_function(X)\nplot_range = (twoclass_output.min(), twoclass_output.max())\nplt.subplot(122)\nfor i, n, c in zip(range(2), class_names, plot_colors):\n    plt.hist(\n        twoclass_output[y == i],\n        bins=10,\n        range=plot_range,\n        facecolor=c,\n        label=\"Class %s\" % n,\n        alpha=0.5,\n        edgecolor=\"k\",\n    )\nx1, x2, y1, y2 = plt.axis()\nplt.axis((x1, x2, y1, y2 * 1.2))\nplt.legend(loc=\"upper right\")\nplt.ylabel(\"Samples\")\nplt.xlabel(\"Score\")\nplt.title(\"Decision Scores\")\n\nplt.tight_layout()\nplt.subplots_adjust(wspace=0.35)\nplt.show()"
+        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nfrom sklearn.datasets import make_gaussian_quantiles\nfrom sklearn.ensemble import AdaBoostClassifier\nfrom sklearn.inspection import DecisionBoundaryDisplay\nfrom sklearn.tree import DecisionTreeClassifier\n\n# Construct dataset\nX1, y1 = make_gaussian_quantiles(\n    cov=2.0, n_samples=200, n_features=2, n_classes=2, random_state=1\n)\nX2, y2 = make_gaussian_quantiles(\n    mean=(3, 3), cov=1.5, n_samples=300, n_features=2, n_classes=2, random_state=1\n)\nX = np.concatenate((X1, X2))\ny = np.concatenate((y1, -y2 + 1))\n\n# Create and fit an AdaBoosted decision tree\nbdt = AdaBoostClassifier(DecisionTreeClassifier(max_depth=1), n_estimators=200)\nbdt.fit(X, y)\n\nplot_colors = \"br\"\nplot_step = 0.02\nclass_names = \"AB\"\n\nplt.figure(figsize=(10, 5))\n\n# Plot the decision boundaries\nax = plt.subplot(121)\ndisp = DecisionBoundaryDisplay.from_estimator(\n    bdt,\n    X,\n    cmap=plt.cm.Paired,\n    response_method=\"predict\",\n    ax=ax,\n    xlabel=\"x\",\n    ylabel=\"y\",\n)\nx_min, x_max = disp.xx0.min(), disp.xx0.max()\ny_min, y_max = disp.xx1.min(), disp.xx1.max()\nplt.axis(\"tight\")\n\n# Plot the training points\nfor i, n, c in zip(range(2), class_names, plot_colors):\n    idx = (y == i).nonzero()\n    plt.scatter(\n        X[idx, 0],\n        X[idx, 1],\n        c=c,\n        s=20,\n        edgecolor=\"k\",\n        label=\"Class %s\" % n,\n    )\nplt.xlim(x_min, x_max)\nplt.ylim(y_min, y_max)\nplt.legend(loc=\"upper right\")\n\nplt.title(\"Decision Boundary\")\n\n# Plot the two-class decision scores\ntwoclass_output = bdt.decision_function(X)\nplot_range = (twoclass_output.min(), twoclass_output.max())\nplt.subplot(122)\nfor i, n, c in zip(range(2), class_names, plot_colors):\n    plt.hist(\n        twoclass_output[y == i],\n        bins=10,\n        range=plot_range,\n        facecolor=c,\n        label=\"Class %s\" % n,\n        alpha=0.5,\n        edgecolor=\"k\",\n    )\nx1, x2, y1, y2 = plt.axis()\nplt.axis((x1, x2, y1, y2 * 1.2))\nplt.legend(loc=\"upper right\")\nplt.ylabel(\"Samples\")\nplt.xlabel(\"Score\")\nplt.title(\"Decision Scores\")\n\nplt.tight_layout()\nplt.subplots_adjust(wspace=0.35)\nplt.show()"
       ]
     }
   ],
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/ensemble/plot_bias_variance.ipynb b/dev/notebooks/auto_examples/ensemble/plot_bias_variance.ipynb
index 22dca1d0ba7c7..252d3e41d442b 100644
--- a/dev/notebooks/auto_examples/ensemble/plot_bias_variance.ipynb
+++ b/dev/notebooks/auto_examples/ensemble/plot_bias_variance.ipynb
@@ -15,7 +15,7 @@
       },
       "outputs": [],
       "source": [
-        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nfrom sklearn.ensemble import BaggingRegressor\nfrom sklearn.tree import DecisionTreeRegressor\n\n# Settings\nn_repeat = 50  # Number of iterations for computing expectations\nn_train = 50  # Size of the training set\nn_test = 1000  # Size of the test set\nnoise = 0.1  # Standard deviation of the noise\nnp.random.seed(0)\n\n# Change this for exploring the bias-variance decomposition of other\n# estimators. This should work well for estimators with high variance (e.g.,\n# decision trees or KNN), but poorly for estimators with low variance (e.g.,\n# linear models).\nestimators = [\n    (\"Tree\", DecisionTreeRegressor()),\n    (\"Bagging(Tree)\", BaggingRegressor(DecisionTreeRegressor())),\n]\n\nn_estimators = len(estimators)\n\n\n# Generate data\ndef f(x):\n    x = x.ravel()\n\n    return np.exp(-(x**2)) + 1.5 * np.exp(-((x - 2) ** 2))\n\n\ndef generate(n_samples, noise, n_repeat=1):\n    X = np.random.rand(n_samples) * 10 - 5\n    X = np.sort(X)\n\n    if n_repeat == 1:\n        y = f(X) + np.random.normal(0.0, noise, n_samples)\n    else:\n        y = np.zeros((n_samples, n_repeat))\n\n        for i in range(n_repeat):\n            y[:, i] = f(X) + np.random.normal(0.0, noise, n_samples)\n\n    X = X.reshape((n_samples, 1))\n\n    return X, y\n\n\nX_train = []\ny_train = []\n\nfor i in range(n_repeat):\n    X, y = generate(n_samples=n_train, noise=noise)\n    X_train.append(X)\n    y_train.append(y)\n\nX_test, y_test = generate(n_samples=n_test, noise=noise, n_repeat=n_repeat)\n\nplt.figure(figsize=(10, 8))\n\n# Loop over estimators to compare\nfor n, (name, estimator) in enumerate(estimators):\n    # Compute predictions\n    y_predict = np.zeros((n_test, n_repeat))\n\n    for i in range(n_repeat):\n        estimator.fit(X_train[i], y_train[i])\n        y_predict[:, i] = estimator.predict(X_test)\n\n    # Bias^2 + Variance + Noise decomposition of the mean squared error\n    y_error = np.zeros(n_test)\n\n    for i in range(n_repeat):\n        for j in range(n_repeat):\n            y_error += (y_test[:, j] - y_predict[:, i]) ** 2\n\n    y_error /= n_repeat * n_repeat\n\n    y_noise = np.var(y_test, axis=1)\n    y_bias = (f(X_test) - np.mean(y_predict, axis=1)) ** 2\n    y_var = np.var(y_predict, axis=1)\n\n    print(\n        \"{0}: {1:.4f} (error) = {2:.4f} (bias^2) \"\n        \" + {3:.4f} (var) + {4:.4f} (noise)\".format(\n            name, np.mean(y_error), np.mean(y_bias), np.mean(y_var), np.mean(y_noise)\n        )\n    )\n\n    # Plot figures\n    plt.subplot(2, n_estimators, n + 1)\n    plt.plot(X_test, f(X_test), \"b\", label=\"$f(x)$\")\n    plt.plot(X_train[0], y_train[0], \".b\", label=\"LS ~ $y = f(x)+noise$\")\n\n    for i in range(n_repeat):\n        if i == 0:\n            plt.plot(X_test, y_predict[:, i], \"r\", label=r\"$\\^y(x)$\")\n        else:\n            plt.plot(X_test, y_predict[:, i], \"r\", alpha=0.05)\n\n    plt.plot(X_test, np.mean(y_predict, axis=1), \"c\", label=r\"$\\mathbb{E}_{LS} \\^y(x)$\")\n\n    plt.xlim([-5, 5])\n    plt.title(name)\n\n    if n == n_estimators - 1:\n        plt.legend(loc=(1.1, 0.5))\n\n    plt.subplot(2, n_estimators, n_estimators + n + 1)\n    plt.plot(X_test, y_error, \"r\", label=\"$error(x)$\")\n    plt.plot(X_test, y_bias, \"b\", label=\"$bias^2(x)$\"),\n    plt.plot(X_test, y_var, \"g\", label=\"$variance(x)$\"),\n    plt.plot(X_test, y_noise, \"c\", label=\"$noise(x)$\")\n\n    plt.xlim([-5, 5])\n    plt.ylim([0, 0.1])\n\n    if n == n_estimators - 1:\n        plt.legend(loc=(1.1, 0.5))\n\nplt.subplots_adjust(right=0.75)\nplt.show()"
+        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nfrom sklearn.ensemble import BaggingRegressor\nfrom sklearn.tree import DecisionTreeRegressor\n\n# Settings\nn_repeat = 50  # Number of iterations for computing expectations\nn_train = 50  # Size of the training set\nn_test = 1000  # Size of the test set\nnoise = 0.1  # Standard deviation of the noise\nnp.random.seed(0)\n\n# Change this for exploring the bias-variance decomposition of other\n# estimators. This should work well for estimators with high variance (e.g.,\n# decision trees or KNN), but poorly for estimators with low variance (e.g.,\n# linear models).\nestimators = [\n    (\"Tree\", DecisionTreeRegressor()),\n    (\"Bagging(Tree)\", BaggingRegressor(DecisionTreeRegressor())),\n]\n\nn_estimators = len(estimators)\n\n\n# Generate data\ndef f(x):\n    x = x.ravel()\n\n    return np.exp(-(x**2)) + 1.5 * np.exp(-((x - 2) ** 2))\n\n\ndef generate(n_samples, noise, n_repeat=1):\n    X = np.random.rand(n_samples) * 10 - 5\n    X = np.sort(X)\n\n    if n_repeat == 1:\n        y = f(X) + np.random.normal(0.0, noise, n_samples)\n    else:\n        y = np.zeros((n_samples, n_repeat))\n\n        for i in range(n_repeat):\n            y[:, i] = f(X) + np.random.normal(0.0, noise, n_samples)\n\n    X = X.reshape((n_samples, 1))\n\n    return X, y\n\n\nX_train = []\ny_train = []\n\nfor i in range(n_repeat):\n    X, y = generate(n_samples=n_train, noise=noise)\n    X_train.append(X)\n    y_train.append(y)\n\nX_test, y_test = generate(n_samples=n_test, noise=noise, n_repeat=n_repeat)\n\nplt.figure(figsize=(10, 8))\n\n# Loop over estimators to compare\nfor n, (name, estimator) in enumerate(estimators):\n    # Compute predictions\n    y_predict = np.zeros((n_test, n_repeat))\n\n    for i in range(n_repeat):\n        estimator.fit(X_train[i], y_train[i])\n        y_predict[:, i] = estimator.predict(X_test)\n\n    # Bias^2 + Variance + Noise decomposition of the mean squared error\n    y_error = np.zeros(n_test)\n\n    for i in range(n_repeat):\n        for j in range(n_repeat):\n            y_error += (y_test[:, j] - y_predict[:, i]) ** 2\n\n    y_error /= n_repeat * n_repeat\n\n    y_noise = np.var(y_test, axis=1)\n    y_bias = (f(X_test) - np.mean(y_predict, axis=1)) ** 2\n    y_var = np.var(y_predict, axis=1)\n\n    print(\n        \"{0}: {1:.4f} (error) = {2:.4f} (bias^2) \"\n        \" + {3:.4f} (var) + {4:.4f} (noise)\".format(\n            name, np.mean(y_error), np.mean(y_bias), np.mean(y_var), np.mean(y_noise)\n        )\n    )\n\n    # Plot figures\n    plt.subplot(2, n_estimators, n + 1)\n    plt.plot(X_test, f(X_test), \"b\", label=\"$f(x)$\")\n    plt.plot(X_train[0], y_train[0], \".b\", label=\"LS ~ $y = f(x)+noise$\")\n\n    for i in range(n_repeat):\n        if i == 0:\n            plt.plot(X_test, y_predict[:, i], \"r\", label=r\"$\\^y(x)$\")\n        else:\n            plt.plot(X_test, y_predict[:, i], \"r\", alpha=0.05)\n\n    plt.plot(X_test, np.mean(y_predict, axis=1), \"c\", label=r\"$\\mathbb{E}_{LS} \\^y(x)$\")\n\n    plt.xlim([-5, 5])\n    plt.title(name)\n\n    if n == n_estimators - 1:\n        plt.legend(loc=(1.1, 0.5))\n\n    plt.subplot(2, n_estimators, n_estimators + n + 1)\n    plt.plot(X_test, y_error, \"r\", label=\"$error(x)$\")\n    plt.plot(X_test, y_bias, \"b\", label=\"$bias^2(x)$\")\n    plt.plot(X_test, y_var, \"g\", label=\"$variance(x)$\")\n    plt.plot(X_test, y_noise, \"c\", label=\"$noise(x)$\")\n\n    plt.xlim([-5, 5])\n    plt.ylim([0, 0.1])\n\n    if n == n_estimators - 1:\n        plt.legend(loc=(1.1, 0.5))\n\nplt.subplots_adjust(right=0.75)\nplt.show()"
       ]
     }
   ],
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/ensemble/plot_ensemble_oob.ipynb b/dev/notebooks/auto_examples/ensemble/plot_ensemble_oob.ipynb
index cd20dedfde1d3..134cf9d6c63de 100644
--- a/dev/notebooks/auto_examples/ensemble/plot_ensemble_oob.ipynb
+++ b/dev/notebooks/auto_examples/ensemble/plot_ensemble_oob.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/ensemble/plot_feature_transformation.ipynb b/dev/notebooks/auto_examples/ensemble/plot_feature_transformation.ipynb
index a976610b8d03d..e5741c43149a4 100644
--- a/dev/notebooks/auto_examples/ensemble/plot_feature_transformation.ipynb
+++ b/dev/notebooks/auto_examples/ensemble/plot_feature_transformation.ipynb
@@ -183,7 +183,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/ensemble/plot_forest_hist_grad_boosting_comparison.ipynb b/dev/notebooks/auto_examples/ensemble/plot_forest_hist_grad_boosting_comparison.ipynb
index 67beed7bb535b..b32953eb73b83 100644
--- a/dev/notebooks/auto_examples/ensemble/plot_forest_hist_grad_boosting_comparison.ipynb
+++ b/dev/notebooks/auto_examples/ensemble/plot_forest_hist_grad_boosting_comparison.ipynb
@@ -132,7 +132,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/ensemble/plot_forest_importances.ipynb b/dev/notebooks/auto_examples/ensemble/plot_forest_importances.ipynb
index 0d66a6794519e..97eb846b2b97c 100644
--- a/dev/notebooks/auto_examples/ensemble/plot_forest_importances.ipynb
+++ b/dev/notebooks/auto_examples/ensemble/plot_forest_importances.ipynb
@@ -150,7 +150,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/ensemble/plot_forest_iris.ipynb b/dev/notebooks/auto_examples/ensemble/plot_forest_iris.ipynb
index b4a24bac049aa..3329b2ab6ba38 100644
--- a/dev/notebooks/auto_examples/ensemble/plot_forest_iris.ipynb
+++ b/dev/notebooks/auto_examples/ensemble/plot_forest_iris.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/ensemble/plot_gradient_boosting_categorical.ipynb b/dev/notebooks/auto_examples/ensemble/plot_gradient_boosting_categorical.ipynb
index acb3f7fafce36..6ed480c43aa25 100644
--- a/dev/notebooks/auto_examples/ensemble/plot_gradient_boosting_categorical.ipynb
+++ b/dev/notebooks/auto_examples/ensemble/plot_gradient_boosting_categorical.ipynb
@@ -175,7 +175,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/ensemble/plot_gradient_boosting_early_stopping.ipynb b/dev/notebooks/auto_examples/ensemble/plot_gradient_boosting_early_stopping.ipynb
index 491922dec9dd9..dd5b0a3fcdd54 100644
--- a/dev/notebooks/auto_examples/ensemble/plot_gradient_boosting_early_stopping.ipynb
+++ b/dev/notebooks/auto_examples/ensemble/plot_gradient_boosting_early_stopping.ipynb
@@ -121,7 +121,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/ensemble/plot_gradient_boosting_oob.ipynb b/dev/notebooks/auto_examples/ensemble/plot_gradient_boosting_oob.ipynb
index ab1657f8bbf64..d1bcf546bbfd4 100644
--- a/dev/notebooks/auto_examples/ensemble/plot_gradient_boosting_oob.ipynb
+++ b/dev/notebooks/auto_examples/ensemble/plot_gradient_boosting_oob.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/ensemble/plot_gradient_boosting_quantile.ipynb b/dev/notebooks/auto_examples/ensemble/plot_gradient_boosting_quantile.ipynb
index 3be354a19103b..75496e60cbc57 100644
--- a/dev/notebooks/auto_examples/ensemble/plot_gradient_boosting_quantile.ipynb
+++ b/dev/notebooks/auto_examples/ensemble/plot_gradient_boosting_quantile.ipynb
@@ -231,7 +231,7 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.experimental import enable_halving_search_cv  # noqa\nfrom sklearn.model_selection import HalvingRandomSearchCV\nfrom sklearn.metrics import make_scorer\nfrom pprint import pprint\n\nparam_grid = dict(\n    learning_rate=[0.05, 0.1, 0.2],\n    max_depth=[2, 5, 10],\n    min_samples_leaf=[1, 5, 10, 20],\n    min_samples_split=[5, 10, 20, 30, 50],\n)\nalpha = 0.05\nneg_mean_pinball_loss_05p_scorer = make_scorer(\n    mean_pinball_loss,\n    alpha=alpha,\n    greater_is_better=False,  # maximize the negative loss\n)\ngbr = GradientBoostingRegressor(loss=\"quantile\", alpha=alpha, random_state=0)\nsearch_05p = HalvingRandomSearchCV(\n    gbr,\n    param_grid,\n    resource=\"n_estimators\",\n    max_resources=250,\n    min_resources=50,\n    scoring=neg_mean_pinball_loss_05p_scorer,\n    n_jobs=2,\n    random_state=0,\n).fit(X_train, y_train)\npprint(search_05p.best_params_)"
+        "from pprint import pprint\n\nfrom sklearn.experimental import enable_halving_search_cv  # noqa: F401\nfrom sklearn.metrics import make_scorer\nfrom sklearn.model_selection import HalvingRandomSearchCV\n\nparam_grid = dict(\n    learning_rate=[0.05, 0.1, 0.2],\n    max_depth=[2, 5, 10],\n    min_samples_leaf=[1, 5, 10, 20],\n    min_samples_split=[5, 10, 20, 30, 50],\n)\nalpha = 0.05\nneg_mean_pinball_loss_05p_scorer = make_scorer(\n    mean_pinball_loss,\n    alpha=alpha,\n    greater_is_better=False,  # maximize the negative loss\n)\ngbr = GradientBoostingRegressor(loss=\"quantile\", alpha=alpha, random_state=0)\nsearch_05p = HalvingRandomSearchCV(\n    gbr,\n    param_grid,\n    resource=\"n_estimators\",\n    max_resources=250,\n    min_resources=50,\n    scoring=neg_mean_pinball_loss_05p_scorer,\n    n_jobs=2,\n    random_state=0,\n).fit(X_train, y_train)\npprint(search_05p.best_params_)"
       ]
     },
     {
@@ -323,7 +323,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/ensemble/plot_gradient_boosting_regression.ipynb b/dev/notebooks/auto_examples/ensemble/plot_gradient_boosting_regression.ipynb
index 03cc1f3731d90..4ba9a4a18ac7a 100644
--- a/dev/notebooks/auto_examples/ensemble/plot_gradient_boosting_regression.ipynb
+++ b/dev/notebooks/auto_examples/ensemble/plot_gradient_boosting_regression.ipynb
@@ -125,7 +125,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/ensemble/plot_gradient_boosting_regularization.ipynb b/dev/notebooks/auto_examples/ensemble/plot_gradient_boosting_regularization.ipynb
index 1b90aa802551f..1ab8600b968f4 100644
--- a/dev/notebooks/auto_examples/ensemble/plot_gradient_boosting_regularization.ipynb
+++ b/dev/notebooks/auto_examples/ensemble/plot_gradient_boosting_regularization.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/ensemble/plot_hgbt_regression.ipynb b/dev/notebooks/auto_examples/ensemble/plot_hgbt_regression.ipynb
index b3f383a6c8a1f..512339e71c16f 100644
--- a/dev/notebooks/auto_examples/ensemble/plot_hgbt_regression.ipynb
+++ b/dev/notebooks/auto_examples/ensemble/plot_hgbt_regression.ipynb
@@ -233,7 +233,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/ensemble/plot_isolation_forest.ipynb b/dev/notebooks/auto_examples/ensemble/plot_isolation_forest.ipynb
index 7b84c2a545148..1e806f6e177ce 100644
--- a/dev/notebooks/auto_examples/ensemble/plot_isolation_forest.ipynb
+++ b/dev/notebooks/auto_examples/ensemble/plot_isolation_forest.ipynb
@@ -125,7 +125,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/ensemble/plot_monotonic_constraints.ipynb b/dev/notebooks/auto_examples/ensemble/plot_monotonic_constraints.ipynb
index c4441367ccdc8..a9fa97a9897ee 100644
--- a/dev/notebooks/auto_examples/ensemble/plot_monotonic_constraints.ipynb
+++ b/dev/notebooks/auto_examples/ensemble/plot_monotonic_constraints.ipynb
@@ -125,7 +125,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/ensemble/plot_random_forest_embedding.ipynb b/dev/notebooks/auto_examples/ensemble/plot_random_forest_embedding.ipynb
index 0fe55e3fc716e..862aeeada4131 100644
--- a/dev/notebooks/auto_examples/ensemble/plot_random_forest_embedding.ipynb
+++ b/dev/notebooks/auto_examples/ensemble/plot_random_forest_embedding.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/ensemble/plot_random_forest_regression_multioutput.ipynb b/dev/notebooks/auto_examples/ensemble/plot_random_forest_regression_multioutput.ipynb
index 0db66c054b24e..95439a715c096 100644
--- a/dev/notebooks/auto_examples/ensemble/plot_random_forest_regression_multioutput.ipynb
+++ b/dev/notebooks/auto_examples/ensemble/plot_random_forest_regression_multioutput.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/ensemble/plot_stack_predictors.ipynb b/dev/notebooks/auto_examples/ensemble/plot_stack_predictors.ipynb
index ccdfdea99dc82..36d80e1ff994d 100644
--- a/dev/notebooks/auto_examples/ensemble/plot_stack_predictors.ipynb
+++ b/dev/notebooks/auto_examples/ensemble/plot_stack_predictors.ipynb
@@ -194,7 +194,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/ensemble/plot_voting_decision_regions.ipynb b/dev/notebooks/auto_examples/ensemble/plot_voting_decision_regions.ipynb
index a1efc7c4e28bf..4de6921bd8bfd 100644
--- a/dev/notebooks/auto_examples/ensemble/plot_voting_decision_regions.ipynb
+++ b/dev/notebooks/auto_examples/ensemble/plot_voting_decision_regions.ipynb
@@ -4,7 +4,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# Plot the decision boundaries of a VotingClassifier\n\n.. currentmodule:: sklearn\n\nPlot the decision boundaries of a :class:`~ensemble.VotingClassifier` for two\nfeatures of the Iris dataset.\n\nPlot the class probabilities of the first sample in a toy dataset predicted by\nthree different classifiers and averaged by the\n:class:`~ensemble.VotingClassifier`.\n\nFirst, three exemplary classifiers are initialized\n(:class:`~tree.DecisionTreeClassifier`,\n:class:`~neighbors.KNeighborsClassifier`, and :class:`~svm.SVC`) and used to\ninitialize a soft-voting :class:`~ensemble.VotingClassifier` with weights `[2,\n1, 2]`, which means that the predicted probabilities of the\n:class:`~tree.DecisionTreeClassifier` and :class:`~svm.SVC` each count 2 times\nas much as the weights of the :class:`~neighbors.KNeighborsClassifier`\nclassifier when the averaged probability is calculated.\n"
+        "\n# Visualizing the probabilistic predictions of a VotingClassifier\n\n.. currentmodule:: sklearn\n\nPlot the predicted class probabilities in a toy dataset predicted by three\ndifferent classifiers and averaged by the :class:`~ensemble.VotingClassifier`.\n\nFirst, three linear classifiers are initialized. Two are spline models with\ninteraction terms, one using constant extrapolation and the other using periodic\nextrapolation. The third classifier is a :class:`~kernel_approximation.Nystroem`\nwith the default \"rbf\" kernel.\n\nIn the first part of this example, these three classifiers are used to\ndemonstrate soft-voting using :class:`~ensemble.VotingClassifier` with weighted\naverage. We set `weights=[2, 1, 3]`, meaning the constant extrapolation spline\nmodel's predictions are weighted twice as much as the periodic spline model's,\nand the Nystroem model's predictions are weighted three times as much as the\nperiodic spline.\n\nThe second part demonstrates how soft predictions can be converted into hard\npredictions.\n"
       ]
     },
     {
@@ -15,7 +15,162 @@
       },
       "outputs": [],
       "source": [
-        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\n\nfrom itertools import product\n\nimport matplotlib.pyplot as plt\n\nfrom sklearn import datasets\nfrom sklearn.ensemble import VotingClassifier\nfrom sklearn.inspection import DecisionBoundaryDisplay\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.svm import SVC\nfrom sklearn.tree import DecisionTreeClassifier\n\n# Loading some example data\niris = datasets.load_iris()\nX = iris.data[:, [0, 2]]\ny = iris.target\n\n# Training classifiers\nclf1 = DecisionTreeClassifier(max_depth=4)\nclf2 = KNeighborsClassifier(n_neighbors=7)\nclf3 = SVC(gamma=0.1, kernel=\"rbf\", probability=True)\neclf = VotingClassifier(\n    estimators=[(\"dt\", clf1), (\"knn\", clf2), (\"svc\", clf3)],\n    voting=\"soft\",\n    weights=[2, 1, 2],\n)\n\nclf1.fit(X, y)\nclf2.fit(X, y)\nclf3.fit(X, y)\neclf.fit(X, y)\n\n# Plotting decision regions\nf, axarr = plt.subplots(2, 2, sharex=\"col\", sharey=\"row\", figsize=(10, 8))\nfor idx, clf, tt in zip(\n    product([0, 1], [0, 1]),\n    [clf1, clf2, clf3, eclf],\n    [\"Decision Tree (depth=4)\", \"KNN (k=7)\", \"Kernel SVM\", \"Soft Voting\"],\n):\n    DecisionBoundaryDisplay.from_estimator(\n        clf, X, alpha=0.4, ax=axarr[idx[0], idx[1]], response_method=\"predict\"\n    )\n    axarr[idx[0], idx[1]].scatter(X[:, 0], X[:, 1], c=y, s=20, edgecolor=\"k\")\n    axarr[idx[0], idx[1]].set_title(tt)\n\nplt.show()"
+        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We first generate a noisy XOR dataset, which is a binary classification task.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nfrom matplotlib.colors import ListedColormap\n\nn_samples = 500\nrng = np.random.default_rng(0)\nfeature_names = [\"Feature #0\", \"Feature #1\"]\ncommon_scatter_plot_params = dict(\n    cmap=ListedColormap([\"tab:red\", \"tab:blue\"]),\n    edgecolor=\"white\",\n    linewidth=1,\n)\n\nxor = pd.DataFrame(\n    np.random.RandomState(0).uniform(low=-1, high=1, size=(n_samples, 2)),\n    columns=feature_names,\n)\nnoise = rng.normal(loc=0, scale=0.1, size=(n_samples, 2))\ntarget_xor = np.logical_xor(\n    xor[\"Feature #0\"] + noise[:, 0] > 0, xor[\"Feature #1\"] + noise[:, 1] > 0\n)\n\nX = xor[feature_names]\ny = target_xor.astype(np.int32)\n\nfig, ax = plt.subplots()\nax.scatter(X[\"Feature #0\"], X[\"Feature #1\"], c=y, **common_scatter_plot_params)\nax.set_title(\"The XOR dataset\")\nplt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Due to the inherent non-linear separability of the XOR dataset, tree-based\nmodels would often be preferred. However, appropriate feature engineering\ncombined with a linear model can yield effective results, with the added\nbenefit of producing better-calibrated probabilities for samples located in\nthe transition regions affected by noise.\n\nWe define and fit the models on the whole dataset.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn.ensemble import VotingClassifier\nfrom sklearn.kernel_approximation import Nystroem\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.pipeline import make_pipeline\nfrom sklearn.preprocessing import PolynomialFeatures, SplineTransformer, StandardScaler\n\nclf1 = make_pipeline(\n    SplineTransformer(degree=2, n_knots=2),\n    PolynomialFeatures(interaction_only=True),\n    LogisticRegression(C=10),\n)\nclf2 = make_pipeline(\n    SplineTransformer(\n        degree=2,\n        n_knots=4,\n        extrapolation=\"periodic\",\n        include_bias=True,\n    ),\n    PolynomialFeatures(interaction_only=True),\n    LogisticRegression(C=10),\n)\nclf3 = make_pipeline(\n    StandardScaler(),\n    Nystroem(gamma=2, random_state=0),\n    LogisticRegression(C=10),\n)\nweights = [2, 1, 3]\neclf = VotingClassifier(\n    estimators=[\n        (\"constant splines model\", clf1),\n        (\"periodic splines model\", clf2),\n        (\"nystroem model\", clf3),\n    ],\n    voting=\"soft\",\n    weights=weights,\n)\n\nclf1.fit(X, y)\nclf2.fit(X, y)\nclf3.fit(X, y)\neclf.fit(X, y)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Finally we use :class:`~inspection.DecisionBoundaryDisplay` to plot the\npredicted probabilities. By using a diverging colormap (such as `\"RdBu\"`), we\ncan ensure that darker colors correspond to `predict_proba` close to either 0\nor 1, and white corresponds to `predict_proba` of 0.5.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from itertools import product\n\nfrom sklearn.inspection import DecisionBoundaryDisplay\n\nfig, axarr = plt.subplots(2, 2, sharex=\"col\", sharey=\"row\", figsize=(10, 8))\nfor idx, clf, title in zip(\n    product([0, 1], [0, 1]),\n    [clf1, clf2, clf3, eclf],\n    [\n        \"Splines with\\nconstant extrapolation\",\n        \"Splines with\\nperiodic extrapolation\",\n        \"RBF Nystroem\",\n        \"Soft Voting\",\n    ],\n):\n    disp = DecisionBoundaryDisplay.from_estimator(\n        clf,\n        X,\n        response_method=\"predict_proba\",\n        plot_method=\"pcolormesh\",\n        cmap=\"RdBu\",\n        alpha=0.8,\n        ax=axarr[idx[0], idx[1]],\n    )\n    axarr[idx[0], idx[1]].scatter(\n        X[\"Feature #0\"],\n        X[\"Feature #1\"],\n        c=y,\n        **common_scatter_plot_params,\n    )\n    axarr[idx[0], idx[1]].set_title(title)\n    fig.colorbar(disp.surface_, ax=axarr[idx[0], idx[1]], label=\"Probability estimate\")\n\nplt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "As a sanity check, we can verify for a given sample that the probability\npredicted by the :class:`~ensemble.VotingClassifier` is indeed the weighted\naverage of the individual classifiers' soft-predictions.\n\nIn the case of binary classification such as in the present example, the\n:term:`predict_proba` arrays contain the probability of belonging to class 0\n(here in red) as the first entry, and the probability of belonging to class 1\n(here in blue) as the second entry.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "test_sample = pd.DataFrame({\"Feature #0\": [-0.5], \"Feature #1\": [1.5]})\npredict_probas = [est.predict_proba(test_sample).ravel() for est in eclf.estimators_]\nfor (est_name, _), est_probas in zip(eclf.estimators, predict_probas):\n    print(f\"{est_name}'s predicted probabilities: {est_probas}\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "print(\n    \"Weighted average of soft-predictions: \"\n    f\"{np.dot(weights, predict_probas) / np.sum(weights)}\"\n)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We can see that manual calculation of predicted probabilities above is\nequivalent to that produced by the `VotingClassifier`:\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "print(\n    \"Predicted probability of VotingClassifier: \"\n    f\"{eclf.predict_proba(test_sample).ravel()}\"\n)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "To convert soft predictions into hard predictions when weights are provided,\nthe weighted average predicted probabilities are computed for each class.\nThen, the final class label is then derived from the class label with the\nhighest average probability, which corresponds to the default threshold at\n`predict_proba=0.5` in the case of binary classification.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "print(\n    \"Class with the highest weighted average of soft-predictions: \"\n    f\"{np.argmax(np.dot(weights, predict_probas) / np.sum(weights))}\"\n)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "This is equivalent to the output of `VotingClassifier`'s `predict` method:\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "print(f\"Predicted class of VotingClassifier: {eclf.predict(test_sample).ravel()}\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Soft votes can be thresholded as for any other probabilistic classifier. This\nallows you to set a threshold probability at which the positive class will be\npredicted, instead of simply selecting the class with the highest predicted\nprobability.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn.model_selection import FixedThresholdClassifier\n\neclf_other_threshold = FixedThresholdClassifier(\n    eclf, threshold=0.7, response_method=\"predict_proba\"\n).fit(X, y)\nprint(\n    \"Predicted class of thresholded VotingClassifier: \"\n    f\"{eclf_other_threshold.predict(test_sample)}\"\n)"
       ]
     }
   ],
@@ -35,7 +190,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/ensemble/plot_voting_probas.ipynb b/dev/notebooks/auto_examples/ensemble/plot_voting_probas.ipynb
deleted file mode 100644
index 4b3c05bb4736e..0000000000000
--- a/dev/notebooks/auto_examples/ensemble/plot_voting_probas.ipynb
+++ /dev/null
@@ -1,43 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "\n# Plot class probabilities calculated by the VotingClassifier\n\n.. currentmodule:: sklearn\n\nPlot the class probabilities of the first sample in a toy dataset predicted by\nthree different classifiers and averaged by the\n:class:`~ensemble.VotingClassifier`.\n\nFirst, three exemplary classifiers are initialized\n(:class:`~linear_model.LogisticRegression`, :class:`~naive_bayes.GaussianNB`,\nand :class:`~ensemble.RandomForestClassifier`) and used to initialize a\nsoft-voting :class:`~ensemble.VotingClassifier` with weights `[1, 1, 5]`, which\nmeans that the predicted probabilities of the\n:class:`~ensemble.RandomForestClassifier` count 5 times as much as the weights\nof the other classifiers when the averaged probability is calculated.\n\nTo visualize the probability weighting, we fit each classifier on the training\nset and plot the predicted class probabilities for the first sample in this\nexample dataset.\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nfrom sklearn.ensemble import RandomForestClassifier, VotingClassifier\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.naive_bayes import GaussianNB\n\nclf1 = LogisticRegression(max_iter=1000, random_state=123)\nclf2 = RandomForestClassifier(n_estimators=100, random_state=123)\nclf3 = GaussianNB()\nX = np.array([[-1.0, -1.0], [-1.2, -1.4], [-3.4, -2.2], [1.1, 1.2]])\ny = np.array([1, 1, 2, 2])\n\neclf = VotingClassifier(\n    estimators=[(\"lr\", clf1), (\"rf\", clf2), (\"gnb\", clf3)],\n    voting=\"soft\",\n    weights=[1, 1, 5],\n)\n\n# predict class probabilities for all classifiers\nprobas = [c.fit(X, y).predict_proba(X) for c in (clf1, clf2, clf3, eclf)]\n\n# get class probabilities for the first sample in the dataset\nclass1_1 = [pr[0, 0] for pr in probas]\nclass2_1 = [pr[0, 1] for pr in probas]\n\n\n# plotting\n\nN = 4  # number of groups\nind = np.arange(N)  # group positions\nwidth = 0.35  # bar width\n\nfig, ax = plt.subplots()\n\n# bars for classifier 1-3\np1 = ax.bar(ind, np.hstack(([class1_1[:-1], [0]])), width, color=\"green\", edgecolor=\"k\")\np2 = ax.bar(\n    ind + width,\n    np.hstack(([class2_1[:-1], [0]])),\n    width,\n    color=\"lightgreen\",\n    edgecolor=\"k\",\n)\n\n# bars for VotingClassifier\np3 = ax.bar(ind, [0, 0, 0, class1_1[-1]], width, color=\"blue\", edgecolor=\"k\")\np4 = ax.bar(\n    ind + width, [0, 0, 0, class2_1[-1]], width, color=\"steelblue\", edgecolor=\"k\"\n)\n\n# plot annotations\nplt.axvline(2.8, color=\"k\", linestyle=\"dashed\")\nax.set_xticks(ind + width)\nax.set_xticklabels(\n    [\n        \"LogisticRegression\\nweight 1\",\n        \"GaussianNB\\nweight 1\",\n        \"RandomForestClassifier\\nweight 5\",\n        \"VotingClassifier\\n(average probabilities)\",\n    ],\n    rotation=40,\n    ha=\"right\",\n)\nplt.ylim([0, 1])\nplt.title(\"Class probabilities for sample 1 by different classifiers\")\nplt.legend([p1[0], p2[0]], [\"class 1\", \"class 2\"], loc=\"upper left\")\nplt.tight_layout()\nplt.show()"
-      ]
-    }
-  ],
-  "metadata": {
-    "kernelspec": {
-      "display_name": "Python 3",
-      "language": "python",
-      "name": "python3"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.10.16"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 0
-}
\ No newline at end of file
diff --git a/dev/notebooks/auto_examples/ensemble/plot_voting_regressor.ipynb b/dev/notebooks/auto_examples/ensemble/plot_voting_regressor.ipynb
index a4a709be87280..36d15f15f6cd7 100644
--- a/dev/notebooks/auto_examples/ensemble/plot_voting_regressor.ipynb
+++ b/dev/notebooks/auto_examples/ensemble/plot_voting_regressor.ipynb
@@ -89,7 +89,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/feature_selection/plot_f_test_vs_mi.ipynb b/dev/notebooks/auto_examples/feature_selection/plot_f_test_vs_mi.ipynb
index 6c47418234a3b..4efac9c7b572d 100644
--- a/dev/notebooks/auto_examples/feature_selection/plot_f_test_vs_mi.ipynb
+++ b/dev/notebooks/auto_examples/feature_selection/plot_f_test_vs_mi.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/feature_selection/plot_feature_selection.ipynb b/dev/notebooks/auto_examples/feature_selection/plot_feature_selection.ipynb
index 157a3eef98d93..58ec67d04dcba 100644
--- a/dev/notebooks/auto_examples/feature_selection/plot_feature_selection.ipynb
+++ b/dev/notebooks/auto_examples/feature_selection/plot_feature_selection.ipynb
@@ -143,7 +143,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/feature_selection/plot_feature_selection_pipeline.ipynb b/dev/notebooks/auto_examples/feature_selection/plot_feature_selection_pipeline.ipynb
index 1753f2dd9bcc9..c138267763371 100644
--- a/dev/notebooks/auto_examples/feature_selection/plot_feature_selection_pipeline.ipynb
+++ b/dev/notebooks/auto_examples/feature_selection/plot_feature_selection_pipeline.ipynb
@@ -132,7 +132,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/feature_selection/plot_rfe_digits.ipynb b/dev/notebooks/auto_examples/feature_selection/plot_rfe_digits.ipynb
index 3ec77eed7c537..2ba9b30daaab7 100644
--- a/dev/notebooks/auto_examples/feature_selection/plot_rfe_digits.ipynb
+++ b/dev/notebooks/auto_examples/feature_selection/plot_rfe_digits.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/feature_selection/plot_rfe_with_cross_validation.ipynb b/dev/notebooks/auto_examples/feature_selection/plot_rfe_with_cross_validation.ipynb
index 361f2160bf1f3..4af5127db4861 100644
--- a/dev/notebooks/auto_examples/feature_selection/plot_rfe_with_cross_validation.ipynb
+++ b/dev/notebooks/auto_examples/feature_selection/plot_rfe_with_cross_validation.ipynb
@@ -33,7 +33,7 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.datasets import make_classification\n\nX, y = make_classification(\n    n_samples=500,\n    n_features=15,\n    n_informative=3,\n    n_redundant=2,\n    n_repeated=0,\n    n_classes=8,\n    n_clusters_per_class=1,\n    class_sep=0.8,\n    random_state=0,\n)"
+        "from sklearn.datasets import make_classification\n\nn_features = 15\nfeat_names = [f\"feature_{i}\" for i in range(15)]\n\nX, y = make_classification(\n    n_samples=500,\n    n_features=n_features,\n    n_informative=3,\n    n_redundant=2,\n    n_repeated=0,\n    n_classes=8,\n    n_clusters_per_class=1,\n    class_sep=0.8,\n    random_state=0,\n)"
       ]
     },
     {
@@ -69,7 +69,7 @@
       },
       "outputs": [],
       "source": [
-        "import matplotlib.pyplot as plt\nimport pandas as pd\n\ncv_results = pd.DataFrame(rfecv.cv_results_)\nplt.figure()\nplt.xlabel(\"Number of features selected\")\nplt.ylabel(\"Mean test accuracy\")\nplt.errorbar(\n    x=cv_results[\"n_features\"],\n    y=cv_results[\"mean_test_score\"],\n    yerr=cv_results[\"std_test_score\"],\n)\nplt.title(\"Recursive Feature Elimination \\nwith correlated features\")\nplt.show()"
+        "import matplotlib.pyplot as plt\nimport pandas as pd\n\ndata = {\n    key: value\n    for key, value in rfecv.cv_results_.items()\n    if key in [\"n_features\", \"mean_test_score\", \"std_test_score\"]\n}\ncv_results = pd.DataFrame(data)\nplt.figure()\nplt.xlabel(\"Number of features selected\")\nplt.ylabel(\"Mean test accuracy\")\nplt.errorbar(\n    x=cv_results[\"n_features\"],\n    y=cv_results[\"mean_test_score\"],\n    yerr=cv_results[\"std_test_score\"],\n)\nplt.title(\"Recursive Feature Elimination \\nwith correlated features\")\nplt.show()"
       ]
     },
     {
@@ -78,6 +78,24 @@
       "source": [
         "From the plot above one can further notice a plateau of equivalent scores\n(similar mean value and overlapping errorbars) for 3 to 5 selected features.\nThis is the result of introducing correlated features. Indeed, the optimal\nmodel selected by the RFE can lie within this range, depending on the\ncross-validation technique. The test accuracy decreases above 5 selected\nfeatures, this is, keeping non-informative features leads to over-fitting and\nis therefore detrimental for the statistical performance of the models.\n\n"
       ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n\nfor i in range(cv.n_splits):\n    mask = rfecv.cv_results_[f\"split{i}_support\"][\n        rfecv.n_features_\n    ]  # mask of features selected by the RFE\n    features_selected = np.ma.compressed(np.ma.masked_array(feat_names, mask=1 - mask))\n    print(f\"Features selected in fold {i}: {features_selected}\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "In the five folds, the selected features are consistent. This is good news,\nit means that the selection is stable across folds, and it confirms that\nthese features are the most informative ones.\n\n"
+      ]
     }
   ],
   "metadata": {
@@ -96,7 +114,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/feature_selection/plot_select_from_model_diabetes.ipynb b/dev/notebooks/auto_examples/feature_selection/plot_select_from_model_diabetes.ipynb
index 6966f81f1109c..3e145f2c4c3d6 100644
--- a/dev/notebooks/auto_examples/feature_selection/plot_select_from_model_diabetes.ipynb
+++ b/dev/notebooks/auto_examples/feature_selection/plot_select_from_model_diabetes.ipynb
@@ -150,7 +150,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/frozen/plot_frozen_examples.ipynb b/dev/notebooks/auto_examples/frozen/plot_frozen_examples.ipynb
index f6bdcafbccd7d..2b7417ddc6724 100644
--- a/dev/notebooks/auto_examples/frozen/plot_frozen_examples.ipynb
+++ b/dev/notebooks/auto_examples/frozen/plot_frozen_examples.ipynb
@@ -114,7 +114,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/gaussian_process/plot_compare_gpr_krr.ipynb b/dev/notebooks/auto_examples/gaussian_process/plot_compare_gpr_krr.ipynb
index e38e80490cd13..74db1e4c6fd68 100644
--- a/dev/notebooks/auto_examples/gaussian_process/plot_compare_gpr_krr.ipynb
+++ b/dev/notebooks/auto_examples/gaussian_process/plot_compare_gpr_krr.ipynb
@@ -320,7 +320,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/gaussian_process/plot_gpc.ipynb b/dev/notebooks/auto_examples/gaussian_process/plot_gpc.ipynb
index cb977189c527a..dd7a92174b5db 100644
--- a/dev/notebooks/auto_examples/gaussian_process/plot_gpc.ipynb
+++ b/dev/notebooks/auto_examples/gaussian_process/plot_gpc.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/gaussian_process/plot_gpc_iris.ipynb b/dev/notebooks/auto_examples/gaussian_process/plot_gpc_iris.ipynb
index 2503b468a0dee..b6f97048422ea 100644
--- a/dev/notebooks/auto_examples/gaussian_process/plot_gpc_iris.ipynb
+++ b/dev/notebooks/auto_examples/gaussian_process/plot_gpc_iris.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/gaussian_process/plot_gpc_isoprobability.ipynb b/dev/notebooks/auto_examples/gaussian_process/plot_gpc_isoprobability.ipynb
index fa5d36eed56ec..74687c24fb876 100644
--- a/dev/notebooks/auto_examples/gaussian_process/plot_gpc_isoprobability.ipynb
+++ b/dev/notebooks/auto_examples/gaussian_process/plot_gpc_isoprobability.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/gaussian_process/plot_gpc_xor.ipynb b/dev/notebooks/auto_examples/gaussian_process/plot_gpc_xor.ipynb
index 1226fc9202050..468fa3b79ee7c 100644
--- a/dev/notebooks/auto_examples/gaussian_process/plot_gpc_xor.ipynb
+++ b/dev/notebooks/auto_examples/gaussian_process/plot_gpc_xor.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/gaussian_process/plot_gpr_co2.ipynb b/dev/notebooks/auto_examples/gaussian_process/plot_gpr_co2.ipynb
index 09ef49e027c0a..ffd99959744bb 100644
--- a/dev/notebooks/auto_examples/gaussian_process/plot_gpr_co2.ipynb
+++ b/dev/notebooks/auto_examples/gaussian_process/plot_gpr_co2.ipynb
@@ -298,7 +298,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/gaussian_process/plot_gpr_noisy.ipynb b/dev/notebooks/auto_examples/gaussian_process/plot_gpr_noisy.ipynb
index ba2a1aa410a21..725c8cd96ada3 100644
--- a/dev/notebooks/auto_examples/gaussian_process/plot_gpr_noisy.ipynb
+++ b/dev/notebooks/auto_examples/gaussian_process/plot_gpr_noisy.ipynb
@@ -234,7 +234,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/gaussian_process/plot_gpr_noisy_targets.ipynb b/dev/notebooks/auto_examples/gaussian_process/plot_gpr_noisy_targets.ipynb
index 0a5973a1e6023..5501ed8ec5d7c 100644
--- a/dev/notebooks/auto_examples/gaussian_process/plot_gpr_noisy_targets.ipynb
+++ b/dev/notebooks/auto_examples/gaussian_process/plot_gpr_noisy_targets.ipynb
@@ -179,7 +179,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/gaussian_process/plot_gpr_on_structured_data.ipynb b/dev/notebooks/auto_examples/gaussian_process/plot_gpr_on_structured_data.ipynb
index c783b46db2728..dba5cb05e77f5 100644
--- a/dev/notebooks/auto_examples/gaussian_process/plot_gpr_on_structured_data.ipynb
+++ b/dev/notebooks/auto_examples/gaussian_process/plot_gpr_on_structured_data.ipynb
@@ -100,7 +100,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/gaussian_process/plot_gpr_prior_posterior.ipynb b/dev/notebooks/auto_examples/gaussian_process/plot_gpr_prior_posterior.ipynb
index bd0be76c31902..e82d30bfc73eb 100644
--- a/dev/notebooks/auto_examples/gaussian_process/plot_gpr_prior_posterior.ipynb
+++ b/dev/notebooks/auto_examples/gaussian_process/plot_gpr_prior_posterior.ipynb
@@ -216,7 +216,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/impute/plot_iterative_imputer_variants_comparison.ipynb b/dev/notebooks/auto_examples/impute/plot_iterative_imputer_variants_comparison.ipynb
index 1a4b1e7c67a55..7cf41d89fd956 100644
--- a/dev/notebooks/auto_examples/impute/plot_iterative_imputer_variants_comparison.ipynb
+++ b/dev/notebooks/auto_examples/impute/plot_iterative_imputer_variants_comparison.ipynb
@@ -15,7 +15,7 @@
       },
       "outputs": [],
       "source": [
-        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\n\nfrom sklearn.datasets import fetch_california_housing\nfrom sklearn.ensemble import RandomForestRegressor\n\n# To use this experimental feature, we need to explicitly ask for it:\nfrom sklearn.experimental import enable_iterative_imputer  # noqa\nfrom sklearn.impute import IterativeImputer, SimpleImputer\nfrom sklearn.kernel_approximation import Nystroem\nfrom sklearn.linear_model import BayesianRidge, Ridge\nfrom sklearn.model_selection import cross_val_score\nfrom sklearn.neighbors import KNeighborsRegressor\nfrom sklearn.pipeline import make_pipeline\n\nN_SPLITS = 5\n\nrng = np.random.RandomState(0)\n\nX_full, y_full = fetch_california_housing(return_X_y=True)\n# ~2k samples is enough for the purpose of the example.\n# Remove the following two lines for a slower run with different error bars.\nX_full = X_full[::10]\ny_full = y_full[::10]\nn_samples, n_features = X_full.shape\n\n# Estimate the score on the entire dataset, with no missing values\nbr_estimator = BayesianRidge()\nscore_full_data = pd.DataFrame(\n    cross_val_score(\n        br_estimator, X_full, y_full, scoring=\"neg_mean_squared_error\", cv=N_SPLITS\n    ),\n    columns=[\"Full Data\"],\n)\n\n# Add a single missing value to each row\nX_missing = X_full.copy()\ny_missing = y_full\nmissing_samples = np.arange(n_samples)\nmissing_features = rng.choice(n_features, n_samples, replace=True)\nX_missing[missing_samples, missing_features] = np.nan\n\n# Estimate the score after imputation (mean and median strategies)\nscore_simple_imputer = pd.DataFrame()\nfor strategy in (\"mean\", \"median\"):\n    estimator = make_pipeline(\n        SimpleImputer(missing_values=np.nan, strategy=strategy), br_estimator\n    )\n    score_simple_imputer[strategy] = cross_val_score(\n        estimator, X_missing, y_missing, scoring=\"neg_mean_squared_error\", cv=N_SPLITS\n    )\n\n# Estimate the score after iterative imputation of the missing values\n# with different estimators\nestimators = [\n    BayesianRidge(),\n    RandomForestRegressor(\n        # We tuned the hyperparameters of the RandomForestRegressor to get a good\n        # enough predictive performance for a restricted execution time.\n        n_estimators=4,\n        max_depth=10,\n        bootstrap=True,\n        max_samples=0.5,\n        n_jobs=2,\n        random_state=0,\n    ),\n    make_pipeline(\n        Nystroem(kernel=\"polynomial\", degree=2, random_state=0), Ridge(alpha=1e3)\n    ),\n    KNeighborsRegressor(n_neighbors=15),\n]\nscore_iterative_imputer = pd.DataFrame()\n# iterative imputer is sensible to the tolerance and\n# dependent on the estimator used internally.\n# we tuned the tolerance to keep this example run with limited computational\n# resources while not changing the results too much compared to keeping the\n# stricter default value for the tolerance parameter.\ntolerances = (1e-3, 1e-1, 1e-1, 1e-2)\nfor impute_estimator, tol in zip(estimators, tolerances):\n    estimator = make_pipeline(\n        IterativeImputer(\n            random_state=0, estimator=impute_estimator, max_iter=25, tol=tol\n        ),\n        br_estimator,\n    )\n    score_iterative_imputer[impute_estimator.__class__.__name__] = cross_val_score(\n        estimator, X_missing, y_missing, scoring=\"neg_mean_squared_error\", cv=N_SPLITS\n    )\n\nscores = pd.concat(\n    [score_full_data, score_simple_imputer, score_iterative_imputer],\n    keys=[\"Original\", \"SimpleImputer\", \"IterativeImputer\"],\n    axis=1,\n)\n\n# plot california housing results\nfig, ax = plt.subplots(figsize=(13, 6))\nmeans = -scores.mean()\nerrors = scores.std()\nmeans.plot.barh(xerr=errors, ax=ax)\nax.set_title(\"California Housing Regression with Different Imputation Methods\")\nax.set_xlabel(\"MSE (smaller is better)\")\nax.set_yticks(np.arange(means.shape[0]))\nax.set_yticklabels([\" w/ \".join(label) for label in means.index.tolist()])\nplt.tight_layout(pad=1)\nplt.show()"
+        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\n\nfrom sklearn.datasets import fetch_california_housing\nfrom sklearn.ensemble import RandomForestRegressor\n\n# To use this experimental feature, we need to explicitly ask for it:\nfrom sklearn.experimental import enable_iterative_imputer  # noqa: F401\nfrom sklearn.impute import IterativeImputer, SimpleImputer\nfrom sklearn.kernel_approximation import Nystroem\nfrom sklearn.linear_model import BayesianRidge, Ridge\nfrom sklearn.model_selection import cross_val_score\nfrom sklearn.neighbors import KNeighborsRegressor\nfrom sklearn.pipeline import make_pipeline\n\nN_SPLITS = 5\n\nrng = np.random.RandomState(0)\n\nX_full, y_full = fetch_california_housing(return_X_y=True)\n# ~2k samples is enough for the purpose of the example.\n# Remove the following two lines for a slower run with different error bars.\nX_full = X_full[::10]\ny_full = y_full[::10]\nn_samples, n_features = X_full.shape\n\n# Estimate the score on the entire dataset, with no missing values\nbr_estimator = BayesianRidge()\nscore_full_data = pd.DataFrame(\n    cross_val_score(\n        br_estimator, X_full, y_full, scoring=\"neg_mean_squared_error\", cv=N_SPLITS\n    ),\n    columns=[\"Full Data\"],\n)\n\n# Add a single missing value to each row\nX_missing = X_full.copy()\ny_missing = y_full\nmissing_samples = np.arange(n_samples)\nmissing_features = rng.choice(n_features, n_samples, replace=True)\nX_missing[missing_samples, missing_features] = np.nan\n\n# Estimate the score after imputation (mean and median strategies)\nscore_simple_imputer = pd.DataFrame()\nfor strategy in (\"mean\", \"median\"):\n    estimator = make_pipeline(\n        SimpleImputer(missing_values=np.nan, strategy=strategy), br_estimator\n    )\n    score_simple_imputer[strategy] = cross_val_score(\n        estimator, X_missing, y_missing, scoring=\"neg_mean_squared_error\", cv=N_SPLITS\n    )\n\n# Estimate the score after iterative imputation of the missing values\n# with different estimators\nestimators = [\n    BayesianRidge(),\n    RandomForestRegressor(\n        # We tuned the hyperparameters of the RandomForestRegressor to get a good\n        # enough predictive performance for a restricted execution time.\n        n_estimators=4,\n        max_depth=10,\n        bootstrap=True,\n        max_samples=0.5,\n        n_jobs=2,\n        random_state=0,\n    ),\n    make_pipeline(\n        Nystroem(kernel=\"polynomial\", degree=2, random_state=0), Ridge(alpha=1e3)\n    ),\n    KNeighborsRegressor(n_neighbors=15),\n]\nscore_iterative_imputer = pd.DataFrame()\n# iterative imputer is sensible to the tolerance and\n# dependent on the estimator used internally.\n# we tuned the tolerance to keep this example run with limited computational\n# resources while not changing the results too much compared to keeping the\n# stricter default value for the tolerance parameter.\ntolerances = (1e-3, 1e-1, 1e-1, 1e-2)\nfor impute_estimator, tol in zip(estimators, tolerances):\n    estimator = make_pipeline(\n        IterativeImputer(\n            random_state=0, estimator=impute_estimator, max_iter=25, tol=tol\n        ),\n        br_estimator,\n    )\n    score_iterative_imputer[impute_estimator.__class__.__name__] = cross_val_score(\n        estimator, X_missing, y_missing, scoring=\"neg_mean_squared_error\", cv=N_SPLITS\n    )\n\nscores = pd.concat(\n    [score_full_data, score_simple_imputer, score_iterative_imputer],\n    keys=[\"Original\", \"SimpleImputer\", \"IterativeImputer\"],\n    axis=1,\n)\n\n# plot california housing results\nfig, ax = plt.subplots(figsize=(13, 6))\nmeans = -scores.mean()\nerrors = scores.std()\nmeans.plot.barh(xerr=errors, ax=ax)\nax.set_title(\"California Housing Regression with Different Imputation Methods\")\nax.set_xlabel(\"MSE (smaller is better)\")\nax.set_yticks(np.arange(means.shape[0]))\nax.set_yticklabels([\" w/ \".join(label) for label in means.index.tolist()])\nplt.tight_layout(pad=1)\nplt.show()"
       ]
     }
   ],
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/impute/plot_missing_values.ipynb b/dev/notebooks/auto_examples/impute/plot_missing_values.ipynb
index 7b195a0d397f1..14a8cb85a5ea6 100644
--- a/dev/notebooks/auto_examples/impute/plot_missing_values.ipynb
+++ b/dev/notebooks/auto_examples/impute/plot_missing_values.ipynb
@@ -51,7 +51,7 @@
       },
       "outputs": [],
       "source": [
-        "rng = np.random.RandomState(0)\n\nfrom sklearn.ensemble import RandomForestRegressor\n\n# To use the experimental IterativeImputer, we need to explicitly ask for it:\nfrom sklearn.experimental import enable_iterative_imputer  # noqa\nfrom sklearn.impute import IterativeImputer, KNNImputer, SimpleImputer\nfrom sklearn.model_selection import cross_val_score\nfrom sklearn.pipeline import make_pipeline\n\nN_SPLITS = 4\nregressor = RandomForestRegressor(random_state=0)"
+        "rng = np.random.RandomState(0)\n\nfrom sklearn.ensemble import RandomForestRegressor\n\n# To use the experimental IterativeImputer, we need to explicitly ask for it:\nfrom sklearn.experimental import enable_iterative_imputer  # noqa: F401\nfrom sklearn.impute import IterativeImputer, KNNImputer, SimpleImputer\nfrom sklearn.model_selection import cross_val_score\nfrom sklearn.pipeline import make_pipeline\n\nN_SPLITS = 4\nregressor = RandomForestRegressor(random_state=0)"
       ]
     },
     {
@@ -204,7 +204,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/inspection/plot_causal_interpretation.ipynb b/dev/notebooks/auto_examples/inspection/plot_causal_interpretation.ipynb
index 0a00851076f83..369eb9747530c 100644
--- a/dev/notebooks/auto_examples/inspection/plot_causal_interpretation.ipynb
+++ b/dev/notebooks/auto_examples/inspection/plot_causal_interpretation.ipynb
@@ -168,7 +168,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/inspection/plot_linear_model_coefficient_interpretation.ipynb b/dev/notebooks/auto_examples/inspection/plot_linear_model_coefficient_interpretation.ipynb
index 62164ce1dbfcb..e9b1c660b7471 100644
--- a/dev/notebooks/auto_examples/inspection/plot_linear_model_coefficient_interpretation.ipynb
+++ b/dev/notebooks/auto_examples/inspection/plot_linear_model_coefficient_interpretation.ipynb
@@ -713,7 +713,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/inspection/plot_partial_dependence.ipynb b/dev/notebooks/auto_examples/inspection/plot_partial_dependence.ipynb
index 97036c3ceeefe..d8b05f879e422 100644
--- a/dev/notebooks/auto_examples/inspection/plot_partial_dependence.ipynb
+++ b/dev/notebooks/auto_examples/inspection/plot_partial_dependence.ipynb
@@ -424,7 +424,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/inspection/plot_permutation_importance.ipynb b/dev/notebooks/auto_examples/inspection/plot_permutation_importance.ipynb
index a940d75a4668b..5984d817e1da3 100644
--- a/dev/notebooks/auto_examples/inspection/plot_permutation_importance.ipynb
+++ b/dev/notebooks/auto_examples/inspection/plot_permutation_importance.ipynb
@@ -237,7 +237,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/inspection/plot_permutation_importance_multicollinear.ipynb b/dev/notebooks/auto_examples/inspection/plot_permutation_importance_multicollinear.ipynb
index d51a96cc129bb..e1d343b449944 100644
--- a/dev/notebooks/auto_examples/inspection/plot_permutation_importance_multicollinear.ipynb
+++ b/dev/notebooks/auto_examples/inspection/plot_permutation_importance_multicollinear.ipynb
@@ -161,7 +161,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/kernel_approximation/plot_scalable_poly_kernels.ipynb b/dev/notebooks/auto_examples/kernel_approximation/plot_scalable_poly_kernels.ipynb
index 9014b4d8d2023..e8b34131a8fd7 100644
--- a/dev/notebooks/auto_examples/kernel_approximation/plot_scalable_poly_kernels.ipynb
+++ b/dev/notebooks/auto_examples/kernel_approximation/plot_scalable_poly_kernels.ipynb
@@ -168,7 +168,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/linear_model/plot_ard.ipynb b/dev/notebooks/auto_examples/linear_model/plot_ard.ipynb
index 076717171f316..da9864d52b475 100644
--- a/dev/notebooks/auto_examples/linear_model/plot_ard.ipynb
+++ b/dev/notebooks/auto_examples/linear_model/plot_ard.ipynb
@@ -175,7 +175,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/linear_model/plot_bayesian_ridge_curvefit.ipynb b/dev/notebooks/auto_examples/linear_model/plot_bayesian_ridge_curvefit.ipynb
index 0f14878463353..9e33ecf54269f 100644
--- a/dev/notebooks/auto_examples/linear_model/plot_bayesian_ridge_curvefit.ipynb
+++ b/dev/notebooks/auto_examples/linear_model/plot_bayesian_ridge_curvefit.ipynb
@@ -89,7 +89,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/linear_model/plot_elastic_net_precomputed_gram_matrix_with_weighted_samples.ipynb b/dev/notebooks/auto_examples/linear_model/plot_elastic_net_precomputed_gram_matrix_with_weighted_samples.ipynb
index f4e18eee9ba49..c0b450cfe188a 100644
--- a/dev/notebooks/auto_examples/linear_model/plot_elastic_net_precomputed_gram_matrix_with_weighted_samples.ipynb
+++ b/dev/notebooks/auto_examples/linear_model/plot_elastic_net_precomputed_gram_matrix_with_weighted_samples.ipynb
@@ -89,7 +89,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/linear_model/plot_huber_vs_ridge.ipynb b/dev/notebooks/auto_examples/linear_model/plot_huber_vs_ridge.ipynb
index 517c5e58d7672..f2bb1e124180a 100644
--- a/dev/notebooks/auto_examples/linear_model/plot_huber_vs_ridge.ipynb
+++ b/dev/notebooks/auto_examples/linear_model/plot_huber_vs_ridge.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/linear_model/plot_lasso_and_elasticnet.ipynb b/dev/notebooks/auto_examples/linear_model/plot_lasso_and_elasticnet.ipynb
index 9077596883940..e0cd8ed17bdf8 100644
--- a/dev/notebooks/auto_examples/linear_model/plot_lasso_and_elasticnet.ipynb
+++ b/dev/notebooks/auto_examples/linear_model/plot_lasso_and_elasticnet.ipynb
@@ -204,7 +204,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/linear_model/plot_lasso_dense_vs_sparse_data.ipynb b/dev/notebooks/auto_examples/linear_model/plot_lasso_dense_vs_sparse_data.ipynb
index b8665e8bd4f5f..68a632e1f6238 100644
--- a/dev/notebooks/auto_examples/linear_model/plot_lasso_dense_vs_sparse_data.ipynb
+++ b/dev/notebooks/auto_examples/linear_model/plot_lasso_dense_vs_sparse_data.ipynb
@@ -71,7 +71,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/linear_model/plot_lasso_lars_ic.ipynb b/dev/notebooks/auto_examples/linear_model/plot_lasso_lars_ic.ipynb
index 6e52b25c08fc9..d5cc569cfae71 100644
--- a/dev/notebooks/auto_examples/linear_model/plot_lasso_lars_ic.ipynb
+++ b/dev/notebooks/auto_examples/linear_model/plot_lasso_lars_ic.ipynb
@@ -147,7 +147,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/linear_model/plot_lasso_lasso_lars_elasticnet_path.ipynb b/dev/notebooks/auto_examples/linear_model/plot_lasso_lasso_lars_elasticnet_path.ipynb
index 353624fc1d11f..7a3c1f8ce8689 100644
--- a/dev/notebooks/auto_examples/linear_model/plot_lasso_lasso_lars_elasticnet_path.ipynb
+++ b/dev/notebooks/auto_examples/linear_model/plot_lasso_lasso_lars_elasticnet_path.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/linear_model/plot_lasso_model_selection.ipynb b/dev/notebooks/auto_examples/linear_model/plot_lasso_model_selection.ipynb
index c0844da11c9e4..f516486912718 100644
--- a/dev/notebooks/auto_examples/linear_model/plot_lasso_model_selection.ipynb
+++ b/dev/notebooks/auto_examples/linear_model/plot_lasso_model_selection.ipynb
@@ -226,7 +226,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/linear_model/plot_logistic.ipynb b/dev/notebooks/auto_examples/linear_model/plot_logistic.ipynb
index 4bfbf3091e721..ab0e34788adb9 100644
--- a/dev/notebooks/auto_examples/linear_model/plot_logistic.ipynb
+++ b/dev/notebooks/auto_examples/linear_model/plot_logistic.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/linear_model/plot_logistic_l1_l2_sparsity.ipynb b/dev/notebooks/auto_examples/linear_model/plot_logistic_l1_l2_sparsity.ipynb
index f7c7f7f99cb73..75890fa75beb3 100644
--- a/dev/notebooks/auto_examples/linear_model/plot_logistic_l1_l2_sparsity.ipynb
+++ b/dev/notebooks/auto_examples/linear_model/plot_logistic_l1_l2_sparsity.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/linear_model/plot_logistic_multinomial.ipynb b/dev/notebooks/auto_examples/linear_model/plot_logistic_multinomial.ipynb
index 2e1d457da988e..2c27d777bdcfc 100644
--- a/dev/notebooks/auto_examples/linear_model/plot_logistic_multinomial.ipynb
+++ b/dev/notebooks/auto_examples/linear_model/plot_logistic_multinomial.ipynb
@@ -125,7 +125,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/linear_model/plot_logistic_path.ipynb b/dev/notebooks/auto_examples/linear_model/plot_logistic_path.ipynb
index 80c690384cf63..520a24df71f6b 100644
--- a/dev/notebooks/auto_examples/linear_model/plot_logistic_path.ipynb
+++ b/dev/notebooks/auto_examples/linear_model/plot_logistic_path.ipynb
@@ -125,7 +125,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/linear_model/plot_multi_task_lasso_support.ipynb b/dev/notebooks/auto_examples/linear_model/plot_multi_task_lasso_support.ipynb
index 7939c7635b304..b583a3498c3f2 100644
--- a/dev/notebooks/auto_examples/linear_model/plot_multi_task_lasso_support.ipynb
+++ b/dev/notebooks/auto_examples/linear_model/plot_multi_task_lasso_support.ipynb
@@ -89,7 +89,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/linear_model/plot_nnls.ipynb b/dev/notebooks/auto_examples/linear_model/plot_nnls.ipynb
index 6740c4410ab68..d995eeb919915 100644
--- a/dev/notebooks/auto_examples/linear_model/plot_nnls.ipynb
+++ b/dev/notebooks/auto_examples/linear_model/plot_nnls.ipynb
@@ -125,7 +125,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/linear_model/plot_ols_ridge.ipynb b/dev/notebooks/auto_examples/linear_model/plot_ols_ridge.ipynb
index 298306a635b71..3df18ae0627bc 100644
--- a/dev/notebooks/auto_examples/linear_model/plot_ols_ridge.ipynb
+++ b/dev/notebooks/auto_examples/linear_model/plot_ols_ridge.ipynb
@@ -139,7 +139,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/linear_model/plot_omp.ipynb b/dev/notebooks/auto_examples/linear_model/plot_omp.ipynb
index 7f098b8e4be74..07369daac74b8 100644
--- a/dev/notebooks/auto_examples/linear_model/plot_omp.ipynb
+++ b/dev/notebooks/auto_examples/linear_model/plot_omp.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/linear_model/plot_poisson_regression_non_normal_loss.ipynb b/dev/notebooks/auto_examples/linear_model/plot_poisson_regression_non_normal_loss.ipynb
index 937e388b34238..6cf39c59c764f 100644
--- a/dev/notebooks/auto_examples/linear_model/plot_poisson_regression_non_normal_loss.ipynb
+++ b/dev/notebooks/auto_examples/linear_model/plot_poisson_regression_non_normal_loss.ipynb
@@ -269,7 +269,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/linear_model/plot_polynomial_interpolation.ipynb b/dev/notebooks/auto_examples/linear_model/plot_polynomial_interpolation.ipynb
index 6c8951ce7dee7..d5d633748023e 100644
--- a/dev/notebooks/auto_examples/linear_model/plot_polynomial_interpolation.ipynb
+++ b/dev/notebooks/auto_examples/linear_model/plot_polynomial_interpolation.ipynb
@@ -143,7 +143,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/linear_model/plot_quantile_regression.ipynb b/dev/notebooks/auto_examples/linear_model/plot_quantile_regression.ipynb
index 005f23eb31b85..fd238b3582703 100644
--- a/dev/notebooks/auto_examples/linear_model/plot_quantile_regression.ipynb
+++ b/dev/notebooks/auto_examples/linear_model/plot_quantile_regression.ipynb
@@ -197,7 +197,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/linear_model/plot_ransac.ipynb b/dev/notebooks/auto_examples/linear_model/plot_ransac.ipynb
index 2ae5ea2b547e1..78b621285fc8e 100644
--- a/dev/notebooks/auto_examples/linear_model/plot_ransac.ipynb
+++ b/dev/notebooks/auto_examples/linear_model/plot_ransac.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/linear_model/plot_ridge_coeffs.ipynb b/dev/notebooks/auto_examples/linear_model/plot_ridge_coeffs.ipynb
index 6ac6a174ee977..0e057f0694b29 100644
--- a/dev/notebooks/auto_examples/linear_model/plot_ridge_coeffs.ipynb
+++ b/dev/notebooks/auto_examples/linear_model/plot_ridge_coeffs.ipynb
@@ -96,7 +96,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/linear_model/plot_ridge_path.ipynb b/dev/notebooks/auto_examples/linear_model/plot_ridge_path.ipynb
index be1df6b33844b..7d1d26aa7ea33 100644
--- a/dev/notebooks/auto_examples/linear_model/plot_ridge_path.ipynb
+++ b/dev/notebooks/auto_examples/linear_model/plot_ridge_path.ipynb
@@ -71,7 +71,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/linear_model/plot_robust_fit.ipynb b/dev/notebooks/auto_examples/linear_model/plot_robust_fit.ipynb
index 5455f79cc5b60..fc2b8ac6a7459 100644
--- a/dev/notebooks/auto_examples/linear_model/plot_robust_fit.ipynb
+++ b/dev/notebooks/auto_examples/linear_model/plot_robust_fit.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/linear_model/plot_sgd_early_stopping.ipynb b/dev/notebooks/auto_examples/linear_model/plot_sgd_early_stopping.ipynb
index e6e6c39d7f67c..2349608649029 100644
--- a/dev/notebooks/auto_examples/linear_model/plot_sgd_early_stopping.ipynb
+++ b/dev/notebooks/auto_examples/linear_model/plot_sgd_early_stopping.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/linear_model/plot_sgd_iris.ipynb b/dev/notebooks/auto_examples/linear_model/plot_sgd_iris.ipynb
index 3dbc9e43c5e51..8ce3016dac5e2 100644
--- a/dev/notebooks/auto_examples/linear_model/plot_sgd_iris.ipynb
+++ b/dev/notebooks/auto_examples/linear_model/plot_sgd_iris.ipynb
@@ -15,7 +15,7 @@
       },
       "outputs": [],
       "source": [
-        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nfrom sklearn import datasets\nfrom sklearn.inspection import DecisionBoundaryDisplay\nfrom sklearn.linear_model import SGDClassifier\n\n# import some data to play with\niris = datasets.load_iris()\n\n# we only take the first two features. We could\n# avoid this ugly slicing by using a two-dim dataset\nX = iris.data[:, :2]\ny = iris.target\ncolors = \"bry\"\n\n# shuffle\nidx = np.arange(X.shape[0])\nnp.random.seed(13)\nnp.random.shuffle(idx)\nX = X[idx]\ny = y[idx]\n\n# standardize\nmean = X.mean(axis=0)\nstd = X.std(axis=0)\nX = (X - mean) / std\n\nclf = SGDClassifier(alpha=0.001, max_iter=100).fit(X, y)\nax = plt.gca()\nDecisionBoundaryDisplay.from_estimator(\n    clf,\n    X,\n    cmap=plt.cm.Paired,\n    ax=ax,\n    response_method=\"predict\",\n    xlabel=iris.feature_names[0],\n    ylabel=iris.feature_names[1],\n)\nplt.axis(\"tight\")\n\n# Plot also the training points\nfor i, color in zip(clf.classes_, colors):\n    idx = np.where(y == i)\n    plt.scatter(\n        X[idx, 0],\n        X[idx, 1],\n        c=color,\n        label=iris.target_names[i],\n        edgecolor=\"black\",\n        s=20,\n    )\nplt.title(\"Decision surface of multi-class SGD\")\nplt.axis(\"tight\")\n\n# Plot the three one-against-all classifiers\nxmin, xmax = plt.xlim()\nymin, ymax = plt.ylim()\ncoef = clf.coef_\nintercept = clf.intercept_\n\n\ndef plot_hyperplane(c, color):\n    def line(x0):\n        return (-(x0 * coef[c, 0]) - intercept[c]) / coef[c, 1]\n\n    plt.plot([xmin, xmax], [line(xmin), line(xmax)], ls=\"--\", color=color)\n\n\nfor i, color in zip(clf.classes_, colors):\n    plot_hyperplane(i, color)\nplt.legend()\nplt.show()"
+        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nfrom sklearn import datasets\nfrom sklearn.inspection import DecisionBoundaryDisplay\nfrom sklearn.linear_model import SGDClassifier\n\n# import some data to play with\niris = datasets.load_iris()\n\n# we only take the first two features. We could\n# avoid this ugly slicing by using a two-dim dataset\nX = iris.data[:, :2]\ny = iris.target\ncolors = \"bry\"\n\n# shuffle\nidx = np.arange(X.shape[0])\nnp.random.seed(13)\nnp.random.shuffle(idx)\nX = X[idx]\ny = y[idx]\n\n# standardize\nmean = X.mean(axis=0)\nstd = X.std(axis=0)\nX = (X - mean) / std\n\nclf = SGDClassifier(alpha=0.001, max_iter=100).fit(X, y)\nax = plt.gca()\nDecisionBoundaryDisplay.from_estimator(\n    clf,\n    X,\n    cmap=plt.cm.Paired,\n    ax=ax,\n    response_method=\"predict\",\n    xlabel=iris.feature_names[0],\n    ylabel=iris.feature_names[1],\n)\nplt.axis(\"tight\")\n\n# Plot also the training points\nfor i, color in zip(clf.classes_, colors):\n    idx = (y == i).nonzero()\n    plt.scatter(\n        X[idx, 0],\n        X[idx, 1],\n        c=color,\n        label=iris.target_names[i],\n        edgecolor=\"black\",\n        s=20,\n    )\nplt.title(\"Decision surface of multi-class SGD\")\nplt.axis(\"tight\")\n\n# Plot the three one-against-all classifiers\nxmin, xmax = plt.xlim()\nymin, ymax = plt.ylim()\ncoef = clf.coef_\nintercept = clf.intercept_\n\n\ndef plot_hyperplane(c, color):\n    def line(x0):\n        return (-(x0 * coef[c, 0]) - intercept[c]) / coef[c, 1]\n\n    plt.plot([xmin, xmax], [line(xmin), line(xmax)], ls=\"--\", color=color)\n\n\nfor i, color in zip(clf.classes_, colors):\n    plot_hyperplane(i, color)\nplt.legend()\nplt.show()"
       ]
     }
   ],
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/linear_model/plot_sgd_loss_functions.ipynb b/dev/notebooks/auto_examples/linear_model/plot_sgd_loss_functions.ipynb
index 83d21264b8448..13a005c4d9df7 100644
--- a/dev/notebooks/auto_examples/linear_model/plot_sgd_loss_functions.ipynb
+++ b/dev/notebooks/auto_examples/linear_model/plot_sgd_loss_functions.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/linear_model/plot_sgd_penalties.ipynb b/dev/notebooks/auto_examples/linear_model/plot_sgd_penalties.ipynb
index d1eaae66e6d26..a89677ca20bb3 100644
--- a/dev/notebooks/auto_examples/linear_model/plot_sgd_penalties.ipynb
+++ b/dev/notebooks/auto_examples/linear_model/plot_sgd_penalties.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/linear_model/plot_sgd_separating_hyperplane.ipynb b/dev/notebooks/auto_examples/linear_model/plot_sgd_separating_hyperplane.ipynb
index 404d9bbc54f41..e5bd69a3dd641 100644
--- a/dev/notebooks/auto_examples/linear_model/plot_sgd_separating_hyperplane.ipynb
+++ b/dev/notebooks/auto_examples/linear_model/plot_sgd_separating_hyperplane.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/linear_model/plot_sgd_weighted_samples.ipynb b/dev/notebooks/auto_examples/linear_model/plot_sgd_weighted_samples.ipynb
index e7da1fde4f6a5..07b5622752ed2 100644
--- a/dev/notebooks/auto_examples/linear_model/plot_sgd_weighted_samples.ipynb
+++ b/dev/notebooks/auto_examples/linear_model/plot_sgd_weighted_samples.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/linear_model/plot_sgdocsvm_vs_ocsvm.ipynb b/dev/notebooks/auto_examples/linear_model/plot_sgdocsvm_vs_ocsvm.ipynb
index ceefe1174cf75..9a9ddd78ef44b 100644
--- a/dev/notebooks/auto_examples/linear_model/plot_sgdocsvm_vs_ocsvm.ipynb
+++ b/dev/notebooks/auto_examples/linear_model/plot_sgdocsvm_vs_ocsvm.ipynb
@@ -68,7 +68,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/linear_model/plot_sparse_logistic_regression_20newsgroups.ipynb b/dev/notebooks/auto_examples/linear_model/plot_sparse_logistic_regression_20newsgroups.ipynb
index 35a82da9587d8..1668916336409 100644
--- a/dev/notebooks/auto_examples/linear_model/plot_sparse_logistic_regression_20newsgroups.ipynb
+++ b/dev/notebooks/auto_examples/linear_model/plot_sparse_logistic_regression_20newsgroups.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/linear_model/plot_sparse_logistic_regression_mnist.ipynb b/dev/notebooks/auto_examples/linear_model/plot_sparse_logistic_regression_mnist.ipynb
index 795fec62b5961..ad13ce0485d27 100644
--- a/dev/notebooks/auto_examples/linear_model/plot_sparse_logistic_regression_mnist.ipynb
+++ b/dev/notebooks/auto_examples/linear_model/plot_sparse_logistic_regression_mnist.ipynb
@@ -15,7 +15,7 @@
       },
       "outputs": [],
       "source": [
-        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\n\nimport time\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nfrom sklearn.datasets import fetch_openml\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.utils import check_random_state\n\n# Turn down for faster convergence\nt0 = time.time()\ntrain_samples = 5000\n\n# Load data from https://www.openml.org/d/554\nX, y = fetch_openml(\"mnist_784\", version=1, return_X_y=True, as_frame=False)\n\nrandom_state = check_random_state(0)\npermutation = random_state.permutation(X.shape[0])\nX = X[permutation]\ny = y[permutation]\nX = X.reshape((X.shape[0], -1))\n\nX_train, X_test, y_train, y_test = train_test_split(\n    X, y, train_size=train_samples, test_size=10000\n)\n\nscaler = StandardScaler()\nX_train = scaler.fit_transform(X_train)\nX_test = scaler.transform(X_test)\n\n# Turn up tolerance for faster convergence\nclf = LogisticRegression(C=50.0 / train_samples, penalty=\"l1\", solver=\"saga\", tol=0.1)\nclf.fit(X_train, y_train)\nsparsity = np.mean(clf.coef_ == 0) * 100\nscore = clf.score(X_test, y_test)\n# print('Best C % .4f' % clf.C_)\nprint(\"Sparsity with L1 penalty: %.2f%%\" % sparsity)\nprint(\"Test score with L1 penalty: %.4f\" % score)\n\ncoef = clf.coef_.copy()\nplt.figure(figsize=(10, 5))\nscale = np.abs(coef).max()\nfor i in range(10):\n    l1_plot = plt.subplot(2, 5, i + 1)\n    l1_plot.imshow(\n        coef[i].reshape(28, 28),\n        interpolation=\"nearest\",\n        cmap=plt.cm.RdBu,\n        vmin=-scale,\n        vmax=scale,\n    )\n    l1_plot.set_xticks(())\n    l1_plot.set_yticks(())\n    l1_plot.set_xlabel(\"Class %i\" % i)\nplt.suptitle(\"Classification vector for...\")\n\nrun_time = time.time() - t0\nprint(\"Example run in %.3f s\" % run_time)\nplt.show()"
+        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\n\nimport time\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nfrom sklearn.datasets import fetch_openml\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.utils import check_random_state\n\n# Turn down for faster convergence\nt0 = time.time()\ntrain_samples = 5000\n\n# Load data from https://www.openml.org/d/554\nX, y = fetch_openml(\"mnist_784\", version=1, return_X_y=True, as_frame=False)\n\nrandom_state = check_random_state(0)\npermutation = random_state.permutation(X.shape[0])\nX = X[permutation]\ny = y[permutation]\nX = X.reshape((X.shape[0], -1))\n\nX_train, X_test, y_train, y_test = train_test_split(\n    X, y, train_size=train_samples, test_size=10000\n)\n\nscaler = StandardScaler()\nX_train = scaler.fit_transform(X_train)\nX_test = scaler.transform(X_test)\n\n# Turn up tolerance for faster convergence\nclf = LogisticRegression(C=50.0 / train_samples, penalty=\"l1\", solver=\"saga\", tol=0.1)\nclf.fit(X_train, y_train)\nsparsity = np.mean(clf.coef_ == 0) * 100\nscore = clf.score(X_test, y_test)\n# print('Best C % .4f' % clf.C_)\nprint(\"Sparsity with L1 penalty: %.2f%%\" % sparsity)\nprint(\"Test score with L1 penalty: %.4f\" % score)\n\ncoef = clf.coef_.copy()\nplt.figure(figsize=(10, 5))\nscale = np.abs(coef).max()\nfor i in range(10):\n    l1_plot = plt.subplot(2, 5, i + 1)\n    l1_plot.imshow(\n        coef[i].reshape(28, 28),\n        interpolation=\"nearest\",\n        cmap=plt.cm.RdBu,\n        vmin=-scale,\n        vmax=scale,\n    )\n    l1_plot.set_xticks(())\n    l1_plot.set_yticks(())\n    l1_plot.set_xlabel(f\"Class {i}\")\nplt.suptitle(\"Classification vector for...\")\n\nrun_time = time.time() - t0\nprint(\"Example run in %.3f s\" % run_time)\nplt.show()"
       ]
     }
   ],
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/linear_model/plot_theilsen.ipynb b/dev/notebooks/auto_examples/linear_model/plot_theilsen.ipynb
index d555e50c1ce25..6b9948ff6ffea 100644
--- a/dev/notebooks/auto_examples/linear_model/plot_theilsen.ipynb
+++ b/dev/notebooks/auto_examples/linear_model/plot_theilsen.ipynb
@@ -71,7 +71,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/linear_model/plot_tweedie_regression_insurance_claims.ipynb b/dev/notebooks/auto_examples/linear_model/plot_tweedie_regression_insurance_claims.ipynb
index 17da54007ac4c..21a0e69289783 100644
--- a/dev/notebooks/auto_examples/linear_model/plot_tweedie_regression_insurance_claims.ipynb
+++ b/dev/notebooks/auto_examples/linear_model/plot_tweedie_regression_insurance_claims.ipynb
@@ -224,7 +224,7 @@
       },
       "outputs": [],
       "source": [
-        "res = []\nfor subset_label, X, df in [\n    (\"train\", X_train, df_train),\n    (\"test\", X_test, df_test),\n]:\n    exposure = df[\"Exposure\"].values\n    res.append(\n        {\n            \"subset\": subset_label,\n            \"observed\": df[\"ClaimAmount\"].values.sum(),\n            \"predicted, frequency*severity model\": np.sum(\n                exposure * glm_freq.predict(X) * glm_sev.predict(X)\n            ),\n            \"predicted, tweedie, power=%.2f\"\n            % glm_pure_premium.power: np.sum(exposure * glm_pure_premium.predict(X)),\n        }\n    )\n\nprint(pd.DataFrame(res).set_index(\"subset\").T)"
+        "res = []\nfor subset_label, X, df in [\n    (\"train\", X_train, df_train),\n    (\"test\", X_test, df_test),\n]:\n    exposure = df[\"Exposure\"].values\n    res.append(\n        {\n            \"subset\": subset_label,\n            \"observed\": df[\"ClaimAmount\"].values.sum(),\n            \"predicted, frequency*severity model\": np.sum(\n                exposure * glm_freq.predict(X) * glm_sev.predict(X)\n            ),\n            \"predicted, tweedie, power=%.2f\" % glm_pure_premium.power: np.sum(\n                exposure * glm_pure_premium.predict(X)\n            ),\n        }\n    )\n\nprint(pd.DataFrame(res).set_index(\"subset\").T)"
       ]
     },
     {
@@ -262,7 +262,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/manifold/plot_compare_methods.ipynb b/dev/notebooks/auto_examples/manifold/plot_compare_methods.ipynb
index a1ce787605acc..7db6453ca1037 100644
--- a/dev/notebooks/auto_examples/manifold/plot_compare_methods.ipynb
+++ b/dev/notebooks/auto_examples/manifold/plot_compare_methods.ipynb
@@ -134,7 +134,7 @@
       },
       "outputs": [],
       "source": [
-        "md_scaling = manifold.MDS(\n    n_components=n_components,\n    max_iter=50,\n    n_init=4,\n    random_state=0,\n    normalized_stress=False,\n)\nS_scaling = md_scaling.fit_transform(S_points)\n\nplot_2d(S_scaling, S_color, \"Multidimensional scaling\")"
+        "md_scaling = manifold.MDS(\n    n_components=n_components,\n    max_iter=50,\n    n_init=1,\n    random_state=0,\n    normalized_stress=False,\n)\nS_scaling = md_scaling.fit_transform(S_points)\n\nplot_2d(S_scaling, S_color, \"Multidimensional scaling\")"
       ]
     },
     {
@@ -190,7 +190,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/manifold/plot_lle_digits.ipynb b/dev/notebooks/auto_examples/manifold/plot_lle_digits.ipynb
index 98aded27adbbd..a92924fa18205 100644
--- a/dev/notebooks/auto_examples/manifold/plot_lle_digits.ipynb
+++ b/dev/notebooks/auto_examples/manifold/plot_lle_digits.ipynb
@@ -87,7 +87,7 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.decomposition import TruncatedSVD\nfrom sklearn.discriminant_analysis import LinearDiscriminantAnalysis\nfrom sklearn.ensemble import RandomTreesEmbedding\nfrom sklearn.manifold import (\n    MDS,\n    TSNE,\n    Isomap,\n    LocallyLinearEmbedding,\n    SpectralEmbedding,\n)\nfrom sklearn.neighbors import NeighborhoodComponentsAnalysis\nfrom sklearn.pipeline import make_pipeline\nfrom sklearn.random_projection import SparseRandomProjection\n\nembeddings = {\n    \"Random projection embedding\": SparseRandomProjection(\n        n_components=2, random_state=42\n    ),\n    \"Truncated SVD embedding\": TruncatedSVD(n_components=2),\n    \"Linear Discriminant Analysis embedding\": LinearDiscriminantAnalysis(\n        n_components=2\n    ),\n    \"Isomap embedding\": Isomap(n_neighbors=n_neighbors, n_components=2),\n    \"Standard LLE embedding\": LocallyLinearEmbedding(\n        n_neighbors=n_neighbors, n_components=2, method=\"standard\"\n    ),\n    \"Modified LLE embedding\": LocallyLinearEmbedding(\n        n_neighbors=n_neighbors, n_components=2, method=\"modified\"\n    ),\n    \"Hessian LLE embedding\": LocallyLinearEmbedding(\n        n_neighbors=n_neighbors, n_components=2, method=\"hessian\"\n    ),\n    \"LTSA LLE embedding\": LocallyLinearEmbedding(\n        n_neighbors=n_neighbors, n_components=2, method=\"ltsa\"\n    ),\n    \"MDS embedding\": MDS(n_components=2, n_init=1, max_iter=120, n_jobs=2),\n    \"Random Trees embedding\": make_pipeline(\n        RandomTreesEmbedding(n_estimators=200, max_depth=5, random_state=0),\n        TruncatedSVD(n_components=2),\n    ),\n    \"Spectral embedding\": SpectralEmbedding(\n        n_components=2, random_state=0, eigen_solver=\"arpack\"\n    ),\n    \"t-SNE embedding\": TSNE(\n        n_components=2,\n        max_iter=500,\n        n_iter_without_progress=150,\n        n_jobs=2,\n        random_state=0,\n    ),\n    \"NCA embedding\": NeighborhoodComponentsAnalysis(\n        n_components=2, init=\"pca\", random_state=0\n    ),\n}"
+        "from sklearn.decomposition import TruncatedSVD\nfrom sklearn.discriminant_analysis import LinearDiscriminantAnalysis\nfrom sklearn.ensemble import RandomTreesEmbedding\nfrom sklearn.manifold import (\n    MDS,\n    TSNE,\n    Isomap,\n    LocallyLinearEmbedding,\n    SpectralEmbedding,\n)\nfrom sklearn.neighbors import NeighborhoodComponentsAnalysis\nfrom sklearn.pipeline import make_pipeline\nfrom sklearn.random_projection import SparseRandomProjection\n\nembeddings = {\n    \"Random projection embedding\": SparseRandomProjection(\n        n_components=2, random_state=42\n    ),\n    \"Truncated SVD embedding\": TruncatedSVD(n_components=2),\n    \"Linear Discriminant Analysis embedding\": LinearDiscriminantAnalysis(\n        n_components=2\n    ),\n    \"Isomap embedding\": Isomap(n_neighbors=n_neighbors, n_components=2),\n    \"Standard LLE embedding\": LocallyLinearEmbedding(\n        n_neighbors=n_neighbors, n_components=2, method=\"standard\"\n    ),\n    \"Modified LLE embedding\": LocallyLinearEmbedding(\n        n_neighbors=n_neighbors, n_components=2, method=\"modified\"\n    ),\n    \"Hessian LLE embedding\": LocallyLinearEmbedding(\n        n_neighbors=n_neighbors, n_components=2, method=\"hessian\"\n    ),\n    \"LTSA LLE embedding\": LocallyLinearEmbedding(\n        n_neighbors=n_neighbors, n_components=2, method=\"ltsa\"\n    ),\n    \"MDS embedding\": MDS(n_components=2, n_init=1, max_iter=120, eps=1e-6),\n    \"Random Trees embedding\": make_pipeline(\n        RandomTreesEmbedding(n_estimators=200, max_depth=5, random_state=0),\n        TruncatedSVD(n_components=2),\n    ),\n    \"Spectral embedding\": SpectralEmbedding(\n        n_components=2, random_state=0, eigen_solver=\"arpack\"\n    ),\n    \"t-SNE embedding\": TSNE(\n        n_components=2,\n        max_iter=500,\n        n_iter_without_progress=150,\n        n_jobs=2,\n        random_state=0,\n    ),\n    \"NCA embedding\": NeighborhoodComponentsAnalysis(\n        n_components=2, init=\"pca\", random_state=0\n    ),\n}"
       ]
     },
     {
@@ -143,7 +143,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/manifold/plot_manifold_sphere.ipynb b/dev/notebooks/auto_examples/manifold/plot_manifold_sphere.ipynb
index 186882bf6ce22..76ceed74f0ef1 100644
--- a/dev/notebooks/auto_examples/manifold/plot_manifold_sphere.ipynb
+++ b/dev/notebooks/auto_examples/manifold/plot_manifold_sphere.ipynb
@@ -15,7 +15,7 @@
       },
       "outputs": [],
       "source": [
-        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\n\nfrom time import time\n\nimport matplotlib.pyplot as plt\n\n# Unused but required import for doing 3d projections with matplotlib < 3.2\nimport mpl_toolkits.mplot3d  # noqa: F401\nimport numpy as np\nfrom matplotlib.ticker import NullFormatter\n\nfrom sklearn import manifold\nfrom sklearn.utils import check_random_state\n\n# Variables for manifold learning.\nn_neighbors = 10\nn_samples = 1000\n\n# Create our sphere.\nrandom_state = check_random_state(0)\np = random_state.rand(n_samples) * (2 * np.pi - 0.55)\nt = random_state.rand(n_samples) * np.pi\n\n# Sever the poles from the sphere.\nindices = (t < (np.pi - (np.pi / 8))) & (t > ((np.pi / 8)))\ncolors = p[indices]\nx, y, z = (\n    np.sin(t[indices]) * np.cos(p[indices]),\n    np.sin(t[indices]) * np.sin(p[indices]),\n    np.cos(t[indices]),\n)\n\n# Plot our dataset.\nfig = plt.figure(figsize=(15, 8))\nplt.suptitle(\n    \"Manifold Learning with %i points, %i neighbors\" % (1000, n_neighbors), fontsize=14\n)\n\nax = fig.add_subplot(251, projection=\"3d\")\nax.scatter(x, y, z, c=p[indices], cmap=plt.cm.rainbow)\nax.view_init(40, -10)\n\nsphere_data = np.array([x, y, z]).T\n\n# Perform Locally Linear Embedding Manifold learning\nmethods = [\"standard\", \"ltsa\", \"hessian\", \"modified\"]\nlabels = [\"LLE\", \"LTSA\", \"Hessian LLE\", \"Modified LLE\"]\n\nfor i, method in enumerate(methods):\n    t0 = time()\n    trans_data = (\n        manifold.LocallyLinearEmbedding(\n            n_neighbors=n_neighbors, n_components=2, method=method, random_state=42\n        )\n        .fit_transform(sphere_data)\n        .T\n    )\n    t1 = time()\n    print(\"%s: %.2g sec\" % (methods[i], t1 - t0))\n\n    ax = fig.add_subplot(252 + i)\n    plt.scatter(trans_data[0], trans_data[1], c=colors, cmap=plt.cm.rainbow)\n    plt.title(\"%s (%.2g sec)\" % (labels[i], t1 - t0))\n    ax.xaxis.set_major_formatter(NullFormatter())\n    ax.yaxis.set_major_formatter(NullFormatter())\n    plt.axis(\"tight\")\n\n# Perform Isomap Manifold learning.\nt0 = time()\ntrans_data = (\n    manifold.Isomap(n_neighbors=n_neighbors, n_components=2)\n    .fit_transform(sphere_data)\n    .T\n)\nt1 = time()\nprint(\"%s: %.2g sec\" % (\"ISO\", t1 - t0))\n\nax = fig.add_subplot(257)\nplt.scatter(trans_data[0], trans_data[1], c=colors, cmap=plt.cm.rainbow)\nplt.title(\"%s (%.2g sec)\" % (\"Isomap\", t1 - t0))\nax.xaxis.set_major_formatter(NullFormatter())\nax.yaxis.set_major_formatter(NullFormatter())\nplt.axis(\"tight\")\n\n# Perform Multi-dimensional scaling.\nt0 = time()\nmds = manifold.MDS(2, max_iter=100, n_init=1, random_state=42)\ntrans_data = mds.fit_transform(sphere_data).T\nt1 = time()\nprint(\"MDS: %.2g sec\" % (t1 - t0))\n\nax = fig.add_subplot(258)\nplt.scatter(trans_data[0], trans_data[1], c=colors, cmap=plt.cm.rainbow)\nplt.title(\"MDS (%.2g sec)\" % (t1 - t0))\nax.xaxis.set_major_formatter(NullFormatter())\nax.yaxis.set_major_formatter(NullFormatter())\nplt.axis(\"tight\")\n\n# Perform Spectral Embedding.\nt0 = time()\nse = manifold.SpectralEmbedding(\n    n_components=2, n_neighbors=n_neighbors, random_state=42\n)\ntrans_data = se.fit_transform(sphere_data).T\nt1 = time()\nprint(\"Spectral Embedding: %.2g sec\" % (t1 - t0))\n\nax = fig.add_subplot(259)\nplt.scatter(trans_data[0], trans_data[1], c=colors, cmap=plt.cm.rainbow)\nplt.title(\"Spectral Embedding (%.2g sec)\" % (t1 - t0))\nax.xaxis.set_major_formatter(NullFormatter())\nax.yaxis.set_major_formatter(NullFormatter())\nplt.axis(\"tight\")\n\n# Perform t-distributed stochastic neighbor embedding.\nt0 = time()\ntsne = manifold.TSNE(n_components=2, random_state=0)\ntrans_data = tsne.fit_transform(sphere_data).T\nt1 = time()\nprint(\"t-SNE: %.2g sec\" % (t1 - t0))\n\nax = fig.add_subplot(2, 5, 10)\nplt.scatter(trans_data[0], trans_data[1], c=colors, cmap=plt.cm.rainbow)\nplt.title(\"t-SNE (%.2g sec)\" % (t1 - t0))\nax.xaxis.set_major_formatter(NullFormatter())\nax.yaxis.set_major_formatter(NullFormatter())\nplt.axis(\"tight\")\n\nplt.show()"
+        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\n\nfrom time import time\n\nimport matplotlib.pyplot as plt\n\n# Unused but required import for doing 3d projections with matplotlib < 3.2\nimport mpl_toolkits.mplot3d  # noqa: F401\nimport numpy as np\nfrom matplotlib.ticker import NullFormatter\n\nfrom sklearn import manifold\nfrom sklearn.utils import check_random_state\n\n# Variables for manifold learning.\nn_neighbors = 10\nn_samples = 1000\n\n# Create our sphere.\nrandom_state = check_random_state(0)\np = random_state.rand(n_samples) * (2 * np.pi - 0.55)\nt = random_state.rand(n_samples) * np.pi\n\n# Sever the poles from the sphere.\nindices = (t < (np.pi - (np.pi / 8))) & (t > (np.pi / 8))\ncolors = p[indices]\nx, y, z = (\n    np.sin(t[indices]) * np.cos(p[indices]),\n    np.sin(t[indices]) * np.sin(p[indices]),\n    np.cos(t[indices]),\n)\n\n# Plot our dataset.\nfig = plt.figure(figsize=(15, 8))\nplt.suptitle(\n    \"Manifold Learning with %i points, %i neighbors\" % (1000, n_neighbors), fontsize=14\n)\n\nax = fig.add_subplot(251, projection=\"3d\")\nax.scatter(x, y, z, c=p[indices], cmap=plt.cm.rainbow)\nax.view_init(40, -10)\n\nsphere_data = np.array([x, y, z]).T\n\n# Perform Locally Linear Embedding Manifold learning\nmethods = [\"standard\", \"ltsa\", \"hessian\", \"modified\"]\nlabels = [\"LLE\", \"LTSA\", \"Hessian LLE\", \"Modified LLE\"]\n\nfor i, method in enumerate(methods):\n    t0 = time()\n    trans_data = (\n        manifold.LocallyLinearEmbedding(\n            n_neighbors=n_neighbors, n_components=2, method=method, random_state=42\n        )\n        .fit_transform(sphere_data)\n        .T\n    )\n    t1 = time()\n    print(\"%s: %.2g sec\" % (methods[i], t1 - t0))\n\n    ax = fig.add_subplot(252 + i)\n    plt.scatter(trans_data[0], trans_data[1], c=colors, cmap=plt.cm.rainbow)\n    plt.title(\"%s (%.2g sec)\" % (labels[i], t1 - t0))\n    ax.xaxis.set_major_formatter(NullFormatter())\n    ax.yaxis.set_major_formatter(NullFormatter())\n    plt.axis(\"tight\")\n\n# Perform Isomap Manifold learning.\nt0 = time()\ntrans_data = (\n    manifold.Isomap(n_neighbors=n_neighbors, n_components=2)\n    .fit_transform(sphere_data)\n    .T\n)\nt1 = time()\nprint(\"%s: %.2g sec\" % (\"ISO\", t1 - t0))\n\nax = fig.add_subplot(257)\nplt.scatter(trans_data[0], trans_data[1], c=colors, cmap=plt.cm.rainbow)\nplt.title(\"%s (%.2g sec)\" % (\"Isomap\", t1 - t0))\nax.xaxis.set_major_formatter(NullFormatter())\nax.yaxis.set_major_formatter(NullFormatter())\nplt.axis(\"tight\")\n\n# Perform Multi-dimensional scaling.\nt0 = time()\nmds = manifold.MDS(2, max_iter=100, n_init=1, random_state=42)\ntrans_data = mds.fit_transform(sphere_data).T\nt1 = time()\nprint(\"MDS: %.2g sec\" % (t1 - t0))\n\nax = fig.add_subplot(258)\nplt.scatter(trans_data[0], trans_data[1], c=colors, cmap=plt.cm.rainbow)\nplt.title(\"MDS (%.2g sec)\" % (t1 - t0))\nax.xaxis.set_major_formatter(NullFormatter())\nax.yaxis.set_major_formatter(NullFormatter())\nplt.axis(\"tight\")\n\n# Perform Spectral Embedding.\nt0 = time()\nse = manifold.SpectralEmbedding(\n    n_components=2, n_neighbors=n_neighbors, random_state=42\n)\ntrans_data = se.fit_transform(sphere_data).T\nt1 = time()\nprint(\"Spectral Embedding: %.2g sec\" % (t1 - t0))\n\nax = fig.add_subplot(259)\nplt.scatter(trans_data[0], trans_data[1], c=colors, cmap=plt.cm.rainbow)\nplt.title(\"Spectral Embedding (%.2g sec)\" % (t1 - t0))\nax.xaxis.set_major_formatter(NullFormatter())\nax.yaxis.set_major_formatter(NullFormatter())\nplt.axis(\"tight\")\n\n# Perform t-distributed stochastic neighbor embedding.\nt0 = time()\ntsne = manifold.TSNE(n_components=2, random_state=0)\ntrans_data = tsne.fit_transform(sphere_data).T\nt1 = time()\nprint(\"t-SNE: %.2g sec\" % (t1 - t0))\n\nax = fig.add_subplot(2, 5, 10)\nplt.scatter(trans_data[0], trans_data[1], c=colors, cmap=plt.cm.rainbow)\nplt.title(\"t-SNE (%.2g sec)\" % (t1 - t0))\nax.xaxis.set_major_formatter(NullFormatter())\nax.yaxis.set_major_formatter(NullFormatter())\nplt.axis(\"tight\")\n\nplt.show()"
       ]
     }
   ],
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/manifold/plot_mds.ipynb b/dev/notebooks/auto_examples/manifold/plot_mds.ipynb
index ddedfb5500ad8..5948f6da8b12d 100644
--- a/dev/notebooks/auto_examples/manifold/plot_mds.ipynb
+++ b/dev/notebooks/auto_examples/manifold/plot_mds.ipynb
@@ -4,7 +4,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# Multi-dimensional scaling\n\nAn illustration of the metric and non-metric MDS on generated noisy data.\n\nThe reconstructed points using the metric MDS and non metric MDS are slightly\nshifted to avoid overlapping.\n"
+        "\n# Multi-dimensional scaling\n\nAn illustration of the metric and non-metric MDS on generated noisy data.\n"
       ]
     },
     {
@@ -15,7 +15,115 @@
       },
       "outputs": [],
       "source": [
-        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\n\nimport numpy as np\nfrom matplotlib import pyplot as plt\nfrom matplotlib.collections import LineCollection\n\nfrom sklearn import manifold\nfrom sklearn.decomposition import PCA\nfrom sklearn.metrics import euclidean_distances\n\n# Generate the data\nEPSILON = np.finfo(np.float32).eps\nn_samples = 20\nrng = np.random.RandomState(seed=3)\nX_true = rng.randint(0, 20, 2 * n_samples).astype(float)\nX_true = X_true.reshape((n_samples, 2))\n\n# Center the data\nX_true -= X_true.mean()\n\n# Compute pairwise Euclidean distances\ndistances = euclidean_distances(X_true)\n\n# Add noise to the distances\nnoise = rng.rand(n_samples, n_samples)\nnoise = noise + noise.T\nnp.fill_diagonal(noise, 0)\ndistances += noise\n\nmds = manifold.MDS(\n    n_components=2,\n    max_iter=3000,\n    eps=1e-9,\n    random_state=42,\n    dissimilarity=\"precomputed\",\n    n_jobs=1,\n)\nX_mds = mds.fit(distances).embedding_\n\nnmds = manifold.MDS(\n    n_components=2,\n    metric=False,\n    max_iter=3000,\n    eps=1e-12,\n    dissimilarity=\"precomputed\",\n    random_state=42,\n    n_jobs=1,\n    n_init=1,\n)\nX_nmds = nmds.fit_transform(distances)\n\n# Rescale the data\nX_mds *= np.sqrt((X_true**2).sum()) / np.sqrt((X_mds**2).sum())\nX_nmds *= np.sqrt((X_true**2).sum()) / np.sqrt((X_nmds**2).sum())\n\n# Rotate the data\npca = PCA(n_components=2)\nX_true = pca.fit_transform(X_true)\nX_mds = pca.fit_transform(X_mds)\nX_nmds = pca.fit_transform(X_nmds)\n\n# Align the sign of PCs\nfor i in [0, 1]:\n    if np.corrcoef(X_mds[:, i], X_true[:, i])[0, 1] < 0:\n        X_mds[:, i] *= -1\n    if np.corrcoef(X_nmds[:, i], X_true[:, i])[0, 1] < 0:\n        X_nmds[:, i] *= -1\n\nfig = plt.figure(1)\nax = plt.axes([0.0, 0.0, 1.0, 1.0])\n\ns = 100\nplt.scatter(X_true[:, 0], X_true[:, 1], color=\"navy\", s=s, lw=0, label=\"True Position\")\nplt.scatter(X_mds[:, 0], X_mds[:, 1], color=\"turquoise\", s=s, lw=0, label=\"MDS\")\nplt.scatter(X_nmds[:, 0], X_nmds[:, 1], color=\"darkorange\", s=s, lw=0, label=\"NMDS\")\nplt.legend(scatterpoints=1, loc=\"best\", shadow=False)\n\n# Plot the edges\nstart_idx, end_idx = np.where(X_mds)\n# a sequence of (*line0*, *line1*, *line2*), where::\n#            linen = (x0, y0), (x1, y1), ... (xm, ym)\nsegments = [\n    [X_true[i, :], X_true[j, :]] for i in range(len(X_true)) for j in range(len(X_true))\n]\nedges = distances.max() / (distances + EPSILON) * 100\nnp.fill_diagonal(edges, 0)\nedges = np.abs(edges)\nlc = LineCollection(\n    segments, zorder=0, cmap=plt.cm.Blues, norm=plt.Normalize(0, edges.max())\n)\nlc.set_array(edges.flatten())\nlc.set_linewidths(np.full(len(segments), 0.5))\nax.add_collection(lc)\n\nplt.show()"
+        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Dataset preparation\n\nWe start by uniformly generating 20 points in a 2D space.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\nfrom matplotlib import pyplot as plt\nfrom matplotlib.collections import LineCollection\n\nfrom sklearn import manifold\nfrom sklearn.decomposition import PCA\nfrom sklearn.metrics import euclidean_distances\n\n# Generate the data\nEPSILON = np.finfo(np.float32).eps\nn_samples = 20\nrng = np.random.RandomState(seed=3)\nX_true = rng.randint(0, 20, 2 * n_samples).astype(float)\nX_true = X_true.reshape((n_samples, 2))\n\n# Center the data\nX_true -= X_true.mean()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Now we compute pairwise distances between all points and add\na small amount of noise to the distance matrix. We make sure\nto keep the noisy distance matrix symmetric.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# Compute pairwise Euclidean distances\ndistances = euclidean_distances(X_true)\n\n# Add noise to the distances\nnoise = rng.rand(n_samples, n_samples)\nnoise = noise + noise.T\nnp.fill_diagonal(noise, 0)\ndistances += noise"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Here we compute metric and non-metric MDS of the noisy distance matrix.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "mds = manifold.MDS(\n    n_components=2,\n    max_iter=3000,\n    eps=1e-9,\n    n_init=1,\n    random_state=42,\n    dissimilarity=\"precomputed\",\n    n_jobs=1,\n)\nX_mds = mds.fit(distances).embedding_\n\nnmds = manifold.MDS(\n    n_components=2,\n    metric=False,\n    max_iter=3000,\n    eps=1e-12,\n    dissimilarity=\"precomputed\",\n    random_state=42,\n    n_jobs=1,\n    n_init=1,\n)\nX_nmds = nmds.fit_transform(distances)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Rescaling the non-metric MDS solution to match the spread of the original data.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "X_nmds *= np.sqrt((X_true**2).sum()) / np.sqrt((X_nmds**2).sum())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "To make the visual comparisons easier, we rotate the original data and both MDS\nsolutions to their PCA axes. And flip horizontal and vertical MDS axes, if needed,\nto match the original data orientation.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# Rotate the data\npca = PCA(n_components=2)\nX_true = pca.fit_transform(X_true)\nX_mds = pca.fit_transform(X_mds)\nX_nmds = pca.fit_transform(X_nmds)\n\n# Align the sign of PCs\nfor i in [0, 1]:\n    if np.corrcoef(X_mds[:, i], X_true[:, i])[0, 1] < 0:\n        X_mds[:, i] *= -1\n    if np.corrcoef(X_nmds[:, i], X_true[:, i])[0, 1] < 0:\n        X_nmds[:, i] *= -1"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Finally, we plot the original data and both MDS reconstructions.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "fig = plt.figure(1)\nax = plt.axes([0.0, 0.0, 1.0, 1.0])\n\ns = 100\nplt.scatter(X_true[:, 0], X_true[:, 1], color=\"navy\", s=s, lw=0, label=\"True Position\")\nplt.scatter(X_mds[:, 0], X_mds[:, 1], color=\"turquoise\", s=s, lw=0, label=\"MDS\")\nplt.scatter(X_nmds[:, 0], X_nmds[:, 1], color=\"darkorange\", s=s, lw=0, label=\"NMDS\")\nplt.legend(scatterpoints=1, loc=\"best\", shadow=False)\n\n# Plot the edges\nstart_idx, end_idx = X_mds.nonzero()\n# a sequence of (*line0*, *line1*, *line2*), where::\n#            linen = (x0, y0), (x1, y1), ... (xm, ym)\nsegments = [\n    [X_true[i, :], X_true[j, :]] for i in range(len(X_true)) for j in range(len(X_true))\n]\nedges = distances.max() / (distances + EPSILON) * 100\nnp.fill_diagonal(edges, 0)\nedges = np.abs(edges)\nlc = LineCollection(\n    segments, zorder=0, cmap=plt.cm.Blues, norm=plt.Normalize(0, edges.max())\n)\nlc.set_array(edges.flatten())\nlc.set_linewidths(np.full(len(segments), 0.5))\nax.add_collection(lc)\n\nplt.show()"
       ]
     }
   ],
@@ -35,7 +143,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/manifold/plot_swissroll.ipynb b/dev/notebooks/auto_examples/manifold/plot_swissroll.ipynb
index 846fbfdf69043..966d5f7521a3a 100644
--- a/dev/notebooks/auto_examples/manifold/plot_swissroll.ipynb
+++ b/dev/notebooks/auto_examples/manifold/plot_swissroll.ipynb
@@ -139,7 +139,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/manifold/plot_t_sne_perplexity.ipynb b/dev/notebooks/auto_examples/manifold/plot_t_sne_perplexity.ipynb
index 3f8e85c923875..2712008f43ec2 100644
--- a/dev/notebooks/auto_examples/manifold/plot_t_sne_perplexity.ipynb
+++ b/dev/notebooks/auto_examples/manifold/plot_t_sne_perplexity.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/miscellaneous/plot_anomaly_comparison.ipynb b/dev/notebooks/auto_examples/miscellaneous/plot_anomaly_comparison.ipynb
index c257f2352a5bd..1385f8ca3b425 100644
--- a/dev/notebooks/auto_examples/miscellaneous/plot_anomaly_comparison.ipynb
+++ b/dev/notebooks/auto_examples/miscellaneous/plot_anomaly_comparison.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/miscellaneous/plot_display_object_visualization.ipynb b/dev/notebooks/auto_examples/miscellaneous/plot_display_object_visualization.ipynb
index c1af0f86e2907..00154a78a131d 100644
--- a/dev/notebooks/auto_examples/miscellaneous/plot_display_object_visualization.ipynb
+++ b/dev/notebooks/auto_examples/miscellaneous/plot_display_object_visualization.ipynb
@@ -125,7 +125,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/miscellaneous/plot_estimator_representation.ipynb b/dev/notebooks/auto_examples/miscellaneous/plot_estimator_representation.ipynb
index 156396230a830..0d59d60a4ad0e 100644
--- a/dev/notebooks/auto_examples/miscellaneous/plot_estimator_representation.ipynb
+++ b/dev/notebooks/auto_examples/miscellaneous/plot_estimator_representation.ipynb
@@ -71,7 +71,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/miscellaneous/plot_isotonic_regression.ipynb b/dev/notebooks/auto_examples/miscellaneous/plot_isotonic_regression.ipynb
index 3b2fdf0d95a20..abf49586e2a9f 100644
--- a/dev/notebooks/auto_examples/miscellaneous/plot_isotonic_regression.ipynb
+++ b/dev/notebooks/auto_examples/miscellaneous/plot_isotonic_regression.ipynb
@@ -78,7 +78,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/miscellaneous/plot_johnson_lindenstrauss_bound.ipynb b/dev/notebooks/auto_examples/miscellaneous/plot_johnson_lindenstrauss_bound.ipynb
index 7e2a9e8c5def1..9e52e14c3c0df 100644
--- a/dev/notebooks/auto_examples/miscellaneous/plot_johnson_lindenstrauss_bound.ipynb
+++ b/dev/notebooks/auto_examples/miscellaneous/plot_johnson_lindenstrauss_bound.ipynb
@@ -114,7 +114,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/miscellaneous/plot_kernel_approximation.ipynb b/dev/notebooks/auto_examples/miscellaneous/plot_kernel_approximation.ipynb
index f6fc72a337896..81439217c92ff 100644
--- a/dev/notebooks/auto_examples/miscellaneous/plot_kernel_approximation.ipynb
+++ b/dev/notebooks/auto_examples/miscellaneous/plot_kernel_approximation.ipynb
@@ -78,7 +78,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/miscellaneous/plot_kernel_ridge_regression.ipynb b/dev/notebooks/auto_examples/miscellaneous/plot_kernel_ridge_regression.ipynb
index 4ad0631ea3c04..312e9d160e8df 100644
--- a/dev/notebooks/auto_examples/miscellaneous/plot_kernel_ridge_regression.ipynb
+++ b/dev/notebooks/auto_examples/miscellaneous/plot_kernel_ridge_regression.ipynb
@@ -153,7 +153,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/miscellaneous/plot_metadata_routing.ipynb b/dev/notebooks/auto_examples/miscellaneous/plot_metadata_routing.ipynb
index 9fa5ed0dde4af..45f2b4f7599b4 100644
--- a/dev/notebooks/auto_examples/miscellaneous/plot_metadata_routing.ipynb
+++ b/dev/notebooks/auto_examples/miscellaneous/plot_metadata_routing.ipynb
@@ -668,7 +668,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/miscellaneous/plot_multilabel.ipynb b/dev/notebooks/auto_examples/miscellaneous/plot_multilabel.ipynb
index d4627b6266cc3..a091d89926663 100644
--- a/dev/notebooks/auto_examples/miscellaneous/plot_multilabel.ipynb
+++ b/dev/notebooks/auto_examples/miscellaneous/plot_multilabel.ipynb
@@ -15,7 +15,7 @@
       },
       "outputs": [],
       "source": [
-        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nfrom sklearn.cross_decomposition import CCA\nfrom sklearn.datasets import make_multilabel_classification\nfrom sklearn.decomposition import PCA\nfrom sklearn.multiclass import OneVsRestClassifier\nfrom sklearn.svm import SVC\n\n\ndef plot_hyperplane(clf, min_x, max_x, linestyle, label):\n    # get the separating hyperplane\n    w = clf.coef_[0]\n    a = -w[0] / w[1]\n    xx = np.linspace(min_x - 5, max_x + 5)  # make sure the line is long enough\n    yy = a * xx - (clf.intercept_[0]) / w[1]\n    plt.plot(xx, yy, linestyle, label=label)\n\n\ndef plot_subfigure(X, Y, subplot, title, transform):\n    if transform == \"pca\":\n        X = PCA(n_components=2).fit_transform(X)\n    elif transform == \"cca\":\n        X = CCA(n_components=2).fit(X, Y).transform(X)\n    else:\n        raise ValueError\n\n    min_x = np.min(X[:, 0])\n    max_x = np.max(X[:, 0])\n\n    min_y = np.min(X[:, 1])\n    max_y = np.max(X[:, 1])\n\n    classif = OneVsRestClassifier(SVC(kernel=\"linear\"))\n    classif.fit(X, Y)\n\n    plt.subplot(2, 2, subplot)\n    plt.title(title)\n\n    zero_class = np.where(Y[:, 0])\n    one_class = np.where(Y[:, 1])\n    plt.scatter(X[:, 0], X[:, 1], s=40, c=\"gray\", edgecolors=(0, 0, 0))\n    plt.scatter(\n        X[zero_class, 0],\n        X[zero_class, 1],\n        s=160,\n        edgecolors=\"b\",\n        facecolors=\"none\",\n        linewidths=2,\n        label=\"Class 1\",\n    )\n    plt.scatter(\n        X[one_class, 0],\n        X[one_class, 1],\n        s=80,\n        edgecolors=\"orange\",\n        facecolors=\"none\",\n        linewidths=2,\n        label=\"Class 2\",\n    )\n\n    plot_hyperplane(\n        classif.estimators_[0], min_x, max_x, \"k--\", \"Boundary\\nfor class 1\"\n    )\n    plot_hyperplane(\n        classif.estimators_[1], min_x, max_x, \"k-.\", \"Boundary\\nfor class 2\"\n    )\n    plt.xticks(())\n    plt.yticks(())\n\n    plt.xlim(min_x - 0.5 * max_x, max_x + 0.5 * max_x)\n    plt.ylim(min_y - 0.5 * max_y, max_y + 0.5 * max_y)\n    if subplot == 2:\n        plt.xlabel(\"First principal component\")\n        plt.ylabel(\"Second principal component\")\n        plt.legend(loc=\"upper left\")\n\n\nplt.figure(figsize=(8, 6))\n\nX, Y = make_multilabel_classification(\n    n_classes=2, n_labels=1, allow_unlabeled=True, random_state=1\n)\n\nplot_subfigure(X, Y, 1, \"With unlabeled samples + CCA\", \"cca\")\nplot_subfigure(X, Y, 2, \"With unlabeled samples + PCA\", \"pca\")\n\nX, Y = make_multilabel_classification(\n    n_classes=2, n_labels=1, allow_unlabeled=False, random_state=1\n)\n\nplot_subfigure(X, Y, 3, \"Without unlabeled samples + CCA\", \"cca\")\nplot_subfigure(X, Y, 4, \"Without unlabeled samples + PCA\", \"pca\")\n\nplt.subplots_adjust(0.04, 0.02, 0.97, 0.94, 0.09, 0.2)\nplt.show()"
+        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nfrom sklearn.cross_decomposition import CCA\nfrom sklearn.datasets import make_multilabel_classification\nfrom sklearn.decomposition import PCA\nfrom sklearn.multiclass import OneVsRestClassifier\nfrom sklearn.svm import SVC\n\n\ndef plot_hyperplane(clf, min_x, max_x, linestyle, label):\n    # get the separating hyperplane\n    w = clf.coef_[0]\n    a = -w[0] / w[1]\n    xx = np.linspace(min_x - 5, max_x + 5)  # make sure the line is long enough\n    yy = a * xx - (clf.intercept_[0]) / w[1]\n    plt.plot(xx, yy, linestyle, label=label)\n\n\ndef plot_subfigure(X, Y, subplot, title, transform):\n    if transform == \"pca\":\n        X = PCA(n_components=2).fit_transform(X)\n    elif transform == \"cca\":\n        X = CCA(n_components=2).fit(X, Y).transform(X)\n    else:\n        raise ValueError\n\n    min_x = np.min(X[:, 0])\n    max_x = np.max(X[:, 0])\n\n    min_y = np.min(X[:, 1])\n    max_y = np.max(X[:, 1])\n\n    classif = OneVsRestClassifier(SVC(kernel=\"linear\"))\n    classif.fit(X, Y)\n\n    plt.subplot(2, 2, subplot)\n    plt.title(title)\n\n    zero_class = (Y[:, 0]).nonzero()\n    one_class = (Y[:, 1]).nonzero()\n    plt.scatter(X[:, 0], X[:, 1], s=40, c=\"gray\", edgecolors=(0, 0, 0))\n    plt.scatter(\n        X[zero_class, 0],\n        X[zero_class, 1],\n        s=160,\n        edgecolors=\"b\",\n        facecolors=\"none\",\n        linewidths=2,\n        label=\"Class 1\",\n    )\n    plt.scatter(\n        X[one_class, 0],\n        X[one_class, 1],\n        s=80,\n        edgecolors=\"orange\",\n        facecolors=\"none\",\n        linewidths=2,\n        label=\"Class 2\",\n    )\n\n    plot_hyperplane(\n        classif.estimators_[0], min_x, max_x, \"k--\", \"Boundary\\nfor class 1\"\n    )\n    plot_hyperplane(\n        classif.estimators_[1], min_x, max_x, \"k-.\", \"Boundary\\nfor class 2\"\n    )\n    plt.xticks(())\n    plt.yticks(())\n\n    plt.xlim(min_x - 0.5 * max_x, max_x + 0.5 * max_x)\n    plt.ylim(min_y - 0.5 * max_y, max_y + 0.5 * max_y)\n    if subplot == 2:\n        plt.xlabel(\"First principal component\")\n        plt.ylabel(\"Second principal component\")\n        plt.legend(loc=\"upper left\")\n\n\nplt.figure(figsize=(8, 6))\n\nX, Y = make_multilabel_classification(\n    n_classes=2, n_labels=1, allow_unlabeled=True, random_state=1\n)\n\nplot_subfigure(X, Y, 1, \"With unlabeled samples + CCA\", \"cca\")\nplot_subfigure(X, Y, 2, \"With unlabeled samples + PCA\", \"pca\")\n\nX, Y = make_multilabel_classification(\n    n_classes=2, n_labels=1, allow_unlabeled=False, random_state=1\n)\n\nplot_subfigure(X, Y, 3, \"Without unlabeled samples + CCA\", \"cca\")\nplot_subfigure(X, Y, 4, \"Without unlabeled samples + PCA\", \"pca\")\n\nplt.subplots_adjust(0.04, 0.02, 0.97, 0.94, 0.09, 0.2)\nplt.show()"
       ]
     }
   ],
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/miscellaneous/plot_multioutput_face_completion.ipynb b/dev/notebooks/auto_examples/miscellaneous/plot_multioutput_face_completion.ipynb
index a7d229bbd48be..f0f447ce8ee21 100644
--- a/dev/notebooks/auto_examples/miscellaneous/plot_multioutput_face_completion.ipynb
+++ b/dev/notebooks/auto_examples/miscellaneous/plot_multioutput_face_completion.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/miscellaneous/plot_outlier_detection_bench.ipynb b/dev/notebooks/auto_examples/miscellaneous/plot_outlier_detection_bench.ipynb
index 918d15d17932d..ec67a01ea06e5 100644
--- a/dev/notebooks/auto_examples/miscellaneous/plot_outlier_detection_bench.ipynb
+++ b/dev/notebooks/auto_examples/miscellaneous/plot_outlier_detection_bench.ipynb
@@ -51,7 +51,7 @@
       },
       "outputs": [],
       "source": [
-        "from time import perf_counter\n\n\ndef fit_predict(estimator, X):\n    tic = perf_counter()\n    if estimator[-1].__class__.__name__ == \"LocalOutlierFactor\":\n        estimator.fit(X)\n        y_pred = estimator[-1].negative_outlier_factor_\n    else:  # \"IsolationForest\"\n        y_pred = estimator.fit(X).decision_function(X)\n    toc = perf_counter()\n    print(f\"Duration for {model_name}: {toc - tic:.2f} s\")\n    return y_pred"
+        "from time import perf_counter\n\n\ndef fit_predict(estimator, X):\n    tic = perf_counter()\n    if estimator[-1].__class__.__name__ == \"LocalOutlierFactor\":\n        estimator.fit(X)\n        y_score = estimator[-1].negative_outlier_factor_\n    else:  # \"IsolationForest\"\n        y_score = estimator.fit(X).decision_function(X)\n    toc = perf_counter()\n    print(f\"Duration for {model_name}: {toc - tic:.2f} s\")\n    return y_score"
       ]
     },
     {
@@ -87,7 +87,7 @@
       },
       "outputs": [],
       "source": [
-        "y_true = {}\ny_pred = {\"LOF\": {}, \"IForest\": {}}\nmodel_names = [\"LOF\", \"IForest\"]\ncat_columns = [\"protocol_type\", \"service\", \"flag\"]\n\ny_true[\"KDDCup99 - SA\"] = y\nfor model_name in model_names:\n    model = make_estimator(\n        name=model_name,\n        categorical_columns=cat_columns,\n        lof_kw={\"n_neighbors\": int(n_samples * anomaly_frac)},\n        iforest_kw={\"random_state\": 42},\n    )\n    y_pred[model_name][\"KDDCup99 - SA\"] = fit_predict(model, X)"
+        "y_true = {}\ny_score = {\"LOF\": {}, \"IForest\": {}}\nmodel_names = [\"LOF\", \"IForest\"]\ncat_columns = [\"protocol_type\", \"service\", \"flag\"]\n\ny_true[\"KDDCup99 - SA\"] = y\nfor model_name in model_names:\n    model = make_estimator(\n        name=model_name,\n        categorical_columns=cat_columns,\n        lof_kw={\"n_neighbors\": int(n_samples * anomaly_frac)},\n        iforest_kw={\"random_state\": 42},\n    )\n    y_score[model_name][\"KDDCup99 - SA\"] = fit_predict(model, X)"
       ]
     },
     {
@@ -116,7 +116,7 @@
       },
       "outputs": [],
       "source": [
-        "y_true[\"forestcover\"] = y\nfor model_name in model_names:\n    model = make_estimator(\n        name=model_name,\n        lof_kw={\"n_neighbors\": int(n_samples * anomaly_frac)},\n        iforest_kw={\"random_state\": 42},\n    )\n    y_pred[model_name][\"forestcover\"] = fit_predict(model, X)"
+        "y_true[\"forestcover\"] = y\nfor model_name in model_names:\n    model = make_estimator(\n        name=model_name,\n        lof_kw={\"n_neighbors\": int(n_samples * anomaly_frac)},\n        iforest_kw={\"random_state\": 42},\n    )\n    y_score[model_name][\"forestcover\"] = fit_predict(model, X)"
       ]
     },
     {
@@ -163,7 +163,7 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.compose import make_column_selector as selector\n\ncategorical_columns_selector = selector(dtype_include=\"category\")\ncat_columns = categorical_columns_selector(X)\n\ny_true[\"ames_housing\"] = y\nfor model_name in model_names:\n    model = make_estimator(\n        name=model_name,\n        categorical_columns=cat_columns,\n        lof_kw={\"n_neighbors\": int(n_samples * anomaly_frac)},\n        iforest_kw={\"random_state\": 42},\n    )\n    y_pred[model_name][\"ames_housing\"] = fit_predict(model, X)"
+        "from sklearn.compose import make_column_selector as selector\n\ncategorical_columns_selector = selector(dtype_include=\"category\")\ncat_columns = categorical_columns_selector(X)\n\ny_true[\"ames_housing\"] = y\nfor model_name in model_names:\n    model = make_estimator(\n        name=model_name,\n        categorical_columns=cat_columns,\n        lof_kw={\"n_neighbors\": int(n_samples * anomaly_frac)},\n        iforest_kw={\"random_state\": 42},\n    )\n    y_score[model_name][\"ames_housing\"] = fit_predict(model, X)"
       ]
     },
     {
@@ -192,7 +192,7 @@
       },
       "outputs": [],
       "source": [
-        "y_true[\"cardiotocography\"] = y\nfor model_name in model_names:\n    model = make_estimator(\n        name=model_name,\n        lof_kw={\"n_neighbors\": int(n_samples * anomaly_frac)},\n        iforest_kw={\"random_state\": 42},\n    )\n    y_pred[model_name][\"cardiotocography\"] = fit_predict(model, X)"
+        "y_true[\"cardiotocography\"] = y\nfor model_name in model_names:\n    model = make_estimator(\n        name=model_name,\n        lof_kw={\"n_neighbors\": int(n_samples * anomaly_frac)},\n        iforest_kw={\"random_state\": 42},\n    )\n    y_score[model_name][\"cardiotocography\"] = fit_predict(model, X)"
       ]
     },
     {
@@ -210,7 +210,7 @@
       },
       "outputs": [],
       "source": [
-        "import math\n\nfrom sklearn.metrics import RocCurveDisplay\n\ncols = 2\npos_label = 0  # mean 0 belongs to positive class\ndatasets_names = y_true.keys()\nrows = math.ceil(len(datasets_names) / cols)\n\nfig, axs = plt.subplots(nrows=rows, ncols=cols, squeeze=False, figsize=(10, rows * 4))\n\nfor ax, dataset_name in zip(axs.ravel(), datasets_names):\n    for model_idx, model_name in enumerate(model_names):\n        display = RocCurveDisplay.from_predictions(\n            y_true[dataset_name],\n            y_pred[model_name][dataset_name],\n            pos_label=pos_label,\n            name=model_name,\n            ax=ax,\n            plot_chance_level=(model_idx == len(model_names) - 1),\n            chance_level_kw={\"linestyle\": \":\"},\n        )\n    ax.set_title(dataset_name)\n_ = plt.tight_layout(pad=2.0)  # spacing between subplots"
+        "import math\n\nfrom sklearn.metrics import RocCurveDisplay\n\ncols = 2\npos_label = 0  # mean 0 belongs to positive class\ndatasets_names = y_true.keys()\nrows = math.ceil(len(datasets_names) / cols)\n\nfig, axs = plt.subplots(nrows=rows, ncols=cols, squeeze=False, figsize=(10, rows * 4))\n\nfor ax, dataset_name in zip(axs.ravel(), datasets_names):\n    for model_idx, model_name in enumerate(model_names):\n        display = RocCurveDisplay.from_predictions(\n            y_true[dataset_name],\n            y_score[model_name][dataset_name],\n            pos_label=pos_label,\n            name=model_name,\n            ax=ax,\n            plot_chance_level=(model_idx == len(model_names) - 1),\n            chance_level_kw={\"linestyle\": \":\"},\n        )\n    ax.set_title(dataset_name)\n_ = plt.tight_layout(pad=2.0)  # spacing between subplots"
       ]
     },
     {
@@ -228,7 +228,7 @@
       },
       "outputs": [],
       "source": [
-        "X = X_forestcover\ny = y_true[\"forestcover\"]\n\nn_samples = X.shape[0]\nn_neighbors_list = (n_samples * np.array([0.2, 0.02, 0.01, 0.001])).astype(np.int32)\nmodel = make_pipeline(RobustScaler(), LocalOutlierFactor())\n\nlinestyles = [\"solid\", \"dashed\", \"dashdot\", \":\", (5, (10, 3))]\n\nfig, ax = plt.subplots()\nfor model_idx, (linestyle, n_neighbors) in enumerate(zip(linestyles, n_neighbors_list)):\n    model.set_params(localoutlierfactor__n_neighbors=n_neighbors)\n    model.fit(X)\n    y_pred = model[-1].negative_outlier_factor_\n    display = RocCurveDisplay.from_predictions(\n        y,\n        y_pred,\n        pos_label=pos_label,\n        name=f\"n_neighbors = {n_neighbors}\",\n        ax=ax,\n        plot_chance_level=(model_idx == len(n_neighbors_list) - 1),\n        chance_level_kw={\"linestyle\": (0, (1, 10))},\n        linestyle=linestyle,\n        linewidth=2,\n    )\n_ = ax.set_title(\"RobustScaler with varying n_neighbors\\non forestcover dataset\")"
+        "X = X_forestcover\ny = y_true[\"forestcover\"]\n\nn_samples = X.shape[0]\nn_neighbors_list = (n_samples * np.array([0.2, 0.02, 0.01, 0.001])).astype(np.int32)\nmodel = make_pipeline(RobustScaler(), LocalOutlierFactor())\n\nlinestyles = [\"solid\", \"dashed\", \"dashdot\", \":\", (5, (10, 3))]\n\nfig, ax = plt.subplots()\nfor model_idx, (linestyle, n_neighbors) in enumerate(zip(linestyles, n_neighbors_list)):\n    model.set_params(localoutlierfactor__n_neighbors=n_neighbors)\n    model.fit(X)\n    y_score = model[-1].negative_outlier_factor_\n    display = RocCurveDisplay.from_predictions(\n        y,\n        y_score,\n        pos_label=pos_label,\n        name=f\"n_neighbors = {n_neighbors}\",\n        ax=ax,\n        plot_chance_level=(model_idx == len(n_neighbors_list) - 1),\n        chance_level_kw={\"linestyle\": (0, (1, 10))},\n        linestyle=linestyle,\n        linewidth=2,\n    )\n_ = ax.set_title(\"RobustScaler with varying n_neighbors\\non forestcover dataset\")"
       ]
     },
     {
@@ -246,7 +246,7 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.preprocessing import MinMaxScaler, SplineTransformer, StandardScaler\n\npreprocessor_list = [\n    None,\n    RobustScaler(),\n    StandardScaler(),\n    MinMaxScaler(),\n    SplineTransformer(),\n]\nexpected_anomaly_fraction = 0.02\nlof = LocalOutlierFactor(n_neighbors=int(n_samples * expected_anomaly_fraction))\n\nfig, ax = plt.subplots()\nfor model_idx, (linestyle, preprocessor) in enumerate(\n    zip(linestyles, preprocessor_list)\n):\n    model = make_pipeline(preprocessor, lof)\n    model.fit(X)\n    y_pred = model[-1].negative_outlier_factor_\n    display = RocCurveDisplay.from_predictions(\n        y,\n        y_pred,\n        pos_label=pos_label,\n        name=str(preprocessor).split(\"(\")[0],\n        ax=ax,\n        plot_chance_level=(model_idx == len(preprocessor_list) - 1),\n        chance_level_kw={\"linestyle\": (0, (1, 10))},\n        linestyle=linestyle,\n        linewidth=2,\n    )\n_ = ax.set_title(\"Fixed n_neighbors with varying preprocessing\\non forestcover dataset\")"
+        "from sklearn.preprocessing import MinMaxScaler, SplineTransformer, StandardScaler\n\npreprocessor_list = [\n    None,\n    RobustScaler(),\n    StandardScaler(),\n    MinMaxScaler(),\n    SplineTransformer(),\n]\nexpected_anomaly_fraction = 0.02\nlof = LocalOutlierFactor(n_neighbors=int(n_samples * expected_anomaly_fraction))\n\nfig, ax = plt.subplots()\nfor model_idx, (linestyle, preprocessor) in enumerate(\n    zip(linestyles, preprocessor_list)\n):\n    model = make_pipeline(preprocessor, lof)\n    model.fit(X)\n    y_score = model[-1].negative_outlier_factor_\n    display = RocCurveDisplay.from_predictions(\n        y,\n        y_score,\n        pos_label=pos_label,\n        name=str(preprocessor).split(\"(\")[0],\n        ax=ax,\n        plot_chance_level=(model_idx == len(preprocessor_list) - 1),\n        chance_level_kw={\"linestyle\": (0, (1, 10))},\n        linestyle=linestyle,\n        linewidth=2,\n    )\n_ = ax.set_title(\"Fixed n_neighbors with varying preprocessing\\non forestcover dataset\")"
       ]
     },
     {
@@ -264,7 +264,7 @@
       },
       "outputs": [],
       "source": [
-        "X = X_cardiotocography\ny = y_true[\"cardiotocography\"]\n\nn_samples, expected_anomaly_fraction = X.shape[0], 0.025\nlof = LocalOutlierFactor(n_neighbors=int(n_samples * expected_anomaly_fraction))\n\nfig, ax = plt.subplots()\nfor model_idx, (linestyle, preprocessor) in enumerate(\n    zip(linestyles, preprocessor_list)\n):\n    model = make_pipeline(preprocessor, lof)\n    model.fit(X)\n    y_pred = model[-1].negative_outlier_factor_\n    display = RocCurveDisplay.from_predictions(\n        y,\n        y_pred,\n        pos_label=pos_label,\n        name=str(preprocessor).split(\"(\")[0],\n        ax=ax,\n        plot_chance_level=(model_idx == len(preprocessor_list) - 1),\n        chance_level_kw={\"linestyle\": (0, (1, 10))},\n        linestyle=linestyle,\n        linewidth=2,\n    )\nax.set_title(\n    \"Fixed n_neighbors with varying preprocessing\\non cardiotocography dataset\"\n)\nplt.show()"
+        "X = X_cardiotocography\ny = y_true[\"cardiotocography\"]\n\nn_samples, expected_anomaly_fraction = X.shape[0], 0.025\nlof = LocalOutlierFactor(n_neighbors=int(n_samples * expected_anomaly_fraction))\n\nfig, ax = plt.subplots()\nfor model_idx, (linestyle, preprocessor) in enumerate(\n    zip(linestyles, preprocessor_list)\n):\n    model = make_pipeline(preprocessor, lof)\n    model.fit(X)\n    y_score = model[-1].negative_outlier_factor_\n    display = RocCurveDisplay.from_predictions(\n        y,\n        y_score,\n        pos_label=pos_label,\n        name=str(preprocessor).split(\"(\")[0],\n        ax=ax,\n        plot_chance_level=(model_idx == len(preprocessor_list) - 1),\n        chance_level_kw={\"linestyle\": (0, (1, 10))},\n        linestyle=linestyle,\n        linewidth=2,\n    )\nax.set_title(\n    \"Fixed n_neighbors with varying preprocessing\\non cardiotocography dataset\"\n)\nplt.show()"
       ]
     }
   ],
@@ -284,7 +284,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/miscellaneous/plot_partial_dependence_visualization_api.ipynb b/dev/notebooks/auto_examples/miscellaneous/plot_partial_dependence_visualization_api.ipynb
index 05f2cd14fe731..35da6fce335c5 100644
--- a/dev/notebooks/auto_examples/miscellaneous/plot_partial_dependence_visualization_api.ipynb
+++ b/dev/notebooks/auto_examples/miscellaneous/plot_partial_dependence_visualization_api.ipynb
@@ -161,7 +161,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/miscellaneous/plot_pipeline_display.ipynb b/dev/notebooks/auto_examples/miscellaneous/plot_pipeline_display.ipynb
index 59ccaeca64b52..865e7a35d4147 100644
--- a/dev/notebooks/auto_examples/miscellaneous/plot_pipeline_display.ipynb
+++ b/dev/notebooks/auto_examples/miscellaneous/plot_pipeline_display.ipynb
@@ -179,7 +179,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/miscellaneous/plot_roc_curve_visualization_api.ipynb b/dev/notebooks/auto_examples/miscellaneous/plot_roc_curve_visualization_api.ipynb
index 7ff61ff63b378..dc11c2aa6b1fc 100644
--- a/dev/notebooks/auto_examples/miscellaneous/plot_roc_curve_visualization_api.ipynb
+++ b/dev/notebooks/auto_examples/miscellaneous/plot_roc_curve_visualization_api.ipynb
@@ -89,7 +89,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/miscellaneous/plot_set_output.ipynb b/dev/notebooks/auto_examples/miscellaneous/plot_set_output.ipynb
index e16a49b35f817..98acd5b57c71d 100644
--- a/dev/notebooks/auto_examples/miscellaneous/plot_set_output.ipynb
+++ b/dev/notebooks/auto_examples/miscellaneous/plot_set_output.ipynb
@@ -269,7 +269,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/mixture/plot_concentration_prior.ipynb b/dev/notebooks/auto_examples/mixture/plot_concentration_prior.ipynb
index 8e102474086fa..fdcc32d774844 100644
--- a/dev/notebooks/auto_examples/mixture/plot_concentration_prior.ipynb
+++ b/dev/notebooks/auto_examples/mixture/plot_concentration_prior.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/mixture/plot_gmm.ipynb b/dev/notebooks/auto_examples/mixture/plot_gmm.ipynb
index 41bbb0183152a..ae0a34db28e39 100644
--- a/dev/notebooks/auto_examples/mixture/plot_gmm.ipynb
+++ b/dev/notebooks/auto_examples/mixture/plot_gmm.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/mixture/plot_gmm_covariances.ipynb b/dev/notebooks/auto_examples/mixture/plot_gmm_covariances.ipynb
index 0a398fb47e2f7..3922a27d40679 100644
--- a/dev/notebooks/auto_examples/mixture/plot_gmm_covariances.ipynb
+++ b/dev/notebooks/auto_examples/mixture/plot_gmm_covariances.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/mixture/plot_gmm_init.ipynb b/dev/notebooks/auto_examples/mixture/plot_gmm_init.ipynb
index 6d30a92230dfe..6d22f98fbddbd 100644
--- a/dev/notebooks/auto_examples/mixture/plot_gmm_init.ipynb
+++ b/dev/notebooks/auto_examples/mixture/plot_gmm_init.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/mixture/plot_gmm_pdf.ipynb b/dev/notebooks/auto_examples/mixture/plot_gmm_pdf.ipynb
index 6c2dec4e6aa37..955c08a20c4f8 100644
--- a/dev/notebooks/auto_examples/mixture/plot_gmm_pdf.ipynb
+++ b/dev/notebooks/auto_examples/mixture/plot_gmm_pdf.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/mixture/plot_gmm_selection.ipynb b/dev/notebooks/auto_examples/mixture/plot_gmm_selection.ipynb
index feb8791a52ec9..602e9335e231b 100644
--- a/dev/notebooks/auto_examples/mixture/plot_gmm_selection.ipynb
+++ b/dev/notebooks/auto_examples/mixture/plot_gmm_selection.ipynb
@@ -136,7 +136,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/mixture/plot_gmm_sin.ipynb b/dev/notebooks/auto_examples/mixture/plot_gmm_sin.ipynb
index 67e665980bb2f..6f45fb118ea10 100644
--- a/dev/notebooks/auto_examples/mixture/plot_gmm_sin.ipynb
+++ b/dev/notebooks/auto_examples/mixture/plot_gmm_sin.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/model_selection/plot_confusion_matrix.ipynb b/dev/notebooks/auto_examples/model_selection/plot_confusion_matrix.ipynb
index e8bc3b9845c48..b7a654462e4c5 100644
--- a/dev/notebooks/auto_examples/model_selection/plot_confusion_matrix.ipynb
+++ b/dev/notebooks/auto_examples/model_selection/plot_confusion_matrix.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/model_selection/plot_cost_sensitive_learning.ipynb b/dev/notebooks/auto_examples/model_selection/plot_cost_sensitive_learning.ipynb
index 869ea6a90239a..6bdbcf53d1f1e 100644
--- a/dev/notebooks/auto_examples/model_selection/plot_cost_sensitive_learning.ipynb
+++ b/dev/notebooks/auto_examples/model_selection/plot_cost_sensitive_learning.ipynb
@@ -695,7 +695,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/model_selection/plot_cv_indices.ipynb b/dev/notebooks/auto_examples/model_selection/plot_cv_indices.ipynb
index 4a705242c87fa..8f3bd3db8198d 100644
--- a/dev/notebooks/auto_examples/model_selection/plot_cv_indices.ipynb
+++ b/dev/notebooks/auto_examples/model_selection/plot_cv_indices.ipynb
@@ -125,7 +125,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/model_selection/plot_cv_predict.ipynb b/dev/notebooks/auto_examples/model_selection/plot_cv_predict.ipynb
index 2d4256a88278b..866f49b888b68 100644
--- a/dev/notebooks/auto_examples/model_selection/plot_cv_predict.ipynb
+++ b/dev/notebooks/auto_examples/model_selection/plot_cv_predict.ipynb
@@ -96,7 +96,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/model_selection/plot_det.ipynb b/dev/notebooks/auto_examples/model_selection/plot_det.ipynb
index 511aff4eb3b22..bf30f161d76de 100644
--- a/dev/notebooks/auto_examples/model_selection/plot_det.ipynb
+++ b/dev/notebooks/auto_examples/model_selection/plot_det.ipynb
@@ -40,7 +40,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Define the classifiers\n\nHere we define two different classifiers. The goal is to visually compare their\nstatistical performance across thresholds using the ROC and DET curves. There\nis no particular reason why these classifiers are chosen other classifiers\navailable in scikit-learn.\n\n"
+        "## Define the classifiers\n\nHere we define two different classifiers. The goal is to visually compare their\nstatistical performance across thresholds using the ROC and DET curves.\n\n"
       ]
     },
     {
@@ -51,14 +51,14 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.ensemble import RandomForestClassifier\nfrom sklearn.pipeline import make_pipeline\nfrom sklearn.svm import LinearSVC\n\nclassifiers = {\n    \"Linear SVM\": make_pipeline(StandardScaler(), LinearSVC(C=0.025)),\n    \"Random Forest\": RandomForestClassifier(\n        max_depth=5, n_estimators=10, max_features=1\n    ),\n}"
+        "from sklearn.dummy import DummyClassifier\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.pipeline import make_pipeline\nfrom sklearn.svm import LinearSVC\n\nclassifiers = {\n    \"Linear SVM\": make_pipeline(StandardScaler(), LinearSVC(C=0.025)),\n    \"Random Forest\": RandomForestClassifier(\n        max_depth=5, n_estimators=10, max_features=1, random_state=0\n    ),\n    \"Non-informative baseline\": DummyClassifier(),\n}"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Plot ROC and DET curves\n\nDET curves are commonly plotted in normal deviate scale. To achieve this the\nDET display transforms the error rates as returned by the\n:func:`~sklearn.metrics.det_curve` and the axis scale using\n`scipy.stats.norm`.\n\n"
+        "## Compare ROC and DET curves\n\nDET curves are commonly plotted in normal deviate scale. To achieve this the\nDET display transforms the error rates as returned by the\n:func:`~sklearn.metrics.det_curve` and the axis scale using\n`scipy.stats.norm`.\n\n"
       ]
     },
     {
@@ -69,14 +69,14 @@
       },
       "outputs": [],
       "source": [
-        "import matplotlib.pyplot as plt\n\nfrom sklearn.metrics import DetCurveDisplay, RocCurveDisplay\n\nfig, [ax_roc, ax_det] = plt.subplots(1, 2, figsize=(11, 5))\n\nfor name, clf in classifiers.items():\n    clf.fit(X_train, y_train)\n\n    RocCurveDisplay.from_estimator(clf, X_test, y_test, ax=ax_roc, name=name)\n    DetCurveDisplay.from_estimator(clf, X_test, y_test, ax=ax_det, name=name)\n\nax_roc.set_title(\"Receiver Operating Characteristic (ROC) curves\")\nax_det.set_title(\"Detection Error Tradeoff (DET) curves\")\n\nax_roc.grid(linestyle=\"--\")\nax_det.grid(linestyle=\"--\")\n\nplt.legend()\nplt.show()"
+        "import matplotlib.pyplot as plt\n\nfrom sklearn.dummy import DummyClassifier\nfrom sklearn.metrics import DetCurveDisplay, RocCurveDisplay\n\nfig, [ax_roc, ax_det] = plt.subplots(1, 2, figsize=(11, 5))\n\nax_roc.set_title(\"Receiver Operating Characteristic (ROC) curves\")\nax_det.set_title(\"Detection Error Tradeoff (DET) curves\")\n\nax_roc.grid(linestyle=\"--\")\nax_det.grid(linestyle=\"--\")\n\nfor name, clf in classifiers.items():\n    (color, linestyle) = (\n        (\"black\", \"--\") if name == \"Non-informative baseline\" else (None, None)\n    )\n    clf.fit(X_train, y_train)\n    RocCurveDisplay.from_estimator(\n        clf, X_test, y_test, ax=ax_roc, name=name, color=color, linestyle=linestyle\n    )\n    DetCurveDisplay.from_estimator(\n        clf, X_test, y_test, ax=ax_det, name=name, color=color, linestyle=linestyle\n    )\n\nplt.legend()\nplt.show()"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Notice that it is easier to visually assess the overall performance of\ndifferent classification algorithms using DET curves than using ROC curves. As\nROC curves are plot in a linear scale, different classifiers usually appear\nsimilar for a large part of the plot and differ the most in the top left\ncorner of the graph. On the other hand, because DET curves represent straight\nlines in normal deviate scale, they tend to be distinguishable as a whole and\nthe area of interest spans a large part of the plot.\n\nDET curves give direct feedback of the detection error tradeoff to aid in\noperating point analysis. The user can then decide the FNR they are willing to\naccept at the expense of the FPR (or vice-versa).\n\n"
+        "Notice that it is easier to visually assess the overall performance of\ndifferent classification algorithms using DET curves than using ROC curves. As\nROC curves are plot in a linear scale, different classifiers usually appear\nsimilar for a large part of the plot and differ the most in the top left\ncorner of the graph. On the other hand, because DET curves represent straight\nlines in normal deviate scale, they tend to be distinguishable as a whole and\nthe area of interest spans a large part of the plot.\n\nDET curves give direct feedback of the detection error tradeoff to aid in\noperating point analysis. The user can then decide the FNR they are willing to\naccept at the expense of the FPR (or vice-versa).\n\n## Non-informative classifier baseline for the ROC and DET curves\n\nThe diagonal black-dotted lines in the plots above correspond to a\n:class:`~sklearn.dummy.DummyClassifier` using the default \"prior\" strategy, to\nserve as baseline for comparison with other classifiers. This classifier makes\nconstant predictions, independent of the input features in `X`, making it a\nnon-informative classifier.\n\nTo further understand the non-informative baseline of the ROC and DET curves,\nwe recall the following mathematical definitions:\n\n$\\text{FPR} = \\frac{\\text{FP}}{\\text{FP} + \\text{TN}}$\n\n$\\text{FNR} = \\frac{\\text{FN}}{\\text{TP} + \\text{FN}}$\n\n$\\text{TPR} = \\frac{\\text{TP}}{\\text{TP} + \\text{FN}}$\n\nA classifier that always predict the positive class would have no true\nnegatives nor false negatives, giving $\\text{FPR} = \\text{TPR} = 1$ and\n$\\text{FNR} = 0$, i.e.:\n\n- a single point in the upper right corner of the ROC plane,\n- a single point in the lower right corner of the DET plane.\n\nSimilarly, a classifier that always predict the negative class would have no\ntrue positives nor false positives, thus $\\text{FPR} = \\text{TPR} = 0$\nand $\\text{FNR} = 1$, i.e.:\n\n- a single point in the lower left corner of the ROC plane,\n- a single point in the upper left corner of the DET plane.\n\n"
       ]
     }
   ],
@@ -96,7 +96,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/model_selection/plot_grid_search_digits.ipynb b/dev/notebooks/auto_examples/model_selection/plot_grid_search_digits.ipynb
index 128cf2383e066..a8ed7680476b1 100644
--- a/dev/notebooks/auto_examples/model_selection/plot_grid_search_digits.ipynb
+++ b/dev/notebooks/auto_examples/model_selection/plot_grid_search_digits.ipynb
@@ -186,7 +186,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/model_selection/plot_grid_search_refit_callable.ipynb b/dev/notebooks/auto_examples/model_selection/plot_grid_search_refit_callable.ipynb
index a8f8d6d8d33dc..138d6551f3dfd 100644
--- a/dev/notebooks/auto_examples/model_selection/plot_grid_search_refit_callable.ipynb
+++ b/dev/notebooks/auto_examples/model_selection/plot_grid_search_refit_callable.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/model_selection/plot_grid_search_stats.ipynb b/dev/notebooks/auto_examples/model_selection/plot_grid_search_stats.ipynb
index e31bd3897243d..303ad01b44749 100644
--- a/dev/notebooks/auto_examples/model_selection/plot_grid_search_stats.ipynb
+++ b/dev/notebooks/auto_examples/model_selection/plot_grid_search_stats.ipynb
@@ -361,7 +361,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/model_selection/plot_grid_search_text_feature_extraction.ipynb b/dev/notebooks/auto_examples/model_selection/plot_grid_search_text_feature_extraction.ipynb
index d9a71cfc9174f..72a7c93917487 100644
--- a/dev/notebooks/auto_examples/model_selection/plot_grid_search_text_feature_extraction.ipynb
+++ b/dev/notebooks/auto_examples/model_selection/plot_grid_search_text_feature_extraction.ipynb
@@ -201,7 +201,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/model_selection/plot_learning_curve.ipynb b/dev/notebooks/auto_examples/model_selection/plot_learning_curve.ipynb
index bbcff38a10742..ef9556a6d8cc3 100644
--- a/dev/notebooks/auto_examples/model_selection/plot_learning_curve.ipynb
+++ b/dev/notebooks/auto_examples/model_selection/plot_learning_curve.ipynb
@@ -125,7 +125,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/model_selection/plot_likelihood_ratios.ipynb b/dev/notebooks/auto_examples/model_selection/plot_likelihood_ratios.ipynb
index 4ef038d757afe..33075327ffdc4 100644
--- a/dev/notebooks/auto_examples/model_selection/plot_likelihood_ratios.ipynb
+++ b/dev/notebooks/auto_examples/model_selection/plot_likelihood_ratios.ipynb
@@ -33,7 +33,7 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.datasets import make_classification\n\nX, y = make_classification(n_samples=10_000, weights=[0.9, 0.1], random_state=0)\nprint(f\"Percentage of people carrying the disease: {100*y.mean():.2f}%\")"
+        "from sklearn.datasets import make_classification\n\nX, y = make_classification(n_samples=10_000, weights=[0.9, 0.1], random_state=0)\nprint(f\"Percentage of people carrying the disease: {100 * y.mean():.2f}%\")"
       ]
     },
     {
@@ -269,7 +269,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/model_selection/plot_multi_metric_evaluation.ipynb b/dev/notebooks/auto_examples/model_selection/plot_multi_metric_evaluation.ipynb
index bbb3f77f4f148..343a59f770005 100644
--- a/dev/notebooks/auto_examples/model_selection/plot_multi_metric_evaluation.ipynb
+++ b/dev/notebooks/auto_examples/model_selection/plot_multi_metric_evaluation.ipynb
@@ -71,7 +71,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/model_selection/plot_nested_cross_validation_iris.ipynb b/dev/notebooks/auto_examples/model_selection/plot_nested_cross_validation_iris.ipynb
index 1c880d8994fdd..9d7a596b96b2f 100644
--- a/dev/notebooks/auto_examples/model_selection/plot_nested_cross_validation_iris.ipynb
+++ b/dev/notebooks/auto_examples/model_selection/plot_nested_cross_validation_iris.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/model_selection/plot_permutation_tests_for_classification.ipynb b/dev/notebooks/auto_examples/model_selection/plot_permutation_tests_for_classification.ipynb
index 0778b36d4a72d..e372bb3fe2c05 100644
--- a/dev/notebooks/auto_examples/model_selection/plot_permutation_tests_for_classification.ipynb
+++ b/dev/notebooks/auto_examples/model_selection/plot_permutation_tests_for_classification.ipynb
@@ -132,7 +132,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/model_selection/plot_precision_recall.ipynb b/dev/notebooks/auto_examples/model_selection/plot_precision_recall.ipynb
index 427dc98d3c32a..251b3ab0191a6 100644
--- a/dev/notebooks/auto_examples/model_selection/plot_precision_recall.ipynb
+++ b/dev/notebooks/auto_examples/model_selection/plot_precision_recall.ipynb
@@ -197,7 +197,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/model_selection/plot_randomized_search.ipynb b/dev/notebooks/auto_examples/model_selection/plot_randomized_search.ipynb
index 6ae507e637b72..350ed4a408c05 100644
--- a/dev/notebooks/auto_examples/model_selection/plot_randomized_search.ipynb
+++ b/dev/notebooks/auto_examples/model_selection/plot_randomized_search.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/model_selection/plot_roc.ipynb b/dev/notebooks/auto_examples/model_selection/plot_roc.ipynb
index f4c6f4e61e040..42953aebee744 100644
--- a/dev/notebooks/auto_examples/model_selection/plot_roc.ipynb
+++ b/dev/notebooks/auto_examples/model_selection/plot_roc.ipynb
@@ -134,7 +134,7 @@
       },
       "outputs": [],
       "source": [
-        "print(f\"y_score:\\n{y_score[0:2,:]}\")\nprint()\nprint(f\"y_score.ravel():\\n{y_score[0:2,:].ravel()}\")"
+        "print(f\"y_score:\\n{y_score[0:2, :]}\")\nprint()\nprint(f\"y_score.ravel():\\n{y_score[0:2, :].ravel()}\")"
       ]
     },
     {
@@ -271,7 +271,7 @@
       },
       "outputs": [],
       "source": [
-        "pair_scores = []\nmean_tpr = dict()\n\nfor ix, (label_a, label_b) in enumerate(pair_list):\n    a_mask = y_test == label_a\n    b_mask = y_test == label_b\n    ab_mask = np.logical_or(a_mask, b_mask)\n\n    a_true = a_mask[ab_mask]\n    b_true = b_mask[ab_mask]\n\n    idx_a = np.flatnonzero(label_binarizer.classes_ == label_a)[0]\n    idx_b = np.flatnonzero(label_binarizer.classes_ == label_b)[0]\n\n    fpr_a, tpr_a, _ = roc_curve(a_true, y_score[ab_mask, idx_a])\n    fpr_b, tpr_b, _ = roc_curve(b_true, y_score[ab_mask, idx_b])\n\n    mean_tpr[ix] = np.zeros_like(fpr_grid)\n    mean_tpr[ix] += np.interp(fpr_grid, fpr_a, tpr_a)\n    mean_tpr[ix] += np.interp(fpr_grid, fpr_b, tpr_b)\n    mean_tpr[ix] /= 2\n    mean_score = auc(fpr_grid, mean_tpr[ix])\n    pair_scores.append(mean_score)\n\n    fig, ax = plt.subplots(figsize=(6, 6))\n    plt.plot(\n        fpr_grid,\n        mean_tpr[ix],\n        label=f\"Mean {label_a} vs {label_b} (AUC = {mean_score :.2f})\",\n        linestyle=\":\",\n        linewidth=4,\n    )\n    RocCurveDisplay.from_predictions(\n        a_true,\n        y_score[ab_mask, idx_a],\n        ax=ax,\n        name=f\"{label_a} as positive class\",\n    )\n    RocCurveDisplay.from_predictions(\n        b_true,\n        y_score[ab_mask, idx_b],\n        ax=ax,\n        name=f\"{label_b} as positive class\",\n        plot_chance_level=True,\n        despine=True,\n    )\n    ax.set(\n        xlabel=\"False Positive Rate\",\n        ylabel=\"True Positive Rate\",\n        title=f\"{target_names[idx_a]} vs {label_b} ROC curves\",\n    )\n\nprint(f\"Macro-averaged One-vs-One ROC AUC score:\\n{np.average(pair_scores):.2f}\")"
+        "pair_scores = []\nmean_tpr = dict()\n\nfor ix, (label_a, label_b) in enumerate(pair_list):\n    a_mask = y_test == label_a\n    b_mask = y_test == label_b\n    ab_mask = np.logical_or(a_mask, b_mask)\n\n    a_true = a_mask[ab_mask]\n    b_true = b_mask[ab_mask]\n\n    idx_a = np.flatnonzero(label_binarizer.classes_ == label_a)[0]\n    idx_b = np.flatnonzero(label_binarizer.classes_ == label_b)[0]\n\n    fpr_a, tpr_a, _ = roc_curve(a_true, y_score[ab_mask, idx_a])\n    fpr_b, tpr_b, _ = roc_curve(b_true, y_score[ab_mask, idx_b])\n\n    mean_tpr[ix] = np.zeros_like(fpr_grid)\n    mean_tpr[ix] += np.interp(fpr_grid, fpr_a, tpr_a)\n    mean_tpr[ix] += np.interp(fpr_grid, fpr_b, tpr_b)\n    mean_tpr[ix] /= 2\n    mean_score = auc(fpr_grid, mean_tpr[ix])\n    pair_scores.append(mean_score)\n\n    fig, ax = plt.subplots(figsize=(6, 6))\n    plt.plot(\n        fpr_grid,\n        mean_tpr[ix],\n        label=f\"Mean {label_a} vs {label_b} (AUC = {mean_score:.2f})\",\n        linestyle=\":\",\n        linewidth=4,\n    )\n    RocCurveDisplay.from_predictions(\n        a_true,\n        y_score[ab_mask, idx_a],\n        ax=ax,\n        name=f\"{label_a} as positive class\",\n    )\n    RocCurveDisplay.from_predictions(\n        b_true,\n        y_score[ab_mask, idx_b],\n        ax=ax,\n        name=f\"{label_b} as positive class\",\n        plot_chance_level=True,\n        despine=True,\n    )\n    ax.set(\n        xlabel=\"False Positive Rate\",\n        ylabel=\"True Positive Rate\",\n        title=f\"{target_names[idx_a]} vs {label_b} ROC curves\",\n    )\n\nprint(f\"Macro-averaged One-vs-One ROC AUC score:\\n{np.average(pair_scores):.2f}\")"
       ]
     },
     {
@@ -334,7 +334,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/model_selection/plot_roc_crossval.ipynb b/dev/notebooks/auto_examples/model_selection/plot_roc_crossval.ipynb
index 7f27c788d7f59..0509f550ebe75 100644
--- a/dev/notebooks/auto_examples/model_selection/plot_roc_crossval.ipynb
+++ b/dev/notebooks/auto_examples/model_selection/plot_roc_crossval.ipynb
@@ -89,7 +89,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/model_selection/plot_successive_halving_heatmap.ipynb b/dev/notebooks/auto_examples/model_selection/plot_successive_halving_heatmap.ipynb
index d81a3705f4b94..846db97c54fef 100644
--- a/dev/notebooks/auto_examples/model_selection/plot_successive_halving_heatmap.ipynb
+++ b/dev/notebooks/auto_examples/model_selection/plot_successive_halving_heatmap.ipynb
@@ -15,7 +15,7 @@
       },
       "outputs": [],
       "source": [
-        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\n\nfrom time import time\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\n\nfrom sklearn import datasets\nfrom sklearn.experimental import enable_halving_search_cv  # noqa\nfrom sklearn.model_selection import GridSearchCV, HalvingGridSearchCV\nfrom sklearn.svm import SVC"
+        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\n\nfrom time import time\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\n\nfrom sklearn import datasets\nfrom sklearn.experimental import enable_halving_search_cv  # noqa: F401\nfrom sklearn.model_selection import GridSearchCV, HalvingGridSearchCV\nfrom sklearn.svm import SVC"
       ]
     },
     {
@@ -78,7 +78,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/model_selection/plot_successive_halving_iterations.ipynb b/dev/notebooks/auto_examples/model_selection/plot_successive_halving_iterations.ipynb
index cffe0888a60f8..e62abefb6bd38 100644
--- a/dev/notebooks/auto_examples/model_selection/plot_successive_halving_iterations.ipynb
+++ b/dev/notebooks/auto_examples/model_selection/plot_successive_halving_iterations.ipynb
@@ -15,7 +15,7 @@
       },
       "outputs": [],
       "source": [
-        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nfrom scipy.stats import randint\n\nfrom sklearn import datasets\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.experimental import enable_halving_search_cv  # noqa\nfrom sklearn.model_selection import HalvingRandomSearchCV"
+        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nfrom scipy.stats import randint\n\nfrom sklearn import datasets\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.experimental import enable_halving_search_cv  # noqa: F401\nfrom sklearn.model_selection import HalvingRandomSearchCV"
       ]
     },
     {
@@ -78,7 +78,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/model_selection/plot_train_error_vs_test_error.ipynb b/dev/notebooks/auto_examples/model_selection/plot_train_error_vs_test_error.ipynb
index f51f72dde7c14..b77f9ebfcaa0b 100644
--- a/dev/notebooks/auto_examples/model_selection/plot_train_error_vs_test_error.ipynb
+++ b/dev/notebooks/auto_examples/model_selection/plot_train_error_vs_test_error.ipynb
@@ -132,7 +132,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/model_selection/plot_tuned_decision_threshold.ipynb b/dev/notebooks/auto_examples/model_selection/plot_tuned_decision_threshold.ipynb
index 1d9dfe4f3fc52..0ab9c6f6c1b4b 100644
--- a/dev/notebooks/auto_examples/model_selection/plot_tuned_decision_threshold.ipynb
+++ b/dev/notebooks/auto_examples/model_selection/plot_tuned_decision_threshold.ipynb
@@ -186,7 +186,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/model_selection/plot_underfitting_overfitting.ipynb b/dev/notebooks/auto_examples/model_selection/plot_underfitting_overfitting.ipynb
index 59e27ecfbd697..61cf65011fc57 100644
--- a/dev/notebooks/auto_examples/model_selection/plot_underfitting_overfitting.ipynb
+++ b/dev/notebooks/auto_examples/model_selection/plot_underfitting_overfitting.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/multiclass/plot_multiclass_overview.ipynb b/dev/notebooks/auto_examples/multiclass/plot_multiclass_overview.ipynb
index 462b6df6b3fa8..065f2a6bd6a95 100644
--- a/dev/notebooks/auto_examples/multiclass/plot_multiclass_overview.ipynb
+++ b/dev/notebooks/auto_examples/multiclass/plot_multiclass_overview.ipynb
@@ -132,7 +132,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/multioutput/plot_classifier_chain_yeast.ipynb b/dev/notebooks/auto_examples/multioutput/plot_classifier_chain_yeast.ipynb
index 478d5c668fc16..ae87b350ece21 100644
--- a/dev/notebooks/auto_examples/multioutput/plot_classifier_chain_yeast.ipynb
+++ b/dev/notebooks/auto_examples/multioutput/plot_classifier_chain_yeast.ipynb
@@ -114,7 +114,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/neighbors/approximate_nearest_neighbors.ipynb b/dev/notebooks/auto_examples/neighbors/approximate_nearest_neighbors.ipynb
index aca1d771daed7..dc161f56f929a 100644
--- a/dev/notebooks/auto_examples/neighbors/approximate_nearest_neighbors.ipynb
+++ b/dev/notebooks/auto_examples/neighbors/approximate_nearest_neighbors.ipynb
@@ -114,7 +114,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/neighbors/plot_caching_nearest_neighbors.ipynb b/dev/notebooks/auto_examples/neighbors/plot_caching_nearest_neighbors.ipynb
index e471be8fc094e..3d86bdffca161 100644
--- a/dev/notebooks/auto_examples/neighbors/plot_caching_nearest_neighbors.ipynb
+++ b/dev/notebooks/auto_examples/neighbors/plot_caching_nearest_neighbors.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/neighbors/plot_classification.ipynb b/dev/notebooks/auto_examples/neighbors/plot_classification.ipynb
index 824bfbd4cfe23..071a663ddf571 100644
--- a/dev/notebooks/auto_examples/neighbors/plot_classification.ipynb
+++ b/dev/notebooks/auto_examples/neighbors/plot_classification.ipynb
@@ -96,7 +96,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/neighbors/plot_digits_kde_sampling.ipynb b/dev/notebooks/auto_examples/neighbors/plot_digits_kde_sampling.ipynb
index 4527d109a8519..13d851171d6f6 100644
--- a/dev/notebooks/auto_examples/neighbors/plot_digits_kde_sampling.ipynb
+++ b/dev/notebooks/auto_examples/neighbors/plot_digits_kde_sampling.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/neighbors/plot_kde_1d.ipynb b/dev/notebooks/auto_examples/neighbors/plot_kde_1d.ipynb
index 0b22b4c7cd7af..6a037df565055 100644
--- a/dev/notebooks/auto_examples/neighbors/plot_kde_1d.ipynb
+++ b/dev/notebooks/auto_examples/neighbors/plot_kde_1d.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/neighbors/plot_lof_novelty_detection.ipynb b/dev/notebooks/auto_examples/neighbors/plot_lof_novelty_detection.ipynb
index e4d1629313726..3999fc65ce3d0 100644
--- a/dev/notebooks/auto_examples/neighbors/plot_lof_novelty_detection.ipynb
+++ b/dev/notebooks/auto_examples/neighbors/plot_lof_novelty_detection.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/neighbors/plot_lof_outlier_detection.ipynb b/dev/notebooks/auto_examples/neighbors/plot_lof_outlier_detection.ipynb
index 6a8d95546a399..fdb3f34b9b20d 100644
--- a/dev/notebooks/auto_examples/neighbors/plot_lof_outlier_detection.ipynb
+++ b/dev/notebooks/auto_examples/neighbors/plot_lof_outlier_detection.ipynb
@@ -89,7 +89,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/neighbors/plot_nca_classification.ipynb b/dev/notebooks/auto_examples/neighbors/plot_nca_classification.ipynb
index 48638ec7f0272..7d49268ada266 100644
--- a/dev/notebooks/auto_examples/neighbors/plot_nca_classification.ipynb
+++ b/dev/notebooks/auto_examples/neighbors/plot_nca_classification.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/neighbors/plot_nca_dim_reduction.ipynb b/dev/notebooks/auto_examples/neighbors/plot_nca_dim_reduction.ipynb
index 3359bdf9cdb08..93693adefa243 100644
--- a/dev/notebooks/auto_examples/neighbors/plot_nca_dim_reduction.ipynb
+++ b/dev/notebooks/auto_examples/neighbors/plot_nca_dim_reduction.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/neighbors/plot_nca_illustration.ipynb b/dev/notebooks/auto_examples/neighbors/plot_nca_illustration.ipynb
index d2584fc0418f5..d2085838606e1 100644
--- a/dev/notebooks/auto_examples/neighbors/plot_nca_illustration.ipynb
+++ b/dev/notebooks/auto_examples/neighbors/plot_nca_illustration.ipynb
@@ -71,7 +71,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/neighbors/plot_nearest_centroid.ipynb b/dev/notebooks/auto_examples/neighbors/plot_nearest_centroid.ipynb
index fd9a4e65e0692..56de89367da4f 100644
--- a/dev/notebooks/auto_examples/neighbors/plot_nearest_centroid.ipynb
+++ b/dev/notebooks/auto_examples/neighbors/plot_nearest_centroid.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/neighbors/plot_regression.ipynb b/dev/notebooks/auto_examples/neighbors/plot_regression.ipynb
index 2e9e2b3c35cbf..5a281c0af5005 100644
--- a/dev/notebooks/auto_examples/neighbors/plot_regression.ipynb
+++ b/dev/notebooks/auto_examples/neighbors/plot_regression.ipynb
@@ -71,7 +71,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/neighbors/plot_species_kde.ipynb b/dev/notebooks/auto_examples/neighbors/plot_species_kde.ipynb
index b5140f6346575..c1c221601cb53 100644
--- a/dev/notebooks/auto_examples/neighbors/plot_species_kde.ipynb
+++ b/dev/notebooks/auto_examples/neighbors/plot_species_kde.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/neural_networks/plot_mlp_alpha.ipynb b/dev/notebooks/auto_examples/neural_networks/plot_mlp_alpha.ipynb
index 3f1a4d235b5e4..70e3a6d8d6134 100644
--- a/dev/notebooks/auto_examples/neural_networks/plot_mlp_alpha.ipynb
+++ b/dev/notebooks/auto_examples/neural_networks/plot_mlp_alpha.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/neural_networks/plot_mlp_training_curves.ipynb b/dev/notebooks/auto_examples/neural_networks/plot_mlp_training_curves.ipynb
index 419cde55dc881..bd9a7757b9a8a 100644
--- a/dev/notebooks/auto_examples/neural_networks/plot_mlp_training_curves.ipynb
+++ b/dev/notebooks/auto_examples/neural_networks/plot_mlp_training_curves.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/neural_networks/plot_mnist_filters.ipynb b/dev/notebooks/auto_examples/neural_networks/plot_mnist_filters.ipynb
index c84416e7788c0..7581cc356aa57 100644
--- a/dev/notebooks/auto_examples/neural_networks/plot_mnist_filters.ipynb
+++ b/dev/notebooks/auto_examples/neural_networks/plot_mnist_filters.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/neural_networks/plot_rbm_logistic_classification.ipynb b/dev/notebooks/auto_examples/neural_networks/plot_rbm_logistic_classification.ipynb
index c164108e91aff..d82f9676c7324 100644
--- a/dev/notebooks/auto_examples/neural_networks/plot_rbm_logistic_classification.ipynb
+++ b/dev/notebooks/auto_examples/neural_networks/plot_rbm_logistic_classification.ipynb
@@ -143,7 +143,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/preprocessing/plot_all_scaling.ipynb b/dev/notebooks/auto_examples/preprocessing/plot_all_scaling.ipynb
index 911b82f430cc2..88c2273ee1df5 100644
--- a/dev/notebooks/auto_examples/preprocessing/plot_all_scaling.ipynb
+++ b/dev/notebooks/auto_examples/preprocessing/plot_all_scaling.ipynb
@@ -215,7 +215,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/preprocessing/plot_discretization.ipynb b/dev/notebooks/auto_examples/preprocessing/plot_discretization.ipynb
index 28a13e6ce4016..1100beee2c39d 100644
--- a/dev/notebooks/auto_examples/preprocessing/plot_discretization.ipynb
+++ b/dev/notebooks/auto_examples/preprocessing/plot_discretization.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/preprocessing/plot_discretization_classification.ipynb b/dev/notebooks/auto_examples/preprocessing/plot_discretization_classification.ipynb
index b77ce35de93a9..e8782f76042d8 100644
--- a/dev/notebooks/auto_examples/preprocessing/plot_discretization_classification.ipynb
+++ b/dev/notebooks/auto_examples/preprocessing/plot_discretization_classification.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/preprocessing/plot_discretization_strategies.ipynb b/dev/notebooks/auto_examples/preprocessing/plot_discretization_strategies.ipynb
index 91b8a092bc856..ccb5fc70129e6 100644
--- a/dev/notebooks/auto_examples/preprocessing/plot_discretization_strategies.ipynb
+++ b/dev/notebooks/auto_examples/preprocessing/plot_discretization_strategies.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/preprocessing/plot_map_data_to_normal.ipynb b/dev/notebooks/auto_examples/preprocessing/plot_map_data_to_normal.ipynb
index f942589c14537..6e4667b549e37 100644
--- a/dev/notebooks/auto_examples/preprocessing/plot_map_data_to_normal.ipynb
+++ b/dev/notebooks/auto_examples/preprocessing/plot_map_data_to_normal.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/preprocessing/plot_scaling_importance.ipynb b/dev/notebooks/auto_examples/preprocessing/plot_scaling_importance.ipynb
index 09fe748a7452e..d60bc4e175e94 100644
--- a/dev/notebooks/auto_examples/preprocessing/plot_scaling_importance.ipynb
+++ b/dev/notebooks/auto_examples/preprocessing/plot_scaling_importance.ipynb
@@ -150,7 +150,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/preprocessing/plot_target_encoder.ipynb b/dev/notebooks/auto_examples/preprocessing/plot_target_encoder.ipynb
index bc58cbb0cb3af..3c2997039d057 100644
--- a/dev/notebooks/auto_examples/preprocessing/plot_target_encoder.ipynb
+++ b/dev/notebooks/auto_examples/preprocessing/plot_target_encoder.ipynb
@@ -186,7 +186,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/preprocessing/plot_target_encoder_cross_val.ipynb b/dev/notebooks/auto_examples/preprocessing/plot_target_encoder_cross_val.ipynb
index e0a4fc1f89d89..a4c2c2ed31a8e 100644
--- a/dev/notebooks/auto_examples/preprocessing/plot_target_encoder_cross_val.ipynb
+++ b/dev/notebooks/auto_examples/preprocessing/plot_target_encoder_cross_val.ipynb
@@ -222,7 +222,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/release_highlights/plot_release_highlights_0_22_0.ipynb b/dev/notebooks/auto_examples/release_highlights/plot_release_highlights_0_22_0.ipynb
index b2c8b5b331e6a..99dfc5e2df0ec 100644
--- a/dev/notebooks/auto_examples/release_highlights/plot_release_highlights_0_22_0.ipynb
+++ b/dev/notebooks/auto_examples/release_highlights/plot_release_highlights_0_22_0.ipynb
@@ -215,7 +215,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/release_highlights/plot_release_highlights_0_23_0.ipynb b/dev/notebooks/auto_examples/release_highlights/plot_release_highlights_0_23_0.ipynb
index b02b7917a0bd0..acad9f2f57858 100644
--- a/dev/notebooks/auto_examples/release_highlights/plot_release_highlights_0_23_0.ipynb
+++ b/dev/notebooks/auto_examples/release_highlights/plot_release_highlights_0_23_0.ipynb
@@ -22,7 +22,7 @@
       },
       "outputs": [],
       "source": [
-        "import numpy as np\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.linear_model import PoissonRegressor\nfrom sklearn.ensemble import HistGradientBoostingRegressor\n\nn_samples, n_features = 1000, 20\nrng = np.random.RandomState(0)\nX = rng.randn(n_samples, n_features)\n# positive integer target correlated with X[:, 5] with many zeros:\ny = rng.poisson(lam=np.exp(X[:, 5]) / 2)\nX_train, X_test, y_train, y_test = train_test_split(X, y, random_state=rng)\nglm = PoissonRegressor()\ngbdt = HistGradientBoostingRegressor(loss=\"poisson\", learning_rate=0.01)\nglm.fit(X_train, y_train)\ngbdt.fit(X_train, y_train)\nprint(glm.score(X_test, y_test))\nprint(gbdt.score(X_test, y_test))"
+        "import numpy as np\n\nfrom sklearn.ensemble import HistGradientBoostingRegressor\nfrom sklearn.linear_model import PoissonRegressor\nfrom sklearn.model_selection import train_test_split\n\nn_samples, n_features = 1000, 20\nrng = np.random.RandomState(0)\nX = rng.randn(n_samples, n_features)\n# positive integer target correlated with X[:, 5] with many zeros:\ny = rng.poisson(lam=np.exp(X[:, 5]) / 2)\nX_train, X_test, y_train, y_test = train_test_split(X, y, random_state=rng)\nglm = PoissonRegressor()\ngbdt = HistGradientBoostingRegressor(loss=\"poisson\", learning_rate=0.01)\nglm.fit(X_train, y_train)\ngbdt.fit(X_train, y_train)\nprint(glm.score(X_test, y_test))\nprint(gbdt.score(X_test, y_test))"
       ]
     },
     {
@@ -40,7 +40,7 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn import set_config\nfrom sklearn.pipeline import make_pipeline\nfrom sklearn.preprocessing import OneHotEncoder, StandardScaler\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.compose import make_column_transformer\nfrom sklearn.linear_model import LogisticRegression\n\nset_config(display=\"diagram\")\n\nnum_proc = make_pipeline(SimpleImputer(strategy=\"median\"), StandardScaler())\n\ncat_proc = make_pipeline(\n    SimpleImputer(strategy=\"constant\", fill_value=\"missing\"),\n    OneHotEncoder(handle_unknown=\"ignore\"),\n)\n\npreprocessor = make_column_transformer(\n    (num_proc, (\"feat1\", \"feat3\")), (cat_proc, (\"feat0\", \"feat2\"))\n)\n\nclf = make_pipeline(preprocessor, LogisticRegression())\nclf"
+        "from sklearn import set_config\nfrom sklearn.compose import make_column_transformer\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.pipeline import make_pipeline\nfrom sklearn.preprocessing import OneHotEncoder, StandardScaler\n\nset_config(display=\"diagram\")\n\nnum_proc = make_pipeline(SimpleImputer(strategy=\"median\"), StandardScaler())\n\ncat_proc = make_pipeline(\n    SimpleImputer(strategy=\"constant\", fill_value=\"missing\"),\n    OneHotEncoder(handle_unknown=\"ignore\"),\n)\n\npreprocessor = make_column_transformer(\n    (num_proc, (\"feat1\", \"feat3\")), (cat_proc, (\"feat0\", \"feat2\"))\n)\n\nclf = make_pipeline(preprocessor, LogisticRegression())\nclf"
       ]
     },
     {
@@ -58,7 +58,7 @@
       },
       "outputs": [],
       "source": [
-        "import scipy\nimport numpy as np\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.cluster import KMeans\nfrom sklearn.datasets import make_blobs\nfrom sklearn.metrics import completeness_score\n\nrng = np.random.RandomState(0)\nX, y = make_blobs(random_state=rng)\nX = scipy.sparse.csr_matrix(X)\nX_train, X_test, _, y_test = train_test_split(X, y, random_state=rng)\nkmeans = KMeans(n_init=\"auto\").fit(X_train)\nprint(completeness_score(kmeans.predict(X_test), y_test))"
+        "import numpy as np\nimport scipy\n\nfrom sklearn.cluster import KMeans\nfrom sklearn.datasets import make_blobs\nfrom sklearn.metrics import completeness_score\nfrom sklearn.model_selection import train_test_split\n\nrng = np.random.RandomState(0)\nX, y = make_blobs(random_state=rng)\nX = scipy.sparse.csr_matrix(X)\nX_train, X_test, _, y_test = train_test_split(X, y, random_state=rng)\nkmeans = KMeans(n_init=\"auto\").fit(X_train)\nprint(completeness_score(kmeans.predict(X_test), y_test))"
       ]
     },
     {
@@ -76,7 +76,7 @@
       },
       "outputs": [],
       "source": [
-        "import numpy as np\nfrom matplotlib import pyplot as plt\nfrom sklearn.model_selection import train_test_split\n\n# from sklearn.inspection import plot_partial_dependence\nfrom sklearn.inspection import PartialDependenceDisplay\nfrom sklearn.ensemble import HistGradientBoostingRegressor\n\nn_samples = 500\nrng = np.random.RandomState(0)\nX = rng.randn(n_samples, 2)\nnoise = rng.normal(loc=0.0, scale=0.01, size=n_samples)\ny = 5 * X[:, 0] + np.sin(10 * np.pi * X[:, 0]) - noise\n\ngbdt_no_cst = HistGradientBoostingRegressor().fit(X, y)\ngbdt_cst = HistGradientBoostingRegressor(monotonic_cst=[1, 0]).fit(X, y)\n\n# plot_partial_dependence has been removed in version 1.2. From 1.2, use\n# PartialDependenceDisplay instead.\n# disp = plot_partial_dependence(\ndisp = PartialDependenceDisplay.from_estimator(\n    gbdt_no_cst,\n    X,\n    features=[0],\n    feature_names=[\"feature 0\"],\n    line_kw={\"linewidth\": 4, \"label\": \"unconstrained\", \"color\": \"tab:blue\"},\n)\n# plot_partial_dependence(\nPartialDependenceDisplay.from_estimator(\n    gbdt_cst,\n    X,\n    features=[0],\n    line_kw={\"linewidth\": 4, \"label\": \"constrained\", \"color\": \"tab:orange\"},\n    ax=disp.axes_,\n)\ndisp.axes_[0, 0].plot(\n    X[:, 0], y, \"o\", alpha=0.5, zorder=-1, label=\"samples\", color=\"tab:green\"\n)\ndisp.axes_[0, 0].set_ylim(-3, 3)\ndisp.axes_[0, 0].set_xlim(-1, 1)\nplt.legend()\nplt.show()"
+        "import numpy as np\nfrom matplotlib import pyplot as plt\n\nfrom sklearn.ensemble import HistGradientBoostingRegressor\n\n# from sklearn.inspection import plot_partial_dependence\nfrom sklearn.inspection import PartialDependenceDisplay\nfrom sklearn.model_selection import train_test_split\n\nn_samples = 500\nrng = np.random.RandomState(0)\nX = rng.randn(n_samples, 2)\nnoise = rng.normal(loc=0.0, scale=0.01, size=n_samples)\ny = 5 * X[:, 0] + np.sin(10 * np.pi * X[:, 0]) - noise\n\ngbdt_no_cst = HistGradientBoostingRegressor().fit(X, y)\ngbdt_cst = HistGradientBoostingRegressor(monotonic_cst=[1, 0]).fit(X, y)\n\n# plot_partial_dependence has been removed in version 1.2. From 1.2, use\n# PartialDependenceDisplay instead.\n# disp = plot_partial_dependence(\ndisp = PartialDependenceDisplay.from_estimator(\n    gbdt_no_cst,\n    X,\n    features=[0],\n    feature_names=[\"feature 0\"],\n    line_kw={\"linewidth\": 4, \"label\": \"unconstrained\", \"color\": \"tab:blue\"},\n)\n# plot_partial_dependence(\nPartialDependenceDisplay.from_estimator(\n    gbdt_cst,\n    X,\n    features=[0],\n    line_kw={\"linewidth\": 4, \"label\": \"constrained\", \"color\": \"tab:orange\"},\n    ax=disp.axes_,\n)\ndisp.axes_[0, 0].plot(\n    X[:, 0], y, \"o\", alpha=0.5, zorder=-1, label=\"samples\", color=\"tab:green\"\n)\ndisp.axes_[0, 0].set_ylim(-3, 3)\ndisp.axes_[0, 0].set_xlim(-1, 1)\nplt.legend()\nplt.show()"
       ]
     },
     {
@@ -94,7 +94,7 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.model_selection import train_test_split\nfrom sklearn.datasets import make_regression\nfrom sklearn.linear_model import Lasso\nimport numpy as np\n\nn_samples, n_features = 1000, 20\nrng = np.random.RandomState(0)\nX, y = make_regression(n_samples, n_features, random_state=rng)\nsample_weight = rng.rand(n_samples)\nX_train, X_test, y_train, y_test, sw_train, sw_test = train_test_split(\n    X, y, sample_weight, random_state=rng\n)\nreg = Lasso()\nreg.fit(X_train, y_train, sample_weight=sw_train)\nprint(reg.score(X_test, y_test, sw_test))"
+        "import numpy as np\n\nfrom sklearn.datasets import make_regression\nfrom sklearn.linear_model import Lasso\nfrom sklearn.model_selection import train_test_split\n\nn_samples, n_features = 1000, 20\nrng = np.random.RandomState(0)\nX, y = make_regression(n_samples, n_features, random_state=rng)\nsample_weight = rng.rand(n_samples)\nX_train, X_test, y_train, y_test, sw_train, sw_test = train_test_split(\n    X, y, sample_weight, random_state=rng\n)\nreg = Lasso()\nreg.fit(X_train, y_train, sample_weight=sw_train)\nprint(reg.score(X_test, y_test, sw_test))"
       ]
     }
   ],
@@ -114,7 +114,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/release_highlights/plot_release_highlights_0_24_0.ipynb b/dev/notebooks/auto_examples/release_highlights/plot_release_highlights_0_24_0.ipynb
index f5848c488f2e3..98492c39ef57f 100644
--- a/dev/notebooks/auto_examples/release_highlights/plot_release_highlights_0_24_0.ipynb
+++ b/dev/notebooks/auto_examples/release_highlights/plot_release_highlights_0_24_0.ipynb
@@ -22,7 +22,7 @@
       },
       "outputs": [],
       "source": [
-        "import numpy as np\nfrom scipy.stats import randint\nfrom sklearn.experimental import enable_halving_search_cv  # noqa\nfrom sklearn.model_selection import HalvingRandomSearchCV\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.datasets import make_classification\n\nrng = np.random.RandomState(0)\n\nX, y = make_classification(n_samples=700, random_state=rng)\n\nclf = RandomForestClassifier(n_estimators=10, random_state=rng)\n\nparam_dist = {\n    \"max_depth\": [3, None],\n    \"max_features\": randint(1, 11),\n    \"min_samples_split\": randint(2, 11),\n    \"bootstrap\": [True, False],\n    \"criterion\": [\"gini\", \"entropy\"],\n}\n\nrsh = HalvingRandomSearchCV(\n    estimator=clf, param_distributions=param_dist, factor=2, random_state=rng\n)\nrsh.fit(X, y)\nrsh.best_params_"
+        "import numpy as np\nfrom scipy.stats import randint\n\nfrom sklearn.datasets import make_classification\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.experimental import enable_halving_search_cv  # noqa: F401\nfrom sklearn.model_selection import HalvingRandomSearchCV\n\nrng = np.random.RandomState(0)\n\nX, y = make_classification(n_samples=700, random_state=rng)\n\nclf = RandomForestClassifier(n_estimators=10, random_state=rng)\n\nparam_dist = {\n    \"max_depth\": [3, None],\n    \"max_features\": randint(1, 11),\n    \"min_samples_split\": randint(2, 11),\n    \"bootstrap\": [True, False],\n    \"criterion\": [\"gini\", \"entropy\"],\n}\n\nrsh = HalvingRandomSearchCV(\n    estimator=clf, param_distributions=param_dist, factor=2, random_state=rng\n)\nrsh.fit(X, y)\nrsh.best_params_"
       ]
     },
     {
@@ -54,7 +54,7 @@
       },
       "outputs": [],
       "source": [
-        "import numpy as np\nfrom sklearn import datasets\nfrom sklearn.semi_supervised import SelfTrainingClassifier\nfrom sklearn.svm import SVC\n\nrng = np.random.RandomState(42)\niris = datasets.load_iris()\nrandom_unlabeled_points = rng.rand(iris.target.shape[0]) < 0.3\niris.target[random_unlabeled_points] = -1\nsvc = SVC(probability=True, gamma=\"auto\")\nself_training_model = SelfTrainingClassifier(svc)\nself_training_model.fit(iris.data, iris.target)"
+        "import numpy as np\n\nfrom sklearn import datasets\nfrom sklearn.semi_supervised import SelfTrainingClassifier\nfrom sklearn.svm import SVC\n\nrng = np.random.RandomState(42)\niris = datasets.load_iris()\nrandom_unlabeled_points = rng.rand(iris.target.shape[0]) < 0.3\niris.target[random_unlabeled_points] = -1\nsvc = SVC(probability=True, gamma=\"auto\")\nself_training_model = SelfTrainingClassifier(svc)\nself_training_model.fit(iris.data, iris.target)"
       ]
     },
     {
@@ -72,7 +72,7 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.feature_selection import SequentialFeatureSelector\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.datasets import load_iris\n\nX, y = load_iris(return_X_y=True, as_frame=True)\nfeature_names = X.columns\nknn = KNeighborsClassifier(n_neighbors=3)\nsfs = SequentialFeatureSelector(knn, n_features_to_select=2)\nsfs.fit(X, y)\nprint(\n    \"Features selected by forward sequential selection: \"\n    f\"{feature_names[sfs.get_support()].tolist()}\"\n)"
+        "from sklearn.datasets import load_iris\nfrom sklearn.feature_selection import SequentialFeatureSelector\nfrom sklearn.neighbors import KNeighborsClassifier\n\nX, y = load_iris(return_X_y=True, as_frame=True)\nfeature_names = X.columns\nknn = KNeighborsClassifier(n_neighbors=3)\nsfs = SequentialFeatureSelector(knn, n_features_to_select=2)\nsfs.fit(X, y)\nprint(\n    \"Features selected by forward sequential selection: \"\n    f\"{feature_names[sfs.get_support()].tolist()}\"\n)"
       ]
     },
     {
@@ -90,7 +90,7 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.datasets import fetch_covtype\nfrom sklearn.pipeline import make_pipeline\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import MinMaxScaler\nfrom sklearn.kernel_approximation import PolynomialCountSketch\nfrom sklearn.linear_model import LogisticRegression\n\nX, y = fetch_covtype(return_X_y=True)\npipe = make_pipeline(\n    MinMaxScaler(),\n    PolynomialCountSketch(degree=2, n_components=300),\n    LogisticRegression(max_iter=1000),\n)\nX_train, X_test, y_train, y_test = train_test_split(\n    X, y, train_size=5000, test_size=10000, random_state=42\n)\npipe.fit(X_train, y_train).score(X_test, y_test)"
+        "from sklearn.datasets import fetch_covtype\nfrom sklearn.kernel_approximation import PolynomialCountSketch\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.pipeline import make_pipeline\nfrom sklearn.preprocessing import MinMaxScaler\n\nX, y = fetch_covtype(return_X_y=True)\npipe = make_pipeline(\n    MinMaxScaler(),\n    PolynomialCountSketch(degree=2, n_components=300),\n    LogisticRegression(max_iter=1000),\n)\nX_train, X_test, y_train, y_test = train_test_split(\n    X, y, train_size=5000, test_size=10000, random_state=42\n)\npipe.fit(X_train, y_train).score(X_test, y_test)"
       ]
     },
     {
@@ -126,7 +126,7 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.ensemble import RandomForestRegressor\nfrom sklearn.datasets import fetch_california_housing\n\n# from sklearn.inspection import plot_partial_dependence\nfrom sklearn.inspection import PartialDependenceDisplay\n\nX, y = fetch_california_housing(return_X_y=True, as_frame=True)\nfeatures = [\"MedInc\", \"AveOccup\", \"HouseAge\", \"AveRooms\"]\nest = RandomForestRegressor(n_estimators=10)\nest.fit(X, y)\n\n# plot_partial_dependence has been removed in version 1.2. From 1.2, use\n# PartialDependenceDisplay instead.\n# display = plot_partial_dependence(\ndisplay = PartialDependenceDisplay.from_estimator(\n    est,\n    X,\n    features,\n    kind=\"individual\",\n    subsample=50,\n    n_jobs=3,\n    grid_resolution=20,\n    random_state=0,\n)\ndisplay.figure_.suptitle(\n    \"Partial dependence of house value on non-location features\\n\"\n    \"for the California housing dataset, with BayesianRidge\"\n)\ndisplay.figure_.subplots_adjust(hspace=0.3)"
+        "from sklearn.datasets import fetch_california_housing\nfrom sklearn.ensemble import RandomForestRegressor\n\n# from sklearn.inspection import plot_partial_dependence\nfrom sklearn.inspection import PartialDependenceDisplay\n\nX, y = fetch_california_housing(return_X_y=True, as_frame=True)\nfeatures = [\"MedInc\", \"AveOccup\", \"HouseAge\", \"AveRooms\"]\nest = RandomForestRegressor(n_estimators=10)\nest.fit(X, y)\n\n# plot_partial_dependence has been removed in version 1.2. From 1.2, use\n# PartialDependenceDisplay instead.\n# display = plot_partial_dependence(\ndisplay = PartialDependenceDisplay.from_estimator(\n    est,\n    X,\n    features,\n    kind=\"individual\",\n    subsample=50,\n    n_jobs=3,\n    grid_resolution=20,\n    random_state=0,\n)\ndisplay.figure_.suptitle(\n    \"Partial dependence of house value on non-location features\\n\"\n    \"for the California housing dataset, with BayesianRidge\"\n)\ndisplay.figure_.subplots_adjust(hspace=0.3)"
       ]
     },
     {
@@ -144,7 +144,7 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.tree import DecisionTreeRegressor\nfrom sklearn.model_selection import train_test_split\nimport numpy as np\n\nn_samples, n_features = 1000, 20\nrng = np.random.RandomState(0)\nX = rng.randn(n_samples, n_features)\n# positive integer target correlated with X[:, 5] with many zeros:\ny = rng.poisson(lam=np.exp(X[:, 5]) / 2)\nX_train, X_test, y_train, y_test = train_test_split(X, y, random_state=rng)\nregressor = DecisionTreeRegressor(criterion=\"poisson\", random_state=0)\nregressor.fit(X_train, y_train)"
+        "import numpy as np\n\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.tree import DecisionTreeRegressor\n\nn_samples, n_features = 1000, 20\nrng = np.random.RandomState(0)\nX = rng.randn(n_samples, n_features)\n# positive integer target correlated with X[:, 5] with many zeros:\ny = rng.poisson(lam=np.exp(X[:, 5]) / 2)\nX_train, X_test, y_train, y_test = train_test_split(X, y, random_state=rng)\nregressor = DecisionTreeRegressor(criterion=\"poisson\", random_state=0)\nregressor.fit(X_train, y_train)"
       ]
     },
     {
@@ -171,7 +171,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/release_highlights/plot_release_highlights_1_0_0.ipynb b/dev/notebooks/auto_examples/release_highlights/plot_release_highlights_1_0_0.ipynb
index 14fdacb71e21e..86e93b7dfc6f6 100644
--- a/dev/notebooks/auto_examples/release_highlights/plot_release_highlights_1_0_0.ipynb
+++ b/dev/notebooks/auto_examples/release_highlights/plot_release_highlights_1_0_0.ipynb
@@ -29,7 +29,7 @@
       },
       "outputs": [],
       "source": [
-        "import numpy as np\nfrom sklearn.preprocessing import SplineTransformer\n\nX = np.arange(5).reshape(5, 1)\nspline = SplineTransformer(degree=2, n_knots=3)\nspline.fit_transform(X)"
+        "import numpy as np\n\nfrom sklearn.preprocessing import SplineTransformer\n\nX = np.arange(5).reshape(5, 1)\nspline = SplineTransformer(degree=2, n_knots=3)\nspline.fit_transform(X)"
       ]
     },
     {
@@ -54,7 +54,7 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.preprocessing import StandardScaler\nimport pandas as pd\n\nX = pd.DataFrame([[1, 2, 3], [4, 5, 6]], columns=[\"a\", \"b\", \"c\"])\nscalar = StandardScaler().fit(X)\nscalar.feature_names_in_"
+        "import pandas as pd\n\nfrom sklearn.preprocessing import StandardScaler\n\nX = pd.DataFrame([[1, 2, 3], [4, 5, 6]], columns=[\"a\", \"b\", \"c\"])\nscalar = StandardScaler().fit(X)\nscalar.feature_names_in_"
       ]
     },
     {
@@ -72,7 +72,7 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.compose import ColumnTransformer\nfrom sklearn.preprocessing import OneHotEncoder\nimport pandas as pd\n\nX = pd.DataFrame({\"pet\": [\"dog\", \"cat\", \"fish\"], \"age\": [3, 7, 1]})\npreprocessor = ColumnTransformer(\n    [\n        (\"numerical\", StandardScaler(), [\"age\"]),\n        (\"categorical\", OneHotEncoder(), [\"pet\"]),\n    ],\n    verbose_feature_names_out=False,\n).fit(X)\n\npreprocessor.get_feature_names_out()"
+        "import pandas as pd\n\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.preprocessing import OneHotEncoder\n\nX = pd.DataFrame({\"pet\": [\"dog\", \"cat\", \"fish\"], \"age\": [3, 7, 1]})\npreprocessor = ColumnTransformer(\n    [\n        (\"numerical\", StandardScaler(), [\"age\"]),\n        (\"categorical\", OneHotEncoder(), [\"pet\"]),\n    ],\n    verbose_feature_names_out=False,\n).fit(X)\n\npreprocessor.get_feature_names_out()"
       ]
     },
     {
@@ -138,7 +138,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/release_highlights/plot_release_highlights_1_1_0.ipynb b/dev/notebooks/auto_examples/release_highlights/plot_release_highlights_1_1_0.ipynb
index a7d21c14addc1..2351acd35d110 100644
--- a/dev/notebooks/auto_examples/release_highlights/plot_release_highlights_1_1_0.ipynb
+++ b/dev/notebooks/auto_examples/release_highlights/plot_release_highlights_1_1_0.ipynb
@@ -22,7 +22,7 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.ensemble import HistGradientBoostingRegressor\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n# Simple regression function for X * cos(X)\nrng = np.random.RandomState(42)\nX_1d = np.linspace(0, 10, num=2000)\nX = X_1d.reshape(-1, 1)\ny = X_1d * np.cos(X_1d) + rng.normal(scale=X_1d / 3)\n\nquantiles = [0.95, 0.5, 0.05]\nparameters = dict(loss=\"quantile\", max_bins=32, max_iter=50)\nhist_quantiles = {\n    f\"quantile={quantile:.2f}\": HistGradientBoostingRegressor(\n        **parameters, quantile=quantile\n    ).fit(X, y)\n    for quantile in quantiles\n}\n\nfig, ax = plt.subplots()\nax.plot(X_1d, y, \"o\", alpha=0.5, markersize=1)\nfor quantile, hist in hist_quantiles.items():\n    ax.plot(X_1d, hist.predict(X), label=quantile)\n_ = ax.legend(loc=\"lower left\")"
+        "import matplotlib.pyplot as plt\nimport numpy as np\n\nfrom sklearn.ensemble import HistGradientBoostingRegressor\n\n# Simple regression function for X * cos(X)\nrng = np.random.RandomState(42)\nX_1d = np.linspace(0, 10, num=2000)\nX = X_1d.reshape(-1, 1)\ny = X_1d * np.cos(X_1d) + rng.normal(scale=X_1d / 3)\n\nquantiles = [0.95, 0.5, 0.05]\nparameters = dict(loss=\"quantile\", max_bins=32, max_iter=50)\nhist_quantiles = {\n    f\"quantile={quantile:.2f}\": HistGradientBoostingRegressor(\n        **parameters, quantile=quantile\n    ).fit(X, y)\n    for quantile in quantiles\n}\n\nfig, ax = plt.subplots()\nax.plot(X_1d, y, \"o\", alpha=0.5, markersize=1)\nfor quantile, hist in hist_quantiles.items():\n    ax.plot(X_1d, hist.predict(X), label=quantile)\n_ = ax.legend(loc=\"lower left\")"
       ]
     },
     {
@@ -47,7 +47,7 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.compose import ColumnTransformer\nfrom sklearn.preprocessing import OneHotEncoder, StandardScaler\nfrom sklearn.pipeline import make_pipeline\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.feature_selection import SelectKBest\nfrom sklearn.datasets import fetch_openml\nfrom sklearn.linear_model import LogisticRegression\n\nX, y = fetch_openml(\n    \"titanic\", version=1, as_frame=True, return_X_y=True, parser=\"pandas\"\n)\nnumeric_features = [\"age\", \"fare\"]\nnumeric_transformer = make_pipeline(SimpleImputer(strategy=\"median\"), StandardScaler())\ncategorical_features = [\"embarked\", \"pclass\"]\n\npreprocessor = ColumnTransformer(\n    [\n        (\"num\", numeric_transformer, numeric_features),\n        (\n            \"cat\",\n            OneHotEncoder(handle_unknown=\"ignore\", sparse_output=False),\n            categorical_features,\n        ),\n    ],\n    verbose_feature_names_out=False,\n)\nlog_reg = make_pipeline(preprocessor, SelectKBest(k=7), LogisticRegression())\nlog_reg.fit(X, y)"
+        "from sklearn.compose import ColumnTransformer\nfrom sklearn.datasets import fetch_openml\nfrom sklearn.feature_selection import SelectKBest\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.pipeline import make_pipeline\nfrom sklearn.preprocessing import OneHotEncoder, StandardScaler\n\nX, y = fetch_openml(\n    \"titanic\", version=1, as_frame=True, return_X_y=True, parser=\"pandas\"\n)\nnumeric_features = [\"age\", \"fare\"]\nnumeric_transformer = make_pipeline(SimpleImputer(strategy=\"median\"), StandardScaler())\ncategorical_features = [\"embarked\", \"pclass\"]\n\npreprocessor = ColumnTransformer(\n    [\n        (\"num\", numeric_transformer, numeric_features),\n        (\n            \"cat\",\n            OneHotEncoder(handle_unknown=\"ignore\", sparse_output=False),\n            categorical_features,\n        ),\n    ],\n    verbose_feature_names_out=False,\n)\nlog_reg = make_pipeline(preprocessor, SelectKBest(k=7), LogisticRegression())\nlog_reg.fit(X, y)"
       ]
     },
     {
@@ -83,7 +83,7 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.preprocessing import OneHotEncoder\nimport numpy as np\n\nX = np.array(\n    [[\"dog\"] * 5 + [\"cat\"] * 20 + [\"rabbit\"] * 10 + [\"snake\"] * 3], dtype=object\n).T\nenc = OneHotEncoder(min_frequency=6, sparse_output=False).fit(X)\nenc.infrequent_categories_"
+        "import numpy as np\n\nfrom sklearn.preprocessing import OneHotEncoder\n\nX = np.array(\n    [[\"dog\"] * 5 + [\"cat\"] * 20 + [\"rabbit\"] * 10 + [\"snake\"] * 3], dtype=object\n).T\nenc = OneHotEncoder(min_frequency=6, sparse_output=False).fit(X)\nenc.infrequent_categories_"
       ]
     },
     {
@@ -126,7 +126,7 @@
       },
       "outputs": [],
       "source": [
-        "import numpy as np\nfrom sklearn.decomposition import MiniBatchNMF\n\nrng = np.random.RandomState(0)\nn_samples, n_features, n_components = 10, 10, 5\ntrue_W = rng.uniform(size=(n_samples, n_components))\ntrue_H = rng.uniform(size=(n_components, n_features))\nX = true_W @ true_H\n\nnmf = MiniBatchNMF(n_components=n_components, random_state=0)\n\nfor _ in range(10):\n    nmf.partial_fit(X)\n\nW = nmf.transform(X)\nH = nmf.components_\nX_reconstructed = W @ H\n\nprint(\n    f\"relative reconstruction error: \",\n    f\"{np.sum((X - X_reconstructed) ** 2) / np.sum(X**2):.5f}\",\n)"
+        "import numpy as np\n\nfrom sklearn.decomposition import MiniBatchNMF\n\nrng = np.random.RandomState(0)\nn_samples, n_features, n_components = 10, 10, 5\ntrue_W = rng.uniform(size=(n_samples, n_components))\ntrue_H = rng.uniform(size=(n_components, n_features))\nX = true_W @ true_H\n\nnmf = MiniBatchNMF(n_components=n_components, random_state=0)\n\nfor _ in range(10):\n    nmf.partial_fit(X)\n\nW = nmf.transform(X)\nH = nmf.components_\nX_reconstructed = W @ H\n\nprint(\n    \"relative reconstruction error: \",\n    f\"{np.sum((X - X_reconstructed) ** 2) / np.sum(X**2):.5f}\",\n)"
       ]
     },
     {
@@ -144,7 +144,7 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.datasets import make_blobs\nfrom sklearn.cluster import KMeans, BisectingKMeans\nimport matplotlib.pyplot as plt\n\nX, _ = make_blobs(n_samples=1000, centers=2, random_state=0)\n\nkm = KMeans(n_clusters=5, random_state=0, n_init=\"auto\").fit(X)\nbisect_km = BisectingKMeans(n_clusters=5, random_state=0).fit(X)\n\nfig, ax = plt.subplots(1, 2, figsize=(10, 5))\nax[0].scatter(X[:, 0], X[:, 1], s=10, c=km.labels_)\nax[0].scatter(km.cluster_centers_[:, 0], km.cluster_centers_[:, 1], s=20, c=\"r\")\nax[0].set_title(\"KMeans\")\n\nax[1].scatter(X[:, 0], X[:, 1], s=10, c=bisect_km.labels_)\nax[1].scatter(\n    bisect_km.cluster_centers_[:, 0], bisect_km.cluster_centers_[:, 1], s=20, c=\"r\"\n)\n_ = ax[1].set_title(\"BisectingKMeans\")"
+        "import matplotlib.pyplot as plt\n\nfrom sklearn.cluster import BisectingKMeans, KMeans\nfrom sklearn.datasets import make_blobs\n\nX, _ = make_blobs(n_samples=1000, centers=2, random_state=0)\n\nkm = KMeans(n_clusters=5, random_state=0, n_init=\"auto\").fit(X)\nbisect_km = BisectingKMeans(n_clusters=5, random_state=0).fit(X)\n\nfig, ax = plt.subplots(1, 2, figsize=(10, 5))\nax[0].scatter(X[:, 0], X[:, 1], s=10, c=km.labels_)\nax[0].scatter(km.cluster_centers_[:, 0], km.cluster_centers_[:, 1], s=20, c=\"r\")\nax[0].set_title(\"KMeans\")\n\nax[1].scatter(X[:, 0], X[:, 1], s=10, c=bisect_km.labels_)\nax[1].scatter(\n    bisect_km.cluster_centers_[:, 0], bisect_km.cluster_centers_[:, 1], s=20, c=\"r\"\n)\n_ = ax[1].set_title(\"BisectingKMeans\")"
       ]
     }
   ],
@@ -164,7 +164,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/release_highlights/plot_release_highlights_1_2_0.ipynb b/dev/notebooks/auto_examples/release_highlights/plot_release_highlights_1_2_0.ipynb
index 1674d7f6804d6..4e4565f4cc2ab 100644
--- a/dev/notebooks/auto_examples/release_highlights/plot_release_highlights_1_2_0.ipynb
+++ b/dev/notebooks/auto_examples/release_highlights/plot_release_highlights_1_2_0.ipynb
@@ -22,7 +22,7 @@
       },
       "outputs": [],
       "source": [
-        "import numpy as np\nfrom sklearn.datasets import load_iris\nfrom sklearn.preprocessing import StandardScaler, KBinsDiscretizer\nfrom sklearn.compose import ColumnTransformer\n\nX, y = load_iris(as_frame=True, return_X_y=True)\nsepal_cols = [\"sepal length (cm)\", \"sepal width (cm)\"]\npetal_cols = [\"petal length (cm)\", \"petal width (cm)\"]\n\npreprocessor = ColumnTransformer(\n    [\n        (\"scaler\", StandardScaler(), sepal_cols),\n        (\n            \"kbin\",\n            KBinsDiscretizer(encode=\"ordinal\", quantile_method=\"averaged_inverted_cdf\"),\n            petal_cols,\n        ),\n    ],\n    verbose_feature_names_out=False,\n).set_output(transform=\"pandas\")\n\nX_out = preprocessor.fit_transform(X)\nX_out.sample(n=5, random_state=0)"
+        "import numpy as np\n\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.datasets import load_iris\nfrom sklearn.preprocessing import KBinsDiscretizer, StandardScaler\n\nX, y = load_iris(as_frame=True, return_X_y=True)\nsepal_cols = [\"sepal length (cm)\", \"sepal width (cm)\"]\npetal_cols = [\"petal length (cm)\", \"petal width (cm)\"]\n\npreprocessor = ColumnTransformer(\n    [\n        (\"scaler\", StandardScaler(), sepal_cols),\n        (\n            \"kbin\",\n            KBinsDiscretizer(encode=\"ordinal\", quantile_method=\"averaged_inverted_cdf\"),\n            petal_cols,\n        ),\n    ],\n    verbose_feature_names_out=False,\n).set_output(transform=\"pandas\")\n\nX_out = preprocessor.fit_transform(X)\nX_out.sample(n=5, random_state=0)"
       ]
     },
     {
@@ -58,7 +58,7 @@
       },
       "outputs": [],
       "source": [
-        "import matplotlib.pyplot as plt\nfrom sklearn.metrics import PredictionErrorDisplay\n\nfig, axs = plt.subplots(nrows=1, ncols=2, figsize=(12, 5))\n_ = PredictionErrorDisplay.from_estimator(\n    hist_no_interact, X, y, kind=\"actual_vs_predicted\", ax=axs[0]\n)\n_ = PredictionErrorDisplay.from_estimator(\n    hist_no_interact, X, y, kind=\"residual_vs_predicted\", ax=axs[1]\n)"
+        "import matplotlib.pyplot as plt\n\nfrom sklearn.metrics import PredictionErrorDisplay\n\nfig, axs = plt.subplots(nrows=1, ncols=2, figsize=(12, 5))\n_ = PredictionErrorDisplay.from_estimator(\n    hist_no_interact, X, y, kind=\"actual_vs_predicted\", ax=axs[0]\n)\n_ = PredictionErrorDisplay.from_estimator(\n    hist_no_interact, X, y, kind=\"residual_vs_predicted\", ax=axs[1]\n)"
       ]
     },
     {
@@ -105,7 +105,7 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.preprocessing import OrdinalEncoder\nfrom sklearn.pipeline import make_pipeline\n\ncategorical_features = [\"pclass\", \"sex\", \"embarked\"]\nmodel = make_pipeline(\n    ColumnTransformer(\n        transformers=[(\"cat\", OrdinalEncoder(), categorical_features)],\n        remainder=\"passthrough\",\n    ),\n    HistGradientBoostingRegressor(random_state=0),\n).fit(X, y)"
+        "from sklearn.pipeline import make_pipeline\nfrom sklearn.preprocessing import OrdinalEncoder\n\ncategorical_features = [\"pclass\", \"sex\", \"embarked\"]\nmodel = make_pipeline(\n    ColumnTransformer(\n        transformers=[(\"cat\", OrdinalEncoder(), categorical_features)],\n        remainder=\"passthrough\",\n    ),\n    HistGradientBoostingRegressor(random_state=0),\n).fit(X, y)"
       ]
     },
     {
@@ -168,7 +168,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/release_highlights/plot_release_highlights_1_3_0.ipynb b/dev/notebooks/auto_examples/release_highlights/plot_release_highlights_1_3_0.ipynb
index 7f6d9bb920324..cde1d7606f132 100644
--- a/dev/notebooks/auto_examples/release_highlights/plot_release_highlights_1_3_0.ipynb
+++ b/dev/notebooks/auto_examples/release_highlights/plot_release_highlights_1_3_0.ipynb
@@ -29,7 +29,7 @@
       },
       "outputs": [],
       "source": [
-        "import numpy as np\nfrom sklearn.cluster import HDBSCAN\nfrom sklearn.datasets import load_digits\nfrom sklearn.metrics import v_measure_score\n\nX, true_labels = load_digits(return_X_y=True)\nprint(f\"number of digits: {len(np.unique(true_labels))}\")\n\nhdbscan = HDBSCAN(min_cluster_size=15).fit(X)\nnon_noisy_labels = hdbscan.labels_[hdbscan.labels_ != -1]\nprint(f\"number of clusters found: {len(np.unique(non_noisy_labels))}\")\n\nprint(v_measure_score(true_labels[hdbscan.labels_ != -1], non_noisy_labels))"
+        "import numpy as np\n\nfrom sklearn.cluster import HDBSCAN\nfrom sklearn.datasets import load_digits\nfrom sklearn.metrics import v_measure_score\n\nX, true_labels = load_digits(return_X_y=True)\nprint(f\"number of digits: {len(np.unique(true_labels))}\")\n\nhdbscan = HDBSCAN(min_cluster_size=15).fit(X)\nnon_noisy_labels = hdbscan.labels_[hdbscan.labels_ != -1]\nprint(f\"number of clusters found: {len(np.unique(non_noisy_labels))}\")\n\nprint(v_measure_score(true_labels[hdbscan.labels_ != -1], non_noisy_labels))"
       ]
     },
     {
@@ -47,7 +47,7 @@
       },
       "outputs": [],
       "source": [
-        "import numpy as np\nfrom sklearn.preprocessing import TargetEncoder\n\nX = np.array([[\"cat\"] * 30 + [\"dog\"] * 20 + [\"snake\"] * 38], dtype=object).T\ny = [90.3] * 30 + [20.4] * 20 + [21.2] * 38\n\nenc = TargetEncoder(random_state=0)\nX_trans = enc.fit_transform(X, y)\n\nenc.encodings_"
+        "import numpy as np\n\nfrom sklearn.preprocessing import TargetEncoder\n\nX = np.array([[\"cat\"] * 30 + [\"dog\"] * 20 + [\"snake\"] * 38], dtype=object).T\ny = [90.3] * 30 + [20.4] * 20 + [21.2] * 38\n\nenc = TargetEncoder(random_state=0)\nX_trans = enc.fit_transform(X, y)\n\nenc.encodings_"
       ]
     },
     {
@@ -65,7 +65,7 @@
       },
       "outputs": [],
       "source": [
-        "import numpy as np\nfrom sklearn.tree import DecisionTreeClassifier\n\nX = np.array([0, 1, 6, np.nan]).reshape(-1, 1)\ny = [0, 0, 1, 1]\n\ntree = DecisionTreeClassifier(random_state=0).fit(X, y)\ntree.predict(X)"
+        "import numpy as np\n\nfrom sklearn.tree import DecisionTreeClassifier\n\nX = np.array([0, 1, 6, np.nan]).reshape(-1, 1)\ny = [0, 0, 1, 1]\n\ntree = DecisionTreeClassifier(random_state=0).fit(X, y)\ntree.predict(X)"
       ]
     },
     {
@@ -101,7 +101,7 @@
       },
       "outputs": [],
       "source": [
-        "import numpy as np\nfrom sklearn.model_selection import cross_val_score\nfrom sklearn.datasets import make_low_rank_matrix\nfrom sklearn.ensemble import HistGradientBoostingRegressor\n\nn_samples, n_features = 500, 10\nrng = np.random.RandomState(0)\nX = make_low_rank_matrix(n_samples, n_features, random_state=rng)\ncoef = rng.uniform(low=-10, high=20, size=n_features)\ny = rng.gamma(shape=2, scale=np.exp(X @ coef) / 2)\ngbdt = HistGradientBoostingRegressor(loss=\"gamma\")\ncross_val_score(gbdt, X, y).mean()"
+        "import numpy as np\n\nfrom sklearn.datasets import make_low_rank_matrix\nfrom sklearn.ensemble import HistGradientBoostingRegressor\nfrom sklearn.model_selection import cross_val_score\n\nn_samples, n_features = 500, 10\nrng = np.random.RandomState(0)\nX = make_low_rank_matrix(n_samples, n_features, random_state=rng)\ncoef = rng.uniform(low=-10, high=20, size=n_features)\ny = rng.gamma(shape=2, scale=np.exp(X @ coef) / 2)\ngbdt = HistGradientBoostingRegressor(loss=\"gamma\")\ncross_val_score(gbdt, X, y).mean()"
       ]
     },
     {
@@ -119,7 +119,7 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.preprocessing import OrdinalEncoder\nimport numpy as np\n\nX = np.array(\n    [[\"dog\"] * 5 + [\"cat\"] * 20 + [\"rabbit\"] * 10 + [\"snake\"] * 3], dtype=object\n).T\nenc = OrdinalEncoder(min_frequency=6).fit(X)\nenc.infrequent_categories_"
+        "import numpy as np\n\nfrom sklearn.preprocessing import OrdinalEncoder\n\nX = np.array(\n    [[\"dog\"] * 5 + [\"cat\"] * 20 + [\"rabbit\"] * 10 + [\"snake\"] * 3], dtype=object\n).T\nenc = OrdinalEncoder(min_frequency=6).fit(X)\nenc.infrequent_categories_"
       ]
     }
   ],
@@ -139,7 +139,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/release_highlights/plot_release_highlights_1_4_0.ipynb b/dev/notebooks/auto_examples/release_highlights/plot_release_highlights_1_4_0.ipynb
index 30bc0c8753644..3ba9a09e6f8a6 100644
--- a/dev/notebooks/auto_examples/release_highlights/plot_release_highlights_1_4_0.ipynb
+++ b/dev/notebooks/auto_examples/release_highlights/plot_release_highlights_1_4_0.ipynb
@@ -40,7 +40,7 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.ensemble import HistGradientBoostingClassifier\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import roc_auc_score\n\nX_train, X_test, y_train, y_test = train_test_split(X_adult, y_adult, random_state=0)\nhist = HistGradientBoostingClassifier(categorical_features=\"from_dtype\")\n\nhist.fit(X_train, y_train)\ny_decision = hist.decision_function(X_test)\nprint(f\"ROC AUC score is {roc_auc_score(y_test, y_decision)}\")"
+        "from sklearn.ensemble import HistGradientBoostingClassifier\nfrom sklearn.metrics import roc_auc_score\nfrom sklearn.model_selection import train_test_split\n\nX_train, X_test, y_train, y_test = train_test_split(X_adult, y_adult, random_state=0)\nhist = HistGradientBoostingClassifier(categorical_features=\"from_dtype\")\n\nhist.fit(X_train, y_train)\ny_decision = hist.decision_function(X_test)\nprint(f\"ROC AUC score is {roc_auc_score(y_test, y_decision)}\")"
       ]
     },
     {
@@ -58,7 +58,7 @@
       },
       "outputs": [],
       "source": [
-        "import polars as pl\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.preprocessing import OneHotEncoder\nfrom sklearn.compose import ColumnTransformer\n\ndf = pl.DataFrame(\n    {\"height\": [120, 140, 150, 110, 100], \"pet\": [\"dog\", \"cat\", \"dog\", \"cat\", \"cat\"]}\n)\npreprocessor = ColumnTransformer(\n    [\n        (\"numerical\", StandardScaler(), [\"height\"]),\n        (\"categorical\", OneHotEncoder(sparse_output=False), [\"pet\"]),\n    ],\n    verbose_feature_names_out=False,\n)\npreprocessor.set_output(transform=\"polars\")\n\ndf_out = preprocessor.fit_transform(df)\ndf_out"
+        "import polars as pl\n\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.preprocessing import OneHotEncoder, StandardScaler\n\ndf = pl.DataFrame(\n    {\"height\": [120, 140, 150, 110, 100], \"pet\": [\"dog\", \"cat\", \"dog\", \"cat\", \"cat\"]}\n)\npreprocessor = ColumnTransformer(\n    [\n        (\"numerical\", StandardScaler(), [\"height\"]),\n        (\"categorical\", OneHotEncoder(sparse_output=False), [\"pet\"]),\n    ],\n    verbose_feature_names_out=False,\n)\npreprocessor.set_output(transform=\"polars\")\n\ndf_out = preprocessor.fit_transform(df)\ndf_out"
       ]
     },
     {
@@ -87,7 +87,7 @@
       },
       "outputs": [],
       "source": [
-        "import numpy as np\nfrom sklearn.ensemble import RandomForestClassifier\n\nX = np.array([0, 1, 6, np.nan]).reshape(-1, 1)\ny = [0, 0, 1, 1]\n\nforest = RandomForestClassifier(random_state=0).fit(X, y)\nforest.predict(X)"
+        "import numpy as np\n\nfrom sklearn.ensemble import RandomForestClassifier\n\nX = np.array([0, 1, 6, np.nan]).reshape(-1, 1)\ny = [0, 0, 1, 1]\n\nforest = RandomForestClassifier(random_state=0).fit(X, y)\nforest.predict(X)"
       ]
     },
     {
@@ -105,7 +105,7 @@
       },
       "outputs": [],
       "source": [
-        "import matplotlib.pyplot as plt\nfrom sklearn.inspection import PartialDependenceDisplay\nfrom sklearn.ensemble import RandomForestRegressor\n\nn_samples = 500\nrng = np.random.RandomState(0)\nX = rng.randn(n_samples, 2)\nnoise = rng.normal(loc=0.0, scale=0.01, size=n_samples)\ny = 5 * X[:, 0] + np.sin(10 * np.pi * X[:, 0]) - noise\n\nrf_no_cst = RandomForestRegressor().fit(X, y)\nrf_cst = RandomForestRegressor(monotonic_cst=[1, 0]).fit(X, y)\n\ndisp = PartialDependenceDisplay.from_estimator(\n    rf_no_cst,\n    X,\n    features=[0],\n    feature_names=[\"feature 0\"],\n    line_kw={\"linewidth\": 4, \"label\": \"unconstrained\", \"color\": \"tab:blue\"},\n)\nPartialDependenceDisplay.from_estimator(\n    rf_cst,\n    X,\n    features=[0],\n    line_kw={\"linewidth\": 4, \"label\": \"constrained\", \"color\": \"tab:orange\"},\n    ax=disp.axes_,\n)\ndisp.axes_[0, 0].plot(\n    X[:, 0], y, \"o\", alpha=0.5, zorder=-1, label=\"samples\", color=\"tab:green\"\n)\ndisp.axes_[0, 0].set_ylim(-3, 3)\ndisp.axes_[0, 0].set_xlim(-1, 1)\ndisp.axes_[0, 0].legend()\nplt.show()"
+        "import matplotlib.pyplot as plt\n\nfrom sklearn.ensemble import RandomForestRegressor\nfrom sklearn.inspection import PartialDependenceDisplay\n\nn_samples = 500\nrng = np.random.RandomState(0)\nX = rng.randn(n_samples, 2)\nnoise = rng.normal(loc=0.0, scale=0.01, size=n_samples)\ny = 5 * X[:, 0] + np.sin(10 * np.pi * X[:, 0]) - noise\n\nrf_no_cst = RandomForestRegressor().fit(X, y)\nrf_cst = RandomForestRegressor(monotonic_cst=[1, 0]).fit(X, y)\n\ndisp = PartialDependenceDisplay.from_estimator(\n    rf_no_cst,\n    X,\n    features=[0],\n    feature_names=[\"feature 0\"],\n    line_kw={\"linewidth\": 4, \"label\": \"unconstrained\", \"color\": \"tab:blue\"},\n)\nPartialDependenceDisplay.from_estimator(\n    rf_cst,\n    X,\n    features=[0],\n    line_kw={\"linewidth\": 4, \"label\": \"constrained\", \"color\": \"tab:orange\"},\n    ax=disp.axes_,\n)\ndisp.axes_[0, 0].plot(\n    X[:, 0], y, \"o\", alpha=0.5, zorder=-1, label=\"samples\", color=\"tab:green\"\n)\ndisp.axes_[0, 0].set_ylim(-3, 3)\ndisp.axes_[0, 0].set_xlim(-1, 1)\ndisp.axes_[0, 0].legend()\nplt.show()"
       ]
     },
     {
@@ -159,7 +159,7 @@
       },
       "outputs": [],
       "source": [
-        "import sklearn\nfrom sklearn.metrics import get_scorer\nfrom sklearn.datasets import make_regression\nfrom sklearn.linear_model import Lasso\nfrom sklearn.model_selection import GridSearchCV, cross_validate, GroupKFold\n\n# For now by default metadata routing is disabled, and need to be explicitly\n# enabled.\nsklearn.set_config(enable_metadata_routing=True)\n\nn_samples = 100\nX, y = make_regression(n_samples=n_samples, n_features=5, noise=0.5)\nrng = np.random.RandomState(7)\ngroups = rng.randint(0, 10, size=n_samples)\nsample_weights = rng.rand(n_samples)\nestimator = Lasso().set_fit_request(sample_weight=True)\nhyperparameter_grid = {\"alpha\": [0.1, 0.5, 1.0, 2.0]}\nscoring_inner_cv = get_scorer(\"neg_mean_squared_error\").set_score_request(\n    sample_weight=True\n)\ninner_cv = GroupKFold(n_splits=5)\n\ngrid_search = GridSearchCV(\n    estimator=estimator,\n    param_grid=hyperparameter_grid,\n    cv=inner_cv,\n    scoring=scoring_inner_cv,\n)\n\nouter_cv = GroupKFold(n_splits=5)\nscorers = {\n    \"mse\": get_scorer(\"neg_mean_squared_error\").set_score_request(sample_weight=True)\n}\nresults = cross_validate(\n    grid_search,\n    X,\n    y,\n    cv=outer_cv,\n    scoring=scorers,\n    return_estimator=True,\n    params={\"sample_weight\": sample_weights, \"groups\": groups},\n)\nprint(\"cv error on test sets:\", results[\"test_mse\"])\n\n# Setting the flag to the default `False` to avoid interference with other\n# scripts.\nsklearn.set_config(enable_metadata_routing=False)"
+        "import sklearn\nfrom sklearn.datasets import make_regression\nfrom sklearn.linear_model import Lasso\nfrom sklearn.metrics import get_scorer\nfrom sklearn.model_selection import GridSearchCV, GroupKFold, cross_validate\n\n# For now by default metadata routing is disabled, and need to be explicitly\n# enabled.\nsklearn.set_config(enable_metadata_routing=True)\n\nn_samples = 100\nX, y = make_regression(n_samples=n_samples, n_features=5, noise=0.5)\nrng = np.random.RandomState(7)\ngroups = rng.randint(0, 10, size=n_samples)\nsample_weights = rng.rand(n_samples)\nestimator = Lasso().set_fit_request(sample_weight=True)\nhyperparameter_grid = {\"alpha\": [0.1, 0.5, 1.0, 2.0]}\nscoring_inner_cv = get_scorer(\"neg_mean_squared_error\").set_score_request(\n    sample_weight=True\n)\ninner_cv = GroupKFold(n_splits=5)\n\ngrid_search = GridSearchCV(\n    estimator=estimator,\n    param_grid=hyperparameter_grid,\n    cv=inner_cv,\n    scoring=scoring_inner_cv,\n)\n\nouter_cv = GroupKFold(n_splits=5)\nscorers = {\n    \"mse\": get_scorer(\"neg_mean_squared_error\").set_score_request(sample_weight=True)\n}\nresults = cross_validate(\n    grid_search,\n    X,\n    y,\n    cv=outer_cv,\n    scoring=scorers,\n    return_estimator=True,\n    params={\"sample_weight\": sample_weights, \"groups\": groups},\n)\nprint(\"cv error on test sets:\", results[\"test_mse\"])\n\n# Setting the flag to the default `False` to avoid interference with other\n# scripts.\nsklearn.set_config(enable_metadata_routing=False)"
       ]
     },
     {
@@ -177,7 +177,7 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.decomposition import PCA\nimport scipy.sparse as sp\nfrom time import time\n\nX_sparse = sp.random(m=1000, n=1000, random_state=0)\nX_dense = X_sparse.toarray()\n\nt0 = time()\nPCA(n_components=10, svd_solver=\"arpack\").fit(X_sparse)\ntime_sparse = time() - t0\n\nt0 = time()\nPCA(n_components=10, svd_solver=\"arpack\").fit(X_dense)\ntime_dense = time() - t0\n\nprint(f\"Speedup: {time_dense / time_sparse:.1f}x\")"
+        "from time import time\n\nimport scipy.sparse as sp\n\nfrom sklearn.decomposition import PCA\n\nX_sparse = sp.random(m=1000, n=1000, random_state=0)\nX_dense = X_sparse.toarray()\n\nt0 = time()\nPCA(n_components=10, svd_solver=\"arpack\").fit(X_sparse)\ntime_sparse = time() - t0\n\nt0 = time()\nPCA(n_components=10, svd_solver=\"arpack\").fit(X_dense)\ntime_dense = time() - t0\n\nprint(f\"Speedup: {time_dense / time_sparse:.1f}x\")"
       ]
     }
   ],
@@ -197,7 +197,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/release_highlights/plot_release_highlights_1_5_0.ipynb b/dev/notebooks/auto_examples/release_highlights/plot_release_highlights_1_5_0.ipynb
index a30bea8f043d6..c706368f94432 100644
--- a/dev/notebooks/auto_examples/release_highlights/plot_release_highlights_1_5_0.ipynb
+++ b/dev/notebooks/auto_examples/release_highlights/plot_release_highlights_1_5_0.ipynb
@@ -22,7 +22,7 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.datasets import make_classification\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.metrics import ConfusionMatrixDisplay\n\n\nX, y = make_classification(n_samples=10_000, weights=[0.9, 0.1], random_state=0)\nX_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)\n\nclassifier_05 = LogisticRegression(C=1e6, random_state=0).fit(X_train, y_train)\n_ = ConfusionMatrixDisplay.from_estimator(classifier_05, X_test, y_test)"
+        "from sklearn.datasets import make_classification\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.metrics import ConfusionMatrixDisplay\nfrom sklearn.model_selection import train_test_split\n\nX, y = make_classification(n_samples=10_000, weights=[0.9, 0.1], random_state=0)\nX_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)\n\nclassifier_05 = LogisticRegression(C=1e6, random_state=0).fit(X_train, y_train)\n_ = ConfusionMatrixDisplay.from_estimator(classifier_05, X_test, y_test)"
       ]
     },
     {
@@ -76,7 +76,7 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.model_selection import TunedThresholdClassifierCV\nfrom sklearn.metrics import make_scorer\n\ncustom_scorer = make_scorer(\n    custom_score, response_method=\"predict\", greater_is_better=True\n)\ntuned_classifier = TunedThresholdClassifierCV(\n    classifier_05, cv=5, scoring=custom_scorer\n).fit(X, y)\n\nprint(f\"Tuned decision threshold: {tuned_classifier.best_threshold_:.3f}\")\nprint(f\"Custom score: {custom_score(y_test, tuned_classifier.predict(X_test)):.2f}\")"
+        "from sklearn.metrics import make_scorer\nfrom sklearn.model_selection import TunedThresholdClassifierCV\n\ncustom_scorer = make_scorer(\n    custom_score, response_method=\"predict\", greater_is_better=True\n)\ntuned_classifier = TunedThresholdClassifierCV(\n    classifier_05, cv=5, scoring=custom_scorer\n).fit(X, y)\n\nprint(f\"Tuned decision threshold: {tuned_classifier.best_threshold_:.3f}\")\nprint(f\"Custom score: {custom_score(y_test, tuned_classifier.predict(X_test)):.2f}\")"
       ]
     },
     {
@@ -144,7 +144,7 @@
       },
       "outputs": [],
       "source": [
-        "import numpy as np\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\n\nX = np.array([[0, 1, 2], [3, 4, 5]])\ncolumn_transformer = ColumnTransformer(\n    [(\"std_scaler\", StandardScaler(), [0]), (\"one_hot\", OneHotEncoder(), [1, 2])]\n)\n\ncolumn_transformer.fit(X)\n\nprint(column_transformer[\"std_scaler\"])\nprint(column_transformer[\"one_hot\"])"
+        "import numpy as np\n\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.preprocessing import OneHotEncoder, StandardScaler\n\nX = np.array([[0, 1, 2], [3, 4, 5]])\ncolumn_transformer = ColumnTransformer(\n    [(\"std_scaler\", StandardScaler(), [0]), (\"one_hot\", OneHotEncoder(), [1, 2])]\n)\n\ncolumn_transformer.fit(X)\n\nprint(column_transformer[\"std_scaler\"])\nprint(column_transformer[\"one_hot\"])"
       ]
     },
     {
@@ -200,7 +200,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/release_highlights/plot_release_highlights_1_6_0.ipynb b/dev/notebooks/auto_examples/release_highlights/plot_release_highlights_1_6_0.ipynb
index 5ede5b9f0a967..33b681d7ce4e8 100644
--- a/dev/notebooks/auto_examples/release_highlights/plot_release_highlights_1_6_0.ipynb
+++ b/dev/notebooks/auto_examples/release_highlights/plot_release_highlights_1_6_0.ipynb
@@ -22,7 +22,7 @@
       },
       "outputs": [],
       "source": [
-        "import time\nfrom sklearn.datasets import make_classification\nfrom sklearn.frozen import FrozenEstimator\nfrom sklearn.linear_model import SGDClassifier\nfrom sklearn.model_selection import FixedThresholdClassifier\n\nX, y = make_classification(n_samples=1000, random_state=0)\n\nstart = time.time()\nclassifier = SGDClassifier().fit(X, y)\nprint(f\"Fitting the classifier took {(time.time() - start) * 1_000:.2f} milliseconds\")\n\nstart = time.time()\nthreshold_classifier = FixedThresholdClassifier(\n    estimator=FrozenEstimator(classifier), threshold=0.9\n).fit(X, y)\nprint(\n    f\"Fitting the threshold classifier took {(time.time() - start) * 1_000:.2f} \"\n    \"milliseconds\"\n)"
+        "import time\n\nfrom sklearn.datasets import make_classification\nfrom sklearn.frozen import FrozenEstimator\nfrom sklearn.linear_model import SGDClassifier\nfrom sklearn.model_selection import FixedThresholdClassifier\n\nX, y = make_classification(n_samples=1000, random_state=0)\n\nstart = time.time()\nclassifier = SGDClassifier().fit(X, y)\nprint(f\"Fitting the classifier took {(time.time() - start) * 1_000:.2f} milliseconds\")\n\nstart = time.time()\nthreshold_classifier = FixedThresholdClassifier(\n    estimator=FrozenEstimator(classifier), threshold=0.9\n).fit(X, y)\nprint(\n    f\"Fitting the threshold classifier took {(time.time() - start) * 1_000:.2f} \"\n    \"milliseconds\"\n)"
       ]
     },
     {
@@ -61,7 +61,7 @@
       },
       "outputs": [],
       "source": [
-        "import numpy as np\nfrom sklearn.ensemble import ExtraTreesClassifier\n\nX = np.array([0, 1, 6, np.nan]).reshape(-1, 1)\ny = [0, 0, 1, 1]\n\nforest = ExtraTreesClassifier(random_state=0).fit(X, y)\nforest.predict(X)"
+        "import numpy as np\n\nfrom sklearn.ensemble import ExtraTreesClassifier\n\nX = np.array([0, 1, 6, np.nan]).reshape(-1, 1)\ny = [0, 0, 1, 1]\n\nforest = ExtraTreesClassifier(random_state=0).fit(X, y)\nforest.predict(X)"
       ]
     },
     {
@@ -116,7 +116,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/semi_supervised/plot_label_propagation_digits.ipynb b/dev/notebooks/auto_examples/semi_supervised/plot_label_propagation_digits.ipynb
index b83c66580a5df..881b6e4ae5a30 100644
--- a/dev/notebooks/auto_examples/semi_supervised/plot_label_propagation_digits.ipynb
+++ b/dev/notebooks/auto_examples/semi_supervised/plot_label_propagation_digits.ipynb
@@ -197,7 +197,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/semi_supervised/plot_label_propagation_digits_active_learning.ipynb b/dev/notebooks/auto_examples/semi_supervised/plot_label_propagation_digits_active_learning.ipynb
index b91fe30eab1c5..fb395d0f428a5 100644
--- a/dev/notebooks/auto_examples/semi_supervised/plot_label_propagation_digits_active_learning.ipynb
+++ b/dev/notebooks/auto_examples/semi_supervised/plot_label_propagation_digits_active_learning.ipynb
@@ -4,7 +4,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# Label Propagation digits active learning\n\nDemonstrates an active learning technique to learn handwritten digits\nusing label propagation.\n\nWe start by training a label propagation model with only 10 labeled points,\nthen we select the top five most uncertain points to label. Next, we train\nwith 15 labeled points (original 10 + 5 new ones). We repeat this process\nfour times to have a model trained with 30 labeled examples. Note you can\nincrease this to label more than 30 by changing `max_iterations`. Labeling\nmore than 30 can be useful to get a sense for the speed of convergence of\nthis active learning technique.\n\nA plot will appear showing the top 5 most uncertain digits for each iteration\nof training. These may or may not contain mistakes, but we will train the next\nmodel with their true labels.\n"
+        "\n# Label Propagation digits: Active learning\n\nDemonstrates an active learning technique to learn handwritten digits\nusing label propagation.\n\nWe start by training a label propagation model with only 10 labeled points,\nthen we select the top five most uncertain points to label. Next, we train\nwith 15 labeled points (original 10 + 5 new ones). We repeat this process\nfour times to have a model trained with 30 labeled examples. Note you can\nincrease this to label more than 30 by changing `max_iterations`. Labeling\nmore than 30 can be useful to get a sense for the speed of convergence of\nthis active learning technique.\n\nA plot will appear showing the top 5 most uncertain digits for each iteration\nof training. These may or may not contain mistakes, but we will train the next\nmodel with their true labels.\n"
       ]
     },
     {
@@ -15,7 +15,7 @@
       },
       "outputs": [],
       "source": [
-        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom scipy import stats\n\nfrom sklearn import datasets\nfrom sklearn.metrics import classification_report, confusion_matrix\nfrom sklearn.semi_supervised import LabelSpreading\n\ndigits = datasets.load_digits()\nrng = np.random.RandomState(0)\nindices = np.arange(len(digits.data))\nrng.shuffle(indices)\n\nX = digits.data[indices[:330]]\ny = digits.target[indices[:330]]\nimages = digits.images[indices[:330]]\n\nn_total_samples = len(y)\nn_labeled_points = 40\nmax_iterations = 5\n\nunlabeled_indices = np.arange(n_total_samples)[n_labeled_points:]\nf = plt.figure()\n\nfor i in range(max_iterations):\n    if len(unlabeled_indices) == 0:\n        print(\"No unlabeled items left to label.\")\n        break\n    y_train = np.copy(y)\n    y_train[unlabeled_indices] = -1\n\n    lp_model = LabelSpreading(gamma=0.25, max_iter=20)\n    lp_model.fit(X, y_train)\n\n    predicted_labels = lp_model.transduction_[unlabeled_indices]\n    true_labels = y[unlabeled_indices]\n\n    cm = confusion_matrix(true_labels, predicted_labels, labels=lp_model.classes_)\n\n    print(\"Iteration %i %s\" % (i, 70 * \"_\"))\n    print(\n        \"Label Spreading model: %d labeled & %d unlabeled (%d total)\"\n        % (n_labeled_points, n_total_samples - n_labeled_points, n_total_samples)\n    )\n\n    print(classification_report(true_labels, predicted_labels))\n\n    print(\"Confusion matrix\")\n    print(cm)\n\n    # compute the entropies of transduced label distributions\n    pred_entropies = stats.distributions.entropy(lp_model.label_distributions_.T)\n\n    # select up to 5 digit examples that the classifier is most uncertain about\n    uncertainty_index = np.argsort(pred_entropies)[::-1]\n    uncertainty_index = uncertainty_index[\n        np.isin(uncertainty_index, unlabeled_indices)\n    ][:5]\n\n    # keep track of indices that we get labels for\n    delete_indices = np.array([], dtype=int)\n\n    # for more than 5 iterations, visualize the gain only on the first 5\n    if i < 5:\n        f.text(\n            0.05,\n            (1 - (i + 1) * 0.183),\n            \"model %d\\n\\nfit with\\n%d labels\" % ((i + 1), i * 5 + 10),\n            size=10,\n        )\n    for index, image_index in enumerate(uncertainty_index):\n        image = images[image_index]\n\n        # for more than 5 iterations, visualize the gain only on the first 5\n        if i < 5:\n            sub = f.add_subplot(5, 5, index + 1 + (5 * i))\n            sub.imshow(image, cmap=plt.cm.gray_r, interpolation=\"none\")\n            sub.set_title(\n                \"predict: %i\\ntrue: %i\"\n                % (lp_model.transduction_[image_index], y[image_index]),\n                size=10,\n            )\n            sub.axis(\"off\")\n\n        # labeling 5 points, remote from labeled set\n        (delete_index,) = np.where(unlabeled_indices == image_index)\n        delete_indices = np.concatenate((delete_indices, delete_index))\n\n    unlabeled_indices = np.delete(unlabeled_indices, delete_indices)\n    n_labeled_points += len(uncertainty_index)\n\nf.suptitle(\n    (\n        \"Active learning with Label Propagation.\\nRows show 5 most \"\n        \"uncertain labels to learn with the next model.\"\n    ),\n    y=1.15,\n)\nplt.subplots_adjust(left=0.2, bottom=0.03, right=0.9, top=0.9, wspace=0.2, hspace=0.85)\nplt.show()"
+        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom scipy import stats\n\nfrom sklearn import datasets\nfrom sklearn.metrics import classification_report, confusion_matrix\nfrom sklearn.semi_supervised import LabelSpreading\n\ndigits = datasets.load_digits()\nrng = np.random.RandomState(0)\nindices = np.arange(len(digits.data))\nrng.shuffle(indices)\n\nX = digits.data[indices[:330]]\ny = digits.target[indices[:330]]\nimages = digits.images[indices[:330]]\n\nn_total_samples = len(y)\nn_labeled_points = 40\nmax_iterations = 5\n\nunlabeled_indices = np.arange(n_total_samples)[n_labeled_points:]\nf = plt.figure()\n\nfor i in range(max_iterations):\n    if len(unlabeled_indices) == 0:\n        print(\"No unlabeled items left to label.\")\n        break\n    y_train = np.copy(y)\n    y_train[unlabeled_indices] = -1\n\n    lp_model = LabelSpreading(gamma=0.25, max_iter=20)\n    lp_model.fit(X, y_train)\n\n    predicted_labels = lp_model.transduction_[unlabeled_indices]\n    true_labels = y[unlabeled_indices]\n\n    cm = confusion_matrix(true_labels, predicted_labels, labels=lp_model.classes_)\n\n    print(\"Iteration %i %s\" % (i, 70 * \"_\"))\n    print(\n        \"Label Spreading model: %d labeled & %d unlabeled (%d total)\"\n        % (n_labeled_points, n_total_samples - n_labeled_points, n_total_samples)\n    )\n\n    print(classification_report(true_labels, predicted_labels))\n\n    print(\"Confusion matrix\")\n    print(cm)\n\n    # compute the entropies of transduced label distributions\n    pred_entropies = stats.distributions.entropy(lp_model.label_distributions_.T)\n\n    # select up to 5 digit examples that the classifier is most uncertain about\n    uncertainty_index = np.argsort(pred_entropies)[::-1]\n    uncertainty_index = uncertainty_index[\n        np.isin(uncertainty_index, unlabeled_indices)\n    ][:5]\n\n    # keep track of indices that we get labels for\n    delete_indices = np.array([], dtype=int)\n\n    # for more than 5 iterations, visualize the gain only on the first 5\n    if i < 5:\n        f.text(\n            0.05,\n            (1 - (i + 1) * 0.183),\n            \"model %d\\n\\nfit with\\n%d labels\" % ((i + 1), i * 5 + 10),\n            size=10,\n        )\n    for index, image_index in enumerate(uncertainty_index):\n        image = images[image_index]\n\n        # for more than 5 iterations, visualize the gain only on the first 5\n        if i < 5:\n            sub = f.add_subplot(5, 5, index + 1 + (5 * i))\n            sub.imshow(image, cmap=plt.cm.gray_r, interpolation=\"none\")\n            sub.set_title(\n                \"predict: %i\\ntrue: %i\"\n                % (lp_model.transduction_[image_index], y[image_index]),\n                size=10,\n            )\n            sub.axis(\"off\")\n\n        # labeling 5 points, remote from labeled set\n        (delete_index,) = (unlabeled_indices == image_index).nonzero()\n        delete_indices = np.concatenate((delete_indices, delete_index))\n\n    unlabeled_indices = np.delete(unlabeled_indices, delete_indices)\n    n_labeled_points += len(uncertainty_index)\n\nf.suptitle(\n    (\n        \"Active learning with Label Propagation.\\nRows show 5 most \"\n        \"uncertain labels to learn with the next model.\"\n    ),\n    y=1.15,\n)\nplt.subplots_adjust(left=0.2, bottom=0.03, right=0.9, top=0.9, wspace=0.2, hspace=0.85)\nplt.show()"
       ]
     }
   ],
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/semi_supervised/plot_label_propagation_structure.ipynb b/dev/notebooks/auto_examples/semi_supervised/plot_label_propagation_structure.ipynb
index ff3139819fdd1..26b5d73a64842 100644
--- a/dev/notebooks/auto_examples/semi_supervised/plot_label_propagation_structure.ipynb
+++ b/dev/notebooks/auto_examples/semi_supervised/plot_label_propagation_structure.ipynb
@@ -4,7 +4,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# Label Propagation learning a complex structure\n\nExample of LabelPropagation learning a complex internal structure\nto demonstrate \"manifold learning\". The outer circle should be\nlabeled \"red\" and the inner circle \"blue\". Because both label groups\nlie inside their own distinct shape, we can see that the labels\npropagate correctly around the circle.\n"
+        "\n# Label Propagation circles: Learning a complex structure\n\nExample of LabelPropagation learning a complex internal structure\nto demonstrate \"manifold learning\". The outer circle should be\nlabeled \"red\" and the inner circle \"blue\". Because both label groups\nlie inside their own distinct shape, we can see that the labels\npropagate correctly around the circle.\n"
       ]
     },
     {
@@ -87,7 +87,7 @@
       },
       "outputs": [],
       "source": [
-        "output_labels = label_spread.transduction_\noutput_label_array = np.asarray(output_labels)\nouter_numbers = np.where(output_label_array == outer)[0]\ninner_numbers = np.where(output_label_array == inner)[0]\n\nplt.figure(figsize=(4, 4))\nplt.scatter(\n    X[outer_numbers, 0],\n    X[outer_numbers, 1],\n    color=\"navy\",\n    marker=\"s\",\n    lw=0,\n    s=10,\n    label=\"outer learned\",\n)\nplt.scatter(\n    X[inner_numbers, 0],\n    X[inner_numbers, 1],\n    color=\"c\",\n    marker=\"s\",\n    lw=0,\n    s=10,\n    label=\"inner learned\",\n)\nplt.legend(scatterpoints=1, shadow=False, loc=\"center\")\nplt.title(\"Labels learned with Label Spreading (KNN)\")\nplt.show()"
+        "output_labels = label_spread.transduction_\noutput_label_array = np.asarray(output_labels)\nouter_numbers = (output_label_array == outer).nonzero()[0]\ninner_numbers = (output_label_array == inner).nonzero()[0]\n\nplt.figure(figsize=(4, 4))\nplt.scatter(\n    X[outer_numbers, 0],\n    X[outer_numbers, 1],\n    color=\"navy\",\n    marker=\"s\",\n    lw=0,\n    s=10,\n    label=\"outer learned\",\n)\nplt.scatter(\n    X[inner_numbers, 0],\n    X[inner_numbers, 1],\n    color=\"c\",\n    marker=\"s\",\n    lw=0,\n    s=10,\n    label=\"inner learned\",\n)\nplt.legend(scatterpoints=1, shadow=False, loc=\"center\")\nplt.title(\"Labels learned with Label Spreading (KNN)\")\nplt.show()"
       ]
     }
   ],
@@ -107,7 +107,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/semi_supervised/plot_self_training_varying_threshold.ipynb b/dev/notebooks/auto_examples/semi_supervised/plot_self_training_varying_threshold.ipynb
index 9b0a0b38070d2..7426e9f7e0fa7 100644
--- a/dev/notebooks/auto_examples/semi_supervised/plot_self_training_varying_threshold.ipynb
+++ b/dev/notebooks/auto_examples/semi_supervised/plot_self_training_varying_threshold.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/semi_supervised/plot_semi_supervised_newsgroups.ipynb b/dev/notebooks/auto_examples/semi_supervised/plot_semi_supervised_newsgroups.ipynb
index 99a9eb9fae66c..2f083c8135070 100644
--- a/dev/notebooks/auto_examples/semi_supervised/plot_semi_supervised_newsgroups.ipynb
+++ b/dev/notebooks/auto_examples/semi_supervised/plot_semi_supervised_newsgroups.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/semi_supervised/plot_semi_supervised_versus_svm_iris.ipynb b/dev/notebooks/auto_examples/semi_supervised/plot_semi_supervised_versus_svm_iris.ipynb
index f760fd0ad615f..974d193fc8284 100644
--- a/dev/notebooks/auto_examples/semi_supervised/plot_semi_supervised_versus_svm_iris.ipynb
+++ b/dev/notebooks/auto_examples/semi_supervised/plot_semi_supervised_versus_svm_iris.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/svm/plot_custom_kernel.ipynb b/dev/notebooks/auto_examples/svm/plot_custom_kernel.ipynb
index ce703f266eaa2..d6459a73a0166 100644
--- a/dev/notebooks/auto_examples/svm/plot_custom_kernel.ipynb
+++ b/dev/notebooks/auto_examples/svm/plot_custom_kernel.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
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   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/svm/plot_iris_svc.ipynb b/dev/notebooks/auto_examples/svm/plot_iris_svc.ipynb
index 60c4d81c41baa..a7fcbb439d373 100644
--- a/dev/notebooks/auto_examples/svm/plot_iris_svc.ipynb
+++ b/dev/notebooks/auto_examples/svm/plot_iris_svc.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
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   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/svm/plot_linearsvc_support_vectors.ipynb b/dev/notebooks/auto_examples/svm/plot_linearsvc_support_vectors.ipynb
index 8247b4d25deab..e8679fc8d86de 100644
--- a/dev/notebooks/auto_examples/svm/plot_linearsvc_support_vectors.ipynb
+++ b/dev/notebooks/auto_examples/svm/plot_linearsvc_support_vectors.ipynb
@@ -15,7 +15,7 @@
       },
       "outputs": [],
       "source": [
-        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nfrom sklearn.datasets import make_blobs\nfrom sklearn.inspection import DecisionBoundaryDisplay\nfrom sklearn.svm import LinearSVC\n\nX, y = make_blobs(n_samples=40, centers=2, random_state=0)\n\nplt.figure(figsize=(10, 5))\nfor i, C in enumerate([1, 100]):\n    # \"hinge\" is the standard SVM loss\n    clf = LinearSVC(C=C, loss=\"hinge\", random_state=42).fit(X, y)\n    # obtain the support vectors through the decision function\n    decision_function = clf.decision_function(X)\n    # we can also calculate the decision function manually\n    # decision_function = np.dot(X, clf.coef_[0]) + clf.intercept_[0]\n    # The support vectors are the samples that lie within the margin\n    # boundaries, whose size is conventionally constrained to 1\n    support_vector_indices = np.where(np.abs(decision_function) <= 1 + 1e-15)[0]\n    support_vectors = X[support_vector_indices]\n\n    plt.subplot(1, 2, i + 1)\n    plt.scatter(X[:, 0], X[:, 1], c=y, s=30, cmap=plt.cm.Paired)\n    ax = plt.gca()\n    DecisionBoundaryDisplay.from_estimator(\n        clf,\n        X,\n        ax=ax,\n        grid_resolution=50,\n        plot_method=\"contour\",\n        colors=\"k\",\n        levels=[-1, 0, 1],\n        alpha=0.5,\n        linestyles=[\"--\", \"-\", \"--\"],\n    )\n    plt.scatter(\n        support_vectors[:, 0],\n        support_vectors[:, 1],\n        s=100,\n        linewidth=1,\n        facecolors=\"none\",\n        edgecolors=\"k\",\n    )\n    plt.title(\"C=\" + str(C))\nplt.tight_layout()\nplt.show()"
+        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nfrom sklearn.datasets import make_blobs\nfrom sklearn.inspection import DecisionBoundaryDisplay\nfrom sklearn.svm import LinearSVC\n\nX, y = make_blobs(n_samples=40, centers=2, random_state=0)\n\nplt.figure(figsize=(10, 5))\nfor i, C in enumerate([1, 100]):\n    # \"hinge\" is the standard SVM loss\n    clf = LinearSVC(C=C, loss=\"hinge\", random_state=42).fit(X, y)\n    # obtain the support vectors through the decision function\n    decision_function = clf.decision_function(X)\n    # we can also calculate the decision function manually\n    # decision_function = np.dot(X, clf.coef_[0]) + clf.intercept_[0]\n    # The support vectors are the samples that lie within the margin\n    # boundaries, whose size is conventionally constrained to 1\n    support_vector_indices = (np.abs(decision_function) <= 1 + 1e-15).nonzero()[0]\n    support_vectors = X[support_vector_indices]\n\n    plt.subplot(1, 2, i + 1)\n    plt.scatter(X[:, 0], X[:, 1], c=y, s=30, cmap=plt.cm.Paired)\n    ax = plt.gca()\n    DecisionBoundaryDisplay.from_estimator(\n        clf,\n        X,\n        ax=ax,\n        grid_resolution=50,\n        plot_method=\"contour\",\n        colors=\"k\",\n        levels=[-1, 0, 1],\n        alpha=0.5,\n        linestyles=[\"--\", \"-\", \"--\"],\n    )\n    plt.scatter(\n        support_vectors[:, 0],\n        support_vectors[:, 1],\n        s=100,\n        linewidth=1,\n        facecolors=\"none\",\n        edgecolors=\"k\",\n    )\n    plt.title(\"C=\" + str(C))\nplt.tight_layout()\nplt.show()"
       ]
     }
   ],
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/svm/plot_oneclass.ipynb b/dev/notebooks/auto_examples/svm/plot_oneclass.ipynb
index 347dc701d7be1..5df4cd8b86920 100644
--- a/dev/notebooks/auto_examples/svm/plot_oneclass.ipynb
+++ b/dev/notebooks/auto_examples/svm/plot_oneclass.ipynb
@@ -57,7 +57,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/svm/plot_rbf_parameters.ipynb b/dev/notebooks/auto_examples/svm/plot_rbf_parameters.ipynb
index feb5a4f86657e..bf2daa479df1a 100644
--- a/dev/notebooks/auto_examples/svm/plot_rbf_parameters.ipynb
+++ b/dev/notebooks/auto_examples/svm/plot_rbf_parameters.ipynb
@@ -179,7 +179,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/svm/plot_separating_hyperplane.ipynb b/dev/notebooks/auto_examples/svm/plot_separating_hyperplane.ipynb
index 0676aea6e3467..d4e435fe76889 100644
--- a/dev/notebooks/auto_examples/svm/plot_separating_hyperplane.ipynb
+++ b/dev/notebooks/auto_examples/svm/plot_separating_hyperplane.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
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   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/svm/plot_separating_hyperplane_unbalanced.ipynb b/dev/notebooks/auto_examples/svm/plot_separating_hyperplane_unbalanced.ipynb
index b7817d74bd68d..73ee80d3ba9d8 100644
--- a/dev/notebooks/auto_examples/svm/plot_separating_hyperplane_unbalanced.ipynb
+++ b/dev/notebooks/auto_examples/svm/plot_separating_hyperplane_unbalanced.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
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   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/svm/plot_svm_anova.ipynb b/dev/notebooks/auto_examples/svm/plot_svm_anova.ipynb
index cf8e62644f713..d0df16c7502b4 100644
--- a/dev/notebooks/auto_examples/svm/plot_svm_anova.ipynb
+++ b/dev/notebooks/auto_examples/svm/plot_svm_anova.ipynb
@@ -89,7 +89,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
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diff --git a/dev/notebooks/auto_examples/svm/plot_svm_kernels.ipynb b/dev/notebooks/auto_examples/svm/plot_svm_kernels.ipynb
index f3cb4526da3f9..7e03f6ba0e00e 100644
--- a/dev/notebooks/auto_examples/svm/plot_svm_kernels.ipynb
+++ b/dev/notebooks/auto_examples/svm/plot_svm_kernels.ipynb
@@ -175,7 +175,7 @@
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       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
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diff --git a/dev/notebooks/auto_examples/svm/plot_svm_margin.ipynb b/dev/notebooks/auto_examples/svm/plot_svm_margin.ipynb
index f6392e06cd009..00e0790e0ab69 100644
--- a/dev/notebooks/auto_examples/svm/plot_svm_margin.ipynb
+++ b/dev/notebooks/auto_examples/svm/plot_svm_margin.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
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diff --git a/dev/notebooks/auto_examples/svm/plot_svm_regression.ipynb b/dev/notebooks/auto_examples/svm/plot_svm_regression.ipynb
index daa961cc9b860..f70b7eb0526c8 100644
--- a/dev/notebooks/auto_examples/svm/plot_svm_regression.ipynb
+++ b/dev/notebooks/auto_examples/svm/plot_svm_regression.ipynb
@@ -89,7 +89,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
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diff --git a/dev/notebooks/auto_examples/svm/plot_svm_scale_c.ipynb b/dev/notebooks/auto_examples/svm/plot_svm_scale_c.ipynb
index 6f38593baed52..54a7d916cfb4b 100644
--- a/dev/notebooks/auto_examples/svm/plot_svm_scale_c.ipynb
+++ b/dev/notebooks/auto_examples/svm/plot_svm_scale_c.ipynb
@@ -136,7 +136,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
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diff --git a/dev/notebooks/auto_examples/svm/plot_svm_tie_breaking.ipynb b/dev/notebooks/auto_examples/svm/plot_svm_tie_breaking.ipynb
index 2a7f00ceb92bc..2e5fca5793fe7 100644
--- a/dev/notebooks/auto_examples/svm/plot_svm_tie_breaking.ipynb
+++ b/dev/notebooks/auto_examples/svm/plot_svm_tie_breaking.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/svm/plot_weighted_samples.ipynb b/dev/notebooks/auto_examples/svm/plot_weighted_samples.ipynb
index eb6ec97afa300..cc4aa6dac91b1 100644
--- a/dev/notebooks/auto_examples/svm/plot_weighted_samples.ipynb
+++ b/dev/notebooks/auto_examples/svm/plot_weighted_samples.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
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diff --git a/dev/notebooks/auto_examples/text/plot_document_classification_20newsgroups.ipynb b/dev/notebooks/auto_examples/text/plot_document_classification_20newsgroups.ipynb
index 826cb8f3095bf..16e3188306567 100644
--- a/dev/notebooks/auto_examples/text/plot_document_classification_20newsgroups.ipynb
+++ b/dev/notebooks/auto_examples/text/plot_document_classification_20newsgroups.ipynb
@@ -247,7 +247,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
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diff --git a/dev/notebooks/auto_examples/text/plot_document_clustering.ipynb b/dev/notebooks/auto_examples/text/plot_document_clustering.ipynb
index 1055efe8b34be..d4199a72f67ce 100644
--- a/dev/notebooks/auto_examples/text/plot_document_clustering.ipynb
+++ b/dev/notebooks/auto_examples/text/plot_document_clustering.ipynb
@@ -287,7 +287,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
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diff --git a/dev/notebooks/auto_examples/text/plot_hashing_vs_dict_vectorizer.ipynb b/dev/notebooks/auto_examples/text/plot_hashing_vs_dict_vectorizer.ipynb
index 142442ac99d74..1c5324210a14e 100644
--- a/dev/notebooks/auto_examples/text/plot_hashing_vs_dict_vectorizer.ipynb
+++ b/dev/notebooks/auto_examples/text/plot_hashing_vs_dict_vectorizer.ipynb
@@ -323,7 +323,7 @@
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       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
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diff --git a/dev/notebooks/auto_examples/tree/plot_cost_complexity_pruning.ipynb b/dev/notebooks/auto_examples/tree/plot_cost_complexity_pruning.ipynb
index 0a5deba1cfb5c..c449c82d26a80 100644
--- a/dev/notebooks/auto_examples/tree/plot_cost_complexity_pruning.ipynb
+++ b/dev/notebooks/auto_examples/tree/plot_cost_complexity_pruning.ipynb
@@ -125,7 +125,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
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   "nbformat": 4,
diff --git a/dev/notebooks/auto_examples/tree/plot_iris_dtc.ipynb b/dev/notebooks/auto_examples/tree/plot_iris_dtc.ipynb
index 36419ccaa7410..17e87023e6105 100644
--- a/dev/notebooks/auto_examples/tree/plot_iris_dtc.ipynb
+++ b/dev/notebooks/auto_examples/tree/plot_iris_dtc.ipynb
@@ -51,7 +51,7 @@
       },
       "outputs": [],
       "source": [
-        "import matplotlib.pyplot as plt\nimport numpy as np\n\nfrom sklearn.datasets import load_iris\nfrom sklearn.inspection import DecisionBoundaryDisplay\nfrom sklearn.tree import DecisionTreeClassifier\n\n# Parameters\nn_classes = 3\nplot_colors = \"ryb\"\nplot_step = 0.02\n\n\nfor pairidx, pair in enumerate([[0, 1], [0, 2], [0, 3], [1, 2], [1, 3], [2, 3]]):\n    # We only take the two corresponding features\n    X = iris.data[:, pair]\n    y = iris.target\n\n    # Train\n    clf = DecisionTreeClassifier().fit(X, y)\n\n    # Plot the decision boundary\n    ax = plt.subplot(2, 3, pairidx + 1)\n    plt.tight_layout(h_pad=0.5, w_pad=0.5, pad=2.5)\n    DecisionBoundaryDisplay.from_estimator(\n        clf,\n        X,\n        cmap=plt.cm.RdYlBu,\n        response_method=\"predict\",\n        ax=ax,\n        xlabel=iris.feature_names[pair[0]],\n        ylabel=iris.feature_names[pair[1]],\n    )\n\n    # Plot the training points\n    for i, color in zip(range(n_classes), plot_colors):\n        idx = np.where(y == i)\n        plt.scatter(\n            X[idx, 0],\n            X[idx, 1],\n            c=color,\n            label=iris.target_names[i],\n            edgecolor=\"black\",\n            s=15,\n        )\n\nplt.suptitle(\"Decision surface of decision trees trained on pairs of features\")\nplt.legend(loc=\"lower right\", borderpad=0, handletextpad=0)\n_ = plt.axis(\"tight\")"
+        "import matplotlib.pyplot as plt\nimport numpy as np\n\nfrom sklearn.datasets import load_iris\nfrom sklearn.inspection import DecisionBoundaryDisplay\nfrom sklearn.tree import DecisionTreeClassifier\n\n# Parameters\nn_classes = 3\nplot_colors = \"ryb\"\nplot_step = 0.02\n\n\nfor pairidx, pair in enumerate([[0, 1], [0, 2], [0, 3], [1, 2], [1, 3], [2, 3]]):\n    # We only take the two corresponding features\n    X = iris.data[:, pair]\n    y = iris.target\n\n    # Train\n    clf = DecisionTreeClassifier().fit(X, y)\n\n    # Plot the decision boundary\n    ax = plt.subplot(2, 3, pairidx + 1)\n    plt.tight_layout(h_pad=0.5, w_pad=0.5, pad=2.5)\n    DecisionBoundaryDisplay.from_estimator(\n        clf,\n        X,\n        cmap=plt.cm.RdYlBu,\n        response_method=\"predict\",\n        ax=ax,\n        xlabel=iris.feature_names[pair[0]],\n        ylabel=iris.feature_names[pair[1]],\n    )\n\n    # Plot the training points\n    for i, color in zip(range(n_classes), plot_colors):\n        idx = np.asarray(y == i).nonzero()\n        plt.scatter(\n            X[idx, 0],\n            X[idx, 1],\n            c=color,\n            label=iris.target_names[i],\n            edgecolor=\"black\",\n            s=15,\n        )\n\nplt.suptitle(\"Decision surface of decision trees trained on pairs of features\")\nplt.legend(loc=\"lower right\", borderpad=0, handletextpad=0)\n_ = plt.axis(\"tight\")"
       ]
     },
     {
@@ -89,7 +89,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
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diff --git a/dev/notebooks/auto_examples/tree/plot_tree_regression.ipynb b/dev/notebooks/auto_examples/tree/plot_tree_regression.ipynb
index 6c944004a3f94..a0cc4dd672feb 100644
--- a/dev/notebooks/auto_examples/tree/plot_tree_regression.ipynb
+++ b/dev/notebooks/auto_examples/tree/plot_tree_regression.ipynb
@@ -200,7 +200,7 @@
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       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
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diff --git a/dev/notebooks/auto_examples/tree/plot_unveil_tree_structure.ipynb b/dev/notebooks/auto_examples/tree/plot_unveil_tree_structure.ipynb
index 61c1558c5854d..2a9bc93df60b1 100644
--- a/dev/notebooks/auto_examples/tree/plot_unveil_tree_structure.ipynb
+++ b/dev/notebooks/auto_examples/tree/plot_unveil_tree_structure.ipynb
@@ -132,7 +132,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.16"
+      "version": "3.10.17"
     }
   },
   "nbformat": 4,
diff --git a/dev/objects.inv b/dev/objects.inv
index 72dc1abb60c77..facdbe161e013 100644
Binary files a/dev/objects.inv and b/dev/objects.inv differ
diff --git a/dev/presentations.html b/dev/presentations.html
index 6ea286a256d98..21c77856002c8 100644
--- a/dev/presentations.html
+++ b/dev/presentations.html
@@ -7,7 +7,7 @@
   <head>
     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="viewport" content="width=device-width, initial-scale=1" />
-<meta property="og:title" content="13. External Resources, Videos and Talks" />
+<meta property="og:title" content="14. External Resources, Videos and Talks" />
 <meta property="og:type" content="website" />
 <meta property="og:url" content="https://scikit-learn/stable/presentations.html" />
 <meta property="og:site_name" content="scikit-learn" />
@@ -16,7 +16,7 @@
 <meta property="og:image:alt" content="scikit-learn" />
 <meta name="description" content="The scikit-learn MOOC: If you are new to scikit-learn, or looking to strengthen your understanding, we highly recommend the scikit-learn MOOC (Massive Open Online Course). The MOOC, created and mai..." />
 
-    <title>13. External Resources, Videos and Talks &#8212; scikit-learn 1.7.dev0 documentation</title>
+    <title>14. External Resources, Videos and Talks &#8212; scikit-learn 1.7.dev0 documentation</title>
   
   
   
@@ -42,7 +42,7 @@
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/copybutton.css?v=76b2166b" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/plot_directive.css" />
     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
+    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=8ee2c72c" />
     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery.css?v=d2d258e8" />
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     <link rel="stylesheet" type="text/css" href="http://github.com/_static/sg_gallery-dataframe.css?v=2082cf3c" />
@@ -80,8 +80,8 @@
     <link rel="icon" href="http://github.com/_static/favicon.ico"/>
     <link rel="author" title="About these documents" href="http://github.com/about.html" />
     <link rel="search" title="Search" href="http://github.com/search.html" />
-    <link rel="next" title="1. Metadata Routing" href="http://github.com/metadata_routing.html" />
-    <link rel="prev" title="12. Choosing the right estimator" href="http://github.com/machine_learning_map.html" />
+    <link rel="next" title="API Reference" href="http://github.com/api/index.html" />
+    <link rel="prev" title="13. Choosing the right estimator" href="http://github.com/machine_learning_map.html" />
   <meta name="viewport" content="width=device-width, initial-scale=1"/>
   <meta name="docsearch:language" content="en"/>
   <meta name="docsearch:version" content="1.7" />
@@ -593,45 +593,46 @@
 <li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/learning_curve.html">3.5. Validation curves: plotting scores to evaluate models</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/inspection.html">4. Inspection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/partial_dependence.html">4.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/permutation_importance.html">4.2. Permutation feature importance</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/metadata_routing.html">4. Metadata Routing</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/inspection.html">5. Inspection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/partial_dependence.html">5.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/permutation_importance.html">5.2. Permutation feature importance</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/visualizations.html">5. Visualizations</a></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/data_transforms.html">6. Dataset transformations</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/compose.html">6.1. Pipelines and composite estimators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/feature_extraction.html">6.2. Feature extraction</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/preprocessing.html">6.3. Preprocessing data</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/impute.html">6.4. Imputation of missing values</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/unsupervised_reduction.html">6.5. Unsupervised dimensionality reduction</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/random_projection.html">6.6. Random Projection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/kernel_approximation.html">6.7. Kernel Approximation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/metrics.html">6.8. Pairwise metrics, Affinities and Kernels</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/preprocessing_targets.html">6.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/visualizations.html">6. Visualizations</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/data_transforms.html">7. Dataset transformations</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/compose.html">7.1. Pipelines and composite estimators</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/feature_extraction.html">7.2. Feature extraction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/preprocessing.html">7.3. Preprocessing data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/impute.html">7.4. Imputation of missing values</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/unsupervised_reduction.html">7.5. Unsupervised dimensionality reduction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/random_projection.html">7.6. Random Projection</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/kernel_approximation.html">7.7. Kernel Approximation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/metrics.html">7.8. Pairwise metrics, Affinities and Kernels</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/preprocessing_targets.html">7.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/datasets.html">7. Dataset loading utilities</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html">7.1. Toy datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html">7.2. Real world datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html">7.3. Generated datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html">7.4. Loading other datasets</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/datasets.html">8. Dataset loading utilities</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html">8.1. Toy datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html">8.2. Real world datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html">8.3. Generated datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html">8.4. Loading other datasets</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/computing.html">8. Computing with scikit-learn</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/scaling_strategies.html">8.1. Strategies to scale computationally: bigger data</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/computational_performance.html">8.2. Computational Performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/parallelism.html">8.3. Parallelism, resource management, and configuration</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/computing.html">9. Computing with scikit-learn</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/scaling_strategies.html">9.1. Strategies to scale computationally: bigger data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/computational_performance.html">9.2. Computational Performance</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/parallelism.html">9.3. Parallelism, resource management, and configuration</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/model_persistence.html">9. Model persistence</a></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/common_pitfalls.html">10. Common pitfalls and recommended practices</a></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/dispatching.html">11. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/array_api.html">11.1. Array API support (experimental)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/model_persistence.html">10. Model persistence</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/common_pitfalls.html">11. Common pitfalls and recommended practices</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/dispatching.html">12. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/array_api.html">12.1. Array API support (experimental)</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/machine_learning_map.html">12. Choosing the right estimator</a></li>
-<li class="toctree-l1 current active"><a class="current reference internal" href="#">13. External Resources, Videos and Talks</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/machine_learning_map.html">13. Choosing the right estimator</a></li>
+<li class="toctree-l1 current active"><a class="current reference internal" href="#">14. External Resources, Videos and Talks</a></li>
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 </div>
 </nav></div>
@@ -668,7 +669,7 @@
     
     <li class="breadcrumb-item"><a href="http://github.com/user_guide.html" class="nav-link">User Guide</a></li>
     
-    <li class="breadcrumb-item active" aria-current="page"><span class="ellipsis"><span class="section-number">13. </span>External Resources, Videos and Talks</span></li>
+    <li class="breadcrumb-item active" aria-current="page"><span class="ellipsis"><span class="section-number">14. </span>External Resources, Videos and Talks</span></li>
   </ul>
 </nav>
 </div>
@@ -686,9 +687,9 @@
                 <article class="bd-article">
                   
   <section id="external-resources-videos-and-talks">
-<span id="external-resources"></span><h1><span class="section-number">13. </span>External Resources, Videos and Talks<a class="headerlink" href="#external-resources-videos-and-talks" title="Link to this heading">#</a></h1>
+<span id="external-resources"></span><h1><span class="section-number">14. </span>External Resources, Videos and Talks<a class="headerlink" href="#external-resources-videos-and-talks" title="Link to this heading">#</a></h1>
 <section id="the-scikit-learn-mooc">
-<h2><span class="section-number">13.1. </span>The scikit-learn MOOC<a class="headerlink" href="#the-scikit-learn-mooc" title="Link to this heading">#</a></h2>
+<h2><span class="section-number">14.1. </span>The scikit-learn MOOC<a class="headerlink" href="#the-scikit-learn-mooc" title="Link to this heading">#</a></h2>
 <p>If you are new to scikit-learn, or looking to strengthen your understanding,
 we highly recommend the <strong>scikit-learn MOOC (Massive Open Online Course)</strong>.</p>
 <p>The MOOC, created and maintained by some of the scikit-learn core-contributors,
@@ -707,7 +708,7 @@ <h2><span class="section-number">13.1. </span>The scikit-learn MOOC<a class="hea
 <a class="reference external" href="https://www.youtube.com/playlist?list=PL2okA_2qDJ-m44KooOI7x8tu85wr4ez4f">playlist</a>.</p>
 </section>
 <section id="videos">
-<span id="id1"></span><h2><span class="section-number">13.2. </span>Videos<a class="headerlink" href="#videos" title="Link to this heading">#</a></h2>
+<span id="id1"></span><h2><span class="section-number">14.2. </span>Videos<a class="headerlink" href="#videos" title="Link to this heading">#</a></h2>
 <ul class="simple">
 <li><p>The <a class="reference external" href="https://www.youtube.com/&#64;scikit-learn">scikit-learn YouTube channel</a>
 features a
@@ -718,14 +719,14 @@ <h2><span class="section-number">13.1. </span>The scikit-learn MOOC<a class="hea
 </ul>
 </section>
 <section id="new-to-scientific-python">
-<h2><span class="section-number">13.3. </span>New to Scientific Python?<a class="headerlink" href="#new-to-scientific-python" title="Link to this heading">#</a></h2>
+<h2><span class="section-number">14.3. </span>New to Scientific Python?<a class="headerlink" href="#new-to-scientific-python" title="Link to this heading">#</a></h2>
 <p>For those that are still new to the scientific Python ecosystem, we highly
 recommend the <a class="reference external" href="https://scipy-lectures.org/">Python Scientific Lecture Notes</a>. This will help you find your footing a
 bit and will definitely improve your scikit-learn experience.  A basic
 understanding of NumPy arrays is recommended to make the most of scikit-learn.</p>
 </section>
 <section id="external-tutorials">
-<h2><span class="section-number">13.4. </span>External Tutorials<a class="headerlink" href="#external-tutorials" title="Link to this heading">#</a></h2>
+<h2><span class="section-number">14.4. </span>External Tutorials<a class="headerlink" href="#external-tutorials" title="Link to this heading">#</a></h2>
 <p>There are several online tutorials available which are geared toward
 specific subject areas:</p>
 <ul class="simple">
@@ -751,15 +752,15 @@ <h2><span class="section-number">13.4. </span>External Tutorials<a class="header
       <i class="fa-solid fa-angle-left"></i>
       <div class="prev-next-info">
         <p class="prev-next-subtitle">previous</p>
-        <p class="prev-next-title"><span class="section-number">12. </span>Choosing the right estimator</p>
+        <p class="prev-next-title"><span class="section-number">13. </span>Choosing the right estimator</p>
       </div>
     </a>
     <a class="right-next"
-       href="http://github.com/metadata_routing.html"
+       href="http://github.com/api/index.html"
        title="next page">
       <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">1. </span>Metadata Routing</p>
+        <p class="prev-next-title">API Reference</p>
       </div>
       <i class="fa-solid fa-angle-right"></i>
     </a>
@@ -786,10 +787,10 @@ <h2><span class="section-number">13.4. </span>External Tutorials<a class="header
   </div>
   <nav class="bd-toc-nav page-toc" aria-labelledby="pst-page-navigation-heading-2">
     <ul class="visible nav section-nav flex-column">
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#the-scikit-learn-mooc">13.1. The scikit-learn MOOC</a></li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#videos">13.2. Videos</a></li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#new-to-scientific-python">13.3. New to Scientific Python?</a></li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#external-tutorials">13.4. External Tutorials</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#the-scikit-learn-mooc">14.1. The scikit-learn MOOC</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#videos">14.2. Videos</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#new-to-scientific-python">14.3. New to Scientific Python?</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#external-tutorials">14.4. External Tutorials</a></li>
 </ul>
   </nav></div>
 
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index c58867bfee12c..db0bb9401e58f 100644
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1045], "complementnbcomplementnb": 266, "complet": [0, 1, 43, 72, 73, 74, 79, 82, 85, 91, 95, 116, 149, 152, 159, 161, 164, 181, 186, 187, 214, 233, 234, 241, 244, 259, 263, 310, 312, 314, 348, 355, 367, 372, 376, 377, 380, 384, 405, 408, 409, 411, 412, 438, 442, 446, 449, 459, 460, 461, 464, 493, 534, 535, 536, 537, 542, 544, 556, 567, 568, 580, 586, 587, 588, 590, 627, 628, 629, 630, 645, 649, 657, 671, 673, 704, 705, 715, 716, 717, 729, 731, 736, 737, 757, 786, 795, 832, 835, 838, 847, 863, 864, 865, 866, 933, 983, 993, 995, 999, 1000, 1002, 1014, 1015, 1018, 1020, 1030, 1033, 1037, 1038, 1039, 1040, 1041, 1042, 1043], "completed_fac": 243, "completeness_scor": [1, 73, 82, 91, 315, 348, 404, 736, 737, 795, 999], "complex": [42, 48, 49, 54, 58, 104, 140, 154, 163, 168, 173, 179, 181, 202, 212, 221, 224, 233, 240, 241, 244, 257, 263, 266, 269, 274, 306, 314, 317, 318, 322, 324, 336, 340, 349, 350, 354, 368, 372, 377, 384, 404, 405, 409, 411, 415, 437, 441, 444, 445, 447, 464, 486, 498, 499, 500, 512, 532, 539, 549, 555, 556, 557, 558, 560, 562, 563, 603, 630, 638, 656, 658, 676, 677, 734, 750, 800, 830, 832, 860, 864, 865, 869, 877, 884, 900, 904, 907, 910, 912, 913, 914, 915, 988, 989, 991, 992, 994, 996, 998, 999, 1000, 1005, 1009, 1012, 1019, 1020, 1021, 1023, 1025, 1031, 1033, 1039, 1040, 1043], "complexity_comput": [46, 49], "complexity_label": [46, 49], "compli": [372, 386, 941, 1019], "complianc": [0, 149], "compliant": [319, 372, 1048, 1049, 1050], "complic": [64, 241, 300, 378, 414, 694, 1002], "compon": [1, 10, 43, 44, 55, 79, 91, 95, 102, 105, 114, 115, 119, 121, 122, 124, 125, 127, 128, 130, 152, 173, 181, 183, 195, 216, 227, 238, 239, 242, 250, 251, 252, 254, 255, 256, 258, 264, 266, 271, 285, 286, 287, 288, 296, 297, 303, 310, 316, 352, 359, 364, 367, 372, 374, 378, 381, 398, 400, 404, 405, 407, 412, 414, 416, 418, 434, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 459, 460, 462, 466, 467, 468, 469, 470, 471, 472, 473, 479, 480, 481, 482, 500, 502, 513, 519, 524, 529, 530, 531, 532, 533, 534, 535, 536, 537, 538, 539, 540, 541, 542, 543, 544, 545, 546, 547, 548, 549, 550, 551, 552, 553, 554, 555, 556, 557, 558, 559, 560, 561, 562, 563, 564, 571, 580, 581, 582, 587, 588, 589, 590, 591, 592, 593, 594, 595, 596, 597, 598, 599, 601, 602, 610, 611, 612, 613, 614, 615, 616, 618, 619, 620, 621, 622, 623, 624, 625, 627, 628, 629, 630, 631, 635, 638, 639, 640, 641, 642, 643, 644, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 670, 671, 672, 673, 674, 675, 676, 677, 678, 679, 680, 688, 689, 690, 691, 692, 695, 797, 798, 799, 800, 803, 804, 814, 822, 830, 832, 833, 834, 835, 836, 837, 838, 839, 840, 841, 842, 843, 846, 847, 848, 849, 850, 851, 852, 853, 854, 855, 856, 860, 861, 862, 864, 865, 867, 868, 869, 870, 871, 872, 873, 874, 875, 876, 877, 878, 879, 880, 881, 882, 883, 884, 885, 894, 895, 896, 897, 898, 899, 900, 901, 904, 905, 906, 907, 908, 909, 910, 912, 913, 914, 915, 919, 947, 948, 991, 996, 998, 1009, 1011, 1014, 1018, 1019, 1020, 1021, 1023, 1024, 1025, 1030, 1032, 1036, 1037, 1038, 1039, 1041, 1042, 1043, 1044, 1045, 1046, 1047, 1048, 1049], "component_1": 255, "component_2": 255, "component_indices_": [639, 1043], "components_": [45, 54, 91, 116, 120, 122, 123, 130, 238, 239, 303, 310, 318, 386, 405, 409, 441, 529, 530, 531, 532, 534, 535, 536, 537, 538, 539, 541, 542, 639, 853, 860, 896, 897, 991, 1011, 1034, 1036, 1042, 1045, 1048], "components_col": 105, "compos": [1, 43, 44, 62, 101, 102, 103, 107, 116, 136, 143, 154, 181, 184, 185, 186, 208, 210, 225, 236, 244, 246, 248, 279, 282, 311, 315, 317, 318, 319, 321, 322, 385, 395, 404, 405, 408, 461, 462, 463, 464, 513, 551, 612, 998, 1000, 1020, 1030], "composit": [6, 35, 236, 315, 364, 408, 658, 788, 989, 995, 1008, 1025, 1031], "compound": [43, 225, 412, 610, 612, 680, 724, 752, 995], "compoundkernel": [1, 610, 1044], "comprehens": [340, 380, 412, 758, 759, 997, 1023, 1038], "compress": [42, 50, 55, 99, 181, 282, 305, 367, 398, 404, 409, 412, 413, 652, 672, 692, 834, 877, 969, 972, 984, 995, 1000, 1009, 1020, 1030, 1039], "compressed_raccoon_kmean": 86, "compressed_raccoon_uniform": 86, "compris": [102, 146, 263, 347, 348, 349, 367, 384, 409, 411, 513, 807, 996], "compromis": [48, 64, 185, 359, 372, 647, 679, 1002, 1033], "comput": [0, 1, 27, 43, 45, 46, 50, 52, 53, 58, 63, 72, 74, 76, 77, 80, 85, 87, 90, 91, 93, 94, 102, 104, 110, 111, 112, 113, 121, 129, 137, 141, 144, 145, 146, 147, 148, 149, 163, 164, 168, 173, 175, 176, 179, 184, 185, 186, 187, 189, 192, 193, 195, 196, 197, 198, 199, 208, 210, 212, 215, 221, 224, 225, 228, 230, 231, 235, 237, 238, 240, 244, 245, 247, 259, 261, 263, 265, 266, 267, 268, 272, 274, 276, 285, 287, 289, 291, 292, 294, 295, 298, 305, 314, 318, 319, 322, 326, 328, 336, 340, 343, 347, 348, 349, 354, 360, 361, 366, 367, 369, 372, 374, 377, 378, 379, 381, 384, 385, 386, 389, 390, 398, 399, 400, 401, 402, 404, 406, 407, 409, 410, 411, 412, 413, 414, 415, 416, 417, 434, 435, 436, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 449, 454, 456, 458, 459, 460, 462, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 480, 514, 529, 530, 532, 533, 535, 536, 537, 538, 539, 540, 541, 542, 543, 544, 545, 546, 547, 548, 549, 551, 552, 553, 554, 555, 556, 557, 558, 559, 560, 561, 562, 563, 565, 566, 567, 568, 572, 581, 584, 585, 588, 589, 590, 592, 593, 598, 599, 602, 603, 604, 605, 606, 607, 608, 610, 612, 613, 614, 615, 616, 619, 620, 621, 622, 623, 624, 625, 627, 629, 630, 631, 632, 633, 634, 637, 638, 639, 640, 641, 642, 644, 645, 646, 647, 648, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 671, 672, 673, 674, 676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 690, 691, 692, 694, 695, 696, 697, 698, 699, 700, 701, 702, 703, 704, 705, 706, 707, 708, 709, 710, 712, 713, 715, 716, 717, 718, 719, 720, 725, 726, 727, 729, 730, 731, 734, 736, 738, 739, 740, 743, 754, 755, 756, 757, 758, 759, 760, 761, 763, 764, 766, 767, 768, 769, 770, 771, 772, 773, 774, 775, 776, 777, 778, 779, 780, 781, 782, 783, 784, 786, 787, 788, 789, 792, 793, 794, 796, 797, 798, 799, 800, 803, 804, 806, 814, 822, 823, 825, 826, 827, 828, 829, 831, 832, 833, 834, 844, 845, 846, 847, 848, 849, 850, 851, 852, 854, 855, 856, 857, 858, 860, 861, 862, 869, 870, 873, 874, 879, 880, 881, 882, 883, 884, 888, 889, 891, 893, 894, 895, 896, 897, 898, 899, 900, 901, 902, 904, 906, 909, 911, 912, 913, 914, 915, 945, 946, 947, 948, 965, 966, 971, 973, 979, 988, 991, 993, 994, 995, 996, 997, 998, 999, 1000, 1001, 1002, 1003, 1004, 1005, 1007, 1009, 1011, 1012, 1013, 1014, 1015, 1018, 1019, 1023, 1025, 1027, 1030, 1031, 1032, 1033, 1034, 1035, 1036, 1037, 1038, 1039, 1040, 1041, 1042, 1043, 1044, 1045, 1046, 1047, 1048, 1049, 1050], "computation": [37, 53, 109, 120, 140, 145, 154, 168, 285, 340, 347, 358, 373, 384, 404, 405, 408, 409, 411, 414, 536, 632, 633, 671, 763, 800, 802, 803, 804, 814, 827, 995, 996, 998, 1006, 1007, 1011, 1025, 1034], "compute_class_weight": [1, 386, 1033, 1044, 1050], "compute_corrected_ttest": 265, "compute_dist": [438, 442, 1042], "compute_full_tre": [438, 442, 1034], "compute_import": 1032, "compute_inverse_compon": [896, 897, 1011], "compute_inverse_transform": 1044, "compute_label": [439, 446], "compute_node_depth": 354, "compute_optics_graph": [1, 452, 453, 1047], "compute_sample_weight": [1, 1044], "compute_scor": [107, 127, 191, 192, 644, 645, 1039], "compute_sourc": 416, "computed_scor": 645, "con": [398, 588, 998], "concat": [43, 179, 183, 199, 225, 877], "concaten": [1, 63, 70, 74, 83, 94, 101, 104, 112, 136, 150, 160, 176, 181, 191, 194, 202, 221, 222, 228, 234, 250, 254, 255, 261, 270, 272, 273, 274, 275, 290, 303, 309, 312, 326, 335, 339, 347, 405, 461, 464, 502, 507, 529, 535, 539, 540, 598, 781, 800, 863, 864, 866, 869, 877, 909, 1000, 1020, 1031], "concav": [164, 322, 369], "concentr": [46, 48, 118, 125, 134, 152, 173, 180, 181, 232, 249, 251, 256, 276, 295, 307, 327, 368, 372, 411, 440, 517, 797, 998, 1005, 1020], "concentrations_prior": 250, "concept": [1, 112, 140, 144, 241, 274, 384, 404, 410, 412, 991, 999, 1002, 1015, 1017, 1023], "conceptu": [369, 411, 997], "concern": [37, 56, 71, 108, 114, 117, 119, 131, 133, 158, 165, 167, 178, 181, 188, 190, 226, 249, 255, 259, 281, 283, 286, 299, 304, 324, 331, 346, 350, 359, 374, 398, 996, 1011], "concis": [64, 208, 372, 377, 1041, 1043], "conclud": [66, 134, 184, 192, 225, 265, 318, 349, 355, 387, 864], "conclus": [43, 125, 184, 186, 208, 210, 265, 267, 355, 411], "concomit": [649, 995], "concret": [212, 373, 387, 403, 413, 674, 675, 896, 897, 995, 1013, 1018, 1049], "concurr": [386, 412, 966, 1042, 1044], "cond": 1049, "conda": [314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 360, 372, 373, 375, 376, 378, 380, 390, 391, 397, 398, 1015], "conda_prefix": 378, "condarc": 370, "condens": [189, 443], "condit": [1, 43, 51, 52, 62, 64, 113, 146, 181, 182, 184, 199, 202, 210, 213, 225, 241, 245, 268, 317, 354, 378, 384, 389, 400, 401, 402, 403, 404, 406, 409, 411, 413, 448, 461, 468, 469, 470, 475, 494, 511, 521, 522, 534, 537, 538, 539, 541, 543, 545, 547, 548, 560, 627, 632, 633, 643, 650, 651, 652, 654, 655, 656, 670, 672, 673, 674, 675, 682, 683, 687, 697, 712, 717, 718, 736, 737, 795, 821, 839, 840, 841, 842, 843, 862, 865, 868, 877, 878, 881, 885, 948, 970, 993, 995, 996, 997, 999, 1001, 1002, 1004, 1009, 1015, 1020, 1025, 1031, 1033, 1034, 1035, 1037, 1038, 1040, 1042, 1044, 1045, 1046, 1048], "condition": [51, 64, 208, 402, 406, 758, 993, 999], "condition2": 154, "conduct": [183, 265, 414, 1022, 1044], "conf": [46, 64, 376, 402, 408, 839, 1001, 1044], "confer": [259, 265, 367, 404, 409, 415, 436, 441, 447, 509, 533, 561, 696, 708, 726, 756, 860, 861, 862, 999, 1005, 1011, 1015], "confid": [52, 61, 62, 63, 64, 66, 149, 173, 175, 251, 265, 268, 328, 387, 402, 414, 636, 658, 659, 666, 668, 671, 674, 675, 676, 698, 702, 707, 720, 726, 727, 739, 740, 756, 789, 832, 871, 904, 906, 909, 995, 998, 999, 1000, 1005, 1012, 1013, 1014, 1023, 1040], "config": [52, 360, 370, 372, 373, 380, 626, 1038], "config_context": [1, 248, 323, 359, 360, 400, 626, 902, 1037, 1043, 1046], "configur": [1, 2, 46, 49, 64, 66, 103, 104, 185, 241, 246, 248, 259, 279, 312, 347, 358, 370, 372, 374, 378, 384, 386, 390, 395, 400, 405, 412, 413, 428, 439, 440, 442, 444, 446, 449, 459, 461, 465, 479, 480, 481, 482, 529, 530, 531, 532, 533, 534, 535, 536, 537, 538, 539, 540, 541, 542, 547, 564, 565, 566, 567, 568, 580, 581, 582, 588, 589, 591, 592, 593, 594, 595, 596, 597, 598, 599, 600, 601, 602, 626, 627, 628, 629, 630, 632, 635, 638, 639, 640, 641, 642, 688, 689, 690, 691, 692, 694, 695, 788, 800, 814, 848, 853, 856, 860, 863, 864, 867, 868, 869, 870, 871, 872, 873, 874, 875, 876, 877, 878, 879, 880, 881, 882, 883, 884, 885, 896, 897, 902, 966, 967, 996, 999, 1009, 1014, 1018, 1025, 1034, 1037, 1038, 1039, 1040, 1042, 1044, 1045, 1046, 1047], "confirm": [43, 52, 66, 116, 137, 143, 146, 149, 186, 208, 210, 259, 268, 271, 274, 310, 349, 376, 988, 1009, 1038], "conflict": [370, 375, 376, 380, 390, 1037, 1038], "conform": [52, 372, 580, 628, 832, 833, 999, 1018, 1019, 1037, 1043], "confound": [183, 184], "confus": [1, 68, 181, 235, 257, 259, 274, 325, 326, 347, 386, 465, 502, 631, 652, 697, 713, 715, 718, 729, 730, 738, 754, 784, 787, 830, 902, 909, 1020, 1030, 1031, 1035, 1039, 1040, 1042, 1043, 1044, 1045], "confusingli": 370, "confusion_matrix": [1, 68, 235, 258, 259, 322, 325, 326, 697, 713, 754, 799, 827, 999, 1031, 1036, 1037, 1040, 1041, 1043, 1047], "confusion_matrix_scor": 999, "confusionmatrixdisplai": [1, 45, 68, 258, 317, 322, 325, 347, 631, 718, 999, 1040, 1041, 1043, 1044, 1045, 1049], "congruenc": [654, 655, 656, 682, 683], "conjug": [265, 449, 459, 672, 674, 687, 695, 995], "conjunct": [395, 404, 593, 806, 822, 823, 825, 826, 827, 828, 831, 967, 989, 995, 1045], "connect": [1, 51, 74, 79, 81, 84, 87, 95, 99, 100, 370, 372, 381, 386, 406, 438, 442, 449, 459, 460, 584, 585, 695, 846, 847, 848, 850, 852, 854, 855, 856, 857, 858, 997, 999, 1002, 1004, 1012, 1022, 1034, 1043, 1047], "connected_compon": 1037, "connectionist": [861, 862], "conner": 1043, "connor": [1038, 1043, 1047, 1048], "connossor": [1038, 1039], "conocophillip": 51, "conort": 1023, "conquer": 948, "conrad": [1030, 1031, 1045, 1048, 1049], "conroi": 1045, "consecut": [134, 144, 209, 384, 402, 408, 412, 440, 444, 446, 447, 449, 453, 456, 459, 535, 536, 537, 544, 601, 645, 666, 667, 668, 676, 677, 678, 797, 798, 805, 839, 840, 841, 842, 843, 861, 862, 988, 1009, 1038], "consensu": [1, 58, 59, 72, 371, 372, 387, 401, 404, 649, 671, 678, 679, 719, 999], "consensus_scor": [1, 58, 59, 401, 1032], "consequ": [90, 127, 225, 265, 266, 305, 323, 333, 355, 402, 403, 409, 411, 559, 560, 562, 563, 564, 655, 656, 989, 995, 999, 1007, 1015, 1039, 1041, 1046, 1048], "conserv": [50, 376, 386, 581, 588, 896, 897, 998, 1011], "consid": [0, 43, 51, 52, 53, 58, 62, 74, 88, 99, 103, 112, 120, 124, 126, 143, 146, 159, 163, 164, 180, 185, 202, 208, 210, 241, 259, 265, 268, 272, 274, 276, 279, 285, 288, 291, 292, 305, 316, 322, 333, 340, 341, 343, 347, 355, 359, 360, 361, 364, 371, 372, 374, 378, 380, 384, 386, 387, 395, 398, 400, 403, 404, 409, 410, 411, 412, 413, 414, 415, 416, 435, 441, 443, 447, 454, 471, 506, 507, 519, 531, 539, 547, 548, 555, 556, 557, 558, 559, 560, 561, 562, 563, 564, 584, 585, 587, 588, 590, 592, 606, 607, 620, 631, 632, 633, 646, 652, 658, 659, 660, 662, 671, 679, 688, 689, 690, 692, 693, 694, 696, 700, 702, 705, 707, 715, 726, 730, 734, 756, 786, 788, 794, 846, 847, 848, 850, 852, 854, 855, 856, 859, 861, 862, 867, 877, 878, 885, 899, 900, 909, 910, 912, 913, 914, 915, 925, 982, 988, 994, 995, 996, 997, 999, 1000, 1002, 1005, 1006, 1007, 1009, 1013, 1014, 1015, 1033, 1034, 1038, 1042, 1043, 1045, 1046, 1047], "consider": [148, 149, 169, 172, 244, 260, 266, 272, 367, 372, 403, 414, 619, 800, 814, 822, 988, 995, 1001, 1005, 1013, 1023, 1047], "consist": [1, 43, 46, 63, 68, 72, 89, 90, 102, 111, 118, 120, 140, 143, 149, 150, 157, 164, 171, 173, 176, 180, 187, 202, 208, 225, 240, 244, 271, 274, 302, 310, 314, 317, 343, 348, 355, 359, 365, 367, 369, 372, 374, 378, 379, 380, 381, 385, 386, 387, 402, 404, 406, 410, 411, 412, 422, 423, 426, 427, 437, 438, 439, 440, 441, 442, 444, 445, 446, 447, 448, 449, 450, 460, 461, 462, 464, 466, 467, 468, 469, 470, 471, 472, 473, 479, 480, 481, 487, 495, 529, 531, 532, 533, 534, 535, 536, 537, 538, 540, 541, 542, 552, 554, 556, 558, 560, 561, 563, 564, 565, 566, 567, 568, 580, 581, 582, 587, 588, 590, 611, 627, 628, 629, 630, 635, 638, 643, 644, 645, 646, 647, 649, 650, 651, 652, 653, 654, 655, 656, 657, 660, 661, 662, 663, 664, 665, 667, 670, 672, 673, 674, 677, 678, 679, 687, 688, 689, 690, 691, 700, 735, 797, 798, 807, 832, 833, 834, 836, 837, 838, 839, 847, 848, 850, 852, 855, 856, 862, 867, 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659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 670, 671, 672, 673, 674, 675, 676, 677, 678, 679, 680, 690, 698, 700, 702, 762, 765, 769, 799, 801, 802, 805, 807, 808, 809, 810, 813, 815, 816, 817, 818, 819, 820, 821, 822, 832, 833, 834, 835, 836, 837, 838, 839, 840, 841, 842, 843, 846, 847, 849, 851, 854, 855, 861, 862, 864, 867, 869, 870, 871, 876, 877, 878, 883, 884, 899, 900, 904, 905, 906, 907, 908, 909, 910, 912, 913, 914, 915, 982, 991, 996, 998, 999, 1003, 1005, 1006, 1009, 1023, 1030, 1037, 1038, 1039, 1040, 1041, 1042, 1043, 1044, 1045, 1046, 1047, 1048, 1049, 1050], "exist_ok": 47, "existing_credit": 259, "exit": [104, 285, 376, 380, 465], "exp": [1, 80, 87, 99, 129, 137, 146, 169, 195, 217, 290, 295, 298, 315, 316, 320, 340, 402, 404, 405, 410, 416, 449, 462, 519, 531, 534, 615, 616, 622, 641, 759, 766, 776, 861, 862, 880, 993, 995, 997, 1001, 1002, 1003, 1012, 1013, 1014], "exp10": 184, "exp_dirichlet_component_": 534, "exp_dist_embed": 295, "expand": [43, 82, 98, 236, 311, 315, 355, 364, 374, 411, 441, 447, 497, 555, 556, 557, 558, 562, 563, 564, 912, 913, 914, 915, 999, 1031, 1038, 1039, 1046], "expand_frame_repr": 225, "expans": [43, 179, 316, 447, 879, 1039], "expect": [43, 44, 49, 52, 61, 63, 66, 72, 79, 86, 116, 118, 125, 134, 137, 139, 141, 143, 146, 149, 161, 162, 168, 174, 181, 182, 186, 196, 201, 208, 209, 210, 212, 215, 225, 238, 241, 244, 245, 251, 252, 255, 256, 259, 271, 272, 285, 310, 323, 343, 345, 347, 348, 355, 359, 360, 372, 374, 376, 377, 378, 379, 380, 381, 384, 385, 386, 387, 389, 395, 400, 402, 404, 405, 406, 408, 411, 412, 413, 427, 461, 462, 464, 465, 466, 479, 480, 481, 494, 521, 530, 534, 536, 539, 550, 552, 554, 556, 558, 560, 561, 563, 565, 566, 568, 570, 573, 587, 588, 590, 594, 596, 605, 611, 627, 632, 633, 635, 643, 644, 645, 646, 647, 649, 650, 651, 652, 653, 654, 655, 656, 657, 660, 661, 662, 663, 664, 665, 667, 670, 672, 673, 674, 675, 678, 679, 695, 704, 705, 716, 735, 788, 794, 800, 814, 837, 838, 839, 840, 841, 842, 843, 846, 847, 848, 850, 854, 855, 856, 857, 862, 865, 870, 877, 878, 881, 884, 885, 894, 895, 902, 904, 905, 906, 907, 908, 909, 910, 913, 915, 925, 941, 942, 963, 969, 988, 995, 998, 999, 1001, 1005, 1009, 1013, 1014, 1015, 1020, 1022, 1025, 1030, 1031, 1032, 1035, 1036, 1038, 1039, 1040, 1041, 1042, 1043, 1044, 1045, 1046, 1047, 1048, 1049, 1050], "expected_anomaly_fract": 244, "expected_failed_check": [941, 942, 943, 1049], "expected_i": 146, "expected_n_anomali": 244, "expected_ri": 705, "expected_to_fail": [573, 941], "expected_to_fail_reason": [573, 941], "expectedli": 347, "expens": [0, 89, 140, 154, 168, 183, 185, 235, 262, 274, 336, 340, 347, 348, 349, 361, 378, 386, 404, 405, 408, 411, 412, 446, 465, 506, 800, 803, 804, 814, 827, 902, 904, 996, 998, 1009, 1014, 1023, 1034], "experi": [30, 134, 171, 175, 183, 184, 185, 186, 189, 210, 259, 282, 310, 343, 348, 349, 360, 366, 372, 374, 375, 387, 408, 513, 728, 785, 995, 1000, 1011, 1017, 1018, 1023], "experienc": 372, 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1033], "explic": 1011, "explicit": [43, 149, 181, 185, 189, 233, 241, 279, 340, 359, 360, 361, 371, 373, 384, 386, 387, 395, 405, 412, 418, 469, 497, 499, 500, 539, 549, 550, 638, 639, 641, 676, 800, 801, 808, 848, 856, 864, 904, 909, 964, 991, 993, 995, 999, 1009, 1015, 1018, 1020, 1030, 1034, 1039, 1040, 1043, 1047, 1050], "explicitli": [43, 80, 141, 168, 175, 179, 180, 209, 237, 241, 321, 340, 349, 355, 360, 366, 368, 372, 373, 374, 376, 384, 386, 395, 398, 400, 408, 412, 414, 491, 533, 547, 548, 578, 579, 596, 627, 648, 658, 659, 669, 680, 709, 727, 782, 788, 789, 803, 804, 835, 838, 844, 845, 870, 894, 895, 987, 988, 989, 991, 993, 995, 996, 999, 1001, 1002, 1009, 1023, 1030, 1033, 1034, 1035, 1036, 1037, 1038, 1039, 1042, 1043, 1044, 1048, 1049], "explod": 311, "exploit": [62, 284, 398, 1000, 1019, 1033], "exploit_incremental_learn": [806, 828], "explor": [52, 72, 137, 138, 149, 187, 231, 232, 244, 259, 266, 267, 273, 274, 282, 312, 316, 336, 348, 355, 366, 369, 371, 378, 404, 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1002, 1015, 1019, 1025, 1038, 1042], "exterqu": [143, 154], "extmath": [1, 253, 347, 381, 450, 945, 946, 947, 948, 949, 950, 1030, 1031, 1036, 1037, 1040, 1044, 1045, 1047], "extr": 47, "extra": [1, 80, 142, 149, 243, 285, 321, 349, 370, 371, 373, 386, 398, 400, 411, 412, 435, 440, 444, 456, 461, 530, 555, 556, 564, 589, 590, 700, 701, 702, 825, 848, 914, 915, 937, 998, 999, 1002, 1030, 1033, 1035, 1036, 1038, 1043, 1045, 1050], "extra_cflag": 378, "extra_tre": [914, 915], "extract": [1, 16, 42, 43, 45, 47, 50, 51, 55, 83, 88, 101, 102, 103, 104, 115, 120, 140, 160, 164, 181, 222, 257, 264, 270, 273, 303, 329, 339, 347, 349, 355, 364, 367, 369, 378, 381, 384, 404, 405, 408, 409, 447, 449, 452, 453, 459, 461, 485, 486, 491, 492, 501, 502, 529, 532, 533, 534, 535, 536, 537, 538, 539, 541, 543, 544, 545, 582, 583, 587, 588, 590, 592, 593, 596, 598, 631, 718, 788, 800, 814, 841, 863, 864, 909, 921, 948, 957, 964, 988, 996, 1000, 1004, 1009, 1013, 1018, 1020, 1023, 1025, 1030, 1031, 1039, 1042], "extract_dbscan": 404, "extract_patches_2d": [1, 83, 123, 412, 586, 1038], "extract_scor": 268, "extractal": 47, "extractor": [208, 303, 361, 367, 386, 412, 501, 580, 581, 587, 588, 590, 988, 1030], "extran": 372, "extrapol": [43, 66, 168, 191, 209, 237, 268, 883, 999, 1009, 1015, 1043, 1045], "extratre": [142, 152, 359, 1033], "extratreeclassifi": [1, 555, 564, 915, 989, 1000, 1015, 1034, 1039, 1040, 1041, 1043, 1044, 1045, 1047, 1049], "extratreeregressor": [1, 556, 561, 564, 914, 989, 1000, 1005, 1015, 1039, 1040, 1041, 1043, 1044, 1045, 1047, 1049], "extratreesclassifi": [1, 142, 152, 323, 411, 413, 556, 562, 564, 914, 915, 989, 1000, 1033, 1034, 1038, 1039, 1040, 1043, 1044, 1045, 1046, 1047, 1049], "extratreesregressor": [1, 243, 323, 411, 555, 563, 564, 914, 915, 989, 1000, 1033, 1036, 1038, 1039, 1040, 1043, 1044, 1045, 1046, 1047, 1049], "extrem": [1, 146, 208, 210, 243, 244, 278, 305, 312, 328, 348, 372, 375, 403, 404, 409, 555, 556, 562, 563, 564, 632, 633, 679, 724, 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"integr": [0, 52, 77, 103, 161, 176, 199, 302, 316, 323, 360, 370, 374, 375, 376, 378, 380, 386, 390, 409, 414, 443, 715, 755, 786, 973, 998, 1006, 1023, 1031, 1033, 1034, 1042, 1043, 1047, 1049], "intel": [999, 1018], "intelex": [390, 1018], "intellectu": 183, "intellig": [96, 164, 259, 347, 369, 404, 411, 445, 553, 554, 638, 696, 725, 834, 861, 862, 1000, 1011, 1019, 1023], "intend": [90, 91, 124, 209, 248, 340, 347, 372, 373, 379, 384, 386, 400, 404, 408, 465, 581, 673, 699, 873, 874, 884, 902, 921, 994, 995, 999, 1003, 1010, 1018, 1049], "intens": [99, 123, 285, 369, 373, 384, 386, 409, 440, 444, 456, 606, 607, 632, 633, 802, 988, 1006, 1023, 1038], "intention": 64, "inter": [1, 75, 141, 355, 408, 411, 716], "interact": [1, 42, 66, 147, 149, 181, 208, 232, 236, 266, 315, 371, 373, 374, 376, 378, 384, 386, 387, 398, 405, 557, 558, 559, 560, 632, 633, 879, 883, 988, 991, 995, 996, 997, 1004, 1006, 1009, 1017, 1019, 1032, 1037, 1044, 1045], "interaction_cst": [185, 319, 411, 559, 560, 1045], "interaction_onli": [43, 66, 879, 995, 1009], "interactiveshellapp": 378, "intercept": [123, 202, 203, 206, 216, 224, 344, 386, 479, 480, 481, 547, 644, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 670, 672, 673, 674, 675, 676, 677, 678, 679, 680, 687, 879, 883, 886, 904, 905, 911, 995, 997, 999, 1004, 1013, 1014, 1033, 1035, 1039, 1040], "intercept_": [194, 200, 202, 216, 242, 334, 341, 344, 386, 479, 480, 481, 547, 644, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 670, 672, 673, 674, 675, 676, 677, 678, 679, 680, 904, 905, 906, 907, 908, 909, 910, 993, 995, 1013, 1014, 1030, 1037, 1042, 1044, 1047], "intercept_hidden_": 860, "intercept_init": [666, 667, 668, 676, 678], "intercept_sc": [658, 659, 904, 905, 911, 995, 1014], "intercept_visible_": 860, "intercepts_": [861, 862, 1003], "interchang": [1018, 1047], 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1042], "interpolated_inverted_cdf": 869, "interpret": [43, 64, 149, 164, 175, 181, 182, 183, 185, 202, 208, 209, 213, 223, 225, 258, 265, 267, 268, 279, 303, 306, 309, 316, 323, 347, 349, 359, 370, 372, 373, 378, 380, 381, 386, 389, 402, 404, 405, 410, 412, 449, 461, 462, 464, 494, 539, 570, 611, 639, 643, 653, 672, 673, 691, 692, 695, 701, 729, 753, 758, 759, 783, 792, 793, 806, 815, 827, 828, 830, 833, 865, 877, 884, 969, 993, 995, 999, 1000, 1006, 1007, 1009, 1015, 1018, 1020, 1030, 1031, 1035, 1038, 1043, 1044, 1045, 1049], "interquartil": [244, 882, 894], "interquartile_rang": 882, "interrog": 412, "interrupt": [995, 999], "intersect": [266, 333, 401, 404, 738], "interv": [1, 52, 64, 133, 149, 175, 181, 195, 208, 209, 210, 225, 265, 367, 386, 402, 408, 411, 414, 435, 436, 514, 515, 516, 555, 556, 558, 560, 562, 563, 635, 636, 638, 677, 742, 748, 750, 804, 830, 867, 869, 883, 906, 907, 908, 934, 991, 995, 999, 1009, 1015, 1020, 1037, 1041], "intervent": [88, 184], "intl": [64, 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"sklearn.mixture", "sklearn.model_selection", "sklearn.multiclass", "sklearn.multioutput", "sklearn.naive_bayes", "sklearn.neighbors", "sklearn.neural_network", "sklearn.pipeline", "sklearn.preprocessing", "sklearn.random_projection", "sklearn.semi_supervised", "sklearn.svm", "sklearn.tree", "sklearn.utils", "Examples based on real world datasets", "Time-related feature engineering", "Image denoising using kernel PCA", "Faces recognition example using eigenfaces and SVMs", "Model Complexity Influence", "Out-of-core classification of text documents", "Outlier detection on a real data set", "Prediction Latency", "Species distribution modeling", "Visualizing the stock market structure", "Lagged features for time series forecasting", "Compressive sensing: tomography reconstruction with L1 prior (Lasso)", "Topic extraction with Non-negative Matrix Factorization and Latent Dirichlet Allocation", "Wikipedia principal eigenvector", "Biclustering", "Biclustering documents with the Spectral Co-clustering algorithm", "A demo of the Spectral Biclustering algorithm", "A demo of the Spectral Co-Clustering algorithm", "Calibration", "Probability calibration of classifiers", "Probability Calibration curves", "Probability Calibration for 3-class classification", "Comparison of Calibration of Classifiers", "Classification", "Plot classification probability", "Classifier comparison", "Recognizing hand-written digits", "Normal, Ledoit-Wolf and OAS Linear Discriminant Analysis for classification", "Linear and Quadratic Discriminant Analysis with covariance ellipsoid", "Clustering", "Adjustment for chance in clustering performance evaluation", "Demo of affinity propagation clustering algorithm", "Agglomerative clustering with and without structure", "Agglomerative clustering with different metrics", "Plot Hierarchical Clustering Dendrogram", "Compare BIRCH and MiniBatchKMeans", "Bisecting K-Means and Regular K-Means Performance Comparison", "Comparing different clustering algorithms on toy datasets", "Segmenting the picture of greek coins in regions", "A demo of structured Ward hierarchical clustering on an image of coins", "Demo of DBSCAN clustering algorithm", "Online learning of a dictionary of parts of faces", "Feature agglomeration", "Various Agglomerative Clustering on a 2D embedding of digits", "Vector Quantization Example", "Feature agglomeration vs. univariate selection", "Demo of HDBSCAN clustering algorithm", "Inductive Clustering", "Demonstration of k-means assumptions", "A demo of K-Means clustering on the handwritten digits data", "An example of K-Means++ initialization", "Selecting the number of clusters with silhouette analysis on KMeans clustering", "Empirical evaluation of the impact of k-means initialization", "Comparing different hierarchical linkage methods on toy datasets", "A demo of the mean-shift clustering algorithm", "Comparison of the K-Means and MiniBatchKMeans clustering algorithms", "Demo of OPTICS clustering algorithm", "Spectral clustering for image segmentation", "Hierarchical clustering: structured vs unstructured ward", "Pipelines and composite estimators", "Column Transformer with Heterogeneous Data Sources", "Column Transformer with Mixed Types", "Selecting dimensionality reduction with Pipeline and GridSearchCV", "Pipelining: chaining a PCA and a logistic regression", "Concatenating multiple feature extraction methods", "Effect of transforming the targets in regression model", "Covariance estimation", "Shrinkage covariance estimation: LedoitWolf vs OAS and max-likelihood", "Ledoit-Wolf vs OAS estimation", "Robust covariance estimation and Mahalanobis distances relevance", "Robust vs Empirical covariance estimate", "Sparse inverse covariance estimation", "Cross decomposition", "Compare cross decomposition methods", "Principal Component Regression vs Partial Least Squares Regression", "Dataset examples", "Plot randomly generated multilabel dataset", "Decomposition", "Faces dataset decompositions", "Blind 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of trees on the iris dataset", "Categorical Feature Support in Gradient Boosting", "Early stopping in Gradient Boosting", "Gradient Boosting Out-of-Bag estimates", "Prediction Intervals for Gradient Boosting Regression", "Gradient Boosting regression", "Gradient Boosting regularization", "Features in Histogram Gradient Boosting Trees", "IsolationForest example", "Monotonic Constraints", "Hashing feature transformation using Totally Random Trees", "Comparing random forests and the multi-output meta estimator", "Combine predictors using stacking", "Plot the decision boundaries of a VotingClassifier", "Plot class probabilities calculated by the VotingClassifier", "Plot individual and voting regression predictions", "Feature Selection", "Comparison of F-test and mutual information", "Univariate Feature Selection", "Pipeline ANOVA SVM", "Recursive feature elimination", "Recursive feature elimination with cross-validation", "Model-based and sequential feature selection", "Frozen Estimators", 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"Examples", "Inspection", "Failure of Machine Learning to infer causal effects", "Common pitfalls in the interpretation of coefficients of linear models", "Partial Dependence and Individual Conditional Expectation Plots", "Permutation Importance vs Random Forest Feature Importance (MDI)", "Permutation Importance with Multicollinear or Correlated Features", "Kernel Approximation", "Scalable learning with polynomial kernel approximation", "Generalized Linear Models", "Comparing Linear Bayesian Regressors", "Curve Fitting with Bayesian Ridge Regression", "Fitting an Elastic Net with a precomputed Gram Matrix and Weighted Samples", "HuberRegressor vs Ridge on dataset with strong outliers", "L1-based models for Sparse Signals", "Lasso on dense and sparse data", "Lasso model selection via information criteria", "Lasso, Lasso-LARS, and Elastic Net paths", "Lasso model selection: AIC-BIC / cross-validation", "Logistic function", "L1 Penalty and Sparsity in Logistic Regression", "Decision Boundaries of Multinomial and One-vs-Rest Logistic Regression", "Regularization path of L1- Logistic Regression", "Joint feature selection with multi-task Lasso", "Non-negative least squares", "Ordinary Least Squares and Ridge Regression", "Orthogonal Matching Pursuit", "Poisson regression and non-normal loss", "Polynomial and Spline interpolation", "Quantile regression", "Robust linear model estimation using RANSAC", "Ridge coefficients as a function of the L2 Regularization", "Plot Ridge coefficients as a function of the regularization", "Robust linear estimator fitting", "Early stopping of Stochastic Gradient Descent", "Plot multi-class SGD on the iris dataset", "SGD: convex loss functions", "SGD: Penalties", "SGD: Maximum margin separating hyperplane", "SGD: Weighted samples", "One-Class SVM versus One-Class SVM using Stochastic Gradient Descent", "Multiclass sparse logistic regression on 20newgroups", "MNIST classification using multinomial logistic + L1", "Theil-Sen Regression", "Tweedie regression on insurance claims", "Manifold learning", "Comparison of Manifold Learning methods", "Manifold learning on handwritten digits: Locally Linear Embedding, Isomap\u2026", "Manifold Learning methods on a severed sphere", "Multi-dimensional scaling", "Swiss Roll And Swiss-Hole Reduction", "t-SNE: The effect of various perplexity values on the shape", "Miscellaneous", "Comparing anomaly detection algorithms for outlier detection on toy datasets", "Visualizations with Display Objects", "Displaying estimators and complex pipelines", "Isotonic Regression", "The Johnson-Lindenstrauss bound for embedding with random projections", "Explicit feature map approximation for RBF kernels", "Comparison of kernel ridge regression and SVR", "Metadata Routing", "Multilabel classification", "Face completion with a multi-output estimators", "Evaluation of outlier detection estimators", "Advanced Plotting With Partial Dependence", "Displaying Pipelines", "ROC Curve with Visualization API", "Introducing the <code class=\"docutils literal notranslate\"><span class=\"pre\">set_output</span></code> API", "Gaussian Mixture Models", "Concentration Prior Type Analysis of Variation Bayesian Gaussian Mixture", "Gaussian Mixture Model Ellipsoids", "GMM covariances", "GMM Initialization Methods", "Density Estimation for a Gaussian mixture", "Gaussian Mixture Model Selection", "Gaussian Mixture Model Sine Curve", "Model Selection", "Confusion matrix", "Post-tuning the decision threshold for cost-sensitive learning", "Visualizing cross-validation behavior in scikit-learn", "Plotting Cross-Validated Predictions", "Detection error tradeoff (DET) curve", "Custom refit strategy of a grid search with cross-validation", "Balance model complexity and cross-validated score", "Statistical comparison of models using grid search", "Sample pipeline for text feature extraction and evaluation", "Plotting Learning Curves and Checking Models\u2019 Scalability", "Class Likelihood Ratios to measure classification performance", "Demonstration of multi-metric evaluation on cross_val_score and GridSearchCV", "Nested versus non-nested cross-validation", "Test with permutations the significance of a classification score", "Precision-Recall", "Comparing randomized search and grid search for hyperparameter estimation", "Multiclass Receiver Operating Characteristic (ROC)", "Receiver Operating Characteristic (ROC) with cross validation", "Comparison between grid search and successive halving", "Successive Halving Iterations", "Effect of model regularization on training and test error", "Post-hoc tuning the cut-off point of decision function", "Underfitting vs. Overfitting", "Multiclass methods", "Overview of multiclass training meta-estimators", "Multioutput methods", "Multilabel classification using a classifier chain", "Approximate nearest neighbors in TSNE", "Nearest Neighbors", "Caching nearest neighbors", "Nearest Neighbors Classification", "Kernel Density Estimation", "Simple 1D Kernel Density Estimation", "Novelty detection with Local Outlier Factor (LOF)", "Outlier detection with Local Outlier Factor (LOF)", "Comparing Nearest Neighbors with and without Neighborhood Components Analysis", "Dimensionality Reduction with Neighborhood Components Analysis", "Neighborhood Components Analysis Illustration", "Nearest Centroid Classification", "Nearest Neighbors regression", "Kernel Density Estimate of Species Distributions", "Neural Networks", "Varying regularization in Multi-layer Perceptron", "Compare Stochastic learning strategies for MLPClassifier", "Visualization of MLP weights on MNIST", "Restricted Boltzmann Machine features for digit classification", "Preprocessing", "Compare the effect of different scalers on data with outliers", "Using KBinsDiscretizer to discretize continuous features", "Feature discretization", "Demonstrating the different strategies of KBinsDiscretizer", "Map data to a normal distribution", "Importance of Feature Scaling", "Comparing Target Encoder with Other Encoders", "Target Encoder\u2019s Internal Cross fitting", "Release Highlights", "Release Highlights for scikit-learn 0.22", "Release Highlights for scikit-learn 0.23", "Release Highlights for scikit-learn 0.24", "Release Highlights for scikit-learn 1.0", "Release Highlights for scikit-learn 1.1", "Release Highlights for scikit-learn 1.2", "Release Highlights for scikit-learn 1.3", "Release Highlights for scikit-learn 1.4", "Release Highlights for scikit-learn 1.5", "Release Highlights for scikit-learn 1.6", "Semi Supervised Classification", "Label Propagation digits: Demonstrating performance", "Label Propagation digits active learning", "Label Propagation learning a complex structure", "Effect of varying threshold for self-training", "Semi-supervised Classification on a Text Dataset", "Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset", "Support Vector Machines", "SVM with custom kernel", "Plot different SVM classifiers in the iris dataset", "Plot the support vectors in LinearSVC", "One-class SVM with non-linear kernel (RBF)", "RBF SVM parameters", "SVM: Maximum margin separating hyperplane", "SVM: Separating hyperplane for unbalanced classes", "SVM-Anova: SVM with univariate feature selection", "Plot classification boundaries with different SVM Kernels", "SVM Margins Example", "Support Vector Regression (SVR) using linear and non-linear kernels", "Scaling the regularization parameter for SVCs", "SVM Tie Breaking Example", "SVM: Weighted samples", "Working with text documents", "Classification of text documents using sparse features", "Clustering text documents using k-means", "FeatureHasher and DictVectorizer Comparison", "Decision Trees", "Post pruning decision trees with cost complexity pruning", "Plot the decision surface of decision trees trained on the iris dataset", "Decision Tree Regression", "Understanding the decision tree structure", "<span class=\"section-number\">10. </span>Common pitfalls and recommended practices", "&lt;no title&gt;", "&lt;no title&gt;", "<span class=\"section-number\">8. </span>Computing with scikit-learn", "<span class=\"section-number\">8.2. </span>Computational Performance", "<span class=\"section-number\">8.3. </span>Parallelism, resource management, and configuration", "<span class=\"section-number\">8.1. </span>Strategies to scale computationally: bigger data", "&lt;no title&gt;", "&lt;no title&gt;", "<span class=\"section-number\">6. </span>Dataset transformations", "<span class=\"section-number\">7. </span>Dataset loading utilities", "<span class=\"section-number\">7.4. </span>Loading other datasets", "<span class=\"section-number\">7.2. </span>Real world datasets", "<span class=\"section-number\">7.3. </span>Generated datasets", "<span class=\"section-number\">7.1. </span>Toy datasets", "Installing the development version of scikit-learn", "Bug triaging and issue curation", "Contributing", "Cython Best Practices, Conventions and Knowledge", "Developing scikit-learn estimators", "Developer\u2019s Guide", "Maintainer Information", "Crafting a minimal reproducer for scikit-learn", "How to optimize for speed", "Developing with the Plotting API", "Developers\u2019 Tips and Tricks", "Utilities for Developers", "<span class=\"section-number\">11. </span>Dispatching", "&lt;no title&gt;", "Frequently Asked Questions", "Getting Started", "Glossary of Common Terms and API Elements", "Scikit-learn governance and decision-making", "Index", "<span class=\"section-number\">4. </span>Inspection", "Installing scikit-learn", "&lt;no title&gt;", "<span class=\"section-number\">12. </span>Choosing the right estimator", "&lt;no title&gt;", "&lt;no title&gt;", "<span class=\"section-number\">1. </span>Metadata Routing", "&lt;no title&gt;", "&lt;no title&gt;", "<span class=\"section-number\">9. </span>Model persistence", "<span class=\"section-number\">3. </span>Model selection and evaluation", "<span class=\"section-number\">11.1. </span>Array API support (experimental)", "<span class=\"section-number\">2.4. </span>Biclustering", "<span class=\"section-number\">1.16. </span>Probability calibration", "<span class=\"section-number\">3.3. </span>Tuning the decision threshold for class prediction", "<span class=\"section-number\">2.3. </span>Clustering", "<span class=\"section-number\">6.1. </span>Pipelines and composite estimators", "<span class=\"section-number\">2.6. </span>Covariance estimation", "<span class=\"section-number\">1.8. </span>Cross decomposition", "<span class=\"section-number\">3.1. </span>Cross-validation: evaluating estimator performance", "<span class=\"section-number\">2.5. </span>Decomposing signals in components (matrix factorization problems)", "<span class=\"section-number\">2.8. </span>Density Estimation", "<span class=\"section-number\">1.11. </span>Ensembles: Gradient boosting, random forests, bagging, voting, stacking", "<span class=\"section-number\">6.2. </span>Feature extraction", "<span class=\"section-number\">1.13. </span>Feature selection", "<span class=\"section-number\">1.7. </span>Gaussian Processes", "dbscan", "fastica", "oas", "BaseEstimator", "BiclusterMixin", "ClassNamePrefixFeaturesOutMixin", "ClassifierMixin", "ClusterMixin", "DensityMixin", "MetaEstimatorMixin", "OneToOneFeatureMixin", "OutlierMixin", "RegressorMixin", "TransformerMixin", "clone", "is_classifier", "is_clusterer", "is_outlier_detector", "is_regressor", "CalibratedClassifierCV", "CalibrationDisplay", "calibration_curve", "AffinityPropagation", "AgglomerativeClustering", "Birch", "BisectingKMeans", "DBSCAN", "FeatureAgglomeration", "HDBSCAN", "KMeans", "MeanShift", "MiniBatchKMeans", "OPTICS", "SpectralBiclustering", "SpectralClustering", "SpectralCoclustering", "affinity_propagation", "cluster_optics_dbscan", "cluster_optics_xi", "compute_optics_graph", "estimate_bandwidth", "k_means", "kmeans_plusplus", "mean_shift", "spectral_clustering", "ward_tree", "ColumnTransformer", "TransformedTargetRegressor", "make_column_selector", "make_column_transformer", "config_context", "EllipticEnvelope", "EmpiricalCovariance", "GraphicalLasso", "GraphicalLassoCV", "LedoitWolf", "MinCovDet", "OAS", "ShrunkCovariance", "empirical_covariance", "graphical_lasso", "ledoit_wolf", "ledoit_wolf_shrinkage", "shrunk_covariance", "CCA", "PLSCanonical", "PLSRegression", "PLSSVD", "clear_data_home", "dump_svmlight_file", "fetch_20newsgroups", "fetch_20newsgroups_vectorized", "fetch_california_housing", "fetch_covtype", "fetch_file", "fetch_kddcup99", "fetch_lfw_pairs", "fetch_lfw_people", "fetch_olivetti_faces", "fetch_openml", "fetch_rcv1", "fetch_species_distributions", "get_data_home", "load_breast_cancer", "load_diabetes", "load_digits", "load_files", "load_iris", "load_linnerud", "load_sample_image", "load_sample_images", "load_svmlight_file", "load_svmlight_files", "load_wine", "make_biclusters", "make_blobs", "make_checkerboard", "make_circles", "make_classification", "make_friedman1", "make_friedman2", "make_friedman3", "make_gaussian_quantiles", "make_hastie_10_2", "make_low_rank_matrix", "make_moons", "make_multilabel_classification", "make_regression", "make_s_curve", "make_sparse_coded_signal", "make_sparse_spd_matrix", "make_sparse_uncorrelated", "make_spd_matrix", "make_swiss_roll", "DictionaryLearning", "FactorAnalysis", "FastICA", "IncrementalPCA", "KernelPCA", "LatentDirichletAllocation", "MiniBatchDictionaryLearning", "MiniBatchNMF", "MiniBatchSparsePCA", "NMF", "PCA", "SparseCoder", "SparsePCA", "TruncatedSVD", "dict_learning", "dict_learning_online", "non_negative_factorization", "sparse_encode", "LinearDiscriminantAnalysis", "QuadraticDiscriminantAnalysis", "DummyClassifier", "DummyRegressor", "AdaBoostClassifier", "AdaBoostRegressor", "BaggingClassifier", "BaggingRegressor", "ExtraTreesClassifier", "ExtraTreesRegressor", "GradientBoostingClassifier", "GradientBoostingRegressor", "HistGradientBoostingClassifier", "HistGradientBoostingRegressor", "IsolationForest", "RandomForestClassifier", "RandomForestRegressor", "RandomTreesEmbedding", "StackingClassifier", "StackingRegressor", "VotingClassifier", "VotingRegressor", "ConvergenceWarning", "DataConversionWarning", "DataDimensionalityWarning", "EfficiencyWarning", "EstimatorCheckFailedWarning", "FitFailedWarning", "InconsistentVersionWarning", "NotFittedError", "UndefinedMetricWarning", "enable_halving_search_cv", "enable_iterative_imputer", "DictVectorizer", "FeatureHasher", "PatchExtractor", "extract_patches_2d", "grid_to_graph", "img_to_graph", "reconstruct_from_patches_2d", "CountVectorizer", "HashingVectorizer", "TfidfTransformer", "TfidfVectorizer", "GenericUnivariateSelect", "RFE", "RFECV", "SelectFdr", "SelectFpr", "SelectFromModel", "SelectFwe", "SelectKBest", "SelectPercentile", "SelectorMixin", "SequentialFeatureSelector", "VarianceThreshold", "chi2", "f_classif", "f_regression", "mutual_info_classif", "mutual_info_regression", "r_regression", "FrozenEstimator", "GaussianProcessClassifier", "GaussianProcessRegressor", "CompoundKernel", "ConstantKernel", "DotProduct", "ExpSineSquared", "Exponentiation", "Hyperparameter", "Kernel", "Matern", "PairwiseKernel", "Product", "RBF", "RationalQuadratic", "Sum", "WhiteKernel", "get_config", "IterativeImputer", "KNNImputer", "MissingIndicator", "SimpleImputer", "DecisionBoundaryDisplay", "PartialDependenceDisplay", "partial_dependence", "permutation_importance", "IsotonicRegression", "check_increasing", "isotonic_regression", "AdditiveChi2Sampler", "Nystroem", "PolynomialCountSketch", "RBFSampler", "SkewedChi2Sampler", "KernelRidge", "ARDRegression", "BayesianRidge", "ElasticNet", "ElasticNetCV", "GammaRegressor", "HuberRegressor", "Lars", "LarsCV", "Lasso", "LassoCV", "LassoLars", "LassoLarsCV", "LassoLarsIC", "LinearRegression", "LogisticRegression", "LogisticRegressionCV", "MultiTaskElasticNet", "MultiTaskElasticNetCV", "MultiTaskLasso", "MultiTaskLassoCV", "OrthogonalMatchingPursuit", "OrthogonalMatchingPursuitCV", "PassiveAggressiveClassifier", "PassiveAggressiveRegressor", "Perceptron", "PoissonRegressor", "QuantileRegressor", "RANSACRegressor", "Ridge", "RidgeCV", "RidgeClassifier", "RidgeClassifierCV", "SGDClassifier", "SGDOneClassSVM", "SGDRegressor", "TheilSenRegressor", "TweedieRegressor", "enet_path", "lars_path", "lars_path_gram", "lasso_path", "orthogonal_mp", "orthogonal_mp_gram", "ridge_regression", "Isomap", "LocallyLinearEmbedding", "MDS", "SpectralEmbedding", "TSNE", "locally_linear_embedding", "smacof", "spectral_embedding", "trustworthiness", "ConfusionMatrixDisplay", "DetCurveDisplay", "DistanceMetric", "PrecisionRecallDisplay", "PredictionErrorDisplay", "RocCurveDisplay", "accuracy_score", "adjusted_mutual_info_score", "adjusted_rand_score", "auc", "average_precision_score", "balanced_accuracy_score", "brier_score_loss", "calinski_harabasz_score", "check_scoring", "class_likelihood_ratios", "classification_report", "contingency_matrix", "pair_confusion_matrix", "cohen_kappa_score", "completeness_score", "confusion_matrix", "consensus_score", "coverage_error", "d2_absolute_error_score", "d2_log_loss_score", "d2_pinball_score", "d2_tweedie_score", "davies_bouldin_score", "dcg_score", "det_curve", "explained_variance_score", "f1_score", "fbeta_score", "fowlkes_mallows_score", "get_scorer", "get_scorer_names", "hamming_loss", "hinge_loss", "homogeneity_completeness_v_measure", "homogeneity_score", "jaccard_score", "label_ranking_average_precision_score", "label_ranking_loss", "log_loss", "make_scorer", "matthews_corrcoef", "max_error", "mean_absolute_error", "mean_absolute_percentage_error", "mean_gamma_deviance", "mean_pinball_loss", "mean_poisson_deviance", "mean_squared_error", "mean_squared_log_error", "mean_tweedie_deviance", "median_absolute_error", "multilabel_confusion_matrix", "mutual_info_score", "ndcg_score", "normalized_mutual_info_score", "additive_chi2_kernel", "chi2_kernel", "cosine_distances", "cosine_similarity", "distance_metrics", "euclidean_distances", "haversine_distances", "kernel_metrics", "laplacian_kernel", "linear_kernel", "manhattan_distances", "nan_euclidean_distances", "paired_cosine_distances", "paired_distances", "paired_euclidean_distances", "paired_manhattan_distances", "pairwise_kernels", "polynomial_kernel", "rbf_kernel", "sigmoid_kernel", "pairwise_distances", "pairwise_distances_argmin", "pairwise_distances_argmin_min", "pairwise_distances_chunked", "precision_recall_curve", "precision_recall_fscore_support", "precision_score", "r2_score", "rand_score", "recall_score", "roc_auc_score", "roc_curve", "root_mean_squared_error", "root_mean_squared_log_error", "silhouette_samples", "silhouette_score", "top_k_accuracy_score", "v_measure_score", "zero_one_loss", "BayesianGaussianMixture", "GaussianMixture", "FixedThresholdClassifier", "GridSearchCV", "GroupKFold", "GroupShuffleSplit", "HalvingGridSearchCV", "HalvingRandomSearchCV", "KFold", "LearningCurveDisplay", "LeaveOneGroupOut", "LeaveOneOut", "LeavePGroupsOut", "LeavePOut", "ParameterGrid", "ParameterSampler", "PredefinedSplit", "RandomizedSearchCV", "RepeatedKFold", "RepeatedStratifiedKFold", "ShuffleSplit", "StratifiedGroupKFold", "StratifiedKFold", "StratifiedShuffleSplit", "TimeSeriesSplit", "TunedThresholdClassifierCV", "ValidationCurveDisplay", "check_cv", "cross_val_predict", "cross_val_score", "cross_validate", "learning_curve", "permutation_test_score", "train_test_split", "validation_curve", "OneVsOneClassifier", "OneVsRestClassifier", "OutputCodeClassifier", "ClassifierChain", "MultiOutputClassifier", "MultiOutputRegressor", "RegressorChain", "BernoulliNB", "CategoricalNB", "ComplementNB", "GaussianNB", "MultinomialNB", "BallTree", "KDTree", "KNeighborsClassifier", "KNeighborsRegressor", "KNeighborsTransformer", "KernelDensity", "LocalOutlierFactor", "NearestCentroid", "NearestNeighbors", "NeighborhoodComponentsAnalysis", "RadiusNeighborsClassifier", "RadiusNeighborsRegressor", "RadiusNeighborsTransformer", "kneighbors_graph", "radius_neighbors_graph", "sort_graph_by_row_values", "BernoulliRBM", "MLPClassifier", "MLPRegressor", "FeatureUnion", "Pipeline", "make_pipeline", "make_union", "Binarizer", "FunctionTransformer", "KBinsDiscretizer", "KernelCenterer", "LabelBinarizer", "LabelEncoder", "MaxAbsScaler", "MinMaxScaler", "MultiLabelBinarizer", "Normalizer", "OneHotEncoder", "OrdinalEncoder", "PolynomialFeatures", "PowerTransformer", "QuantileTransformer", "RobustScaler", "SplineTransformer", "StandardScaler", "TargetEncoder", "add_dummy_feature", "binarize", "label_binarize", "maxabs_scale", "minmax_scale", "normalize", "power_transform", "quantile_transform", "robust_scale", "scale", "GaussianRandomProjection", "SparseRandomProjection", "johnson_lindenstrauss_min_dim", "LabelPropagation", "LabelSpreading", "SelfTrainingClassifier", "set_config", "show_versions", "LinearSVC", "LinearSVR", "NuSVC", "NuSVR", "OneClassSVM", "SVC", "SVR", "l1_min_c", "DecisionTreeClassifier", "DecisionTreeRegressor", "ExtraTreeClassifier", "ExtraTreeRegressor", "export_graphviz", "export_text", "plot_tree", "Bunch", "ClassifierTags", "InputTags", "RegressorTags", "Tags", "TargetTags", "TransformerTags", "_safe_indexing", "min_pos", "as_float_array", "assert_all_finite", "check_X_y", "check_array", "check_consistent_length", "check_random_state", "check_scalar", "compute_class_weight", "compute_sample_weight", "deprecated", "all_displays", "all_estimators", "all_functions", "check_estimator", "estimator_checks_generator", "parametrize_with_checks", "estimator_html_repr", "density", "fast_logdet", "randomized_range_finder", "randomized_svd", "safe_sparse_dot", "weighted_mode", "gen_batches", "gen_even_slices", "get_tags", "single_source_shortest_path_length", "indexable", "MetadataRequest", "MetadataRouter", "MethodMapping", "get_routing_for_object", "process_routing", "available_if", "is_multilabel", "type_of_target", "unique_labels", "murmurhash3_32", "Parallel", "delayed", "sample_without_replacement", "resample", "safe_mask", "safe_sqr", "shuffle", "incr_mean_variance_axis", "inplace_column_scale", "inplace_csr_column_scale", "inplace_row_scale", "inplace_swap_column", "inplace_swap_row", "mean_variance_axis", "inplace_csr_row_normalize_l1", "inplace_csr_row_normalize_l2", "check_is_fitted", "check_memory", "check_symmetric", "column_or_1d", "has_fit_parameter", "validate_data", "<span class=\"section-number\">3.2. </span>Tuning the hyper-parameters of an estimator", "<span class=\"section-number\">6.4. </span>Imputation of missing values", "<span class=\"section-number\">1.15. </span>Isotonic regression", "<span class=\"section-number\">6.7. </span>Kernel Approximation", "<span class=\"section-number\">1.3. </span>Kernel ridge regression", "<span class=\"section-number\">1.2. </span>Linear and Quadratic Discriminant Analysis", "<span class=\"section-number\">3.5. </span>Validation curves: plotting scores to evaluate models", "<span class=\"section-number\">1.1. </span>Linear Models", "<span class=\"section-number\">2.2. </span>Manifold learning", "<span class=\"section-number\">6.8. </span>Pairwise metrics, Affinities and Kernels", "<span class=\"section-number\">2.1. </span>Gaussian mixture models", "<span class=\"section-number\">3.4. </span>Metrics and scoring: quantifying the quality of predictions", "<span class=\"section-number\">1.12. </span>Multiclass and multioutput algorithms", "<span class=\"section-number\">1.9. </span>Naive Bayes", "<span class=\"section-number\">1.6. </span>Nearest Neighbors", "<span class=\"section-number\">1.17. </span>Neural network models (supervised)", "<span class=\"section-number\">2.9. </span>Neural network models (unsupervised)", "<span class=\"section-number\">2.7. </span>Novelty and Outlier Detection", "<span class=\"section-number\">4.1. </span>Partial Dependence and Individual Conditional Expectation plots", "<span class=\"section-number\">4.2. </span>Permutation feature importance", "&lt;no title&gt;", "<span class=\"section-number\">6.3. </span>Preprocessing data", "<span class=\"section-number\">6.9. </span>Transforming the prediction target (<code class=\"docutils literal notranslate\"><span class=\"pre\">y</span></code>)", "<span class=\"section-number\">6.6. </span>Random Projection", "<span class=\"section-number\">1.14. </span>Semi-supervised learning", "<span class=\"section-number\">1.5. </span>Stochastic Gradient Descent", "<span class=\"section-number\">1.4. </span>Support Vector Machines", "<span class=\"section-number\">1.10. </span>Decision Trees", "<span class=\"section-number\">6.5. </span>Unsupervised dimensionality reduction", "<span class=\"section-number\">13. </span>External Resources, Videos and Talks", "Related Projects", "Roadmap", "Computation times", "<span class=\"section-number\">1. </span>Supervised learning", "Support", "Testimonials", "<span class=\"section-number\">2. </span>Unsupervised learning", "User Guide", "Available documentation for scikit-learn", "<span class=\"section-number\">5. </span>Visualizations", "Release History", "&lt;no title&gt;", "Older Versions", "Version 0.13", "Version 0.14", "Version 0.15", "Version 0.16", "Version 0.17", "Version 0.18", "Version 0.19", "Version 0.20", "Version 0.21", "Version 0.22", "Version 0.23", "Version 0.24", "Version 1.0", "Version 1.1", "Version 1.2", "Version 1.3", "Version 1.4", "Version 1.5", "Version 1.6", "Version 1.7"], "titleterms": {"": [186, 310, 312, 375, 384, 411, 999], "0": [180, 314, 315, 316, 317, 1015, 1030, 1031, 1032, 1033, 1034, 1035, 1036, 1037, 1038, 1039, 1040, 1041, 1042, 1043, 1044, 1045, 1046, 1047, 1048, 1049], "1": [185, 317, 318, 319, 320, 321, 322, 323, 384, 1030, 1031, 1033, 1034, 1035, 1036, 1037, 1038, 1039, 1040, 1041, 1042, 1043, 1044, 1045, 1046, 1047, 1048, 1049, 1050], "10": [100, 1030], "11": 1030, "12": 1030, "13": [323, 1031, 1049], 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\ No newline at end of file
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"combinator": 809, "combined_featur": 106, "combsccod": 1043, "comcast": 51, "come": [82, 100, 102, 111, 116, 125, 163, 167, 201, 238, 278, 313, 346, 347, 358, 360, 364, 368, 371, 372, 373, 375, 379, 383, 385, 389, 397, 402, 403, 407, 410, 411, 435, 446, 452, 468, 547, 704, 794, 820, 905, 908, 952, 987, 988, 992, 995, 997, 1001, 1004, 1013, 1029, 1040, 1043, 1045], "comedi": 874, "comfort": [88, 410], "command": [237, 358, 359, 369, 371, 375, 377, 379, 389, 411, 505, 1017, 1031], "comment": [80, 346, 369, 370, 371, 372, 374, 375, 379, 483, 577, 998], "commerc": 1022, "commerci": [407, 1022], "commit": [374, 375, 379, 1021, 1029, 1030, 1031, 1032], "committ": 1029, "commod": 998, "common": [1, 52, 54, 55, 57, 123, 160, 163, 180, 181, 182, 205, 211, 212, 259, 264, 271, 281, 315, 322, 339, 346, 348, 353, 358, 365, 371, 373, 379, 381, 383, 384, 388, 389, 394, 400, 401, 403, 404, 407, 409, 412, 413, 443, 461, 463, 493, 572, 588, 631, 652, 671, 672, 700, 745, 752, 768, 800, 801, 804, 814, 816, 817, 818, 819, 820, 826, 829, 864, 866, 874, 875, 876, 881, 883, 888, 889, 891, 892, 893, 894, 920, 922, 949, 952, 987, 988, 994, 998, 1001, 1004, 1008, 1013, 1014, 1017, 1018, 1019, 1022, 1024, 1029, 1037, 1039, 1040, 1044], "common_nod": 353, "common_node_id": 353, "common_param": [90, 146, 149, 184, 239, 266, 267, 339], "common_scatter_plot_param": 155, "commonli": [207, 261, 277, 339, 364, 385, 403, 404, 413, 613, 757, 758, 796, 832, 875, 996, 998, 999, 1001, 1045], "commun": [240, 364, 370, 376, 379, 385, 386, 403, 410, 535, 537, 544, 691, 709, 875, 988, 995, 1001, 1008, 1012, 1016, 1017, 1021, 1022, 1037, 1038], "commut": [43, 52], "comp": [57, 115, 116, 123, 130, 302, 328, 346, 347, 348, 366], "comp_cov": [465, 466, 467, 468, 469, 470, 471, 472], "compact": [163, 188, 304, 358, 368, 843, 844, 1014, 1031], "compani": [207, 224, 402, 403, 1021, 1022], "companion": [371, 1008], "companioni": 1029, "compar": [43, 44, 48, 52, 57, 58, 61, 62, 64, 70, 71, 72, 74, 75, 76, 78, 81, 82, 85, 87, 88, 89, 90, 91, 97, 100, 103, 104, 105, 106, 111, 112, 114, 116, 123, 127, 129, 133, 134, 135, 137, 139, 142, 143, 144, 146, 148, 149, 150, 152, 154, 156, 163, 171, 178, 179, 180, 183, 184, 185, 186, 189, 191, 193, 194, 196, 197, 198, 201, 202, 204, 207, 208, 210, 211, 213, 214, 216, 220, 223, 224, 226, 227, 230, 232, 235, 238, 241, 243, 244, 245, 246, 251, 252, 256, 259, 262, 263, 265, 267, 269, 271, 273, 275, 276, 277, 279, 281, 284, 285, 286, 287, 289, 290, 291, 293, 294, 295, 296, 298, 301, 303, 305, 308, 309, 311, 315, 339, 344, 346, 347, 348, 352, 353, 354, 360, 366, 368, 371, 373, 397, 399, 400, 401, 402, 403, 405, 406, 407, 408, 410, 412, 413, 436, 437, 438, 440, 441, 442, 444, 445, 446, 448, 454, 459, 460, 465, 466, 467, 468, 469, 470, 471, 472, 478, 479, 480, 486, 493, 499, 501, 509, 511, 519, 521, 525, 528, 534, 536, 538, 542, 543, 548, 549, 558, 559, 560, 561, 562, 630, 638, 643, 644, 652, 654, 656, 662, 675, 676, 683, 694, 704, 711, 714, 730, 737, 742, 785, 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"completeness_scor": [1, 73, 82, 91, 314, 347, 403, 735, 736, 794, 998], "complex": [42, 48, 49, 54, 58, 104, 140, 154, 162, 167, 172, 178, 180, 201, 211, 220, 223, 232, 239, 240, 243, 256, 262, 265, 268, 273, 305, 313, 316, 317, 321, 323, 335, 339, 348, 349, 353, 367, 371, 376, 383, 403, 404, 408, 410, 414, 436, 440, 443, 444, 446, 463, 485, 497, 498, 499, 511, 531, 538, 548, 554, 555, 556, 557, 559, 561, 562, 602, 629, 637, 655, 657, 675, 676, 733, 749, 799, 829, 831, 859, 863, 864, 868, 876, 883, 899, 903, 906, 909, 911, 912, 913, 914, 987, 988, 990, 991, 993, 995, 997, 998, 999, 1004, 1008, 1011, 1018, 1019, 1020, 1022, 1024, 1030, 1032, 1038, 1039, 1042], "complexity_comput": [46, 49], "complexity_label": [46, 49], "compli": [371, 385, 940, 1018], "complianc": [0, 149, 371], "compliant": [318, 371, 1047, 1048, 1049], "complic": [64, 240, 299, 377, 413, 693, 1001], "compon": [1, 10, 43, 44, 55, 79, 91, 95, 102, 105, 114, 115, 119, 121, 122, 124, 125, 127, 128, 130, 152, 172, 180, 182, 194, 215, 226, 237, 238, 241, 249, 250, 251, 253, 254, 255, 257, 263, 265, 270, 284, 285, 286, 287, 295, 296, 302, 309, 315, 332, 351, 358, 363, 366, 371, 373, 377, 380, 397, 399, 403, 404, 406, 411, 413, 415, 417, 433, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 458, 459, 461, 465, 466, 467, 468, 469, 470, 471, 472, 478, 479, 480, 481, 499, 501, 512, 518, 523, 528, 529, 530, 531, 532, 533, 534, 535, 536, 537, 538, 539, 540, 541, 542, 543, 544, 545, 546, 547, 548, 549, 550, 551, 552, 553, 554, 555, 556, 557, 558, 559, 560, 561, 562, 563, 570, 579, 580, 581, 586, 587, 588, 589, 590, 591, 592, 593, 594, 595, 596, 597, 598, 600, 601, 609, 610, 611, 612, 613, 614, 615, 617, 618, 619, 620, 621, 622, 623, 624, 626, 627, 628, 629, 630, 634, 637, 638, 639, 640, 641, 642, 643, 644, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 670, 671, 672, 673, 674, 675, 676, 677, 678, 679, 687, 688, 689, 690, 691, 694, 796, 797, 798, 799, 802, 803, 813, 821, 829, 831, 832, 833, 834, 835, 836, 837, 838, 839, 840, 841, 842, 845, 846, 847, 848, 849, 850, 851, 852, 853, 854, 855, 859, 860, 861, 863, 864, 866, 867, 868, 869, 870, 871, 872, 873, 874, 875, 876, 877, 878, 879, 880, 881, 882, 883, 884, 893, 894, 895, 896, 897, 898, 899, 900, 903, 904, 905, 906, 907, 908, 909, 911, 912, 913, 914, 918, 946, 947, 990, 995, 997, 1008, 1010, 1013, 1017, 1018, 1019, 1020, 1022, 1023, 1024, 1029, 1031, 1035, 1036, 1037, 1038, 1040, 1041, 1042, 1043, 1044, 1045, 1046, 1047, 1048], "component_1": 254, "component_2": 254, "component_indices_": [638, 1042], "components_": [45, 54, 91, 116, 120, 122, 123, 130, 237, 238, 302, 309, 317, 385, 404, 408, 440, 528, 529, 530, 531, 533, 534, 535, 536, 537, 538, 540, 541, 638, 852, 859, 895, 896, 990, 1010, 1033, 1035, 1041, 1044, 1047], "components_col": 105, "compos": [1, 43, 44, 62, 101, 102, 103, 107, 116, 136, 143, 154, 180, 183, 184, 185, 207, 209, 224, 235, 243, 245, 247, 278, 281, 310, 314, 316, 317, 318, 320, 321, 384, 394, 403, 404, 407, 460, 461, 462, 463, 512, 550, 611, 997, 999, 1019, 1029], "composit": [6, 35, 235, 314, 363, 407, 657, 787, 988, 994, 1007, 1024, 1030], "compound": [43, 224, 411, 609, 611, 679, 723, 751, 994], "compoundkernel": [1, 609, 1043], "comprehens": [339, 379, 411, 757, 758, 996, 1022, 1037], "compress": [42, 50, 55, 99, 162, 180, 281, 304, 366, 397, 403, 408, 411, 412, 651, 671, 691, 833, 876, 968, 971, 983, 994, 999, 1008, 1019, 1029, 1038], "compressed_raccoon_kmean": 86, "compressed_raccoon_uniform": 86, "compris": [102, 146, 262, 346, 347, 348, 366, 383, 408, 410, 512, 806, 995], "compromis": [48, 64, 184, 358, 371, 646, 678, 1001, 1032], "comput": [0, 1, 27, 43, 45, 46, 50, 52, 53, 58, 63, 72, 74, 76, 77, 80, 85, 87, 90, 91, 93, 94, 102, 104, 110, 111, 112, 113, 121, 129, 137, 141, 144, 145, 146, 147, 148, 149, 155, 162, 163, 167, 172, 174, 175, 178, 183, 184, 185, 186, 188, 191, 192, 194, 195, 196, 197, 198, 207, 209, 211, 214, 220, 223, 224, 227, 229, 230, 234, 236, 237, 239, 243, 244, 246, 258, 260, 262, 264, 265, 266, 267, 271, 273, 275, 284, 286, 288, 290, 291, 293, 294, 297, 304, 313, 317, 318, 321, 325, 327, 335, 339, 342, 346, 347, 348, 353, 359, 360, 365, 366, 368, 371, 373, 376, 377, 378, 380, 383, 384, 385, 388, 389, 397, 398, 399, 400, 401, 403, 405, 406, 408, 409, 410, 411, 412, 413, 414, 415, 416, 433, 434, 435, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 448, 453, 455, 457, 458, 459, 461, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 479, 513, 528, 529, 531, 532, 534, 535, 536, 537, 538, 539, 540, 541, 542, 543, 544, 545, 546, 547, 548, 550, 551, 552, 553, 554, 555, 556, 557, 558, 559, 560, 561, 562, 564, 565, 566, 567, 571, 580, 583, 584, 587, 588, 589, 591, 592, 597, 598, 601, 602, 603, 604, 605, 606, 607, 609, 611, 612, 613, 614, 615, 618, 619, 620, 621, 622, 623, 624, 626, 628, 629, 630, 631, 632, 633, 636, 637, 638, 639, 640, 641, 643, 644, 645, 646, 647, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 670, 671, 672, 673, 675, 676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 690, 691, 693, 694, 695, 696, 697, 698, 699, 700, 701, 702, 703, 704, 705, 706, 707, 708, 709, 711, 712, 714, 715, 716, 717, 718, 719, 724, 725, 726, 728, 729, 730, 733, 735, 737, 738, 739, 742, 753, 754, 755, 756, 757, 758, 759, 760, 762, 763, 765, 766, 767, 768, 769, 770, 771, 772, 773, 774, 775, 776, 777, 778, 779, 780, 781, 782, 783, 785, 786, 787, 788, 791, 792, 793, 795, 796, 797, 798, 799, 802, 803, 805, 813, 821, 822, 824, 825, 826, 827, 828, 830, 831, 832, 833, 843, 844, 845, 846, 847, 848, 849, 850, 851, 853, 854, 855, 856, 857, 859, 860, 861, 868, 869, 872, 873, 878, 879, 880, 881, 882, 883, 887, 888, 890, 892, 893, 894, 895, 896, 897, 898, 899, 900, 901, 903, 905, 908, 910, 911, 912, 913, 914, 944, 945, 946, 947, 964, 965, 970, 972, 978, 987, 990, 992, 993, 994, 995, 996, 997, 998, 999, 1000, 1001, 1002, 1003, 1004, 1006, 1008, 1010, 1011, 1012, 1013, 1014, 1017, 1018, 1022, 1024, 1026, 1029, 1030, 1031, 1032, 1033, 1034, 1035, 1036, 1037, 1038, 1039, 1040, 1041, 1042, 1043, 1044, 1045, 1046, 1047, 1048, 1049], "computation": [37, 53, 109, 120, 140, 145, 154, 167, 284, 339, 346, 357, 372, 383, 403, 404, 407, 408, 410, 413, 535, 631, 632, 670, 762, 799, 801, 802, 803, 813, 826, 994, 995, 997, 1005, 1006, 1010, 1024, 1033], "compute_class_weight": [1, 385, 1032, 1043, 1049], "compute_corrected_ttest": 264, "compute_dist": [437, 441, 1041], "compute_full_tre": [437, 441, 1033], "compute_import": 1031, "compute_inverse_compon": [895, 896, 1010], "compute_inverse_transform": 1043, "compute_label": [438, 445], "compute_node_depth": 353, "compute_optics_graph": [1, 451, 452, 1046], "compute_sample_weight": [1, 1043], "compute_scor": [107, 127, 190, 191, 643, 644, 1038], "compute_sourc": 415, "computed_scor": 644, "con": [397, 587, 997], "concat": [43, 178, 182, 198, 224, 876], "concaten": [1, 63, 70, 74, 83, 94, 101, 104, 112, 136, 150, 159, 175, 180, 190, 193, 201, 220, 221, 227, 233, 249, 253, 254, 260, 269, 271, 272, 273, 274, 289, 302, 308, 311, 325, 334, 338, 346, 404, 460, 463, 501, 506, 528, 534, 538, 539, 597, 780, 799, 862, 863, 865, 868, 876, 908, 999, 1019, 1030], "concav": [163, 321, 368], "concentr": [46, 48, 118, 125, 134, 152, 172, 179, 180, 231, 248, 250, 255, 275, 294, 306, 326, 367, 371, 410, 439, 516, 796, 997, 1004, 1019], "concentrations_prior": 249, "concept": [1, 112, 140, 144, 240, 273, 383, 403, 409, 411, 990, 998, 1001, 1014, 1016, 1022], "conceptu": [368, 410, 996], "concern": [37, 56, 71, 108, 114, 117, 119, 131, 133, 157, 164, 166, 177, 180, 187, 189, 225, 248, 254, 258, 280, 282, 285, 298, 303, 323, 330, 345, 349, 358, 373, 397, 995, 1010], "concis": [64, 207, 371, 376, 1040, 1042], "conclud": [66, 134, 183, 191, 224, 264, 317, 348, 354, 386, 863], "conclus": [43, 125, 183, 185, 207, 209, 264, 266, 354, 410], "concomit": [648, 994], "concret": [211, 372, 386, 402, 412, 673, 674, 895, 896, 994, 1012, 1017, 1048], "concurr": [385, 411, 965, 1041, 1043], "cond": 1048, "conda": [313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 359, 371, 372, 374, 375, 377, 379, 389, 390, 396, 397, 1014], "conda_prefix": 377, "condarc": 369, "condens": [188, 442], "condit": [1, 43, 51, 52, 62, 64, 113, 146, 180, 181, 183, 198, 201, 209, 212, 224, 240, 244, 267, 316, 353, 377, 383, 388, 399, 400, 401, 402, 403, 405, 408, 410, 412, 447, 460, 467, 468, 469, 474, 493, 510, 520, 521, 533, 536, 537, 538, 540, 542, 544, 546, 547, 559, 626, 631, 632, 642, 649, 650, 651, 653, 654, 655, 669, 671, 672, 673, 674, 681, 682, 686, 696, 711, 716, 717, 735, 736, 794, 820, 838, 839, 840, 841, 842, 861, 864, 867, 876, 877, 880, 884, 947, 969, 992, 994, 995, 996, 998, 1000, 1001, 1003, 1008, 1014, 1019, 1024, 1030, 1032, 1033, 1034, 1036, 1037, 1039, 1041, 1043, 1044, 1045, 1047], "condition": [51, 64, 207, 401, 405, 757, 992, 998], "condition2": 154, "conduct": [182, 264, 413, 1021, 1043], "conf": [46, 64, 375, 401, 407, 838, 1000, 1043], "confer": [258, 264, 366, 403, 408, 414, 435, 440, 446, 508, 532, 560, 695, 707, 725, 755, 859, 860, 861, 998, 1004, 1010, 1014], "confid": [52, 61, 62, 63, 64, 66, 149, 172, 174, 250, 264, 267, 327, 386, 401, 413, 635, 657, 658, 665, 667, 670, 673, 674, 675, 697, 701, 706, 719, 725, 726, 738, 739, 755, 788, 831, 870, 903, 905, 908, 994, 997, 998, 999, 1004, 1011, 1012, 1013, 1022, 1039], "config": [52, 359, 369, 371, 372, 379, 625, 1037], "config_context": [1, 247, 322, 358, 359, 399, 625, 901, 1036, 1042, 1045], "configur": [1, 2, 46, 49, 64, 66, 103, 104, 184, 240, 245, 247, 258, 278, 311, 346, 357, 369, 371, 373, 377, 383, 385, 389, 394, 399, 404, 411, 412, 427, 438, 439, 441, 443, 445, 448, 458, 460, 464, 478, 479, 480, 481, 528, 529, 530, 531, 532, 533, 534, 535, 536, 537, 538, 539, 540, 541, 546, 563, 564, 565, 566, 567, 579, 580, 581, 587, 588, 590, 591, 592, 593, 594, 595, 596, 597, 598, 599, 600, 601, 625, 626, 627, 628, 629, 631, 634, 637, 638, 639, 640, 641, 687, 688, 689, 690, 691, 693, 694, 787, 799, 813, 847, 852, 855, 859, 862, 863, 866, 867, 868, 869, 870, 871, 872, 873, 874, 875, 876, 877, 878, 879, 880, 881, 882, 883, 884, 895, 896, 901, 965, 966, 995, 998, 1008, 1013, 1017, 1024, 1033, 1036, 1037, 1038, 1039, 1041, 1043, 1044, 1045, 1046], "confirm": [43, 52, 66, 116, 137, 143, 146, 149, 162, 185, 207, 209, 258, 267, 270, 273, 309, 348, 375, 987, 1008, 1037], "conflict": [369, 374, 375, 379, 389, 1036, 1037], "conform": [52, 371, 579, 627, 831, 832, 998, 1017, 1018, 1036, 1042], "confound": [182, 183], "confus": [1, 68, 180, 234, 256, 258, 273, 324, 325, 346, 385, 464, 501, 630, 651, 696, 712, 714, 717, 728, 729, 737, 753, 783, 786, 829, 901, 908, 1019, 1029, 1030, 1034, 1038, 1039, 1041, 1042, 1043, 1044], "confusingli": 369, "confusion_matrix": [1, 68, 234, 257, 258, 321, 324, 325, 696, 712, 753, 798, 826, 998, 1030, 1035, 1036, 1039, 1040, 1042, 1046], "confusion_matrix_scor": 998, "confusionmatrixdisplai": [1, 45, 68, 257, 316, 321, 324, 346, 630, 717, 998, 1039, 1040, 1042, 1043, 1044, 1048], "congruenc": [653, 654, 655, 681, 682], "conjug": [264, 448, 458, 671, 673, 686, 694, 994], "conjunct": [394, 403, 592, 805, 821, 822, 824, 825, 826, 827, 830, 966, 988, 994, 1044], "connect": [1, 51, 74, 79, 81, 84, 87, 95, 99, 100, 369, 371, 380, 385, 405, 437, 441, 448, 458, 459, 583, 584, 694, 845, 846, 847, 849, 851, 853, 854, 855, 856, 857, 996, 998, 1001, 1003, 1011, 1021, 1033, 1042, 1046], "connected_compon": 1036, "connectionist": [860, 861], "conner": 1042, "connor": [1037, 1042, 1046, 1047, 1049], "connossor": [1037, 1038], "conocophillip": 51, "conort": 1022, "conquer": 947, "conrad": [1029, 1030, 1044, 1047, 1048], "conroi": 1044, "consecut": [134, 144, 208, 383, 401, 407, 411, 439, 443, 445, 446, 448, 452, 455, 458, 534, 535, 536, 543, 600, 644, 665, 666, 667, 675, 676, 677, 796, 797, 804, 838, 839, 840, 841, 842, 860, 861, 987, 1008, 1037], "consensu": [1, 58, 59, 72, 370, 371, 386, 400, 403, 648, 670, 677, 678, 718, 998], "consensus_scor": [1, 58, 59, 400, 1031], "consequ": [90, 127, 224, 264, 265, 304, 322, 332, 354, 401, 402, 408, 410, 558, 559, 561, 562, 563, 654, 655, 988, 994, 998, 1006, 1014, 1038, 1040, 1045, 1047], "conserv": [50, 375, 385, 580, 587, 895, 896, 997, 1010], "consid": [0, 43, 51, 52, 53, 58, 62, 74, 88, 99, 103, 112, 120, 124, 126, 143, 146, 158, 162, 163, 179, 184, 201, 207, 209, 240, 258, 264, 267, 271, 273, 275, 278, 284, 287, 290, 291, 304, 315, 321, 332, 339, 340, 342, 346, 354, 358, 359, 360, 363, 370, 371, 373, 377, 379, 383, 385, 386, 394, 397, 399, 402, 403, 408, 409, 410, 411, 412, 413, 414, 415, 434, 440, 442, 446, 453, 470, 505, 506, 518, 530, 538, 546, 547, 554, 555, 556, 557, 558, 559, 560, 561, 562, 563, 583, 584, 586, 587, 589, 591, 605, 606, 619, 630, 631, 632, 645, 651, 657, 658, 659, 661, 670, 678, 687, 688, 689, 691, 692, 693, 695, 699, 701, 704, 706, 714, 725, 729, 733, 755, 785, 787, 793, 845, 846, 847, 849, 851, 853, 854, 855, 858, 860, 861, 866, 876, 877, 884, 898, 899, 908, 909, 911, 912, 913, 914, 924, 981, 987, 993, 994, 995, 996, 998, 999, 1001, 1004, 1005, 1006, 1008, 1012, 1013, 1014, 1032, 1033, 1037, 1041, 1042, 1044, 1045, 1046], "consider": [148, 149, 168, 171, 243, 259, 265, 271, 366, 371, 402, 413, 618, 799, 813, 821, 987, 994, 1000, 1004, 1012, 1022, 1046], "consist": [1, 43, 46, 63, 68, 72, 89, 90, 102, 111, 118, 120, 140, 143, 149, 150, 156, 162, 163, 170, 172, 175, 179, 186, 201, 207, 224, 239, 243, 270, 273, 301, 309, 313, 316, 342, 347, 354, 358, 364, 366, 368, 371, 373, 377, 378, 379, 380, 384, 385, 386, 401, 403, 405, 409, 410, 411, 421, 422, 425, 426, 436, 437, 438, 439, 440, 441, 443, 444, 445, 446, 447, 448, 449, 459, 460, 461, 463, 465, 466, 467, 468, 469, 470, 471, 472, 478, 479, 480, 486, 494, 528, 530, 531, 532, 533, 534, 535, 536, 537, 539, 540, 541, 551, 553, 555, 557, 559, 560, 562, 563, 564, 565, 566, 567, 579, 580, 581, 586, 587, 589, 610, 626, 627, 628, 629, 634, 637, 642, 643, 644, 645, 646, 648, 649, 650, 651, 652, 653, 654, 655, 656, 659, 660, 661, 662, 663, 664, 666, 669, 671, 672, 673, 676, 677, 678, 686, 687, 688, 689, 690, 699, 734, 796, 797, 806, 831, 832, 833, 835, 836, 837, 838, 846, 847, 849, 851, 854, 855, 861, 866, 867, 868, 870, 874, 875, 878, 879, 881, 882, 884, 895, 896, 899, 903, 904, 906, 907, 909, 912, 914, 920, 929, 931, 954, 968, 971, 986, 987, 990, 991, 992, 994, 995, 997, 998, 999, 1001, 1002, 1008, 1011, 1013, 1014, 1018, 1022, 1029, 1030, 1031, 1033, 1034, 1035, 1036, 1037, 1038, 1039, 1041, 1042, 1043, 1044, 1045, 1046, 1047, 1048, 1049], "consol": 142, "consolid": [0, 385, 386, 1029], "consolidate_scor": 52, "consortium": [0, 1022], "constant": [1, 43, 129, 137, 149, 155, 170, 173, 174, 179, 183, 196, 208, 209, 211, 224, 235, 240, 245, 261, 267, 296, 300, 302, 305, 307, 314, 342, 344, 354, 363, 373, 380, 385, 400, 410, 413, 426, 442, 461, 478, 479, 480, 508, 510, 531, 535, 537, 544, 546, 547, 548, 549, 551, 553, 555, 557, 559, 562, 565, 567, 588, 604, 607, 610, 612, 626, 629, 631, 632, 634, 639, 642, 643, 644, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 671, 672, 675, 676, 677, 678, 679, 686, 688, 692, 694, 720, 722, 723, 727, 731, 774, 776, 784, 798, 821, 836, 837, 846, 854, 860, 861, 868, 879, 882, 891, 903, 904, 905, 906, 907, 908, 909, 910, 912, 914, 990, 992, 994, 998, 1000, 1001, 1008, 1012, 1014, 1024, 1032, 1037, 1039, 1041, 1042, 1043, 1045, 1047, 1048], "constant_": 549, "constant_valu": [413, 612, 616], "constant_value_bound": [413, 610, 612, 616], "constantini": 1045, "constantkernel": [1, 170, 176, 413, 610, 616, 620, 623], "constantli": [72, 207, 1022], "constitu": 368, "constitut": [83, 385, 407, 410, 446, 452, 586, 587, 589, 1002], "constrain": [25, 81, 120, 143, 149, 151, 184, 200, 277, 299, 314, 320, 333, 335, 364, 385, 403, 408, 410, 506, 558, 559, 634, 657, 658, 689, 693, 821, 828, 994, 997, 1033], "constrained_layout": [120, 184, 226, 310, 311, 318], "constraint": [88, 90, 100, 120, 133, 180, 197, 204, 211, 243, 244, 259, 300, 301, 302, 314, 371, 383, 408, 411, 483, 505, 506, 554, 555, 556, 557, 558, 559, 561, 562, 631, 634, 656, 817, 818, 911, 912, 913, 914, 967, 987, 989, 994, 995, 1012, 1019, 1033, 1036, 1037, 1040, 1042, 1044, 1046], "constru": 411, "construct": [1, 43, 50, 102, 104, 134, 136, 138, 154, 163, 224, 234, 240, 245, 247, 297, 305, 307, 314, 317, 365, 368, 373, 380, 385, 403, 404, 407, 408, 410, 411, 414, 428, 437, 438, 440, 441, 446, 448, 453, 459, 461, 463, 516, 532, 538, 541, 552, 553, 579, 585, 638, 687, 689, 690, 813, 843, 844, 845, 846, 847, 849, 851, 853, 854, 855, 862, 863, 864, 865, 867, 908, 928, 930, 946, 947, 958, 962, 987, 988, 990, 994, 995, 998, 999, 1001, 1008, 1011, 1013, 1014, 1018, 1034, 1036, 1037, 1040, 1043], "construct_grid": [50, 297], 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464, 799, 813, 824, 825, 826, 901, 965, 966, 1024, 1044, 1047, 1048], "dispatch_next": 965, "dispatch_one_batch": 965, "dispers": [70, 278, 403, 709, 724, 994], "displai": [1, 51, 62, 64, 66, 70, 72, 80, 93, 103, 112, 113, 115, 116, 120, 140, 150, 151, 154, 178, 180, 183, 184, 197, 224, 232, 233, 243, 247, 253, 261, 265, 266, 271, 273, 274, 277, 279, 294, 310, 314, 315, 334, 351, 358, 371, 373, 374, 378, 380, 389, 391, 404, 408, 411, 412, 417, 434, 460, 463, 464, 493, 538, 561, 629, 630, 631, 657, 686, 696, 697, 699, 700, 701, 712, 717, 781, 788, 799, 805, 813, 822, 829, 863, 864, 876, 878, 883, 901, 908, 915, 916, 917, 937, 965, 984, 998, 1002, 1017, 1019, 1024, 1038, 1040, 1041, 1043, 1044], "display_label": [45, 257, 696, 1041], "dispos": 987, "disproportion": 409, "disput": 140, "disregard": [346, 385, 411, 426, 461, 478, 479, 480, 549, 551, 553, 555, 557, 559, 562, 565, 567, 610, 634, 642, 643, 644, 645, 646, 648, 649, 650, 651, 652, 653, 654, 655, 656, 659, 660, 661, 662, 663, 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353, 369, 410, 827, 1008, 1021, 1025], "zisserman": [637, 990, 998], "zito": [1029, 1042], "ziv": 1035, "zivori": 1035, "zj": [1037, 1038], "zlabel": 126, "zoj613": 1041, "zolisa": 1041, "zoom": [43, 139, 304, 391], "zoom_in_percentile_rang": 304, "zorder": [51, 91, 122, 151, 168, 176, 199, 205, 227, 229, 236, 239, 249, 314, 320, 340], "zou": [134, 196, 410, 516, 550, 655, 994, 1012], "zou_et_al_criterion_resc": 196, "zoubin": 898, "zoubir": 112, "zouhar": 1042, "zsh": 359, "zuckerberg": 0, "zwinck": 1031, "zxcvbniu": 1036, "zy": 1048, "zz": 111, "zzrh2009": 410, "z\u00e9": 1037, "\u00e4yr\u00e4m\u00f6": 994, "\u00f3scar": [1033, 1034, 1035, 1036], "\u00f6zer": 1042, "\u0142ukasz": 1044, "\u015fahin": 1043, "\u0161t\u011bp\u00e1n": [1047, 1048], "\u675c\u4e16\u6a4b": [0, 393], "\u8d75\u4e30": 1043}, "titles": ["About us", "API Reference", "sklearn", "sklearn.base", "sklearn.calibration", "sklearn.cluster", "sklearn.compose", "sklearn.covariance", "sklearn.cross_decomposition", "sklearn.datasets", "sklearn.decomposition", "sklearn.discriminant_analysis", "sklearn.dummy", "sklearn.ensemble", "sklearn.exceptions", "sklearn.experimental", "sklearn.feature_extraction", "sklearn.feature_selection", "sklearn.frozen", "sklearn.gaussian_process", "sklearn.impute", "sklearn.inspection", "sklearn.isotonic", "sklearn.kernel_approximation", "sklearn.kernel_ridge", "sklearn.linear_model", "sklearn.manifold", "sklearn.metrics", "sklearn.mixture", "sklearn.model_selection", "sklearn.multiclass", "sklearn.multioutput", "sklearn.naive_bayes", "sklearn.neighbors", "sklearn.neural_network", "sklearn.pipeline", "sklearn.preprocessing", "sklearn.random_projection", "sklearn.semi_supervised", "sklearn.svm", "sklearn.tree", "sklearn.utils", "Examples based on real world datasets", "Time-related feature engineering", "Image denoising using kernel PCA", "Faces recognition example using eigenfaces and SVMs", "Model Complexity Influence", "Out-of-core classification of text documents", "Outlier detection on a real data set", "Prediction Latency", "Species distribution modeling", "Visualizing the stock market structure", "Lagged features for time series forecasting", "Compressive sensing: tomography reconstruction with L1 prior (Lasso)", "Topic extraction with Non-negative Matrix Factorization and Latent Dirichlet Allocation", "Wikipedia principal eigenvector", "Biclustering", "Biclustering documents with the Spectral Co-clustering algorithm", "A demo of the Spectral Biclustering algorithm", "A demo of the Spectral Co-Clustering algorithm", "Calibration", "Probability calibration of classifiers", "Probability Calibration curves", "Probability Calibration for 3-class classification", "Comparison of Calibration of Classifiers", "Classification", "Plot classification probability", "Classifier comparison", "Recognizing hand-written digits", "Normal, Ledoit-Wolf and OAS Linear Discriminant Analysis for classification", "Linear and Quadratic Discriminant Analysis with covariance ellipsoid", "Clustering", "Adjustment for chance in clustering performance evaluation", "Demo of affinity propagation clustering algorithm", "Agglomerative clustering with and without structure", "Agglomerative clustering with different metrics", "Plot Hierarchical Clustering Dendrogram", "Compare BIRCH and MiniBatchKMeans", "Bisecting K-Means and Regular K-Means Performance Comparison", "Comparing different clustering algorithms on toy datasets", "Segmenting the picture of greek coins in regions", "A demo of structured Ward hierarchical clustering on an image of coins", "Demo of DBSCAN clustering algorithm", "Online learning of a dictionary of parts of faces", "Feature agglomeration", "Various Agglomerative Clustering on a 2D embedding of digits", "Vector Quantization Example", "Feature agglomeration vs. univariate selection", "Demo of HDBSCAN clustering algorithm", "Inductive Clustering", "Demonstration of k-means assumptions", "A demo of K-Means clustering on the handwritten digits data", "An example of K-Means++ initialization", "Selecting the number of clusters with silhouette analysis on KMeans clustering", "Empirical evaluation of the impact of k-means initialization", "Comparing different hierarchical linkage methods on toy datasets", "A demo of the mean-shift clustering algorithm", "Comparison of the K-Means and MiniBatchKMeans clustering algorithms", "Demo of OPTICS clustering algorithm", "Spectral clustering for image segmentation", "Hierarchical clustering: structured vs unstructured ward", "Pipelines and composite estimators", "Column Transformer with Heterogeneous Data Sources", "Column Transformer with Mixed Types", "Selecting dimensionality reduction with Pipeline and GridSearchCV", "Pipelining: chaining a PCA and a logistic regression", "Concatenating multiple feature extraction methods", "Effect of transforming the targets in regression model", "Covariance estimation", "Shrinkage covariance estimation: LedoitWolf vs OAS and max-likelihood", "Ledoit-Wolf vs OAS estimation", "Robust covariance estimation and Mahalanobis distances relevance", "Robust vs Empirical covariance estimate", "Sparse inverse covariance estimation", "Cross decomposition", "Compare cross decomposition methods", "Principal Component Regression vs Partial Least Squares Regression", "Dataset examples", "Plot randomly generated multilabel dataset", "Decomposition", "Faces dataset decompositions", "Blind source separation using FastICA", "FastICA on 2D point clouds", "Image denoising using dictionary learning", "Incremental PCA", "Kernel PCA", "Principal Component Analysis (PCA) on Iris Dataset", "Model selection with Probabilistic PCA and Factor Analysis (FA)", "Comparison of LDA and PCA 2D projection of Iris dataset", "Sparse coding with a precomputed dictionary", "Factor Analysis (with rotation) to visualize patterns", "Developing Estimators", "<code class=\"docutils literal notranslate\"><span class=\"pre\">__sklearn_is_fitted__</span></code> as Developer API", "Ensemble methods", "Multi-class AdaBoosted Decision Trees", "Decision Tree Regression with AdaBoost", "Two-class AdaBoost", "Single estimator versus bagging: bias-variance decomposition", "OOB Errors for Random Forests", "Feature transformations with ensembles of trees", "Comparing Random Forests and Histogram Gradient Boosting models", "Feature importances with a forest of trees", "Plot the decision surfaces of ensembles of trees on the iris dataset", "Categorical Feature Support in Gradient Boosting", "Early stopping in Gradient Boosting", "Gradient Boosting Out-of-Bag estimates", "Prediction Intervals for Gradient Boosting Regression", "Gradient Boosting regression", "Gradient Boosting regularization", "Features in Histogram Gradient Boosting Trees", "IsolationForest example", "Monotonic Constraints", "Hashing feature transformation using Totally Random Trees", "Comparing random forests and the multi-output meta estimator", "Combine predictors using stacking", "Visualizing the probabilistic predictions of a VotingClassifier", "Plot individual and voting regression predictions", "Feature Selection", "Comparison of F-test and mutual information", "Univariate Feature Selection", "Pipeline ANOVA SVM", "Recursive feature elimination", "Recursive feature elimination with cross-validation", "Model-based and sequential feature selection", "Frozen Estimators", "Examples of Using <code class=\"docutils literal notranslate\"><span class=\"pre\">FrozenEstimator</span></code>", "Gaussian Process for Machine Learning", "Comparison of kernel ridge and Gaussian process regression", "Probabilistic predictions with Gaussian process classification (GPC)", "Gaussian process classification (GPC) on iris dataset", "Iso-probability lines for Gaussian Processes classification (GPC)", "Illustration of Gaussian process classification (GPC) on the XOR dataset", "Forecasting of CO2 level on Mona Loa dataset using Gaussian process regression (GPR)", "Ability of Gaussian process regression (GPR) to estimate data noise-level", "Gaussian Processes regression: basic introductory example", "Gaussian processes on discrete data structures", "Illustration of prior and posterior Gaussian process for different kernels", "Missing Value Imputation", "Imputing missing values with variants of IterativeImputer", "Imputing missing values before building an estimator", "Examples", "Inspection", "Failure of Machine Learning to infer causal effects", "Common pitfalls in the interpretation of coefficients of linear models", "Partial Dependence and Individual Conditional Expectation Plots", "Permutation Importance vs Random Forest Feature Importance (MDI)", "Permutation Importance with Multicollinear or Correlated Features", "Kernel Approximation", "Scalable learning with polynomial kernel approximation", "Generalized Linear Models", "Comparing Linear Bayesian Regressors", "Curve Fitting with Bayesian Ridge Regression", "Fitting an Elastic Net with a precomputed Gram Matrix and Weighted Samples", "HuberRegressor vs Ridge on dataset with strong outliers", "L1-based models for Sparse Signals", "Lasso on dense and sparse data", "Lasso model selection via information criteria", "Lasso, Lasso-LARS, and Elastic Net paths", "Lasso model selection: AIC-BIC / cross-validation", "Logistic function", "L1 Penalty and Sparsity in Logistic Regression", "Decision Boundaries of Multinomial and One-vs-Rest Logistic Regression", "Regularization path of L1- Logistic Regression", "Joint feature selection with multi-task Lasso", "Non-negative least squares", "Ordinary Least Squares and Ridge Regression", "Orthogonal Matching Pursuit", "Poisson regression and non-normal loss", "Polynomial and Spline interpolation", "Quantile regression", "Robust linear model estimation using RANSAC", "Ridge coefficients as a function of the L2 Regularization", "Plot Ridge coefficients as a function of the regularization", "Robust linear estimator fitting", "Early stopping of Stochastic Gradient Descent", "Plot multi-class SGD on the iris dataset", "SGD: convex loss functions", "SGD: Penalties", "SGD: Maximum margin separating hyperplane", "SGD: Weighted samples", "One-Class SVM versus One-Class SVM using Stochastic Gradient Descent", "Multiclass sparse logistic regression on 20newgroups", "MNIST classification using multinomial logistic + L1", "Theil-Sen Regression", "Tweedie regression on insurance claims", "Manifold learning", "Comparison of Manifold Learning methods", "Manifold learning on handwritten digits: Locally Linear Embedding, Isomap\u2026", "Manifold Learning methods on a severed sphere", "Multi-dimensional scaling", "Swiss Roll And Swiss-Hole Reduction", "t-SNE: The effect of various perplexity values on the shape", "Miscellaneous", "Comparing anomaly detection algorithms for outlier detection on toy datasets", "Visualizations with Display Objects", "Displaying estimators and complex pipelines", "Isotonic Regression", "The Johnson-Lindenstrauss bound for embedding with random projections", "Explicit feature map approximation for RBF kernels", "Comparison of kernel ridge regression and SVR", "Metadata Routing", "Multilabel classification", "Face completion with a multi-output estimators", "Evaluation of outlier detection estimators", "Advanced Plotting With Partial Dependence", "Displaying Pipelines", "ROC Curve with Visualization API", "Introducing the <code class=\"docutils literal notranslate\"><span class=\"pre\">set_output</span></code> API", "Gaussian Mixture Models", "Concentration Prior Type Analysis of Variation Bayesian Gaussian Mixture", "Gaussian Mixture Model Ellipsoids", "GMM covariances", "GMM Initialization Methods", "Density Estimation for a Gaussian mixture", "Gaussian Mixture Model Selection", "Gaussian Mixture Model Sine Curve", "Model Selection", "Confusion matrix", "Post-tuning the decision threshold for cost-sensitive learning", "Visualizing cross-validation behavior in scikit-learn", "Plotting Cross-Validated Predictions", "Detection error tradeoff (DET) curve", "Custom refit strategy of a grid search with cross-validation", "Balance model complexity and cross-validated score", "Statistical comparison of models using grid search", "Sample pipeline for text feature extraction and evaluation", "Plotting Learning Curves and Checking Models\u2019 Scalability", "Class Likelihood Ratios to measure classification performance", "Demonstration of multi-metric evaluation on cross_val_score and GridSearchCV", "Nested versus non-nested cross-validation", "Test with permutations the significance of a classification score", "Precision-Recall", "Comparing randomized search and grid search for hyperparameter estimation", "Multiclass Receiver Operating Characteristic (ROC)", "Receiver Operating Characteristic (ROC) with cross validation", "Comparison between grid search and successive halving", "Successive Halving Iterations", "Effect of model regularization on training and test error", "Post-hoc tuning the cut-off point of decision function", "Underfitting vs. Overfitting", "Multiclass methods", "Overview of multiclass training meta-estimators", "Multioutput methods", "Multilabel classification using a classifier chain", "Approximate nearest neighbors in TSNE", "Nearest Neighbors", "Caching nearest neighbors", "Nearest Neighbors Classification", "Kernel Density Estimation", "Simple 1D Kernel Density Estimation", "Novelty detection with Local Outlier Factor (LOF)", "Outlier detection with Local Outlier Factor (LOF)", "Comparing Nearest Neighbors with and without Neighborhood Components Analysis", "Dimensionality Reduction with Neighborhood Components Analysis", "Neighborhood Components Analysis Illustration", "Nearest Centroid Classification", "Nearest Neighbors regression", "Kernel Density Estimate of Species Distributions", "Neural Networks", "Varying regularization in Multi-layer Perceptron", "Compare Stochastic learning strategies for MLPClassifier", "Visualization of MLP weights on MNIST", "Restricted Boltzmann Machine features for digit classification", "Preprocessing", "Compare the effect of different scalers on data with outliers", "Using KBinsDiscretizer to discretize continuous features", "Feature discretization", "Demonstrating the different strategies of KBinsDiscretizer", "Map data to a normal distribution", "Importance of Feature Scaling", "Comparing Target Encoder with Other Encoders", "Target Encoder\u2019s Internal Cross fitting", "Release Highlights", "Release Highlights for scikit-learn 0.22", "Release Highlights for scikit-learn 0.23", "Release Highlights for scikit-learn 0.24", "Release Highlights for scikit-learn 1.0", "Release Highlights for scikit-learn 1.1", "Release Highlights for scikit-learn 1.2", "Release Highlights for scikit-learn 1.3", "Release Highlights for scikit-learn 1.4", "Release Highlights for scikit-learn 1.5", "Release Highlights for scikit-learn 1.6", "Semi Supervised Classification", "Label Propagation digits: Demonstrating performance", "Label Propagation digits: Active learning", "Label Propagation circles: Learning a complex structure", "Effect of varying threshold for self-training", "Semi-supervised Classification on a Text Dataset", "Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset", "Support Vector Machines", "SVM with custom kernel", "Plot different SVM classifiers in the iris dataset", "Plot the support vectors in LinearSVC", "One-class SVM with non-linear kernel (RBF)", "RBF SVM parameters", "SVM: Maximum margin separating hyperplane", "SVM: Separating hyperplane for unbalanced classes", "SVM-Anova: SVM with univariate feature selection", "Plot classification boundaries with different SVM Kernels", "SVM Margins Example", "Support Vector Regression (SVR) using linear and non-linear kernels", "Scaling the regularization parameter for SVCs", "SVM Tie Breaking Example", "SVM: Weighted samples", "Working with text documents", "Classification of text documents using sparse features", "Clustering text documents using k-means", "FeatureHasher and DictVectorizer Comparison", "Decision Trees", "Post pruning decision trees with cost complexity pruning", "Plot the decision surface of decision trees trained on the iris dataset", "Decision Tree Regression", "Understanding the decision tree structure", "<span class=\"section-number\">11. </span>Common pitfalls and recommended practices", "&lt;no title&gt;", "&lt;no title&gt;", "<span class=\"section-number\">9. </span>Computing with scikit-learn", "<span class=\"section-number\">9.2. </span>Computational Performance", "<span class=\"section-number\">9.3. </span>Parallelism, resource management, and configuration", "<span class=\"section-number\">9.1. </span>Strategies to scale computationally: bigger data", "&lt;no title&gt;", "&lt;no title&gt;", "<span class=\"section-number\">7. </span>Dataset transformations", "<span class=\"section-number\">8. </span>Dataset loading utilities", "<span class=\"section-number\">8.4. </span>Loading other datasets", "<span class=\"section-number\">8.2. </span>Real world datasets", "<span class=\"section-number\">8.3. </span>Generated datasets", "<span class=\"section-number\">8.1. </span>Toy datasets", "Installing the development version of scikit-learn", "Bug triaging and issue curation", "Contributing", "Cython Best Practices, Conventions and Knowledge", "Developing scikit-learn estimators", "Developer\u2019s Guide", "Maintainer Information", "Crafting a minimal reproducer for scikit-learn", "How to optimize for speed", "Developing with the Plotting API", "Developers\u2019 Tips and Tricks", "Utilities for Developers", "<span class=\"section-number\">12. </span>Dispatching", "&lt;no title&gt;", "Frequently Asked Questions", "Getting Started", "Glossary of Common Terms and API Elements", "Scikit-learn governance and decision-making", "Index", "<span class=\"section-number\">5. </span>Inspection", "Installing scikit-learn", "&lt;no title&gt;", "<span class=\"section-number\">13. </span>Choosing the right estimator", "&lt;no title&gt;", "&lt;no title&gt;", "<span class=\"section-number\">4. </span>Metadata Routing", "&lt;no title&gt;", "&lt;no title&gt;", "<span class=\"section-number\">10. </span>Model persistence", "<span class=\"section-number\">3. </span>Model selection and evaluation", "<span class=\"section-number\">12.1. </span>Array API support (experimental)", "<span class=\"section-number\">2.4. </span>Biclustering", "<span class=\"section-number\">1.16. </span>Probability calibration", "<span class=\"section-number\">3.3. </span>Tuning the decision threshold for class prediction", "<span class=\"section-number\">2.3. </span>Clustering", "<span class=\"section-number\">7.1. </span>Pipelines and composite estimators", "<span class=\"section-number\">2.6. </span>Covariance estimation", "<span class=\"section-number\">1.8. </span>Cross decomposition", "<span class=\"section-number\">3.1. </span>Cross-validation: evaluating estimator performance", "<span class=\"section-number\">2.5. </span>Decomposing signals in components (matrix factorization problems)", "<span class=\"section-number\">2.8. </span>Density Estimation", "<span class=\"section-number\">1.11. </span>Ensembles: Gradient boosting, random forests, bagging, voting, stacking", "<span class=\"section-number\">7.2. </span>Feature extraction", "<span class=\"section-number\">1.13. </span>Feature selection", "<span class=\"section-number\">1.7. </span>Gaussian Processes", "dbscan", "fastica", "oas", "BaseEstimator", "BiclusterMixin", "ClassNamePrefixFeaturesOutMixin", "ClassifierMixin", "ClusterMixin", "DensityMixin", "MetaEstimatorMixin", "OneToOneFeatureMixin", "OutlierMixin", "RegressorMixin", "TransformerMixin", "clone", "is_classifier", "is_clusterer", "is_outlier_detector", "is_regressor", "CalibratedClassifierCV", "CalibrationDisplay", "calibration_curve", "AffinityPropagation", "AgglomerativeClustering", "Birch", "BisectingKMeans", "DBSCAN", "FeatureAgglomeration", "HDBSCAN", "KMeans", "MeanShift", "MiniBatchKMeans", "OPTICS", "SpectralBiclustering", "SpectralClustering", "SpectralCoclustering", "affinity_propagation", "cluster_optics_dbscan", "cluster_optics_xi", "compute_optics_graph", "estimate_bandwidth", "k_means", "kmeans_plusplus", "mean_shift", "spectral_clustering", "ward_tree", "ColumnTransformer", "TransformedTargetRegressor", "make_column_selector", "make_column_transformer", "config_context", "EllipticEnvelope", "EmpiricalCovariance", "GraphicalLasso", "GraphicalLassoCV", "LedoitWolf", "MinCovDet", "OAS", "ShrunkCovariance", "empirical_covariance", "graphical_lasso", "ledoit_wolf", "ledoit_wolf_shrinkage", "shrunk_covariance", "CCA", "PLSCanonical", "PLSRegression", "PLSSVD", "clear_data_home", "dump_svmlight_file", "fetch_20newsgroups", "fetch_20newsgroups_vectorized", "fetch_california_housing", "fetch_covtype", "fetch_file", "fetch_kddcup99", "fetch_lfw_pairs", "fetch_lfw_people", "fetch_olivetti_faces", "fetch_openml", "fetch_rcv1", "fetch_species_distributions", "get_data_home", "load_breast_cancer", "load_diabetes", "load_digits", "load_files", "load_iris", "load_linnerud", "load_sample_image", "load_sample_images", "load_svmlight_file", "load_svmlight_files", "load_wine", "make_biclusters", "make_blobs", "make_checkerboard", "make_circles", "make_classification", "make_friedman1", "make_friedman2", "make_friedman3", "make_gaussian_quantiles", "make_hastie_10_2", "make_low_rank_matrix", "make_moons", "make_multilabel_classification", "make_regression", "make_s_curve", "make_sparse_coded_signal", "make_sparse_spd_matrix", "make_sparse_uncorrelated", "make_spd_matrix", "make_swiss_roll", "DictionaryLearning", "FactorAnalysis", "FastICA", "IncrementalPCA", "KernelPCA", "LatentDirichletAllocation", "MiniBatchDictionaryLearning", "MiniBatchNMF", "MiniBatchSparsePCA", "NMF", "PCA", "SparseCoder", "SparsePCA", "TruncatedSVD", "dict_learning", "dict_learning_online", "non_negative_factorization", "sparse_encode", "LinearDiscriminantAnalysis", "QuadraticDiscriminantAnalysis", "DummyClassifier", "DummyRegressor", "AdaBoostClassifier", "AdaBoostRegressor", "BaggingClassifier", "BaggingRegressor", "ExtraTreesClassifier", "ExtraTreesRegressor", "GradientBoostingClassifier", "GradientBoostingRegressor", "HistGradientBoostingClassifier", "HistGradientBoostingRegressor", "IsolationForest", "RandomForestClassifier", "RandomForestRegressor", "RandomTreesEmbedding", "StackingClassifier", "StackingRegressor", "VotingClassifier", "VotingRegressor", "ConvergenceWarning", "DataConversionWarning", "DataDimensionalityWarning", "EfficiencyWarning", "EstimatorCheckFailedWarning", "FitFailedWarning", "InconsistentVersionWarning", "NotFittedError", "UndefinedMetricWarning", "enable_halving_search_cv", "enable_iterative_imputer", "DictVectorizer", "FeatureHasher", "PatchExtractor", "extract_patches_2d", "grid_to_graph", "img_to_graph", "reconstruct_from_patches_2d", "CountVectorizer", "HashingVectorizer", "TfidfTransformer", "TfidfVectorizer", "GenericUnivariateSelect", "RFE", "RFECV", "SelectFdr", "SelectFpr", "SelectFromModel", "SelectFwe", "SelectKBest", "SelectPercentile", "SelectorMixin", "SequentialFeatureSelector", "VarianceThreshold", "chi2", "f_classif", "f_regression", "mutual_info_classif", "mutual_info_regression", "r_regression", "FrozenEstimator", "GaussianProcessClassifier", "GaussianProcessRegressor", "CompoundKernel", "ConstantKernel", "DotProduct", "ExpSineSquared", "Exponentiation", "Hyperparameter", "Kernel", "Matern", "PairwiseKernel", "Product", "RBF", "RationalQuadratic", "Sum", "WhiteKernel", "get_config", "IterativeImputer", "KNNImputer", "MissingIndicator", "SimpleImputer", "DecisionBoundaryDisplay", "PartialDependenceDisplay", "partial_dependence", "permutation_importance", "IsotonicRegression", "check_increasing", "isotonic_regression", "AdditiveChi2Sampler", "Nystroem", "PolynomialCountSketch", "RBFSampler", "SkewedChi2Sampler", "KernelRidge", "ARDRegression", "BayesianRidge", "ElasticNet", "ElasticNetCV", "GammaRegressor", "HuberRegressor", "Lars", "LarsCV", "Lasso", "LassoCV", "LassoLars", "LassoLarsCV", "LassoLarsIC", "LinearRegression", "LogisticRegression", "LogisticRegressionCV", "MultiTaskElasticNet", "MultiTaskElasticNetCV", "MultiTaskLasso", "MultiTaskLassoCV", "OrthogonalMatchingPursuit", "OrthogonalMatchingPursuitCV", "PassiveAggressiveClassifier", "PassiveAggressiveRegressor", "Perceptron", "PoissonRegressor", "QuantileRegressor", "RANSACRegressor", "Ridge", "RidgeCV", "RidgeClassifier", "RidgeClassifierCV", "SGDClassifier", "SGDOneClassSVM", "SGDRegressor", "TheilSenRegressor", "TweedieRegressor", "enet_path", "lars_path", "lars_path_gram", "lasso_path", "orthogonal_mp", "orthogonal_mp_gram", "ridge_regression", "Isomap", "LocallyLinearEmbedding", "MDS", "SpectralEmbedding", "TSNE", "locally_linear_embedding", "smacof", "spectral_embedding", "trustworthiness", "ConfusionMatrixDisplay", "DetCurveDisplay", "DistanceMetric", "PrecisionRecallDisplay", "PredictionErrorDisplay", "RocCurveDisplay", "accuracy_score", "adjusted_mutual_info_score", "adjusted_rand_score", "auc", "average_precision_score", "balanced_accuracy_score", "brier_score_loss", "calinski_harabasz_score", "check_scoring", "class_likelihood_ratios", "classification_report", "contingency_matrix", "pair_confusion_matrix", "cohen_kappa_score", "completeness_score", "confusion_matrix", "consensus_score", "coverage_error", "d2_absolute_error_score", "d2_log_loss_score", "d2_pinball_score", "d2_tweedie_score", "davies_bouldin_score", "dcg_score", "det_curve", "explained_variance_score", "f1_score", "fbeta_score", "fowlkes_mallows_score", "get_scorer", "get_scorer_names", "hamming_loss", "hinge_loss", "homogeneity_completeness_v_measure", "homogeneity_score", "jaccard_score", "label_ranking_average_precision_score", "label_ranking_loss", "log_loss", "make_scorer", "matthews_corrcoef", "max_error", "mean_absolute_error", "mean_absolute_percentage_error", "mean_gamma_deviance", "mean_pinball_loss", "mean_poisson_deviance", "mean_squared_error", "mean_squared_log_error", "mean_tweedie_deviance", "median_absolute_error", "multilabel_confusion_matrix", "mutual_info_score", "ndcg_score", "normalized_mutual_info_score", "additive_chi2_kernel", "chi2_kernel", "cosine_distances", "cosine_similarity", "distance_metrics", "euclidean_distances", "haversine_distances", "kernel_metrics", "laplacian_kernel", "linear_kernel", "manhattan_distances", "nan_euclidean_distances", "paired_cosine_distances", "paired_distances", "paired_euclidean_distances", "paired_manhattan_distances", "pairwise_kernels", "polynomial_kernel", "rbf_kernel", "sigmoid_kernel", "pairwise_distances", "pairwise_distances_argmin", "pairwise_distances_argmin_min", "pairwise_distances_chunked", "precision_recall_curve", "precision_recall_fscore_support", "precision_score", "r2_score", "rand_score", "recall_score", "roc_auc_score", "roc_curve", "root_mean_squared_error", "root_mean_squared_log_error", "silhouette_samples", "silhouette_score", "top_k_accuracy_score", "v_measure_score", "zero_one_loss", "BayesianGaussianMixture", "GaussianMixture", "FixedThresholdClassifier", "GridSearchCV", "GroupKFold", "GroupShuffleSplit", "HalvingGridSearchCV", "HalvingRandomSearchCV", "KFold", "LearningCurveDisplay", "LeaveOneGroupOut", "LeaveOneOut", "LeavePGroupsOut", "LeavePOut", "ParameterGrid", "ParameterSampler", "PredefinedSplit", "RandomizedSearchCV", "RepeatedKFold", "RepeatedStratifiedKFold", "ShuffleSplit", "StratifiedGroupKFold", "StratifiedKFold", "StratifiedShuffleSplit", "TimeSeriesSplit", "TunedThresholdClassifierCV", "ValidationCurveDisplay", "check_cv", "cross_val_predict", "cross_val_score", "cross_validate", "learning_curve", "permutation_test_score", "train_test_split", "validation_curve", "OneVsOneClassifier", "OneVsRestClassifier", "OutputCodeClassifier", "ClassifierChain", "MultiOutputClassifier", "MultiOutputRegressor", "RegressorChain", "BernoulliNB", "CategoricalNB", "ComplementNB", "GaussianNB", "MultinomialNB", "BallTree", "KDTree", "KNeighborsClassifier", "KNeighborsRegressor", "KNeighborsTransformer", "KernelDensity", "LocalOutlierFactor", "NearestCentroid", "NearestNeighbors", "NeighborhoodComponentsAnalysis", "RadiusNeighborsClassifier", "RadiusNeighborsRegressor", "RadiusNeighborsTransformer", "kneighbors_graph", "radius_neighbors_graph", "sort_graph_by_row_values", "BernoulliRBM", "MLPClassifier", "MLPRegressor", "FeatureUnion", "Pipeline", "make_pipeline", "make_union", "Binarizer", "FunctionTransformer", "KBinsDiscretizer", "KernelCenterer", "LabelBinarizer", "LabelEncoder", "MaxAbsScaler", "MinMaxScaler", "MultiLabelBinarizer", "Normalizer", "OneHotEncoder", "OrdinalEncoder", "PolynomialFeatures", "PowerTransformer", "QuantileTransformer", "RobustScaler", "SplineTransformer", "StandardScaler", "TargetEncoder", "add_dummy_feature", "binarize", "label_binarize", "maxabs_scale", "minmax_scale", "normalize", "power_transform", "quantile_transform", "robust_scale", "scale", "GaussianRandomProjection", "SparseRandomProjection", "johnson_lindenstrauss_min_dim", "LabelPropagation", "LabelSpreading", "SelfTrainingClassifier", "set_config", "show_versions", "LinearSVC", "LinearSVR", "NuSVC", "NuSVR", "OneClassSVM", "SVC", "SVR", "l1_min_c", "DecisionTreeClassifier", "DecisionTreeRegressor", "ExtraTreeClassifier", "ExtraTreeRegressor", "export_graphviz", "export_text", "plot_tree", "Bunch", "ClassifierTags", "InputTags", "RegressorTags", "Tags", "TargetTags", "TransformerTags", "_safe_indexing", "min_pos", "as_float_array", "assert_all_finite", "check_X_y", "check_array", "check_consistent_length", "check_random_state", "check_scalar", "compute_class_weight", "compute_sample_weight", "deprecated", "all_displays", "all_estimators", "all_functions", "check_estimator", "estimator_checks_generator", "parametrize_with_checks", "estimator_html_repr", "density", "fast_logdet", "randomized_range_finder", "randomized_svd", "safe_sparse_dot", "weighted_mode", "gen_batches", "gen_even_slices", "get_tags", "single_source_shortest_path_length", "indexable", "MetadataRequest", "MetadataRouter", "MethodMapping", "get_routing_for_object", "process_routing", "available_if", "is_multilabel", "type_of_target", "unique_labels", "murmurhash3_32", "Parallel", "delayed", "sample_without_replacement", "resample", "safe_mask", "safe_sqr", "shuffle", "incr_mean_variance_axis", "inplace_column_scale", "inplace_csr_column_scale", "inplace_row_scale", "inplace_swap_column", "inplace_swap_row", "mean_variance_axis", "inplace_csr_row_normalize_l1", "inplace_csr_row_normalize_l2", "check_is_fitted", "check_memory", "check_symmetric", "column_or_1d", "has_fit_parameter", "validate_data", "<span class=\"section-number\">3.2. </span>Tuning the hyper-parameters of an estimator", "<span class=\"section-number\">7.4. </span>Imputation of missing values", "<span class=\"section-number\">1.15. </span>Isotonic regression", "<span class=\"section-number\">7.7. </span>Kernel Approximation", "<span class=\"section-number\">1.3. </span>Kernel ridge regression", "<span class=\"section-number\">1.2. </span>Linear and Quadratic Discriminant Analysis", "<span class=\"section-number\">3.5. </span>Validation curves: plotting scores to evaluate models", "<span class=\"section-number\">1.1. </span>Linear Models", "<span class=\"section-number\">2.2. </span>Manifold learning", "<span class=\"section-number\">7.8. </span>Pairwise metrics, Affinities and Kernels", "<span class=\"section-number\">2.1. </span>Gaussian mixture models", "<span class=\"section-number\">3.4. </span>Metrics and scoring: quantifying the quality of predictions", "<span class=\"section-number\">1.12. </span>Multiclass and multioutput algorithms", "<span class=\"section-number\">1.9. </span>Naive Bayes", "<span class=\"section-number\">1.6. </span>Nearest Neighbors", "<span class=\"section-number\">1.17. </span>Neural network models (supervised)", "<span class=\"section-number\">2.9. </span>Neural network models (unsupervised)", "<span class=\"section-number\">2.7. </span>Novelty and Outlier Detection", "<span class=\"section-number\">5.1. </span>Partial Dependence and Individual Conditional Expectation plots", "<span class=\"section-number\">5.2. </span>Permutation feature importance", "&lt;no title&gt;", "<span class=\"section-number\">7.3. </span>Preprocessing data", "<span class=\"section-number\">7.9. </span>Transforming the prediction target (<code class=\"docutils literal notranslate\"><span class=\"pre\">y</span></code>)", "<span class=\"section-number\">7.6. </span>Random Projection", "<span class=\"section-number\">1.14. </span>Semi-supervised learning", "<span class=\"section-number\">1.5. </span>Stochastic Gradient Descent", "<span class=\"section-number\">1.4. </span>Support Vector Machines", "<span class=\"section-number\">1.10. </span>Decision Trees", "<span class=\"section-number\">7.5. </span>Unsupervised dimensionality reduction", "<span class=\"section-number\">14. </span>External Resources, Videos and Talks", "Related Projects", "Roadmap", "Computation times", "<span class=\"section-number\">1. </span>Supervised learning", "Support", "Testimonials", "<span class=\"section-number\">2. </span>Unsupervised learning", "User Guide", "Available documentation for scikit-learn", "<span class=\"section-number\">6. </span>Visualizations", "Release History", "&lt;no title&gt;", "Older Versions", "Version 0.13", "Version 0.14", "Version 0.15", "Version 0.16", "Version 0.17", "Version 0.18", "Version 0.19", "Version 0.20", "Version 0.21", "Version 0.22", "Version 0.23", "Version 0.24", "Version 1.0", "Version 1.1", "Version 1.2", "Version 1.3", "Version 1.4", "Version 1.5", 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diff --git a/dev/sg_execution_times.html b/dev/sg_execution_times.html
index 378901e26e41d..b75facf1b64fb 100644
--- a/dev/sg_execution_times.html
+++ b/dev/sg_execution_times.html
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-<meta property="og:description" content="20:11.455 total execution time for 279 files from all galleries:,,, Example, Time, Mem (MB),,, Comparing Random Forests and Histogram Gradient Boosting models(../examples/ensemble/plot_forest_hist_..." />
+<meta property="og:description" content="21:57.480 total execution time for 278 files from all galleries:,,, Example, Time, Mem (MB),,, Comparing Random Forests and Histogram Gradient Boosting models(../examples/ensemble/plot_forest_hist_..." />
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-<meta name="description" content="20:11.455 total execution time for 279 files from all galleries:,,, Example, Time, Mem (MB),,, Comparing Random Forests and Histogram Gradient Boosting models(../examples/ensemble/plot_forest_hist_..." />
+<meta name="description" content="21:57.480 total execution time for 278 files from all galleries:,,, Example, Time, Mem (MB),,, Comparing Random Forests and Histogram Gradient Boosting models(../examples/ensemble/plot_forest_hist_..." />
 
     <title>Computation times &#8212; scikit-learn 1.7.dev0 documentation</title>
   
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-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
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@@ -594,7 +594,7 @@
                   
   <section id="computation-times">
 <span id="sphx-glr-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Link to this heading">#</a></h1>
-<p><strong>20:11.455</strong> total execution time for 279 files <strong>from all galleries</strong>:</p>
+<p><strong>21:57.480</strong> total execution time for 278 files <strong>from all galleries</strong>:</p>
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 <link href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/5.3.0/css/bootstrap.min.css" rel="stylesheet" />
@@ -616,1118 +616,1114 @@
 </thead>
 <tbody>
 <tr class="row-even"><td><p><a class="reference internal" href="http://github.com/auto_examples/ensemble/plot_forest_hist_grad_boosting_comparison.html#sphx-glr-auto-examples-ensemble-plot-forest-hist-grad-boosting-comparison-py"><span class="std std-ref">Comparing Random Forests and Histogram Gradient Boosting models</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/ensemble/plot_forest_hist_grad_boosting_comparison.py</span></code>)</p></td>
-<td><p>00:54.339</p></td>
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 <td><p>0.0</p></td>
 </tr>
 <tr class="row-odd"><td><p><a class="reference internal" href="http://github.com/auto_examples/feature_selection/plot_select_from_model_diabetes.html#sphx-glr-auto-examples-feature-selection-plot-select-from-model-diabetes-py"><span class="std std-ref">Model-based and sequential feature selection</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/feature_selection/plot_select_from_model_diabetes.py</span></code>)</p></td>
-<td><p>00:50.137</p></td>
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 <td><p>0.0</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="reference internal" href="http://github.com/auto_examples/miscellaneous/plot_outlier_detection_bench.html#sphx-glr-auto-examples-miscellaneous-plot-outlier-detection-bench-py"><span class="std std-ref">Evaluation of outlier detection estimators</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/miscellaneous/plot_outlier_detection_bench.py</span></code>)</p></td>
-<td><p>00:48.298</p></td>
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 </tr>
 <tr class="row-odd"><td><p><a class="reference internal" href="http://github.com/auto_examples/compose/plot_compare_reduction.html#sphx-glr-auto-examples-compose-plot-compare-reduction-py"><span class="std std-ref">Selecting dimensionality reduction with Pipeline and GridSearchCV</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/compose/plot_compare_reduction.py</span></code>)</p></td>
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 <td><p>0.0</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="reference internal" href="http://github.com/auto_examples/kernel_approximation/plot_scalable_poly_kernels.html#sphx-glr-auto-examples-kernel-approximation-plot-scalable-poly-kernels-py"><span class="std std-ref">Scalable learning with polynomial kernel approximation</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/kernel_approximation/plot_scalable_poly_kernels.py</span></code>)</p></td>
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 <tr class="row-odd"><td><p><a class="reference internal" href="http://github.com/auto_examples/model_selection/plot_tuned_decision_threshold.html#sphx-glr-auto-examples-model-selection-plot-tuned-decision-threshold-py"><span class="std std-ref">Post-hoc tuning the cut-off point of decision function</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/model_selection/plot_tuned_decision_threshold.py</span></code>)</p></td>
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 <tr class="row-even"><td><p><a class="reference internal" href="http://github.com/auto_examples/model_selection/plot_grid_search_text_feature_extraction.html#sphx-glr-auto-examples-model-selection-plot-grid-search-text-feature-extraction-py"><span class="std std-ref">Sample pipeline for text feature extraction and evaluation</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/model_selection/plot_grid_search_text_feature_extraction.py</span></code>)</p></td>
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 </tr>
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-<td><p>00:22.640</p></td>
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-<td><p>00:22.370</p></td>
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 <td><p>0.0</p></td>
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 <tr class="row-even"><td><p><a class="reference internal" href="http://github.com/auto_examples/linear_model/plot_poisson_regression_non_normal_loss.html#sphx-glr-auto-examples-linear-model-plot-poisson-regression-non-normal-loss-py"><span class="std std-ref">Poisson regression and non-normal loss</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/linear_model/plot_poisson_regression_non_normal_loss.py</span></code>)</p></td>
-<td><p>00:19.253</p></td>
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 <td><p>0.0</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="http://github.com/auto_examples/manifold/plot_compare_methods.html#sphx-glr-auto-examples-manifold-plot-compare-methods-py"><span class="std std-ref">Comparison of Manifold Learning methods</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/manifold/plot_compare_methods.py</span></code>)</p></td>
-<td><p>00:17.663</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="http://github.com/auto_examples/multiclass/plot_multiclass_overview.html#sphx-glr-auto-examples-multiclass-plot-multiclass-overview-py"><span class="std std-ref">Overview of multiclass training meta-estimators</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/multiclass/plot_multiclass_overview.py</span></code>)</p></td>
+<td><p>00:19.729</p></td>
 <td><p>0.0</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="http://github.com/auto_examples/applications/plot_prediction_latency.html#sphx-glr-auto-examples-applications-plot-prediction-latency-py"><span class="std std-ref">Prediction Latency</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/applications/plot_prediction_latency.py</span></code>)</p></td>
-<td><p>00:17.477</p></td>
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+<td><p>00:19.192</p></td>
 <td><p>0.0</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="http://github.com/auto_examples/manifold/plot_swissroll.html#sphx-glr-auto-examples-manifold-plot-swissroll-py"><span class="std std-ref">Swiss Roll And Swiss-Hole Reduction</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/manifold/plot_swissroll.py</span></code>)</p></td>
-<td><p>00:17.003</p></td>
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 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="http://github.com/auto_examples/cluster/plot_hdbscan.html#sphx-glr-auto-examples-cluster-plot-hdbscan-py"><span class="std std-ref">Demo of HDBSCAN clustering algorithm</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/cluster/plot_hdbscan.py</span></code>)</p></td>
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 <td><p>0.0</p></td>
 </tr>
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-<td><p>00:12.891</p></td>
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+<td><p>00:15.012</p></td>
 <td><p>0.0</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="http://github.com/auto_examples/inspection/plot_linear_model_coefficient_interpretation.html#sphx-glr-auto-examples-inspection-plot-linear-model-coefficient-interpretation-py"><span class="std std-ref">Common pitfalls in the interpretation of coefficients of linear models</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/inspection/plot_linear_model_coefficient_interpretation.py</span></code>)</p></td>
-<td><p>00:12.806</p></td>
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+<td><p>00:14.892</p></td>
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 </tr>
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-<td><p>00:12.772</p></td>
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+<td><p>00:14.675</p></td>
 <td><p>0.0</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="http://github.com/auto_examples/applications/plot_cyclical_feature_engineering.html#sphx-glr-auto-examples-applications-plot-cyclical-feature-engineering-py"><span class="std std-ref">Time-related feature engineering</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/applications/plot_cyclical_feature_engineering.py</span></code>)</p></td>
-<td><p>00:12.148</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="http://github.com/auto_examples/linear_model/plot_sparse_logistic_regression_mnist.html#sphx-glr-auto-examples-linear-model-plot-sparse-logistic-regression-mnist-py"><span class="std std-ref">MNIST classification using multinomial logistic + L1</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/linear_model/plot_sparse_logistic_regression_mnist.py</span></code>)</p></td>
+<td><p>00:13.966</p></td>
 <td><p>0.0</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="http://github.com/auto_examples/miscellaneous/plot_johnson_lindenstrauss_bound.html#sphx-glr-auto-examples-miscellaneous-plot-johnson-lindenstrauss-bound-py"><span class="std std-ref">The Johnson-Lindenstrauss bound for embedding with random projections</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/miscellaneous/plot_johnson_lindenstrauss_bound.py</span></code>)</p></td>
-<td><p>00:11.506</p></td>
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+<td><p>00:13.462</p></td>
 <td><p>0.0</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="http://github.com/auto_examples/applications/plot_topics_extraction_with_nmf_lda.html#sphx-glr-auto-examples-applications-plot-topics-extraction-with-nmf-lda-py"><span class="std std-ref">Topic extraction with Non-negative Matrix Factorization and Latent Dirichlet Allocation</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/applications/plot_topics_extraction_with_nmf_lda.py</span></code>)</p></td>
-<td><p>00:11.217</p></td>
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+<td><p>00:12.848</p></td>
 <td><p>0.0</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="http://github.com/auto_examples/linear_model/plot_sparse_logistic_regression_mnist.html#sphx-glr-auto-examples-linear-model-plot-sparse-logistic-regression-mnist-py"><span class="std std-ref">MNIST classification using multinomial logistic + L1</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/linear_model/plot_sparse_logistic_regression_mnist.py</span></code>)</p></td>
-<td><p>00:10.741</p></td>
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+<td><p>00:12.624</p></td>
 <td><p>0.0</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="http://github.com/auto_examples/ensemble/plot_gradient_boosting_oob.html#sphx-glr-auto-examples-ensemble-plot-gradient-boosting-oob-py"><span class="std std-ref">Gradient Boosting Out-of-Bag estimates</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/ensemble/plot_gradient_boosting_oob.py</span></code>)</p></td>
-<td><p>00:10.376</p></td>
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 </tr>
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 </tr>
 <tr class="row-even"><td><p><a class="reference internal" href="http://github.com/auto_examples/ensemble/plot_gradient_boosting_quantile.html#sphx-glr-auto-examples-ensemble-plot-gradient-boosting-quantile-py"><span class="std std-ref">Prediction Intervals for Gradient Boosting Regression</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/ensemble/plot_gradient_boosting_quantile.py</span></code>)</p></td>
-<td><p>00:09.901</p></td>
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 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="http://github.com/auto_examples/applications/plot_tomography_l1_reconstruction.html#sphx-glr-auto-examples-applications-plot-tomography-l1-reconstruction-py"><span class="std std-ref">Compressive sensing: tomography reconstruction with L1 prior (Lasso)</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/applications/plot_tomography_l1_reconstruction.py</span></code>)</p></td>
-<td><p>00:09.552</p></td>
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 </tr>
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 </tr>
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 </tr>
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 </tr>
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 </tr>
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 </tr>
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 </tr>
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 </tr>
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 </tr>
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+<tr class="row-odd"><td><p><a class="reference internal" href="http://github.com/auto_examples/ensemble/plot_gradient_boosting_regularization.html#sphx-glr-auto-examples-ensemble-plot-gradient-boosting-regularization-py"><span class="std std-ref">Gradient Boosting regularization</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/ensemble/plot_gradient_boosting_regularization.py</span></code>)</p></td>
+<td><p>00:08.808</p></td>
 <td><p>0.0</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="http://github.com/auto_examples/semi_supervised/plot_semi_supervised_newsgroups.html#sphx-glr-auto-examples-semi-supervised-plot-semi-supervised-newsgroups-py"><span class="std std-ref">Semi-supervised Classification on a Text Dataset</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/semi_supervised/plot_semi_supervised_newsgroups.py</span></code>)</p></td>
-<td><p>00:07.667</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="http://github.com/auto_examples/applications/plot_species_distribution_modeling.html#sphx-glr-auto-examples-applications-plot-species-distribution-modeling-py"><span class="std std-ref">Species distribution modeling</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/applications/plot_species_distribution_modeling.py</span></code>)</p></td>
+<td><p>00:08.461</p></td>
 <td><p>0.0</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="http://github.com/auto_examples/linear_model/plot_tweedie_regression_insurance_claims.html#sphx-glr-auto-examples-linear-model-plot-tweedie-regression-insurance-claims-py"><span class="std std-ref">Tweedie regression on insurance claims</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/linear_model/plot_tweedie_regression_insurance_claims.py</span></code>)</p></td>
-<td><p>00:07.625</p></td>
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+<td><p>00:08.333</p></td>
 <td><p>0.0</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="http://github.com/auto_examples/text/plot_document_classification_20newsgroups.html#sphx-glr-auto-examples-text-plot-document-classification-20newsgroups-py"><span class="std std-ref">Classification of text documents using sparse features</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/text/plot_document_classification_20newsgroups.py</span></code>)</p></td>
-<td><p>00:07.425</p></td>
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+<td><p>00:08.331</p></td>
 <td><p>0.0</p></td>
 </tr>
 <tr class="row-odd"><td><p><a class="reference internal" href="http://github.com/auto_examples/decomposition/plot_faces_decomposition.html#sphx-glr-auto-examples-decomposition-plot-faces-decomposition-py"><span class="std std-ref">Faces dataset decompositions</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/decomposition/plot_faces_decomposition.py</span></code>)</p></td>
-<td><p>00:07.399</p></td>
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 <td><p>0.0</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="http://github.com/auto_examples/model_selection/plot_successive_halving_heatmap.html#sphx-glr-auto-examples-model-selection-plot-successive-halving-heatmap-py"><span class="std std-ref">Comparison between grid search and successive halving</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/model_selection/plot_successive_halving_heatmap.py</span></code>)</p></td>
-<td><p>00:07.352</p></td>
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 <td><p>0.0</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="http://github.com/auto_examples/model_selection/plot_nested_cross_validation_iris.html#sphx-glr-auto-examples-model-selection-plot-nested-cross-validation-iris-py"><span class="std std-ref">Nested versus non-nested cross-validation</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/model_selection/plot_nested_cross_validation_iris.py</span></code>)</p></td>
-<td><p>00:07.050</p></td>
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 <td><p>0.0</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="http://github.com/auto_examples/applications/plot_digits_denoising.html#sphx-glr-auto-examples-applications-plot-digits-denoising-py"><span class="std std-ref">Image denoising using kernel PCA</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/applications/plot_digits_denoising.py</span></code>)</p></td>
-<td><p>00:06.435</p></td>
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 <td><p>0.0</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="http://github.com/auto_examples/text/plot_hashing_vs_dict_vectorizer.html#sphx-glr-auto-examples-text-plot-hashing-vs-dict-vectorizer-py"><span class="std std-ref">FeatureHasher and DictVectorizer Comparison</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/text/plot_hashing_vs_dict_vectorizer.py</span></code>)</p></td>
-<td><p>00:06.275</p></td>
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 <td><p>0.0</p></td>
 </tr>
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-<td><p>00:06.220</p></td>
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 <td><p>0.0</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="http://github.com/auto_examples/neural_networks/plot_mnist_filters.html#sphx-glr-auto-examples-neural-networks-plot-mnist-filters-py"><span class="std std-ref">Visualization of MLP weights on MNIST</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/neural_networks/plot_mnist_filters.py</span></code>)</p></td>
-<td><p>00:06.189</p></td>
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 <td><p>0.0</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="http://github.com/auto_examples/bicluster/plot_bicluster_newsgroups.html#sphx-glr-auto-examples-bicluster-plot-bicluster-newsgroups-py"><span class="std std-ref">Biclustering documents with the Spectral Co-clustering algorithm</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/bicluster/plot_bicluster_newsgroups.py</span></code>)</p></td>
-<td><p>00:06.088</p></td>
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 <td><p>0.0</p></td>
 </tr>
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-<td><p>00:06.081</p></td>
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 <td><p>0.0</p></td>
 </tr>
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-<td><p>00:06.021</p></td>
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 <td><p>0.0</p></td>
 </tr>
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-<td><p>00:05.982</p></td>
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 <td><p>0.0</p></td>
 </tr>
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-<td><p>00:05.843</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="http://github.com/auto_examples/model_selection/plot_nested_cross_validation_iris.html#sphx-glr-auto-examples-model-selection-plot-nested-cross-validation-iris-py"><span class="std std-ref">Nested versus non-nested cross-validation</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/model_selection/plot_nested_cross_validation_iris.py</span></code>)</p></td>
+<td><p>00:06.625</p></td>
 <td><p>0.0</p></td>
 </tr>
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-<td><p>00:05.772</p></td>
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 <td><p>0.0</p></td>
 </tr>
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-<td><p>00:05.734</p></td>
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 <td><p>0.0</p></td>
 </tr>
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 </tr>
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-<td><p>00:05.553</p></td>
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 <td><p>0.0</p></td>
 </tr>
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 <td><p>0.0</p></td>
 </tr>
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-<td><p>00:05.523</p></td>
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 <td><p>0.0</p></td>
 </tr>
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-<td><p>00:05.471</p></td>
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 </tr>
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-<td><p>00:05.434</p></td>
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 </tr>
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 </tr>
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 <tr class="row-even"><td><p><a class="reference internal" href="http://github.com/auto_examples/gaussian_process/plot_gpr_co2.html#sphx-glr-auto-examples-gaussian-process-plot-gpr-co2-py"><span class="std std-ref">Forecasting of CO2 level on Mona Loa dataset using Gaussian process regression (GPR)</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/gaussian_process/plot_gpr_co2.py</span></code>)</p></td>
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 </tr>
 <tr class="row-odd"><td><p><a class="reference internal" href="http://github.com/auto_examples/ensemble/plot_feature_transformation.html#sphx-glr-auto-examples-ensemble-plot-feature-transformation-py"><span class="std std-ref">Feature transformations with ensembles of trees</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/ensemble/plot_feature_transformation.py</span></code>)</p></td>
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-<td><p>00:02.541</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="http://github.com/auto_examples/covariance/plot_robust_vs_empirical_covariance.html#sphx-glr-auto-examples-covariance-plot-robust-vs-empirical-covariance-py"><span class="std std-ref">Robust vs Empirical covariance estimate</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/covariance/plot_robust_vs_empirical_covariance.py</span></code>)</p></td>
+<td><p>00:03.272</p></td>
 <td><p>0.0</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="http://github.com/auto_examples/neural_networks/plot_rbm_logistic_classification.html#sphx-glr-auto-examples-neural-networks-plot-rbm-logistic-classification-py"><span class="std std-ref">Restricted Boltzmann Machine features for digit classification</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/neural_networks/plot_rbm_logistic_classification.py</span></code>)</p></td>
-<td><p>00:02.517</p></td>
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+<td><p>00:03.180</p></td>
 <td><p>0.0</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="http://github.com/auto_examples/compose/plot_column_transformer.html#sphx-glr-auto-examples-compose-plot-column-transformer-py"><span class="std std-ref">Column Transformer with Heterogeneous Data Sources</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/compose/plot_column_transformer.py</span></code>)</p></td>
-<td><p>00:02.508</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="http://github.com/auto_examples/manifold/plot_t_sne_perplexity.html#sphx-glr-auto-examples-manifold-plot-t-sne-perplexity-py"><span class="std std-ref">t-SNE: The effect of various perplexity values on the shape</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/manifold/plot_t_sne_perplexity.py</span></code>)</p></td>
+<td><p>00:03.158</p></td>
 <td><p>0.0</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="http://github.com/auto_examples/covariance/plot_robust_vs_empirical_covariance.html#sphx-glr-auto-examples-covariance-plot-robust-vs-empirical-covariance-py"><span class="std std-ref">Robust vs Empirical covariance estimate</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/covariance/plot_robust_vs_empirical_covariance.py</span></code>)</p></td>
-<td><p>00:02.489</p></td>
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 <td><p>0.0</p></td>
 </tr>
 <tr class="row-odd"><td><p><a class="reference internal" href="http://github.com/auto_examples/covariance/plot_lw_vs_oas.html#sphx-glr-auto-examples-covariance-plot-lw-vs-oas-py"><span class="std std-ref">Ledoit-Wolf vs OAS estimation</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/covariance/plot_lw_vs_oas.py</span></code>)</p></td>
-<td><p>00:02.455</p></td>
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 <td><p>0.0</p></td>
 </tr>
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-<td><p>00:02.450</p></td>
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 <td><p>0.0</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="http://github.com/auto_examples/gaussian_process/plot_gpc_iris.html#sphx-glr-auto-examples-gaussian-process-plot-gpc-iris-py"><span class="std std-ref">Gaussian process classification (GPC) on iris dataset</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/gaussian_process/plot_gpc_iris.py</span></code>)</p></td>
-<td><p>00:02.348</p></td>
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 <td><p>0.0</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="http://github.com/auto_examples/applications/plot_stock_market.html#sphx-glr-auto-examples-applications-plot-stock-market-py"><span class="std std-ref">Visualizing the stock market structure</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/applications/plot_stock_market.py</span></code>)</p></td>
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 <td><p>0.0</p></td>
 </tr>
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-<td><p>00:02.282</p></td>
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 </tr>
 <tr class="row-even"><td><p><a class="reference internal" href="http://github.com/auto_examples/cluster/plot_dict_face_patches.html#sphx-glr-auto-examples-cluster-plot-dict-face-patches-py"><span class="std std-ref">Online learning of a dictionary of parts of faces</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/cluster/plot_dict_face_patches.py</span></code>)</p></td>
-<td><p>00:02.139</p></td>
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 <td><p>0.0</p></td>
 </tr>
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 <td><p>0.0</p></td>
 </tr>
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-<td><p>00:02.104</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="http://github.com/auto_examples/calibration/plot_calibration_curve.html#sphx-glr-auto-examples-calibration-plot-calibration-curve-py"><span class="std std-ref">Probability Calibration curves</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/calibration/plot_calibration_curve.py</span></code>)</p></td>
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 <td><p>0.0</p></td>
 </tr>
 <tr class="row-odd"><td><p><a class="reference internal" href="http://github.com/auto_examples/classification/plot_classifier_comparison.html#sphx-glr-auto-examples-classification-plot-classifier-comparison-py"><span class="std std-ref">Classifier comparison</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/classification/plot_classifier_comparison.py</span></code>)</p></td>
-<td><p>00:02.097</p></td>
+<td><p>00:02.187</p></td>
 <td><p>0.0</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="reference internal" href="http://github.com/auto_examples/release_highlights/plot_release_highlights_1_4_0.html#sphx-glr-auto-examples-release-highlights-plot-release-highlights-1-4-0-py"><span class="std std-ref">Release Highlights for scikit-learn 1.4</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/release_highlights/plot_release_highlights_1_4_0.py</span></code>)</p></td>
-<td><p>00:02.090</p></td>
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 <td><p>0.0</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="http://github.com/auto_examples/decomposition/plot_pca_iris.html#sphx-glr-auto-examples-decomposition-plot-pca-iris-py"><span class="std std-ref">Principal Component Analysis (PCA) on Iris Dataset</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/decomposition/plot_pca_iris.py</span></code>)</p></td>
-<td><p>00:02.047</p></td>
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 <td><p>0.0</p></td>
 </tr>
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-<td><p>00:02.025</p></td>
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 <td><p>0.0</p></td>
 </tr>
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-<td><p>00:01.968</p></td>
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 </tr>
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 <td><p>0.0</p></td>
 </tr>
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 <td><p>0.0</p></td>
 </tr>
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 <td><p>0.0</p></td>
 </tr>
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 </tr>
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 <tr class="row-odd"><td><p><a class="reference internal" href="http://github.com/auto_examples/cluster/plot_linkage_comparison.html#sphx-glr-auto-examples-cluster-plot-linkage-comparison-py"><span class="std std-ref">Comparing different hierarchical linkage methods on toy datasets</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/cluster/plot_linkage_comparison.py</span></code>)</p></td>
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+<tr class="row-even"><td><p><a class="reference internal" href="http://github.com/auto_examples/model_selection/plot_likelihood_ratios.html#sphx-glr-auto-examples-model-selection-plot-likelihood-ratios-py"><span class="std std-ref">Class Likelihood Ratios to measure classification performance</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/model_selection/plot_likelihood_ratios.py</span></code>)</p></td>
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 <td><p>0.0</p></td>
 </tr>
 <tr class="row-odd"><td><p><a class="reference internal" href="http://github.com/auto_examples/miscellaneous/plot_kernel_approximation.html#sphx-glr-auto-examples-miscellaneous-plot-kernel-approximation-py"><span class="std std-ref">Explicit feature map approximation for RBF kernels</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/miscellaneous/plot_kernel_approximation.py</span></code>)</p></td>
-<td><p>00:01.734</p></td>
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 <td><p>0.0</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="http://github.com/auto_examples/neighbors/plot_nca_dim_reduction.html#sphx-glr-auto-examples-neighbors-plot-nca-dim-reduction-py"><span class="std std-ref">Dimensionality Reduction with Neighborhood Components Analysis</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/neighbors/plot_nca_dim_reduction.py</span></code>)</p></td>
-<td><p>00:01.630</p></td>
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+<td><p>00:01.785</p></td>
 <td><p>0.0</p></td>
 </tr>
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-<td><p>00:01.629</p></td>
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 <td><p>0.0</p></td>
 </tr>
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-<td><p>00:01.570</p></td>
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 <td><p>0.0</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="http://github.com/auto_examples/release_highlights/plot_release_highlights_1_3_0.html#sphx-glr-auto-examples-release-highlights-plot-release-highlights-1-3-0-py"><span class="std std-ref">Release Highlights for scikit-learn 1.3</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/release_highlights/plot_release_highlights_1_3_0.py</span></code>)</p></td>
-<td><p>00:01.489</p></td>
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 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="http://github.com/auto_examples/svm/plot_svm_kernels.html#sphx-glr-auto-examples-svm-plot-svm-kernels-py"><span class="std std-ref">Plot classification boundaries with different SVM Kernels</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/svm/plot_svm_kernels.py</span></code>)</p></td>
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 </tr>
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 <td><p>0.0</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="reference internal" href="http://github.com/auto_examples/gaussian_process/plot_gpr_prior_posterior.html#sphx-glr-auto-examples-gaussian-process-plot-gpr-prior-posterior-py"><span class="std std-ref">Illustration of prior and posterior Gaussian process for different kernels</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/gaussian_process/plot_gpr_prior_posterior.py</span></code>)</p></td>
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 <td><p>0.0</p></td>
 </tr>
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-<td><p>00:01.388</p></td>
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 <td><p>0.0</p></td>
 </tr>
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-<td><p>00:01.368</p></td>
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 </tr>
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-<td><p>00:01.356</p></td>
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 <td><p>0.0</p></td>
 </tr>
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 </tr>
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 </tr>
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 </tr>
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 </tr>
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 <td><p>0.0</p></td>
 </tr>
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-<td><p>00:01.220</p></td>
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 </tr>
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 </tr>
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 </tr>
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 </tr>
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 </tr>
 <tr class="row-even"><td><p><a class="reference internal" href="http://github.com/auto_examples/cluster/plot_agglomerative_clustering_metrics.html#sphx-glr-auto-examples-cluster-plot-agglomerative-clustering-metrics-py"><span class="std std-ref">Agglomerative clustering with different metrics</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/cluster/plot_agglomerative_clustering_metrics.py</span></code>)</p></td>
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 <td><p>0.0</p></td>
 </tr>
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-<tr class="row-odd"><td><p><a class="reference internal" href="http://github.com/auto_examples/cluster/plot_bisect_kmeans.html#sphx-glr-auto-examples-cluster-plot-bisect-kmeans-py"><span class="std std-ref">Bisecting K-Means and Regular K-Means Performance Comparison</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/cluster/plot_bisect_kmeans.py</span></code>)</p></td>
-<td><p>00:01.045</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="http://github.com/auto_examples/cluster/plot_kmeans_assumptions.html#sphx-glr-auto-examples-cluster-plot-kmeans-assumptions-py"><span class="std std-ref">Demonstration of k-means assumptions</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/cluster/plot_kmeans_assumptions.py</span></code>)</p></td>
+<td><p>00:01.141</p></td>
 <td><p>0.0</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="http://github.com/auto_examples/linear_model/plot_lasso_dense_vs_sparse_data.html#sphx-glr-auto-examples-linear-model-plot-lasso-dense-vs-sparse-data-py"><span class="std std-ref">Lasso on dense and sparse data</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/linear_model/plot_lasso_dense_vs_sparse_data.py</span></code>)</p></td>
-<td><p>00:01.039</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="http://github.com/auto_examples/cluster/plot_bisect_kmeans.html#sphx-glr-auto-examples-cluster-plot-bisect-kmeans-py"><span class="std std-ref">Bisecting K-Means and Regular K-Means Performance Comparison</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/cluster/plot_bisect_kmeans.py</span></code>)</p></td>
+<td><p>00:01.114</p></td>
 <td><p>0.0</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="http://github.com/auto_examples/semi_supervised/plot_semi_supervised_versus_svm_iris.html#sphx-glr-auto-examples-semi-supervised-plot-semi-supervised-versus-svm-iris-py"><span class="std std-ref">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/semi_supervised/plot_semi_supervised_versus_svm_iris.py</span></code>)</p></td>
-<td><p>00:01.022</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="http://github.com/auto_examples/model_selection/plot_grid_search_refit_callable.html#sphx-glr-auto-examples-model-selection-plot-grid-search-refit-callable-py"><span class="std std-ref">Balance model complexity and cross-validated score</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/model_selection/plot_grid_search_refit_callable.py</span></code>)</p></td>
+<td><p>00:01.095</p></td>
 <td><p>0.0</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="reference internal" href="http://github.com/auto_examples/cluster/plot_adjusted_for_chance_measures.html#sphx-glr-auto-examples-cluster-plot-adjusted-for-chance-measures-py"><span class="std std-ref">Adjustment for chance in clustering performance evaluation</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/cluster/plot_adjusted_for_chance_measures.py</span></code>)</p></td>
-<td><p>00:01.019</p></td>
+<td><p>00:01.060</p></td>
 <td><p>0.0</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="http://github.com/auto_examples/ensemble/plot_forest_importances.html#sphx-glr-auto-examples-ensemble-plot-forest-importances-py"><span class="std std-ref">Feature importances with a forest of trees</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/ensemble/plot_forest_importances.py</span></code>)</p></td>
-<td><p>00:01.013</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="http://github.com/auto_examples/ensemble/plot_voting_regressor.html#sphx-glr-auto-examples-ensemble-plot-voting-regressor-py"><span class="std std-ref">Plot individual and voting regression predictions</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/ensemble/plot_voting_regressor.py</span></code>)</p></td>
+<td><p>00:01.051</p></td>
 <td><p>0.0</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="http://github.com/auto_examples/ensemble/plot_voting_regressor.html#sphx-glr-auto-examples-ensemble-plot-voting-regressor-py"><span class="std std-ref">Plot individual and voting regression predictions</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/ensemble/plot_voting_regressor.py</span></code>)</p></td>
-<td><p>00:01.001</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="http://github.com/auto_examples/linear_model/plot_lasso_model_selection.html#sphx-glr-auto-examples-linear-model-plot-lasso-model-selection-py"><span class="std std-ref">Lasso model selection: AIC-BIC / cross-validation</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/linear_model/plot_lasso_model_selection.py</span></code>)</p></td>
+<td><p>00:01.013</p></td>
 <td><p>0.0</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="http://github.com/auto_examples/release_highlights/plot_release_highlights_1_1_0.html#sphx-glr-auto-examples-release-highlights-plot-release-highlights-1-1-0-py"><span class="std std-ref">Release Highlights for scikit-learn 1.1</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/release_highlights/plot_release_highlights_1_1_0.py</span></code>)</p></td>
-<td><p>00:00.947</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="http://github.com/auto_examples/svm/plot_svm_tie_breaking.html#sphx-glr-auto-examples-svm-plot-svm-tie-breaking-py"><span class="std std-ref">SVM Tie Breaking Example</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/svm/plot_svm_tie_breaking.py</span></code>)</p></td>
+<td><p>00:00.989</p></td>
 <td><p>0.0</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="http://github.com/auto_examples/linear_model/plot_quantile_regression.html#sphx-glr-auto-examples-linear-model-plot-quantile-regression-py"><span class="std std-ref">Quantile regression</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/linear_model/plot_quantile_regression.py</span></code>)</p></td>
-<td><p>00:00.870</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="http://github.com/auto_examples/semi_supervised/plot_semi_supervised_versus_svm_iris.html#sphx-glr-auto-examples-semi-supervised-plot-semi-supervised-versus-svm-iris-py"><span class="std std-ref">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/semi_supervised/plot_semi_supervised_versus_svm_iris.py</span></code>)</p></td>
+<td><p>00:00.958</p></td>
 <td><p>0.0</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="http://github.com/auto_examples/linear_model/plot_lasso_model_selection.html#sphx-glr-auto-examples-linear-model-plot-lasso-model-selection-py"><span class="std std-ref">Lasso model selection: AIC-BIC / cross-validation</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/linear_model/plot_lasso_model_selection.py</span></code>)</p></td>
-<td><p>00:00.863</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="http://github.com/auto_examples/release_highlights/plot_release_highlights_1_1_0.html#sphx-glr-auto-examples-release-highlights-plot-release-highlights-1-1-0-py"><span class="std std-ref">Release Highlights for scikit-learn 1.1</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/release_highlights/plot_release_highlights_1_1_0.py</span></code>)</p></td>
+<td><p>00:00.946</p></td>
 <td><p>0.0</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="reference internal" href="http://github.com/auto_examples/tree/plot_iris_dtc.html#sphx-glr-auto-examples-tree-plot-iris-dtc-py"><span class="std std-ref">Plot the decision surface of decision trees trained on the iris dataset</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/tree/plot_iris_dtc.py</span></code>)</p></td>
-<td><p>00:00.856</p></td>
+<td><p>00:00.938</p></td>
 <td><p>0.0</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="http://github.com/auto_examples/linear_model/plot_elastic_net_precomputed_gram_matrix_with_weighted_samples.html#sphx-glr-auto-examples-linear-model-plot-elastic-net-precomputed-gram-matrix-with-weighted-samples-py"><span class="std std-ref">Fitting an Elastic Net with a precomputed Gram Matrix and Weighted Samples</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/linear_model/plot_elastic_net_precomputed_gram_matrix_with_weighted_samples.py</span></code>)</p></td>
-<td><p>00:00.794</p></td>
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+<td><p>00:00.922</p></td>
 <td><p>0.0</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="http://github.com/auto_examples/linear_model/plot_lasso_lasso_lars_elasticnet_path.html#sphx-glr-auto-examples-linear-model-plot-lasso-lasso-lars-elasticnet-path-py"><span class="std std-ref">Lasso, Lasso-LARS, and Elastic Net paths</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/linear_model/plot_lasso_lasso_lars_elasticnet_path.py</span></code>)</p></td>
-<td><p>00:00.789</p></td>
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+<td><p>00:00.888</p></td>
 <td><p>0.0</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="http://github.com/auto_examples/neighbors/plot_nca_classification.html#sphx-glr-auto-examples-neighbors-plot-nca-classification-py"><span class="std std-ref">Comparing Nearest Neighbors with and without Neighborhood Components Analysis</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/neighbors/plot_nca_classification.py</span></code>)</p></td>
-<td><p>00:00.737</p></td>
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+<td><p>00:00.843</p></td>
 <td><p>0.0</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="http://github.com/auto_examples/ensemble/plot_adaboost_twoclass.html#sphx-glr-auto-examples-ensemble-plot-adaboost-twoclass-py"><span class="std std-ref">Two-class AdaBoost</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/ensemble/plot_adaboost_twoclass.py</span></code>)</p></td>
-<td><p>00:00.710</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="http://github.com/auto_examples/feature_selection/plot_rfe_with_cross_validation.html#sphx-glr-auto-examples-feature-selection-plot-rfe-with-cross-validation-py"><span class="std std-ref">Recursive feature elimination with cross-validation</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/feature_selection/plot_rfe_with_cross_validation.py</span></code>)</p></td>
+<td><p>00:00.838</p></td>
 <td><p>0.0</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="http://github.com/auto_examples/release_highlights/plot_release_highlights_1_5_0.html#sphx-glr-auto-examples-release-highlights-plot-release-highlights-1-5-0-py"><span class="std std-ref">Release Highlights for scikit-learn 1.5</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/release_highlights/plot_release_highlights_1_5_0.py</span></code>)</p></td>
-<td><p>00:00.701</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="http://github.com/auto_examples/mixture/plot_gmm_init.html#sphx-glr-auto-examples-mixture-plot-gmm-init-py"><span class="std std-ref">GMM Initialization Methods</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/mixture/plot_gmm_init.py</span></code>)</p></td>
+<td><p>00:00.836</p></td>
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 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="http://github.com/auto_examples/neighbors/plot_lof_novelty_detection.html#sphx-glr-auto-examples-neighbors-plot-lof-novelty-detection-py"><span class="std std-ref">Novelty detection with Local Outlier Factor (LOF)</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/neighbors/plot_lof_novelty_detection.py</span></code>)</p></td>
-<td><p>00:00.686</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="http://github.com/auto_examples/neighbors/plot_nca_classification.html#sphx-glr-auto-examples-neighbors-plot-nca-classification-py"><span class="std std-ref">Comparing Nearest Neighbors with and without Neighborhood Components Analysis</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/neighbors/plot_nca_classification.py</span></code>)</p></td>
+<td><p>00:00.779</p></td>
 <td><p>0.0</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="http://github.com/auto_examples/feature_selection/plot_rfe_with_cross_validation.html#sphx-glr-auto-examples-feature-selection-plot-rfe-with-cross-validation-py"><span class="std std-ref">Recursive feature elimination with cross-validation</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/feature_selection/plot_rfe_with_cross_validation.py</span></code>)</p></td>
-<td><p>00:00.674</p></td>
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 </tr>
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 </tr>
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-<td><p>00:00.657</p></td>
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+<td><p>00:00.745</p></td>
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 </tr>
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-<td><p>00:00.651</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="http://github.com/auto_examples/release_highlights/plot_release_highlights_1_5_0.html#sphx-glr-auto-examples-release-highlights-plot-release-highlights-1-5-0-py"><span class="std std-ref">Release Highlights for scikit-learn 1.5</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/release_highlights/plot_release_highlights_1_5_0.py</span></code>)</p></td>
+<td><p>00:00.727</p></td>
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 </tr>
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-<td><p>00:00.627</p></td>
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+<td><p>00:00.704</p></td>
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 </tr>
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-<td><p>00:00.625</p></td>
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 </tr>
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-<td><p>00:00.614</p></td>
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 </tr>
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 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="http://github.com/auto_examples/mixture/plot_gmm_init.html#sphx-glr-auto-examples-mixture-plot-gmm-init-py"><span class="std std-ref">GMM Initialization Methods</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/mixture/plot_gmm_init.py</span></code>)</p></td>
-<td><p>00:00.597</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="http://github.com/auto_examples/preprocessing/plot_discretization_strategies.html#sphx-glr-auto-examples-preprocessing-plot-discretization-strategies-py"><span class="std std-ref">Demonstrating the different strategies of KBinsDiscretizer</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/preprocessing/plot_discretization_strategies.py</span></code>)</p></td>
+<td><p>00:00.654</p></td>
 <td><p>0.0</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="http://github.com/auto_examples/linear_model/plot_logistic_multinomial.html#sphx-glr-auto-examples-linear-model-plot-logistic-multinomial-py"><span class="std std-ref">Decision Boundaries of Multinomial and One-vs-Rest Logistic Regression</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/linear_model/plot_logistic_multinomial.py</span></code>)</p></td>
-<td><p>00:00.569</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="http://github.com/auto_examples/ensemble/plot_monotonic_constraints.html#sphx-glr-auto-examples-ensemble-plot-monotonic-constraints-py"><span class="std std-ref">Monotonic Constraints</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/ensemble/plot_monotonic_constraints.py</span></code>)</p></td>
+<td><p>00:00.644</p></td>
 <td><p>0.0</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="http://github.com/auto_examples/ensemble/plot_random_forest_regression_multioutput.html#sphx-glr-auto-examples-ensemble-plot-random-forest-regression-multioutput-py"><span class="std std-ref">Comparing random forests and the multi-output meta estimator</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/ensemble/plot_random_forest_regression_multioutput.py</span></code>)</p></td>
-<td><p>00:00.563</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="http://github.com/auto_examples/bicluster/plot_spectral_biclustering.html#sphx-glr-auto-examples-bicluster-plot-spectral-biclustering-py"><span class="std std-ref">A demo of the Spectral Biclustering algorithm</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/bicluster/plot_spectral_biclustering.py</span></code>)</p></td>
+<td><p>00:00.641</p></td>
 <td><p>0.0</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="http://github.com/auto_examples/decomposition/plot_kernel_pca.html#sphx-glr-auto-examples-decomposition-plot-kernel-pca-py"><span class="std std-ref">Kernel PCA</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/decomposition/plot_kernel_pca.py</span></code>)</p></td>
-<td><p>00:00.541</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="http://github.com/auto_examples/neighbors/plot_kde_1d.html#sphx-glr-auto-examples-neighbors-plot-kde-1d-py"><span class="std std-ref">Simple 1D Kernel Density Estimation</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/neighbors/plot_kde_1d.py</span></code>)</p></td>
+<td><p>00:00.629</p></td>
 <td><p>0.0</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="http://github.com/auto_examples/linear_model/plot_theilsen.html#sphx-glr-auto-examples-linear-model-plot-theilsen-py"><span class="std std-ref">Theil-Sen Regression</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/linear_model/plot_theilsen.py</span></code>)</p></td>
-<td><p>00:00.540</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="http://github.com/auto_examples/decomposition/plot_kernel_pca.html#sphx-glr-auto-examples-decomposition-plot-kernel-pca-py"><span class="std std-ref">Kernel PCA</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/decomposition/plot_kernel_pca.py</span></code>)</p></td>
+<td><p>00:00.626</p></td>
 <td><p>0.0</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="http://github.com/auto_examples/semi_supervised/plot_label_propagation_digits_active_learning.html#sphx-glr-auto-examples-semi-supervised-plot-label-propagation-digits-active-learning-py"><span class="std std-ref">Label Propagation digits active learning</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/semi_supervised/plot_label_propagation_digits_active_learning.py</span></code>)</p></td>
-<td><p>00:00.537</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="http://github.com/auto_examples/cross_decomposition/plot_pcr_vs_pls.html#sphx-glr-auto-examples-cross-decomposition-plot-pcr-vs-pls-py"><span class="std std-ref">Principal Component Regression vs Partial Least Squares Regression</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/cross_decomposition/plot_pcr_vs_pls.py</span></code>)</p></td>
+<td><p>00:00.606</p></td>
 <td><p>0.0</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="http://github.com/auto_examples/neighbors/plot_kde_1d.html#sphx-glr-auto-examples-neighbors-plot-kde-1d-py"><span class="std std-ref">Simple 1D Kernel Density Estimation</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/neighbors/plot_kde_1d.py</span></code>)</p></td>
-<td><p>00:00.527</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="http://github.com/auto_examples/covariance/plot_sparse_cov.html#sphx-glr-auto-examples-covariance-plot-sparse-cov-py"><span class="std std-ref">Sparse inverse covariance estimation</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/covariance/plot_sparse_cov.py</span></code>)</p></td>
+<td><p>00:00.574</p></td>
 <td><p>0.0</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="http://github.com/auto_examples/neighbors/plot_classification.html#sphx-glr-auto-examples-neighbors-plot-classification-py"><span class="std std-ref">Nearest Neighbors Classification</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/neighbors/plot_classification.py</span></code>)</p></td>
-<td><p>00:00.523</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="http://github.com/auto_examples/ensemble/plot_random_forest_regression_multioutput.html#sphx-glr-auto-examples-ensemble-plot-random-forest-regression-multioutput-py"><span class="std std-ref">Comparing random forests and the multi-output meta estimator</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/ensemble/plot_random_forest_regression_multioutput.py</span></code>)</p></td>
+<td><p>00:00.561</p></td>
 <td><p>0.0</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="http://github.com/auto_examples/cross_decomposition/plot_pcr_vs_pls.html#sphx-glr-auto-examples-cross-decomposition-plot-pcr-vs-pls-py"><span class="std std-ref">Principal Component Regression vs Partial Least Squares Regression</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/cross_decomposition/plot_pcr_vs_pls.py</span></code>)</p></td>
-<td><p>00:00.507</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="http://github.com/auto_examples/gaussian_process/plot_gpr_noisy_targets.html#sphx-glr-auto-examples-gaussian-process-plot-gpr-noisy-targets-py"><span class="std std-ref">Gaussian Processes regression: basic introductory example</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/gaussian_process/plot_gpr_noisy_targets.py</span></code>)</p></td>
+<td><p>00:00.551</p></td>
 <td><p>0.0</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="http://github.com/auto_examples/ensemble/plot_adaboost_regression.html#sphx-glr-auto-examples-ensemble-plot-adaboost-regression-py"><span class="std std-ref">Decision Tree Regression with AdaBoost</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/ensemble/plot_adaboost_regression.py</span></code>)</p></td>
-<td><p>00:00.504</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="http://github.com/auto_examples/linear_model/plot_theilsen.html#sphx-glr-auto-examples-linear-model-plot-theilsen-py"><span class="std std-ref">Theil-Sen Regression</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/linear_model/plot_theilsen.py</span></code>)</p></td>
+<td><p>00:00.545</p></td>
 <td><p>0.0</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="http://github.com/auto_examples/bicluster/plot_spectral_biclustering.html#sphx-glr-auto-examples-bicluster-plot-spectral-biclustering-py"><span class="std std-ref">A demo of the Spectral Biclustering algorithm</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/bicluster/plot_spectral_biclustering.py</span></code>)</p></td>
-<td><p>00:00.501</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="http://github.com/auto_examples/cluster/plot_feature_agglomeration_vs_univariate_selection.html#sphx-glr-auto-examples-cluster-plot-feature-agglomeration-vs-univariate-selection-py"><span class="std std-ref">Feature agglomeration vs. univariate selection</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/cluster/plot_feature_agglomeration_vs_univariate_selection.py</span></code>)</p></td>
+<td><p>00:00.540</p></td>
 <td><p>0.0</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="http://github.com/auto_examples/cluster/plot_feature_agglomeration_vs_univariate_selection.html#sphx-glr-auto-examples-cluster-plot-feature-agglomeration-vs-univariate-selection-py"><span class="std std-ref">Feature agglomeration vs. univariate selection</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/cluster/plot_feature_agglomeration_vs_univariate_selection.py</span></code>)</p></td>
-<td><p>00:00.474</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="http://github.com/auto_examples/neighbors/plot_classification.html#sphx-glr-auto-examples-neighbors-plot-classification-py"><span class="std std-ref">Nearest Neighbors Classification</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/neighbors/plot_classification.py</span></code>)</p></td>
+<td><p>00:00.525</p></td>
 <td><p>0.0</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="http://github.com/auto_examples/compose/plot_feature_union.html#sphx-glr-auto-examples-compose-plot-feature-union-py"><span class="std std-ref">Concatenating multiple feature extraction methods</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/compose/plot_feature_union.py</span></code>)</p></td>
-<td><p>00:00.459</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="http://github.com/auto_examples/covariance/plot_covariance_estimation.html#sphx-glr-auto-examples-covariance-plot-covariance-estimation-py"><span class="std std-ref">Shrinkage covariance estimation: LedoitWolf vs OAS and max-likelihood</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/covariance/plot_covariance_estimation.py</span></code>)</p></td>
+<td><p>00:00.524</p></td>
 <td><p>0.0</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="http://github.com/auto_examples/gaussian_process/plot_gpr_noisy_targets.html#sphx-glr-auto-examples-gaussian-process-plot-gpr-noisy-targets-py"><span class="std std-ref">Gaussian Processes regression: basic introductory example</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/gaussian_process/plot_gpr_noisy_targets.py</span></code>)</p></td>
-<td><p>00:00.458</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="http://github.com/auto_examples/tree/plot_cost_complexity_pruning.html#sphx-glr-auto-examples-tree-plot-cost-complexity-pruning-py"><span class="std std-ref">Post pruning decision trees with cost complexity pruning</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/tree/plot_cost_complexity_pruning.py</span></code>)</p></td>
+<td><p>00:00.500</p></td>
 <td><p>0.0</p></td>
 </tr>
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-<td><p>00:00.451</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="http://github.com/auto_examples/mixture/plot_gmm_sin.html#sphx-glr-auto-examples-mixture-plot-gmm-sin-py"><span class="std std-ref">Gaussian Mixture Model Sine Curve</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/mixture/plot_gmm_sin.py</span></code>)</p></td>
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 <td><p>0.0</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="http://github.com/auto_examples/linear_model/plot_polynomial_interpolation.html#sphx-glr-auto-examples-linear-model-plot-polynomial-interpolation-py"><span class="std std-ref">Polynomial and Spline interpolation</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/linear_model/plot_polynomial_interpolation.py</span></code>)</p></td>
-<td><p>00:00.436</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="http://github.com/auto_examples/cluster/plot_segmentation_toy.html#sphx-glr-auto-examples-cluster-plot-segmentation-toy-py"><span class="std std-ref">Spectral clustering for image segmentation</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/cluster/plot_segmentation_toy.py</span></code>)</p></td>
+<td><p>00:00.496</p></td>
 <td><p>0.0</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="http://github.com/auto_examples/covariance/plot_sparse_cov.html#sphx-glr-auto-examples-covariance-plot-sparse-cov-py"><span class="std std-ref">Sparse inverse covariance estimation</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/covariance/plot_sparse_cov.py</span></code>)</p></td>
-<td><p>00:00.436</p></td>
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 <td><p>0.0</p></td>
 </tr>
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-<td><p>00:00.433</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="http://github.com/auto_examples/ensemble/plot_isolation_forest.html#sphx-glr-auto-examples-ensemble-plot-isolation-forest-py"><span class="std std-ref">IsolationForest example</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/ensemble/plot_isolation_forest.py</span></code>)</p></td>
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 <td><p>0.0</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="http://github.com/auto_examples/tree/plot_cost_complexity_pruning.html#sphx-glr-auto-examples-tree-plot-cost-complexity-pruning-py"><span class="std std-ref">Post pruning decision trees with cost complexity pruning</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/tree/plot_cost_complexity_pruning.py</span></code>)</p></td>
-<td><p>00:00.433</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="http://github.com/auto_examples/ensemble/plot_adaboost_regression.html#sphx-glr-auto-examples-ensemble-plot-adaboost-regression-py"><span class="std std-ref">Decision Tree Regression with AdaBoost</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/ensemble/plot_adaboost_regression.py</span></code>)</p></td>
+<td><p>00:00.474</p></td>
 <td><p>0.0</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="reference internal" href="http://github.com/auto_examples/linear_model/plot_logistic_l1_l2_sparsity.html#sphx-glr-auto-examples-linear-model-plot-logistic-l1-l2-sparsity-py"><span class="std std-ref">L1 Penalty and Sparsity in Logistic Regression</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/linear_model/plot_logistic_l1_l2_sparsity.py</span></code>)</p></td>
-<td><p>00:00.430</p></td>
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 <td><p>0.0</p></td>
 </tr>
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-<td><p>00:00.425</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="http://github.com/auto_examples/linear_model/plot_lasso_and_elasticnet.html#sphx-glr-auto-examples-linear-model-plot-lasso-and-elasticnet-py"><span class="std std-ref">L1-based models for Sparse Signals</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/linear_model/plot_lasso_and_elasticnet.py</span></code>)</p></td>
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 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="http://github.com/auto_examples/covariance/plot_covariance_estimation.html#sphx-glr-auto-examples-covariance-plot-covariance-estimation-py"><span class="std std-ref">Shrinkage covariance estimation: LedoitWolf vs OAS and max-likelihood</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/covariance/plot_covariance_estimation.py</span></code>)</p></td>
-<td><p>00:00.421</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="http://github.com/auto_examples/compose/plot_feature_union.html#sphx-glr-auto-examples-compose-plot-feature-union-py"><span class="std std-ref">Concatenating multiple feature extraction methods</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/compose/plot_feature_union.py</span></code>)</p></td>
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 <td><p>0.0</p></td>
 </tr>
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 </tr>
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 </tr>
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+<tr class="row-odd"><td><p><a class="reference internal" href="http://github.com/auto_examples/linear_model/plot_ols_ridge.html#sphx-glr-auto-examples-linear-model-plot-ols-ridge-py"><span class="std std-ref">Ordinary Least Squares and Ridge Regression</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/linear_model/plot_ols_ridge.py</span></code>)</p></td>
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 </tr>
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 </tr>
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-<td><p>00:00.404</p></td>
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 </tr>
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 </tr>
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-<td><p>00:00.391</p></td>
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 </tr>
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 </tr>
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 </tr>
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 </tr>
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 </tr>
 <tr class="row-odd"><td><p><a class="reference internal" href="http://github.com/auto_examples/linear_model/plot_ridge_path.html#sphx-glr-auto-examples-linear-model-plot-ridge-path-py"><span class="std std-ref">Plot Ridge coefficients as a function of the regularization</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/linear_model/plot_ridge_path.py</span></code>)</p></td>
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 <td><p>00:00.334</p></td>
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 </tr>
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 </tr>
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 <td><p>00:00.326</p></td>
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 </tr>
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 <td><p>00:00.320</p></td>
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 </tr>
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 </tr>
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 </tr>
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 </tr>
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-<td><p>00:00.226</p></td>
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 </tr>
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 </tr>
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 </tr>
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 </tr>
 <tr class="row-odd"><td><p><a class="reference internal" href="http://github.com/auto_examples/gaussian_process/plot_gpr_on_structured_data.html#sphx-glr-auto-examples-gaussian-process-plot-gpr-on-structured-data-py"><span class="std std-ref">Gaussian processes on discrete data structures</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/gaussian_process/plot_gpr_on_structured_data.py</span></code>)</p></td>
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 </tr>
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 </tr>
 <tr class="row-odd"><td><p><a class="reference internal" href="http://github.com/auto_examples/linear_model/plot_omp.html#sphx-glr-auto-examples-linear-model-plot-omp-py"><span class="std std-ref">Orthogonal Matching Pursuit</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/linear_model/plot_omp.py</span></code>)</p></td>
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 <td><p>0.0</p></td>
 </tr>
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-<td><p>00:00.196</p></td>
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 </tr>
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 </tr>
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 <td><p>0.0</p></td>
 </tr>
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 </tr>
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 <td><p>0.0</p></td>
 </tr>
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 </tr>
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 </tr>
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 </tr>
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 </tr>
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 </tr>
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 </tr>
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-<td><p>00:00.169</p></td>
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 <td><p>0.0</p></td>
 </tr>
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 <td><p>00:00.166</p></td>
 <td><p>0.0</p></td>
 </tr>
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-<td><p>00:00.164</p></td>
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 </tr>
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-<td><p>00:00.160</p></td>
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 <td><p>0.0</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="http://github.com/auto_examples/neighbors/plot_nearest_centroid.html#sphx-glr-auto-examples-neighbors-plot-nearest-centroid-py"><span class="std std-ref">Nearest Centroid Classification</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/neighbors/plot_nearest_centroid.py</span></code>)</p></td>
-<td><p>00:00.157</p></td>
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 <td><p>0.0</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="reference internal" href="http://github.com/auto_examples/miscellaneous/plot_set_output.html#sphx-glr-auto-examples-miscellaneous-plot-set-output-py"><span class="std std-ref">Introducing the set_output API</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/miscellaneous/plot_set_output.py</span></code>)</p></td>
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 <td><p>0.0</p></td>
 </tr>
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-<td><p>00:00.147</p></td>
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 <td><p>0.0</p></td>
 </tr>
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-<td><p>00:00.144</p></td>
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 <td><p>0.0</p></td>
 </tr>
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 </tr>
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-<td><p>00:00.126</p></td>
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 <td><p>0.0</p></td>
 </tr>
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 <td><p>0.0</p></td>
 </tr>
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 <td><p>0.0</p></td>
 </tr>
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 </tr>
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 </tr>
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 </tr>
 <tr class="row-odd"><td><p><a class="reference internal" href="http://github.com/auto_examples/miscellaneous/plot_pipeline_display.html#sphx-glr-auto-examples-miscellaneous-plot-pipeline-display-py"><span class="std std-ref">Displaying Pipelines</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/miscellaneous/plot_pipeline_display.py</span></code>)</p></td>
 <td><p>00:00.100</p></td>
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 </tr>
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 <td><p>00:00.099</p></td>
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 </tr>
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 </tr>
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 <td><p>0.0</p></td>
 </tr>
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 </tr>
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+<tr class="row-odd"><td><p><a class="reference internal" href="http://github.com/auto_examples/linear_model/plot_sgd_separating_hyperplane.html#sphx-glr-auto-examples-linear-model-plot-sgd-separating-hyperplane-py"><span class="std std-ref">SGD: Maximum margin separating hyperplane</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/linear_model/plot_sgd_separating_hyperplane.py</span></code>)</p></td>
 <td><p>00:00.072</p></td>
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-<td><p>00:00.071</p></td>
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 <td><p>0.0</p></td>
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-<td><p>00:00.070</p></td>
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-<td><p>00:00.063</p></td>
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-<tr class="row-even"><td><p><a class="reference internal" href="http://github.com/auto_examples/release_highlights/plot_release_highlights_1_0_0.html#sphx-glr-auto-examples-release-highlights-plot-release-highlights-1-0-0-py"><span class="std std-ref">Release Highlights for scikit-learn 1.0</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/release_highlights/plot_release_highlights_1_0_0.py</span></code>)</p></td>
-<td><p>00:00.016</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="http://github.com/auto_examples/release_highlights/plot_release_highlights_1_0_0.html#sphx-glr-auto-examples-release-highlights-plot-release-highlights-1-0-0-py"><span class="std std-ref">Release Highlights for scikit-learn 1.0</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/release_highlights/plot_release_highlights_1_0_0.py</span></code>)</p></td>
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 <td><p>0.0</p></td>
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-<tr class="row-odd"><td><p><a class="reference internal" href="http://github.com/auto_examples/feature_selection/plot_feature_selection_pipeline.html#sphx-glr-auto-examples-feature-selection-plot-feature-selection-pipeline-py"><span class="std std-ref">Pipeline ANOVA SVM</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/feature_selection/plot_feature_selection_pipeline.py</span></code>)</p></td>
-<td><p>00:00.013</p></td>
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 <td><p>0.0</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="http://github.com/auto_examples/applications/wikipedia_principal_eigenvector.html#sphx-glr-auto-examples-applications-wikipedia-principal-eigenvector-py"><span class="std std-ref">Wikipedia principal eigenvector</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/applications/wikipedia_principal_eigenvector.py</span></code>)</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="http://github.com/auto_examples/applications/wikipedia_principal_eigenvector.html#sphx-glr-auto-examples-applications-wikipedia-principal-eigenvector-py"><span class="std std-ref">Wikipedia principal eigenvector</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/applications/wikipedia_principal_eigenvector.py</span></code>)</p></td>
 <td><p>00:00.000</p></td>
 <td><p>0.0</p></td>
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+<tr class="row-even"><td><p><a class="reference internal" href="http://github.com/auto_examples/developing_estimators/sklearn_is_fitted.html#sphx-glr-auto-examples-developing-estimators-sklearn-is-fitted-py"><span class="std std-ref">__sklearn_is_fitted__ as Developer API</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/developing_estimators/sklearn_is_fitted.py</span></code>)</p></td>
 <td><p>00:00.000</p></td>
 <td><p>0.0</p></td>
 </tr>
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+<tr class="row-odd"><td><p><a class="reference internal" href="http://github.com/auto_examples/neighbors/approximate_nearest_neighbors.html#sphx-glr-auto-examples-neighbors-approximate-nearest-neighbors-py"><span class="std std-ref">Approximate nearest neighbors in TSNE</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/neighbors/approximate_nearest_neighbors.py</span></code>)</p></td>
 <td><p>00:00.000</p></td>
 <td><p>0.0</p></td>
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diff --git a/dev/supervised_learning.html b/dev/supervised_learning.html
index 4f42f5ad7502b..23f558b457533 100644
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/learning_curve.html">3.5. Validation curves: plotting scores to evaluate models</a></li>
 </ul>
 </details></li>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/permutation_importance.html">4.2. Permutation feature importance</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/partial_dependence.html">5.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/preprocessing_targets.html">6.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/visualizations.html">6. Visualizations</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/data_transforms.html">7. Dataset transformations</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/compose.html">7.1. Pipelines and composite estimators</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/preprocessing.html">7.3. Preprocessing data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/impute.html">7.4. Imputation of missing values</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/unsupervised_reduction.html">7.5. Unsupervised dimensionality reduction</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/kernel_approximation.html">7.7. Kernel Approximation</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html">7.4. Loading other datasets</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/datasets.html">8. Dataset loading utilities</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html">8.1. Toy datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html">8.2. Real world datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html">8.3. Generated datasets</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/parallelism.html">8.3. Parallelism, resource management, and configuration</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/computing.html">9. Computing with scikit-learn</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/scaling_strategies.html">9.1. Strategies to scale computationally: bigger data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/computational_performance.html">9.2. Computational Performance</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/parallelism.html">9.3. Parallelism, resource management, and configuration</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/array_api.html">11.1. Array API support (experimental)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/model_persistence.html">10. Model persistence</a></li>
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 <li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/learning_curve.html">3.5. Validation curves: plotting scores to evaluate models</a></li>
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+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/inspection.html">5. Inspection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/partial_dependence.html">5.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html">7.3. Generated datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html">7.4. Loading other datasets</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/datasets.html">8. Dataset loading utilities</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html">8.1. Toy datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html">8.2. Real world datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html">8.3. Generated datasets</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/scaling_strategies.html">9.1. Strategies to scale computationally: bigger data</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/partial_dependence.html">5.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
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+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/data_transforms.html">7. Dataset transformations</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/compose.html">7.1. Pipelines and composite estimators</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/unsupervised_reduction.html">7.5. Unsupervised dimensionality reduction</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/kernel_approximation.html">7.7. Kernel Approximation</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/preprocessing_targets.html">7.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html">7.3. Generated datasets</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html">7.4. Loading other datasets</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/datasets.html">8. Dataset loading utilities</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html">8.1. Toy datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html">8.2. Real world datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html">8.3. Generated datasets</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/computational_performance.html">8.2. Computational Performance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/parallelism.html">8.3. Parallelism, resource management, and configuration</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/computing.html">9. Computing with scikit-learn</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/scaling_strategies.html">9.1. Strategies to scale computationally: bigger data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/computational_performance.html">9.2. Computational Performance</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/parallelism.html">9.3. Parallelism, resource management, and configuration</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/model_persistence.html">9. Model persistence</a></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/common_pitfalls.html">10. Common pitfalls and recommended practices</a></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/dispatching.html">11. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/array_api.html">11.1. Array API support (experimental)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/model_persistence.html">10. Model persistence</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/common_pitfalls.html">11. Common pitfalls and recommended practices</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/dispatching.html">12. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/array_api.html">12.1. Array API support (experimental)</a></li>
 </ul>
 </details></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/machine_learning_map.html">12. Choosing the right estimator</a></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/presentations.html">13. External Resources, Videos and Talks</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/machine_learning_map.html">13. Choosing the right estimator</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/presentations.html">14. External Resources, Videos and Talks</a></li>
 </ul>
 </div>
 </nav></div>
@@ -951,218 +952,222 @@
 </li>
 </ul>
 </li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/inspection.html">4. Inspection</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/partial_dependence.html">4.1. Partial Dependence and Individual Conditional Expectation plots</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="http://github.com/modules/partial_dependence.html#partial-dependence-plots">4.1.1. Partial dependence plots</a></li>
-<li class="toctree-l3"><a class="reference internal" href="http://github.com/modules/partial_dependence.html#individual-conditional-expectation-ice-plot">4.1.2. Individual conditional expectation (ICE) plot</a></li>
-<li class="toctree-l3"><a class="reference internal" href="http://github.com/modules/partial_dependence.html#mathematical-definition">4.1.3. Mathematical Definition</a></li>
-<li class="toctree-l3"><a class="reference internal" href="http://github.com/modules/partial_dependence.html#computation-methods">4.1.4. Computation methods</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/metadata_routing.html">4. Metadata Routing</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/metadata_routing.html#usage-examples">4.1. Usage Examples</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="http://github.com/metadata_routing.html#weighted-scoring-and-fitting">4.1.1. Weighted scoring and fitting</a></li>
+<li class="toctree-l3"><a class="reference internal" href="http://github.com/metadata_routing.html#weighted-scoring-and-unweighted-fitting">4.1.2. Weighted scoring and unweighted fitting</a></li>
+<li class="toctree-l3"><a class="reference internal" href="http://github.com/metadata_routing.html#unweighted-feature-selection">4.1.3. Unweighted feature selection</a></li>
+<li class="toctree-l3"><a class="reference internal" href="http://github.com/metadata_routing.html#different-scoring-and-fitting-weights">4.1.4. Different scoring and fitting weights</a></li>
 </ul>
 </li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/permutation_importance.html">4.2. Permutation feature importance</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="http://github.com/modules/permutation_importance.html#outline-of-the-permutation-importance-algorithm">4.2.1. Outline of the permutation importance algorithm</a></li>
-<li class="toctree-l3"><a class="reference internal" href="http://github.com/modules/permutation_importance.html#relation-to-impurity-based-importance-in-trees">4.2.2. Relation to impurity-based importance in trees</a></li>
-<li class="toctree-l3"><a class="reference internal" href="http://github.com/modules/permutation_importance.html#misleading-values-on-strongly-correlated-features">4.2.3. Misleading values on strongly correlated features</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/metadata_routing.html#api-interface">4.2. API Interface</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/metadata_routing.html#metadata-routing-support-status">4.3. Metadata Routing Support Status</a></li>
 </ul>
 </li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/inspection.html">5. Inspection</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/partial_dependence.html">5.1. Partial Dependence and Individual Conditional Expectation plots</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="http://github.com/modules/partial_dependence.html#partial-dependence-plots">5.1.1. Partial dependence plots</a></li>
+<li class="toctree-l3"><a class="reference internal" href="http://github.com/modules/partial_dependence.html#individual-conditional-expectation-ice-plot">5.1.2. Individual conditional expectation (ICE) plot</a></li>
+<li class="toctree-l3"><a class="reference internal" href="http://github.com/modules/partial_dependence.html#mathematical-definition">5.1.3. Mathematical Definition</a></li>
+<li class="toctree-l3"><a class="reference internal" href="http://github.com/modules/partial_dependence.html#computation-methods">5.1.4. Computation methods</a></li>
 </ul>
 </li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/visualizations.html">5. Visualizations</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/visualizations.html#available-plotting-utilities">5.1. Available Plotting Utilities</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="http://github.com/visualizations.html#display-objects">5.1.1. Display Objects</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/permutation_importance.html">5.2. Permutation feature importance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="http://github.com/modules/permutation_importance.html#outline-of-the-permutation-importance-algorithm">5.2.1. Outline of the permutation importance algorithm</a></li>
+<li class="toctree-l3"><a class="reference internal" href="http://github.com/modules/permutation_importance.html#relation-to-impurity-based-importance-in-trees">5.2.2. Relation to impurity-based importance in trees</a></li>
+<li class="toctree-l3"><a class="reference internal" href="http://github.com/modules/permutation_importance.html#misleading-values-on-strongly-correlated-features">5.2.3. Misleading values on strongly correlated features</a></li>
 </ul>
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 </ul>
 </li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/data_transforms.html">6. Dataset transformations</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/compose.html">6.1. Pipelines and composite estimators</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="http://github.com/modules/compose.html#pipeline-chaining-estimators">6.1.1. Pipeline: chaining estimators</a></li>
-<li class="toctree-l3"><a class="reference internal" href="http://github.com/modules/compose.html#transforming-target-in-regression">6.1.2. Transforming target in regression</a></li>
-<li class="toctree-l3"><a class="reference internal" href="http://github.com/modules/compose.html#featureunion-composite-feature-spaces">6.1.3. FeatureUnion: composite feature spaces</a></li>
-<li class="toctree-l3"><a class="reference internal" href="http://github.com/modules/compose.html#columntransformer-for-heterogeneous-data">6.1.4. ColumnTransformer for heterogeneous data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="http://github.com/modules/compose.html#visualizing-composite-estimators">6.1.5. Visualizing Composite Estimators</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/visualizations.html">6. Visualizations</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/visualizations.html#available-plotting-utilities">6.1. Available Plotting Utilities</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="http://github.com/visualizations.html#display-objects">6.1.1. Display Objects</a></li>
 </ul>
 </li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/feature_extraction.html">6.2. Feature extraction</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="http://github.com/modules/feature_extraction.html#feature-hashing">6.2.2. Feature hashing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="http://github.com/modules/feature_extraction.html#text-feature-extraction">6.2.3. Text feature extraction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="http://github.com/modules/feature_extraction.html#image-feature-extraction">6.2.4. Image feature extraction</a></li>
 </ul>
 </li>
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-<li class="toctree-l3"><a class="reference internal" href="http://github.com/modules/preprocessing.html#standardization-or-mean-removal-and-variance-scaling">6.3.1. Standardization, or mean removal and variance scaling</a></li>
-<li class="toctree-l3"><a class="reference internal" href="http://github.com/modules/preprocessing.html#non-linear-transformation">6.3.2. Non-linear transformation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="http://github.com/modules/preprocessing.html#normalization">6.3.3. Normalization</a></li>
-<li class="toctree-l3"><a class="reference internal" href="http://github.com/modules/preprocessing.html#encoding-categorical-features">6.3.4. Encoding categorical features</a></li>
-<li class="toctree-l3"><a class="reference internal" href="http://github.com/modules/preprocessing.html#discretization">6.3.5. Discretization</a></li>
-<li class="toctree-l3"><a class="reference internal" href="http://github.com/modules/preprocessing.html#imputation-of-missing-values">6.3.6. Imputation of missing values</a></li>
-<li class="toctree-l3"><a class="reference internal" href="http://github.com/modules/preprocessing.html#generating-polynomial-features">6.3.7. Generating polynomial features</a></li>
-<li class="toctree-l3"><a class="reference internal" href="http://github.com/modules/preprocessing.html#custom-transformers">6.3.8. Custom transformers</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/data_transforms.html">7. Dataset transformations</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/compose.html">7.1. Pipelines and composite estimators</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="http://github.com/modules/compose.html#pipeline-chaining-estimators">7.1.1. Pipeline: chaining estimators</a></li>
+<li class="toctree-l3"><a class="reference internal" href="http://github.com/modules/compose.html#transforming-target-in-regression">7.1.2. Transforming target in regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="http://github.com/modules/compose.html#featureunion-composite-feature-spaces">7.1.3. FeatureUnion: composite feature spaces</a></li>
+<li class="toctree-l3"><a class="reference internal" href="http://github.com/modules/compose.html#columntransformer-for-heterogeneous-data">7.1.4. ColumnTransformer for heterogeneous data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="http://github.com/modules/compose.html#visualizing-composite-estimators">7.1.5. Visualizing Composite Estimators</a></li>
 </ul>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/impute.html">6.4. Imputation of missing values</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="http://github.com/modules/impute.html#univariate-vs-multivariate-imputation">6.4.1. Univariate vs. Multivariate Imputation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="http://github.com/modules/impute.html#univariate-feature-imputation">6.4.2. Univariate feature imputation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="http://github.com/modules/impute.html#multivariate-feature-imputation">6.4.3. Multivariate feature imputation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="http://github.com/modules/impute.html#nearest-neighbors-imputation">6.4.4. Nearest neighbors imputation</a></li>
-<li class="toctree-l3"><a class="reference internal" href="http://github.com/modules/impute.html#keeping-the-number-of-features-constant">6.4.5. Keeping the number of features constant</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="http://github.com/modules/impute.html#estimators-that-handle-nan-values">6.4.7. Estimators that handle NaN values</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/feature_extraction.html">7.2. Feature extraction</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="http://github.com/modules/feature_extraction.html#loading-features-from-dicts">7.2.1. Loading features from dicts</a></li>
+<li class="toctree-l3"><a class="reference internal" href="http://github.com/modules/feature_extraction.html#feature-hashing">7.2.2. Feature hashing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="http://github.com/modules/feature_extraction.html#text-feature-extraction">7.2.3. Text feature extraction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="http://github.com/modules/feature_extraction.html#image-feature-extraction">7.2.4. Image feature extraction</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="http://github.com/modules/unsupervised_reduction.html#random-projections">6.5.2. Random projections</a></li>
-<li class="toctree-l3"><a class="reference internal" href="http://github.com/modules/unsupervised_reduction.html#feature-agglomeration">6.5.3. Feature agglomeration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/preprocessing.html">7.3. Preprocessing data</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="http://github.com/modules/preprocessing.html#standardization-or-mean-removal-and-variance-scaling">7.3.1. Standardization, or mean removal and variance scaling</a></li>
+<li class="toctree-l3"><a class="reference internal" href="http://github.com/modules/preprocessing.html#non-linear-transformation">7.3.2. Non-linear transformation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="http://github.com/modules/preprocessing.html#normalization">7.3.3. Normalization</a></li>
+<li class="toctree-l3"><a class="reference internal" href="http://github.com/modules/preprocessing.html#encoding-categorical-features">7.3.4. Encoding categorical features</a></li>
+<li class="toctree-l3"><a class="reference internal" href="http://github.com/modules/preprocessing.html#discretization">7.3.5. Discretization</a></li>
+<li class="toctree-l3"><a class="reference internal" href="http://github.com/modules/preprocessing.html#imputation-of-missing-values">7.3.6. Imputation of missing values</a></li>
+<li class="toctree-l3"><a class="reference internal" href="http://github.com/modules/preprocessing.html#generating-polynomial-features">7.3.7. Generating polynomial features</a></li>
+<li class="toctree-l3"><a class="reference internal" href="http://github.com/modules/preprocessing.html#custom-transformers">7.3.8. Custom transformers</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="http://github.com/modules/random_projection.html#sparse-random-projection">6.6.3. Sparse random projection</a></li>
-<li class="toctree-l3"><a class="reference internal" href="http://github.com/modules/random_projection.html#inverse-transform">6.6.4. Inverse Transform</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/impute.html">7.4. Imputation of missing values</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="http://github.com/modules/impute.html#univariate-vs-multivariate-imputation">7.4.1. Univariate vs. Multivariate Imputation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="http://github.com/modules/impute.html#univariate-feature-imputation">7.4.2. Univariate feature imputation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="http://github.com/modules/impute.html#multivariate-feature-imputation">7.4.3. Multivariate feature imputation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="http://github.com/modules/impute.html#nearest-neighbors-imputation">7.4.4. Nearest neighbors imputation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="http://github.com/modules/impute.html#keeping-the-number-of-features-constant">7.4.5. Keeping the number of features constant</a></li>
+<li class="toctree-l3"><a class="reference internal" href="http://github.com/modules/impute.html#marking-imputed-values">7.4.6. Marking imputed values</a></li>
+<li class="toctree-l3"><a class="reference internal" href="http://github.com/modules/impute.html#estimators-that-handle-nan-values">7.4.7. Estimators that handle NaN values</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="http://github.com/modules/kernel_approximation.html#radial-basis-function-kernel">6.7.2. Radial Basis Function Kernel</a></li>
-<li class="toctree-l3"><a class="reference internal" href="http://github.com/modules/kernel_approximation.html#additive-chi-squared-kernel">6.7.3. Additive Chi Squared Kernel</a></li>
-<li class="toctree-l3"><a class="reference internal" href="http://github.com/modules/kernel_approximation.html#skewed-chi-squared-kernel">6.7.4. Skewed Chi Squared Kernel</a></li>
-<li class="toctree-l3"><a class="reference internal" href="http://github.com/modules/kernel_approximation.html#polynomial-kernel-approximation-via-tensor-sketch">6.7.5. Polynomial Kernel Approximation via Tensor Sketch</a></li>
-<li class="toctree-l3"><a class="reference internal" href="http://github.com/modules/kernel_approximation.html#mathematical-details">6.7.6. Mathematical Details</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/unsupervised_reduction.html">7.5. Unsupervised dimensionality reduction</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="http://github.com/modules/unsupervised_reduction.html#pca-principal-component-analysis">7.5.1. PCA: principal component analysis</a></li>
+<li class="toctree-l3"><a class="reference internal" href="http://github.com/modules/unsupervised_reduction.html#random-projections">7.5.2. Random projections</a></li>
+<li class="toctree-l3"><a class="reference internal" href="http://github.com/modules/unsupervised_reduction.html#feature-agglomeration">7.5.3. Feature agglomeration</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="http://github.com/modules/metrics.html#linear-kernel">6.8.2. Linear kernel</a></li>
-<li class="toctree-l3"><a class="reference internal" href="http://github.com/modules/metrics.html#polynomial-kernel">6.8.3. Polynomial kernel</a></li>
-<li class="toctree-l3"><a class="reference internal" href="http://github.com/modules/metrics.html#sigmoid-kernel">6.8.4. Sigmoid kernel</a></li>
-<li class="toctree-l3"><a class="reference internal" href="http://github.com/modules/metrics.html#rbf-kernel">6.8.5. RBF kernel</a></li>
-<li class="toctree-l3"><a class="reference internal" href="http://github.com/modules/metrics.html#laplacian-kernel">6.8.6. Laplacian kernel</a></li>
-<li class="toctree-l3"><a class="reference internal" href="http://github.com/modules/metrics.html#chi-squared-kernel">6.8.7. Chi-squared kernel</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/random_projection.html">7.6. Random Projection</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="http://github.com/modules/random_projection.html#the-johnson-lindenstrauss-lemma">7.6.1. The Johnson-Lindenstrauss lemma</a></li>
+<li class="toctree-l3"><a class="reference internal" href="http://github.com/modules/random_projection.html#gaussian-random-projection">7.6.2. Gaussian random projection</a></li>
+<li class="toctree-l3"><a class="reference internal" href="http://github.com/modules/random_projection.html#sparse-random-projection">7.6.3. Sparse random projection</a></li>
+<li class="toctree-l3"><a class="reference internal" href="http://github.com/modules/random_projection.html#inverse-transform">7.6.4. Inverse Transform</a></li>
 </ul>
 </li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/preprocessing_targets.html">6.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="http://github.com/modules/preprocessing_targets.html#label-binarization">6.9.1. Label binarization</a></li>
-<li class="toctree-l3"><a class="reference internal" href="http://github.com/modules/preprocessing_targets.html#label-encoding">6.9.2. Label encoding</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/kernel_approximation.html">7.7. Kernel Approximation</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="http://github.com/modules/kernel_approximation.html#nystroem-method-for-kernel-approximation">7.7.1. Nystroem Method for Kernel Approximation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="http://github.com/modules/kernel_approximation.html#radial-basis-function-kernel">7.7.2. Radial Basis Function Kernel</a></li>
+<li class="toctree-l3"><a class="reference internal" href="http://github.com/modules/kernel_approximation.html#additive-chi-squared-kernel">7.7.3. Additive Chi Squared Kernel</a></li>
+<li class="toctree-l3"><a class="reference internal" href="http://github.com/modules/kernel_approximation.html#skewed-chi-squared-kernel">7.7.4. Skewed Chi Squared Kernel</a></li>
+<li class="toctree-l3"><a class="reference internal" href="http://github.com/modules/kernel_approximation.html#polynomial-kernel-approximation-via-tensor-sketch">7.7.5. Polynomial Kernel Approximation via Tensor Sketch</a></li>
+<li class="toctree-l3"><a class="reference internal" href="http://github.com/modules/kernel_approximation.html#mathematical-details">7.7.6. Mathematical Details</a></li>
 </ul>
 </li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/metrics.html">7.8. Pairwise metrics, Affinities and Kernels</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="http://github.com/modules/metrics.html#cosine-similarity">7.8.1. Cosine similarity</a></li>
+<li class="toctree-l3"><a class="reference internal" href="http://github.com/modules/metrics.html#linear-kernel">7.8.2. Linear kernel</a></li>
+<li class="toctree-l3"><a class="reference internal" href="http://github.com/modules/metrics.html#polynomial-kernel">7.8.3. Polynomial kernel</a></li>
+<li class="toctree-l3"><a class="reference internal" href="http://github.com/modules/metrics.html#sigmoid-kernel">7.8.4. Sigmoid kernel</a></li>
+<li class="toctree-l3"><a class="reference internal" href="http://github.com/modules/metrics.html#rbf-kernel">7.8.5. RBF kernel</a></li>
+<li class="toctree-l3"><a class="reference internal" href="http://github.com/modules/metrics.html#laplacian-kernel">7.8.6. Laplacian kernel</a></li>
+<li class="toctree-l3"><a class="reference internal" href="http://github.com/modules/metrics.html#chi-squared-kernel">7.8.7. Chi-squared kernel</a></li>
 </ul>
 </li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/datasets.html">7. Dataset loading utilities</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html">7.1. Toy datasets</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html#iris-plants-dataset">7.1.1. Iris plants dataset</a></li>
-<li class="toctree-l3"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html#diabetes-dataset">7.1.2. Diabetes dataset</a></li>
-<li class="toctree-l3"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html#optical-recognition-of-handwritten-digits-dataset">7.1.3. Optical recognition of handwritten digits dataset</a></li>
-<li class="toctree-l3"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html#linnerrud-dataset">7.1.4. Linnerrud dataset</a></li>
-<li class="toctree-l3"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html#wine-recognition-dataset">7.1.5. Wine recognition dataset</a></li>
-<li class="toctree-l3"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html#breast-cancer-wisconsin-diagnostic-dataset">7.1.6. Breast cancer Wisconsin (diagnostic) dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/preprocessing_targets.html">7.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="http://github.com/modules/preprocessing_targets.html#label-binarization">7.9.1. Label binarization</a></li>
+<li class="toctree-l3"><a class="reference internal" href="http://github.com/modules/preprocessing_targets.html#label-encoding">7.9.2. Label encoding</a></li>
 </ul>
 </li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html">7.2. Real world datasets</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="http://github.com/datasets/real_world.html#the-olivetti-faces-dataset">7.2.1. The Olivetti faces dataset</a></li>
-<li class="toctree-l3"><a class="reference internal" href="http://github.com/datasets/real_world.html#the-20-newsgroups-text-dataset">7.2.2. The 20 newsgroups text dataset</a></li>
-<li class="toctree-l3"><a class="reference internal" href="http://github.com/datasets/real_world.html#the-labeled-faces-in-the-wild-face-recognition-dataset">7.2.3. The Labeled Faces in the Wild face recognition dataset</a></li>
-<li class="toctree-l3"><a class="reference internal" href="http://github.com/datasets/real_world.html#forest-covertypes">7.2.4. Forest covertypes</a></li>
-<li class="toctree-l3"><a class="reference internal" href="http://github.com/datasets/real_world.html#rcv1-dataset">7.2.5. RCV1 dataset</a></li>
-<li class="toctree-l3"><a class="reference internal" href="http://github.com/datasets/real_world.html#kddcup-99-dataset">7.2.6. Kddcup 99 dataset</a></li>
-<li class="toctree-l3"><a class="reference internal" href="http://github.com/datasets/real_world.html#california-housing-dataset">7.2.7. California Housing dataset</a></li>
-<li class="toctree-l3"><a class="reference internal" href="http://github.com/datasets/real_world.html#species-distribution-dataset">7.2.8. Species distribution dataset</a></li>
 </ul>
 </li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html">7.3. Generated datasets</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="http://github.com/datasets/sample_generators.html#generators-for-classification-and-clustering">7.3.1. Generators for classification and clustering</a></li>
-<li class="toctree-l3"><a class="reference internal" href="http://github.com/datasets/sample_generators.html#generators-for-regression">7.3.2. Generators for regression</a></li>
-<li class="toctree-l3"><a class="reference internal" href="http://github.com/datasets/sample_generators.html#generators-for-manifold-learning">7.3.3. Generators for manifold learning</a></li>
-<li class="toctree-l3"><a class="reference internal" href="http://github.com/datasets/sample_generators.html#generators-for-decomposition">7.3.4. Generators for decomposition</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/datasets.html">8. Dataset loading utilities</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html">8.1. Toy datasets</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html#iris-plants-dataset">8.1.1. Iris plants dataset</a></li>
+<li class="toctree-l3"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html#diabetes-dataset">8.1.2. Diabetes dataset</a></li>
+<li class="toctree-l3"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html#optical-recognition-of-handwritten-digits-dataset">8.1.3. Optical recognition of handwritten digits dataset</a></li>
+<li class="toctree-l3"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html#linnerrud-dataset">8.1.4. Linnerrud dataset</a></li>
+<li class="toctree-l3"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html#wine-recognition-dataset">8.1.5. Wine recognition dataset</a></li>
+<li class="toctree-l3"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html#breast-cancer-wisconsin-diagnostic-dataset">8.1.6. Breast cancer Wisconsin (diagnostic) dataset</a></li>
 </ul>
 </li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html">7.4. Loading other datasets</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html#sample-images">7.4.1. Sample images</a></li>
-<li class="toctree-l3"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html#datasets-in-svmlight-libsvm-format">7.4.2. Datasets in svmlight / libsvm format</a></li>
-<li class="toctree-l3"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html#downloading-datasets-from-the-openml-org-repository">7.4.3. Downloading datasets from the openml.org repository</a></li>
-<li class="toctree-l3"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html#loading-from-external-datasets">7.4.4. Loading from external datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html">8.2. Real world datasets</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="http://github.com/datasets/real_world.html#the-olivetti-faces-dataset">8.2.1. The Olivetti faces dataset</a></li>
+<li class="toctree-l3"><a class="reference internal" href="http://github.com/datasets/real_world.html#the-20-newsgroups-text-dataset">8.2.2. The 20 newsgroups text dataset</a></li>
+<li class="toctree-l3"><a class="reference internal" href="http://github.com/datasets/real_world.html#the-labeled-faces-in-the-wild-face-recognition-dataset">8.2.3. The Labeled Faces in the Wild face recognition dataset</a></li>
+<li class="toctree-l3"><a class="reference internal" href="http://github.com/datasets/real_world.html#forest-covertypes">8.2.4. Forest covertypes</a></li>
+<li class="toctree-l3"><a class="reference internal" href="http://github.com/datasets/real_world.html#rcv1-dataset">8.2.5. RCV1 dataset</a></li>
+<li class="toctree-l3"><a class="reference internal" href="http://github.com/datasets/real_world.html#kddcup-99-dataset">8.2.6. Kddcup 99 dataset</a></li>
+<li class="toctree-l3"><a class="reference internal" href="http://github.com/datasets/real_world.html#california-housing-dataset">8.2.7. California Housing dataset</a></li>
+<li class="toctree-l3"><a class="reference internal" href="http://github.com/datasets/real_world.html#species-distribution-dataset">8.2.8. Species distribution dataset</a></li>
 </ul>
 </li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html">8.3. Generated datasets</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="http://github.com/datasets/sample_generators.html#generators-for-classification-and-clustering">8.3.1. Generators for classification and clustering</a></li>
+<li class="toctree-l3"><a class="reference internal" href="http://github.com/datasets/sample_generators.html#generators-for-regression">8.3.2. Generators for regression</a></li>
+<li class="toctree-l3"><a class="reference internal" href="http://github.com/datasets/sample_generators.html#generators-for-manifold-learning">8.3.3. Generators for manifold learning</a></li>
+<li class="toctree-l3"><a class="reference internal" href="http://github.com/datasets/sample_generators.html#generators-for-decomposition">8.3.4. Generators for decomposition</a></li>
 </ul>
 </li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/computing.html">8. Computing with scikit-learn</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/scaling_strategies.html">8.1. Strategies to scale computationally: bigger data</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="http://github.com/computing/scaling_strategies.html#scaling-with-instances-using-out-of-core-learning">8.1.1. Scaling with instances using out-of-core learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html">8.4. Loading other datasets</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html#sample-images">8.4.1. Sample images</a></li>
+<li class="toctree-l3"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html#datasets-in-svmlight-libsvm-format">8.4.2. Datasets in svmlight / libsvm format</a></li>
+<li class="toctree-l3"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html#downloading-datasets-from-the-openml-org-repository">8.4.3. Downloading datasets from the openml.org repository</a></li>
+<li class="toctree-l3"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html#loading-from-external-datasets">8.4.4. Loading from external datasets</a></li>
 </ul>
 </li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/computational_performance.html">8.2. Computational Performance</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="http://github.com/computing/computational_performance.html#prediction-latency">8.2.1. Prediction Latency</a></li>
-<li class="toctree-l3"><a class="reference internal" href="http://github.com/computing/computational_performance.html#prediction-throughput">8.2.2. Prediction Throughput</a></li>
-<li class="toctree-l3"><a class="reference internal" href="http://github.com/computing/computational_performance.html#tips-and-tricks">8.2.3. Tips and Tricks</a></li>
 </ul>
 </li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/parallelism.html">8.3. Parallelism, resource management, and configuration</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="http://github.com/computing/parallelism.html#parallelism">8.3.1. Parallelism</a></li>
-<li class="toctree-l3"><a class="reference internal" href="http://github.com/computing/parallelism.html#configuration-switches">8.3.2. Configuration switches</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/computing.html">9. Computing with scikit-learn</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/scaling_strategies.html">9.1. Strategies to scale computationally: bigger data</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="http://github.com/computing/scaling_strategies.html#scaling-with-instances-using-out-of-core-learning">9.1.1. Scaling with instances using out-of-core learning</a></li>
 </ul>
 </li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/computational_performance.html">9.2. Computational Performance</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="http://github.com/computing/computational_performance.html#prediction-latency">9.2.1. Prediction Latency</a></li>
+<li class="toctree-l3"><a class="reference internal" href="http://github.com/computing/computational_performance.html#prediction-throughput">9.2.2. Prediction Throughput</a></li>
+<li class="toctree-l3"><a class="reference internal" href="http://github.com/computing/computational_performance.html#tips-and-tricks">9.2.3. Tips and Tricks</a></li>
 </ul>
 </li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/model_persistence.html">9. Model persistence</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/model_persistence.html#workflow-overview">9.1. Workflow Overview</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="http://github.com/model_persistence.html#train-and-persist-the-model">9.1.1. Train and Persist the Model</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/parallelism.html">9.3. Parallelism, resource management, and configuration</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="http://github.com/computing/parallelism.html#parallelism">9.3.1. Parallelism</a></li>
+<li class="toctree-l3"><a class="reference internal" href="http://github.com/computing/parallelism.html#configuration-switches">9.3.2. Configuration switches</a></li>
 </ul>
 </li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/model_persistence.html#onnx">9.2. ONNX</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/model_persistence.html#skops-io">9.3. <code class="docutils literal notranslate"><span class="pre">skops.io</span></code></a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/model_persistence.html#pickle-joblib-and-cloudpickle">9.4. <code class="docutils literal notranslate"><span class="pre">pickle</span></code>, <code class="docutils literal notranslate"><span class="pre">joblib</span></code>, and <code class="docutils literal notranslate"><span class="pre">cloudpickle</span></code></a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/model_persistence.html#security-maintainability-limitations">9.5. Security &amp; Maintainability Limitations</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="http://github.com/model_persistence.html#replicating-the-training-environment-in-production">9.5.1. Replicating the training environment in production</a></li>
-<li class="toctree-l3"><a class="reference internal" href="http://github.com/model_persistence.html#serving-the-model-artifact">9.5.2. Serving the model artifact</a></li>
 </ul>
 </li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/model_persistence.html#summarizing-the-key-points">9.6. Summarizing the key points</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/model_persistence.html">10. Model persistence</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/model_persistence.html#workflow-overview">10.1. Workflow Overview</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="http://github.com/model_persistence.html#train-and-persist-the-model">10.1.1. Train and Persist the Model</a></li>
 </ul>
 </li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/common_pitfalls.html">10. Common pitfalls and recommended practices</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/common_pitfalls.html#inconsistent-preprocessing">10.1. Inconsistent preprocessing</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/common_pitfalls.html#data-leakage">10.2. Data leakage</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="http://github.com/common_pitfalls.html#how-to-avoid-data-leakage">10.2.1. How to avoid data leakage</a></li>
-<li class="toctree-l3"><a class="reference internal" href="http://github.com/common_pitfalls.html#data-leakage-during-pre-processing">10.2.2. Data leakage during pre-processing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/model_persistence.html#onnx">10.2. ONNX</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/model_persistence.html#skops-io">10.3. <code class="docutils literal notranslate"><span class="pre">skops.io</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/model_persistence.html#pickle-joblib-and-cloudpickle">10.4. <code class="docutils literal notranslate"><span class="pre">pickle</span></code>, <code class="docutils literal notranslate"><span class="pre">joblib</span></code>, and <code class="docutils literal notranslate"><span class="pre">cloudpickle</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/model_persistence.html#security-maintainability-limitations">10.5. Security &amp; Maintainability Limitations</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="http://github.com/model_persistence.html#replicating-the-training-environment-in-production">10.5.1. Replicating the training environment in production</a></li>
+<li class="toctree-l3"><a class="reference internal" href="http://github.com/model_persistence.html#serving-the-model-artifact">10.5.2. Serving the model artifact</a></li>
 </ul>
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-<li class="toctree-l3"><a class="reference internal" href="http://github.com/common_pitfalls.html#using-none-or-randomstate-instances-and-repeated-calls-to-fit-and-split">10.3.1. Using <code class="docutils literal notranslate"><span class="pre">None</span></code> or <code class="docutils literal notranslate"><span class="pre">RandomState</span></code> instances, and repeated calls to <code class="docutils literal notranslate"><span class="pre">fit</span></code> and <code class="docutils literal notranslate"><span class="pre">split</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="http://github.com/common_pitfalls.html#common-pitfalls-and-subtleties">10.3.2. Common pitfalls and subtleties</a></li>
-<li class="toctree-l3"><a class="reference internal" href="http://github.com/common_pitfalls.html#general-recommendations">10.3.3. General recommendations</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/model_persistence.html#summarizing-the-key-points">10.6. Summarizing the key points</a></li>
 </ul>
 </li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/common_pitfalls.html">11. Common pitfalls and recommended practices</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/common_pitfalls.html#inconsistent-preprocessing">11.1. Inconsistent preprocessing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/common_pitfalls.html#data-leakage">11.2. Data leakage</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="http://github.com/common_pitfalls.html#how-to-avoid-data-leakage">11.2.1. How to avoid data leakage</a></li>
+<li class="toctree-l3"><a class="reference internal" href="http://github.com/common_pitfalls.html#data-leakage-during-pre-processing">11.2.2. Data leakage during pre-processing</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="http://github.com/modules/array_api.html#common-estimator-checks">11.1.3. Common estimator checks</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/common_pitfalls.html#controlling-randomness">11.3. Controlling randomness</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="http://github.com/common_pitfalls.html#using-none-or-randomstate-instances-and-repeated-calls-to-fit-and-split">11.3.1. Using <code class="docutils literal notranslate"><span class="pre">None</span></code> or <code class="docutils literal notranslate"><span class="pre">RandomState</span></code> instances, and repeated calls to <code class="docutils literal notranslate"><span class="pre">fit</span></code> and <code class="docutils literal notranslate"><span class="pre">split</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="http://github.com/common_pitfalls.html#common-pitfalls-and-subtleties">11.3.2. Common pitfalls and subtleties</a></li>
+<li class="toctree-l3"><a class="reference internal" href="http://github.com/common_pitfalls.html#general-recommendations">11.3.3. General recommendations</a></li>
 </ul>
 </li>
 </ul>
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-<li class="toctree-l1"><a class="reference internal" href="http://github.com/machine_learning_map.html">12. Choosing the right estimator</a></li>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/presentations.html">13. External Resources, Videos and Talks</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/presentations.html#the-scikit-learn-mooc">13.1. The scikit-learn MOOC</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/presentations.html#new-to-scientific-python">13.3. New to Scientific Python?</a></li>
-<li class="toctree-l2"><a class="reference internal" href="http://github.com/presentations.html#external-tutorials">13.4. External Tutorials</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/dispatching.html">12. Dispatching</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/array_api.html">12.1. Array API support (experimental)</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="http://github.com/modules/array_api.html#example-usage">12.1.1. Example usage</a></li>
+<li class="toctree-l3"><a class="reference internal" href="http://github.com/modules/array_api.html#support-for-array-api-compatible-inputs">12.1.2. Support for <code class="docutils literal notranslate"><span class="pre">Array</span> <span class="pre">API</span></code>-compatible inputs</a></li>
+<li class="toctree-l3"><a class="reference internal" href="http://github.com/modules/array_api.html#common-estimator-checks">12.1.3. Common estimator checks</a></li>
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-<ul>
-<li class="toctree-l1"><a class="reference internal" href="http://github.com/metadata_routing.html">1. Metadata Routing</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="http://github.com/machine_learning_map.html">13. Choosing the right estimator</a></li>
+<li class="toctree-l1"><a class="reference internal" href="http://github.com/presentations.html">14. External Resources, Videos and Talks</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/presentations.html#the-scikit-learn-mooc">14.1. The scikit-learn MOOC</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/presentations.html#videos">14.2. Videos</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/presentations.html#new-to-scientific-python">14.3. New to Scientific Python?</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/presentations.html#external-tutorials">14.4. External Tutorials</a></li>
+</ul>
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-    class="page-toc tocsection onthispage">
-    <i class="fa-solid fa-list"></i> On this page
-  </div>
-  <nav class="bd-toc-nav page-toc" aria-labelledby="pst-page-navigation-heading-2">
-    <ul class="visible nav section-nav flex-column">
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#under-development">Under Development</a><ul class="simple nav section-nav flex-column">
-</ul>
-</li>
-</ul>
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diff --git a/dev/versions.html b/dev/versions.html
index 158411fdd86c4..812a82fcd10fb 100644
--- a/dev/versions.html
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 <meta property="og:type" content="website" />
 <meta property="og:url" content="https://scikit-learn/stable/versions.html" />
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+<meta property="og:description" content="Web-based documentation is available for versions listed below: scikit-learn 1.7.dev0 (dev) documentation( ZIP 90.3 MB), scikit-learn 1.6.1 (stable) documentation( ZIP 91.2 MB), scikit-learn 1.5.2 ..." />
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 <meta property="og:image:alt" content="scikit-learn" />
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+<meta name="description" content="Web-based documentation is available for versions listed below: scikit-learn 1.7.dev0 (dev) documentation( ZIP 90.3 MB), scikit-learn 1.6.1 (stable) documentation( ZIP 91.2 MB), scikit-learn 1.5.2 ..." />
 
     <title>Available documentation for scikit-learn &#8212; scikit-learn 1.7.dev0 documentation</title>
   
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@@ -596,7 +596,7 @@
 <h1>Available documentation for scikit-learn<a class="headerlink" href="#available-documentation-for-scikit-learn" title="Link to this heading">#</a></h1>
 <p>Web-based documentation is available for versions listed below:</p>
 <ul class="simple">
-<li><p><a class="reference external" href="https://scikit-learn.org/dev/">scikit-learn 1.7.dev0 (dev) documentation</a> (<a class="reference external" href="https://scikit-learn.org/dev//_downloads/scikit-learn-docs.zip">ZIP 90.0 MB</a>)</p></li>
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 <li><p><a class="reference external" href="https://scikit-learn.org/1.5/">scikit-learn 1.5.2 documentation</a> (<a class="reference external" href="https://scikit-learn.org/1.5//_downloads/scikit-learn-docs.zip">ZIP 90.8 MB</a>)</p></li>
 <li><p><a class="reference external" href="https://scikit-learn.org/1.4/">scikit-learn 1.4.2 documentation</a> (<a class="reference external" href="https://scikit-learn.org/1.4//_downloads/scikit-learn-docs.zip">ZIP 74.4 MB</a>)</p></li>
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index 745ec70dcedf2..6ec1a614da64c 100644
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-<meta property="og:title" content="5. Visualizations" />
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-    <title>5. Visualizations &#8212; scikit-learn 1.7.dev0 documentation</title>
+    <title>6. Visualizations &#8212; scikit-learn 1.7.dev0 documentation</title>
   
   
   
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     <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
-    <link rel="stylesheet" type="text/css" href="http://github.com/_static/jupyterlite_sphinx.css?v=2c9f8f05" />
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-    <link rel="prev" title="4.2. Permutation feature importance" href="http://github.com/modules/permutation_importance.html" />
+    <link rel="next" title="7. Dataset transformations" href="http://github.com/data_transforms.html" />
+    <link rel="prev" title="5.2. Permutation feature importance" href="http://github.com/modules/permutation_importance.html" />
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+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/permutation_importance.html">5.2. Permutation feature importance</a></li>
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+<li class="toctree-l1 current active"><a class="current reference internal" href="#">6. Visualizations</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/data_transforms.html">7. Dataset transformations</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/compose.html">7.1. Pipelines and composite estimators</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/feature_extraction.html">7.2. Feature extraction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/preprocessing.html">7.3. Preprocessing data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/impute.html">7.4. Imputation of missing values</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/unsupervised_reduction.html">7.5. Unsupervised dimensionality reduction</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/kernel_approximation.html">7.7. Kernel Approximation</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/loading_other_datasets.html">7.4. Loading other datasets</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/datasets.html">8. Dataset loading utilities</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/toy_dataset.html">8.1. Toy datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/real_world.html">8.2. Real world datasets</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/datasets/sample_generators.html">8.3. Generated datasets</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/parallelism.html">8.3. Parallelism, resource management, and configuration</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="http://github.com/computing.html">9. Computing with scikit-learn</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/scaling_strategies.html">9.1. Strategies to scale computationally: bigger data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/computational_performance.html">9.2. Computational Performance</a></li>
+<li class="toctree-l2"><a class="reference internal" href="http://github.com/computing/parallelism.html">9.3. Parallelism, resource management, and configuration</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="http://github.com/modules/array_api.html">11.1. Array API support (experimental)</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="http://github.com/common_pitfalls.html">11. Common pitfalls and recommended practices</a></li>
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-    <li class="breadcrumb-item active" aria-current="page"><span class="ellipsis"><span class="section-number">5. </span>Visualizations</span></li>
+    <li class="breadcrumb-item active" aria-current="page"><span class="ellipsis"><span class="section-number">6. </span>Visualizations</span></li>
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@@ -686,7 +687,7 @@
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-<span id="id1"></span><h1><span class="section-number">5. </span>Visualizations<a class="headerlink" href="#visualizations" title="Link to this heading">#</a></h1>
+<span id="id1"></span><h1><span class="section-number">6. </span>Visualizations<a class="headerlink" href="#visualizations" title="Link to this heading">#</a></h1>
 <p>Scikit-learn defines a simple API for creating visualizations for machine
 learning. The key feature of this API is to allow for quick plotting and
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@@ -745,9 +746,9 @@
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 <section id="available-plotting-utilities">
-<h2><span class="section-number">5.1. </span>Available Plotting Utilities<a class="headerlink" href="#available-plotting-utilities" title="Link to this heading">#</a></h2>
+<h2><span class="section-number">6.1. </span>Available Plotting Utilities<a class="headerlink" href="#available-plotting-utilities" title="Link to this heading">#</a></h2>
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       <i class="fa-solid fa-angle-left"></i>
       <div class="prev-next-info">
         <p class="prev-next-subtitle">previous</p>
-        <p class="prev-next-title"><span class="section-number">4.2. </span>Permutation feature importance</p>
+        <p class="prev-next-title"><span class="section-number">5.2. </span>Permutation feature importance</p>
       </div>
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@@ -811,7 +812,7 @@ <h3><span class="section-number">5.1.1. </span>Display Objects<a class="headerli
        title="next page">
       <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">6. </span>Dataset transformations</p>
+        <p class="prev-next-title"><span class="section-number">7. </span>Dataset transformations</p>
       </div>
       <i class="fa-solid fa-angle-right"></i>
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-<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#display-objects">5.1.1. Display Objects</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#available-plotting-utilities">6.1. Available Plotting Utilities</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#display-objects">6.1.1. Display Objects</a></li>
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 those estimators as part of parallel parameter search or cross-validation.
 <a class="reference external" href="https://github.com/scikit-learn/scikit-learn/issues/12122">#12122</a> by <a class="reference external" href="https://github.com/ogrisel">Olivier Grisel</a>.</p></li>
 <li><p><span class="raw-html"><span class="badge text-bg-danger">Fix</span></span>  Fixed a bug affecting <a class="reference internal" href="http://github.com/modules/generated/sklearn.linear_model.SGDClassifier.html#sklearn.linear_model.SGDClassifier" title="sklearn.linear_model.SGDClassifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">linear_model.SGDClassifier</span></code></a> in the multiclass
-case. Each one-versus-all step is run in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.Parallel.html#joblib.Parallel" title="(in joblib v1.5.dev0)"><code class="xref py py-class docutils literal notranslate"><span class="pre">joblib.Parallel</span></code></a> call and
+case. Each one-versus-all step is run in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.Parallel.html#joblib.Parallel" title="(in joblib v1.6.dev0)"><code class="xref py py-class docutils literal notranslate"><span class="pre">joblib.Parallel</span></code></a> call and
 mutating a common parameter, causing a segmentation fault if called within a
 backend using processes and not threads. We now use <code class="docutils literal notranslate"><span class="pre">require=sharedmem</span></code>
-at the <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.Parallel.html#joblib.Parallel" title="(in joblib v1.5.dev0)"><code class="xref py py-class docutils literal notranslate"><span class="pre">joblib.Parallel</span></code></a> instance creation. <a class="reference external" href="https://github.com/scikit-learn/scikit-learn/issues/12518">#12518</a> by
+at the <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.Parallel.html#joblib.Parallel" title="(in joblib v1.6.dev0)"><code class="xref py py-class docutils literal notranslate"><span class="pre">joblib.Parallel</span></code></a> instance creation. <a class="reference external" href="https://github.com/scikit-learn/scikit-learn/issues/12518">#12518</a> by
 <a class="reference external" href="https://github.com/pierreglaser">Pierre Glaser</a> and <a class="reference external" href="https://github.com/ogrisel">Olivier Grisel</a>.</p></li>
 </ul>
 </section>
@@ -2034,7 +2034,7 @@ <h4>Miscellaneous<a class="headerlink" href="#id40" title="Link to this heading"
 to control memory consumption limits in chunked operations, such as the new
 <a class="reference internal" href="http://github.com/modules/generated/sklearn.metrics.pairwise_distances_chunked.html#sklearn.metrics.pairwise_distances_chunked" title="sklearn.metrics.pairwise_distances_chunked"><code class="xref py py-func docutils literal notranslate"><span class="pre">metrics.pairwise_distances_chunked</span></code></a>. See <a class="reference internal" href="http://github.com/computing/computational_performance.html#working-memory"><span class="std std-ref">Limiting Working Memory</span></a>.
 <a class="reference external" href="https://github.com/scikit-learn/scikit-learn/issues/10280">#10280</a> by <a class="reference external" href="https://joelnothman.com/">Joel Nothman</a> and <a class="reference external" href="https://github.com/dalmia">Aman Dalmia</a>.</p></li>
-<li><p><span class="raw-html"><span class="badge text-bg-success">Feature</span></span>  The version of <a class="reference external" href="https://joblib.readthedocs.io/en/latest/index.html#module-joblib" title="(in joblib v1.5.dev0)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">joblib</span></code></a> bundled with Scikit-learn is now 0.12.
+<li><p><span class="raw-html"><span class="badge text-bg-success">Feature</span></span>  The version of <a class="reference external" href="https://joblib.readthedocs.io/en/latest/index.html#module-joblib" title="(in joblib v1.6.dev0)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">joblib</span></code></a> bundled with Scikit-learn is now 0.12.
 This uses a new default multiprocessing implementation, named <a class="reference external" href="https://github.com/tomMoral/loky">loky</a>. While this may incur some memory and
 communication overhead, it should provide greater cross-platform stability
 than relying on Python standard library multiprocessing. <a class="reference external" href="https://github.com/scikit-learn/scikit-learn/issues/11741">#11741</a> by
diff --git a/dev/whats_new/v0.21.html b/dev/whats_new/v0.21.html
index 7cd88d5991385..5b268d064fa04 100644
--- a/dev/whats_new/v0.21.html
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@@ -42,7 +42,7 @@
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@@ -1256,7 +1256,7 @@ <h4><a class="reference internal" href="../api/sklearn.linear_model.html#module-
 <li><p><span class="raw-html"><span class="badge text-bg-danger">Fix</span></span>  Fixed a bug in <a class="reference internal" href="http://github.com/modules/generated/sklearn.linear_model.HuberRegressor.html#sklearn.linear_model.HuberRegressor" title="sklearn.linear_model.HuberRegressor"><code class="xref py py-class docutils literal notranslate"><span class="pre">linear_model.HuberRegressor</span></code></a> that was
 broken when <code class="docutils literal notranslate"><span class="pre">X</span></code> was of dtype bool. <a class="reference external" href="https://github.com/scikit-learn/scikit-learn/pull/13328">#13328</a> by <a class="reference external" href="http://alexandre.gramfort.net/">Alexandre Gramfort</a>.</p></li>
 <li><p><span class="raw-html"><span class="badge text-bg-danger">Fix</span></span>  Fixed a performance issue of <code class="docutils literal notranslate"><span class="pre">saga</span></code> and <code class="docutils literal notranslate"><span class="pre">sag</span></code> solvers when called
-in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.Parallel.html#joblib.Parallel" title="(in joblib v1.5.dev0)"><code class="xref py py-class docutils literal notranslate"><span class="pre">joblib.Parallel</span></code></a> setting with <code class="docutils literal notranslate"><span class="pre">n_jobs</span> <span class="pre">&gt;</span> <span class="pre">1</span></code> and
+in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.Parallel.html#joblib.Parallel" title="(in joblib v1.6.dev0)"><code class="xref py py-class docutils literal notranslate"><span class="pre">joblib.Parallel</span></code></a> setting with <code class="docutils literal notranslate"><span class="pre">n_jobs</span> <span class="pre">&gt;</span> <span class="pre">1</span></code> and
 <code class="docutils literal notranslate"><span class="pre">backend=&quot;threading&quot;</span></code>, causing them to perform worse than in the sequential
 case. <a class="reference external" href="https://github.com/scikit-learn/scikit-learn/pull/13389">#13389</a> by <a class="reference external" href="https://github.com/pierreglaser">Pierre Glaser</a>.</p></li>
 <li><p><span class="raw-html"><span class="badge text-bg-danger">Fix</span></span>  Fixed a bug in
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index 4e05f0c7f029e..976ce2a17f068 100644
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index 7cb2b96a0e752..dfc49fdeb57a2 100644
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index c2c7234129e96..ed1ddc99fb69f 100644
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index 748235d0a11cc..cc62951bb9161 100644
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index c620ef04043cf..581ed382aa6e1 100644
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 </ul>
 <section id="version-1-7-dev0">
 <span id="changes-1-7-dev0"></span><h2>Version 1.7.dev0<a class="headerlink" href="#version-1-7-dev0" title="Link to this heading">#</a></h2>
-<p><strong>April 2025</strong></p>
+<p><strong>May 2025</strong></p>
 <section id="changes-impacting-many-modules">
 <h3>Changes impacting many modules<a class="headerlink" href="#changes-impacting-many-modules" title="Link to this heading">#</a></h3>
 <ul class="simple">
@@ -671,8 +672,12 @@ <h3>Support for Array API<a class="headerlink" href="#support-for-array-api" tit
 <a class="reference internal" href="http://github.com/modules/generated/sklearn.metrics.precision_score.html#sklearn.metrics.precision_score" title="sklearn.metrics.precision_score"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.metrics.precision_score</span></code></a> and
 <a class="reference internal" href="http://github.com/modules/generated/sklearn.metrics.recall_score.html#sklearn.metrics.recall_score" title="sklearn.metrics.recall_score"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.metrics.recall_score</span></code></a> now support Array API compatible inputs.
 By <a class="reference external" href="https://github.com/OmarManzoor">Omar Salman</a> <a class="reference external" href="https://github.com/scikit-learn/scikit-learn/pull/30395">#30395</a></p></li>
+<li><p><span class="raw-html"><span class="badge text-bg-success">Feature</span></span>  <a class="reference internal" href="http://github.com/modules/generated/sklearn.utils.extmath.randomized_svd.html#sklearn.utils.extmath.randomized_svd" title="sklearn.utils.extmath.randomized_svd"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.utils.extmath.randomized_svd</span></code></a> now support Array API compatible inputs.
+By <a class="reference external" href="https://github.com/clane9">Connor Lane</a> and <a class="reference external" href="https://github.com/jeremiedbb">Jérémie du Boisberranger</a>. <a class="reference external" href="https://github.com/scikit-learn/scikit-learn/pull/30819">#30819</a></p></li>
 <li><p><span class="raw-html"><span class="badge text-bg-success">Feature</span></span>  <a class="reference internal" href="http://github.com/modules/generated/sklearn.metrics.hamming_loss.html#sklearn.metrics.hamming_loss" title="sklearn.metrics.hamming_loss"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.metrics.hamming_loss</span></code></a> now support Array API compatible inputs.
 By <a class="reference external" href="https://github.com/lithomas1">Thomas Li</a> <a class="reference external" href="https://github.com/scikit-learn/scikit-learn/pull/30838">#30838</a></p></li>
+<li><p><span class="raw-html"><span class="badge text-bg-success">Feature</span></span>  <a class="reference internal" href="http://github.com/modules/generated/sklearn.preprocessing.Binarizer.html#sklearn.preprocessing.Binarizer" title="sklearn.preprocessing.Binarizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">preprocessing.Binarizer</span></code></a> now supports Array API compatible inputs.
+By <a class="reference external" href="https://github.com/Tialo">Yaroslav Korobko</a>, <a class="reference external" href="https://github.com/ogrisel">Olivier Grisel</a>, and <a class="reference external" href="https://github.com/lithomas1">Thomas Li</a>. <a class="reference external" href="https://github.com/scikit-learn/scikit-learn/pull/31190">#31190</a></p></li>
 <li><p>array-api-compat and array-api-extra are now vendored within the
 scikit-learn source. Users of the experimental array API standard
 support no longer need to install array-api-compat in their environment.
@@ -702,6 +707,15 @@ <h3><a class="reference internal" href="../api/sklearn.calibration.html#module-s
 Grisel</a> <a class="reference external" href="https://github.com/scikit-learn/scikit-learn/pull/30873">#30873</a></p></li>
 </ul>
 </section>
+<section id="sklearn-compose">
+<h3><a class="reference internal" href="http://github.com/api/sklearn.compose.html#module-sklearn.compose" title="sklearn.compose"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.compose</span></code></a><a class="headerlink" href="#sklearn-compose" title="Link to this heading">#</a></h3>
+<ul class="simple">
+<li><p><span class="raw-html"><span class="badge text-bg-warning">API Change</span></span>  The <code class="docutils literal notranslate"><span class="pre">force_int_remainder_cols</span></code> parameter of <a class="reference internal" href="http://github.com/modules/generated/sklearn.compose.ColumnTransformer.html#sklearn.compose.ColumnTransformer" title="sklearn.compose.ColumnTransformer"><code class="xref py py-class docutils literal notranslate"><span class="pre">compose.ColumnTransformer</span></code></a> and
+<a class="reference internal" href="http://github.com/modules/generated/sklearn.compose.make_column_transformer.html#sklearn.compose.make_column_transformer" title="sklearn.compose.make_column_transformer"><code class="xref py py-func docutils literal notranslate"><span class="pre">compose.make_column_transformer</span></code></a> is deprecated and will be removed in 1.9.
+It has no effect.
+By <a class="reference external" href="https://github.com/jeremiedbb">Jérémie du Boisberranger</a> <a class="reference external" href="https://github.com/scikit-learn/scikit-learn/pull/31167">#31167</a></p></li>
+</ul>
+</section>
 <section id="sklearn-covariance">
 <h3><a class="reference internal" href="http://github.com/api/sklearn.covariance.html#module-sklearn.covariance" title="sklearn.covariance"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.covariance</span></code></a><a class="headerlink" href="#sklearn-covariance" title="Link to this heading">#</a></h3>
 <ul class="simple">
@@ -729,10 +743,34 @@ <h3><a class="reference internal" href="../api/sklearn.decomposition.html#module
 <section id="sklearn-ensemble">
 <h3><a class="reference internal" href="http://github.com/api/sklearn.ensemble.html#module-sklearn.ensemble" title="sklearn.ensemble"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.ensemble</span></code></a><a class="headerlink" href="#sklearn-ensemble" title="Link to this heading">#</a></h3>
 <ul class="simple">
+<li><p><span class="raw-html"><span class="badge text-bg-success">Feature</span></span>  <a class="reference internal" href="http://github.com/modules/generated/sklearn.ensemble.HistGradientBoostingClassifier.html#sklearn.ensemble.HistGradientBoostingClassifier" title="sklearn.ensemble.HistGradientBoostingClassifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">ensemble.HistGradientBoostingClassifier</span></code></a> and
+<a class="reference internal" href="http://github.com/modules/generated/sklearn.ensemble.HistGradientBoostingRegressor.html#sklearn.ensemble.HistGradientBoostingRegressor" title="sklearn.ensemble.HistGradientBoostingRegressor"><code class="xref py py-class docutils literal notranslate"><span class="pre">ensemble.HistGradientBoostingRegressor</span></code></a> allow for more control over the
+validation set used for early stopping. You can now pass data to be used for
+validation directly to <code class="docutils literal notranslate"><span class="pre">fit</span></code> via the arguments <code class="docutils literal notranslate"><span class="pre">X_val</span></code>, <code class="docutils literal notranslate"><span class="pre">y_val</span></code> and
+<code class="docutils literal notranslate"><span class="pre">sample_weight_val</span></code>.
+By <a class="reference external" href="https://github.com/lorentzenchr">Christian Lorentzen</a>. <a class="reference external" href="https://github.com/scikit-learn/scikit-learn/pull/27124">#27124</a></p></li>
 <li><p><span class="raw-html"><span class="badge text-bg-danger">Fix</span></span>  <a class="reference internal" href="http://github.com/modules/generated/sklearn.ensemble.VotingClassifier.html#sklearn.ensemble.VotingClassifier" title="sklearn.ensemble.VotingClassifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">ensemble.VotingClassifier</span></code></a> and <a class="reference internal" href="http://github.com/modules/generated/sklearn.ensemble.VotingRegressor.html#sklearn.ensemble.VotingRegressor" title="sklearn.ensemble.VotingRegressor"><code class="xref py py-class docutils literal notranslate"><span class="pre">ensemble.VotingRegressor</span></code></a>
 validate <code class="docutils literal notranslate"><span class="pre">estimators</span></code> to make sure it is a list of tuples. By <a class="reference external" href="https://github.com/thomasjpfan">Thomas Fan</a>. <a class="reference external" href="https://github.com/scikit-learn/scikit-learn/pull/30649">#30649</a></p></li>
 </ul>
 </section>
+<section id="sklearn-feature-selection">
+<h3><a class="reference internal" href="http://github.com/api/sklearn.feature_selection.html#module-sklearn.feature_selection" title="sklearn.feature_selection"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.feature_selection</span></code></a><a class="headerlink" href="#sklearn-feature-selection" title="Link to this heading">#</a></h3>
+<ul class="simple">
+<li><p><span class="raw-html"><span class="badge text-bg-info">Enhancement</span></span>  <a class="reference internal" href="http://github.com/modules/generated/sklearn.feature_selection.RFECV.html#sklearn.feature_selection.RFECV" title="sklearn.feature_selection.RFECV"><code class="xref py py-class docutils literal notranslate"><span class="pre">feature_selection.RFECV</span></code></a> now gives access to the ranking and support in each
+iteration and cv step of feature selection.
+By <a class="reference external" href="https://github.com/MarieSacksick">Marie S.</a> <a class="reference external" href="https://github.com/scikit-learn/scikit-learn/pull/30179">#30179</a></p></li>
+<li><p><span class="raw-html"><span class="badge text-bg-danger">Fix</span></span>  <a class="reference internal" href="http://github.com/modules/generated/sklearn.feature_selection.SelectFromModel.html#sklearn.feature_selection.SelectFromModel" title="sklearn.feature_selection.SelectFromModel"><code class="xref py py-class docutils literal notranslate"><span class="pre">feature_selection.SelectFromModel</span></code></a> now correctly works when the estimator
+is an instance of <a class="reference internal" href="http://github.com/modules/generated/sklearn.linear_model.ElasticNetCV.html#sklearn.linear_model.ElasticNetCV" title="sklearn.linear_model.ElasticNetCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">linear_model.ElasticNetCV</span></code></a> with its <code class="docutils literal notranslate"><span class="pre">l1_ratio</span></code> parameter
+being an array-like.
+By <a class="reference external" href="https://github.com/vasco-s-pereira">Vasco Pereira</a>. <a class="reference external" href="https://github.com/scikit-learn/scikit-learn/pull/31107">#31107</a></p></li>
+</ul>
+</section>
+<section id="sklearn-gaussian-process">
+<h3><a class="reference internal" href="http://github.com/api/sklearn.gaussian_process.html#module-sklearn.gaussian_process" title="sklearn.gaussian_process"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.gaussian_process</span></code></a><a class="headerlink" href="#sklearn-gaussian-process" title="Link to this heading">#</a></h3>
+<ul class="simple">
+<li><p><span class="raw-html"><span class="badge text-bg-info">Enhancement</span></span>  <a class="reference internal" href="http://github.com/modules/generated/sklearn.gaussian_process.GaussianProcessClassifier.html#sklearn.gaussian_process.GaussianProcessClassifier" title="sklearn.gaussian_process.GaussianProcessClassifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">gaussian_process.GaussianProcessClassifier</span></code></a> now includes a <code class="docutils literal notranslate"><span class="pre">latent_mean_and_variance</span></code> method that exposes the mean and the variance of the latent function, <span class="math notranslate nohighlight">\(f\)</span>, used in the Laplace approximation. By <a class="reference external" href="https://github.com/miguelgondu">Miguel González Duque</a> <a class="reference external" href="https://github.com/scikit-learn/scikit-learn/pull/22227">#22227</a></p></li>
+</ul>
+</section>
 <section id="sklearn-inspection">
 <h3><a class="reference internal" href="http://github.com/api/sklearn.inspection.html#module-sklearn.inspection" title="sklearn.inspection"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.inspection</span></code></a><a class="headerlink" href="#sklearn-inspection" title="Link to this heading">#</a></h3>
 <ul class="simple">
@@ -744,6 +782,10 @@ <h3><a class="reference internal" href="../api/sklearn.inspection.html#module-sk
 plotting all classes for multi-class problems when <code class="docutils literal notranslate"><span class="pre">response_method</span></code> is
 ‘decision_function’, ‘predict_proba’ or ‘auto’.
 By <a class="reference external" href="https://github.com/lucyleeow">Lucy Liu</a> <a class="reference external" href="https://github.com/scikit-learn/scikit-learn/pull/29797">#29797</a></p></li>
+<li><p><span class="raw-html"><span class="badge text-bg-danger">Fix</span></span>  <a class="reference internal" href="http://github.com/modules/generated/sklearn.inspection.partial_dependence.html#sklearn.inspection.partial_dependence" title="sklearn.inspection.partial_dependence"><code class="xref py py-func docutils literal notranslate"><span class="pre">inspection.partial_dependence</span></code></a> now raises an informative error when passing
+an empty list as the <code class="docutils literal notranslate"><span class="pre">categorical_features</span></code> parameter. <code class="docutils literal notranslate"><span class="pre">None</span></code> should be used instead
+to indicate that no categorical features are present.
+By <a class="reference external" href="https://github.com/pedroL0pes">Pedro Lopes</a>. <a class="reference external" href="https://github.com/scikit-learn/scikit-learn/pull/31146">#31146</a></p></li>
 <li><p><span class="raw-html"><span class="badge text-bg-warning">API Change</span></span>  <a class="reference internal" href="http://github.com/modules/generated/sklearn.inspection.partial_dependence.html#sklearn.inspection.partial_dependence" title="sklearn.inspection.partial_dependence"><code class="xref py py-func docutils literal notranslate"><span class="pre">inspection.partial_dependence</span></code></a> does no longer accept integer dtype for
 numerical feature columns. Explicit conversion to floating point values is
 now required before calling this tool (and preferably even before fitting the
@@ -754,33 +796,57 @@ <h3><a class="reference internal" href="../api/sklearn.inspection.html#module-sk
 <section id="sklearn-linear-model">
 <h3><a class="reference internal" href="http://github.com/api/sklearn.linear_model.html#module-sklearn.linear_model" title="sklearn.linear_model"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.linear_model</span></code></a><a class="headerlink" href="#sklearn-linear-model" title="Link to this heading">#</a></h3>
 <ul class="simple">
+<li><p><span class="raw-html"><span class="badge text-bg-info">Enhancement</span></span>  <a class="reference internal" href="http://github.com/modules/generated/sklearn.linear_model.SGDClassifier.html#sklearn.linear_model.SGDClassifier" title="sklearn.linear_model.SGDClassifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">linear_model.SGDClassifier</span></code></a> and <a class="reference internal" href="http://github.com/modules/generated/sklearn.linear_model.SGDRegressor.html#sklearn.linear_model.SGDRegressor" title="sklearn.linear_model.SGDRegressor"><code class="xref py py-class docutils literal notranslate"><span class="pre">linear_model.SGDRegressor</span></code></a> now accept
+<code class="docutils literal notranslate"><span class="pre">l1_ratio=None</span></code> when <code class="docutils literal notranslate"><span class="pre">penalty</span></code> is not <code class="docutils literal notranslate"><span class="pre">&quot;elasticnet&quot;</span></code>.
+By <a class="reference external" href="https://github.com/MarcBresson">Marc Bresson</a>. <a class="reference external" href="https://github.com/scikit-learn/scikit-learn/pull/30730">#30730</a></p></li>
 <li><p><span class="raw-html"><span class="badge text-bg-danger">Fix</span></span>  <a class="reference internal" href="http://github.com/modules/generated/sklearn.linear_model.LogisticRegression.html#sklearn.linear_model.LogisticRegression" title="sklearn.linear_model.LogisticRegression"><code class="xref py py-class docutils literal notranslate"><span class="pre">linear_model.LogisticRegression</span></code></a> and
 <a class="reference internal" href="http://github.com/modules/generated/sklearn.linear_model.LogisticRegressionCV.html#sklearn.linear_model.LogisticRegressionCV" title="sklearn.linear_model.LogisticRegressionCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">linear_model.LogisticRegressionCV</span></code></a> now properly pass sample weights to
 <a class="reference internal" href="http://github.com/modules/generated/sklearn.utils.class_weight.compute_class_weight.html#sklearn.utils.class_weight.compute_class_weight" title="sklearn.utils.class_weight.compute_class_weight"><code class="xref py py-func docutils literal notranslate"><span class="pre">utils.class_weight.compute_class_weight</span></code></a> when fit with
 <code class="docutils literal notranslate"><span class="pre">class_weight=&quot;balanced&quot;</span></code>.
 By <a class="reference external" href="https://github.com/snath-xoc">Shruti Nath</a> and <a class="reference external" href="https://github.com/ogrisel">Olivier Grisel</a> <a class="reference external" href="https://github.com/scikit-learn/scikit-learn/pull/30057">#30057</a></p></li>
-<li><dl class="simple">
-<dt><span class="raw-html"><span class="badge text-bg-danger">Fix</span></span>   <span class="raw-html"><span class="badge text-bg-info">Enhancement</span></span>  Added a new parameter <code class="docutils literal notranslate"><span class="pre">tol</span></code> to</dt><dd><p><a class="reference internal" href="http://github.com/modules/generated/sklearn.linear_model.LinearRegression.html#sklearn.linear_model.LinearRegression" title="sklearn.linear_model.LinearRegression"><code class="xref py py-class docutils literal notranslate"><span class="pre">linear_model.LinearRegression</span></code></a> that determines the precision of the
+<li><p><span class="raw-html"><span class="badge text-bg-danger">Fix</span></span>  Added a new parameter <code class="docutils literal notranslate"><span class="pre">tol</span></code> to
+<a class="reference internal" href="http://github.com/modules/generated/sklearn.linear_model.LinearRegression.html#sklearn.linear_model.LinearRegression" title="sklearn.linear_model.LinearRegression"><code class="xref py py-class docutils literal notranslate"><span class="pre">linear_model.LinearRegression</span></code></a> that determines the precision of the
 solution <code class="docutils literal notranslate"><span class="pre">coef_</span></code> when fitting on sparse data.
-By <a class="reference external" href="https://github.com/SuccessMoses">Success Moses</a> <a class="reference external" href="https://github.com/scikit-learn/scikit-learn/pull/30521">#30521</a></p>
-</dd>
-</dl>
-</li>
-<li><dl class="simple">
-<dt><span class="raw-html"><span class="badge text-bg-danger">Fix</span></span>   The update and initialization of the hyperparameters now properly handle</dt><dd><p>sample weights in <a class="reference internal" href="http://github.com/modules/generated/sklearn.linear_model.BayesianRidge.html#sklearn.linear_model.BayesianRidge" title="sklearn.linear_model.BayesianRidge"><code class="xref py py-class docutils literal notranslate"><span class="pre">linear_model.BayesianRidge</span></code></a>.
-By <a class="reference external" href="https://github.com/antoinebaker">Antoine Baker</a>. <a class="reference external" href="https://github.com/scikit-learn/scikit-learn/pull/30644">#30644</a></p>
-</dd>
-</dl>
-</li>
+By <a class="reference external" href="https://github.com/SuccessMoses">Success Moses</a> <a class="reference external" href="https://github.com/scikit-learn/scikit-learn/pull/30521">#30521</a></p></li>
+<li><p><span class="raw-html"><span class="badge text-bg-danger">Fix</span></span>  The update and initialization of the hyperparameters now properly handle
+sample weights in <a class="reference internal" href="http://github.com/modules/generated/sklearn.linear_model.BayesianRidge.html#sklearn.linear_model.BayesianRidge" title="sklearn.linear_model.BayesianRidge"><code class="xref py py-class docutils literal notranslate"><span class="pre">linear_model.BayesianRidge</span></code></a>.
+By <a class="reference external" href="https://github.com/antoinebaker">Antoine Baker</a>. <a class="reference external" href="https://github.com/scikit-learn/scikit-learn/pull/30644">#30644</a></p></li>
+<li><p><span class="raw-html"><span class="badge text-bg-danger">Fix</span></span>  <a class="reference internal" href="http://github.com/modules/generated/sklearn.linear_model.BayesianRidge.html#sklearn.linear_model.BayesianRidge" title="sklearn.linear_model.BayesianRidge"><code class="xref py py-class docutils literal notranslate"><span class="pre">linear_model.BayesianRidge</span></code></a> now uses the full SVD to correctly estimate
+the posterior covariance matrix <code class="docutils literal notranslate"><span class="pre">sigma_</span></code> when <code class="docutils literal notranslate"><span class="pre">n_samples</span> <span class="pre">&lt;</span> <span class="pre">n_features</span></code>.
+By <a class="reference external" href="https://github.com/antoinebaker">Antoine Baker</a> <a class="reference external" href="https://github.com/scikit-learn/scikit-learn/pull/31094">#31094</a></p></li>
+<li><p><span class="raw-html"><span class="badge text-bg-warning">API Change</span></span>  The parameter <code class="docutils literal notranslate"><span class="pre">n_alphas</span></code> has been deprecated in the following classes:
+<a class="reference internal" href="http://github.com/modules/generated/sklearn.linear_model.ElasticNetCV.html#sklearn.linear_model.ElasticNetCV" title="sklearn.linear_model.ElasticNetCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">linear_model.ElasticNetCV</span></code></a> and <a class="reference internal" href="http://github.com/modules/generated/sklearn.linear_model.LassoCV.html#sklearn.linear_model.LassoCV" title="sklearn.linear_model.LassoCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">linear_model.LassoCV</span></code></a>
+and <a class="reference internal" href="http://github.com/modules/generated/sklearn.linear_model.MultiTaskElasticNetCV.html#sklearn.linear_model.MultiTaskElasticNetCV" title="sklearn.linear_model.MultiTaskElasticNetCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">linear_model.MultiTaskElasticNetCV</span></code></a>
+and <a class="reference internal" href="http://github.com/modules/generated/sklearn.linear_model.MultiTaskLassoCV.html#sklearn.linear_model.MultiTaskLassoCV" title="sklearn.linear_model.MultiTaskLassoCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">linear_model.MultiTaskLassoCV</span></code></a>, and will be removed in 1.9. The parameter
+<code class="docutils literal notranslate"><span class="pre">alphas</span></code> now supports both integers and array-likes, removing the need for <code class="docutils literal notranslate"><span class="pre">n_alphas</span></code>.
+From now on, only <code class="docutils literal notranslate"><span class="pre">alphas</span></code> should be set to either indicate the number of alphas to
+automatically generate (int) or to provide a list of alphas (array-like) to test along
+the regularization path.
+By <a class="reference external" href="https://github.com/KANNAHWORLD">Siddharth Bansal</a>. <a class="reference external" href="https://github.com/scikit-learn/scikit-learn/pull/30616">#30616</a></p></li>
+<li><p><span class="raw-html"><span class="badge text-bg-warning">API Change</span></span>  Using the <code class="docutils literal notranslate"><span class="pre">&quot;liblinear&quot;</span></code> solver for multiclass classification with a one-versus-rest
+scheme in <a class="reference internal" href="http://github.com/modules/generated/sklearn.linear_model.LogisticRegression.html#sklearn.linear_model.LogisticRegression" title="sklearn.linear_model.LogisticRegression"><code class="xref py py-class docutils literal notranslate"><span class="pre">linear_model.LogisticRegression</span></code></a> and
+<a class="reference internal" href="http://github.com/modules/generated/sklearn.linear_model.LogisticRegressionCV.html#sklearn.linear_model.LogisticRegressionCV" title="sklearn.linear_model.LogisticRegressionCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">linear_model.LogisticRegressionCV</span></code></a> is deprecated and will raise an error in
+version 1.8. Either use a solver which supports the multinomial loss or wrap the
+estimator in a <a class="reference internal" href="http://github.com/modules/generated/sklearn.multiclass.OneVsRestClassifier.html#sklearn.multiclass.OneVsRestClassifier" title="sklearn.multiclass.OneVsRestClassifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.multiclass.OneVsRestClassifier</span></code></a> to keep applying a
+one-versus-rest scheme.
+By <a class="reference external" href="https://github.com/jeremiedbb">Jérémie du Boisberranger</a>. <a class="reference external" href="https://github.com/scikit-learn/scikit-learn/pull/31241">#31241</a></p></li>
 </ul>
 </section>
 <section id="sklearn-manifold">
 <h3><a class="reference internal" href="http://github.com/api/sklearn.manifold.html#module-sklearn.manifold" title="sklearn.manifold"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.manifold</span></code></a><a class="headerlink" href="#sklearn-manifold" title="Link to this heading">#</a></h3>
 <ul class="simple">
+<li><p><span class="raw-html"><span class="badge text-bg-info">Enhancement</span></span>  <a class="reference internal" href="http://github.com/modules/generated/sklearn.manifold.MDS.html#sklearn.manifold.MDS" title="sklearn.manifold.MDS"><code class="xref py py-class docutils literal notranslate"><span class="pre">manifold.MDS</span></code></a> will switch to use <code class="docutils literal notranslate"><span class="pre">n_init=1</span></code> by default,
+starting from version 1.9.
+By <a class="reference external" href="https://github.com/dkobak">Dmitry Kobak</a> <a class="reference external" href="https://github.com/scikit-learn/scikit-learn/pull/31117">#31117</a></p></li>
 <li><p><span class="raw-html"><span class="badge text-bg-danger">Fix</span></span>  <a class="reference internal" href="http://github.com/modules/generated/sklearn.manifold.MDS.html#sklearn.manifold.MDS" title="sklearn.manifold.MDS"><code class="xref py py-class docutils literal notranslate"><span class="pre">manifold.MDS</span></code></a> now correctly handles non-metric MDS. Furthermore,
 the returned stress value now corresponds to the returned embedding and
 normalized stress is now allowed for metric MDS.
 By <a class="reference external" href="https://github.com/dkobak">Dmitry Kobak</a> <a class="reference external" href="https://github.com/scikit-learn/scikit-learn/pull/30514">#30514</a></p></li>
+<li><p><span class="raw-html"><span class="badge text-bg-danger">Fix</span></span>  <a class="reference internal" href="http://github.com/modules/generated/sklearn.manifold.MDS.html#sklearn.manifold.MDS" title="sklearn.manifold.MDS"><code class="xref py py-class docutils literal notranslate"><span class="pre">manifold.MDS</span></code></a> now uses <code class="docutils literal notranslate"><span class="pre">eps=1e-6</span></code> by default and the convergence
+criterion was adjusted to make sense for both metric and non-metric MDS
+and to follow the reference R implementation. The formula for normalized
+stress was adjusted to follow the original definition by Kruskal.
+By <a class="reference external" href="https://github.com/dkobak">Dmitry Kobak</a> <a class="reference external" href="https://github.com/scikit-learn/scikit-learn/pull/31117">#31117</a></p></li>
 </ul>
 </section>
 <section id="sklearn-metrics">
@@ -792,6 +858,12 @@ <h3><a class="reference internal" href="../api/sklearn.metrics.html#module-sklea
 classifiers. See the docstrings for more details. By
 <a class="reference external" href="https://github.com/aggvarun01">Varun Aggarwal</a>, <a class="reference external" href="https://github.com/ogrisel">Olivier Grisel</a> and
 <a class="reference external" href="https://github.com/antoinebaker">Antoine Baker</a>. <a class="reference external" href="https://github.com/scikit-learn/scikit-learn/pull/22046">#22046</a></p></li>
+<li><p><span class="raw-html"><span class="badge text-bg-info">Enhancement</span></span>  <a class="reference internal" href="http://github.com/modules/generated/sklearn.metrics.det_curve.html#sklearn.metrics.det_curve" title="sklearn.metrics.det_curve"><code class="xref py py-func docutils literal notranslate"><span class="pre">metrics.det_curve</span></code></a>, <a class="reference internal" href="http://github.com/modules/generated/sklearn.metrics.DetCurveDisplay.html#sklearn.metrics.DetCurveDisplay.from_estimator" title="sklearn.metrics.DetCurveDisplay.from_estimator"><code class="xref py py-class docutils literal notranslate"><span class="pre">metrics.DetCurveDisplay.from_estimator</span></code></a>,
+and <a class="reference internal" href="http://github.com/modules/generated/sklearn.metrics.DetCurveDisplay.html#sklearn.metrics.DetCurveDisplay.from_estimator" title="sklearn.metrics.DetCurveDisplay.from_estimator"><code class="xref py py-class docutils literal notranslate"><span class="pre">metrics.DetCurveDisplay.from_estimator</span></code></a> now accept a
+<code class="docutils literal notranslate"><span class="pre">drop_intermediate</span></code> option to drop thresholds where true positives (tp) do not
+change from the previous or subsequent thresholds. All points with the same tp
+value have the same <code class="docutils literal notranslate"><span class="pre">fnr</span></code> and thus same y coordinate in a DET curve.
+By <a class="reference external" href="https://github.com/ArturoAmorQ">Arturo Amor</a> <a class="reference external" href="https://github.com/scikit-learn/scikit-learn/pull/29151">#29151</a></p></li>
 <li><p><span class="raw-html"><span class="badge text-bg-info">Enhancement</span></span>  <a class="reference internal" href="http://github.com/modules/generated/sklearn.metrics.class_likelihood_ratios.html#sklearn.metrics.class_likelihood_ratios" title="sklearn.metrics.class_likelihood_ratios"><code class="xref py py-func docutils literal notranslate"><span class="pre">class_likelihood_ratios</span></code></a> now has a <code class="docutils literal notranslate"><span class="pre">replace_undefined_by</span></code> param.
 When there is a division by zero, the metric is undefined and the set values are
 returned for <code class="docutils literal notranslate"><span class="pre">LR+</span></code> and <code class="docutils literal notranslate"><span class="pre">LR-</span></code>.
@@ -799,15 +871,33 @@ <h3><a class="reference internal" href="../api/sklearn.metrics.html#module-sklea
 <li><p><span class="raw-html"><span class="badge text-bg-danger">Fix</span></span>  <a class="reference internal" href="http://github.com/modules/generated/sklearn.metrics.log_loss.html#sklearn.metrics.log_loss" title="sklearn.metrics.log_loss"><code class="xref py py-func docutils literal notranslate"><span class="pre">metrics.log_loss</span></code></a> now raises a <code class="docutils literal notranslate"><span class="pre">ValueError</span></code> if values of <code class="docutils literal notranslate"><span class="pre">y_true</span></code>
 are missing in <code class="docutils literal notranslate"><span class="pre">labels</span></code>. By <a class="reference external" href="https://github.com/aggvarun01">Varun Aggarwal</a>,
 <a class="reference external" href="https://github.com/ogrisel">Olivier Grisel</a> and <a class="reference external" href="https://github.com/antoinebaker">Antoine Baker</a>. <a class="reference external" href="https://github.com/scikit-learn/scikit-learn/pull/22046">#22046</a></p></li>
+<li><p><span class="raw-html"><span class="badge text-bg-danger">Fix</span></span>  <a class="reference internal" href="http://github.com/modules/generated/sklearn.metrics.det_curve.html#sklearn.metrics.det_curve" title="sklearn.metrics.det_curve"><code class="xref py py-func docutils literal notranslate"><span class="pre">metrics.det_curve</span></code></a> and <a class="reference internal" href="http://github.com/modules/generated/sklearn.metrics.DetCurveDisplay.html#sklearn.metrics.DetCurveDisplay" title="sklearn.metrics.DetCurveDisplay"><code class="xref py py-class docutils literal notranslate"><span class="pre">metrics.DetCurveDisplay</span></code></a> now return an
+extra threshold at infinity where the classifier always predicts the negative
+class i.e. tps = fps = 0.
+By <a class="reference external" href="https://github.com/ArturoAmorQ">Arturo Amor</a> <a class="reference external" href="https://github.com/scikit-learn/scikit-learn/pull/29151">#29151</a></p></li>
 <li><p><span class="raw-html"><span class="badge text-bg-danger">Fix</span></span>  <a class="reference internal" href="http://github.com/modules/generated/sklearn.metrics.class_likelihood_ratios.html#sklearn.metrics.class_likelihood_ratios" title="sklearn.metrics.class_likelihood_ratios"><code class="xref py py-func docutils literal notranslate"><span class="pre">class_likelihood_ratios</span></code></a> now raises <code class="docutils literal notranslate"><span class="pre">UndefinedMetricWarning</span></code> instead
 of <code class="docutils literal notranslate"><span class="pre">UserWarning</span></code> when a division by zero occurs.
 By <a class="reference external" href="https://github.com/StefanieSenger">Stefanie Senger</a> <a class="reference external" href="https://github.com/scikit-learn/scikit-learn/pull/29288">#29288</a></p></li>
 <li><p><span class="raw-html"><span class="badge text-bg-danger">Fix</span></span>  <a class="reference internal" href="http://github.com/modules/generated/sklearn.metrics.RocCurveDisplay.html#sklearn.metrics.RocCurveDisplay" title="sklearn.metrics.RocCurveDisplay"><code class="xref py py-class docutils literal notranslate"><span class="pre">metrics.RocCurveDisplay</span></code></a> will no longer set a legend when
 <code class="docutils literal notranslate"><span class="pre">label</span></code> is <code class="docutils literal notranslate"><span class="pre">None</span></code> in both the <code class="docutils literal notranslate"><span class="pre">line_kwargs</span></code> and the <code class="docutils literal notranslate"><span class="pre">chance_level_kw</span></code>.
 By <a class="reference external" href="https://github.com/ArturoAmorQ">Arturo Amor</a> <a class="reference external" href="https://github.com/scikit-learn/scikit-learn/pull/29727">#29727</a></p></li>
+<li><p><span class="raw-html"><span class="badge text-bg-danger">Fix</span></span>  <a class="reference internal" href="http://github.com/modules/generated/sklearn.metrics.d2_log_loss_score.html#sklearn.metrics.d2_log_loss_score" title="sklearn.metrics.d2_log_loss_score"><code class="xref py py-func docutils literal notranslate"><span class="pre">d2_log_loss_score</span></code></a> now properly handles the case when <code class="docutils literal notranslate"><span class="pre">labels</span></code> is
+passed and not all of the labels are present in <code class="docutils literal notranslate"><span class="pre">y_true</span></code>.
+By <a class="reference external" href="https://github.com/vmargonis">Vassilis Margonis</a> <a class="reference external" href="https://github.com/scikit-learn/scikit-learn/pull/30903">#30903</a></p></li>
 <li><p><span class="raw-html"><span class="badge text-bg-danger">Fix</span></span>  Fix <a class="reference internal" href="http://github.com/modules/generated/sklearn.metrics.adjusted_mutual_info_score.html#sklearn.metrics.adjusted_mutual_info_score" title="sklearn.metrics.adjusted_mutual_info_score"><code class="xref py py-func docutils literal notranslate"><span class="pre">metrics.adjusted_mutual_info_score</span></code></a> numerical issue when number of
 classes and samples is low.
 By <a class="reference external" href="https://github.com/glevv">Hleb Levitski</a> <a class="reference external" href="https://github.com/scikit-learn/scikit-learn/pull/31065">#31065</a></p></li>
+<li><p><span class="raw-html"><span class="badge text-bg-warning">API Change</span></span>  The <code class="docutils literal notranslate"><span class="pre">sparse</span></code> parameter of <a class="reference internal" href="http://github.com/modules/generated/sklearn.metrics.fowlkes_mallows_score.html#sklearn.metrics.fowlkes_mallows_score" title="sklearn.metrics.fowlkes_mallows_score"><code class="xref py py-func docutils literal notranslate"><span class="pre">metrics.fowlkes_mallows_score</span></code></a> is deprecated and
+will be removed in 1.9. It has no effect.
+By <a class="reference external" href="https://github.com/cynddl">Luc Rocher</a>. <a class="reference external" href="https://github.com/scikit-learn/scikit-learn/pull/28981">#28981</a></p></li>
+<li><p><span class="raw-html"><span class="badge text-bg-warning">API Change</span></span>  The <code class="docutils literal notranslate"><span class="pre">raise_warning</span></code> parameter of <a class="reference internal" href="http://github.com/modules/generated/sklearn.metrics.class_likelihood_ratios.html#sklearn.metrics.class_likelihood_ratios" title="sklearn.metrics.class_likelihood_ratios"><code class="xref py py-func docutils literal notranslate"><span class="pre">metrics.class_likelihood_ratios</span></code></a> is deprecated
+and will be removed in 1.9. An <code class="docutils literal notranslate"><span class="pre">UndefinedMetricWarning</span></code> will always be raised in case
+of a division by zero.
+By <a class="reference external" href="https://github.com/StefanieSenger">Stefanie Senger</a>. <a class="reference external" href="https://github.com/scikit-learn/scikit-learn/pull/29288">#29288</a></p></li>
+<li><p><span class="raw-html"><span class="badge text-bg-warning">API Change</span></span>  In <a class="reference internal" href="http://github.com/modules/generated/sklearn.metrics.RocCurveDisplay.html#sklearn.metrics.RocCurveDisplay.from_predictions" title="sklearn.metrics.RocCurveDisplay.from_predictions"><code class="xref py py-meth docutils literal notranslate"><span class="pre">sklearn.metrics.RocCurveDisplay.from_predictions</span></code></a>,
+the argument <code class="docutils literal notranslate"><span class="pre">y_pred</span></code> has been renamed to <code class="docutils literal notranslate"><span class="pre">y_score</span></code> to better reflect its purpose.
+<code class="docutils literal notranslate"><span class="pre">y_pred</span></code> will be removed in 1.9.
+By <a class="reference external" href="https://github.com/bagustris">Bagus Tris Atmaja</a> in <a class="reference external" href="https://github.com/scikit-learn/scikit-learn/pull/29865">#29865</a></p></li>
 </ul>
 </section>
 <section id="sklearn-mixture">
@@ -836,6 +926,16 @@ <h3><a class="reference internal" href="../api/sklearn.model_selection.html#modu
 By <a class="reference external" href="https://github.com/antoinebaker">Antoine Baker</a> <a class="reference external" href="https://github.com/scikit-learn/scikit-learn/pull/30743">#30743</a></p></li>
 </ul>
 </section>
+<section id="sklearn-multiclass">
+<h3><a class="reference internal" href="http://github.com/api/sklearn.multiclass.html#module-sklearn.multiclass" title="sklearn.multiclass"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.multiclass</span></code></a><a class="headerlink" href="#sklearn-multiclass" title="Link to this heading">#</a></h3>
+<ul class="simple">
+<li><p><span class="raw-html"><span class="badge text-bg-danger">Fix</span></span>  The <code class="docutils literal notranslate"><span class="pre">predict_proba</span></code> method of <a class="reference internal" href="http://github.com/modules/generated/sklearn.multiclass.OneVsRestClassifier.html#sklearn.multiclass.OneVsRestClassifier" title="sklearn.multiclass.OneVsRestClassifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.multiclass.OneVsRestClassifier</span></code></a> now
+returns zero for all classes when all inner estimators never predict their positive
+class.
+By <a class="reference external" href="https://github.com/Luis-Varona">Luis M. B. Varona</a>, <a class="reference external" href="https://github.com/MarcBresson">Marc Bresson</a>, and
+<a class="reference external" href="https://github.com/jeremiedbb">Jérémie du Boisberranger</a>. <a class="reference external" href="https://github.com/scikit-learn/scikit-learn/pull/31228">#31228</a></p></li>
+</ul>
+</section>
 <section id="sklearn-multioutput">
 <h3><a class="reference internal" href="http://github.com/api/sklearn.multioutput.html#module-sklearn.multioutput" title="sklearn.multioutput"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.multioutput</span></code></a><a class="headerlink" href="#sklearn-multioutput" title="Link to this heading">#</a></h3>
 <ul class="simple">
@@ -853,6 +953,9 @@ <h3><a class="reference internal" href="../api/sklearn.neural_network.html#modul
 <li><p><span class="raw-html"><span class="badge text-bg-success">Feature</span></span>  Added parameter for <code class="docutils literal notranslate"><span class="pre">loss</span></code> in <a class="reference internal" href="http://github.com/modules/generated/sklearn.neural_network.MLPRegressor.html#sklearn.neural_network.MLPRegressor" title="sklearn.neural_network.MLPRegressor"><code class="xref py py-class docutils literal notranslate"><span class="pre">neural_network.MLPRegressor</span></code></a> with options
 <code class="docutils literal notranslate"><span class="pre">&quot;squared_error&quot;</span></code> (default) and <code class="docutils literal notranslate"><span class="pre">&quot;poisson&quot;</span></code> (new).
 By <a class="reference external" href="https://github.com/lorentzenchr">Christian Lorentzen</a> <a class="reference external" href="https://github.com/scikit-learn/scikit-learn/pull/30712">#30712</a></p></li>
+<li><p><span class="raw-html"><span class="badge text-bg-danger">Fix</span></span>  <a class="reference internal" href="http://github.com/modules/generated/sklearn.neural_network.MLPRegressor.html#sklearn.neural_network.MLPRegressor" title="sklearn.neural_network.MLPRegressor"><code class="xref py py-class docutils literal notranslate"><span class="pre">neural_network.MLPRegressor</span></code></a> now raises an informative error when
+<code class="docutils literal notranslate"><span class="pre">early_stopping</span></code> is set and the computed validation set is too small.
+By <a class="reference external" href="https://github.com/davidshumway">David Shumway</a>. <a class="reference external" href="https://github.com/scikit-learn/scikit-learn/pull/24788">#24788</a></p></li>
 </ul>
 </section>
 <section id="sklearn-pipeline">
@@ -870,13 +973,19 @@ <h3><a class="reference internal" href="../api/sklearn.preprocessing.html#module
 <li><p><span class="raw-html"><span class="badge text-bg-info">Enhancement</span></span>  <a class="reference internal" href="http://github.com/modules/generated/sklearn.preprocessing.KBinsDiscretizer.html#sklearn.preprocessing.KBinsDiscretizer" title="sklearn.preprocessing.KBinsDiscretizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">preprocessing.KBinsDiscretizer</span></code></a> with <code class="docutils literal notranslate"><span class="pre">strategy=&quot;uniform&quot;</span></code> now
 accepts <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code>. Additionally with <code class="docutils literal notranslate"><span class="pre">strategy=&quot;quantile&quot;</span></code> the
 <code class="docutils literal notranslate"><span class="pre">quantile_method</span></code> can now be specified (in the future
-<code class="docutils literal notranslate"><span class="pre">quantile_method=&quot;averaged_inverted_cdf&quot;</span></code> will become the default)
-<a class="reference external" href="https://github.com/scikit-learn/scikit-learn/pull/29907">#29907</a> by <a class="reference external" href="https://github.com/snath-xoc">Shruti Nath</a> and <a class="reference external" href="https://github.com/ogrisel">Olivier Grisel</a> <a class="reference external" href="https://github.com/scikit-learn/scikit-learn/pull/29907">#29907</a></p></li>
+<code class="docutils literal notranslate"><span class="pre">quantile_method=&quot;averaged_inverted_cdf&quot;</span></code> will become the default).
+By <a class="reference external" href="https://github.com/snath-xoc">Shruti Nath</a> and <a class="reference external" href="https://github.com/ogrisel">Olivier Grisel</a> <a class="reference external" href="https://github.com/scikit-learn/scikit-learn/pull/29907">#29907</a></p></li>
 <li><p><span class="raw-html"><span class="badge text-bg-danger">Fix</span></span>  <a class="reference internal" href="http://github.com/modules/generated/sklearn.preprocessing.KBinsDiscretizer.html#sklearn.preprocessing.KBinsDiscretizer" title="sklearn.preprocessing.KBinsDiscretizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">preprocessing.KBinsDiscretizer</span></code></a> now uses weighted resampling when
 sample weights are given and subsampling is used. This may change results
 even when not using sample weights, although in absolute and not in terms
 of statistical properties.
-<a class="reference external" href="https://github.com/scikit-learn/scikit-learn/pull/29907">#29907</a> by <a class="reference external" href="https://github.com/snath-xoc">Shruti Nath</a> and <a class="reference external" href="https://github.com/jeremiedbb">Jérémie du Boisberranger</a> <a class="reference external" href="https://github.com/scikit-learn/scikit-learn/pull/29907">#29907</a></p></li>
+By <a class="reference external" href="https://github.com/snath-xoc">Shruti Nath</a> and <a class="reference external" href="https://github.com/jeremiedbb">Jérémie du Boisberranger</a> <a class="reference external" href="https://github.com/scikit-learn/scikit-learn/pull/29907">#29907</a></p></li>
+<li><p><span class="raw-html"><span class="badge text-bg-danger">Fix</span></span>  Now using <code class="docutils literal notranslate"><span class="pre">scipy.stats.yeojohnson</span></code> instead of our own implementation of the Yeo-Johnson transform.
+Fixed numerical stability (mostly overflows) of the Yeo-Johnson transform with
+<code class="docutils literal notranslate"><span class="pre">PowerTransformer(method=&quot;yeo-johnson&quot;)</span></code> when scipy version is <code class="docutils literal notranslate"><span class="pre">&gt;=</span> <span class="pre">1.12</span></code>.
+Initial PR by <a class="reference external" href="https://github.com/xuefeng-xu">Xuefeng Xu</a> completed by <a class="reference external" href="https://github.com/yaichm">Mohamed Yaich</a>,
+<a class="reference external" href="https://github.com/eroussama">Oussama Er-rabie</a>, <a class="reference external" href="https://github.com/Dlimim">Mohammed Yaslam Dlimi</a>,
+<a class="reference external" href="https://github.com/HamzaLuffy">Hamza Zaroual</a>, <a class="reference external" href="https://github.com/AmineHannoun">Amine Hannoun</a> and <a class="reference external" href="https://github.com/smarie">Sylvain Marié</a>. <a class="reference external" href="https://github.com/scikit-learn/scikit-learn/pull/31227">#31227</a></p></li>
 </ul>
 </section>
 <section id="sklearn-svm">
@@ -891,28 +1000,31 @@ <h3><a class="reference internal" href="../api/sklearn.svm.html#module-sklearn.s
 <section id="sklearn-utils">
 <h3><a class="reference internal" href="http://github.com/api/sklearn.utils.html#module-sklearn.utils" title="sklearn.utils"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.utils</span></code></a><a class="headerlink" href="#sklearn-utils" title="Link to this heading">#</a></h3>
 <ul class="simple">
+<li><p><span class="raw-html"><span class="badge text-bg-info">Enhancement</span></span>  <a class="reference internal" href="http://github.com/modules/generated/sklearn.utils.multiclass.type_of_target.html#sklearn.utils.multiclass.type_of_target" title="sklearn.utils.multiclass.type_of_target"><code class="xref py py-func docutils literal notranslate"><span class="pre">utils.multiclass.type_of_target</span></code></a> raises a warning when the number
+of unique classes is greater than 50% of the number of samples. This warning is raised
+only if <code class="docutils literal notranslate"><span class="pre">y</span></code> has more than 20 samples.
+By <a class="reference external" href="https://github.com/rprkh">Rahil Parikh</a>. <a class="reference external" href="https://github.com/scikit-learn/scikit-learn/pull/26335">#26335</a></p></li>
+<li><p><span class="raw-html"><span class="badge text-bg-info">Enhancement</span></span>  :func: <code class="docutils literal notranslate"><span class="pre">resample</span></code> now handles sample weights which allows
+weighted resampling.
+By <a class="reference external" href="https://github.com/snath-xoc">Shruti Nath</a> and <a class="reference external" href="https://github.com/ogrisel">Olivier Grisel</a> <a class="reference external" href="https://github.com/scikit-learn/scikit-learn/pull/29907">#29907</a></p></li>
 <li><p><span class="raw-html"><span class="badge text-bg-info">Enhancement</span></span>  <a class="reference internal" href="http://github.com/modules/generated/sklearn.utils.class_weight.compute_class_weight.html#sklearn.utils.class_weight.compute_class_weight" title="sklearn.utils.class_weight.compute_class_weight"><code class="xref py py-func docutils literal notranslate"><span class="pre">utils.class_weight.compute_class_weight</span></code></a> now properly accounts for
 sample weights when using strategy “balanced” to calculate class weights.
 By <a class="reference external" href="https://github.com/snath-xoc">Shruti Nath</a> <a class="reference external" href="https://github.com/scikit-learn/scikit-learn/pull/30057">#30057</a></p></li>
 <li><p><span class="raw-html"><span class="badge text-bg-info">Enhancement</span></span>  Warning filters from the main process are propagated to joblib workers.
 By <a class="reference external" href="https://github.com/thomasjpfan">Thomas Fan</a> <a class="reference external" href="https://github.com/scikit-learn/scikit-learn/pull/30380">#30380</a></p></li>
-</ul>
-<blockquote>
-<div><dl class="simple">
-<dt><span class="raw-html"><span class="badge text-bg-info">Enhancement</span></span> - :func: <code class="docutils literal notranslate"><span class="pre">resample</span></code> now handles sample weights which allows</dt><dd><p>weighted resampling.
-<a class="reference external" href="https://github.com/scikit-learn/scikit-learn/pull/29907">#29907</a> by <a class="reference external" href="https://github.com/snath-xoc">Shruti Nath</a> and <a class="reference external" href="https://github.com/ogrisel">Olivier Grisel</a> <a class="reference external" href="https://github.com/scikit-learn/scikit-learn/pull/29907">#29907</a></p>
-</dd>
-</dl>
-</div></blockquote>
-<ul class="simple">
-<li><dl class="simple">
-<dt><span class="raw-html"><span class="badge text-bg-danger">Fix</span></span>   In <code class="xref py py-mod docutils literal notranslate"><span class="pre">utils.estimator_checks</span></code> we now enforce for binary classifiers a</dt><dd><p>binary <code class="docutils literal notranslate"><span class="pre">y</span></code> by taking the minimum as the negative class instead of the first
+<li><p><span class="raw-html"><span class="badge text-bg-info">Enhancement</span></span>  The private helper function <a class="reference internal" href="http://github.com/modules/generated/sklearn.utils._safe_indexing.html#sklearn.utils._safe_indexing" title="sklearn.utils._safe_indexing"><code class="xref py py-func docutils literal notranslate"><span class="pre">utils._safe_indexing</span></code></a> now officially supports
+pyarrow data. For instance, passing a pyarrow <code class="docutils literal notranslate"><span class="pre">Table</span></code> as <code class="docutils literal notranslate"><span class="pre">X</span></code> in a
+<a class="reference internal" href="http://github.com/modules/generated/sklearn.compose.ColumnTransformer.html#sklearn.compose.ColumnTransformer" title="sklearn.compose.ColumnTransformer"><code class="xref py py-class docutils literal notranslate"><span class="pre">compose.ColumnTransformer</span></code></a> is now possible.
+By <a class="reference external" href="https://github.com/lorentzenchr">Christian Lorentzen</a> <a class="reference external" href="https://github.com/scikit-learn/scikit-learn/pull/31040">#31040</a></p></li>
+<li><p><span class="raw-html"><span class="badge text-bg-danger">Fix</span></span>  In <code class="xref py py-mod docutils literal notranslate"><span class="pre">utils.estimator_checks</span></code> we now enforce for binary classifiers a
+binary <code class="docutils literal notranslate"><span class="pre">y</span></code> by taking the minimum as the negative class instead of the first
 element, which makes it robust to <code class="docutils literal notranslate"><span class="pre">y</span></code> shuffling. It prevents two checks from
 wrongly failing on binary classifiers.
-By <a class="reference external" href="https://github.com/antoinebaker">Antoine Baker</a>. <a class="reference external" href="https://github.com/scikit-learn/scikit-learn/pull/30775">#30775</a></p>
-</dd>
-</dl>
-</li>
+By <a class="reference external" href="https://github.com/antoinebaker">Antoine Baker</a>. <a class="reference external" href="https://github.com/scikit-learn/scikit-learn/pull/30775">#30775</a></p></li>
+<li><p><span class="raw-html"><span class="badge text-bg-danger">Fix</span></span>  <a class="reference internal" href="http://github.com/modules/generated/sklearn.utils.extmath.randomized_svd.html#sklearn.utils.extmath.randomized_svd" title="sklearn.utils.extmath.randomized_svd"><code class="xref py py-func docutils literal notranslate"><span class="pre">utils.extmath.randomized_svd</span></code></a> and <a class="reference internal" href="http://github.com/modules/generated/sklearn.utils.extmath.randomized_range_finder.html#sklearn.utils.extmath.randomized_range_finder" title="sklearn.utils.extmath.randomized_range_finder"><code class="xref py py-func docutils literal notranslate"><span class="pre">utils.extmath.randomized_range_finder</span></code></a>
+now validate their input array to fail early with an informative error message on
+invalid input.
+By <a class="reference external" href="https://github.com/clane9">Connor Lane</a>. <a class="reference external" href="https://github.com/scikit-learn/scikit-learn/pull/30819">#30819</a></p></li>
 </ul>
 <p class="rubric">Code and documentation contributors</p>
 <p>Thanks to everyone who has contributed to the maintenance and improvement of
@@ -978,16 +1090,20 @@ <h3><a class="reference internal" href="../api/sklearn.utils.html#module-sklearn
 <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#support-for-array-api">Support for Array API</a></li>
 <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#metadata-routing">Metadata routing</a></li>
 <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn-calibration"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.calibration</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn-compose"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.compose</span></code></a></li>
 <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn-covariance"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.covariance</span></code></a></li>
 <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn-datasets"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.datasets</span></code></a></li>
 <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn-decomposition"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.decomposition</span></code></a></li>
 <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn-ensemble"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.ensemble</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn-feature-selection"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.feature_selection</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn-gaussian-process"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.gaussian_process</span></code></a></li>
 <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn-inspection"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.inspection</span></code></a></li>
 <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn-linear-model"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.linear_model</span></code></a></li>
 <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn-manifold"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.manifold</span></code></a></li>
 <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn-metrics"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.metrics</span></code></a></li>
 <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn-mixture"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.mixture</span></code></a></li>
 <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn-model-selection"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.model_selection</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn-multiclass"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.multiclass</span></code></a></li>
 <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn-multioutput"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.multioutput</span></code></a></li>
 <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn-neural-network"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.neural_network</span></code></a></li>
 <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn-pipeline"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.pipeline</span></code></a></li>